54168 WORLD DEVELOPMENT INDICATORS 2 2009 The world by income Low ($935 or less) Classified according to Lower middle ($936­$3,705) World Bank estimates of 2007 GNI per capita Upper middle ($3,706­$11,455) High ($11,456 or more) No data Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London WORLD DEVELOPMENT 2009 INDICATORS Copyright 2009 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW, Washington, D.C. 20433 USA All rights reserved Manufactured in the United States of America First printing April 2009 This volume is a product of the staff of the Development Data Group of the World Bank's Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank's Board of Execu- tive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsi- bility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth's surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemina- tion of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA. Photo credit: Front cover, Gavin Hellier/Robert Harding World Imagery/Getty Images. If you have questions or comments about this product, please contact: Development Data Group The World Bank 1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: data@worldbank.org Web site: www.worldbank.org or www.worldbank.org/data ISBN 978-0-8213-7829-8 ECO -AUDIT Environmental Benefits Statement The World Bank is committed to preserving endangered forests and natural resources. The Office of the Publisher has chosen to print World Development Indicators 2009 on recycled paper with 30 percent post-consumer fiber in accordance with the recommended standards for paper usage set by the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endangered forests. For more information, visit www. greenpressinitiative.org. Saved: 62 trees 43 million Btu of total energy 5,452 pounds of net greenhouse gases 22,631 gallons of waste water 2,906 pounds of solid waste WORLD DEVELOPMENT 2009 INDICATORS PREFACE World Development Indicators 2009 arrives at a moment of great uncertainty for the global economy. The crisis that began more than a year ago in the U.S. housing market spread to the global financial system and is now taking its toll on real output and incomes. As a consequence, an additional 50 million people will be left in extreme poverty. And if the crisis deepens and widens or is prolonged, other development indicators--school enrollments, women's employ- ment, child mortality--will be affected, jeopardizing progress toward the Millennium Development Goals. Statistics help us understand the events that triggered the crisis and measure its impact. Along with this year's 91 data tables, each section of the World Development Indicators 2009 has an introduction that shows statistics in action, describing the history of the current crisis, its effect on developing economies, and the challenges they face. World view reviews the housing bubble and other asset bubbles that preceded it, the global macroeconomic imbalances that fed the bubbles, and the role of financial innovation. Economy looks at the record growth of developing economies preceding the crisis. Environment reviews the increasing impact of developing economies on the global environment. Global links discusses the transmission of the global crisis through the avenues of global integration: trade, finance, migration, and remittances. States and markets reminds us that as information and communication technologies change the way we work, they will be part of the solution to the current crisis. People contains most of the statistics for measuring progress toward the Millennium Development Goals. Its introduction, prepared by our partners at the International Labour Organization, examines new measures of decent work and productive employment now included in the Millennium Development Goals. High quality, timely, and publicly available data will be central to managing the response to the crisis. We need high frequency--quarterly or monthly--data on labor markets to better track the impacts of macroeconomic events on people. We also need to know more about the characteristics of households and their response to economic condi- tions. While income distribution data are improving, they are weak at both ends of the spectrum, missing the very rich and the very poor. We know little about household assets in most developing economies. There is little information on housing markets, and financial data need to be enriched with more information on nonbank financial institutions (such as insurance companies, pension funds, investment banks, and hedge funds) in many countries. Official statistical agencies need to take a long range view of their public role--to think broadly about data needs and build strategic partnerships with academia and the private sector. In a time of crisis the careful, systematic accumu- lation of statistical information may seem a luxury. It is not. We need better data now to guide our responses to the current crisis and to plot our course in the future. The World Bank stands ready to support countries with their statistical capacity-building efforts. We will also continue to maintain the World Development Indicators as a rich source of development information, bringing to you new and critical data areas as availability and quality improve. And as always, we welcome your comments and suggestions for making World Development Indicators more useful to you. Shaida Badiee Director Development Data Group 2009 World Development Indicators v ACKNOWLEDGMENTS This book and its companion volumes, The Little Data Book and The Little Green Data Book, are prepared by a team led by Sulekha Patel under the supervision of Eric Swanson and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, David Cielikowski, Richard Fix, Masako Hiraga, Kiyomi Horiuchi, Nino Kostava, K. Sarwar Lateef, Soong Sup Lee, Ibrahim Levent, Raymond Muhula, M.H. Saeed Ordoubadi, Beatriz Prieto-Oramas, Changqing Sun, and K.M. Vijayalakshmi, working closely with other teams in the Development Economics Vice Presidency's Develop ment Data Group. The CD-ROM development team included Azita Amjadi, Ramgopal Erabelly, Reza Farivari, Buyant Erdene Khaltarkhuu, and William Prince. The work was carried out under the management of Shaida Badiee. The choice of indicators and the contents of the explanatory text was shaped through close consultation with and substantial contributions from staff in the world Bank's four thematic networks--Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Financial and Private Sector Development--and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book's contents, please see Credits. For a listing of key partners, see Partners. Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott. Elaine Wilson created the graphics and typeset the book. Joseph Caponio and Amye Kenall provided proofreading and production assistance. Communications Development's London partner, Peter Grundy of Peter Grundy Art & Design, provided art direction and design. Staff from External Affairs oversaw printing and dissemination of the book. 2009 World Development Indicators vii TABLE OF CONTENTS FRONT 1jj 1kk 1ll Fiscal positions have generally improved but remain weak for some developing economies Finding fiscal space in low-income economies Recent World Bank Group initiatives 11 11 11 Preface v Acknowledgments vii 1.2a Location of indicators for Millennium Development Goals 1­4 21 Partners xii 1.3a Location of indicators for Millennium Development Goals 5­7 25 Users guide xx 1.4a Location of indicators for Millennium Development Goal 8 27 1. WORLD VIEW Introduction 1 2. PEOPLE Introduction 35 Tables Tables 1.1 Size of the economy 14 2.1 Population dynamics 40 1.2 Millennium Development Goals: eradicating poverty and 2.2 Labor force structure 44 saving lives 18 2.3 Employment by economic activity 48 1.3 Millennium Development Goals: protecting our common 2.4 Decent work and productive employment 52 environment 22 2.5 Unemployment 56 1.4 Millennium Development Goals: overcoming obstacles 26 2.6 Children at work 60 1.5 Women in development 28 2.7 Poverty rates at national poverty lines 64 1.6 Key indicators for other economies 32 2.8 Poverty rates at international poverty lines 67 Text figures, tables, and boxes 2.9 Distribution of income or consumption 72 1a Developing economies had their best decade of growth in 2000­07 2 2.10 Assessing vulnerability and security 76 1b Long-term trends reached new heights 2 2.11 Education inputs 80 1c Most developing economy exports go to high-income economies 2 2.12 Participation in education 84 1d Increased investment led to faster growth in low- and middle- 2.13 Education efficiency 88 income economies 2 2.14 Education completion and outcomes 92 1e Large current account surpluses and deficits were concentrated 2.15 Education gaps by income and gender 96 in a few economies during 2005­07 3 2.16 Health systems 98 1f Current account surpluses and deficits increased 3 2.17 Disease prevention coverage and quality 102 1g Trade surpluses led to large build-ups in reserves 3 2.18 Reproductive health 106 1h Trade deficits were financed by foreign investors 3 2.19 Nutrition 110 1i Private capital flows to developing economies took off in 2002 . . . 4 2.20 Health risk factors and future challenges 114 1j . . . And investors perceived less risk 4 2.21 Health gaps by income and gender 118 1k Prices of assets, especially in real estate, were rising rapidly in 2.22 Mortality 122 some countries . . . 4 Text figures, tables, and boxes 1l . . . And so were equity asset valuations 4 2a Different goals--different progress 35 1m Indebtedness ratios have improved for most economies 5 2b What is decent work? 36 1n Growing reserves comfortably covered short-term debt liabilities 5 2c Employment to population ratios have not changed much 1o Commodity price rises accelerated in recent years 5 over time . . . 36 1p Food and fuel importers were hurt by rising prices 5 2d . . . But variations are wide across regions 36 1q Output in the largest economies slowed or declined in the 2e High employment to population ratios in some countries 4th quarter of 2008 6 reflect high numbers of working poor 37 1r U.S. household debt rose rapidly after 2000 6 2f Fewer women than men are employed all over the world 37 1s U.S. house prices peaked in 2006 6 2g Many young people are in the workforce, at the expense of 1t As housing bubbles burst, investors lost confidence 6 higher education 37 1u Savings and investment in China . . . 7 2h For many poor countries, there is a tradeoff between 1v . . . And the United States 7 education and employment 37 1w The five largest current account surpluses and deficits 7 2i Although there are large regional variations in vulnerable 1x U.S. foreign assets and liabilities doubled 7 employment . . . 38 1y Assets underlying over the counter derivatives rose sevenfold . . . 8 2j . . . Women are more likely than men to be in vulnerable 1z . . . While the market value of derivatives rose ninefold 8 employment 38 1aa U.S. domestic financial sector profits averaged almost 2k Share of working poor in total employment is highest in South 30 percent of before-tax profits during 2000­06 8 Asia and Sub-Saharan Africa 38 1bb Derivatives can undermine capital controls, leading to linkages 2l Labor productivity has increased across the world 38 that make market dynamics difficult to predict 8 2m Scenarios for 2008 39 1cc The number of banking crises rose after the 1970s 9 2.6a Children work long hours 63 1dd The latest crisis is affecting a large portion of global income 9 2.8a While the number of people living on less than $1.25 a day has 1ee The cost of systemic financial crises can be very high 9 fallen, the number living on $1.25­$2.00 a day has increased 69 1ff Borrowing costs have climbed, reflecting perceived risk 10 2.8b Poverty rates have begun to fall 69 1gg Equity markets have suffered large losses 10 2.8c Regional poverty estimates 70 1hh Low-income economies depend the most on official aid, 2.9a The Gini coefficient and ratio of income or consumption of the workers' remittances, and foreign direct investment 10 richest quintile to the poorest quintiles are closely correlated 75 1ii Remittances are significant for many low-income economies 10 2.15a There is a large gap in educational attainment across gender and urban-rural lines 97 2.16a There is a wide gap in health expenditure per capita between high-income economies and developing economies 101 viii 2009 World Development Indicators 3. ENVIRONMENT Introduction 127 3o Reductions in energy-related carbon dioxide emissions by region in the 550 and 450 parts per million Policy Scenarios Tables relative to the Trend Scenario 131 3.1 Rural population and land use 134 3p Energy efficiency has been improving 132 3.2 Agricultural inputs 138 3q Electricity generated from renewables is projected to more 3.3 Agricultural output and productivity 142 than double by 2030 132 3.4 Deforestation and biodiversity 146 3r Top 10 users of wind to generate electricity 133 3.5 Freshwater 150 3s Cost and savings under the Policy Scenarios 133 3.6 Water pollution 154 3.1a What is rural? Urban? 137 3.7 Energy production and use 158 3.2a Nearly 40 percent of land globally is devoted to agriculture 141 3.8 Energy dependency and efficiency and carbon dioxide emissions 162 3.2b Developing regions lag in agricultural machinery, which 3.9 Trends in greenhouse gas emissions 166 reduces their agricultural productivity 141 3.10 Sources of electricity 170 3.3a Cereal yield in low-income economies was less than 40 percent 3.11 Urbanization 174 of the yield in high-income countries 145 3.12 Urban housing conditions 178 3.3b Sub-Saharan Africa had the lowest yield, while East Asia 3.13 Traffic and congestion 182 and Pacific is closing the gap with high-income economies 145 3.14 Air pollution 186 3.5a Agriculture is still the largest user of water, accounting for 3.15 Government commitment 188 some 70 percent of global withdrawals 153 3.16 Toward a broader measure of savings 192 3.5b The share of withdrawals for agriculture approaches Text figures, tables, and boxes 90 percent in some developing regions 153 3a Energy use has doubled since 1971 128 3.6a Emissions of organic water pollutants declined in most 3b High-income economies use almost half of all global energy 128 economies from 1990 to 2005, even in some of the top 3c The top six energy consumers use 55 percent of global energy 128 emitters 157 3d High-income economies use more than 11 times the energy 3.7a A person in a high-income economy uses an average of that low-income economies do 128 more than 11 times as much energy as a person in a 3e Nonrenewable fuels are projected to account for 80 percent low-income economy 161 of energy use in 2030--about the same as in 2006 129 3.8a High-income economies depend on imported energy . . . 165 3f Fossil fuels will remain the main sources of energy 3.8b . . . mostly from middle-income economies in the Middle East through 2030 129 and North Africa and Latin America and the Caribbean 165 3g Known global oil reserves and countries with highest 3.9a The 10 largest contributors to methane emissions account endowments in 2006 129 for about 62 percent of emissions 169 3h Production declines from existing oil fields have been rapid 129 3.9b The 10 largest contributors to nitrous oxide emissions 3i Economic activity, energy use, and greenhouse gas account for about 56 percent of emissions 169 emissions move together 130 3.10a Sources of electricity generation have shifted since 1999 . . . 173 3j Decarbonization of energy reversed at the beginning of the 3.10b . . . with developing economies relying more on coal 173 21st century 130 3.11a Developing economies had the largest increase in urban 3k The top six carbon dioxide emitters in 2005 130 population between 1990 and 2007 177 3l High-income economies are by far the greatest emitters of 3.11b Latin America and the Caribbean had the same share of carbon dioxide 130 urban population as high-income economies in 2007 177 3m Carbon dioxide emissions embedded in international trade 131 3.12a Selected housing indicators for smaller economies 181 3n Impact of Policy Scenarios: carbon dioxide concentration, 3.13a Particulate matter concentration has fallen in all income temperature increase, emissions, and energy demand 131 groups, and the higher the income, the lower the concentration 185 2009 World Development Indicators ix TABLE OF CONTENTS 4. ECONOMY 5. STATES AND MARKETS Introduction 197 Introduction 265 Tables Tables 4.1 Growth of output 204 5.1 Private sector in the economy 270 4.2 Structure of output 208 5.2 Business environment: enterprise surveys 274 4.3 Structure of manufacturing 212 5.3 Business environment: Doing Business indicators 278 4.4 Structure of merchandise exports 216 5.4 Stock markets 282 4.5 Structure of merchandise imports 220 5.5 Financial access, stability, and efficiency 286 4.6 Structure of service exports 224 5.6 Tax policies 290 4.7 Structure of service imports 228 5.7 Military expenditures and arms transfers 294 4.8 Structure of demand 232 5.8 Public policies and institutions 298 4.9 Growth of consumption and investment 236 5.9 Transport services 302 4.10 Central government finances 240 5.10 Power and communications 306 4.11 Central government expenses 244 5.11 The information age 310 4.12 Central government revenues 248 5.12 Science and technology 314 4.13 Monetary indicators 252 Text figures, tables, and boxes 4.14 Exchange rates and prices 256 5a Improving governance and contributing to growth 265 4.15 Balance of payments current account 260 5b Seventy percent of mobile phone subscribers are in Text figures, tables, and boxes developing economies, 2000­07 266 4a Economic growth slowed in 2007 197 5d Internet use in developing economies is growing, but still 4b Large middle-income economies with economic growth lags behind use in developed economies 266 above 10 percent 197 5c Competition can spur growth in mobile phone service 266 4c Asian countries invested more 198 5e Broadband access in developed and developing economies 267 4d East Asia and Pacific is the largest saver 198 5f International bandwidth has increased rapidly in Europe and 4e High-income economies still produce the largest share of Central Asia and Latin America and the Caribbean 267 manufactured goods . . . 198 5g Prices for mobile phone services have declined in many 4f . . . And account for the largest share of manufactures exports 198 countries 267 4g Twelve developing economies had a cash deficit greater than 5h Internet service prices have fallen in some Sub-Saharan 3 percent of GDP 199 African countries, 2005­07 267 4h Five developing economies had a public debt to GDP ratio 5i East Asia & Pacific leads in share of information and greater than 60 percent over 2005­07 199 communication technology goods exports 268 4i Modest inflationary pressure affected 74 countries 199 5j India leads developing economies in information and 4j Real interest rates declined in 66 countries 199 communications technology service export shares, 2007 268 4k­4p Growth in GDP and investment 2007­08, selected major 5k Developing economies have only about 4 percent of the developing economies 200 world's secure servers, 2008 268 4q­4v Growth in industrial production 2007­08, selected major 5l Partnership on Measuring ICT for Development 269 developing economies 200 4w­4bb Lending and inflation rates 2007­08, selected major developing economies 200 4cc­4hh Central government debt 2007­08, selected major developing economies 200 4.3a Manufacturing continues to show strong growth in East Asia through 2007 215 4.4a Developing economies' share of world merchandise exports continues to expand 219 4.5a Top 10 developing economy exporters of merchandise goods in 2007 223 4.6a Top 10 developing economy exporters of commercial services in 2007 227 4.7a The mix of commercial service imports by developing economies is changing 231 4.9a GDP per capita is still lagging in some regions 239 4.10a Fifteen developing economies had a government expenditure to GDP ratio of 30 percent or higher 243 4.11a Interest payments are a large part of government expenses for some developing economies 247 4.12a Rich economies rely more on direct taxes 251 4.15a Top 15 economies with the largest reserves in 2007 263 x 2009 World Development Indicators 6. GLOBAL LINKS Introduction 319 6r Net nonconcessional lending to middle-income economies from international financial institutions, declining since Tables 2002, recently increased 324 6.1 Integration with the global economy 328 6s Aid is equivalent to 5 percent of the GNI of low-income 6.2 Growth of merchandise trade 332 economies s 324 6.3 Direction and growth of merchandise trade 336 6t Aid for long-term development has remained about the 6.4 High-income economy trade with low- and middle-income same as in the 1970s 324 economies 339 6.5 Direction of trade of developing economies 342 6u Aid flows declined after the Nordic banking crisis in 1991 325 6.6 Primary commodity prices 345 6v Two U.S. financial crises in the late 20th century--aid down, then up 325 6.7 Regional trade blocs 348 6w Migration to high-income economies has increased 325 6.8 Tariff barriers 352 6x More remittance flows are now going to developing economies 325 6.9 External debt 356 6y­6dd Merchandise trade 2006­08, selected major developing 6.10 Ratios for external debt 360 economies 326 6.11 Global private financial flows 364 6ee­6jj Equity price indices 2007­09, selected major developing 6.12 Net official financial flows 368 economies 326 6.13 Financial flows from Development Assistance Committee 6kk­6pp Bond spreads 2007­09, selected major developing economies 326 members 372 6.14 Allocation of bilateral aid fromDevelopment Assistance 6qq­6vv Financing through international capital markets 2007­09, selected major developing economies 326 Committee members 374 6.15 Aid dependency 376 6.1a Estimating the global emigrant stock 331 6.16 Distribution of net aid by Development Assistance 6.3a In 2007 around 70 percent of exports from low- and middle- income economies and from high-income economies were Committee members 380 directed to high-income economies 338 6.17 Movement of people 384 6.4a High-income economies' tariffs on imports from low- and 6.18 Characteristics of immigrants in selected OECD countries 388 middle-income economies fell between 1997 and 2007 but 6.19 Travel and tourism 390 remain high for some products 341 Text figures, tables, and boxes 6.5a Trading partners vary by region 344 6a The importance of trade to developing economies has increased 320 6.6a Commodity prices increased between 2000 and the last 6b High-income economies and a few large middle-income quarter of 2008--the longest boom since 1960 347 economies account for a majority of world exports 320 6.7a The number of trade agreements has increased rapidly since 6c Most developing economy exports were directed to 1990, especially bilateral agreements 351 high-income economies in 2007 320 6.9a The levels and the composition of external debt vary by regions 359 6d Merchandise imports of Group of Seven industrial economies 6.10a The burden of external debt service declined for most regions have declined, reflecting slowing demand for imports 320 over 1995­2007 363 6e Primary commodity prices have been volatile over the past year 321 6.11a In 2007 middle-income economies received nearly 20 times more 6f For some economies food imports were equivalent to more private capital flows than low-income economies did 367 than 7 percent of household consumption, 2005­07 average 321 6.12a Net nonconcessional lending from international financial 6g Large middle-income economies received increasing amount institutions has declined in recent years as countries have of portfolio equity flows in recent years 321 paid off previous loans 371 6h Other developing economies borrowed increasing amounts 6.15a Official development assistance from non-DAC donors, 2003­07 379 from private creditors 321 6.16a Most donors increased their proportions of untied aid 6i Much global FDI is directed to high-income economies and between 2000 and 2007 383 a few large middle-income economies . . . 322 6.19a High-income economies remain the main destination for 6j . . . But as a share of GDP, FDI net inflows are a large source international travelers, but the share of tourists visiting of private financing for low-income economies 322 developing economies is rising 393 6k FDI net inflows to Indonesia and Malaysia declined immediately after the East Asian financial crisis hit 322 6l FDI net inflows to the Republic of Korea and Thailand 6m remained resilient for several years after the plunge in GDP Net portfolio equity flows to large middle-income economies increased considerably 322 323 BACK 6n Stock market capitalizations declined after the financial crisis 323 Primary data documentation 395 6o Spreads on emerging market sovereign and corporate bonds Statistical methods 406 have widened, increasing the cost of borrowing 323 Credits 408 6p Private lending to Europe and Central Asia increased Bibliography 410 ninefold between 2003 and 2007 323 Index of indicators 418 6q For middle-income economies nearly 80 percent of long-term debt was from private creditors while for low-income economies 90 percent was from official creditors 324 2009 World Development Indicators xi PARTNERS Defining, gathering, and disseminating international statistics is a collective effort of many people and organiza- tions. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and stan- dards fundamental to an international statistical system. Nongovernmental organizations and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank's efforts, and to those of many others, to improve the quality of life of the world's people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, Web addresses are included for each listed organization. The addresses shown were active on March 1, 2009. Information about the World Bank is also provided. International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and infor- mation analysis center of the U.S. Department of Energy. The CDIAC's scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere; the role of the ter- restrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases; emissions of carbon dioxide to the atmosphere; long-term climate trends; the effects of elevated carbon dioxide on vegetation; and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.esd.ornl.gov/. Deutsche Gesellschaft für Technische Zusammenarbeit The Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH is a German government-owned corpora- tion for international cooperation with worldwide operations. GTZ's aim is to positively shape political, economic, eco- logical, and social development in partner countries, thereby improving people's living conditions and prospects. For more information, see www.gtz.de/. Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org/. xii 2009 World Development Indicators International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is respon- sible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. ICAO's strategic objectives include enhancing global aviation safety and security and the efficiency of aviation operations, minimizing the adverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations, and strengthening laws governing international civil aviation. For more information, see www.icao.int/. International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. ILO helps advance the creation of decent jobs and the kinds of economic and working conditions that give working people and business people a stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org/. International Monetary Fund The International Monetary Fund (IMF) is an international organization of 185 member countries established to promote international monetary cooperation, a stable system of exchange rates, and the balanced expan- sion of international trade and to foster economic growth and high levels of employment. The IMF reviews national, regional, and global economic and financial developments, provides policy advice to member countries and serves as a forum where they can discuss the national, regional, and global consequences of their policies. The IMF also makes financing temporarily available to member countries to help them address balance of payments problems. Among the IMF's core missions are the collection and dissemination of high-quality macroeconomic and financial statistics as an essential prerequisite for formulating appropriate policies. The IMF provides technical assistance and training to member countries in areas of its core expertise, including the development of economic and financial data in accordance with international standards. For more information, see www.imf.org. International Telecommunication Union The International Telecommunication Union (ITU) is the leading UN agency for information and com- munication technologies. ITU's mission is to enable the growth and sustained development of telecom- munications and information networks and to facilitate universal access so that people everywhere can participate in, and benefit from, the emerging information society and global economy. A key priority lies in bridging the so-called Digital Divide by building information and communication infrastructure, promot- ing adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster preven- tion and mitigation. For more information, see www.itu.int/. 2009 World Development Indicators xiii PARTNERS National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. NSF's goals--discovery, learning, research infrastructure, and stewardship--provide an integrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive science and engineering workforce, expand the scientific literacy of all citizens, build the nation's research capabil- ity through investments in advanced instrumentation and facilities, and support excellence in science and engineering research and education through a capable and responsive organization. For more information, see www.nsf.gov/. Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 30 member countries shar- ing a commitment to democratic government and the market economy to support sustainable economic growth, boost employment, raise living standards, maintain financial stability, assist other coun- tries' economic development, and contribute to growth in world trade. With active relationships with some 100 other countries it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac/) is one of the principal bodies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum of major bilateral donors, who work together to increase the effectiveness of their common efforts to sup- port sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.oecd.org/. Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an under- standing of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI's main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/. Understanding Children's Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labor Organization, the United Nations Children's Fund (UNICEF), and the World Bank initiated the joint interagency research program "Understanding Children's Work and Its Impact" in December 2000. The Understanding Children's Work (UCW) project was located at UNICEF's Innocenti Research Centre in Flor- ence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. xiv 2009 World Development Indicators The UCW project addresses the crucial need for more and better data on child labor. UCW's online data- base contains data by country on child labor and the status of children. For more information, see www.ucw-project.org/. United Nations The United Nations currently has 192 member states. The purposes of the United Nations, as set forth in the Charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promot- ing respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org/. United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/. United Nations Children's Fund The United Nations Children's Fund (UNICEF) works with other UN bodies and with governments and nongovern- mental organizations to improve children's lives in more than 190 countries through various programs in educa- tion and health. UNICEF focuses primarily on five areas: child survival and development, basic Education and gender equality (including girls' education), child protection, HIV/AIDS, and policy advocacy and partnerships. For more information, see www.unicef.org/. United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations that promotes international cooperation among member states and associate members 2009 World Development Indicators xv PARTNERS in education, science, culture and communications. The UNESCO Institute for Statistics is the organization's statistical branch, established in July 1999 to meet the growing needs of UNESCO member states and the international community for a wider range of policy-relevant, timely, and reliable statistics on these topics. For more information, see www.uis.unesco.org/. United Nations Environment Programme The mandate of the United Nations Environment Programme is to provide leadership and encourage partner- ship in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org/. United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/. The UN Refugee Agency The UN Refugee Agency (UNHCR) is mandated to lead and coordinate international action to protect refugees and resolve refugee problems worldwide. Its primary purpose is to safeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees. For more information, see www.unhcr.org World Bank The World Bank is a vital source of financial and technical assistance for developing countries. The World Bank is made up of two unique development institutions owned by 185 member countries--the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA). These institutions play different but collaborative roles to advance the vision of an inclusive and sustainable globalization. The IBRD focuses on middle-income and creditworthy poor countries, while IDA focuses on the poorest countries. Together they provide low-interest loans, interest-free credits, and grants to developing countries for a wide array of purposes, including investments in education, health, public administration, infrastructure, financial and private sector development, agriculture, and environmental and natural resource management. The World Bank's work focuses on achieving the Millennium Development Goals by working with partners to alleviate poverty. For more information, see www.worldbank.org/data/. xvi 2009 World Development Indicators World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends. For more information, see www.who.int/. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations dedicated to developing a balanced and accessible international intellectual property (IP) system, which rewards creativ- ity, stimulates innovation, and contributes to economic development while safeguarding the public interest. WIPO carries out a wide variety of tasks related to the protection of IP rights. These include developing international IP laws and standards, delivering global IP protection services, encouraging the use of IP for economic development, promoting better understanding of IP, and providing a forum for debate. For more information, see www.wipo.int/. World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.unwto.org/. World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as pos- sible. It does this by administering trade agreements, acting as a forum for trade negotiations, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues--through technical assistance and training programs--and cooperating with other international organizations. At the heart of the system--known as the multilateral trading system--are the WTO's agreements, negotiated and signed by a large majority of the world's trading nations and ratified by their parliaments. For more information, see www.wto.org/. Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk/. 2009 World Development Indicators xvii PARTNERS International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/. International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization whose mission is to encourage and promote development and maintenance of better, safer, and more sustainable roads and road networks. Working together with its members and associates, the IRF promotes social and economic benefits that flow from well planned and environmentally sound road transport networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF works in all aspects of road policy and development worldwide with governments and financial institutions, members, and the community of road professionals. For more information, see www.irfnet.org/. Netcraft Netcraft provides Internet security services such as antifraud and antiphishing services, application testing, code reviews, and automated penetration testing. Netcraft also provides research data and analysis on many aspects of the Internet and is a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, script- ing languages, and content technologies on the Internet. For more information, see http://news.netcraft.com/. PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused services in the fields of assurance, tax, human resources, transactions, performance improvement, and crisis management services to help address client and stake- holder issues. For more information, see www.pwc.com/. Standard & Poor's Standard & Poor's is the world's foremost provider of independent credit ratings, indexes, risk evaluation, investment research, and data. S&P's Global Stock Markets Factbook draw on data from S&P's Emerging Markets Database (EMDB) and other sources covering data on more than 100 markets with comprehensive market profiles for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor's calculates indices to serve as benchmarks that are consistent across national boundaries. For more information, see www.standardandpoors.com/. xviii 2009 World Development Indicators World Conservation Monitoring Centre The World Conservation Monitoring Centre provides information on the conservation and sustainable use of the world's living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the information needed for wise management of the world's living resources. For more information, see www.unep-wcmc.org/. World Information Technology and Services Alliance The World Information Technology and Services Alliance (WITSA) is a consortium of more than 60 informa- tion technology (IT) industry associations from economies around the world. WITSA members represent over 90 percent of the world IT market. As the global voice of the IT industry, WITSA has an active role in international public policy issues affecting the creation of a robust global information infrastructure, includ- ing advocating policies that advance the industry's growth and development, facilitating international trade and investment in IT products and services, increasing competition through open markets and regulatory reform, strengthening national industry associations through the sharing of knowledge, protecting intel- lectual property, encouraging cross-industry and government cooperation to enhance information security, bridging the education and skills gap, and safeguarding the viability and continued growth of the Internet and electronic commerce. For more information, see www.witsa.org/. World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides--and helps other institutions provide-- objective information and practical proposals for policy and institutional change that will foster environmen- tally sound, socially equitable development. The institute's current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/. 2009 World Development Indicators xix USERS GUIDE Tables simple totals, where they do not), median values (m), not be complete because of special circumstances The tables are numbered by section and display the weighted averages (w), or simple (unweighted) aver- affecting the collection and reporting of data, such identifying icon of the section. Countries and econo- ages (u). Gap filling of amounts not allocated to coun- as problems stemming from conflicts. mies are listed alphabetically (except for Hong Kong, tries may result in discrepancies between subgroup For these reasons, although data are drawn from China, which appears after China). Data are shown aggregates and overall totals. For further discussion the sources thought to be most authoritative, they for 153 economies with populations of more than of aggregation methods, see Statistical methods. should be construed only as indicating trends and 1 million, as well as for Taiwan, China, in selected characterizing major differences among economies tables. Table 1.6 presents selected indicators for Aggregate measures for regions rather than as offering precise quantitative measures 56 other economies--small economies with popu- The aggregate measures for regions cover only low- and of those differences. Discrepancies in data presented lations between 30,000 and 1 million and smaller middle-income economies, including economies with in different editions of World Development Indicators economies if they are members of the International populations of less than 1 million listed in table 1.6. reflect updates by countries as well as revisions to his- Bank for Reconstruction and Development (IBRD) or, The country composition of regions is based on torical series and changes in methodology. Thus read- as it is commonly known, the World Bank. A complete the World Bank's analytical regions and may differ ers are advised not to compare data series between set of indicators for these economies is available from common geographic usage. For regional clas- editions of World Development Indicators or between on the World Development Indicators CD-ROM and in sifications, see the map on the inside back cover and different World Bank publications. Consistent time- WDI Online. The term country, used interchangeably the list on the back cover flap. For further discussion series data for 1960­2007 are available on the World with economy, does not imply political independence, of aggregation methods, see Statistical methods. Development Indicators CD-ROM and in WDI Online. but refers to any territory for which authorities report Except where otherwise noted, growth rates are separate social or economic statistics. When avail- Statistics in real terms. (See Statistical methods for information able, aggregate measures for income and regional Data are shown for economies as they were con- on the methods used to calculate growth rates.) Data groups appear at the end of each table. stituted in 2007, and historical data are revised to for some economic indicators for some economies Indicators are shown for the most recent year reflect current political arrangements. Exceptions are are presented in fiscal years rather than calendar or period for which data are available and, in most noted throughout the tables. years; see Primary data documentation. All dollar fig- tables, for an earlier year or period (usually 1990 or Additional information about the data is provided ures are current U.S. dollars unless otherwise stated. 1995 in this edition). Time-series data for all 209 in Primary data documentation. That section sum- The methods used for converting national currencies economies are available on the World Development marizes national and international efforts to improve are described in Statistical methods. Indicators CD-ROM and in WDI Online. basic data collection and gives country-level informa- Known deviations from standard definitions or tion on primary sources, census years, fiscal years, Country notes breaks in comparability over time or across countries statistical methods and concepts used, and other · Unless otherwise noted, data for China do not are either footnoted in the tables or noted in About background information. Statistical methods provides include data for Hong Kong, China; Macao, China; the data. When available data are deemed to be technical information on some of the general calcula- or Taiwan, China. too weak to provide reliable measures of levels and tions and formulas used throughout the book. · Data for Indonesia include Timor-Leste through trends or do not adequately adhere to international 1999 unless otherwise noted standards, the data are not shown. Data consistency, reliability, and comparability · Montenegro declared independence from Serbia Considerable effort has been made to standardize and Montenegro on June 3, 2006. When avail- Aggregate measures for income groups the data, but full comparability cannot be assured, able, data for each country are shown separately. The aggregate measures for income groups include and care must be taken in interpreting the indicators. However, some indicators for Serbia continue to 209 economies (the economies listed in the main Many factors affect data availability, comparability, include data for Montenegro through 2005; these tables plus those in table 1.6) whenever data are and reliability: statistical systems in many develop- data are footnoted in the tables. Moreover, data available. To maintain consistency in the aggregate ing economies are still weak; statistical methods, for most indicators from 1999 onward for Serbia measures over time and between tables, missing coverage, practices, and definitions differ widely; and exclude data for Kosovo, which in 1999 became data are imputed where possible. The aggregates cross-country and intertemporal comparisons involve a territory under international administration pur- are totals (designated by a t if the aggregates include complex technical and conceptual problems that can- suant to UN Security Council Resolution 1244 gap-filled estimates for missing data and by an s, for not be resolved unequivocally. Data coverage may (1999); any exceptions are noted. xx 2009 World Development Indicators Classification of economies Symbols For operational and analytical purposes the World .. Bank's main criterion for classifying economies is means that data are not available or that aggregates gross national income (GNI) per capita (calculated cannot be calculated because of missing data in the by the World Bank Atlas method). Every economy is years shown. classified as low income, middle income (subdivided into lower middle and upper middle), or high income. 0 or 0.0 For income classifications see the map on the inside means zero or small enough that the number would front cover and the list on the front cover fl ap. Low- round to zero at the displayed number of decimal and middle-income economies are sometimes places. referred to as developing economies. The term is used for convenience; it is not intended to imply / that all economies in the group are experiencing in dates, as in 2003/04, means that the period of time, similar development or that other economies have usually 12 months, straddles two calendar years and reached a preferred or final stage of development. refers to a crop year, a survey year, or a fiscal year. Note that classifi cation by income does not neces- sarily refl ect development status. Because GNI per $ capita changes over time, the country composition means current U.S. dollars unless otherwise noted. of income groups may change from one edition of World Development Indicators to the next. Once the > classifi cation is fi xed for an edition, based on GNI means more than. per capita in the most recent year for which data are available (2007 in this edition), all historical < data presented are based on the same country means less than. grouping. Low-income economies are those with a GNI Data presentation conventions per capita of $935 or less in 2007. Middle-income · A blank means not applicable or, for an aggre- economies are those with a GNI per capita of more gate, not analytically meaningful. than $935 but less than $11,456. Lower middle- · A billion is 1,000 million. income and upper middle-income economies are · A trillion is 1,000 billion. separated at a GNI per capita of $3,705. High- · Figures in italics refer to years or periods other income economies are those with a GNI per capita than those specified or to growth rates calculated of $11,456 or more. The 16 participating mem- for less than the full period specified. ber countries of the euro area are presented as · Data for years that are more than three years a subgroup under high-income economies. Note from the range shown are footnoted. that the Slovak Republic joined the euro area on January 1, 2009. The cutoff date for data is February 1, 2009. 2009 World Development Indicators xxi Introduction T he world seems to be entering an economic crisis unlike any seen since the founding of the Bretton Woods institutions. Indeed, simultaneous crises. The bursting of a real estate bubble. The liquidity and solvency problems for major banks. The liquidity trap as consumers and businesses prefer holding cash to spending on consumption or investment. The disrup- tions in international capital flows. And for some countries a currency crisis. Plummeting global output and trade in the last quarter of 2008 brought the global economy to a standstill after years of remarkable growth, throwing millions out of work. The United States, as the epicenter, has seen unemployment rising to more than 11 million, an unem- ployment rate of 7.2 percent. Most forecasts show world GDP growth slowing to near zero or negative values, after a 3.4 percent increase in 2008. What brought about the crisis? Why is it so severe? How quickly has it spread? In this intro- duction, and in the introductions to sections four (Economy) and six (Global links), the data describe the events that have brought us to this point. Could the crisis have been anticipated by looking more closely at the same data? Perhaps. Perhaps not. But there is still much we can learn about how these events unfolded. The crisis must be seen in the context of dramatic changes in the global economy. First, record export-led economic growth in emerging market economies shifted the balance of global economic power, evidenced by their growing share in world output, trade, and international reserves. High savings rates outstripped their capacity to invest in their own economies while policies to sterilize large inflows and protect against financial shocks led to a large build up in international reserves. So poorer economies were financing the current account deficits of high-income economies. Second, financial integration has accompanied expanding trade, spurred by remarkable developments in information technology and financial innovation. This extended the reach of global markets, lowering costs and increasing their efficiency, but also spreading systemic shocks farther and faster. The financial crisis had its origins in a U.S. real estate asset bubble fed by a boom in sub- prime mortgage lending. The availability of cheap credit fed asset bubbles in other developed economies and among major emerging market economies. The rapid and massive growth of long-term, illiquid, and risky assets financed by short-term liabilities contributed to the speed with which the crisis spread across the world economy and to its severity. Global growth will be negative in 2009, and growth in developing economies will fall sharply from the 6 percent or higher rates of 2008. This reflects both a sharp decline in export demand from high-income economies and a major reduction in access to commercial finance and an increase in its cost. Slower growth will inevitably affect the ability of low-income economies to reach the Millennium Development Goals. How far the global recession extends and how long it lasts will depend on the effectiveness of policies adopted by rich and poor economies alike in the months ahead. 2009 World Development Indicators 1 Growth accelerated in the 2000s Exports led growth The years preceding the 2008 global crisis saw the strongest Integration of the global economy was marked by a rapid in- economic growth in decades (figure 1a). Global economic crease in trade. Growth in low- and middle-income economies output grew 4 percent a year from 2000 to 2007, led by re- was led by exports, which grew at an average annual rate of cord growth in low- and middle-income economies. Develop- 12 percent over 2000­07. China and India were among the ing economies averaged 6.5 percent annual growth of GDP fastest- growing exporters. Export growth was led by manu- from 2000 to 2007, and growth in every region was the high- factures in China and by services in India. Some smaller est in three decades (figure 1b). Europe and Central Asia and economies with exports of oil, gas, metals, minerals, or manu- South Asia had their best decade in the most recent period factures were also among the fastest growing. Exports from (2000­07). East Asia and Pacific almost equaled its previous low- and middle-income economies in 2007 made up 29 per- peak, reached before the 1997 crisis. For others the peak cent of the world total, up from 21 percent in 2000. Although was in 1976-- before the oil price shocks of the late 1970s trade between low- and middle-income economies has been and the debt crisis of the 1980s. But growth rates in high- growing, 70 percent of low- and middle-income economies' ex- income economies have been on a downward path since the ports still went to high-income economies in 2007 (figure 1c). 1970s. Fast-growing, export-oriented economies attracted new China and India have emerged in recent years as drivers of investment (figure 1d). Some of it came from domestic sav- global economic growth, accounting for 2.9 percentage points ing. In low- and middle-income economies savings rose from of the 5 percent growth in global output in 2007. Low- and 25 percent of GDP in 2000 to 32 percent in 2007. But growth middle-income economies now contribute 43 percent of also attracted foreign direct investment. The contribution of global output, up from 36 percent in 2000. China and India investment to GDP growth in these economies averaged less than 1 percentage point before 2000 but rose to 2.4 percent- account for 5 percentage points of that increased share. age points over 2000­07. Developing economies had their Most developing economy exports best decade of growth in 2000­07 1a go to high-income economies 1c Average annual growth in purchasing power parity GDP (%) Low income Middle income High income Merchandise exports from developing economies, by destination ($ trillions) 5 8 4 To low-income economies 6 3 4 To middle-income economies 2 2 1 0 To high-income economies 1970­80 1980­90 1990­2000 2000­07 0 Note: Data for 1970­80 are based on GDP in constant 2000 U.S. dollars converted 1990 1995 2000 2005 2007 using market exchange rates. Source: World Development Indicators data files. Source: International Monetary Fund's Direction of Trade database. Long-term trends Increased investment led to faster growth reached new heights 1b in low- and middle-income economies 1d Contributions to GDP growth (%) Net exports Investment Consumption Annual growth in GDP per capita, 10-year moving average (%) 10 8 East Asia & Pacific 6 8 4 South Asia 6 2 High-income OECD 4 0 Latin America & Caribbean ­2 2 Middle East & North Africa Sub-Saharan Africa Europe & Central Asia ­4 0 ­6 1970 1975 1980 1985 1990 1995 2000 2007 ­2 1990 1995 2000 2005 2007 Source: World Development Indicators data files. Source: World Development Indicators data files. 2 2009 World Development Indicators Structural imbalances emerged Countries became more interdependent Countries with trade surpluses accumulated capital beyond As the global imbalance between savings and investment their capacity to absorb it. Many ran large current account grew, countries with large deficits borrowed from countries surpluses and accumulated record reserves. Countries with with surpluses, while fast-growing exporters depended on ex- trade deficits financed their current account by increased bor- panding markets in deficit countries. China and other surplus rowing abroad. From 2005 to 2007 the five largest surplus economies accumulated record reserves (figure 1g) and sent economies accounted for 71 percent of total current account capital overseas. The United States and other deficit coun- surpluses, and the five largest deficit economies, for 79 per- cent of total current account deficits (table 1e). tries consumed more and financed their deficits by issuing China's current account surplus rose from 2 percent of more debt and equity (figure 1h). GDP in 2000 to an average of 10 percent during 2005­07 Savings and investment trends for China, the largest sur- (figure 1f). Oil and gas exporters such as the Russian Fed- plus country, and the United States, the largest deficit coun- eration and Saudi Arabia also saw surpluses balloon. Unlike try, illustrate the growing imbalances. China's savings rate many high-income economies, Germany went from a deficit increased, exceeding investment by 11.5 percent of GDP in of 1.5 percent of GDP in 2000 to a surplus of 6 percent over 2007. In the United States private savings almost disappeared, 2000­07. But some countries with strong export growth had and investment exceeded savings by 4.6 percent of GDP. equally strong import growth, with India and Mexico maintain- ing small current account deficits. Countries with large reserves invested large portions of The largest deficits were in high-income economies, with their holdings in U.S. Treasury securities, widely regarded as the United States accounting for more than half the world's very low risk. At the end of 2008 China was the largest foreign current account deficits. The U.S. current account deficit holder of U.S. Treasury securities, at $696 billion, followed by increased from 4.3 percent of GDP in 2000 to an average Japan, at $578 billion. Total foreign holdings of U.S. Treasury of 6 percent in 2005­07. Spain's rose from 4 percent to 9 securities were $3.1 trillion, up from $2.4 trillion in 2007. percent of GDP. Large current account surpluses and deficits were Trade surpluses led to concentrated in a few economies during 2005­07 1e large build-ups in reserves 1g 2005­07 Share of all average deficit/surplus Percent Reserves ($ billions) 2000 2007 Economy ($ billions) economies (%) of GDP 1,600 All deficit economies ­1,303 United States ­749 57 ­6 Spain ­113 9 ­9 1,200 United Kingdom ­74 6 ­3 Australia ­47 4 ­6 800 Italy ­43 3 ­2 All surplus economies 1,428 400 China 372 26 10 Germany 256 18 6 Japan 210 15 4 0 Saudi Arabia 95 7 27 Taiwan, Russian Euro Japan China Russian Federation 76 5 8 China Federation zone Source: International Monetary Fund balance of payments data files and World Development Indicators data files. Source: International Monetary Fund balance of payments data files. Current account surpluses Trade deficits were and deficits increased 1f financed by foreign investors 1h Current account balance (% of GDP) Net flows of portfolio debt and equity securities ($ billions) 2000 2007 30 Saudi Arabia 1,000 750 20 Russian Federation China 500 10 250 0 United Kingdom 0 United States Spain ­250 ­10 2000 2001 2002 2003 2004 2005 2006 2007 Brazil Japan Euro United United area Kingdom States Source: World Development Indicators data files. Source: International Monetary Fund balance of payments data files. 2009 World Development Indicators 3 Foreign investments grew Asset prices rose rapidly as well Private capital flows to low- and middle-income economies more Stock market capitalization in low- and middle-income econo- than quadrupled from $200 billion in 2000 to over $900 billion mies increased nearly eightfold, rising from $2 trillion in 2000 in 2007, reaching 6.6 percent of the economies' collective GDP to $15 trillion in 2007, or from 35 percent of GDP to 114 (figure 1i). Foreign domestic investment accounts for most of percent. Stock markets in Brazil, China, India, and the Rus- those flows, as multinational corporations established footholds sian Federation accounted for $11 trillion. Foreign investors in new markets, shifted production sites to take advantage of increased their stakes in these markets, which outperformed lower costs, or sought access to supplies of natural resources. more developed markets. Foreign holdings of portfolio equity Portfolio investment in bond and equity markets also grew. securities increased from $37 billion in 2001 to $364 billion Foreign investors were drawn to emerging equity markets as the in 2007 in Brazil, from $11 billion in 2000 to $292 billion in prospects for these economies improved substantially and the 2007 in the Russian Federation and from $17 billion to $103 returns outpaced those in more developed markets. Net inflows billion in India, and from $43 billion in 2004 to $125 billion in from bonds and commercial bank lending grew from $12 billion 2007 in China. Other classes of assets such as housing also in 2000 to $269 billion in 2007 as globalization of the banking appreciated rapidly (figure 1k). industry continued and perceived risk in many low- and middle- Asset prices rose in part due to more optimistic expecta- income economies dropped to all-time lows (figure 1j). tions for future earnings. Price-earnings ratios, a measure of Brazil, China, India, and the Russian Federation attracted valuation for equities, rose rapidly in low- and middle-income the largest shares of capital flows among developing econo- economy stock markets (figure 1l). From 2000 to 2007 ratios mies. But foreign domestic investment flows to low-income rose from 11.5 to 16.6 in Brazil, from 21.6 to 50.5 in China, economies also increased in recent years--some of them com- ing from developing economies with large current account sur- from 16.8 to 31.6 in India, and from 3.8 to 18.4 in the Rus- pluses--drawn by rising commodity prices into the oil, mineral, sian Federation. And rising housing prices reflected expecta- and other commodity sectors and into infrastructure projects. tions for continuing appreciation. Private capital flows to developing Prices of assets, especially in real estate, economies took off in 2002 . . . 1i were rising rapidly in some countries . . . 1k Private financial flows (% of GDP) House price indices (2002 = 100) 8 500 Russian Federation: average housing prices 6 400 4 300 South Africa: ABSA House Price Index 2 Indonesia: residential property 200 price index (14-city composite) Taiwan, China: Sinyi Purchase Price Index 0 Singapore: property 1990 1995 2000 2005 2007 100 Malaysia: house prices price index 2002 2003 2004 2005 2006 2007 2008 Source: Global Development Finance data files and World Development Indicators data files. Source: Haver Analytics. . . . And investors . . . And so were perceived less risk 1j equity asset valuations 1l Spread on emerging market sovereign bonds against 10-year U.S. Treasury notes Price-earnings ratio (Standard & Poor's IFCG index) (basis points) 1,800 50 China 1,500 40 India 1,200 30 Russian Federation 900 20 600 Brazil 10 300 0 0 2000 2001 2002 2003 2004 2005 2006 2007 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Source: JPMorgan-Chase. Source: Standard & Poor's 2008. 4 2009 World Development Indicators External debt declined and Demand for primary changed composition commodities increased The Debt Initiative for Heavily Indebted Poor Countries and Rapid global economic growth drove demand for commodi- the Multilateral Debt Relief Initiative have helped some of ties, boosting prices, especially for oil, metals, and minerals the poorest and most indebted countries, especially in Sub- used as inputs to manufacturing. After increasing gradually Saharan Africa, significantly reduce their outstanding debt. from 2000 to 2006, prices rose more rapidly in 2007 and into External debt to GNI ratios for Sub-Saharan Africa went from 2008. Food prices also rose, due in part to the production of more than 80 percent in the mid-1990s to less than 30 per- ethanol from corn and other food crops (figure 1o). cent today (figure 1m). Elsewhere, especially in Europe and Rising commodity prices benefited exporters, especially in Latin America and the Caribbean and Sub-Saharan Africa. Central Asia, debt increased in recent years. For Croatia, Ka- Aside from the terms of trade gains, the higher commodity zakhstan, Latvia, Romania, and a few small island economies prices increased government revenues from taxes on com- external debt to GNI ratios reached all-time highs in 2007. modity exports and attracted foreign domestic investment into As debt ratios fell, many countries gained access to pri- commodity exports and supporting infrastructure projects. vate financing. Private nonguaranteed debt of low- and middle- But for food and fuel importers the spike in prices has income economies rose from 24 percent of total debt in 2000 been costly. Current account balances of most oil-importing to 37 percent in 2007. In Europe and Central Asia private non- low- and middle-income economies worsened (figure 1p). Price guaranteed debt made up 55 percent of total external debt in increases have also pushed up inflation and interest rates, with 2007. Short-term debt in low- and middle-income economies the impacts especially severe for poor people. In eight countries rose from 13 percent of total debt in 2000 to 24 percent in higher food prices between 2005 and 2007 increased poverty 2007. In 2007 in East Asia and Pacific short-term debt made rates by 3 percentage points on average (Ivanic and Martin up 39 percent of total debt and 55 percent in China. But grow- 2008). Globally, the number of people living on less than $1.25 ing international reserves helped offset the risk of short-term a day may have risen by more than 100 million before commod- financing in foreign currencies (figure 1n). ity prices began to fall in the latter half of 2008. Indebtedness ratios have Commodity price rises improved for most economies 1m accelerated in recent years 1o External debt to GNI ratio, by region (%) Commodity prices (index, 2000 = 100) 100 400 Metals and minerals 75 Sub-Saharan Africa 300 Latin America & Caribbean Energy 50 Middle East & North Africa 200 Food 25 South Asia 100 Europe & Central Asia East Asia & Pacific 0 0 1990 1995 2000 2005 2007 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Dec-07 Source: Global Development Finance data files and World Development Indicators data files. Source: World Development Indicators data files. Growing reserves comfortably Food and fuel importers covered short-term debt liabilities 1n were hurt by rising prices 1p Short-term debt, by region (% of total reserves) 2000 2007 Current account balance of low- and middle-income economies, excluding 100 China and oil exporters (% of GDP) 0 75 ­1 50 25 ­2 0 East Asia Europe & Latin Middle East & South Sub-Saharan ­3 & Pacific Central Asia America & North Africa Asia Africa 2000 2001 2002 2003 2004 2005 2006 2007 Caribbean Source: World Development Indicators data files. Source: World Development Indicators data files. 2009 World Development Indicators 5 A perfect storm? The current global financial and economic crisis is unlike any- the subprime mortgage crisis became a fully fledged financial thing the world has seen since the Great Depression nearly crisis (Lin 2009). eight decades ago. It embraces simultaneous crises in the The impacts were felt throughout the increasingly inte- housing, equity, and financial markets, triggering what could grated global financial markets, attacking stock markets become a global recession. Output and trade declined sharp- globally and reducing credit availability. Global stock markets ly in the last quarter of 2008 (figure 1q). Projections for 2009 lost an estimated $30 trillion in market capitalization in 2008 suggest global growth close to zero percent, with strong down- over their inflated 2007 levels. Rising unemployment and the side risks. Unemployment is rising sharply in both developed wealth effects of falling asset prices contributed to a sharp and emerging market economies. The International Labour decline in consumer spending. Developing countries sud- Organization estimates job losses of up to 50 million in 2009. denly faced a sharp decline in demand for their exports and a The United States lost as many as 3.6 million jobs in 2008. drop in commodity prices. As recessionary trends developed, The crisis had its superficial roots in the rise in U.S. remittances from migrant workers declined, and migrants household debt (figure 1r), financed largely by home mort- began returning home. gages, many of which did not meet prime underwriting guide- Three major factors account for the scale of the crisis. lines. When home prices began to fall from their peak in Underlying the bubbles in global real estate and stock mar- 2006 (figure 1s), mortgage default rates rose sharply and kets were growing macroeconomic imbalances that fed liquid- triggered a collapse in mortgage-backed securities. Sub- ity into the system, lowering real interest rates and fueling the prime lending came to an abrupt halt, further driving down asset price bubbles. Financial innovations pioneered by major the prices of U.S. homes. Investors, their confidence under- global investment banks turned out to be transmission mech- mined, withdrew funds from other illiquid markets (figure 1t), anisms for instability (Lin 2009). And the failure of national and investment banks had to liquidate assets or withdraw financial regulators to effectively regulate global financial mar- financing from customers, forcing further deleveraging. Thus, kets encouraged investors to take exorbitant risk. Output in the largest economies slowed U.S. house prices or declined in the 4th quarter of 2008 1q peaked in 2006 1s 2008 Q1 2008 Q2 GDP (% change from previous year) 2008 Q3 2008 Q4 U.S. house price index (1980 = 100) 15 400 10 300 5 0 200 ­5 100 ­10 0 ­15 United States Japan Euro area China 2000 Q1 2002 Q1 2004 Q1 2006 Q1 2008 Q4 Source: U.S. Department of Commerce, Japan Cabinet Office, Eurostat, China National Bureau of Statistics, Haver Analytics, and World Bank staff calculations. Source: U.S. Office of Federal Housing Enterprise Oversight. U.S. household debt As housing bubbles burst, rose rapidly after 2000 1r investors lost confidence 1t Household debt as share of disposable personal income (%) Stock market capitalization ($ trillions) 160 80 Total household debt 120 60 Home mortgages outstanding 80 40 40 20 Consumer credit 0 0 1998 2000 2002 2004 2006 2007 Jan-07 Jun-07 Jan-08 Jun-08 Jan-09 Source: Board of Governors of the Federal Reserve System data files. Source: World Federation of Exchanges data files. 6 2009 World Development Indicators Macroeconomic imbalances Global savings and investment rates have been fairly stable direct investment and portfolio flows (figure 1x). U.S. liabilities in recent years at 20­22 percent. But these global rates rose from 77 percent of GDP to 145 percent over the same masked a significant shift in the source of savings. In devel- period, increasing the negative net liabilities to 17 percent of oping economies savings rates rose 7 percentage points of 2007 GDP. Foreign official assets held in the United States aggregate GDP between 2000 and 2007, exceeding invest- more than tripled, to $3.3 trillion, or 24 percent of GDP, the ment rates, while in high-income, Organisation for Economic bulk of it in U.S. government securities, the counterpart to the Co-operation and Development countries, savings rates fell buildup in reserves in developing and high-income Asia. This by about 2 percentage points of aggregate GDP. kept U.S. interest rates low and stimulated a global "search The growing pool of savings in part of the world reflected for yield." It also led investors to underprice risk and shift the rising new incomes of oil exporters, boosted by record to risky assets, stimulating a boom in real estate and stock prices, deliberate policies to build up foreign exchange markets globally. reserves by Asian countries wishing to avoid repeating the Some see the savings surpluses in developing economies experience of the late 1990s, and the excess household and as policy driven, ascribing a passive role to policymakers in corporate savings in China (figure 1u). Surpluses in Germany, industrial economies who benefited from these surpluses. Japan, and some Asian countries were matched by substan- Others see U.S. structural deficits as reflecting profligate tial savings deficits, mainly in the United States (figures 1v spending. Indeed, personal savings in the United States were and 1w). U.S. savings rates fell nearly 4 percentage points a mere 1.7 percent of GDP in 2000, falling further to 0.4 between 2000 and 2007, producing a savings-investment percent by 2007 reflecting consumption growth in "substan- imbalance of close to 5 percentage points of GDP. tial excess of income growth" (Summers 2006). But until the The rest of the world's savings surpluses left the United crisis rudely interrupted the party, both surplus and deficit States awash in liquidity. U.S.-owned assets abroad doubled countries benefited: the first from high export-led growth; the between 2000 and 2007 to 128 percent of GDP, reflecting the second from low interest rates and cheap consumer goods, importance of the United States as a source of both foreign which held inflation down despite large fiscal deficits. Savings and investment The five largest current in China . . . 1u account surpluses and deficits 1w Current account balance, 2005­07 average ($ billions) Gross savings and investment (% of GDP) 400 60 Gross savings (% of GDP) 200 50 0 ­200 40 ­400 ­600 Gross capital formation (% of GDP) 30 ­800 1980 1985 1990 1995 2000 2007 China Germany Japan Saudi Russian Italy Australia United Spain United Arabia Federation Kingdom States Source: World Development Indicators data files. Source: International Monetary Fund balance of payments data files. . . . And the U.S. foreign assets United States 1v and liabilities doubled 1x Gross savings and investment (% of GDP) U.S. international investment position (% of GDP) 30 160 Foreign-owned assets in the United States (% of GDP) Gross capital formation (% of GDP) 120 20 80 U.S.-owned assets abroad (% of GDP) Gross savings (% of GDP) 10 40 0 0 1980 1985 1990 1995 2000 2006 2000 2001 2002 2003 2004 2005 2006 2007 Source: World Development Indicators data files. Source: Department of Commerce, Bureau of Economic Analysis data files. 2009 World Development Indicators 7 The role of financial innovation What distinguishes this crisis from previous crises is the Derivatives were pioneered globally by investment banks, speed and depth of the transmission channels, as a U.S.- stimulated by high fees. Financial sector profits in the U.S. based crisis turned global in a matter of months. This reflects averaged 29 percent of before-tax profi ts between 2000 the transformation of the financial system during this boom and 2006 (figure 1aa). U.S investment banks quickly grew period by the dramatic growth in the share of assets held to rival commercial banks but were not subject to the same outside the traditional banking system. The mortgage market, regulation. "The scale of long-term risky and illiquid assets for example, was transformed by an "originate and distribute" financed by very short-term liabilities made many of the model. Mortgage loans, made by loan originators, were re- vehicles and institutions in this parallel financial system vul- sold to financial institutions, which "sliced and diced" pools nerable to a classic type of run, but without the protections, of mortgages and aggregated them into collateralized debt such as deposit insurance, that the banking system has in obligations resold in turn to investors all over the world. place to reduce such risks" (Geithner 2008). Following the Derivatives, or financial instruments whose value is derived collapse of the real estate market and the loss of confidence from the value of an underlying asset (commodities, equities, in mortgage- backed securities, investors began pulling out stocks, mortgages, real estate, loans, bonds) or an index of these markets, creating liquidity and solvency crises for (of interest rates, stock prices, or consumer prices), enable investment banks. those who trade in them to mitigate risk through hedging or to Underlying these developments lay the failure to prop- speculate. Derivatives can be bought and sold through over the erly regulate financial institutions, weaknesses in internal counter trades between two parties, or they can be exchange risk management systems, and the failure of credit rating traded. Over the counter derivatives had a notional value of agencies to correctly rate risk. At the end of 2007, there some $684 trillion in June 2008 (figure 1y), representing the were reportedly 12 triple-A rated companies in the world, value of the underlying assets against which the derivatives but as many as 64,000 structured finance instruments were were issued. But the risk is better measured by the cost of rated triple-A (Blankfein 2009). Given the size of the market replacing all such contracts at the prevailing market price: their in these new instruments, it is questionable whether any gross global market value rose from $2.5 trillion in June 2000 single national authority can regulate cross-border transac- to a still astronomical $20.4 trillion in June 2008 (figure 1z). tions (figure 1bb). Assets underlying over the counter U.S. domestic financial sector profits averaged almost derivatives rose sevenfold . . . 1y 30 percent of before-tax profits during 2000­06 1aa Notional amounts oustanding ($ trillions) Share of before-tax profits (%) 800 40 Unallocateda 600 Credit default swaps 30 Commodity contracts Equity linked contracts 400 20 200 10 Interest rate contracts Foreign exchange contracts 0 Jun-00 Jun-01 Jun-02 Jun-03 Jun-04 Jun-05 Jun-06 Jun-07 Jun-08 0 1995 1997 1999 2001 2003 2005 2007 a. Includes over the counter derivatives of nonreporting institutions, based on the latest Triennial Central Bank Survey of Foreign Exchange and Derivatives Market Activity in 2007. Source: Bank for International Settlements data files. Source: Department of Commerce, Bureau of Economic Analysis data files. . . . While the market value Derivatives can undermine capital controls, leading to of derivatives rose ninefold 1z linkages that make market dynamics difficult to predict 1bb Gross market values ($ trillions) Market average daily turnover in over Total Foreign exchange 25 the counter derivatives, 2007 ($ billions) Interest rate 500 20 Unallocateda 400 Credit default swaps 15 Commodity contracts 300 Equity linked contracts 10 200 5 100 Interest rate contracts Foreign exchange contracts 0 0 Jun-00 Jun-01 Jun-02 Jun-03 Jun-04 Jun-05 Jun-06 Jun-07 Jun-08 Emerging China India Korea, Latin Brazil Mexico Central Russian South Turkey Asia Rep. America Europe Feder- Africa a. Includes over the counter derivatives of nonreporting institutions, based on the latest ation Triennial Central Bank Survey of Foreign Exchange and Derivatives Market Activity in 2007. Source: Bank for International Settlements data files. Source: Bank for International Settlements 2007. 8 2009 World Development Indicators Why financial crises occur so often Banking systems are inherently prone to crises. Banks borrow savings and loan crisis of 1984, Japan's asset bust in 1992, short (take in deposits) and lend long. They rely on deposi- and the Asian financial crisis of 1997­98 (figure 1dd). tors not to withdraw their deposits all at the same time. But Some key characteristics of banking crises include: depositor confidence in banks can be shaken by the rising · Periods of high international capital mobility, which threat of nonperforming loans or by political and economic have repeatedly produced international banking cri- developments. Even sound banks can be hurt by rumors and ses, possibly because they were accompanied by a general loss of confidence. When that happens, depositors inadequate regulation and supervision (Caprio and may demand their deposits, causing a run on banks or a li- Klingebiel 1996). quidity crisis. If banks sell their assets to maintain their ability · Preceding period of sustained surges in capital inflows. to repay depositors, that may reduce the price of their assets · Preceding boom in real housing prices, followed by a and thus their equity base, a solvency crisis. marked decline in the year of the crisis and beyond. A recent study notes that banking crises "have long been · Preceding expansion in the number of fi nancial an equal opportunity menace," (Reinhart and Rogoff 2008, institutions. p. 2) affecting developed and developing economies alike. It The cost of bailing out banks following a systemic crisis examines crises beginning with Denmark's financial panic dur- (the exhaustion of much or all of banking capital) is often high ing the Napoleonic war to the current global financial crisis in (figure 1ee). A study of 117 systemic banking crises in 93 66 economies. The Great Depression of the 1930s was the economies between the late 1970s and 2002 shows that the crisis that affected the greatest number of countries in the 109 cost to countries of major crises could amount to as much years ending in 2008. The period immediately after World War as 55% of GDP (as with Indonesia in its 1997­2002 crisis; II, between the late 1940s and the early 1970s, when financial Caprio and Klingebiel 2003). This does not include the cost markets were repressed and capital controls were extensive, to depositors and borrowers of wider interest rate spreads was marked by relative calm. Banking crises recurred again from bad loans on balance sheets. The data need to be used after the 1970s following financial and international capital with caution, however, because in some cases costs include account liberalization (figure 1cc). Major crises in this period corporate restructuring while in others they relate to restruc- included the Latin American debt crisis of the 1980s, the U.S. turing and capitalization of banks. The number of banking crises The cost of systemic rose after the 1970s 1cc financial crises can be very high 1ee Cost of crisis (% of GDP) Countries with banking crisis 60 Tanzania 1980­87 Hungary 1991­95 Finland 1991­94 40 Taiwan, China 1997­98 Czech Republic 1991­1994 Paraguay 1995­99 20 Bulgaria 1995­1997 Mauritania 1984­93 Malaysia 1997­2002 0 Spain 1977­85 1970 1975 1980 1985 1990 1995 2000 2007 Senegal 1988­91 Benin 1988­90 Source: Reinhart and Rogoff 2008. Venezuela 1994­95 Mexico 1994­97 The latest crisis is affecting a Ecuador 1998­2002 large portion of global income 1dd Uruguay 1981­84 Japan 1991­2002 Proportion of global income of countries with banking crises (%) Côte d'Ivoire 1988­91 60 U.S.-led global Korea, Rep. 1997­2002 U.S. savings Japan financial crisis Israel 1977­83 and loan crisis banking crisis Asian Turkey 2000­2002 crisis 40 Macedonia 1993­94 Thailand 1997­2002 Chile 1981­86 20 Jamaica 1995­2000 China 1990s Indonesia 1997­2002 0 Argentina 1980­82 1970 1975 1980 1985 1990 1995 2000 2007 0 10 20 30 40 50 60 Source: Reinhart and Rogoff 2008 and World Development Indicators data files. Source: Caprio and Klingebiel 2003. 2009 World Development Indicators 9 The crisis spreads quickly . . . . . . And developing economies feel the pain Past crises show that equity prices typically fall 55 percent Low-income economies are the most vulnerable to potential over 3.5 years (Reinhart and Rogoff 2008). Housing prices losses of official aid, workers' remittances, and foreign direct in- decline an average 35 percent over 6 years. Unemployment vestment, which often make up a large share of their GDP (table rises 7 percentage points over 4 years. Output falls by 9 per- 1hh). But the slowdown in trade could also hurt low-income com- cent over 2 years. And the real value of government debt rises modity exporters such as The Gambia, Guinea-Bissau, Nigeria, an average of 86 percent. This pattern is beginning to play out Mauritania, Mongolia, Papua New Guinea, and Zimbabwe, which in the major high-income economies. In the fourth quarter of are expected to suffer large terms of trade losses as prices 2008 U.S. gross domestic output contracted by an annual- fall. Lower commodity prices will reduce both export revenues ized rate of 6.2 percent, Euro zone countries by 5.9 percent, and fiscal revenues--and discourage foreign direct investment. and Japan by 12.1 percent. But food and fuel importers, which have endured soaring prices Many low- and middle-income economies have begun to since January 2007, will get some relief--oil importers such as feel the impact as high-income economy demand for their Kyrgyz Republic and Tajikistan and food importers such as Be- exports declines. The troubles of the financial sector have nin, Eritrea, Ghana, Guinea, Haiti, Madagascar, Niger, Senegal, increased risk aversion and reduced liquidity, impairing or and Togo may benefit from improved terms of trade. reversing capital flows to low- and middle-income economy Remittances have proved surprisingly resilient, rising again borrowers and equity markets (figures 1ff and 1gg). The sub- in 2008. But they are expected to fall as unemployment rises sidiaries of troubled banks are likely to curtail lending, push- in high-income economies and some migrants return home. ing corporations with debt falling due into risk of insolvency. For many low-income economies, remittances are a big part of Foreign direct investment flows may also decline as corpora- total capital flows--10 percent of GDP in more than a quarter tions adjust to an increasingly uncertain environment and as of them (figure 1ii). And if the pattern of past financial crises plunging commodity prices make some ventures less appeal- is a guide, there is also a risk that official aid will decline. ing. Higher unemployment will also reduce workers' remit- Low-income economies rely heavily on official aid flows, with tance flows to low- and middle-income economies. median official aid at 15 percent of GDP in 2005­07. Borrowing costs have climbed, Low-income economies depend the most on official aid, reflecting perceived risk 1ff workers' remittances, and foreign direct investment 1hh Spread on emerging market sovereign bonds against 10-year U.S. Treasury notes External financing, 2007 (% of GDP) (basis points) Low- Middle- 1,000 income income Source economies economies 750 Workers' remittances and compensation of employees, receipts 5.7 1.8 500 Official aid 5.0 0.3 Foreign direct investment, net inflows 4.2 3.7 250 Portfolio equity investment 1.6 0.9 Bonds 0.2 0.6 0 Commercial banks and other lending, net flows ­0.1 1.6 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Net exports of goods and services ­6.3 2.6 Source: JPMorgan-Chase. Source: World Development Indicators data files. Equity markets have Remittances are significant suffered large losses 1gg for many low-income economies 1ii Number of low-income countries MSCI equity price indices (January 2008 = 100) 15 120 90 10 Group of Seven 60 5 Emerging market economies 30 0 0 0­5 6­10 11­15 16­20 21­25 26­30 31­35 36­40 41­45 46­50 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Private current transfers (% of GDP) Source: International Monetary Fund balance of payments data files and World Source: Morgan-Stanley. Development Indicators data files. 10 2009 World Development Indicators Coping with the crisis Protecting the vulnerable In November 2008 China introduced a $585 billion eco- Poor people in developing economies are highly exposed to nomic stimulus package to counter the global crisis. Other the global crisis. World Bank estimates for 2009 suggest middle-income economies also have stimulus plans. Fiscal re- that lower growth rates will trap 46 million more people be- sponses to the crisis must address short-term risks to macro- low the $1.25 a day poverty line than expected before the economic stability and long-term fiscal sustainability--while crisis. An extra 53 million people will be living on less than protecting the vulnerable segments of society and the longer $2 a day, and child mortality rates could soar. It is esti- term investments that sustain economic growth and human mated that 200,000­400,000 more children a year, a total development. About 40 percent of low- and middle-income of 1.4­2.8 million from 2009 to 2015, may die if the crisis economies have good fiscal and current account positions, persists. including many larger economies, and may be able to expand Poor consumers are the first to be hurt by lower demand fiscal policy without jeopardizing solvency (table 1jj). for labor and falling remittances. In addition, shrinking fis- In addition to strong fiscal and external positions, a suc- cal revenues and potential decreases in offi cial aid flows cessful fiscal stimulus requires administrative capability to threaten to reduce access to social safety nets and to such design and implement new programs, or expand existing ones social services as health care and education. Households (box 1kk). Getting the timing and size right for a discretionary may have to sell productive assets, pull children out of fiscal stimulus is not easy. Packages often cannot be delivered school, and reduce calorie intake, which can lead to acute quickly enough, and expenditures may go to wasteful projects, malnutrition. The long-term consequences can be severe especially when subject to political pressure. Where administra- and in some cases irreversible, especially for women and tive capacity is weak, easier to implement options are boosting children. existing safety net programs, supplementing or replacing falter- Almost 40 percent of low- and middle-income economies ing foreign financing of infrastructure projects already under way are highly exposed to the poverty effects of the crisis. Yet with domestic financing, creating jobs through public works proj- three-quarters of them cannot raise funds domestically or ects, increasing fiscal transfers to subnational governments, internationally to finance programs to curb the effects of the and facilitating central bank support of trade financing. downturn. Fiscal positions have generally improved but Recent World Bank remain weak for some developing economies 1jj Group initiatives 1ll Fiscal position (% of GDP; median) Establish a vulnerability fund. The World Bank has proposed a vulnera- Maximum Fiscal bility fund financed by high-income economies to assist countries that Public debt debt balance Country group 2007 2002­07 2007a cannot afford to protect the vulnerable. The fund's priorities would Low-income economiesb 40.5 87.9 ­2.4 be to invest in safety net programs and infrastructure and to finance Large economies 36.7 87.9 ­1.9 small and medium-size enterprises and microfinance institutions. East Asia and Pacific 40.6 70.1 ­2.5 Substantially increase lending by the International Bank for Recon- Europe and Central Asia 31.7 77.5 ­2.6 Latin America and Caribbean 37.6 55.0 ­0.4 struction and Development (IBRD). IBRD could make new commit- Middle East and North Africa 41.2 57.8 ­4.8 ments of up to $100 billion over the next three years. South Asia 57.1 66.4 ­3.4 Fast track funds from the International Development Association (IDA). Sub-Saharan Africa 28.0 93.9 ­1.7 A facility is now in place to speed $2 billion to help the poorest Small economies 61.0 87.1 ­3.6 countries deal with the effects of the crisis. Middle-income economies 34.1 51.1 ­0.6 a. After official grants. Respond to the food crisis. Nearly $900 million is approved or in the b. IDA-eligible economies. pipeline to help developing countries cope with the impact of high Source: IMF 2008b; World Development Indicators data files. food prices through a $1.2 billion food facility. Ensure trade flows. The International Finance Corporation (IFC), a Finding fiscal space in member of the World Bank Group that focuses on the private sector, low-income economies 1kk plans to double its existing Global Trade Finance Program to $3 bil- lion over three years and to mobilize funds from other sources. Low-income economies can allow fiscal deficits to temporarily increase if they can access financing, but this generally has not been Bolster distressed banking systems. IFC is putting in place a global the case in past downturns. Median public debt among low-income equity fund to recapitalize distressed banks. IFC expects to invest $1 economies was 41 percent of GDP in 2007. A quarter of developing billion over three years, and Japan plans to invest $2 billion. economies had public debt of less than 21 percent. Among larger Sub-Saharan African economies median debt was 28 percent. Keep infrastructure projects on track. IFC expects to invest at least The ability to borrow depends on the size of the fiscal deficit, the $300 million over three years and mobilize $1.5 billion to provide level of government debt, the country's growth prospects, the gov- rollover financing and recapitalize viable infrastructure projects in ernment's reputation for fiscal management, the structure of debt distress. (maturity, currency), and recent debt history. Financing a larger fiscal deficit is generally easier if the country's Support microfinance. IFC and Germany have launched a $500 mil- starting external balance and reserve position are strong. A fiscal lion facility to support microfinance institutions facing difficulties as stimulus package tends to increase the external deficit by bolster- a result of the crisis. ing domestic demand. For commodity-exporting countries current account and fiscal deficits tend to rise when commodity prices fall, Shift advisory support to help companies weather the crisis. IFC is refo- as at present. Thus a large imbalance or a low level of reserves will cusing advisory services to help clients cope with the crisis. It esti- tend to limit the size of the fiscal stimulus that is possible. mates a financing need of at least $40 million over three years. 2009 World Development Indicators 11 Millennium Development Goals Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of 1.1 Proportion of population below $1 purchasing power people whose income is less than $1 a day parity (PPP) a day1 1.2 Poverty gap ratio [incidence × depth of poverty] 1.3 Share of poorest quintile in national consumption Target 1.B Achieve full and productive employment and decent 1.4 Growth rate of GDP per person employed work for all, including women and young people 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 (PPP) a day 1.7 Proportion of own-account and contributing family workers in total employment Target 1.C Halve, between 1990 and 2015, the proportion of 1.8 Prevalence of underweight children under five years of age people who suffer from hunger 1.9 Proportion of population below minimum level of dietary energy consumption Goal 2 Achieve universal primary education Target 2.A Ensure that by 2015 children everywhere, boys and 2.1 Net enrollment ratio in primary education girls alike, will be able to complete a full course of 2.2 Proportion of pupils starting grade 1 who reach last primary schooling grade of primary education 2.3 Literacy rate of 15- to 24-year-olds, women and men Goal 3 Promote gender equality and empower women Target 3.A Eliminate gender disparity in primary and secondary 3.1 Ratios of girls to boys in primary, secondary, and tertiary education, preferably by 2005, and in all levels of education education no later than 2015 3.2 Share of women in wage employment in the nonagricultural sector 3.3 Proportion of seats held by women in national parliament Goal 4 Reduce child mortality Target 4.A Reduce by two-thirds, between 1990 and 2015, the 4.1 Under-five mortality rate under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Target 5.A Reduce by three-quarters, between 1990 and 2015, 5.1 Maternal mortality ratio the maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel Target 5.B Achieve by 2015 universal access to reproductive 5.3 Contraceptive prevalence rate health 5.4 Adolescent birth rate 5.5 Antenatal care coverage (at least one visit and at least four visits) 5.6 Unmet need for family planning Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 6.A Have halted by 2015 and begun to reverse the 6.1 HIV prevalence among population ages 15­24 years spread of HIV/AIDS 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15­24 years with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10­14 years Target 6.B Achieve by 2010 universal access to treatment for 6.5 Proportion of population with advanced HIV infection with HIV/AIDS for all those who need it access to antiretroviral drugs Target 6.C Have halted by 2015 and begun to reverse the 6.6 Incidence and death rates associated with malaria incidence of malaria and other major diseases 6.7 Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www. un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on 14 November 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries "to create an environment--at the national and global levels alike--which is conducive to development and the elimination of poverty." All indicators should be disaggregated by sex and urban-rural location as far as possible. 12 2009 World Development Indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development 7.1 Proportion of land area covered by forest into country policies and programs and reverse the 7.2 Carbon dioxide emissions, total, per capita and loss of environmental resources per $1 GDP (PPP) 7.3 Consumption of ozone-depleting substances Target 7.B Reduce biodiversity loss, achieving, by 2010, a 7.4 Proportion of fish stocks within safe biological limits significant reduction in the rate of loss 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction Target 7.C Halve by 2015 the proportion of people without 7.8 Proportion of population using an improved drinking water sustainable access to safe drinking water and basic source sanitation 7.9 Proportion of population using an improved sanitation facility Target 7.D Achieve by 2020 a significant improvement in the 7.10 Proportion of urban population living in slums2 lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 8.A Develop further an open, rule-based, predictable, Some of the indicators listed below are monitored separately nondiscriminatory trading and financial system for the least developed countries (LDCs), Africa, landlocked developing countries, and small island developing states. (Includes a commitment to good governance, development, and poverty reduction--both Official development assistance (ODA) nationally and internationally.) 8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donors' gross national income 8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic Target 8.B Address the special needs of the least developed education, primary health care, nutrition, safe water, and countries sanitation) 8.3 Proportion of bilateral official development assistance of (Includes tariff and quota-free access for the least OECD/DAC donors that is untied developed countries' exports; enhanced program of 8.4 ODA received in landlocked developing countries as a debt relief for heavily indebted poor countries (HIPC) proportion of their gross national incomes and cancellation of official bilateral debt; and more 8.5 ODA received in small island developing states as a generous ODA for countries committed to poverty proportion of their gross national incomes reduction.) Market access Target 8.C Address the special needs of landlocked 8.6 Proportion of total developed country imports (by value developing countries and small island developing and excluding arms) from developing countries and least states (through the Programme of Action for developed countries, admitted free of duty the Sustainable Development of Small Island 8.7 Average tariffs imposed by developed countries on Developing States and the outcome of the 22nd agricultural products and textiles and clothing from special session of the General Assembly) developing countries 8.8 Agricultural support estimate for OECD countries as a percentage of their GDP 8.9 Proportion of ODA provided to help build trade capacity Target 8.D Deal comprehensively with the debt problems of developing countries through national and Debt sustainability international measures in order to make debt 8.10 Total number of countries that have reached their HIPC sustainable in the long term decision points and number that have reached their HIPC completion points (cumulative) 8.11 Debt relief committed under HIPC Initiative and Multilateral Debt Relief Initiative (MDRI) 8.12 Debt service as a percentage of exports of goods and services Target 8.E In cooperation with pharmaceutical companies, 8.13 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 8.F In cooperation with the private sector, make 8.14 Telephone lines per 100 population available the benefits of new technologies, 8.15 Cellular subscribers per 100 population especially information and communications 8.16 Internet users per 100 population 1. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. 2. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material. 2009 World Development Indicators 13 1.1 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2007 2007 2007 2007b 2007 2007b 2007 2007 2007 2007 2006­07 2006­07 Afghanistan .. 652 .. 8.1 120 ..c .. 26d ..d .. 5.3 .. Albania 3 29 116 10.5 111 3,300 116 23 7,240 107 6.0 5.7 Algeria 34 2,382 14 122.5 48 3,620 108 259d 7,640d 104 3.1 1.6 Angola 17 1,247 14 43.0 67 2,540 128 72.3 4,270 135 21.1 18.3 Argentina 40 2,780 14 238.7 30 6,040 85 512.4 12,970 78 8.7 7.6 Armenia 3 30 107 7.9 123 2,630 125 17.7 5,870 117 13.8 13.8 Australia 21 7,741 3 751.5 15 35,760 29 702.0 33,400 34 3.3 1.7 Austria 8 84 101 348.9 25 41,960 19 305.6 36,750 21 3.4 2.9 Azerbaijan 9 87 104 22.6 84 2,640 124 56.3 6,570 114 25.0 23.9 Bangladesh 159 144 1,218 74.9 56 470 184 211.4 1,330 177 6.4 4.7 Belarus 10 208 47 40.9 69 4,220 100 104.3 10,750 90 8.2 8.5 Belgium 11 31 351 436.9 20 41,110 21 375.3 35,320 26 2.8 2.0 Benin 9 113 82 5.1 141 570 178 11.8 1,310 180 4.6 1.5 Bolivia 10 1,099 9 12.0 106 1,260 151 39.5 4,150 138 4.6 2.8 Bosnia and Herzegovina 4 51 74 14.3 101 3,790e 105 30.3 8,020 102 6.8 7.0 Botswana 2 582 3 11.5 107 6,120 84 24.2 12,880 79 5.3 4.0 Brazil 192 8,515 23 1,122.1 10 5,860 86 1,775.6 9,270 97 5.4 4.2 Bulgaria 8 111 71 35.1 73 4,580 97 85.0 11,100 87 6.2 6.7 Burkina Faso 15 274 54 6.4 130 430 186 16.5 1,120 186 4.0 1.0 Burundi 8 28 331 0.9 188 110 209 2.8 330 206 3.6 ­0.3 Cambodia 14 181 82 8.0 122 550 179 24.9 1,720 170 10.2 8.3 Cameroon 19 475 40 19.5 90 1,050 156 39.3 2,120 161 3.5 1.5 Canada 33 9,985 4 1,307.5 9 39,650 22 1,170.7 35,500 25 2.7 1.7 Central African Republic 4 623 7 1.6 174 370 189 3.1 710 201 4.2 2.3 Chad 11 1,284 9 5.8 137 540 180 13.8 1,280 181 0.6 ­2.1 Chile 17 757 22 135.8 46 8,190 76 204.7 12,330 82 5.1 4.1 China 1,318 9,598 141 3,126.0 4 2,370 132 7,150.5 5,420 120 13.0 12.4 Hong Kong, China 7 1 6,647 218.6 32 31,560 33 304.3 43,940 13 6.4 5.3 Colombia 44 1,142 40 180.4 37 4,100e 103 363.4 8,260 101 7.5 6.2 Congo, Dem. Rep. 62 2,345 28 8.6 121 140 207 17.9 290 207 6.5 3.5 Congo, Rep. 4 342 11 5.8 136 1,540 145 10.4 2,750 152 ­1.6 ­3.6 Costa Rica 4 51 87 24.7 81 5,520 90 46.9d 10,510d 92 7.8 6.3 Cote d'Ivoire 19 322 61 17.8 95 920 161 31.1 1,620 175 1.7 ­0.2 Croatia 4 57 79 46.4 65 10,460 66 68.9 15,540 68 5.6 5.6 Cuba 11 111 103 .. .. ..f .. .. .. .. .. .. Czech Republic 10 79 134 150.7 40 14,580 56 234.5 22,690 53 6.6 5.9 Denmark 5 43 129 302.8 26 55,440 9 201.0 36,800 20 1.8 1.3 Dominican Republic 10 49 201 34.6 74 3,560 109 61.8d 6,350d 115 8.5 7.3 Ecuador 13 284 48 41.5 68 3,110 120 94.8 7,110 112 2.7 1.6 Egypt, Arab Rep. 75 1,001 76 119.5 49 1,580 144 405.3 5,370 121 7.1 5.2 El Salvador 7 21 331 19.6 89 2,850 121 38.6d 5,640d 118 4.7 3.3 Eritrea 5 118 48 1.3 178 270 202 3.0d 620d 204 1.3 ­1.8 Estonia 1 45 32 17.2 97 12,830 61 25.3 18,830 62 6.3 6.5 Ethiopia 79 1,104 79 17.6 96 220 205 61.7 780 196 11.1 8.4 Finland 5 338 17 234.3 31 44,300 18 183.9 34,760 28 4.4 4.0 France 62 552 112 2,466.6g 6 38,810g 25 2,088.8 33,850 32 2.2 1.6 Gabon 1 268 5 9.3 116 7,020 80 17.8 13,410 76 5.6 4.0 Gambia, The 2 11 171 0.5 193 320 195 1.9 1,140 185 6.3 3.6 Georgia 4 70 63 9.3 117 2,120 135 21.0 4,760 129 12.4 13.3 Germany 82 357 236 3,207.3 3 38,990 24 2,857.7 34,740 30 2.5 2.6 Ghana 23 239 103 13.8 104 590 177 31.0 1,320 178 6.3 4.2 Greece 11 132 87 288.1 27 25,740 40 311.5 27,830 43 4.0 3.6 Guatemala 13 109 123 32.8 79 2,450 130 60.3d 4,520d 131 5.7 3.2 Guinea 9 246 38 3.7 150 400 188 10.5 1,120 186 1.5 ­0.6 Guinea-Bissau 2 36 60 0.3 202 200 206 0.8 470 205 2.7 ­0.3 Haiti 10 28 349 5.0 143 520 182 10.1d 1,050d 189 3.2 1.4 14 2009 World Development Indicators WORLD VIEW Population Surface Population Size of the economy Gross national Gross national PPP gross national 1.1Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2007 2007 2007 2007b 2007 2007b 2007 2007 2007 2007 2006­07 2006­07 Honduras 7 112 63 11.3 109 1,590 143 25.6d 3,610d 143 6.3 4.3 Hungary 10 93 112 117.5 51 11,680 64 175.6 17,470 64 1.1 1.3 India 1,125 3,287 378 1,071.0 11 950 159 3,082.5 2,740 153 9.1 7.6 Indonesia 226 1,905 125 372.6 23 1,650 141 804.5 3,570 144 6.3 5.1 Iran, Islamic Rep. 71 1,745 44 251.5 29 3,540 110 769.7 10,840 89 7.8 6.4 Iraq .. 438 .. .. .. ..h .. .. .. .. .. .. Ireland 4 70 63 207.9 34 47,610 13 164.6 37,700 18 6.0 3.4 Israel 7 22 332 159.2 39 22,170 45 188.9 26,310 45 5.4 3.5 Italy 59 301 202 1,988.2 7 33,490 30 1,792.6 30,190 38 1.5 0.7 Jamaica 3 11 247 8.9 119 3,330i 115 14.2d 5,300d 123 ­7.3 ­7.7 Japan 128 378 351 4,828.9 2 37,790 26 4,440.2 34,750 29 2.1 2.1 Jordan 6 89 65 16.3 99 2,840 122 29.5 5,150 124 6.0 2.6 Kazakhstan 15 2,725 6 77.7 55 5,020 94 148.7 9,600 95 8.9 7.7 Kenya 38 580 66 24.0 82 640 174 58.1 1,550 176 7.0 4.2 Korea, Dem. Rep. 24 121 198 .. .. ..c .. .. .. .. .. .. Korea, Rep. 48 99 491 955.8 14 19,730 48 1,203.6 24,840 50 5.0 4.6 Kuwait 3 18 149 99.9 53 38,420 23 136.7 52,610 4 6.3 3.7 Kyrgyz Republic 5 200 27 3.2 157 610 176 10.4 1,980 165 8.2 7.3 Lao PDR 6 237 25 3.7 151 630 175 12.2 2,080 162 7.9 6.0 Latvia 2 65 37 22.6 85 9,920 69 35.9 15,790 67 10.3 10.9 Lebanon 4 10 400 23.8 83 5,800 87 41.2 10,040 93 2.0 1.0 Lesotho 2 30 66 2.1 171 1,030 157 3.9 1,940 166 4.9 4.3 Liberia 4 111 39 0.5 194 140 207 1.0 280 208 9.4 5.4 Libya 6 1,760 3 55.5 62 9,010 73 90.6d 14,710d 71 6.8 4.8 Lithuania 3 65 54 33.0 77 9,770 71 56.8 16,830 66 8.8 9.4 Macedonia, FYR 2 26 80 7.1 126 3,470 111 18.4 9,050 100 5.0 4.9 Madagascar 20 587 34 6.4 131 320 195 18.2 930 193 6.2 3.4 Malawi 14 118 148 3.5 153 250 204 10.5 760 198 7.9 5.2 Malaysia 27 330 81 170.5 38 6,420 81 351.2 13,230 77 6.3 4.6 Mali 12 1,240 10 6.1 134 500 183 12.8 1,040 190 2.8 ­0.2 Mauritania 3 1,031 3 2.6 166 840 167 6.3 2,000 164 1.9 ­0.6 Mauritius 1 2 621 7.0 127 5,580 89 14.4 11,410 86 4.7 4.0 Mexico 105 1,964 54 989.5 13 9,400 72 1,464.4 13,910 74 3.2 2.2 Moldova 4 34 116 4.1 147 1,210j 153 10.6 2,800 151 3.0 3.8 Mongolia 3 1,567 2 3.4 156 1,290 149 8.3 3,170 146 10.2 9.2 Morocco 31 447 69 70.7 57 2,290 133 125.1 4,050 139 2.7 1.5 Mozambique 21 799 27 7.1 125 330 194 15.5 730 199 7.3 5.3 Myanmar 49 677 74 .. .. ..c .. .. .. .. 5.0 4.1 Namibia 2 824 3 7.2 124 3,450 112 10.6 5,100 125 5.9 4.2 Nepal 28 147 197 9.9 115 350 193 29.8 1,060 188 3.2 1.5 Netherlands 16 42 484 747.8 16 45,650 17 646.5 39,470 17 3.5 3.3 New Zealand 4 268 16 114.5 52 27,080 39 107.3 25,380 48 3.0 1.9 Nicaragua 6 130 46 5.5 138 990 158 14.1d 2,510d 157 3.9 2.6 Niger 14 1,267 11 4.0 148 280 200 9.0 630 203 3.2 ­0.1 Nigeria 148 924 162 136.3 45 920 161 260.8 1,760 169 5.9 3.6 Norway 5 324 15 364.3 24 77,370 3 252.6 53,650 5 3.7 2.6 Oman 3 310 8 32.8 75 12,860 59 55.1 21,650 57 7.2 5.6 Pakistan 162 796 211 140.2 43 860 165 412.9 2,540 155 6.0 3.7 Panama 3 76 45 18.4 93 5,500 92 35.5d 10,610d 91 11.5 9.8 Papua New Guinea 6 463 14 5.4 139 850 166 11.8d 1,870d 168 6.2 4.2 Paraguay 6 407 15 10.5 112 1,710 139 27.7 4,520 131 6.8 4.9 Peru 28 1,285 22 95.0 54 3,410 113 200.9 7,200 108 8.9 7.6 Philippines 88 300 295 142.1 42 1,620 142 326.4 3,710 142 7.2 5.2 Poland 38 313 124 375.3 21 9,850 70 590.9 15,500 69 6.6 6.7 Portugal 11 92 116 201.1 36 18,950 50 231.1 21,790 59 1.8 1.5 Puerto Rico 4 9 445 .. .. ..k .. .. .. .. .. .. 2009 World Development Indicators 15 1.1 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2007 2007 2007 2007b 2007 2007b 2007 2007 2007 2007 2006­07 2006­07 Romania 22 238 94 137.7 44 6,390 82 266.2 12,350 81 6.0 6.2 Russian Federation 142 17,098 9 1,069.8 12 7,530 79 2,036.5 14,330 73 8.1 8.4 Rwanda 10 26 395 3.1 161 320 195 8.4 860 194 6.0 3.0 Saudi Arabia 24 2,000l 12 373.7 22 15,450 54 554.4 22,910 52 3.4 1.2 Senegal 12 197 64 10.3 113 830 168 20.5 1,650 173 4.8 1.9 Serbia 7 78 95 33.5 76 4,540 98 72.6 9,830 94 7.5 8.0 Sierra Leone 6 72 82 1.5 176 260 203 3.9 660 202 6.8 4.9 Singapore 5 1 6,660 148.4 41 32,340 31 220.0 47,950 10 7.7 3.3 Slovak Republic 5 49 112 63.3 59 11,720 63 103.7 19,220 61 10.4 10.3 Slovenia 2 20 100 43.4 66 21,510 46 52.9 26,230 46 6.8 6.2 Somalia 9 638 14 .. .. ..c .. .. .. .. .. .. South Africa 48 1,219 39 273.9 28 5,720 88 452.3 9,450 96 5.1 4.1 Spain 45 505 90 1,314.5 8 29,290 36 1,380.0 30,750 37 3.8 2.1 Sri Lanka 20 66 310 30.8 80 1,540 145 84.0 4,200 137 6.8 6.1 Sudan 39 2,506 16 36.7 70 950 159 72.5 1,880 167 10.2 7.7 Swaziland 1 17 67 2.9 164 2,560 126 5.6 4,890 128 3.5 2.8 Sweden 9 450 22 437.9 19 47,870 12 343.0 37,490 19 2.7 2.0 Switzerland 8 41 189 459.2 18 60,820 7 335.3 44,410 12 3.3 2.4 Syrian Arab Republic 20 185 108 35.3 72 1,780 137 88.1 4,430 133 6.6 4.0 Tajikistan 7 143 48 3.1 160 460 185 11.5 1,710 171 7.8 6.2 Tanzania 40 947 46 16.3m 98 410m 187 48.7 1,200 182 7.1 4.5 Thailand 64 513 125 217.2 33 3,400 114 502.8 7,880 103 4.8 4.1 Timor-Leste 1 15 71 1.6 175 1,510 147 3.3d 3,090d 147 7.8 4.5 Togo 7 57 121 2.4 168 360 191 5.1 770 197 1.9 ­0.7 Trinidad and Tobago 1 5 260 19.3 92 14,480 57 29.9d 22,420d 56 5.5 5.1 Tunisia 10 164 66 32.8 78 3,210 118 73.0 7,140 110 6.3 5.3 Turkey 74 784 96 593.0 17 8,030 77 946.7 12,810 80 4.6 3.3 Turkmenistan 5 488 11 .. .. ..h .. 21.0d 4,350d 130 .. .. Uganda 31 241 157 11.3 108 370 189 32.1 1,040 190 7.9 4.3 Ukraine 47 604 80 118.9 50 2,560 126 316.7 6,810 113 7.6 8.2 United Arab Emirates 4 84 52 .. .. ..k .. .. .. .. 9.4 5.7 United Kingdom 61 244 252 2,464.3 5 40,660 20 2,063.8 34,050 27 3.0 2.4 United States 302 9,632 33 13,886.4 1 46,040 16 13,827.2 45,840 11 2.0 1.0 Uruguay 3 176 19 21.2 86 6,390 82 36.6 11,020 88 7.4 7.1 Uzbekistan 27 447 63 19.7 87 730 172 65.3d 2,430d 158 9.5 7.9 Venezuela, RB 27 912 31 207.6 35 7,550 78 337.8 12,290 83 8.4 6.6 Vietnam 85 329 275 65.4 58 770 169 215.4 2,530 156 8.5 7.2 West Bank and Gaza 4 6 616 4.5 142 1,290 148 .. .. .. 6.3 2.7 Yemen, Rep. 22 528 42 19.4 91 870 163 49.3 2,200 160 3.6 0.6 Zambia 12 753 16 9.2 118 770 169 14.2 1,190 183 6.0 4.0 Zimbabwe 13 391 35 4.5 145 340 191 .. .. .. ­5.3 ­6.0 World 6,610 s 133,946 s 51 w 52,850.4 t 7,995 w 65,752.3 t 9,947 t 3.8 w 2.6 w Low income 1,296 21,846 61 744.3 574 1,929.7 1,489 6.4 4.2 Middle income 4,258 77,006 57 12,393.5 2,910 25,666.2 6,027 8.2 7.2 Lower middle income 3,435 35,510 100 6,542.9 1,905 15,748.8 4,585 10.2 9.1 Upper middle income 824 41,497 20 5,853.9 7,107 9,943.8 12,072 5.8 5.0 Low & middle income 5,554 98,852 58 13,141.1 2,366 27,592.5 4,968 8.1 6.8 East Asia & Pacific 1,912 16,299 121 4,172.8 2,182 9,503.1 4,969 11.4 10.5 Europe & Central Asia 446 23,972 19 2,697.2 6,052 5,018.7 11,262 6.9 6.7 Latin America & Carib. 561 20,421 28 3,252.1 5,801 5,426.0 9,678 5.7 4.4 Middle East & N. Africa 313 8,778 36 883.5 2,820 2,318.7 7,402 5.9 4.1 South Asia 1,522 5,140 318 1,338.7 880 3,853.6 2,532 8.4 6.8 Sub-Saharan Africa 800 24,242 34 761.0 951 1,495.5 1,869 6.2 3.8 High income 1,056 35,094 32 39,685.9 37,570 38,386.0 36,340 2.5 1.8 Euro area 324 2,585 129 11,611.1 35,818 10,554.9 32,560 2.7 2.1 a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be low income ($935 or less). d. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. e. Included in the aggregates for lower middle-income economies based on earlier data. f. Estimated to be upper middle income ($3,706­$11,455). g. Includes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. h. Estimated to be lower middle income ($936­$3,705). i. Included in the aggregates for upper middle-income economies based on earlier data. j. Excludes Transnistria. k. Estimated to be high income ($11,456 or more). l. Provisional estimate. m. Covers mainland Tanzania only. 16 2009 World Development Indicators WORLD VIEW Size of the economy 1.1 About the data Definitions Population, land area, income, output, and growth in allowing comparison of real levels of expenditure · Population is based on the de facto definition of output are basic measures of the size of an economy. between countries, just as conventional price indexes population, which counts all residents regardless of They also provide a broad indication of actual and allow comparison of real values over time. legal status or citizenship--except for refugees not potential resources. Population, land area, income PPP rates are calculated by simultaneously compar- permanently settled in the country of asylum, who (as measured by gross national income, GNI), and ing the prices of similar goods and services among a are generally considered part of the population of output (as measured by gross domestic product, large number of countries. In the most recent round their country of origin. The values shown are midyear GDP) are therefore used throughout World Develop- of price surveys conducted by the International Com- estimates. See also table 2.1. · Surface area is ment Indicators to normalize other indicators. parison Program (ICP), 146 countries and territories a country's total area, including areas under inland Population estimates are generally based on participated in the data collection, including China bodies of water and some coastal waterways. · Pop- extrapolations from the most recent national cen- for the first time, India for the first time since 1985, ulation density is midyear population divided by land sus. For further discussion of the measurement of and almost all African countries. The PPP conver- area in square kilometers. · Gross national income population and population growth, see About the data sion factors presented in the table come from three (GNI) is the sum of value added by all resident pro- for table 2.1 and Statistical methods. sources. For 45 high- or upper middle-income coun- ducers plus any product taxes (less subsidies) not The surface area of an economy includes inland tries conversion factors are provided by Eurostat included in the valuation of output plus net receipts bodies of water and some coastal waterways. Sur- and the Organisation for Economic Co-operation of primary income (compensation of employees and face area thus differs from land area, which excludes and Development (OECD), with PPP estimates for property income) from abroad. Data are in current bodies of water, and from gross area, which may 34 European countries incorporating new price data U.S. dollars converted using the World Bank Atlas include offshore territorial waters. Land area is par- collected since 2005. For the remaining 2005 ICP method (see Statistical methods). · GNI per capita is ticularly important for understanding an economy's countries the PPP estimates are extrapolated from GNI divided by midyear population. GNI per capita in agricultural capacity and the environmental effects the 2005 ICP benchmark results, which account for U.S. dollars is converted using the World Bank Atlas of human activity. (For measures of land area and relative price changes between each economy and method. · Purchasing power parity (PPP) GNI is GNI data on rural population density, land use, and agri- the United States. For countries that did not partici- converted to international dollars using PPP rates. An cultural productivity, see tables 3.1­3.3.) Innova- pate in the 2005 ICP round, the PPP estimates are international dollar has the same purchasing power tions in satellite mapping and computer databases imputed using a statistical model. over GNI that a U.S. dollar has in the United States. have resulted in more precise measurements of land For more information on the results of the 2005 · Gross domestic product (GDP) is the sum of value and water areas. ICP, see the introduction to World View. The final added by all resident producers plus any product GNI measures total domestic and foreign value report of the program is available at www.worldbank. taxes (less subsidies) not included in the valuation added claimed by residents. GNI comprises GDP org/data/icp. of output. Growth is calculated from constant price plus net receipts of primary income (compensation All 209 economies shown in World Development GDP data in local currency. · GDP per capita is GDP of employees and property income) from nonresident Indicators are ranked by size, including those that divided by midyear population. sources. The World Bank uses GNI per capita in U.S. appear in table 1.6. The ranks are shown only in dollars to classify countries for analytical purposes table 1.1. No rank is shown for economies for which and to determine borrowing eligibility. For definitions numerical estimates of GNI per capita are not pub- of the income groups in World Development Indica- lished. Economies with missing data are included in tors, see Users guide. For discussion of the useful- the ranking at their approximate level, so that the rel- ness of national income and output as measures of ative order of other economies remains consistent. productivity or welfare, see About the data for tables 4.1 and 4.2. Data sources When calculating GNI in U.S. dollars from GNI Population estimates are prepared by World Bank reported in national currencies, the World Bank fol- staff from a variety of sources (see Data sources lows the World Bank Atlas conversion method, using for table 2.1). Data on surface and land area are a three-year average of exchange rates to smooth from the Food and Agriculture Organization (see the effects of transitory fluctuations in exchange Data sources for table 3.1). GNI, GNI per capita, rates. (For further discussion of the World Bank Atlas GDP growth, and GDP per capita growth are esti- method, see Statistical methods.) GDP and GDP per mated by World Bank staff based on national capita growth rates are calculated from data in con- accounts data collected by World Bank staff during stant prices and national currency units. economic missions or reported by national statis- Because exchange rates do not always reflect dif- tical offices to other international organizations ferences in price levels between countries, the table such as the OECD. PPP conversion factors are also converts GNI and GNI per capita estimates into estimates by Eurostat/OECD and by World Bank international dollars using purchasing power parity staff based on data collected by the ICP. (PPP) rates. PPP rates provide a standard measure 2009 World Development Indicators 17 Millennium Development Goals: 1.2 eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary school mortality rate % 1995­ % of total employment under age 5 % % per 1,000 2007a,b 1990 2007 1990 2000­07a 1991 2007c 1991 2007c 1990 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 7.8 .. .. .. 17.0 .. 96 96 97 46 15 Algeria 6.9 .. 35 .. 10.2 80 95 83 99 69 37 Angola 2.0 .. .. .. 27.5 35 .. .. .. 258 158 Argentina 3.4 d .. 20 .. 2.3 .. 97 .. 104 29 16 Armenia 8.6 .. .. .. 4.2 .. 98 .. 104 56 24 Australia 5.9 10 9 .. .. .. .. 101 97 10 6 Austria 8.6 .. 9 .. .. .. 103 95 97 10 4 Azerbaijan 13.3 .. 53 .. 14.0 .. .. 100 .. 98 39 Bangladesh 9.4 .. 85 64.3 39.2 .. 72 .. 103 151 61 Belarus 8.8 .. .. .. 1.3 94 92 .. 101 24 13 Belgium 8.5 .. 10 .. .. 79 87 101 98 10 5 Benin 6.9 .. .. .. 21.5 21 64 49 73 184 123 Bolivia 1.8 40 .. 8.9 5.9 71 101 .. 98 125 57 Bosnia and Herzegovina 6.9 .. .. .. 1.6 .. .. .. 99 22 14 Botswana 3.1 .. .. .. 10.7 89 95 109 101 57 40 Brazil 3.0 29 27 .. 2.2 90 106 .. 103 58 22 Bulgaria 8.7 .. 8 .. 1.6 90 98 99 97 19 12 Burkina Faso 7.0 .. .. 29.6 35.2 20 33 62 82 206 191 Burundi 9.0 .. .. .. 38.9 46 39 82 90 189 180 Cambodia 7.1 .. 87 .. 28.4 .. 85 73 90 119 91 Cameroon 5.6 .. .. 18.0 15.1 53 55 83 85 139 148 Canada 7.2 .. 10 .. .. .. .. 99 98 8 6 Central African Republic 5.2 .. .. .. 21.8 27 24 60 .. 171 172 Chad 6.3 94 .. .. 33.9 18 31 42 64 201 209 Chile 4.1 .. 25 .. 0.6 .. 95 100 99 21 9 China 5.7 .. .. .. 6.8 105 .. 87 100 45 22 Hong Kong, China 5.3 6 7 .. .. 102 102 103 98 .. .. Colombia 2.3 28 41 .. 5.1 70 107 108 104 35 20 Congo, Dem. Rep. 5.5 .. .. .. 33.6 46 51 .. 73 200 161 Congo, Rep. 5.0 .. .. .. 11.8 54 72 85 90 104 125 Costa Rica 4.2 25 20 .. .. 79 91 101 102 18 11 Côte d'Ivoire 5.0 .. .. .. 16.7 43 45 65 .. 151 127 Croatia 8.7 .. 18 .. .. .. 96 .. 96 13 6 Cuba .. .. .. .. .. 99 93 106 99 13 7 Czech Republic 10.2 7 12 .. 2.1 .. 94 98 101 13 4 Denmark 8.3 .. .. .. .. 98 101 101 101 9 4 Dominican Republic 4.0 39 42 8.4 4.2 62 89 .. 104 66 38 Ecuador 3.4 36 34 .. 6.2 .. 106 .. 100 57 22 Egypt, Arab Rep. 9.0 28 25 8.2 5.4 .. 98 81 95 93 36 El Salvador 3.3 35 36 11.1 6.1 61 91 102 101 60 24 Eritrea .. .. .. .. 34.5 .. 46 .. 78 147 70 Estonia 6.8 2 6 .. .. .. 100 .. 100 18 6 Ethiopia 9.3 .. 52 .. 34.6 .. 46 68 83 204 119 Finland 9.6 .. .. .. .. 97 97 109 102 7 4 France 7.2 .. 7 .. .. 104 .. 102 100 9 4 Gabon 6.1 48 .. .. 8.8 .. .. .. .. 92 91 Gambia, The 4.8 .. .. .. 15.8 .. 72 66 100 153 109 Georgia 5.4 .. 62 .. .. .. 92 98 98 47 30 Germany 8.5 .. .. .. .. .. 97 99 98 9 4 Ghana 5.2 .. .. 24.1 18.8 61 71 79 95 120 115 Greece 6.7 40 28 .. .. .. 103 99 98 11 4 Guatemala 3.4 .. .. 27.8 17.7 .. 77 .. 93 82 39 Guinea 5.8 .. .. .. 22.5 17 64 45 74 231 150 Guinea-Bissau 7.2 .. .. .. 21.9 .. .. .. .. 240 198 Haiti 2.5 .. .. .. 18.9 27 .. 94 .. 152 76 18 2009 World Development Indicators WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender 1.2 Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary school mortality rate % 1995­ % of total employment under age 5 % % per 1,000 2007a,b 1990 2007 1990 2000­07a 1991 2007c 1991 2007c 1990 2007 Honduras 2.5 49 .. .. 8.6 64 88 106 106 58 24 Hungary 8.6 7 7 2.3 .. 87 96 100 99 17 7 India 8.1 .. .. .. 43.5 64 86 70 91 117 72 Indonesia 7.1 .. 63 31.0 24.4 91 99 93 98 91 31 Iran, Islamic Rep. 6.4 .. 43 .. .. 91 105 85 114 72 33 Iraq .. .. .. .. .. .. .. 78 .. 53 .. Ireland 7.4 20 11 .. .. .. 96 104 103 9 4 Israel 5.7 .. 7 .. .. .. 101 105 101 12 5 Italy 6.5 16 22 .. .. 104 100 100 99 9 4 Jamaica 5.2 42 35 .. 3.1 90 82 102 101 33 31 Japan 10.6 19 11 .. .. 101 .. 101 100 6 4 Jordan 7.2 .. .. 4.8 3.6 101 99 101 102 40 24 Kazakhstan 7.4 .. 36 .. 4.9 .. 104 e 102 99e 60 32 Kenya 4.7 .. .. 20.1 16.5 .. 93 94 96 97 121 Korea, Dem. Rep. .. .. .. .. 17.8 .. .. .. .. 55 55 Korea, Rep. 7.9 .. 25 .. .. 98 101 99 96 9 5 Kuwait .. .. .. .. .. .. 98 97 100 15 11 Kyrgyz Republic 8.1 .. 47 .. 2.7 .. 95 .. 100 74 38 Lao PDR 8.5 .. .. .. 36.4 45 77 76 86 163 70 Latvia 6.8 .. 7 .. .. .. 92 101 100 17 9 Lebanon .. .. .. .. .. .. 82 .. 103 37 29 Lesotho 3.0 38 .. .. 16.6 59 78 123 104 102 84 Liberia 6.4 .. .. .. 20.4 .. 55e .. .. 205 133 Libya .. .. .. .. .. .. .. .. 105 41 18 Lithuania 6.8 .. .. .. .. 89 93 .. 100 16 8 Macedonia, FYR 6.1 .. 22 .. 1.8 .. 97 .. 99 38 17 Madagascar 6.2 84 86 35.5 36.8 33 62 98 96 168 112 Malawi 7.0 .. .. 24.4 18.4 29 55 81 100 209 111 Malaysia 6.4 29 22 .. .. 91 98 101 104 22 11 Mali 6.5 .. .. 29.0 27.9 13 49 57 78 250 196 Mauritania 6.2 .. .. .. 30.4 34 59 71 102 130 119 Mauritius .. 12 17 .. .. 107 94 102 102 24 15 Mexico 4.6 26 29 13.9 3.4 88 104 97 99 52 35 Moldova 7.3 .. 32 .. 3.2 .. 93 106 102 37 18 Mongolia 7.2 .. .. .. 5.3 .. 110 109 107 98 43 Morocco 6.5 .. 52 8.1 9.9 48 83 70 87 89 34 Mozambique 5.4 .. .. .. 21.2 26 46 71 85 201 168 Myanmar .. .. .. .. 29.6 .. .. 97 .. 130 103 Namibia 1.5 .. 21 21.5 17.5 .. 77 106 104 87 68 Nepal 6.1 .. .. .. 38.8 51 78e 59 98e 142 55 Netherlands 7.6 .. .. .. .. .. .. 97 98 9 5 New Zealand 6.4 13 12 .. .. 100 .. 100 103 11 6 Nicaragua 3.8 .. 45 9.6 7.8 42 73 109 102 68 35 Niger 5.9 .. .. 41.0 39.9 18 40 53 71 304 176 Nigeria 5.1 .. .. 35.1 27.2 .. 72 77 84 230 189 Norway 9.6 .. 6 .. .. 100 96 102 100 9 4 Oman .. .. .. .. .. 74 88 89 99 32 12 Pakistan 9.1 .. 62 39.0 31.3 .. 62 .. 78 132 90 Panama 2.5 34 28 .. .. .. 99 .. 101 34 23 Papua New Guinea 4.5 .. .. .. .. 46 .. 80 .. 94 65 Paraguay 3.4 23 47 2.8 .. 68 95 98 99 41 29 Peru 3.9 36 40 8.8 5.2 .. 101 96 101 78 20 Philippines 5.6 .. 45 .. 20.7 88 94 100 102 62 28 Poland 7.3 28 19 .. .. 96 97 100 99 17 7 Portugal 5.8 19 19 .. .. 95 104 103 101 15 4 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 19 Millennium Development Goals: 1.2 eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary school mortality rate % 1995­ % of total employment under age 5 % % per 1,000 2007a,b 1990 2007 1990 2000­07a 1991 2007c 1991 2007c 1990 2007 Romania 8.2 9 32 .. 3.5 100 101 99 100 32 15 Russian Federation 6.4 1 6 .. .. .. .. 104 99 27 15 Rwanda 5.4 .. .. 24.3 18.0 35 35 92 100 195 181 Saudi Arabia .. .. .. .. .. 55 93 84 94 44 25 Senegal 6.2 83 .. 21.9 14.5 43 49 69 92 149 114 Serbia 8.3f .. 23 .. 1.8 .. .. .. 102 .. 8 Sierra Leone 6.1 .. .. .. 28.3 .. 81 67 86 290 262 Singapore 5.0 8 10 .. 3.3 .. .. .. .. 8 3 Slovak Republic 8.8 .. 10 .. .. .. 93 .. 100 15 8 Slovenia 8.2 12 13 .. .. .. .. .. 100 11 4 Somalia .. .. .. .. 32.8 .. .. .. .. 203 142 South Africa 3.1 .. 3 .. .. 76 92 104 100 64 59 Spain 7.0 22 12 .. .. 103 99 104 103 9 4 Sri Lanka 6.8 .. 41 29.3 22.8 102 106 102 .. 32 21 Sudan .. .. .. .. 38.4 42 50 77 89e 125 109 Swaziland 4.5 .. .. .. 9.1 60 67 98 95 96 91 Sweden 9.1 .. .. .. .. 96 .. 102 100 7 3 Switzerland 7.6 9 10 .. .. 53 88 97 97 9 5 Syrian Arab Republic .. .. .. .. .. 89 114 85 96 37 17 Tajikistan 7.7 .. .. .. 14.9 .. 95 .. 89 117 67 Tanzania 7.3 .. 88 25.1 16.7 62 112e 97 .. 157 116 Thailand 6.1 70 53 17.4 7.0 .. 101 97 104 31 7 Timor-Leste 6.7 .. .. .. 40.6 .. 69 .. 95 184 97 Togo 7.6 .. .. 21.2 .. 35 57 59 75 150 100 Trinidad and Tobago 5.5 22 16 4.7 4.4 101 88 101 101 34 35 Tunisia 5.9 .. .. 8.5 .. 74 120 86 104 52 21 Turkey 5.2 .. 36 8.7 3.5 90 96 81 90 82 23 Turkmenistan 6.0 .. .. .. .. .. .. .. .. 99 50 Uganda 6.1 .. .. 19.7 19.0 .. 54 82 98 175 130 Ukraine 9.0 .. .. .. 4.1 94 101 .. 100 25 24 United Arab Emirates .. .. .. .. .. 103 105 104 101 15 8 United Kingdom 6.1 .. .. .. .. .. .. 102 102 10 6 United States 5.4 .. .. .. 1.3 .. 95 100 100 11 8 Uruguay 4.5 .. 25 .. 6.0 94 99 .. 106 25 14 Uzbekistan 7.1 .. .. .. 4.4 .. 97 94 98 74 41 Venezuela, RB 4.9 .. 30 .. .. 81 98 105 103 32 19 Vietnam 7.1 .. 74 36.9 20.2 .. .. .. .. 56 15 West Bank and Gaza .. .. 36 .. .. .. 83 .. 104 38 27 Yemen, Rep. 7.2 .. .. .. .. .. 60 .. 66 127 73 Zambia 3.6 65 .. 21.2 23.3 .. 88 .. 96 163 170 Zimbabwe 4.6 .. .. 8.0 14.0 97 .. 92 97 95 90 World .. w .. w .. w 23.2 w 79 w 86 w 86 w 96 w 93 w 68 w Low income .. .. .. 28.0 .. 65 .. 87 164 126 Middle income .. .. .. 22.0 84 93 85 97 75 45 Lower middle income .. .. .. 24.8 83 91 82 95 81 50 Upper middle income .. 22 .. .. 90 101 99 103 47 24 Low & middle income .. .. .. 24.1 78 85 83 95 101 74 East Asia & Pacific .. .. .. 12.8 101 98 89 99 56 27 Europe & Central Asia .. 19 .. .. 93 98 100 102 49 23 Latin America & Carib. 30 31 .. 4.4 84 100 98 103 55 26 Middle East & N. Africa .. 37 .. .. 78 90 78 96 77 38 South Asia .. .. .. 41.1 62 80 70 89 125 78 Sub-Saharan Africa .. .. .. 26.6 51 60 79 86 183 146 High income .. .. .. .. .. 97 100 104 12 7 Euro area .. 12 .. .. 101 .. 101 .. 10 4 a. Data are for the most recent year available. b. See table 2.9 for survey year and whether share is based on income or consumption expenditure. c. Provisional data. d. Urban data. e. Data are for 2008. f. Includes Montenegro. 20 2009 World Development Indicators WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives 1.2 About the data Definitions Tables 1.2­1.4 present indicators for 17 of the 21 and undernourished mothers who give birth to under- · Share of poorest quintile in national consumption targets specified by the Millennium Development weight children. or income is the share of the poorest 20 percent of Goals. Each of the eight goals includes one or more Progress toward universal primary education is the population in consumption or, in some cases, targets, and each target has several associated measured by the primary completion rate. Because income. · Vulnerable employment is the sum of indicators for monitoring progress toward the target. many school systems do not record school comple- unpaid family workers and own-account workers as Most of the targets are set as a value of a specific tion on a consistent basis, it is estimated from the a percentage of total employment. · Prevalence of indicator to be attained by a certain date. In some gross enrollment rate in the final grade of primary malnutrition is the percentage of children under age cases the target value is set relative to a level in school, adjusted for repetition. Official enrollments five whose weight for age is more than two standard 1990. In others it is set at an absolute level. Some sometimes differ significantly from attendance, and deviations below the median for the international of the targets for goals 7 and 8 have not yet been even school systems with high average enrollment reference population ages 0­59 months. The data quantified. ratios may have poor completion rates. are based on the new international child growth stan- The indicators in this table relate to goals 1­4. Eliminating gender disparities in education would dards for infants and young children, called the Child Goal 1 has three targets between 1990 and 2015: help increase the status and capabilities of women. Growth Standards, released in 2006 by the World to halve the proportion of people whose income is The ratio of female to male enrollments in primary Health Organization. · Primary completion rate is less than $1 a day, to achieve full and productive and secondary school provides an imperfect measure the percentage of students completing the last year employment and decent work for all, and to halve the of the relative accessibility of schooling for girls. of primary school. It is calculated as the total num- proportion of people who suffer from hunger. Esti- The targets for reducing under-five mortality rates ber of students in the last grade of primary school, mates of poverty rates are in tables 2.7 and 2.8. are among the most challenging. Under-five mortal- minus the number of repeaters in that grade, divided The indicator shown here, the share of the poorest ity rates are harmonized estimates produced by a by the total number of children of official graduation quintile in national consumption, is a distributional weighted least squares regression model and are age. · Ratio of girls to boys enrollments in primary measure. Countries with more unequal distribu- available at regular intervals for most countries. and secondary school is the ratio of the female to tions of consumption (or income) have a higher rate Most of the 60 indicators relating to the Millennium male gross enrollment rate in primary and secondary of poverty for a given average income. Vulnerable Development Goals can be found in World Develop- school. · Under-five mortality rate is the probability employment measures the portion of the labor force ment Indicators. Table 1.2a shows where to find the that a newborn baby will die before reaching age five, that receives the lowest wages and least security in indicators for the first four goals. For more informa- if subject to current age-specific mortality rates. The employment. No single indicator captures the con- tion about data collection methods and limitations, probability is expressed as a rate per 1,000. cept of suffering from hunger. Child malnutrition is a see About the data for the tables listed there. For symptom of inadequate food supply, lack of essen- information about the indicators for goals 5­8, see tial nutrients, illnesses that deplete these nutrients, About the data for tables 1.3 and 1.4. Location of indicators for Millennium Development Goals 1­4 1.2a Goal 1. Eradicate extreme poverty and hunger Table 1.1 Proportion of population below $1.25 a day 2.8 1.2 Poverty gap ratio 2.8 1.3 Share of poorest quintile in national consumption 1.2, 2.9 1.4 Growth rate of GDP per person employed 2.4 1.5 Employment to population ratio 2.4 1.6 Proportion of employed people living below $1 per day -- 1.7 Proportion of own-account and unpaid family workers in total employment 1.2, 2.4 1.8 Prevalence of underweight in children under age five 1.2, 2.19, 2.21 1.9 Proportion of population below minimum level of dietary energy consumption 2.19 Goal 2. Achieve universal primary education Data sources 2.1 Net enrollment ratio in primary education 2.12 The indicators here and throughout this book have 2.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.13 2.3 Literacy rate of 15- to 24-year-olds 2.14 been compiled by World Bank staff from primary Goal 3. Promote gender equality and empower women and secondary sources. Efforts have been made 3.1 Ratio of girls to boys in primary, secondary, and tertiary education 1.2, 2.12* to harmonize the data series used to compile this 3.2 Share of women in wage employment in the nonagricultural sector 1.5, 2.3* table with those published on the United Nations 3.3 Proportion of seats held by women in national parliament 1.5 Millennium Development Goals Web site (www. Goal 4. Reduce child mortality 4.1 Under-five mortality rate 1.2, 2.21, 2.22 un.org/millenniumgoals), but some differences in 4.2 Infant mortality rate 2.21, 2.22 timing, sources, and definitions remain. For more 4.3 Proportion of one-year-old children immunized against measles 2.17, 2.21 information see the data sources for the indica- -- No data are available in the World Development Indicators database. * Table shows information on related indicators. tors listed in table 1.2a. 2009 World Development Indicators 21 Millennium Development Goals: 1.3 protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15­49 ages 15­49 people metric tons % % of population peoplea 2005 1990 2002­07b 2007 2007 1990 2005 2008 1990 2006 2007 Afghanistan .. .. .. .. .. .. .. 0.7 .. .. .. Albania 92 .. 60 .. 17 2.2 1.1 1.5 .. 97 14.9 Algeria 180 47 61 0.1 57 3.0 4.2 2.1 88 94 10.3 Angola 1,400 .. .. 2.1 287 0.4 0.6 1.4 26 50 2.9 Argentina 77 .. .. 0.5 31 3.4 3.9 1.9 81 91 25.9 Armenia 76 .. 53 0.1 72 1.2 1.4 0.9 .. 91 5.7 Australia 4 .. .. 0.2 6 17.2 18.1 4.7 100 100 68.1 Austria 4 .. .. 0.2 12 7.5 8.9 1.9 100 100 67.4 Azerbaijan 82 .. 51 0.2 77 6.4 4.4 0.8 .. 80 10.8 Bangladesh 570 40 56 .. 223 0.1 0.3 1.9 26 36 0.3 Belarus 18 .. 73 0.2 61 10.6 6.5 0.7 .. 93 29.0 Belgium 8 78 .. 0.2 12 9.9 9.8 1.3 .. .. 65.9 Benin 840 .. 17 1.2 91 0.1 0.3 1.5 12 30 1.7 Bolivia 290 30 58 0.2 155 0.8 1.0 0.8 33 43 10.5 Bosnia and Herzegovina 3 .. 36 <0.1 51 1.6 6.9 13.1 .. 95 28.0 Botswana 380 33 .. 23.9 731 1.6 2.5 0.5 38 47 5.3 Brazil 110 59 .. 0.6 48 1.4 1.7 1.3 71 77 35.2 Bulgaria 11 .. .. .. 39 8.6 5.7 1.1 99 99 30.9 Burkina Faso 700 .. 17 1.6 226 0.1 0.1 1.0 5 13 0.6 Burundi 1,100 .. 9 2.0 367 0.0 0.0 1.5 44 41 0.7 Cambodia 540 .. 40 0.8 495 0.0 0.0 29.8 8 28 0.5 Cameroon 1,000 16 29 5.1 192 0.1 0.2 5.4 39 51 2.0 Canada 7 .. .. 0.4 5 15.4 16.6 1.8 100 100 72.8 Central African Republic 980 .. 19 6.3 345 0.1 0.1 0.6 11 31 0.3 Chad 1,500 .. 3 3.5 299 0.0 0.0 1.0 5 9 0.6 Chile 16 56 58 0.3 12 2.7 4.1 2.4 84 94 31.1 China 45 85 85 0.1 98 2.1 4.3 2.4 48 65 16.1 Hong Kong, China .. 86 .. .. 62 4.6 5.7 13.2 .. .. 57.2 Colombia 130 66 78 0.6 35 1.7 1.4 1.2 68 78 27.5 Congo, Dem. Rep. 1,100 8 .. .. 392 0.1 0.0 2.5 15 31 0.4 Congo, Rep. 740 .. 21 3.5 403 0.5 0.6 1.0 .. 20 1.9 Costa Rica 30 .. 96 0.4 11 0.9 1.7 1.9 94 96 33.6 Côte d'Ivoire 810 .. 13 3.9 420 0.4 0.5 3.9 20 24 1.6 Croatia 7 .. .. <0.1 40 5.1 5.2 1.8 99 99 44.7 Cuba 45 .. 77 0.1 6 3.0 2.2 4.2 98 98 11.6 Czech Republic 4 78 .. .. 9 15.6 11.7 1.5 100 99 48.3 Denmark 3 78 .. 0.2 8 9.7 8.5 1.6 100 100 80.7 Dominican Republic 150 56 73 1.1 69 1.3 2.0 2.1 68 79 17.2 Ecuador 210 53 73 0.3 101 1.6 2.2 10.4 71 84 13.2 Egypt, Arab Rep. 130 47 59 .. 21 1.4 2.4 4.1 50 66 14.0 El Salvador 170 47 67 0.8 40 0.5 1.0 1.8 73 86 11.1 Eritrea 450 .. 8 1.3 95 .. 0.2 15.0 3 5 2.5 Estonia 25 .. .. 1.3 38 18.1 13.5 0.6 95 95 63.7 Ethiopia 720 4 15 2.1 378 0.1 0.1 1.3 4 11 0.4 Finland 7 77 .. 0.1 6 10.1 10.1 1.3 100 100 78.8 France 8 81 .. 0.4 14 6.4 6.2 2.5 .. .. 51.2 Gabon 520 .. .. 5.9 406 6.5 1.2 2.1 .. 36 6.2 Gambia, The 690 12 .. 0.9 258 0.2 0.2 2.2 .. 52 5.9 Georgia 66 .. 47 0.1 84 3.2 1.1 1.0 94 93 8.2 Germany 4 75 .. 0.1 6 12.3 9.5 2.2 100 100 72.3 Ghana 560 13 17 1.9 203 0.2 0.3 3.7 6 10 3.8 Greece 3 .. .. 0.2 18 7.1 8.6 2.1 97 98 32.9 Guatemala 290 .. 43 0.8 63 0.6 0.9 2.4 70 84 10.1 Guinea 910 .. 9 1.6 287 0.2 0.2 2.2 13 19 0.5 Guinea-Bissau 1,100 .. 10 1.8 220 0.2 0.2 2.4 .. 33 2.2 Haiti 670 10 32 2.2 306 0.1 0.2 2.3 29 19 10.4 22 2009 World Development Indicators WORLD VIEW Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental 1.3 Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15­49 ages 15­49 people metric tons % % of population peoplea 2005 1990 2002­07b 2007 2007 1990 2005 2008 1990 2006 2007 Honduras 280 47 65 0.7 59 0.5 1.1 3.5 45 66 6.0 Hungary 6 .. .. 0.1 17 5.8 5.6 1.8 100 100 51.9 India 450 43 56 0.3 168 0.8 1.3 3.3 14 28 7.2 Indonesia 420 50 61 0.2 228 0.8 1.9 3.4 51 52 5.8 Iran, Islamic Rep. 140 49 79 0.2 22 4.0 6.5 1.0 83 .. 32.4 Iraq .. 14 .. .. .. 2.6 .. 11.0 .. .. .. Ireland 1 60 .. 0.2 13 8.7 10.2 1.8 .. .. 56.1 Israel 4 68 .. 0.1 8 7.1 9.2 4.3 .. .. 27.9 Italy 3 .. .. 0.4 7 7.0 7.7 2.2 .. .. 53.9 Jamaica 170 55 69 1.6 7 3.3 3.8 7.7 83 83 56.1 Japan 6 58 .. .. 21 8.7 9.6 4.9 100 100 69.0 Jordan 62 40 57 .. 7 3.2 3.8 3.4 .. 85 19.7 Kazakhstan 140 .. 51 0.1 129 17.6 11.9 1.1 97 97 12.3 Kenya 560 27 39 .. 353 0.2 0.3 3.9 39 42 8.0 Korea, Dem. Rep. 370 62 .. .. 344 12.1 3.5 1.3 .. .. 0.0 Korea, Rep. 14 79 .. <0.1 90 5.6 9.4 1.7 .. .. 75.9 Kuwait 4 .. .. .. 24 20.4 36.9 6.3 .. .. 33.8 Kyrgyz Republic 150 .. 48 0.1 121 2.8 1.1 0.8 .. 93 14.3 Lao PDR 660 .. 38 0.2 151 0.1 0.3 1.2 .. 48 1.7 Latvia 10 .. .. 0.8 53 5.4 2.8 1.4 .. 78 55.0 Lebanon 150 .. 58 0.1 19 3.1 4.2 1.2 .. .. 38.3 Lesotho 960 23 37 23.2 637 .. .. 0.6 .. 36 3.5 Liberia 1,200 .. 11 1.7 277 0.2 0.1 3.8 40 32 0.5 Libya 97 .. .. .. 17 8.7 9.5 1.6 97 97 4.3 Lithuania 11 .. .. 0.1 68 6.6 4.1 0.9 .. .. 49.2 Macedonia, FYR 10 .. 14 <0.1 29 8.1 5.1 0.9 .. 89 27.3 Madagascar 510 17 27 0.1 251 0.1 0.2 6.4 8 12 0.6 Malawi 1,100 13 42 11.9 346 0.1 0.1 3.3 46 60 1.0 Malaysia 62 50 .. 0.5 103 3.1 9.3 6.9 .. 94 55.7 Mali 970 .. 8 1.5 319 0.1 0.0 1.0 35 45 0.8 Mauritania 820 3 .. 0.8 318 1.4 0.6 2.9 20 24 1.0 Mauritius 15 75 76 1.7 22 1.4 2.7 24.3 94 94 27.0 Mexico 60 .. 71 0.3 20 4.5 4.1 3.2 56 81 22.7 Moldova 22 .. 68 0.4 141 5.4 2.1 1.3 .. 79 18.4 Mongolia 46 .. 66 0.1 205 4.7 3.4 1.1 .. 50 12.3 Morocco 240 42 63 0.1 92 1.0 1.6 1.9 52 72 21.4 Mozambique 520 .. 17 12.5 431 0.1 0.1 2.9 20 31 0.9 Myanmar 380 17 34 0.7 171 0.1 0.2 2.7 23 82 0.1 Namibia 210 29 55 15.3 767 0.0 1.3 2.1 26 35 4.9 Nepal 830 23 48 0.5 173 0.0 0.1 1.1 9 27 1.4 Netherlands 6 76 .. 0.2 8 9.3 7.7 1.3 100 100 84.2 New Zealand 9 .. .. 0.1 7 6.5 7.2 5.1 .. .. 69.2 Nicaragua 170 .. 72 0.2 49 0.6 0.7 1.3 42 48 2.8 Niger 1,800 4 11 0.8 174 0.1 0.1 1.0 3 7 0.3 Nigeria 1,100 6 13 3.1 311 0.5 0.8 4.3 26 30 6.8 Norway 7 74 .. 0.1 6 7.1 11.4 1.5 .. .. 84.8 Oman 64 9 .. .. 13 5.6 12.5 4.2 85 .. 13.1 Pakistan 320 15 30 0.1 181 0.6 0.9 1.7 33 58 10.8 Panama 130 .. .. 1.0 47 1.3 1.8 2.9 .. 74 22.3 Papua New Guinea 470 .. .. 1.5 250 0.6 0.7 3.6 44 45 1.8 Paraguay 150 48 73 0.6 58 0.5 0.7 0.5 60 70 8.7 Peru 240 59 71 0.5 126 1.0 1.4 2.8 55 72 27.4 Philippines 230 36 51 .. 290 0.7 0.9 6.6 58 78 6.0 Poland 8 49 .. 0.1 25 9.1 7.9 1.2 .. .. 44.0 Portugal 11 .. .. 0.5 30 4.3 5.9 2.8 92 99 40.1 Puerto Rico 18 .. .. .. 4 .. .. 3.6 .. .. 25.4 2009 World Development Indicators 23 Millennium Development Goals: 1.3 protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15­49 ages 15­49 people metric tons % % of population peoplea 2005 1990 2002­07b 2007 2007 1990 2005 2008 1990 2006 2007 Romania 24 .. 70 0.1 115 6.7 4.1 1.6 72 72 23.9 Russian Federation 28 34 .. 1.1 110 15.3 10.5 1.3 87 87 21.1 Rwanda 1,300 21 17 2.8 397 0.1 0.1 1.6 29 23 1.1 Saudi Arabia 18 .. .. .. 46 12.1 16.5 3.8 91 99 26.4 Senegal 980 .. 12 1.0 272 0.4 0.4 2.2 26 28 6.6 Serbia 14 c .. 41 0.1 32 6.2d 6.5d .. .. 92 20.3 Sierra Leone 2,100 .. 5 1.7 574 0.1 0.2 3.2 .. 11 0.2 Singapore 14 65 .. 0.2 27 13.8 13.2 9.7 100 100 65.7 Slovak Republic 6 74 .. <0.1 17 9.7 6.8 1.1 100 100 55.9 Slovenia 6 .. .. <0.1 13 9.0 7.4 2.1 .. .. 52.6 Somalia 1,400 1 15 0.5 249 0.0 0.1 3.2 .. 23 1.1 South Africa 400 57 60 18.1 948 9.4 8.7 1.6 55 59 8.3 Spain 4 .. .. 0.5 30 5.5 7.9 3.8 100 100 51.3 Sri Lanka 58 .. 68 .. 60 0.2 0.6 14.0 71 86 3.9 Sudan 450 9 8 1.4 243 0.2 0.3 2.4 33 35 9.1 Swaziland 390 20 51 26.1 1,198 0.6 0.8 0.8 .. 50 3.7 Sweden 3 .. .. 0.1 6 5.8 5.4 1.4 100 100 79.7 Switzerland 5 .. .. 0.6 6 6.4 5.5 1.4 100 100 76.3 Syrian Arab Republic 130 .. 58 .. 24 2.8 3.6 2.0 81 92 17.4 Tajikistan 170 .. 38 0.3 231 4.4 0.8 0.8 .. 92 7.2 Tanzania 950 10 26 6.2 297 0.1 0.1 5.1 35 33 1.0 Thailand 110 .. 77 1.4 142 1.8 4.3 3.4 78 96 21.0 Timor-Leste 380 .. 20 .. 322 .. 0.2 .. .. 41 0.1 Togo 510 34 17 3.3 429 0.2 0.2 1.2 13 12 5.0 Trinidad and Tobago 45 .. 43 1.5 11 13.8 24.7 1.7 93 92 16.0 Tunisia 100 50 .. 0.1 26 1.6 2.2 2.1 74 85 16.8 Turkey 44 63 71 .. 30 2.5 3.4 1.4 85 88 16.5 Turkmenistan 130 .. 48 <0.1 68 8.7 8.6 10.7 .. .. 1.4 Uganda 550 5 24 5.4 330 0.0 0.1 2.5 29 33 2.5 Ukraine 18 .. 67 1.6 102 13.2 6.9 1.1 96 93 21.5 United Arab Emirates 37 .. .. .. 16 29.3 30.1 14.1 97 97 51.8 United Kingdom 8 .. 84 0.2 15 9.9 9.1 2.8 .. .. 71.7 United States 11 71 .. 0.6 4 19.2 19.5 5.7 100 100 73.5 Uruguay 20 .. .. 0.6 22 1.3 1.7 2.6 100 100 29.1 Uzbekistan 24 .. 65 0.1 113 6.1 4.3 1.0 93 96 4.5 Venezuela, RB 57 .. .. .. 34 5.9 5.6 1.1 83 .. 20.8 Vietnam 150 53 76 0.5 171 0.3 1.2 3.5 29 65 21.0 West Bank and Gaza .. .. 50 .. 20 .. .. .. .. 80 9.6 Yemen, Rep. 430 10 28 .. 76 0.8 1.0 12.6 28 46 1.4 Zambia 830 15 34 15.2 506 0.3 0.2 0.7 42 52 4.2 Zimbabwe 880 43 60 15.3 782 1.6 0.9 0.9 44 46 10.1 World 400 w 57 w 60 w 0.8 w 139 w 4.3e w 4.5e w 51 w 60 w 21.8 w Low income 780 22 33 2.1 269 0.7 0.6 26 39 5.2 Middle income 260 61 68 0.6 129 2.8 3.3 48 60 15.2 Lower middle income 300 63 69 0.3 134 1.8 2.8 41 55 12.4 Upper middle income 97 52 .. 1.7 108 6.9 5.5 77 83 26.6 Low & middle income 440 54 60 0.9 162 2.4 2.7 44 55 13.1 East Asia & Pacific 150 75 78 0.2 136 1.9 3.6 48 66 14.6 Europe & Central Asia 44 .. .. 0.6 84 10.4 7.0 88 89 21.4 Latin America & Carib. 130 56 .. 0.5 50 2.3 2.5 68 78 26.9 Middle East & N. Africa 200 42 62 0.1 41 2.5 3.7 67 77 17.1 South Asia 500 40 53 0.3 174 0.7 1.1 18 33 6.6 Sub-Saharan Africa 900 15 23 5.0 369 0.9 0.8 26 31 4.4 High income 10 72 .. 0.3 16 11.8 12.6 100 100 65.7 Euro area 5 .. .. 0.3 13 8.4 8.1 .. .. 59.2 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite ITU for third-party use of these data. b. Data are for the most recent year available. c. Includes Montenegro. d. Includes Kosovo and Montenegro. e. Includes emissions not allocated to specific countries. 24 2009 World Development Indicators WORLD VIEW Millennium Development Goals: protecting our common environment 1.3 About the data Definitions The Millennium Development Goals address con- between contraction of the virus and the appearance · Maternal mortality ratio is the number of women cerns common to all economies. Diseases and envi- of symptoms, or malaria, which has periods of dor- who die from pregnancy-related causes during preg- ronmental degradation do not respect national bound- mancy, can be particularly difficult. The table shows nancy and childbirth, per 100,000 live births. Data aries. Epidemic diseases, wherever they occur, pose the estimated prevalence of HIV among adults ages are from various years and adjusted to a common a threat to people everywhere. And environmental 15­49. Prevalence among older populations can be 2005 base year. The values are modeled estimates damage in one location may affect the well-being of affected by life-prolonging treatment. The incidence of (see About the data for table 2.18). · Contracep- plants, animals, and humans far away. The indicators tuberculosis is based on case notifications and esti- tive prevalence rate is the percentage of women in the table relate to goals 5, 6, and 7 and the targets mates of cases detected in the population. ages 15­49 married or in-union who are practicing, of goal 8 that address access to new technologies. Carbon dioxide emissions are the primary source or whose sexual partners are practicing, any form of For the other targets of goal 8, see table 1.4. of greenhouse gases, which contribute to global contraception. · HIV prevalence is the percentage The target of achieving universal access to repro- warming, threatening human and natural habitats. of people ages 15­49 who are infected with HIV. ductive health has been added to goal 5 to address In recognition of the vulnerability of animal and plant · Incidence of tuberculosis is the estimated number the importance of family planning and health services species, a new target of reducing biodiversity loss of new tuberculosis cases (pulmonary, smear posi- in improving maternal health and preventing maternal has been added to goal 7. tive, and extrapulmonary). · Carbon dioxide emis- death. Women with multiple pregnancies are more Access to reliable supplies of safe drinking water and sions are those stemming from the burning of fossil likely to die in childbirth. Access to contraception is sanitary disposal of excreta are two of the most impor- an important way to limit and space births. tant means of improving human health and protecting fuels and the manufacture of cement. They include Measuring disease prevalence or incidence can be the environment. Improved sanitation facilities prevent emissions produced during consumption of solid, difficult. Most developing economies lack reporting human, animal, and insect contact with excreta. liquid, and gas fuels and gas flaring (see table 3.8). systems for monitoring diseases. Estimates are often Internet use includes narrowband and broadband · Proportion of species threatened with extinction derived from survey data and report data from sentinel Internet. Narrowband is often limited to basic applica- is the total number of threatened mammal (exclud- sites, extrapolated to the general population. Tracking tions; broadband is essential to promote e-business, ing whales and porpoises), bird, and higher native, diseases such as HIV/AIDS, which has a long latency e-learning, e-government, and e-health. vascular plant species as a percentage of the total number of known species of the same categories. Location of indicators for Millennium Development Goals 5­7 1.3a · Access to improved sanitation facilities is the percentage of the population with at least adequate Goal 5. Improve maternal health Table access to excreta disposal facilities (private or 5.1 Maternal mortality ratio 1.3, 2.18 5.2 Proportion of births attended by skilled health personnel 2.18, 2.21 shared, but not public) that can effectively prevent 5.3 Contraceptive prevalence rate 1.3, 2.18, 2.21 human, animal, and insect contact with excreta 5.4 Adolescent fertility rate 2.18 (facilities do not have to include treatment to ren- 5.5 Antenatal care coverage 1.5, 2.18, 2.21 der sewage outflows innocuous). Improved facilities 5.6 Unmet need for family planning 2.18 range from simple but protected pit latrines to flush Goal 6. Combat HIV/AIDS, malaria, and other diseases toilets with a sewerage connection. To be effective, 6.1 HIV prevalence among population ages 15­24 1.3*, 2.20* facilities must be correctly constructed and properly 6.2 Condom use at last high-risk sex 2.20* maintained. · Internet users are people with access 6.3 Proportion of population ages 15­24 with comprehensive, correct -- knowledge of HIV/AIDS to the worldwide network. 6.4 Ratio of school attendance of orphans to school attendance of -- nonorphans ages 10­14 6.5 Proportion of population with advanced HIV infection with access -- to antiretroviral drugs 6.6 Incidence and death rates associated with malaria -- 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets 2.17 6.8 Proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 2.17 6.9 Incidence, prevalence, and death rates associated with tuberculosis 1.3, 2.20 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course 2.17 Data sources Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 3.1 The indicators here and throughout this book have 7.2 Carbon dioxide emissions, total, per capita, and per $1 purchasing power been compiled by World Bank staff from primary parity GDP 3.8 and secondary sources. Efforts have been made 7.3 Consumption of ozone-depleting substances 3.9* 7.4 Proportion of fish stocks within safe biological limits -- to harmonize the data series used to compile this 7.5 Proportion of total water resources used 3.5 table with those published on the United Nations 7.6 Proportion of terrestrial and marine areas protected -- Millennium Development Goals Web site (www. 7.7 Proportion of species threatened with extinction 1.3 un.org/millenniumgoals), but some differences in 7.8 Proportion of population using an improved drinking water source 1.3, 2.17, 3.5 timing, sources, and definitions remain. For more 7.9 Proportion of population using an improved sanitation facility 1.3, 2.17, 3.11 7.10 Proportion of urban population living in slums -- information see the data sources for the indica- -- No data are available in the World Development Indicators database. * Table shows information on related indicators. tors listed in table 1.3a. 2009 World Development Indicators 25 Millennium Development Goals: 1.4 overcoming obstacles Development Assistance Committee members Official development Least developed countries' access Support to assistance (ODA) to high-income markets agriculture by donor For basic Goods Average tariff on exports of Net social servicesa (excluding arms) least developed countries % of % of total admitted free of tariffs donor sector-allocable % of exports from least Agricultural products Textiles Clothing GNI ODA developed countries % % % % of GDP 2007 2007 2000 2006 2000 2006 2000 2006 2000 2006 2007b Australia 0.32 9.1 95.9 100.0 0.2 0.0 5.7 0.0 22.5 0.0 0.28 Canada 0.29 31.2 39.0 99.7 0.3 0.1 6.0 0.2 19.3 1.7 0.68 European Union 97.8 97.8 3.0 2.4 0.0 0.1 0.0 1.2 0.91 Austria 0.50 9.0 Belgium 0.43 20.6 Denmark 0.81 10.1 Finland 0.39 13.9 France 0.38 6.0 Germany 0.37 10.0 Greece 0.16 15.0 Ireland 0.55 35.1 Italy 0.19 9.8 Luxembourg 0.91 33.9 Netherlands 0.81 18.1 Portugal 0.22 3.1 Spain 0.37 15.6 Sweden 0.93 13.1 United Kingdom 0.36 57.6 Japan 0.17 3.8 49.1 26.7 4.7 4.4 5.0 2.7 0.4 0.1 1.04 New Zealand 0.27 32.0 85.9c 99.2c 0.0 c 13.1c 9.3c 0.0 c 12.9c 0.0 c 0.22 Norway 0.95 21.2 99.0 99.0 3.6 0.2 4.6 0.0 1.4 1.0 0.79 Switzerland 0.37 6.3 99.4 96.7 6.1 2.6 0.0 0.0 0.0 0.0 1.11 United States 0.16 31.6 50.3 76.6 6.9 6.4 7.0 5.8 14.1 11.3 0.73 Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC MDRI HIPC HIPC HIPC MDRI decision completion Initiative assistancef decision completion Initiative assistancef pointd pointd assistancee pointd pointd assistancee $ millions $ millions $ millions $ millions Afghanistan Jul. 2007 Floating 571 .. Honduras Jul. 2000 Apr. 2005 776 1,543 Benin Jul. 2000 Mar. 2003 366 604 Liberia Mar. 2008 Floating 2,845 .. Boliviag Feb. 2000 Jun. 2001 1,856 1,596 Madagascar Dec. 2000 Oct. 2004 1,167 1,292 Burkina Fasog,h Jul. 2000 Apr. 2002 772 603 Malawih Dec. 2000 Aug. 2006 1,310 705 Burundi Aug. 2005 Jan. 2009 908 53i Malig Sep. 2000 Mar. 2003 752 1,043 Cameroon Oct. 2000 Apr. 2006 1,768 747 Mauritania Feb. 2000 Jun. 2002 868 450 Central African Republic Sep. 2007 Floating 611 .. Mozambiqueg Apr. 2000 Sep. 2001 2,992 1,057 Chad May 2001 Floating 227 .. Nicaragua Dec. 2000 Jan. 2004 4,618 954 Congo, Dem. Rep. Jul. 2003 Floating 7,636 .. Nigerh Dec. 2000 Apr.2004 899 519 Congo, Rep. Apr. 2006 Floating 1,847 .. Rwandah Dec. 2000 Apr. 2005 908 225 Ethiopiah Nov. 2001 Apr. 2004 2,575 1,458 São Tomé & Principeh Dec. 2000 Mar. 2007 163 26 Gambia, The Dec. 2000 Dec. 2007 93 199 Senegal Jun. 2000 Apr. 2004 682 1,374 Ghana Feb. 2002 Jul. 2004 2,910 2,095 Sierra Leone Mar. 2002 Dec. 2006 857 352 Guinea Dec. 2000 Floating 761 .. Tanzania Apr. 2000 Nov. 2001 2,828 2,038 Guinea-Bissau Dec. 2000 Floating 581 .. Togo Nov. 2008 Floating 270 .. Guyanag Nov. 2000 Dec. 2003 852 402 Ugandag Feb. 2000 May 2000 1,434 1,805 Haiti Nov. 2006 Floating 147 .. Zambia Dec. 2000 Apr. 2005 3,489 1,632 a. Includes primary education, basic life skills for youth, adult and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education, health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease control including HIV/AIDS, personnel development for population and reproductive health, basic drinking water supply and basic sanitation, and multisector aid for basic social services. b. Provisional data. c. Calculated by World Bank staff using the World Integrated Trade Solution based on the United Nations Conference on Trade and Development's Trade Analysis and Information Systems database. d. Refers to the Enhanced HIPC Initiative. e. Total HIPC assistance (committed debt relief) assuming of full participation of creditors, in end-2007 net present value terms. Topping-up assistance and assistance provided under the original HIPC Initiative were committed in net present value terms as of the decision point and are converted to end-2007 terms. f. Multilateral Debt Relief Initiative (MDRI) assistance has been delivered in full to all post-completion point countries, shown in end-2007 net present value terms. g. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. h. Assistance includes topping up at completion point. i. Excludes $15 million (in nominal terms) of committed debt relief by the International Monetary Fund. 26 2009 World Development Indicators WORLD VIEW Millennium Development Goals: overcoming obstacles 1.4 About the data Definitions Achieving the Millennium Development Goals requires lines with "international peaks"). The averages in the · Net offi cial development assistance (ODA) is an open, rule-based global economy in which all table include ad valorem duties and equivalents. grants and loans (net of repayments of principal) that countries, rich and poor, participate. Many poor Subsidies to agricultural producers and exporters meet the DAC definition of ODA and are made to coun- countries, lacking the resources to finance develop- in OECD countries are another barrier to developing tries on the DAC list of recipients. · ODA for basic ment, burdened by unsustainable debt, and unable economies' exports. Agricultural subsidies in OECD social services is aid reported by DAC donors for to compete globally, need assistance from rich coun- economies are estimated at $365 billion in 2007. basic education, primary health care, nutrition, pop- tries. For goal 8--develop a global partnership for The Debt Initiative for Heavily Indebted Poor Coun- ulation policies and programs, reproductive health, development--many indicators therefore monitor the tries (HIPCs), an important step in placing debt relief and water and sanitation services. · Goods admitted actions of members of the Organisation for Economic within the framework of poverty reduction, is the first free of tariffs are exports of goods (excluding arms) Co-operation and Development's (OECD) Develop- comprehensive approach to reducing the external from least developed countries admitted without tar- ment Assistance Committee (DAC). debt of the world's poorest, most heavily indebted iff. · Average tariff is the unweighted average of the Official development assistance (ODA) has risen countries. A 1999 review led to an enhancement of effectively applied rates for all products subject to in recent years as a share of donor countries' gross the framework. In 2005, to further reduce the debt tariffs. · Agricultural products are plant and animal national income (GNI), but the poorest economies need of HIPCs and provide resources for meeting the Mil- products, including tree crops but excluding timber additional assistance to achieve the Millennium Devel- lennium Development Goals, the Multilateral Debt and fish products. · Textiles and clothing are natural opment Goals. After rising to a record $107 billion in Relief Initiative (MDRI), proposed by the Group of and synthetic fibers and fabrics and articles of cloth- 2005, net ODA disbursements from DAC donors fell Eight countries, was launched. ing made from them. · Support to agriculture is the 3.3 percent in 2007 to $103.5 billion in nominal terms. Under the MDRI four multilateral institutions--the value of gross transfers from taxpayers and consum- One important action that high-income economies can International Development Association (IDA), Interna- ers arising from policy measures, net of associated take is to reduce barriers to exports from low- and mid- tional Monetary Fund (IMF), African Development Fund budgetary receipts, regardless of their objectives and dle-income economies. The European Union has begun (AfDF), and Inter-American Development Bank (IDB) impacts on farm production and income or consump- to eliminate tariffs on developing economy exports of --provide 100 percent debt relief on eligible debts tion of farm products. · HIPC decision point is the "everything but arms," and the United States offers due to them from countries having completed the HIPC date when a heavily indebted poor country with an special concessions to Sub-Saharan African exports. Initiative process. Data in the table refer to status as of established track record of good performance under However, these programs still have many restrictions. February 2009 and might not show countries that have adjustment programs supported by the IMF and the Average tariffs in the table refl ect high-income since reached the decision or completion point. Debt World Bank commits to additional reforms and a OECD member tariff schedules for exports of coun- relief under the HIPC Initiative has reduced future debt poverty reduction strategy. · HIPC completion point tries designated least developed countries by the payments by $51.3 billion for 34 countries that have is the date when a country successfully completes United Nations. Although average tariffs have been reached the decision point. And 23 countries that have the key structural reforms agreed on at the decision falling, averages may disguise high tariffs on specific reached the completion point have received additional point, including implementing a poverty reduction goods (see table 6.8 for each country's share of tariff assistance of $22.8 billion under the MDRI. strategy. The country then receives the bulk of debt relief under the HIPC Initiative without further policy Location of indicators for Millennium Development Goal 8 1.4a conditions. · HIPC Initiative assistance is the debt Goal8. Develop a global partnership for development Table relief committed as of the decision point in end-2007 8.1 Net ODA as a percentage of DAC donors' gross national income 1.4, 6.13 net present value. · MDRI assistance is the debt 8.2 Proportion of ODA for basic social services 1.4, 6.14b* relief from IDA, IMF, AfDF, and IDB, delivered to coun- 8.3 Proportion of ODA that is untied 6.14b tries having reached the HIPC completion point in 8.4 Proportion of ODA received in landlocked countries as a percentage of GNI -- end-2007 net present value. 8.5 Proportion of ODA received in small island developing states as a percentage of GNI -- 8.6 Proportion of total developed country imports (by value, excluding arms) from least developed countries admitted free of duty 1.4 Data sources 8.7 Average tariffs imposed by developed countries on agricultural products and Data on ODA are from the OECD. Data on goods textiles and clothing from least developed countries 1.4, 6.8* admitted free of tariffs and average tariffs are 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 1.4 8.9 Proportion of ODA provided to help build trade capacity -- from the World Trade Organization, in collabora- 8.10 Number of countries reaching HIPC decision and completion points 1.4 tion with the United Nations Conference on Trade 8.11 Debt relief committed under new HIPC initiative 1.4 and Development and the International Trade Cen- 8.12 Debt services as a percentage of exports of goods and services 6.10* tre. These data are available at www.mdg-trade. 8.13 Proportion of population with access to affordable, essential drugs on a org. Data on subsidies to agriculture are from sustainable basis -- 8.14 Telephone lines per 100 people 1.3*, 5.10 the OECD's Producer and Consumer Support Esti- 8.15 Cellular subscribers per 100 people 1.3*, 5.10 mates, OECD Database 1986­2007. Data on the 8.16 Internet users per 100 people 5.11 HIPC Initiative and MDRI are from the World Bank's -- No data are available in the World Development Indicators database. * Table shows information on related indicators. Economic Policy and Debt Department. 2009 World Development Indicators 27 1.5 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of nonagricultural % of male % of female % of total Male Female % ages 15­19 wage employment employment employment % of total seats 2007 2007 2007 2002­07a 2002­07a 2006 2002­07a 2002­07a 1990 2008 Afghanistan .. .. .. .. .. .. .. .. 4 28 Albania 50.3 73 80 97 .. .. .. .. 29 7 Algeria 49.5 71 74 89 .. 17 7.1 13.6 2 8 Angola 50.7 41 44 80 .. .. .. .. 15 37 Argentina 51.1 72 79 99 .. 45 0.7b 1.6b 6 40 Armenia 53.5 68 75 93 5 46 .. .. 36 8 Australia 50.2 79 84 .. .. 49 0.2 0.4 6 27 Austria 51.0 77 83 .. .. 47 2.0 2.9 12 32 Azerbaijan 51.4 64 71 77 6 50 16.8 16.8 .. 11 Bangladesh 48.8 63 65 51 33 .. 9.7 60.1 10 15 Belarus 53.5 65 76 99 .. .. .. .. .. 29 Belgium 51.0 77 83 .. .. 46 0.4 2.9 9 35 Benin 49.6 56 58 84 21 .. .. .. 3 11 Bolivia 50.2 63 68 79 16 .. 12.6 34.8 9 17 Bosnia and Herzegovina 51.4 72 77 99 .. 35 3.0 11.0 .. 12 Botswana 50.3 50 51 .. .. 42 2.2 2.2 5 11 Brazil 50.7 69 76 97 .. .. 4.6b 8.1b 5 9 Bulgaria 51.6 69 76 .. .. 53 0.7 1.6 21 22 Burkina Faso 50.0 51 54 85 23 .. .. .. .. 15 Burundi 51.1 48 51 92 .. .. .. .. .. 31 Cambodia 51.2 57 62 69 8 52 .. .. .. 16 Cameroon 50.0 50 51 82 28 .. .. .. 14 14 Canada 50.5 78 83 .. .. 50 0.1 0.2 13 21 Central African Republic 51.2 43 46 69 .. .. .. .. 4 11 Chad 50.3 49 52 39 37 .. .. .. .. 5 Chile 50.5 75 82 .. .. 39 0.9 2.8 .. 15 China 48.4 71 75 90 .. .. .. .. 21 21 Hong Kong, China 52.1 79 85 .. .. 48 0.1b 1.1b .. .. Colombia 50.8 69 77 94 21 49 3.2 6.1 5 8 Congo, Dem. Rep. 50.5 45 48 85 24 .. .. .. 5 8 Congo, Rep. 50.4 54 57 86 27 .. .. .. 14 7 Costa Rica 49.2 76 81 92 .. 41 1.3 2.8 11 37 Côte d'Ivoire 49.3 48 49 85 .. .. .. .. 6 9 Croatia 51.9 72 79 100 4 44 c 1.1 3.7 .. 22 Cuba 50.0 76 80 100 .. 43 .. .. 34 43 Czech Republic 51.2 74 80 .. .. 46 0.2 1.1 .. 16 Denmark 50.5 76 81 .. .. 49 0.3 1.0 31 38 Dominican Republic 49.9 69 75 99 21 39 2.8 4.9 8 20 Ecuador 49.9 72 78 84 .. 42 4.4 11.1 5 25 Egypt, Arab Rep. 49.9 69 74 70 9 21 8.6b 32.6b 4 2 El Salvador 50.9 69 75 86 .. 49 8.8 9.9 12 17 Eritrea 50.9 56 60 70 14 .. .. .. .. 22 Estonia 53.9 67 79 .. .. 53 0.0d 0.0 d .. 21 Ethiopia 50.3 52 54 28 17 47 7.8 12.7 .. 22 Finland 51.0 76 83 .. .. 51 0.6 0.4 32 42 France 51.3 78 85 .. .. 48 0.3 1.0 7 18 Gabon 50.0 56 57 .. .. .. .. .. 13 17 Gambia, The 49.9 59 60 98 .. .. .. .. 8 9 Georgia 52.8 67 75 94 .. 49 19.0 39.0 .. 6 Germany 51.1 77 82 .. .. 47 0.4 1.8 .. 32 Ghana 49.3 60 60 92 14 .. .. .. .. 11 Greece 50.5 77 82 .. .. 42 3.7 10.7 7 15 Guatemala 51.2 67 74 84 .. 38 21.3 24.5 7 12 Guinea 49.5 54 58 82 32 .. .. .. .. 19 Guinea-Bissau 50.6 45 48 78 .. .. .. .. 20 14 Haiti 50.5 59 63 85 14 .. .. .. .. 4 28 2009 World Development Indicators WORLD VIEW Female Life Women in development Pregnant Teenage Women in Unpaid family 1.5 Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of nonagricultural % of male % of female % of total Male Female % ages 15­19 wage employment employment employment % of total seats 2007 2007 2007 2002­07a 2002­07a 2006 2002­07a 2002­07a 1990 2008 Honduras 50.3 67 74 92 22 45 12.1 8.3 10 23 Hungary 52.4 69 77 .. .. 48 0.3 0.7 21 11 India 48.2 63 66 74 16 18 .. .. 5 9 Indonesia 50.1 69 73 93 10 29 7.8 33.6 12 12 Iran, Islamic Rep. 49.3 69 73 .. .. .. 5.4 32.7 2 3 Iraq .. .. .. .. .. .. .. .. 11 26 Ireland 50.1 77 82 .. .. 48 0.4 0.9 8 13 Israel 50.5 79 83 .. .. 49 0.1 0.4 7 14 Italy 51.4 79 84 .. .. 43 1.3 2.6 13 21 Jamaica 50.7 70 75 91 .. 48 0.5 2.2 5 13 Japan 51.2 79 86 .. .. 42 1.1 7.3 1 9 Jordan 48.6 71 74 99 4 26 .. .. 0 6 Kazakhstan 52.2 61 72 100 7 49 1.0 1.3 .. 16 Kenya 50.2 53 55 88 23 .. .. .. 1 9 Korea, Dem. Rep. 50.7 65 69 .. .. .. .. .. 21 20 Korea, Rep. 50.0 76 82 .. .. 42 1.2 12.7 2 14 Kuwait 40.1 76 80 .. .. .. .. .. .. 3 Kyrgyz Republic 50.7 64 72 97 .. 52 8.8 19.3 .. 26 Lao PDR 50.2 63 66 .. .. 50 .. .. 6 25 Latvia 53.9 66 77 .. .. 53 1.5 1.6 .. 20 Lebanon 51.0 70 74 96 .. .. .. .. 0 5 Lesotho 52.9 43 42 90 20 .. .. .. .. 25 Liberia 50.0 45 47 .. 32 .. .. .. .. 13 Libya 48.2 72 77 .. .. .. .. .. .. 8 Lithuania 53.4 65 77 .. .. 54 1.1 2.4 .. 23 Macedonia, FYR 50.1 72 77 98 .. 40 7.0 14.9 .. 32 Madagascar 50.3 58 61 80 34 38 32.1 73.0 7 8 Malawi 50.3 48 48 92 34 .. .. .. 10 13 Malaysia 49.2 72 77 79 .. 38 2.7 8.8 5 11 Mali 51.3 52 57 70 36 35 18.4 10.2 .. 10 Mauritania 49.4 62 66 .. .. .. .. .. .. 22 Mauritius 50.4 69 76 .. .. 38 0.9 4.7 7 17 Mexico 51.2 73 77 .. .. 39 4.9 10.0 12 23 Moldova 52.1 65 72 98 6 54 1.3 3.4 .. 22 Mongolia 50.1 64 70 99 .. 53 18.4 31.7 25 4 Morocco 50.8 69 73 68 7 28 17.0 55.3 0 11 Mozambique 51.5 42 42 85 41 .. .. .. 16 35 Myanmar 50.5 59 65 .. .. .. .. .. .. .. Namibia 50.7 52 53 95 .. 47 3.2 5.8 7 27 Nepal 50.4 63 64 44 19 .. .. .. 6 33 Netherlands 50.5 78 82 .. .. 47 0.2 1.0 21 39 New Zealand 50.7 78 82 .. .. 47 0.8 1.5 14 33 Nicaragua 50.2 70 76 90 .. .. 12.2 9.1 15 19 Niger 49.3 58 56 46 39 .. .. .. 5 12 Nigeria 50.0 46 47 58 25 21 .. .. .. 7 Norway 50.3 78 83 .. .. 49 0.2 0.3 36 36 Oman 44.1 74 77 .. .. .. .. .. .. 0 Pakistan 48.6 65 66 61 9 11 18.6 61.9 10 23 Panama 49.6 73 78 .. .. 43 2.3 4.0 8 17 Papua New Guinea 49.3 55 60 .. .. .. .. .. 0 1 Paraguay 49.5 70 74 94 .. .. 10.8b 8.9b 6 13 Peru 49.9 69 74 91 26 36 4.7b 9.9b 6 29 Philippines 49.6 70 74 88 8 42 9.0 18.0 9 21 Poland 51.7 71 80 .. .. 47 2.8 6.0 14 20 Portugal 51.6 75 82 .. .. 47 0.7 1.5 8 28 Puerto Rico 52.1 74 83 .. .. 41 0.0 d 0.0 d .. .. 2009 World Development Indicators 29 1.5 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of nonagricultural % of male % of female % of total Male Female % ages 15­19 wage employment employment employment % of total seats 2007 2007 2007 2002­07a 2002­07a 2006 2002­07a 2002­07a 1990 2008 Romania 51.3 69 76 94 .. 47 6.5 19.9 34 9 Russian Federation 53.7 62 74 .. .. 51 0.1 0.1 .. 14 Rwanda 51.8 45 48 94 4 .. .. .. 17 56 Saudi Arabia 45.0 71 75 .. .. 13 .. .. .. 0 Senegal 50.2 61 65 87 19 .. .. .. 13 22 Serbia 50.5 71 76 98 .. 42 3.1 11.9 .. 22 Sierra Leone 50.7 41 44 81 .. 23 14.8 21.6 .. 13 Singapore 49.7 78 83 .. .. 50 0.4 1.3 5 25 Slovak Republic 51.5 71 78 .. .. 50 0.1b 0.1b .. 19 Slovenia 51.2 74 82 .. .. 48 3.1 7.1 .. 13 Somalia 50.4 47 49 26 .. .. .. .. 4 8 South Africa 50.8 49 52 92 .. 43 0.3 0.6 3 33 Spain 50.7 78 84 .. .. 43 0.7 1.6 15 36 Sri Lanka 50.7 69 76 99 .. 45 4.4b 21.7b 5 6 Sudan 49.6 57 60 70 .. .. .. .. .. 18 Swaziland 51.6 40 39 85 23 .. .. .. 4 11 Sweden 50.4 79 83 .. .. 50 0.3 0.3 38 47 Switzerland 51.3 79 84 .. .. 47 1.7 3.2 14 29 Syrian Arab Republic 49.5 72 76 84 .. .. .. .. 9 12 Tajikistan 50.4 64 69 79 .. .. .. .. .. 18 Tanzania 50.3 51 54 78 26 .. 9.7 13.0 .. 30 Thailand 51.3 66 75 98 .. 47 14.0 29.9 3 12 Timor-Leste 49.2 60 62 61 .. .. .. .. .. 29 Togo 50.5 57 60 84 .. .. .. .. 5 11 Trinidad and Tobago 50.8 68 72 96 .. 43 0.3 1.7 17 27 Tunisia 49.7 72 76 .. .. .. .. .. 4 23 Turkey 49.6 69 74 81 .. 21 5.6 38.2 1 9 Turkmenistan 50.8 59 68 99 .. .. .. .. 26 16 Uganda 50.0 51 52 94 25 .. 10.3b 40.5b 12 31 Ukraine 53.9 63 74 99 .. 55 0.4 0.3 .. 8 United Arab Emirates 32.4 77 81 .. .. .. .. .. 0 23 United Kingdom 51.0 77 82 .. .. 50 0.2 0.5 6 20 United States 50.8 75 81 .. .. 47 0.1 0.1 7 17 Uruguay 51.7 72 80 .. .. 45 0.9b 3.0 b 6 12 Uzbekistan 50.3 64 70 99 .. .. .. .. .. 18 Venezuela, RB 49.8 71 77 .. .. 41 0.6 1.6 10 19 Vietnam 50.0 72 76 91 3 46 18.9 47.2 18 26 West Bank and Gaza 49.1 72 75 99 .. 17 6.6 31.5 .. .. Yemen, Rep. 49.4 61 64 41 .. .. .. .. 4 0d Zambia 50.2 42 42 93 32 .. .. .. 7 15 Zimbabwe 50.3 44 43 94 21 .. 10.4 13.6 11 15 World 49.6 w 67 w 71 w 81 w .. w .. w .. w 13 w 18 w Low income 49.8 56 59 67 .. .. .. .. 18 Middle income 49.3 67 72 86 .. .. .. 13 17 Lower middle income 48.9 67 71 84 .. .. .. 13 15 Upper middle income 51.2 67 75 .. 45 3.3 7.3 13 19 Low & middle income 49.4 65 69 81 .. .. .. 13 17 East Asia & Pacific 48.9 70 74 90 .. .. .. 17 18 Europe & Central Asia 52.1 65 74 .. 48 2.4 6.3 .. 15 Latin America & Carib. 50.6 70 76 95 .. 4.0 7.5 12 22 Middle East & N. Africa 49.7 68 72 76 .. .. .. 4 9 South Asia 48.4 63 66 69 18 .. .. 6 20 Sub-Saharan Africa 50.2 50 52 72 .. .. .. .. 18 High income 50.6 77 82 .. 46 0.5 2.4 12 22 Euro area 51.1 77 83 .. 45 0.8 2.1 12 25 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2007. d. Less than 0.5. 30 2009 World Development Indicators WORLD VIEW Women in development 1.5 About the data Definitions Despite much progress in recent decades, gender Women's wage work is important for economic · Female population is the percentage of the popu- inequalities remain pervasive in many dimensions of growth and the well-being of families. But restricted lation that is female. · Life expectancy at birth is life--worldwide. But while disparities exist through- access to education and vocational training, heavy the number of years a newborn infant would live if out the world, they are most prevalent in developing workloads at home and in nonpaid domestic and prevailing patterns of mortality at the time of its birth countries. Gender inequalities in the allocation of market activities, and labor market discrimination were to stay the same throughout its life. · Pregnant such resources as education, health care, nutrition, often limit women's participation in paid economic women receiving prenatal care are the percentage and political voice matter because of the strong activities, lower their productivity, and reduce their of women attended at least once during pregnancy association with well-being, productivity, and eco- wages. When women are in salaried employment, by skilled health personnel for reasons related to nomic growth. These patterns of inequality begin at they tend to be concentrated in the nonagricultural pregnancy. · Teenage mothers are the percentage of an early age, with boys routinely receiving a larger sector. However, in many developing countries women ages 15­19 who already have children or are share of education and health spending than do girls, women are a large part of agricultural employment, currently pregnant. · Women in nonagricultural sec- for example. often as unpaid family workers. Among people who tor are female wage employees in the nonagricultural Because of biological differences girls are are unsalaried, women are more likely than men to sector as a percentage of total nonagricultural wage expected to experience lower infant and child mor- be unpaid family workers, while men are more likely employment. · Unpaid family workers are those who tality rates and to have a longer life expectancy than women to be self-employed or employers. There work without pay in a market-oriented establishment than boys. This biological advantage may be over- are several reasons for this. or activity operated by a related person living in the shadowed, however, by gender inequalities in nutri- Few women have access to credit markets, capital, same household. · Women in parliaments are the tion and medical interventions and by inadequate land, training, and education, which may be required percentage of parliamentary seats in a single or care during pregnancy and delivery, so that female to start a business. Cultural norms may prevent lower chamber held by women. rates of illness and death sometimes exceed male women from working on their own or from super- rates, particularly during early childhood and the vising other workers. Also, women may face time reproductive years. In high-income countries women constraints due to their traditional family respon- tend to outlive men by four to eight years on aver- sibilities. Because of biases and misclassification age, while in low-income countries the difference is substantial numbers of employed women may be narrower--about two to three years. The difference underestimated or reported as unpaid family workers in child mortality rates (table 2.22) is another good even when they work in association or equally with indicator of female social disadvantage because their husbands in the family enterprise. nutrition and medical interventions are particularly Women are vastly underrepresented in decision- important for the 1­4 age group. Female child mor- making positions in government, although there is tality rates that are as high as or higher than male some evidence of recent improvement. Gender parity child mortality rates may indicate discrimination in parliamentary representation is still far from being against girls. realized. In 2008 women accounted for 18 percent Having a child during the teenage years limits of parliamentarians worldwide, compared with 9 per- girls' opportunities for better education, jobs, and cent in 1987. Without representation at this level, it income. Pregnancy is more likely to be unintended is difficult for women to influence policy. during the teenage years, and births are more likely For information on other aspects of gender, see to be premature and are associated with greater tables 1.2 (Millennium Development Goals: eradicat- Data sources risks of complications during delivery and of death. ing poverty and saving lives), 1.3 (Millennium Devel- Data on female population and life expectancy are In many countries maternal mortality (tables 1.3 and opment Goals: protecting our common environment), from the World Bank's population database. Data 2.18) is a leading cause of death among women of 2.3 (Employment by economic activity), 2.4 (Decent on pregnant women receiving prenatal care are reproductive age. Most maternal deaths result from work and productive employment), 2.5 (Unemploy- from household surveys, including Demographic preventable causes--hemorrhage, infection, and ment), 2.6 (Children at work), 2.10 (Assessing vulner- and Health Surveys by Macro International and complications from unsafe abortions. Prenatal care ability and security), 2.13 (Education efficiency), 2.14 Multiple Indicator Cluster Surveys by the United is essential for recognizing, diagnosing, and promptly (Education completion and outcomes), 2.15 (Educa- Nations Children's Fund (UNICEF), and UNICEF's treating complications that arise during pregnancy. tion gaps by income and gender), 2.18 (Reproductive State of the World's Children 2009. Data on teen- In high-income countries most women have access health), 2.20 (Health risk factors and future chal- age mothers are from Demographic and Health to health care during pregnancy, but in developing lenges), 2.21 (Health gaps by income and gender), Surveys by Macro International. Data on labor countries an estimated 200 million women suffer and 2.22 (Mortality). force and employment are from the International pregnancy-related complications, and over half a mil- Labour Organization's Key Indicators of the Labour lion die every year (Glasier and others 2006). This Market, fi fth edition. Data on women in parlia- is reflected in the differences in maternal mortality ments are from the Inter-Parliamentary Union. ratios between high- and low-income countries. 2009 World Development Indicators 31 1.6 Key indicators for other economies Population Surface Population Gross national Gross domestic Life Adult Carbon area density income product expectancy literacy dioxide at birth rate emissions PPPa thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2007 2007 2007 2007b 2007b 2007 2007 2006­07 2006­07 2007 2007 2005 American Samoa 65 0.2 325 .. ..c .. .. .. .. .. .. .. Andorra 82 0.5 175 .. ..d .. .. .. .. .. .. .. Antigua and Barbuda 85 0.4 193 980 11,650 1,487e 17,680e ­1.2 ­2.1 .. 99 421 Aruba 101 0.2 561 .. ..d .. .. .. .. .. 98 2,308 Bahamas, The 331 13.9 33 .. ..d .. .. 2.8 1.6 73 .. 2,107 Bahrain 753 0.7 1,060 12,607 17,390 19,720 27,210 7.8 5.6 76 89 19,668 Barbados 294 0.4 684 .. ..d 4,711e 16,140e .. .. 77 .. 1,315 Belize 304 23.0 13 1,144 3,760 1,847e 6,080e 1.2 ­0.9 76 .. 817 Bermuda 64 0.1 1,280 .. ..d .. .. 4.6 4.3 79 .. 572 Bhutan 657 47.0 14 1,166 1,770 3,275 4,980 19.1 17.5 66 53 414 Brunei Darussalam 389 5.8 74 10,211 26,740 19,540 50,200 0.6 ­1.3 77 95 5,892 Cape Verde 530 4.0 132 1,287 2,430 1,557 2,940 7.0 4.6 71 84 286 Cayman Islands 54 0.3 206 .. ..d .. .. .. .. .. 99 315 Channel Islands 149 0.2 785 10,241 68,640 .. .. 5.9 5.7 79 .. .. Comoros 628 1.9 338 425 680 721 1,150 ­1.0 ­3.3 65 75 88 Cyprus 855 9.3 92 19,617f 24,940 f 20,549 24,040 4.4 3.3 79 98 7,017 Djibouti 833 23.2 36 908 1,090 1,885 2,260 4.0 2.2 55 .. 374 Dominica 73 0.8 97 292 4,030 501e 6,930e 3.2 2.6 .. .. 114 Equatorial Guinea 508 28.1 18 6,527 12,860 10,770 21,220 12.5 9.9 52 .. 4,335 Faeroe Islands 48 1.4 35 .. ..d .. .. .. .. 79 .. 656 Fiji 834 18.3 46 3,125 3,750 3,538 4,240 ­6.6 ­7.1 69 .. 1,641 French Polynesia 263 4.0 72 .. ..d .. .. .. .. 74 .. 685 Greenland 57 410.5 0g .. ..d .. .. .. .. .. .. 557 Grenada 106 0.3 311 414 3,920 579e 5,480e 3.0 2.9 69 .. 234 Guam 173 0.5 321 .. ..d .. .. .. .. 76 .. .. Guyana 739 215.0 4 926 1,250 1,907e 2,580e 9.1 9.2 67 .. 1,491 Iceland 311 103.0 3 17,959 57,750 10,596 34,070 3.8 1.4 81 .. 2,184 Isle of Man 77 0.6 136 3,517 45,810 .. .. 7.7 6.7 .. .. .. About the data Definitions The table shows data for 56 economies with popula- · Population is based on the de facto definition of net receipts of primary income (compensation of tions between 30,000 and 1 million and for smaller population, which counts all residents regardless employees and property income) from abroad. Data economies if they are members of the World Bank. of legal status or citizenship--except for refugees are in current U.S. dollars converted using the World Where data on gross national income (GNI) per capita not permanently settled in the country of asylum, Bank Atlas method (see Statistical methods). · GNI are not available, the estimated range is given. For who are generally considered part of the popula- per capita is GNI divided by midyear population. more information on the calculation of GNI (gross tion of their country of origin. The values shown are GNI per capita in U.S. dollars is converted using national product, or GNP, in the System of National midyear estimates. See also table 2.1. · Surface the World Bank Atlas method. · Purchasing power Accounts 1968) and purchasing power parity (PPP) area is a country's total area, including areas under parity (PPP) GNI is GNI converted to international conversion factors, see About the data for table inland bodies of water and some coastal waterways. dollars using PPP rates. An international dollar has 1.1. Additional data for the economies in the table · Population density is midyear population divided the same purchasing power over GNI that a U.S. are available on the World Development Indicators by land area in square kilometers. · Gross national dollar has in the United States. · Gross domestic CD-ROM or at WDI Online. income (GNI) is the sum of value added by all resi- product (GDP) is the sum of value added by all dent producers plus any product taxes (less sub- resident producers plus any product taxes (less sidies) not included in the valuation of output plus subsidies) not included in the valuation of output. 32 2009 World Development Indicators WORLD VIEW Key indicators for other economies Population Surface Population Gross national Gross domestic Life 1.6 Adult Carbon area density income product expectancy literacy dioxide at birth rate emissions PPPa thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2007 2007 2007 2007b 2007b 2007 2007 2006­07 2006­07 2007 2007 2005 Kiribati 95 0.8 117 107 1,120 194 e 2,040e 1.7 0.1 61 .. 26 Liechtenstein 35 0.2 220 .. ..d .. .. .. .. .. .. .. Luxembourg 480 2.6 185 34,234 72,430 29,239 61,860 4.5 2.9 79 .. 11,314 Macao, China 480 0.0 17,026 .. ..d .. .. 27.3 26.6 81 94 2,235 Maldives 305 0.3 1,018 974 3,190 1,500 4,910 6.6 4.9 68 97 714 Malta 409 0.3 1,279 6,825 16,680 9,192 22,460 3.8 3.1 80 92 2,550 Marshall Islands 58 0.2 324 189 3,240 .. .. 3.5 1.2 .. .. 84 Mayotte 186 0.4 497 .. ..c .. .. .. .. .. .. .. Micronesia, Fed. Sts. 111 0.7 159 253 2,280 334 e 3,010e ­3.2 ­3.5 69 .. .. Monaco 33 0.0 16,769 .. ..d .. .. .. .. .. .. .. Montenegro 599 14.0 43 3,154 5,270 7,056 11,780 10.7 11.1 75 .. .. Netherlands Antilles 191 0.8 239 .. ..d .. .. .. .. 75 96 3,891 New Caledonia 242 18.6 13 .. ..d .. .. .. .. 76 96 2,638 Northern Mariana Islands 84 0.5 182 .. ..d .. .. .. .. .. .. .. Palau 20 0.5 44 167 8,270 .. .. 2.5 1.9 69 .. 114 Qatar 836 11.0 76 .. ..d .. .. 6.1 1.8 76 93 49,816 Samoa 181 2.8 64 489 2,700 789e 4,350e 6.1 5.6 72 99 150 San Marino 31 0.1 510 1,430 46,770 .. .. 4.5 3.1 82 .. .. Sao Tome and Principe 158 1.0 165 138 870 258 1,630 6.0 4.1 65 88 103 Seychelles 85 0.5 185 762 8,960 1,313e 15,440e 6.3 5.8 73 .. 579 Solomon Islands 495 28.9 18 374 750 845e 1,710e 10.2 7.7 64 .. 176 St. Kitts and Nevis 49 0.3 188 488 9,990 668e 13,680e 3.3 2.5 .. .. 136 St. Lucia 168 0.6 275 928 5,520 1,552e 9,240e 3.2 2.0 74 .. 370 St. Vincent & Grenadines 120 0.4 309 507 4,210 863e 7,170e 6.7 6.2 72 .. 191 Suriname 458 163.3 3 2,166 4,730 3,498e 7,640e 5.3 4.7 70 90 2,374 Tonga 102 0.8 142 254 2,480 397e 3,880e ­0.3 ­0.6 73 99 117 Vanuatu 226 12.2 19 417 1,840 771e 3,410e 5.0 2.6 70 78 88 Virgin Islands (U.S.) 108 0.4 310 .. ..d .. .. .. .. 79 .. .. a. PPP is purchasing power parity, see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be upper middle income ($3,706­$11,455). d. Estimated to be high income ($11,456 or more). e. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. f. Excludes Turkish Cypriot side. g. Less than 0.5. Growth is calculated from constant price GDP data in local currency. · GDP per capita is GDP divided by midyear population. · Life expectancy at birth is the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. Data sources · Adult literacy rate is the percentage of adults The indicators here and throughout the book ages 15 and older who can, with understanding, are compiled by World Bank staff from primary read and write a short, simple statement about their and secondary sources. More information about everyday life. · Carbon dioxide emissions are those the indicators and their sources can be found in stemming from the burning of fossil fuels and the the About the data, Definitions, and Data sources manufacture of cement. They include carbon dioxide entries that accompany each table in subsequent produced during consumption of solid, liquid, and sections. gas fuels and gas fl aring. 2009 World Development Indicators 33 Text figures, tables, and boxes Introduction D ecent and productive work Sustainable development is about improving the quality of people's lives and expanding their ability to shape their futures. These generally call for higher per capita incomes, but they also involve equitable education and job opportunities, better health and nutrition, and a more sustainable natural environment. The Millennium Development Goals are the world's time-bound targets to measure and moni- tor the progress of countries in improving people's welfare. They address extreme poverty in its many dimensions--income, hunger, and disease--while promoting education, gender equality, health, and sanitation. At the midpoint between their adoption in 2000 and the target date of 2015, the goals related to human development (primary school completion rate, under-five and maternal mortality) have recorded slower progress than those related to economic growth and infrastructure development (income poverty, gender parity, access to clean water and sanitation; figure 2a). Income from work is the main determinant of living conditions and well-being (World Bank 1995). Therefore, breaking the cycle of poverty involves creating local wealth and new cycles of opportu- nity through decent and productive employment. Economic growth is a vehicle for poverty reduc- tion, but economic advances in a variety of countries over the past decade have not helped lift a majority of people and their families out of poverty because many poor people were deprived of opportunities to benefit from economic expansion. That is why decent and productive work has been added as a target, with four specific indicators. Target 1b under Goal 1 is to achieve full and productive employment and decent work for all, including women and young people. The indicators cover employment, vulnerable employment, the working poor, and labor productivity. Different goals--different progress 2a Distance to goal achieved in 2006 Distance to goal to be on track in 2006 Goal 1a. Deep poverty Goal 1b. Hunger Goal 2. Primary education Goal 3. Gender parity at school Goal 4. Child mortality Goal 5. Maternal mortality Goal 7c. Access to safe water Goal 7c. Access to sanitation 0% 20% 40% 60% 80% 100% Source: World Bank and IMF 2008a. 2009 World Development Indicators 35 New indicators on decent and Employment to population ratios productive work The employment to population ratio--the number of people Measuring the achievement of decent and productive work in employed as a percentage of the population for the corre- its many facets (box 2b) is challenging. Nevertheless, it is clear sponding age group (ages 15 and older or ages 15­24) and that increased opportunities for decent and productive employ- ment have led to greater earnings for many workers in high- sex--is a good indicator of the efficiency of an economy in income economies. In contrast, jobs in the formal economy are providing jobs. Although there is no optimal employment to often beyond the reach of most people in low-income econo- population ratio, most economies have ratios in the range of mies, where many workers still continue to toil long hours in 55­75 percent. Ratios above 80 percent could point to an poor conditions with low remuneration and low productivity. A set of indicators has been adopted to assess the abundance of low-quality jobs. achievement of the Millennium Development Goal target The ratios for people ages 15 and older changed little for full and productive employment and decent work for all: between 1991 and 2007 (figure 2c), but they hide wide varia- employment to population ratios, the share of vulnerable tions across regions (figure 2d). Developed economies have employment in total employment, the share of working poor (earning less than $1.25 a day) in total employment, and lower ratios than developing economies because their higher labor productivity growth rates. The four indicators should: productivity and incomes require fewer workers to meet the · Provide relevant measures of progress toward the needs of the entire population. new target. For developing economies there is no correct, or opti- · Provide a basis for international comparison. · Link to country monitoring systems. mal, ratio. During the development process employment to · Be based on international standards, recommenda- population ratios and poverty indicators can both be high tions, and best practices. because people must work to survive, which is the case for · Be constructed from well established data sources, some countries in South Asia and Sub-Saharan Africa (fig- quantifiable and consistent, enabling measurement ure 2e). When unemployment rates are very high, signifying over time. What is Employment to population ratios decent work? 2b have not changed much over time. . . 2c The International Labour Organization defines decent work as pro- Employment to population ratio, ages 15 and older (%) 1991 2007 100 ductive work for women and men in conditions of freedom, equity, security, and human dignity. Endorsed by the international commu- 75 nity, decent work involves opportunities for productive work and 50 delivers a fair income, guarantees equal opportunities and equal treatment for all, provides security in the workplace and protection 25 for workers and their families, offers better prospects for personal 0 development and social integration, and gives people the freedom to Low Lower Upper Low and High income middle income middle income middle income income express their concerns, to organize, and to participate in decisions that affect their lives. Source: ILO database Key Indicators of the Labour Market, 5th edition. The Decent Work Agenda strives for equitable economic growth . . . But variations are through a coherent blend of social and economic goals, balanced wide across regions 2d and integrated at the global, regional, national, sectoral, and local Employment to population ratio, ages 15 and older (%) 1991 2008 levels. Its four strategic objectives are mutually supportive: 100 · Employment--the principal route out of poverty is productive 75 work. · Rights--without them, men and women will not be empowered to 50 escape poverty. 25 · Protection--social protection safeguards against poverty. · Dialogue--participation of employer and worker organizations are a 0 East Asia Europe Latin Middle East South Sub-Saharan High key element in shaping government policy for poverty reduction. & Pacific & Central America & & North Asia Africa income Asia Caribbean Africa Source: Adapted from International Labour Organization (www.ilo.org/decentwork/). Source: ILO 2008. 36 2009 World Development Indicators that people are looking for work but not finding it, efforts and Pacific, where the high employment to population ratio is are needed to increase the employment to population ratio. in part explained by the high employment to population ratio Efforts to boost ratios are also needed when unemployment for women, and the gender gap in employment to popula- rates are low as a result of discouragement, indicating that tion ratios is the lowest of all regions. Gender gaps in these people may have given up hope of finding a job. On the other ratios may be the result of women choosing not to work, but hand, increases in employment to population ratios should especially in developing countries women are likely to face be moderate, since sharp spikes could be the result of a cultural or other constraints to labor market participation. If decline in productivity. such constraints become less binding over time, ratios for Two regions experienced increases in employment to women will gradually increase. population ratios over this period: Latin America and the High ratios among youth, as in some East Asian and Caribbean and Middle East and North Africa. In both, the Pacific economies, indicate that more young people are work- increases were fueled by increases in the labor force partici- ing rather than attending school and investing in their future pation of women. But despite this, the employment to popu- (figure 2g). A reduction in youth employment to population lation ratio in the Middle East and North Africa remained the ratios can be a positive trend if related to increased enroll- lowest in the world. ments. Ratios generally are negatively correlated to school Globally, employment to population ratios are lower for enrollment: the higher the enrollment, the lower the employ- women than for men, resulting in a large untapped potential ment (figure 2h). But Nigeria and Sudan have low second- of female labor (figure 2f). The Middle East and North Africa ary school enrollments and low youth employment, indicat- has the largest gender gap, attributable to low participation ing that young people are neither working nor preparing for of women in the workforce. The opposite is true in East Asia future work. High employment to population ratios in some Many young people are in the workforce, countries reflect high numbers of working poor 2e at the expense of higher education 2g Enrollment ratios and employment to population Employment to population ratio, ages 15­24, total (%) Employment to population ratio, ages 15 and older, selected countries (%) 1991 2007 ratio, ages 15­24, Gross enrollment ratio, secondary by region, 2007 (%) Gross enrollment ratio, tertiary, total 100 100 75 75 50 50 25 25 0 0 East Asia Europe Latin Middle East South Sub-Saharan Burundi Uganda Madagascar Guinea & Pacific& Central America & & North Asia Africa Asia Caribbean Africa Source: UNESCO Institute for Statistics and ILO database Key Indicators of the Source: ILO database Key Indicators of the Labour Market, 5th edition. Labour Market, 5th edition. Fewer women than men are For many poor countries, there is a employed all over the world 2f tradeoff between education and employment 2h Employment to population ratio, ages 15 and older, by sex and region, 2008 (%) Men Women Employment to population ratio, ages 15­24, 2007 (%) 100 80 Uganda Cambodia 60 75 Niger 40 50 Sudan Nigeria 25 20 Namibia 0 0 East Asia Europe Latin Middle East South Sub-Saharan High 0 20 40 60 80 100 120 & Pacific & Central America & & North Asia Africa income Gross enrollment ratio, secondary, 2005­07 (% of relevant age group) Asia Caribbean Africa Source: UNESCO Institute for Statistics and ILO database Key Indicators of the Source: ILO 2008. Labour Market, 5th edition. 2009 World Development Indicators 37 Share of vulnerable employment in The share of working poor in total employment total employment Working poor are employed people living in a household in The share of vulnerable employment in total employment which each member is estimated to be below the poverty line captures the proportion of workers who are less likely to have ($1.25 a day). Measurements of the working poor indicate access to social security, income protection, and effective the lack of decent work: if a person's work does not provide coverage under labor legislation--and are thus more likely to a sufficient income to lift the family out of poverty, this work lack critical elements of decent work. Such elements include does not fulfill the income component of decent work. Unem- mechanisms for dialogue that could improve their working ployment is not an option for the poor, who have no savings or other sources of income and cannot rely on safety nets. conditions or ensure rights at work. The fact that the vulner- Measuring decent work remains a challenge. able are most likely to lack social protection and safety nets Harsh labor market conditions in South Asia and Sub- in times of low economic demand can increase their poverty, Saharan Africa are reflected in the share of working poor a big concern in the current global economic crisis. in total employment (figure 2k). Almost 60 percent of work- Vulnerable employment accounted for just over half of ers in Sub-Saharan Africa and 40 percent in South Asia are world employment in 2007 (50.5 percent), down from 52.6 extremely poor ($1.25 a day). It will take many years in these percent in 2000. It is very high in South Asia and Sub- regions to make decent work for all a realistic objective. Saharan Africa (figure 2i), accounting for three-quarters of East Asia and Pacific advanced most toward improving the all jobs, and in East Asia and Pacific. The lowest share of incomes of workers, reducing the share of working poor from vulnerable employment outside high-income economies is in 36 percent to 13 percent. Europe and Central Asia. Women are more likely than men The global financial crisis, which has turned into a global to be vulnerable (figure 2j), and the difference is more than jobs crisis, will likely increase the share of working poor and 10 percentage points in Sub-Saharan Africa, South Asia, and the share of vulnerable employment in developing econo- the Middle East and North Africa. Shares of women in vulner- mies. But with 2008 data not yet available for many coun- able employment are very low in high-income regions. tries, it is difficult to estimate the impact (box 2m). Although there are large regional Share of working poor in total employment is variations in vulnerable employment . . . 2i highest in South Asia and Sub-Saharan Africa 2k Vulnerable employment as a share of total employment, by region (%) 1991 2007 Share of working poor in total employment, by region (%) 1991 2007 100 100 75 75 50 50 25 25 0 0 East Asia Europe Latin Middle East South Sub-Saharan High East Asia Europe Latin Middle East South Sub-Saharan High & Pacific & Central America & & North Asia Africa income & Pacific & Central America & & North Asia Africa income Asia Caribbean Africa Asia Caribbean Africa Source: ILO 2008. Source: ILO 2008. . . . Women are more likely than men Labor productivity has to be in vulnerable employment 2j increased across the world 2l Vulnerable employment as a share of 1991 men 1991 women total employment, by sex and region (%) 2007 men 2007 women GDP per person employed, by region ($ thousands) 1991 2000 2007 100 80 75 60 40 50 20 25 0 0 East Asia Europe Latin Middle East South Sub-Saharan High East Asia Europe Latin Middle East South Sub-Saharan High & Pacific & Central America & & North Asia Africa income & Pacific & Central America & & North Asia Africa income Asia Caribbean Africa Asia Caribbean Africa Source: ILO 2008. Source: ILO 2008. 38 2009 World Development Indicators Labor productivity growth rates Challenge of measuring decent work Labor productivity assesses the likelihood that an economy The multifaceted Decent Work Agenda links full and produc- provides the opportunity to create and sustain decent em- tive employment with rights at work, social protection, and a ployment with fair and equitable remuneration and better social dialogue. A major challenge lies in refining indicators working conditions. Higher productivity improves the social of the qualitative elements of decent work and collecting the and economic environment, reducing poverty through invest- data. Future action will include: ments in human and physical capital, social protection, and · Compiling definitions for statistical indicators based technological progress. on agreed international statistical standards and pro- Higher productivity comes from enterprises' combining of viding guidance on interpreting indicators, including capital, labor, and technology. There has been an increase in limitations and possible pitfalls. labor productivity since 2000 across the world. · Carrying out developmental work on statistical indica- Productivity gains have been greatest in high-income tors in areas highlighted by experts, such as mater- economies and in Europe and Central Asia (figure 2l). Produc- nity protection, paid annual leave, sick leave, and tivity increased slightly in Latin America and the Caribbean, sustainable enterprises. and the share of working poor subsequently fell. The fairly · Generating reliable and reproducible indicators to com- low and often volatile productivity changes in Sub-Saharan ply with fundamental principles and rights at work. Africa may explain the limited decline in workers in vulnerable The indicators are a start at measuring progress toward employment there. The number of working poor is unlikely to decent work, but challenges remain, particularly for develop- decline without increased productivity. ing economies, at different stages of statistical development and with different statistical capacities. Scenarios for 2008 2m In response to the financial crisis and dwindling access to funding, employment would fall below 50 percent (figure 1). But a second sce- many businesses are reducing operating costs by postponing invest- nario finds it likely to rise to 52.6 percent. The projections in figure 2 ments and shedding workers. The economic weight and market size would result in a decrease in the share of extreme working poverty in of the high-income economies, and the global linkages of the financial total employment from 2007 in the first and second scenarios. However, sector, mean that the crisis is hitting funding and export markets in in the third scenario, the share would increase 4 percent over the share other parts of the world, especially those for commodities. from 2007 (figure 2). Given the sharp decline in economic growth for Since data on working poverty and vulnerable employment are lack- many economies in 2008, the third scenario may be most likely. ing for 2008, global scenarios for 2008 are presented instead (ILO The negative impact in these scenarios is realistic, since people 2009). who lose their wage and salaried employment will most likely end up Both the share of working poor and workers in vulnerable employ- out of work altogether or working as own-account workers and unpaid ment will have increased in 2008, reversing the encouraging trends to contributing family workers. And new entrants to labor markets will 2007. The slight decline in vulnerable employment in recent years raised have fewer opportunities in wage and salaried jobs, most likely ending hopes that in 2008, for the first time, the share of workers in vulnerable up in vulnerable employment. Two global scenarios for Global scenarios for working vulnerable employment in 2008 Figure 1 poor ($1.25 a day) in 2008 Figure 2 Percent Percent 60 30 2007 (50.5) 2007 (23.0) 50 27.0 49.6 52.6 40 20 21.2 20.2 30 20 10 10 0 0 Scenario 1 Scenario 2 Scenario 1 Scenario 2 Scenario 3 Source: ILO 2009. Source: ILO 2009. Scenario 1. Based on labor market data to date and IMF November Scenario 1. Based on labor market data to date and IMF November 2008 revised estimates for economic growth. 2008 revised estimates for economic growth. Scenario 2. Based on a simultaneous increase in vulnerable employ- Scenario 2. Based on a 5 percent higher poverty line. ment in all economies equal to half the largest increase since 1991 and Scenario 3. Based on a 10 percent higher poverty line. IMF November 2008 revised estimates for economic growth. 2009 World Development Indicators 39 Tables 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2007 2015 1990­2007 2007­15 2007 2007 2007 2007 2007 2007 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.3 3.2 3.3 ­0.2 0.4 25 66 9 38 14 6 16 Algeria 25.3 33.9 38.0 1.7 1.5 28 67 5 42 7 5 21 Angola 10.5 16.9 20.7 2.8 2.5 46 51 2 90 5 21 47 Argentina 32.6 39.5 42.5 1.1 0.9 26 64 10 40 16 8 18 Armenia 3.5 3.0 3.0 ­1.0 0.1 19 69 12 28 17 10 13 Australia 17.1 21.0 22.5 1.2 0.8 19 67 13 28 20 7 14 Austria 7.7 8.3 8.4 0.4 0.1 15 68 17 23 25 9 9 Azerbaijan 7.2 8.6 9.1 1.0 0.8 23 69 7 34 10 6 18 Bangladesh 113 158.6 180.0 2.0 1.6 34 62 4 55 6 8 25 Belarus 10.2 9.7 9.3 ­0.3 ­0.6 15 71 14 21 20 14 10 Belgium 10 10.6 10.7 0.4 0.1 17 66 17 25 26 9 11 Benin 5.2 9.0 11.3 3.3 2.8 44 54 3 82 5 11 40 Bolivia 6.7 9.5 10.9 2.1 1.6 37 58 5 64 8 8 27 Bosnia and Herzegovina 4.3 3.8 3.7 ­0.8 ­0.1 17 69 14 25 21 10 9 Botswana 1.4 1.9 2.1 1.9 1.2 35 62 3 56 6 14 25 Brazil 149.5 191.6 209.4 1.5 1.1 27 66 6 41 10 6 19 Bulgaria 8.7 7.7 7.2 ­0.8 ­0.8 13 69 17 19 25 15 10 Burkina Faso 8.9 14.8 18.6 3.0 2.9 46 51 3 90 6 14 44 Burundi 5.7 8.5 11.2 2.4 3.5 44 53 3 84 5 16 47 Cambodia 9.7 14.4 16.6 2.3 1.8 36 61 3 59 5 9 26 Cameroon 12.2 18.5 21.5 2.4 1.9 41 55 4 74 6 14 35 Canada 27.8 33.0 35.3 1.0 0.8 17 70 13 24 19 7 11 Central African Republic 3 4.3 5.0 2.2 1.8 42 54 4 78 7 18 36 Chad 6.1 10.8 13.4 3.3 2.7 46 51 3 91 6 15 45 Chile 13.2 16.6 17.8 1.4 0.9 24 68 9 35 13 5 15 China 1,135.2 1,318.3 1,375.7 0.9 0.5 21 71 8 29 11 7 12 Hong Kong, China 5.7 6.9 7.4 1.1 0.9 14 73 12 20 16 6 10 Colombia 33.2 44.0 48.4 1.7 1.2 29 65 5 45 8 6 19 Congo, Dem. Rep. 37.9 62.4 78.5 2.9 2.9 47 50 3 95 5 18 50 Congo, Rep. 2.4 3.8 4.5 2.6 2.1 42 55 3 76 6 11 35 Costa Rica 3.1 4.5 5.0 2.2 1.3 27 67 6 41 9 4 18 Côte d'Ivoire 12.8 19.3 22.3 2.4 1.9 41 56 3 74 6 15 35 Croatia 4.8 4.4 4.4 ­0.4 ­0.2 15 68 17 22 26 12 9 Cuba 10.6 11.3 11.2 0.4 ­0.1 18 70 12 26 17 8 10 Czech Republic 10.4 10.3 10.3 0.0a ­0.1 14 71 15 20 20 10 11 Denmark 5.1 5.5 5.5 0.4 0.1 19 66 16 28 24 10 12 Dominican Republic 7.3 9.7 10.6 1.7 1.1 33 61 6 54 9 6 23 Ecuador 10.3 13.3 14.6 1.5 1.1 32 62 6 51 10 5 21 Egypt, Arab Rep. 55.1 75.5 86.2 1.8 1.7 33 62 5 52 8 6 24 El Salvador 5.1 6.9 7.6 1.7 1.3 33 61 6 55 9 6 23 Eritrea 3.2 4.8 6.2 2.5 3.0 43 55 2 78 4 9 39 Estonia 1.6 1.3 1.3 ­0.9 ­0.3 15 68 17 22 24 13 12 Ethiopia 48.0 79.1 96.0 2.9 2.4 44 53 3 82 6 13 38 Finland 5.0 5.3 5.4 0.3 0.2 17 67 16 26 24 9 11 France 56.7 61.7 63.3 0.5 0.3 18 65 16 28 25 8 13 Gabon 0.9 1.3 1.5 2.2 1.5 35 61 5 58 8 12 26 Gambia, The 1.0 1.7 2.1 3.4 2.5 41 55 4 74 7 10 35 Georgia 5.5 4.4 4.2 ­1.3 ­0.6 18 68 14 26 21 12 11 Germany 79.4 82.3 81.1 0.2 ­0.2 14 66 20 21 30 10 8 Ghana 15.6 23.5 27.3 2.4 1.9 38 58 4 66 6 9 30 Greece 10.2 11.2 11.2 0.6 0.0 b 14 67 19 21 28 10 10 Guatemala 8.9 13.3 16.2 2.4 2.4 43 53 4 80 8 6 33 Guinea 6.0 9.4 11.4 2.6 2.4 43 54 3 80 6 12 40 Guinea-Bissau 1.0 1.7 2.2 3.0 3.0 48 49 3 97 6 18 50 Haiti 7.1 9.6 11.0 1.8 1.7 37 59 4 63 7 9 28 40 2009 World Development Indicators PEOPLE Population Population dynamics Average annual Population age Dependency 2.1 Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2007 2015 1990­2007 2007­15 2007 2007 2007 2007 2007 2007 2007 Honduras 4.9 7.1 8.3 2.2 1.9 39 57 4 68 7 6 28 Hungary 10.4 10.1 9.8 ­0.2 ­0.3 15 69 16 22 22 13 10 India 849.5 1,124.8 1,249.6 1.7 1.3 32 63 5 51 8 8 24 Indonesia 178.2 225.6 245.1 1.4 1.0 28 67 6 42 9 6 19 Iran, Islamic Rep. 54.4 71.0 78.9 1.6 1.3 27 69 4 39 6 6 18 Iraq 18.5 .. .. .. .. .. .. .. .. .. .. .. Ireland 3.5 4.4 4.8 1.3 1.2 21 68 11 30 16 6 16 Israel 4.7 7.2 8.1 2.5 1.6 28 62 10 45 16 6 21 Italy 56.7 59.4 58.4 0.3 ­0.2 14 66 20 21 30 10 9 Jamaica 2.4 2.7 2.8 0.7 0.5 31 62 7 50 12 6 17 Japan 123.5 127.8 124.5 0.2 ­0.3 14 66 21 21 32 9 9 Jordan 3.2 5.7 6.8 3.5 2.1 36 61 3 59 5 4 29 Kazakhstan 16.3 15.5 16.8 ­0.3 1.0 24 69 8 35 11 10 20 Kenya 23.4 37.5 46.1 2.8 2.6 43 55 3 78 5 12 39 Korea, Dem. Rep. 20.1 23.8 24.4 1.0 0.3 23 68 9 34 13 10 13 Korea, Rep. 42.9 48.5 49.2 0.7 0.2 18 72 10 24 14 5 10 Kuwait 2.1 2.7 3.2 1.3 2.1 23 75 2 31 3 2 18 Kyrgyz Republic 4.4 5.2 5.8 1.0 1.2 30 65 6 46 9 7 23 Lao PDR 4.1 5.9 6.7 2.1 1.7 38 58 4 65 6 7 27 Latvia 2.7 2.3 2.2 ­0.9 ­0.5 14 69 17 20 25 15 10 Lebanon 3.0 4.1 4.4 1.9 1.0 28 65 7 43 11 7 18 Lesotho 1.6 2.0 2.1 1.3 0.6 40 55 5 72 9 19 29 Liberia 2.1 3.7 4.7 3.3 2.8 47 51 2 93 4 18 50 Libya 4.4 6.2 7.1 2.0 1.8 30 66 4 46 6 4 23 Lithuania 3.7 3.4 3.3 ­0.5 ­0.5 16 69 16 23 23 14 10 Macedonia, FYR 1.9 2.0 2.0 0.4 0.0a 19 70 11 27 16 9 11 Madagascar 12.0 19.7 24.1 2.9 2.5 43 54 3 81 6 10 36 Malawi 9.4 13.9 17.0 2.3 2.5 47 50 3 94 6 15 41 Malaysia 18.1 26.5 30.0 2.3 1.5 30 65 5 47 7 4 21 Mali 7.7 12.3 15.7 2.8 3.0 48 49 4 97 7 15 48 Mauritania 1.9 3.1 3.8 2.8 2.4 40 57 4 70 6 8 32 Mauritius 1.1 1.3 1.3 1.0 0.6 24 70 7 34 10 7 14 Mexico 83.2 105.3 113.7 1.4 1.0 30 64 6 46 10 5 19 Moldova 4.4 3.8 3.7 ­0.8 ­0.4 19 70 11 27 16 12 11 Mongolia 2.1 2.6 2.8 1.3 1.0 27 69 4 39 6 6 22 Morocco 24.2 30.9 33.9 1.4 1.2 29 65 5 45 8 6 21 Mozambique 13.5 21.4 24.7 2.7 1.8 44 52 3 85 6 20 39 Myanmar 40.1 48.8 51.9 1.1 0.8 26 68 6 39 8 10 18 Namibia 1.4 2.1 2.3 2.3 1.5 37 59 4 64 6 12 26 Nepal 19.1 28.1 32.2 2.3 1.7 38 58 4 65 6 8 28 Netherlands 15.0 16.4 16.5 0.5 0.1 18 67 15 27 22 8 11 New Zealand 3.4 4.2 4.5 1.2 0.8 21 67 12 31 19 7 15 Nicaragua 4.1 5.6 6.3 1.8 1.4 37 59 4 62 7 5 25 Niger 7.8 14.2 18.5 3.5 3.3 48 49 3 98 7 14 49 Nigeria 94.5 148.0 175.6 2.6 2.1 44 53 3 82 6 17 40 Norway 4.2 4.7 4.9 0.6 0.5 19 66 15 29 22 9 12 Oman 1.8 2.6 3.0 2.0 2.0 32 65 3 50 4 3 22 Pakistan 108.0 162.5 192.3 2.4 2.1 36 60 4 59 7 7 27 Panama 2.4 3.3 3.8 1.9 1.5 30 64 6 47 10 5 21 Papua New Guinea 4.1 6.3 7.3 2.5 1.8 40 58 2 69 4 10 30 Paraguay 4.2 6.1 7.0 2.2 1.6 35 60 5 58 8 6 25 Peru 21.8 27.9 30.7 1.5 1.2 31 64 6 48 9 6 21 Philippines 61.2 87.9 101.0 2.1 1.7 35 61 4 59 7 5 26 Poland 38.1 38.1 37.5 0.0 b ­0.2 15 71 13 22 19 10 10 Portugal 9.9 10.6 10.7 0.4 0.1 16 67 17 23 25 10 10 Puerto Rico 3.5 3.9 4.1 0.6 0.5 21 66 13 32 20 8 13 2009 World Development Indicators 41 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2007 2015 1990­2007 2007­15 2007 2007 2007 2007 2007 2007 2007 Romania 23.2 21.5 20.5 ­0.4 ­0.6 15 70 15 22 21 12 10 Russian Federation 148.3 142.1 135.6 ­0.3 ­0.6 15 72 13 21 19 15 11 Rwanda 7.3 9.7 12.1 1.7 2.8 43 55 2 78 4 17 44 Saudi Arabia 16.4 24.2 28.3 2.3 2.0 33 65 3 50 4 4 25 Senegal 7.9 12.4 15.4 2.7 2.7 42 54 4 77 8 9 35 Serbia 7.6 7.4 7.3 ­0.2 ­0.1 18 c 67c 15c 27c 22c 14 9 Sierra Leone 4.1 5.8 6.9 2.1 2.1 43 54 3 80 6 22 46 Singapore 3.0 4.6 4.8 2.4 0.5 18 73 9 25 12 5 10 Slovak Republic 5.3 5.4 5.4 0.1 0.0a 16 72 12 22 16 10 10 Slovenia 2.0 2.0 2.0 0.1 ­0.2 14 70 16 20 23 9 10 Somalia 6.7 8.7 10.9 1.5 2.8 44 53 3 83 5 17 43 South Africa 35.2 47.9 49.5 1.8 0.4 32 64 4 50 7 17 22 Spain 38.8 44.9 45.7 0.9 0.2 15 69 17 21 25 9 11 Sri Lanka 17.1 20.0 20.5 0.9 0.3 23 70 7 33 10 6 19 Sudan 25.9 38.6 45.6 2.3 2.1 40 56 4 71 6 10 32 Swaziland 0.8 1.1 1.2 2.3 0.7 39 58 3 67 6 21 29 Sweden 8.6 9.1 9.4 0.4 0.3 17 66 18 26 27 10 12 Switzerland 6.7 7.6 7.7 0.7 0.2 16 68 16 24 24 8 10 Syrian Arab Republic 12.7 19.9 23.5 2.6 2.1 36 61 3 58 5 3 27 Tajikistan 5.3 6.7 7.7 1.4 1.6 38 58 4 65 7 6 27 Tanzania 25.5 40.4 48.9 2.7 2.4 44 53 3 84 6 13 39 Thailand 54.3 63.8 66.6 1.0 0.5 21 71 8 30 12 8 15 Timor-Leste 0.7 1.1 1.4 2.1 3.2 45 53 3 85 5 9 42 Togo 4.0 6.6 8.0 3.0 2.5 43 54 3 79 6 10 37 Trinidad and Tobago 1.2 1.3 1.4 0.5 0.4 21 72 7 30 9 8 15 Tunisia 8.2 10.2 11.2 1.3 1.1 25 69 6 36 9 6 17 Turkey 56.2 73.9 81.0 1.6 1.2 27 67 6 41 9 7 19 Turkmenistan 3.7 5.0 5.5 1.8 1.3 30 65 5 46 7 8 22 Uganda 17.8 30.9 40.6 3.2 3.4 49 48 2 101 5 13 47 Ukraine 51.9 46.5 43.6 ­0.6 ­0.8 14 70 16 20 23 16 10 United Arab Emirates 1.9 4.4 5.3 5.0 2.4 20 79 1 25 1 1 16 United Kingdom 57.2 61.0 62.5 0.4 0.3 18 66 16 27 25 9 13 United States 249.6 301.6 324.1 1.1 0.9 20 67 12 31 18 8 14 Uruguay 3.1 3.3 3.4 0.4 0.2 23 63 14 37 22 9 15 Uzbekistan 20.5 26.9 30.0 1.6 1.4 32 64 5 49 7 5 21 Venezuela, RB 19.8 27.5 31.1 1.9 1.5 31 64 5 47 8 5 22 Vietnam 66.2 85.2 94.1 1.5 1.3 28 66 6 42 8 5 19 West Bank and Gaza 2.0 3.7 4.5 3.7 2.5 45 52 3 88 6 4 36 Yemen, Rep. 12.3 22.4 28.2 3.5 2.9 45 53 2 85 4 7 38 Zambia 8.1 11.9 13.8 2.3 1.9 46 52 3 88 6 19 39 Zimbabwe 10.5 13.4 14.8 1.4 1.3 38 58 4 66 6 18 28 World 5,259.1 s 6,610.3 s 7,210.6 s 1.3 w 1.1 w 28 w 65 w 7w 43 w 12 w 8w 20 w Low income 866.5 1,295.8 1,532.9 2.4 2.1 39 57 4 69 6 11 33 Middle income 3,457.9 4,258.2 4,586.3 1.2 0.9 27 67 7 40 10 7 18 Lower middle income 2,751.9 3,434.5 3,721.4 1.3 1.0 27 67 6 41 10 7 19 Upper middle income 706.0 823.7 865.0 0.9 0.6 24 67 9 36 13 9 17 Low & middle income 4,324.4 5,554.0 6,119.2 1.5 1.2 30 64 6 46 9 8 22 East Asia & Pacific 1,596.0 1,912.4 2,026.0 1.1 0.7 23 70 7 33 10 7 14 Europe & Central Asia 436.2 445.6 447.3 0.1 0.0 b 19 69 11 28 17 12 14 Latin America & Carib. 435.1 560.6 614.2 1.5 1.1 29 64 6 45 10 6 20 Middle East & N. Africa 223.7 313.2 358.7 2.0 1.7 32 63 4 51 7 6 24 South Asia 1,120.2 1,522.0 1,711.6 1.8 1.5 33 62 5 53 8 8 25 Sub-Saharan Africa 513.2 800.0 961.4 2.6 2.3 43 54 3 80 6 15 39 High income 934.7 1,056.3 1,091.3 0.7 0.4 18 67 15 26 22 8 12 Euro area 301.6 324.2 325.5 0.4 0.1 15 67 18 23 27 9 10 a. More than ­0.05. b. Less than 0.05. c. Includes Kosovo. 42 2009 World Development Indicators PEOPLE Population dynamics 2.1 About the data Definitions Population estimates are usually based on national Dependency ratios account for variations in the · Population is based on the de facto definition of population censuses, but the frequency and quality proportions of children, elderly people, and working- population, which counts all residents regardless of vary by country. Most countries conduct a complete age people in the population. Calculations of young legal status or citizenship--except for refugees not enumeration no more often than once a decade. and old-age dependency suggest the dependency permanently settled in the country of asylum, who Estimates for the years before and after the census burden that the working-age population bears in are generally considered part of the population of are interpolations or extrapolations based on demo- relation to children and the elderly. But dependency their country of origin. The values shown are mid- graphic models. Errors and undercounting occur ratios show only the age composition of a popula- year estimates for 1990 and 2007 and projections even in high-income countries; in developing coun- tion, not economic dependency. Some children and for 2015. · Average annual population growth is tries errors may be substantial because of limits in elderly people are part of the labor force, and many the exponential change for the period indicated. See the transport, communications, and other resources working-age people are not. Statistical methods for more information. · Popula- required to conduct and analyze a full census. Vital rates are based on data from birth and death tion age composition is the percentage of the total The quality and reliability of official demographic registration systems, censuses, and sample surveys population that is in specific age groups. · Depen- data are also affected by public trust in the govern- by national statistical offices and other organiza- dency ratio is the ratio of dependents--people ment, government commitment to full and accurate tions, or on demographic analysis. The 2007 esti- younger than 15 or older than 64--to the working- enumeration, confidentiality and protection against mates for many countries are projections based on age population--those ages 15­64. · Crude death misuse of census data, and census agencies' indepen- extrapolations of levels and trends from earlier years rate and crude birth rate are the number of deaths dence from political influence. Moreover, comparability or interpolations of population estimates and projec- and the number of live births occurring during the of population indicators is limited by differences in the tions from the United Nations Population Division. year, per 1,000 people, estimated at midyear. Sub- concepts, definitions, collection procedures, and esti- Data for most high-income countries are provisional tracting the crude death rate from the crude birth mation methods used by national statistical agencies estimates based on vital registers. rate provides the rate of natural increase, which is and other organizations that collect the data. Vital registers are the preferred source for these equal to the population growth rate in the absence Of the 153 economies in the table and the 56 econo- data, but in many developing countries systems for of migration. mies in table 1.6, 180 (about 86 percent) conducted a registering births and deaths are absent or incom- census during the 2000 census round (1995­2004). A plete because of defi ciencies in the coverage of quarter of countries have completed a census for the events or geographic areas. Many developing coun- 2010 census round (2005­14). All told, 195 countries tries carry out special household surveys that ask (93 percent) have conducted a census during 1995­ respondents about recent births and deaths. Esti- 2008. The currentness of a census and the availability mates derived in this way are subject to sampling of complementary data from surveys or registration errors and recall errors. systems are objective ways to judge demographic The United Nations Statistics Division monitors data quality. Some European countries' registration the completeness of vital registration systems. The systems offer complete information on population in share of countries with at least 90 percent complete the absence of a census. See Primary data documenta- vital registration rose from 45 percent in 1988 to tion for the most recent census or survey year and for 61 percent in 2007. Still, some of the most populous the completeness of registration. developing countries--China, India, Indonesia, Bra- Data sources Current population estimates for developing coun- zil, Pakistan, Bangladesh, Nigeria--lack complete tries that lack recent census data and pre- and post- vital registration systems. From 2000 to 2007, on The World Bank's population estimates are com- census estimates for countries with census data are average 64 percent of births, 62 percent of deaths, piled and produced by its Human Development provided by the United Nations Population Division and 45 percent of infant deaths were registered and Network and Development Data Group in consulta- and other agencies. The standard estimation method reported to the United Nations Statistics Division. tion with its operational staff and country offices. requires fertility, mortality, and net migration data, International migration is the only other factor Important inputs to the World Bank's demographic often collected from sample surveys, which can be besides birth and death rates that directly deter- work come from the United Nations Population small or limited in coverage. Population estimates mines a country's population growth. From 1990 to Division's World Population Prospects: The 2006 are from demographic modeling and so are suscep- 2005 the number of migrants in high-income coun- Revision; census reports and other statistical tible to biases and errors from shortcomings in the tries rose 40 million. About 190 million people (3 publications from national statistical offi ces; model and in the data. Population projections use percent of the world population) live outside their household surveys conducted by national agen- the cohort component method. home country. Estimating migration is difficult. At cies, Macro International, and the U.S. Centers for The growth rate of the total population conceals any time many people are located outside their home Disease Control and Prevention; Eurostat, Demo- age-group differences in growth rates. In many devel- country as tourists, workers, or refugees or for other graphic Statistics (various years); Secretariat of oping countries the once rapidly growing under-15 reasons. Standards for the duration and purpose of the Pacific Community, Statistics and Demography population is shrinking. Previously high fertility rates international moves that qualify as migration vary, Programme; and U.S. Bureau of the Census, Inter- and declining mortality rates are now reflected in the and estimates require information on flows into and national Database. larger share of the working-age population. out of countries that is difficult to collect. 2009 World Development Indicators 43 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2007 1990 2007 1990 2007 1990­2007 1990 2007 Afghanistan .. .. .. .. .. .. .. .. .. Albania 84 71 67 50 1.7 1.4 ­0.9 43.4 41.9 Algeria 75 78 23 37 7.0 13.9 4.0 23.6 31.9 Angola 90 89 74 75 4.5 7.5 2.9 46.4 46.6 Argentina 79 76 29 50 12.0 18.3 2.5 28.4 41.1 Armenia 79 68 66 56 1.8 1.5 ­1.1 48.0 49.9 Australia 76 72 52 57 8.5 10.9 1.5 41.2 44.8 Austria 70 68 43 52 3.5 4.2 1.0 40.8 44.7 Azerbaijan 78 71 66 60 3.4 4.3 1.4 48.2 48.1 Bangladesh 89 85 62 57 50.9 74.3 2.2 39.5 39.2 Belarus 75 66 60 53 5.3 4.9 ­0.5 48.9 48.8 Belgium 61 60 36 46 3.9 4.7 1.0 39.0 44.4 Benin 88 86 51 59 1.9 3.7 3.9 38.2 40.5 Bolivia 85 83 46 66 2.5 4.4 3.3 36.1 45.0 Bosnia and Herzegovina 83 67 69 53 2.5 1.9 ­1.7 46.7 46.1 Botswana 78 63 44 48 0.5 0.7 2.4 37.7 43.7 Brazil 85 82 39 60 59.3 97.7 2.9 32.1 43.3 Bulgaria 64 57 57 46 4.2 3.4 ­1.3 48.4 46.2 Burkina Faso 90 90 76 77 3.9 6.7 3.2 47.4 46.8 Burundi 90 90 91 90 2.8 4.2 2.4 52.5 51.4 Cambodia 85 87 77 75 4.3 7.5 3.2 52.4 48.7 Cameroon 79 75 53 52 4.4 7.0 2.7 40.8 41.3 Canada 76 73 58 63 14.7 18.5 1.3 44.1 46.6 Central African Republic 88 87 69 67 1.3 1.9 2.2 46.6 45.5 Chad 84 77 57 71 2.3 4.3 3.6 41.4 48.7 Chile 77 72 32 39 5.0 7.0 2.0 30.6 36.0 China 85 80 73 71 650.6 785.7 1.1 44.8 45.7 Hong Kong, China 80 70 47 53 2.9 3.6 1.4 36.3 45.3 Colombia 77 79 44 64 13.4 22.1 2.9 35.9 44.2 Congo, Dem. Rep. 86 90 60 54 14.6 23.5 2.8 42.5 38.6 Congo, Rep. 84 83 57 56 0.9 1.5 2.8 41.4 41.1 Costa Rica 85 79 36 43 1.2 2.0 3.0 29.2 34.5 Côte d'Ivoire 89 85 42 39 4.6 7.1 2.5 29.6 30.6 Croatia 75 60 52 45 2.4 2.0 ­1.2 43.2 44.5 Cuba 73 69 36 45 4.5 5.2 0.9 33.2 39.2 Czech Republic 80 68 61 51 5.7 5.2 ­0.5 45.5 44.2 Denmark 75 71 62 61 2.9 2.9 0.0a 46.1 46.7 Dominican Republic 82 73 26 57 2.5 4.2 3.1 24.0 43.8 Ecuador 78 79 33 52 3.5 5.9 3.2 29.5 40.0 Egypt, Arab Rep. 74 71 24 24 15.9 24.0 2.4 24.4 25.3 El Salvador 80 79 51 47 2.0 2.8 2.2 41.2 38.8 Eritrea 88 86 55 55 1.2 1.9 2.8 40.4 41.0 Estonia 72 65 61 54 0.8 0.7 ­1.0 50.3 49.8 Ethiopia 89 91 63 80 19.7 37.9 3.8 42.4 47.3 Finland 71 65 59 58 2.6 2.7 0.2 47.5 48.0 France 65 62 46 50 24.8 28.0 0.7 43.3 46.0 Gabon 83 80 63 62 0.4 0.6 2.6 44.0 43.8 Gambia, The 86 84 70 70 0.4 0.8 3.5 45.4 45.8 Georgia 83 74 67 55 3.1 2.3 ­1.6 48.2 46.3 Germany 73 66 46 51 39.3 41.4 0.3 40.9 44.8 Ghana 74 73 73 72 6.4 10.5 3.0 49.4 48.9 Greece 67 65 36 43 4.2 5.2 1.3 36.2 40.4 Guatemala 89 85 28 45 2.8 4.9 3.2 23.7 37.1 Guinea 90 89 80 79 2.8 4.5 2.7 47.2 47.1 Guinea-Bissau 87 90 56 54 0.4 0.6 2.8 40.3 38.4 Haiti 81 83 49 39 2.6 3.6 2.0 39.4 33.4 44 2009 World Development Indicators PEOPLE Labor force structure Labor force participation rate Labor force 2.2 Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2007 1990 2007 1990 2007 1990­2007 1990 2007 Honduras 87 82 37 37 1.6 2.6 2.6 30.0 31.7 Hungary 66 59 47 44 4.6 4.3 ­0.4 44.6 45.2 India 85 82 35 34 321.9 447.7 1.9 27.6 28.2 Indonesia 81 86 50 50 75.3 110.5 2.3 38.4 36.9 Iran, Islamic Rep. 81 75 22 32 15.6 27.8 3.4 20.2 29.2 Iraq 74 .. 12 .. 4.4 .. .. 13.3 .. Ireland 70 73 36 53 1.3 2.2 2.9 34.3 42.5 Israel 62 61 41 50 1.7 2.9 3.2 40.6 46.3 Italy 66 61 36 39 24.0 25.3 0.3 37.0 40.4 Jamaica 80 74 66 55 1.1 1.2 0.3 46.6 44.0 Japan 77 72 50 48 63.9 65.7 0.2 40.6 41.2 Jordan 68 72 11 16 0.7 1.6 5.0 12.6 16.9 Kazakhstan 78 75 62 65 7.8 8.2 0.3 47.0 49.3 Kenya 90 87 75 74 9.9 17.4 3.3 46.0 46.4 Korea, Dem. Rep. 79 78 51 58 9.6 12.4 1.5 40.6 44.0 Korea, Rep. 73 73 47 49 19.1 24.3 1.4 39.3 40.8 Kuwait 81 81 34 43 0.8 1.4 2.9 21.6 24.0 Kyrgyz Republic 74 75 58 53 1.8 2.3 1.5 46.1 42.8 Lao PDR 83 80 80 79 1.8 2.9 2.6 49.6 50.5 Latvia 77 69 63 54 1.4 1.2 ­1.2 49.6 48.2 Lebanon 83 77 22 25 1.0 1.5 2.4 22.8 25.6 Lesotho 85 75 68 68 0.7 0.9 1.5 50.9 52.4 Liberia 85 85 54 55 0.8 1.4 3.3 39.3 39.8 Libya 78 78 17 26 1.2 2.3 3.6 15.5 23.5 Lithuania 74 61 59 51 1.9 1.6 ­1.1 48.1 49.5 Macedonia, FYR 73 66 54 42 0.9 0.9 ­0.1 42.7 39.3 Madagascar 85 88 80 82 5.5 9.5 3.3 48.8 48.8 Malawi 80 80 76 76 3.9 5.7 2.3 50.7 50.1 Malaysia 81 80 43 45 7.0 11.6 2.9 34.4 35.2 Mali 69 65 34 37 2.0 3.2 2.8 34.9 38.3 Mauritania 84 80 58 60 0.8 1.3 3.1 42.0 42.9 Mauritius 82 77 40 42 0.5 0.6 1.4 33.1 36.1 Mexico 84 80 34 41 29.9 44.4 2.3 30.0 35.6 Moldova 74 48 61 45 2.1 1.4 ­2.2 48.6 51.3 Mongolia 65 61 55 58 0.7 1.1 2.5 46.2 49.1 Morocco 82 80 24 25 7.7 11.2 2.3 23.5 24.7 Mozambique 84 77 86 88 6.2 9.9 2.8 54.7 56.1 Myanmar 88 86 69 69 20.2 27.9 1.9 44.7 45.3 Namibia 65 59 49 49 0.4 0.7 2.7 45.2 46.3 Nepal 80 76 48 59 7.1 11.7 2.9 37.9 45.2 Netherlands 70 71 43 57 6.9 8.5 1.2 39.1 45.0 New Zealand 74 75 54 61 1.7 2.3 1.7 43.1 45.9 Nicaragua 85 87 39 38 1.4 2.2 2.8 32.1 31.0 Niger 87 88 41 39 2.6 4.7 3.6 32.5 30.7 Nigeria 75 71 37 39 28.4 45.3 2.7 34.0 35.9 Norway 73 71 57 62 2.2 2.5 0.8 44.7 47.1 Oman 81 77 20 26 0.6 1.0 3.0 14.2 19.6 Pakistan 86 85 11 21 30.1 56.2 3.7 10.9 18.7 Panama 81 80 37 48 0.9 1.5 2.9 30.9 37.1 Papua New Guinea 75 73 71 71 1.8 2.7 2.6 46.7 49.2 Paraguay 83 84 52 71 1.7 3.1 3.6 38.0 45.2 Peru 76 82 48 64 8.3 14.1 3.1 39.1 43.9 Philippines 83 80 47 50 23.5 36.9 2.7 36.6 38.3 Poland 72 61 55 47 18.1 17.3 ­0.2 45.4 45.4 Portugal 73 70 50 56 4.8 5.6 0.9 42.8 46.3 Puerto Rico 61 58 31 38 1.2 1.5 1.4 35.8 42.3 2009 World Development Indicators 45 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2007 1990 2007 1990 2007 1990­2007 1990 2007 Romania 67 60 55 46 10.8 9.7 ­0.6 46.7 45.0 Russian Federation 76 69 60 57 77.0 75.8 ­0.1 48.5 49.5 Rwanda 88 79 86 81 3.2 4.4 2.0 51.7 52.9 Saudi Arabia 80 80 15 19 5.1 8.7 3.2 11.2 14.9 Senegal 90 86 61 62 3.3 5.3 2.9 40.1 42.2 Serbia .. 60 .. 43 .. 3.1 .. .. 42.5 Sierra Leone 65 67 66 65 1.6 2.2 2.0 51.6 50.3 Singapore 79 76 51 54 1.6 2.4 2.7 39.1 41.1 Slovak Republic 79 69 66 52 2.9 2.7 ­0.3 47.2 44.6 Slovenia 76 65 60 52 1.1 1.0 ­0.3 46.3 45.9 Somalia 89 89 52 54 2.6 3.4 1.7 37.6 38.9 South Africa 64 60 44 47 11.6 17.4 2.4 41.9 45.2 Spain 69 68 34 47 15.9 22.0 1.9 34.4 41.5 Sri Lanka 79 75 46 43 7.3 9.0 1.2 36.1 37.2 Sudan 78 72 24 31 7.3 11.9 2.9 23.4 30.4 Swaziland 79 69 66 62 0.3 0.5 2.7 51.1 50.1 Sweden 72 69 63 61 4.7 4.9 0.2 47.7 47.0 Switzerland 79 75 49 60 3.6 4.2 1.0 39.4 45.8 Syrian Arab Republic 81 78 18 21 3.3 6.4 4.0 18.3 20.8 Tajikistan 84 67 75 56 2.4 2.6 0.4 48.2 46.6 Tanzania 93 90 89 87 12.5 19.9 2.7 50.1 49.8 Thailand 87 81 76 66 31.6 36.6 0.9 47.3 46.8 Timor-Leste 81 83 52 58 0.3 0.4 2.0 38.1 40.4 Togo 89 87 53 52 1.5 2.6 3.2 38.6 38.3 Trinidad and Tobago 76 77 39 55 0.5 0.7 2.4 34.9 42.6 Tunisia 76 71 21 26 2.4 3.7 2.5 21.6 26.5 Turkey 81 71 34 24 21.0 24.2 0.8 29.4 26.8 Turkmenistan 75 71 63 59 1.5 2.2 2.4 47.3 46.9 Uganda 92 90 80 82 8.0 13.5 3.1 47.4 47.8 Ukraine 72 65 57 53 26.0 23.3 ­0.6 49.4 49.3 United Arab Emirates 92 93 25 40 1.0 2.7 6.2 9.8 14.5 United Kingdom 75 70 53 56 29.5 31.4 0.4 43.3 45.5 United States 76 72 57 59 129.3 156.6 1.1 44.3 45.7 Uruguay 72 75 43 53 1.3 1.6 1.2 39.8 43.5 Uzbekistan 85 70 76 58 9.7 11.8 1.1 48.3 46.0 Venezuela, RB 82 81 32 52 7.0 12.7 3.5 27.8 38.9 Vietnam 81 76 74 69 31.4 44.4 2.0 48.4 47.9 West Bank and Gaza 67 67 10 14 0.4 0.8 4.1 11.9 16.9 Yemen, Rep. 70 66 15 22 2.5 5.4 4.6 17.5 24.4 Zambia 81 81 59 60 3.1 4.6 2.3 42.6 43.2 Zimbabwe 80 80 68 60 4.2 5.7 1.9 46.4 43.3 World 81 w 78 w 52 w 53 w 2,352.2 t 3,098.8 t 1.6 w 39.3 w 40.3 w Low income 84 82 56 56 342.8 542.8 2.7 40.1 40.7 Middle income 83 79 53 52 1,565.5 2,039.7 1.6 38.6 39.5 Lower middle income 84 80 55 53 1,270.7 1,661.6 1.6 38.6 38.9 Upper middle income 78 73 46 50 294.8 378.1 1.5 38.7 42.1 Low & middle income 83 79 53 53 1,908.3 2,582.5 1.8 38.9 39.7 East Asia & Pacific 84 80 69 67 858.7 1,081.5 1.4 44.2 44.5 Europe & Central Asia 76 67 57 50 206.6 207.2 0.0a 46.1 45.5 Latin America & Carib. 82 80 38 53 165.1 262.2 2.7 32.1 40.8 Middle East & N. Africa 77 74 21 26 62.4 106.2 3.1 21.3 26.1 South Asia 85 82 36 36 421.5 607.9 2.2 28.2 29.1 Sub-Saharan Africa 82 80 58 60 194.1 317.5 2.9 42.3 43.5 High income 74 70 49 52 443.9 516.3 0.9 41.4 43.4 Euro area 69 65 42 48 135.8 154.3 0.7 39.9 43.8 a. Less than 0.05. 46 2009 World Development Indicators PEOPLE Labor force structure 2.2 About the data Definitions The labor force is the supply of labor available for pro- further information on source, reference period, or · Labor force participation rate is the proportion ducing goods and services in an economy. It includes definition, consult the original source. of the population ages 15 and older that is eco- people who are currently employed and people who The labor force participation rates in the table are nomically active: all people who supply labor for the are unemployed but seeking work as well as first-time from the ILO database, Key Indicators of the Labour production of goods and services during a specified job-seekers. Not everyone who works is included, Market, 5th edition. These harmonized estimates period. · Total labor force is people ages 15 and however. Unpaid workers, family workers, and stu- use strict data selection criteria and enhanced older who meet the ILO definition of the economi- dents are often omitted, and some countries do not methods to ensure comparability across countries cally active population. It includes both the employed count members of the armed forces. Labor force size and over time, including collection and tabulation and the unemployed. · Average annual percentage tends to vary during the year as seasonal workers methodologies and methods applied to such country- growth of the labor force is calculated using the enter and leave. specifi c factors as military service requirements. exponential endpoint method (see Statistical meth- Data on the labor force are compiled by the Inter- Estimates are based mainly on labor force surveys, ods for more information). · Female labor force as as national Labour Organization (ILO) from labor force with other sources (population censuses and nation- a percentage of the labor force shows the extent to surveys, censuses, establishment censuses and ally reported estimates) used only when no survey which women are active in the labor force. surveys, and administrative records such as employ- data are available. ment exchange registers and unemployment insur- Participation rates indicate the relative size of ance schemes. For some countries a combination the labor supply. Beginning in the 2008 edition of of these sources is used. Labor force surveys are World Development Indicators, the indicator covers the most comprehensive source for internationally the population ages 15 and older, to include peo- comparable labor force data. They can cover all ple who continue working past age 65. In previous noninstitutionalized civilians, all branches and sec- editions the indicator was for the population ages tors of the economy, and all categories of workers, 15­64, so participation rates are not comparable including people holding multiple jobs. By contrast, across editions. labor force data from population censuses are often The labor force estimates in the table were calcu- based on a limited number of questions on the eco- lated by applying labor force participation rates from nomic characteristics of individuals, with little scope the ILO database to World Bank population estimates to probe. The resulting data often differ from labor to create a series consistent with these population force survey data and vary considerably by country, estimates. This procedure sometimes results in depending on the census scope and coverage. Estab- labor force estimates that differ slightly from those lishment censuses and surveys provide data only on in the ILO's Yearbook of Labour Statistics and its data- the employed population, not unemployed workers, base Key Indicators of the Labour Market. workers in small establishments, or workers in the Estimates of women in the labor force and employ- informal sector (ILO, Key Indicators of the Labour ment are generally lower than those of men and are Market 2001­2002). not comparable internationally, reflecting that demo- The reference period of a census or survey is graphic, social, legal, and cultural trends and norms another important source of differences: in some determine whether women's activities are regarded countries data refer to people's status on the day as economic. In many countries many women work of the census or survey or during a specific period on farms or in other family enterprises without pay, before the inquiry date, while in others data are and others work in or near their homes, mixing work recorded without reference to any period. In devel- and family activities during the day. oping countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Data sources Differing definitions of employment age also affect comparability. For most countries the working age is Data on labor force participation rates are from 15 and older, but in some countries children younger the ILO database Key Indicators of the Labour than 15 work full- or part-time and are included in Market, 5th edition. Labor force numbers were the estimates. Similarly, some countries have an calculated by World Bank staff, applying labor upper age limit. As a result, calculations may sys- force participation rates from the ILO database tematically over- or underestimate actual rates. For to population estimates. 2009 World Development Indicators 47 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. 23 .. 11 .. 24 .. 25 .. 53 .. 64 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0 b,c 2c 0 b,c 1c 40 c 33c 18 c 11c 59c 66c 81c 88 c Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 6 5 4 3 32 31 12 9 61 65 84 88 Austria 6 6c 8 6c 47 40 c 20 13c 46 55c 72 81c Azerbaijan .. 41 .. 37 .. 15 .. 9 .. 44 .. 54 Bangladesh 54 50 85 59 16 12 9 18 25 38 2 23 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 3c 2c 2c 2c 41c 35c 16c 11c 56c 62c 81c 86c Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 3c .. 1c .. 42c .. 17c .. 55c .. 82c .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 29 .. 13 .. 28 .. 17 .. 43 .. 71 Brazil 31c 25c 25c 16c 27c 27c 10 c 13c 43c 48 c 65c 71c Bulgaria .. 11 .. 7 .. 39 .. 29 .. 50 .. 64 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 53 .. 68 .. 14 .. 4 .. 26 .. 23 .. Canada 6c 4c 2c 2c 31c 32c 11c 11c 64 c 64 c 87c 88 c Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 24 17 6 6 32 29 15 12 45 54 79 83 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China 1 0b 0b 0b 37 22 27 7 63 77 73 93 Colombia 2c 32 1c 8 35c 21 25c 16 63c 48 74 c 76 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 32 21 5 5 27 26 25 13 41 52 69 82 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 12d .. 14 d .. 40 d .. 18d .. 48d .. 67d Cuba .. 28 .. 10 .. 23 .. 14 .. 50 .. 76 Czech Republic 9 5 7 3 55 49 33 27 36 46 61 71 Denmark 7 4 3 2 37 34 16 12 56 62 81 86 Dominican Republic 26 21 3 3 23 26 21 15 52 53 76 82 Ecuador 10 c 11c 2c 4c 29c 27c 17c 12c 62c 62c 81c 84 c Egypt, Arab Rep. 35 28 52 39 25 23 10 6 41 49 37 55 El Salvador 48 30 15 3 23 25 23 22 29 45 63 75 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 23c 7c 13c 4c 42c 44 c 30 c 24 c 36c 49c 57c 72c Ethiopia .. 84 .. 76 .. 5 .. 8 .. 10 .. 16 Finland 11 7 6 3 38 38 15 12 51 56 78 84 France .. 5 .. 2 .. 35 .. 12 .. 60 .. 85 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 52 .. 57 .. 14 .. 4 .. 34 .. 38 Germany 4 3 4 2 50 41 24 16 47 56 72 82 Ghana 66 .. 59 .. 10 .. 10 .. 23 .. 32 .. Greece 20 c 12c 26c 14 c 32c 30 c 17c 10 c 48 c 58 c 56c 76c Guatemala .. .. .. .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 48 2009 World Development Indicators PEOPLE Employment by economic activity Agriculture Industry 2.3 Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a Honduras 53c 51c 6c 13c 18 c 20 c 25c 23c 29c 29c 69c 63c Hungary .. 7c .. 3c .. 42c .. 21c .. 51c .. 76c India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 54 41d 57 41d 15 21d 13 15d 31 38d 31 44 d Iran, Islamic Rep. .. 23 .. 34 .. 31 .. 28 .. 46 .. 37 Iraq .. 14 .. 33 .. 20 .. 7 .. 66 .. 60 Ireland 19 9 3 1 33 39 18 12 48 51 78 86 Israel 5 3 2 1 38 31 15 11 57 65 83 88 Italy 8 5 9 3 37 39 22 18 55 56 70 79 Jamaica 36 25 16 9 25 27 12 5 39 48 72 86 Japan 6 4 7 5 40 35 27 18 54 59 65 77 Jordan .. 4 .. 2 .. 23 .. 12 .. 73 .. 84 Kazakhstan .. 35 .. 32 .. 24 .. 10 .. 41 .. 58 Kenya 19 c .. 20 c .. 23c .. 9c .. 58 c .. 71c .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 14 7 18 9 40 34 28 17 46 59 54 74 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 39 .. 39 .. 23 .. 11 .. 38 .. 50 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 15c .. 8c .. 35c .. 16c .. 49c .. 75c Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 25 17c 15 11c 46 37c 31 21c 29 46c 54 68 c Macedonia, FYR .. 20 .. 19 .. 34 .. 30 .. 46 .. 51 Madagascar .. 77 .. 79 .. 7 .. 6 .. 16 .. 15 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23 16 20 11 31 35 32 27 46 49 48 62 Mali .. 50 .. 30 .. 18 .. 15 .. 32 .. 55 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 15 11 13 9 36 34 48 29 48 55 39 62 Mexico 34 21 11 5 25 30 19 19 41 49 70 76 Moldova .. 41 .. 40 .. 21 .. 12 .. 38 .. 48 Mongolia .. 43 .. 37 .. 19 .. 15 .. 38 .. 48 Morocco .. 40 .. 61 .. 21 .. 16 .. 39 .. 23 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 .. 52 .. 21 .. 8 .. 34 .. 40 .. Nepal 75 .. 91 .. 4 .. 1 .. 20 .. 8 .. Netherlands 5 4 3 2 33 30 10 8 60 62 82 86 New Zealand 13 9 8 5 31 32 13 11 56 59 80 84 Nicaragua .. 41 .. 10 .. 19 .. 17 .. 33 .. 52 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 7 5 3 2 34 32 10 8 58 63 86 90 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 45 38 69 67 20 21 15 15 35 41 16 18 Panama 35 22 3 4 20 22 11 9 45 56 85 86 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 3c 39c 0 b,c 20 c 33c 19c 19c 10 c 64 c 42c 80 c 70 c Peru 1c 1c 0 b,c 0 b,c 30 c 31c 13c 13c 69c 68 c 87c 86c Philippines 53c 45 32c 25 17c 17 14 c 12 29c 39 55c 64 Poland .. 18 c .. 17c .. 39c .. 17c .. 43c .. 66c Portugal 10 c 11c 13c 13c 39c 41c 24 c 19c 51c 48 c 63c 68 c Puerto Rico 5 3 0b 0b 27 25 19 11 67 72 80 89 2009 World Development Indicators 49 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a Romania 29 31 38 33 44 35 30 25 28 34 33 42 Russian Federation .. 12 .. 8 .. 38 .. 21 .. 50 .. 71 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5 .. 0b .. 11 .. 1 .. 85 .. 99 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia .. 21e .. 20e .. 37e .. 20e .. 42e .. 60e Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 1 0 0b 0 36 36 32 21 63 63 68 79 Slovak Republic .. 6c .. 3c .. 50 c .. 25c .. 44 c .. 72c Slovenia .. 9 .. 9 .. 47 .. 25 .. 43 .. 65 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 13 .. 7 .. 33 .. 14 .. 54 .. 79 Spain 11c 6c 8c 4c 41c 41c 16c 12c 49c 52c 76c 84 c Sri Lanka .. .. .. .. .. .. .. .. .. .. .. .. Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5c 3c 2c 1c 40 c 34 c 12c 9c 55c 63c 86c 90 c Switzerland 4c 5c 4c 3c 37c 32c 15c 11c 59c 63c 81c 86c Syrian Arab Republic 23 23 54 49 28 29 8 8 49 48 38 43 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 78 c .. 90 c .. 7c .. 1c .. 15c .. 8c .. Thailand 60 44 62 41 18 22 13 19 22 34 25 41 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 6 6 2 34 41 14 16 51 52 80 82 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 22 72 52 26 28 11 15 41 50 17 33 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 91 60c 91 77c 4 11c 6 5c 5 29c 3 18 c Ukraine .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 3 2 1 1 41 33 16 9 55 65 82 90 United States 4 2 1 1 34 30 14 10 62 68 85 90 Uruguay 7c 7c 1c 2c 36c 29c 21c 13c 57c 64 c 78 c 86c Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 16c 2 2c 32 25c 16 11c 52 59c 82 86c Vietnam .. 56 .. 60 .. 21 .. 14 .. 23 .. 26 West Bank and Gaza .. 12 .. 34 .. 28 .. 8 .. 59 .. 56 Yemen, Rep. 44 .. 83 .. 14 .. 2 .. 38 .. 13 .. Zambia .. .. .. .. .. .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 20 .. 14 .. 30 .. 17 .. 49 .. 68 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 19 .. 19 .. 34 .. 20 .. 47 .. 62 Latin America & Carib. 21 21 14 10 30 27 14 15 49 52 71 76 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 5 3 38 34 19 13 56 62 76 84 Euro area 7 5 7 3 42 38 21 14 50 56 72 82 Note: Data across sectors may not sum to 100 percent because of workers not classified by sectors. a. Data are for the most recent year available. b. Less than 0.5. c. Limited coverage. d. Data are for 2007. e. Data are for 2008. 50 2009 World Development Indicators PEOPLE Employment by economic activity 2.3 About the data Definitions The International Labour Organization (ILO) classi- aggregated into three broad groups: agriculture, · Agriculture corresponds to division 1 (ISIC revi- fies economic activity using the International Stan- industry, and services. Such broad classification may sion 2) or tabulation categories A and B (ISIC revi- dard Industrial Classification (ISIC) of All Economic obscure fundamental shifts within countries' indus- sion 3) and includes hunting, forestry, and fishing. Activities, revision 2 (1968) and revision 3 (1990). trial patterns. A slight majority of countries report · Industry corresponds to divisions 2­5 (ISIC revi- Because this classification is based on where work economic activity according to the ISIC revision 2 sion 2) or tabulation categories C­F (ISIC revision is performed (industry) rather than type of work per- instead of revision 3. The use of one classification or 3) and includes mining and quarrying (including oil formed (occupation), all of an enterprise's employees the other should not have a significant impact on the production), manufacturing, construction, and public are classified under the same industry, regardless information for the three broad sectors presented utilities (electricity, gas, and water). · Services corre- of their trade or occupation. The categories should in the table. spond to divisions 6­9 (ISIC revision 2) or tabulation sum to 100 percent. Where they do not, the differ- The distribution of economic wealth in the world categories G­P (ISIC revision 3) and include whole- ences are due to workers who cannot be classified remains strongly correlated with employment by sale and retail trade and restaurants and hotels; by economic activity. economic activity. The wealthier economies are transport, storage, and communications; financing, Data on employment are drawn from labor force those with the largest share of total employment in insurance, real estate, and business services; and surveys, household surveys, official estimates, cen- services, whereas the poorer economies are largely community, social, and personal services. suses and administrative records of social insurance agriculture based. schemes, and establishment surveys when no other The distribution of economic activity by gender information is available. The concept of employment reveals some clear patterns. Men still make up the generally refers to people above a certain age who majority of people employed in all three sectors, but worked, or who held a job, during a reference period. the gender gap is biggest in industry. Employment in Employment data include both full-time and part-time agriculture is also male-dominated, although not as workers. much as industry. Segregating one sex in a narrow There are many differences in how countries define range of occupations significantly reduces economic and measure employment status, particularly mem- efficiency by reducing labor market flexibility and thus bers of the armed forces, self-employed workers, and the economy's ability to adapt to change. This seg- unpaid family workers. Where members of the armed regation is particularly harmful for women, who have forces are included, they are allocated to the service a much narrower range of labor market choices and sector, causing that sector to be somewhat over- lower levels of pay than men. But it is also detri- stated relative to the service sector in economies mental to men when job losses are concentrated where they are excluded. Where data are obtained in industries dominated by men and job growth is from establishment surveys, data cover only employ- centered in service occupations, where women have ees; thus self-employed and unpaid family workers better chances, as has been the recent experience are excluded. In such cases the employment share in many countries. of the agricultural sector is severely underreported. There are several explanations for the rising impor- Caution should be also used where the data refer tance of service jobs for women. Many service jobs-- only to urban areas, which record little or no agricul- such as nursing and social and clerical work--are tural work. Moreover, the age group and area covered considered "feminine" because of a perceived simi- could differ by country or change over time within a larity to women's traditional roles. Women often do country. For detailed information on breaks in series, not receive the training needed to take advantage of consult the original source. changing employment opportunities. And the greater Countries also take different approaches to the availability of part-time work in service industries may treatment of unemployed people. In most countries lure more women, although it is unclear whether this unemployed people with previous job experience are is a cause or an effect. classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribu- tion of employment by economic activity may not be fully comparable across countries. Data sources The ILO's Yearbook of Labour Statistics and its data- base Key Indicators of the Labour Market report data Data on employment are from the ILO database by major divisions of the ISIC revision 2 or revision 3. Key Indicators of the Labour Market, 5th edition. In the table the reported divisions or categories are 2009 World Development Indicators 51 2.4 Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15­24 % of relevant age group % of male employment % of female employment % growth 1991 2007 1991 2007 1991 2007a 1990 2007 1990 2007 1992 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 53 51 39 36 88 .. .. .. .. .. ­3.9 4.9 Algeria 39 51 25 34 60 83 .. .. .. .. ­0.1 0.1 Angola 76 76 69 68 11 17 .. .. .. .. ­10.3 7.8 Argentina 54 58 43 39 72 84 .. 22b .. 17b 9.6 5.0 Armenia 39 40 24 26 .. 89 .. .. .. .. ­39.5 11.2 Australia 57 62 58 64 83 150 12 11 9 7 0.4 0.6 Austria 54 57 61 53 102 102 .. 9 .. 9 0.3 0.2 Azerbaijan 57 61 38 39 88 .. .. 41 .. 66 ­23.7 9.3 Bangladesh 75 68 67 56 20 .. .. 85 .. 87 5.2 2.3 Belarus 59 53 40 34 93 95 .. .. .. .. ­8.7 9.6 Belgium 46 49 31 27 101 110 .. 11 .. 9 0.3 0.3 Benin 71 72 64 59 10 32 .. .. .. .. ­2.1 1.6 Bolivia 62 70 48 49 44 82 32b .. 50 b .. 0.0 2.7 Bosnia and Herzegovina 52 41 28 16 .. 85 .. .. .. .. 0.8 4.7 Botswana 49 46 39 26 48 76 .. .. .. .. 0.8 ­0.7 Brazil 56 65 54 53 58 105 29b 30 30 b 24 ­8.2 3.8 Bulgaria 46 47 28 26 86 105 .. 10 .. 7 ­11.6 4.6 Burkina Faso 81 81 77 74 7 16 .. .. .. .. ­3.0 1.2 Burundi 84 83 73 72 5 15 .. .. .. .. ­1.0 0.2 Cambodia 78 79 68 75 25 42 .. .. .. .. 4.7 3.8 Cameroon 59 59 39 35 26 25 .. .. .. .. ­5.8 1.1 Canada 59 64 57 61 101 .. .. 12b .. 9b 2.1 ­0.6 Central African Republic 72 71 57 57 11 .. .. .. .. .. ­9.0 0.4 Chad 66 69 50 49 7 19 .. .. .. .. 3.0 ­3.5 Chile 51 51 34 24 73 91 .. 25 .. 24 6.9 2.8 China 76 73 72 56 40 76 .. .. .. .. 11.8 8.6 Hong Kong, China 63 59 54 39 80 86 .. 10 .. 4 5.7 1.8 Colombia 52 63 37 44 50 85 30 b 41 26 b 41 ­0.1 2.0 Congo, Dem. Rep. 67 66 58 61 21 33 .. .. .. .. ­13.6 5.0 Congo, Rep. 65 64 48 45 46 .. .. .. .. .. ­0.4 5.9 Costa Rica 57 59 48 44 45 87 26 20 21 20 4.2 1.7 Côte d'Ivoire 62 60 51 45 21 .. .. .. .. .. ­3.1 0.6 Croatia 46 47 25 29 .. 91 .. 18b .. 18b ­11.5 1.6 Cuba 53 56 39 32 94 93 .. .. .. .. .. .. Czech Republic 58 56 45 29 91 96 .. 15 .. 9 ­1.1 3.6 Denmark 62 63 65 62 109 120 .. .. .. .. 1.7 0.7 Dominican Republic 44 53 28 33 .. 79 42 49 30 30 6.4 4.4 Ecuador 52 60 39 39 55 70 33b 29 b 41b 41b ­3.1 2.8 Egypt, Arab Rep. 43 42 22 22 71 .. .. 20 .. 44 1.8 4.5 El Salvador 59 58 42 41 36 64 .. 29 .. 44 6.9 1.6 Eritrea 65 65 58 53 .. 29 .. .. .. .. 15.3 ­2.5 Estonia 63 57 44 30 104 100 2 8 3 4 ­18.5 2.1 Ethiopia 72 81 65 74 13 30 .. 48 .. 56 ­11.7 7.7 Finland 59 57 45 44 116 112 .. .. .. .. 3.8 1.1 France 50 51 28 29 98 114 .. 8 .. 5 1.7 0.5 Gabon 58 59 37 34 39 .. .. .. .. .. ­7.5 ­0.7 Gambia, The 72 72 58 54 17 49 .. .. .. .. ­0.3 2.3 Georgia 58 56 28 22 95 90 .. 64 .. 65 ­43.3 4.1 Germany 56 54 58 43 98 102 .. .. .. .. 3.8 ­0.7 Ghana 69 65 40 40 34 49 .. .. .. .. ­1.2 3.8 Greece 46 50 31 28 94 103 .. 28 .. 27 ­2.0 1.6 Guatemala 56 63 52 53 23 56 .. .. .. .. 3.4 1.0 Guinea 82 82 75 73 10 35 .. .. .. .. ­0.9 2.2 Guinea-Bissau 66 66 57 62 6 .. .. .. .. .. ­2.2 0.5 Haiti 56 56 37 48 21 .. .. .. .. .. ­10.2 0.0 52 2009 World Development Indicators PEOPLE Decent work and productive employment Employment to Gross enrollment Vulnerable 2.4 Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15­24 % of relevant age group % of male employment % of female employment % growth 1991 2007 1991 2007 1991 2007a 1990 2007 1990 2007 1992 2008 Honduras 59 56 49 43 33 61 48b .. 50 b .. ­0.4 1.9 Hungary 50 47 39 21 86 96 8 8 7 6 ­3.0 2.3 India 59 55 47 39 42 55 .. .. .. .. 2.4 5.4 Indonesia 63 62 46 41 45 66 .. 60 .. 68 4.3 4.2 Iran, Islamic Rep. 46 48 33 35 57 73 .. 40 .. 56 2.6 1.4 Iraq 35 .. 23 .. 44 .. .. .. .. .. .. .. Ireland 45 60 38 47 100 112 25 16 9 5 2.5 0.0 Israel 46 51 25 27 92 92 .. 9 .. 5 1.3 2.3 Italy 45 46 30 26 83 100 .. 27 .. 16 0.9 ­0.4 Jamaica 62 58 39 32 65 87 46 38 37 31 0.9 ­0.7 Japan 63 57 43 41 97 101 15 10 26 12 ­0.3 0.4 Jordan 36 39 24 19 82 89 .. .. .. .. 8.2 1.7 Kazakhstan 64 64 46 42 100 92d .. .. .. .. ­1.5 2.8 Kenya 72 73 61 59 46 50 .. .. .. .. ­5.2 ­1.1 Korea, Dem. Rep. 62 65 46 40 .. .. .. .. .. .. .. .. Korea, Rep. 59 59 36 29 90 98 .. 23 .. 28 3.9 1.9 Kuwait 62 66 30 32 43 91 .. .. .. .. 52.3 3.0 Kyrgyz Republic 60 59 41 40 100 86 .. 47 .. 47 ­14.3 5.6 Lao PDR 80 78 74 64 23 44 .. .. .. .. 3.8 4.3 Latvia 59 57 42 35 92 99 .. 9 .. 6 ­31.7 1.7 Lebanon 46 46 32 28 62 81 .. .. .. .. 1.0 6.2 Lesotho 51 56 43 43 24 37 .. .. .. .. 5.6 4.8 Liberia 66 66 57 57 .. .. .. .. .. .. ­34.1 4.3 Libya 46 49 28 28 .. 94 .. .. .. .. ­5.5 4.5 Lithuania 55 53 35 20 92 99 .. .. .. .. ­20.0 8.3 Macedonia, FYR 38 35 19 14 .. 84 .. 24 .. 20 ­5.7 4.3 Madagascar 79 83 65 70 17 26 .. 84 .. 89 ­2.8 3.1 Malawi 72 72 49 49 8 28 .. .. .. .. ­10.6 5.0 Malaysia 60 61 47 44 57 69 31 23 25 21 6.2 3.6 Mali 48 46 39 34 8 32 .. .. .. .. 5.2 1.8 Mauritania 54 47 43 31 14 25 .. .. .. .. ­0.9 2.7 Mauritius 56 55 46 37 55 88 13 18 7 15 ­2.5 2.9 Mexico 57 58 50 43 53 87 29 28 15 32 ­0.2 0.6 Moldova 59 44 38 17 78 89 .. 35 .. 30 ­28.6 7.6 Mongolia 51 52 40 35 82 92 .. .. .. .. ­12.8 7.9 Morocco 46 46 40 34 36 56 .. 47 .. 65 ­7.1 4.2 Mozambique 79 77 66 64 7 18 .. .. .. .. ­13.1 4.3 Myanmar 75 75 62 54 23 .. .. .. .. .. 7.1 3.0 Namibia 46 42 24 14 45 59 .. .. .. .. 3.6 0.2 Nepal 61 62 54 45 34 48d .. .. .. .. ­0.6 1.7 Netherlands 53 61 55 65 120 118 .. .. .. .. ­0.3 0.5 New Zealand 57 65 55 56 90 120 15 14 10 10 0.1 ­0.4 Nicaragua 53 59 41 48 42 66 .. 45 .. 46 ­5.5 0.4 Niger 60 60 50 51 7 11 .. .. .. .. ­10.0 2.4 Nigeria 52 51 28 24 24 32 .. .. .. .. 0.2 2.4 Norway 60 65 49 56 103 113 .. 8 .. 3 4.1 1.0 Oman 53 51 30 29 45 90 .. .. .. .. 1.6 2.8 Pakistan 48 51 39 43 25 33 .. 58 .. 75 4.6 1.8 Panama 50 60 34 40 62 70 44 30 19 24 1.9 7.4 Papua New Guinea 70 70 58 55 12 .. .. .. .. .. 11.8 4.2 Paraguay 62 73 51 58 31 66 17b 45 31b 50 ­2.1 2.2 Peru 58 68 45 52 67 94 30 b 33b 46b 47b ­3.6 6.7 Philippines 59 61 42 40 71 83 .. 44 .. 47 ­3.4 3.1 Poland 54 49 32 26 87 100 .. 21 .. 18 4.1 1.3 Portugal 59 58 53 36 66 97 18b 18 21b 19 3.3 ­0.2 Puerto Rico 38 42 21 29 .. .. .. .. .. .. .. .. 2009 World Development Indicators 53 2.4 Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15­24 % of relevant age group % of male employment % of female employment % growth 1991 2007 1991 2007 1991 2007a 1990 2007 1990 2007 1992 2008 Romania 57 50 42 24 92 86 7b 32 11b 33 ­13.3 9.4 Russian Federation 58 59 34 33 93 84 1 6 1 6 ­19.2 6.3 Rwanda 87 80 79 64 9 18 .. .. .. .. 13.7 5.2 Saudi Arabia 51 51 27 25 44 94 .. .. .. .. 2.0 2.7 Senegal 67 66 59 54 15 24 77 .. 91 .. ­1.6 0.7 Serbia 50 c 49c 28 c 30 c .. 88 .. 25 .. 20 .. .. Sierra Leone 64 64 39 42 17 32 .. .. .. .. ­19.5 3.8 Singapore 64 62 56 37 .. .. 10 12 6 7 3.5 ­0.9 Slovak Republic 56 53 43 30 .. 96 .. 13b .. 5b ­8.0 4.6 Slovenia 56 56 38 33 89 95 .. 14 .. 13 1.4 3.6 Somalia 65 66 58 57 .. .. .. .. .. .. .. .. South Africa 40 41 20 14 69 96 .. 2 .. 3 ­6.0 2.7 Spain 43 53 36 38 105 119 20 13 24 10 3.7 7.1 Sri Lanka 52 55 32 35 71 .. .. 39b .. 44b 5.1 4.3 Sudan 46 47 29 24 21 35d .. .. .. .. 4.3 5.2 Swaziland 54 51 34 26 42 47 .. .. .. .. ­0.5 1.3 Sweden 65 61 59 45 90 103 .. .. .. .. 3.3 0.6 Switzerland 68 64 69 63 99 93 8 10 11 11 0.3 1.5 Syrian Arab Republic 47 45 38 33 48 72 .. .. .. .. 8.1 1.5 Tajikistan 54 55 36 55 102 84 .. .. .. .. ­27.4 3.2 Tanzania 88 78 79 70 5 .. .. 82b .. 93b ­2.8 4.2 Thailand 78 72 70 47 33 83 67 51 74 56 6.8 3.3 Timor-Leste 64 67 51 58 .. 53 .. .. .. .. .. .. Togo 65 64 57 52 20 39 .. .. .. .. ­6.5 ­2.4 Trinidad and Tobago 45 62 33 47 82 76 22 17 21 13 ­4.8 2.7 Tunisia 41 42 29 23 45 85 .. .. .. .. 4.7 2.1 Turkey 53 43 48 31 48 79 .. 32 .. 50 4.4 0.1 Turkmenistan 56 59 34 34 .. .. .. .. .. .. ­8.7 7.2 Uganda 85 83 78 76 11 18 .. .. .. .. 0.2 6.1 Ukraine 59 54 37 34 94 94 .. .. .. .. ­8.8 3.0 United Arab Emirates 71 75 43 47 68 92 .. .. .. .. ­3.3 4.7 United Kingdom 58 59 66 56 87 98 .. .. .. .. 2.7 1.0 United States 61 62 56 52 92 94 .. .. .. .. 2.7 3.0 Uruguay 54 58 43 38 84 101 .. 26 .. 24 4.4 9.7 Uzbekistan 55 58 36 38 99 102 .. .. .. .. ­11.4 5.7 Venezuela, RB 52 60 36 38 53 79 .. 28 .. 33 0.7 2.7 Vietnam 76 71 75 52 32 .. .. .. .. .. 5.8 4.0 West Bank and Gaza 29 32 19 16 .. 92 .. 34 .. 47 .. .. Yemen, Rep. 38 39 23 22 .. 46 .. .. .. .. 2.6 ­0.2 Zambia 57 61 40 47 23 43 56 .. 81 .. ­3.9 3.2 Zimbabwe 70 67 48 51 49 40 .. .. .. .. .. .. World 63 w 61 w 53 w 45 w 51 w 66 w .. w .. w .. w .. w ­0.4 w 3.1 w Low income 66 65 55 51 25 38 .. .. .. .. ­0.5 3.8 Middle income 64 62 54 43 51 70 .. .. .. .. ­1.7 6.1 Lower middle income 66 63 56 44 47 65 .. .. .. .. 3.1 7.7 Upper middle income 55 56 43 38 68 91 .. 23 .. 20 ­7.2 3.9 Low & middle income 64 62 54 45 45 61 .. .. .. .. ­1.6 5.8 East Asia & Pacific 74 71 67 52 47 73 .. .. .. .. 8.5 8.6 Europe & Central Asia 56 54 38 32 85 88 .. 19 .. 18 ­11.9 4.6 Latin America & Carib. 55 61 47 46 51 89 30 31 28 31 ­1.8 3.4 Middle East & N. Africa 43 45 28 28 57 71 .. 34 .. 52 1.2 3.2 South Asia 59 56 48 42 38 49 .. .. .. .. 3.0 6.5 Sub-Saharan Africa 64 64 50 49 21 32 .. .. .. .. ­4.0 3.7 High income 57 58 47 44 92 101 .. .. .. .. 2.0 1.9 Euro area 50 52 41 37 .. .. .. 14 .. 9 2.2 1.4 a. Provisional data. b. Limited coverage. c. Includes Montenegro. d. Data are for 2008. 54 2009 World Development Indicators PEOPLE Decent work and productive employment 2.4 About the data Definitions Four targets were added to the UN Millennium Dec- small group of countries. The labor force survey is · Employment to population ratio is the proportion laration at the 2005 World Summit High-Level Ple- the most comprehensive source for internationally of a country's population that is employed. People nary Meeting of the 60th Session of the UN General comparable employment, but there are still some ages 15 and older are generally considered the work- Assembly. One was full and productive employment limitations for comparing data across countries and ing-age population. People ages 15­24 are generally and decent work for all, which is seen as the main over time even within a country. Information from considered the youth population. · Gross enrollment route for people to escape poverty. The four indi- labor force surveys is not always consistent in what ratio, secondary, is the ratio of total enrollment in cators for this target have an economic focus, and is included in employment. For example, informa- secondary education, regardless of age, to the popu- three of them are presented in the table. tion provided by the Organisation for Economic Co- lation of the age group that officially corresponds to The employment to population ratio indicates operation and Development relates only to civilian secondary education. · Vulnerable employment is how efficiently an economy provides jobs for people employment, which can result in an underestimation unpaid family workers and own-account workers as a who want to work. A high ratio means that a large of "employees" and "workers not classified by sta- percentage of total employment. · Labor productiv- propor tion of the population is employed. But a tus," especially in countries with large armed forces. ity is the growth rate of gross domestic product lower employment to population ratio can be seen While the categories of unpaid family workers and (GDP) divided by total employment in the economy. as a positive sign, especially for young people, if it self-employed workers, which include own-account is caused by an increase in their education. This workers, would not be affected, their relative shares indicator has a gender bias because women who do would be. Geographic coverage is another factor that not consider their work employment or who are not can limit cross-country comparisons. The employment perceived as working tend to be undercounted. This by status data for most Latin American countries cov- bias has different effects across countries. ers urban areas only. Similarly, in some countries Comparability of employment ratios across coun- in Sub-Saharan Africa, where limited information is tries is also affected by variations in definitions of available anyway, the members of producer coopera- employment and population (see About the data for tives are usually excluded from the self-employed table 2.3). The biggest difference results from the category. For detailed information on definitions and age range used to define labor force activity. The coverage, consult the original source. population base for employment ratios can also vary Labor productivity is used to assess a country's (see table 2.1). Most countries use the resident, economic ability to create and sustain decent employ- noninstitutionalized population of working age living ment opportunities with fair and equitable remunera- in private households, which excludes members of tion. Productivity increases obtained through invest- the armed forces and individuals residing in men- ment, trade, technological progress, or changes in tal, penal, or other types of institutions. But some work organization can increase social protection countries include members of the armed forces in and reduce poverty, which in turn reduce vulner- the population base of their employment ratio while able employment and working poverty. Productivity excluding them from employment data (International increases do not guarantee these improvements, Labour Organization, Key Indicators of the Labour but without them--and the economic growth they Market, 5th edition). bring--improvements are highly unlikely. For compa- The proportion of unpaid family workers and own- rability of individual sectors labor productivity is esti- account workers in total employment is derived from mated according to national accounts conventions. information on status in employment. Each status However, there are still significant limitations on the group faces different economic risks, and unpaid availability of reliable data. Information on consis- family workers and own-account workers are the tent series of output in both national currencies and most vulnerable--and therefore the most likely to purchasing power parity dollars is not easily avail- fall into poverty. They are the least likely to have for- able, especially in developing countries, because the mal work arrangements, are the least likely to have definition, coverage, and methodology are not always social protection and safety nets to guard against consistent across countries. For example, countries Data sources economic shocks, and often are incapable of gen- employ different methodologies for estimating the erating sufficient savings to offset these shocks. A missing values for the nonmarket service sectors Data on employment to population ratio, vulner- high proportion of unpaid family workers in a country and use different definitions of the informal sector. able employment, and labor productivity are from indicates weak development, little job growth, and the International Labour Organization database often a large rural economy. Key Indicators of the Labour Market, 5th edition. Data on employment by status are drawn from Data on gross enrollment ratios are from the labor force surveys and household surveys, supple- UNESCO Institute for Statistics. mented by offi cial estimates and censuses for a 2009 World Development Indicators 55 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990­92a 2004­07a 1990­92a 2004­07a 1990­92a 2004­07a 2004­07a 2004­07a 2004­07a 2004­07a 2004­07a 2004­07a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. 98.3 .. 1.7 Algeria 23.0 12.3 24.2 .. 20.3 .. .. .. .. 59.3 23.0 11.4 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.7b 9.5b 6.4b 7.8b 7.0 b 11.6b .. .. .. 37.3b 41.8b 19.7b Armenia .. 9.6 .. 5.7 .. 13.8 .. .. .. 5.2 83.0 11.9 Australia 10.8 4.4 11.4 4.0 10.0 4.8 15.5b 16.5b 14.4b 48.0 34.1 17.9 Austria 3.6 4.4 3.5 3.9 3.8 5.0 26.8 26.6 27.1 37.9 b 52.1b 10.0 b Azerbaijan .. .. .. .. .. .. .. .. .. 6.3 78.9 14.9 Bangladesh .. 4.3 .. 3.4 .. 7.0 .. .. .. 33.0 24.4 15.9 Belarus .. .. .. .. .. .. .. .. .. 10.0 39.0 51.0 Belgium 6.7 7.6 4.8 6.7 9.5 8.7 50.0 49.1 51.0 42.1 38.2 19.7 Benin 1.5 .. 2.2 .. 0.6 .. .. .. .. .. .. .. Bolivia 5.5b .. 5.5b .. 5.6b .. .. .. .. .. .. .. Bosnia and Herzegovina 17.6 31.1 15.5 28.9 21.6 34.8 .. .. .. .. .. .. Botswana .. 17.6 .. 15.3 .. 19.9 .. .. .. .. .. .. Brazil 6.4b 8.9b 5.4b 6.8b 7.9b 11.7b .. .. .. 51.6b 33.6b 3.6b Bulgaria .. 8.9 .. 8.6 .. 9.3 .. .. .. 41.8 49.7 8.6 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi 0.5 .. 0.7 .. 0.3 .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 11.2b 6.0 b 12.0 b 6.4b 10.2b 5.6b 7.5b 8.4b 6.3b 27.7b 41.1b 31.2b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 4.4 8.9 3.9 .. 5.3 .. .. .. .. 17.0 57.9 24.8 China 2.3b 4.0 b .. .. .. .. .. .. .. .. .. .. Hong Kong, China 2.0 4.0 2.0 4.5 1.9 3.4 .. .. .. 40.8 41.4 16.6 Colombia 9.5 10.9 6.8 8.7 13.0 13.8 .. .. .. 76.6 .. 20.6 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 4.1 4.6 3.5 3.3 5.4 6.8 .. .. .. 65.2 27.3 6.4 Côte d'Ivoire 6.7 .. .. .. .. .. .. .. .. .. .. .. Croatia .. 9.6 .. 8.3 .. 11.2 58.8 54.6 61.8 20.4 67.8 11.8 Cuba .. 1.9 .. 1.7 .. 2.2 .. .. .. 43.0 52.4 4.6 Czech Republic .. 5.3 .. 4.2 .. 6.7 53.4 51.7 54.7 26.8 68.8 4.3 Denmark 9.0 3.6 8.3 3.2 9.9 4.0 18.2 18.4 17.9 35.9 35.1 23.0 Dominican Republic 20.7 17.9 12.0 11.3 35.2 28.8 .. .. .. .. .. .. Ecuador 8.9 7.9 6.0 5.8 13.2 10.8 .. .. .. 74.0 .. 23.6 Egypt, Arab Rep. 9.0 9.0 6.4 6.0 17.0 18.6 .. .. .. .. .. .. El Salvador 7.9 6.6 8.4 8.5 7.2 3.9 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.7 4.7 3.9 5.4 3.5 3.9 .. .. .. 23.1 57.8 16.6 Ethiopia .. 5.4 .. 2.7 .. 8.2 .. .. .. 35.9 13.3 3.2 Finland 11.6 6.8 13.3 6.4 9.6 7.3 23.0 26.5 19.5 35.5 45.9 18.6 France 10.0 8.0 7.9 7.4 12.7 8.5 40.4 40.6 40.1 39.9 39.6 19.9 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 13.3 .. 13.9 .. 12.6 .. .. .. 5.1 52.5 42.3 Germany 6.6 8.6 5.3 8.5 8.4 8.8 56.6 57.5 55.6 33.1 56.3 10.6 Ghana 4.7 .. 3.7 .. 5.5 .. .. .. .. .. .. .. Greece 7.8 8.1 4.9 5.0 12.9 12.6 50.3 42.1 54.9 29.3 48.4 21.8 Guatemala .. 3.1 .. 2.8 .. 3.7 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 12.7 .. 11.9 .. 13.8 .. .. .. .. .. .. .. 56 2009 World Development Indicators PEOPLE Unemployment Unemployment Long-term 2.5 Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990­92a 2004­07a 1990­92a 2004­07a 1990­92a 2004­07a 2004­07a 2004­07a 2004­07a 2004­07a 2004­07a 2004­07a Honduras 3.2 4.2 3.3 3.2 3.0 6.2 .. .. .. .. .. .. Hungary 9.9 7.4 11.0 7.1 8.7 7.7 47.5 47.3 47.9 33.1 58.7 8.1 India .. 5.0 b .. 4.9b .. 5.3b .. .. .. 29.0 37.7 33.3 Indonesia 2.8 9.1 2.7 8.1 3.0 10.8 .. .. .. 44.4 40.7 9.6 Iran, Islamic Rep. 11.1 10.5 9.5 9.3 24.4 15.7 .. .. .. 41.8 34.7 19.6 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 15.2 4.6 15.2 4.8 15.2 4.3 30.3 36.0 21.9 39.8 37.2 18.2 Israel 11.2b 7.3b 9.2b 6.7b 13.9 b 7.9b .. .. .. 12.2 12.8 72.5 Italy 11.5 6.1 8.1 4.9 17.3 7.9 49.9 47.3 52.3 46.5 40.6 11.3 Jamaica 15.7 9.4 9.5 5.5 22.8 14.3 .. .. .. 9.7 4.3 8.4 Japan 2.2 3.9 2.1 4.0 2.2 3.7 32.0 40.3 19.4 67.2 .. 32.8 Jordan .. 12.4 .. 11.8 .. 16.5 .. .. .. .. .. .. Kazakhstan .. 8.4 .. 7.0 .. 9.8 .. .. .. 7.1 49.0 43.9 Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.5 3.2 2.8 3.7 2.1 2.6 0.6 0.7 0.3 15.2 49.7 35.2 Kuwait .. 1.7 .. .. .. .. .. .. .. 19.4 41.4 9.6 Kyrgyz Republic .. 8.3 .. 7.7 .. 9.0 .. .. .. 13.3 77.1 9.6 Lao PDR .. 1.4 .. 1.3 .. 1.4 .. .. .. .. .. .. Latvia .. 6.0 .. 6.3 .. 5.4 .. .. .. 24.3 59.9 14.6 Lebanon .. 8.1 .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. 5.6 .. 6.8 .. 4.2 .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 4.3 .. 4.3 .. 4.4 .. .. .. 14.2 70.4 15.4 Macedonia, FYR .. 34.9 .. 34.5 .. 35.5 .. .. .. .. .. .. Madagascar .. 2.6 .. 1.7 .. 3.5 .. .. .. 67.7 .. 9.3 Malawi .. 7.8 .. 5.4 .. 10.0 .. .. .. .. .. .. Malaysia 3.7 3.1 .. 3.2 .. 3.4 .. .. .. 13.3 61.6 25.1 Mali .. 8.8 .. 7.2 .. 10.9 .. .. .. .. .. .. Mauritania .. 33.0 .. 25.2 .. .. .. .. .. .. .. .. Mauritius .. 8.5 .. 5.3 .. 14.4 .. .. .. 44.2 48.5 6.4 Mexico 3.1 3.4 2.7 3.2 4.0 3.7 2.7b 3.0 b 2.3b 50.7 24.5 22.9 Moldova .. 5.1 .. 6.2 .. 3.9 .. .. .. .. .. .. Mongolia .. 2.8 .. .. .. .. .. .. .. 35.1 45.8 18.5 Morocco 16.0 b 10.0 13.0 b 10.1 25.3b 10.0 .. .. .. 51.1b 22.4b 21.6b Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.0 .. 4.7 .. 8.8 .. .. .. .. .. .. .. Namibia 19.0 21.9 20.0 19.4 19.0 25.0 .. .. .. .. .. .. Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 5.5 3.6 4.3 3.2 7.3 4.1 41.7 43.9 39.8 41.3 39.7 17.0 New Zealand 10.4b 3.6b 11.0 b 3.3b 9.6b 3.9b 5.7b 6.1b 5.4b 30.6 38.8 26.9 Nicaragua 14.4 5.2 11.3 5.4 19.5 4.9 .. .. .. 72.8 2.1 18.0 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 5.9 2.5 6.6 2.5 5.1 2.4 8.8 10.2 7.1 25.4 49.2 20.6 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 5.2 5.3 3.8 4.5 14.0 8.4 .. .. .. 14.3 11.4 26.0 Panama 14.7 6.8 10.8 5.3 22.3 9.3 .. .. .. 36.0 39.6 24.0 Papua New Guinea 7.7 .. 9.0 .. 5.9 .. .. .. .. .. .. .. Paraguay 5.3b 5.6b 6.4b 4.2b 3.8b 7.6b .. .. .. 49.9 38.0 9.9 Peru 9.4b 6.7b 7.5b 5.6b 12.5b 8.0 b .. .. .. 30.0 b 31.9b 37.6b Philippines 8.6 6.3 7.9 6.4 9.9 6.0 .. .. .. 13.6 46.2 39.4 Poland 13.3 9.6 12.2 9.0 14.7 10.3 45.9 45.8 46.0 16.4 73.2 10.4 Portugal 4.1b 8.0 3.5b 6.6 5.0 b 9.6 47.3 48.2 46.7 68.1 15.4 13.2 Puerto Rico 16.9 10.9 19.1 12.0 13.3 9.5 .. .. .. .. .. .. 2009 World Development Indicators 57 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990­92a 2004­07a 1990­92a 2004­07a 1990­92a 2004­07a 2004­07a 2004­07a 2004­07a 2004­07a 2004­07a 2004­07a Romania .. 6.4 .. 7.2 .. 5.4 .. .. .. 25.8 66.3 6.1 Russian Federation 5.3 6.1 5.4 6.4 5.2 5.8 .. .. .. 13.7 54.2 32.1 Rwanda 0.3 .. 0.6 .. 0.2 .. .. .. .. .. .. .. Saudi Arabia .. 5.6 .. 4.2 .. 13.2 .. .. .. 12.3 43.9 40.0 Senegal .. .. .. .. .. .. .. .. .. 40.2 6.9 2.5 Serbia .. 13.3c .. 11.7c .. 15.2c 70.5 79.3 82.2 .. .. .. Sierra Leone .. 3.4 .. 4.5 .. 2.3 .. .. .. .. .. .. Singapore 2.7 4.0 2.7 3.7 2.6 4.3 .. .. .. 31.0 25.6 43.2 Slovak Republic .. 11.0 .. 9.8 .. 12.5 70.8 72.3 69.4 29.2b 65.3b 5.3b Slovenia .. 4.6 .. 3.9 .. 6.1 .. .. .. 25.0 60.4 12.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 23.0 .. 20.0 .. 26.6 .. .. .. 36.2 56.3 4.5 Spain 18.1 8.3 13.9 6.4 25.8 10.9 27.6 23.9 30.5 54.8 23.6 20.4 Sri Lanka 13.3b 6.0 b 10.1b 4.3b 19.9b 9.0 b .. .. .. 45.4b 22.0 b 32.6b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5.7 6.1 6.7 5.8 4.6 6.4 13.0 14.5 11.4 32.2 46.0 17.1 Switzerland 2.8 3.6 2.3 2.9 3.5 4.5 40.8 37.9 43.0 28.8 53.2 17.9 Syrian Arab Republic 6.8 .. 5.2 .. 14.0 .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. 66.5 28.8 4.6 Tanzania 3.6b 4.7 2.8b .. 4.3b .. .. .. .. .. .. .. Thailand 1.4 1.2 1.3 1.3 1.5 1.1 .. .. .. 40.5 45.5 0.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 19.6 6.5 17.0 4.4 23.9 9.6 .. .. .. .. .. .. Tunisia .. 14.2 .. 13.1 .. 17.3 .. .. .. 79.1 .. 13.6 Turkey 8.5 9.9 8.8 9.8 7.8 10.2 30.4 27.1 39.5 52.3 28.2 12.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine .. 6.8 .. 7.0 .. 6.6 .. .. .. 8.5 52.2 39.3 United Arab Emirates .. 3.1 .. 2.5 .. 7.1 .. .. .. 24.3 36.0 21.6 United Kingdom 9.7 5.2 11.5 5.5 7.3 4.9 24.7 29.7 18.2 37.3 47.7 14.3 United States 7.5b 4.6b 7.9b 4.7b 7.0 b 4.5b 10.0 b 10.7b 9.0 b 18.7b 35.5b 45.7b Uruguay 9.0 b 9.2b 6.8b 6.6b 11.8b 12.4b .. .. .. 59.1 27.0 13.8 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 7.7 7.5 8.2 7.1 6.8 8.1 .. .. .. .. .. .. Vietnam .. 2.1 .. 1.9 .. 2.4 .. .. .. .. .. .. West Bank and Gaza .. 21.6 .. 22.1 .. 19.0 .. .. .. 54.3 14.2 23.5 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 18.9 .. 16.3 .. 22.4 .. .. .. .. .. .. .. Zimbabwe .. 4.2 .. 4.2 .. 4.1 .. .. .. .. .. .. World .. w 6.4 w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. 6.4 .. .. .. .. .. .. .. .. .. .. Lower middle income .. 5.7 .. .. .. .. .. .. .. .. .. .. Upper middle income 6.3 8.7 5.9 8.0 7.1 9.6 .. .. .. 37.8 43.6 13.7 Low & middle income .. 6.4 .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 2.5 4.5 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 7.8 .. 8.1 .. 7.4 .. .. .. 23.4 53.1 22.3 Latin America & Carib. 6.7 8.8 5.4 6.9 8.4 11.5 .. .. .. 53.4 32.2 12.9 Middle East & N. Africa 12.8 12.1 10.8 10.4 21.7 18.4 .. .. .. .. .. .. South Asia .. 5.3 .. 5.1 .. 6.0 .. .. .. 27.8 34.5 32.2 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.4 5.5 7.0 5.2 7.9 5.8 25.6 27.3 23.1 35.3 41.3 26.7 Euro area 9.5 7.5 7.5 6.6 12.5 8.6 45.2 44.4 45.5 41.4 42.9 14.9 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2008. 58 2009 World Development Indicators PEOPLE Unemployment 2.5 About the data Definitions Unemployment and total employment are the broad- generate statistics that are more comparable inter- · Unemployment is the share of the labor force with- est indicators of economic activity as reflected by nationally. But the age group, geographic coverage, out work but available for and seeking employment. the labor market. The International Labour Organiza- and collection methods could differ by country or Definitions of labor force and unemployment may tion (ILO) defines the unemployed as members of the change over time within a country. For detailed infor- differ by country (see About the data). · Long-term economically active population who are without work mation, consult the original source. unemployment is the number of people with continu- but available for and seeking work, including people Women tend to be excluded from the unemploy- ous periods of unemployment extending for a year or who have lost their jobs or who have voluntarily left ment count for various reasons. Women suffer more longer, expressed as a percentage of the total unem- work. Some unemployment is unavoidable. At any from discrimination and from structural, social, and ployed. · Unemployment by educational attainment time some workers are temporarily unemployed-- cultural barriers that impede them from seeking is the unemployed by level of educational attainment between jobs as employers look for the right workers work. Also, women are often responsible for the as a percentage of the total unemployed. The levels and workers search for better jobs. Such unemploy- care of children and the elderly and for household of educational attainment accord with the ISCED97 ment, often called frictional unemployment, results affairs. They may not be available for work during of the United Nations Educational, Scientific, and from the normal operation of labor markets. the short reference period, as they need to make Cultural Organization. Changes in unemployment over time may reflect arrangements before starting work. Furthermore, changes in the demand for and supply of labor; they women are considered to be employed when they may also refl ect changes in reporting practices. are working part-time or in temporary jobs, despite Paradoxically, low unemployment rates can disguise the instability of these jobs or their active search for substantial poverty in a country, while high unemploy- more secure employment. ment rates can occur in countries with a high level of Long-term unemployment is measured by the economic development and low rates of poverty. In length of time that an unemployed person has been countries without unemployment or welfare benefits without work and looking for a job. The data in the people eke out a living in vulnerable employment. In table are from labor force surveys. The underlying countries with well developed safety nets workers assumption is that shorter periods of joblessness can afford to wait for suitable or desirable jobs. But are of less concern, especially when the unem- high and sustained unemployment indicates serious ployed are covered by unemployment benefi ts or inefficiencies in resource allocation. similar forms of support. The length of time that a The ILO definition of unemployment notwithstand- person has been unemployed is difficult to measure, ing, reference periods, the criteria for people consid- because the ability to recall that time diminishes as ered to be seeking work, and the treatment of people the period of joblessness extends. Women's long- temporarily laid off or seeking work for the first time term unemployment is likely to be lower in countries vary across countries. In many developing countries where women constitute a large share of the unpaid it is especially difficult to measure employment and family workforce. unemployment in agriculture. The timing of a survey, Unemployment by level of educational attainment for example, can maximize the effects of seasonal provides insights into the relation between the edu- unemployment in agriculture. And informal sector cational attainment of workers and unemployment employment is difficult to quantify where informal and may be used to draw inferences about changes activities are not tracked. in employment demand. Information on educational Data on unemployment are drawn from labor force attainment is the best available indicator of skill sample surveys and general household sample levels of the labor force. Besides the limitations to surveys, censuses, and offi cial estimates, which comparability raised for measuring unemployment, are generally based on information from different the different ways of classifying the education level sources and can be combined in many ways. Admin- may also cause inconsistency. Education level is istrative records, such as social insurance statistics supposed to be classifi ed according to Interna- and employment office statistics, are not included tional Standard Classifi cation of Education 1997 in the table because of their limitations in cover- (ISCED97). For more information on ISCED97, see age. Labor force surveys generally yield the most About the data for table 2.11. comprehensive data because they include groups not covered in other unemployment statistics, par- Data sources ticularly people seeking work for the first time. These surveys generally use a definition of unemployment Data on unemployment are from the ILO database that follows the international recommendations more Key Indicators of the Labour Market, 5th edition. closely than that used by other sources and therefore 2009 World Development Indicators 59 2.6 Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7­14 % of children ages 7­14 % of children ages 7­14 % of children in employment in employment in employment ages 7­14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 2000 36.6 41.1 31.8 43.1 56.9 .. .. .. .. 1.4 93.1 Algeria .. .. .. .. .. .. .. .. .. .. .. Angolab 2001 30.1 30.0 30.1 26.6 73.4 .. .. .. .. 6.2 80.1 Argentina 2004 12.9 15.7 9.8 4.8 95.2 .. .. .. 34.2 8.1 56.2 Armenia .. .. .. .. .. .. .. .. .. .. .. Australia .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2000 9.7 12.0 7.3 4.2 95.8 .. .. .. .. 2.1 88.9 Bangladesh 2006 16.2 25.7 6.4 37.8 62.2 .. .. .. ­ 17.0 77.8 Belarus 2005 11.7 12.1 11.2 0.0 100.0 .. .. .. .. 9.2 78.8 Belgium .. .. .. .. .. .. .. .. .. .. .. Benin 2006 74.4 72.8 76.1 36.1 63.9 .. .. .. .. .. .. Bolivia 2005 22.0 23.9 20.1 8.1 91.9 84.4 4.3 10.1 1.2 4.4 92.9 Bosnia and Herzegovina 2006 10.6 11.7 9.5 0.1 99.9 .. .. .. .. 1.6 92.1 Botswana .. .. .. .. .. .. .. .. .. .. .. Brazil 2004 7.0 9.4 4.6 7.2 92.8 60.8 6.6 30.9 6.8 21.5 58.0 Bulgaria .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 2004 50.0 49.0 51.0 98.1 1.9 97.2 0.4 2.2 1.3 0.4 98.3 Burundi 2000 37.0 38.4 35.7 48.3 51.7 .. .. .. 3.9 85.3 .. Cambodia 2001 52.3 52.4 52.1 16.5 83.5 76.1 5.0 18.0 1.5 4.7 90.2 Cameroonc 2001 15.9 14.5 17.4 52.5 47.5 88.2 2.1 7.1 .. .. .. Canada .. .. .. .. .. .. .. .. .. .. .. Central African Republic 2000 67.0 66.5 67.6 54.9 45.1 .. .. .. .. 2.0 56.4 Chad 2004 60.4 64.4 56.2 49.1 50.9 .. .. .. .. 1.8 77.2 Chile 2003 4.1 5.1 3.1 3.2 96.8 24.1 6.9 66.9 .. .. .. China .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 2005 4.0 6.2 1.8 32.8 67.2 .. .. .. 12.6 39.1 48.3 Congo, Dem. Rep.c 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. 6.6 76.7 Congo, Rep 2005 30.1 29.9 30.2 9.9 90.1 .. .. .. .. 4.2 84.5 Costa Ricac 2004 5.7 8.1 3.5 44.6 55.4 40.3 9.5 49.0 15.8 57.7 26.6 Côte d'Ivoire 2006 45.7 47.7 43.6 46.8 53.2 .. .. .. .. 2.4 88.0 Croatia .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. Dominican Republicc 2005 5.8 9.0 2.7 6.2 93.8 18.5 9.8 57.5 23.8 19.5 56.2d Ecuador 2004 12.0 14.6 9.3 27.0 73.0 70.0 4.7 23.7 6.0 15.8 75.5 Egypt, Arab Rep. 2005 7.9 11.5 4.3 21.0 79.0 .. .. .. 11.4 87.4 El Salvador 2003 12.7 17.1 8.1 19.5 80.5 51.0 12.5 35.4 1.5 15.3 78.4 Eritrea .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. Ethiopia 2005 56.0 64.3 47.1 69.4 30.6 94.6 1.5 3.7 1.7 2.4 95.8 Finland .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. Gambia, The 2005 43.5 33.9 52.3 32.1 67.9 .. .. .. .. 1.1 87.3 Georgia .. .. .. .. .. .. .. .. .. .. .. Germany .. .. .. .. .. .. .. .. .. .. .. Ghana 2003 6.0 6.0 5.9 71.2 28.8 78.8 2.8 15.2 10.8 5.5 78.4 Greece .. .. .. .. .. .. .. .. .. .. .. Guatemala 2004 16.8 23.1 10.5 31.3 68.7 66.1 9.1 23.5 3.4 17.8 78.9 Guinea 1994 48.3 47.2 49.5 98.6 1.4 .. .. .. .. .. .. Guinea-Bissau 2000 67.5 67.4 67.5 63.7 36.3 .. .. .. .. 0.9 81.1 Haiti 2005 33.4 37.3 29.6 17.7 82.3 .. .. .. .. 1.8 79.4 60 2009 World Development Indicators PEOPLE Survey Children in employment Children at work Employment by 2.6 Status in year economic activitya employmenta % of children ages 7­14 % of children ages 7­14 % of children ages 7­14 % of children in employment in employment in employment ages 7­14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Honduras 2004 6.8 10.4 3.2 48.6 51.4 63.4 8.3 24.7 2.7 19.9 73.8 Hungary .. .. .. .. .. .. .. .. .. .. .. India 2004­05 4.2 4.2 4.2 84.9 15.2 69.4 16.0 12.4 7.1 6.8 59.3 Indonesia 2000 8.9 8.8 9.1 24.9 75.1 .. .. .. .. 17.8 75.8d Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. Iraq 2006 14.7 17.9 11.3 32.4 67.6 .. .. .. .. 7.0 85.3 Ireland .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. Jamaica 2002 1.1 1.5 0.6 17.1 82.9 31.3 7.7 51.4 21.6 35.1 43.3 Japan .. .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. .. Kazakhstan 2006 3.6 4.4 2.8 1.6 98.4 .. .. .. .. 4.0 75.0 Kenya 2000 37.7 40.1 35.2 14.1 85.9 .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2006 5.2 5.8 4.6 7.9 92.1 .. .. .. .. 3.7 81.9 Lao PDR .. .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. .. .. .. .. Lebanon .. .. .. .. .. .. .. .. .. .. .. Lesotho 2000 30.8 34.2 27.5 17.6 82.4 .. .. .. .. 3.6 83.3 Liberia 2007 37.4 37.8 37.1 45.0 55.0 .. .. .. .. 1.7 79.3 Libya .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 2005 11.8 14.8 8.6 2.8 97.2 .. .. .. .. 3.9 89.5 Madagascar 2001 25.6 26.1 25.1 85.1 14.9 94.0 1.0 2.4 6.8 1.5 91.4 Malawi 2006 40.3 41.3 39.4 10.5 89.5 .. .. .. .. 6.7 75.5 Malaysia .. .. .. .. .. .. .. .. .. .. .. Mali 2006 49.5 55.0 44.1 59.5 40.5 .. .. .. .. 1.6 80.4 Mauritania .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. Mexicoe 2004 8.9 12.2 5.6 34.1 65.9 38.1 12.3 48.0 3.7 52.0 44.2 Moldova 2000 33.5 34.1 32.8 3.8 96.2 .. .. .. .. 2.9 82.0 Mongolia 2005 12.4 14.1 10.7 8.7 91.3 .. .. .. .. 3.9 91.3 Morocco 1998­99 13.2 13.5 12.8 93.2 6.8 60.6 8.3 10.1 2.1 10.0 81.7 Mozambiquec 1996 1.8 1.9 1.7 100.0 0.0 .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 1999 15.4 16.2 14.7 9.5 90.5 91.5 0.4 8.0 0.1 4.5 95.0 Nepal 1999 47.2 42.2 52.4 35.6 64.4 87.0 1.4 11.1 4.2 3.3 92.4 Netherlands .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2005 10.1 16.2 3.9 30.8 69.2 70.5 9.7 19.3 1.2 13.8 85.0 f Niger 2006 47.1 49.2 45.0 66.5 33.5 .. .. .. 4.8 74.5 .. Nigeria .. .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. Pakistan .. .. .. .. .. .. .. .. .. .. .. Panamac 2003 5.1 7.7 2.2 38.4 61.6 57.6 3.1 38.1 12.4 24.9 50.3f Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. Paraguayc 2005 15.3 22.6 7.7 24.2 75.7 60.8 6.2 32.1 9.3 24.8 65.8 Peru 2000 24.1 25.7 22.3 4.8 95.2 72.6 2.8 24.5 1.9 6.8 91.4 Philippines 2001 13.3 16.3 10.0 14.8 85.2 64.3 4.1 30.6 4.1 22.8 73.1 Poland .. .. .. .. .. .. .. .. .. .. .. Portugal 2001 3.6 4.6 2.6 3.6 96.4 48.5 11.2 33.3 .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 61 2.6 Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7­14 % of children ages 7­14 % of children ages 7­14 % of children in employment in employment in employment ages 7­14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Romania 2000 1.4 1.7 1.1 20.7 79.3 97.1 0.0 2.3 4.5 .. 92.9d Russian Federation .. .. .. .. .. .. .. .. .. .. .. Rwanda 2000 33.1 36.1 30.3 27.5 72.5 .. .. .. .. 2.9 85.7 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. Senegal 2005 18.5 24.4 12.6 61.9 38.1 79.1 5.0 14.0 6.3 4.4 84.1 Serbia 2005 6.9 7.2 6.6 2.1 97.9 .. .. .. .. 5.2 89.4 Sierra Leone 2005 62.7 63.6 61.8 29.9 70.1 .. .. .. .. 1.0 71.1 Singapore .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. Somalia 2006 43.5 45.5 41.5 53.5 46.5 .. .. .. .. 1.6 94.8 South Africa 1999 27.7 29.0 26.4 5.1 94.9 .. .. .. 7.1 7.1 85.8 Spain .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 1999 17.0 20.4 13.4 5.4 94.6 71.2 13.1 15.0 2.9 8.3 88.0 Sudang 2000 19.1 21.5 16.8 55.9 44.1 .. .. .. .. 7.3 81.3 Swaziland 2000 11.2 11.4 10.9 14.0 86.0 .. .. .. .. 10.4 85.9 Sweden .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 2006 6.6 8.8 4.3 34.6 65.4 .. .. .. .. 21.5 68.8 Tajikistan 2005 8.9 8.7 9.1 9.0 91.0 .. .. .. .. 24.2 71.3 Tanzania 2001 40.4 41.5 39.2 40.0 60.0 78.5 0.2 21.3 0.9 1.0 98.2d Thailand 2005 15.1 15.7 14.4 4.2 95.8 .. .. .. .. 13.5 80.0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 2006 38.7 39.8 37.4 29.8 70.2 82.9 1.3 15.1 5.0 1.6 93.4 Trinidad and Tobago 2000 3.9 5.2 2.8 12.8 87.2 .. .. .. .. 29.8 64.9 Tunisia .. .. .. .. .. .. .. .. .. .. .. Turkey 1999 4.5 5.2 3.8 66.8 33.2 65.4 15.9 18.7 3.7 34.9 61.4 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. Uganda 2005­06 38.2 39.8 36.5 7.7 92.3 95.5 1.4 3.0 1.4 1.5 97.1 Ukraine 2005 17.3 18.0 16.6 0.1 99.9 .. .. .. .. 3.1 79.3 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. .. .. .. .. .. Uzbekistan 2005 5.1 5.3 4.9 1.0 99.0 .. .. .. .. 3.8 78.6 Venezuela, RBc 2005 5.4 7.1 3.6 24.7 75.3 28.3 8.0 61.1 18.9 25.3 54.0 Vietnam 2006 21.3 21.0 21.6 11.9 88.1 .. .. .. .. 5.9 91.2 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1999 13.1 12.4 14.0 64.3 35.7 92.0 1.0 6.2 4.1 5.4 86.8 Zambia 2005 47.9 48.9 46.8 25.9 74.1 95.9 0.6 3.5 2.6 0.7 96.5 Zimbabwe 1999 14.3 15.3 13.3 12.0 88.0 .. .. .. 3.4 28.4 68.2 a. Shares may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Covers children ages 10­14. d. Refers to family workers, regardless of whether they are paid. e. Covers children ages 12­14. f. Refers to unpaid workers, regardless of whether they are family workers. g. Covers northern Sudan only. 62 2009 World Development Indicators PEOPLE Children at work 2.6 About the data Definitions The data in the table refer to children's work in working for payment in cash or in kind or is involved in · Survey year is the year in which the underlying the sense of "economic activity"--that is, children unpaid work, whether a child is working for someone data were collected. · Children in employment are in employment, which is a broader concept than who is not a member of the household, whether a children involved in any economic activity for at least child labor (see ILO forthcoming for details on this child is involved in any type of family work (on the one hour in the reference week of the survey. · Work distinction). farm or in a business), and the like. The ages used only refers to children who are employed and not In line with the definition of economic activity in country surveys to define child labor range from 5 attending school. · Study and work refer to chil- adopted by the 13th International Conference of to 17 years. The data in the table have been recalcu- dren attending school in combination with employ- Labour Statisticians, the threshold for classifying a lated to present statistics for children ages 7­14. ment. · Employment by economic activity is the person as employed is to have been engaged at least Although efforts are made to harmonize the defi - distribution of children in employment by the major one hour in any activity during the reference period nition of employment and the questions on employ- industrial categories (ISIC revision 2 or revision 3). relating to the production of goods and services ment used in survey questionnaires, significant dif- · Agriculture corresponds to division 1 (ISIC revi- set by the 1993 United Nations System of National ferences remain in the survey instruments used to sion 2) or categories A and B (ISIC revision 3) and Accounts. Children seeking work are thus not included collect data on children in employment and in the includes agriculture and hunting, forestry and log- in employment. Economic activity covers all market sampling design underlying these surveys. Differ- ging, and fishing. · Manufacturing corresponds to production and certain types of nonmarket production, ences exist not only across different household sur- division 3 (ISIC revision 2) or category D (ISIC revi- including production of goods for own use. It excludes veys in the same country, but also across the same sion 3). · Services correspond to divisions 6­9 (ISIC unpaid household services (commonly called "house- type of survey carried out in different countries. revision 2) or categories G­P (ISIC revision 3) and hold chores")--that is, the production of domestic Because of the differences in the underlying sur- include wholesale and retail trade, hotels and restau- and personal services by household members for vey instruments and dates, estimates of working rants, transport, financial intermediation, real estate, consumption within their own household. children are not fully comparable across countries. public administration, education, health and social The data are from household surveys conducted Caution should be used in drawing conclusions work, other community services, and private house- by the International Labor Organization (ILO), the concerning relative levels of child economic activity hold activity. · Self-employed workers are people United Nations Children's Fund (UNICEF), the World across countries or regions based on the data. whose remuneration depends directly on the profits Bank, and national statistical offi ces. These sur- The table aggregates the distribution of children derived from the goods and services they produce, veys yield a variety of data on education, employ- in employment by the industrial categories of the with or without other employees, and include employ- ment, health, expenditure, and consumption indica- International Standard Industrial Classification ers, own-account workers, and members of produc- tors that relate to children's work. (ISIC): agriculture, manufacturing, and services. ers cooperatives. · Wage workers (also known as Household survey data generally include informa- A residual category--which includes mining and employees) are people who hold explicit (written or tion on work type--for example, whether a child is quarrying; electricity, gas, and water; construction; oral) or implicit employment contracts that provide extraterritorial organization and other inadequately basic remuneration that does not depend directly on Children work long hours 2.6a defined activities--is not presented. Both ISIC revi- the revenue of the unit for which they work. · Unpaid sion 2 and revision 3 are used, depending solely on family workers are people who work without pay in a Average work time among children ages 7­14 who the codification applied by each country in describ- market-oriented establishment operated by a related study and work, 2005 (hours per week) 40 ing the economic activity. The use of two different person living in the same household. classifications does not affect the definition of the Data sources groups presented in the table. 30 The table aggregates the distribution of children Data on children at work are estimates produced in employment by status in employment. Status in by the Understanding Children's Work project 20 employment is based on the International Classifica- based on household survey data sets made avail- tion of Status in Employment (1993), which shows able by the ILO's International Programme on the the distribution of children in employment by three Elimination of Child Labour under its Statistical 10 major categories: self-employed workers, wage work- Monitoring Programme on Child Labour, UNICEF ers (also known as employees), and unpaid family under its Multiple Indicator Cluster Survey pro- 0 workers. A residual category--which includes those gram, the World Bank under its Living Standards Paraguay Somalia Zambia not classifiable by status--is not presented. Measurement Study program, and national sta- Children in many countries work long hours, tistical offices. Information on how the data were often combining studying with working. In Para- collected and some indication of their reliability guay children work more than 30 hours a week, can be found at www.ilo.org/public/english/ leaving very little time for studying or any other standards/ipec/simpoc/, www.childinfo.org, and activities. www.worldbank.org/lsms. Detailed country statis- Source: Understanding Children's Work Project. tics can be found at www.ucw-project.org. 2009 World Development Indicators 63 2.7 Poverty rates at national poverty lines Population below national poverty line Poverty gap at national poverty line Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year % % % year % % % year % % % Afghanistan 2007 45.0 27.0 42.0 .. .. .. .. .. .. Albania 2002 29.6 19.5 25.4 2005 24.2 11.2 18.5 2005 5.3 2.3 4.0 Algeria 1988 16.6 7.3 12.2 1995 30.3 14.7 22.6 1995 4.5 1.8 3.2 Argentina 1998 .. 28.8 .. 2002 .. 53.0 .. 2002 .. 28.5 .. Armenia 1998­99 50.8 58.3 55.1 2001 48.7 51.9 50.9 2001 .. .. 15.1 Azerbaijan 1995 .. .. 68.1 2001 42.0 55.0 49.6 2001 .. .. 15.5 Bangladesh 2000 52.3 35.1 48.9 2005 43.8 28.4 40.0 2005 9.8 6.5 9.0 Belarus 2002 .. .. 30.5 2004 .. .. 17.4 .. .. .. Benin 1999 33.0 23.3 29.0 2003 46.0 29.0 39.0 2003 14.0 8.0 12.0 Bolivia 1999 80.1 51.4 62.0 2002 82.2 53.9 64.6 2002 43.4 23.8 31.2 Bosnia and Herzegovina 2001­02 19.9 13.8 19.5 .. .. .. 2001­02 4.9 2.8 4.6 Brazil 1998 51.4 14.7 22.0 2002­03 41.0 17.5 21.5 2002­03 28.4 17.8 19.6 Bulgaria 1997 .. .. 36.0 2001 .. .. 12.8 2001 .. .. 4.2 Burkina Faso 1998 61.1 22.4 54.6 2003 52.4 19.2 46.4 2003 17.6 5.1 15.3 Burundi 1998 64.6 66.5 68.0 .. .. .. .. .. .. Cambodia 1994 .. .. 47.0 2004 38.0 18.0 35.0 2004 7.8 1.2 6.7 Cameroon 1996 59.6 41.4 53.3 2001 49.9 22.1 40.2 .. .. .. Chad 1995­96 48.6 .. 43.4 .. .. .. 1995­96 26.3 .. 27.5 Chile 1996 .. .. 19.9 1998 .. .. 17.0 1998 .. .. 5.7 China 1998 4.6 .. 4.6 2004 .. .. 2.8 .. .. .. Colombia 1995 79.0 48.0 60.0 1999 79.0 55.0 64.0 1999 44.0 26.0 34.0 Congo, Dem. Rep. 2004­05 75.7 61.5 71.3 .. .. .. 2004­05 34.9 26.2 32.2 Congo, Rep. 2005 49.2 .. 42.3 .. .. .. .. .. .. Costa Rica 1989 35.8 26.2 31.7 2004 28.3 20.8 23.9 2004 10.8 7.0 8.6 Croatia 2002 .. .. 11.2 2004 .. .. 11.1 .. .. .. Dominican Republic 2000 45.3 18.2 27.7 2004 55.7 34.7 42.2 2004 24.0 12.9 16.8 Ecuador 1998 69.0 30.0 46.0 2001 .. .. 45.2 2001 .. .. 18.0 Egypt, Arab Rep. 1995­96 23.3 22.5 22.9 1999­20 00 .. .. 16.7 1999­2000 .. .. 3.0 El Salvador 1995 64.8 38.9 50.6 2002 49.8 28.5 37.2 2002 24.2 11.1 16.5 Eritrea 1993­94 .. .. 53.0 .. .. .. .. .. .. Estonia 1995 14.7 6.8 8.9 .. .. .. 1995 6.6 1.8 3.1 Ethiopia 1995­96 47.0 33.3 45.5 1999­2000 45.0 37.0 44.2 1999­2000 12.0 10.0 12.0 Gambia, The 1998 61.0 48.0 57.6 2003 63.0 57.0 61.3 2003 .. .. 25.9 Georgia 2002 55.4 48.5 52.1 2003 52.7 56.2 54.5 .. .. .. Ghana 1998­99 49.6 19.4 39.5 2005­06 39.2 10.8 28.5 2005­06 13.5 3.1 9.6 Guatemala 1989 71.9 33.7 57.9 2000 74.5 27.1 56.2 2000 .. .. 22.6 Guinea 1994 .. .. 40.0 .. .. .. .. .. .. Guinea-Bissau 2002 .. 52.6 65.7 .. .. .. 2000 .. 17.5 25.7 Haiti 1987 .. .. 65.0 1995 66.0 .. .. .. .. .. Honduras 1998­99 71.2 28.6 52.5 2004 70.4 29.5 50.7 2004 34.5 9.1 22.3 Hungary 1993 .. .. 14.5 1997 .. .. 17.3 1997 4.1 .. .. India 1993­94 37.3 32.4 36.0 1999­2000 30.2 24.7 28.6 1999­2000 5.6 6.9 .. Indonesia 1996 19.8 13.6 17.6 2005 .. .. 16.0 2004 .. .. 2.9 Jamaica 1995 37.0 18.7 27.5 2000 25.1 12.8 18.7 .. .. .. Jordan 1997 27.0 19.7 21.3 2002 18.7 12.9 14.2 2002 4.7 2.9 3.3 Kazakhstan 2001 .. .. 17.6 2002 .. .. 15.4 2002 4.5 2.0 3.1 Kenya 1994 47.0 29.0 40.0 1997 53.0 49.0 52.0 .. .. .. Kyrgyz Republic 2003 57.5 35.7 49.9 2005 50.8 29.8 43.1 2005 12.0 7.0 10.0 Lao PDR 1997­98 41.0 26.9 38.6 2002­03 .. .. 33.0 2002­03 .. .. 8.0 Latvia 2002 11.6 .. 7.5 2004 12.7 .. 5.9 2004 .. .. 1.2 Lesotho 1994/95 68.9 36.7 66.6 2002/03 60.5 41.5 56.3 .. .. .. Macedonia, FYR 2002 25.3 .. 21.4 2003 22.3 .. 21.7 2003 6.5 .. 6.7 Madagascar 1997 76.0 63.2 73.3 1999 76.7 52.1 71.3 1999 36.1 21.4 32.8 Malawi 1990­91 .. .. 54.0 1997­98 66.5 54.9 65.3 .. .. .. Malaysia 1989 .. .. 15.5 .. .. .. .. .. .. Mali 1998 75.9 30.1 63.8 .. .. .. .. .. .. 64 2009 World Development Indicators PEOPLE Poverty rates at national poverty lines Population below national poverty line 2.7 Poverty gap at national poverty line Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year % % % year % % % year % % % Mauritania 1996 65.5 30.1 50.0 2000 61.2 25.4 46.3 .. .. .. Mauritius 1992 .. .. 10.6 .. .. .. .. .. .. Mexico 2002 34.8 11.4 20.3 2004 27.9 11.3 17.6 2002 12.2 2.8 6.3 Moldova 2001 64.1 58.0 62.4 2002 67.2 42.6 48.5 2002 .. .. 16.5 Mongolia 1998 32.6 39.4 35.6 2002 43.4 30.3 36.1 2002 13.2 9.2 11.0 Morocco 1990­91 18.0 7.6 13.1 1998­99 27.2 12.0 19.0 1998­99 6.7 2.5 4.4 Mozambique 1996­97 71.3 62.0 69.4 2002­03 55.3 51.5 54.1 2002­03 20.9 19.7 20.5 Nepal 1995­96 43.3 21.6 41.8 2003­04 34.6 9.6 30.9 2003­04 8.5 2.2 7.5 Nicaragua 1998 68.5 30.5 47.9 2001 64.3 28.7 45.8 2001 25.9 8.7 17.0 Niger 1989­93 66.0 52.0 63.0 .. .. .. .. .. .. Nigeria 1985 49.5 31.7 43.0 1992­93 36.4 30.4 34.1 .. .. .. Pakistan 1993 33.4 17.2 28.6 1998­99 35.9 24.2 32.6 1998­99 7.9 5.0 7.0 Panama 1997 64.9 15.3 37.3 .. .. .. 1997 32.1 3.9 16.4 Papua New Guinea 1996 41.3 16.1 37.5 .. .. .. 1996 13.8 4.3 12.4 Paraguaya 1990 28.5 19.7 20.5 .. .. .. 1990 10.5 5.6 6.0 Peru 2001 77.1 42.0 54.3 2004 72.1 42.9 53.1 2004 28.3 12.4 18.0 Philippines 1994 45.4 18.6 32.1 1997 36.9 11.9 25.1 1997 10.0 2.6 6.4 Poland 1996 .. .. 14.6 2001 .. .. 14.8 .. .. .. Romania 1995 .. .. 25.4 2002 .. .. 28.9 2002 .. .. 7.6 Russian Federation 1998 .. .. 31.4 2002 .. .. 19.6 2002 .. .. 5.1 Rwanda 1993 .. .. 51.2 1999­2000 65.7 14.3 60.3 .. .. .. Senegal 1992 40.4 23.7 33.4 .. .. .. 1992 16.4 3.1 13.9 Sierra Leone 1989 .. .. 82.8 2003­04 79.0 56.4 70.2 2003­04 34.0 .. 29.0 Slovak Republic 2004 .. .. 16.8 .. .. .. 2004 .. .. 5.5 Sri Lanka 1995­96 27.0 15.0 25.0 2002 7.9 24.7 22.7 2002 5.6 1.7 5.1 Swaziland 2000­01 75.0 49.0 69.2 .. .. .. 2000­01 .. .. 32.9 Tajikistan 1999 .. .. 74.9 2003 .. .. 44.4 2003 .. .. 12.7 Tanzania 1991 40.8 31.2 38.6 2000­01 38.7 29.5 35.7 .. .. .. Thailand 1994 .. .. 9.8 1998 .. .. 13.6 1998 .. .. 3.0 Timor-Leste 2001 .. .. 39.7 .. .. .. 2001 .. .. 11.9 Togo 1987­89 .. .. 32.3 .. .. .. 1987­89 .. .. 10.0 Trinidad and Tobago 1992 20.0 24.0 21.0 .. .. .. 1992 6.2 7.4 7.3 Tunisia 1990 13.1 3.5 7.4 1995 13.9 3.6 7.6 1990 3.3 0.9 1.7 Turkey 1994 .. .. 28.3 2002 34.5 22.0 27.0 2002 .. .. 0.3 Uganda 1999­2000 37.4 9.6 33.8 2002­03 41.7 12.2 37.7 2002­03 12.6 3.0 11.3 Ukraine 2000 34.9 .. 31.5 2003 28.4 .. 19.5 .. .. .. Uruguay 1994 .. 20.2 .. 1998 .. 24.7 .. 1998 .. 8.6 .. Uzbekistan 2000­01 33.6 27.8 31.5 2003 29.8 22.6 27.2 .. .. .. Venezuela, RB 1989 .. .. 31.3 1997­99 .. .. 52.0 1997­99 .. .. 24.0 Vietnam 1998 45.5 9.2 37.4 2002 35.6 6.6 28.9 2002 8.7 1.3 6.9 Yemen, Rep. 1998 45.0 30.8 41.8 .. .. .. 1998 14.7 8.2 13.2 Zambia 1998 83.1 56.0 72.9 2004 78.0 53.0 68.0 2004 44.0 22.0 36.0 Zimbabwe 1990­91 35.8 3.4 25.8 1995­96 48.0 7.9 34.9 .. .. .. a. Covers Asunción metropolitan area only. 2009 World Development Indicators 65 2.7 Poverty rates at national poverty lines About the data Definitions The World Bank periodically prepares poverty Data quality · Survey year is the year in which the underlying data assessments of countries in which it has an active Poverty assessments are based on surveys fielded to were collected. · Rural population below national program, in close collaboration with national institu- collect, among other things, information on income poverty line is the percentage of the rural population tions, other development agencies, and civil society or consumption from a sample of households. To be living below the national rural poverty line. · Urban groups, including poor people's organizations. Pov- useful for poverty estimates, surveys must be nation- population below national poverty line is the per- erty assessments report the extent and causes of ally representative and include sufficient information centage of the urban population living below the poverty and propose strategies to reduce it. Since to compute a comprehensive estimate of total house- national urban poverty line. · National population 1992 the World Bank has conducted about 200 pov- hold consumption or income (including consumption below national poverty line is the percentage of the erty assessments, which are the main source of the or income from own production), from which it is pos- country's population living below the national poverty poverty estimates presented in the table. Countries sible to construct a correctly weighted distribution line. National estimates are based on population- report similar assessments as part of their Poverty of consumption or income per person. There remain weighted subgroup estimates from household sur- Reduction Strategies. many potential problems with household survey data, veys. · Poverty gap at national poverty line is the The poverty assessments are the best available including selective nonresponse and differences in mean shortfall from the poverty line (counting the source of information on poverty estimates using the menu of consumption items presented and the nonpoor as having zero shortfall) as a percentage of national poverty lines. They often include separate length of the period over which respondents must the poverty line. This measure reflects the depth of assessments of urban and rural poverty. Data are recall their expenditures. These issues are dis- poverty as well as its incidence. derived from nationally representative household cussed in About the data for table 2.8. surveys conducted by national statistical offices or by private agencies under the supervision of govern- National poverty lines ment or international agencies and obtained from National poverty lines are used to make estimates government statistical offices and World Bank Group of poverty consistent with the country's specific eco- country departments. nomic and social circumstances and are not intended Some poverty assessments analyze the current for international comparisons of poverty rates. The poverty status of a country using the latest available setting of national poverty lines reflects local percep- household survey data, while others use survey data tions of the level of consumption or income needed for several years to analyze poverty trends. Thus, not to be poor. The perceived boundary between poverty estimates for more than one year might be poor and not poor rises with the average income of derived from a single poverty assessment. A poverty a country and so does not provide a uniform measure assessment might not use all available household for comparing poverty rates across countries. Never- surveys, or survey data might become available at theless, national poverty estimates are clearly the a later date even though data were collected before appropriate measure for setting national policies for the poverty assessment date. Thus poverty assess- poverty reduction and for monitoring their results. ments may not fully represent all household survey Almost all the national poverty lines use a food data. bundle based on prevailing diets that attains pre- Over the last 20 years there has been considerable determined nutritional requirements for good health expansion in the number of countries that field sur- and normal activity levels, plus an allowance for non- veys and in the frequency of the surveys. The quality food spending. The rise in poverty lines with average Data sources of their data has improved greatly as well. income is driven more by the gradient in the non- food component of the poverty lines than in the food The poverty measures are prepared by the World Data availability component, although there is still an appreciable Bank's Development Research Group, based on The number of data sets within two years of any given share attributable to the gradient in food poverty data from World Bank's country poverty assess- year rose dramatically, from 13 between 1978 and lines. While nutritional requirements tend to be fairly ments and country Poverty Reduction Strategies. 1982 to 158 between 2001 and 2006. Data cover- similar even across countries at different levels of Summaries of poverty assessments are available age is improving in all regions, but the Middle East economic development, richer countries tend to use at www.worldbank.org/povertynet, by selecting and North Africa and Sub-Saharan Africa continue to a more expensive food bundle--more meat and veg- "Poverty assessments" from the left side bar. lag. The database, maintained by a team in the World etables, less starchy staples, and more processed Poverty assessment documents are available at Bank's Development Research Group, is updated foods generally--for attaining the same nutritional www-wds.worldbank.org, under "By topic," "Pov- annually as new survey data become available, and needs. erty reduction," "Poverty assessment." Further a major reassessment of progress against poverty is discussion of how national poverty lines vary made about every three years. A complete overview across countries can be found in Ravallion, Chen, of data availability by year and country is available at and Sangraula's "Dollar a Day Revisited" (2008). http://iresearch.worldbank.org/povcalnet/. 66 2009 World Development Indicators PEOPLE Poverty rates at international poverty lines International poverty International poverty line 2.8 line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year % % % % year % % % % Albania 75.51 120.82 2002a <2 <0.5 8.7 1.4 2005a <2 <0.5 7.8 1.4 Algeria 48.42b 77.48b 1988a 6.6 1.8 23.8 6.6 1995a 6.8 1.4 23.6 6.4 Angola 88.13 141.01 .. .. .. .. 2000a 54.3 29.9 70.2 42.3 Argentina 1.69 2.71 2002c,d 9.9 2.9 19.7 7.4 2005c,d 4.5 1.0 11.3 3.6 Armenia 245.24 392.38 2002a 15.0 3.1 46.7 13.6 2003a 10.6 1.9 43.4 11.3 Azerbaijan 2,170.94 3,473.51 2001a 6.3 1.1 27.1 6.8 2005a <2 <0.5 <2 <0.5 Bangladesh 31.87 50.99 2000a 57.8e 17.3e 85.4 e 38.7e 2005a 49.6e 13.1e 81.3e 33.8e Belarus 949.53 1,519.25 2002a <2 <0.5 <2 <0.5 2005a <2 <0.5 <2 <0.5 Benin 343.99 550.38 .. .. .. .. 2003a 47.3 15.7 75.3 33.5 Bhutan 23.08 36.93 .. .. .. .. 2003a 26.2 7.0 49.5 18.8 Bolivia 3.21 5.14 2002d 22.8 12.4 34.2 18.5 2005a 19.6 9.7 30.3 15.5 Bosnia and Herzegovina 1.09 1.74 2001a <2 <0.5 <2 <0.5 2004a <2 <0.5 <2 <0.5 Botswana 4.23 6.77 1985­86a 35.6 13.8 54.7 25.8 1993­94a 31.2 11.0 49.4 22.3 Brazil 1.96 3.14 2005d 7.8 1.6 18.3 5.9 2007d 5.2 1.3 12.7 4.1 Bulgaria 0.92 1.47 2001a 2.6 <0.5 7.8 2.2 2003a <2 <0.5 <2 0.9 Burkina Faso 303.02 484.83 1998a 70.0 30.2 87.6 49.1 2003a 56.5 20.3 81.2 39.2 Burundi 558.79 894.07 1998a 86.4 47.3 95.4 64.1 2006a 81.3 36.4 93.4 56.0 Cambodia 2,019.12 3,230.60 1993­94a,f 48.6 13.8 77.8 33.3 2004a 40.2 11.3 68.2 28.0 Cameroon 368.12 588.99 1996a 51.5 18.9 74.4 36.0 2001a 32.8 10.2 57.7 23.6 Cape Verde 97.72 156.35 .. .. .. .. 2001a 20.6 5.9 40.2 14.9 Central African Republic 384.33 614.93 1993a 82.8 57.0 90.7 68.4 2003a 62.4 28.3 81.9 45.3 Chad 409.46 655.14 .. .. .. .. 2002­03a 61.9 25.6 83.3 43.9 Chile 484.20 774.72 2003d <2 <0.5 5.3 1.3 2006d <2 <0.5 2.4 0.39 China 5.11g 8.17g 2002a 28.4h 8.7h 51.1h 20.6h 2005a 15.9h 4.0h 36.3h 12.2h Colombia 1,489.68 2,383.48 2003d 15.4 6.1 26.3 10.9 2006d 16.0 5.7 27.9 11.9 Comoros 368.01 588.82 .. .. .. .. 2004a 46.1 20.8 65.0 34.2 Congo, Dem. Rep. 395.29 632.46 .. .. .. .. 2005­06a 59.2 25.3 79.5 42.4 Congo, Rep. 469.46 751.14 .. .. .. .. 2005a 54.1 22.8 74.4 38.8 Costa Rica 348.70 b 557.92b 2003d 5.6 2.4 11.5 4.7 2005d 2.4 <0.5 8.6 2.3 Croatia 5.58 8.92 2001a <2 <0.5 <2 <0.5 2005a <2 <0.5 <2 <0.5 Czech Republic 19.00 30.39 1993d <2 <0.5 <2 <0.5 1996d <2 <0.5 <2 <0.5 Côte d'Ivoire 407.26 651.62 1998a 24.1 6.7 49.1 18.1 2002a 23.3 6.8 46.8 17.6 Djibouti 134.76 215.61 1996a 4.8 1.6 15.1 4.5 2002a 18.8 5.3 41.2 14.6 Dominican Republic 25.50 b 40.79b 2003d 6.1 1.5 16.3 5.1 2005d 5.0 0.9 15.1 4.3 Ecuador 0.63 1.00 2005d 9.8 3.2 20.4 7.6 2007d 4.7 1.2 12.8 4.0 Egypt, Arab Rep. 2.53 4.04 1999­2000a <2 <0.5 19.3 3.5 2004­05a <2 <0.5 18.4 3.5 El Salvador 6.02b 9.62b 2003d 14.3 6.7 25.3 11.6 2005d 11.0 4.8 20.5 8.9 Estonia 11.04 17.66 2002a <2 <0.5 2.5 0.6 2004a <2 <0.5 <2 <0.5 Ethiopia 3.44 5.50 1999­2000a 55.6 16.2 86.4 37.9 2005a 39.0 9.6 77.5 28.8 Gabon 554.69 887.50 .. .. .. .. 2005a 4.8 0.9 19.6 5.0 Gambia, The 12.93 20.69 1998a 66.7 34.7 82.0 50.0 2003­04a 34.3 12.1 56.7 24.9 Georgia 0.98 1.57 2002a 15.1 4.7 34.2 12.2 2005a 13.4 4.4 30.4 10.9 Ghana 5,594.78 8,951.64 1998­99a 39.1 14.4 63.3 28.5 2006a 30.0 10.5 53.6 22.3 Guatemala 5.68b 9.08b 2002d 16.9 6.5 29.8 12.9 2006d 11.7 3.5 24.3 8.9 Guinea-Bissau 355.34 568.55 1993­94a 52.1 20.6 75.7 37.4 2002­03a 48.8 16.5 77.9 34.8 Guinea 1,849.46 2,959.13 1994a 36.8 11.5 63.8 26.4 2002­03a 70.1 32.2 87.2 50.2 Guyana 131.47b 210.35b 1993d 5.8 2.6 15.0 5.4 1998d 7.7 3.9 16.8 6.9 Haiti 24.21b 38.73b .. .. .. .. 2001d 54.9 28.2 72.1 41.8 Honduras 12.08b 19.32b 2005d 22.2 10.2 34.8 16.7 2006d 18.2 8.2 29.7 14.2 Hungary 171.90 275.03 2002a <2 <0.5 <2 <0.5 2004a <2 <0.5 <2 <0.5 India 19.50i 31.20i 1993­94a 49.4h 14.4h 81.7h 35.3h 2004­05a 41.6h 10.8h 75.6h 30.4h Iran, Islamic Rep. 3,393.53 5,429.65 1998a <2 <0.5 8.3 1.8 2005a <2 <0.5 8.0 1.8 Jamaica 54.20 b 86.72b 2002a <2 <0.5 8.7 1.6 2004a <2 <0.5 5.8 0.9 Jordan 0.62 0.99 2002­03a <2 <0.5 11.0 2.1 2006a <2 <0.5 3.5 0.6 Kazakhstan 81.21 129.93 2002a 5.2 0.9 21.5 5.4 2003a 3.1 <0.5 17.2 3.9 2009 World Development Indicators 67 2.8 Poverty rates at international poverty lines International poverty International poverty line line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year % % % % year % % % % Kenya 40.85 65.37 1997a 19.6 4.6 42.7 14.7 2005­06a 19.7 6.1 39.9 15.1 Kyrgyz Republic 16.25 26.00 2002a 34.0 8.8 66.6 24.9 2004a 21.8 4.4 51.9 16.8 Lao PDR 4,677.02 7,483.24 1997­98 49.3e 14.9e 79.9e 34.4 e 2002­03a 44.0e 12.1e 76.8e 31.0e Latvia 0.43 0.69 2002a <2 <0.5 <2 0.6 2004a <2 <0.5 <2 <0.5 Lesotho 4.28 6.85 1995a 47.6 26.7 61.1 37.3 2002­03a 43.4 20.8 62.2 33.0 Liberia 0.64 1.02 .. .. .. .. 2007a 83.7 40.8 94.8 59.5 Lithuania 2.08 3.32 2002a <2 <0.5 <2 <0.5 2004a <2 <0.5 <2 <0.5 Macedonia, FYR 29.47 47.16 2002a <2 <0.5 3.1 0.7 2003a <2 <0.5 3.2 0.7 Madagascar 945.48 1,512.76 2001a 76.3 41.4 88.7 57.2 2005a 67.8 26.5 89.6 46.9 Malawi 71.15 113.84 1997­98d 83.1 46.0 93.5 62.3 2004­05a,j 73.9 32.3 90.4 51.8 Malaysia 2.64 4.23 1997d <2 <0.5 6.8 1.3 2004­05d <2 <0.5 7.8 1.4 Mali 362.10 579.36 2001a 61.2 25.8 82.0 43.6 2006a 51.4 18.8 77.1 36.5 Mauritania 157.08 251.33 1995­96a 23.4 7.1 48.3 17.8 2000a 21.2 5.7 44.1 15.9 Mexico 9.56 15.30 2004a 2.8 1.4 7.0 2.6 2006a <2 <0.5 4.8 1.0 Moldova 6.03 9.65 2002a 17.1 4.0 40.3 13.2 2004a 8.1 1.7 28.9 7.9 Mongolia 653.12 1,044.99 2002a 15.5 3.6 38.8 12.3 2005a 22.4 6.2 49.0 17.2 Morocco 6.89 11.02 2000a 6.3 0.9 24.3 6.3 2007a 2.5 0.5 14.0 3.1 Mozambique 14,532.12 23,251.39 1996­97a 81.3 42.0 92.9 59.4 2002­03a 74.7 35.4 90.0 53.5 Namibia 6.33 10.13 .. .. .. .. 1993d 49.1 24.6 62.2 36.5 Nepal 33.08 52.93 1995­96a 68.4 26.7 88.1 46.8 2003­04a 55.1 19.7 77.6 37.8 Nicaragua 9.12b 14.59b 2001d 19.4 6.7 37.5 14.4 2005d 15.8 5.2 31.8 12.3 Niger 334.16 534.66 1994a 78.2 38.6 91.5 56.5 2005a 65.9 28.1 85.6 46.6 Nigeria 98.23 157.17 1996­97a 68.5 32.1 86.4 49.7 2003­04a 64.4 29.6 83.9 46.9 Pakistan 25.89 41.42 2001­02a 35.9 7.9 73.9 26.4 2004­05a 22.6 4.4 60.3 18.7 Panama 0.76b 1.22b 2004d 9.2 2.7 18.0 6.8 2006d 9.5 3.1 17.8 7.1 Papua New Guinea 2.11b 3.37b .. .. .. .. 1996a 35.8 12.3 57.4 25.5 Paraguay 2,659.74 4,255.59 2005d 9.3 3.4 18.4 7.3 2007d 6.5 2.7 14.2 5.5 Peru 2.07 3.31 2005d 8.2 2.0 19.4 6.3 2006d 7.9 1.9 18.5 6.0 Philippines 30.22 48.36 2003a 22.0 5.5 43.8 16.0 2006a 22.6 5.5 45.0 16.3 Poland 2.69 4.31 2002a <2 <0.5 <2 <0.5 2005a <2 <0.5 <2 <0.5 Romania 2.15 3.44 2002a 2.9 0.8 13.0 3.2 2005a <2 <0.5 3.4 0.9 Russian Federation 16.74 26.78 2002a <2 <0.5 3.7 0.6 2005a <2 <0.5 <2 <0.5 Rwanda 295.93 473.49 1984­85a 63.3 19.7 88.4 41.8 2000a 76.6 38.2 90.3 55.7 Senegal 372.81 596.49 2001a 44.2 14.3 71.3 31.2 2005a 33.5 10.8 60.3 24.6 Sierra Leone 1,745.26 2,792.42 1989­90a 62.8 44.8 75.0 54.0 2002­03a 53.4 20.3 76.1 37.5 Slovak Republic 23.53 37.66 1992d <2 <0.5 <2 <0.5 1996d <2 <0.5 <2 <0.5 Slovenia 198.25 317.20 2002a <2 <0.5 <2 <0.5 2004a <2 <0.5 <2 <0.5 South Africa 5.71 9.14 1995a 21.4 5.2 39.9 15.0 2000a 26.2 8.2 42.9 18.3 Sri Lanka 50.05 80.08 1995­96a 16.3 3.0 46.7 13.7 2002a 14.0 2.6 39.7 11.8 St. Lucia 2.37b 3.80 b .. .. .. .. 1995d 20.9 7.2 40.6 15.5 Suriname 2.29b 3.67b .. .. .. .. 1999d 15.5 5.9 27.2 11.7 Swaziland 4.66 7.45 1994­95a 78.6 47.7 89.3 61.6 2000­01a 62.9 29.4 81.0 45.8 Tajikistan 1.16 1.85 2003a 36.3 10.3 68.8 26.7 2004a 21.5 5.1 50.8 16.8 Tanzania 603.06 964.90 1991­92a 72.6 29.7 91.3 50.1 2000­01a 88.5 46.8 96.6 64.4 Thailand 21.83 34.93 2002a <2 <0.5 15.1 2.8 2004a <2 <0.5 11.5 2.0 Timor-Leste 0.61b 0.98b .. .. .. .. 2001a 52.9 19.1 77.5 37.0 Togo 352.82 564.51 .. .. .. .. 2006a 38.7 11.4 69.3 27.9 Trinidad and Tobago 5.77b 9.23b 1988d <2 <0.5 8.6 1.9 1992d 4.2 1.1 13.5 3.9 Tunisia 0.87 1.39 1995a 6.5 1.3 20.4 5.8 2000a 2.6 <0.5 12.8 3.0 Turkey 1.25 2.00 2002a 2.0 <0.5 9.6 2.3 2005a 2.7 0.9 9.0 2.6 Turkmenistan 5,961.06b 9,537.69b 1993d 63.5 25.8 85.7 44.8 1998a 24.8 7.0 49.6 18.4 Uganda 930.77 1,489.24 2002a 57.4 22.7 79.8 40.6 2005a 51.5 19.1 75.6 36.4 Ukraine 2.14 3.42 2002a <2 <0.5 3.4 0.7 2005a <2 <0.5 <2 <0.5 Uruguay 19.14 30.62 2005c,d <2 <0.5 4.5 0.7 2006d <2 <0.5 4.2 0.6 Uzbekistan 470.09b 752.14b 2002a 42.3 12.4 75.6 30.6 2003a 46.3 15.0 76.7 33.2 Venezuela, RB 1,563.90 2,502.24 2003d 18.4 8.8 31.7 14.6 2006d 3.5 1.2 10.2 3.2 68 2009 World Development Indicators PEOPLE Poverty rates at international poverty lines International poverty International poverty line 2.8 line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year % % % % year % % % % Vietnam 7,399.87 11,839.79 2004a 24.2 5.1 52.5 17.9 2006a 21.5 4.6 48.4 16.2 Yemen, Rep. 113.83 182.12 1998a 12.9 3.0 36.3 11.1 2005a 17.5 4.2 46.6 14.8 Zambia 3,537.91 5,660.65 2002­03a 64.6 27.1 85.1 45.8 2004­05a 64.3 32.8 81.5 48.3 a. Expenditure based. b. PPP imputed using regression. c. Covers urban area only. d. Income based. e. Adjusted by spatial consumer price index information. f. Due to security concerns, the survey covered only 56 percent of rural villages and 65 percent of the rural population. g. PPP conversion factor based on urban prices. h. Weighted average of urban and rural estimates. i. Weighted average of urban and rural poverty lines. j. Due to change in survey design, the most recent survey is not strictly comparable with the previous one. Regional poverty estimates and progress toward 84 percent to 16 percent, leaving 620 million fewer $1.25 a day poverty line than had been expected the Millennium Development Goals people in poverty. before the crisis. Global poverty measured at the $1.25 a day poverty Over the same period the poverty rate in South Most of the people who have escaped extreme line has been decreasing since the 1980s. The share Asia fell from 59 percent to 40 percent (table 2.8c). poverty remain very poor by the standards of mid- of population living on less than $1.25 a day fell In contrast, the poverty rate fell only slightly in Sub- dle-income economies. The median poverty line for 10 percentage points, to 42 percent, in 1990 and Saharan Africa--from less than 54 percent in 1981 developing countries in 2005 was $2.00 a day. The then fell nearly 17 percentage points between 1990 to more than 58 percent in 1999 then down to poverty rate for all developing countries measured and 2005. The number of people living in extreme 51 percent in 2005. But the number of people living at this line fell from nearly 70 percent in 1981 to poverty fell from 1.9 billion in 1981 to 1.8 billion below the poverty line has nearly doubled. 47 percent in 2005, but the number of people liv- in 1990 to about 1.4 billion in 2005 (figure 2.8a). Only East Asia and Pacific is consistently on track ing on less than $2.00 a day has remained nearly This substantial reduction in extreme poverty over to meet the Millennium Development Goal target of constant at 2.5 billion. The largest decrease, both the past quarter century, however, disguises large reducing 1990 poverty rates by half by 2015. A slight in number and proportion, occurred in East Asia regional differences. acceleration over historical growth rates could lift and Pacifi c, led by China. Elsewhere, the number of The greatest reduction in poverty occurred in East Latin America and the Caribbean and South Asia people living on less than $2.00 a day increased, Asia and Pacific, where the poverty rate declined to the target. However, the recent slowdown in the and the number of people living between $1.25 from 78 percent in 1981 to 17 percent in 2005 and global economy may leave these regions and many and $2.00 a day nearly doubled, to 1.18 billion. the number of people living on less than $1.25 a day countries short of the target. Preliminary estimates In 2009 the global growth deceleration will likely dropped more than 750 million (figure 2.8b). Much for 2009 suggest that lower economic growth rates leave 53 million more people below the $2 a day of this decline was in China, where poverty fell from will likely leave 46 million more people below the poverty line. While the number of people living on less than $1.25 a day has Poverty rates fallen, the number living on $1.25­$2.00 a day has increased 2.8a have begun to fall 2.8b People living in poverty (billions) Share of population living on less than $1.25 a day, by region (%) 3.0 80 2.5 People living on more than $1.25 and less than $2.00 Sub-Saharan Africa People living on less than a day, all developing regions 60 2.0 $1.25 a day, other developing regions 1.5 40 People living on less than South Asia $1.25 a day, East Asia & Pacific 1.0 East Asia Europe & Central Asia & Pacific 20 People living on less than Middle East & North Africa 0.5 Latin America & Caribbean $1.25 a day, South Asia People living on less than $1.25 a day, Sub-Saharan Africa 0 0 1981 1984 1987 1990 1993 1996 1999 2002 2005 1981 1984 1987 1990 1993 1996 1999 2002 2005 Source: PovcalNet, World Bank. Source: PovcalNet, World Bank. 2009 World Development Indicators 69 2.8 Poverty rates at international poverty lines Regional poverty estimates 2.8c Region 1981 1984 1987 1990 1993 1996 1999 2002 2005 People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific 1,072 947 822 873 845 622 635 507 316 China 835 720 586 683 633 443 447 363 208 Europe & Central Asia 7 6 5 9 20 21 24 21 17 Latin America & Caribbean 47 59 56 49 46 53 55 56 45 Middle East & North Africa 14 12 12 10 10 10 11 10 11 South Asia 548 548 569 579 559 594 589 616 596 India 420 416 428 435 444 442 447 460 456 Sub-Saharan Africa 211 241 256 295 317 355 382 390 388 Total 1,898 1,812 1,721 1,816 1,797 1,657 1,696 1,600 1,373 Share of people living on less than 2005 PPP $1.25 a day (%) East Asia & Pacific 77.7 65.5 54.2 54.7 50.8 36.0 35.5 27.6 16.8 China 84.0 69.4 54.0 60.2 53.7 36.4 35.6 28.4 15.9 Europe & Central Asia 1.8 1.4 1.1 2.1 4.4 4.8 5.3 4.8 3.8 Latin America & Caribbean 12.9 15.3 13.7 11.3 10.1 10.9 10.9 10.7 8.2 Middle East & North Africa 7.9 6.1 5.7 4.3 4.1 4.1 4.2 3.6 3.6 South Asia 59.4 55.6 54.2 51.7 46.9 47.1 44.1 43.8 40.3 India 59.8 55.5 53.6 51.3 49.4 46.6 44.8 43.9 41.6 Sub-Saharan Africa 53.4 55.8 54.5 57.6 56.9 58.8 58.4 55.0 50.9 Total 52.2 47.0 42.1 42.0 39.5 34.7 33.9 30.7 25.3 People living on less than 2005 PPP $2.00 a day (millions) East Asia & Pacific 1,278 1,280 1,238 1,273 1,262 1,108 1,105 954 728 China 972 963 907 961 926 792 770 655 473 Europe & Central Asia 35 28 25 31 47 55 66 55 41 Latin America & Caribbean 89 109 102 95 95 106 110 113 94 Middle East & North Africa 46 43 47 44 48 52 51 50 51 South Asia 799 836 881 926 950 1,008 1,030 1,083 1,091 India 609 635 669 702 735 757 783 813 827 Sub-Saharan Africa 291 325 348 390 423 471 508 535 555 Total 2,538 2,622 2,642 2,760 2,825 2,800 2,870 2,791 2,560 Share of people living on less than 2005 PPP $2.00 a day (%) East Asia & Pacific 92.6 88.5 81.5 79.8 75.8 64.1 61.8 51.9 38.6 China 97.8 92.9 83.7 84.6 78.6 65.0 61.4 51.1 36.3 Europe & Central Asia 8.7 6.8 5.9 7.1 10.8 12.4 14.9 12.5 9.2 Latin America & Caribbean 24.6 28.1 24.9 21.9 20.7 22.0 21.8 21.5 17.1 Middle East & North Africa 26.7 23.0 22.7 19.7 19.8 20.2 18.9 17.6 16.9 South Asia 86.5 84.8 83.9 82.7 79.7 79.8 77.2 77.0 73.9 India 86.6 84.8 83.8 82.6 81.7 79.8 78.4 77.5 75.6 Sub-Saharan Africa 73.8 75.5 74.0 76.0 75.9 77.9 77.6 75.6 72.9 Total 69.9 68.1 64.7 63.8 62.0 58.6 57.4 53.6 47.3 Source: World Bank PovcalNet. 70 2009 World Development Indicators PEOPLE Poverty rates at international poverty lines 2.8 About the data The World Bank produced its first global poverty esti- The statistics reported here are based on con- PPP exchange rates are used to estimate global mates for developing countries for World Development sumption data or, when unavailable, on income poverty, because they take into account the local Report 1990: Poverty using household survey data for surveys. Analysis of some 20 countries for which prices of goods and services not traded internation- 22 countries (Ravallion, Datt, and van de Walle 1991). income and consumption expenditure data were ally. But PPP rates were designed for comparing Since then there has been considerable expansion in both available from the same surveys found income aggregates from national accounts, not for mak- the number of countries that field household income to yield a higher mean than consumption but also ing international poverty comparisons. As a result, and expenditure surveys. The World Bank's poverty higher inequality. When poverty measures based on there is no certainty that an international poverty line monitoring database now includes more than 600 consumption and income were compared, the two measures the same degree of need or deprivation surveys representing 115 developing countries. More effects roughly cancelled each other out: there was across countries. So called poverty PPPs, designed than 1.2 million randomly sampled households were no significant statistical difference. to compare the consumption of the poorest people interviewed in these surveys, representing 96 per- in the world, might provide a better basis for com- cent of the population of developing countries. International poverty lines parison of poverty across countries. Work on these International comparisons of poverty estimates entail measures is ongoing. Data availability both conceptual and practical problems. Countries Definitions The number of data sets within two years of any given have different definitions of poverty, and consistent year rose dramatically, from 13 between 1978 and comparisons across countries can be difficult. Local · International poverty line in local currency is the international poverty lines of $1.25 and $2.00 a day 1982 to 158 between 2001 and 2006. Data cover- poverty lines tend to have higher purchasing power in in 2005 prices, converted to local currency using age is improving in all regions, but the Middle East rich countries, where more generous standards are the PPP conversion factors estimated by the Interna- and North Africa and Sub-Saharan Africa continue to used, than in poor countries. tional Comparison Program. · Survey year is the year lag. The database, maintained by a team in the World Poverty measures based on an international pov- in which the underlying data were collected. · Popu- Bank's Development Research Group, is updated erty line attempt to hold the real value of the poverty lation below $1.25 a day and population below $2 annually as new survey data become available, and line constant across countries, as is done when mak- a day are the percentages of the population living a major reassessment of progress against poverty is ing comparisons over time. Since World Development on less than $1.25 a day and $2.00 a day at 2005 made about every three years. A complete overview Report 1990 the World Bank has aimed to apply a international prices. As a result of revisions in PPP of data availability by year and country is available at common standard in measuring extreme poverty, exchange rates, poverty rates for individual countries http://iresearch.worldbank.org/povcalnet/. anchored to what poverty means in the world's poor- cannot be compared with poverty rates reported in est countries. The welfare of people living in different earlier editions. · Poverty gap is the mean shortfall Data quality countries can be measured on a common scale by from the poverty line (counting the nonpoor as having Besides the frequency and timeliness of survey data, adjusting for differences in the purchasing power of zero shortfall), expressed as a percentage of the pov- erty line. This measure reflects the depth of poverty other data quality issues arise in measuring house- currencies. The commonly used $1 a day standard, as well as its incidence. hold living standards. The surveys ask detailed ques- measured in 1985 international prices and adjusted tions on sources of income and how it was spent, to local currency using purchasing power parities which must be carefully recorded by trained person- (PPPs), was chosen for World Development Report nel. Income is generally more difficult to measure 1990 because it was typical of the poverty lines in accurately, and consumption comes closer to the low-income countries at the time. Data sources notion of living standards. And income can vary over Early editions of World Development Indicators time even if living standards do not. But consumption used PPPs from the Penn World Tables to convert The poverty measures are prepared by the World data are not always available: the latest estimates values in local currency to equivalent purchasing Bank's Development Research Group. The interna- reported here use consumption for about two-thirds power measured in U.S dollars. Later editions used tional poverty lines are based on nationally repre- of countries. 1993 consumption PPP estimates produced by the sentative primary household surveys conducted by However, even similar surveys may not be strictly World Bank. International poverty lines were recently national statistical offices or by private agencies comparable because of differences in timing or in the revised using the new data on PPPs compiled in under the supervision of government or interna- quality and training of enumerators. Comparisons the 2005 round of the International Comparison tional agencies and obtained from government of countries at different levels of development also Program, along with data from an expanded set of statistical offices and World Bank Group country pose a potential problem because of differences household income and expenditure surveys. The new departments. The World Bank Group has prepared in the relative importance of the consumption of extreme poverty line is set at $1.25 a day in 2005 an annual review of its poverty work since 1993. nonmarket goods. The local market value of all con- PPP terms, which represents the mean of the poverty For details on data sources and methods used in sumption in kind (including own production, particu- lines found in the poorest 15 countries ranked by per deriving the World Bank's latest estimates, and fur- larly important in underdeveloped rural economies) capita consumption. The new poverty line maintains ther discussion of the results, see Shaohua Chen should be included in total consumption expenditure, the same standard for extreme poverty--the poverty and Martin Ravallion's "The Developing World Is but may not be. Most survey data now include valu- line typical of the poorest countries in the world--but Poorer Than We Thought, but No Less Successful ations for consumption or income from own produc- updates it using the latest information on the cost of in the Fight against Poverty?" (2008). tion, but valuation methods vary. living in developing countries. 2009 World Development Indicators 71 2.9 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Afghanistan .. .. .. .. .. .. .. .. Albania 2005b 33.0 3.2 7.8 12.2 16.6 22.6 40.9 25.9 Algeria 1995b 35.3 2.8 6.9 11.5 16.3 22.8 42.4 26.9 Angola 2000 b 58.6 0.6 2.0 5.7 10.8 19.7 61.9 44.7 Argentinac 2005d 50.0 1.2 3.4 7.8 13.3 21.6 53.9 37.3 Armenia 2003b 33.8 3.7 8.6 12.3 15.7 20.7 42.8 28.9 Australia 1994 d 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 2000 d 29.1 3.3 8.6 13.3 17.4 22.9 37.8 23.0 Azerbaijan 2005b 16.8 6.1 13.3 16.2 18.7 21.7 30.2 17.5 Bangladesh 2005b 31.0 4.3 9.4 12.6 16.1 21.1 40.8 26.6 Belarus 2005b 27.9 3.6 8.8 13.6 17.8 23.1 36.7 22.0 Belgium 2000 d 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1 Benin 2003b 38.6 2.9 6.9 10.9 15.1 21.2 45.9 31.0 Bolivia 2005b 58.2 0.5 1.8 5.9 11.4 20.2 60.7 44.1 Bosnia and Herzegovina 2004b 35.8 2.8 6.9 11.5 16.2 22.6 42.8 27.4 Botswana 1993­95b 61.0 1.3 3.1 5.8 9.6 16.4 65.0 51.2 Brazil 2007d 55.0 1.1 3.0 6.9 11.8 19.6 58.7 43.0 Bulgaria 2003b 29.2 3.5 8.7 13.5 17.4 22.3 38.1 23.8 Burkina Faso 2003b 39.6 3.0 7.0 10.6 14.7 20.6 47.1 32.4 Burundi 2006b 33.3 4.1 9.0 11.9 15.4 21.0 42.8 28.0 Cambodia 2007b 40.7 3.0 7.1 10.6 14.0 19.6 48.8 34.2 Cameroon 2001b 44.6 2.4 5.6 9.3 13.7 20.5 50.9 35.5 Canada 2000 d 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Central African Republic 2003b 43.6 2.1 5.2 9.4 14.3 21.7 49.4 33.0 Chad 2002­03b 39.8 2.6 6.3 10.4 15.0 21.8 46.6 30.8 Chile 2006d 52.0 1.6 4.1 7.7 12.2 19.3 56.8 41.7 China 2005d 41.5 2.4 5.7 9.8 14.7 22.0 47.8 31.4 Hong Kong, China 1996d 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2006d 58.5 0.8 2.3 6.0 11.0 19.1 61.6 45.9 Congo, Dem. Rep. 2005­06b 44.4 2.3 5.5 9.2 13.8 20.9 50.6 34.7 Congo, Rep. 2005b 47.3 2.1 5.0 8.4 13.0 20.5 53.1 37.1 Costa Rica 2005d 47.2 1.5 4.2 8.6 13.9 21.7 51.8 35.5 Côte d'Ivoire 2002b 48.4 2.0 5.0 8.7 12.9 19.3 54.1 39.6 Croatia 2005b 29.0 3.6 8.7 13.3 17.5 22.8 37.7 23.1 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996d 25.8 4.3 10.2 14.3 17.5 21.7 36.2 22.7 Denmark 1997d 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Dominican Republic 2005d 50.0 1.5 4.0 8.0 12.9 20.6 54.5 38.7 Ecuador 2007d 54.4 1.2 3.4 7.2 11.8 19.2 58.5 43.3 Egypt, Arab Rep. 2004­05b 32.1 3.9 9.0 12.6 16.1 20.9 41.5 27.6 El Salvador 2005d 49.7 1.0 3.3 8.1 13.6 21.6 53.4 37.0 Eritrea .. .. .. .. .. .. .. .. Estonia 2004b 36.0 2.7 6.8 11.6 16.2 22.5 43.0 27.7 Ethiopia 2005b 29.8 4.1 9.3 13.2 16.8 21.4 39.4 25.6 Finland 2000 d 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 France 1995d 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon 2005b 41.5 2.5 6.1 10.1 14.6 21.2 47.9 32.7 Gambia, The 2003b 47.3 2.0 4.8 8.6 13.2 20.6 52.8 36.9 Georgia 2005b 40.8 1.9 5.4 10.4 15.4 22.4 46.4 30.6 Germany 2000 d 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1 Ghana 2006b 42.8 1.9 5.2 9.8 14.8 21.9 48.3 32.5 Greece 2000 d 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 Guatemala 2006d 53.7 1.3 3.4 7.2 12.0 19.5 57.8 42.4 Guinea 2003b 43.3 2.4 5.8 9.6 14.1 20.8 49.7 34.4 Guinea-Bissau 2002b 35.5 2.9 7.2 11.6 16.0 22.1 43.0 28.0 Haiti 2001d 59.5 0.9 2.5 5.9 10.5 18.1 63.0 47.8 72 2009 World Development Indicators PEOPLE Distribution of income or consumption Survey Gini Percentage share of 2.9 year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Honduras 2006d 55.3 0.7 2.5 6.7 12.1 20.4 58.4 42.2 Hungary 2004b 30.0 3.5 8.6 13.1 17.1 22.5 38.7 24.1 India 2004­05b 36.8 3.6 8.1 11.3 14.9 20.4 45.3 31.1 Indonesia 2005b 39.4 3.0 7.1 10.7 14.4 20.5 47.3 32.3 Iran, Islamic Rep. 2005b 38.3 2.6 6.4 10.9 15.6 22.2 45.0 29.6 Iraq .. .. .. .. .. .. .. .. Ireland 2000 d 34.3 2.9 7.4 12.3 16.3 21.9 42.0 27.2 Israel 2001d 39.2 2.1 5.7 10.5 15.9 23.0 44.9 28.8 Italy 2000 d 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8 Jamaica 2004b 45.5 2.1 5.2 9.0 13.8 20.9 51.2 35.6 Japan 1993d 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2006b 37.7 3.0 7.2 11.1 15.2 21.1 45.4 30.7 Kazakhstan 2003b 33.9 3.1 7.4 11.9 16.6 23.0 41.3 25.9 Kenya 2005b 47.7 1.8 4.7 8.8 13.3 20.3 53.0 37.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998d 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2004b 32.9 3.6 8.1 12.0 16.2 22.3 41.4 25.9 Lao PDR 2002­03b 32.6 3.7 8.5 12.3 16.2 21.6 41.4 27.0 Latvia 2004b 35.7 2.7 6.8 11.6 16.3 22.6 42.7 27.4 Lebanon .. .. .. .. .. .. .. .. Lesotho 2003b 52.5 1.0 3.0 7.2 12.5 21.0 56.4 39.4 Liberia 2007b 52.6 2.4 6.4 11.4 15.7 21.6 45.0 30.1 Libya .. .. .. .. .. .. .. .. Lithuania 2004b 35.8 2.7 6.8 11.5 16.3 22.7 42.8 27.4 Macedonia, FYR 2003b 39.0 2.4 6.1 10.6 15.6 22.5 45.2 29.5 Madagascar 2005b 47.2 2.6 6.2 9.6 13.1 17.7 53.5 41.5 Malawi 2004­05b 39.0 2.9 7.0 10.8 14.9 20.9 46.4 31.7 Malaysia 2004 d 37.9 2.6 6.4 10.8 15.8 22.8 44.4 28.5 Mali 2006b 39.0 2.7 6.5 10.7 15.2 21.6 46.0 30.5 Mauritania 2000 b 39.0 2.5 6.2 10.5 15.4 22.3 45.7 29.6 Mauritius .. .. .. .. .. .. .. .. Mexico 2006b 48.1 1.8 4.6 8.6 13.2 20.3 53.3 37.9 Moldova 2004b 35.6 3.0 7.3 11.6 16.0 22.0 43.1 28.2 Mongolia 2005b 33.0 2.9 7.2 12.2 17.1 23.4 40.2 24.8 Morocco 2007b 40.9 2.7 6.5 10.5 14.5 20.6 47.9 33.2 Mozambique 2002­03b 47.1 2.1 5.4 9.2 13.1 19.0 53.3 39.2 Myanmar .. .. .. .. .. .. .. .. Namibia 1993d 74.3 0.6 1.5 2.8 5.5 12.0 78.3 65.0 Nepal 2003­04b 47.3 2.7 6.1 8.9 12.5 18.4 54.2 40.4 Netherlands 1999d 30.9 2.5 7.6 13.2 17.2 23.3 38.7 22.9 New Zealand 1997d 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8 Nicaragua 2005d 52.3 1.4 3.8 7.7 12.3 19.4 56.9 41.8 Niger 2005b 43.9 2.3 5.9 9.8 13.9 20.1 50.3 35.7 Nigeria 2003­04b 42.9 2.0 5.1 9.7 14.7 21.9 48.6 32.4 Norway 2000 d 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 2004­05b 31.2 3.9 9.1 12.8 16.3 21.3 40.5 26.5 Panama 2006d 54.9 0.8 2.5 6.6 12.1 20.8 58.0 41.4 Papua New Guinea 1996b 50.9 1.9 4.5 7.7 12.1 19.3 56.4 40.9 Paraguay 2007d 53.2 1.1 3.4 7.6 12.2 19.4 57.4 42.3 Peru 2006d 49.6 1.5 3.9 8.0 13.2 21.0 54.0 37.9 Philippines 2006b 44.0 2.4 5.6 9.1 13.7 21.2 50.4 33.9 Poland 2005b 34.9 3.0 7.3 11.7 16.2 22.4 42.4 27.2 Portugal 1997d 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. 2009 World Development Indicators 73 2.9 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Romania 2005b 31.5 3.3 8.2 12.8 16.8 22.3 39.9 25.3 Russian Federation 2005b 37.5 2.6 6.4 11.0 15.9 22.7 44.1 28.4 Rwanda 2000 b 46.7 2.3 5.4 9.0 13.2 19.6 52.8 38.2 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 2005b 39.2 2.5 6.2 10.6 15.3 22.0 45.9 30.1 Serbiae 2003b 30.0 3.4 8.3 13.0 17.3 23.0 38.4 23.4 Sierra Leone 2003b 42.5 2.6 6.1 9.7 14.0 20.9 49.3 33.6 Singapore 1998d 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8 Slovak Republic 1996d 25.8 3.1 8.8 14.9 18.6 22.9 34.8 20.8 Slovenia 2004b 31.2 3.4 8.2 12.8 17.0 22.6 39.4 24.6 Somalia .. .. .. .. .. .. .. .. South Africa 2000 b 57.8 1.3 3.1 5.6 9.9 18.8 62.7 44.9 Spain 2000 d 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6 Sri Lanka 2002b 41.1 2.9 6.8 10.4 14.4 20.5 48.0 33.3 Sudan .. .. .. .. .. .. .. .. Swaziland 2001b 50.7 1.8 4.5 8.0 12.3 19.4 55.9 40.8 Sweden 2000 d 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2 Switzerland 2000 d 33.7 2.9 7.6 12.2 16.3 22.6 41.3 25.9 Syrian Arab Republic .. .. .. .. .. .. .. .. Tajikistan 2004b 33.6 3.2 7.7 12.0 16.4 22.4 41.4 26.4 Tanzania 2000­01b 34.6 3.1 7.3 11.8 16.3 22.3 42.3 27.0 Thailand 2004b 42.5 2.6 6.1 9.8 14.2 21.0 49.0 33.7 Timor-Leste 2001b 39.5 2.9 6.7 10.4 14.8 21.3 46.8 31.3 Togo 2006b 34.4 3.3 7.6 11.7 16.1 22.2 42.4 27.1 Trinidad and Tobago 1992d 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 Tunisia 2000 b 40.8 2.4 5.9 10.2 14.9 21.8 47.2 31.6 Turkey 2005b 43.2 1.9 5.2 9.8 14.6 21.6 48.8 33.2 Turkmenistan 1998b 40.8 2.5 6.0 10.2 14.9 21.7 47.2 31.8 Uganda 2005b 42.6 2.6 6.1 9.8 14.1 20.7 49.3 34.1 Ukraine 2005b 28.2 3.8 9.0 13.4 17.6 22.9 37.2 22.5 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 1999d 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5 United States 2000d 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9 Uruguay 2006d 46.2 1.7 4.5 8.7 14.0 21.8 51.1 34.8 Uzbekistan 2003b 36.7 2.9 7.1 11.5 15.7 21.5 44.2 29.5 Venezuela, RB 2006d 43.4 1.7 4.9 9.6 14.8 22.1 48.6 32.7 Vietnam 2006b 37.8 3.1 7.1 10.8 15.2 21.6 45.4 29.8 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 2005b 37.7 2.9 7.2 11.3 15.3 21.0 45.3 30.8 Zambia 2004­05b 50.7 1.3 3.6 7.8 12.8 20.6 55.2 38.9 Zimbabwe 1995b 50.1 1.8 4.6 8.1 12.2 19.3 55.7 40.3 a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Urban data. d. Refers to income shares by percentiles of population, ranked by per capita income. e. Includes Montenegro. 74 2009 World Development Indicators PEOPLE Distribution of income or consumption 2.9 About the data Definitions Inequality in the distribution of income is reflected but achieving strict comparability is still impossible · Survey year is the year in which the underlying in the percentage shares of income or consumption (see About the data for tables 2.7 and 2.8). data were collected. · Gini index measures the accruing to portions of the population ranked by Two sources of noncomparability should be noted extent to which the distribution of income (or con- income or consumption levels. The portions ranked in particular. First, the surveys can differ in many sumption expenditure) among individuals or house- lowest by personal income receive the smallest respects, including whether they use income or con- holds within an economy deviates from a perfectly shares of total income. The Gini index provides a con- sumption expenditure as the living standard indi- equal distribution. A Lorenz curve plots the cumula- venient summary measure of the degree of inequal- cator. The distribution of income is typically more tive percentages of total income received against the ity. Data on the distribution of income or consump- unequal than the distribution of consumption. In cumulative number of recipients, starting with the tion come from nationally representative household addition, the definitions of income used differ more poorest individual. The Gini index measures the area surveys. Where the original data from the house- often among surveys. Consumption is usually a between the Lorenz curve and a hypothetical line hold survey were available, they have been used to much better welfare indicator, particularly in devel- of absolute equality, expressed as a percentage of directly calculate the income or consumption shares oping countries. Second, households differ in size the maximum area under the line. Thus a Gini index by quintile. Otherwise, shares have been estimated (number of members) and in the extent of income of 0 represents perfect equality, while an index of from the best available grouped data. sharing among members. And individuals differ in 100 implies perfect inequality. · Percentage share The distribution data have been adjusted for age and consumption needs. Differences among of income or consumption is the share of total household size, providing a more consistent mea- countries in these respects may bias comparisons income or consumption that accrues to subgroups of sure of per capita income or consumption. No adjust- of distribution. population indicated by deciles or quintiles. ment has been made for spatial differences in cost World Bank staff have made an effort to ensure of living within countries, because the data needed that the data are as comparable as possible. Wher- for such calculations are generally unavailable. For ever possible, consumption has been used rather further details on the estimation method for low- and than income. Income distribution and Gini indexes for middle-income economies, see Ravallion and Chen high-income economies are calculated directly from (1996). the Luxembourg Income Study database, using an Because the underlying household surveys differ estimation method consistent with that applied for in method and type of data collected, the distribu- developing countries. tion data are not strictly comparable across coun- tries. These problems are diminishing as survey methods improve and become more standardized, The Gini coefficient and ratio of income or consumption of the richest quintile to the poorest quintiles are closely correlated 2.9a Gini coefficient (%) 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Ratio of income or consumption of richest quintile to poorest quintile Data sources There are many ways to measure income or consumption inequality. The Gini coefficient shows inequal- Data on distribution are compiled by the World ity over the entire population; the ratio of income or consumption of the richest quintile to the poorest Bank's Development Research Group using pri- quintiles shows differences only at the tails of the population distribution. Both measures are closely mary household survey data obtained from govern- correlated and provide similar information. At low levels of inequality the Gini coefficient is a more sensi- ment statistical agencies and World Bank country tive measure, but above a Gini value of 45­55 percent the inequality ratio rises faster. departments. Data for high-income economies are Source: World Development Indicators data files. from the Luxembourg Income Study database. 2009 World Development Indicators 75 2.10 Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15­24 ages 15­24 total % of labor age % of average 2003­05a 2003­05a 2004­07a Year force population Year GDP Year wage Afghanistan .. .. .. .. .. 2005 0.5 .. Albania .. .. .. 2004 48.9 33.0 2005 5.4 .. Algeria 43 46 .. 2002 36.7 22.1 2002 3.2 .. Angola .. .. .. .. .. .. .. Argentina 22b 28b .. 2004 35.0 25.9 2007 8.0 2000 43.8 Armenia .. .. 36 2002 64.4 48.3 2004 3.4 2007 20.3 Australia 11b 11b .. 2005 92.6 69.6 2004 4.9 .. Austria 11 10 .. 2005 96.4 68.7 2005 14.7 .. Azerbaijan .. .. 25 2007 36.8 30.2 2000 3.3 2006 24.3 Bangladesh 7 6 10 2004 2.8 2.1 2001 0.5 .. Belarus .. .. 54 1992 97.0 94.0 2002 12.1 2002 41.6 Belgium 21 19 .. 2005 94.2 61.6 2003 11.0 .. Benin .. .. 23 1996 4.8 .. 2006 1.5 .. Bolivia .. .. .. 2002 10.1 7.8 2000 4.5 .. Bosnia and Herzegovina .. .. .. 2004 36.0 27.0 2004 8.8 .. Botswana .. .. .. .. .. .. .. Brazil 14b 23b .. 2004 52.6 39.1 2004 12.6 .. Bulgaria 23 21 .. 1994 64.0 63.0 2005 8.9 2004 42.9 Burkina Faso .. .. .. 1993 3.1 3.0 1992 0.3 .. Burundi .. .. .. 1993 3.3 3.0 1991 0.2 .. Cambodia .. .. 24 .. .. .. .. Cameroon .. .. 24 1993 13.7 11.5 2001 0.8 .. Canada 14b 11b .. 2005 90.5 71.4 2004 4.8 .. Central African Republic .. .. .. 2004 1.5 1.3 2004 0.8 .. Chad .. .. 20 1990 1.1 1.0 1997 0.1 .. Chile 15 21 .. 2003 58.0 35.2 2001 2.9 2006 53.5 China .. .. .. 2005 20.5 17.2 1996 2.7 .. Hong Kong, China 14 8 .. .. .. .. .. Colombia 12 19 19 2006 24.5 18.8 2006 2.7 .. Congo, Dem. Rep. .. .. 21 .. .. .. .. Congo, Rep. .. .. 23 1992 5.8 5.6 2004 0.9 .. Costa Rica 11 22 .. 2004 55.3 37.6 2006 2.4 .. Côte d'Ivoire .. .. .. 1997 9.3 9.1 1997 0.3 .. Croatia 21c 28 c .. 2007 75.2 51.0 2007 11.3 2005 32.4 Cuba .. .. 46 .. .. 1992 12.6 .. Czech Republic 19 19 .. 2007 84.5 67.3 2005 9.4 2005 40.7 Denmark 6 10 .. 2007 94.4 86.9 2005 8.5 .. Dominican Republic .. .. 35 2000 31.0 20.7 2000 0.8 .. Ecuador 12b 21b .. 2004 27.0 20.8 2002 2.5 .. Egypt, Arab Rep. .. .. 12 2004 55.5 27.7 2004 4.1 .. El Salvador 13 9 .. 2005 29.8 19.7 2006 1.9 .. Eritrea .. .. .. .. .. 2001 0.3 .. Estonia 16 15 .. 2004 95.2 68.6 2003 6.0 2007 35.4 Ethiopia 4 11 23 .. .. 2007 0.3 .. Finland 21 19 .. 2005 88.7 67.2 2005 8.0 .. France 21b 25b .. 2005 89.9 61.4 2005 14.0 .. Gabon .. .. .. 1995 15.0 14.0 .. .. Gambia, The .. .. .. 2003 3.8 2.9 .. .. Georgia 27 31 .. 2004 29.9 22.7 2004 3.0 2003 13.0 Germany 16 14 .. 2005 88.2 65.5 2005 12.6 .. Ghana .. .. .. 2004 9.1 7.1 2002 1.3 .. Greece 18 35 .. 2005 85.2 58.5 2005 12.0 .. Guatemala .. .. .. 2005 24.0 18.0 2005 1.0 .. Guinea .. .. 17 1993 1.5 1.8 .. .. Guinea-Bissau .. .. .. 2004 1.9 1.5 2005 2.1 .. Haiti .. .. 44 .. .. .. .. 76 2009 World Development Indicators PEOPLE Assessing vulnerability and security Youth Female-headed Pension 2.10 Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15­24 ages 15­24 total % of labor age % of average 2003­05a 2003­05a 2004­07a Year force population Year GDP Year wage Honduras 5b 11b 26 2006 16.1 12.4 1994 0.6 .. Hungary 20 19 .. 2007 93.0 62.6 2005 11.1 2005 39.8 India 10 b 11b 14 2004 9.0 5.7 2007 2.0 .. Indonesia 25 34 .. 2002 15.5 11.3 .. .. Iran, Islamic Rep. 20 32 .. 2001 35.1 20.0 2000 1.1 .. Iraq .. .. .. .. .. .. .. Ireland 9 7 .. 2005 88.0 63.9 2003 3.4 .. Israel 17 19 .. 1992 82.0 63.0 1996 5.9 .. Italy 22 27 .. 2005 92.4 58.4 2005 14.7 .. Jamaica 22 36 41 2004 17.4 12.6 1996 .. .. Japan 10 b 7b .. 2005 95.3 75.0 2005 9.5 .. Jordan .. .. .. 2004 32.2 18.6 2001 2.2 .. Kazakhstan 13 16 .. 2004 33.8 26.4 2004 4.9 2003 24.9 Kenya .. .. .. 2005 8.0 6.7 2003 1.1 .. Korea, Dem. Rep. .. .. .. .. .. .. .. Korea, Rep. 12 9 .. 2005 78.0 55.0 2005 2.0 .. Kuwait .. .. .. .. .. 1990 3.5 .. Kyrgyz Republic 14 18 25 2006 42.2 28.9 2006 4.8 2003 27.5 Lao PDR .. .. .. .. .. .. .. Latvia 12 14 .. 2003 92.4 66.5 2002 7.5 2005 33.1 Lebanon .. .. .. 2003 33.1 19.9 2003 2.1 .. Lesotho .. .. 37 2005 5.7 3.6 .. .. Liberia .. .. 31 .. .. .. .. Libya .. .. .. 2004 65.5 38.1 2001 2.1 .. Lithuania 16 15 .. 2004 79.7 56.0 2003 6.8 2005 30.9 Macedonia, FYR 63 62 8 2000 63.8 38.9 2006 8.5 2006 55.0 Madagascar 7 7 22 1993 5.4 4.8 1990 0.2 .. Malawi .. .. 25 .. .. .. .. Malaysia .. .. .. 2000 65.0 .. 1999 6.5 .. Mali .. .. 12 1990 2.5 2.0 1991 0.4 .. Mauritania .. .. .. 1995 5.0 4.0 1992 0.2 .. Mauritius 21 34 .. 2000 51.4 33.6 1999 4.4 .. Mexico 6 7 .. 2002 34.5 22.7 2005 0.9 .. Moldova 19 18 34 2000 60.6 43.1 2003 8.0 2003 20.9 Mongolia 20 21 17 2002 61.4 49.1 2002 5.8 .. Morocco 16 14 17 2003 22.4 12.8 2003 1.9 .. Mozambique .. .. .. 1995 2.0 2.1 1996 0.0 .. Myanmar .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. .. Nepal .. .. 23 2003 2.1 1.4 2003 0.3 .. Netherlands 10 10 .. 2005 90.3 70.4 2005 8.4 .. New Zealand 9b 10 b .. .. .. 2005 7.2 .. Nicaragua 11 16 .. 2005 17.9 11.5 1996 2.5 .. Niger .. .. 19 2006 1.3 1.2 2006 0.7 .. Nigeria .. .. .. 2005 1.7 1.2 1991 0.1 .. Norway 13 12 .. 2005 90.8 75.7 2003 8.4 .. Oman .. .. .. .. .. .. .. Pakistan 11 15 10 2004 6.4 4.0 1993 0.9 .. Panama 19 30 .. 1998 51.6 40.7 1996 4.3 .. Papua New Guinea .. .. .. .. .. .. .. Paraguay 12b 21b .. 2004 11.6 9.1 2001 1.2 .. Peru 21b 21b 22 2003 16.3 12.3 2000 2.6 .. Philippines 15 19 .. 2007 20.8 15.5 1993 1.0 .. Poland 37 39 .. 2005 84.9 54.5 2005 14.1 2007 47.1 Portugal 14 19 .. 2005 91.4 71.9 2004 10.4 .. Puerto Rico 25b 21b .. .. .. .. .. 2009 World Development Indicators 77 2.10 Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15­24 ages 15­24 total % of labor age % of average 2003­05a 2003­05a 2004­07a Year force population Year GDP Year wage Romania 21 18 .. 2007 53.4 36.3 2003 6.9 2005 41.5 Russian Federation .. .. .. .. .. 2004 5.8 2003 29.2 Rwanda .. .. 34 2004 4.8 4.1 .. .. Saudi Arabia .. .. .. .. .. 1998 0.2 .. Senegal .. .. 23 2003 5.3 3.9 2003 1.3 .. Serbia .. .. 29 2003 46.0 d 32.2d 2003 12.4 d .. Sierra Leone .. .. .. 2004 4.6 3.6 .. .. Singapore 4 6 .. 2000 70.0 .. 1996 1.4 .. Slovak Republic 31 29 .. 2003 78.5 55.3 2005 6.5 2005 44.7 Slovenia 11 12 .. 1995 86.0 68.7 2003 10.1 2005 44.3 Somalia .. .. .. .. .. .. .. South Africa 56 65 .. .. .. .. .. Spain 17 24 .. 2005 91.0 63.2 2005 10.4 2006 58.6 Sri Lanka 20 b 37b .. 2004 35.6 22.2 2002 2.0 .. Sudan .. .. 19 1995 12.1 12.0 .. .. Swaziland .. .. 48 .. .. .. .. Sweden 23 22 .. 2005 91.0 72.3 2005 11.4 .. Switzerland 9 9 .. 2005 100.0 79.1 2005 9.2 2000 40.0 Syrian Arab Republic .. .. .. 2004 17.4 11.4 2004 1.3 .. Tajikistan .. .. .. .. .. 1996 3.0 2003 25.7 Tanzania .. .. 25 1996 2.0 2.0 .. .. Thailand 5 5 30 2005 27.2 21.8 .. .. Timor-Leste .. .. .. .. .. .. .. Togo .. .. .. 1997 15.9 15.0 1997 0.6 .. Trinidad and Tobago .. .. .. 2004 55.6 .. 1996 0.6 .. Tunisia 31 29 .. 2004 45.3 25.4 2003 4.3 .. Turkey 19 19 .. 2007 55.0 30.5 2003 3.2 2007 61.3 Turkmenistan .. .. .. .. .. 1996 2.3 .. Uganda .. .. 30 2004 10.7 9.3 2003 0.3 .. Ukraine 15 14 .. 2007 68.2 47.4 2005 15.4 2007 48.3 United Arab Emirates .. .. .. .. .. .. .. United Kingdom 13 10 .. 2005 92.7 71.4 2005 7.6 .. United States 12b 10 b .. 2005 92.5 72.5 2003 7.3 2006 29.2 Uruguay 25 35 .. 2004 55.0 44.3 2007 10.0 .. Uzbekistan .. .. 18 .. .. 2005 6.5 2005 40.0 Venezuela, RB 24 35 .. 2004 31.8 23.8 2001 2.7 .. Vietnam 4 5 .. 2005 13.2 10.8 1998 1.6 .. West Bank and Gaza 39 45 .. 2000 18.8 7.8 2001 0.8 .. Yemen, Rep. .. .. .. 2005 10.0 5.5 1999 0.9 .. Zambia .. .. .. 2000 5.9 4.9 2006 0.2 .. Zimbabwe .. .. 38 1995 12.0 10.0 2002 2.3 .. World .. w .. w Low income .. .. Middle income .. .. Lower middle income .. .. Upper middle income 21 26 Low & middle income .. .. East Asia & Pacific .. .. Europe & Central Asia .. .. Latin America & Carib. 14 21 Middle East & N. Africa .. .. South Asia 11 12 Sub-Saharan Africa .. .. High income 14 13 Euro area 18 21 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2007. d. Includes Montenegro. 78 2009 World Development Indicators PEOPLE Assessing vulnerability and security 2.10 About the data Definitions As traditionally measured, poverty is a static con- residency, or income status. In contribution-related · Youth unemployment is the share of the labor force cept, and vulnerability a dynamic one. Vulnerabil- schemes, however, eligibility is usually restricted ages 15­24 without work but available for and seek- ity reflects a household's resilience in the face of to individuals who have contributed for a minimum ing employment. · Female-headed households are shocks and the likelihood that a shock will lead to a number of years. Definitional issues--relating to the the percentage of households with a female head. decline in well-being. Thus, it depends primarily on labor force, for example--may arise in comparing · Pension contributors are the share of the labor the household's assets and insurance mechanisms. coverage by contribution-related schemes over time force or working-age population (here defined as Because poor people have fewer assets and less and across countries (for country-specific informa- ages 15 and older) covered by a pension scheme. diversified sources of income than do the better-off, tion, see Hinz and Pallares-Miralles forthcoming). · Public expenditure on pensions is all government fluctuations in income affect them more. The share of the labor force covered by a pension expenditures on cash transfers to the elderly, the Enhancing security for poor people means reduc- scheme may be overstated in countries that do not disabled, and survivors and the administrative costs ing their vulnerability to such risks as ill health, pro- try to count informal sector workers as part of the of these programs. · Average pension is the aver- viding them the means to manage risk themselves, labor force. age pension payment of all pensioners of the main and strengthening market or public institutions for Public interventions and institutions can provide pension schemes divided by the average wage of all managing risk. Tools include microfinance programs, services directly to poor people, although whether formal sector workers. public provision of education and basic health care, these interventions and institutions work well for the and old age assistance (see tables 2.11 and 2.16). poor is debated. State action is often ineffective, Poor households face many risks, and vulnerability in part because governments can influence only a is thus multidimensional. The indicators in the table few of the many sources of well-being and in part focus on individual risks--youth unemployment, because of difficulties in delivering goods and ser- female-headed households, income insecurity in vices. The effectiveness of public provision is further old age--and the extent to which publicly provided constrained by the fiscal resources at governments' services may be capable of mitigating some of these disposal and the fact that state institutions may not risks. Poor people face labor market risks, often hav- be responsive to the needs of poor people. ing to take up precarious, low-quality jobs and to The data on public pension spending cover the increase their household's labor market participa- pension programs of the social insurance schemes tion by sending their children to work (see tables for which contributions had previously been made. 2.4 and 2.6). Income security is a prime concern In many cases noncontributory pensions or social for the elderly. assistance targeted to the elderly and disabled are Youth unemployment is an important policy issue also included. A country's pattern of spending is cor- for many economies. Experiencing unemployment related with its demographic structure--spending may permanently impair a young person's produc- increases as the population ages. tive potential and future employment opportunities. The table presents unemployment among youth ages 15­24, but the lower age limit for young people in a country could be determined by the minimum age for leaving school, so age groups could dif- fer across countries. Also, since this age group is likely to include school leavers, the level of youth unemployment varies considerably over the year as a result of different school opening and closing dates. The youth unemployment rate shares similar limita- tions on comparability as the general unemployment Data sources rate. For further information, see About the data for table 2.5 and the original source. Data on youth unemployment are from the ILO The definition of female-headed household differs database Key Indicators of the Labour Market, greatly across countries, making cross-country com- 5th edition. Data on female-headed household are parison difficult. In some cases it is assumed that a from Demographic and Health Surveys by Macro woman cannot be the head of any household with an International. Data on pension contributors and adult male, because of sex-biased stereotype. Cau- pension spending are from Richard Paul Hinz and tion should be used in interpreting the data. Montserrat Pallares-Miralles' International Pat- Pension scheme coverage may be broad or even uni- terns of Pension Provision II (forthcoming). versal where eligibility is determined by citizenship, 2009 World Development Indicators 79 2.11 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil-teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2007a 1999 2007a 1999 2007a 2007a 2007a 2007a 2007a Afghanistan .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. Algeria 26.5 .. .. .. .. .. .. .. 99.2 24 Angola .. 3.7 .. 36.9 .. 78.3 2.6 .. .. 41 Argentina .. 12.0 .. 19.6 17.7 .. .. .. .. 17 Armenia .. .. .. .. .. .. 2.7 15.0 77.5 19 Australia .. 17.3 15.4 15.4 27.2 23.1 4.8 .. .. .. Austria 18.2 23.5 29.9 26.3 51.6 50.0 5.4 10.9 .. 12 Azerbaijan .. .. 15.4 .. .. .. 2.6 12.6 .. 13 Bangladesh .. .. 13.4 .. 50.1 46.2 2.6 15.8 .. .. Belarus 30.1 14.4 .. 27.0 .. 18.3 5.2 9.3 99.8 16 Belgium 15.8 20.2 23.7 33.4 38.3 35.1 6.0 12.1 .. 11 Benin .. 13.4 26.3 .. 170.4 165.4 3.9 18.0 71.8 44 Bolivia .. .. 11.7 .. 44.1 .. .. .. .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 16.1 .. 41.2 .. 449.6 8.1 21.0 86.9 24 Brazil .. 15.4 9.5 13.2 57.1 35.1 4.5 14.5 .. 21 Bulgaria .. 24.5 18.8 23.4 17.9 24.8 4.5 .. .. 16 Burkina Faso .. 36.0 .. 23.3 .. 236.5 4.5 15.4 87.7 48 Burundi 13.4 19.9 .. 77.5 1,078.2 363.1 5.1 17.7 87.4 52 Cambodia .. .. 11.5 .. 43.7 8.5 1.6 12.4 98.4 51 Cameroon .. 7.6 16.8 41.6 64.4 126.3 3.9 17.0 36.3 44 Canada .. .. .. .. 47.1 .. 4.9 .. .. .. Central African Republic 11.9 7.5 .. .. .. 305.2 1.4 .. .. 102b Chad 8.0 7.1 28.3 29.2 .. 348.2 1.9 10.1 26.8 60 Chile .. 11.1 14.8 12.4 19.4 11.8 3.2 16.0 .. 26 China .. .. 11.5 .. 90.1 .. .. .. .. 18 Hong Kong, China .. 12.5 17.7 16.5 .. 47.3 3.5 23.2 94.6 17 Colombia .. 15.6 16.9 12.6 39.6 52.7 4.9 12.6 .. 28 Congo, Dem. Rep. .. .. .. .. .. .. .. .. 96.0 38 Congo, Rep. .. 3.0 .. .. 379.5 .. 1.8 8.1 86.6 58 Costa Rica 7.8 .. 23.2 .. 55.0 .. 4.9 20.6 89.5 19 Côte d'Ivoire .. .. 55.5 .. 216.6 .. .. .. 100.0 41 Croatia .. .. .. .. 41.5 .. 4.6 9.3 .. 17 Cuba 21.6 51.1 41.4 60.1 86.6 43.5 13.3 20.6 100.0 10 Czech Republic .. 12.6 21.7 22.9 33.7 27.2 4.3 9.5 .. 16 Denmark .. 25.1 38.1 35.0 65.9 55.3 8.3 15.5 .. .. Dominican Republic .. 10.3 .. 4.7 .. .. 2.4 11.0 88.3 24 Ecuador .. .. 9.7 .. .. .. .. .. 71.6 23 Egypt, Arab Rep. .. .. .. .. .. .. 3.8 12.6 .. 27 El Salvador .. 9.0 7.9 10.5 9.4 15.5 3.0 13.1 93.3 40 Eritrea .. 9.6 38.5 9.6 444.1 .. 2.4 .. 87.1 48 Estonia .. 19.4 27.9 23.0 32.6 18.3 4.9 14.6 .. 11 Ethiopia 22.1 12.5 .. 8.9 .. 785.5 5.5 23.3 .. .. Finland 21.7 18.0 25.8 32.3 40.3 34.4 6.3 12.5 .. 16 France 11.8 17.4 28.5 27.0 29.7 33.3 5.7 10.6 .. 19 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The 13.2 .. .. .. .. .. .. .. 76.3 41 Georgia .. .. .. .. .. .. 2.7 7.8 .. .. Germany .. 16.3 20.5 21.5 .. .. 4.5 9.7 .. 14 Ghana .. 18.4 .. 29.1 .. 213.4 5.4 .. 56.3 35 Greece 7.5 14.1 13.5 18.2 22.8 21.5 3.5 9.2 .. 11 Guatemala .. 10.5 4.3 6.0 .. 19.3 3.1 .. .. 30 Guinea .. .. .. .. .. 192.9 1.7 .. 98.8 45 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 9.1 .. .. .. .. .. .. .. .. .. 80 2009 World Development Indicators PEOPLE Public expenditure Education inputs Public expenditure 2.11 Trained Primary per student on education teachers school in primary pupil-teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2007a 1999 2007a 1999 2007a 2007a 2007a 2007a 2007a Honduras .. .. .. .. .. .. .. .. .. 28 Hungary .. 25.7 19.1 23.1 34.2 23.8 5.5 10.9 .. 10 India .. 8.9 24.7 16.7 90.8 57.8 3.2 .. .. .. Indonesia .. .. .. .. .. .. 3.5 17.5 .. 20 Iran, Islamic Rep. .. 15.4 9.9 22.3 34.8 27.7 5.5 19.5 70.8 19 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 11.5 14.7 16.8 21.8 28.5 24.8 4.8 13.9 .. 17 Israel .. 20.7 22.4 20.5 31.7 23.1 6.3 .. .. 13 Italy 14.9 23.1 27.7 26.9 27.6 22.3 4.4 9.2 .. 11 Jamaica 9.9 14.6 23.6 21.5 79.0 .. 5.3 8.8 .. 28 Japan .. 22.2 20.9 22.4 15.1 19.2 3.5 9.5 .. 19 Jordan .. 15.4 15.8 19.0 .. .. .. .. .. .. Kazakhstan .. .. .. .. .. 8.0 2.9 .. .. 17b Kenya .. 22.4 15.2 22.1 209.4 .. 7.1 17.9 .. 40 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 11.8 18.8 15.7 23.4 8.4 9.3 4.4 15.3 .. 27 Kuwait 35.4 9.2 .. 14.1 .. 79.8 3.6 12.9 100.0 10 Kyrgyz Republic .. .. .. .. 27.7 22.3 5.6 .. 62.4 24 Lao PDR .. 9.1 4.3 4.7 66.5 25.2 3.2 15.8 89.7 30 Latvia .. .. 23.7 .. 27.9 .. .. .. .. 12 Lebanon .. 8.3 .. 8.8 14.2 14.8 2.7 9.6 12.2 14 Lesotho .. 25.0 71.6 49.8 1,295.1 1,141.5 13.3 29.8 66.1 40 Liberia .. 6.0 .. .. .. .. .. .. 40.2b 24b Libya .. .. .. .. 23.9 .. .. .. .. .. Lithuania .. 15.9 .. 20.2 34.2 18.2 5.0 14.7 .. 14 Macedonia, FYR .. .. .. .. .. .. .. .. .. 19 Madagascar .. 9.5 .. 12.7 182.1 145.2 3.4 16.4 55.2 49 Malawi 7.2 .. .. .. .. .. .. .. .. .. Malaysia 10.1 .. 22.6 .. 84.3 .. .. .. .. 17 Mali .. 21.3 53.0 31.7 227.7 .. 4.6 16.8 100.0 52 Mauritania .. 9.6 35.3 24.2 77.8 39.2 2.9 10.1 100.0 43 Mauritius 10.1 10.3 15.3 17.4 40.4 29.8 3.9 12.7 100.0 22 Mexico 4.8 15.1 14.2 15.6 47.8 40.0 5.5 .. .. 28 Moldova .. 33.6 .. 40.7 .. 41.4 8.3 19.8 .. 16 Mongolia .. 14.9 .. 14.8 .. .. 5.1 .. 98.7 32 Morocco 15.4 14.6 44.5 39.3 94.8 73.9 5.5 26.1 100.0 27 Mozambique .. 15.1 .. 86.9 .. .. 5.2 21.0 63.2 65 Myanmar .. .. 6.8 .. 27.5 .. .. .. 99.0 29 Namibia .. 21.4 36.2 22.0 156.9 141.3 .. .. 94.8 30 Nepal .. 15.3b 13.1 11.3b 141.6 .. 3.8b .. 61.4b 38b Netherlands 12.1 17.7 21.1 24.2 42.8 39.9 5.2 11.5 .. .. New Zealand 17.2 17.8 24.3 20.6 41.6 26.4 6.2 15.5 .. 16 Nicaragua .. 9.8 .. 4.5 .. .. .. .. 73.6 33 Niger .. 28.7 60.9 46.1 .. 371.4 3.4 17.6 98.2 40 Nigeria .. .. .. .. .. .. .. .. 51.2 40 Norway 32.7 18.9 26.8 28.8 45.8 49.2 7.0 16.7 .. .. Oman 10.5 15.1 21.9 12.7 .. 14.0 4.0 31.1 100.0 14 Pakistan .. .. .. .. .. .. 2.9 11.2 84.6 39 Panama 11.3 12.4 19.1 15.1 33.6 .. .. .. 90.8 25 Papua New Guinea .. .. .. .. .. .. .. .. .. 36 Paraguay .. .. 18.4 .. 58.9 .. .. .. .. .. Peru .. 7.0 10.7 9.0 20.9 10.5 2.5 15.4 .. 22 Philippines .. 8.6 10.8 9.1 15.1 11.5 2.5 15.2 .. 35 Poland .. 23.7 16.5 22.2 21.1 21.4 5.5 .. .. 11 Portugal 16.3 23.2 27.5 34.7 28.1 27.1 5.4 11.3 .. 11 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 81 2.11 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil-teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2007a 1999 2007a 1999 2007a 2007a 2007a 2007a 2007a Romania .. 10.7 16.0 16.0 32.6 23.7 3.5 .. .. 17 Russian Federation .. .. .. .. .. 12.6 3.1 .. .. 17 Rwanda .. 10.2 30.2 35.1 699.4 372.8 4.9 19.0 98.1 69 Saudi Arabia .. 18.5 .. 18.4 .. .. .. .. 91.5 11 Senegal 18.9 17.9 .. 32.9 .. 225.2 4.8 26.3 100.0 34 Serbia .. .. .. .. .. .. 4.2b 9.4b .. 13 Sierra Leone .. .. .. .. .. .. 3.8 .. 49.4 44 Singapore .. 9.3b .. 14.1b .. .. 2.9b 15.3b 96.1 20 Slovak Republic .. 14.8 18.3 15.2 32.6 24.2 3.9 .. .. 17 Slovenia 17.4 25.1 26.0 32.0 28.3 22.7 5.8 12.7 .. 15 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 20.2 15.6 20.0 16.7 60.7 44.3 5.4 17.4 .. 30 Spain 11.3 19.1 24.4 23.4 19.6 22.8 4.2 11.0 .. 14 Sri Lanka .. .. .. .. .. .. .. .. .. 23 Sudan .. .. .. .. .. .. .. .. 58.7 37 Swaziland 6.7 15.4 26.1 43.7 388.4 343.6 7.6 .. 90.8 33 Sweden 45.8 25.7 26.6 33.5 52.7 41.5 7.1 .. .. 10 Switzerland 36.1 24.5 27.7 28.3 54.5 56.2 5.8 .. .. 13 Syrian Arab Republic .. 20.3 21.7 .. .. .. .. .. .. .. Tajikistan .. 9.4b 6.5 14.4b 27.4 11.8 3.7b 19.3b 87.4 22 Tanzania .. .. .. .. .. .. .. .. 99.4b 53b Thailand 11.6 .. 15.5 .. 35.1 28.0 4.3 25.0 .. 18 Timor-Leste .. 27.6 .. .. .. .. .. .. .. 31 Togo .. 9.8 31.1 20.0 .. 162.5 3.7 15.8b 14.6 39 Trinidad and Tobago .. .. 12.3 .. 149.3 .. .. .. .. 17 Tunisia .. 20.9 27.1 24.2 89.4 55.9 7.2 20.8 .. 19 Turkey 10.7 .. 14.3 .. 45.5 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. 84.8 49 Ukraine .. 15.8 11.2 24.3 36.5 25.5 5.4 20.2 99.8 16 United Arab Emirates .. 4.4 11.5 6.2 41.5 .. 1.4 28.3 100.0 17 United Kingdom 15.0 18.9 24.3 20.3 26.2 32.3 5.5 12.5 .. 18 United States .. 22.2 22.5 24.6 27.0 25.4 5.7 13.7 .. 14 Uruguay 7.8 8.8 11.3 10.8 19.1 18.8 2.9 11.6 .. 20 Uzbekistan .. .. .. .. .. .. .. .. 100.0 18 Venezuela, RB .. 9.1 .. 8.1 .. 24.4 3.7 .. 84.0 19 Vietnam .. .. .. .. .. .. .. .. 95.6 21 West Bank and Gaza .. .. .. .. .. .. .. .. 100.0 30 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. Zambia .. 2.3 19.4 8.1 164.3 .. 1.5 .. .. 49 Zimbabwe 20.7 .. 19.6 .. 196.1 .. .. .. .. 38 World .. m 15.3 m .. m .. m .. m .. m 4.5 m 14.2 m 25 w Low income .. .. .. .. .. .. .. .. 41 Middle income .. .. .. .. .. .. 4.5 14.2 24 Lower middle income .. .. .. .. .. .. 3.2 .. 26 Upper middle income .. 14.5 18.1 19.7 38.6 24.2 4.5 13.0 20 Low & middle income .. .. .. .. .. .. .. .. 28 East Asia & Pacific .. .. 8.1 .. 37.8 .. .. .. 20 Europe & Central Asia .. .. .. .. .. .. 4.1 13.1 18 Latin America & Carib. .. 12.0 13.1 13.2 37.1 .. 3.5 13.1 23 Middle East & N. Africa .. .. .. .. .. .. .. .. 24 South Asia .. .. 13.4 .. 90.8 .. .. .. .. Sub-Saharan Africa .. 11.8 .. .. .. .. 4.1 .. 45 High income 15.8 18.9 22.4 23.1 32.6 25.4 5.1 12.5 15 Euro area 14.9 18.0 24.4 26.3 29.1 26.0 5.2 11.2 14 a. Provisional data. b. Data are for 2008. 82 2009 World Development Indicators PEOPLE Education inputs 2.11 About the data Definitions Data on education are compiled by the United private spending adds to the complexity of collecting · Public expenditure per student is public current Nations Educational, Scientific, and Cultural Organi- accurate data on public spending. and capital spending on education divided by the zation (UNESCO) Institute for Statistics from official The share of trained teachers in primary educa- number of students by level as a percentage of gross responses to surveys and from reports provided by tion measures the quality of the teaching staff. It domestic product (GDP) per capita. · Public expen- education authorities in each country. The data are does not take account of competencies acquired by diture on education is current and capital public used for monitoring, policymaking, and resource teachers through their professional experience or expenditure on education as a percentage of GDP allocation. However, coverage and data collection self-instruction or of such factors as work experi- and as a percentage of total government expendi- methods vary across countries and over time within ence, teaching methods and materials, or classroom ture. · Trained teachers in primary education are countries, so comparisons should be made with conditions, which may affect the quality of teaching. the percentage of primary school teachers who have caution. Since the training teachers receive varies greatly received the minimum organized teacher training For most countries the data on education spending (pre-service or in-service), care should be taken in (pre-service or in-service) required for teaching in in the table refer to public spending--government making comparisons across countries. their country. · Primary school pupil-teacher ratio spending on public education plus subsidies for pri- The primary school pupil-teacher ratio refl ects is the number of pupils enrolled in primary school vate education--and generally exclude foreign aid for the average number of pupils per teacher. It differs divided by the number of primary school teachers education. They may also exclude spending by reli- from the average class size because of the differ- (regardless of their teaching assignment). gious schools, which play a significant role in many ent practices countries employ, such as part-time developing countries. Data for some countries and teachers, school shifts, and multigrade classes. The some years refer to ministry of education spending comparability of pupil-teacher ratios across coun- only and exclude education expenditures by other tries is affected by the definition of teachers and by ministries and local authorities. differences in class size by grade and in the number Many developing countries seek to supplement of hours taught, as well as the different practices public funds for education, some with tuition fees mentioned above. Moreover, the underlying enroll- to recover part of the cost of providing education ment levels are subject to a variety of reporting errors services or to encourage development of private (for further discussion of enrollment data, see About schools. Fees raise diffi cult questions of equity, the data for table 2.12). While the pupil-teacher ratio efficiency, access, and taxation, however, and some is often used to compare the quality of schooling governments have used scholarships, vouchers, and across countries, it is often weakly related to the other public finance methods to counter criticism. value added of schooling systems. For greater detail, consult the country- and indicator- In 1998 UNESCO introduced the new International specific notes in the original source. Standard Classification of Education 1997 (ISCED The share of public expenditure devoted to edu- 1997). Consistent historical time series with reclas- cation allows an assessment of the priority a gov- sification of the pre­ISCED 1997 series were pro- ernment assigns to education relative to other duced for a selection of indicators in 2008. The full public investments, as well as a government's set of the historical series is forthcoming. commitment to investing in human capital develop- In 2006 the UNESCO Institute for Statistics also ment. It also reflects the development status of a changed its convention for citing the reference year country's education system relative to that of oth- of education data and indicators to the calendar year ers. However, returns on investment to education, in which the academic or financial year ends. Data especially primary and lower secondary education, that used to be listed for 2006, for example, are cannot be understood simply by comparing current now listed for 2007. This change was implemented education indicators with national income. It takes to present the most recent data available and to a long time before currently enrolled children can align the data reporting with that of other interna- productively contribute to the national economy tional organizations (in particular the Organisation (Hanushek 2002). for Economic Co-operation and Development and Data on education finance are generally of poor Eurostat). Data sources quality. This is partly because ministries of education, from which the UNESCO Institute for Statistics col- Data on education inputs are from the UNESCO lects data, may not be the best source for education Institute for Statistics, which compiles inter- finance data. Other agencies, particularly ministries national data on education in cooperation with of finance, need to be consulted, but coordination is national commissions and national statistical not easy. It is also difficult to track actual spending services. from the central government to local institutions. And 2009 World Development Indicators 83 2.12 Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio ratio enrollment school ratio, primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2007a 2007a 2007a 2007a 1991 2007a 1991 2007a 2007a 2007a 2007a 2007a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. 96 .. .. .. .. .. .. .. Algeria 30 110 83 24 89 95 53 .. 97 95 61 88 Angola 61 199 17 3 50 .. .. .. .. .. .. .. Argentina 66 112 84 64 94 99 .. 78 100 98 5 31 Armenia 37 110 89 34 .. 85 .. 86 92 95 5 2 Australia 104 105 150 73 99 96 80 87 96 97 36 27 Austria 90 102 102 50 88 97 .. .. 97 98 6 3 Azerbaijan .. .. .. .. 89 .. .. .. .. .. .. .. Bangladesh .. .. .. 7 76 .. .. .. .. .. .. .. Belarus 103 97 95 69 85 91 .. 87 91 89 18 21 Belgium 121 102 110 63 96 97 86 87 97 98 9 8 Benin 6 96 32 5 41 80 .. .. 90 75 71 173 Bolivia 50 109 82 .. .. 95 .. 71 96 97 30 22 Bosnia and Herzegovina 10 98 85 .. 79 .. .. .. .. .. .. .. Botswana 15 107 76 5 88 84 39 56 83 86 27 22 Brazil 69 137 105 25 84 94 .. 79 94 97 383 214 Bulgaria 82 100 105 46 .. 92 .. 88 94 94 8 9 Burkina Faso 3 65 16 3 27 52 .. 12 58 48 514 608 Burundi 2 114 15 2 53 81 .. .. 82 80 116 128 Cambodia 13 119 42 5 72 89 .. 31 .. .. .. .. Cameroon 21 110 25 7 69 .. .. .. .. .. .. .. Canada 68 98 .. .. 98 .. 89 .. .. .. .. .. Central African Republic .. 80 b .. 1 52 61b .. .. 71b 51b 103b 174b Chad 1 74 19 1 34 .. .. .. .. .. .. .. Chile 55 104 91 47 89 .. 55 .. .. .. .. .. China 39 111 76 22 98 .. .. .. .. .. .. .. Hong Kong, China 66 98 86 34 92 91 .. 79 97 93 7 16 Colombia 41 116 85 32 68 87 34 67 91 91 219 194 Congo, Dem. Rep. 3 85 33 4 54 .. .. .. .. .. .. .. Congo, Rep. 10 106 .. .. 82 54 .. .. 56 52 129 142 Costa Rica 61 110 87 25 87 .. 38 64 .. .. .. .. Côte d'Ivoire 3 72 .. 8 45 .. .. .. .. .. .. .. Croatia 50 99 91 44 70 90 .. 87 98 100 2 0c Cuba 111 102 93 109 94 98 73 86 99 99 4 6 Czech Republic 114 100 96 50 87 93 .. .. 91 94 22 15 Denmark 95 99 120 80 98 96 87 89 95 97 10 7 Dominican Republic 32 107 79 .. .. 82 .. 61 84 86 104 90 Ecuador 100 118 70 .. 98 97 .. 59 .. .. .. .. Egypt, Arab Rep. 17 105 .. 35 86 96 .. .. 100 95 10 222 El Salvador 49 118 64 22 .. 92 .. 54 93 94 32 27 Eritrea 14 55 29 .. 15 41 .. 25 45 40 167 181 Estonia 93 99 100 65 .. 94 .. 91 97 97 1 1 Ethiopia 3 91 30 3 22 71 .. 24 75 69 1,667 2,054 Finland 62 98 112 93 98 97 93 96 97 97 6 5 France 116 110 114 56 100 99 .. 99 99 99 18 9 Gabon .. .. .. .. 94 .. .. .. .. .. .. .. Gambia, The 22 86 49 .. 46 76 .. 36 74 78 33 27 Georgia 57 99 90 37 97 94 .. 82 96 93 7 11 Germany 106 103 102 .. 84 98 .. .. 98 99 28 19 Ghana 60 98 49 6 54 72 .. 45 73 71 477 490 Greece 69 102 103 95 95 99 83 92 100 100 1 1 Guatemala 29 113 56 18 64 95 .. 38 98 95 17 53 Guinea 10 91 35 5 27 74 .. 28 80 70 146 216 Guinea-Bissau .. .. .. .. 38 .. .. .. .. .. .. .. Haiti .. .. .. .. 21 .. .. .. .. .. .. .. 84 2009 World Development Indicators PEOPLE Gross enrollment Participation in education Net enrollment Adjusted net 2.12 Children out of ratio ratio enrollment school ratio, primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2007a 2007a 2007a 2007a 1991 2007a 1991 2007a 2007a 2007a 2007a 2007a Honduras 36 117 61 .. 88 96 21 .. 96 98 21 12 Hungary 86 97 96 69 87 88 .. 90 94 95 12 11 India 40 112 55 12 .. 89 .. .. 96 92 2,529 4,613 Indonesia 37 114 66 17 96 95 39 60 99 96 142 544 Iran, Islamic Rep. 54 121 73 31 92 94 .. 77 .. .. .. .. Iraq .. .. .. .. 94 .. .. .. .. .. .. .. Ireland .. 104 112 59 90 95 80 87 94 95 13 10 Israel 91 110 92 58 .. 97 .. 89 96 98 13 9 Italy 104 103 100 67 100 99 .. 94 100 99 5 12 Jamaica 92 95 87 .. 96 90 64 78 91 91 16 15 Japan 86 100 101 57 100 100 97 99 .. .. .. .. Jordan 32 97 89 39 95 90 .. 82 93 95 31 22 Kazakhstan 39b 109b 92b 47b 88 90 b .. 86b 99b 100 b 6b 2b Kenya 49 106 50 .. .. 75 .. 43 76 77 708 662 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 101 105 98 93 100 98 86 96 .. .. .. .. Kuwait 77 98 91 18 49 88 .. 77 95 93 5 7 Kyrgyz Republic 16 95 86 43 92 84 .. 81 93 92 16 16 Lao PDR 13 118 44 9 62 86 .. 35 88 84 44 59 Latvia 89 95 99 74 94 90 .. .. 90 94 4 3 Lebanon 66 95 81 52 66 83 .. 73 84 83 38 38 Lesotho 18 114 37 4 72 72 15 24 71 74 54 47 Liberia 125b 83b .. .. .. 31b .. .. 32b 30 b 221b 226b Libya 9 110 94 .. .. .. .. .. .. .. .. .. Lithuania 69 95 99 76 .. 89 .. 92 92 92 7 6 Macedonia, FYR 33 98 84 30 .. 92 .. 81 97 97 2 1 Madagascar 8 141 26 3 64 98 .. 17 99 100 17 3 Malawi .. 116 28 0c 49 87 .. 24 84 91 198 117 Malaysia 63 100 69 29 93 100 .. 69 .. .. .. .. Mali 3 83 32 4 25 63 6 .. 70 56 312 452 Mauritania 2 103 25 4 36 80 .. 16 79 83 51 38 Mauritius 99 101 88 14b 91 95 .. 73 95 96 3 2 Mexico 106 113 87 26 98 98 45 70 100 99 12 61 Moldova 70 94 89 41 86 88 .. 81 90 90 8 9 Mongolia 54 100 92 48 90 89 .. 81 96 99 5 1 Morocco 60 107 56 11 56 89 .. .. 92 87 157 237 Mozambique .. 111 18 1 42 70 .. 3 72 67 578 671 Myanmar .. .. .. .. 99 .. .. .. .. .. .. .. Namibia 32 109 59 6 86 87 .. 49 84 89 30 21 Nepal 57b 124b 48b .. 63 80 b .. .. 82b 78b 338b 376b Netherlands 90 107 118 60 95 98 84 88 99 98 7 14 New Zealand 92 102 120 80 98 99 85 .. 99 100 1 1 Nicaragua 52 116 66 .. 70 90 .. 43 91 92 38 34 Niger 2 53 11 1 24 45 6 9 52 39 574 689 Nigeria 15 97 32 10 55 63 .. .. 69 60 3,608 4,582 Norway 90 98 113 78 100 98 88 96 98 98 5 4 Oman 31 80 90 25 69 73 .. 79 74 76 46 41 Pakistan 52 84 33 5 33 66 .. 32 73 57 2,705 4,116 Panama 70 113 70 45 92 98 .. 64 99 99 2 3 Papua New Guinea .. 55 .. .. 66 .. .. .. .. .. .. .. Paraguay 34 111 66 26 94 94 26 57 95 95 23 19 Peru 68 116 94 35 88 96 .. 72 .. .. .. .. Philippines 45 110 83 28 96 91 .. 60 91 93 553 400 Poland 57 98 100 66 96 96 .. 94 96 97 55 44 Portugal 79 115 97 55 98 98 .. 82 99 99 2 3 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 85 2.12 Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio ratio enrollment school ratio, primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2007a 2007a 2007a 2007a 1991 2007a 1991 2007a 2007a 2007a 2007a 2007a Romania 72 105 86 52 81 93 .. 73 95 96 21 19 Russian Federation 83 96 84 70 98 .. .. .. .. .. .. .. Rwanda .. 147 18 3 67 94 8 .. 92 95 56 38 Saudi Arabia 11 98 94 30 59 85 31 73 85 84 245 252 Senegal 9 84 24 6 45 72 .. 20 73 73 254 253 Serbia 59 97 88 .. .. 95 .. .. 95 95 8 7 Sierra Leone 5 147 32 .. 43 .. .. 23 .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 93 100 96 45 .. 92 .. .. 92 92 10 9 Slovenia 81 100 95 83 96 95 .. 90 97 97 2 1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 43 103 96 15 90 86 45 72 91 92 332 274 Spain 121 105 119 67 100 100 .. 94 100 99 1 6 Sri Lanka .. 108 .. .. 84 .. .. .. .. .. .. .. Sudan 27b 72b 35b .. .. .. .. .. .. .. .. .. Swaziland 17 106 47 4 75 78 30 32 78 79 23 22 Sweden 95 96 103 79 100 95 85 99 95 95 17 17 Switzerland 99 97 93 46 84 89 80 82 93 94 18 17 Syrian Arab Republic 10 126 72 .. 91 .. 43 66 .. .. .. .. Tajikistan 9b 100 b 84 20 b 77 98b .. 81 99 96 2 15 Tanzania 35b 112b .. 1 51 98 .. .. 99 97 50 93 Thailand 94 106 83 50 88 94 .. 76 .. .. .. .. Timor-Leste 10 91 53 .. .. 63 .. .. 64 62 35 36 Togo 4 97 39 5 64 77 15 .. 84 74 83 139 Trinidad and Tobago 85 95 76 11 89 85 .. 65 89 90 8 7 Tunisia .. 108 85 31 93 96 .. .. 97 98 18 9 Turkey 13 94 79 35 89 91 42 69 93 89 291 439 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 3 117 18 .. 51 .. .. 16 .. .. .. .. Ukraine 94 100 94 76 81 89 .. 84 90 90 85 82 United Arab Emirates 85 107 92 23 99 91 60 79 99 98 2 3 United Kingdom 72 105 98 59 98 98 80 92 99 100 16 0c United States 61 98 94 82 97 92 84 88 92 94 1,004 723 Uruguay 79 115 101 46 91 100 .. .. 98 97 4 4 Uzbekistan 27 95 102 10 78 91 .. 92 95 92 60 86 Venezuela, RB 62 106 79 52 91 92 .. 68 94 94 105 90 Vietnam .. .. .. .. 90 .. .. .. .. .. .. .. West Bank and Gaza 30 80 92 46 .. 73 .. 89 77 78 56 52 Yemen, Rep. 1 87 46 9 .. 75 .. 37 85 65 275 632 Zambia .. 119 43 .. 78 94 .. 41 95 96 60 48 Zimbabwe .. 101 40 .. 84 88 .. 37 88 89 149 132 World 41 w 105 w 66 w 25 w 81 w 86 w .. w 58 w 90 87 w Low income 22 94 38 6 56 73 .. 34 77 70 Middle income 44 111 70 24 87 91 .. 62 95 93 Lower middle income 39 111 65 19 85 90 .. .. 94 92 Upper middle income 68 111 91 42 90 94 .. 76 96 96 Low & middle income 37 106 61 19 79 85 .. 54 89 86 East Asia & Pacific 42 110 73 21 96 93 .. .. 94 94 Europe & Central Asia 52 97 88 53 89 91 .. 81 94 92 Latin America & Carib. 65 118 89 31 86 94 .. 70 95 96 Middle East & N. Africa 33 105 71 25 83 90 .. 67 93 90 South Asia 36 108 49 10 69 85 .. .. 92 87 Sub-Saharan Africa 14 94 32 5 53 70 .. 25 73 68 High income 78 101 101 67 95 95 .. 90 96 96 Euro area 106 .. .. .. .. .. .. .. .. .. a. Provisional data. b. Data are for 2008. c. Less than 0.5. 86 2009 World Development Indicators PEOPLE Participation in education 2.12 About the data Definitions School enrollment data are reported to the United enrolled in each grade because of repetition rather · Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural Organi- than a successful education system. The net enroll- ment, regardless of age, to the population of the age zation (UNESCO) Institute for Statistics by national ment ratio excludes overage and underage students group that officially corresponds to the level of educa- education authorities and statistical offices. Enroll- to capture more accurately the system's coverage tion shown. · Preprimary education refers to the ini- ment ratios help monitor whether a country is on and internal efficiency but does not account for chil- tial stage of organized instruction, designed primarily track to achieve the Millennium Development Goal dren who fall outside the official school age because to introduce very young children to a school-type envi- of universal primary education by 2015 (a net pri- of late or early entry rather than grade repetition. ronment. · Primary education provides children with mary enrollment ratio of 100 percent), and whether Differences between gross and net enrollment basic reading, writing, and mathematics skills along an education system has the capacity to meet the ratios show the incidence of overage and underage with an elementary understanding of such subjects needs of universal primary education, as indicated enrollments. as history, geography, natural science, social sci- in part by gross enrollment ratios. Adjusted net primary enrollment (called total net ence, art, and music. · Secondary education com- Enrollment ratios, while a useful measure of par- primary enrollment in the 2008 edition), recently pletes the provision of basic education that began ticipation in education, have limitations. They are added as a Millennium Development Goal indica- at the primary level and aims at laying the founda- based on annual school surveys, which are typically tor, captures primary-school-age children who have tions for lifelong learning and human development conducted at the beginning of the school year and do progressed to secondary education, which the tradi- by offering more subject- or skill-oriented instruction not reflect actual attendance or dropout rates during tional net enrollment ratio excludes. using more specialized teachers. · Tertiary educa- the year. And school administrators may exaggerate The data on children out of school (primary-school- tion refers to a wide range of post-secondary educa- enrollments, especially if there is a financial incen- age children not enrolled in primary or secondary tion institutions, including technical and vocational tive to do so. education) are compiled by the UNESCO Institute for education, colleges, and universities, whether or not Also, as international indicators, the gross and net Statistics using administrative data. Children out of leading to an advanced research qualification, that primary enrollment ratios have an inherent weak- school include dropouts, children never enrolled, and normally require as a minimum condition of admis- ness: the length of primary education differs across children of primary age enrolled in preprimary educa- sion the successful completion of education at the countries, although the International Standard Clas- tion. Large numbers of children out of school create secondary level. · Net enrollment ratio is the ratio sification of Education tries to minimize the differ- pressure to enroll children and provide classrooms, of total enrollment of children of official school age ence. A relatively short duration for primary educa- teachers, and educational materials, a task made based on the International Standard Classification of tion tends to increase the ratio; a relatively long one difficult in many countries by limited education bud- Education 1997 to the population of the age group to decrease it (in part because more older children gets. However, getting children into school is a high that officially corresponds to the level of education drop out). priority for countries and crucial for achieving the shown. · Adjusted net enrollment ratio, primary, Overage or underage enrollments are frequent, par- Millennium Development Goal of universal primary is the ratio of total enrollment of children of official ticularly when parents prefer children to start school education. school age for primary education who are enrolled in at other than the official age. Age at enrollment may In 2006 the UNESCO Institute for Statistics primary or secondary education to the total primary- be inaccurately estimated or misstated, especially changed its convention for citing the reference school-age population. · Children out of school in communities where registration of births is not year. For more information, see About the data for are the number of primary-school-age children not strictly enforced. table 2.11. enrolled in primary or secondary school. Other problems of cross-country comparison stem from errors in school-age population estimates. Age- sex structures drawn from censuses or vital registra- tions, the primary data sources on school-age popu- lation, commonly underenumerate (especially young children) to circumvent laws or regulations. Errors are also introduced when parents round children's ages. While census data are often adjusted for age bias, adjustments are rarely made for inadequate vital registration systems. Compounding these prob- lems, pre- and postcensus estimates of school-age children are model interpolations or projections that may miss important demographic events (see dis- Data sources cussion of demographic data in About the data for table 2.1). Data on gross and net enrollment ratios and out Gross enrollment ratios indicate the capacity of of school children are from the UNESCO Institute each level of the education system, but a high ratio for Statistics. may reflect a substantial number of overage children 2009 World Development Indicators 87 2.13 Education efficiency Gross intake rate Cohort Repeaters in Transition to in grade 1 survival rate primary school secondary school % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5 primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2007a 2007a 1991 2006a 1991 2006a 2006a 2006a 2007a 2007a 2006a 2006a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria 102 100 95 95 94 97 89 95 14 8 78 84 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 109 108 .. 88 .. 91 85 89 8 5 92 94 Armenia 130 133 .. .. .. .. 98 97 0b 0b 99 100 Australia 106 105 98 .. 99 .. .. .. .. .. .. .. Austria 102 100 .. .. .. .. .. .. .. .. .. .. Azerbaijan 94 92 .. .. .. .. 98 100 .. .. 99 100 Bangladesh .. .. .. .. .. .. .. .. .. .. .. .. Belarus 103 101 .. .. .. .. 99 100 0b 0b 100 100 Belgium 98 99 90 96 92 97 93 94 3 3 .. .. Benin 122 108 54 72 56 71 67 63 8 8 72 70 Bolivia 122 122 .. .. .. .. .. .. 1 1 .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 124 120 81 80 87 85 71 78 .. .. 97 98 Brazil .. .. .. .. .. .. .. .. .. .. .. .. Bulgaria 100 100 .. .. .. .. 95 95 3 2 96 96 Burkina Faso 86 76 71 79 68 82 71 74 12 12 47 44 Burundi 144 137 65 65 58 68 56 61 32 32 37 24 Cambodia 141 132 .. 61 .. 64 53 56 13 10 81 78 Cameroon 118 103 .. .. .. .. .. .. 20 20 35 37 Canada .. .. 95 .. 98 .. .. .. .. .. .. .. Central African Republic 99c 73c 24 53 22 45 43 35 28 28 44 51 Chad 109 79 56 34 41 32 27 23 22 24 56 42 Chile 100 99 94 99 91 99 .. .. 3 2 96 98 China 88 87 58 .. 78 .. .. .. 0b 0b .. .. Hong Kong, China 88 83 .. 99 .. 100 99 100 1 1 100 100 Colombia 123 121 .. 85 .. 92 85 92 4 3 99 100 Congo, Dem. Rep. 114 99 58 .. 50 .. .. .. 16 16 .. .. Congo, Rep. 89 86 56 .. 65 .. .. .. 21 21 58 58 Costa Rica 101 102 83 86 85 89 82 86 9 6 100 97 Côte d'Ivoire 76 64 75 83 70 73 83 66 22 21 49 48 Croatia 97 97 .. .. .. .. 99 100 0b 0b 100 100 Cuba 98 98 .. 97 .. 97 97 97 1 0b 98 98 Czech Republic 109 108 .. 100 .. 100 100 100 1 1 99 99 Denmark 97 98 94 100 94 100 92 92 0 0 100 100 Dominican Republic 123 116 .. 66 .. 71 58 65 7 4 93 98 Ecuador 141 139 .. 80 .. 83 79 82 2 1 81 77 Egypt, Arab Rep. 105 102 .. 96 .. 97 94 96 4 2 .. .. El Salvador 111 107 .. 72 .. 76 67 71 8 5 91 92 Eritrea 44 38 .. 59 .. 61 59 61 15 14 78 76 Estonia 96 95 .. 97 .. 97 96 97 3 1 96 99 Ethiopia 144 128 16 64 23 65 57 59 7 5 90 87 Finland 97 96 100 99 100 100 99 100 1 0b 98 99 France .. .. 69 .. 95 .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 88 95 .. .. .. .. .. .. 6 6 95 93 Georgia 109 103 .. 86 .. 90 83 89 0b 0b 98 100 Germany 104 103 .. .. .. .. 98 99 1 1 100 99 Ghana 105 110 81 .. 79 .. .. .. 6 6 .. .. Greece 100 99 100 97 100 100 97 100 1 1 100 99 Guatemala 124 122 .. 69 .. 67 63 62 13 11 94 90 Guinea 97 90 64 87 48 79 82 72 9 10 69 59 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 88 2009 World Development Indicators PEOPLE Gross intake rate Education efficiency Cohort Repeaters in 2.13 Transition to in grade 1 survival rate primary school secondary school % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5 primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2007a 2007a 1991 2006a 1991 2006a 2006a 2006a 2007a 2007a 2006a 2006a Honduras 136 131 .. 64 .. 69 58 64 8 6 68 74 Hungary 97 96 .. .. .. .. 97 98 2 2 99 99 India 133 126 .. 66 .. 65 66 65 3 3 86 82 Indonesia 123 119 34 83 78 86 78 81 4 3 88 89 Iran, Islamic Rep. 109 106 91 .. 89 .. .. .. 3 1 89 77 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 97 99 99 97 100 100 .. .. 1 1 .. .. Israel 95 98 .. 100 .. 99 100 99 2 1 73 72 Italy 105 104 .. 99 .. 100 99 100 0b 0b 100 99 Jamaica 94 92 .. .. .. .. .. .. 3 2 100 97 Japan 99 99 100 .. 100 .. .. .. .. .. .. .. Jordan 89 90 .. 97 .. 96 96 95 1 1 97 96 Kazakhstan 117c 117c .. .. .. .. 99d 100 d 0 b,c 0 b,c 100 d 100 d Kenya 112 108 .. 81 .. 85 .. .. 6 6 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 106 109 99 99 100 99 99 99 0b 0b 99 99 Kuwait 97 94 .. 100 .. 99 100 99 1 1 100 100 Kyrgyz Republic 97 97 .. .. .. .. 96 97 0b 0b 99 99 Lao PDR 135 126 .. 62 .. 61 62 61 18 16 79 76 Latvia 95 95 .. .. .. .. 98 98 4 2 97 97 Lebanon 89 87 .. 97 .. 100 94 99 11 8 85 90 Lesotho 105 99 58 68 73 80 53 71 24 18 68 68 Liberia 100 c 100 c .. .. .. .. .. .. 7c 6c .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 98 94 .. .. .. .. 97 97 1 0b 98 99 Macedonia, FYR 99 99 .. .. .. .. 98 99 0b 0b 100 99 Madagascar 171 168 22 42 21 43 42 43 20 18 61 60 Malawi 137 147 71 44 57 43 37 35 21 20 76 71 Malaysia 98 98 97 99 97 100 .. .. .. .. 100 99 Mali 92 79 71 83 67 79 75 70 17 17 52 47 Mauritania 115 120 76 63 75 65 54 55 3 3 57 47 Mauritius 100 102 97 99 98 99 98 98 4 3 65 77 Mexico 112 110 35 94 71 95 91 93 5 3 95 93 Moldova 96 96 .. .. .. .. 96 96 0b 0b 99 99 Mongolia 124 126 .. 86 .. 83 86 83 1 0b 95 97 Morocco 116 112 75 85 76 83 79 76 14 10 80 79 Mozambique 166 156 36 68 32 60 48 41 6 6 56 61 Myanmar 136 135 .. 68 .. 72 68 72 1 0b 75 70 Namibia 102 103 60 84 65 90 73 80 19 14 75 80 Nepal 125c 127c 51 60 d 51 64 d 60 d 64 d 17c 17c 81d 81d Netherlands 103 101 .. 99 .. 100 .. .. .. .. .. .. New Zealand 105 104 .. .. .. .. .. .. .. .. .. .. Nicaragua 173 163 11 50 37 57 46 55 11 8 .. .. Niger 72 58 61 74 65 69 72 67 5 5 42 37 Nigeria 106 90 .. .. .. .. .. .. 3 3 .. .. Norway 100 100 99 100 100 100 99 100 0 0 99 100 Oman 77 78 97 98 96 99 97 98 1 2 97 97 Pakistan 125 100 .. 68 .. 72 68 72 2 2 69 75 Panama 115 113 .. 90 .. 90 88 89 7 4 100 98 Papua New Guinea .. .. 70 .. 68 .. .. .. .. .. .. .. Paraguay 113 110 73 86 75 90 82 86 6 4 89 89 Peru 109 110 .. 90 .. 89 86 84 9 8 97 94 Philippines 131 121 .. 70 .. 78 66 75 3 2 100 98 Poland 97 98 .. .. .. .. .. .. 1 0b .. .. Portugal 108 109 .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 89 2.13 Education efficiency Gross intake rate Cohort Repeaters in Transition to in grade 1 survival rate primary school secondary school % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5 primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2007a 2007a 1991 2006a 1991 2006a 2006a 2006a 2007a 2007a 2006a 2006a Romania 97 97 .. .. .. .. 93 94 3 2 98 98 Russian Federation 101 100 .. .. .. .. .. .. 1 1 .. .. Rwanda 209 205 61 .. 59 .. .. .. 15 15 .. .. Saudi Arabia 98 99 82 .. 84 .. .. .. 3 3 .. .. Senegal 98 103 .. 65 .. 65 54 53 11 10 52 48 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 188 172 .. .. .. .. .. .. 10 10 .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 102 101 .. .. .. .. 97 98 3 2 98 98 Slovenia 95 96 .. .. .. .. .. .. 1 0b .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 117 109 .. .. .. .. .. .. 8 8 87 89 Spain 104 104 .. 100 .. 100 100 100 3 2 .. .. Sri Lanka 112 112 92 93 93 94 93 94 1 1 96 97 Sudan 90 c 80 c 90 72 99 69 64 60 3 3 90d 98d Swaziland 111 103 74 81 80 87 66 75 19 15 88 89 Sweden 96 95 100 .. 100 .. .. .. .. .. .. .. Switzerland 88 92 .. .. .. .. .. .. 2 1 99 100 Syrian Arab Republic 123 119 97 .. 95 .. 95 96 8 6 95 96 Tajikistan 106 102 .. .. .. .. 100 97 0b 0b 100 c 97c Tanzania 116c 114 c 81 85 82 89 81 85 4 4 64 d 52d Thailand 71 83 .. .. .. .. .. .. 12 6 85 89 Timor-Leste 113 111 .. .. .. .. .. .. 15 14 .. .. Togo 97 90 52 58 42 50 49 39 23 24 56 49 Trinidad and Tobago 96 92 .. 90 .. 92 80 87 6 4 94 92 Tunisia 97 100 94 96 77 97 94 95 7 5 86 90 Turkey 95 92 98 89 97 90 95 93 3 3 93 90 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 145 147 .. 49 .. 49 26 25 13 13 42 43 Ukraine 101 100 .. .. .. .. 97 99 0b 0b 100 100 United Arab Emirates 108 106 80 100 80 100 100 100 2 2 98 99 United Kingdom .. .. .. .. .. .. .. .. 0 0 .. .. United States 105 102 .. 96 .. 98 .. .. 0 0 .. .. Uruguay 100 101 96 92 98 95 91 94 8 6 76 87 Uzbekistan 95 92 .. .. .. .. 99 99 0b 0b 100 100 Venezuela, RB 106 104 .. 96 .. 100 95 100 6 4 98 98 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 80 79 .. .. .. .. 99 99 1 1 97 98 Yemen, Rep. 122 102 .. 67 .. 65 61 57 5 4 83 82 Zambia 126 129 .. 94 .. 84 83 67 7 6 54 64 Zimbabwe .. .. 72 .. 81 .. .. .. .. .. .. .. World 114 w 108 w .. w .. w .. w .. w .. w .. w 4w 3w .. w .. w Low income 118 108 .. .. .. .. .. .. 8 8 .. .. Middle income 114 110 .. .. .. .. .. .. 3 3 .. .. Lower middle income 114 110 .. .. .. .. .. .. 3 3 .. .. Upper middle income 111 107 .. .. .. .. .. .. .. .. .. .. Low & middle income 115 109 .. .. .. .. .. .. 4 4 .. .. East Asia & Pacific 99 97 55 .. 78 .. .. .. 2 1 .. .. Europe & Central Asia 99 97 .. .. .. .. .. .. 1 1 .. .. Latin America & Carib. 122 117 .. .. .. .. .. .. .. .. .. .. Middle East & N. Africa 108 105 .. .. .. .. .. .. 7 4 86 83 South Asia 131 122 .. 67 .. 66 67 66 4 4 83 81 Sub-Saharan Africa 115 105 .. .. .. .. .. .. 9 9 .. .. High income 103 102 .. .. .. .. .. .. .. .. .. .. Euro area 104 103 .. .. .. .. 98 99 2 1 .. .. a. Provisional data. b. Less than 0.5. c. Data are for 2008. d. Data are for 2007. 90 2009 World Development Indicators PEOPLE Education efficiency 2.13 About the data Definitions The United Nations Educational, Scientific, and Cul- and measuring students' learning progress against · Gross intake rate in grade 1 is the number of tural Organization (UNESCO) Institute for Statistics those standards through standardized assessments, new entrants in the first grade of primary education estimates indicators of students' progress through actions that many countries do not systematically regardless of age as a percentage of the population school. These indicators measure an education sys- undertake. of the official primary school entrance age. · Cohort tem's success in reaching all students, efficiently Data on repeaters are often used to indicate an survival rate is the percentage of children enrolled moving students from one grade to the next, and education system's internal efficiency. Repeaters not in the first grade of primary school who eventually imparting a particular level of education. only increase the cost of education for the family reach grade 5 or the last grade of primary educa- The gross intake rate indicates the level of access and the school system, but also use limited school tion. The estimate is based on the reconstructed to primary education and the education system's resources. Country policies on repetition and promo- cohort method (see About the data). · Repeaters in capacity to provide access to primary education. tion differ; in some cases the number of repeaters is primary school are the number of students enrolled Low gross intake rates in grade 1 reflect the fact controlled because of limited capacity. Care should in the same grade as in the previous year as a per- that many children do not enter primary school even be taken in interpreting this indicator. centage of all students enrolled in primary school. though school attendance, at least through the pri- The transition rate from primary to secondary · Transition to secondary school is the number of mary level, is mandatory in all countries. Because school conveys the degree of access or transition new entrants to the first grade of secondary school the gross intake rate includes all new entrants between the two levels. As completing primary edu- in a given year as a percentage of the number of regardless of age, it can exceed 100 percent. Once cation is a prerequisite for participating in lower students enrolled in the final grade of primary school enrolled, students drop out for a variety of reasons, secondary school, growing numbers of primary in the previous year. including low quality schooling, lack of relevant cur- completers will inevitably create pressure for more riculum (real or perceived by parents or students), available places at the secondary level. A low transi- repetition, discouragement over poor performance, tion rate can signal such problems as an inadequate and direct and indirect schooling costs. Students' examination and promotion system or insufficient progress to higher grades may also be limited by the secondary school capacity. The quality of data on availability of teachers, classrooms, and materials. the transition rate is affected when new entrants and The cohort survival rate is the estimated proportion repeaters are not correctly distinguished in the first of an entering cohort of grade 1 students that even- grade of secondary school. Students who interrupt tually reaches grade 5 or the last grade of primary their studies after completing primary school could education. It measures an education system's hold- also affect data quality. ing power and internal efficiency. Rates approaching In 2006 the UNESCO Institute for Statistics 100 percent indicate high retention and low dropout changed its convention for citing the reference levels. Cohort survival rates are typically estimated year. For more information, see About the data for from data on enrollment and repetition by grade for table 2.11. two consecutive years. This procedure, called the reconstructed cohort method, makes three simplify- ing assumptions: dropouts never return to school; promotion, repetition, and dropout rates remain con- stant over the period in which the cohort is enrolled in school; and the same rates apply to all pupils enrolled in a grade, regardless of whether they previ- ously repeated a grade (Fredricksen 1993). Cross- country comparisons should thus be made with cau- tion, because other flows--caused by new entrants, reentrants, grade skipping, migration, or transfers during the school year--are not considered. Research suggests that five to six years of school- ing, which is how long primary education lasts in most countries, is a critical threshold for achiev- ing sustainable basic literacy and numeracy skills. But the indicator only indirectly reflects the quality Data sources of schooling, and a high rate does not guarantee these learning outcomes. Measuring actual learn- Data on education efficiency are from the UNESCO ing outcomes requires setting curriculum standards Institute for Statistics. 2009 World Development Indicators 91 2.14 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2007a 1991 2007a 1991 2007a 1990 2005­07b 1990 2005­07b 2005­07b 2005­07b Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 96 .. 97 .. 96 .. 99 .. 99 99 99 Algeria 80 95 86 94 73 96 86 94 62 91 84 66 Angola 35 .. .. .. .. .. .. .. .. .. .. .. Argentina .. 97 .. 95 .. 99 98 99 99 99 98 98 Armenia .. 98 .. 96 .. 100 100 100 100 100 100 99 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. 103 .. 103 .. 102 .. .. .. .. .. .. Azerbaijan .. .. .. .. .. .. .. 100 .. 100 100 99 Bangladesh .. 72 .. 70 .. 74 52 71 38 73 59 48 Belarus 94 92 .. 93 .. 92 100 100 100 100 100 100 Belgium 79 87 76 86 82 88 .. .. .. .. .. .. Benin 21 64 28 76 13 52 55 63 27 41 53 28 Bolivia 71 101 78 102 64 100 96 100 92 99 96 86 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 89 95 82 91 97 98 86 93 92 95 83 83 Brazil 90 106 .. .. .. .. .. 97 .. 99 90 90 Bulgaria 90 98 88 98 92 98 .. 98 .. 97 99 98 Burkina Faso 20 33 24 37 15 29 27 47 14 33 37 22 Burundi 46 39 49 42 43 36 59 .. 48 .. .. .. Cambodia .. 85 .. 85 .. 85 .. 90 .. 83 86 68 Cameroon 53 55 57 61 49 50 .. .. .. .. .. .. Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 27 24 35 30 18 19 63 .. 35 .. .. .. Chad 18 31 29 41 7 21 .. 53 .. 35 43 21 Chile .. 95 .. 96 .. 95 98 99 99 99 97 96 China 105 .. .. .. .. .. 97 99 91 99 96 90 Hong Kong, China 102 102 .. 104 .. 99 .. .. .. .. .. .. Colombia 70 107 67 105 73 109 89 97 92 98 92 93 Congo, Dem. Rep. 46 51 58 61 34 41 .. .. .. .. .. .. Congo, Rep. 54 72 59 75 49 70 .. .. .. .. .. .. Costa Rica 79 91 77 90 81 93 .. 98 .. 98 96 96 Côte d'Ivoire 43 45 55 53 32 36 60 .. 38 .. .. .. Croatia .. 96 .. 97 .. 95 100 100 100 100 99 98 Cuba 99 93 .. 93 .. 93 .. 100 .. 100 100 100 Czech Republic .. 94 .. 95 .. 93 .. .. .. .. .. .. Denmark 98 101 98 101 98 102 .. .. .. .. .. .. Dominican Republic 62 89 .. 87 .. 91 .. 95 .. 97 89 90 Ecuador .. 106 .. 105 .. 107 97 95 96 96 87 82 Egypt, Arab Rep. .. 98 .. 101 .. 96 71 88 54 82 75 58 El Salvador 61 91 60 89 62 93 85 93 85 94 85 80 Eritrea .. 46 .. 52 .. 41 .. .. .. .. .. .. Estonia .. 100 .. 102 .. 98 100 100 100 100 100 100 Ethiopia .. 46 .. 51 .. 41 39 .. 28 .. .. .. Finland 97 97 98 97 97 97 .. .. .. .. .. .. France 104 .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. 94 98 92 96 90 82 Gambia, The .. 72 .. 70 .. 73 .. .. .. .. .. .. Georgia .. 92 .. .. .. .. .. .. .. .. .. .. Germany .. 97 .. 97 .. 98 .. .. .. .. .. .. Ghana 61 71 69 73 54 68 .. 80 .. 76 72 58 Greece .. 103 .. 104 .. 103 99 99 99 99 98 96 Guatemala .. 77 .. 80 .. 74 82 88 71 83 79 68 Guinea 17 64 25 73 9 55 .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 27 .. 29 .. 26 .. .. .. .. .. .. .. 92 2009 World Development Indicators PEOPLE Education completion and outcomes Primary completion Youth literacy 2.14 Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2007a 1991 2007a 1991 2007a 1990 2005­07b 1990 2005­07b 2005­07b 2005­07b Honduras 64 88 67 85 61 90 .. 93 .. 95 84 83 Hungary 87 96 93 96 95 96 99 98 99 99 99 99 India 64 86 75 88 52 83 74c 87 49c 77 77 54 Indonesia 91 99 .. 99 .. 99 97 97 95 96 95 89 Iran, Islamic Rep. 91 105 97 98 85 113 92 97 81 96 87 77 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. 96 .. 91 .. 101 .. .. .. .. .. .. Israel .. 101 .. 100 .. 101 .. .. .. .. .. .. Italy 104 100 104 100 104 99 .. 100 .. 100 99 99 Jamaica 90 82 86 81 94 84 .. 91 .. 98 81 91 Japan 101 .. 101 .. 102 .. .. .. .. .. .. .. Jordan 101 99 101 100 101 98 .. 99 .. 99 95 87 Kazakhstan .. 104 d .. 104 d .. 105d 100 100 100 100 100 99 Kenya .. 93 .. 94 .. 92 .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 98 101 98 106 98 95 .. .. .. .. .. .. Kuwait .. 98 .. 98 .. 98 .. 98 .. 99 95 93 Kyrgyz Republic .. 95 .. 95 .. 94 .. 100 .. 100 100 99 Lao PDR 45 77 .. 81 .. 72 .. 89 .. 79 82 63 Latvia .. 92 .. 93 .. 91 100 100 100 100 100 100 Lebanon .. 82 .. 80 .. 83 .. 98 .. 99 93 86 Lesotho 59 78 42 65 76 92 .. .. .. .. .. .. Liberia .. 55d .. 60 d .. 50 d 56 68 47 76 60 51 Libya .. .. .. .. .. .. 99 100 91 98 94 78 Lithuania 89 93 .. 92 .. 93 100 100 100 100 100 100 Macedonia, FYR .. 97 .. 96 .. 98 99 99 99 99 99 95 Madagascar 33 62 33 62 34 61 .. .. .. .. .. .. Malawi 29 55 36 55 21 56 70 84 49 82 79 65 Malaysia 91 98 91 98 91 98 96 98 95 98 94 90 Mali 13 49 15 59 10 40 .. 47 .. 31 35 18 Mauritania 34 59 41 59 27 60 .. 70 .. 62 63 48 Mauritius 107 94 107 92 107 95 91 95 92 97 90 85 Mexico 88 104 .. 104 .. 104 96 98 95 98 94 91 Moldova .. 93 .. 93 .. 93 100 100 100 100 100 99 Mongolia .. 110 .. 108 .. 113 .. 94 .. 97 97 98 Morocco 48 83 57 87 39 79 71 84 46 67 69 43 Mozambique 26 46 32 53 21 39 .. 58 .. 47 57 33 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia .. 77 .. 73 .. 81 86 91 90 94 89 87 Nepal 51 78d .. 79d .. 78d 68 85 33 73 70 44 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand 100 .. 101 .. 99 .. .. .. .. .. .. .. Nicaragua 42 73 .. 70 .. 77 .. 85 .. 89 78 78 Niger 18 40 22 47 13 32 .. 52 .. 23 43 15 Nigeria .. 72 .. 80 .. 65 81 89 62 85 80 64 Norway 100 96 100 95 100 97 .. .. .. .. .. .. Oman 74 88 78 88 70 88 .. 99 .. 98 89 77 Pakistan .. 62 .. 70 .. 53 .. 79 .. 58 68 40 Panama .. 99 .. 98 .. 99 95 97 95 96 94 93 Papua New Guinea 46 .. 51 .. 42 .. .. 63 .. 65 62 53 Paraguay 68 95 68 94 69 96 96 99 95 99 96 93 Peru .. 101 .. 101 .. 101 97 98 94 97 95 85 Philippines 88 94 .. 90 .. 97 96 94 97 95 93 94 Poland 96 97 .. .. .. .. 100 100 100 99 100 99 Portugal 95 104 94 102 95 107 99 100 99 100 97 93 Puerto Rico .. .. .. .. .. .. 92 .. 94 .. .. .. 2009 World Development Indicators 93 2.14 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2007a 1991 2007a 1991 2007a 1990 2005­07b 1990 2005­07b 2005­07b 2005­07b Romania 100 101 100 101 100 101 99 97 99 98 98 97 Russian Federation .. .. .. .. .. .. 100 100 100 100 100 99 Rwanda 35 35 40 36 31 35 .. .. .. .. .. .. Saudi Arabia 55 93 60 96 51 91 94 98 81 96 89 79 Senegal 43 49 52 51 33 47 49 58 28 45 52 33 Serbia .. .. .. .. .. .. 99e .. 98e .. .. .. Sierra Leone .. 81 .. 92 .. 70 .. 64 .. 44 50 27 Singapore .. .. .. .. .. .. 99 100 99 100 97 92 Slovak Republic .. 93 .. 94 .. 92 .. .. .. .. .. .. Slovenia .. .. .. .. .. .. 100 100 100 100 100 100 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 76 92 71 92 80 92 .. 95 .. 96 89 87 Spain 103 99 104 99 103 99 100 100 100 100 99 97 Sri Lanka 102 106 103 106 102 107 .. 97 .. 98 93 89 Sudan 42 50 47 54 37 46 .. .. .. .. .. .. Swaziland 60 67 57 64 63 69 83 .. 84 .. .. .. Sweden 96 .. 96 .. 96 .. .. .. .. .. .. .. Switzerland 53 88 53 88 54 89 .. .. .. .. .. .. Syrian Arab Republic 89 114 94 116 84 113 .. 95 .. 92 90 76 Tajikistan .. 95 .. .. .. .. 100 100 100 100 100 100 Tanzania 62 112d 62 115d 63 109d 86 79 78 76 79 66 Thailand .. 101 .. 99 .. 104 .. 98 .. 98 96 93 Timor-Leste .. 69 .. 69 .. 69 .. .. .. .. .. .. Togo 35 57 48 67 22 48 .. .. .. .. .. .. Trinidad and Tobago 101 88 98 86 104 90 99 100 99 100 99 98 Tunisia 74 120 79 122 70 117 .. 97 .. 94 86 69 Turkey 90 96 93 101 86 91 97 99 88 94 96 81 Turkmenistan .. .. .. .. .. .. .. 100 .. 100 100 99 Uganda .. 54 .. 57 .. 51 77 88 63 84 82 66 Ukraine 94 101 .. 101 .. 101 .. 100 .. 100 100 100 United Arab Emirates 103 105 104 103 103 106 .. 94 .. 97 89 91 United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. 95 .. 94 .. 96 .. .. .. .. .. .. Uruguay 94 99 91 98 96 100 .. 98 .. 99 97 98 Uzbekistan .. 97 .. 99 .. 96 .. .. .. .. .. .. Venezuela, RB 81 98 76 96 86 100 95 98 96 99 95 95 Vietnam .. .. .. .. .. .. 94 .. 93 .. .. .. West Bank and Gaza .. 83 .. 83 .. 83 .. 99 .. 99 97 90 Yemen, Rep. .. 60 .. 74 .. 46 83 93 35 67 77 40 Zambia .. 88 .. 94 .. 83 67 82 66 68 81 61 Zimbabwe 97 .. 99 .. 96 .. 97 94 94 88 94 88 World 79 w 86 w 86 w 88 w 75 w 84 w 88 w 91 w 79 w 87 w 88 w 79 w Low income .. 65 .. 70 .. 60 70 79 56 69 72 55 Middle income 84 93 90 94 79 92 90 94 81 91 90 80 Lower middle income 83 91 90 93 76 90 89 93 78 89 88 77 Upper middle income 90 101 90 101 90 101 96 98 96 98 95 93 Low & middle income 78 85 85 87 73 83 97 90 92 85 86 75 East Asia & Pacific 101 98 105 98 97 98 99 98 98 98 96 90 Europe & Central Asia 93 98 93 99 92 96 93 99 94 99 99 90 Latin America & Carib. 84 100 84 100 85 101 86 97 76 97 92 90 Middle East & N. Africa 78 90 84 93 72 88 85 93 69 86 82 65 South Asia 62 80 75 83 52 77 71 84 48 74 74 52 Sub-Saharan Africa 51 60 57 65 47 55 71 77 59 67 71 54 High income .. 97 .. 97 .. 97 100 100 99 100 99 99 Euro area 101 .. 100 .. 100 .. .. .. .. .. .. .. a. Provisional data. b. Data are for the most recent year available. c. Includes the Indian-held part of Jammu and Kashmir. d. Data are for 2008. e. Includes Montenegro. 94 2009 World Development Indicators PEOPLE Education completion and outcomes 2.14 About the data Definitions Many governments publish statistics that indi- Institute for Statistics has established literacy as · Primary completion rate is the percentage of stu- cate how their education systems are working and an outcome indicator based on an internationally dents completing the last year of primary school. It is developing--statistics on enrollment and such effi - agreed definition. calculated by taking the total number of students in ciency indicators as repetition rates, pupil-teacher The literacy rate is the percentage of people who the last grade of primary school, minus the number of ratios, and cohort progression. The World Bank and can, with understanding, both read and write a repeaters in that grade, divided by the total number the United Nations Educational, Scientific, and Cul- short, simple statement about their everyday life. In of children of official completing age. · Youth literacy tural Organization (UNESCO) Institute for Statistics practice, literacy is difficult to measure. To estimate rate is the percentage of people ages 15­24 that jointly developed the primary completion rate indica- literacy using such a definition requires census or can, with understanding, both read and write a short, tor. Increasingly used as a core indicator of an educa- survey measurements under controlled conditions. simple statement about their everyday life. · Adult tion system's performance, it reflects an education Many countries estimate the number of literate literacy rate is the literacy rate among people ages system's coverage and the educational attainment people from self-reported data. Some use educa- 15 and older. of students. The indicator is a key measure of edu- tional attainment data as a proxy but apply different cation outcome at the primary level and of progress lengths of school attendance or levels of completion. toward the Millennium Development Goals and the Because definitions and methodologies of data col- Education for All initiative. However, because curri- lection differ across countries, data should be used cula and standards for school completion vary across cautiously. countries, a high primary completion rate does not The reported literacy data are compiled by the necessarily mean high levels of student learning. UNESCO Institute for Statistics based on national The primary completion rate reflects the primary censuses and household surveys during 1985­2007. cycle as defined by the International Standard Clas- For countries that have not reported national esti- sification of Education, ranging from three or four mates, the UNESCO Institute for Statistics derived years of primary education (in a very small number the modeled estimates. For detailed information on of countries) to five or six years (in most countries) sources, definitions, and methodology, consult the and seven (in a small number of countries). original source. The table shows the proxy primary completion rate, Literacy statistics for most countries cover the pop- calculated by subtracting the number of repeaters ulation ages 15 and older, but some include younger in the last grade of primary school from the total ages or are confined to age ranges that tend to inflate number of students in that grade and dividing by the literacy rates. The literacy data in the narrower age total number of children of official graduation age. range of 15­24 better captures the ability of partici- Data limitations preclude adjusting for students who pants in the formal education system and reflects drop out during the final year of primary school. Thus recent progress in education. The youth literacy rate proxy rates should be taken as an upper estimate of reported in the table measures the accumulated out- the actual primary completion rate. comes of primary education over the previous 10 There are many reasons why the primary comple- years or so by indicating the proportion of people who tion rate can exceed 100 percent. The numerator have passed through the primary education system may include late entrants and overage children who and acquired basic literacy and numeracy skills. have repeated one or more grades of primary school as well as children who entered school early, while the denominator is the number of children of official completing age. Other data limitations contribute to completion rates exceeding 100 percent, such as the use of population estimates of varying reliability, the conduct of school and population surveys at dif- ferent times of year, and other discrepancies in the numbers used in the calculation. Basic student outcomes include achievements in reading and mathematics judged against established standards. In many countries national assessments Data sources are enabling the ministry of education to monitor Data on primary completion rates and lit- progress in these outcomes. Internationally compa- eracy rates are from the UNESCO Institute for rable assessments are not yet available, except for Statistics. a few, mostly industrialized, countries. The UNESCO 2009 World Development Indicators 95 2.15 Education gaps by income and gender Survey Gross intake Gross primary Average years Primary Children year rate in grade 1 participation rate of schooling completion rate out of school % of relevant % of relevant % of relevant % of children age group age group Ages 15­24 age group ages 6­11 Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile Male Female quintile quintile Armenia 2000 105 93 177 181 9 11 96 98 96 98 14 13 Bangladesh 2004 193 156 107 120 3 8 26 70 47 58 25 10 Benin 2001 74 112 51 115 1 6 7 45 23 15 66 21 Bolivia 2003 98 95 92 98 6 11 48 90 75 75 24 5 Burkina Faso 2003 24 97 20 98 1 6 8 52 24 20 87 32 Cambodia 2000 146 187 78 134 2 7 4 45 18 17 50 12 Cameroon 2004 115 100 94 122 3 9 12 69 36 37 42 4 Central African Republic 1994­95 103 118 57 130 2 6 0a 18 8 6 65 21 Chad 2004 3 14 15 98 0a 5 1 36 15 8 91 36 Colombia 2005 157 85 126 99 6 11 50 90 70 77 8 1 Comoros 1996 84 119 56 147 2 6 4 29 12 12 72 26 Côte d'Ivoire 1994 26 39 41 103 2 6 6 41 25 17 70 23 Dominican Republic 2002 170 103 149 156 6 11 38 87 57 69 14 4 Egypt, Arab Rep. 2003 87 120 96 103 6 11 58 87 77 71 24 5 Eritrea 1995 55 117 42 154 1 7 3 65 21 24 84 10 Ethiopia 2000 87 257 61 186 1 5 4 44 15 12 87 42 Gabon 2000 .. .. 155 140 5 8 12 60 35 40 8 3 Ghana 2003 90 90 71 108 4 9 15 66 38 41 57 20 Guatemala 1995 114 124 62 122 2 9 9 76 41 40 58 8 Guinea 1999 13 39 10 38 1 5 3 27 18 9 95 77 Haiti 2000 141 200 94 152 3 8 1 40 13 18 64 21 India 1999 99 72 87 122 3 10 31 87 64 55 35 2 Indonesia 2002­03 85 92 103 104 7 11 75 97 86 89 19 6 Jordan 2002 .. .. 101 99 10 12 93 98 97 97 11 9 Kazakhstan 1999 .. .. 125 130 10 11 98 100 98 99 24 18 Kenya 2003 128 123 104 118 5 9 14 57 30 36 24 4 Kyrgyz Republic 1997 .. .. 133 138 10 10 86 88 85 87 21 18 Madagascar 1997 84 87 59 134 2 7 1 47 13 16 60 6 Malawi 2002 180 226 103 126 4 8 10 52 32 14 29 9 Mali 2001 45 89 36 101 1 5 3 37 16 11 75 29 Morocco 2003­04 109 85 98 116 2 9 17 78 47 46 26 2 Mozambique 2003 104 134 79 150 2 5 2 17 8 7 59 13 Namibia 1992 .. .. 138 116 5 8 15 65 25 34 22 9 Nepal 2001 240 249 116 160 3 7 18 59 37 28 33 6 Nicaragua 2001 127 108 79 104 3 10 14 88 47 59 46 5 Niger 1998 11 69 15 77 1 4 8 46 22 13 90 44 Nigeria 2003 77 106 67 111 4 10 16 70 39 37 56 5 Pakistan 1990­91 68 173 45 127 2 8 11 55 32 22 72 13 Paraguay 1990 137 106 103 114 5 10 29 77 49 54 21 10 Peru 2000 114 94 112 109 6 11 41 93 72 72 9 1 Philippines 2003 131 102 103 102 6 11 46 88 67 79 17 2 Rwanda 2000 216 197 100 126 3 6 7 28 14 14 43 23 Tanzania 1999 95 231 63 119 4 7 27 55 34 34 74 27 Uganda 2000­01 145 127 106 120 4 8 7 43 19 21 28 6 Uzbekistan 1996 .. .. 102 114 10 10 84 87 84 86 29 23 Vietnam 2002 121 105 139 127 5 10 58 97 84 84 8 2 Zambia 2001­02 83 119 74 112 4 9 16 79 38 43 61 18 Zimbabwe 1994 138 114 104 109 7 10 36 80 51 57 22 8 a. Less than 0.5. 96 2009 World Development Indicators PEOPLE Education gaps by income and gender 2.15 About the data Definitions The data in the table describe basic information on collected was assigned a weight generated through · Survey year is the year in which the underlying data school participation and educational attainment by principal-component analysis. The resulting scores were collected. · Gross intake rate in grade 1 is individuals in different socioeconomic groups within were standardized to a standard normal distribution the number of students in the first grade of primary countries. The data are from Demographic and with a mean of zero and a standard deviation of one. education regardless of age as a percentage of the Health Surveys conducted by Macro International The standardized scores were then used to create population of the official primary school entrance with the support of the U.S. Agency for International break-points defining wealth quintiles, expressed as age. These data may differ from those in table 2.13. Development. These large-scale household sample quintiles of individuals in the population. · Gross primary participation rate is the ratio of surveys, conducted periodically in developing coun- The selection of the asset index for defining socio- total students attending primary school regardless tries, collect information on a large number of health, economic status was based on pragmatic rather than of age to the population of the age group that offi - nutrition, and population measures as well as on conceptual considerations: Demographic and Health cially corresponds to primary education. · Average respondents' social, demographic, and economic Surveys do not collect income or consumption data years of schooling are the years of formal school- characteristics using a standard set of question- but do have detailed information on households' own- ing received, on average, by youths and adults ages naires. The data presented here draw on responses ership of consumer goods and access to a variety 15­24. · Primary completion rate is the percentage to individual and household questionnaires. of goods and services. Like income or consumption, of children of the official primary school completing Typically, Demographic and Health Surveys collect the asset index defines disparities primarily in eco- age to the official primary school completing age plus basic information on educational attainment and nomic terms. It therefore excludes other possibilities four who have completed the last year of primary enrollment levels from every household member of disparities among groups, such as those based school or higher. These data differ from those in ages 5 or 6 and older as background characteris- on gender, education, ethnic background, or other table 2.14 because the definition and methodology tics. As the surveys are intended for the collection of facets of social exclusion. To that extent the index are different. · Children out of school are the per- demographic and health information, the education provides only a partial view of the multidimensional centage of children ages 6­11 who are not in school. section of the survey is not as robust and detailed concepts of poverty, inequality, and inequity. These data differ from those in table 2.12 because as the health section; however, it still provides useful Creating one index that includes all asset indica- the definition and methodology are different. micro-level information on education that cannot be tors limits the types of analysis that can be per- explained by aggregate national-level data. formed. In particular, the use of a unified index does Socioeconomic status as displayed in the table not permit a disaggregated analysis to examine is based on a household's assets, including owner- which asset indicators have a more or less important ship of consumer items, features of the household's association with education status. In addition, some dwelling, and other characteristics related to wealth. asset indicators may reflect household wealth better Each household asset on which information was in some countries than in others--or reflect differ- ent degrees of wealth in different countries. Taking There is a large gap in educational such information into account and creating country- attainment across gender and specific asset indexes with country-specific choices urban-rural lines 2.15a of asset indicators might produce a more effective Highest level of education, Liberia, 2007 (%) and accurate index for each country. The asset index 75 used in the table does not have this flexibility. Urban men Rural men The analysis was carried out for 48 countries. The Urban women Rural women table shows the estimates for the poorest and rich- est quintiles only; the full set of estimates for 32 indi- 50 cators is available in the country reports (see Data sources). The data in the table differ from data for similar indicators in preceding tables either because 25 the indicator refers to a period a few years preceding the survey date or because the indicator definition or methodology is different. Findings should be used 0 with caution because of measurement error inherent No education Primary Secondary or higher Data sources in the use of survey data. Rural women are the most disadvantaged in Data on education gaps by income and gender are Liberia, with more than 55 percent having no from an analysis of Demographic and Health Sur- education and only 10 percent having second- veys by Macro International and the World Bank. ary or higher education. Country reports are available at www.worldbank. Source: Demographic and Health Surveys. org/education/edstats/. 2009 World Development Indicators 97 2.16 Health systems Health Year last Health workers Year Hospital Outpatient expenditure national of last beds visits health health account surveya completed per 1,000 people Total Public Out of pocket Per capita Nurses and per 1,000 % of GDP % of total % of private $ PPP$ Physicians midwives people per capita 2006 2006 2006 2006 2006 2002­07b 2002­07b 2002­07b 2000­07b Afghanistan 9.2 32.4 78.5 .. .. .. .. 2003 .. .. Albania 6.5 37.3 94.9 187 1,332 2003 1.2 4.1 2005 3.0 1.5 Algeria 4.2 81.1 94.6 148 .. 2001 1.1 2.2 2006 1.7 .. Angola 2.6 86.8 100.0 71 187 0.1 1.4 2001 0.8 .. Argentina 10.1 45.5 43.8 551 2,723 1997 .. .. .. .. Armenia 4.7 41.2 87.6 98 763 2007 3.7 4.9 2005 4.4 2.8 Australia 8.7 67.7 56.4 3,302 4,152 2005 2.5 .. 4.0 6.2 Austria 10.2 75.9 65.8 3,974 5,424 2004 3.7 6.6 7.6 6.7 Azerbaijan 4.1 26.1 86.4 102 1,031 3.6 8.4 2006 8.0 4.6 Bangladesh 3.2 31.8 88.3 12 119 2005 0.3 0.3 2006 0.3 .. Belarus 6.4 74.9 68.8 243 1,997 4.8 12.5 2005 11.3 13.2 Belgium 9.9 72.5 79.0 3,726 4,821 2006 4.2 14.2 5.3 7.0 Benin 4.7 50.2 94.9 26 154 2003 0.0 c 0.8 2006 0.5 .. Bolivia 6.4 62.8 81.0 79 699 2007 .. .. 2003 1.1 .. Bosnia and Herzegovina 9.5 55.2 100.0 296 1,102 2006 1.4 4.7 2006 3.0 3.3 Botswana 7.1 76.5 27.5 379 1,054 2003 0.4 2.7 2000 2.4 .. Brazil 7.5 47.9 63.8 427 1,460 2005 .. .. 1996 2.4 .. Bulgaria 7.2 56.7 97.1 297 1,709 2006 3.7 4.6 6.4 .. Burkina Faso 6.3 56.9 91.5 27 161 2005 0.1 0.5 2003 0.9 .. Burundi 8.7 8.6 57.4 10 92 0.0 c 0.2 2000 0.7 .. Cambodia 5.9 26.0 84.7 30 437 .. .. 2005 0.1 .. Cameroon 4.6 21.2 94.8 45 202 1995 0.2 1.6 2006 1.5 .. Canada 10.0 70.4 49.0 3,917 4,651 2007 1.9 10.1 3.4 6.3 Central African Republic 4.0 38.3 95.0 14 56 0.1 0.4 2006 1.2 .. Chad 4.9 53.9 96.2 29 379 0.0 c 0.3 2004 0.4 .. Chile 5.3 52.7 54.8 473 1,290 2006 1.1 0.6 2.3 .. China 4.6 40.7 83.1 94 1,124 2006 1.5 1.0 2006 2.2 .. Hong Kong, China .. .. .. .. .. .. .. .. .. Colombia 7.3 85.4 43.9 217 989 2003 1.4d 0.6 2005 1.0 .. Congo, Dem. Rep. 6.8 18.7 48.9 10 47 0.1 0.5 2007 0.8 .. Congo, Rep. 2.1 71.7 100.0 44 188 0.2 1.0 2005 1.6 .. Costa Rica 7.7 68.4 86.7 402 .. 2003 .. .. 1993 1.3 .. Côte d'Ivoire 3.8 23.6 87.8 35 .. 0.1 0.6 2006 0.4 .. Croatia 8.2 76.8e 92.2 996e 2,101 2.7 5.5 5.3 6.4 Cuba 7.7 91.6 93.3 362 .. 5.9 7.4 2006 4.9 .. Czech Republic 6.9 88.0 95.5 953 3,270 2006 3.6 8.9 1993 8.2 15.0 Denmark 10.8 85.9 90.1 5,447 5,165 2006 3.6 10.1 3.8 4.1 Dominican Republic 5.6 37.0 64.3 206 .. 2002 .. .. 2007 1.0 .. Ecuador 5.3 43.6 85.6 166 928 2005 .. .. 2004 1.7 .. Egypt, Arab Rep. 6.3 41.4 94.9 92 1,174 2002 2.4 3.4 2005 2.1 .. El Salvador 6.6 61.8 88.9 181 .. 2007 1.5 0.8 2002/03 0.7 .. Eritrea 3.6 45.9 100.0 8 .. 0.1 0.6 2002 1.2 .. Estonia 5.2 73.3 93.3 632 2,043 2006 3.3 7.0 5.7 6.9 Ethiopia 3.9 59.3 80.6 7 77 2005 0.0 c 0.2 2005 0.2 .. Finland 8.2 76.0 77.6 3,232 3,607 2006 3.3 8.9 6.8 4.3 France 11.0 79.7 33.2 3,937 5,189 2006 3.4 8.0 7.3 6.9 Gabon 4.5 73.0 100.0 351 1,338 0.3 5.0 2000 2.0 .. Gambia, The 5.0 56.8 71.2 15 142 2004 0.1 1.3 2005/06 0.8 .. Georgia 8.4 21.5 91.9 147 1,063 2007 4.7 4.0 2005 3.3 2.2 Germany 10.6 76.9 57.1 3,718 5,210 2006 3.4 8.0 8.3 7.0 Ghana 5.1 34.2 77.8 33 214 2002 0.2 0.9 2006 0.9 .. Greece 9.5 62.0 94.8 2,280 3,745 5.0 3.6 4.8 .. Guatemala 5.8 28.7 92.5 157 .. 2007 .. .. 2002 0.7 .. Guinea 5.8 14.1 99.5 20 105 0.1 0.5 2005 0.3 .. Guinea-Bissau 5.8 26.3 55.8 12 69 0.1 0.7 2006 0.7 .. Haiti 8.4 67.6 89.6 42 .. 2006 .. .. 2005 1.3 .. 98 2009 World Development Indicators PEOPLE Health Health systems Year last Health workers Year 2.16 Hospital Outpatient expenditure national of last beds visits health health account surveya completed per 1,000 people Total Public Out of pocket Per capita Nurses and per 1,000 % of GDP % of total % of private $ PPP$ Physicians midwives people per capita 2006 2006 2006 2006 2006 2002­07b 2002­07b 2002­07b 2000­07b Honduras 6.4 47.8 87.1 99 .. 2005 .. .. 2005 1.0 .. Hungary 8.3 70.9 77.6 929 2,761 2006 3.0 9.2 7.1 12.9 India 3.6 25.0 91.4 29 426 2001 0.6 1.3 2005/06 0.9 .. Indonesia 2.5 50.5 70.4 39 213 2004 0.1 0.8 2002/03 0.6 .. Iran, Islamic Rep. 6.8 50.7 94.8 215 3,057 2001 0.9 1.6 2000 1.7 .. Iraq 3.5f 78.1f 100.0 f .. .. .. .. 2006 .. .. Ireland 7.5 78.3 57.2 3,871 4,270 2.9 19.5 5.6 .. Israel 8.0 56.0 75.3 1,675 3,028 3.7 6.2 6.0 7.1 Italy 9.0 77.2 88.5 2,813 3,190 3.7 7.2 3.9 6.1 Jamaica 4.7 53.1 63.7 180 .. 2006 0.9 1.7 2005 2.0 .. Japan 8.1 81.3 80.8 2,759 4,693 2006 2.1 9.5 14.0 14.4 Jordan 9.7g 43.3g 75.9 238g 988g 2001 2.4 3.2 2007 1.9 .. Kazakhstan 3.6 64.3 98.4 190 1,608 2007 3.9 7.6 2006 8.1 6.6 Kenya 4.6 47.8 80.0 29 205 2002 0.1 1.2 2004 1.4 .. Korea, Dem. Rep. 3.5 85.6 100.0 .. .. 3.3 4.1 2000 13.2 .. Korea, Rep. 6.4 55.7 81.0 1,168 3,341 2007 1.6 1.9 8.6 .. Kuwait 2.2 78.2 91.6 803 1,614 1.8 3.7 1996 1.9 .. Kyrgyz Republic 6.4 43.0 94.1 35 524 2006 2.4 5.8 2005/06 4.9 3.6 Lao PDR 4.0 18.6 76.1 24 401 0.4 1.0 2006 1.2 .. Latvia 6.6 59.2 97.2 582 2,320 2005 3.1 5.6 7.5 5.5 Lebanon 8.8 44.3 76.1 494 1,694 2005 2.4 1.3 2000 3.4 .. Lesotho 6.8 58.9 68.9 51 403 0.1 0.6 2004 1.3 .. Liberia 4.8 25.8 65.7 7 46 0.0 c 0.3 2007 .. .. Libya 2.4 66.3 100.0 219 .. 1.3 4.8 2000 3.7 .. Lithuania 6.2 70.0 98.3 547 2,046 2006 4.0 7.7 8.1 6.6 Macedonia, FYR 8.0 70.6 100.0 249 1,522 2.6 4.3 2005 4.6 6.0 Madagascar 3.2 62.8 52.5 9 60 2003 0.3 0.3 2003/04 0.3 0.5 Malawi 12.9 69.0 28.4 21 114 2006 0.0 c 0.6 2006 1.1 .. Malaysia 4.3 44.6 73.2 259 1,518 2006 0.7 1.8 1.8 .. Mali 5.8 49.6 99.5 31 152 2004 0.1 0.6 2006 0.3 .. Mauritania 2.2 69.5 100.0 19 128 0.1 0.6 2000/01 0.4 .. Mauritius 3.9 51.1 80.6 230 998 2004 1.1 3.7 3.0 .. Mexico 6.6 44.2 93.9 527 1,208 2006 1.5 .. 1995 1.6 2.5 Moldova 9.4 46.9 97.7 90 862 2.7 6.2 2005 5.2 6.0 Mongolia 5.7 73.7 44.0 70 984 2003 2.6 3.5 2005 6.1 .. Morocco 5.3 26.2 77.3 113 288 2001 0.5 0.8 2003/04 0.9 .. Mozambique 5.0 70.8 40.6 16 75 1997 0.0 c 0.3 2003 0.8 .. Myanmar 2.2 13.1 99.4 5 .. 2001 0.4 1.0 2000 0.6 .. Namibia 8.7 43.5 5.7 281 957 2000 0.3 3.1 2006/07 3.3 .. Nepal 5.1 30.5 85.2 17 215 2003 0.2 0.5 2006 0.2 .. Netherlands 9.4 80.0 29.3 3,872 5,520 2007 3.7 14.6 4.8 5.4 New Zealand 9.3 77.8 74.6 2,421 3,370 2006 2.2 8.9 6.2 4.4 Nicaragua 9.6 48.2 98.1 92 .. 2004 0.4 1.1 2001 1.0 .. Niger 5.9 54.7 96.5 16 78 2004 0.0 c 0.2 2006 0.3 .. Nigeria 3.8 29.7 90.4 33 184 2002 0.3 1.7 2007 0.5 .. Norway 8.7 83.6 95.2 6,267 5,952 2005 3.8 16.2 4.0 .. Oman 2.3 82.3 57.7 332 906 1998 1.7 3.7 1995 2.0 .. Pakistan 2.0 16.4 97.9 16 187 0.8 0.5 2006/07 1.0 .. Panama 7.3 68.8 80.6 380 .. 2003 .. .. 2003 2.2 .. Papua New Guinea 3.2 82.0 41.5 29 .. 2000 .. .. 1996 .. .. Paraguay 7.6 38.3 87.7 117 777 2005 1.1 1.8 2004 1.3 .. Peru 4.4 58.3 77.5 149 587 2005 .. .. 2004 1.2 .. Philippines 3.8 32.9 83.5 52 314 2007 1.2 6.1 2003 1.1 .. Poland 6.2 70.0 85.4 555 2,031 2006 2.0 5.2 5.2 6.1 Portugal 10.2 70.5 77.3 1,864 3,014 2006 3.4 4.6 3.5 3.9 Puerto Rico .. .. .. .. .. .. .. 1995/96 .. .. 2009 World Development Indicators 99 2.16 Health systems Health Year last Health workers Year Hospital Outpatient expenditure national of last beds visits health health account surveya completed per 1,000 people Total Public Out of pocket Per capita Nurses and per 1,000 % of GDP % of total % of private $ PPP$ Physicians midwives people per capita 2006 2006 2006 2006 2006 2002­07b 2002­07b 2002­07b 2000­07b Romania 4.5 76.9 96.8 256 1,244 2006 1.9 4.2 1999 6.5 5.6 Russian Federation 5.3 63.2 81.5 367 2,217 2007 4.3 8.5 1996 9.7 9.0 Rwanda 10.9 42.5 38.6 33 270 2006 0.1 0.4 2005 1.6 .. Saudi Arabia 3.3 77.0 13.4 492 1,386 1.4 3.0 2007 2.2 .. Senegal 5.8 56.9 77.0 44 199 2005 0.1 0.3 2005 0.1 .. Serbia 8.2 69.7 87.9 336 1,717 2005 2.0 4.3 2005-06 4.1 .. Sierra Leone 4.0 36.4 56.4 12 88 0.0 c 0.5 2005 0.4 .. Singapore 3.3 33.1 93.8 1,017 3,037 1.5 4.4 2005 3.2 .. Slovak Republic 7.1 70.6 79.8 735 2,788 2006 3.1 6.6 6.8 12.5 Slovenia 8.4 72.2 42.5 1,607 3,230 2006 2.4 8.0 4.8 6.6 Somalia .. .. .. .. .. .. .. 2006 .. .. South Africa 8.0 37.7 17.5 425 1,100 1998 0.8 4.1 1998 2.8 .. Spain 8.4 71.2 74.7 2,328 3,935 2006 3.3 7.6 3.4 9.5 Sri Lanka 4.2 47.5 86.7 62 677 2006 0.6 1.7 1987 3.1 .. Sudan 3.8 36.8 100.0 37 167 0.3 0.9 2006 0.7 .. Swaziland 6.3 65.8 41.4 155 1,420 0.2 6.3 2000 2.1 .. Sweden 9.2 81.7 87.9 3,973 4,588 2006 3.3 10.9 .. 2.8 Switzerland 10.8 59.1 75.3 5,660 5,446 2007 4.0 .. 5.5 .. Syrian Arab Republic 3.9 47.8 100.0 66 482 0.5 1.4 2006 1.5 .. Tajikistan 5.0 23.8e 96.6 25e 455 2.0 5.0 2005 5.4 d 8.3d Tanzania 6.4 57.8 54.3 23 324 2006 0.0 c 0.4 2006 1.1 .. Thailand 3.5 64.5 76.6 113 825 2006 .. .. 2005/06 2.2 .. Timor-Leste 17.7 86.0 37.2 52 .. 0.1 2.2 .. .. Togo 6.0 21.2 84.2 21 67 2002 0.0 c 0.4 2006 0.9 .. Trinidad and Tobago 4.4 56.5 88.0 600 .. 2000 .. 1.8 2006 2.7 .. Tunisia 5.1 44.2 81.7 156 624 2005 1.3 2.9 2006 1.8 .. Turkey 4.8 72.5 84.2 352 866 2005 1.6 2.9 2003 2.7 4.6 Turkmenistan 3.8 66.5 100.0 146 .. 2.5 4.7 2006 4.3 3.7 Uganda 7.0 25.4 51.0 24 165 2001 0.1 0.7 2006 1.1 .. Ukraine 6.9 55.4 88.8 160 1,327 2004 3.1 8.5 2007 8.7 10.8 United Arab Emirates 2.5 70.4 69.4 1,018 .. 1.7 3.5 1.9 .. United Kingdom 8.2 87.3 91.7 3,332 4,259 2000 2.2 .. 3.9 4.9 United States 15.3 45.8 23.5 6,719 6,719 2007 2.3 .. monthly 3.1 9.0 Uruguay 8.2 43.5 31.1 476 1,616 2006 3.7 0.9 2.9 .. Uzbekistan 4.7 50.2 97.1 30 .. 2.7 10.9 2006 4.7 8.7 Venezuela, R.B. 4.9 49.5 88.6 332 .. .. .. 2000 0.9 .. Vietnam 6.6 32.3 90.2 46 658 2006 0.6 0.8 2006 2.7 .. West Bank and Gaza .. .. .. .. .. .. .. 2006 .. .. Yemen, Rep. 4.5 46.0 95.2 40 367 2003 0.3 0.7 2006 0.7 .. Zambia 6.2 60.7 67.2 58 244 2006 0.1 2.0 2005 2.0 .. Zimbabwe 9.3 48.7 50.3 38 .. 2001 0.2 0.7 2005/06 3.0 .. World 9.8 w 60.0 w 43.5 w 722 w 1,466 w .. w .. w 2.6 w .. w Low income 4.3 36.8 84.6 23 205 .. .. .. .. Middle income 5.4 50.1 77.6 140 945 .. .. 2.3 .. Lower middle income 4.5 44.1 85.0 75 794 .. .. 1.8 .. Upper middle income 6.3 54.7 70.7 412 1,581 .. .. 5.0 .. Low & middle income 5.3 49.5 78.0 114 789 .. .. 1.7 .. East Asia & Pacific 4.3 42.1 82.1 83 939 1.5 1.0 2.2 .. Europe & Central Asia 5.5 66.2 85.6 304 1,631 3.2 6.7 7.1 7.5 Latin America & Carib. 6.9 50.0 72.2 374 1,355 .. .. .. .. Middle East & N. Africa 5.7 51.3 90.5 133 1,364 .. .. .. .. South Asia 3.5 25.8 91.4 26 368 0.6 1.3 0.9 .. Sub-Saharan Africa 5.7 41.6 46.8 53 224 .. .. .. .. High income 11.2 61.6 36.5 4,033 4,969 2.6 .. 6.1 8.6 Euro area 9.9 76.9 60.0 3,268 4,460 3.5 .. 5.7 6.8 a. Survey name can be found in Primary data documentation. b. Data are for the most recent year available. c. Less than 0.05. d. Data are for 2008. e. Data are for 2007. f. Excludes northern Iraq. g. Includes contributions from the United Nations Relief and Works Agency for Palestine Refugees. 100 2009 World Development Indicators PEOPLE Health systems 2.16 About the data Health systems--the combined arrangements of expenditures. In general, low-income economies international agencies and nongovernmental organi- institutions and actions whose primary purpose is to have a higher share of private health expenditure zations), and social (or compulsory) health insurance promote, restore, or maintain health (WHO 2000)-- than do middle- and high-income countries. High funds. · Out-of-pocket health expenditure, part of are increasingly being recognized as key to combating out-of-pocket expenditures may discourage people private health expenditure, is direct household out- disease and improving the health status of popula- from accessing preventive or curative care and can lays, including gratuities and in-kind payments, for tions. The World Bank's 2007 "Healthy Development: impoverish households that cannot afford needed health practitioners and pharmaceutical suppliers, Strategy for Health, Nutrition, and Population care. Health financing data are collected through therapeutic appliances, and other goods and ser- Results" emphasizes the need to strengthen health national health accounts, which systematically, vices whose primary intent is to restore or enhance systems, which are weak in many countries, in order comprehensively, and consistently monitoring health health. · Health expenditure per capita is total to increase the effectiveness of programs aimed at system resource flows. To establish a national health health expenditure divided by population in U.S. reducing specific diseases and further reduce mor- account, countries must define the boundaries of the dollars and in international dollars converted using bidity and mortality (World Bank 2007c). To evaluate health system and classify health expenditure infor- 2005 purchasing power parity (PPP) rates for health health systems, the World Health Organization (WHO) mation along several dimensions, including sources expenditure. · Year last national health account has recommended that key components--such as of financing, providers of health services, functional completed is the latest year for which the health financing, service delivery, workforce, information, use of health expenditures, and benefi ciaries of expenditure data are available using the national and governance--be monitored using several key expenditures. The accounting system can then pro- health account approach. · Physicians include gen- indicators (WHO 2008a). The data in the table are vide an accurate picture of resource envelopes and eralist and specialist medical practitioners.· Nurses a subset of these indicators. Monitoring health sys- financial flows and allow analysis of the equity and and midwives include professional nurses and tems allows the effectiveness, efficiency, and equity efficiency of financing to inform policy. midwives, auxiliary nurses and midwives, enrolled of different health system models to be compared. Many low-income countries use Demographic and nurses and midwives, and other personnel, such as Health system data also help identify weaknesses Health Surveys or Multiple Indicator Cluster Surveys dental nurses and primary care nurses. · Year of and strengths and areas that need investment, such funded by donors to obtain health system data. Data last health survey is the latest year the national sur- as additional health facilities, better health informa- on health worker (physicians, nurses, and midwives) vey that collects health information was conducted. tion systems, or better trained human resources. density shows the availability of medical personnel. · Hospital beds are inpatient beds for both acute Health expenditure data are broken down into The WHO estimates that at least 2.5 physicians, and chronic care available in public, private, general, public and private expenditures, with private expen- nurses, and midwives per 1,000 people are needed and specialized hospitals and rehabilitation centers. diture further broken down into out-of-pocket expen- to provide adequate coverage with primary care inter- · Outpatient visits per capita are the number of diture (direct payments by households to providers), ventions associated with achieving the Millennium visits to health care facilities per capita, including which make up the largest proportion of private Development Goals (WHO 2006). The WHO compiles repeat visits. data from household and labor force surveys, cen- There is a wide gap in health suses, and administrative records. Data comparabil- expenditure per capita between ity is limited by differences in definitions and train- high-income economies and ing of medical personnel varies. In addition, human developing economies 2.16a Data sources resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of Data on health expenditures and year last national Health expenditure per capita ($) 2002 2006 5,000 health personnel available to the entire population. health account completed are mostly from the Availability and use of health services, shown by WHO's National Health Account database (www. 4,000 hospital beds per 1,000 people and outpatient visits who.int/nha/en), supplemented by country data. per capita, reflect both demand and supply side fac- Data on health expenditure per capita in current 3,000 tors. In the absence of a consistent definition these dollars are from WHO's National Health Account are crude indicators of the extent of physical, finan- database. Data on health expenditure per capita 2,000 cial, and other barriers to health care. in PPP dollars are World Bank staff estimates 1,000 based on the WHO's National Health Account Definitions database and the 2005 round of the International 0 · Total health expenditure is the sum of public and Comparison Program. Data on physicians, nurses High income Middle income Low income private health expenditure. It covers the provision of and midwives, hospital beds, and outpatient vis- Health expenditure per capita by high-income health services (preventive and curative), family plan- its are from the WHO, OECD, and TransMONEE, economies is 300 times more than that by ning and nutrition activities, and emergency aid for supplemented by country data. Information on developing economies, and the gap has been health but excludes provision of water and sanitation. health survey is from various sources including increasing. · Public health expenditure is recurrent and capital Macro International and the United Nations Chil- Source: World Health Organization. spending from central and local governments, exter- dren's Fund. nal borrowing and grants (including donations from 2009 World Development Indicators 101 2.17 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS an improved improved immunization with acute diarrhea who sleeping with fever treatment detection water source sanitation rate respiratory received oral under receiving success rate facilities infection rehydration treated antimalarial rate taken to and continuous bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of new % of new % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2006 1990 2006 2007 2007 2002­07c 2002­07c 2002­07c 2002­07c 2006 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. 84 64 Albania .. 97 .. 97 97 98 45 50 .. .. 93 54 Algeria 94 85 88 94 92 95 53 24 .. .. 91 98 Angola 39 51 26 50 88 83 .. .. 17.7 29.3 18 102 Argentina 94 96 81 91 99 96 .. .. .. .. 63 76 Armenia .. 98 .. 91 92 88 36 59 .. .. 69 51 Australia 100 100 100 100 94 92 .. .. .. .. 85 49 Austria 100 100 100 100 79 85 .. .. .. .. 71 41 Azerbaijan 68 78 .. 80 97 95 33 45 .. .. 60 46 Bangladesh 78 80 26 36 88 90 30 49 .. .. 92 66 Belarus 100 100 .. 93 99 95 90 54 .. .. 70 40 Belgium .. .. .. .. 92 99 .. .. .. .. 73 58 Benin 63 65 12 30 61 67 36 42 20.1 54.0 87 86 Bolivia 72 86 33 43 81 81 52 54 .. .. 83 71 Bosnia and Herzegovina 97 99 .. 95 96 95 91 53 .. .. 97 81 Botswana 93 96 38 47 90 97 .. .. .. .. 72 57 Brazil 83 91 71 77 99 98 .. .. .. .. 72 69 Bulgaria 99 99 99 99 96 95 .. .. .. .. 80 81 Burkina Faso 34 72 5 13 94 99 39 42 9.6 48.0 73 18 Burundi 70 71 44 41 75 74 38 23 8.3 30.0 83 27 Cambodia 19 65 8 28 79 82 48 50 4.2 0.2 93 61 Cameroon 49 70 39 51 74 82 35 22 13.1 57.8 74 91 Canada 100 100 100 100 94 94 .. .. .. .. 57 62 Central African Republic 58 66 11 31 62 54 32 47 15.1 57.0 65 71 Chad .. 48 5 9 23 20 12 27 0.6 44.0 54 18 Chile 91 95 84 94 91 94 .. .. .. .. 85 105 China 67 88 48 65 94 93 .. .. .. .. 94 80 Hong Kong, China .. .. .. .. .. .. .. .. .. .. 78 60 Colombia 89 93 68 78 95 93 62 39 .. .. 71 81 Congo, Dem. Rep. 43 46 15 31 79 87 42 .. 5.8 29.8 86 61 Congo, Rep. .. 71 .. 20 67 80 48 39 6.1 48.0 53 56 Costa Rica .. 98 94 96 90 89 .. .. .. .. 88 120 Côte d'Ivoire 67 81 20 24 67 76 35 45 3.0 36.0 73 42 Croatia 99 99 99 99 96 96 .. .. .. .. 30 46 Cuba .. 91 98 98 99 93 .. .. .. .. 90 109 Czech Republic 100 100 100 99 97 99 .. .. .. .. 69 67 Denmark 100 100 100 100 89 75 .. .. .. .. 77 69 Dominican Republic 84 95 68 79 96 79 67 42 .. .. 78 66 Ecuador 73 95 71 84 99 99 .. .. .. .. 74 46 Egypt, Arab Rep. 94 98 50 66 97 98 63 27 .. .. 87 72 El Salvador 69 84 73 86 98 96 62 .. .. .. 91 65 Eritrea 43 60 3 5 95 97 44 54 4.2 3.6 90 35 Estonia 100 100 95 95 96 95 .. .. .. .. 68 76 Ethiopia 13 42 4 11 65 73 19 15 33.1 9.5 84 28 Finland 100 100 100 100 98 99 .. .. .. .. .. 0 France .. 100 .. .. 87 98 .. .. .. .. .. 0 Gabon .. 87 .. 36 55 38 .. .. .. .. 46 66 Gambia, The .. 86 .. 52 85 90 69 38 49.0 62.6 58 64 Georgia 76 99 94 93 97 98 74 37 .. .. 75 113 Germany 100 100 100 100 94 97 .. .. .. .. 71 54 Ghana 56 80 6 10 95 94 34 29 21.8 60.8 76 36 Greece 96 100 97 98 88 88 .. .. .. .. .. 0 Guatemala 79 96 70 84 93 82 64 22 .. .. 47 40 Guinea 45 70 13 19 71 75 42 38 1.4 43.5 75 53 Guinea-Bissau .. 57 .. 33 76 63 57 25 39.0 45.7 69 68 Haiti 52 58 29 19 58 53 31 43 .. 5.1 82 49 102 2009 World Development Indicators PEOPLE Disease prevention coverage and quality Access to Access to Child Children Children with Children Children 2.17 Tuberculosis DOTS an improved improved immunization with acute diarrhea who sleeping with fever treatment detection water source sanitation rate respiratory received oral under receiving success rate facilities infection rehydration treated antimalarial rate taken to and continuous bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of new % of new % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2006 1990 2006 2007 2007 2002­07c 2002­07c 2002­07c 2002­07c 2006 2007 Honduras 72 84 45 66 89 86 56 49 .. 0.5 86 87 Hungary 96 100 100 100 99 99 .. .. .. .. 46 51 India 71 89 14 28 67 62 69 33 .. 8.2 86 68 Indonesia 72 80 51 52 80 75 61 56 .. .. 91 91 Iran, Islamic Rep. 92 .. 83 .. 97 99 .. .. .. .. 83 68 Iraq 83 .. .. .. .. .. .. .. .. .. 84 37 Ireland .. .. .. .. 87 92 .. .. .. .. .. 0 Israel 100 100 .. .. 97 96 .. .. .. .. 74 61 Italy .. .. .. .. 87 96 .. .. .. .. 67 0 Jamaica 92 93 83 83 76 85 75 39 .. .. 41 83 Japan 100 100 100 100 98 98 .. .. .. .. 53 77 Jordan 97 98 .. 85 95 98 75 44 .. .. 71 81 Kazakhstan 96 96 97 97 99 93 71 48 .. .. 72 69 Kenya 41 57 39 42 80 81 49 33 6.0 26.5 85 72 Korea, Dem. Rep. .. 100 .. .. 99 92 93 .. .. .. 86 64 Korea, Rep. .. .. .. .. 92 91 .. .. .. .. 81 14 Kuwait .. .. .. .. 99 99 .. .. .. .. 78 90 Kyrgyz Republic .. 89 .. 93 99 94 62 22 .. .. 82 60 Lao PDR .. 60 .. 48 40 50 .. .. .. .. 92 78 Latvia 99 99 .. 78 97 98 .. .. .. .. 73 89 Lebanon 100 100 .. .. 53 74 .. .. .. .. 90 62 Lesotho .. 78 .. 36 85 83 59 53 .. .. 66 16 Liberia 57 64 40 32 95 88 70 .. .. 58.5 76 69 Libya 71 .. 97 97 98 98 .. .. .. .. 77 162 Lithuania .. .. .. .. 97 95 .. .. .. .. 74 90 Macedonia, FYR .. 100 .. 89 96 95 93 45 .. .. 87 74 Madagascar 39 47 8 12 81 82 48 47 0.2 34.2 78 69 Malawi 41 76 46 60 83 87 52 27 24.7 24.9 78 41 Malaysia 98 99 .. 94 90 96 .. .. .. .. 48 80 Mali 33 60 35 45 68 68 38 38 27.1 31.7 76 23 Mauritania 37 60 20 24 67 75 45 .. .. 20.7 41 39 Mauritius 100 100 94 94 98 97 .. .. .. .. 92 69 Mexico 88 95 56 81 96 98 .. .. .. .. 80 99 Moldova .. 90 .. 79 96 92 60 48 .. .. 62 67 Mongolia 64 72 .. 50 98 95 63 47 .. .. 88 76 Morocco 75 83 52 72 95 95 38 46 .. .. 87 93 Mozambique 36 42 20 31 77 72 55 47 .. 14.9 83 49 Myanmar 57 80 23 82 81 86 66 65 .. .. 84 116 Namibia 57 93 26 35 69 86 72 .. 10.5 9.8 76 84 Nepal 72 89 9 27 81 82 43 37 .. 0.1 88 66 Netherlands 100 100 100 100 96 96 .. .. .. .. 84 11 New Zealand 97 .. .. .. 79 88 .. .. .. .. 70 60 Nicaragua 70 79 42 48 99 87 .. .. .. .. 89 97 Niger 41 42 3 7 47 39 47 34 7.4 33.0 77 53 Nigeria 50 47 26 30 62 54 33 28 1.2 33.9 76 23 Norway 100 100 .. .. 92 93 .. .. .. .. 93 33 Oman 81 .. 85 .. 97 99 .. .. .. .. 86 125 Pakistan 86 90 33 58 80 83 69 37 .. 3.3 88 67 Panama .. 92 .. 74 89 88 .. .. .. .. 79 98 Papua New Guinea 39 40 44 45 58 60 .. .. .. .. 73 15 Paraguay 52 77 60 70 80 66 .. .. .. .. 83 58 Peru 75 84 55 72 99 80 67 71 .. .. 78 93 Philippines 83 93 58 78 92 87 55 76 .. 0.2 88 75 Poland .. .. .. .. 98 99 .. .. .. .. 75 66 Portugal 96 99 92 99 95 97 .. .. .. .. 87 87 Puerto Rico .. .. .. .. .. .. .. .. .. .. 80 77 2009 World Development Indicators 103 2.17 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS an improved improved immunization with acute diarrhea who sleeping with fever treatment detection water source sanitation rate respiratory received oral under receiving success rate facilities infection rehydration treated antimalarial rate taken to and continuous bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of new % of new % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2006 1990 2006 2007 2007 2002­07c 2002­07c 2002­07c 2002­07c 2006 2007 Romania 76 88 72 72 97 97 .. .. .. .. 83 85 Russian Federation 94 97 87 87 99 98 .. .. .. .. 58 49 Rwanda 65 65 29 23 99 97 28d 24 13.0 12.3 86 25 Saudi Arabia 94 96 91 99 96 96 .. .. .. .. 69 39 Senegal 67 77 26 28 84 94 47 43 16.4 22.0 76 48 Serbia .. 99e .. 92e 95 94 93 31 .. .. 84 80 Sierra Leone .. 53 .. 11 67 64 48 31 5.3 51.9 87 37 Singapore 100 100 100 100 95 96 .. .. .. .. 84 96 Slovak Republic 100 100 100 100 99 99 .. .. .. .. 81 44 Slovenia .. .. .. .. 96 97 .. .. .. .. 92 77 Somalia .. 29 .. 23 34 39 13 7 11.4 7.9 89 64 South Africa 81 93 55 59 83 97 .. .. .. .. 74 78 Spain 100 100 100 100 97 96 .. .. .. .. .. 0 Sri Lanka 67 82 71 86 98 98 58 .. 2.9 0.3 87 85 Sudan 64 70 33 35 79 84 .. 56 27.6 54.2 82 31 Swaziland .. 60 .. 50 91 95 73 22 0.6 0.6 43 55 Sweden 100 100 100 100 96 99 .. .. .. .. 63 0 Switzerland 100 100 100 100 86 93 .. .. .. .. .. 0 Syrian Arab Republic 83 89 81 92 98 99 77 34 .. .. 86 80 Tajikistan .. 67 .. 92 85 86 64 22 1.3 1.2 84 30 Tanzania 49 55 35 33 90 83 59 53 16.0 58.2 85 51 Thailand 95 98 78 96 96 98 84 46 .. .. 77 72 Timor-Leste .. 62 .. 41 63 70 24 .. 8.3 47.4 79 61 Togo 49 59 13 12 80 88 23 22 38.4 47.7 67 15 Trinidad and Tobago 88 94 93 92 91 88 74 32 .. .. .. .. Tunisia 82 94 74 85 98 98 .. .. .. .. 91 78 Turkey 85 97 85 88 96 96 41 .. .. .. 91 76 Turkmenistan .. .. .. .. 99 98 83 25 .. .. 84 84 Uganda 43 64 29 33 68 64 73 39 9.7 61.3 70 51 Ukraine .. 97 96 93 98 98 .. .. .. .. 59 55 United Arab Emirates 100 100 97 97 92 92 .. .. .. .. 79 18 United Kingdom 100 100 .. .. 86 92 .. .. .. .. .. 0 United States 99 99 100 100 93 96 .. .. .. .. 64 87 Uruguay 100 100 100 100 96 94 .. .. .. .. 87 95 Uzbekistan 90 88 93 96 99 96 68 28 .. .. 81 45 Venezuela, RB 89 .. 83 .. 55 71 .. .. .. .. 82 68 Vietnam 52 92 29 65 83 92 83 65 5.1 2.6 92 82 West Bank and Gaza .. 89 .. 80 .. .. .. .. .. .. 94 5 Yemen, Rep. .. 66 28 46 74 87 47 48 .. .. 83 46 Zambia 50 58 42 52 85 80 68 48 22.8 57.9 85 58 Zimbabwe 78 81 44 46 66 62 25 47 2.9 4.7 60 27 World 76 w 86 w 51 w 60 w 82 w 82 w .. w .. w 85 w 63 w Low income 58 68 26 39 76 77 .. 26.4 84 51 Middle income 75 89 48 60 84 82 .. .. 85 72 Lower middle income 72 88 41 55 82 79 .. .. 87 72 Upper middle income 88 95 77 83 94 96 .. .. 72 72 Low & middle income 72 84 44 55 81 80 .. .. 85 64 East Asia & Pacific 68 87 48 66 90 89 .. .. 91 77 Europe & Central Asia 90 95 88 89 97 96 .. .. 70 56 Latin America & Carib. 84 91 68 78 93 92 .. .. 76 72 Middle East & N. Africa 89 89 67 77 90 92 .. .. 86 72 South Asia 73 87 18 33 72 69 .. 7.3 87 67 Sub-Saharan Africa 49 58 26 31 73 73 12.3 34.9 76 47 High income 99 100 100 100 93 95 .. .. 68 37 Euro area .. 100 .. .. 91 96 .. .. .. 17 a. For malaria prevention only. b. Refers to children who were immunized before age 12 months or in some cases at any time before the survey (12­23 months). c. Data are for the most recent year available. d. Data are for 2008. e. Includes Kosovo. 104 2009 World Development Indicators PEOPLE Disease prevention coverage and quality 2.17 About the data Definitions People's health is influenced by the environment rehydration salts at home. However, recommenda- · Access to an improved water source refers to peo- in which they live. Lack of clean water and basic tions for the use of oral rehydration therapy have ple with reasonable access to water from an improved sanitation is the main reason diseases transmitted changed over time based on scientific progress, so source, such as piped water into a dwelling, public tap, by feces are so common in developing countries. it is difficult to accurately compare use rates across tubewell, protected dug well, and rainwater collection. Access to drinking water from an improved source countries. Until the current recommended method Reasonable access is the availability of at least 20 and access to improved sanitation do not ensure for home management of diarrhea is adopted and liters a person a day from a source within 1 kilometer safety or adequacy, as these characteristics are applied in all countries, the data should be used of the dwelling. · Access to improved sanitation facil- not tested at the time of the surveys. But improved with caution. Also, the prevalence of diarrhea may ities refers to people with at least adequate access drinking water technologies and improved sanitation vary by season. Since country surveys are adminis- to excreta disposal facilities that can effectively pre- facilities are more likely than those characterized tered at different times, data comparability is further vent human, animal, and insect contact with excreta. as unimproved to provide safe drinking water and to affected. Improved facilities range from protected pit latrines prevent contact with human excreta. The data are Malaria is endemic to the poorest countries in the to flush toilets. · Child immunization rate refers to derived by the Joint Monitoring Programme (JMP) world, mainly in tropical and subtropical regions of children ages 12­23 months who, before 12 months of the World Health Organization (WHO) and United Africa, Asia, and the Americas. Insecticide-treated or at any time before the survey, had received one Nations Children's Fund (UNICEF) based on national bednets, properly used and maintained, are one of dose of measles vaccine and three doses of diphthe- censuses and nationally representative household the most important malaria-preventive strategies to ria, pertussis (whooping cough), and tetanus (DTP3) surveys. The coverage rates for water and sanita- limit human-mosquito contact. Studies have empha- vaccine. · Children with acute respiratory infection tion are based on information from service users sized that mortality rates could be reduced by about taken to health provider are children under age 5 with on the facilities their households actually use rather 25­30 percent if every child under age 5 in malaria- acute respiratory infection in the two weeks before than on information from service providers, which risk areas such as Africa slept under a treated bed- the survey who were taken to an appropriate health may include nonfunctioning systems. While the esti- net every night. provider. · Children with diarrhea who received oral mates are based on use, the JMP reports use as Prompt and effective treatment of malaria is a criti- rehydration and continuous feeding are children access, because access is the term used in the Mil- cal element of malaria control. It is vital that suffer- under age 5 with diarrhea in the two weeks before the lennium Development Goal target for drinking water ers, especially children under age 5, start treatment survey who received either oral rehydration therapy or and sanitation. within 24 hours of the onset of symptoms, to pre- increased fluids, with continuous feeding. · Children Governments in developing countries usually vent progression--often rapid--to severe malaria sleeping under treated bednets are children under finance immunization against measles and diphthe- and death. age 5 who slept under an insecticide-treated bed- ria, pertussis (whooping cough), and tetanus (DTP) Data on the success rate of tuberculosis treatment net to prevent malaria the night before the survey. as part of the basic public health package. In many are provided for countries that have implemented · Children with fever receiving antimalarial drugs are developing countries lack of precise information on DOTS, the internationally recommended tubercu- children under age 5 who were ill with fever in the two the size of the cohort of one-year-old children makes losis control strategy. The treatment success rate weeks before the survey and received any appropri- immunization coverage diffi cult to estimate from for tuberculosis provides a useful indicator of the ate (locally defined) antimalarial drugs. · Tuberculosis program statistics. The data shown here are based quality of health services. A low rate or no success treatment success rate refers to new registered infec- on an assessment of national immunization cover- suggests that infectious patients may not be receiv- tious tuberculosis cases that were cured or completed age rates by the WHO and UNICEF. The assessment ing adequate treatment. An essential complement a full course of treatment. · DOTS detection rate considered both administrative data from service to the tuberculosis treatment success rate is the refers to estimated new infectious tuberculosis cases providers and household survey data on children's DOTS detection rate, which indicates whether there detected by DOTS, the internationally recommended immunization histories. Based on the data available, is adequate coverage by the recommended case tuberculosis detection and treatment strategy. consideration of potential biases, and contributions detection and treatment strategy. A country with a Data sources of local experts, the most likely true level of immuni- high treatment success rate may still face big chal- Data on access to water and sanitation are from zation coverage was determined for each year. lenges if its DOTS detection rate remains low. the WHO and UNICEF's Progress on Drinking Water Acute respiratory infection continues to be a lead- For indicators that are from household surveys, the and Sanitation (2008). Data on immunization are ing cause of death among young children, killing year in the table refers to the survey year. For more from WHO and UNICEF estimates (www.who.int/ about 2 million children under age 5 in developing information, consult the original sources. immunization_monitoring). Data on children with countries each year. Data are drawn mostly from acute respiratory infection, with diarrhea, sleeping household health surveys in which mothers report under treated bednets, and receiving antimalarial on number of episodes and treatment for acute respi- drugs are from UNICEF's State of the World's Chil- ratory infection. dren 2009, Childinfo, and Demographic and Health Since 1990 diarrhea-related deaths among chil- Surveys by Macro International. Data on tuberculo- dren have declined tremendously. Most diarrhea- sis are from the WHO's Global Tuberculosis Control related deaths are due to dehydration, and many of Report 2009. these deaths can be prevented with the use of oral 2009 World Development Indicators 105 2.18 Reproductive health Total fertility Adolescent Unmet Contraceptive Newborns Pregnant Births attended Maternal rate fertility rate need for prevalence rate protected women by skilled mortality contraception against receiving health staff ratio tetanus prenatal care births per % of married % of married per 100,000 live births births per 1,000 women women ages women ages National Modeled woman ages 15­19 15­49 15­49 % of births % % of total estimates estimates 1990 2007 2007 2002­07a 2002­07a 2007 2002­07a 1990 2002­07a 1990­2007a 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 2.9 1.8 16 .. 60 87 97 .. 100 20 92 Algeria 4.6 2.4 7 .. 61 70 89 77 95 117 180 Angola 7.1 5.8 138 .. .. 81 80 .. 47 .. 1,400 Argentina 3.0 2.3 57 .. .. .. 99 96 99 48 77 Armenia 2.5 1.7 30 13 53 .. 93 .. 98 16 76 Australia 1.9 1.9 14 .. .. .. .. 100 100 .. 4 Austria 1.5 1.4 12 .. .. .. .. .. .. .. 4 Azerbaijan 2.7 2.0 29 23 51 .. 77 .. 89 29 82 Bangladesh 4.3 2.8 124 11 56 91 51 .. 18 322 570 Belarus 1.9 1.3 22 .. 73 .. 99 .. 100 12 18 Belgium 1.6 1.8 7 .. .. 94 .. .. .. .. 8 Benin 6.7 5.4 120 30 17 93 84 .. 74 397 840 Bolivia 4.9 3.5 78 23 58 71 79 43 67 229 290 Bosnia and Herzegovina 1.7 1.2 20 23 36 85 99 97 100 9 3 Botswana 4.6 2.9 52 .. .. 78 .. 77 .. 326 380 Brazil 2.8 2.2 89 .. .. 93 97 72 97 53 110 Bulgaria 1.8 1.4 40 .. .. 65 .. .. 99 7 11 Burkina Faso 7.3 6.0 126 29 17 80 85 .. 54 484 700 Burundi 6.8 6.8 55 .. 9 78 92 .. 34 615 1,100 Cambodia 5.7 3.2 42 25 40 87 69 .. 44 472 540 Cameroon 5.9 4.3 118 20 29 81 82 58 63 669 1,000 Canada 1.8 1.6 14 .. .. 82 .. .. 100 .. 7 Central African Republic 5.6 4.6 115 .. 19 54 69 .. 53 543 980 Chad 6.7 6.2 164 21 3 60 39 .. 14 1,099 1,500 Chile 2.6 1.9 60 .. 58 .. .. .. 100 20 16 China 2.1 1.7 8 .. 85 .. 90 50 98 41 45 Hong Kong, China 1.3 1.0 5 .. .. .. .. .. 100 .. .. Colombia 3.0 2.5 76 6 78 78 94 82 96 75 130 Congo, Dem. Rep. 6.7 6.3 222 24 .. 81 85 .. 74 1,289 1,100 Congo, Rep. 5.3 4.5 115 16 21 90 86 .. 83 781 740 Costa Rica 3.1 2.1 71 .. 96 .. 92 98 99 36 30 Côte d'Ivoire 6.5 4.5 107 29 13 76 85 .. 57 543 810 Croatia 1.6 1.4 14 .. .. 97 100 100 100 10 7 Cuba 1.7 1.5 47 8 77 .. 100 .. 100 21 45 Czech Republic 1.9 1.4 11 .. .. .. .. .. 100 8 4 Denmark 1.7 1.9 6 .. .. .. .. .. .. 10 3 Dominican Republic 3.3 2.4 108 11 73 85 99 93 98 159 150 Ecuador 3.6 2.6 83 .. 73 67 84 .. 75 107 210 Egypt, Arab Rep. 4.3 2.9 39 10 59 85 70 37 74 84 130 El Salvador 3.7 2.7 81 .. 67 87 86 52 92 71 170 Eritrea 6.2 5.0 72 27 8 80 70 .. 28 998 450 Estonia 2.0 1.6 21 .. .. .. .. .. 100 7 25 Ethiopia 6.8 5.3 94 34 15 85 28 .. 6 673 720 Finland 1.8 1.8 9 .. .. .. .. .. 100 6 7 France 1.8 2.0 7 .. .. .. .. .. .. 10 8 Gabon 4.7 3.1 82 .. .. 67 .. .. .. 519 520 Gambia, The 6.0 4.7 104 .. .. 90 98 44 57 730 690 Georgia 2.1 1.4 30 .. 47 87 94 .. 98 23 66 Germany 1.5 1.4 9 .. .. .. .. .. 100 8 4 Ghana 5.7 3.8 55 34 17 88 92 40 50 .. 560 Greece 1.4 1.4 9 .. .. 69 .. .. .. 1 3 Guatemala 5.6 4.2 107 .. 43 80 84 .. 41 133 290 Guinea 6.6 5.4 149 21 9 95 82 31 38 980 910 Guinea-Bissau 7.1 7.1 188 .. 10 92 78 .. 39 405 1,100 Haiti 5.4 3.8 46 38 32 43 85 23 26 630 670 106 2009 World Development Indicators PEOPLE Total fertility Adolescent Unmet Reproductive health Contraceptive Newborns Pregnant Births attended 2.18 Maternal rate fertility rate need for prevalence rate protected women by skilled mortality contraception against receiving health staff ratio tetanus prenatal care births per % of married % of married per 100,000 live births births per 1,000 women women ages women ages National Modeled woman ages 15­19 15­49 15­49 % of births % % of total estimates estimates 1990 2007 2007 2002­07a 2002­07a 2007 2002­07a 1990 2002­07a 1990­2007a 2005 Honduras 5.1 3.3 93 17 65 94 92 45 67 108 280 Hungary 1.8 1.3 19 .. .. .. .. .. 100 8 6 India 4.0 2.7 62 13 56 86 74 .. 47 301 450 Indonesia 3.1 2.2 40 9 61 83 93 32 72 307 420 Iran, Islamic Rep. 4.8 2.0 20 .. 79 83 .. .. 97 25 140 Iraq 5.9 .. .. .. .. .. .. 54 .. 294 .. Ireland 2.1 1.9 16 .. .. .. .. .. 100 6 1 Israel 2.8 2.9 14 .. .. .. .. .. .. 5 4 Italy 1.3 1.3 6 .. .. 52 .. .. 99 7 3 Jamaica 2.9 2.4 78 .. 69 54 91 79 97 95 170 Japan 1.5 1.3 3 .. .. 86 .. 100 100 8 6 Jordan 5.4 3.6 25 11 57 87 99 87 99 41 62 Kazakhstan 2.7 2.4 31 .. 51 .. 100 .. 100 70 140 Kenya 5.8 5.0 104 25 39 74 88 50 42 414 560 Korea, Dem. Rep. 2.4 1.9 1 .. .. 91 .. .. 97 105 370 Korea, Rep. 1.6 1.3 4 .. .. .. .. 98 100 20 14 Kuwait 3.5 2.2 13 .. .. 83 .. .. 100 5 4 Kyrgyz Republic 3.7 2.7 31 1 48 82 97 .. 98 104 150 Lao PDR 6.1 3.2 72 .. 38 47 .. .. .. 405 660 Latvia 2.0 1.4 14 .. .. .. .. .. 100 9 10 Lebanon 3.1 2.2 25 .. 58 72 96 .. 98 .. 150 Lesotho 4.9 3.4 73 31 37 76 90 .. 55 762 960 Liberia 6.9 5.2 219 36 11 89 .. .. 46 .. 1,200 Libya 4.7 2.7 3 .. .. .. .. .. .. 77 97 Lithuania 2.0 1.4 18 .. .. .. .. .. 100 13 11 Macedonia, FYR 2.0 1.4 21 34 14 .. 98 .. 98 21 10 Madagascar 6.2 4.8 133 24 27 72 80 57 51 469 510 Malawi 6.9 5.6 135 28 42 86 92 55 54 807 1,100 Malaysia 3.7 2.6 13 .. .. 89 79 .. 98 28 62 Mali 7.4 6.5 179 31 8 89 70 .. 45 464 970 Mauritania 5.8 4.4 85 .. .. 60 .. 40 .. 747 820 Mauritius 2.3 1.7 41 .. 76 86 .. 91 99 22 15 Mexico 3.4 2.1 65 .. 71 87 .. .. 93 62 60 Moldova 2.3 1.7 32 7 68 .. 98 .. 100 16 22 Mongolia 4.0 1.9 45 14 66 87 99 .. 99 90 46 Morocco 4.0 2.4 19 10 63 85 68 31 63 227 240 Mozambique 6.2 5.1 149 18 17 82 85 .. 48 408 520 Myanmar 3.4 2.1 16 .. 34 91 .. .. 68 316 380 Namibia 5.7 3.6 59 .. 55 82 95 68 81 271 210 Nepal 5.1 3.0 115 25 48 83 44 7 19 281 830 Netherlands 1.6 1.7 5 .. .. .. .. .. 100 7 6 New Zealand 2.2 2.2 22 .. .. .. .. .. .. 15 9 Nicaragua 4.7 2.8 113 .. 72 94 90 .. 74 87 170 Niger 7.9 7.0 196 16 11 72 46 15 33 648 1,800 Nigeria 6.7 5.3 126 17 13 53 58 33 35 .. 1,100 Norway 1.9 1.9 8 .. .. .. .. 100 .. 6 7 Oman 6.5 3.0 10 .. .. 95 .. .. 98 13 64 Pakistan 6.1 3.9 36 25 30 81 61 19 39 533 320 Panama 3.0 2.6 83 .. .. .. .. .. 91 66 130 Papua New Guinea 4.8 3.8 51 .. .. 60 .. .. 42 .. 470 Paraguay 4.5 3.1 72 .. 73 81 94 66 77 121 150 Peru 3.9 2.5 60 8 71 82 91 80 71 185 240 Philippines 4.3 3.2 47 17 51 65 88 .. 60 162 230 Poland 2.0 1.3 13 .. .. .. .. .. 100 3 8 Portugal 1.4 1.3 13 .. .. .. .. 98 .. 8 11 Puerto Rico 2.2 1.8 47 .. .. .. .. .. 100 .. 18 2009 World Development Indicators 107 2.18 Reproductive health Total fertility Adolescent Unmet Contraceptive Newborns Pregnant Births attended Maternal rate fertility rate need for prevalence rate protected women by skilled mortality contraception against receiving health staff ratio tetanus prenatal care births per % of married % of married per 100,000 live births births per 1,000 women women ages women ages National Modeled woman ages 15­19 15­49 15­49 % of births % % of total estimates estimates 1990 2007 2007 2002­07a 2002­07a 2007 2002­07a 1990 2002­07a 1990­2007a 2005 Romania 1.8 1.3 32 .. 70 .. 94 .. 98 15 24 Russian Federation 1.9 1.4 28 .. .. .. .. .. 100 24 28 Rwanda 7.4 5.9 40 38 17 82 94 26 39 750 1,300 Saudi Arabia 5.9 3.2 28 .. .. 56 .. .. 96 10 18 Senegal 6.5 5.1 87 32 12 86 87 .. 52 401 980 Serbia 1.8 1.4 24 29 41 .. 98 .. 99 13 14b Sierra Leone 6.5 6.5 160 .. 5 94 81 .. 43 1,800 2,100 Singapore 1.9 1.3 5 .. .. 4 .. .. 100 6 14 Slovak Republic 2.1 1.3 20 .. .. 73 .. .. 100 4 6 Slovenia 1.5 1.4 7 .. .. 74 .. 100 100 17 6 Somalia 6.8 6.0 66 .. 15 68 26 .. 33 1,044 1,400 South Africa 3.5 2.7 61 .. 60 72 92 .. 92 166 400 Spain 1.3 1.4 9 .. .. 72 .. .. .. 6 4 Sri Lanka 2.5 1.9 25 .. 68 91 99 .. 99 43 58 Sudan 5.9 4.2 57 6 8 72 70 69 49 .. 450 Swaziland 5.6 3.6 33 24 51 86 85 .. 69 589 390 Sweden 2.1 1.9 4 .. .. 86 .. .. .. 5 3 Switzerland 1.6 1.5 4 .. .. 93 .. .. 100 5 5 Syrian Arab Republic 5.4 3.1 35 .. 58 92 84 .. 93 65 130 Tajikistan 5.1 3.3 28 .. 38 88 80 .. 83 97 170 Tanzania 6.1 5.2 121 22 26 88 78 53 43 578 950 Thailand 2.1 1.9 42 .. 77 89 98 .. 97 12 110 Timor-Leste 5.5 6.5 54 .. 20 59 61 .. 18 .. 380 Togo 6.4 4.8 89 .. 17 82 84 31 62 478 510 Trinidad and Tobago 2.4 1.6 35 .. 43 .. 96 .. 98 45 45 Tunisia 3.5 2.0 7 .. .. 96 .. 69 .. 69 100 Turkey 3.0 2.2 37 .. 71 69 81 .. 83 29 44 Turkmenistan 4.2 2.5 16 .. 48 .. 99 .. 100 14 130 Uganda 7.1 6.7 152 41 24 85 94 38 42 435 550 Ukraine 1.8 1.2 28 .. 67 .. 99 .. 99 17 18 United Arab Emirates 4.3 2.3 18 .. .. .. .. .. 100 3 37 United Kingdom 1.8 1.9 24 .. 84 .. .. .. .. 7 8 United States 2.1 2.1 42 .. .. .. .. 99 99 8 11 Uruguay 2.5 2.0 61 .. .. .. .. .. 99 35 20 Uzbekistan 4.1 2.4 34 8 65 87 99 .. 100 28 24 Venezuela, RB 3.4 2.6 90 .. .. 51 .. .. 95 61 57 Vietnam 3.6 2.1 17 5 76 86 91 .. 88 162 150 West Bank and Gaza 6.3 4.6 79 .. 50 .. 99 .. 99 .. .. Yemen, Rep. 8.0 5.5 71 .. 28 52 41 16 36 365 430 Zambia 6.4 5.2 125 27 34 89 93 51 43 729 830 Zimbabwe 5.1 3.7 59 13 60 78 94 70 69 555 880 World 3.2 w 2.5 w 52 w .. w 60 w .. w 81 w 50 w 65 w 400 w Low income 5.6 4.2 95 21 33 78 67 .. 42 780 Middle income 3.0 2.2 42 .. 68 .. 86 49 74 260 Lower middle income 3.1 2.3 39 .. 69 .. 84 45 69 300 Upper middle income 2.7 2.0 56 .. .. .. .. .. 95 97 Low & middle income 3.5 2.7 56 .. 60 81 81 46 62 440 East Asia & Pacific 2.4 1.9 17 .. 78 .. 90 48 87 150 Europe & Central Asia 2.3 1.7 29 .. .. .. .. .. 95 44 Latin America & Carib. 3.2 2.4 77 .. .. 83 95 72 89 130 Middle East & N. Africa 4.8 2.8 30 .. 62 78 76 48 80 200 South Asia 4.2 2.9 67 14 53 85 69 32 41 500 Sub-Saharan Africa 6.3 5.1 118 24 23 75 72 .. 45 900 High income 1.8 1.8 22 .. .. .. .. .. 99 10 Euro area 1.5 1.5 8 .. .. .. .. .. .. 5 a. Data are for most recent year available. b. Includes Montenegro. 108 2009 World Development Indicators PEOPLE Reproductive health 2.18 About the data Definitions Reproductive health is a state of physical and men- indicator has changed, these data cannot be com- · Total fertility rate is the number of children that tal well-being in relation to the reproductive system pared with those in editions before 2008. would be born to a woman if she were to live to the and its functions and processes. Means of achieving Good prenatal and postnatal care improve mater- end of her childbearing years and bear children in reproductive health include education and services nal health and reduce maternal and infant mortality. accordance with current age-specific fertility rates. during pregnancy and childbirth, safe and effec- But data may not reflect such improvements because · Adolescent fertility rate is the number of births per tive contraception, and prevention and treatment health information systems are often weak, mater- 1,000 women ages 15­19. · Unmet need for contra- of sexually transmitted diseases. Complications of nal deaths are underreported, and rates of maternal ception is the percentage of fertile, married women pregnancy and childbirth are the leading cause of mortality are difficult to measure. of reproductive age who do not want to become preg- death and disability among women of reproductive The share of births attended by skilled health staff nant and are not using contraception. · Contracep- age in developing countries. is an indicator of a health system's ability to provide tive prevalence rate is the percentage of women Total and adolescent fertility rates are based on adequate care for pregnant women. Maternal mor- married or in-union ages 15­49 who are practicing, data on registered live births from vital registration tality ratios are generally of unknown reliability, as or whose sexual partners are practicing, any form systems or, in the absence of such systems, from are many other cause-specific mortality indicators. of contraception. · Newborns protected against censuses or sample surveys. The estimated rates Household surveys such as Demographic and Health tetanus are the percentage of births by women of are generally considered reliable measures of fertility Surveys attempt to measure maternal mortality by child-bearing age who are immunized against teta- in the recent past. Where no empirical information asking respondents about survivorship of sisters. nus. · Pregnant women receiving prenatal care are on age- specific fertility rates is available, a model is The main disadvantage of this method is that the the percentage of women attended at least once used to estimate the share of births to adolescents. estimates of maternal mortality that it produces during pregnancy by skilled health personnel for For countries without vital registration systems fertil- pertain to 12 years or so before the survey, making reasons related to pregnancy. · Births attended by ity rates are generally based on extrapolations from them unsuitable for monitoring recent changes or skilled health staff are the percentage of deliveries trends observed in censuses or surveys from earlier observing the impact of interventions. In addition, attended by personnel trained to give the necessary years. measurement of maternal mortality is subject to care to women during pregnancy, labor, and post- More couples in developing countries want to limit many types of errors. Even in high-income countries partum; to conduct deliveries on their own; and to or postpone childbearing but are not using effec- with vital registration systems, misclassification of care for newborns. · Maternal mortality ratio is the tive contraception. These couples have an unmet maternal deaths has been found to lead to serious number of women who die from pregnancy-related need for contraception. Common reasons are lack underestimation. causes during pregnancy and childbirth per 100,000 of knowledge about contraceptive methods and The national estimates of maternal mortality live births. concerns about possible side effects. This indica- ratios in the table are based on national surveys, tor excludes women not exposed to the risk of unin- vital registration records, and surveillance data or Data sources tended pregnancy because of menopause, infertility, are derived from community and hospital records. or postpartum anovulation. The modeled estimates are based on an exercise by Data on fertility rates are compiled and estimated Contraceptive prevalence reflects all methods-- the World Health Organization (WHO), United Nations by the World Bank's Development Data Group. ineffective traditional methods as well as highly Children's Fund (UNICEF), United Nations Population Inputs come from the United Nations Population effective modern methods. Contraceptive prevalence Fund (UNFPA), and World Bank. For countries with Division's World Population Prospects: The 2006 rates are obtained mainly from household surveys, complete vital registration systems with good attribu- Revision, census reports and other statistical including Demographic and Health Surveys, Multiple tion of cause of death, the data are used as reported. publications from national statistical offi ces, Indicator Cluster Surveys, and contraceptive preva- For countries with national data either from complete and household surveys such as Demographic lence surveys (see Primary data documentation for vital registration systems with uncertain or poor attri- and Health Surveys. Data on women with unmet the most recent survey year). Unmarried women are bution of cause of death or from household surveys need for contraception and contraceptive preva- often excluded from such surveys, which may bias reported maternal mortality was adjusted, usually by lence rates are from household surveys, including the estimates. a factor of underenumeration and misclassification. Demographic and Health Surveys by Macro Inter- An important cause of infant mortality in some For countries with no empirical national data (about national and Multiple Indicator Cluster Surveys by developing countries, neonatal tetanus can be pre- 35 percent of countries), maternal mortality was esti- UNICEF. Data on tetanus vaccinations, pregnant vented through immunization of the mother during mated with a regression model using socioeconomic women receiving prenatal care, births attended pregnancy. As in last year's edition, the data on information, including fertility, birth attendants, and by skilled health staff, and national estimates of tetanus in the table are estimated by the "protec- GDP. Neither set of ratios can be assumed to provide maternal mortality ratios are from UNICEF's State tion at birth" model, which tracks the immunization an exact estimate of maternal mortality for any of the of the World's Children 2009 and Childinfo and status of women of child-bearing age. The estimates countries in the table. Demographic and Health Surveys by Macro Inter- account for the number of vaccine doses received For the indicators that are from household surveys, national. Modeled estimates for maternal mortal- and the time since the mother's last immunization. the year in the table refers to the survey year. For ity ratios are from WHO, UNICEF, UNFPA and the A currently immune woman's child is considered more information, consult the original sources. World Bank's Maternal Mortality in 2005 (2007). protected. Because the methodology behind this 2009 World Development Indicators 109 2.19 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A Prevalence undernourishment malnutrition of overweight birthweight breast- of iodized supplemen- of anemia children babies feeding salt tation % % of children under age 5 % of children % of children % of % of children Children Pregnant % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months under age 5 women 1990­92 2003­05 2000­07a 2000­07a 2000­07a 2002­07a 2002­07a 2002­07a 2007 2000­05 a 2000­05a Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania <5 <5 17.0 39.2 30.0 7 40 60 .. 31 34 Algeria <5 <5 10.2 21.6 15.4 6 7 61 .. 43 43 Angola 66 46 27.5 50.8 5.3 .. .. .. 36 .. 57 Argentina <5 <5 2.3 8.2 9.9 7 .. .. .. 18 25 Armenia 46 21 4.2 18.2 11.7 8 33 97 .. 24 12 Australia <5 <5 .. .. .. .. .. .. .. 8 12 Austria <5 <5 .. .. .. .. .. .. .. 11 15 Azerbaijan 27 12 14.0 24.1 6.2 .. 12 54 95b 32 38 Bangladesh 36 27 39.2 47.8 0.9 22 37 84 95 47 47 Belarus <5 <5 1.3 4.5 9.7 4 9 55 .. 27 26 Belgium <5 <5 .. .. .. .. .. .. .. 9 13 Benin 28 19 21.5 39.1 3.0 15 43 55 73 82 73 Bolivia 24 22 5.9 32.5 9.2 7 54 90 39 52 37 Bosnia and Herzegovina <5 <5 1.6 11.8 25.6 5 18 62 .. 27 35 Botswana 20 26 10.7 29.1 10.4 .. .. .. .. .. 21 Brazil 10 6 2.2 7.1 7.3 8 .. .. .. 55 29 Bulgaria <5 <5 1.6 8.8 13.6 .. .. 100 .. 27 30 Burkina Faso 14 10 35.2 43.1 5.4 16 7 34 95 92 68 Burundi 44 63 38.9 63.1 1.4 11 45 98 83 56 47 Cambodia 38 26 28.4 43.7 1.7 14 60 73 76 63 66 Cameroon 34 23 15.1 35.4 8.7 11 21 49 95 68 51 Canada <5 <5 .. .. .. .. .. .. .. 8 12 Central African Republic 47 43 21.8 44.6 10.8 13 23 62 78 .. .. Chad 59 39 33.9 44.8 4.4 22 2 56 54 71 60 Chile 7 <5 0.6 2.1 9.8 6 85 .. .. 24 28 China 15c 9c 6.8 21.8 9.2 2 51 94 .. 20 29 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 15 10 5.1 16.2 4.2 6 47 .. .. 28 31 Congo, Dem. Rep. 29 76 33.6 44.4 6.5 .. 36 .. 79 71 67 Congo, Rep. 40 22 11.8 31.2 8.5 13 19 82 95 66 55 Costa Rica <5 <5 .. .. .. 7 .. .. .. .. .. Côte d'Ivoire 15 14 16.7 40.1 9.0 17 4 84 63 69 55 Croatia <5 <5 .. .. .. 5 .. .. .. 23 28 Cuba 5 <5 .. .. .. 5 26 88 .. 27 39 Czech Republic <5 <5 2.1 2.6 4.4 .. .. .. .. 18 22 Denmark <5 <5 .. .. .. .. .. .. .. 9 12 Dominican Republic 27 21 4.2 11.7 8.6 11 8 19 .. 35 40 Ecuador 24 15 6.2 29.0 5.1 .. 40 .. .. 38 38 Egypt, Arab Rep. <5 <5 5.4 23.8 14.1 14 38 78 87b 30 45 El Salvador 9 10 6.1 24.6 5.8 7 24 62 20 18 .. Eritrea 67 68 34.5 43.7 1.6 14 52 68 51 70 55 Estonia <5 <5 .. .. .. .. .. .. .. 23 23 Ethiopia 71 46 34.6 50.7 5.1 20 49 20 88 75 63 Finland <5 <5 .. .. .. .. .. .. .. 11 15 France <5 <5 .. .. .. .. .. .. .. 8 11 Gabon 5 <5 8.8 26.3 5.6 .. .. .. 90 44 46 Gambia, The 20 30 15.8 27.6 2.7 20 41 7 93 .. .. Georgia 47 13 .. .. .. 5 11 87 .. 41 42 Germany <5 <5 .. .. .. .. .. .. .. 8 12 Ghana 34 9 18.8 35.6 4.5 9 54 32 95 76 65 Greece <5 <5 .. .. .. .. .. .. .. 12 19 Guatemala 14 16 17.7 54.3 5.6 12 51 40 33 38 22 Guinea 19 17 22.5 39.3 5.1 12 27 51 95 79 63 Guinea-Bissau 20 32 21.9 36.1 5.1 24 16 1 66 75 58 Haiti 63 58 18.9 29.7 3.9 25 41 3 42 65 63 110 2009 World Development Indicators PEOPLE Prevalence of Prevalence of child Prevalence Low- Exclusive Nutrition Consumption Vitamin A 2.19 Prevalence undernourishment malnutrition of overweight birthweight breast- of iodized supplemen- of anemia children babies feeding salt tation % % of children under age 5 % of children % of children % of % of children Children Pregnant % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months under age 5 women 1990­92 2003­05 2000­07a 2000­07a 2000­07a 2002­07a 2002­07a 2002­07a 2007 2000­05 a 2000­05a Honduras 19 12 8.6 29.9 5.8 10 30 .. 40 30 .. Hungary <5 <5 .. .. .. .. .. .. .. 19 21 India 24 21 43.5 47.9 1.9 28 46 51 33 74 50 Indonesia 19 17 24.4 28.6 5.1 9 40 73 87 44 44 Iran, Islamic Rep. <5 <5 .. .. .. .. 23 99 .. 35 .. Iraq .. .. .. .. .. .. .. .. .. .. .. Ireland <5 <5 .. .. .. .. .. .. .. 10 15 Israel <5 <5 .. .. .. .. .. .. .. 12 17 Italy <5 <5 .. .. .. .. .. .. .. 11 15 Jamaica 11 5 3.1 4.5 7.5 12 15 .. .. .. .. Japan <5 <5 .. .. .. .. .. .. .. 11 15 Jordan <5 <5 3.6 12.0 4.7 12 22 .. .. 28 39 Kazakhstan <5 <5 4.9 17.5 14.8 6 17 92 .. .. 26 Kenya 33 32 16.5 35.8 5.8 10 13 .. 22 .. .. Korea, Dem. Rep. 21 32 17.8 44.7 0.9 7 65 40 95 .. .. Korea, Rep. <5 <5 .. .. .. .. .. .. .. .. 23 Kuwait 20 <5 .. .. .. .. .. .. .. 32 31 Kyrgyz Republic 17 <5 2.7 18.1 10.7 5 32 76 95 .. 34 Lao PDR 27 19 36.4 48.2 2.7 .. .. .. 83 48 56 Latvia <5 <5 .. .. .. .. .. .. .. 27 25 Lebanon <5 <5 .. .. .. .. .. 92 .. .. 32 Lesotho 15 15 16.6 45.2 6.8 13 36 91 85 49 25 Liberia 30 40 20.4 39.4 4.2 .. 29 .. 85 .. .. Libya <5 <5 .. .. .. .. .. .. .. 34 34 Lithuania <5 <5 .. .. .. .. .. .. .. 24 24 Macedonia, FYR <5 <5 1.8 11.5 16.2 6 16 94 .. .. 32 Madagascar 32 37 36.8 52.8 6.2 17 67 75 95 68 50 Malawi 45 29 18.4 52.5 10.2 14 57 50 90 73 47 Malaysia <5 <5 .. .. .. 9 .. .. .. 32 38 Mali 14 11 27.9 38.5 4.7 19 38 79 95 83 73 Mauritania 10 8 30.4 39.5 3.8 .. .. .. 95 68 53 Mauritius 7 6 .. .. .. 14 21 .. .. .. .. Mexico <5 <5 3.4 15.5 7.6 8 .. 91 68 .. .. Moldova <5 <5 3.2 11.3 9.1 6 46 60 .. 41 36 Mongolia 30 29 5.3 27.5 14.2 6 57 83 95 21 37 Morocco 5 <5 9.9 23.1 13.3 15 31 21 .. 32 37 Mozambique 59 38 21.2 47.0 6.3 15 30 54 48 75 52 Myanmar 44 19 29.6 40.6 2.4 .. 15 60 94 63 50 Namibia 29 19 17.5 29.6 4.6 .. 24 .. 68 41 31 Nepal 21 15 38.8 49.3 0.6 21 53 .. 95 .. .. Netherlands <5 <5 .. .. .. .. .. .. .. 9 13 New Zealand <5 <5 .. .. .. .. .. .. .. 11 18 Nicaragua 52 22 7.8 25.2 7.1 .. .. 97 95 17 33 Niger 38 29 39.9 54.8 3.5 27 9 46 95 81 66 Nigeria 15 9 27.2 43.0 6.2 14 17 97 74 .. .. Norway <5 <5 .. .. .. .. .. .. .. 6 9 Oman .. .. .. .. .. 9 .. .. .. .. 43 Pakistan 22 23 31.3 41.5 4.8 .. 37 17 95 51 39 Panama 18 17 .. .. .. 10 .. .. 4 .. .. Papua New Guinea .. .. .. .. .. .. .. .. 7 60 55 Paraguay 16 11 .. .. .. 9 22 94 .. 30 39 Peru 28 15 5.2 31.3 11.8 10 63 91 .. 50 43 Philippines 21 16 20.7 33.8 2.4 20 34 45 83 36 44 Poland <5 <5 .. .. .. .. .. .. .. 23 25 Portugal <5 <5 .. .. .. .. .. .. .. 13 17 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 111 2.19 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A Prevalence undernourishment malnutrition of overweight birthweight breast- of iodized supplemen- of anemia children babies feeding salt tation % % of children under age 5 % of children % of children % of % of children Children Pregnant % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months under age 5 women 1990­92 2003­05 2000­07a 2000­07a 2000­07a 2002­07a 2002­07a 2002­07a 2007 2000­05 a 2000­05a Romania <5 <5 3.5 12.8 8.3 8 16 74 .. 40 30 Russian Federation <5 <5 .. .. .. 6 .. 35 .. 27 21 Rwanda 45 40 18.0 51.7 6.7 6 88 88 89 .. .. Saudi Arabia <5 <5 .. .. .. .. .. .. .. 33 32 Senegal 28 26 14.5 20.1 2.4 19 34 41 94 70 58 Serbia <5d <5d 1.8 8.1 19.3 5 15 .. .. .. .. Sierra Leone 45 47 28.3 46.9 5.9 24 8 45 95 83 60 Singapore .. .. 3.3 4.4 2.6 .. .. .. .. 19 24 Slovak Republic <5 <5 .. .. .. .. .. .. .. 23 25 Slovenia <5 <5 .. .. .. .. .. .. .. 14 19 Somalia .. .. 32.8 42.1 4.7 11 9 1 89 .. .. South Africa <5 <5 .. .. .. .. 7 .. 33 .. 22 Spain <5 <5 .. .. .. .. .. .. .. 13 18 Sri Lanka 27 21 22.8 18.4 1.0 .. .. 94 64 30 29 Sudan 31 21 38.4 47.6 5.2 .. 34 11 90 85 58 Swaziland 12 18 9.1 36.6 14.9 9 32 80 59 47 24 Sweden <5 <5 .. .. .. .. .. .. .. 9 13 Switzerland <5 <5 .. .. .. .. .. .. .. 6 .. Syrian Arab Republic <5 <5 .. .. .. 9 29 79 .. 41 39 Tajikistan 34 34 14.9 33.1 6.7 10 25 46 92 38 45 Tanzania 28 35 16.7 44.4 4.9 10 41 43 93 72 58 Thailand 29 17 7.0 15.7 8.0 9 5 47 .. .. .. Timor-Leste 18 22 40.6 55.7 5.7 12 31 58 57 32 23 Togo 45 37 .. .. .. 12 28 25 95 52 50 Trinidad and Tobago 11 10 4.4 5.3 4.9 19 13 28 .. 30 30 Tunisia <5 <5 .. .. .. .. .. .. .. .. .. Turkey <5 <5 3.5 15.6 9.1 .. 21 64 .. 33 40 Turkmenistan 9 6 .. .. .. 4 11 87 .. 36 30 Uganda 19 15 19.0 44.8 4.9 14 60 96 64 64 41 Ukraine <5 <5 4.1 22.9 26.5 4 6 18 .. 22 27 United Arab Emirates <5 <5 .. .. .. .. .. .. .. 28 28 United Kingdom <5 <5 .. .. .. .. .. .. .. .. 15 United States <5 <5 1.3 3.9 8.0 8 .. .. .. 3 6 Uruguay 5 <5 6.0 13.9 9.4 8 54 .. .. 19 27 Uzbekistan 5 14 4.4 19.6 12.8 5 26 53 84 38 .. Venezuela, RB 10 12 .. .. .. 9 .. .. .. 33 40 Vietnam 28 14 20.2 35.8 2.5 7 17 93 95b 34 32 West Bank and Gaza .. 15 .. .. .. 7 27 86 .. .. .. Yemen, Rep. 30 32 .. .. .. .. 12 30 47b 68 58 Zambia 40 45 23.3 52.5 5.9 12 61 77 95 53 .. Zimbabwe 40 40 14.0 35.8 9.1 11 22 91 83 .. .. World 17 w 14 w 23.2 w 34.7 w 5.7 w 14 w 38 w 69 w .. w .. w .. w Low income 31 27 28.0 43.8 4.8 15 33 61 82 .. .. Middle income 16 13 22.0 31.8 6.1 15 41 71 .. .. .. Lower middle income 19 14 24.8 34.9 5.8 16 43 71 .. .. .. Upper middle income 6 6 .. .. .. 8 .. 72 .. 38 29 Low & middle income 19 16 24.1 36.0 5.7 15 38 69 .. .. .. East Asia & Pacific 18 11 12.8 25.8 7.1 6 43 86 .. 20 29 Europe & Central Asia 7 6 .. .. .. 6 .. 50 .. 29 30 Latin America & Carib. 12 9 4.4 15.8 7.4 9 .. 85 .. .. .. Middle East & N. Africa 7 7 .. .. .. 12 26 68 .. .. .. South Asia 25 22 41.1 47.3 2.2 27 44 51 50 74 50 Sub-Saharan Africa 31 29 26.6 44.3 5.8 14 31 62 77 .. .. High income 5 5 .. .. .. .. .. .. .. .. 13 Euro area 5 5 .. .. .. .. .. .. .. 10 14 a. Data are for the most recent year available. b. Country's vitamin A supplementation programs do not target children all the way up to 59 months of age. c. Includes Hong Kong, China; Macao, China; and Taiwan, China. d. Includes Montenegro. 112 2009 World Development Indicators PEOPLE Nutrition 2.19 About the data Definitions Data on undernourishment are from the Food and Low birthweight, which is associated with maternal · Prevalence of undernourishment is the percentage Agriculture Organization (FAO) of the United Nations malnutrition, raises the risk of infant mortality and of the population whose dietary energy consump- and measure food deprivation based on average food stunts growth in infancy and childhood. There is also tion is continuously below a minimum requirement available for human consumption per person, the emerging evidence that low-birthweight babies are for maintaining a healthy life and carrying out light level of inequality in access to food, and the mini- more prone to noncommunicable diseases such as physical activity with an acceptable minimum weight mum calories required for an average person. diabetes and cardiovascular diseases. Estimates of for height. · Prevalence of child malnutrition is the From a policy and program standpoint, however, low-birthweight infants are drawn mostly from hos- percentage of children under age 5 whose weight for this measure has its limits. First, food insecurity pital records and household surveys. Many births age (underweight) or height for age (stunting) is more exists even where food availability is not a problem in developing countries take place at home and are than two standard deviations below the median for because of inadequate access of poor households seldom recorded. A hospital birth may indicate higher the international reference population ages 0­59 to food. Second, food insecurity is an individual income and therefore better nutrition, or it could indi- months. Height is measured by recumbent length or household phenomenon, and the average food cate a higher risk birth, possibly skewing the data on for children up to two years old and by stature while available to each person, even corrected for possible birthweights downward. The data should therefore be standing for older children. Data are for the WHO child effects of low income, is not a good predictor of food used with caution. growth standards released in 2006. · Prevalence of insecurity among the population. And third, nutrition Improved breastfeeding can save an estimated 1.3 over weight children is the percentage of children security is determined not only by food security but million children a year. Breast milk alone contains under age 5 whose weight for height is more than also by the quality of care of mothers and children all the nutrients, antibodies, hormones, and antioxi- two standard deviations above the median for the and the quality of the household's health environ- dants an infant needs to thrive. It protects babies international reference population of the correspond- ment (Smith and Haddad 2000). from diarrhea and acute respiratory infections, stimu- ing age as established by the WHO child growth stan- Estimates of child malnutrition, based on weight for lates their immune systems and response to vaccina- dards released in 2006. · Low-birthweight babies age (underweight) and height for age (stunting), are tion, and may confer cognitive benefits. The data on are the percentage of newborns weighing less than from national survey data. The proportion of under- breastfeeding are derived from national surveys. 2.5 kilograms within the first hours of life, before sig- weight children is the most common malnutrition Iodine defi ciency is the single most important nificant postnatal weight loss has occurred. · Exclu- indicator. Being even mildly underweight increases cause of preventable mental retardation, and it sive breastfeeding is the percentage of children less the risk of death and inhibits cognitive development contributes significantly to the risk of stillbirth and than six months old who were fed breast milk alone in children. And it perpetuates the problem across miscarriage. Widely used and inexpensive, iodized (no other liquids) in the past 24 hours. · Consump- generations, as malnourished women are more salt is the best source of iodine, and a global cam- tion of iodized salt is the percentage of households likely to have low-birthweight babies. Height for age paign to iodize edible salt is significantly reducing the that use edible salt fortified with iodine. · Vitamin A reflects linear growth achieved pre- and postnatally; risks (www.childinfo.org). The data on iodized salt are supplementation is the percentage of children ages a deficit indicates long-term, cumulative effects of derived from household surveys. 6­59 months old who received at least one dose of inadequate health, diet, or care. Stunting is often Vitamin A is essential for immune system function- vitamin A in the previous six months, as reported by used as a proxy for multifaceted deprivation and as ing. Vitamin A deficiency, a leading cause of blind- mothers. · Prevalence of anemia, children under an indicator of long-term changes in malnutrition. ness, also causes a 23 percent greater risk of dying age 5, is the percentage of children under age 5 Estimates of overweight children are also from from a range of childhood ailments such as measles, whose hemoglobin level is less than 110 grams per national survey data. Overweight children have malaria, and diarrhea. Giving vitamin A to new breast- liter at sea level. · Prevalence of anemia, pregnant become a growing concern in developing countries. feeding mothers helps protect their children during women, is the percentage of pregnant women whose Research shows an association between childhood the first months of life. Food fortification with vitamin hemoglobin level is less than 110 grams per liter obesity and a high prevalence of diabetes, respiratory A is being introduced in many developing countries. at sea level. disease, high blood pressure, and psychosocial and Data on anemia are compiled by the WHO based orthopedic disorders (de Onis and Blössner 2000). mainly on nationally representative surveys between Data sources New international growth reference standards for 1993 and 2005, which measured hemoglobin in the infants and young children were released in 2006 by blood. WHO's hemoglobin thresholds were then used Data on undernourishment are from ww.fao.org/ the World Health Organization (WHO) to monitor chil- to determine anemia status based on age, sex, and faostat/foodsecurity/index_en.htm. Data on dren's nutritional status. They are also key in moni- physiological status. Children under age 5 and preg- malnutrition and overweight children are from toring health targets for the Millennium Development nant women have the highest risk for anemia. Data the WHO's Global Database on Child Growth and Goals. Differences in growth to age 5 are influenced should be used with caution because surveys dif- Malnutrition (www.who.int/nutgrowthdb). Data on more by nutrition, feeding practices, environment, fer in quality, coverage, age group interviewed, and low-birthweight babies, breastfeeding, iodized salt and healthcare than by genetics or ethnicity. The treatment of missing values across countries and consumption, and vitamin A supplementation are previously reported data were based on the U.S. over time. from the United Nations Children's Fund's State of National Center for Health Statistics­WHO growth For indicators from household surveys, the year in the World's Children 2009 and Childinfo. Data on reference. Because of the change in standards, the the table refers to the survey year. For more informa- anemia are from the WHO's Worldwide Prevalence data in this edition should not be compared with data tion, consult the original sources. of Anemia 1993­2005 (2008). in editions prior to 2008. 2009 World Development Indicators 113 2.20 Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIV Condom use of smoking tuberculosis of diabetes Female Youth per % of Total % of total % of population % of population % of adults 100,000 population % of population population ages 15­24 ages 15­24 Male Female people ages 20­79 ages 15­49 with HIV Male Female Male Female 2008 2008 2007 2007 1990 2007 2001 2007 2007 2007 2000­07a 2000­07a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 41 4 17 4.5 .. .. .. .. .. .. .. .. Algeria 27 0b 57 8.4 .. 0.1 25.0 28.6 0.1 0.1 .. .. Angola .. .. 287 3.3 0.3 2.1 60.9 61.1 0.2 0.3 .. .. Argentina 34 24 31 5.6 0.2 0.5 25.0 26.7 0.6 0.3 .. .. Armenia 55 4 72 7.7 .. 0.1 <27.8 <41.7 0.2 0.1 32 7 Australia 28 22 6 5.0 0.1 0.2 <7.1 6.7 0.2 <0.1 .. .. Austria 46 40 12 7.9 <0.1 0.2 27.3 29.6 0.2 0.1 .. .. Azerbaijan .. 1 77 7.3 .. 0.2 .. 16.7 0.3 0.1 25 1 Bangladesh 43 1 223 5.3 .. .. .. 16.7 .. .. .. .. Belarus 64 21 61 7.6 .. 0.2 27.5 30.0 0.3 0.1 .. .. Belgium 30 24 12 5.2 0.1 0.2 26.2 27.3 0.2 0.1 .. .. Benin .. .. 91 4.4 0.1 1.2 63.3 62.7 0.3 0.9 39 10 Bolivia 34 26 155 5.8 0.1 0.2 24.6 27.8 0.2 0.1 29 10 Bosnia and Herzegovina 49 35 51 7.0 .. <0.1 .. .. .. .. .. .. Botswana .. .. 731 5.2 4.7 23.9 59.3 60.7 5.1 15.3 .. .. Brazil 20 13 48 6.2 0.4 0.6 34.4 33.8 1.0 0.6 .. .. Bulgaria 48 28 39 7.6 .. .. .. .. .. .. .. .. Burkina Faso 14 1 226 3.7 1.9 1.6 45.4 50.8 0.5 0.9 54 17 Burundi 16 11 367 1.7 1.7 2.0 59.2 58.9 0.4 1.3 .. .. Cambodia 38 6 495 5.0 0.7 0.8 25.8 28.6 0.8 0.3 31 3 Cameroon 10 1 192 3.7 0.8 5.1 61.2 60.0 1.2 4.3 52 24 Canada 19 18 5 7.4 0.2 0.4 26.5 27.4 0.4 0.2 .. .. Central African Republic .. .. 345 4.4 1.8 6.3 66.7 65.0 1.1 5.5 .. .. Chad 13 1 299 3.6 0.7 3.5 60.7 61.1 2.0 2.8 18 7 Chile 42 31 12 5.6 <0.1 0.3 26.0 28.1 0.3 0.2 .. .. China 60 4 98 4.1 .. 0.1c 25.5c 29.0 c 0.1c 0.1c .. .. Hong Kong, China 22 4 62 8.2 .. .. .. .. .. .. .. .. Colombia 27 11 35 5.0 0.1 0.6 26.9 29.4 0.7 0.3 .. 23 Congo, Dem. Rep. 11 1 392 3.0 .. .. .. .. .. .. .. .. Congo, Rep. 10 0b 403 5.0 5.1 3.5 58.4 58.9 0.8 2.3 36 16 Costa Rica 26 7 11 9.3 0.1 0.4 27.5 28.1 0.4 0.2 .. .. Côte d'Ivoire 12 1 420 4.6 2.2 3.9 58.2 59.5 0.8 2.4 .. .. Croatia 39 29 40 7.1 .. <0.1 .. .. .. .. .. .. Cuba 36 26 6 9.3 .. 0.1 <43.5 29.0 0.1 0.1 .. .. Czech Republic 37 25 9 7.6 .. .. <38.5 <33.3 <0.1 .. .. .. Denmark 36 31 8 5.5 0.1 0.2 .. 22.9 0.2 0.1 .. .. Dominican Republic 16 11 69 8.7 0.6 1.1 54.0 50.8 0.3 0.6 58 19 Ecuador 24 6 101 5.7 0.1 0.3 25.8 28.4 0.4 0.2 .. .. Egypt, Arab Rep. 25 1 21 11.0 .. .. 26.8 28.9 .. .. .. .. El Salvador 22 3 40 9.0 0.1 0.8 25.7 28.5 0.9 0.5 .. .. Eritrea 16 1 95 2.3 0.1 1.3 60.0 60.0 0.3 0.9 .. 2 Estonia 50 28 38 7.6 .. 1.3 <28.6 24.2 1.6 0.7 .. .. Ethiopia 7 1 378 2.3 0.7 2.1 59.5 59.6 0.5 1.5 18 2 Finland 32 24 6 5.9 .. 0.1 <50.0 <41.7 0.1 <0.1 .. .. France 37 27 14 5.9 0.1 0.4 25.0 27.1 0.4 0.2 .. .. Gabon .. .. 406 4.9 0.9 5.9 58.3 58.7 1.3 3.9 .. .. Gambia, The 17 1 258 4.1 .. 0.9 59.0 60.0 0.2 0.6 .. .. Georgia 57 6 84 7.4 .. 0.1 20.0 37.0 0.1 0.1 .. .. Germany 37 26 6 7.9 <0.1 0.1 27.3 28.8 0.1 0.1 .. .. Ghana 7 1 203 4.2 0.1 1.9 58.3 60.0 0.4 1.3 45 19 Greece 64 40 18 5.9 0.1 0.2 26.5 27.3 0.2 0.1 .. .. Guatemala 25 4 63 8.6 <0.1 0.8 97.9 98.1 .. 1.5 .. .. Guinea .. 9 287 4.1 0.2 1.6 59.6 59.3 0.4 1.2 35 10 Guinea-Bissau .. .. 220 3.8 0.2 1.8 59.2 58.0 0.4 1.2 .. .. Haiti .. 4 306 9.0 1.2 2.2 45.7 52.7 0.6 1.4 28 20 114 2009 World Development Indicators PEOPLE Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIV 2.20 Condom use of smoking tuberculosis of diabetes Female Youth per % of Total % of total % of population % of population % of adults 100,000 population % of population population ages 15­24 ages 15­24 Male Female people ages 20­79 ages 15­49 with HIV Male Female Male Female 2008 2008 2007 2007 1990 2007 2001 2007 2007 2007 2000­07a 2000­07a Honduras .. 3 59 9.1 1.3 0.7 25.7 28.5 0.7 0.4 .. 7 Hungary 46 34 17 7.6 .. 0.1 <35.7 <30.3 0.1 <0.1 .. .. India 28 1 168 6.7 0.1 0.3 38.5 38.3 0.3 0.3 .. .. Indonesia 62 4 228 2.3 .. 0.2 10.8 20.0 0.3 0.1 .. 1 Iran, Islamic Rep. 24 2 22 7.8 .. 0.2 26.7 28.2 0.2 0.1 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 27 26 13 5.1 .. 0.2 26.1 27.3 0.2 0.1 .. .. Israel 31 18 8 6.9 <0.1 0.1 60.0 59.2 <0.1 0.1 .. .. Italy 33 19 7 5.8 0.4 0.4 25.7 27.3 0.4 0.2 .. .. Jamaica 19 8 7 10.3 0.3 1.6 26.4 29.2 1.7 0.9 .. .. Japan 44 14 21 4.9 .. .. 22.2 24.0 .. .. .. .. Jordan 62 10 7 9.8 .. .. .. .. .. .. .. 4 Kazakhstan 43 10 129 5.6 .. 0.1 <29.4 27.5 0.2 0.1 .. .. Kenya 24 1 353 3.3 .. .. .. .. .. .. 39 9 Korea, Dem. Rep. 59 .. 344 5.2 .. .. .. .. .. .. .. .. Korea, Rep. 53 6 90 7.8 .. <0.1 26.5 27.7 <0.1 <0.1 .. .. Kuwait 34 2 24 14.4 .. .. .. .. .. .. .. .. Kyrgyz Republic 47 2 121 5.1 .. 0.1 <50 26.2 0.2 0.1 .. .. Lao PDR 61 14 151 3.1 .. 0.2 <45.5 24.1 0.2 0.1 .. .. Latvia 54 24 53 7.6 .. 0.8 <23.8 27.0 0.9 0.5 .. .. Lebanon 29 7 19 7.7 <0.1 0.1 <45.5 <33.3 0.1 0.1 .. .. Lesotho 48 34 637 3.8 0.8 23.2 58.3 57.7 5.9 14.9 44 26 Liberia .. .. 277 4.6 0.4 1.7 59.1 59.4 0.4 1.3 19 9 Libya 32 2 17 4.4 .. .. .. .. .. .. .. .. Lithuania 45 21 68 7.6 .. 0.1 <35.7 <45.5 0.1 0.1 .. .. Macedonia, FYR 40 32 29 7.1 .. <0.1 .. .. .. .. .. .. Madagascar .. .. 251 3.0 .. 0.1 23.8 26.2 0.2 0.1 8 2 Malawi 19 2 346 2.1 2.1 11.9 56.4 58.3 2.4 8.4 32 9 Malaysia 51 3 103 10.7 0.1 0.5 23.3 26.6 0.6 0.3 .. .. Mali 14 1 319 4.1 0.2 1.5 60.5 60.2 0.4 1.1 29 4 Mauritania 16 1 318 4.6 <0.1 0.8 25.8 27.9 0.9 0.5 .. .. Mauritius 36 1 22 11.1 <0.1 1.7 <27.8 29.2 1.8 1.0 .. .. Mexico 37 12 20 10.6 0.2 0.3 27.1 28.5 0.3 0.2 .. .. Moldova 46 6 141 7.6 .. 0.4 <50.0 29.5 0.4 0.2 55 22 Mongolia 46 7 205 1.9 .. 0.1 .. <20.0 0.1 .. .. .. Morocco 26 0b 92 8.1 .. 0.1 27.5 28.1 0.1 0.1 .. .. Mozambique 20 2 431 3.7 1.4 12.5 59.4 57.9 2.9 8.5 27 12 Myanmar 44 12 171 3.2 0.4 0.7 33.4 41.7 0.7 0.6 .. .. Namibia 36 9 767 4.2 1.2 15.3 60.7 61.1 3.4 10.3 65 42 Nepal 29 26 173 4.2 <0.1 0.5 21.8 25.0 0.5 0.3 24 8 Netherlands 38 30 8 5.2 0.1 0.2 25.6 27.2 0.2 0.1 .. .. New Zealand 30 28 7 6.4 0.1 0.1 <16.7 <35.7 0.1 .. .. .. Nicaragua .. 5 49 10.1 <0.1 0.2 25.6 28.0 0.3 0.1 .. 7 Niger 41 11 174 3.7 0.1 0.8 29.3 30.4 0.9 0.5 .. .. Nigeria 9 0b 311 4.5 0.7 3.1 60.0 58.3 0.8 2.3 38 8 Norway 34 30 6 3.6 <0.1 0.1 <41.7 <33.3 0.1 0.1 .. .. Oman 24 0b 13 13.1 .. .. .. .. .. .. .. .. Pakistan 30 3 181 9.6 .. 0.1 26.0 28.7 0.1 0.1 .. .. Panama 52 20 47 9.7 0.4 1.0 26.9 28.9 1.1 0.6 .. .. Papua New Guinea 46 28 250 2.9 .. 1.5 34.7 39.6 0.6 0.7 .. .. Paraguay 33 14 58 4.8 <0.1 0.6 26.4 29.0 0.7 0.3 .. .. Peru 43 23 126 6.0 0.1 0.5 26.8 28.4 0.5 0.3 .. 9 Philippines 39 9 290 7.6 .. .. <50 26.8 .. .. 13 3 Poland 44 27 25 7.6 .. 0.1 26.0 28.9 0.1 0.1 .. .. Portugal 41 31 30 5.7 0.2 0.5 26.6 27.6 0.5 0.3 .. .. Puerto Rico .. .. 4 10.7 .. .. .. .. .. .. .. .. 2009 World Development Indicators 115 2.20 Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIV Condom use of smoking tuberculosis of diabetes Female Youth per % of Total % of total % of population % of population % of adults 100,000 population % of population population ages 15­24 ages 15­24 Male Female people ages 20­79 ages 15­49 with HIV Male Female Male Female 2008 2008 2007 2007 1990 2007 2001 2007 2007 2007 2000­07a 2000­07a Romania 41 25 115 7.6 .. 0.1 50.7 50.0 0.2 0.2 .. .. Russian Federation 70 27 110 7.6 .. 1.1 22.1 25.5 1.3 0.6 .. .. Rwanda .. 8 397 1.5 9.2 2.8 60.6 60.0 0.5 1.4 19 5 Saudi Arabia 25 3 46 16.7 .. .. .. .. .. .. .. .. Senegal 14 1 272 4.6 0.1 1.0 60.9 59.4 0.3 0.8 48 5 Serbia 44 d 44 d 32 7.1d <0.1 0.1 25.5 28.1 0.1 0.1 .. .. Sierra Leone 32 4 574 4.3 0.2 1.7 59.4 58.8 0.4 1.3 .. .. Singapore 26 5 27 10.1 .. 0.2 <34.5 29.3 0.2 0.1 .. .. Slovak Republic 42 20 17 7.6 .. <0.1 .. .. .. .. .. .. Slovenia 32 21 13 7.6 .. <0.1 .. .. .. .. .. .. Somalia .. .. 249 2.8 <0.1 0.5 26.5 27.9 0.6 0.3 .. .. South Africa 25 8 948 4.4 0.8 18.1 58.7 59.3 4.0 12.7 57 46 Spain 36 31 30 5.7 0.4 0.5 20.8 20.0 0.6 0.2 .. .. Sri Lanka 25 0b 60 8.4 .. .. <33.3 37.8 <0.1 .. .. .. Sudan 24 2 243 4.0 0.8 1.4 56.0 58.6 0.3 1.0 .. .. Swaziland 13 3 1,198 4.0 0.9 26.1 60.7 58.8 5.8 22.6 66 44 Sweden 20 25 6 5.2 0.1 0.1 43.4 46.8 0.1 0.1 .. .. Switzerland 31 22 6 7.9 0.4 0.6 33.2 36.8 0.4 0.5 .. .. Syrian Arab Republic 43 .. 24 10.6 .. .. .. .. .. .. .. .. Tajikistan .. .. 231 4.9 .. 0.3 <20.8 21.0 0.4 0.1 .. .. Tanzania 21 2 297 2.9 4.8 6.2 61.7 58.5 0.5 0.9 36 13 Thailand 37 3 142 6.9 1.0 1.4 36.9 41.7 1.2 1.2 .. .. Timor-Leste 26 1 322 1.7 .. .. .. .. .. .. .. .. Togo .. .. 429 4.1 0.7 3.3 61.0 57.5 0.8 2.4 .. .. Trinidad and Tobago 32 6 11 11.5 0.2 1.5 57.5 59.2 0.3 1.0 .. .. Tunisia 47 1 26 5.2 .. 0.1 <45.5 27.8 0.1 <0.1 .. .. Turkey 52 19 30 7.8 .. .. .. .. .. .. .. .. Turkmenistan 27 1 68 5.2 .. <0.1 .. .. .. .. .. 1 Uganda 18 2 330 2.0 13.7 5.4 58.9 59.3 1.3 3.9 38 15 Ukraine 64 23 102 7.6 .. 1.6 35.7 44.2 1.5 1.5 .. .. United Arab Emirates 26 2 16 19.5 .. .. .. .. .. .. .. .. United Kingdom 37 35 15 2.9 <0.1 0.2 .. .. .. .. .. .. United States 26 22 4 7.8 0.5 0.6 18.0 20.9 0.7 0.3 .. .. Uruguay 37 28 22 5.6 0.1 0.6 25.4 28.0 0.6 0.3 .. .. Uzbekistan 24 1 113 5.1 .. 0.1 <35.7 28.8 0.1 0.1 18 2 Venezuela, RB 33 27 34 5.4 .. .. .. .. .. .. .. .. Vietnam 43 2 171 2.9 0.1 0.5 24.7 27.1 0.6 0.3 16 8 West Bank and Gaza 41 3 20 8.4 .. .. .. .. .. .. .. .. Yemen, Rep. 77 29 76 2.9 .. .. .. .. .. .. .. .. Zambia 18 2 506 3.8 8.9 15.2 54.7 57.1 3.6 11.3 36 19 Zimbabwe 21 2 782 4.0 14.2 15.3 58.8 56.7 2.9 7.7 52 9 World 40 w 8w 139 w 5.8 w 0.3 w 0.8 w 30.8 w 32.9 w 0.5 w 0.7 w Low income 29 4 269 4.7 1.5 2.1 37.5 40.6 0.7 1.6 Middle income 44 6 129 5.9 0.1 0.6 30.8 32.6 0.4 0.5 Lower middle income 44 3 134 5.5 0.1 0.3 30.7 32.7 0.3 0.3 Upper middle income 41 18 108 7.4 .. 1.7 31.1 32.2 1.0 1.4 Low & middle income 41 6 162 5.6 0.4 0.9 32.1 34.2 0.5 0.8 East Asia & Pacific 57 4 136 4.2 0.1 0.2 25.4 28.5 0.2 0.2 Europe & Central Asia 56 21 84 7.3 .. 0.6 28.6 30.5 0.8 0.5 Latin America & Carib. 29 15 50 7.1 0.3 0.5 32.1 32.8 0.7 0.4 Middle East & N. Africa 31 3 41 8.6 .. 0.1 28.0 28.6 .. .. South Asia 30 2 174 6.9 0.1 0.3 32.9 34.6 0.3 0.3 Sub-Saharan Africa 15 2 369 3.6 2.1 5.0 57.0 56.9 1.1 3.3 High income 34 22 16 6.8 0.3 0.3 23.3 24.9 0.5 0.2 Euro area 37 27 13 6.4 0.2 0.3 25.8 26.9 0.3 0.2 a. Data are for the most recent year available. b. Less than 0.5. c. Includes Hong Kong, China. d. Includes Montenegro. 116 2009 World Development Indicators PEOPLE Health risk factors and future challenges 2.20 About the data Definitions The limited availability of data on health status is a occur in young adults, with young women especially · Prevalence of smoking is the percentage of men major constraint in assessing the health situation in vulnerable. and women who smoke cigarettes. The age range var- developing countries. Surveillance data are lacking The Joint United Nations Programme on HIV/AIDS ies, but in most countries is 18 and older or 15 and for many major public health concerns. Estimates (UNAIDS) and the WHO estimate HIV prevalence from older. · Incidence of tuberculosis is the estimated of prevalence and incidence are available for some sentinel surveillance, population-based surveys, and number of new tuberculosis cases (pulmonary, smear diseases but are often unreliable and incomplete. special studies. The estimates in the table are more positive, extrapulmonary). · Prevalence of diabetes National health authorities differ widely in capacity reliable than previous estimates because of expanded refers to the percentage of people ages 20­79 who and willingness to collect or report information. To sentinel surveillance and improved data quality. Find- have type 1 or type 2 diabetes. · Prevalence of HIV compensate for this and improve reliability and inter- ings from population-based HIV surveys, which are is the percentage of people who are infected with national comparability, the World Health Organization geographically more representative than sentinel sur- HIV. Total and youth rates are percentages of the (WHO) prepares estimates in accordance with epide- veillance and include both men and women, influenced relevant age group. Female rate is as a percentage miological models and statistical standards. a downward adjustment to prevalence rates based on of the total population with HIV. · Condom use is the Smoking is the most common form of tobacco use sentinel surveillance. And assumptions about the aver- percentage of the population ages 15­24 who used a and the prevalence of smoking is therefore a good age time people living with HIV survive without antiret- condom at last intercourse in the last 12 months. measure of the tobacco epidemic (Corrao and others roviral treatment were improved in the most recent 2000). Tobacco use causes heart and other vascular model. Thus, estimates in this edition should not be diseases and cancers of the lung and other organs. compared with estimates in previous editions. Given the long delay between starting to smoke and Estimates from recent Demographic and Health the onset of disease, the health impact of smoking Surveys that have collected data on HIV/AIDS dif- in developing countries will increase rapidly only in fer somewhat from those of UNAIDS and the WHO, the next few decades. Because the data present a which are based on surveillance systems that focus one-time estimate, with no information on intensity on pregnant women who attend sentinel antenatal or duration of smoking, and because the definition of clinics. Caution should be used in comparing the adult varies, the data should be used with caution. two sets of estimates. Demographic and Health Sur- Tuberculosis is one of the main causes of adult veys are household surveys that use a representative deaths from a single infectious agent in developing sample from the whole population, whereas surveil- countries. In developed countries tuberculosis has lance data from antenatal clinics are limited to preg- reemerged largely as a result of cases among immi- nant women. Household surveys also frequently pro- grants. The estimates of tuberculosis incidence in vide better coverage of rural populations. However, the table are based on an approach in which reported respondents who refuse to participate or are absent cases are adjusted using the ratio of case notifi - from the household add considerable uncertainty to cations to the estimated share of cases detected survey-based HIV estimates, because the possible by panels of 80 epidemiologists convened by the association of absence or refusal with higher HIV WHO. prevalence is unknown. UNAIDS and the WHO esti- Diabetes, an important cause of ill health and a mate HIV prevalence for the adult population (ages risk factor for other diseases in developed countries, 15­49) by assuming that prevalence among pregnant is spreading rapidly in developing countries. Highest women is a good approximation of prevalence among among the elderly, prevalence rates are rising among men and women. However, this assumption might not younger and productive populations in developing apply to all countries or over time. Other potential countries. Economic development has led to the biases are associated with the use of antenatal clinic Data sources spread of Western lifestyles and diet to develop- data, such as differences among women who attend ing countries, resulting in a substantial increase in antenatal clinics and those who do not. Data on smoking are from Omar Shafey, Michael diabetes. Without effective prevention and control Data on condom use are from household surveys Eriksen, Hana Ross, and Judith Mackay's Tobacco programs, diabetes will likely continue to increase. and refer to condom use at last intercourse. How- Atlas, 3rd edition (2009). Data on tuberculosis are Data are estimated based on sample surveys. ever, condoms are not as effective at preventing the from the WHO's Global Tuberculosis Control Report Adult HIV prevalence rates reflect the rate of HIV transmission of HIV unless used consistently. Some 2009. Data on diabetes are from the International infection in each country's population. Low national surveys have asked directly about consistent use, Diabetes Federation's Diabetes Atlas, 3rd edition. prevalence rates can be misleading, however. They but the question is subject to recall and other biases. Data on prevalence of HIV are from UNAIDS and often disguise epidemics that are initially concen- Caution should be used in interpreting the data. the WHO's 2008 Report on the Global AIDS Epi- trated in certain localities or population groups and For indicators from household surveys, the year in demic. Data on condom use are from Demographic threaten to spill over into the wider population. In the table refers to the survey year. For more informa- and Health Surveys by Macro International. many developing countries most new infections tion, consult the original sources. 2009 World Development Indicators 117 2.21 Health gaps by income and gender Survey Prevalence of Child Infant Under-five year child malnutrition immunization rate mortality rate mortality rate Moderate underweight % of children % of children under age 5 ages 12­23 monthsa New reference Old reference Measles DTP3 per 1,000 live births per 1,000 Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile Armenia 2000 3 2 3 1 68 74b 89 84b 52 27 61 30 Bangladesh 2004 36 19 41 24 60 91 71 91 90 65 121 71 Benin 2001 18 6 21 9 57 83 63 89 112 50 198 93 Bolivia 2003 7 1 10 1 62 74 64 85 87 32 119 37 Brazil 1996 7 2 10 3 78 90 66 82 83 29 99 33 Burkina Faso 2003 19 13 26 16 48 71 45 73 97 78 206 144 Cambodia 2000 27 23 35 28 44 82 39 75 110 50 155 64 Cameroon 2004 .. .. .. .. 57 86 55 86 101 52 189 88 Central African Republic 1994­95 20 11 25 15 31 80 27 76 132 54 193 98 Chad 2004 24 16 27 19 8 38 5 42 109 101 176 187 Colombia 2005 7 2 11 3 70 91 73 91 32 14 39 16 Comores 1996 15 12 22 14 51 86 58 92 87 65 129 87b Côte d'Ivoire 1994 17 7 21 10 31 79 26 74 117 63 190 97 Dominican Republic 2002 7 1 9 1 83 94 46 66 50 20 66 22 Egypt, Arab Rep. 2000 4 2 5 2 95 99 94 93 76 30 98 34 Eritrea 1995 .. .. .. .. 37 92 30 89 74 68 152 104 Ethiopia 2000 25 22 32 29 18 52 14 43 93 95 159 147 Gabon 2000 10 4 14 7 34 71 18 49 57 36 93 55 Ghana 2003 17 6 22 10 74 88 64 87 61 58 128 88 Guatemala 1998­99 21 9 26 10 80 91 74 76 58 39 78 39 Guinea 1999 17 9 22 13 33 73 30 69 119 70 230 133 Haiti 2000 14 4 18 6 43 63 31 58 100 97 164 109 India 1998­99 28 16 33 21 28 81 36 85 97 38 141 46 Indonesia 2002­03 .. .. .. .. 59 85 42 72 61 17 77 22 Jordan 1997 .. .. .. .. 90 93 98 93 35 23 42 25 Kazakhstan 1999 3 5 5 6 74 76b 90 82b 68 42 82 45 Kenya 2003 17 6 22 7 54 88 56 73 96 62 149 91 Kyrgyz Republic 1997 6 5 10 7 82 81 82 87 83 46 96 49 Madagascar 1997 24 18 29 24 32 79 32 81 119 58 195 101 Malawi 2000 18 9 24 12 80 90 79 93 132 86 231 149 Mali 2001 20 10 26 13 40 77 28 71 137 90 248 148 Mauritania 2000­01 18 11 23 15 42 86 18 61 61 62 98 79 Morocco 2003­04 11 2 13 3 83 98 89 98 62 24 78 26 Mozambique 2003 16 5 21 7 61 96 52 96 143 71 196 108 Namibia 2000 17 6 22 9 76 86 76 83 36 23 55 31 Nepal 2001 34 20 40 26 61 83 62 85 86 53 130 68 Nicaragua 2001 9 2 13 2 76 94 77 83 50 16 64 19 Niger 1998 27 18 30 26 23 66 9 68 131 86 282 184 Nigeria 2003 20 9 24 10 16 71 7 61 133 52 257 79 Pakistan 1990­91 28 14 33 19 28 75 24 64 89 63 125 74 Paraguay 1990 3 1 5 1 48 69 40 69 43 16 57 20 Peru 2000 9 1 13 1 81 92 76 93 64 14 93 18 Philippines 2003 .. .. .. .. 70 89 64 92 42 19 66 21 Rwanda 2000 15 8 19 12 84 89 80 89 139 88 246 154 Senegal 1997 .. .. .. .. .. .. .. .. 85 45 181 70 South Africa 1998 .. .. .. .. 74 85 64 85 62 17 87 22 Tanzania 2004 14 8 20 11 65 91 34 36 88 64 137 93 Togo 1998 17 8 23 10 35 63 29 68 84 66 168 97 Turkey 1998 .. .. .. .. 64 89 45 81 68 30 85 33 Turkmenistan 2000 .. .. .. .. 91 80 97 86 89 58 106 70 Uganda 2000­01 16 7 21 10 49 65 35 55 106 60 192 106 Uzbekistan 1996 11 8 15 10 96 93 89 82 54 46 70 50 Vietnam 2002 .. .. .. .. 64 98 53 94 39 14 53 16 Yemen, Rep. 1997 .. .. 36 24 16 73 14 71 109 60 163 73 Zambia 2001­02 18 12 24 17 81 88 74 89 115 57 192 92 Zimbabwe 1999 10 5 16 6 80 86 81 86 59 44 100 62 a. Refers to children who were immunized at any time before the survey. b. The data contain large sampling errors because of the small number of cases. 118 2009 World Development Indicators PEOPLE Health gaps by income and gender Survey Prevalence of child Child Infant 2.21Under-five year malnutrition immunization rate mortality rate mortality rate Old reference Moderate underweight % of children % of children ages 12­23 monthsa under age 5 Measles DTP3 per 1,000 live births per 1,000 Male Female Male Female Male Female Male Female Male Female Armenia 2000 2 3 71 79 90 89 46 42 51 45 Bangladesh 2004 34 35 76 76 81 81 80 64 102 91 Benin 2001 19 17 69 67 74 71 98 92 162 163 Bolivia 2003 6 6 65 63 70 73 71 64 94 91 Brazil 1996 6 5 87 87 82 80 52 44 60 53 Burkina Faso 2003 25 23 54 58 57 57 95 89 195 192 Cambodia 2000 32 33 57 54 50 47 103 82 133 110 Cameroon 2004 14 15 65 66 65 68 88 74 154 141 Central African Republic 1994­95 21 19 52 53 49 46 109 94 165 152 Chad 2004 23 23 23 23 20 21 122 108 207 198 Colombia 2005 6 6 83 82 84 81 26 18 30 21 Comores 1996 19 17 63 64 68 69 93 75 122 103 Côte d'Ivoire 1994 19 16 54 52 49 45 99 83 163 137 Dominican Republic 2002 5 5 89 88 54 61 38 31 46 40 Egypt, Arab Rep. 2000 4 3 97 97 94 94 55 55 69 70 Eritrea 1995 26 27 52 50 49 49 82 69 163 141 Ethiopia 2000 32 31 28 26 22 19 124 101 197 178 Gabon 2000 10 9 55 55 40 33 74 49 103 80 Ghana 2003 17 17 82 83 81 77 70 59 111 108 Guatemala 1998­99 21 18 82 87 73 74 50 48 64 65 Guinea 1999 17 19 52 52 46 47 112 101 202 188 Haiti 2000 14 13 54 54 43 43 97 83 143 132 India 1998­99 28 30 52 50 56 54 75 71 98 105 Indonesia 2002­03 .. .. 73 71 58 59 46 40 58 51 Jordan 1997 4 5 90 90 96 96 34 23 38 30 Kazakhstan 1999 4 4 79 78 89 88 62 47 72 53 Kenya 2003 18 14 73 72 71 74 84 67 122 103 Kyrgyz Republic 1997 11 8 84 85 83 81 72 60 81 70 Madagascar 1997 27 27 47 45 48 49 109 90 176 152 Malawi 2000 20 19 83 83 84 85 117 108 207 199 Mali 2001 24 21 49 48 41 38 136 116 250 226 Mauritania 2000­01 22 22 61 63 39 41 74 59 110 94 Morocco 2003­04 9 8 88 92 95 95 51 37 59 48 Mozambique 2003 18 17 77 76 73 71 127 120 181 176 Namibia 2000 19 18 79 82 78 81 45 34 67 54 Nepal 2001 35 36 73 69 74 70 79 75 105 112 Nicaragua 2001 9 7 87 86 84 81 39 32 48 41 Niger 1998 29 30 36 34 25 25 141 131 299 306 Nigeria 2003 19 20 34 38 19 24 116 102 222 212 Pakistan 1990­91 27 27 55 46 45 40 102 86 122 119 Paraguay 1990 3 4 56 61 50 57 39 33 49 45 Peru 2000 6 6 84 85 85 84 46 40 64 57 Philippines 2003 .. .. 78 81 78 80 35 25 48 34 Rwanda 2000 19 19 86 88 85 87 123 112 215 198 Senegal 1997 .. .. .. .. .. .. 74 65 144 134 South Africa 1998 .. .. 84 81 74 78 49 35 66 48 Tanzania 2004 18 18 80 80 37 33 83 82 135 130 Togo 1998 19 18 45 40 43 41 89 71 156 132 Turkey 1998 7 7 79 78 60 57 51 46 61 58 Turkmenistan 2000 11 10 87 88 93 92 83 60 101 76 Uganda 2000­01 18 17 56 57 45 48 93 85 164 149 Uzbekistan 1996 15 13 91 92 87 90 50 37 65 46 Vietnam 2002 .. .. 84 82 72 73 25 25 34 31 Yemen, Rep. 1997 33 30 45 40 41 39 98 80 128 114 Zambia 2001­02 21 21 83 86 78 82 95 93 176 160 Zimbabwe 1999 12 11 77 81 80 82 63 56 95 85 a. Refers to children who were immunized at any timebefore the survey. 2009 World Development Indicators 119 2.21 Health gaps by income and gender Survey Pregnant women Contraceptive Births attended by Total fertility Exclusive year receiving prevalence skilled health staffa rate breastfeeding prenatal care rate modern methods % of married women % of children % ages 15­49 % of total births per woman under 4 months Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile Armenia 2000 85 97 16 29 93 100 2.5 1.6 .. .. Bangladesh 2004 25 81 45 50 3 39 4.1 2.2 62 31 Benin 2001 73 100 4 15 50 99 7.2 3.5 50 42b Bolivia 2003 62 98 23 49 27 98 6.7 2.0 79 31 Brazil 1996 72 98 56 77 72 99 4.8 1.7 33 60 b Burkina Faso 2003 56 96 2 27 19 84 6.6 3.6 17 28 Cambodia 2000 22 80 13 25 15 81 4.7 2.2 14 18 Cameroon 2004 65 97 2 27 29 95 6.5 3.2 33 30 b Central African Republic 1994­95 39 91 1 9 14 82 5.1 4.9 9 4 Chad 2004 9 77 0 7 1 51 5.1 6.0 1 2 Colombia 2005 84 99 60 72 72 99 4.1 1.4 60 64 Comoros 1996 67 95 7 19 26 85 6.4 3.0 3b .. Côte d'Ivoire 1994 62 98 1 13 17 84 6.4 3.7 0 5 Dominican Republic 2002 97 99 59 70 94 100 4.5 2.1 18 6 Egypt, Arab Rep. 2000 31 84 43 61 31 94 4.0 2.9 72 57 Eritrea 1995 34 90 0c 19 5 74 8.0 3.7 64 73 Ethiopia 2000 15 60 3 23 1 25 6.3 3.6 63 46 Gabon 2000 85 98 6 18 67 97 6.3 3.0 6 5b Ghana 2003 83 98 9 26 21 90 6.4 2.8 62b .. Guatemala 1998­99 37 97 5 60 9 92 7.6 2.9 62 .. Guinea 1999 58 97 1 9 12 82 5.8 4.0 9 8 Haiti 2000 65 91 17 24 4 70 6.8 2.7 40 15b India 1998­99 44 93 29 55 16 84 3.4 1.8 64 37 Indonesia 2002­03 78 99 49 58 40 94 3.0 2.2 58 35 Jordan 1997 93 97 28 47 91 99 5.2 3.1 14 14b Kazakhstan 1999 97 91 49 55 99 99 3.4 1.2 .. .. Kenya 2003 75 94 12 44 17 75 7.6 3.1 22 17 Kyrgyz Republic 1997 96 99 44 54 96 100 4.6 2.0 18b .. Madagascar 1997 67 96 2 24 30 89 8.1 3.4 57 65 Malawi 2000 89 98 20 40 43 83 7.1 4.8 53 72 Mali 2001 42 92 4 18 22 89 7.3 5.3 38 18 Mauritania 2000­01 33 89 0c 17 15 93 5.4 3.5 28 30 Morocco 2003­04 40 93 51 57 29 95 3.3 1.9 53 36 Mozambique 2003 67 98 14 37 25 89 6.3 3.8 47 27 Namibia 2000 81 96 29 64 55 97 6.0 2.7 100 b 85b Nepal 2001 30 80 24 55 4 45 5.3 2.3 76 67 Nicaragua 2001 69 97 50 71 78 99 5.6 2.1 53 15b Niger 1998 24 85 1 18 4 63 8.4 5.7 1 3 Nigeria 2003 37 96 4 21 13 85 6.5 4.2 15 34 Pakistan 1990­91 8 72 1 23 5 55 5.1 4.0 36 9 Paraguay 1990 73 98 21 46 41 98 7.9 2.7 7 0 Peru 2000 41 74 37 58 13 88 5.5 1.6 88 59 Philippines 2003 72 97 24 35 25 92 5.9 2.0 60 20 Rwanda 2000 90 95 2 15 17 60 6.0 5.4 89 79 Senegal 1997 67 97 1 24 20 86 7.4 3.6 13 19 South Africa 1998 96 94 34 70 68 98 4.8 1.9 15 11b Tanzania 2004 91 97 11 36 31 87 7.3 3.3 58 55 Togo 1998 69 97 3 13 25 91 7.3 2.9 7 34 Turkey 1998 38 96 24 48 53 98 3.9 1.7 10 4b Turkmenistan 2000 98 97 51 50 97 98 3.4 2.1 11 28b Uganda 2000­01 88 98 11 41 20 77 8.5 4.1 73 59 Uzbekistan 1996 93 96 46 52 92 100 4.4 2.2 .. .. Vietnam 2002 68 100 58 52 58 100 2.2 1.4 18 .. Yemen, Rep. 1997 17 68 1 24 7 50 7.3 4.7 20 13 Zambia 2001­02 89 99 11 53 20 91 7.3 3.6 39 70 b Zimbabwe 1999 94 97 41 67 57 94 4.9 2.6 36 46b a. Based on births in the fi ve years before the survey. b. The data contain large sampling errors because of the small number of cases. c. Less than 0.5. 120 2009 World Development Indicators PEOPLE Health gaps by income and gender 2.21 About the data Definitions The data in the table describe the health status and specific asset indexes with country-specific choices · Survey year is the year in which the underlying use of health services by individuals in different of asset indicators might produce a more effective data were collected. · Prevalence of child malnutri- socioeconomic groups and by sex within countries. and accurate index for each country. The asset index tion is the percentage of children under age 5 whose The data are from Demographic and Health Surveys used in the table does not have this flexibility. weight for age is two to three standard deviations conducted by Macro International with the support The analysis was carried out for 56 countries, below the median reference standard for their age. of the U.S. Agency for International Development. with the results issued in country reports. The table The table presents malnutrition data using both the These large-scale household sample surveys, con- shows the estimates for the poorest and richest quin- old reference standards and the new international ducted periodically in developing countries, collect tiles and by sex only; the full set of estimates for up child growth standards released in 2006 by the information on many health, nutrition, and popula- to 117 indicators is available in the country reports World Health Organization. For more information tion measures as well as on respondents' social, (see Data sources). about the change in standards, see About the data demographic, and economic characteristics using a Demographic and Health Surveys try to collect for table 2.19. · Child immunization rate is the per- standard set of questionnaires. The data presented internationally comparable data, but the age group centage of children ages 12­23 months at the time here draw on responses to individual and household of the reference population could differ across coun- of the survey who, at any time before the survey, questionnaires. tries. Caution should be used when comparing the had received measles vaccine and three doses of Socioeconomic status as displayed in the table is data. The estimates in the table are based on survey diphtheria, tetanus, and pertussis (whooping cough) based on a household's assets, including ownership data, which refer to a period preceding the survey vaccine (DTP3). · Infant mortality rate is the num- of consumer items, features of the household's dwell- date, or use a definition or methodology different ber of infants dying before reaching one year of age, ing, and other characteristics related to wealth. Each from the estimates in tables 2.17­2.19 and 2.22. per 1,000 live births. · Under-five mortality rate is household asset on which information was collected Thus the estimates may differ from those in the other the probability that a newborn baby will die before was assigned a weight generated through principal- tables, and caution should be used in interpreting reaching age 5, per 1,000, if subject to current age- component analysis. The resulting scores were stan- the data. specific mortality rates. · Pregnant women receiv- dardized in relation to a standard normal distribution ing prenatal care are the percentage of women with with a mean of zero and a standard deviation of one. one or more births during the five years preceding the The standardized scores were then used to create survey who were attended by skilled health personnel break-points defining wealth quintiles, expressed as at least once during pregnancy for reasons related quintiles of individuals in the population rather than to pregnancy. · Contraceptive prevalence rate is quintiles of individuals at risk with respect to any the percentage of women married or in-union ages one health indicator. 15­49 who are practicing, or whose sexual partners The choice of the asset index for defining socio- are practicing, any modern method of contracep- economic status was based on pragmatic rather than tion. · Births attended by skilled health staff are conceptual considerations: Demographic and Health the percentage of deliveries attended by personnel Surveys do not collect income or consumption data trained to give the necessary supervision, care, and but do have detailed information on households' own- advice to women during pregnancy, labor, and the ership of consumer goods and access to a variety postpartum period; to conduct deliveries on their of goods and services. Like income or consumption, own; and to care for newborns. Skilled health staff the asset index defines disparities primarily in eco- include doctors, nurses, and trained midwives, but nomic terms. It therefore excludes other possibilities exclude trained or untrained traditional birth atten- of disparities among groups, such as those based dants. · Total fertility rate is the number of children on gender, education, ethnic background, or other that would be born to a woman if she were to live to facets of social exclusion. To that extent the index the end of her childbearing years and bear children provides only a partial view of the multidimensional in accordance with current age-specific fertility rates. concepts of poverty, inequality, and inequity. · Exclusive breastfeeding refers to the percentage Creating one index that includes all asset indicators of children ages 0­3 months who received only limits the types of analysis that can be performed. breast milk in the 24 hours preceding the survey. In particular, the use of a unified index does not per- Data sources mit a disaggregated analysis to examine which asset indicators are more closely associated with health Data on health gaps by income and gender are from status or use of health services. In addition, some Davidson R. Gwatkin and others' Socio- Economic asset indicators may reflect household wealth better Differences in Health, Nutrition, and Population in some countries than in others--or reflect differ- (2007). Country reports are available at www. ent degrees of wealth in different countries. Taking worldbank.org/povertyandhealth/countrydata. such information into account and creating country- 2009 World Development Indicators 121 2.22 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2007 1990 2007 1990 2007 2000­07a,b 2000­07a,b 2005­07a 2005­07a 2007 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 72 76 37 13 46 15 3 1 106 51 81 90 Algeria 67 72 54 33 69 37 .. .. 121 102 78 81 Angola 40 43 150 116 258 158 .. .. 479 434 30 36 Argentina 72 75 25 15 29 16 .. .. 166 79 74 87 Armenia 68 72 48 22 56 24 8 3 195 87 68 83 Australia 77 81 8 5 10 6 .. .. 84 48 88 93 Austria 76 80 8 4 10 4 .. .. 111 55 84 93 Azerbaijan 66 67 78 34 98 39 9 5 216 102 62 77 Bangladesh 55 64 105 47 151 61 24 29 231 198 62 67 Belarus 71 70 20 12 24 13 .. .. 330 115 52 82 Belgium 76 80 8 4 10 5 .. .. 111 61 84 92 Benin 53 57 111 78 184 123 64 65 279 235 53 59 Bolivia 59 66 89 48 125 57 20 26 235 176 63 71 Bosnia and Herzegovina 72 75 18 13 22 14 .. .. 145 76 76 86 Botswana 63 51 45 33 57 40 .. .. 567 567 32 36 Brazil 67 72 49 20 58 22 .. .. 229 120 67 80 Bulgaria 72 73 15 10 19 12 .. .. 221 92 70 86 Burkina Faso 50 52 112 104 206 191 110 113 283 183 47 58 Burundi 46 49 113 108 189 180 .. .. 404 370 40 45 Cambodia 55 60 87 70 119 91 20 20 346 243 50 61 Cameroon 55 50 85 87 139 148 73 72 414 420 41 43 Canada 77 81 7 5 8 6 .. .. 94 57 86 92 Central African Republic 50 45 113 113 171 172 74 82 559 533 28 34 Chad 51 51 120 124 201 209 96 101 355 308 43 49 Chile 74 78 18 8 21 9 .. .. 129 64 80 89 China 68 73 36 19 45 22 .. .. 151 90 75 83 Hong Kong, China 77 82 .. .. .. .. .. .. 77 33 87 94 Colombia 68 73 28 17 35 20 4 3 202 95 71 83 Congo, Dem. Rep. 46 46 127 108 200 161 70 64 435 400 36 40 Congo, Rep. 57 55 67 79 104 125 49 43 391 367 45 50 Costa Rica 76 79 16 10 18 11 .. .. 114 61 82 89 Côte d'Ivoire 53 48 104 89 151 127 .. .. 420 403 38 43 Croatia 72 76 11 5 13 6 .. .. 156 61 76 89 Cuba 75 78 11 5 13 7 .. .. 117 72 82 88 Czech Republic 71 77 11 3 13 4 .. .. 148 66 78 90 Denmark 75 78 8 4 9 4 .. .. 116 69 83 89 Dominican Republic 68 72 53 31 66 38 6 4 219 131 68 79 Ecuador 69 75 43 20 57 22 5 5 169 90 75 85 Egypt, Arab Rep. 62 71 68 30 93 36 10 10 155 91 73 83 El Salvador 66 72 47 21 60 24 .. .. 207 125 70 80 Eritrea 49 58 88 46 147 70 55 50 418 319 42 54 Estonia 69 73 12 4 18 6 .. .. 283 92 59 85 Ethiopia 48 53 122 75 204 119 56 56 361 325 45 50 Finland 75 79 6 3 7 4 .. .. 133 57 83 92 France 77 81 7 4 9 4 .. .. 123 57 84 93 Gabon 61 57 60 60 92 91 32 33 379 383 49 51 Gambia, The 51 59 104 82 153 109 46 39 219 181 58 63 Georgia 70 71 41 27 47 30 5 4 213 81 67 84 Germany 75 80 7 4 9 4 .. .. 107 56 84 92 Ghana 57 60 76 73 120 115 51 35 285 281 57 59 Greece 77 80 9 4 11 4 .. .. 91 41 85 93 Guatemala 63 70 60 29 82 39 .. .. 236 130 67 79 Guinea 47 56 137 93 231 150 89 86 272 235 52 59 Guinea-Bissau 42 46 142 118 240 198 110 88 447 401 35 41 Haiti 55 61 105 57 152 76 33 36 306 237 55 63 122 2009 World Development Indicators PEOPLE Life expectancy Infant mortality Under-five Mortality Child mortality Adult mortality 2.22 Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2007 1990 2007 1990 2007 2000­07a,b 2000­07a,b 2005­07a 2005­07a 2007 2007 Honduras 66 70 45 20 58 24 8 9 238 139 66 78 Hungary 69 73 15 6 17 7 .. .. 256 107 67 86 India 60 65 80 54 117 72 9 12 257 164 60 69 Indonesia 62 71 60 25 91 31 13 11 168 118 72 80 Iran, Islamic Rep. 65 71 54 29 72 33 .. .. 152 101 73 81 Iraq 62 .. 42 .. 53 .. .. .. .. .. .. .. Ireland 75 79 8 4 9 4 .. .. 88 56 85 91 Israel 77 81 10 4 12 5 .. .. 80 38 87 93 Italy 77 81 8 3 9 4 .. .. 84 44 86 93 Jamaica 72 73 28 26 33 31 5 6 220 139 70 79 Japan 79 83 5 3 6 4 .. .. 90 44 87 94 Jordan 67 73 33 21 40 24 3 2 164 113 73 81 Kazakhstan 68 66 51 28 60 32 5 4 361 144 50 77 Kenya 59 54 64 80 97 121 42 39 417 396 43 48 Korea, Dem. Rep. 70 67 42 42 55 55 .. .. 179 127 66 75 Korea, Rep. 71 79 8 4 9 5 .. .. 109 45 82 92 Kuwait 75 78 13 9 15 11 .. .. 86 52 85 90 Kyrgyz Republic 68 68 62 34 74 38 8 4 279 131 57 75 Lao PDR 55 64 120 56 163 70 .. .. 233 190 62 68 Latvia 69 71 14 7 17 9 .. .. 311 114 64 86 Lebanon 69 72 32 26 37 29 .. .. 153 101 74 82 Lesotho 59 43 81 68 102 84 22 19 723 720 19 22 Liberia 43 46 138 93 205 133 57 51 460 425 33 38 Libya 68 74 35 17 41 18 .. .. 148 92 75 84 Lithuania 71 71 10 7 16 8 .. .. 346 116 63 86 Macedonia, FYR 71 74 33 15 38 17 2 1 135 80 77 85 Madagascar 51 59 103 70 168 112 45 45 287 227 55 62 Malawi 49 48 124 71 209 111 52 54 526 519 34 37 Malaysia 70 74 16 10 22 11 .. .. 152 87 75 85 Mali 48 54 148 117 250 196 117 114 255 178 49 59 Mauritania 58 64 81 75 130 119 50 48 172 106 64 74 Mauritius 69 72 20 13 24 15 .. .. 207 106 68 82 Mexico 71 75 42 29 52 35 .. .. 142 79 78 86 Moldova 67 69 30 16 37 18 7 4 294 138 59 77 Mongolia 61 67 71 35 98 43 11 10 262 171 59 71 Morocco 64 71 69 32 89 34 9 11 148 98 74 82 Mozambique 44 42 135 115 201 168 61 64 625 613 24 27 Myanmar 59 62 91 74 130 103 .. .. 298 190 54 67 Namibia 62 53 57 47 87 68 24 19 518 512 37 41 Nepal 54 64 99 43 142 55 21 18 230 206 62 66 Netherlands 77 80 7 4 9 5 .. .. 81 59 86 91 New Zealand 75 80 8 5 11 6 .. .. 92 59 87 91 Nicaragua 64 73 52 28 68 35 10 9 209 120 70 80 Niger 47 57 143 83 304 176 138 135 166 180 60 58 Nigeria 47 47 120 97 230 189 57 57 434 416 37 39 Norway 77 80 7 3 9 4 .. .. 86 53 87 92 Oman 70 76 25 11 32 12 .. .. 99 73 82 87 Pakistan 60 65 102 73 132 90 14 22 174 142 66 69 Panama 72 76 27 18 34 23 .. .. 139 74 78 87 Papua New Guinea 55 57 69 50 94 65 .. .. 422 306 41 55 Paraguay 68 72 34 24 41 29 .. .. 174 128 72 79 Peru 66 71 58 17 78 20 11 8 200 123 70 80 Philippines 66 72 43 23 62 28 14 9 158 104 73 82 Poland 71 75 19 6 17 7 .. .. 209 80 72 89 Portugal 74 78 11 3 15 4 .. .. 128 53 83 91 Puerto Rico 75 78 .. .. .. .. .. .. 134 53 80 91 2009 World Development Indicators 123 2.22 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2007 1990 2007 1990 2007 2000­07a,b 2000­07a,b 2005­07a 2005­07a 2007 2007 Romania 70 73 27 13 32 15 .. .. 201 85 69 85 Russian Federation 69 68 23 13 27 15 .. .. 429 158 43 77 Rwanda 32 46 117 109 195 181 90 87 456 408 34 40 Saudi Arabia 68 73 35 20 44 25 3 4 140 90 76 84 Senegal 57 63 72 59 149 114 69 69 171 102 63 73 Serbia 71 73 .. 7 .. 8 4 3 157c 83c 74 c 85c Sierra Leone 39 43 169 155 290 262 134 124 405 345 33 40 Singapore 74 80 7 2 8 3 .. .. 81 46 86 92 Slovak Republic 71 74 12 7 15 8 .. .. 196 76 71 88 Slovenia 73 78 8 3 11 4 .. .. 149 57 80 91 Somalia 42 48 121 88 203 142 53 54 385 335 39 44 South Africa 62 50 49 46 64 59 13 9 623 598 27 33 Spain 77 81 8 4 9 4 .. .. 106 44 85 94 Sri Lanka 70 72 26 17 32 21 .. .. 233 99 67 83 Sudan 53 59 79 69 125 109 38 30 305 265 53 59 Swaziland 58 40 70 66 96 91 32 30 772 760 15 18 Sweden 78 81 6 3 7 3 .. .. 78 48 88 93 Switzerland 77 82 7 4 9 5 .. .. 78 46 87 93 Syrian Arab Republic 68 74 30 15 37 17 5 3 124 84 78 85 Tajikistan 63 67 91 57 117 67 18 13 211 139 63 73 Tanzania 51 52 96 73 157 116 56 52 433 401 41 46 Thailand 67 71 26 6 31 7 .. .. 264 159 64 77 Timor-Leste 47 61 138 77 184 97 .. .. 266 232 57 62 Togo 58 58 89 65 150 100 55 43 278 236 54 61 Trinidad and Tobago 70 70 30 31 34 35 .. .. 240 190 66 73 Tunisia 70 74 41 18 52 21 .. .. 124 72 78 86 Turkey 66 72 67 21 82 23 9 9 152 85 73 84 Turkmenistan 63 63 81 45 99 50 19 17 298 142 53 72 Uganda 50 51 106 82 175 130 75 62 429 416 41 44 Ukraine 70 68 22 20 25 24 4 1 385 142 51 80 United Arab Emirates 73 79 13 7 15 8 .. .. 74 49 86 91 United Kingdom 76 79 8 5 10 6 .. .. 96 60 85 91 United States 75 78 9 7 11 8 .. .. 141 82 81 88 Uruguay 73 76 21 12 25 14 .. .. 142 67 77 88 Uzbekistan 69 67 61 36 74 41 11 7 240 136 61 74 Venezuela, RB 71 74 27 17 32 19 .. .. 179 94 73 84 Vietnam 66 74 40 13 56 15 5 4 137 91 78 84 West Bank and Gaza 69 73 33 24 38 27 3 3 129 92 78 84 Yemen, Rep. 54 63 90 55 127 73 10 11 254 205 59 66 Zambia 48 42 99 103 163 170 89 74 620 619 25 27 Zimbabwe 61 43 62 59 95 90 21 21 687 719 22 22 World 65 w 69 w 63 w 47 w 93 w 68 w 219 w 155 w 68 w 76 w Low income 54 57 103 80 164 126 306 269 53 58 Middle income 65 70 55 35 75 45 201 127 68 78 Lower middle income 64 69 58 38 81 50 197 125 68 77 Upper middle income 69 71 39 21 47 24 225 138 64 80 Low & middle income 63 67 69 51 101 74 224 159 65 74 East Asia & Pacific 67 72 42 22 56 27 163 102 74 81 Europe & Central Asia 69 70 41 21 49 23 303 125 58 81 Latin America & Carib. 68 73 44 22 55 26 196 107 71 82 Middle East & N. Africa 64 70 58 32 77 38 164 112 72 80 South Asia 59 64 87 59 125 78 248 169 60 69 Sub-Saharan Africa 50 51 108 89 183 146 417 390 41 45 High income 76 79 10 6 12 7 117 63 83 91 Euro area 76 80 8 4 10 4 112 54 84 92 a. Data are for the most recent year available. b. Refers to a survey year. Values were estimated directly from surveys and cover the 5 or 10 years preceding the survey. c. Includes Kosovo. 124 2009 World Development Indicators PEOPLE Mortality 2.22 About the data Definitions Mortality rates for different age groups (infants, chil- their reference dates and then extrapolate the trend · Life expectancy at birth is the number of years a dren, and adults) and overall mortality indicators (life to the present. (For further discussion of childhood newborn infant would live if prevailing patterns of mor- expectancy at birth or survival to a given age) are mortality estimates, see UNICEF, WHO, World Bank, tality at the time of its birth were to stay the same important indicators of health status in a country. and United Nations Population Division 2007; for a throughout its life. · Infant mortality rate is the num- Because data on the incidence and prevalence of graphic presentation and detailed background data, ber of infants dying before reaching one year of age, diseases are frequently unavailable, mortality rates see www.childmortality.org/). per 1,000 live births in a given year. · Under-five mor- are often used to identify vulnerable populations. Infant and child mortality rates are higher for boys tality rate is the probability per 1,000 that a newborn And they are among the indicators most frequently than for girls in countries in which parental gender baby will die before reaching age 5, if subject to current used to compare socioeconomic development across preferences are insignificant. Child mortality cap- age-specific mortality rates. · Child mortality rate is countries. tures the effect of gender discrimination better than the probability per 1,000 of dying between ages 1 and The main sources of mortality data are vital reg- infant mortality does, as malnutrition and medical 5--that is, the probability of a 1-year-old dying before istration systems and direct or indirect estimates interventions are more important in this age group. reaching age 5--if subject to current age-specific mor- based on sample surveys or censuses. A "complete" Where female child mortality is higher, as in some tality rates. · Adult mortality rate is the probability per vital registration system--covering at least 90 per- countries in South Asia, girls probably have unequal 1,000 of dying between the ages of 15 and 60--that cent of vital events in the population--is the best access to resources. Child mortality rates in the is, the probability of a 15-year-old dying before reach- source of age-specific mortality data. Where reliable table are not compatible with infant mortality and ing age 60--if subject to current age-specific mortality age-specific mortality data are available, life expec- under-fi ve mortality rates because of differences rates between those ages. · Survival to age 65 refers tancy at birth is directly estimated from the life table in methodology and reference year. Child mortality to the percentage of a hypothetical cohort of newborn constructed from age- specific mortality data. data were estimated directly from surveys and cover infants that would survive to age 65, if subject to cur- But complete vital registration systems are fairly the 10 years preceding the survey. In addition to rent age-specific mortality rates. uncommon in developing countries. Thus estimates estimates from Demographic Health Surveys, new Data sources must be obtained from sample surveys or derived estimates derived from Multiple Indicator Cluster by applying indirect estimation techniques to reg- Surveys (MICS) 3 have been added to the table; they Data on infant and under-five mortality rates are istration, census, or survey data (see Primary data cover the 5 years preceding the survey. the estimates by the Inter-agency Group for Child documentation). Survey data are subject to recall Rates for adult mortality and survival to age 65 Mortality Estimation (which comprises the World error, and surveys estimating infant deaths require come from life tables. Adult mortality rates increased Health Organization, UNICEF, United Nations Popu- large samples because households in which a birth notably in a dozen countries in Sub-Saharan Africa lation Division, World Bank, Harvard University, or an infant death has occurred during a given year between 1995­2000 and 2000­05 and in several U.S. Census Bureau, Economic Commission for cannot ordinarily be pre-selected for sampling. Indi- countries in Europe and Central Asia during the first Latin America and the Caribbean, Measure DHS, rect estimates rely on model life tables that may be half of the 1990s. In Sub-Saharan Africa the increase and other universities and research institutes) inappropriate for the population concerned. Because stems from AIDS-related mortality and affects both and are based mainly on household surveys, cen- life expectancy at birth is estimated using infant mor- sexes, though women are more affected. In Europe suses, and vital registration data, supplemented tality data and model life tables for many develop- and Central Asia the causes are more diverse (high by the World Bank's estimates based on house- ing countries, similar reliability issues arise for this prevalence of smoking, high-fat diet, excessive alco- hold surveys and vital registration and sample reg- indicator. Extrapolations based on outdated surveys hol use, stressful conditions related to the economic istration data. Data on child mortality rates are may not be reliable for monitoring changes in health transition) and affect men more. from Demographic and Health Surveys by Macro status or for comparative analytical work. The percentage of a hypothetical cohort surviv- International (Measure DHS) and Multiple Indica- Estimates of infant and under-five mortality tend ing to age 65 reflects both child and adult mortality tor Cluster Surveys by UNICEF. Other estimates to vary by source and method for a given time and rates. Like life expectancy, it is a synthetic mea- are compiled and produced by the World Bank's place. Years for available estimates also vary by sure based on current age-specific mortality rates. Human Development Network and Development country, making comparison across countries and It shows that even in countries where mortality is Data Group in consultation with its operational over time diffi cult. To make infant and under-fi ve high, a certain share of the current birth cohort will staff and country offices. Important inputs to the mortality estimates comparable and to ensure con- live well beyond the life expectancy at birth, while in World Bank's demographic work come from the sistency across estimates by different agencies, low-mortality countries close to 90 percent will reach United Nations Population Division's World Popula- the United Nations Children's Fund (UNICEF) and at least age 65. tion Prospects: The 2006 Revision, census reports the World Bank (now working together with other and other statistical publications from national organizations as the Inter-agency Group for Child statistical offices and Eurostat, Demographic and Mortality Estimation) developed and adopted a Health Surveys by Macro International, and the statistical method that uses all available informa- Human Mortality Database by the University of tion to reconcile differences. The method uses the California, Berkeley, and the Max Planck Institute weighted least squares method to fit a regression for Demographic Research (www.mortality.org). line to the relationship between mortality rates and 2009 World Development Indicators 125 Text figures, tables, and boxes Introduction E nergy and a changing climate The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today's trends in energy use are not sustain- able. As the world's population grows and economies become more industrialized, nonrenew- able energy sources will become scarcer and more costly. And carbon dioxide emissions from the use of fossil fuels will continue to build in the atmosphere, accelerating global warming. Energy-related carbon dioxide now accounts for 61­65 percent of global greenhouse gas emissions (IEA 2008a; IPCC 2007a; WRI 2005). Global warming will have particularly perni- cious effects for developing economies, with their high exposure and low adaptive capacity. Where energy comes from, how we produce it, and how much we use will profoundly affect development in the 21st century. This introduction focuses on recent trends in energy use and carbon dioxide emissions--and projections through 2030. There is now a consensus that action is needed to curb the growth in human-made greenhouse-gas emissions (IPCC 2007b; IEA 2008a). A new post-2012 policy regime on global climate change--to be agreed in Copenhagen in late 2009--aims to set a quantified global goal for stabilizing greenhouse gases in the atmosphere and to establish robust policy mechanisms that ensure the goal is achieved. Without government initiatives on energy or climate change, global temperatures may rise as much as 6°C by the end of the century. This outcome of the Intergovernmental Panel on Climate Change Trend Scenario can be compared with a 3°C rise under a Policy Scenario in which greenhouse gasses are stabilized at 550 parts per million (ppm) of carbon dioxide equivalent and a 2°C rise under a Policy Scenario in which concentrations are stabilized at 450 ppm. The consequences of the Trend Scenario go well beyond what the international community regards as acceptable. The global financial and economic crisis, while reducing the demand for energy in the short run, may also slow efforts at energy saving by lowering the price of oil and other fossil energy sources. And by discouraging investments in fossil fuel substitutes and more energy- efficient production processes, the crisis may leave the world on a higher carbon dioxide emission path. The world's largest economy and biggest contributor to carbon dioxide emissions has new leadership that could make climate change a top priority and commit resources to find- ing alternative sources of cleaner energy. 2009 World Development Indicators 127 Energy use: unsustainable trend, unacceptable future particularly oil, make projections of energy demand diffi - In 2006 global energy use from all sources reached 11.5 bil- cult. The International Energy Agency forecasts that energy lion metric tons of oil equivalent--twice as high as its 1971 demand in 2030 will be 45 percent higher than energy use level (figure 3a). High-income economies, with just 15 per- in 2006, for an average annual growth of 1.6 percent, or just cent of world population, use almost half of global energy (fig- a little slower than the 1.9 percent from 1980 to 2006 (IEA ure 3b). Energy use grew by 2.4 percent a year in low-income 2008a, b). More than 80 percent of the energy used in 2006 economies, 2.0 percent in middle-income economies, and was from nonrenewable fuels--carbon dioxide­emitting oil, 1.6 percent in high-income economies over 1990­2006. It coal, and gas. In the absence of new policies this share is rose 4.4 percent a year in China and 3.5 percent in India. The projected to remain above 80 percent in 2030, with demand United States, Russian Federation, Germany, Japan, China, for coal--cheaper and more abundant--growing faster than and India are the top energy consumers, accounting for 55 that for oil and gas (figure 3e and table 3f). percent of global energy use (figure 3c and table 3.7). On Growing 2 percent a year on average, world demand for average, high-income economies use more than 11 times the coal is projected to be 60 percent higher in 2030 than in energy per capita of low-income economies, with huge dif- 2006. Most of the increase in demand comes from the power ferences across countries and within countries and regions generation sector. China and India together account for 85 (figure 3d). percent of this increase. Oil demand grows far more slowly The accelerating trend in energy use and the potential than demand for other fossil fuels, mainly because of high consequences have been matters of concern for the interna- final prices. Yet, oil remains the dominant fuel in the primary tional community. The recent global financial crisis, economic mix, even with the drop in its share from 34 percent in 2006 downturn, and significant fluctuations in the price of energy, to 30 percent in 2030. Energy use has The top six energy consumers doubled since 1971 3a use 55 percent of global energy 3c Energy use (billions of metric tons of oil equivalent) Energy use per capita 12 (thousands of kilograms of oil equivalent) 1971 1990 2006 8 9 Other high income 6 6 United States 4 China India 3 2 Russian Federation 0 Rest of the world 0 United Russian Germany Japan China India 1971 1975 1980 1985 1990 1995 2000 2006 States Federation Source: World Development Indicators data files. Source: Table 3.7. High-income economies use High-income economies use more than 11 times almost half of all global energy 3b the energy that low-income economies do 3d Energy use, 2006 Energy use per capita, by income group Low income 5% (thousands of kilograms of oil equivalent) 1971 1990 2006 India 5% 6 Other China high income 4 16% 29% 2 Other United middle income States 29% 20% 0 Low Lower Upper High World income middle income middle income income Source: Table 3.7. Source: Table 3.7. 128 2009 World Development Indicators Uncertain supply It is not going to be easy to meet the expected growth in fields, which are typically smaller, more complex, and more energy demand, particularly for oil, despite seemingly large costly to develop. available reserves (table 3g). Based on a field by field analy- Despite the improved environment for emerging, climate- sis of production trends, the cost of investment, opportu- friendly, renewable energy sources and technologies, many nities for expanding capacity, and possible constraints and barriers remain. The costs of some technologies are high risks--both above and below ground--the International at their early stages, when economies of scale cannot be Energy Agency has drawn attention to the possibility of an realized. Research and development were limited until the oil-supply crunch by the middle of the 2010s, if upstream recent oil price rise. Concerns are growing about the impact investments fall short of requirements. A growing number of on food supplies with more use of crops for energy. And there oil companies and analysts have suggested that oil produc- is skepticism about the net contribution of biofuels to lower tion may peak within the next two decades, a result of ris- greenhouse gas emissions (FAO 2008). ing costs, political and geological factors, and limits on the In many countries climate change has risen to the top of investment that can be mobilized (IEA 2008a). The rate of the political agenda--the result of a growing body of evidence decline in production from existing fields--especially large, on global warming and ever more startling predictions of the mature fields that have been the mainstay of global output ecological consequences (IPCC 2007b). The commitment of for several decades--has been faster than anticipated (fig- the new U.S. administration to containing the impact of global ure 3h). How output from these fields evolves--with or with- climate change and the changing attitudes toward wind and out the deployment of enhanced recovery techniques--will solar energy offer promise of reducing the carbon footprint of have major implications for the required investment in new energy use. Nonrenewable fuels are projected to account for 80 percent Known global oil reserves and countries of energy use in 2030--about the same as in 2006 3e with highest endowments in 2006 3g Energy demand, by source Oil reserves Share of world (thousands of megatons of oil equivalent) 2006 2030 Country (billions of barrels) total (%) 6 Saudi Arabia 264.3 20.4 Canada 178.9 13.8 4 Iran 132.5 10.3 Iraq 115.0 8.9 Kuwait 101.5 7.9 2 United Arab Emirates 97.8 7.6 Venezuela, RB 79.7 6.2 Russian Federation 60.0 4.6 0 Coal Oil Gas Nuclear Hydro Biomass Other Rest of the world 262.8 20.3 and waste renewables Total 1,292.5 Source: Table 3.7. Source: Deutch, Lauvergeon, and Prawiraatmadja 2007. Fossil fuels will remain the main Production declines from sources of energy through 2030 3f existing oil fields have been rapid 3h Annual growth, Production-weighted average post-peak observed decline, OPEC Fuel 1980 2006 2030 2006­30 (%) by type of producer and year of first production (%) Non-OPEC Total (million metric 0 tons oil equivalent) 7,224 11,730 17,014 1.6 Share (% of total) ­4 Coal 24.8 26.0 28.8 2.0 Oil 43.0 34.3 30.0 1.0 ­8 Gas 17.1 20.5 21.6 1.8 Nuclear 2.6 6.2 5.3 0.9 ­12 Hydropower 2.0 2.2 2.4 1.9 ­16 Biomass and waste 10.4 10.1 9.8 1.4 Pre-1970s 1970s 1980s 1990s 2000­07 Other renewables 0.2 0.6 2.1 7.2 Source: IEA 2008a. Source: IEA 2008a. 2009 World Development Indicators 129 Energy and climate change Economic activity, energy use, and carbon dioxide emissions again cause emissions growth to fall below the rate of growth move together (figure 3i). The world is already experiencing of primary energy use (figure 3j). But as the world becomes the impact of rising average global temperature on physical wealthier, energy-related carbon dioxide emissions continue and biological systems, and the situation is worsening. The to rise in absolute terms. 13 warmest years since 1880 have occurred in the last 16 World carbon dioxide emissions per capita fell until around years (IPCC 2007a; Rosenzweig and others 2008). There is a 2000, but have since risen rapidly. In the absence of new risk of reaching unpredictable tipping points, such as a rise in policies, this upward trend is projected to continue through Arctic temperatures precipitating a massive release of meth- 2030. Government policies, including those to address cli- ane from permafrost zones. Thawing permafrost could also mate change, air pollution, and energy security, have slowed threaten oil and gas extraction infrastructure and pipeline the growth in emissions in some countries. But in most, stability. emissions are still rising fast. In 2005 per capita emissions were greatest in the United States, followed by the Russian Current carbon dioxide levels Federation, Japan, and Germany (table 3.8 and figure 3k). In the 1980s global energy-related carbon dioxide emissions China's per capita emissions were 4.3 tons--close to the (up 1.7 percent annually) rose more slowly than primary en- global average and about one-third of the level of high-income ergy demand (up 1.9 percent annually), mainly because the economies (figure 3l)--while India's were 1.3 tons. shares of natural gas, nuclear power, and renewables in the Carbon dioxide emissions are attributed to the country power mix expanded. But this decarbonization of energy or region consuming the fossil fuel. Yet the consumption reversed at the beginning of the 21st century as the share benefits from the goods and services produced using the of nuclear energy fell while that of coal rose. In the Trend fossil fuel are often realized in a country other than that in Scenario recarbonization of the energy sector is projected which the emissions arise. This concerns some emerging to continue until after 2020, when changing supply patterns market economies, which tend to be more export-oriented, Economic activity, energy use, and The top six carbon greenhouse gas emissions move together 3i dioxide emitters in 2005 3k Index (1971 = 100) Carbon dioxide emissions per capita (metric tons) 1971 1990 2005 350 25 Real GDP 20 300 250 15 Energy use 200 Fossil fuel 10 energy consumption 150 5 Carbon dioxide emissions 0 100 United Russian Japan Germany China India 1971 1975 1980 1985 1990 1995 2000 2006 States Federation Source: World Development Indicators data files. Source: Table 3.8. Decarbonization of energy reversed High-income economies are by far at the beginning of the 21st century 3j the greatest emitters of carbon dioxide 3l Average annual growth in world primary energy Energy demand Carbon dioxide emissions, by income group (metric tons) 1971 1990 2005 demand and energy-related carbon dioxide Energy-related emissions in the Trend Scenario (%) carbon dioxide emissions 15 3 12 9 2 6 1 3 0 Low Lower Upper High World 0 income middle income middle income income 1980­90 1990­2000 2000­10 2010­20 2020­30 Source: IEA 2008a. Source: Table 3.8. 130 2009 World Development Indicators with energy-intensive manufactured exports. A detailed input- In the Trend Scenario rising global use of fossil energy output analysis in China tracked the distribution of fuels, continues to drive up energy-related carbon dioxide emis- raw materials, and intermediate goods to and from indus- sions over at least the next two decades. Emissions grew by tries throughout the economy. Taking carbon intensities and 2.5 gigatons from 1990 to 2000, when their growth acceler- trade data into account, it estimated the energy-related car- ated, and increased a further 4.5 gigatons to 28 gigatons by bon dioxide emissions embedded in domestic production for 2006. They are projected to increase a further 45 percent by export at 34 percent of its 2004 emissions (IEA 2007). With 2030, approaching 41 gigatons in the Trend Scenario. This China's production facilities expanding rapidly, the figures for acceleration in carbon dioxide emissions calls for urgent sta- later years could be higher (box 3m). bilization measures. There is not yet an international consensus on long- Trend and Policy Scenarios term stabilization targets. Most discussions center on sta- Annual greenhouse gas emissions are projected to grow bilization levels between 450 ppm and 550 ppm of carbon from 44 gigatons of carbon dioxide equivalent in 2005 to dioxide equivalent and their consequences (table 3n and fig- 60 gigatons in 2030, a 35 percent increase. The share of ure 3o; IPCC 2007). The required reduction in energy-related energy- related carbon dioxide emissions in total emissions is emissions varies with level of international participation by forecast to increase from 61 percent in 2005 to 68 percent economies sorted by income groups. In both the 450 ppm in 2030 (IEA 2008a). With emissions of greenhouse gases and 550 ppm Policy Scenarios, even after allowing for inter- building in the atmosphere faster than natural processes can national emissions trading and active engagement by non­ remove them, concentrations rise. The Trend Scenario puts Organisation for Economic Co-operation and Development us on a path to doubling aggregate concentrations by the end (OECD) countries, International Energy Agency projections of the century, increasing global average temperatures up to show that OECD countries would have to substantially reduce 6°C (IPCC 2007a; IEA 2008a). emissions domestically (IEA 2008a). Carbon dioxide emissions Impact of Policy Scenarios: carbon dioxide concentration, embedded in international trade 3m temperature increase, emissions, and energy demand 3n Energy and energy-related carbon dioxide emissions are embedded Global emissions by in imports as well as exports, and some goods and services are 2030 (gigatons) Global energy more emissions-intensive than others. There are ways of calculating Carbon dioxide Energy- demand (metric concentration related Total tons oil equivalent) the emissions embedded in international trade, none of them fully (parts per Temperature carbon greenhouse accurate because of the lack of complete, reliable, and up-to-date million) increase dioxide gases 2020 2030 data. A detailed input-output analysis for China reveals the complex- 550 3ºC 33 48 14,360 15,480 ity, involving calculating carbon intensity all along the production 450 2ºC 26a 36 14,280 14,360 chain and across the economy, including outsourcing (IEA 2007; Houser and others 2008). a. Emissions peak in 2020 at 32.5 gigatons and then decline to 25.7 gigatons in 2030. At the global level the percentage of exports in GDP can be used Source: IPCC 2007b; IEA 2008a. as a simple proxy for the share of energy-related carbon dioxide emissions embedded in domestic production for export. The coun- tries for which up-to-date trade data are available represent 83 percent of total world energy-related carbon dioxide emissions (IEA Reductions in energy-related carbon dioxide emissions 2008a). The International Energy Agency estimate of the share of by region in the 550 and 450 parts per million Policy emissions embedded in exports in 2006 ranges from 15 percent Scenarios relative to the Trend Scenario 3o for North America to 48 percent for the Middle East. The differ- Carbon dioxide emissions (gigatons) ence reflects variations in the amount and type of exports and the 45 carbon intensity of energy use. The shares for China (44 percent) Trend Scenario 40 and Asian countries other than China and India (41 percent) are next highest. Of the 23 gigatons of energy-related carbon dioxide 35 550 Policy Scenario emissions in the International Energy Agency sample, one-third 30 were embedded in production for export. China alone accounted 450 Policy Scenario for 2.3 gigatons (31 percent) of this, and Europe and the Russian 25 Federation combined for another 1.7 gigatons (23 percent). Africa 20 and Latin America each accounted for just 2 percent of embedded 2006 2009 2010 2015 2020 2025 2030 emissions (IEA 2008a). Source: IEA 2008a. 2009 World Development Indicators 131 Need for cleaner, more efficient energy Adequate energy supplies are required for economies to and other industrial facilities, but it has not yet been grow and poverty to be reduced, but the current reliance on deployed on a significant scale (IEA 2008a). The basic fossil fuels is not sustainable. Transitioning to new energy technology already exists to capture carbon dioxide sources poses a significant challenge to all economies. Hu- gas and transport and store it permanently in geologi- manity's future on this planet may depend on finding ways to cal formations. Four large-scale carbon capture and supply the world's growing energy needs without irreparably storage projects are operating around the world, each harming the environment. This could be achieved through separating around 1 megaton of carbon dioxide per new energy technologies, greater energy efficiency, and al- year from produced natural gas: Sleipner and Snohvit ternative renewable sources that provide a low-carbon path in Norway, Weyburn in Canada (with the carbon diox- to growth. ide sourced in the United States), and In Salah in For the low-carbon growth needed to stabilize carbon Algeria. Yet there are technical, economic, and legal dioxide emissions, technological innovations are crucial. barriers to more widespread deployment, particularly Because much of today's energy-using capital stock will be high energy intensity and the cost. replaced only gradually, it will take time before most of the · Second-generation biofuels. New biofuel technologies impact of recent and future technological developments that --notably hydrolysis and gasification of woody ligno- improve energy efficiency are felt. Rates of capital-stock turn- cellulosic feedstock to produce ethanol--are expected over differ greatly by industry and sector. Most of today's to reach commercialization by around 2020. Although cars, trucks, heating and cooling systems, and industrial the technology already exists, experts believe that boilers will be replaced by 2030. But most buildings, roads, more research is needed to improve process efficien- railways, and airports and many power stations and refiner- cies. There is virtually no commercial production of ies will still be in use unless governments encourage or force ethanol yet from cellulosic biomass, but several OECD early retirement. Despite the slow turnover, refurbishment in countries are researching it. A recent Food and Agri- some cases could significantly improve energy efficiency at culture Organization report is skeptical about the net an acceptable net economic cost. contribution of biofuels to reduction of greenhouse On the supply side technological advances can improve gasses and blames biofuels production for last year's the technical and economic efficiency of producing and sup- large food price increases (FAO 2008). plying energy. In some cases they are expected to reduce · Coal-to-liquids. The conversion of coal to oil products unit costs and to lead to new and cleaner ways of producing through gasification and synthesis--much like gas to and delivering energy services. Some major new supply-side liquid production--has been done commercially for technologies that are approaching commercialization are many decades. Yet global production remains limited expected to become available to some degree before 2030 because it has been uneconomical, mainly because (IEA 2008a). of the large amounts of energy and water used in the · Carbon capture and storage. This technology miti- process, the high cost of building plants, and the vola- gates emissions of carbon dioxide from power plants tility of oil and coal prices. Energy efficiency Electricity generated from renewables has been improving 3p is projected to more than double by 2030 3q 2005 PPP$ per kilogram of oil equivalent 1990 2006 Electricity generated Hydropower Wind Biomass and waste 8 (terrawatt hours) Solar Geothermal Tide and wave 1,200 6 900 4 600 2 300 0 Low Lower Upper High Euro World 0 income middle middle income area 2006­15 2015­30 income income Source: Table 3.8. Source: IEA 2008a. 132 2009 World Development Indicators Energy efficiency · Biomass, geothermal, and solar thermal met around In recent years there has been an encouraging trend in pro- 6 percent of global heating demand in 2006, a ducing more from each unit of energy (figure 3p), a powerful share projected to rise to 7 percent by 2030. Where and cost-effective way to get on the path to a sustainable resources are abundant and conventional energy energy future. Greater energy efficiency can reduce the need sources expensive, renewables-based heating can be for investing in energy infrastructure, cut fuel costs, increase cost competitive with conventional heating systems. competitiveness, and improve consumer welfare. And by re- · The share of biofuels in road transport fuels world- ducing greenhouse gas emissions and air pollution, it can be wide is projected to rise from 1.5 percent in 2006 good for the environment. The International Energy Agency to 5 percent in 2030, spurred by subsidies and high estimates that implementing a host of 25 policy recommen- oil prices. Most of the growth comes from the United dations for promoting energy efficiency could reduce annual States, European Union, China, and Brazil. carbon dioxide emissions 8.2 gigatons by 2030--equivalent The cost of power generation from renewables is expected to to one-fifth of global energy-related carbon dioxide emissions fall. Greater deployment spurs technological progress and in- in the Trend Scenario (IEA 2008b). The recommendations creases economies of scale, lowering investment costs. The cover policies and technologies for buildings, appliances, costs of the more mature technologies, including geothermal transport, and industry as well as end-use applications such and onshore wind, are assumed to fall least. Renewables ac- as lighting. count for just under half of the total projected investment in electricity generation. The cost of stabilizing carbon dioxide Renewable energy is significant, but there are also significant savings (box 3s). The share of renewables in global primary energy demand, And the cost of inaction would be far higher. excluding traditional biomass, is projected to climb from 7 percent in 2006 to 10 percent by 2030 in the Trend Sce- Cost and savings under nario (IEA 2008a). This assumes that costs come down as the Policy Scenarios 3s renewable technologies mature, that higher fossil fuel prices The 550 parts per million (ppm) Policy Scenario requires spend- make renewables more competitive, and that policy support ing $4.1 trillion more on energy efficiency and power plants and reducing consumption of fossil fuels by 22 gigatons of oil equivalent is strong. The renewables industry could eliminate its reli- over 2010­30 through more efficient energy use. The International ance on subsidies and bring emerging technologies into the Energy Agency estimates that the net undiscounted savings in the 550 ppm Policy Scenario, compared with the Trend Scenario, amount mainstream. to more than $4 trillion. · World renewables-based electricity generation-- The 450 ppm Policy Scenario requires additional investment of $3.6 mostly hydro and wind power--is projected to more trillion in power plants and $5.7 trillion in energy efficiency over 2010­ 30 relative to the Trend Scenario. This additional investment is much than double by 2030 (figure 3q). higher in 2021­30 than in 2010­20 (see figure). In the 450 ppm Policy · Many countries have already begun exploiting wind to Scenario substantially higher investment is needed in power plants. Also, investment in energy efficiency rises considerably, particularly generate electricity (figure 3r). Global wind power is beyond 2020. During that period improving energy efficiency in build- projected to increase 11-fold, becoming the second ings will require the highest investment. In the 450 ppm Policy Scenario the additional investment in power plants and demand-side efficiency largest source renewable after hydropower by 2030. corresponds to 0.55 percent of cumulative world GDP over 2010­30, compared with 0.24 percent in the 550 ppm Policy Scenario. Top 10 users of wind Change in power plant and energy efficiency investments to generate electricity 3r in the Policy Scenarios relative to the Trend Scenario Share of wind in total power generation, 2006 (%) 2007 $ trillions 450 ppm Scenario (additional to 550) 550 ppm Scenario 5 Denmark United States 4 Portugal Germany 3 United Kingdom India 2 Italy 1 Spain France 0 China 2010­20 2020­30 2010­20 2020­30 Power plants Energy efficiency 0 3 6 9 12 15 Source: IEA 2008a. Source: IEA 2008a. 2009 World Development Indicators 133 Tables 3.1 Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares per % of total % growth sq. km Forest area Permanent cropland Arable land 100 people 1990 2007 1990­2007 2007 1990 2005 1990 2005 1990 2005 1990­92 2003­05 Afghanistan .. .. .. 652.1 2.0 1.3 0.2 0.2 12.1 12.1 .. .. Albania 64 54 ­1.2 27.4 28.8 29.0 4.6 4.5 21.1 21.1 18.7 18.4 Algeria 48 35 ­0.1 2,381.7 0.8 1.0 0.2 0.4 3.0 3.1 24.5 23.1 Angola 63 44 0.7 1,246.7 48.9 47.4 0.4 0.2 2.3 2.6 21.2 21.1 Argentina 13 8 ­1.6 2,736.7 12.9 12.1 0.4 0.4 9.6 10.4 75.2 74.0 Armenia 33 36 ­0.4 28.2 12.0 10.0 2.7 2.1 17.7 17.6 16.1a 16.4 Australia 15 11 ­0.2 7,682.3 21.9 21.3 0.0 0.0 6.2 6.4 248.9 240.6 Austria 34 33 0.2 82.5 45.8 46.8 1.0 0.8 17.3 16.8 17.3 17.0 Azerbaijan 46 48 1.3 82.7 11.2 11.3 3.7 2.7 20.5 22.3 22.6a 22.2 Bangladesh 80 73 1.5 130.2 6.8 6.7 2.3 3.5 70.2 61.1 5.7 5.3 Belarus 34 27 ­1.7 207.5 36.0 38.0 0.9 0.6 29.3 26.3 58.4 a 56.2 Belgium 4 3 ­1.4 30.2 23.2b 22.1 0.5b 0.8 23.9b 27.9 8.2 8.1 Benin 66 59 2.7 110.6 30.0 21.3 0.9 2.4 14.6 24.9 33.0 33.0 Bolivia 44 35 0.7 1,084.4 57.9 54.2 0.1 0.2 1.9 2.8 34.9 33.9 Bosnia and Herzegovina 61 53 ­1.6 51.2 43.1 42.7 2.9 1.9 16.6 19.5 27.0a 26.9 Botswana 58 41 ­0.1 566.7 24.2 21.1 0.0 0.0 0.7 0.7 21.5 20.8 Brazil 25 15 ­1.6 8,459.4 61.5 56.5 0.8 0.9 6.0 7.0 33.1 32.0 Bulgaria 34 29 ­1.6 108.6 30.1 33.4 2.7 1.9 34.9 29.2 43.4 42.0 Burkina Faso 86 81 2.6 273.6 26.1 24.8 0.2 0.2 12.9 17.7 35.9 35.9 Burundi 94 90 2.1 25.7 11.3 5.9 14.0 14.2 36.2 37.8 14.2 13.0 Cambodia 87 79 1.8 176.5 73.3 59.2 0.6 0.9 20.9 21.0 28.4 27.0 Cameroon 59 44 0.7 465.4 52.7 45.6 2.6 2.6 12.8 12.8 36.7 34.2 Canada 23 20 0.0 9,093.5 34.1 34.1 0.7 0.7 5.0 5.0 147.4 142.7 Central African Republic 63 62 2.0 623.0 37.2 36.5 0.1 0.1 3.1 3.1 49.1 46.8 Chad 79 74 2.9 1,259.2 10.4 9.5 0.0 0.0 2.6 3.3 40.7 40.1 Chile 17 12 ­0.7 748.8 20.4 21.5 0.3 0.5 3.7 2.6 12.7 12.2 China 73 58 ­0.5 9,327.5 16.8 21.2 0.8 1.4 13.3 15.4 11.1 11.0 Hong Kong, China 1 0 .. 1.0 .. .. .. .. .. .. .. .. Colombia 32 26 0.5 1,109.5 55.4 54.7 1.5 1.5 3.0 1.8 6.2 5.1 Congo, Dem. Rep. 72 67 2.5 2,267.1 62.0 58.9 0.5 0.5 2.9 3.0 12.9 11.8 Congo, Rep. 46 39 1.7 341.5 66.5 65.8 0.1 0.1 1.4 1.4 15.0 14.0 Costa Rica 49 37 0.5 51.1 50.2 46.8 4.9 6.5 5.1 4.4 5.6 5.3 Côte d'Ivoire 60 52 1.5 318.0 32.1 32.7 11.0 11.3 7.6 11.0 18.2 18.8 Croatia 46 43 ­0.8 55.9 37.9 38.2 2.0 2.1 21.7 19.8 32.9a 27.6 Cuba 27 24 ­0.2 109.8 18.7 24.7 7.4 6.1 27.6 33.4 32.8 32.7 Czech Republic 25 27 0.4 77.3 34.1 34.3 3.1 3.1 41.1 39.4 30.1 29.9 Denmark 15 14 ­0.3 42.4 10.5 11.8 0.2 0.2 60.4 52.7 42.6 41.8 Dominican Republic 45 32 ­0.3 48.4 28.4 28.4 9.3 10.3 18.6 16.9 9.2 8.8 Ecuador 45 35 0.1 276.8 49.9 39.2 4.8 4.4 5.8 4.9 12.0 10.1 Egypt, Arab Rep. 57 57 1.9 995.5 0.0 0.1 0.4 0.5 2.3 3.0 4.2 4.1 El Salvador 51 40 0.3 20.7 18.1 14.4 12.5 12.1 26.5 31.9 10.4 10.0 Eritrea 84 80 2.2 101.0 15.9 15.4 0.0 0.0 4.9 6.3 14.6 14.0 Estonia 29 31 ­0.6 42.4 51.4 53.9 0.3 0.3 26.3 13.9 52.1a 40.9 Ethiopia 87 83 2.7 1,000.0 14.7 13.0 0.6 0.8 10.0 13.1 15.1 16.7 Finland 39 37 0.1 304.6 72.9 73.9 0.0 0.0 7.4 7.3 42.2 42.5 France 26 23 ­0.2 550.1 26.4 28.3 2.2 2.1 32.7 33.6 31.1 30.5 Gabon 31 15 ­1.9 257.7 85.1 84.5 0.6 0.7 1.1 1.3 27.0 25.6 Gambia, The 62 44 1.4 10.0 44.2 47.1 0.5 0.5 18.2 35.0 21.3 21.9 Georgia 45 47 ­1.0 69.5 39.7 39.7 4.8 3.8 11.4 11.5 17.1a 17.8 Germany 27 26 0.1 348.8 30.8 31.8 1.3 0.6 34.3 34.1 14.3 14.4 Ghana 64 51 1.1 227.5 32.7 24.2 6.6 9.7 11.9 18.4 19.7 19.0 Greece 41 39 0.3 128.9 25.6 29.1 8.3 8.8 22.5 20.4 24.9 24.1 Guatemala 59 52 1.6 108.4 43.8 36.3 4.5 5.6 12.0 13.3 12.2 11.6 Guinea 72 66 2.1 245.7 30.1 27.4 2.0 2.7 3.0 4.9 12.1 13.2 Guinea-Bissau 72 70 2.9 28.1 78.8 73.7 4.2 8.9 10.7 10.7 21.2 19.4 Haiti 72 55 0.2 27.6 4.2 3.8 11.6 11.6 28.3 28.3 8.9 8.5 134 2009 World Development Indicators ENVIRONMENT Rural population and land use Rural population Land area Land use 3.1 average % of land area Arable land annual thousand hectares per % of total % growth sq. km Forest area Permanent cropland Arable land 100 people 1990 2007 1990­2007 2007 1990 2005 1990 2005 1990 2005 1990­92 2003­05 Honduras 60 53 1.4 111.9 66.0 41.5 3.2 3.2 13.1 9.5 16.9 15.9 Hungary 34 33 ­0.4 89.6 20.0 22.1 2.6 2.3 56.2 51.3 45.2 45.5 India 75 71 1.3 2,973.2 21.5 22.8 2.2 3.4 54.8 53.7 15.5 14.8 Indonesia 69 50 ­0.6 1,811.6 64.3 48.8 6.5 7.5 11.2 12.7 10.3 10.6 Iran, Islamic Rep. 44 32 ­0.3 1,628.6 6.8 6.8 0.8 1.0 9.3 10.2 24.0 24.0 Iraq 30 .. .. 437.4 1.8 1.9 0.7 0.6 12.1 13.1 22.0 .. Ireland 43 39 0.7 68.9 6.4 9.7 0.0 0.0 15.1 17.6 29.7 29.5 Israel 10 8 1.7 21.6 7.1 7.9 4.1 3.5 15.9 14.6 5.3 4.8 Italy 33 32 0.0 294.1 28.5 33.9 10.1 8.6 30.6 26.3 14.7 13.6 Jamaica 51 47 0.2 10.8 31.9 31.3 9.2 10.2 11.0 16.1 6.7 6.6 Japan 37 34 ­0.3 364.5 68.4 68.2 1.3 0.9 13.1 12.0 3.5 3.4 Jordan 28 22 2.0 88.2 0.9 0.9 0.8 1.0 2.0 2.1 3.9 3.6 Kazakhstan 44 42 ­0.5 2,699.7 1.3 1.2 0.1 0.1 13.0 8.3 148.7a 149.3 Kenya 82 79 2.5 569.1 6.5 6.2 0.8 0.8 8.8 9.2 15.7 15.1 Korea, Dem. Rep. 42 38 0.4 120.4 68.1 51.4 1.5 1.7 19.0 23.3 11.4 11.7 Korea, Rep. 26 19 ­1.2 98.7 64.5 63.5 1.6 2.0 19.8 16.4 3.6 3.4 Kuwait 2 2 0.2 17.8 0.2 0.3 0.1 0.2 0.2 0.8 0.6 0.6 Kyrgyz Republic 62 64 1.1 191.8 4.4 4.5 0.4 0.4 6.9 6.7 27.2a 25.9 Lao PDR 85 70 1.0 230.8 75.0 69.9 0.3 0.4 3.5 4.3 17.0 17.8 Latvia 31 32 ­0.7 62.3 45.1 47.2 0.4 0.2 27.2 17.5 41.0a 44.1 Lebanon 17 13 0.4 10.2 11.8 13.3 11.9 13.9 17.9 18.2 4.7 4.7 Lesotho 86 75 0.5 30.4 0.2 0.3 0.1 0.1 10.4 10.9 17.3 16.8 Liberia 55 41 1.6 96.3 42.1 32.7 2.2 2.3 4.2 4.0 12.0 11.4 Libya 24 23 1.6 1,759.5 0.1 0.1 0.2 0.2 1.0 1.0 33.3 30.6 Lithuania 32 33 ­0.4 62.7 31.3 33.5 0.7 0.6 46.0 30.4 58.8a 49.0 Macedonia, FYR 42 34 ­1.0 25.4 35.6 35.6 2.2 1.8 23.8 22.3 27.9a 27.9 Madagascar 76 71 2.4 581.5 23.5 22.1 1.0 1.0 4.7 5.1 17.6 16.3 Malawi 88 82 1.8 94.1 41.4 36.2 1.2 1.5 19.3 27.6 18.4 19.8 Malaysia 50 31 ­0.7 328.6 68.1 63.6 16.0 17.6 5.2 5.5 7.6 7.1 Mali 77 68 2.1 1,220.2 11.5 10.3 0.0 0.0 1.7 3.9 45.3 42.6 Mauritania 60 59 2.7 1,030.7 0.4 0.3 0.0 0.0 0.4 0.5 18.5 17.1 Mauritius 56 58 1.2 2.0 19.2 18.2 3.0 3.0 49.3 49.3 8.3 8.1 Mexico 29 23 0.1 1,944.0 35.5 33.0 1.0 1.3 12.5 12.9 25.4 24.6 Moldova 53 58 ­0.4 32.9 9.7 10.0 14.2 9.1 52.8 56.2 45.1a 47.1 Mongolia 43 43 1.3 1,566.5 7.3 6.5 0.0 0.0 0.9 0.7 49.1 46.7 Morocco 52 44 0.5 446.3 9.6 9.8 1.6 2.1 19.5 19.0 29.7 28.4 Mozambique 79 64 1.4 786.4 25.4 24.5 0.3 0.3 4.4 5.6 21.6 21.8 Myanmar 75 68 0.6 657.6 59.6 49.0 0.8 1.4 14.5 15.3 21.4 21.1 Namibia 72 64 1.5 823.3 10.6 9.3 0.0 0.0 0.8 1.0 42.7 40.9 Nepal 91 83 1.7 143.0 33.7 25.4 0.5 0.9 16.0 16.5 9.4 8.9 Netherlands 31 19 ­2.5 33.9 10.2 10.8 0.9 1.0 25.9 26.8 5.7 5.6 New Zealand 15 14 0.5 267.7 28.8 31.0 5.1 7.1 9.9 5.6 38.5 36.7 Nicaragua 48 44 1.2 121.4 53.9 42.7 1.6 1.9 10.7 15.9 37.1 35.7 Niger 85 84 3.4 1,266.7 1.5 1.0 0.0 0.0 8.7 11.4 125.7 113.1 Nigeria 65 52 1.4 910.8 18.9 12.2 2.8 3.3 32.4 35.1 22.6 22.6 Norway 28 23 ­0.7 304.3 30.0 30.8 .. .. 2.8 2.8 19.6 19.0 Oman 34 28 1.0 309.5 0.0 0.0 0.1 0.1 0.1 0.2 1.6 2.2 Pakistan 69 64 1.9 770.9 3.3 2.5 0.6 1.0 26.6 27.6 15.2 14.1 Panama 46 28 ­1.1 74.4 58.8 57.7 2.1 2.0 6.7 7.4 18.1 17.3 Papua New Guinea 85 87 2.7 452.9 69.6 65.0 1.3 1.4 0.4 0.5 3.8 3.9 Paraguay 51 40 0.8 397.3 53.3 46.5 0.2 0.2 5.3 10.6 61.2 70.2 Peru 31 29 1.0 1,280.0 54.8 53.7 0.3 0.5 2.7 2.9 14.2 13.7 Philippines 51 36 0.0 298.2 35.5 24.0 14.8 16.8 18.4 19.1 7.3 6.9 Poland 39 39 0.0 306.3 29.2 30.0 1.1 1.2 47.3 39.6 35.3 32.6 Portugal 52 41 ­1.0 91.5 33.9 41.3 8.5 7.1 25.6 13.8 15.4 13.3 Puerto Rico 28 2 ­15.1 8.9 45.5 46.0 5.6 4.7 7.3 8.0 1.7 1.8 2009 World Development Indicators 135 3.1 Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares per % of total % growth sq. km Forest area Permanent cropland Arable land 100 people 1990 2007 1990­2007 2007 1990 2005 1990 2005 1990 2005 1990­92 2003­05 Romania 47 46 ­0.5 230.0 27.8 27.7 2.6 2.3 41.2 40.4 42.4 43.2 Russian Federation 27 27 ­0.1 16,381.4 49.4 49.4 0.1 0.1 8.1 7.4 84.9a 84.9 Rwanda 95 82 0.9 24.7 12.9 19.5 12.4 11.1 35.7 48.6 11.8 13.2 Saudi Arabia 23 17 0.5 2,000.0 c 1.4 1.4 0.0 0.1 1.6 1.6 17.0 15.7 Senegal 61 58 2.4 192.5 48.6 45.0 0.1 0.2 12.1 13.2 22.9 21.8 Serbia 50 48 ­0.3 91.0 .. 26.3d .. 3.2d .. 33.6d .. 42.4 d Sierra Leone 67 63 1.7 71.6 42.5 38.5 0.8 1.1 6.8 8.4 10.8 11.0 Singapore 0 0 .. 0.7 3.4 3.3 1.5 0.3 1.5 0.9 0.0 0.0 Slovak Republic 44 44 0.1 48.1 40.0 40.1 1.0 0.5 32.5 28.9 27.1 26.0 Slovenia 50 51 0.2 20.1 59.5 62.8 1.8 1.3 9.9 8.7 8.6a 8.7 Somalia 70 64 1.0 627.3 13.2 11.4 0.0 0.0 1.6 2.2 15.1 16.5 South Africa 48 40 0.7 1,214.5 7.6 7.6 0.7 0.8 11.1 12.1 33.0 31.8 Spain 25 23 0.5 499.2 27.0 35.9 9.7 9.9 30.7 27.4 32.2 32.0 Sri Lanka 83 85 1.1 64.6 36.4 29.9 15.9 15.5 13.5 14.2 4.8 4.7 Sudan 73 57 0.9 2,376.0 32.1 28.4 0.0 0.1 5.4 8.2 48.1 51.2 Swaziland 77 75 2.2 17.2 27.4 31.5 0.7 0.8 10.5 10.3 16.7 15.9 Sweden 17 16 ­0.1 410.3 66.7 67.1 0.0 0.0 6.9 6.6 30.3 29.8 Switzerland 27 27 0.6 40.0 28.9 30.5 0.5 0.6 9.8 10.3 5.7 5.5 Syrian Arab Republic 51 46 2.0 183.8 2.0 2.5 4.0 4.7 26.6 26.5 27.1 25.9 Tajikistan 68 74 1.8 140.0 2.9 2.9 0.9 0.9 6.1 6.6 14.9a 14.4 Tanzania 81 75 2.2 885.8 46.8 39.8 1.1 1.3 10.2 10.4 25.9 24.5 Thailand 71 67 0.6 510.9 31.2 28.4 6.1 7.0 34.2 27.8 25.9 22.7 Timor-Leste 79 73 1.7 14.9 65.0 53.7 3.9 4.6 7.4 8.2 15.2 13.2 Togo 70 59 2.0 54.4 12.6 7.1 1.7 2.6 38.6 45.8 45.1 41.2 Trinidad and Tobago 92 87 0.2 5.1 45.8 44.1 9.0 9.2 14.4 14.6 5.7 5.7 Tunisia 42 34 0.1 155.4 4.1 6.8 12.5 13.9 18.7 17.6 29.0 27.9 Turkey 41 32 0.1 769.6 12.6 13.2 3.9 3.6 32.0 31.0 34.8 33.2 Turkmenistan 55 52 1.4 469.9 8.8 8.8 0.1 0.1 2.9 4.9 40.5a 46.9 Uganda 89 87 3.1 197.1 25.0 18.4 9.4 11.2 25.4 27.4 20.0 18.9 Ukraine 33 32 ­0.8 579.4 16.1 16.5 1.9 1.6 57.6 56.0 66.9a 68.4 United Arab Emirates 21 22 5.3 83.6 2.9 3.7 0.2 2.3 0.4 0.8 2.0 1.6 United Kingdom 11 10 ­0.3 241.9 10.8 11.8 0.3 0.2 27.4 23.7 9.8 9.6 United States 25 19 ­0.6 9,161.9 32.6 33.1 0.2 0.3 20.3 19.0 61.6 59.7 Uruguay 11 8 ­1.6 175.0 5.2 8.6 0.3 0.2 7.2 7.8 41.5 41.5 Uzbekistan 60 63 1.9 425.4 7.2 7.7 0.9 0.8 10.5 11.0 18.0a 18.2 Venezuela, RB 16 7 ­2.8 882.1 59.0 54.1 0.9 0.9 3.2 3.0 10.5 10.1 Vietnam 80 73 0.9 310.1 28.8 41.7 3.2 7.6 16.4 21.3 8.2 8.0 West Bank and Gaza 32 28 3.0 6.0 .. 1.5 19.1 19.1 18.4 17.8 3.5 3.2 Yemen, Rep. 79 70 2.8 528.0 1.0 1.0 0.2 0.3 2.9 2.9 8.1 7.4 Zambia 61 65 2.6 743.4 66.1 57.1 0.0 0.0 7.1 7.1 49.3 46.7 Zimbabwe 71 63 0.8 386.9 57.5 45.3 0.3 0.3 7.5 8.3 25.2 24.7 World 57 w 51 w 0.6 w 129,644.6 s 31.2 w 30.4 w 0.9 w 1.1 w 10.8 w 11.0 w 23.0 w 22.3 w Low income 75 68 1.8 21,216.9 26.8 24.7 1.0 1.2 9.0 10.2 18.1 17.6 Middle income 61 52 0.3 74,923.2 33.7 32.7 1.0 1.2 11.1 11.2 20.8 20.2 Lower middle income 68 58 0.4 34,404.7 25.6 25.0 1.5 1.8 13.5 14.3 15.0 14.7 Upper middle income 31 25 ­0.4 40,518.4 40.6 39.3 0.6 0.7 9.1 8.6 44.7 43.3 Low & middle income 63 56 0.7 96,140.1 32.2 31.0 1.0 1.2 10.6 11.0 20.2 19.6 East Asia & Pacific 71 57 ­0.3 15,870.6 28.8 28.4 2.2 2.9 12.1 13.5 11.6 11.4 Europe & Central Asia 37 36 0.0 23,109.9 38.2 38.3 0.4 0.4 12.4 11.0 58.4 57.7 Latin America & Carib. 29 22 ­0.2 20,156.5 48.8 45.4 0.9 1.0 6.5 7.2 27.6 26.8 Middle East & N. Africa 48 43 1.3 8,643.7 2.3 2.4 0.8 0.9 5.8 6.1 18.3 17.6 South Asia 75 71 1.5 4,781.3 16.5 16.8 1.8 2.6 42.6 41.9 14.5 13.8 Sub-Saharan Africa 72 64 1.9 23,578.1 28.5 26.5 0.8 0.9 6.6 8.0 25.5 25.0 High income 27 23 ­0.3 33,504.5 28.4 28.8 0.7 0.7 11.4 11.0 37.3 36.4 Euro area 29 27 ­0.1 2,513.0 32.5 37.2 4.7 4.3 26.9 25.5 20.6 20.2 a. Data are not available for all three years. b. Includes Luxembourg. c. Provisional estimate. d. Includes Montenegro. 136 2009 World Development Indicators ENVIRONMENT Rural population and land use 3.1 About the data Definitions With 3 billion people, including 70 percent of the Satellite images show land use that differs from · Rural population is calculated as the difference world's poor people, living in rural areas, adequate that of ground-based measures in area under cultiva- between the total population and the urban popula- indicators to monitor progress in rural areas are tion and type of land use. Moreover, land use data tion (see Definitions for tables 2.1 and 3.11). · Land essential. However, few indicators are disaggre- in some countries (India is an example) are based area is a country's total area, excluding area under gated between rural and urban areas (for some that on reporting systems designed for collecting tax rev- inland water bodies and national claims to the con- are, see tables 2.7, 3.5, and 3.11). The table shows enue. With land taxes no longer a major source of tinental shelf and to exclusive economic zones. indicators of rural population and land use. Rural government revenue, the quality and coverage of land In most cases definitions of inland water bodies population is approximated as the midyear nonurban use data have declined. Data on forest area may be includes major rivers and lakes. (See table 1.1 for population. While a practical means of identifying the particularly unreliable because of irregular surveys the total surface area of countries.) · Land use can rural population, it is not precise (see box 3.1a for and differences in definitions (see About the data be broken into several categories, three of which further discussion). for table 3.4). FAO's Global Forest Resources Assess- are presented in the table (not shown are land used The data in the table show that land use patterns ment 2005 aims to address this limitation. The as permanent pasture and land under urban devel- are changing. They also indicate major differences FAO has been coordinating global forest resources opments). · Forest area is land under natural or in resource endowments and uses among countries. assessments every 5­10 years since 1946. Global planted stands of trees, whether productive or not. True comparability of the data is limited, however, Forest Resources Assessment 2005, conducted dur- · Permanent cropland is land cultivated with crops by variations in definitions, statistical methods, and ing 2003­05, covers 229 countries and territories that occupy the land for long periods and need not quality of data. Countries use different definitions of at three points: 1990, 2000, and 2005. The most be replanted after each harvest, such as cocoa, cof- rural and urban population and land use. The Food comprehensive assessment of forests, forestry, and fee, and rubber. Land under flowering shrubs, fruit and Agriculture Organization of the United Nations the benefits of forest resources in both scope and trees, nut trees, and vines is included, but land under (FAO), the primary compiler of the data, occasion- number of countries and people involved, it exam- trees grown for wood or timber is not. · Arable land ally adjusts its definitions of land use categories ines status and trends for about 40 variables on the is land defined by the FAO as under temporary crops and revises earlier data. Because the data reflect extent, condition, uses, and values of forests and (double-cropped areas are counted once), temporary changes in reporting procedures as well as actual other wooded land. meadows for mowing or for pasture, land under mar- changes in land use, apparent trends should be inter- ket or kitchen gardens, and land temporarily fallow. preted cautiously. Land abandoned as a result of shifting cultivation is excluded. What is rural? Urban? 3.1a The rural population identified in table 3.1 is approximated as the difference between total population and urban population, calculated using the urban share reported by the United Nations Population Division. There is no universal standard for distinguishing rural from urban areas, and any urban-rural dichotomy is an oversimplification (see About the data for table 3.11). The two distinct images--isolated farm, thriving metropolis--represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income. Because rurality has many dimensions, for policy purposes the rural-urban dichotomy presented in tables 3.1, 3.5, and 3.11 is inadequate. A 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the Data sources unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and Data on urban population shares used to estimate it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population rural population come from the United Nations density and remoteness together define a set of rural areas that face special development challenges. Population Division's World Urbanization Pros- Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors' estimates of pects: The 2007 Revision. Data on land area and the rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Their land use are from the FAO's electronic files. The estimates range from 13 percent of the population, based on a population density of less than 20 people FAO gathers these data from national agencies per square kilometer, to 64 percent, based on a population density of more than 500 people per square through annual questionnaires and country official kilometer. Taking remoteness into account, the estimated rural population would be 13­52 percent. The publications and websites and by analyzing the estimate for Latin America and the Caribbean in table 3.1 is 22 percent. results of national agricultural censuses. 2009 World Development Indicators 137 3.2 Agricultural inputs Agricultural Irrigated Land under Fertilizer Agricultural Agricultural landa land cereal production consumption employment machinery hundred grams Tractors % of % of thousand per hectare % of total per 100 sq. km land area cropland hectares of arable land employment of arable land 1990­92 2003­05 1990­92b 2003­05b 1990­92 2005­07 1990­92b 2003­05b 1990­92 2003­05 1990­92 2003­05 Afghanistan 58.3 58.3 33.9 33.8 c 2,283.3 2,943.7 58.5 47.5 .. .. 0 0 Albania 41.1 40.9 55.6 50.5c 242.6 141.0 903.3 924.3 .. 58.3 177 125 Algeria 16.3 17.1 6.4 6.9c 3,104.9 2,664.7 144.5 166.1 .. 21.1c 128 133 Angola 46.1 46.2 2.3 2.2c 892.6 1,476.4 28.8 28.5 .. .. 35 31 Argentina 46.6 47.2 5.6 .. 8,509.6 9,332.5 72.7 479.7 0.4 1.2 99 86 Armenia 44.7c 49.3 49.9c 51.5c 162.8c 180.3 502.0 c 232.0 .. 46.5c 293c 289 Australia 60.5 57.5 4.2 4.9 12,813.8 18,999.9 274.8 492.8 5.5 3.8 67 65 Austria 42.5 40.0 0.3 2.5c 903.2 802.2 1,994.7 3,192.1 7.5 5.4 2,367 2,396 Azerbaijan 53.4 c 57.5 68.0 c 69.1 627.0c 764.7 440.0 c 109.9 32.5 39.6 195c 126 Bangladesh 73.5 69.2 33.8 56.1c 10,985.4 11,547.8 1,135.6 1,723.4 66.4c 51.7c 2 1 Belarus 45.3c 42.7 2.1c 2.0 c 2,578.0c 2,261.7 2,292.9c 1,455.0 21.7 .. 207c 101 Belgium 44.0 d 46.0 2.2d 4.7c 354.3d 319.7 4,545.8d .. 2.8 1.9 1,474 d 1,137 Benin 20.6 31.9 0.6 0.4 c 659.9 938.1 78.4 2.9 .. .. 1 1 Bolivia 32.9 34.6 5.5 4.1c 632.9 839.9 41.7 60.8 1.7 .. 25 20 Bosnia and Herzegovina 43.0 c 42.1 0.2c 0.3c 305.1c 313.4 0.0 c 452.7 .. .. 235c 287 Botswana 45.9 45.8 0.2 0.3c 140.1 84.3 21.6 .. .. 21.2c 143 159 Brazil 28.9 31.2 4.6 4.4 c 19,632.5 19,055.2 655.6 1,570.0 25.6c 20.9c 142 134 Bulgaria 55.7 48.5 29.6 16.6c 2,179.3 1,602.5 1,194.1 1,608.2 19.7 9.6 128 100 Burkina Faso 34.9 39.8 0.6 0.5c 2,724.5 3,259.9 60.3 75.0 .. .. 3 4 Burundi 82.9 90.9 1.2 1.5c 218.8 211.8 33.7 16.4 .. .. 2 2 Cambodia 25.5 29.6 6.6 7.0 c 1,800.8 2,586.3 18.7 30.7 .. .. 3 9 Cameroon 19.7 19.7 0.3 0.4 c 816.1 1,138.9 34.1 94.3 60.6c .. 1 1 Canada 7.5 7.4 1.4 1.5c 20,864.4 16,038.3 476.3 581.5 4.2 2.7 162 161 Central African Republic 8.0 8.4 0.0 0.1c 104.0 182.3 5.1 .. .. .. 0 0 Chad 38.4 38.9 0.5 0.8 c 1,241.9 2,510.6 24.6 .. .. .. 1 0 Chile 21.0 20.4 57.1 81.0 c 741.6 619.1 1,214.7 2,910.5 18.8 13.4 144 274 China 57.0 59.5 36.9 35.6c 93,430.3 83,522.6 2,321.0 3,148.0 53.5 .. 64 71 Hong Kong, China .. .. .. .. .. .. .. .. 0.8 0.3 .. .. Colombia 40.5 38.2 14.3 24.0 c 1,598.1 1,083.1 1,822.5 3,310.0 1.4 21.3 98 97 Congo, Dem. Rep. 10.1 10.1 0.1 0.1c 1,867.6 1,972.9 8.3 .. .. .. 4 4 Congo, Rep. 30.8 30.9 0.3 0.4 c 9.1 17.0 34.6 .. .. .. 15 14 Costa Rica 55.7 56.5 15.2 20.2c 83.1 57.5 4,521.9 8,528.3 25.2 15.0 259 311 Côte d'Ivoire 59.8 63.4 1.1 1.1c 1,434.0 788.5 150.8 203.0 .. .. 20 27 Croatia 43.0 c 50.8 0.2c 0.7c 592.7c 556.2 1,514.2c 1,368.6 .. 16.8 35c 1,574 Cuba 61.5 60.0 22.6 19.5c 235.0 287.9 1,288.2 193.0 25.1c 21.5c 246 205 Czech Republic .. 55.2 .. 0.7c .. 1,569.5 .. 1,404.1 10.1 4.3 .. 292 Denmark 65.4 62.0 16.9 9.7 1,581.3 1,485.1 2,249.3 1,159.3 5.4 3.0 625 511 Dominican Republic 71.6 70.7 16.5 20.8 c 134.2 160.0 1,003.3 .. 19.5c 14.4 25 23 Ecuador 28.6 26.9 27.9 28.3 861.0 849.7 508.5 1,731.2 7.0 8.9 54 112 Egypt, Arab Rep. 2.7 3.5 100.0 100.0 c 2,410.2 2,975.4 3,977.2 6,706.7 36.2 29.9c 251 324 El Salvador 71.1 82.2 4.9 4.9c 452.6 344.6 1,335.5 904.3 17.9 18.7c 60 52 Eritrea .. 75.1 .. 3.5c .. 402.9 .. 12.8 .. .. .. 8 Estonia 32.4 c 19.1 0.5c 0.7c 453.6c 282.0 1,010.9c 724.0 19.5 5.8 455c 646 Ethiopia .. 33.0 .. 2.5c 4,585.8 8,727.3 .. 119.7 .. 44.1c .. 2 Finland 7.9 7.4 2.8 2.9 1,050.5 1,155.9 1,647.0 1,286.0 8.8 4.9 900 784 France 55.3 53.9 11.0 13.3c 9,211.6 9,142.7 2,918.1 2,081.2 .. 4.0 784 653 Gabon 20.0 20.0 1.1 1.4 c 14.4 19.8 25.1 56.9 .. .. 28 29 Gambia, The 63.2 80.7 0.9 0.6c 89.5 207.7 43.7 81.5 .. .. 2 3 Georgia 46.5c 43.3 39.9c 44.0 c 248.5c 258.8 905.7c 195.7 .. 54.4 296c 222 Germany 49.8 48.8 4.0 4.0 c 6,673.0 6,710.2 2,615.8 2,145.3 4.0 c 2.4 1,253 795 Ghana 55.7 64.8 0.7 0.5c 1,077.6 1,397.7 37.6 78.0 62.0 c .. 15 9 Greece 71.3 65.2 31.1 37.9c 1,455.2 1,167.5 2,289.4 1,691.3 22.7 13.4 774 970 Guatemala 39.5 42.9 6.8 6.3c 768.2 810.4 1,072.1 1,285.4 .. .. 33 30 Guinea 48.9 51.0 7.0 5.4 c 774.2 1,705.7 16.3 26.6 .. .. 48 48 Guinea-Bissau 53.2 58.0 4.1 4.5c 112.4 138.3 15.2 .. .. .. 1 1 Haiti 57.9 57.7 8.0 8.4 c 406.5 440.2 35.0 .. .. .. 2 2 138 2009 World Development Indicators ENVIRONMENT Agricultural Irrigated Agricultural inputs Land under Fertilizer Agricultural 3.2Agricultural landa land cereal production consumption employment machinery hundred grams Tractors % of % of thousand per hectare % of total per 100 sq. km land area cropland hectares of arable land employment of arable land 1990­92 2003­05 1990­92b 2003­05b 1990­92 2005­07 1990­92b 2003­05b 1990­92 2003­05 1990­92 2003­05 Honduras 29.8 26.2 3.8 5.6c 502.3 367.8 203.2 544.8 42.1 37.2 31 50 Hungary 70.7 65.4 4.1 2.5 2,803.5 2,903.3 796.3 1,152.6 11.3c 5.3 158 259 India 60.9 60.6 28.3 32.9c 100,759.8 99,409.0 757.6 1,140.1 68.1 .. 65 165 Indonesia 23.5 26.3 14.5 12.4 c 13,861.2 15,404.7 1,329.8 1,548.1 54.9 44.5 3 2 Iran, Islamic Rep. 38.5 36.1 39.9 47.2 9,611.9 9,074.1 749.6 928.7 25.6 24.9c 136 162 Iraq 21.9 22.9 63.0 58.6c 3,506.1 4,110.7 347.0 .. .. 17.0 c 72 127 Ireland 70.2 62.4 .. .. 298.0 278.3 6,591.0 4,529.4 14.1 6.3 1,667 1,409 Israel 26.7 24.4 44.4 .. 107.8 86.3 2,835.7 20,007.8 3.7 2.0 763 757 Italy 55.4 50.7 22.9 25.8 c 4,346.9 3,899.0 2,195.5 1,726.2 8.4 4.6c 1,619 2,312 Jamaica 44.0 47.4 11.0 0.0 c 2.6 1.6 1,737.0 611.4 27.3c 19.0 242 177 Japan 15.5 12.9 54.3 35.8 2,438.6 2,006.9 3,778.5 4,108.8 6.8 4.5 4,297 4,417 Jordan 12.0 11.5 25.0 27.5 111.9 64.5 969.4 7,294.8 .. 3.6c 352 328 Kazakhstan 82.0 c 76.9 9.8 c 15.7c 22,152.4c 14,512.3 135.5c 68.2 .. 34.4 c 62c 21 Kenya 47.3 47.4 1.1 1.8 c 1,765.9 2,190.8 209.2 375.5 19.0 c .. 20 25 Korea, Dem. Rep. 21.0 24.9 58.2 50.3c 1,569.0 1,293.8 3,522.3 .. .. .. 297 234 Korea, Rep. 21.9 19.2 47.1 47.6c 1,367.8 1,045.2 4,931.8 5,011.5 16.7 8.3 275 1,344 Kuwait 7.9 8.6 60.0 72.2c 0.4 1.4 666.7 10,631.1 .. 0.0 c 215 70 Kyrgyz Republic 52.6c 56.2 72.6c 73.1 578.0c 601.0 242.4 c 236.7 35.5 43.4 189c 159 Lao PDR 7.2 8.5 16.2 16.5c 625.3 888.8 31.0 .. .. .. 11 11 Latvia 40.8 c 26.5 1.1c 2.1c 696.7c .. 995.3c 435.2 .. 13.0 364 c 548 Lebanon 31.1 38.1 28.1 31.3c 41.5 64.9 1,639.3 1,467.0 .. .. 188 446 Lesotho 76.7 76.9 0.6 0.9c 177.6 160.7 167.3 .. .. .. 57 61 Liberia 27.1 27.0 0.5 0.5c 135.0 120.0 2.5 .. .. .. 8 9 Libya 8.8 8.8 21.8 21.9c 355.0 352.8 457.6 506.2 .. .. 187 224 Lithuania 54.1c 42.5 0.5c 0.4 c 1,134.0c 974.1 540.7c 1,470.0 18.8 15.9 256c 660 Macedonia, FYR 51.4 c 48.8 12.1c 9.0 c 235.2c 180.3 0.0 c 399.8 .. 19.4 730 c 954 Madagascar 62.5 70.2 30.7 30.6c 1,321.0 1,590.9 34.0 32.4 .. 78.0 c 5 2 Malawi 40.2 48.3 1.2 2.2c 1,442.6 1,791.8 350.6 235.6 .. .. 8 6 Malaysia 22.7 24.0 4.8 0.0 c 699.3 685.7 .. 8,199.7 23.9c 14.6c 161 241 Mali 26.3 32.4 3.7 4.9c 2,392.7 3,184.4 91.0 .. .. 41.5c 11 5 Mauritania 38.5 38.6 11.8 .. 132.9 222.7 132.2 .. .. .. 8 8 Mauritius 55.7 55.7 16.0 20.8 c 0.5 0.1 2,731.5 2,300.9 14.7c 10.0 36 52 Mexico 53.8 55.3 22.0 22.8 c 10,075.0 9,918.6 686.4 714.4 24.7c 15.9 128 130 Moldova 77.9c 76.7 .. 10.7c 675.6c 915.4 776.5c 121.8 .. 41.4 310 c 222 Mongolia 79.9 83.3 5.8 7.0 c 620.0 135.7 111.5 38.5 .. 40.6 73 35 Morocco 68.2 68.1 13.2 15.4 c 5,373.9 5,308.6 352.9 569.7 .. 45.0 46 58 Mozambique 60.7 61.8 2.8 2.6c 1,508.6 2,180.5 12.0 51.0 .. .. 14 14 Myanmar 15.8 17.1 10.1 17.0 c 5,282.9 8,454.5 78.8 10.5 69.4 c .. 12 11 Namibia 47.0 47.2 0.7 1.0 c 206.4 284.0 0.0 21.6 48.2c .. 47 39 Nepal 29.0 29.5 .. 47.0 2,957.2 3,339.1 339.6 262.3 81.9 .. 23 24 Netherlands 58.9 56.8 61.0 60.2c 185.0 215.5 6,297.8 9,126.6 4.3 2.9 2,056 1,647 New Zealand 65.0 64.5 .. .. 153.5 123.5 1,911.4 6,740.8 10.7 7.6 323 499 Nicaragua 33.5 43.5 4.0 2.8 c 299.3 500.7 269.9 317.2 38.7 29.0 c 20 16 Niger 27.0 30.4 0.5 0.5c 7,010.6 8,798.4 1.2 3.7 .. .. 0 0 Nigeria 79.4 80.4 0.7 0.8 c 16,416.7 19,572.3 142.2 63.9 .. .. 8 10 Norway 3.3 3.4 .. .. 361.4 320.9 2,362.0 2,461.0 5.9 3.5 1,723 1,526 Oman 3.5 5.1 71.6 90.0 c 2.4 4.5 2,440.6 3,424.2 .. .. 42 39 Pakistan 33.7 35.2 .. 84.2 11,776.8 12,871.3 962.0 1,594.9 48.9 42.7 133 185 Panama 28.7 30.0 4.8 6.2c 182.4 181.9 666.2 426.1 25.8 c 16.4 103 148 Papua New Guinea 2.0 2.3 .. .. 1.9 3.2 621.5 1,805.9 .. .. 59 52 Paraguay 56.0 60.7 2.9 1.7c 454.7 801.0 91.9 580.9 1.7 31.5c 72 43 Peru 17.1 16.6 29.9 27.8 c 682.5 1,160.7 245.9 853.6 1.0 0.7 36 36 Philippines 37.4 40.9 15.7 14.5c 6,957.4 6,737.7 935.5 1,449.1 45.3 37.1 72 111 Poland 61.6 52.8 .. 0.6c 8,522.7 8,371.6 894.8 1,296.5 25.2 17.9 821 1,119 Portugal 42.8 41.2 .. 23.8 c 780.1 359.5 1,122.9 2,018.6 15.6 12.1 569 1,259 Puerto Rico 47.5 25.1 .. .. 0.5 0.3 .. .. 3.5 2.1 478 438 2009 World Development Indicators 139 3.2 Agricultural inputs Agricultural Irrigated Land under Fertilizer Agricultural Agricultural landa land cereal production consumption employment machinery hundred grams Tractors % of % of thousand per hectare % of total per 100 sq. km land area cropland hectares of arable land employment of arable land 1990­92 2003­05 1990­92b 2003­05b 1990­92 2005­07 1990­92b 2003­05b 1990­92 2003­05 1990­92 2003­05 Romania 64.4 63.8 .. 3.2 5,842.3 5,178.7 788.5 428.8 30.6 33.1 146 183 Russian Federation 13.5c 13.2 .. 3.6 59,541.3c 41,831.1 417.4 c 114.1 .. 10.4 98 c 44 Rwanda 75.6 78.6 0.3 0.6c 258.2 333.8 19.9 .. .. .. 1 0 Saudi Arabia 57.5 80.8 44.2 42.7c 1,061.8 664.1 1,445.8 1,060.3 .. .. 20 28 Senegal 41.9 42.6 3.3 4.8 c 1,153.8 1,130.9 65.1 134.4 .. .. 2 3 Serbiae .. 54.8 .. 0.9 .. 1,945.8c .. 1,199.5 .. .. .. 948 Sierra Leone 38.3 40.0 5.2 4.7c 451.7 883.5 22.6 .. .. .. 3 1 Singapore 2.2 1.2 .. .. .. .. 54,333.3 141,594.4 0.3c 0.2 637 1,083 Slovak Republic .. 42.3 .. 3.8 .. 772.3 .. 766.2 .. 5.2 .. 158 Slovenia 28.0 c 25.0 .. 1.2 112.5c 97.3 3,167.8 c 3,522.5 .. 8.9 .. .. Somalia 70.2 70.7 19.2 15.7c 531.4 624.6 8.8 .. .. .. 16 10 South Africa 80.2 82.0 8.3 9.5c 5,735.9 3,570.9 548.6 520.7 .. 10.3c 101 43 Spain 60.8 58.3 .. 20.3c 7,588.5 6,357.7 1,185.9 1,601.6 10.7 5.5 494 705 Sri Lanka 36.2 36.5 28.0 38.8 c 834.3 908.0 2,016.4 2,873.1 44.3 33.9c 180 221 Sudan 51.9 57.2 14.1 10.2c 6,266.9 9,192.4 51.2 35.6 .. .. 8 9 Swaziland 75.8 80.9 24.1 26.0 c 69.1 59.4 688.5 .. .. .. 251 222 Sweden 8.2 7.8 4.1 4.3c 1,184.3 989.4 1,112.0 1,050.6 3.3 2.1 604 616 Switzerland 46.9 38.1 6.0 5.8 c 207.3 163.2 4,031.8 2,147.6 4.2 4.0 2,870 2,632 Syrian Arab Republic 73.7 75.6 14.3 24.3c 3,811.9 3,222.8 621.5 856.8 28.2c 27.0 c 137 220 Tajikistan 32.1c 30.4 72.9c 68.3c 266.0c 392.8 1,488.4 c .. 57.9 67.2 415c 239 Tanzania 38.4 38.8 1.4 1.8 c 3,003.3 4,906.7 53.5 69.7 84.2c .. 8 23 Thailand 41.9 36.3 21.0 28.2c 10,593.6 11,402.1 598.2 1,406.9 61.7 43.3 39 256 Timor-Leste 21.9 22.9 .. .. 83.7 107.7 .. .. .. .. 8 7 Togo 58.7 66.7 0.3 0.3c 610.2 736.6 56.3 61.1 .. .. 0 0 Trinidad and Tobago 25.7 25.9 3.3 3.3c 6.4 2.0 1,111.4 1,848.1 11.8 4.9 606 683 Tunisia 58.4 63.0 .. 7.2 1,524.7 1,484.7 329.9 460.9 .. .. 88 140 Turkey 51.8 53.3 .. 19.7 13,759.9 13,328.6 756.7 1,095.5 46.5 32.5 287 426 Turkmenistan 68.6c 70.2 .. 79.5c 331.3c 1,022.1 1,296.3c .. .. .. 465c 224 Uganda 61.0 63.9 0.1 0.1c 1,097.6 1,669.7 1.5 15.0 91.5c 69.1c 9 9 Ukraine 72.4 c 71.4 7.6c 6.6c 12,542.3c 13,875.5 806.9c 186.5 20.0 19.8 153c 114 United Arab Emirates 3.7 6.7 .. 29.9c 1.4 0.0 4,810.4 5,531.3 .. .. 50 59 United Kingdom 75.0 70.2 2.5 3.0 c 3,548.5 2,880.3 3,323.1 3,020.0 2.2 1.3 762 872 United States 46.6 45.3 11.3 12.5c 64,547.3 57,213.3 1,014.8 1,562.1 2.9 1.6 245 272 Uruguay 84.7 85.4 10.2 14.9c 509.4 571.2 610.2 1,387.4 1.5 4.6c 259 266 Uzbekistan 65.2c 65.6 87.3c 84.9c 1,225.3c 1,601.1 1,631.6c .. .. .. 402c 362 Venezuela, RB 24.7 24.6 13.9 16.9c 798.7 1,092.0 1,388.3 1,747.5 12.6 10.7c 176 186 Vietnam 21.0 30.8 44.6 33.7c 6,726.1 8,399.2 1,299.3 3,308.9 73.8 c 58.8 c 60 247 West Bank and Gaza 62.5 61.8 .. 7.0 c .. 32.7c .. .. .. 15.8 c 441 715 Yemen, Rep. 33.4 33.6 24.3 33.0 c 738.2 746.2 127.2 50.6 52.6c .. 40 43 Zambia 31.4 34.4 0.7 2.9c 813.4 837.2 131.1 0.0 .. .. 11 11 Zimbabwe 34.1 39.9 3.6 5.2c 1,430.8 2,095.0 508.1 328.6 .. .. 61 75 World 37.3 w 38.2 w 17.0 w 18.0 w 704,455.8 s 689,120.1 s 945.2 w 1,192.9 w 42.6 w .. w 186 w 208 w Low income 36.7 38.7 9.5 18.6 103,608.2 131,441.1 339.8 448.7 .. .. 40 47 Middle income 37.1 38.2 22.8 20.8 454,663.6 419,524.9 965.8 1,233.0 51.6 .. 115 144 Lower middle income 45.5 47.0 27.5 28.1 287,054.6 283,677.2 1,169.7 1,615.2 54.6 .. 102 122 Upper middle income 30.1 30.7 10.1 8.9 167,609.0 135,847.6 603.6 703.9 .. 17.5 75 127 Low & middle income 37.0 38.3 20.0 20.4 558,271.9 550,965.9 833.7 1,090.7 52.4 .. 166 163 East Asia & Pacific 48.3 50.7 .. .. 142,265.1 139,631.3 1,821.0 2,627.6 54.5 .. 56 96 Europe & Central Asia 28.1 28.4 16.7 10.9 137,268.0 110,119.5 564.7 359.7 .. 20.8 190 186 Latin America & Carib. 34.4 35.7 11.3 12.5 47,712.7 48,655.5 585.7 1,108.1 18.6 16.5 121 118 Middle East & N. Africa 23.5 23.6 31.6 33.7 30,590.3 30,102.1 641.4 1,055.7 .. .. 116 151 South Asia 54.7 54.7 28.8 39.2 129,690.1 131,101.8 767.2 1,166.3 65.6 .. 67 153 Sub-Saharan Africa 42.1 43.9 3.4 3.5 70,745.7 91,355.7 129.0 119.2 .. .. 19 13 High income 37.9 38.0 .. .. 146,184.0 138,154.2 1,200.2 1,474.7 5.6 3.4 413 432 Euro area 49.7 47.3 13.9 16.5 32,674.3 31,370.1 2,302.5 2,046.1 7.3 4.5 993 1,002 a. Includes permanent pastures, arable land, and land under permanent crops. b. Data for two periods may not be comparable (see About the data). c. Data are not available for all three years. d. Includes Luxembourg. e. Includes Montenegro. 140 2009 World Development Indicators ENVIRONMENT Agricultural inputs 3.2 About the data Definitions Agriculture is still a major sector in many economies, machinery. There is no single correct mix of inputs: · Agricultural land is the share of land area that and agricultural activities provide developing coun- appropriate levels and application rates vary by coun- is permanent pastures, arable, or under permanent tries with food and revenue. But agricultural activities try and over time and depend on the type of crops, crops. Permanent pasture is land used for fi ve or also can degrade natural resources. Poor farming the climate and soils, and the production process more years for forage, including natural and culti- practices can cause soil erosion and loss of soil used. vated crops. Arable land includes land defined by the fertility. Efforts to increase productivity by using The data shown here and in table 3.3 are collected FAO as land under temporary crops (double-cropped chemical fertilizers, pesticides, and intensive irriga- by the Food and Agriculture Organization of the areas are counted once), temporary meadows for tion have environmental costs and health impacts. United Nations (FAO) through annual questionnaires. mowing or for pasture, land under market or kitchen Excessive use of chemical fertilizers can alter the The FAO tries to impose standard definitions and gardens, and land temporarily fallow. Land aban- chemistry of soil. Pesticide poisoning is common in reporting methods, but complete consistency across doned as a result of shifting cultivation is excluded. developing countries. And salinization of irrigated countries and over time is not possible. For example, Land under permanent crops is land cultivated with land diminishes soil fertility. Thus, inappropriate use permanent pastures are quite different in nature and crops that occupy the land for long periods and need of inputs for agricultural production has far-reaching intensity in African countries and dry Middle Eastern not be replanted after each harvest, such as cocoa, effects. countries. Thus, despite standard definitions, data coffee, and rubber. Land under flowering shrubs, fruit The table provides indicators of major inputs to on agricultural land in different climates may not be trees, nut trees, and vines is included, but land under agricultural production: land, fertilizer, labor, and comparable. Data on agricultural employment, in par- trees grown for wood or timber is not. · Irrigated land ticular, should be used with caution. In many coun- refers to areas purposely provided with water, includ- Nearly 40 percent of land globally tries much agricultural employment is informal and ing land irrigated by controlled flooding. · Cropland is is devoted to agriculture 3.2a unrecorded, including substantial work performed arable land and permanent cropland (see table 3.1). by women and children. To address some of these · Land under cereal production refers to harvested Total land area in 2005: 130 million sq. km concerns, this indicator is heavily footnoted in the areas, although some countries report only sown or database in sources, definition, and coverage. cultivated area. · Fertilizer consumption is the quan- Fertilizer consumption measures the quantity of tity of plant nutrients per unit of arable land. Fertil- Permanent Others pastures 31.4% plant nutrients. Consumption is calculated as pro- izer products cover nitrogen, potash, and phosphate 26.1% duction plus imports minus exports. Because some (including ground rock phosphate). Traditional nutri- Arable land chemical compounds used for fertilizers have other ents--animal and plant manures--are not included. 11.0% industrial applications, the consumption data may The time reference for fertilizer consumption is the Forests 30.4% overstate the quantity available for crops. The FAO crop year (July through June). · Agricultural employ- Permanent recently revised the time series for fertilizer con- ment is employment in agriculture, forestry, hunting, crops 1.1% sumption and irrigation but only for 2002 onward, and fishing (see table 2.3). · Agricultural machinery Note: Agricultural land includes permanent pastures, and data for 2002­05 are not available for all coun- refers to wheel and crawler tractors (excluding gar- arable land, and land under permanent crops. Source: Tables 3.1 and 3.2. tries. The fertilizer data from the FAO's previous den tractors) in use in agriculture at the end of the releases are not necessarily comparable with later calendar year specified or during the first quarter of Developing regions lag in agricultural data. In the previous release data were based on the following year. machinery, which reduces their the total consumption of fertilizers, but in the recent agricultural productivity 3.2b release data are based on the nutrients in fertilizers. Tractors per 100 square Caution should thus be used when comparing data kilometers of arable land 1990­92 2003­05 over time. 500 To smooth annual fluctuations in agricultural activ- ity, the indicators in the table have been averaged 400 over three years. 300 200 100 0 East Europe Latin Middle South Sub- High Data sources Asia & & America East & Asia Saharan income Pacific Central & North Africa Asia Caribbean Africa Data on agricultural inputs are from electronic files Source: Table 3.2. that the FAO makes available to the World Bank. 2009 World Development Indicators 141 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production index production index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2003­05 1990­92 2003­05 1990­92 2003­05 1990­92 2005­07 1990­92 2003­05 Afghanistan 97.4 135.9 78.3 116.5 64.1 99.5 1,153 1,650 .. .. Albania 87.5 102.4 72.0 109.4 63.9 113.0 2,372 3,580 778 1,449 Algeria 86.3 136.6 83.3 123.1 80.7 106.6 915 1,465 1,911 2,225 Angola 60.3 154.3 64.9 136.3 75.6 99.9 378 526 165 174 Argentina 67.3 112.6 73.9 107.0 89.1 97.3 2,652 4,077 6,767 10,072 Armenia 106.4 a 126.6 104.4 a 122.6 101.3a 115.5 1,843a 1,605 1,476a 3,692 Australia 60.3 97.9 68.7 98.9 83.3 94.9 1,739 1,411 20,839 29,908 Austria 93.4 98.5 95.8 98.1 97.3 97.8 5,400 5,917 12,048 21,920 Azerbaijan 135.4 a 129.3 103.3a 127.9 94.7a 122.0 2,113a 2,637 1,084 a 1,143 Bangladesh 75.6 107.7 74.0 107.5 73.8 105.1 2,567 3,769 254 338 Belarus 113.0a 126.0 127.2a 114.6 141.2a 104.2 2,739a 2,767 1,977a 3,153 Belgium 77.6b 107.3 87.8b 100.1 93.8b 96.9 6,122b 8,248 .. 39,243 Benin 58.4 118.2 62.6 119.7 87.9 112.4 880 1,173 326 519 Bolivia 63.7 119.6 68.9 115.3 76.9 108.4 1,384 1,939 670 773 Bosnia and Herzegovina 106.5a 106.7 115.4 a 106.9 137.8a 107.6 3,548a 3,922 .. 8,270 Botswana 96.4 112.4 114.4 106.2 118.2 104.7 312 508 536 390 Brazil 77.3 125.2 71.0 122.6 65.3 121.1 1,916 3,206 1,507 3,119 Bulgaria 148.7 102.6 144.9 99.2 143.3 97.6 3,633 3,010 2,500 7,159 Burkina Faso 74.0 124.6 73.3 114.7 68.0 115.6 783 1,132 110 173 Burundi 112.6 105.3 112.3 105.3 134.7 99.9 1,370 1,316 108 70 Cambodia 65.4 109.6 64.6 110.8 65.9 110.3 1,356 2,482 .. 314 Cameroon 71.5 108.9 74.2 108.2 85.5 100.6 1,166 1,355 389 648 Canada .. .. .. .. .. .. 2,559 3,083 28,243 44,133 Central African Republic 74.0 96.5 69.8 108.8 68.1 117.6 883 1,100 287 381 Chad 68.4 111.2 72.1 110.7 84.8 107.6 636 907 173 215 Chile 78.5 115.9 73.8 114.2 67.9 111.1 3,949 6,073 3,573 5,309 China 70.0 113.9 61.3 116.7 49.4 122.5 4,307 5,322 258 407 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 96.6 112.1 82.5 112.2 80.4 112.6 2,492 3,792 3,080 2,749 Congo, Dem. Rep. 124.5 96.8 121.3 97.1 101.4 96.3 794 772 184 149 Congo, Rep. 80.0 106.4 78.9 108.8 75.8 118.8 688 788 .. .. Costa Rica 71.3 103.8 71.3 104.5 79.7 100.7 3,188 3,110 3,143 4,506 Côte d'Ivoire 73.9 96.5 74.1 101.7 75.6 124.7 863 1,785 598 795 Croatia 80.3a 90.6 92.3a 93.3 126.4 a 101.0 3,975a 5,215 4,915a 9,975 Cuba 112.1 112.3 116.6 107.5 130.4 89.0 2,092 2,742 .. .. Czech Republic .. 95.8 .. 94.6 .. 93.4 .. 4,480 .. 5,521 Denmark 103.7 99.8 93.8 102.3 89.0 103.5 5,448 5,863 15,190 38,441 Dominican Republic 118.9 106.4 99.5 108.9 79.1 111.6 4,078 4,426 2,268 4,586 Ecuador 79.5 103.1 72.9 110.1 65.8 117.2 1,724 2,751 1,686 1,676 Egypt, Arab Rep. 69.5 110.7 66.7 113.3 65.4 119.0 5,738 7,624 1,528 2,072 El Salvador 102.2 91.3 85.2 103.5 74.3 108.3 1,871 2,785 1,633 1,638 Eritrea .. 80.4 .. 90.9 .. 100.3 .. 436 .. 71 Estonia 123.5a 92.0 151.3a 99.0 167.5a 103.0 1,304 a 2,644 3,002a 3,129 Ethiopia .. 108.6 .. 111.0 .. 116.6 1,234 1,557 .. 158 Finland 97.8 104.0 103.6 105.5 106.6 106.3 3,246 3,471 18,818 31,276 France 94.1 97.4 95.6 97.7 97.5 98.2 6,370 6,731 22,234 44,080 Gabon 87.3 102.3 89.2 101.8 86.8 100.0 1,712 1,648 1,176 1,592 Gambia, The 56.0 97.2 60.1 97.8 98.7 103.4 1,114 1,122 224 235 Georgia 115.6a 94.3 97.3a 104.3 79.3a 113.7 1,998 a 1,650 2,443a 1,791 Germany 84.0 96.5 97.5 98.9 108.4 100.6 5,578 6,538 13,724 25,657 Ghana 59.1 118.4 61.1 118.3 90.7 111.0 1,084 1,365 293 320 Greece 86.0 89.1 91.5 91.7 101.4 98.1 3,589 3,867 7,536 8,818 Guatemala 77.7 103.2 75.8 106.7 76.5 103.9 1,882 1,501 2,120 2,623 Guinea 74.0 110.8 72.9 113.7 60.6 115.6 1,423 1,433 142 190 Guinea-Bissau 71.3 104.6 73.3 105.5 81.1 109.8 1,529 1,544 205 238 Haiti 108.7 98.3 98.5 101.6 69.8 111.8 997 921 .. .. 142 2009 World Development Indicators ENVIRONMENT Agricultural output and productivity Crop Food Livestock Cereal 3.3Agricultural production index production index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2003­05 1990­92 2003­05 1990­92 2003­05 1990­92 2005­07 1990­92 2003­05 Honduras 92.5 129.4 87.2 158.2 69.1 180.3 1,371 1,519 1,193 1,483 Hungary 115.0 111.2 119.5 105.1 126.7 95.8 4,551 5,135 4,105 6,987 India 80.2 103.2 76.3 105.3 69.3 112.6 1,947 2,464 324 392 Indonesia 82.7 118.8 83.1 120.4 85.5 134.2 3,826 4,374 484 583 Iran, Islamic Rep. 74.8 117.1 72.8 114.1 68.2 105.2 1,523 2,481 1,954 2,561 Iraq 102.0 118.5 99.0 118.8 92.6 117.8 872 794 .. 1,756 Ireland 94.1 105.2 94.1 98.2 94.3 97.1 6,653 7,185 .. 17,107 Israel 97.6 111.9 83.2 115.2 72.9 117.5 3,132 3,153 .. .. Italy 97.5 97.2 96.2 96.8 95.0 95.8 4,340 5,311 11,528 23,967 Jamaica 85.0 97.2 85.3 98.9 86.8 102.0 1,298 1,211 2,016 1,889 Japan 113.0 93.0 109.4 96.5 106.9 99.7 5,713 6,013 20,445 35,668 Jordan 100.2 134.9 85.9 119.3 70.4 99.6 1,167 1,263 1,892 1,360 Kazakhstan 168.7a 110.9 173.9a 112.7 178.0a 117.2 1,338a 1,158 1,795a 1,557 Kenya 84.9 103.3 85.3 106.4 85.4 110.4 1,645 1,723 334 333 Korea, Dem. Rep. 125.7 110.1 128.7 112.0 145.2 120.9 5,073 3,638 .. .. Korea, Rep. 88.3 93.1 80.3 96.8 68.1 101.7 5,885 6,291 5,679 11,286 Kuwait 33.9 122.3 30.0 115.0 27.9 110.9 3,112 2,621 .. 13,521a Kyrgyz Republic 67.4 a 103.7 82.8a 103.7 109.1a 101.8 2,772a 2,523 675a 979 Lao PDR 62.3 113.8 59.3 115.4 60.2 112.9 2,355 3,517 360 459 Latvia 137.3a 120.3 199.4 a 111.1 253.7a 102.9 1,641a 2,669 1,790a 2,704 Lebanon 109.7 94.1 100.0 103.0 65.6 126.7 2,001 2,666 .. 29,950 Lesotho 67.9 105.6 85.7 102.8 108.7 100.0 703 546 428 423 Liberia 62.4 99.2 77.7 97.5 89.9 109.7 951 1,156 .. .. Libya 79.5 98.5 77.8 99.2 75.8 100.5 706 617 .. .. Lithuania 77.5a 104.5 133.1a 103.7 184.7a 103.0 1,938a 2,625 .. 4,760 Macedonia, FYR 107.1a 99.9 106.2a 101.1 103.8a 104.2 2,652a 2,938 2,256a 3,487 Madagascar 92.3 105.6 91.6 104.6 93.2 101.0 1,935 2,493 186 174 Malawi 57.3 94.4 48.9 95.7 85.3 102.1 871 1,416 72 116 Malaysia 74.4 121.4 71.0 120.1 81.0 118.2 2,827 3,336 386 525 Mali 73.9 118.2 78.7 113.6 81.7 116.8 840 1,109 208 241 Mauritania 64.5 90.5 85.7 107.1 90.0 110.5 802 763 574 356 Mauritius 110.7 103.3 98.6 106.5 70.7 116.4 4,117 7,666 3,942 5,011 Mexico 83.0 106.8 76.6 108.8 71.2 110.1 2,520 3,145 2,256 2,793 Moldova 134.6a 112.5 152.9a 113.1 214.5a 105.2 2,928a 2,113 1,286a 816 Mongolia 260.7 114.2 103.3 72.2 93.2 69.6 967 839 870 907 Morocco 100.9 139.3 93.5 126.3 81.0 102.2 1,094 986 1,430 1,746 Mozambique 64.2 108.7 69.4 106.5 94.5 101.3 330 949 107 148 Myanmar 61.7 119.6 61.8 121.3 64.2 128.5 2,739 3,629 .. .. Namibia 72.0 113.2 106.2 130.8 114.1 135.2 388 420 820 1,103 Nepal 73.7 114.2 75.3 112.9 80.1 110.0 1,831 2,271 191 207 Netherlands 93.4 98.0 100.9 93.4 104.4 91.4 7,145 7,753 24,914 42,049 New Zealand 78.7 105.1 78.1 115.5 81.0 115.2 5,257 7,071 19,155 27,189 Nicaragua 76.2 118.6 63.7 123.6 57.1 124.0 1,543 1,880 .. 2,071 Niger 73.3 110.7 73.5 105.3 71.7 92.3 323 437 152 157a Nigeria 68.7 105.0 69.2 105.2 76.8 107.0 1,135 1,460 .. .. Norway 121.1 104.5 102.8 100.1 98.1 99.0 3,744 3,924 19,500 37,039 Oman 62.8 87.3 63.0 93.0 65.6 104.1 2,206 3,214 1,005 1,302a Pakistan 80.9 106.6 71.0 109.5 67.4 112.5 1,818 2,639 594 696 Panama 109.9 108.2 87.8 103.1 69.5 98.1 1,862 1,939 2,363 3,904 Papua New Guinea 78.5 102.5 79.9 107.7 80.9 113.1 2,504 3,672 500 595 Paraguay 85.3 124.6 77.2 116.3 86.7 97.8 1,905 2,201 1,596 2,052 Peru 52.4 109.2 56.6 112.1 68.0 116.5 2,463 3,603 930 1,481 Philippines 84.4 113.0 77.5 114.6 61.9 118.2 2,070 3,164 905 1,075 Poland 110.9 91.1 112.5 97.8 115.4 106.7 2,958 3,029 1,502a 2,182 Portugal 104.0 97.7 95.6 97.9 85.5 98.2 1,939 2,882 4,642 6,220 Puerto Rico 167.7 117.4 127.5 97.6 118.3 93.5 1,100 1,826 .. .. 2009 World Development Indicators 143 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production index production index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2003­05 1990­92 2003­05 1990­92 2003­05 1990­92 2005­07 1990­92 2003­05 Romania 93.0 116.4 103.0 114.3 120.3 110.3 2,777 2,680 2,196 4,646 Russian Federation 125.9a 115.9 142.0a 110.3 161.8a 103.8 1,743a 1,865 1,825a 2,519 Rwanda 110.2 115.1 106.7 116.4 81.2 123.6 1,088 1,117 167 182 Saudi Arabia 121.4 115.0 93.9 111.6 67.5 108.4 4,212 4,464 7,875 15,780 Senegal 72.5 88.9 74.0 90.1 87.5 104.4 803 968 225 215 Serbia .. 116.3c .. 107.0 c .. 95.1c .. 3,839 .. 1,679c Sierra Leone 128.9 115.7 121.3 114.5 93.4 106.9 1,223 1,023 .. .. Singapore 157.1 105.3 402.4 114.0 413.8 114.4 .. .. 22,695 40,419 Slovak Republic .. 105.6 .. 101.4 .. 97.2 1,031a 4,081 .. 5,026 Slovenia 94.0a 111.8 82.1a 104.8 75.7a 101.1 3,279a 5,498 12,042a .. Somalia 102.8 106.3 85.6 105.1 83.3 104.9 622 432 .. .. South Africa 79.8 104.9 84.7 107.7 93.9 109.8 1,602 3,081 1,786 2,495 Spain 88.1 103.4 85.3 106.2 80.0 110.5 2,310 3,050 9,511 18,619 Sri Lanka 86.9 101.3 89.1 103.0 93.6 115.5 2,950 3,636 679 702 Sudan 68.8 115.9 66.6 112.0 67.4 109.4 596 681 414 667 Swaziland 105.6 99.9 107.1 104.6 129.6 110.2 1,299 1,196 1,231 1,330 Sweden 102.0 101.5 98.3 99.4 95.9 98.1 4,272 4,791 22,533 35,378 Switzerland 112.2 97.4 107.4 99.6 105.7 100.4 6,102 6,364 19,884 23,588 Syrian Arab Republic 73.9 115.7 75.4 120.6 75.4 121.7 947 1,749 2,344 3,261 Tajikistan 122.8a 147.7 132.7a 150.1 192.6a 152.3 1,037a 2,275 346a 409 Tanzania 92.8 107.3 89.0 106.9 82.9 109.5 1,276 1,162 238 295 Thailand 81.9 106.3 83.5 104.5 86.2 103.8 2,186 2,907 497 624 Timor-Leste 93.7 106.8 102.0 112.0 101.4 129.0 1,694 1,230 .. .. Togo 73.8 113.4 75.1 108.2 93.4 109.1 791 1,152 312 347 Trinidad and Tobago 116.4 78.5 92.7 112.9 73.4 142.6 3,159 2,647 1,666 1,745 Tunisia 105.6 122.7 90.0 115.0 60.5 99.1 1,401 1,320 2,422 2,700 Turkey 88.3 103.8 89.2 105.3 92.1 108.0 2,192 2,529 1,770 1,846 Turkmenistan 110.9a 126.0 63.0a 142.2 56.1a 139.7 2,210a 3,065 1,222a .. Uganda 77.9 107.2 80.0 107.8 82.3 111.4 1,487 1,527 155 175 Ukraine 128.5a 117.2 145.5a 111.7 171.7a 103.5 2,834 a 2,368 1,195a 1,702 United Arab Emirates 23.5 55.7 29.7 64.5 71.5 122.2 2,042 2,000 10,454 25,841 United Kingdom 105.0 98.4 105.4 98.0 105.8 97.6 6,321 7,082 22,664 26,942 United States 88.4 105.8 85.7 104.2 83.4 103.1 4,875 6,512 20,793 42,744 Uruguay 71.3 .. 76.7 .. 83.5 .. 2,445 4,223 5,714 7,973 Uzbekistan 108.0a 115.6 97.6a 118.1 100.1a 115.8 1,777a 4,042 1,272a 1,800 Venezuela, RB 79.6 98.6 74.6 94.8 73.4 91.7 2,561 3,365 4,483 6,331 Vietnam 60.2 120.4 62.1 122.1 57.9 131.1 3,097 4,726 214 305 West Bank and Gaza .. 104.6 .. 109.9 .. 117.9 1,105 2,091a .. .. Yemen, Rep. 75.2 100.9 72.3 105.3 66.3 112.0 906 885 271 328 a Zambia 80.8 104.3 84.2 105.3 79.9 98.9 1,251 1,697 159 204 Zimbabwe 69.4 65.1 76.2 84.7 90.0 99.8 1,125 674 240 222 World 82.0 w 109.4 w 81.1 w 109.7 w 82.2 w 110.1 w 2,839 w 3,301 w 746 w 908 w Low income 76.1 109.3 75.1 110.4 76.5 112.4 1,596 2,105 270 319 Middle income 80.5 112.0 78.6 112.9 78.3 114.9 2,639 3,093 467 650 Lower middle income 76.7 111.7 71.5 113.5 64.6 117.6 2,789 3,354 371 513 Upper middle income 91.7 113.0 96.0 111.5 103.6 110.0 1,992 2,553 1,987 2,819 Low & middle income 79.8 111.6 78.1 112.6 78.1 114.7 2,421 2,858 433 579 East Asia & Pacific 71.9 114.4 64.8 116.6 53.1 122.0 3,816 4,681 295 438 Europe & Central Asia 113.8 109.8 128.3 108.7 152.2 106.8 1,961 2,211 1,756 2,108 Latin America & Carib. 78.0 116.3 74.2 114.8 72.5 112.9 2,234 3,308 2,125 3,055 Middle East & N. Africa 79.4 117.3 76.5 115.7 71.2 110.3 1,544 2,206 1,583 2,204 South Asia 80.0 104.2 75.6 106.3 69.2 112.1 1,977 2,581 335 406 Sub-Saharan Africa 75.5 106.1 76.9 107.2 83.1 108.8 1,003 1,228 262 278 High income 89.9 101.9 89.6 101.9 90.0 101.8 4,259 5,072 14,543 25,594 Euro area 90.6 98.5 93.6 99.8 97.1 100.8 4,621 5,419 12,587 22,134 a. Data are not available for all three years. b. Includes Luxembourg. c. Includes Montenegro. 144 2009 World Development Indicators ENVIRONMENT Agricultural output and productivity 3.3 About the data Definitions The agricultural production indexes in the table are single enterprise, estimates of the amounts retained · Crop production index is agricultural production prepared by the Food and Agriculture Organization of for seed and feed are subtracted from the produc- for each period relative to the base period 1999­ the United Nations (FAO). The FAO obtains data from tion data to avoid double counting. The resulting 2001. It includes all crops except fodder crops. The official and semiofficial reports of crop yields, area aggregate represents production available for any regional and income group aggregates for the FAO's under production, and livestock numbers. If data are use except as seed and feed. The FAO's indexes production indexes are calculated from the under- unavailable, the FAO makes estimates. The indexes may differ from those from other sources because lying values in international dollars, normalized to the are calculated using the Laspeyres formula: produc- of differences in coverage, weights, concepts, time base period 1999­2001. · Food production index tion quantities of each commodity are weighted by periods, calculation methods, and use of interna- covers food crops that are considered edible and average international commodity prices in the base tional prices. that contain nutrients. Coffee and tea are excluded period and summed for each year. Because the FAO's To facilitate cross-country comparisons, the FAO because, although edible, they have no nutritive indexes are based on the concept of agriculture as a uses international commodity prices to value produc- value. · Livestock production index includes meat tion. These prices, expressed in international dollars and milk from all sources, dairy products such as (equivalent in purchasing power to the U.S. dollar), cheese, and eggs, honey, raw silk, wool, and hides Cereal yield in low-income economies are derived using a Geary-Khamis formula applied and skins. · Cereal yield, measured in kilograms was less than 40 percent of the yield in high-income countries 3.3a to agricultural outputs (see System of National per hectare of harvested land, includes wheat, rice, Accounts 1993, sections 16.93­96). This method maize, barley, oats, rye, millet, sorghum, buckwheat, Kilograms per hectare assigns a single price to each commodity so that, for and mixed grains. Production data on cereals refer (thousands) 1990­92 2005­07 6 example, one metric ton of wheat has the same price to crops harvested for dry grain only. Cereal crops regardless of where it was produced. The use of inter- harvested for hay or harvested green for food, feed, 5 national prices eliminates fluctuations in the value or silage, and those used for grazing, are excluded. of output due to transitory movements of nominal The FAO allocates production data to the calendar 4 exchange rates unrelated to the purchasing power year in which the bulk of the harvest took place. But 3 of the domestic currency. most of a crop harvested near the end of a year will Data on cereal yield may be affected by a variety of be used in the following year. · Agricultural produc- 2 reporting and timing differences. Millet and sorghum, tivity is the ratio of agricultural value added, mea- which are grown as feed for livestock and poultry in sured in 2000 U.S. dollars, to the number of workers 1 Europe and North America, are used as food in Africa, in agriculture. Agricultural productivity is measured 0 Asia, and countries of the former Soviet Union. So by value added per unit of input. (For further discus- World Low Lower Upper High Euro some cereal crops are excluded from the data for sion of the calculation of value added in national income middle middle income area income income some countries and included elsewhere, depending accounts, see About the data for tables 4.1 and 4.2.) Source: Table 3.3. on their use. To smooth annual fluctuations in agri- Agricultural value added includes that from forestry cultural activity, the indicators in the table have been and fishing. Thus interpretations of land productivity Sub-Saharan Africa had the averaged over three years. should be made with caution. lowest yield, while East Asia and Pacific is closing the gap with high-income economies 3.3b Kilograms per hectare (thousands) 1990­92 2005­07 6 5 4 3 Data sources 2 Data on agricultural production indexes, cereal 1 yield, and agricultural workers are from electronic files that the FAO makes available to the World 0 Bank. The files may contain more recent informa- East Europe Latin Middle South Sub- High Asia & & America East & Asia Saharan income tion than published versions. Data on agricultural Pacific Central & North Africa Asia Caribbean Africa value added are from the World Bank's national Source: Table 3.3. accounts files. 2009 World Development Indicators 145 3.4 Deforestation and biodiversity Forest Average annual Animal Higher GEF Nationally Marine area deforestationa species plantsb benefits protected areas protected index for areas biodiversity 0­100 (no Total Total biodiversity % of % of thousand known Threatened known Threatened to maximum thousand total land thousand surface sq. km % species species species species biodiversity) sq. km area sq. km area 1990 2005 1990­2000 2000­05 2004 2008 2004 2008 2008 2006c 2006c 2004 2004 Afghanistan 13 9 2.5 3.1 578 30 4,000 2 3.4 2.2 0.3 .. .. Albania 8 8 0.3 ­0.6 376 45 3,031 0 0.2 0.2 0.7 0.3 1.0 Algeria 18 23 ­1.8 ­1.2 472 72 3,164 3 2.9 118.8 5.0 0.9 0.0 Angola 610 591 0.2 0.2 1,226 62 5,185 26 8.3 125.5 10.1 29.1 2.3 Argentina 353 330 0.4 0.4 1,413 152 9,372 44 17.7 173.6 6.3 7.8 0.3 Armenia 3 3 1.0 1.5 380 35 3,553 1 0.2 2.4 8.7 .. .. Australia 1,679 1,637 0.2 0.1 1,227 568 15,638 55 87.7 734.1 9.6 680.8 8.8 Austria 38 39 ­0.2 ­0.1 513 62 3,100 4 0.3 23.5 28.5 .. .. Azerbaijan 9 9 0.0 0.0 446 38 4,300 0 0.8 4.0 4.8 1.2 1.4 Bangladesh 9 9 0.0 0.3 735 89 5,000 12 1.4 0.9 0.7 0.3 0.2 Belarus 75 79 ­0.5 ­0.1 297 17 2,100 .. 0.0 10.8 5.2 .. .. Belgium 8d 7 .. .. 519 29 1,550 1 0.0 1.0 3.2 0.0 0.0 Benin 33 24 2.1 2.5 644 34 2,500 14 0.2 26.1 23.6 .. .. Bolivia 628 587 0.4 0.5 1,775 80 17,367 71 12.5 218.5 20.2 .. .. Bosnia and Herzegovina 22 22 0.1 0.0 390 55 .. 1 0.4 0.2 0.5 .. .. Botswana 137 119 0.9 1.0 739 18 2,151 0 1.4 174.4 30.8 .. .. Brazil 5,200 4,777 0.5 0.6 2,290 343 56,215 382 100.0 1,515.2 17.9 47.4 0.6 Bulgaria 33 36 ­0.1 ­1.4 485 47 3,572 0 0.8 11.0 10.1 0.0 0.0 Burkina Faso 72 68 0.3 0.3 581 13 1,100 2 0.3 38.2 14.0 .. .. Burundi 3 2 3.7 5.2 713 48 2,500 2 0.3 1.5 6.0 .. .. Cambodia 129 104 1.1 2.0 648 82 .. 31 3.5 41.5 23.5 1.9 1.1 Cameroon 245 212 0.9 1.0 1,258 157 8,260 355 12.5 39.9 8.6 3.9 0.8 Canada 3,101 3,101 0.0 0.0 683 77 3,270 2 21.5 473.2 5.2 362.7 3.6 Central African Republic 232 228 0.1 0.1 850 17 3,602 15 1.5 94.7 15.2 .. .. Chad 131 119 0.6 0.7 635 21 1,600 2 2.2 114.9 9.1 .. .. Chile 153 161 ­0.4 ­0.4 604 95 5,284 40 15.3 27.8 3.7 114.5 15.1 China 1,571 1,973 ­1.2 ­2.2 1,801 351 32,200 446 66.6 1,437.8 15.4 16.0 0.2 Hong Kong, China .. .. .. .. 363 37 .. 6 0.0 0.3 24.7 0.3 26.5 Colombia 614 607 0.1 0.1 2,288 382 51,220 223 51.5 282.7 25.5 8.1 0.7 Congo, Dem. Rep. 1,405 1,336 0.4 0.2 1,578 126 11,007 65 19.9 196.1 8.6 .. .. Congo, Rep. 227 225 0.1 0.1 763 37 6,000 35 3.6 48.7 14.3 .. .. Costa Rica 26 24 0.8 ­0.1 1,070 131 12,119 111 9.7 11.2 21.8 4.8 9.4 Côte d'Ivoire 102 104 ­0.1 ­0.1 931 73 3,660 105 3.4 38.9 12.2 0.3 0.1 Croatia 21 21 0.0 ­0.1 461 78 4,288 1 0.6 3.1 5.6 2.5 4.4 Cuba 21 27 ­1.7 ­2.2 423 115 6,522 163 12.5 1.5 1.4 31.7 28.6 Czech Republic 26 26 0.0 ­0.1 474 39 1,900 4 0.1 12.5 16.1 .. .. Denmark 4 5 ­0.9 ­0.6 508 28 1,450 3 0.2 2.5 5.8 5.1 11.8 Dominican Republic 14 14 0.0 0.0 260 81 5,657 30 6.0 11.8 24.4 8.6 17.6 Ecuador 138 109 1.5 1.7 1,856 340 19,362 1,839 29.3 62.6 22.6 141.0 49.7 Egypt, Arab Rep. 0e 1 ­3.0 ­2.6 599 59 2,076 2 2.9 53.2 5.3 76.7 7.7 El Salvador 4 3 1.5 1.7 571 29 2,911 26 0.9 0.2 1.0 0.1 0.4 Eritrea 16 16 0.2 0.3 607 38 .. 3 0.8 5.0 5.0 .. .. Estonia 22 23 ­0.3 ­0.4 334 14 1,630 0 0.1 20.0 47.1 .. .. Ethiopia 147 130 0.7 1.1 1,127 86 6,603 22 8.4 186.2 18.6 .. .. Finland 222 225 ­0.1 0.0 501 19 1,102 1 0.2 29.5 9.7 1.1 0.3 France 145 156 ­0.5 ­0.3 665 117 4,630 8 5.3 55.6 10.1 0.5 0.1 Gabon 219 218 0.0 0.0 798 43 6,651 108 3.0 34.9 13.5 1.0 0.4 Gambia, The 4 5 ­0.4 ­0.4 668 31 974 4 0.1 0.3 3.5 0.2 1.9 Georgia 28 28 0.0 0.0 366 46 4,350 0 0.6 2.7 3.9 0.0 0.1 Germany 107 111 ­0.3 0.0 613 59 2,682 12 0.6 75.8 21.7 9.1 2.6 Ghana 74 55 2.0 2.0 978 56 3,725 117 1.9 36.3 15.9 .. .. Greece 33 38 ­0.9 ­0.8 530 95 4,992 11 2.8 4.0 3.1 2.5 1.9 Guatemala 47 39 1.2 1.3 877 133 8,681 83 8.0 35.4 32.6 0.1 0.1 Guinea 74 67 0.7 0.5 855 61 3,000 22 2.3 15.0 6.1 .. .. Guinea-Bissau 22 21 0.4 0.5 560 29 1,000 4 0.6 2.9 10.2 .. .. Haiti 1 1 0.6 0.7 312 91 5,242 29 5.2 0.1 0.3 .. .. 146 2009 World Development Indicators ENVIRONMENT Forest Deforestation and biodiversity Average annual Animal Higher GEF Nationally 3.4 Marine area deforestationa species plantsb benefits protected areas protected index for areas biodiversity 0­100 (no Total Total biodiversity % of % of thousand known Threatened known Threatened to maximum thousand total land thousand surface sq. km % species species species species biodiversity) sq. km area sq. km area 1990 2005 1990­2000 2000­05 2004 2008 2004 2008 2008 2006c 2006c 2004 2004 Honduras 74 46 3.0 3.1 900 102 5,680 110 7.2 21.9 19.6 1.9 1.7 Hungary 18 20 ­0.6 ­0.7 455 55 2,214 1 0.2 5.2 5.8 .. .. India 639 677 ­0.6 0.0 1,602 313 18,664 246 39.9 151.7 5.1 16.1 0.5 Indonesia 1,166 885 1.7 2.0 2,271 464 29,375 386 81.0 203.1 11.2 130.1 6.8 Iran, Islamic Rep. 111 111 0.0 0.0 656 75 8,000 1 7.3 103.9 6.4 6.2 0.4 Iraq 8 8 ­0.2 ­0.1 498 40 .. 0 1.6 0.0 0.0 .. .. Ireland 4 7 ­3.3 ­1.9 471 15 950 1 0.6 0.8 1.1 0.0 0.0 Israel 2 2 ­0.6 ­0.8 649 79 2,317 0 0.8 3.4 15.6 0.1 0.6 Italy 84 100 ­1.2 ­1.1 610 119 5,599 19 3.8 19.4 6.6 1.5 0.5 Jamaica 3 3 0.1 0.1 333 61 3,308 209 4.4 1.6 15.0 8.2 74.5 Japan 250 249 0.0 0.0 763 190 5,565 12 36.0 34.5 9.5 10.6 2.8 Jordan 1 1 0.0 0.0 490 43 2,100 0 0.4 9.3 10.6 0.0 0.0 Kazakhstan 34 33 0.1 0.2 642 55 6,000 16 5.1 77.4 2.9 0.5 0.0 Kenya 37 35 0.3 0.3 1,510 172 6,506 103 8.8 69.1 12.1 3.1 0.5 Korea, Dem. Rep. 82 62 1.8 1.9 474 44 2,898 3 0.7 3.2 2.6 .. .. Korea, Rep. 64 63 0.1 0.1 512 54 2,898 0 1.7 3.5 3.5 3.5 3.5 Kuwait 0e 0e ­3.4 ­2.7 381 23 234 .. 0.1 0.0 0.0 0.3 1.5 Kyrgyz Republic 8 9 ­0.2 ­0.3 265 22 4,500 14 1.1 6.2 3.2 .. .. Lao PDR 173 161 0.5 0.5 919 77 8,286 21 5.0 37.5 16.3 .. .. Latvia 28 29 ­0.3 ­0.4 393 23 1,153 0 0.0 10.4 16.7 0.2 0.2 Lebanon 1 1 ­0.8 ­0.8 447 38 3,000 0 0.2 0.0 0.4 0.0 0.0 Lesotho 0 0 ­3.4 ­2.7 370 11 1,591 1 0.3 0.1 0.2 .. .. Liberia 41 32 1.6 1.8 759 60 2,200 46 2.6 15.2 15.8 0.6 0.5 Libya 2 2 0.0 0.0 413 31 1,825 1 1.6 1.2 0.1 0.5 0.0 Lithuania 20 21 ­0.3 ­0.8 298 20 1,796 .. 0.0 3.6 5.7 0.5 0.8 Macedonia, FYR 9 9 0.0 0.0 380 34 3,500 0 0.2 1.8 7.1 .. .. Madagascar 137 128 0.5 0.3 427 262 9,505 281 29.2 15.2 2.6 0.2 0.0 Malawi 39 34 0.9 0.9 865 141 3,765 14 3.5 18.4 19.5 .. .. Malaysia 224 209 0.4 0.7 1,083 225 15,500 686 13.9 59.7 18.2 5.0 1.5 Mali 141 126 0.7 0.8 758 21 1,741 6 1.5 26.0 2.1 .. .. Mauritania 4 3 2.7 3.4 615 44 1,100 .. 1.3 2.5 0.2 15.0 1.5 Mauritius 0e 0e 0.3 0.5 151 65 750 88 3.3 0.1 3.3 0.1 4.4 Mexico 690 642 0.5 0.4 1,570 579 26,071 261 68.7 102.5 5.3 82.1 4.2 Moldova 3 3 ­0.2 ­0.2 253 28 1,752 0 0.0 0.5 1.4 .. .. Mongolia 115 103 0.7 0.8 527 38 2,823 0 4.2 217.9 13.9 .. .. Morocco 43 44 ­0.1 ­0.2 559 76 3,675 2 3.5 4.7 1.1 0.5 0.1 Mozambique 200 193 0.3 0.3 913 93 5,692 46 7.2 45.3 5.8 22.5 2.8 Myanmar 392 322 1.3 1.4 1,335 118 7,000 38 10.0 35.5 5.4 0.2 0.0 Namibia 88 77 0.9 0.9 811 55 3,174 24 5.2 42.8 5.2 74.0 9.0 Nepal 48 36 2.1 1.4 477 72 6,973 7 2.1 22.8 16.0 .. .. Netherlands 3 4 ­0.4 ­0.3 539 26 1,221 0 0.2 4.3 12.7 0.8 1.9 New Zealand 77 83 ­0.6 ­0.2 424 124 2,382 21 20.2 64.7 24.2 22.7 8.4 Nicaragua 65 52 1.6 1.3 813 59 7,590 39 3.3 21.3 17.6 1.3 1.0 Niger 19 13 3.7 1.0 616 20 1,460 2 0.9 84.1 6.6 .. .. Nigeria 172 111 2.7 3.3 1,189 79 4,715 171 6.0 56.5 6.2 .. .. Norway 91 94 ­0.2 ­0.2 525 32 1,715 2 1.3 15.5 5.1 1.3 0.4 Oman 0e 0e 0.0 0.0 557 50 1,204 6 3.7 0.2 0.1 29.6 9.6 Pakistan 25 19 1.8 2.1 820 78 4,950 2 4.9 65.3 8.5 2.2 0.3 Panama 44 43 0.2 0.1 1,145 121 9,915 194 10.9 7.6 10.2 10.0 13.3 Papua New Guinea 315 294 0.5 0.5 980 158 11,544 142 25.4 36.2 8.0 3.5 0.8 Paraguay 212 185 0.9 0.9 864 39 7,851 10 2.8 23.4 5.9 .. .. Peru 702 687 0.1 0.1 2,222 238 17,144 275 33.4 175.9 13.7 3.4 0.3 Philippines 106 72 2.8 2.1 812 253 8,931 216 32.3 30.0 10.1 16.6 5.5 Poland 89 92 ­0.2 ­0.3 534 38 2,450 4 0.5 75.4 24.6 0.7 0.2 Portugal 31 38 ­1.5 ­1.1 606 147 5,050 16 5.5 4.6 5.0 2.0 2.2 Puerto Rico 4 4 ­0.1 0.0 348 47 2,493 53 4.0 0.3 3.3 1.7 19.1 2009 World Development Indicators 147 3.4 Deforestation and biodiversity Forest Average annual Animal Higher GEF Nationally Marine area deforestationa species plantsb benefits protected areas protected index for areas biodiversity 0­100 (no Total Total biodiversity % of % of thousand known Threatened known Threatened to maximum thousand total land thousand surface sq. km % species species species species biodiversity) sq. km area sq. km area 1990 2005 1990­2000 2000­05 2004 2008 2004 2008 2008 2006c 2006c 2004 2004 Romania 64 64 0.0 0.0 466 64 3,400 1 0.7 5.2 2.2 6.1 2.6 Russian Federation 8,090 8,088 0.0 0.0 941 153 11,400 7 34.1 1,113.4 6.8 301.8 1.8 Rwanda 3 5 ­0.8 ­6.9 871 49 2,288 3 0.9 2.0 8.1 .. .. Saudi Arabia 27 27 0.0 0.0 527 45 2,028 3 3.2 818.3 38.1 5.2 0.3 Senegal 93 87 0.5 0.5 803 55 2,086 7 1.0 21.6 11.2 0.9 0.4 Serbia 26f 27f ­0.3f ­0.3f 477f 42 4,082 f 1 0.2f 3.3f 3.2f 0.1f 0.1f Singapore 0e 0e 0.0 0.0 473 44 2,282 54 0.1 0.0 4.2 0.0 0.1 Slovak Republic 19 19 0.0 ­0.1 419 44 3,124 2 0.1 9.6 20.0 .. .. Slovenia 12 13 ­0.3 ­0.4 437 80 3,200 .. 0.2 1.3 6.7 0.0 0.0 Somalia 83 71 1.0 1.0 824 55 3,028 17 6.1 1.9 0.3 3.3 0.5 South Africa 92 92 0.0 0.0 1,149 323 23,420 74 20.7 73.7 6.1 3.4 0.3 Spain 135 179 ­2.0 ­1.7 647 170 5,050 49 6.8 41.4 8.3 1.8 0.4 Sri Lanka 24 19 1.2 1.5 504 177 3,314 280 7.9 11.3 17.5 2.3 3.5 Sudan 764 675 0.8 0.8 1,254 47 3,137 17 5.1 114.1 4.8 0.3 0.0 Swaziland 5 5 ­0.9 ­0.9 614 16 2,715 11 0.1 0.5 3.1 .. .. Sweden 274 275 0.0 0.0 542 30 1,750 3 0.3 42.4 10.3 4.3 1.0 Switzerland 12 12 ­0.4 ­0.4 475 44 3,030 3 0.2 11.8 29.5 .. .. Syrian Arab Republic 4 5 ­1.5 ­1.3 432 59 3,000 0 0.9 1.2 0.7 .. .. Tajikistan 4 4 0.0 0.0 427 27 5,000 14 0.7 19.6 14.0 .. .. Tanzania 414 353 1.0 1.1 1,431 299 10,008 240 14.8 342.7 38.7 2.3 0.2 Thailand 160 145 0.7 0.4 1,271 157 11,625 86 8.0 101.7 19.9 5.8 1.1 Timor-Leste 10 8 1.2 1.3 .. .. .. 0 0.6 0.9 6.3 .. .. Togo 7 4 3.4 4.5 740 33 3,085 10 0.3 6.0 11.1 .. .. Trinidad and Tobago 2 2 0.3 0.2 551 38 2,259 1 2.2 0.2 4.7 0.1 1.3 Tunisia 6 11 ­4.1 ­1.9 438 52 2,196 0 0.5 2.4 1.5 0.2 0.1 Turkey 97 102 ­0.4 ­0.2 581 121 8,650 3 6.2 12.7 1.6 4.5 0.6 Turkmenistan 41 41 0.0 0.0 421 44 .. 3 1.8 12.6 2.7 .. .. Uganda 49 36 1.9 2.2 1,375 131 4,900 38 2.8 62.9 31.9 .. .. Ukraine 93 96 ­0.2 ­0.1 445 58 5,100 1 0.5 19.4 3.3 3.1 0.5 United Arab Emirates 2 3 ­2.4 ­0.1 298 27 .. .. 0.2 0.2 0.2 .. .. United Kingdom 26 28 ­0.7 ­0.4 660 38 1,623 14 3.5 47.5 19.6 22.5 9.2 United States 2,986 3,031 ­0.1 ­0.1 1,356 937 19,473 244 94.2 1,379.2 15.1 909.5 9.4 Uruguay 9 15 ­4.5 ­1.3 532 66 2,278 1 1.2 0.6 0.3 0.1 0.0 Uzbekistan 31 33 ­0.4 ­0.5 434 33 4,800 15 1.1 8.7 2.0 .. .. Venezuela, RB 520 477 0.6 0.6 1,745 166 21,073 69 25.3 638.1 72.3 21.3 2.3 Vietnam 94 129 ­2.3 ­2.0 1,116 152 10,500 147 12.1 16.0 5.2 0.7 0.2 West Bank and Gaza .. 0e .. 0.0 .. .. .. 0 0.0 .. .. .. .. Yemen, Rep. 5 5 0.0 0.0 459 47 1,650 159 3.2 0.0 0.0 .. .. Zambia 491 425 0.9 1.0 1,025 38 4,747 8 3.8 300.5 40.4 .. .. Zimbabwe 222 175 1.5 1.7 883 35 4,440 17 1.9 57.2 14.8 .. .. World 40,457 s 39,426 s 0.2 w 0.2 w 14,042.4 s 11.0 w 4,348.8 s 3.8 w Low income 5,697 5,251 0.5 0.7 2,179.4 10.8 57.5 .. Middle income 25,266 24,520 0.2 0.2 7,908.5 10.6 1,218.4 1.7 Lower middle income 8,822 8,609 0.2 0.0 3,734.7 11.0 559.4 1.8 Upper middle income 16,443 15,911 0.2 0.2 4,173.8 10.3 659.0 1.6 Low & middle income 30,962 29,771 0.3 0.3 10,087.9 10.7 1,275.8 1.6 East Asia & Pacific 4,580 4,507 0.3 ­0.2 2,221.7 14.0 192.0 1.3 Europe & Central Asia 8,834 8,857 0.0 0.0 1,401.3 6.1 321.4 1.4 Latin America & Carib. 9,834 9,147 0.5 0.5 3,365.2 16.7 495.6 2.7 Middle East & N. Africa 200 211 ­0.4 ­0.3 294.8 3.6 85.1 1.1 South Asia 789 801 ­0.2 0.1 266.6 5.6 20.9 0.5 Sub-Saharan Africa 6,727 6,247 0.4 0.6 2,538.3 11.3 160.7 .. High income 9,495 9,656 ­0.1 ­0.1 3,954.5 11.8 3,073.0 8.9 Euro area 817 936 ­1.1 ­0.6 262.5 10.6 19.6 0.8 a. Negative values indicate an increase in forest area. b. Flowering plants only. c. Data reported by the World Conservation Monitoring Centre in 2006 are the most recent year available. d. Includes Luxembourg. e. Less than 0.5. f. Includes Montenegro. 148 2009 World Development Indicators ENVIRONMENT Deforestation and biodiversity 3.4 About the data Definitions Biological diversity is defined in terms of variability in Global Environment Facility's (GEF) benefits index for · Forest area is land under natural or planted stands genes, species, and ecosystems. A 2008 comprehen- biodiversity is a comprehensive indicator of national of trees, whether productive or not. · Average sive assessment of world species shows that at least biodiversity status and is used to guide its biodiversity annual deforestation is the permanent conversion 1,141 of 5,487 known mammals are threatened with priorities. The indicator incorporates information on of natural forest area to other uses, including agri- extinction. As threats to biodiversity mount, the inter- individual species range maps available from the IUCN culture, ranching, settlements, and infrastructure. national community is increasingly focusing on con- for virtually all mammals (5,487), amphibians (5,915), Deforested areas do not include areas logged but serving diversity. Deforestation is a major cause of and endangered birds (1,098); country data from the intended for regeneration or areas degraded by fuel- loss of biodiversity, and habitat conservation is vital World Resources Institute for reptiles and vascular wood gathering, acid precipitation, or forest fires. for stemming this loss. Conservation efforts have plants; country data from FishBase for 31,190 fish · Animal species are mammals (excluding whales focused on protecting areas of high biodiversity. species; and the ecological characteristics of 867 and porpoises) and birds (included within a country's The Food and Agriculture Organization of the United world terrestrial ecoregions from WWF International. breeding or wintering ranges). · Higher plants are Nations (FAO) Global Forest Resources Assessment For each country the biodiversity indicator incorpo- native vascular plant species. · Threatened species 2005 provides detailed information on forest cover in rates the best available and comparable information are the number of species classified by the IUCN as 2005 and adjusted estimates of forest cover in 1990 in four relevant dimensions: represented species, endangered and vulnerable. · GEF benefits index and 2000. The current survey uses a uniform defini- threatened species, represented ecoregions, and for biodiversity is a composite index of relative bio- tion of forest. Because of space limitations, the table threatened ecoregions. To combine these dimensions diversity potential based on the species represented does not break down forest cover between natural for- into one measure, the indicator uses dimensional in each country and their threat status and diversity est and plantation, a breakdown the FAO provides for weights that reflect the consensus of conservation of habitat types. The index has been normalized from developing countries. Thus the deforestation data in scientists at the GEF, IUCN, WWF International, and 0 (no biodiversity potential) to 100 (maximum bio- the table may underestimate the rate at which natural other nongovernmental organizations. diversity potential). · Nationally protected areas are forest is disappearing in some countries. The World Conservation Monitoring Centre (WCMC) totally or partially protected areas of at least 1,000 Measures of species richness are a straight- compiles data on protected areas, numbers of cer- hectares that are designated as scientific reserves forward way to indicate an area's importance for tain species, and numbers of those species under with limited public access, national parks, natural biodiversity. The number of threatened species is threat from various sources. Because of differences monuments, nature reserves or wildlife sanctuaries, also an important measure of the immediate need in definitions, reporting practices, and reporting peri- protected landscapes, and areas managed mainly for for conservation in an area. Global analyses of the ods, cross-country comparability is limited. sustainable use. Marine areas, unclassified areas, status of threatened species have been carried out Nationally protected areas are defined using the littoral (intertidal) areas, and sites protected under for few groups of organisms. Only for mammals, six IUCN management categories for areas of at local or provincial law are excluded. Total area pro- birds, and amphibians has the status of virtually all least 1,000 hectares: scientific reserves and strict tected is a percentage of total land area (see table known species been assessed. Threatened species nature reserves with limited public access; national 3.1). · Marine protected areas are areas of intertidal are defined using the World Conservation Union's parks of national or international significance and not or subtidal terrain--and overlying water and asso- (IUCN) classifi cation: endangered (in danger of materially affected by human activity; natural monu- ciated flora and fauna and historical and cultural extinction and unlikely to survive if causal factors ments and natural landscapes with unique aspects; features--that have been reserved to protect part continue operating) and vulnerable (likely to move managed nature reserves and wildlife sanctuaries; or all of the enclosed environment. into the endangered category in the near future if protected landscapes (which may include cultural causal factors continue operating). landscapes); and areas managed mainly for the sus- Unlike birds and mammals, it is difficult to accu- tainable use of natural systems to ensure long-term rately count plants. The number of plant species is protection and maintenance of biological diversity. Data sources highly debated. The 2008 IUCN Red List of Threat- The data in the table cover these six categories as Data on forest area and deforestation are from the ened Species, the result of more than 20 years' work well as terrestrial protected areas that were not FAO's Global Forest Resources Assessment 2005. by botanists worldwide, is the most comprehensive assigned to a category by the IUCN. Designating land Data on species are from the electronic files of list of threatened species on a global scale. Only 5 as a protected area does not mean that protection the United Nations Environment Programme and percent of plant species have been evaluated, and is in force. And for small countries that only have WCMC and 2008 IUCN Red List of Threatened Spe- 70 percent of these are threatened with extinction. protected areas smaller than 1,000 hectares, the cies. The GEF benefits index for biodiversity is from Plant species data may not be comparable across size limit in the definition leads to an underestimate Kiran Dev Pandey, Piet Buys, Ken Chomitz, and countries because of differences in taxonomic con- of protected areas. David Wheeler's "Biodiversity Conservation Indica- cepts and coverage and so should be used with cau- Due to variations in consistency and methods of tors: New Tools for Priority Setting at the Global tion. However, the data identify countries that are collection, data quality is highly variable across coun- Environment Facility" (2006). Data on protected major sources of global biodiversity and that show tries. Some countries update their information more areas are from the United Nations Environment national commitments to habitat protection. frequently than others, some have more accurate Programme and WCMC, as compiled by the World More than information about species richness is data on extent of coverage, and many underreport Resources Institute. needed to set priorities for conserving biodiversity. The the number or extent of protected areas. 2009 World Development Indicators 149 3.5 Freshwater Renewable internal Annual freshwater Water Access to an improved freshwater resourcesa withdrawals productivity water source GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per % of urban % of rural billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2007 2007 2007b 2007b 2007b 2007b 2007b 2007b 2006 2006 Afghanistan 55 .. 23.3 42.3 98 0 2 .. .. .. Albania 27 8,456 1.7 6.4 62 11 27 2.2 97 97 Algeria 11 332 6.1 54.0 65 13 22 9.0 87 81 Angola 148 8,696 0.4 0.2 60 17 23 26.1 62 39 Argentina 276 6,987 29.2 10.6 74 9 17 9.7 98 80 Armenia 9 3,023 3.0 32.5 66 4 30 0.6 99 96 Australia 492 23,412 23.9 4.9 75 10 15 16.9 100 100 Austria 55 6,614 2.1 3.8 1 64 35 91.9 100 100 Azerbaijan 8 947 12.2 150.5 76 19 4 0.8 95 59 Bangladesh 105 662 79.4 75.6 96 1 3 0.6 85 78 Belarus 37 3,834 2.8 7.5 30 47 23 4.6 100 99 Belgium 12 1,129 .. .. .. .. .. .. 100 .. Benin 10 1,141 0.1 1.3 45 23 32 18.2 78 57 Bolivia 304 31,892 1.4 0.5 81 7 13 5.8 96 69 Bosnia and Herzegovina 36 9,409 .. .. .. .. .. .. 100 98 Botswana 2 1,276 0.2 8.1 41 18 41 31.8 100 90 Brazil 5,418 28,277 59.3 1.1 62 18 20 10.9 97 58 Bulgaria 21 2,742 10.5 50.0 19 78 3 1.2 100 97 Burkina Faso 13 846 0.8 6.4 86 1 13 3.3 97 66 Burundi 10 1,184 0.3 2.9 77 6 17 2.5 84 70 Cambodia 121 8,346 4.1 3.4 98 0 1 0.9 80 61 Cameroon 273 14,731 1.0 0.4 74 8 18 10.2 88 47 Canada 2,850 86,426 46.0 1.6 12 69 20 15.8 100 99 Central African Republic 141 32,463 0.0 0.0 4 16 80 38.4 90 51 Chad 15 1,394 0.2 1.5 83 0 17 6.0 71 40 Chile 884 53,270 12.6 1.4 64 25 11 6.0 98 72 China 2,812 2,132 630.3 22.4 68 26 7 1.9 98 81 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 2,112 48,014 10.7 0.5 46 4 50 8.8 99 77 Congo, Dem. Rep. 900 14,423 0.4 0.0 31 17 53 12.0 82 29 Congo, Rep. 222 58,937 0.0 0.0 9 22 70 76.1 95 35 Costa Rica 112 25,189 2.7 2.4 53 17 29 6.0 99 96 Côte d'Ivoire 77 3,988 0.9 1.2 65 12 24 11.2 98 66 Croatia 38 8,499 .. .. .. .. .. .. 100 98 Cuba 38 3,386 8.2 21.5 69 12 19 .. 95 78 Czech Republic 13 1,272 2.6 19.6 2 57 41 22.0 100 100 Denmark 6 1,099 1.3 21.2 43 25 32 126.0 100 100 Dominican Republic 21 2,153 3.4 16.1 66 2 32 5.8 97 91 Ecuador 432 32,385 17.0 3.9 82 5 12 0.9 98 91 Egypt, Arab Rep. 2 24 68.3 3,794.4 86 6 8 1.5 99 98 El Salvador 18 2,590 1.3 7.2 59 16 25 10.3 94 68 Eritrea 3 578 0.6 20.8 95 0 5 1.2 74 57 Estonia 13 9,475 0.2 1.2 5 38 57 35.6 100 99 Ethiopia 122 1,543 5.6 4.6 94 0 6 1.6 96 31 Finland 107 20,232 2.5 2.3 3 84 14 49.2 100 100 France 179 2,893 40.0 22.4 10 74 16 33.2 100 100 Gabon 164 123,291 0.1 0.1 42 8 50 42.2 95 47 Gambia, The 3 1,758 0.0 1.0 65 12 23 13.8 91 81 Georgia 58 13,224 1.6 2.8 65 13 22 2.7 100 97 Germany 107 1,301 47.1 44.0 20 68 12 40.4 100 100 Ghana 30 1,291 1.0 3.2 66 10 24 5.1 90 71 Greece 58 5,182 7.8 13.4 80 3 16 16.2 100 99 Guatemala 109 8,181 2.0 1.8 80 13 6 9.6 99 94 Guinea 226 24,093 1.5 0.7 90 2 8 2.1 91 59 Guinea-Bissau 16 9,441 0.2 1.1 82 5 13 1.2 82 47 Haiti 13 1,354 1.0 7.6 94 1 5 3.9 70 51 150 2009 World Development Indicators ENVIRONMENT Renewable internal Annual freshwater Freshwater Water 3.5 Access to an improved freshwater resourcesa withdrawals productivity water source GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per % of urban % of rural billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2007 2007 2007b 2007b 2007b 2007b 2007b 2007b 2006 2006 Honduras 96 13,527 0.9 0.9 80 12 8 8.3 95 74 Hungary 6 597 7.6 127.3 32 59 9 6.3 100 100 India 1,261 1,122 645.8 51.2 86 5 8 0.7 96 86 Indonesia 2,838 12,578 82.8 2.9 91 1 8 2.0 89 71 Iran, Islamic Rep. 129 1,809 93.3 72.6 92 1 7 1.4 99 84 Iraq 35 .. 66.0 187.5 79 15 7 0.4 .. .. Ireland 49 11,223 1.1 2.3 0 77 23 85.3 100 .. Israel 1 104 2.0 260.5 58 6 36 67.0 100 100 Italy 183 3,074 44.4 24.3 45 37 18 24.7 100 .. Jamaica 9 3,514 0.4 4.4 49 17 34 19.6 97 88 Japan 430 3,365 88.4 20.6 62 18 20 52.8 100 100 Jordan 1 119 0.9 138.0 65 4 31 12.1 99 91 Kazakhstan 75 4,871 35.0 46.4 82 17 2 0.5 99 91 Kenya 21 552 2.7 13.2 79 4 17 5.0 85 49 Korea, Dem. Rep. 67 2,817 9.0 13.5 55 25 20 .. 100 100 Korea, Rep. 65 1,338 18.6 28.7 48 16 36 27.5 97 71 Kuwait .. .. 0.9 .. 54 2 44 42.9 .. .. Kyrgyz Republic 46 8,873 10.1 21.7 94 3 3 0.1 99 83 Lao PDR 190 32,495 3.0 1.6 90 6 4 0.6 86 53 Latvia 17 7,355 0.3 1.8 13 33 53 26.1 100 96 Lebanon 5 1,172 1.3 27.3 60 11 29 15.7 100 100 Lesotho 5 2,607 0.1 1.0 20 40 40 17.1 93 74 Liberia 200 53,290 0.1 0.1 55 18 27 5.1 72 52 Libya 1 97 4.3 721.0 83 3 14 8.0 72 68 Lithuania 16 4,610 0.3 1.7 7 15 78 42.3 .. .. Macedonia, FYR 5 2,651 .. .. .. .. .. .. 100 99 Madagascar 337 17,133 15.0 4.4 96 2 3 0.3 76 36 Malawi 16 1,159 1.0 6.3 80 5 15 1.7 96 72 Malaysia 580 21,846 9.0 1.6 62 21 17 10.4 100 96 Mali 60 4,865 6.5 10.9 90 1 9 0.4 86 48 Mauritania 0 128 1.7 425.0 88 3 9 0.6 70 54 Mauritius 3 2,182 0.7 26.4 68 3 30 6.9 100 100 Mexico 409 3,885 78.2 19.1 77 5 17 7.4 98 85 Moldova 1 264 2.3 231.0 33 58 10 0.6 96 85 Mongolia 35 13,322 0.4 1.3 52 27 20 2.5 90 48 Morocco 29 940 12.6 43.4 87 3 10 2.9 100 58 Mozambique 100 4,693 0.6 0.6 87 2 11 6.7 71 26 Myanmar 881 18,051 33.2 3.8 98 1 1 .. 80 80 Namibia 6 2,971 0.3 4.9 71 5 24 11.4 99 90 Nepal 198 7,051 10.2 5.1 96 1 3 0.5 94 88 Netherlands 11 672 7.9 72.2 34 60 6 48.5 100 100 New Zealand 327 77,336 2.1 0.6 42 9 48 24.1 100 .. Nicaragua 190 33,854 1.3 0.7 83 2 15 3.0 90 63 Niger 4 247 2.2 62.3 95 0 4 0.8 91 32 Nigeria 221 1,493 8.0 3.6 69 10 21 5.7 65 30 Norway 382 81,119 2.2 0.6 11 67 23 76.8 100 100 Oman 1 539 1.3 94.4 88 1 10 16.9 85 73 Pakistan 55 339 169.4 308.0 96 2 2 0.4 95 87 Panama 147 44,130 0.8 0.6 28 5 67 14.2 96 81 Papua New Guinea 801 126,658 0.1 0.0 1 42 56 49.6 88 32 Paraguay 94 15,358 0.5 0.5 71 8 20 14.4 94 52 Peru 1,616 57,925 20.1 1.2 82 10 8 2.6 92 63 Philippines 479 5,450 28.5 6.0 74 9 17 2.7 96 88 Poland 54 1,406 16.2 30.2 8 79 13 10.6 100 .. Portugal 38 3,582 11.3 29.6 78 12 10 10.0 99 100 Puerto Rico 7 1,801 .. .. .. .. .. .. .. .. 2009 World Development Indicators 151 3.5 Freshwater Renewable internal Annual freshwater Water Access to an improved freshwater resourcesa withdrawals productivity water source GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per % of urban % of rural billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2007 2007 2007b 2007b 2007b 2007b 2007b 2007b 2006 2006 Romania 42 1,963 23.2 54.8 57 34 9 1.6 99 76 Russian Federation 4,313 30,350 76.7 1.8 18 63 19 3.4 100 88 Rwanda 10 976 0.2 1.6 68 8 24 11.6 82 61 Saudi Arabia 2 99 23.7 986.1 88 3 9 9.9 97 .. Senegal 26 2,079 2.2 8.6 93 3 4 2.2 93 65 Serbia 44 c 5,419c .. .. .. .. .. .. 99d .. Sierra Leone 160 27,358 0.4 0.2 92 3 5 1.7 83 32 Singapore 1 131 .. .. .. .. .. .. 100 .. Slovak Republic 13 2,334 .. .. .. .. .. .. 100 100 Slovenia 19 9,251 .. .. .. .. .. .. .. .. Somalia 6 690 3.3 55.0 99 0 0 .. 63 10 South Africa 45 936 12.5 27.9 63 6 31 10.6 100 82 Spain 111 2,478 35.6 32.0 68 19 13 16.3 100 100 Sri Lanka 50 2,499 12.6 25.2 95 2 2 1.3 98 79 Sudan 30 778 37.3 124.4 97 1 3 0.3 78 64 Swaziland 3 2,306 1.0 39.5 97 1 2 1.4 87 51 Sweden 171 18,692 3.0 1.7 9 54 37 83.0 100 100 Switzerland 40 5,351 2.6 6.4 2 74 24 97.2 100 100 Syrian Arab Republic 7 352 16.7 238.4 88 4 9 1.3 95 83 Tajikistan 66 9,837 12.0 18.0 92 5 4 0.1 93 58 Tanzania 84 2,078 5.2 6.2 89 0 10 2.0 81 46 Thailand 210 3,290 87.1 41.5 95 2 2 1.4 99 97 Togo 12 1,748 0.2 1.5 45 2 53 8.2 86 40 Trinidad and Tobago 4 2,881 0.3 8.1 6 26 68 26.3 97 93 Tunisia 4 410 2.6 62.9 82 4 14 7.4 99 84 Turkey 227 3,072 40.1 17.7 74 11 15 7.0 98 95 Turkmenistan 1 274 24.7 1,812.5 98 1 2 0.1 .. .. Uganda 39 1,261 .. .. .. .. .. .. 90 60 Ukraine 53 1,142 37.5 70.7 52 35 12 0.8 97 97 United Arab Emirates 0 34 4.0 2,665.3 83 2 15 24.5 100 100 United Kingdom 145 2,377 9.5 6.6 3 75 22 152.1 100 100 United States 2,800 9,283 479.3 17.1 41 46 13 20.4 100 94 Uruguay 59 17,750 3.2 5.3 96 1 3 6.6 100 100 Uzbekistan 16 608 58.3 357.0 93 2 5 0.2 98 82 Venezuela, RB 722 26,287 8.4 1.2 47 7 46 14.0 .. .. Vietnam 367 4,304 71.4 19.5 68 24 8 0.4 98 90 West Bank and Gaza .. .. .. .. .. .. .. .. 90 88 Yemen, Rep. 2 94 3.4 161.9 90 2 8 2.8 68 65 Zambia 80 6,728 1.7 2.2 76 7 17 1.9 90 41 Zimbabwe 12 915 4.2 34.3 79 7 14 1.6 98 72 World 43,464 s 6,624 w 3,850.0 s 9.0 w 70 w 20 w 10 w 10.3 w 96 w 77 w Low income 5,985 4,619 554.6 9.4 90 5 5 1.0 84 60 Middle income 27,963 6,589 2,374.8 8.5 76 15 9 3.7 97 83 Lower middle income 14,116 4,117 2,929.5 8.7 78 13 8 3.2 96 82 Upper middle income 13,847 16,993 1,937.8 13.8 80 12 8 2.5 98 83 Low & middle income 33,947 6,128 437.0 3.2 57 26 17 8.8 94 76 East Asia & Pacific 9,454 4,945 959.0 10.2 74 20 7 3.4 96 81 Europe & Central Asia 5,167 11,806 368.4 7.2 60 30 10 3.7 99 88 Latin America & Carib. 13,425 23,965 264.9 2.0 71 10 19 9.9 97 73 Middle East & N. Africa 225 728 275.6 122.3 86 6 8 2.7 95 81 South Asia 1,819 1,196 941.1 51.7 89 4 6 1.1 94 84 Sub-Saharan Africa 3,858 4,823 120.5 3.2 87 3 10 4.1 81 46 High income 9,516 9,313 920.5 10.4 43 42 15 31.6 100 98 Euro area 930 2,907 200.0 22.3 38 48 15 33.7 100 100 a. Excludes river flows from other countries because of data unreliability. b. Refers to data reported to the Food and Agriculture Organization as of 2007. See Primary data documentation for year of most recent water withdrawals survey. c. Includes Montenegro. d. Includes Kosovo. 152 2009 World Development Indicators ENVIRONMENT Freshwater 3.5 About the data Definitions The data on freshwater resources are based on Caution should also be used in comparing data · Renewable internal freshwater resources flows estimates of runoff into rivers and recharge of on annual freshwater withdrawals, which are subject are internal renewable resources (internal river groundwater. These estimates are based on differ- to variations in collection and estimation methods. flows and groundwater from rainfall) in the country. ent sources and refer to different years, so cross- In addition, inflows and outflows are estimated at · Renewable internal freshwater resources per cap- country comparisons should be made with caution. different times and at different levels of quality and ita are calculated using the World Bank's population Because the data are collected intermittently, they precision, requiring caution in interpreting the data, estimates (see table 2.1). · Annual freshwater with- may hide significant variations in total renewable particularly for water-short countries, notably in the drawals are total water withdrawals, not counting water resources from year to year. The data also Middle East and North Africa. evaporation losses from storage basins. Withdraw- fail to distinguish between seasonal and geographic Water productivity is an indication only of the als also include water from desalination plants in variations in water availability within countries. Data effi ciency by which each country uses its water countries where they are a significant source. With- for small countries and countries in arid and semiarid resources. Given the different economic structure drawals can exceed 100 percent of total renewable zones are less reliable than those for larger countries of each country, these indicators should be used resources where extraction from nonrenewable aqui- and countries with greater rainfall. carefully, taking into account the countries' sectoral fers or desalination plants is considerable or where activities and natural resource endowments. water reuse is significant. Withdrawals for agriculture The data on access to an improved water source and industry are total withdrawals for irrigation and Agriculture is still the largest user measure the percentage of the population with ready livestock production and for direct industrial use of water, accounting for some access to water for domestic purposes. The data (including for cooling thermoelectric plants). With- 70 percent of global withdrawals 3.5a are based on surveys and estimates provided by drawals for domestic uses include drinking water, Percent Industry Domestic Agriculture governments to the Joint Monitoring Programme of municipal use or supply, and use for public services, 100 the World Health Organization (WHO) and the United commercial establishments, and homes. · Water Nations Children's Fund (UNICEF). The coverage productivity is calculated as GDP in constant prices 80 rates are based on information from service users divided by annual total water withdrawal. · Access on actual household use rather than on information to an improved water source is the percentage of the 60 from service providers, which may include nonfunc- population with reasonable access to an adequate tioning systems. Access to drinking water from an amount of water from an improved source, such as 40 improved source does not ensure that the water is piped water into a dwelling, plot, or yard; public tap safe or adequate, as these characteristics are not or standpipe; tubewell or borehole; protected dug 20 tested at the time of survey. While information on well or spring; and rainwater collection. Unimproved access to an improved water source is widely used, sources include unprotected dug wells or springs, 0 it is extremely subjective, and such terms as safe, carts with small tank or drum, bottled water, and Low Lower Upper High World income middle middle income improved, adequate, and reasonable may have dif- tanker trucks. Reasonable access is defined as the income income ferent meaning in different countries despite offi - availability of at least 20 liters a person a day from Source: Table 3.5. cial WHO definitions (see Definitions). Even in high- a source within 1 kilometer of the dwelling. The share of withdrawals for income countries treated water may not always be agriculture approaches 90 percent safe to drink. Access to an improved water source is in some developing regions 3.5b equated with connection to a supply system; it does Percent Industry Domestic Agriculture not take into account variations in the quality and 100 cost (broadly defined) of the service. 80 60 Data sources 40 Data on freshwater resources and withdrawals are from the Food and Agriculture Organization 20 of the United Nations AQUASTAT data. The GDP estimates used to calculate water productivity 0 are from the World Bank national accounts data- East Europe Latin Middle South Sub- Asia & America East & Asia Saharan base. Data on access to water are from WHO and & Central & North Africa Pacific Asia Caribbean Africa UNICEF's Progress on Drinking Water and Sanita- Source: Table 3.5. tion (2008). 2009 World Development Indicators 153 3.6 Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total thousand kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2005a 1990 2005a 2005a 2005a 2005a 2005a 2005a 2005a 2005a 2005a Afghanistan 5.9 0.2 0.16 0.21 .. 19.7 27.9 14.1 11.7 23.3 .. 3.1 Albania 2.4 2.5 0.25 0.23 0.0 0.0 0.0 33.4 0.0 66.6 0.0 0.0 Algeria 107.0 .. 0.25 .. .. .. .. .. .. .. .. .. Angola 4.5 .. 0.19 .. .. .. .. .. .. .. .. .. Argentina 181.4 155.5 0.21 0.23 3.8 8.4 15.8 30.5 3.5 14.3 2.1 21.6 Armenia 37.9 7.1 0.11 0.28 .. .. .. 77.6 .. 22.4 .. .. Australia 186.1 111.7 0.18 0.18 12.4 22.8 6.7 43.5 0.2 5.3 2.8 6.3 Austria 90.5 85.2 0.15 0.14 5.5 7.2 9.2 12.4 5.2 4.8 5.9 49.7 Azerbaijan 41.3 18.1 0.15 0.18 9.6 2.4 19.3 18.1 6.5 13.7 1.2 29.3 Bangladesh 250.8 303.0 0.15 0.14 0.7 2.3 3.0 7.6 2.6 79.3 0.5 4.2 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 107.8 99.6 0.17 0.17 6.2 7.7 17.5 15.7 5.4 7.4 2.2 37.9 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 11.3 11.5 0.24 0.25 0.9 9.8 13.1 35.4 7.7 18.4 5.3 9.5 Bosnia and Herzegovina 50.7 .. 0.14 .. .. .. .. .. .. .. .. .. Botswana 2.5 3.4 0.30 0.34 0.0 2.9 0.0 70.5 0.0 5.6 0.0 21.1 Brazil 780.4 .. 0.19 .. .. .. .. .. .. .. .. .. Bulgaria 124.3 100.6 0.17 0.17 3.6 4.2 7.1 17.6 4.3 31.4 3.0 28.7 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi 1.6 .. 0.24 .. .. .. .. .. .. .. .. .. Cambodia 3.6 .. 0.21 .. .. .. .. .. .. .. .. .. Cameroon 14.0 10.0 0.28 0.19 0.4 5.2 36.1 48.8 0.0 3.8 5.0 0.8 Canada 300.9 310.3 0.17 0.16 4.4 9.1 10.6 13.9 2.8 7.9 6.7 44.6 Central African Republic 1.0 .. 0.18 .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 96.5 .. 0.25 7.1 6.4 13.5 35.5 3.6 9.3 6.7 18.0 China 7,038.1 6,088.7 0.14 0.14 20.4 10.9 14.8 28.1 0.5 15.5 0.9 8.8 Hong Kong, China 86.1 34.3 0.12 0.20 1.2 43.5 3.9 30.5 0.1 16.2 0.2 4.6 Colombia .. 87.0 .. 0.20 2.3 8.9 17.3 21.3 5.3 24.1 0.9 19.9 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 2.5 .. 0.32 .. .. .. .. .. .. .. .. .. Costa Rica 27.2 31.2 0.20 0.22 1.6 10.0 8.2 65.7 0.1 10.2 1.3 2.9 Côte d'Ivoire 7.9 .. 0.22 .. .. .. .. .. .. .. .. .. Croatia 48.5 41.2 0.17 0.17 3.3 7.2 9.5 17.9 5.9 16.2 4.8 35.1 Cuba 173.0 .. 0.25 .. .. .. .. .. .. .. .. .. Czech Republic 176.8 152.4 0.15 0.13 5.4 4.6 9.9 11.4 4.9 8.3 4.0 51.6 Denmark 84.3 62.0 0.18 0.17 1.0 12.3 13.8 17.6 4.2 2.4 4.0 44.8 Dominican Republic 88.6 88.6 0.18 0.18 0.1 1.3 2.3 18.6 1.4 73.1 0.1 3.1 Ecuador 28.6 44.7 0.24 0.28 1.8 7.8 12.8 46.4 4.4 12.3 2.2 12.3 Egypt, Arab Rep. 206.5 206.5 0.19 0.19 5.8 4.0 13.9 20.0 8.2 31.1 0.6 16.4 El Salvador 5.5 .. 0.22 .. .. .. .. .. .. .. .. .. Eritrea 2.4 2.9 0.19 0.21 0.3 4.1 8.6 31.8 14.8 24.1 0.0 16.4 Estonia 21.7 16.5 0.15 0.15 0.3 7.3 7.8 15.8 4.7 10.9 16.9 36.4 Ethiopia 18.5 24.1 0.23 0.22 1.6 6.9 10.7 29.7 8.3 28.6 1.4 12.8 Finland 72.0 59.2 0.19 0.15 1.6 17.0 8.5 9.4 4.1 3.1 7.1 49.3 France 326.5 604.7 0.11 0.16 3.2 7.4 16.1 16.1 3.7 5.4 2.3 45.8 Gabon 2.0 .. 0.25 .. .. .. .. .. .. .. .. .. Gambia, The 0.8 .. 0.34 .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany 806.6 960.3 0.13 0.14 3.8 7.1 12.1 11.7 3.4 2.6 2.1 57.2 Ghana .. 15.4 .. 0.17 3.1 2.8 15.0 19.2 4.2 10.0 34.3 11.4 Greece 50.9 46.5 0.19 0.20 4.5 7.8 13.2 22.8 6.9 20.0 2.0 22.9 Guatemala 21.6 .. 0.23 .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 0.1 0.0 0.01 0.01 0.0 2.0 0.0 0.0 0.0 0.0 0.0 98.0 154 2009 World Development Indicators ENVIRONMENT Emissions of organic Water pollution Industry shares of emissions 3.6 water pollutants of organic water pollutants % of total thousand kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2005a 1990 2005a 2005a 2005a 2005a 2005a 2005a 2005a 2005a 2005a Honduras 17.8 .. 0.23 .. .. .. .. .. .. .. .. .. Hungary 7.0 123.2 0.36 0.15 2.5 6.5 10.3 16.1 3.7 12.1 3.5 45.3 India 1,410.6 1,519.8 0.20 0.20 12.2 7.6 9.2 53.7 0.3 12.7 0.3 3.9 Indonesia 721.8 731.0 0.18 0.18 1.3 3.8 13.1 21.7 3.5 30.5 8.0 18.1 Iran, Islamic Rep. 131.6 163.2 0.16 0.15 7.0 2.9 12.5 16.1 14.0 11.8 0.7 34.9 Iraq 7.7 7.7 0.27 0.27 13.1 25.6 29.9 16.9 5.4 9.1 .. .. Ireland 36.0 33.6 0.19 0.20 1.2 11.9 12.1 24.5 2.7 2.1 3.7 41.8 Israel 43.9 42.8 0.18 0.18 2.2 8.5 15.0 19.7 0.0 9.1 1.5 43.9 Italy 378.3 481.3 0.13 0.12 3.5 5.3 10.4 8.7 5.4 15.0 2.9 48.9 Jamaica 18.7 .. 0.29 .. .. .. .. .. .. .. .. .. Japan 1,451.4 1,133.1 0.14 0.15 3.1 7.2 11.2 15.2 3.7 5.6 2.0 52.0 Jordan 15.0 27.1 0.18 0.18 2.7 6.4 15.0 21.9 11.3 16.1 2.7 23.9 Kazakhstan 1.3 1.7 0.40 0.41 0.0 50.0 0.0 47.6 0.0 0.0 0.0 2.4 Kenya 42.6 56.1 0.23 0.24 .. 11.5 5.4 66.8 0.1 12.8 1.7 1.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 366.9 317.0 0.12 0.12 4.3 5.5 12.3 6.5 3.1 10.2 0.9 57.2 Kuwait 9.1 11.9 0.16 0.17 2.1 16.6 11.1 50.2 0.4 11.6 2.8 5.2 Kyrgyz Republic 28.9 11.5 0.14 0.19 7.1 6.2 8.3 23.6 15.2 12.0 1.8 25.9 Lao PDR 0.5 0.5 0.44 0.44 0.0 26.3 0.0 73.7 0.0 0.0 0.0 0.0 Latvia 39.8 29.9 0.12 0.18 2.4 7.3 5.2 21.7 3.6 13.5 20.2 26.1 Lebanon 14.7 14.7 0.19 0.19 0.5 7.5 6.0 25.5 12.9 16.7 4.5 26.3 Lesotho .. 13.2 .. 0.13 1.0 0.5 1.4 3.4 0.5 90.8 .. 2.4 Liberia 0.6 .. 0.30 .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 54.0 42.9 0.15 0.17 0.8 4.9 7.1 20.3 4.2 21.3 11.1 30.3 Macedonia, FYR 32.4 .. 0.18 .. .. .. .. .. .. .. .. .. Madagascar 11.0 88.9 .. 0.14 0.3 1.6 12.4 7.6 2.8 58.9 6.3 10.0 Malawi 37.2 32.7 0.40 0.39 .. 1.4 3.7 82.1 0.6 7.5 1.1 3.6 Malaysia 104.7 187.6 .. 0.12 2.8 4.7 15.1 9.2 3.7 8.1 7.6 48.9 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 0.3 0.4 0.05 0.06 0.0 13.7 0.0 0.0 .. 0.0 0.0 86.3 Mexico 370.8 .. 0.19 0.20 7.8 12.5 10.4 55.6 0.2 7.5 0.9 5.1 Moldova 29.2 22.4 0.44 0.45 0.0 3.4 0.0 95.6 0.0 0.0 .. 1.0 Mongolia 10.2 .. 0.18 .. .. .. .. .. .. .. .. .. Morocco .. 72.8 .. 0.16 0.9 3.0 9.3 16.0 7.1 45.5 1.9 16.3 Mozambique 20.4 10.2 0.27 0.31 1.1 7.1 2.7 81.2 0.1 5.8 1.4 0.7 Myanmar 7.7 6.2 0.17 0.18 56.5 4.6 13.2 14.9 0.4 2.9 1.7 5.8 Namibia 7.4 .. 0.35 .. .. .. .. .. .. .. .. .. Nepal 26.4 26.8 0.14 0.16 1.6 3.9 7.2 19.2 29.9 29.4 2.0 6.8 Netherlands 137.0 119.2 0.20 0.18 1.3 13.1 15.6 18.8 3.7 2.9 2.6 42.1 New Zealand 46.7 55.6 0.24 0.22 2.2 10.4 8.4 28.9 3.0 7.2 8.3 31.7 Nicaragua 10.5 .. 0.27 .. .. .. .. .. .. .. .. .. Niger .. 0.4 .. 0.32 .. 17.0 4.4 76.9 0.3 .. 0.8 0.6 Nigeria 70.8 .. 0.22 .. .. .. .. .. .. .. .. .. Norway 51.8 49.2 0.20 0.20 3.9 14.6 7.6 21.0 3.8 2.1 5.7 41.2 Oman 3.8 6.5 0.15 0.18 3.9 6.2 16.1 22.1 23.4 6.2 2.2 19.8 Pakistan 104.1 .. 0.18 .. .. .. .. .. .. .. .. .. Panama 10.3 12.9 0.30 0.31 1.6 10.2 8.2 53.8 5.6 7.5 1.7 11.4 Papua New Guinea 5.7 .. 0.25 .. .. .. .. .. .. .. .. .. Paraguay 15.3 10.8 0.20 0.28 3.1 9.3 16.7 42.6 5.9 11.0 4.5 6.9 Peru 56.1 .. 0.20 .. .. .. .. .. .. .. .. .. Philippines 118.4 98.5 0.26 0.24 6.7 6.9 15.2 34.7 7.3 3.4 0.0 25.9 Poland 446.7 364.2 0.16 0.16 3.1 5.1 10.7 19.0 5.6 11.8 5.2 39.6 Portugal 140.6 107.2 0.14 0.15 2.3 7.0 6.2 14.5 8.6 25.3 3.9 32.2 Puerto Rico 19.0 9.2 0.15 0.18 1.9 14.9 21.9 34.4 0.2 15.5 1.4 9.7 2009 World Development Indicators 155 3.6 Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total thousand kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2005a 1990 2005a 2005a 2005a 2005a 2005a 2005a 2005a 2005a 2005a Romania 407.0 235.1 0.12 0.15 4.8 3.2 6.7 12.7 4.0 28.9 5.2 34.4 Russian Federation 1,521.4 1,425.9 0.16 0.17 9.8 4.8 11.7 17.5 7.9 6.8 4.3 37.3 Rwanda 7.1 7.1 0.44 0.44 .. .. 0.0 97.0 0.0 0.0 0.0 3.0 Saudi Arabia 18.5 .. 0.15 .. .. .. .. .. .. .. .. .. Senegal 6.1 6.6 0.30 0.29 4.9 6.3 23.8 44.6 3.9 10.5 0.8 5.3 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 4.2 .. 0.32 .. .. .. .. .. .. .. .. .. Singapore 32.4 34.3 0.09 0.10 1.4 24.6 16.0 25.4 0.1 3.9 1.6 26.9 Slovak Republic 72.8 54.6 0.13 0.14 7.3 4.8 8.0 10.5 5.7 14.3 3.2 46.3 Slovenia 55.6 38.4 0.16 0.16 33.7 14.7 8.3 23.7 0.2 10.8 2.0 6.7 Somalia 6.2 .. 0.38 .. .. .. .. .. .. .. .. .. South Africa 260.5 183.8 0.17 0.17 6.7 7.3 11.7 16.5 5.0 7.0 4.6 41.3 Spain 348.0 372.5 0.16 0.15 3.1 7.8 10.6 14.9 7.6 9.6 3.8 42.6 Sri Lanka 53.0 78.4 0.19 0.18 0.5 7.2 6.6 51.5 0.2 31.6 1.1 1.2 Sudan .. 38.6 .. 0.29 0.6 1.9 7.0 57.5 14.2 8.0 1.7 9.1 Swaziland 146.0 .. 0.16 .. .. .. .. .. .. .. .. .. Sweden 116.8 100.1 0.15 0.15 5.3 12.4 9.7 9.0 2.5 1.4 5.4 54.3 Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 6.6 4.5 0.45 0.45 0.0 6.2 0.0 93.8 0.0 0.0 0.0 0.0 Tajikistan 29.1 16.1 0.17 0.23 21.9 1.4 5.1 20.2 7.6 37.5 0.4 5.9 Tanzania 31.1 35.2 0.24 0.25 1.5 9.4 2.7 69.3 0.1 14.0 1.5 1.4 Thailand 369.4 333.8 0.15 0.16 1.8 4.1 13.2 16.5 3.4 22.5 2.4 36.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 7.0 7.6 0.23 0.29 0.0 18.1 21.4 39.1 0.4 7.6 8.5 4.9 Tunisia 44.6 55.8 0.18 0.14 2.5 6.1 5.5 35.8 0.4 43.3 1.9 4.6 Turkey 174.9 177.7 0.18 0.16 5.2 3.0 9.8 15.2 6.2 35.7 1.0 24.0 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 2.7 2.1 0.27 0.23 .. 7.8 7.3 34.8 13.3 17.2 0.0 19.6 Ukraine .. 527.2 .. 0.19 14.6 4.1 10.3 19.0 6.4 6.6 2.2 36.8 United Arab Emirates 5.6 .. 0.14 .. .. .. .. .. .. .. .. .. United Kingdom 599.9 539.7 0.16 0.17 2.5 12.4 13.6 14.4 3.8 4.6 2.4 46.2 United States 2,307.0 1,960.3 0.14 0.14 3.4 9.0 13.0 11.8 3.6 5.0 4.0 50.3 Uruguay 38.7 15.8 0.23 0.28 1.2 3.7 6.6 79.2 0.1 7.4 0.6 1.2 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 96.5 .. 0.21 .. .. .. .. .. .. .. .. .. Vietnam 141.0 470.2 0.16 0.15 1.4 3.7 6.6 14.3 7.1 40.3 3.7 22.9 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.5 1.0 0.43 0.41 .. 79.9 0.0 20.1 0.0 0.0 0.0 0.0 Zambia 15.9 .. 0.23 .. .. .. .. .. .. .. .. .. Zimbabwe 29.3 29.3 0.20 0.20 8.0 4.7 11.0 21.5 6.3 25.2 1.7 21.5 a. Data are derived using the United Nations Industrial Development Organization's (UNIDO) industry database four-digit International Standard Industrial Classification (ISIC). Data in italics are for the most recent year available and are derived using UNIDO's industry database at the three-digit ISIC. 156 2009 World Development Indicators ENVIRONMENT Water pollution 3.6 About the data Definitions Emissions of organic pollutants from industrial emissions of organic water pollutants. Such data · Emissions of organic water pollutants are mea- activities are a major cause of degradation of water are fairly reliable because sampling techniques for sured as biochemical oxygen demand, or the amount quality. Water quality and pollution levels are gener- measuring water pollution are more widely under- of oxygen that bacteria in water will consume in ally measured as concentration or load--the rate of stood and much less expensive than those for air breaking down waste, a standard water treatment occurrence of a substance in an aqueous solution. pollution. test for the presence of organic pollutants. Emis- Polluting substances include organic matter, metals, Hettige, Mani, and Wheeler (1998) used plant- and sions per worker are total emissions divided by the minerals, sediment, bacteria, and toxic chemicals. sector-level information on emissions and employ- number of industrial workers. · Industry shares of The table focuses on organic water pollution result- ment from 13 national environmental protection emissions of organic water pollutants are emissions ing from industrial activities. Because water pollu- agencies and sector-level information on output from manufacturing activities as defined by two-digit tion tends to be sensitive to local conditions, the and employment from the United Nations Industrial divisions of the International Standard Industrial national-level data in the table may not reflect the Development Organization (UNIDO). Their economet- Classification (ISIC) revision 3. quality of water in specific locations. ric analysis found that the ratio of BOD to employ- The data in the table come from an international ment in each industrial sector is about the same study of industrial emissions that may have been across countries. This finding allowed the authors to the first to include data from developing countries estimate BOD loads across countries and over time. (Hettige, Mani, and Wheeler 1998). These data were The estimated BOD intensities per unit of employ- updated through 2005 by the World Bank's Develop- ment were multiplied by sectoral employment num- ment Research Group. Unlike estimates from earlier bers from UNIDO's industry database for 1980­98. studies based on engineering or economic models, These estimates of sectoral emissions were then these estimates are based on actual measurements used to calculate kilograms of emissions of organic of plant-level water pollution. The focus is on organic water pollutants per day for each country and year. water pollution caused by organic waste, measured in The data in the table were derived by updating these terms of biochemical oxygen demand (BOD), because estimates through 2005. the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treat- ment, by contrast, reduces BOD. Data on water pollution are more readily available than are other emissions data because most indus- trial pollution control programs start by regulating Emissions of organic water pollutants declined in most economies from 1990 to 2005, even in some of the top emitters 3.6a Kilograms per day (millions) 1990­98 2000­05 8 6 Data sources 4 Data on water pollutants are from the 1998 study by Hemamala Hettige, Muthukumara Mani, and David Wheeler, "Industrial Pollution in Economic 2 Development: Kuznets Revisited" (available at www.worldbank.org/nipr). The data were updated 0 through 2005 by the World Bank's Development China United States Russian Japan India Germany Indonesia Federation Research Group using the same methodology as Note: Data are for the most recent year available during the period specified. the initial study. Data on industrial sectoral employ- Source: Table 3.6. ment are from UNIDO's industry database. 2009 World Development Indicators 157 3.7 Energy production and use Energy Energy Clean energy production use production Total Total % of total million million average Per capita metric tons of metric tons of annual kilograms of Combustible % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel renewables and waste energy use 1990 2006 1990 2006 1990­2006 1990 2006 1990 2006 1990 2006 1990 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. .. Albania 2.4 1.2 2.7 2.3 2.2 809 715 76.5 68.5 13.6 10.1 9.2 19.0 Algeria 104.4 173.2 23.9 36.7 2.5 946 1,100 99.9 99.7 0.1 0.2 0.1 0.1 Angola 28.7 79.2 6.3 10.3 3.2 597 620 30.2 33.9 68.8 63.9 1.0 2.2 Argentina 48.4 83.9 46.1 69.1 2.0 1,414 1,766 88.7 88.4 3.7 3.7 7.5 7.6 Armenia 0.1 0.8 7.9 2.6 ­4.8 2,228 859 97.3 68.3 0.1 0.0 1.7 32.7 Australia 157.5 267.8 87.7 122.5 2.2 5,138 5,917 94.0 94.7 4.5 4.1 1.5 1.3 Austria 8.1 9.9 25.1 34.2 2.0 3,249 4,132 79.4 75.6 9.9 13.1 10.9 9.6 Azerbaijan 21.3 38.1 26.1 14.1 ­3.3 3,643 1,659 .. 97.9 0.0 0.0 0.2 1.5 Bangladesh 10.8 20.3 12.8 25.0 4.5 113 161 45.9 65.8 53.5 33.7 0.6 0.5 Belarus 3.3 3.9 42.3 28.6 ­2.3 4,153 2,939 95.5 91.9 0.5 5.0 0.0 0.0 Belgium 13.6 15.5 49.7 61.0 1.4 4,988 5,782 75.6 72.5 2.6 5.9 22.4 20.0 Benin 1.8 1.7 1.7 2.8 3.0 324 321 5.8 37.1 93.2 61.1 0.0 0.0 Bolivia 4.9 14.3 2.8 5.8 4.2 416 625 69.1 83.1 27.2 13.8 3.6 3.2 Bosnia and Herzegovina 4.6 4.0 7.0 5.4 1.7 1,633 1,427 93.9 90.6 2.3 3.4 3.7 9.4 Botswana 0.9 1.1 1.3 2.0 2.6 931 1,054 66.3 69.2 33.1 23.2 0.1 0.0 Brazil 103.7 206.7 140.0 224.1 3.0 936 1,184 51.2 53.7 34.0 29.6 13.1 15.0 Bulgaria 9.6 11.1 28.8 20.7 ­1.5 3,305 2,688 84.4 72.8 0.6 3.9 13.8 26.5 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 3.6 .. 5.0 3.6 .. 351 .. 28.4 .. 71.3 .. 0.1 Cameroon 11.0 10.3 5.0 7.1 2.3 411 390 19.5 16.3 75.9 79.2 4.5 4.5 Canada 273.7 411.7 209.5 269.7 1.7 7,539 8,262 74.7 75.0 3.9 4.7 21.4 20.9 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. .. Chile 7.6 10.0 14.1 29.8 5.0 1,067 1,812 74.8 73.5 19.0 15.9 6.2 9.9 China 886.3 1,749.3 863.2 1,878.7 4.4 760 1,433 75.5 85.1 23.2 12.0 1.3 3.0 Hong Kong, China 0.0 0.0 10.7 18.2 3.1 1,872 2,653 99.5 96.7 0.5 0.3 0.0 0.0 Colombia 48.2 84.6 24.7 30.2 0.5 747 695 68.1 73.4 22.3 14.9 9.6 12.2 Congo, Dem. Rep. 12.0 17.8 11.9 17.5 2.4 314 289 12.0 4.6 84.0 92.4 4.1 3.9 Congo, Rep. 8.7 15.4 0.8 1.2 2.6 329 327 35.0 36.8 59.5 57.6 5.3 2.7 Costa Rica 1.0 2.3 2.0 4.6 5.0 658 1,040 48.3 48.6 36.6 15.5 14.4 35.8 Côte d'Ivoire 3.4 9.3 4.4 7.3 3.4 345 385 24.9 35.7 72.0 63.8 2.6 1.8 Croatia 5.1 4.1 9.1 9.0 1.3 1,905 2,017 86.7 84.7 3.4 4.1 3.6 5.8 Cuba 6.6 5.0 16.8 10.6 ­1.6 1,587 944 65.1 88.0 34.9 11.9 0.0 0.1 Czech Republic 40.1 33.4 49.0 46.1 0.1 4,726 4,485 93.2 83.0 0.0 4.0 6.9 15.3 Denmark 10.1 29.6 17.9 20.9 0.4 3,486 3,850 89.9 87.3 6.4 12.9 0.3 2.6 Dominican Republic 1.0 1.5 4.1 7.8 4.4 567 816 75.1 80.4 24.2 18.0 0.7 1.5 Ecuador 16.5 29.8 6.1 11.2 3.8 597 851 79.5 88.2 13.5 5.2 7.0 5.5 Egypt, Arab Rep. 54.9 77.8 32.0 62.5 4.6 580 843 94.0 95.9 3.3 2.3 2.7 1.9 El Salvador 1.7 2.6 2.5 4.7 3.8 496 697 32.0 44.0 48.2 31.6 19.8 24.4 Eritrea 0.7 0.5 0.9 0.7 ­2.2 277 150 19.7 27.0 80.3 73.0 0.0 0.0 Estonia 5.1 3.6 9.6 4.9 ­2.8 6,122 3,638 .. 88.7 2.0 10.7 0.0 0.2 Ethiopia 14.1 20.4 15.0 22.3 2.6 313 289 6.6 8.8 92.8 90.0 0.6 1.3 Finland 12.1 18.0 28.7 37.4 1.8 5,758 7,108 56.0 52.2 15.9 20.4 20.7 18.6 France 112.4 137.0 227.6 272.7 1.2 4,012 4,444 58.6 52.6 5.1 4.4 38.1 44.9 Gabon 14.6 12.1 1.2 1.8 2.2 1,354 1,391 35.4 39.2 59.8 56.3 4.9 4.4 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. .. Georgia 1.8 1.2 12.3 3.3 ­8.2 2,255 754 88.7 65.1 3.7 19.3 5.3 14.0 Germany 186.2 136.8 355.6 348.6 0.0 4,477 4,231 87.0 81.8 1.3 4.6 11.6 13.9 Ghana 4.4 6.5 5.3 9.5 3.6 343 413 19.0 31.7 73.1 63.3 9.2 5.1 Greece 9.2 10.0 22.2 31.1 2.5 2,188 2,792 94.8 93.1 4.0 3.3 0.9 2.5 Guatemala 3.4 5.4 4.5 8.2 4.1 503 628 28.8 44.5 67.9 51.5 3.4 4.0 Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1.3 2.0 1.6 2.6 3.3 223 272 20.9 23.3 76.5 75.8 2.5 0.9 158 2009 World Development Indicators ENVIRONMENT Energy Energy production and use Energy 3.7Clean energy production use production Total Total % of total million million average Per capita metric tons of metric tons of annual kilograms of Combustible % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel renewables and waste energy use 1990 2006 1990 2006 1990­2006 1990 2006 1990 2006 1990 2006 1990 2006 Honduras 1.7 2.0 2.4 4.3 3.2 494 621 31.1 53.3 62.0 41.5 8.1 5.1 Hungary 14.3 10.3 28.6 27.6 0.1 2,753 2,740 82.4 80.3 1.3 4.3 12.9 13.2 India 291.1 435.6 319.9 565.8 3.5 377 510 55.8 69.0 41.7 28.3 2.4 2.7 Indonesia 170.0 307.7 102.8 179.1 3.4 577 803 54.7 67.1 43.8 29.2 1.5 3.7 Iran, Islamic Rep. 179.8 309.3 68.8 170.9 5.5 1,265 2,438 98.2 98.6 1.0 0.5 0.8 0.9 Iraq 104.9 101.1 19.1 32.0 3.2 1,029 1,123 98.7 99.4 0.1 0.1 1.2 0.1 Ireland 3.5 1.6 10.3 15.5 3.2 2,943 3,628 85.3 91.8 1.0 1.4 0.6 1.3 Israel 0.4 2.7 12.1 21.3 3.6 2,599 3,017 97.3 97.3 0.0 0.0 3.0 3.4 Italy 25.3 27.4 148.1 184.2 1.6 2,611 3,125 93.5 90.7 0.6 2.6 3.8 4.6 Jamaica 0.5 0.5 2.9 4.6 2.5 1,233 1,724 83.5 89.2 16.2 10.5 0.3 0.3 Japan 75.2 101.1 443.9 527.6 1.1 3,593 4,129 84.7 81.6 1.1 1.3 14.2 17.1 Jordan 0.2 0.3 3.5 7.2 4.1 1,103 1,294 98.3 98.0 0.1 0.0 1.7 1.4 Kazakhstan 90.5 131.0 73.6 61.4 ­2.3 4,505 4,012 97.0 98.7 0.2 0.1 0.9 1.1 Kenya 9.0 14.3 11.2 17.9 2.9 479 491 19.5 20.6 75.9 73.6 4.4 5.9 Korea, Dem. Rep. 28.9 22.2 33.2 21.7 ­2.4 1,649 913 93.1 90.2 2.9 4.8 4.0 5.0 Korea, Rep. 22.6 43.7 93.4 216.5 5.3 2,178 4,483 83.9 80.8 0.8 1.1 15.4 18.1 Kuwait 50.4 150.6 8.0 25.3 8.0 3,762 9,729 99.9 100.0 0.1 0.0 0.0 0.0 Kyrgyz Republic 2.5 1.5 7.6 2.8 ­4.9 1,713 542 93.6 62.0 0.1 0.1 11.3 45.5 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. .. Latvia 1.1 1.8 7.9 4.6 ­3.0 2,941 2,017 81.8 64.2 8.4 25.9 4.9 5.1 Lebanon 0.1 0.2 2.3 4.8 4.6 777 1,173 93.7 94.2 4.5 2.7 1.9 1.4 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. .. Libya 73.2 102.0 11.5 17.8 2.3 2,645 2,943 98.9 99.1 1.1 0.9 0.0 0.0 Lithuania 4.9 3.5 16.2 8.5 ­2.8 4,394 2,517 76.0 62.1 1.8 8.8 27.9 27.4 Macedonia, FYR 1.5 1.5 2.7 2.8 ­0.3 1,423 1,355 98.2 82.9 0.0 6.0 1.5 5.5 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 50.3 97.9 23.3 68.3 6.1 1,288 2,617 89.4 95.3 9.1 4.1 1.5 0.9 Mali .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. .. Mexico 193.4 256.0 123.0 177.4 2.2 1,478 1,702 88.3 89.1 6.0 4.6 5.8 6.4 Moldova 0.1 0.1 9.9 3.4 ­6.1 2,265 884 .. 88.7 0.4 2.2 0.2 0.2 Mongolia 2.7 3.0 3.4 2.8 ­1.9 1,624 1,080 96.9 95.7 2.5 3.8 0.0 0.0 Morocco 0.8 0.7 7.2 14.0 3.9 298 458 94.0 94.5 4.4 3.2 1.5 1.1 Mozambique 5.6 10.7 6.0 8.8 2.7 441 420 6.2 6.9 93.2 81.6 0.4 14.4 Myanmar 10.7 22.1 10.7 14.3 1.9 266 295 14.6 25.9 84.4 72.1 1.0 2.0 Namibia 0.2 0.3 0.7 1.5 5.1 443 721 62.0 67.3 16.0 12.7 17.5 8.8 Nepal 5.5 8.3 5.8 9.4 3.2 304 340 5.3 11.3 93.4 86.2 1.3 2.4 Netherlands 60.5 60.8 67.1 80.1 1.2 4,489 4,901 96.0 92.9 1.4 3.3 1.4 1.5 New Zealand 12.0 13.1 13.8 17.5 1.6 3,991 4,192 65.4 70.0 4.0 6.0 30.7 24.0 Nicaragua 1.5 2.1 2.1 3.5 3.1 512 624 29.2 39.0 53.2 52.2 17.3 8.7 Niger .. .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 150.5 235.3 70.9 105.1 2.4 751 726 19.7 19.8 79.8 79.6 0.5 0.6 Norway 119.1 222.9 21.4 26.1 1.9 5,050 5,598 52.8 54.9 4.8 5.1 48.6 39.6 Oman 38.3 60.6 4.6 15.4 7.1 2,475 6,057 100.0 100.0 0.0 0.0 0.0 0.0 Pakistan 34.4 61.3 43.4 79.3 3.7 402 499 53.3 60.9 43.2 34.9 3.5 4.2 Panama 0.6 0.8 1.5 2.8 3.6 618 845 58.4 71.7 28.3 17.4 12.8 11.1 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 4.6 6.7 3.1 4.0 1.5 731 660 21.6 30.5 72.3 52.0 75.8 116.5 Peru 10.6 11.5 10.0 13.6 2.1 457 491 64.1 68.5 26.9 17.4 9.0 14.0 Philippines 13.7 24.7 26.2 43.0 3.6 427 498 50.8 51.0 29.2 26.1 20.0 22.9 Poland 99.4 77.9 99.9 97.7 ­0.6 2,620 2,562 97.7 95.3 2.2 5.5 0.1 0.2 Portugal 3.4 4.3 17.2 25.4 3.1 1,742 2,402 81.0 81.1 14.4 11.9 4.7 5.2 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 159 3.7 Energy production and use Energy Energy Clean energy production use production Total Total % of total million million average Per capita metric tons of metric tons of annual kilograms of Combustible % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel renewables and waste energy use 1990 2006 1990 2006 1990­2006 1990 2006 1990 2006 1990 2006 1990 2006 Romania 40.8 28.0 62.5 40.1 ­2.3 2,693 1,860 96.2 85.1 1.0 8.1 1.6 7.6 Russian Federation 1,280.3 1,220.0 878.9 676.2 ­1.5 5,927 4,745 93.4 89.4 1.4 1.1 5.2 8.3 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 370.8 570.7 61.3 146.1 4.7 3,744 6,170 100.0 100.0 0.0 0.0 0.0 0.0 Senegal 1.0 1.2 1.8 3.0 3.8 233 250 48.0 59.5 52.0 39.6 0.0 0.7 Serbia 13.4 a 10.6 19.5a 17.1 .. 2,569a 2,303 90.7a 90.2 6.0a 4.7 4.2a 5.5 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. .. Singapore 0.0 0.0 13.4 30.7 3.8 4,384 6,968 100.0 100.0 0.0 0.0 0.0 0.0 Slovak Republic 5.3 6.6 21.3 18.7 ­0.1 4,035 3,465 81.6 70.9 0.8 2.6 15.5 27.5 Slovenia 2.9 3.3 5.6 7.3 2.2 2,792 3,618 70.6 69.2 4.8 6.5 26.2 24.2 Somalia .. .. .. .. .. .. .. .. .. .. .. .. .. South Africa 114.5 158.7 91.2 129.8 2.1 2,592 2,739 86.2 87.1 11.4 10.5 2.5 2.7 Spain 34.6 31.4 91.2 144.6 3.3 2,349 3,277 77.7 82.8 4.5 3.6 17.9 13.8 Sri Lanka 4.2 5.5 5.5 9.4 3.7 322 472 24.1 41.4 71.0 54.3 4.9 4.2 Sudan 8.8 30.7 10.7 17.7 3.5 411 470 17.7 21.8 81.5 77.5 0.8 0.7 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. .. Sweden 29.7 32.8 47.6 51.3 0.5 5,557 5,650 37.8 34.9 11.6 18.4 50.5 44.5 Switzerland 9.7 12.1 24.8 28.2 0.7 3,695 3,770 61.5 56.1 3.7 7.2 35.5 35.9 Syrian Arab Republic 22.3 26.5 11.7 18.9 2.9 918 975 98.0 98.2 0.0 0.0 2.0 1.8 Tajikistan 2.0 1.5 5.6 3.6 ­2.7 1,051 548 72.7 59.5 0.0 0.0 25.5 39.1 Tanzania 9.1 19.4 9.8 20.8 4.8 385 527 7.6 8.3 91.0 91.0 1.4 0.6 Thailand 26.5 56.2 43.9 103.4 5.2 809 1,630 65.5 82.3 33.4 16.6 1.0 0.7 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. .. Togo 1.1 2.0 1.3 2.4 4.6 328 375 17.3 13.4 80.6 84.5 0.6 0.3 Trinidad and Tobago 12.6 34.6 6.0 14.3 5.8 4,934 10,768 99.2 99.8 0.8 0.2 0.0 0.0 Tunisia 5.7 6.6 5.1 8.7 3.7 630 863 87.5 86.6 12.4 13.3 0.1 0.1 Turkey 25.8 26.3 52.9 94.0 3.5 943 1,288 81.9 89.1 13.6 5.5 4.6 5.5 Turkmenistan 74.9 61.6 19.6 17.3 1.1 5,352 3,524 .. 100.0 0.0 0.0 0.3 0.0 Uganda .. .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 135.8 82.8 253.8 137.4 ­3.5 4,891 2,937 91.9 82.2 0.1 0.4 8.2 17.9 United Arab Emirates 110.2 177.3 23.2 46.9 4.4 12,416 11,036 100.0 100.0 0.0 0.0 0.0 0.0 United Kingdom 208.0 186.6 212.3 231.1 0.5 3,708 3,814 90.9 89.2 0.3 1.7 8.3 8.9 United States 1,649.4 1,654.2 1,926.3 2,320.7 1.3 7,717 7,768 86.5 85.7 3.2 3.4 10.2 10.8 Uruguay 1.1 0.8 2.3 3.2 1.2 725 962 58.7 67.8 24.3 14.9 26.8 9.7 Uzbekistan 38.6 58.2 46.4 48.5 0.6 2,261 1,829 99.2 98.9 0.0 0.0 1.2 1.1 Venezuela, RB 148.9 195.5 43.9 62.2 1.6 2,224 2,302 91.5 88.2 1.2 0.9 7.2 11.0 Vietnam 24.7 71.9 24.3 52.3 5.1 367 621 20.4 49.8 77.7 46.4 1.9 3.9 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 9.4 18.7 2.6 7.1 6.4 208 326 97.0 98.9 3.0 1.1 0.0 0.0 Zambia 4.9 6.7 5.5 7.3 1.7 673 625 16.6 11.1 73.4 78.2 12.5 11.0 Zimbabwe 8.6 8.8 9.4 9.6 ­0.2 895 724 45.3 29.2 50.4 63.3 4.0 5.0 World 8,821.7 t 11,786.0 t 8,637.3 t 11,525.2 t 1.8 w 1,686 w 1,820 w 81.2 w 80.9 w 10.0 w 9.8 w 8.7 w 9.2 w Low income 451.2 734.4 400.2 575.5 2.4 482 478 44.5 43.1 52.9 53.8 2.8 3.2 Middle income 4,623.1 6,447.4 3,797.2 5,348.7 2.0 1,097 1,267 81.2 82.4 14.6 12.3 4.1 5.2 Lower middle income 2,272.0 3,782.9 1,973.3 3,468.8 3.3 716 1,019 74.4 81.0 22.7 15.2 3.0 3.9 Upper middle income 2,351.1 2,664.4 1,823.9 1,879.8 0.2 2,584 2,300 88.4 84.8 5.9 7.0 5.3 7.6 Low & middle income 5,058.6 7,147.8 4,181.1 5,899.7 2.1 990 1,108 78.1 79.0 17.9 15.9 4.0 5.0 East Asia & Pacific 1,223.4 2,372.6 1,140.5 2,382.5 4.2 718 1,258 71.8 82.1 26.4 14.7 1.8 3.2 Europe & Central Asia 1,862.0 1,772.5 1,695.2 1,302.8 ­1.5 3,887 2,930 93.2 89.1 1.6 2.2 5.0 8.1 Latin America & Carib. 608.6 926.1 457.6 685.9 2.4 1,054 1,240 71.4 73.0 19.5 15.9 9.1 11.0 Middle East & N. Africa 563.0 827.2 190.2 385.5 4.3 849 1,254 97.3 97.8 1.6 1.2 1.1 0.9 South Asia 348.8 535.6 390.8 694.8 3.5 350 468 54.0 66.8 43.5 30.4 2.5 2.8 Sub-Saharan Africa 475.6 762.2 313.3 467.6 2.5 685 670 41.8 41.1 56.1 56.3 2.2 2.6 High income 3,780.4 4,663.1 4,479.4 5,659.1 1.6 4,807 5,416 84.1 82.9 2.8 3.4 13.0 13.5 Euro area 477.4 463.1 1,075.7 1,268.9 1.2 3,567 3,936 80.0 76.4 3.2 4.9 16.4 18.3 a. Includes Kosovo and Montenegro. 160 2009 World Development Indicators ENVIRONMENT Energy production and use 3.7 About the data Definitions In developing economies growth in energy use is assumed for converting nuclear electricity into oil · Energy production refers to forms of primary closely related to growth in the modern sectors-- equivalents and 100 percent efficiency for converting energy--petroleum (crude oil, natural gas liquids, industry, motorized transport, and urban areas-- hydroelectric power. and oil from nonconventional sources), natural gas, but energy use also reflects climatic, geographic, The IEA makes these estimates in consultation solid fuels (coal, lignite, and other derived fuels), and economic factors (such as the relative price with national statistical offices, oil companies, elec- and combustible renewables and waste--and pri- of energy). Energy use has been growing rapidly in tric utilities, and national energy experts. The IEA mary electricity, all converted into oil equivalents low- and middle-income economies, but high-income occasionally revises its time series to reflect politi- (see About the data). · Energy use refers to the use economies still use almost five times as much energy cal changes, and energy statistics undergo contin- of primary energy before transformation to other on a per capita basis. ual changes in coverage or methodology as more end-use fuels, which is equal to indigenous produc- Energy data are compiled by the International detailed energy accounts become available. Breaks tion plus imports and stock changes, minus exports Energy Agency (IEA). IEA data for economies that in series are therefore unavoidable. and fuels supplied to ships and aircraft engaged in are not members of the Organisation for Economic international transport (see About the data). · Fos- Co-operation and Development (OECD) are based sil fuel comprises coal, oil, petroleum, and natu- on national energy data adjusted to conform to ral gas products. · Combustible renewables and annual questionnaires completed by OECD member waste comprise solid biomass, liquid biomass, bio- governments. gas, industrial waste, and municipal waste. · Clean Total energy use refers to the use of primary energy energy production is noncarbohydrate energy that before transformation to other end-use fuels (such does not produce carbon dioxide when generated. It as electricity and refined petroleum products). It includes hydropower and nuclear, geothermal, and includes energy from combustible renewables and solar power, among others. waste--solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences (see Data sources). All forms of energy--primary energy and primary electricity--are converted into oil equiva- lents. A notional thermal efficiency of 33 percent is A person in a high-income economy uses an average of more than 11 times as much energy as a person in a low-income economy 3.7a Energy use per capita (thousands of kilograms of oil equivalent) 1990 2006 6 4 Data sources 2 Data on energy production and use are from IEA electronic files and are published in IEA's annual publications, Energy Statistics and Bal- 0 ances of Non-OECD Countries, Energy Statistics Low income Lower Upper High income World middle income middle income of OECD Countries, and Energy Balances of OECD Source: Table 3.7. Countries. 2009 World Development Indicators 161 Energy dependency and efficiency 3.8 and carbon dioxide emissions Net energy GDP per unit of Carbon dioxide importsa energy use emissions Carbon intensity 2005 PPP $ kilograms per kilograms per per kilogram Total kilogram of oil Per capita 2005 PPP $ % of energy use of oil equivalent million metric tons equivalent energy use metric tons of GDP 1990 2006 1990 2006 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan .. .. .. .. 2.6 0.7 .. .. .. .. .. 0.0 Albania 8 47 4.8 8.9 7.3 3.5 2.7 1.5 2.2 1.1 0.6 0.2 Algeria ­337 ­372 6.6 6.5 77.0 137.5 3.2 4.0 3.0 4.2 0.5 0.6 Angola ­356 ­671 5.4 6.9 4.6 9.0 0.7 0.9 0.4 0.6 0.1 0.2 Argentina ­5 ­21 5.3 6.6 109.7 152.7 2.4 2.4 3.4 3.9 0.5 0.4 Armenia 98 67 1.3 5.5 4.2 4.3 0.5 1.7 1.2 1.4 0.4 0.3 Australia ­80 ­119 4.6 5.4 293.1 368.9 3.3 3.1 17.2 18.1 0.7 0.6 Austria 68 71 8.0 8.4 57.6 73.6 2.3 2.2 7.5 8.9 0.3 0.3 Azerbaijan 18 ­171 1.3 3.6 46.1 36.6 1.8 2.6 6.4 4.4 1.4 1.0 Bangladesh 16 19 6.1 7.0 15.4 40.0 1.2 1.7 0.1 0.3 0.2 0.2 Belarus 92 86 1.5 3.2 107.8 63.3 2.5 2.4 10.6 6.5 1.6 0.8 Belgium 73 75 5.0 5.7 99.1 102.6 2.0 1.7 9.9 9.8 0.4 0.3 Benin ­6 39 3.2 3.8 0.7 2.6 0.4 1.0 0.1 0.3 0.1 0.2 Bolivia ­77 ­144 7.4 6.2 5.5 9.3 2.0 1.7 0.8 1.0 0.3 0.3 Bosnia and Herzegovina 35 27 .. 4.6 6.9 26.3 1.0 5.2 1.6 6.9 .. 1.1 Botswana 29 45 7.3 11.7 2.2 4.6 1.7 2.4 1.6 2.5 0.2 0.2 Brazil 26 8 7.7 7.3 202.6 325.5 1.4 1.5 1.4 1.7 0.2 0.2 Bulgaria 67 46 2.3 3.7 75.3 44.4 2.6 2.2 8.6 5.7 1.1 0.6 Burkina Faso .. .. .. .. 0.6 0.7 .. .. 0.1 0.1 0.1 0.1 Burundi .. .. .. .. 0.2 0.2 .. .. 0.0 0.0 0.1 0.1 Cambodia .. 29 .. 4.5 0.5 0.5 .. 0.1 0.0 0.0 .. 0.0 Cameroon ­118 ­46 5.0 5.1 1.6 3.7 0.3 0.5 0.1 0.2 0.1 0.1 Canada ­31 ­53 3.6 4.3 428.5 537.5 2.0 2.0 15.4 16.6 0.6 0.5 Central African Republic .. .. .. .. 0.2 0.3 .. .. 0.1 0.1 0.1 0.1 Chad .. .. .. .. 0.1 0.1 .. .. 0.0 0.0 0.0 0.0 Chile 46 67 6.2 7.0 35.3 66.1 2.5 2.2 2.7 4.1 0.4 0.3 China ­3 7 1.4 3.2 2,399.2 5,547.8 2.8 3.2 2.1 4.3 1.9 1.0 Hong Kong, China 100 100 12.7 14.3 26.2 38.6 2.5 2.1 4.6 5.7 0.2 0.2 Colombia ­95 ­180 8.1 11.0 57.4 58.6 2.3 2.0 1.7 1.4 0.3 0.2 Congo, Dem. Rep. ­1 ­2 1.9 0.9 4.0 2.1 0.3 0.1 0.1 0.0 0.2 0.1 Congo, Rep. ­997 ­1,180 10.5 10.5 1.2 2.0 1.5 1.6 0.5 0.6 0.1 0.2 Costa Rica 49 49 9.5 9.3 2.9 7.3 1.4 1.8 0.9 1.7 0.2 0.2 Cote d'Ivoire 23 ­28 5.4 4.1 5.4 8.7 1.2 1.1 0.4 0.5 0.2 0.3 Croatia 43 54 6.0 6.9 24.6 22.9 2.7 2.6 5.1 5.2 0.5 0.4 Cuba 61 53 .. .. 32.0 24.3 1.9 2.5 3.0 2.2 .. .. Czech Republic 18 27 3.5 4.8 161.7 119.7 3.3 2.6 15.6 11.7 1.0 0.6 Denmark 44 ­41 7.3 8.9 49.8 46.1 2.8 2.3 9.7 8.5 0.4 0.3 Dominican Republic 75 80 5.9 7.2 9.6 18.8 2.3 2.4 1.3 2.0 0.4 0.4 Ecuador ­169 ­165 9.2 8.1 16.6 29.3 2.7 2.8 1.6 2.2 0.3 0.3 Egypt, Arab Rep. ­72 ­25 5.8 5.7 75.4 173.5 2.4 2.8 1.4 2.4 0.4 0.5 El Salvador 32 44 7.8 7.6 2.6 6.4 1.0 1.4 0.5 1.0 0.1 0.2 Eritrea 20 27 1.9 4.0 .. 0.8 .. 1.0 .. 0.2 .. 0.3 Estonia 47 27 1.7 5.0 28.3 18.2 3.0 3.6 18.1 13.5 1.8 0.8 Ethiopia 7 9 1.8 2.3 3.0 7.9 0.2 0.4 0.1 0.1 0.1 0.2 Finland 58 52 4.1 4.5 50.6 53.2 1.8 1.5 10.1 10.1 0.4 0.3 France 51 50 6.2 7.0 363.3 377.7 1.6 1.4 6.4 6.2 0.3 0.2 Gabon ­1,077 ­566 11.2 9.9 6.0 1.5 4.8 0.8 6.5 1.2 0.4 0.1 Gambia, The .. .. .. .. 0.2 0.3 .. .. 0.2 0.2 0.2 0.2 Georgia 85 64 2.4 5.2 17.3 4.8 1.4 1.5 3.2 1.1 0.6 0.3 Germany 48 61 5.7 7.6 980.6 784.0 2.8 2.3 12.3 9.5 0.5 0.3 Ghana 18 32 2.5 2.9 3.8 7.3 0.7 0.8 0.2 0.3 0.3 0.3 Greece 59 68 8.0 9.3 72.2 95.4 3.2 3.1 7.1 8.6 0.4 0.3 Guatemala 24 34 6.6 6.6 5.1 11.4 1.1 1.4 0.6 0.9 0.2 0.2 Guinea .. .. .. .. 1.0 1.4 .. .. 0.2 0.2 0.2 0.1 Guinea-Bissau .. .. .. .. 0.2 0.3 .. .. 0.2 0.2 0.3 0.4 Haiti 21 23 7.3 4.0 1.0 1.8 0.6 0.7 0.1 0.2 0.1 0.2 162 2009 World Development Indicators ENVIRONMENT Energy dependency and efficiency and carbon dioxide emissions Net energy GDP per unit of Carbon dioxide 3.8 importsa energy use emissions Carbon intensity 2005 PPP $ kilograms per kilograms per per kilogram Total kilogram of oil Per capita 2005 PPP $ % of energy use of oil equivalent million metric tons equivalent energy use metric tons of GDP 1990 2006 1990 2006 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 30 53 5.4 5.5 2.6 7.4 1.1 1.9 0.5 1.1 0.2 0.3 Hungary 50 63 4.5 6.5 60.1 56.4 2.1 2.0 5.8 5.6 0.5 0.3 India 9 23 3.2 4.7 679.9 1,402.4 2.1 2.6 0.8 1.3 0.7 0.6 Indonesia ­65 ­72 3.6 4.2 149.3 419.6 1.5 2.4 0.8 1.9 0.4 0.6 Iran, Islamic Rep. ­161 ­81 4.9 4.0 218.3 451.6 3.2 2.9 4.0 6.5 0.6 0.7 Iraq ­451 ­216 .. .. 48.5 84.5 2.5 2.8 2.6 .. .. .. Ireland 66 90 6.0 10.9 30.6 42.3 3.0 2.8 8.7 10.2 0.5 0.3 Israel 96 88 6.9 7.9 33.1 63.6 2.7 3.0 7.1 9.2 0.4 0.4 Italy 83 85 9.1 9.1 395.7 452.1 2.7 2.4 7.0 7.7 0.3 0.3 Jamaica 84 89 4.2 3.6 8.0 10.2 2.7 2.6 3.3 3.8 0.6 0.6 Japan 83 81 7.2 7.5 1,080.7 1,230.0 2.4 2.3 8.7 9.6 0.3 0.3 Jordan 95 96 3.0 3.5 10.2 20.5 2.9 2.9 3.2 3.8 1.0 0.9 Kazakhstan ­23 ­113 1.6 2.4 288.1 180.9 3.9 3.2 17.6 11.9 2.5 1.4 Kenya 20 21 3.0 2.8 5.8 11.1 0.5 0.6 0.2 0.3 0.2 0.2 Korea, Dem. Rep. 13 ­3 .. .. 244.6 82.6 7.4 3.9 12.1 3.5 .. .. Korea, Rep. 76 80 4.9 5.0 241.6 452.2 2.6 2.1 5.6 9.4 0.5 0.4 Kuwait ­530 ­495 2.8 4.6 43.4 93.6 5.4 3.3 20.4 36.9 0.6 0.8 Kyrgyz Republic 67 47 1.5 3.3 12.6 5.6 1.7 2.0 2.8 1.1 1.1 0.6 Lao PDR .. .. .. .. 0.2 1.4 .. .. 0.1 0.3 0.1 0.1 Latvia 86 60 3.4 7.3 14.5 6.5 1.8 1.4 5.4 2.8 0.5 0.2 Lebanon 94 96 6.8 8.1 9.1 16.9 3.9 3.0 3.1 4.2 0.6 0.4 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. 0.5 0.5 .. .. 0.2 0.1 0.4 0.4 Libya ­534 ­474 .. 4.4 37.8 56.1 3.3 3.2 8.7 9.5 .. 0.8 Lithuania 70 59 2.9 6.1 24.3 14.0 1.5 1.6 6.6 4.1 0.5 0.3 Macedonia, FYR 47 47 5.9 5.9 15.5 10.3 5.7 3.7 8.1 5.1 1.0 0.7 Madagascar .. .. .. .. 0.9 2.8 .. .. 0.1 0.2 0.1 0.2 Malawi .. .. .. .. 0.6 1.0 .. .. 0.1 0.1 0.1 0.1 Malaysia ­116 ­43 5.2 4.7 55.3 239.8 2.4 3.6 3.1 9.3 0.5 0.8 Mali .. .. .. .. 0.4 0.6 .. .. 0.1 0.0 0.1 0.0 Mauritania .. .. .. .. 2.6 1.6 .. .. 1.4 0.6 0.9 0.3 Mauritius .. .. .. .. 1.5 3.4 .. .. 1.4 2.7 0.2 0.3 Mexico ­57 ­44 6.8 7.7 375.2 421.5 3.1 2.4 4.5 4.1 0.4 0.3 Moldova 99 97 1.7 2.6 23.8 8.1 2.4 2.3 5.4 2.1 1.4 0.9 Mongolia 20 ­7 1.4 2.6 10.0 8.8 2.9 3.4 4.7 3.4 2.0 1.3 Morocco 89 95 9.3 8.3 23.5 48.0 3.3 3.6 1.0 1.6 0.4 0.4 Mozambique 6 ­22 0.9 1.7 1.0 1.9 0.2 0.2 0.1 0.1 0.2 0.1 Myanmar 0 ­55 1.3 2.9 4.3 11.3 0.4 0.8 0.1 0.2 0.3 0.3 Namibia 67 79 8.1 6.5 0.0 2.6 0.0 1.8 0.0 1.3 0.0 0.3 Nepal 5 11 2.3 2.9 0.6 3.1 0.1 0.3 0.0 0.1 0.0 0.1 Netherlands 10 24 5.8 7.3 139.7 125.8 2.1 1.5 9.3 7.7 0.4 0.2 New Zealand 13 26 4.6 5.9 22.5 29.9 1.6 1.7 6.5 7.2 0.4 0.3 Nicaragua 29 39 3.7 3.8 2.6 3.9 1.2 1.2 0.6 0.7 0.3 0.3 Niger .. .. .. .. 1.0 1.1 .. .. 0.1 0.1 0.2 0.1 Nigeria ­112 ­124 2.0 2.5 45.3 114.3 0.6 1.1 0.5 0.8 0.3 0.5 Norway ­456 ­755 6.4 8.6 30.3 52.9 1.4 1.6 7.1 11.4 0.2 0.2 Oman ­740 ­293 5.7 3.6 10.3 31.4 2.3 2.2 5.6 12.5 0.4 0.6 Pakistan 21 23 4.2 4.6 68.0 134.3 1.6 1.8 0.6 0.9 0.4 0.4 Panama 59 72 9.8 11.6 3.1 5.9 2.1 2.3 1.3 1.8 0.2 0.2 Papua New Guinea .. .. .. .. 2.4 4.4 .. .. 0.6 0.7 0.3 0.4 Paraguay ­48 ­69 5.5 6.0 2.3 3.9 0.7 1.0 0.5 0.7 0.1 0.2 Peru ­6 15 9.8 14.0 21.0 37.0 2.1 2.7 1.0 1.4 0.2 0.2 Philippines 48 43 5.7 6.1 43.9 75.0 1.7 1.7 0.7 0.9 0.3 0.3 Poland 1 20 3.1 5.7 347.6 302.4 3.5 3.3 9.1 7.9 1.1 0.6 Portugal 80 83 9.1 8.7 42.3 62.4 2.5 2.3 4.3 5.9 0.3 0.3 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 163 Energy dependency and efficiency 3.8 and carbon dioxide emissions Net energy GDP per unit of Carbon dioxide importsa energy use emissions Carbon intensity 2005 PPP $ kilograms per kilograms per per kilogram Total kilogram of oil Per capita 2005 PPP $ % of energy use of oil equivalent million metric tons equivalent energy use metric tons of GDP 1990 2006 1990 2006 1990 2005 1990 2005 1990 2005 1990 2005 Romania 35 30 2.9 5.4 155.1 89.1 2.5 2.3 6.7 4.1 0.9 0.4 Russian Federation ­46 ­80 2.1 2.7 2,261.7 1,503.3 2.6 2.3 15.3 10.5 1.2 0.9 Rwanda .. .. .. .. 0.5 0.6 .. .. 0.1 0.1 0.1 0.1 Saudi Arabia ­505 ­291 5.1 3.5 197.4 381.1 3.2 2.7 12.1 16.5 0.6 0.8 Senegal 48 59 5.8 6.2 3.1 5.1 1.7 1.7 0.4 0.4 0.3 0.3 Serbia 31b 38 5.1b 4.1 65.4b 52.5b 3.0 b .. 6.2b 6.5b .. .. Sierra Leone .. .. .. .. 0.3 0.9 .. .. 0.1 0.2 0.1 0.3 Singapore 100 100 5.4 6.5 41.9 56.3 3.1 1.8 13.8 13.2 0.6 0.3 Slovak Republic 75 65 3.1 5.1 51.4 36.6 2.4 1.9 9.7 6.8 0.8 0.4 Slovenia 48 54 5.9 6.8 18.0 14.8 3.2 2.0 9.0 7.4 0.6 0.3 Somalia .. .. .. .. 0.0 0.6 .. .. 0.0 0.1 .. .. South Africa ­26 ­22 3.0 3.2 331.9 408.8 3.6 3.2 9.4 8.7 1.2 1.0 Spain 62 78 8.4 8.5 211.8 343.7 2.3 2.4 5.5 7.9 0.3 0.3 Sri Lanka 24 41 6.3 8.0 3.8 11.0 0.7 1.2 0.2 0.6 0.1 0.2 Sudan 18 ­73 2.5 3.9 5.4 10.6 0.5 0.6 0.2 0.3 0.2 0.2 Swaziland .. .. .. .. 0.4 1.0 .. .. 0.6 0.8 0.1 0.2 Sweden 38 36 4.4 5.9 49.5 48.5 1.0 0.9 5.8 5.4 0.2 0.2 Switzerland 61 57 9.1 9.7 42.7 41.2 1.7 1.5 6.4 5.5 0.2 0.2 Syrian Arab Republic ­91 ­40 3.2 4.2 35.8 68.4 3.1 3.7 2.8 3.6 1.0 0.9 Tajikistan 64 59 2.9 2.8 23.4 5.2 4.2 1.5 4.4 0.8 1.4 0.5 Tanzania 8 7 2.2 2.1 2.3 4.7 0.2 0.2 0.1 0.1 0.1 0.1 Thailand 40 46 5.1 4.5 95.7 270.9 2.2 2.7 1.8 4.3 0.4 0.6 Timor-Leste .. .. .. .. .. 0.2 .. .. .. 0.2 .. 0.2 Togo 19 15 2.6 2.0 0.8 1.3 0.6 0.6 0.2 0.2 0.2 0.3 Trinidad and Tobago ­109 ­142 2.1 2.0 16.9 32.7 2.8 2.6 13.8 24.7 1.4 1.3 Tunisia ­11 24 6.4 7.8 13.3 22.0 2.6 2.6 1.6 2.2 0.4 0.3 Turkey 51 72 8.3 8.9 141.5 247.9 2.7 2.9 2.5 3.4 0.3 0.3 Turkmenistan ­281 ­257 .. 1.4 32.0 41.6 1.6 2.5 8.7 8.6 .. 1.8 Uganda .. .. .. .. 0.8 2.3 .. .. 0.0 0.1 0.1 0.1 Ukraine 47 40 1.6 2.1 684.0 327.1 2.7 2.3 13.2 6.9 1.6 1.2 United Arab Emirates ­375 ­278 4.1 4.7 54.7 123.7 2.4 2.7 29.3 30.1 0.6 0.6 United Kingdom 2 19 6.4 8.6 569.2 546.4 2.7 2.3 9.9 9.1 0.4 0.3 United States 14 29 4.1 5.5 4,797.5 5,776.4 2.5 2.5 19.2 19.5 0.6 0.5 Uruguay 49 75 9.7 10.3 3.9 5.6 1.7 1.9 1.3 1.7 0.2 0.2 Uzbekistan 17 ­20 0.9 1.2 125.3 112.4 2.7 2.4 6.1 4.3 3.1 2.1 Venezuela, RB ­239 ­214 4.3 4.7 117.4 148.1 2.7 2.5 5.9 5.6 0.6 0.6 Vietnam ­2 ­38 2.5 3.7 21.4 101.8 0.9 2.0 0.3 1.2 0.4 0.6 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. ­266 ­164 8.5 6.7 9.6 20.1 3.7 2.9 0.8 1.0 0.4 0.4 Zambia 10 9 1.8 2.0 2.4 2.4 0.4 0.3 0.3 0.2 0.2 0.2 Zimbabwe 9 9 .. .. 16.6 11.5 1.8 1.2 1.6 0.9 .. .. World ­2c w ­2c w 4.2 w 5.2 w 22,584.9d t 29,257.0d t 2.6d w 2.6d w 4.3d w 4.5d w 0.6d w 0.5d w Low income ­13 ­28 2.5 3.2 518.7 722.5 1.3 1.3 0.7 0.6 0.3 0.4 Middle income ­22 ­21 3.0 4.2 9,675.3 13,842.1 2.5 2.7 2.8 3.3 0.9 0.7 Lower middle income ­15 ­9 2.6 3.9 4,882.2 9,447.7 2.5 2.9 1.8 2.8 1.0 0.8 Upper middle income ­29 ­42 3.5 4.8 4,793.1 4,393.3 2.6 2.4 6.9 5.5 0.8 0.5 Low & middle income ­21 ­21 3.0 4.1 10,193.8 14,564.1 2.4 2.6 2.4 2.7 0.8 0.6 East Asia & Pacific ­7 0 2.0 3.4 3,029.7 6,769.2 2.7 3.1 1.9 3.6 1.3 0.9 Europe & Central Asia ­10 ­36 2.3 3.5 4,365.8 3,087.2 2.6 2.4 10.4 7.0 1.1 0.7 Latin America & Carib. ­33 ­35 6.8 7.3 1,020.2 1,360.7 2.2 2.1 2.3 2.5 0.3 0.3 Middle East & N. Africa ­196 ­115 5.5 5.0 565.4 1,112.6 3.0 3.0 2.5 3.7 0.5 0.6 South Asia 11 23 3.4 4.8 770.5 1,592.6 2.0 2.4 0.7 1.1 0.6 0.5 Sub-Saharan Africa ­52 ­63 2.7 3.0 463.4 649.3 1.5 1.4 0.9 0.8 0.6 0.5 High income 16 18 5.2 6.3 11,003.2 13,099.7 2.5 2.3 11.8 12.6 0.5 0.4 Euro area 56 64 6.6 7.7 2,529.7 2,585.0 2.4 2.0 8.4 8.1 0.4 0.3 a. Negative values indicate that a country is a net exporter. b. Includes Kosovo and Montenegro. c. Deviation from zero is due to statistical errors and changes in stock. d. Includes emissions not allocated to specific countries. 164 2009 World Development Indicators ENVIRONMENT Energy dependency and efficiency and carbon dioxide emissions 3.8 About the data Because commercial energy is widely traded, its pro- combustion different fossil fuels release different estimated from a consistent time series tend to be duction and use need to be distinguished. Net energy amounts of carbon dioxide for the same level of more accurate than individual values. Each year imports show the extent to which an economy's use energy use: oil releases about 50 percent more car- the CDIAC recalculates the entire time series since exceeds its production. High-income economies are bon dioxide than natural gas, and coal releases about 1949, incorporating recent findings and corrections. net energy importers; middle-income economies are twice as much. Cement manufacturing releases Estimates exclude fuels supplied to ships and aircraft their main suppliers. about half a metric ton of carbon dioxide for each in international transport because of the difficulty of The ratio of gross domestic product (GDP) to energy metric ton of cement produced. apportioning the fuels among benefiting countries. use indicates energy efficiency. To produce compa- The U.S. Department of Energy's Carbon Diox- The ratio of carbon dioxide per unit of energy shows rable and consistent estimates of real GDP across ide Information Analysis Center (CDIAC) calculates carbon intensity, which is the amount of carbon diox- economies relative to physical inputs to GDP--that annual anthropogenic emissions from data on fossil ide emitted as a result of using one unit of energy in is, units of energy use--GDP is converted to 2005 fuel consumption (from the United Nations Statistics the process of production. The proportion of carbon constant international dollars using purchasing Division's World Energy Data Set) and world cement dioxide per unit of GDP indicates how clean produc- power parity (PPP) rates. Differences in this ratio manufacturing (from the U.S. Bureau of Mines's tion processes are. over time and across economies reflect structural Cement Manufacturing Data Set). Carbon dioxide changes in an economy, changes in sectoral energy emissions, often calculated and reported as elemen- Definitions efficiency, and differences in fuel mixes. tal carbon, were converted to actual carbon dioxide · Net energy imports are estimated as energy use Carbon dioxide emissions, largely by-products of mass by multiplying them by 3.664 (the ratio of the less production, both measured in oil equivalents. energy production and use (see table 3.7), account mass of carbon to that of carbon dioxide). Although · GDP per unit of energy use is the ratio of gross for the largest share of greenhouse gases, which estimates of global carbon dioxide emissions are domestic product (GDP) per kilogram of oil equivalent are associated with global warming. Anthropogenic probably accurate within 10 percent (as calculated of energy use, with GDP converted to 2005 constant carbon dioxide emissions result primarily from fos- from global average fuel chemistry and use), coun- international dollars using purchasing power parity sil fuel combustion and cement manufacturing. In try estimates may have larger error bounds. Trends (PPP) rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has High-income economies depend on imported energy . . . 3.8a in the United States. Energy use refers to the use of primary energy before transformation to other Net energy imports (% of energy use) 1990 2006 end-use fuel, which is equal to indigenous produc- Low income tion plus imports and stock changes minus exports and fuel supplied to ships and aircraft engaged in Lower middle income international transport (see About the data for table Upper middle income 3.7). · Carbon dioxide emissions are emissions from the burning of fossil fuels and the manufacture of High income cement and include carbon dioxide produced dur- Euro area ing consumption of solid, liquid, and gas fuels and gas flaring. ­60 ­40 ­20 0 20 40 60 80 Note: Negative values indicate that the income group is a net energy exporter. Source: Table 3.8. . . . mostly from middle-income economies in the Middle East and North Africa and Latin America and the Caribbean 3.8b Net energy imports (% of energy use) 1990 2006 East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa Data sources South Asia Data on energy use are from the electronic files Sub-Saharan Africa of the International Energy Agency. Data on car- bon dioxide emissions are from the CDIAC, Envi- ­250 ­200 ­150 ­100 ­50 0 50 100 ronmental Sciences Division, Oak Ridge National Note: Negative values indicate that the region is a net energy exporter. Source: Table 3.8. Laboratory, Tennessee, United States. 2009 World Development Indicators 165 3.9 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand thousand average metric tons metric tons metric tons annual of carbon of carbon of carbon dioxide % of total dioxide % of total dioxide % growtha % changeb % changeb % changeb % changeb equivalent Industrial Agricultural equivalent Industrial Agricultural equivalent 1990­ 1990­ 1990­ 1990­ 1990­ 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Afghanistan ­9.3 ­73.5 .. .. .. .. .. .. .. .. .. .. Albania ­0.9 ­51.9 2,170 ­2.7 11.5 70.0 1,390 ­40.6 0.0 97.1 50 .. Algeria 3.9 78.6 24,310 30.9 66.3 15.3 10,330 17.7 7.2 89.1 110 ­52.2 Angola 4.1 93.5 37,020 171.6 11.6 39.1 28,350 454.8 0.0 35.9 0 .. Argentina 2.0 39.2 94,340 14.9 13.0 63.9 83,410 28.2 0.2 97.7 930 ­50.5 Armenia 0.3 3.6 2,300 ­25.6 25.2 50.9 450 ­50.5 0.0 93.3 10 .. Australia 1.3 25.9 116,840 12.3 24.6 61.5 114,500 7.9 1.3 94.9 4,580 74.8 Austria 1.5 27.8 7,210 ­12.2 14.6 50.1 4,620 ­19.5 9.1 85.3 3,310 173.6 Azerbaijan ­3.2 ­31.8 11,550 ­20.4 45.2 45.4 4,040 ­0.5 0.0 93.6 50 ­72.2 Bangladesh 6.8 160.1 92,530 13.4 11.6 69.2 37,100 65.5 0.0 91.9 0 .. Belarus ­3.3 ­41.3 16,620 ­13.6 43.0 38.8 10,360 ­32.2 32.3 65.6 440 .. Belgium ­0.2 3.6 7,610 ­25.6 17.0 59.7 9,650 ­14.2 12.0 65.4 9,380 7,115.4 Benin 8.0 259.0 4,840 77.3 8.9 47.5 4,660 119.8 0.0 68.0 0 .. Bolivia 3.0 68.2 27,120 74.4 2.8 34.5 28,300 97.8 0.0 43.3 0 .. Bosnia and Herzegovina 16.0 280.4 2,850 42.5 51.9 32.6 1,020 ­10.5 0.0 82.4 850 84.8 Botswana 4.2 110.0 4,480 3,346.2 17.9 71.9 2,460 .. 0.0 96.3 0 .. Brazil 3.3 60.6 421,820 47.7 3.0 67.1 300,300 31.8 3.2 74.4 7,760 46.7 Bulgaria ­3.1 ­41.0 6,140 ­35.8 31.9 32.7 5,880 ­55.6 29.9 64.5 650 .. Burkina Faso 1.8 33.6 .. .. .. .. .. .. .. .. .. .. Burundi 1.1 15.1 .. .. .. .. .. .. .. .. .. .. Cambodia 1.0 19.5 14,890 .. 5.4 71.5 3,820 .. 0.0 74.1 0 .. Cameroon 4.5 131.5 15,110 43.9 17.9 56.0 14,540 75.4 0.0 85.0 890 9.9 Canada 1.8 25.4 103,830 25.1 46.6 22.2 51,390 1.4 4.5 86.7 11,010 ­14.1 Central African Republic 1.8 27.7 .. .. .. .. .. .. .. .. .. .. Chad 2.9 ­2.6 .. .. .. .. .. .. .. .. .. .. Chile 4.7 87.1 19,560 37.8 12.1 29.9 12,590 54.1 6.9 88.7 10 .. China 4.6 131.2 995,760 11.2 34.2 50.0 566,680 24.5 3.0 92.7 119,720 1,285.6 Hong Kong, China 2.4 47.3 1,090 ­7.6 40.4 0.9 200 ­4.8 0.0 5.0 330 .. Colombia ­0.7 2.1 61,690 25.4 15.7 55.1 24,530 16.0 0.9 78.0 330 73.7 Congo, Dem. Rep. ­5.3 ­46.0 5,750 115.4 49.6 11.8 2,250 174.4 0.0 15.6 0 .. Congo, Rep. 1.9 70.6 50,320 81.5 7.7 26.3 38,680 99.5 0.0 23.2 0 .. Costa Rica 5.3 150.4 2,450 ­34.1 1.6 58.0 2,850 ­17.2 0.0 98.9 0 .. Cote d'Ivoire 2.0 61.5 15,320 183.2 11.2 20.6 12,350 402.0 0.0 25.0 0 .. Croatia 1.8 ­6.9 3,690 ­6.6 44.2 29.8 3,590 5.9 22.3 63.8 720 7.5 Cuba ­1.8 ­24.1 9,490 ­4.0 6.4 62.4 8,330 ­39.0 8.0 87.4 110 .. Czech Republic ­1.5 ­26.0 14,930 ­32.9 58.7 17.2 6,570 ­38.8 16.4 75.0 3,530 17,550.0 Denmark ­1.3 ­7.4 4,920 ­12.9 16.3 67.7 7,380 ­26.2 7.7 78.6 1,460 461.5 Dominican Republic 5.4 96.3 5,960 12.9 4.0 62.1 2,850 ­31.2 0.0 96.1 0 .. Ecuador 3.2 76.8 12,890 5.9 16.3 57.4 8,500 ­3.8 0.0 97.6 0 .. Egypt, Arab Rep. 5.9 130.0 32,960 41.8 31.2 44.2 27,810 63.8 11.5 85.6 1,820 ­19.1 El Salvador 5.4 144.5 3,200 16.8 12.5 48.1 2,250 9.8 0.0 95.1 0 .. Eritrea 11.2 .. 2,410 15.3 7.5 77.6 2,350 75.4 0.0 99.1 0 .. Estonia ­2.8 ­35.8 1,230 ­52.9 43.1 35.0 610 ­62.6 0.0 83.6 60 .. Ethiopia 8.0 165.9 47,740 22.1 10.0 77.2 63,130 24.4 0.0 98.6 0 .. Finland 1.5 5.1 5,470 ­26.1 10.2 30.3 5,330 ­11.6 27.0 59.5 1,030 368.2 France 0.1 4.0 43,520 ­23.3 10.7 71.1 78,090 ­11.7 12.1 77.3 27,010 151.5 Gabon ­8.9 ­74.9 2,040 ­34.6 79.9 4.4 420 ­77.3 0.0 57.1 0 .. Gambia, The 3.2 50.0 .. .. .. .. .. .. .. .. .. .. Georgia ­8.8 ­72.3 4,330 ­25.2 29.6 51.7 3,390 0.0 17.4 49.3 10 .. Germany 1.8 178.1 58,100 ­47.1 45.7 39.2 69,470 ­10.3 13.3 74.2 41,980 273.8 Ghana 4.7 94.3 8,630 62.5 10.7 49.6 10,520 131.7 0.0 88.6 170 ­10.5 Greece 2.3 32.0 7,410 16.0 9.7 39.1 13,090 0.2 3.3 91.3 1,620 105.1 Guatemala 6.2 125.0 8,990 51.9 11.3 42.7 7,980 66.9 0.0 70.8 0 .. Guinea 2.0 34.1 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 1.9 29.8 .. .. .. .. .. .. .. .. .. .. Haiti 6.9 77.9 3,740 30.3 6.4 61.2 4,290 73.7 0.0 98.4 0 .. 166 2009 World Development Indicators ENVIRONMENT Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide 3.9 Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand thousand average metric tons metric tons metric tons annual of carbon of carbon of carbon dioxide % of total dioxide % of total dioxide % growtha % changeb % changeb % changeb % changeb equivalent Industrial Agricultural equivalent Industrial Agricultural equivalent 1990­ 1990­ 1990­ 1990­ 1990­ 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Honduras 7.6 186.9 5,380 7.2 5.8 71.9 3,860 8.7 0.0 97.9 0 .. Hungary ­0.5 ­6.2 11,050 ­22.3 52.6 18.3 8,760 ­26.7 20.7 76.0 1,540 102.6 India 4.8 106.3 712,330 13.9 14.7 64.8 300,680 33.5 0.5 93.0 9,510 18.7 Indonesia 5.7 181.0 224,330 24.5 36.2 41.2 69,910 16.1 0.3 72.6 900 ­34.8 Iran, Islamic Rep. 4.9 106.9 95,060 73.7 64.7 21.8 66,140 36.0 0.9 97.6 1,560 ­26.8 Iraq 3.6 74.2 10,980 ­1.3 48.7 14.7 3,990 ­39.3 0.0 93.0 470 20.5 Ireland 2.6 38.3 3,660 ­68.3 24.3 32.0 12,320 ­4.0 0.2 92.6 2,050 1,763.6 Israel 4.9 92.0 1,170 15.8 9.4 36.8 1,820 ­4.2 0.0 83.5 1,140 35.7 Italy 0.9 14.2 36,670 ­13.3 19.1 37.7 37,200 4.6 23.7 70.5 27,710 480.9 Jamaica 2.0 27.6 1,160 ­4.9 3.4 47.4 1,020 ­16.4 0.0 96.1 0 .. Japan 0.9 13.8 53,480 ­7.3 30.0 13.4 23,590 ­26.2 8.4 49.3 70,570 165.7 Jordan 4.0 101.4 1,610 49.1 13.0 24.2 1,240 6.9 0.0 93.5 10 .. Kazakhstan ­3.6 ­37.2 28,270 ­48.9 49.1 37.9 5,530 ­76.6 0.0 90.2 0 .. Kenya 4.8 90.5 20,310 4.6 18.0 65.0 19,060 ­12.7 0.0 96.4 0 .. Korea, Dem. Rep. ­10.4 ­66.2 10,650 8.7 29.0 36.4 23,160 152.0 0.0 97.5 860 186.7 Korea, Rep. 4.2 87.2 31,280 14.0 18.5 31.1 22,020 132.3 56.6 36.1 8,700 61.1 Kuwait 2.7 115.6 11,200 64.7 93.9 1.5 540 116.0 0.0 81.5 390 56.0 Kyrgyz Republic ­5.5 ­55.8 3,520 ­24.8 10.5 72.2 3,260 ­23.1 0.0 98.8 60 .. Lao PDR 15.5 520.7 .. .. .. .. .. .. .. .. .. .. Latvia ­5.9 ­55.5 2,290 ­47.0 40.6 29.3 1,390 ­48.3 0.0 88.5 110 .. Lebanon 4.1 85.7 980 34.2 12.2 18.4 1,020 37.8 0.0 93.1 0 .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia 3.1 1.6 .. .. .. .. .. .. .. .. .. .. Libya 2.9 48.5 8,540 ­2.4 77.6 8.9 2,050 ­28.3 0.0 91.7 290 190.0 Lithuania ­4.2 ­42.6 3,650 ­52.8 44.1 38.1 2,860 ­31.3 0.0 90.2 150 .. Macedonia, FYR ­0.8 ­33.9 .. .. .. .. .. .. .. .. .. .. Madagascar 8.4 198.9 .. .. .. .. .. .. .. .. .. .. Malawi 4.1 65.2 .. .. .. .. .. .. .. .. .. .. Malaysia 7.4 333.9 25,510 19.8 57.2 22.3 9,920 ­14.5 3.9 64.3 530 ­44.8 Mali 2.1 33.9 .. .. .. .. .. .. .. .. .. .. Mauritania ­6.1 ­38.1 .. .. .. .. .. .. .. .. .. .. Mauritius 6.2 133.1 .. .. .. .. .. .. .. .. .. .. Mexico 0.5 12.3 120,100 25.3 22.2 39.6 75,500 7.5 1.2 90.1 3,160 63.7 Moldova ­8.1 ­66.2 2,590 ­45.8 43.6 30.9 970 ­70.3 0.0 94.8 360 .. Mongolia ­1.7 ­12.0 4,840 ­34.4 2.9 83.9 22,850 128.5 0.0 99.6 0 .. Morocco 4.0 104.4 13,240 46.0 2.6 41.6 15,510 7.9 0.0 75.2 0 .. Mozambique 4.1 88.6 11,680 23.9 16.9 64.3 9,930 236.6 0.0 99.7 0 .. Myanmar 6.1 165.3 60,840 51.5 6.8 70.0 25,900 80.0 0.0 66.8 10 .. Namibia 51.7 34,883.6 4,260 ­1.4 4.7 89.9 4,620 9.0 0.0 99.1 0 .. Nepal 9.4 395.9 36,040 6.6 10.4 80.5 7,100 24.6 0.0 88.5 0 .. Netherlands ­0.2 ­10.0 15,180 ­21.4 23.6 49.2 16,800 ­13.0 33.8 51.5 5,300 ­10.9 New Zealand 2.4 33.4 27,490 0.4 10.4 82.3 27,960 ­17.6 0.1 99.4 820 105.0 Nicaragua 4.4 47.9 6,350 35.4 4.7 80.2 3,210 ­14.4 0.0 96.9 0 .. Niger ­0.4 1.0 .. .. .. .. .. .. .. .. .. .. Nigeria 6.2 152.1 78,290 31.2 45.5 33.7 39,030 39.1 0.0 87.1 80 ­33.3 Norway 3.4 74.7 12,080 58.5 61.8 14.3 4,680 ­11.5 37.8 53.0 1,770 ­64.5 Oman 8.3 206.3 4,260 110.9 76.1 12.9 1,140 31.0 0.0 96.5 0 .. Pakistan 4.5 97.4 110,300 33.2 14.1 66.3 80,040 44.5 0.8 96.4 620 ­11.4 Panama 4.4 88.1 3,040 2.4 4.3 72.4 2,070 ­17.9 0.0 95.7 0 .. Papua New Guinea 3.6 82.7 .. .. .. .. .. .. .. .. .. .. Paraguay 3.7 71.5 17,750 51.8 1.7 70.9 12,870 29.0 0.0 81.8 0 .. Peru 3.3 76.0 21,510 24.6 6.4 48.1 18,720 30.9 0.0 89.4 80 .. Philippines 3.9 70.7 44,860 15.5 8.0 66.7 18,940 5.3 0.1 95.6 350 250.0 Poland ­1.2 ­13.0 60,060 ­33.3 67.0 18.4 26,110 ­17.3 22.3 72.5 1,270 176.1 Portugal 2.7 47.3 7,140 ­4.2 8.0 52.9 7,000 1.2 9.9 80.7 1,050 707.7 Puerto Rico ­8.9 ­82.1 .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 167 3.9 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand thousand average metric tons metric tons metric tons annual of carbon of carbon of carbon dioxide % of total dioxide % of total dioxide % growtha % changeb % changeb % changeb % changeb equivalent Industrial Agricultural equivalent Industrial Agricultural equivalent 1990­ 1990­ 1990­ 1990­ 1990­ 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Romania ­3.5 ­42.6 23,260 ­45.0 52.4 30.1 11,790 ­52.3 25.9 69.6 2,220 48.0 Russian Federation ­2.6 ­33.5 501,380 ­20.6 77.3 7.9 42,650 ­67.0 8.0 76.2 56,600 192.1 Rwanda 1.6 14.6 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 2.2 93.0 63,500 59.9 91.8 1.9 7,720 ­6.2 0.0 92.1 1,530 ­32.3 Senegal 2.9 61.9 6,340 14.2 4.7 75.9 10,250 64.8 0.0 99.0 10 .. Serbiac ­2.4 ­59.8 6,720 ­47.7 16.4 59.2 4,700 ­48.2 11.1 81.5 840 147.1 Sierra Leone 5.3 181.3 .. .. .. .. .. .. .. .. .. .. Singapore 0.8 34.2 1,260 70.3 27.0 4.8 7,970 4,327.8 95.7 0.8 1,300 225.0 Slovak Republic ­1.6 ­28.8 5,290 ­29.0 54.3 19.5 2,760 ­40.6 32.2 58.0 710 7,000.0 Slovenia 0.8 ­17.6 1,630 ­6.3 20.9 47.9 1,100 2.8 0.0 88.2 210 ­63.8 Somalia 44.7 3,123.5 .. .. .. .. .. .. .. .. .. .. South Africa 1.2 23.2 59,200 13.3 54.3 23.8 29,250 10.5 7.3 82.7 2,600 79.3 Spain 3.4 62.2 38,010 20.1 11.3 44.1 48,520 37.5 3.5 85.7 15,050 239.0 Sri Lanka 8.0 193.1 10,280 0.0 12.3 61.8 3,130 29.9 0.0 89.1 0 .. Sudan 6.5 97.3 67,310 69.3 21.5 73.3 59,750 51.6 0.0 96.2 0 .. Swaziland 10.8 125.0 .. .. .. .. .. .. .. .. .. .. Sweden ­0.2 ­1.9 6,460 ­15.8 6.5 41.5 6,070 ­4.1 8.4 76.8 1,620 63.6 Switzerland ­0.2 ­3.6 4,150 ­13.4 8.7 68.0 2,840 ­10.4 8.1 78.2 3,310 335.5 Syrian Arab Republic 2.9 90.9 7,960 37.0 33.8 34.7 9,430 20.0 2.8 94.9 0 .. Tajikistan ­10.0 ­77.7 3,270 ­11.4 10.1 68.5 1,590 ­48.9 0.0 99.4 120 50.0 Tanzania 3.9 100.2 39,460 46.9 20.3 63.5 31,690 36.0 0.0 84.3 0 .. Thailand 6.2 182.9 78,840 14.4 9.4 76.1 27,990 31.2 0.7 87.9 940 ­40.5 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 4.7 79.0 2,840 58.7 14.8 48.6 5,470 174.9 0.0 88.8 0 .. Trinidad and Tobago 4.0 93.1 3,820 52.2 78.0 1.0 360 5.9 0.0 91.7 0 .. Tunisia 3.2 65.7 4,390 17.4 32.1 34.2 7,230 69.7 4.1 94.2 30 .. Turkey 3.6 75.2 23,140 ­14.5 15.3 59.5 47,950 8.3 9.0 88.0 1,480 ­47.9 Turkmenistan 2.5 30.2 23,060 ­30.6 81.8 15.2 3,200 ­22.9 20.0 78.8 250 .. Uganda 7.4 183.8 .. .. .. .. .. .. .. .. .. .. Ukraine ­5.1 ­52.2 75,640 ­48.3 68.9 15.7 23,270 ­66.5 41.6 54.2 1,390 2,216.7 United Arab Emirates 6.9 126.3 34,250 79.2 96.8 1.7 2,730 193.5 0.0 90.5 480 118.2 United Kingdom ­0.3 ­4.0 39,400 ­41.8 35.7 50.7 65,480 ­4.4 37.1 52.2 14,030 138.6 United States 1.3 20.4 810,280 ­5.5 56.4 18.4 456,210 10.5 5.5 74.7 108,420 18.8 Uruguay 1.2 42.2 17,700 25.4 0.6 90.3 15,630 3.0 0.0 99.6 20 .. Uzbekistan 13.1 1,602.7 51,480 23.7 70.1 23.2 14,660 2.3 0.3 98.3 760 .. Venezuela, RB 2.6 26.2 65,730 58.3 42.0 33.6 26,460 21.9 0.1 77.8 2,300 72.9 Vietnam 11.9 376.0 75,080 41.7 17.8 66.8 37,470 169.2 0.0 94.9 10 .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 4.5 110.2 9,040 95.7 44.5 27.7 7,080 38.6 0.0 98.9 10 .. Zambia ­1.1 ­3.2 16,770 70.8 5.7 68.6 11,410 137.7 3.7 65.1 0 .. Zimbabwe ­3.2 ­31.2 10,400 ­4.1 24.8 60.4 10,160 13.3 0.0 97.1 20 .. World 1.6 w 29.5 w 6,607,490 s 7.0 w 34.8 w 43.1 w 3,787,800 s 14.0 w 5.2 w 82.6 w 601,890 s 126.9 w Low income 0.9 39.4 742,160 32.7 21.1 59.3 477,730 53.9 0.2 89.6 2,730 96.4 Middle income 2.0 43.1 4,255,740 8.6 32.2 46.4 2,179,990 13.7 3.3 84.8 221,040 244.3 Lower middle income 3.8 93.5 2,731,100 14.2 27.8 51.7 1,448,600 25.7 2.4 86.7 139,690 428.9 Upper middle income ­0.6 ­8.3 1,524,640 ­0.2 39.9 36.9 731,390 ­4.3 5.1 81.0 81,350 115.3 Low & middle income 1.9 42.9 4,997,900 11.6 30.5 48.3 2,657,720 19.3 2.8 85.7 223,770 241.2 East Asia & Pacific 4.2 123.4 .. .. 30.5 51.9 .. .. 2.2 90.0 .. .. Europe & Central Asia ­2.3 ­29.3 851,260 ­26.2 69.1 16.2 215,350 ­46.5 15.6 77.5 67,550 163.6 Latin America & Carib. 1.9 33.4 929,970 36.0 10.7 57.9 645,520 24.6 1.9 80.8 14,700 38.4 Middle East & N. Africa 4.4 96.8 209,070 47.8 52.0 25.9 151,830 29.4 3.4 92.0 4,300 ­15.7 South Asia 4.8 106.7 961,480 15.3 14.1 66.0 428,050 37.6 0.5 93.4 10,130 16.3 Sub-Saharan Africa 2.0 40.3 .. .. 24.4 49.4 .. .. 0.6 78.5 .. .. High income 1.3 26.9 1,609,590 ­5.0 47.9 26.9 1,130,080 3.2 10.7 75.3 378,120 89.4 Euro area 1.2 43.2 232,220 ­25.9 22.2 47.6 303,960 ­3.1 12.8 76.4 135,750 236.8 a. Calculated using the least squares method, which accounts for ups and downs of all data points in the period (see Statistical methods). b. Calculated as the change in emission since 1990, which is the baseline for Kyoto Protocol requirements. c. Includes Kosovo and Montenegro. 168 2009 World Development Indicators ENVIRONMENT Trends in greenhouse gas emissions 3.9 About the data Definitions Greenhouse gases--which include carbon dioxide, compared. A kilogram of methane is 21 times as · Carbon dioxide emissions are emissions from methane, nitrous oxide, hydrofluorocarbons, per- effective at trapping heat in the earth's atmosphere the burning of fossil fuels and the manufacture of fluorocarbons, and sulfur hexafluoride--contribute as a kilogram of carbon dioxide within 100 years. cement and include carbon dioxide produced during to climate change. Nitrous oxide emissions are mainly from fossil fuel consumption of solid, liquid, and gas fuels and gas Carbon dioxide emissions, largely a by-product of combustion, fertilizers, rainforest fires, and animal flaring. · Methane emissions are emissions from energy production and use (see table 3.7), account waste. Nitrous oxide is a powerful greenhouse gas, human activities such as agriculture and from indus- for the largest share of greenhouse gases. Anthro- with an estimated atmospheric lifetime of 114 years, trial methane production. · Industrial methane emis- pogenic carbon dioxide emissions result primarily compared with 12 years for methane. The per kilo- sions are emissions from the handling, transmission, from fossil fuel combustion and cement manufactur- gram global warming potential of nitrous oxide is and combustion of fossil fuels and biofuels. · Agri- ing. Burning oil releases more carbon dioxide than nearly 310 times that of carbon dioxide within 100 cultural methane emissions are emissions from burning natural gas, and burning coal releases even years. animals, animal waste, rice production, agricultural more for the same level of energy use. Cement manu- Other greenhouse gases covered under the Kyoto waste burning (nonenergy, on-site), and savannah facturing releases about half a metric ton of carbon Protocol are hydrofluorocarbons, perfluorocarbons, burning. · Nitrous oxide emissions are emissions dioxide for each metric ton of cement produced. and sulfur hexafluoride. Although emissions of these from agricultural biomass burning, industrial activi- Methane emissions result largely from agricultural artificial gases are small, they are more powerful ties, and livestock management. · Industrial nitrous activities, industrial production landfills and waste- greenhouse gases than carbon dioxide, with much oxide emissions are emissions produced during the water treatment, and other sources such as tropi- higher atmospheric lifetimes and high global warm- manufacturing of adipic acid and nitric acid. · Agri- cal forest and other vegetation fires. The emissions ing potential. cultural nitrous oxide emissions are emissions pro- are usually expressed in carbon dioxide equivalents For a discussion of carbon dioxide sources and duced through fertilizer use (synthetic and animal using the global warming potential, which allows the methodology behind emissions calculation, see manure), animal waste management, agricultural the effective contributions of different gases to be About the data for table 3.8. waste burning (nonenergy, on-site), and savannah burning. · Other greenhouse gas emissions are by- The 10 largest contributors to methane emissions product emissions of hydrofluorocarbons, perfluoro- account for about 62 percent of emissions 3.9a carbons, and sulfur hexafluoride. Methane emissions, 2005 (million metric tons of carbon dioxide equivalent) 1,000 750 500 250 0 China United India Russian Brazil Indonesia Mexico Australia Pakistan Canada States Federation Source: Table 3.9. The 10 largest contributors to nitrous oxide emissions account for about 56 percent of emissions 3.9b Nitrous oxide emissions, 2005 (million metric tons of carbon dioxide equivalent) 600 400 Data sources Data on carbon dioxide emissions are from the Carbon Dioxide Information Analysis Center, Envi- 200 ronmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data on 0 methane, nitrous oxide, and other greenhouse China United States India Brazil Australia Argentina Pakistan France Mexico Indonesia gases emissions are compiled by the International Source: Table 3.9. Energy Agency. 2009 World Development Indicators 169 3.10 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.2 5.1 0.0 0.0 0.0 0.0 10.9 1.8 89.1 98.2 0.0 0.0 Algeria 16.1 35.2 0.0 0.0 93.7 97.2 5.4 2.2 0.8 0.6 0.0 0.0 Angola 0.8 3.0 0.0 0.0 0.0 0.0 13.8 9.9 86.2 90.1 0.0 0.0 Argentina 50.7 115.0 1.3 1.8 39.2 50.2 9.8 7.0 35.2 33.0 14.3 6.7 Armenia 10.4 5.9 0.0 0.0 16.4 24.8 68.6 0.0 15.0 30.7 0.0 44.4 Australia 154.3 251.3 77.1 79.2 10.6 12.2 2.7 0.9 9.2 6.2 0.0 0.0 Austria 49.3 60.7 14.2 13.7 15.7 17.6 3.8 2.7 63.9 57.4 0.0 0.0 Azerbaijan 23.2 23.6 0.0 0.0 0.0 63.5 97.0 25.9 3.0 10.7 0.0 0.0 Bangladesh 7.7 24.3 0.0 0.0 84.3 87.6 4.3 6.7 11.4 5.7 0.0 0.0 Belarus 39.5 31.8 0.0 0.0 58.1 95.0 41.8 4.6 0.1 0.1 0.0 0.0 Belgium 70.3 84.3 28.2 10.9 7.7 27.3 1.9 1.6 0.4 0.4 60.8 55.3 Benin 0.0 0.1 0.0 0.0 0.0 0.0 100.0 100.0 0.0 0.0 0.0 0.0 Bolivia 2.1 5.3 0.0 0.0 37.6 39.3 5.3 16.7 55.3 40.8 0.0 0.0 Bosnia and Herzegovina 14.6 13.3 71.8 54.9 0.0 0.0 7.3 1.2 20.9 43.9 0.0 0.0 Botswana 0.9 1.0 88.1 99.4 0.0 0.0 11.9 0.6 0.0 0.0 0.0 0.0 Brazil 222.8 419.3 2.0 2.4 0.0 4.4 2.2 3.0 92.8 83.2 1.0 3.3 Bulgaria 42.1 45.5 50.3 42.2 7.6 4.7 2.9 0.8 4.5 9.3 34.8 42.8 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 1.2 .. 0.0 .. 0.0 .. 95.7 .. 4.1 .. 0.0 Cameroon 2.7 4.0 0.0 0.0 0.0 0.0 1.5 5.9 98.5 94.1 0.0 0.0 Canada 481.9 612.5 17.1 17.1 2.0 5.5 3.4 1.5 61.6 58.0 15.1 16.0 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 18.4 57.6 34.3 17.1 1.3 19.9 7.6 1.6 55.3 59.5 0.0 0.0 China 621.2 2,864.2 71.3 80.4 0.4 0.5 7.9 1.8 20.4 15.2 0.0 1.9 Hong Kong, China 28.9 38.6 98.3 68.9 0.0 30.8 1.7 0.3 0.0 0.0 0.0 0.0 Colombia 36.4 54.3 10.1 7.5 12.4 12.4 1.0 0.2 75.6 78.7 0.0 0.0 Congo, Dem. Rep. 5.7 7.9 0.0 0.0 0.0 0.0 0.4 0.3 99.6 99.7 0.0 0.0 Congo, Rep. 0.5 0.5 0.0 0.0 0.0 17.9 0.6 0.0 99.4 82.1 0.0 0.0 Costa Rica 3.5 8.7 0.0 0.0 0.0 0.0 2.5 6.1 97.5 75.9 0.0 0.0 Côte d'Ivoire 2.0 5.5 0.0 0.0 0.0 72.7 33.3 0.0 66.7 27.3 0.0 0.0 Croatia 9.2 12.3 6.8 18.3 20.2 16.7 31.6 15.9 41.3 48.8 0.0 0.0 Cuba 15.0 16.5 0.0 0.0 0.2 0.0 91.5 96.7 0.6 0.6 0.0 0.0 Czech Republic 62.3 83.7 76.4 60.4 0.6 3.9 0.9 0.3 1.9 3.0 20.2 31.1 Denmark 26.0 45.7 90.7 53.9 2.7 20.6 3.4 3.5 0.1 0.1 0.0 0.0 Dominican Republic 3.7 14.2 1.2 13.4 0.0 9.1 88.6 67.3 9.4 10.0 0.0 0.0 Ecuador 6.3 15.4 0.0 0.0 0.0 9.6 21.5 44.1 78.5 46.3 0.0 0.0 Egypt, Arab Rep. 42.3 115.4 0.0 0.0 39.6 72.1 36.9 16.1 23.5 11.2 0.0 0.0 El Salvador 2.2 5.6 0.0 0.0 0.0 0.0 6.9 44.2 73.5 35.1 0.0 0.0 Eritrea 0.1 0.3 0.0 0.0 0.0 0.0 100.0 99.3 0.0 0.0 0.0 0.0 Estonia 17.4 9.7 85.8 90.2 5.9 8.0 8.3 0.3 0.0 0.1 0.0 0.0 Ethiopia 1.2 3.3 0.0 0.0 0.0 0.0 11.6 0.3 88.4 99.7 0.0 0.0 Finland 54.4 82.3 18.5 20.6 8.6 15.0 3.1 0.6 20.0 14.0 35.3 27.8 France 417.2 569.2 8.5 4.6 0.7 3.9 2.1 1.2 12.9 9.8 75.3 79.1 Gabon 1.0 1.7 0.0 0.0 16.4 15.3 11.2 29.5 72.1 54.8 0.0 0.0 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 13.7 7.3 0.0 0.0 15.6 26.7 29.2 0.3 55.2 72.9 0.0 0.0 Germany 547.7 629.4 58.7 48.0 7.4 12.1 1.9 1.5 3.2 3.2 27.8 26.6 Ghana 5.7 8.4 0.0 0.0 0.0 0.0 0.0 33.3 100.0 66.7 0.0 0.0 Greece 34.8 60.2 72.4 53.6 0.3 17.6 22.3 16.0 5.1 9.7 0.0 0.0 Guatemala 2.3 7.9 0.0 13.6 0.0 0.0 9.0 25.3 76.0 48.3 0.0 0.0 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 0.6 0.6 0.0 0.0 0.0 0.0 20.6 52.5 76.5 47.5 0.0 0.0 170 2009 World Development Indicators ENVIRONMENT Electricity Sources of electricity Sources of 3.10 production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 Honduras 2.3 6.0 0.0 0.0 0.0 0.0 1.7 56.1 98.3 43.2 0.0 0.0 Hungary 28.4 35.9 30.5 19.8 15.7 36.7 4.8 1.5 0.6 0.5 48.3 37.5 India 289.4 744.1 66.2 68.3 3.4 8.3 3.5 4.2 24.8 15.3 2.1 2.5 Indonesia 33.3 133.1 31.5 44.1 2.3 14.6 42.7 29.1 20.2 7.2 0.0 0.0 Iran, Islamic Rep. 59.1 201.0 0.0 0.0 52.5 73.7 37.3 17.2 10.3 9.1 0.0 0.0 Iraq 24.0 31.9 0.0 0.0 0.0 0.0 89.2 98.5 10.8 1.5 0.0 0.0 Ireland 14.2 27.7 41.6 21.3 27.7 52.3 10.0 9.8 4.9 2.6 0.0 0.0 Israel 20.9 51.8 50.1 69.3 0.0 17.5 49.9 13.1 0.0 0.1 0.0 0.0 Italy 213.1 307.7 16.8 16.4 18.6 51.4 48.2 14.9 14.8 12.0 0.0 0.0 Jamaica 2.5 7.5 0.0 0.0 0.0 0.0 92.4 96.4 3.6 2.2 0.0 0.0 Japan 835.5 1,090.5 14.0 27.4 20.0 23.3 18.5 8.5 10.7 7.9 24.2 27.8 Jordan 3.6 11.6 0.0 0.0 11.9 70.4 87.8 29.2 0.3 0.4 0.0 0.0 Kazakhstan 87.4 71.7 71.1 70.3 10.5 11.8 10.0 7.0 8.4 10.8 0.0 0.0 Kenya 3.2 6.5 0.0 0.0 0.0 0.0 7.1 30.5 76.6 50.6 0.0 0.0 Korea, Dem. Rep. 27.7 22.4 40.1 40.9 0.0 0.0 3.6 2.8 56.3 56.2 0.0 0.0 Korea, Rep. 105.4 402.3 16.8 38.0 9.1 18.1 17.9 5.6 6.0 0.9 50.2 37.0 Kuwait 18.5 47.6 0.0 0.0 45.7 27.5 54.3 72.5 0.0 0.0 0.0 0.0 Kyrgyz Republic 15.7 17.1 13.1 3.3 23.5 9.6 0.0 0.0 63.5 87.2 0.0 0.0 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 6.6 4.9 0.0 0.0 26.1 42.9 5.4 0.1 67.6 55.2 0.0 0.0 Lebanon 1.5 9.3 0.0 0.0 0.0 0.0 66.7 92.5 33.3 7.5 0.0 0.0 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 10.2 24.0 0.0 0.0 0.0 40.9 100.0 59.1 0.0 0.0 0.0 0.0 Lithuania 28.4 12.1 0.0 0.0 23.8 20.4 14.6 1.7 1.5 3.3 60.0 71.6 Macedonia, FYR 5.8 7.0 89.7 72.9 0.0 0.0 1.8 3.5 8.5 23.6 0.0 0.0 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23.0 91.6 12.3 25.3 22.0 64.0 48.4 3.0 17.3 7.7 0.0 0.0 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico 124.1 249.6 6.3 12.7 11.6 45.5 56.7 21.6 18.9 12.2 2.4 4.4 Moldova 16.2 3.8 30.8 0.0 42.3 97.5 25.4 0.0 1.6 2.0 0.0 0.0 Mongolia 3.5 3.6 92.4 96.9 0.0 0.0 7.6 3.1 0.0 0.0 0.0 0.0 Morocco 9.6 23.2 23.0 58.1 0.0 12.8 64.4 21.4 12.7 6.9 0.0 0.0 Mozambique 0.5 14.7 13.9 0.0 0.0 0.1 23.6 0.1 62.6 99.9 0.0 0.0 Myanmar 2.5 6.2 1.6 0.0 39.3 40.2 10.9 5.8 48.1 53.9 0.0 0.0 Namibia 1.4 1.6 1.5 5.2 0.0 0.0 3.3 0.6 95.2 94.1 0.0 0.0 Nepal 0.9 2.7 0.0 0.0 0.0 0.0 0.1 0.4 99.9 99.6 0.0 0.0 Netherlands 71.9 98.4 38.3 26.9 50.9 57.6 4.3 2.1 0.1 0.1 4.9 3.5 New Zealand 32.3 43.5 1.9 12.5 17.6 22.6 0.0 0.1 72.3 53.9 0.0 0.0 Nicaragua 1.4 3.0 0.0 0.0 0.0 0.0 39.8 72.2 28.8 12.5 0.0 0.0 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 13.5 23.1 0.1 0.0 53.7 57.8 13.7 8.8 32.6 33.4 0.0 0.0 Norway 121.6 121.3 0.1 0.1 0.0 0.4 0.0 0.0 99.6 98.5 0.0 0.0 Oman 4.5 13.6 0.0 0.0 81.6 82.0 18.4 18.0 0.0 0.0 0.0 0.0 Pakistan 37.7 98.4 0.1 0.1 33.6 36.4 20.6 28.6 44.9 32.5 0.8 2.3 Panama 2.7 6.0 0.0 0.0 0.0 0.0 14.7 38.9 83.2 59.8 0.0 0.0 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 27.2 53.8 0.0 0.0 0.0 0.0 0.0 0.0 99.9 100.0 0.0 0.0 Peru 13.8 27.4 0.0 3.0 1.7 9.5 21.5 8.4 75.8 78.5 0.0 0.0 Philippines 25.2 56.7 7.7 27.0 0.0 28.8 46.7 8.2 24.0 17.5 0.0 0.0 Poland 134.4 160.8 97.5 93.6 0.1 1.9 1.2 1.5 1.1 1.3 0.0 0.0 Portugal 28.4 48.6 32.1 30.8 0.0 25.4 33.1 10.8 32.3 22.6 0.0 0.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 171 3.10 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 Romania 64.3 62.7 28.8 40.3 35.1 18.9 18.4 2.6 17.7 29.3 0.0 9.0 Russian Federation 1,082.2 993.9 14.3 17.9 47.3 46.1 11.9 2.5 15.3 17.4 10.9 15.7 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 69.2 179.8 0.0 0.0 48.1 47.7 51.9 52.3 0.0 0.0 0.0 0.0 Senegal 0.9 2.4 0.0 0.0 2.3 1.9 93.0 85.1 0.0 9.6 0.0 0.0 Serbia 40.9b 36.5 69.1b 68.7 3.2b 0.3 4.6b 0.9 23.1b 30.1 0.0 b 0.0 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 15.7 39.4 0.0 0.0 0.0 78.0 100.0 22.0 0.0 0.0 0.0 0.0 Slovak Republic 25.5 31.3 31.9 18.3 7.1 6.1 6.4 2.3 7.4 14.1 47.2 57.6 Slovenia 12.0 15.1 31.9 36.0 0.0 2.5 4.8 0.3 24.7 23.8 38.7 36.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 165.4 251.9 94.3 93.5 0.0 0.0 0.0 0.0 0.6 1.5 5.1 4.7 Spain 151.2 299.1 40.1 22.8 1.0 30.2 5.7 8.0 16.8 8.5 35.9 20.1 Sri Lanka 3.2 9.4 0.0 0.0 0.0 0.0 0.2 50.6 99.8 49.4 0.0 0.0 Sudan 1.5 4.2 0.0 0.0 0.0 0.0 36.8 67.5 63.2 32.5 0.0 0.0 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 146.0 143.3 1.1 1.1 0.3 0.4 0.9 1.2 49.7 43.1 46.7 46.7 Switzerland 55.0 62.1 0.1 0.0 0.6 1.3 0.7 0.3 54.2 49.8 43.0 44.8 Syrian Arab Republic 11.6 37.3 0.0 0.0 20.5 38.0 56.0 51.2 23.5 10.7 0.0 0.0 Tajikistan 18.1 16.9 0.0 0.0 9.1 2.3 0.0 0.0 90.9 97.7 0.0 0.0 Tanzania 1.6 2.8 0.0 3.8 0.0 43.8 4.9 0.6 95.1 51.7 0.0 0.0 Thailand 44.2 138.7 25.0 18.0 40.2 67.8 23.5 6.1 11.3 5.9 0.0 0.0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 0.2 0.2 0.0 0.0 0.0 0.0 39.9 57.5 60.1 41.2 0.0 0.0 Trinidad and Tobago 3.6 7.0 0.0 0.0 99.0 99.4 0.1 0.2 0.0 0.0 0.0 0.0 Tunisia 5.8 14.1 0.0 0.0 63.7 84.9 35.5 14.2 0.8 0.7 0.0 0.0 Turkey 57.5 176.3 35.1 26.5 17.7 45.8 6.9 2.5 40.2 25.1 0.0 0.0 Turkmenistan 14.6 13.7 0.0 0.0 95.2 100.0 0.0 0.0 4.8 0.0 0.0 0.0 Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 298.6 193.2 38.2 33.6 16.7 12.7 16.1 0.4 3.5 6.7 25.5 46.7 United Arab Emirates 17.1 66.8 0.0 0.0 96.3 98.0 3.7 2.0 0.0 0.0 0.0 0.0 United Kingdom 317.8 394.5 65.0 38.5 1.6 35.8 10.9 1.3 1.6 1.2 20.7 19.1 United States 3,202.8 4,274.3 53.1 49.8 11.9 19.6 4.1 1.9 8.5 6.8 19.1 19.1 Uruguay 7.4 5.6 0.0 0.0 0.0 0.1 5.1 35.1 94.2 64.0 0.0 0.0 Uzbekistan 56.3 49.3 7.4 4.7 76.4 69.6 4.4 12.8 11.8 12.8 0.0 0.0 Venezuela, RB 59.3 110.4 0.0 0.0 26.2 13.4 11.5 14.6 62.3 72.0 0.0 0.0 Vietnam 8.7 56.5 23.1 17.2 0.1 37.0 15.0 4.1 61.8 41.8 0.0 0.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.7 5.3 0.0 0.0 0.0 0.0 100.0 100.0 0.0 0.0 0.0 0.0 Zambia 8.0 9.4 0.5 0.2 0.0 0.0 0.3 0.4 99.2 99.4 0.0 0.0 Zimbabwe 9.4 9.8 53.3 43.0 0.0 0.0 0.0 0.2 46.7 56.8 0.0 0.0 World 11,845.0 t 18,977.0 t 37.3 w 40.8 w 14.6 w 20.1 w 10.3 w 5.5 w 18.0 w 15.9 w 17.0 w 14.7 w Low income 261.0 449.1 9.4 5.8 29.0 30.2 7.3 12.4 41.9 38.8 0.1 0.5 Middle income 4,017.2 7,919.8 35.3 48.3 20.6 18.6 14.4 6.1 22.3 20.6 6.3 5.1 Lower middle income 1,705.1 4,921.3 47.0 61.2 10.6 11.9 15.9 6.0 20.7 16.3 4.8 3.4 Upper middle income 2,312.1 2,998.3 26.6 27.0 28.0 29.5 13.4 6.2 23.5 27.6 7.4 7.8 Low & middle income 4,278.5 8,376.6 33.7 45.9 21.1 19.2 14.0 6.4 23.5 21.5 5.9 4.8 East Asia & Pacific 794.1 3,394.2 61.1 72.1 3.4 6.7 12.5 3.3 21.5 15.0 0.0 1.6 Europe & Central Asia 2,085.5 1,966.8 27.7 29.4 34.3 35.5 13.0 2.8 13.9 17.4 10.8 14.4 Latin America & Carib. 606.2 1,192.1 3.8 5.2 9.2 19.3 18.9 12.5 63.7 57.3 2.1 2.7 Middle East & N. Africa 187.9 515.0 1.2 2.6 36.9 60.7 48.3 27.8 12.4 7.4 0.0 0.0 South Asia 341.7 886.2 56.1 57.4 8.5 13.5 5.3 7.5 27.4 17.4 1.9 2.4 Sub-Saharan Africa 260.2 416.8 62.2 57.8 2.8 4.6 1.9 3.2 15.9 18.0 3.2 2.8 High income 7,585.5 10,644.0 39.2 36.6 10.9 20.7 8.2 4.7 14.9 11.4 23.2 22.5 Euro area 1,693.6 2,324.4 33.7 24.6 8.6 21.2 9.5 5.0 11.1 9.1 35.6 33.3 a. Shares may not sum to 100 percent because some sources of generated electricity (such as wind, solar, and geothermal) are not shown. b. Includes Kosovo and Montenegro. 172 2009 World Development Indicators ENVIRONMENT Sources of electricity 3.10 About the data Definitions Use of energy is important in improving people's adjustments are made to compensate for differences · Electricity production is measured at the termi- standard of living. But electricity generation also in definitions. The IEA makes these estimates in con- nals of all alternator sets in a station. In addition to can damage the environment. Whether such damage sultation with national statistical offices, oil compa- hydropower, coal, oil, gas, and nuclear power gen- occurs depends largely on how electricity is gener- nies, electric utilities, and national energy experts. It eration, it covers generation by geothermal, solar, ated. For example, burning coal releases twice as occasionally revises its time series to reflect political wind, and tide and wave energy as well as that from much carbon dioxide--a major contributor to global changes. For example, the IEA has constructed his- combustible renewables and waste. Production warming--as does burning an equivalent amount torical energy statistics for countries of the former includes the output of electric plants designed to of natural gas (see About the data for table 3.8). Soviet Union. In addition, energy statistics for other produce electricity only, as well as that of combined Nuclear energy does not generate carbon dioxide countries have undergone continuous changes in heat and power plants. · Sources of electricity are emissions, but it produces other dangerous waste coverage or methodology in recent years as more the inputs used to generate electricity: coal, gas, oil, products. The table provides information on electric- detailed energy accounts have become available. hydropower, and nuclear power. · Coal is all coal and ity production by source. Breaks in series are therefore unavoidable. brown coal, both primary (including hard coal and The International Energy Agency (IEA) compiles lignite-brown coal) and derived fuels (including pat- data on energy inputs used to generate electricity. ent fuel, coke oven coke, gas coke, coke oven gas, IEA data for countries that are not members of the and blast furnace gas). Peat is also included in this Organisation for Economic Co-operation and Devel- category. · Gas is natural gas but not natural gas opment (OECD) are based on national energy data liquids. · Oil is crude oil and petroleum products. adjusted to conform to annual questionnaires com- · Hydropower is electricity produced by hydroelectric pleted by OECD member governments. In addition, power plants. · Nuclear power is electricity produced estimates are sometimes made to complete major by nuclear power plants. aggregates from which key data are missing, and Sources of electricity generation have shifted since 1990 . . . 3.10a World 1990 2006 Other 3% Other 3% Nuclear Nuclear power power 17% 15% Coal 37% Coal Hydropower 41% Hydropower 16% 18% Oil Gas Oil 5% Gas 10% 15% 20% Source: Table 3.10. . . . with developing economies relying more on coal 3.10b Developing economies 1990 2006 Nuclear power 7% Other 1% Nuclear power 5% Other 2% Coal Hydropower Hydropower 34% 23% 22% Coal 46% Data sources Oil Data on electricity production are from the IEA's 6% Oil electronic files and its annual publications Energy 14% Gas Gas 21% 19% Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries. Source: Table 3.10. 2009 World Development Indicators 173 3.11 Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2007 1990 2007 1990­2007 1990 2007 1990 2007 1990 2006 1990 2006 Afghanistan 2.3 6.4 18 24 6.0 11 12 62 51 .. 45 .. 25 Albania 1.2 1.5 36 46 1.2 .. .. .. .. 97 98 .. 97 Algeria 13.2 21.9 52 65 3.0 8 10 14 15 99 98 77 87 Angola 3.9 9.5 37 56 5.2 15 23 40 42 55 79 9 16 Argentina 28.3 36.3 87 92 1.4 39 39 37 35 86 92 45 83 Armenia 2.4 1.9 68 64 ­1.3 33 37 49 57 94 96 .. 81 Australia 14.6 18.6 85 89 1.4 60 61 25 24 100 100 100 100 Austria 5.1 5.6 66 67 0.5 27 28 41 41 100 100 100 100 Azerbaijan 3.8 4.4 54 52 0.8 24 22 45 43 .. 90 .. 70 Bangladesh 22.4 42.3 20 27 3.7 8 12 29 32 56 48 18 32 Belarus 6.7 7.1 66 73 0.3 16 19 24 26 .. 91 .. 97 Belgium 9.6 10.3 96 97 0.4 10 17 10 17 .. .. .. .. Benin 1.8 3.7 35 41 4.3 .. .. .. .. 32 59 2 11 Bolivia 3.7 6.2 56 65 3.0 25 32 29 26 47 54 15 22 Bosnia and Herzegovina 1.7 1.8 39 47 0.3 .. .. .. .. 99 99 .. 92 Botswana 0.6 1.1 42 59 3.9 .. .. .. .. 60 60 22 30 Brazil 111.8 163.1 75 85 2.2 34 38 13 12 82 84 37 37 Bulgaria 5.8 5.4 66 71 ­0.4 14 16 21 22 100 100 96 96 Burkina Faso 1.2 2.8 14 19 4.9 .. 8 49 41 23 41 2 6 Burundi 0.4 0.9 6 10 5.1 .. .. .. .. 41 44 44 41 Cambodia 1.2 3.0 13 21 5.3 6 10 49 48 .. 62 2 19 Cameroon 5.0 10.4 41 56 4.3 14 19 19 18 47 58 34 42 Canada 21.3 26.5 77 80 1.3 40 44 18 20 100 100 99 99 Central African Republic 1.1 1.7 37 38 2.4 .. .. .. .. 21 40 5 25 Chad 1.3 2.8 21 26 4.7 .. .. 38 35 19 23 1 4 Chile 11.0 14.6 83 88 1.7 35 34 42 39 91 97 48 74 China 311.0 556.3 27 42 3.4 13 18 3 3 61 74 43 59 Hong Kong, China 5.7 6.9 100 100 1.2 100 100 100 100 .. .. .. .. Colombia 22.6 32.6 68 74 2.2 32 35 22 23 81 85 39 58 Congo, Dem. Rep. 10.5 20.8 28 33 4.0 15 17 35 38 53 42 1 25 Congo, Rep. 1.3 2.3 54 61 3.3 29 36 53 59 .. 19 .. 21 Costa Rica 1.6 2.8 51 63 3.4 24 29 47 46 96 96 92 95 Côte d'Ivoire 5.1 9.3 40 48 3.5 16 20 41 41 39 38 8 12 Croatia 2.6 2.5 54 57 ­0.1 .. .. .. .. 99 99 98 98 Cuba 7.8 8.5 73 76 0.5 20 19 27 26 99 99 95 95 Czech Republic 7.8 7.6 75 74 ­0.2 12 11 16 16 100 100 98 98 Denmark 4.4 4.7 85 86 0.5 26 20 31 23 100 100 100 100 Dominican Republic 4.0 6.7 55 68 3.0 21 22 38 32 77 81 57 74 Ecuador 5.7 8.7 55 65 2.5 26 32 28 29 88 91 50 72 Egypt, Arab Rep. 24.0 32.2 44 43 1.7 22 21 38 37 68 85 37 52 El Salvador 2.5 4.1 49 60 2.9 19 21 39 35 88 90 59 80 Eritrea 0.5 1.0 16 20 4.0 .. .. .. .. 20 14 0 3 Estonia 1.1 0.9 71 69 ­1.1 .. .. .. .. 96 96 94 94 Ethiopia 6.1 13.2 13 17 4.6 4 4 30 22 19 27 2 8 Finland 3.1 3.3 61 63 0.5 17 21 28 34 100 100 100 100 France 42.0 47.6 74 77 0.7 23 22 22 21 .. .. .. .. Gabon 0.6 1.1 69 85 3.4 .. .. .. .. .. 37 .. 30 Gambia, The 0.4 0.9 38 56 5.6 .. .. .. .. .. 50 .. 55 Georgia 3.0 2.3 55 53 ­1.5 22 25 41 48 96 94 91 92 Germany 58.1 60.5 73 74 0.2 8 9 6 6 100 100 100 100 Ghana 5.7 11.6 36 49 4.2 12 16 21 18 11 15 3 6 Greece 6.0 6.8 59 61 0.8 30 29 51 48 100 99 93 97 Guatemala 3.7 6.4 41 48 3.3 .. 8 22 16 87 90 58 79 Guinea 1.7 3.2 28 34 3.7 15 16 53 47 19 33 10 12 Guinea-Bissau 0.3 0.5 28 30 3.3 .. .. .. .. .. 48 .. 26 Haiti 2.0 4.4 29 45 4.5 16 21 56 46 49 29 20 12 174 2009 World Development Indicators ENVIRONMENT Urban Population Urbanization Population in 3.11 Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2007 1990 2007 1990­2007 1990 2007 1990 2007 1990 2006 1990 2006 Honduras 2.0 3.4 40 47 3.1 .. .. 29 29 68 78 29 55 Hungary 6.8 6.7 66 67 ­0.1 19 17 29 25 100 100 100 100 India 216.6 329.1 26 29 2.5 10 12 6 6 44 52 4 18 Indonesia 54.5 113.6 31 50 4.3 9 9 14 8 73 67 42 37 Iran, Islamic Rep. 30.6 48.3 56 68 2.7 23 23 21 16 86 .. 78 .. Iraq 12.9 19.4 70 67 2.4 26 22 32 26 75 80 .. 69 Ireland 2.0 2.7 57 61 1.7 26 25 46 40 .. .. .. .. Israel 4.2 6.6 90 92 2.6 43 60 48 49 100 100 .. .. Italy 37.8 40.3 67 68 0.4 19 17 9 8 .. .. .. .. Jamaica 1.2 1.4 49 53 1.1 .. .. .. .. 82 82 83 84 Japan 78.0 84.7 63 66 0.5 46 48 42 42 100 100 100 100 Jordan 2.3 4.5 72 78 4.0 27 18 37 30 .. 88 .. 71 Kazakhstan 9.2 8.9 56 58 ­0.2 7 8 12 14 97 97 96 98 Kenya 4.3 8.0 18 21 3.7 6 8 32 38 18 19 44 48 Korea, Dem. Rep. 11.8 14.8 58 62 1.4 15 19 21 22 .. .. .. .. Korea, Rep. 31.6 39.4 74 81 1.3 51 48 33 25 .. .. .. .. Kuwait 2.1 2.6 98 98 1.3 65 72 67 74 .. .. .. .. Kyrgyz Republic 1.7 1.9 38 36 0.7 .. .. 38 43 .. 94 .. 93 Lao PDR 0.6 1.7 15 30 6.0 .. .. .. .. .. 87 .. 38 Latvia 1.9 1.5 69 68 ­1.0 .. .. .. .. .. 82 .. 71 Lebanon 2.5 3.6 83 87 2.1 43 45 52 52 100 100 .. .. Lesotho 0.2 0.5 14 25 4.7 .. .. .. .. .. 43 30 34 Liberia 1.0 2.2 45 59 4.9 .. 28 55 47 59 49 24 7 Libya 3.3 4.8 76 77 2.2 48 55 45 46 97 97 96 96 Lithuania 2.5 2.3 68 67 ­0.6 .. .. .. .. .. .. .. .. Macedonia, FYR 1.1 1.4 58 66 1.2 .. .. .. .. .. 92 .. 81 Madagascar 2.8 5.7 24 29 4.1 8 9 33 30 15 18 6 10 Malawi 1.1 2.5 12 18 5.0 .. .. .. .. 50 51 46 62 Malaysia 9.0 18.4 50 69 4.2 6 5 12 8 95 95 .. 93 Mali 1.8 3.9 23 32 4.6 10 12 42 38 53 59 30 39 Mauritania 0.8 1.3 40 41 2.9 .. .. .. .. 33 44 11 10 Mauritius 0.5 0.5 44 42 0.8 .. .. .. .. 95 95 94 94 Mexico 59.4 81.0 71 77 1.8 32 34 26 23 74 91 8 48 Moldova 2.1 1.6 47 42 ­1.5 .. .. .. .. .. 85 .. 73 Mongolia 1.2 1.5 57 57 1.3 .. .. 48 60 .. 64 .. 31 Morocco 11.7 17.2 48 56 2.3 16 19 23 18 80 85 25 54 Mozambique 2.9 7.7 21 36 5.8 6 7 27 19 .. 53 12 19 Myanmar 10.0 15.6 25 32 2.6 7 8 29 26 47 85 15 81 Namibia 0.4 0.8 28 36 3.8 .. .. .. .. 73 66 8 18 Nepal 1.7 4.7 9 17 6.0 .. .. 23 19 36 45 6 24 Netherlands 10.3 13.3 69 81 1.5 14 12 10 8 100 100 100 100 New Zealand 2.9 3.7 85 86 1.3 25 30 30 34 .. .. 88 .. Nicaragua 2.2 3.2 52 56 2.2 18 21 34 38 59 57 23 34 Niger 1.2 2.3 15 16 3.9 .. .. 36 39 16 27 1 3 Nigeria 33.3 70.5 35 48 4.4 11 14 14 13 33 35 22 25 Norway 3.1 3.6 72 77 1.0 .. .. 22 22 .. .. .. .. Oman 1.2 1.9 66 72 2.5 .. .. .. .. 97 97 61 .. Pakistan 33.0 58.0 31 36 3.3 16 18 22 21 76 90 14 40 Panama 1.3 2.4 54 72 3.7 35 38 65 53 .. 78 .. 63 Papua New Guinea 0.6 0.8 15 13 1.5 .. .. .. .. 67 67 41 41 Paraguay 2.1 3.7 49 60 3.4 22 31 45 51 88 89 34 42 Peru 15.0 19.9 69 71 1.7 27 29 39 40 73 85 15 36 Philippines 29.9 56.4 49 64 3.7 14 14 27 20 71 81 46 72 Poland 23.4 23.4 61 61 0.0 4 4 7 7 .. .. .. .. Portugal 4.7 6.2 48 59 1.6 37 39 54 45 97 99 88 98 Puerto Rico 2.6 3.9 72 98 2.4 44 67 60 69 .. .. .. .. 2009 World Development Indicators 175 3.11 Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2007 1990 2007 1990­2007 1990 2007 1990 2007 1990 2006 1990 2006 Romania 12.3 11.6 53 54 ­0.3 8 9 14 17 88 88 52 54 Russian Federation 108.8 103.5 73 73 ­0.3 18 18 8 10 93 93 70 70 Rwanda 0.4 1.8 5 18 8.8 .. .. 56 48 31 34 29 20 Saudi Arabia 12.5 19.9 77 81 2.7 30 40 19 22 100 100 .. .. Senegal 3.1 5.2 39 42 3.1 18 21 45 50 52 54 9 9 Serbia 3.8 3.8 50 52 0.0 .. 11 .. 21 .. 96a .. 88a Sierra Leone 1.3 2.2 33 37 2.9 .. .. 40 39 .. 20 .. 5 Singapore 3.0 4.6 100 100 2.4 99 100 99 100 100 100 .. .. Slovak Republic 3.0 3.0 57 56 0.1 .. .. .. .. 100 100 99 99 Slovenia 1.0 1.0 50 49 ­0.1 .. .. .. .. .. .. .. .. Somalia 2.0 3.1 30 36 2.7 14 13 47 35 .. 51 .. 7 South Africa 18.3 28.8 52 60 2.7 25 33 10 12 64 66 45 49 Spain 29.3 34.5 75 77 1.0 22 24 15 16 100 100 100 100 Sri Lanka 2.9 3.0 17 15 0.2 .. .. .. .. 85 89 68 86 Sudan 6.9 16.4 27 43 5.1 9 12 34 29 53 50 26 24 Swaziland 0.2 0.3 23 25 2.8 .. .. .. .. .. 64 .. 46 Sweden 7.1 7.7 83 84 0.5 17 14 21 16 100 100 100 100 Switzerland 4.9 5.5 73 73 0.7 14 15 19 20 100 100 100 100 Syrian Arab Republic 6.2 10.7 49 54 3.2 26 31 25 25 94 96 69 88 Tajikistan 1.7 1.8 32 26 0.3 .. .. .. .. .. 95 .. 91 Tanzania 4.8 10.1 19 25 4.4 5 7 27 29 29 31 36 34 Thailand 16.0 21.1 29 33 1.6 11 10 37 32 92 95 72 96 Timor-Leste 0.2 0.3 21 27 3.7 .. .. .. .. .. 64 .. 32 Togo 1.2 2.7 30 41 4.8 16 22 52 53 25 24 8 3 Trinidad and Tobago 0.1 0.2 9 13 2.9 .. .. .. .. 93 92 93 92 Tunisia 4.7 6.8 58 66 2.1 .. .. .. .. 95 96 44 64 Turkey 33.2 50.4 59 68 2.4 22 27 20 20 96 96 69 72 Turkmenistan 1.7 2.4 45 48 2.2 .. .. .. .. .. .. .. .. Uganda 2.0 4.0 11 13 4.1 4 5 38 36 27 29 29 34 Ukraine 34.7 31.6 67 68 ­0.5 12 11 7 9 98 97 93 83 United Arab Emirates 1.5 3.4 79 78 4.9 25 31 32 40 98 98 95 95 United Kingdom 50.8 54.8 89 90 0.5 26 26 15 16 .. .. .. .. United States 188.0 245.5 75 81 1.6 41 43 9 8 100 100 99 99 Uruguay 2.8 3.1 89 92 0.6 41 45 46 49 100 100 99 99 Uzbekistan 8.2 9.9 40 37 1.1 10 8 25 22 97 97 91 95 Venezuela, RB 16.6 25.6 84 93 2.5 34 32 17 12 90 .. 47 .. Vietnam 13.4 23.3 20 27 3.2 13 13 30 22 62 88 21 56 West Bank and Gaza 1.3 2.7 68 72 4.0 .. .. .. .. .. 84 .. 69 Yemen, Rep. 2.6 6.7 21 30 5.7 5 9 25 30 79 88 14 30 Zambia 3.2 4.2 39 35 1.6 9 11 24 32 49 55 38 51 Zimbabwe 3.0 4.9 29 37 2.9 10 12 34 32 65 63 35 37 World 2,252.1 s 3,260.9 s 43 w 50 w 2.2 w 18 w 20 w 17 w 16 w 76 w 78 w 34 w 44 w Low income 219.6 410.5 25 32 3.7 11 12 28 26 50 54 19 33 Middle income 1,361.3 2,049.5 39 48 2.4 15 18 14 12 72 76 33 45 Lower middle income 873.3 1,430.3 32 42 2.9 13 16 12 10 64 71 31 43 Upper middle income 488.0 619.3 69 75 1.4 .. 28 17 17 86 89 53 64 Low & middle income 1,580.9 2,460.1 37 44 2.6 14 17 16 14 69 73 30 41 East Asia & Pacific 460.0 827.7 29 43 3.5 .. .. 9 7 65 75 42 59 Europe & Central Asia 273.7 283.3 63 64 0.2 15 16 13 14 95 94 77 79 Latin America & Carib. 308.0 438.8 71 78 2.1 32 34 24 22 81 86 35 51 Middle East & N. Africa 115.7 179.3 52 57 2.6 20 21 27 24 83 88 51 62 South Asia 279.2 443.9 25 29 2.7 10 12 10 11 49 57 8 23 Sub-Saharan Africa 144.3 287.1 28 36 4.0 .. 13 26 25 41 42 20 24 High income 671.1 800.9 73 77 1.0 .. .. 20 19 100 100 99 99 Euro area 213.0 236.7 71 73 0.6 18 18 15 15 .. .. .. .. a. Includes Kosovo. 176 2009 World Development Indicators ENVIRONMENT Urbanization 3.11 About the data Definitions There is no consistent and universally accepted populous nations were to change their definition of · Urban population is the midyear population of standard for distinguishing urban from rural areas, in urban centers. According to China's State Statis- areas defined as urban in each country and reported part because of the wide variety of situations across tical Bureau, by the end of 1996 urban residents to the United Nations (see About the data). · Popula- countries. Most countries use an urban classification accounted for about 43 percent of China's popula- tion in urban agglomerations of more than 1 million related to the size or characteristics of settlements. tion, more than double the 20 percent considered is the percentage of a country's population living in Some define urban areas based on the presence of urban in 1994. In addition to the continuous migra- metropolitan areas that in 2005 had a population of certain infrastructure and services. And other coun- tion of people from rural to urban areas, one of the more than 1 million. · Population in largest city is tries designate urban areas based on administrative main reasons for this shift was the rapid growth in the percentage of a country's urban population living arrangements. the hundreds of towns reclassified as cities in recent in that country's largest metropolitan area. · Access The population of a city or metropolitan area years. to improved sanitation facilities is the percentage depends on the boundaries chosen. For example, in Because the estimates in the table are based on of the urban or rural population with access to at 1990 Beijing, China, contained 2.3 million people in national definitions of what constitutes a city or met- least adequate excreta disposal facilities (private or 87 square kilometers of "inner city" and 5.4 million in ropolitan area, cross-country comparisons should be shared but not public) that can effectively prevent 158 square kilometers of "core city." The population made with caution. To estimate urban populations, human, animal, and insect contact with excreta. of "inner city and inner suburban districts" was 6.3 UN ratios of urban to total population were applied Improved facilities range from simple but protected million and that of "inner city, inner and outer sub- to the World Bank's estimates of total population pit latrines to flush toilets with a sewerage connec- urban districts, and inner and outer counties" was (see table 2.1). tion. To be effective, facilities must be correctly con- 10.8 million. (Most countries use the last definition.) The table shows access to improved sanitation structed and properly maintained. For further discussion of urban-rural issues see box facilities for both urban and rural populations to 3.1a in About the data for table 3.1. allow comparison of access. Definitions of access Estimates of the world's urban population would and urban areas vary, however, so comparisons change significantly if China, India, and a few other between countries can be misleading. Developing economies had the largest increase in urban population between 1990 and 2007 3.11a Urban population (millions) 1990 2007 1,600 1,200 800 400 0 Low income Lower middle income Upper middle income High income Source: Table 3.11. Latin America and the Caribbean had the same share of urban population as high-income economies in 2007 3.11b Percent Urban Rural 100 Data sources 75 Data on urban population and the population in urban agglomerations and in the largest city are 50 from the United Nations Population Division's World Urbanization Prospects: The 2007 Revi- 25 sion. Data on total population are World Bank estimates. Data on access to sanitation are from 0 the World Health Organization and United Nations East Asia Europe & Latin America Middle East South Sub-Saharan High income & Pacific Central Asia & Caribbean & North Africa Asia Africa Children's Fund's Progress on Drinking Water and Source: Table 3.11. Sanitation (2008). 2009 World Development Indicators 177 3.12 Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units Households living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings people % of total % of total % of total % of total % of total National Urban National Urban National Urban National Urban National Urban National Urban Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 2001 4.2 3.9 .. .. .. .. 65b 30 b .. .. 12 13 Algeria 1998 4.9 .. .. .. .. .. 67 .. .. .. 19 .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2001 3.6 .. 19 .. 97 .. .. .. 4 .. 16b .. Armenia 2001 4.1 4.0 4 6 93 93 95 90 1 1 .. .. Australia 2001 3.8 .. 1 .. .. .. .. .. .. .. .. .. Austria 1991 2.6 .. 2 .. .. .. .. .. 50 .. 13 .. Azerbaijan 1999 4.7 4.4 .. .. .. .. 74 62 4 5 .. .. Bangladesh 2001 4.8 4.8 .. .. 21b 42b 88b 61b .. .. .. .. Belarus 1999 .. .. .. .. .. .. .. .. .. .. .. .. Belgium 2001 2.6 .. 0b .. .. .. 67 .. 32b .. .. .. Benin 1992 5.9 .. .. .. 26 .. 59 .. .. .. .. .. Bolivia 2001 4.2 4.3 40 .. 43 58 70 59 3b 5b 6 4 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 2001 4.2 3.9 27 47 88 90 b 61 47 1 .. .. .. Brazil 2000 3.8 3.7 .. .. .. .. 74 75 .. .. .. .. Bulgaria 2001 2.7 2.7 .. .. 79 89 98 98 .. .. 23 17 Burkina Faso 1996 6.2 5.8 30 53 .. .. .. .. .. .. .. .. Burundi 1990 4.7 .. .. .. .. .. .. .. .. .. .. .. Cambodia 1998 5.2 .. .. .. .. .. .. .. .. .. .. .. Cameroon 1987 5.2 5.1 67 77 77 .. 73 48 27 42 .. .. Canada 2001 2.6 .. .. .. .. .. 64 .. 32 .. 8 .. Central African Republic 2003 5.2 5.8 32 36b 78 92 85 74 .. .. .. .. Chad 1993 5.1 5.1 .. .. .. .. .. .. .. .. .. .. Chile 2002 3.4 3.5 .. .. 91 92 66 65 13 15 11 10 China 2000 3.4 3.2 .. .. 82 .. 88 74 .. .. 1 .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 1993 4.8 .. 27b .. 83b .. 68b .. 13 .. 10 b .. Congo Dem Rep 1984 5.4 .. 55 .. .. .. .. .. .. .. .. .. Congo Rep 1984 10.5 .. .. .. .. .. 76 .. .. .. .. .. Costa Rica 2000 4.0 .. 22 .. 88 .. 72 .. 2 3 9 6 Côte d'Ivoire 1998 5.4 .. .. .. .. .. .. .. .. .. .. .. Croatia 2001 3.0 .. .. .. .. .. .. .. .. .. 12 .. Cuba 1981 4.2 4.2 .. .. .. .. .. .. 15 21 0 0 Czech Republic 2001 2.4 .. .. .. .. .. 52 .. 49 .. 12 .. Denmark 2001 2.2 .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 2002 3.9 .. .. .. 97 .. .. .. 8 .. 11 .. Ecuador 2001 3.5 3.7 30 .. 81 88 68 b 58b 9 14 12 7 Egypt 1996 4.7 .. .. .. .. .. .. .. 75 .. .. .. El Salvador 1992 .. .. 63 .. 67 83 70 68 3 6 11 11 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 2000 2.4 2.3 3 .. .. .. .. .. 72 .. 13 .. Ethiopia 1994 4.8 4.7 .. .. .. 23 .. 54 .. .. .. .. Finland 2000 2.2 .. .. .. .. .. 64 .. 44 .. .. .. France 1999 2.5 .. .. .. .. .. 55 .. .. .. 7 .. Gabon 2003 5.2 .. .. .. .. .. .. .. .. .. .. .. Gambia 1993 8.9 .. .. .. 18 .. 68 .. .. .. .. .. Georgia 2002 3.5 3.5 .. .. .. .. .. .. .. .. .. .. Germany 2001 2.3 .. .. .. .. .. 43 .. .. .. 7 .. Ghana 2000 5.1 5.1 .. .. 45 .. 57 .. 53 .. 5 .. Greece 2001 3.0 .. 1 .. .. .. .. .. .. .. .. .. Guatemala 2002 4.4 4.7 .. .. 67 80 81 74 2 4 13 11 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1982 4.2 .. 26 .. .. .. 92 68 .. .. 9 19 178 2009 World Development Indicators ENVIRONMENT Census Household Urban housing conditions Overcrowding Durable Home Multiunit 3.12 Vacancy year size dwelling ownership dwellings rate units Households living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings people % of total % of total % of total % of total % of total National Urban National Urban National Urban National Urban National Urban National Urban Honduras 2001 4.4 .. .. .. 69 85 .. .. .. .. 14 .. Hungary 1990 2.7 .. .. .. .. .. .. .. .. .. 4 .. India 2001 5.3 5.3 77 71 83 81 87 67 .. .. 6 9 Indonesia 2000 4.0 .. .. .. .. .. .. .. .. .. .. .. Iran, Islamic Rep. 1996 4.8 4.6 33b 26b 72 76 73 67 .. .. .. .. Iraq 1997 7.7 7.2 .. .. 88 96 70 66 4 5 13 15 Ireland 2002 3.0 .. .. .. .. .. .. .. 8b .. .. .. Israel 1995 3.5 .. .. .. .. .. .. .. .. .. .. .. Italy 2001 2.8 .. .. .. .. .. .. .. .. .. 21 .. Jamaica 2001 3.5 .. .. .. 98b .. 58b .. 2b .. .. .. Japan 2000 2.7 .. .. .. .. .. 61 .. 37 .. .. .. Jordan 1994 6.2 6.0 1 .. 97 97 69 64 57 67 .. .. Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya 1999 4.6 3.4 .. .. 35 72 72 25 .. .. 39 17 Korea, Dem Rep 2000 3.8 .. 23 .. .. .. 50 .. 15 .. .. .. Korea, Rep. 1993 4.4 .. .. .. .. .. .. .. .. .. .. .. Kuwait 1995 6.4 .. .. .. .. .. .. .. 9b .. 11 .. Kyrgyz Republic 1999 4.4 3.6 .. .. .. .. .. .. .. .. .. .. Laos 1995 6.1 6.1 .. .. 49 77 96 86 .. .. .. .. Latvia 2000 3.0 2.6 4 .. 88 .. 58 .. 74 .. 0 .. Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho 2001 5.0 .. 10 b .. .. .. 84 .. 0 .. .. .. Liberia 1974 4.8 .. 31 .. 20 .. 1 .. .. .. .. .. Libya 6.4 .. .. .. .. .. .. .. .. .. 7 .. Lithuania 2001 2.6 .. 7 .. .. .. .. .. .. .. .. .. Macedonia, FYR 2002 3.6 3.6b 8b .. 95b 95b 48b .. .. .. 7b 3b Madagascar 1993 4.9 4.8 64 57 .. .. 81 59 .. .. .. .. Malawi 1998 4.4 4.4 30 .. 48 84 86 47 .. .. .. .. Malaysia 2000 4.5 4.4 .. .. .. .. .. .. 10 b 16b .. .. Mali 1998 5.6 .. .. .. .. .. .. .. .. .. .. .. Mauritania 1988 .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 2000 3.9 3.8 6 7 91 94 87 81 .. .. 7 6 Mexico 2000 4.4 .. 27b .. 87 .. 78 .. 6 .. .. .. Moldova 2003 .. .. .. .. .. .. .. .. .. .. .. .. Mongolia 2000 4.4 4.5 .. .. .. .. .. .. 48 56 .. .. Morocco 1982 5.9 5.3 .. .. .. .. .. .. .. .. .. .. Mozambique 1997 4.4 4.9 37 28 7 20 92 83 1 1 0 .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 2001 5.3 .. .. .. .. .. .. .. .. .. .. .. Nepal 2001 5.4 4.9 .. .. .. .. 88 .. .. .. 0 .. Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand 2001 2.8 .. 1b .. .. .. 65 .. 17 .. 10 .. Nicaragua 1995 5.3 .. .. .. 79 87 84 86 0 0 8 .. Niger 2001 6.4 6.0 .. .. .. .. 77 40 .. .. .. .. Nigeria 1991 5.0 4.7 .. .. .. .. .. .. .. .. .. .. Norway 1980 2.7 .. 1 .. .. .. 67 .. 38 .. .. .. Oman 2003 7.1 .. .. .. .. .. .. .. .. .. .. .. Pakistan 1998 6.8 6.8 .. .. 58 86 81 .. .. .. .. .. Panama 2000 4.1 .. 28b .. 88 98b 80 66b 10 b 10 b 14 .. Papua New Guinea 1990 4.5b 6.5 .. .. .. .. .. 44 .. 8 .. .. Paraguay 2002 4.6 4.5 38b ..b 95b 98b 79 75 1b 2b 6b 6b Peru 1993 .. .. .. .. 49 64 .. .. .. .. 7 3 Philippines 1990 5.3 5.3 .. .. 62 .. 83 76 6 11 4 4 Poland 1988 3.2 .. .. .. .. .. .. .. .. .. 1 .. Portugal 2001 2.8 .. .. .. .. .. 76 .. 86 .. .. .. Puerto Rico 1990 3.3 .. .. .. .. .. 72 .. .. .. 11 .. 2009 World Development Indicators 179 3.12 Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units Households living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings people % of total % of total % of total % of total % of total National Urban National Urban National Urban National Urban National Urban National Urban Romania 1992 3.1 3.1 .. .. 58 .. 87 77 39 71 6 4 Russia 2002 2.8 2.7 7 5 .. .. .. .. 73 86 .. .. Rwanda 1991 4.7 .. .. .. 79 78 92 73 19 25 .. .. Saudi Arabia 2004 5.5 .. .. .. 92b .. 43 .. .. .. .. .. Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia 2001 2.9 2.2 .. .. .. .. .. .. .. .. .. .. Sierra Leone 1985 6.8 .. .. .. 34 .. 68 .. .. .. .. .. Singapore 2000 4.4 .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia 1991 3.1 .. .. .. .. .. 69 .. 37 .. 9 .. Somalia 1975 .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2001 4.0 .. .. .. .. .. .. .. 7 .. .. .. Spain 1991 3.3 .. 0 .. .. .. 78 .. .. .. .. .. Sri Lanka 2001 3.8 .. .. .. 93b 92b 70 b 58b 1 14b 13 1b Sudan 1993 5.8 6.0 .. .. .. .. 86b 58b 0b 1b .. .. Swaziland 1997 5.4 3.7 .. .. .. .. .. .. .. .. .. .. Sweden 1990 2.0 .. .. .. .. .. .. .. 54 .. 1 .. Switzerland 1990 2.4 2.1 .. .. .. .. 31 24 28 32 11 7 Syrian Arab Republic 1981 6.3 6.0 .. .. .. .. .. .. .. .. .. .. Tajikistan 2000 .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 2002 4.9 4.5b 33b 7b .. .. 82b 43b .. .. .. .. Thailand 2000 3.8 .. .. .. 93 93 81 62 3 .. 3 .. Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 2000 3.7 .. 9b .. 98b .. 74b .. 17b .. .. .. Tunisia 1994 8.0 .. .. .. 99 .. 71 89b 6 10 b 15 12b Turkey 1990 5.0 .. .. .. .. .. 70 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 1991 4.9 4.0b .. .. 21b .. 80 b 24b 0b 2b .. .. Ukraine 2003 .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 2001 .. 2.4 .. .. .. .. .. 69 .. 19 .. .. United States 2000 2.7 .. .. .. .. .. 66 .. .. .. 9 7 Uruguay 1996 3.3 3.4b 22b .. .. .. 57b 57b .. .. 13b 13b Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela. RB 2001 4.4 .. .. .. .. .. 78 .. 14 .. 16 .. Vietnam 1999 4.6 4.5 .. .. 77 89 95 86 .. .. .. .. West Bank and Gaza 1997 7.1 .. .. .. .. .. 78 .. 45 .. .. .. Yemen 1994 6.7 6.8 54b 6b .. .. 88b 68b 3b 11b .. .. Zambia 2000 5.3 5.9 .. .. .. .. 94 30 .. .. .. .. Zimbabwe 1992 4.8 4.2 .. .. .. .. 94 30 6 .. .. .. a. More than two people per room. b. Data are from a previous census. 180 2009 World Development Indicators ENVIRONMENT Urban housing conditions 3.12 About the data Definitions Urbanization can yield important social benefi ts, There is a strong demand for quantitative indi- · Census year is the year in which the underlying improving access to public services and the job mar- cators that can measure housing conditions on a data were collected. · Household size is the average ket. It also leads to significant demands for services. regular basis to monitor progress. However, data number of people within a household, calculated by Inadequate living quarters and demand for housing deficiencies and lack of rigorous quantitative analy- dividing total population by the number of households and shelter are major concerns for policymakers. sis hamper informed decisionmaking on desirable in the country and in urban areas. · Overcrowding The unmet demand for affordable housing, along policies to improve housing conditions. The data refers to the number of households living in dwell- with urban poverty, has led to the emergence of in the table are from housing and population cen- ings with two or more people per room as a percent- slums in many poor countries. Improving the shel- suses, collected using similar definitions. The table age of total households in the country and in urban ter situation requires a better understanding of the will incorporate household survey data in future edi- areas. · Durable dwelling units are the number of mechanisms governing housing markets and the pro- tions. The table focuses attention on urban areas, housing units in structures made of durable building cesses governing housing availability. That requires where housing conditions are typically most severe. materials (concrete, stone, cement, brick, asbestos, good data and adequate policy-oriented analysis so Not all the compiled indicators are presented in the zinc, and stucco) expected to maintain their stability that housing policy can be formulated in a global table because of space limitations. for 20 years or longer under local conditions with comparative perspective and drawn from lessons normal maintenance and repair, taking into account learned in other countries. Housing policies and location and environmental hazards such as floods, outcomes affect such broad socioeconomic condi- mudslides, and earthquakes, as a percentage of tions as the infant mortality rate, performance in total dwellings. · Home ownership refers to the school, household saving, productivity levels, capital number of privately owned dwellings as a percent- formation, and government budget deficits. A good age of total dwellings. When the number of private understanding of housing conditions thus requires dwellings is not available from the census data, the an extensive set of indicators within a reasonable share of households that own their housing unit is framework. used. Privately owned and owner-occupied units are included, depending on the definition used in the Selected housing indicators for smaller economies 3.12a census data. State- and community-owned units and rented, squatted, and rent-free units are excluded. Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate · Multiunit dwellings are the number of multiunit units dwellings, such as apartments, flats, condominiums, Households living in Buildings Privately barracks, boardinghouses, orphanages, retirement overcrowded with durable owned Unoccupied houses, hostels, hotels, and collective dwellings, number of dwellingsa structure dwellings dwellings people % of total % of total % of total % of total % of total as a percentage of total dwellings. · Vacancy rate Antigua and Barbuda 2001 3.0 .. 99b 65b 3b 22 is the percentage of completed dwelling units that Bahamas 1990 3.8 12 99 55 13 14 are currently unoccupied. It includes all vacant Bahrain 2001 5.9 .. 94b 51 28 6 units, whether on the market or not (such as second Barbados 1990 3.5 3 100 76 9 9 Belize 2000 4.6 .. 93 63 4 .. homes). Cape Verde 1990 5.1 28 78 72 2 .. Cayman Islands 1999 3.1 .. 100 53 38 19 Equatorial Guinea 1993 7.5 14 56b 75 14 .. Fiji 1996 5.4 .. 60 65 7 .. Guam 2000 4.0 2b 93 48 29 19 Isle of Man 2001 2.4 0 .. 68 16 .. Maldives 2000 6.6 .. 93 .. 1 15 Marshall Islands 1999 7.8 .. 95 72 12 8 Netherlands Antilles 2001 2.9 24b 99 60 16 12 New Caledonia 1989 4.1 .. 77 53 9 13 Northern Mariana Islands 1995 4.9 9b 99 33 27 17 Palau 2000 5.7 8 76 79 11 3 Seychelles 1997 4.2 15b 97 78 .. 0 Solomon Islands 1999 6.3 51 23 85 1 .. St. Vincent & Grenadines 1991 3.9 .. 98 71 7 .. Turks and Caicos 1990 3.3 4 96 66 11 .. Virgin Islands (UK) 1991 3.0 2 99 40 46 .. Data sources Western Samoa 1991 7.3 .. 42 90 47 30 Data on urban housing conditions are from a. More than two people per room. b. Data are from a previous census. Source: National population and housing censuses. national population and housing censuses. 2009 World Development Indicators 181 3.13 Traffic and congestion Motor Passenger Road Road sector Transport sector Fuel Particulate matter vehicles cars density fuel consumption fuel consumption price concentration km. of road per Urban-population- per per per 100 sq. liters $ per liter weighted PM10 1,000 kilometer 1,000 km. of % of total liters per capita Super grade micrograms per people of road people land area consumption per capita Diesel Gasoline gasoline Diesel cubic meter 2006 2006 2006 2006 2006 2006 2006 2006 2008 2008 1990 2006 Afghanistan .. 9 .. 6 .. .. .. .. 1.05 0.96 78 41 Albania 97 15 71 63 27 225 156 77 1.36 1.31 92 44 Algeria 91 27 58 5 14 179 98 60 0.34 0.20 115 71 Angola .. .. 8 4 11 82 48 31 0.53 0.39 142 66 Argentina .. .. 146 8 19 396 217 94 0.78 0.58 105 73 Armenia .. .. .. 25 7 71 0 67 1.08 1.11 453 59 Australia 671 17 542 11 19 1,321 418 781 0.74 0.94 23 15 Austria 552 43 507 128 20 964 631 275 1.37 1.43 38 33 Azerbaijan 61 10 57 68 11 211 92 100 0.74 0.56 226 60 Bangladesh 2 1 1 166 4 8 9 2 1.17 0.70 231 135 Belarus .. .. 183 46 5 171 129 62 1.33 1.06 23 6 Belgium 535 36 474 499 13 897 731 163 1.50 1.34 30 22 Benin .. .. 13 17 22 83 29 50 1.03 1.03 75 46 Bolivia 49 7 15 6 20 149 70 56 0.68 0.53 120 94 Bosnia and Herzegovina .. .. .. 43 15 250 159 87 1.13 1.18 36 19 Botswana 113 7 47 4 26 327 114 199 0.88 1.02 95 67 Brazil 170 18 136 20 22 308 166 86 1.26 1.03 40 23 Bulgaria 360 63 314 37 12 383 220 92 1.28 1.37 111 57 Burkina Faso 7 7 5 34 .. .. .. .. 1.38 1.33 151 84 Burundi .. .. 1 48 .. .. .. .. 1.39 1.23 56 29 Cambodia 36 37 25 22 8 32 19 12 0.94 0.89 86 46 Cameroon 11 3 11 11 10 46 25 19 1.14 1.04 116 62 Canada 582 13 561 14 16 1,537 452 1,084 0.76 0.90 25 17 Central African Republic .. .. 1 4 .. .. .. .. 1.44 1.44 62 44 Chad 6 2 .. 3 .. .. .. .. 1.30 1.32 217 109 Chile 157 26 97 11 18 378 232 149 0.95 0.95 88 48 China 28 11 18 36 5 76 48 45 0.99 1.01 114 73 Hong Kong, China 70 245 52 180 8 250 187 53 1.95 1.16 .. .. Colombia 59 16 37 15 23 186 83 90 1.04 0.73 39 22 Congo, Dem. Rep. .. .. .. 7 1 3 0 3 1.23 1.21 73 47 Congo, Rep. .. .. 8 5 22 85 55 27 0.81 0.57 135 64 Costa Rica 198 24 146 70 29 358 175 164 1.24 1.10 45 36 Côte d'Ivoire .. .. 7 25 5 24 19 9 1.33 1.20 94 36 Croatia 366 56 323 51 21 487 295 184 1.27 1.37 44 30 Cuba .. .. .. 55 7 79 23 47 1.67 1.51 44 17 Czech Republic 394 31 399 163 12 650 399 230 1.37 1.45 67 21 Denmark 437 33 354 168 20 904 520 390 1.54 1.54 30 19 Dominican Republic 115 .. 78 26 20 192 62 119 1.04 0.94 44 20 Ecuador 66 20 39 15 31 310 180 148 0.51 0.27 38 25 Egypt, Arab Rep. .. .. 29 9 16 157 93 52 0.49 0.20 223 119 El Salvador .. .. 24 48 20 161 84 69 0.78 0.81 46 33 Eritrea .. .. .. 3 6 11 10 1 2.53 1.07 118 56 Estonia 477 11 367 126 14 619 375 269 1.18 1.30 45 13 Ethiopia 2 4 1 3 5 16 13 2 0.92 0.89 112 68 Finland 542 36 470 23 11 898 499 416 1.57 1.39 23 18 France 598 39 496 141 15 809 590 191 1.52 1.45 18 13 Gabon .. .. .. 3 8 128 91 32 1.14 0.90 10 8 Gambia, The 7 3 5 33 .. .. .. .. 0.79 0.75 144 86 Georgia 71 16 56 29 16 139 44 87 1.09 1.16 208 47 Germany 598 213 565 185 15 748 374 312 1.56 1.56 27 19 Ghana 18 9 12 25 12 58 29 29 0.90 0.90 39 34 Greece 522 47 409 89 21 674 269 415 1.23 1.41 67 36 Guatemala 68 53 53 13 20 149 66 76 0.86 0.82 63 62 Guinea 14 4 8 10 .. .. .. .. 1.02 1.02 108 70 Guinea-Bissau 1 1 .. 12 .. .. .. .. 0.00 0.00 119 72 Haiti .. .. .. 15 5 16 19 15 1.16 0.89 70 37 182 2009 World Development Indicators ENVIRONMENT Motor Passenger Road Traffic and congestion Road sector Transport sector Fuel 3.13 Particulate matter vehicles cars density fuel consumption fuel consumption price concentration km. of road per Urban-population- per per per 100 sq. liters $ per liter weighted PM10 1,000 kilometer 1,000 km. of % of total liters per capita Super grade micrograms per people of road people land area consumption per capita Diesel Gasoline gasoline Diesel cubic meter 2006 2006 2006 2006 2006 2006 2006 2006 2008 2008 1990 2006 Honduras 67 31 52 12 17 127 64 57 0.80 0.80 45 43 Hungary 374 20 292 172 15 496 307 176 1.27 1.38 36 19 India 12 3 8 100 6 33 23 10 1.09 0.70 112 65 Indonesia 109 62 .. 20 12 117 50 69 0.60 0.46 137 83 Iran, Islamic Rep. .. .. 24 10 21 612 239 331 0.53 0.03 86 51 Iraq .. .. 30 10 32 428 161 244 0.03 0.01 146 115 Ireland 447 20 382 132 29 1,222 676 518 1.56 1.64 25 16 Israel 293 115 239 85 16 574 185 357 1.47 1.27 71 31 Italy 667 81 595 162 21 779 480 252 1.57 1.63 42 27 Jamaica 188 24 138 201 12 246 163 230 0.74 0.84 59 43 Japan 586 63 441 316 14 689 252 406 1.74 1.54 43 30 Jordan 127 91 88 9 22 332 167 156 0.61 0.61 110 45 Kazakhstan 139 23 114 3 5 241 46 207 0.83 0.72 43 19 Kenya 18 10 9 11 6 34 21 13 1.20 1.14 67 36 Korea, Dem. Rep. .. .. .. 21 2 16 9 7 0.76 0.95 165 68 Korea, Rep. 328 156 240 102 12 653 363 171 1.65 1.33 51 35 Kuwait 422 181 349 32 13 1,441 356 1,002 0.24 0.20 75 97 Kyrgyz Republic .. .. 39 9 9 55 0 52 0.80 0.88 75 22 Lao PDR 57 10 .. 13 .. .. .. .. 0.92 0.76 91 49 Latvia 415 14 357 108 22 528 342 191 1.12 1.23 38 16 Lebanon .. .. 403 67 28 384 3 355 0.76 0.76 43 36 Lesotho .. .. .. 20 .. .. .. .. 0.79 0.93 86 41 Liberia .. .. 6 10 .. .. .. .. 0.74 1.03 61 40 Libya 257 .. 232 5 18 620 364 227 0.14 0.12 106 88 Lithuania 513 22 468 123 16 475 284 119 1.13 1.22 53 19 Macedonia, FYR 163 25 150 52 12 196 109 62 1.15 1.12 46 21 Madagascar .. .. .. 8 .. .. .. .. 1.55 1.43 78 34 Malawi .. .. .. 16 .. .. .. .. 1.78 1.67 75 33 Malaysia 272 72 225 28 19 588 211 355 0.53 0.53 37 23 Mali .. .. .. 1 .. .. .. .. 1.30 1.10 274 152 Mauritania .. .. .. 1 .. .. .. .. 1.49 1.06 147 86 Mauritius 138 89 104 99 .. .. .. .. 0.74 0.56 23 18 Mexico 222 65 147 18 26 514 151 335 0.74 0.54 69 36 Moldova 94 31 84 38 7 75 55 22 1.20 1.04 97 36 Mongolia 43 2 28 3 11 144 39 127 1.38 1.42 198 110 Morocco 59 29 46 13 3 16 8 15 1.29 0.83 34 21 Mozambique .. .. .. 4 4 19 16 4 1.71 1.37 111 28 Myanmar 6 .. 4 4 8 29 19 9 0.43 0.52 107 58 Namibia 85 4 42 5 37 316 101 196 0.78 0.88 74 47 Nepal .. .. 3 12 3 12 8 2 1.13 0.82 67 34 Netherlands 486 62 429 372 14 826 492 300 1.68 1.45 46 34 New Zealand 722 32 609 35 25 1,237 536 660 1.09 0.85 16 14 Nicaragua 46 13 18 14 14 101 63 37 0.87 0.82 48 28 Niger 5 4 4 1 .. .. .. .. 0.99 0.97 220 132 Nigeria .. .. 17 21 7 64 8 52 0.59 1.13 175 45 Norway 546 27 439 29 13 863 644 378 1.63 1.63 24 15 Oman .. .. 156 11 9 649 61 548 0.31 0.38 148 108 Pakistan 14 8 10 34 10 56 47 8 0.84 0.77 224 120 Panama 103 27 73 15 16 164 140 153 0.67 0.68 58 35 Papua New Guinea .. .. 5 4 .. .. .. .. 0.94 0.90 34 21 Paraguay 85 15 50 7 27 206 165 32 1.17 0.96 106 77 Peru 47 16 30 6 25 145 103 32 1.42 0.99 98 54 Philippines 34 14 9 67 14 82 53 36 0.91 0.81 55 23 Poland 416 35 351 135 13 388 198 125 1.43 1.40 59 37 Portugal 507 67 471 90 24 683 473 186 1.61 1.47 51 23 Puerto Rico .. .. .. 289 .. .. .. .. 0.65 0.78 27 21 2009 World Development Indicators 183 3.13 Traffic and congestion Motor Passenger Road Road sector Transport sector Fuel Particulate matter vehicles cars density fuel consumption fuel consumption price concentration km. of road per Urban-population- per per per 100 sq. liters $ per liter weighted PM10 1,000 kilometer 1,000 km. of % of total liters per capita Super grade micrograms per people of road people land area consumption per capita Diesel Gasoline gasoline Diesel cubic meter 2006 2006 2006 2006 2006 2006 2006 2006 2008 2008 1990 2006 Romania 180 20 156 83 10 218 138 78 1.11 1.22 36 14 Russian Federation 228 35 188 5 6 338 119 230 0.89 0.86 41 18 Rwanda 3 .. 1 57 .. .. .. .. 1.37 1.37 49 26 Saudi Arabia .. 20 415 10 18 1,329 590 672 0.16 0.09 161 113 Senegal 14 9 10 7 16 47 38 9 1.35 1.26 97 95 Serbia 244 46 204 44 14 236 160 68 1.11 1.29 33a 15a Sierra Leone 4 2 2 16 .. .. .. .. 0.91 0.91 92 50 Singapore 141 194 105 461 7 608 374 206 1.07 0.90 106 41 Slovak Republic 287 35 247 89 9 379 227 132 1.57 1.68 41 15 Slovenia 531 28 493 190 21 879 485 372 1.18 1.26 40 30 Somalia .. .. .. 3 .. .. .. .. 1.12 1.15 78 31 South Africa 151 16 103 30 11 345 136 198 0.87 0.95 34 21 Spain 550 35 445 132 22 866 708 185 1.23 1.28 42 32 Sri Lanka 55 11 17 148 16 92 63 27 1.43 0.75 94 82 Sudan .. .. .. 1 12 68 44 21 0.65 0.45 296 165 Swaziland 84 25 40 21 .. .. .. .. 0.86 0.93 56 33 Sweden 516 7 462 155 14 948 414 485 1.38 1.52 15 12 Switzerland 564 59 520 173 20 871 291 548 1.30 1.52 37 26 Syrian Arab Republic 48 23 19 21 24 277 148 115 0.85 0.53 159 75 Tajikistan .. .. 19 19 37 238 0 226 1.03 1.00 103 50 Tanzania .. .. 1 8 4 28 20 6 1.11 1.30 57 25 Thailand .. .. 54 35 17 328 217 98 0.87 0.64 88 71 Timor-Leste .. .. .. .. .. .. .. .. 1.22 1.35 .. .. Togo .. .. 10 13 8 35 18 16 0.89 0.88 57 35 Trinidad and Tobago .. .. .. 162 5 640 250 357 0.36 0.24 142 101 Tunisia 95 49 83 12 17 174 118 47 0.96 0.84 74 30 Turkey 124 20 84 55 13 199 133 44 1.87 1.63 68 40 Turkmenistan .. .. .. 5 5 228 0 217 0.22 0.20 177 55 Uganda 5 .. 2 17 .. .. .. .. 1.30 1.22 28 12 Ukraine 128 36 118 28 5 186 70 119 0.88 0.96 72 21 United Arab Emirates .. .. 228 5 17 2,147 1,108 936 0.37 0.52 266 127 United Kingdom 517 80 457 171 17 775 427 352 1.44 1.65 25 15 United States 814b 31 461b,c 68 23 2,104 548 1,468 0.56 0.78 30 21 Uruguay 176 .. 151 102 24 274 182 76 1.18 1.17 237 175 Uzbekistan .. .. .. 18 3 71 15 53 1.35 0.75 85 55 Venezuela, RB .. .. 94 11 25 678 120 489 0.02 0.01 22 11 Vietnam 8 .. .. 68 12 87 47 38 0.80 0.77 123 55 West Bank and Gaza 36 18 29 80 .. .. .. .. 1.34 1.25 .. .. Yemen, Rep. .. .. 19 14 31 119 34 73 0.30 0.17 .. .. Zambia .. .. .. 12 4 30 16 16 1.70 1.61 96 40 Zimbabwe .. .. 45 25 4 34 21 13 1.30 1.05 35 27 World .. w .. w 118 w 26 w 14 w 301 w 128 w 164 w 1.11 m 1.03 m 80 w 50 w Low income .. .. .. .. 7 42 21 20 1.12 1.03 143 69 Middle income 46 13 41 23 10 146 72 72 0.91 0.90 91 56 Lower middle income 19 10 14 40 8 95 53 46 0.88 0.83 112 67 Upper middle income 172 .. 139 .. 13 360 154 178 1.12 1.03 53 30 Low & middle income 38 .. 38 15 10 125 62 61 1.03 0.95 98 58 East Asia & Pacific 27 11 18 35 6 95 55 51 0.92 0.85 112 69 Europe & Central Asia 162 30 184 9 8 263 116 139 1.13 1.13 63 27 Latin America & Carib. 155 .. 115 18 23 329 143 152 0.87 0.83 59 35 Middle East & N. Africa .. .. 33 .. 20 297 131 146 0.61 0.53 124 72 South Asia 12 3 8 75 6 33 24 9 1.09 0.76 134 78 Sub-Saharan Africa .. .. .. .. 8 66 27 37 1.14 1.06 113 53 High income 630 41 455 76 19 1,210 468 691 1.28 1.36 37 26 Euro area 604 66 418d 123 17 801 514 257 1.54 1.44 33 23 a. Includes Montenegro. b. Data are from the U.S. Federal Highway Administration. c. Excludes personal passenger vans, passenger minivans, and utility-type vehicles, which are all treated as trucks. d. Data are from the European Commission and the European Road Federation. 184 2009 World Development Indicators ENVIRONMENT Traffic and congestion 3.13 About the data Definitions Traffic congestion in urban areas constrains eco- be comparable. Another reason is coverage. For · Motor vehicles include cars, buses, and freight nomic productivity, damages people's health, and example, for the United States the 2005 estimate vehicles but not two-wheelers. Population fi gures degrades the quality of life. The particulate air pollu- for passenger cars from the U.S. Federal Highway refer to the midyear population in the year for which tion emitted by motor vehicles--the dust and soot in Administration excludes personal passenger vans, data are available. Roads refer to motorways, high- exhaust--is far more damaging to human health than passenger minivans, and utility-type vehicles, which ways, main or national roads, and secondary or once believed. (For information on particulate matter are all treated as trucks. Moreover, the data do not regional roads. A motorway is a road designed and and other air pollutants, see table 3.14.) cover vehicle quality or age. Road density is a rough built for motor traffic that separates the traffic flow- In recent years ownership of passenger cars has indicator of accessibility and does not capture road ing in opposite directions. · Passenger cars are road increased, and expanded economic activity has led width, type, or condition. Thus comparisons over time motor vehicles, other than two-wheelers, intended to more goods and services transported by road over and across countries should be made with caution. for the carriage of passengers and designed to seat greater distances (see table 5.9). These develop- Data on fuel prices are compiled by the German no more than nine people (including the driver). ments have increased demand for roads and vehicles, Agency for Technical Cooperation (GTZ), from its global · Road density is the ratio of the length of the adding to urban congestion, air pollution, health haz- network, and other sources, including the Allgemeiner country's total road network to the country's land ards, and traffic accidents and injuries. Congestion, Deutscher Automobile Club (for Europe) and the Latin area. The road network includes all roads in the the most visible cost of expanding vehicle ownership, American Energy Organization (for Latin America). country-- motorways, highways, main or national is reflected in the indicators in the table. Other rel- Local prices are converted to U.S. dollars using the roads, secondary or regional roads, and other urban evant indicators--such as average vehicle speed in exchange rate in the Financial Times international mon- and rural roads. · Road sector fuel consumption is major cities and the cost of congestion, which takes a etary table on the survey date. When multiple exchange the average fuel used per capita in the roads sector. heavy toll on economic productivity--are not included rates exist, the market, parallel, or black market rate · Transport sector fuel consumption is the average because data are incomplete or difficult to compare. is used. Prices were compiled in mid-November 2008, volume of fuel consumed per capita in the trans- The data in the table--except those on fuel prices when crude oil prices had dropped to $48 a barrel port sector. · Fuel price is the pump price of super and particulate matter--are compiled by the Interna- Brent (from a high of $148 in July). grade gasoline (usually 95 octane) and of diesel fuel. tional Road Federation (IRF) through questionnaires Considerable uncertainty surrounds estimates Prices are converted from the local currency to U.S. sent to national organizations. The IRF uses a hier- of particulate matter concentrations, and caution dollars (see About the data). · Particulate matter archy of sources to gather as much information as should be used in interpreting them. They allow for concentration is fine suspended particulates of less possible. Primary sources are national road asso- cross-country comparisons of the relative risk of par- than 10 microns in diameter (PM10) that are capable ciations. If they lack data or do not respond, other ticulate matter pollution facing urban residents. Major of penetrating deep into the respiratory tract and agencies are contacted, including road directorates, sources of urban outdoor particulate matter pollution causing significant health damage. Data are urban- ministries of transport or public works, and central are traffic and industrial emissions, but nonanthro- population-weighted PM10 levels in residential areas statistical offices. As a result, data quality is uneven. pogenic sources such as dust storms may also be of cities with more than 100,000 residents. The esti- Coverage of each indicator may differ across coun- a substantial contributor for some cities. Country mates represent the average annual exposure level tries because of different definitions. Comparability technology and pollution controls are important deter- of the average urban resident to outdoor particulate is also limited when time series data are reported. minants of particulate matter. Data on particulate matter. The IRF took steps to improve the quality of the data matter for selected cities are in table 3.14. Estimates in its World Road Statistics 2008. Because this effort of economic damages from death and illness due to covers 1999­2006 only, time series data may not particulate matter pollution are in table 3.16. Particulate matter concentration has fallen in all income groups, and the higher the income, the lower the concentration 3.13a Data sources Urban-population-weighted particulate matter (PM10, micrograms per cubic meter) 1990 2006 Data on vehicles, road density, and fuel consump- 150 tion are from the IRF's electronic files and its annual World Road Statistics, except where noted. 100 Data on fuel prices are from the GTZ's electronic files. Data on particulate matter concentrations are from Kiran Dev Pandey, David Wheeler, Bart 50 Ostro, Uwe Deichmann, Kirk Hamilton, and Katie Bolt's "Ambient Particulate Matter Concentrations 0 in Residential and Pollution Hotspot Areas of World Low income Lower Upper High income Euro area Cities: New Estimates Based on the Global Model middle income middle income Source: Table 3.13. of Ambient Particulates (GMAPS)" (2006). 2009 World Development Indicators 185 3.14 Air pollution City City Particulate Sulfur Nitrogen About the data population matter dioxide dioxide concentration Urban- Indoor and outdoor air pollution place a major burden population- on world health. More than half the world's people weighted PM10 rely on dung, wood, crop waste, or coal to meet basic micrograms per micrograms per micrograms per thousands cubic meter cubic meter cubic meter energy needs. Cooking and heating with these fuels on 2007 2006 2001 a 2001 a open fires or stoves without chimneys lead to indoor air Argentina Córdoba 1,452 55 .. 97 pollution, which is responsible for 1.6 million deaths a Australia Melbourne 3,728 12 .. 30 year--one every 20 seconds. In many urban areas air Perth 1,532 12 5 19 pollution exposure is the main environmental threat to Sydney 4,327 19 28 81 health. Long-term exposure to high levels of soot and Austria Vienna 2,315 39 14 42 small particles contributes to a range of health effects, Belgium Brussels 1,743 25 20 48 including respiratory diseases, lung cancer, and heart Brazil Rio de Janeiro 11,748 29 129 .. São Paulo 18,845 34 43 83 disease. Particulate pollution, alone or with sulfur diox- Bulgaria Sofia 1,185 63 39 122 ide, creates an enormous burden of ill health. Canada Montréal 3,678 17 10 42 Sulfur dioxide and nitrogen dioxide emissions Toronto 5,213 20 17 43 lead to deposition of acid rain and other acidic com- Vancouver 2,146 12 14 37 pounds over long distances, which can lead to the Chile Santiago 5,720 54 29 81 leaching of trace minerals and nutrients critical to China Anshan 1,639 83 115 88 trees and plants. Sulfur dioxide emissions can dam- Beijing 11,106 90 90 122 Changchun 3,183 75 21 64 age human health, particularly that of the young and Chengdu 4,123 87 77 74 old. Nitrogen dioxide is emitted by bacteria, motor Chongqing 6,461 124 340 70 vehicles, industrial activities, nitrogen fertilizers, fuel Dalian 3,167 50 61 100 and biomass combustion, and aerobic decomposi- Guangzhou 8,829 64 57 136 tion of organic matter in soils and oceans. Guiyang 3,662 71 424 53 Where coal is the primary fuel for power plants Harbin 3,621 77 23 30 Jinan 2,798 95 132 45 without effective dust controls, steel mills, industrial Kunming 2,931 71 19 33 boilers, and domestic heating, high levels of urban Lanzhou 2,561 92 102 104 air pollution are common-- especially particulates Liupanshui 1,221 60 102 .. and sulfur dioxide. Elsewhere the worst emissions Nanchang 2,350 79 69 29 are from petroleum product combustion. Pingxiang 905 67 75 .. Sulfur dioxide and nitrogen dioxide concentration Quingdao 2,817 62 190 64 data are based on average observed concentrations Shanghai 14,987 74 53 73 Shenyang 4,787 102 99 73 at urban monitoring sites, which not all cities have. Taiyuan 2,794 89 211 55 The data on particulate matter are estimated aver- Tianjin 7,180 126 82 50 age annual concentrations in residential areas away Wulumqi 2,025 57 60 70 from air pollution "hotspots," such as industrial Wuhan 7,243 80 40 43 districts and transport corridors. The data are from Zhengzhou 2,636 98 63 95 the World Bank's Development Research Group and Zibo 3,061 75 198 43 Environment Department estimates of annual ambi- Colombia Bogotá 7,772 30 .. .. Croatia Zagreb 908 32 31 .. ent concentrations of particulate matter in cities Cuba Havana 2,174 20 1 5 with populations exceeding 100,000 (Pandey and Czech Republic Prague 1,162 21 14 33 others 2006b). A country's technology and pollution Denmark Copenhagen 1,085 19 7 54 controls are important determinants of particulate Ecuador Guayaquil 2,514 23 15 .. matter concentrations. Quito 1,701 30 22 .. Pollutant concentrations are sensitive to local con- Egypt, Arab Rep. Cairo 11,893 149 69 .. Finland Helsinki 1,115 19 4 35 ditions, and even monitoring sites in the same city France Paris 9,904 11 14 57 may register different levels. Thus these data should Germany Berlin 3,406 21 18 26 be considered only a general indication of air qual- Frankfurt 668 18 11 45 ity, and comparisons should be made with caution. Munich 1,275 19 8 53 Current World Health Organization (WHO) air quality Ghana Accra 2,121 33 .. .. guidelines are annual mean concentrations of 20 Greece Athens 3,242 38 34 64 micrograms per cubic meter for particulate matter Hungary Budapest 1,679 20 39 51 Iceland Reykjavik 164 18 5 42 less than 10 microns in diameter and 40 micrograms India Ahmadabad 5,375 76 30 21 for nitrogen dioxide and daily mean concentrations of Bengaluru 6,787 41 .. .. 20 micrograms per cubic meter for sulfur dioxide. 186 2009 World Development Indicators ENVIRONMENT City City Particulate Sulfur Air pollution Nitrogen 3.14 Definitions population matter dioxide dioxide concentration Urban- · City population is the number of residents of population- the city or metropolitan area as defined by national weighted PM10 micrograms per micrograms per micrograms per authorities and reported to the United Nations. · Par- thousands cubic meter cubic meter cubic meter ticulate matter concentration is fine suspended par- 2007 2006 2001 a 2001 a ticulates of less than 10 microns in diameter (PM10) India Chennai 7,163 34 15 17 that are capable of penetrating deep into the respi- Delhi 15,926 136 24 41 ratory tract and causing significant health damage. Hyderabad 6,376 37 12 17 Data are urban- population-weighted PM10 levels in Kanpur 3,162 99 15 14 Kolkata 14,787 116 49 34 residential areas of cities with more than 100,000 Lucknow 2,695 99 26 25 residents. The estimates represent the average Mumbai 18,978 57 33 39 annual exposure level of the average urban resident Nagpur 2,454 50 6 13 to outdoor particulate matter. · Sulfur dioxide is an Pune 4,672 42 .. .. air pollutant produced when fossil fuels containing Indonesia Jakarta 9,125 84 .. .. Iran, Islamic Rep. Tehran 7,873 50 209 .. sulfur are burned. · Nitrogen dioxide is a poisonous, Ireland Dublin 1,059 16 20 .. pungent gas formed when nitric oxide combines with Italy Milan 2,945 30 31 248 hydrocarbons and sunlight, producing a photochemi- Rome 3,339 29 .. .. cal reaction. These conditions occur in both natural Turin 1,652 43 .. .. and anthropogenic activities. Japan Osaka-Kobe 11,294 33 19 63 Tokyo 35,676 38 18 68 Yokohama 3,366 29 100 13 Kenya Nairobi 3,010 40 .. .. Korea, Rep. Pusan 3,480 35 60 51 Seoul 9,796 37 44 60 Taegu 2,460 40 81 62 Malaysia Kuala Lumpur 1,448 23 24 .. Mexico Mexico City 19,028 48 74 130 Netherlands Amsterdam 1,031 34 10 58 New Zealand Auckland 1,245 13 3 20 Norway Oslo 802 18 8 43 Philippines Manila 11,100 28 33 .. Poland Katowice 2,914 39 83 79 Lódz 776 38 21 43 Warsaw 1,707 42 16 32 Portugal Lisbon 2,812 21 8 52 Romania Bucharest 1,942 16 10 71 Russian Federation Moscow 10,452 19 109 .. Omsk 1,135 19 20 34 Singapore Singapore 4,436 41 20 30 Slovak Republic Bratislava 456 15 21 27 South Africa Cape Town 3,215 13 21 72 Durban 2,729 25 31 .. Johannesburg 3,435 26 19 31 Data sources Spain Barcelona 4,920 33 11 43 Madrid 5,567 29 24 66 Data on city population are from the United Sweden Stockholm 1,264 11 3 20 Nations Population Division. Data on particulate Switzerland Zurich 1,108 24 11 39 matter concentrations are from Kiran D. Pandey, Thailand Bangkok 6,704 76 11 23 David Wheeler, Bart Ostro, Uwe Deichman, Kirk Turkey Ankara 3,716 39 55 46 Istanbul 10,061 46 120 .. Hamilton, and Kathrine Bolt's "Ambient Particulate Ukraine Kiev 2,709 26 14 51 Matter Concentration in Residential and Pollution United Kingdom Birmingham 2,285 14 9 45 Hotspot Areas of World Cities: New Estimates London 8,567 19 25 77 Based on the Global Model of Ambient Particu- Manchester 2,230 15 26 49 lates (GMAPS)" (2006). Data on sulfur dioxide United States Chicago 8,990 23 14 57 Los Angeles 12,500 32 9 74 and nitrogen dioxide concentrations are from the New York-Newark 19,040 20 26 79 WHO's Healthy Cities Air Management Information Venezuela, RB Caracas 2,985 16 33 57 System and the World Resources Institute. a. Data are for the most recent year available. 2009 World Development Indicators 187 3.15 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of Biological Kyoto Stockholm changeb layer control the Seac diversity b Protocol CITES CCD Convention 1992 1985 1987 1982 1992 1997 1973 1994 2001 Afghanistan .. .. 2002 2004 d 2004 d .. 2002 1985d 1995d .. Albania 1993 .. 1995 1999d 1999d 2003d 1994 d 2005d 2003d 2000 d 2004 Algeria 2001 .. 1994 1992d 1992d 1996 1995 2005d 1983d 1996 2006 Angola .. .. 2000 2000 d 2000 d 1994 1998 2007 .. 1997 2006 Argentina 1992 .. 1994 1990 1990 1995 1994 2001 1981 1997 2005 Armenia .. .. 1994 1999d 1999d 2002d 1993e 2008e .. 1997 2003 Australia 1992 1994 1994 1987d 1989 1994 1993 .. 1976 2000 2004 Austria .. .. 1994 1987 1989 1995 1994 2002 1982d 1997d 2002 Azerbaijan 1998 .. 1995 1996d 1996d .. 2000 f 2000 d 1998d 1998d 2004 d Bangladesh 1991 1990 1994 1990 d 1990 d 2001 1994 2001d 1981 1996 2007 Belarus .. .. 2000 1986e 1988e 2006d 1993 2007e 1995d 2001d 2004 d Belgium .. .. 1996 1988 1988 1998 1996 2002 1983 1997d 2006 Benin 1993 .. 1994 1993d 1993d 1997 1994 2002d 1984 d 1996 2004 Bolivia 1994 1988 1995 1994 d 1994 d 1995 1994 1999 1979 1996 2003 Bosnia and Herzegovina .. .. 2000 1992g 1992g 1994g 2002d 2007 2002 2002d .. Botswana 1990 1991 1994 1991d 1991d 1994 1995 2003d 1977d 1996 2002d Brazil .. 1988 1994 1990 d 1990 d 1994 1994 2002 1975 1997 2004 Bulgaria .. 1994 1995 1990 d 1990 d 1996 1996 2002 1991d 2001d 2004 Burkina Faso 1993 .. 1994 1989 1989 2005 1993 2005d 1989d 1996 2004 Burundi 1994 1989 1997 1997d 1997d .. 1997 2001d 1988d 1997 2005 Cambodia 1999 .. 1996 2001d 2001d .. 1995d 2002d 1997 1997 2006 Cameroon .. 1989 1995 1989d 1989d 1994 1994 2002d 1981d 1997 .. Canada 1990 1994 1994 1986 1988 2003 1992 2002 1975 1995 2001 Central African Republic .. .. 1995 1993d 1993d .. 1995 2008 1980 d 1996 .. Chad 1990 .. 1994 1989d 1994 .. 1994 .. 1989d 1996 2004 Chile .. 1993 1995 1990 1990 1997 1994 2002 1975 1997 2005 China 1994 1994 1994 1989d 1991d 1996 1993 2002f 1981d 1997 2004 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 1998 1988 1995 1990 d 1993d 1994 2001d 1981 1999 .. Congo, Dem. Rep. .. 1990 1995 1994 d 1994 d 1995 1996 2005d 1976d 1997 2005d Congo, Rep. .. 1990 1997 1994 d 1994 d 2008 1994 2007 1983d 1999 2007 Costa Rica 1990 1992 1994 1991d 1991d 1994 1994 2002 1975 1998 2007 Côte d'Ivoire 1994 1991 1995 1993d 1993d 1994 1994 2007 1994 d 1997 2004 Croatia 2001 2000 1996 1991e 1991e 1994g 1996 .. 2000 d 2000e 2007 Cuba .. .. 1994 1992d 1992d 1994 1994 2002 1990 d 1997 2007 Czech Republic 1994 .. 1994 1993e 1993e 1996 1993f 2007e 993g 2000 d 2002 Denmark 1994 .. 1994 1988 1988 2004 1993 2002 1977 1995d 2003 Dominican Republic .. 1995 1999 1993d 1993d .. 1996 2002d 1986d 1997d 2007 Ecuador 1993 1995 1994 1990 d 1990 d .. 1993 2000 1975 1995 2004 Egypt, Arab Rep. 1992 1988 1995 1988 1988 1994 1994 2005d 1978 1995 2003 El Salvador 1994 1988 1996 1992 1992 .. 1994 1998 1987d 1997d .. Eritrea 1995 .. 1995 2005d 2005d .. 1996d 2005d 1994 d 1996 2005d Estonia 1998 .. 1994 1996d 1996d 2005d 1994 2002 1992d .. .. Ethiopia 1994 1991 1994 1994 d 1994 d .. 1994 2005d 1989d 1997 2003 Finland 1995 .. 1994 1986 1988 1996 1994 e 2002 1976d 1995e 2002e France 1990 .. 1994 1987f 1988f 1996 1994 2002f 1978 1997 2004f Gabon .. 1990 1998 1994 d 1994 d 1998 1997 .. 1989d 1996d 2007 Gambia, The 1992 1989 1994 1990 d 1990 d 1994 1994 2001d 1977d 1996 2006 Georgia 1998 .. 1994 1996d 1996d 1996d 1994 d 1999d 1996d 1999 2006 Germany .. .. 1994 1988 1988 1994 d 1993 2002 1976 1996 2002 Ghana 1992 1988 1995 1989d 1989 1994 1994 2003d 1975 1996 2003 Greece .. .. 1994 1988 1988 1995 1994 2002 1992d 1997 2006 Guatemala 1994 1988 1996 1987d 1989d 1997 1995 1999 1979 1998d .. Guinea 1994 1988 1994 1992d 1992d 1994 1993 2000 d 1981d 1997 .. Guinea-Bissau 1993 1991 1996 2002d 2002d 1994 1995 .. 1990 d 1995 2008 Haiti 1999 .. 1996 2000 d 2000 d 1996 1996 2005d .. 1996 .. 188 2009 World Development Indicators ENVIRONMENT Environ- Biodiversity Government commitment Participation 3.15 mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of Biological Kyoto Stockholm changeb layer control the Seac diversity b Protocol CITES CCD Convention 1992 1985 1987 1982 1992 1997 1973 1994 2001 Honduras 1993 .. 1996 1993d 1993d 1994 1995 2000 1985d 1997 2005 Hungary 1995 .. 1994 1988d 1989d 2002 1994 2002d 1985d 1999d 2008 India 1993 1994 1994 1991d 1992d 1995 1994 2008e 1976 1996 2006 Indonesia 1993 1993 1994 1992d 1992 1994 1994 2004 1978d 1998 .. Iran, Islamic Rep. .. .. 1996 1990 d 1990 d .. 1996 2005d 1976 1997 2006 Iraq .. .. .. .. .. 1994 .. .. .. .. .. Ireland .. .. 1994 1988d 1988 1996 1996 2002 2002 1997 .. Israel .. .. 1996 1992d 1992 .. 1995 2004 1979 1996 .. Italy .. .. 1994 1988 1988 1995 1994 2002 1979 1997 .. Jamaica 1994 .. 1995 1993d 1993d 1994 1995 1999d 1997d 1997d 2007 Japan .. .. 1994 1988d 1988 1996 1993e 2002e 1980 1998e 2002d Jordan 1991 .. 1994 1989d 1989d 1995d 1993 2003d 1978d 1996 2004 Kazakhstan .. .. 1995 1998d 1998d .. 1994 .. 2000 d 1997 .. Kenya 1994 1992 1994 1988d 1988 1994 1994 2005d 1978 1997 2004 Korea, Dem. Rep. .. .. 1995 1995d 1995d .. 1994f 2005d .. 2003d 2002d Korea, Rep. .. .. 1994 1992 1992 1996 1994 2002 1993d 1999 2007 Kuwait .. .. 1995 1992d 1992d 1994 2002 2005d 2002 1997 2006 Kyrgyz Republic 1995 .. 2000 2000 d 2000 d .. 1996f 2003d .. 1997d 2006 Lao PDR 1995 .. 1995 1998d 1998d 1998 1996f 2003d 2004 d 1996e 2006 Latvia .. .. 1995 1995d 1995d 2004 d 1995 2002 1997d 2002d 2004 Lebanon .. .. 1995 1993d 1993d 1995 1994 2006 .. 1996 2003 Lesotho 1989 .. 1995 1994 d 1994 d 2007 1995 2000d 2003 1995 2002 Liberia 2003 1996d 1996d 2008 2000 2002d 2005d 1998d 2002d Libya .. .. 1999 1990 d 1990 d .. 2001 2006 2003d 1996 2005d Lithuania .. .. 1995 1995d 1995d 2003d 1996 2003 2001d 2003d 2006 Macedonia, FYR .. .. 1998 1994g 1994g 1994g 1997d 2004 d 2000 d 2002d 2004 Madagascar 1988 1991 1999 1996d 1996d 2001 1996 2003d 1975 1997 .. Malawi 1994 .. 1994 1991d 1991d .. 1994 2001d 1982d 1996 .. Malaysia 1991 1988 1994 1989d 1989d 1996 1994 2002 1977d 1997 .. Mali .. 1989 1995 1994 d 1994 d 1994 1995 2002 1994 d 1995 2003 Mauritania 1988 .. 1994 1994 d 1994 d 1996 1996 2005d 1998d 1996 2005 Mauritius 1990 .. 1994 1992d 1992d 1994 1992 2001d 1975 1996 2004 Mexico .. 1988 1994 1987 1988 1994 1993 2000 1991d 1995 2003 Moldova 2002 .. 1995 1996d 1996d 2007 1995 2008e 2001d 1999d 2004 Mongolia 1995 .. 1994 1996d 1996d 1996 1993 1999d 1996d 1996 2004 Morocco .. 1988 1996 1995 1995 2007 1995 2002d 1975 1996 2004 Mozambique 1994 .. 1995 1994 d 1994 d 1997 1995 2005d 1981d 1997 2005 Myanmar .. 1989 1995 1993d 1993d 1996 1995 2003d 1997d 1997d 2004 d Namibia 1992 .. 1995 1993d 1993d 1994 1997 2003d 1990 d 1997 2005d Nepal 1993 .. 1994 1994 d 1994 d 1998 1993 2005d 1975d 1996 2007 Netherlands 1994 .. 1994 1988d 1988e 1996 1994 e 2002d 1984 1995e 2002e New Zealand 1994 .. 1994 1987 1988 1996 1993 2002 1989d 2000 d 2004 Nicaragua 1994 .. 1996 1993d 1993d 2000 1995 1999 1977d 1998 .. Niger .. 1991 1995 1992d 1992d .. 1995 2004 1975 1996 2006 Nigeria 1990 1992 1994 1988d 1988d 1994 1994 2004 d 1974 1997 2004 Norway .. 1994 1994 1986 1988 1996 1993 2008e 1976 1996 2002 Oman .. .. 1995 1999d 1999d 1994 1995 2005d .. 1996d 2005 Pakistan 1994 1991 1994 1992d 1992d 1997 1994 2005d 1976d 1997 .. Panama 1990 .. 1995 1989d 1989 1996 1995 1999 1978 1996 2003 Papua New Guinea 1992 1993 1994 1992d 1992d 1997 1993 2002 1975d 2000 d 2003 Paraguay .. .. 1994 1992d 1992d 1994 1994 1999 1976 1997 2004 Peru .. 1988 1994 1989 1993d .. 1993 2002 1975 1995 2005 Philippines 1989 1989 1994 1991d 1991 1994 1993 2003 1981 2000 2004 Poland 1993 1991 1994 1990 d 1990 d 1998 1996 2002 1989 2001d 2008 Portugal 1995 .. 1994 1988d 1988 1997 1993 2002f 1980 1996 2004 e Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 189 3.15 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of Biological Kyoto Stockholm changeb layer control the Seac diversity b Protocol CITES CCD Convention 1992 1985 1987 1982 1992 1997 1973 1994 2001 Romania 1995 .. 1994 1993d 1993d 1996 1994 2001 1994 d 1998d 2004 Russian Federation 1999 1994 1995 1986e 1988e 1997 1995 2008e 1992 2003d .. Rwanda 1991 .. 1998 2001d 2001d .. 1996 2004 d 1980 d 1998 2002d Saudi Arabia .. .. 1995 1993d 1993d 1996 2001f 2005d 1996d 1997d .. Senegal 1984 1991 1995 1993d 1993 1994 1994 2001d 1977d 1995 2003 Serbia and Montenegro 2001 2001g 2001g 2001g 2002 2007 2002 .. 2002 Sierra Leone 1994 .. 1995 2001d 2001d 1994 1994f 2006d 1994 d 1997 2003d Singapore 1993 1995 1997 1989d 1989d 1994 1995 2006d 1986d 1999d 2005 Slovak Republic .. .. 1994 1993g 1993g 1996 1994f 2002 1993 2002d 2002 Slovenia 1994 .. 1996 1992g 1992g 1995g 1996 2002 2000d 2001d 2004 Somalia .. 2001d 2001d 1994 .. .. 1985d 2002d .. South Africa 1993 .. 1997 1990 d 1990 d 1997 1995 2002d 1975 1997 2002 Spain .. .. 1994 1988d 1988 1997 1995 2002 1986d 1996 2004 Sri Lanka 1994 1991 1994 1989d 1989d 1994 1994 2002d 1979d 1998d .. Sudan .. .. 1994 1993d 1993d 1994 1995 2004 d 1982 1995 2006 Swaziland 1997 1992d 1992d .. 1994 .. 1997d 1996 2006 Sweden .. .. 1994 1986 1988 1996 1993 2002 1974 1995 2002 Switzerland .. .. 1994 1987 1988 .. 1994 2006d 1974 1996 2003 Syrian Arab Republic 1999 .. 1996 1989d 1989d .. 1996 2006d 2003d 1997 2005 Tajikistan .. .. 1998 1996d 1998d .. 1997f .. .. 1997d 2007 Tanzania 1994 1988 1996 1993d 1993d 1994 1996 2002d 1979 1997 2004 Thailand .. .. 1995 1989d 1989 .. 2004 2002 1983 2001d 2005 Togo 1991 .. 1995 1991d 1991 1994 1995e 2004 d 1978 1995e 2004 Trinidad and Tobago .. .. 1994 1989d 1989d 1994 1996 1999 1984 d 2000 d 2002d Tunisia 1994 1988 1994 1989d 1989d 1994 1993 2003d 1974 1995 2004 Turkey 1998 .. 2004 1991d 1991d .. 1997 .. 1996d 1998 .. Turkmenistan .. .. 1995 1993d 1993d .. 1996f 2008e .. 1996 .. Uganda 1994 1988 1994 1988d 1988 1994 1993 2002d 1991d 1997 2004 d Ukraine 1999 .. 1997 1986e 1988e 1999 1995 2004 1999d 2002d .. United Arab Emirates .. .. 1996 1989d 1989d .. 2000 2005d 1990 d 1998d 2002 United Kingdom 1995 1994 1994 1987 1988 1997d 1994 2002 1976 1996 2005 United States 1995 1995 1994 1986 1988 .. .. .. 1974 2000 .. Uruguay .. .. 1994 1989d 1991d 1994 1993 2001 1975 1999d 2004 Uzbekistan .. .. 1994 1993d 1993d .. 1995f 2007e 1997d 1995 .. Venezuela .. .. 1995 1988d 1989 .. 1994 .. 1977 1998d 2005 Vietnam .. 1993 1995 1994 d 1994 d 2006d 1994 2008e 1994 d 1998d 2002 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1996 1992 1996 1996d 1996d 1994 1996 2004 d 1997d 1997d 2004 Zambia 1994 .. 1994 1990 d 1990 d 1994 1993 2006d 1980 d 1996 2006 Zimbabwe 1987 .. 1994 1992d 1992d 1994 1994 .. 1981d 1997 .. a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention became effective November 16, 1994. d. Accession. e. Acceptance. f. Approval. g. Succession. 190 2009 World Development Indicators ENVIRONMENT Government commitment 3.15 About the data Definitions National environmental strategies and participation Environment and Development (the Earth Summit) in · Environmental strategies or action plans pro- in international treaties on environmental issues pro- Rio de Janeiro, which produced Agenda 21--an array vide a comprehensive analysis of conservation and vide some evidence of government commitment to of actions to address environmental challenges: resource management issues that integrate envi- sound environmental management. But the signing · The Framework Convention on Climate Change ronmental concerns with development. They include of these treaties does not always imply ratification, aims to stabilize atmospheric concentrations of national conservation strategies, environmental nor does it guarantee that governments will comply greenhouse gases at levels that will prevent human action plans, environmental management strategies, with treaty obligations. activities from interfering dangerously with the and sustainable development strategies. The date In many countries efforts to halt environmental global climate. is the year a country adopted a strategy or action degradation have failed, primarily because govern- · The Vienna Convention for the Protection of the plan. · Biodiversity assessments, strategies, or ments have neglected to make this issue a priority, a Ozone Layer aims to protect human health and the action plans include biodiversity profiles (see About reflection of competing claims on scarce resources. environment by promoting research on the effects the data). · Participation in treaties covers nine To address this problem, many countries are prepar- of changes in the ozone layer and on alternative international treaties (see About the data). · Climate ing national environmental strategies--some focus- substances (such as substitutes for chlorofluoro- change refers to the Framework Convention on Cli- ing narrowly on environmental issues, and others carbon) and technologies, monitoring the ozone mate Change (signed in 1992). · Ozone layer refers integrating environmental, economic, and social layer, and taking measures to control the activities to the Vienna Convention for the Protection of the concerns. Among such initiatives are conservation that produce adverse effects. Ozone Layer (signed in 1985). · CFC control refers to strategies and environmental action plans. Some · The Montreal Protocol for Chlorofl uorocarbon the Protocol on Substances That Deplete the Ozone countries have also prepared country environmental Control requires that countries help protect the Layer (the Montreal Protocol for Chlorofluorocarbon profiles and biodiversity strategies and profiles. earth from excessive ultraviolet radiation by cut- Control) (signed in 1987). · Law of the Sea refers National conservation strategies--promoted by ting chlorofluorocarbon consumption by 20 per- to the United Nations Convention on the Law of the the World Conservation Union (IUCN)--provide a cent over their 1986 level by 1994 and by 50 Sea (signed in 1982). · Biological diversity refers comprehensive, cross-sectoral analysis of conser- percent over their 1986 level by 1999, with allow- to the Convention on Biological Diversity (signed at vation and resource management issues to help inte- ances for increases in consumption by developing the Earth Summit in 1992). · Kyoto Protocol refers grate environmental concerns with the development countries. to the protocol on climate change adopted at the process. Such strategies discuss current and future · The United Nations Convention on the Law of the third conference of the parties to the United Nations needs, institutional capabilities, prevailing technical Sea, which became effective in November 1994, Framework Convention on Climate Change in Decem- conditions, and the status of natural resources in establishes a comprehensive legal regime for seas ber 1997. · CITES is the Convention on International a country. and oceans, establishes rules for environmental Trade in Endangered Species of Wild Fauna and Flora, National environmental action plans, supported by standards and enforcement provisions, and devel- an agreement among governments to ensure that the World Bank and other development agencies, ops international rules and national legislation to the survival of wild animals and plants is not threat- describe a country's main environmental concerns, prevent and control marine pollution. ened by uncontrolled exploitation. Adopted in 1973, identify the principal causes of environmental prob- · The Convention on Biological Diversity promotes it entered into force in 1975. · CCD is the United lems, and formulate policies and actions to deal with conservation of biodiversity through scientifi c Nations Convention to Combat Desertification, an them. These plans are a continuing process in which and technological cooperation among countries, international convention addressing the problems governments develop comprehensive environmental access to financial and genetic resources, and of land degradation in the world's drylands. Adopted policies, recommend specific actions, and outline transfer of ecologically sound technologies. in 1994, it entered into force in 1996. · Stockholm the investment strategies, legislation, and institu- But 10 years after the Earth Summit in Rio de Convention is an international legally binding instru- tional arrangements required to implement them. Janeiro the World Summit on Sustainable Develop- ment to protect human health and the environment Biodiversity profiles--prepared by the World Con- ment in Johannesburg recognized that many of the from persistent organic pollutants. Adopted in 2001, servation Monitoring Centre and the IUCN--provide proposed actions had yet to materialize. To help it entered into force in 2004. basic background on species diversity, protected developing countries comply with their obligations areas, major ecosystems and habitat types, and under these agreements, the Global Environment legislative and administrative support. In an effort Facility (GEF) was created to focus on global improve- Data sources to establish a scientific baseline for measuring prog- ment in biodiversity, climate change, international Data on environmental strategies and participation ress in biodiversity conservation, the United Nations waters, and ozone layer depletion. The UNEP, United in international environmental treaties are from Environment Programme (UNEP) coordinates global Nations Development Programme, and World Bank the Secretariat of the United Nations Framework biodiversity assessments. manage the GEF according to the policies of its gov- Convention on Climate Change, the Ozone Secre- To address global issues, many governments have erning body of country representatives. The World tariat of the UNEP, the World Resources Institute, also signed international treaties and agreements Bank is responsible for the GEF Trust Fund and chairs the UNEP, the Center for International Earth Sci- launched in the wake of the 1972 United Nations the GEF. ence Information Network, and the United Nations Conference on the Human Environment in Stock- Treaty Series. holm and the 1992 United Nations Conference on 2009 World Development Indicators 191 3.16 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed national expenditure depletion depletion forest dioxide emission net capital savings depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 19.2 10.9 8.3 2.8 0.0 0.0 0.0 0.2 0.2 10.7 Algeria 57.9 11.6 46.3 4.5 29.7 0.1 0.1 1.2 0.3 19.4 Angola 31.8 14.3 17.5 2.3 55.6 0.0 0.0 0.2 1.3 ­37.3 Argentina 27.2 12.4 14.8 4.0 7.7 0.6 0.0 0.5 1.6 8.3 Armenia 29.7 10.7 18.9 2.2 0.0 1.1 0.0 0.4 1.6 18.1 Australia 22.8 15.3 7.5 4.8 2.9 3.8 0.0 0.3 0.1 5.2 Austria 26.2 15.1 11.1 5.3 0.2 0.0 0.0 0.1 0.3 15.7 Azerbaijan 59.9 13.5 46.4 2.8 52.6 0.0 0.0 2.0 1.2 ­6.6 Bangladesh 32.2 7.7 24.5 1.8 2.9 0.0 0.7 0.4 0.5 21.8 Belarus 26.9 11.8 15.1 4.9 0.1 0.0 0.0 1.4 .. 18.5a Belgium 24.8 14.6 10.2 5.8 0.0 0.0 0.0 0.2 0.2 15.7 Benin .. 8.8 .. 3.6 0.0 0.0 0.9 0.3 0.4 .. Bolivia 30.1 10.1 20.0 6.3 21.6 2.4 0.0 0.5 1.4 0.4 Bosnia and Herzegovina 8.9 11.1 ­2.2 .. 0.2 0.0 .. 0.9 0.1 .. Botswana 57.9 12.8 45.0 6.6 0.2 8.2 0.0 0.3 .. 42.9a,b Brazil 17.0 12.6 4.4 4.4 2.3 1.6 0.0 0.2 0.2 4.5 Bulgaria 17.8 11.9 5.9 4.1 0.6 1.1 0.0 1.0 1.5 5.7 Burkina Faso .. 8.4 .. 4.3 0.0 0.0 1.1 0.1 1.3 .. Burundi .. 6.6 .. 5.1 0.0 0.8 11.5 0.2 0.1 .. Cambodia 15.9 9.1 6.8 1.7 0.0 0.0 0.2 0.1 0.3 7.9 Cameroon 19.7 9.7 10.0 2.6 6.4 0.1 0.0 0.2 0.8 5.3 Canada 23.0 14.9 8.1 4.8 4.1 0.9 0.0 0.4 0.1 7.4 c Central African Republic 4.5 8.2 ­3.7 1.3 0.0 0.0 0.0 0.1 0.4 ­2.9 Chad 26.9 10.2 16.6 1.2 40.7 0.0 0.0 0.0 1.1 ­24.0 Chile 28.7 14.3 14.4 3.4 0.2 16.7 0.0 0.4 0.6 ­0.1 China 54.4 10.7 43.7 1.8 4.5 1.3 0.0 1.4 1.6 36.8 Hong Kong, China 33.8 13.8 20.1 3.0 0.0 0.0 0.0 0.2 .. 22.9a Colombia 19.6 12.1 7.5 4.8 6.6 1.7 0.0 0.3 0.1 3.6 Congo, Dem. Rep. 12.1 7.0 5.1 0.9 3.1 2.9 0.0 0.2 0.6 ­0.8 Congo, Rep. 45.4 13.4 32.0 2.3 56.5 0.0 0.0 0.4 0.7 ­23.4 Costa Rica 19.2 12.4 6.8 4.1 0.0 0.0 0.1 0.2 0.3 10.2 Cote d'Ivoire 9.6 10.0 ­0.4 4.7 7.0 0.0 0.0 0.2 0.3 ­3.2 Croatia 24.6 13.4 11.3 4.3 0.7 0.0 0.2 0.4 0.5 13.8 Cuba .. .. .. 8.2 .. .. .. .. 0.1 .. Czech Republic 27.0 14.4 12.6 4.0 0.4 0.0 0.1 0.6 0.1 15.4 Denmark 24.0 14.9 9.1 7.8 2.3 0.0 0.0 0.1 0.1 14.4 Dominican Republic 21.0 12.0 9.0 3.5 0.0 3.5 0.0 0.6 0.1 8.4 Ecuador 26.9 11.7 15.2 1.4 18.4 0.5 0.0 0.5 0.1 ­2.9 Egypt, Arab Rep. 22.4 10.2 12.2 4.4 13.4 0.1 0.2 1.0 1.0 0.9 El Salvador 12.5 11.3 1.2 2.8 0.0 0.0 0.5 0.3 0.2 3.0 Eritrea .. 7.8 .. 1.9 0.0 0.0 0.9 0.4 0.4 .. Estonia 21.9 14.5 7.4 4.6 0.8 0.0 0.1 0.9 0.0 10.2 Ethiopia 20.9 7.5 13.4 3.7 0.0 0.4 5.4 0.4 0.3 10.6 Finland 26.5 14.8 11.6 5.9 0.0 0.1 0.0 0.2 0.1 17.1 France 19.2 13.3 5.9 5.1 0.0 0.0 0.0 0.1 0.0 10.9 Gabon 46.3 14.2 32.1 3.1 33.3 0.0 0.0 0.1 .. 1.7a Gambia, The 12.6 8.7 3.9 2.0 0.0 0.0 0.6 0.4 0.7 4.2 Georgia 17.0 10.4 6.5 2.8 0.0 0.0 0.0 0.4 1.3 7.7 Germany 24.9 14.6 10.4 4.4 0.2 0.0 0.0 0.2 0.1 14.3 Ghana 23.2 8.9 14.2 4.7 0.0 4.5 2.3 0.4 0.1 11.5 Greece 9.5 14.6 ­5.1 2.8 0.2 0.2 0.0 0.3 0.7 ­3.7 Guatemala 16.8 10.9 5.9 2.8 0.6 0.0 0.8 0.3 0.5 6.5 Guinea 8.8 8.6 0.2 2.0 0.0 4.9 1.7 0.2 0.3 ­4.9 Guinea-Bissau 24.0 7.5 16.5 2.3 0.0 0.0 0.0 0.6 0.9 17.3 Haiti .. 9.8 .. 1.5 0.0 0.0 0.6 0.2 0.4 .. 192 2009 World Development Indicators ENVIRONMENT Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon 3.16 Particulate Adjusted savings of fixed national expenditure depletion depletion forest dioxide emission net capital savings depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Honduras 23.8 10.8 13.0 3.5 0.0 2.0 0.0 0.5 0.4 13.6 Hungary 17.7 14.2 3.5 5.4 0.6 0.0 0.0 0.4 0.1 7.9 India 38.8 9.6 29.2 3.2 2.7 0.7 0.9 1.1 0.7 26.4 Indonesia 27.2 10.8 16.3 1.1 6.9 2.0 0.0 0.8 1.1 6.7 Iran, Islamic Rep. 43.4 11.6 31.8 4.9 26.8 0.6 0.0 1.3 0.7 7.3 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 25.6 18.1 7.6 5.1 0.0 0.2 0.0 0.2 0.0 12.3c Israel .. 13.9 .. 6.0 0.2 0.0 0.0 0.4 0.4 .. Italy 19.8 14.6 5.2 4.2 0.2 0.0 0.0 0.2 0.2 8.9 Jamaica .. 13.2 .. 5.4 0.0 1.9 0.0 0.7 0.3 .. Japan 31.0 14.0 17.0 3.2 0.0 0.0 0.0 0.2 0.5 19.5c Jordan 8.2 10.4 ­2.2 5.6 0.3 0.5 0.0 0.9 0.6 1.1 Kazakhstan 32.5 13.8 18.7 4.4 28.3 2.4 0.0 2.0 0.3 ­9.9 Kenya 17.1 8.8 8.3 6.6 0.0 0.1 1.2 0.3 0.1 13.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 29.9 13.7 16.2 3.9 0.0 0.0 0.0 0.4 0.6 19.1 Kuwait .. 13.3 .. 3.0 32.5 0.0 0.0 0.5 1.4 .. Kyrgyz Republic 6.7 9.1 ­2.4 5.2 0.1 0.0 0.0 1.2 0.2 1.2 Lao PDR 23.5 9.3 14.2 1.3 0.0 0.0 0.0 0.3 1.2 14.0 Latvia 15.4 13.9 1.6 5.6 0.0 0.0 0.6 0.3 0.0 6.2 Lebanon 0.4 12.1 ­11.7 2.5 0.0 0.0 0.0 0.5 0.8 ­10.6 Lesotho 32.7 7.3 25.4 10.0 0.0 0.0 1.3 .. 0.2 .. Liberia ­19.3 9.4 ­28.7 .. 0.0 0.0 6.6 0.6 0.4 .. Libya .. 12.4 .. .. 45.1 0.0 0.0 0.9 .. .. Lithuania 17.0 13.5 3.5 4.8 0.1 0.0 0.1 0.3 0.2 7.6 Macedonia, FYR 21.1 11.4 9.6 4.9 0.0 0.0 0.2 1.2 0.1 13.1 Madagascar 13.4 8.2 5.2 3.1 0.0 0.0 0.2 0.3 0.2 7.7 Malawi 9.6 7.6 2.0 3.5 0.0 0.0 0.8 0.3 0.2 4.3 Malaysia 38.4 12.5 25.9 5.5 10.3 0.1 0.0 0.8 0.1 20.2 Mali 13.6 9.0 4.6 3.6 0.0 0.0 0.0 0.1 1.6 6.5 Mauritania 28.0 8.9 19.1 2.8 0.0 17.0 0.5 0.8 2.3 1.2 Mauritius 19.7 11.8 7.9 3.4 0.0 0.0 0.0 0.4 .. 10.9a Mexico 25.7 12.9 12.8 5.5 6.9 0.4 0.0 0.4 0.4 10.3 Moldova 20.4 8.8 11.6 6.6 0.0 0.0 0.1 1.4 0.6 16.0 Mongolia 42.5 10.3 32.3 4.6 2.5 14.0 0.0 2.2 2.0 16.3 Morocco 32.8 10.9 21.9 5.2 0.0 1.0 0.0 0.5 0.1 25.6 Mozambique 3.1 8.9 ­5.8 3.8 7.1 0.0 0.6 0.2 0.2 ­10.2 Myanmar .. .. .. 0.8 .. .. .. .. 0.6 .. Namibia 40.3 11.3 29.0 7.3 0.0 4.2 0.0 0.3 0.1 31.7 Nepal 28.2 8.0 20.2 2.4 0.0 0.0 4.4 0.2 0.1 17.9 Netherlands 27.6 14.6 12.9 4.8 1.4 0.0 0.0 0.1 0.6 15.6 New Zealand .. 15.5 .. 6.7 1.3 0.2 0.0 0.2 0.0 .. Nicaragua 14.6 9.7 4.9 3.0 0.0 0.7 0.0 0.6 0.1 6.5 Niger .. 7.7 .. 2.6 0.0 0.0 2.4 0.2 0.9 .. Nigeria .. 10.8 .. 0.9 25.2 0.0 0.1 0.7 0.5 .. Norway 38.3 15.7 22.6 6.5 13.4 0.0 0.0 0.2 0.0 15.5 Oman .. .. .. 3.9 .. .. 0.0 .. 1.4 .. Pakistan 24.5 9.1 15.4 2.1 3.3 0.0 0.9 0.7 1.5 11.0 Panama 24.7 12.9 11.8 4.4 0.0 0.0 0.0 0.3 0.2 15.7 Papua New Guinea 39.2 10.6 28.7 .. 18.0 30.0 0.0 0.4 0.0 .. Paraguay 19.6 10.3 9.4 3.9 0.0 0.0 0.0 0.3 0.7 12.3 Peru 25.7 12.4 13.3 2.6 1.5 10.5 0.0 0.3 0.6 3.1 Philippines 31.6 9.3 22.3 2.2 0.4 1.6 0.1 0.5 0.2 21.7 Poland 22.0 13.3 8.7 5.3 0.9 0.5 0.1 0.7 0.4 11.5 Portugal 12.6 14.4 ­1.8 5.4 0.0 0.1 0.0 0.2 0.3 3.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 193 3.16 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed national expenditure depletion depletion forest dioxide emission net capital savings depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Romania 21.1 12.4 8.7 3.4 2.1 0.1 0.0 0.5 0.0 9.3 Russian Federation 31.3 12.9 18.4 3.5 17.9 1.3 0.0 1.1 0.2 1.4 Rwanda 16.2 8.0 8.2 4.6 0.0 0.0 3.6 0.2 0.1 9.0 Saudi Arabia .. 13.4 .. 7.2 42.1 0.0 0.0 0.7 1.4 .. Senegal 21.8 9.4 12.4 4.5 0.0 0.1 0.0 0.3 1.2 15.2 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leone 9.8 7.9 2.0 3.9 0.0 0.9 1.6 0.4 0.9 2.1 Singapore .. 15.1 .. 2.7 0.0 0.0 0.0 0.3 0.9 .. Slovak Republic 24.0 13.8 10.2 3.8 0.1 0.0 0.4 0.5 0.0 13.1 Slovenia 28.0 14.2 13.7 5.5 0.1 0.0 0.2 0.3 0.2 18.4 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 14.5 12.4 2.1 5.3 3.1 2.2 0.2 1.3 0.1 0.4 Spain 21.9 14.8 7.1 3.9 0.0 0.0 0.0 0.2 0.4 10.4 Sri Lanka 23.3 10.3 13.0 2.6 0.0 0.0 0.6 0.3 0.3 14.3 Sudan 13.2 10.8 2.4 0.9 15.7 0.1 0.0 0.2 0.4 ­13.2 Swaziland 19.8 10.6 9.2 6.4 0.0 0.0 0.0 0.3 0.1 15.2 Sweden 27.5 14.7 12.8 7.2 0.0 0.3 0.0 0.1 .. 19.6a Switzerland .. 13.9 .. 4.8 0.0 0.0 0.0 0.1 0.2 .. Syrian Arab Republic 19.7 10.6 9.1 2.6 19.2 0.0 0.0 1.3 0.9 ­9.7 Tajikistan 13.9 8.9 5.0 3.2 0.3 0.0 0.0 1.3 0.4 6.3 Tanzania .. 8.2 .. 2.4 0.5 5.6 0.0 0.2 0.1 .. Thailand 34.0 11.8 22.2 4.8 4.1 0.0 0.2 0.9 0.4 21.4 Timor-Leste .. 1.9 .. .. .. 0.0 .. 0.1 .. .. Togo .. 8.3 .. 2.5 0.0 0.6 2.6 0.6 0.2 .. Trinidad and Tobago 31.0 14.0 16.9 4.0 41.9 0.0 0.0 1.6 0.3 ­22.8 Tunisia 23.9 11.8 12.0 6.7 4.6 0.6 0.1 0.6 0.2 12.5 Turkey 16.0 12.7 3.2 3.7 0.2 0.1 0.0 0.3 1.1 5.3 Turkmenistan .. 11.1 .. .. 92.6 0.0 .. 2.5 1.0 .. Uganda 14.0 8.3 5.7 4.0 0.0 0.0 4.6 0.1 .. 4.9a Ukraine 23.1 11.2 11.9 4.4 3.0 0.0 0.0 2.2 0.3 10.7 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 15.7 14.7 1.0 5.0 1.5 0.0 0.0 0.2 0.0 4.3c United States 14.0 14.8 ­0.8 4.8 1.2 0.1 0.0 0.3 0.3 2.0 c Uruguay 13.4 12.5 1.0 2.6 0.0 0.0 0.3 0.2 1.9 1.2 Uzbekistan 38.6 9.2 29.4 9.4 38.5 0.0 0.0 5.8 0.7 ­6.2 Venezuela, RB 34.8 12.3 22.5 3.4 18.7 0.7 0.0 0.7 0.0 5.9 Vietnam 35.5 9.4 26.1 2.8 11.6 0.1 0.4 1.2 0.5 15.2 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. 10.1 .. .. 22.5 0.0 0.0 0.8 .. .. Zambia 26.2 10.7 15.5 2.1 0.1 19.8 0.0 0.2 0.6 ­3.0 Zimbabwe .. .. .. 6.9 .. .. .. .. 0.1 .. World 22.7 w 13.7 w 8.9 w 4.3 w 3.0 w 0.4 w 0.0 w 0.4 w 0.4 w 8.8 w Low income 25.4 9.3 16.2 2.6 9.8 0.9 0.8 0.7 0.7 5.8 Middle income 32.3 11.7 20.6 3.5 7.1 1.2 0.1 0.9 0.8 14.0 Lower middle income 41.7 10.7 31.0 2.6 6.6 1.2 0.2 1.2 1.1 23.5 Upper middle income 23.2 12.8 10.5 4.4 7.6 1.3 0.0 0.6 0.4 4.9 Low & middle income 32.0 11.6 20.4 3.4 7.2 1.2 0.1 0.9 0.8 13.6 East Asia & Pacific 48.0 10.7 37.3 2.1 4.9 1.3 0.0 1.3 1.3 30.6 Europe & Central Asia 24.0 12.8 11.2 4.0 9.8 0.7 0.0 1.0 0.5 3.2 Latin America & Carib. 22.9 12.6 10.3 4.5 5.4 1.9 0.0 0.3 0.4 6.7 Middle East & N. Africa 33.3 11.3 22.0 4.7 21.3 0.4 0.0 1.0 0.6 3.4 South Asia 36.2 9.5 26.8 3.0 2.7 0.6 0.9 1.0 0.8 23.9 Sub-Saharan Africa 17.4 11.1 6.3 3.6 11.7 1.5 0.5 0.7 0.4 ­5.0 High income 20.6 14.5 6.1 4.6 1.5 0.2 0.0 0.3 0.3 8.5 Euro area 22.3 14.4 7.8 4.6 0.2 0.0 0.0 0.2 0.2 11.9 a. Excludes particulate emissions damage. b. Likely to be overestimated because mineral depletion excludes diamonds. c. World Bank staff estimate. 194 2009 World Development Indicators ENVIRONMENT Toward a broader measure of savings 3.16 About the data Definitions Adjusted net savings measure the change in value of future rents from resource extractions. An economic · Gross savings are the difference between gross a specified set of assets, excluding capital gains. If rent represents an excess return to a given factor national income and public and private consumption, a country's net savings are positive and the account- of production. Natural resources give rise to rents plus net current transfers. · Consumption of fixed ing includes a sufficiently broad range of assets, because they are not produced; in contrast, for pro- capital is the replacement value of capital used up in economic theory suggests that the present value duced goods and services competitive forces will production. · Net national savings are gross savings of social welfare is increasing. Conversely, persis- expand supply until economic profits are driven to minus consumption of fixed capital. · Education expen- tently negative adjusted net savings indicate that an zero. For each type of resource and each country, unit diture is public current operating expenditures in edu- economy is on an unsustainable path. resource rents are derived by taking the difference cation, including wages and salaries and excluding capi- The table provides a check on the extent to which between world prices (to reflect the social oppor- tal investments in buildings and equipment. · Energy today's rents from a number of natural resources tunity cost of resource extraction) and the average depletion is the ratio of the value of the stock of energy and changes in human capital are balanced by unit extraction or harvest costs (including a "normal" resources to the remaining reserve lifetime (capped at net savings, or this generation's bequest to future return on capital). Unit rents are then multiplied by 25 years). It covers coal, crude oil, and natural gas. generations. the physical quantity extracted or harvested to arrive · Mineral depletion is the ratio of the value of the stock Adjusted net savings are derived from standard at total rent. To estimate the value of the resource, of mineral resources to the remaining reserve lifetime national accounting measures of gross savings by rents are assumed to be constant over the life of the (capped at 25 years). It covers tin, gold, lead, zinc, iron, making four adjustments. First, estimates of capital resource (the El Serafy approach), and the present copper, nickel, silver, bauxite, and phosphate. · Net for- consumption of produced assets are deducted to value of the rent flow is calculated using a 4 percent est depletion is unit resource rents times the excess of obtain net savings. Second, current public expen- social discount rate. For details on the estimation of roundwood harvest over natural growth. · Carbon diox- ditures on education are added to net savings (in natural wealth see World Bank (2006a). ide damage is estimated at $20 per ton of carbon (the standard national accounting these expenditures A positive net depletion figure for forest resources unit damage in 1995 U.S. dollars) times tons of carbon are treated as consumption). Third, estimates of implies that the harvest rate exceeds the rate of emitted. · Particulate emission damage is the will- the depletion of a variety of natural resources are natural growth; this is not the same as deforesta- ingness to pay to avoid illness and death attributable deducted to reflect the decline in asset values asso- tion, which represents a change in land use (see to particulate emissions.· Adjusted net savings are ciated with their extraction and harvest. And fourth, Definitions for table 3.4). In principle, there should net savings plus education expenditure minus energy deductions are made for damages from carbon diox- be an addition to savings in countries where growth depletion, mineral depletion, net forest depletion, and ide and particulate emissions. exceeds harvest, but empirical estimates suggest carbon dioxide and particulate emissions damage. The exercise treats public education expenditures that most of this net growth is in forested areas that as an addition to savings. However, because of the cannot currently be exploited economically. Because Data sources wide variability in the effectiveness of public edu- the depletion estimates reflect only timber values, Data on gross savings are from World Bank cation expenditures, these figures cannot be con- they ignore all the external and nontimber benefits national accounts data files (see table 4.8). Data strued as the value of investments in human capital. associated with standing forests. on consumption of fixed capital are from the United A current expenditure of $1 on education does not Pollution damage from emissions of carbon dioxide Nations Statistics Division's National Accounts necessarily yield $1 of human capital. The calcula- is calculated as the marginal social cost per unit mul- Statistics: Main Aggregates and Detailed Tables, tion should also consider private education expen- tiplied by the increase in the stock of carbon dioxide. 1997, extrapolated to 2007. Data on education diture, but data are not available for a large number The unit damage figure represents the present value expenditure are from the United Nations Statistics of countries. of global damage to economic assets and to human Division's Statistical Yearbook 1997 and from the While extensive, the accounting of natural resource welfare over the time the unit of pollution remains United Nations Educational, Scientific, and Cultural Organization Institute for Statistics online data- depletion and pollution costs still has some gaps. in the atmosphere. base. Missing data are estimated by World Bank Key estimates missing on the resource side include Pollution damage from particulate emissions is staff. Data on energy, mineral, and forest depletion the value of fossil water extracted from aquifers, net estimated by valuing the human health effects from are estimates based on sources and methods in depletion of fish stocks, and depletion and degrada- exposure to particulate matter pollution in urban Arundhati Kunte and others' "Estimating National tion of soils. Important pollutants affecting human areas. The estimates are calculated as willingness Wealth: Methodology and Results" (1998). Data on health and economic assets are excluded because to pay to avoid illness and death from cardiopulmo- carbon dioxide damage are from Samuel Fankhaus- no internationally comparable data are widely avail- nary disease and lung cancer in adults and acute er's Valuing Climate Change: The Economics of the able on damage from ground-level ozone or sulfur respiratory infections in children that is attributable Greenhouse (1995). Data on particulate emission oxides. to particulate emissions. damage are from Kiran D. Pandey and others' "The Estimates of resource depletion are based on the For a detailed note on methodology, see www. Human Costs of Air Pollution: New Estimates for "change in real wealth" method described in Hamil- worldbank.org/data. Developing Countries" (2006). The conceptual ton and Ruta (2008), which estimates depletion as underpinnings of the savings measure appear in the ratio between the total value of the resource Kirk Hamilton and Michael Clemens' "Genuine Sav- and the remaining reserve lifetime. The total value ings Rates in Developing Countries" (1999). of the resource is the present value of current and 2009 World Development Indicators 195 Text figures, tables, and boxes Introduction T he global economy in 2007 Global output grew 3.8 percent in 2007, receding slightly from 4 percent in 2006. The down- turn was greatest in high-income economies, where growth fell from 3 percent to 2.5 per- cent, affected by the cooling of the housing market, a precursor to the 2008 financial crisis. Low- and middle-income economies, which have grown faster on average, reached a peak of 8.1 percent annual growth in 2007. Their strong performance was led by the economies of East Asia and Pacific and South Asia (figure 4a), dominated by China at 13 percent annual growth and India at 9 percent. After a decade of sustained growth India's gross national income (GNI) per capita (using the World Bank Atlas method) now places it with China among the lower middle-income economies. Cambodia, Lao PDR, Malaysia, Mongolia, the Philippines, and Vietnam in East Asia and Pacific all grew faster than 6 percent, as did all South Asian economies except Afghanistan and Nepal (figure 4b). Sub-Saharan Africa achieved 6.2 percent growth for the second year in a row, thanks to higher prices for its oil and commodity exports. The commodity boom also helped the Middle East and North Africa and Latin America and the Caribbean achieve their highest growth rate since 2004. Egypt, Iran, Jordan, Libya, Syria, and Tunisia grew more than 6 percent. About half the countries in Latin America and the Caribbean grew more than 6 percent, with Argentina at 9 percent and Venezuela at 8 percent. Similarly, 28 of the 47 Sub-Saharan countries grew 5 percent or more. Growth slowed in Europe and Central Asia, but annual rates remained above 5 percent, except for Moldova. Economic growth Large middle-income economies with slowed in 2007 4a economic growth above 10 percent 4b GDP growth, by region (%) 2006 2007 2008a GDP growth, 2007 (%) 12 30 9 20 6 10 3 0 0 East Europe & Latin Middle South Sub- High n la ia a a a a ia a lia n ic ija in gi m ak iopi di da go en tv bl go Asia & Central America & East & Asia Saharan income or Ch bo na ba La Su pu An m on h Ge m Pa er Pacific Asia Caribbean North Africa Et Ar Re M Ca Az Africa ov Sl a. Preliminary estimate as of November 2008. Source: World Development indicators data files. Source: World Development indicators data files. 2009 World Development Indicators 197 Savings and investment High-income economies still dominate were higher in 2007 manufactured output and exports The rapid growth of developing economies since 2000 has During their industrial revolutions today's developed economies been marked by large increases in investment (figure 4c). Be- transformed themselves from agrarian economies into produc- tween 2000 and 2007 investment rates rose from 32 percent ers and exporters of manufactured goods. Manufacturing has of gross domestic product (GDP) to 38 percent in East Asia yet to take off in most developing economies. Value added in and Pacific and from 23 percent to 34 percent in South Asia. manufacturing accounted for as much as 20 percent of GDP in Investment in China, India, Lao PDR, Mongolia, and Vietnam only about a dozen developing countries in 2007, among them exceeded 37 percent of GDP during 2005­07. Sub-Saharan China, Indonesia, Lao PDR, Philippines, and Vietnam. China's Africa saw its investment grow 73 percent, Europe and Cen- share was 32 percent. Some developing economies have been tral Asia 91 percent, and the Middle East and North Africa 63 investing in and expanding their services, which in 38 countries percent. At 34 percent, investment growth in Latin America accounted for more then 50 percent of GDP, among them Ban- and the Caribbean was not as rapid. gladesh, India, Pakistan, and Sri Lanka. Resource extraction Most of the increase was financed by rising savings. remains important for many countries: minerals and petroleum Annual gross savings in developing economies grew from were the leading sources of growth in many Sub-Saharan, and 25 percent of GDP to 31 percent between 2000 and 2007. Middle Eastern and North African economies. East Asia and Pacific saw the largest increase, from 36 per- Services are now the largest sector in high-income econo- cent of GDP to 47 percent, followed by South Asia, from mies, accounting for more than 70 percent of GDP. Although 25 percent to 34 percent (figure 4d). Countries enjoying a their manufacturing share has fallen, they still accounted for surge in demand for their exports of fuels, other commodities, 73 percent of global manufactured output in 2006, down or manufactures--such as Algeria, Azerbaijan, Botswana, from 79 percent in 2000 (figure 4e). They also accounted for China, Gabon, Iran, and Mongolia--saved more than 40 per- the largest share of manufactures exports (figure 4f). East cent of their GDP during 2005­07. Asia and Pacific made the largest inroads, improving its share of global manufactured output by 4 percentage points, from 9 percent to 13 percent. Asian countries High-income economies still produce invested more 4c the largest share of manufactured goods . . . 4e Share of world manufacturing value added Gross capital formation, by region (index, 2000 = 100) East Asia & Pacific 9% 2000 Europe & Central Asia 3% 250 South Asia Latin America & Caribbean 6% Middle East & North Africa 1% East Asia & Pacific South Asia 1% 200 Sub-Saharan Africa 1% Europe & Central Asia High income 2006 East Asia & Pacific 13% 150 79% Europe & Central Asia 4% Latin America & Caribbean 6% 100 Sub-Saharan Africa Middle East & North Africa 1% Middle East & North Africa South Asia 2% Latin America & Caribbean Sub-Saharan Africa 1% High income 50 73% 2000 2001 2002 2003 2004 2005 2006 2007 Source: World Development Indicators data files. Source: World Development Indicators data files. East Asia and Pacific . . . And account for the largest is the largest saver 4d share of manufactures exports 4f Share of world manufactures exports Savings and investment, by region, 2005­07 (% of GDP) Savings Investment East Asia & Pacific 9% 50 2000 Europe & Central Asia 2% Latin America & Caribbean 4% Middle East & North Africa 1% 40 South Asia 1% Sub-Saharan Africa 1% 30 High income 2006 East Asia & Pacific 14% 82% 20 Europe & Central Asia 4% Latin America & Caribbean 4% 10 Middle East & North Africa 1% South Asia 1% Sub-Saharan Africa 1% 0 High income East Europe & Latin Middle East South Sub- High 76% Asia & Central America & & North Asia Saharan income Pacific Asia Caribbean Africa Africa Source: World Development Indicators data files. Source: World Development Indicators data files. 198 2009 World Development Indicators Macroeconomic stability and fiscal space Macroeconomic stability is good for economic growth. Despite countries may also be able to undertake fiscal stimulus pro- concerns that strict fiscal and monetary policies were prevent- grams by taking on additional debt within the available fiscal ing countries from pursuing aggressive antipoverty programs space. Among large economies with data for 2005­07, 34 and progressing toward the Millennium Development Goals, had a cash surplus and 16 had a cash deficit greater than 2 most developing economies have reduced their fiscal deficits, percent and 12 developing economies had a deficit greater controlled inflation, and kept real interest rates low. than 3 percent (figure 4g). In 2007, five developing econo- In periods of rapid growth monetary policy is crucial in mies had a public debt to GDP ratio greater than 60 percent maintaining macroeconomic stability. But in a deep recession (figure 4h). it has limitations because interest rates cannot go below zero. Maintaining fiscal solvency is one aspect of fiscal policy. The current financial crisis has renewed interest in fis- A second is macroeconomic stability. The ability to increase cal policy to stimulate economic growth. Discretionary fiscal public spending while maintaining macroeconomic stability spending is possible when a country has "fiscal space," the has been referred to as having "macroeconomic space." difference between current government spending and the High inflation limits the possibility of fiscal expansion. Rapid maximum spending a government can undertake without growth, supply constraints, and rising demand for commodities jeopardizing its fiscal solvency--that is, without jeopardizing by developed and developing economies boosted prices glob- its current and future ability to service its debt. Indicators ally, and inflation rose modestly in many countries (figure 4i), that assess fiscal space include the ratios of public debt to even as the highest rates were being brought under control. GDP, external debt to GDP, and fiscal balance to GDP. The Some countries responded by raising interest rates ratio of the current account balance to GDP and of private between 2005 and 2007, but real interest rates continued capital flows excluding foreign direct investment to GDP are to fall in many others (figure 4j). Now, faced with a crisis that also relevant to determining the extent of fiscal space. began in the United States in 2007 and spread to other econ- Fiscal space can be created without issuing new debt, omies in 2008, developing economies will face difficult policy through improved efficiency and public expenditure, increased choices as they continue to grow while maintaining a stable-- revenue mobilization, and additional grant aid. But some and sustainable--fiscal stance. Twelve developing economies had a Modest inflationary pressure cash deficit greater than 3 percent of GDP 4g affected 74 countries 4i Change in inflation from 2000­05 to 2005­07 (percentage points) Cash deficit, 2005­07 (% of GDP) 10 0 In 74 countries inflation increased 0 ­10 ­10 ­20 ­20 ­30 Jamaica Lebanon Ghana Sri Burkina Mali Egypt, Jordan Pakistan Kenya Lao Madagascar Lanka Faso Arab Rep. PDR In 58 countries inflation decreased ­30 Source: World Development Indicators data files. Source: International Monetary Fund and World Development Indicators data files. Five developing economies had a public debt to Real interest rates GDP ratio greater than 60 percent over 2005­07 4h declined in 66 countries 4j Public debt to GDP ratio, 2005­07 (%) Change in real interest rate, 2002 to 2007 (percentage points) 150 40 33 countries had higher real interest rates in 2007 than in 2002 20 100 0 ­20 50 ­40 ­60 66 countries had lower real interest 0 rates in 2007 than in 2002 Jamaica Cuba Bolivia Sri Lanka Jordan ­80 Source: World Development Indicators data files. Source: International Monetary Fund and World Development Indicators data files. 2009 World Development Indicators 199 Growth in GDP and investment Brazil 4k China 4l Year on year change in GDP and Year on year change in GDP and Quarterly data for selected major economies in each of the six investment (%) investment (%) developing regions show that the 2008 global crisis has af- 40 40 fected countries differently (figure 4k­4p). In Brazil GDP rose 30 30 slightly in 2008 over 2007. In China and India GDP growth Investment has declined but remains above 7 percent. The GDP growth 20 Investment 20 rate fell significantly in the Russian Federation in the fourth quarter of 2008, to 1.1 percent. South Africa and Egypt saw 10 10 their GDP growth rates decline rapidly in the second half of GDP GDP 0 0 2008. However, overall GDP growth remained positive in all Q1-07 Q3-07 Q1-08 Q3-08 Q1-07 Q3-07 Q1-08 Q4-08 six countries. Source: Haver Analytics and China, Source: Haver Analytics. National Bureau of Statistics. Growth in industrial production Brazil 4q China 4r Year on year change in industrial Year on year change in industrial In the current global crisis growth in the industrial sector, production (%) production (%) 20 20 particularly the manufacturing sector, has fallen sharply. The large developing economies shown here, with the exception 10 10 of China, saw their industrial production fall into the negative range by the end of 2008 (figures 4q­4v). Even China saw a 0 0 large decline in its industrial output due to falling demand. ­10 ­10 ­20 ­20 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Feb-07 Jan-08 Jun-08 Dec-08 Source: Haver Analytics. Source: Haver Analytics. Lending and inflation rates Brazil 4w China 4x Countries need macroeconomic space to pursue monetary Lending and inflation rates (%) Lending and inflation rates (%) 30 30 policies to stimulate their economies. But many countries are still experiencing double-digit inflation despite cooling economies (figure 4w­4bb). Egypt has a high inflation rate. 20 20 But China's lending rate and inflation rate are both below 10 Lending rate percent. 10 10 Lending rate Inflation rate Inflation rate 0 0 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Source: World Development Indicators Source: Haver Analytics. data files. Central government debt Brazil 4cc China 4dd Central government Domestic debt Central government Domestic debt debt (% of GDP) Foreign debt debt (% of GDP) Foreign debt With monetary policy limited in scope during the current cri- 60 60 sis, countries are looking to fiscal policy to lift them out of the economic crisis (figures 4cc­4hh). China seems to have the 40 40 fiscal space to afford a large stimulus package. Brazil and India have a more constrained fiscal environment, limiting the actions they can take without affecting fiscal solvency. For 20 20 the Russian Federation the falling price of oil and the depre- ciation of the ruble may also limit the scope for fiscal policy. 0 0 Q1-07 Q3-07 Q1-08 Q4-08 2000 2001 2002 2003 2004 2005 2006 Source: Banco Central do Brasil. Source: Haver Analytics. 200 2009 World Development Indicators Arab Republic of Egypt 4m India 4n Russian Federation 4o South Africa 4p Year on year change in GDP and Year on year change in GDP and Year on year change in GDP and Year on year change in GDP and investment (%) investment (%) investment (%) investment (%) 40 40 40 40 30 30 30 30 Investment 20 20 20 20 Investment Investment Investment 10 10 10 10 GDP GDP GDP GDP 0 0 0 0 Q3-07 Q4-07 Q1-08 Q2-08 Q3-08 Q1-07 Q3-07 Q1-08 Q4-08 Q1-07 Q3-07 Q1-08 Q4-08 Q1-07 Q3-07 Q1-08 Q4-08 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Arab Republic of Egypt 4s India 4t Russian Federation 4u South Africa 4v Year on year change in industrial Year on year change in industrial Year on year change in industrial Year on year change in industrial production (%) production (%) production (%) production (%) 20 20 20 20 10 10 10 10 0 0 0 0 ­10 ­10 ­10 ­10 ­20 ­20 ­20 ­20 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Arab Republic of Egypt 4y India 4z Russian Federation 4aa South Africa 4bb Lending and inflation rates (%) Lending and inflation rates (%) Lending and inflation rates (%) Lending and inflation rates (%) 30 30 30 30 Inflation rate 20 20 20 20 Lending rate Lending rate Inflation rate 10 10 10 10 Lending rate Inflation rate Inflation rate Lending rate 0 0 0 0 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Jan-07 Jun-07 Jan-08 Jun-08 Dec-08 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Arab Republic of Egypt 4ee India 4ff Russian Federation 4gg South Africa 4hh Central government Domestic debt Central government Domestic debt Central government Domestic debt Central government Domestic debt debt (% of GDP) Foreign debt debt (% of GDP) Foreign debt debt (% of GDP) Foreign debt debt (% of GDP) Foreign debt 60 60 60 60 40 40 40 40 20 20 20 20 0 0 0 0 Q1-07 Q3-07 Q1-08 Q4-08 Q1-07 Q3-07 Q1-08 Q3-08 Q1-07 Q3-07 Q1-08 Q4-08 Q1-07 Q3-07 Q1-08 Q4-08 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. 2009 World Development Indicators 201 Tables 4.a Recent economic performance of selected developing countries Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross international product and services and services balance reserves months average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2007 2008a 2007 2008a 2007 2008a 2007 2008a 2007 2008a 2008a 2008a Algeria 3.1 3.0 ­0.6 4.4 7.6 5.6 7.5 9.0 .. 20.2 138,945 31.0 Angola 21.1 14.8 .. .. .. .. 7.1 32.5 15.3 19.5 .. .. Argentina 8.7 6.0 9.0 .. 20.6 .. 14.2 10.0 2.7 0.9 .. .. Armenia 13.8 6.8 ­3.5 ­13.8 13.0 9.6 4.2 8.5 ­6.4 ­12.8 1,405 3.0 Azerbaijan 25.0 10.8 43.3 6.0 14.0 20.9 14.4 20.8 28.9 29.5 18,038 28.9 Bangladesh 6.4 6.2 13.0 8.7 16.0 14.5 6.8 8.0 1.3 0.9 5,788 2.8 Belarus 8.2 10.0 7.3 1.2 0.0 16.8 12.1 20.6 ­6.8 ­7.6 3,061 0.9 Bolivia 4.6 6.1 3.3 9.4 4.8 16.3 7.4 14.9 13.7 11.9 7,722 16.6 Bosnia and Herzegovina 6.8 6.0 12.6 13.1 16.6 17.2 6.0 7.4 ­12.7 ­15.5 4,497 5.4 Botswana 5.3 2.4 8.8 ­20.8 13.6 19.6 11.7 12.2 19.8 ­1.9 9,200 21.3 Brazil 5.4 5.5 6.6 2.9 20.7 23.0 4.1 7.5 0.1 ­1.8 207,467 10.2 Bulgaria 6.2 6.0 5.2 2.8 9.9 4.8 7.8 10.3 ­22.0 ­24.3 17,923 5.4 Burundi 3.6 4.5 .. 34.0 .. 4.3 9.5 24.5 ­11.9 ­13.0 154 3.9 Cameroon 3.5 3.9 ­12.1 4.7 6.2 5.1 2.0 1.7 ­2.6 1.3 3,991 6.5 Chile 5.1 3.5 7.8 1.8 14.3 13.4 4.9 8.7 4.4 ­2.6 23,162 4.8 China 11.9 9.0 19.9 7.8 13.9 3.7 5.2 7.2 12.0 8.9 1,950,000 19.1 Colombia 8.2 3.5 12.2 7.9 18.4 3.2 2.9 8.7 ­2.9 ­3.2 23,671 6.9 Congo, Dem. Rep. 6.8 8.2 9.8 ­0.2 9.1 12.1 17.0 19.7 0.0 ­12.3 83 0.2 Congo, Rep. ­1.6 7.6 .. 6.2 .. ­0.6 ­7.9 33.3 ­28.5 6.5 5,507 22.2 Costa Rica 7.3 2.9 8.4 3.2 4.5 7.0 9.6 13.0 ­6.0 ­8.0 4,216 2.9 Côte d'Ivoire 1.7 2.8 ­9.9 7.4 1.3 0.3 2.7 5.0 ­0.7 ­0.5 .. .. Croatia 5.6 2.2 5.7 12.9 5.8 18.0 4.0 6.6 ­8.7 ­10.0 14,327 4.9 Dominican Republic 8.5 5.3 7.6 ­10.9 6.7 12.7 5.7 10.6 ­6.1 ­9.6 2,644 1.8 Ecuador 2.6 3.1 ­1.7 1.5 6.5 5.4 4.7 11.9 3.6 2.8 4,473 2.4 Egypt, Arab Rep. 7.1 7.2 23.3 28.8 28.8 26.3 12.6 12.3 0.3 0.5 34,603 6.6 El Salvador 4.7 3.8 3.9 4.0 8.1 10.8 4.4 6.3 ­5.5 ­6.1 2,515 2.7 Ethiopia 11.1 11.6 10.2 0.6 3.8 10.7 16.8 25.9 ­4.3 ­10.5 906 .. Gabon 5.6 2.1 4.2 ­0.9 22.0 10.9 5.2 14.2 .. 17.1 1,954 5.1 Ghana 6.3 7.2 2.6 4.7 8.9 8.9 14.8 18.1 ­14.2 19.9 1,896 2.1 Guatemala 5.7 4.6 10.8 8.1 7.0 12.5 6.1 8.5 ­5.0 ­5.5 4,564 3.5 Honduras 6.3 4.0 3.6 11.0 8.0 17.6 7.0 9.8 ­10.0 ­13.3 2,532 3.8 India 9.0 7.0 7.5 9.7 7.7 4.7 4.3 9.5 .. ­2.6 254,613 9.2 Indonesia 6.3 6.1 8.0 9.5 8.9 10.0 11.5 18.3 2.4 .. 51,639 4.9 Iran, Islamic Rep. 7.8 5.0 2.8 ­40.0 4.9 ­13.0 20.5 25.0 .. 10.3 63,450 7.4 Jamaica .. ­1.3 .. .. .. .. .. 21.1 .. ­13.8 1,600 2.7 Jordan 6.0 5.5 0.8 ­3.5 6.5 11.1 6.0 14.9 ­17.5 ­14.0 8,344 4.8 Kazakhstan 8.9 3.2 9.0 8.0 25.5 3.2 15.5 20.0 ­7.0 6.7 19,401 4.7 Kenya 7.0 3.6 6.0 ­4.2 12.7 8.2 4.7 27.0 ­4.6 ­7.1 2,928 3.0 Latvia 10.3 ­4.6 12.5 ­1.8 16.9 ­13.7 13.3 15.2 ­23.9 ­13.2 5,248 7.1 Lebanon 2.0 5.5 6.6 32.6 8.9 7.5 4.9 7.5 ­8.4 ­17.4 17,062 12.6 Lesotho 4.9 3.9 14.6 ­21.8 13.3 7.7 6.2 8.3 13.2 ­3.7 982 6.6 202 2009 World Development Indicators ECONOMY Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross international product and services and services balance reserves months average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2007 2008a 2007 2008a 2007 2008a 2007 2008a 2007 2008a 2008a 2008a Lithuania 8.8 3.2 4.7 13.1 9.1 11.6 8.6 12.1 ­13.7 ­12.6 6,441 2.4 Macedonia, FYR 5.0 5.3 11.8 6.4 15.2 20.0 5.1 6.6 .. ­13.2 2,115 3.2 Malawi 7.9 8.7 ­1.1 ­5.4 ­4.2 ­4.6 7.4 8.6 .. ­17.9 209 1.3 Malaysia 6.3 5.1 4.2 9.2 5.4 3.5 5.2 1.3 15.5 12.8 91,400 7.4 Mauritius 4.7 5.3 4.7 5.4 5.9 2.9 7.0 7.6 ­6.0 ­6.3 2,570 4.9 Mexico 3.2 1.3 6.2 ­3.5 7.0 1.3 4.7 7.4 ­0.5 ­1.7 85,421 2.9 Moldova 3.0 7.2 9.5 ­7.8 13.4 ­6.1 15.8 9.7 ­15.8 ­15.4 1,672 4.2 Mongolia 10.2 8.9 .. 19.6 .. 50.3 12.3 22.4 .. ­9.6 637 2.1 Montenegro 10.3 7.5 10.3 6.1 10.3 12.3 7.2 9.0 .. ­31.3 468 .. Morocco 2.7 5.8 5.2 ­8.0 15.0 5.6 3.8 3.0 ­0.2 ­4.6 25,350 7.0 Nicaragua 3.9 3.5 9.7 3.9 16.3 11.4 9.2 16.8 ­18.3 ­24.4 1,206 2.5 Nigeria 5.9 6.2 .. .. .. .. 5.1 15.8 13.3 6.2 52,800 12.0 Pakistan 6.0 6.0 2.3 ­8.9 ­2.8 ­2.1 7.9 13.4 ­5.8 ­8.3 9,385 2.2 Panama 11.5 9.0 15.0 14.4 19.3 16.5 1.9 9.3 ­7.3 ­9.8 2,602 3.5 Papua New Guinea 6.2 6.9 ­13.4 13.6 ­1.4 13.9 2.4 11.4 .. 2.8 2,090 4.4 Paraguay 6.8 5.8 9.6 32.4 10.8 39.3 10.2 7.1 1.0 ­2.0 2,864 3.7 Peru 8.9 9.0 6.2 10.1 21.3 24.1 2.0 5.8 1.4 ­1.6 37,297 10.0 Philippines 7.2 4.0 5.6 2.5 ­4.5 3.6 2.8 8.5 4.4 1.5 36,659 5.4 Poland 6.6 4.8 8.5 10.8 15.0 11.4 3.3 3.5 ­4.4 ­5.5 62,180 3.8 Romania 6.0 7.8 12.0 13.8 12.0 9.4 10.8 13.5 ­13.9 ­12.9 37,972 5.7 Russian Federation 8.1 7.3 6.4 5.7 27.3 19.4 13.5 15.0 5.9 7.6 614,638 16.5 Senegal 4.8 2.7 ­1.8 14.1 8.7 13.5 5.2 7.4 .. ­12.4 1,610 3.2 Serbia 7.5 6.0 16.3 12.1 23.9 12.0 6.8 11.1 .. ­17.4 14,383 5.7 Seychelles 6.3 0.1 30.6 16.2 43.6 19.2 7.3 29.6 ­36.2 ­32.1 51 0.7 South Africa 5.1 3.1 8.3 3.0 10.4 .. 8.9 10.5 ­7.3 ­8.1 34,089 3.0 Sri Lanka 6.8 6.3 .. 5.2 .. 4.0 14.0 23.5 ­4.2 ­7.5 1,753 1.5 Sudan 10.2 8.3 33.6 23.0 ­4.4 0.3 7.0 15.8 ­7.1 ­8.3 .. .. Swaziland 3.5 2.0 ­1.9 6.4 3.0 3.5 9.0 9.7 ­2.3 ­1.3 685 2.9 Syrian Arab Republic 6.6 5.1 2.5 ­2.4 8.4 2.5 3.5 12.3 .. 0.0 5,516 3.4 Thailand 4.8 2.6 7.1 5.5 3.5 7.5 3.2 4.5 6.4 ­0.1 111,008 6.6 Tunisia 6.3 5.0 8.5 1.1 6.1 6.0 2.4 5.0 ­2.6 ­4.6 8,769 4.2 Togo 1.9 0.8 .. 5.6 .. 8.9 1.3 4.8 .. ­7.0 .. 3.7 Turkey 4.6 1.0 7.3 .. 10.7 .. 7.6 10.1 ­5.7 ­5.5 72,946 4.3 Uganda 7.9 9.5 12.2 7.3 16.7 28.1 6.9 6.3 ­6.3 ­9.1 2,673 6.4 Ukraine 7.6 2.1 3.2 ­0.4 19.9 6.7 21.8 28.2 ­3.7 ­6.7 31,543 3.9 Uruguay 7.4 10.6 10.2 12.0 12.5 20.0 8.5 6.5 ­0.8 ­2.7 6,329 8.6 Uzbekistan 9.5 8.0 32.4 15.8 36.8 20.0 24.0 17.0 .. 17.9 2,684 3.6 Venezuela, RB 8.4 4.9 ­5.6 0.0 33.6 2.7 14.0 38.0 8.8 12.0 43,127 9.3 Vietnam 8.5 6.2 21.0 5.6 30.8 9.2 8.2 21.7 ­10.2 ­10.2 22,420 4.0 Zambia 6.0 5.8 21.2 ­5.9 16.2 13.1 9.4 11.0 ­4.4 ­8.9 976 2.3 Zimbabwe .. .. .. .. 29.4 .. .. .. .. .. .. .. a. Data are preliminary estimates. Source: World Development Indicators data files. 2009 World Development Indicators 203 4.1 Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 Afghanistan .. 10.7 .. .. .. .. .. .. .. .. Albania 3.5 5.3 4.3 1.4 ­0.5 3.6 .. ­0.2 6.9 7.2 Algeria 1.9 4.5 3.6 6.3 1.8 3.9 ­2.1 2.3 1.8 5.0 Angolaa 1.6 12.9 ­1.4 14.3 4.4 14.0 ­0.3 20.2 ­2.2 9.1 Argentina 4.3 4.7 3.5 3.7 3.8 6.2 2.7 5.8 4.5 3.6 Armenia ­1.9 12.7 0.5 7.8 ­7.8 15.9 ­4.3 6.5 6.4 13.7 Australia 3.6 3.2 3.1 0.2 2.7 2.5 1.8 1.2 4.2 3.7 Austria 2.4 2.0 1.6 ­0.4 2.7 3.0 2.7 2.3 2.3 1.8 Azerbaijan ­6.3 17.6 ­2.1 6.1 ­2.1 23.0 ­15.7 11.0 ­2.3 15.1 Bangladesh 4.8 5.7 2.9 3.1 7.3 7.9 7.2 7.7 4.5 5.9 Belarus ­1.7 8.3 ­4.0 6.4 ­1.8 11.5 ­0.7 11.3 ­0.4 6.3 Belgium 2.1 2.0 2.7 ­1.7 1.8 1.3 3.1 0.7 1.9 2.2 Benina 4.8 3.8 5.8 4.6 4.1 3.8 5.8 2.7 4.2 3.2 Bolivia 4.0 3.6 2.9 3.4 4.1 4.4 3.8 4.1 4.3 2.6 Bosnia and Herzegovina .. 5.3 .. 5.0 .. 7.1 .. 8.1 .. 3.9 Botswana 6.0 5.3 ­1.2 ­1.5 5.8 4.6 4.4 2.8 7.8 5.9 Brazil 2.7 3.3 3.6 4.0 2.4 3.1 2.0 3.2 3.8 3.4 Bulgaria ­1.8 5.7 3.0 ­4.1 ­5.0 5.9 .. 6.6 ­5.2 6.3 Burkina Faso 5.5 5.8 5.9 6.2 5.9 7.3 5.9 6.3 3.9 5.5 Burundi ­2.9 2.7 ­1.9 ­1.5 ­4.3 ­6.2 ­8.7 .. ­2.8 10.4 Cambodia 7.0 9.9 3.7 5.5 14.3 14.2 18.6 13.8 7.1 10.2 Cameroon 1.7 3.5 5.5 3.4 ­0.9 ­0.4 1.4 5.8 0.2 6.2 Canada 3.1 2.7 1.1 2.3 3.2 1.5 .. 0.1 3.0 3.2 Central African Republic 2.0 0.0 3.8 0.7 0.7 0.8 ­0.2 1.1 0.2 ­1.6 Chad 2.2 12.2 4.9 3.3 0.6 33.2 .. .. 0.8 8.4 Chile 6.6 4.5 2.2 5.9 5.6 3.6 4.4 4.0 6.9 4.7 Chinaa 10.6 10.3 4.1 4.2 13.7 11.6 12.9 10.9 10.2 10.6 Hong Kong, China 3.6 5.2 .. ­2.2 .. ­2.8 .. ­3.3 .. 4.9 Colombia 2.8 4.9 ­2.6 3.0 1.5 5.0 ­2.5 5.5 4.1 4.7 Congo, Dem. Rep. ­4.9 5.0 1.4 1.2 ­8.0 9.6 ­8.7 6.3 ­13.0 11.5 Congo, Rep.a 1.0 4.1 0.7 .. 1.7 .. ­2.4 .. ­0.7 .. Costa Rica 5.3 5.4 4.1 4.1 6.2 5.9 6.8 5.8 4.7 5.6 Côte d'Ivoirea 3.2 0.3 3.5 1.2 6.3 ­1.0 5.5 ­2.8 2.0 0.4 Croatia 0.6 4.8 ­2.1 1.3 ­1.1 5.9 ­3.5 5.5 1.3 4.7 Cubaa 4.2 3.4 .. .. .. .. .. .. .. .. Czech Republic 1.1 4.6 0.0 0.4 0.2 6.4 4.3 8.2 1.2 4.3 Denmark 2.7 1.8 4.6 0.6 2.5 0.1 2.5 ­0.4 2.7 2.0 Dominican Republica 6.0 4.8 3.9 4.7 7.0 2.2 4.9 2.3 6.0 6.2 Ecuador 1.9 5.0 ­1.7 4.8 2.6 5.4 1.5 5.4 2.4 2.7 Egypt, Arab Rep. 4.4 4.3 3.1 3.3 5.1 4.5 6.4 4.0 4.1 4.8 El Salvador 4.8 2.8 1.2 3.2 5.1 2.3 5.2 2.4 4.0 2.9 Eritrea 5.7 1.4 1.5 9.3 15.0 0.8 10.6 ­4.9 5.7 0.1 Estonia 0.4 8.1 ­6.2 ­3.0 ­2.4 9.8 7.3 10.5 3.3 7.7 Ethiopia 3.8 7.5 2.6 6.2 4.1 9.0 3.9 6.4 5.2 8.4 Finland 2.6 3.0 ­1.1 1.3 4.1 4.5 6.4 4.7 2.5 2.0 France 1.9 1.8 2.0 ­0.3 1.0 1.4 .. 1.2 2.2 2.0 Gabona 2.3 2.0 2.0 1.4 1.6 1.2 3.0 3.4 3.1 2.7 Gambia, The 3.0 4.9 3.3 2.6 1.0 7.3 0.9 4.2 3.7 6.0 Georgia ­7.1 8.3 ­11.0 1.6 ­8.1 12.5 .. 10.4 ­0.3 9.5 Germany 1.8 1.0 0.1 ­0.4 ­0.1 1.5 0.2 1.6 2.9 1.1 Ghanaa 4.3 5.5 3.4 2.8 2.7 5.2 ­4.5 .. 5.6 7.6 Greece 2.2 4.3 0.5 ­2.6 1.0 4.8 .. 4.0 2.6 4.1 Guatemalaa 4.2 3.6 2.8 3.0 4.3 2.9 2.8 2.8 4.7 4.1 Guinea 4.4 2.8 4.3 4.0 4.9 3.7 4.0 2.6 3.6 1.4 Guinea-Bissau 1.2 0.4 3.9 4.8 ­3.1 4.0 ­2.0 4.0 ­0.6 0.3 Haiti ­1.5 0.2 .. .. .. .. .. .. .. .. 204 2009 World Development Indicators ECONOMY Gross domestic product Agriculture Growth of output Industry Manufacturing 4.1 Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 Honduras 3.2 5.3 2.2 3.9 3.6 4.3 4.0 5.6 3.8 6.3 Hungary 1.5 4.0 ­2.4 4.8 3.6 3.8 7.9 5.6 1.3 4.0 India 5.9 7.8 3.2 3.1 6.1 8.6 6.7 8.0 7.7 9.3 Indonesiaa 4.2 5.1 2.0 3.2 5.2 4.1 6.7 5.1 4.0 6.8 Iran, Islamic Rep. 3.1 5.9 3.2 5.9 2.6 6.9 5.1 9.9 3.8 5.3 Iraq .. ­11.4 .. .. .. .. .. .. .. .. Ireland 7.4 5.5 0.8 ­4.3 12.7 4.9 .. .. 7.5 6.1 Israela 5.4 3.2 .. .. .. .. .. .. .. .. Italy 1.5 1.0 2.1 ­0.2 1.0 0.3 1.4 ­1.2 1.6 1.4 Jamaica 1.8 1.0 ­0.3 ­1.4 ­1.0 1.7 ­2.2 ­0.2 2.3 0.4 Japan 1.1 1.7 ­1.3 ­1.7 ­0.3 1.7 .. 1.9 2.0 1.6 Jordan 5.0 6.3 ­3.0 7.5 5.2 8.4 5.6 10.2 5.0 5.8 Kazakhstan ­4.1 10.0 ­8.0 5.0 0.6 11.1 2.7 8.6 0.3 10.9 Kenya 2.2 4.4 1.9 3.2 1.2 4.7 1.3 4.2 3.2 4.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 5.8 4.7 1.6 0.3 6.0 6.3 7.3 7.4 5.6 3.7 Kuwaita 4.9 9.2 1.0 .. 0.3 .. ­0.1 .. 3.5 .. Kyrgyz Republic ­4.1 4.1 1.5 2.0 ­10.3 ­0.4 ­7.5 ­2.4 ­4.9 8.3 Lao PDR 6.5 6.7 4.8 3.1 11.1 12.8 11.7 10.1 6.6 7.1 Latvia ­1.5 9.0 ­5.2 2.9 ­8.3 8.4 ­7.3 6.3 2.7 9.5 Lebanon 6.1 3.3 1.9 0.5 ­1.8 4.0 ­0.8 2.8 3.7 2.6 Lesotho 5.1 3.8 2.4 ­5.5 5.3 5.8 6.6 6.7 7.1 3.7 Liberia 4.1 ­2.7 .. .. .. .. .. .. .. .. Libya .. 3.7 .. .. .. .. .. .. .. .. Lithuania ­2.7 8.0 ­0.8 1.9 3.2 10.3 5.5 9.9 5.5 7.4 Macedonia, FYR ­0.8 2.7 0.2 1.3 ­2.3 2.0 ­5.3 1.1 0.5 3.2 Madagascar 2.0 3.2 1.9 1.9 2.4 2.7 2.0 3.8 2.3 3.6 Malawi 3.7 3.3 8.6 0.2 2.0 4.6 0.5 2.8 1.6 4.0 Malaysiaa 7.0 5.4 0.3 3.8 8.6 4.6 9.5 5.8 7.3 6.6 Mali 4.1 5.4 2.6 4.8 6.4 4.5 ­1.4 5.1 3.0 6.5 Mauritania 2.9 5.1 ­0.2 0.6 3.4 4.2 5.8 ­1.4 4.9 6.9 Mauritius 5.2 4.0 ­0.5 0.6 5.5 1.4 5.3 0.2 6.4 6.0 Mexico 3.1 2.6 1.5 2.0 3.8 1.7 4.3 1.7 2.9 3.0 Moldova ­9.6 6.5 ­11.2 ­1.8 ­13.6 0.8 ­7.1 4.8 0.7 11.5 Mongolia 1.0 7.5 2.5 4.6 ­2.5 7.9 ­9.7 8.1 0.7 8.4 Morocco 2.4 5.0 ­0.4 5.4 3.2 4.4 2.6 3.3 3.1 5.1 Mozambique 6.1 8.1 5.2 7.5 12.3 11.2 10.2 11.0 5.0 6.7 Myanmar a 6.9 9.2 5.7 .. 10.5 .. 7.9 .. 7.2 .. Namibia 4.0 4.8 3.8 1.4 2.4 5.9 2.6 3.5 4.5 5.4 Nepal 4.9 3.4 2.5 3.3 7.1 2.7 8.9 0.9 6.2 3.4 Netherlands 3.2 1.6 1.8 1.1 1.7 0.6 2.6 0.7 3.6 2.1 New Zealand 3.2 3.4 2.9 2.5 2.4 2.8 2.2 2.6 3.6 3.9 Nicaragua 3.7 3.4 4.7 3.0 5.5 4.4 5.3 5.5 5.0 3.5 Niger a 2.4 3.9 3.0 .. 2.0 .. 2.6 .. 1.9 .. Nigeria 2.5 6.6 .. 7.0 .. 3.8 .. .. .. 14.3 Norway 3.9 2.4 2.6 3.7 3.8 0.6 1.5 3.7 3.9 3.1 Omana 4.5 4.7 5.0 2.2 3.9 ­0.5 6.0 9.3 5.0 7.5 Pakistan 3.8 5.6 4.4 3.4 4.1 7.9 3.8 9.9 4.4 6.0 Panama 4.7 6.0 3.1 4.1 6.0 4.0 2.7 0.6 4.5 6.5 Papua New Guinea 3.8 2.3 4.5 1.4 5.4 3.2 4.6 3.2 ­0.6 2.7 Paraguaya 2.2 3.3 3.3 5.4 0.6 1.7 1.4 1.2 2.5 3.0 Peru 4.7 5.4 5.5 3.6 5.4 6.3 3.8 6.2 4.0 5.3 Philippinesa 3.3 5.1 1.7 3.8 3.5 4.0 3.0 4.5 4.0 6.5 Poland 4.7 4.1 0.5 3.9 7.1 4.7 9.9 6.9 5.1 3.5 Portugal 2.8 0.9 ­0.4 0.5 3.2 ­0.4 2.6 ­0.1 2.4 1.6 Puerto Ricoa 4.2 .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 205 4.1 Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 Romania ­0.6 6.1 ­1.9 7.7 ­1.2 6.5 .. .. 0.9 4.8 Russian Federation ­4.7 6.6 ­4.9 3.9 ­7.1 5.8 .. .. ­1.7 7.2 Rwandaa ­0.2 5.8 2.5 4.0 ­3.8 8.2 ­5.8 6.7 ­0.9 6.7 Saudi Arabiaa 2.1 4.1 1.6 1.4 2.2 4.5 5.6 6.1 2.2 4.1 Senegal 3.0 4.5 2.4 0.7 3.8 4.1 3.1 1.9 3.0 6.1 Serbia ­4.7 5.6 .. .. .. .. .. .. .. .. Sierra Leone ­5.0 11.2 ­13.0 .. ­4.5 .. 6.1 .. ­2.9 .. Singapore 7.6 5.8 ­2.4 2.9 7.8 5.5 7.0 7.0 7.8 6.0 Slovak Republic 2.2 6.0 0.4 1.4 3.8 7.0 9.3 10.7 5.3 5.4 Slovenia 2.7 4.3 0.4 ­1.8 1.6 5.3 1.8 5.3 3.3 4.2 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2.1 4.3 1.0 0.4 1.1 3.4 1.6 3.2 2.7 5.0 Spain 2.7 3.4 3.1 ­1.6 2.3 2.8 .. 1.1 2.7 3.7 Sri Lankaa 5.3 5.3 1.8 1.8 6.9 5.0 8.1 4.0 5.7 6.3 Sudan 5.5 7.1 7.4 1.8 8.5 10.2 7.5 3.5 1.9 10.3 Swaziland 3.4 2.6 0.9 1.2 3.2 1.7 2.8 1.6 3.9 4.1 Sweden 2.2 3.0 ­0.8 2.6 4.3 4.3 .. .. 1.8 2.5 Switzerland 1.0 1.8 ­0.9 ­1.1 0.3 1.7 .. .. 1.2 1.4 Syrian Arab Republic 5.1 4.5 6.0 3.6 9.2 2.8 .. 16.2 1.5 7.9 Tajikistan ­10.4 8.8 ­6.8 8.8 ­11.4 9.8 ­12.6 9.5 ­10.8 8.3 Tanzaniab 2.9 6.7 3.2 4.9 3.1 9.6 2.7 8.0 2.7 6.2 Thailanda 4.2 5.3 1.0 2.6 5.7 6.6 6.9 6.8 3.7 4.7 Timor-Lestea .. 0.9 .. .. .. .. .. .. .. .. Togoa 3.5 2.6 4.0 2.8 1.8 8.1 1.8 7.5 3.9 ­0.7 Trinidad and Tobago 3.2 8.8 2.7 ­10.8 3.2 12.3 4.9 8.7 3.2 5.2 Tunisiaa 4.7 4.8 2.3 2.9 4.6 3.3 5.5 3.2 5.3 6.0 Turkey 3.9 5.9 1.3 1.5 4.7 6.9 4.7 6.5 4.0 6.1 Turkmenistan ­4.8 .. ­5.7 .. ­3.4 .. .. .. ­5.4 .. Uganda 7.1 7.1 3.7 1.3 12.1 10.3 14.1 6.6 8.2 9.8 Ukraine ­9.3 7.6 ­5.6 2.6 ­12.6 6.8 ­11.2 11.1 ­8.1 7.2 United Arab Emirates 4.8 7.7 13.2 3.6 3.0 6.0 11.9 8.1 7.2 9.6 United Kingdom 2.7 2.6 ­0.3 1.4 1.5 0.1 1.3 ­0.4 3.3 3.4 United States 3.5 2.6 3.7 3.2 3.7 1.3 .. 2.1 3.4 2.9 Uruguay 3.4 3.3 2.8 6.1 1.1 3.7 ­0.1 5.3 3.7 2.3 Uzbekistan ­0.2 6.2 0.5 6.8 ­3.4 4.2 0.7 1.9 0.4 7.0 Venezuela, RB 1.6 4.6 1.2 3.8 1.2 2.4 4.5 2.4 ­0.1 6.3 Vietnama 7.9 7.8 4.3 3.8 11.9 10.3 11.2 11.9 7.5 7.4 West Bank and Gaza 7.3 ­0.9 .. .. .. .. .. .. .. .. Yemen, Rep.a 6.0 4.0 5.6 .. 8.2 .. 5.7 .. 5.0 .. Zambia 0.5 5.1 4.2 2.0 ­4.2 9.4 0.8 5.3 2.5 6.5 Zimbabwe 2.1 ­5.7 4.3 ­8.5 0.4 ­10.0 0.4 ­12.0 2.9 ­10.0 World 2.9 w 3.2 w 2.0 w 2.5 w 2.4 w 3.0 w .. w 3.3 w 3.1 w 3.0 w Low income 3.4 5.6 3.3 3.5 4.5 7.3 4.7 7.8 3.6 5.8 Middle income 3.9 6.2 2.3 3.6 4.7 7.1 6.3 7.1 4.3 6.2 Lower middle income 6.2 8.0 2.9 3.8 7.9 9.1 8.4 9.1 6.3 8.4 Upper middle income 2.2 4.3 0.8 3.2 1.6 4.3 3.7 4.2 3.0 4.4 Low & middle income 3.9 6.2 2.5 3.6 4.7 7.2 6.2 7.2 4.2 6.2 East Asia & Pacific 8.5 9.0 3.5 4.0 11.0 10.1 10.9 9.7 8.0 9.3 Europe & Central Asia ­0.8 6.1 ­1.8 3.3 ­2.6 6.5 .. .. 0.9 6.1 Latin America & Carib. 3.2 3.6 2.1 3.5 3.1 3.3 2.9 3.3 3.5 3.6 Middle East & N. Africa 3.8 4.5 2.9 4.4 4.1 3.6 4.2 6.0 3.4 5.1 South Asia 5.5 7.3 3.3 3.1 6.0 8.3 6.4 8.1 6.9 8.6 Sub-Saharan Africa 2.5 5.1 3.2 3.0 2.0 5.2 2.1 3.2 2.5 5.1 High income 2.7 2.4 1.3 0.7 1.9 1.7 .. 2.1 2.9 2.5 Euro area 2.1 1.7 1.6 ­0.6 1.1 1.6 1.2 1.1 2.5 1.9 a. Components are at producer prices. b. Covers mainland Tanzania only. 206 2008 World Development Indicators ECONOMY Growth of output 4.1 About the data Definitions An economy's growth is measured by the change in Rebasing national accounts · Gross domestic product (GDP) at purchaser prices the volume of its output or in the real incomes of When countries rebase their national accounts, they is the sum of gross value added by all resident pro- its residents. The 1993 United Nations System of update the weights assigned to various components ducers in the economy plus any product taxes (less National Accounts (1993 SNA) offers three plausible to better reflect current patterns of production or subsidies) not included in the valuation of output. It indicators for calculating growth: the volume of gross uses of output. The new base year should represent is calculated without deducting for depreciation of domestic product (GDP), real gross domestic income, normal operation of the economy--it should be a year fabricated capital assets or for depletion and degra- and real gross national income. The volume of GDP without major shocks or distortions. Some developing dation of natural resources. Value added is the net is the sum of value added, measured at constant countries have not rebased their national accounts output of an industry after adding up all outputs and prices, by households, government, and industries for many years. Using an old base year can be mis- subtracting intermediate inputs. The industrial origin operating in the economy. leading because implicit price and volume weights of value added is determined by the International Each industry's contribution to growth in the econ- become progressively less relevant and useful. Standard Industrial Classifi cation (ISIC) revision omy's output is measured by growth in the industry's To obtain comparable series of constant price data, 3. · Agriculture is the sum of gross output less value added. In principle, value added in constant the World Bank rescales GDP and value added by the value of intermediate input used in production prices can be estimated by measuring the quantity industrial origin to a common reference year. This for industries classified in ISIC divisions 1­5 and of goods and services produced in a period, valu- year's World Development Indicators continues to includes forestry and fishing. · Industry is the sum ing them at an agreed set of base year prices, and use 2000 as the reference year. Because rescaling of gross output less the value of intermediate input subtracting the cost of intermediate inputs, also in changes the implicit weights used in forming regional used in production for industries classified in ISIC constant prices. This double-deflation method, rec- and income group aggregates, aggregate growth rates divisions 10­45, which cover mining, manufactur- ommended by the 1993 SNA and its predecessors, in this year's edition are not comparable with those ing (also reported separately), construction, electric- requires detailed information on the structure of from earlier editions with different base years. ity, water, and gas. · Manufacturing is the sum of prices of inputs and outputs. Rescaling may result in a discrepancy between gross output less the value of intermediate input In many industries, however, value added is the rescaled GDP and the sum of the rescaled com- used in production for industries classified in ISIC extrapolated from the base year using single volume ponents. Because allocating the discrepancy would divisions 15­37. · Services correspond to ISIC divi- indexes of outputs or, less commonly, inputs. Par- cause distortions in the growth rates, the discrep- sions 50­99. This sector is derived as a residual ticularly in the services industries, including most of ancy is left unallocated. As a result, the weighted (from GDP less agriculture and industry) and may not government, value added in constant prices is often average of the growth rates of the components gen- properly reflect the sum of services output, including imputed from labor inputs, such as real wages or erally will not equal the GDP growth rate. banking and financial services. For some countries number of employees. In the absence of well defined it includes product taxes (minus subsidies) and may measures of output, measuring the growth of ser- Computing growth rates also include statistical discrepancies. vices remains difficult. Growth rates of GDP and its components are calcu- Moreover, technical progress can lead to improve- lated using the least squares method and constant ments in production processes and in the quality of price data in the local currency. Constant price U.S. goods and services that, if not properly accounted dollar series are used to calculate regional and for, can distort measures of value added and thus income group growth rates. Local currency series are of growth. When inputs are used to estimate output, converted to constant U.S. dollars using an exchange Data sources as for nonmarket services, unmeasured technical rate in the common reference year. The growth rates progress leads to underestimates of the volume of in the table are average annual compound growth Data on national accounts for most developing output. Similarly, unmeasured improvements in qual- rates. Methods of computing growth rates and the countries are collected from national statistical ity lead to underestimates of the value of output and alternative conversion factor are described in Sta- organizations and central banks by visiting and value added. The result can be underestimates of tistical methods. resident World Bank missions. Data for high- growth and productivity improvement and overesti- income economies are from Organisation for mates of inflation. Changes in the System of National Accounts Economic Co-operation and Development (OECD) Informal economic activities pose a particular World Development Indicators adopted the termi- data files. The World Bank rescales constant price measurement problem, especially in developing nology of the 1993 SNA in 2001. Although many data to a common reference year. The complete countries, where much economic activity is unre- countries continue to compile their national accounts national accounts time series is available on the corded. A complete picture of the economy requires according to the SNA version 3 (referred to as the World Development Indicators 2009 CD-ROM. estimating household outputs produced for home 1968 SNA), more and more are adopting the 1993 The United Nations Statistics Division publishes use, sales in informal markets, barter exchanges, SNA. Some low-income countries still use concepts detailed national accounts for UN member coun- and illicit or deliberately unreported activities. The from the even older 1953 SNA guidelines, including tries in National Accounts Statistics: Main Aggre- consistency and completeness of such estimates valuations such as factor cost, in describing major gates and Detailed Tables and publishes updates depend on the skill and methods of the compiling economic aggregates. Countries that use the 1993 in the Monthly Bulletin of Statistics. statisticians. SNA are identified in Primary data documentation. 2008 World Development Indicators 207 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan .. 8,399 .. 36 .. 24 .. 15 .. 39 Albania 2,424 10,831 56 21 22 20 14 .. 22 59 Algeria 41,764 135,285 10 8 50 61 11 5 39 31 Angolaa 5,040 61,403 7 9 66 70 4 5 26 21 Argentina 258,032 262,451 6 9 28 34 18 21 66 57 Armenia 1,468 9,204 42 20 32 44 25 17 26 36 Australia 361,306 820,974 3 2 29 29 15 11 68 69 Austria 239,561 373,192 3 2 30 31 19 20 67 67 Azerbaijan 3,052 31,248 27 6 34 73 13 6 39 21 Bangladesh 37,940 68,415 26 19 25 28 15 18 49 52 Belarus 13,973 44,773 17 9 37 42 31 32 46 48 Belgium 284,321 452,754 2 1 28 24 20 17 70 75 Benina 2,009 5,428 34 32 15 13 9 8 51 54 Bolivia 6,715 13,120 17 13 33 36 19 15 50 51 Bosnia and Herzegovina 1,867 15,144 21 10 26 22 11 13 54 69 Botswana 4,774 12,311 4 2 51 49 5 3 45 49 Brazil 768,951 1,313,361 6 6 28 29 19 18 67 66 Bulgaria 13,107 39,549 14 6 35 32 24 17 50 61 Burkina Faso 2,380 6,767 35 33 21 22 15 14 43 44 Burundi 1,000 974 48 35 19 20 9 9 33 45 Cambodia 3,239 8,350 50 32 15 27 10 19 36 41 Cameroon 8,733 20,686 24 19 31 31 22 17 45 50 Canada 590,517 1,329,885 3 .. 31 .. 18 .. 66 .. Central African Republic 1,122 1,712 46 54 21 14 10 8 33 32 Chad 1,446 7,085 36 23 14 44 11 6 51 32 Chile 71,349 163,913 9 4 35 47 18 14 55 49 Chinaa 728,007 3,205,507 20 11 47 49 34 32 33 40 Hong Kong, China 144,230 207,169 0 0 15 8 8 3 85 92 Colombia 92,503 207,786 15 9 32 35 16 18 53 56 Congo, Dem. Rep. 5,643 8,953 57 42 17 28 9 6 26 29 Congo, Rep.a 2,116 7,646 10 5 45 60 8 6 45 35 Costa Rica 11,722 26,267 14 9 30 29 22 21 57 63 Côte d'Ivoirea 11,000 19,796 25 24 21 25 15 18 55 51 Croatia 18,808 51,278 11 7 34 32 24 21 55 61 Cubaa .. .. 6 .. 45 .. 38 .. 49 .. Czech Republic 55,257 174,998 5 3 38 39 24 27 57 59 Denmark 181,985 311,580 3 1 25 26 17 14 71 73 Dominican Republica 12,585 36,686 13 12 33 28 18 13 55 60 Ecuador 20,206 44,490 .. 7 .. 37 .. 10 .. 56 Egypt, Arab Rep. 60,159 130,476 17 14 32 36 17 16 51 50 El Salvador 9,500 20,373 14 12 30 29 23 22 56 59 Eritrea 578 1,375 21 24 17 19 9 5 62 56 Estonia 4,343 20,901 6 3 33 30 21 18 61 67 Ethiopia 7,606 19,395 57 46 10 13 5 5 33 40 Finland 130,605 244,661 4 3 33 32 25 24 63 65 France 1,569,983 2,589,839 3 2 25 21 .. 12 72 77 Gabona 4,959 11,568 8 5 52 60 5 4 40 35 Gambia, The 382 644 30 29 13 15 6 5 57 56 Georgia 2,694 10,175 52 11 16 24 17 12 32 65 Germany 2,522,792 3,317,365 1 1 32 30 23 23 67 69 Ghanaa 6,457 15,147 39 34 24 26 9 8 37 41 Greece 131,718 313,354 9 4 21 23 .. 13 70 73 Guatemalaa 14,657 33,855 24 11 20 28 14 18 56 61 Guinea 3,694 4,564 19 17 29 45 4 4 52 38 Guinea-Bissau 254 357 55 64 12 12 8 8 33 24 Haiti 2,908 6,715 25 .. 32 .. 20 .. 44 .. 208 2009 World Development Indicators ECONOMY Gross domestic product Agriculture Structure of output Industry Manufacturing 4.2 Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 3,911 12,234 22 13 31 28 18 20 48 59 Hungary 44,656 138,429 7 4 32 30 24 22 61 66 India 356,299 1,176,890 26 18 28 30 18 16 46 52 Indonesiaa 202,132 432,817 17 14 42 47 24 27 41 39 Iran, Islamic Rep. 90,829 286,058 18 10 34 44 12 11 47 45 Iraq 10,114 .. 9 .. 75 .. 1 .. 16 .. Ireland 67,090 259,018 7 2 38 35 30 23 55 63 Israela 95,907 163,957 .. .. .. .. .. .. .. .. Italy 1,126,042 2,101,637 3 2 30 27 22 18 66 71 Jamaica 5,813 11,430 9 6 37 35 16 15 54 59 Japan 5,247,609 4,384,255 2 1 34 30 23 21 64 68 Jordan 6,727 15,833 4 3 29 29 15 19 67 67 Kazakhstan 20,374 104,853 13 6 32 41 15 12 55 53 Kenya 9,046 24,190 31 26 16 18 10 11 53 56 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 517,118 969,795 6 3 42 39 28 28 52 58 Kuwaita 27,192 112,116 0 .. 55 .. 4 .. 45 .. Kyrgyz Republic 1,661 3,745 44 34 20 19 9 11 37 47 Lao PDR 1,764 4,108 56 42 19 32 14 21 25 26 Latvia 5,236 27,155 9 3 30 22 21 11 61 75 Lebanon 11,719 24,352 7 6 27 24 15 11 66 70 Lesotho 1,009 1,600 16 12 36 47 15 19 48 41 Liberia 135 735 82 54 5 19 3 13 13 27 Libya 25,541 58,333 .. .. .. .. .. .. .. .. Lithuania 7,507 38,332 12 5 34 33 21 19 55 61 Macedonia, FYR 4,449 7,674 13 12 30 30 23 19 57 59 Madagascar 3,160 7,382 27 26 9 17 8 16 64 56 Malawi 1,397 3,563 30 34 20 20 16 14 50 45 Malaysiaa 88,832 186,719 13 10 41 48 26 28 46 42 Mali 2,466 6,863 50 37 19 24 8 3 32 39 Mauritania 1,415 2,644 37 13 25 47 8 5 37 41 Mauritius 3,820 6,786 10 5 32 28 23 20 58 67 Mexico 286,698 1,022,815 6 4 28 36 21 19 66 60 Moldova 1,753 4,396 33 12 32 15 26 14 35 73 Mongolia 1,227 3,930 41 23 29 41 12 4 30 36 Morocco 32,986 75,119 15 14 34 27 19 15 51 59 Mozambique 2,247 7,790 35 28 15 26 8 15 51 47 Myanmar a .. .. 60 .. 10 .. 7 .. 30 .. Namibia 3,503 7,015 12 11 28 30 13 11 60 59 Nepal 4,401 10,315 42 34 23 17 10 8 35 49 Netherlands 418,969 765,818 3 2 27 24 17 13 69 74 New Zealand 62,049 135,667 7 .. 27 .. 19 .. 66 .. Nicaragua 3,191 5,726 23 19 27 30 19 19 49 51 Niger a 1,881 4,170 40 .. 17 .. 6 .. 43 .. Nigeria 28,109 165,469 .. 33 .. 39 .. 3 .. 28 Norway 148,920 388,413 3 1 34 43 13 10 63 56 Omana 13,803 35,729 3 .. 46 .. 5 .. 51 .. Pakistan 60,636 142,893 26 21 24 27 16 19 50 53 Panama 7,906 19,485 8 7 18 17 9 7 74 77 Papua New Guinea 4,636 6,259 35 35 34 45 8 6 31 20 Paraguaya 8,066 12,222 21 22 23 20 16 13 56 58 Peru 53,674 107,297 9 7 31 37 17 16 60 56 Philippinesa 74,120 144,062 22 14 32 32 23 22 46 54 Poland 139,062 422,090 8 4 35 31 21 18 57 65 Portugal 112,960 222,758 6 3 28 24 18 15 66 73 Puerto Ricoa 42,647 .. 1 .. 44 .. 42 .. 55 .. 2009 World Development Indicators 209 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 35,477 165,976 21 9 43 36 29 22 36 55 Russian Federation 395,529 1,290,082 7 5 37 38 .. 19 56 57 Rwandaa 1,293 3,339 44 40 16 14 10 6 40 46 Saudi Arabiaa 142,458 381,683 6 3 49 65 10 10 45 32 Senegal 4,879 11,165 21 14 24 23 17 14 55 62 Serbia 19,681 40,122 .. 13 .. 28 .. .. .. 59 Sierra Leone 871 1,664 43 45 39 24 9 .. 18 31 Singapore 84,291 161,347 0 0 35 31 27 25 65 69 Slovak Republic 19,579 74,972 6 3 38 36 27 22 56 61 Slovenia 20,814 47,182 4 2 35 34 26 23 60 63 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 151,113 283,007 4 3 35 31 21 18 61 66 Spain 596,751 1,436,891 5 3 29 30 .. 16 66 67 Sri Lankaa 13,030 32,346 23 12 27 30 16 19 50 58 Sudan 13,830 46,228 39 28 11 31 5 6 51 41 Swaziland 1,699 2,894 12 7 45 49 39 44 43 43 Sweden 253,706 454,310 3 2 31 29 .. .. 67 70 Switzerland 315,940 424,367 2 1 30 28 .. .. 68 71 Syrian Arab Republic 11,397 37,745 32 18 20 35 15 12 48 47 Tajikistan 1,232 3,712 38 21 39 28 28 20 22 51 Tanzaniab 5,255 16,181 47 45 14 17 7 7 38 37 Thailanda 167,896 245,351 10 11 41 44 30 35 50 45 Timor-Lestea .. 395 .. .. .. .. .. .. .. .. Togoa 1,309 2,499 38 44 22 24 10 10 40 32 Trinidad and Tobago 5,329 20,886 2 0 47 59 9 6 51 41 Tunisiaa 18,031 35,020 11 10 29 30 19 17 59 60 Turkey 244,946 655,881 11 9 23 28 .. 19 66 63 Turkmenistan 2,482 12,933 17 .. 63 .. 40 .. 20 .. Uganda 5,756 11,771 49 24 14 26 7 8 36 50 Ukraine 48,214 141,177 15 8 43 37 35 23 42 55 United Arab Emirates 42,807 163,296 3 2 52 59 10 12 45 39 United Kingdom 1,141,045 2,772,024 2 1 32 23 22 14 66 76 United States 7,342,300 13,751,400 2 1 26 22 19 14 72 77 Uruguay 18,348 23,136 9 10 29 32 20 23 62 58 Uzbekistan 13,350 22,308 32 23 28 31 12 12 40 46 Venezuela, RB 74,889 228,071 6 4 41 58 15 16 53 38 Vietnama 20,736 68,643 27 20 29 42 15 21 44 38 West Bank and Gaza 3,220 4,016 .. .. .. .. .. .. .. .. Yemen, Rep.a 4,236 22,523 20 .. 32 .. 14 .. 48 .. Zambia 3,478 11,363 18 22 36 38 11 11 46 40 Zimbabwe 7,111 3,418 15 19 29 24 22 14 56 57 World 29,669,867 t 54,583,788 t 4w 3w 30 w 28 w 20 w 18 w 65 w 69 w Low income 301,247 801,382 32 25 23 30 13 16 45 46 Middle income 4,878,804 13,490,034 13 9 35 37 23 19 52 53 Lower middle income 2,149,301 6,896,111 20 13 39 41 25 24 41 46 Upper middle income 2,731,355 6,594,607 7 6 31 33 20 19 62 61 Low & middle income 5,181,211 14,296,294 14 10 34 37 22 18 52 53 East Asia & Pacific 1,312,340 4,365,487 19 12 44 47 31 30 36 41 Europe & Central Asia 998,317 3,156,118 11 7 33 34 .. 19 56 60 Latin America & Carib. 1,751,109 3,615,910 7 6 29 33 19 18 64 61 Middle East & N. Africa 315,655 850,182 16 11 34 40 15 12 50 49 South Asia 476,196 1,443,539 26 18 27 29 17 17 46 53 Sub-Saharan Africa 327,582 847,438 18 15 29 32 16 14 53 53 High income 24,484,804 40,309,714 2 1 30 26 20 17 68 72 Euro area 7,274,362 12,277,625 3 2 29 27 22 18 68 71 a. Components are at producer prices. b. Covers mainland Tanzania only. 210 2008 World Development Indicators ECONOMY Structure of output 4.2 About the data Definitions An economy's gross domestic product (GDP) repre- Ideally, industrial output should be measured · Gross domestic product (GDP) at purchaser prices sents the sum of value added by all producers in through regular censuses and surveys of fi rms. is the sum of gross value added by all resident pro- the economy. Value added is the value of the gross But in most developing countries such surveys are ducers in the economy plus any product taxes (less output of producers less the value of intermediate infrequent, so earlier survey results must be extrapo- subsidies) not included in the valuation of output. goods and services consumed in production, before lated using an appropriate indicator. The choice of It is calculated without deducting for depreciation taking account of the consumption of fixed capital sampling unit, which may be the enterprise (where of fabricated assets or for depletion and degrada- in the production process. The United Nations Sys- responses may be based on financial records) or tion of natural resources. Value added is the net tem of National Accounts calls for estimates of value the establishment (where production units may be output of an industry after adding up all outputs and added to be valued at either basic prices (excluding recorded separately), also affects the quality of subtracting intermediate inputs. The industrial origin net taxes on products) or producer prices (including the data. Moreover, much industrial production is of value added is determined by the International net taxes on products paid by producers but excluding organized in unincorporated or owner-operated ven- Standard Industrial Classifi cation (ISIC) revision sales or value added taxes). Both valuations exclude tures that are not captured by surveys aimed at the 3. · Agriculture is the sum of gross output less transport charges that are invoiced separately by pro- formal sector. Even in large industries, where regu- the value of intermediate input used in production ducers. Total GDP shown in the table and elsewhere lar surveys are more likely, evasion of excise and for industries classified in ISIC divisions 1­5 and in this volume is measured at purchaser prices. Value other taxes and nondisclosure of income lower the includes forestry and fishing. · Industry is the sum added by industry is normally measured at basic estimates of value added. Such problems become of gross output less the value of intermediate input prices. When value added is measured at producer more acute as countries move from state control used in production for industries classified in ISIC prices, this is noted in Primary data documentation. of industry to private enterprise, because new firms divisions 10­45, which cover mining, manufactur- While GDP estimates based on the production enter business and growing numbers of established ing (also reported separately), construction, electric- approach are generally more reliable than estimates firms fail to report. In accordance with the System ity, water, and gas. · Manufacturing is the sum of compiled from the income or expenditure side, dif- of National Accounts, output should include all such gross output less the value of intermediate input ferent countries use different definitions, methods, unreported activity as well as the value of illegal used in production for industries classified in ISIC and reporting standards. World Bank staff review the activities and other unrecorded, informal, or small- divisions 15­37. · Services correspond to ISIC divi- quality of national accounts data and sometimes scale operations. Data on these activities need to be sions 50­99. This sector is derived as a residual make adjustments to improve consistency with collected using techniques other than conventional (from GDP less agriculture and industry) and may not international guidelines. Nevertheless, significant surveys of firms. properly reflect the sum of services output, including discrepancies remain between international stan- In industries dominated by large organizations and banking and financial services. For some countries dards and actual practice. Many statistical offices, enterprises, such as public utilities, data on output, it includes product taxes (minus subsidies) and may especially those in developing countries, face severe employment, and wages are usually readily available also include statistical discrepancies. limitations in the resources, time, training, and bud- and reasonably reliable. But in the services industry gets required to produce reliable and comprehensive the many self-employed workers and one-person busi- series of national accounts statistics. nesses are sometimes difficult to locate, and they have little incentive to respond to surveys, let alone Data problems in measuring output to report their full earnings. Compounding these prob- Among the difficulties faced by compilers of national lems are the many forms of economic activity that accounts is the extent of unreported economic activ- go unrecorded, including the work that women and ity in the informal or secondary economy. In develop- children do for little or no pay. For further discussion Data sources ing countries a large share of agricultural output is of the problems of using national accounts data, see either not exchanged (because it is consumed within Srinivasan (1994) and Heston (1994). Data on national accounts for most developing the household) or not exchanged for money. countries are collected from national statistical Agricultural production often must be estimated Dollar conversion organizations and central banks by visiting and indirectly, using a combination of methods involv- To produce national accounts aggregates that are resident World Bank missions. Data for high- ing estimates of inputs, yields, and area under cul- measured in the same standard monetary units, income economies are from Organisation for Eco- tivation. This approach sometimes leads to crude the value of output must be converted to a single nomic Co-operation and Development (OECD) data approximations that can differ from the true values common currency. The World Bank conventionally files. The complete national accounts time series over time and across crops for reasons other than uses the U.S. dollar and applies the average official is available on the World Development Indicators climate conditions or farming techniques. Similarly, exchange rate reported by the International Monetary 2009 CD-ROM. The United Nations Statistics Divi- agricultural inputs that cannot easily be allocated to Fund for the year shown. An alternative conversion sion publishes detailed national accounts for UN specific outputs are frequently "netted out" using factor is applied if the official exchange rate is judged member countries in National Accounts Statistics: equally crude and ad hoc approximations. For further to diverge by an exceptionally large margin from the Main Aggregates and Detailed Tables and publishes discussion of the measurement of agricultural pro- rate effectively applied to transactions in foreign cur- updates in the Monthly Bulletin of Statistics. duction, see About the data for table 3.3. rencies and traded products. 2008 World Development Indicators 211 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1995 2007 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Afghanistan .. 1,053 .. .. .. .. .. .. .. .. .. .. Albania 405 1,830 .. 20 .. .. .. .. .. .. .. 80 Algeria 4,366 6,393 .. .. .. .. .. .. .. .. .. .. Angola 202 3,074 .. .. .. .. .. .. .. .. .. .. Argentina 44,502 51,305 30 .. 6 .. 10 .. 9 .. 54 .. Armenia 356 1,380 .. .. .. .. .. .. .. .. .. .. Australia 50,044 81,096 20 17 6 1 11 5 .. 7 63 69 Austria 41,681 58,005 11 10 5 2 27 31 2 6 56 52 Azerbaijan 352 1,604 .. 28 .. 1 .. 11 .. 7 .. 53 Bangladesh 5,586 11,755 28 .. 44 .. 4 .. 11 .. 13 .. Belarus 3,909 12,194 .. .. .. .. .. .. .. .. .. .. Belgium 51,721 59,893 13 13 6 4 22 21 8 20 51 42 Benin 174 322 .. .. .. .. .. .. .. .. .. .. Bolivia 1,123 1,498 36 .. 5 .. 1 .. 3 .. 55 .. Bosnia and Herzegovina 213 1,614 .. .. .. .. .. .. .. .. .. .. Botswana 242 381 44 23 1 0 15 .. 5 .. 55 77 Brazil 124,976 199,714 21 18 6 4 23 22 13 12 38 44 Bulgaria 2,015 5,395 23 17 12 15 20 18 15 7 30 44 Burkina Faso 336 775 .. .. .. .. .. .. .. .. .. .. Burundi 83 64 .. .. .. .. .. .. .. .. .. .. Cambodia 296 1,447 20 .. 3 .. .. .. .. .. 80 .. Cameroon 1,758 3,328 .. .. .. .. .. .. .. .. .. .. Canada 100,393 .. 13 .. 4 .. 23 .. 10 .. 50 .. Central African Republic 108 129 .. .. .. .. .. .. .. .. .. .. Chad 159 398 .. .. .. .. .. .. .. .. .. .. Chile 10,594 21,488 .. 16 .. 2 .. 3 .. 8 .. 72 China 245,002 1,341,337 4 4 2 2 2 3 .. .. 93 93 Hong Kong, China 10,524 5,034 .. .. .. .. .. .. .. .. .. .. Colombia 13,506 33,565 .. 27 .. 9 .. 7 .. 13 .. 44 Congo, Dem. Rep. 510 543 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 172 456 .. .. .. .. .. .. .. .. .. .. Costa Rica 2,339 4,900 .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 1,655 3,471 .. .. .. .. .. .. .. .. .. .. Croatia 3,666 8,832 .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 12,124 42,681 12 10 6 4 24 29 4 3 55 54 Denmark 26,924 31,100 20 14 2 2 25 18 1 2 52 64 Dominican Republic 2,286 4,852 .. .. .. .. .. .. .. .. .. .. Ecuador 2,830 4,063 26 30 7 4 4 3 4 5 59 58 Egypt, Arab Rep. 9,829 19,520 19 .. 13 .. 12 .. 18 .. 38 .. El Salvador 2,026 4,209 .. .. .. .. .. .. .. .. .. .. Eritrea 47 72 55 43 12 12 1 2 19 8 13 35 Estonia 804 3,193 .. 13 .. 6 .. 14 .. 5 .. 63 Ethiopia 344 923 51 48 20 10 2 1 4 5 23 35 Finland 28,814 43,121 10 7 3 2 27 35 4 4 57 52 France .. 283,186 13 14 5 4 28 29 12 12 41 41 Gabon 224 470 .. .. .. .. .. .. .. .. .. .. Gambia, The 20 28 65 .. .. .. .. .. .. .. 35 .. Georgia 523 1,080 .. 41 .. 2 .. 5 .. 13 .. 39 Germany 516,542 595,045 .. 9 .. 2 .. 42 .. 10 .. 38 Ghana 602 1,199 .. 32 .. 6 .. 0 .. 12 .. 49 Greece .. 31,426 25 .. 15 .. 13 .. 10 .. 38 .. Guatemala 2,069 6,203 .. .. .. .. .. .. .. .. .. .. Guinea 142 178 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 19 26 .. .. .. .. .. .. .. .. .. .. Haiti 558 .. .. .. .. .. .. .. .. .. .. .. 212 2009 World Development Indicators ECONOMY Manufacturing Structure of manufacturing Food, Textiles and Machinery Chemicals 4.3 Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1995 2007 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Honduras 607 2,218 .. .. .. .. .. .. .. .. .. .. Hungary 8,839 25,977 19 13 3 4 10 41 13 10 55 33 India 57,917 175,691 .. 9 .. 9 .. 20 .. 16 .. 46 Indonesia 48,781 116,894 .. 23 .. 13 .. 18 .. 9 .. 36 Iran, Islamic Rep. 10,918 29,832 15 9 12 4 18 25 15 15 40 47 Iraq 67 .. 12 .. 7 .. 2 .. .. .. 80 .. Ireland 18,096 44,801 15 16 0 0 24 7 13 27 49 50 Israel .. .. 13 11 5 2 15 16 6 10 61 61 Italy 225,514 299,459 9 9 14 11 27 26 8 7 41 46 Jamaica 865 1,631 .. .. .. .. .. .. .. .. .. .. Japan 1,077,348 933,818 11 11 4 2 37 41 10 11 39 35 Jordan 866 2,665 30 23 7 11 5 6 15 16 44 43 Kazakhstan 2,976 12,049 .. .. .. .. .. .. .. .. .. .. Kenya 757 2,355 .. 29 .. 2 .. .. .. 5 .. 64 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 128,839 240,325 8 7 10 5 39 47 8 8 34 33 Kuwait 1,032 .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 142 367 .. 14 .. 5 .. 5 .. 1 .. 74 Lao PDR 245 867 .. .. .. .. .. .. .. .. .. .. Latvia 965 2,589 39 20 9 8 18 10 4 4 30 58 Lebanon 1,577 2,284 .. .. .. .. .. .. .. .. .. .. Lesotho 129 272 .. .. .. .. .. .. .. .. .. .. Liberia 4 95 .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 1,390 6,548 .. 20 .. 11 .. 11 .. 6 .. 52 Macedonia, FYR 873 1,244 35 .. .. .. .. .. .. .. 65 .. Madagascar 233 1,062 .. 40 .. 27 .. 1 .. 2 .. 30 Malawi 195 442 .. .. .. .. .. .. .. .. .. .. Malaysia 23,432 52,223 .. 8 .. 3 .. 37 .. 12 .. 40 Mali 174 195 .. .. .. .. .. .. .. .. .. .. Mauritania 107 84 .. .. .. .. .. .. .. .. .. .. Mauritius 765 1,198 25 30 .. .. .. .. .. .. 75 70 Mexico 54,546 182,916 26 .. 4 .. 22 .. 15 .. 33 .. Moldova 400 517 .. 50 .. .. .. 0 .. .. .. 50 Mongolia 143 158 23 .. 62 .. 1 .. 1 .. 12 .. Morocco 6,056 10,019 .. 36 .. 14 .. 8 .. 14 .. 27 Mozambique 166 1,080 .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 403 728 .. .. .. .. .. .. .. .. .. .. Nepal 393 740 35 .. 34 .. 2 .. .. .. 29 .. Netherlands 65,999 79,146 18 15 3 1 15 14 16 10 48 59 New Zealand 10,645 .. 29 25 .. .. .. .. .. .. 71 75 Nicaragua 533 946 .. .. .. .. .. .. .. .. .. .. Niger 120 .. .. .. .. .. .. .. .. .. .. .. Nigeria .. 3,760 .. .. .. .. .. .. .. .. .. .. Norway 17,018 34,306 17 19 2 1 24 23 9 9 48 47 Oman 643 .. 15 7 8 1 3 3 6 13 68 76 Pakistan 8,864 25,654 .. .. .. .. .. .. .. .. .. .. Panama 694 1,290 54 .. 7 .. .. .. 7 .. 32 .. Papua New Guinea 372 361 .. .. .. .. .. .. .. .. .. .. Paraguay 1,280 1,570 .. .. .. .. .. .. .. .. .. .. Peru 8,105 15,600 28 31 9 14 7 2 9 11 48 42 Philippines 17,043 31,718 29 23 1 1 3 6 2 2 66 68 Poland 25,885 64,821 18 22 8 4 0 19 3 7 81 47 Portugal 18,249 23,509 13 14 22 3 18 15 6 2 41 66 Puerto Rico 17,867 .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 213 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1995 2007 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Romania 9,387 32,925 28 15 7 16 10 19 7 6 48 44 Russian Federation .. 210,692 .. 15 .. 2 .. 8 .. 8 .. 67 Rwanda 132 203 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 13,714 36,349 .. .. .. .. .. .. .. .. .. .. Senegal 730 1,424 .. .. .. .. .. .. .. .. .. .. Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 75 .. .. .. .. .. .. .. .. .. .. .. Singapore 20,799 38,275 4 3 1 1 60 47 9 24 26 50 Slovak Republic 4,704 10,923 11 7 4 5 14 23 9 2 63 63 Slovenia 4,573 9,677 .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 29,274 45,674 15 17 5 1 19 .. 10 7 50 76 Spain .. 175,881 16 15 7 5 23 22 10 8 43 49 Sri Lanka 1,836 5,985 .. .. .. .. .. .. .. .. .. .. Sudan 640 2,679 .. .. .. .. .. .. .. .. .. .. Swaziland 557 1,095 .. .. .. .. .. .. .. .. .. .. Sweden .. .. 7 7 1 1 33 30 3 12 56 51 Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 1,574 5,145 .. .. .. .. .. .. .. .. .. .. Tajikistan 331 659 .. .. .. .. .. .. .. .. .. .. Tanzaniab 349 819 .. .. .. .. .. .. .. .. .. .. Thailand 50,194 85,451 21 .. 9 .. 29 .. 6 .. 35 .. Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 130 214 .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 439 1,157 .. 13 .. 1 .. 2 .. 35 .. 50 Tunisia 3,419 6,009 .. .. .. .. .. .. .. .. .. .. Turkey .. 109,200 15 .. 17 .. 16 .. 10 .. 42 .. Turkmenistan 948 .. .. .. .. .. .. .. .. .. .. .. Uganda 359 851 .. .. .. .. .. .. .. .. .. .. Ukraine 14,922 29,003 .. .. .. .. .. .. .. .. .. .. United Arab Emirates 4,452 19,995 .. .. .. .. .. .. .. .. .. .. United Kingdom 219,282 269,610 13 15 5 3 28 27 11 11 42 45 United States 1,289,100 1,700,000 12 14 4 2 34 29 12 14 38 40 Uruguay 3,614 5,269 .. 40 .. 10 .. 1 .. 9 .. 39 Uzbekistan 1,376 2,541 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 10,668 21,941 .. .. .. .. .. .. .. .. .. .. Vietnam 3,109 14,673 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 599 .. .. 55 .. .. .. .. .. .. .. 45 Zambia 344 1,187 .. .. .. .. .. .. .. .. .. .. Zimbabwe 1,370 324 30 .. 7 .. 29 .. 6 .. 29 .. World 5,486,528 t 7,998,553 t Low income 35,085 90,319 Middle income 1,002,230 2,355,279 Lower middle income 514,247 1,409,173 Upper middle income 499,190 1,100,621 Low & middle income 1,037,197 2,434,141 East Asia & Pacific 390,709 1,160,250 Europe & Central Asia .. .. Latin America & Carib. 290,974 580,099 Middle East & N. Africa 39,388 86,834 South Asia 75,044 221,220 Sub-Saharan Africa 45,959 82,642 High income 4,467,711 5,544,426 Euro Area 1,343,206 1,691,606 a. Includes unallocated data. b. Covers mainland Tanzania only. 214 2008 World Development Indicators ECONOMY Structure of manufacturing 4.3 About the data Definitions The data on the distribution of manufacturing value accord with revision 3. Concordances matching ISIC · Manufacturing value added is the sum of gross added by industry are provided by the United Nations categories to national classification systems and to output less the value of intermediate inputs used in Industrial Development Organization (UNIDO). UNIDO related systems such as the Standard International production for industries classified in ISIC major divi- obtains the data from a variety of national and inter- Trade Classification are available. sion 3. · Food, beverages, and tobacco correspond national sources, including the United Nations Sta- In establishing classifi cations systems compil- to ISIC divisions 15 and 16. · Textiles and clothing tistics Division, the World Bank, the Organisation for ers must define both the types of activities to be correspond to ISIC divisions 17­19. · Machinery and Economic Co-operation and Development, and the described and the units whose activities are to transport equipment correspond to ISIC divisions International Monetary Fund. To improve comparabil- be reported. There are many possibilities, and the 29, 30, 32, 34, and 35. · Chemicals correspond to ity over time and across countries, UNIDO supple- choices affect how the statistics can be interpreted ISIC division 24. · Other manufacturing, a residual, ments these data with information from industrial and how useful they are in analyzing economic covers wood and related products (ISIC division 20), censuses, statistics from national and international behavior. The ISIC emphasizes commonalities in the paper and related products (ISIC divisions 21 and organizations, unpublished data that it collects in the production process and is explicitly not intended to 22), petroleum and related products (ISIC division field, and estimates by the UNIDO Secretariat. Nev- measure outputs (for which there is a newly devel- 23), basic metals and mineral products (ISIC divi- ertheless, coverage may be incomplete, particularly oped Central Product Classification). Nevertheless, sion 27), fabricated metal products and professional for the informal sector. When direct information on the ISIC views an activity as defined by "a process goods (ISIC division 28), and other industries (ISIC inputs and outputs is not available, estimates may resulting in a homogeneous set of products" (UN divisions 25, 26, 31, 33, 36, and 37). be used, which may result in errors in industry totals. 1990 [ISIC, series M, no. 4, rev. 3], p. 9). Moreover, countries use different reference periods Firms typically use multiple processes to produce (calendar or fiscal year) and valuation methods (basic a product. For example, an automobile manufacturer or producer prices) to estimate value added. (See engages in forging, welding, and painting as well as also About the data for table 4.2.) advertising, accounting, and other service activities. The data on manufacturing value added in U.S. dol- Collecting data at such a detailed level is not practical, lars are from the World Bank's national accounts files nor is it useful to record production data at the highest and may differ from those UNIDO uses to calculate level of a large, multiplant, multiproduct firm. The ISIC shares of value added by industry, in part because has therefore adopted as the definition of an estab- of differences in exchange rates. Thus value added lishment "an enterprise or part of an enterprise which in a particular industry estimated by applying the independently engages in one, or predominantly one, shares to total manufacturing value added will not kind of economic activity at or from one location . . . match those from UNIDO sources. Classification of for which data are available . . ." (UN 1990, p. 25). manufacturing industries in the table accords with By design, this definition matches the reporting unit the United Nations International Standard Industrial required for the production accounts of the United Classification (ISIC) revision 3 for the first time. Pre- Nations System of National Accounts. The ISIC sys- vious editions of World Development Indicators used tem is described in the United Nations' International revision 2, first published in 1948. Revision 3 was Standard Industrial Classification of All Economic Activi- completed in 1989, and many countries now use it. ties, Third Revision (1990). The discussion of the ISIC But revision 2 is still widely used for compiling cross- draws on Jacob Ryten's "Fifty Years of ISIC: Historical country data. UNIDO has converted these data to Origins and Future Perspectives" (1998). Manufacturing continues to show strong growth in East Asia through 2007 4.3a Value added in manufacturing (index, 1990 = 100) 500 East Asia & Pacific 400 South Asia 300 Middle East & North Africa Latin America & Caribbean 200 Data sources Sub-Saharan Africa Data on manufacturing value added are from the 100 World Bank's national accounts files. Data used 0 to calculate shares of industry value added are 1990 1995 2000 2005 2007 provided to the World Bank in electronic files Manufacturing continues to be the dominant sector in East Asia and Pacific, growing an average of about by UNIDO. The most recent published source is 10 percent a year between 1990 and 2007. UNIDO's International Yearbook of Industrial Sta- Source: World Development Indicators data files. tistics 2008. 2008 World Development Indicators 215 4.4 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan 156 480 .. .. .. .. .. .. .. .. .. .. Albania 202 1,072 11 5 9 3 3 7 12 14 65 70 Algeria 10,258 60,163 1 0 0 0 95 98 1 1 3 1 Angola 3,642 39,900 .. .. .. .. .. .. .. .. .. .. Argentina 20,967 55,933 50 50 4 1 10 11 2 4 34 31 Armenia 271 1,157 11 8 5 3 1 1 26 31 54 56 Australia 53,111 141,317 21 13 9 3 18 23 20 29 24 19 Austria 57,738 162,920 4 6 3 2 1 3 3 3 88 82 Azerbaijan 635 10,500 4 8 8 1 66 81 1 3 20 6 Bangladesh 3,501 12,453 10 6 3 2 0 1 0 0 85 91 Belarus 4,803 24,339 .. 7 .. 2 .. 35 .. 1 .. 53 Belgium 178,265a 430,779 10a 8 1a 1 3a 7 4a 4 76a 78 Benin 420 650 20 26 64 64 7 0 0 1 10 9 Bolivia 1,100 4,490 19 15 9 2 13 48 31 26 17 7 Bosnia and Herzegovina 152 4,166 .. 5 .. 8 .. 8 .. 18 .. 61 Botswana 2,142 5,117 .. 3 .. 0 .. 0 .. 23 .. 73 Brazil 46,506 160,649 29 26 5 4 1 8 10 12 53 47 Bulgaria 5,355 18,466 18 9 3 2 7 13 10 18 60 55 Burkina Faso 276 607 23 .. 63 .. 0 .. 0 .. 6 .. Burundi 105 62 60 35 3 4 0 4 1 2 2 21 Cambodia 855 4,100 .. .. .. .. .. .. .. .. .. .. Cameroon 1,651 3,604 27 12 28 16 29 62 8 5 8 3 Canada 192,197 418,974 8 8 9 4 9 22 7 9 62 53 Central African Republic 171 195 4 1 20 41 1 0 30 17 45 36 Chad 243 3,450 .. .. .. .. .. .. .. .. .. .. Chile 16,024 68,296 24 15 14 6 0 1 47 65 12 10 China 148,780 1,217,776 8 3 2 0 4 2 2 2 84 93 Hong Kong, Chinab 173,871 349,386 3 3 0 1 0 2 1 4 94 68 Colombia 10,056 29,991 31 15 5 4 27 36 1 2 34 39 Congo, Dem. Rep. 1,563 2,650 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 1,172 6,100 1 .. 8 .. 88 .. 0 .. 3 .. Costa Rica 3,453 9,353 63 30 5 2 1 1 1 1 25 63 Côte d'Ivoire 3,806 8,500 63 39 20 9 10 33 0 0 7 18 Croatia 4,517 12,360 11 10 5 4 8 13 2 5 74 68 Cuba 1,600 3,701 .. 11 .. 0 .. 0 .. 2 .. 24 Czech Republic 21,335 122,421 6 4 4 1 4 3 3 2 82 90 Denmark 50,906 103,453 24 17 3 3 3 10 1 2 60 66 Dominican Republic 3,780 7,237 11 .. 0 .. 0 .. 0 .. 8 .. Ecuador 4,307 13,785 52 27 3 4 35 60 0 1 8 8 Egypt, Arab Rep. 3,450 16,201 10 8 6 2 37 52 6 3 40 19 El Salvador 1,652 3,980 57 35 1 1 0 5 3 4 39 55 Eritrea 86 15 .. .. .. .. .. .. .. .. .. .. Estonia 1,840 10,996 16 8 10 6 7 12 3 3 64 64 Ethiopia 422 1,284 73 61 13 20 3 0 0 3 11 13 Finland 40,490 89,705 2 2 8 5 2 5 3 5 83 81 France 301,162 553,398 14 11 1 1 2 4 3 3 79 79 Gabon 2,713 6,150 0 1 13 7 83 86 2 3 2 4 Gambia, The 16 13 60 82 1 6 0 .. 1 0 36 12 Georgia 151 1,240 29 24 3 2 19 4 8 20 41 45 Germany 523,461 1,326,411 5 4 1 1 1 2 3 3 85 83 Ghana 1,724 4,214 41 47 10 5 3 1 6 2 9 11 Greece 11,054 23,809 30 20 4 2 7 12 8 11 50 52 Guatemala 2,155 6,926 65 37 4 4 2 5 0 4 28 50 Guinea 702 1,100 7 .. 1 .. 0 .. 65 .. 24 .. Guinea-Bissau 24 95 89 .. 11 .. 0 .. 0 .. 0 .. Haiti 110 522 37 .. 0 .. 0 .. 0 .. 62 .. Data for Taiwan, China 113,047 246,377 3 1 2 1 1 6 1 3 93 89 216 2009 World Development Indicators ECONOMY Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and 4.4 Manufactures exports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 1,769 5,594 87 52 3 2 0 5 0 7 9 29 Hungary 12,865 94,618 21 6 2 1 3 3 5 2 68 81 India 30,630 145,325 19 9 1 2 2 16 4 8 73 64 Indonesia 45,417 118,014 11 15 7 6 25 26 6 11 51 42 Iran, Islamic Rep. 18,360 86,000 4 4 1 0 86 83 1 2 9 10 Iraq 496 41,600 .. 0 .. 0 .. 100 .. 0 .. 0 Ireland 44,705 121,024 19 10 1 1 0 1 1 1 71 84 Israel 19,046 54,065 5 3 2 1 0 0 1 1 89 76 Italy 233,766 491,507 7 6 1 1 1 4 1 2 89 84 Jamaica 1,427 1,942 22 16 0 0 1 15 51 64 26 5 Japan 443,116 712,769 0 1 1 1 1 1 1 2 95 90 Jordan 1,769 5,700 25 15 2 0 0 1 24 7 49 76 Kazakhstan 5,250 47,755 10 4 3 1 25 66 24 15 38 13 Kenya 1,878 4,080 56 43 7 12 6 4 3 3 28 37 Korea, Dem. Rep. 959 1,690 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 125,058 371,489 2 1 1 1 2 7 1 2 92 89 Kuwait 12,785 62,376 0 .. 0 .. 95 .. 0 .. 5 .. Kyrgyz Republic 409 1,135 23 17 13 5 11 12 12 4 41 35 Lao PDR 311 923 .. .. .. .. .. .. .. .. .. .. Latvia 1,305 8,311 14 13 23 16 2 4 1 4 58 60 Lebanon 816 3,574 20 .. 1 .. 0 .. 8 .. 69 .. Lesotho 160 805 .. .. .. .. .. .. .. .. .. .. Liberia 820 184 .. .. .. .. .. .. .. .. .. .. Libya 8,975 45,400 0 .. 0 .. 95 .. 0 .. 5 .. Lithuania 2,705 17,161 18 17 8 3 11 13 5 2 58 64 Macedonia, FYR 1,204 3,356 18 14 5 1 0 5 18 5 58 76 Madagascar 507 1,190 69 31 6 3 1 5 7 3 14 57 Malawi 405 710 90 86 2 4 0 0 0 0 7 11 Malaysia 73,914 176,211 10 9 6 2 7 14 1 2 75 71 Mali 441 1,480 19 7 61 14 0 0 0 0 2 3 Mauritania 488 1,510 .. 25 .. 0 .. .. .. 69 .. 0 Mauritius 1,538 2,231 29 31 1 1 0 0 0 1 70 67 Mexico 79,542 271,990 8 5 1 0 10 16 3 3 77 72 Moldova 745 1,342 72 57 2 1 1 0 3 9 23 32 Mongolia 473 1,889 2 2 28 11 0 9 60 61 10 5 Morocco 6,881 14,656 31 19 3 2 2 4 12 10 51 65 Mozambique 168 2,700 66 11 16 3 2 15 2 64 13 6 Myanmar 860 6,257 .. .. .. .. .. .. .. .. .. .. Namibia 1,409 2,919 .. 24 .. 1 .. 0 .. 35 .. 39 Nepal 345 888 7 .. 1 .. 0 .. 0 .. 91 .. Netherlands 203,171 551,250 20 13 4 3 7 9 3 3 62 60 New Zealand 13,645 26,974 44 52 19 10 2 4 5 5 28 25 Nicaragua 466 1,202 74 81 3 1 1 1 1 2 20 10 Niger 288 733 19 14 1 3 0 2 80 63 1 6 Nigeria 12,342 65,500 2 0 2 0 96 98 0 0 1 1 Norway 41,992 136,377 8 5 2 1 47 64 9 8 27 18 Oman 6,068 24,722 5 2 0 0 79 89 2 1 14 7 Pakistan 8,029 17,838 12 12 4 1 1 6 0 1 83 79 Panama 625 1,164 74 83 0 1 3 1 1 4 20 11 Papua New Guinea 2,654 4,671 13 .. 20 .. 38 .. 25 .. 4 .. Paraguay 919 2,785 44 80 36 6 0 0 0 1 19 14 Peru 5,575 27,956 29 14 2 1 5 9 42 49 14 12 Philippines 17,502 50,466 13 6 1 1 2 2 4 5 41 51 Poland 22,895 138,806 10 9 3 1 8 4 7 5 71 80 Portugal 22,783 51,455 7 9 5 2 3 4 2 4 83 74 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 217 4.4 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 7,910 40,286 7 4 3 2 8 8 3 5 78 80 Russian Federation 81,095 355,175 2 2 3 3 43 61 10 8 26 17 Rwanda 54 177 57 45 16 5 0 0 12 46 14 5 Saudi Arabia 50,040 233,174 1 1 0 0 88 90 1 0 11 9 Senegal 993 1,698 9 37 7 3 22 19 12 4 48 36 Serbia .. 8,825 28 19 4 2 2 3 15 10 49 66 Sierra Leone 42 244 .. .. .. .. .. .. .. .. .. .. Singaporeb 118,268 299,272 4 2 1 0 7 14 2 2 84 76 Slovak Republic 8,580 58,171 6 4 4 1 4 5 4 3 82 87 Slovenia 8,316 30,054 4 3 2 2 1 2 3 6 90 88 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 27,853c 69,788 8c 7 4c 2 9c 11 8c 29 43c 51 Spain 97,849 241,018 15 13 2 1 2 5 2 3 78 75 Sri Lanka 3,798 7,740 21 22 4 2 0 0 1 4 73 70 Sudan 555 8,879 44 6 46 2 0 90 0 0 6 0 Swaziland 866 2,450 .. 21 .. 7 .. 1 .. 1 .. 70 Sweden 80,440 169,084 2 4 7 4 2 5 3 4 79 77 Switzerland 81,641 172,060 3 3 1 0 0 2 3 3 93 91 Syrian Arab Republic 3,563 11,700 .. 17 .. 2 .. 40 .. 1 .. 32 Tajikistan 750 1,468 .. .. .. .. .. .. .. .. .. .. Tanzania 682 2,022 65 35 23 7 0 1 0 13 10 17 Thailand 56,439 153,103 19 12 5 5 1 4 1 2 73 76 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 378 690 19 16 42 9 0 0 32 13 7 62 Trinidad and Tobago 2,455 15,100 8 3 0 0 48 66 0 3 43 28 Tunisia 5,475 15,029 10 10 1 0 8 16 2 1 79 70 Turkey 21,637 107,215 20 8 1 0 1 5 3 3 74 81 Turkmenistan 1,880 8,920 1 .. 13 .. 77 .. 1 .. 8 .. Uganda 460 1,623 86 62 4 8 0 1 1 2 4 21 Ukraine 13,128 49,248 19 13 1 1 4 5 8 6 66 74 United Arab Emirates 28,364 173,000 8 1 0 0 9 62 55 1 28 3 United Kingdom 237,953 437,807 8 5 1 1 6 10 3 4 82 74 United States 584,743 1,162,479 11 8 4 2 2 4 3 4 77 77 Uruguay 2,106 4,485 44 53 15 11 1 4 1 1 39 30 Uzbekistan 3,430 8,029 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 18,457 69,165 3 0 0 0 76 93 7 2 14 5 Vietnam 5,449 48,387 30 19 3 4 18 24 0 1 44 51 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,945 7,310 .. 5 .. 0 .. 93 .. 0 .. 1 Zambia 1,040 4,619 3 7 1 1 3 1 87 78 7 13 Zimbabwe 2,118 2,060 43 16 7 11 1 1 12 19 37 48 World 5,172,481 t 13,952,366 t 9w 7w 3w 2w 6w 10 w 3w 4w 75 w 72 w Low income 62,712 230,764 20 14 4 3 36 44 3 2 35 35 Middle income 884,363 3,939,704 14 9 3 2 11 18 5 6 64 61 Lower middle income 413,659 2,196,661 14 8 3 2 11 15 3 4 66 68 Upper middle income 470,822 1,741,884 15 11 4 2 12 21 6 8 62 55 Low & middle income 947,082 4,170,475 14 10 4 2 11 18 5 6 63 60 East Asia & Pacific 354,784 1,784,975 11 7 4 2 6 7 2 3 73 77 Europe & Central Asia 183,585 878,671 9 6 3 2 25 35 8 7 47 45 Latin America & Carib. 223,927 753,753 20 17 3 2 15 14 8 10 54 54 Middle East & N. Africa 62,002 307,393 6 5 1 0 72 75 3 2 18 16 South Asia 46,647 185,551 17 10 2 2 1 14 3 7 76 66 Sub-Saharan Africa 76,554 262,784 17 11 6 3 37 39 8 14 27 30 High income 4,225,020 9,784,163 8 6 2 2 6 8 3 4 78 75 Euro area 1,742,200 4,158,254 10 8 2 1 2 4 3 3 80 78 Note: Components may not sum to 100 percent because of unclassified trade. Exports of gold are excluded. a. Includes Luxembourg. b. Includes re-exports. c. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 218 2008 World Development Indicators ECONOMY Structure of merchandise exports 4.4 About the data Definitions Data on merchandise trade are from customs c. In some compilations categories b and c are · Merchandise exports are the f.o.b. value of goods reports of goods moving into or out of an economy classified as re-exports. Because of differences in provided to the rest of the world. · Food corresponds or from reports of financial transactions related to reporting practices, data on exports may not be fully to the commodities in SITC sections 0 (food and live merchandise trade recorded in the balance of pay- comparable across economies. animals), 1 (beverages and tobacco), and 4 (animal ments. Because of differences in timing and defi - The data on total exports of goods (merchandise) and vegetable oils and fats) and SITC division 22 nitions, trade flow estimates from customs reports are from the World Trade Organization (WTO), which (oil seeds, oil nuts, and oil kernels). · Agricultural and balance of payments may differ. Several inter- obtains data from national statistical offices and the raw materials correspond to SITC section 2 (crude national agencies process trade data, each correct- IMF's International Financial Statistics, supplemented materials except fuels), excluding divisions 22, 27 ing unreported or misreported data, leading to other by the Comtrade database and publications or data- (crude fertilizers and minerals excluding coal, petro- differences. bases of regional organizations, specialized agen- leum, and precious stones), and 28 (metalliferous The most detailed source of data on international cies, economic groups, and private sources (such as ores and scrap). · Fuels correspond to SITC section trade in goods is the United Nations Statistics Divi- Eurostat, the Food and Agriculture Organization, and 3 (mineral fuels). · Ores and metals correspond to sion's Commodity Trade (Comtrade) database. The country reports of the Economist Intelligence Unit). the commodities in SITC divisions 27, 28, and 68 International Monetary Fund (IMF) also collects Country websites and email contact have improved (nonferrous metals). · Manufactures correspond to customs-based data on trade in goods. Exports are collection of up-to-date statistics, reducing the pro- the commodities in SITC sections 5 (chemicals), 6 recorded as the cost of the goods delivered to the portion of estimates. The WTO database now covers (basic manufactures), 7 (machinery and transport frontier of the exporting country for shipment--the most major traders in Africa, Asia, and Latin America, equipment), and 8 (miscellaneous manufactured free on board (f.o.b.) value. Many countries report which together with high-income countries account goods), excluding division 68. trade data in U.S. dollars. When countries report in for nearly 95 percent of world trade. Reliability of local currency, the United Nations Statistics Division data for countries in Europe and Central Asia has applies the average official exchange rate to the U.S. also improved. dollar for the period shown. Export shares by major commodity group are from Countries may report trade according to the gen- Comtrade. The values of total exports reported eral or special system of trade (see Primary data here have not been fully reconciled with the esti- documentation). Under the general system exports mates from the national accounts or the balance comprise outward-moving goods that are (a) goods of payments. wholly or partly produced in the country; (b) foreign The classification of commodity groups is based goods, neither transformed nor declared for domestic on the Standard International Trade Classification consumption in the country, that move outward from (SITC) revision 3. Previous editions contained data customs storage; and (c) goods previously included based on the SITC revision 1. Data for earlier years in as imports for domestic consumption but subse- previous editions may differ because of this change quently exported without transformation. Under the in methodology. Concordance tables are available to special system exports comprise categories a and convert data reported in one system to another. Developing economies' share of world merchandise exports continues to expand 4.4a 1995 2007 Data sources ($5.2 billion) ($13.9 billion) East Asia & Pacific 13% Data on merchandise exports are from the WTO. East Asia & Pacific 7% Data on shares of exports by major commodity Europe & Central Asia 4% Europe & Central Asia 6% Latin America & Caribbean 4% group are from Comtrade. The WTO publishes Sub-Saharan Africa 1% Latin America Middle East & N. Africa 1% & Caribbean 5% data on world trade in its Annual Report. The IMF South Asia 1% Sub-Saharan Africa 2% publishes estimates of total exports of goods in High income Middle East & N. Africa 2% 82% High income South Asia 1% its International Financial Statistics and Direction 71% of Trade Statistics, as does the United Nations Statistics Division in its Monthly Bulletin of Sta- tistics. And the United Nations Conference on Trade and Development publishes data on the Developing economies' share of world merchandise exports increased 11 percentage points from 1995 structure of exports in its Handbook of Statistics. to 2007. East Asia and Pacific was the biggest gainer, capturing an additional 5 percentage points. Every Tariff line records of exports are compiled in the region except South Asia increased its share in world trade. United Nations Statistics Division's Comtrade Source: World Development Indicators data files and World Trade Organization. database. 2008 World Development Indicators 219 4.5 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan 387 2,950 .. .. .. .. .. .. .. .. .. .. Albania 714 4,196 34 16 1 1 3 15 1 3 61 65 Algeria 10,100 27,631 29 20 3 2 1 1 2 2 65 75 Angola 1,468 11,400 .. .. .. .. .. .. .. .. .. .. Argentina 20,122 44,780 5 4 2 1 4 6 3 3 86 85 Armenia 674 3,282 31 17 0 1 27 16 0 3 39 59 Australia 61,283 165,334 5 5 2 1 5 13 1 1 86 76 Austria 66,237 162,351 6 6 3 2 4 10 4 5 82 75 Azerbaijan 668 5,712 39 16 1 1 5 3 2 2 53 78 Bangladesh 6,694 18,595 17 17 3 7 8 13 2 3 69 60 Belarus 5,564 28,674 .. 7 .. 1 .. 35 .. 4 .. 48 Belgium 164,934a 413,163 11a 8 2a 1 6a 11 5a 5 71a 74 Benin 746 1,500 36 30 4 4 0 20 1 1 59 45 Bolivia 1,424 3,444 10 10 2 1 5 8 3 1 81 79 Bosnia and Herzegovina 1,082 9,772 .. 16 .. 1 .. 14 .. 4 .. 64 Botswana 1,911 4,035 .. 13 .. 1 .. 16 .. 2 .. 67 Brazil 54,137 126,581 11 5 3 1 12 19 3 5 71 64 Bulgaria 5,660 29,983 8 6 3 1 34 5 4 9 48 62 Burkina Faso 455 1,650 21 .. 2 .. 14 .. 1 .. 62 .. Burundi 234 319 21 12 2 1 11 28 1 1 64 58 Cambodia 1,187 5,500 .. .. .. .. .. .. .. .. .. .. Cameroon 1,199 3,760 17 18 3 2 2 31 6 3 72 46 Canada 168,426 389,600 6 6 2 1 4 9 3 3 83 77 Central African Republic 175 230 16 17 10 27 9 17 2 2 64 37 Chad 365 1,500 24 .. 1 .. 18 .. 1 .. 57 .. Chile 15,900 47,114 7 7 2 1 9 26 2 4 79 62 China 132,084 955,950 7 4 5 4 4 12 4 12 79 68 Hong Kong, China 196,072 370,132 5 3 2 1 2 3 2 2 87 90 Colombia 13,853 32,897 9 9 3 1 3 3 2 3 78 83 Congo, Dem. Rep. 871 3,700 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 670 2,900 21 .. 1 .. 20 .. 1 .. 58 .. Costa Rica 4,036 12,955 10 8 1 1 9 12 2 2 77 74 Côte d'Ivoire 2,931 6,160 21 17 1 1 19 30 1 1 57 49 Croatia 7,352 25,830 12 8 2 1 12 15 3 3 67 73 Cuba 2,825 10,083 .. 12 .. 0 .. 0 .. 1 .. 50 Czech Republic 25,085 117,900 7 5 3 1 8 8 4 4 77 81 Denmark 45,939 99,621 12 12 3 2 3 6 2 2 73 77 Dominican Republic 5,170 13,817 .. .. .. .. .. .. .. .. .. .. Ecuador 4,152 13,565 8 8 3 1 6 21 2 1 82 67 Egypt, Arab Rep. 11,760 27,064 28 19 7 4 1 15 3 4 61 42 El Salvador 3,329 8,677 15 17 2 2 9 18 2 1 72 62 Eritrea 454 515 .. .. .. .. .. .. .. .. .. .. Estonia 2,546 15,516 14 8 3 3 11 14 1 1 71 66 Ethiopia 1,145 5,395 14 7 2 1 11 13 1 1 72 77 Finland 29,470 81,491 6 5 4 3 9 14 6 10 74 66 France 289,391 615,229 11 8 3 1 7 14 3 3 76 74 Gabon 882 2,250 19 17 1 0 3 4 1 1 76 78 Gambia, The 182 315 36 31 1 2 14 17 0 1 46 49 Georgia 392 5,217 36 16 0 1 39 18 0 1 24 60 Germany 463,872 1,058,580 10 7 3 1 6 11 4 5 70 67 Ghana 1,906 8,043 8 15 1 1 6 2 3 2 75 81 Greece 25,898 76,149 16 11 2 1 7 15 3 4 71 68 Guatemala 3,292 13,578 12 11 2 1 12 18 1 1 73 68 Guinea 819 1,190 31 .. 1 .. 19 .. 1 .. 47 .. Guinea-Bissau 133 140 44 .. 0 .. 16 .. 0 .. 40 .. Haiti 653 1,682 .. .. .. .. .. .. .. .. .. .. Data for Taiwan, China 103,558 219,649 5 3 4 2 7 20 6 9 74 65 220 2009 World Development Indicators ECONOMY Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and 4.5 Manufactures imports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 1,879 8,556 13 15 1 1 12 20 1 1 74 64 Hungary 15,465 95,041 6 4 3 1 12 9 4 3 75 76 India 34,707 216,622 4 3 4 2 24 33 7 6 53 46 Indonesia 40,630 92,381 9 11 6 4 7 30 5 4 73 53 Iran, Islamic Rep. 13,882 46,000 21 2 2 1 2 4 3 0 70 16 Iraq 665 32,000 .. .. .. .. .. .. .. .. .. .. Ireland 32,340 82,472 8 9 1 1 3 9 2 2 76 73 Israel 29,578 59,039 7 6 2 1 6 16 2 2 82 74 Italy 205,990 504,454 11 8 6 2 7 12 5 6 67 65 Jamaica 2,818 5,899 14 13 2 1 13 34 1 0 68 49 Japan 335,882 621,091 16 9 6 2 16 28 7 9 53 51 Jordan 3,697 13,511 21 15 2 1 13 22 2 3 61 57 Kazakhstan 3,807 32,756 10 7 2 1 25 11 5 2 58 79 Kenya 2,991 8,989 10 11 2 2 15 21 2 2 71 62 Korea, Dem. Rep. 1,380 3,460 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 135,119 356,846 5 4 5 2 14 27 6 8 67 58 Kuwait 7,790 23,642 16 .. 1 .. 1 .. 2 .. 81 .. Kyrgyz Republic 522 2,417 18 15 3 2 36 31 3 2 41 50 Lao PDR 589 1,065 .. .. .. .. .. .. .. .. .. .. Latvia 1,815 15,285 10 10 2 3 21 11 1 2 66 70 Lebanon 7,278 12,251 19 .. 2 .. 8 .. 2 .. 63 .. Lesotho 1,107 1,730 .. .. .. .. .. .. .. .. .. .. Liberia 510 499 .. .. .. .. .. .. .. .. .. .. Libya 5,392 7,750 .. .. .. .. .. .. .. .. .. .. Lithuania 3,650 24,207 13 9 4 2 19 16 4 2 58 70 Macedonia, FYR 1,719 5,228 17 12 3 1 12 19 3 6 54 62 Madagascar 628 2,590 16 15 2 1 14 17 1 0 65 67 Malawi 475 1,450 14 11 1 1 11 14 1 1 73 74 Malaysia 77,691 146,982 5 6 1 1 2 9 3 5 84 75 Mali 772 2,255 20 15 1 1 16 22 1 1 63 62 Mauritania 431 1,510 .. 25 .. 1 .. 27 .. 0 .. 47 Mauritius 1,976 3,895 17 19 3 2 7 18 1 1 72 58 Mexico 75,858 296,275 6 6 2 1 2 7 2 3 80 76 Moldova 840 3,690 8 12 3 2 46 21 2 1 42 64 Mongolia 415 2,117 14 12 1 0 19 27 1 1 65 60 Morocco 10,023 31,695 20 12 6 3 14 20 4 4 56 61 Mozambique 704 3,300 22 18 3 1 10 16 1 0 62 47 Myanmar 1,348 3,250 .. .. .. .. .. .. .. .. .. .. Namibia 1,616 3,420 .. 15 .. 1 .. 10 .. 1 .. 73 Nepal 1,333 2,904 12 .. 6 .. 13 .. 5 .. 63 .. Netherlands 185,232 491,583 14 9 2 1 8 14 4 4 72 61 New Zealand 13,957 30,890 7 8 1 1 5 14 3 3 82 73 Nicaragua 975 3,579 18 16 1 0 18 23 1 0 63 60 Niger 374 970 32 24 1 5 13 17 3 1 51 53 Nigeria 8,222 29,500 18 18 1 1 1 3 2 3 77 76 Norway 32,968 80,281 7 7 3 2 3 4 7 10 80 77 Oman 4,379 16,100 20 10 1 1 2 3 2 5 68 81 Pakistan 11,515 32,590 18 9 5 5 16 26 3 4 57 55 Panama 2,510 6,872 11 10 1 0 14 18 1 1 73 68 Papua New Guinea 1,452 2,909 .. .. .. .. .. .. .. .. .. .. Paraguay 3,144 7,280 19 7 0 0 7 13 1 1 74 78 Peru 7,584 20,180 14 10 2 2 9 19 1 1 75 65 Philippines 28,341 57,985 8 7 2 1 9 17 3 2 58 48 Poland 29,050 162,674 10 6 3 2 9 10 3 4 74 75 Portugal 32,610 78,138 14 12 4 2 8 14 2 3 72 65 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 221 4.5 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 10,278 69,863 8 6 2 1 21 11 4 3 63 77 Russian Federation 60,945 223,421 18 13 1 1 3 1 3 2 44 77 Rwanda 236 737 19 14 3 2 12 9 3 3 64 73 Saudi Arabia 28,091 90,157 16 13 1 1 0 0 3 5 75 81 Senegal 1,412 4,452 25 25 2 1 30 27 1 1 42 45 Serbia .. 18,350 14 6 4 2 14 17 7 6 60 69 Sierra Leone 133 445 .. .. .. .. .. .. .. .. .. .. Singapore 124,507 263,155 5 3 1 0 8 20 2 2 83 71 Slovak Republic 8,770 60,218 9 5 3 1 13 11 6 3 70 79 Slovenia 9,492 31,534 8 6 5 3 7 9 4 7 74 74 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 30,546b 90,990 7b 5 2b 1 8b 19 2b 3 78b 65 Spain 113,537 372,569 14 9 3 1 8 15 4 4 71 70 Sri Lanka 5,306 11,300 16 12 2 1 6 13 1 3 75 69 Sudan 1,218 8,775 24 5 2 0 14 0 0 0 59 93 Swaziland 1,008 2,650 .. 21 .. 1 .. 16 .. 1 .. 61 Sweden 65,036 151,269 7 7 2 2 6 11 4 5 80 72 Switzerland 80,152 161,232 6 5 2 1 3 7 3 4 85 82 Syrian Arab Republic 4,709 14,500 .. 13 .. 3 .. 27 .. 3 .. 52 Tajikistan 810 2,455 .. .. .. .. .. .. .. .. .. .. Tanzania 1,675 5,337 10 12 1 1 1 30 4 1 84 55 Thailand 70,786 140,795 4 4 4 2 7 18 3 5 80 69 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 594 1,440 18 15 2 1 30 27 1 2 49 55 Trinidad and Tobago 1,714 7,450 16 8 1 1 1 33 6 7 77 50 Tunisia 7,902 18,980 13 10 4 2 7 13 3 3 73 71 Turkey 35,709 170,057 7 3 6 3 13 14 6 8 69 63 Turkmenistan 1,365 4,460 24 .. 0 .. 3 .. 2 .. 71 .. Uganda 1,056 3,466 16 12 3 1 2 19 2 1 78 63 Ukraine 15,484 60,670 8 7 2 1 48 26 3 4 38 62 United Arab Emirates 23,778 132,000 15 6 0 1 4 1 6 4 75 62 United Kingdom 267,250 619,575 10 9 2 1 4 10 3 4 80 72 United States 770,852 2,020,403 5 4 2 1 8 18 3 3 79 70 Uruguay 2,867 5,726 10 9 4 3 10 22 1 1 74 65 Uzbekistan 2,750 4,848 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 12,649 46,097 14 7 4 1 1 0 4 1 76 64 Vietnam 8,155 60,830 5 6 2 4 10 15 2 4 76 66 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,582 6,500 .. 25 .. 1 .. 22 .. 1 .. 51 Zambia 700 3,971 10 5 2 0 13 12 2 5 72 76 Zimbabwe 2,660 2,420 6 11 2 4 9 18 2 6 78 54 World 5,228,953 t 14,144,532 t 9w 6w 3w 1w 7w 15 w 4w 5w 75 w 69 w Low income 74,778 257,014 14 13 3 3 12 17 2 3 66 62 Middle income 948,310 3,651,462 8 6 3 2 7 14 4 5 75 68 Lower middle income 452,835 1,955,705 9 6 4 3 8 17 4 7 72 63 Upper middle income 495,317 1,691,013 8 6 3 1 7 11 3 4 77 72 Low & middle income 1,023,140 3,908,450 8 7 3 2 7 14 3 5 74 68 East Asia & Pacific 366,057 1,476,968 6 5 4 3 5 14 4 8 78 67 Europe & Central Asia 200,774 939,184 11 7 3 2 14 12 4 4 61 70 Latin America & Carib. 241,363 734,326 8 7 2 1 5 11 2 3 78 72 Middle East & N. Africa 77,167 238,291 22 12 4 2 6 14 3 2 64 50 South Asia 60,322 286,538 8 4 4 2 21 32 6 6 55 48 Sub-Saharan Africa 78,377 242,243 11 10 2 1 10 18 2 2 73 64 High income 4,205,548 10,242,758 9 6 3 1 7 15 4 4 75 70 Euro area 1,644,739 4,068,352 11 8 3 2 7 12 4 5 72 69 Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 222 2008 World Development Indicators ECONOMY Structure of merchandise imports 4.5 About the data Definitions Data on imports of goods are derived from the domestic consumption from bonded warehouses · Merchandise imports are the c.i.f. value of goods same sources as data on exports. In principle, world and free trade zones. Goods transported through a purchased from the rest of the world valued in U.S. exports and imports should be identical. Similarly, country en route to another are excluded. dollars. · Food corresponds to the commodities in exports from an economy should equal the sum of The data on total imports of goods (merchandise) SITC sections 0 (food and live animals), 1 (beverages imports by the rest of the world from that economy. in the table come from the World Trade Organization and tobacco), and 4 (animal and vegetable oils and But differences in timing and definitions result in dis- (WTO). For further discussion of the WTO's sources fats) and SITC division 22 (oil seeds, oil nuts, and oil crepancies in reported values at all levels. For further and methodology, see About the data for table 4.4. kernels). · Agricultural raw materials correspond to discussion of indicators of merchandise trade, see The import shares by major commodity group are SITC section 2 (crude materials except fuels), exclud- About the data for tables 4.4 and 6.2. from the United Nations Statistics Division's Com- ing divisions 22, 27 (crude fertilizers and minerals The value of imports is generally recorded as the modity Trade (Comtrade) database. The values of excluding coal, petroleum, and precious stones), and cost of the goods when purchased by the importer total imports reported here have not been fully rec- 28 (metalliferous ores and scrap). · Fuels correspond plus the cost of transport and insurance to the fron- onciled with the estimates of imports of goods and to SITC section 3 (mineral fuels). · Ores and met- tier of the importing country--the cost, insurance, services from the national accounts (shown in table als correspond to the commodities in SITC divisions and freight (c.i.f.) value, corresponding to the landed 4.8) or those from the balance of payments (table 27, 28, and 68 (nonferrous metals). · Manufactures cost at the point of entry of foreign goods into the 4.15). correspond to the commodities in SITC sections 5 country. A few countries, including Australia, Canada, The classification of commodity groups is based (chemicals), 6 (basic manufactures), 7 (machinery and the United States, collect import data on a free on the Standard International Trade Classification and transport equipment), and 8 (miscellaneous on board (f.o.b.) basis and adjust them for freight and (SITC) revision 3. Previous editions contained data manufactured goods), excluding division 68. insurance costs. Many countries report trade data in based on the SITC revision 1. Data for earlier years in U.S. dollars. When countries report in local currency, previous editions may differ because of this change the United Nations Statistics Division applies the in methodology. Concordance tables are available to average official exchange rate to the U.S. dollar for convert data reported in one system to another. the period shown. Countries may report trade according to the gen- eral or special system of trade (see Primary data documentation). Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for Top 10 developing economy exporters of merchandise goods in 2007 4.5a Merchandise exports ($ billions) 1995 2007 1,500 Data sources 1,200 Data on merchandise imports are from the WTO. Data on shares of imports by major commodity group are from Comtrade. The WTO publishes data 900 on world trade in its Annual Report. The Interna- tional Monetary Fund publishes estimates of total 600 imports of goods in its International Financial Sta- tistics and Direction of Trade Statistics, as does the 300 United Nations Statistics Division in its Monthly Bulletin of Statistics. And the United Nations Con- 0 China Russian Mexico Malaysia Brazil Thailand India Poland Indonesia Turkey ference on Trade and Development publishes Federation data on the structure of imports in its Handbook China continues to dominate merchandise exports among developing economies. Even when developed of Statistics. Tariff line records of imports are com- economies are included, China ranks as the third leading merchandise exporter. piled in the United Nations Statistics Division's Source: World Development Indicators data files and World Trade Organization. Comtrade database. 2008 World Development Indicators 223 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 94 1,399 19.1 8.2 69.3 71.6 1.4 2.5 10.2 17.6 Algeria .. .. .. .. .. .. .. .. .. .. Angola 113 311 31.8 5.4 0.7 72.4 9.2 .. 59.0 22.2 Argentina 3,676 10,175 27.4 16.5 60.5 42.4 0.2 0.1 11.9 41.0 Armenia 27 571 53.4 23.3 5.2 53.4 6.7 3.3 41.3 20.0 Australia 16,076 39,727 29.3 18.2 50.6 56.4 5.4 3.7 14.8 21.7 Austria 31,692 55,210 11.8 21.7 42.4 34.0 3.9 5.1 41.9 39.2 Azerbaijan 166 1,172 45.9 51.6 42.3 15.2 0.1 0.7 11.7 32.6 Bangladesh 469 685 15.0 11.7 5.3 11.1 0.1 4.6 79.6 72.6 Belarus 466 3,235 64.8 72.5 5.0 10.0 0.5 0.4 29.7 17.1 Belgium 33,619a 76,875 29.4 a 32.3 17.4 a 14.2 14.8a 6.1 38.4 a 47.4 Benin 159 196 25.8 14.9 53.2 59.2 6.9 2.5 14.1 23.5 Bolivia 174 453 44.8 13.3 31.5 57.2 9.8 12.9 13.9 16.6 Bosnia and Herzegovina 457 1,361 3.8 12.2 54.1 53.6 2.6 3.4 39.5 30.8 Botswana 236 922 16.2 9.0 68.5 59.2 7.8 3.7 7.5 28.1 Brazil 6,005 22,555 43.3 18.0 16.2 22.0 16.9 7.2 23.6 52.8 Bulgaria 1,431 6,333 34.5 30.0 33.0 49.4 7.6 1.3 32.5 19.3 Burkina Faso 38 .. 17.3 .. 47.8 .. .. .. 34.8 .. Burundi 4 7 46.2 21.2 32.4 20.2 0.5 0.0 21.0 58.6 Cambodia 103 1,510 30.5 13.9 51.7 75.1 .. 0.9 17.7 10.1 Cameroon 242 476 48.3 38.7 14.8 37.2 7.2 5.1 29.7 19.0 Canada 25,425 61,386 20.7 18.5 31.1 25.4 11.4 9.1 36.8 46.9 Central African Republic 0 .. 34.1 .. 33.9 .. 19.6 .. 12.5 .. Chad 23 .. 4.5 .. 49.8 .. 1.7 .. 43.9 .. Chile 3,249 8,677 36.8 59.1 28.0 16.4 7.4 3.0 27.8 21.5 China 18,430 121,654 18.2 25.7 47.4 30.6 10.1 0.9 24.4 42.7 Hong Kong, China 33,790 83,563 32.5 30.9 16.8 16.0 9.2 13.3 41.5 39.8 Colombia 1,641 3,559 34.4 31.0 40.0 46.9 6.5 1.9 19.1 20.2 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 61 303 52.2 4.0 22.4 18.0 0.0 31.4 25.4 46.6 Costa Rica 957 3,598 14.0 9.5 71.2 56.4 -0.2 0.3 14.9 33.8 Côte d'Ivoire 426 787 28.9 28.7 20.9 13.2 12.3 12.8 37.9 58.1 Croatia 2,223 12,524 31.8 12.3 60.7 73.7 1.3 0.7 6.2 13.3 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 6,638 17,144 22.0 29.4 43.4 38.7 1.1 1.8 33.5 30.1 Denmark 15,171 61,608 44.6 47.1 24.3 15.6 .. .. 31.0 37.4 Dominican Republic 1,894 4,740 2.2 7.4 82.9 86.1 0.1 0.9 14.9 5.5 Ecuador 687 1,087 46.8 31.9 37.1 57.4 0.0 .. 16.0 10.7 Egypt, Arab Rep. 8,262 19,660 38.8 35.3 32.5 47.3 1.0 0.9 27.8 16.5 El Salvador 342 1,454 28.3 25.2 25.0 58.2 7.8 2.7 39.0 13.8 Eritrea 49 .. 70.4 .. 3.1 .. 1.0 .. 26.5 .. Estonia 868 4,336 43.0 41.4 41.1 23.9 0.4 3.3 15.5 31.4 Ethiopia 310 1,185 76.9 61.9 5.3 14.9 1.5 4.9 16.4 18.3 Finland 7,334 20,154 28.1 13.8 22.4 14.0 2.0 1.6 47.5 70.7 France 83,108 144,680 24.6 23.1 33.2 37.4 5.3 2.1 36.9 37.3 Gabon 191 120 46.4 22.0 9.0 7.7 3.3 24.1 41.3 46.2 Gambia, The 38 114 21.7 17.4 73.4 65.3 0.3 0.3 4.7 17.0 Georgia 188 976 48.2 52.4 25.0 39.3 .. 2.3 26.9 6.0 Germany 73,576 210,820 27.0 24.4 24.5 17.1 5.0 8.3 43.5 50.1 Ghana 139 1,614 58.7 19.4 7.9 56.3 3.0 0.8 30.3 23.6 Greece 9,528 42,984 3.9 54.2 43.4 36.2 0.3 1.3 52.4 8.3 Guatemala 628 1,599 8.6 11.6 33.9 65.9 4.0 1.9 53.6 20.6 Guinea 17 44 75.3 13.6 5.1 0.4 1.4 0.6 18.2 85.4 Guinea-Bissau 2 6 18.2 22.9 14.0 16.6 .. 19.5 81.8 41.0 Haiti 98 152 5.1 .. 91.9 91.8 0.6 .. 2.4 8.2 224 2009 World Development Indicators ECONOMY Commercial Structure of service exports Transport Travel Insurance and 4.6 Computer, information, service exports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 221 732 25.6 5.6 36.3 76.0 2.0 3.1 36.1 15.2 Hungary 5,086 16,642 8.0 19.4 57.6 28.5 3.2 1.7 31.3 50.4 India 6,763 89,746 28.0 10.2 38.2 11.9 2.5 4.2 31.4 73.7 Indonesia 5,342 12,065 1.1 18.3 97.9 44.3 .. 2.5 2.1 34.9 Iran, Islamic Rep. 533 .. 25.9 .. 12.6 .. 8.8 .. 52.7 .. Iraq .. 353 .. 57.8 .. 40.8 .. 0.4 .. 1.0 Ireland 4,799 88,994 22.2 3.9 46.1 6.9 17.9 24.9 31.7 64.2 Israel 7,906 21,091 25.5 21.3 37.9 14.5 0.2 0.1 36.5 64.1 Italy 61,173 110,468 17.7 16.1 47.0 38.6 6.6 4.9 28.8 40.3 Jamaica 1,568 2,665 16.0 16.8 68.2 71.5 1.1 3.0 14.7 8.7 Japan 63,966 127,060 35.2 33.1 5.0 7.4 0.9 5.9 58.8 53.6 Jordan 1,689 3,298 24.8 19.3 39.1 70.1 0.2 .. 36.1 10.6 Kazakhstan 535 3,242 65.7 53.5 22.7 31.3 0.0 3.4 11.6 11.8 Kenya 1,183 2,177 59.4 51.7 35.7 41.8 1.4 0.3 3.4 6.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 22,133 61,536 41.9 54.9 23.3 9.4 0.4 7.3 34.5 28.5 Kuwait 1,124 8,572 83.6 34.1 10.7 2.6 5.7 1.4 .. 61.9 Kyrgyz Republic 39 654 39.6 21.3 11.9 52.9 0.6 1.3 48.4 24.5 Lao PDR 68 278 22.8 .. 76.0 .. 0.6 .. 0.6 .. Latvia 718 3,633 91.9 50.8 2.8 18.5 2.4 7.7 3.0 23.0 Lebanon .. 12,516 .. 4.6 .. 39.9 .. 3.1 .. 52.4 Lesotho 30 68 7.0 1.0 90.9 63.0 1.4 -0.1 0.7 36.2 Liberia .. 156 .. 13.0 .. 84.3 .. .. .. 2.8 Libya 20 99 62.7 25.4 12.0 74.6 .. .. 25.3 .. Lithuania 482 3,980 59.6 59.0 16.0 29.0 0.9 0.6 23.5 11.4 Macedonia, FYR 151 581 32.0 32.0 13.6 22.2 3.6 1.9 50.7 43.9 Madagascar 219 420 29.8 28.2 26.3 43.7 2.2 0.1 41.6 28.1 Malawi 24 .. 27.6 .. 72.4 .. 0.3 .. .. .. Malaysia 11,438 28,184 21.6 25.0 34.7 45.8 0.1 1.6 43.7 27.6 Mali 68 291 32.5 13.4 37.3 60.1 5.1 2.4 25.2 24.1 Mauritania 19 .. 9.1 .. 57.9 .. .. .. 33.0 .. Mauritius 773 2,194 25.8 19.6 55.6 59.4 0.0 1.9 18.5 19.0 Mexico 9,585 17,726 12.1 11.3 64.5 72.8 6.7 11.3 16.7 4.6 Moldova 143 631 29.5 46.3 39.8 26.0 11.6 1.0 19.1 26.7 Mongolia 47 483 31.7 44.4 43.6 46.6 5.3 2.0 19.5 7.0 Morocco 2,020 11,490 20.3 15.8 64.2 62.5 1.4 0.6 14.2 21.1 Mozambique 242 404 24.8 31.8 .. 40.4 .. 1.6 75.2 26.2 Myanmar 353 321 6.5 35.1 42.7 26.8 0.0 .. 50.9 38.1 Namibia 301 580 .. 20.7 92.4 74.9 1.5 0.9 6.2 3.5 Nepal 592 340 9.3 10.9 30.0 58.9 .. 0.9 60.7 29.3 Netherlands 44,646 94,212 40.4 29.2 14.7 14.2 1.2 2.1 43.7 54.5 New Zealand 4,401 9,178 34.7 21.7 52.7 58.9 0.1 1.2 12.6 18.2 Nicaragua 94 332 17.7 12.8 52.5 76.8 2.5 1.2 27.4 9.2 Niger 12 84 3.3 10.5 57.8 43.1 0.0 4.4 38.9 42.0 Nigeria 608 1,333 16.4 82.8 2.8 16.1 0.6 0.3 80.2 0.8 Norway 13,458 40,357 63.3 47.3 16.6 10.5 3.7 3.2 16.4 39.0 Oman 13 1,163 .. 33.1 .. 55.5 .. 0.7 .. 10.7 Pakistan 1,432 2,221 58.0 48.2 7.7 12.4 1.0 4.6 33.4 34.7 Panama 1,298 4,854 60.4 53.7 23.8 24.4 6.1 8.1 9.6 13.8 Papua New Guinea 321 285 10.8 10.9 7.8 1.3 1.2 5.4 80.2 82.4 Paraguay 566 774 13.3 16.6 24.3 13.1 5.0 4.3 57.4 65.9 Peru 1,042 3,209 32.5 19.7 41.1 60.4 7.2 10.0 19.3 9.8 Philippines 9,323 8,448 2.9 15.6 12.2 58.4 0.7 1.2 84.2 24.8 Poland 10,637 28,694 28.6 32.2 21.7 36.9 8.3 1.2 41.4 29.6 Portugal 8,161 22,906 18.6 25.7 59.2 44.4 4.5 2.1 17.7 27.9 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 225 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 1,476 10,398 31.9 24.4 40.0 14.1 5.4 16.6 22.7 44.9 Russian Federation 10,567 39,119 35.8 30.2 40.8 24.6 0.6 4.0 22.8 41.2 Rwanda 11 126 60.6 28.5 21.9 51.8 1.1 0.7 17.6 19.0 Saudi Arabia 3,475 7,901 .. .. .. .. .. .. .. .. Senegal 364 712 15.4 15.9 46.1 35.1 0.6 2.6 37.9 46.4 Serbia .. 3,140 .. 23.1 .. 27.6 .. 1.8 .. 47.6 Sierra Leone 71 42 13.7 36.7 80.5 52.0 0.3 2.0 5.6 9.3 Singapore 25,404 69,712 32.7 33.3 30.0 12.5 8.5 11.6 28.9 42.7 Slovak Republic 2,378 7,022 25.9 32.1 26.2 28.9 4.9 4.8 43.0 34.3 Slovenia 2,016 5,643 25.1 30.6 53.8 39.3 0.6 1.4 20.6 28.7 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 4,414 13,242 24.2 13.6 48.2 63.8 9.9 8.2 17.7 14.4 Spain 40,019 128,340 15.8 16.8 63.4 45.1 3.9 5.9 16.9 32.2 Sri Lanka 800 1,691 41.9 49.6 28.2 22.8 3.4 3.3 26.5 24.3 Sudan 82 342 0.9 3.1 9.7 76.6 3.7 7.9 85.8 12.4 Swaziland 150 447 18.2 2.0 32.2 7.1 0.0 6.9 49.6 84.0 Sweden 15,336 63,054 32.2 17.6 22.6 19.0 2.4 4.4 42.7 58.9 Switzerland 25,179 64,947 15.1 8.4 37.6 18.8 27.8 39.1 19.5 33.8 Syrian Arab Republic 1,632 2,649 14.5 8.2 77.1 76.4 .. 2.4 8.4 12.9 Tajikistan .. 116 .. 53.9 .. 2.8 .. 10.7 .. 32.6 Tanzania 566 1,675 0.3 19.8 88.6 61.9 0.0 1.6 11.1 16.6 Thailand 14,652 30,124 16.8 21.1 54.8 55.3 0.7 1.0 27.7 22.5 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 64 175 33.9 38.6 19.9 11.8 1.8 3.8 44.3 45.8 Trinidad and Tobago 331 883 58.6 24.4 23.4 51.3 9.2 15.3 8.8 9.0 Tunisia 2,401 4,757 24.9 30.2 63.7 54.1 1.5 2.5 9.8 13.1 Turkey 14,475 28,253 11.8 21.9 34.2 65.4 1.5 3.7 52.4 9.0 Turkmenistan 79 .. 79.9 .. 9.3 .. 0.9 .. 10.0 .. Uganda 104 483 17.9 3.1 75.1 73.6 .. 7.5 7.0 15.8 Ukraine 2,846 13,651 75.6 44.8 6.7 33.7 2.7 3.0 15.0 18.5 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 77,549 277,647 20.7 11.8 26.4 13.6 17.5 29.9 35.4 44.7 United States 198,501 472,679 22.7 16.3 37.7 25.2 4.2 14.5 35.5 43.9 Uruguay 1,309 1,735 30.5 35.1 46.7 46.6 1.5 4.6 21.3 13.7 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 1,529 1,552 38.2 26.0 55.5 52.6 0.1 0.1 6.1 21.2 Vietnam 2,243 6,030 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 141 562 21.9 5.5 35.3 75.6 .. .. 42.8 18.9 Zambia 112 279 64.3 34.5 25.9 49.5 .. 6.5 9.8 9.5 Zimbabwe 353 .. 26.4 .. 50.6 .. 0.3 .. 22.7 .. World 1,211,160 t 3,355,922 t 26.9 w 23.7 w 32.5 w 26.6 w 5.9 w 8.4 w 36.2 w 42.1 w Low income 11,661 33,841 27.3 26.4 15.9 19.8 .. 2.6 55.4 51.3 Middle income 182,607 640,179 24.8 23.3 45.2 45.0 5.9 3.8 26.6 28.0 Lower middle income 90,944 380,479 21.4 23.5 47.6 41.7 6.3 1.7 28.3 33.1 Upper middle income 91,769 265,566 27.5 23.1 43.0 47.5 5.5 5.5 25.0 24.0 Low & middle income 194,194 675,065 24.9 24.4 43.8 44.1 5.7 3.7 27.9 27.9 East Asia & Pacific 62,745 211,292 17.4 23.3 49.2 40.6 7.1 1.3 30.6 34.8 Europe & Central Asia 48,999 168,014 37.2 32.9 32.6 32.9 2.3 3.8 28.1 30.5 Latin America & Carib. 37,663 93,750 24.0 18.2 51.3 55.4 6.9 7.1 17.9 19.3 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. South Asia 10,333 95,745 31.8 19.3 29.7 13.7 2.1 4.2 36.4 62.8 Sub-Saharan Africa 12,142 38,309 26.2 32.5 31.3 46.3 5.8 5.5 40.1 16.6 High income 1,014,833 2,679,427 27.5 23.5 29.3 21.7 6.0 9.6 38.5 46.0 Euro Area 422,602 1,083,420 25.6 23.2 31.5 25.1 5.6 5.9 37.6 45.8 a. Includes Luxembourg. 226 2008 World Development Indicators ECONOMY Structure of service exports 4.6 About the data Definitions Balance of payments statistics, the main source of affiliates. Another important dimension of service · Commercial service exports are total service information on international trade in services, have trade not captured by conventional balance of pay- exports minus exports of government services not many weaknesses. Some large economies--such ments statistics is establishment trade--sales in included elsewhere. · Transport covers all transport as the former Soviet Union--did not report data on the host country by foreign affiliates. By contrast, services (sea, air, land, internal waterway, space, trade in services until recently. Disaggregation of cross-border intrafirm transactions in merchandise and pipeline) performed by residents of one economy important components may be limited and varies may be reported as exports or imports in the balance for those of another and involving the carriage of considerably across countries. There are inconsis- of payments. passengers, movement of goods (freight), rental of tencies in the methods used to report items. And the The data on exports of services in the table and carriers with crew, and related support and auxiliary recording of major flows as net items is common (for on imports of services in table 4.7, unlike those in services. Excluded are freight insurance, which is example, insurance transactions are often recorded editions before 2000, include only commercial ser- included in insurance services; goods procured in as premiums less claims). These factors contribute vices and exclude the category "government services ports by nonresident carriers and repairs of trans- to a downward bias in the value of the service trade not included elsewhere." The data are compiled by port equipment, which are included in goods; repairs reported in the balance of payments. the IMF based on returns from national sources. of harbors, railway facilities, and airfield facilities, Efforts are being made to improve the coverage, Data on total trade in goods and services from the which are included in construction services; and quality, and consistency of these data. Eurostat and IMF's Balance of Payments database are shown in rental of carriers without crew, which is included the Organisation for Economic Co-operation and table 4.15. in other services. · Travel covers goods and ser- Development, for example, are working together International transactions in services are defined vices acquired from an economy by travelers in that to improve the collection of statistics on trade in by the IMF's Balance of Payments Manual (1993) as economy for their own use during visits of less than services in member countries. In addition, the Inter- the economic output of intangible commodities that one year for business or personal purposes. · Insur- national Monetary Fund (IMF) has implemented may be produced, transferred, and consumed at the ance and financial services cover freight insurance the new classifi cation of trade in services intro- same time. Definitions may vary among reporting on goods exported and other direct insurance such duced in the fifth edition of its Balance of Payments economies. Travel services include the goods and as life insurance; financial intermediation services Manual (1993). services consumed by travelers, such as meals, such as commissions, foreign exchange transac- Still, difficulties in capturing all the dimensions of lodging, and transport (within the economy visited), tions, and brokerage services; and auxiliary services international trade in services mean that the record including car rental. such as financial market operational and regulatory is likely to remain incomplete. Cross-border intrafirm services. · Computer, information, communica- service transactions, which are usually not captured tions, and other commercial services cover such in the balance of payments, have increased in recent activities as international telecommunications and years. An example is transnational corporations' use postal and courier services; computer data; news- of mainframe computers around the clock for data related service transactions between residents and processing, exploiting time zone differences between nonresidents; construction services; royalties and their home country and the host countries of their license fees; miscellaneous business, professional, and technical services; and personal, cultural, and Top 10 developing economy exporters of commercial services in 2007 4.6a recreational services. Commercial service exports ($ billions) 1995 2007 150 120 90 60 30 0 Data sources China India Russian Thailand Poland Turkey Malaysia Brazil Egypt, Mexico Federation Arab. Rep Data on exports of commercial services are from The top 10 developing economy exporters of commercial services accounted for almost 63 percent of the IMF, which publishes balance of payments developing economy commercial service exports and 13 percent of world commercial service exports. data in its International Financial Statistics and Source: International Monetary Fund balance of payments data files. Balance of Payments Statistics Yearbook. 2008 World Development Indicators 227 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 98 1,391 61.4 12.8 6.7 66.3 22.1 4.7 9.8 16.1 Algeria .. .. .. .. .. .. .. .. .. .. Angola 1,665 11,610 18.2 21.6 4.5 1.8 2.7 4.9 74.6 71.7 Argentina 6,992 10,522 30.1 28.6 46.9 37.3 7.1 4.4 15.9 29.7 Armenia 52 772 82.6 46.8 6.2 38.1 10.3 7.0 0.9 8.1 Australia 16,979 38,540 36.9 33.9 30.4 37.0 7.2 3.3 25.6 25.8 Austria 27,552 38,909 11.9 30.7 39.5 27.2 5.6 6.6 43.0 35.6 Azerbaijan 297 3,324 31.1 16.5 49.1 7.9 0.8 5.7 19.0 69.9 Bangladesh 1,192 2,673 65.0 78.6 19.6 5.8 5.6 8.4 9.7 7.2 Belarus 276 1,999 35.9 44.9 31.5 30.3 3.6 2.9 29.0 21.9 Belgium 32,511a 72,383 24.1a 28.2 27.7a 23.9 10.2a 4.8 38.0a 43.2 Benin 235 342 59.2 63.4 14.7 10.0 10.4 9.9 15.7 16.7 Bolivia 321 810 65.9 35.6 15.0 30.8 9.3 13.8 9.9 19.9 Bosnia and Herzegovina 262 550 51.5 44.1 30.9 35.4 9.5 8.0 8.1 12.6 Botswana 440 970 42.6 41.1 33.0 28.7 8.1 4.3 16.3 25.8 Brazil 13,161 34,776 44.1 24.7 25.8 23.6 9.6 6.1 20.6 45.6 Bulgaria 1,278 4,812 41.5 33.9 15.3 38.0 9.1 4.9 43.2 23.2 Burkina Faso 116 .. 56.0 .. 19.6 .. 4.8 .. 19.6 .. Burundi 62 168 49.4 32.0 41.0 61.7 5.9 1.9 3.8 4.3 Cambodia 181 859 46.4 59.4 4.6 14.3 4.3 5.5 44.7 20.8 Cameroon 485 1,413 35.4 49.7 21.7 22.5 7.2 6.7 35.7 21.0 Canada 32,985 79,824 24.1 23.5 31.1 31.2 11.3 11.3 33.5 34.1 Central African Republic 114 .. 43.7 .. 38.0 .. 7.9 .. 10.4 .. Chad 174 .. 55.0 .. 14.9 .. 1.5 .. 28.6 .. Chile 3,524 9,718 54.0 54.3 19.9 18.1 4.1 8.7 21.9 18.9 China 24,635 129,255 38.7 33.5 15.0 23.0 17.3 8.7 29.0 34.8 Hong Kong, China 24,962 41,234 22.2 31.5 54.0 38.1 6.2 7.1 17.6 23.3 Colombia 2,813 6,170 42.4 42.5 31.2 24.9 11.9 8.1 14.5 24.5 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 690 3,523 18.6 15.0 7.5 4.8 7.3 5.2 66.6 75.0 Costa Rica 895 1,799 41.4 34.8 36.1 34.9 4.6 7.3 17.9 23.0 Côte d'Ivoire 1,235 2,223 50.5 57.3 15.4 17.8 11.0 9.3 23.2 24.9 Croatia 1,327 3,858 29.5 22.6 31.8 25.5 3.4 4.0 35.3 47.9 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 4,860 14,308 16.5 25.3 33.7 25.5 5.2 6.9 44.7 42.3 Denmark 13,945 53,889 45.1 43.4 30.8 21.8 .. .. 24.1 34.8 Dominican Republic 957 1,692 61.1 65.5 18.1 19.3 10.2 7.9 10.6 7.3 Ecuador 1,141 2,481 42.4 53.5 20.6 20.3 5.9 5.8 31.1 20.4 Egypt, Arab Rep. 4,511 13,088 35.1 46.0 28.3 18.7 4.6 10.4 32.0 24.9 El Salvador 488 1,706 55.1 42.6 14.9 35.5 11.0 8.7 19.0 13.2 Eritrea 45 .. 1.6 .. 6.9 .. 0.3 .. 93.1 .. Estonia 420 3,022 52.9 42.0 21.5 22.2 4.7 2.9 20.9 33.0 Ethiopia 337 1,740 63.4 64.7 7.5 6.2 7.4 4.1 21.7 25.1 Finland 9,418 21,745 22.8 24.2 24.2 18.3 5.0 2.2 48.0 55.3 France 64,523 129,464 32.9 29.1 25.4 28.4 6.1 4.6 35.6 37.9 Gabon 832 1,020 17.7 31.4 16.5 26.9 8.6 6.5 57.2 35.2 Gambia, The 47 77 59.6 47.2 30.4 9.6 5.8 8.1 4.2 35.1 Georgia 249 872 27.0 58.2 62.8 20.2 8.4 14.1 1.8 7.6 Germany 128,865 257,096 18.4 23.6 46.8 32.3 1.6 4.2 33.2 39.8 Ghana 331 1,808 61.3 47.3 6.2 30.9 6.5 4.6 26.0 17.3 Greece 4,003 19,783 29.9 54.0 33.1 17.3 4.5 8.5 32.5 20.2 Guatemala 672 2,005 41.4 52.2 21.0 29.8 8.7 10.6 28.9 7.5 Guinea 252 248 58.4 38.9 8.4 11.7 7.2 6.2 26.0 43.2 Guinea-Bissau 27 42 53.1 53.5 14.1 30.9 4.7 0.4 28.1 15.1 Haiti 236 476 77.6 83.9 14.7 11.7 1.7 1.5 5.9 3.0 228 2009 World Development Indicators ECONOMY Commercial Structure of service imports Transport Travel Insurance and 4.7 Computer, information, service imports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 326 1,027 60.4 60.9 17.5 29.8 2.5 1.5 19.7 7.8 Hungary 3,765 15,034 12.8 20.3 39.8 19.6 4.9 3.6 42.5 56.5 India 10,062 77,200 56.7 40.0 9.9 11.7 5.6 6.3 27.9 42.1 Indonesia 13,230 24,022 36.7 39.6 16.4 20.4 3.4 4.3 43.4 35.7 Iran, Islamic Rep. 2,192 .. 43.0 .. 11.0 .. 9.9 .. 36.1 .. Iraq .. 5,030 .. 51.3 .. 7.8 .. 19.4 .. 21.4 Ireland 11,252 94,472 15.9 3.0 18.1 9.2 1.4 17.2 64.6 70.6 Israel 8,131 17,587 44.9 33.1 26.1 18.5 3.0 2.2 26.0 46.2 Italy 54,613 118,261 24.5 23.2 27.2 23.1 9.7 4.0 38.6 49.7 Jamaica 1,073 2,205 .. .. 13.8 13.5 9.2 10.5 77.1 76.0 Japan 121,547 148,685 29.6 33.0 30.2 17.8 2.4 5.2 37.8 44.0 Jordan 1,385 3,317 52.3 54.1 30.7 26.6 6.1 8.1 10.9 11.1 Kazakhstan 776 11,370 38.4 18.6 36.4 9.2 1.8 3.9 25.2 68.3 Kenya 900 1,268 46.4 63.8 21.4 20.7 10.4 -6.6 21.8 22.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 25,394 82,523 38.0 36.2 25.0 25.3 1.5 2.2 35.5 36.3 Kuwait 3,826 10,431 39.4 38.1 58.8 58.7 1.7 1.9 0.1 1.3 Kyrgyz Republic 193 577 27.1 58.4 3.4 15.6 4.3 3.2 65.3 22.8 Lao PDR 119 76 43.3 .. 25.0 .. 4.0 .. 27.7 .. Latvia 225 2,678 68.2 29.3 10.8 34.6 7.0 4.0 14.0 32.0 Lebanon .. 9,970 .. 17.2 .. 31.2 .. 3.0 .. 48.6 Lesotho 58 88 74.9 78.5 22.6 18.5 0.2 .. 2.4 3.1 Liberia .. 214 .. 57.3 .. 9.8 .. 2.3 .. 30.6 Libya 510 2,438 60.4 51.8 15.0 36.4 .. 7.8 24.7 3.9 Lithuania 457 3,282 63.9 47.7 23.3 34.8 1.1 3.2 11.7 14.3 Macedonia, FYR 300 548 49.6 42.3 8.8 12.9 20.7 4.4 20.9 40.4 Madagascar 277 462 55.6 48.5 21.1 15.9 3.7 1.0 19.6 34.7 Malawi 151 .. 66.8 .. 26.0 .. 0.1 .. 7.2 .. Malaysia 14,821 27,784 37.8 39.5 15.6 18.9 .. 3.2 46.5 38.3 Mali 412 672 59.6 60.3 11.9 17.9 1.4 5.0 27.1 16.7 Mauritania 197 .. 61.5 .. 11.6 .. 1.4 .. 25.4 .. Mauritius 630 1,538 39.9 37.4 25.2 23.5 4.6 5.5 30.3 33.6 Mexico 9,021 22,990 38.0 12.0 35.1 36.4 12.5 48.7 14.4 2.9 Moldova 193 593 51.6 41.3 29.2 35.9 9.3 2.1 9.9 20.7 Mongolia 87 514 69.6 49.5 22.3 36.5 1.8 3.9 8.1 10.1 Morocco 1,350 4,527 48.1 48.8 22.4 19.4 3.5 2.5 25.9 29.2 Mozambique 350 819 32.7 36.0 .. 22.0 2.2 2.9 65.1 39.2 Myanmar 233 635 11.0 48.6 7.7 5.8 0.5 .. 80.8 45.6 Namibia 538 505 36.5 47.7 16.7 26.2 9.5 6.3 37.3 19.8 Nepal 305 716 36.3 40.1 44.7 38.2 3.0 4.3 15.9 17.4 Netherlands 43,618 83,769 28.9 24.9 26.8 22.8 3.0 3.2 41.3 49.2 New Zealand 4,571 8,940 41.2 33.1 27.5 34.3 5.2 4.1 26.1 28.6 Nicaragua 207 525 39.1 54.3 19.3 23.0 3.3 10.8 38.3 11.9 Niger 120 327 74.4 69.4 11.1 8.5 2.6 3.1 12.0 19.0 Nigeria 4,398 11,837 22.4 28.5 20.6 20.6 2.5 0.0 54.4 50.8 Norway 13,052 38,941 38.2 33.8 32.4 36.1 5.6 3.5 23.7 26.7 Oman 985 4,996 41.8 34.5 4.8 14.9 4.6 7.7 48.8 42.9 Pakistan 2,431 8,409 67.0 38.7 18.4 18.9 4.3 3.2 10.3 39.2 Panama 1,049 2,050 71.0 59.1 11.5 15.0 8.8 14.3 8.7 11.7 Papua New Guinea 642 1,151 25.2 24.2 9.1 4.8 2.8 10.3 63.0 60.7 Paraguay 676 442 66.4 59.1 19.7 24.6 12.4 12.0 1.4 4.3 Peru 1,781 4,133 50.8 45.7 16.7 24.4 10.2 8.1 22.3 21.8 Philippines 6,906 7,245 29.7 52.7 6.1 22.3 1.6 6.7 62.6 18.3 Poland 7,008 23,696 25.2 23.9 5.9 32.7 13.6 3.9 55.3 39.5 Portugal 6,339 13,686 26.8 32.3 33.1 28.7 8.9 4.1 31.1 35.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 229 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 1,801 10,047 33.5 32.6 38.7 15.3 5.3 17.8 22.4 34.3 Russian Federation 20,205 57,810 16.4 16.2 57.4 38.5 0.4 4.0 25.9 41.3 Rwanda 58 259 72.8 33.2 17.1 26.7 0.8 0.7 10.1 39.4 Saudi Arabia 8,670 30,798 25.3 28.9 .. .. 2.8 3.2 71.9 67.8 Senegal 405 805 57.1 58.5 17.7 6.7 7.0 9.8 18.2 25.0 Serbia .. 3,456 .. 28.7 .. 30.2 .. 2.5 .. 38.6 Sierra Leone 79 86 17.4 56.3 62.5 16.5 3.8 9.9 16.3 17.3 Singapore 20,728 72,214 44.8 34.5 22.5 16.4 10.1 6.7 22.6 42.4 Slovak Republic 1,800 6,449 17.0 28.5 17.8 23.8 4.9 9.6 60.2 38.1 Slovenia 1,429 4,186 30.6 24.0 40.2 26.3 1.8 2.6 27.4 47.1 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 5,756 16,258 39.9 46.9 32.1 24.2 14.1 5.1 13.8 23.9 Spain 22,354 98,431 31.1 25.1 20.3 20.0 7.4 8.1 41.2 46.8 Sri Lanka 1,169 2,569 58.1 62.8 15.9 15.3 5.4 6.0 20.5 15.9 Sudan 150 2,873 27.3 45.6 28.7 51.4 0.3 0.3 43.7 2.7 Swaziland 206 494 15.7 11.7 20.7 10.4 4.3 12.2 59.2 65.7 Sweden 17,112 47,813 28.4 15.9 31.8 29.2 1.4 2.8 38.4 52.1 Switzerland 14,899 33,209 35.2 21.3 49.8 30.9 1.1 8.1 13.9 39.7 Syrian Arab Republic 1,358 2,437 57.2 51.5 36.7 22.2 5.6 15.2 6.1 11.2 Tajikistan .. 590 .. 24.1 .. 1.1 .. 4.7 .. 70.1 Tanzania 729 1,424 29.8 34.1 49.4 45.3 2.7 5.4 18.0 15.2 Thailand 18,629 38,173 41.8 47.6 22.9 13.5 5.2 5.0 30.2 33.9 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 148 295 70.8 78.6 12.5 1.8 4.4 11.4 12.3 8.2 Trinidad and Tobago 223 471 .. .. 31.0 38.2 7.9 6.5 61.0 55.3 Tunisia 1,245 2,662 45.3 54.8 20.1 16.4 6.5 8.3 28.1 20.5 Turkey 4,654 14,160 30.3 46.0 19.6 23.0 8.4 15.3 41.7 15.6 Turkmenistan 403 .. 40.4 .. 18.2 .. 6.9 .. 34.6 .. Uganda 563 1,159 38.2 53.8 14.3 9.7 4.2 6.9 43.3 29.6 Ukraine 1,334 11,055 34.0 35.3 15.7 32.3 7.3 9.5 42.9 22.9 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 62,524 197,188 27.1 19.1 39.9 36.3 4.4 8.1 28.7 36.5 United States 129,227 341,673 32.3 28.0 35.8 23.7 5.9 18.1 26.0 30.3 Uruguay 814 1,208 46.2 51.4 29.0 19.8 4.5 5.3 20.2 23.5 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 4,654 7,243 30.7 51.3 36.8 19.2 2.6 9.8 29.9 19.6 Vietnam 2,304 6,924 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 604 2,069 35.6 42.7 12.5 8.9 7.1 7.9 44.8 40.5 Zambia 282 886 78.9 46.5 9.2 6.3 0.0 8.7 11.9 38.6 Zimbabwe 645 .. 56.0 .. 18.7 .. 2.9 .. 22.5 .. World 1,218,748 t 3,075,521 t 31.2 w 28.4 w 30.9 w 25.7 w 6.2 w 9.7 w 32.1 w 36.3 w Low income 22,663 59,403 49.0 47.4 18.0 15.7 4.8 .. 29.0 31.4 Middle income 214,348 676,675 38.7 33.7 23.0 25.8 9.9 13.8 29.4 26.8 Lower middle income 107,442 386,677 41.8 40.9 16.3 21.8 10.4 7.5 31.5 29.8 Upper middle income 106,960 291,319 36.0 28.2 28.7 28.9 9.3 18.5 27.6 24.6 Low & middle income 236,387 735,826 39.2 34.4 22.7 25.4 9.6 13.4 29.4 27.0 East Asia & Pacific 82,593 239,360 38.0 38.8 15.5 20.6 12.1 6.7 36.7 34.0 Europe & Central Asia 43,870 165,739 29.3 31.2 26.4 29.7 7.1 8.0 37.7 31.1 Latin America & Carib. 52,171 116,734 41.3 25.9 31.2 30.5 10.1 27.7 17.8 16.1 Middle East & N. Africa 19,235 52,758 44.9 46.4 21.4 18.8 .. 9.9 28.2 24.9 South Asia 15,377 92,697 58.6 45.4 13.4 13.1 5.3 6.1 22.6 35.3 Sub-Saharan Africa 24,584 73,653 39.6 43.8 24.1 22.2 8.8 4.0 28.1 30.2 High income 981,892 2,345,145 29.1 27.0 33.1 25.8 5.4 8.9 32.7 38.5 Euro area 421,783 1,001,166 24.9 25.8 31.8 26.0 5.4 5.0 37.9 43.2 a. Includes Luxembourg. 230 2008 World Development Indicators ECONOMY Structure of service imports 4.7 About the data Definitions Trade in services differs from trade in goods because · Commercial service imports are total service services are produced and consumed at the same imports minus imports of government services not time. Thus services to a traveler may be consumed included elsewhere. · Transport covers all transport in the producing country (for example, use of a hotel services (sea, air, land, internal waterway, space, room) but are classified as imports of the traveler's and pipeline) performed by residents of one economy country. In other cases services may be supplied for those of another and involving the carriage of from a remote location; for example, insurance passengers, movement of goods (freight), rental of services may be supplied from one location and carriers with crew, and related support and auxiliary consumed in another. For further discussion of the services. Excluded are freight insurance, which is problems of measuring trade in services, see About included in insurance services; goods procured in the data for table 4.6. ports by nonresident carriers and repairs of trans- The data on imports of services in the table and on port equipment, which are included in goods; repairs exports of services in table 4.6, unlike those in edi- of harbors, railway facilities, and airfield facilities, tions before 2000, include only commercial services which are included in construction services; and and exclude the category "government services not rental of carriers without crew, which is included included elsewhere." The data are compiled by the in other services. · Travel covers goods and ser- International Monetary Fund (IMF) based on returns vices acquired from an economy by travelers in that from national sources. economy for their own use during visits of less than International transactions in services are defined one year for business or personal purposes. · Insur- by the IMF's Balance of Payments Manual (1993) as ance and financial services cover freight insurance the economic output of intangible commodities that on goods imported and other direct insurance such may be produced, transferred, and consumed at the as life insurance; financial intermediation services same time. Definitions may vary among reporting such as commissions, foreign exchange transac- economies. tions, and brokerage services; and auxiliary services Travel services include the goods and services such as financial market operational and regulatory consumed by travelers, such as meals, lodging, and services. · Computer, information, communica- transport (within the economy visited), including car tions, and other commercial services cover such rental. activities as international telecommunications, and postal and courier services; computer data; news- related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and The mix of commercial service imports by developing economies is changing 4.7a recreational services. 1995 2007 ($236 million) ($736 million) Other 29% Transport Other 39% 27% Transport 34% Insurance and Travel financial 10% 23% Insurance and financial 13% Travel 25% Data sources Between 1995 and 2007 developing economies' commercial service imports more than doubled. Insur- Data on imports of commercial services are from ance and financial services and travel services are displacing transport and other services as the most the IMF, which publishes balance of payments important services imported. data in its International Financial Statistics and Source: International Monetary Fund balance of payments data files. Balance of Payments Statistics Yearbook. 2008 World Development Indicators 231 4.8 Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan .. 110 .. 9 .. 25 .. 12 .. 56 .. 24 Albania 87 88 14 9 21 30 12 28 35 54 21 20 Algeria 55 31 17 12 31 33 26 47 29 23 26 57 Angola 51 54 40 ..a 35 14 82 71 68 39 30 25 Argentina 69 59 13 13 18 24 10 25 10 20 16 27 Armenia 109 72 11 11 18 37 24 19 62 39 ­7 31 Australia 59 56 18 18 24 27 18 21 20 22 18 22 Austria 57 54 20 18 23 21 35 59 35 52 21 26 Azerbaijan 77 26 13 11 24 21 28 72 42 30 14 50 Bangladesh 83 77 5 6 19 24 11 20 17 27 21 35 Belarus 59 54 21 19 25 33 50 62 54 68 21 27 Belgium 54 52 22 22 20 22 68 89 63 87 25 25 Benin 82 78 11 15 20 20 20 13 33 26 8 11 Bolivia 76 63 14 14 15 15 23 42 27 34 11 30 Bosnia and Herzegovina .. 90 .. 22 20 23 20 39 71 74 10 9 Botswana 34 29 29 20 25 41 51 47 38 37 36 55 Brazil 62 61 21 20 18 18 7 14 9 12 16 17 Bulgaria 71 69 15 16 16 37 45 63 46 85 12 18 Burkina Faso 63 75 25 22 24 18 14 12 27 27 18 6 Burundi 89 91 19 29 6 17 13 11 27 48 4 1 Cambodia 95 83 6 3 15 21 31 65 47 73 5 15 Cameroon 72 73 9 9 13 17 24 22 18 21 14 20 Canada 57 56 21 19 19 23 37 36 34 34 18 24 Central African Republic 79 96 15 3 14 9 20 15 28 22 6 4 Chad 91 60 7 6 13 19 22 49 34 34 5 23 Chile 61 55 10 10 26 21 29 47 27 33 25 25 China 42 33 14 14 42 43 23 42 21 32 43 55 Hong Kong, China 62 60 8 8 34 21 143 207 148 196 30 35 Colombia 65 63 15 17 26 24 15 17 21 21 18 19 Congo, Dem. Rep. 81 80 5 11 9 20 28 28 24 39 1 12 Congo, Rep. 49 29 13 14 37 27 65 73 64 43 ­3 36 Costa Rica 71 67 14 13 18 25 38 49 40 54 15 19 Côte d'Ivoire 66 77 11 8 16 9 42 47 34 41 12 9 Croatia 64 56 29 20 18 33 39 48 49 56 11 24 Cuba 71 .. 24 .. 7 .. 13 .. 16 .. .. .. Czech Republic 51 48 21 20 33 27 51 80 55 75 29 25 Denmark 51 50 25 26 20 23 38 52 34 51 22 24 Dominican Republic 79 79 5 7 19 22 31 35 34 41 18 20 Ecuador 68 66 13 11 22 24 26 34 28 34 17 26 Egypt, Arab Rep. 74 72 11 11 20 21 23 30 28 35 22 23 El Salvador 87 96 9 7 20 20 22 26 38 50 15 12 Eritrea 94 86 44 31 23 11 22 6 83 34 4 10 Estonia 54 56 26 17 28 38 68 74 76 85 24 20 Ethiopia 80 84 8 11 18 25 10 13 16 32 21 21 Finland 52 52 23 21 18 22 36 45 29 40 22 27 France 57 57 24 23 19 22 23 27 22 28 19 19 Gabon 41 36 12 9 23 26 59 65 36 36 33 41 Gambia, The 90 77 14 16 20 23 49 33 73 50 6 12 Georgia 102 70 11 22 4 35 26 32 42 58 ­7 17 Germany 58 57 20 18 22 18 24 47 23 40 20 25 Ghana 76 79 12 14 20 34 24 33 33 60 18 23 Greece 75 71 15 17 19 26 17 22 27 35 18 9 Guatemala 86 88 6 8 15 21 19 26 25 42 11 17 Guinea 74 84 8 6 21 13 21 37 25 39 14 9 Guinea-Bissau 95 70 6 16 22 17 12 43 35 46 5 23 Haiti 87 91 7 9 24 29 9 14 27 43 10 .. 232 2009 World Development Indicators ECONOMY Household General Structure of demand Gross Exports Imports 4.8 Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 64 77 9 16 32 33 44 51 48 78 27 23 Hungary 66 66 11 10 23 22 45 80 45 79 19 16 India 64 54 11 10 27 39 11 21 12 24 27 39 Indonesia 62 63 8 8 32 25 26 29 28 25 28 26 Iran, Islamic Rep. 46 45 16 11 29 33 22 32 13 22 37 43 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 54 46 16 16 18 27 76 80 64 69 23 23 Israel 56 56 28 25 25 20 29 43 37 45 13 .. Italy 58 59 18 20 20 21 26 29 22 30 22 20 Jamaica 70 72 11 16 29 30 51 45 61 63 24 15 Japan 55 57 15 18 28 24 9 16 8 15 30 28 Jordan 65 91 24 23 33 27 52 58 73 99 29 9 Kazakhstan 68 47 14 10 23 36 39 49 44 43 18 29 Kenya 70 74 15 17 22 20 33 26 39 37 16 17 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 52 55 11 15 38 29 29 46 30 45 36 30 Kuwait 43 30 32 15 15 20 52 65 42 30 .. .. Kyrgyz Republic 75 101 20 18 18 26 29 45 42 90 9 7 Lao PDR .. 68 .. 8 .. 40 23 37 37 53 15 23 Latvia 63 65 24 18 14 37 43 44 45 65 14 15 Lebanon 101 92 15 15 36 18 11 25 62 50 ­3 0 Lesotho 104 98 24 26 56 28 19 55 103 107 27 41 Liberia .. 116 .. 15 .. 20 9 33 72 84 .. 40 Libya 59 .. 22 .. 12 .. 29 .. 22 .. .. .. Lithuania 67 66 22 17 22 30 50 55 61 67 13 16 Macedonia, FYR 70 78 19 18 21 23 33 55 43 75 14 21 Madagascar 90 84 7 5 11 27 24 30 32 47 1 13 Malawi 79 84 21 12 17 26 30 24 48 45 ­4 10 Malaysia 48 46 12 12 44 22 94 110 98 90 34 38 Mali 83 76 10 11 23 23 21 27 36 37 14 13 Mauritania 77 61 11 20 20 26 37 58 45 65 17 29 Mauritius 63 69 13 14 29 27 58 62 64 71 26 20 Mexico 67 65 10 10 20 26 30 28 28 30 19 25 Moldova 57 95 27 19 25 38 49 46 58 98 19 22 Mongolia 56 48 13 13 32 40 48 64 49 66 35 42 Morocco 68 58 17 18 21 33 27 36 34 45 17 32 Mozambique 90 76 8 12 27 19 16 39 41 46 ­6 3 Myanmar 87 .. ..a .. 14 .. 1 .. 2 .. 14 .. Namibia 54 50 30 25 22 30 49 50 56 54 32 40 Nepal 75 81 9 9 25 28 25 13 35 31 23 29 Netherlands 49 47 24 25 21 20 59 75 54 67 27 28 New Zealand 58 60 17 19 23 23 29 29 28 31 18 15 Nicaragua 83 90 11 12 22 32 19 33 35 67 ­1 14 Niger 86 75 14 12 7 23 17 15 24 25 ­4 .. Nigeria .. .. .. .. .. .. 44 40 42 30 .. .. Norway 50 42 22 20 22 23 38 46 32 30 26 38 Oman 51 39 25 18 15 19 44 63 36 38 .. .. Pakistan 72 75 12 9 19 23 17 14 19 21 21 25 Panama 52 60 15 11 30 23 101 80 98 75 30 23 Papua New Guinea 44 47 17 11 22 20 61 90 44 68 36 35 Paraguay 76 74 10 11 26 18 59 51 71 54 18 20 Peru 71 61 10 9 25 23 13 29 18 22 25 24 Philippines 74 75 11 10 22 15 36 43 44 42 19 34 Poland 60 60 20 19 19 24 23 41 21 44 20 21 Portugal 65 65 18 20 23 22 29 33 35 40 23 12 Puerto Rico .. .. .. .. .. .. 72 .. 97 .. .. .. 2009 World Development Indicators 233 4.8 Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 68 69 14 13 24 30 28 31 33 44 19 21 Russian Federation 52 49 19 18 25 25 29 30 26 22 28 31 Rwanda 97 86 10 11 13 21 5 10 26 28 12 16 Saudi Arabia 47 28 24 23 20 22 38 65 28 38 20 .. Senegal 80 78 13 10 14 33 31 24 37 44 8 22 Serbia 73 81 23 18 12 23 17 29 24 51 6 8 Sierra Leone 88 83 14 10 6 13 19 21 26 28 ­3 10 Singapore 41 38 8 10 34 23 .. 231 .. 202 52 .. Slovak Republic 52 56 22 17 24 28 58 86 56 87 27 23 Slovenia 60 52 19 18 24 31 50 70 52 71 23 27 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 63 62 18 20 18 21 23 32 22 35 17 14 Spain 60 57 18 18 22 31 22 26 22 33 22 21 Sri Lanka 73 68 11 15 26 27 36 29 46 40 20 23 Sudan 85 65 5 15 14 24 5 20 10 24 4 12 Swaziland 82 73 15 15 16 13 60 80 74 81 7 20 Sweden 50 47 27 26 17 20 40 52 33 45 20 28 Switzerland 60 59 12 11 23 22 36 52 31 45 30 35 Syrian Arab Republic 66 68 13 12 27 20 31 41 38 41 23 19 Tajikistan 62 114 16 9 29 22 66 21 72 66 23 14 Tanzaniab 86 73 12 16 20 17 24 22 42 28 0 11 Thailand 55 53 10 13 42 27 42 73 49 66 34 33 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 77 81 12 19 16 18 32 40 37 57 11 7 Trinidad and Tobago 53 55 12 12 21 13 54 58 39 37 26 30 Tunisia 63 63 16 14 25 25 45 54 49 57 20 23 Turkey 78 71 7 13 18 22 14 22 17 27 15 16 Turkmenistan 44 46 8 13 49 23 75 65 75 48 50 34 Uganda 85 79 11 13 12 22 12 17 21 31 8 14 Ukraine 55 60 21 19 27 27 47 45 50 51 23 23 United Arab Emirates 48 45 16 10 30 21 69 91 63 68 .. .. United Kingdom 63 63 20 22 17 19 28 26 29 29 16 15 United States 68 70 15 16 18 20 11 11 12 17 16 14 Uruguay 73 74 12 11 15 15 19 29 19 30 14 13 Uzbekistan 51 54 22 17 27 19 28 40 28 30 27 39 Venezuela, RB 69 54 7 12 18 28 27 31 22 25 21 35 Vietnam 74 66 8 6 27 42 33 77 42 90 19 35 West Bank and Gaza 98 96 18 33 35 26 16 14 68 68 11 13 Yemen, Rep. 71 .. 14 .. 22 .. 51 .. 58 .. 20 .. Zambia 72 59 15 10 16 24 36 42 40 36 5 23 Zimbabwe 65 72 18 27 20 17 38 57 41 73 17 0 World 61 w 61 w 17 w 17 w 22 w 22 w 21 w 28 w 21 w 29 w 21 w 22 w Low income 75 74 11 9 20 25 24 32 29 38 17 26 Middle income 60 55 14 14 27 29 23 33 24 31 26 32 Lower middle income 55 48 13 12 33 35 23 37 24 33 32 42 Upper middle income 64 61 15 15 20 23 23 30 23 28 19 23 Low & middle income 61 55 14 14 26 29 23 33 24 31 25 32 East Asia & Pacific 47 41 13 13 40 38 29 48 29 39 38 48 Europe & Central Asia 63 60 16 16 21 25 26 34 27 36 21 23 Latin America & Carib. 66 62 15 14 20 22 18 24 19 23 18 22 Middle East & N. Africa 63 58 15 13 25 28 26 36 29 34 25 33 South Asia 67 59 10 10 25 35 12 21 15 25 25 36 Sub-Saharan Africa 69 66 15 16 18 22 28 34 30 37 14 17 High income 61 62 17 18 21 21 21 27 20 28 21 20 Euro area 57 57 20 20 21 22 29 41 28 39 21 22 a. Data for general government final consumption expenditure are not available separately; they are included in household final consumption expenditure. b. Covers mainland Tanzania only. 234 2008 World Development Indicators ECONOMY Structure of demand 4.8 About the data Definitions Gross domestic product (GDP) from the expenditure capital outlays on defense establishments that may · Household final consumption expenditure is the side is made up of household final consumption be used by the general public, such as schools, air- market value of all goods and services, including expenditure, general government final consumption fields, and hospitals, and intangibles such as com- durable products (such as cars and computers), expenditure, gross capital formation (private and puter software and mineral exploration outlays. Data purchased by households. It excludes purchases public investment in fixed assets, changes in inven- on capital formation may be estimated from direct of dwellings but includes imputed rent for owner- tories, and net acquisitions of valuables), and net surveys of enterprises and administrative records occupied dwellings. It also includes government fees exports (exports minus imports) of goods and ser- or based on the commodity flow method using data for permits and licenses. Expenditures of nonprofit vices. Such expenditures are recorded in purchaser from production, trade, and construction activities. institutions serving households are included, even prices and include net taxes on products. The quality of data on government fixed capital forma- when reported separately. Household consumption Because policymakers have tended to focus on tion depends on the quality of government account- expenditure may include any statistical discrepancy fostering the growth of output, and because data ing systems (which tend to be weak in developing in the use of resources relative to the supply of on production are easier to collect than data on countries). Measures of fixed capital formation by resources. · General government fi nal consump- spending, many countries generate their primary households and corporations--particularly capital tion expenditure is all government current expendi- estimate of GDP using the production approach. outlays by small, unincorporated enterprises--are tures for purchases of goods and services (including Moreover, many countries do not estimate all the usually unreliable. compensation of employees). It also includes most components of national expenditures but instead Estimates of changes in inventories are rarely expenditures on national defense and security but derive some of the main aggregates indirectly using complete but usually include the most important excludes military expenditures with potentially wider GDP (based on the production approach) as the activities or commodities. In some countries these public use that are part of government capital forma- control total. Household final consumption expen- estimates are derived as a composite residual along tion. · Gross capital formation is outlays on addi- diture (private consumption in the 1968 System of with household fi nal consumption expenditure. tions to fixed assets of the economy, net changes in National Accounts, or SNA) is often estimated as According to national accounts conventions, adjust- inventories, and net acquisitions of valuables. Fixed a residual, by subtracting all other known expendi- ments should be made for appreciation of the value assets include land improvements (fences, ditches, tures from GDP. The resulting aggregate may incor- of inventory holdings due to price changes, but this drains); plant, machinery, and equipment purchases; porate fairly large discrepancies. When household is not always done. In highly inflationary economies and construction (roads, railways, schools, buildings, consumption is calculated separately, many of the this element can be substantial. and so on). Inventories are goods held to meet tem- estimates are based on household surveys, which Data on exports and imports are compiled from porary or unexpected fluctuations in production or tend to be one-year studies with limited coverage. customs reports and balance of payments data. sales, and "work in progress." · Exports and imports Thus the estimates quickly become outdated and Although the data from the payments side provide of goods and services are the value of all goods must be supplemented by estimates using price- and reasonably reliable records of cross-border transac- and other market services provided to or received quantity-based statistical procedures. Complicating tions, they may not adhere strictly to the appropriate from the rest of the world. They include the value of the issue, in many developing countries the distinc- definitions of valuation and timing used in the bal- merchandise, freight, insurance, transport, travel, tion between cash outlays for personal business ance of payments or correspond to the change-of- royalties, license fees, and other services (com- and those for household use may be blurred. World ownership criterion. This issue has assumed greater munication, construction, financial, information, Development Indicators includes in household con- significance with the increasing globalization of inter- business, personal, government services, and so sumption the expenditures of nonprofit institutions national business. Neither customs nor balance of on). They exclude compensation of employees and serving households. payments data usually capture the illegal transac- investment income (factor services in the 1968 SNA) General government final consumption expenditure tions that occur in many countries. Goods carried and transfer payments. · Gross savings are gross (general government consumption in the 1968 SNA) by travelers across borders in legal but unreported national income less total consumption, plus net includes expenditures on goods and services for shuttle trade may further distort trade statistics. transfers. individual consumption as well as those on services Gross savings represent the difference between for collective consumption. Defense expenditures, disposable income and consumption and replace including those on capital outlays (with certain excep- gross domestic savings, a concept used by the World tions), are treated as current spending. Bank and included in World Development Indicators Data sources Gross capital formation (gross domestic investment editions before 2006. The change was made to con- in the 1968 SNA) consists of outlays on additions form to SNA concepts and definitions. For further Data on national accounts indicators for most to the economy's fixed assets plus net changes in discussion of the problems in compiling national developing countries are collected from national the level of inventories. It is generally obtained from accounts, see Srinivasan (1994), Heston (1994), statistical organizations and central banks by vis- industry reports of acquisitions and distinguishes only and Ruggles (1994). For an analysis of the reliability iting and resident World Bank missions. Data for the broad categories of capital formation. The 1993 of foreign trade and national income statistics, see high-income economies are from Organisation for SNA recognizes a third category of capital forma- Morgenstern (1963). Economic Co-operation and Development (OECD) tion: net acquisitions of valuables. Included in gross data files. capital formation under the 1993 SNA guidelines are 2008 World Development Indicators 235 4.9 Growth of consumption and investment Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 4.3 4.3 5.2 3.8 2.4 2.2 25.8 5.5 18.9 11.0 15.7 14.2 Algeria ­0.1 5.5 ­1.9 3.9 3.6 4.3 ­0.6 9.4 3.2 3.3 ­1.0 7.9 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2.8 3.8 1.5 2.8 2.2 2.3 7.4 11.2 8.7 7.5 15.6 8.1 Armenia ­0.5 8.6 1.1 9.0 ­1.5 11.8 ­1.9 24.5 ­18.4 13.2 ­12.7 11.3 Australia 3.2 3.9 2.0 2.5 2.9 3.2 5.1 7.4 7.7 2.0 7.6 8.8 Austria 1.9 1.4 1.5 0.9 2.5 1.5 2.4 0.8 5.5 6.1 5.0 4.9 Azerbaijan ­1.7 12.4 ­2.7 11.4 ­1.7 14.2 41.7 26.6 5.7 22.9 14.1 24.0 Bangladesh 2.6 4.1 0.5 2.2 4.7 10.2 9.2 8.6 13.1 11.8 9.7 9.5 Belarus ­0.5 11.2 ­0.3 11.7 ­1.9 2.2 ­7.5 15.2 ­4.8 7.8 ­8.7 9.6 Belgium 1.8 1.4 1.5 0.8 1.4 1.5 2.7 3.6 4.7 3.3 4.5 3.3 Benin 2.6 2.3 ­0.8 ­0.9 4.4 8.3 12.2 7.7 1.8 2.7 2.1 1.8 Bolivia 3.6 2.8 1.3 0.8 3.6 3.4 8.5 0.2 4.5 10.2 6.0 6.2 Bosnia and Herzegovina .. 1.7 .. .. .. 4.5 .. 6.4 .. 9.8 .. 2.4 Botswana 2.5 4.2 0.1 2.9 6.5 3.9 6.7 ­0.3 4.7 4.5 3.8 1.8 Brazila 3.7 2.6 2.2 1.2 1.0 3.0 4.2 2.5 5.9 9.5 11.6 6.0 Bulgaria ­3.7 6.1 ­3.0 6.8 ­8.4 3.6 ­5.0 16.5 3.9 9.5 2.7 12.7 Burkina Faso 5.7 4.5 2.7 1.2 2.9 8.7 3.1 9.0 4.4 10.9 1.9 7.2 Burundi ­4.9 .. .. .. ­2.6 .. ­0.5 .. ­1.2 .. ­1.6 .. Cambodiaa 6.0 8.9 3.4 7.0 7.2 1.9 10.3 13.5 21.7 16.9 14.8 15.4 Cameroon 3.1 4.5 0.5 2.2 0.7 2.8 0.4 3.9 3.2 0.7 5.1 4.0 Canada 2.6 3.4 1.6 2.4 0.3 2.8 4.6 6.5 8.7 0.9 7.1 4.2 Central African Republica .. ­0.9 .. ­2.5 .. ­1.3 .. 1.2 .. ­1.5 .. ­1.8 Chada 1.5 2.2 ­1.8 ­1.3 ­8.3 5.7 4.0 1.5 2.3 41.9 ­1.8 6.2 Chile 7.3 5.6 5.6 4.5 3.7 4.6 9.3 8.8 9.4 6.7 11.7 11.5 China 8.9 7.8 7.8 7.2 9.7 9.1 11.5 13.4 12.9 24.4 14.3 18.6 Hong Kong, China 3.8 3.1 2.0 2.6 3.7 0.9 4.8 2.1 7.8 10.2 8.4 9.0 Colombia 2.2 4.4 0.4 2.9 10.5 4.5 2.0 13.3 5.3 5.2 9.0 10.8 Congo, Dem. Rep.a ­4.5 .. ­7.2 .. ­17.4 .. ­0.7 .. ­0.5 7.7 ­2.4 20.3 Congo, Rep.a ­1.8 .. .. .. ­4.4 .. 10.4 .. 3.0 .. 2.0 .. Costa Ricaa 5.1 3.9 2.5 2.0 2.0 1.4 5.1 8.8 10.9 7.8 9.2 6.6 Côte d'Ivoire 4.1 ­1.2 1.2 ­2.9 0.8 2.7 8.1 ­0.8 1.9 4.5 8.2 4.3 Croatia 2.7 4.8 3.4 4.8 1.3 1.1 5.4 12.2 5.9 6.2 4.6 7.9 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 3.0 3.8 3.0 3.7 ­0.9 2.2 4.6 4.7 8.7 11.9 12.0 10.6 Denmark 2.2 3.0 1.8 2.6 2.4 1.5 5.7 3.9 5.1 4.2 6.1 6.8 Dominican Republica 5.3 3.8 3.4 2.2 5.2 5.1 10.4 1.2 9.1 4.2 9.4 1.2 Ecuador a 2.1 5.9 0.3 4.7 ­1.5 2.9 ­0.6 8.1 5.3 7.1 2.8 9.9 Egypt, Arab Rep. 3.7 3.8 1.8 2.0 4.4 2.8 5.8 5.7 3.5 16.7 3.0 13.5 El Salvador 5.3 3.5 3.3 2.0 2.8 1.3 7.1 2.5 13.4 4.9 11.6 5.0 Eritrea ­5.0 1.6 ­6.7 ­2.3 22.6 1.2 19.1 ­1.0 ­2.5 ­6.3 7.5 ­3.7 Estonia 0.6 10.0 2.1 10.3 5.7 1.8 0.5 14.0 11.0 9.0 12.0 12.2 Ethiopia 3.6 9.5 0.4 6.7 9.0 0.4 6.5 9.3 7.1 12.8 5.8 12.9 Finland 1.7 3.5 1.4 3.1 0.6 1.6 2.2 4.2 10.3 5.1 6.5 6.1 France 1.6 2.4 1.2 1.7 1.4 1.7 1.8 2.7 6.9 2.5 5.7 4.4 Gabona ­0.3 4.3 ­2.8 2.6 3.7 0.4 3.0 7.3 2.1 ­1.6 0.1 4.5 Gambia, The 3.6 1.8 ­0.1 ­1.4 ­2.2 4.2 1.9 10.7 0.1 ­0.4 0.1 0.9 Georgia 6.1 9.2 7.5 10.3 12.0 7.3 ­12.5 18.4 12.2 6.4 11.2 8.4 Germany 1.9 0.2 1.6 0.2 1.9 0.4 1.1 ­0.2 6.0 7.2 5.8 5.4 Ghana 4.1 5.8 1.4 3.5 4.8 ­1.0 4.3 14.4 10.1 3.9 10.4 7.2 Greece 2.1 4.4 1.4 4.0 2.1 2.3 4.1 7.6 7.6 3.0 7.4 4.8 Guatemalaa 4.2 4.0 1.8 1.5 5.1 0.4 6.1 4.1 6.1 2.3 9.2 3.4 Guinea 5.2 3.7 2.0 1.8 ­0.5 ­0.9 0.1 ­5.6 0.3 1.3 ­1.1 ­1.6 Guinea-Bissau 2.6 6.9 ­0.4 3.7 1.9 ­0.8 ­6.5 ­0.4 15.4 4.5 ­0.4 0.7 Haiti .. .. .. .. .. .. .. .. .. .. .. .. 236 2009 World Development Indicators ECONOMY Growth of consumption and investment Household final General government Gross capital 4.9 Goods and consumption final consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 Hondurasa 3.0 5.4 0.6 3.4 2.0 5.8 6.9 6.6 1.6 6.9 3.8 7.4 Hungary ­0.1 4.5 0.1 4.7 0.9 2.3 9.6 0.8 9.9 11.2 11.4 10.0 India 4.8 6.0 2.9 4.5 6.6 3.4 6.9 15.1 12.3 15.7 14.4 19.4 Indonesia 6.6 4.1 5.0 2.7 0.1 7.9 ­0.6 5.7 5.9 8.3 5.7 9.8 Iran, Islamic Rep. 3.2 7.4 1.6 5.8 1.6 3.6 ­0.1 8.3 1.2 5.0 ­6.8 13.2 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 4.6 4.7 3.7 2.7 6.1 6.0 10.2 5.6 15.7 4.9 14.5 4.4 Israel 4.8 3.2 2.2 1.3 2.7 1.0 1.8 1.7 10.9 5.6 7.5 3.4 Italy 1.5 0.9 1.5 0.2 ­0.2 2.0 1.6 1.6 5.9 1.8 4.4 2.8 Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan 1.5 1.3 1.3 1.1 2.9 1.9 ­0.8 0.1 4.1 7.7 4.2 4.3 Jordan 4.9 6.7 1.1 4.1 4.7 2.9 0.3 8.7 2.6 8.9 1.5 8.4 Kazakhstana ­8.1 10.3 ­7.0 9.7 ­7.1 8.1 ­18.3 21.3 ­2.6 7.4 ­11.2 7.4 Kenya 3.6 4.1 0.6 1.4 6.9 2.3 6.1 7.7 1.0 7.7 9.4 8.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 4.9 2.9 3.9 2.4 4.7 4.9 3.4 3.3 16.0 12.3 10.0 10.0 Kuwait 4.5 5.9 0.6 2.9 ­2.4 6.6 1.0 13.7 ­1.6 5.4 0.8 9.4 Kyrgyz Republic ­6.5 12.6 ­7.4 11.6 ­8.8 ­0.1 ­3.9 ­2.4 ­1.6 4.6 ­8.2 16.0 Lao PDR .. 3.2 .. 1.5 .. 3.9 .. 15.2 .. ­11.5 .. ­11.5 Latvia ­3.9 11.0 ­2.7 11.7 1.8 2.8 ­3.7 16.5 4.3 9.4 7.6 13.8 Lebanon 1.8 3.5 0.0 2.3 10.2 1.1 ­7.1 0.1 15.5 9.9 ­1.0 4.2 Lesotho 1.5 12.0 ­0.2 11.1 9.0 2.0 2.8 ­4.4 9.7 21.1 2.1 15.0 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuaniaa 5.2 10.1 6.0 10.6 1.6 4.3 11.1 13.1 4.9 11.7 7.5 14.5 Macedonia, FYR 2.2 4.4 1.7 4.1 ­0.4 ­0.3 3.6 3.4 4.2 3.6 7.5 4.2 Madagascar 2.2 3.6 ­0.8 0.8 0.0 7.6 3.3 17.0 3.8 3.9 4.1 7.3 Malawi 5.4 4.2 3.4 1.6 ­4.4 5.2 ­8.4 26.2 4.0 ­10.2 ­1.1 2.7 Malaysia 5.3 7.5 2.6 5.5 4.8 8.4 5.3 2.7 12.0 6.8 10.3 7.8 Mali 3.0 0.9 0.3 ­2.1 3.2 22.2 0.4 6.2 9.9 6.3 3.5 3.9 Mauritania .. 7.4 .. 4.4 .. 3.1 .. 23.8 ­1.3 11.5 0.6 14.1 Mauritius 5.1 5.3 3.9 4.4 4.8 4.5 4.7 5.1 5.4 2.7 5.2 2.9 Mexico 3.9 3.8 2.2 2.7 1.8 0.1 4.7 0.6 14.6 5.7 12.3 6.0 Moldovaa 9.9 10.1 10.7 11.5 ­12.4 7.0 ­15.5 11.9 0.7 13.0 5.6 15.4 Mongoliaa .. .. .. .. .. .. .. .. .. .. .. .. Morocco 1.8 4.6 0.1 3.5 3.9 3.1 2.5 8.7 5.9 7.6 5.1 8.3 Mozambiquea 5.7 7.2 2.6 4.8 3.2 ­9.3 8.6 3.1 13.1 15.9 7.6 4.4 Myanmar 3.9 .. .. .. .. .. 15.3 .. 10.0 .. 5.8 .. Namibia 4.8 2.1 1.9 0.7 3.3 2.0 6.9 8.9 3.8 6.4 5.4 4.7 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 3.1 0.6 2.4 0.2 2.0 2.9 4.4 0.5 7.3 4.6 7.6 4.2 New Zealand 3.2 4.8 2.0 3.3 2.5 3.8 5.9 6.4 5.2 3.2 6.2 7.5 Nicaraguaa 6.1 3.2 3.9 1.9 ­1.5 2.7 11.3 2.2 9.3 8.8 12.2 5.5 Niger 1.8 .. .. .. 0.8 .. 4.0 .. 3.1 .. ­2.1 .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 3.5 4.1 2.9 3.4 2.7 2.2 6.0 6.5 5.5 1.0 5.8 5.6 Oman 5.4 1.3 2.6 0.6 2.4 6.1 4.0 17.0 6.2 7.0 5.9 12.8 Pakistan 4.9 5.0 2.3 2.5 0.7 8.5 1.8 6.3 1.7 10.1 2.5 9.5 Panamaa 6.4 6.7 4.2 4.8 1.7 4.0 10.4 6.7 ­0.4 5.8 1.2 6.2 Papua New Guinea 2.5 10.1 ­0.2 7.6 2.5 ­0.3 1.9 0.7 5.1 ­0.4 3.4 4.5 Paraguay 2.6 2.4 0.2 0.5 2.5 2.5 0.7 1.8 3.1 7.4 2.9 5.0 Perua 4.0 4.6 2.3 3.3 5.2 4.5 7.4 8.6 8.5 8.8 9.0 8.6 Philippines 3.7 5.1 1.5 3.0 3.8 2.2 4.1 0.5 7.8 7.1 7.8 4.4 Polanda 5.2 3.4 5.1 3.5 3.7 3.3 10.6 4.8 11.3 10.3 16.7 8.6 Portugal 3.0 1.4 2.7 0.8 2.9 1.6 5.8 ­1.7 5.3 4.1 7.3 2.8 Puerto Rico .. .. .. .. .. .. .. .. 1.6 .. 4.5 .. 2009 World Development Indicators 237 4.9 Growth of consumption and investment Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 Romaniaa 1.3 8.0 1.7 8.5 0.8 6.8 ­5.1 9.1 8.1 10.8 6.0 11.9 Russian Federation ­0.9 10.1 ­0.7 10.6 ­2.2 2.1 ­19.1 11.7 0.8 9.0 ­6.1 19.6 Rwandaa 0.4 .. .. .. ­2.6 .. 0.4 .. ­6.4 .. 6.1 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2.6 4.2 ­0.2 1.5 0.9 0.2 3.5 11.8 4.1 3.0 2.0 6.4 Serbia .. 6.2 .. 6.4 .. 0.1 .. 17.4 .. 12.1 .. 13.2 Sierra Leone ­4.4 .. .. .. 10.4 .. ­5.6 .. ­11.2 .. ­0.2 .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 5.6 5.0 5.4 4.9 1.3 3.2 8.1 7.1 8.8 11.4 12.1 10.1 Slovenia 3.9 3.0 4.0 2.8 2.2 3.2 10.4 7.2 1.7 9.2 5.2 8.8 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2.9 5.6 0.6 4.4 0.3 5.2 5.0 9.1 5.6 3.8 7.1 10.3 Spain 2.4 3.6 2.0 1.9 2.7 5.1 3.2 5.4 10.5 3.7 9.4 6.9 Sri Lankaa 5.3 .. 4.4 .. 10.5 .. 6.9 .. 7.5 .. 8.6 .. Sudan 3.7 6.4 1.1 4.2 5.5 8.1 22.0 13.1 11.6 13.0 8.4 13.6 Swazilanda 7.3 5.7 4.0 4.3 7.1 0.2 ­4.7 ­3.0 6.4 2.8 6.2 4.0 Sweden 1.4 2.3 1.0 1.9 0.6 0.8 1.8 4.3 8.5 5.9 6.3 4.8 Switzerland 1.1 1.3 0.5 0.6 0.5 1.2 0.7 1.1 4.1 3.9 4.3 3.8 Syrian Arab Republic 3.0 7.6 0.3 4.7 2.0 6.9 3.3 3.0 12.0 6.6 4.4 13.1 Tajikistan ­11.8 12.0 ­13.1 10.7 ­12.6 0.8 ­17.6 9.3 ­5.3 10.0 ­6.0 11.2 Tanzaniab 4.9 2.8 2.0 0.2 ­7.0 16.9 ­1.6 7.3 9.3 12.0 3.9 5.7 Thailand 3.7 4.8 2.5 4.1 5.1 4.9 ­4.0 7.6 9.5 6.9 4.5 7.6 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 5.0 0.5 1.7 ­2.4 0.0 1.3 ­0.1 5.9 1.2 6.0 1.1 3.1 Trinidad and Tobago 0.7 13.3 0.1 12.9 0.3 4.3 12.5 4.2 6.9 5.8 9.9 9.5 Tunisia 4.3 5.1 2.6 4.1 4.1 4.3 3.6 1.3 5.1 4.1 3.8 2.4 Turkey 3.8 6.3 1.9 5.0 4.6 3.6 4.7 11.2 11.1 7.6 10.8 12.3 Turkmenistan .. .. .. .. .. .. 1.9 .. ­6.1 13.9 0.6 12.3 Uganda 6.7 7.2 3.3 3.8 7.1 4.1 8.9 11.3 14.7 12.9 10.0 9.6 Ukraine ­6.9 13.7 ­6.4 14.6 ­4.1 2.5 ­18.5 8.8 ­3.6 3.8 ­6.6 7.1 United Arab Emirates 7.1 12.9 1.1 7.5 6.8 0.8 5.5 5.5 5.5 12.2 6.4 13.6 United Kingdom 2.9 2.7 2.6 2.2 1.0 2.8 4.7 3.8 6.6 4.1 6.8 5.1 United States 3.6 3.0 2.4 2.1 0.7 2.5 7.5 2.5 7.3 3.4 9.8 5.2 Uruguaya 5.0 2.2 4.3 2.1 2.3 ­1.3 6.3 5.6 6.0 8.3 9.9 5.2 Uzbekistan .. .. .. .. .. .. ­2.5 7.0 2.5 6.7 ­0.4 8.3 Venezuela, RB 0.6 8.1 ­1.5 6.2 3.7 7.1 11.0 10.8 1.0 ­0.9 8.2 14.0 Vietnam 5.4 7.5 3.9 6.1 3.2 7.5 19.8 12.5 19.2 19.5 19.5 20.2 West Bank and Gaza 5.3 ­1.5 1.2 ­4.8 12.7 1.3 9.2 ­3.0 8.7 ­3.1 7.5 ­2.3 Yemen, Rep. 3.2 .. ­0.7 .. 1.7 .. 11.4 .. 16.6 .. 8.3 .. Zambia 2.4 0.1 ­0.2 ­1.7 ­8.1 24.9 13.3 6.6 6.7 21.9 15.5 15.6 Zimbabwe 0.0 ­3.8 ­1.9 ­4.5 ­2.2 ­3.0 ­2.5 ­10.6 10.5 ­7.5 9.4 ­3.3 World 3.0 w 2.9 w 1.5 w 1.7 w 1.7 w 2.6 w 3.3 w 3.7 w 6.9 w 7.1 w 7.0 w 6.6 w Low income 3.5 4.9 1.0 2.6 0.1 6.6 5.2 8.7 7.1 11.8 6.9 11.9 Middle income 4.1 5.6 2.7 4.5 3.5 4.9 2.8 9.8 7.4 12.0 6.6 11.8 Lower middle income 5.3 6.3 3.8 5.2 6.6 6.7 5.9 12.0 8.0 16.3 6.9 13.9 Upper middle income 3.1 4.9 2.0 4.1 1.3 3.0 ­0.3 6.3 6.9 7.2 6.3 9.6 Low & middle income 4.0 5.5 2.4 4.2 3.4 4.9 2.9 9.8 7.4 12.0 6.6 11.8 East Asia & Pacific 7.4 7.0 6.1 6.1 8.1 8.5 8.1 12.0 10.9 17.3 10.2 14.1 Europe & Central Asia 1.3 7.4 1.1 7.4 0.2 3.1 ­8.4 10.5 2.6 8.8 0.0 12.9 Latin America & Carib. 3.6 3.8 1.9 2.4 2.1 2.7 5.4 4.7 8.5 6.2 10.8 7.1 Middle East & N. Africa 2.9 5.2 0.7 3.3 3.5 3.5 1.2 7.2 4.0 7.6 0.0 10.0 South Asia 4.6 5.7 2.6 4.0 5.9 4.2 6.5 13.9 10.0 14.5 11.2 17.4 Sub-Saharan Africa 3.2 4.9 0.4 2.4 0.4 4.9 4.6 8.0 .. .. 6.0 8.4 High income 2.8 2.4 2.0 1.7 1.5 2.2 3.4 2.1 6.8 5.1 7.1 5.3 Euro area 1.9 1.5 1.6 0.9 1.5 1.8 2.2 2.0 6.8 4.7 6.2 4.7 a. Household final consumption expenditure includes statistical discrepancy. b. Covers mainland Tanzania only. 238 2008 World Development Indicators ECONOMY Growth of consumption and investment 4.9 About the data Definitions Measures of growth in consumption and capital for- the change in government employment. Neither · Household final consumption expenditure is the mation are subject to two kinds of inaccuracy. The technique captures improvements in productivity market value of all goods and services, including first stems from the difficulty of measuring expendi- or changes in the quality of government services. durable products (such as cars and computers), tures at current price levels, as described in About Deflators for household consumption are usually cal- purchased by households. It excludes purchases of the data for table 4.8. The second arises in deflat- culated on the basis of the consumer price index. dwellings but includes imputed rent for owner-occu- ing current price data to measure volume growth, Many countries estimate household consumption pied dwellings. It also includes government fees for where results depend on the relevance and reliability as a residual that includes statistical discrepancies permits and licenses. Expenditures of nonprofit insti- of the price indexes and weights used. Measuring associated with the estimation of other expenditure tutions serving households are included, even when price changes is more difficult for investment goods items, including changes in inventories; thus these reported separately. Household consumption expen- than for consumption goods because of the one-time estimates lack detailed breakdowns of household diture may include any statistical discrepancy in the nature of many investments and because the rate consumption expenditures. use of resources relative to the supply of resources. of technological progress in capital goods makes · Household fi nal consumption expenditure per capturing change in quality diffi cult. (An example capita is household final consumption expenditure is computers--prices have fallen as quality has divided by midyear population. · General government improved.) Several countries estimate capital forma- final consumption expenditure is all government cur- tion from the supply side, identifying capital goods rent expenditures for goods and services (including entering an economy directly from detailed produc- compensation of employees). It also includes most tion and international trade statistics. This means expenditures on national defense and security but that the price indexes used in deflating production excludes military expenditures with potentially wider and international trade, reflecting delivered or offered public use that are part of government capital forma- prices, will determine the deflator for capital forma- tion. · Gross capital formation is outlays on addi- tion expenditures on the demand side. tions to fixed assets of the economy, net changes in Growth rates of household final consumption expen- inventories, and net acquisitions of valuables. Fixed diture, household final consumption expenditure per assets include land improvements (fences, ditches, capita, general government final consumption expen- drains); plant, machinery, and equipment purchases; diture, gross capital formation, and exports and and construction (roads, railways, schools, buildings, imports of goods and services are estimated using and so on). Inventories are goods held to meet tem- constant price data. (Consumption, capital forma- porary or unexpected fluctuations in production or tion, and exports and imports of goods and services sales, and "work in progress." · Exports and imports as shares of GDP are shown in table 4.8.) of goods and services are the value of all goods To obtain government consumption in constant and other market services provided to or received prices, countries may defl ate current values by from the rest of the world. They include the value of applying a wage (price) index or extrapolate from merchandise, freight, insurance, transport, travel, royalties, license fees, and other services (commu- GDP per capita is still lagging in some regions 4.9a nication, construction, financial, information, busi- ness, personal, government services, and so on). GDP per capita (2000 $) 5,000 They exclude compensation of employees and invest- Latin America & Caribbean ment income (factor services in the 1968 System of 4,000 National Accounts) and transfer payments. Europe & Central Asia 3,000 2,000 Middle East & North Africa East Asia & Pacific Data sources 1,000 South Asia Data on national accounts indicators for most Sub-Saharan Africa 0 developing countries are collected from national 1990 1995 2000 2005 2007 statistical organizations and central banks by vis- Although GDP per capita more than tripled in East Asia and Pacific between 1990 and 2007, it is still iting and resident World Bank missions. Data for less than GDP per capita in Latin America and Caribbean, Europe and Central Asia, and Middle East high-income economies are from Organisation for and North Africa. Economic Co-operation and Development (OECD) Source: World Development Indicators data files. data files. 2008 World Development Indicators 239 4.10 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 2007 2007 Afghanistanb .. 7.4 .. 17.1 .. ­1.7 .. 0.3 .. 2.1 9.3 0.1 Albaniab 21.2 23.6 25.6 21.9 ­8.9 ­3.0 7.4 1.9 2.1 1.0 .. 15.5 Algeriab 30.2 40.1 24.2 18.6 ­1.3 6.1 ­7.4 ­3.6 8.6 ­1.2 .. 2.1 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. 18.1 .. 18.3 .. ­0.5 .. 0.5 .. 1.5 .. 26.5 Armeniab .. 21.0 .. 16.6 .. ­0.6 .. 0.3 .. 1.8 .. 1.5 Australia .. 27.5 .. 25.6 .. 1.7 .. .. .. .. 21.0 3.5 Austria 37.1 37.1 41.7 38.4 ­4.2 ­0.9 .. .. .. .. 59.2 7.3 Azerbaijanb 18.0 .. 19.8 .. ­3.1 .. .. .. .. .. .. .. Bangladeshb .. 10.3 .. 10.1 .. ­1.3 .. 2.4 .. 0.5 .. 20.7 Belarusb 30.0 38.7 28.7 35.0 ­2.7 0.4 2.2 0.3 0.4 3.2 9.0 0.9 Belgium 41.5 40.8 45.6 41.1 ­3.8 0.3 .. .. .. .. 84.1 9.2 Beninb .. 17.2 .. 13.9 .. 0.3 .. ­2.7 .. 2.5 .. 1.3 Bolivia .. 23.3 .. 21.8 .. 1.2 .. ­0.2 .. ­0.1 .. 8.0 Bosnia and Herzegovina .. 40.3 .. 37.5 .. 1.0 .. 0.3 .. 0.3 .. 1.2 Botswanab 40.5 .. 30.4 .. 4.9 .. 0.2 .. ­0.4 .. .. .. Brazilb 26.9 .. 32.9 .. ­2.7 .. .. .. .. .. .. .. Bulgariab 35.5 37.2 39.4 32.0 ­5.1 3.5 7.4 ­0.6 ­0.8 ­0.8 .. 2.8 Burkina Faso .. 13.0 .. 12.8 .. ­6.1 .. 0.1 .. 4.3 .. 3.1 Burundib 19.3 .. 23.6 .. ­4.7 .. 3.1 .. 4.0 .. .. .. Cambodia .. 9.8 .. 8.6 .. ­1.7 .. ­0.3 .. 2.1 .. 1.5 Cameroonb 11.8 .. 10.6 .. 0.2 .. ­0.3 .. 0.3 .. .. .. Canadab 20.3 21.0 24.2 19.1 ­4.3 1.8 4.9 ­0.9 0.0 0.2 45.1 6.1 Central African Republicb .. 8.3 .. 9.7 .. ­0.5 .. 1.3 .. 0.2 .. 8.0 Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 27.5 .. 17.3 .. 8.8 .. ­1.1 .. ­0.3 .. 2.2 Chinab 5.4 10.3 .. 11.4 .. ­1.4 1.6 1.2 .. ­0.1 .. 4.3 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 24.0 .. 25.5 .. ­1.8 .. 1.5 .. ­1.0 49.1 26.5 Congo, Dem. Rep.b 5.3 .. 8.2 .. 0.0 .. 0.0 .. 0.2 .. .. .. Congo, Rep. .. 39.9 .. 24.8 .. 9.6 .. .. .. .. .. 6.5 Costa Ricab 20.3 24.7 21.3 21.7 ­2.1 1.7 .. .. ­0.8 .. .. 12.6 Cote d'Ivoireb 20.1 19.2 .. 20.5 .. ­0.8 ­1.2 ­0.1 3.8 1.2 107.7 8.9 Croatiab 43.1 41.0 42.5 39.7 ­1.3 ­1.3 ­2.7 0.7 0.8 ­0.5 .. 4.8 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republicb 33.2 31.0 32.6 33.6 ­0.9 ­1.7 ­0.5 1.8 ­0.4 0.8 25.0 3.0 Denmark 39.1 40.4 38.2 36.3 1.5 4.8 .. .. .. .. 23.9 4.5 Dominican Republicb .. 18.1 .. 17.2 .. ­1.8 .. 0.1 .. 2.5 .. 8.4 Ecuador b 30.9 .. 26.3 .. 0.1 .. .. .. .. .. .. .. Egypt, Arab Rep.b 34.8 27.1 28.1 29.3 3.4 ­4.6 .. 7.3 .. 0.5 .. 18.7 El Salvador .. 19.2 .. 17.2 .. 0.8 .. 0.4 .. ­1.0 40.5 10.8 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 32.7 .. 27.5 .. 3.2 .. .. .. .. 4.2 0.2 Ethiopiab .. .. .. .. .. .. .. .. .. .. .. .. Finland 40.6 38.9 49.9 34.0 ­7.5 5.5 8.9 ­0.4 0.2 ­0.8 37.5 3.2 France 43.3 41.8 47.6 44.5 ­4.1 ­2.3 .. .. .. .. 66.7 5.9 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Theb 23.7 .. .. .. .. .. .. .. .. .. .. .. Georgiab 12.2 24.0 15.4 22.9 ­4.3 0.8 2.2 ­0.1 2.4 0.2 22.7 2.3 Germany 29.9 28.5 38.6 29.0 ­8.3 ­0.4 .. 0.2 .. 0.1 40.8 6.0 Ghanab 17.0 25.5 .. 29.1 .. ­7.6 .. 5.0 .. 2.3 .. 9.6 Greece 35.1 38.9 42.6 41.7 ­9.1 ­3.7 .. .. .. .. 113.7 11.1 Guatemalab 8.4 12.5 7.6 13.5 ­0.5 ­2.0 .. 1.9 0.4 1.2 22.2 10.7 Guineab 11.2 .. 12.1 .. ­4.3 .. ­0.1 .. 4.5 .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 240 2009 World Development Indicators ECONOMY Revenuea Central government finances Expense Cash surplus Net incurrence 4.10 Debt and interest or deficit of liabilities payments Interest % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 2007 2007 Honduras .. 22.1 .. 22.5 .. ­1.1 .. 1.3 .. 1.1 .. 2.6 Hungary 42.7 38.1 49.6 42.9 ­4.7 ­4.9 3.9 ­0.8 ­0.7 4.5 69.4 10.2 Indiab 12.3 13.6 14.4 15.3 ­2.2 ­1.4 5.1 2.8 0.0 0.2 53.7 23.9 Indonesiab 17.7 18.4 9.7 16.9 3.0 ­1.1 ­0.6 0.0 ­0.4 ­0.4 28.8 14.8 Iran, Islamic Rep.b 24.2 37.2 15.8 20.5 1.1 10.6 .. ­0.6 0.1 0.0 .. 0.8 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 33.6 33.3 37.5 32.2 ­2.2 0.4 .. .. .. .. 27.4 2.8 Israel .. 40.2 .. 42.1 .. 0.3 .. .. .. .. .. 10.4 Italy 40.4 37.9 48.0 39.8 ­7.5 ­1.8 .. .. .. .. 104.8 12.4 Jamaicab .. 64.4 33.3 63.1 .. ­28.9 .. .. .. .. 128.7 20.1 Japan 20.7 .. .. .. .. .. 1.5 .. .. .. .. .. Jordanb 28.2 32.3 26.1 36.6 0.9 ­5.1 ­2.5 3.1 6.1 ­3.0 77.5 8.1 Kazakhstanb 14.0 15.9 18.7 14.1 ­1.8 1.2 0.8 1.0 2.8 ­0.4 5.3 1.5 Kenyab 21.6 18.9 25.9 19.7 ­5.1 ­3.0 3.9 2.1 ­1.3 0.1 .. 10.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.b 17.8 26.6 14.3 20.1 2.4 4.6 ­0.3 ­2.6 ­0.1 ­0.1 .. 5.6 Kuwait 36.8 48.3 46.4 32.0 ­13.6 16.3 .. .. .. .. .. 0.1 Kyrgyz Republicb 16.7 21.0 25.6 18.4 ­10.8 ­1.5 .. 0.1 .. 1.3 .. 2.6 Lao PDR .. 13.6 .. 10.8 .. ­3.0 .. 0.1 .. 3.8 .. 3.1 Latviab 25.8 28.1 28.3 27.8 ­2.7 0.9 2.4 ­0.2 1.5 0.4 .. 1.1 Lebanon .. 21.5 .. 32.2 .. ­11.5 .. 3.9 .. 2.7 .. 56.0 Lesothob 46.1 63.0 31.8 47.4 4.7 9.2 0.0 .. 5.8 .. .. 4.0 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 29.3 .. 29.7 .. ­0.9 .. ­0.7 .. 1.8 19.3 2.2 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. 11.8 .. 11.2 .. ­2.7 .. 0.7 .. 2.2 .. 7.0 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiab 24.4 .. 17.2 .. 2.4 .. .. .. ­0.8 .. .. .. Mali .. 16.2 .. 15.2 .. ­5.6 .. ­1.0 .. 3.5 .. 1.7 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusb 21.6 20.9 19.9 21.3 ­1.3 ­2.3 3.1 ­0.6 ­0.6 2.1 40.4 14.8 Mexicob 15.3 .. 15.0 .. ­0.6 .. .. .. 5.5 .. .. .. Moldovab 28.4 34.3 38.4 32.5 ­6.3 ­0.3 3.0 0.1 2.7 0.2 23.3 3.2 Mongolia .. 40.5 .. 25.0 .. 7.7 .. 2.6 .. 2.8 46.9 1.0 Moroccob .. 34.8 .. 29.2 .. 2.5 .. ­2.9 .. 0.1 .. 5.6 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.4 8.0 .. 3.4 .. ­1.8 .. 1.8 .. 0.0 .. .. Namibiab 31.7 .. 35.7 .. ­5.0 .. .. .. .. .. .. .. Nepalb 10.5 11.9 .. 15.1 .. ­1.0 0.6 1.2 2.5 0.3 43.0 6.0 Netherlands 41.5 41.3 50.8 40.8 ­9.2 0.3 .. .. .. .. 44.0 4.4 New Zealand .. 36.8 .. 32.7 .. 3.1 .. ­1.7 .. 2.8 38.6 3.4 Nicaraguab 12.8 19.5 14.2 19.0 0.6 0.4 .. .. 3.4 .. .. 6.5 Niger .. 13.9 .. 12.0 .. ­1.0 .. ­2.0 .. 2.5 .. 1.8 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 50.3 .. 31.7 .. 18.0 .. ­1.3 .. 1.6 46.1 1.6 Omanb 27.8 .. 32.4 .. ­8.9 .. ­0.1 .. 0.0 .. .. .. Pakistanb 17.2 14.4 19.1 16.2 ­5.3 ­4.1 .. .. .. .. .. 29.2 Panamab 26.1 .. 22.0 .. 1.5 .. .. .. .. .. .. .. Papua New Guineab 22.7 .. 24.5 .. ­0.5 .. 1.5 .. ­0.7 .. .. .. Paraguay b .. 20.3 .. 16.8 .. 1.8 .. 0.9 .. ­0.3 .. 4.0 Perub 17.4 20.0 17.4 17.0 ­1.3 2.0 .. 2.1 3.9 ­2.0 27.2 8.7 Philippinesb 17.7 15.8 15.9 17.2 ­0.8 ­1.5 ­0.5 1.2 ­0.7 0.9 77.7 26.5 Poland .. 32.7 .. 34.5 .. ­2.0 .. 1.9 .. 1.7 47.4 5.9 Portugal 35.3 39.4 37.8 41.9 ­3.0 ­2.6 ­3.5 ­0.2 4.1 2.5 70.9 7.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 241 4.10 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 2007 2007 Romania .. 25.8 .. 26.8 .. ­2.4 .. 2.0 .. 1.2 .. 2.6 Russian Federation .. 31.6 .. 23.2 .. 6.2 .. 0.5 .. ­0.8 7.2 1.4 Rwandab 10.6 .. 15.0 .. ­5.6 .. 2.9 .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegalb 15.2 .. .. .. .. .. .. .. .. .. .. .. Serbiab .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leoneb 9.4 12.3 .. 23.7 .. ­2.5 0.3 .. .. .. .. 21.0 Singaporeb 26.7 21.6 12.4 13.6 19.8 12.5 10.3 13.6 0.0 .. 86.7 0.1 Slovak Republic .. 29.1 .. 30.6 .. ­1.8 .. ­1.2 .. 1.7 31.3 5.2 Sloveniab 35.8 38.6 34.3 38.6 ­0.1 ­0.7 ­0.4 1.5 0.3 ­0.4 .. 3.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 32.0 .. 30.1 .. 1.7 .. 0.3 .. ­0.2 .. 8.3 Spain 32.0 27.8 37.1 25.1 ­5.8 2.6 .. .. .. .. 35.4 4.4 Sri Lankab 20.4 15.8 26.0 20.1 ­7.6 ­6.5 5.2 4.2 3.2 2.8 85.0 30.7 Sudanb 7.2 .. 6.8 .. ­0.4 .. 0.3 .. .. .. .. .. Swazilandb .. .. .. .. .. .. .. .. .. .. .. .. Sweden 35.0 .. 44.1 .. ­9.3 .. .. .. ­1.2 .. 47.4 .. Switzerlandb 22.6 18.6 25.7 18.4 ­0.6 0.6 ­0.5 ­1.1 .. .. 25.4 4.6 Syrian Arab Republicb 22.9 .. .. .. .. .. .. .. .. .. .. .. Tajikistanb 9.3 13.5 11.4 13.7 ­3.3 ­6.6 0.1 .. 2.3 .. .. 5.1 Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 19.6 .. 17.7 .. 0.1 .. 2.7 .. ­1.0 26.2 5.6 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togob .. 17.0 .. 17.5 .. ­0.8 .. ­0.5 .. 0.7 .. 5.6 Trinidad and Tobagob 27.2 33.4 25.3 28.4 ­0.1 1.8 2.8 ­0.9 2.6 ­0.3 .. 6.1 Tunisiab 30.0 30.0 28.4 29.0 ­2.5 ­2.2 0.9 0.3 2.9 ­1.0 50.9 8.8 Turkey b .. 25.5 .. 24.2 .. 1.4 .. 1.3 .. ­0.3 43.8 22.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandab 10.6 12.7 .. 16.5 .. ­1.9 .. 1.6 .. 1.5 .. 7.8 Ukraineb .. 34.7 .. 35.0 .. ­0.6 .. 0.5 .. 0.4 12.4 1.5 United Arab Emiratesb 10.1 .. 9.3 .. 0.5 .. .. .. .. .. .. .. United Kingdom 37.0 38.1 36.9 40.8 0.3 ­2.7 ­0.3 3.5 0.0 0.0 48.4 5.7 United States .. 19.6 .. 21.6 .. ­2.1 .. 0.7 .. 1.7 47.3 11.6 Uruguay b 27.6 26.9 27.1 26.9 ­1.2 ­1.6 7.9 ­0.4 1.1 4.4 59.3 14.2 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBb 16.9 28.3 18.5 25.1 ­2.3 2.2 1.1 1.2 0.1 3.3 .. 10.4 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep.b 17.3 .. 19.1 .. ­3.9 .. .. .. .. .. .. .. Zambiab 20.0 17.7 21.4 23.0 ­3.1 ­0.8 28.0 .. 16.2 .. .. 7.2 Zimbabweb 26.7 .. 32.1 .. ­5.4 .. ­1.4 .. 1.6 .. .. .. World .. w 26.8 w .. w 27.4 w .. w ­0.8 w .. m .. m .. m .. m .. m 5.6 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income 16.4 18.2 .. 18.6 .. ­1.5 .. 1.0 .. 0.0 .. 5.6 Lower middle income 11.9 16.2 .. 16.1 .. ­1.1 .. 1.1 .. 0.1 .. 5.6 Upper middle income .. .. .. .. .. .. .. 0.3 .. 0.4 .. 4.8 Low & middle income .. 17.9 .. 18.4 .. ­1.5 .. .. .. .. .. 7.1 East Asia & Pacific 8.4 11.6 .. 12.2 .. ­1.1 .. 2.1 .. .. .. .. Europe & Central Asia .. 30.1 .. 27.4 .. 1.8 .. 0.3 .. 0.4 .. 2.2 Latin America & Carib. 21.2 .. 23.4 .. ­1.5 .. .. 1.7 .. 1.2 .. 8.7 Middle East & N. Africa 28.8 33.0 .. 25.2 .. 2.4 .. ­0.2 .. 0.1 .. 8.1 South Asia 13.1 13.5 15.3 15.1 ­2.7 ­1.8 3.8 2.6 1.1 0.4 53.7 22.3 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income .. 27.0 .. 28.0 .. ­1.0 .. .. .. .. 44.0 5.2 Euro area 34.8 35.0 42.4 35.9 ­7.5 ­0.7 .. .. .. .. 44.0 5.9 a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework. 242 2008 World Development Indicators ECONOMY Central government finances 4.10 About the data Definitions Tables 4.10­4.12 present an overview of the size borrowing for temporary periods can also be used. · Revenue is cash receipts from taxes, social con- and role of central governments relative to national Government excludes public corporations and quasi tributions, and other revenues such as fines, fees, economies. The tables are based on the concepts corporations (such as the central bank). rent, and income from property or sales. Grants, usu- and recommendations of the second edition of the Units of government at many levels meet this defini- ally considered revenue, are excluded. · Expense is International Monetary Fund's (IMF) Government tion, from local administrative units to the national cash payments for government operating activities in Finance Statistics Manual 2001. Before 2005 World government, but inadequate statistical coverage pre- providing goods and services. It includes compensa- Development Indicators reported data derived on the cludes presenting subnational data. Although data tion of employees, interest and subsidies, grants, basis of the 1986 manual's cash-based method. The for general government under the 2001 manual are social benefi ts, and other expenses such as rent 2001 manual, harmonized with the 1993 System of available for a few countries, only data for the cen- and dividends. · Cash surplus or deficit is revenue National Accounts, recommends an accrual account- tral government are shown to minimize disparities. (including grants) minus expense, minus net acquisi- ing method, focusing on all economic events affect- Still, different accounting concepts of central govern- tion of nonfinancial assets. In editions before 2005 ing assets, liabilities, revenues, and expenses, not ment make cross-country comparisons potentially nonfinancial assets were included under revenue only those represented by cash transactions. It takes misleading. and expenditure in gross terms. This cash surplus all stocks into account, so that stock data at the Central government can refer to consolidated or bud- or deficit is close to the earlier overall budget balance end of an accounting period equal stock data at the getary accounting. For most countries central govern- (still missing is lending minus repayments, which are beginning of the period plus flows over the period. ment finance data have been consolidated into one included as a financing item under net acquisition The 1986 manual considered only the debt stock account, but for others only budgetary central gov- of financial assets). · Net incurrence of liabilities data. Further, the new manual no longer distinguishes ernment accounts are available. Countries reporting is domestic financing (obtained from residents) and between current and capital revenue or expenditures, budgetary data are noted in Primary data documenta- foreign financing (obtained from nonresidents), or and it introduces the concepts of nonfinancial and tion. Because budgetary accounts may not include the means by which a government provides financial financial assets. Most countries still follow the 1986 all central government units (such as social security resources to cover a budget deficit or allocates finan- manual, however. The IMF has reclassified histori- funds), they usually provide an incomplete picture. cial resources arising from a budget surplus. The net cal Government Finance Statistics Yearbook data to Data on government revenue and expense are col- incurrence of liabilities should be offset by the net conform to the 2001 manual's format. Because of lected by the IMF through questionnaires to member acquisition of financial assets (a third financing item). reporting differences, the reclassified data under- countries and by the Organisation for Economic Co- The difference between the cash surplus or deficit state both revenue and expense. operation and Development. Despite IMF efforts to stan- and the three financing items is the net change in The 2001 manual describes government's eco- dardize data collection, statistics are often incomplete, the stock of cash. · Total debt is the entire stock of nomic functions as the provision of goods and ser- untimely, and not comparable across countries. direct government fixed-term contractual obligations vices on a nonmarket basis for collective or individual Government finance statistics are reported in local to others outstanding on a particular date. It includes consumption, and the redistribution of income and currency. The indicators here are shown as percent- domestic and foreign liabilities such as currency and wealth through transfer payments. Government ages of GDP. Many countries report government finance money deposits, securities other than shares, and activities are financed mainly by taxation and other data by fiscal year; see Primary data documentation for loans. It is the gross amount of government liabili- income transfers, though other financing such as information on fiscal year end by country. ties reduced by the amount of equity and financial derivatives held by the government. Because debt Fifteen developing economies had a government expenditure is a stock rather than a flow, it is measured as of to GDP ratio of 30 percent or higher 4.10a a given date, usually the last day of the fiscal year. · Interest payments are interest payments on gov- Central government expense, 2007 (% of GDP) 75 ernment debt--including long-term bonds, long-term loans, and other debt instruments --to domestic and foreign residents. 50 Data sources 25 Data on central government finances are from the IMF's Government Finance Statistics Yearbook 0 2008 and data files. Each country's accounts ca an nd s s o tia a s e ia va a n ia ve ru lle th in in no ric an r do ai la rd oa lga ra ov la so are reported using the system of common defi - di he Af ba m Po hu Jo ol Be Cr Uk al eg Bu Le yc Ja h Le M Lit M ut rz Se He So nitions and classifications in the IMF's Govern- d an ia ment Finance Statistics Manual 2001. See these sn Bo sources for complete and authoritative explana- Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. tions of concepts, definitions, and data sources. 2008 World Development Indicators 243 4.11 Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistana .. 67 .. 28 .. 0 .. 5 .. 0 Albaniaa 18 12 14 30 9 17 59 42 0 0 Algeriaa 6 5 39 28 13 5 34 32 8 29 Angola .. .. .. .. .. .. .. .. .. .. Argentina .. 5 .. 12 .. 26 .. 50 .. 7 Armeniaa .. 37 .. 22 .. 2 .. 34 .. 5 Australia .. 11 .. 10 .. 4 .. 70 .. 6 Austria 5 6 8 13 9 7 77 70 3 5 Azerbaijana 49 .. 10 .. 0 .. 41 .. 0 .. Bangladesha .. 13 .. 25 .. 22 .. 29 .. 11 Belarusa 39 14 5 11 1 1 55 68 0 5 Belgium 3 2 7 7 18 9 71 79 3 3 Benina .. 31 .. 40 .. 2 .. 8 .. 20 Bolivia .. 14 .. 22 .. 10 .. 47 .. 7 Bosnia and Herzegovina .. 24 .. 28 .. 1 .. 43 .. 4 Botswanaa 32 .. 30 .. 2 .. 36 .. 2 .. Brazila 5 .. 8 .. 45 .. 45 .. 1 .. Bulgariaa 18 14 7 18 37 3 38 58 2 6 Burkina Faso .. 21 .. 39 .. 4 .. 35 .. 0 Burundia 20 .. 30 .. 6 .. 14 .. 10 .. Cambodia .. 41 .. 33 .. 2 .. 19 .. 5 Cameroona 17 .. 40 .. 26 .. 14 .. .. .. Canadaa 8 8 10 12 18 7 64 67 .. 6 Central African Republica .. 27 .. 53 .. 9 .. .. .. 11 Chad .. .. .. .. .. .. .. .. .. .. Chile .. 11 .. 21 .. 4 .. 57 .. 11 Chinaa .. 27 .. 5 .. 4 .. 60 .. 4 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia .. 5 .. 19 .. 25 .. 45 .. 7 Congo, Dem. Rep.a 37 .. 58 .. 1 .. 2 .. .. .. Congo, Rep. .. 18 .. 18 .. 11 .. 53 .. 0 Costa Ricaa 12 12 38 42 20 14 26 18 4 15 Côte d'Ivoirea .. 34 .. 33 .. 9 .. 18 .. 7 Croatiaa 35 10 27 26 3 5 32 53 3 6 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republica 7 6 9 9 3 3 75 71 5 12 Denmark 8 9 13 13 13 5 64 70 4 4 Dominican Republica .. 17 .. 31 .. 9 .. 30 .. 13 Ecuador a 6 .. 49 .. 26 .. .. .. .. .. Egypt, Arab Rep.a 18 8 22 24 26 18 6 39 .. 11 El Salvador .. 16 .. 40 .. 12 .. 22 .. 11 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia .. 14 .. 22 .. 0 .. 43 .. 4 Ethiopiaa .. .. .. .. .. .. .. .. .. .. Finland 8 10 9 10 7 4 68 71 11 7 France 8 6 23 22 6 6 59 62 6 6 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, Thea .. .. .. .. .. .. .. .. .. .. Georgiaa 52 38 11 16 10 3 26 35 .. 9 Germany 4 5 5 5 6 6 67 82 20 3 Ghanaa .. 15 .. 38 .. 11 .. 37 .. 0 Greece 10 11 22 24 27 10 36 45 5 4 Guatemalaa 15 13 50 24 12 10 18 25 6 27 Guineaa 17 .. 34 .. 28 .. 9 .. 1 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 244 2009 World Development Indicators ECONOMY Goods and Central government expenses Compensation Interest Subsidies and 4.11 Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras .. 17 .. 49 .. 3 .. 16 .. 16 Hungary 8 9 10 13 20 9 57 61 13 10 Indiaa 14 12 10 7 27 21 33 36 0 1 Indonesiaa 21 8 20 13 16 16 41 63 2 0 Iran, Islamic Rep.a 21 10 56 37 0 1 .. 32 .. 18 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 5 12 15 24 14 3 33 37 1 1 Israel .. 27 .. 25 .. 11 .. 30 .. 9 Italy 4 4 14 16 24 12 54 64 6 6 Jamaicaa 22 47 24 20 32 21 1 1 21 11 Japan .. .. .. .. .. .. .. .. .. .. Jordana 7 5 67 26 11 8 12 26 4 35 Kazakhstana .. 23 .. 8 3 2 58 64 .. 3 Kenyaa 15 15 28 44 46 11 .. 9 2 2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 16 7 15 12 3 7 63 58 3 15 Kuwait 33 15 31 24 5 0 24 40 7 20 Kyrgyz Republica 32 25 36 27 5 3 27 34 .. 11 Lao PDR .. 37 .. 38 .. 5 .. 18 .. 3 Latviaa 20 12 20 20 3 1 56 66 0 0 Lebanon .. 3 .. 27 .. 40 .. 22 .. 8 Lesothoa 32 37 45 33 5 5 8 11 3 6 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. 13 .. 18 .. 2 .. 62 .. 9 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar .. 14 .. 46 .. 10 .. 14 .. 16 Malawi .. .. .. .. .. .. .. .. .. .. Malaysiaa 23 .. 34 .. 17 .. 27 .. 1 .. Mali .. 38 .. 33 .. 2 .. 16 .. 11 Mauritania .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 12 45 35 12 15 28 33 2 5 Mexicoa 9 .. 19 .. 19 .. .. .. .. .. Moldovaa 10 18 8 15 11 4 71 56 1 7 Mongolia .. 37 .. 24 .. 2 .. 36 .. 1 Moroccoa .. 9 .. 46 .. 7 .. 29 .. 10 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. Namibiaa 28 .. 53 .. 1 .. .. .. 4 .. Nepala .. .. .. .. .. 7 .. .. .. .. Netherlands 5 8 8 8 9 4 77 79 3 3 New Zealand .. 30 .. 25 .. 4 .. 38 .. 7 Nicaraguaa 14 13 25 36 17 8 29 38 14 6 Niger .. 30 .. 30 .. 3 .. 9 .. 28 Nigeria .. .. .. .. .. .. .. .. .. .. Norway .. 11 .. 17 .. 2 .. 67 .. 6 Omana 55 .. 30 .. 7 .. 8 .. 0 .. Pakistana .. 31 .. 4 28 26 2 31 .. 8 Panamaa 16 .. 45 .. 8 .. 30 .. 1 .. Papua New Guineaa 19 .. 36 .. 20 .. 26 .. 1 .. Paraguaya .. 11 .. 52 .. 5 .. 25 .. 8 Perua 20 19 19 18 19 10 33 50 8 3 Philippinesa 15 18 34 31 33 24 15 19 .. 8 Poland .. 8 .. 12 .. 6 .. 69 .. 7 Portugal 7 7 30 27 10 7 43 49 11 2 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 245 4.11 Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania .. 15 .. 23 .. 3 .. 50 .. 13 Russian Federation .. 14 .. 17 .. 2 .. 61 .. 15 Rwandaa 52 .. 36 .. 12 .. 5 .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegala .. .. .. .. .. .. .. .. .. .. Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leonea .. 28 .. 26 .. 19 .. 9 .. 18 Singaporea 38 44 39 32 8 0 15 24 .. .. Slovak Republic .. 9 .. 13 .. 5 .. 68 .. 5 Sloveniaa 19 12 21 19 3 4 55 62 3 3 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. 10 .. 14 .. 9 .. 59 .. 8 Spain 5 4 14 9 11 5 42 78 2 5 Sri Lankaa 23 11 20 30 22 25 24 23 10 10 Sudana 44 .. 38 .. 8 .. 10 .. .. .. Swazilanda .. .. .. .. .. .. .. .. .. .. Sweden 10 .. 5 .. 13 .. 71 .. 1 .. Switzerlanda 24 8 6 7 4 5 66 75 0 5 Syrian Arab Republica .. .. .. .. .. .. .. .. .. .. Tajikistana 47 29 8 9 12 5 33 27 .. 30 Tanzania .. .. .. .. .. .. .. .. .. .. Thailand .. 27 .. 37 .. 6 .. 30 .. 3 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togoa .. 25 .. 31 .. 6 .. 27 .. 12 Trinidad and Tobagoa 20 16 36 22 20 7 24 36 1 19 Tunisiaa 7 6 37 38 13 9 36 37 7 10 Turkeya .. 7 .. 24 .. 24 .. 37 .. 1 Turkmenistan .. .. .. .. .. .. .. .. .. .. Ugandaa .. 30 .. 12 .. 8 .. 49 .. 0 Ukrainea .. 12 .. 14 .. 1 .. 67 .. 6 United Arab Emiratesa 50 .. 37 .. .. .. .. .. .. .. United Kingdom 22 19 7 15 9 5 54 53 9 10 United States .. 15 .. 13 .. 10 .. 60 .. 2 Uruguaya 13 16 17 23 6 14 64 48 0 .. Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 6 6 22 16 27 12 61 64 2 3 Vietnam .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep.a 8 .. 67 .. 16 .. 8 .. 0 .. Zambiaa 32 32 35 30 16 7 19 24 0 7 Zimbabwea 16 .. 34 .. 31 .. 19 .. .. .. World .. m 13 m .. m 24 m .. m 6m .. m 40 m .. m 6m Low income .. .. .. .. .. .. .. .. .. .. Middle income .. 13 .. 24 .. 6 .. 37 .. 7 Lower middle income .. 13 .. 28 .. 6 .. 34 .. 7 Upper middle income 17 12 25 20 14 5 .. 57 .. 8 Low & middle income .. 16 .. 28 .. 7 .. 35 .. 6 East Asia & Pacific .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 14 .. 18 .. 2 .. 57 .. 6 Latin America & Carib. 13 14 26 23 20 10 .. 38 .. 9 Middle East & N. Africa 8 6 47 28 13 8 .. 32 .. 10 South Asia .. 13 .. 25 27 22 24 29 .. 8 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. High income 8 9 14 13 9 5 63 62 3 6 Euro area 5 7 11 13 10 6 61 68 3 5 Note: Components may not sum to 100 percent because of rounding or missing data. a. Data were reported on a cash basis and have been adjusted to the accrual framework. 246 2008 World Development Indicators ECONOMY Central government expenses 4.11 About the data Definitions The term expense has replaced expenditure in the to households are shown as subsidies and other · Goods and services are all government payments table since the 2005 edition of World Development transfers, and other expenses. The economic clas- in exchange for goods and services used for the Indicators in accordance with use in the International sification can be problematic. For example, the dis- production of market and nonmarket goods and ser- Monetary Fund's (IMF) Government Finance Statis- tinction between current and capital expense may vices. Own-account capital formation is excluded. tics Manual 2001. Government expenses include all be arbitrary, and subsidies to public corporations or · Compensation of employees is all payments in nonrepayable payments, whether current or capital, banks may be disguised as capital financing. Subsi- cash, as well as in kind (such as food and hous- requited or unrequited. The concept of total central dies may also be hidden in special contractual pric- ing), to employees in return for services rendered, government expense as presented in the IMF's Gov- ing for goods and services. For further discussion of and government contributions to social insurance ernment Finance Statistics Yearbook is comparable government finance statistics, see About the data for schemes such as social security and pensions that to the concept used in the 1993 System of National tables 4.10 and 4.12. provide benefits to employees. · Interest payments Accounts. are payments made to nonresidents, to residents, Expenses can be measured either by function and to other general government units for the use of (health, defense, education) or by economic type borrowed money. (Repayment of principal is shown (interest payments, wages and salaries, purchases as a financing item, and commission charges are of goods and services). Functional data are often shown as purchases of services.) · Subsidies and incomplete, and coverage varies by country because other transfers include all unrequited, nonrepayable functional responsibilities stretch across levels of transfers on current account to private and public government for which no data are available. Defense enterprises; grants to foreign governments, inter- expenses, usually the central government's respon- national organizations, and other government units; sibility, are shown in table 5.7. For more information and social security, social assistance benefits, and on education expenses, see table 2.10; for more on employer social benefits in cash and in kind. · Other health expenses, see table 2.15. expense is spending on dividends, rent, and other The classification of expenses by economic type in miscellaneous expenses, including provision for con- the table shows whether the government produces sumption of fixed capital. goods and services and distributes them, purchases the goods and services from a third party and dis- tributes them, or transfers cash to households to make the purchases directly. When the government produces and provides goods and services, the cost is reflected in compensation of employees, use of goods and services, and consumption of fixed capi- tal. Purchases from a third party and cash transfers Interest payments are a large part of government expenses for some developing economies 4.11a Central government interest payments as a share of total expense, 2007 (%) 40 30 20 Data sources 10 Data on central government expenses are from 0 the IMF's Government Finance Statistics Yearbook na an ka a es y sh a ca p. s s ca y r do ke ua iu ll e bi di 2008 and data files. Each country's accounts Re no Ri ai an in de st rit m In lva r ug he Tu m pp ki ba iL lo la a au ab Ur Ja yc Sa Pa st ili Co ng Sr Le M Ar are reported using the system of common defi - Se Co Ph Ba El t, yp Eg nitions and classifications in the IMF's Govern- Interest payments accounted for more than 12 percent of total expenses in 2007 for 15 countries. ment Finance Statistics Manual 2001. See these sources for complete and authoritative explana- a. Data are for 2005. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. tions of concepts, definitions, and data sources. 2008 World Development Indicators 247 4.12 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistana .. 4 .. 4 .. 8 .. 0 .. 0 .. 83 Albaniaa 8 15 39 49 14 8 1 1 15 18 22 10 Algeriaa 65 7 10 63 18 4 1 1 .. .. 5 26 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. 19 .. 29 .. 16 .. 14 .. 17 .. 5 Armeniaa .. 18 .. 42 .. 4 .. 10 .. 13 .. 14 Australia .. 66 .. 23 .. 2 .. 0 .. .. .. 9 Austria 26 26 24 24 0 0 2 4 40 40 8 7 Azerbaijana 31 .. 34 .. 33 .. 2 .. 23 .. 0 .. Bangladesha .. 17 .. 28 .. 27 .. 4 .. .. .. 24 Belarusa 16 6 33 34 6 17 11 5 31 30 3 8 Belgium 36 37 23 25 .. .. 2 1 36 35 3 2 Benina .. 19 .. 36 .. 24 .. 6 .. .. .. 15 Bolivia .. 10 .. 43 .. 3 .. 9 .. 7 .. 28 Bosnia and Herzegovina .. 3 .. 49 .. 0 .. 2 .. 33 .. 12 Botswanaa 21 .. 4 .. 15 .. 0 .. .. .. 59 .. Brazila 14 .. 24 .. 2 .. 4 .. 31 .. 26 .. Bulgariaa 17 16 28 46 8 1 3 0 21 22 23 15 Burkina Faso .. 15 .. 35 .. 13 .. 2 .. .. .. 35 Burundia 14 .. 30 .. 20 .. 1 .. 5 .. 30 .. Cambodia .. 10 .. 40 .. 22 .. 0 .. .. .. 28 Cameroona 17 .. 25 .. 28 .. 3 .. 2 .. 25 .. Canadaa 50 55 17 16 2 1 .. .. 22 21 10 7 Central African Republica .. 14 .. 23 .. 19 .. 4 .. 6 .. 34 Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 40 .. 34 .. 1 .. 2 .. 5 .. 17 Chinaa 9 25 61 57 7 5 0 1 .. .. 22 12 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 17 .. 26 .. 6 .. 8 .. 4 .. 39 Congo, Dem. Rep.a 21 .. 12 .. 21 .. 5 .. 1 .. 41 .. Congo, Rep. .. 5 .. 6 .. 3 .. 1 .. 1 .. 84 Costa Ricaa 11 16 32 38 15 5 1 2 28 30 12 9 Côte d'Ivoirea 15 12 14 15 58 41 3 10 5 7 5 15 Croatiaa 11 9 42 45 9 1 1 1 33 33 4 11 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republica 15 19 32 27 4 0 1 1 40 45 8 8 Denmark 34 44 40 40 .. .. 7 2 5 3 14 10 Dominican Republica .. 20 .. 53 .. 14 .. 4 .. 1 .. 9 Ecuador a 50 .. 26 .. 11 .. 1 .. .. .. 12 .. Egypt, Arab Rep.a 17 28 13 19 10 5 10 3 10 .. 41 44 El Salvador .. 24 .. 42 .. 5 .. 1 .. 10 .. 18 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 11 .. 41 .. 0 .. 0 .. 34 .. .. Ethiopiaa .. .. .. .. .. .. .. .. .. .. .. .. Finland 16 21 31 32 0 0 1 2 34 31 17 14 France 17 25 25 23 0 0 3 4 47 43 8 6 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Thea 14 .. 32 .. 42 .. 0 .. 0 .. 7 .. Georgiaa 7 12 48 56 10 1 .. 1 13 17 22 13 Germany 16 18 20 23 .. .. 0 .. 58 55 6 4 Ghanaa 15 19 31 34 24 18 .. .. .. .. 9 30 Greece 17 19 32 29 0 0 3 3 31 36 16 14 Guatemalaa 19 28 46 55 23 9 3 1 2 2 6 4 Guineaa 8 .. 4 .. 62 .. 2 .. 1 .. 23 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 248 2009 World Development Indicators ECONOMY Taxes on income, Central government revenues Taxes on Taxes on Other Social 4.12 Grants and profits, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras .. 21 .. 42 .. 5 .. 1 .. 11 .. 20 Hungary 18 21 28 33 8 0 1 2 33 35 12 9 Indiaa 23 41 28 29 24 15 0 0 0 0 25 15 Indonesiaa 46 28 33 32 4 3 1 4 6 3 9 30 Iran, Islamic Rep.a 12 12 5 2 9 5 1 1 6 12 66 69 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 37 37 35 34 0 0 2 6 17 18 9 4 Israel .. 32 .. 29 .. 1 .. 5 .. 16 .. 17 Italy 32 35 21 21 .. .. 5 5 35 35 6 4 Jamaicaa .. 9 .. 18 .. 5 .. 13 .. 5 .. 51 Japan 35 .. 14 .. 1 .. 5 .. 26 .. 18 .. Jordana 10 12 23 40 22 9 9 14 .. 0 36 24 Kazakhstana 11 35 28 30 3 7 5 0 48 .. 6 29 Kenyaa 35 37 40 43 14 11 1 1 0 0 10 9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 31 31 32 25 7 3 10 8 8 15 12 18 Kuwait 1 1 0 .. 2 1 0 0 .. .. 97 98 Kyrgyz Republica 26 9 56 50 5 12 1 .. .. .. 11 29 Lao PDR .. 18 .. 36 .. 9 .. 1 .. .. .. 36 Latviaa 7 13 41 39 3 1 0 0 35 29 13 18 Lebanon .. 13 .. 35 .. 7 .. 12 .. 1 .. 32 Lesothoa 15 17 12 14 49 57 1 0 .. .. 24 12 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 21 .. 37 .. 0 .. 0 .. 30 .. 11 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. 9 .. 18 .. 35 .. 9 .. .. .. 29 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiaa 37 .. 26 .. 12 .. 5 .. 1 .. 19 .. Mali .. 18 .. 38 .. 9 .. 8 .. .. .. 27 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 16 25 47 34 14 6 7 6 5 16 12 Mexicoa 27 .. 54 .. 4 .. 2 .. 14 .. 16 .. Moldovaa 6 3 38 49 5 5 1 0 38 27 2 16 Mongolia .. 15 .. 20 .. 5 .. 20 .. 9 .. 31 Moroccoa .. 27 .. 31 .. 7 .. 6 .. 13 .. 15 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 20 25 26 31 12 2 .. .. .. .. 42 42 Namibiaa 27 .. 32 .. 28 .. 2 .. .. .. 11 .. Nepala 10 13 33 36 26 16 4 4 .. .. 27 30 Netherlands 26 27 24 28 .. 1 2 3 40 34 8 8 New Zealand .. 57 .. 26 .. 3 .. 0 .. 0 .. 15 Nicaraguaa 9 23 52 50 7 4 0 0 11 19 31 22 Niger .. 12 .. 18 .. 26 .. 3 .. .. .. 41 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 32 .. 24 .. 0 .. 1 .. 18 .. 25 Omana 21 .. 1 .. 3 .. 2 .. .. .. 74 .. Pakistana 18 25 27 30 24 10 7 1 .. .. 24 33 Panamaa 20 .. 17 .. 11 .. 3 .. 16 .. 34 .. Papua New Guineaa 40 .. 8 .. 27 .. 2 .. 0 .. 23 .. Paraguaya .. 10 .. 38 .. 7 .. 1 .. 16 .. 28 Perua 15 34 46 36 10 2 8 6 10 8 11 14 Philippinesa 33 41 26 28 29 20 4 6 .. .. 8 11 Poland .. 16 .. 39 .. 0 .. 1 .. 36 .. 8 Portugal 23 23 32 32 0 0 2 2 29 32 14 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 249 4.12 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania .. 14 .. 30 .. 1 .. 0 .. 40 .. 14 Russian Federation .. 6 .. 24 .. 23 .. 0 .. 19 .. 28 Rwandaa 11 .. 25 .. 23 .. 3 .. 2 .. 36 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegala 17 .. 19 .. 36 .. 2 .. .. .. 26 .. Serbiaa .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leonea 15 16 34 9 39 27 0 .. .. .. 12 48 Singaporea 26 28 20 23 1 0 15 15 .. .. 38 33 Slovak Republic .. 12 .. 36 .. 0 .. 0 .. 40 .. 12 Sloveniaa 13 18 33 32 9 0 0 3 42 38 3 9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 52 .. 32 .. 4 .. 3 .. 2 .. 7 Spain 28 33 21 16 0 0 0 0 40 46 .. 5 Sri Lankaa 12 18 49 48 17 14 4 5 1 1 18 13 Sudana 17 .. 41 .. 27 .. 1 .. .. .. 14 .. Swazilanda .. .. .. .. .. .. .. .. .. .. .. .. Sweden 12 .. 31 .. 1 .. 7 .. 37 .. 13 .. Switzerlanda 11 19 21 32 1 1 2 2 49 36 17 9 Syrian Arab Republica 23 .. 37 .. 13 .. 8 .. 0 .. 19 .. Tajikistana 6 3 63 54 12 11 0 1 13 12 5 18 Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 37 .. 40 .. 6 .. 0 .. 5 .. 12 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togoa .. 19 .. 46 .. 20 .. 3 .. .. .. 12 Trinidad and Tobagoa 50 60 26 13 6 5 1 9 2 4 15 9 Tunisiaa 16 27 20 32 28 6 4 5 15 18 17 11 Turkeya .. 23 .. 42 .. 1 .. 6 .. .. .. 28 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandaa 10 19 45 30 7 21 2 0 .. .. 37 30 Ukrainea .. 13 .. 29 .. 4 .. 0 .. 37 .. 16 United Arab Emiratesa .. .. 15 .. .. .. .. .. 1 .. 84 .. United Kingdom 39 37 31 28 .. .. 6 7 19 21 5 6 United States .. 57 .. 3 .. 1 .. 1 .. 35 .. 3 Uruguaya 10 13 32 50 4 5 10 1 31 21 8 10 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 38 21 33 25 9 5 0 4 4 2 19 43 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep.a 17 .. 10 .. 18 .. 3 .. .. .. 51 .. Zambiaa 27 33 22 36 36 8 0 0 0 .. 15 23 Zimbabwea 36 .. 22 .. 17 .. 3 .. 2 .. 19 .. World .. m 19 m .. m 32 m .. m 5m .. m 2m .. m .. m .. m 14 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. 16 .. 38 .. 5 .. 1 .. 13 .. 15 Lower middle income 19 17 34 39 14 5 .. 1 .. 10 16 16 Upper middle income .. 16 .. 37 .. 4 .. 1 .. 22 .. 14 Low & middle income .. 15 .. 35 .. 7 .. 2 .. .. .. 16 East Asia & Pacific 35 26 26 35 12 6 .. .. .. .. 20 22 Europe & Central Asia .. 13 .. 40 .. 1 .. 0 .. 30 .. 15 Latin America & Carib. .. 17 .. 38 .. 5 .. 2 .. 8 .. 20 Middle East & N. Africa 17 13 13 32 18 6 4 5 .. .. 36 26 South Asia 15 18 31 29 24 15 4 2 .. 0 25 27 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 26 28 24 26 .. 0 3 2 35 34 9 9 Euro area 26 25 24 28 0 0 2 3 38 36 8 6 Note: Components may not sum to 100 percent because of missing data or adjustment to tax revenue. a. Data were reported on a cash basis and have been adjusted to the accrual framework. 250 2008 World Development Indicators ECONOMY Central government revenues 4.12 About the data Definitions The International Monetary Fund (IMF) classifi es Direct taxes tend to be progressive, whereas indirect · Taxes on income, profits, and capital gains are government revenues as taxes, grants, and property taxes are proportional. levied on the actual or presumptive net income income. Taxes are classified by the base on which Social security taxes do not reflect compulsory pay- of individuals, on the profi ts of corporations and the tax is levied, grants by the source, and property ments made by employers to provident funds or other enterprises, and on capital gains, whether real- income by type (for example, interest, dividends, agencies with a like purpose. Similarly, expenditures ized or not, on land, securities, and other assets. or rent). The most important source of revenue is from such funds are not reflected in government Intragovernmental payments are eliminated in con- taxes. Grants are unrequited, nonrepayable, non- expenses (see table 4.11). For further discussion of solidation. · Taxes on goods and services include compulsory receipts from other government units taxes and tax policies, see About the data for table general sales and turnover or value added taxes, and foreign governments or from international orga- 5.6. For further discussion of government revenues selective excises on goods, selective taxes on ser- nizations. Transactions are generally recorded on an and expenditures, see About the data for tables 4.10 vices, taxes on the use of goods or property, taxes accrual basis. and 4.11. on extraction and production of minerals, and prof- The IMF's Government Finance Statistics Manual its of fiscal monopolies. · Taxes on international 2001 describes taxes as compulsory, unrequited trade include import duties, export duties, profi ts payments made to governments by individuals, busi- of export or import monopolies, exchange profi ts, nesses, or institutions. Taxes are classified in six and exchange taxes. · Other taxes include employer major groups by the base on which the tax is levied: payroll or labor taxes, taxes on property, and taxes income, profits, and capital gains; payroll and work- not allocable to other categories, such as penalties force; property; goods and services; international for late payment or nonpayment of taxes. · Social trade and transactions; and other. However, the dis- contributions include social security contributions by tinctions are not always clear. Taxes levied on the employees, employers, and self-employed individu- income and profits of individuals and corporations als, and other contributions whose source cannot are classified as direct taxes, and taxes and duties be determined. They also include actual or imputed levied on goods and services are classified as indi- contributions to social insurance schemes operated rect taxes. This distinction may be a useful simplifica- by governments. · Grants and other revenue include tion, but it has no particular analytical significance grants from other foreign governments, international except with respect to the capacity to fix tax rates. organizations, and other government units; interest; dividends; rent; requited, nonrepayable receipts for Rich economies rely more on direct taxes 4.12a public purposes (such as fines, administrative fees, and entrepreneurial income from government owner- Taxes on income and capital gains as a share of central government revenue, 2007 (%) ship of property); and voluntary, unrequited, nonre- 70 payable receipts other than grants. United States 60 South Africa 50 India 40 30 Norway Data sources 20 Data on central government revenues are from 10 the IMF's Government Finance Statistics Yearbook 2008 and data files. Each country's accounts 0 are reported using the system of common defini- 100 1,000 10,000 100,000 tions and classifications in the IMF's Government GNI per capita ($, log scale) Finance Statistics Manual 2001. The IMF receives Low income Middle income High income additional information from the Organisation for High-income economies tend to tax income and property, whereas low-income economies tend to rely on Economic Co-operation and Development on the indirect taxes on international trade and goods and services. But there are exceptions in all groups. tax revenues of some of its members. See the IMF sources for complete and authoritative explana- Note: Data are for the most recent year for 2005­07. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. tions of concepts, definitions, and data sources. 2008 World Development Indicators 251 4.13 Monetary indicators Money and Claims on Claims on Interest rate quasi money private sector governments and other public entities Annual growth Annual growth % annual % growth % of M2 % of M2 Deposit Lending Real 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan .. 46.0 .. 20.1 .. ­7.8 .. .. .. 18.1 .. 7.5 Albania 51.8 13.7 1.8 14.2 ­8.3 3.3 15.3 5.7 19.7 14.1 8.9 10.6 Algeria 9.6 22.8 1.0 3.9 ­10.0 ­19.9 16.6 1.8 18.4 8.0 ­7.9 0.5 Angola 4,105.6 38.6 471.4 33.7 119.5 0.7 125.9 6.8 206.3 17.7 ­84.7 9.9 Argentina ­2.8 24.5 ­1.1 15.2 7.8 ­1.1 11.9 8.0 17.9 11.1 14.2 ­2.7 Armenia 64.3 42.3 70.3 39.7 7.2 ­4.7 63.2 6.3 111.9 17.5 ­18.9 12.8 Australia 8.5 29.9 12.5 23.6 0.4 5.9 6.1 4.7 10.7 10.0 9.1 5.1 Austriaa .. .. .. .. .. .. 2.2 .. 6.4 .. 6.1 .. Azerbaijan 25.4 73.2 6.1 56.4 ­32.7 2.8 .. 11.6 .. 19.1 .. 4.1 Bangladesh 12.1 13.6 25.0 10.5 4.8 4.3 6.0 9.2 14.0 16.0 6.2 8.6 Belarus 158.4 34.7 61.4 46.6 44.7 ­31.8 100.8 8.3 175.0 8.6 ­63.9 ­3.1 Belgiuma .. .. .. .. .. .. 4.0 .. 8.4 8.6 7.1 6.8 Benin ­1.8 19.6 2.2 14.3 6.0 ­17.6 3.5 3.5 16.8 .. 13.0 .. Bolivia 7.7 26.2 13.7 6.6 1.1 ­4.5 18.9 3.5 51.0 12.9 35.5 5.1 Bosnia and Herzegovina 22.0 32.6 23.9 24.1 ­0.4 0.8 51.9 3.6 73.5 7.2 76.3 1.1 Botswana 12.3 31.2 ­1.7 12.7 10.0 ­26.9 9.8 8.6 14.4 16.2 5.2 4.0 Brazil 44.3 18.6 40.5 21.8 14.6 4.6 52.2 10.6 78.2 43.7 65.5 38.1 Bulgaria 40.5 31.3 22.1 44.0 ­7.2 ­6.5 35.9 3.7 79.4 10.0 10.1 2.0 Burkina Faso 22.3 23.8 2.9 0.9 ­7.3 ­11.1 3.5 3.5 16.8 .. 16.5 .. Burundi ­8.0 13.8 ­7.1 4.5 0.2 ­1.4 .. .. 15.3 16.8 ­0.7 6.7 Cambodia 43.6 61.8 12.5 40.2 1.2 ­13.1 8.7 1.9 18.7 16.4 6.4 11.2 Cameroon ­6.2 14.9 0.3 3.0 ­2.2 ­17.1 5.5 4.3 16.0 15.0 6.0 12.7 Canada 4.8 ­25.3 3.8 ­2.5 0.2 ­0.4 5.3 2.1 8.7 6.1 6.2 10.3 Central African Republic 4.3 ­3.6 3.9 3.0 ­7.9 3.5 5.5 4.3 16.0 15.0 5.2 12.8 Chad 48.8 9.8 6.4 3.5 ­18.6 ­28.6 5.5 4.3 16.0 15.0 6.6 12.3 Chile 24.3 18.2 34.9 27.1 ­2.0 1.6 13.7 5.6 18.2 8.7 7.0 3.6 China 29.5 16.7 22.5 13.4 0.8 3.7 11.0 4.1 12.1 7.5 ­1.5 0.0 Hong Kong, China 10.6 18.8 9.8 4.8 ­2.4 ­3.8 5.6 2.4 8.8 6.8 4.4 3.7 Colombia 28.2 11.9 34.3 30.8 2.9 ­6.9 32.3 8.0 42.7 15.4 20.1 10.1 Congo, Dem. Rep. 357.6 50.7 59.6 17.6 ­7.9 5.1 60.0 .. 293.9 .. ­30.5 .. Congo, Rep. ­0.1 7.1 6.3 1.0 2.0 ­4.2 5.5 4.3 16.0 15.0 12.2 24.9 Costa Rica 4.7 18.5 ­1.4 53.7 5.6 ­7.0 23.9 6.4 36.7 12.8 11.9 3.2 Cote d'Ivoire 18.1 23.6 13.3 10.5 0.3 3.7 3.5 3.5 16.8 .. 16.8 .. Croatia 40.4 18.2 30.5 14.2 ­2.4 ­0.8 5.5 2.3 20.2 9.3 14.1 5.1 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 29.3 12.8 15.8 16.3 2.1 ­2.6 7.0 1.3 12.8 5.8 ­3.6 2.1 Denmark 6.2 12.1 2.6 38.4 ­1.5 ­2.2 3.9 .. 10.3 .. 9.0 .. Dominican Republic 16.6 22.1 14.4 21.8 ­1.7 9.4 14.9 7.0 30.7 15.8 16.0 9.6 Ecuador 6.8 18.4 15.1 13.1 ­74.8 ­3.3 43.3 5.0 55.7 12.1 45.7 7.1 Egypt, Arab Rep. 9.9 19.1 12.1 5.7 0.6 2.2 10.9 6.1 16.5 12.5 4.6 ­0.1 El Salvador 13.5 17.8 22.6 9.5 ­0.9 3.1 14.4 .. 19.1 .. 7.8 .. Eritrea 21.0 12.1 27.8 2.4 20.5 11.3 .. .. .. .. .. .. Estonia 27.5 13.6 28.9 52.5 ­9.3 0.9 8.7 4.4 19.0 6.5 ­9.3 ­2.9 Ethiopia 9.0 ­46.8 13.4 14.7 ­3.5 5.4 11.5 3.6 15.1 7.0 2.1 ­4.1 Finlanda .. .. .. .. .. .. 3.2 1.0 7.8 3.7 2.9 3.0 Francea .. .. .. .. .. .. 4.5 2.9 8.1 6.6 6.7 4.9 Gabon 10.1 6.9 11.9 20.7 5.8 ­47.2 5.5 4.3 16.0 15.0 14.5 9.3 Gambia, The 14.2 6.7 ­5.0 4.8 15.2 ­5.4 12.5 12.9 25.0 27.9 20.3 21.0 Georgia 40.2 49.7 ­11.1 79.0 73.8 ­1.4 31.0 9.5 58.2 20.4 10.6 9.8 Germanya .. .. .. .. .. .. 3.9 .. 10.9 .. 8.9 .. Ghana 43.2 42.8 10.2 20.1 28.1 10.9 28.7 8.9 .. .. .. .. Greecea .. .. .. .. .. .. 15.8 2.2 23.1 .. 12.1 .. Guatemala 15.6 11.3 36.1 18.2 ­7.1 ­1.4 7.9 4.8 21.2 12.8 11.5 6.3 Guinea 11.3 33.4 12.1 19.8 8.4 18.1 17.5 14.4 21.5 .. 14.7 .. Guinea-Bissau 43.0 24.9 ­6.7 7.5 ­20.4 ­0.3 3.5 3.5 32.9 .. ­8.2 .. Haiti 27.1 11.1 15.7 2.7 0.1 ­3.6 4.2 1.5 24.8 47.0 ­2.4 22.9 252 2009 World Development Indicators ECONOMY Money and Claims on Monetary indicators Claims on Interest rate 4.13 quasi money private sector governments and other public entities Annual growth Annual growth % annual % growth % of M2 % of M2 Deposit Lending Real 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 28.9 19.3 18.0 27.5 ­7.5 0.4 12.0 7.8 27.0 16.6 1.7 9.0 Hungary 20.9 9.5 4.9 19.6 20.2 1.9 24.4 6.8 32.6 9.1 4.6 3.2 India 11.0 22.3 6.0 13.1 3.4 1.5 .. .. 15.5 13.0 5.9 7.8 Indonesia 27.5 19.3 25.9 13.3 ­2.3 0.5 16.7 8.0 18.9 13.9 8.3 2.2 Iran, Islamic Rep. 30.1 30.6 9.8 37.1 17.3 ­0.5 .. 11.6 .. 12.0 .. ­7.0 Iraq .. 37.1 .. 3.0 .. ­44.3 .. 11.4 .. 19.7 .. .. Irelanda .. .. .. .. .. .. 0.4 0.0 6.6 2.7 3.4 0.1 Israel 21.7 ­4.4 18.3 7.5 ­0.5 ­1.8 14.1 3.5 20.2 6.3 ­0.2 6.5 Italya .. .. .. .. .. .. 6.4 .. 13.2 6.3 7.9 4.0 Jamaica 28.0 12.6 18.0 15.7 6.1 ­1.6 23.2 7.1 43.6 17.2 17.5 ­7.9 Japan 4.1 0.8 1.3 ­0.1 2.5 ­0.4 0.9 0.8 3.5 1.9 4.0 2.5 Jordan 5.7 12.4 9.6 9.4 ­3.8 5.3 7.7 5.4 10.7 8.7 8.6 2.6 Kazakhstan 108.2 25.9 ­72.5 73.2 24.7 ­20.0 .. .. .. .. .. .. Kenya 29.0 20.4 26.7 10.8 6.6 0.8 13.6 5.2 28.8 13.3 15.8 8.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 15.6 0.3 21.6 18.1 ­1.2 ­3.7 8.8 5.2 9.0 6.6 1.5 5.3 Kuwait 9.4 19.3 10.9 31.2 ­2.0 5.4 6.5 5.4 8.4 8.5 3.4 ­7.7 Kyrgyz Republic 14.8 33.2 0.1 29.2 62.6 ­8.8 36.7 5.4 65.0 25.3 21.9 10.4 Lao PDR 16.4 38.7 18.1 8.4 ­9.7 ­1.8 14.0 5.0 25.7 28.5 5.0 24.2 Latvia ­21.4 13.5 ­23.8 59.2 6.5 ­2.1 14.8 6.1 34.6 10.9 5.5 ­2.1 Lebanon 16.4 12.4 13.1 4.9 5.1 0.4 16.3 8.0 24.7 10.3 12.8 5.1 Lesotho 9.8 16.4 ­2.3 8.2 ­18.7 ­52.0 13.3 6.5 16.4 14.1 15.3 7.5 Liberia 29.5 42.4 ­6.0 16.6 37.2 67.9 6.4 3.8 15.6 15.1 8.5 ­0.8 Libya 9.6 38.0 3.1 2.9 3.6 ­38.9 5.5 2.5 7.0 6.0 .. 0.6 Lithuania 28.9 21.7 12.7 48.8 ­2.4 2.3 20.1 5.4 27.1 6.9 ­13.2 ­1.6 Macedonia, FYR 1.8 30.7 ­138.9 26.7 ­229.7 9.0 24.1 4.9 46.0 10.2 24.6 4.9 Madagascar 16.2 20.9 9.4 8.0 ­10.3 1.0 18.5 16.5 37.5 45.0 ­5.3 31.5 Malawi 56.2 36.6 2.8 9.9 ­10.4 1.7 37.3 6.0 47.3 27.7 ­16.9 18.9 Malaysia 18.5 10.5 29.2 8.1 ­0.7 ­1.4 5.9 3.2 8.7 6.4 4.9 1.2 Mali 7.3 13.7 18.9 10.2 ­11.6 ­2.2 3.5 3.5 16.8 .. 14.5 .. Mauritania ­5.1 .. ­42.5 .. ­28.9 .. 9.0 8.0 20.3 23.5 17.0 26.7 Mauritius 18.6 15.4 8.7 14.6 3.0 ­0.4 12.2 11.8 20.8 21.9 16.1 13.9 Mexico 31.9 13.8 ­2.9 12.0 27.6 1.8 39.8 3.2 59.4 7.6 15.6 2.7 Moldova 65.3 39.8 34.6 37.8 19.1 ­5.6 25.4 15.0 36.7 18.8 7.7 2.7 Mongolia 32.6 56.7 14.4 57.5 ­31.8 ­17.0 74.6 13.5 134.4 17.5 46.9 4.6 Morocco 7.0 16.1 6.9 17.3 5.1 0.2 7.3 3.7 11.3 11.5 3.1 10.4 Mozambique 47.7 26.2 21.8 8.1 ­12.5 3.8 38.8 11.9 24.4 19.5 18.0 12.6 Myanmar 36.5 30.0 13.4 4.2 19.7 28.9 9.8 12.0 16.5 17.0 ­2.4 ­2.2 Namibia 22.6 10.2 30.5 15.7 1.7 ­10.0 10.8 7.5 18.5 12.9 12.1 12.0 Nepal 15.4 18.6 18.1 14.6 3.3 4.8 9.6 2.3 12.9 8.0 4.7 0.3 Netherlandsa .. .. .. .. .. .. 4.4 3.9 7.2 4.6 5.0 3.4 New Zealand 9.3 9.8 15.8 18.7 ­3.9 ­2.7 8.5 7.8 12.1 12.8 9.9 7.5 Nicaragua 35.1 18.5 30.3 27.4 ­21.5 ­4.8 11.1 6.1 19.9 13.0 5.7 3.5 Niger 3.8 24.7 ­22.8 11.5 10.2 ­14.7 3.5 3.5 16.8 .. 15.5 .. Nigeria 19.4 64.2 22.3 79.2 ­9.1 4.7 13.5 10.3 20.2 16.9 ­22.9 11.3 Norway 3.8 .. 9.5 .. ­1.9 .. 5.0 4.9 7.6 6.7 4.4 5.0 Oman 7.7 34.7 9.3 29.9 ­2.3 ­11.2 6.5 4.1 9.4 7.3 7.5 ­0.3 Pakistan 13.8 19.5 10.8 10.0 8.7 7.1 .. 5.3 .. 11.8 .. 3.5 Panama 8.4 17.4 14.5 18.9 ­4.3 ­6.6 7.2 4.8 11.1 8.3 10.6 6.2 Papua New Guinea 13.7 27.8 0.2 14.4 5.0 ­11.3 7.3 1.1 13.1 9.8 ­2.3 7.2 Paraguay 0.5 29.5 4.9 28.0 0.1 ­7.7 21.2 5.0 33.9 25.0 17.9 13.4 Peru 29.3 23.0 31.1 19.7 ­8.1 ­9.0 9.6 3.2 36.2 22.9 20.5 20.4 Philippines 23.9 5.4 27.9 2.1 3.0 0.1 8.4 3.7 14.7 8.7 6.6 5.7 Poland 35.6 13.0 19.1 22.3 3.1 ­2.5 26.8 2.2 33.5 5.5 ­5.2 3.9 Portugala .. .. .. .. .. .. 8.4 .. 13.8 .. 10.0 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 253 4.13 Monetary indicators Money and Claims on Claims on Interest rate quasi money private sector governments and other public entities Annual growth Annual growth % annual % growth % of M2 % of M2 Deposit Lending Real 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 69.6 33.9 23.1 50.3 11.6 5.0 44.7 6.7 50.7 13.4 11.4 2.3 Russian Federation 112.6 44.2 46.2 41.7 73.6 ­17.7 102.0 5.1 320.3 10.0 72.3 ­3.1 Rwanda 69.5 18.0 32.7 14.5 ­41.0 ­13.8 11.1 6.8 18.5 15.8 6.9 6.4 Saudi Arabia 3.4 20.1 3.4 15.4 1.4 ­23.3 6.2 4.8 .. .. .. .. Senegal 7.4 13.1 1.2 6.9 1.0 5.0 3.5 3.5 16.8 .. 17.8 .. Serbia 33.0 42.5 88.5 36.2 34.1 ­1.0 19.1 4.1 78.0 11.1 23.0 4.1 Sierra Leone 19.6 22.6 1.6 8.5 ­101.6 ­4.5 7.0 10.0 28.8 25.0 ­3.6 13.3 Singapore 8.5 13.4 19.7 12.8 ­8.1 3.0 3.5 0.5 6.4 5.3 4.0 1.2 Slovak Republica 18.4 11.1 3.4 14.8 ­4.8 ­1.4 9.0 3.7 16.8 8.0 7.1 6.8 Sloveniaa 30.4 8.4 31.6 40.5 5.0 ­2.3 15.4 3.6 23.4 5.9 ­4.0 1.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 16.0 20.2 18.9 26.2 ­4.1 ­0.9 13.5 9.2 17.9 13.2 6.9 3.9 Spaina .. .. .. .. .. .. 7.7 .. 10.1 .. 4.9 .. Sri Lanka 35.8 16.5 75.4 15.9 5.4 1.4 12.1 9.1 18.0 17.1 8.0 2.7 Sudan 72.7 10.3 10.6 8.0 389.1 4.2 .. .. .. .. .. .. Swaziland 3.9 21.5 1.3 21.5 ­14.8 ­50.9 9.4 7.1 17.1 13.2 ­1.5 3.9 Sweden 3.1 11.4 ­1.1 31.1 ­4.0 0.3 6.2 0.8 11.1 3.3 7.2 2.4 Switzerland 4.6 3.3 4.0 10.1 0.2 ­0.1 1.3 2.1 5.5 3.2 4.7 1.7 Syrian Arab Republic 9.2 12.4 3.9 4.4 6.1 ­18.8 4.0 8.3 9.0 10.2 2.2 6.5 Tajikistan .. 169.9 .. 253.7 .. ­13.8 23.9 8.4 75.5 22.9 6.2 ­3.9 Tanzania 33.0 20.5 ­3.9 16.1 16.3 ­0.7 24.6 8.7 42.8 16.0 12.6 9.5 Thailand 17.7 2.5 40.3 4.3 ­4.2 0.4 11.6 2.9 13.3 7.1 7.3 3.7 Timor-Leste .. 43.9 .. ­11.0 .. ­135.6 .. 0.8 .. 15.1 .. 2.5 Togo 22.3 16.8 17.6 15.5 14.9 0.8 3.5 3.5 17.5 .. 13.8 .. Trinidad and Tobago 4.0 10.8 9.0 12.0 0.6 8.8 6.9 5.9 15.2 11.8 10.7 7.6 Tunisia 6.6 12.4 10.4 9.8 ­1.2 1.2 .. .. .. .. .. .. Turkey 104.2 15.2 66.9 16.2 30.1 3.9 76.0 22.6 .. .. .. .. Turkmenistan 449.5 .. 76.3 .. ­573.1 .. .. .. .. .. .. .. Uganda 13.9 22.0 9.6 10.7 ­41.2 ­13.4 7.6 9.3 20.2 19.1 9.9 11.4 Ukraine 115.5 50.8 7.7 68.6 95.4 1.3 70.3 8.1 122.7 13.9 ­56.8 ­6.5 United Arab Emirates 10.2 41.7 10.7 36.3 ­4.3 0.7 .. .. .. .. .. .. United Kingdom 20.3 15.9 19.6 19.9 1.0 ­0.3 4.1 .. 6.7 5.5 3.9 2.4 United States 6.9 12.1 6.0 7.1 0.2 1.4 .. .. 8.8 8.1 6.7 5.3 Uruguay 36.9 5.0 35.2 7.6 1.0 ­10.6 57.7 2.4 93.1 8.9 36.9 0.4 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 36.6 28.7 15.3 39.6 32.8 ­10.7 24.7 10.7 39.7 17.1 ­7.9 2.7 Vietnam 25.8 49.1 18.9 44.5 0.7 ­1.0 8.5 7.5 20.1 11.2 10.5 2.7 West Bank and Gaza .. 5.6 .. 2.9 .. 2.4 .. 3.0 .. 7.7 .. 2.3 Yemen, Rep. 50.7 17.0 6.0 7.1 13.3 12.6 23.8 13.0 31.5 18.0 ­3.2 2.6 Zambia 55.5 25.3 34.2 20.5 185.8 ­8.7 30.2 9.2 45.5 18.9 5.4 8.7 Zimbabwe 25.5 64,472.5 25.5 67,582.1 ­0.3 11,566.1 25.9 121.5 34.7 579.0 23.0 ­0.7 a. As members of the European Monetary Union, these countries share a single currency, the euro. 254 2008 World Development Indicators ECONOMY Monetary indicators 4.13 About the data Definitions Money and the financial accounts that record the reporting period. The valuation of financial deriva- · Money and quasi money are the sum of currency supply of money lie at the heart of a country's tives and the net liabilities of the banking system outside banks, demand deposits other than those of financial system. There are several commonly used can also be difficult. The quality of commercial bank the central government, and the time, savings, and defi nitions of the money supply. The narrowest, reporting also may be adversely affected by delays in foreign currency deposits of resident sectors other M1, encompasses currency held by the public and reports from bank branches, especially in countries than the central government. This definition of the demand deposits with banks. M2 includes M1 plus where branch accounts are not computerized. Thus money supply, often called M2, corresponds to lines time and savings deposits with banks that require the data in the balance sheets of commercial banks 34 and 35 in the IMF's International Financial Statis- prior notice for withdrawal. M3 includes M2 as well may be based on preliminary estimates subject to tics (IFS). The change in money supply is measured as various money market instruments, such as cer- constant revision. This problem is likely to be even as the difference in end-of-year totals relative to M2 tificates of deposit issued by banks, bank deposits more serious for nonbank financial intermediaries. in the preceding year. · Claims on private sector denominated in foreign currency, and deposits with Many interest rates coexist in an economy, reflect- (IFS line 32 d) include gross credit from the financial fi nancial institutions other than banks. However ing competitive conditions, the terms governing loans system to individuals, enterprises, nonfinancial pub- defined, money is a liability of the banking system, and deposits, and differences in the position and lic entities not included under net domestic credit, distinguished from other bank liabilities by the spe- status of creditors and debtors. In some economies and financial institutions not included elsewhere. cial role it plays as a medium of exchange, a unit of interest rates are set by regulation or administra- · Claims on governments and other public enti- account, and a store of value. tive fiat. In economies with imperfect markets, or ties (IFS line 32 an + 32 b + 32 bx + 32 c) usually The banking system's assets include its net for- where reported nominal rates are not indicative of comprise direct credit for specific purposes, such eign assets and net domestic credit. Net domestic effective rates, it may be difficult to obtain data on as financing the government budget deficit; loans credit includes credit extended to the private sector interest rates that reflect actual market transactions. to state enterprises; advances against future credit and general government and credit extended to the Deposit and lending rates are collected by the Inter- authorizations; and purchases of treasury bills and nonfinancial public sector in the form of investments national Monetary Fund (IMF) as representative inter- bonds, net of deposits by the public sector. Public in short- and long-term government securities and est rates offered by banks to resident customers. sector deposits with the banking system also include loans to state enterprises; liabilities to the public The terms and conditions attached to these rates sinking funds for the service of debt and temporary and private sectors in the form of deposits with the differ by country, however, limiting their comparabil- deposits of government revenues. · Deposit interest banking system are netted out. Net domestic credit ity. Real interest rates are calculated by adjusting rate is the rate paid by commercial or similar banks also includes credit to banking and nonbank financial nominal rates by an estimate of the inflation rate in for demand, time, or savings deposits. · Lending institutions. the economy. A negative real interest rate indicates interest rate is the rate charged by banks on loans to Domestic credit is the main vehicle through which a loss in the purchasing power of the principal. The prime customers. · Real interest rate is the lending changes in the money supply are regulated, with cen- real interest rates in the table are calculated as interest rate adjusted for inflation as measured by tral bank lending to the government often playing the (i ­ P) / (1 + P), where i is the nominal lending inter- the GDP deflator. most important role. The central bank can regulate est rate and P is the inflation rate (as measured by lending to the private sector in several ways--for the GDP deflator). example, by adjusting the cost of the refinancing facilities it provides to banks, by changing market interest rates through open market operations, or by Data sources controlling the availability of credit through changes in the reserve requirements imposed on banks and Data on monetary and financial statistics are ceilings on the credit provided by banks to the pri- published by the IMF in its monthly International vate sector. Financial Statistics and annual International Finan- Monetary accounts are derived from the balance cial Statistics Yearbook. The IMF collects data on sheets of financial institutions--the central bank, the financial systems of its member countries. The commercial banks, and nonbank financial interme- World Bank receives data from the IMF in elec- diaries. Although these balance sheets are usually tronic files that may contain more recent revisions reliable, they are subject to errors of classification, than the published sources. The discussion of valuation, and timing and to differences in account- monetary indicators draws from an IMF publication ing practices. For example, whether interest income by Marcello Caiola, A Manual for Country Econo- is recorded on an accrual or a cash basis can make mists (1995). Also see the IMF's Monetary and a substantial difference, as can the treatment of non- Financial Statistics Manual (2000) for guidelines performing assets. Valuation errors typically arise for the presentation of monetary and financial sta- for foreign exchange transactions, particularly in tistics. Data on real interest rates are derived from countries with flexible exchange rates or in countries World Bank data on the GDP deflator. that have undergone currency devaluation during the 2008 World Development Indicators 255 4.14 Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion market rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2007 2008a 1995 2007 2007 2007 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 Afghanistan 49.96 50.52 .. 16.1 0.3 .. .. 11.8 .. .. .. .. Albania 90.43 96.84 24.4 43.7 0.5 .. 38.0 3.7 27.8 2.9 .. 4.8 Algeria 69.29 70.91 15.3 35.8 0.5 82.3 18.5 8.8 17.3 2.6 .. 3.5 Angola 76.71 75.13 0.0 51.6 0.7 .. 739.4 56.1 711.0 54.4 .. .. Argentina 3.10 3.41 0.9 1.6 0.5 .. 5.2 12.3 8.9 10.6 0.1 18.0 Armenia 342.08 307.83 116.6 183.8 0.5 122.6 212.5 4.3 72.8 3.5 .. 0.9 Australia 1.20 1.49 1.3 1.4 1.1 133.3 1.5 3.7 2.1 2.9 1.1 3.5 Austriab 0.73 0.73 0.9 0.9 1.2 106.7 1.7 1.7 2.2 1.9 0.3 2.4 Azerbaijan 0.86 0.81 0.2 0.4 0.5 .. 203.0 8.9 170.9 6.8 .. .. Bangladesh 68.88 68.89 19.2 24.0 0.3 .. 4.0 4.4 5.5 6.4 .. .. Belarus 2,146.08 2,189.82 3.5 913.5 0.4 .. 355.1 27.6 271.3 22.2 267.8 26.6 Belgiumb 0.73 0.73 0.9 0.9 1.2 110.8 1.8 2.0 1.9 2.0 1.2 2.3 Benin 479.27 481.53 187.5 219.7 0.5 .. 8.7 2.9 8.7 2.7 .. .. Bolivia 7.85 7.02 1.6 2.6 0.3 81.8 8.6 6.8 8.7 4.0 .. .. Bosnia and Herzegovina 1.43 1.44 0.6 0.7 0.5 .. 3.7 3.7 .. .. .. .. Botswana 6.14 7.84 1.4 3.0 0.5 .. 9.7 7.4 10.4 8.4 .. .. Brazil 1.95 2.39 0.7 1.4 0.7 .. 211.8 8.5 199.5 7.7 204.9 11.6 Bulgaria 1.43 1.46 0.0 0.7 0.5 133.9 103.3 5.0 117.5 5.7 85.7 5.5 Burkina Faso 479.27 481.53 189.6 195.3 0.4 .. 3.7 2.2 5.5 2.4 .. .. Burundi 1,081.87 1,234.53 126.6 363.9 0.3 68.7 13.4 8.4 16.1 7.4 .. .. Cambodia 4,056.17 4,091.00 1,142.8 1,345.7 0.3 95.3 4.4 3.8 6.3 3.5 .. .. Cameroon 479.27 481.53 241.2 251.4 0.5 114.6 6.3 2.2 6.5 2.1 .. .. Canada 1.07 1.24 1.2 1.2 1.1 132.3 1.5 2.0 1.7 2.2 2.7 1.2 Central African Republic 479.27 481.53 272.0 264.8 0.6 115.1 4.5 2.0 5.3 2.4 6.0 4.4 Chad 479.27 481.53 135.5 213.6 0.4 126.7 7.1 8.2 6.9 2.0 .. .. Chile 522.46 649.32 263.8 371.8 0.7 95.2 7.9 7.0 8.9 2.7 7.0 5.8 China 7.61 6.84 3.4 3.6 0.5 99.1 7.9 3.8 8.6 1.8 .. .. Hong Kong, China 7.80 7.75 7.9 5.5 0.7 .. 4.5 ­2.3 5.9 ­0.5 0.6 0.5 Colombia 2,078.29 2,273.16 423.8 1,143.3 0.6 115.8 22.3 7.0 20.3 5.9 16.4 5.4 Congo, Dem. Rep. 516.75 560.84 0.0 267.8 0.5 31.8 964.9 31.0 932.8 29.5 .. .. Congo, Rep. 479.27 481.53 150.9 277.1 0.6 .. 9.0 5.7 9.3 2.8 .. .. Costa Rica 516.62 549.80 103.1 280.5 0.5 95.3 15.9 10.0 15.6 11.1 14.1 12.2 Côte d'Ivoire 479.27 481.53 261.9 291.4 0.6 117.9 9.2 3.0 7.2 2.9 .. .. Croatia 5.37 5.38 3.1 3.9 0.7 112.9 86.0 3.7 86.2 2.5 74.2 2.5 Cuba .. .. .. .. .. .. 3.0 2.6 .. .. .. .. Czech Republic 20.29 19.48 11.1 14.2 0.7 136.8 12.8 2.2 7.8 2.1 8.2 2.3 Denmark 5.44 5.58 8.5 8.6 1.6 109.7 1.6 2.2 2.1 1.9 1.1 2.3 Dominican Republic 33.26 35.44 6.7 18.7 0.6 100.9 9.4 17.4 8.7 17.3 .. .. Ecuador 1.00 1.00 0.4 0.4 0.4 138.5 4.3 9.6 37.1 7.5 .. 8.7 Egypt, Arab Rep. 5.73 5.30 1.2 1.8 0.3 .. 8.7 7.2 8.8 6.2 6.1 9.5 El Salvador 1.00 1.00 0.4 0.5 0.5 .. 6.2 3.5 8.5 3.6 .. 4.2 Eritrea 15.38 15.38 1.9 7.0 0.5 .. 7.9 18.6 .. .. .. .. Estonia 11.43 11.66 4.8 8.7 0.8 .. 53.6 5.2 21.6 3.7 8.1 2.7 Ethiopia 8.95 9.32 2.1 2.8 0.3 99.6 6.4 6.6 5.5 8.3 .. .. Finlandb 0.73 0.73 1.0 1.0 1.3 106.2 2.0 0.9 1.5 1.2 1.0 1.9 Franceb 0.73 0.73 1.0 0.9 1.2 109.0 1.3 2.1 1.6 1.9 .. 1.9 Gabon 479.27 481.53 188.0 274.8 0.6 105.6 7.0 5.0 4.6 1.1 .. .. Gambia, The 24.88 21.64 3.9 7.7 0.3 59.6 4.2 11.9 4.1 9.7 .. .. Georgia 1.67 1.66 0.4 0.8 0.5 109.7 356.7 6.9 24.7 6.7 .. 6.6 Germany b 0.73 0.73 1.0 0.9 1.2 108.9 1.7 1.1 2.0 1.6 0.4 2.6 Ghana 0.94 1.07 0.1 0.5 0.5 115.5 26.7 19.5 28.4 17.0 .. .. Greeceb 0.73 0.73 0.6 0.7 1.0 116.8 9.2 3.3 9.0 3.3 3.6 4.0 Guatemala 7.67 7.71 2.9 4.2 0.6 .. 10.4 4.7 10.1 7.1 .. .. Guinea 3,644.33 4,478.00 666.8 1,857.1 0.4 .. 5.5 18.1 .. .. .. .. Guinea-Bissau 479.27 481.53 114.9 211.4 0.4 .. 32.5 1.6 34.0 1.5 .. .. Haiti 36.86 39.82 6.0 22.3 0.6 .. 22.8 17.4 21.9 18.9 .. .. 256 2009 World Development Indicators ECONOMY Official Exchange rates and prices Purchasing Ratio of PPP Real GDP implicit Consumer price 4.14 Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion market rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2007 2008a 1995 2007 2007 2007 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 Honduras 18.90 18.90 3.0 8.6 0.5 .. 19.8 6.2 22.8 7.8 .. .. Hungary 183.63 196.78 61.6 134.8 0.7 142.5 19.6 5.1 20.3 5.5 16.8 3.2 India 41.35 48.64 11.1 15.3 0.4 .. 8.1 4.3 9.1 4.4 7.4 4.9 Indonesia 9,141.00 11,836.00 1,031.8 4,724.6 0.5 .. 15.8 10.1 13.7 9.3 15.4 9.4 Iran, Islamic Rep. 9,281.15 9,896.38 567.5 3,412.4 0.4 143.1 27.7 17.4 26.0 14.3 28.4 10.7 Iraq 1,254.57 1,203.00 .. 558.7 .. .. .. .. .. .. .. .. Irelandb 0.73 0.73 0.8 1.0 1.3 133.1 3.7 2.9 2.3 3.5 1.6 0.3 Israel 4.11 3.87 3.1 3.6 0.9 79.4 11.0 1.1 9.7 1.5 8.1 4.6 Italy b 0.73 0.73 0.8 0.9 1.2 112.3 3.8 2.6 3.7 2.3 2.9 2.7 Jamaica 68.95 73.88 16.8 47.8 0.7 .. 23.0 11.2 23.5 10.7 .. .. Japan 117.75 91.32 174.3 120.1 1.0 66.6 0.1 ­1.2 0.8 ­0.2 ­0.9 0.3 Jordan 0.71 0.71 0.4 0.4 0.6 .. 3.2 3.0 3.5 3.3 .. 9.0 Kazakhstan 122.55 120.58 17.5 76.4 0.6 .. 204.7 14.3 67.8 7.5 16.3 11.5 Kenya 67.32 78.04 15.8 31.3 0.4 .. 16.6 5.1 15.6 9.4 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 929.26 1,373.84 690.0 749.9 0.8 .. 5.7 1.7 5.1 3.1 3.7 2.1 Kuwait 0.28 0.28 0.1 0.2 0.8 .. 1.5 8.7 2.0 2.3 1.4 2.5 Kyrgyz Republic 37.32 39.38 3.5 13.3 0.4 .. 110.6 6.3 23.3 4.6 35.6 7.9 Lao PDR 9,603.16 8,640.71 309.7 3,096.8 0.3 .. 27.0 9.6 28.2 9.5 .. .. Latvia 0.51 0.52 0.2 0.4 0.7 .. 48.0 6.9 29.2 5.1 12.0 6.6 Lebanon 1,507.50 1,507.50 837.2 886.4 0.6 .. 17.9 2.1 .. .. .. .. Lesotho 7.05 9.97 2.3 3.6 0.5 128.8 7.7 5.4 9.8 7.8 .. .. Liberia 61.27 62.57 0.6 33.5 0.5 .. 51.8 10.1 .. .. .. .. Libya 1.26 1.30 .. 0.8 0.7 .. .. 21.0 5.6 ­3.0 .. .. Lithuania 2.52 2.57 1.2 1.6 0.6 .. 75.0 2.9 32.6 1.6 24.7 4.3 Macedonia, FYR 44.73 45.73 17.9 18.5 0.4 100.4 79.3 2.8 10.6 1.9 8.4 0.7 Madagascar 1,873.88 1,841.20 287.6 754.2 0.4 .. 19.1 11.6 18.7 10.8 .. .. Malawi 139.96 140.60 3.9 47.1 0.3 65.0 33.6 21.2 33.8 13.2 .. .. Malaysia 3.44 3.55 1.4 1.8 0.5 102.1 4.0 3.8 3.6 2.1 3.4 4.6 Mali 479.27 481.53 226.8 246.2 0.5 .. 7.0 3.6 5.2 1.7 .. .. Mauritania 258.59 235.99 62.4 118.0 0.4 .. 8.7 11.3 6.1 7.5 .. .. Mauritius 31.31 32.10 10.5 15.4 0.5 .. 6.4 5.4 6.9 5.9 .. .. Mexico 10.93 13.37 2.9 7.5 0.7 .. 19.0 8.6 19.4 4.5 18.4 6.2 Moldova 12.14 10.40 1.2 5.5 0.5 101.9 119.6 11.5 19.2 11.0 .. .. Mongolia 1,170.97 1,228.87 158.7 544.9 0.5 .. 57.8 14.1 35.7 6.8 .. .. Morocco 8.19 8.10 4.9 4.9 0.6 92.6 4.0 1.3 3.8 1.8 2.9 .. Mozambique 25.84 24.13 3.9 11.7 0.5 .. 34.1 7.9 31.8 11.8 .. .. Myanmar 5.56 5.65 40.2 249.7 .. .. 25.5 21.1 25.9 23.6 .. .. Namibia 7.05 9.97 2.5 4.4 0.7 .. 10.4 4.8 .. 4.7 .. .. Nepal 66.42 78.07 15.4 24.7 0.4 .. 8.0 5.6 8.7 5.0 .. .. Netherlandsb 0.73 0.73 0.9 0.9 1.2 113.5 2.1 2.2 2.4 2.0 1.3 2.5 New Zealand 1.36 1.80 1.5 1.5 1.2 137.8 1.7 2.7 1.7 2.6 1.4 2.8 Nicaragua 18.45 19.81 3.5 7.3 0.4 88.6 42.4 7.7 .. 7.5 .. .. Niger 479.27 481.53 209.8 224.4 0.5 .. 6.0 2.0 6.1 1.8 .. .. Nigeria 125.81 117.73 15.5 71.4 0.6 130.6 29.5 17.9 32.5 13.5 .. .. Norway 5.86 7.01 9.2 9.0 1.5 112.2 2.7 4.2 2.2 1.6 1.6 6.2 Oman 0.39 0.39 0.2 0.2 0.6 .. 0.1 5.7 .. 1.3 .. .. Pakistan 60.74 79.09 10.1 21.5 0.4 95.6 11.1 6.7 9.7 6.0 10.4 7.0 Panama 1.00 1.00 0.5 0.5 0.5 .. 3.6 1.7 1.1 1.5 1.0 2.7 Papua New Guinea 2.97 2.66 0.7 1.4 0.5 96.5 7.6 7.2 9.3 6.1 .. .. Paraguay 5,032.72 4,837.00 949.3 2,267.1 0.5 97.6 11.5 10.8 13.1 8.7 .. 11.8 Peru 3.13 3.11 1.2 1.5 0.5 .. 26.7 3.6 27.3 2.0 23.7 2.3 Philippines 46.15 48.09 14.1 22.2 0.5 112.3 8.3 5.1 7.7 5.2 5.6 8.2 Poland 2.77 2.97 1.2 1.9 0.7 114.1 24.7 2.4 25.3 2.3 19.8 2.7 Portugalb 0.73 0.73 0.6 0.7 0.9 113.5 5.2 3.0 4.5 2.9 .. 2.6 Puerto Rico 1.00 1.00 .. .. .. .. 3.0 .. .. .. .. .. 2009 World Development Indicators 257 4.14 Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion market rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2007 2008a 1995 2007 2007 2007 1990­2000 2000­07 1990­2000 2000­07 1990­2000 2000­07 Romania 2.44 2.90 0.1 1.5 0.6 140.5 98.0 18.0 100.5 13.8 93.8 17.5 Russian Federation 25.58 28.13 1.5 15.8 0.6 172.7 161.5 16.7 99.1 12.9 99.8 16.9 Rwanda 546.96 551.35 129.3 216.5 0.4 .. 14.3 9.7 16.2 7.8 .. .. Saudi Arabia 3.75 3.75 1.8 2.6 0.7 78.5 1.6 8.1 1.0 0.9 1.3 1.9 Senegal 479.27 481.53 252.0 258.5 0.5 .. 6.0 2.2 5.4 1.7 .. .. Serbia 58.45 64.71 2.6 31.0 0.5 .. .. 18.8 50.2 18.0 .. .. Sierra Leone 2,985.19 2,990.63 387.6 1,251.2 0.4 73.8 31.9 8.8 29.3 8.1 .. .. Singapore 1.51 1.48 1.3 1.1 0.7 95.8 1.3 0.9 1.7 0.8 ­1.0 3.4 Slovak Republicb 24.69c 22.65c 13.0 17.1 0.7 158.1 11.1 4.0 8.4 5.4 9.5 5.2 Sloveniab 0.73 0.73 0.4 0.6 0.9 .. 29.3 4.4 12.0 4.5 9.1 4.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 7.05 9.97 2.3 4.3 0.6 94.9 9.9 6.7 8.7 4.9 7.7 6.0 Spainb 0.73 0.73 0.7 0.7 1.0 117.3 3.9 4.1 3.8 3.2 2.4 3.0 Sri Lanka 110.62 111.37 18.3 42.1 0.4 .. 9.1 10.0 9.9 9.9 8.1 11.1 Sudan 2.02 2.03 0.3 1.2 0.6 .. 65.5 9.6 71.9 7.8 .. .. Swaziland 7.05 9.97 2.2 3.7 0.5 .. 10.5 7.3 9.4 6.5 .. .. Sweden 6.76 8.02 9.4 9.1 1.4 98.9 2.2 1.5 1.9 1.4 2.4 2.6 Switzerland 1.20 1.15 2.0 1.7 1.4 98.0 1.1 0.8 1.6 0.9 ­0.4 0.8 Syrian Arab Republic 11.23 11.23 12.8 21.0 0.4 .. 7.9 6.2 6.4 5.1 4.7 2.2 Tajikistan 3.44 3.42 0.0 1.1 0.3 .. 235.0 20.4 .. 12.7 .. .. Tanzania 1,245.04 1,273.83 154.8 412.4 0.3 .. 21.6 9.0 20.9 4.9 .. .. Thailand 34.52 35.08 15.1 16.3 0.5 .. 4.2 2.9 4.9 2.7 3.8 5.3 Timor-Leste 1.00 1.00 .. 0.5 0.5 .. .. 2.6 .. 4.4 .. .. Togo 479.27 481.53 238.6 230.9 0.5 113.5 7.0 0.8 8.5 2.2 .. .. Trinidad and Tobago 6.33 6.26 2.9 4.2 0.7 115.5 5.4 6.4 5.7 5.5 2.8 2.0 Tunisia 1.28 1.34 0.5 0.6 0.5 82.2 4.4 2.7 4.4 3.0 3.6 3.7 Turkey 1.30 1.54 0.0 0.9 0.7 .. 74.7 18.7 79.9 20.6 .. 8.7 Turkmenistan .. .. .. 3,950.3 0.4 .. 408.0 .. .. .. .. .. Uganda 1,723.49 1,959.26 472.8 640.1 0.4 90.0 12.0 4.8 10.5 5.0 .. .. Ukraine 5.05 7.58 0.3 2.2 0.4 112.6 271.0 14.0 155.7 8.5 161.6 12.2 United Arab Emirates 3.67 3.67 1.7 2.7 0.7 .. 2.2 7.7 .. .. .. .. United Kingdom 0.50 0.64 0.6 0.6 1.3 107.9 2.9 2.7 2.9 2.8 2.4 1.3 United States 1.00 1.00 1.0 1.0 1.0 88.8 2.0 2.6 2.7 2.7 1.2 4.2 Uruguay 23.47 24.35 5.7 14.5 0.6 79.6 31.1 9.4 33.9 9.9 27.2 15.3 Uzbekistan .. .. 11.2 432.7 0.3 .. 245.8 26.5 .. .. .. .. Venezuela, RB 2.15 2.15 0.1 1.5 0.7 81.2 45.3 26.8 49.0 20.2 44.1 27.6 Vietnam 16,105.13 16,600.00 3,170.2 5,167.5 0.3 .. 15.2 6.6 4.1 5.8 .. .. West Bank and Gaza .. .. .. .. .. 5.7 3.4 .. 3.8 .. .. Yemen, Rep. 198.95 200.03 22.0 85.7 0.4 .. 22.4 13.5 26.3 12.9 .. .. Zambia 4,002.52 4,882.97 393.5 2,808.8 0.7 151.5 52.1 18.3 57.0 17.6 101.4 .. Zimbabwe 9,675.78 30,000.00 .. .. .. .. 26.7 232.0 29.0 497.7 25.9 .. Note: The differences in the growth rates of the GDP deflator and consumer and wholesale price indexes are due mainly to differences in data availability for each of the indexes during the period. a. Average for December or latest monthly data available. b. As members of the euro area, these countries share a single currency, the euro. c. Koruny. 258 2008 World Development Indicators ECONOMY Exchange rates and prices 4.14 About the data Definitions In a market-based economy, household, producer, for currencies of selected countries and the euro · Official exchange rate is the exchange rate deter- and government choices about resource allocation area. For most high-income countries weights are mined by national authorities or the rate determined are influenced by relative prices, including the real derived from industrial country trade in manufac- in the legally sanctioned exchange market. It is cal- exchange rate, real wages, real interest rates, and tured goods. Data are compiled from the nominal culated as an annual average based on monthly other prices in the economy. Relative prices also effective exchange rate index and a cost indicator averages (local currency units relative to the U.S. largely reflect these agents' choices. Thus relative of relative normalized unit labor costs in manufactur- dollar). · Purchasing power parity (PPP) conversion prices convey vital information about the interaction ing. For selected other countries the nominal effec- factor is the number of units of a country's currency of economic agents in an economy and with the rest tive exchange rate index is based on manufactured required to buy the same amount of goods and ser- of the world. goods and primary products trade with partner or vices in the domestic market that a U.S. dollar would The exchange rate is the price of one currency competitor countries. For these countries the real buy in the United States. · Ratio of PPP conver- in terms of another. Offi cial exchange rates and effective exchange rate index is the nominal index sion factor to market exchange rate is the result exchange rate arrangements are established by adjusted for relative changes in consumer prices; an obtained by dividing the PPP conversion factor by the governments. Other exchange rates recognized by increase represents an appreciation of the local cur- market exchange rate. · Real effective exchange governments include market rates, which are deter- rency. Because of conceptual and data limitations, rate is the nominal effective exchange rate (a mea- mined largely by legal market forces, and for coun- changes in real effective exchange rates should be sure of the value of a currency against a weighted tries with multiple exchange arrangements, principal interpreted with caution. average of several foreign currencies) divided by rates, secondary rates, and tertiary rates. (Also see Inflation is measured by the rate of increase in a a price deflator or index of costs. · GDP implicit Statistical methods for alternative conversion factors price index, but actual price change can be nega- deflator measures the average annual rate of price in the World Bank Atlas method of calculating gross tive. The index used depends on the prices being change in the economy as a whole for the periods national income [GNI] per capita in U.S. dollars.) examined. The GDP deflator reflects price changes shown. · Consumer price index reflects changes Official or market exchange rates are often used for total GDP. The most general measure of the over- in the cost to the average consumer of acquiring a to convert economic statistics in local currencies to all price level, it accounts for changes in government basket of goods and services that may be fixed or a common currency in order to make comparisons consumption, capital formation (including inventory may change at specified intervals, such as yearly. across countries. Since market rates reflect at best appreciation), international trade, and the main com- The Laspeyres formula is generally used. · Whole- the relative prices of tradable goods, the volume of ponent, household final consumption expenditure. sale price index refers to a mix of agricultural and goods and services that a U.S. dollar buys in the The GDP deflator is usually derived implicitly as the industrial goods at various stages of production and United States may not correspond to what a U.S. ratio of current to constant price GDP--or a Paasche distribution, including import duties. The Laspeyres dollar converted to another country's currency at index. It is defective as a general measure of inflation formula is generally used. the official exchange rate would buy in that country, for policy use because of long lags in deriving esti- particularly when nontradable goods and services mates and because it is often an annual measure. account for a significant share of a country's output. Consumer price indexes are produced more fre- An alternative exchange rate--the purchasing power quently and so are more current. They are also con- parity (PPP) conversion factor--is preferred because structed explicitly, based on surveys of the cost of it reflects differences in price levels for both tradable a defined basket of consumer goods and services. and nontradable goods and services and therefore Nevertheless, consumer price indexes should be provides a more meaningful comparison of real out- interpreted with caution. The definition of a house- put. See table 1.1 for further discussion. hold, the basket of goods, and the geographic (urban The ratio of the PPP conversion factor to the official or rural) and income group coverage of consumer exchange rate--the national price level or compara- price surveys can vary widely by country. In addi- tive price level--measures differences in the price tion, weights are derived from household expendi- level at the gross domestic product (GDP) level. The ture surveys, which, for budgetary reasons, tend to price level index tends to be lower in poorer coun- be conducted infrequently in developing countries, tries and to rise with income. The market exchange impairing comparability over time. Although useful for rate (or alternative conversion factor) is the official measuring consumer price inflation within a country, exchange rate adjusted for some countries by World consumer price indexes are of less value in compar- Bank staff to reflect actual price changes. National ing countries. price levels vary systematically, rising with GNI per Wholesale price indexes are based on the prices at Data sources capita. The real effective exchange rate is a nominal the first commercial transaction of commodities that effective exchange rate index adjusted for relative are important in a country's output or consumption. Data on official and real effective exchange rates movements in national price or cost indicators of Prices are farm-gate for agricultural commodities and and consumer and wholesale price indexes are the home country, selected countries, and the euro ex-factory for industrial goods. Preference is given to from the International Monetary Fund's Interna- area. A nominal effective exchange rate index is the indexes with the broadest coverage of the economy. tional Financial Statistics. PPP conversion factors ratio (expressed on the base 2000 = 100) of an The least-squares method is used to calculate and GDP deflators are from the World Bank's data index of a currency's period-average exchange rate growth rates of the GDP implicit deflator, consumer files. to a weighted geometric average of exchange rates price index, and wholesale price index. 2008 World Development Indicators 259 4.15 Balance of payments current account Goods and Net Net current Current account Total services income transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 304 2,201 836 4,292 44 217 477 1,043 ­12 ­831 265 2,162 Algeria .. .. .. .. .. .. .. .. .. .. 4,164 114,972 Angola 3,836 44,707 3,519 26,305 ­767 ­8,778 156 ­222 ­295 9,402 213 11,197 Argentina 24,987 66,085 26,066 53,353 ­4,636 ­5,929 597 318 ­5,118 7,122 15,979 46,149 Armenia 300 1,777 726 3,589 40 278 168 945 ­218 ­590 111 1,659 Australia 69,710 182,872 74,841 199,457 ­14,036 ­40,817 ­109 ­280 ­19,277 ­57,682 14,952 26,908 Austria 89,906 217,883 92,055 199,334 ­1,597 ­5,166 ­1,702 ­1,352 ­5,448 12,031 23,369 18,194 Azerbaijan 785 22,517 1,290 9,424 ­6 ­5,079 111 1,005 ­401 9,019 121 4,273 Bangladesh 4,431 14,091 7,589 19,553 68 ­968 2,265 7,287 ­824 857 2,376 5,277 Belarus 5,269 27,583 5,752 30,421 ­51 ­411 76 189 ­458 ­3,060 377 4,179 Belgium 190,686b 402,821 178,798b 394,601 6,808b 5,816 ­4,463b ­6,819 14,232b 7,216 24,120 16,485 Benin 614 953 895 1,398 ­8 ­30 121 259 ­167 ­217 198 1,209 Bolivia 1,234 4,959 1,574 4,078 ­207 ­173 244 1,091 ­303 1,800 1,005 5,314 Bosnia and Herzegovina .. 5,608 .. 10,514 .. 400 .. 2,576 .. ­1,930 80 4,525 Botswana 2,421 6,092 2,050 4,417 ­32 ­346 ­39 1,105 300 2,434 4,695 9,790 Brazil 52,641 184,544 63,293 157,871 ­11,105 ­29,242 3,621 4,029 ­18,136 1,460 51,477 180,334 Bulgaria 6,776 24,911 6,502 33,467 ­432 ­624 132 464 ­26 ­8,716 1,635 17,545 Burkina Faso 272 .. 483 .. ­29 .. 255 .. 15 .. 347 1,029 Burundi 129 84 259 435 ­13 ­6 153 241 10 ­116 216 177 Cambodia 969 5,637 1,375 6,327 ­57 ­360 277 544 ­186 ­506 192 2,140 Cameroon 2,040 4,952 1,608 5,531 ­412 ­385 69 416 90 ­547 15 2,932 Canada 219,501 495,016 200,991 468,502 ­22,721 ­12,988 ­117 ­888 ­4,328 12,639 16,369 41,082 Central African Republic 179 .. 244 .. ­23 .. 63 .. ­25 .. 238 92 Chad 190 .. 411 .. ­7 .. 191 .. ­38 .. 147 964 Chile 19,358 76,429 18,301 53,938 ­2,714 ­18,265 307 2,974 ­1,350 7,200 14,860 16,843 China 147,240 1,342,206 135,282 1,034,729 ­11,774 25,688 1,435 38,668 1,618 371,833 80,288 1,546,365 Hong Kong, China .. 429,542 .. 406,913 .. 5,693 .. ­2,576 .. 25,746 55,424 152,693 Colombia 12,294 34,213 16,012 37,416 ­1,596 ­7,894 799 5,231 ­4,516 ­5,866 8,452 20,951 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 157 181 Congo, Rep. 1,374 6,127 1,346 6,386 ­695 ­1,885 42 ­38 ­625 ­2,181 64 2,184 Costa Rica 4,451 12,895 4,717 14,092 ­226 ­850 134 470 ­358 ­1,578 1,060 4,115 Cote d'Ivoire 4,337 9,419 3,806 8,376 ­787 ­810 ­237 ­379 ­492 ­146 529 2,519 Croatia 6,972 25,148 9,106 29,481 ­53 ­1,545 802 1,431 ­1,385 ­4,447 1,896 13,675 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 28,202 139,971 30,044 131,275 ­104 ­11,041 572 ­887 ­1,374 ­3,232 14,613 34,907 Denmark 65,655 162,058 57,860 154,725 ­4,549 176 ­1,391 ­5,130 1,855 2,379 11,652 34,318 Dominican Republic 5,731 11,972 6,137 15,370 ­769 ­2,079 992 3,409 ­183 ­2,068 373 2,562 Ecuador 5,196 16,038 5,708 15,638 ­930 ­2,048 442 3,246 ­1,000 1,598 1,788 3,521 Egypt, Arab Rep. 13,260 44,398 17,140 53,697 ­405 1,388 4,031 8,322 ­254 412 17,122 32,214 El Salvador 2,040 5,527 3,623 9,842 ­67 ­579 1,389 3,776 ­262 ­1,119 940 2,304 Eritrea 135 .. 498 .. 8 .. 324 .. ­32 .. 40 25 Estonia 2,573 15,471 2,860 17,828 3 ­1,574 126 159 ­158 ­3,772 583 3,269 Ethiopia 768 2,658 1,446 6,920 ­19 40 736 3,395 39 ­827 815 833 Finland 47,973 110,338 37,705 99,954 ­4,440 1,560 ­597 ­1,897 5,231 10,048 10,657 8,380 France 362,717 691,775 333,746 731,671 ­8,964 39,305 ­9,167 ­30,658 10,840 ­31,249 58,510 115,487 Gabon 2,945 5,610 1,723 2,400 ­665 ­958 ­42 ­269 515 1,983 153 1,238 Gambia, The 177 233 232 322 ­5 ­40 52 76 ­8 ­53 106 143 Georgia 575 3,182 1,413 5,917 127 39 197 689 ­514 ­2,006 199 1,361 Germany 600,347 1,571,251 586,662 1,334,445 ­2,814 57,894 ­38,768 ­41,771 ­27,897 252,929 121,816 135,932 Ghana 1,582 6,004 2,120 10,060 ­129 ­139 523 2,043 ­144 ­2,151 804 2,269 Greece 15,523 67,071 24,711 101,311 ­1,684 ­12,469 8,008 2,122 ­2,864 ­44,587 16,119 3,648 Guatemala 2,823 8,721 3,728 14,511 ­159 ­770 491 4,863 ­572 ­1,697 783 4,315 Guinea 700 1,252 1,011 1,514 ­85 ­63 179 ­131 ­216 ­456 87 97 Guinea-Bissau 30 .. 89 .. ­21 .. 46 .. ­35 .. 20 113 Haiti 192 729 802 2,321 ­31 7 553 1,505 ­87 ­80 199 453 Data for Taiwan, China 128,369 277,811 124,171 251,157 4,188 10,132 ­2,912 ­3,807 5,474 32,979 95,559 281,658 260 2009 World Development Indicators ECONOMY Balance of payments current account Goods and Net Net current Current account 4.15 Total services income transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 1,635 6,344 1,852 9,594 ­226 ­598 243 2,622 ­201 ­1,225 270 2,546 Hungary 19,765 111,005 19,916 107,499 ­1,701 ­10,702 203 452 ­1,650 ­6,743 12,017 24,052 India 38,013 198,971 48,225 230,232 ­3,734 ­4,264 8,382 26,109 ­5,563 ­9,415 22,865 276,578 Indonesia 52,923 130,492 54,461 109,571 ­5,874 ­15,524 981 4,968 ­6,431 10,365 14,908 56,936 Iran, Islamic Rep. 18,953 .. 15,113 .. ­478 .. ­4 .. 3,358 .. .. .. Iraq .. 30,887 .. 24,198 .. ­3,546 .. ­462 .. 2,681 8,347 19,655 Ireland 49,439 204,512 42,169 178,698 ­7,325 ­36,851 1,776 ­1,658 1,721 ­12,695 8,770 925 Israel 27,478 70,901 35,287 73,631 ­2,654 ­25 5,673 7,278 ­4,790 4,523 8,123 28,519 Italy 295,618 614,384 250,319 619,592 ­15,644 ­26,918 ­4,579 ­18,905 25,076 ­51,032 60,690 94,109 Jamaica 3,394 4,928 3,729 8,050 ­371 ­662 607 2,040 ­99 ­1,744 681 1,879 Japan 493,991 807,207 419,556 723,705 44,285 138,502 ­7,676 ­11,514 111,044 210,490 192,620 973,297 Jordan 3,479 9,110 4,903 15,500 ­279 835 1,444 2,779 ­259 ­2,776 2,279 7,925 Kazakhstan 5,975 51,906 6,102 44,887 ­146 ­12,193 59 ­2,160 ­213 ­7,333 1,660 17,641 Kenya 3,526 6,822 5,922 9,840 ­219 ­192 1,037 2,108 ­1,578 ­1,102 384 3,355 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 147,761 442,016 155,104 433,181 ­1,303 769 ­19 ­3,649 ­8,665 5,954 32,804 262,533 Kuwait 14,215 73,317 12,615 33,707 4,881 12,937 ­1,465 ­5,076 5,016 47,471 4,543 18,776 Kyrgyz Republic 448 2,021 726 3,218 ­35 ­52 79 1,020 ­235 ­228 134 1,177 Lao PDR 408 1,201 748 1,141 ­6 ­50 110 98 ­237 107 99 708 Latvia 2,088 11,898 2,193 17,828 19 ­937 71 382 ­16 ­6,485 602 5,761 Lebanon .. 16,603 .. 21,912 .. 377 .. 2,886 .. ­2,046 8,100 20,599 Lesotho 199 881 1,046 1,715 314 420 210 625 ­323 212 457 658 Liberia .. 542 .. 1,742 .. ­150 .. 1,139 .. ­211 28 119 Libya 7,513 47,069 5,755 20,368 133 1,971 ­220 ­219 1,672 28,454 7,415 83,260 Lithuania 3,191 21,187 3,902 26,429 ­13 ­1,614 109 1,163 ­614 ­5,692 829 7,721 Macedonia, FYR 1,302 2,998 1,773 4,258 ­30 ­3 213 1,239 ­288 ­24 275 2,264 Madagascar 749 1,332 987 2,042 ­167 ­80 129 236 ­276 ­554 109 847 Malawi 470 .. 660 .. ­44 .. 157 .. ­78 .. 115 227 Malaysia 83,369 204,674 86,851 167,061 ­4,144 ­3,995 ­1,017 ­4,687 ­8,644 28,931 24,699 101,995 Mali 529 1,864 991 2,150 ­41 ­269 219 325 ­284 ­231 323 1,087 Mauritania 504 .. 510 .. ­48 .. 76 .. 22 .. 90 207 Mauritius 2,349 4,436 2,454 5,202 ­19 239 101 119 ­22 ­408 887 1,832 Mexico 89,321 289,612 82,168 305,775 ­12,689 ­13,684 3,960 24,323 ­1,576 ­5,525 17,046 87,208 Moldova 884 2,018 1,006 4,306 ­18 414 56 1,179 ­85 ­695 257 1,334 Mongolia 508 2,031 521 1,880 ­25 ­145 77 215 39 222 158 1,396 Morocco 9,044 27,311 11,243 34,732 ­1,318 ­405 2,330 7,703 ­1,186 ­122 3,874 24,714 Mozambique 411 2,871 1,055 3,667 ­140 ­592 339 592 ­445 ­795 195 1,524 Myanmar 1,307 4,870 2,020 2,890 ­110 ­1,290 562 123 ­261 813 651 1,383 Namibia 1,734 3,521 2,100 3,615 139 ­158 403 1,000 176 747 221 896 Nepal 1,029 1,436 1,624 3,655 9 137 230 2,088 ­356 6 646 1,565 Netherlands 241,517 558,952 216,558 491,853 7,247 4,813 ­6,434 ­12,326 25,773 59,586 47,162 26,928 New Zealand 17,882 36,591 17,248 38,111 ­3,957 ­9,164 255 449 ­3,068 ­10,235 4,410 17,247 Nicaragua 662 2,685 1,150 4,673 ­372 ­135 138 1,075 ­722 ­1,048 142 1,103 Niger 321 599 457 1,077 ­47 1 31 163 ­152 ­314 95 593 Nigeria 12,342 66,768 12,841 46,066 ­2,878 ­16,746 799 18,016 ­2,578 21,972 1,709 51,907 Norway 56,058 177,889 46,848 116,784 ­1,919 2,001 ­2,059 ­2,647 5,233 60,459 22,976 60,840 Oman 6,078 25,886 5,035 19,339 ­374 ­959 ­1,469 ­3,670 ­801 1,918 1,943 9,524 Pakistan 10,214 21,879 14,185 37,525 ­1,939 ­3,735 2,562 11,086 ­3,349 ­8,295 2,528 15,798 Panama 7,610 14,263 7,768 14,627 ­466 ­1,311 153 253 ­471 ­1,422 781 1,935 Papua New Guinea 2,992 3,580 1,905 2,692 ­488 ­538 75 291 674 640 267 2,106 Paraguay 4,802 6,317 5,200 6,471 110 ­93 195 373 ­92 126 1,106 2,462 Peru 6,622 31,298 9,597 23,870 ­2,482 ­8,418 832 2,495 ­4,625 1,505 8,653 27,786 Philippines 26,795 57,960 33,317 65,094 3,662 ­542 880 13,977 ­1,980 6,301 7,781 33,740 Poland 35,716 173,399 33,825 184,234 ­1,995 ­16,253 958 8,493 854 ­18,595 14,957 65,725 Portugal 32,260 74,902 39,545 89,803 21 ­10,135 7,132 3,618 ­132 ­21,418 22,063 11,512 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 261 4.15 Balance of payments current account Goods and Net Net current Current account Total services income transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 9,404 50,794 11,306 74,858 ­241 ­5,788 369 6,716 ­1,774 ­23,136 2,624 39,974 Russian Federation 92,987 393,817 82,809 282,673 ­3,372 ­31,396 157 ­3,506 6,963 76,241 18,024 477,950 Rwanda 75 363 374 909 7 ­15 350 413 57 ­147 99 553 Saudi Arabia 53,450 242,046 34,286 113,396 2,800 238 ­27,282 ­33,808 ­5,318 95,080 10,399 37,592 Senegal 1,506 2,398 1,821 4,032 ­124 ­63 195 837 ­244 ­861 272 1,660 Serbia .. 11,945 .. 21,432 .. ­829 .. 3,970 .. ­6,346 .. 14,214 Sierra Leone 128 335 260 490 ­30 ­104 43 78 ­118 ­181 35 217 Singapore 157,658 372,964 144,520 326,455 2,130 ­5,752 ­894 ­1,696 14,373 39,062 68,816162,957 Slovak Republic 10,969 64,869 10,658 65,245 ­14 ­3,288 93 ­438 390 ­4,103 3,863 18,973 Slovenia 10,377 32,772 10,749 33,663 201 ­1,000 95 ­402 ­75 ­2,293 1,821 1,066 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 34,402 89,746 33,375 98,502 ­2,875 ­8,923 ­645 ­2,953 ­2,493 ­20,631 4,464 32,919 Spain 133,910 385,984 135,000 479,096 ­5,402 ­43,243 4,525 ­9,000 ­1,967 ­145,355 40,531 19,029 Sri Lanka 4,617 9,452 5,982 12,773 ­137 ­358 732 2,311 ­770 ­1,368 2,112 3,654 Sudan 681 9,264 1,238 10,661 ­3 ­2,253 60 382 ­500 ­3,268 163 1,378 Swaziland 1,020 2,199 1,274 2,523 81 64 144 194 ­30 ­66 298 774 Sweden 95,525 234,049 81,142 200,184 ­6,473 9,534 ­2,970 ­4,983 4,940 38,416 25,870 31,033 Switzerland 123,320 266,864 108,916 221,114 10,708 7,600 ­4,409 ­9,405 20,703 43,946 68,620 75,172 Syrian Arab Republic 5,757 13,169 5,541 11,879 ­560 ­935 607 565 263 920 .. .. Tajikistan .. 1,706 .. 3,707 .. ­51 .. 1,557 .. ­495 39 204 Tanzania 1,265 3,941 2,139 6,334 ­110 ­79 395 617 ­590 ­1,856 270 2,886 Thailand 70,292 180,382 82,246 162,904 ­2,114 ­5,661 487 3,938 ­13,582 15,755 36,939 87,472 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. 230 Togo 465 970 671 1,517 ­34 ­38 118 244 ­122 ­340 130 438 Trinidad and Tobago 2,799 10,569 2,110 6,265 ­390 ­760 ­4 50 294 3,594 379 6,745 Tunisia 7,979 20,057 8,811 20,826 ­716 ­1,754 774 1,619 ­774 ­904 1,689 8,032 Turkey 36,581 144,209 40,113 176,999 ­3,204 ­7,143 4,398 2,236 ­2,338 ­37,697 13,891 76,496 Turkmenistan 1,774 .. 1,796 .. 17 .. 5 .. 0 .. 1,168 .. Uganda 664 2,185 1,490 4,161 ­96 ­279 639 1,509 ­281 ­745 459 2,560 Ukraine 17,090 64,001 18,280 72,153 ­434 ­659 472 3,539 ­1,152 ­5,272 1,069 32,484 United Arab Emirates .. .. .. .. .. .. .. .. .. .. 7,778 77,239 United Kingdom 322,114 723,781 327,000 824,103 3,393 48,667 ­11,943 ­27,111 ­13,436 ­78,765 49,144 57,275 United States 794,397 1,645,726 890,784 2,345,982 20,899 81,751 ­38,073 ­112,705 ­113,561 ­731,209 175,996277,549 Uruguay 3,507 6,825 3,568 6,803 ­227 ­342 76 134 ­213 ­186 1,813 4,121 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 20,753 70,838 16,905 52,987 ­1,943 2,565 109 ­415 2,014 20,001 10,715 33,759 Vietnam 9,498 54,591 12,334 65,845 ­384 ­2,168 1,200 6,430 ­2,020 ­6,992 1,324 23,479 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2,160 7,774 2,471 9,337 ­561 ­1,152 1,056 1,387 184 ­1,328 638 7,757 Zambia 1,222 4,872 1,338 4,524 ­249 ­1,383 182 530 ­182 ­505 223 1,090 Zimbabwe 2,344 .. 2,515 .. ­294 .. 40 .. ­425 .. 888 .. World 6,392,988 t 17,070,490 t 6,233,515 t 16,620,157 t Low income 72,352 270,815 99,243 308,995 Middle income 1,087,015 4,680,736 1,127,240 4,202,021 Lower middle income 512,935 2,687,262 543,345 2,265,062 Upper middle income 572,897 2,040,686 584,277 1,943,357 Low & middle income 1,159,335 4,959,944 1,223,867 4,509,716 East Asia & Pacific 397,583 1,997,264 413,802 1,634,141 Europe & Central Asia 238,763 1,079,371 248,679 1,099,718 Latin America & Carib. 272,861 863,832 288,143 827,588 Middle East & N. Africa .. .. 103,655 303,535 South Asia 58,893 244,054 78,652 300,537 Sub-Saharan Africa 89,262 322,767 99,763 301,623 High income 5,228,660 12,191,150 5,008,580 12,186,397 Euro area 2,097,739 5,031,654 1,974,212 4,839,351 a. International reserves including gold valued at London gold price. b. Includes Luxembourg. 262 2008 World Development Indicators ECONOMY Balance of payments current account 4.15 About the data Definitions The balance of payments records an economy's trans- system, external debt records, information provided · Exports and imports of goods and services are all actions with the rest of the world. Balance of payments by enterprises, surveys to estimate service transac- transactions between residents of an economy and accounts are divided into two groups: the current tions, and foreign exchange records. Differences in the rest of the world involving a change in ownership account, which records transactions in goods, ser- collection methods--such as in timing, definitions of general merchandise, goods sent for processing vices, income, and current transfers, and the capital of residence and ownership, and the exchange rate and repairs, nonmonetary gold, and services. · Net and financial account, which records capital transfers, used to value transactions--contribute to net errors income is receipts and payments of employee com- acquisition or disposal of nonproduced, nonfinancial and omissions. In addition, smuggling and other ille- pensation for nonresident workers, and investment assets, and transactions in financial assets and liabili- gal or quasi-legal transactions may be unrecorded or income (receipts and payments on direct investment, ties. The table presents data from the current account misrecorded. For further discussion of issues relat- portfolio investment, and other investments and plus gross international reserves. ing to the recording of data on trade in goods and receipts on reserve assets). Income derived from The balance of payments is a double-entry account- services, see About the data for tables 4.4­4.7. the use of intangible assets is recorded under busi- ing system that shows all flows of goods and services The concepts and definitions underlying the data in ness services. · Net current transfers are recorded into and out of an economy; all transfers that are the the table are based on the fifth edition of the Inter- in the balance of payments whenever an economy counterpart of real resources or financial claims pro- national Monetary Fund's (IMF) Balance of Payments provides or receives goods, services, income, or vided to or by the rest of the world without a quid pro Manual (1993). That edition redefined as capital trans- financial items without a quid pro quo. All transfers quo, such as donations and grants; and all changes fers some transactions previously included in the cur- not considered to be capital are current. · Current in residents' claims on and liabilities to nonresidents rent account, such as debt forgiveness, migrants' cap- account balance is the sum of net exports of goods that arise from economic transactions. All transac- ital transfers, and foreign aid to acquire capital goods. and services, net income, and net current transfers. tions are recorded twice--once as a credit and once Thus the current account balance now reflects more · Total reserves are holdings of monetary gold, as a debit. In principle the net balance should be accurately net current transfer receipts in addition to special drawing rights, reserves of IMF members zero, but in practice the accounts often do not bal- transactions in goods, services (previously nonfac- held by the IMF, and holdings of foreign exchange ance, requiring inclusion of a balancing item, net tor services), and income (previously factor income). under the control of monetary authorities. The gold errors and omissions. Many countries maintain their data collection systems component of these reserves is valued at year-end Discrepancies may arise in the balance of pay- according to the fourth edition of the Balance of Pay- (December 31) London prices ($386.75 an ounce in ments because there is no single source for balance ments Manual (1977). Where necessary, the IMF con- 1995 and $696.70 an ounce in 2007). of payments data and therefore no way to ensure verts such reported data to conform to the fifth edition that the data are fully consistent. Sources include (see Primary data documentation). Values are in U.S. customs data, monetary accounts of the banking dollars converted at market exchange rates. Top 15 economies with the largest reserves in 2007 4.15a Total reserves Share of world Annual Months of ($ billions) total (%) change (%) imports 2006 2007 2007 2006­07 2007 China 1,081 1,546 21.8 43.1 17.0 Japan 895 973 13.7 8.7 14.9 Russian Federation 304 478 6.8 57.3 15.9 Data sources Taiwan, China 275 282 4.0 2.5 12.8 Data on the balance of payments are published in United States 221 278 3.9 25.5 1.1 the IMF's Balance of Payments Statistics Yearbook India 178 277 3.9 55.3 8.8a and International Financial Statistics. The World Korea, Rep. 239 263 3.7 9.8 7.0 Bank exchanges data with the IMF through elec- Brazil 86 180 2.5 110.1 10.9 tronic files that in most cases are more timely and Singapore 136 163 2.3 19.6 5.2 cover a longer period than the published sources. Hong Kong, China 133 153 2.2 14.6 3.6 More information about the design and compila- Germany 112 136 1.9 21.8 1.0 tion of the balance of payments can be found in France 98 115 1.6 17.6 1.5 the IMF's Balance of Payments Manual, fifth edition Algeria 81 115 1.6 41.1 .. (1993), Balance of Payments Textbook (1996), and Malaysia 83 102 1.4 23.1 6.7 Balance of Payments Compilation Guide (1995). Italy 76 94 1.3 24.2 1.5 The IMF's International Financial Statistics and Balance of Payments databases are available on a. Data are for 2006. Source: International Monetary Fund, International Financial Statistics data files. CD-ROM. 2006 World Development Indicators 263 STATES AND MARKETS Introduction I nformation and communication technology for development Rapid advances in information and communication technology (ICT) have connected people, businesses, and governments around the world, enabling knowledge sharing across cultures and countries. ICTs used in e-government projects can reduce corruption, and some ICTs, such as broadband, can contribute to economic growth (box 5a). Good government policies and regulations are creating competitive ICT markets, increasing access to ICT services for people everywhere. Recognizing the need to analyze ICT's impact on development, many statistical offi ces in developing countries are beginning to conduct household and business surveys to improve their ICT policy and analysis. Information on ICT infrastructure, access, use, quality, affordability, applications, and trade are included in tables 5.10 and 5.11. The well known success of mobile telephony worldwide has been achieved through high demand, low-cost technologies, and market liberalization. Research on the diffusion of advanced telecom- munications services in developing economies finds that the rate of adoption depends on an appro- priate business environment--which depends in turn on the regulatory and policy environment. Many countries that have created a competitive market environment for ICTs have more people using ICT services. Competition lowers prices for ICT services and expands markets. Prices for ICT services, such as mobile cellular phone tariffs, have been falling rapidly. But the services are still unaffordable for many people in low-income economies, leaving them yet to realize the potential of ICT for economic and social development. ICT services range from telecommunication infrastructure (voice, data, and media services) to information applications tailored to specifi c sectors and functions (such Improving governance and contributing to growth 5a as services in banking and fi nance, E-government projects increase revenues and improve governance Successful e-government projects have reduced transaction costs land management, education, and and processing time and increased government revenue. The health), to electronic government e-Customs System in Ghana (GCNet) increased revenues 49 per- cent in the first 18 months of operation and reduced clearance (e-government), and to the production times to two days from three weeks. And a land registration system of equipment. has cut bribes $18.3 million a year in the Indian state of Karnataka, where an overwhelming proportion of supervisors now sense that the abuse of discretionary power in providing services to citizens In developing economies innovative has narrowed. use of ICT services is changing peo- Broadband increases productivity and contributes to growth ple's lives and providing new opportu- Although the benefits of broadband are not yet available to most people in developing economies, access to information and com- nities. For example, banking services munication technology, especially broadband, supports the growth and job search text messaging ser- of firms by lowering costs and raising productivity. Analysis of broad- band access in developed economies suggests a robust and notice- vices can be delivered through mobile able growth dividend. For developing economies the growth benefit phones and portable devices. Farm- of broadband is about the same as that for developed economies-- about a 1.4 percentage point increase in per capita GDP for each ers and fishers also use these tech- 10 percent increase in broadband coverage. nologies to track prices and market Source: World Bank forthcoming. demand. 2009 World Development Indicators 265 Mobile phones have captured the market in developing economies a plan to issue a new license has been enough to trigger At the end of 2007 there were about 1.1 billion fixed telephone growth, encouraging the existing mobile phone operator to lines and 3.3 billion mobile phone subscribers worldwide. De- improve service, reduce prices, and increase market penetra- veloping economies increased their share of mobile phone tion before the new entrant starts operations. subscribers from about 30 percent of the world total in 2000 The demand for always-on, high-capacity Internet services to more than 50 percent in 2004 and to about 70 percent in is increasing. Advanced Internet service--beyond what can 2007 (figure 5b). But access to mobile phones is still low in be achieved through dial-up connections--has become more many countries, including Burundi, Central African Republic, important as the demand for data and value-added services Eritrea, Ethiopia, and Papua New Guinea, with fewer than 5 grows. Broadband allows for large volumes of data to be trans- mobile phone subscribers per 100 people. mitted and for cheaper voice communications (say, by routing Mobile phone service in developing economies has over- calls over the Internet). Broadband also enables voice, data, taken fixed-line service. Wireless technology can be deployed and media services to be transmitted over the same network. more quickly than fixed-line telephone systems and requires This convergence of services can be very good for economic and less upfront investment in infrastructure. This translates to social development--increasing productivity, lowering transac- lower prices and stronger customer demand. Liberalization tion costs, facilitating trade, and boosting retail sales and tax started later for fixed-line markets (previously dominated by revenues. Where broadband has been introduced in rural areas state-owned monopolies), while mobile phone markets were of developing economies, villagers and farmers have gained bet- generally opened to one or more new entrants from the start. ter access to training, job opportunities, and market prices of And even where the opening of mobile phone markets was crops. But in 2007 broadband reached just 2 percent of the delayed, once they were opened competition often led to an population on average in developing economies, concentrated in immediate growth in mobile subscribers (figure 5c). Countries urban areas. Why? Because of the relatively high cost of comput- that have taken decisive steps to establish independent regu- ers and, in rural areas, the limited access to electricity. Internet lators and foster competition have seen greater improvements use (narrowband and broadband) in developing economies is in sector performance. In some cases the announcement of only about one-fifth that in developed economies (figure 5d). Seventy percent of mobile phone subscribers Competition can spur growth are in developing economies, 2000­07 5b in mobile phone service 5c Developing economies Mobile cellular phone subscribers per 100 people Mobile cellular phone subscribers (billions) Developed economies 60 Tunisia 4 45 Belarus 3 Honduras 30 Tonga 2 15 Afghanistan 1 Kenya 0 0 ­3 ­2 ­1 0 1 2 3 2000 2001 2002 2003 2004 2005 2006 2007 Note: Year 0 is the year of entry of a second mobile operator. Source: International Telecommunication Union, World Telecommunication/ICT Source: International Telecommunication Union, World Telecommunication/ICT Indicators database. Indicators database. Internet use in developing economies is growing, but still lags behind use in developed economies 5d Internet users per 100 people Developing economies Developed economies 80 60 40 20 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Source: International Telecommunication Union, World Telecommunication/ICT Indicators database. 266 2009 World Development Indicators To unleash ICT's potential impact on growth, services must be affordable to Broadband access is also limited in rural areas of some developed economies, such as the United States, where there more people is a renewed commitment to improve access to rural areas as The price of ICT access continues to fall with technological part of an economic stimulus package. The goal is to create advances, market growth, and greater competition, a trend jobs, bring broadband to every community, and improve the that is especially important in allowing people in developing U.S. ranking for per capita broadband access (with about 24 economies to take full advantage of ICT services. In recent broadband subscribers per 100 people in 2007, the U.S. is years steep price reductions have contributed to the rapid below the top tier of developed economies; figure 5e). expansion of mobile phone use in many economies (figures Although the capacity of broadband service is measured 5b and 5g). Prepaid services allow mobile customers to pay by the advertised speed available to consumers, speed may in small amounts instead of committing to fixed monthly sub- be constrained by bandwidth availability (effective rate of data scriptions. Prepaid cards give even low-income consumers transfer), which is increasing faster in developed economies, access to mobile communications, increasing penetration in with their robust infrastructure, than in developing economies. poor and rural areas. In high-income economies, average per capita international Pricing for Internet access has also been falling in many bandwidth increased from 586 bits per second (bps) in 2000 countries, including many Sub-Saharan countries (figure 5h). to 18,240 bps in 2007. Among developing regions Europe Still, the average price in Sub-Saharan Africa as a whole con- and Central Asia and Latin America and the Caribbean have tinues to be well above the world average and, as measured the greatest capacity. Over 2000­07 bandwidth per capita against income, is not affordable for most people. In 2006 increased from 12 bps to 1,114 bps in Europe and Central Asia the Internet access tariff for Sub-Saharan Africa was about 62 and from 8 bps to 1,126 bps in Latin America and the Carib- percent of average monthly per capita income, far more than bean. With improved fiber-optic connectivity some countries the roughly 12 percent in South Asia, and less than the 9 per- in South Asia are seeing a rise in international bandwidth, yet cent average for all other developing regions. In high-income South Asia and Sub- Saharan Africa are still well behind other economies Internet service costs less than 1 percent of the regions in international bandwidth per capita (figure 5f). average monthly income. Broadband access in Prices for mobile phone services developed and developing economies 5e have declined in many countries 5g Mobile cellular tariff, average annual change, 2004­06 (%) Subscribers per 100 people 0 40 Developed economies ­10 ­20 30 ­30 20 ­40 Developing economies ­50 10 Benin China Armenia Chile Moldova Chad Uzbekistan Sri Lanka Note: Tariff is based on the prepaid price for 25 calls per month spread over the 0 same mobile network, other mobile networks, and mobile to fi xed calls and during Liechten- Sweden Denmark Nether- Monaco Lithuania Hungary Romania Poland Slovak peak, off-peak, and weekend times (Organisation for Economic Co-operation and stein lands Republic Development low user definition). It also includes 30 text messages per month. Countries that have experienced significant reductions in mobile phone service Source: International Telecommunication Union, World Telecommunication/ICT prices do not necessarily have the lowest prices. Indicators database. Source: International Telecommunication Union, World Telecommunication/ICT Indicators database. International bandwidth has increased rapidly in Europe Internet service prices have fallen in some and Central Asia and Latin America and the Caribbean 5f Sub-Saharan African countries, 2005­07 5h Bits per second per capita, by region Internet access tariff ($ per month) 2005 2006 2007 1,200 100 Latin America & Caribbean 75 900 Europe & World average, 2006 ($22) Central Asia 50 600 25 300 Middle East & North Africa East Asia & Pacific 0 Sub-Saharan Africa Sub-Saharan Tanzania Burkina South Mauritania Nigeria Ethiopia South Asia Africa average Faso Africa 0 2000 2001 2002 2003 2004 2005 2006 2007 Note: Tariff is based on the cheapest available tariff for accessing the Internet for 20 hours a month (10 hours peak and 10 hours off-peak). The basket does not Source: International Telecommunication Union, World Telecommunication/ICT include the telephone line rental but does include any usage charges. Indicators database and World Development Indicators data files. Source: International Telecommunication Union, World Telecommunication/ICT Indicators database. 2009 World Development Indicators 267 Developing economies benefit from ICT applications are transforming how ICT exports information is shared and transactions Although ICT exports do not necessarily reflect high rates of are made ICT use, they indicate the importance of a country's ICT sec- Governments are becoming increasingly important users of tor and its international competitiveness. As barriers to ICT ICT, particularly for e-government--using Internet technology trade are removed, opportunities for developing economies as a platform for exchanging information, providing services, to benefit from such exports will likely grow. and transacting with citizens, businesses, and other arms Some developing economies have already become key of government. That makes them major actors in fostering exporters of ICT goods. China leads in dollar values of ICT ICT uptake and setting information technology standards. export goods in 2006, with $299 billion. For many countries in E-government initiatives can make public administration more East Asia and Pacific ICT export goods make up a large share of efficient, improve delivery of public services, and increase total goods exports (figure 5i). The share is 56 percent for the government accountability and transparency. They can also Philippines, 46 percent for Singapore, 45 percent for Malaysia, reduce transaction costs and processing times and increase 42 percent for Hong Kong, China, and 31 percent for China. government revenues. Some e-government projects have also Trade in ICT services includes communications services improved governance, so vital for development. (telecommunications, business network services, teleconfer- A secure, reliable business-enabling environment is a encing, support services, and postal services) and computer key element of e-commerce. Privacy and security concerns and information services (databases, data processing, soft- about the transmission of personal or financial information ware design and development, maintenance and repair, and over the Internet are major issues for both consumers and news agency services). India's software exports jumped from firms, perhaps explaining why they can be reluctant to use the about $1 billion in 1995 to $22 billion in 2006, generating Internet to make transactions. The number of secure serv- employment for about 1.6 million people. India leads all other ers indicates how many companies are conducting encrypted developing economies in exports of communication, computer, transactions over the Internet. Developing economies have and information services as a share of total service exports, only a fraction of the world's secure servers--about 4 percent at 42 percent in 2006 (table 5j). (figure 5k). East Asia & Pacific leads in share of information and Developing economies have only about communication technology goods exports 5i 4 percent of the world's secure servers, 2008 5k Information and communication technology goods exports as a share of Developing economies 3.9% total goods exports, by region (%) 40 East Asia & Pacific 30 20 High income Latin America & Caribbean 10 High income 96.1% Sub-Saharan Africa South Asia Europe & Central Asia 0 2000 2001 2002 2003 2004 2005 2006 Source: United Nations Statistics Division Commodity Trade (Comtrade) database. Source: Netcraft (www.netcraft.com). India leads developing economies in information and communications technology service export shares, 2007 5j ICT service exports as a share of total service exports (%) Top five developing economies Top five developed economies Country Share (percent) Country Share (percent) India 41.6a Kuwait 48.8 Niger 38.8a Ireland 30.1 Guyana 21.5 Israel 28.5 Yemen, Rep. 18.9a Canada 11.1 Romania 16.3 Finland 8.4 a. Data are for 2006. Source: International Monetary Fund, Balance of Payments Statistics Yearbook database. 268 2009 World Development Indicators Progress in measuring ICT Improving ICT indicators to analyze the impact of ICT on de- In May 2008 the partnership published the Global Informa- velopment was highlighted at the World Summits on the Infor- tion Society: A Statistical View, with information on more than mation Society, held in Geneva in 2003 and Tunis in 2005. 40 core ICT indicators covering ICT infrastructure; access and Attending were 50 heads of state, prime ministers, and vice use of ICT by households, individuals, and businesses; ICT in presidents--and 80 ministers and vice ministers from 180 education; and ICT sector activity and trade in ICT goods (see countries. The challenge has been taken up by the Partnership http://measuring-ict.unctad.org for a complete list). on Measuring ICT for Development, with country statistical offices and ICT agencies (box 5l). Partnership on Measuring The partnership was launched in 2004 with the following ICT for Development 5l objectives to improve ICT measures: Members of the Partnership on Measuring ICT for Development · Continue to raise awareness among policymakers include a wide range of organizations: about the importance of statistical indicators for · Eurostat. monitoring ICT policies and carrying out impact analysis. · International Telecommunication Union. · Expand the core list of indicators to other areas · Organisation for Economic Co-operation and Development. of interest such as ICT in education, government, · United Nations Conference on Trade and Development. and health, building on the original core ICT list of access and use by individuals, households and busi- · United Nations Economic Commission for Africa. nesses, and production and trade in ICT goods and · United Nations Economic Commission for Asia and the services. Pacific. · Conduct regional workshops to exchange national · United Nations Economic Commission for Latin America and experiences and discuss methodologies, definitions, the Caribbean. survey vehicles, and data collection efforts. · Assist statistical agencies in developing economies in · United Nations Economic Commission for Western Asia. ICT data collection and dissemination, including national · United Nations Educational, Scientific, and Cultural Organiza- databases to store and analyze survey results. tion's Institute for Statistics. · Develop a global database of ICT indicators and make · World Bank. it available on the World Wide Web. 2009 World Development Indicators 269 Tables 5.1 Private sector in the economy Investment commitments in infrastructure Domestic Businesses projects with private participationa credit to registered private sector $ millions Water and Telecommunications Energy Transport sanitation % of GDP New Total 2000­05 2006­07 2000­05 2006­07 2000­05 2006­07 2000­05 2006­07 2007 2007 2007 Afghanistan 466.1 795.4 1.6 .. .. .. .. .. 4.0 .. .. Albania 569.2 267.0 790.6 .. 308.0 .. 8.0 0.0 29.6 2,176 16,110 Algeria 3,422.5 1,263.0 962.0 2,320.0 120.9 161.0 510.0 230.0 13.3 10,662 105,128 Angola 278.7 448.0 45.0 9.4 .. 53.0 .. .. 10.2 .. .. Argentina 5,836.8 2,134.2 3,826.9 2,320.7 203.6 1,065.7 791.6 .. 14.5 16,400 218,700 Armenia 317.1 77.0 74.0 57.0 63.0 10.0 0.0 .. 13.6 3,822 56,461 Australia .. .. .. .. .. .. .. .. 127.5 89,960 641,538 Austria .. .. .. .. .. .. .. .. 114.2 3,484 76,374 Azerbaijan 355.6 601.6 375.2 .. .. .. 0.0 .. 15.3 4,945 69,309 Bangladesh 1,294.3 2,461.8 501.5 .. 0.0 0.0 .. .. 37.3 5,328 67,459 Belarus 735.4 881.3 .. .. .. .. .. .. 25.1 .. .. Belgium .. .. .. .. .. .. .. .. 92.3 28,016 354,489 Benin 116.9 222.0 590.0 .. .. .. .. .. 20.0 .. .. Bolivia 520.5 181.0 884.4 117.3 16.6 .. .. .. 37.0 1,625 24,649 Bosnia and Herzegovina 0.0 901.5 277.9 800.0 .. .. .. .. 54.7 314 23,634 Botswana 104.0 46.0 .. .. .. .. .. .. 20.1 7,301 .. Brazil 41,053.8 12,667.3 25,489.2 11,470.5 4,206.4 1,966.2 1,215.3 639.6 49.8 490,542 5,668,003 Bulgaria 2,179.1 1,116.0 3,094.1 909.3 2.1 531.6 152.0 .. 66.8 49,328 315,037 Burkina Faso 41.9 378.8 .. .. .. .. .. .. 16.8 639 .. Burundi 53.6 0.0 .. .. .. .. .. .. 23.6 .. .. Cambodia 148.1 217.0 108.1 648.7 125.3 200.0 .. .. 18.2 .. .. Cameroon 394.4 212.4 91.8 440.0 0.0 .. .. 0.0 9.2 .. .. Canada .. .. .. .. .. .. .. .. 136.4 207,000 2,500,000 Central African Republic 0.0 12.0 .. .. .. .. .. .. 6.7 .. .. Chad 11.0 79.9 0.0 .. .. .. .. .. 2.9 .. .. Chile 3,561.6 1,930.7 1,393.2 458.8 4,821.2 538.0 1,495.2 3.1 88.5 25,124 223,345 China 8,548.0 0.0 10,493.2 2,765.2 13,796.8 11,455.2 3,477.0 2,616.6 111.0 .. .. Hong Kong, China .. .. .. .. .. .. .. .. 139.6 80,935 524,445 Colombia 1,570.9 2,704.0 351.6 639.0 1,005.4 2,142.4 314.3 305.0 32.7 28,801 497,778 Congo, Dem. Rep. 473.4 394.0 .. .. .. .. .. .. 3.9 .. .. Congo, Rep. 61.8 100.0 .. .. .. .. 0.0 .. 2.5 237 .. Costa Rica .. .. 80.0 80.0 465.2 373.0 .. .. 44.3 10,567 102,311 Cote d'Ivoire 134.9 266.7 0.0 .. 176.4 .. .. .. 16.1 .. .. Croatia 1,205.7 2,401.0 7.1 .. 451.0 492.0 298.7 .. 72.1 11,055 200,955 Cuba 100.8 0.0 .. .. 0.0 .. 600.0 .. .. .. .. Czech Republic 8,508.0 488.0 3,865.3 .. 106.7 .. 263.7 0.0 47.7 16,395 244,417 Denmark .. .. .. .. .. .. .. .. 202.4 28,811 200,060 Dominican Republic 393.0 77.1 1,306.6 0.0 898.9 250.0 .. .. 33.4 .. 20,808 Ecuador 357.8 439.7 302.0 129.0 685.0 1,166.0 500.0 .. 25.5 3,196 37,434 Egypt, Arab Rep. 3,471.9 6,509.0 678.0 469.0 821.5 730.0 .. .. 50.6 9,595 367,559 El Salvador 1,110.6 583.0 85.0 0.0 .. .. .. .. 42.8 1,802 .. Eritrea 40.0 0.0 .. .. .. .. .. .. 25.6 .. .. Estonia 334.7 132.4 .. .. 298.4 .. 115.0 .. 96.1 .. .. Ethiopia .. .. .. .. .. .. .. .. 23.8 .. .. Finland .. .. .. .. .. .. .. .. 82.0 10,424 120,294 France .. .. .. .. .. .. .. .. 105.2 137,481 1,267,419 Gabon 26.6 131.4 0.0 0.0 177.4 .. .. .. 12.0 .. .. Gambia, The 6.6 0.0 .. 0.0 .. .. .. .. 16.2 .. .. Georgia 173.8 484.2 40.0 557.3 .. 228.0 .. 435.0 28.3 5,260 59,641 Germany .. .. .. .. .. .. .. .. 105.5 66,747 573,985 Ghana 156.5 635.0 590.0 100.0 10.0 .. 0.0 .. 17.8 .. .. Greece .. .. .. .. .. .. .. .. 91.5 .. .. Guatemala 560.1 780.1 110.0 226.8 .. .. .. .. 35.2 4,251 .. Guinea 50.6 66.0 .. .. .. .. .. .. 5.1 .. .. Guinea-Bissau 21.9 39.5 .. .. .. .. .. .. 6.1 .. .. Haiti 18.0 306.0 5.5 .. .. .. .. .. 11.3 9 300 270 2009 World Development Indicators STATES AND MARKETS Private sector in the economy Investment commitments in infrastructure Domestic 5.1Businesses projects with private participationa credit to registered private sector $ millions Water and Telecommunications Energy Transport sanitation % of GDP New Total 2000­05 2006­07 2000­05 2006­07 2000­05 2006­07 2000­05 2006­07 2007 2007 2007 Honduras 135.0 319.9 358.8 .. 120.0 .. 207.9 .. 53.1 .. .. Hungary 5,172.8 1,523.3 851.6 1,707.0 3,297.5 1,588.0 0.0 0.0 61.5 28,153 273,549 India 20,642.0 14,849.4 8,285.8 16,537.1 4,281.3 13,164.7 112.9 142.3 47.3 20,000 732,000 Indonesia 6,557.2 5,192.7 1,763.5 1,116.1 159.2 3,495.9 44.8 20.2 25.4 18,960 271,255 Iran, Islamic Rep. 695.0 221.0 650.0 .. .. .. .. .. 49.2 .. .. Iraq 984.0 3,790.0 .. 590.0 .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. 199.6 18,704 180,891 Israel .. .. .. .. .. .. .. .. 90.1 18,814 162,910 Italy .. .. .. .. .. .. .. .. 101.8 77,587 638,987 Jamaica 700.3 166.7 201.0 78.0 565.0 .. .. .. 31.1 2,023 54,116 Japan .. .. .. .. .. .. .. .. 171.6 145,151 2,572,088 Jordan 1,589.0 394.3 .. 420.0 0.0 675.0 169.0 .. 99.0 2,361 .. Kazakhstan 1,153.7 1,473.2 300.0 .. 231.0 0.0 100.0 .. 58.9 .. .. Kenya 1,434.0 1,496.0 .. 116.7 .. 404.0 .. .. 27.2 7,371 125,102 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. 107.8 .. .. Kuwait .. .. .. .. .. .. .. .. 69.6 .. .. Kyrgyz Republic 11.5 40.9 .. .. .. .. 0.0 .. 15.3 3,987 .. Lao PDR 87.7 10.0 1,250.0 800.0 0.0 .. .. .. 6.8 .. .. Latvia 700.0 283.1 71.1 .. .. 135.0 .. .. 93.9 12,017 .. Lebanon 138.1 0.0 .. .. 153.0 .. 0.0 .. 75.6 1,030 .. Lesotho 88.4 10.3 0.0 .. .. .. .. .. 10.5 .. .. Liberia 70.3 27.5 .. .. .. .. .. .. 10.0 .. .. Libya .. .. .. .. .. .. .. .. 7.2 .. .. Lithuania 993.0 326.2 446.3 96.0 .. .. .. .. 61.2 6,578 67,095 Macedonia, FYR 706.6 371.0 .. 391.0 .. .. .. .. 38.0 .. .. Madagascar 12.6 119.6 0.0 .. 48.5 0.0 .. .. 10.1 1,234 19,305 Malawi 36.3 67.5 0.0 .. .. .. .. .. 10.6 420 5,595 Malaysia 3,756.9 1,130.0 6,637.6 203.0 4,461.4 954.0 6,502.2 0.0 105.3 43,279 .. Mali 82.6 87.0 365.9 .. 55.4 .. .. .. 18.8 .. .. Mauritania 92.1 50.1 .. .. .. .. .. .. .. .. .. Mauritius 413.0 26.1 0.0 .. .. .. .. .. 83.5 .. .. Mexico 18,191.4 5,642.2 6,749.3 1,081.0 2,970.4 8,808.4 523.7 303.8 22.0 306,400 4,290,000 Moldova 46.1 197.3 127.2 434.0 0.0 .. .. .. 36.9 6,806 73,532 Mongolia 22.1 0.0 .. .. .. .. .. .. 45.5 .. .. Morocco 6,139.5 1,466.6 1,049.0 .. 200.0 140.0 .. .. 69.9 24,811 .. Mozambique 123.0 81.2 1,205.8 .. 334.6 .. .. .. 13.9 .. .. Myanmar .. .. .. 556.1 .. .. .. .. 5.6 .. .. Namibia 35.0 8.5 1.0 .. .. .. 0.0 .. 65.3 .. .. Nepal 109.3 26.0 39.0 .. .. .. .. .. 36.3 .. .. Netherlands .. .. .. .. .. .. .. .. 195.0 116,000 1,030,000 New Zealand .. .. .. .. .. .. .. .. 148.1 74,247 474,212 Nicaragua 218.5 171.2 126.3 95.0 104.0 .. .. .. 39.1 2,070 .. Niger 85.5 110.0 .. .. .. .. 3.4 .. 9.6 .. .. Nigeria 6,950.7 5,296.1 1,920.0 280.0 2,355.4 262.1 .. .. 25.4 .. .. Norway .. .. .. .. .. .. .. .. .. 18,082 132,788 Oman 1,005.0 306.0 1,364.3 600.0 473.8 .. .. 173.0 32.0 6,362 38,864 Pakistan 6,595.1 5,213.4 524.6 1,494.3 71.0 801.0 .. .. 29.4 4,840 .. Panama 211.4 182.5 429.8 495.7 51.4 .. .. .. 92.0 .. .. Papua New Guinea .. 150.0 .. .. .. .. .. .. 21.4 .. .. Paraguay 199.0 319.7 .. .. .. .. .. .. 20.0 .. .. Peru 2,233.4 1,117.0 2,498.9 399.7 522.5 2,290.8 152.0 .. 21.0 .. .. Philippines 4,616.4 1,929.4 3,428.4 3,370.8 1,060.5 550.0 0.0 503.9 28.8 18,189 .. Poland 16,800.1 4,970.7 2,341.5 11.2 1,672.0 .. 64.3 0.8 39.7 26,388 523,584 Portugal .. .. .. .. .. .. .. .. 169.0 30,934 423,719 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 271 5.1 Private sector in the economy Investment commitments in infrastructure Domestic Businesses projects with private participationa credit to registered private sector $ millions Water and Telecommunications Energy Transport sanitation % of GDP New Total 2000­05 2006­07 2000­05 2006­07 2000­05 2006­07 2000­05 2006­07 2007 2007 2007 Romania 3,693.9 2,230.7 1,240.8 2,645.0 .. 116.8 1,116.0 0.0 35.8 103,733 870,195 Russian Federation 22,049.4 11,757.4 1,726.0 14,011.2 109.4 144.0 935.4 396.2 39.0 489,955 3,267,325 Rwanda 72.3 124.4 1.6 .. .. .. .. .. 12.2 .. 455 Saudi Arabia .. .. .. .. .. .. .. .. 40.4 .. .. Senegal 593.1 779.0 93.3 .. 55.4 .. 0.0 0.0 23.0 23 1,000 Serbia 563.5 2,864.1 .. .. .. .. 0.0 .. 34.2 10,876 83,499 Sierra Leone 48.8 66.3 .. 1.2 .. .. .. .. 5.3 .. .. Singapore .. .. .. .. .. .. .. .. 99.9 25,904 133,235 Slovak Republic 2,709.9 581.7 3,384.6 1,272.0 .. 42.0 .. 13.6 42.4 16,025 135,330 Slovenia .. .. .. .. .. .. .. .. 79.0 4,957 47,312 Somalia 13.4 0.0 .. .. .. .. .. .. .. .. .. South Africa 10,519.5 2,574.0 1,251.3 9.9 504.7 3,483.0 31.3 0.0 164.3 41,356 553,425 Spain .. .. .. .. .. .. .. .. 182.7 145,593 2,435,689 Sri Lanka 766.1 723.1 270.8 .. .. .. .. .. 33.3 4,529 .. Sudan 747.7 1,184.3 .. .. .. 30.0 .. 120.7 12.6 .. .. Swaziland 27.7 3.8 .. .. .. .. .. .. 25.4 .. .. Sweden .. .. .. .. .. .. .. .. 123.7 27,994 326,052 Switzerland .. .. .. .. .. .. .. .. 177.6 18,284 162,326 Syrian Arab Republic 583.0 104.3 .. .. .. 37.0 .. .. 16.2 216 2,268 Tajikistan 8.5 11.0 16.0 .. .. .. .. .. 28.9 794 .. Tanzania 515.3 485.5 348.0 28.4 27.7 134.0 8.5 .. 15.5 3,933 59,163 Thailand 5,602.7 2,180.0 4,693.3 .. 939.0 .. 287.7 18.8 92.4 25,184 297,084 Timor-Leste 0.0 0.0 .. .. .. .. .. .. 25.4 .. .. Togo 0.0 0.0 657.7 .. .. .. .. .. 21.3 .. .. Trinidad and Tobago .. 190.0 .. 39.0 .. .. 120.0 .. 33.8 .. .. Tunisia 751.0 2,419.0 30.0 .. .. 840.0 .. .. 64.3 6,675 63,584 Turkey 12,788.6 4,206.7 5,854.8 328.7 3,118.6 2,598.0 .. .. 29.1 93,634 764,240 Turkmenistan 20.0 48.1 .. .. .. .. .. .. .. .. .. Uganda 387.6 500.6 113.9 822.6 .. 404.0 0.0 .. 10.6 8,906 89,503 Ukraine 3,162.9 2,211.0 160.0 .. .. .. 100.0 .. 58.8 41,809 528,864 United Arab Emirates .. .. .. .. .. .. .. .. 64.3 .. .. United Kingdom .. .. .. .. .. .. .. .. 190.0 449,700 2,546,200 United States .. .. .. .. .. .. .. .. 210.1 676,830 5,156,000 Uruguay 114.2 60.9 330.0 .. 251.1 .. 368.0 .. 23.7 .. .. Uzbekistan 285.6 362.1 .. .. .. .. 0.0 .. .. 10,264 56,465 Venezuela, RB 3,337.0 1,683.0 39.5 .. 34.0 .. 15.0 .. 23.6 .. .. Vietnam 430.0 1,326.7 2,360.6 287.0 20.0 400.0 174.0 .. 93.3 .. 52,506 West Bank and Gaza 279.8 0.0 150.0 .. .. .. .. .. 7.5 .. .. Yemen, Rep. 376.8 292.1 .. 15.8 .. .. .. .. 7.9 50 .. Zambia 208.3 379.0 3.0 .. 15.6 .. 0.0 .. 12.0 5,300 .. Zimbabwe 72.0 20.0 .. .. .. .. .. .. 26.6 .. .. World .. s .. s .. s .. s .. s .. s .. s .. s 135.6 w Low income 21,743.0 23,001.5 10,746.1 5,150.7 3,295.8 2,605.1 185.9 0.0 31.2 Middle income 230,758.0 116,028.5 102,036.5 66,150.7 49,769.0 58,924.6 20,141.7 5,735.9 60.4 Lower middle income 79,126.8 54,904.0 40,467.3 31,883.7 25,282.6 37,718.9 5,883.5 4,392.4 74.7 Upper middle income 151,631.2 61,124.5 61,569.2 34,267.0 24,486.5 21,205.7 14,258.2 1,343.5 45.5 Low & middle income 252,501.0 139,029.9 112,782.6 71,301.4 53,064.8 61,529.7 20,327.6 5,735.9 59.0 East Asia & Pacific 29,854.1 12,191.0 30,741.7 9,746.8 20,562.2 17,055.1 10,485.7 3,159.5 97.5 Europe & Central Asia 68,767.4 38,203.5 16,942.6 20,240.6 5,955.1 4,255.4 2,774.4 832.0 39.5 Latin America & Carib. 80,867.3 31,561.1 44,627.8 17,659.6 16,958.3 18,600.4 6,232.5 1,251.5 36.6 Middle East & N. Africa 18,430.6 16,459.3 3,519.0 3,814.8 1,475.4 2,883.0 679.0 230.0 42.1 South Asia 29,926.2 24,086.6 9,623.3 18,031.4 4,352.3 13,965.7 112.9 142.3 44.7 Sub-Saharan Africa 24,655.4 16,528.5 7,328.3 1,808.2 3,761.6 4,770.1 43.2 120.7 70.4 High income .. .. .. .. .. .. .. .. 163.2 Euro area .. .. .. .. .. .. .. .. 121.6 a. Data refer to total for the period shown. Includes infrastructure projects with private sector participation that reached financial closure in 1990­2007. 272 2009 World Development Indicators STATES AND MARKETS Private sector in the economy 5.1 About the data Definitions Private sector development and investment--tapping and small-scale operators--may be omitted because · Investment commitments in infrastructure proj- private sector initiative and investment for socially they are not publicly reported. The database is a joint ects with private participation refers to infrastruc- useful purposes--are critical for poverty reduction. product of the World Bank's Finance, Economics, and ture projects in telecommunications, energy (electric- In parallel with public sector efforts, private invest- Urban Development Department and the Public- ity and natural gas transmission and distribution), ment, especially in competitive markets, has tre- Private Infrastructure Advisory Facility. Geographic transport, and water and sanitation that have reached mendous potential to contribute to growth. Private and income aggregates are calculated by the World financial closure and directly or indirectly serve the markets are the engine of productivity growth, creat- Bank's Development Data Group. For more informa- public. Incinerators, movable assets, standalone ing productive jobs and higher incomes. And with gov- tion, see http://ppi.worldbank.org/. solid waste projects, and small projects such as ernment playing a complementary role of regulation, Credit is an important link in money transmission; windmills are excluded. Included are operation and funding, and service provision, private initiative and it finances production, consumption, and capital for- management contracts, operation and management investment can help provide the basic services and mation, which in turn affect economic activity. The contracts with major capital expenditure, greenfield conditions that empower poor people--by improving data on domestic credit to the private sector are projects (new facilities built and operated by a private health, education, and infrastructure. taken from the banking survey of the International entity or a public-private joint venture), and dives- Investment in infrastructure projects with private Monetary Fund's (IMF) International Financial Statis- titures. Investment commitments are the sum of participation has made important contributions to tics or, when unavailable, from its monetary survey. investments in facilities and investments in govern- easing fiscal constraints, improving the efficiency The monetary survey includes monetary authorities ment assets. Investments in facilities are resources of infrastructure services, and extending delivery (the central bank), deposit money banks, and other the project company commits to invest during the to poor people. Developing countries have been in banking institutions, such as finance companies, contract period in new facilities or in expansion and the forefront, pioneering better approaches to infra- development banks, and savings and loan institu- modernization of existing facilities. Investments in structure services and reaping the benefits of greater tions. Credit to the private sector may sometimes government assets are the resources the project competition and customer focus. include credit to state-owned or partially state-owned company spends on acquiring government assets The data on investment in infrastructure projects enterprises. such as state-owned enterprises, rights to provide with private participation refer to all investment (pub- Entrepreneurship is essential to the dynamism of services in a specific area, or use of specific radio lic and private) in projects in which a private com- the modern market economy, and a greater entry rate spectrums. · Domestic credit to private sector is pany assumes operating risk during the operating of new businesses can foster competition and eco- financial resources provided to the private sector-- period or development and operating risk during the nomic growth. The table includes data on business such as through loans, purchases of nonequity contract period. Investment refers to commitments registrations from the 2008 World Bank Group Entre- securities, and trade credits and other accounts not disbursements. Foreign state-owned companies preneurship Survey, which includes entrepreneurial receivable--that establish a claim for repayment. are considered private entities for the purposes of activity in more than 100 countries for 2000­08. For some countries these claims include credit to this measure. Survey data are used to analyze firm creation, its public enterprises. · New businesses registered Investments are classified into two types: invest- relationship to economic growth and poverty reduc- are the number of limited liability firms registered ments in physical assets--the resources a com- tion, and the impact of regulatory and institutional in the calendar year. · Total businesses registered pany commits to invest in expanding and modern- reforms. The 2008 survey improves on earlier sur- are the year-end stock of total registered limited izing facilities--and payments to the government to veys' methodology and country coverage for better liability firms. acquire state-owned enterprises or rights to provide cross-country comparability. Data on total and newly services in a specific area or to use part of the radio registered businesses were collected directly from spectrum. national registrars of companies. For cross-country The data are from the World Bank's Private Partici- comparability, only limited liability corporations pation in Infrastructure (PPI) Project database, which that operate in the formal sector are included. For tracks infrastructure projects with private participa- additional information on sources, methodology, tion in developing countries. It provides information calculation of entrepreneurship rates, and data limi- on more than 4,100 infrastructure projects in 141 tations see http://econ.worldbank.org/research/ Data sources developing economies from 1984 to 2007. The data- entrepreneurship. Data on investment commitments in infra- base contains more than 30 fields per project record, structure projects with private participation are including country, financial closure year, infrastruc- from the World Bank's PPI Project database ture services provided, type of private participation, (http://ppi.worldbank.org). Data on domestic investment, technology, capacity, project location, credit are from the IMF's International Financial contract duration, private sponsors, bidding process, Statistics. Data on business registration and are and development bank support. Data on the projects from the World Bank's Entrepreneurship Survey are compiled from publicly available information. The and database (http://econ.worldbank.org/ database aims to be as comprehensive as possible, research/entrepreneurship). but some projects--particularly those involving local 2009 World Development Indicators 273 5.2 Business environment: enterprise surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Inter- Average Time Losses due Firms that nationally time to dealing with Time required Informal to theft, do not Firms with Firms using recognized clear direct Firms Average officials to obtain payments robbery, report all female banks to Value lost due quality exports offering number operating to public vandalism, sales for tax participation finance to electrical certification through formal % of of times license officials and arson purposes in ownership investment outages ownership customs training management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 2007 18.7 5.5 21.2 57.7 3.3 .. 10.8 12.4 13.7 24.6 1.9 19.9 Algeriaa 2007 25.1 3.4 19.3 64.7 6.3 .. 15.0 8.9 4.0 5.0 14.1 17.3 Angola 2006 7.1 5.2 24.1 46.8 2.4 67.8 23.4 2.1 3.7 5.1 16.5 19.4 Argentina 2006 14.1 4.6 175.8 18.7 3.7 49.1 30.3 6.9 1.4 26.9 5.5 52.2 Armenia 2005 3.0 2.9 .. 24.6 0.0 26.2 12.5 35.0 2.5 5.7 5.0 35.9 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2005 5.2 1.3 .. 37.8 0.2 38.7 14.4 0.6 5.9 10.3 1.6 16.3 Bangladesh 2007 3.2 1.4 6.1 85.1 1.2 .. 16.1 24.7 10.6 7.8 8.4 16.2 Belarusa 2008 13.6 2.1 38.2 13.5 1.8 20.0 52.9 35.8 0.8 13.9 2.6 44.4 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 2004 6.5 6.3 39.9 57.7 0.3 39.6 .. 20.8 6.5 2.7 6.3 35.3 Bolivia 2006 13.5 3.5 30.0 32.4 3.3 51.4 41.1 22.2 4.4 13.8 15.3 53.9 Bosnia and Herzegovina 2005 4.3 1.9 .. 24.1 0.4 29.2 25.2 17.5 2.4 14.5 2.0 47.2 Botswana 2006 5.0 2.4 13.7 27.6 3.2 65.3 40.9 11.3 1.4 12.7 1.4 37.7 Brazil 2003 7.2 .. .. .. 0.4 82.8 .. 22.9 1.6 19.1 8.2 67.1 Bulgaria 2007 17.4 4.1 48.2 16.1 1.3 39.7 39.2 40.5 1.2 22.3 2.7 36.5 Burkina Faso 2006 9.5 2.5 5.8 87.0 1.8 58.8 23.3 23.1 3.9 7.4 2.8 43.1 Burundi 2006 5.7 2.1 27.3 56.5 4.9 42.7 34.8 12.3 10.7 7.1 0.0 22.1 Cambodia 2007 5.6 2.3 .. 61.2 0.4 .. .. 11.3 2.4 .. 1.7 48.4 Cameroon 2006 12.8 6.4 15.6 77.6 3.8 38.7 35.3 19.5 3.9 16.4 4.3 42.4 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 2006 9.0 5.4 67.7 8.2 1.3 27.9 27.8 29.1 1.8 22.0 5.8 46.9 China 2003 18.3 14.4 11.8 72.6 0.1 49.5 .. 9.8 1.3 35.9 6.7 84.8 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2006 14.3 2.5 28.2 8.2 2.9 38.7 43.0 30.6 2.3 5.9 7.1 39.5 Congo, Dem. Rep. 2006 6.3 10.0 17.8 83.8 6.5 65.4 21.2 3.3 5.6 4.3 3.6 11.4 Congo, Rep. 2009 5.9 2.9 .. 48.3 17.3 86.8 27.5 6.3 15.7 23.8 .. 38.5 Costa Rica 2005 9.6 0.7 .. 33.8 0.4 68.3 34.7 9.3 1.9 10.5 3.5 46.4 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia 2007 10.9 1.7 26.5 14.5 0.9 33.3 33.5 60.0 0.8 16.5 1.3 28.0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 2005 2.1 1.7 .. 25.5 0.4 51.1 21.8 11.4 1.6 12.5 3.6 60.3 Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 2005 8.8 2.7 .. 26.3 0.7 73.6 .. 3.6 15.2 9.6 11.4 53.3 Ecuador 2006 17.3 2.6 19.9 21.5 3.0 37.6 32.7 24.0 2.7 18.2 7.0 61.6 Egypt, Arab Rep.a 2007 .. 3.8 81.5 7.3 .. 34.8 20.9 9.5 4.7 20.0 6.4 21.2 El Salvador 2006 9.2 4.1 35.4 34.3 5.6 42.3 39.6 17.3 2.9 11.0 2.6 49.6 Eritrea 2002 .. .. .. .. .. .. .. .. .. .. .. .. Estonia 2005 2.3 2.2 .. 16.2 0.4 24.7 34.1 17.8 1.1 13.2 1.8 64.9 Ethiopia 2006 3.8 1.8 11.4 12.4 1.4 51.6 30.9 11.0 0.9 4.2 4.3 38.2 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 2009 3.0 22.6 12.7 24.1 3.7 62.7 26.9 5.9 1.8 22.3 3.9 33.7 Gambia, The 2006 7.3 3.2 9.1 52.4 8.7 88.1 21.3 7.6 11.8 22.2 5.0 25.6 Georgiaa 2008 2.1 1.7 11.8 4.1 7.6 36.0 40.8 38.2 1.4 16.0 3.8 14.5 Germany 2005 4.5 1.3 .. .. 0.5 .. 20.3 44.3 .. .. 4.7 35.4 Ghana 2007 4.0 4.6 6.4 38.8 3.7 59.2 44.0 16.0 6.0 6.8 7.8 33.0 Greece 2005 3.7 1.7 .. 21.6 0.0 53.2 24.4 16.1 .. 11.7 5.5 20.0 Guatemala 2006 9.2 3.9 75.4 15.7 5.2 44.2 28.4 12.8 4.5 8.0 4.5 28.1 Guinea 2006 2.7 3.6 13.0 84.8 8.3 95.4 25.4 0.9 14.0 5.2 4.3 21.1 Guinea-Bissau 2006 2.9 4.4 30.4 62.7 3.3 68.2 19.9 0.7 5.3 8.4 5.6 12.4 Haiti .. .. .. .. .. .. .. .. .. .. .. .. 274 2009 World Development Indicators STATES AND MARKETS Business environment: enterprise surveys Survey Regulations Permits Corruption Crime Informality Gender Finance 5.2 Infrastructure Innovation Trade Workforce year and tax and licenses Inter- Average Time Losses due Firms that nationally time to dealing with Time required Informal to theft, do not Firms with Firms using recognized clear direct Firms Average officials to obtain payments robbery, report all female banks to Value lost due quality exports offering number operating to public vandalism, sales for tax participation finance to electrical certification through formal % of of times license officials and arson purposes in ownership investment outages ownership customs training management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Honduras 2006 4.6 2.4 31.6 16.7 6.1 36.0 39.9 8.5 3.8 16.5 6.0 33.3 Hungary 2005 4.0 2.5 .. 32.1 0.1 40.0 40.1 22.3 1.4 23.1 4.5 39.9 India 2006 6.7 3.1 .. 47.5 0.1 59.2 9.1 19.4 6.6 22.5 15.6 15.9 Indonesia 2003 4.0 2.0 18.6 44.2 0.2 44.0 .. 13.9 3.3 22.1 4.1 23.8 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 2005 2.3 1.3 .. 8.3 0.3 28.8 41.6 20.8 1.5 17.2 2.6 73.2 Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 2005 6.3 2.2 .. 17.7 1.1 28.8 32.2 10.6 11.8 16.4 4.3 53.5 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 2006 6.7 2.2 6.4 18.1 1.3 13.0 13.1 8.6 1.7 15.5 3.8 23.9 Kazakhstan 2005 3.1 4.0 .. 45.1 0.3 23.2 36.1 15.4 2.2 9.9 6.8 30.7 Kenyaa 2007 5.1 9.0 23.4 79.2 3.9 45.9 37.1 22.9 6.4 9.8 5.6 40.7 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2005 3.2 2.4 .. 14.1 0.0 43.7 19.1 11.5 .. 17.6 7.2 39.5 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2005 6.1 3.5 43.9 66.3 0.7 43.2 27.3 7.9 4.1 11.9 4.1 47.0 Lao PDR 2005 4.5 3.8 .. 31.2 1.5 14.9 .. 13.8 4.3 3.3 2.0 28.2 Latvia 2005 2.9 2.2 .. 31.3 0.5 26.3 42.3 15.1 1.4 9.3 2.0 51.7 Lebanon 2006 12.0 4.7 .. 51.2 0.5 67.5 27.9 26.8 6.0 20.9 7.4 67.8 Lesotho 2009 5.7 3.1 10.9 12.9 6.7 .. 22.5 26.7 6.0 31.9 8.0 52.7 Liberia 2009 8.5 7.1 16.9 52.9 7.9 90.0 27.6 11.5 3.7 4.7 .. 29.4 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 2005 5.1 4.2 55.5 44.6 0.4 39.0 25.5 15.6 1.2 15.1 1.8 52.6 Macedonia, FYR 2005 8.2 2.7 .. 26.0 0.3 52.2 17.5 9.0 1.8 11.0 2.4 37.4 Madagascar 2005 20.8 2.7 .. 24.5 1.9 21.0 .. 13.0 6.6 6.6 3.5 48.5 Malawi 2006 5.8 8.9 17.4 35.7 2.3 55.3 15.8 20.6 22.6 17.2 3.5 51.6 Malaysia 2002 7.3 5.2 .. .. 0.3 .. .. 23.8 1.8 31.4 2.5 42.0 Malia 2007 2.4 2.3 41.0 28.9 3.7 39.7 18.4 7.0 1.8 8.6 4.8 22.5 Mauritania 2006 5.8 1.9 10.7 82.1 5.6 82.5 17.3 3.2 1.6 5.9 3.9 25.5 Mauritius 2005 9.6 2.1 .. 17.5 0.1 26.3 .. 36.3 2.9 28.4 4.4 62.1 Mexico 2006 20.5 2.3 11.9 22.6 3.4 57.7 24.8 2.6 2.4 20.3 5.4 24.6 Moldova 2005 3.6 2.7 .. 36.0 0.1 40.2 27.5 17.7 2.7 6.9 2.6 32.5 Mongolia 2004 6.0 7.3 .. .. 0.6 80.4 .. 32.8 1.5 20.5 3.5 46.2 Moroccoa 2007 11.4 5.1 3.4 13.4 0.4 .. 13.1 12.3 1.3 17.3 1.8 24.7 Mozambiquea 2007 3.3 2.7 34.3 14.7 5.1 73.5 24.4 11.3 2.5 18.7 10.0 22.5 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 2006 2.9 1.6 9.6 11.4 3.0 45.5 33.4 8.1 0.7 17.6 1.5 44.5 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2006 9.3 2.5 19.7 17.2 3.8 60.4 41.4 13.0 8.7 18.7 5.0 28.9 Niger 2006 11.5 4.3 10.9 69.7 6.1 29.7 10.0 14.6 2.5 4.8 7.4 34.4 Nigeriaa 2007 6.1 3.7 12.8 40.9 4.1 68.0 20.0 2.7 8.9 8.5 7.5 25.7 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. 5.2 11.8 33.2 .. 42.5 .. 6.5 4.2 10.8 4.2 20.9 Pakistan 2002 8.7 4.2 35.2 57.0 0.1 .. .. 3.6 4.9 17.0 9.7 11.1 Panama 2006 10.3 2.7 41.2 25.4 2.7 54.2 37.1 19.2 2.4 14.7 5.7 43.9 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 2006 7.9 2.2 37.8 84.8 3.1 42.8 44.8 8.2 2.5 7.1 5.5 46.9 Peru 2006 13.5 2.5 81.1 11.3 2.4 27.2 32.8 30.9 3.2 14.6 5.6 57.7 Philippines 2003 6.9 3.9 25.0 44.7 0.9 57.9 .. 5.5 5.9 15.8 6.6 21.7 Poland 2005 3.0 2.7 .. 23.7 0.4 43.9 33.6 20.7 1.6 13.9 3.3 48.4 Portugal 2005 3.3 1.7 .. 14.5 0.2 37.3 50.8 9.5 .. 12.7 7.2 31.9 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 275 5.2 Business environment: enterprise surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Inter- Average Time Losses due Firms that nationally time to dealing with Time required Informal to theft, do not Firms with Firms using recognized clear direct Firms Average officials to obtain payments robbery, report all female banks to Value lost due quality exports offering number operating to public vandalism, sales for tax participation finance to electrical certification through formal % of of times license officials and arson purposes in ownership investment outages ownership customs training management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Romania 2005 1.1 1.8 .. 33.1 0.2 26.9 27.7 23.2 2.1 16.8 2.4 32.7 Russian Federation 2005 6.3 2.5 .. 59.9 0.5 40.3 28.6 10.2 2.0 9.3 8.2 37.3 Rwanda 2006 5.9 4.0 6.5 20.0 7.1 28.9 41.0 15.9 8.7 10.8 6.7 27.6 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegala 2007 2.9 1.8 21.4 18.1 4.1 21.6 26.3 19.8 5.0 6.1 8.9 16.3 Serbia 2005 8.1 4.1 .. 31.8 0.6 33.3 25.0 16.7 2.4 11.7 3.2 47.5 Sierra Leone 2009 7.9 2.6 12.0 18.7 4.2 77.3 10.0 12.5 7.8 17.3 .. 25.0 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 2005 3.0 1.8 .. 34.3 0.4 22.0 18.2 13.2 1.2 10.0 5.8 79.4 Slovenia 2005 3.7 1.4 .. 11.2 0.2 35.6 34.5 29.6 1.1 20.2 2.9 69.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2003 9.2 3.3 6.4 2.1 0.5 15.9 .. 24.2 0.4 42.4 4.5 64.0 Spain 2005 0.8 1.5 .. 4.4 0.2 18.3 34.1 32.2 3.0 21.3 4.9 51.3 Sri Lanka 2004 3.5 5.1 49.5 16.3 0.5 42.0 .. 16.2 .. .. 7.6 32.6 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 2006 4.4 1.9 24.0 40.6 3.4 74.6 28.6 7.7 2.5 22.1 4.0 51.0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 2003 10.3 6.0 .. .. .. 79.9 .. 2.9 8.6 7.4 6.3 21.0 Tajikistana 2008 11.7 2.7 22.6 40.5 4.5 .. 34.4 21.4 15.1 16.7 20.4 21.1 Tanzania 2006 4.0 3.3 15.9 49.5 3.9 71.0 30.9 6.8 9.6 14.7 5.7 36.5 Thailand 2006 0.4 1.1 32.1 .. 0.1 .. .. 74.4 1.5 39.0 1.5 75.3 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 2005 10.8 2.2 .. 45.7 0.2 63.1 8.9 7.5 2.2 12.6 4.5 25.5 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 2006 5.2 2.9 9.3 51.7 4.1 74.5 34.7 7.7 10.2 15.5 4.7 35.0 Ukrainea 2008 11.3 3.8 31.0 22.9 3.6 .. 47.1 32.1 4.4 13.0 3.5 25.1 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 2006 7.0 2.2 133.8 7.3 2.1 45.5 41.6 6.9 0.9 6.8 2.8 24.6 Uzbekistana 2008 11.1 1.7 9.1 56.2 18.3 .. 39.8 8.2 5.4 1.3 5.1 9.6 Venezuela, RB 2006 33.6 3.4 41.6 .. 6.8 .. .. 35.7 4.4 12.5 14.1 42.3 Vietnam 2005 3.1 2.2 .. 67.2 0.1 70.3 27.4 29.2 .. 11.4 4.9 44.0 West Bank and Gaza 2006 5.7 5.2 21.3 13.3 7.5 25.7 18.0 4.2 4.6 18.2 6.0 26.5 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambiaa 2007 4.6 2.9 47.3 14.8 3.3 .. 37.4 10.1 3.6 17.2 3.1 25.4 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. a. Representative sample of the nonagricultural economy, excluding financial and public services. 276 2009 World Development Indicators STATES AND MARKETS Business environment: enterprise surveys 5.2 About the data Definitions The World Bank Group's Enterprise Survey gath- with access to modern and efficient infrastructure-- · Survey year is the year in which the underlying ers firm-level data on the business environment telecommunications, electricity, and transport--can data were collected. · Time dealing with officials to assess constraints to private sector growth and be more productive. Firm-level innovation and use of is the time senior management spends dealing with enterprise performance. Standardized surveys are modern technology may help firms compete. the requirements of government regulation. · Aver- conducted all over the world, and data are available Delays in clearing customs can be costly, deterring age number of times meeting with tax offi cials on almost 85,000 firms in 106 countries. The survey firms from engaging in trade or making them uncom- is the average number of visits with tax officials. covers 11 dimensions of the business environment, petitive globally. Ill-considered labor regulations dis- · Time required to obtain operating license is the including corruption, crime, informality, regulation, courage firms from creating jobs, and while employed average wait to obtain an operating license from and finance. For some countries, firm-level panel data workers may benefit, unemployed, low-skilled, and the day applied for to the day granted. · Informal are available, making it possible to track changes in informally employed workers will not. A trained labor payments to public offi cials are the percentage the business environment over time. force enables firms to thrive, compete, innovate, and of firms expected to make informal payments to Firms evaluating investment options, governments adopt new technology. public offi cials to "get things done" for customs, interested in improving business conditions, and Most of the data in the table are from the World taxes, licenses, regulations, services, and the like. economists seeking to explain economic perfor- Bank Financial and Private Sector Development · Losses due to theft, robbery, vandalism, and mance have all grappled with defining and measuring Group's Enterprise Surveys. Data for 27 countries in arson are the estimated losses from those causes the business environment. The firm-level data from Europe and Central Asia and 2 comparator countries that occurred on establishments' premises as a per- Enterprise Surveys provide a useful tool for bench- in Asia (Republic of Korea and Vietnam) are based on centage of annual sales. · Firms that do not report marking performance and monitoring progress. the joint European Bank for Reconstruction and Devel- all sales for tax purposes are the percentage of firms Most countries can improve regulation and taxa- opment (EBRD)­World Bank Business Environment that expressed that a typical firm reports less than tion without compromising broader social interests. and Enterprise Performance Surveys (BEEPS). 100 percent of sales for tax purposes; such firms are Excessive regulation may harm business perfor- All BEEPS economies plus the Latin American and termed "informal firms." · Firms with female partici- mance and growth. For example, time spent with Caribbean countries, the North African countries for pation in ownership are the percentage of firms with tax officials is a burden firms may face in paying 2007, the Sub-Saharan African countries for 2006 a woman among the principal owners. · Firms using taxes. The business environment suffers when gov- and 2007 (except Burkina Faso, Cameroon, and Cape banks to finance investment are the percentage of ernments increase uncertainty and risks or impose Verde), Jordan, and Bangladesh for 2007 draw a sam- firms using banks to finance investments. · Value unnecessary costs and unsound regulation and taxa- ple from the universe of registered nonagricultural lost due to electrical outages is the percentage of tion. Time to obtain licenses and permits and the businesses excluding the financial and public sectors. sales lost due to power outages. · Internationally associated red tape constrain firm operations. Economies with samples that are representative of recognized quality certifi cation ownership is the In some countries doing business requires infor- the economy are footnoted. Samples for most of the percentage of firms that have earned a quality certi- mal payments to "get things done" in customs, taxes, remaining economies were drawn from the manufac- fication recognized by the International Organization licenses, regulations, services, and the like. Such cor- turing sector. Typical Enterprise Survey sample sizes for Standardization (ISO). · Average time to clear ruption harms the business environment by distorting range from 100 to 1,800, depending on the size of direct exports through customs is the average num- policymaking, undermining government credibility, and the economy. Samples are selected by simple random ber of days to clear direct exports through customs. diverting public resources. Crime, theft, and disorder sampling or stratified random sampling. BEEPS 2005 · Firms offering formal training are the percentage also impose costs on businesses and society. use a simple random sample method based on GDP of firms offering formal training programs for their In many developing countries informal businesses contributions, so samples are self-weighted. BEEPS permanent, full-time employees. operate without licenses. These firms have less 2008 economies, Latin American and Caribbean, North access to financial and public services and can African, and Sub-Saharan African countries (except engage in fewer types of contracts and investments, Burkina Faso, Cameroon, and Cape Verde), Bangladesh, constraining growth. and Jordan use stratified random sampling. Stratified Equal opportunities for men and women contrib- random sampling allows indicators to be computed by ute to development. Female participation in firm sector, firm size, and geographic region. ownership is a measure of women's integration as At the sector level the strata are composed of decisionmakers. selected manufacturing industries and one service Financial markets connect firms to lenders and sector (retail), plus the rest of the economy, a resid- investors, allowing firms to grow their businesses: ual stratum. Firm size is stratified into small (5­19 creditworthy firms can obtain credit from financial employees), medium (20­100 employees), and large intermediaries at competitive prices. But too often (more than 100 employees). Geographic stratifica- Data sources market imperfections and government-induced distor- tion is defined by country. Economy wide indicators Data on the business environment are from the tions limit access to credit and thus restrain growth. can be computed with more precision under stratified World Bank Group's Enterprise Surveys website The reliability and availability of infrastructure random sampling than under simple random sampling (www.enterprisesurveys.org). benefit households and support development. Firms when individual observations are properly weighted. 2009 World Development Indicators 277 Business environment: 5.3 Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property construction workers contracts investors business permits Time Rigidity of Disclosure Cost Number of required employment index Time to Time % of per Time procedures to build a index Time 0­10 (least resolve Number of required capita Number of required to build a warehouse 0­100 (least Number of required to most insolvency procedures days income procedures days warehouse days to most rigid) procedures days disclosure) years June June June June June June June June June June June June 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Afghanistan 4 9 59.5 9 250 13 340 27 47 1,642 0 .. Albania 6 8 25.8 6 42 24 331 35 39 390 8 .. Algeria 14 24 10.8 14 51 22 240 48 47 630 6 2.5 Angola 8 68 196.8 7 334 12 328 66 46 1,011 5 6.2 Argentina 15 32 9.0 5 51 28 338 35 36 590 6 2.8 Armenia 9 18 3.6 3 4 19 116 31 49 285 5 1.9 Australia 2 2 0.8 5 5 16 221 3 28 395 8 1.0 Austria 8 28 5.1 3 32 13 194 33 25 397 3 1.1 Azerbaijan 6 16 3.2 4 11 31 207 3 39 237 7 2.7 Bangladesh 7 73 25.7 8 245 14 231 35 41 1,442 6 4.0 Belarus 8 31 7.8 4 21 17 210 27 28 225 5 5.8 Belgium 3 4 5.2 7 132 14 169 20 25 505 8 0.9 Benin 7 31 196.0 4 120 15 410 40 42 825 6 4.0 Bolivia 15 50 112.4 7 92 17 249 79 40 591 1 1.8 Bosnia and Herzegovina 12 60 30.8 7 128 16 296 46 38 595 3 3.3 Botswana 10 78 2.3 4 11 24 167 20 29 987 7 1.7 Brazil 18 152 8.2 14 42 18 411 46 45 616 6 4.0 Bulgaria 4 49 2.0 8 19 24 139 29 39 564 10 3.3 Burkina Faso 5 16 62.3 6 136 15 214 21 37 446 6 4.0 Burundi 11 43 215.0 5 94 20 384 30 44 832 4 .. Cambodia 9 85 151.7 7 56 23 709 45 44 401 5 .. Cameroon 13 37 137.1 5 93 15 426 46 43 800 6 3.2 Canada 1 5 0.5 6 17 14 75 4 36 570 8 0.8 Central African Republic 10 14 232.3 5 75 21 239 61 43 660 6 4.8 Chad 19 75 175.0 6 44 9 181 46 41 743 6 .. Chile 9 27 7.5 6 31 18 155 24 36 480 7 4.5 China 14 40 8.4 4 29 37 336 27 34 406 10 1.7 Hong Kong, China 5 11 2.0 5 54 15 119 0 24 211 10 1.1 Colombia 9 36 14.1 9 23 13 114 24 34 1,346 8 3.0 Congo, Dem. Rep. 13 155 435.4 8 57 14 322 74 43 645 3 5.2 Congo, Rep. 10 37 106.4 7 116 14 169 69 44 560 6 3.0 Costa Rica 12 60 20.5 6 21 23 191 28 40 877 2 3.5 Côte d'Ivoire 10 40 135.1 6 62 21 628 38 33 770 6 2.2 Croatia 8 40 11.5 5 174 19 410 50 38 561 1 3.1 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 8 15 9.6 4 123 36 180 28 27 820 2 6.5 Denmark 4 6 0.0 6 42 6 69 10 34 380 7 1.1 Dominican Republic 8 19 19.4 7 60 17 214 28 34 460 5 3.5 Ecuador 14 65 38.5 9 16 19 155 51 39 588 1 5.3 Egypt, Arab Rep. 6 7 18.3 7 72 28 249 27 42 1,010 8 4.2 El Salvador 8 17 49.6 5 31 34 155 24 30 786 5 4.0 Eritrea 13 84 102.2 12 101 .. .. 20 39 405 4 .. Estonia 5 7 1.7 3 51 14 118 58 36 425 8 3.0 Ethiopia 7 16 29.8 13 43 12 128 34 39 690 4 3.0 Finland 3 14 1.0 3 14 18 38 48 32 235 6 0.9 France 5 7 1.0 9 113 13 137 56 30 331 10 1.9 Gabon 9 58 20.3 8 60 16 210 52 38 1,070 6 5.0 Gambia, The 8 27 254.9 5 371 17 146 27 32 434 2 3.0 Georgia 3 3 4.0 2 3 12 113 7 36 285 8 3.3 Germany 9 18 5.6 4 40 12 100 44 30 394 5 1.2 Ghana 9 34 32.7 5 34 18 220 37 36 487 7 1.9 Greece 15 19 10.2 11 22 15 169 51 39 819 1 2.0 Guatemala 11 26 50.6 5 30 22 215 28 31 1,459 3 3.0 Guinea 13 41 135.7 6 104 32 255 44 50 276 6 3.8 Guinea-Bissau 17 233 257.7 9 211 15 167 66 41 1,140 6 .. Haiti 13 195 159.6 5 405 11 1,179 21 35 508 2 5.7 278 2009 World Development Indicators STATES AND MARKETS Business environment: Starting a Doing Business indicators Registering Dealing with Employing Enforcing 5.3 Protecting Closing a business property construction workers contracts investors business permits Time Rigidity of Disclosure Cost Number of required employment index Time to Time % of per Time procedures to build a index Time 0­10 (least resolve Number of required capita Number of required to build a warehouse 0­100 (least Number of required to most insolvency procedures days income procedures days warehouse days to most rigid) procedures days disclosure) years June June June June June June June June June June June June 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Honduras 13 20 52.6 7 23 17 125 53 45 900 1 3.8 Hungary 4 5 8.4 4 17 31 204 30 33 335 2 2.0 India 13 30 70.1 6 45 20 224 30 46 1,420 7 10.0 Indonesia 11 76 77.9 6 39 18 176 40 39 570 9 5.5 Iran, Islamic Rep. 8 47 4.6 9 36 19 670 40 39 520 5 4.5 Iraq 11 77 150.7 5 8 14 215 38 51 520 4 .. Ireland 4 13 0.3 5 38 11 185 17 20 515 10 0.4 Israel 5 34 4.4 7 144 20 235 24 35 890 7 4.0 Italy 6 10 18.5 8 27 14 257 38 41 1,210 7 1.8 Jamaica 6 8 7.9 5 54 10 156 4 35 655 4 1.1 Japan 8 23 7.5 6 14 15 187 17 30 316 7 0.6 Jordan 10 14 60.4 8 22 18 122 30 39 689 5 4.3 Kazakhstan 8 21 5.2 5 40 38 231 23 38 230 7 3.3 Kenya 12 30 39.7 8 64 10 100 17 44 465 3 4.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 10 17 16.9 7 11 13 34 45 35 230 7 1.5 Kuwait 13 35 1.3 8 55 25 104 13 50 566 7 4.2 Kyrgyz Republic 4 15 7.4 7 8 13 159 38 39 177 9 4.0 Lao PDR 8 103 14.1 9 135 24 172 34 42 443 0 .. Latvia 5 16 2.3 7 50 25 187 43 27 279 5 3.0 Lebanon 5 11 87.5 8 25 20 211 25 37 721 9 4.0 Lesotho 7 40 37.8 6 101 15 601 21 41 695 2 2.6 Liberia 8 27 100.2 13 50 25 321 31 41 1,280 4 3.0 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 7 26 2.7 2 3 17 162 48 30 210 5 1.7 Macedonia, FYR 7 9 3.8 6 66 21 198 47 38 385 5 3.7 Madagascar 5 7 11.0 7 74 16 178 63 38 871 5 .. Malawi 10 39 125.9 6 88 21 213 25 42 432 4 2.6 Malaysia 9 13 14.7 5 144 25 261 10 30 600 10 2.3 Mali 11 26 121.5 5 29 14 208 38 39 860 6 3.6 Mauritania 9 19 33.9 4 49 25 201 45 46 370 5 8.0 Mauritius 5 6 5.0 4 210 18 107 23 37 750 6 1.7 Mexico 9 28 12.5 5 74 12 138 48 38 415 8 1.8 Moldova 9 15 8.9 6 48 30 292 41 31 365 7 2.8 Mongolia 7 13 4.0 5 11 21 215 34 32 314 5 4.0 Morocco 6 12 10.2 8 47 19 163 63 40 615 6 1.8 Mozambique 10 26 22.9 8 42 17 381 49 30 730 5 5.0 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 10 66 22.1 9 23 12 139 20 33 270 5 1.5 Nepal 7 31 60.2 3 5 15 424 42 39 735 6 5.0 Netherlands 6 10 5.9 2 5 18 230 42 25 514 4 1.1 New Zealand 1 1 0.4 2 2 7 65 7 30 216 10 1.3 Nicaragua 6 39 121.0 8 124 17 219 27 35 540 4 2.2 Niger 11 19 170.1 4 35 17 265 70 39 545 6 5.0 Nigeria 8 31 90.1 14 82 18 350 7 39 457 5 2.0 Norway 6 10 2.1 1 3 14 252 47 33 310 7 0.9 Oman 7 14 3.6 2 16 16 242 24 51 598 8 4.0 Pakistan 11 24 12.6 6 50 12 223 43 47 976 6 2.8 Panama 7 13 19.6 7 44 21 131 66 31 686 1 2.5 Papua New Guinea 8 56 23.6 4 72 24 217 10 43 591 5 3.0 Paraguay 7 35 67.9 6 46 13 291 59 38 591 6 3.9 Peru 10 65 25.7 5 33 21 210 48 41 468 8 3.1 Philippines 15 52 29.8 8 33 24 203 35 37 842 2 5.7 Poland 10 31 18.8 6 197 30 308 37 38 830 7 3.0 Portugal 6 6 2.9 5 42 21 328 48 34 577 6 2.0 Puerto Rico 7 7 0.8 8 194 22 209 25 39 620 7 3.8 2009 World Development Indicators 279 Business environment: 5.3 Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property construction workers contracts investors business permits Time Rigidity of Disclosure Cost Number of required employment index Time to Time % of per Time procedures to build a index Time 0­10 (least resolve Number of required capita Number of required to build a warehouse 0­100 (least Number of required to most insolvency procedures days income procedures days warehouse days to most rigid) procedures days disclosure) years June June June June June June June June June June June June 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Romania 6 10 3.6 8 83 17 243 62 31 512 9 3.3 Russian Federation 8 29 2.6 6 52 54 704 44 37 281 6 3.8 Rwanda 8 14 108.9 4 315 14 210 38 24 310 2 .. Saudi Arabia 7 12 14.9 2 2 18 125 13 44 635 8 1.5 Senegal 4 8 72.7 6 124 16 220 61 44 780 6 3.0 Serbia 11 23 7.6 6 111 20 279 39 36 635 7 2.7 Sierra Leone 7 17 56.2 7 86 25 283 51 40 515 3 2.6 Singapore 4 4 0.7 3 9 11 38 0 21 150 10 0.8 Slovak Republic 6 16 3.3 3 17 13 287 36 30 565 3 4.0 Slovenia 5 19 0.1 6 391 15 208 59 32 1,350 3 2.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 6 22 6.0 6 24 17 174 42 30 600 8 2.0 Spain 10 47 14.9 4 18 11 233 56 39 515 5 1.0 Sri Lanka 4 38 7.1 8 83 21 214 27 40 1,318 4 1.7 Sudan 10 39 50.8 6 9 19 271 36 53 810 0 .. Swaziland 13 61 35.1 11 46 13 93 13 40 972 0 2.0 Sweden 3 15 0.6 1 2 8 116 44 30 508 6 2.0 Switzerland 6 20 2.1 4 16 14 154 17 32 417 0 3.0 Syrian Arab Republic 8 17 18.2 4 19 26 128 34 55 872 6 4.1 Tajikistan 13 49 27.6 6 37 32 351 51 34 295 4 3.0 Tanzania 12 29 41.5 9 73 21 308 63 38 462 3 3.0 Thailand 8 33 4.9 2 2 11 156 18 35 479 10 2.7 Timor-Leste 10 83 6.6 .. .. 22 208 34 51 1,800 3 .. Togo 13 53 251.3 5 295 15 277 57 41 588 6 3.0 Trinidad and Tobago 9 43 0.9 8 162 20 261 7 42 1,340 4 .. Tunisia 10 11 7.9 4 39 20 84 49 39 565 0 1.3 Turkey 6 6 14.9 6 6 25 188 38 35 420 9 3.3 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 18 25 100.7 13 227 16 143 3 38 535 2 2.2 Ukraine 10 27 5.5 10 93 30 471 45 30 354 1 2.9 United Arab Emirates 8 17 13.4 3 6 21 125 13 50 607 4 5.1 United Kingdom 6 13 0.8 2 21 19 144 14 30 404 10 1.0 United States 6 6 0.7 4 12 19 40 0 32 300 7 1.5 Uruguay 11 44 43.5 8 66 30 234 31 40 720 3 2.1 Uzbekistan 7 15 10.3 12 78 26 260 34 42 195 4 4.0 Venezuela, RB 16 141 26.8 8 47 11 395 79 29 510 3 4.0 Vietnam 11 50 16.8 4 57 13 194 24 34 295 6 5.0 West Bank and Gaza 11 49 69.1 7 63 21 199 31 44 700 6 .. Yemen, Rep. 7 13 93.0 6 19 13 107 33 37 520 6 3.0 Zambia 6 18 28.6 6 39 17 254 34 35 471 3 2.7 Zimbabwe 10 96 432.7 4 30 19 1,426 33 38 410 8 3.3 World 9u 38 u 47.1 u 6u 72 u 18 u 222 u 33 u 38 u 613 u 5u 3.0 u Low income 10 49 110.1 7 108 18 304 39 40 626 5 3.6 Middle income 9 42 34.9 6 67 19 213 33 39 652 5 3.2 Lower middle income 9 35 45.9 6 69 19 209 33 39 669 5 3.5 Upper middle income 9 53 19.1 6 64 20 219 32 38 627 5 2.9 Low & middle income 9 44 60.3 6 81 19 243 35 39 643 5 3.3 East Asia & Pacific 9 44 38.0 5 113 19 183 20 37 591 5 3.1 Europe & Central Asia 8 23 9.3 6 59 24 265 37 37 385 6 3.2 Latin America & Carib. 10 70 43.9 7 66 17 233 33 39 714 4 3.2 Middle East & N. Africa 9 27 60.9 7 37 20 215 39 43 716 6 3.5 South Asia 7 33 31.9 6 106 16 245 26 44 1,053 4 5.0 Sub-Saharan Africa 10 46 111.4 7 97 17 273 41 39 662 5 3.4 High income 7 21 7.2 5 47 17 160 29 35 522 6 2.1 Euro area 7 17 5.9 6 69 14 190 44 31 591 6 1.4 280 2009 World Development Indicators STATES AND MARKETS Business environment: Doing Business indicators 5.3 About the data Definitions The economic health of a country is measured not only The Doing Business project encompasses two · Number of procedures for starting a business is in macroeconomic terms but also by other factors that types of data: data from readings of laws and regu- the number of procedures required to start a busi- shape daily economic activity such as laws, regula- lations and data on time and motion indicators that ness, including interactions to obtain necessary per- tions, and institutional arrangements. The Doing Busi- measure efficiency in achieving a regulatory goal. mits and licenses and to complete all inscriptions, ness indicators measure business regulation, gauge Within the time and motion indicators cost estimates verifications, and notifications to start operations for regulatory outcomes, and measure the extent of legal are recorded from official fee schedules where appli- businesses with specific characteristics of owner- protection of property, the flexibility of employment cable. The data from surveys are subjected to numer- ship, size, and type of production. · Time required regulation, and the tax burden on businesses. ous tests for robustness, which lead to revision or for starting a business is the number of calendar The table presents a subset of Doing Business expansion of the information collected. days to complete the procedures for legally operating indicators covering 7 of the 10 sets of indicators: The Doing Business methodology has limitations a business using the fastest procedure, independent starting a business, registering property, dealing with that should be considered when interpreting the of cost. · Cost for starting a business is normalized construction permits, employing workers, enforcing data. First, the data collected refer to businesses as a percentage of gross national income (GNI) per contracts, protecting investors, and closing a busi- in the economy's largest city and may not represent capita. · Number of procedures for registering prop- ness. Table 5.5 includes Doing Business measures regulations in other locations of the economy. To erty is the number of procedures required for a busi- of getting credit, and table 5.6 presents data on address this limitation, subnational indicators are ness to legally transfer property. · Time required for paying taxes. being collected for six economies, and data collec- registering property is the number of calendar days The fundamental premise of the Doing Business tion is under way in six more. These subnational stud- for a business to legally transfer property. · Number project is that economic activity requires good rules ies point to significant differences in the speed of of procedures for dealing with licenses to build a and regulations that are efficient, accessible to all reform and the ease of doing business across cities warehouse is the number of interactions of a com- who need to use them, and simple to implement. in the same economy. Second, the data often focus pany's employees or managers with external parties, Thus some Doing Business indicators give a higher on a specifi c business form--generally a limited including government staff, public inspectors, nota- score for more regulation, such as stricter disclosure liability company of a specified size--and may not ries, land registry and cadastre staff, and technical requirements in related-party transactions, and oth- represent regulation for other types of businesses experts apart from architects and engineers. · Time ers give a higher score for simplified regulations, such as sole proprietorships. Third, transactions required for dealing with construction permits to such as a one-stop shop for completing business described in a standardized business case refer to build a warehouse is the number of calendar days startup formalities. a specific set of issues and may not represent the to complete the required procedures for building a In constructing the indicators, it is assumed that full set of issues a business encounters. Fourth, the warehouse using the fastest procedure, independent entrepreneurs know about all regulations and comply time measures involve an element of judgment by the of cost. · Rigidity of employment index, a measure with them; in practice, entrepreneurs may not be expert respondents. When sources indicate different of employment regulation, is the average of three aware of all required procedures or may avoid legally estimates, the Doing Business time indicators repre- subindexes: a difficulty of hiring index, a rigidity of required procedures altogether. But where regula- sent the median values of several responses given hours index, and a difficulty of firing index. Higher tion is particularly onerous, levels of informality are under the assumptions of the standardized case. values indicate more rigid regulations. · Number of higher, which comes at a cost: firms in the informal Fifth, the methodology assumes that a business has procedures for enforcing contracts is the number sector usually grow more slowly, have less access full information on what is required and does not of independent actions, mandated by law or court to credit, and employ fewer workers--and those waste time when completing procedures. regulation, that demand interaction between the par- workers remain outside the protections of labor law. ties to a contract or between them and the judge or The indicators in the table can help policymakers court officer. · Time required for enforcing contracts understand the business environment in a country is the number of calendar days from the time of the and--along with information from other sources such filing of a lawsuit in court to the final determination as the World Bank's Enterprise Surveys--provide and payment. · Extent of disclosure index measures insights into potential areas of reform. the degree to which investors are protected through Doing Business data are collected with a stan- disclosure of ownership and financial information. dardized survey that uses a simple business case Higher values indicate more disclosure. · Time to to ensure comparability across economies and over resolve insolvency is the number of years from time time--with assumptions about the legal form of the of filing for insolvency in court until resolution of dis- business, its size, its location, and nature of its tressed assets and payment of creditors. operation. Surveys in 181 countries are administered through more than 6,700 local experts, including Data sources lawyers, business consultants, accountants, freight Data on the business environment are from forwarders, government officials, and other profes- the World Bank's Doing Business project (www. sionals who routinely administer or advise on legal doingbusiness.org). and regulatory requirements. 2009 World Development Indicators 281 5.4 Stock markets Market Market Turnover Listed domestic S&P/Global capitalization liquidity ratio companies Equity Indices Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2008 2000 2007 2000 2007 2000 2008 2000 2008 2007 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 166,068 52,309 58.4 33.0 2.1 3.1 4.8 19.3 127 107 0.7 ­56.2 Armenia 2 105 0.1 1.1 0.0 0.1 4.6 6.1 105 29 .. .. Australia 372,794 1,298,429 92.0 158.2 55.9 161.1 56.5 110.5 1,330 1,913 .. .. Austria 29,935 228,707 15.4 61.3 4.8 32.5 29.8 57.8 97 96 .. .. Azerbaijan 4 .. 0.1 .. .. .. .. .. 2 .. .. .. Bangladesh 1,186 6,671 2.5 9.9 1.6 7.0 74.4 137.3 221 290 126.4 a 4.3a Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 182,481 386,362 78.7 85.3 16.4 56.5 20.7 65.3 174 153 .. .. Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 1,742 2,263 20.7 17.2 0.8 0.0 0.1 0.0 26 37 .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 978 3,556 15.8 47.8 0.8 0.9 4.8 3.1 16 19 37.2a ­38.4 a Brazil 226,152 589,384 35.1 104.3 15.7 44.5 43.5 74.3 459 432 74.7 ­57.2 Bulgaria 617 8,858 4.9 55.1 0.5 13.9 9.2 10.8 503 334 39.0a ­70.2a Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 841,385 2,186,550 116.1 164.4 87.6 123.7 77.3 84.7 1,418 3,881 .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 60,401 132,428 80.3 129.9 8.1 27.1 9.4 21.2 258 235 22.6 ­41.2 China 580,991 2,793,613 48.5 194.2 60.2 243.1 158.3 121.3 1,086 1,604 66.6 ­52.7 Hong Kong, China 623,398 1,162,566 368.6 561.2 223.4 442.6 61.3 89.1 779 1,029 .. .. Colombia 9,560 87,032 10.2 49.1 0.4 5.0 3.8 13.2 126 96 12.7a .. Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2,924 2,035 18.3 7.7 0.7 0.2 12.0 3.1 21 12 .. .. Cote d'Ivoire 1,185 7,071 11.4 42.2 0.3 0.8 2.6 4.1 41 38 115.6a ­16.9a Croatia 2,742 26,790 14.9 128.7 1.0 8.0 7.4 7.4 64 376 68.1a ­59.3a Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 11,002 48,850 19.4 42.0 11.6 24.0 60.3 70.4 131 28 49.7 ­45.9 Denmark 107,666 277,746 67.3 89.1 57.2 77.7 86.0 99.1 225 201 .. .. Dominican Republic 141 .. 0.8 .. .. .. .. .. 6 .. .. .. Ecuador 704 4,562 4.4 9.6 0.1 0.7 5.5 3.6 30 38 3.8a ­8.8a Egypt, Arab Rep. 28,741 85,885 28.8 106.8 11.1 40.7 34.7 58.6 1,076 373 52.2 ­55.8 El Salvador 2,041 6,743 15.5 33.1 0.2 0.9 1.3 3.7 40 51 .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 1,846 1,951 32.8 28.9 5.8 10.0 18.9 23.2 23 18 ­15.5a ­65.5a Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 293,635 369,168 241.0 150.9 169.6 222.1 64.3 182.0 154 134 .. .. France 1,446,634 2,771,217 108.9 107.0 81.6 132.0 74.1 131.5 808 707 .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 24 1,389 0.8 13.7 0.1 0.4 .. 4.4 269 161 .. .. Germany 1,270,243 2,105,506 66.8 63.5 56.3 101.4 79.1 179.7 1,022 658 .. .. Ghana 502 3,394 10.1 15.7 0.2 0.7 1.5 5.2 22 35 21.6a ­10.4a Greece 110,839 264,942 88.3 84.6 75.7 48.4 63.7 64.0 329 292 .. .. Guatemala 240 .. 1.2 .. 0.0 .. 0.0 .. 44 .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 282 2009 World Development Indicators STATES AND MARKETS Market Market Stock markets Turnover Listed domestic 5.4 S&P/Global capitalization liquidity ratio companies Equity Indices Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2008 2000 2007 2000 2007 2000 2008 2000 2008 2007 2008 Honduras 458 .. 8.8 .. .. .. .. .. 46 .. .. .. Hungary 12,021 18,579 25.1 34.4 25.3 34.3 90.7 93.0 60 41 13.1 ­62.5 India 148,064 645,478 32.2 154.6 110.8 94.1 133.6 85.2 5,937 4,921 78.6 ­64.1 Indonesia 26,834 98,761 16.3 48.9 8.7 26.1 32.9 71.3 290 396 49.3 ­61.1 Iran, Islamic Rep. 7,350 45,574 7.3 15.9 1.1 2.9 12.7 19.7 304 329 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 81,882 144,026 85.0 55.6 15.0 52.7 19.2 88.9 76 57 .. .. Israel 64,081 134,463 51.8 144.2 18.9 69.2 36.3 60.2 654 630 34.3 ­33.1 Italy 768,364 1,072,692 70.0 51.0 70.9 110.1 104.0 220.4 291 301 .. .. Jamaica 3,582 7,513 44.6 107.9 0.9 3.1 2.5 3.6 46 39 0.3a ­38.2a Japan 3,157,222 4,453,475 67.6 101.6 57.7 148.2 69.9 141.6 2,561 3,844 ­5.2b ­39.7b Jordan 4,943 35,847 58.4 260.3 4.9 110.1 7.7 72.7 163 262 32.6a .. Kazakhstan 1,342 31,075 7.3 39.5 0.5 8.5 25.1 22.2 23 74 ­47.0a ­47.0a Kenya 1,283 10,917 10.1 55.3 0.4 5.4 3.6 11.8 57 53 11.8a ­40.3a Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 171,587 494,631 33.5 115.9 208.7 203.5 233.2 181.2 1,308 1,798 27.7 ­55.6 Kuwait 20,772 107,168 55.1 167.7 11.2 107.7 21.3 83.2 77 202 39.9a .. Kyrgyz Republic 4 121 0.3 3.2 1.7 3.7 .. 131.2 80 10 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 563 1,609 7.2 11.5 2.9 0.5 48.6 1.8 64 35 1.9a ­58.7a Lebanon 1,583 9,641 9.4 44.6 0.7 4.1 6.7 6.9 12 11 40.5a ­25.3a Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 1,588 3,625 13.9 26.4 1.8 2.7 14.8 59.9 54 41 14.3a ­73.0a Macedonia, FYR 7 2,715 0.2 35.4 3.3 6.6 6.6 26.5 1 38 .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. 587 .. 18.6 .. 0.5 13.8 3.5 .. 9 .. .. Malaysia 116,935 187,066 124.7 174.4 62.4 80.3 44.6 31.4 795 977 44.6 ­43.7 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania 1,090 .. 97.2 .. .. .. .. .. 40 .. .. .. Mauritius 1,331 3,443 29.8 83.5 1.7 5.4 5.0 8.9 40 41 94.0a ­49.2a Mexico 125,204 232,581 21.5 38.9 7.8 11.3 32.3 34.3 179 125 12.8 ­45.1 Moldova 392 .. 30.4 .. 1.9 2.3 5.8 .. 36 .. .. .. Mongolia 37 612 3.4 15.6 0.7 1.4 7.3 14.7 410 384 .. .. Morocco 10,899 65,748 29.4 100.5 3.0 35.0 9.2 31.4 53 77 45.3 ­17.0 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 311 619 9.1 10.0 0.6 0.3 4.5 2.8 13 7 39.4 a ­9.9a Nepal 790 4,909 14.4 47.6 0.6 2.2 6.9 6.9 110 144 .. .. Netherlands 640,456 956,469 166.3 124.9 175.9 235.5 101.4 207.8 234 226 .. .. New Zealand 18,866 47,454 37.1 35.0 21.2 16.0 45.9 46.9 142 154 .. .. Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 4,237 49,803 9.2 52.2 0.6 10.1 7.3 29.3 195 213 108.3a .. Norway 65,034 357,420 38.6 92.0 35.7 121.5 93.4 147.8 191 195 .. .. Oman 3,463 14,914 17.4 45.2 2.8 9.3 14.2 44.2 131 127 67.0a .. Pakistan 6,581 23,491 8.9 49.2 44.6 70.3 475.5 116.0 762 653 41.7a .. Panama 2,794 6,568 24.0 31.9 1.3 0.6 1.7 4.0 29 31 ­15.7a .. Papua New Guinea 1,520 6,632 49.3 118.9 0.0 0.4 .. 0.5 7 15 .. .. Paraguay 224 409 3.5 4.4 0.1 0.0 3.5 0.5 56 55 .. .. Peru 10,562 55,625 19.8 98.8 2.9 6.8 12.6 6.3 230 199 66.4 ­41.1 Philippines 25,957 52,101 34.2 71.7 10.8 20.3 15.8 22.2 228 244 36.0 ­53.7 Poland 31,279 90,233 18.3 49.1 8.5 20.0 49.9 45.7 225 349 23.2 ­57.8 Portugal 60,681 132,258 53.9 59.4 48.3 64.9 85.5 122.2 109 47 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 283 5.4 Stock markets Market Market Turnover Listed domestic S&P/Global capitalization liquidity ratio companies Equity Indices Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2008 2000 2007 2000 2007 2000 2008 2000 2008 2007 2008 Romania 1,069 19,923 2.9 27.1 0.6 4.9 23.1 11.3 5,555 1,824 32.8a ­72.2a Russian Federation 38,922 397,183 15.0 116.5 7.8 58.5 36.9 75.0 249 314 21.9 ­73.4 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 67,171 246,337 35.6 135.0 9.2 178.1 27.1 137.8 75 127 35.6 .. Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia 734 23,934 4.6 59.7 0.1 6.4 0.0 14.6 6 1,771 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 152,827 353,489 164.8 219.1 98.7 238.1 52.1 122.0 418 472 .. .. Slovak Republic 1,217 5,079 6.0 9.3 4.4 0.0 129.8 0.4 493 120 57.4 a ­36.0a Slovenia 2,547 11,772 12.8 61.4 2.3 5.8 20.7 6.9 38 84 95.0a ­66.9a Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 204,952 491,282 154.2 294.5 58.3 150.4 33.9 60.6 616 425 15.5 ­41.7 Spain 504,219 1,800,097 86.8 125.3 169.8 206.1 210.7 189.7 1,019 3,498 .. .. Sri Lanka 1,074 4,326 6.6 23.4 0.9 2.9 11.0 17.2 239 234 ­10.6a .. Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 73 203 4.9 7.0 0.0 0.0 9.8 0.0 6 6 .. .. Sweden 328,339 612,497 133.7 134.8 158.8 213.3 111.2 147.4 292 272 .. .. Switzerland 792,316 1,274,516 317.0 300.3 243.7 418.9 82.0 143.0 252 257 .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 233 541 2.6 3.8 0.4 0.1 2.4 2.1 4 7 .. .. Thailand 29,489 102,594 24.0 79.9 19.0 44.1 53.2 78.2 381 476 39.4 ­50.5 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 4,330 12,157 53.1 74.7 1.7 1.7 3.1 2.6 27 37 ­2.8 ­9.9 Tunisia 2,828 6,374 14.5 15.3 3.2 1.9 23.3 25.5 44 49 15.6a ­3.1a Turkey 69,659 117,930 26.1 43.7 67.1 46.1 206.2 118.5 315 284 74.8a ­62.4 a Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 35 116 0.6 1.2 0.0 0.1 .. 5.2 2 5 .. .. Ukraine 1,881 24,358 6.0 79.2 0.9 1.4 19.6 3.7 139 251 112.2a ­82.2a United Arab Emirates 5,727 97,852 8.1 84.8 0.2 69.2 3.9 89.8 54 96 52.1a .. United Kingdom 2,576,992 3,858,505 177.6 139.2 126.5 372.5 66.6 270.1 1,904 2,588 5.6c ­31.3c United States 15,104,037 19,947,284 154.7 145.1 326.3 309.9 200.8 216.5 7,524 5,130 3.5d ­38.5d Uruguay 161 159 0.8 0.7 0.0 0.1 0.5 12.0 16 8 .. .. Uzbekistan 32 715 0.2 4.2 0.1 0.4 .. 5.9 5 114 .. .. Venezuela, RB 8,128 8,251 6.9 4.5 0.6 0.4 8.9 1.3 85 60 79.0 .. Vietnam .. 9,589 .. 28.5 .. 18.3 .. 28.8 .. 171 10.7a ­68.2a West Bank and Gaza 765 2,475 18.6 111.1 4.6 52.2 10.0 31.3 24 35 .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 236 2,346 7.3 20.6 0.2 0.6 20.8 4.1 9 15 .. .. Zimbabwe 2,432 5,333 32.9 70.3 3.8 9.7 10.8 5.1 69 81 ­83.8a .. World 32,187,756 s ..e s 102.4 w 121.3 w 152.4 w 186.6 122.3 w ..e w 47,877 s ..e s Low income 18,702 110,935 7.9 40.5 14.7 24.8 .. 69.3 1,575 1,534 Middle income 1,964,730 6,475,916 36.5 117.0 33.8 94.3 78.5 78.2 20,998 15,300 Lower middle income 895,510 4,062,921 35.0 144.9 50.6 146.7 112.7 96.2 11,241 9,227 Upper middle income 1,069,220 2,412,995 37.9 88.5 18.3 40.6 48.6 61.0 9,757 6,073 Low & middle income 1,983,431 6,586,851 35.3 113.9 33.0 91.3 82.8 77.8 22,573 16,834 East Asia & Pacific 780,487 3,243,723 47.1 165.1 49.8 191.2 125.0 112.0 3,190 3,868 Europe & Central Asia 150,122 721,582 17.5 77.3 24.9 38.7 92.4 68.8 7,588 3,882 Latin America & Carib. 620,263 1,168,004 31.6 71.4 8.4 24.3 27.2 47.0 1,762 1,302 Middle East & N. Africa 57,110 203,494 19.9 56.1 5.1 18.8 12.4 28.7 1,676 772 South Asia 157,695 679,965 26.1 133.4 90.2 84.8 167.9 89.3 7,269 6,098 Sub-Saharan Africa 217,754 570,083 89.9 149.0 32.3 60.9 22.2 29.1 1,088 912 High income 30,204,325 49,648,498 116.9 123.8 178.2 217.8 130.5 180.5 25,304 29,505 Euro area 5,433,547 10,468,592 87.0 85.3 80.4 124.0 90.6 162.8 5,028 5,711 a. Refers to the S&P Frontier BMI index. b. Refers to the Nikkei 225 index. c. Refers to the FT 100 index. d. Refers to the S&P 500 index. e. Aggregates not presented because data for high-income economies are not available for 2008. 284 2009 World Development Indicators STATES AND MARKETS Stock markets 5.4 About the data Definitions The development of an economy's financial markets size of the stock market in U.S. dollars and as a · Market capitalization (also known as market is closely related to its overall development. Well percentage of GDP. The number of listed domestic value) is the share price times the number of shares functioning financial systems provide good and eas- companies is another measure of market size. Mar- outstanding. · Market liquidity is the total value ily accessible information. That lowers transaction ket size is positively correlated with the ability to of shares traded during the period divided by gross costs, which in turn improves resource allocation and mobilize capital and diversify risk. domestic product (GDP). This indicator complements boosts economic growth. Both banking systems and Market liquidity, the ability to easily buy and sell the market capitalization ratio by showing whether stock markets enhance growth, the main factor in securities, is measured by dividing the total value market size is matched by trading. · Turnover ratio poverty reduction. At low levels of economic develop- of shares traded by GDP. The turnover ratio--the is the total value of shares traded during the period ment commercial banks tend to dominate the finan- value of shares traded as a percentage of market divided by the average market capitalization for the cial system, while at higher levels domestic stock capitalization--is also a measure of liquidity as well period. Average market capitalization is calculated as markets tend to become more active and efficient as of transaction costs. (High turnover indicates low the average of the end-of-period values for the cur- relative to domestic banks. transaction costs.) The turnover ratio complements rent period and the previous period. · Listed domes- Open economies with sound macroeconomic poli- the ratio of value traded to GDP, because the turn- tic companies are the domestically incorporated cies, good legal systems, and shareholder protection over ratio is related to the size of the market and the companies listed on the country's stock exchanges attract capital and therefore have larger financial mar- value traded ratio to the size of the economy. A small, at the end of the year. This indicator does not include kets. Recent research on stock market development liquid market will have a high turnover ratio but a low investment companies, mutual funds, or other col- shows that modern communications technology and value of shares traded ratio. Liquidity is an impor- lective investment vehicles. · S&P/Global Equity increased financial integration have resulted in more tant attribute of stock markets because, in theory, Indices measure the U.S. dollar price change in the cross-border capital flows, a stronger presence of liquid markets improve the allocation of capital and stock markets. financial firms around the world, and the migration of enhance prospects for long-term economic growth. stock exchange activities to international exchanges. A more comprehensive measure of liquidity would Many firms in emerging markets now cross-list on include trading costs and the time and uncertainty international exchanges, which provides them with in finding a counterpart in settling trades. lower cost capital and more liquidity-traded shares. The S&P/EMDB, the source for all the data in the However, this also means that exchanges in emerg- table, provides regular updates on 58 emerging ing markets may not have enough financial activity stock markets and 35 frontier markets. Standard to sustain them, putting pressure on them to rethink & Poor's maintains a series of indexes for investors their operations. interested in investing in stock markets in developing The stock market indicators in the table are from countries. The S&P/IFCI index, Standard & Poor's Standard & Poor's Emerging Markets Data Base. The leading emerging markets index, is designed to be indicators include measures of size (market capital- sufficiently investable to support index tracking port- ization, number of listed domestic companies) and folios in emerging market stocks that are legally and liquidity (value of shares traded as a percentage of practically open to foreign portfolio investment. The gross domestic product, value of shares traded as a S&P/Frontier BMI measures the performance of 35 percentage of market capitalization). The comparabil- small and illiquid markets. The individual country indi- ity of such indicators across countries may be limited ces include all publicly listed equities representing by conceptual and statistical weaknesses, such as an aggregate of at least 80 percent or more of mar- inaccurate reporting and differences in accounting ket capitalization in each market. These indexes are standards. The percentage change in stock market widely used benchmarks for international portfolio prices in U.S. dollars are from Standard & Poor's management. See www.standardandpoors.com for Global Equity Indices (S&P IFCI) and Standard & further information on the indexes. Poor's Frontier Broad Market Index (BMI) and is an Because markets included in Standard & Poor's Data sources important measure of overall performance. Regula- emerging markets category vary widely in level of Data on stock markets are from Standard & Poor's tory and institutional factors that can affect investor development, it is best to look at the entire category Global Stock Markets Factbook 2008, which draws confidence, such as entry and exit restrictions, the to identify the most significant market trends. And on the Emerging Markets Data Base, supple- existence of a securities and exchange commission, it is useful to remember that stock market trends mented by other data from Standard & Poor's. and the quality of laws to protect investors, may influ- may be distorted by currency conversions, espe- The firm collects data through an annual survey ence the functioning of stock markets but are not cially when a currency has registered a significant of the world's stock exchanges, supplemented by included in the table. devaluation. information provided by its network of correspon- Stock market size can be measured in various About the data is based on Demirgüç-Kunt and dents and by Reuters. Data on GDP are from the ways, and each may produce a different ranking of Levine (1996), Beck and Levine (2001), and Claes- World Bank's national accounts data files. countries. Market capitalization shows the overall sens, Klingebiel, and Schmukler (2002). 2009 World Development Indicators 285 5.5 Financial access, stability, and efficiency Getting Bank Ratio of bank Domestic Interest Risk premium credit capital to nonperforming credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Strength of Depth of Lending Prime lending legal rights credit % of adult population rate minus rate minus index information Public Private deposit rate treasury bill rate 0­10 (weak index credit registry credit bureau percentage percentage to strong) 0­6 (low to high) coverage coverage % % % of GDP points points June 2008 June 2008 June 2008 June 2008 2007 2007 2007 2007 2007 Afghanistan 1 0 0.0 0.0 .. .. ­1.6 .. .. Albania 9 4 8.3 0.0 5.8 3.4 62.1 8.4 8.2 Algeria 3 2 0.2 0.0 .. .. ­3.5 6.3 7.0 Angola 4 4 2.7 0.0 .. .. 1.9 10.9 .. Argentina 4 6 31.2 100.0 13.1 2.7 28.5 3.1 .. Armenia 7 5 2.6 24.4 22.5 2.4 12.1 11.3 11.4 Australia 9 5 0.0 100.0 4.6 0.2 141.8 5.4 .. Austria 7 6 1.3 40.9 6.5 2.1 125.8 .. .. Azerbaijan 8 5 3.1 0.0 14.2 7.2 18.2 7.6 8.5 Bangladesh 8 2 0.9 0.0 6.5 14.0 58.2 6.8 .. Belarus 2 5 2.4 0.0 15.9 0.7 27.2 0.3 .. Belgium 7 4 57.7 0.0 4.3 1.2 113.4 .. 4.8 Benin 3 1 10.5 0.0 .. .. 8.9 .. .. Bolivia 1 6 11.9 29.7 9.6 5.6 53.5 9.3 6.8 Bosnia and Herzegovina 5 5 0.0 69.2 13.1 3.0 54.7 3.6 .. Botswana 7 4 0.0 52.9 .. .. ­16.4 7.6 .. Brazil 3 5 20.2 62.2 9.9 3.0 95.9 33.1 32.2 Bulgaria 8 6 30.7 5.0 7.7 2.1 59.2 6.3 6.2 Burkina Faso 3 1 1.9 0.0 .. .. 12.4 .. .. Burundi 2 1 0.3 0.0 .. .. 38.5 .. 8.2 Cambodia 9 0 0.0 0.0 .. .. 12.9 14.6 .. Cameroon 3 2 4.9 0.0 .. .. 5.8 10.8 .. Canada 6 6 0.0 100.0 5.5 0.7 165.1 4.0 2.0 Central African Republic 3 2 1.2 0.0 .. .. 17.4 10.8 .. Chad 3 1 0.6 0.0 .. .. 1.1 10.8 .. Chile 4 5 28.1 34.5 6.7 0.8 90.0 3.1 .. China 6 4 58.8 0.0 5.5 6.7 132.0 3.3 .. Hong Kong, China 10 5 0.0 69.9 12.0 0.9 125.3 4.3 4.8 Colombia 5 5 0.0 42.5 11.4 3.2 41.6 7.4 .. Congo, Dem. Rep. 3 0 0.0 0.0 .. .. 5.6 .. .. Congo, Rep. 3 2 6.9 0.0 .. .. ­10.1 10.8 .. Costa Rica 5 5 5.9 51.6 10.7 1.2 47.4 6.4 .. Côte d'Ivoire 3 1 2.9 0.0 .. .. 20.7 .. .. Croatia 6 3 0.0 71.8 12.5 4.8 82.9 7.0 .. Cuba .. .. .. .. .. .. .. .. .. Czech Republic 6 5 4.6 65.2 6.0 2.6 52.9 4.5 2.2 Denmark 9 4 0.0 5.0 6.1 0.6 205.1 .. .. Dominican Republic 3 6 33.9 35.0 9.5 4.0 54.0 8.9 .. Ecuador 3 5 37.7 46.8 8.5 2.9 18.7 7.1 .. Egypt, Arab Rep. 3 5 2.2 4.7 5.1 24.7 89.5 6.4 5.7 El Salvador 5 6 18.4 83.0 11.8 2.1 46.3 .. .. Eritrea 2 0 0.0 0.0 .. .. 124.5 .. .. Estonia 6 5 0.0 20.6 8.6 0.5 95.1 2.1 .. Ethiopia 4 2 0.1 0.0 .. .. 47.2 3.4 6.9 Finland 7 5 0.0 14.8 9.2 0.3 85.6 .. .. France 7 4 28.3 0.0 5.5 2.7 122.0 .. .. Gabon 3 2 20.7 0.0 7.0 7.6 2.9 10.8 .. Gambia, The 5 0 0.0 0.0 .. .. 23.9 15.0 .. Georgia 6 6 0.0 4.5 20.4 2.6 31.6 10.9 .. Germany 7 6 0.7 98.4 4.3 3.4 124.9 .. .. Ghana 7 0 0.0 0.0 11.8 6.4 32.9 .. .. Greece 3 4 0.0 39.0 6.6 4.5 108.6 .. .. Guatemala 7 5 16.1 19.7 9.2 5.8 41.4 8.1 .. Guinea 3 0 0.0 0.0 .. .. 16.0 .. .. Guinea-Bissau 3 1 1.0 0.0 .. .. 12.1 .. .. Haiti 2 2 0.7 0.0 .. .. 22.8 45.5 32.7 286 2009 World Development Indicators STATES AND MARKETS Financial access, stability, and efficiency Getting Bank Ratio of bank Domestic 5.5 Interest Risk premium credit capital to nonperforming credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Strength of Depth of Lending Prime lending legal rights credit % of adult population rate minus rate minus index information Public Private deposit rate treasury bill rate 0­10 (weak index credit registry credit bureau percentage percentage to strong) 0­6 (low to high) coverage coverage % % % of GDP points points June 2008 June 2008 June 2008 June 2008 2007 2007 2007 2007 2007 Honduras 6 6 11.3 60.5 8.4 6.6 51.0 8.8 .. Hungary 7 5 0.0 10.0 8.3 2.4 74.4 2.3 1.4 India 8 4 0.0 10.5 6.4 2.5 64.2 .. .. Indonesia 3 4 26.1 0.0 10.0 9.3 40.5 5.9 .. Iran, Islamic Rep. 5 3 21.7 0.0 .. .. 50.5 0.4 .. Iraq 3 0 0.0 0.0 .. .. .. 8.4 ­1.3 Ireland 8 5 0.0 100.0 4.5 0.7 195.7 2.6 .. Israel 9 5 0.0 91.0 6.2 1.7 74.8 2.8 1.9 Italy 3 5 11.8 74.9 7.7 4.8 129.7 .. 2.3 Jamaica 8 0 0.0 0.0 8.7 2.6 63.9 10.1 4.6 Japan 7 6 0.0 76.2 5.0 1.5 294.1 1.1 1.3 Jordan 4 2 1.0 0.0 6.7 4.1 123.5 3.2 .. Kazakhstan 5 6 0.0 25.6 15.2 2.7 41.0 .. .. Kenya 10 4 0.0 2.1 12.4 22.7 37.6 8.2 6.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. 7 6 0.0 90.4 9.0 0.7 110.2 1.4 .. Kuwait 4 4 0.0 31.2 12.0 3.2 72.5 3.1 5.5 Kyrgyz Republic 7 5 0.0 3.7 .. .. 14.2 19.9 20.4 Lao PDR 4 0 0.0 0.0 .. .. 6.8 23.5 9.7 Latvia 9 4 3.7 0.0 7.9 0.4 94.8 4.8 6.7 Lebanon 3 5 6.8 0.0 8.1 10.1 186.9 2.3 5.0 Lesotho 8 0 0.0 0.0 14.6 3.0 ­18.4 7.7 6.3 Liberia 4 1 0.3 0.0 .. .. 160.6 11.3 .. Libya .. .. .. .. .. .. ­66.3 3.5 .. Lithuania 5 6 8.9 7.2 7.4 1.0 61.1 1.5 2.6 Macedonia, FYR 7 4 6.5 0.0 .. 9.1 35.5 5.4 .. Madagascar 2 0 0.1 0.0 .. 10.1 9.9 28.5 33.2 Malawi 8 0 0.0 0.0 .. .. 16.1 21.7 13.8 Malaysia 10 6 52.9 .. 7.5 6.6 113.4 3.2 3.0 Mali 3 1 4.1 0.0 .. .. 14.9 .. .. Mauritania 3 1 0.2 0.0 .. .. .. 15.5 13.1 Mauritius 5 3 20.6 0.0 .. .. 110.5 10.1 .. Mexico 4 6 0.0 70.8 14.4 2.5 37.7 4.4 0.4 Moldova 8 0 0.0 0.0 17.3 3.7 40.2 3.8 5.7 Mongolia 6 3 22.7 0.0 .. .. 30.1 4.1 .. Morocco 3 2 2.4 0.0 6.9 7.9 90.1 .. .. Mozambique 2 4 1.9 0.0 6.4 2.6 10.4 7.7 4.4 Myanmar .. .. .. .. .. .. 28.1 5.0 .. Namibia 8 5 0.0 59.6 7.9 2.8 63.7 5.3 4.3 Nepal 5 2 0.0 0.2 .. .. 49.3 5.8 4.4 Netherlands 6 5 0.0 81.0 3.3 0.8 204.4 0.7 .. New Zealand 9 5 0.0 100.0 .. .. 144.4 5.0 5.3 Nicaragua 3 5 13.4 100.0 8.8 8.0 73.5 7.0 .. Niger 3 1 0.9 0.0 .. .. 7.1 .. .. Nigeria 8 0 0.1 0.0 16.3 8.4 20.2 6.7 10.1 Norway 7 4 0.0 100.0 5.0 0.6 .. 1.8 .. Oman 4 2 23.4 0.0 13.2 3.2 28.7 3.1 .. Pakistan 6 4 4.9 1.5 10.2 8.4 45.7 6.5 2.8 Panama 6 6 0.0 43.7 13.7 1.3 89.3 3.5 .. Papua New Guinea 5 0 0.0 0.0 .. .. 22.8 8.7 5.1 Paraguay 3 6 9.7 48.6 11.6 1.3 19.5 20.0 .. Peru 7 6 23.7 33.2 8.8 1.3 16.2 19.6 .. Philippines 3 3 0.0 5.4 11.7 5.8 46.0 5.0 5.3 Poland 8 4 0.0 50.0 7.4 3.1 46.6 3.3 1.3 Portugal 3 4 76.4 11.3 6.2 0.8 172.3 .. .. Puerto Rico 8 5 0.0 61.4 .. .. .. .. .. 2009 World Development Indicators 287 5.5 Financial access, stability, and efficiency Getting Bank Ratio of bank Domestic Interest Risk premium credit capital to nonperforming credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Strength of Depth of Lending Prime lending legal rights credit % of adult population rate minus rate minus index information Public Private deposit rate treasury bill rate 0­10 (weak index credit registry credit bureau percentage percentage to strong) 0­6 (low to high) coverage coverage % % % of GDP points points June 2008 June 2008 June 2008 June 2008 2007 2007 2007 2007 2007 Romania 8 5 4.5 24.7 7.3 9.7 35.7 6.6 6.2 Russian Federation 3 4 0.0 10.0 13.3 2.5 25.7 4.9 .. Rwanda 2 2 0.3 0.0 9.2 27.2 8.7 9.1 8.6 Saudi Arabia 4 6 0.0 14.1 9.9 2.1 17.5 .. .. Senegal 3 1 4.4 0.0 10.4 18.6 24.8 .. .. Serbia 7 5 0.0 91.9 17.1 3.8 30.8 7.1 6.7 Sierra Leone 4 0 0.0 0.0 17.7 31.7 9.8 15.0 6.6 Singapore 10 4 0.0 48.3 9.3 1.8 80.7 4.8 3.0 Slovak Republic 9 4 1.4 39.9 10.6 2.5 51.5 4.3 .. Slovenia 6 2 2.7 0.0 8.4 2.5 82.0 2.3 2.0 Somalia .. .. .. .. .. .. .. .. .. South Africa 9 6 0.0 64.8 7.9 1.4 198.1 4.0 4.1 Spain 6 5 45.8 8.1 7.0 0.7 192.5 .. .. Sri Lanka 4 5 0.0 8.7 .. 9.6 45.0 8.0 0.5 Sudan 5 0 0.0 0.0 .. .. 0.2 .. .. Swaziland 6 5 0.0 43.5 22.9 4.0 6.8 6.1 4.1 Sweden 5 4 0.0 100.0 4.0 0.5 131.7 2.5 1.6 Switzerland 8 5 0.0 22.5 4.9 0.3 189.7 1.0 1.0 Syrian Arab Republic 1 0 0.0 0.0 .. .. 38.8 1.8 .. Tajikistan 2 0 0.0 0.0 .. .. 15.4 14.4 .. Tanzania 8 0 0.0 0.0 .. .. 14.1 7.3 2.6 Thailand 4 5 0.0 31.8 9.5 7.9 104.9 4.2 3.6 Timor-Leste 1 0 0.0 0.0 .. .. ­29.9 14.3 .. Togo 3 1 2.6 0.0 .. .. 22.0 .. .. Trinidad and Tobago 8 4 0.0 37.6 .. .. 26.2 5.9 4.8 Tunisia 3 5 14.9 0.0 7.7 17.3 71.5 .. .. Turkey 4 5 12.7 26.3 13.0 3.6 48.7 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. Uganda 7 0 0.0 0.0 10.3 4.1 5.2 9.8 10.1 Ukraine 9 3 0.0 3.0 12.5 13.2 61.7 5.8 .. United Arab Emirates 4 5 6.5 7.7 12.6 6.3 66.6 .. .. United Kingdom 9 6 0.0 100.0 8.9 0.9 190.5 .. 0.0 United States 8 6 0.0 100.0 10.3 1.4 240.6 .. 3.6 Uruguay 5 6 15.4 98.0 10.5 1.1 24.8 6.6 1.8 Uzbekistan 3 3 2.3 2.2 .. .. .. .. .. Venezuela, RB 3 0 0.0 0.0 8.3 1.2 22.7 6.4 .. Vietnam 7 4 13.4 0.0 .. .. 96.2 3.7 7.0 West Bank and Gaza 0 3 7.8 0.0 .. .. 8.3 4.8 .. Yemen, Rep. 2 0 0.1 0.0 .. .. 9.9 5.0 2.1 Zambia 9 0 0.0 0.1 .. 10.8 16.6 9.7 6.9 Zimbabwe 8 0 0.0 0.0 .. .. 93.1 457.5 330.2 World 5.3 u 2.9 u 5.6 u 20.9 u 8.9 m 2.7 m 162.5 w 6.5 m Low income 4.4 1.0 1.2 0.2 .. .. 36.9 10.3 Middle income 5.2 3.2 7.6 19.1 9.6 3.0 75.0 6.6 Lower middle income 4.7 3.0 7.4 14.4 9.5 3.9 90.5 7.3 Upper middle income 5.9 3.4 7.9 25.9 9.9 2.5 58.7 6.4 Low & middle income 4.9 2.4 5.4 12.6 9.7 3.5 73.2 7.1 East Asia & Pacific 5.4 1.7 8.7 4.2 .. .. 116.3 5.9 Europe & Central Asia 6.3 4.1 4.9 18.2 13.0 3.1 38.8 6.3 Latin America & Carib. 5.2 3.5 9.9 34.3 10.2 2.3 59.9 7.0 Middle East & N. Africa 2.6 2.3 4.8 0.4 .. .. 47.8 4.3 South Asia 4.8 2.1 0.7 2.6 6.5 8.4 61.5 6.7 Sub-Saharan Africa 4.5 1.4 2.5 5.0 .. .. 80.6 10.0 High income 6.5 4.2 6.0 45.7 6.5 1.4 194.8 4.0 Euro area 5.9 4.2 17.3 36.0 6.4 1.9 138.7 .. 288 2009 World Development Indicators STATES AND MARKETS Financial access, stability, and efficiency 5.5 About the data Definitions Financial sector development has positive impacts The size and mobility of international capital · Strength of legal rights index measures the degree on economic growth and poverty. The size of the flows make it increasingly important to monitor the to which collateral and bankruptcy laws protect the sector determines the resources mobilized for invest- strength of financial systems. Robust financial sys- rights of borrowers and lenders and thus facilitate ment. Access to finance can expand opportunities for tems can increase economic activity and welfare, lending. Higher values indicate that the laws are bet- all with higher levels of access and use of banking but instability in the financial system can disrupt ter designed to expand access to credit. · Depth of services associated with lower financing obstacles financial activity and impose widespread costs on credit information index measures rules affecting the for people and businesses. A stable financial sys- the economy. The ratio of bank capital to assets, scope, accessibility, and quality of information avail- tem that promotes efficient savings and investment a measure of bank solvency and resiliency, shows able through public or private credit registries. Higher is also crucial for a thriving democracy and market the extent to which banks can deal with unexpected values indicate the availability of more credit informa- economy. The banking system is the largest sector losses. Capital includes tier 1 capital (paid-up shares tion. · Public credit registry coverage is the number in the financial system in most countries, so most and common stock), a common feature in all coun- of individuals and firms listed in a public credit reg- indicators in the table cover the banking system. tries' banking systems, and total regulatory capital, istry with current information on repayment history, There are several aspects of access to financial which includes several types of subordinated debt unpaid debts, or credit outstanding as a percentage services: availability, cost, and quality of services. instruments that need not be repaid if the funds of the adult population. · Private credit bureau cov- The development and growth of credit markets are required to maintain minimum capital levels erage is the number of individuals or firms listed by depend on access to timely, reliable, and accurate (tier 2 and tier 3 capital). Total assets include all a private credit bureau with current information on data on borrowers' credit experiences. For secured nonfinancial and financial assets. Data are from repayment history, unpaid debts, or credit outstand- transactions, such as mortgages or vehicle loans, internally consistent financial statements. ing as a percentage of the adult population. · Bank rapid access to information in property registries is The ratio of bank nonperforming loans to total gross capital to asset ratio is the ratio of bank capital and also vital, and for small business loans corporate loans, a measure of bank health and efficiency, helps reserves to total assets. Capital and reserves include registry data are needed. Access to credit can be to identify problems with asset quality in the loan port- funds contributed by owners, retained earnings, gen- improved by increasing information about potential folio. A high ratio may signal deterioration of the credit eral and special reserves, provisions, and valuation borrowers' creditworthiness and making it easy to portfolio. International guidelines recommend that adjustments. · Ratio of bank nonperforming loans to create and enforce collateral agreements. Lenders loans be classified as nonperforming when payments total gross loans is the value of nonperforming loans look at a borrower's credit history and collateral. of principal and interest are 90 days or more past divided by the total value of the loan portfolio (includ- Where credit registries and effective collateral laws due or when future payments are not expected to be ing nonperforming loans before the deduction of loan are absent--as in many developing countries-- received in full. See the International Monetary Fund's loss provisions). The amount recorded as nonperform- banks make fewer loans. Indicators that cover finan- (IMF) Global Financial Stability Report for details. ing should be the gross value of the loan as recorded cial access, or getting credit, include the strength Domestic credit by the banking sector as a share of on the balance sheet, not just the amount overdue. of legal rights index (ranges from 0, weak, to 10, GDP is a measure of banking sector depth and finan- · Domestic credit provided by banking sector is all strong), depth of credit information index (ranges cial sector development in terms of size. In a few coun- credit to various sectors on a gross basis, except to from 0, low, to 6, high), public registry coverage, and tries governments may hold international reserves the central government, which is net. The banking private bureau coverage. as deposits in the banking system rather than in the sector includes monetary authorities, deposit money The strength of legal rights index is based on eight central bank. Since the claims on the central govern- banks, and other banking institutions for which data aspects related to legal rights in collateral law and ment are a net item (claims on the central govern- are available. · Interest rate spread is the interest two aspects in bankruptcy law. The methodology for ment minus central government deposits), this net rate charged by banks on loans to prime customers the index in this edition includes three improvements. figure may be negative, resulting in a negative figure of minus the interest rate paid by commercial or similar First, a standardized case scenario with assumptions domestic credit provided by the banking sector. banks for demand, time, or savings deposits. · Risk was introduced to bring the indicator in line with The interest rate spread--the margin between premium on lending is the interest rate charged by other Doing Business indicators. Second, the indica- the cost of mobilizing liabilities and the earnings on banks on loans to prime private sector customers tor focuses on revolving movable collateral, such as assets--is a measure of financial sector efficiency in minus the "risk-free" treasury bill interest rate at accounts receivable and inventory, rather than tangi- intermediation. A narrow interest rate spread means which short-term government securities are issued ble movable collateral, such as equipment. Third, the low transaction costs, which lowers the cost of funds or traded in the market. indicator no longer considers whether management for investment, crucial to economic growth. remains in place during reorganization, thus better The risk premium on lending is the spread between Data sources accommodating economies that adopt reorganization the lending rate to the private sector and the "risk- Data on getting credit are from the World Bank's procedures similar to U.S. Chapter 11 reorganization free" government rate. Spreads are expressed as Doing Business project (www.doingbusiness.org). or redressement procedures in civil law systems. The annual averages. A small spread indicates that the Data on bank capital and nonperforming loans are depth of credit information index assesses six fea- market considers its best corporate customers to be from the IMF's Global Financial Stability Report. tures of the public registry or the private credit bureau low risk. A negative rate indicates that the market Data on credit and interest rates are from the (or both). For more information on these indexes, see considers its best corporate clients to be lower risk IMF's International Financial Statistics. www.doingbusiness.org/MethodologySurveys/. than the government. 2009 World Development Indicators 289 5.6 Tax policies Tax revenue collected Taxes payable Highest marginal by central government by businesses tax ratea Time to prepare, Individual Number file, and pay taxes Total tax rate On income Corporate % of GDP of payments hours % of profi t % over $ % 2000 2007 June 2008 June 2008 June 2008 2006­08b 2006­08b 2006­08b Afghanistanc .. 5.8 8 275 36.4 .. .. .. Albaniac 16.1 .. 44 244 50.5 20 2,413 10 Algeriac 36.9 29.6 34 451 74.2 .. .. .. Angola .. .. 31 272 53.2 .. .. .. Argentina 9.8 .. 9 453 108.1 35 38,339 35 Armeniac .. 16 50 958 36.6 .. .. .. Australia 23.7 25.1 12 107 50.3 45 104,167 30 Austria 19.6 20.1 22 170 54.5 50 69,473 25 Azerbaijanc 12.7 .. 23 376 41.1 35 13,793 22 Bangladeshc 7.6 8 21 302 39.5 .. .. .. Belarusc 16.6 24 112 1,188 117.5 .. .. .. Belgium 27.4 25.8 11 156 58.1 50 47,048 33 Beninc 15.5 16.3 55 270 73.2 35 .. 38 Bolivia 13.2 17 41 1,080 78.1 13 .. 25 Bosnia and Herzegovina .. 22.3 51 428 44.1 15 .. 30 Botswanac .. .. 19 140 17.1 25 19,967 15 Brazilc 11.3 .. 11 2,600 69.4 28 13,462 15 Bulgariac 18.3 24.6 17 616 34.9 24 5,414 10 Burkina Faso .. 12 45 270 44.6 .. .. .. Burundic 13.6 .. 32 140 278.7 .. .. .. Cambodia 8.2 8.2 27 137 22.6 20 36,973 20 Cameroonc 11.2 .. 41 1,400 51.4 .. .. .. Canadac 15 15.2 9 119 45.4 29 100,970 38 Central African Republicc .. .. 54 504 203.8 .. .. .. Chad .. .. 54 122 60.5 .. .. .. Chile 16.7 21.5 10 316 25.9 40 9,722 35 Chinac 6.8 9.4 9 504 79.9 45 175,695 25 Hong Kong, China .. .. 4 80 24.2 17 15,484 18 Colombia 11.7 13.6 31 256 78.4 22 45,564 33 Congo, Dem. Rep.c 3.5 .. 32 308 229.8 50 4,631 40 Congo, Rep. 9.2 6.2 61 606 65.5 .. 4,681 .. Costa Ricac 12.1 15.2 43 282 55.7 15 14,575 30 Côte d'Ivoirec 14.6 15.5 66 270 45.4 10 5,386 35 Croatiac 26.2 23.4 17 196 32.5 45 4,014 .. Cuba .. .. .. .. .. .. .. .. Czech Republicc 15.4 15.1 12 930 48.6 15 53,482 21 Denmark 31 35.5 9 135 29.9 59 63,598 25 Dominican Republicc .. 16.6 9 480 35.7 30 26,209 30 Ecuadorc .. .. 8 600 34.9 25 62,800 25 Egypt, Arab Rep.c 13.4 15.4 29 711 46.1 20 .. 20 El Salvador 10.7 14 53 320 34.9 .. 13,600 25 Eritrea .. .. 18 216 84.5 .. .. .. Estonia 15.9 17.2 10 81 48.6 21 .. 21 Ethiopiac 10.2 .. 20 198 31.1 35 .. 30 Finland 24.6 21.8 20 269 47.8 32 84,457 26 France 23.2 21.8 11 132 65.4 .. .. 33 Gabon .. .. 26 272 44.7 .. .. .. Gambia, Thec .. .. 50 376 292.4 .. .. .. Georgiac 7.7 17.7 30 387 38.6 12 .. 20 Germany 11.9 11.8 16 196 50.5 45 340,553 15 Ghanac 17.2 22.6 33 224 32.7 25 10,435 22 Greece 23.3 19.9 10 224 47.4 40 102,650 25 Guatemalac 10.1 11.9 39 344 36.5 31 38,714 31 Guineac 11.1 .. 56 416 49.9 .. .. .. Guinea-Bissau .. .. 46 208 45.9 .. .. .. Haiti .. .. 42 160 40.1 .. .. .. 290 2009 World Development Indicators STATES AND MARKETS Tax revenue collected Taxes payable Tax policies 5.6 Highest marginal by central government by businesses tax ratea Time to prepare, Individual Number file, and pay taxes Total tax rate On income Corporate % of GDP of payments hours % of profi t % over $ % 2000 2007 June 2008 June 2008 June 2008 2006­08b 2006­08b 2006­08b Honduras .. 16.4 47 224 49.3 25 26,455 25 Hungary 21.9 21.5 14 330 57.5 36 .. 16 Indiac 9 11.6 60 271 71.5 30 6,344 30 Indonesiac 11.6 .. 51 266 37.3 35 22,173 30 Iran, Islamic Rep.c 6.3 7.3 22 344 44.2 35 9,183 25 Iraq .. .. 13 312 24.7 .. .. .. Ireland 26.1 25.6 9 76 28.8 42 43,591 13 Israel 29 28.4 33 230 33.9 49 111,695 27 Italy 23.2 23.3 15 334 73.3 43 102,166 28 Jamaicac 24.7 28.9 72 414 51.3 .. .. .. Japan .. .. 13 355 55.4 40 157,895 30 Jordanc 19 26.7 26 101 31.1 .. .. .. Kazakhstanc 10.2 12.3 9 271 36.4 20 .. 30 Kenyac 16.8 18 41 417 50.9 .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep.c 16.1 17.9 14 250 33.7 35 69,980 25 Kuwait 1.3 0.9 14 118 14.4 0 .. .. Kyrgyz Republicc 11.7 16.6 75 202 61.4 .. .. .. Lao PDR .. 10.5 34 560 33.7 .. .. .. Latviac 14.2 16.6 7 279 33.0 25 .. 15 Lebanon 12.2 15.2 19 180 36.0 .. .. .. Lesothoc 32.7 56.8 21 324 18.0 .. .. .. Liberia .. .. 32 158 35.8 .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 14.6 18.1 15 166 46.4 27 .. 15 Macedonia, FYRc .. .. 40 75 18.4 24 17,283 15 Madagascar 11.3 11.4 25 238 42.8 .. .. .. Malawi .. .. 19 292 31.4 .. .. .. Malaysiac 13.8 .. 12 145 34.5 28 72,254 26 Mali 13.2 15.5 58 270 51.4 .. .. .. Mauritania .. .. 38 696 98.7 .. .. .. Mauritiusc 18.2 17.6 7 161 22.2 15 .. 15 Mexicoc 11.7 .. 27 549 51.5 28 9,496 28 Moldovac 14.7 20.6 53 234 42.1 18 2,423 0 Mongolia .. 25.3 42 204 30.3 .. .. .. Moroccoc 19.9 25.2 28 358 44.6 .. .. .. Mozambique .. .. 37 230 34.3 32 38,814 32 Myanmar 3 4.7 .. .. .. .. .. .. Namibiac 30 .. 37 375 25.3 35 28,694 35 Nepalc 8.7 9.8 34 408 34.1 .. .. .. Netherlands 22.3 23.9 9 180 39.1 52 72,631 26 New Zealand 29.5 31.4 8 70 35.6 39 40,463 30 Nicaraguac 13.8 18 64 240 63.2 30 26,455 30 Niger .. 11.7 42 270 42.3 .. .. .. Nigeria .. .. 35 938 32.2 .. .. .. Norway 27.4 28.9 4 87 41.6 .. 120,148 28 Omanc 7.2 .. 14 62 21.6 0 .. .. Pakistanc 10.1 9.8 47 560 28.9 35 11,490 37 Panamac 10.2 .. 59 482 50.6 27 30,000 30 Papua New Guineac 19 .. 33 194 41.7 .. .. .. Paraguayc .. 11.5 35 328 35.0 8 .. 10 Peruc 12.2 15.6 9 424 41.2 30 62,100 30 Philippinesc 13.7 14 47 195 50.8 32 12,077 35 Poland 16 18.4 40 418 40.2 40 35,052 19 Portugal 21.5 22.6 8 328 43.6 42 85,202 25 Puerto Rico .. .. 16 218 64.7 33 50,000 19 2009 World Development Indicators 291 5.6 Tax policies Tax revenue collected Taxes payable Highest marginal by central government by businesses tax ratea Time to prepare, Individual Number file, and pay taxes Total tax rate On income Corporate % of GDP of payments hours % of profi t % over $ % 2000 2007 June 2008 June 2008 June 2008 2006­08b 2006­08b 2006­08b Romania 11.7 12 113 202 48.0 16 .. 16 Russian Federation 13.6 16.7 22 448 48.7 13 .. 24 Rwandac .. .. 34 160 33.7 .. .. .. Saudi Arabia .. .. 14 79 14.5 0 .. .. Senegalc 16.1 .. 59 666 46.0 .. .. .. Serbiac .. .. 66 279 34.0 15 .. 10 Sierra Leonec 10.2 .. 28 399 233.5 .. .. .. Singaporec 15.4 14.4 5 84 27.9 20 222,222 18 Slovak Republic .. 14.1 31 325 47.4 19 .. 19 Sloveniac 20.6 21 22 260 36.7 41 19,582 22 Somalia .. .. .. .. .. .. .. .. South Africa 24 29.1 9 200 34.2 40 52,688 28 Spain 16.2 13.9 8 234 60.2 27 72,752 30 Sri Lankac 14.5 14.2 62 256 63.7 35 4,419 35 Sudanc 6.4 .. 42 180 31.6 .. .. .. Swazilandc .. .. 33 104 36.6 33 10,760 30 Sweden 20.6 .. 2 122 54.5 32 77,239 28 Switzerlandc 11.1 10.5 24 63 28.9 .. .. .. Syrian Arab Republicc 17.4 .. 20 336 43.5 .. .. .. Tajikistanc 7.7 .. 54 224 85.5 .. .. .. Tanzania .. .. 48 172 45.1 30 .. 30 Thailand .. 16.2 23 264 37.8 37 118,624 30 Timor-Leste .. .. 15 640 28.3 .. .. .. Togoc .. 16.4 53 270 48.2 .. .. .. Trinidad and Tobagoc 22.1 29.1 40 114 33.1 25 .. 25 Tunisiac 21.3 21.2 22 228 59.1 .. .. .. Turkeyc .. 18.5 15 223 45.5 35 36,752 20 Turkmenistan .. .. .. .. .. .. .. .. Ugandac 10.4 12.3 32 222 34.5 30 2,899 30 Ukrainec 14.1 16.7 99 848 58.4 15 .. 25 United Arab Emiratesc 1.7 .. 14 12 14.4 0 .. .. United Kingdom 28.9 28 8 105 35.3 40 50,435 28 United States 12.7 12.2 10 187 42.3 35 326,450 35 Uruguayc 16.7 18.7 53 336 58.5 25 3,970 25 Uzbekistan .. .. 106 196 90.6 25 .. 10 Venezuela, RBc 13.3 15.5 70 864 56.6 34 93,767 34 Vietnam .. .. 32 1,050 40.1 40 .. 28 West Bank and Gaza .. .. 27 154 16.8 .. .. .. Yemen, Rep.c 9.4 .. 44 248 47.8 .. .. .. Zambiac 18.6 17.2 37 132 16.1 .. .. .. Zimbabwec .. .. 52 256 63.7 .. .. .. World 15.7 w 16.8 w 31 u 300 u 49.3 u .. .. .. Low income .. .. 41 314 68.4 .. .. .. Middle income 11.7 14.1 34 352 44.1 .. .. .. Lower middle income 8.9 11.6 34 346 42.9 .. .. .. Upper middle income .. .. 34 361 45.8 .. .. .. Low & middle income 11.6 14.0 36 339 52.3 .. .. .. East Asia & Pacific 7.7 10.1 28 270 39.9 .. .. .. Europe & Central Asia 14.8 17.3 50 384 48.5 .. .. .. Latin America & Carib. 11.4 .. 35 428 48.6 .. .. .. Middle East & N. Africa 15.1 16.4 27 295 42.2 .. .. .. South Asia 9.3 11.3 32 293 40.4 .. .. .. Sub-Saharan Africa .. .. 38 312 66.9 .. .. .. High income 16.5 17.0 16 181 40.3 .. .. .. Euro area 19.1 18.5 14 201 48.2 .. .. .. a. Data are from PriceWaterhouseCooper's Worldwide Tax Summaries online. b. Data are for the most recent year available. c. Data on central government taxes were reported on a cash basis and have been adjusted to the accrual framework of the International Monetary Fund's Government Finance Statistics Manual 2001. 292 2009 World Development Indicators STATES AND MARKETS Tax policies 5.6 About the data Definitions Taxes are the main source of revenue for most gov- they have certain levels of start-up capital, employ- · Tax revenue collected by central government ernments. The sources of tax revenue and their rel- ees, and turnover. For details about the assump- is compulsory transfers to the central government ative contributions are determined by government tions, see the World Bank's Doing Business 2009. for public purposes. Certain compulsory transfers policy choices about where and how to impose taxes A potentially important influence on both domestic such as fines, penalties, and most social security and by changes in the structure of the economy. Tax and international investors is a tax system's progres- contributions are excluded. Refunds and corrections policy may refl ect concerns about distributional sivity, as reflected in the highest marginal tax rate of erroneously collected tax revenue are treated as effects, economic efficiency (including corrections levied at the national level on individual and corpo- negative revenue. The analytic framework of the for externalities), and the practical problems of rate income. Data for individual marginal tax rates International Monetary Fund's (IMF) Government administering a tax system. There is no ideal level generally refer to employment income. In some coun- Finance Statistics Manual 2001 (GFSM 2001) is of taxation. But taxes influence incentives and thus tries the highest marginal tax rate is also the basic based on accrual accounting and balance sheets. the behavior of economic actors and the economy's or flat rate, and other surtaxes, deductions, and the For countries still reporting government finance data competitiveness. like may apply. And in many countries several differ- on a cash basis, the IMF adjusts reported data to the The level of taxation is typically measured by tax ent corporate tax rates may be levied, depending on GFSM 2001 accrual framework. These countries are revenue as a share of gross domestic product (GDP). the type of business (mining, banking, insurance, footnoted in the table. · Number of tax payments Comparing levels of taxation across countries pro- agriculture, manufacturing), ownership (domestic or by businesses is the total number of taxes paid by vides a quick overview of the fiscal obligations and foreign), volume of sales, and whether surtaxes or businesses during one year. When electronic filing is incentives facing the private sector. The table shows exemptions are included. The corporate tax rates in available, the tax is counted as paid once a year even only central government data, which may significantly the table are mainly general rates applied to domes- if payments are more frequent. · Time to prepare, understate the total tax burden, particularly in coun- tic companies. For more detailed information, see file, and pay taxes is the time, in hours per year, it tries where provincial and municipal governments are the country's laws, regulations, and tax treaties and takes to prepare, file, and pay (or withhold) three large or have considerable tax authority. PricewaterhouseCoopers's Worldwide Tax Summaries major types of taxes: the corporate income tax, the Low ratios of tax revenue to GDP may reflect weak Online (www.pwc.com). value-added or sales tax, and labor taxes, includ- administration and large-scale tax avoidance or eva- ing payroll taxes and social security contributions. sion. Low ratios may also reflect a sizable parallel · Total tax rate is the total amount of taxes pay- economy with unrecorded and undisclosed incomes. able by businesses (except for consumption taxes) Tax revenue ratios tend to rise with income, with after accounting for deductions and exemptions as higher income countries relying on taxes to finance a percentage of profi t. For further details on the a much broader range of social services and social method used for assessing the total tax payable, see security than lower income countries are able to. the World Bank's Doing Business 2009. · Highest The indicators covering taxes payable by busi- marginal tax rate is the highest rate shown on the nesses measure all taxes and contributions that national schedule of tax rates applied to the annual are government mandated (at any level--federal, taxable income of individuals and corporations. Also state, or local), apply to standardized businesses, presented are the income levels for individuals above and have an impact in their income statements. The which the highest marginal tax rates levied at the taxes covered go beyond the definition of a tax for national level apply. government national accounts (compulsory, unre- quited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value-added taxes. The indicators account for government-mandated contributions paid by the employer to a requited private pension fund or work- ers insurance fund but exclude value-added taxes Data sources because they do not affect the accounting profits of Data on central government tax revenue are from the business--that is, they are not reflected in the print and electronic editions of the IMF's Govern- income statement. ment Finance Statistics Yearbook. Data on taxes To make the data comparable across countries, payable by businesses are from Doing Business several assumptions are made about businesses. 2009 (www.doingbusiness.org). Data on individ- The main assumptions are that they are limited liabil- ual and corporate tax rates are from Pricewater- ity companies, they operate in the country's most houseCoopers's Worldwide Tax Summaries Online populous city, they are domestically owned, they per- (www.pwc.com). form general industrial or commercial activities, and 2009 World Development Indicators 293 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers % of central government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 Afghanistan .. 1.5 .. 8.7 400 51 6.3 0.6 .. .. 33 37 Albania 1.2 1.8 5.4 .. 68 15 5.2 1.0 .. .. 3 5 Algeria 3.4 2.9 16.5 15.7 305 334 2.8 2.4 .. .. 428 700 Angola 2.4 3.7 .. .. 118 117 1.9 1.6 1 .. 157 4 Argentina 1.3 0.7 6.2 .. 102 107 0.6 0.6 2 .. 224 41 Armenia 3.6 3.0 .. 18.1 42 42 3.1 2.8 .. .. 2 .. Australia 1.9 2.0 7.5 7.8 52 55 0.5 0.5 43 1 366 685 Austria 1.0 1.0 2.5 2.5 41 35 1.0 0.8 21 86 25 335 Azerbaijan 2.3 3.0 13.8 .. 87 82 2.4 1.9 .. .. 3 27 Bangladesh 1.4 1.1 14.9 11.7 137 221 0.2 0.3 .. .. 205 17 Belarus 1.3 1.6 5.3 4.7 91 183 1.9 3.8 293 35 41 254 Belgium 1.4 1.1 3.2 2.6 39 39 0.9 0.8 22 10 39 171 Benin 0.6 1.1 4.7 7.6 7 8 0.3 0.2 .. .. 6 3 Bolivia 1.7 1.2 6.5 5.7 70 83 2.0 1.9 .. .. 19 5 Bosnia and Herzegovina 3.6 1.3 .. 3.5 76 9 4.1 0.5 4 .. 25 .. Botswana 3.0 2.6 .. .. 10 11 1.6 1.6 .. .. 50 .. Brazil 1.6 1.6 7.1 .. 673 721 0.8 0.7 26 24 126 175 Bulgaria 2.5 2.0 7.8 6.3 114 75 3.5 2.2 2 7 7 38 Burkina Faso 1.2 1.4 .. 9.6 11 11 0.2 0.2 .. .. .. 4 Burundi 6.0 4.8 30.3 .. 46 51 1.4 1.2 .. .. 1 .. Cambodia 2.2 0.9 16.8 12.8 360 191 6.1 2.6 .. .. .. 36 Cameroon 1.3 1.4 12.0 .. 22 23 0.4 0.3 .. .. 1 0 Canada 1.1 1.3 6.0 7.0 69 64 0.4 0.3 109 343 560 623 Central African Republic 1.0 1.1 .. .. 5 3 0.3 0.2 .. .. .. 9 Chad 1.9 1.0 .. .. 35 35 1.0 0.8 .. .. 15 3 Chile 3.7 3.4 17.7 19.5 117 103 1.9 1.5 1 .. 177 615 China 1.8a 2.0a 19.6a 17.9a 3,910 2,885 0.5 0.4 228 355 1,874 1,424 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 3.6 3.1 19.0 12.3 247 411 1.3 1.8 .. .. 62 38 Congo, Dem. Rep. 1.0 1.7 11.4 .. 93 143 0.5 0.6 .. .. 41 17 Congo, Rep. 1.4 1.1 5.9 5.2 15 12 1.2 0.8 .. .. 0 1 Costa Rica .. .. .. .. 15 10 0.9 0.5 .. .. .. .. Côte d'Ivoire .. 1.5 .. 7.1 15 19 0.2 0.3 .. .. 32 .. Croatia 3.6 2.0 7.8 5.0 101 21 5.0 1.1 2 .. 70 14 Cuba .. .. .. .. 85 76 1.6 1.5 .. .. .. .. Czech Republic 2.0 1.5 6.1 4.5 63 27 1.2 0.5 78 13 16 15 Denmark 1.5 1.3 4.2 3.7 22 30 0.8 1.0 20 5 64 201 Dominican Republic 0.9 0.5 .. 3.5 40 65 1.1 1.5 .. .. 13 2 Ecuador 1.7 2.8 .. .. 58 58 1.2 1.0 .. .. 12 45 Egypt, Arab Rep. 3.2 2.5 12.3 8.4 679 866 3.4 3.6 38 .. 826 418 El Salvador 0.9 0.6 4.3 3.3 29 33 1.2 1.2 .. .. 16 .. Eritrea 36.4 .. .. .. 200 202 14.1 10.4 0 .. 4 271 Estonia 1.4 1.9 4.7 7.0 8 7 1.1 1.0 .. .. 27 30 Ethiopia 7.6 1.9 18.0 .. 353 138 1.2 0.4 .. .. 125 .. Finland 1.3 1.3 3.7 3.7 35 32 1.4 1.2 9 24 518 110 France 2.5 2.3 5.7 5.3 389 353 1.5 1.3 1,033 2,690 58 63 Gabon 1.8 1.1 .. .. 7 7 1.3 1.1 .. .. .. 21 Gambia, The 0.8 0.6 .. .. 1 1 0.1 0.1 .. .. .. .. Georgia 0.6 7.5 5.3 32.7 33 33 1.4 1.4 22 .. 6 4 Germany 1.5 1.3 4.7 4.4 221 244 0.5 0.6 1,622 3,395 135 85 Ghana 1.0 0.7 3.3 2.6 8 14 0.1 0.1 .. .. 1 13 Greece 4.3 3.5 9.8 8.3 163 161 3.4 3.1 2 23 651 2,089 Guatemala 0.8 0.5 7.5 3.6 53 35 1.7 0.7 .. .. 1 .. Guinea 1.5 .. 11.8 .. 19 19 0.5 0.4 .. .. 19 .. Guinea-Bissau 4.4 4.0 .. .. 9 9 1.8 1.4 .. .. .. .. Haiti .. .. .. .. 5 0 0.2 0.0 .. .. .. .. 294 2009 World Development Indicators STATES AND MARKETS Military expenditures and arms transfers Military expenditures Armed forces personnel 5.7 Arms transfers % of central government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 Honduras 0.5 0.6 .. 2.8 14 20 0.6 0.8 .. .. .. .. Hungary 1.7 1.1 4.1 2.6 58 37 1.4 0.9 .. 6 14 192 India 3.1 2.5 19.5 16.5 2,372 2,576 0.6 0.6 16 14 826 1,318 Indonesia 1.0 1.2 5.7 .. 492 582 0.5 0.5 16 8 170 475 Iran, Islamic Rep. 3.8 3.0 22.5 14.7 753 563 3.6 2.0 0 10 413 297 Iraq .. .. .. .. 479 362 7.8 5.0 .. .. .. 244 Ireland 0.7 0.5 2.6 1.6 12 10 0.7 0.5 .. .. 0 13 Israel 7.9 8.3 17.6 19.8 181 185 7.3 6.5 316 238 364 891 Italy 2.0 1.8 5.2 4.5 503 436 2.1 1.7 192 562 241 176 Jamaica 0.5 0.7 1.5 1.1 3 3 0.3 0.3 .. .. 5 1 Japan 1.0 0.9 .. .. 249 242 0.4 0.4 .. .. 431 519 Jordan 6.2 6.9 23.1 19.0 149 111 11.6 6.8 .. 13 130 83 Kazakhstan 0.8 1.2 5.7 8.5 99 81 1.3 1.0 16 12 144 21 Kenya 1.3 1.8 7.8 9.3 27 29 0.2 0.2 .. .. .. 25 Korea, Dem. Rep. .. .. .. .. 1,244 1,295 11.2 10.4 13 .. 19 9 Korea, Rep. 2.5 2.7 14.4 13.3 688 692 3.0 2.8 8 214 1,266 1,807 Kuwait 7.1 4.3 18.9 13.5 20 23 1.8 1.7 99 .. 245 117 Kyrgyz Republic 2.9 3.3 18.0 18.1 14 21 0.7 0.9 .. .. .. 1 Lao PDR 2.0 .. .. .. 129 129 5.4 4.5 .. .. 7 4 Latvia 0.9 1.8 3.2 6.6 9 17 0.8 1.4 .. .. 3 51 Lebanon 5.5 5.8 17.7 17.9 77 76 5.8 5.1 45 .. 4 3 Lesotho 3.6 2.5 7.8 5.3 2 2 0.3 0.2 .. .. 6 1 Liberia .. 0.8 .. .. 15 2 1.3 0.1 .. .. 8 .. Libya 3.1 1.1 .. .. 77 76 4.1 3.4 11 9 145 3 Lithuania 1.4 1.2 5.2 3.9 17 24 1.0 1.5 3 .. 5 4 Macedonia, FYR 1.9 2.1 .. .. 24 19 2.8 2.1 0 .. 11 0 Madagascar 1.2 1.1 11.5 10.0 29 22 0.4 0.2 .. .. .. .. Malawi 0.7 1.2 .. .. 6 7 0.1 0.1 1 .. .. .. Malaysia 1.6 2.1 10.5 .. 116 134 1.2 1.2 8 .. 40 550 Mali 2.4 2.3 20.7 15.3 15 12 0.6 0.4 .. .. 7 7 Mauritania 3.5 3.1 .. .. 21 21 2.0 1.6 .. .. 31 .. Mauritius 0.2 0.2 1.0 0.8 2 2 0.3 0.3 .. .. .. 4 Mexico 0.5 0.4 3.4 .. 208 286 0.5 0.6 .. .. 227 11 Moldova 0.4 0.4 1.4 1.3 13 9 0.7 0.6 3 4 .. .. Mongolia 2.2 1.2 .. 5.0 16 16 1.7 1.4 .. .. .. .. Morocco 2.3 3.2 12.0 11.0 241 246 2.4 2.2 .. .. 123 44 Mozambique 1.3 0.9 .. .. 6 11 0.1 0.1 .. .. 0 .. Myanmar 2.3 .. .. .. 429 513 1.7 1.8 .. .. 3 20 Namibia 2.7 3.4 8.6 .. 9 15 1.5 2.2 .. .. 18 72 Nepal 1.0 1.5 .. 12.6 90 131 1.0 1.1 .. .. 11 5 Netherlands 1.6 1.5 4.0 3.6 57 41 0.7 0.5 259 1,355 142 210 New Zealand 1.2 1.1 3.5 3.2 9 9 0.5 0.4 1 .. 45 70 Nicaragua 0.8 0.7 4.7 3.5 16 14 0.9 0.6 .. .. .. .. Niger 1.1 1.0 .. 10.6 11 10 0.3 0.2 .. .. .. 0 Nigeria 0.8 0.6 .. .. 107 162 0.3 0.4 .. .. 42 15 Norway 1.7 1.4 5.3 4.5 27 19 1.1 0.8 3 14 263 483 Oman 10.6 11.3 40.4 .. 48 47 5.4 4.9 .. 1 120 4 Pakistan 4.0 3.5 23.4 21.7 900 921 2.2 1.6 3 9 .. .. Panama 1.0 .. 4.6 .. 12 12 0.9 0.8 .. .. 0 .. Papua New Guinea 0.9 0.5 2.9 .. 4 3 0.2 0.1 .. .. .. .. Paraguay 1.1 0.8 .. 4.5 35 26 1.4 0.8 .. .. 6 1 Peru 1.7 1.1 9.7 6.7 193 198 1.7 1.4 4 .. 24 172 Philippines 1.1 0.8 6.2 4.7 149 147 0.5 0.4 .. 4 9 28 Poland 1.9 2.0 5.7 5.8 239 142 1.4 0.8 43 135 159 985 Portugal 2.0 1.7 5.1 4.2 91 91 1.7 1.6 .. 30 2 2 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 295 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers % of central government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 Romania 2.5 1.8 8.9 6.8 283 153 2.4 1.6 3 16 23 70 Russian Federation 3.7 3.6 19.3 15.4 1,427 1,476 2.0 1.9 4,190 4,588 .. 4 Rwanda 3.5 1.7 .. .. 76 35 2.1 0.8 .. .. 14 3 Saudi Arabia 10.6 9.3 .. .. 217 238 3.2 2.7 .. 36 81 72 Senegal 1.3 1.7 10.4 .. 15 19 0.4 0.4 .. .. .. 15 Serbia 5.4 2.4 .. .. 136 24 .. 0.8 7 5 1 .. Sierra Leone 3.7 1.8 12.8 .. 4 11 0.2 0.5 .. .. 13 .. Singapore 4.7 4.3 28.7 31.5 169 167 8.2 6.8 10 3 612 707 Slovak Republic 1.7 1.7 .. 5.4 41 17 1.6 0.6 92 7 2 4 Slovenia 1.1 1.5 2.9 4.0 14 12 1.4 1.2 .. .. 1 2 Somalia .. .. .. .. 50 .. 1.8 .. .. .. 1 .. South Africa 1.6 1.4 5.6 4.7 72 62 0.5 0.4 18 80 16 855 Spain 1.2 1.2 3.9 4.7 242 222 1.3 1.0 46 529 332 385 Sri Lanka 4.5 2.9 19.7 14.6 204 213 2.6 2.4 .. .. 226 1 Sudan 4.7 4.3 53.0 .. 120 127 1.2 1.1 .. .. 146 49 Swaziland 1.8 .. .. .. 3 .. 0.8 .. .. .. 1 .. Sweden 2.0 1.3 5.5 .. 88 18 1.9 0.4 308 413 210 85 Switzerland 1.1 0.8 4.2 4.4 28 23 0.7 0.5 104 211 14 126 Syrian Arab Republic 5.4 3.9 .. .. 425 401 8.8 6.3 .. 3 439 30 Tajikistan 1.2 .. 13.4 .. 7 17 0.4 0.7 .. .. .. 13 Tanzania 1.5 1.0 .. .. 35 28 0.2 0.1 .. .. .. 9 Thailand 1.4 1.4 .. 7.8 417 420 1.2 1.1 .. .. 93 9 Timor-Leste .. .. .. .. .. 1 .. 0.2 .. .. .. .. Togo .. 1.6 .. 9.8 8 10 0.4 0.4 .. .. .. .. Trinidad and Tobago .. .. .. .. 8 4 1.3 0.6 .. .. 10 .. Tunisia 1.7 1.4 6.2 4.9 47 48 1.5 1.3 .. .. 11 18 Turkey 3.7 2.1 .. 8.6 828 612 3.5 2.5 15 33 1,042 944 Turkmenistan 2.9 .. .. .. 15 22 0.8 1.0 .. .. .. .. Uganda 2.5 1.7 16.0 12.4 51 47 0.5 0.3 .. .. 6 5 Ukraine 3.6 2.9 13.5 8.3 420 215 1.8 0.9 280 109 0 .. United Arab Emirates 3.4 1.9 45.7 .. 66 51 3.5 1.9 .. 3 309 1,040 United Kingdom 2.4 2.5 6.7 6.2 213 160 0.7 0.5 1,356 1,151 808 698 United States 3.1 4.2 15.6 19.4 1,455 1,555 1.0 1.0 7,505 7,454 268 587 Uruguay 1.5 1.3 5.0 4.8 25 26 1.6 1.6 1 .. 4 33 Uzbekistan 0.8 .. .. .. 79 87 0.8 0.7 73 4 6 .. Venezuela, RB 1.2 1.1 5.4 5.2 79 115 0.8 0.9 .. 1 89 887 Vietnam .. .. .. .. 524 495 1.4 1.1 .. .. 5 1 West Bank and Gaza .. .. .. .. .. 56 .. 6.8 .. .. .. 2 Yemen, Rep. 5.0 4.7 23.9 .. 136 138 3.4 2.6 .. .. 158 57 Zambia 1.8 1.8 10.3 7.6 23 16 0.6 0.4 .. .. 27 3 Zimbabwe 0.0 0.0 .. .. 62 51 1.2 0.9 3 .. 2 20 World 2.3 w 2.5 w 10.2 w 11.2 w 29,353 s 27,254 s 1.0 w 0.9 w 18,266 s 24,192 s 18,066 s 23,493 s Low income 2.4 1.8 .. .. 5,806 5,359 1.3 1.0 17 9 724 148 Middle income 2.0 2.0 15.3 14.2 17,507 16,185 0.9 0.8 5,135 5,459 8,181 10,718 Lower middle income 2.2 2.1 17.3 15.6 12,481 11,528 0.8 0.7 549 481 5,846 5,396 Upper middle income 1.9 1.9 .. .. 5,026 4,657 1.4 1.2 4,586 4,978 2,335 5,322 Low & middle income 2.1 2.0 15.3 14.2 23,313 21,544 1.0 0.8 5,152 5,459 8,905 10,866 East Asia & Pacific 1.7 1.8 18.4 16.8 7,794 6,815 0.8 0.6 241 359 2,211 2,532 Europe & Central Asia 3.1 2.7 12.6 11.0 4,220 3,394 2.1 1.6 4,869 4,973 1,397 2,163 Latin America & Carib. 1.4 1.3 6.5 .. 2,084 2,408 0.9 0.9 4 25 966 1,978 Middle East & N. Africa 3.5 3.0 13.1 13.5 3,379 3,289 4.0 3.1 0 22 2,522 1,824 South Asia 3.1 2.6 19.9 16.9 4,114 4,113 0.8 0.7 19 23 1,257 1,373 Sub-Saharan Africa 1.8 1.5 .. .. 1,724 1,525 0.7 0.5 19 80 552 996 High income 2.3 2.6 10.0 10.6 6,040 5,710 1.2 1.1 13,114 18,733 9,161 12,627 Euro area 1.8 1.6 4.8 4.6 1,820 1,690 1.3 1.1 3,204 8,681 2,146 3,641 Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2008). a. Estimates differ from official statistics of the government of China, which has published the following estimates: military expenditure as 1.2 percent of GDP in 2000 and 1.4 percent in 2006 and 7.6 percent of central government expenditure in 2000 and 7.4 percent in 2006 (see National Bureau of Statistics of China, www.stats.gov.cn). 296 2009 World Development Indicators STATES AND MARKETS Military expenditures and arms transfers 5.7 About the data Definitions Although national defense is an important function of completeness of data, data on military expenditures · Military expenditures are SIPRI data derived from government and security from external threats that are not strictly comparable across countries. More the NATO definition, which includes all current and contributes to economic development, high levels of information on SIPRI's military expenditure project capital expenditures on the armed forces, including military expenditures for defense or civil conflicts bur- can be found at www.sipri.org/contents/milap/. peacekeeping forces; defense ministries and other den the economy and may impede growth. Data on Data on armed forces refer to military personnel on government agencies engaged in defense projects; military expenditures as a share of gross domestic active duty, including paramilitary forces. Because paramilitary forces, if judged to be trained and product (GDP) are a rough indicator of the portion of data exclude personnel not on active duty, they equipped for military operations; and military space national resources used for military activities and of underestimate the share of the labor force working activities. Such expenditures include military and civil the burden on the national economy. As an "input" for the defense establishment. Governments rarely personnel, including retirement pensions and social measure military expenditures are not directly related report the size of their armed forces, so such data services for military personnel; operation and main- to the "output" of military activities, capabilities, or typically come from intelligence sources. tenance; procurement; military research and develop- security. Comparisons of military spending between SIPRI's Arms Transfers Project collects data on ment; and military aid (in the military expenditures countries should take into account the many fac- arms transfers from open sources. Since publicly of the donor country). Excluded are civil defense and tors that influence perceptions of vulnerability and available information is inadequate for tracking all current expenditures for previous military activities, risk, including historical and cultural traditions, the weapons and other military equipment, SIPRI covers such as for veterans benefits, demobilization, and length of borders that need defending, the quality of only what it terms major conventional weapons. Data weapons conversion and destruction. This definition relations with neighbors, and the role of the armed cover the supply of weapons through sales, aid, gifts, cannot be applied for all countries, however, since forces in the body politic. and manufacturing licenses; therefore the term arms that would require more detailed information than is Data on military spending reported by governments transfers rather than arms trade is used. SIPRI data available about military budgets and off-budget mili- are not compiled using standard definitions. They are also cover weapons supplied to or from rebel forces tary expenditures (for example, whether military bud- often incomplete and unreliable. Even in countries in an armed conflict as well as arms deliveries for gets cover civil defense, reserves and auxiliary forces, where the parliament vigilantly reviews budgets and which neither the supplier nor the recipient can be police and paramilitary forces, and military pensions). spending, military expenditures and arms transfers identified with acceptable certainty; these data are · Armed forces personnel are active duty military per- rarely receive close scrutiny or full, public disclosure available in SIPRI's database. sonnel, including paramilitary forces if the training, (see Ball 1984 and Happe and Wakeman-Linn 1994). SIPRI's estimates of arms transfers are designed organization, equipment, and control suggest they Therefore, SIPRI has adopted a definition of military as a trend-measuring device in which similar weap- may be used to support or replace regular military expenditure derived from the North Atlantic Treaty ons have similar values, reflecting both the value and forces. Reserve forces, which are not fully staffed or Organization (NATO) definition (see Definitions). The quality of weapons transferred. SIPRI cautions that operational in peace time, are not included. The data data on military expenditures as a share of GDP and the estimated values do not reflect financial value also exclude civilians in the defense establishment as a share of central government expenditure are (payments for weapons transferred) because reliable and so are not consistent with the data on military estimated by the Stockholm International Peace data on the value of the transfer are not available, expenditures on personnel. · Arms transfers cover Research Institute (SIPRI). Central government and even when values are known, the transfer usually the supply of military weapons through sales, aid, expenditures are from the International Monetary includes more than the actual conventional weapons, gifts, and manufacturing licenses. Weapons must be Fund (IMF). Therefore the data in the table may such as spares, support systems, and training, and transferred voluntarily by the supplier, have a military differ from comparable data published by national details of the financial arrangements (such as credit purpose, and be destined for the armed forces, para- governments. and loan conditions and discounts) are usually not military forces, or intelligence agencies of another SIPRI's primary source of military expenditure data known. country. The trends shown in the table are based on is official data provided by national governments. Given these measurement issues, SIPRI's method actual deliveries only. Data cover major conventional These data are derived from national budget docu- of estimating the transfer of military resources weapons such as aircraft, armored vehicles, artil- ments, defense white papers, and other public docu- includes an evaluation of the technical parameters lery, radar systems, missiles, and ships designed for ments from official government agencies, including of the weapons. Weapons for which a price is not military use. Excluded are transfers of other military governments' responses to questionnaires sent by known are compared with the same weapons for equipment such as small arms and light weapons, SIPRI, the United Nations, or the Organization for which actual acquisition prices are available (core trucks, small artillery, ammunition, support equip- Security and Co-operation in Europe. Secondary weapons) or for the closest match. These weapons ment, technology transfers, and other services. sources include international statistics, such as are assigned a value in an index that reflects their Data sources those of NATO and the IMF's Government Finance military resource value in relation to the core weap- Statistics Yearbook. Other secondary sources include ons. These matches are based on such characteris- Data on military expenditures are from SIPRI's country reports of the Economist Intelligence Unit, tics as size, performance, and type of electronics, Yearbook 2008: Armaments, Disarmament, and country reports by IMF staff, and specialist journals and adjustments are made for secondhand weapons. International Security. Data on armed forces per- and newspapers. More information on SIPRI's Arms Transfers Project sonnel are from the International Institute for Stra- In the many cases where SIPRI cannot make is available at www.sipri.org/contents/armstrad/. tegic Studies' The Military Balance 2009. Data on independent estimates, it uses the national data arms transfers are from SIPRI's Arms Transfer provided. Because of the differences in defi ni- Project (www.sipri.org/contents/armstrad/). tions and the difficulty in verifying the accuracy and 2009 World Development Indicators 297 5.8 Public policies and institutions IDA Economic management Structural policies Resource 1­6 (low to high) 1­6 (low to high) Allocation Index 1­6 (low to high) Business Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2007 2007 2007 2007 2007 2007 2007 2007 2007 Afghanistan 2.5 3.5 3.0 3.0 3.2 2.5 2.0 2.5 2.3 Angola 2.7 3.0 3.0 3.0 3.0 4.0 2.5 2.0 2.8 Armenia 4.4 5.5 5.0 6.0 5.5 4.5 3.5 4.0 4.0 Azerbaijan 3.8 4.5 4.5 5.0 4.7 4.0 3.0 3.5 3.5 Bangladesh 3.5 4.0 3.5 4.5 4.0 3.5 3.0 3.5 3.3 Benin 3.6 4.5 4.0 3.5 4.0 4.0 3.5 3.5 3.7 Bhutan 3.9 4.5 4.5 4.5 4.5 3.0 3.0 3.5 3.2 Bolivia 3.7 4.0 4.0 4.5 4.2 5.0 3.5 2.5 3.7 Bosnia and Herzegovina 3.7 4.5 3.5 4.0 4.0 3.5 4.0 4.0 3.8 Burkina Faso 3.7 4.5 4.5 4.0 4.3 4.0 3.0 3.0 3.3 Burundi 3.0 3.5 3.5 2.5 3.2 3.5 3.0 2.5 3.0 Cambodia 3.2 4.5 3.0 3.5 3.7 3.5 2.5 3.5 3.2 Cameroon 3.2 4.0 4.0 3.0 3.7 3.5 3.0 3.0 3.2 Cape Verde 4.2 4.5 4.5 4.5 4.5 4.0 4.0 3.5 3.8 Central African Republic 2.5 3.5 3.0 2.0 2.8 3.5 2.5 2.0 2.7 Chad 2.6 3.0 2.5 2.5 2.7 3.0 3.0 2.5 2.8 Comoros 2.4 2.5 1.5 2.0 2.0 3.0 2.5 2.5 2.7 Congo, Dem. Rep. 2.8 3.5 3.5 2.5 3.2 4.0 2.0 3.0 3.0 Congo, Rep. 2.7 3.0 2.0 2.5 2.5 3.5 2.5 2.5 2.8 Côte d'Ivoire 2.6 3.0 2.5 1.5 2.3 3.5 3.0 3.0 3.2 Djibouti 3.1 3.5 2.5 2.5 2.8 4.0 3.5 3.5 3.7 Dominica 3.9 4.0 4.5 3.0 3.8 4.0 4.0 4.5 4.2 Eritrea 2.4 2.0 2.0 2.5 2.2 1.5 2.0 2.0 1.8 Ethiopia 3.4 3.0 4.0 3.5 3.5 3.0 3.0 3.5 3.2 Gambia, The 3.2 4.0 3.5 2.5 3.3 4.0 3.0 3.5 3.5 Georgia 4.3 4.5 4.5 5.0 4.7 5.5 3.5 5.0 4.7 Ghana 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 Grenada 3.7 3.5 2.5 3.0 3.0 4.0 3.5 4.5 4.0 Guinea 3.0 3.0 3.5 2.5 3.0 4.0 3.0 3.0 3.3 Guinea-Bissau 2.6 2.0 2.5 1.5 2.0 4.0 3.0 2.5 3.2 Guyana 3.4 3.5 3.5 4.0 3.7 4.0 3.5 3.0 3.5 Haiti 2.9 3.5 3.5 2.5 3.2 4.0 3.0 2.5 3.2 Honduras 3.8 4.0 3.5 4.0 3.8 5.0 3.5 4.5 4.3 India 3.9 4.5 3.5 4.5 4.2 4.0 4.0 3.5 3.8 Kenya 3.6 4.5 4.0 4.0 4.2 4.0 3.5 4.0 3.8 Kiribati 3.1 2.5 2.0 5.0 3.2 3.0 3.0 3.0 3.0 Kyrgyz Republic 3.7 4.5 4.0 4.0 4.2 5.0 3.5 3.5 4.0 Lao PDR 3.1 4.5 3.5 3.5 3.8 3.5 2.0 3.0 2.8 About the data The International Development Association (IDA) is the exercise even though they are IDA eligible. Albania and extent, based on its per capita gross national income. part of the World Bank Group that helps the poorest Indonesia are no longer included in the table because This ensures that good performers receive a higher countries reduce poverty by providing concessional they have graduated from IDA. Country assessments IDA allocation in per capita terms. The IRAI is a key loans and grants for programs aimed at boosting have been carried out annually since the mid-1970s element in the country performance rating. economic growth and improving living conditions. by World Bank staff. Over time the criteria have been The CPIA exercise is intended to capture the quality IDA funding helps these countries deal with the com- revised from a largely macroeconomic focus to include of a country's policies and institutional arrangements, plex challenges they face in meeting the Millennium governance aspects and a broader coverage of social focusing on key elements that are within the country's Development Goals. and structural dimensions. Country performance is control, rather than on outcomes (such as economic The World Bank's IDA Resource Allocation Index assessed against a set of 16 criteria grouped into four growth rates) that are influenced by events beyond (IRAI), presented in the table, is based on the results of clusters: economic management, structural policies, the country's control. More specifically, the CPIA the annual Country Policy and Institutional Assessment policies for social inclusion and equity, and public sec- measures the extent to which a country's policy and (CPIA) exercise, which covers the IDA-eligible coun- tor management and institutions. IDA resources are institutional framework supports sustainable growth tries. The table does not include Liberia, Myanmar, allocated to a country on per capita terms based on and poverty reduction and, consequently, the effective and Somalia because they were not rated in the 2007 its IDA country performance rating and, to a limited use of development assistance. 298 2009 World Development Indicators STATES AND MARKETS IDA Public policies and institutions Economic management Structural policies 5.8 Resource 1­6 (low to high) 1­6 (low to high) Allocation Index 1­6 (low to high) Business Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2007 2007 2007 2007 2007 2007 2007 2007 2007 Lesotho 3.5 4.0 4.0 4.0 4.0 3.5 3.5 3.0 3.3 Madagascar 3.7 4.0 3.0 4.0 3.7 4.0 3.5 4.0 3.8 Malawi 3.4 3.5 3.5 3.0 3.3 4.0 3.0 3.5 3.5 Maldives 3.6 3.0 2.5 3.0 2.8 4.0 4.0 4.0 4.0 Mali 3.7 4.5 4.0 4.5 4.3 4.0 3.0 3.5 3.5 Mauritania 3.4 3.5 3.0 4.0 3.5 4.5 2.5 3.5 3.5 Moldova 3.8 4.0 4.0 4.0 4.0 4.5 3.5 3.5 3.8 Mongolia 3.4 3.5 3.0 3.0 3.2 4.5 3.0 3.5 3.7 Mozambique 3.6 4.0 4.0 4.5 4.2 4.5 3.5 3.0 3.7 Nepal 3.4 4.5 3.5 3.5 3.8 4.0 3.0 3.0 3.3 Nicaragua 3.8 4.0 4.0 4.5 4.2 4.5 3.5 3.5 3.8 Niger 3.3 4.0 3.5 3.5 3.7 4.0 3.0 3.0 3.3 Nigeria 3.4 4.0 4.5 4.5 4.3 3.0 3.5 3.0 3.2 Pakistan 3.6 3.5 3.5 4.5 3.8 4.0 4.5 4.0 4.2 Papua New Guinea 3.3 4.5 3.5 4.5 4.2 4.5 3.0 3.0 3.5 Rwanda 3.7 4.0 4.0 3.5 3.8 3.5 3.5 3.5 3.5 Samoa 3.9 4.0 3.5 4.0 3.8 4.5 4.0 3.5 4.0 São Tome and Principe 3.0 3.0 3.0 2.5 2.8 4.0 2.5 3.0 3.2 Senegal 3.7 4.5 4.0 4.0 4.2 4.0 3.5 4.0 3.8 Sierra Leone 3.1 4.0 3.5 3.5 3.7 3.5 3.0 2.5 3.0 Solomon Islands 2.7 3.5 3.0 2.5 3.0 3.0 3.0 2.5 2.8 Sri Lanka 3.5 2.5 3.0 3.5 3.0 3.5 4.0 4.0 3.8 St. Lucia 4.0 4.5 3.5 4.0 4.0 4.0 4.0 4.5 4.2 St. Vincent & Grenadines 3.8 4.0 3.5 3.5 3.7 4.0 4.0 4.5 4.2 Sudan 2.5 3.5 3.0 1.5 2.7 2.5 2.5 3.0 2.7 Tajikistan 3.2 4.0 4.0 3.0 3.7 4.0 3.0 3.5 3.5 Tanzania 3.9 4.5 4.5 4.0 4.3 4.0 3.5 3.5 3.7 Timor-Leste 2.7 2.5 3.0 3.5 3.0 3.5 2.5 1.5 2.5 Togo 2.5 2.5 2.5 1.5 2.2 4.0 2.5 3.0 3.2 Tonga 3.0 3.0 2.5 3.0 2.8 3.5 3.0 3.0 3.2 Uganda 3.9 4.5 4.5 4.5 4.5 4.0 3.5 4.0 3.8 Uzbekistan 3.1 3.5 3.5 4.0 3.7 2.5 2.5 3.0 2.7 Vanuatu 3.3 4.0 3.0 4.0 3.7 3.5 3.0 3.5 3.3 Vietnam 3.8 4.5 4.5 4.0 4.3 3.5 3.0 3.5 3.3 Yemen, Rep. 3.2 3.5 3.0 4.0 3.5 4.5 2.5 3.5 3.5 Zambia 3.5 4.0 3.5 3.5 3.7 4.0 3.5 3.5 3.7 Zimbabwe 1.7 1.0 1.0 1.0 1.0 2.0 2.5 1.5 2.0 All criteria within each cluster receive equal weight, on relevant publicly available indicators. In interpreting information. To ensure that scores are consistent and each cluster has a 25 percent weight in the over- the assessment scores, it should be noted that the across countries, the process involves two key phases. all score, which is obtained by averaging the average criteria are designed in a developmentally neutral man- In the benchmarking phase a small representative sam- scores of the four clusters. For each of the 16 criteria ner. Accordingly, higher scores can be attained by a ple of countries drawn from all regions is rated. Country countries are rated on a scale of 1 (low) to 6 (high). country that, given its stage of development, has a teams prepare proposals that are reviewed first at the The scores depend on the level of performance in policy and institutional framework that more strongly regional level and then in a Bankwide review process. a given year assessed against the criteria, rather fosters growth and poverty reduction. A similar process is followed to assess the perfor- than on changes in performance compared with the The country teams that prepare the ratings are very mance of the remaining countries, using the benchmark previous year. All 16 CPIA criteria contain a detailed familiar with the country, and their assessments are countries' scores as guideposts. The final ratings are description of each rating level. In assessing country based on country diagnostic studies prepared by the determined following a Bankwide review. The overall performance, World Bank staff evaluate the country's World Bank or other development organizations and numerical IRAI score and the separate criteria scores performance on each of the criteria and assign a rat- on their own professional judgment. An early consul- were first publicly disclosed in June 2006. ing. The ratings reflect a variety of indicators, observa- tation is conducted with country authorities to make See IDA's website at www.worldbank.org/ida for tions, and judgments based on country knowledge and sure that the assessments are informed by up-to-date more information. 2009 World Development Indicators 299 5.8 Public policies and institutions Policies for social inclusion and equity Public sector management and institutions 1­6 (low to high) 1­6 (low to high) Quality of budgetary Transparency, Equity Policies and Property and accountability, of public Building Social institutions for rights and financial Efficiency Quality and corruption Gender resource human protection environmental rule-based manage- of revenue of public in the public equality use resources and labor sustainability Average governance ment mobilization administration sector Average 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Afghanistan 2.0 2.5 3.0 2.0 2.0 2.3 1.5 3.0 2.5 2.0 2.0 2.2 Angola 3.0 2.5 2.5 2.5 3.0 2.7 2.0 2.5 2.5 2.5 2.5 2.4 Armenia 4.5 4.5 4.0 4.5 3.5 4.2 3.5 4.0 3.5 4.0 3.5 3.7 Azerbaijan 4.0 4.0 3.5 4.0 3.0 3.7 3.0 4.0 3.5 3.0 2.5 3.2 Bangladesh 4.0 3.5 4.0 3.5 3.0 3.6 3.0 3.0 3.0 3.0 3.0 3.0 Benin 3.5 3.0 3.5 3.0 3.5 3.3 3.0 3.5 3.5 3.0 3.5 3.3 Bhutan 4.0 4.0 4.5 3.5 4.5 4.1 3.5 3.5 4.0 4.0 4.0 3.8 Bolivia 4.0 4.0 4.0 3.5 3.5 3.8 2.5 3.5 4.0 3.0 3.5 3.3 Bosnia and Herzegovina 4.5 3.0 3.5 3.5 3.5 3.6 3.0 3.5 4.0 3.0 3.0 3.3 Burkina Faso 3.5 4.0 3.5 3.5 3.5 3.6 3.5 4.0 3.5 3.5 3.0 3.5 Burundi 4.0 3.5 3.0 3.0 3.0 3.3 2.5 3.0 3.0 2.5 2.0 2.6 Cambodia 4.0 3.0 3.5 3.0 3.0 3.3 2.5 3.0 3.0 2.5 2.5 2.7 Cameroon 3.0 3.0 3.5 3.0 3.0 3.1 2.5 3.5 3.5 3.0 2.5 3.0 Cape Verde 4.5 4.5 4.5 4.5 3.5 4.3 4.0 4.0 3.5 4.0 4.5 4.0 Central African Republic 2.5 2.0 2.0 2.0 2.5 2.2 2.0 2.0 2.5 2.5 2.5 2.3 Chad 2.5 3.0 2.5 2.5 2.5 2.6 2.0 2.0 2.5 2.5 2.0 2.2 Comoros 3.0 3.0 3.0 2.5 2.0 2.7 2.5 1.5 2.5 2.0 2.5 2.2 Congo, Dem. Rep. 3.0 3.0 3.0 3.0 2.5 2.9 2.0 2.5 2.5 2.5 2.0 2.3 Congo, Rep. 3.0 2.5 3.0 2.5 2.5 2.7 2.5 2.5 3.0 2.5 2.5 2.6 Côte d'Ivoire 2.5 1.5 2.5 2.5 2.5 2.3 2.0 2.0 4.0 2.0 2.0 2.4 Djibouti 2.5 3.0 3.5 3.0 3.0 3.0 2.5 3.0 3.5 2.5 2.5 2.8 Dominica 3.5 3.5 4.0 3.5 3.5 3.6 4.0 3.5 4.0 3.5 4.0 3.8 Eritrea 3.5 3.0 3.5 3.0 2.0 3.0 2.5 2.5 3.5 3.0 2.0 2.7 Ethiopia 3.0 4.5 4.0 3.5 3.5 3.7 3.0 4.0 4.0 3.0 2.5 3.3 Gambia, The 3.5 3.0 3.5 2.5 3.0 3.1 3.5 3.0 3.5 3.0 2.0 3.0 Georgia 4.5 4.5 4.0 4.0 3.0 4.0 3.5 4.0 4.5 3.5 3.0 3.7 Ghana 4.0 4.0 4.5 3.5 3.5 3.9 3.5 4.0 4.5 3.5 4.0 3.9 Grenada 5.0 3.5 4.0 3.5 4.0 4.0 3.5 4.0 3.5 3.5 4.0 3.7 Guinea 3.5 3.0 3.0 3.0 2.5 3.0 2.0 3.0 3.0 3.0 2.5 2.7 Guinea-Bissau 2.5 3.0 2.5 2.5 2.5 2.6 2.5 2.5 3.0 2.5 2.5 2.6 Guyana 4.0 3.5 3.5 3.0 3.0 3.4 3.0 3.5 3.5 2.5 3.0 3.1 Haiti 3.0 3.0 2.5 2.5 2.5 2.7 2.0 3.0 2.5 2.5 2.0 2.4 Honduras 4.0 4.0 4.0 3.5 3.0 3.7 3.5 4.0 4.0 3.0 3.0 3.5 India 3.5 4.0 4.0 3.5 3.5 3.7 3.5 4.0 4.0 3.5 3.5 3.7 Kenya 3.0 3.0 3.5 3.0 3.5 3.2 2.5 3.5 4.0 3.5 3.0 3.3 Kiribati 3.0 3.0 2.5 3.0 3.0 2.9 3.5 3.0 3.0 3.0 3.5 3.2 Kyrgyz Republic 4.5 3.5 3.5 3.5 3.0 3.6 2.5 3.0 3.5 3.0 2.5 2.9 Lao PDR 3.5 3.5 3.0 2.5 3.5 3.2 3.0 3.0 2.5 3.0 2.0 2.7 Definitions · IDA Resource Allocation Index is obtained by sustainability. · Structural policies cluster: Trade · Equity of public resource use assesses the extent calculating the average score for each cluster and assesses how the policy framework fosters trade in to which the pattern of public expenditures and rev- then by averaging those scores. For each of 16 cri- goods. · Financial sector assesses the structure of enue collection affects the poor and is consistent teria countries are rated on a scale of 1 (low) to the financial sector and the policies and regulations with national poverty reduction priorities. · Build- 6 (high) · Economic management cluster: Macro- that affect it. · Business regulatory environment ing human resources assesses the national policies economic management assesses the monetary, assesses the extent to which the legal, regulatory, and public and private sector service delivery that exchange rate, and aggregate demand policy frame- and policy environments help or hinder private busi- affect the access to and quality of health and edu- work. · Fiscal policy assesses the short- and nesses in investing, creating jobs, and becoming cation services, including prevention and treatment medium-term sustainability of fiscal policy (taking more productive. · Policies for social inclusion of HIV/AIDS, tuberculosis, and malaria. · Social into account monetary and exchange rate policy and equity cluster: Gender equality assesses the protection and labor assess government policies in and the sustainability of the public debt) and its extent to which the country has installed institutions social protection and labor market regulations that impact on growth. · Debt policy assesses whether and programs to enforce laws and policies that pro- reduce the risk of becoming poor, assist those who the debt management strategy is conducive to mini- mote equal access for men and women in educa- are poor to better manage further risks, and ensure mizing budgetary risks and ensuring long-term debt tion, health, the economy, and protection under law. a minimal level of welfare to all people. · Policies 300 2009 World Development Indicators STATES AND MARKETS Public policies and institutions Policies for social inclusion and equity Public sector management and institutions 5.8 1­6 (low to high) 1­6 (low to high) Quality of budgetary Transparency, Equity Policies and Property and accountability, of public Building Social institutions for rights and financial Efficiency Quality and corruption Gender resource human protection environmental rule-based manage- of revenue of public in the public equality use resources and labor sustainability Average governance ment mobilization administration sector Average 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Lesotho 4.0 3.0 3.5 3.0 3.5 3.4 3.5 3.0 4.0 3.0 3.5 3.4 Madagascar 3.5 4.0 3.5 3.5 4.0 3.7 3.5 3.5 3.5 3.5 3.5 3.5 Malawi 3.5 3.5 3.0 3.5 3.5 3.4 3.5 3.0 4.0 3.5 3.0 3.4 Maldives 4.0 4.0 4.0 3.5 4.0 3.9 4.0 3.0 4.0 4.0 2.5 3.5 Mali 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 4.0 3.0 3.5 3.5 Mauritania 4.0 3.5 3.5 3.0 3.5 3.5 3.0 2.5 3.5 3.0 3.0 3.0 Moldova 5.0 3.5 4.0 3.5 3.5 3.9 3.5 4.0 3.5 3.0 3.0 3.4 Mongolia 3.5 3.5 3.5 3.5 3.0 3.4 3.0 4.0 3.5 3.5 3.0 3.4 Mozambique 3.5 3.5 3.5 3.0 3.0 3.3 3.0 3.5 4.0 3.0 3.0 3.3 Nepal 3.5 3.5 4.0 3.0 3.0 3.4 3.0 3.5 3.5 3.0 3.0 3.2 Nicaragua 3.5 4.0 3.5 3.5 3.5 3.6 3.0 4.0 4.0 3.0 3.0 3.4 Niger 2.5 3.5 3.0 3.0 3.0 3.0 3.0 3.5 3.5 3.0 3.0 3.2 Nigeria 3.0 3.5 3.0 3.5 3.0 3.2 2.5 3.0 3.0 3.0 3.0 2.9 Pakistan 2.0 3.5 3.5 3.0 3.5 3.1 3.0 3.5 3.5 3.5 2.5 3.2 Papua New Guinea 2.5 3.5 2.5 3.0 2.0 2.7 2.0 3.5 3.5 2.5 3.0 2.9 Rwanda 3.5 4.5 4.5 3.5 3.0 3.8 3.0 4.0 3.5 3.5 3.5 3.5 Samoa 3.5 4.0 4.0 3.5 4.0 3.8 4.0 3.5 4.0 4.0 4.0 3.9 São Tome and Principe 3.0 3.0 3.0 2.5 2.5 2.8 2.5 3.0 3.5 3.0 3.5 3.1 Senegal 3.5 3.5 3.5 3.0 3.5 3.4 3.5 3.5 4.0 3.5 3.0 3.5 Sierra Leone 3.0 3.0 3.5 3.0 2.0 2.9 2.5 3.5 2.5 3.0 2.5 2.8 Solomon Islands 3.0 2.5 3.0 2.5 2.0 2.6 2.5 2.5 2.5 2.0 3.0 2.5 Sri Lanka 4.0 3.5 4.0 3.5 3.5 3.7 3.0 4.0 3.5 3.0 3.0 3.3 St. Lucia 3.5 4.0 4.0 4.0 3.5 3.8 4.0 3.5 4.0 3.5 4.5 3.9 St. Vincent & Grenadines 4.0 3.5 4.0 3.5 3.5 3.7 4.0 3.5 4.0 3.5 4.0 3.8 Sudan 2.0 2.5 2.5 2.5 2.5 2.4 2.0 2.0 3.0 2.5 2.0 2.3 Tajikistan 3.5 3.5 3.0 3.5 2.5 3.2 2.5 3.0 3.0 2.5 2.0 2.6 Tanzania 4.0 4.0 4.0 3.5 3.5 3.8 3.5 4.0 4.0 3.5 3.5 3.7 Timor-Leste 3.5 3.0 2.5 2.0 2.5 2.7 1.5 3.0 3.0 2.5 3.0 2.6 Togo 3.0 2.0 3.0 2.5 2.5 2.6 2.5 2.0 2.5 2.0 2.0 2.2 Tonga 2.5 3.5 4.0 3.0 3.0 3.2 3.5 2.5 3.0 3.0 2.5 2.9 Uganda 3.5 4.5 4.0 3.5 4.0 3.9 3.5 4.0 3.0 3.0 3.0 3.3 Uzbekistan 4.0 3.5 4.0 3.5 3.5 3.7 2.5 3.0 3.0 2.5 1.5 2.5 Vanuatu 3.5 3.5 2.5 2.0 3.0 2.9 3.0 3.5 3.5 2.5 3.0 3.1 Vietnam 4.5 4.5 4.0 3.5 3.5 4.0 3.5 4.0 3.5 3.5 3.0 3.5 Yemen, Rep. 2.0 3.5 3.0 3.5 3.0 3.0 2.5 3.0 3.0 3.0 3.0 2.9 Zambia 3.5 3.5 3.5 3.0 3.5 3.4 3.0 3.5 3.5 3.0 3.0 3.2 Zimbabwe 2.5 1.5 1.5 1.0 2.5 1.8 1.0 2.0 3.5 1.5 1.0 1.8 and institutions for environmental sustainability accurate accounting and fiscal reporting, including to which public employees within the executive are assess the extent to which environmental policies timely and audited public accounts. · Effi ciency required to account for administrative decisions, foster the protection and sustainable use of natural of revenue mobilization assesses the overall pat- use of resources, and results obtained. The three resources and the management of pollution. · Public tern of revenue mobilization--not only the de facto main dimensions assessed are the accountability sector management and institutions cluster: Prop- tax structure, but also revenue from all sources as of the executive to oversight institutions and of pub- erty rights and rule-based governance assess the actually collected. · Quality of public administration lic employees for their performance, access of civil extent to which private economic activity is facili- assesses the extent to which civilian central govern- society to information on public affairs, and state tated by an effective legal system and rule-based ment staff is structured to design and implement capture by narrow vested interests. governance structure in which property and contract government policy and deliver services effectively. rights are reliably respected and enforced. · Quality · Transparency, accountability, and corruption in Data sources of budgetary and financial management assesses the public sector assess the extent to which the Data on public policies and institutions are from the extent to which there is a comprehensive and executive can be held accountable for its use of the World Bank Group's CPIA database available credible budget linked to policy priorities, effective funds and for the results of its actions by the elector- at www.worldbank.org/ida. fi nancial management systems, and timely and ate, the legislature, and the judiciary and the extent 2009 World Development Indicators 301 5.9 Transport services Roads Railways Ports Air Passengers Passengers Port Registered carried Goods carried Goods container carrier Total road Paved million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 2000­06a 2000­06a 2000­06a 2000­06a 2000­07a 2000­07a 2000­07a 2007 2007 2007 2007 Afghanistan 42,150 29.3 .. .. .. .. .. .. .. .. .. Albania 18,000 39.0 197 2,200 423 51 53 .. 4 213 0 Algeria 108,302 70.2 .. .. 3,572 821 1,429 .. 44 2,813 17 Angola 51,429 10.4 166,045 4,709 .. .. .. .. 5 277 73 Argentina 231,374 30.0 .. .. 35,753 .. 12,871 1,874 79 7,037 133 Armenia 7,504 89.0 2,344 432 711 27 678 .. 6 606 7 Australia 812,972 .. 290,280 168,630 9,639 1,309 46,036 6,229 354 48,729 2,348 Austria 107,262 100.0 69,000 26,411 5,818 9,051 18,996 .. 151 9,141 454 Azerbaijan 59,141 49.4 10,892 7,536 2,122 1,109 10,374 .. 13 1,441 12 Bangladesh 239,226 9.5 .. .. 2,855 4,164 817 978 11 1,243 89 Belarus 94,797 88.6 9,343 15,779 5,494 9,366 47,933 .. 6 344 1 Belgium 152,256 78.2 130,868 51,572 3,374 9,932 8,149 10,258 158 3,641 740 Benin 19,000 9.5 .. .. .. .. .. .. .. .. .. Bolivia 62,479 7.0 .. .. .. .. .. .. 24 1,745 9 Bosnia and Herzegovina 21,846 52.3 .. 300 1,103 88 1,138 .. .. .. .. Botswana 25,798 32.6 .. .. .. .. .. .. 7 228 0 Brazil 1,751,868 5.5 .. .. 29,487 .. 232,297 6,454 650 45,287 1,478 Bulgaria 40,231 98.4 13,688 11,843 4,027 2,424 5,242 .. 12 855 3 Burkina Faso 92,495 4.2 .. .. .. .. .. .. 2 78 0 Burundi 12,322 10.4 .. .. .. .. .. .. .. .. .. Cambodia 38,257 6.3 201 3 650 45 92 .. 4 308 2 Cameroon 51,346 8.4 .. .. 974 370 1,055 .. 12 453 26 Canada 1,408,900 39.9 493,814 184,774 57,042 2,858 353,227 4,605 1,189 52,104 1,430 Central African Republic 24,307 .. .. .. .. .. .. .. .. .. .. Chad 40,000 0.8 .. .. .. .. .. .. .. .. .. Chile 79,604 20.2 .. .. 6,008 732 3,957 2,681 101 7,191 1,295 China 3,456,999 70.7 1,013,085 975,420 63,637 689,616 2,211,246 104,559 1,754 183,613 11,190 Hong Kong, China 1,983 100.0 .. .. .. .. .. 23,998 130 21,796 8,326 Colombia 164,278 .. 157 39,726 2,137 .. 7,751 1,897 186 11,631 1,070 Congo, Dem. Rep. 153,497 1.8 .. .. 3,641 79 331 .. .. .. .. Congo, Rep. 17,289 5.0 .. .. 795 211 234 .. .. .. .. Costa Rica 35,983 25.2 .. .. .. .. .. 977 37 1,017 10 Côte d'Ivoire 80,000 8.1 .. .. 639 10 675 710 .. .. .. Croatia 28,788 89.0 3,537 10,502 2,722 1,611 3,574 175 22 2,288 3 Cuba 60,856 49.0 5,121 2,133 .. .. .. .. 12 857 31 Czech Republic 128,512 100.0 90,055 46,600 9,491 6,855 16,972 .. 76 4,870 33 Denmark 72,361 100.0 70,635 11,058 2,133 5,724 2,030 775 14 582 1 Dominican Republic 12,600 49.4 .. .. .. .. .. 884 .. .. .. Ecuador 43,670 14.8 11,410 5,453 .. .. .. 671 45 2,631 6 Egypt, Arab Rep. 92,370 81.0 .. .. 5,195 40,837 3,917 5,311 51 5,829 207 El Salvador 10,029 19.8 .. .. .. .. .. .. 22 2,537 22 Eritrea 4,010 21.8 .. .. .. .. .. .. .. .. .. Estonia 57,016 22.7 3,190 7,641 962 273 8,153 .. 9 651 1 Ethiopia 39,477 12.7 219,113 2,456 .. .. .. .. 38 2,290 160 Finland 78,941 65.3 70,900 26,400 5,899 3,778 10,434 1,564 119 8,289 490 France 951,500 100.0 724,000 197,000 29,488 83,299 40,635 4,928 825 61,551 6,425 Gabon 9,170 10.2 .. .. 810 76 2,202 .. 9 535 70 Gambia, The 3,742 19.3 16 .. .. .. .. .. .. .. .. Georgia 20,329 38.6 5,200 570 1,513 809 7,379 .. 5 272 3 Germany 644,480 100.0 1,062,700 251,372 33,897 74,740 91,013 16,713 1,127 106,102 8,529 Ghana 57,614 14.9 .. .. 977 85 242 .. .. .. .. Greece 117,533 91.8 .. 18,360 2,551 1,954 835 1,820 138 10,206 72 Guatemala 14,095 34.5 .. .. .. .. .. 853 .. .. .. Guinea 44,348 9.8 .. .. .. .. .. .. .. .. .. Guinea-Bissau 3,455 27.9 .. .. .. .. .. .. .. .. .. Haiti 4,160 24.3 .. .. .. .. .. .. .. .. .. 302 2009 World Development Indicators STATES AND MARKETS Roads Transport services Railways Ports 5.9Air Passengers Passengers Port Registered carried Goods carried Goods container carrier Total road Paved million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 2000­06a 2000­06a 2000­06a 2000­06a 2000­07a 2000­07a 2000­07a 2007 2007 2007 2007 Honduras 13,600 20.4 .. .. .. .. .. 553 .. .. .. Hungary 159,600 43.9 13,300 9,090 7,730 6,953 8,537 .. 46 2,592 20 India 3,316,452 47.4 .. .. 63,327 694,764 480,993 7,372 569 51,897 968 Indonesia 391,009 55.4 .. .. .. 25,535 4,698 4,481 358 30,406 485 Iran, Islamic Rep. 172,927 72.8 .. .. 7,265 12,549 20,542 1,723 138 13,916 95 Iraq 45,550 84.3 .. .. .. .. .. .. .. .. .. Ireland 96,602 100.0 .. 15,900 1,919 2,007 129 1,175 350 50,738 131 Israel 17,719 100.0 .. .. 958 1,831 1,175 1,957 47 4,663 1,133 Italy 487,700 100.0 97,560 192,700 16,668 47,113 22,340 10,435 432 37,831 1,550 Jamaica 22,056 73.3 .. .. .. .. .. 2,017 21 1,527 15 Japan 1,196,999 79.3 947,562 327,632 20,050 252,579 23,145 19,008 657 99,842 8,435 Jordan 7,694 100.0 .. .. 293 .. 517 .. 29 2,246 277 Kazakhstan 91,563 91.4 100,865 53,816 14,205 13,613 191,189 .. 19 1,295 17 Kenya 63,265 14.1 .. 22 1,917 226 1,399 .. 32 2,857 298 Korea, Dem. Rep. 25,554 2.8 .. .. .. .. .. .. 2 105 2 Korea, Rep. 102,062 88.6 97,854 12,545 3,399 31,596 10,927 16,640 243 36,655 9,040 Kuwait 5,749 85.0 .. .. .. .. .. 750 21 2,628 239 Kyrgyz Republic 18,500 91.1 5,874 1,336 .. .. .. .. 5 275 1 Lao PDR 29,811 13.4 .. .. .. .. .. .. 10 328 3 Latvia 69,675 100.0 2,780 2,729 2,269 983 16,735 .. 29 1,410 13 Lebanon 6,970 .. .. .. .. .. .. 948 11 969 74 Lesotho 5,940 18.3 .. .. .. .. .. .. .. .. .. Liberia 10,600 6.2 .. .. .. .. .. .. .. .. .. Libya 83,200 57.2 .. .. .. .. .. .. 13 1,204 0 Lithuania 79,987 28.3 43,167 18,134 1,766 409 14,373 .. 11 424 1 Macedonia, FYR 13,182 .. 842 4,100 947 109 961 .. 2 209 0 Madagascar 49,827 11.6 .. .. .. .. .. .. 37 616 24 Malawi 15,451 45.0 .. .. 710 26 38 .. 6 155 1 Malaysia 93,109 79.8 .. .. 1,667 2,193 1,355 14,873 185 21,326 2,662 Mali 18,709 18.0 .. .. 733 196 189 .. .. .. .. Mauritania 11,066 26.8 .. .. .. .. .. .. 2 155 0 Mauritius 2,021 100.0 .. .. .. .. .. .. 12 1,278 203 Mexico 356,945 50.0 436,999 209,392 26,662 84 75,600 3,071 310 20,953 482 Moldova 12,838 85.5 1,640 1,577 .. .. .. .. 4 274 1 Mongolia 49,250 3.5 557 242 1,810 1,289 9,219 .. 6 381 5 Morocco 57,625 61.9 .. 1,256 1,907 3,659 5,837 .. 69 4,624 45 Mozambique 30,400 18.7 .. .. .. .. .. .. 11 443 6 Myanmar 27,000 11.9 .. .. .. .. .. .. 29 1,663 3 Namibia 42,237 12.8 47 591 .. .. .. .. 7 431 0 Nepal 17,280 56.9 .. .. .. .. .. .. 7 528 8 Netherlands 126,100 90.0 .. 77,100 2,776 15,546 4,331 11,287 260 28,857 5,006 New Zealand 93,576 65.6 .. .. .. .. 4,078 2,029 219 12,546 868 Nicaragua 18,669 11.4 .. .. .. .. .. .. .. .. .. Niger 18,550 20.5 .. .. .. .. .. .. .. .. .. Nigeria 193,200 15.0 .. .. 3,528 174 77 .. 17 1,363 10 Norway 92,864 77.5 58,247 14,966 .. .. .. .. .. .. .. Oman 34,965 27.7 .. .. .. .. .. 2,877 .. .. .. Pakistan 260,420 65.4 263,788 129,249 7,791 25,621 5,907 1,936 51 5,439 314 Panama 11,643 34.6 .. .. .. .. .. 4,070 33 2,029 36 Papua New Guinea 19,600 3.5 .. .. .. .. .. .. 22 919 23 Paraguay 29,500 50.8 .. .. .. .. .. .. 10 459 0 Peru 78,986 13.9 .. .. .. 119 1,159 1,178 62 5,273 162 Philippines 200,037 9.9 .. .. 491 144 1 3,835 65 8,818 286 Poland 423,997 69.7 247,388 136,490 19,419 17,081 43,548 .. 89 4,270 83 Portugal 82,900 86.0 .. 23,187 2,838 3,610 2,585 1,138 138 10,320 323 Puerto Rico 25,645 95.0 .. 10 .. .. .. 1,695 .. .. .. 2009 World Development Indicators 303 5.9 Transport services Roads Railways Ports Air Passengers Passengers Port Registered carried Goods carried Goods container carrier Total road Paved million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 2000­06a 2000­06a 2000­06a 2000­06a 2000­07a 2000­07a 2000­07a 2007 2007 2007 2007 Romania 198,817 30.2 7,985 51,531 10,646 7,417 13,471 1,411 51 3,004 6 Russian Federation 933,000 80.9 .. 25,200 84,158 173,411 2,090,337 2,657 468 33,188 1,224 Rwanda 14,008 19.0 .. .. .. .. .. .. .. .. .. Saudi Arabia 221,372 21.5 .. .. 1,412 345 1,630 4,209 151 17,141 1,230 Senegal 13,576 29.3 .. .. 906 88 384 .. 0 539 0 Serbia 38,799 62.7 3,865 452 3,819 762 4,234 .. 20 1,042 4 Sierra Leone 11,300 8.0 .. .. .. .. .. .. 0 19 10 Singapore 3,262 100.0 .. .. .. .. .. 27,932 85 19,566 7,981 Slovak Republic 43,761 87.0 32,214 22,114 3,629 2,148 9,331 .. 24 2,679 45 Slovenia 38,562 100.0 850 12,112 1,228 812 3,603 .. 20 861 2 Somalia 22,100 11.8 .. .. .. .. .. .. .. .. .. South Africa 364,131 17.3 .. 434 24,487 14,856 108,513 3,734 153 12,870 939 Spain 666,292 99.0 397,117 132,868 14,832 21,225 11,064 11,148 658 60,665 1,204 Sri Lanka 97,286 81.0 21,067 .. 1,200 4,682 135 3,382 21 3,101 325 Sudan 11,900 36.3 .. .. 5,478 40 766 .. 9 598 46 Swaziland 3,594 30.0 .. .. .. .. .. .. .. .. .. Sweden 697,794 58.2 106,583 40,123 9,821 6,467 11,500 1,439 .. .. .. Switzerland 71,298 100.0 93,480 16,337 3,619 15,771 16,736 .. 139 12,298 1,105 Syrian Arab Republic 38,923 100.0 589 .. 2,043 744 2,552 .. 19 1,371 18 Tajikistan 27,767 .. 414 25,604 .. .. 14,529 .. 8 581 2 Tanzania 78,891 8.6 .. .. 4,460 b 433b 728b .. 5 251 1 Thailand 180,053 98.5 .. .. 4,044 9,195 4,037 6,200 130 21,192 2,455 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 7,520 31.6 .. .. .. .. .. .. .. .. .. Trinidad and Tobago 8,320 51.1 .. .. .. .. .. .. 15 1,086 47 Tunisia 19,232 65.8 .. 16,611 2,218 1,407 2,197 .. 22 2,055 19 Turkey 426,951 .. 187,592 177,399 8,697 5,553 9,680 4,488 197 22,895 466 Turkmenistan 24,000 81.2 .. .. .. .. .. .. 16 1,851 11 Uganda 70,746 23.0 .. .. 259 .. 218 .. 0 55 34 Ukraine 169,323 97.4 51,820 23,895 21,891 53,230 240,810 979 30 1,736 18 United Arab Emirates 4,030 100.0 .. .. .. .. .. 13,160 .. .. .. United Kingdom 398,351 100.0 736,000 163,000 16,208 48,511 22,110 9,383 1,045 101,623 6,154 United States 6,544,257 65.3 7,814,575 2,116,532 191,771 .. 2,820,061c 41,625 9,816d 744,302d 40,618d Uruguay 77,732 10.0 .. .. 3,003 12 331 .. 9 569 4 Uzbekistan 81,600 87.3 .. 1,200 4,005 2,339 19,281 .. 22 1,940 66 Venezuela, RB 96,155 33.6 .. .. 336 .. 81 1,332 141 5,495 2 Vietnam 222,179 .. .. .. 3,147 4,659 3,881 3,937 60 7,194 258 West Bank and Gaza 5,147 100.0 .. .. .. .. .. .. .. .. .. Yemen, Rep. 71,300 8.7 .. .. .. .. .. .. 14 1,073 41 Zambia 91,440 22.0 .. .. 1,273 183 .. .. 6 62 0 Zimbabwe 97,267 19.0 .. .. .. .. .. .. 7 255 8 World 35.9 m .. m .. m .. s .. m .. m 465,594 s 24,654 s 2,058,936 s 124,628 s Low income 12.1 .. .. .. .. .. .. 422 30,709 1,320 Middle income 47.7 .. .. .. 1,295 6,608 193,415 6,368 554,986 26,769 Lower middle income 39.0 .. .. .. 1,025 3,046 142,654 3,711 359,286 17,503 Upper middle income 41.8 .. .. .. 2,309 13,471 50,761 2,658 195,701 9,266 Low & middle income 23.1 .. .. .. .. .. 200,266 6,791 585,695 28,088 East Asia & Pacific 11.4 .. .. .. 2,893 1,902 137,886 2,699 277,535 17,475 Europe & Central Asia .. 10,062 15,477 187,104 1,975 13,471 .. 970 76,387 1,916 Latin America & Carib. 22.0 .. .. .. .. .. 27,286 1,684 112,436 4,726 Middle East & N. Africa 81.0 .. .. .. 2,533 2,552 .. 378 33,077 699 South Asia 56.9 .. .. .. 25,621 5,907 13,668 638 59,108 1,378 Sub-Saharan Africa 11.9 .. .. .. .. .. .. 421 27,153 1,895 High income 87.0 .. 46,600 .. 6,467 11,064 265,328 17,864 1,473,241 96,540 Euro area 100.0 97,840 39,474 124,917 9,051 9,883 72,409 3,871 335,641 24,098 a. Data are for the latest year available in the period shown. b. Includes Tazara railway. c. Refers to class 1 railways only. d. Covers only carriers designated by the U.S. Department of Transportation as major and national air carriers. 304 2009 World Development Indicators STATES AND MARKETS Transport services 5.9 About the data Definitions Transport infrastructure--highways, railways, ports some indication of economic growth in a country. · Total road network covers motorways, highways, and waterways, and airports and air traffic control But when traffic is merely transshipment, much of main or national roads, secondary or regional roads, systems--and the services that fl ow from it are the economic benefit goes to the terminal operator and all other roads in a country. · Paved roads are crucial to the activities of households, producers, and ancillary services for ships and containers rather roads surfaced with crushed stone (macadam) and and governments. Because performance indicators than to the country more broadly. In transshipment hydrocarbon binder or bituminized agents, with con- vary widely by transport mode and focus (whether centers empty containers may account for as much crete, or with cobblestones. · Passengers carried physical infrastructure or the services flowing from as 40 percent of traffic. by road are the number of passengers transported that infrastructure), highly specialized and carefully The air transport data represent the total (interna- by road times kilometers traveled. · Goods hauled specified indicators are required. The table provides tional and domestic) scheduled traffic carried by the by road are the volume of goods transported by road selected indicators of the size, extent, and produc- air carriers registered in a country. Countries submit vehicles, measured in millions of metric tons times tivity of roads, railways, and air transport systems air transport data to ICAO on the basis of standard kilometers traveled. · Rail lines are the length of rail- and of the volume of traffic in these modes as well instructions and definitions issued by ICAO. In many way route available for train service, irrespective of as in ports. cases, however, the data include estimates by ICAO the number of parallel tracks. · Passengers carried Data for transport sectors are not always inter- for nonreporting carriers. Where possible, these esti- by railway are the number of passengers transported nationally comparable. Unlike for demographic sta- mates are based on previous submissions supple- by rail times kilometers traveled. · Goods hauled tistics, national income accounts, and international mented by information published by the air carriers, by railway are the volume of goods transported by trade data, the collection of infrastructure data has such as flight schedules. railway, measured in metric tons times kilometers not been "internationalized." But data on roads are The data cover the air traffic carried on scheduled traveled. · Port container traffic measures the flow collected by the International Road Federation (IRF), services, but changes in air transport regulations of containers from land to sea transport modes and and data on air transport by the International Civil in Europe have made it more diffi cult to classify vice versa in twenty-foot-equivalent units (TEUs), a Aviation Organization (ICAO). traffic as scheduled or nonscheduled. Thus recent standard-size container. Data cover coastal shipping National road associations are the primary source increases shown for some European countries may as well as international journeys. Transshipment traf- of IRF data. In countries where a national road asso- be due to changes in the classification of air traffic fic is counted as two lifts at the intermediate port ciation is lacking or does not respond, other agencies rather than actual growth. For countries with few air (once to off-load and again as an outbound lift) and are contacted, such as road directorates, ministries carriers or only one, the addition or discontinuation includes empty units. · Registered carrier depar- of transport or public works, or central statistical of a home-based air carrier may cause significant tures worldwide are domestic takeoffs and takeoffs offices. As a result, definitions and data collection changes in air traffic. abroad of air carriers registered in the country. · Pas- methods and quality differ, and the compiled data sengers carried by air include both domestic and are of uneven quality. Moreover, the quality of trans- international passengers of air carriers registered port service (reliability, transit time, and condition of in the country. · Air freight is the volume of freight, goods delivered) is rarely measured, though it may be express, and diplomatic bags carried on each flight as important as quantity in assessing an economy's stage (operation of an aircraft from takeoff to its next transport system. landing), measured in metric tons times kilometers Unlike the road sector, where numerous qualified traveled. motor vehicle operators can operate anywhere on the road network, railways are a restricted transport system with vehicles confined to a fixed guideway. Considering the cost and service characteristics, railways generally are best suited to carry--and can effectively compete for--bulk commodities and con- Data sources tainerized freight for distances of 500­5,000 kilo- Data on roads are from the IRF's World Road meters, and passengers for distances of 50­1,000 Statistics, supplemented by World Bank staff kilometers. Below these limits road transport estimates. Data on railways are from a database tends to be more competitive, while above these maintained by the World Bank's Transport and limits air transport for passengers and freight and Urban Development Department, Transport Divi- sea transport for freight tend to be more competi- sion, based on data from the International Union tive. The railways indicators in the table focus on of Railways. Data on port container traffic are from scale and output measures: total route-kilometers, Containerisation International's Containerisation passenger- kilometers, and goods (freight) hauled in International Yearbook. Data on air transport are ton-kilometers. from the ICAO's Civil Aviation Statistics of the World Measures of port container traffi c, much of it and ICAO staff estimates. commodities of medium to high value added, give 2009 World Development Indicators 305 5.10 Power and communications Electric power Telephones Affordability and efficiency Access and use Quality Population Transmission Inter national covered by $ per month Telecom- Mobile cellular Consumption and distribution per 100 people voice traffic a mobile cellular Residential Mobile cellular munications and fi xed-line per capita losses Fixed Mobile cellular minutes per networka fi xed-line prepaid revenue a subscribers kWh % of output lines a subscriptions a person % tariff a tariff a % of GDP per employeea 2006 2006 2007 2007 2007 2007 2008 2008 2007 2007 Afghanistan .. .. .. .. .. .. .. .. 5.1 861 Albania 961 52 9 72 125 97 4.3 22.7 6.0 710 Algeria 870 18 9 81 18 82 4.6 8.2 2.7 285 Angola 153 14 1 29 .. 40 20.2 11.8 2.0 586 Argentina 2,620 13 24 102 3 94 4.8 12.5 3.1 1,929 Armenia 1,612 13 20 62 128 88 5.1 8.4 3.0 173 Australia 11,332 7 46 101 .. 99 27.5 26.5 3.6 310 Austria 8,090 6 41 119 .. 99 28.7 24.3 2.1 747 Azerbaijan 2,514 13 15 53 .. 99 2.4 15.2 2.6 413 Bangladesh 146 6 1 22 6 90 1.3 1.3 .. .. Belarus 3,322 12 38 72 .. 93 .. 11.8 2.1 280 Belgium 8,684 5 44 101 .. 100 36.4 21.9 3.0 690 Benin 69 .. 1 21 11 80 7.5 15.5 1.1 1,539 Bolivia 485 14 7 34 80 46 22.7 5.9 6.8 376 Bosnia and Herzegovina 2,385 17 28 65 241 99 9.5 9.9 5.7 657 Botswana 1,419 15 7 61 93 99 16.9 8.3 3.0 1,074 Brazil 2,060 17 21 63 .. 91 29.1 37.0 4.7 358 Bulgaria 4,311 11 30 129 31 100 9.2 18.6 5.9 522 Burkina Faso .. .. 1 11 11 61 10.3 16.9 4.0 440 Burundi .. .. 0 3 .. 82 .. 12.2 .. .. Cambodia 88 8 0 18 10 87 8.0 5.0 .. .. Cameroon 186 15 1 24 4 58 14.8 17.8 3.1 1,050 Canada 16,753 8 55 61 .. 98 32.8 19.2 2.7 424 Central African Republic .. .. 0 3 .. 19 10.6 12.6 1.1 293 Chad .. .. 0 9 .. 24 .. 13.2 .. .. Chile 3,207 12 21 84 40 100 27.0 13.7 .. 1,311 China 2,041 6 28 42 9 97 3.7 3.6 2.9 1,310 Hong Kong, China 5,883 12 60 155 1,387 100 11.3 2.6 3.5 813 Colombia 968 19 18 77 106 83 7.6 9.6 3.9 .. Congo, Dem. Rep. 96 4 0 11 4 50 .. 11.0 7.6 3,628 Congo, Rep. 155 64 0 34 .. 53 .. .. .. .. Costa Rica 1,801 10 32 34 119 87 4.6 4.5 2.2 470 Côte d'Ivoire 182 19 1 37 .. 59 22.8 14.8 5.5 1,442 Croatia 3,636 16 42 113 208 100 16.4 18.7 5.3 778 Cuba 1,231 16 9 2 31 77 13.2 22.7 .. 58 Czech Republic 6,509 6 23 123 74 100 30.9 18.6 3.7 796 Denmark 6,864 3 52 114 307 114 28.5 5.8 2.6 512 Dominican Republic 1,309 11 9 57 .. 90 14.4 9.1 0.5 .. Ecuador 759 45 14 76 90 84 1.1 9.0 4.1 512 Egypt, Arab Rep. 1,382 11 15 40 42 94 3.0 4.7 3.8 538 El Salvador 721 13 16 90 515 95 10.4 10.5 5.7 1,657 Eritrea 49 .. 1 2 7 2 .. .. 2.0 105 Estonia 5,883 11 37 148 .. 100 13.7 13.6 4.8 707 Ethiopia 38 10 1 2 3 10 1.5 3.1 2.2 142 Finland 17,177 4 33 115 .. 99 19.3 14.1 2.5 584 France 7,813 6 56 90 243 99 30.9 35.7 2.2 695 Gabon 1,083 18 2 88 74 79 .. 14.9 2.0 .. Gambia, The .. .. 4 47 .. 85 4.0 6.0 .. 481 Georgia 1,549 14 13 59 .. 96 7.3 8.5 6.5 355 Germany 7,174 5 65 118 .. 100 28.8 10.1 2.6 703 Ghana 304 16 2 32 1 68 4.7 5.9 .. 1,261 Greece 5,372 8 54 110 182 100 26.7 25.1 3.7 802 Guatemala 529 12 10 76 .. 76 8.7 4.5 .. .. Guinea .. .. 1 21 .. 80 3.4 3.5 .. .. Guinea-Bissau .. .. 0 17 .. 65 .. 21.9 .. .. Haiti 37 38 1 26 .. 32 .. 4.5 .. .. 306 2009 World Development Indicators STATES AND MARKETS Electric power Power and communications Telephones 5.10 Affordability and efficiency Access and use Quality Population Transmission Inter national covered by $ per month Telecom- Mobile cellular Consumption and distribution per 100 people voice traffic a mobile cellular Residential Mobile cellular munications and fi xed-line per capita losses Fixed Mobile cellular minutes per networka fi xed-line prepaid revenue a subscribers kWh % of output lines a subscriptions a person % tariff a tariff a % of GDP per employeea 2006 2006 2007 2007 2007 2007 2008 2008 2007 2007 Honduras 642 26 12 59 33 90 .. 10.8 6.6 391 Hungary 3,882 11 32 110 120 99 30.2 16.1 4.2 1,009 India 503 25 4 21 .. 61 3.5 1.6 2.0 .. Indonesia 530 11 8 36 .. 90 4.5 5.3 .. .. Iran, Islamic Rep. 2,290 20 34 42 9 95 0.2 3.8 1.4 913 Iraq .. 6 .. .. .. .. .. .. .. 941 Ireland 6,488 8 48 114 .. 99 42.2 18.7 2.4 .. Israel 6,889 3 43 124 364 100 .. 9.3 4.1 .. Italy 5,755 6 46 152 .. 100 27.4 17.1 3.2 1,228 Jamaica 2,453 13 14 100 .. 95 10.8 7.0 .. .. Japan 8,220 5 40 84 46 100 18.3 32.2 3.1 1,334 Jordan 1,904 13 10 83 32 99 8.3 4.5 8.3 1,026 Kazakhstan 4,293 9 21 80 47 81 .. 11.4 2.9 308 Kenya 145 17 1 30 3 77 11.6 13.4 6.1 1,782 Korea, Dem. Rep. 797 16 5 0 .. 0 .. .. .. .. Korea, Rep. 8,063 4 46 90 29 90 6.4 14.6 5.0 637 Kuwait 16,311 11 20 104 .. 100 9.3 7.9 .. 372 Kyrgyz Republic 2,015 24 9 41 30 24 .. 6.4 4.8 311 Lao PDR .. .. 2 25 7 55 3.9 3.0 1.7 748 Latvia 2,876 17 28 97 67 99 11.9 7.3 4.0 697 Lebanon 2,141 16 17 31 279 100 10.9 22.2 8.0 .. Lesotho .. .. 3 23 18 55 12.5 12.6 0.6 1,111 Liberia .. .. 0 15 .. .. .. .. 8.2 .. Libya 3,688 7 14 73 66 71 .. 6.1 .. .. Lithuania 3,233 9 24 146 54 100 15.0 8.7 3.1 .. Macedonia, FYR 3,495 24 23 96 125 100 8.7 13.2 6.8 1,065 Madagascar .. .. 1 11 1 23 18.3 12.4 3.9 394 Malawi .. .. 1 8 .. 93 3.3 12.0 3.3 .. Malaysia 3,388 1 16 88 .. 93 5.1 5.9 .. 571 Mali .. .. 1 21 2 22 9.9 10.0 6.0 1,490 Mauritania .. .. 1 42 5 51 12.9 9.9 7.5 1,272 Mauritius .. .. 29 74 125 99 5.5 4.4 3.6 492 Mexico 2,003 16 19 63 185 100 22.3 15.0 2.8 789 Moldova 1,516 40 28 49 149 98 3.1 8.9 10.1 294 Mongolia 1,298 12 6 30 5 41 .. 5.4 3.9 190 Morocco 685 19 8 65 22 98 27.4 22.2 4.8 .. Mozambique 461 14 0 15 13 44 17.7 10.1 1.2 980 Myanmar 93 27 1 0 .. 10 .. .. 0.6 81 Namibia 1,546 22 7 38 .. 95 14.5 11.5 .. 435 Nepal 80 21 2 12 6 10 3.4 2.9 1.0 565 Netherlands 7,055 5 45 118 .. 100 31.2 17.7 .. .. New Zealand 9,646 7 41 101 310 98 34.4 23.1 3.0 598 Nicaragua 426 22 4 38 65 70 5.1 13.8 .. .. Niger .. .. 0 6 .. 45 13.6 13.8 2.2 328 Nigeria 116 27 1 27 .. 60 10.3 12.1 3.1 .. Norway 24,296 8 42 110 193 .. 37.6 9.7 1.4 .. Oman 4,456 16 10 96 37 96 32.6 5.5 2.7 858 Pakistan 480 22 3 39 10 90 3.6 1.9 2.7 50 Panama 1,506 17 15 90 66 81 9.1 5.1 3.5 229 Papua New Guinea .. .. 1 5 .. .. 4.0 12.8 .. .. Paraguay 900 5 6 77 35 .. 7.2 5.7 4.8 799 Peru 899 9 10 55 99 92 15.4 8.0 2.9 624 Philippines 578 12 4 59 .. 99 14.2 5.7 4.4 .. Poland 3,585 9 27 109 .. 99 28.0 12.5 3.7 566 Portugal 4,799 8 40 127 178 99 25.7 26.4 4.5 1,365 Puerto Rico .. .. 27 86 .. 100 .. .. .. .. 2009 World Development Indicators 307 5.10 Power and communications Electric power Telephones Affordability and efficiency Access and use Quality Population Transmission Inter national covered by $ per month Telecom- Mobile cellular Consumption and distribution per 100 people voice traffic a mobile cellular Residential Mobile cellular munications and fi xed-line per capita losses Fixed Mobile cellular minutes per networka fi xed-line prepaid revenue a subscribers kWh % of output lines a subscriptions a person % tariff a tariff a % of GDP per employeea 2006 2006 2007 2007 2007 2007 2008 2008 2007 2007 Romania 2,402 10 20 106 41 98 12.2 11.9 3.5 617 Russian Federation 6,122 11 31 115 .. 95 11.7 8.6 2.6 439 Rwanda .. .. 0 7 11 90 7.3 10.0 3.2 1,040 Saudi Arabia 7,080 7 17 117 216 98 9.2 8.8 3.0 933 Senegal 150 26 2 29 26 85 17.4 8.4 9.9 1,859 Serbia 4,040 16 41 115 144 92 4.9 4.9 5.0 787 Sierra Leone .. .. .. 13 .. 70 .. 70.9 .. .. Singapore 8,520 5 41 129 1,531 100 7.1 4.0 2.9 .. Slovak Republic 5,136 4 21 112 97 100 24.5 16.1 3.4 748 Slovenia 7,124 6 42 96 92 100 20.5 12.4 3.2 587 Somalia .. .. 1 7 .. .. .. .. .. .. South Africa 4,810 9 10 88 .. 100 22.4 12.3 7.5 1,145 Spain 6,206 9 45 108 .. 99 30.8 33.3 4.2 809 Sri Lanka 400 15 14 40 34 90 4.8 2.4 2.5 755 Sudan 95 15 1 21 7 60 4.4 4.8 3.7 1,557 Swaziland .. .. 4 33 .. 90 4.8 12.1 12.7 .. Sweden 15,231 8 60 113 .. 98 22.8 7.5 2.7 905 Switzerland 8,360 7 65 109 .. 100 29.0 35.5 3.2 549 Syrian Arab Republic 1,466 24 17 31 79 96 1.2 9.1 3.0 409 Tajikistan 2,241 16 5 35 .. .. .. 23.3 2.9 114 Tanzania 59 21 0 21 0 65 10.9 11.1 .. .. Thailand 2,080 8 11 124 14 38 5.8 3.9 4.0 2,808 Timor-Leste .. .. 0 7 18 69 .. .. 8.0 645 Togo 97 46 2 18 5 85 13.1 18.0 7.4 1,059 Trinidad and Tobago 5,006 6 23 113 .. 100 19.7 7.9 2.6 .. Tunisia 1,221 13 12 77 73 100 3.0 7.2 4.3 915 Turkey 2,053 14 25 84 30 98 .. 12.7 2.5 1,782 Turkmenistan 2,123 14 9 7 .. 14 .. 17.2 0.7 72 Uganda .. .. 1 14 7 80 12.6 10.4 3.2 255 Ukraine 3,400 12 28 119 57 100 4.2 8.2 5.7 .. United Arab Emirates 14,567 7 32 177 .. 100 5.0 4.1 2.7 852 United Kingdom 6,185 8 55 118 .. 100 27.3 20.5 3.7 .. United States 13,564 6 54 85 .. 100 17.2 15.3 3.1 389 Uruguay 2,042 30 29 90 127 100 13.0 13.8 3.7 661 Uzbekistan 1,694 9 7 22 12 75 .. 1.8 2.5 117 Venezuela, RB 3,174 22 18 87 .. 90 7.0 24.7 3.8 .. Vietnam 598 11 34 28 .. 70 2.3 4.2 4.7 .. West Bank and Gaza .. .. 9 28 .. 95 .. 9.6 0.8 880 Yemen, Rep. 190 23 4 14 .. 68 0.8 4.9 .. .. Zambia 730 6 1 22 .. 50 27.7 12.3 2.5 .. Zimbabwe 900 7 3 9 21 75 .. 3.4 .. 381 World 2,751 w 9w 20 w 51 w .. w 80 w 10.9 m 10.1 m 3.2 w 664 m Low income 309 16 4 22 .. 54 9.0 10.1 3.3 301 Middle income 1,651 12 17 48 .. 83 8.7 8.9 3.2 595 Lower middle income 1,269 11 15 39 .. 80 5.4 8.4 3.1 624 Upper middle income 3,242 12 23 84 .. 95 11.9 12.3 3.3 566 Low & middle income 1,380 12 14 42 .. 76 8.7 9.1 3.3 624 East Asia & Pacific 1,669 7 23 44 9 93 4.5 5.0 3.0 546 Europe & Central Asia 3,835 12 26 95 .. 92 9.0 9.4 2.9 532 Latin America & Carib. 1,808 16 18 67 .. 91 10.4 9.6 3.8 530 Middle East & N. Africa 1,418 16 17 51 32 93 3.0 7.2 3.1 691 South Asia 453 24 3 23 .. 61 3.5 1.9 2.1 660 Sub-Saharan Africa 531 11 2 23 .. 56 11.6 11.8 4.7 499 High income 9,675 6 50 100 .. 99 27.3 16.1 3.1 747 Euro area 6,956 6 53 117 .. 99 28.7 18.7 2.8 725 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. 308 2009 World Development Indicators STATES AND MARKETS Power and communications 5.10 About the data Definitions The quality of an economy's infrastructure, includ- Operators have traditionally been the main source · Electric power consumption per capita measures ing power and communications, is an important ele- of telecommunications data, so information on sub- the production of power plants and combined heat ment in investment decisions for both domestic and scribers has been widely available for most coun- and power plants less transmission, distribution, foreign investors. Government effort alone is not tries. This gives a general idea of access, but a and transformation losses and own use by heat and enough to meet the need for investments in modern more precise measure is the penetration rate--the power plants divided by midyear population. · Elec- infrastructure; public-private partnerships, especially share of households with access to telecommunica- tric power transmission and distribution losses are those involving local providers and financiers, are tions. During the past few years more information losses in transmission between sources of supply critical for lowering costs and delivering value for on information and communication technology use and points of distribution and in distribution to con- money. In telecommunications, competition in the has become available from household and business sumers, including pilferage. · Fixed telephone lines marketplace, along with sound regulation, is lower- surveys. Also important are data on actual use of are telephone lines connecting a subscriber to the ing costs, improving quality, and easing access to telecommunications equipment. Ideally, statistics telephone exchange equipment. · Mobile cellular services around the globe. on telecommunications (and other information and telephone subscriptions are subscriptions to a pub- An economy's production and consumption of elec- communications technologies) should be compiled lic mobile telephone service using cellular technol- tricity are basic indicators of its size and level of for all three measures: subscription and possession, ogy, which provide access to the public switched development. Although a few countries export elec- access, and use. The quality of data varies among telephone network. Post-paid and prepaid subscrip- tric power, most production is for domestic consump- reporting countries as a result of differences in regu- tions are included. · International voice traffic is tion. Expanding the supply of electricity to meet the lations covering data provision and availability. the sum of international incoming and outgoing tele- growing demand of increasingly urbanized and indus- Globally, there have been huge improvements in phone traffic (in minutes) divided by total population. trialized economies without incurring unacceptable access to telecommunications, driven mainly by · Population covered by mobile cellular network is social, economic, and environmental costs is one of mobile telephony. By 2007 worldwide mobile cel- the percentage of people that live in areas served by the great challenges facing developing countries. lular phone subscribers numbered 3.3 billion, far a mobile cellular signal regardless of whether they Data on electric power production and consump- outpacing the 1.1 billion fixed-line subscribers. By use it. · Residential fixed-line tariff is the monthly tion are collected from national energy agencies by 2006 approximately 99 percent of the population subscription charge plus the cost of 30 three-minute the International Energy Agency (IEA) and adjusted in high-income countries and about 77 percent of local calls (15 peak and 15 off-peak). · Mobile cel- by the IEA to meet international definitions (for data the population in developing countries were covered lular prepaid tariff is based on the Organisation for on electricity production, see table 3.10). Electricity by a mobile cellular network (within areas served by Economic Co-operation and Development's low-user consumption is equivalent to production less power a mobile cellular signal). Indeed, in many develop- definition, which includes the cost of monthly mobile plants' own use and transmission, distribution, and ing countries, especially in Sub-Saharan Africa, the use for 25 outgoing calls per month spread over the transformation losses less exports plus imports. It number of mobile phones has overtaken the number same mobile network, other mobile networks, and includes consumption by auxiliary stations, losses of fixed-line phones. mobile to fixed-line calls and during peak, off-peak, in transformers that are considered integral parts Although access is the key to delivering telecom- and weekend times as well as 30 text messages of those stations, and electricity produced by pump- munications services to people, if the service is not per month. · Telecommunications revenue is the ing installations. Where data are available, it covers affordable to most people, then goals of universal revenue from the provision of telecommunications electricity generated by primary sources of energy-- usage will not be met. Two indicators of telecom- services such as fixed-line, mobile, and data divided coal, oil, gas, nuclear, hydro, geothermal, wind, tide munications affordability are presented in the table: by GDP. · Mobile cellular and fixed-line subscribers and wave, and combustible renewables. Neither pro- fixed-line telephone service tariff and prepaid mobile per employee are telephone subscribers (fixed-line duction nor consumption data capture the reliability cellular service tariff. Telecommunications efficiency plus mobile) divided by the total number of telecom- of supplies, including breakdowns, load factors, and is measured by total telecommunications revenue munications employees. frequency of outages. divided by GDP and by mobile cellular and fixed-line Over the past decade new financing and technol- telephone subscribers per employee. ogy, along with privatization and liberalization, have spurred dramatic growth in telecommunications Data sources in many countries. With the rapid development of Data on electricity consumption and losses are mobile telephony and the global expansion of the from the IEA's Energy Statistics and Balances of Internet, information and communication technolo- Non-OECD Countries 2008, the IEA's Energy Sta- gies are increasingly recognized as essential tools of tistics of OECD Countries 2008, and the United development, contributing to global integration and Nations Statistics Division's Energy Statistics Year- enhancing public sector effectiveness, efficiency, book. Data on telecommunications are from the and transparency. The table presents telecommuni- International Telecommunication Union's World cations indicators covering access and use, quality, Telecommunication Development Report data- and affordability and efficiency. base and World Bank estimates. 2009 World Development Indicators 309 5.11 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisionb Access and use Quality Affordability Application technology trade Fixed International Fixed Information broadband Internet broadband Secure and com- Goods Services Internet bandwidthb Internet Internet munications Exports Imports Exports per 100 people subscribersb bits per access servers technology % of total % of total % of total per 1,000 Personal Internet per 100 second per tariffb per million expenditures goods goods service people % computersb usersb people capita $ per month people % of GDP exports imports exports 2000­07a 2006 2007 2007 2007 2007 2008 December 2008 2007 2007 2007 2007 Afghanistan .. 62 .. .. .. .. .. .. .. .. .. .. Albania 24 90 3.8 14.9 0.31 216 31 5 .. 1.0 3.5 3.6 Algeria .. 91 1.1 10.3 0.85 89 17 1 2.5 0.0 6.9 .. Angola 2 9 0.7 2.9 0.07 17 164 1 .. .. .. .. Argentina 36 95 9.0 25.9 6.58 2,320 38 18 6.0 0.6 13.1 7.9 Armenia 8 91 31.9 5.7 0.07 .. 39 5 .. 0.6 5.8 14.6 Australia 155 99c .. 68.1 22.98 5,472 28 993 6.6 1.8 12.8 4.6 Austria 311 98 60.7 67.4 19.51 20,288 61 481 5.6 6.3 8.2 6.3 Azerbaijan 16 99 2.4 10.8 0.07 701 85 2 .. 0.1 6.1 3.1 Bangladesh .. 48 2.2 0.3 0.00 4 54 0 8.0 .. .. 5.7 Belarus 81 97 0.8 29.0 0.12 264 .. 2 .. 0.8 3.0 6.8 Belgium 165 99 41.7 65.9 25.55 24,945 31 251 5.8 3.7 4.8 8.7 Benin 0 13 0.7 1.7 0.02 17 105 0 .. 0.0 3.3 5.4 Bolivia .. 63 2.4 10.5 0.36 42 34 4 5.8 0.1 4.9 12.5 Bosnia and Herzegovina .. 96 6.4 28.0 2.24 530 15 7 .. 0.5 3.8 .. Botswana 41 9 4.8 5.3 0.19 43 30 2 .. 0.2 5.5 6.8 Brazil 36 91 16.1 35.2 3.54 1,041 47 24 5.8 3.2 14.5 1.8 Bulgaria 79 92 8.9 30.9 8.21 4,909 16 26 7.7 1.8 6.0 4.4 Burkina Faso .. 12 0.6 0.6 0.01 15 1,861 0 .. .. .. .. Burundi .. 15 0.8 0.7 0.00 1 .. 0 .. 0.5 2.5 0.0 Cambodia .. 43 0.4 0.5 0.06 17 91 1 .. .. .. 3.1 Cameroon .. 25 1.1 2.0 0.00 11 184 0 5.0 0.0 3.2 13.0 Canada 175 99 94.3 72.8 27.52 16,193 20 907 6.4 4.7 10.1 11.1 Central African Republic .. 5 0.3 0.3 0.00 0 1,396 0 .. .. .. .. Chad .. 4 0.2 0.6 0.00 1 .. .. .. .. .. .. Chile 51 97 14.1 31.1 7.90 4,086 53 35 4.2 0.1 9.0 2.7 China 74 89 5.7 16.1 5.04 280 19 1 7.9 30.9 28.6 4.5 Hong Kong, China 222 100 68.6 57.2 27.42 15,892 25 287 4.7 42.1 41.8 1.6 Colombia 23 84 8.0 27.5 2.74 971 36 11 4.4 0.3 13.3 7.9 Congo, Dem. Rep. .. 4 0.0 0.4 0.00 0 .. 0 .. .. .. .. Congo, Rep. .. 27 0.5 1.9 0.00 0 .. 0 .. .. .. .. Costa Rica 65 94 23.1 33.6 2.83 820 17 99 3.9 29.4 25.3 16.4 Côte d'Ivoire .. 35 1.7 1.6 0.05 16 47 1 .. 0.4 4.2 11.0 Croatia .. 94 .. 44.7 8.73 3,380 21 92 .. 5.3 7.5 4.2 Cuba 65 70 3.6 11.6 0.02 19 1,630 0 .. 1.9 2.9 .. Czech Republic 183 83 27.4 48.3 12.72 7,075 29 151 7.1 14.2 15.0 8.0 Denmark 353 96 54.9 80.7 35.87 34,506 30 1,041 5.8 7.1 11.9 .. Dominican Republic 39 78 3.5 17.2 1.58 154 28 13 .. .. .. 3.7 Ecuador 99 87 13.0 13.2 2.39 324 40 10 6.1 0.3 7.7 6.2 Egypt, Arab Rep. .. 96c 4.9 14.0 0.63 143 8 1 5.8 .. .. 4.2 El Salvador 38 83 5.2 11.1 1.31 18 18 10 .. 0.6 8.4 9.6 Eritrea .. 18 0.8 2.5 0.00 2 .. .. .. .. .. .. Estonia 191 .. 52.2 63.7 20.70 11,925 39 280 .. 14.2 11.1 6.1 Ethiopia 5 5 0.7 0.4 0.00 3 644 0 .. 0.3 7.1 6.3 Finland 431 87 50.0 78.8 30.57 17,221 38 686 5.2 18.9 14.4 8.4 France 164 97 65.2 51.2 25.20 29,466 38 172 5.7 8.0 9.8 4.1 Gabon .. 58 3.6 6.2 0.15 150 .. 4 .. 0.1 6.6 .. Gambia, The .. 12 3.3 5.9 0.02 36 384 2 .. 0.2 4.6 .. Georgia 4 89 5.4 8.2 1.06 745 48 6 .. 0.4 7.1 1.5 Germany 267 94 65.6 72.3 23.82 25,654 38 549 6.2 9.6 12.3 7.8 Ghana .. 26c 0.6 3.8 0.07 21 64 1 .. 0.0 6.3 0.0 Greece .. 100 9.4 32.9 9.09 4,537 25 61 5.4 3.3 6.0 1.6 Guatemala .. 50 2.1 10.1 0.21 187 34 8 .. 0.5 9.1 14.8 Guinea .. 10 0.5 0.5 0.00 0 800 0 .. .. .. .. Guinea-Bissau .. 31 0.2 2.2 0.00 1 .. .. .. .. .. .. Haiti .. 27 5.2 10.4 0.00 17 .. 1 .. .. .. 4.9 310 2009 World Development Indicators STATES AND MARKETS Daily Households The information age Personal computers and the Internet 5.11 Information and newspapers with communications televisionb Access and use Quality Affordability Application technology trade Fixed International Fixed Information broadband Internet broadband Secure and com- Goods Services Internet bandwidthb Internet Internet munications Exports Imports Exports per 100 people subscribersb bits per access servers technology % of total % of total % of total per 1,000 Personal Internet per 100 second per tariffb per million expenditures goods goods service people % computersb usersb people capita $ per month people % of GDP exports imports exports 2000­07a 2006 2007 2007 2007 2007 2008 December 2008 2007 2007 2007 2007 Honduras .. 61 2.0 6.0 0.00 244 .. 6 11.2 0.3 6.9 11.5 Hungary 217 101 25.6 51.9 14.21 4,773 25 83 5.9 26.1 19.9 6.8 India 71 53 3.3 7.2 0.28 32 6 1 5.6 1.3 8.3 41.6 Indonesia .. 65 2.0 5.8 0.11 53 22 1 3.9 5.3 5.4 11.9 Iran, Islamic Rep. .. .. 10.6 32.4 .. 153 43 0 3.5 0.1 1.9 .. Iraq .. .. .. .. .. .. .. .. .. .. .. 1.9 Ireland 182 119 58.2 56.1 18.46 15,229 38 679 5.9 22.4 24.1 30.1 Israel .. 92 .. 27.9 21.29 2,003 .. 273 6.5 10.9 11.4 28.5 Italy 137 98 36.7 53.9 18.29 10,302 26 93 5.8 3.7 7.0 3.5 Jamaica .. 70 6.8 56.1 3.47 19,151 30 32 6.6 0.2 3.6 6.8 Japan 551 99c .. 69.0 22.14 3,734 32 472 7.2 19.3 13.7 1.2 Jordan .. 96 6.7 19.7 1.50 164 31 9 9.3 4.8 7.0 0.0 Kazakhstan .. .. .. 12.3 1.75 129 .. 2 .. 0.1 5.2 2.5 Kenya .. 32c 1.4 8.0 0.05 9 168 1 8.2 1.0 5.6 4.1 Korea, Dem. Rep. .. .. .. 0.0 0.00 .. .. .. .. .. .. .. Korea, Rep. .. 100 57.6 75.9 30.36 1,027 20 696 7.1 27.2 16.5 1.4 Kuwait .. 95 23.7 33.8 0.99 871 46 65 4.5 .. .. 48.4 Kyrgyz Republic 1 .. 1.9 14.3 0.06 114 .. 1 .. 0.8 5.1 1.9 Lao PDR 3 30 1.8 1.7 0.06 32 268 0 .. .. .. .. Latvia 154 .. 32.7 55.0 6.42 3,537 26 98 .. 3.4 6.9 4.9 Lebanon 54 95 10.4 38.3 4.88 227 23 13 .. .. .. 2.2 Lesotho .. 13 0.3 3.5 0.00 2 49 0 .. .. .. .. Liberia .. .. .. 0.5 .. .. .. .. .. .. .. .. Libya .. 50 2.2 4.3 0.16 50 .. 0 .. .. .. 2.5 Lithuania 108 98 18.3 49.2 15.04 4,656 16 83 .. 4.8 6.4 3.1 Macedonia, FYR 89 98 36.8 27.3 4.93 17 15 12 .. 0.4 4.4 14.1 Madagascar .. 18 0.5 0.6 0.01 8 120 0 .. 0.5 4.7 0.5 Malawi .. 5 0.2 1.0 0.01 5 900 0 .. 0.4 3.8 .. Malaysia 109 95 23.1 55.7 3.81 998 20 27 6.8 41.5 36.0 4.9 Mali .. 15 0.8 0.8 0.03 17 58 1 .. 0.2 4.2 .. Mauritania .. 25 4.6 1.0 0.13 70 62 2 .. .. 2.1 .. Mauritius 77 96c 17.6 27.0 4.88 226 51 60 .. 4.7 6.1 2.8 Mexico 93 98 14.4 22.7 4.32 178 37 16 4.0 19.6 14.9 2.3 Moldova .. 74 11.1 18.4 1.24 931 23 7 .. 2.6 4.3 15.4 Mongolia 20 33 13.9 12.3 0.28 116 .. 9 .. 0.1 5.9 3.7 Morocco 12 78 3.6 21.4 1.55 814 20 1 8.3 5.7 6.7 3.3 Mozambique 3 9 1.4 0.9 0.00 3 100 0 .. 0.1 5.1 5.0 Myanmar .. 3 0.9 0.1 0.00 2 .. 0 .. .. .. .. Namibia 28 41 24.0 4.9 0.01 27 46 9 .. 0.5 7.3 2.7 Nepal .. 13 0.5 1.4 0.04 5 23 1 .. .. .. .. Netherlands 307 99 91.2 84.2 33.62 78,159 38 1,108 6.6 18.9 19.8 11.0 New Zealand 182 99 52.6 69.2 20.17 4,544 31 980 5.7 2.3 9.7 5.4 Nicaragua .. 60 4.0 2.8 0.34 144 30 7 .. 0.2 7.3 8.2 Niger 0 7 0.1 0.3 0.00 2 58 0 .. 0.4 4.4 32.8 Nigeria .. 26 0.8 6.8 0.00 5 690 1 3.4 0.0 6.9 .. Norway 516 97 62.9 84.8 30.57 26,904 57 851 4.4 1.8 9.7 4.2 Oman .. 79 7.1 13.1 0.78 142 31 12 .. 0.8 3.8 .. Pakistan 50 .. .. 10.8 0.03 44 18 1 5.6 0.5 7.2 6.8 Panama 65 87 4.6 22.3 4.31 15,977 15 87 5.9 0.0 6.7 4.6 Papua New Guinea 9 10 6.4 1.8 0.00 1 144 1 .. .. .. 2.2 Paraguay .. 79 7.8 8.7 0.84 163 35 6 .. 0.4 28.6 2.2 Peru .. 73 10.3 27.4 2.04 2,704 36 10 3.9 0.1 8.0 2.6 Philippines 79 63 7.3 6.0 0.56 114 23 5 5.7 29.1 20.6 7.0 Poland 114 89 16.9 44.0 8.99 2,748 27 85 6.0 5.6 9.6 4.0 Portugal .. 99 17.2 40.1 14.37 4,790 30 115 5.7 9.0 9.3 4.8 Puerto Rico .. 97 0.8 25.4 3.02 511 .. 54 .. .. .. .. 2009 World Development Indicators 311 5.11 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisionb Access and use Quality Affordability Application technology trade Fixed International Fixed Information broadband Internet broadband Secure and com- Goods Services Internet bandwidthb Internet Internet munications Exports Imports Exports per 100 people subscribersb bits per access servers technology % of total % of total % of total per 1,000 Personal Internet per 100 second per tariffb per million expenditures goods goods service people % computersb usersb people capita $ per month people % of GDP exports imports exports 2000­07a 2006 2007 2007 2007 2007 2008 December 2008 2007 2007 2007 2007 Romania 70 90 19.2 23.9 9.05 2,945 23 16 5.3 3.1 7.6 16.3 Russian Federation 92 98 13.3 21.1 2.81 573 14 7 4.1 0.5 10.1 6.0 Rwanda .. 2 0.3 1.1 0.03 16 92 0 .. 0.8 8.0 1.9 Saudi Arabia .. 99 14.8 26.4 2.58 510 40 8 4.7 0.3 7.8 .. Senegal 9 41 2.1 6.6 0.31 137 29 1 10.9 0.5 4.0 18.0 Serbia .. 80 24.4 20.3 4.41 2,861 9 2 .. .. .. .. Sierra Leone .. .. .. 0.2 .. .. .. 1 .. .. .. 0.2 Singapore 361 98 74.3 65.7 19.51 22,783 22 390 6.5 45.6 38.3 3.1 Slovak Republic 126 78 51.4 55.9 8.75 5,555 28 58 6.0 13.2 10.3 6.6 Slovenia 173 97 42.5 52.6 17.09 6,720 27 171 4.7 3.0 5.3 5.2 Somalia .. 8 0.9 1.1 0.00 0 .. 0 .. .. .. .. South Africa 30 59 8.5 8.3 0.79 71 26 37 9.7 1.8 11.3 3.9 Spain 144 96 39.3 51.3 17.98 11,008 29 171 5.5 4.0 7.9 5.4 Sri Lanka 26 32 3.7 3.9 0.32 118 21 3 6.0 1.7 4.9 10.6 Sudan .. 16 11.2 9.1 0.11 345 29 0 .. 0.0 7.5 5.4 Swaziland 24 18 3.7 3.7 0.00 1 1,877 5 .. 0.0 3.8 1.4 Sweden 481 94 88.1 79.7 35.85 49,828 32 775 6.4 11.2 12.2 13.1 Switzerland 420 86 91.8 76.3 31.52 29,417 32 982 8.0 3.7 7.4 .. Syrian Arab Republic .. 105 9.0 17.4 0.03 53 51 0 .. 0.1 2.5 5.8 Tajikistan .. 79 1.3 7.2 0.00 0 .. .. .. .. .. 12.6 Tanzania 2 7c 0.9 1.0 0.00 3 68 0 .. 0.4 6.2 2.5 Thailand .. 92 7.0 21.0 1.43 346 18 10 6.1 24.2 20.0 .. Timor-Leste .. .. .. 0.1 0.00 9 .. .. .. .. .. .. Togo 2 14 3.0 5.0 0.00 4 106 1 .. 0.1 4.2 6.9 Trinidad and Tobago 149 88 13.2 16.0 2.66 675 13 46 .. 0.2 5.9 .. Tunisia 23 93 7.5 16.8 1.12 303 13 11 6.0 4.2 5.9 1.2 Turkey .. 112 6.0 16.5 6.16 1,381 .. 57 5.5 2.0 4.0 1.8 Turkmenistan 9 .. 7.2 1.4 .. 16 .. .. .. .. .. .. Uganda .. 10 1.7 2.5 0.01 11 170 0 .. 6.9 10.0 7.6 Ukraine 131 97 4.5 21.5 1.72 206 21 4 7.1 1.5 3.3 3.6 United Arab Emirates .. 86 33.0 51.8 8.70 2,785 22 126 5.1 4.3 8.6 .. United Kingdom 290 98 80.2 71.7 25.58 39,650 29 908 6.7 20.5 13.6 7.8 United States 193 95 80.5 73.5 24.27 11,277 15 1,174 7.5 16.3 14.6 4.3 Uruguay .. 92 13.6 29.1 4.96 903 24 43 6.0 0.1 6.5 8.8 Uzbekistan .. 99 3.1 4.5 0.03 9 .. 0 .. .. .. .. Venezuela, RB 93 90 9.3 20.8 3.12 628 31 7 3.9 0.0 12.1 11.1 Vietnam .. 89 9.6 21.0 1.52 148 17 1 6.1 5.1 7.6 .. West Bank and Gaza 10 93 5.6 9.6 1.50 324 .. 1 .. .. .. .. Yemen, Rep. 4 43 2.8 1.4 .. 28 226 0 .. 0.1 3.7 18.9 Zambia 5 .. 1.1 4.2 0.02 3 91 0 .. 0.1 5.1 8.5 Zimbabwe .. 32 6.5 10.1 0.11 4 .. 1 3.5 0.3 2.0 .. World 105 w 89 m 15.3 w 21.8 w 6.03 w 3,297 w 31.4 m 112 w 6.5 w 15.4 w 15.2 w 6.7 w Low income .. 16 1.5 5.2 0.02 26 102.4 0 .. 1.4 6.7 .. Middle income 71 89 5.6 15.2 2.72 389 28.0 7 5.9 16.9 18.0 4.9 Lower middle income 72 79 4.6 12.4 2.33 199 30.5 2 6.5 20.6 20.2 5.0 Upper middle income 65 92 12.4 26.6 4.28 1,185 26.0 26 5.2 13.5 16.2 4.8 Low & middle income 66 63 5.3 13.1 2.35 318 36.3 5 5.9 16.1 17.5 4.9 East Asia & Pacific 74 53 5.6 14.6 3.70 247 21.7 2 7.3 30.9 28.1 5.2 Europe & Central Asia 109 96 10.8 21.4 4.39 1,114 21.8 24 5.0 1.8 7.0 5.0 Latin America & Carib. 64 84 11.3 26.9 3.64 1,126 34.0 18 4.9 11.4 15.9 4.7 Middle East & N. Africa .. 94 6.3 17.1 .. 186 23.0 1 4.5 .. .. .. South Asia 68 42 3.3 6.6 0.24 31 21.0 1 5.7 1.2 8.1 39.0 Sub-Saharan Africa .. 18 1.8 4.4 0.03 36 100.1 3 .. 1.1 8.2 .. High income 261 98 67.4 65.7 22.84 18,242 30.2 663 6.7 15.2 14.6 7.0 Euro area 201 97 55.5 59.2 21.60 32,560 30.5 321 5.9 9.4 10.9 8.2 a. Data are for the most recent year available. b. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. c. Data are for 2007. 312 2009 World Development Indicators STATES AND MARKETS The information age 5.11 About the data The digital and information revolution has changed the generally derive their estimates by multiplying sub- notebooks and excluding terminals connected to way the world learns, communicates, does business, scriber counts reported by Internet service providers mainframe and minicomputers intended primarily and treats illnesses. New information and communi- by a multiplier. This method may undercount actual for shared use and devices such as smart phones cations technologies (ICT) offer vast opportunities for users, particularly in developing countries, where and personal digital assistants. · Internet users are progress in all walks of life in all countries--opportu- many commercial subscribers rent out computers people with access to the worldwide network. · Fixed nities for economic growth, improved health, better connected to the Internet or prepaid cards are used broadband Internet subscribers are the number of service delivery, learning through distance education, to access the Internet. broadband subscribers with a digital subscriber and social and cultural advances. Broadband refers to technologies that provide line, cable modem, or other high-speed technology. The table presents indicators of the penetration of Internet speeds of at least 256 kilobits a second of · International Internet bandwidth is the contracted the information economy, quality, and secure Internet upstream and downstream capacity and includes digi- capacity of international connections between coun- servers), and some of the economics of the informa- tal subscriber lines, cable modems, satellite broad- tries for transmitting Internet traffic. · Fixed broad- tion age. band Internet, fiber-to-home Internet access, ethernet band Internet access tariff is the lowest sampled Comparable statistics on access, use, quality, local access networks, and wireless area networks. cost per 100 kilobits a second per month and are and affordability of ICT are needed to formulate Bandwidth refers to the range of frequencies available calculated from low- and high-speed monthly service growth-enabling policies for the sector and to moni- for signals. The higher the bandwidth, the more infor- charges. Monthly charges do not include installation tor and evaluate the sector's impact on development. mation that can be transmitted at one time. Reporting fees or modem rentals. · Secure Internet servers Although basic access data are available for many countries may have different definitions of broadband, are servers using encryption technology in Internet countries, in most developing countries little is known so data are not strictly comparable. transactions. · Information and communications about who uses ICT; what they are used for (school, The number of secure Internet servers, from the technology expenditures include computer hard- work, business, research, government); and how they Netcraft Secure Server Survey, indicates how many ware (computers, storage devices, printers, and other affect people and businesses. The global Partner- companies conduct encrypted transactions over the peripherals); computer software (operating systems, ship on Measuring ICT for Development is helping to Internet. The survey examines the use of encrypted programming tools, utilities, applications, and inter- set standards, harmonize information and commu- transactions through extensive automated explora- nal software development); computer services (infor- nications technology statistics, and build statistical tion, tallying the number of Web sites using a secure mation technology consulting, computer and network capacity in developing countries. For more informa- socket layer (SSL). Some countries, such as the systems integration, Web hosting, data processing tion see www.itu.int/ITU-D/ict/partnership/. Republic of Korea, use application layers to establish services, and other services); and communications Data on daily newspapers in circulation are from the encryption channel, which is SSL equivalent. services (voice and data communications services) United Nations Educational, Scientific, and Cultural According to the World Information Technology and and wired and wireless communications equipment. Organization (UNESCO) Institute for Statistics surveys Services Alliance's (WITSA) Digital Planet 2008, the · Information and communication technology on circulation, online newspapers, journalists, com- global marketplace for information and communica- goods exports and imports include telecommunica- munity newspapers, and news agencies. tions technologies was expected to be about $3.4 tril- tions, audio and video, computer and related equip- Estimates of households with television are derived lion in 2007 and to rise to almost $3.8 trillion in ment; electronic components; and other information from household surveys. Some countries report only 2008. The data on information and communications and communication technology goods. Software is the number of households with a color television set, technology expenditures cover the world's 75 largest excluded. · Information and communication technol- and so the true number may be higher than reported. buyers among countries and regions. ogy service exports include computer and communi- Estimates of personal computers are from an Information and communication technology goods cations services (telecommunications and postal and annual International Telecommunication Union (ITU) exports and imports are defi ned by the Working courier services) and information services (computer questionnaire sent to member states, supplemented Party on Indicators for the Information Society and data and news-related service transactions). by other sources. Many governments lack the capac- are reported in the Organisation for Economic Co- Data sources ity to survey all places where personal computers operation and Development's Guide to Measuring the Data on newspapers are compiled by the UNESCO are used (homes, schools, businesses, government Information Society. Information and communication Institute for Statistics. Data on personal computers offices, libraries, Internet cafes) so most estimates technology service exports data are based on the and the Internet are from the ITU's World Telecom- are derived from the number of personal computers International Monetary Fund's (IMF) Balance of Pay- munication Development Report database. Data sold each year. Annual shipment data can also be mul- ments Statistics Yearbook classification. on secure Internet servers are from Netcraft (www. tiplied by an estimated average useful lifespan before Definitions netcraft.com/) and official government sources. replacement to approximate the number of personal Data on information and communication technology computers. There is no precise method for determin- · Daily newspapers are newspapers issued at least goods trade are from the United Nations Statistics ing replacement rates, but in general personal com- four times a week that report mainly on events in the Division's Commodity Trade (Comtrade) database. puters are replaced every three to five years. 24-hour period before going to press. The indicator Data on information and communication technology Data on Internet users and related indicators are is average circulation (or copies printed) per 1,000 expenditures are from WITSA's Digital Planet 2008 based on nationally reported data. Some countries people. · Households with television are the percent- and Global Insight, Inc. Data on information and com- derive these data from surveys, but since survey age of households with a television set. · Personal munication technology service exports are from the questions and definitions differ, the estimates may computers are self-contained computers designed IMF's Balance of Payments Statistics database. not be strictly comparable. Countries without surveys for use by a single individual, including laptops and 2009 World Development Indicators 313 5.12 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda,b fileda journal articles % of manu- per million per million factured $ millions Non- Non- people people % of GDP $ millions exports Receipts Payments Residents residents Residents residents 2000­06c 2000­06c 2005 2000­06c 2007 2007 2007 2007 2007 2007 2007 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. 8 12 5 8 .. .. 186 809 Algeria 170 35 350 0.07 11 2 .. .. 84 765 2,235 1,415 Angola .. .. .. .. .. .. 12 1 .. .. .. .. Argentina 895 366 3,058 0.49 1,144 7 85 1,036 0 0 55,252 18,465 Armenia .. .. 180 0.21 9 2 .. .. 192 1 883 594 Australia 4,053 904 15,957 1.78 3,541 14 686 2,821 2,717 24,626 40,001 11,176 Austria 3,657 1,462 4,566 2.46 14,566 11 757 1,471 2,271 378 7,844 820 Azerbaijan .. .. 116 0.22 15 4 0 5 281 6 751 1,025 Bangladesh .. .. 193 .. .. .. 0 8 22 288 .. .. Belarus .. .. 490 0.68 346 3 7 69 1,188 337 3,666 1,409 Belgium 3,252 1,447 6,841 1.85 25,178 7 1,640 1,867 454 163 22,964 d 1,695d Benin .. .. .. .. 0 0 0 2 .. .. .. .. Bolivia 120 6 .. 0.28 15 5 2 16 .. .. 1,873 4,208 Bosnia and Herzegovina .. .. .. .. 76 3 .. .. 55 162 320 870 Botswana .. .. .. 0.39 16 0 0 11 .. .. .. .. Brazil 461 394 9,889 0.82 9,295 12 319 2,259 3,810 20,264 76,827 17,842 Bulgaria 1,344 488 764 0.48 618 6 10 73 211 28 6,868 674 Burkina Faso 22 16 .. 0.17 .. .. .. .. .. .. .. .. Burundi .. .. .. .. 1 4 0 .. .. .. .. .. Cambodia 17 13 .. 0.05 .. .. 0 10 .. .. 467 1,847 Cameroon 26 .. 131 .. 3 3 0 6 .. .. .. .. Canada 3,922 1,467 25,836 1.97 29,593 14 3,635 7,552 4,998 35,133 21,101 26,657 Central African Republic .. .. .. .. 0 0 .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 833 302 1,559 0.67 401 7 61 434 291 2,924 30,847 13,473 China 926 .. 41,596 1.42 336,988 30 343 8,192 153,060 92,101 669,276 56,840 Hong Kong, China 2,090 416 .. 0.74 2,370 19 259 1,357 160 13,606 7,902 15,627 Colombia 127 97 400 0.17 338 3 17 188 121 1,860 14,118 9,876 Congo, Dem. Rep. .. .. .. 0.48 .. .. .. .. .. .. .. .. Congo, Rep. 32 35 .. .. .. .. .. .. .. .. .. .. Costa Rica 122 .. 105 0.37 2,088 45 0 53 .. .. 5,872 5,882 Côte d'Ivoire 68 .. .. .. 460 32 0 10 .. .. .. .. Croatia 1,148 538 953 0.87 769 9 40 214 344 93 1,486 946 Cuba .. .. 261 0.51 248 35 .. .. 74 210 454 602 Czech Republic 2,578 1,555 3,169 1.54 15,410 14 35 653 716 192 9,156 1,006 Denmark 5,277 1,990 5,040 2.44 11,247 17 .. .. 1,660 197 4,444 662 Dominican Republic .. .. .. .. .. .. 0 32 0 0 .. .. Ecuador 51 .. .. 0.06 73 7 0 45 0 794 6,078 6,527 Egypt, Arab Rep. .. .. 1,658 0.19 5 0 122 241 516 1,589 .. .. El Salvador 47 .. .. .. 42 4 1 24 .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 2,622 579 439 1.15 840 12 10 40 44 19 1,537 443 Ethiopia 20 10 88 0.20 4 3 0 2 .. .. .. .. Finland 7,681 .. 4,811 3.43 15,565 21 1,216 1,326 1,804 211 3,504 629 France 3,353 1,746 30,309 2.12 80,465 19 8,827 4,603 14,722 2,387 70,432 3,151 Gabon .. .. .. .. 71 32 .. .. .. .. .. .. Gambia, The 28 17 .. .. 0 2 .. .. .. .. .. .. Georgia .. .. 145 0.18 39 7 11 5 83 79 554 605 Germany 3,386 1,144 44,145 2.52 155,922 14 7,249 9,698 47,853 13,139 72,788 3,377 Ghana .. .. 81 .. 5 1 0 .. .. .. .. .. Greece 1,790 795 4,291 0.50 1,005 8 52 600 772 3,889 6,416 889 Guatemala 31 11 .. 0.03 119 3 9 79 9 99 5,955 5,048 Guinea .. .. .. .. .. .. 0 0 .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. 3 0 .. .. .. .. 314 2009 World Development Indicators STATES AND MARKETS Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent 5.12 Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda,b fileda journal articles % of manu- per million per million factured $ millions Non- Non- people people % of GDP $ millions exports Receipts Payments Residents residents Residents residents 2000­06c 2000­06c 2005 2000­06c 2007 2007 2007 2007 2007 2007 2007 2007 Honduras .. .. .. 0.05 8 1 .. 25 .. .. 2,369 5,034 Hungary 1,745 491 2,614 1.00 19,349 25 841 1,596 689 102 3,615 631 India 111 86 14,608 0.69 4,944 5 112 949 4,521 19,984 73,308 12,361 Indonesia 199 .. 205 0.05 5,225 11 31 1,052 282 4,324 36,644 16,005 Iran, Islamic Rep. .. .. 2,635 0.59 375 6 .. .. .. .. .. .. Iraq .. .. .. .. 0 0 0 29 .. .. .. .. Ireland 2,882 740 2,120 1.31 28,720 28 1,110 24,669 847 78 1,905 1,116 Israel .. .. 6,309 4.53 3,088 8 784 946 257 7,239 3,293 7,285 Italy 1,407 .. 24,645 1.10 27,817 7 1,050 1,680 9,255 870 50,604 4,490 Jamaica .. .. .. 0.07 2 2 15 60 21 132 594 1,114 Japan 5,546 561 55,471 3.40 121,425 19 23,229 16,678 333,498 62,793 118,130 12,796 Jordan .. .. 275 0.34 38 1 0 0 .. .. .. .. Kazakhstan 783 83 96 0.28 1,470 23 0 68 1,433 124 .. .. Kenya .. .. 226 .. 81 5 23 24 38 33 1,451 1,187 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 4,162 583 16,396 3.23 110,633 33 1,920 5,075 128,701 43,768 112,157 20,131 Kuwait 74 94 233 0.18 .. .. 0 0 .. .. .. .. Kyrgyz Republic .. .. .. 0.20 8 2 2 12 155 3 191 424 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 1,758 648 134 0.69 353 7 13 40 114 37 1,398 479 Lebanon .. .. 234 .. .. .. .. .. .. .. .. .. Lesotho 10 11 .. 0.06 .. .. 20 .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 361 493 .. .. .. .. .. 0 .. .. .. .. Lithuania 2,358 411 406 0.80 1,214 11 0 22 62 20 2,218 431 Macedonia, FYR 547 83 .. 24.77 21 1 3 9 .. .. .. .. Madagascar 43 6 .. 0.16 7 1 2 9 4 40 445 432 Malawi .. .. .. .. 2 2 .. .. .. .. 222 582 Malaysia 503 63 615 0.60 64,584 52 36 1,195 531 4,269 12,289 13,605 Mali .. .. .. .. 3 7 0 1 .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. 0.38 112 8 0 6 .. .. .. .. Mexico 464 260 3,902 0.50 33,314 17 120 503 629 15,970 54,610 28,606 Moldova .. .. 89 .. 14 5 6 7 333 14 1,262 717 Mongolia .. .. .. 0.26 7 8 .. .. 103 110 339 277 Morocco .. .. 443 0.66 858 9 4 36 178 732 5,637 1,365 Mozambique .. .. .. 0.50 3 2 0 2 .. .. 553 943 Myanmar 18 139 .. 0.16 .. .. .. .. .. .. .. .. Namibia .. .. .. .. 83 5 .. 2 .. .. .. .. Nepal 59 137 .. .. .. .. .. .. .. .. .. .. Netherlands 2,524 1,863 13,885 1.69 74,369 26 4,322 3,662 2,079 367 .. .. New Zealand 4,207 768 2,983 1.17 628 10 141 553 1,892 5,952 9,665 9,945 Nicaragua .. .. .. 0.05 5 4 0 .. .. .. 1,195 4,780 Niger 8 10 .. .. 4 14 0 0 .. .. .. .. Nigeria .. .. 362 .. 62 8 .. 174 .. .. .. .. Norway 4,668 .. 3,644 1.49 4,391 18 700 622 1,223 5,431 3,326 3,286 Oman .. .. 111 .. 8 0 .. .. .. .. .. .. Pakistan 80 41 492 0.44 188 1 37 107 0 0 9,033 4,952 Panama 87 206 .. 0.25 0 0 0 47 .. .. 3,530 6,079 Papua New Guinea .. .. .. .. .. .. .. .. .. .. 76 536 Paraguay 71 122 .. 0.09 24 6 206 6 .. .. .. .. Peru .. .. 133 0.15 69 2 2 90 28 1,331 12,778 8,867 Philippines .. .. 178 0.14 13,792 54 5 364 231 3,034 8,398 6,335 Poland 1,562 227 6,844 0.56 4,177 4 108 1,575 2,392 361 13,951 1,100 Portugal 2,007 277 2,910 0.83 3,285 9 105 451 250 31 15,288 959 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 315 5.12 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda,b fileda journal articles % of manu- per million per million factured $ millions Non- Non- people people % of GDP $ millions exports Receipts Payments Residents residents Residents residents 2000­06c 2000­06c 2005 2000­06c 2007 2007 2007 2007 2007 2007 2007 2007 Romania 952 209 887 0.46 1,178 4 41 242 827 59 10,988 883 Russian Federation 3,255 574 14,412 1.08 4,144 7 396 2,806 27,505 11,934 31,502 10,372 Rwanda .. .. .. .. 1 16 0 1 .. .. .. .. Saudi Arabia .. .. 575 .. 121 1 0 0 128 642 .. .. Senegal .. .. 83 0.09 22 4 0 5 .. .. .. .. Serbia .. .. 849 1.65 176 4 .. .. 395 121 2,102 2,030 Sierra Leone .. .. .. .. .. .. .. 2 .. .. .. .. Singapore 5,713 549 3,609 2.39 105,549 46 716 9,905 696 9,255 5,383 11,170 Slovak Republic 2,186 424 919 0.49 2,517 5 149 124 239 106 2,889 1,031 Slovenia 2,924 1,476 1,035 1.63 1,246 5 18 169 331 15 1,493 428 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 361 109 2,392 0.92 1,859 6 53 1,596 0 5,781 17,106 12,811 Spain 2,639 919 18,336 1.21 9,916 5 536 3,402 3,267 265 55,909 1,924 Sri Lanka 141 77 136 0.19 99 2 0 0 151 279 3,382 2,835 Sudan .. .. .. 0.28 0 1 .. .. .. .. .. .. Swaziland .. .. .. .. 0 0 0 121 .. .. .. .. Sweden 6,139 .. 10,012 3.82 20,369 16 4,753 1,810 2,527 398 10,510 800 Switzerland 3,436 2,317 8,749 2.93 33,655 22 .. .. 1,692 342 11,723 4,670 Syrian Arab Republic .. .. 77 .. 29 1 0 20 124 133 .. .. Tajikistan .. .. .. 0.10 .. .. 1 1 26 0 170 612 Tanzania .. .. 107 .. 5 1 0 5 .. .. .. .. Thailand 292 211 1,249 0.26 30,925 27 54 2,287 877 511 20,140 13,415 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. 0 0 0 7 .. .. .. .. Trinidad and Tobago .. .. .. 0.12 60 2 .. .. 0 551 .. .. Tunisia 1,450 41 571 1.03 565 5 15 10 56 282 .. .. Turkey 577 65 7,815 0.76 328 0 .. 647 1,810 211 59,028 4,020 Turkmenistan .. .. .. .. .. .. .. .. .. .. 146 380 Uganda .. .. 93 0.19 24 11 1 5 6 1 .. .. Ukraine .. .. 2,105 1.03 1,314 4 53 577 3,474 2,416 19,888 3,858 United Arab Emirates .. .. 229 .. 23 1 .. .. .. .. .. .. United Kingdom 3,033 .. 45,572 1.80 63,066 19 15,108 10,121 17,375 7,624 28,976 4,999 United States 4,651 .. 205,320 2.61 228,655 28 82,614 25,047 241,347 214,807 256,429 33,065 Uruguay 373 51 204 0.26 41 3 0 7 .. .. 3,804 8,991 Uzbekistan .. .. 157 .. .. .. .. .. 324 198 1,382 680 Venezuela, RB 86 2 534 0.23 80 3 0 276 .. .. .. .. Vietnam 115 .. 221 0.19 1,273 6 .. .. 180 1,767 12,884 5,134 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. .. .. 1 1 149 9 .. .. .. .. Zambia .. .. .. 0.03 9 2 0 1 .. .. .. .. Zimbabwe .. .. .. .. 48 3 .. .. .. .. .. .. World 1,173 w .. w 708,086 s 2.30 w 1,807,189 s 18 w 165,115 s 164,279 s 1,012,033 s575,469 s 1,381,943 s 405,931 s Low income .. .. 2,103 .. .. 7 68 362 485 202 10,852 7,204 Middle income 510 .. 123,683 0.94 466,128 19 2,270 26,188 189,511 128,440 408,371 199,853 Lower middle income 336 .. 67,280 1.00 339,542 23 964 13,441 155,262 99,433 90,521 66,626 Upper middle income 1,107 304 56,403 0.73 126,586 14 1,306 12,747 34,249 29,007 317,850 133,227 Low & middle income .. .. 125,786 0.92 391,161 19 2,337 26,550 189,996 128,642 419,223 207,057 East Asia & Pacific 926 .. 44,064 1.42 .. 31 469 13,100 153,937 92,623 32,534 27,686 Europe & Central Asia 2,014 330 36,442 0.86 16,092 6 693 6,369 34,441 13,121 157,537 29,834 Latin America & Carib. 429 283 20,045 0.61 48,714 12 880 4,710 861 20,264 199,614 128,807 Middle East & N. Africa .. .. 6,243 .. .. 4 141 296 600 2,354 7,872 2,780 South Asia 111 86 15,429 0.68 .. 5 44 115 151 279 12,432 7,919 Sub-Saharan Africa .. .. 3,563 .. 2,717 8 110 1,960 6 1 17,106 12,811 High income 3,890 .. 582,300 2.48 1,312,001 18 162,778 137,730 822,037 446,827 962,720 198,874 Euro area 2,767 1,237 158,066 2.01 440,779 14 27,601 54,216 81,901 21,591 312,991 21,892 a. Original information was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. b. Excludes applications filed under the auspices of the European Patent Office (140,763 by nonresidents) and the Eurasian Patent Organization (2,293 by nonresidents). c. Data are for the most recent year available. d. Includes Luxembourg and the Netherlands. 316 2009 World Development Indicators STATES AND MARKETS Science and technology 5.12 About the data Definitions Technological innovation, often fueled by government- many low-technology products, the product approach · Researchers in R&D are professionals engaged in led research and development (R&D), has been the is more appropriate than the sectoral approach for conceiving of or creating new knowledge, products, pro- driving force for industrial growth. The best opportuni- analyzing international trade. This method takes only cesses, methods, and systems and in managing the ties to improve living standards, including new ways R&D intensity into account, but other characteristics projects concerned. Postgraduate doctoral students of reducing poverty, will come from science and tech- of high technology are also important, such as know- (ISCED97 level 6) engaged in R&D are considered nology. Countries able to access, generate, and apply how, scientific and technical personnel, and technol- researchers. · Technicians in R&D and equivalent scientific knowledge will have a competitive edge. And ogy embodied in patents. Considering these character- staff are people whose main tasks require technical there is greater appreciation of the need for high- istics would yield a different list (see Hatzichronoglou knowledge and experience in engineering, physical quality scientific input into public policy issues such 1997). Moreover, the R&D for high-technology exports and life sciences (technicians), and social sciences as regional and global environmental concerns. may not have occurred in the reporting country. and humanities (equivalent staff). They engage in R&D Science and technology cover a range of issues A patent is an exclusive right granted for an inven- by performing scientific and technical tasks involving too broad and complex to be quantified by a single tion (a product or process that provides a new way the application of concepts and operational methods, set of indicators, but those in the table shed light on of doing something or a new technical solution to a normally under researcher supervision. · Scientific countries' technology base. problem). The invention must be of practical use and and technical journal articles are published articles in The United Nations Educational, Scientific, and Cul- display a characteristic unknown in the body of exist- physics, biology, chemistry, mathematics, clinical med- tural Organization (UNESCO) Institute for Statistics col- ing knowledge in its technical field. A patent grants icine, biomedical research, engineering and technol- lects data on researchers, technicians, and expenditure protection for a specified period, generally 20 years. ogy, and earth and space sciences. · Expenditures for on R&D from around the world, through questionnaires Most countries have systems to protect patentable R&D are current and capital expenditures on creative and surveys and from other international sources. R&D inventions. The Patent Cooperation Treaty provides a work undertaken to increase the stock of knowledge, covers basic research, applied research, and experi- system for filing patent applications. It consists of an including on humanity, culture, and society, and the mental development. Data on researchers and techni- international phase followed by a national or regional use of knowledge to devise new applications. · High- cians are normally calculated as full-time equivalents. phase. An applicant files an international application technology exports are products with high R&D inten- Scientific and technical article counts are from a set and designates the countries in which patent protection sity, such as in aerospace, computers, pharmaceuti- of journals classified and covered by the Institute for is sought (since 2004 all eligible countries are automat- cals, scientific instruments, and electrical machinery. Scientific Information's Science Citation Index (SCI) and ically designated in every application under the treaty). · Royalty and license fees are payments and receipts Social Sciences Citation Index (SSCI). Counts are based The application is searched and published, and, option- between residents and nonresidents for authorized on fractional assignments; for example, an article with ally, an international preliminary examination is con- use of intangible, nonproduced, nonfinancial assets two authors from different countries is counted as one- ducted. In the national (or regional) phase the applicant and proprietary rights (such as patents, copyrights, half of an article for each country (see Definitions for requests national processing of the application, pays trademarks, and industrial processes) and for the use, fields covered). The SCI and SSCI databases cover the additional fees, and initiates the national search and through licensing, of produced originals of prototypes core set of scientific journals but may exclude some granting procedure. International applications under the (such as films and manuscripts). · Patent applications of regional or local importance. They may also reflect treaty provide for a national patent grant only--there is filed are worldwide patent applications filed through some bias toward English-language journals. no international patent. The national phase filing repre- the Patent Cooperation Treaty procedure or with a R&D expenditures include all expenditures for R&D sents the applicant's seeking of patent protection for a national patent office. · Trademark applications filed performed within a country, including capital expendi- given territory, whereas international filings, while they are applications to register a trademark with a national tures and current costs (annual wages and salaries represent a legal right, do not accurately reflect where or regional trademark office. and all associated costs of researchers, technicians, patent protection is eventually sought. Resident filings and supporting staff and other current costs, includ- are those from residents of the country or region con- Data sources ing noncapital purchases of materials, supplies, and cerned. Nonresident filings are from applicants outside Data on R&D are provided by the UNESCO Insti- R&D equipment such as utilities, books, journals, the country or region. For regional offices such as the tute for Statistics. Data on scientific and technical reference materials, subscriptions to libraries and European Patent Office, applications from residents of journal articles are from the U.S. National Science scientific societies, and materials for laboratories). any member state of the regional patent convention are Board's Science and Engineering Indicators 2008. The method for determining a country's high-tech- considered a resident filing. Some offices (notably the Data on high-technology exports are from the nology exports was developed by the Organisation for U.S. Patent and Trademark Office) use the residence United Nations Statistics Division's Commodity Economic Co-operation and Development in collabora- of the inventor rather than the applicant to classify resi- Trade (Comtrade) database. Data on royalty and tion with Eurostat. Termed the "product approach" to dent and nonresident filings. A trademark protects its license fees are from the International Monetary distinguish it from a "sectoral approach," the method owner by ensuring exclusive right to use it to identify Fund's Balance of Payments Statistics Yearbook. is based on R&D intensity (R&D expenditure divided goods or services or to authorize another to use it in Data on patents and trademarks are from the by total sales) for groups of products from Germany, return for payment. The period of protection varies, but World Intellectual Property Organization's WIPO Italy, Japan, the Netherlands, Sweden, and the United a trademark can be renewed indefinitely. Trademarks Patent Report: Statistics on Worldwide Patent Activ- States. Because industrial sectors specializing in a help consumers identify a product or service whose ity (2008) and www.wipo.int. few high-technology products may also produce nature and quality meet their needs. 2009 World Development Indicators 317 Text figures, tables, and boxes Introduction I n a more integrated world the financial crisis reaches more economies faster Although high-income economies remain the principal source and destination of international trade and investment, globalization has allowed more developing countries to participate in the growth of the global economy. They now account for almost 30 percent of world trade, and their share has been increasing. Developing economies attracted 20 times more foreign direct investment in nominal terms in 2007 than in 1990 and raised 40 times more net port- folio equity. The 12 largest developing economies, which produce 70 percent of developing country output, accounted for 67 percent of developing country exports in 2007. They also received 69 percent of the net private financial inflows to developing economies. The financial crisis that originated in high-income economies has spread rapidly to develop- ing economies through the same channels that connect them to the global economy: trade, investment, aid, and the movement of people. Although developing economies have previously encountered financial and economic crises, the current one is larger and may last longer. And because the world is more integrated, the crisis will affect more economies and more people. Even before the current crisis many developing economies' finances suffered from hikes in commodity prices in the first half of 2008, while net exporters of commodities gained. Since then, the prices of primary commodities have declined rapidly. While the net effect is difficult to measure, some developing economies have benefited from improved terms of trade, but price volatility undermines investment in the commodity-producing and exporting sectors-- and reduces government revenue. Export revenues constitute about a third of developing country GDP. But global demand is declining, as economies in recession import less and less. Economies that benefited from growing exports in the past decade will be hurt as export revenues decline. Weak prospects for international capital markets, foreign investments, and aid flows pose an immediate danger for developing economies. Equity prices plunged during the financial crisis, and developing country asset values declined. Credit conditions have tightened, and cross- border lending has become more expensive. Foreign direct investment is likely to decline as businesses around the world suffer from shrinking profits and growing pressure to raise cash. Lower financial flows affect countries differently, depending on their integration with capital markets and their dependence on foreign capital. In the past, as donor economies entered recession, their aid to developing economies fell. Countries dependent on official flows are likely to face fiscal difficulties if donors fail to keep their aid commitments. High-income economies are the primary destination for migrant workers. The financial crisis has distressed labor markets in many high-income economies, and millions of workers-- including many migrants--are losing their jobs. This may diminish workers' remittances, an important source of foreign capital in many developing economies. Data in this section provide snapshots of the world's integration and a framework for measuring it, to show how developing economies are likely to be affected by the recent financial crisis. Figures 6y­6vv illustrate just how quickly the financial crisis has spread. 2009 World Development Indicators 319 Global trade slows The importance of trade to low- and middle-income econo- middle-income economies' merchandise exports went to mies can be seen in the ratio of trade (imports plus exports) high-income economies (figure 6c). Low-income economies to GDP, which has risen rapidly, from 47 percent in 1990 to exported 67 percent of their goods to high-income markets. 70 percent in 2007 for low-income economies and 39 per- And some of the exports to other developing economies are cent to 64 percent for middle-income economies, surpassing primary goods, which are in turn used for manufactured goods the share of high-income economies (figure 6a). Increased destined for high-income markets. exports drove many developing economies' GDP growth in Since the onset of the financial crisis, the output of high- the past few years. income economies has fallen and with it global trade. In Developing economies' share of world trade increased the third quarter of 2008 the volume of imports by Group from 18 percent in 1990 to 28 percent in 2007. The 12 larg- of Seven industrial economies declined 1.4 percent over the est developing economies-- China, India, Russian Federation, same quarter in 2007. The sharpest reductions were in Italy Brazil, Mexico, Turkey, Indonesia, Islamic Republic of Iran, (7.1 percent), the United Kingdom (5.2 percent), the United Poland, Argentina, Thailand, and South Africa--accounted States (3.6 percent), and Japan (1.3 percent) (figure 6d). for 67 percent of developing economies' exports in 2007, a Developing economies' exports fell sharply starting in the share that has increased over time (figure 6b). China alone last quarter of 2008. Merchandise exports in January 2009 accounted for 27 percent. Low-income economies' share of fell 17 percent over exports in January 2008 for China, 31 per- global exports in 2007 was a mere 1.8 percent, but export cent for Mexico, and 43 percent for the Russian Federation. revenues constituted 33 percent of their GDP. Imports by large developing economies from other developing Although trade between developing economies increased economies have also declined, and the ripple effect is likely in the last decade, trade with high-income economies still to hurt the low-income economies whose main exports are accounts for the largest portion of developing economies' primary commodities such as fuels, metals, minerals, and total merchandise exports. In 2007 about 70 percent of agricultural raw materials. The importance of trade to Most developing economy exports were developing economies has increased 6a directed to high-income economies in 2007 6c Trade as a percentage of GDP, by income group Middle-income economies Low-income economies 80 Low-income economies Middle-income economies Exports to Exports to 60 middle-income middle-income economies economies 27% 25% High-income economies 40 Exports to Exports to high-income high-income economies economies 20 70% 67% Exports to Exports to low-income low-income economies economies 0 3% 8% 1990 1995 2000 2005 2007 Source: World Bank staff calculations based on data from IMF's Direction of Trade Source: World Development Indicators data files. database. High-income economies and a few large middle-income Merchandise imports of Group of Seven industrial economies economies account for a majority of world exports 6b have declined, reflecting slowing demand for imports 6d Percentage change over same quarter of previous year, seasonally adjusted Exports of goods and services (2000 $ billions) 15 15 Low-income economies 10 12 Germany Other middle-income economies The largest 12 developing economies 5 9 United States 6 0 Japan United Kingdom 3 ­5 Italy High-income economies ­10 0 2006 2006 2007 2007 2007 2007 2008 2008 2008 1990 1995 2000 2005 2007 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Source: World Development Indicators data files. Source: Organisation for Economic Co-operation and Development. 320 2009 World Development Indicators Primary commodity prices Private financial flows-- have been volatile greater access for some Commodity prices rose rapidly in early 2008 before collaps- Developing economies now have greater access to interna- ing in the second half of the year (figure 6e). Oil prices rose tional capital markets and attract more foreign direct invest- 48 percent between December 2007 and July 2008 and then ment (FDI). In nominal terms private capital flows to devel- plunged 69 percent by December 2008. Prices of nonenergy oping economies increased from $208 billion in 2003 to commodities increased an average of 32 percent then dropped $961 billion in 2007, but 70 percent of that went to the 12 39 percent over the same period. Food, fertilizers, and metals largest economies. and minerals have been among the most volatile. FDI was the source of 55 percent of private financial flows to The price hikes in early 2008 threatened to impover- developing economies in 2007. The 12 largest economies, with ish around 200 million people. They also weakened the greater access to international capital markets, also received fiscal positions of developing economies that import large large amounts of portfolio equity investment, at 18 percent of quantities of food and fuel (table 6f), as governments spent total private flows in 2007 (figure 6g). For developing econo- more on subsidies and safety nets to offset higher costs. mies with limited or no access to international capital markets, The sharp price decline in the second half of 2008 eased borrowing from private creditors was the second largest source the pressure on net importers of fuel and other commodi- of private flows, at 25 percent in 2007 (figure 6h). ties, but hurt the export revenues of economies that export Larger middle-income economies were directly hit by fall- mostly oil. Net importers of food and fuel may temporar- ing equity prices. Sovereign and corporate bond spreads wid- ily benefi t from lower prices, but producers of export com- ened, indicating more costly borrowing terms. Cross-border modities are likely to suffer. Low prices and uncertain long- lending by private creditors also slowed, restricting credit to term prospects may diminish further investment in primary developing economies, especially to the least creditworthy commodities. borrowers. Primary commodity prices have Large middle-income economies received increasing been volatile over the past year 6e amount of portfolio equity flows in recent years 6g World Bank commodity price indices, current prices, 2000 = 100 500 Private financial net inflows to largest 12 developing economies ($ billions) Energy 800 400 Commercial banks and other lending Nonenergy commodities 600 300 Food Bonds 200 400 Raw materials 100 Portfolio equity 200 0 Foreign direct investment Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 0 Source: World Bank. 2003 2004 2005 2006 2007 Source: Global Development Finance data files. For some economies food imports were equivalent to more than 7 percent of household consumption, 2005­07 average 6f Other developing economies borrowed Merchandise imports Food imports Fuel imports increasing amounts from private creditors 6h Share of Share of PPP household Share of Value PPP GDP consumption PPP GDP Private financial net inflows to other developing economies ($ billions) Economy ($ millions) (percent) (percent) (percent) 300 Namibia 3,003 30.1 11.3 1.9 Commercial banks and other lending Botswana 3,419 14.3 9.4 2.2 Gambia, The 270 14.1 9.4 2.4 Bonds 200 Jordan 11,852 45.9 9.1 10.5 Mauritius 3,560 26.7 8.5 4.6 Portfolio equity Senegal 3,694 19.0 8.0 5.2 100 Swaziland 2,461 47.5 7.6 3.5 Jamaica 5,430 32.8 7.3 9.9 Foreign direct investment Gabon 1,815 9.6 7.0 0.3 0 2003 2004 2005 2006 2007 PPP is purchasing power parity. Source: World Development Indicators data files. Source: Global Development Finance data files. 2009 World Development Indicators 321 Foreign direct investment-- Foreign direct investment-- largest source of private financing stable source of financing? In 2007 high-income economies received about 75 percent of FDI is considered a fairly stable source of external financ- global FDI inflows. The 12 largest developing economies re- ing, but in past financial crises it declined, sometimes ceived more than 66 percent of the remainder (figure 6i), with sharply. During the East Asian financial crisis net inflows of China alone receiving nearly 26 percent. Low-income econo- FDI fell 105 percent between 1997 and 1998 to Indonesia mies received a mere 1.5 percent of global FDI. and 58 percent to Malaysia in parallel with falling output (fig- Despite accounting for a small part of global flows, FDI ure 6k). For Thailand and the Republic of Korea FDI net inflows is the largest source of private financing for many develop- remained resilient for two to three years (figure 6l). ing economies, especially important for low-income econo- The current financial crisis originated in high-income mies with limited or no access to international capital mar- economies, so the effect on FDI flows to developing econo- kets. Between 2000 and 2007 FDI net inflows to low-income mies may be more drastic than in past crises originating in economies more than doubled, from 1.7 percent of GDP to developing economies. Businesses around the world are low- 4.2 percent of GDP (figure 6j). The increase was due partly to ering their capital spending in response to tighter credit and increased investments in oil, mineral, and other primary com- weaker global demand. And reinvested earnings, which have modity production sectors driven by high commodity prices accounted for a rising share of FDI net inflows, have started and partly to an increase in infrastructure projects (many to plunge as profits weakened. Likewise, weak commodity through public-private partnerships). In 2007 FDI net inflows prices are likely to undermine new investments in commodity- were more than 20 percent of GDP for nine small economies-- producing industries, and low real estate prices will weaken Republic of Congo, Seychelles, St. Kitts and Nevis, St. Lucia, FDI in the construction sector. For countries already running Montenegro, São Tomé and Principe, Djibouti, Grenada, and current account deficits, a shortfall in FDI may further under- Bulgaria. mine their ability to manage their balance of payments. Much global FDI is directed to high-income economies FDI net inflows to Indonesia and Malaysia declined and a few large middle-income economies . . . 6i immediately after the East Asian financial crisis hit 6k Indonesia Malaysia $ trillions FDI net inflows ($ billions) 2.5 GDP growth rate (%) To other developing economies 15 15 2.0 To the largest 12 developing economies 10 10 1.5 5 5 1.0 0 0 0.5 ­5 ­5 To high-income economies ­10 ­10 0.0 1990 1995 2000 2005 2007 ­15 ­15 1995 1997 1999 2001 2003 2005 1995 1997 1999 2001 2003 2005 Source: World Development Indicators data files. Source: World Development Indicators data files. . . . But as a share of GDP, FDI net inflows are a large FDI net inflows to the Republic of Korea and Thailand source of private financing for low-income economies 6j remained resilient for several years after the plunge in GDP 6l Korea, Rep. Thailand Share of GDP, by income group (%) FDI net inflows ($ billions) 6 GDP growth rate (%) High-income economies 15 15 5 4 10 10 Middle-income economies 3 5 5 2 0 0 Low-income economies 1 ­5 ­5 0 ­10 ­10 ­1 1980 1985 1990 1995 2000 2007 ­15 ­15 1995 1997 1999 2001 2003 2005 1995 1997 1999 2001 2003 2005 Source: World Development Indicators data files. Source: World Development Indicators data files. 322 2009 World Development Indicators Declining portfolio equity flows Private debt flows have become more costly In 2007 high-income economies received $577 billion--more Developing economies raised net capital of $85.4 billion than 80 percent of global portfolio equity flows. Developing through bond issuance in 2007, up from $20.4 billion in 2003. economies received nearly $140 billion, more than 87 per- The five largest bond issuers (Russian Federation, Kazakhstan, cent of it going to the 12 largest developing economies (figure India, Turkey, and Ukraine) accounted for 74 percent. Nearly 6m). And low-income economies received only 1.7 percent of 90 percent of the bond issuance by low-income economies in global equity flows. 2007 was public and publicly guaranteed, while almost 70 per- For larger middle-income economies with developed capital cent by middle-income economies was private nonguaranteed. markets, portfolio equity flows are the second largest source of private financial flows. Equities from these economies (emerg- Following the financial crisis, sovereign bond spreads (the ing market equities) became an attractive investment for high- spread over 10-year U.S. Treasury notes) widened for most de- income investors with appetites for risk, and their prices soared veloping economies, raising the cost of borrowing. Corporate after 2005. Equity markets, declining since October 2007, fell bond spreads jumped even more (figure 6o). By January 2009 sharply in the last quarter of 2008; investors became more risk sovereign bond spreads in 15 developing economies exceed- averse and were pressed to liquidate their holdings. ed the "distressed debt" threshold of 1,000 basis points. Developing economies now play a bigger role in international Borrowing from private creditors is (after FDI) the second- capital markets. Stock market capitalization of listed compa- largest source of private financial flows to developing econ- nies from developing economies accounted for 23 percent of omies. It is especially important for low-income economies global market capitalization in end-2007, up from 6 percent in with limited or no access to global equity markets. The finan- end-2000. In more mature developing markets the stock mar- cial crisis has made cross-border borrowing more costly. And ket capitalization to GDP ratio, at its peak, approached that of high-income economies, around 120 percent. But the ratio has trade finance tightened in the last quarter of 2008. been volatile over the past few years and may be unsustainable Most affected by the slump in debt flows is Europe and for some countries (figure 6n). By December 2008 the stock Central Asia, where net borrowing from foreign private credi- market capitalization in 42 developing economies where data tors rapidly increased during the last few years, from $14.5 bil- are available had fallen to 52 percent of GDP, down more than lion (1 percent of GDP) in 2003 to $131.2 billion (4 percent of 59 percentage points from its October 2007 peak. GDP) in 2007 (figure 6p). Net portfolio equity flows to large Spreads on emerging market sovereign and corporate middle-income economies increased considerably 6m bonds have widened, increasing the cost of borrowing 6o Low-income economies Spreads over U.S. Treasury notes (basis points) Other middle-income economies $ billions The largest 12 middle-income economies 1,200 150 Corporate Emerging Market Bond Index 900 100 600 50 300 Sovereign Emerging Market Bond Index 0 0 1990 1992 1994 1996 1998 2000 2002 2004 2007 Jan-07 Jun-07 Jan-08 Jun-08 Jan-09 Source: World Development Indicators data files. Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance. Stock market capitalizations Private lending to Europe and Central Asia declined after the financial crisis 6n increased ninefold between 2003 and 2007 6p December 2003 Peak (October 2007) Percent of GDP December 2008a Net commercial bank and other private lending ($ billions) 400 150 Russian Federation 120 300 Romania 90 Ukraine 200 Kazakhstan 60 Poland 100 30 Turkey Rest of Eastern Europe and Central Asia 0 0 South China India Brazil Russian Thailand Indonesia Turkey Mexico Africa Federation 2003 2004 2005 2006 2007 a. Data are as a percentage of 2007 GDP. Source: World Development Indicators data files and Standard & Poor's. Source: Global Development Finance data files. 2009 World Development Indicators 323 External debt declined but the share Official development assistance-- of private debt has increased a lifeline to poor countries External debt of low-income economies declined from 65 per- Official development assistance is the main source of exter- cent of GNI in 2000 to 29 percent in 2007, partly due to debt nal financing for low-income economies (figure 6s). For some relief (figure 6q). Public debt from official creditors accounted countries (Liberia, Burundi, Micronesia, Solomon Islands, Af- for nearly 90 percent of the long-term external debt of low- ghanistan, Guinea-Bissau, and Sierra Leone) it is equivalent income countries in 2007, and public debt from private credi- to more than 30 percent of GNI. tors for 7 percent. Private nonguaranteed debt was less than Official development assistance has risen 38 percent in 1 percent of GNI. constant prices since 2000, but most of the increase was for External debt of middle-income economies declined from 36 debt relief, technical assistance, and emergency relief, which do percent of GNI in 2000 to less than 25 percent in 2007. More not provide long-term investment to raise productive capacity. than half their long-term external debt was private nonguaran- Official development assistance to finance long-term develop- teed debt, which amounted to 10 percent of GNI in 2007. Public ment projects has not increased much since the 1970s (figure debt from private creditors was 6 percent of GNI and short-term 6t). After reaching a record $107 billion in 2005--mainly driven debt 5 percent of GNI, putting some middle-income economies by one-time debt relief--aid declined by 13 percent in 2007. at greater risk from tightening global credit markets. Official development assistance is a small part of the Net lending of international financial institutions to middle- budgets of the Development Assistance Committee members income economies had been declining since 2002 (figure 6r). of the Organisation for Economic Co-operation and Develop- But with private capital flows drying up, middle-income econo- ment, averaging only 0.28 percent of their GNI, a small frac- mies are again turning to the major international financial institu- tion of what they will spend on bailouts and fiscal stimulus in tions. The International Monetary Fund made new commitments their domestic economies. But for low-income economies that to several countries totaling $45 billion as of February 2009. depend on aid for their development, even a small decline can The World Bank created a $2 billion fast-track fund to assist the be devastating. To counter the financial crisis and progress poorest countries affected by the crisis and plans to increase toward their development goals, the poorest countries will commitments to all eligible countries in the next few years. need more assistance from external donors. For middle-income economies nearly 80 percent of long- Aid is equivalent to 5 percent of term debt was from private creditors while for low-income the GNI of low-income economies 6s economies 90 percent was from official creditors 6q Net aid received (% of GNI) Low-income economies Middle-income economies 10 Short-term debt Private nonguaranteed debt Public and publicly guaranteed debt from private creditors 8 Percent of GNI Public and publicly guaranteed debt from official creditors Low-income economies 100 100 6 75 75 4 Sub-Saharan African economies 50 50 2 Middle-income economies 25 25 0 1975 1980 1985 1990 1995 2000 2007 0 0 1990 1995 2000 2005 2007 1990 1995 2000 2005 2007 Source: Organisation for Economic Co-operation and Development, Development Assistance Committee International Development Statistics database and World Source: Global Development Finance data files. Development Indicators data files. Net nonconcessional lending to middle-income Aid for long-term development has economies from international financial institutions, remained about the same as in the 1970s 6t declining since 2002, recently increased 6r Net aid disbursements from Development Assistance Committee member economies (2006 $ billions) Regional development banks $ billions International Monetary Fund World Bank 120 Debt-related aid 50 100 Humanitarian aid Administrative costs 25 80 60 0 Technical cooperation 40 Bilateral aid for development programs and projects ­25 20 Contribution to multilateral institutions 0 ­50 1970 1975 1980 1985 1990 1995 2000 2007 1990 1992 1994 1996 1998 2000 2002 2004 2007 Source: Organisation for Economic Co-operation and Development, Development Source: Global Development Finance data files. Assistance Committee International Development Statistics database. 324 2009 World Development Indicators Official development assistance-- Migration and remittances--of increased in historical perspective importance for developing economies Aid flows usually decline after an economic crisis in the do- Net migration to high-income economies totaled 18.5 mil- nor economy. After Japan's bubble economy burst in 1990 lion people during 2000­05, almost twice the number for aid flows declined continually through 1996 by 24 percent. 1985­90 (figure 6w). Global flows of workers' remittances After the U.S. savings and loan crisis in the 1980s aid flows and compensation of employees also increased, from $68.6 dropped 46 percent over the next 10 years. After the Nordic billion in 1990 to $371.3 billion in 2007, more than 75 per- banking crisis in 1991 net aid flows fell 61 percent from Fin- cent of it received by developing economies, up from 45 per- land, 14 percent from Sweden, and 7 percent from Norway cent in 1990 (figure 6x). (figure 6u). In Norway aid flows recovered immediately, while Remittances have become an important source of foreign Sweden's aid flows continued to decline through 1998. exchange earnings for developing economies, at 2 percent of But donor economies can increase aid flows to developing their GDP in 2007. For low-income economies remittances economies even with difficult conditions at home. After the equaled 7 percent of GDP in 2007, up from 3 percent in 2000. dot-com bubble burst in 2000, the United States--despite And for Guyana, Lesotho, Liberia, Moldova, Seychelles, Tajiki- significant economic difficulties--increased its aid flows con- stan, and Tonga remittances accounted for more than 25 per- tinually through 2005 (figure 6v). The increase was partly a cent of GDP in 2007. response to international pressure to increase aid following The deepening global recession has reduced demand the 2000 UN Millennium Summit and the 2002 Financing for for migrant workers. As labor markets tighten, thousands of Development Conference and partly a response to the human- workers are losing their jobs, and some migrants are return- itarian needs of countries at war, such as Afghanistan and ing home. After a final surge associated with the repatriation Iraq. of savings and capital assets, remittances are expected to decline, leaving many families with fewer means of support. Aid flows declined after the Migration to high-income Nordic banking crisis in 1991 6u economies has increased 6w Index, 1991 = 100 Net migration (millions) 1985­90 1990­95 1995­2000 2000­05 120 20 Norway 100 Sweden 7% drop 10 80 14% drop 60 0 Finland 40 61% drop ­10 20 0 ­20 1985 1987 1989 1991 1993 1995 1997 1999 Low-income Middle-income High-income economies economies economies Source: Organisation for Economic Co-operation and Development, Development Assistance Committee's International Development Statistics database. Source: United Nations Population Division and World Development Indicators data files. Two U.S. financial crises in the More remittance flows are now late 20th century--aid down, then up 6v going to developing economies 6x Percentage of global workers' remittances and compensation of employees Index, 1988 = 100 1990: $68.6 billion 2007: $371.3 billion 200 East Asia & Pacific 5% Europe & Central Asia 5% Latin America & 150 Caribbean 8% East Asia 148% increase & Pacific High income 18% Banking crisis in 1988 24% 100 Europe & Middle East & Central Asia 27% drop Stock market crash in 2000 High income North Africa Sub-Saharan 14% 50 55% 17% Africa 5% 46% drop South Latin South Asia America & Asia 14% Caribbean 0 8% 17% 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 Sub-Saharan Africa 3% Middle East & North Africa 9% Source: Organisation for Economic Co-operation and Development, Development Assistance Committee's International Development Statistics database. Source: World Development Indicators data files. 2009 World Development Indicators 325 Merchandise trade Brazil 6y China 6z Goods make up 70­90 percent of most countries' total ex- Merchandise trade (percent of GDP) Merchandise trade (percent of GDP) 50 50 ports and imports. Since 2006 Brazil, China, and the Rus- Exports sian Federation have been running current account surpluses, 40 40 while Egypt, India, and South Africa have had deficits. In the 30 30 last quarter of 2008 merchandise exports and imports in ab- 20 20 Exports Imports solute values and as a share of GDP declined for China, India, 10 10 the Russian Federation, and South Africa (figures 6y­6dd). Imports Merchandise exports and imports also declined for Brazil, but 0 0 Q1-06 Q1-07 Q1-08 Q4-08 Q1-06 Q1-07 Q1-08 Q4-08 its GDP declined more. Source: Haver Analytics. Source: Haver Analytics. Equity price indices Brazil 6ee China 6ff MSCI equity price index MSCI equity price index (January 2007 = 100) (January 2007 = 100) Equities of large developing economies had become attrac- 250 250 tive investments, and their prices soared through October 200 200 2007. But equity prices fell back in late 2007 and plunged in 150 150 the last quarter of 2008 (figures 6ee­6jj). Declines in equity prices undermine the values of developing country assets. 100 100 50 50 0 0 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Source: MSCI Barra and Thomson Source: MSCI Barra and Thomson Datastream Advance. Datastream Advance. Bond spreads Brazil 6kk China 6ll Spreads over U.S. Treasury notes Spreads over U.S. Treasury notes Following the financial crisis, sovereign bond spreads have (basis points) (basis points) 2,500 2,500 widened. Corporate spreads have jumped even more. Bond 2,000 2,000 spreads, measured by J.P. Morgan's Emerging Markets Bond 1,500 1,500 Index Global (EMBI Global) and Corporate Emerging Markets Bond Index (CEMBI) and benchmarked against the yield of 10- 1,000 1,000 CEMBI CEMBI year U.S. Treasury notes, indicate that the cost of external bor- 500 500 EMBI Global rowing for developing countries is rising (figures 6kk­6pp). 0 0 EMBI Global ­500 ­500 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Source: JP Morgan Chase, Bloomberg, Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance. and Thomson Datastream Advance. Financing through international Brazil 6qq China 6rr capital markets Gross capital inflows over previous Gross capital inflows over previous 12 months ($ billions) 12 months ($ billions) 160 160 In 2007 developing economies received increasing amounts of gross private debt and equity flows. But in 2008, even be- 120 120 fore the intensification of the financial crisis, credit conditions 80 80 tightened, and transactions slowed. Since October 2008 there has been virtually no new equity issuance, and only a 40 40 limited amount of new bond issuance and syndicated bank 0 0 loan commitments (figures 6qq­6vv). Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Source: Dealogic. Source: Dealogic. 326 2009 World Development Indicators Arab Republic of Egypt 6aa India 6bb Russian Federation 6cc South Africa 6dd Merchandise trade (percent of GDP) Merchandise trade (percent of GDP) Merchandise trade (percent of GDP) Merchandise trade (percent of GDP) 50 50 50 50 40 40 40 40 Imports Imports Exports 30 30 30 30 Imports Exports 20 20 20 20 Exports Imports 10 10 10 10 Exports 0 0 0 0 Q1-06 Q1-07 Q1-08 Q4-08 Q1-06 Q1-07 Q1-08 Q4-08 Q1-06 Q1-07 Q1-08 Q4-08 Q1-06 Q1-07 Q1-08 Q4-08 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Arab Republic of Egypt 6gg India 6hh Russian Federation 6ii South Africa 6jj MSCI equity price index MSCI equity price index MSCI equity price index MSCI equity price index (January 2007 = 100) (January 2007 = 100) (January 2007 = 100) (January 2007 = 100) 250 250 250 250 200 200 200 200 150 150 150 150 100 100 100 100 50 50 50 50 0 0 0 0 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Source: MSCI Barra and Thomson Source: MSCI Barra and Thomson Source: MSCI Barra and Thomson Source: MSCI Barra and Thomson Datastream Advance. Datastream Advance. Datastream Advance. Datastream Advance. Arab Republic of Egypt 6mm India 6nn Russian Federation 6oo South Africa 6pp Spreads over U.S. Treasury notes Spreads over U.S. Treasury notes Spreads over U.S. Treasury notes Spreads over U.S. Treasury notes (basis points) (basis points) (basis points) (basis points) 2,500 2,500 2,500 2,500 CEMBI 2,000 2,000 2,000 2,000 CEMBI 1,500 1,500 1,500 1,500 CEMBI 1,000 1,000 1,000 1,000 EMBI Global 500 500 500 500 EMBI Global EMBI Global 0 0 0 0 ­500 ­500 ­500 ­500 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Source: JP Morgan Chase, Bloomberg, Source: JP Morgan Chase, Bloomberg, Source: JP Morgan Chase, Bloomberg, Source: JP Morgan Chase, Bloomberg, and Thomson Datastream Advance. and Thomson Datastream Advance. and Thomson Datastream Advance. and Thomson Datastream Advance. Arab Republic of Egypt 6ss India 6tt Russian Federation 6uu South Africa 6vv Gross capital inflows over previous Gross capital inflows over previous Gross capital inflows over previous Gross capital inflows over previous 12 months ($ billions) 12 months ($ billions) 12 months ($ billions) 12 months ($ billions) 160 160 160 160 120 120 120 120 80 80 80 80 40 40 40 40 0 0 0 0 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Jan-07 Jan-08 Jan-09 Source: Dealogic. Source: Dealogic. Source: Dealogic. Source: Dealogic. 2009 World Development Indicators 327 Tables 6.1 Integration with the global economy Trade International finance Movement of people Communication % of GDP Financing Emigration of through Workers' people with tertiary International international remittances education to International Internet capital Foreign direct and International OECD countries voice bandwidtha markets investment compensation migrant stock % of population age traffic a bits per % of GDP Gross Net Net of employees Net migration % of total 25 and older with minutes second Merchandise Services inflows inflows outflows received thousands population tertiary education per person per capita 2007 2007 2007 2007 2007 2007 2000­05 2005 2000 2007 2007 Afghanistan 35.6 .. 0.0 2.9 .. .. 1,112 .. 22.6 1 0 Albania 48.6 26.0 0.0 4.4 0.1 9.9 ­110 2.6 17.4 125 216 Algeria 64.9 .. 0.3 1.2 .. 1.6b ­140 0.7 9.4 18 89 Angola 83.5 21.1 5.8 ­1.5 1.5 .. 175 0.4 3.6 .. 17 Argentina 38.4 8.1 4.4 2.5 0.6 0.2 ­100 3.9 2.8 3 2,320 Armenia 48.2 14.9 0.2 7.6 0.0 9.2 ­100 7.8 8.9 128 .. Australia 37.4 9.7 .. 4.8 3.0 0.5 593 20.1 2.7 .. 5,472 Austria 87.2 25.4 .. 8.2 8.4 0.8 180 15.0 13.5 .. 20,288 Azerbaijan 51.9 14.8 2.9 ­15.2 0.9 4.1 ­100 2.2 1.8 .. 701 Bangladesh 45.4 6.6 1.0 1.0 0.0 9.6 ­500 0.7 4.4 6 4 Belarus 118.4 11.8 0.8 4.0 0.0 0.8 0 12.2 3.2 .. 264 Belgium 186.4 33.5 .. 15.9 12.2 1.9 180 6.9 5.5 .. 24,945 Benin 39.6 12.3 0.0 0.9 0.0 4.1b 99 2.1 8.6 11 17 Bolivia 60.5 9.9 0.0 1.6 0.0 7.1 ­100 1.3 5.8 80 42 Bosnia and Herzegovina 92.0 12.8 0.9 13.9 0.2 16.6 115 1.1 20.3 241 530 Botswana 74.3 15.5 0.0 ­0.2 0.0 1.1 20 4.4 5.1 93 43 Brazil 21.9 4.7 5.3 2.6 0.5 0.3 ­229 0.3 2.0 .. 1,041 Bulgaria 122.5 28.2 11.9 22.7 0.7 5.3 ­43 1.3 9.6 31 4,909 Burkina Faso 33.4 .. 0.2 8.9 .. 0.7b 100 5.5 2.5 11 15 Burundi 39.2 21.4 0.0 0.1 0.0 0.0 192 1.3 7.3 .. 1 Cambodia 115.0 29.4 3.2 10.4 0.0 4.2 10 2.2 21.4 10 17 Cameroon 35.6 10.1 0.0 2.1 ­0.8 0.8 6 0.8 17.1 4 11 Canada 60.8 10.8 .. 8.4 4.1 .. 1,041 18.9 4.7 .. 16,193 Central African Republic 24.8 .. 17.8 1.6 .. .. ­45 1.8 7.2 .. 0 Chad 69.9 .. 0.0 8.5 .. .. 219 4.3 9.0 .. 1 Chile 70.4 11.4 3.4 8.8 2.3 0.0 b 30 1.4 6.0 40 4,086 China 67.8 7.9 2.9 4.3 0.5 1.0 b ­1,900 0.0 3.8 9 280 Hong Kong, China 347.3 60.2 .. 26.2 29.5 0.2 300 44.0 29.6 1,387 15,892 Colombia 30.3 4.8 3.1 4.4 0.4 2.2 ­120 0.3 10.4 106 971 Congo, Dem. Rep. 70.9 .. 0.0 8.0 .. .. ­237 0.9 9.0 4 0 Congo, Rep. 117.7 50.3 0.0 56.1 .. 0.2 ­10 8.0 22.9 .. 0 Costa Rica 84.9 20.7 0.1 7.2 1.0 2.4 84 10.2 7.1 119 820 Côte d'Ivoire 74.1 17.1 0.4 2.2 .. 0.9 ­339 12.8 6.1 .. 16 Croatia 74.5 32.1 9.9 9.6 0.5 2.7 100 14.9 24.6 208 3,380 Cuba .. .. .. .. .. .. ­129 0.7 28.8 31 19 Czech Republic 137.3 18.0 .. 5.3 0.8 0.8 67 4.4 8.5 74 7,075 Denmark 65.2 37.1 .. 3.8 6.4 0.3 46 7.2 7.8 307 34,506 Dominican Republic 57.4 18.0 2.9 4.6 0.0 9.3 ­148 1.7 22.4 .. 154 Ecuador 61.5 8.4 0.2 0.4 0.0 7.0 ­400 0.9 9.5 90 324 Egypt, Arab Rep. 33.2 26.3 5.2 8.9 0.5 5.9 ­525 0.2 4.7 42 143 El Salvador 62.1 15.8 0.0 7.5 0.5 18.2 ­143 0.4 31.7 515 18 Eritrea 38.6 .. 0.0 ­0.2 .. .. 229 0.3 35.2 7 2 Estonia 126.8 35.7 2.9 12.9 7.5 2.0 1 15.0 9.9 .. 11,925 Ethiopia 34.4 16.1 0.0 1.1 0.0 1.8 ­140 0.7 9.8 3 3 Finland 70.0 17.2 .. 4.7 3.5 0.3 33 3.0 7.2 .. 17,221 France 45.1 10.7 .. 6.2 8.8 0.5 722 10.6 3.4 243 29,466 Gabon 72.6 13.7 9.6 2.3 0.9 0.1b 10 18.9 14.4 74 150 Gambia, The 51.0 29.7 0.0 10.6 .. 7.4 31 14.3 67.8 .. 36 Georgia 63.5 19.9 7.8 17.0 0.7 6.8 ­248 4.3 2.8 .. 745 Germany 71.9 14.4 .. 1.6 5.2 0.3 1,000 12.3 5.7 .. 25,654 Ghana 80.9 25.3 15.2 6.4 0.0 0.8 12 7.4 44.6 1 21 Greece 31.9 20.2 .. 0.6 1.7 0.8 154 8.8 12.1 182 4,537 Guatemala 60.6 11.0 0.0 2.1 0.2 12.6 ­300 0.4 23.9 .. 187 Guinea 50.2 7.5 0.0 2.4 .. 3.3 ­425 4.5 4.6 .. 0 Guinea-Bissau 65.8 .. 0.0 2.0 .. 8.1b 1 1.2 27.7 .. 1 Haiti 32.8 13.5 0.0 1.1 .. 18.2 ­140 0.3 83.4 .. 17 328 2009 World Development Indicators GLOBAL LINKS Integration with the global economy Trade International finance Movement of people 6.1 Communication % of GDP Financing Emigration of through Workers' people with tertiary International international remittances education to International Internet capital Foreign direct and International OECD countries voice bandwidtha markets investment compensation migrant stock % of population age traffic a bits per % of GDP Gross Net Net of employees Net migration % of total 25 and older with minutes second Merchandise Services inflows inflows outflows received thousands population tertiary education per person per capita 2007 2007 2007 2007 2007 2007 2000­05 2005 2000 2007 2007 Honduras 115.7 14.6 0.0 6.7 0.0 21.5 ­150 0.4 24.8 33 244 Hungary 137.0 23.1 8.2 26.9 25.8 0.3 65 3.1 12.8 120 4,773 India 30.8 15.2 5.2 2.0 1.1 3.0 b ­1,350 0.5 4.3 .. 32 Indonesia 48.6 8.5 2.9 1.6 1.1 1.4 ­1,000 0.1 2.9 .. 53 Iran, Islamic Rep. 46.1 .. 0.1 0.3 .. 0.4b ­1,250 2.8 14.3 9 153 Iraq .. .. .. .. .. .. ­375 .. 10.9 .. 4 Ireland 78.6 70.8 .. 10.1 8.5 0.2 188 14.1 33.7 .. 15,229 Israel 69.0 23.8 .. 5.9 4.3 0.6 115 38.4 7.8 364 2,003 Italy 47.4 11.1 .. 1.9 4.4 0.2 1,125 4.3 9.6 .. 10,302 Jamaica 68.6 43.4 33.4 7.6 1.0 18.8 ­100 0.7 84.7 .. 19,151 Japan 30.4 6.4 .. 0.5 1.7 0.0 270 1.6 1.2 46 3,734 Jordan 121.3 43.5 8.9 11.6 0.3 21.7 130 41.1 7.4 32 164 Kazakhstan 76.8 14.5 26.2 9.7 3.1 0.2 ­200 16.5 1.2 47 129 Kenya 54.0 17.2 0.0 3.0 0.1 6.6b 25 1.0 38.5 3 9 Korea, Dem. Rep. .. .. .. .. .. .. 0 0.2 .. .. .. Korea, Rep. 75.1 15.1 .. 0.2 1.6 0.1 ­80 1.1 7.5 29 1,027 Kuwait 76.7 20.3 .. 0.1 12.2 .. 264 65.8 7.1 .. 871 Kyrgyz Republic 94.9 33.8 0.0 5.6 0.0 19.1 ­75 5.6 0.9 30 114 Lao PDR 48.4 8.6 5.3 7.9 .. 0.0 b ­115 0.4 37.2 7 32 Latvia 86.9 23.5 7.8 8.3 1.2 2.0 ­20 19.5 8.5 67 3,537 Lebanon 65.0 92.4 11.8 11.7 ­1.0 23.7 0 16.4 43.8 279 227 Lesotho 158.4 11.7 1.2 8.1 .. 27.7 ­36 0.3 4.1 18 2 Liberia 92.9 216.4 158.1 17.9 0.0 8.8 ­119 1.5 44.3 .. .. Libya 91.1 4.7 0.2 8.0 1.3 0.0 b 10 10.4 4.3 66 50 Lithuania 107.9 19.4 5.3 5.3 1.6 3.7 ­30 4.8 8.3 54 4,656 Macedonia, FYR 111.9 18.5 0.5 4.2 0.0 3.5b ­10 6.0 29.4 125 17 Madagascar 51.2 22.1 5.7 13.5 .. 0.1b ­5 0.3 7.7 1 8 Malawi 60.6 .. 0.2 1.5 .. 0.0 b ­30 2.1 20.9 .. 5 Malaysia 173.1 30.1 7.9 4.5 5.9 1.0 b 150 6.4 10.5 .. 998 Mali 54.4 16.8 2.6 5.2 0.0 3.1b ­134 0.4 14.7 2 17 Mauritania 114.2 .. 0.0 5.8 .. 0.1b 30 2.2 8.5 5 70 Mauritius 90.3 55.3 16.2 5.0 0.9 3.2b 0 1.7 55.8 125 226 Mexico 55.6 4.1 4.3 2.4 0.8 2.7b ­3,983 0.6 15.5 185 178 Moldova 114.5 29.1 0.0 11.2 0.3 34.1 ­250 11.4 4.1 149 931 Mongolia 101.9 32.2 1.9 8.3 .. 4.9b ­50 0.4 7.4 5 116 Morocco 61.7 23.4 4.1 3.7 0.8 9.0 ­550 0.4 18.0 22 814 Mozambique 77.0 16.9 11.0 5.5 0.0 1.3 ­20 2.0 22.5 13 3 Myanmar .. .. .. .. .. ..b ­99 0.2 3.9 .. 2 Namibia 90.4 15.9 0.0 2.4 0.0 0.2 ­1 7.1 3.4 .. 27 Nepal 36.8 12.0 0.0 0.1 .. 16.8 ­100 3.0 4.0 6 5 Netherlands 136.2 23.7 .. 16.1 3.5 0.3 110 10.0 9.5 .. 78,159 New Zealand 42.7 13.5 .. 2.0 2.1 0.5 102 15.5 21.8 310 4,544 Nicaragua 83.5 16.2 0.0 6.7 0.0 12.9 ­210 0.5 30.2 65 144 Niger 40.8 11.7 0.0 0.6 0.0 1.9 ­28 0.9 5.4 .. 2 Nigeria 57.4 9.5 4.3 3.7 0.3 5.6b ­170 0.7 10.5 .. 5 Norway 55.8 20.7 .. 1.0 3.2 0.2 84 7.4 6.2 193 26,904 Oman 91.3 13.4 6.6 4.5 0.9 0.1 ­150 25.0 0.4 37 142 Pakistan 35.3 8.8 1.7 3.7 0.1 4.2 ­1,239 2.1 12.7 10 44 Panama 41.2 36.1 15.3 9.8 0.0 0.9 8 3.2 16.7 66 15,977 Papua New Guinea 121.1 29.9 13.2 1.5 0.1 0.2b 0 0.4 27.8 .. 1 Paraguay 82.3 10.8 0.0 1.6 0.1 3.8 ­45 2.9 3.8 35 163 Peru 44.9 7.1 3.1 5.0 .. 2.0 ­510 0.2 5.8 99 2,704 Philippines 75.3 11.0 6.3 2.0 2.4 11.3 ­900 0.4 13.5 .. 114 Poland 71.4 12.5 2.1 5.4 1.2 2.5 ­200 1.8 14.2 .. 2,748 Portugal 58.2 16.7 .. 2.5 2.8 1.8 276 7.2 18.9 178 4,790 Puerto Rico .. .. .. .. .. .. ­10 10.7 .. .. 511 2009 World Development Indicators 329 6.1 Integration with the global economy Trade International finance Movement of people Communication % of GDP Financing Emigration of through Workers' people with tertiary International international remittances education to International Internet capital Foreign direct and International OECD countries voice bandwidtha markets investment compensation migrant stock % of population age traffic a bits per % of GDP Gross Net Net of employees Net migration % of total 25 and older with minutes second Merchandise Services inflows inflows outflows received thousands population tertiary education per person per capita 2007 2007 2007 2007 2007 2007 2000­05 2005 2000 2007 2007 Romania 66.4 12.4 1.0 5.7 0.0 5.1 ­270 0.6 11.2 41 2,945 Russian Federation 44.8 7.6 12.2 4.3 3.6 0.3 917 8.4 1.4 .. 573 Rwanda 27.4 13.5 0.4 2.0 ­0.4 1.5 43 1.3 26.3 11 16 Saudi Arabia 85.0 10.1 .. ­2.1 0.0 .. 285 27.5 0.9 216 510 Senegal 55.1 17.7 0.6 0.7 0.1 8.3b ­100 2.8 17.1 26 137 Serbia 67.7 .. 0.0 7.8 .. 12.2b,c ­339 6.4 c 14.6c 144 2,861 Sierra Leone 41.4 8.2 0.0 5.7 ­0.6 8.9 472 2.1 49.2 .. .. Singapore 348.6 88.2 .. 15.0 7.6 .. 200 43.2 14.5 1,531 22,783 Slovak Republic 157.9 18.1 3.8 4.5 0.5 2.0 3 2.3 14.3 97 5,555 Slovenia 130.5 20.9 .. 3.1 3.3 0.6 22 8.4 10.9 92 6,720 Somalia .. .. .. .. .. .. 100 3.4 34.5 .. 0 South Africa 56.8 10.7 9.9 2.0 1.3 0.3 75 2.4 7.4 .. 71 Spain 42.7 15.9 .. 4.2 8.9 0.7 2,846 11.0 4.2 .. 11,008 Sri Lanka 58.9 13.3 3.4 1.9 0.2 7.8 ­442 1.9 28.2 34 118 Sudan 38.2 7.2 0.3 5.2 0.0 3.8 ­532 1.7 6.8 7 345 Swaziland 176.3 33.2 0.0 1.3 0.8 3.5 ­6 4.0 5.3 .. 1 Sweden 70.5 24.6 .. 2.7 6.3 0.2 152 12.4 4.5 .. 49,828 Switzerland 78.5 23.5 .. 11.7 11.9 0.5 100 22.3 9.5 .. 29,417 Syrian Arab Republic 69.4 16.3 0.0 1.8 0.0 2.2b 200 5.2 6.1 79 53 Tajikistan 105.7 20.0 0.0 9.7 0.0 45.5 ­345 4.7 0.6 .. 0 Tanzania 45.5 19.7 0.5 4.0 0.0 0.1 ­345 2.1 12.1 0 3 Thailand 119.8 28.0 1.1 3.9 0.9 0.7 231 1.7 2.2 14 346 Timor-Leste .. .. 0.0 .. .. .. 100 0.6 16.5 18 9 Togo 85.2 23.2 0.0 2.8 ­0.6 9.2b ­4 2.9 16.3 5 4 Trinidad and Tobago 108.0 9.5 6.1 .. ­2.3 0.4b ­20 2.8 78.9 .. 675 Tunisia 97.1 22.0 1.6 4.6 0.3 4.9 ­29 0.4 12.4 73 303 Turkey 42.3 6.7 5.5 3.4 0.3 0.2 ­30 1.8 5.8 30 1,381 Turkmenistan 103.5 .. 0.0 6.2 .. .. ­10 4.6 0.4 .. 16 Uganda 43.2 14.2 10.1 4.1 0.0 7.2 ­5 1.8 36.0 7 11 Ukraine 77.9 18.3 9.6 7.0 0.5 3.2 ­173 14.5 4.3 57 206 United Arab Emirates 150.4 .. .. .. .. .. 577 78.3 0.7 .. 2,785 United Kingdom 38.1 17.5 .. 7.1 9.9 0.3 948 9.0 17.1 .. 39,650 United States 23.1 6.3 .. 1.7 2.4 0.0 6,493 13.0 0.5 .. 11,277 Uruguay 44.1 13.0 5.9 3.8 0.0 0.4 ­104 2.5 9.0 127 903 Uzbekistan 57.7 .. 0.0 1.2 .. .. ­300 4.8 0.8 12 9 Venezuela, RB 50.5 4.0 5.2 0.3 1.0 0.1 40 3.8 3.8 .. 628 Vietnam 159.1 18.9 8.5 9.8 0.2 8.0 b ­200 0.0 26.9 .. 148 West Bank and Gaza .. .. 0.0 .. .. 14.9 b 11 48.5 12.0 .. 324 Yemen, Rep. 61.3 12.3 0.4 4.1 0.0 5.7b ­100 1.3 6.0 .. 28 Zambia 75.6 10.5 2.7 8.7 0.0 0.5 ­82 2.4 16.4 .. 3 Zimbabwe 122.0 .. 0.0 3.0 .. .. ­75 3.9 13.1 21 4 World 51.0 w 12.0 w .. w 4.0 w 4.3 w 0.7 w ..d s 3.0 w 5.4 w .. w 3,297 w Low income 62.6 12.5 3.3 4.2 0.1 5.7 ­2,858 1.7 12.8 .. 26 Middle income 55.9 9.6 5.0 3.7 1.1 1.8 ­15,770 1.3 6.7 .. 389 Lower middle income 59.6 10.6 3.2 3.5 0.6 2.4 ­11,295 0.8 6.6 .. 199 Upper middle income 52.0 8.8 6.8 3.9 1.5 1.2 ­4,475 3.6 6.8 .. 1,185 Low & middle income 56.2 9.8 4.9 3.7 1.1 2.0 ­18,629 1.4 7.2 .. 318 East Asia & Pacific 75.3 10.4 3.2 4.1 0.9 1.5 ­3,847 0.2 7.0 9 247 Europe & Central Asia 56.6 10.4 8.3 5.0 1.9 1.6 ­1,798 6.8 4.4 .. 1,114 Latin America & Carib. 41.2 6.0 4.5 3.0 0.7 1.8 ­6,811 1.0 10.6 .. 1,126 Middle East & N. Africa 57.5 .. 1.9 3.7 .. 3.7 ­2,618 3.0 10.4 32 186 South Asia 32.7 14.0 4.6 2.1 0.9 3.6 ­2,484 0.8 5.3 .. 31 Sub-Saharan Africa 59.7 13.6 5.7 3.4 0.7 2.5 ­1,070 2.1 12.3 .. 36 High income 49.1 12.7 .. 4.1 5.2 0.2 18,522 11.2 3.9 .. 18,242 Euro area 67.0 17.2 .. 6.3 8.4 0.5 6,887 9.5 6.9 .. 32,560 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. World Bank estimates. c. Includes Montenegro. d. World totals computed by the United Nations sum to zero, but because the aggregates shown here refer to World Bank definitions, regional and income group totals do not equal zero. 330 2009 World Development Indicators GLOBAL LINKS Integration with the global economy 6.1 About the data Globalization--the integration of the world definition of long-term loans differs across countries. syndicated bank lending, and new equity place- economy--has been a persistent theme of the past However, the quality and coverage of the data are ments. · Foreign direct investment net infl ows quarter century. Growth of cross-border economic improving as a result of continuous efforts by inter- are net inflows of FDI in the reporting economy. FDI activity has changed the structure of economies and national and national statistics agencies. See About is the sum of equity capital, reinvestment of earn- the political and social organization of countries. Not the data for table 6.11 for more information. ings, and other short- and long-term capital. · For- all effects of globalization can be measured directly. Workers' remittances are current private transfers eign direct investment net outflows are net outflows But the scope and pace of change can be monitored from migrant workers resident in the host country for of investment from the reporting economy to the rest along four key dimensions: trade in goods and ser- more than a year, irrespective of their immigration of the world. · Workers' remittances and compen- vices, fi nancial fl ows, movement of people, and status, to recipients in their country of origin. Com- sation of employees received are current transfers communication. pensation of employees is the income of migrants by migrant workers and wages and salaries of non- Trade data are based on gross flows that capture who have lived in the host country for less than a resident workers. · Net migration is the total number the two-way flow of goods and services. In conven- year. Migration has increased in importance, now of immigrants minus the total number of emigrants, tional balance of payments accounting, exports are accounting for a substantial part of global integra- including citizens and noncitizens, for the five-year recorded as a credit and imports as a debit. See tion. The estimates of the international migrant stock period. · International migrant stock is the number of tables 4.4 and 4.5 for data on the main trade com- are derived from data on foreign-born population-- people born in a country other than that in which they ponents of merchandise trade and tables 4.6 and people who reside in one country but were born in live, including refugees. · Emigration of people with 4.7 for the same data on services trade. another--mainly from population censuses. See tertiary education to OECD countries is the stock Financing through international capital markets About the data and Definitions for table 6.17 for of emigrants ages 25 and older, residing in an OECD includes gross bond issuance, bank lending, and new more information. One negative effect of migration country other than that in which they were born, with equity placement as reported by Dealogic, a com- is "brain drain"--emigration of highly educated peo- at least one year of tertiary education. · International pany specializing in the investment banking industry. ple. The table shows data on emigration of people voice traffic is the sum of international incoming and In financial accounting inward investment is a credit with tertiary education, drawn from Docquier, Mar- outgoing telephone traffic (in minutes) divided by and outward investment a debit. Gross flow is a bet- fouk, and Lowell (2007). The study analyzes skilled total population. · International Internet bandwidth ter measure of integration than net flow because migration using data from censuses and registers is the contracted capacity of international connections gross flow shows the total value of financial trans- of Organisation for Economic Development and Co- between countries for transmitting Internet traffic. actions over a period, while net flow is the sum of operation (OECD) countries and provides data disag- Data sources credits and debits and represents a balance in which gregated by gender for 1990 and 2000. many transactions are canceled out. Components of Well developed communications infrastructure Data on merchandise trade are from the World financing through international capital markets are attracts investments and allows investors to capi- Trade Organization's Annual Report. Data on trade in reported in U.S. dollars by market sources. talize on benefi ts offered by the digital age. See services are from the International Monetary Fund's Foreign direct investment (FDI) includes equity About the data for tables 5.10 and 5.11 for more (IMF) Balance of Payments database. Data on inter- investment, reinvested earnings, and short- and information. national capital market financing are based on data long-term loans between parent firms and foreign reported by Dealogic. Data on FDI are based on bal- Definitions affiliates. Distinguished from other kinds of interna- ance of payments data reported by the IMF, supple- tional investment, FDI establishes a lasting interest · Trade in merchandise is the sum of merchandise mented by staff estimates using data reported by in or effective management control over an enter- exports and imports. · Trade in services is the the United Nations Conference on Trade and Devel- prise in another country. FDI may be understated in sum of services exports and imports. · Financ- opment and official national sources. Data on work- many developing countries because some countries ing through international capital markets is the ers' remittances are World Bank staff estimates fail to report reinvested earnings and because the sum of the absolute values of new bond issuance, based on IMF balance of payments data. Data on net migration are from the United Nations Popu- Estimating the global emigrant stock 6.1a lation Division's World Population Prospects: The Internationally comparable estimates of migrant stock by country of origin are vital for making policies on 2006 Revision. Data on international migrant stock international migration. The World Bank's Development Research Group and the United Nations Population are from the United Nations Population Division's Division's Department of Economic and Social Affairs are developing estimates of international migrants Trends in Total Migrant Stock: The 2005 Revision. by country of origin. They have created the Global Migration database, which contains all publicly available Data on emigration of people with tertiary educa- data on international migrants, classified by age, sex, place of birth, and country of citizenship enumer- tion are from Frédéric Docquier, Abdeslam Marfouk, ated by censuses, population registers, and surveys. Available at www.unmigration.org, the database and B. Lindsay Lowell's, "A Gendered Assessment uses many sources of data, including the United Nations Statistics Division, the United Nations Popula- of the Brain Drain" (2007). Data on international tion Division, and the World Bank's Development Research Group, in collaboration with the University voice traffic and international Internet bandwidth are of Nottingham and the University of Sussex. The next step is to develop an appropriate methodology for from the International Telecommunication Union's estimating the emigrant stock for each country at specific points in time. International Development Report database. 2009 World Development Indicators 331 6.2 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1990­2000 2000­07a 1990­2000 2000­07a 1990­2000 2000­07a 1990­2000 2000­07a 1995 2007a Afghanistan .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. Algeria 2.8 0.7 ­0.8 10.8 2.1 18.6 ­1.3 16.3 57.9 183.8 Angola 6.2 11.9 7.1 16.3 6.1 31.5 7.8 20.0 80.8 204.1 Argentina 8.4 7.2 17.7 11.0 10.1 12.1 17.0 13.4 91.6 116.2 Armenia .. .. .. .. .. .. .. .. .. .. Australiab 7.3 5.6 9.2 7.1 5.7 17.5 8.7 11.8 99.4 152.4 Austriab 4.1 8.2 1.9 8.0 .. .. .. .. .. .. Azerbaijan .. .. .. .. .. .. .. .. .. .. Bangladesh 12.9 11.1 5.9 5.0 15.7 11.7 10.4 12.2 111.8 69.1 Belarus .. .. .. .. .. .. .. .. .. .. Belgiumb 6.0 4.7 5.7 5.0 8.7 6.4 9.1 6.8 104.3 101.2 Benin 1.0 3.6 8.2 0.9 3.3 7.7 9.7 8.5 106.6 80.3 Bolivia 2.8 11.2 9.1 5.0 4.3 22.7 9.7 9.9 89.4 137.2 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 4.8 4.9 4.0 0.8 4.7 9.1 2.7 6.5 89.3 89.6 Brazil 5.1 11.1 16.7 6.0 5.9 18.3 12.5 11.8 110.4 107.8 Bulgaria .. .. .. .. .. .. .. .. .. .. Burkina Faso 13.2 15.1 3.6 9.6 12.9 20.3 3.6 16.6 131.0 89.6 Burundi 8.6 ­3.9 4.0 12.0 ­4.3 6.7 ­6.9 17.8 163.6 128.6 Cambodia .. 16.0 .. 12.0 26.8 17.4 25.2 16.8 .. 84.8 Cameroon 0.3 ­1.6 5.0 2.0 ­3.6 12.1 2.1 10.4 90.4 132.1 Canadab 9.1 1.1 9.0 4.6 12.4 2.1 11.9 3.2 103.2 117.6 Central African Republic 20.0 ­1.2 4.3 5.0 3.5 1.7 0.2 11.5 193.0 81.3 Chad .. .. .. .. .. .. .. .. .. .. Chile 11.1 6.7 10.7 12.5 9.4 23.2 10.3 16.3 135.6 194.4 China 13.8 26.2 12.8 18.8 14.5 27.5 13.0 25.0 101.9 98.9 Hong Kong, China 8.4 9.6 8.9 8.8 8.3 9.5 8.8 9.5 99.1 96.8 Colombia 4.5 6.5 8.5 12.3 7.3 14.2 9.7 16.2 86.8 121.4 Congo, Dem. Rep. ­1.8 9.4 4.6 17.8 ­7.2 19.4 ­0.5 24.8 79.8 119.2 Congo, Rep. 6.6 3.0 4.9 17.5 7.5 20.5 8.7 22.6 52.0 187.1 Costa Rica 14.0 8.3 14.9 8.1 17.0 8.2 13.9 10.8 104.6 84.9 Côte d'Ivoire 5.0 2.0 ­0.3 7.9 6.0 13.2 2.4 16.1 122.0 134.5 Croatia .. .. .. .. .. .. .. .. .. .. Cuba .. ­0.6 .. 4.1 ­1.7 12.5 2.5 12.7 .. 143.5 Czech Republic .. .. .. .. .. .. .. .. .. .. Denmarkb 5.4 3.4 5.8 5.1 5.9 4.7 6.8 6.2 102.1 98.9 Dominican Republic 3.9 1.3 11.6 1.4 4.2 3.9 12.0 5.1 98.1 94.7 Ecuador 6.3 10.1 5.9 14.0 6.8 18.8 7.9 19.0 80.6 114.2 Egypt, Arab Rep. ­0.2 8.3 1.8 3.0 0.7 22.8 4.7 10.6 116.3 130.6 El Salvador 2.9 2.4 7.6 5.5 8.9 4.2 10.9 8.5 121.1 95.5 Eritrea ­28.3 ­14.5 ­3.2 ­2.4 ­31.1 ­13.7 ­0.2 2.6 101.7 70.6 Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia 10.5 7.8 7.3 16.5 10.7 17.0 7.3 23.8 151.0 110.2 Finland .. .. .. .. .. .. .. .. .. .. Franceb 8.4 4.8 6.6 6.3 6.8 3.7 5.6 5.1 106.3 101.4 Gabon 5.2 1.7 2.5 6.2 0.8 18.3 2.2 10.6 125.4 182.8 Gambia, The ­11.6 ­7.8 0.1 2.5 ­12.3 ­4.0 0.2 9.9 100.0 82.5 Georgia .. .. .. .. .. .. .. .. .. .. Germany b .. .. .. .. .. .. .. .. 107.5 97.2 Ghana 7.7 5.3 8.6 11.6 9.0 14.7 8.3 17.2 106.7 143.3 Greeceb 8.9 .. 9.3 .. 8.2 .. 8.2 .. 89.6 93.3 Guatemala 8.5 4.8 10.0 7.1 10.1 8.3 10.4 12.5 117.9 88.7 Guinea 5.0 ­10.4 ­1.4 1.0 0.6 5.5 ­2.6 7.9 89.6 173.1 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 12.6 8.0 13.3 3.6 12.2 10.4 14.4 8.5 113.2 83.9 Data for Taiwan, China 3.1 7.9 4.8 4.2 7.2 10.0 8.5 10.1 89.9 80.0 332 2009 World Development Indicators GLOBAL LINKS Export Growth of merchandise trade Import Export Import 6.2 Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1990­2000 2000­07a 1990­2000 2000­07a 1990­2000 2000­07a 1990­2000 2000­07a 1995 2007a Honduras 2.5 5.8 12.7 8.8 7.2 7.2 13.8 13.4 96.3 80.8 Hungary b 10.1 12.5 11.6 9.9 10.1 19.3 11.8 17.4 104.3 95.5 India 6.9 12.4 9 17.6 5.3 21.1 7.9 25.9 108.0 99.3 Indonesia 9.1 1.9 2.9 5.7 7.9 10.1 1.0 15.4 90.4 104.9 Iran, Islamic Rep. .. 1.9 .. 10.5 1.2 18.9 ­4.8 17.9 .. 160.2 Iraq .. .. .. .. .. .. .. .. .. .. Irelandb 15.2 2.6 11.4 3.1 17.3 ­0.4 13.8 1.2 98.9 91.3 Israelb 9.7 5.1 8.9 2.5 17.9 9.5 16.0 7.9 92.1 91.7 Italy b 4.8 1.6 4.2 2.1 10.0 4.4 8.4 5.5 96.8 98.0 Jamaica 2.2 1.4 .. 1.3 2.2 8.5 6.9 9.4 .. 101.6 Japanb 2.6 4.8 5.3 3.5 2.4 7.8 2.9 9.1 105.5 86.0 Jordan 4.7 8.7 3.8 8.2 6.6 17.2 5.1 18.6 115.6 92.4 Kazakhstan .. .. .. .. .. .. .. .. .. .. Kenya 3.9 6.2 7.4 8.6 6.3 13.1 6.0 17.9 103.9 83.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 15.8 13.6 10.0 8.3 10.1 14.2 7.1 14.5 138.5 72.0 Kuwait .. 5.8 .. 12.3 16.5 23.9 5.5 16.3 .. 194.3 Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. Lao PDR .. 11.4 .. 6.7 15.4 20.5 12.7 13.4 .. 114.0 Latviab 7.2 .. .. .. 11.6 .. .. .. .. .. Lebanon .. 14.4 .. 2.0 4.1 21.6 8.7 9.0 .. 98.8 Lesotho 13.3 20.3 3.1 9.0 12.8 21.2 1.9 14.1 100.0 78.9 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. 5.9 0.0 11.4 ­2.6 24.4 ­1.4 22.4 .. 172.0 Lithuania .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar 4.1 1.2 4.5 8.3 9.0 3.3 6.3 15.1 79.6 74.8 Malawi 2.7 6.4 ­2.4 8.0 0.9 9.3 ­0.6 14.3 105.7 84.6 Malaysia 13.6 7.4 10.6 7.2 12.2 10.7 9.5 10.4 108.5 100.1 Mali 10.3 2.8 6.4 6.2 6.3 14.6 4.7 14.0 109.6 132.3 Mauritania 1.9 9.1 4.2 14.2 ­1.9 24.3 ­1.6 20.3 102.2 153.6 Mauritius 2.7 4.7 3.4 9.0 2.2 3.9 3.3 10.4 88.5 86.4 Mexico 15.5 4.1 13.2 4.8 16.1 8.3 14.2 8.0 92.5 104.6 Moldova .. .. .. .. .. .. .. .. .. .. Mongolia .. 5.4 .. 10.3 0.7 21.8 0.5 18.6 .. 160.3 Morocco 7.5 2.6 7.2 8.1 7.2 9.5 5.5 15.4 89.1 98.4 Mozambique 15.2 17.4 1.0 10.7 10.2 30.1 1.1 16.7 151.1 127.6 Myanmar 15.5 6.4 13.8 ­6.3 14.4 17.3 22.6 ­0.5 214.3 122.8 Namibia 2.4 7.8 7.7 8.6 0.9 16.9 3.9 12.6 82.6 139.3 Nepal .. ­1.5 .. 0.1 10.7 2.8 9.3 8.9 .. 80.0 Netherlandsb 6.8 4.4 7.0 4.9 7.2 5.9 7.3 5.2 103.2 111.7 New Zealandb 4.6 3.3 5.9 8.1 3.9 10.9 5.7 13.9 101.8 118.1 Nicaragua 10.4 8.6 9.3 5.6 10.3 10.9 11.6 11.0 128.9 78.3 Niger 3.1 ­8.7 ­2.1 11.8 0.0 13.9 0.8 20.0 121.4 313.9 Nigeria 3.3 1.8 2.5 11.6 1.1 19.6 3.1 18.8 55.6 168.4 Norway b 6.6 0.6 7.8 6.9 8.6 7.4 7.2 7.6 60.3 134.0 Oman 4.0 ­4.5 .. 9.6 5.7 12.3 6.1 15.4 .. 180.8 Pakistan 2.5 9.3 2.4 10.4 4.3 11.6 3.1 20.3 119.2 65.5 Panama 6.0 ­0.5 7.8 5.1 9.4 4.4 8.7 10.8 100.0 94.0 Papua New Guinea ­7.7 ­2.7 .. 4.9 3.7 15.1 ­0.8 12.2 .. 177.2 Paraguay ­0.2 15.9 5.4 17.9 1.7 19.2 7.0 21.7 118.3 99.2 Peru 9.4 9.7 10.6 8.2 9.0 24.5 10.8 13.9 123.4 161.9 Philippines 16.0 4.6 11.3 4.3 18.8 4.8 12.5 7.3 80.2 81.7 Polandb 9.8 13.9 19.0 10.6 9.4 24.7 16.9 19.6 101.7 109.4 Portugalb 0.3 .. 0.5 .. ­3.0 .. ­2.6 .. 104.7 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 333 6.2 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1990­2000 2000­07a 1990­2000 2000­07a 1990­2000 2000­07a 1990­2000 2000­07a 1995 2007a Romania .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. Rwanda ­8.0 5.7 0.8 8.1 ­4.0 17.6 ­1.7 14.7 110.1 127.2 Saudi Arabia 2.9 2.2 .. 12.7 3.1 21.6 0.8 17.6 .. 236.5 Senegal 10.6 2.2 4.9 7.1 4.0 10.2 3.6 15.8 156.3 97.4 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore 11.7 13.4 8.3 8.9 9.9 14.8 7.8 12.9 104.3 84.5 Slovak Republic .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. South Africa 4.5 2.9 7.6 10.5 2.5 14.3 5.8 19.1 106.0 126.7 Spainb 11.4 4.2 9.3 7.2 14.5 5.8 12.0 8.3 104.3 104.7 Sri Lanka 7.4 4.1 8.0 2.3 11.3 6.4 8.9 9.9 99.0 75.8 Sudan 12.6 8.4 8.4 23.9 14.0 26.4 9.8 29.5 100.0 180.7 Swaziland 4.0 10.8 3.1 8.5 5.9 14.9 5.0 14.7 100.0 86.6 Swedenb 8.9 3.9 6.4 2.1 7.4 10.2 5.4 10.3 109.5 89.3 Switzerlandb 3.7 4.9 4.2 3.0 4.4 6.3 3.6 5.1 96.4 96.4 Syrian Arab Republic 2.2 ­0.4 .. 12.3 0.9 12.8 3.6 19.8 .. 134.4 Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania 6.0 5.8 ­2.0 11.8 6.4 15.9 0.1 20.6 98.0 109.4 Thailand 9.6 9.1 2.6 10.1 10.5 13.6 5.0 14.6 116.0 96.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 9.1 8.0 6.0 4.3 6.6 13.7 5.5 16.5 99.1 85.5 Trinidad and Tobago .. 5.3 .. 3.3 6.8 21.1 12.1 12.6 .. 132.2 Tunisia 5.7 9.3 4.3 5.1 6.0 13.7 5.2 11.2 95.8 91.5 Turkey 10.7 13.6 11.1 12.1 9.2 22.0 10.3 21.3 105.7 94.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 17.8 10.8 22.4 5.5 15.4 19.5 21.0 12.6 197.2 104.2 Ukraine .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. 7.8 .. 16.9 6.5 21.8 10.7 21.7 .. 154.0 United Kingdomb 6.2 2.5 6.5 5.2 6.2 9.0 6.5 11.0 100.1 104.5 United Statesb 6.6 4.4 9.1 5.2 7.2 6.7 9.5 8.7 103.3 96.6 Uruguay 6.1 9.3 10.5 4.1 5.2 12.6 10.1 9.9 116.2 88.8 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 5.2 ­1.2 4.8 10.1 5.4 15.5 5.3 16.6 63.4 161.6 Vietnam .. 13.2 .. 14.0 22.7 20.4 22.7 22.2 .. 92.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. ­3.5 4.4 9.1 20.6 13.4 0.6 17.5 .. 154.8 Zambia 6.1 8.0 2.9 17.6 ­4.6 28.3 1.3 24.8 189.7 193.2 Zimbabwe 8.8 ­6.6 8.0 ­7.5 3.4 2.4 1.9 2.7 96.8 94.0 a. Data for 2007 are provisional and may differ from data published elsewhere. b. Data are from the International Monetary Fund's International Financial Statistics database. 334 2009 World Development Indicators GLOBAL LINKS Growth of merchandise trade 6.2 About the data Definitions Data on international trade in goods are available from national and international sources such as the · Export and import volumes are indexes of the quan- from each country's balance of payments and IMF's International Financial Statistics database, the tity of goods traded. They are derived from UNCTAD's customs records. While the balance of payments United Nations Economic Commission for Latin Amer- quantum index series and are the ratio of the export focuses on the financial transactions that accom- ica and the Caribbean, the United Nations Statistics or import value indexes to the corresponding unit pany trade, customs data record the direction of Division's Monthly Bulletin of Statistics database, value indexes. Unit value indexes are based on data trade and the physical quantities and value of goods the World Bank Africa Database, the U.S. Bureau reported by countries that demonstrate consistency entering or leaving the customs area. Customs data of Labor Statistics, Japan Customs, and UNCTAD's under UNCTAD quality controls, supplemented by may differ from data recorded in the balance of pay- Commodity Price Statistics. The IMF also compiles UNCTAD's estimates using the previous year's trade ments because of differences in valuation and time data on trade prices and volumes in its International values at the Standard International Trade Classifi - of recording. The 1993 United Nations System of Financial Statistics (IFS) database. cation three-digit level as weights. For economies National Accounts and the fifth edition of the Inter- Unless otherwise noted, the growth rates and for which UNCTAD does not publish data, the export national Monetary Fund's (IMF) Balance of Payments terms of trade in the table were calculated from and import volume indexes (lines 72 and 73) in the Manual (1993) attempted to reconcile definitions and index numbers compiled by UNCTAD. The growth IMF's International Financial Statistics are used to reporting standards for international trade statistics, rates and terms of trade for selected economies calculate the average annual growth rates. · Export but differences in sources, timing, and national prac- were calculated from index numbers compiled in and import values are the current value of exports tices limit comparability. Real growth rates derived the IMF's International Financial Statistics. In some (f.o.b.) or imports (c.i.f.), converted to U.S. dollars from trade volume indexes and terms of trade based cases price and volume indexes from different and expressed as a percentage of the average for on unit price indexes may therefore differ from those sources vary significantly as a result of differences the base period (2000). UNCTAD's export or import derived from national accounts aggregates. in estimation procedures. Because the IMF does not value indexes are reported for most economies. For Trade in goods, or merchandise trade, includes all publish trade value indexes, for selected economies selected economies for which UNCTAD does not pub- goods that add to or subtract from an economy's the trade value indexes were derived from the vol- lish data, the value indexes are derived from export material resources. Trade data are collected on the ume and price indexes. All indexes are rescaled to or import volume indexes (lines 72 and 73) and cor- basis of a country's customs area, which in most a 2000 base year. responding unit value indexes of exports or imports cases is the same as its geographic area. Goods The terms of trade measures the relative prices of (lines 74 and 75) in the IMF's International Financial provided as part of foreign aid are included, but a country's exports and imports. There are several Statistics. · Net barter terms of trade index is calcu- goods destined for extraterritorial agencies (such ways to calculate it. The most common is the net lated as the percentage ratio of the export unit value as embassies) are not. barter (or commodity) terms of trade index, or the indexes to the import unit value indexes, measured Collecting and tabulating trade statistics are dif- ratio of the export price index to the import price relative to the base year 2000. ficult. Some developing countries lack the capacity index. When a country's net barter terms of trade to report timely data, especially landlocked coun- index increases, its exports become more valuable tries and countries whose territorial boundaries are or its imports cheaper. porous. Their trade has to be estimated from the data reported by their partners. (For further discussion of the use of partner country reports, see About the data for table 6.3.) Countries that belong to common customs unions may need to collect data through direct inquiry of companies. Economic or political concerns may lead some national authorities to sup- press or misrepresent data on certain trade flows, such as oil, military equipment, or the exports of a dominant producer. In other cases reported trade data may be distorted by deliberate under- or over- invoicing to affect capital transfers or avoid taxes. And in some regions smuggling and black market trading result in unreported trade flows. By international agreement customs data are Data sources reported to the United Nations Statistics Division, which maintains the Commodity Trade (Comtrade) Data on trade indexes are from UNCTAD's annual and Monthly Bulletin of Statistics databases. The Handbook of Statistics for most economies and United Nations Conference on Trade and Develop- from the IMF's International Financial Statistics for ment (UNCTAD) compiles international trade sta- selected economies. tistics, including price, value, and volume indexes, 2009 World Development Indicators 335 6.3 Direction and growth of merchandise trade Direction of trade High-income importers % of world trade, 2007 European United Other high- Source of exports Union Japan States income Total High-income economies 29.0 2.4 7.9 12.1 51.5 European Union 22.7 0.4 2.6 3.8 29.5 Japan 0.7 .. 1.1 1.6 3.4 United States 1.7 0.5 .. 3.3 5.5 Other high-income economies 3.8 1.5 4.3 3.5 13.1 Low- and middle-income economies 7.6 1.7 5.8 5.5 20.6 East Asia & Pacific 2.2 1.3 2.3 3.8 9.6 China 1.7 0.7 1.7 2.7 6.8 Europe & Central Asia 3.0 0.1 0.1 0.5 3.7 Russian Federation 1.2 0.1 0.1 0.2 1.5 Latin America & Caribbean 0.8 0.1 2.5 0.4 3.8 Brazil 0.3 0.0 0.2 0.1 0.6 Middle East & N. Africa 0.9 0.1 0.3 0.3 1.5 Algeria 0.2 0.0 0.1 0.0 0.4 South Asia 0.3 0.0 0.2 0.3 0.9 India 0.2 0.0 0.2 0.3 0.7 Sub-Saharan Africa 0.4 0.1 0.4 0.2 1.1 South Africa 0.1 0.1 0.1 0.1 0.3 World 36.7 4.1 13.7 17.6 72.1 Low- and middle-income importers % of world trade, 2007 Europe Latin Middle East Asia & Central America East & South Sub-Saharan Source of exports & Pacific Asia & Caribbean N. Africa Asia Africa Total High-income economies 7.5 3.9 3.1 1.1 1.1 1.0 17.7 European Union 1.0 3.1 0.7 0.7 0.4 0.5 6.4 Japan 1.3 0.1 0.2 0.0 0.1 0.1 1.8 United States 0.7 0.2 1.7 0.1 0.2 0.1 2.9 Other high-income economies 4.5 0.4 0.5 0.3 0.5 0.3 6.6 Low- and middle-income economies 2.5 2.5 1.6 0.7 0.7 0.7 8.7 East Asia & Pacific 1.4 0.6 0.4 0.2 0.4 0.2 3.2 China 0.5 0.5 0.4 0.2 0.3 0.2 2.0 Europe & Central Asia 0.2 1.7 0.1 0.2 0.1 0.0 2.3 Russian Federation 0.1 0.7 0.0 0.1 0.0 0.0 1.0 Latin America & Caribbean 0.3 0.1 1.0 0.1 0.0 0.1 1.6 Brazil 0.1 0.0 0.3 0.0 0.0 0.0 0.5 Middle East & N. Africa 0.2 0.1 0.0 0.2 0.0 0.0 0.5 Algeria 0.0 0.0 0.0 0.0 0.0 0.0 0.1 South Asia 0.2 0.0 0.0 0.0 0.1 0.1 0.5 India 0.1 0.0 0.0 0.0 0.1 0.1 0.4 Sub-Saharan Africa 0.2 0.0 0.1 0.0 0.0 0.2 0.6 South Africa 0.0 0.0 0.0 0.0 0.0 0.1 0.1 World 10.0 6.5 4.8 1.8 1.8 1.6 26.4 336 2009 World Development Indicators GLOBAL LINKS Direction and growth of merchandise trade 6.3 Nominal growth of trade High-income importers annual % growth, 1997­2007 European United Other high- Source of exports Union Japan States income Total High-income economies 9.2 4.6 6.0 6.8 7.8 European Union 9.7 3.7 8.2 7.6 9.2 Japan 4.4 .. 2.1 5.3 4.0 United States 5.4 ­0.5 .. 5.0 4.5 Other high-income economies 9.4 7.2 6.0 8.6 7.7 Low- and middle-income economies 15.0 9.5 12.2 14.0 13.4 East Asia & Pacific 17.7 9.6 15.3 14.1 14.3 China 25.2 12.4 21.7 18.4 19.5 Europe & Central Asia 17.4 10.1 8.5 15.0 16.4 Russian Federation 17.4 9.9 5.3 14.3 15.7 Latin America & Caribbean 11.2 6.9 9.4 12.9 10.0 Brazil 11.5 3.5 10.4 14.2 11.0 Middle East & N. Africa 12.4 15.4 27.4 16.7 14.8 Algeria 11.7 16.5 22.5 23.5 15.4 South Asia 11.6 4.7 10.6 16.0 12.4 India 13.3 7.0 13.0 17.9 14.5 Sub-Saharan Africa 7.6 9.6 14.4 6.3 9.7 South Africaa 7.2 9.6 11.2 3.6 7.2 World 10.2 6.4 8.2 8.5 9.1 Low- and middle-income importers annual % growth, 1997­2007 Europe Latin Middle East Asia & Central America East & South Sub-Saharan Source of exports & Pacific Asia & Caribbean N. Africa Asia Africa Total High-income economies 11.5 13.4 6.5 9.5 11.9 9.7 10.6 European Union 10.0 13.6 6.6 8.5 11.8 9.5 11.0 Japan 9.3 17.7 5.1 6.2 8.8 8.7 8.9 United States 8.6 8.2 6.1 7.6 14.7 8.8 7.2 Other high-income economies 13.3 12.9 8.6 13.9 11.8 11.0 12.6 Low- and middle-income economies 19.3 16.2 12.8 18.4 19.0 17.1 16.6 East Asia & Pacific 18.3 30.3 22.7 22.3 22.9 24.0 21.6 China 22.2 33.4 27.2 28.5 29.4 27.5 27.3 Europe & Central Asia 13.9 14.2 13.7 18.2 17.5 17.2 14.6 Russian Federation 13.8 14.7 14.0 22.9 16.9 18.3 15.0 Latin America & Caribbean 22.1 14.0 9.9 10.2 20.8 18.4 12.3 Brazil 18.8 15.1 10.4 13.2 15.7 20.2 12.8 Middle East & N. Africa 24.0 12.6 10.1 21.3 12.0 20.7 17.7 Algeria 59.3 8.6 10.4 23.5 14.4 3.3 14.4 South Asia 22.1 13.0 20.8 18.4 18.4 17.3 18.8 India 23.6 14.1 23.8 21.7 16.9 18.7 20.1 Sub-Saharan Africa 25.0 13.0 19.1 8.8 4.1 11.6 15.6 South Africaa 14.2 8.1 5.2 10.9 11.4 7.9 9.5 World 13.0 14.5 8.2 12.2 14.1 12.2 12.2 a. Data for 1997 are based on imports from South Africa reported by other economies because data on exports for South Africa were not available. 2009 World Development Indicators 337 6.3 Direction and growth of merchandise trade About the data Definitions The table provides estimates of the flow of trade in using the IMF's published period average exchange · Merchandise trade includes all trade in goods; goods between groups of economies. The data are rate (series rf or rh, monthly averages of the market trade in services is excluded. · High-income econo- from the International Monetary Fund's (IMF) Direc- or official rates) for the reporting country or, if unavail- mies are those classifi ed as such by the World tion of Trade database. All high-income economies able, monthly average rates in New York. Because Bank (see inside front cover). · European Union is and major developing economies report trade on imports are reported at cost, insurance, and freight defined as all high-income EU members: Austria, a timely basis, covering about 85 percent of trade (c.i.f.) valuations, and exports at free on board (f.o.b.) Belgium, Cyprus, Czech Republic, Denmark, Esto- for recent years. Trade by less timely reporters and valuations, the IMF adjusts country reports of import nia, Finland, France, Germany, Greece, Hungary, by countries that do not report is estimated using values by dividing them by 1.10 to estimate equiva- Ireland, Italy, Luxembourg, Malta, the Netherlands, reports of trading partner countries. Because the lent export values. The accuracy of this approxima- Portugal, Slovenia, Spain, Sweden, and the United largest exporting and importing countries are reli- tion depends on the set of partners and the items Kingdom. · Other high-income economies include able reporters, a large portion of the missing trade traded. Other factors affecting the accuracy of trade all high-income economies (both Organisation for flows can be estimated from partner reports. Part- data include lags in reporting, recording differences Economic Co-operation and Development members ner country data may introduce discrepancies due to across countries, and whether the country reports and others) except the high-income European Union, smuggling, confidentiality, different exchange rates, trade according to the general or special system of Japan, and the United States. · Low- and middle- overreporting of transit trade, inclusion or exclusion trade. (For further discussion of the measurement of income regional groupings are based on World Bank of freight rates, and different points of valuation and exports and imports, see About the data for tables classifications (see inside back cover for regional times of recording. 4.4 and 4.5.) groupings) and may differ from those used by other In addition, estimates of trade within the European The regional trade flows in the table are calculated organizations. Union (EU) have been significantly affected by changes from current price values. The growth rates are in in reporting methods following the creation of a cus- nominal terms; that is, they include the effects of toms union. The current system for collecting data on changes in both volumes and prices. trade between EU members--Intrastat, introduced in 1993--has less exhaustive coverage than the previ- ous customs-based system and has resulted in some problems of asymmetry (estimated imports are about 5 percent less than exports). Despite these issues, only a small portion of world trade is estimated to be omitted from the IMF's Direction of Trade Statistics Yearbook and Direction of Trade database. Most countries report their trade data in national currencies, which are converted into U.S. dollars In 2007 around 70 percent of exports from low- and middle-income economies and from high-income economies were directed to high-income economies 6.3a Destination of exports from Destination of exports from low- and middle-income economies high-income economies South Asia 2.3% Sub-Saharan Africa 2.2% South Asia 1.5% Sub-Saharan Africa 1.4% Middle East & Unspecified 1.9% Middle East & Unspecified 1.3% North Africa 2.4% North Africa 1.6% Latin America & Latin America & Caribbean 5.4% Caribbean 4.5% Europe & Central Europe & Central Asia 5.5% Asia 8.5% East Asia & Pacific 10.7% East Asia & Pacific 8.3% High income High income 69.0% 73.4% Data sources East Asia and Pacific and Europe and Central Asia were the two largest developing region importers from Data on the direction and growth of merchandise both low- and middle-income economies and high-income economies. trade were calculated using the IMF's Direction of Source: World Bank staff calculations based on data from the International Monetary Fund's Direction of Trade database. Trade database. 338 2009 World Development Indicators GLOBAL LINKS High-income economy trade with low- and middle-income economies 6.4 Exports to low-income economies High-income economies European Union Japan United States 1997 2007 1997 2007 1997 2007 1997 2007 Total ($ billions) 47.8 121.4 21.1 47.2 4.9 12.2 5.2 12.6 % of total exports Food 11.7 8.7 13.8 9.4 0.9 0.7 23.8 20.5 Cereals 4.2 2.6 3.7 1.7 0.4 0.1 17.7 13.6 Agricultural raw materials 2.1 2.1 1.5 1.4 1.2 1.6 5.1 6.6 Ores and nonferrous metals 1.2 2.3 0.9 1.2 0.5 1.8 0.7 1.9 Fuels 4.4 13.0 2.7 11.8 1.1 1.1 1.4 3.4 Crude petroleum 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 3.6 11.9 2.4 11.4 1.0 1.0 1.1 3.0 Manufactured goods 78.4 69.1 78.5 72.5 94.0 92.3 65.6 63.3 Chemical products 12.0 11.7 14.6 12.0 5.3 5.8 9.2 7.2 Iron and steel 3.6 3.5 3.5 2.7 7.8 9.6 1.5 1.3 Machinery and transport equipment 43.5 40.0 43.0 44.5 64.9 64.0 44.2 46.0 Furniture 0.3 0.2 0.5 0.4 0.1 0.2 0.2 0.2 Textiles 6.5 4.1 2.3 1.7 6.1 3.7 3.1 1.0 Footwear 0.5 0.2 0.4 0.2 0.0 0.0 0.4 0.4 Other 12.1 9.4 14.2 11.0 9.9 9.0 7.0 7.3 Miscellaneous goods 1.7 4.2 1.5 3.1 2.3 2.6 3.5 4.2 Imports from low-income economies Total ($ billions) 55.5 154.4 26.3 58.2 5.2 10.2 12.8 61.3 % of total imports Food 20.6 12.2 28.0 19.5 26.5 15.4 8.8 4.2 Cereals 0.3 0.4 0.1 0.2 0.0 0.4 0.2 0.1 Agricultural raw materials 7.2 2.5 8.7 4.4 10.9 3.1 0.9 0.5 Ores and nonferrous metals 4.5 5.4 5.1 8.1 9.0 10.9 1.9 0.4 Fuels 29.8 40.1 17.6 22.9 20.6 25.6 54.3 60.8 Crude petroleum 28.4 35.2 17.3 16.6 17.8 17.0 51.3 57.3 Petroleum products 1.1 1.5 0.2 0.6 1.1 3.0 2.9 1.9 Manufactured goods 36.4 37.9 39.5 44.4 32.3 42.7 33.7 33.6 Chemical products 0.6 1.0 0.5 1.4 0.3 1.2 0.7 0.4 Iron and steel 0.5 0.2 0.5 0.3 1.2 0.4 0.3 0.1 Machinery and transport equipment 2.5 3.3 1.8 2.3 2.2 16.9 0.2 1.2 Furniture 0.5 2.2 0.4 1.9 1.6 2.8 0.1 2.6 Textiles 22.1 22.5 21.2 25.7 21.1 10.8 27.6 24.9 Footwear 2.8 3.9 4.5 6.7 1.8 3.7 0.8 1.8 Other 7.3 4.8 10.5 6.1 4.2 6.8 4.1 2.6 Miscellaneous goods 1.5 1.7 1.0 0.6 0.7 2.4 0.4 0.4 Simple applied tariff rates on imports from low-income economies (%)a Food 7.6 6.9 6.9 4.4 10.2 4.0 3.2 2.9 Cereals 12.9 11 41.5 20.7 4.2 7.9 1.5 2.2 Agricultural raw materials 2.5 3.7 0.2 0.1 1.8 0.2 0.4 0.5 Ores and nonferrous metals 1.5 0.9 0.3 0.4 0.7 0.1 0.2 0.6 Fuels 3.1 0.8 0.0 0.0 1.6 0.2 0.5 0.8 Crude petroleum 1.7 0.5 0.0 0.0 0.9 0.0 0.3 0.0 Petroleum products 5.0 1.1 0.0 0.1 5.6 0.6 1.1 1.4 Manufactured goods 4.9 3.6 1.2 1.0 2.4 1.8 5.9 4.0 Chemical products 3.6 2.6 1.3 1.0 3.0 0.3 1.0 0.8 Iron and steel 5.1 1.8 0.6 0.1 0.1 0.1 1.7 0.5 Machinery and transport equipment 2.3 1.5 0.3 0.2 0.1 0.0 0.5 0.4 Furniture 4.0 3.2 0.1 0.0 0.0 0.0 1.2 1.1 Textiles 8.1 5.9 3.1 2.9 4.5 3.4 11.4 7.5 Footwear 7.9 6.4 3.0 2.5 8.4 9.1 14.2 8.3 Other 3.0 2.14 0.6 0.2 1.4 0.7 3.2 1.1 Miscellaneous goods 0.8 0.3 0.0 0.0 0.0 0.0 0.3 0.1 Average 5.1 4.0 1.9 1.4 3.6 3.2 5.1 3.7 2009 World Development Indicators 339 High-income economy trade with 6.4 low- and middle-income economies Exports to middle-income economies High-income economies European Union Japan United States 1997 2007 1997 2007 1997 2007 1997 2007 Total ($ billions) 737.3 2,001.2 287.0 837.3 100.4 235.5 189.2 357.5 % of total exports Food 7.4 5.1 8.7 5.0 0.4 0.3 9.1 10.1 Cereals 1.6 1.0 1.3 0.6 0.0 0.0 2.5 3.3 Agricultural raw materials 2.0 1.9 1.3 1.4 1.1 0.9 2.7 3.7 Ores and nonferrous metals 1.9 4.0 1.6 2.6 1.3 3.6 1.6 4.3 Fuels 3.1 5.6 1.6 2.6 0.9 1.2 2.6 5.3 Crude petroleum 0.6 0.8 0.1 0.1 0.0 0.0 0.1 0.0 Petroleum products 1.8 3.7 1.3 2.1 0.7 1.0 1.6 4.0 Manufactured goods 83.2 79.2 84.2 84.2 94.6 89.1 80.7 72.7 Chemical products 11.1 13.2 12.7 13.4 6.9 9.8 10.8 13.1 Iron and steel 2.8 3.5 2.7 3.6 6.3 6.6 1.0 1.3 Machinery and transport equipment 49.1 45.8 45.9 47.0 68.1 60.9 49.7 43.3 Furniture 0.6 0.4 0.9 0.7 0.1 0.2 0.6 0.3 Textiles 5.7 2.9 5.4 3.7 2.9 1.6 4.9 2.4 Footwear 0.4 0.2 0.7 0.5 0.0 0.0 0.2 0.1 Other 13.7 13.2 16.1 15.2 10.2 10.1 13.5 12.2 Miscellaneous goods 2.1 3.4 1.9 2.9 1.7 4.8 3.4 3.9 Imports from middle-income economies Total ($ billions) 916.3 3,053.0 273.8 1,123.6 109.0 264.9 291.3 895.4 % of total imports Food 10.6 6.3 14.1 7.9 15.5 7.8 7.7 4.8 Cereals 0.4 0.4 0.3 0.5 0.3 0.3 0.2 0.2 Agricultural raw materials 2.9 1.4 3.8 1.9 5.5 2.4 1.5 0.9 Ores and nonferrous metals 5.6 5.6 7.3 6.1 10.0 12.3 2.7 2.8 Fuels 13.7 18.5 18.7 22.5 15.8 17.8 12.4 18.4 Crude petroleum 8.6 12.2 11.9 15.1 7.4 8.8 9.6 14.6 Petroleum products 2.2 3.5 2.9 3.9 1.2 2.2 2.5 3.2 Manufactured goods 65.2 65.7 53.7 58.6 51.8 58.1 73.4 70.9 Chemical products 3.2 3.5 4.3 3.6 2.7 4.2 2.2 2.7 Iron and steel 2.5 3.2 2.2 4.0 1.7 1.6 2.1 2.0 Machinery and transport equipment 25.9 32.3 15.8 25.4 18.0 26.9 34.2 36.5 Furniture 1.4 2.0 1.5 2.0 1.4 1.5 1.8 3.0 Textiles 13.8 8.6 14.9 9.3 13.5 9.6 13.0 8.5 Footwear 3.1 1.5 2.1 1.5 1.9 1.2 4.0 1.9 Other 15.2 14.5 12.9 12.9 12.6 13.2 16.2 16.3 Miscellaneous goods 1.9 1.7 2.2 1.3 1.4 1.6 2.3 2.2 Simple applied tariff rates on imports from middle-income economies (%)a Food 9.9 9.1 16.4 8.9 12.4 7.7 3.7 3.8 Cereals 13.8 13.9 47.6 25.2 16.8 12.0 1.5 1.4 Agricultural raw materials 2.6 2.6 1.0 0.3 1.3 0.6 0.5 0.6 Ores and nonferrous metals 1.8 1.3 0.9 0.5 0.1 0.1 0.4 0.4 Fuels 3.3 1.4 0.0 0.0 1.3 0.3 0.4 1.3 Crude petroleum 7.1 0.6 0.0 0.0 0.9 0.0 0.4 0.0 Petroleum products 6.2 2.1 0.1 0.1 5.6 1.2 1.3 3.4 Manufactured goods 4.9 3.6 2.0 0.9 1.5 2.4 3.8 2.6 Chemical products 3.4 2.4 1.4 1.0 0.6 0.4 1.4 0.8 Iron and steel 3.5 1.8 1.1 0.1 0.1 0.2 2.9 0.2 Machinery and transport equipment 3.3 2.3 0.7 0.2 0.0 0.0 0.5 0.2 Furniture 5.4 4.3 0.3 0.0 0.0 0.1 0.4 0.3 Textiles 8.7 6.7 5.6 2.9 4.4 6.6 11.0 7.4 Footwear 9.1 8.1 4.6 2.7 13.2 19.3 12.9 7.8 Other 3.2 2.8 1.1 0.3 0.5 0.7 1.0 0.7 Miscellaneous goods 0.7 1.5 0.0 0.0 0.0 0.0 0.4 0.0 Average 5.3 4.1 3.3 1.7 2.7 2.8 3.6 2.6 a. Includes ad valorem equivalents of specific rates. 340 2009 World Development Indicators GLOBAL LINKS High-income economy trade with low- and middle-income economies 6.4 About the data Definitions Developing economies are becoming increasingly manufactures as a share of goods imports from both The product groups in the table are defined in accor- important in the global trading system. Since the low- and middle-income economies have grown. And dance with SITC revision 3: food (0, 1, 22, and 4) and early 1990s trade between high-income economies trade between developing economies has grown cereals (04); agricultural raw materials (2 excluding and low- and middle-income economies has grown substantially over the past decade, a result of their 22, 27, and 28); ores and nonferrous metals (27, 28, faster than trade among high-income economies. increasing share of world output and liberalization of and 68); fuels (3), crude petroleum (crude petroleum The increased trade benefi ts consumers and pro- trade, among other influences. oils and oils obtained from bituminous minerals; ducers. But as was apparent at the World Trade Orga- Yet trade barriers remain high. The table includes 333), and petroleum products (noncrude petroleum nization's (WTO) Ministerial Conferences in Doha, information about tariff rates by selected product and preparations; 334); manufactured goods (5­8 Qatar, in October 2001; Cancun, Mexico, in Sep- groups. Applied tariff rates are the tariffs in effect excluding 68), chemical products (5), iron and steel tember 2003; and Hong Kong, China, in December for partners in preferential trade agreements such (67), machinery and transport equipment (7), furni- 2005, achieving a more pro-development outcome as the North American Free Trade Agreement. When ture (82), textiles (65 and 84), footwear (85), and from trade remains a challenge. Doing so will require these rates are unavailable, most favored nation other manufactured goods (6 and 8 excluding 65, strengthening international consultation. After the rates are used. The difference between most favored 67, 68, 82, 84, and 85); and miscellaneous goods Doha meetings negotiations were launched on ser- nation and applied rates can be substantial. Simple (9). · Exports are all merchandise exports by high- vices, agriculture, manufactures, WTO rules, the averages of applied rates are shown because they income economies to low-income and middle-income environment, dispute settlement, intellectual prop- are generally a better indicator of tariff protection economies as recorded in the United Nations Sta- erty rights protection, and disciplines on regional than weighted average rates are. tistics Division's Comtrade database. Exports are integration. At the most recent negotiations in Hong The data are from the United Nations Conference recorded free on board (f.o.b.). · Imports are all Kong, China, trade ministers agreed to eliminate on Trade and Development (UNCTAD). Partner coun- merchandise imports by high-income economies subsidies of agricultural exports by 2013; to abolish try reports by high-income economies were used for from low-income and middle-income economies as cotton export subsidies and grant unlimited export both exports and imports. Because of differences in recorded in the United Nations Statistics Division's access to selected cotton-growing countries in Sub- sources of data, timing, and treatment of missing data, Commodity Trade (Comtrade) database. Imports Saharan Africa; to cut more domestic farm supports the numbers in the table may not be fully comparable include insurance and freight charges (c.i.f.). · High-, in the European Union, Japan, and the United States; with those used to calculate the direction of trade middle-, and low-income economies are those and to offer more aid to developing countries to help statistics in table 6.3 or the aggregate flows in tables classified as such by the World Bank (see inside them compete in global trade. 4.4, 4.5, and 6.2. Tariff line data were matched to front cover). · European Union is defined as all Trade flows between high-income and low- and Standard International Trade Classification (SITC) revi- high-income EU members: Austria, Belgium, Cyprus, middle-income economies reflect the changing mix of sion 3 codes to define commodity groups. For further Czech Republic, Denmark, Estonia, Finland, France, exports to and imports from developing economies. discussion of merchandise trade statistics, see About Germany, Greece, Hungary, Ireland, Italy, Luxem- While food and primary commodities have continued the data for tables 4.4, 4.5, 6.2, 6.3, and 6.5, and for bourg, Malta, the Netherlands, Portugal, Slovenia, to fall as a share of high-income economies' imports, information about tariff barriers, see table 6.8. Spain, Sweden, and the United Kingdom. High-income economies' tariffs on imports from low- and middle-income economies fell between 1997 and 2007 but remain high for some products 6.4a Simple applied tariff rates (%) 1997 2007 10 8 6 4 2 0 Data sources Food Agricultural Fuels Ores and Manu- Machinery Textiles Food Agricultural Fuels Ores and Manu- Machinery Textiles raw nonferrous factured and raw nonferrous factured and materials metals goods transport materials metals goods transport Data on trade values are from United Nations equipment equipment Imports from low-income economies Imports from middle-income economies Statistics Division's Comtrade database. Data Food and textile products are subject to higher tariff rates than other products are. And tariff rates on on tariffs are from UNCTAD's Trade Analysis and agricultural raw material imports from low-income countries have increased significantly. Information System database and are calculated by World Bank staff using the World Integrated Source: United Nations Statistics Division's Comtrade database and the United Nations Conference on Trade and Development's Trade Analysis and Information System database. Trade Solution system. 2009 World Development Indicators 341 6.5 Direction of trade of developing economies Exports Imports % of total merchandise exports % of total merchandise imports To developing economies To high-income From developing economies From high-income Within region Outside region economies Within region Outside region economies 1997 2007 1997 2007 1997 2007 1997 2007 1997 2007 1997 2007 East Asia & Pacific 8.5 10.6 6.9 14.6 84.6 74.8 9.4 16.9 7.5 13.2 84.6 69.9 Cambodia 55.5 7.3 1.0 1.9 43.5 90.8 34.3 59.7 0.8 1.4 43.5 38.9 China 4.9 5.5 8.4 17.0 86.6 77.5 6.0 10.7 10.1 17.0 86.6 72.3 Indonesia 11.2 18.9 6.7 12.7 82.1 68.4 8.6 27.8 8.3 10.3 82.1 61.9 Korea, Dem. Rep. 16.8 40.7 33.5 49.6 49.7 9.6 43.0 51.9 18.7 38.6 49.7 9.5 Lao PDR 26.2 71.0 0.0 1.2 73.8 27.8 91.7 85.5 0.1 0.7 73.8 13.8 Malaysia 10.1 19.7 6.5 9.8 83.5 70.5 10.2 26.1 4.4 5.4 83.5 68.5 Mongolia 22.5 72.0 10.8 3.9 66.6 24.1 14.5 34.5 38.9 34.7 66.6 30.8 Myanmar 15.4 61.3 20.2 19.1 64.4 19.6 42.6 63.9 3.0 4.7 64.4 31.4 Papua New Guinea 11.2 15.3 0.7 2.4 88.2 82.2 7.2 18.0 1.0 1.3 88.2 80.7 Philippines 8.4 21.1 1.2 2.2 90.4 76.6 10.4 19.5 5.1 3.4 90.4 77.2 Thailand 13.6 25.1 5.4 11.0 81.0 64.0 11.6 25.6 7.3 7.3 81.0 67.0 Vietnam 14.4 19.8 5.4 3.4 80.2 76.8 13.4 33.9 3.6 4.6 80.2 61.4 Europe & Central Asia 32.1 28.3 10.0 10.2 57.8 61.5 27.7 27.5 7.9 14.0 57.8 58.4 Albania 8.1 8.6 0.1 3.9 91.8 87.6 10.5 23.4 0.5 10.7 91.8 65.9 Armenia 44.1 36.0 21.8 4.9 34.2 59.2 41.4 42.6 12.1 15.1 34.2 42.3 Azerbaijan 55.0 38.2 25.0 21.3 20.0 40.5 69.0 45.3 7.7 11.6 20.0 43.1 Belarus 80.7 58.9 7.3 6.7 12.0 34.4 73.5 72.6 3.3 5.1 12.0 22.3 Bosnia and Herzegovina 39.3 26.9 4.9 2.3 55.9 70.8 33.7 37.2 0.1 1.1 55.9 61.7 Bulgaria 36.3 29.5 9.1 5.9 54.6 64.6 38.2 35.7 9.3 7.7 54.6 56.6 Croatia 23.6 22.9 4.8 2.8 71.6 74.4 9.0 20.3 7.4 9.7 71.6 70.0 Georgia 77.3 46.8 5.6 5.6 17.1 47.6 55.8 56.8 2.5 8.1 17.1 35.2 Kazakhstan 49.2 26.2 11.7 22.3 39.1 51.5 61.1 48.4 3.0 23.5 39.1 28.0 Kyrgyz Republic 54.7 56.6 7.5 17.2 37.8 26.1 68.7 66.5 7.0 16.4 37.8 17.1 Latvia 38.4 37.0 2.3 2.3 59.3 60.7 30.1 35.4 1.6 3.2 59.3 61.4 Lithuania 58.6 45.2 1.0 2.0 40.4 52.9 38.4 39.1 2.9 4.3 40.4 56.6 Macedonia, FYR 20.4 42.4 1.2 0.5 78.4 57.1 28.5 37.5 6.2 4.7 78.4 57.8 Moldova 80.5 65.0 0.5 2.0 19.0 32.9 69.1 67.0 1.6 2.9 19.0 30.2 Poland 27.8 34.4 6.1 10.6 66.1 55.0 21.4 36.5 7.6 23.0 66.1 40.5 Romania 13.6 19.5 14.4 5.8 72.0 74.7 19.0 20.9 7.7 7.1 72.0 72.1 Russian Federation 30.4 28.7 9.9 10.5 59.7 60.8 33.3 20.8 9.2 19.9 59.7 59.3 Tajikistan 43.3 50.9 2.7 6.7 53.9 42.4 66.6 70.3 2.3 17.5 53.9 12.1 Turkey 18.4 19.1 11.7 14.8 69.9 66.1 9.7 22.8 12.6 22.8 69.9 54.5 Turkmenistan 48.2 64.6 31.7 22.9 20.2 12.4 71.5 41.4 4.7 17.7 20.2 40.9 Ukraine 51.2 51.1 22.5 20.1 26.3 28.7 65.4 45.4 3.2 12.1 26.3 42.5 Uzbekistan 62.8 62.8 10.1 14.1 27.1 23.1 46.5 52.4 6.1 14.9 27.1 32.7 Latin America & Carib. 19.5 18.2 5.8 11.1 74.7 70.7 17.7 20.4 5.4 16.9 74.7 62.6 Argentina 50.8 41.1 17.6 27.2 31.6 31.7 31.6 44.5 7.5 18.3 31.6 37.2 Bolivia 43.5 65.2 0.3 2.5 56.2 32.3 43.8 71.3 1.0 4.8 56.2 23.8 Brazil 28.7 25.3 13.0 20.5 58.2 54.2 22.1 17.3 10.2 27.9 58.2 54.8 Chile 21.5 16.9 7.6 23.2 70.9 59.9 29.4 35.8 8.4 21.1 70.9 43.0 Colombia 27.7 35.4 1.9 4.6 70.4 60.0 25.8 31.5 3.7 15.3 70.4 53.2 Costa Rica 26.1 19.5 2.1 18.2 71.8 62.3 32.4 27.6 4.1 7.6 71.8 64.8 Cuba 6.1 13.0 42.7 36.8 51.2 50.2 10.9 39.9 24.7 18.6 51.2 41.5 Dominican Republic 2.9 6.4 0.4 2.8 96.7 90.8 21.2 28.2 1.4 6.2 96.7 65.7 Ecuador 25.9 28.3 7.3 9.4 66.8 62.3 34.2 36.1 4.5 13.4 66.8 50.6 El Salvador 48.0 40.3 0.8 1.4 51.2 58.3 38.9 42.1 1.8 7.1 51.2 50.8 Guatemala 30.1 39.6 3.5 3.0 66.4 57.4 31.2 33.1 2.4 9.5 66.4 57.4 Haiti 0.4 11.9 0.0 3.7 99.6 84.3 14.9 16.8 4.5 10.4 99.6 72.8 Honduras 17.8 17.5 0.0 0.8 82.2 81.7 30.5 27.1 0.0 5.9 82.2 66.9 Jamaica 4.3 2.0 8.1 9.1 87.6 88.9 10.5 29.1 2.3 5.4 87.6 65.5 Mexico 5.1 6.0 0.3 1.6 94.6 92.4 2.3 5.1 3.2 15.4 94.6 79.5 Nicaragua 28.6 44.6 0.1 0.8 71.3 54.6 46.9 58.8 0.5 0.8 71.3 40.4 Panama 23.6 16.9 0.2 8.1 76.2 75.1 30.0 24.2 1.6 7.3 76.2 68.6 Paraguay 61.0 70.0 0.4 8.1 38.6 21.9 53.9 53.6 2.9 10.6 38.6 35.8 Peru 17.9 20.5 13.5 15.9 68.6 63.6 30.4 38.4 2.6 18.3 68.6 43.3 Uruguay 56.6 41.9 9.6 18.6 33.9 39.5 50.7 48.2 7.6 21.3 33.9 30.5 Venezuela, RB 21.9 13.3 0.6 4.8 77.4 81.9 22.2 43.4 1.4 10.2 77.4 46.5 342 2009 World Development Indicators GLOBAL LINKS Direction of trade of developing economies Exports Imports 6.5 % of total merchandise exports % of total merchandise imports To developing economies To high-income From developing economies From high-income Within region Outside region economies Within region Outside region economies 1997 2007 1997 2007 1997 2007 1997 2007 1997 2007 1997 2007 Middle East & N. Africa 4.6 7.5 16.9 18.5 78.5 74.0 4.8 8.3 20.7 31.5 78.5 60.2 Algeria 1.2 2.5 13.5 11.2 85.3 86.4 3.6 3.3 15.6 31.6 85.3 65.1 Egypt, Arab Rep. 7.0 14.7 12.9 15.3 80.1 70.1 1.3 4.1 24.3 33.6 80.1 62.3 Iran, Islamic Rep. 0.3 2.7 19.6 38.0 80.1 59.3 0.8 0.8 29.4 46.0 80.1 53.2 Iraq 20.2 3.0 17.2 7.0 62.6 90.0 25.8 39.2 28.7 31.9 62.6 28.9 Jordan 28.4 27.5 25.4 17.1 46.3 55.4 17.7 8.6 19.5 25.9 46.3 65.4 Lebanon 16.9 39.6 14.7 11.9 68.4 48.5 7.0 16.5 16.7 22.2 68.4 61.3 Libya 3.5 3.1 10.9 7.1 85.7 89.8 9.2 11.4 10.7 25.5 85.7 63.1 Morocco 6.2 2.7 19.4 21.1 74.4 76.2 4.6 6.3 21.3 20.0 74.4 73.8 Syrian Arab Republic 13.5 53.4 15.6 4.3 70.9 42.3 4.4 21.2 26.9 30.4 70.9 48.5 Tunisia 7.3 9.5 8.3 6.5 84.4 84.0 4.8 7.0 11.8 15.3 84.4 77.7 Yemen, Rep. 0.3 2.1 56.5 66.8 43.2 31.1 3.6 3.3 25.5 34.7 43.2 62.0 South Asia 4.4 6.4 17.5 26.5 78.1 67.1 3.9 4.9 21.7 32.3 78.1 62.8 Afghanistan 24.0 45.9 20.6 21.1 55.4 33.0 9.7 41.8 33.3 28.1 55.4 30.0 Bangladesh 2.5 2.8 9.9 7.1 87.6 90.1 15.0 16.9 23.2 31.3 87.6 51.9 India 4.8 5.2 19.6 29.2 75.5 65.6 0.6 1.1 22.1 33.0 75.5 65.9 Nepal 25.9 72.8 0.4 2.8 73.8 24.4 28.2 66.2 8.2 18.6 73.8 15.2 Pakistan 2.9 13.0 14.1 21.3 83.0 65.7 2.3 3.0 21.3 33.9 83.0 63.1 Sri Lanka 2.8 9.3 15.1 14.3 82.0 76.4 12.1 25.6 21.2 24.9 82.0 49.5 Sub-Saharan Africa 12.3 12.3 12.4 23.3 75.4 64.4 13.3 12.2 13.5 28.9 75.4 59.0 Angola 1.3 4.6 16.4 38.4 82.3 57.0 9.6 7.4 7.7 27.6 82.3 65.0 Benin 7.8 31.5 61.1 44.6 31.1 23.9 15.4 9.0 15.8 59.8 31.1 31.2 Burkina Faso 19.9 16.3 11.0 50.1 69.1 33.6 26.2 41.7 12.0 15.6 69.1 42.7 Burundi 2.5 18.4 0.0 16.6 97.5 65.0 27.0 27.2 6.6 16.5 97.5 56.3 Cameroon 6.6 9.6 9.3 9.5 84.1 80.9 19.2 19.8 10.0 25.1 84.1 55.1 Central African Republic 17.0 8.7 0.1 33.3 82.9 58.0 23.1 24.9 6.8 10.9 82.9 64.2 Chad 6.5 0.5 27.9 4.6 65.7 95.0 22.8 22.0 7.5 14.3 65.7 63.7 Congo, Dem. Rep. 8.6 10.5 4.6 34.1 86.9 55.3 40.6 53.9 9.4 6.7 86.9 39.4 Congo, Rep. 1.1 1.4 11.6 43.1 87.3 55.5 12.2 4.8 6.5 27.8 87.3 67.4 Ethiopia 3.0 2.4 15.3 30.1 81.7 67.4 2.3 2.4 18.7 42.4 81.7 55.2 Gabon 1.0 3.1 11.2 31.1 87.8 65.8 16.0 9.3 5.1 13.0 87.8 77.7 Gambia, The 8.5 8.4 15.3 57.4 76.2 34.2 10.5 21.8 19.7 51.9 76.2 26.3 Ghana 8.2 11.6 13.0 20.3 78.8 68.1 22.9 24.8 13.1 33.2 78.8 42.1 Guinea 5.7 1.9 0.5 35.3 93.8 62.8 12.5 12.2 16.4 33.1 93.8 54.6 Guinea-Bissau 1.6 8.9 55.0 90.2 43.4 0.9 13.3 25.8 20.7 19.7 43.4 54.5 Kenya 41.5 42.8 13.5 16.1 45.0 41.1 13.5 9.1 14.7 29.2 45.0 61.7 Liberia 1.4 2.3 14.0 57.6 84.5 40.0 1.1 2.4 9.1 17.7 84.5 79.9 Madagascar 7.5 2.7 6.3 5.8 86.2 91.6 11.8 14.4 25.0 37.8 86.2 47.8 Malawi 21.6 26.1 13.1 29.1 65.3 44.8 68.0 55.1 5.2 18.0 65.3 27.0 Mali 4.0 6.6 42.7 62.4 53.3 31.0 36.1 43.2 6.5 14.3 53.3 42.5 Mauritania 9.6 13.0 4.6 37.2 85.8 49.8 4.1 6.0 26.2 35.3 85.8 58.7 Mauritius 5.8 12.1 1.2 3.1 93.0 84.9 15.2 10.5 22.1 44.2 93.0 45.3 Mozambique 31.0 21.0 8.0 9.8 61.0 69.2 64.1 47.1 7.2 19.3 61.0 33.5 Niger 28.5 32.8 0.3 2.8 71.2 64.3 27.2 23.3 16.9 17.0 71.2 59.6 Nigeria 8.1 10.2 13.7 14.0 78.2 75.8 3.3 6.0 20.9 28.6 78.2 65.4 Rwanda 5.8 5.5 14.2 40.0 80.0 54.4 30.9 46.6 7.7 11.4 80.0 42.0 Senegal 29.6 49.6 19.5 12.2 51.0 38.2 11.8 9.3 20.8 27.7 51.0 63.0 Sierra Leone 0.0 2.9 0.0 6.2 100.0 90.9 21.2 19.5 9.3 33.6 100.0 46.9 Somalia 0.3 4.0 13.1 29.3 86.6 66.7 13.6 10.9 66.7 66.7 86.6 22.4 South Africa 14.2 14.2 11.7 16.0 74.0 69.8 3.7 6.2 11.6 27.9 74.0 65.9 Sudan 1.3 0.3 30.5 84.2 68.2 15.5 3.2 2.4 41.6 52.8 68.2 44.8 Tanzania 14.6 18.7 25.8 32.4 59.5 48.9 16.5 20.1 23.3 30.5 59.5 49.3 Togo 17.4 55.7 37.5 17.3 45.1 27.0 23.9 6.1 8.9 47.7 45.1 46.2 Uganda 2.4 23.4 7.0 11.1 90.6 65.5 44.2 40.2 9.4 17.4 90.6 42.4 Zambia 13.7 24.1 25.8 18.5 60.5 57.5 55.6 59.1 5.8 11.6 60.5 29.2 Zimbabwe 35.1 55.7 9.6 11.6 55.4 32.7 50.6 67.8 5.6 11.6 55.4 20.7 Note: Bilateral trade data are not available for Timor-Leste, Serbia, West Bank and Gaza, Botswana, Côte d'Ivoire, Eritrea, Lesotho, Namibia, and Swaziland. 2009 World Development Indicators 343 6.5 Direction of trade of developing economies About the data Definitions Developing economies are an increasingly important share of intraregional trade is increasing. Geographic · Exports to developing economies within region part of the global trading system. Their share of world patterns of trade vary widely by country and commod- are the sum of merchandise exports from the report- trade rose from 18 percent in 1990 to 28 percent ity. Larger shares of exports from oil- and resource- ing economy to other developing economies in the in 2007. And trade between high-income economies rich economies are to high-income economies. same World Bank region as a percentage of total and low- and middle-income economies has grown The relative importance of intraregional trade merchandise exports by the economy. · Exports to faster than trade between high-income economies. is higher for both landlocked countries and small developing economies outside region are the sum This increased trade benefits both producers and con- countries with close trade links to the largest of merchandise exports from the reporting economy sumers in developing and high-income economies. regional economy. For most developing economies-- to other developing economies in other World Bank The table shows trade in goods between develop- especially smaller ones--there is a "geographic regions as a percentage of total merchandise exports ing economies in the same region and other regions bias" favoring intraregional trade. Despite the broad by the economy. · Exports to high-income econo- and between developing economies and high-income trend toward globalization and the reduction of trade mies are the sum of merchandise exports from the economies. Data on exports and imports are from the barriers, the relative share of intraregional trade reporting economy to high-income economies as a International Monetary Fund's (IMF) Direction of Trade increased for most economies between 1997 and percentage of total merchandise exports by the econ- database and should be broadly consistent with data 2007. This is due partly to trade-related advantages, omy. · Imports from developing economies within from other sources, such as the United Nations Statis- such as proximity, lower transport costs, increased region are the sum of merchandise imports by the tics Division's Commodity Trade (Comtrade) database. knowledge from repeated interaction, and cultural reporting economy from other developing economies Generally, data on trade between developing and high- and historical affinity. The direction of trade is also in the same World Bank region as a percentage of income economies are complete. But trade flows influenced by preferential trade agreements that a total merchandise imports by the economy. · Imports between many developing economies--particularly country has made with other economies. Though from developing economies outside region are the those in Sub-Saharan Africa--are not well recorded, formal agreements on trade liberalization do not sum of merchandise imports by the reporting econ- and the value of trade among developing economies automatically increase trade, they nevertheless omy from other developing economies in other World may be understated. The table does not include some affect the direction of trade between the participat- Bank regions as a percentage of total merchandise developing economies because data on their bilateral ing economies. Table 6.7 illustrates the size of exist- imports by the economy. · Imports from high-income trade flows are not available. Data on the direction of ing regional trade blocs that have formal preferential economies are the sum of merchandise imports by trade between selected high-income economies are trade agreements. the reporting economy from high-income economies presented and discussed in tables 6.3 and 6.4. Although global integration has increased, develop- as a percentage of total merchandise imports by the At the regional level most exports from developing ing economies still face trade barriers when access- economy. economies are to high-income economies, but the ing other markets (see table 6.8). Trading partners vary by region 6.5a Merchandise exports, 2007 (%) Merchandise imports, 2007 (%) To developing economies within region From developing economies within region To developing economies outside region From developing economies outside region To the United States From the United States To other high-income economies From other high-income economies 100 100 75 75 50 50 25 25 0 0 East Europe & Latin Middle South Sub- East Europe & Latin Middle South Sub- Data sources Asia & Central America East & Asia Saharan Asia & Central America East & Asia Saharan Pacific Asia & Carib. N. Africa Africa Pacific Asia & Carib. N. Africa Africa Data on merchandise trade flows are published in In 2007 most developing economy merchandise trade was with high-income partners, but the degree of the IMF's Direction of Trade Statistics Yearbook and dependence varied by region. Latin America and Caribbean is highly integrated with the United States Direction of Trade Statistics Quarterly; the data in and most likely to be affected by the U.S. recession. Most merchandise exports of Latin America and the table were calculated using the IMF's Direction Caribbean and Europe and Central Asia to developing economies stayed within the same region. Most of Trade database. Regional and income group merchandise imports of East Asia and Pacific and South Asia from developing economies were from within classifications are according to the World Bank the same region, reflecting strong presence of large regional economies such as China and India. classification of economies as of July 1, 2008 and Source: World Bank staff calculations based on data from International Monetary Fund's Direction of Trade database. are as shown on the inside covers of this report. 344 2009 World Development Indicators GLOBAL LINKS Primary commodity prices 6.6 1970 1980 1990 1995 2000 2002 2003 2004 2005 2006 2007 2008 World Bank commodity price index (2000 = 100) Energy 19 153 79 53 100 92 101 123 171 197 207 269 Nonenergy commodities 183 177 115 117 100 105 108 121 135 172 190 215 Agriculture 188 195 113 122 100 112 114 118 121 134 153 181 Beverages 230 273 117 136 100 124 117 109 125 130 144 165 Food 201 199 116 117 100 115 117 123 121 131 156 195 Fats and oils 237 196 105 126 100 115 129 134 120 123 177 219 Grains 204 199 121 124 100 117 112 115 115 134 160 222 Other food 151 205 124 101 100 114 105 117 129 140 126 140 Raw materials 136 143 105 125 100 97 107 109 119 144 149 155 Timber 97 92 88 105 100 92 91 90 100 113 116 119 Other raw materials 179 198 124 146 100 104 124 130 141 179 185 195 Fertilizers 82 177 98 110 100 98 110 125 148 151 203 452 Metals and minerals 185 141 122 106 100 92 96 126 162 251 266 257 Steel productsa 0 134 131 118 100 92 100 153 170 162 154 228 Commodity prices (2000 prices) Energy Coal, Australian ($/mt) .. 49 39 33 26 26 25 48 43 44 56 100 Natural gas, Europe ($/mmBtu) .. 5 2 2 4 3 4 4 6 8 7 11 Natural gas, U.S. ($/mmBtu) 1 2 2 1 4 4 5 5 8 6 6 7 Natural gas, liquefied, Japan ($/mmBtu) .. 7 4 3 5 4 5 5 5 6 7 10 Petroleum, avg, spot ($/bbl) 4 45 22 14 28 26 28 34 48 57 60 76 Beverages (cents/kg) Cocoa 233 321 123 119 91 185 170 141 140 142 165 203 Coffee, Arabica 397 427 192 277 192 142 137 161 230 225 231 243 Coffee, robusta 321 400 115 230 91 69 79 72 101 133 162 183 Tea, avg., 3 auctions 289 205 200 124 188 157 147 153 150 168 172 191 Tea, Colombo auctions 217 137 182 118 179 163 150 162 167 171 214 220 Tea, Kolkata auctions 343 253 273 145 181 153 142 156 147 157 163 178 Tea, Mombasa auctions 307 224 144 108 203 156 150 141 134 175 141 175 Food Fats and oils ($/mt) Coconut oil 1,376 831 327 556 450 439 454 600 560 542 778 964 Copraa 779 558 224 364 305 278 291 409 376 360 514 643 Groundnut oil 1,312 1,059 937 823 714 717 1,207 1,054 963 867 1,145 1,679 Palm oil 901 719 282 521 310 407 430 428 383 427 661 747 Palm kernel oila .. .. .. .. 444 434 445 588 569 519 753 890 Soybeans 405 365 240 215 212 222 256 278 249 240 325 412 Soybean meal 357 323 195 164 189 183 205 219 195 187 260 339 Soybean oil 992 737 435 519 338 474 538 559 495 535 747 991 Grains ($/mt) Barley .. 96 78 86 77 114 102 90 86 104 146 158 Maize 202 154 106 103 89 104 102 102 90 109 139 176 Rice, Thailand, 5% 438 506 263 266 202 200 192 216 260 272 277 512 Sorghuma 179 159 101 99 88 106 103 100 87 110 138 164 Wheat, Canadaa 218 235 152 172 147 183 172 169 179 194 254 358 Wheat, U.S., hard red winter 190 213 132 147 114 155 142 142 138 172 216 257 Wheat, U.S., soft red winter a 197 208 125 139 99 136 134 131 123 142 202 214 2009 World Development Indicators 345 6.6 Primary commodity prices 1970 1980 1990 1995 2000 2002 2003 2004 2005 2006 2007 2008 Commodity prices (continued) (2000 prices) Food (continued) Other food Bananas, U.S. ($/mt) 573 467 526 369 424 552 364 476 547 605 572 665 Beef (cents/kg) 452 340 249 158 193 220 192 228 238 228 220 247 Chicken meat (cents/kg) .. 85 96 92 119 132 129 138 135 124 133 134 Fishmeal ($/mt)a 682 621 401 411 413 632 593 589 664 1,040 997 .. Oranges ($/mt) 582 482 516 441 363 589 661 780 794 741 810 872 Shrimp, Mexico (cents/kg) .. 1,420 1,039 1,253 1,515 1,097 1,110 928 939 915 855 840 Sugar, EU domestic (cents/kg) 39 60 57 57 56 57 58 61 60 58 58 55 Sugar, U.S. domestic (cents/kg) 57 82 50 42 43 48 46 41 43 44 39 37 Sugar, world (cents/kg) 29 78 27 24 18 16 15 14 20 29 19 22 Agricultural raw materials Cotton A index (cents/kg) 219 252 177 177 130 106 136 124 110 113 118 124 Logs, Cameroon ($/cu. m)a 149 310 334 282 275 246 271 301 304 285 323 415 Logs, Malaysian ($/cu. m) 149 241 172 212 190 170 182 179 184 214 227 230 Rubber, Singapore (cents/kg) 141 176 84 131 67 80 105 118 136 188 194 206 Plywood (cents/sheet)a 357 338 345 485 448 410 419 422 462 532 543 511 Sawnwood, Malaysian ($/cu. m) 608 489 518 614 595 549 535 528 599 670 683 701 Tobacco ($/mt)a 3,727 2,806 3,297 2,194 2,976 2,864 2,568 2,488 2,533 2,653 2,808 2,801 Woodpulp ($/mt)a 615 661 792 708 664 472 510 582 577 624 650 652 Fertilizers ($/mt) Diammonium phosphate 187 274 167 180 154 164 174 201 224 233 366 762 Phosphate rock 38 58 39 29 44 42 37 37 38 40 60 272 Potassium chloride 109 143 95 98 123 118 110 113 144 156 170 449 Triple superphosphate 147 222 128 124 138 139 145 169 183 180 287 749 Urea .. .. 116 155 101 98 135 159 199 199 262 388 Metals and minerals Aluminum ($/mt) 1,926 1,795 1,593 1,499 1,549 1,408 1,389 1,558 1,724 2,297 2,235 2,027 Copper ($/mt) 4,895 2,690 2,586 2,437 1,813 1,627 1,727 2,602 3,340 6,007 6,030 5,481 Gold ($/toz)a 125 750 373 319 279 323 353 372 404 540 590 687 Iron ore (cents/dmtu) 34 35 32 24 29 31 31 34 59 69 72 111 Lead (cents/kg) 105 112 79 52 45 47 50 80 89 115 219 165 Nickel ($/mt) 9,860 8,037 8,614 6,830 8,638 7,066 9,346 12,551 13,387 21,675 31,532 16,635 Silver (cents/toz)a 614 2,544 475 431 500 483 477 607 666 1,034 1,136 1,182 Tin (cents/kg) 1,273 2,068 591 516 544 424 475 773 670 785 1,231 1,459 Zinc (cents/kg) 102 94 147 86 113 81 80 95 125 293 275 148 MUV G-5 index (2000 = 100) 29 81 103 120 100 96 103 110 110 112 118 127 Note: bbl = barrel, cu. m = cubic meter, dmtu = dry metric ton unit, kg = kilogram, mmBtu = million British thermal units, mt = metric ton, toz = troy ounce. a. Series not included in the nonenergy index. 346 2009 World Development Indicators GLOBAL LINKS Primary commodity prices 6.6 About the data Definitions Primary commodities--raw or partially processed International Trade Classification (SITC) revision 3, · Energy price index is the composite price index for materials that will be transformed into fi nished the Food Agriculture Organization's FAOSTAT data- coal, petroleum, and natural gas, weighted by exports goods--are often developing countries' most signifi - base, the International Energy Agency database, BP's of each commodity from low- and middle-income cant exports, and the revenues obtained from them Statistical Review of World Energy, the World Bureau of countries. · Nonenergy commodity price index cov- have an important effect on living standards. Price Metal Statistics, and World Bank staff estimates. ers the 34 nonenergy primary commodities that make data for primary commodities are collected from a Each index in the table represents a fixed basket up the agriculture, fertilizer, and metals and miner- variety of sources, including international commodity of primary commodity exports over time. The non- als indexes. · Agriculture includes beverages, food, study groups, government agencies, industry trade energy commodity price index contains 41 price and agricultural raw materials. · Beverages include journals, and Bloomberg and Datastream data feed series for 34 nonenergy commodities. The index cocoa, coffee, and tea. · Food includes fats and oils, systems. Prices are either compiled in U.S. dollars in previous editions contained only 31 nonenergy grains, and other food items. Fats and oils include or converted to U.S. dollars when quoted in local commodities. In response to changes in commodity coconut oil, groundnut oil, palm oil, soybeans, soy- currencies. trade shares, minor adjustments have been made to bean oil, and soybean meal. Grains include barley, The table is based on frequently updated price the commodities basket, with barley, poultry meat, maize, rice, and wheat. Other food items include reports. When available, the prices received by and potassium and nitrogen fertilizers added and bananas, beef, chicken, oranges, shrimp, and sugar. exporters are used; otherwise, the prices paid by sorghum dropped. · Agricultural raw materials include timber and importers or trade unit values are used. Annual Separate indices are compiled for energy and steel other raw materials. Timber includes tropical hard price series are generally simple averages based on products, which are not included in the nonenergy logs and sawnwood. Other raw materials include higher frequency data. The constant price series in commodity price index. The previous petroleum index cotton, natural rubber, and tobacco. · Fertilizers the table are deflated using the manufactures unit has been replaced with a new energy index that include phosphate, phosphate rock, potassium, and value (MUV) index for the Group of Five (G-5) coun- includes coal, petroleum, and natural gas. The new nitrogenous products. · Metals and minerals include tries (see below). and old energy indices are similar because petroleum aluminum, copper, iron ore, lead, nickel, tin, and zinc. The commodity price indices are calculated as exports account for almost 85 percent of total energy · Steel products price index is the composite price Laspeyres index numbers, in which the fixed weights commodity exports from developing countries. index for eight steel products based on quotations are the 2002­04 average export values for low- and The MUV index is a composite index of prices for free on board (f.o.b.) Japan excluding shipments to middle-income economies (based on 2001 gross manufactured exports from the five major (G-5) indus- the United States for all years and to China prior national income) rebased to 2000. As of April 2008 trial economies (France, Germany, Japan, the United to 2001, weighted by product shares of apparent the weights were changed from 1987­89 average Kingdom, and the United States) to low- and middle- combined consumption (volume of deliveries) for Ger- export values to 2002­04 averages in order to include income economies, valued in U.S. dollars. The index many, Japan, and the United States. · Commodity the most recent available complete data. Data for covers products in groups 5­8 of SITC revision 1. To prices--for definitions and sources, see "Commod- exports are collected from various sources, includ- construct the MUV G-5 index, unit value indexes for ity price data" (also known as the "Pink Sheet") at ing the United Nations Statistics Division's Commod- each country are combined using weights determined the World Bank Prospects for Development website ity Trade Statistics (Comtrade) database Standard by each country's export share in a base year. (www.worldbank.org/prospects, click on Products). · MUV G-5 index is the manufactures unit value Commodity prices increased between 2000 and the last quarter of 2008-- index for G-5 country exports to low- and middle- the longest boom since 1960 6.6a income economies. World Bank commodity price index (2000 = 100) 300 250 Nonenergy commodities 200 150 100 50 Energy commodities 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2008 Data sources The recent commodity price boom threatened to impoverish millions of people around the world. Although there have been other commodity price booms, the recent boom lasted the longest. The average price of Data on commodity prices and the MUV G-5 index nonenergy commodities increased 115 percent over 2000­08. The increase in energy prices was even are compiled by the World Bank's Development more remarkable--169 percent. In the last quarter of 2008, commodity prices declined significantly. Prospects Group. Monthly updates of commod- ity prices are available at www.worldbank.org/ Note: Dotted lines are projections for 2009. Source: World Bank commodity price data. prospects. 2009 World Development Indicators 347 6.7 Regional trade blocs Merchandise exports within bloc Year of entry into Type force of the of most $ millions Year of most recent recent creation agreement agreementa 1990 1995 2000 2004 2005 2006 2007 High-income and low- and middle-income economies APECb 1989 None 1,000,616 1,688,708 2,261,791 2,924,272 3,310,461 3,775,728 4,191,536 EEA 1994 1994 EIA 998,015 1,330,493 1,702,877 2,643,117 2,846,278 3,218,165 3,774,508 EFTA 1960 2002 EIA 717 925 831 1,128 1,252 1,524 2,196 European Union 1957 1958 EIA, CU 951,373 1,272,211 1,630,509 2,535,600 2,714,582 3,069,912 3,596,135 NAFTA 1994 1994 FTA, EIA 249,474 394,472 676,141 737,591 824,710 902,298 951,587 SPARTECA 1981 1981 PS 5,637 9,101 8,554 13,585 15,181 15,536 18,582 Trans-Pacific SEP 2006 2006 EIA, FTA 1,195 2,614 1,438 2,096 2,345 2,927 3,290 East Asia and Pacific and South Asia APTA 1975 1976 PS 5,475 21,728 37,895 99,369 127,277 156,957 198,000 ASEAN 1967 1992 FTA 32,785 79,544 98,060 141,931 165,458 191,392 216,424 PICTA 2001 2003 FTA 5 42 65 130 122 151 187 SAARC 1985 2006 FTA 1,013 2,024 2,680 5,830 7,266 8,310 10,222 Europe, Central Asia,and Middle East CEFTA 1992 1994 FTA .. 534 1,038 2,009 2,434 2,819 3,641 CIS 1991 1994 FTA .. 31,529 28,753 43,425 59,423 66,689 97,545 COZ 2003 2004 FTA .. 24,398 22,985 32,629 45,973 49,695 75,700 EAEC 1997 1997 CU .. 13,556 15,467 17,292 27,297 27,930 50,079 ECO 1985 2003 PS 1,232 4,746 4,518 9,982 13,936 19,053 24,584 GCC 1981 2003c CU 4,760 6,832 7,954 12,532 16,635 20,693 24,728 PAFTA (GAFTA) 1997 1998 FTA 10,028 12,948 16,088 35,328 44,511 54,827 65,818 UMA 1989 1994 c NNA 1,071 1,109 1,041 1,448 1,934 2,478 3,076 Latin America and the Caribbean Andean Community 1969 1988 CU 788 1,788 2,046 3,435 4,572 5,011 5,509 CACM 1961 1961 CU 779 1,594 2,586 3,574 4,342 4,697 5,562 CARICOM 1973 1997 EIA 445 877 1,078 1,746 2,090 2,429 3,759 LAIA (ALADI) 1980 1981 PS 15,769 35,986 44,253 57,741 71,720 90,357 109,130 MERCOSUR 1991 2005 EIA 6,166 16,811 20,082 19,675 24,211 31,197 39,486 OECS 1981 1981c NNA 29 39 38 60 68 84 104 Sub-Saharan Africa CEMAC 1994 1999 CU 114 120 96 174 198 245 304 COMESA 1994 1994 FTA 830 1,367 1,443 2,420 2,866 3,468 4,582 EAC 1996 2000 CU 132 628 689 930 1,043 1,279 1,587 ECCAS 1983 2004 c NNA 133 157 181 221 251 310 385 ECOWAS 1975 1993 PS 1,384 1,875 2,715 4,366 5,497 5,957 7,341 Indian Ocean Commission 1984 2005c NNA 75 113 106 155 159 172 204 SADC 1992 2000 FTA 1,720 3,615 4,427 6,655 7,798 8,694 11,952 UEMOA 1994 2000 CU 499 560 741 1,233 1,390 1,545 1,917 Note: Regional bloc memberships are as follows: Andean Community, Bolivia, Colombia, Ecuador, and Peru; Arab Maghreb Union (UMA), Algeria, Libyan Arab Republic, Mauritania, Morocco, and Tunisia; Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand, the United States, and Vietnam; Asia-Pacific Trade Agreement (APTA; formerly Bangkok Agreement), Bangladesh, China, India, the Republic of Korea, the Lao People's Democratic Republic, and Sri Lanka; Association of South East Asian Nations (ASEAN), Brunei Darussalam, Cambodia, Indonesia, the Lao People's Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas, Barbados, Belize, Dominica, Grenada, Guyana, Haiti, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Central European Free Trade Area (CEFTA), Albania, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Moldova, Montenegro, and Serbia; Common Economic Zone (COZ), Belarus, Kazakhstan, the Russian Federation, and Ukraine; Common Market for Eastern and Southern Africa (COMESA), Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Libyan Arab Republic, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Zambia, and Zimbabwe; Commonwealth of Independent States (CIS), Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic, Mol- dova, the Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan; East African Community (EAC), Burundi, Kenya, Rwanda, Tanzania, and Uganda; Economic and Monetary Community of Central Africa (CEMAC; formerly Central African Customs and Economic Union [UDEAC]), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Principe; Economic Com- munity of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d'Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, 348 2009 World Development Indicators GLOBAL LINKS Regional trade blocs 6.7 Merchandise exports within bloc Year of entry into Type force of the of most % of total bloc exports Year of most recent recent creation agreement agreementa 1990 1995 2000 2004 2005 2006 2007 High-income and low- and middle-income economies APECb 1989 None 68.7 71.7 73.1 72.2 70.8 69.5 67.4 EEA 1994 1994 EIA 69.4 67.3 68.6 68.9 68.4 68.6 69.0 EFTA 1960 2002 EIA 0.7 0.7 0.6 0.5 0.5 0.6 0.7 European Union 1957 1958 EIA, CU 67.8 65.8 67.3 67.6 67.0 67.2 67.5 NAFTA 1994 1994 FTA, EIA 42.2 46.2 55.7 55.9 55.8 53.9 51.3 SPARTECA 1981 1981 PS 10.5 12.9 10.7 12.1 11.4 10.2 10.5 Trans-Pacific SEP 2006 2006 EIA, FTA 1.5 1.7 0.8 0.8 0.8 0.8 0.8 East Asia and Pacific and South Asia APTA 1975 1976 PS 3.3 6.8 8.0 10.6 11.0 10.9 11.2 ASEAN 1967 1992 FTA 19.8 24.5 23.0 24.9 25.3 25.0 25.2 PICTA 2001 2003 FTA 0.2 1.0 1.7 2.3 1.8 1.9 2.0 SAARC 1985 2006 FTA 3.6 4.4 4.2 5.7 5.6 5.2 5.3 Europe, Central Asia, and Middle East CEFTA 1992 1994 FTA .. 7.8 15.3 15.9 16.6 16.1 16.8 CIS 1991 1994 FTA .. 28.6 20.0 17.6 18.0 16.5 19.8 COZ 2003 2004 FTA .. 23.8 17.1 14.0 14.7 13.0 16.2 EAEC 1997 1997 CU .. 14.8 12.5 8.5 9.6 8.0 11.8 ECO 1985 2003 PS 3.2 7.9 5.6 6.7 7.6 8.5 9.2 GCC 1981 2003c CU 5.8 6.8 4.8 5.0 4.9 5.0 5.4 PAFTA (GAFTA) 1997 1998 FTA 8.9 9.8 7.2 10.0 9.9 9.9 10.6 UMA 1989 1994 c NNA 3.3 3.8 2.2 2.0 2.0 2.1 2.3 Latin America and the Caribbean Andean Community 1969 1988 CU 5.6 8.6 7.7 8.7 9.0 7.8 7.4 CACM 1961 1961 CU 17.6 21.8 19.1 20.9 20.1 15.8 17.0 CARICOM 1973 1997 EIA 8.2 12.1 14.4 12.2 11.6 11.3 15.7 LAIA (ALADI) 1980 1981 PS 12.2 17.3 13.2 13.2 13.6 14.3 15.1 MERCOSUR 1991 2005 EIA 9.9 18.9 16.4 11.1 11.0 12.2 12.8 OECS 1981 1981c NNA 9.0 12.6 10.0 11.7 11.4 8.2 8.1 Sub-Saharan Africa CEMAC 1994 1999 CU 2.0 2.1 1.0 1.2 0.9 0.9 1.1 COMESA 1994 1994 FTA 3.6 6.1 4.6 5.0 4.5 4.2 4.7 EAC 1996 2000 CU 7.4 19.5 22.6 18.9 17.6 19.3 20.4 ECCAS 1983 2004 c NNA 1.3 1.5 1.0 0.8 0.6 0.5 0.6 ECOWAS 1975 1993 PS 9.7 9.0 7.6 9.3 9.3 8.4 9.4 Indian Ocean Commission 1984 2005c NNA 4.8 5.9 4.4 4.3 4.6 4.8 5.7 SADC 1992 2000 FTA 17.9 32.8 9.5 9.7 9.3 9.1 10.1 UEMOA 1994 2000 CU 11.3 10.3 13.1 12.9 13.4 13.1 15.2 Pakistan, Tajikistan, Turkey, Turkmenistan, and Uzbekistan; Eurasian Economic Community (EAEC), Belarus, Kazakhstan, Kyrgyz Republic, the Russian Federation, Tajikistan, and Uzbekistan; European Economic Area (EEA), European Union plus Iceland, Liechtenstein, and Norway; European Free Trade Association (EFTA), Ice- land, Liechtenstein, Norway, and Switzerland; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom; Gulf Cooperation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Ara- bia, and the United Arab Emirates; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Bolivarian Republic of Venezuela; North American Free Trade Agreement (NAFTA), Canada, Mexico, and the United States; Organization of Eastern Caribbean States (OECS), Anguilla, Antigua and Barbuda, British Virgin Islands, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Pacific Island Countries Trade Agreement (PICTA), Cook Islands, Fiji, Federated States of Micronesia, Nauru, Niue, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu; Pan-Arab Free Trade Area (PAFTA; also known as Greater Arab Trade Area [GAFTA]), Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, the United Arab Emirates, and Yemen; South Asian Association for Regional Cooperation (SAARC), Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; South Pacific Regional Trade and Economic Cooperation Agreement (SPARTECA), Australia, Cook Islands, Fiji, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Zealand, Niue, Papua New Guinea, Solomon Islands, Tonga, Tuvalu, Vanu- atu, and Western Samoa; Southern African Development Community (SADC), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Southern Common Market (MERCOSUR), Argentina, Brazil, Paraguay, Uruguay, and Bolivarian Republic of Venezuela; Trans-Pacific Strategic Economic Partnership (Trans-Pacific SEP), Brunei Darussalam, Chile, New Zea- land, and Singapore; West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo. 2009 World Development Indicators 349 6.7 Regional trade blocs Merchandise exports by bloc Year of entry into Type force of the of most % of world exports Year of most recent recent creation agreement agreementa 1990 1995 2000 2004 2005 2006 2007 High-income and low- and middle-income economies APECb 1989 None 41.7 46.3 48.5 44.4 45.1 45.5 45.0 EEA 1994 1994 EIA 41.1 38.9 38.9 42.0 40.2 39.2 39.6 EFTA 1960 2002 EIA 2.8 2.4 2.2 2.3 2.3 2.3 2.3 European Union 1957 1958 EIA, CU 40.1 38.1 38.0 41.0 39.1 38.2 38.6 NAFTA 1994 1994 FTA, EIA 16.9 16.8 19.0 14.5 14.3 14.0 13.4 SPARTECA 1981 1981 PS 1.5 1.4 1.3 1.2 1.3 1.3 1.3 Trans-Pacific SEP 2006 2006 EIA, FTA 2.3 3.0 2.7 2.8 2.9 3.0 2.9 East Asia and Pacific and South Asia APTA 1975 1976 PS 4.8 6.3 7.4 10.3 11.2 12.0 12.8 ASEAN 1967 1992 FTA 4.7 6.4 6.7 6.2 6.3 6.4 6.2 PICTA 2001 2003 FTA 0.1 0.1 0.1 0.1 0.1 0.1 0.1 SAARC 1985 2006 FTA 0.8 0.9 1.0 1.1 1.3 1.3 1.4 Europe, Central Asia, and Middle East CEFTA 1992 1994 FTA .. 0.1 0.1 0.1 0.1 0.1 0.2 CIS 1991 1994 FTA .. 2.2 2.2 2.7 3.2 3.4 3.6 COZ 2003 2004 FTA .. 2.0 2.1 2.5 3.0 3.2 3.4 EAEC 1997 1997 CU .. 1.8 1.9 2.2 2.7 2.9 3.1 ECO 1985 2003 PS 1.1 1.2 1.3 1.6 1.8 1.9 1.9 GCC 1981 2003c CU 2.4 2.0 2.6 2.7 3.3 3.5 3.3 PAFTA (GAFTA) 1997 1998 FTA 3.2 2.6 3.5 3.9 4.3 4.7 4.5 UMA 1989 1994 c NNA 0.9 0.6 0.8 0.8 0.9 1.0 1.0 Latin America and the Caribbean Andean Community 1969 1988 CU 0.4 0.4 0.4 0.4 0.5 0.5 0.5 CACM 1961 1961 CU 0.1 0.1 0.2 0.2 0.2 0.2 0.2 CARICOM 1973 1997 EIA 0.2 0.1 0.1 0.2 0.2 0.2 0.2 LAIA (ALADI) 1980 1981 PS 3.7 4.1 5.3 4.8 5.1 5.3 5.2 MERCOSUR 1991 2005 EIA 1.8 1.8 1.9 1.9 2.1 2.1 2.2 OECS 1981 1981c NNA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Sub-Saharan Africa CEMAC 1994 1999 CU 0.2 0.1 0.1 0.1 0.2 0.2 0.2 COMESA 1994 1994 FTA 0.7 0.4 0.5 0.5 0.6 0.7 0.7 EAC 1996 2000 CU 0.1 0.1 0.0 0.1 0.1 0.1 0.1 ECCAS 1983 2004 c NNA 0.3 0.2 0.3 0.3 0.4 0.5 0.5 ECOWAS 1975 1993 PS 0.4 0.4 0.6 0.5 0.6 0.6 0.6 Indian Ocean Commission 1984 2005c NNA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 SADC 1992 2000 FTA 0.3 0.2 0.7 0.7 0.8 0.8 0.9 UEMOA 1994 2000 CU 0.1 0.1 0.1 0.1 0.1 0.1 0.1 a. CU is customs union; EIA is economic integration agreement; FTA is free trade agreement; NNA is not notified agreement, which refers to preferential trade arrangements established among member countries that are not notified to the World Trade Organization (these agreements may be functionally equivalent to any of the other agreements); and PS is partial scope agreement. b. No preferential trade agreement c. Years of the most recent agreement are collected from the official website of the trade bloc. 350 2009 World Development Indicators GLOBAL LINKS Regional trade blocs 6.7 About the data Trade blocs are groups of countries that have estab- The table shows the value of merchandise intra- affected by the same preferences as in recent years. lished preferential arrangements governing trade trade (service exports are excluded) for important In addition, some countries belong to more than between members. Although in some cases the pref- regional trade blocs and the size of intratrade rela- one trade bloc, so shares of world exports exceed erences--such as lower tariff duties or exemptions tive to each bloc's exports of goods and the share 100 percent. Exports of blocs include all commod- from quantitative restrictions--may be no greater of the bloc's exports in world exports. Although the ity trade, which may include items not specified in than those available to other trading partners, such Asia Pacific Economic Cooperation (APEC) has no trade bloc agreements. Differences from previously arrangements are intended to encourage exports by preferential arrangements, it is included because of published estimates may be due to changes in mem- bloc members to one another--sometimes called the volume of trade between its members. bership or revisions in underlying data. intratrade. The data on country exports are from the Inter- Definitions Most countries are members of a regional trade national Monetary Fund's (IMF) Direction of Trade bloc, and more than a third of the world's trade takes database and should be broadly consistent with · Merchandise exports within bloc are the sum of place within such arrangements. While trade blocs those from sources such as the United Nations merchandise exports by members of a trade bloc to vary in structure, they all have the same objective: Statistics Division's Commodity Trade (Comtrade) other members of the bloc. They are shown both in to reduce trade barriers between member countries. database. However, trade flows between many devel- U.S. dollars and as a percentage of total merchan- But effective integration requires more than reducing oping countries, particularly in Sub-Saharan Africa, dise exports by the bloc. · Merchandise exports by tariffs and quotas. Economic gains from competition are not well recorded, so the value of intratrade for bloc as a share of world exports are the bloc's total and scale may not be achieved unless other barri- certain groups may be understated. Data on trade merchandise exports (within the bloc and to the rest ers that divide markets and impede the free flow between developing and high-income countries are of the world) as a share of total merchandise exports of goods, services, and investments are lifted. For generally complete. by all economies in the world. · Type of most recent example, many regional trade blocs retain contingent Membership in the trade blocs shown is based agreement includes customs union, under which protections on intrabloc trade, including antidumping, on the most recent information available (see Data members substantially eliminate all tariff and nontariff countervailing duties, and "emergency protection" to sources). Other types of preferential trade agree- barriers among themselves and establish a common address balance of payments problems or protect an ments may have entered into force earlier than those external tariff for nonmembers; economic integration industry from import surges. Other barriers include shown in the table and may still be effective. Unless agreement, which liberalizes trade in services among differing product standards, discrimination in public otherwise indicated in the footnotes, information on members and covers a substantial number of sectors, procurement, and cumbersome border formalities. the type of agreement and date of enforcement are affects a sufficient volume of trade, includes substan- Membership in a regional trade bloc may reduce based on the World Trade Organization's (WTO) list tial modes of supply, and is nondiscriminatory (in the the frictional costs of trade, increase the cred- of regional trade agreements. sense that similarly situated service suppliers are ibility of reform initiatives, and strengthen security Although bloc exports have been calculated back treated the same); free trade agreement, under which among partners. But making it work effectively is to 1990 on the basis of current membership, several members substantially eliminate all tariff and nontariff challenging. All economic sectors may be affected, blocs came into existence after that and membership barriers but set tariffs on imports from nonmembers; and some may expand while others contract, so it is may have changed over time. For this reason, and partial scope agreement, which is a preferential trade important to weigh the potential costs and benefits because systems of preferences also change over agreement notified to the WTO that is not a free trade of membership. time, intratrade in earlier years may not have been agreement, a customs union, or an economic integra- tion agreement; and not notified agreement, which is The number of trade agreements has increased rapidly since 1990, a preferential trade arrangement established among especially bilateral agreements 6.7a member countries that is not notified to the World Trade Organization (the agreement may be functionally Cumulative agreements Bilateral Multilateral 180 equivalent to any of the other agreements). 150 Data sources 120 Data on merchandise trade flows are published in 90 the IMF's Direction of Trade Statistics Yearbook and 60 Direction of Trade Statistics Quarterly; the data in 30 the table were calculated using the IMF's Direc- tion of Trade database. Data on trade bloc mem- 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2008 bership are from the World Bank Policy Research Report Trade Blocs (2000a), UNCTAD's Trade and Note: Data are cumulative number of bilateral and multilateral trade agreements notified to the General Agreement on Tariffs and Trade/World Trade Organization (GATT/WTO) at the time they entered into force. Only agreements Development Report 2007, WTO's Regional Trade that are that are currently in force are included. Agreements on accessions of new members to an existing Agreements Information System, and the World agreement are not included. Agreements that are in force but have not been notified to GATT/WTO may be excluded. Source: World Bank staff calculations based on the World Trade Organization's Regional Trade Agreements Information System. Bank's International Trade Unit. 2009 World Development Indicators 351 6.8 Tariff barriers All Primary Manufactured products products products % Share of tariff Share of Most Simple Simple Weighted lines with tariff lines % % recent Binding mean mean mean international with specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 2007 100.0 7.0 5.7 5.7 0.0 0.0 7.0 5.2 5.5 6.0 Algeria 2007 .. .. 16.2 9.9 40.0 0.0 16.2 9.2 16.2 10.1 Angola 2006 100.0 59.2 7.6 6.5 10.4 0.0 11.5 13.1 6.9 5.0 Antigua and Barbuda 2007 97.9 58.7 11.6 12.6 47.9 0.0 13.7 12.3 11.2 12.7 Argentina 2007 100.0 31.9 10.8 5.7 24.8 0.0 7.8 1.3 11.1 6.4 Armenia 2006 100.0 8.5 3.6 1.8 0.1 0.3 5.5 1.5 3.4 2.0 Australia 2007 97.1 9.9 2.8 1.8 2.6 0.1 0.9 0.3 3.1 2.3 Azerbaijan 2007 .. .. 8.6 4.0 47.9 0.5 10.1 3.7 8.4 4.1 Bahamas, The 2006 .. .. 28.5 23.9 77.4 0.0 24.4 15.1 29.4 29.7 Bahrain 2007 73.4 34.4 4.2 4.8 0.2 0.0 5.8 5.6 3.9 3.4 Bangladesh 2007 15.9 163.1 14.5 11.0 41.1 0.0 15.2 7.3 14.4 13.0 Barbados 2007 97.9 78.1 15.1 14.8 44.9 0.6 26.4 21.9 13.5 12.3 Belarus 2002 .. .. 11.3 8.9 16.4 0.0 11.1 7.1 11.3 10.3 Belize 2007 97.9 58.2 11.6 9.3 43.3 0.0 15.6 6.5 11.1 11.0 Benin 2007 39.0 28.6 13.4 11.8 53.8 0.0 13.4 11.9 13.4 11.7 Bermuda 2007 .. .. 18.5 30.2 66.5 0.8 14.0 18.6 19.4 30.9 Bhutan 2007 .. .. 18.2 17.8 50.7 0.0 43.7 44.9 15.5 16.0 Bolivia 2007 100.0 40.0 6.2 4.3 0.0 0.0 6.1 4.4 6.2 4.3 Bosnia and Herzegovina 2007 .. .. 6.8 4.9 11.4 0.0 3.3 2.0 7.2 6.3 Botswana 2007 96.3 18.9 8.3 8.9 19.0 0.2 4.3 0.9 8.6 9.5 Brazil 2007 100.0 31.4 12.3 6.8 26.4 0.0 7.9 1.2 12.7 9.4 Brunei 2007 95.3 24.3 3.1 6.1 21.6 0.1 0.9 13.2 3.4 4.5 Bulgaria 2006 100.0 24.6 4.1 2.1 13.3 2.0 9.5 4.8 3.5 1.3 Burkina Faso 2007 39.2 41.9 12.1 10.3 44.4 0.0 11.3 7.6 12.2 11.0 Burundi 2007 21.8 68.3 13.5 12.3 27.4 0.0 12.8 13.4 13.7 11.5 Cambodia 2007 .. .. 12.5 10.0 49.2 0.0 14.8 10.5 12.1 10.0 Cameroon 2007 13.3 79.9 18.6 12.5 52.6 0.0 21.9 10.8 18.2 14.6 Canada 2007 99.7 5.1 4.2 1.6 7.9 3.6 5.1 3.2 4.0 1.2 Central African Republic 2007 .. .. 17.5 13.6 47.4 0.0 19.0 13.8 17.3 13.5 Chad 2007 .. .. 17.0 13.6 44.8 0.0 20.8 18.3 16.5 12.7 Chile 2007 100.0 25.1 2.0 1.8 0.0 0.0 2.4 2.2 1.9 1.5 China 2007 100.0 10.0 8.9 5.1 14.9 0.0 9.0 3.0 8.9 6.3 Hong Kong, China 2007 45.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Colombia 2007 100.0 42.8 10.8 8.8 41.0 0.0 9.7 8.2 10.9 9.0 Congo, Dem. Rep. 2007 .. .. 13.0 11.2 43.4 0.0 14.2 10.9 12.8 11.3 Congo, Rep. 2007 16.1 27.3 18.6 14.7 52.6 0.0 22.1 18.6 18.1 14.1 Costa Rica 2007 100.0 42.9 6.1 4.1 0.4 0.0 8.5 5.1 5.8 3.8 Côte d'Ivoire 2007 33.1 11.2 13.4 7.2 50.2 0.0 15.5 4.1 13.0 10.7 Croatia 2007 100.0 6.0 2.5 1.2 4.1 0.0 4.4 1.9 2.3 0.9 Cuba 2007 30.9 21.3 11.3 7.4 34.4 0.0 10.9 5.0 11.3 9.5 Djibouti 2006 100.0 41.0 30.2 29.1 87.9 0.0 23.0 23.1 31.3 31.0 Dominica 2007 94.8 58.7 11.9 7.9 43.3 0.0 19.3 5.7 10.6 9.3 Dominican Republic 2006 100.0 34.9 9.3 8.5 28.6 0.0 12.7 7.3 8.9 9.0 Ecuador 2007 100.0 21.8 10.0 5.9 34.1 0.0 8.7 3.3 10.1 7.1 Egypt, Arab Rep. 2005 99.3 36.8 19.1 13.3 23.0 0.0 86.2 17.7 12.0 11.7 El Salvador 2007 100.0 36.6 5.1 4.6 1.9 0.0 6.9 3.5 4.9 5.3 Equatorial Guinea 2007 .. .. 18.3 15.6 52.3 0.0 21.7 21.5 17.8 14.3 Ethiopia 2006 .. .. 18.6 12.0 57.4 0.0 18.1 7.8 18.6 13.9 European Union 2007 100.0 4.2 2.4 1.8 5.5 6.4 6.1 1.8 1.6 1.8 Fiji 51.3 40.1 .. .. .. .. .. .. .. .. Gabon 2007 100.0 21.4 18.0 14.5 52.0 0.0 20.0 14.2 17.7 14.6 Gambia, The 2007 13.7 101.8 18.7 15.1 90.7 0.0 17.1 13.3 19.1 17.0 Georgia 2007 100.0 7.2 0.6 0.5 0.0 0.0 4.4 1.3 0.1 0.1 Ghana 2007 14.3 92.5 13.0 9.9 40.8 0.0 16.8 14.4 12.5 8.8 Grenada 2007 100.0 56.8 10.5 8.6 43.0 0.0 13.7 8.4 10.0 8.7 Guatemala 2007 100.0 42.2 5.3 4.6 1.1 0.0 6.7 4.2 5.2 4.9 Data for Taiwan, China 2007 100.0 5.9 5.7 2.0 8.9 0.6 11.1 2.4 4.9 1.9 352 2009 World Development Indicators GLOBAL LINKS All Tariff barriers Primary 6.8Manufactured products products products % Share of tariff Share of Most Simple Simple Weighted lines with tariff lines % % recent Binding mean mean mean international with specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Guinea 2005 38.6 20.3 14.2 12.7 58.6 0.0 16.4 14.3 13.9 11.2 Guinea-Bissau 2007 .. .. 14.0 14.5 55.3 0.0 16.6 17.6 13.5 12.4 Guyana 2006 100.0 56.7 11.4 6.2 34.5 0.0 17.9 4.1 10.6 7.9 Haiti 2007 100.0 32.4 2.9 3.0 4.7 0.0 5.6 4.4 2.4 2.0 Honduras 2007 .. .. 5.4 4.5 0.3 0.0 7.1 5.4 5.2 4.0 Hungary 2002 96.2 9.7 8.9 7.9 10.9 0.0 18.1 6.7 7.8 8.1 Iceland 2007 95.0 13.5 4.1 2.1 10.2 3.4 16.4 5.4 2.4 1.0 India 2005a 73.8 49.6 17.0 13.4 15.4 3.9 25.2 14.3 15.9 12.3 Indonesia 2007 96.6 37.1 5.9 3.9 12.7 0.0 6.6 2.5 5.8 4.4 Iran, Islamic Rep. 2007 .. .. 21.3 17.6 54.8 0.0 16.9 15.3 21.7 18.5 Israel 2007 75.0 21.5 2.3 1.1 1.0 0.7 4.8 1.3 2.1 1.1 Jamaica 2006 100.0 49.6 9.2 8.9 35.8 0.0 16.0 9.5 8.3 8.5 Japan 2007 99.7 2.9 4.2 3.1 10.1 3.6 11.4 3.8 2.9 2.2 Jordan 2007 100.0 16.3 10.7 5.9 32.7 0.0 14.4 3.8 10.1 7.3 Kazakhstan 2004 .. .. 2.4 1.9 0.0 0.0 3.5 3.4 2.3 1.5 Kenya 2007 14.8 95.4 12.3 7.0 37.4 0.0 16.1 7.0 11.9 7.0 Korea, Rep. 2007 94.6 15.8 8.5 8.0 5.1 0.1 20.8 11.5 6.6 4.8 Kuwait 2007 99.9 100.0 4.3 3.9 0.0 0.0 3.4 2.9 4.4 4.0 Kyrgyz Republic 2007 99.9 7.4 2.9 1.1 1.5 1.7 4.7 0.7 2.6 1.4 Lao PDR 2007 .. .. 5.8 8.3 15.1 0.0 9.9 8.3 5.3 8.3 Latvia 2001 100.0 12.8 3.3 2.6 3.0 0.0 8.1 5.4 2.6 1.6 Lebanon 2007 .. .. 5.6 4.8 11.6 0.0 8.2 5.0 5.2 5.1 Lesotho 2007 .. .. 9.0 13.9 21.3 0.5 7.8 5.0 9.0 14.3 Libya 2006 .. .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Lithuania 2003 100.0 9.2 1.3 0.6 3.1 0.0 3.3 1.3 1.0 0.4 Macao 2007 28.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Macedonia, FYR 2007 100.0 6.9 8.6 5.7 31.7 0.0 9.3 5.4 8.6 5.9 Madagascar 2007 30.0 27.3 12.1 8.4 40.4 0.0 14.1 4.4 11.8 10.4 Malawi 2006 31.6 75.4 12.9 8.1 40.4 0.0 12.8 6.1 12.9 8.9 Malaysia 2007 83.7 14.5 5.9 3.1 24.8 0.0 2.8 2.3 6.5 3.4 Maldives 2006 97.1 36.9 21.4 21.1 72.3 0.0 18.1 19.5 22.2 22.0 Mali 2007 40.2 28.5 12.5 8.7 47.2 0.0 11.6 9.8 12.6 8.4 Mauritania 2007 39.3 19.6 12.6 10.1 49.0 0.0 11.2 9.2 12.9 11.0 Mauritius 2007 17.9 94.0 4.3 2.5 9.2 0.0 6.3 2.8 4.0 2.4 Mexico 2006 100.0 35.0 8.0 2.4 10.9 0.3 7.3 1.8 8.1 2.5 Moldova 2006 .. .. 4.4 1.7 16.0 1.3 7.2 1.4 4.0 1.9 Mongolia 2007 100.0 17.5 4.9 5.1 0.4 0.0 5.2 5.4 4.9 4.9 Montserrat 1999 .. .. 18.2 13.3 41.2 0.0 22.4 15.6 16.4 12.2 Morocco 2007 100.0 41.3 13.3 10.0 45.1 0.0 20.0 11.6 12.6 9.1 Mozambique 2007 .. .. 11.0 7.7 36.7 0.0 13.9 8.0 10.5 7.5 Myanmar 2007 17.4 83.6 4.1 3.9 8.1 0.0 5.8 4.5 3.9 3.7 Namibia 2007 96.3 19.2 6.6 1.5 18.0 0.5 4.2 0.7 7.0 1.9 Nepal 2007 .. .. 12.6 13.7 42.1 0.0 12.4 9.7 12.7 15.8 New Zealand 2007 99.9 10.0 3.8 2.7 7.5 0.0 2.1 0.4 4.0 3.6 Nicaragua 2007 100.0 41.7 5.4 3.6 0.4 0.0 7.8 3.9 5.1 3.4 Niger 2007 96.7 44.7 12.9 10.1 51.0 0.0 13.2 10.6 12.9 9.6 Nigeria 2006 19.3 118.4 10.6 9.4 33.9 0.0 12.8 13.1 10.3 8.2 Norway 2007 100.0 3.0 4.1 2.6 5.0 5.4 29.8 10.2 0.5 0.2 Oman 2007 100.0 13.8 3.8 3.4 0.2 0.0 4.5 3.0 3.7 3.4 Pakistan 2007 98.7 59.9 14.9 11.4 52.7 0.0 14.2 8.7 15.0 13.3 Panama 2007 99.9 23.4 7.3 7.0 33.6 0.0 11.3 7.8 6.9 6.6 Papua New Guinea 2007 100.0 31.7 5.2 1.6 25.6 0.8 16.2 2.4 3.7 1.1 Paraguay 2007 100.0 33.5 8.0 3.3 17.3 0.0 5.7 0.8 8.2 4.0 Peru 2007 100.0 30.1 8.5 5.2 10.5 0.0 9.3 2.7 8.5 6.4 Philippines 2007 67.0 25.7 5.0 3.6 15.8 0.0 6.0 5.2 4.8 2.7 Poland 2003 96.3 11.9 4.3 2.3 8.8 0.1 18.2 6.7 2.5 1.2 Qatar 2007 100.0 15.9 3.9 4.0 0.1 0.0 3.5 3.8 3.9 4.0 2009 World Development Indicators 353 6.8 Tariff barriers All Primary Manufactured products products products % Share of tariff Share of Most Simple Simple Weighted lines with tariff lines % % recent Binding mean mean mean international with specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Romania 2005 .. .. 6.5 3.1 20.6 0.0 13.4 7.2 5.6 1.8 Russian Federation 2007 .. .. 9.9 7.2 34.4 15.2 8.8 9.1 10.1 6.8 Rwanda 2008 100.0 89.5 19.3 11.6 55.2 0.0 17.0 8.8 19.5 12.6 Saudi Arabia 2007 .. .. 4.0 3.9 0.1 0.0 3.4 2.8 4.1 4.2 Serbiab 2005 .. .. 8.1 6.0 17.8 0.0 11.0 4.5 7.8 6.8 Senegal 2007 100.0 30.0 13.5 9.3 51.4 0.0 14.5 7.8 13.3 10.6 Seychelles 2007 .. .. 6.5 28.3 12.8 0.0 14.1 50.5 4.8 6.4 Sierra Leone 2004 100.0 47.4 .. .. .. .. .. .. .. .. Singapore 2007 69.7 7.0 0.0 0.0 0.1 0.1 0.2 0.0 0.0 0.0 Slovak Republic 2002 100.0 5.0 5.0 4.6 4.3 0.0 5.6 3.7 4.9 4.9 Solomon Islands 2007 100.0 78.7 10.3 13.3 2.5 1.2 16.2 19.9 9.4 7.8 South Africa 2007 96.3 19.2 8.1 5.0 19.1 0.6 5.9 1.9 8.4 6.5 Sri Lanka 2006a 38.1 29.8 11.3 7.1 23.5 0.8 17.8 9.0 10.6 6.4 St. Kitts and Nevis 2007 97.9 75.9 12.1 12.1 43.9 0.2 13.0 11.1 11.9 12.5 St. Lucia 2007 99.6 61.9 9.6 9.0 39.9 0.0 12.8 4.9 9.1 12.3 St. Vincent & Grenadines 2007 .. .. 11.3 8.4 44.4 0.2 15.1 7.8 10.6 8.6 Sudan 2006 .. .. 17.1 15.3 38.1 0.0 23.0 19.7 16.6 14.7 Suriname 2007 .. .. 11.5 11.8 39.4 0.0 17.8 16.0 10.6 10.9 Swaziland 2007 96.3 19.2 9.9 7.9 24.0 0.8 10.3 3.1 9.8 8.7 Switzerland 2007 99.8 0.0 3.9 1.9 10.6 30.9 20.0 9.6 1.1 0.2 Syrian Arab Republic 2002 .. .. 14.7 15.5 23.3 0.0 14.4 11.7 14.7 17.1 Tajikistan 2006 .. .. 4.9 3.8 0.1 0.7 5.4 2.1 4.8 5.2 Tanzania 2007 13.4 120.0 12.5 7.2 38.0 0.0 16.8 7.5 12.1 7.0 Thailand 2006 75.0 25.7 10.8 4.6 22.9 0.9 13.6 2.1 10.4 5.8 Togo 2007 14.0 80.0 13.9 10.4 55.1 0.0 14.2 9.6 13.8 10.8 Trinidad and Tobago 2007 100.0 55.8 8.8 10.6 43.6 0.4 17.0 3.2 7.6 18.4 Tunisia 2006 57.9 57.9 23.0 18.3 55.5 0.0 32.4 13.9 22.2 20.0 Turkey 2007 50.4 28.6 2.5 2.0 4.7 0.3 14.0 3.8 1.4 1.3 Turkmenistan 2002 .. .. 5.4 2.9 14.8 2.8 14.9 12.6 3.8 1.1 Uganda 2007 15.7 73.4 12.1 8.4 37.4 0.0 15.9 9.6 11.6 7.8 Ukraine 2006 .. .. 4.9 3.1 4.8 3.7 5.2 0.9 4.8 4.5 United Arab Emirates 2007 100.0 14.7 4.2 3.8 0.2 0.0 4.3 2.9 4.2 4.4 United States 2007 100.0 3.6 2.9 1.6 5.5 4.8 3.0 1.3 2.9 1.7 Uruguay 2007 100.0 31.6 9.5 3.6 25.9 0.0 5.8 1.3 9.8 4.9 Uzbekistan 2007 .. .. 10.8 6.6 18.8 0.0 10.2 2.6 10.9 7.4 Vanuatu 2007 .. .. 16.9 11.0 64.8 0.0 19.4 18.7 16.4 9.9 Venezuela 2007 99.9 36.5 12.3 10.7 45.6 0.0 11.1 8.5 12.4 11.0 Vietnam 2007 .. .. 11.7 10.6 32.2 0.0 14.5 10.2 11.3 11.0 Yemen 2006 .. .. 6.7 6.9 1.8 0.0 9.6 8.6 6.3 5.6 Zambia 2005 16.8 106.5 14.6 9.4 34.5 0.0 15.0 9.3 14.6 9.4 Zimbabwe 2007a 22.4 89.8 16.6 .. 34.3 5.6 17.4 .. 16.4 .. World 79.6 32.0 7.0 3.0 15.7 0.5 8.9 2.5 6.7 3.2 Low income 45.9 52.4 3.9 1.9 5.6 0.0 13.8 9.2 11.6 10.3 Middle income 89.0 32.1 4.6 1.7 7.1 0.1 10.8 4.0 7.5 5.4 Lower middle income 88.7 31.0 2.9 1.8 3.4 0.0 12.9 4.0 8.9 6.5 Upper middle income 89.0 33.9 8.2 4.9 21.5 0.1 8.7 3.9 6.2 4.1 Low & middle income 75.3 36.2 5.3 4.2 13.0 2.4 11.2 4.3 8.2 5.6 East Asia & Pacific 79.1 32.5 7.9 4.6 21.7 0.0 9.2 3.2 8.0 5.7 Europe & Central Asia 93.8 11.0 14.0 11.0 40.2 0.0 8.1 4.8 4.9 4.0 Latin America & Carib. 97.0 41.6 13.7 8.0 35.8 0.0 9.1 2.9 7.8 5.1 Middle East & N. Africa 91.4 36.9 9.3 5.7 22.9 0.4 24.1 10.7 12.8 11.2 South Asia 64.7 52.3 8.6 5.2 21.8 0.7 16.6 8.2 13.3 7.8 Sub-Saharan Africa 48.0 43.2 11.8 9.9 34.3 0.1 13.6 7.0 11.7 8.3 High income 92.1 21.7 7.9 5.0 20.0 0.9 5.1 1.7 3.7 1.9 OECD 98.7 7.2 11.9 7.9 36.4 0.0 3.7 1.7 2.8 1.9 Non-OECD 86.3 33.4 6.5 4.0 17.0 1.3 5.5 1.6 4.4 1.8 Note: Tariff rates include ad valorem equivalents of specific rates whenever available. a. Rates are most favored nation rates. b. Includes Montenegro. 354 2009 World Development Indicators GLOBAL LINKS Tariff barriers 6.8 About the data Definitions Poor people in developing countries work primarily in nation or applied rates are calculated using all traded · Binding coverage is the percentage of product agriculture and labor-intensive manufactures, sectors items. Weighted mean tariffs are weighted by the lines with an agreed bound rate. · Simple mean that confront the greatest trade barriers. Removing value of the country's trade with each trading part- bound rate is the unweighted average of all the lines barriers to merchandise trade could increase growth ner. Simple averages are often a better indicator of in the tariff schedule in which bound rates have been in these countries--even more if trade in services tariff protection than weighted averages, which are set. · Simple mean tariff is the unweighted average (retailing, business, financial, and telecommunica- biased downward because higher tariffs discourage of effectively applied rates or most favored nation tions services) were also liberalized. trade and reduce the weights applied to these tariffs. rates for all products subject to tariffs calculated In general, tariffs in high-income countries on Bound rates result from trade negotiations incorpo- for all traded goods. · Weighted mean tariff is the imports from developing countries, though low, are rated into a country's schedule of concessions and average of effectively applied rates or most favored twice those collected from other high-income coun- are thus enforceable. nation rates weighted by the product import shares tries. But protection is also an issue for developing Some countries set fairly uniform tariff rates across corresponding to each partner country. · Share of countries, which maintain high tariffs on agricultural all imports. Others are selective, setting high tariffs tariff lines with international peaks is the share commodities, labor-intensive manufactures, and to protect favored domestic industries. The share of lines in the tariff schedule with tariff rates that other products and services. In some developing of tariff lines with international peaks provides an exceed 15 percent. · Share of tariff lines with spe- regions new trade policies could make the difference indication of how selectively tariffs are applied. The cific rates is the share of lines in the tariff schedule between achieving important Millennium Develop- effective rate of protection--the degree to which the that are set on a per unit basis or that combine ad ment Goals--reducing poverty, lowering maternal value added in an industry is protected--may exceed valorem and per unit rates. · Primary products are and child mortality rates, improving educational the nominal rate if the tariff system systematically commodities classified in SITC revision 3 sections attainment--and falling far short. differentiates among imports of raw materials, inter- 0­4 plus division 68 (nonferrous metals). · Manu- Countries use a combination of tariff and nontariff mediate products, and finished goods. factured products are commodities classified in measures to regulate imports. The most common The share of tariff lines with specific rates shows SITC revision 3 sections 5­8 excluding division 68. form of tariff is an ad valorem duty, based on the the extent to which countries use tariffs based on value of the import, but tariffs may also be levied physical quantities or other, non­ad valorem mea- on a specific, or per unit, basis or may combine ad sures. Some countries such as Switzerland apply valorem and specific rates. Tariffs may be used to mainly specific duties. To the extent possible, these raise fiscal revenues or to protect domestic indus- specifi c rates have been converted to their ad tries from foreign competition--or both. Nontariff valorem equivalent rates and have been included in barriers, which limit the quantity of imports of a par- the calculation of simple and weighted tariffs. ticular good, include quotas, prohibitions, licensing Data are classified using the Harmonized System schemes, export restraint arrangements, and health of trade at the six- or eight-digit level. Tariff line data and quarantine measures. Because of the difficulty were matched to Standard International Trade Clas- of combining nontariff barriers into an aggregate indi- sification (SITC) revision 3 codes to define commod- cator, they are not included in the table. ity groups and import weights. Import weights were Unless specified as most favored nation rates, the calculated using the United Nations Statistics Divi- tariff rates used in calculating the indicators in the sion's Commodity Trade (Comtrade) database. Data table are effectively applied rates. Effectively applied are shown only for the last year for which complete rates are those in effect for partners in preferen- data are available. tial trade arrangements such as the North Ameri- can Free Trade Agreement. The difference between most favored nation and applied rates can be sub- stantial. As more countries report their free trade agreements, suspensions of tariffs, or other spe- cial preferences, World Development Indicators will Data sources include their effectively applied rates. All estimates are calculated using the most recent information, All indicators in the table were calculated by World which is not necessarily revised every year. As a Bank staff using the World Integrated Trade Solu- result, data for the same year may differ from data tion system. Data on tariffs were provided by the in last year's edition. United Nations Conference on Trade and Develop- Three measures of average tariffs are shown: sim- ment and the World Trade Organization. Data on ple bound rates and the simple and the weighted global imports are from the United Nations Statis- tariffs. Bound rates are based on all products in a tics Division's Comtrade database. country's tariff schedule, while the most favored 2009 World Development Indicators 355 6.9 External debt Total external Long-term Short-term Use of IMF debt debt debt credit $ millions Public and publicly guaranteed IBRD loans Private $ millions Total and IDA credits nonguaranteed $ millions $ millions 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan .. 2,041 .. 1,961 .. 411 .. 0 .. 21 .. 59 Albania 456 2,697 330 1,787 109 809 0 72 62 748 65 90 Algeria 33,042 5,541 31,303 3,756 2,049 113 0 1,068 261 717 1,478 0 Angola 11,500 12,730 9,543 10,474 81 365 0 0 1,958 2,256 0 0 Argentina 98,465 127,758 54,913 66,110 4,913 5,674 16,066 23,581 21,355 38,067 6,131 0 Armenia 371 2,888 298 1,272 96 970 0 999 2 459 70 158 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 321 3,068 206 1,748 30 682 0 143 14 1,074 101 103 Bangladesh 15,927 21,998 15,106 20,151 5,692 10,077 0 0 199 1,347 622 501 Belarus 1,694 9,470 1,301 2,338 116 42 0 1,125 110 6,007 283 0 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 1,614 857 1,483 852 498 176 0 0 47 1 84 4 Bolivia 5,272 4,947 4,459 2,150 865 259 239 2,621 307 176 268 0 Bosnia and Herzegovina .. 6,378 .. 3,014 472 1,521 .. 1,774 .. 1,587 48 2 Botswana 717 396 707 380 108 6 0 0 10 16 0 0 Brazil 160,469 237,472 98,260 79,957 6,038 9,676 30,830 118,267 31,238 39,248 142 0 Bulgaria 10,379 32,968 8,808 5,243 444 1,604 342 13,689 512 14,036 717 0 Burkina Faso 1,271 1,472 1,140 1,268 608 468 0 0 56 166 75 37 Burundi 1,162 1,456 1,099 1,344 591 830 0 0 15 14 48 98 Cambodia 2,284 3,755 2,110 3,537 65 535 0 0 102 218 72 0 Cameroon 10,807 3,091 9,477 2,204 1,067 239 288 636 991 234 51 17 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 942 972 850 836 414 403 0 0 57 87 35 49 Chad 843 1,793 777 1,712 379 994 0 0 17 25 49 56 Chile 22,038 58,649 7,178 9,378 1,383 357 11,429 35,969 3,431 13,302 0 0 China 118,090 373,635 94,674 87,653 14,248 21,912 1,090 82,284 22,325 203,698 0 0 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 25,044 44,976 13,946 27,689 2,559 4,758 5,553 11,938 5,545 5,349 0 0 Congo, Dem. Rep. 13,239 12,287 9,636 10,853 1,413 2,402 0 0 3,118 625 485 808 Congo, Rep. 5,896 5,156 4,874 4,807 279 303 0 0 1,004 312 19 37 Costa Rica 3,802 7,846 3,133 3,750 303 46 214 1,134 430 2,962 24 0 Côte d'Ivoire 18,899 13,938 11,902 11,651 2,386 2,388 2,660 591 3,910 1,523 427 173 Croatia 3,830 48,584 1,860 14,212 117 1,101 1,257 29,273 492 5,099 221 0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 4,447 10,312 3,653 6,546 300 482 19 845 616 2,374 160 548 Ecuador 13,903 17,525 11,977 10,447 1,108 697 440 5,184 1,312 1,894 173 0 Egypt, Arab Rep. 33,475 30,444 30,687 26,940 2,356 2,671 313 2,053 2,372 1,451 103 0 El Salvador 2,509 8,790 1,979 5,444 327 411 5 2,376 525 970 0 0 Eritrea 37 875 37 856 24 457 0 0 0 19 0 0 Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 10,308 2,634 9,774 2,544 1,470 711 0 0 460 90 73 0 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 4,360 5,746 3,976 5,177 110 12 0 0 287 545 97 25 Gambia, The 426 732 385 704 162 219 0 0 15 21 26 6 Georgia 1,240 2,257 1,039 1,543 84 885 0 241 85 222 116 252 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 5,495 4,486 4,200 3,047 2,434 1,104 27 0 620 1,272 648 167 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 3,282 6,271 2,328 4,214 158 740 142 78 812 1,979 0 0 Guinea 3,242 3,261 2,987 3,048 847 1,305 0 0 161 148 94 65 Guinea-Bissau 898 744 798 730 210 315 0 0 95 8 6 5 Haiti 821 1,598 766 1,500 389 518 0 0 27 42 29 56 356 2009 World Development Indicators GLOBAL LINKS Total external Long-term External debt Short-term 6.9 Use of IMF debt debt debt credit $ millions Public and publicly guaranteed IBRD loans Private $ millions Total and IDA credits nonguaranteed $ millions $ millions 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 4,797 3,232 4,193 1,942 828 401 123 624 382 634 99 32 Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 94,464 220,956 80,422 74,419 27,348 33,432 6,618 102,876 5,049 43,662 2,374 0 Indonesia 124,398 140,783 65,309 68,708 13,259 8,371 33,123 37,132 25,966 34,943 0 0 Iran, Islamic Rep. 21,879 20,058 15,116 11,146 316 699 314 91 6,449 8,821 0 0 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 4,577 10,063 3,716 6,372 595 360 128 2,189 492 1,502 240 0 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 7,661 8,397 6,624 7,318 806 908 0 0 785 991 251 88 Kazakhstan 3,750 96,133 2,834 1,698 295 427 103 82,690 381 11,745 432 0 Kenya 7,309 7,327 5,857 6,122 2,412 2,968 445 0 634 936 374 269 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 609 2,401 472 1,895 141 651 0 280 13 76 124 150 Lao PDR 2,165 3,337 2,091 2,446 285 686 0 860 10 5 64 26 Latvia 463 39,342 271 1,809 55 85 0 20,858 31 16,676 160 0 Lebanon 2,966 24,634 1,550 19,789 113 437 50 530 1,365 4,235 0 80 Lesotho 684 680 642 645 207 297 0 0 4 0 38 35 Liberia 2,154 2,475 1,161 910 269 77 0 0 657 1,214 336 352 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 1,277 3,760 788 1,520 181 587 289 1,044 143 1,196 57 0 Madagascar 4,302 1,661 3,687 1,425 1,121 865 0 0 542 194 73 43 Malawi 2,238 870 2,078 807 1,306 178 0 0 44 32 116 31 Malaysia 34,343 53,717 16,023 18,441 1,059 135 11,046 20,026 7,274 15,250 0 0 Mali 2,958 2,018 2,739 1,989 863 452 0 0 72 17 147 13 Mauritania 2,396 1,704 2,127 1,437 347 203 0 0 169 254 100 13 Mauritius 1,757 4,253 1,148 572 157 90 267 62 342 3,618 0 0 Mexico 165,379 178,108 93,902 105,379 13,823 4,540 18,348 63,723 37,300 9,006 15,828 0 Moldova 695 3,203 450 779 152 432 9 1,169 6 1,095 230 160 Mongolia 520 1,596 472 1,566 59 331 0 5 0 0 47 26 Morocco 23,771 20,293 23,190 15,670 3,999 2,595 331 2,673 198 1,949 52 0 Mozambique 7,458 3,123 5,209 2,533 890 902 1,769 0 279 575 202 15 Myanmar 5,771 7,372 5,378 5,516 777 793 0 0 393 1,856 0 0 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 2,410 3,645 2,339 3,485 1,023 1,524 0 0 23 81 48 79 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 10,390 3,399 8,566 2,144 341 321 0 361 1,785 809 39 85 Niger 1,576 972 1,319 863 598 235 133 20 72 49 52 40 Nigeria 34,092 9,008 28,140 3,815 3,489 2,309 301 175 5,651 5,018 0 0 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 30,229 40,685 23,788 35,917 6,403 11,161 1,593 1,153 3,235 2,233 1,613 1,381 Panama 6,099 9,862 3,782 8,267 175 216 0 1,061 2,207 529 111 5 Papua New Guinea 2,506 2,254 1,668 1,156 407 256 711 998 78 100 50 0 Paraguay 2,574 3,561 1,453 2,195 189 244 338 572 784 794 0 0 Peru 30,833 32,154 18,931 19,669 1,729 2,649 1,288 6,679 9,659 5,806 955 0 Philippines 39,379 65,845 28,525 37,895 5,185 2,921 4,847 20,867 5,279 7,084 728 0 Poland 44,080 195,374 40,890 43,598 2,067 1,870 1,012 91,412 2,178 60,365 0 0 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 357 6.9 External debt Total external Long-term Short-term Use of IMF debt debt debt credit $ millions Public and publicly guaranteed IBRD loans Private $ millions Total and IDA credits nonguaranteed $ millions $ millions 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 6,832 85,380 3,957 15,238 844 2,604 534 39,636 1,303 30,505 1,038 0 Russian Federation 121,401 370,172 101,582 70,396 1,524 4,292 0 220,673 10,201 79,103 9,617 0 Rwanda 1,029 495 971 456 512 204 0 0 32 31 26 8 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 3,916 2,579 3,266 2,029 1,160 663 44 200 260 323 347 27 Serbia 10,785a 26,280 6,788a 8,224 1,252a 2,951 1,773a 15,404 2,139a 2,652 84 a 0 Sierra Leone 1,220 348 1,028 308 234 84 0 0 27 3 165 37 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia 2,678 2,944 1,961 1,979 432 448 0 0 551 788 166 177 South Africa 25,358 43,380 9,837 13,868 0 27 4,935 12,954 9,673 16,558 913 0 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 8,395 14,037 7,175 11,879 1,512 2,357 90 259 535 1,649 595 251 Sudan 17,603 19,224 9,779 12,337 1,279 1,307 496 0 6,368 6,405 960 482 Swaziland 249 393 238 357 25 23 0 0 11 36 0 0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. 471 19 .. .. .. .. .. .. Tajikistan 634 1,228 590 1,065 0 360 0 40 43 76 0 46 Tanzania 7,421 5,024 6,217 3,684 2,269 1,585 44 4 963 1,319 197 18 Thailand 100,039 63,067 16,826 9,841 1,906 129 39,117 31,585 44,095 21,640 0 0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 1,476 1,967 1,286 1,655 541 723 0 0 85 310 105 2 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 10,818 20,231 9,215 16,579 1,766 1,595 0 0 1,310 3,652 293 0 Turkey 73,781 251,477 50,317 75,171 5,069 7,601 7,079 127,344 15,701 41,803 685 7,158 Turkmenistan 402 739 385 648 1 15 0 2 17 89 0 0 Uganda 3,609 1,611 3,089 1,575 1,792 840 0 0 103 26 417 9 Ukraine 8,429 73,600 6,581 10,568 491 2,309 84 39,687 223 22,914 1,542 431 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 5,318 12,346 3,833 9,616 513 666 127 357 1,336 2,373 21 0 Uzbekistan 1,799 3,871 1,415 3,086 157 359 15 594 212 191 157 0 Venezuela, RB 35,538 43,148 28,223 27,494 1,639 0 2,013 3,954 3,063 11,700 2,239 0 Vietnam 25,428 24,207 21,778 19,372 231 4,549 0 0 3,272 4,672 377 164 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6,217 5,930 5,528 5,343 827 2,058 0 0 689 417 0 169 Zambia 6,958 2,783 5,291 1,136 1,434 323 13 990 415 569 1,239 87 Zimbabwe 4,989 5,290 3,462 3,735 896 977 381 16 685 1,421 461 118 World .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s Low income 253,626 222,664 207,566 182,906 48,542 59,927 8,185 5,945 28,143 28,452 9,732 5,361 Middle income 1,632,033 3,242,545 1,078,726 1,129,584 131,030 145,775 202,778 1,283,874 300,461 818,908 50,068 10,179 Lower middle income 782,620 1,264,349 528,904 512,685 88,208 100,440 95,156 357,936 147,477 390,837 11,084 2,891 Upper middle income 849,412 1,978,196 549,822 616,899 42,822 45,335 107,622 925,938 152,984 428,071 38,984 7,288 Low & middle income 1,885,659 3,465,210 1,286,291 1,312,490 179,572 205,702 210,963 1,289,819 328,604 847,360 59,800 15,540 East Asia & Pacific 455,608 741,426 255,393 256,942 37,604 40,778 90,050 193,785 108,828 290,485 1,337 215 Europe & Central Asia 293,781 1,262,445 231,636 268,016 13,699 32,471 12,497 688,158 33,902 297,721 15,747 8,550 Latin America & Carib. 608,461 825,567 371,667 403,303 38,485 33,226 87,303 281,527 122,859 139,932 26,632 804 Middle East & N. Africa 140,110 136,000 123,482 106,981 12,279 11,222 1,008 6,415 13,443 22,250 2,177 353 South Asia 151,740 304,698 129,135 149,012 42,036 59,126 8,301 104,287 9,051 49,121 5,252 2,277 Sub-Saharan Africa 235,959 195,074 174,978 128,235 35,468 28,878 11,804 15,647 40,522 47,850 8,654 3,341 High income Euro area a. Includes Montenegro. 358 2009 World Development Indicators GLOBAL LINKS External debt 6.9 About the data Definitions A country's external indebtedness affects its credit- Because debt data are normally reported in the · Total external debt is debt owed to nonresidents worthiness and investor perceptions. Data on the currency of repayment, they have to be converted into repayable in foreign currency, goods, or services. It external debt of developing countries are gathered by a single currency (U.S. dollars) to produce summary is the sum of public, publicly guaranteed, and private the World Bank through its Debtor Reporting System. tables. Stock figures (amount of debt outstanding) nonguaranteed long-term debt, short-term debt, and Indebtedness is calculated using loan-by-loan reports are converted using end-of-period exchange rates, use of IMF credit. · Long-term debt is debt that has submitted by countries on long-term public and pub- as published in the IMF's International Financial Sta- an original or extended maturity of more than one licly guaranteed borrowing and information on short- tistics (line ae). Flow figures are converted at annual year. It has three components: public, publicly guar- term debt collected by the countries or from creditors average exchange rates (line rf). Projected debt anteed, and private nonguaranteed debt. · Public through the reporting systems of the Bank for Inter- service is converted using end-of-period exchange and publicly guaranteed debt comprises the long- national Settlements and the Organisation for Eco- rates. Debt repayable in multiple currencies, goods, term external obligations of public debtors, including nomic Co-operation and Development. These data are or services and debt with a provision for maintenance the national government and political subdivisions supplemented by information from major multilateral of the value of the currency of repayment are shown (or an agency of either) and autonomous public bod- banks and official lending agencies in major creditor at book value. ies, and the external obligations of private debtors countries and by estimates by World Bank and Inter- Because flow data are converted at annual aver- that are guaranteed for repayment by a public entity. national Monetary Fund (IMF) staff. The table includes age exchange rates and stock data at end-of-period · IBRD loans and IDA credits are extended by the data on long-term private nonguaranteed debt reported exchange rates, year-to-year changes in debt out- World Bank. The International Bank for Reconstruc- to the World Bank or estimated by its staff. standing and disbursed are sometimes not equal to tion and Development (IBRD) lends at market rates. The coverage, quality, and timeliness of data vary net flows (disbursements less principal repayments); The International Development Association (IDA) pro- across countries. Coverage varies for both debt instru- similarly, changes in debt outstanding, including vides credits at concessional rates. · Private non- ments and borrowers. The widening spectrum of debt undisbursed debt, differ from commitments less guaranteed debt consists of the long-term external instruments and investors alongside the expansion of repayments. Discrepancies are particularly notable obligations of private debtors that are not guaranteed private nonguaranteed borrowing makes comprehen- when exchange rates have moved sharply during for repayment by a public entity. · Short-term debt is sive coverage of external debt more complex. Report- the year. Cancellations and reschedulings of other debt owed to nonresidents having an original matu- ing countries differ in their capacity to monitor debt, liabilities into long-term public debt also contribute rity of one year or less and interest in arrears on long- especially private nonguaranteed debt. Even data on to the differences. term debt. · Use of IMF credit denotes members' public and publicly guaranteed debt are affected by Variations in reporting rescheduled debt also affect drawings on the IMF other than those drawn against coverage and reporting accuracy--again because of cross-country comparability. For example, reschedul- the country's reserve tranche position and includes monitoring capacity and sometimes because of an ing of official Paris Club creditors may be subject to purchases and drawings under Stand-By, Extended, unwillingness to provide information. A key part often lags between completion of the general rescheduling Structural Adjustment, Enhanced Structural Adjust- underreported is military debt. Currently, 128 devel- agreement and completion of the specific bilateral ment, and Systemic Transformation Facility Arrange- oping countries report to the Debtor Reporting Sys- agreements that define the terms of the rescheduled ments, together with Trust Fund loans. tem. Nonreporting countries might have outstanding debt. Other areas of inconsistency include country debt with the World Bank, other international financial treatment of arrears and of nonresident national institutions, and private creditors. deposits denominated in foreign currency. The levels and the composition of external debt vary by regions 6.9a $ billions Public and publicly-guaranteed long-term debt Private nonguaranteed long-term debt Short-term debt Data sources 1,500 Data on external debt are mainly from reports to 1,200 the World Bank through its Debtor Reporting Sys- 900 tem from member countries that have received IBRD loans or IDA credits, with additional infor- 600 mation from the files of the World Bank, the IMF, 300 the African Development Bank and African Devel- 0 opment Fund, the Asian Development Bank and 1990 2000 2007 1990 2000 2007 1990 2000 2007 1990 2000 2007 1990 2000 2007 1990 2000 2007 Asian Development Fund, and the Inter-American East Asia & Europe & Latin America & Middle East & South Sub-Saharan Pacific Central Asia Caribbean North Africa Asia Africa Development Bank. Summary tables of the exter- The share of private nonguaranteed debt has increased for East Asia and Pacific, Europe and Central nal debt of developing countries are published Asia, Latin America and Caribbean, and South Asia. Public and publicly guaranteed debt remain the main annually in the World Bank's Global Development source of external borrowing for the Middle East and North Africa and Sub-Saharan Africa. Finance and on its Global Development Finance Source: Global Development Finance data files. CD-ROM. 2009 World Development Indicators 359 6.10 Ratios for external debt Total Total debt Multilateral Short-term Present value external debt service debt service debt of debt % of exports % of exports of % of public and of goods, goods and services publicly guaranteed services, % of GNI and incomea debt service % of total debt % of total reserves % of GNI and incomea 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 2007b 2007b Afghanistan .. .. .. .. .. 52.2 .. 1.0 .. .. 18 c 80 c Albania 18.4 24.2 1.4 4.1 11.4 51.2 13.7 27.7 23.5 34.6 22 61 Algeria 83.5 4.1 .. .. 17.7 3.0 0.8 12.9 6.3 0.6 4 9 Angola 311.9 26.2 12.0 10.2 0.6 0.2 17.0 17.7 919.7 20.2 32 35 Argentina 38.9 49.7 30.1 13.0 21.6 72.2 21.7 29.8 133.6 82.5 63 219 Armenia 25.3 30.5 3.1 7.0 69.8 86.8 0.6 15.9 1.9 27.7 38 117 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 10.6 11.7 1.3 0.7 21.8 49.2 4.4 35.0 11.6 25.1 14 16 Bangladesh 40.7 29.9 13.2 3.9 27.1 70.0 1.3 6.1 8.4 25.5 22 84 Belarus 12.2 21.3 3.4 3.9 55.4 8.3 6.5 63.4 29.2 143.8 25 40 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 82.1 15.8 6.8 .. 54.8 35.9 2.9 0.1 23.7 0.0 12c 58 c Bolivia 81.2 38.2 29.4 11.9 75.5 88.2 5.8 3.6 30.5 3.3 24 c 52c Bosnia and Herzegovina .. 40.8 .. 8.0 .. 54.0 .. 24.9 .. 35.1 42 80 Botswana 15.1 3.4 3.1 0.9 76.0 70.8 1.4 4.0 0.2 0.2 3 5 Brazil 21.2 18.7 36.6 27.8 18.5 13.5 19.5 16.5 60.7 21.8 25 155 Bulgaria 81.8 84.3 16.5 15.5 10.5 49.7 4.9 42.6 31.3 80.0 100 144 Burkina Faso 53.6 21.9 .. .. 76.7 53.2 4.4 11.3 16.1 16.1 14 c 108 c Burundi 117.6 154.6 27.6 42.6 70.6 94.1 1.3 0.9 6.9 7.8 97c 882c Cambodia 71.8 47.0 0.7 0.5 11.9 80.0 4.5 5.8 53.1 10.2 46 63 Cameroon 131.7 15.0 20.8 9.9 60.8 19.6 9.2 7.6 6,444.5 8.0 5c 19c Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 85.5 57.1 .. .. 100.0 100.0 6.0 9.0 23.8 95.0 48 c 325c Chad 58.5 29.1 .. .. 86.1 83.2 2.0 1.4 11.6 2.6 19c 28 c Chile 32.1 40.3 24.5 14.2 76.2 7.3 15.6 22.7 23.1 79.0 45 85 China 16.5 11.6 9.9 2.2 7.6 28.1 18.9 54.5 27.8 13.2 13 32 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 27.5 22.5 31.5 22.0 32.7 39.0 22.1 11.9 65.6 25.5 28 133 Congo, Dem. Rep. 271.4 142.9 .. .. .. 37.6 23.6 5.1 1,980.9 346.1 111c 326c Congo, Rep. 480.0 86.1 13.1 1.2 21.2 44.5 17.0 6.0 1,578.0 14.3 93c 88 c Costa Rica 33.1 30.8 13.8 4.4 50.7 60.8 11.3 37.8 40.6 72.0 35 62 Côte d'Ivoire 188.7 73.6 23.1 4.5 59.3 95.8 20.7 10.9 739.1 60.4 67c 123c Croatia 20.4 97.7 4.8 33.0 73.1 14.6 12.8 10.5 25.9 37.3 109 197 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 37.8 29.7 6.1 8.6 39.8 29.1 13.8 23.0 165.3 92.7 33 70 Ecuador 72.1 41.3 24.8 18.7 32.0 53.7 9.4 10.8 73.4 53.8 50 115 Egypt, Arab Rep. 55.8 23.2 13.2 4.4 26.3 22.2 7.1 4.8 13.9 4.5 25 60 El Salvador 26.7 44.4 8.9 11.0 55.1 52.9 20.9 11.0 55.9 42.1 50 104 Eritrea 6.3 64.1 0.1 .. 100.0 94.4 0.0 2.2 0.0 .. 41c 660 c Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 136.6 13.6 18.4 4.1 41.7 38.6 4.5 3.4 56.5 .. 8c 47c Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 101.6 56.2 15.3 .. 17.9 93.6 6.6 9.5 187.8 44.0 73 99 Gambia, The 113.0 122.7 15.5 12.4 49.1 70.1 3.5 2.9 14.0 15.1 34 c 63c Georgia 48.2 21.7 .. 4.6 0.4 22.3 6.9 9.8 43.0 16.3 20 52 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 86.9 29.9 24.0 3.1 48.4 32.4 11.3 28.4 77.1 .. 22c 55c Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 22.6 18.7 11.1 5.2 47.7 49.4 24.7 31.6 103.7 45.9 21 54 Guinea 89.8 72.5 25.0 13.1 30.4 50.6 5.0 4.5 185.6 .. 64 c 210 c Guinea-Bissau 380.7 213.6 51.9 .. 88.3 49.8 10.5 1.1 467.0 7.5 263c 529c Haiti 28.1 26.1 51.0 4.7 92.2 81.1 3.2 2.6 13.4 9.3 20 c 57c 360 2009 World Development Indicators GLOBAL LINKS Total Ratios for external debt Total debt Multilateral Short-term 6.10 Present value external debt service debt service debt of debt % of exports % of exports of % of public and of goods, goods and services publicly guaranteed services, % of GNI and incomea debt service % of total debt % of total reserves % of GNI and incomea 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 2007b 2007b Honduras 131.5 27.8 34.0 3.7 52.6 45.4 8.0 19.6 141.7 24.9 21c 26c Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 26.8 18.9 29.7 .. 24.3 20.8 5.3 19.8 22.1 15.8 20 82 Indonesia 63.4 33.9 29.9 10.5 28.4 33.7 20.9 24.8 174.2 61.4 43 120 Iran, Islamic Rep. 24.3 7.1 30.2 .. 1.3 3.7 29.5 44.0 .. .. 8 22 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 82.2 101.0 16.2 17.3 40.6 21.7 10.7 14.9 72.2 80.0 131 183 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 118.8 50.5 12.4 5.7 33.5 42.5 10.2 11.8 34.4 12.5 54 69 Kazakhstan 18.6 103.7 3.9 49.6 7.8 36.9 10.2 12.2 23.0 66.6 131 218 Kenya 83.8 30.2 24.7 6.0 32.5 60.7 8.7 12.8 164.9 27.9 26 85 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 37.5 65.0 13.2 6.7 59.0 83.9 2.1 3.2 9.7 6.5 43c 65c Lao PDR 123.2 84.4 6.3 18.9 37.4 91.6 0.5 0.2 10.2 0.7 84 267 Latvia 8.8 150.3 1.6 73.3 60.3 49.7 6.7 42.4 5.2 289.5 192 373 Lebanon 24.3 101.8 .. 18.7 13.2 4.1 46.0 17.2 16.9 20.6 111 115 Lesotho 50.9 33.7 6.1 7.0 60.3 29.0 0.6 0.0 0.9 .. 23 35 Liberia .. 442.1 .. 111.6 .. 100.0 30.5 49.0 2,340.6 1,016.9 978 c 976c Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 29.0 49.2 .. .. 99.9 58.1 11.2 31.8 51.9 52.8 54 100 Madagascar 143.3 22.7 7.6 .. 74.3 84.7 12.6 11.7 497.1 22.9 21c 70 c Malawi 165.8 24.6 24.9 .. 51.4 35.8 1.9 3.7 37.8 14.1 9c 37c Malaysia 40.6 29.4 7.0 4.6 15.5 8.0 21.2 28.4 29.5 15.0 34 28 Mali 122.3 30.6 13.4 .. 45.5 51.5 2.4 0.8 22.2 1.6 16c 51c Mauritania 175.3 62.0 22.9 .. 49.6 89.5 7.1 14.9 187.9 122.5 85c 150 c Mauritius 46.2 62.1 9.4 4.9 34.5 25.1 19.5 85.1 38.5 197.5 65 94 Mexico 60.5 17.7 27.0 12.5 19.5 6.7 22.6 5.1 218.8 10.3 20 62 Moldova 40.3 66.5 7.9 9.5 79.1 56.2 0.9 34.2 2.3 82.1 72 98 Mongolia 43.3 41.5 10.1 .. 2.8 38.1 0.1 0.0 0.3 0.0 37 52 Morocco 75.1 27.4 33.4 11.4 30.3 34.2 0.8 9.6 5.1 7.9 29 66 Mozambique 360.6 44.3 34.5 1.3 17.4 62.2 3.7 18.4 142.8 37.7 15c 34 c Myanmar .. .. 17.8 .. 15.0 0.5 6.8 25.2 60.4 .. 46 119 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 54.7 35.0 7.5 4.5 54.2 73.6 0.9 2.2 3.5 .. 22c 70 c Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 368.3 60.7 38.7 11.7 30.3 76.3 17.2 23.8 1,256.8 73.3 31c 52c Niger 86.1 23.0 16.7 .. 95.5 94.8 4.5 5.1 75.6 8.3 12c 70 c Nigeria 131.7 6.1 13.8 1.4 45.4 43.7 16.6 55.7 330.7 9.7 6 12 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 49.5 28.0 26.5 8.9 43.2 53.7 10.7 5.5 128.0 14.1 25 123 Panama 80.9 54.3 3.4 5.3 52.7 26.6 36.2 5.4 282.4 27.4 70 81 Papua New Guinea 57.3 40.2 20.8 .. 31.7 78.2 3.1 4.4 29.1 4.7 42 47 Paraguay 31.5 28.6 5.6 6.2 48.0 59.2 30.4 22.3 70.8 32.3 35 60 Peru 60.3 32.6 15.9 25.0 49.9 19.4 31.3 18.1 111.6 20.9 42 125 Philippines 51.7 41.9 16.1 13.7 29.2 17.7 13.4 10.8 67.8 21.0 51 97 Poland 32.2 47.7 11.0 25.6 13.5 8.1 4.9 30.9 14.6 91.8 53 121 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2009 World Development Indicators 361 6.10 Ratios for external debt Total Total debt Multilateral Short-term Present value external debt service debt service debt of debt % of exports % of exports of % of public and of goods, goods and services publicly guaranteed services, % of GNI and incomea debt service % of total debt % of total reserves % of GNI and incomea 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 2007b 2007b Romania 19.4 51.5 10.5 19.1 21.3 35.3 19.1 35.7 49.7 76.3 67 175 Russian Federation 31.0 29.4 6.3 9.1 9.7 11.1 8.4 21.4 56.6 16.6 39 105 Rwanda 79.2 14.9 20.5 3.2 99.0 72.9 3.1 6.3 32.3 5.6 8c 69c Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 82.9 23.3 16.8 .. 62.2 61.2 6.6 12.5 95.6 19.5 21c 59c Serbia .. 68.3 .. .. 100.0d 44.9 19.8d 10.1 .. 18.7 86 198 Sierra Leone 149.0 21.4 53.7 2.5 8.4 40.7 2.2 1.0 77.8 1.6 10 c 37c Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. 20.6 26.8 .. .. .. .. South Africa 17.1 15.8 9.5 5.9 0.0 1.5 38.1 38.2 216.7 50.3 19 58 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 65.3 43.9 8.0 6.7 14.0 25.1 6.4 11.7 25.3 45.1 42 105 Sudan 136.3 46.1 6.7 3.2 100.0 25.3 36.2 33.3 3,898.2 464.8 93c 382c Swaziland 14.0 13.3 1.5 1.9 64.0 58.2 4.5 9.3 3.7 4.7 14 17 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan 53.6 34.0 .. 2.3 .. 59.2 6.8 6.2 .. .. 30 33 Tanzania 144.6 31.1 17.9 2.5 66.7 37.4 13.0 26.2 356.6 45.7 15c 62c Thailand 60.6 26.5 11.6 8.1 20.9 16.2 44.1 34.3 119.4 24.7 29 37 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 116.7 80.1 6.0 .. 75.5 60.9 5.8 15.8 65.1 70.8 80 c 148 c Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 63.0 60.8 16.9 11.3 43.8 45.7 12.1 18.1 77.6 45.5 65 106 Turkey 30.5 38.8 27.7 32.1 20.7 11.8 21.3 16.6 113.0 54.6 47 200 Turkmenistan 16.1 5.9 .. .. 1.9 4.7 4.3 12.1 1.5 .. 7 10 Uganda 63.3 14.0 19.8 2.1 69.7 58.4 2.8 1.6 22.4 1.0 9c 37c Ukraine 17.8 52.9 6.6 16.9 13.6 25.3 2.6 31.1 20.9 70.5 66 131 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 29.4 54.3 22.1 19.1 27.3 28.3 25.1 19.2 73.7 57.6 69 196 Uzbekistan 13.5 17.3 .. .. 1.9 17.0 11.8 4.9 .. .. 20 51 Venezuela, RB 48.7 18.7 22.9 7.4 11.5 23.9 8.6 27.1 28.6 34.7 26 66 Vietnam 124.0 36.3 .. 2.3 2.9 12.0 12.9 19.3 247.2 19.9 35 45 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 169.0 27.9 3.1 2.7 78.3 59.8 11.1 7.0 107.9 5.4 23 46 Zambia 215.1 27.9 .. 2.5 50.6 91.0 6.0 20.5 186.2 52.2 7c 16c Zimbabwe 73.5 .. .. .. 33.6 5.6 13.7 26.9 77.2 .. 121 326 World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income 87.1 28.9 19.2 4.0 41.7 50.2 11.1 12.8 149.8 16.7 Middle income 34.7 24.8 17.1 10.6 21.4 19.8 18.4 25.3 66.8 22.4 Lower middle income 37.6 18.7 16.8 6.2 22.0 25.9 18.8 30.9 63.2 16.3 Upper middle income 32.3 31.5 17.4 16.4 20.8 15.4 18.0 21.6 70.6 33.5 Low & middle income 37.7 25.1 17.2 10.2 22.9 21.4 17.4 24.5 70.2 22.2 East Asia & Pacific 35.5 17.0 12.7 4.0 18.2 23.6 23.9 39.2 64.9 15.5 Europe & Central Asia 30.0 41.5 10.5 18.7 16.5 16.7 11.5 23.6 53.1 37.9 Latin America & Carib. 35.9 23.7 26.2 16.0 26.2 21.6 20.2 16.9 88.6 31.1 Middle East & N. Africa 53.6 18.9 19.8 5.8 19.3 19.7 9.6 16.4 18.4 6.2 South Asia 32.0 21.1 25.5 12.9 27.4 28.8 6.0 16.1 29.5 16.2 Sub-Saharan Africa 76.1 24.9 15.9 5.0 35.0 22.4 17.2 24.5 193.5 31.4 High income Euro area a. Includes workers' remittances. b. The numerator refers to 2007, whereas the denominator is a three year average of 2005­07 data. c. Data are from debt sustainability analyses for low-income countries. Present value estimates for these countries are for public and publicly guaranteed debt only. d. Includes Montenegro. 362 2009 World Development Indicators GLOBAL LINKS Ratios for external debt 6.10 About the data Definitions A country's external debt burden, both debt outstand- value of external debt provides a measure of future · Total external debt is debt owed to nonresidents ing and debt service, affects its creditworthiness and debt service obligations. and comprises public, publicly guaranteed, and pri- vulnerability. The table shows total external debt rela- The present value of external debt is calculated vate nonguaranteed long-term debt, short-term debt, tive to a country's size--gross national income (GNI). by discounting the debt service (interest plus amor- and use of IMF credit. It is presented as a share of Total debt service is contrasted with countries' ability tization) due on long-term external debt over the GNI. · Total debt service is the sum of principal to obtain foreign exchange through exports of goods, life of existing loans. Short-term debt is included at repayments and interest actually paid on total long- services, income, and workers' remittances. face value. The data on debt are in U.S. dollars con- term debt (public and publicly guaranteed and private Multilateral debt service (shown as a share of the verted at official exchange rates (see About the data nonguaranteed), use of IMF credit, and interest on country's total public and publicly guaranteed debt for table 6.9). The discount rate on long-term debt short-term debt. · Exports of goods, services, and service) are obligations to international fi nancial depends on the currency of repayment and is based income refer to international transactions involv- institutions, such as the World Bank, the Interna- on commercial interest reference rates established ing a change in ownership of general merchandise, tional Monetary Fund (IMF), and regional develop- by the Organisation for Economic Co-operation and goods sent for processing and repairs, nonmonetary ment banks. Multilateral debt service takes priority Development. Loans from the International Bank gold, services, receipts of employee compensation over private and bilateral debt service, and bor- for Reconstruction and Development (IBRD), cred- for nonresident workers, investment income, and rowers must stay current with multilateral debts its from the International Development Association workers' remittances. · Multilateral debt service is to remain creditworthy. While bilateral and private (IDA), and obligations to the IMF are discounted using the repayment of principal and interest to the World creditors often write off debts, international financial a special drawing rights reference rate. When the Bank, regional development banks, and other multi- institution bylaws prohibit granting debt relief or can- discount rate is greater than the loan interest rate, lateral and intergovernmental agencies. · Short-term celing debts directly. However, the recent decrease the present value is less than the nominal sum of debt includes all debt having an original maturity of in multilateral debt service ratios for some countries future debt service obligations. one year or less and interest in arrears on long-term reflects debt relief from special programs, such as Debt ratios are used to assess the sustainability of debt. · Total reserves comprise holdings of mon- the Heavily Indebted Poor Countries (HIPC) Debt a country's debt service obligations, but no absolute etary gold, special drawing rights, reserves of IMF Initiative and the Multilateral Debt Relief Initiative rules determine what values are too high. Empirical members held by the IMF, and holdings of foreign (MDRI) (see table 1.4.) Other countries have accel- analysis of developing countries' experience and exchange under the control of monetary authorities. erated repayment of debt outstanding. Indebted debt service performance shows that debt service · Present value of debt is the sum of short-term countries may also apply to the Paris and London difficulties become increasingly likely when the pres- external debt plus the discounted sum of total debt Clubs to renegotiate obligations to public and private ent value of debt reaches 200 percent of exports. service payments due on public, publicly guaranteed, creditors. Still, what constitutes a sustainable debt burden var- and private nonguaranteed long-term external debt Because short-term debt poses an immediate ies by country. Countries with fast-growing econo- over the life of existing loans. burden and is particularly important for monitoring mies and exports are likely to be able to sustain vulnerability, it is compared with the total debt and higher debt levels. foreign exchange reserves that are instrumental in providing coverage for such obligations. The present Data sources Data on external debt are mainly from reports to The burden of external debt service declined for most regions over 1995­2007 6.10a the World Bank through its Debtor Reporting Sys- Percent Total debt service (% of exports of goods, services, and income) Total debt service (% of GNI) tem from member countries that have received 40 IBRD loans or IDA credits, with additional infor- mation from the files of the World Bank, the IMF, 30 the African Development Bank and African Devel- 20 opment Fund, the Asian Development Bank and Asian Development Fund, and the Inter- American 10 Development Bank. Data on GNI, exports of goods and services, and total reserves are from 0 the World Bank's national accounts files and the 1995 2000 2007 1995 2000 2007 1995 2000 2007 1995 2000 2007 1995 2000 2007 1995 2000 2007 East Asia & Europe & Latin America & Middle East & South Sub-Saharan IMF's Balance of Payments and International Pacific Central Asia Caribbean North Africa Asia Africa Financial Statistics databases. Summary tables Declines in external debt service ratios for the Middle East and North Africa and Sub-Saharan Africa of the external debt of developing countries are were due partly to debt relief. Ratios for Europe and Central Asia in 2007 are nearly the same as in 2000 published annually in the World Bank's Global because debt service, GNI, and export revenue increased at a similar rate. Development Finance and on its Global Develop- Source: Global Development Finance data files. ment Finance CD-ROM. 2009 World Development Indicators 363 6.11 Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan .. 288 .. 0 .. 0 .. 0 Albania 70 477 0 0 0 0 0 ­7 Algeria 0 1,665 0 0 ­278 0 788 ­642 Angola 472 ­893 0 0 0 0 123 1,536 Argentina 5,609 6,462 1,552 1,785 3,705 4,227 754 ­135 Armenia 25 699 0 0 0 0 0 502 Australia 12,026 39,596 2,585 17,104 .. .. .. .. Austria 1,901 30,717 1,262 3,684 .. .. .. .. Azerbaijan 330 ­4,749 0 2 0 0 0 94 Bangladesh 2 653 ­15 153 0 0 ­21 ­21 Belarus 15 1,785 0 5 0 19 103 257 Belgium 10,689a 72,195 .. 3,360 .. .. .. .. Benin 13 48 0 0 0 0 0 0 Bolivia 393 204 0 0 0 0 41 230 Bosnia and Herzegovina 0 2,111 0 0 .. 0 .. ­242 Botswana 70 ­29 6 9 0 0 ­6 ­2 Brazil 4,859 34,585 2,775 26,217 2,636 570 8,283 18,629 Bulgaria 90 8,974 0 101 ­6 ­87 ­93 5,079 Burkina Faso 10 600 0 0 0 0 0 11 Burundi 2 1 0 0 0 0 ­1 0 Cambodia 151 867 0 0 0 0 13 0 Cameroon 7 433 0 ­13 0 0 ­65 ­120 Canada 9,319 111,772 ­3,077 ­42,041 .. .. .. .. Central African Republic 6 27 0 0 0 0 0 0 Chad 33 603 0 0 0 0 0 ­1 Chile 2,957 14,457 ­249 404 489 ­862 1,773 4,664 China 35,849 138,413 0 18,510 317 1,718 4,696 13,898 Hong Kong, China .. 54,365 .. 43,625 .. .. .. .. Colombia 968 9,040 165 790 1,008 210 1,250 3,068 Congo, Dem. Rep. 122 720 0 0 0 0 0 ­9 Congo, Rep. 125 4,289 0 0 0 0 ­50 0 Costa Rica 337 1,896 0 0 ­4 ­25 ­20 272 Côte d'Ivoire 211 427 1 148 0 0 14 ­167 Croatia 114 4,916 4 435 0 92 265 5,270 Cuba .. .. .. .. .. .. .. .. Czech Republic 2,568 9,294 1,236 ­268 .. .. .. .. Denmark 4,139 11,858 .. 3,017 .. .. .. .. Dominican Republic 414 1,698 0 0 0 411 ­31 ­15 Ecuador 452 183 13 1 0 0 59 338 Egypt, Arab Rep. 598 11,578 0 ­3,199 0 0 ­311 ­103 El Salvador 38 1,526 0 0 0 ­30 ­31 ­80 Eritrea .. ­3 0 0 0 0 0 0 Estonia 201 2,687 10 260 .. .. .. .. Ethiopia 14 223 0 0 0 0 ­48 ­44 Finland 1,044 11,568 2,027 5,279 .. .. .. .. France 23,736 159,463 6,823 ­68,583 .. .. .. .. Gabon ­315 269 0 0 0 1,000 ­75 55 Gambia, The 8 68 0 0 0 0 0 0 Georgia 6 1,728 0 34 0 0 0 75 Germany 11,985 51,543 ­1,513 14,314 .. .. .. .. Ghana 107 970 0 0 0 750 38 47 Greece 1,053 1,959 0 10,865 .. .. .. .. Guatemala 75 724 0 0 44 ­150 ­32 ­13 Guinea 1 111 0 0 0 0 ­15 0 Guinea-Bissau 0 7 0 0 0 0 0 0 Haiti 7 75 0 0 0 0 0 0 364 2009 World Development Indicators GLOBAL LINKS Global private financial flows Equity flows Debt flows 6.11 $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 50 816 0 0 ­13 0 38 25 Hungary 4,804 37,231 ­62 ­5,014 .. .. .. .. India 2,144 22,950 1,591 34,986b 286 8,227 967 17,704 Indonesia 4,346 6,928 1,493 3,559 2,248 2,843 58 2,317 Iran, Islamic Rep. 17 754 0 0 0 0 ­115 ­1,284 Iraq .. .. .. .. .. .. .. .. Ireland 1,447 26,085 0 137,155 .. .. .. .. Israel 1,350 9,664 991 4,306 .. .. .. .. Italy 4,842 40,040 5,358 ­14,874 .. .. .. .. Jamaica 147 866 0 0 13 1,796 15 ­65 Japan 39 22,180 50,597 45,455 .. .. .. .. Jordan 13 1,835 0 346 0 ­2 ­201 ­16 Kazakhstan 964 10,189 0 841 0 10,243 240 11,029 Kenya 32 728 6 1 0 0 ­163 ­14 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1,776 1,579 4,219 ­28,726 .. .. .. .. Kuwait 7 119 0 0 .. .. .. .. Kyrgyz Republic 96 208 0 2 0 0 0 ­19 Lao PDR 95 324 0 0 0 0 0 305 Latvia 180 2,247 0 ­13 43 0 3 6,174 Lebanon 35 2,845 0 791 350 94 333 270 Lesotho 275 130 0 0 0 0 12 ­5 Liberia 5 132 0 0 0 0 0 0 Libya ­88 4,689 .. 0 .. .. .. .. Lithuania 73 2,017 6 ­166 .. .. .. .. Macedonia, FYR 9 320 0 0 0 0 0 200 Madagascar 10 997 0 0 0 0 ­4 ­1 Malawi 6 55 0 0 0 0 ­23 ­1 Malaysia 4,178 8,456 0 ­669 2,440 ­2,170 1,231 873 Mali 111 360 0 0 0 0 0 ­1 Mauritania 7 153 0 0 0 0 0 ­1 Mauritius 19 339 22 50 150 0 126 ­37 Mexico 9,526 24,686 519 ­482 3,758 ­514 1,401 8,678 Moldova 26 493 ­1 2 0 ­6 24 364 Mongolia 10 328 0 0 0 75 ­14 5 Morocco 92 2,807 20 ­64 0 0 158 ­170 Mozambique 45 427 0 0 0 0 24 6 Myanmar 280 428 0 0 0 0 36 ­137 Namibia 153 170 46 5 .. .. .. .. Nepal .. 6 0 0 0 0 ­5 0 Netherlands 12,206 123,609 ­743 ­98,086 .. .. .. .. New Zealand 3,316 2,753 .. 192 .. .. .. .. Nicaragua 89 382 0 0 0 0 ­81 77 Niger 7 27 0 0 0 0 ­24 ­7 Nigeria 1,079 6,087 0 4,648 0 175 ­448 ­487 Norway 2,393 3,788 636 6,444 .. .. .. .. Oman 46 2,376 0 2,056 .. .. .. .. Pakistan 723 5,333 10 1,276 0 750 317 ­247 Panama 223 1,907 0 0 0 930 ­12 ­17 Papua New Guinea 455 96 0 0 ­32 0 ­311 103 Paraguay 103 196 0 0 0 0 ­16 14 Peru 2,557 5,343 171 814 0 1,003 43 2,086 Philippines 1,478 2,928 0 3,285 1,110 28 ­215 2,523 Poland 3,659 22,959 219 ­453 250 2,821 228 15,036 Portugal 685 5,534 ­179 ­664 .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. 2009 World Development Indicators 365 6.11 Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2007 1995 2007 1995 2007 1995 2007 Romania 419 9,492 0 746 0 0 413 12,534 Russian Federation 2,065 55,073 47 18,844 ­810 36,505 444 22,824 Rwanda 2 67 0 0 0 0 0 0 Saudi Arabia ­1,875 ­8,069 0 0 .. .. .. .. Senegal 32 78 4 0 0 0 ­25 ­32 Serbia 45c 3,110 0c 0 0c 165 0c 4,072 Sierra Leone 7 94 0 0 0 0 ­28 0 Singapore 11,535 24,137 ­159 6,743 .. .. .. .. Slovak Republic 236 3,363 ­16 232 .. .. .. .. Slovenia 150 1,483 .. 275 .. .. .. .. Somalia 1 141 0 0 0 0 0 0 South Africa 1,248 5,746 2,914 8,670 731 4,645 748 305 Spain 8,086 60,122 4,216 15,393 .. .. .. .. Sri Lanka 56 603 0 ­322 0 500 103 283 Sudan 12 2,426 0 ­17 0 0 0 0 Swaziland 52 37 1 1 0 0 0 ­3 Sweden 14,939 12,286 1,853 4,371 .. .. .. .. Switzerland 4,158 49,730 5,851 689 .. .. .. .. Syrian Arab Republic 100 .. .. .. .. .. .. .. Tajikistan 10 360 0 0 0 0 0 3 Tanzania 120 647 0 3 0 0 15 8 Thailand 2,068 9,498 2,253 4,241 2,123 ­183 3,702 5,840 Timor-Leste .. .. .. .. .. .. .. .. Togo 26 69 0 0 0 0 0 0 Trinidad and Tobago 299 .. 17 .. .. .. .. .. Tunisia 264 1,620 12 30 588 5 ­96 29 Turkey 885 22,195 195 5,138 627 4,415 174 34,243 Turkmenistan 233 804 0 0 0 0 20 ­42 Uganda 121 484 0 ­48 0 0 ­9 ­1 Ukraine 267 9,891 0 715 ­200 4,068 ­19 13,975 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 21,731 197,766 8,070 31,483 .. .. .. .. United States 57,800 237,541 16,523 197,517 .. .. .. .. Uruguay 157 879 0 ­27 144 814 39 ­58 Uzbekistan ­24 262 0 0 0 0 201 ­222 Venezuela, RB 985 646 270 66 ­468 760 ­247 ­1,232 Vietnam 1,780 6,700 0 6,243 0 ­26 356 ­60 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. ­218 917 0 0 0 0 ­2 0 Zambia 97 984 0 4 0 0 ­37 198 Zimbabwe 118 69 0 0 ­30 0 140 ­3 World 328,380 s 2,139,338 s 120,570 s 715,869 s .. s .. s .. s .. s Low income 5,713 31,995 6 12,429 .. 1,649 ­10 ­776 Middle income 93,118 494,617 14,043 126,002 21,280 83,745 26,886 211,782 Lower middle income 54,387 241,019 5,763 63,710 7,233 18,717 10,799 62,434 Upper middle income 38,731 253,598 8,281 62,292 14,047 65,028 16,087 149,348 Low & middle income 98,831 526,612 14,049 138,431 21,218 85,395 26,876 211,006 East Asia & Pacific 50,798 175,340 3,746 35,168 8,206 2,286 9,532 25,832 Europe & Central Asia 9,558 156,437 471 26,232 .. 58,236 2,003 131,197 Latin America & Carib. 30,181 107,270 5,216 29,569 11,311 8,699 13,211 36,949 Middle East & N. Africa 817 28,905 32 ­2,096 660 97 555 ­1,916 South Asia 2,931 29,926 1,585 36,093 286 9,477 1,362 17,719 Sub-Saharan Africa 4,546 28,734 2,999 13,465 851 6,600 213 1,225 High income 229,549 1,612,726 106,520 577,438 .. .. .. .. Euro area 78,432 778,971 17,232 289,878 .. .. .. .. a. Includes Luxembourg. b. Based on data from the Reserve Bank of India. c. Includes Montenegro. 366 2009 World Development Indicators GLOBAL LINKS Global private financial flows 6.11 About the data Private fi nancial fl ows account for the bulk of payments data on FDI do not include capital raised edition includes portfolio equity flows data for non- development finance and are split into two broad locally, which has become an important source of reporting countries based on data from the IMF's categories--equity and debt. Equity flows comprise investment financing in some developing countries. International Financial Statistics database. Bonds, foreign direct investment (FDI) and portfolio equity. In addition, FDI data capture only cross-border invest- bank lending, and supplier credits are shown for only Debt flows are financing raised through bond issu- ment flows involving equity participation and thus 128 developing countries that report to the Debtor ance, bank lending, and supplier credits. omit nonequity crossborder transactions such as Reporting System; nonreporting countries may also The data on FDI and portfolio equity are based on intrafirm flows of goods and services. For a detailed receive debt flows. balance of payments data reported by the Interna- discussion of the data issues, see the World Bank's The volume of global private financial flows reported tional Monetary Fund (IMF). These data are supple- World Debt Tables 1993­94 (vol. 1, chap. 3). by the World Bank generally differs from that reported mented by staff estimates using data from the United Statistics on bonds, bank lending, and supplier by other sources because of differences in sources, Nations Conference on Trade and Development and credits are produced by aggregating individual trans- classifi cation of economies, and method used to official national sources for FDI data and from market actions of public and publicly guaranteed debt and adjust and disaggregate reported information. In sources for portfolio equity data. private nonguaranteed debt. Data on public and pub- addition, particularly for debt financing, differences Under the internationally accepted definition of FDI, licly guaranteed debt are reported through the Debtor may also result based on whether particular install- provided in the fifth edition of the IMF's Balance of Reporting System by World Bank member economies ments of the transactions are included and how cer- Payments Manual (1993), FDI has three components: that have received either loans from the International tain offshore issuances are treated. equity investment, reinvested earnings, and short- and Bank for Reconstruction and Development or cred- Definitions long-term loans between parent firms and foreign affili- its from the International Development Association. ates. Distinguished from other kinds of international These reports are cross-checked with data reported · Foreign direct investment is net inflows of invest- investment, FDI is made to establish a lasting interest from market sources that also provide transactions ment to acquire a lasting interest in or management in or effective management control over an enterprise data. Information on private nonguaranteed bonds control over an enterprise operating in an economy in another country. The IMF suggests as a guideline and bank lending is collected from market sources, other than that of the investor. It is the sum of equity that investments should account for at least 10 per- because official national sources reporting to the capital, reinvested earnings, other long-term capital, cent of voting stock to be counted as FDI. In practice Debtor Reporting System are not asked to report the and short-term capital, as shown in the balance of many countries set a higher threshold. Also, many breakdown between private nonguaranteed bonds payments. · Portfolio equity includes net inflows countries fail to report reinvested earnings, and the and private nonguaranteed loans. from equity securities other than those recorded definition of long-term loans differs among countries. Previous editions of the table included portfolio as direct investment and including shares, stocks, FDI data do not give a complete picture of inter- equity flows data only for countries that report to depository receipts, and direct purchases of shares national investment in an economy. Balance of the Debtor Reporting System. The table in this year's in local stock markets by foreign investors · Bonds are securities issued with a fixed rate of interest for a In 2007 middle-income economies received nearly 20 times period of more than one year. They include net flows more private capital flows than low-income economies did 6.11a through cross-border public and publicly guaranteed and private nonguaranteed bond issues. · Commer- Middle-income economies Low-income economies Net inflows Net inflows cial bank and other lending includes net commercial ($ billions) ($ billions) bank lending (public and publicly guaranteed and pri- 1,000 50 Commercial bank vate nonguaranteed) and other private credits. and other lending 800 Bonds 40 Portfolio equity Foreign direct investment Official development assistance 600 30 400 20 200 10 0 0 Data sources Data on equity and debt flows are compiled from ­200 ­10 1990 1995 2000 2007 1990 1995 2000 2007 a variety of public and private sources, including Net private flows to middle-income economies have increased since 2003, reaching $916 billion in the World Bank's Debtor Reporting System, the 2007--or 24 times the value of aid received. But aid remains the main source of external financing for IMF's International Financial Statistics and Bal- low-income economies. ance of Payments databases, and Dealogic. These data are also published in the World Bank's Global Source: Global Development Finance data files and Organisation for Economic Cooperation and Development, Development Assistance Committee's International Development Statistics. Development Finance 2009. 2009 World Development Indicators 367 6.12 Net official financial flows Total International financial institutions United Nationsb,c $ millions $ millions Regional From IMF development banksb $ millions From bilateral multilateral World Banka Conces- Non- Conces- Non- Other sources sourcesa,b IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA UNTA Others 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Afghanistan 6.9 238.9 41.8 0.0 54.8 0.0 94.3 0.0 0.3 26.4 0.0 3.1 18.2 Albania 6.7 102.8 42.9 5.4 ­10.7 3.7 0.0 8.3 48.3 0.7 0.0 0.9 3.3 Algeria ­134.1 ­4.3 0.0 ­5.9 0.0 0.0 0.0 0.0 ­4.3 1.1 0.0 2.3 2.5 Angola 665.0 31.8 3.0 0.0 0.0 0.0 1.0 ­0.5 0.0 12.1 0.0 2.6 13.6 Argentina 0.2 ­215.2 0.0 ­531.6 0.0 0.0 0.0 ­5.2 316.3 0.6 0.0 2.0 2.7 Armenia 94.8 83.3 85.8 ­0.7 ­13.6 0.0 0.0 ­6.9 7.2 0.7 0.0 1.2 9.6 Australia Austria Azerbaijan 3.1 157.3 52.0 16.0 ­25.6 ­11.2 13.1 76.0 24.9 1.4 0.0 0.7 10.0 Bangladesh ­69.3 677.4 408.3 0.0 0.0 0.0 127.2 55.4 21.2 16.0 0.0 7.7 41.6 Belarus 1,512.3 ­10.4 0.0 ­8.7 0.0 0.0 0.0 ­5.3 0.0 0.7 0.0 0.8 2.1 Belgium Benin ­35.8 121.1 42.5 0.0 1.3 0.0 45.3 0.0 16.4 5.4 0.0 1.9 8.3 Bolivia 36.5 71.2 15.7 0.0 0.0 ­14.8 43.8 ­45.8 62.1 1.7 0.0 1.4 7.1 Bosnia and Herzegovina ­52.4 42.4 48.0 ­23.8 0.0 ­18.4 0.0 18.3 7.8 0.8 0.0 0.9 8.8 Botswana 5.1 ­20.6 ­0.5 ­1.1 0.0 0.0 ­2.3 ­15.2 ­8.2 1.2 0.0 1.7 3.8 Brazil ­518.3 811.9 0.0 ­198.6 0.0 0.0 0.0 1,031.1 ­30.0 2.1 0.0 3.8 3.5 Bulgaria 8.5 ­292.7 0.0 136.6 0.0 ­347.0 0.0 ­14.8 ­67.5 .. .. .. .. Burkina Faso 35.9 191.2 80.0 0.0 0.8 0.0 55.4 0.0 24.4 11.4 0.0 2.1 17.1 Burundi 1.0 52.9 1.7 0.0 10.9 0.0 ­0.1 0.0 3.4 9.6 0.0 2.2 25.2 Cambodia 74.5 105.2 13.6 0.0 0.0 0.0 52.2 0.0 1.1 6.7 0.0 1.8 29.8 Cameroon ­31.9 109.9 19.9 ­5.4 8.1 0.0 17.7 12.6 35.3 6.1 0.0 2.1 13.5 Canada Central African Republic 0.0 ­6.1 ­7.9 0.0 24.2 ­19.2 ­16.9 ­3.6 0.0 6.1 0.0 2.1 9.1 Chad 6.1 25.0 4.9 ­4.7 ­14.8 0.0 8.2 0.0 1.4 11.8 0.0 2.1 16.1 Chile ­5.0 35.2 ­0.7 8.3 0.0 0.0 ­0.2 25.1 0.0 0.4 0.0 1.5 0.8 China ­1,038.4 1,094.3 ­261.3 285.3 0.0 0.0 0.0 983.3 20.8 13.3 0.0 7.8 45.1 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. .. Colombia ­112.6 334.5 ­0.7 132.4 0.0 0.0 ­10.0 476.9 ­272.2 1.6 0.0 1.8 4.7 Congo, Dem. Rep. ­138.2 44.3 61.3 0.0 ­64.3 0.0 4.5 ­24.7 ­3.6 43.3 0.0 4.4 23.4 Congo, Rep. ­19.0 0.9 ­3.1 0.0 0.0 0.0 0.4 ­13.0 3.5 1.8 0.0 4.0 7.3 Costa Rica 2.2 ­135.6 ­0.2 ­8.0 0.0 0.0 ­12.3 ­56.2 ­61.5 0.6 0.0 0.8 1.2 Côte d'Ivoire 16.4 ­23.6 ­15.9 ­29.7 ­46.9 62.2 ­1.1 ­37.6 20.6 9.7 0.0 2.7 12.4 Croatia ­159.6 201.4 0.0 2.3 0.0 0.0 0.0 25.5 169.1 0.3 0.0 1.1 3.1 Cuba .. .. .. .. .. .. .. .. .. 0.8 0.0 1.4 3.5 Czech Republic .. .. 0.0 ­9.9 .. .. .. .. .. .. .. .. .. Denmark Dominican Republic 125.7 30.8 ­0.7 34.4 0.0 63.0 ­21.1 ­46.4 ­2.0 1.1 0.0 1.0 1.5 Ecuador ­222.8 558.3 ­1.1 ­62.2 0.0 ­23.1 ­26.5 116.8 550.6 0.8 0.0 1.9 1.1 Egypt, Arab Rep. ­1,140.8 1,212.1 ­33.3 626.0 0.0 0.0 0.2 572.2 28.9 3.2 0.0 2.9 12.0 El Salvador ­15.6 ­7.8 ­0.8 ­16.3 0.0 0.0 ­22.5 30.6 ­5.1 0.7 0.0 1.0 4.6 Eritrea 13.4 44.7 19.6 0.0 0.0 0.0 4.0 0.0 3.5 2.7 0.0 2.0 12.9 Estonia .. .. 0.0 ­6.6 .. .. .. .. .. .. .. .. .. Ethiopia 102.5 425.6 132.3 0.0 0.0 0.0 137.2 ­18.2 59.0 51.4 0.0 3.8 60.1 Finland France Gabon 20.8 ­37.4 0.0 ­7.8 0.0 ­34.0 ­0.2 ­26.4 27.3 0.7 0.0 1.1 1.9 Gambia, The 9.1 38.7 ­3.9 0.0 2.4 0.0 10.1 0.0 21.2 1.5 0.0 1.5 5.9 Georgia ­55.3 79.8 65.4 0.0 3.2 0.0 0.0 1.2 0.3 0.8 0.0 1.1 7.8 Germany Ghana ­3.2 292.3 247.5 0.0 0.0 0.0 17.9 ­14.4 14.3 7.7 0.0 2.1 17.2 Greece .. .. 0.0 0.0 .. .. .. .. .. .. .. .. .. Guatemala ­40.1 464.1 0.0 98.2 0.0 0.0 ­17.6 199.0 176.0 1.1 0.0 1.3 6.1 Guinea ­36.9 4.7 ­5.2 0.0 ­10.7 0.0 13.9 ­7.4 ­15.5 5.8 0.0 2.1 21.7 Guinea-Bissau ­8.7 20.4 5.2 0.0 ­3.3 0.0 1.8 0.0 0.6 2.5 0.0 1.7 11.9 Haiti ­5.3 117.4 ­14.6 0.0 51.6 ­30.9 92.0 0.0 1.8 5.0 0.0 1.4 11.1 368 2009 World Development Indicators GLOBAL LINKS Total Net official financial flows International financial institutions 6.12 United Nationsb,c $ millions $ millions Regional From IMF development banksb $ millions From bilateral multilateral World Banka Conces- Non- Conces- Non- Other sources sourcesa,b IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA UNTA Others 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Honduras 10.6 127.7 45.6 0.0 0.0 0.0 65.4 ­18.8 20.6 1.1 0.0 1.5 12.3 Hungary .. .. 0.0 ­36.6 .. .. .. .. .. .. .. .. .. India 525.1 1,385.3 44.6 651.8 0.0 0.0 0.0 606.1 ­10.8 37.0 0.0 9.6 47.0 Indonesia ­2,013.5 192.3 192.1 ­601.5 0.0 0.0 100.9 471.9 0.0 5.5 0.0 6.8 16.6 Iran, Islamic Rep. ­152.3 147.7 0.0 139.5 0.0 0.0 0.0 0.0 0.0 2.2 0.0 2.6 3.4 Iraq .. .. .. .. .. .. .. .. .. 9.4 0.0 1.9 2.9 Ireland Israel .. .. .. .. .. .. .. .. .. .. .. .. .. Italy Jamaica ­52.2 ­58.1 0.0 ­28.9 0.0 0.0 ­5.2 ­32.1 4.9 0.6 0.0 0.7 1.9 Japan .. .. .. .. .. .. .. .. .. .. .. .. .. Jordan ­165.0 ­0.9 ­2.6 ­38.4 0.0 ­76.2 0.0 0.0 ­0.2 2.3 110.9 1.6 1.7 Kazakhstan ­4.5 ­109.8 0.0 ­76.3 0.0 0.0 ­49.0 ­10.4 21.6 1.1 0.0 1.0 2.2 Kenya 48.6 151.2 92.8 0.0 104.5 0.0 27.6 ­11.0 ­111.5 11.0 0.0 2.8 35.0 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. 5.1 0.0 3.4 2.1 Korea, Rep. .. .. 0.0 ­469.8 .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 5.5 21.3 13.1 0.0 ­20.8 0.0 26.5 ­6.9 2.3 1.1 0.0 1.0 5.0 Lao PDR 34.3 95.2 16.4 0.0 ­3.0 0.0 57.1 0.9 3.8 2.6 0.0 2.1 15.3 Latvia ­0.5 71.6 0.0 ­22.2 0.0 0.0 0.0 ­1.8 95.6 .. .. .. .. Lebanon ­106.9 236.9 0.0 114.6 0.0 77.7 0.0 0.0 ­45.2 2.2 84.0 1.3 2.3 Lesotho ­3.1 28.5 8.0 ­3.6 ­2.7 0.0 3.3 ­1.0 8.5 2.0 0.0 1.4 12.6 Liberia 0.0 ­232.9 ­35.3 ­162.5 0.0 ­0.7 ­5.9 ­52.3 0.0 6.1 0.0 2.1 15.6 Libya .. .. .. .. .. .. .. .. .. 0.0 0.0 0.9 0.3 Lithuania .. .. 0.0 ­58.5 .. .. .. .. .. .. .. .. .. Macedonia, FYR ­84.1 ­114.5 ­4.6 ­79.6 ­10.5 ­46.2 0.0 23.4 ­2.5 0.8 0.0 0.7 4.0 Madagascar 7.2 290.2 198.1 0.0 12.0 0.0 36.7 4.2 8.1 12.5 0.0 2.6 16.0 Malawi 6.7 99.7 13.7 0.0 10.2 0.0 26.7 ­2.0 7.6 11.9 0.0 2.4 29.2 Malaysia ­1,150.4 ­326.9 0.0 ­301.8 0.0 0.0 0.0 ­36.5 5.7 0.5 0.0 1.5 3.7 Mali 45.5 205.9 138.9 0.0 4.1 0.0 28.4 0.0 2.3 14.5 0.0 2.6 15.1 Mauritania 40.4 81.5 66.1 0.0 12.8 0.0 9.9 ­9.1 ­20.8 2.4 0.0 1.7 18.5 Mauritius 4.0 24.8 ­0.6 19.8 0.0 0.0 ­0.1 11.8 ­9.0 0.0 0.0 1.6 1.3 Mexico ­213.2 844.9 0.0 329.0 0.0 0.0 0.0 511.2 0.0 1.8 0.0 2.6 0.3 Moldova ­11.9 48.8 38.2 ­15.0 27.6 ­16.6 0.0 ­4.6 7.6 0.7 0.0 1.1 9.8 Mongolia 10.7 44.6 16.4 0.0 ­6.6 0.0 17.5 0.0 5.7 1.2 0.0 3.2 7.2 Morocco 240.2 604.2 ­1.4 124.1 0.0 0.0 ­0.9 113.2 365.1 1.4 0.0 2.8 ­0.1 Mozambique ­1.9 372.4 211.9 0.0 5.0 0.0 73.2 7.6 29.9 14.3 0.0 2.4 28.1 Myanmar ­142.2 38.2 0.0 0.0 0.0 0.0 0.0 0.0 ­1.3 14.3 0.0 4.7 20.5 Namibia .. .. 0.0 0.0 .. .. .. .. .. 0.8 0.0 1.6 4.8 Nepal ­26.6 132.4 0.3 0.0 33.2 0.0 57.1 0.0 ­3.0 7.7 0.0 5.5 31.6 Netherlands New Zealand Nicaragua 1.8 200.7 53.2 0.0 18.2 0.0 112.1 ­5.7 8.0 1.7 0.0 1.7 11.5 Niger ­0.2 121.1 36.6 0.0 11.9 0.0 27.9 ­2.5 12.7 19.9 0.0 2.6 12.0 Nigeria 68.2 223.5 315.4 ­175.9 0.0 0.0 48.0 ­33.2 0.0 33.8 0.0 4.8 30.6 Norway .. .. .. .. .. .. .. .. .. .. .. .. .. Oman .. .. 0.0 0.0 .. .. .. .. .. 0.0 0.0 0.7 0.5 Pakistan ­149.9 1,374.6 867.3 ­109.0 ­122.6 ­29.5 279.4 422.5 8.8 17.0 0.0 5.6 35.1 Panama ­3.1 45.3 0.0 30.4 0.0 ­10.2 ­7.6 1.7 24.5 0.4 0.0 1.0 5.1 Papua New Guinea ­18.5 ­88.6 ­3.7 ­66.1 0.0 0.0 ­2.9 ­21.2 ­3.1 2.2 0.0 2.3 3.9 Paraguay ­40.2 ­26.0 ­1.5 ­14.4 0.0 0.0 ­15.1 1.2 1.1 0.9 0.0 0.6 1.2 Peru ­2,404.6 ­34.8 0.0 15.3 0.0 ­20.5 ­7.5 173.2 ­204.5 1.4 0.0 2.1 5.7 Philippines 86.9 202.2 ­7.0 ­30.9 0.0 0.0 ­27.4 241.5 3.1 2.6 0.0 3.5 16.8 Poland ­2,275.1 ­258.3 0.0 ­258.3 0.0 0.0 0.0 0.0 0.0 .. .. .. .. Portugal Puerto Rico 2009 World Development Indicators 369 6.12 Net official financial flows Total International financial institutions United Nationsb,c $ millions $ millions Regional From IMF development banksb $ millions From bilateral multilateral World Banka Conces- Non- Conces- Non- Other sources sourcesa,b IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA UNTA Others 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Romania ­10.8 50.3 0.0 21.4 0.0 ­105.5 14.0 26.4 94.0 .. .. .. .. Russian Federation ­850.0 ­450.0 0.0 ­510.1 0.0 0.0 0.0 3.9 56.2 .. .. .. .. Rwanda ­3.4 123.9 27.6 0.0 3.5 0.0 61.2 0.0 ­4.7 9.1 0.0 1.8 25.4 Saudi Arabia .. .. .. .. .. .. .. .. .. 0.1 0.0 0.7 0.8 Senegal 51.7 395.0 132.9 0.0 0.0 0.0 50.3 ­5.3 188.7 4.8 0.0 2.5 21.1 Serbia ­36.0 213.0 47.4 ­17.5 0.0 0.0 0.0 71.2 95.6 0.6 0.0 1.3 14.4 Sierra Leone ­3.0 57.5 14.2 0.0 0.0 0.0 0.9 0.0 6.9 10.7 0.0 2.1 22.7 Singapore .. .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. 0.0 ­10.8 .. .. .. .. .. .. .. .. .. Slovenia .. .. 0.0 ­13.0 .. .. .. .. .. .. .. .. .. Somalia 0.0 30.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.0 0.0 3.0 15.2 South Africa 0.0 ­16.5 0.0 ­2.5 0.0 0.0 0.0 ­26.3 0.0 1.7 0.0 2.9 7.7 Spain .. .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 124.7 161.4 24.9 0.0 0.0 ­5.3 81.3 ­2.7 49.7 0.8 0.0 3.7 9.0 Sudan 266.0 157.3 0.0 0.0 0.0 ­60.0 0.0 0.0 174.3 18.4 0.0 4.3 20.3 Swaziland ­5.2 3.5 ­0.3 ­2.3 0.0 0.0 ­1.0 0.6 0.2 2.4 0.0 1.3 2.6 Sweden Switzerland Syrian Arab Republic .. .. ­1.5 0.0 .. .. .. .. .. 3.8 42.1 2.5 7.6 Tajikistan 180.6 50.9 7.0 0.0 0.0 0.0 38.3 ­0.4 ­4.6 3.0 0.0 1.6 6.0 Tanzania 9.4 671.8 474.4 0.0 4.3 0.0 119.4 ­0.9 20.0 15.0 0.0 3.0 36.6 Thailand ­826.1 ­332.4 ­3.4 ­272.4 0.0 0.0 ­3.8 ­42.3 ­26.7 1.1 0.0 4.5 10.6 Timor-Leste .. .. .. .. .. .. .. .. .. 2.2 0.0 1.4 4.5 Togo ­2.0 5.7 0.0 0.0 ­6.7 0.0 1.0 ­1.4 0.3 4.0 0.0 1.5 7.0 Trinidad and Tobago .. .. .. .. .. .. .. .. .. 0.0 0.0 0.3 2.5 Tunisia ­90.3 68.7 ­2.1 ­8.0 0.0 0.0 0.0 ­131.9 204.6 0.7 0.0 1.6 3.8 Turkey ­367.5 ­2,421.0 ­5.9 557.1 0.0 ­4,016.7 0.0 0.0 1,035.0 1.2 0.0 0.9 7.4 Turkmenistan ­78.3 ­3.6 0.0 ­5.6 0.0 0.0 0.0 0.0 ­1.9 1.6 0.0 0.2 2.1 Uganda ­0.6 498.6 373.6 0.0 0.0 0.0 81.2 ­2.5 ­2.9 18.5 0.0 2.4 28.3 Ukraine ­246.3 ­492.8 0.0 ­74.4 0.0 ­427.1 0.0 ­33.9 32.4 1.2 0.0 1.7 7.3 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom United States Uruguay ­13.2 ­2.7 0.0 13.2 0.0 0.0 ­2.4 ­17.5 0.8 0.5 0.0 0.9 1.8 Uzbekistan ­75.3 48.1 15.8 ­7.4 0.0 0.0 0.2 18.3 11.9 2.8 0.0 1.2 5.3 Venezuela, RB 141.3 ­589.2 0.0 ­50.4 0.0 0.0 0.0 ­430.1 ­116.2 1.1 0.0 1.2 5.2 Vietnam 335.3 940.7 718.1 0.0 ­25.4 0.0 159.2 47.5 10.6 4.3 0.0 5.3 21.1 West Bank and Gaza .. .. .. .. .. .. .. .. .. 6.4 463.3 0.0 3.6 Yemen, Rep. ­23.3 162.3 87.9 0.0 ­73.0 ­13.5 0.0 0.0 136.6 5.7 0.0 3.5 15.1 Zambia 36.2 140.9 51.2 0.0 42.1 0.0 19.0 5.2 ­7.1 10.0 0.0 2.6 17.9 Zimbabwe 126.4 12.7 0.0 0.0 ­0.1 0.0 0.0 0.0 ­2.4 4.7 0.0 2.0 8.5 World .. s .. s .. s .. s .. s .. s .. s .. s .. s 984.1 s 700.3 s 461.7 s 1,699.4 s Low income 510.8 8,409.6 4,849.5 ­555.3 ­1.2 ­31.6 1,866.4 297.3 456.8 505.5 0.0 129.0 893.2 Middle income ­10,875.0 5,108.9 539.7 ­17.5 ­15.5 ­5,092.9 298.0 4,820.3 3,015.4 196.0 700.3 167.0 498.1 Lower middle income ­6,793.6 7,488.6 481.3 867.9 ­15.5 ­652.6 364.1 3,770.0 1,371.1 164.5 616.3 109.5 412.0 Upper middle income ­4,081.4 ­2,406.5 58.4 ­885.4 0.0 ­4,440.3 ­66.1 1,050.3 1,644.3 26.9 84.0 36.6 84.8 Low & middle income ­10,364.3 14,260.7 5,389.2 ­572.8 ­16.8 ­5,124.4 2,164.4 5,117.6 3,472.2 979.7 700.3 458.4 1,692.9 East Asia & Pacific ­4,660.1 2,024.0 689.9 ­989.1 ­34.9 0.0 351.5 1,651.0 27.5 68.8 0.0 55.5 203.8 Europe & Central Asia ­2,427.9 ­3,049.3 411.5 ­458.6 ­50.4 ­4,984.7 43.1 187.5 1,651.3 23.5 0.0 17.2 110.3 Latin America & Carib. ­3,362.9 2,731.2 106.9 ­244.2 69.8 ­41.2 212.7 1,922.2 506.1 28.6 0.0 64.9 105.4 Middle East & N. Africa ­1,595.8 3,030.8 55.1 952.0 ­75.8 ­12.1 0.3 553.6 693.9 42.0 700.3 58.4 63.1 South Asia 398.8 4,032.8 1,399.2 542.8 ­34.6 ­34.7 651.0 1,081.3 91.7 107.1 0.0 38.6 190.4 Sub-Saharan Africa 1,283.6 4,840.8 2,726.5 ­375.7 109.2 ­51.7 905.8 ­277.9 501.7 439.6 0.0 128.7 734.6 High income .. .. .. .. .. .. .. .. .. 4.4 0.0 3.3 6.5 Euro area .. .. .. .. .. .. .. .. .. .. .. .. .. a. Aggregates include amounts for economies that do not report to the World Bank's Debtor Reporting System and may differ from aggregates published in Global Development Finance 2009. b. Aggregates include amounts for economies not specified elsewhere. c. World and income group aggregates include flows not allocated by country or region. 370 2009 World Development Indicators GLOBAL LINKS Net official financial flows 6.12 About the data Definitions The table shows concessional and nonconcessional Bank for Reconstruction and Development (IBRD) lend- · Total net official financial flows are disbursements financial flows from official bilateral sources and from ing. Exceptions are also made for small island econo- of public or publicly guaranteed loans and credits, the major international financial institutions and UN mies. The IBRD lends to creditworthy countries at a less repayments of principal. · IDA is the Interna- agencies. The international financial institutions fund variable base rate of six-month LIBOR plus a spread, tional Development Association, the concessional nonconcessional lending operations primarily by sell- either variable or fixed, for the life of the loan. The lend- loan window of the World Bank Group. · IBRD is ing low-interest, highly rated bonds backed by prudent ing rate is reset every six months and applies to the the International Bank for Reconstruction and lending and financial policies and the strong financial interest period beginning on that date. Although some Development, the founding and largest member of support of their members. Funds are then on-lent to outstanding IBRD loans have a low enough interest the World Bank Group. · IMF is the International developing countries at slightly higher interest rates rate to be classified as concessional under the DAC Monetary Fund, which provides concessional lending with 15- to 20-year maturities. Lending terms vary definition, all IBRD loans in the table are classified as through the Poverty Reduction and Growth Facility with market conditions and institutional policies. nonconcessional. Lending by the International Finance and the IMF Trust Fund and nonconcessional lending Concessional flows from international financial Corporation is not included. through credit to its members, mainly for balance of institutions are credits provided through concessional The International Monetary Fund makes conces- payments needs. · Regional development banks lending facilities. Subsidies from donors or other sional funds available through its Poverty Reduction are the African Development Bank, which serves all resources reduce the cost of these loans. Grants and Growth Facility and the IMF Trust Fund. Eligibility of Africa, including North Africa; the Asian Develop- are not included in net flows. The Organisation for is based principally on a country's per capita income ment Bank, which serves South and Central Asia Economic Co-operation and Development's (OECD) and eligibility under IDA. and East Asia and Pacific; the European Bank for Development Assistance Committee (DAC) defines Regional development banks also maintain conces- Reconstruction and Development, which serves concessional flows from bilateral donors as flows with sional windows. Loans from the major regional devel- Europe and Central Asia; and the Inter-American a grant element of at least 25 percent, evaluated opment banks are recorded in the table according to Development Bank, which serves the Americas. assuming a 10 percent nominal discount rate. each institution's classification and not according to · Concessional financial flows are disbursements World Bank concessional lending is done by the the DAC definition. made through concessional lending facilities. · Non- International Development Association (IDA) based Data for flows from international financial institutions concessional financial flows are all disbursements on gross national income (GNI) per capita and perfor- are available for 128 countries that report to the World that are not concessional. · Other institutions, a mance standards assessed by World Bank staff. The Bank's Debtor Reporting System. World Bank flows for residual category in the World Bank's Debtor Report- cutoff for IDA eligibility, set at the beginning of the nonreporting countries were collected from its opera- ing System, includes other multilateral institutions World Bank's fiscal year, has been $1,095 since July 1, tional records. Nonreporting countries may have net such as the Caribbean Development Fund, Council 2008, measured in 2007 U.S. dollars using the World flows from other international financial institutions. of Europe, European Development Fund, Islamic Bank Atlas method (see Users guide). In exceptional Official flows from the United Nations are mainly con- Development Bank, and Nordic Development Fund. circumstances IDA extends temporary eligibility to cessional flows classified as official development assis- · United Nations includes the United Nations Chil- countries above the cutoff that are undertaking major tance but may include nonconcessional flows classified dren's Fund (UNICEF), United Nations Relief and adjustments but are not creditworthy for International as other official flows in OECD-DAC databases. Works Agency for Palestine Refugees in the Near East (UNRWA), United Nations Regular Programme Net nonconcessional lending from international financial institutions has for Technical Assistance (UNTA), and other UN agen- declined in recent years as countries have paid off previous loans 6.12a cies, such as the International Fund for Agricultural Net inflows ($ billions) World Bank International Monetary Fund Regional development banks Development, Joint United Nations Programme on 6 HIV/AIDS, United Nations Development Programme, 3 United Nations Population Fund, United Nations 0 Refugee Agency, and World Food Programme. ­3 Data sources ­6 Data on net fi nancial fl ows from international ­9 financial institutions are from the World Bank's ­12 Debtor Reporting System and published in the 1990 2000 2007 1990 2000 2007 1990 2000 2007 1990 2000 2007 1990 2000 2007 1990 2000 2007 East Asia & Europe & Latin America & Middle East & South Sub-Saharan World Bank's Global Development Finance 2009 Pacific Central Asia Caribbean North Africa Asia Africa and electronically as GDF Online. Data on official Latin America and the Caribbean paid off International Monetary Fund (IMF) loans in the early 2000s but still flows from UN agencies are from the OECD-DAC receives positive net disbursements from regional development banks, as do East Asia and Pacific, Middle annual Development Cooperation Report and are East and North Africa, and South Asia. Europe and Central Asia paid off loans from the IMF and World Bank in available electronically on the OECD-DAC's Inter- 2007. Nonconcessional lending to Sub-Saharan Africa is small because most borrowing is concessional. national Development Statistics CD-ROM and at Source: Global Development Finance data files. www.oecd.org/dac/stats/idsonline. 2009 World Development Indicators 371 Financial flows from Development 6.13 Assistance Committee members Net disbursements Total net Official Other Private Net flowsa development assistancea official flowsa grants by flowsa NGOsa Contributions Foreign Bilateral Multilateral Private Bilateral Bilateral to multilateral direct portfolio portfolio export Total grants loans institutions Total investment investment investment credits 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 $ millions Australia 10,307 2,669 2,265 3 400 36 6,948 2,367 4,379 .. 202 655 Austria 20,553 1,808 1,351 ­26 484 ­624 19,247 15,802 .. .. 3,445 123 Belgium 3,820 1,953 1,268 ­29 713 ­161 1,686 1,488 .. .. 198 342 Canada 17,161 4,080 3,192 ­40 928 ­4 11,731 7,932 2,386 .. 1,413 1,355 Denmark 4,807 2,562 1,722 ­72 912 ­91 2,242 2,242 .. .. .. 94 Finland 2,149 981 575 9 397 96 1,051 11 1,040 .. .. 20 France 43,126 9,884 6,690 ­431 3,625 ­1,179 34,422 14,337 21,925 .. ­1,840 .. Germany 39,339 12,291 8,091 ­141 4,341 ­2,525 28,302 13,521 11,101 ­56 3,736 1,271 Greece 3,391 501 249 .. 252 4 2,880 2,880 .. .. .. 7 Ireland 5,840 1,192 824 .. 368 .. 4,329 .. 4,329 .. .. 318 Italy 4,422 3,971 1,252 19 2,700 ­261 649 1,353 ­3,547 .. 2,843 63 Japan 30,315 7,679 5,983 ­205 1,901 211 21,979 18,037 3,251 ­1,896 2,586 446 Luxembourg 384 376 253 .. 122 .. .. .. .. .. .. 8 Netherlands 18,142 6,224 4,813 ­169 1,580 .. 11,575 ­1,028 11,951 795 ­143 343 New Zealand 404 320 247 .. 73 8 26 26 .. .. .. 50 Norway 5,221 3,728 2,624 258 845 5 1,488 1,488 .. .. 0 .. Portugal 2,215 471 252 18 200 ­237 1,980 1,550 .. .. 430 2 Spain 21,662 5,140 3,257 82 1,801 6 16,516 16,626 2 .. ­111 .. Sweden 6,911 4,339 2,862 71 1,407 ­46 2,541 2,232 0 .. 309 78 Switzerland 12,561 1,689 1,256 18 416 .. 10,368 11,199 .. ­833 3 504 United Kingdom 58,319 9,849 6,572 ­971 4,247 ­43 47,846 31,043 16,587 .. 217 667 United States 129,862 21,787 19,729 ­827 2,886 ­1,632 97,545 45,591 59,796 ­7,737 ­105 12,161 Total 440,912 103,491 75,326 ­2,433 30,598 ­6,438 325,350 188,696 133,199 ­9,727 13,182 18,508 Official development assistance Commitmentsb Gross Net disbursementsb disbursements % of general Per capitab government $ millions $ millions $ millionsb $ % of GNIa disbursementsa 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 Australia 1,863 1,886 1,605 2,317 1,605 2,317 83 110 0.27 0.32 0.73 0.89 Austria 863 1,695 666 1,648 662 1,622 82 195 0.23 0.50 0.46 1.05 Belgium 1,297 1,968 1,297 1,828 1,262 1,756 123 166 0.36 0.43 0.74 0.90 Canada 3,013 4,244 2,669 3,766 2,632 3,729 86 113 0.25 0.29 0.60 0.75 Denmark 2,461 2,116 2,625 2,394 2,597 2,301 486 420 1.06 0.81 1.96 1.64 Finland 512 948 547 887 537 887 104 167 0.31 0.39 0.64 0.85 France 7,181 10,651 7,657 10,315 6,287 8,867 107 144 0.30 0.38 0.62 0.75 Germany 8,287 12,854 8,412 12,326 7,288 11,069 89 135 0.27 0.37 0.57 0.87 Greece 370 446 370 446 370 446 34 40 0.20 0.16 0.42 0.38 Ireland 388 1,070 388 1,070 388 1,070 102 247 0.29 0.55 0.87 1.47 Italy 2,586 3,787 2,558 3,832 2,202 3,547 39 60 0.13 0.19 0.28 0.41 Japan 14,679 14,424 13,982 13,801 11,587 7,812 91 61 0.28 0.17 0.86 0.50 Luxembourg 202 334 202 334 202 334 459 727 0.72 0.91 1.76 2.19 Netherlands 5,475 6,686 5,135 5,986 4,989 5,629 313 343 0.84 0.81 1.86 1.81 New Zealand 199 308 187 272 187 272 49 64 0.25 0.27 0.57 0.64 Norway 1,954 3,339 2,206 3,350 2,195 3,350 489 707 0.76 0.95 1.83 2.41 Portugal 681 425 681 425 443 420 43 41 0.26 0.22 0.60 0.48 Spain 2,412 4,834 2,412 4,834 2,077 4,566 52 101 0.22 0.37 0.57 1.01 Sweden 1,949 3,333 2,438 3,857 2,438 3,857 275 420 0.80 0.93 1.35 1.90 Switzerland 1,279 1,654 1,260 1,611 1,257 1,605 175 211 0.34 0.37 1.11 1.24 United Kingdom 6,470 10,358 6,470 10,358 6,398 8,774 109 145 0.32 0.36 0.80 0.83 United States 14,698 26,933 12,662 22,111 11,604 21,231 42 70 0.10 0.16 0.31 0.44 Total 78,821 114,295 76,430 107,770 69,210 95,462 82 107 0.22 0.28 0.60 0.71 Note: Components may not sum to totals because of gaps in reporting. a. At current prices and exchange rates. b. At 2006 prices and exchange rates. 372 2009 World Development Indicators GLOBAL LINKS Financial flows from Development Assistance Committee members 6.13 About the data The flows of official and private financial resources dropped. ODA recipients now comprise all low- and official flows are transactions by the official sector from the members of the Development Assistance middle-income countries except those that are mem- whose main objective is other than development or Committee (DAC) of the Organisation for Economic bers of the Group of Eight or the European Union whose grant element is less than 25 percent. · Pri- Co-operation and Development (OECD) to developing (including countries with a firm date for EU acces- vate flows are flows at market terms financed from economies are compiled by DAC, based principally on sion). The content and structure of tables 6.13­6.16 private sector resources in donor countries. They reporting by DAC members using standard question- were revised to reflect this change. Because official include changes in holdings of private long-term naires issued by the DAC Secretariat. aid flows are quite small relative to ODA, the net assets by reporting country residents. · Foreign The table shows data reported by DAC member effect of these changes is believed to be minor. direct investment is investment by residents of DAC economies and does not include aid provided by the Flows are transfers of resources, either in cash or member countries to acquire a lasting management Commission of the European Communities--a multi- in the form of commodities or services measured on interest (at least 10 percent of voting stock) in an lateral member of DAC. a cash basis. Short-term capital transactions (with enterprise operating in the recipient country. The DAC exists to help its members coordinate their one year or less maturity) are not counted. Repay- data reflect changes in the net worth of subsidiaries development assistance and to encourage the ments of the principal (but not interest) of ODA loans in recipient countries whose parent company is in expansion and improve the effectiveness of the are recorded as negative flows. Proceeds from offi - the DAC source country. · Bilateral portfolio invest- aggregate resources flowing to recipient economies. cial equity investments in a developing country are ment is bank lending and the purchase of bonds, In this capacity DAC monitors the flow of all financial reported as ODA, while proceeds from their later sale shares, and real estate by residents of DAC member resources, but its main concern is official develop- are recorded as negative flows. countries in recipient countries. · Multilateral port- ment assistance (ODA). Grants or loans to countries The table is based on donor country reports and folio investment is transactions of private banks and and territories on the DAC list of aid recipients have does not provide a complete picture of the resources nonbanks in DAC member countries in the securities to meet three criteria to be counted as ODA. They received by developing economies for two reasons. issued by multilateral institutions. · Private export are undertaken by the official sector. They promote First, flows from DAC members are only part of the credits are loans extended to recipient countries economic development and welfare as the main aggregate resource flows to these economies. Sec- by the private sector in DAC member countries to objective. And they are provided on concessional ond, the data that record contributions to multilateral promote trade; they may be supported by an offi - financial terms (loans must have a grant element of institutions measure the flow of resources made cial guarantee. · Net grants by nongovernmental at least 25 percent, calculated at a discount rate of available to those institutions by DAC members, not organizations (NGOs) are private grants by NGOs, 10 percent). The DAC Statistical Reporting Directives the flow of resources from those institutions to devel- net of subsidies from the offi cial sector. · Com- provide the most detailed explanation of this defini- oping and transition economies. mitments are obligations, expressed in writing and tion and all ODA-related rules. Aid as a share of gross national income (GNI), aid backed by funds, undertaken by an official donor to This definition excludes nonconcessional fl ows per capita, and ODA as a share of the general gov- provide specified assistance to a recipient country from official creditors, which are classified as "other ernment disbursements of the donor are calculated or multilateral organization. · Gross disbursements official flows," and aid for military purposes. Trans- by the OECD. The denominators used in calculating are the international transfer of financial resources fer payments to private individuals, such as pen- these ratios may differ from corresponding values and goods and services, valued at the cost to the sions, reparations, and insurance payouts, are in elsewhere in this book because of differences in tim- donor. general not counted. In addition to financial flows, ing or definitions. ODA includes technical cooperation, most expen- Definitions ditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral · Net disbursements are gross disbursements of institutions such as the United Nations and its spe- grants and loans minus repayments of principal on cialized agencies, and concessional funding to multi- earlier loans. · Total net flows are ODA or official lateral development banks. aid flows, other official flows, private flows, and net A DAC revision of the list of countries and terri- grants by nongovernmental organizations. · Official tories counted as aid recipients has governed aid development assistance refers to flows that meet Data sources reporting for the three years starting in 2005. In the the DAC definition of ODA and are made to coun- past DAC distinguished aid going to Part I and Part II tries and territories on the DAC list of aid recipients. Data on financial flows are compiled by OECD- countries. Part I countries, the recipients of ODA, · Bilateral grants are transfers of money or in kind DAC and published in its annual statistical report, comprised many of the countries classified by the for which no repayment is required. · Bilateral loans Geographical Distribution of Financial Flows to Aid World Bank as low- and middle-income economies. are loans extended by governments or official agen- Recipients, and its annual Development Coopera- Part II countries, whose assistance was designated cies that have a grant element of at least 25 percent tion Report. Data are available electronically on official aid, included the more advanced countries (calculated at a 10 percent discount rate). · Contri- the OECD-DAC's International Development Sta- of Central and Eastern Europe, countries of the for- butions to multilateral institutions are concessional tistics CD-ROM and at www.oecd.org/dac/stats/ mer Soviet Union, and certain advanced developing funding received by multilateral institutions from DAC idsonline. countries and territories. This distinction has been members as grants or capital subscriptions. · Other 2009 World Development Indicators 373 Allocation of bilateral aid from 6.14 Development Assistance Committee members 6.14a Aid by purpose Net disbursements Share of bilateral ODA net disbursements % Development projects, programs, and other Technical Debt-related Humanitarian Administrative $ millionsa resource provisions cooperationb aid assistance costs 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 Australia 758 2,268 28.0 27.3 55.1 51.2 0.8 10.9 9.7 6.6 6.2 4.0 Austria 273 1,324 36.4 26.1 41.8 18.9 12.7 51.1 2.7 1.1 6.4 2.7 Belgium 477 1,240 36.4 28.9 46.9 49.5 3.9 9.5 5.4 7.4 7.5 4.7 Canada 1,160 3,152 39.9 58.8 43.0 24.6 0.7 0.4 5.0 8.7 11.4 7.5 Denmark 1,024 1,651 66.1 70.3 25.3 8.7 0.6 4.8 0.0 8.5 8.0 7.7 Finland 217 584 40.8 28.5 41.4 45.6 0.0 0.0 10.5 18.0 7.2 7.8 France 2,829 6,258 30.7 26.3 50.6 52.3 11.7 15.2 0.4 0.6 6.7 5.7 Germany 2,687 7,950 19.8 25.4 63.8 44.8 3.5 23.0 4.1 3.5 8.7 3.3 Greece 99 249 69.6 28.2 23.8 57.1 0.0 0.0 6.4 5.1 0.2 9.6 Ireland 154 824 79.1 67.3 0.4 4.7 0.0 0.0 15.5 23.1 5.1 5.0 Italy 377 1,270 52.0 61.4 8.1 13.8 15.7 14.3 18.3 6.5 5.9 3.9 Japan 9,768 5,778 61.6 34.8 24.9 31.4 3.1 20.6 0.9 1.6 9.5 11.6 Luxembourg 99 253 84.6 77.5 3.2 3.9 0.6 0.0 10.4 12.0 1.2 6.6 Netherlands 2,243 4,644 43.0 67.1 33.7 13.6 4.9 6.3 9.1 7.3 9.4 5.7 New Zealand 85 247 39.7 51.2 48.1 28.9 0.0 0.0 3.4 11.7 8.8 8.2 Norway 934 2,883 58.1 60.8 23.0 18.8 0.7 1.6 11.3 12.3 6.9 6.5 Portugal 179 270 35.4 37.0 50.4 57.5 9.6 0.1 1.9 0.3 2.7 5.2 Spain 720 3,339 70.2 70.2 17.9 14.5 1.4 4.7 3.7 6.8 6.8 3.8 Sweden 1,242 2,932 61.9 65.4 13.6 15.1 2.1 1.7 14.6 10.5 7.7 7.3 Switzerland 627 1,274 58.8 48.5 19.4 25.6 0.7 3.8 20.2 13.5 0.9 8.5 United Kingdom 2,710 5,602 49.9 67.3 25.5 16.0 3.4 0.7 12.7 6.3 8.4 9.7 United States 7,405 18,901 15.1 71.5 64.4 6.3 1.3 0.5 9.6 15.8 9.7 5.9 Total 36,064 72,894 42.3 53.1 39.4 23.3 3.6 8.7 6.1 8.6 8.6 6.3 a. At current exchange rates and prices. b. Includes aid for promoting development awareness and aid provided to refugees in donor economies. About the data Aid can be used in many ways. The sector to which provide debt relief on liabilities that recipient coun- human resources from donors or action directed to aid goes, the form it takes, and the procurement tries have difficulty servicing. Thus, this type of aid human resources (such as training or advice). Also restrictions attached to it are important influences may not provide a full value of new resource flows included are aid for promoting development aware- on aid effectiveness. The data on allocation of offi - for development, in particular for heavily indebted ness and aid provided to refugees in the donor econ- cial development assistance (ODA) in the table are poor countries. Humanitarian assistance provides omy. Assistance specifically to facilitate a capital based principally on reporting by members of the relief following sudden disasters and supports food project is not included. · Debt-related aid groups Organisation for Economic Co-operation and Devel- programs in emergency situations. This type of aid all actions relating to debt, including forgiveness, opment (OECD) Development Assistance Committee does not generally contribute to financing long-term swaps, buybacks, rescheduling, and refinancing. (DAC). For more detailed explanation of ODA, see development. · Humanitarian assistance is emergency and dis- About the data for table 6.13. tress relief (including aid to refugees and assistance Definitions The form in which an ODA contribution reaches for disaster preparedness). · Administrative costs the benefiting sector or the economy is important. A · Net disbursements are gross disbursements of are the total current budget outlays of institutions distinction is made between resource provision and grants and loans minus repayments of principal on responsible for the formulation and implementation technical cooperation. Resource provision involves earlier loans · Development projects, programs, of donor's aid programs and other administrative mainly cash or in-kind transfers and financing of and other resource provisions are aid provided as costs incurred by donors in aid delivery. capital projects, with the deliverables being finan- cash transfers, aid in kind, development food aid, cial support and the provision of commodities and and the financing of capital projects, intended to Data sources supplies. Technical cooperation includes grants to increase or improve the recipient's stock of physical nationals of aid-recipient countries receiving educa- capital and to support recipient's development plans Data on aid flows are published by OECD-DAC in tion or training at home or abroad, and payments and other activities with finance and commodity its annual statistical report, Geographical Distribu- to consultants, advisers, and similar personnel and supply. · Technical cooperation is the provision of tion of Financial Flows to Aid Recipients, and its to teachers and administrators serving in recipient resources whose main aim is to augment the stock of annual Development Cooperation Report. Data are countries. Technical cooperation is spent mostly in human intellectual capital, such as the level of knowl- available electronically on the OECD-DAC's Interna- the donor economy. edge, skills, and technical know-how in the recipient tional Development Statistics CD-ROM and at www. Two other types of aid are presented because they country (including the cost of associated equipment). oecd.org/dac/stats/idsonline. serve distinctive purposes. Debt-related aid aims to Contributions take the form mainly of the supply of 374 2009 World Development Indicators GLOBAL LINKS Allocation of bilateral aid from Development Assistance Committee members 6.14 6.14b Aid by sector Total Social infrastructure and services Economic infrastructure, Multi- Untied sector- services, and production sector sector or aida allocable Water Government Transport cross- aid supply and and civil and com- cutting Share of bilateral Total Education Health Population sanitation society Total munication Agriculture ODA commitment (%) 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Australia 73.8 48.0 8.9 6.3 1.7 0.7 28.7 9.8 3.4 4.4 15.9 98.4 Austria 26.0 20.3 10.6 2.0 0.3 1.7 4.8 4.4 1.8 1.0 1.4 86.6 Belgium 54.3 39.1 13.7 7.6 1.9 3.6 8.7 10.5 2.0 3.3 4.7 92.0 Canada 63.9 47.3 7.1 14.4 2.5 0.7 21.6 9.6 1.5 2.1 7.0 74.6 Denmark 63.9 33.8 3.6 5.2 4.2 2.1 16.5 23.6 8.6 5.7 6.5 95.5 Finland 58.2 31.7 4.4 3.2 1.9 4.7 14.7 17.4 1.0 6.3 9.0 90.7 France 62.0 35.9 22.8 2.1 0.0 4.6 1.2 16.6 6.5 7.9 9.5 92.6 Germany 62.0 37.9 15.2 2.6 1.3 6.2 10.2 17.4 0.5 2.4 6.7 93.4 Greece 82.9 67.6 24.4 11.3 2.3 1.1 24.3 6.1 0.3 2.6 9.2 42.3b Ireland 64.6 55.5 12.1 15.1 6.1 2.8 15.7 6.1 0.8 4.4 3.0 100.0 b Italy 43.9 22.2 3.4 6.3 1.0 4.1 5.7 11.2 2.6 3.8 10.5 59.8 Japan 68.6 26.7 5.5 2.3 0.2 14.9 2.3 33.7 11.2 8.2 8.2 95.1 Luxembourg 68.9 47.5 10.8 14.6 6.7 5.1 6.0 14.9 2.2 4.8 6.6 100.0 b Netherlands 56.6 33.8 12.4 2.5 1.0 7.5 9.7 14.7 0.8 1.5 8.0 81.1 New Zealand 53.4 39.3 17.3 3.2 2.2 1.4 14.2 9.5 2.4 2.1 4.6 87.8 Norway 69.9 41.3 9.3 5.1 2.3 1.6 20.1 18.8 0.8 3.5 9.8 99.9 Portugal 91.7 73.3 25.8 3.9 0.1 0.6 35.3 12.4 11.0 0.7 5.9 58.0 b Spain 71.7 46.2 10.1 5.3 1.6 3.3 13.8 13.1 4.7 3.2 12.4 89.1b Sweden 52.0 31.3 2.8 6.1 3.2 1.6 14.5 12.4 0.8 4.0 8.2 100.0 Switzerland 49.1 23.6 3.5 3.4 0.2 2.8 12.8 14.8 0.9 5.1 10.7 99.7 United Kingdom 67.4 44.7 12.1 8.1 5.7 1.7 14.3 18.9 1.0 1.6 3.8 100.0b United States 75.3 51.4 3.4 4.6 18.1 1.7 18.6 19.2 5.4 4.9 4.7 68.5 Total 66.3 40.5 9.1 4.7 6.1 4.7 12.5 18.8 4.4 4.6 7.1 84.6 a. Excludes technical cooperation and administrative costs. b. Gross disbursements. About the data Definitions The Development Assistance Committee (DAC) · Bilateral official development assistance (ODA) and planning and activities promoting good gover- records the sector classifi cation of aid using a commitments are firm obligations, expressed in nance and civil society. · Economic infrastructure, three-level hierarchy. The top level is grouped by writing and backed by the necessary funds, under- services, and production sector group assistance themes, such as social infrastructure and services; taken by official bilateral donors to provide specified for networks, utilities, services that facilitate eco- economic infrastructure, services, and production; assistance to a recipient country or a multilateral nomic activity, and contributions to all directly pro- and multisector or cross-cutting areas. The second organization. Bilateral commitments are recorded ductive sectors. · Transport and communication level is more specifi c. Education and health and in the full amount of expected transfer, irrespective refer to road, rail, water, and air transport; post transport and storage are examples. The third level of the time required for completing disbursements. and telecommunications; and television and print comprises subsectors such as basic education and · Total sector-allocable aid is the sum of aid that media. · Agriculture refers to sector policy, devel- basic health. Some contributions are reported as can be assigned to specific sectors or multisector opment, and inputs; crop and livestock production; non-sector-allocable aid. activities. · Social infrastructure and services refer and agricultural credit, cooperatives, and research. Reporting on the sectoral destination and the to efforts to develop the human resources poten- · Multisector or cross-cutting refers to support for form of aid by donors may not be complete. Also, tial of aid recipients. · Education refers to general projects that straddle several sectors. · Untied aid measures of aid allocation may differ from the per- teaching and instruction at all levels, as well as con- is ODA not subject to restrictions by donors on pro- spectives of donors and recipients because of dif- struction to improve or adapt educational establish- curement sources. ference in classification, available information, and ments. Training in a particular field is reported for the Data sources recording time. sector concerned. · Health refers to assistance to The proportion of untied aid is reported because hospitals, clinics, other medical and dental services, Data on aid flows are published annually by the tying arrangements may prevent recipients from public health administration, and medical insur- Organisation for Economic Co-operation and Devel- obtaining the best value for their money. Tying ance programs. · Population refers to all activities opment (OECD) DAC in Geographical Distribution of requires recipients to purchase goods and services related to family planning and research into popula- Financial Flows to Aid Recipients and Development from the donor country or from a specified group of tion problems. · Water supply and sanitation refer Cooperation Report. Data are available electroni- countries. Such arrangements prevent a recipient to assistance for water supply and use, sanitation, cally on the OECD-DAC's International Develop- from misappropriating or mismanaging aid receipts, and water resources development (including rivers). ment Statistics CD-ROM and at www.oecd.org/ but they may also be motivated by a desire to benefit · Government and civil society refer to assistance dac/stats/idsonline. donor country suppliers. to strengthen government administrative apparatus 2009 World Development Indicators 375 6.15 Aid dependency Net official Aid per Aid dependency development capita ratios assistancea Aid as % of imports Aid as Aid as % of gross of goods, services, Aid as % of central $ millions $ % of GNI capital formation and income government expense 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 Afghanistan 136 3,951 .. .. .. .. .. .. .. .. .. 167.7 Albania 317 305 103 96 8.4 2.7 34.8 9.4 21.0 7.0 .. .. Algeria 201 390 7 12 0.4 0.3 1.5 0.9 .. .. 1.8 1.5 Angola 302 241 22 14 4.1 0.5 22.0 2.8 4.1 0.7 .. .. Argentina 53 82 1 2 0.0 0.0 0.1 0.1 0.1 0.1 .. .. Armenia 216 352 70 117 11.0 3.7 60.6 10.3 21.2 8.5 .. 23.0 Australia Austria Azerbaijan 139 225 17 26 2.8 0.9 12.8 3.4 5.8 1.5 .. .. Bangladesh 1,172 1,502 8 9 2.4 2.0 10.8 9.0 11.7 7.2 .. 21.7 Belarus 40 83 4 9 0.3 0.2 1.2 0.6 0.5 0.3 1.5 0.5 Belgium Benin 241 470 33 52 10.7 8.7 56.4 .. 32.4 .. .. .. Bolivia 482 476 58 50 5.9 3.7 31.6 23.9 19.7 10.3 .. 16.7 Bosnia and Herzegovina 737 443 199 117 12.4 2.8 65.1 12.9 17.4 4.0 .. 7.8 Botswana 31 104 18 56 0.5 0.9 1.4 2.1 1.0 2.0 .. .. Brazil 232 297 1 2 0.0 0.0 0.2 0.1 0.2 0.1 .. .. Bulgariab 311 .. 39 .. 2.5 .. 13.5 .. 3.7 .. 7.6 .. Burkina Faso 338 930 28 63 13.0 13.8 77.2 .. 48.9 .. .. .. Burundi 93 466 14 55 12.9 49.5 213.8 .. 56.5 103.8 .. .. Cambodia 396 672 31 46 10.9 8.4 60.3 38.7 16.1 9.9 .. .. Cameroon 381 1,933 24 104 4.0 9.4 22.6 54.0 12.9 32.4 .. .. Canada Central African Republic 75 176 19 41 8.0 10.4 82.4 116.1 .. .. .. .. Chad 130 352 15 33 9.5 5.7 40.4 26.0 .. .. .. .. Chile 49 120 3 7 0.1 0.1 0.3 0.3 0.2 0.2 0.3 0.4 China 1,728 1,439 1 1 0.1 0.0 0.4 0.1 0.6 0.1 .. .. Hong Kong, Chinab 4 .. 1 .. 0.0 .. 0.0 .. 0.0 .. .. .. Colombia 187 731 5 17 0.2 0.4 1.3 1.4 1.1 1.5 .. 1.4 Congo, Dem. Rep. 177 1,217 3 19 4.5 14.2 119.1 67.3 .. .. 15.2 .. Congo, Rep. 33 127 10 34 1.5 2.1 4.6 6.1 1.6 1.5 .. .. Costa Rica 11 53 3 12 0.1 0.2 0.4 0.8 0.1 0.3 0.3 0.9 Côte d'Ivoire 351 165 21 9 3.6 0.9 31.2 9.7 7.9 1.8 .. 4.1 Croatia 66 164 15 37 0.4 0.3 1.8 1.0 0.6 0.5 0.8 0.8 Cuba 44 92 4 8 .. .. .. .. .. .. .. .. Czech Republicb 438 .. 43 .. 0.8 .. 2.6 .. 1.1 .. 2.3 .. Denmark Dominican Republic 56 128 6 13 0.3 0.4 1.2 1.6 0.5 0.7 .. .. Ecuador 146 215 12 16 1.0 0.5 4.6 2.0 2.3 1.2 .. .. Egypt, Arab Rep. 1,328 1,083 20 14 1.3 0.8 6.8 4.0 5.6 1.9 .. .. El Salvador 180 88 29 13 1.4 0.4 8.1 2.1 3.0 0.8 .. 22.1 Eritrea 176 155 48 32 27.7 11.3 116.7 106.7 34.5 .. .. .. Estoniab 64 .. 47 .. 1.2 .. 4.0 .. 1.2 .. 3.8 .. Ethiopia 686 2,422 10 31 8.5 12.5 41.4 50.1 41.0 34.8 .. .. Finland France Gabon 12 48 10 36 0.3 0.5 1.1 1.6 0.5 .. .. .. Gambia, The 50 72 36 42 12.4 12.1 67.8 48.5 .. 19.6 .. .. Georgia 169 382 36 87 5.3 3.7 20.8 10.9 13.6 6.0 47.9 16.4 Germany Ghana 600 1,151 30 49 12.4 7.7 50.2 22.5 17.3 11.2 .. 26.0 Greece Guatemala 263 450 23 34 1.4 1.3 7.7 6.4 4.4 2.8 12.5 .. Guinea 153 224 19 24 5.0 5.0 24.9 39.0 15.7 13.7 .. .. Guinea-Bissau 80 123 59 73 39.5 35.4 329.8 200.7 .. .. .. .. Haiti 208 701 24 73 5.4 11.4 20.8 40.6 15.1 30.0 .. .. 376 2009 World Development Indicators GLOBAL LINKS Net official Aid per Aid dependency Aid dependency 6.15 development capita ratios assistancea Aid as % of imports Aid as Aid as % of gross of goods, services, Aid as % of central $ millions $ % of GNI capital formation and income government expense 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 Honduras 449 464 72 65 6.5 4.0 22.3 11.3 8.9 4.5 .. 16.7 Hungary b 252 .. 25 .. 0.6 .. 2.0 .. 0.6 .. 1.3 .. India 1,463 1,298 1 1 0.3 0.1 1.3 0.3 1.8 .. 2.0 0.7 Indonesia 1,654 796 8 4 1.1 0.2 4.5 0.7 2.5 0.6 .. .. Iran, Islamic Rep. 130 102 2 1 0.1 0.0 0.4 0.1 0.7 .. 0.2 0.2 Iraq 100 9,115 .. .. .. .. .. .. .. .. .. .. Ireland Israelb 800 .. 127 .. 0.7 .. 3.2 .. 1.4 .. 1.5 .. Italy Jamaica 10 26 4 10 0.1 0.3 0.5 .. 0.2 0.3 0.4 0.4 Japan Jordan 552 504 115 88 6.4 3.0 29.2 11.6 8.7 3.1 24.1 8.7 Kazakhstan 189 202 13 13 1.1 0.2 5.7 0.5 1.8 0.3 7.5 1.4 Kenya 510 1,275 16 34 4.1 5.3 23.1 26.1 12.9 12.6 23.9 24.0 Korea, Dem. Rep. 73 98 3 4 .. .. .. .. .. .. .. .. Korea, Rep.b ­198 .. ­4 .. 0.0 .. ­0.1 .. ­0.1 .. ­0.2 .. Kuwaitb 3 .. 1 .. 0.0 .. 0.1 .. 0.0 .. .. .. Kyrgyz Republic 215 274 44 52 16.7 7.4 78.3 28.1 28.5 8.3 99.2 39.8 Lao PDR 282 396 54 68 16.9 10.0 57.4 24.2 44.1 32.1 .. 89.7 Latviab 91 .. 38 .. 1.2 .. 4.9 .. 2.3 .. 4.1 .. Lebanon 199 939 53 229 1.2 3.9 5.9 21.5 .. 3.9 3.8 12.0 Lesotho 37 130 19 65 3.4 6.4 10.1 29.0 4.4 7.1 .. 17.1 Liberia 67 696 22 187 17.4 124.3 .. 473.6 .. 36.4 .. .. Libya 14 19 3 3 .. 0.0 0.3 .. 0.2 0.1 .. .. Lithuaniab 99 .. 28 .. 0.9 .. 4.4 .. 1.6 .. 3.2 .. Macedonia, FYR 251 213 125 105 7.1 2.8 31.5 12.0 10.6 .. .. .. Madagascar 322 892 20 45 8.4 12.2 55.1 44.2 20.3 .. 78.1 108.1 Malawi 446 735 38 53 26.1 20.8 188.7 79.3 65.7 .. .. .. Malaysia 45 200 2 8 0.1 0.1 0.2 0.5 0.0 0.1 0.3 .. Mali 360 1,017 36 82 15.0 15.4 60.5 63.7 34.4 .. 128.0 97.4 Mauritania 216 364 84 117 19.8 13.2 103.3 53.1 .. .. .. .. Mauritius 20 75 17 59 0.5 1.1 1.8 4.1 0.7 1.3 2.2 5.0 Mexico ­56 121 ­1 1 0.0 0.0 0.0 0.0 0.0 0.0 ­0.1 .. Moldova 123 269 30 71 9.4 5.6 39.7 16.0 11.3 5.8 32.9 18.9 Mongolia 217 228 91 87 20.1 5.9 68.8 14.4 27.5 .. .. 23.2 Morocco 419 1,090 15 35 1.2 1.5 4.4 4.5 3.1 3.0 .. 5.0 Mozambique 906 1,777 50 83 22.5 25.2 68.9 118.9 51.4 39.9 .. .. Myanmar 106 190 2 4 .. .. .. .. 4.0 .. .. .. Namibia 152 205 81 99 4.4 3.0 22.8 9.7 8.2 5.1 14.1 .. Nepal 387 598 16 21 7.0 5.7 29.0 20.7 21.2 16.0 .. .. Netherlands New Zealand Nicaragua 561 834 110 149 15.0 14.9 47.2 45.8 23.5 17.2 86.5 76.4 Niger 208 542 19 38 11.7 12.8 101.4 .. 43.0 .. .. 108.4 Nigeria 174 2,042 1 14 0.4 1.4 .. .. 1.1 3.1 .. .. Norway Oman 45 ­31 19 ­12 0.2 .. 1.9 .. 0.6 ­0.1 0.9 .. Pakistan 700 2,212 5 14 1.0 1.5 5.5 6.8 4.8 5.2 5.7 9.5 Panama 16 ­135 5 ­40 0.1 ­0.7 0.6 ­2.9 0.2 ­0.8 0.6 .. Papua New Guinea 275 317 51 50 8.3 5.7 35.7 25.7 13.7 .. 26.2 .. Paraguay 82 108 15 18 1.1 0.9 6.1 4.9 2.3 1.6 .. 5.3 Peru 398 263 15 9 0.8 0.3 3.7 1.1 3.4 0.8 4.2 1.4 Philippines 575 634 8 7 0.7 0.4 3.6 2.9 1.1 0.9 4.3 2.6 Polandb 1,396 .. 36 .. 0.8 .. 3.3 .. 2.3 .. .. .. Portugal Puerto Rico 2009 World Development Indicators 377 6.15 Aid dependency Net official Aid per Aid dependency development capita ratios assistancea Aid as % of imports Aid as Aid as % of gross of goods, services, Aid as % of central $ millions $ % of GNI capital formation and income government expense 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 2000 2007 Romaniab 432 .. 19 .. 1.2 .. 6.0 .. 2.9 .. .. .. Russian Federationb 1,561 .. 11 .. 0.6 .. 3.2 .. 2.2 .. .. .. Rwanda 321 713 39 73 18.7 21.5 101.2 100.9 71.2 73.4 .. .. Saudi Arabia 22 ­131 1 ­5 0.0 0.0 0.1 ­0.2 0.1 ­0.1 .. .. Senegal 425 843 41 68 9.2 7.6 44.3 23.2 22.0 .. 71.2 .. Serbia 1,134 c 834 151c 113 12.6c 2.2 150.1c 9.0 .. .. .. .. Sierra Leone 181 535 40 92 29.3 32.9 413.2 239.5 68.8 84.0 98.8 .. Singaporeb 1 .. 0 .. 0.0 .. 0.0 .. 0.0 .. 0.0 .. Slovak Republicb 113 .. 21 .. 0.6 .. 2.1 .. 0.7 .. .. .. Sloveniab 61 .. 31 .. 0.3 .. 1.1 .. 0.5 .. 0.9 .. Somalia 101 384 14 44 .. .. .. .. .. .. .. .. South Africa 487 794 11 17 0.4 0.3 2.3 1.3 1.3 0.7 1.3 0.9 Spain Sri Lanka 276 589 15 29 1.7 1.8 6.0 6.7 3.2 4.3 7.3 9.1 Sudan 220 2,104 7 55 1.9 5.0 9.7 18.8 8.5 16.1 .. .. Swaziland 13 63 13 55 0.9 2.1 5.1 16.7 0.9 2.3 .. .. Sweden Switzerland Syrian Arab Republic 158 75 10 4 0.9 0.2 4.7 1.0 2.4 .. .. .. Tajikistan 124 221 20 33 15.8 6.1 125.1 27.1 .. 5.9 160.3 .. Tanzania 1,035 2,811 31 70 11.6 17.4 64.7 .. 46.4 43.3 .. .. Thailand 698 ­312 12 ­5 0.6 ­0.1 2.5 ­0.5 0.9 ­0.2 .. ­0.7 Timor-Leste 231 278 297 262 71.6 16.3 285.9 .. .. .. .. .. Togo 70 121 13 18 5.4 4.9 29.4 .. 10.5 .. .. 27.6 Trinidad and Tobago ­2 18 ­1 14 0.0 0.1 ­0.1 0.7 0.0 .. .. .. Tunisia 222 310 23 30 1.2 0.9 4.2 3.6 2.1 1.3 4.1 3.1 Turkey 327 797 5 11 0.1 0.1 0.6 0.6 0.5 0.4 .. 0.5 Turkmenistan 31 28 7 6 1.2 0.2 3.1 .. .. .. .. .. Uganda 845 1,728 34 56 13.9 15.0 70.0 65.7 53.7 38.1 95.5 .. Ukraine 541 405 11 9 1.8 0.3 8.8 1.1 2.8 0.5 6.4 0.8 United Arab Emiratesb 3 .. 1 .. 0.0 .. 0.0 .. .. .. .. .. United Kingdom United States Uruguay 17 34 5 10 0.1 0.1 0.6 1.0 0.3 0.4 0.3 0.5 Uzbekistan 186 166 8 6 1.4 0.7 8.3 3.8 .. .. .. .. Venezuela, RB 76 71 3 3 0.1 0.0 0.3 0.1 0.3 0.1 0.3 .. Vietnam 1,681 2,497 22 29 5.5 3.7 18.2 8.7 9.3 3.6 .. .. West Bank and Gaza 637 1,868 219 504 13.3 .. 47.4 .. .. .. .. .. Yemen, Rep. 263 225 14 10 3.0 1.1 14.3 .. 6.2 2.1 .. .. Zambia 795 1,045 76 88 25.8 10.5 140.8 38.1 53.1 17.6 .. 39.9 Zimbabwe 176 465 14 35 2.5 .. 17.5 .. .. .. .. .. World 57,878 s 105,056 s 10 w 16 w 0.2 w 0.2 w 0.8 w .. w 0.6 w 0.5 w .. w .. w Low income 16,632 40,259 15 31 4.6 5.2 23.4 23.3 14.0 11.7 .. .. Middle income 25,713 38,538 6 9 0.5 0.3 1.9 0.9 1.5 0.8 .. .. Lower middle income 17,757 31,700 6 9 0.6 0.5 2.3 1.2 2.2 1.3 .. .. Upper middle income 7,241 6,011 9 7 0.3 0.1 1.2 0.4 0.8 0.3 .. .. Low & middle income 55,145 105,130 11 19 0.9 0.7 3.8 2.5 3.0 2.1 .. .. East Asia & Pacific 8,589 8,611 5 5 0.5 0.2 1.6 0.5 1.4 0.5 .. .. Europe & Central Asia 9,962 5,785 22 13 1.1 0.2 5.3 0.7 3.1 0.5 .. .. Latin America & Carib. 4,850 6,826 9 12 0.2 0.2 1.2 0.8 0.9 0.7 .. .. Middle East & N. Africa 4,489 17,578 16 56 1.0 1.8 4.0 6.7 3.4 5.5 .. .. South Asia 4,206 10,379 3 7 0.7 0.7 3.0 2.0 3.6 .. .. .. Sub-Saharan Africa 13,261 35,362 20 44 4.1 4.5 23.1 21.6 11.0 10.0 .. .. High income 2,732 ­74 3 0 0.0 0.0 0.0 .. 0.0 0.0 .. .. Euro area .. .. .. .. .. .. .. .. .. .. .. .. Note: Regional aggregates include data for economies not listed in the table. World and income group totals include aid not allocated by country or region--including administrative costs, research on development issues, and aid to nongovernmental organizations. Thus regional and income group totals do not sum to the world total. a. The distinction between official aid, for countries on the Part II list of the Organisation for Economic Co-operation and Development Development Assistance Committee (DAC), and official development assistance was dropped in 2005. b. No longer on the DAC list of eligible official development assistance recipients. Data for 2000 are official aid. c. Includes Montenegro. 378 2009 World Development Indicators GLOBAL LINKS Aid dependency 6.15 About the data Unless otherwise noted, aid includes official devel- provide measures of recipient country dependency The nominal values used here may overstate the opment assistance (ODA; see About the data for on aid. But care must be taken in drawing policy con- real value of aid to recipients. Changes in interna- table 6.13). The data cover loans and grants from clusions. For foreign policy reasons some countries tional prices and exchange rates can reduce the pur- Development Assistance Committee (DAC) member have traditionally received large amounts of aid. Thus chasing power of aid. Tying aid, still prevalent though countries, multilateral organizations, and non-DAC aid dependency ratios may reveal as much about declining in importance, also tends to reduce its pur- donors. They do not refl ect aid given by recipient a donor's interests as about a recipient's needs. chasing power (see About the data for table 6.14). countries to other developing countries. As a result, Ratios are generally much higher in Sub-Saharan The aggregates refer to World Bank definitions. some countries that are net donors (such as Saudi Africa than in other regions, and they increased in Therefore the ratios shown may differ from those Arabia) are shown in the table as aid recipients (see the 1980s. High ratios are due only in part to aid of the Organisation for Economic Co-operation and table 6.15a). Aid given before 2005 to countries flows. Many African countries saw severe erosion Development (OECD). that were Part II recipients (see About the data in their terms of trade in the 1980s, which, along Definitions for table 6.13 for more information) is defined as with weak policies, contributed to falling incomes, official aid. imports, and investment. Thus the increase in aid · Net official development assistance is flows (net The table does not distinguish types of aid (pro- dependency ratios reflects events affecting both the of repayment of principal) that meet the DAC defini- gram, project, or food aid; emergency assistance; numerator (aid) and the denominator (GNI). tion of ODA and are made to countries and territories postconflict peacekeeping assistance; or technical Because the table relies on information from on the DAC list of aid recipients. See About the data cooperation), which may have different effects on the donors, it is not necessarily consistent with infor- for table 6.13. · Aid per capita is ODA divided by economy. Expenditures on technical cooperation do mation recorded by recipients in the balance of pay- midyear population. · Aid dependency ratios are not always directly benefit the economy to the extent ments, which often excludes all or some technical calculated using values in U.S. dollars converted at that they defray costs incurred outside the country on assistance--particularly payments to expatriates official exchange rates. Imports of goods, services, salaries and benefits of technical experts and over- made directly by the donor. Similarly, grant commod- and income refer to international transactions involv- head costs of firms supplying technical services. ity aid may not always be recorded in trade data or in ing a change in ownership of general merchandise, Ratios of aid to gross national income (GNI), gross the balance of payments. Moreover, DAC statistics goods sent for processing and repairs, nonmonetary capital formation, imports, and government spending exclude purely military aid. gold, services, receipts of employee compensation for nonresident workers, and investment income. For Official development assistance from non-DAC donors, 2003­07 6.15a definitions of GNI, gross capital formation, and cen- tral government expense, see Definitions for tables Net disbursements ($ millions) 1.1, 4.8, and 4.10. 2003 2004 2005 2006 2007 OECD members (non-DAC) Czech Republic 91 108 135 161 179 Hungary 21 70 100 149 103 Iceland 18 21 27 41 48 Korea, Rep. 366 423 752 455 699 Poland 27 118 205 297 363 Slovak Republic 15 28 56 55 67 Turkey 67 339 601 714 602 Arab countries Kuwait 138 161 218 158 110 Data sources Saudi Arabia 2,391 1,734 1,005 2,095 2,079 Data on fi nancial fl ows are compiled by DAC United Arab Emirates 188 181 141 249 429 and published in its annual statistical report, Other donors Geographical Distribution of Financial Flows to Israela 112 84 95 90 111 Aid Recipients, and in its annual Development Taiwan, China .. 421 483 513 514 Cooperation Report. Data are available electroni- Others 4 22 86 195 255 cally on the OECD-DAC's International Develop- Total 3,436 3,712 3,905 5,172 5,560 ment Statistics CD-ROM and at www.oecd.org/ Note: The table does not reflect aid provided by several major emerging non­Organisation for Economic Co-operation dac/stats/idsonline. Data on population, GNI, and Development donors because information on their aid has not been disclosed. gross capital formation, imports of goods and a. Includes $68.8 million in 2003, $47.9 million in 2004, $49.2 million in 2005, $45.5 million in 2006, and $42.9 million in 2007 for first-year sustenance expenses for people arriving from developing countries (many of which are services, and central government expense used experiencing civil war or severe unrest) or people who have left their country for humanitarian or political reasons. in computing the ratios are from World Bank and Source: Organisation for Economic Co-operation and Development. International Monetary Fund databases. 2009 World Development Indicators 379 Distribution of net aid by Development 6.16 Assistance Committee members Ten major DAC donors $ millions Other Total United European United DAC donors $ millions States Commission Germany France Japan Kingdom Netherlands Spain Canada Sweden $ millions 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Afghanistan 3,300.2 1,514.3 307.5 217.2 19.5 101.0 268.7 88.8 43.5 345.4 56.2 338.2 Albania 251.0 32.1 50.3 46.0 5.0 ­1.6 6.8 6.8 19.5 0.8 11.9 73.4 Algeria 375.4 1.7 86.2 9.4 185.2 7.3 0.6 0.0 60.5 0.9 0.9 22.9 Angola 150.5 39.6 64.9 12.3 3.2 23.1 10.0 ­49.3 17.6 4.5 6.7 17.9 Argentina 69.9 7.8 6.1 11.9 16.8 15.1 1.0 0.0 21.6 3.6 0.9 ­14.9 Armenia 251.2 79.9 20.5 22.5 8.5 85.2 7.5 5.8 0.1 1.9 3.6 15.7 Australia Austria Azerbaijan 118.8 49.0 9.0 24.3 9.5 11.4 0.4 0.1 0.0 1.5 1.4 12.3 Bangladesh 765.3 49.1 101.5 43.1 ­0.9 ­6.6 245.6 99.5 12.2 60.2 11.8 150.0 Belarus 55.7 8.1 6.9 18.9 1.2 0.4 0.8 0.0 0.2 0.5 10.5 8.3 Belgium Benin 319.7 25.3 81.4 29.6 56.4 6.8 0.0 34.7 2.1 7.0 0.3 76.0 Bolivia 396.6 122.4 43.9 39.8 7.2 36.9 ­105.2 48.3 74.6 22.8 26.0 79.9 Bosnia and Herzegovina 352.1 31.6 63.7 29.0 5.6 5.4 9.5 21.1 30.2 10.3 37.0 108.6 Botswana 101.0 44.8 37.3 2.5 9.2 ­2.2 0.4 0.1 0.0 2.2 3.7 2.9 Brazil 295.5 3.9 25.7 76.8 112.9 ­9.9 3.1 0.2 32.8 9.2 4.0 36.9 Bulgaria Burkina Faso 611.2 21.8 199.4 39.9 114.8 20.4 0.0 65.7 4.8 22.7 21.1 100.6 Burundi 321.4 25.9 121.7 23.0 18.3 8.5 13.2 23.1 2.3 6.9 6.0 72.4 Cambodia 462.1 87.2 44.8 37.6 35.0 113.6 24.6 0.2 8.5 15.3 17.9 77.4 Cameroon 1,791.7 30.7 94.9 754.5 596.2 18.6 51.7 2.6 15.3 12.4 73.6 141.3 Canada Central African Republic 147.8 18.4 30.0 5.3 54.2 2.6 5.1 6.3 1.0 5.7 7.6 11.8 Chad 298.6 59.6 75.2 29.5 47.9 9.9 5.1 6.8 4.1 11.6 5.9 43.0 Chile 110.5 ­1.1 12.5 27.5 10.2 8.8 0.5 0.1 6.7 3.5 0.3 41.6 China 1,387.2 40.8 56.0 289.3 132.3 435.7 162.4 24.8 67.5 32.9 10.6 135.0 Hong Kong, China Colombia 702.7 403.5 73.8 23.9 34.4 0.4 1.5 28.0 64.3 20.1 18.6 34.3 Congo, Dem. Rep. 946.4 132.4 158.0 63.0 27.6 22.9 121.2 50.7 17.7 33.0 33.4 286.4 Congo, Rep. 97.8 9.6 50.3 5.8 18.5 5.0 0.2 0.0 1.0 2.5 3.2 1.9 Costa Rica 56.3 ­2.9 7.9 3.2 23.1 17.3 ­12.0 1.8 10.0 3.9 1.1 2.9 Côte d'Ivoire 179.6 37.0 68.1 19.8 50.7 6.5 ­37.1 1.5 3.1 3.9 5.7 20.3 Croatia 155.9 21.1 100.9 7.5 3.2 0.2 1.1 0.1 0.0 0.3 6.1 15.5 Cuba 59.1 12.4 2.1 3.0 2.5 1.8 ­4.9 0.2 24.0 8.4 0.1 9.6 Czech Republic Denmark Dominican Republic 132.3 4.5 107.3 8.7 16.5 3.0 ­37.4 0.0 27.3 2.9 0.2 ­0.7 Ecuador 215.4 42.7 34.9 22.0 4.0 3.0 ­1.3 0.5 71.3 3.5 0.5 34.4 Egypt, Arab Rep. 995.2 462.4 208.1 153.9 77.1 ­27.0 0.1 14.6 11.4 17.7 2.4 74.5 El Salvador 96.6 39.0 25.2 9.2 5.7 26.8 ­96.7 0.4 61.1 4.2 4.2 17.6 Eritrea 81.6 1.6 36.1 3.9 0.9 8.4 5.2 4.4 0.2 1.4 2.6 16.8 Estonia Ethiopia 1,606.8 371.7 364.8 96.5 20.1 36.0 291.5 50.8 27.1 90.5 44.7 213.2 Finland France Gabon 40.3 1.1 6.7 ­1.4 32.2 0.3 0.0 0.0 0.2 1.7 0.0 ­0.2 Gambia, The 42.3 1.7 9.2 0.9 0.7 6.4 5.0 10.1 4.8 1.5 0.8 1.1 Georgia 272.3 86.8 28.1 38.3 4.5 7.0 8.7 7.9 0.0 4.2 10.8 76.1 Germany Ghana 802.3 70.7 93.9 52.7 41.6 46.5 152.3 142.2 5.8 78.6 1.7 116.5 Greece Guatemala 443.2 45.7 30.8 17.3 2.9 17.7 ­27.6 25.2 252.9 11.2 28.7 38.5 Guinea 153.1 24.7 30.9 15.8 55.1 12.0 1.1 0.0 2.3 7.1 0.4 3.8 Guinea-Bissau 88.5 6.3 44.9 0.4 3.4 1.1 0.1 0.0 12.6 1.0 0.0 18.9 Haiti 531.8 202.2 97.5 2.7 48.2 6.8 0.0 0.0 15.4 119.2 2.3 37.4 380 2009 World Development Indicators GLOBAL LINKS Distribution of net aid by Development Assistance Committee members Ten major DAC donors 6.16 $ millions Other Total United European United DAC donors $ millions States Commission Germany France Japan Kingdom Netherlands Spain Canada Sweden $ millions 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Honduras 330.9 71.1 41.3 26.2 1.6 20.8 0.0 0.4 110.8 13.1 19.8 25.9 Hungary India 992.8 84.9 89.6 128.0 ­48.0 99.9 510.5 6.9 12.6 21.4 13.5 73.5 Indonesia 418.0 117.3 55.9 31.4 ­84.0 ­222.5 69.7 42.4 ­16.2 53.4 16.4 354.0 Iran, Islamic Rep. 77.4 2.2 10.0 42.3 18.1 ­12.1 0.5 2.2 6.9 0.1 0.0 7.2 Iraq 9,016.1 3,749.3 24.5 2,095.0 759.2 858.8 60.2 5.9 33.2 43.0 17.4 1,369.6 Ireland Israel Italy Jamaica 20.7 ­5.5 37.7 ­8.1 ­1.2 ­8.0 2.1 ­5.3 0.5 6.8 0.2 1.4 Japan Jordan 344.8 259.5 55.4 27.9 ­3.8 ­28.3 0.5 0.1 10.3 8.8 0.2 14.3 Kazakhstan 190.2 77.7 9.4 49.6 3.5 43.3 0.7 0.4 0.3 0.6 0.7 4.2 Kenya 938.1 325.2 114.0 62.5 47.8 57.1 111.3 11.0 44.9 22.4 45.5 96.3 Korea, Dem. Rep. 88.3 32.5 16.6 3.1 0.3 0.0 1.2 0.7 1.6 2.1 7.8 22.5 Korea, Rep. Kuwait Kyrgyz Republic 138.5 39.8 19.9 25.0 1.0 15.7 13.0 0.4 0.3 0.7 7.4 15.5 Lao PDR 230.7 1.4 8.9 23.8 35.5 81.5 1.7 0.1 0.0 4.7 19.8 53.4 Latvia Lebanon 524.3 127.1 60.5 32.6 102.6 15.8 7.5 0.6 37.3 10.3 9.1 120.8 Lesotho 79.7 19.5 17.4 6.8 ­1.0 4.9 8.1 0.0 1.2 1.4 0.2 21.5 Liberia 265.9 102.7 39.5 10.0 1.1 12.5 10.0 2.9 3.6 2.9 19.8 61.1 Libya 16.3 4.0 1.1 3.9 1.1 0.4 0.3 0.0 0.1 0.0 0.0 5.3 Lithuania Macedonia, FYR 210.0 31.3 76.0 18.4 3.5 20.2 1.9 9.0 1.4 0.2 14.1 34.1 Madagascar 554.9 66.9 168.4 14.0 142.0 111.2 1.7 11.9 5.4 3.2 0.5 29.8 Malawi 475.5 79.0 75.0 24.4 0.9 40.3 133.7 6.8 ­0.3 16.0 20.4 79.3 Malaysia 192.1 2.3 0.4 9.6 ­4.8 223.0 ­20.2 0.0 0.0 1.0 0.4 ­19.6 Mali 736.7 54.0 178.7 40.6 214.0 9.7 0.0 64.9 17.5 55.9 26.5 75.0 Mauritania 235.2 10.2 101.9 12.9 37.9 23.5 0.1 0.1 39.1 1.7 0.8 7.0 Mauritius 77.3 0.3 33.8 ­0.1 39.8 2.8 0.1 0.0 0.0 0.6 0.0 0.2 Mexico 89.8 83.6 10.9 28.2 16.0 ­45.2 2.3 ­0.3 ­17.2 6.7 0.2 4.6 Moldova 159.4 18.9 66.3 9.3 7.3 5.7 6.8 8.0 0.7 0.4 17.1 19.1 Mongolia 142.6 12.7 2.2 30.3 0.7 51.6 1.2 11.1 9.0 2.1 2.2 19.4 Morocco 952.6 5.5 324.7 142.8 218.8 64.7 0.3 ­0.3 84.8 8.9 1.3 101.1 Mozambique 1,313.0 153.4 239.8 61.8 25.7 27.8 115.7 80.7 53.8 57.3 103.6 393.5 Myanmar 149.0 15.4 19.7 5.8 1.7 30.5 18.0 2.4 0.0 0.4 11.4 43.8 Namibia 159.8 58.8 16.3 21.2 2.8 5.7 0.9 1.0 28.5 1.9 4.1 18.7 Nepal 402.0 54.0 24.7 48.9 ­2.4 48.6 88.4 3.3 0.1 14.8 ­17.7 139.3 Netherlands New Zealand Nicaragua 581.8 76.5 87.8 30.8 2.9 30.6 ­6.9 37.0 115.1 22.2 41.9 143.7 Niger 350.2 41.3 117.4 21.4 56.7 28.3 2.4 0.1 8.2 14.5 1.2 58.6 Nigeria 1,558.6 240.6 173.4 25.5 11.8 26.8 286.0 344.0 0.5 20.9 1.1 428.1 Norway Oman 9.6 7.3 0.0 0.4 0.9 0.9 0.2 0.0 0.0 0.0 0.0 0.0 Pakistan 1,044.3 433.6 67.9 62.4 52.4 53.2 197.8 36.2 5.7 44.7 14.2 76.1 Panama ­136.5 7.3 3.0 1.1 0.2 2.0 ­162.3 0.1 10.6 1.2 0.1 0.3 Papua New Guinea 307.9 0.8 20.4 ­1.0 0.1 ­10.6 1.0 0.0 0.1 1.2 0.2 295.7 Paraguay 105.7 24.9 23.0 4.8 3.5 28.9 ­0.2 0.1 13.3 3.3 1.8 2.4 Peru 236.4 94.1 65.2 7.6 6.2 39.8 ­251.0 ­0.1 109.4 20.1 5.8 139.4 Philippines 581.8 84.8 34.4 28.2 ­4.1 222.2 0.6 4.8 29.2 22.5 6.1 153.3 Poland Portugal Puerto Rico 2009 World Development Indicators 381 Distribution of net aid by Development 6.16 Assistance Committee members Ten major DAC donors $ millions Other Total United European United DAC donors $ millions States Commission Germany France Japan Kingdom Netherlands Spain Canada Sweden $ millions 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Romania Russian Federation Rwanda 453.1 90.8 79.2 23.1 5.4 19.5 95.0 27.8 8.4 9.7 21.8 72.4 Saudi Arabia ­144.1 0.2 0.0 1.5 7.0 ­154.0 0.8 0.0 0.0 0.0 0.0 0.4 Senegal 532.2 39.2 81.3 27.1 176.7 32.0 11.7 22.4 41.6 47.9 0.2 52.2 Serbia 747.2 105.1 271.1 78.4 7.3 7.2 15.5 3.1 2.5 8.0 33.5 215.6 Sierra Leone 452.8 20.9 72.0 36.5 41.7 30.1 88.1 47.1 3.2 5.7 1.7 105.6 Singapore Slovak Republic Slovenia Somalia 335.3 58.7 78.6 13.6 6.2 3.9 26.4 12.4 2.3 12.9 25.8 94.7 South Africa 741.8 227.1 144.7 101.5 105.0 4.7 ­20.4 44.9 1.1 14.0 19.4 100.0 Spain Sri Lanka 351.8 33.5 53.9 21.3 6.5 44.2 11.5 14.6 14.7 30.7 23.1 97.8 Sudan 1,920.8 710.5 254.7 36.9 13.8 51.6 206.2 202.5 28.4 70.8 68.1 277.5 Swaziland 35.9 3.5 23.7 ­5.9 0.3 7.3 2.2 0.0 0.0 1.4 0.0 3.4 Sweden Switzerland Syrian Arab Republic 49.3 2.5 40.2 8.0 31.7 ­45.6 0.1 0.1 3.0 0.5 1.1 8.0 Tajikistan 121.9 34.9 16.0 12.6 4.7 9.4 4.5 0.1 3.0 5.8 13.9 17.2 Tanzania 2,017.8 166.9 187.1 65.0 3.0 721.7 231.8 128.2 8.0 56.7 107.8 341.7 Thailand ­366.0 44.5 30.2 ­2.0 5.2 ­477.4 0.2 0.7 0.7 5.2 9.1 17.7 Timor-Leste 265.8 25.1 39.6 6.2 0.5 13.1 4.0 0.0 11.4 3.2 6.4 156.3 Togo 95.8 7.4 31.1 12.1 33.7 0.5 0.3 0.0 1.6 2.3 0.8 6.1 Trinidad and Tobago 15.4 0.3 8.5 0.4 4.4 0.1 0.1 0.0 0.0 1.5 0.0 0.1 Tunisia 310.7 ­10.9 116.8 27.5 127.9 20.6 0.1 ­2.0 21.3 ­0.8 0.9 9.4 Turkey 783.4 ­11.8 545.9 ­55.6 134.2 86.6 1.4 ­0.8 55.9 ­1.7 7.2 22.0 Turkmenistan 3.8 0.1 2.5 0.8 0.4 ­0.5 0.2 0.0 0.0 0.3 0.0 0.0 Uganda 1,119.4 301.6 117.0 47.6 9.0 27.5 167.2 70.4 2.7 20.0 56.6 300.1 Ukraine 333.2 91.1 89.0 69.1 6.5 5.7 7.8 0.0 0.1 16.0 22.1 25.8 United Arab Emirates United Kingdom United States Uruguay 29.1 0.5 9.2 ­2.9 2.9 2.6 0.1 0.0 12.7 2.0 0.3 1.7 Uzbekistan 112.7 19.1 10.4 16.5 2.8 56.3 0.1 0.1 0.0 0.5 0.8 6.2 Venezuela, RB 63.0 10.1 18.5 5.6 6.8 2.4 0.1 0.0 15.9 1.6 0.0 2.0 Vietnam 1,556.1 40.6 67.7 97.6 154.5 640.0 97.2 47.7 31.5 28.9 47.0 303.3 West Bank and Gaza 1,369.8 212.3 533.3 75.2 55.9 48.7 22.7 30.3 72.7 42.3 54.3 222.1 Yemen, Rep. 185.1 19.9 17.7 60.8 5.8 9.8 25.3 31.7 0.2 1.6 0.8 11.4 Zambia 834.2 165.3 121.3 40.7 1.1 94.6 74.2 71.5 0.9 23.8 53.7 187.0 Zimbabwe 423.5 139.1 52.1 19.5 15.5 11.7 94.1 7.1 0.7 13.0 19.7 51.2 World 83,989.0 s 18,901.2 s 11,095.5 s 7,949.8 s 6,258.4 s 5,778.2 s 5,601.5 s 4,643.9 s 3,338.9 s 3,152.2 s 2,932.2 s 14,337.2 s Low income 27,993.9 5,405.8 4,201.8 1,600.9 1,702.6 2,634.3 2,999.3 1,616.8 452.9 1,306.1 831.3 5,242.3 Middle income 33,464.3 8,326.2 5,104.0 4,873.1 3,570.5 1,995.6 552.4 717.5 1,772.4 707.9 697.2 5,147.4 Lower middle income 27,519.4 7,536.3 3,284.8 4,394.4 2,441.9 1,596.7 694.3 530.2 1,500.8 562.6 588.4 4,389.1 Upper middle income 5,182.4 743.4 1,466.8 393.7 1,044.0 398.4 ­152.6 170.2 215.8 112.6 103.4 686.7 Low & middle income 84,053.1 18,893.2 11,072.0 7,947.5 6,242.3 5,930.9 5,597.3 4,643.9 3,318.7 3,138.9 2,932.2 14,336.3 East Asia & Pacific 6,548.1 734.8 450.2 604.4 431.6 1,184.3 365.8 136.1 144.4 179.9 172.9 2,143.8 Europe & Central Asia 4,608.9 900.3 1,450.3 436.1 216.8 362.0 86.8 65.8 121.7 51.1 207.3 710.9 Latin America & Carib. 5,805.6 1,398.1 1,042.7 474.0 355.1 225.2 ­572.6 268.8 1,181.4 450.2 203.1 779.7 Middle East & N. Africa 14,819.7 4,958.1 1,688.3 2,746.1 1,668.1 917.5 118.2 85.0 386.8 136.8 113.2 2,001.5 South Asia 7,029.5 2,237.2 655.9 544.3 30.0 362.7 1,323.5 256.6 89.4 523.7 103.5 902.9 Sub-Saharan Africa 26,801.6 4,557.6 4,409.5 2,059.4 2,869.0 1,696.8 2,415.5 1,663.0 520.9 1,164.9 995.2 4,449.9 High income ­64.1 8.1 23.5 2.3 16.2 ­152.7 4.3 0.0 20.2 13.3 0.0 0.9 Euro area Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. 382 2009 World Development Indicators GLOBAL LINKS Distribution of net aid by Development Assistance Committee members 6.16 About the data Definitions The table shows net bilateral aid to low- and middle- information on such aid expenditures as development- · Net aid refers to net bilateral official development income economies from members of the Develop- oriented research, stipends and tuition costs for aid- assistance that meets the DAC definition of official ment Assistance Committee (DAC) of the Organisa- financed students in donor countries, and payment development assistance and is made to countries tion for Economic Co-operation and Development of experts hired by donor countries. Moreover, a full and territories on the DAC list of aid recipients. See (OECD). The data include aid to some countries and accounting would include donor country contributions About the data for table 6.13 · Other DAC donors territories not shown in the table and aid to unspeci- to multilateral institutions, the flow of resources from are Australia, Austria, Belgium, Denmark, Finland, fied economies recorded only at the regional or global multilateral institutions to recipient countries, and Greece, Ireland, Italy, Luxembourg, New Zealand, level. Aid to countries and territories not shown in the flows from countries that are not members of DAC. Norway, Portugal, and Switzerland. table has been assigned to regional totals based Previous editions of the table included only DAC on the World Bank's regional classification system. member economies. The table also includes net Aid to unspecified economies is included in regional aid from the European Commission--a multilateral totals and, when possible, income group totals. Aid member of DAC. not allocated by country or region--including admin- The expenditures that countries report as official istrative costs, research on development, and aid to development assistance (ODA) have changed. For nongovernmental organizations--is included in the example, some DAC members have reported as world total. Thus regional and income group totals ODA the aid provided to refugees during the first 12 do not sum to the world total. months of their stay within the donor's borders. The table is based on donor country reports of Some of the aid recipients shown in the table are bilateral programs, which may differ from reports by also aid donors. See table 6.15a for a summary of recipient countries. Recipients may lack access to ODA from non-DAC countries. Most donors increased their proportions of untied aid between 2000 and 2007 6.16a Proportion of bilateral official development assistance commitment that was untied, 2007 (percent) 100 Australia DAC donors whose proportion of Japan France Denmark untied aid increased over 2000­07 Germany Spain Belgium Finland Austria New Zealand Netherlands Canada 75 United States Italy Portugal 50 Greece 25 DAC donors whose proportion of untied aid decreased over 2000­07 0 0 25 50 75 100 Data sources Proportion of bilateral official development assistance commitment that was untied, 2000 (percent) Data on financial flows are compiled by DAC and Six Development Assistance Committee (DAC) donor countries provided nearly 100 percent untied aid in 2007: Sweden, the United Kingdom, Norway, Luxembourg, Ireland, and Switzerland (data points at top published in its annual statistical report, Geo- right of figure, left to right). Tying aid may prevent recipients from getting the best value for their money. graphical Distribution of Financial Flows to Aid Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may Recipients, and its annual Development Coopera- also be motivated by a desire to benefit donor country suppliers. On average, 85 percent of bilateral tion Report. Data are available electronically on commitments by DAC donors in 2007 were untied, up from 81 percent in 2000. the OECD-DAC's International Development Sta- Note: Data for Ireland are for 2001 and 2007 and for New Zealand for 2002 and 2007. The United States did not tistics CD-ROM and at www.oecd.org/dac/stats/ report amount of untied aid until 2006. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. idsonline. 2009 World Development Indicators 383 6.17 Movement of people Net migration International Refugees Workers' remittances and migrant stock compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 2000­05 1995 2005 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan 3,313 1,112 35 43 2,679.1 3,057.7 19.6 .. .. .. .. .. Albania ­409 ­110 71 83 5.8 15.3 4.7 0.1 427 1,071 .. 7 Algeria ­50 ­140 299 242 1.5 10.6 192.5 94.1 1,120a 2,120a .. .. Angola 143 175 38 56 246.7 186.2 10.9 12.1 5 .. 210 603 Argentina 50 ­100 1,590 1,500 0.3 1.2 10.3 3.3 56 604 190 472 Armenia ­500 ­100 455 235 201.4 15.4 219.0 4.6 65 846 17 176 Australia 519 593 4,068 4,097 .. 0.1 62.2 22.2 1,651 3,862 700 3,559 Austria 262 180 717 1,234 0.1 0.1 34.4 30.8 1,012 2,945 346 2,985 Azerbaijan ­116 ­100 292 182 200.5 15.9 233.7 2.4 3 1,287 9 435 Bangladesh ­260 ­500 1,006 1,032 57.0 10.2 51.1 27.6 1,202 6,562 1 3 Belarus 0 0 1,269 1,191 0.1 5.0 29.0 0.6 29 354 12 109 Belgium 85 180 909 719 .. 0.1 31.7 17.6 4,937 8,562 3,252 3,192 Benin 105 99 146 175 0.1 0.3 23.8 7.6 100a 224a 26a 67a Bolivia ­100 ­100 70 116 0.2 0.4 0.7 0.6 7 927 9 72 Bosnia and Herzegovina ­1,000 115 73 41 769.8 78.3 40.0 7.4 .. 2,520 .. 65 Botswana 14 20 39 80 .. .. 0.3 2.5 59 141 200 120 Brazil ­184 ­229 730 641 0.1 1.6 2.1 20.8 3,315 4,382 347 896 Bulgaria ­349 ­43 47 104 4.2 3.3 1.3 4.8 42 2,086 34 86 Burkina Faso ­128 100 464 773 0.1 0.6 29.8 0.5 80a 50a 51a 44 a Burundi ­250 192 295 100 350.6 375.7 173.0 24.5 .. 0 5 0 Cambodia 150 10 116 304 61.2 17.7 .. 0.2 12 353 52 157 Cameroon ­5 6 159 137 2.0 11.5 45.8 60.1 11 167 22 103 Canada 643 1,041 5,003 6,106 .. 0.5 152.1 175.7 .. .. .. .. Central African Republic 37 ­45 67 76 0.2 98.1 33.9 7.5 0 .. 27 .. Chad ­10 219 78 437 59.7 55.7 0.1 294.0 1 .. 15 .. Chile 90 30 136 231 14.3 1.0 0.3 1.4 .. 3a 7a 6a China ­1,281 ­1,900 441 596 104.7 149.1 288.3 301.1 1,053a 32,833a 19 4,372 Hong Kong, China 300 300 2,432 2,999 0.2 .. 1.5 0.1 .. 348 .. 380 Colombia ­250 ­120 108 123 1.9 551.7 0.2 0.2 815 4,523 150 95 Congo, Dem. Rep. 1,208 ­237 2,049 539 89.7 370.4 1,433.8 177.4 .. .. .. .. Congo, Rep. 14 ­10 169 288 0.2 19.7 19.4 38.5 4 15 27 102 Costa Rica 62 84 228 441 0.2 0.4 24.2 17.2 123 635 36 271 Côte d'Ivoire 214 ­339 2,314 2,371 0.2 22.2 297.9 24.6 151 179 457 19 Croatia 153 100 721 661 245.6 100.4 198.7 1.6 544 1,394 17 86 Cuba ­98 ­129 90 74 24.9 7.5 1.8 0.6 .. .. .. .. Czech Republic 8 67 454 453 2.0 1.4 2.7 2.0 191 1,332 101 2,625 Denmark 58 46 250 389 .. .. 64.8 26.8 523 989 209 2,958 Dominican Republic ­129 ­148 118 156 .. 0.4 1.0 .. 839 3,414 7 28 Ecuador ­50 ­400 88 114 0.2 1.3 0.2 264.9 386 3,094 4 83 Egypt, Arab Rep. ­600 ­525 172 166 0.9 6.8 5.4 97.6 3,226 7,656 223 180 El Salvador ­90 ­143 26 24 23.5 6.0 0.2 .. 1,064 3,711 1 29 Eritrea ­359 229 12 15 286.7 208.7 1.1 5.0 .. .. .. .. Estonia ­108 1 309 202 0.4 0.3 .. .. 1 426 3 96 Ethiopia 868 ­140 795 555 101.0 59.9 393.5 85.2 27 359 1 15 Finland 43 33 103 156 .. .. 10.2 6.2 74 772 54 391 France 424 722 6,089 6,471 0.1 0.1 155.3 151.8 4,640 13,746 4,935 4,380 Gabon 20 10 164 245 .. 0.1 0.8 8.8 4a 11a 99a 186a Gambia, The 45 31 148 232 0.2 1.3 6.6 14.9 19 47 .. 12 Georgia ­560 ­248 250 191 0.3 11.8 0.1 1.0 284 696 12 28 Germany 2,688 1,000 9,092 10,144 0.4 0.1 1,267.9 578.9 4,523 8,570 11,270 13,860 Ghana 40 12 1,038 1,669 13.6 5.1 83.2 35.0 17 117 5a 6a Greece 470 154 549 974 0.2 0.1 4.4 2.2 3,286 2,484 300 1,460 Guatemala ­360 ­300 45 53 42.9 6.2 1.5 0.4 358 4,254 8 18 Guinea 350 ­425 870 406 0.4 8.3 672.3 25.2 1 151 10 119 Guinea-Bissau 20 1 32 19 0.8 1.0 15.4 7.9 2a 29a 3a 5a Haiti ­133 ­140 22 30 13.9 22.3 .. .. 109 1,222 .. 96 384 2009 World Development Indicators GLOBAL LINKS Net migration International Movement of people Refugees 6.17 Workers' remittances and migrant stock compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 2000­05 1995 2005 1995 2007 1995 2007 1995 2007 1995 2007 Honduras ­120 ­150 31 26 1.2 1.2 0.1 .. 124 2,625 8 2 Hungary 101 65 293 316 2.3 3.4 11.4 8.1 152 413 146 235 India ­960 ­1,350 6,951 5,700 5.0 20.5 227.5 161.5 6,223a 35,262a 419a 1,580a Indonesia ­725 ­1,000 219 160 9.8 20.6 0.1 0.3 651 6,174 .. 1,654 Iran, Islamic Rep. ­1,587 ­1,250 2,478 1,959 112.4 68.4 2,072.0 963.5 1,600a 1,115a .. .. Iraq 170 ­375 134 28 718.7 2,309.2 116.7 42.4 .. 389 .. 781a Ireland ­1 188 264 585 .. .. 0.4 9.3 347 580 173 2,554 Israel 484 115 1,919 2,661 0.9 1.5 .. 1.2 702 1,041 1,408 2,770 Italy 573 1,125 1,483 2,519 0.1 0.1 74.3 38.1 2,364 3,165 1,824 11,287 Jamaica ­100 ­100 20 18 .. 0.8 .. .. 653 2,144 74 454 Japan 248 270 1,261 2,048 .. 0.5 5.4 1.8 1,151 1,577 1,820 4,037 Jordan 509 130 1,618 2,225 0.5 1.8 1,288.9b 2,403.8b 1,441 3,434 107 479 Kazakhstan ­1,509 ­200 3,295 2,502 0.1 5.2 15.6 4.3 116 223 503 4,303 Kenya 222 25 366 345 9.3 7.5 234.7 265.7 298a 1,588a 4 16 Korea, Dem. Rep. 0 0 35 37 .. 0.6 .. .. .. .. .. .. Korea, Rep. ­115 ­80 584 551 .. 1.2 .. 0.1 1,080 1,128 634 4,070 Kuwait ­598 264 996 1,669 0.8 0.7 3.3 38.2 .. .. 1,354 3,824 Kyrgyz Republic ­273 ­75 482 288 17.1 2.3 13.4 0.7 1 715 41 220 Lao PDR ­30 ­115 23 25 58.2 10.0 .. .. 22a 1a 9a 1a Latvia ­134 ­20 713 449 0.2 0.7 .. .. 41 552 1 45 Lebanon 230 0 594 657 13.5 13.1 348.1b 464.3b 1,225 5,769 .. 2,845 Lesotho ­84 ­36 5 6 .. .. .. .. 411 443 75 21 Liberia ­283 ­119 199 50 744.6 91.5 120.1 10.5 .. 65 .. 0 Libya 10 10 506 618 0.6 2.0 4.0 4.1 .. 16a 222a 945a Lithuania ­99 ­30 272 165 0.1 0.5 .. 0.7 1 1,427 1 566 Macedonia, FYR ­27 ­10 114 121 12.9 8.1 9.1 1.2 68a 267a 1a 18a Madagascar ­7 ­5 60 63 0.1 0.3 .. .. 14a 11a 11a 21a Malawi ­920 ­30 325 279 .. 0.1 1.0 2.9 1a 1a 1a 1a Malaysia 287 150 1,135 1,639 0.1 0.6 5.3 32.7 716a 1,803a 1,329 6,385 Mali ­260 ­134 63 46 77.2 4.5 17.9 9.2 112a 212a 42a 57a Mauritania ­15 30 118 66 84.3 33.1 34.4 30.5 5a 2a 14 .. Mauritius ­7 0 12 21 .. 0.1 .. .. 132a 215a 1 12 Mexico ­1,792 ­3,983 467 644 0.4 5.6 38.7 1.6 4,368a 27,144a .. .. Moldova ­121 ­250 473 440 0.5 4.9 .. 0.2 1 1,498 1 87 Mongolia ­59 ­50 7 9 .. 1.1 .. .. .. 194a .. 77a Morocco ­450 ­550 103 132 0.3 4.0 0.1 0.8 1,970 6,730 20 52 Mozambique 650 ­20 246 406 125.6 0.2 0.1 2.8 59 99 21 45 Myanmar ­126 ­99 112 117 152.3 191.3 .. .. 81a 125a .. 32a Namibia 3 ­1 124 143 .. 1.1 1.7 6.5 16 16 11 16 Nepal ­101 ­100 625 819 0.1 3.4 124.8 130.7 57 1,734 9 4 Netherlands 190 110 1,387 1,638 0.1 0.2 80.0 86.6 1,359 2,548 2,802 7,830 New Zealand 94 102 732 642 .. .. 3.8 2.7 1,858 650 584 1,207 Nicaragua ­115 ­210 27 28 23.9 1.9 0.6 0.2 75 740 .. .. Niger ­3 ­28 139 124 10.3 0.8 27.6 0.3 8 78 29a 29a Nigeria ­96 ­170 582 971 1.9 13.9 8.1 8.5 804 a 9,221a 5 103 Norway 42 84 231 344 .. .. 47.6 34.5 239 613 603 3,642 Oman 23 ­150 573 628 .. .. .. .. 39 39 1,537 3,670 Pakistan ­2,611 ­1,239 4,077 3,254 5.3 31.9 1,202.5 2,035.0 1,712 5,998 4a 3a Panama 8 8 73 102 0.2 0.1 0.9 16.9 112 180 20 151 Papua New Guinea 0 0 32 25 2.0 .. 9.6 10.0 16a 13a 16a 135a Paraguay ­30 ­45 183 168 0.1 0.1 0.1 0.1 287 469 .. .. Peru ­441 ­510 51 42 5.9 7.7 0.6 1.0 599 2,131 34 137 Philippines ­900 ­900 214 374 0.5 1.6 0.8 0.1 5,360 16,291 151 35 Poland ­77 ­200 963 703 19.7 2.9 0.6 9.8 724 10,496 262 1,278 Portugal ­7 276 528 764 0.2 0.1 0.3 0.4 3,953 3,945 527 1,311 Puerto Rico ­4 ­10 351 418 .. .. .. .. .. .. .. .. 2009 World Development Indicators 385 6.17 Movement of people Net migration International Refugees Workers' remittances and migrant stock compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 2000­05 1995 2005 1995 2007 1995 2007 1995 2007 1995 2007 Romania ­529 ­270 135 133 17.0 5.3 0.2 1.8 9 8,533 2 351 Russian Federation 2,263 917 11,707 12,080 207.0 92.9 246.7 1.7 2,503 4,100 3,939 17,716 Rwanda ­1,714 43 60 121 1,819.4 81.0 7.8 53.6 21 51 1 68 Saudi Arabia ­500 285 4,611 6,361 0.3 0.8 13.2 240.7 .. .. 16,594 16,068 Senegal ­100 ­100 320 326 17.6 15.9 66.8 20.4 146a 925a 76a 96a Serbia 451 ­339 760c 512c 86.1c 165.6 650.7c 98.0 105a,c 4,910a,c .. .. Sierra Leone ­380 472 55 119 379.5 32.1 4.7 8.8 24 148 .. 136 Singapore 250 200 992 1,843 .. 0.1 0.1 .. .. .. .. .. Slovak Republic 9 3 114 124 0.1 0.3 2.3 0.3 1 1,483 3 73 Slovenia 38 22 200 167 12.9 0.1 22.3 0.3 272 284 31 207 Somalia ­1,193 100 18 282 638.7 457.4 0.6 0.9 .. .. .. .. South Africa 1,125 75 1,098 1,106 0.5 0.5 101.4 36.7 26 834 629 1,186 Spain 292 2,846 1,009 4,790 0.2 2.4 5.9 5.1 3,235 10,687 868 14,728 Sri Lanka ­256 ­442 428 368 107.6 135.0 .. 0.2 809 2,527 16 314 Sudan ­168 ­532 1,111 639 445.3 523.0 674.1 222.7 346 1,769 1 2 Swaziland ­38 ­6 38 45 .. .. 0.7 0.8 83 100 4 8 Sweden 151 152 906 1,117 .. 0.1 199.2 75.1 288 775 336 1,142 Switzerland 200 100 1,471 1,660 0.1 .. 82.9 45.7 1,473 2,035 10,114 16,273 Syrian Arab Republic ­70 200 801 985 8.0 13.7 373.5b 1,955.3b 339a 824a 15a 235a Tajikistan ­313 ­345 305 306 59.0 0.9 0.6 1.1 .. 1,691 .. 184 Tanzania 591 ­345 1,130 792 0.1 1.3 829.7 435.6 1 14 1 46 Thailand 172 231 568 1,050 0.2 2.3 106.6 125.6 1,695 1,635 .. .. Timor-Leste 0 100 6 6 .. 0.3 .. .. .. .. .. .. Togo ­122 ­4 169 183 93.2 22.5 10.9 1.3 15a 229a 5a 35a Trinidad and Tobago ­24 ­20 46 38 .. 0.2 .. .. 32a 92a 14 .. Tunisia ­22 ­29 38 38 0.3 2.5 0.2 0.1 680 1,716 36 15 Turkey 109 ­30 1,210 1,328 44.9 221.9 12.8 7.0 3,327 1,209 .. 106 Turkmenistan 50 ­10 260 224 2.9 0.7 23.3 0.1 4 .. 7 .. Uganda 120 ­5 610 518 24.2 21.3 229.4 229.0 .. 849 .. 364 Ukraine 100 ­173 7,063 6,833 1.7 26.0 5.2 7.3 6 4,503 1 42 United Arab Emirates 340 577 1,716 3,212 .. 0.3 0.4 0.2 .. .. .. .. United Kingdom 167 948 4,198 5,408 0.1 0.2 90.9 299.7 2,469 8,234 2,581 5,048 United States 5,200 6,493 28,522 38,355 0.2 2.2 623.3 281.2 2,179 2,972 22,181 45,643 Uruguay ­20 ­104 93 84 0.3 0.2 0.1 0.1 .. 97 .. 4 Uzbekistan ­340 ­300 1,474 1,268 0.1 5.7 2.6 1.1 .. .. .. .. Venezuela, RB 40 40 1,019 1,010 0.5 5.1 1.6 200.9 2 136 203 598 Vietnam ­256 ­200 27 21 543.5 327.8 34.4 2.4 .. 5,500a .. .. West Bank and Gaza 1 11 1,201 1,680 72.8 341.2 1,201.0b 1,793.9b 626a 598a .. 16a Yemen, Rep. 650 ­100 228 265 0.4 1.6 53.5 117.4 1,080a 1,283a 61a 120a Zambia ­11 ­82 271 275 0.1 0.2 130.0 112.9 .. 59 59 124 Zimbabwe ­192 ­75 638 511 .. 14.4 0.5 4.0 44 .. 7 .. World ..d s ..d s 164,017 s 189,693 s 18,068.7b,e s 15,953.5b,e s 18,068.7b,f s 15,953.5b,f s 101,562 s 371,263 s 98,648 s 248,066 s Low income ­1,910 ­2,858 22,337 20,756 8,561.3 5,688.8 6,402.8 4,232.4 6,207 39,940 922 2,388 Middle income ­10,752 ­15,770 55,625 54,857 3,935.3 5,342.5 8,611.2 9,530.4 51,121 241,233 9,763 51,507 Lower middle income ­10,710 ­11,295 27,196 26,298 3,253.1 4,695.1 7,153.7 8,579.4 32,898 161,264 1,643 12,172 Upper middle income ­42 ­4,475 28,429 28,559 682.2 647.4 1,457.6 951.1 18,223 79,970 8,120 39,335 Low & middle income ­12,662 ­18,629 77,963 75,613 12,496.6 11,031.3 15,014.0 13,762.8 57,328 281,174 10,685 53,895 East Asia & Pacific ­2,828 ­3,847 2,996 4,427 932.7 724.5 447.0 472.4 9,701 65,340 1,618 12,909 Europe & Central Asia ­3,216 ­1,798 31,643 29,529 1,877.0 789.6 1,420.6 166.0 7,855 50,377 4,771 25,908 Latin America & Carib. ­3,847 ­6,811 5,280 5,713 155.8 624.7 94.0 530.6 13,335 63,107 1,114 3,582 Middle East & N. Africa ­1,224 ­2,618 8,207 9,014 948.0 2,775.5 5,683.1 7,943.9 13,319 31,678 702 5,673 South Asia ­976 ­2,484 13,133 11,229 2,958.8 3,369.3 1,625.5 2,355.0 10,005 52,086 475 2,007 Sub-Saharan Africa ­572 ­1,070 16,704 15,701 5,624.3 2,747.7 5,743.8 2,294.9 3,113 18,586 2,005 3,816 High income 12,645 18,522 86,054 114,080 21.3 14.9 3,055.6 2,190.8 44,234 90,089 87,963 194,171 Euro area 5,099 6,887 22,528 30,461 13.7 0.6 1,688.3 934.2 30,072 61,551 26,446 73,962 a. World Bank estimates. b. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East, who are not included in data from the UN Refugee Agency (UNHCR). c. Includes Montenegro. d. World totals computed by the United Nations sum to zero, but because the aggregates refer to World Bank definitions, regional and income group totals do not. e. Includes refugees without specified country of origin. f. Regional and income group totals do not sum to the world total because of rounding. 386 2009 World Development Indicators GLOBAL LINKS Movement of people 6.17 About the data Definitions Movement of people, most often through migration, registrations varies greatly. Many refugees may not · Net migration is the net total of migrants during the is a significant part of global integration. Migrants be aware of the need to register or may choose not period. It is the total number of immigrants less the contribute to the economies of both their host coun- to do so. And administrative records tend to over- total number of emigrants, including both citizens and try and their country of origin. Yet reliable statistics estimate the number of refugees because it is eas- noncitizens. Data are five-year estimates. · Interna- on migration are difficult to collect and are often ier to register than to de-register. The UN Refugee tional migrant stock is the number of people born in incomplete, making international comparisons a Agency (UNHCR) collects and maintains data on a country other than that in which they live. It includes challenge. refugees, except for Palestinian refugees residing refugees. · Refugees are people who are recognized The United Nations Population Division provides in areas under the mandate of the United Nations as refugees under the 1951 Convention Relating to data on net migration and migrant stock. To derive Relief and Works Agency for Palestine Refugees in the Status of Refugees or its 1967 Protocol, the estimates of net migration, the organization takes the Near East (UNRWA). The UNRWA provides ser- 1969 Organization of African Unity Convention Gov- into account the past migration history of a country vices to Palestinian refugees who live in certain erning the Specific Aspects of Refugee Problems in or area, the migration policy of a country, and the areas and who register with the agency. Registra- Africa, people recognized as refugees in accordance influx of refugees in recent periods. The data to cal- tion is voluntary, and estimates by the UNRWA are with the UNHCR statute, people granted refugee-like culate these official estimates come from a variety not an accurate count of the Palestinian refugee humanitarian status, and people provided temporary of sources, including border statistics, administra- population. The table shows estimates of refugees protection. Asylum seekers are people who have tive records, surveys, and censuses. When no offi - collected by the UNHCR, complemented by estimates applied for asylum or refugee status and who have cial estimates can be made because of insufficient of Palestinian refugees under the UNRWA mandate. not yet received a decision or who are registered as data, net migration is derived through the balance Thus, the aggregates differ from those published by asylum seekers. Palestinian refugees are people (and equation, which is the difference between overall the UNHCR. their descendants) whose residence was Palestine population growth and the natural increase during Workers' remittances and compensation of employ- between June 1946 and May 1948 and who lost their the 1990­2000 intercensal period. ees are World Bank staff estimates based on data homes and means of livelihood as a result of the The data used to estimate the international migrant from the International Monetary Fund's (IMF) Balance 1948 Arab-Israeli conflict. · Country of origin refers stock at a particular time are obtained mainly from of Payments Statistics Yearbook. The IMF data are to the nationality or country of citizenship of a claim- population censuses. The estimates are derived supplemented by World Bank staff estimates for ant. · Country of asylum is the country where an from the data on foreign-born population--people missing data for countries where workers' remit- asylum claim was filed. · Workers' remittances and who have residence in one country but were born in tances are important. The data reported here are the compensation of employees received and paid com- another country. When data on the foreign-born popu- sum of three items defined in the fifth edition of the prise current transfers by migrant workers and wages lation are not available, data on foreign population-- IMF's Balance of Payments Manual: workers' remit- and salaries earned by nonresident workers. Remit- that is, people who are citizens of a country other tances, compensation of employees, and migrants' tances are classified as current private transfers from than the country in which they reside--are used as transfers. migrant workers resident in the host country for more estimates. The distinction among these three items is not than a year, irrespective of their immigration status, After the breakup of the Soviet Union in 1991 peo- always consistent in the data reported by countries to recipients in their country of origin. Migrants' trans- ple living in one of the newly independent countries to the IMF. In some cases countries compile data on fers are defined as the net worth of migrants who are who were born in another were classified as interna- the basis of the citizenship of migrant workers rather expected to remain in the host country for more than tional migrants. Estimates of migrant stock in the than their residency status. Some countries also one year that is transferred to another country at the newly independent states from 1990 on are based report remittances entirely as workers' remittances time of migration. Compensation of employees is the on the 1989 census of the Soviet Union. or compensation of employees. Following the fifth income of migrants who have lived in the host country For countries with information on the interna- edition of the Balance of Payments Manual in 1993, for less than a year. tional migrant stock for at least two points in time, migrants' transfers are considered a capital transac- Data sources interpolation or extrapolation was used to estimate tion, but previous editions regarded them as current the international migrant stock on July 1 of the refer- transfers. For these reasons the figures presented in Data on net migration are from the United Nations ence years. For countries with only one observation, the table take all three items into account. Population Division's World Population Prospects: estimates for the reference years were derived using The 2006 Revision. Data on migration stock are rates of change in the migrant stock in the years pre- from the United Nations Population Division's ceding or following the single observation available. Trends in Total Migrant Stock: The 2005 Revision. A model was used to estimate migrants for countries Data on refugees are from the UNHCR's Statisti- that had no data. cal Yearbook 2007, complemented by statistics Registrations, together with other sources-- on Palestinian refugees under the mandate of including estimates and surveys--are the main the UNRWA as published on its website. Data on sources of refugee data. But there are difficulties remittances are World Bank staff estimates based in collecting accurate statistics. Although refugees on IMF balance of payments data. are often registered individually, the accuracy of 2009 World Development Indicators 387 Characteristics of immigrants in 6.18 selected OECD countries Foreign-born population by country of origin 6.18a Country of Country of birth % of Country of Country of birth % of Country of Country of birth % of residence foreign-born residence foreign-born residence foreign-born population population population Australia United Kingdom 26.1 Germany Former USSR 17.5 Norway Sweden 9.6 New Zealand 8.2 Turkey 15.2 Former Yugoslavia 7.5 Italy 5.6 Poland 13.1 Denmark 7.1 Austria Former Yugoslavia 34.5 Greece Albania 33.7 Portugal Angola 28.4 Germany 14.1 Former USSR 18.5 France 14.0 Turkey 12.2 Germany 9.1 Mozambique 12.9 Belgium France 13.9 Hungary Romania 49.5 Slovak Czech Republic 63.2 Italy 12.7 Slovak Republic 13.5 Republic Hungary 15.2 Morocco 11.2 Former Yugoslavia 11.2 Former USSR 8.1 Canada United Kingdom 11.4 Ireland United Kingdom 62.3 Spain Morocco 14.5 China 5.9 United States 4.4 Ecuador 9.9 Italy 5.9 Former USSR 2.9 France 7.8 Czech Slovak Republic 63.8 Italy Switzerland 8.9 Sweden Finland 18.4 Republic Former USSR 11.2 Former Yugoslavia 8.7 Former Yugoslavia 13.1 Poland 5.6 Germany 8.3 Iraq 5.7 Denmark Turkey 9.1 Japana Koreab 40.9 Switzerland Former Yugoslavia 16.1 Former Yugoslavia 8.5 China 19.9 Italy 15.9 Germany 7.8 Brazil 13.8 Germany 12.1 Finland Former USSR 33.6 Netherlands Indonesia 12.5 United Ireland 11.7 Sweden 21.9 Turkey 11.2 Kingdom India 10.1 Former Yugoslavia 3.5 Morocco 9.3 Pakistan 6.7 France Algeria 21.6 New Zealand United Kingdom 33.3 United States Mexico 26.3 Morocco 12.3 Samoa 6.9 Philippines 4.3 Portugal 10.1 Australia 6.7 Puerto Rico 4.1 a. Refers to individuals living in Japan not of Japanese nationality because data based on the country of birth are not available. b. Democratic People's Republic of Korea and Republic of Korea combined. Foreign-born population by gender, educational attainment, occupation, and sector of employment 6.18b Gender Educational attainment Occupationa Sector of employment % of the foreign- born population % of the foreign-born population % of the employed foreign-born % of the employed foreign-born ages 15 and older ages 15 and older population ages 15 and older population ages 15 and older Personal Agriculture Distributive and social Producer Male Female Primary Secondary Tertiary Professionals Technicians Operators and industry services services services Australia 49.4 50.6 41.3 32.8 25.8 31.2 24.2 44.7 26.6 24.6 31.1 17.7 Austria 47.9 52.1 49.4 39.3 11.3 13.3 19.7 67.1 32.9 21.4 31.9 13.8 Belgium 48.1 51.9 53.3 23.8 23.0 31.6 22.0 46.4 28.7 21.2 37.0 13.1 Canada 48.1 51.9 30.1 31.9 38.0 28.8 26.0 45.2 26.9 21.9 32.0 19.3 Czech Republic 45.5 54.5 38.6 48.7 12.8 18.6 21.4 60.0 44.2 20.8 27.4 7.6 Denmark 48.6 51.4 36.9 39.2 23.9 16.9 23.3 59.8 22.9 21.1 38.4 17.6 Finland 49.6 50.4 52.6 28.5 18.9 21.6 20.2 58.1 27.3 19.5 36.3 16.8 France 49.5 50.5 54.8 27.2 18.1 22.1 22.4 55.5 28.3b 30.5b 36.9b 4.3b Germany 50.3 49.7 45.8 39.3 14.9 10.2 20.5 69.3 .. .. .. .. Greece 50.1 49.9 42.7 41.4 15.9 11.2 9.0 79.9 49.7 15.4 28.8 6.1 Hungary 44.1 55.9 41.1 39.1 19.8 31.8 20.1 48.1 33.3 24.5 31.8 10.3 Ireland 49.6 50.4 29.6 29.3 41.1 38.1 19.9 42.0 28.0 17.9 36.0 18.1 Italy 45.6 54.4 54.3 33.5 12.2 17.5 19.9 62.6 45.2 15.5 33.3 6.1 Japan 46.8 53.2 25.9 44.2 30.0 15.6c 8.5c 75.9c .. .. .. .. Netherlands 48.6 51.4 49.2 31.6 19.2 25.3 27.6 47.1 26.2 19.7 35.6 18.5 New Zealand 48.1 51.9 18.7 50.4 31.0 33.4 25.3 41.3 25.2 23.6 34.2 17.0 Norway 48.9 51.1 18.3 51.2 30.5 20.9 26.0 53.1 18.8 19.2 47.8 14.2 Portugal 49.1 50.9 54.7 25.9 19.3 21.3 24.8 53.9 33.1 19.2 36.4 11.3 Slovak Republic 43.7 56.3 29.3 55.0 15.7 23.8 26.1 50.0 37.4 19.6 34.8 8.2 Spain 50.3 49.7 56.3 22.6 21.1 15.5 14.0 70.5 37.6 17.8 35.9 8.6 Sweden 48.6 51.4 29.5 46.2 24.3 19.0 21.2 59.8 25.0 17.5 43.2 14.3 Switzerland 47.8 52.2 41.6 34.7 23.7 23.1 27.2 49.8 33.0 19.6 32.3 15.1 United Kingdom 46.7 53.3 40.6 24.5 34.8 34.2 26.0 39.8 16.7 22.2 40.2 20.9 United States 49.6 50.4 39.2 34.7 26.1 28.9c 11.2c 59.9c 26.9 18.0 43.8 11.2 a. Excludes armed forces (International Standard Classification of Occupations, ISCO-88, group 0). b. Does not refer to International Standard Industrial Classification revision 3 classifications. c. Does not refer to ISCO-88 classifications. 388 2009 World Development Indicators GLOBAL LINKS Characteristics of immigrants in selected OECD countries 6.18 About the data International migration has become an important and Southern African countries also have a signifi - science occupations; architecture and engineering element of global integration over the last 20 years. cant share of migrant workers in their labor force. occupations; life, physical, and social science occu- Many countries have adopted policies that encour- Census and population register data generally pations; community and social services occupations; age the entrance of foreign labor. At the same time are the most relevant sources for small population legal occupations; education, training, and library remittances--transfers of gifts, wages, and salaries groups, but the data are subject to two limitations. occupations; arts, design, entertainment, sports, earned by migrants working abroad--have fueled First, to ensure international comparability, immi- and media occupations; and healthcare practitioner private financing in developing countries. And demo- grants are identified as people whose place of birth and technical occupations; technicians refer to office graphic trends in developing and high-income econo- differs from their current country of residence. Thus, and administrative support occupations; operators mies are likely to influence future migration patterns. nationals born abroad may be included in the immi- refer to healthcare support occupations; protective Despite the importance of international migration, grant population. This could be an issue for countries service occupations; food preparation and servic- the quality and comparability of data remain limited, with large repatriated communities (such as France ing occupations; building and grounds cleaning and in part because of differing national definitions of who and Portugal) or with large expatriate communities maintenance occupations; personal care and service is an immigrant. Many countries define immigrants (such as Germany and the United Kingdom). Sec- occupations; sales and related occupations; farming, as people with foreign nationality, but others focus ond, coverage of undocumented migrants, short-term fishing, and forestry occupations; construction and on birthplace, considering all those born abroad as migrants, and asylum seekers may be incomplete. extraction occupations; installation, maintenance, immigrants. The lack of comparable data hinders Table 6.18a presents the top three countries of and repair occupations; production occupations; and the design and implementation of sound migration birth along with their share of the foreign-born popu- transportation and material moving occupations. policies by both receiving and sending countries. Sys- lation in the reporting country. Because censuses Definitions tematic recording of immigration stock is difficult, of different countries apply different rules when especially for poor countries and countries affected addressing countries that split, recomposed, or are · Foreign-born population is the population ages 15 by civil disorder and natural disasters. newly constituted, the coding from the national data and older who were born in a country other than the The table presents the main characteristics of collection were maintained. For some countries peo- country of residence when the census data were col- immigrants in selected Organisation for Economic ple born in the Democratic People's Republic of Korea lected. · Primary education refers to International Co-operation and Development (OECD) countries, or the Republic of Korea could not be distinguished. Standard Classification of Education 1997 (ISCED) using data from the Database on Immigrants in In many OECD country censuses countries of the levels 0­2. · Secondary education refers to ISCED OECD Countries, the OECD's effort, in cooperation former Union of Soviet Socialist Republics (USSR) are levels 3­4. · Tertiary education refers to ISCED 5­6. with national statistical offices, to compile interna- grouped under the name "former USSR." The same · Professionals refer to ISCO major groups 1 and 2. tionally comparable data on foreign-born populations applies to the countries of the former Yugoslavia. · Technicians refer to ISCO major groups 3 and 4. in OECD countries. The database provides compre- Table 6.18b presents the main characteristics of the · Operators refer to ISCO major groups 5­9. · Agri- hensive information on many demographic and foreign-born population by gender, educational attain- culture and industry refer to International Standard labor market characteristics of OECD immigrants. ment, occupation, and sector of employment. The Industrial Classification revision 3 (ISIC) major groups Its main sources of data are population censuses database tries to harmonize the classification of vari- A­F. · Producer services refer to ISIC major groups (for 22 countries), population registers (for Denmark, ables that are not systematically collected according J and K. · Distributive services refer to ISIC major Finland, Norway, and Sweden), and labor force sur- to international classifications. For example, occupa- groups G and I. · Personal and social services refer veys (Germany and the Netherlands). The database tions based on national classifications were mapped to ISIC major groups H and L­Q. includes information on demographic characteristics to International Standard Classification of Occupations (age and gender), duration of stay, labor market out- (ISCO-88) classifications, generally with help from comes (employment status, occupations, sectors of national statistical offices. Details of the mapping are activity), fields of study, educational attainment, and in OECD's A Profile of Immigrant Populations in the 21st place of birth. The database provides comparable Century: Data from OECD Countries (2008). data on foreign-born population for around 2000; Because the database does not provide mapping time series data are not available. Other national for Japan and the United States, their data were sources that contain time series data use differ- mapped for the table. For Japan professionals refer Data sources ent definitions of immigrant, making comparability to managers and officials and professional and tech- across countries difficult. nical workers; technicians refer to clerical and related Data on characteristics of migrants in OECD coun- Although the database includes data on all OECD workers; operators refer to agricultural, forestry, and tries are from OECD's Database on Immigrants countries except Iceland and the Republic of Korea, fisheries workers; production process workers and in OECD Countries. The methodology of data col- the table presents selected data only for high-income laborers; protective service workers; sales workers; lection and compilation and the summary report OECD countries with populations of more than 1 mil- service workers; and workers in transport and com- can be found in OECD's A Profile of Immigrant lion. More data are available from the original source. munications. For the United States professionals refer Populations in the 21st Century: Data from OECD Though OECD countries are believed to receive the to management occupations; business and financial Countries (2008). largest number of migrant workers, Gulf countries operations occupations; computer and mathematical 2009 World Development Indicators 389 6.19 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Afghanistan .. .. .. .. .. .. .. .. 1 .. .. .. Albania 41a 57a 12 2,979 70 1,055 23.2 47.9 19 940 2.3 21.9 Algeria 520b,c 1,743b,c 1,090 1,499 32d 219d .. .. 186d 377d .. .. Angola 9 195 3 .. 27 236 0.7 0.5 113 473 3.2 1.8 Argentina 2,289 4,562 3,815 4,167 2,550 4,984 10.2 7.5 4,013 5,071 15.4 9.5 Armenia 12 381 .. 329 14 343 4.7 19.3 12 345 1.7 9.6 Australia 3,825e 5,064e 2,519 5,462 11,915 29,065 17.1 15.9 7,260 19,844 9.7 9.9 Austria 17,173f 20,766f 3,713 9,876 14,529 21,292 16.2 9.8 11,686 12,839 12.7 6.4 Azerbaijan .. 1,010 432 1,631 87 317 11.1 1.4 165 381 12.8 4.0 Bangladesh 156 289 830 2,327 25g 76g 0.6g 0.5g 234g 514 3.1g 2.6 Belarus 161 105 626 517 28 479 0.5 1.7 101 724 1.8 2.4 Belgium 5,560 f 7,045f 5,645 8,371 4,548g 12,176 2.4g 3.0 8,115g 19,095 4.5g 4.8 Benin 138 186 .. .. 85g 124 13.8g 12.8 48 75 5.4 5.1 Bolivia 284 556 249 476 92 294 7.5 5.9 72 325 4.6 8.0 Bosnia and Herzegovina 115f 306f .. .. 257 798 22.9 14.2 97 232 2.4 2.2 Botswana 521 1,675 .. .. 176 549 7.3 9.0 153 281 7.5 6.4 Brazil 1,991 5,026 2,600 5,141 1,085 5,284 2.1 2.9 3,982 10,434 6.3 6.6 Bulgaria 3,466 5,151 3,524 4,515 662 3,975 9.8 16.0 312 2,597 4.8 7.8 Burkina Faso 124h 289h .. .. .. 55 .. .. .. 84 .. .. Burundi 34 c 201c 36 .. 2 2 1.9 2.7 25g 106 9.7g 24.4 Cambodia .. 1,873 31 996 71 1,284 7.3 22.8 22 194 1.6 3.1 Cameroon 100h 185h .. .. 75 221 3.7 4.5 140 476 8.7 8.6 Canada 16,932 17,931 18,206 25,163 9,176 17,985 4.2 3.6 12,658 31,365 6.3 6.7 Central African Republic 26e 14e .. 11 4d 4 .. .. 43d 32d .. .. Chad 19h 25h .. .. 43d .. .. .. 38d .. .. .. Chile 1,540 2,507 1,070 3,234 1,186 2,172 6.1 2.8 934 2,140 5.1 4.0 China 20,034 54,720 4,520 40,954 8,730 g 41,126 5.9g 3.1 3,688g 33,264 2.7g 3.2 Hong Kong, China 7,137 17,154 3,023 80,682 9,604g 18,015d 3.5g 4.2d 10,497d,g 15,086d,g 6.5d,g 3.7d,g Colombia 1,433b 1,195b 1,057 1,767 887 2,262 7.2 6.6 1,162 2,093 7.3 5.6 Congo, Dem. Rep. 35 47e 50 .. .. .. .. .. .. .. .. .. Congo, Rep. 37h .. .. .. 15 54g 1.1 0.9g 69 168g 5.1 2.6g Costa Rica 785 1,980 273 577 763 2,224 17.1 17.2 336 731 7.1 5.2 Côte d'Ivoire 188 .. .. .. 103 104g 2.4 1.1g 312 396g 8.2 4.7g Croatia 1,485f 9,307f .. .. 1,349g 9,576 19.3g 38.1 422g 1,025 4.6g 3.5 Cuba 742e 2,119e 72 194 1,100 d 2,415d .. .. .. .. .. .. Czech Republic 3,381f 6,680f .. .. 2,880 g 7,496 10.2g 5.4 1,635g 3,771 5.4g 2.9 Denmark 2,124f 4,716f 5,035 6,142 3,691g 5,587g 5.6g 3.9g 4,288g 7,428g 7.4g 5.6g Dominican Republic 1,776c,e 3,980c,e 168 443 1,571g 4,026g 27.4g 33.6g 267 517 4.4 3.4 Ecuador 440b,i 937b,i 271 801 315 626 6.1 3.9 331 733 5.8 4.7 Egypt, Arab Rep. 2,871 10,610 2,683 4,531 2,954 10,327 22.3 23.3 1,371 2,886 8.0 5.4 El Salvador 235 1,339 348 1,012 152 1,158 7.5 21.0 99 690 2.7 7.0 Eritrea 315b,c 78b,c .. .. 58d 60d 43.1d .. .. .. .. .. Estonia 530 1,900 1,764 .. 452 1,415 17.6 9.1 121 804 4.2 4.5 Ethiopia 103e 303c 120 .. 177 792 23.1 29.8 30 107g 2.1 1.5g Finland 2,644 3,519 5,147 5,749 2,383 3,890 5.0 3.5 2,853 4,632 7.6 4.6 France 60,033 81,940 18,686 22,467 31,295 63,609 8.6 9.2 20,699 44,544 6.2 6.1 Gabon 125e .. 203 .. 94 13 3.2 0.2 182 346 10.6 14.4 Gambia, The 45 143 .. 387 28g 77 15.8g 33.1 16 7g 6.9 2.2g Georgia 85b 1,052b 228 .. 75 441 13.1 13.9 171 277 12.1 4.7 Germany 14,847f 24,421f 55,800 70,400 24,052 46,860 4.0 3.0 66,527 93,515 11.3 7.0 Ghana 286c 497c .. .. 30 990 1.9 16.5 74 816 3.5 8.1 Greece 10,130 17,518 .. .. 4,182 15,687 26.9 23.4 1,495 3,430 6.0 3.4 Guatemala 563b 1,628b 333 1,168 216 1,055g 7.7 12.1g 167 736 4.5 5.1 Guinea 12e 46e .. .. 1 1 0.1 0.1 29 96 2.9 6.3 Guinea-Bissau .. 30e .. .. 3 3 5.3 2.6 6 16g 6.7 17.3g Haiti 145 112 .. .. 90 g 140 g 46.8g 19.2g 35g 332 4.4g 14.3 390 2009 World Development Indicators GLOBAL LINKS International tourists Travel and tourism Inbound tourism expenditure 6.19 Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Honduras 271 831 149 315 85 559 5.2 8.8 99 385 5.3 4.0 Hungary .. 8,638 13,083 18,471 2,938 5,693 14.9 5.1 1,501 3,468 7.5 3.2 India 2,124i 5,082i 3,056 9,780 2,582g 10,729g 6.8g 4.5g 996g 9,296 2.1g 4.0 Indonesia 4,324 5,506 1,782 4,341 5,229g 5,833 9.9g 4.5 2,172g 6,120 4.0 g 5.6 Iran, Islamic Rep. 489 2,735 1,000 .. 205 1,834 1.1 .. 247 6,526 1.6 .. Iraq 61b .. .. .. 18d,g 170 .. 0.6 117d,g 526 .. 2.2 Ireland 4,818 8,332 2,547 6,848 2,698 8,863 5.5 4.3 2,034g 8,811 4.8g 4.9 Israel 2,215i 2,067i 2,259 4,147 3,491 3,712 12.7 5.2 2,626 4,250 7.4 5.8 Italy 31,052 43,654 18,173 27,734 30,426 46,144 10.3 7.5 17,219 32,754 6.9 5.3 Jamaica 1,147c,e 1,701c,e .. .. 1,199 2,137 35.3 43.4 173 340 4.6 4.2 Japan 3,345b,i 8,347b,i 15,298 17,295 4,894 12,422 1.0 1.5 46,966 37,261 11.2 5.1 Jordan 1,075 3,431c 1,128 2,094 973 2,755 28.0 30.2 719 1,024 14.7 6.6 Kazakhstan .. 3,876 523 4,544 155 1,213 2.6 2.3 296 1,355 4.9 3.0 Kenya 896 1,644 .. .. 590 1,507 16.7 22.1 183g 262g 3.1g 2.7g Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 3,753b,c 6,448b,c 3,819 13,325 6,670 8,947 4.5 2.0 6,947 23,359 4.5 5.4 Kuwait 72h 293h 878 2,529 307 512 2.2 0.7 2,514 6,678 19.9 19.8 Kyrgyz Republic 36 1,654 42 454 5g 392 1.1g 19.4 7g 193 1.0 g 6.0 Lao PDR 60 842 .. .. 52 189g 12.8 15.7g 34 .. 4.5 .. Latvia 539 1,653 1,812 3,398 37 880 1.8 7.4 62 1,021 2.8 5.7 Lebanon 450 1,017 .. .. 710 5,573 .. 33.6 .. 3,914 .. 17.9 Lesotho 87 292 .. .. 29 43g 14.6 4.9g 17 24 1.6 1.4 Liberia .. .. .. .. .. 131g .. 24.2g .. 48 .. 2.8 Libya 56 149 484 .. 4 244 0.1 0.6 98 915 1.7 5.8 Lithuania 650 1,486 1,925 3,696 102 1,192 3.2 5.6 107 1,167 2.7 4.4 Macedonia, FYR 147f 230 f .. .. 19g 219 2.7 5.2 27g 147 1.7 2.6 Madagascar 75e 344 e 39 .. 106 506 14.2 21.8 79 94g 8.0 3.9g Malawi 192 714 .. .. 22 48 4.7 .. 53 84 8.0 .. Malaysia 7,469 20,973 20,642 30,761 5,044 16,798 6.1 8.2 2,722 6,245 3.1 3.7 Mali 42e,h 164e,h .. .. 26 175 4.9 9.4 74 196 7.5 9.1 Mauritania .. .. .. .. 11g .. 2.2g .. 30 .. 5.9 .. Mauritius 422 907 107 213 616 1,663 26.2 37.5 184 388 7.5 7.5 Mexico 20,241c 21,424 c 8,450 15,089 6,847 14,072 7.7 4.9 3,587 9,843 4.4 3.2 Moldova 32 13 71 82 71 221 8.0 11.0 73 270 7.3 6.3 Mongolia 108 452 .. .. 33 261 6.5 12.9 22 212 4.2 11.3 Morocco 2,602c 7,408 c 1,317 2,320 1,469 8,307 16.2 30.4 356 1,418 3.2 4.1 Mozambique .. 664 .. .. 49g 182 10.2g 6.3 68 209 6.6 5.7 Myanmar 117 248 .. .. 169 59 12.9 1.2 18 40 0.9 1.4 Namibia 272 929 .. .. 278g 542 16.0 g 15.4 90 g 132g 4.3g 3.7g Nepal 363 527 100 469 232 234 22.5 16.3 167 402 10.3 11.0 Netherlands 6,574f 11,008f 12,313 17,556 10,611 19,981 4.4 3.6 13,151 19,475 6.1 4.0 New Zealand 1,475b 2,434b 920 1,978 2,318g 5,406g 13.0 g 14.8g 1,259g 3,066g 7.3g 8.0 g Nicaragua 281 800 c 255 868 51 255 7.7 9.5 56 195 4.9 4.2 Niger 35 60 10 .. 7g 39 2.2g 6.5 26 42 5.7 3.9 Nigeria 656 1,111 .. .. 47 340 0.4 0.5 939 3,494 7.3 7.6 Norway 2,880a 4,290 590 3,395 2,730 5,021 4.9 2.8 4,481 14,109 9.6 12.1 Oman 279h 1,144h .. .. 193g 902 2.5g 3.5 47g 944 0.9g 4.9 Pakistan 378 840 .. .. 582 900 5.7 4.1 654 2,043 4.6 5.4 Panama 345 1,103 185 314 372 1,796 4.9 12.6 181 457 2.3 3.1 Papua New Guinea 42 104 51 .. 25g 4 0.8g 0.1 58g 56 3.0 g 2.1 Paraguay 438i 416i 427 242 162 121 3.4 1.9 173 184 3.3 2.8 Peru 479 1,812 508 1,857 521 2,222 7.9 7.1 428 1,274 4.5 5.3 Philippines 1,760 c 3,092c 1,615 2,745 1,141 5,518 4.3 9.5 551 2,007 1.7 3.1 Poland 19,215 14,975 36,387 47,561 6,927 11,686 19.4 6.7 5,865 8,341 17.3 4.5 Portugal 9,511i 12,321c .. 20,989 5,646 12,917 17.5 17.2 2,540 4,836 6.4 5.4 Puerto Rico 3,131e 3,687e 1,237 1,441 1,828d 3,414 d .. .. 1,155d 1,743d .. .. 2009 World Development Indicators 391 6.19 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 1995 2007 Romania 5,445b 7,722b 5,737 10,980 689 1,922 7.3 3.8 749 1,719 6.6 2.3 Russian Federation 10,290 b 22,909b 21,329 34,285 4,312g 12,587 4.6g 3.2 11,599g 24,289 14.0 g 8.6 Rwanda .. .. .. .. 4 66 5.4 18.2 13 69 3.5 7.6 Saudi Arabia 3,325 11,531 .. 4,817 .. 6,020d .. 2.5d .. 6,279d .. 5.5d Senegal .. 866 .. .. 168 329 11.2 13.7 154 139 8.5 3.4 Serbia .. 696f .. .. .. 1,011d .. .. .. 1,194 d .. .. Sierra Leone 38e 32e 6 71 57g 22g 44.4g 6.6g 51 17 19.4 3.5 Singapore 6,070 7,957 2,867 6,024 7,611g 8,680 g 4.8g 2.3g 4,663g 11,844g 3.2g 3.6g Slovak Republic 903f 1,685f 218 23,837 630 2,352 5.7 3.6 338 1,825 3.2 2.8 Slovenia 732f 1,751f .. 2,496 1,128 2,400 10.9 7.3 606 1,219 5.6 3.6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 4,488 9,091 2,520 4,433 2,655 9,890 7.7 11.0 2,414 6,103 7.2 6.2 Spain 34,920 58,973 3,648 11,276 27,369 65,136 20.4 16.9 5,826 24,179 4.3 5.0 Sri Lanka 403i 494i 504 862 367 750 7.9 7.9 279 709 4.7 5.6 Sudan 29i 436c 195 .. 8g 262g 1.2g 2.8g 43g 1,477g 3.5g 13.9g Swaziland 300a 870h .. 1,130 54 32 5.3 1.5 45 63 3.5 2.5 Sweden 2,310 f 3,434f 10,127 12,681 4,390 13,706 4.6 5.9 6,816 15,696 8.4 7.8 Switzerland 6,946h 8,448h 11,148 .. 11,354 14,777 9.2 5.5 9,478 12,449 8.7 5.6 Syrian Arab Republic .. 4,566 1,746 4,042 1,258g 2,113 21.9g 16.0 498g 585 9.0 g 4.9 Tajikistan .. .. .. .. .. 16 .. 0.9 .. 7g .. 0.2g Tanzania 285 692 157 .. 502g 1,053 39.7g 26.7 360 g 666 16.8g 10.5 Thailand 6,952c 14,464i 1,820 4,018 9,257 20,623 13.2 11.4 4,791 6,887 5.8 4.2 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 53h 86h .. .. 13g 23 2.8g 2.4 40 42 6.0 2.8 Trinidad and Tobago 260e 449e 261 .. 232 693 8.3 5.6 91 206 4.3 3.7 Tunisia 4,120i 6,758i 1,778 2,302 1,838 3,373 23.0 16.8 294 530 3.3 2.5 Turkey 7,083 22,248 3,981 8,938 4,957g 20,649 13.6g 14.3 911g 3,720 2.3g 2.1 Turkmenistan 218 8 21 38 13 .. 0.7 .. 74 .. 4.1 .. Uganda 160 642 148 272 78g 359 11.7g 16.4 80 g 200 5.4g 4.8 Ukraine 3,716 23,122 6,552 16,875 191g 5,317 1.1g 8.3 210 g 3,743 1.1g 5.2 United Arab Emirates 2,315a,c 7,126a,c .. .. 632d 4,972d .. .. .. 8,827d .. .. United Kingdom 21,719 30,870 41,345 69,450 27,577 47,109 8.6 6.5 30,749 88,478 9.4 10.7 United States 43,490 55,986 51,285 64,052 93,700 144,808 11.8 8.8 60,924 109,578 6.8 4.7 Uruguay 2,022 1,752 562 635 725 927 20.7 13.6 332 349 9.3 5.1 Uzbekistan 92 281 246 893 15d 43d .. .. .. .. .. .. Venezuela, RB 700 771 534 1,410 995 894 4.8 1.3 1,852 2,101 11.0 4.0 Vietnam 1,351b 4,244b .. .. .. 3,200d .. 7.1d .. .. .. .. West Bank and Gaza 220h 264h .. .. 255g 121g .. .. 162g 265g .. .. Yemen, Rep. 61h 379h .. .. 50 g 425g 2.3g 5.5g 76g 247 3.1g 2.6 Zambia 163 897 .. .. 29g 138g 2.4g 2.8g 83 98 6.2 2.2 Zimbabwe 1,416b 2,508b 256 .. 145d 365d .. .. 106d .. .. .. World 535,972 t 911,470 t 579,267 t 1,100,372 t 486,148 t 1,028,350 t 7.6 w 5.9 w 458,208 t 918,692 t 7.4 w 5.5 w Low income 9,007 25,153 .. .. 4,632 15,333 6.2 5.7 6,137 16,856 5.4 5.5 Middle income 156,729 336,249 164,054 341,298 89,012 279,099 8.3 6.3 63,726 186,323 5.7 4.5 Lower middle income 59,900 165,468 36,644 112,566 42,143 139,002 8.7 5.8 20,688 90,554 3.9 3.9 Upper middle income 97,095 171,571 121,149 203,223 46,881 140,091 7.9 6.8 43,323 95,178 7.4 5.1 Low & middle income 168,466 365,821 195,054 415,707 93,858 295,275 8.2 6.2 69,343 201,988 5.7 4.5 East Asia & Pacific 43,654 108,445 36,055 81,138 31,197 96,536 7.8 4.8 14,769 57,505 3.5 3.5 Europe & Central Asia 55,679 122,293 87,492 151,898 20,529 76,692 8.7 7.1 22,793 57,564 9.3 5.1 Latin America & Carib. 38,965 57,814 21,780 40,970 21,591 50,739 7.5 5.6 18,751 39,665 6.5 4.8 Middle East & N. Africa 13,329 39,685 13,236 25,636 9,771 35,786 12.8 24.6 4,422 15,481 5.2 6.4 South Asia 3,819 8,088 5,151 15,408 4,016 13,343 6.8 5.3 2,393 12,922 3.0 5.1 Sub-Saharan Africa 12,871 29,688 .. .. 6,729 22,231 7.6 7.1 6,761 19,233 6.7 6.0 High income 362,515 540,487 336,254 601,882 392,250 733,064 7.5 5.8 388,174 717,338 7.8 5.8 Euro area 202,533 297,435 140,127 225,488 164,023 329,568 7.8 6.5 154,993 276,680 7.8 5.6 Note: Aggregates are based on World Bank country classifications and differ from those of the World Tourism Organization. Regional and income group totals include countries not shown in the table for which data are available. a. Arrivals in hotels only. b. Arrivals of nonresident visitors at national borders. c. Includes nationals residing abroad. d. Country estimates. e. Arrivals by air only. f. Arrivals in all types of accommodation establishments. g. Expenditure of travel related items only; excludes passenger transport items. h. Arrivals in hotels and similar establishments. i. Excludes nationals residing abroad. 392 2009 World Development Indicators GLOBAL LINKS Travel and tourism 6.19 About the data Definitions Tourism is defined as the activities of people trav- the arrivals of international visitors, which include · International inbound tourists (overnight visitors) eling to and staying in places outside their usual tourists, same-day visitors, cruise passengers, and are the number of tourists who travel to a country environment for no more than one year for leisure, crew members. other than that in which they usually reside, and out- business, and other purposes not related to an activ- Sources and collection methods for arrivals differ side their usual environment, for a period not exceed- ity remunerated from within the place visited. The across countries. In some cases data are from bor- ing 12 months and whose main purpose in visiting social and economic phenomenon of tourism has der statistics (police, immigration, and the like) and is other than an activity remunerated from within the grown substantially over the past quarter century. supplemented by border surveys. In other cases data country visited. When data on number of tourists are Statistical information on tourism is based mainly are from tourism accommodation establishments. For not available, the number of visitors, which includes on data on arrivals and overnight stays along with some countries number of arrivals is limited to arriv- tourists, same-day visitors, cruise passengers, and balance of payments information. These data do not als by air and for others to arrivals staying in hotels. crew members, is shown instead. · International out- completely capture the economic phenomenon of Some countries include arrivals of nationals residing bound tourists are the number of departures that tourism or provide the information needed for effec- abroad while others do not. Caution should thus be people make from their country of usual residence tive public policies and efficient business operations. used in comparing arrivals across countries. to any other country for any purpose other than an Data are needed on the scale and significance of The World Tourism Organization is improving its activity remunerated in the country visited. · Inbound tourism. Information on the role of tourism in national coverage of tourism expenditure data, using balance tourism expenditure is expenditures by international economies is particularly defi cient. Although the of payments data from the International Monetary inbound visitors, including payments to national carri- World Tourism Organization reports progress in har- Fund (IMF) supplemented by data from individual ers for international transport. These receipts include monizing definitions and measurement, differences countries. These data, shown in the table, include any other prepayment made for goods or services in national practices still prevent full comparability. travel and passenger transport items as defined in received in the destination country. They may include The data in the table are from the World Tourism the IMF's (1993) Balance of Payments Manual. When receipts from same-day visitors, except when these Organization, a United Nations agency. The data on the IMF does not report data on passenger transport are important enough to justify separate classifica- inbound and outbound tourists refer to the number of items, expenditure data for travel items are shown. tion. For some countries they do not include receipts arrivals and departures, not to the number of people The aggregates are calculated using the World for passenger transport items. Their share in exports traveling. Thus a person who makes several trips to Bank's weighted aggregation methodology (see Sta- is calculated as a ratio to exports of goods and ser- a country during a given period is counted each time tistical methods) and differ from the World Tourism vices (all transactions between residents of a coun- as a new arrival. Unless otherwise indicated in the Organization's aggregates. try and the rest of the world involving a change of footnotes, the data on inbound tourism show the ownership from residents to nonresidents of general arrivals of nonresident tourists (overnight visitors) merchandise, goods sent for processing and repairs, at national borders. When data on international tour- nonmonetary gold, and services). · Outbound tour- ists are unavailable or incomplete, the table shows ism expenditure is expenditures of international out- bound visitors in other countries, including payments to foreign carriers for international transport. These High-income economies remain the main destination for international travelers, expenditures may include those by residents travel- but the share of tourists visiting developing economies is rising 6.19a ing abroad as same-day visitors, except when these 1995 2007 are important enough to justify separate classifica- Total international tourist arrivals: 536 million Total international tourist arrivals: 911 million tion. For some countries they do not include expen- Middle East & South Asia 0.7% Middle East & South Asia 0.9% North Africa 2.5% North Africa 4.4% ditures for passenger transport items. Their share in Sub-Saharan Africa 2.4% Sub-Saharan Africa 3.3% Latin America & imports is calculated as a ratio to imports of goods Caribbean 7.3% Latin America & Caribbean 6.4% and services (all transactions between residents of a Europe & country and the rest of the world involving a change of Central Asia 10.5% ownership from nonresidents to residents of general Europe & merchandise, goods sent for processing and repairs, East Asia & Central Asia Pacific 8.2% High income 13.5% nonmonetary gold, and services). 68.3% High income 59.6% Data sources East Asia & Pacific 12.0% Data on visitors and tourism expenditure are from the World Tourism Organization's Yearbook of Tourism Statistics and Compendium of Tourism Statistics 2009. Data in the table are updated Although nearly 60 percent of international tourists traveled to high-income economies in 2007, the share from electronic files provided by the World Tour- traveling to developing economies has increased since 1995. The share of tourists going to East Asia ism Organization. Data on exports and imports and Pacific and Europe and Central Asia increased the most--about 3 percentage points. are from the IMF's Balance of Payments Statistics Source: World Bank staff calculations based on World Tourism Organization data. Yearbook and data files. 2009 World Development Indicators 393 Text figures, tables, and boxes PRIMARY DATA DOCUMENTATION The World Bank is not a primary data collection agency for most areas other than business and enterprise surveys, living standards surveys, and external debt. As a major user of socioeconomic data, however, the World Bank recognizes the importance of data documentation to inform users of differences in the methods and conventions used by primary data collectors--usually national statistical agencies, central banks, and customs services--and by international organiza- tions, which compile the statistics that appear in the World Development Indica- tors database. These differences may give rise to significant discrepancies over time both within countries and across them. Delays in reporting data and the use of old surveys as the base for current estimates may further compromise the quality of data reported here. The tables in this section provide information on sources, methods, and reporting standards of the principal demographic, economic, and environmental indicators in World Development Indicators. Additional documentation is avail- able from the World Bank's Country Statistical Information Database at www. worldbank.org/data. The demand for good quality statistical data is increasing. Timely and reliable statistics are key to the broad development strategy often referred to as "manag- ing for results." Monitoring and reporting on publicly agreed indicators are central to implementing poverty reduction strategies and lie at the heart of the Millen- nium Development Goals and the new Results Measurement System adopted for the 14th replenishment of the International Development Association. A global action plan to improve national and international statistics was agreed on during the Second Roundtable on Managing for Development Results in February 2004 in Marrakech, Morocco. The plan, now referred to as the Mar- rakech Action Plan for Statistics, or MAPS, has been widely endorsed and forms the overarching framework for statistical capacity building. The third roundtable conference, held in February 2007 in Hanoi, Vietnam, reaffirmed MAPS as the guiding strategy for improving the capacity of the national and international sta- tistical systems. See www.mfdr.org/RT3 for reports from the conference. 2009 World Development Indicators 395 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Afghanistan Afghan afghani 2002/03 VAB Preliminary G C G Albania Albanian lek a 1996 b VAB 2005 BPM5 Actual G C G Algeria Algerian dinar 1980 VAB BPM5 Actual S B American Samoa U.S. dollar Andorra Euro G Angola Angolan kwanza 1997 VAP 1991­96 2005 BPM5 Actual S G Antigua and Barbuda Eastern Caribbean dollar 1990 VAB BPM5 G G Argentina Argentine peso 1993 b VAB 1971­84 2005 BPM5 Actual S C S Armenia Armenian dram a 1996 b VAB 1990­95 2005 BPM5 Actual S C S Aruba Aruban florins 1995 S Australia Australian dollar a 2007 b VAB 2005 BPM5 G C S Austria Euro 2000 b VAB 2005 BPM5 S C S Azerbaijan New Azeri manat a 2003 b VAB 1992­95 2005 BPM5 Actual G C G Bahamas, The Bahamian dollar 1991 b VAB BPM5 G B G Bahrain Dinar 1985 VAP 2005 BPM5 G C G Bangladesh Bangladesh taka 1995/96 b VAB 2005 BPM5 Preliminary G C G Barbados Barbados dollar 1974 VAB BPM5 G C G Belarus Belarusian rubel a 2000 b VAB 1990­95 2005 BPM5 Actual G C S Belgium Euro 2000 b VAB 2005 BPM5 S C S Belize Belize dollar 2000 b VAB BPM5 Actual G G Benin CFA franc 1985 VAP 1992 2005 BPM5 Preliminary S B G Bermuda Bermuda dollar 1996 VAB Bhutan Ngultrum 2000 b VAB 2005 Actual C Bolivia Boliviano 1990 b VAB 1960­85 2005 BPM5 Actual S C G Bosnia and Herzegovina Konvertible mark a 1996 b VAB 2005 BPM5 Actual G C Botswana Botswana pula 1993/94 b VAB 2005 BPM5 Preliminary G B G Brazil Brazilian real 2000 b VAB 2005 BPM5 Actual S C S Brunei Darussalam Brunei dollar 2000 VAP 2005 G G Bulgaria Bulgarian lev a 2002 b VAB 1978­89, 2005 BPM5 Actual G C S 1991­92 Burkina Faso CFA franc 1999 VAB 1992­93 2005 BPM4 Actual G B G Burundi Burundi franc 1980 VAB 2005 BPM5 Actual S C Cambodia Cambodian riel 2000 VAB 2005 BPM5 Actual G C G Cameroon CFA franc 2000 b VAB 2005 BPM5 Actual S B G Canada Canadian dollar 2000 b VAB 2005 BPM5 G C S Cape Verde Escudos 1980 VAP 2005 BPM5 Actual S G Cayman Islands Cayman Islands dollar Central African Republic CFA franc 2000 VAB 2005 BPM4 Preliminary S B G Chad CFA franc 1995 b VAB 2005 BPM5 Actual S C G Channel Islands Jersey pound & 2007 & 2007 b VAB Guernsey pound 2003 Chile Chilean peso 2003 b VAB 2005 BPM5 Actual S C S China Chinese yuan 2000 b VAP 1978­93 2005 BPM5 Preliminary S B G Hong Kong, China Hong Kong dollar 2006 b VAB 2005 BPM5 G C S Colombia Colombian peso 2000 b VAB 1992­94 2005 BPM5 Actual S B S Comoros CFA franc 1990 VAP 2005 Preliminary Congo, Dem. Rep. Congo franc 1987 b VAB 1999­2001 2005 BPM5 Estimate S C G Congo, Rep. CFA franc 1978 VAP 1993 2005 BPM5 Preliminary S C G Costa Rica Costa Rican colon 1991 b VAB BPM5 Actual S C S Côte d'Ivoire CFA franc 1996 VAP 2005 BPM5 Actual S C G Croatia Croatian kuna a 1997 b VAB 2005 BPM5 Actual G C S Cuba Cuban peso 1984 VAP G Cyprus Euro a 2000 VAB 2005 BPM5 G C Czech Republic Czech koruna 2000 1995 b VAB 2005 BPM5 G C S Denmark Danish krone 2000 b VAB 2005 BPM5 G C S Djibouti CFA franc 1990 VAB 2005 Actual 396 2009 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Afghanistan 1979 MICS, 2003 1977 2000 Albania 2001 MICS, 2005 LSMS, 2005 Yes 1998 2005 2007 2000 Algeria 2008 MICS, 2006 IHS, 1995 2001 2006 2000 American Samoa 2000 Yes Andorra 2000 Yes 2006 Angola 1970 MICS, 2001 IHS, 2000 1964­65 1991 2000 Antigua and Barbuda 2001 Yes 2007 1990 Argentina 2001 IHS, 2005 Yes 2002 2002 2006 2000 Armenia 2001 DHS, 2005 IHS, 2003 Yes 2006 2000 Aruba 2000 2007 Australia 2006 ES/BS, 1994 Yes 2001 2005 2006 2000 Austria 2001 IS 2000 Yes 1999­2000 2004 2006 2000 Azerbaijan 1999 DHS, 2006 ES/BS, 2005 Yes 2005 2006 2005 Bahamas, The 2000 1998 2007 Bahrain 2001 Yes 2007 2003 Bangladesh 2001 MICS 2006 IHS, 2005 2005 1998 2006 2000 Barbados 2000 Yes 2007 2000 Belarus 1999 MICS, 2005 ES/BS 2005 Yes 1994 2007 2000 Belgium 2001 IHS, 2000 Yes 1999­2000c 2004 2006 Belize 2000 MICS, 2006 2007 2000 Benin 2002 DHS, 2006 CWIQ, 2003 1992 2005 2001 Bermuda 2000 Yes Bhutan 2005 IHS, 2003 2000 2000 Bolivia 2001 DHS, 2003 IHS, 2005 1984­88 2001 2006 2000 Bosnia and Herzegovina 1991 MICS, 2006 LSMS, 2004 Yes 2007 Botswana 2001 MICS, 2000 ES/BS, 1993/94 1993 2005 2007 2000 Brazil 2000 DHS, 1996 IHS, 2007 1996 2004 2006 2000 Brunei Darussalam 2001 Yes 2006 Bulgaria 2001 ES/BS, 2003 Yes 2005 2006 2000 Burkina Faso 2006 DHS, 2003 CWIQ, 2003 1993 2004 2000 Burundi 1990 MICS, 2000 CWIQ, 2006 2007 2000 Cambodia 2008 DHS, 2005 IHS, 2004 2000 2004 2000 Cameroon 1987 MICS, 2006 PS, 2001 1984 2006 2000 Canada 2006 LFS, 2000 Yes 1996/2001 2002 2006 2000 Cape Verde 2000 ES/BS, 2001 Yes 2004 2007 Cayman Islands 1999 Yes Central African Republic 2003 MICS, 2006 PS, 2003 1985 2005 2000 Chad 1993 DHS, 2004 PS, 2002 1995 2000 Channel Islands 2001 Yes Chile 2002 IHS, 2006 Yes 1997 2005 2006 2000 China 2000 NSS, 2006 IHS, 2003 1997 2005 2006 2000 Hong Kong, China 2006 Yes 2006 Colombia 2005 DHS, 2005 IHS, 2006 2001 2005 2006 2000 Comoros 2003 MICS, 2000 IHS, 2004 2007 Congo, Dem. Rep. 1984 DHS 2007 1­2-3, 2005 1990 1986 2000 Congo, Rep. 1996 DHS, 2005 CWIQ/ PS, 2005 1985­86 1995 2002 Costa Rica 2000 RHS, 1993 LFS, 2005 Yes 1973 2006 2000 Côte d'Ivoire 1998 MICS, 2006 IHS, 2002 2001 2007 Croatia 2001 ES/BS, 2005 Yes 2003 2006 Cuba 2002 MICS, 2006 Yes 2006 2000 Cyprus 2001 Yes 2005 2006 2000 Czech Republic 2001 RHS, 1993 IS 1996/97 Yes 2000 2004 2006 2000 Denmark 2001 ITR 1997 Yes 1999­2000 2004 2006 2000 Djibouti 1983 MICS, 2006 PS, 2002 2000 2009 World Development Indicators 397 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Dominica Eastern Caribbean dollar 1990 b VAB BPM5 Actual G G Dominican Republic Dominican peso 1990 VAP BPM5 Actual G C G Ecuador U.S. dollar 2000 b VAB 2005 BPM5 Actual S B S Egypt, Arab Rep. Egyptian pound 1991/92 VAB 2005 BPM5 Actual S B S El Salvador Salvadoran colon 1990 VAB BPM5 Actual S C S Equatorial Guinea CFA franc 2000 VAB 1965­84 2005 Eritrea Eritrean nakfa 1992 VAB BPM4 Actual G Estonia Estonian kroon 2000 b VAB 1987­95 2005 BPM5 G C S Ethiopia Ethiopian birr 1999/2000 b VAB 2005 BPM5 Actual G C G Faeroe Islands Danish krone VAB BPM5 G Fiji Fijian dollar 1995 VAB 2005 BPM4 Actual G B G Finland Euro 2000 b VAB 2005 BPM5 G C S France Euro a 2000 b VAB 2005 BPM5 S C S French Polynesia CFP franc G Gabon CFA franc 1991 VAP 1993 2005 BPM5 Preliminary S B G Gambia, The Gambian dalasi 1987 VAB 2005 BPM5 Estimate G B G Georgia Georgian lari a 1996 b VAB 1990­95 2005 BPM5 Actual G C G Germany Euro 2000 b VAB 2005 BPM5 S C S Ghana Ghanaian cedi 1975 VAP 1973­87 2005 BPM5 Preliminary G B G Greece Euro a 2000 VAB 2005 BPM5 S C S Greenland Danish krone G Grenada Eastern Caribbean dollar 1990 VAB BPM5 Actual G G Guam U.S. dollar Guatemala Guatemalan quetzal 2001 b VAP BPM5 Actual S B G Guinea Guinean franc 1996 VAB 2005 BPM5 Estimate S B G Guinea-Bissau CFA franc 1986 VAB 2005 BPM5 Preliminary G G Guyana Guyana dollar 1988 VAB BPM5 Actual S Haiti Haitian gourde 1975/76 VAB 1991 BPM5 Preliminary G Honduras Honduran lempira 2000 b VAB 1988­89 BPM5 Actual S B G Hungary Hungarian forint a 2000 b VAB 2005 BPM5 S C S Iceland Iceland kronur 2000 VAB 2005 BPM5 G C S India Indian rupee 1999/ b VAB 2005 BPM5 Actual G C S 2000 Indonesia Indonesian rupiah 2000 VAP 2005 BPM5 Actual S C S Iran, Islamic Rep. Iranian rial 1997/98 VAB 1980­02 2005 BPM5 Actual G C Iraq Iraqi dinar 1997 VAB 1997, 2004 2005 BPM5 S Ireland Euro 2000 b VAB 2005 BPM5 G C S Isle of Man Manx pound 2005 2003 Israel Israeli new shekel 2005 b VAP 2005 BPM5 S C S Italy Euro 2000 b VAB 2005 BPM5 S C S Jamaica Jamaica dollar 1996 VAB BPM5 Actual G C G Japan Japanese yen 2000 VAB 2005 BPM5 G C S Jordan Jordan dinar 1994 VAB 2005 BPM5 Actual G B G Kazakhstan Kazakh tenge a 1995 b VAB 1987­95 2005 BPM5 Actual G C S Kenya Kenya shilling 2001 b VAB 2005 BPM5 Actual G B G Kiribati Australian dollar 1991 VAB G G Korea, Dem. Rep. Democratic Republic BPM5 of Korea won Korea, Rep. Korean won 2000 b VAB 2005 BPM5 S C S Kuwait Kuwaiti dinar 1995 VAP 2005 BPM5 S C G Kyrgyz Republic Kyrgyz som a 1995 b VAB 1990­95 2005 BPM5 Actual G C S Lao PDR Lao kip 1990 VAB 2005 BPM5 Preliminary G Latvia Latvian lat 2000 b VAB 1987­95 2005 BPM5 Actual S C S Lebanon Lebanese pound 2005 VAB 2005 BPM5 Actual G B G Lesotho Lesotho loti 1995 b VAB 2005 BPM5 Actual G C G Liberia Liberian dollar 1992 VAB 2005 BPM5 Estimate G 398 2009 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Dominica 2001 Yes 2006 Dominican Republic 2002 DHS, 2007 IHS, 2005 1971 2001 2000 Ecuador 2001 RHS, 2004 LFS, 2007 1999­2000 2005 2007 2000 Egypt, Arab Rep. 2006 DHS, 2005, SPA 2004 ES/BS, 2005 Yes 1999­2000 2002 2007 2000 El Salvador 2007 RHS, 2002/03 IHS, 2005 Yes 1970­71 2006 2000 Equatorial Guinea 2002 2000 Eritrea 1984 DHS, 2002 2005 2003 2004 Estonia 2000 ES/BS, 2004 Yes 2001 2005 2006 2000 Ethiopia 2007 DHS, 2005 ES/BS, 2004­05 2001­02 2005 2007 2002 Faeroe Islands 2002 2006 Fiji 2007 Yes 2007 2000 Finland 2000 IS, 2000 Yes 1999­2000 2004 2006 2000 France 2006 ES/BS, 1994/95 Yes 1999­2000 2004 2006 2000 French Polynesia 2007 Yes 2007 Gabon 2003 DHS, 2000 CWIQ/ IHS, 2005 1974­75 2006 2000 Gambia, The 2003 MICS, 2005/06 IHS, 2003/04 2001­02 2007 2000 Georgia 2002 MICS, 2005 IHS, 2005 Yes 2004 2005 2007 2005 Germany 2004 IHS, 2000 Yes 1999­2000 2004 2006 2000 Ghana 2000 MICS, 2006 LSMS, 2005 1984 2003 2007 2000 Greece 2001 IHS, 2000 Yes 1999­2000 1998 2006 2000 Greenland 2000 Yes 2006 Grenada 2001 Yes 2006 Guam 2000 Yes Guatemala 2002 RHS, 2002 LSMS, 2006 Yes 2003 2006 2000 Guinea 1996 DHS, 2005 CWIQ/, 2002/03 2000­01 2002 2000 Guinea-Bissau 1991 MICS, 2006 CWIQ, 2002 1988 1995 2000 Guyana 2002 MICS, 2006 IHS, 1998 2006 2000 Haiti 2003 DHS, 2005 IHS, 2001 1971 1997 2000 Honduras 2001 DHS, 2005 IHS, 2006 1993 2006 2000 Hungary 2001 ES/BS, 2004 Yes 2000 2004 2006 2000 Iceland 2000 Yes 2005 2006 2000 India 2001 DHS, 2005/06 IHS, 2004/05 1995­96/ 2004 2007 2000 2000­01 Indonesia 2000 DHS, 2002/03 IHS, 2005 2003 2003 2007 2000 Iran, Islamic Rep. 2006 DHS, 2000 ES/BS, 2005 Yes 2003 2004 2006 2004 Iraq 1997 MICS, 2006 1981 1998 2007 2000 Ireland 2006 IHS, 2000 Yes 2000 2004 2006 2000 Isle of Man 2006 Yes Israel 1995 ES/BS, 2001 Yes 1981 2004 2006 2004 Italy 2001 ES/BS, 2000 Yes 2000 2004 2006 2000 Jamaica 2001 MICS 2005 LSMS, 2004 1996 2007 2000 Japan 2005 Yes 2000 2005 2006 2000 Jordan 2004 DHS, 2007 ES/BS, 2004 1997 2005 2006 2005 Kazakhstan 1999 MICS, 2006 ES/BS, 2003 Yes 2007 2000 Kenya 1999 DHS, 2003, SPA, 2004 IHS, 2005 1977­79 2005 2007 2003 Kiribati 2005 2005 Korea, Dem. Rep. 1993 MICS, 2000 2000 Korea, Rep. 2005 ES/BS, 1998/99 Yes 2000 2005 2006 2000 Kuwait 2005 FHS, 1996 Yes 1970 2007 2002 Kyrgyz Republic 1999 MICS 2005/06 ES/BS, 2004 Yes 2002 2005 2006 2000 Lao PDR 2005 MICS, 2006 ES/BS, 2002 1998­99 1999 1975 2000 Latvia 2000 IHS, 2004 Yes 2001 2005 2006 2000 Lebanon 1970 MICS, 2000 1998­99 1998 2004 2005 Lesotho 2006 DHS, 2004 ES/BS, 2002­03 1999­2000 2004 2000 Liberia 2008 DHS, 2007 1984 2000 2009 World Development Indicators 399 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Libya Libyan dinar 1999 VAB 1986 BPM5 G Liechtenstein Swiss franc VAB S Lithuania Lithuanian litas 2000 b VAB 1990­95 2005 BPM5 G C S Luxembourg Euro 2000 VAB 2005 BPM5 S C S Macao, China Pataca 2002 VAB 2005 BPM5 G C G Macedonia, FYR Macedonian denar 1997 1995 b VAB 2005 BPM5 Actual G G Madagascar Malagasy ariary 1984 VAB 2005 BPM5 Actual S C G Malawi Malawi kwacha 1994 VAB 2005 BPM5 Actual G B G Malaysia Malaysian ringgit 2000 VAP 2005 BPM5 Preliminary G C S Maldives Rufiyaa 1995 VAB 2005 BPM5 Actual G C Mali CFA franc 1987 VAB 2005 BPM4 Actual G B G Malta Euro 1973 VAB 2005 BPM5 G C G Marshall Islands U.S. dollar 1991 VAB Mauritania Mauritanian ouguiya 1998 VAB 2005 BPM4 Actual G G Mauritius Mauritian rupee 1997/98 VAB 2005 BPM5 Actual G C G Mayotte Mexico Mexican new peso 2003 b VAB 2005 BPM5 Actual G C S Micronesia, Fed. Sts. U.S. dollar 1998 VAB Moldova Moldovan leu a 1996 b VAB 1990­95 2005 BPM5 Actual G C S Monaco Euro Mongolia Mongolian tugrik 2005 b VAB 2005 BPM5 Estimate S C G Montenegro Euro 2000 b VAB 2005 Actual Morocco Moroccan dirham 1998 VAB 2005 BPM5 Actual S C S Mozambique Mozambican 2003 VAB 1992­95 2005 BPM5 Preliminary S G metical (New) Myanmar Myanmar kyat 1985/86 VAP BPM5 Estimate G C Namibia Namibia dollar 1995/96 b VAB 2005 BPM5 G B G Nepal Nepalese rupee 2000/01 VAB 2005 BPM5 Actual S C G Netherlands Antilles Netherlands BPM5 S Antilles guilder Netherlands Euro a 2000 b VAB 2005 BPM5 S C S New Caledonia CFP franc S New Zealand New Zealand dollar 2000/01 VAB 2005 BPM5 G C Nicaragua Nicaraguan gold 1994 b VAB 1965­95 BPM5 Actual S B G cordoba Niger CFA franc 1987 VAP 1993 2005 BPM5 Preliminary S G Nigeria Nigerian naira 2002 VAB 1971­98 2005 BPM5 Preliminary G G Northern Mariana Islands U.S. dollar Norway Norwegian krone a 2000 b VAB 2005 BPM5 G C S Oman Rial Omani 1988 VAP 2005 BPM5 G B G Pakistan Pakistan rupee 1999/ b VAB 2005 BPM5 Actual G C G 2000 Palau U.S. dollar 1995 VAB Panama Panamanian balboa 1996 b VAB BPM5 Actual S C G Papua New Guinea Papua New Guinea kina 1998 VAB 1989 BPM5 Actual G B Paraguay Paraguayan guarani 1994 VAP 2005 BPM5 Actual S C G Peru Peruvian new sol 1994 VAB 1985­90 2005 BPM5 Actual S C S Philippines Philippine peso 1985 VAP 2005 BPM5 Actual G B S Poland Polish zloty a 2002 b VAB 2005 BPM5 Actual S C S Portugal Euro 2000 b VAB 2005 BPM5 S C S Puerto Rico U.S. dollar 1954 VAP G Qatar Qatar riyals 2001 VAP 2005 G B G Romania New Romanian leu a 2005 b VAB 1987­89, 2005 BPM5 Actual S C S 1992 Russian Federation Russian ruble 2000 b VAB 1987­95 2005 BPM5 Preliminary G C S Rwanda Rwanda franc 1995 VAP 1994 2005 BPM5 Preliminary G C G 400 2009 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Libya 1995 MICS, 2000 2001 2004 2000 Liechtenstein 2000 Yes Lithuania 2001 ES/BS, 2004 Yes 2003 2005 2006 2000 Luxembourg 2001 Yes 1999­2000c 2003 2007 Macao, China 2006 Yes 2007 Macedonia, FYR 2002 MICS, 2005 ES/BS, 2003 Yes 1994 2001 2007 Madagascar 1993 DHS, 2003/04 PS 2005 2004 2005 2007 2000 Malawi 2008 MICS 2006 LSMS, 2004 1993 2001 2006 2000 Malaysia 2000 ES/BS, 2004/05 Yes 2004 2006 2000 Maldives 2006 MICS, 2001 Yes 2007 Mali 1998 DHS, 2006 IHS, 2006 1984 2007 2000 Malta 2005 Yes 2001 2005 2007 2000 Marshall Islands 1999 Mauritania 2000 DHS, 2000/01 IHS, 2000 1984­85 2006 2000 Mauritius 2000 Yes 2004 2007 2003 Mayotte 2007 Mexico 2005 ENPF, 1995 LFS, 2006 1991 2000 2007 2000 Micronesia, Fed. Sts. 2000 Moldova 2004 DHS, 2005 ES/BS, 2004 Yes 2005 2007 2000 Monaco 2000 Mongolia 2000 MICS, 2005 LSMS, 2005 Yes 2000 2006 2000 Montenegro 2003 MICS, 2005/06 Yes Morocco 2004 DHS, 2003/04 ES/BS, 2007 1996 2005 2006 2000 Mozambique 2007 DHS, 2003 ES/BS, 2002/03 1999­2000 2007 2000 Myanmar 1983 MICS, 2000 2003 1992 2000 Namibia 2001 DHS, 2006/07 ES/BS, 1993/94 1996­97 2006 2000 Nepal 2001 DHS, 2006 LSMS, 2003/04 2002 2002 2007 2000 Netherlands Antilles 2001 Yes 2004 2000 Netherlands 2001 IHS, 1999 Yes 1999­2000c 2004 2006 New Caledonia 2004 Yes 2006 New Zealand 2006 Yes 2002 2004 2006 2000 Nicaragua 2005 DHS, 2001 LSMS, 2005 2001 2007 2000 Niger 2001 DHS/MICS, 2006 1980 2007 2000 Nigeria 2006 MICS, 2007 IHS, 2003 1960 2006 2000 Northern Mariana Islands 2000 Norway 2001 IS, 2000 Yes 1999 2004 2006 2000 Oman 2003 FHS, 1995 1978­79 2005 2007 2003 Pakistan 1998 DHS, 2006/07 LSMS, 2004/05 2000 2007 2000 Palau 2005 Yes Panama 2000 LSMS, 2003 LFS, 2006 2001 2001 2006 2000 Papua New Guinea 2000 DHS, 1996 IHS, 1996 2001 2004 2000 Paraguay 2002 RHS, 2004 IHS, 2007 1991 2002 2006 2000 Peru 2007 DHS, 2004 LSMS, 2006 1994 2005 2006 2000 Philippines 2007 DHS, 2003 ES/BS, 2006 Yes 2002 2003 2006 2000 Poland 2002 ES/BS, 2005 Yes 1996/2002 2004 2007 2000 Portugal 2001 Yes 1999 2004 2006 2000 Puerto Rico 2000 RHS, 1995/96 Yes 1997/2002 Qatar 2004 Yes 2000­01 2004 2006 2005 Romania 2002 RHS, 1999 LFS, 2005 Yes 2002 2005 2007 2000 Russian Federation 2002 RHS, 1996 IHS, 2005 Yes 1994­95 2005 2007 2000 Rwanda 2002 DHS, 2005 IHS, 1999 1984 1999 2005 2000 2009 World Development Indicators 401 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Samoa U.S. dollar 2002 VAB BPM5 Actual G San Marino Euro 1995 2000 b VAB S C G São Tomé and Principe Dobras 2001 VAP 2005 Actual S G Saudi Arabia Saudi Arabian riyal 1999 VAP 2005 BPM4 G G Senegal CFA franc 1999 1987 b VAB 2005 BPM5 Preliminary S B G Serbia Serbian dinar a 2002 b VAB 2005 Actual S C Seychelles Seychelles rupees 1986 VAP BPM5 Actual G C G Sierra Leone Sierra Leonean leone 1990 b VAB 2005 BPM5 Preliminary S B G Singapore Singapore dollar 2000 b VAB 2005 BPM5 G C S Slovak Republic Slovak koruna 2000 1995 b VAB 2005 BPM5 G C S Slovenia Euro a 2000 b VAB 2005 BPM5 S C S Solomon Islands Solomon Islands dollar 1990 VAB BPM5 Actual Somalia Somali shilling 1985 VAB 1977­90 Estimate South Africa South African rand 2000 b VAB 2005 BPM5 Preliminary G C S Spain Euro 2000 b VAB 2005 BPM5 S C S Sri Lanka Sri Lankan rupee 2002 VAP 2005 BPM5 Actual G B G St. Kitts and Nevis Eastern Caribbean dollar 1990 b VAB BPM5 Actual G C G St. Lucia Eastern Caribbean dollar 1990 VAB BPM5 Actual G G St. Vincent & Grenadines Eastern Caribbean dollar 1990 VAB BPM5 Actual G G Sudan Sudanese pound 1981/82d 1996 VAB 2005 BPM5 Actual G B G Suriname Suriname guilder 1990 b VAB BPM5 G G Swaziland Lilangeni 2000 VAB 2005 Actual G C G Sweden Swedish krona a 2000 VAB 2005 BPM5 G C S Switzerland Swiss franc 2000 VAB 2005 BPM5 S C S Syrian Arab Republic Syrian pound 2000 VAB 1970­07 2005 BPM5 S C G Tajikistan Tajik somoni a 2000 b VAB 1990­95 2005 BPM5 Preliminary G C G Tanzania Tanzania shilling 1992 VAB 2005 BPM5 Estimate S G Thailand Thai baht 1988 VAP 2005 BPM5 Preliminary G C S Timor-Leste U.S. dollar 2000 VAP Togo CFA franc 1978 VAP 2005 BPM5 Actual S B G Tonga Pa'anga 2000/01 VAB BPM5 Actual G Trinidad and Tobago Trinidad and 2000 b VAB BPM5 S C G Tobago dollar Tunisia Tunisian dinar 1990 VAP 2005 BPM5 Actual G C S Turkey Turkish lira 1998 VAB 2005 BPM5 Actual S B S Turkmenistan Turkmen manat a 1987 b VAB 1987­95, BPM5 Actual G 1997­07 Uganda Uganda shilling 2001/02 VAB 2005 BPM5 Actual G B G Ukraine Ukrainian hryvnia a 2003 b VAB 1987­95 2005 BPM5 Preliminary G C S United Arab Emirates U.A.E. dirham 1995 VAB BPM4 G C G United Kingdom Pound sterling 2000 b VAB 2005 BPM5 G C S United States U.S. dollar a 2000 VAB 2005 BPM5 G C S Uruguay Uruguayan peso 1983 VAB 2005 BPM5 Actual S C S Uzbekistan Uzbek sum a 1997 b VAB 1990­95 BPM5 Estimate G Vanuatu Vatu 1983 VAP BPM5 Actual G Venezuela, RB Bolivar fuerte 1997 VAB 2005 BPM5 Actual G C G Vietnam Vietnamese dong 1994 b VAP 1991 2005 BPM4 Estimate G C G Virgin Islands (U.S.) U.S. dollar 1982 G West Bank and Gaza Israeli new shekel 1997 VAB B G Yemen, Rep. Yemen rial 1990 VAP 1990­96 2005 BPM5 Actual G B G Zambia Zambian kwacha 1994 VAB 1990­92 2005 BPM5 Preliminary G B G Zimbabwe Zimbabwe dollar 1990 VAB 1991, 1998 2005 BPM5 Actual G C G 402 2009 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Samoa 2006 1999 2005 San Marino 2000 Yes São Tomé and Principe 2001 Yes 2005 Saudi Arabia 2004 Demographic survey, 2007 1999 2005 2006 Senegal 2002 DHS, 2005 PS 2005 1998­1999 2002 2006 2002 Serbia 2002 MICS, 2005/06 Yes 2006 Seychelles 2002 Yes 1998 2006 2003 Sierra Leone 2004 MICS, 2005, DHS 2008 IHS, 2002/03 1984­1985 2007 2000 Singapore 2000 General household, 2005 Yes 2004 2006 Slovak Republic 2001 IS, 1997 Yes 2001 2004 2006 Slovenia 2002 ES/BS, 2004 Yes 2000 2006 Solomon Islands 1999 Somalia 1987 MICS, 2006 1982 2003 South Africa 2001 DHS, 1998 ES/BS, 2000 2000 2005 2007 2000 Spain 2001 IHS, 2000 Yes 1999 2004 2006 2000 Sri Lanka 2001 DHS, 1987 ES/BS, 2002 Yes 2002 2005 2000 St. Kitts and Nevis 2001 Yes 2006 St. Lucia 2001 IHS, 1995 Yes 2007 St. Vincent & Grenadines 2001 Yes Sudan 1993 MICS-PAPFAM 2006 2001 2006 2000 Suriname 2004 MICS, 2000 ES/BS, 1999 Yes 2004 2001 2000 Swaziland 2007 DHS, 2006 ES/BS, 2000/01 2003 2007 2000 Sweden 2005 IS, 2000 Yes 1999­2000 2004 2007 2000 Switzerland 2000 ES/BS, 2000 Yes 2000 2003 2006 2000 Syrian Arab Republic 2004 MICS, 2006 1981 2007 2003 Tajikistan 2000 MICS, 2005 LSMS, 2004 Yes 1994 2000 2000 Tanzania 2002 DHS, 2004, SPA, 2006 ES/BS, 2000/01 2002­2003 2007 2002 Thailand 2000 MICS 2005/06 IHS, 2004 2003 2000 2006 2000 Timor-Leste 2004 DGHS, 2003 LSMS, 2001 Togo 1981 MICS, 2006 CWIQ, 2006 1996 2007 2002 Tonga 2006 Yes 2001 2007 Trinidad and Tobago 2000 MICS, 2006 IHS, 1992 Yes 2004 2005 2006 2000 Tunisia 2004 MICS, 2006 IHS, 2000 2004 2006 2000 Turkey 2000 DHS, 2003 LFS, 2005 2001 2001 2006 2003 Turkmenistan 1995 DHS,2006 LSMS, 1998 Yes 2000 2000 Uganda 2002 DHS, 2006 PS, 2005 1991 2000 2006 Ukraine 2001 DHS, 2007 ES/BS, 2005 Yes 2004 2007 2000 United Arab Emirates 2005 1998 2006 2005 United Kingdom 2001 IS, 1999 Yes 1999­2000c 2004 2006 2000 United States 2000 CPS(monthly) LFS 2000 Yes 1997/2002 2004 2007 2000 Uruguay 2004 IHS, 2006 Yes 2000 2004 2006 2000 Uzbekistan 1989 MICS, 2006 ES/BS, 2003 Yes 2000 Vanuatu 1999 2006 Venezuela, RB 2001 MICS, 2000 IHS, 2006 Yes 1997 2006 Vietnam 1999 MICS, 2006 IHS, 2006 2001 2000 2006 2000 Virgin Islands (U.S.) 2000 Yes West Bank and Gaza 2007 PAPFAM, 2006 1971 Yemen, Rep. 2004 MICS, 2006 ES/BS, 2005 2002 2004 2006 2000 Zambia 2000 DHS, 2001/02, SPA, 2005 IHS, 2004 1990 2006 2000 Zimbabwe 2002 DHS, 2005/06 1960 1996 2006 2002 Note: For explanation of the abbreviations used in the table see notes following the table. a. Original chained constant price data are rescaled. b. Country uses the 1993 System of National Accounts methodology. c. Conducted annually. d. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year. 2009 World Development Indicators 403 Primary data documentation notes · Base year is the base or pricing period used for system (G) or special trade system (S). Under the gen- Faeroe Islands, Greenland, Iceland, San Marino, and constant price calculations in the country's national eral trade system goods entering directly for domestic Sweden. These countries produce similar census accounts. Price indices derived from national accounts consumption and goods entered into customs storage tables every 5 or 10 years instead of conducting tra- aggregates, such as the implicit deflator for gross are recorded as imports at arrival. Under the special ditional censuses. A rare case, France has been con- domestic product (GDP), express the price level rela- trade system goods are recorded as imports when ducting a rolling census every year since 2004; the tive to base year prices. · Reference year is the year declared for domestic consumption whether at time 1999 general population census was the last to cover in which the local currency, constant price series of a of entry or on withdrawal from customs storage. the entire population simultaneously (www.insee.fr/ country is valued. The reference year is usually the Exports under the general system comprise outward- en/recensement/page_accueil_rp.htm). · Latest same as the base year used to report the constant moving goods: (a) national goods wholly or partly pro- demographic, education, or health household survey price series. However, when the constant price data duced in the country; (b) foreign goods, neither trans- indicates the household surveys used to compile the are chain linked, the base year is changed annually, formed nor declared for domestic consumption in the demographic, education, and health data in section so the data are rescaled to a specific reference year country, that move outward from customs storage; 2. CPS is Current Population Survey, DGHS is Demo- to provide a consistent time series. When the country and (c) nationalized goods that have been declared for graphic and General Health Survey, DHS is Demo- has not rescaled following a change in base year, domestic consumption and move outward without graphic and Health Survey, ENPF is National Family World Bank staff rescale the data to maintain a longer being transformed. Under the special system of trade, Planning Survey (Encuesta Nacional de Planificacion historical series. To allow for cross-country compari- exports are categories a and c. In some compilations Familiar), FHS is Family Health Survey, LSMS is Living son and data aggregation, constant price data categories b and c are classified as re-exports. Direct Standards Measurement Survey, MICS is Multiple reported in World Development Indicators are rescaled transit trade--goods entering or leaving for transport Indicator Cluster Survey, NSS is National Sample Sur- to a common reference year (2000) and currency (U.S. only--is excluded from both import and export statis- vey on Population Change, PAPFAM is Pan Arab Project dollars). · System of National Accounts identifies tics. See About the data for tables 4.4, 4.5, and 6.2 for Family Health, RHS is Reproductive Health Survey, countries that use the 1993 System of National for further discussion. · Government finance account- and SPA is Service Provision Assessments. Detailed Accounts (1993 SNA), the terminology applied in ing concept is the accounting basis for reporting cen- information for DHS and SPA are available at www. World Development Indicators since 2001, to compile tral government financial data. For most countries measuredhs.com/aboutsurveys; for MICS at www. national accounts. Although more countries are adopt- government finance data have been consolidated (C) childinfo.org; and for RHS at www.cdc.gov/ ing the 1993 SNA, many still follow the 1968 SNA, into one set of accounts capturing all central govern- reproductivehealth/surveys. · Source of most recent and some low-income countries use concepts from ment fiscal activities. Budgetary central government income and expenditure data shows household sur- the 1953 SNA. · SNA price valuation shows whether accounts (B) exclude some central government units. veys that collect income and expenditure data. Names value added in the national accounts is reported at See About the data for tables 4.10, 4.11, and 4.12 for and detailed information on household surveys can basic prices (VAB) or producer prices (VAP). Producer further details. · IMF data dissemination standard be found on the website of the International House- prices include taxes paid by producers and thus tend shows the countries that subscribe to the IMF's Spe- hold Survey Network (www.surveynetwork.org). Core to overstate the actual value added in production. cial Data Dissemination Standard (SDDS) or General Welfare Indicator Questionnaire Surveys (CWIQ), However, VAB can be higher than VAP in countries with Data Dissemination System (GDDS). S refers to coun- developed by the World Bank, measure changes in key high agricultural subsidies. See About the data for tries that subscribe to the SDDS and have posted data social indicators for different population groups-- tables 4.1 and 4.2 for further discussion of national on the Dissemination Standards Bulletin Board at specifically indicators of access, utilization, and sat- accounts valuation. · Alternative conversion factor http://dsbb.imf.org. G refers to countries that sub- isfaction with core social and economic services. identifies the countries and years for which a World scribe to the GDDS. The SDDS was established for Expenditure survey/budget surveys (ES/BS) collect Bank­estimated conversion factor has been used in member countries that have or might seek access to detailed information on household consumption as place of the official exchange rate (line rf in the Inter- international capital markets to guide them in provid- well as on general demographic, social, and economic national Monetary Fund's [IMF] International Financial ing their economic and financial data to the public. characteristics. Integrated household surveys (IHS) Statistics). See Statistical methods for further discus- The GDDS helps countries disseminate comprehen- collect detailed information on a wide variety of topics, sion of alternative conversion factors. · Purchasing sive, timely, accessible, and reliable economic, finan- including health, education, economic activities, hous- power parity (PPP) survey year is the latest available cial, and sociodemographic statistics. IMF member ing, and utilities. Income surveys (IS) collect informa- survey year for the International Comparison Pro- countries elect to participate in either the SDDS or tion on the income and wealth of households as well gram's estimates of PPPs. See About the data for the GDDS. Both standards enhance the availability of as various social and economic characteristics. Labor table 1.1 for a more detailed description of PPPs. timely and comprehensive data and therefore contrib- force surveys (LFS) collect information on employ- · Balance of Payments Manual in use refers to the ute to the pursuit of sound macroeconomic policies. ment, unemployment, hours of work, income, and classification system used to compile and report data The SDDS is also expected to improve the functioning wages. Living Standards Measurement Studies on balance of payments items in table 4.15. BPM4 of financial markets. · Latest population census (LSMS), developed by the World Bank, provide a com- refers to the 4th edition of the IMF's Balance of Pay- shows the most recent year in which a census was prehensive picture of household welfare and the fac- ments Manual (1977), and BPM5 to the 5th edition conducted and in which at least preliminary results tors that affect it; they typically incorporate data col- (1993). · External debt shows debt reporting status have been released. The preliminary results from the lection at the individual, household, and community for 2007 data. Actual indicates that data are as very recent censuses could be reflected in timely revi- levels. Priority surveys (PS) are a light monitoring sur- reported, preliminary that data are preliminary and sions if basic data are available, such as population vey, designed by the World Bank, for collecting data include an element of staff estimation, and estimate by age and sex, as well as the detailed definition of from a large number of households cost-effectively that data are World Bank staff estimates. · System counting, coverage and completeness. The census and quickly. Income tax registers (ITR) provide infor- of trade refers to the United Nations general trade includes registration-based censuses for Andorra, mation on a population's income and allowance, such 404 2009 World Development Indicators Primary data documentation notes as gross income, taxable income, and taxes by socio- Economies with exceptional reporting periods of Geography and Statistics revised its national economic group. 1-2-3 surveys (1-2-3) are imple- Reporting period accounts data. Among the changes are new sources Fiscal for national mented in three phases and collect sociodemographic Economy year end accounts data and a change in base year to 2000. · Burkina Faso. and employment data, data on the informal sector, Afghanistan Mar. 20 FY National accounts value added and expenditure and information on living conditions and household Australia Jun. 30 FY data have been revised for 1985­2006 according to consumption. · Vital registration complete identifies Bangladesh Jun. 30 FY recently released data from the Ministry of Economy countries judged to have at least 90 percent complete Botswana Jun. 30 FY and Finance. Constant price series have been linked registries of vital (birth and death) statistics by the Canada Mar. 31 CY back since 1984. Valuation is value added at basic United Nations Statistics Division and reported in Egypt, Arab Rep. Jun. 30 FY prices, and the new base year is 1999. · Chile. Data Population and Vital Statistics Reports. Countries with Ethiopia Jul. 7 FY from 2003 onward reflect the Central Bank's new complete vital statistics registries may have more Gambia, The Jun. 30 CY series using 2003 as the base year. · China. The accurate and more timely demographic indicators Haiti Sep. 30 FY base year for constant price data changed from 1990 than other countries. · Latest agricultural census India Mar. 31 FY to 2000. · Côte d'Ivoire. Data for 1999­2006 were shows the most recent year in which an agricultural Indonesia Mar. 31 CY revised using data from the IMF, national authorities, census was conducted and reported to the Food and Iran, Islamic Rep. Mar. 20 FY and World Bank staff estimates. · Egypt. Constant Agriculture Organization of the United Nations. · Lat- Japan Mar. 31 CY price data are updated from official published national est industrial data show the most recent year for Kenya Jun. 30 CY accounts. Constant price import and export data which manufacturing value added data at the three- Kuwait Jun. 30 CY have been revised based on data from the Central digit level of the International Standard Industrial Clas- Lesotho Mar. 31 CY Bank website (www.cbe.org.eg), which lists the con- Malawi Mar. 31 CY sification (ISIC, revision 2 or 3) are available in the stant price expenditure components of GDP. · Fiji. Mauritius Jun. 30 FY United Nations Industrial Development Organization Data revisions reflect changes in sources. Data for Myanmar Mar. 31 FY database. · Latest trade data show the most recent 1996­2005 were revised using data from the Asian Namibia Mar. 31 CY year for which structure of merchandise trade data Development Bank's Key Indicators 2007. · India. In Nepal Jul. 14 FY from the United Nations Statistics Division's Commod- May 2007 the Central Statistical Organization pub- New Zealand Mar. 31 FY ity Trade (Comtrade) database are available. · Latest lished revised national accounts data for 1951­99 Pakistan Jun. 30 FY water withdrawal data show the most recent year for consistent with the new series of national accounts Puerto Rico Jun. 30 FY which data on freshwater withdrawals have been com- statistics released on January 31, 2006. · Jordan. Sierra Leone Jun. 30 CY piled from a variety of sources. See About the data for Data have been revised by the Central Bank and the Singapore Mar. 31 CY table 3.5 for more information. Department of Statistics. · Lebanon. Data have been South Africa Mar. 31 CY Swaziland Mar. 31 CY revised by the Central Bank. · Malawi. The National Exceptional reporting periods Sweden Jun. 30 CY Statistical Offi ce, with assistance from Norway, In most economies the fiscal year is concurrent with Thailand Sep. 30 CY revised its national accounts data. The initial out- the calendar year. Exceptions are shown in the table Uganda Jun. 30 FY come is that GDP will increase by approximately 37 at right. The ending date reported here is for the fis- United States Sep. 30 CY percent. · Morocco. The government revised national cal year of the central government. Fiscal years for Zimbabwe Jun. 30 CY accounts data from 1998 onward. National accounts other levels of government and reporting years for value added data switched from producer prices to statistical surveys may differ. And some countries Revisions to national accounts data basic prices. The new base year is 1998. · São Tomé that follow a fiscal year report their national accounts National accounts data are revised by national sta- and Principe. Data have been revised by the National data on a calendar year basis as shown in the report- tistical offices when methodologies change or data Statistics Institute. Revised GDP estimates are much ing period column. sources improve. National accounts data in World higher (47.5 percent for the new base year 2001) than The reporting period for national accounts data Development Indicators are also revised when data those of the previous series and reflect improvements is designated as either calendar year basis (CY) or sources change. The following notes, while not com- in coverage. · Senegal. National accounts data have fiscal year basis (FY). Most economies report their prehensive, provide information on revisions from been revised to conform to 1993 SNA methodology, national accounts and balance of payments data previous data. and the base year has changed to 1999. Value added using calendar years, but some use fiscal years. In · Bhutan. Data revisions refl ect changes in data are now in basic prices. Agricultural sector data World Development Indicators fiscal year data are sources. Current and constant price value added data are entered in the year of production (N) in the 1999 assigned to the calendar year that contains the larger from 1980 to 2006 are from the government of Bhu- base year of the SNA as opposed to the year follow- share of the fiscal year. If a country's fiscal year ends tan. Current price expenditure data for 1989­2005 ing the year of production (N+1) in base year 1987. before June 30, data are shown in the first year of and constant price expenditure data for 2000­05 · Sudan. Expenditure items in both current and con- the fiscal period; if the fiscal year ends on or after are from the Asian Development Bank's Key Indica- stant prices for 1988­95 were revised using recent June 30, data are shown in the second year of the tors 2007. · Botswana. Large changes in constant United Nations Statistics Division and IMF World period. Balance of payments data are reported in price consumption indicators from 1998­2006 are Economic Outlook estimates. · Tanzania. National World Development Indicators by calendar year and due to statistical discrepancy. The Central Statisti- accounts expenditure data in current and constant so are not comparable to the national accounts data cal Office published large-scale revisions of constant prices have been revised from 1995 onward. Data of the countries that report their national accounts price discrepancies in GDP for 1996/97­2004/05 are from IMF and World Bank staff estimates and on a fiscal year basis. in April 2006 and May 2007. · Brazil. The Institute Tanzanian authorities. 2009 World Development Indicators 405 STATISTICAL METHODS This section describes some of the statistical procedures used in preparing the World indicator as a weight) and denoted by a u when calculated as unweighted Development Indicators. It covers the methods employed for calculating regional and averages. The aggregate ratios are based on available data, including data income group aggregates and for calculating growth rates, and it describes the World for economies not shown in the main tables. Missing values are assumed Bank Atlas method for deriving the conversion factor used to estimate gross national to have the same average value as the available data. No aggregate is cal- income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures and culated if missing data account for more than a third of the value of weights calculations are described in the About the data sections following each table. in the benchmark year. In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing values for missing data according Aggregation rules to the above rules for computing totals. Aggregates based on the World Bank's regional and income classifications of econo- · Aggregate growth rates are denoted by a w when calculated as a weighted mies appear at the end of most tables. The countries included in these classifica- average of growth rates. In a few cases growth rates may be computed from tions are shown on the flaps on the front and back covers of the book. Most tables time series of group totals. Growth rates are not calculated if more than half also include the aggregate euro area. This aggregate includes the member states of the observations in a period are missing. For further discussion of methods the Economic and Monetary Union (EMU) of the European Union that have adopted of computing growth rates see below. the euro as their currency: Austria, Belgium, Cyprus, Finland, France, Germany, · Aggregates denoted by an m are medians of the values shown in the table. Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic, No value is shown if more than half the observations for countries with a Slovenia, and Spain. Other classifications, such as the European Union and regional population of more than 1 million are missing. trade blocs, are documented in About the data for the tables in which they appear. Exceptions to the rules occur throughout the book. Depending on the judgment Because of missing data, aggregates for groups of economies should be of World Bank analysts, the aggregates may be based on as little as 50 percent of treated as approximations of unknown totals or average values. Regional and the available data. In other cases, where missing or excluded values are judged to be income group aggregates are based on the largest available set of data, including small or irrelevant, aggregates are based only on the data shown in the tables. values for the 153 economies shown in the main tables, other economies shown in table 1.6, and Taiwan, China. The aggregation rules are intended to yield esti- Growth rates mates for a consistent set of economies from one period to the next and for all Growth rates are calculated as annual averages and represented as percentages. indicators. Small differences between sums of subgroup aggregates and overall Except where noted, growth rates of values are computed from constant price totals and averages may occur because of the approximations used. In addition, series. Three principal methods are used to calculate growth rates: least squares, compilation errors and data reporting practices may cause discrepancies in theo- exponential endpoint, and geometric endpoint. Rates of change from one period retically identical aggregates such as world exports and world imports. to the next are calculated as proportional changes from the earlier period. Five methods of aggregation are used in World Development Indicators: · For group and world totals denoted in the tables by a t, missing data are Least-squares growth rate. Least-squares growth rates are used wherever imputed based on the relationship of the sum of available data to the total there is a sufficiently long time series to permit a reliable calculation. No growth in the year of the previous estimate. The imputation process works forward rate is calculated if more than half the observations in a period are missing. and backward from 2000. Missing values in 2000 are imputed using one of The least-squares growth rate, r, is estimated by fitting a linear regression trend several proxy variables for which complete data are available in that year. The line to the logarithmic annual values of the variable in the relevant period. The imputed value is calculated so that it (or its proxy) bears the same relation- regression equation takes the form ship to the total of available data. Imputed values are usually not calculated if missing data account for more than a third of the total in the benchmark ln Xt = a + bt year. The variables used as proxies are GNI in U.S. dollars, total population, exports and imports of goods and services in U.S. dollars, and value added which is equivalent to the logarithmic transformation of the compound growth in agriculture, industry, manufacturing, and services in U.S. dollars. equation, · Aggregates marked by an s are sums of available data. Missing values are Xt = Xo (1 + r ) t. not imputed. Sums are not computed if more than a third of the observations in the series or a proxy for the series are missing in a given year. In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are · Aggregates of ratios are denoted by a w when calculated as weighted averages parameters to be estimated. If b* is the least-squares estimate of b, then the of the ratios (using the value of the denominator or, in some cases, another average annual growth rate, r, is obtained as [exp(b*) ­ 1] and is multiplied by 100 406 2009 World Development Indicators for expression as a percentage. The calculated growth rate is an average rate that The inflation rate for Japan, the United Kingdom, the United States, and the is representative of the available observations over the entire period. It does not euro area, representing international inflation, is measured by the change in the necessarily match the actual growth rate between any two periods. "SDR deflator". (Special drawing rights, or SDRs, are the International Monetary Fund's unit of account.) The SDR deflator is calculated as a weighted average of Exponential growth rate. The growth rate between two points in time for cer- these countries' GDP deflators in SDR terms, the weights being the amount of tain demographic indicators, notably labor force and population, is calculated each country's currency in one SDR unit. Weights vary over time because both from the equation the composition of the SDR and the relative exchange rates for each currency change. The SDR deflator is calculated in SDR terms first and then converted r = ln(pn/p 0)/n to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conver- sion factor is then applied to a country's GNI. The resulting GNI in U.S. dollars is where pn and p 0 are the last and first observations in the period, n is the number divided by the midyear population to derive GNI per capita. of years in the period, and ln is the natural logarithm operator. This growth rate is When official exchange rates are deemed to be unreliable or unrepresenta- based on a model of continuous, exponential growth between two points in time. tive of the effective exchange rate during a period, an alternative estimate of the It does not take into account the intermediate values of the series. Nor does it exchange rate is used in the Atlas formula (see below). correspond to the annual rate of change measured at a one-year interval, which The following formulas describe the calculation of the Atlas conversion fac- is given by (pn ­ pn­1)/pn­1. tor for year t: Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at intervals, in which case the compound growth model is appropriate. The average and the calculation of GNI per capita in U.S. dollars for year t: growth rate over n periods is calculated as Yt$ = (Yt/Nt)/et* r = exp[ln(pn/p 0)/n] ­ 1. where et* is the Atlas conversion factor (national currency to the U.S. dollar) for Like the exponential growth rate, it does not take into account intermediate year t, et is the average annual exchange rate (national currency to the U.S. dollar) values of the series. for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. dollar terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current World Bank Atlas method GNI (local currency) for year t, and Nt is the midyear population for year t. In calculating GNI and GNI per capita in U.S. dollars for certain operational purposes, the World Bank uses the Atlas conversion factor. The purpose of the Alternative conversion factors Atlas conversion factor is to reduce the impact of exchange rate fluctuations in The World Bank systematically assesses the appropriateness of official exchange the cross-country comparison of national incomes. rates as conversion factors. An alternative conversion factor is used when the The Atlas conversion factor for any year is the average of a country's exchange official exchange rate is judged to diverge by an exceptionally large margin from rate (or alternative conversion factor) for that year and its exchange rates for the rate effectively applied to domestic transactions of foreign currencies and the two preceding years, adjusted for the difference between the rate of infla- traded products. This applies to only a small number of countries, as shown tion in the country and that in Japan, the United Kingdom, the United States, in Primary data documentation. Alternative conversion factors are used in the and the euro area. A country's inflation rate is measured by the change in its Atlas methodology and elsewhere in World Development Indicators as single-year GDP deflator. conversion factors. 2009 World Development Indicators 407 CREDITS World Development Indicators 2009 draws on a wide range of World Bank reports and Seyed Mehran Hosseini (tuberculosis); Omar Shafey of the American Cancer and numerous external sources, listed in the bibliography following this section. Society (tobacco); Delice Gan of the International Diabetes Federation (diabetes), Many people inside and outside the World Bank helped in writing and producing and Nyein Nyein Lwin of the United Nations Children's Fund (health). Valuable com- the book. The team would like to particularly acknowledge the help and encour- ments and inputs at all stages of the production process came from Eric Swanson agement of Justin Lin, Senior Vice President and Chief Economist of the World and on the introduction from Sarwar Lateef. Bank, and Shaida Badiee, Director, Development Data Group. The team is also grateful to the people who provided valuable comments on the entire book. This 3. Environment note identifies many of those who made specific contributions. Many others, Section 3 was prepared by Mehdi Akhlaghi and M.H. Saeed Ordoubadi in partner- too numerous to acknowledge here, helped in many ways for which the team is ship with the World Bank's Sustainable Development Network. Important contri- extremely grateful. butions were made by Carola Fabi and Edward Gillin of the Food and Agriculture Organization of the United Nations; Ricardo Quercioli of the International Energy 1. World view Agency; Amy Cassara, Christian Layke, Daniel Prager, and Robin White of the The introduction to section 1 was prepared by Sarwar Lateef, Soong Sup Lee, World Resources Institute; Laura Battlebury of the World Conservation Monitor- and Eric Swanson. Valuable suggestions were provided by Amar Bhattacharya, ing Centre; and Gerhard Metchies of German Technical Cooperation (GTZ). The Milan Brahmbhatt, Mansoor Dailami, Alan Gelb, and Claudia Paz Sepulveda. World Bank's Environment Department devoted substantial staff resources to Maurizio Bussolo and Rafael de Hoyos of the Development Economics Pros- the book, for which the team is very grateful. M.H. Saeed Ordoubadi wrote the pects Group helped in com puting the inequality estimates. Changqing Sun introduction with valuable comments from Sarwar Lateef, Bruce Ross-Larson, and prepared the estimates of gross national income in purchasing power parity Eric Swanson. Other contributions were made by Susmita Dasgupta, Kirk Hamil- (PPP) terms. K.M. Vijayalakshmi prepared tables 1.1 and 1.6. Uranbileg Bat- ton, Craig Meisner, Kiran Pandey, Giovanni Ruta, and Jana Stover. jargal prepared table 1.4, with valuable assistance from Azita Amjadi. Tables 1.2, 1.3, and 1.5 were prepared by Masako Hiraga. Juan Pedro Schmid and 4. Economy Mona Prasad of the World Bank's Economic Policy and Debt Department pro- Section 4 was prepared by K.M. Vijayalakshmi in close collaboration with the Sus- vided the estimates of debt relief for the Heavily Indebted Poor Countries tainable Development and Economic Data Team of the World Bank's Development Debt Initiative and Multilateral Debt Relief Initiative. Data Group, led by Soong Sup Lee. K.M. Vijayalakshmi wrote the introduction with valuable suggestions from Sarwar Lateef, Soong Sup Lee, and Eric Swanson. 2. People Useful comments were provided by Hinh Dinh and Alice Kuegler. Contributions to Section 2 was prepared by Masako Hiraga and Sulekha Patel in partnership with the section were provided by Azita Amjadi (trade). The national accounts data for the World Bank's Human Development Network and the Development Research low- and middle-income economies were gathered by the World Bank's regional Group in the Development Economics Vice Presidency. Shota Hatakeyama and staff through the annual Unified Survey. Maja Bresslauer, Mahyar Eshragh-Tabary, William Prince provided invaluable assistance in data and table preparation, Victor Gabor, Bala Bhaskar Naidu Kalimili, and Soong Sup Lee worked on updat- and Kiyomi Horiuchi prepared the demographic estimates and projections. The ing, estimating, and validating the databases for national accounts. The team introduction was written by the International Labour Organization's (ILO) Employ- is grateful to the International Monetary Fund, Organisation for Economic Co- ment Trends Team. The poverty estimates were prepared by Shaohua Chen operation and Development, United Nations Industrial Development Organization, and Prem Sangraula of the World Bank's Poverty Monitoring Group and and World Trade Organization for access to their databases. Changquin Sun. The data on children at work were prepared by Lorenzo Guarcello and Furio Rosati from the Understanding Children's Work project. The data on 5. States and markets health gaps by income and gender were based on data prepared by Darcy Gallucio Section 5 was prepared by David Cieslikowski and Raymond Muhula, in partner- and Davidson Gwatkin of the Human Development Network. Other contribu- ship with the World Bank's Financial and Private Sector Development Network, tions were provided by Eduard Bos, Charu Garg, Inez Mikkelsen-Lopez, and Poverty Reduction and Economic Management Network, Sustainable Devel- Emi Suzuki (population, health, and nutrition); Montserrat Pallares-Miralles opment Network, International Finance Corporation, and external partners. (pension); Lawrence Jeffrey Johnson of the ILO (labor force); Juan Cruz Perusia David Cieslikowski wrote the introduction with input from Sarwar Lateef and and Olivier Labeof the United Nations Educational, Scientific, and Cultural Orga- Eric Swanson. Other contributors include Ada Karina Izaguirre (privatization and nization Institute for Statistics (education and literacy); the World Health Orga- infrastructure projects); Leora Klapper (business registration); Federica Saliola nization's Chandika Indikadahena (health expenditure), Monika Bloessner and (Enterprise Surveys); Svetlana Bagaudinova (Doing Business); Alka Banerjee and Mercedes de Onis (malnutrition and overweight), Neeru Gupta (health workers), Isilay Cabuk (Standard & Poor's global stock market indexes); Satish Mannan Mie Inoue and Jessica Ho (hospital beds), Rifat Hossain (water and sanitation), (public policies and institutions); Nigel Adderley of the International Institute 408 2009 World Development Indicators for Strategic Studies (military personnel); Bjorn Hagelin and Petter Stålenheim Design, production, and editing of the Stockholm International Peace Research Institute (military expenditures Richard Fix and Beatriz Prieto-Oramas coordinated all stages of production with and arms transfers); Imed Ben Hamadi of the International Road Federation, Communications Development Incorporated, which provided overall design direc- Ananthanaryan Sainarayan of the International Civil Aviation Organization, and tion, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, Helene Stephan (transport); Jane Degerlund of Containerisation International and Christopher Trott. Elaine Wilson created the graphics and typeset the book. (ports); Vanessa Grey and Esperanza Magpantay of the International Telecom- Joseph Caponio and Amye Kenall provided proofreading and production assis- munication Union; Ernesto Fernandez Polcuch of the United Nations Educa- tance. Communications Development's London partner, Peter Grundy of Peter tional, Scientific, and Cultural Organization Institute for Statistics (research and Grundy Art & Design, provided art direction and design. Staff from External Affairs development, researchers, and technicians); and Anders Halvorsen of the World oversaw printing and dissemination of the book. Information Technology and Services Alliance (information and communication technology expenditures). Client services The Development Data Group's Client Services and Communications Team 6. Global links (Azita Amjadi, Richard Fix, Buyant Erdene Khaltarkhuu, Alison Kwong, and Section 6 was prepared by Uranbileg Batjargal in partnership with the Finan- Beatriz Prieto-Oramas) contributed to the design and planning of World Development cial Data Team of the World Bank's Development Data Group, Development Indicators 2009 and helped coordinate work with the Office of the Publisher. Research Group (trade), Prospects Group (commodity prices), and external partners. Uranbileg Batjargal wrote the introduction, with valuable comments Administrative assistance and office technology support from Sarwar Lateef and Eric Swanson. Olga Akcadag and Nino Kostava pro- Awatif Abuzeid and Estela Zamora provided administrative assistance. Jean- vided research assistance. Substantial input for the data and tables came from Pierre Djomalieu, Gytis Kanchas, Nacer Megherbi, and Shahin Outadi provided Azita Amjadi (trade and tariffs) and Nino Kostava (external debt and financial information technology support. data). Eric Swanson provided guidance on table contents and organization. Other contributors include Frederic Docquier (emigration rates), Flavine Creppy Publishing and dissemination and Yumiko Mochizuki of the United Nations Conference on Trade and Develop- The Offi ce of the Publisher, under the direction of Carlos Rossel, provided ment, and Francis Ng (trade); Betty Dow (commodity prices); Dilek Aykut (for- valu able assistance throughout the production process. Denise Bergeron, eign direct investment flows); Eung Ju Kim (financing through capital markets); Stephen McGroarty, and Nora Ridolfi coordinated printing and supervised mar- Olga Akcadag (Bloomberg, external debt, and financial data); Yasmin Ahmad keting and distribution. Merrell Tuck-Primdahl of the Development Economics and Cecile Sangare of the Organisation for Economic Co-operation and Devel- Vice President's Office managed the communications strategy. opment (aid); Ibrahim Levent and Alagiriswamy Venkatesan (external debt); Henrik Pilgaard of the United Nations Refugee Agency (refugees); Bela Hovy of the World Development Indicators CD-ROM United Nations Population Division (migration); K.M. Vijayalakshmi (remittances); Programming and testing were carried out under the coordination of Reza Farivari and Teresa Ciller of the World Tourism Organization (tourism). Ramgopal Erabelly, by Azita Amjadi, Shelley Fu, Buyant Erdene Khaltarkhuu, Vilas K. Mandlekar, Shelley Lai Fu, and William Prince provided valuable technical assistance. Nacer Megherbi, William Prince, and Malarvizhi Veerappan. Masako Hiraga produced the social indicators tables. Kiyomi Horiuchi produced the population projection Other parts of the book tables. William Prince coordinated user interface design and overall production and Jeff Lecksell of the World Bank's Map Design Unit coordinated preparation of provided quality assurance. Photo credits belong to the World Bank photo library. the maps on the inside covers. David Cieslikowski prepared the Users guide. Eric Swanson wrote Statistical methods. K.M. Vijayalakshmi coordinated preparation WDI Online of Primary data documentation, and Awatif Abuzeid and Buyant Erdene Khaltarkhuu Design, programming, and testing were carried out by Reza Farivari and his team: assisted in updating the Primary data documentation table. Richard Fix and Azita Amjadi, Ramgopal Erabelly, Shelley Fu, Buyant Erdene Khaltarkhuu, and Beatriz Prieto-Oramas prepared Partners and Index of indicators. Shahin Outadi. William Prince coordinated production and provided quality assur- ance. Valentina Kalk and Malika Khek of the Office of the Publisher were responsible Database management for implementation of WDI Online and management of the subscription service. Mehdi Akhlaghi and William Prince coordinated management of the integrated World Development Indicators database. Development and operation of the Client feedback database management system was made possible by the Data and Informa- The team is grateful to the many people who have taken the time to provide tion Systems Team, which included Ying Chi, Ramgopal Erabelly, Shelley Fu, assistance on its publications. Their feedback and suggestions have helped Shahin Outadi, and Atsushi Shimo, under the leadership of Reza Farivari. improve this year's edition. 2009 World Development Indicators 409 BIBLIOGRAPHY Aminian, Nathalie, K.C. Fung, and Francis Ng. 2008. "Integration of Markets vs. Caiola, Marcello. 1995. A Manual for Country Economists. Training Series 1, Vol. Integration by Agreements." 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[www.wto.org/english/tratop_e/region_e/region_e.htm]. 2009 World Development Indicators 417 INDEX OF INDICATORS References are to table numbers. per worker 3.3 A Agriculture Aid by recipient agricultural raw materials aid dependency ratios 6.15 commodity prices 6.6 per capita 6.15 exports total 6.15 as share of total exports 4.4 net concessional flows from high-income economies as share of total exports 6.4 from international financial institutions 6.12 imports from UN agencies 6.12 as share of total imports 4.4 official development assistance by DAC members by high-income economies as share of total exports 6.4 administrative costs, as share of net bilateral ODA tariff rates applied by high-income countries 6.4 disbursements 6.14a cereal bilateral aid 6.14a, 6.14b, 6.16 area under production 3.2 by purpose 6.14a exports from high-income economies as share of total exports 6.4 by sector 6.14b imports, by high-income economies as share of total imports 6.4 commitments 6.13, 6.14b tariff rates applied by high-income countries 6.4 debt-related aid, as share of net bilateral ODA disbursements 6.14a yield 3.3 development projects, programs, and other resource provisions, employment, as share of total 3.2 as share of net bilateral ODA disbursements 6.14a fertilizer for basic social services, as share of sector-allocable bilateral commodity prices 6.6 ODA commitments 1.4 consumption, per hectare of arable land 3.2 gross disbursements 6.13 food humanitarian assistance, as share of net bilateral ODA beverages and tobacco 4.3 disbursements 6.14a commodity prices 6.6 net disbursements exports from high-income economies as share of total exports 4.4, 6.4 as share of general government disbursements 6.13 imports by high-income economies as share of total imports 4.5, 6.4 as share of GNI of donor country 1.4, 6.13 tariff rates applied by high-income countries 6.4 from major donors, by recipient 6.16 freshwater withdrawals for, as share of total 3.5 per capita of donor country 6.13 land total 6.13, 6.14a agricultural, as share of land area 3.2 technical cooperation, as share of net bilateral ODA arable, as share of land area 3.1 disbursements 6.14a arable, per 100 people 3.1 total sector allocable, as share of bilateral ODA commitments 6.14b area under cereal production 3.2 untied aid 6.14b irrigated, as share of cropland 3.2 official development assistance by non-DAC members 6.15a permanent cropland, as share of land area 3.1 machinery AIDS--see HIV, prevalence tractors per 100 square kilometers of arable land 3.2 production indexes Air pollution--see Pollution crop 3.3 food 3.3 Air transport livestock 3.3 air freight 5.9 value added passengers carried 5.9 annual growth 4.1 registered carrier departures worldwide 5.9 as share of GDP 4.2 418 2009 World Development Indicators Animal species closing a business threatened 3.4 time to resolve insolvency 5.3 total known 3.4 corruption informal payments to public officials 5.2 Asylum seekers--see Migration; Refugees crime losses due to theft, robbery, vandalism, and arson 5.2 B Balance of payments customs average time to clear exports dealing with construction permits to build a warehouse 5.2 current account balance 4.15 number of procedures 5.3 exports and imports of goods and services 4.15 time required 5.3 net current transfers 4.15 employing workers net income 4.15 rigidity of employment index 5.3 total reserves 4.15 enforcing contracts See also Exports; Imports; Investment; Private financial flows; Trade number of procedures 5.3 time required 5.3 Beverages finance commodity prices 6.6 firms using banks to finance investment 5.2 gender Biodiversity--see Biological diversity female participation in ownership 5.2 informality Biological diversity firms that do not report all sales for tax purposes 5.2 assessment, date prepared, by country 3.15 infrastructure GEF benefits index 3.4 value lost due to electrical outages 5.2 threatened species 3.4 innovation animal 3.4 ISO certification ownership 5.2 higher plants 3.4 permits and licenses treaty 3.15 time required to obtain operating license 5.2 protecting investors disclosure, index 5.3 Birth rate, crude 2.1 registering property number of procedures 5.3 Births attended by skilled health staff 2.18, 2.21 time to register 5.3 regulation and tax Birthweight, low 2.19 average number of times firms spend meeting with tax officials 5.2 time dealing with officials 5.2 Bonds--see Debt flows; Private financial flows starting a business cost to start a business 5.3 Brain drain--see Emigration of people with tertiary education to OECD number of start-up procedures 5.3 countries time to start a business 5.3 workforce, firms offering formal training 5.2 Breastfeeding, exclusive 2.19, 2.21 Business environment businesses registered C Carbon dioxide new 5.1 damage 3.16 total 5.1 2009 World Development Indicators 419 INDEX OF INDICATORS emissions See also Purchasing power parity (PPP) per 2005 PPP dollar of GDP 3.8 per capita 1.3, 3.8 Contraceptive prevalence rate 1.3, 2.17, 2.20 total 1.6, 3.8 intensity 3.8 Contract enforcement number of procedures 5.3 Children at work time required for 5.3 by economic activity 2.6 male and female 2.6 Corruption, informal payments to public officials 5.2 study and work 2.6 status in employment 2.6 Country Policy and Institutional Assessment (CPIA)--see Economic total 2.6 management; Social inclusion and equity policies; Public sector management work only 2.6 and institutions; Structural policies Cities Credit air pollution 3.14 getting credit population depth of credit information index 5.5 in largest city 3.11 strength of legal rights index 5.5 in selected cities 3.14 private credit registry coverage 5.5 in urban agglomerations of more than 1 million 3.11 public credit registry coverage 5.5 urban population 3.11 provided by banking sector 5.5 See also Urban environment to private sector 5.1 Closing a business--see Business environment Crime, losses due to 5.2 Commercial bank and other lending 6.11 Current account balance 4.15 See also Debt flows; Private financial flows See also Balance of payments Commodity prices and price indexes 6.6 Customs, average time to clear 5.2 Communications--see Internet; Newspapers, daily; Telephones; Television, households with D Compensation of government employees 4.11 DAC (Development Assistance Committee)--see Aid Computers (personal) per 100 people 5.11 Death rate, crude 2.1 See also Mortality rate Consumption distribution--see Income distribution Debt, external fixed capital 3.16 as share of GNI 6.10 government, general debt ratios 6.10 annual growth 4.9 debt service as share of GDP 4.8 multilateral, as share of public and publicly guaranteed debt household service 6.10 average annual growth 4.9 total, as share of exports of goods and services and income 6.10 per capita 4.9 IMF credit, use of 6.9 as share of GDP 4.8 420 2009 World Development Indicators long-term out of school children, male and female 2.12, 2.15 private nonguaranteed 6.9 primary completion rate 1.2, 2.14, 2.15 public and publicly guaranteed male and female 2.14, 2.15 IBRD loans and IDA credits 6.9 progression total 6.9 share of cohort reaching grade 5, male and female 2.13 present value share of cohort reaching last grade of primary, male and female 2.13 as share of GNI 6.10 public expenditure on as share of exports of goods and services and income 6.10 as share of GDP 2.11 short-term 6.9 as share of total government expenditure 2.11 as share of total debt 6.10 per student, as share of GDP per capita, by level 2.11 as share of total reserves 6.10 pupil-teacher ratio, primary level 2.11 total 6.9 repeaters, primary level, male and female 2.13 total 6.9 teachers, primary, trained 2.11 transition to secondary school, male and female 2.13 Debt flows unemployment by level of educational attainment 2.5 bonds 6.11 years of schooling, average 2.15 commercial banks and other lending 6.11 See also Private financial flows Electricity consumption 5.10 Deforestation, average annual 3.4 production share of total 3.10 Density--see Population, density sources 3.10 transmissions and distribution losses 5.10 Dependency ratio--See Population value lost due to outages 5.2 Development assistance--see Aid Emigration of people with tertiary education to OECD countries 6.1 Disease--see Health risks Emissions carbon dioxide Distribution of income or consumption--see Income distribution average annual growth 3.9 intensity 3.8 E Economic management (Country Policy and Institutional Assessment) per capita total methane 3.8 3.8 debt policy 5.8 agricultural as share of total 3.9 economic management cluster average 5.8 industrial as share of total 3.9 fiscal policy 5.8 total 3.9 macroeconomic management 5.8 nitrous oxide agricultural as share of total 3.9 Education industrial as share of total 3.9 enrollment ratio total 3.9 girls to boys enrollment in primary and secondary schools 1.2 other greenhouse gases 3.9 gross, by level 2.12 net, by level 2.12 Employment adjusted net, primary 2.12 children in employment 2.6 gross intake rate, grade 1 2.13, 2.15 in agriculture gross primary participation rate 2.15 as share of total employment 3.2 2009 World Development Indicators 421 INDEX OF INDICATORS male and female 2.3 ratio of PPP conversion factor to official exchange rate 4.14 in industry, male and female 2.3 real effective 4.14 in informal sector, urban, male and female 2.9 See also Purchasing power parity (PPP) in services, male and female 2.3 rigidity index 5.3 Export credits to population ratio 2.4 private, from DAC members 6.13 vulnerable 2.4 See also Labor force; Unemployment Exports arms 5.7 Employing workers goods and services rigidity of employment index 5.3 as share of GDP 4.8 average annual growth 4.9 Endangered species--see Animal species; Biological diversity; Plants, higher total 4.15 high-technology Energy share of manufactured exports 5.12 commodity prices 6.6 total 5.12 depletion, as share of GNI 3.16 merchandise emissions--see Pollution annual growth 6.2, 6.3 imports, net 3.8 by high-income countries, by product 6.4 production 3.7 by developing countries, by partner 6.5 use by regional trade blocs 6.7 2005 PPP dollar of GDP per unit 3.8 direction of trade 6.3 average annual growth 3.8 structure 4.4 clean energy consumption as share of total 3.7 total 4.4 combustible renewables and waste as share of total 3.7 value, average annual growth 6.2 fossil fuel consumption as share of total 3.7 volume, average annual growth 6.2 total 3.7 services See also Electricity; Fuels structure 4.6 total 4.6 Enforcing contracts--see Business environment transport 4.6 travel 4.6, 6.19 Enrollment--see Education See also Trade Entry regulations for business--see Business environment Environmental strategy, year adopted 3.15 F Female-headed households 2.9 Equity flows Fertility rate foreign direct investment, net inflows 6.11 adolescent 2.18 portfolio equity 6.11 total 2.18, 2.21 See also Private financial flows Finance, firms using banks to finance investment 5.2 European Commission distribution of net aid from 6.16 Financial access, stability, and efficiency bank capital to asset ratio 5.5 Exchange rates bank nonperforming loans 5.5 official, local currency units to U.S. dollar 4.14 422 2009 World Development Indicators Financial flows, net Fuels from DAC members 6.13 consumption official road sector 3.13 from bilateral sources 6.12 transportation sector 3.13 from international financial institutions 6.12 exports from multilateral sources 6.12 as share of total exports 4.4 from UN agencies 6.12 crude petroleum, from high-income economies, as share of total total 6.12 exports 6.4 official development assistance and official aid from high-income economies, as share of total exports 6.4 grants from NGOs 6.13 petroleum products, from high-income economies, as share of other official flows 6.13 total exports 6.4 private 6.13 imports total 6.13 as share of total imports 4.4 See also Aid crude petroleum, by high-income economies, as share of total imports 6.4 Financing through international capital markets 6.1 by high-income economies, as share of total imports 6.4 See also Private financial flows petroleum products, by high-income economies, as share of total imports 6.4 Food--see Agriculture, production indexes; Commodity prices and price prices 3.13 indexes tariff rates applied by high-income countries 6.4 Foreign-born population in OECD countries by country of origin by gender 6.18a 6.18b G GEF benefits index for biodiversity 3.4 by education attainment 6.18b by occupation 6.18b Gender, female participation in ownership 5.2 by sector of employment 6.18b Gender differences Foreign direct investment, net--see Investment; Private financial flows in children in employment 2.6 in education Forest enrollment, primary and secondary 1.2, 2.12, 2.13, 2.14 area, as share of total land area 3.1 in employment 2.3 deforestation, average annual 3.4 in HIV prevalence 2.20 net depletion 3.16 in labor force participation 2.2 in life expectancy at birth 1.5 Freshwater in literacy annual withdrawals adult 2.14 amount 3.5 youth 2.14 as share of internal resources 3.5 in mortality for agriculture 3.5 adult 2.22 for domestic use 3.5 child 2.22 for industry 3.5 in smoking 2.20 renewable internal resources in survival to age 65 2.22 flows 3.5 in youth unemployment 2.10 per capita 3.5 unpaid family workers 1.5 See also Water, access to improved source of women in nonagricultural sector 1.5 women in parliaments 1.5 2009 World Development Indicators 423 INDEX OF INDICATORS Gini index 2.9 Gross savings as share of GDP 4.8 Government, central as share of GNI 3.16 cash surplus or deficit 4.10 debt as share of GDP interest, as share of revenue 4.10 4.10 H Health care interest, as share of total expenses 4.11 children sleeping under treated bednets 2.17 expense children with acute respiratory infection taken to health provider 2.17 as share of GDP 4.10 children with diarrhea who received oral rehydration and continued by economic type 4.11 feeding 2.17 military 5.7 children with fever receiving antimalarial drugs 2.17 net incurrence of liabilities, as share of GDP hospital beds per 1,000 people 2.16 domestic 4.10 immunization 2.17, 2.21 foreign 4.10revenues, current newborns protected against tetanus 2.18 as share of GDP 4.10 physicians, nurses, and midwives 2.16 grants and other 4.12 outpatient visits per capita 2.16 social contributions 4.12 physicians per 1,000 people 2.16 tax, as share of GDP 5.6 pregnant women receiving prenatal care 1.5, 2.18, 2.21 tax, by source 4.12 reproductive anemia, prevalence of, pregnant women 2.19 Greenhouse gases--see Emissions births attended by skilled health staff 1.2, 2.18, 2.21 contraceptive prevalence rate 1.3, 2.18, 2.21 Gross capital formation fertility rate annual growth 4.9 adolescent 2.18 as share of GDP 4.8 total 2.18, 2.21 low-birthweight babies 2.19 Gross domestic product (GDP) maternal mortality ratio 1.3, 2.18 annual growth 1.1, 1.6, 4.1 unmet need for contraception 2.18 implicit deflator--see Prices tuberculosis per capita, annual growth 1.1, 1.6 DOTS detection rate 2.17 total 4.2 incidence 1.3, 2.20 treatment success rate 2.17 Gross enrollment--see Education Health expenditure Gross national income (GNI) as share of GDP 2.16 per capita out of pocket 2.16 PPP dollars 1.1, 1.6 per capita 2.16 rank 1.1 public 2.16 U.S. dollars 1.1, 1.6 total 2.16 rank year last national health account completed 2.16 PPP dollars 1.1 U.S. dollars 1.1 Health surveys total year of most recent health survey 2.16 PPP dollars 1.1, 1.6 U.S. dollars 1.1, 1.6 424 2009 World Development Indicators Health risks Imports anemia, prevalence of arms 5.7 children ages under 5 2.19 energy, net, as share of total energy use 3.8 pregnant women 2.19 goods and services child malnutrition, prevalence 1.2, 2.19, 2.21 as share of GDP 4.8 condom use 2.20 average annual growth 4.9 diabetes, prevalence 2.20 total 4.15 HIV prevalence 1.3, 2.20 merchandise overweight children, prevalence 2.19 annual growth 6.3 smoking, prevalence 2.20 by high-income countries, by product 6.4 tuberculosis, incidence 1.3, 2.20 by developing countries, by partner 6.5 undernourishment, prevalence 2.19 structure 4.5 tariffs 6.4, 6.8 Heavily indebted poor countries (HIPCs) total 4.5 assistance 1.4 value, average annual growth 6.2 completion point 1.4 volume, average annual growth 6.2 decision point 1.4 services Multilateral Debt Relief Initiative (MDRI) assistance 1.4 structure 4.7 total 4.7 HIV transport 4.7 prevalence 1.3, 2.20 travel 4.7, 6.17 female 2.20 See also Trade population ages 15­24, male and female 2.20 total 2.20 Income distribution prevention Gini index 2.9 condom use, male and female 2.20 percentage of 1.2, 2.9 Hospital beds--see Health care Industry annual growth 4.1 Housing conditions, national and urban as share of GDP 4.2 durable dwelling units 3.12 employment, male and female 2.3 home ownership 3.12 household size 3.12 Inflation--see Prices multiunit dwellings 3.12 overcrowding 3.12 Informal economy, firms that do not report all sales for tax purposes 5.2 vacancy rate 3.12 Information and communications technology expenditures I IDA Resource Allocation Index (IRAI) 5.8 as share of GDP Innovation, ISO certification ownership 5.11 5.2 Immigrants in selected OECD countries 6.18 Integration, global economic, indicators 6.1 Immunization rate, child Interest payments--see Government, central, debt DPT, share of children ages 12­23 months 2.17, 2.21 measles, share of children ages 12­23 months 2.17, 2.21 Interest rates tetanus, newborns protected against 2.18 deposit 4.13 2009 World Development Indicators 425 INDEX OF INDICATORS lending real risk premium on lending 4.13 4.13 5.5 L Labor force spread 5.5 annual growth 2.2 armed forces 5.7 International Bank for Reconstruction and Development (IBRD) children at work 2.6 IBRD loans and IDA credits 6.9 female 2.2 net financial flows from 6.12 participation of population ages 15+, male female 2.2 total 2.2 International Development Association (IDA) See also Employment; Migration; Unemployment IBRD loans and IDA credits 6.9 net concessional flows from 6.12 Land area arable--see Agriculture, land; Land use International migrant stock See also Protected areas; Surface area as share of total population 6.1 total 6.17 Land use See also Migration arable land, as share of total land 3.1 area under cereal production 3.2 International Monetary Fund (IMF) by type 3.1 net financial flows from 6.12 forest area, as share of total land 3.1 use of IMF credit 6.9 irrigated land 3.2 permanent cropland, as share of total land 3.1 Internet total area 3.1 broadband subscribers 5.11 fixed broadband access tariff 5.11 Life expectancy at birth secure servers 5.11 male and female 1.5 users 5.11 total 1.6, 2.22 international Internet bandwidth 5.11, 6.1 Literacy Investment adult, male and female 1.6, 2.14 foreign direct, net inflows youth, male and female 1.6, 2.14 as share of GDP 6.1 from DAC members total foreign direct, net outflows 6.13 6.11 M Malnutrition, in children under age 5 1.2, 2.19, 2.21 as share of GDP 6.1 infrastructure, private participation in Malaria energy 5.1 children sleeping under treated bednets 2.17 telecommunications 5.1 children with fever receiving antimalarial drugs 2.17 transport 5.1 water and sanitation 5.1 Management time dealing with officials 5.2 See also Gross capital formation; Private financial flows Manufacturing Iodized salt, consumption of 2.19 chemicals 4.3 exports 4.4, 6.4 food 4.3 imports 4.5, 6.4 426 2009 World Development Indicators machinery 4.3 food 4.5 structure 4.3 footwear 6.4 textile 4.3 fuels 4.5 value added furniture 6.4 annual growth 4.1 information and communications technology goods 5.11 as share of GDP 4.2 iron and steel 6.4 total 4.3 machinery and transport equipment 6.4 See also Merchandise manufactures 4.5 ores and metals 4.5 Market access to high-income countries ores and nonferrous materials 6.4 goods admitted free of tariffs 1.4 petroleum products 6.4 support to agriculture 1.4 textiles 6.4 tariffs on exports from low- and middle-income countries total 4.5 agricultural products 1.4 value, average annual growth 6.2 textiles and clothing 1.4 volume, average annual growth 6.2 trade Merchandise as share of GDP 6.1 exports by developing countries, by partner 6.5 agricultural raw materials 4.4, 6.4 direction 6.3 by regional trade blocs 6.7 growth 6.3 by developing countries, by partner 6.5 regional trade blocs 6.7 cereals 6.4 chemicals 6.4 Metals and minerals crude petroleum 6.4 commodity prices and price index 6.6 food 4.4, 6.4 footwear 6.4 Methane emissions fuels 4.4 agricultural as share of total 3.9 furniture 6.4 industrial as share of total 3.9 information and communications technology goods 5.11 total 3.9 information and communications technology services 5.11 iron and steel 6.4 Migration machinery and transport equipment 6.4 characteristics of immigrants in OECD countries 6.18 manufactures 4.4 emigration of people with tertiary education to OECD countries 6.1 ores and metals 4.4 international migrant stock ores and nonferrous materials 6.4 as share of total population 6.1 petroleum products 6.4 total 6.17 textiles 6.4 net 6.1, 6.17 total 4.4 See also Refugees; Remittances value, average annual growth 6.2 volume, average annual growth 6.2 Military within regional trade blocs 6.7 armed forces personnel imports as share of labor force 5.7 agricultural raw materials 4.5 total 5.7 by developing countries, by partner 6.5 arms transfers cereals 6.4 exports 5.7 chemicals 6.4 imports 5.7 crude petroleum 6.4 2009 World Development Indicators 427 INDEX OF INDICATORS military expenditure unmet need for contraception 2.18 as share of central government expenditure 5.7 vulnerable employment 1.2, 2.4 as share of GDP 5.7 women in wage employment in the nonagricultural sector 1.5 Millennium Development Goals, indicators for Minerals, depletion of 3.15 access to improved sanitation facilities 1.3, 2.17 access to improved water source 2.17, 3.5 Monetary indicators aid claims on governments and other public entities 4.13 as share of GNI of donor country 1.4, 6.13 claims on private sector 4.13 for basic social services as share of total sector allocable ODA commitments 1.4 Money and quasi money, annual growth 4.13 births attended by skilled health staff 2.18 carbon dioxide emissions per capita 1.3, 3.8 Mortality rate children sleeping under treated bednets 2.17 adult, male and female 2.22 contraceptive prevalence rate 1.3, 2.18 child, male and female 2.22 employment to population ratio 2.4 children under age 5 1.2, 2.21, 2.22 enrollment ratio, net, primary 2.12 infant 2.22 female to male enrollments, primary and secondary 1.2 maternal 1.3, 2.18 fertility rate, adolescent 2.18 heavily indebted poor countries (HIPCs) Motor vehicles completion point 1.4 passenger cars 3.13 decision point 1.4 per 1,000 people 3.13 nominal debt service relief 1.4 per kilometer of road 3.13 immunization road density 3.13 DPT 2.17, 2.21 See also Roads; Traffic measles 2.17, 2.21 income or consumption, national share of poorest quintile 1.2, 2.9 MUV G-5 index 6.6 infant mortality rate 2.21, 2.22 labor productivity, GDP per person employed literacy rate of 15- to 24-year-olds malnutrition, prevalence 1.2, 2.19, 2.21 2.4 2.14 N Net enrollment--see Education malaria children under age 5 sleeping under insecticide treated bednets 2.17 Net national savings 3.16 children under age 5 with fever who are treated with appropriate antimalarial drugs 2.17 Newspapers, daily 5.11 maternal mortality ratio 1.3, 2.18 national parliament seats held by women 1.5 Nitrous oxide emissions poverty gap 2.7, 2.8 agricultural as share of total 3.9 pregnant women receiving prenatal care 1.5, 2.18, 2.21 industrial as share of total 3.9 share of cohort reaching last grade of primary 2.13 total 3.9 telephone lines, fixed-line and mobile 1.3, 5.10 tuberculosis Nutrition DOTS detection rate 2.17 anemia, prevalence of incidence 1.3, 2.20 children ages under 5 2.19 treatment success rate 2.17 pregnant women 2.19 under-five mortality rate 1.2, 2.22 breastfeeding 2.19, 2.21 undernourishment, prevalence 2.19 iodized salt consumption 2.19 428 2009 World Development Indicators malnutrition, child 1.2, 2.19, 2.11 per 2005 PPP dollar of GDP 3.8 overweight children, prevalence 2.19 per capita 3.8 undernourishment, prevalence 2.19 total 3.8 vitamin A supplementation 2.19 methane emissions agricultural as share of total 3.9 O Official development assistance--see Aid industrial as share of total total nitrogen dioxide, selected cities 3.9 3.9 3.14 nitrous oxide emissions Official flows agricultural as share of total 3.9 net industrial as share of total 3.9 from bilateral sources 6.12 total 3.9 from international financial institutions 6.12 organic water pollutants, emissions from multilateral sources 6.12 by industry 3.6 from United Nations 6.12 per day 3.6 other 6.13 per worker 3.6 particulate matter, selected cities 3.14 P Passenger cars per 1,000 people 3.13 sulfur dioxide, selected cities urban-population-weighted PM10 3.14 3.13 Population Particulate matter age dependency ratio 2.1 emission damage 3.16 annual growth 2.1 selected cities 3.14 by age group urban-population-weighted PM10 3.13 0­14 2.11 5­64 2.1 Patent applications filed 5.12 65 and older 2.1 density 1.1, 1.6 Pension female, as share of total 1.5 average, as share of per capita income 2.10 rural contributors annual growth 3.1 as share of labor force 2.10 as share of total 3.1 as share of working age population 2.10 total 1.1, 1.6, 2.1 public expenditure on, as share of GDP 2.10 urban as share of total 3.11 Permits and licenses, time required to obtain operating license 5.2 average annual growth 3.11 in largest city 3.11 Physicians--see Health care in selected cities 3.14 in urban agglomerations 3.11 Plants, higher total 3.11 species 3.4 See also Migration threatened species 3.4 Portfolio--see Equity flows; Private financial flows Pollution carbon dioxide Ports, container traffic in 5.9 damage, as share of GNI 3.16 emissions 2009 World Development Indicators 429 INDEX OF INDICATORS Poverty national international poverty line as share of total land area 3.4 local currency 2.8 total 3.4 population living below $1.25 a day 2.8 Protecting investors disclosure index 5.3 $2 a day 2.8 national poverty line Public sector management and institutions (Country Policy and Institutional population living below 2.7 Assessment) national 2.7 efficiency of revenue mobilization 5.8 rural 2.7 property rights and rule-based governance 5.8 urban 2.7 public sector management and institutions cluster average 5.8 quality of budgetary and financial management 5.8 Power--see Electricity, production quality of public administration 5.8 transparency, accountability, and corruption in the public sector 5.8 Prenatal care, pregnant women receiving 1.5, 2.18, 2.21 Purchasing power parity (PPP) Prices conversion factor 4.14 commodity prices and price indexes 6.6 gross national income 1.1, 1.6 consumer, annual growth 4.14 fuel GDP implicit deflator, annual growth terms of trade 3.18 4.14 6.2 R Railways wholesale, annual growth 4.14 goods hauled by 5.9 lines, total 5.9 Primary education--see Education passengers carried 5.9 Private financial flows Refugees debt flows by country of asylum 6.17 bonds 6.11 by country of origin 6.17 commercial bank and other lending 6.11 equity flows Regional development banks, net financial flows from 6.12 foreign direct investment, net inflows 6.11 portfolio equity 6.11 Regional trade agreements--see Trade blocs, regional financing through international capital markets, as share of GDP 6.1 from DAC members 6.13 Registering property See also Investment number of procedures 5.3 time to register 5.3 Productivity in agriculture Regulation and tax administration value added per worker 3.3 management time dealing with officials 5.2 labor productivity, GDP per person employed 2.4 meeting with tax officials, number of times 5.2 water productivity, total 3.5 Relative prices (PPP)--see Purchasing power parity (PPP) Protected areas marine Remittances as share of total surface area 3.4 workers' remittances and compensation of employees total 3.4 as share of GDP 6.1 430 2009 World Development Indicators paid 6.17 Secondary education--see Education received 6.17 Services Research and development employment, male and female 2.3 expenditures 5.12 exports researchers 5.12 structure 4.6 technicians 5.12 total 4.6 imports Reserves, gross international--see Balance of payments structure 4.7 total 4.7 Roads trade, as share of GDP 6.1 goods hauled by 5.9 value added passengers carried 5.9 annual growth 4.1 paved, as share of total 5.9 as share of GDP 4.2 total network 5.9 traffic 3.13 Smoking, prevalence, male and female 2.20 Royalty and license fees Social inclusion and equity policies (Country Policy and Institutional payments 5.12 Assessment) receipts 5.12 building human resources 5.8 equity of public resource use 5.8 Rural environment gender equity 5.8 access to improved sanitation facilities 3.11 policy and institutions for environmental sustainability 5.8 population social inclusion and equity cluster average 5.8 annual growth 3.1 social protection and labor 5.8 as share of total 3.1 Starting a business--see Business environment S S&P/EMDB Indexes 5.4 Stock markets listed domestic companies 5.4 market capitalization Sanitation, access to improved facilities, population with as share of GDP 5.4 rural 3.11 total 5.4 total 1.3, 2.17 market liquidity 5.4 urban 3.11 S&P/EMDB Indices 5.4 turnover ratio 5.4 Savings gross, as share of GDP 4.8 Steel products, commodity prices and price index 6.6 gross, as share of GNI 3.16 net 3.16 Structural policies (Country Policy and Institutional Assessment) business regulating environment 5.8 Schooling--see Education financial sector 5.8 structural policies cluster average 5.8 Science and technology trade 5.8 scientific and technical journal articles 5.12 See also Research and development Sulfur dioxide emissions--see Pollution 2009 World Development Indicators 431 INDEX OF INDICATORS Surface area 1.1, 1.6 international voice traffic 5.10, 6.1 See also Land use per 100 people 5.10 mobile cellular Survival to age 65, male and female 2.22 per 100 people 1.3, 5.10 population covered 5.10 Suspended particulate matter--see Pollution prepaid tariff 5.10 mobile cellular and fixed-line subscribers per employee 5.10 T Tariffs total revenue Television, households with 5.10 5.11 all products binding coverage 6.8 Terms of trade, net barter 6.2 simple mean board rate 6.8 simple mean tariff 6.8 Tertiary education--see Education weighted mean tariff 6.8 applied rates on imports from low- and middle-income economies 6.4 Tetanus vaccinations, newborns protected against 2.18 manufactured products simple mean tariff 6.8 Threatened species--see Animal species; Biological diversity; Plants, higher weighted mean tariff 6.8 on exports of least developed countries 1.4 Tourism, international primary products expenditures in the country simple mean tariff 6.8 as share of exports 6.19 weighted mean tariff 6.8 total 6.19 expenditures in other countries Taxes and tax policies as share of imports 6.19 business taxes total 6.19 average number of times firms spent meeting tax officials 5.2 inbound tourists, by country 6.19 number of payments 5.6 outbound tourists, by country 6.19 time to prepare, file, and pay 5.6 total tax rate, percent profit 5.6 Trade goods and services taxes, domestic 4.12 arms 5.7 highest marginal tax rate merchandise corporate 5.6 as share of GDP 6.1 individual 5.6 direction of, by developing countries 6.5 income, profit, and capital gains taxes as share of revenue 4.12 direction of, by region 6.3 international trade taxes 4.12 high-income economy with low- and middle-income economies, other taxes 4.12 by product 6.4 social contributions 4.12 nominal growth, by region 6.3 tax revenue, as share of GDP 5.6 regional trading blocs 6.7 services Technology--see Computers; Exports, high-technology; Internet; Research and as share of GDP 6.1 development; Science and technology computer, information, communications, and other 4.6, 4.7 insurance and financial 4.6, 4.7 Telephones transport 4.6, 4.7 fixed line travel 4.6, 4.7 per 100 people 5.10 See also Balance of payments; Exports; Imports; Manufacturing; residential tariff 5.10 Merchandise; Terms of trade; Trade blocs 432 2009 World Development Indicators Trade blocs, regional UNICEF, net official financial flows from 6.12 exports within bloc 6.7 total exports, by bloc 6.7 UNTA, net official financial flows from 6.12 type of agreement 6.7 year of creation 6.7 UNRWA year of entry into force of the most recent agreement 6.7 net official financial flows from 6.12 refugees under the mandate of 6.17 Trademark applications filed 5.12 Urban environment Trade policies--see Tariffs access to sanitation 3.11 employment, informal sector 2.8 Traffic population road traffic 3.13 as share of total 3.11 road traffic injury and mortality 2.18 average annual growth 3.11 See also Roads in largest city 3.11 in urban agglomerations 3.11 Transport--see Air transport; Railways; Roads; Traffic; Urban environment total 3.11 selected cities Travel--see Tourism, international nitrogen dioxide 3.14 particulate matter 3.14 Treaties, participation in population 3.14 biological diversity 3.15 sulfur dioxide 3.14 CFC control 3.15 See also Pollution; Population; Sanitation; Water climate change 3.15 Convention on International Trade on Endangered Species (CITES) Convention to Combat Desertification (CCD) Kyoto Protocol 3.15 3.15 3.15 V Value added Law of the Sea 3.15 as share of GDP ozone layer 3.15 in agriculture 4.2 Stockholm Convention on Persistent Organic Pollutants 3.15 in industry 4.2 in manufacturing 4.2 Tuberculosis, incidence 1.3, 2.20 in services 4.2 growth U UN agencies, net official financial flows from 6.12 in agriculture in industry in manufacturing 4.1 4.1 4.1 in services 4.1 Undernourishment, prevalence of 2.19 per worker in agriculture 3.3 Unemployment total, in manufacturing 4.3 incidence of long-term, total, male, and female 2.5 by level of educational attainment, primary, secondary, tertiary 2.5 Vulnerable employment 1.2, 2.4 total, male, and female 2.5 youth, male, and female UNHCR, refugees under the mandate of 1.3, 2.10 6.17 W Water access to improved source of, population with 1.3, 2.17 2009 World Development Indicators 433 INDEX OF INDICATORS pollution--see Pollution, organic water pollutants women in parliaments 1.5 productivity 3.5 Workforce, firms offering formal training 5.2 Women in development female-headed households 2.10 World Bank commodity price index female population 1.5 energy 6.6 life expectancy at birth 1.5 nonenergy commodities 6.6 pregnant women receiving prenatal care 1.5 steel products 6.6 teenage mothers 1.5 unpaid family workers 1.5 World Bank, net financial flows from 6.12 vulnerable employment 2.4 See also International Bank for Reconstruction and Development; women in nonagricultural sector 1.5 International Development Association 434 2009 World Development Indicators The world by region Classified according to Low- and middle-income economies World Bank analytical East Asia and Pacific Middle East and North Africa High-income economies grouping Europe and Central Asia South Asia OECD Latin America and the Caribbean Sub-Saharan Africa Other No data The World Bank 1818 H Street N.W. ISBN 978-0-8213-7829-8 Washington, D.C. 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: www.worldbank.org SKU 17829 Email: feedback@worldbank.org The World Development Indicators Includes more than 800 indicators for 153 economies Provides definitions, sources, and other information about the data Organizes the data into six thematic areas 1 2 3 WORLD VIEW PEOPLE ENVIRONMENT Living standards Natural resources and development Gender, health, and and environmental progress employment changes 4 5 6 ECONOMY STATES & MARKETS GLOBAL LINKS New opportunities Elements of a good Evidence on for growth investment climate globalization Saved: 62 trees 43 million Btu of total energy 5,452 pounds of net greenhouse gases 22,631 gallons of waste water 2,906 pounds of solid waste