07 WORLD DEVELOPMENT INDICATORS 54166 STATES & MARKETS WORLD VIEW ENVIRONMENT GLOBAL LINKS ECONOMY PEOPLE INCOME MAP The world by income Low income Armenia Belize Greece Afghanistan Azerbaijan Botswana Greenland Bangladesh Belarus Chile Guam Benin Bolivia Costa Rica Hong Kong, China Bhutan Bosnia and Herzegovina Croatia Iceland Burkina Faso Brazil Czech Republic Ireland Burundi Bulgaria Dominica Isle of Man Cambodia Cameroon Equatorial Guinea Israel Central African Republic Cape Verde Estonia Italy Chad China Gabon Japan Comoros Colombia Grenada Korea, Rep. Congo, Dem. Rep. Congo, Rep. Hungary Kuwait Côte d'Ivoire Cuba Latvia Liechtenstein Eritrea Djibouti Lebanon Luxembourg Ethiopia Dominican Republic Libya Macao, China Gambia, The Ecuador Lithuania Malta Ghana Egypt, Arab Rep. Malaysia Monaco Guinea El Salvador Mauritius Netherlands Guinea-Bissau Fiji Mayotte Netherlands Antilles Haiti Georgia Mexico New Caledonia India Guatemala Northern Mariana Islands New Zealand Kenya Guyana Oman Norway Korea, Dem. Rep. Honduras Palau Portugal Kyrgyz Republic Indonesia Panama Puerto Rico Lao PDR Iran, Islamic Rep. Poland Qatar Liberia Iraq Romania San Marino Madagascar Jamaica Russian Federation Saudi Arabia Malawi Jordan Seychelles Singapore Mali Kazakhstan Slovak Republic Slovenia Mauritania Kiribati South Africa Spain Mongolia Lesotho St. Kitts and Nevis Sweden Mozambique Macedonia, FYR St. Lucia Switzerland Myanmar Maldives St. Vincent and the United Arab Emirates Nepal Marshall Islands Grenadines United Kingdom Niger Micronesia, Fed. Sts. Trinidad and Tobago United States Nigeria Moldova Turkey Virgin Islands (U.S.) Pakistan Morocco Uruguay Papua New Guinea Namibia Venezuela, RB Rwanda Nicaragua São Tomé and Principe Paraguay High income Senegal Peru Andorra Sierra Leone Philippines Antigua and Barbuda Solomon Islands Samoa Aruba Somalia Serbia and Montenegro Australia Sudan Sri Lanka Austria Tajikistan Suriname Bahamas, The Tanzania Swaziland Bahrain Timor-Leste Syrian Arab Republic Belgium Togo Thailand Bermuda Uganda Tonga Brunei Darussalam Uzbekistan Tunisia Canada Vietnam Turkmenistan Cayman Islands Yemen, Rep. Ukraine Channel Islands Zambia Vanuatu Cyprus Zimbabwe West Bank and Gaza Denmark Faeroe Islands Lower middle income Upper middle income Finland Albania American Samoa France Algeria Argentina French Polynesia Angola Barbados Germany The world by income Low ($875 or less) Classified according to Lower middle ($876­$3,465) World Bank estimates of 2005 GNI per capita Upper middle ($3,466­$10,725) High ($10,726 or more) No data Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London 2007 WORLD DEVELOPMENT INDICATORS Copyright 2007 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 2007 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 credits: Front cover, clockwise from top left, World Bank photo library, Alfredo Caliz/Panos Pictures, World Bank photo library, and Digital Vision. 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 0-8213-6959-8 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 2007 on recycled paper with 30 percent post-consumer waste, 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: 93 trees 4,354 pounds of solid waste 33,908 gallons of waste water 8,169 pounds of net greenhouse gases 65 million BTUs of total energy 2007 WORLD DEVELOPMENT INDICATORS PREFACE You can't monitor development progress without good data. The point may seem obvious, but it bears repeating. What we know about development--successes and failures--depends on the availability and quality of data. Data are the evidence for evidence-based decisionmaking. When we talk about managing for development results, we are talking about using data to plan, implement, guide, and evaluate development programs. We won't know when we have achieved the Millennium Development Goals unless we have the data to measure progress. Strong statistical systems, based on institutional autonomy, professional integrity, and commitment to high standards, provide the basis for producing credible statistics for informed decisionmaking. That is why we are working with our partners to improve international databases, which provide the data for World Development Indicators, and to strengthen national statistical systems, the ultimate source of the data. Three years ago in Marrakech, Morocco, the Second Roundtable on Managing for Development Results endorsed a new strategy for improving development statistics, the Marrakech Action Plan for Statistics (MAPS). Since then, countries and donor agencies have united behind those joint goals. Much has been accomplished. With support from the Partnership for Statistics in Development in the 21st Century (PARIS21), regional bodies, international agencies, and bilateral donors, 88 countries have adopted National Statistical Development Strategies to guide the maturation of their statistical systems. Many are also subscribers to the General Data Dissemination System. Based on these plans, countries and donors have begun to increase their investments in statistics. MAPS also called for actions to improve the quality and availability of data needed in the near term to measure progress on national development plans and the Millennium Development Goals. An Accelerated Data Program, piloted in six African countries, is demonstrating that even existing data sets can yield valuable information. Work on the next round of population and housing censuses has begun. The United Nations Statistics Division has initiated an intergovernmental process to increase support for censuses in developing countries. Along with censuses, surveys are a major source of development statistics. In 2005 the International Household Survey Network was formed to coordinate activities and provide tools for documenting and archiving surveys, thus ensuring that investments in surveys will continue to pay dividends into the future. All of these are important steps in building national and international statistical systems that respond to the demand for evidence to guide development. But more remains to be done, and the need is urgent. The challenges to us--national and international statisticians, donors, data users, and everyone concerned with measuring results--are threefold: · How to accelerate investment in statistics. · How to produce statistics that meet the needs of users. · And how to harmonize donor efforts in support of developing countries as they build their statistical systems. Building statistical systems is a long-term process. So is our commitment. As we plan for the future, we are learning from our experience and realizing the results of past investments. 2007 World Development Indicators v PREFACE This year the preliminary results of the International Comparison Program are being released, providing new comparisons of price levels for more than 140 countries. The program, the largest single data collection effort ever undertaken, is a salutary example of what can be accomplished through global partnership, technical innovation, and systematic attention to building local statistical capacity. When the final results become available in next year's World Development Indicators, we will know more about the size of the world's economy and the welfare of its people than ever before. And what we have learned by working together through the program will help us to manage new large-scale efforts to improve development statistics. As always, we welcome your comments and suggestions for making World Development Indicators, its data- bases, and related publications more useful to you. Shaida Badiee Director Development Data Group vi 2007 World Development Indicators ACKNOWLEDGMENTS This book and its companion volumes, The Little Data Book and The Little Green Data Book, are prepared by a team led by Eric Swanson and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, David Cieslikowski, Sebastien Dessus, Richard Fix, Masako Hiraga, Kiyomi Horiuchi, Raymond Muhula, M.H. Saeed Ordoubadi, Brian Pascual, Sulekha Patel, Changqing Sun, and K.M. Vijayalakshmi, working closely with other teams in the Develop- ment Economics Vice Presidency's Development Data Group. The CD-ROM development team included Azita Amjadi, Ramgopal Erabelly, Saurabh Gupta, Reza Farivari, and William Prince. The work was carried out under the management of Shaida Badiee. The choice of indicators and text content 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 content, please see Credits. For a listing of our 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. Amy Ditzel, Laura Peterson Nussbaum, and Zachary Schauf provided copyediting, proofreading, and production assis- tance. Communications Development's London partner, Peter Grundy of Peter Grundy Art & Design, provided art direc- tion and design. Staff from External Affairs oversaw printing and dissemination of the book. 2007 World Development Indicators vii TABLE OF CONTENTS FRONT Preface v Acknowledgments vii Partners xii Users guide xx 1. WORLD VIEW Introduction 1 1q Countries with high initial mortality rates progress more slowly 6 Goal, targets, and indicators for the Millennium Development 1r Under-five mortality reduction performance is associated with Goals 12 good growth performance 6 Tables 1s Important synergies between health- and education-related Millennium Development Goals 7 1.1 Size of the economy 14 1t Performance in maternal health and under-five mortality 1.2 Millennium Development Goals: eradicating poverty and are associated 7 improving lives 18 1.3 Millennium Development Goals: protecting our common 1u Best and worst performers in reducing child mortality 7 environment 22 1v Most countries are progressing in primary school completion 8 1.4 Millennium Development Goals: overcoming obstacles 26 1w The number of countries with large gender disparity gaps in school is falling rapidly 8 1.5 Women in development 28 1x Countries starting from low levels progress faster in primary 1.6 Key indicators for other economies 32 school completion 8 Text figures, tables, and boxes 1y Countries starting from low levels improve gender parity 1a Faster growth, less dispersion among developing economies more rapidly 8 in the last decade 2 1z The worst gender parity performance is associated with poor 1b Growth accelerated in low- and middle-income countries 2 school completion performance 9 1c Poor developing countries are not systematically catching up 1aa The worst performers on school completion were poor with richer ones 2 growth performers 9 1d Countries that opened up to trade also performed better on growth 2 1bb Best and worst primary school completion performers 9 1e Price inflation dropped in most developing countries in the 1cc More than a billion people still lack access to safe drinking water 10 last decade 3 1dd Carbon dioxide emissions are mounting and accumulating in 1f The worst growth performers have much higher costs to start the atmosphere 10 a business 3 1ee Access to water improved almost everywhere 10 1g Best and worst growth performers in annual per capita GDP 1ff Growth and water access performance are not growth, 1995­2005 3 systematically associated 10 1h The number of poor people declined, mostly in East Asia and Pacific 4 1gg Growth and carbon content reduction performance are 1i Poverty rates are on the decline in South and East Asia 4 correlated . . . 11 1j Inequality has increased in many countries, with or without growth 4 1hh . . . But not enough to claim that growth is good for mitigating 1k Changes in income growth and distribution both affect growth in carbon emissions 11 poverty reduction 4 1ii Best and worst water access performers 11 1l Poverty reduction and per capita income growth performances 1.1a Developing countries produce slightly less than half the are correlated 5 world's output 17 1m The worst poverty reduction performers record very poor 1.2a Location of indicators for Millennium Development Goals 1­5 21 income growth 5 1.3a Location of indicators for Millennium Development Goals 6­7 25 1n Best and worst poverty reduction performers 5 1.4a Location of indicators for Millennium Development Goal 8 27 1o Under-five mortality rates have improved almost everywhere 6 1p The proportion of births attended by skilled staff increased greatly in many countries 6 viii 2007 World Development Indicators 2. PEOPLE 3. ENVIRONMENT Introduction 35 Introduction 121 Tables Tables 2.1 Population dynamics 40 3.1 Rural population and land use 126 2.2 Labor force structure 44 3.2 Agricultural inputs 130 2.3 Employment by economic activity 48 3.3 Agricultural output and productivity 134 2.4 Children at work 52 3.4 Deforestation and biodiversity 138 2.5 Unemployment 56 3.5 Freshwater 142 2.6 Poverty 60 3.6 Water pollution 146 2.7 Distribution of income or consumption 66 3.7 Energy production and use 150 2.8 Assessing vulnerability and security 70 3.8 Energy efficiency and emissions 154 2.9 Education inputs 74 3.9 Sources of electricity 158 2.10 Participation in education 78 3.10 Urbanization 162 2.11 Education efficiency 82 3.11 Urban housing conditions 166 2.12 Education completion and outcomes 86 3.12 Traffic and congestion 170 2.13 Education gaps by income and gender 90 3.13 Air pollution 174 2.14 Health expenditure, services, and use 92 3.14 Government commitment 176 2.15 Disease prevention coverage and quality 96 3.15 Toward a broader measure of savings 180 2.16 Reproductive health 100 Text figures, tables, and boxes 2.17 Nutrition 104 3a Agriculture's share in GDP--declining, but still more than a 2.18 Health risk factors and public health challenges 108 fifth in low-income economies 122 2.19 Health gaps by income and gender 112 3b Agricultural productivity has increased, yielding more output 2.20 Mortality 116 for all 122 Text figures, tables, and boxes 3c More people will experience water scarcity and water stress 123 2a Child mortality has fallen in the past 25 years for countries at 3d Agriculture is the biggest consumer of water . . . 123 all incomes 35 3e . . . and the least productive user 123 2b Under-five mortality is 15 times higher in low-income countries 3f Irrigation has increased, demanding more water 123 than in high-income countries 36 3g Cereal yields have increased in most regions--East Asia has 2c Little reduction in risks for poor children 36 almost reached the high-income economies 124 2d In Sierra Leone most deaths occur before age 5 36 3h Forested areas are shrinking in Latin America and 2e A child born in Denmark can expect to live to be 78 36 Sub-Saharan Africa--recovering in East Asia 124 2f A health gap becomes a life gap 37 3i Agriculture accounts for a seventh of all greenhouse 2g Health inequalities by social, cultural, and geographic factors 37 gas emissions 125 2h Under-five mortality falls with rising income 37 3j Less rain is falling in the Sahel, with dire consequences 125 2i Health inequalities in developing countries 37 3k Horn of Africa suffers floods after parching drought 125 2j Why do the poor receive and seek less healthcare than the rich? 38 3.1a What is rural? Urban? 129 2k Rich people use health services more than poor people 38 3.2a Nearly 40 percent of land globally is used for agriculture 133 2l Some countries have reduced inequalities in use of 3.3a The five countries with the highest agricultural productivity 137 professional healthcare in childbirth 38 3.3b The 10 countries with the highest cereal yield in 2003­05-- 2m Differences in healthcare spending contribute to global and the 10 with the lowest 137 disparities 39 3.5a The rural-urban divide in access to an improved water source 145 2n Where are healthcare workers hiding? 39 3.6a Emissions of organic water pollutants declined in most countries 2o Public health spending benefits the rich most 39 from 1990 to 2003, even among the top emitters 149 2p Health shocks can push households into poverty 39 3.7a Energy use per capita varies widely among the top energy users 153 2.3a Lower wages and less rewarding employment opportunities 3.8a High-income countries contribute more than half of global mean higher risk of poverty for women 51 carbon dioxide emissions 157 2.4a Child labor is an obstacle to education for all 55 3.8b The five largest contributors to carbon dioxide emissions 2.6a Regional poverty estimates 63 differ considerably in per capita emissions 157 2.12a Children from poorer families are less likely to complete 3.9a Coal is still the major source of electricity in all income groups, their schooling 89 with low-income countries increasingly relying on this source 161 2.14a Differences in healthcare expenditures contribute to global 3.10a Population of the world's largest metropolitan areas in 1000, disparities in health outcomes 95 1900, 2000, and 2015 165 2.20a Under-five mortality rates improve as mothers' education 3.11a Selected housing indicators for smaller economies 169 levels rise 119 3.12a The 15 economies with the most expensive gasoline-- and the 15 with the cheapest, 2006 173 2007 World Development Indicators ix TABLE OF CONTENTS 4. ECONOMY 5. STATES AND MARKETS Introduction 185 Introduction 259 Tables Tables 4.a Recent economic performance 188 5.1 Private sector in the economy 264 4.1 Growth of output 190 5.2 Investment climate: enterprise surveys 268 4.2 Structure of output 194 5.3 Business environment: Doing Business indicators 272 4.3 Structure of manufacturing 198 5.4 Stock markets 276 4.4 Structure of merchandise exports 202 5.5 Financial access, stability, and efficiency 280 4.5 Structure of merchandise imports 206 5.6 Tax policies 284 4.6 Structure of service exports 210 5.7 Defense expenditures and arms transfers 288 4.7 Structure of service imports 214 5.8 Public policies and institutions 292 4.8 Structure of demand 218 5.9 Transport services 296 4.9 Growth of consumption, investment, and trade 222 5.10 Power and communications 300 4.10 Central government finances 226 5.11 The information age 304 4.11 Central government expenses 230 5.12 Science and technology 308 4.12 Central government revenues 234 Text figures, tables, and boxes 4.13 Monetary indicators 238 5a Governance and growth go together 260 4.14 Exchange rates and prices 242 5b Criteria for measuring economic and sector policies and 4.15 Balance of payments current account 246 governance system 260 4.16 External debt 250 5c The IDA Resource Allocation Index is a key element of a 4.17 Debt ratios 254 country's IDA performance rating 261 Text figures, tables, and boxes 5d On public sector management, countries bunch around 4a Developing economies increase their share of global output 185 the middle 262 4b Growth is accelerating in the low-income economies 186 5e Strong performance on economic management, weaker on 4c Patterns of regional growth vary widely 186 public sector management 262 4d Inflation is now less than 10 percent in all developing regions 186 5f Worldwide Governance Indicators--Six key dimensions 4e Economies with high growth rates generally have lower rates of governance 262 of inflation 186 5g Other selected sources of data for monitoring governance 263 4f Top 10 economies with largest reserves 187 4g More reserves to cover debt 187 4.3a Manufacturing continues to show strong growth in East Asia 201 4.4a Developing economies' share of world merchandise exports continues to expand 205 4.5a Top 10 developing country exporters of merchandise in 2005 209 4.6a Top 10 developing country exporters of commercial services in 2005 213 4.7a The mix of commercial service imports by developing countries is changing 217 4.9a Investment is rising rapidly in Asia 225 4.10a Fourteen developing economies had a cash deficit greater than 4 percent of GDP 229 4.11a Interest payments are a large part of government expenses for some developing countries 233 4.12a Rich countries rely more on direct taxes 237 4.15a Top 15 economies with the largest current account surplus-- and top 15 economies with the largest current account deficit in 2005 249 4.16a External debt started to decline in the Sub-Saharan African economies in 2005 253 4.17a The debt burden of Sub-Saharan Africa rose slightly in 2005, after falling 257 x 2007 World Development Indicators 6. GLOBAL LINKS BACK Introduction 313 Primary data documentation 369 Statistical methods 378 Tables Credits 380 6.1 Integration with the global economy 316 Bibliography 382 6.2 Growth of merchandise trade 320 Index of indicators 389 6.3 Direction and growth of merchandise trade 324 6.4 High-income economy trade with low- and middle-income economies 327 6.5 Primary commodity prices 330 6.6 Regional trade blocs 332 6.7 Tariff barriers 336 6.8 Global private financial flows 340 6.9 Financial flows from Development Assistance Committee members 344 6.10 Allocation of bilateral aid from Development Assistance Committee members 346 6.11 Aid dependency 348 6.12 Distribution of net aid by Development Assistance Committee members 352 6.13 Net financial flows from multilateral institutions 356 6.14 Movement of people 360 6.15 Travel and tourism 364 Text figures, tables, and boxes 6a Trade growth outpaces GDP growth 314 6b Exports from developing countries have grown fast 314 6c Foreign direct investment leads resource flows to developing economies 314 6d Developing economies differ greatly in external resource flows 314 6e Aid flows are rising 315 6f Only 41 percent of aid finances development projects and general budget support 315 6g Fast growth in tourism, especially for low-income countries 315 6h Migration to developing economies accounts for almost half of all migrants 315 6.1a Private capital flows are rising, but they remain below the peak of 2000 319 6.2a Terms of trade are deteriorating for non-oil-exporting developing countries 323 6.3a Three regions account from more than 75 percent of exports to other developing regions, 2005 326 6.4a Imports from low- and middle-income economies to high-income economies vary considerably 329 6.6a Preferential regional trade agreements have a mixed impact on trade 335 6.8a Private capital flows to developing countries are rising 343 6.11a Official development assistance from non-DAC donors, 2001­05 351 6.12a The flow of bilateral aid from DAC members reflects global events and priorities 355 6.13a Maintaining financial flows from multilateral institutions to developing countries 359 6.14a High-skill workers in developing countries are increasingly emigrating to high-income countries 363 6.15a International tourism generated more than $2 billion a day in 2005 367 2007 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, 2007. 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 corporation for international cooperation with worldwide operations. GTZ's aim is to positively shape political, economic, ecologi- cal, 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 2007 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 promo- tion of social justice and internationally recognized human and labor rights. 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) was established to promote international monetary cooperation, facilitate the expansion and balanced growth of international trade, promote exchange rate stability, help establish a multilateral payments system, make the general resources of the IMF temporarily available to its members under adequate safeguards, and shorten the duration and lessen the degree of disequilibrium in the international balance of payments of members. For more information, see www.imf.org/. International Telecommunication Union The International Telecommunication Union (ITU), a specialized agency of the United Nations, covers all aspects of telecommunication, from setting standards that facilitate seamless interworking of equip- ment and systems on a global basis to adopting operational procedures for the vast and growing array of wireless services and designing programs to improve telecommunication infrastructure in the devel- oping world. The ITU is also a catalyst for forging development partnerships between government and private industry. For more information, see www.itu.int/. 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. It is responsible for promoting science and engineering through almost 20,000 research and education projects. In addition, the NSF fosters the exchange of scientific information among scien- tists and engineers in the United States and other countries, supports programs to strengthen scientific and engineering research potential, and evaluates the impact of research on industrial development and general welfare. For more information, see www.nsf.gov/. 2007 World Development Indicators xiii PARTNERS 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. With active relationships with some 70 other countries, nongovernmental organizations, and civil society, 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 bod- ies 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 support 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. 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/. xiv 2007 World Development Indicators 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 works with other UN bodies and with governments and nongovernmental organizations to improve children's lives in more than 140 developing countries through community-based services in primary health care, basic education, and safe water and sanitation. 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 is a specialized agency of the United Nations that promotes "collaboration among nations through education, science, and culture in order to further universal respect for justice, for the rule of law, and for the human rights and fundamental freedoms . . . for the peoples of the world, without distinction of race, sex, language, or religion." 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, 2007 World Development Indicators xv PARTNERS 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/. World Bank Group The World Bank Group is the world's largest source of development assistance. Its mission is to fight poverty and improve the living standards of people in the developing world. It is a development bank, providing loans, policy advice, technical assistance, and knowledge sharing services to low- and middle-income countries to reduce poverty. The Bank promotes growth to create jobs and to empower poor people to take advantage of these opportunities. It uses its financial resources, trained staff, and extensive knowledge base to help each developing country onto a path of stable, sustainable, and equitable growth in the fight against poverty. The World Bank Group has 185 member countries. For more information, see www.worldbank.org/data/. 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. The WHO carries out a wide range of func- tions, including coordinating international health work; helping governments strengthen health services; providing technical assistance and emergency aid; working for the prevention and control of disease; pro- moting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promoting and coordinating biomedical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international standards for biological, pharmaceuti- cal, and similar products; and standardizing diagnostic procedures. For more information, see www.who.int/. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is an international organization dedicated to helping to ensure that the rights of creators and owners of intellectual property are protected worldwide and that inventors and authors are thus recognized and rewarded for their ingenuity. WIPO's main tasks include harmonizing national intellectual property legislation and procedures, providing services for international applications for industrial property rights, exchanging intellectual property information, providing legal and technical assistance to developing and other countries facilitating the resolution of private intellectual property disputes, and marshalling information technology as a tool for storing, accessing, and using valuable intellectual property information. A substantial part of its activities and resources is devoted to development cooperation with developing countries. 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.world-tourism.org/. xvi 2007 World Development Indicators 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 possible. It does this by administering trade agreements, acting as a forum for trade negotia- tions, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues--through technical assistance and training programs--and cooperating with other inter- national 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 con- tainer 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/. 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 with a mission to encourage and promote development and maintenance of better and safer roads and road networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF has a major role to play in all aspects of road policy and development worldwide. For govern- ments and fi nancial institutions, the IRF provides a wide base of expertise for planning road develop- ment strategy and policy. For its members, the IRF is a business network, a link to external institutions and agencies and a business card of introduction to government offi cials and decisionmakers. For the community of road professionals, the IRF is a source of support and information for national road associations, advocacy groups, companies, and institutions dedicated to the development of road infrastructure. For more information, see www.irfnet.org/. 2007 World Development Indicators xvii PARTNERS Netcraft Netcraft's work includes the provision of network security services and research data and analysis of the Internet. It is an authority on the market share of Web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scripting languages, and content technologies on the Internet. For more information, see www.netcraft.com/. PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused assurance, tax, and advisory services for public and private clients in corporate accountability, risk management, structuring and mergers and acquisitions, and performance and process improvement. For more information, see www.pwcglobal.com/. Standard & Poor's Emerging Markets Data Base Standard & Poor's Emerging Markets Data Base (EMDB) is the world's leading source for information and indices on stock markets in developing countries. It currently covers 53 markets and more than 2,600 stocks. Drawing a sample of stocks in each EMDB market, Standard & Poor's calculates indices to serve as benchmarks that are consistent across national boundaries. Standard & Poor's calculates one index, the S&P/IFCG (Global) index, that refl ects the perspective of local investors and those inter- ested in broad trends in emerging markets and another, the S&P/IFCI (Investable) index, that provides a broad, neutral, and historically consistent benchmark for the growing emerging market investment community. For more information, see www.standardandpoors.com/. 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 the global voice of the information tech- nology industry. It is dedicated to advocating policies that advance the industry's growth and development; facilitating international trade and investment in information technology products and services; strengthening WITSA's national industry associations; and providing members with a broad network of contacts. WITSA also hosts the World Congress on Information Technology and other worldwide events. For more information, see www.witsa.org/. xviii 2007 World Development Indicators 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 environmentally sound, socially equitable development. The institute's current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agricul- ture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/. 2007 World Development Indicators xix USERS GUIDE Tables weighted averages (w), or simple averages (u). Gap fill- coverage, practices, and definitions differ widely; and The tables are numbered by section and display the ing of amounts not allocated to countries may result cross-country and intertemporal comparisons involve identifying icon of the section. Countries and econo- in discrepancies between subgroup aggregates and complex technical and conceptual problems that can- mies are listed alphabetically (except for Hong Kong, overall totals. For further discussion of aggregation not be resolved unequivocally. Data coverage may China, which appears after China). Data are shown methods, see Statistical methods. not be complete because of special circumstances for 152 economies with populations of more than affecting the collection and reporting of data, such 1 million, as well as for Taiwan, China, in selected as problems stemming from conflicts. tables. Table 1.6 presents selected indicators for Aggregate measures for regions For these reasons, although data are drawn from 56 other economies--small economies with popu- The aggregate measures for regions include only the sources thought to be most authoritative, they lations between 30,000 and 1 million and smaller low- and middle-income economies (note that these should be construed only as indicating trends and economies if they are members of the International measures include developing economies with popu- characterizing major differences among economies Bank for Reconstruction and Development (IBRD) or, lations of less than 1 million, including those listed rather than as offering precise quantitative mea- as it is commonly known, the World Bank. The term in table 1.6). sures of those differences. Discrepancies in data country, used interchangeably with economy, does The country composition of regions is based on presented in different editions of World Development not imply political independence, but refers to any the World Bank's analytical regions and may differ Indicators reflect updates by countries as well as territory for which authorities report separate social from common geographic usage. For regional clas- revisions to historical series and changes in meth- or economic statistics. When available, aggregate sifications, see the map on the inside back cover and odology. Thus readers are advised not to compare measures for income and regional groups appear at the list on the back cover flap. For further discussion data series between editions of World Development the end of each table. of aggregation methods, see Statistical methods. Indicators or between different World Bank publica- Indicators are shown for the most recent year tions. Consistent time-series data for 1960­2005 or period for which data are available and, in most are available on the World Development Indicators tables, for an earlier year or period (usually 1990 in Statistics CD-ROM and in WDI Online. this edition). Time-series data are available on the Data are shown for economies as they were con- Except where otherwise noted, growth rates are World Development Indicators CD-ROM and in WDI stituted in 2005, and historical data are revised to in real terms. (See Statistical methods for information Online. reflect current political arrangements. Exceptions are on the methods used to calculate growth rates.) Data Known deviations from standard definitions or noted throughout the tables. for some economic indicators for some economies breaks in comparability over time or across countries Additional information about the data is provided are presented in fiscal years rather than calendar are either footnoted in the tables or noted in About in Primary data documentation. That section sum- years; see Primary data documentation. All dollar fig- the data. When available data are deemed to be marizes national and international efforts to improve ures are current U.S. dollars unless otherwise stated. too weak to provide reliable measures of levels and basic data collection and gives country-level informa- The methods used for converting national currencies trends or do not adequately adhere to international tion on primary sources, census years, fiscal years, are described in Statistical methods. standards, the data are not shown. statistical methods and concepts used, and other background information. Statistical methods provides technical information on some of the general calcula- Country notes Aggregate measures for income groups tions and formulas used throughout the book. · Unless otherwise noted, data for China do not The aggregate measures for income groups include include data for Hong Kong, China; Macao, China; 208 economies (the economies listed in the main or Taiwan, China. tables plus those in table 1.6) whenever data are Data consistency, reliability, and comparability · Data for Indonesia include Timor-Leste through available. To maintain consistency in the aggregate Considerable effort has been made to standardize 1999 unless otherwise noted. measures over time and between tables, missing the data, but full comparability cannot be assured, · Although Montenegro declared independence data are imputed where possible. The aggregates and care must be taken in interpreting the indicators. from Serbia and Montenegro on June 3, 2006, are totals (designated by a t if the aggregates include Many factors affect data availability, comparability, this edition of World Development Indicators con- gap-filled estimates for missing data and by an s, for and reliability: statistical systems in many develop- tinues to list and show data for Serbia and Monte- simple totals, where they do not), median values (m), ing economies are still weak; statistical methods, negro together; any exceptions are noted. Data xx 2007 World Development Indicators from 1999 onward for Serbia and Montenegro Symbols for most indicators exclude data for Kosovo, a .. territory within Serbia that is currently under inter- means that data are not available or that aggregates national administration pursuant to UN Security cannot be calculated because of missing data in the Council Resolution 1244 (1999); any exceptions years shown. are noted. 0 or 0.0 means zero or small enough that the number would Classification of economies round to zero at the displayed number of decimal For operational and analytical purposes the World places. Bank's main criterion for classifying economies is gross national income (GNI) per capita (calculated / by the World Bank Atlas method). Every economy is in dates, as in 2003/04, means that the period of classified as low income, middle income (subdivided time, usually 12 months, straddles two calendar into lower middle and upper middle), or high income. years and refers to a crop year, a survey year, or a For income classifications see the map on the inside fiscal year. front cover and the list on the front cover fl ap. Low- and middle-income economies are sometimes $ referred to as developing economies. The term is means current U.S. dollars unless otherwise noted. used for convenience; it is not intended to imply that all economies in the group are experiencing > similar development or that other economies have means more than. reached a preferred or final stage of development. Note that classifi cation by income does not neces- < sarily refl ect development status. Because GNI per means less than. capita changes over time, the country composition of income groups may change from one edition of World Development Indicators to the next. Once the Data presentation conventions classifi cation is fi xed for an edition, based on GNI · A blank means not applicable or, for an aggre- per capita in the most recent year for which data gate, not analytically meaningful. are available (2005 in this edition), all historical · A billion is 1,000 million. data presented are based on the same country · A trillion is 1,000 billion. grouping. · Figures in italics refer to years or periods other Low-income economies are those with a GNI per than those specified or to growth rates calculated capita of $875 or less in 2005. Middle-income econ- for less than the full period specified. omies are those with a GNI per capita of more than · Data for years that are more than three years $875 but less than $10,726. Lower middle-income from the range shown are footnoted. and upper middle-income economies are separated at a GNI per capita of $3,465. High-income econo- The cutoff date for data is February 1, 2007. mies are those with a GNI per capita of $10,726 or more. The 13 participating member countries of the European Monetary Union (EMU) are presented as a subgroup under high-income economies. Note that Slovenia joined the EMU on January 1, 2007. 2007 World Development Indicators xxi WORLD VIEW 1 Introduction M easuring development--in ways familiar and new To achieve the Millennium Development Goals by 2015 many countries need to quickly improve their economic growth and their education and health systems, their management of environmental resources, and their infrastructure for water, sanitation, telecommunica- tions, and transportation. Over the last 10 years developing economies have grown faster than in any period since 1965--and even faster since 2000. While the global picture is dominated by the larger economies--Brazil, China, India, Russia, and South Africa, recently joined by the major oil exporters--more are now doing well and fewer have suffered severe recessions, raising average growth rates. Economic growth is a clear marker of development, and countries that grow usually reduce poverty. But if the fruits of growth are not widely shared many poor people can be left behind even as average incomes rise. Nor does economic growth guarantee that access to water will improve or that more children will attend school. But failing to grow almost always makes matters worse. In considering the recent progress of developing countries on many social, economic, and environmental indicators, the Millennium Development Goals set one standard for all coun- tries. But country performance is influenced by many factors. One is the starting point. Countries starting from worse positions have the potential to make faster progress, as they may benefit from the experience and technologies of more advanced economies. But poor countries may also face unusual obstacles in reaching their development goals. In either case, comparing a country's progress over the last decade with the average progress of those starting from a similar position can help to identify countries that have made exceptional progress--and those whose progress has been unexpectedly slow. This section compares the progress of developing countries measured by the rate of change of selected indicators after first taking into account countries' starting points. The difference between actual progress and the average progress of countries starting from a similar posi- tion is referred to as country performance, and countries are classified as follows: · Best performers are significantly above the average of countries with similar starting points. · Good performers are above average, yet not significantly so in a statistical sense. · Poor performers are below the average, yet not significantly so in a statistical sense. · Worst performers are significantly below the average of countries with similar starting points. Those that perform well on one indicator may not perform well on another. The patterns are complex, but they begin to highlight more of the diversity--and sometimes the commonality-- of outcomes in development. 2007 World Development Indicators 1 Economic growth Per capita GDP growth accelerated in low- and middle-income Although developing economies as a whole are catching countries in the last decade (1995­2005), as more coun- up with high-income economies, there is little evidence of tries grew at a moderate pace and fewer experienced severe convergence between low- and middle-income economies. recessions (figure 1a). And it was systematically faster in de- For them, the relationship between per capita growth rates veloping countries than in high-income countries in the last and initial levels of per capita GDP shows that lower initial five years--for the first time since the de-colonization period per capita GDP was not systematically associated with higher (figure 1b). per capita GDP growth (figure 1c). This tells us that coun- Current projections suggest that developing countries tries start out with roughly the same potential for economic will continue to grow more rapidly than high-income ones in growth. Differences in performance are likely to be associ- the next 25 years. Based on these scenarios, the develop- ated with policies and institutions that encourage productive ing country share of the global economy could rise from 23 investment in human, social, and physical capital. But luck percent of world GDP today to 31 percent in 2030, and devel- also plays an important role, particularly in the small and oping country average incomes could increase from 16 per- poor countries, which are more sensitive to external shocks, cent to 24 percent of those of high-income countries (World good and bad: conflicts, terms of trade, and the like. Bank, Global Economic Prospects 2007). But the income gap Globalization's intense pace in the last decade--in trade, between developing and high-income economies will remain finance, technology, ideas, and migration--has changed the substantial, and the absolute difference in per capita incomes external environment for countries. Most developing countries will continue to widen. have further integrated into world markets, notably through a reduction in trade barriers and transport costs. Here, trade integration is measured by the ratio of imports and exports of goods and services to GDP. For countries starting from Faster growth, less dispersion among Poor developing countries are not developing economies in the last decade 1a systematically catching up with richer ones 1c Number of countries 1985­95 1995­2005 Per capita GDP growth rate, 1995­2005 (%) 25 12 20 8 15 4 10 0 5 ­4 0 ­8 ­7 ­6 ­5 ­4 ­3 ­2 ­1 0 1 2 3 4 5 6 7 More 100 1,000 10,000 100,000 Per capita GDP growth rate (%) than 7 Per capita GDP, 1995 (PPP $, log) Note: Based on 100 country observations. Note: Based on 125 country observations. Source: World Bank staff calculations. Source: World Bank staff calculations. Growth accelerated in low- and Countries that opened up to trade middle-income countries 1b also performed better on growth 1d Annual growth in GDP per capita (%) Per capita GDP growth performance (percentage points) 7 0.6 6 0.4 Low-income Middle-income 0.2 5 0.0 4 ­0.2 3 ­0.4 2 ­0.6 1 ­0.8 High-income ­1.0 0 -­1.2 ­1 Worst Poor Good Best ­2 Trade integration performance 1980 1985 1990 1995 2000 2005 Note: Based on 109 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in trade integration or per capita GDP. Trade integration is measured by the Note: Based on market exchange rates. ratio of imports and exports of goods and services to GDP. Source: World Bank staff calculations. Source: World Bank staff calculations. 2 2007 World Development Indicators similar positions, countries integrating less rapidly recorded Country growth performance is benchmarked against the much lower per capita GDP growth (figure 1d). But that does average growth rate for countries that started with a similar not mean that trade integration necessarily causes growth. per capita GDP in 1995 (in purchasing power parity terms). Other factors, such as gains in competitiveness caused Because initial levels of per capita GDP had little influence by domestic policies, can cause both faster growth and on growth rates over the period, potential average growth is increased trade. almost identical for all countries (figure 1g). The best and Macroeconomic management also improved in the devel- worst performers, which significantly deviated from averages oping world, reflected in the sharp drop in the number of coun- in one direction or the other, are marked with an asterisk. tries with very high price inflation (figure 1e). The best growth Among rapidly growing countries, many are in Eastern performers recorded average annual inflation of 12 percent Europe or are oil exporters. One can also find some post- over the last decade--worst performers, 29 percent. conflict countries. At the slow end of the spectrum are coun- Cumbersome business environments also hamper tries that experienced major conflicts or financial crises in growth. The cost of starting a private business, as a percent- the last decade, are landlocked, or are far from major trade age of per capita income, is an indicator of the opportunity routes. Most of them are located in Sub-Saharan Africa. for entrepreneurs to develop new economic activities and to compete with existing businesses, an important force driving economic growth. That cost varies from less than 5 percent to a striking 1,440 percent--or 14 years of per capita income in 2005. Countries that performed worst on growth in the last decade also had much higher startup costs than other countries in 2005 (figure 1f). Price inflation dropped in most Best and worst growth performers in developing countries in the last decade 1e annual per capita GDP growth, 1995­2005 1g Number of countries 1985­95 1995­2005 Per capita GDP growth Actual Average growth of countries performance (percentage points) growth starting from similar position 50 10 40 8 30 6 20 4 10 2 0 Less than 5 5­10 11­20 More than 20 0 Average annual inflation (%) Azerbaijan* Bosnia and Herzegovina* Armenia* Kazakhstan* Latvia* China* Estonia* Belarus* Georgia* Lithuania* Chad* Mozambique* Trinidad and Tobago Albania Vietnam* Botswana Cambodia Russian Federation Tajikistan Hungary Note: Based on 107 country observations. Source: World Bank staff calculations. The worst growth performers have 4 much higher costs to start a business 1f Business startup costs as share of per capita income, 2005 (%) 2 350 300 0 Honduras Niger Uruguay Madagascar Central African Rep. Haiti Togo 250 Burundi Gabon* Paraguay* Venezuela, RB* ­2 Côte d'Ivoire* Vanuatu* Djibouti* Eritrea* 200 Congo, Rep.* 150 ­4 Congo, Dem. Rep.* Guinea-Bissau* 100 Zimbabwe* Solomon Islands* 50 ­6 0 Worst Poor Good Best Per capita GDP growth performance, 1995­2005 Note: Based on 109 country observations. Performance is the difference between Note: Based on 125 country observations. Asterisks indicate performers that actual growth and average growth of countries starting from similar positions in per significantly deviated, positively or negatively, from average per capita GDP growth capita GDP. of countries with similar starting points. Source: World Bank staff calculations. Source: World Bank staff calculations. 2007 World Development Indicators 3 Poverty reduction The number of people living on less than $1 a day in develop- The responsiveness of poverty to growth depends on the ing countries fell by more than 260 million over 1990­2004, distribution of income (or consumption) and how it changes. thanks in large part to massive poverty reduction in China. In Many factors influence how the benefits of growth are shared: contrast, the number of poor people continued to increase in health, education, infrastructure, gender parity, social safety Sub- Saharan Africa, rising by almost 60 million (figure 1h). In nets, rule of law, political voice and participation, and access turn, the share of the population in Sub-Saharan Africa living to markets, technology, information, and credit (World Bank on less than $1 a day dropped from 47 percent in 1990 to 2005d). In the last decade poverty reduction was not always 41 percent in 2004 (figure 1i). or everywhere commensurate with income growth. In some The Millennium Development Goal of halving the pro- countries and regions, inequality worsened, as poor people portion of poor people is still within reach at the worldwide did not reap the fruits of economic expansion, lacking oppor- level--with a projected decline from 29 percent to 10 percent tunities to do so. between 1990 and 2015. But many countries will most likely Fifty-nine countries with comparable $1 or $2 a day pov- not reach it, particularly those in Sub-Saharan Africa, where erty data measured at two points in time (with a gap of at average poverty rates remain above 40 percent, raising con- least 10 years) over the last two decades show that growth cerns of widening inequalities between regions. and changes in income distribution can reinforce or offset their effects on poverty reduction (figures 1j and 1k). In 26 cases income growth was accompanied by increased inequal- ity, and in 20 more income distribution worsened as average incomes fell. The number of poor people declined, Inequality has increased in many mostly in East Asia and Pacific 1h countries, with or without growth 1j Change in the number of poor people, 1990­2004 (millions) Positive income growth and 100 decreasing inequalities 50 10 countries (17%) Negative income growth and increasing inequalities 0 20 countries (34%) ­50 ­100 ­150 ­200 Positive income growth ­250 and increasing inequalities ­300 Negative income growth 26 countries (44%) and decreasing inequalities ­350 3 countries (5%) East Asia Europe & Latin America Middle East & South Sub-Saharan & Pacific Central Asia & Caribbean North Africa Asia Africa Note: Based on 59 country observations. Source: World Bank staff calculations. Source: World Bank staff calculations. Poverty rates are on the Changes in income growth and decline in South and East Asia 1i distribution both affect poverty reduction 1k Share of population living on less than $1 a day (%) Change in poverty due to change in income distribution (percentage points) 60 2 Poverty increase 50 Sub-Saharan Africa 1 40 South Asia 0 30 East Asia & Pacific 20 ­1 Latin America & Caribbean Poverty decrease 10 Middle East & North Africa Europe & Central Asia ­2 0 ­4 ­3 ­2 ­1 0 1 2 3 4 5 6 1981 1984 1987 1990 1993 1996 1999 2002 2004 Change in poverty due to growth (percentage points) Note: Based on 59 country observations. Source: World Bank staff calculations. Source: World Bank staff calculations. 4 2007 World Development Indicators But this is not to say that growth is bad for poverty reduc- Countries are ranked here by poverty reduction in the tion. In 17 cases the contribution of growth to poverty reduc- most recent 10-year period with data (figure 1n; periods vary tion surpassed the negative impact of worsening inequality, from country to country depending on the availability of pov- and in another 11 cases reduction in inequality added to the erty surveys). Also shown is the average poverty reduction poverty-reducing effect of positive growth. In only one case-- of countries starting from a similar initial poverty rate. The out of 60--was poverty reduced despite negative income best and worst performers, which significantly deviated from growth. expectations in one direction or the other, are marked with Looking at the relationship between countries' per capita an asterisk. income growth and performance in reducing $1 a day pov- There is great diversity in the characteristics of good per- erty (controlling for starting points) also suggests a posi- formers. Among them are low- and middle-income countries tive and significant statistical relationship between the two from most regions and with varying population sizes. Note (figure 1l). too that the best and worst performers are not necessarily The worst poverty reduction performers recorded particu- the countries that recorded the largest absolute changes in larly weak income growth performance (figure 1m). But the poverty rates. Mauritania, for example, recorded a substan- distinction among the three other groups of performers (poor, tial reduction but still fell short of the average performance good, and best) is less pronounced. This suggests that the of countries with similar initial poverty rates. Mexico experi- relationship between income growth and poverty reduction is enced a smaller poverty reduction but significantly exceeded more diverse when the economy is not in deep recession. In the average benchmark. other words, income growth is necessary but may not be suf- ficient for sustained poverty reduction. Poverty reduction and per capita income Best and worst poverty growth performances are correlated 1l reduction performers 1n Poverty reduction performance (percentage points) Absolute changes in the 15 poverty headcount index Actual Average progress of countries ($1 a day, PPP) per year (%) progress starting from similar position 10 2 5 0 1 ­5 0 ­10 ­15 Kyrgyz Republic 1993­2003* Brazil 1993­2003 ­1 Honduras 1986­99 ­20 Mexico 1992­2002* Nepal 1985­2004 Indonesia 1987­2002 ­12 ­10 ­8 ­6 ­4 ­2 0 2 4 6 8 China 1990­2001 Guatemala 1989­2002 Mauritania 1987­2000 India 1987­99 Per capita income growth performance (percentage points) ­2 Note: Based on 41 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in poverty rates or per capita incomes. ­3 Source: World Bank staff calculations. 2 The worst poverty reduction performers record very poor income growth 1m 1 Per capita income growth performance (percentage points) 3 2 0 Turkmenistan 1988­98* Lao PDR 1992­2002* Peru 1990­2002 Paraguay 1990­2002 Uzbekistan 1988­2000* Moldova 1988­2001* Bolivia 1991­2002 1 0 ­1 Rwanda 1985­2000 ­1 ­2 Madagascar 1980­2001 ­3 ­2 ­4 Nigeria 1993­2003 ­5 ­6 ­3 ­7 Worst Poor Good Best Poverty reduction performance Note: Based on 41 country observations. Performance is the difference between Note: Based on 41 country observations. Asterisks indicate performers that signifi - actual rate of change and average rate of change of countries starting from similar cantly deviated, positively or negatively, from average rate of change in poverty rate positions in poverty rates or per capita incomes. of countries with similar initial position over the period indicated. Source: World Bank staff calculations. Source: World Bank staff calculations. 2007 World Development Indicators 5 Health More than 10 million children in developing countries die be- Performance in reducing child mortality is measured by fore the age of five every year, mostly from preventable ill- progress from a given starting position. Worrying--and unlike nesses. Child mortality has declined in every region since other development goals--countries with high initial mortal- 1990 (figure 1o), but progress is slow: only 35 countries ity rates face greater difficulties in reducing them (in rela- are on track to meet the Millennium Development Goal of tive terms) than do countries starting from more favorable reducing under-five mortality by two-thirds between 1990 and positions (figure 1q). HIV/AIDS and other communicable dis- 2015. Progress is particularly slow in Sub-Saharan Africa, eases are probably behind this, as countries with higher HIV where AIDS, malaria, and malnutrition are driving up mortal- prevalence rates record significantly lower reductions in child ity rates. mortality. Countries with high under-five mortality rates are Improving maternal health, itself a goal, is a powerful also often countries where malaria is prevalent and difficult instrument for reducing child mortality. More than 500,000 to curb. women in developing countries die in childbirth each year, Economic growth is associated with improving mortality and at least 10 million suffer injuries, infections, and disabili- outcomes. On average, good and best performers in reduc- ties. High mortality results from malnutrition, frequent preg- ing under-five mortality had significantly higher growth per- nancies, and inadequate healthcare during pregnancy and formance than did poor and worst performers (figure 1r). delivery. Women are receiving better care during childbirth, Accordingly, country case studies emphasize the influence of with the proportion of births attended by skilled health staff poverty in determining child mortality. Because poor children going up from 60 percent to 70 percent between 1990 and are more likely to be malnourished and to receive less health- 2004 (figure 1p). Countries in Africa and South Asia neverthe- care, they are more exposed to the risk of dying before the less lag behind, with much lower ratios. age of five. Under-five mortality rates have Countries with high initial mortality improved almost everywhere 1o rates progress more slowly 1q Number of countries 1990 2004 Change in under-five mortality rate (percentage points) 40 10 35 8 6 30 4 25 2 20 0 15 ­2 ­4 10 ­6 5 ­8 0 ­10 Less than 10 10­25 26­50 51­100 101­150 151­200 More than 1 10 100 1,000 Under-five mortality rate (per 1,000) 200 Initial under-five mortality rate (per 1,000, log) Note: Based on 147 country observations. Note: Based on 147 country observations. Source: World Bank staff calculations. Source: World Bank staff calculations. The proportion of births attended by skilled Under-five mortality reduction performance is staff increased greatly in many countries 1p associated with good growth performance 1r Number of countries 1986­95 1999­2005 Per capita GDP growth performance (percentage points) 25 10 8 20 6 4 2 15 0 ­2 10 ­4 ­6 ­8 5 ­10 Worst Poor Good Best 0 Under-five mortality reduction performance Less than 10 10­30 31­50 51­70 71­90 More than 90 Note: Based on 116 country observations. Performance is the difference between Share of births attended by skilled health staff (%) actual rate of change and average rate of change of countries starting from similar Note: Based on 66 country observations. positions in under-fi ve mortality rates or per capita GDP. Source: World Bank staff calculations. Source: World Bank staff calculations. 6 2007 World Development Indicators Performance in reducing under-five mortality rates is sig- Countries are ranked here by their reduction in under-five nificantly associated with education (primary school comple- mortality rates over 1990­2004 (figure 1u). Also shown is tion) and gender (equal access to schooling), suggesting the average reduction of countries starting from a similar that there are synergies among the Millennium Development position. The best and worst performers, which far exceeded Goals (figure 1s). averages in one direction or the other, are marked with an The relationship between per capita GDP growth perfor- asterisk. mance and improvements in maternal healthcare performance Most of the worst performers are in Sub-Saharan Africa, (as measured by the proportion of births attended by skilled where HIV is rampant, particularly in the east and south. But health staff) is not straightforward--no direct statistical rela- Sub-Saharan Africa also hosts some of the countries that tionship can be observed between the two. But performance recorded the largest drops in under-five mortality. In South in improving maternal healthcare is strongly associated with Asia 4 of the 8 countries are among the 10 countries that performance in reducing under-five mortality (figure 1t). This recorded the largest improvements in mortality rates. Three might not reflect any direct causal relationship between these of them are among the best performers, after accounting for two indicators. Rather, it could reflect the impact of health their starting positions. Iraq, starting from a favorable initial infrastructure and policies on these two indicators. position, saw its under-five mortality rate grow from 50 to 125 per 1,000 over the period 1990­2004. Important synergies between health- and Best and worst performers education-related Millennium Development Goals 1s in reducing child mortality 1u Under-five mortality reduction performance (percentage points) Absolute annual changes in mortality 10 (per 1,000 children ages 1­5), Actual Average progress of countries 1990­2004 progress starting from similar position 8 0 6 ­1 4 ­2 2 ­3 0 ­2 ­4 ­4 ­5 Malawi Maldives* Egypt, Arab Rep.* Nepal Bangladesh* ­6 ­6 Lao PDR* Mozambique Bhutan* Guinea ­8 ­6 ­4 ­2 0 2 4 6 Timor-Leste* ­7 Primary school completion performance (percentage points) Note: Based on 70 country observations. Performance is the difference between ­8 actual rate of change and average rate of change of countries starting from similar positions in under-fi ve mortality rates or primary school completion rates. ­9 Source: World Bank staff calculations. 6 5 Performance in maternal health and under-five mortality are associated 1t 4 3 Under-five mortality reduction performance (percentage points) 1.0 2 0.5 1 0.0 0 ­0.5 ­1 Iraq* ­1.0 Botswana* Zimbabwe* ­2 Kenya* Swaziland* ­1.5 Cambodia* Côte d'Ivoire* Central African Republic Rwanda Equatorial Guinea ­2.0 ­3 ­2.5 ­3.0 Worst Poor Good Best Maternal health performance Note: Based on 66 country observations. Performance is the difference between Note: Based on 147 country observations. Asterisks indicate performers that sig- actual rate of change and average rate of change of countries starting from similar nificantly deviated, positively or negatively, from average rate of change in under-fi ve positions in maternal healthcare or under-fi ve mortality rates. mortality rate of countries with similar initial position. Source: World Bank staff calculations. Source: World Bank staff calculations. 2007 World Development Indicators 7 Education and gender As a result of significant progress over the last decade, the The ability of countries to raise their primary school average primary completion rate has risen from 62 percent completion rates in the last decade was determined largely to 72 percent (figure 1v). But even at this pace Sub-Saharan by their starting point. Countries with lower initial primary Africa and South Asia may not reach the Millennium Devel- completion rates made faster progress (figure 1x), probably opment Goals target of having all children of relevant age reflecting the fact that it becomes more difficult and costly to complete primary school by 2015. In 2001­02 it was esti- enroll and keep all children in school as the number of those mated that about 100 million primary-school-age children left out falls. Country case studies suggest that girls, poor were not attending school, three-quarters of them in these children, and children living in rural areas are less likely to two regions. complete schooling. These are the areas where faster prog- Beyond the necessity of educating all children, eliminat- ress must be made to achieve education for all. ing discrimination against girls' participation in school is a Improvements in gender parity in school are also signifi - powerful instrument for empowering half the world's people, cantly associated with initial conditions. On average coun- improving the health of children, and reducing poverty. Prog- tries starting with greater initial gender disparity have made ress in eliminating gender disparities in primary and second- faster progress (figure 1y). ary school has been remarkable in the last decade (figure When all children are enrolled and complete school, there 1w). On average the deviation from perfect parity (a gender will be no gender disparity in school. Over the last decade the parity index of 100 percent) shrank from 14 percent in 1991 number of countries in which the number of boys in primary to 8 percent in 2003­05. and secondary schools exceed that of girls by more than 40 percent (a gender parity index below 60 percent) fell--from Most countries are progressing Countries starting from low levels progress in primary school completion 1v faster in primary school completion 1x Number of countries 1994 2004 Growth in school completion rate (percentage points) 35 15 30 10 25 5 20 15 0 10 ­5 5 0 ­10 Less than 20 20­40 41­60 61­80 More than 80 0 20 40 60 80 100 Primary school completion rate (%) Initial primary school completion rate (%) Note: Based on 68 country observations. Note: Based on 70 country observations. Source: World Bank staff calculations. Source: World Bank staff calculations. The number of countries with large gender Countries starting from low levels disparity gaps in school is falling rapidly 1w improve gender parity more rapidly 1y Number of countries 1991 2003­05 Growth in gender parity index (percentage points) 60 4 50 3 2 40 1 30 0 20 ­1 10 ­2 0 ­3 Less than 60 60­70 71­80 81­90 91­95 More than 95 40 60 80 100 Gender parity index at school (%) Initial gender parity index at school (%) Note: Based on 97 country observations. The gender parity index is equal to 100 Note: Based on 97 country observations. The gender parity index is equal to 100 minus the relative excess or deficit of boys over girls in primary and secondary school. minus the relative excess or deficit of boys over girls in primary and secondary school. Source: World Bank staff calculations. Source: World Bank staff calculations. 8 2007 World Development Indicators 17 (of 97) to 3. And the number of countries with gender par- Countries are ranked here by their primary school com- ity index above 90 percent increased from 54 to 69. But the pletion progress in the last decade (figure 1bb). Also shown relationship between school completion and improvements in is the average progress of countries starting from a similar gender parity performance (accounting for initial conditions) position. The best and worst performers, which far exceeded appears to be more pronounced and uniform on the negative averages in one direction or the other, are marked with an side than it is on the positive side (figure 1z). Countries that asterisk. most improved their gender parity index did not record sig- The two groups of performers, best and worst, both nificantly higher school completion performances. But coun- include a large number of Sub-Saharan African countries, tries in which gender parity declined the most were countries illustrating the diversity of performance in the region. Devel- where school completion performance was also particularly oping countries improved their primary completion rates by 1 poor, possibly reflecting the fact that dropout rates are higher percentage point every year on average over the last decade for girls than for boys during difficult periods. or so. The best performers all recorded yearly increases There is not a statistically significant correlation between exceeding 2.8 percentage points. performance in per capita GDP growth and primary school completion. While the relationship shows up at the extremes-- the best and worst school completion performers record very distinct growth performances--the growth performance of poor school completion performers cannot be clearly distin- guished from that of good performers (figure 1aa). The worst gender parity performance is associated Best and worst primary with poor school completion performance 1z school completion performers 1bb School completion performance (percentage points) Absolute changes in school 0.5 completion rate per year, Actual Average progress of countries various periods (percentage points) progress starting from similar position 0.0 4 ­0.5 3 ­1.0 ­1.5 2 ­2.0 ­2.5 1 Worst Poor Good Best Gender parity performance 0 Togo, 1994­2004* Ethiopia, 1995­2005* Cape Verde, 1994­2004 Mali, 1994­2004* Dominican Republic, 1994­2004 Morocco, 1994­2004 Guinea, 1994­2004* Benin, 1994­2004 Nicaragua, 1994­2004 Lao PDR, 1994­2004 Note: Based on 58 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in gender parity or primary school completion rates. Source: World Bank staff calculations. The worst performers on school 2 completion were poor growth performers 1aa Per capita GDP growth performance (percentage points) 1 0.2 0.1 0 Turkey, 1991­2004 Iran, Islamic Rep., 1994­2004 0.0 Macedonia, FYR, 1994­2004 Tanzania, 1994­2005 Ukraine, 1991­2001 Jamaica, 1993­2004 Trinidad and Tobago, 1993­2003 ­0.1 Zimbabwe, 1993­2003 ­1 Rwanda, 1992-2004* ­0.2 Burundi, 1993­2004* ­0.3 ­2 ­0.4 ­0.5 Worst Poor Good Best Primary school completion performance Note: Based on 67 country observations. Performance is the difference between Note: Based on 70 country observations. Asterisks indicate performers that signifi - actual rate of change and average rate of change of countries starting from similar cantly deviated, positively or negatively, over the period indicated from the average rate positions in primary school completion rates or per capita GDP. of change in primary school completion rate of countries with similar initial position. Source: World Bank staff calculations. Source: World Bank staff calculations. 2007 World Development Indicators 9 Environment Access to improved water sources and emissions of car- Between 1990 and 2004 the proportion of people in bon dioxide are among the indicators that the international developing countries with access to an improved water source community uses to monitor progress toward environmental increased from 73 percent to 80 percent, and the number of sustainability. countries with more than half the population lacking access Today, more than a billion people in developing countries fell from 24 to 11 (figure 1ee). Countries starting from lower lack access to an adequately protected source of water close positions advanced faster. to their dwellings (figure 1cc). Progress to improve access has Economic activity, agriculture, and industry in particular been significant in the last decade, but probably insufficient compete with human needs for access to water sources. But in Africa to meet the 2015 Millennium Development Goal tar- greater wealth and urbanization allow more of the popula- get of halving the proportion of people in 1990 without sus- tion to connect to safe drinking water networks. The data tainable access to safe drinking water. do not reveal a statistically significant correlation between The role of carbon dioxide in climate change is now well water access and growth performance overall. But the worst documented, but the use of carbon-based energy has addi- growth performers distinctively record poor water access tional effects on human health through local air pollution. Yet performance (figure 1ff). Such countries may also be those emissions mount as countries grow economically, unless they with degraded water infrastructure and poor management reduce the carbon content of their economic activity through capacity. technological progress or shift away from carbon-intensive production and consumption (figure 1dd). More than a billion people still Access to water improved lack access to safe drinking water 1cc almost everywhere 1ee Millions of people 1990 2004 Number of countries 1990 2004 500 80 70 400 60 300 50 40 200 30 20 100 10 0 0 Europe & East Asia Latin America Middle East South Sub-Saharan Less than 20 20­40 41­60 61­80 More than 80 Central Asia & Pacific & Caribbean & North Africa Asia Africa Share of population with access to improved water source (%) Note: Based on 113 country observations. Source: World Bank staff calculations. Source: World Bank staff calculations. Carbon dioxide emissions are mounting Growth and water access performance and accumulating in the atmosphere 1dd are not systematically associated 1ff Billions of tons Water access performance (percentage points) 25 0.2 0.1 20 0.0 Low- and middle-income countries ­0.1 15 ­0.2 ­0.3 10 High-income countries ­0.4 ­0.5 5 Worst Poor Good Best Per capita GDP growth performance 0 Note: Based on 84 country observations. Performance is the difference between 1960 1965 1970 1975 1980 1985 1990 1995 2002 actual rate of change and average rate of change of countries starting from similar positions in per capita GDP or water access. Source: World Bank staff calculations. Source: World Bank staff calculations. 10 2007 World Development Indicators In the next decades all countries need to make impor- Countries are ranked here by their progress in water tant efforts to reduce their carbon emissions. In developing access in 1990­2004. Also shown is the average progress economies such a commitment might be perceived as at of countries starting from a similar position (figure 1ii). The odds with that of fostering growth. But recent history sug- best and worst performers, which far exceeded averages in gests that developing countries that have grown the fastest one direction or the other, are marked with an asterisk. also made the greatest reductions in the carbon content of A number of poor performers suffered from particularly their economic activities (measured by carbon dioxide emis- difficult geographical constraints--small Pacific island or des- sions per unit of GDP in PPP terms; figure 1gg). It is likely ert countries with low rainfall, for instance. But others, also that growth was accompanied by more rapid adoption of new, facing difficult geographical constraints, greatly improved more energy efficient technologies and a shift toward less access to safe water. The best and worst performers are carbon-intensive production and consumption. not necessarily countries that registered the largest abso- This is not enough, however, to claim that growth is good lute changes. Indeed, the initial rate of access to improved for mitigating carbon dioxide emissions: the best growth water sources can alone explain almost half the differences performers recorded much higher growth in carbon dioxide in progress across countries. Accounting for starting points emissions than other groups (figure 1hh). Technical efficiency thus portrays a different picture of relative performances gains were not sufficient to compensate for the growth in across countries. output. Growth and carbon content Best and worst water reduction performance are correlated . . . 1gg access performers 1ii Carbon content performance (percentage points) Annual changes in share of people 15 with permanent access to improved Actual Average progress of countries water source, 1990­2004 (%) progress starting from similar position 10 2.8 5 0 2.1 ­5 ­10 1.4 ­15 ­10 ­5 0 5 10 0.7 GDP growth performance (percentage points) Note: Based on 122 country observations. Performance is the difference between actual rate of change and average rate of change of countries starting from similar positions in carbon content levels or GDP. 0.0 Central African Rep. Afghanistan* Malawi* Namibia* Paraguay Burkina Faso Ghana Myanmar Ecuador Chad Source: World Bank staff calculations. . . . But not enough to claim that growth is good for mitigating growth in carbon emissions 1hh 1.4 Carbon dioxide emissions growth performance (percentage points) 5 4 0.7 3 2 0.0 1 Iraq Philippines Trinidad and Tobago Samoa Yemen, Rep. 0 Comoros ­0.7 Algeria Marshall Islands ­1 Uzbekistan Maldives ­2 Worst Poor Good Best ­1.4 GDP growth performance Note: Based on 122 country observations. Performance is the difference between Note: Based on 102 country observations. Asterisks indicate performers that signifi - actual rate of change and average rate of change of countries starting from similar cantly deviated, positively or negatively, from average rate of change in water access positions in GDP or carbon dioxide emissions. of countries with similar initial position. Source: World Bank staff calculations. Source: World Bank staff calculations. 2007 World Development Indicators 11 Goals, targets, and indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1 Halve, between 1990 and 2015, the proportion of 1 Proportion of population below $1 (PPP) a daya people whose income is less than $1 a day 1a Poverty headcount ratio (percentage of population below the national poverty line) 2 Poverty gap ratio [incidence × depth of poverty] 3 Share of poorest quintile in national consumption Target 2 Halve, between 1990 and 2015, the proportion of 4 Prevalence of underweight children under five years of age people who suffer from hunger 5 Proportion of population below minimum level of dietary energy consumption Goal 2 Achieve universal primary education Target 3 Ensure that, by 2015, children everywhere, boys and 6 Net enrollment ratio in primary education girls alike, will be able to complete a full course of 7 Proportion of pupils starting grade 1 who reach grade 5b primary schooling 8 Literacy rate of 15- to 24-year-olds Goal 3 Promote gender equality and empower women Target 3 Ensure that, by 2015, children everywhere, boys and 6 Net enrollment ratio in primary education girls alike, will be able to complete a full course of 7 Proportion of pupils starting grade 1 who reach grade 5b primary schooling 8 Literacy rate of 15- to 24-year-olds Target 4 Eliminate gender disparity in primary and secondary 9 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 10 Ratio of literate women to men ages 15­24 11 Share of women in wage employment in the nonagricultural sector 12 Proportion of seats held by women in national parliaments Goal 4 Reduce child mortality Target 5 Reduce by two-thirds, between 1990 and 2015, the 13 Under-five mortality rate under-five mortality rate 14 Infant mortality rate 15 Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Target 6 Reduce by three-quarters, between 1990 and 2015, 16 Maternal mortality ratio the maternal mortality ratio 17 Proportion of births attended by skilled health personnel Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 7 Have halted by 2015 and begun to reverse the 18 HIV prevalence among pregnant women ages 15­24 spread of HIV/AIDS 19 Condom use rate of the contraceptive prevalence ratec 19a Condom use at last high-risk sex 19b Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDSd 19c Contraceptive prevalence rate 20 Ratio of school attendance of orphans to school attendance of nonorphans ages 10­14 Target 8 Have halted by 2015 and begun to reverse the 21 Prevalence and death rates associated with malaria incidence of malaria and other major diseases 22 Proportion of population in malaria-risk areas using effective malaria prevention and treatment measurese 23 Prevalence and death rates associated with tuberculosis 24 Proportion of tuberculosis cases detected and cured under directly observed treatment, short course (DOTS) Goal 7 Ensure environmental sustainability Target 9 Integrate the principles of sustainable development 25 Proportion of land area covered by forest into country policies and programs and reverse the 26 Ratio of area protected to maintain biological diversity to loss of environmental resources surface area 27 Energy use (kilograms of oil equivalent) per $1 GDP (PPP) 28 Carbon dioxide emissions per capita and consumption of ozone-depleting chlorofluorocarbons (ODP tons) 29 Proportion of population using solid fuels Target 10 Halve, by 2015, the proportion of people without 30 Proportion of population with sustainable access to an sustainable access to safe drinking water and basic improved water source, urban and rural sanitation 31 Proportion of population with access to improved sanitation, urban and rural 12 2007 World Development Indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Target 11 By 2020, to have achieved a significant improvement 32 Proportion of households with access to secure tenure in the lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 12 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 countries and small island developing states. Includes a commitment to good governance, development and poverty reduction--both nationally Official development assistance (ODA) and internationally 33 Net ODA, total and to the least developed countries, as a percentage of OECD/DAC donors' gross national income 34 Proportion of total bilateral, sector-allocable ODA of OECD/ Target 13 Address the special needs of the least developed DAC donors to basic social services (basic education, countries primary healthcare, nutrition, safe water and sanitation) 35 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 programme 36 ODA received in landlocked countries as a proportion of of debt relief for heavily indebted poor countries their gross national incomes (HIPC) and cancellation of official bilateral debt; 37 ODA received in small island developing states as and more generous ODA for countries committed to proportion of their gross national incomes poverty reduction Market access 38 Proportion of total developed country imports (by value and excluding arms) from developing countries and from the Target 14 Address the special needs of landlocked countries least developed countries, admitted free of duty and small island developing states (through 39 Average tariffs imposed by developed countries on the Programme of Action for the Sustainable agricultural products and textiles and clothing from Development of Small Island Developing States developing countries and the outcome of the 22nd special session of the 40 Agricultural support estimate for OECD countries as a General Assembly) percentage of their gross domestic product 41 Proportion of ODA provided to help build trade capacity Debt sustainability Target 15 Deal comprehensively with the debt problems 42 Total number of countries that have reached their HIPC of developing countries through national and decision points and number that have reached their HIPC international measures in order to make debt completion points (cumulative) sustainable in the long term 43 Debt relief committed under HIPC Debt Initiative 44 Debt service as a percentage of exports of goods and services Target 16 In cooperation with developing countries, develop 45 Unemployment rate of 15- to 24-year-olds, male and and implement strategies for decent and productive female and totalf work for youth Target 17 In cooperation with pharmaceutical companies, 46 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 18 In cooperation with the private sector, make 47 Telephone lines and cellular subscribers per 100 people available the benefits of new technologies, 48a Personal computers in use per 100 people especially information and communications 48b Internet users per 100 people Note: Goals, targets, and indicators effective September 8, 2003. a. For monitoring country poverty trends, indicators based on national poverty lines should be used, where available. b. An alternative indicator under development is "primary completion rate." c. Among contraceptive methods, only condoms are effective in preventing HIV transmission. Since the condom use rate is only measured among women in union, it is supplemented by an indicator on condom use in high-risk situations (indicator 19a) and an indicator on HIV/AIDS knowledge (indicator 19b). Indicator 19c (contraceptive prevalence rate) is also useful in tracking progress in other health, gender, and poverty goals. d. This indicator is defined as the percentage of 15- to 24-year-olds who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can transmit HIV. However, since there are currently not a sufficient number of surveys to be able to calculate the indicator as defined above, UNICEF, in collaboration with UNAIDS and WHO, produced two proxy indicators that represent two components of the actual indicator. They are the percentage of women and men ages 15­24 who know that a person can protect herself from HIV infection by "consistent use of condom," and the percentage of women and men ages 15­24 who know a healthy-looking person can transmit HIV. e. Prevention to be measured by the percentage of children under age fi ve sleeping under insecticide-treated bednets; treatment to be measured by percentage of children under age fi ve who are appropriately treated. f. An improved measure of the target for future years is under development by the International Labour Organization. 2007 World Development Indicators 13 Tables 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 2005 2005 2005 2005b 2005 2005b 2005 2005 2005 2005 2004­05 2004­05 Afghanistan .. 652 .. 7.0 114 ..c .. .. .. .. 14.0 .. Albania 3 29 114 8.0 109 2,570 115 17.0 5,420 121 5.5 4.9 Algeria 33 2,382 14 89.6 49 2,730 108 222.4 d 6,770 d 103 5.3 3.7 Angola 16 1,247 13 22.5 80 1,410 134 35.2d 2,210 d 160 20.6 17.2 Argentina 39 2,780 14 173.1 34 4,470 89 539.4 13,920 64 9.2 8.1 Armenia 3 30 107 4.4 137 1,470 132 15.3 5,060 127 14.0 14.4 Australia 20 7,741 3 673.2 13 33,120 20 622.3 30,610 21 2.8 1.6 Austria 8 84 100 306.2 21 37,190 16 272.9 33,140 12 1.8 1.1 Azerbaijan 8 87 101 10.4 102 1,240 142 41.0 4,890 130 26.2 25.0 Bangladesh 142 144 1,090 66.7 55 470 175 296.4 2,090 165 6.0 4.0 Belarus 10 208 47 27.0 69 2,760 107 77.1 7,890 95 9.2 9.8 Belgium 10 31 347 378.7 18 36,140 17 342.0 32,640 14 1.2 0.7 Benin 8 113 76 4.3 138 510 173 9.4 1,110 189 3.9 0.7 Bolivia 9 1,099 8 9.3 105 1,010 148 25.2 2,740 151 4.1 2.1 Bosnia and Herzegovina 4 51 76 10.5 101 2,700 111 30.4 7,790 96 5.0 5.1 Botswana 2 582 3 9.9 104 5,590 77 18.1 10,250 80 6.2 6.4 Brazil 186 8,515 22 662.0 14 3,550 e 96 1,534.1 8,230 89 2.3 0.9 Bulgaria 8 111 71 26.7 70 3,450 98 66.8 8,630 86 5.5 6.1 Burkina Faso 13 274 48 5.2 131 400 183 16.1d 1,220 d 186 4.8 1.6 Burundi 8 28 294 0.7 188 100 208 4.8d 640 d 208 0.9 ­2.6 Cambodia 14 181 80 6.1 121 430 180 35.0 d 2,490 d 154 13.4 11.2 Cameroon 16 475 35 16.4 86 1,000 150 35.1 2,150 162 2.0 0.3 Canada 32 9,985 4 1,052.6 9 32,590 21 1,040.7 32,220 16 2.9 1.9 Central African Republic 4 623 6 1.4 168 350 186 4.6d 1,140 d 188 2.2 0.9 Chad 10 1,284 8 3.9 143 400 183 14.3d 1,470 d 182 5.6 2.3 Chile 16 757 22 95.7 47 5,870 76 186.9 11,470 76 6.3 5.2 China 1,305 9,634f 140 2,269.7 5 1,740 128 8,609.7g 6,600 g 107 10.2 9.5 Hong Kong, China 7 1 6,664 192.1 30 27,670 29 240.7 34,670 9 7.3 6.3 Colombia 46 1,139 41 104.5 45 2,290 123 338.4 d 7,420 d 98 5.1 3.5 Congo, Dem. Rep. 58 2,345 25 7.0 115 120 207 41.4d 720 d 204 6.5 3.4 Congo, Rep. 4 342 12 3.8 144 950 151 3.2 810 200 9.2 6.0 Costa Rica 4 51 85 20.3 82 4,700 87 41.9d 9,680 d 83 5.9 4.1 Côte d'Ivoire 18 322 57 15.7 87 870 156 27.0 1,490 181 1.8 0.2 Croatia 4 57 79 36.9 61 8,290 65 56.7 12,750 69 4.3 4.3 Cuba 11 111 103 .. .. ..h .. .. .. .. 5.4 5.2 Czech Republic 10 79 132 114.8 41 11,220i 56 206.1 20,140 49 6.1 5.8 Denmark 5 43 128 261.8 26 48,330 6 181.8 33,570 11 3.1 2.8 Dominican Republic 9 49 184 21.9 81 2,460 117 63.6d 7,150 d 101 9.3 7.7 Ecuador 13 284 48 34.7 63 2,620 113 53.8 4,070 138 4.7 3.3 Egypt, Arab Rep. 74 1,001 74 93.0 48 1,260 140 328.7 4,440 133 4.9 3.0 El Salvador 7 21 332 16.8 85 2,450 119 35.2d 5,120 d 125 2.8 1.0 Eritrea 4 118 44 0.8 187 170 201 4.4 d 1,010 d 192 0.5 ­3.4 Estonia 1 45 32 12.2 98 9,060 63 20.8 15,420 60 9.8 10.0 Ethiopia 71 1,104 71 11.1 99 160 202 71.3d 1,000 d 193 8.7 6.8 Finland 5 338 17 196.9 29 37,530 14 163.5 31,170 20 2.1 1.7 France 61 552 111 2,169.2j 6 34,600j 19 1,859.1 30,540 22 1.2 0.6 Gabon 1 268 5 6.9 116 5,010 81 8.2 5,890 115 2.2 0.6 Gambia, The 2 11 152 0.4 192 290 192 2.9d 1,920 d 172 5.0 2.3 Georgia 4 70 64 5.9 124 1,320 137 14.6 3,270 147 9.3 10.3 Germany 82 357 236 2,875.6 3 34,870 18 2,408.9 29,210 27 1.0 1.0 Ghana 22 239 97 10.0 103 450 176 52.4 d 2,370 d 155 5.9 3.8 Greece 11 132 86 220.3 28 19,840 38 262.3 23,620 41 3.7 3.3 Guatemala 13 109 116 30.3 66 2,400 120 55.6d 4,410 d 134 3.2 0.8 Guinea 9 246 38 3.9 140 420 182 21.1 2,240 158 3.3 1.1 Guinea-Bissau 2 36 56 0.3 201 180 200 1.1d 700 d 206 3.5 0.5 Haiti 9 28 309 3.9 142 450 176 15.7d 1,840 d 175 2.0 0.5 14 2007 World Development Indicators 1.1 WORLD VIEW 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 2005 2005 2005 2005b 2005 2005b 2005 2005 2005 2005 2004­05 2004­05 Honduras 7 112 64 8.0 110 1,120 145 20.9d 2,900 d 150 4.0 1.8 Hungary 10 93 113 101.6 46 10,070 59 170.9 16,940 56 4.1 4.3 India 1,095 3,287 368 804.1 10 730 158 3,787.3d 3,460 d 143 9.2 7.7 Indonesia 221 1,905 122 282.2 23 1,280 139 820.5 3,720 140 5.6 4.2 Iran, Islamic Rep. 68 1,648 42 177.3 32 2,600 114 549.4 8,050 91 4.4 2.9 Iraq .. 438 .. .. .. ..h .. .. .. .. 46.5 .. Ireland 4 70 60 171.1 35 41,140 9 144.4 34,720 8 5.5 3.2 Israel 7 22 320 128.7 36 18,580 43 175.0 25,280 37 5.2 3.3 Italy 59 301 199 1,772.9 7 30,250 26 1,690.2 28,840 28 0.0 ­0.8 Jamaica 3 11 245 9.0 106 3,390 99 10.9 4,110 137 1.8 1.3 Japan 128 378 351 4,976.5 2 38,950 12 4,013.4 31,410 18 2.6 2.6 Jordan 5 89 62 13.5 94 2,460 117 28.9 5,280 123 7.3 4.8 Kazakhstan 15 2,725 6 44.6 59 2,940 103 117.1 7,730 97 9.7 8.7 Kenya 34 580 60 18.4 83 540 171 40.1 1,170 187 5.8 3.4 Korea, Dem. Rep. 22 121 187 .. .. ..c .. .. .. .. .. .. Korea, Rep. 48 99 489 765.0 11 15,840 49 1,055.2 21,850 45 4.0 3.5 Kuwait 3 18 142 77.7 51 30,630 25 59.1d 24,010d 36 8.5 5.3 Kyrgyz Republic 5 200 27 2.3 157 450 176 9.6 1,870 174 ­0.6 ­1.6 Lao PDR 6 237 26 2.6 154 430 180 12.0 2,020 166 7.0 4.6 Latvia 2 65 37 15.6 88 6,770 74 31.0 13,480 67 10.2 10.8 Lebanon 4 10 350 22.6 79 6,320 75 20.5 5,740 118 1.0 0.0 Lesotho 2 30 59 1.7 165 950 151 6.1d 3,410 d 144 1.2 1.4 Liberia 3 111 34 0.4 193 130 206 .. .. .. 5.3 3.9 Libya 6 1,760 3 32.4 64 5,530 78 .. .. .. 3.5 1.5 Lithuania 3 65 54 24.6 76 7,210 72 48.6 14,220 62 7.5 8.1 Macedonia, FYR 2 26 80 5.8 126 2,830 106 14.4 7,080 102 4.0 3.8 Madagascar 19 587 32 5.4 130 290 192 16.4 880 197 4.6 1.8 Malawi 13 118 137 2.1 161 160 202 8.4 650 207 2.6 0.4 Malaysia 25 330 77 125.9 38 4,970 82 261.6 10,320 79 5.2 3.3 Mali 14 1,240 11 5.2 132 380 185 13.5 1,000 193 6.1 3.0 Mauritania 3 1,026 3 1.8 163 580 169 6.6d 2,150 d 162 5.4 2.4 Mauritius 1 2 612 6.5 118 5,250 79 15.5 12,450 71 4.6 3.7 Mexico 103 1,958 54 753.4 12 7,310 71 1,034.0 10,030 81 3.0 1.9 Moldova 4 34 128 3.2k 148 930k 154 9.0 2,150 162 7.1 7.4 Mongolia 3 1,567 2 1.8 164 690 160 5.6 2,190 161 6.2 4.6 Morocco 30 447 68 52.6 56 1,740 128 131.5 4,360 135 1.7 0.6 Mozambique 20 802 25 6.2 119 310 191 25.1d 1,270 d 184 7.7 5.7 Myanmar 51 677 77 .. .. ..c .. .. .. .. 5.0 3.9 Namibia 2 824 2 6.1 122 2,990 102 16.1d 7,910 d 93 3.5 2.4 Nepal 27 147 190 7.3 113 270 195 41.5 1,530 179 2.7 0.7 Netherlands 16 42 482 642.0 15 39,340 11 530.1 32,480 15 1.1 0.9 New Zealand 4 271 15 106.3 44 25,920 32 94.4 23,030 42 1.9 1.0 Nicaragua 5 130 42 4.9 133 950 151 18.8 d 3,650 d 141 4.0 3.4 Niger 14 1,267 11 3.3 146 240 196 11.2d 800 d 201 4.5 1.1 Nigeria 132 924 144 74.0 52 560 170 136.8 1,040 191 6.9 4.7 Norway 5 324 15 281.5 24 60,890 2 186.9 40,420 4 2.3 1.6 Oman 3 310 8 23.0 .. 9,070 .. 37.2 14,680 .. 3.1 2.2 Pakistan 156 796 202 107.3 43 690 160 366.1 2,350 157 7.8 5.2 Panama 3 76 43 15.0 90 4,630 88 23.6 7,310 99 6.4 4.5 Papua New Guinea 6 463 13 2.8 141 500 162 14.0 d 2,370 d 155 3.3 1.3 Paraguay 6 407 15 6.1 120 1,040 146 29.3d 4,970 d 129 2.9 1.0 Peru 28 1,285 22 74.0 53 2,650 112 163.1 5,830 117 6.4 4.9 Philippines 83 300 279 109.7 42 1,320 137 440.2 5,300 122 5.0 3.2 Poland 38 313 125 273.1 25 7,160 73 514.9 13,490 66 3.4 3.4 Portugal 11 92 115 181.3 31 17,190 47 208.1 19,730 50 0.4 ­0.1 Puerto Rico 4 9 441 .. .. ..l .. .. .. .. .. .. 2007 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 2005 2005 2005 2005b 2005 2005b 2005 2005 2005 2005 2004­05 2004­05 Romania 22 238 94 84.6 50 3,910 93 193.4 8,940 85 4.1 4.3 Russian Federation 143 17,098 9 638.1 16 4,460 90 1,522.7 10,640 78 6.4 6.9 Rwanda 9 26 366 2.1 160 230 197 11.9d 1,320 d 183 6.0 4.2 Saudi Arabia 23 2,000 m 12 289.2 22 12,510 55 340.8d 14,740 d 61 6.6 3.8 Senegal 12 197 61 8.2 107 700 159 20.6 1,770 176 5.1 2.7 Serbia and Montenegro 8 102 79 26.3n 72 3,220 n 100 .. .. .. 4.7 5.0 Sierra Leone 6 72 77 1.2 174 220 199 4.3 780 202 7.5 3.8 Singapore 4 1 6,302 119.8 39 27,580 30 129.3 29,780 24 6.4 3.9 Slovak Republic 5 49 112 42.8 60 7,950 68 84.9 15,760 58 6.0 5.9 Slovenia 2 20 99 34.9 62 17,440 45 44.3 22,160 44 4.0 3.8 Somalia 8 638 13 .. .. ..c .. .. .. .. .. .. South Africa 47 1,219 39 223.5 27 4,770 85 568.3d 12,120 d 73 4.9 3.7 Spain 43 505 87 1,095.9 8 25,250 34 1,120.5 25,820 33 3.4 1.7 Sri Lanka 20 66 304 22.8 78 1,160 144 88.7 4,520 132 5.3 4.4 Sudan 36 2,506 15 23.1 77 640 164 72.5d 2,000 d 169 8.0 5.9 Swaziland 1 17 66 2.6 153 2,280 124 5.9 5,190 124 1.8 0.8 Sweden 9 450 22 369.1 19 40,910 10 283.5 31,420 17 2.7 2.3 Switzerland 7 41 186 411.4 17 55,320 3 275.8 37,080 5 1.9 1.2 Syrian Arab Republic 19 185 104 26.3 71 1,380 136 71.2 3,740 139 5.1 2.5 Tajikistan 7 143 46 2.2 158 330 190 8.2 1,260 185 7.5 6.2 Tanzania 38 945 43 12.7o 96 340o 189 28.0 730 203 7.0 5.0 Thailand 64 513 126 175.0 33 2,720 110 542.1 8,440 87 4.5 3.6 Togo 6 57 113 2.2 159 350 186 9.5d 1,550 d 178 2.8 0.2 Trinidad and Tobago 1 5 254 13.4 95 10,300 58 17.2 13,170 68 7.0 6.7 Tunisia 10 164 65 28.8 68 2,880 105 79.2 7,900 94 4.2 3.2 Turkey 72 784 94 342.0 20 4,750 86 606.8 8,420 88 7.4 6.0 Turkmenistan 5 488 10 .. .. ..h .. .. .. .. .. .. Uganda 29 241 146 8.0 111 280 194 43.2d 1,500 d 180 6.6 2.9 Ukraine 47 604 81 71.7 54 1,520 131 316.3 6,720 105 2.6 3.4 United Arab Emirates 5 84 54 103.5 .. 23,950 .. 104.1d 24,090d .. 8.5 3.4 United Kingdom 60 244 249 2,272.7 4 37,740 13 1,968.8 32,690 13 1.8 1.2 United States 296 9,629 32 12,912.9 1 43,560 7 12,434.4 41,950 3 3.2 2.2 Uruguay 3 176 20 15.1 89 4,360 91 34.0 9,810 82 6.6 5.8 Uzbekistan 26 447 62 13.6 93 520 172 52.9 2,020 166 7.0 5.8 Venezuela, RB 27 912 30 128.1 37 4,820 83 171.2 6,440 110 9.3 7.5 Vietnam 83 332 268 51.3 57 620 165 250.2 3,010 149 8.4 7.2 West Bank and Gaza 4 6 602 4.5 136 1,230 143 .. .. .. 6.3 2.8 Yemen, Rep. 21 528 40 12.6 97 600 167 19.3 920 196 2.6 ­0.6 Zambia 12 753 16 5.8 125 500 174 11.1 950 195 5.2 3.5 Zimbabwe 13 391 34 4.5 135 350 186 25.2 1,940 171 ­6.5 ­7.0 World 6,438 s 133,841 s 50 w 45,135.2 t 7,011 w 60,669.6 t 9,424 w 3.5 w 2.3 w Low income 2,352 29,265 83 1,377.2 585 5,848.6 2,486 8.0 6.1 Middle income 3,074 70,081 45 8,137.8 2,647 22,133.7 7,199 6.4 5.5 Lower middle income 2,475 39,946 63 4,759.9 1,923 15,624.3 6,314 7.0 6.0 Upper middle income 600 30,135 21 3,379.3 5,634 6,557.0 10,931 5.5 4.9 Low & middle income 5,427 99,346 56 9,514.8 1,753 27,972.5 5,154 6.6 5.3 East Asia & Pacific 1,885 16,301 119 3,073.0 1,630 11,149.9 5,914 8.9 8.0 Europe & Central Asia 472 24,238 20 1,954.7 4,143 4,317.9 9,152 6.0 5.9 Latin America & Carib. 551 20,418 27 2,227.9 4,045 4,469.9 8,116 4.5 3.1 Middle East & N. Africa 306 8,984 34 672.7 2,198 1,861.9 6,084 4.3 2.4 South Asia 1,470 5,140 307 1,016.9 692 4,618.6 3,142 8.7 6.9 Sub-Saharan Africa 743 24,265 31 554.4 746 1,489.4 2,004 5.7 3.4 High income 1,011 34,595 31 35,643.4 35,264 32,899.9 32,550 2.7 1.9 Europe EMU 314 2,506 128 10,075.3 32,098 9,076.2 28,915 1.3 0.7 a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be low-income ($875 or less). d. Based on regression; others are extrapolated from the latest International Comparison Program benchmark estimates. e. Included in the aggregates for lower middle-income economies based on earlier data. f. Includes Taiwan, China; Macao, China; and Hong Kong, China. g. Based on a 1986 bilateral comparison between China and the United States (Ruoen and Kai 1995) employing a different methodology than that used for other countries. This interim methodology will be revised in the next few years. h. Estimated to be lower middle-income ($876­$3,465). i. Included in the aggregates for upper middle-income economies based on earlier data. j. Includes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. k. Excludes data for Transnistria. l. Estimated to be high-income ($10,726 or more). m. Provisional estimate. n. Excludes data for Kosovo. o. Data are for mainland Tanzania only. 16 2007 World Development Indicators 1.1 WORLD VIEW Size of the economy About the data Definitions Population, land area, income, output, and growth in GNI measures the total domestic and foreign value · Population is based on the de facto definition of output are basic measures of the size of an economy. added claimed by residents. GNI comprises GDP population, which counts all residents regardless of They also provide a broad indication of actual and plus net receipts of primary income (compensation legal status or citizenship--except for refugees not potential resources. Population, land area, income of employees and property income) from nonresident permanently settled in the country of asylum, who (as measured by gross national income, GNI) and out- sources. The World Bank uses GNI per capita in U.S. are generally considered part of the population of put (as measured by gross domestic product, GDP) dollars to classify countries for analytical purposes their country of origin. The values shown are midyear are therefore used throughout World Development and to determine borrowing eligibility. For definitions estimates for 2005. See also table 2.1. · Surface Indicators to normalize other indicators. of the income groups in World Development Indica- area is a country's total area, including areas under Population estimates are generally based on tors, see Users guide. For discussion of the useful- inland bodies of water and some coastal water- extrapolations from the most recent national census. ness of national income and output as measures of ways. · Population density is midyear population For further discussion of the measurement of popula- productivity or welfare, see About the data for tables divided by land area in square kilometers. · Gross tion and population growth, see About the data for 4.1 and 4.2. national income (GNI) is the sum of value added by table 2.1 and Statistical methods. When calculating GNI in U.S. dollars from GNI all resident producers plus any product taxes (less The surface area of an economy includes inland reported in national currencies, the World Bank fol- subsidies) not included in the valuation of output bodies of water and some coastal waterways. Sur- lows its Atlas conversion method, using a three-year plus net receipts of primary income (compensation face area thus differs from land area, which excludes average of exchange rates to smooth the effects of of employees and property income) from abroad. bodies of water, and from gross area, which may transitory fluctuations in exchange rates. (For fur- Data are in current U.S. dollars converted using the include offshore territorial waters. Land area is par- ther discussion of the Atlas method, see Statistical World Bank Atlas method (see Statistical methods). ticularly important for understanding an economy's methods.) GDP and GDP per capita growth rates are · GNI per capita is gross national income divided by agricultural capacity and the environmental effects calculated from data in constant prices and national midyear population. GNI per capita in U.S. dollars is of human activity. (For measures of land area and currency units. converted using the World Bank Atlas method. · PPP data on rural population density, land use, and agri- Because exchange rates do not always reflect dif- GNI is gross national income converted to interna- cultural productivity, see tables 3.1­3.3.) Innova- ferences in price levels between countries, this table tional dollars using purchasing power parity rates. An tions in satellite mapping and computer databases also converts GNI and GNI per capita estimates into international dollar has the same purchasing power have resulted in more precise measurements of land international dollars using purchasing power parity over GNI as a U.S. dollar has in the United States. and water areas. (PPP) rates. PPP rates provide a standard measure · Gross domestic product (GDP) is the sum of value allowing comparison of real levels of expenditure added by all resident producers plus any product Developing countries produce slightly between countries, just as conventional price indexes taxes (less subsidies) not included in the valuation less than half the world's output 1.1a allow comparison of real values over time. The PPP of output. Growth is calculated from constant price conversion factors used here are derived from price GDP data in local currency. · GDP per capita is gross Share of PPP GNI, 2005 surveys covering 118 countries conducted by the domestic product divided by midyear population. East Asia & Pacific 18% International Comparison Program. For Organisation for Economic Co-operation and Development (OECD) countries data come from the most recent round of High-income South Asia 8% 54% surveys, completed in 2002; the rest are from either the 1996 or the 1993 survey or earlier round and Europe & Central Asia 7% Data sources extrapolated to the 1996 benchmark. Estimates for countries not included in the surveys are derived Population estimates are prepared by World Bank Latin America & Caribbean 7% from statistical models using available data. staff from a variety of sources (see Data sources Middle East & Sub-Saharan North Africa 3% All 208 economies shown in World Development for table 2.1). Data on surface and land area are Africa 2% Indicators are ranked by size, including those that from the Food and Agriculture Organization (see When measured by purchasing power parities appear in table 1.6. The ranks are shown only in Data sources for table 3.1). GNI, GNI per capita, (PPPs), which take into account national dif- table 1.1. No rank is shown for economies for which GDP growth, and GDP per capita growth are esti- ferences in the cost of living, developing coun- numerical estimates of GNI per capita are not pub- mated by World Bank staff based on national tries produce a large part of the world's output. lished. Economies with missing data are included in accounts data collected by World Bank staff dur- Much of this is in the form of nontradable goods the ranking at their approximate level, so that the rel- ing economic missions or reported by national and services, which are undervalued at market ative order of other economies remains consistent. statistical offices to other international organiza- exchange rates. For this reason PPPs are used in tions such as the OECD. Purchasing power parity international comparisons of well-being such as conversion factors are estimates by World Bank $1 and $2 a day measures of absolute poverty. staff based on data collected by the International Source: World Bank staff estimates. Comparison Program. 2007 World Development Indicators 17 1.2 Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme Achieve universal Promote gender Reduce child Improve maternal poverty and hunger primary education equality mortality health Share of Maternal poorest mortality quintile in Prevalence of child ratio national malnutrition Ratio of female to male Modeled consumption Underweight Primary completion enrollments in primary Under-fi ve estimates Births attended by or income % of children ratea and secondary schoola mortality rate per 100,000 skilled health staff % 1993­ under age 5 % % per 1,000 live births % of total 2005b,c 1990­95b 2000­05b 1991 2005d 1991 2005d 1990 2005d 2000 1990­95b 2000­05b Afghanistan .. .. 39 25 32 .. 55 .. .. 1,900 .. 14 Albania 8.2 .. 14 .. 97 96 99 45 18 55 .. 98 Algeria 7.0 13 10 79 96 .. 102 69 39 140 77 96 Angola .. 31 35 .. .. .. 260 260 1,700 .. 45 Argentina 3.1e 2 4f .. 100 .. 111 29 18 82 96 95 Armenia 8.5 .. 3 90 91 .. 108 54 29 55 .. 98 Australia 5.9 .. .. .. .. 103 102 10 6 8 100 99 Austria 8.6 .. .. .. .. 94 102 10 5 4 100 .. Azerbaijan 7.4 .. 7 .. 94 96 98 105 89 94 .. 88 Bangladesh 9.0 68 48 49 77 .. 101 149 73 380 10 13 Belarus 8.5 .. .. 95 100 .. 105 19 12 35 .. 100 Belgium 8.5 .. .. 79 .. 100 103 10 5 10 .. .. Benin 7.4 .. 30 21 65 49 73 185 150 850 .. 75 Bolivia 1.5 15 8 .. 101 .. 93 125 65 420 47 67 Bosnia and Herzegovina 9.5 .. 4 .. .. .. .. 22 15 31 97 100 Botswana 3.2 .. 13 83 92 108 102 58 120 100 .. 94 Brazil 2.8 .. .. 93 108 .. 105 60 33 260 72 97 Bulgaria 8.7 .. .. 85 98 100 100 19 15 32 .. 99 Burkina Faso 6.9 33 38 21 31 61 77 210 191 1,000 42 38 Burundi 5.1 .. 45 46 36 81 83 190 190 1,000 .. 25 Cambodia 6.8 .. 36 .. 92 .. 87 115 87 450 .. 44 Cameroon 5.6 15 18 56 62 .. 83 139 149 730 58 62 Canada 7.2 .. .. .. .. 106 106 8 6 6 98 98 Central African Republic 2.0 23 24 27 23 59 65 168 193 1,100 46 44 Chad .. .. 37 18 32 .. 60 201 208 1,100 .. 14 Chile 3.8 1 1 .. 95 .. 98 21 10 31 100 100 China 4.7 13 8 103 98 86 98 49 27 56 .. 97 Hong Kong, China 5.3 .. .. 102 110 .. 93 .. .. .. .. 100 Colombia 2.5 8 7 70 98 107 104 35 21 130 86 96 Congo, Dem. Rep. .. 34 31 46 39 .. 73 205 205 990 .. 61 Congo, Rep. .. .. .. 54 57 83 89 110 108 510 .. 86 Costa Rica 3.5 2 .. 79 92 .. 104 18 12 43 98 99 Côte d'Ivoire 5.2 24 17 43 .. .. 67 157 195 690 45 68 Croatia 8.3 1 .. 85 91 .. 104 12 7 8 100 100 Cuba .. .. 4 96 94 109 110 13 7 33 100 100 Czech Republic 10.3 1 .. .. 104 97 101 13 4 9 99 100 Denmark 8.3 .. .. 98 99 103 109 9 5 5 .. .. Dominican Republic 4.0 10 5 61 92 .. 111 65 31 150 93 99 Ecuador 3.3 .. 12 91 101 .. .. 57 25 130 .. 75 Egypt, Arab Rep. 8.6 17 9 .. 95 79 .. 104 33 84 46 74 El Salvador 2.7 11 10 41 87 .. 100 60 27 150 51 92 Eritrea .. 44 40 19 51 .. 70 147 78 630 21 28 Estonia 6.7 .. .. 93 101 105 114 16 7 63 .. 100 Ethiopia 9.1 48 38 26 55 68 76 204 127 850 .. 6 Finland 9.6 .. .. 97 100 110 107 7 4 6 100 100 France 7.2 .. .. 104 .. 104 105 9 5 17 99 .. Gabon .. .. 12 58 66 .. .. 92 91 420 .. 86 Gambia, The 4.8 .. 17 44 .. .. 97 151 137 540 44 55 Georgia 5.6 .. .. .. 87 101 103 47 45 32 .. 92 Germany 8.5 .. .. 100 96 .. .. 9 5 8 .. .. Ghana 5.6 27 22 63 72 78 91 122 112 540 44 47 Greece 6.7 .. .. 99 102 98 105 11 5 9 .. .. Guatemala 2.9 27 23 .. 74 .. 91 82 43 240 34 41 Guinea 7.0 27 33 17 55 45 74 234 160 740 31 56 Guinea-Bissau 5.2 .. 25 .. .. .. .. 253 200 1,100 25 35 Haiti 2.4 28 17 27 .. .. .. 150 120 680 20 24 18 2007 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme Achieve universal Promote gender Reduce child Improve maternal poverty and hunger primary education equality mortality health Share of Maternal poorest mortality quintile in Prevalence of child ratio national malnutrition Ratio of female to male Modeled consumption Underweight Primary completion enrollments in primary Under-fi ve estimates Births attended by or income % of children ratea and secondary schoola mortality rate per 100,000 skilled health staff % 1993­ under age 5 % % per 1,000 live births % of total 2005b,c 1990­95b 2000­05b 1991 2005d 1991 2005d 1990 2005d 2000 1990­95b 2000­05b Honduras 3.4 18 17 65 79 106 109 59 40 110 45 56 Hungary 9.5 .. .. 93 95 101 107 17 8 16 .. 100 India 8.9 53 .. 68 89 69 87 123 74 540 34 43 Indonesia 8.4 34 28 91 101 .. 97 91 36 230 37 72 Iran, Islamic Rep. 5.1 16 .. 91 96 83 99 72 36 76 .. 90 Iraq .. 12 16 58 74 .. 76 50 .. 250 .. 72 Ireland 7.4 .. .. .. 101 103 103 9 6 5 .. 100 Israel 5.7 .. .. .. 105 104 105 12 6 17 .. .. Italy 6.5 .. .. 104 101 98 106 9 4 5 .. .. Jamaica 5.3 5 4 90 84 100 104 20 20 87 .. 97 Japan 10.6 .. .. 101 .. 96 98 6 4 10 100 .. Jordan 6.7 6 4 72 97 104 102 40 26 41 87 100 Kazakhstan 7.4 8 .. .. 114 .. 106 63 73 210 100 .. Kenya 6.0 23 20 .. 95 .. 94 97 120 1,000 45 42 Korea, Dem. Rep. .. .. 24 .. .. .. .. 55 55 67 .. 97 Korea, Rep. 7.9 .. .. 98 104 89 87 9 5 20 98 100 Kuwait .. .. .. .. 100 .. 110 16 11 5 .. 100 Kyrgyz Republic 8.9 .. 7 .. 97 .. 105 80 67 110 .. 99 Lao PDR 8.1 40 40 43 76 .. 84 163 79 650 .. 19 Latvia 6.6 .. .. .. 92 103 115 18 11 42 100 100 Lebanon .. .. 4 .. 90 .. 104 37 30 150 .. 93 Lesotho 1.5 21 18 59 67 121 103 101 132 550 50 55 Liberia .. .. 27 .. .. .. .. 235 235 760 .. 51 Libya .. 5 .. .. .. .. 106 41 19 97 94 .. Lithuania 6.8 .. .. 89 98 .. 110 13 9 13 .. 100 Macedonia, FYR 6.1 .. .. 98 96 99 103 38 17 23 .. 99 Madagascar 4.9 34 42 33 58 97 96 168 119 550 57 51 Malawi 7.0 30 22 28 61 80 98 221 125 1,800 55 56 Malaysia 4.4 20 11 91 94 .. 109 22 12 41 .. 97 Mali 6.1 .. 33 11 38 58 75 250 218 1,200 .. 41 Mauritania 6.2 48 32 33 45 65 96 133 125 1,000 40 57 Mauritius .. 15 .. 107 97 101 98 23 15 24 98 99 Mexico 4.3 .. .. 86 99 95 101 46 27 83 .. 83 Moldova 7.8 .. 4 .. 92 .. 109 35 16 36 .. 100 Mongolia 7.5 12 13 .. 97 113 116 108 49 110 .. 97 Morocco 6.5 10 10 47 80 69 88 89 40 220 40 63 Mozambique 5.4 27 24 27 42 .. 82 235 145 1,000 .. 48 Myanmar .. 43 32 .. 79 .. 104 130 105 360 .. 57 Namibia 1.4 26 24 78 75 108 101 86 62 300 68 76 Nepal 6.0 49 45g 51 76g 58 88 145 74 740 7 15 Netherlands 7.6 .. .. .. 100 95 99 9 5 16 .. .. New Zealand 6.4 .. .. 100 .. 102 113 11 6 7 100 .. Nicaragua 5.6 11 10 44 76 108 103 68 37 230 .. 67 Niger 2.6 43 40 17 28 .. 72 320 256 1,600 15 16 Nigeria 5.0 39 29 .. 82 .. 82 230 194 800 31 35 Norway 9.6 .. .. 100 101 104 109 9 4 16 .. .. Oman .. 23 .. 74 93 91 99 32 12 87 91 95 Pakistan 9.3 38 38 .. 63 .. 75 130 99 500 19 31 Panama 2.5 6 .. 86 97 .. 109 34 24 160 86 93 Papua New Guinea 4.5 .. .. 47 54 .. 87 94 74 300 .. 41 Paraguay 2.4 4 5 71 91 .. 101 41 23 170 67 77 Peru 3.7 11 7 .. 100 .. 103 78 27 410 .. 73 Philippines 5.4 30 28 86 97 104 106 62 33 200 53 60 Poland 7.5 .. .. 98 100 103 109 18 7 13 .. 100 Portugal 5.8 .. .. 95 104 105 108 14 5 5 .. 100 Puerto Rico .. .. .. .. .. .. .. .. .. 25 .. 100 2007 World Development Indicators 19 1.2 Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme Achieve universal Promote gender Reduce child Improve maternal poverty and hunger primary education equality mortality health Share of Maternal poorest mortality quintile in Prevalence of child ratio national malnutrition Ratio of female to male Modeled consumption Underweight Primary completion enrollments in primary Under-fi ve estimates Births attended by or income % of children ratea and secondary schoola mortality rate per 100,000 skilled health staff % 1993­ under age 5 % % per 1,000 live births % of total 2005b,c 1990­95b 2000­05b 1991 2005d 1991 2005d 1990 2005d 2000 1990­95b 2000­05b Romania 8.1 6 3 96 93 99 105 31 19 49 99 99 Russian Federation 6.1 3 6 93 94 108 110 27 18 67 .. 99 Rwanda 5.3 29 23 33 39 .. 99 173 203 1,400 26 39 Saudi Arabia .. 15 .. 56 85 86 101 44 26 23 .. 93 Senegal 6.6 22 23 39 52 .. 90 149 119 690 47 58 Serbia and Montenegro .. 2 71 .. .. .. 28 15 11 .. 92 Sierra Leone 1.1 29 27 .. .. .. 71 302 282 2,000 .. 42 Singapore 5.0 .. 3 .. .. 90 .. 8 3 30 .. 100 Slovak Republic 8.8 .. .. 96 99 .. 104 14 8 3 .. 99 Slovenia 9.1 .. .. 95 102 .. 109 10 4 17 100 100 Somalia .. .. 33g .. .. .. .. 225 225 1,100 .. 25 South Africa 3.5 9 .. 75 99 103 101 60 68 230 82 92 Spain 7.0 .. .. .. 109 104 107 9 5 4 .. .. Sri Lanka 7.0 33 29 97 .. 101 102 32 14 92 94 96 Sudan .. 34 41 41 50 78 89 120 90 590 86 87 Swaziland 4.3 .. 10 60 64 94 94 110 160 370 56 74 Sweden 9.1 .. .. 96 .. 105 112 7 4 2 .. .. Switzerland 7.6 .. .. 53 97 92 94 9 5 7 .. .. Syrian Arab Republic .. 13 7 94 111 83 94 39 15 160 77 70 Tajikistan 7.9 .. .. .. 102 .. 84 115 71 100 .. 71 Tanzania 7.3 29 22 61 54 96 95 161 122 1,500 44 43 Thailand 6.3 18 .. .. 82 .. 101 37 21 44 .. 99 Togo .. .. .. 35 65 58 72 152 139 570 .. 61 Trinidad and Tobago .. .. 6 100 99 101 104 33 19 160 .. 96 Tunisia 6.0 9 4 74 97 84 105 52 24 120 81 90 Turkey 5.3 10 4 90 88 79 84 82 29 70 76 83 Turkmenistan 6.1 .. 12 .. .. .. .. 97 104 31 .. 97 Uganda 5.7 26 23 .. 57 81 96 160 136 880 38 39 Ukraine 9.2 .. 1 94 114 .. 102 26 17 35 .. 100 United Arab Emirates .. 14 .. 103 76 120 126 15 9 54 99 100 United Kingdom 6.1 .. .. .. .. 96 107 10 6 13 .. .. United States 5.4 1 2 .. .. 105 109 11 7 17 .. 99 Uruguay 5.0 d 5 .. 94 91 .. 114 23 15 27 .. 99 Uzbekistan 7.2 .. 8 .. 97 .. 96 79 68 24 .. 96 Venezuela, RB 3.3 5 4 43 92 .. 104 33 21 96 .. 95 Vietnam 9.0 45 28 .. 94 .. 94 53 19 130 .. 90 West Bank and Gaza .. .. 5 .. 98 .. 104 40 23 .. .. 97 Yemen, Rep. 7.4 39 46 .. 62 .. 61 139 102 570 16 27 Zambia 3.6 25 23 .. 78 .. 92 180 182 750 51 43 Zimbabwe 4.6 16 .. 99 80 91 95 80 132 1,100 69 .. World 30 w .. w .. w 85 w .. w 94 w 95 w 75 w 410 w .. w 63 w Low income 46 .. 60 74 .. 87 147 114 684 33 41 Middle income 15 11 93 96 .. 99 58 37 150 .. 87 Lower middle income 17 12 94 97 90 99 62 39 163 .. 86 Upper middle income 7 .. 87 95 99 99 41 27 91 .. 92 Low & middle income 32 22 79 84 .. 93 103 82 450 .. 61 East Asia & Pacific 20 15 100 98 .. 99 59 33 117 .. 87 Europe & Central Asia .. 5 91 92 98 96 48 32 58 .. 94 Latin America & Carib. .. .. 83 98 99 102 54 31 194 .. 87 Middle East & N. Africa 16 15 77 89 79 99 80 53 183 .. 74 South Asia 53 .. 76 82 69 87 129 83 564 30 37 Sub-Saharan Africa 32 30 50 58 .. 86 185 163 921 .. 45 High income .. .. .. 97 100 100 11 7 14 .. .. a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Data are for the most recent year available. c. See table 2.6 for survey year and whether share is based on income or consumption expenditure. d. Provisional data. e. Urban data. f. Data are for 2005­06. g. Data are for 2005. 20 2007 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and improving lives About the data This table and the following two present indicators rate. Because many school systems do not record see About the data for the tables listed there. For for 17 of the 18 targets specified by the Millennium school completion on a consistent basis, it is esti- information about the indicators for goals 6, 7, and Development Goals. Each of the eight goals com- mated from the gross enrollment rate in the final 8, see About the data for tables 1.3 and 1.4. prises one or more targets, and each target has grade of primary school, adjusted for repetition. Definitions associated with it several indicators for monitoring Official enrollments sometimes differ significantly progress toward the target. Most of the targets are from actual attendance, and even school systems · Share of poorest quintile in national consump- set as a value of a specific indicator to be attained with high average enrollment ratios may have poor tion or income is the share of consumption or, in by a certain date. In some cases the target value is completion rates. Estimates of primary school some cases, income that accrues to the poorest set relative to a level in 1990. In others it is set at completion rates are provided by the United Nations 20 percent of the population. · Prevalence of child an absolute level. Some of the targets for goals 7 Educational, Scientifi c, and Cultural Organization malnutrition is the percentage of children under age and 8 have not yet been quantified Institute of Statistics and national sources. fi ve whose weight for age is more than two standard The indicators in this table relate to goals 1­5. Eliminating gender disparities in education would deviations below the median for the international ref- Goal 1 has two targets between 1990 and 2015: help to increase the status and capabilities of erence population ages 0­59 months. The reference to reduce by half the proportion of people whose women. The ratio of girls' to boys' enrollments in population, adopted by the World Health Organization income is less than $1 a day and to reduce by half primary and secondary school provides an imperfect in 1983, is based on children from the United States, the proportion of people who suffer from hunger. measure of the relative accessibility of schooling for who are assumed to be well nourished. · Primary Estimates of poverty rates can be found in table 2.6. girls. With a target date of 2005, this is the first of completion rate is the percentage of students com- The indicator shown here, the share of the poorest the goals to fall due. pleting the last year of primary school. It is calculated quintile in national consumption, is a distributional The targets for reducing under-five and maternal as the total number of students in the last grade of measure. Countries with more unequal distributions mortality are among the most challenging. Although primary school, minus the number of repeaters in of consumption (or income) will have a higher rate of estimates of under-five mortality rates are available that grade, divided by the total number of children poverty for a given average income. No single indica- at regular intervals for most countries, maternal of official graduation age. · Ratio of female to male tor captures the concept of suffering from hunger. mortality is difficult to measure, in part because it enrollments in primary and secondary school is the Child malnutrition is a symptom of inadequate food is relatively rare. ratio of female to male gross enrollment rate in pri- supply, lack of essential nutrients, illnesses that Most of the 48 indicators relating to the Millennium mary and secondary school. · Under-five mortality deplete these nutrients, and undernourished moth- Development Goals can be found in World Develop- rate is the probability that a newborn baby will die ers who give birth to underweight children. ment Indicators. Table 1.2a shows where to find the before reaching age five, if subject to current age- Progress toward achieving universal primary educa- indicators for the first five goals. For more informa- specific mortality rates. The probability is expressed tion is measured by the primary school completion tion about data collection methods and limitations, as a rate per 1,000. · Maternal mortality ratio is the number of women who die from pregnancy-related Location of indicators for Millennium Development Goals 1­5 1.2a causes during pregnancy and childbirth, per 100,000 live births. The data shown here have been collected Goal 1. Eradicate extreme poverty and hunger Table in various years and adjusted to a common 2000 1. Proportion of population below $1 a day 2.6 base year. The values are modeled estimates (see 2. Poverty gap ratio 2.6 3. Share of poorest quintile in national consumption 1.2, 2.7 About the data for table 2.16). · Births attended by 4. Prevalence of underweight in children under age five 1.2, 2.17 skilled health staff are the percentage of deliveries 5. Proportion of population below minimum level of dietary energy consumption 2.17 attended by personnel trained to give the necessary Goal 2. Achieve universal primary education supervision, care, and advice to women during preg- 6. Net enrollment ratio 2.10 nancy, labor, and the postpartum period; to conduct 7. Proportion of pupils starting grade 1 who reach grade 5 2.11 8. Literacy rate of 15- to 24-year-olds 2.12 deliveries on their own; and to care for newborns. Goal 3. Promote gender equality and empower women 9. Ratio of girls to boys in primary, secondary, and tertiary education 1.2* 10. Ratio of literate females to males among 15- to 24-year-olds 2.12* Data sources 11. Share of women in wage employment in the nonagricultural sector 1.5, 2.2* 12. Proportion of seats held by women in national parliament 1.5 The indicators here and throughout this book have Goal 4. Reduce child mortality been compiled by World Bank staff from primary 13. Under-five mortality rate 1.2, 2.20 and secondary sources. Efforts have been made to 14. Infant mortality rate 2.20 harmonize these data series with those published 15. Proportion of one-year-old children immunized against measles 2.15 on the United Nations Millennium Development Goal 4. Improve maternal health 16. Maternal mortality ratio 1.2, 2.16 Goals Web site (www.un.org/millenniumgoals), but 17. Proportion of births attended by skilled health personnel 1.2, 2.16 some differences in timing, sources, and defini- * Table shows information on related indicators. tions remain. 2007 World Development Indicators 21 1.3 Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Fixed-line and Incidence of Youth mobile phone HIV prevalence tuberculosis Carbon dioxide emissions Access to an improved Access to improved unemployment subscribers % of population per 100,000 per capita water source sanitation facilities % ages per 1,000 ages 15­49 people metric tons % of population % of population 15­24 peoplea 2005 2005 1990 2003 1990 2004 1990 2004 2005 2005 Afghanistan <0.1 168 0.2 0.0 4 39 3 34 .. 44 Albania .. 20 2.2 1.0 96 96 .. 91 .. 493 Algeria 0.1 55 3.0 5.1 94 85 88 92 43 494 Angola 3.7 269 0.4 0.6 36 53 29 31 .. 75 Argentina 0.6 41 3.4 3.4 94 96 81 91 24 797 Armenia 0.1 71 1.2 1.1 .. 92 .. 83 .. 260 Australia 0.1 6 15.9 17.8 100 100 100 100 11 1,470 Austria 0.3 11 7.5 8.7 100 100 100 100 10 1,441 Azerbaijan <0.1 76 7.5 3.5 68 77 .. 54 .. 397 Bangladesh <0.1 227 0.1 0.3 72 74 20 39 7 71 Belarus 0.3 62 10.6 6.3 100 100 .. 84 .. 755 Belgium 0.3 13 10.1 9.9 100 100 100 100 18 1,337 Benin 1.8 88 0.1 0.3 63 67 12 33 .. 98 Bolivia 0.1 211 0.8 0.9 72 85 33 46 .. 334 Bosnia and Herzegovina 0.1 52 1.6 4.9 97 97 .. 95 .. 656 Botswana 24.1 654 1.5 2.3 93 95 38 42 .. 541 Brazil 0.5 60 1.4 1.6 83 90 71 75 18 587 Bulgaria 0.1 39 8.6 5.6 99 99 99 99 22 1,128 Burkina Faso 2.0 223 0.1 0.1 38 61 7 13 .. 51 Burundi 3.3 334 0.0 0.0 69 79 44 36 .. 18 Cambodia 1.6 506 0.0 0.0 .. 41 .. 17 .. 40 Cameroon 5.5b 174 0.1 0.2 50 66 48 51 .. 102 Canada 0.3 5 15.0 17.9 100 100 100 100 12 1,080 Central African Republic 10.7 314 0.1 0.1 52 75 23 27 .. 27 Chad 3.5 272 0.0 0.0 19 42 7 9 .. 14 Chile 0.3 15 2.7 3.7 90 95 84 91 17 859 China 0.1c 100 2.1 3.2 70 77 23 44 .. 570 Hong Kong, China .. 75 4.6 5.6 .. .. .. .. 11 1,798 Colombia 0.6 45 1.6 1.3 92 93 82 86 25 648 Congo, Dem. Rep. 3.2 356 0.1 0.0 43 46 16 30 .. 48 Congo, Rep. 5.3 367 0.5 0.4 .. 58 .. 27 .. 102 Costa Rica 0.3 14 0.9 1.5 .. 97 .. 92 15 575 Côte d'Ivoire 7.1 382 0.4 0.3 69 84 21 37 .. 108 Croatia 0.1 41 5.1 5.4 100 100 100 100 33 1,097 Cuba 0.1 9 3.0 2.3 .. 91 98 98 .. 87 Czech Republic <0.1 10 15.6 11.4 100 100 99 98 19 1,465 Denmark 0.2 7 9.7 10.1 100 100 100 100 9 1,628 Dominican Republic 1.1 91 1.3 2.5 84 95 52 78 .. 508 Ecuador 0.3 131 1.6 1.8 73 94 63 89 16 601 Egypt, Arab Rep. <0.1 25 1.4 2.0 94 98 54 70 27 325 El Salvador 0.9 51 0.5 1.0 67 84 51 62 12 492 Eritrea 2.4 282 0.0 0.2 43 60 7 9 .. 18 Estonia 1.3 43 18.1 13.5 100 100 97 97 16 1,402 Ethiopia .. 344 0.1 0.1 23 22 3 13 8 14 Finland 0.1 6 10.3 13.0 100 100 100 100 19 1,401 France 0.4 13 6.4 6.2 100 100 .. .. 23 1,376 Gabon 7.9 308 6.3 0.9 .. 88 .. 36 .. 498 Gambia, The 2.4 242 0.2 0.2 .. 82 .. 53 .. 192 Georgia 0.2 83 3.2 0.8 80 82 97 94 28 337 Germany 0.1 7 12.3 9.8 100 100 100 100 15 1,628 Ghana 2.3 205 0.2 0.4 55 75 15 18 .. 143 Greece 0.2 17 7.1 8.7 .. .. .. .. 25 1,472 Guatemala 0.9 78 0.6 0.9 79 95 58 86 .. 457 Guinea 1.5 236 0.2 0.1 44 50 14 18 .. 20 Guinea-Bissau 3.8 206 0.2 0.2 .. 59 .. 35 .. 8 Haiti 3.8 305 0.1 0.2 47 54 24 30 .. 64 22 2007 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Fixed-line and Incidence of Youth mobile phone HIV prevalence tuberculosis Carbon dioxide emissions Access to an improved Access to improved unemployment subscribers % of population per 100,000 per capita water source sanitation facilities % ages per 1,000 ages 15­49 people metric tons % of population % of population 15­24 peoplea 2005 2005 1990 2003 1990 2004 1990 2004 2005 2005 Honduras 1.5 78 0.5 0.9 84 87 50 69 7 246 Hungary 0.1 22 5.8 5.7 99 99 .. 95 19 1,257 India 0.9 168 0.8 1.2 70 86 14 33 11 128 Indonesia 0.1 239 0.8 1.4 72 77 46 55 .. 271 Iran, Islamic Rep. 0.2 23 4.0 5.7 92 94 83 .. 23 384 Iraq .. 56 2.6 2.7 83 81 81 79 .. 57 Ireland 0.2 12 8.7 10.4 .. .. .. .. 8 1,501 Israel .. 8 7.1 10.2 100 100 .. .. 18 1,544 Italy 0.5 7 6.9 7.7 .. .. .. .. 23 1,659 Jamaica 1.5 7 3.3 4.1 92 93 75 80 28 1,146 Japan <0.1 28 8.7 9.6 100 100 100 100 9 1,202 Jordan .. 5 3.2 3.3 97 97 93 93 30 423 Kazakhstan 0.1 144 17.6 10.7 87 86 72 72 14 350 Kenya 6.1 641 0.2 0.3 45 61 40 43 .. 143 Korea, Dem. Rep. .. 178 12.4 3.5 100 100 .. 59 .. 41 Korea, Rep. <0.1 96 5.6 9.5 .. 92 .. .. 10 1,286 Kuwait .. 24 21.3 32.7 .. .. .. .. .. 1,140 Kyrgyz Republic <0.1 121 2.8 1.1 78 77 60 59 20 190 Lao PDR 0.1 155 0.1 0.2 .. 51 .. 30 .. 120 Latvia 0.8 63 5.4 2.9 99 99 .. 78 13 1,131 Lebanon 0.1 11 3.3 5.4 100 100 .. 98 .. 554 Lesotho 23.2 696 .. .. .. 79 37 37 .. 163 Liberia .. 301 0.2 0.1 55 61 39 27 .. .. Libya .. 18 8.7 8.9 71 .. 97 97 .. 156 Lithuania 0.2 63 6.6 3.7 .. .. .. .. 16 1,510 Macedonia, FYR <0.1 30 8.1 5.2 .. .. .. .. 63 882 Madagascar 0.5 234 0.1 0.1 40 46 14 32 .. 31 Malawi 14.1 409 0.1 0.1 40 73 47 61 .. 41 Malaysia 0.5 102 3.1 6.4 98 99 .. 94 .. 943 Mali 1.7 278 0.0 0.0 34 50 36 46 .. 70 Mauritania 0.7 298 1.3 0.9 38 53 31 34 .. 256 Mauritius 0.6 62 1.4 2.6 100 100 .. 94 26 862 Mexico 0.3 23 4.5 4.1 82 97 58 79 7 650 Moldova 1.1 138 5.5 1.7 .. 92 .. 68 19 480 Mongolia <0.1 191 4.7 3.2 63 62 .. 59 20 279 Morocco 0.1 89 1.0 1.3 75 81 56 73 17 455 Mozambique 16.1 447 0.1 0.1 36 43 20 32 .. 40 Myanmar 1.3 171 0.1 0.2 57 78 24 77 .. 13 Namibia 19.6 697 0.0 1.2 57 87 24 25 .. 206 Nepal 0.5 180 0.0 0.1 70 90 11 35 .. 26 Netherlands 0.2 7 9.3 8.7 100 100 100 100 10 1,436 New Zealand 0.1 9 6.8 8.7 97 .. .. .. 9 1,283 Nicaragua 0.2 58 0.7 0.8 70 79 45 47 13 260 Niger 1.1 164 0.1 0.1 39 46 7 13 .. 23 Nigeria 3.9 283 0.5 0.4 49 48 39 44 .. 151 Norway 0.1 5 8.3 9.9 100 100 100 100 12 1,489 Oman .. 11 5.6 12.8 80 .. 83 .. .. 623 Pakistan 0.1 181 0.6 0.8 83 91 37 59 12 116 Panama 0.9 45 1.3 1.9 90 90 71 73 23 555 Papua New Guinea 1.8 250 0.6 0.4 39 39 44 44 .. 15 Paraguay 0.4 68 0.5 0.7 62 86 58 80 .. 374 Peru 0.6 172 1.0 1.0 74 83 52 63 21 280 Philippines <0.1 291 0.7 1.0 87 85 57 72 16 459 Poland 0.1 26 9.1 8.0 .. .. .. .. 38 1,073 Portugal 0.4 33 4.3 5.5 .. .. .. .. 16 1,486 Puerto Rico .. 5 3.3 0.5 .. .. .. .. 23 974 2007 World Development Indicators 23 1.3 Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Fixed-line and Incidence of Youth mobile phone HIV prevalence tuberculosis Carbon dioxide emissions Access to an improved Access to improved unemployment subscribers % of population per 100,000 per capita water source sanitation facilities % ages per 1,000 ages 15­49 people metric tons % of population % of population 15­24 peoplea 2005 2005 1990 2003 1990 2004 1990 2004 2005 2005 Romania 0.1 134 6.7 4.2 .. 57 .. .. 20 820 Russian Federation 1.1 119 15.3 10.3 94 97 87 87 .. 1,119 Rwanda 3.1 361 0.1 0.1 59 74 37 42 .. 18 Saudi Arabia .. 41 12.0 13.7 90 .. .. .. .. 740 Senegal 0.9 255 0.4 0.4 65 76 33 57 .. 171 Serbia and Montenegro 0.2 33 6.2 6.2 93 93 87 87 .. 917 Sierra Leone 1.6 475 0.1 0.1 .. 57 .. 39 .. 19 Singapore 0.3 29 14.8 11.4 100 100 100 100 5 1,435 Slovak Republic <0.1 17 9.7 7.0 100 100 99 99 30 1,065 Slovenia <0.1 15 9.0 7.7 .. .. .. .. 13 1,287 Somalia 0.9 224 0.0 .. .. 29 .. 26 .. 73 South Africa 18.8 600 8.1 7.9 83 88 69 65 60 825 Spain <0.1 27 5.5 7.4 100 100 100 100 20 1,374 Sri Lanka 0.1 60 0.2 0.5 68 79 69 91 26 235 Sudan 1.6 228 0.2 0.3 64 70 33 34 .. 69 Swaziland 33.4 1,262 0.6 0.9 .. 62 .. 48 .. 208 Sweden 0.2 6 5.8 5.9 100 100 100 100 14 1,804 Switzerland 0.4 7 6.4 5.5 100 100 100 100 9 1,609 Syrian Arab Republic .. 37 2.8 2.7 80 93 73 90 26 307 Tajikistan <0.1 198 4.4 0.7 .. 59 .. 51 .. 46 Tanzania 7.0 b 342 0.1 0.1 46 62 47 47 .. 56 Thailand 1.4 142 1.8 3.9 95 99 80 99 5 537 Togo 3.2 373 0.2 0.4 50 52 37 35 .. 82 Trinidad and Tobago 2.6 9 13.9 22.1 92 91 100 100 21 861 Tunisia 0.1 24 1.6 2.1 81 93 75 85 31 692 Turkey .. 29 2.6 3.1 85 96 85 88 19 868 Turkmenistan 0.1 70 8.7 9.2 .. 72 .. 62 .. 82 Uganda 6.4d 369 0.0 0.1 44 60 42 43 .. 56 Ukraine 1.4 99 13.2 6.6 96 96 96 96 17 545 United Arab Emirates .. 16 30.8 33.4 100 100 97 98 .. 1,273 United Kingdom .. 14 9.9 9.4 100 100 .. .. 12 1,616 United States 0.6 5 19.3 19.9 100 100 100 100 11 1,227 Uruguay 0.5 28 1.3 1.3 100 100 100 100 30 624 Uzbekistan 0.2 113 6.3 4.8 94 82 51 67 .. 80 Venezuela, RB 0.7 42 5.9 5.6 .. 83 .. 68 28 606 Vietnam 0.5e 175 0.3 0.9 65 85 36 61 5 306 West Bank and Gaza .. 21 .. .. .. 92 .. 73 40 398 Yemen, Rep. .. 82 0.8 0.9 71 67 32 43 .. 92 Zambia 17.0 600 0.3 0.2 50 58 44 55 .. 89 Zimbabwe 20.1 601 1.6 0.9 78 81 50 53 25 79 World 1.0 w 136 w 4.3 w 4.3 w 77 w 83 w 45 w 57 w .. w 523 w Low income 1.7 220 0.8 0.8 64 75 21 38 .. 114 Middle income 0.6 111 3.5 3.6 78 84 48 62 .. 590 Lower middle income 0.3 113 2.4 2.9 76 82 42 57 .. 511 Upper middle income 2.2 104 8.1 6.4 90 94 79 84 25 901 Low & middle income 1.1 158 2.4 2.4 73 80 37 52 .. 382 East Asia & Pacific 0.2 136 1.9 2.7 72 79 30 51 .. 496 Europe & Central Asia 0.7 84 10.2 6.9 93 92 86 85 .. 898 Latin America & Carib. 0.6 61 2.4 2.4 83 91 67 77 17 496 Middle East & N. Africa 0.1 43 2.5 3.4 88 89 70 76 .. 389 South Asia 0.7 174 0.7 1.0 71 84 17 37 11 119 Sub-Saharan Africa 6.2 348 0.8 0.8 49 56 31 37 .. 142 High income 0.4 17 11.8 12.8 100 100 100 100 13 1,338 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. b. Survey data, 2004. c. Includes Hong Kong, China. d. Survey data, 2004­05. e. Survey data, 2005. 24 2007 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment About the data The Millennium Development Goals address issues collected from sentinel sites or through targeted mies. Fixed telephone lines and mobile phones are of common concern to all nations. Diseases and surveys. In older, generalized epidemics antenatal among the telecommunications technologies that environmental degradation do not respect national clinics are a key site for monitoring HIV and other are changing the way the global economy works. boundaries. Epidemic diseases, wherever they per- sexually transmitted diseases. Recently, household Definitions sist, pose a threat to people everywhere. And dam- surveys have been used to track the disease. The age to the environment in one location may affect table shows the estimated prevalence among adults · HIV prevalence is the percentage of people ages the well-being of plants, animals, and humans far ages 15­49. Prevalence rates in the older popula- 15­49 who are infected with HIV. · Incidence of away. The indicators in the table relate to goals 6 tion can be affected by life-prolonging treatment. tuberculosis is the estimated number of new tuber- and 7 and the targets of goal 8 that address youth The incidence of tuberculosis is based on data on culosis cases (pulmonary, smear positive, and extra- employment and access to new technologies. For the case notifications and estimates of the proportion pulmonary). · Carbon dioxide emissions are those other targets of goal 8, see table 1.4. of cases detected in the population. stemming from the burning of fossil fuels and the Measuring the prevalence or incidence of a disease Carbon dioxide emissions are the primary source manufacture of cement. They include carbon dioxide can be difficult. Much of the developing world lacks of greenhouse gases, which contribute to global produced during consumption of solid, liquid, and gas reporting systems for monitoring diseases. Estimates warming. fuels and gas flaring. · Access to an improved water are often derived from surveys and reports from sen- Access to reliable supplies of safe drinking water source refers to the percentage of the population tinel sites that must be extrapolated to the general and sanitary disposal of excreta are two of the most with reasonable access to an adequate amount of population. Tracking diseases such as HIV/AIDS, important means of improving human health and water from an improved source, such as piped water which has a long latency between contraction of the protecting the environment. There is no widespread into a dwelling, plot, or yard; public tap or standpipe; virus and the appearance of symptoms, or malaria, program for testing the quality of water. The indicator tubewell or borehole; protected dug well or spring; which has periods of dormancy, can be particularly shown here measures the proportion of households and rainwater collection. Unimproved sources include difficult. For some of the most serious illnesses inter- with access to an improved source, such as piped unprotected dug well or spring, cart with small tank or national organizations have formed coalitions such water or protected wells. Improved sanitation facili- drum, bottled water, and tanker trucks. Reasonable as the Joint United Nations Programme on HIV/AIDS ties prevent human, animal, and insect contact with access is defined as the availability of at least 20 and the Roll Back Malaria campaign to gather infor- excreta but do not include treatment to render sew- liters a person a day from a source within 1 kilome- mation and coordinate global efforts to treat victims age outflows innocuous. ter of the dwelling. · Access to improved sanitation and prevent the spread of disease. The eighth goal--to develop a global partnership facilities refers to the percentage of the population The models and data used to estimate HIV preva- for development--takes note of the need for decent with at least adequate access to excreta disposal lence depend on the nature of the epidemic in each and productive work for youth. Labor market informa- facilities (private or shared, but not public) that country. In early stages infections are usually con- tion, such as unemployment rates, is still generally can effectively prevent human, animal, and insect centrated in high-risk groups for which data are unavailable for most low- and middle-income econo- contact with excreta. Improved facilities range from simple but protected pit latrines to flush toilets with a Location of indicators for Millennium Development Goals 6­7 1.3a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained. Goal 6. Combat HIV/AIDS, malaria, and other diseases Table · Youth unemployment refers to the share of the 18. HIV prevalence among pregnant women ages 15­24 1.3*, 2.18* labor force ages 15­24 without work but available 19. Condom use rate of the contraceptive prevalence rate -- for and seeking employment. Definitions of labor 19a. Condom use at last high-risk sex -- force and unemployment differ by country. · Fixed- 19b. Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDS -- 19c. Contraceptive prevalence rate 2.16 line and mobile phone subscribers are telephone 20. Ratio of school attendance of orphans to school attendance of nonorphans ages mainlines connecting a customer's equipment to 10­14 -- the public switched telephone network, and users 21. Prevalence and death rates associated with malaria -- of portable telephones subscribing to an automatic 22. Proportion of population in malaria-risk areas using effective malaria prevention public mobile telephone service using cellular tech- and treatment measures 2.15* nology that provides access to the public switched 23. Prevalence and death rates associated with tuberculosis 1.3* telephone network. 24. Proportion of tuberculosis cases detected and cured under DOTS 2.15 Goal 7. Ensure environmental sustainability 25. Proportion of land area covered by forest 3.4 Data sources 26. Ratio of area protected to maintain biological diversity to surface area 3.4 27. Energy use (kilograms of oil equivalent) per $1 of GDP (PPP) 3.8 The indicators here and throughout this book have 28. Carbon dioxide emissions per capita and consumption of ozone-depleting been compiled by World Bank staff from primary chlorofluorocarbons 3.8* and secondary sources. Efforts have been made to 29. Proportion of population using solid fuels 3.7* harmonize these data series with those published 30. Proportion of population with sustainable access to an improved water source, urban and rural 2.15, 3.5 on the United Nations Millennium Development 31. Proportion of population with access to improved sanitation, urban and rural 2.15, 3.10 Goals Web site (www.un.org/millenniumgoals), but 32. Proportion of population with access to secure tenure 3.11 some differences in timing, sources, and defini- -- No data are available in the World Development Indicators database. * Table shows information on related indicators. tions remain. 2007 World Development Indicators 25 1.4 Millennium Development Goals: 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 Average tariff on exports of Net social servicesa Goods least developed countries % of % of total (excluding arms) donor sector-allocable admitted free of tariffs Agricultural products Textiles Clothing GNI ODA % % % % % of GDP 2005 2004­05 1998 2004 1998 2004 1998 2004 1998 2004 2005 Australia 0.25 10.7 95.4 97.3 0.2 0.4 8.9 0.9 25.4 0.0 0.29 Canada 0.34 30.4 62.9 98.9 0.5 0.2 9.8 0.3 20.5 1.4 0.75 European Union 97.5 96.0 3.4 2.8 0.0 0.2 0.0 1.0 1.14 Austria 0.52 13.9 Belgium 0.53 16.5 Denmark 0.81 17.6 Finland 0.46 13.4 France 0.47 6.3 Germany 0.36 12.1 Greece 0.17 18.8 Ireland 0.42 32.0 Italy 0.29 9.4 Luxembourg 0.82 29.5 Netherlands 0.82 22.0 Portugal 0.21 2.7 Spain 0.27 18.3 Sweden 0.94 15.2 United Kingdom 0.47 30.2 Japan 0.28 4.6 58.0 33.7 7.0 6.7 3.8 1.7 0.5 0.1 1.28 New Zealandb 0.27 29.9 94.8 99.2 0.5 0.2 12.8 0.2 18.6 0.2 0.40 Norway b 0.94 14.3 90.7 99.1 17.2 3.5 15.6 0.0 16.3 0.0 1.11 Switzerland 0.44 7.2 99.9 99.4 4.1 6.7 0.0 0.0 0.0 0.0 1.68 United States 0.22 18.4 57.7 69.4 4.2 3.5 6.8 5.7 14.4 12.3 0.88 Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC MDRI HIPC HIPC HIPC MDRI decision completion Inititaive assistancef decision completion Inititaive assistancef pointc pointd assistancee pointc pointd assistancee $ millions $ millions $ millions $ millions Benin Jul. 2000 Mar. 2003 328 571 Honduras Jul. 2000 Apr. 2005 688 733 Bolivia Feb. 2000 Jun. 2001 1,663 1,004 Madagascar Dec. 2000 Oct. 2004 1,035 1,219 Burkina Faso Jul. 2000 Apr. 2002 672 573 Malawi Dec. 2000 Aug. 2006 1,211 618 Burundi Aug. 2005 Floating 826 .. Mali Sep. 2000 Mar. 2003 667 985 Cameroon Oct. 2000 Apr. 2006 1,569 707 Mauritania Feb. 2000 Jun. 2002 770 424 Chad May 2001 Floating 202 .. Mozambique Apr. 2000 Sep. 2001 2,599 1,004 Congo, Dem. Rep. Jul. 2003 Floating 6,875 .. Nicaragua Dec. 2000 Jan. 2004 4,098 466 Congo, Rep. Apr. 2006 Floating 1,679 .. Niger Dec. 2000 Apr.2004 798 489 Ethiopia Nov. 2001 Apr. 2004 2,284 1,383 Rwanda Dec. 2000 Apr. 2005 814 206 Gambia, The Dec. 2000 Floating 83 .. São Tomé & Principe Dec. 2000 Floating 120 .. Ghana Feb. 2002 Jul. 2004 2,595 1,963 Senegal Jun. 2000 Apr. 2004 605 1,297 Guinea Dec. 2000 Floating 676 .. Sierra Leone Mar. 2002 Dec. 2006 683 .. Guinea-Bissau Dec. 2000 Floating 515 .. Tanzania Apr. 2000 Nov. 2001 2,511 1,919 Guyana Nov. 2002 Dec. 2003 732 140 Uganda Feb. 2000 May 2000 1,282 1,705 Haiti Nov. 2006 Floating .. .. Zambia Dec. 2000 Apr. 2005 3,096 1,522 a. Includes basic health, education, nutrition, and water and sanitation services. b. Estimates of market access to New Zealand and Norway for least developed countries are 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 System database. c. The date refers to the Enhanced Heavily Indebted Poor Countries (HIPC) initiative. The following countries reached their decision point under the original HIPC framework: Bolivia in September 1997, Burkina Faso in September 1997, Côte d'Ivoire in March 1998, Guyana in December 1997, Mali in September 1998, Mozambique in April 1998, and Uganda in April 1997. d. The date refers to the Enhanced HIPC framework. The following countries also reached completion points under the original framework: Bolivia in September 1998, Burkina Faso in July 2000, Guyana in May 1999, Mali in September 2000, Mozambique in July 1999, and Uganda in April 1998. e. HIPC debt relief is committed in net present value (NPV) terms as of the decision point (plus topping-up assistance at completion point in the cases of Burkina Faso, Ethiopia, Niger, and Rwanda) and is converted to end-2005 NPV terms. f. Multilateral Debt Relief Initiative (MDRI) assistance has been delivered in full to all post-completion point countries, shown in end-2005 NPV terms. 26 2007 World Development Indicators 1.4 WORLD VIEW Millennium Development Goals: overcoming obstacles About the data Achieving the Millennium Development Goals will developing economies. Although average tariffs have relief. Nineteen of these countries have reached the require an open, rule-based global economy in which been falling, averages may disguise high tariffs tar- completion point and have received nearly $29 billion all countries, rich and poor, participate. Many poor geted at specific goods (see table 6.7 for estimates in HIPC Initiative assistance and have received or are countries, lacking the resources to finance their devel- of the share of tariff lines with "international peaks" expected to receive $18 billion in MDRI assistance. opment, burdened by unsustainable levels of debt, in each country's tariff schedule). The averages in Definitions and unable to compete in the global marketplace, the table include ad valorem duties and ad valorem need assistance from rich countries. For goal 8-- equivalents of non-ad valorem duties. Subsidies to · Net official development assistance (ODA) com- develop a global partnership for development--many agricultural producers and exporters in OECD countries prises grants and loans (net of repayments of princi- of the indicators therefore monitor the actions of are another form of barrier to developing economies' pal) that meet the DAC definition of ODA and are made members of the Development Assistance Committee exports. The table shows the value of total support to to countries and territories on the DAC list of recipi- (DAC) of the Organisation for Economic Co-operation agriculture as a share of the economy's gross domes- ent countries. · ODA for basic social services is aid and Development (OECD). tic product (GDP). Agricultural subsidies in OECD econ- reported by DAC donors for basic health, education, Official development assistance (ODA) has risen omies are estimated at $385 billion in 2005. nutrition, and water and sanitation services. · Goods in recent years as a share of donor countries' gross The Debt Initiative for Heavily Indebted Poor Coun- admitted free of tariffs refer to the value of exports of national income (GNI), but the poorest countries will tries (HIPCs) is the first comprehensive approach to goods (excluding arms) from least developed countries need additional assistance to achieve the Millennium reducing the external debt of the world's poorest, most admitted without tariff, as a share of total exports Development Goals. Official aid rose to a record of heavily indebted countries. It represents an important from least developed countries. · Average tariff is $106 billion in 2005, and donor countries have step in placing debt relief within an overall framework the simple mean tariff, the unweighted average of the pledged to increase ODA to more than $130 billion of poverty reduction. A major review in 1999 led to effectively applied rates for all products subject to (in 2004 dollars) by 2010. However, this would still an enhancement of the original framework. The Mul- tariffs.· Agricultural products comprise plant and ani- fall short of levels considered necessary to achieve tilateral Debt Relief Initiative (MDRI), proposed by the mal products, including tree crops but excluding tim- the Millennium Development Goals. Group of Eight countries, was launched in 2005 to ber and fish products. · Textiles and clothing include One of the most important actions that high-income further reduce the debt of HIPCs and provide addi- natural and synthetic fibers and fabrics and articles economies can take to help is to reduce barriers to tional resources to help them meet the Millennium of clothing made from them. · Support to agriculture the exports of low- and middle-income economies. Development Goals. Under the MDRI three multilateral is the annual monetary value of all gross transfers The European Union has launched a program to institutions--the International Development Associa- from taxpayers and consumers arising from policy eliminate tariffs on developing country exports of tion (IDA), International Monetary Fund (IMF), and Afri- measures that support agriculture, net of the asso- "everything but arms," and the United States offers can Development Fund (AfDF)--provide 100 percent ciated budgetary receipts, regardless of their objec- special concessions to exports from Sub-Saharan debt relief on eligible debts due to them from countries tives and impacts on farm production and income, or Africa. However, there are still many restrictions built having completed the HIPC Initiative process. Debt consumption of farm products. · HIPC decision point into these programs. relief under the two initiatives is expected to reduce is the date at which a heavily indebted poor country Average tariffs in the table reflect tariff schedules the debt stocks of the 29 HIPCs that have reached the with an established track record of good performance applied by high-income OECD members to exports of decision point by almost 90 percent. Debt service paid under adjustment programs supported by the Inter- countries designated least developed countries by the by these countries declined by about 2 percent of GDP national Monetary Fund and the World Bank com- United Nations. Agricultural commodities, textiles, and between 1999 and 2005 and is expected to decline mits to undertake additional reforms and to develop clothing are three of the most important exports of further in the medium term as a result of MDRI debt and implement a poverty reduction strategy.· HIPC completion point is the date at which the country Location of indicators for Millennium Development Goal 8 1.4a successfully completes the key structural reforms agreed on at the decision point, including developing Goal 8. Develop a global partnership for development Table and implementing its poverty reduction strategy. The 33. Net ODA as a percentage of DAC donors' gross national income 6.9 country then receives the bulk of debt relief under 34. Proportion of ODA for basic social services 1.4 the HIPC Initiative without further policy conditions. · HIPC Initiative assistance is the present value of 35. Proportion of ODA that is untied 6.10 debt relief committed as of the decision point and 36. Proportion of ODA received in landlocked countries as a percentage of GNI -- measured in end-2005 terms.· MDRI assistance is 37. Proportion of ODA received in small island developing states as a percentage of GNI -- the present value of debt relief under the Multilateral 38. Proportion of total developed country imports (by value, excluding arms) from devel- Debt Relief Initiative from IDA, IMF, and AfDB delivered oping countries admitted free of duty 1.4 to countries having reached the HIPC completion point 39. Average tariffs imposed by developed countries on agricultural products and tex- and measured in end-2005 terms. tiles and clothing from developing countries 1.4, 6.7* Data sources 40. Agricultural support estimate for OECD countries as a percentage of GDP 1.4 41. Proportion of ODA provided to help build trade capacity -- The indicators here, and where they appear 42. Number of countries reaching HIPC decision and completion points 1.4 throughout the rest of the book, are compiled by World Bank staff from primary and secondary 43. Debt relief committed under new HIPC initiative 1.4 sources. Data on ODA and support to agriculture 44. Debt services as a percentage of exports of goods and services 4.17* are from the OECD. The World Trade Organization, 45. Unemployment rate of 15- to 24-year-olds 1.3, 2.8 in collaboration with the United Nations Confer- 46. Proportion of population with access to affordable, essential drugs on a sustain- -- ence on Trade and Development and the Inter- able basis national Trade Centre, provided the estimates of 47. Telephone lines and cellular subscribers per 100 people 1.3, 5.10 goods admitted free of tariffs and average tariffs. 48a. Personal computers in use per 100 people 5.11 Data on the HIPC Initiative and MDRI are from the 48b. Internet users per 100 people 5.11 August 2006 report "Heavily Indebted Poor Coun- tries (HIPC) Initiative and Multilateral Debt Relief -- No data are available in the World Development Indicators database. * Table shows information on related indicators. Initiative (MDRI)-- Status of Implementation." 2007 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 male % of female % of total Male Female % ages 15­19 % of total employment employment % of total seats 2005 2005 2005 2000­05a 2000­05a 2004 2000­05a 2000­05a 1990 2006 Afghanistan .. .. .. 16 .. .. .. .. 4 27 Albania 50.4 73 79 91 .. 31.7 .. .. 29 7 Algeria 49.5 70 73 81 .. 17.0 7.1 13.6 2 6 Angola 50.7 40 43 66 .. .. .. .. 15 15 Argentina 51.1 71 79 98 .. 45.5 0.8 1.6 6 35 Armenia 53.4 70 76 93 6 46.5 1.1 0.8 36 5 Australia 50.6 78 83 .. .. 48.6 0.2 0.4 6 25 Austria 51.1 77 82 .. .. 46.2 0.6 1.6 12 32 Azerbaijan 51.5 70 75 70 .. 48.8 .. .. .. 11 Bangladesh 48.9 63 65 49 33b 23.1 9.9 48.0 10 15 Belarus 53.3 63 74 .. .. 56.0 .. .. .. 29 Belgium 50.9 77 82 .. .. 44.8 .. .. 9 35 Benin 49.6 54 56 81 22 .. .. .. 3 7 Bolivia 50.2 63 67 79 16 36.5 5.2 11.1 9 17 Bosnia and Herzegovina 51.4 72 77 99 .. .. .. .. .. .. Botswana 50.9 35 34 97 .. 43.0 1.4 1.2 5 11 Brazil 50.7 67 75 97 .. 46.7 5.5 9.3 5 .. Bulgaria 51.6 69 76 .. .. 53.0 1.2 2.6 21 22 Burkina Faso 49.7 48 49 73 23 14.6 .. .. .. 12 Burundi 51.2 44 46 78 .. .. .. .. .. 31 Cambodia 51.7 54 61 38 8 51.3 31.6 53.3 .. 10 Cameroon 50.3 46 47 83 28 21.6 9.5 27.2 14 9 Canada 50.4 78 83 .. .. 49.4 0.1 0.2 13 21 Central African Republic 51.2 39 40 62 .. .. .. .. 4 11 Chad 50.5 43 45 39 37 12.8 .. .. .. 7 Chile 50.5 75 81 .. .. 38.1 1.4 3.2 .. 15 China 48.6 70 74 90 .. 40.9 .. .. 21 20 Hong Kong, China 52.9 79 85 .. .. 47.3 0.2 1.4 .. .. Colombia 50.6 70 76 94 21 48.3 3.5 7.7 5 .. Congo, Dem. Rep. 50.4 43 45 68 .. 20.1 .. .. 5 8 Congo, Rep. 50.4 52 54 88 .. .. .. .. 14 9 Costa Rica 49.2 77 81 92 .. 38.5 1.8 3.5 11 39 Côte d'Ivoire 49.2 45 47 88 .. .. .. .. 6 9 Croatia 51.9 72 79 .. .. 46.2 1.3 4.4 .. 22 Cuba 50.0 75 79 100 .. 37.7 .. .. 34 36 Czech Republic 51.3 73 79 .. .. 47.1 0.3 1.3 .. 16 Denmark 50.5 76 80 .. .. 48.8 0.2 1.3 31 37 Dominican Republic 49.5 65 72 99 23 38.2 .. .. 8 20 Ecuador 49.9 72 78 84 .. 42.7 3.7 11.0 5 .. Egypt, Arab Rep. 49.9 68 73 70 10 20.6 9.0 25.8 4 2 El Salvador 50.8 68 74 86 .. 34.8 7.7 7.7 12 17 Eritrea 50.9 53 57 70 14 .. .. .. .. 22 Estonia 54.0 67 78 .. .. 52.2 0.5 0.5 .. 19 Ethiopia 50.3 42 43 28 17 40.6 5.2 9.9 .. 22 Finland 51.0 76 82 .. .. 50.7 0.5 0.4 32 38 France 51.3 77 84 .. .. 47.2 .. .. 7 12 Gabon 50.2 53 54 94 33 .. .. .. 13 9 Gambia, The 50.4 55 58 91 .. .. .. .. 8 13 Georgia 52.7 68 75 95 .. 50.3 19.0 39.0 .. 9 Germany 51.2 76 82 .. .. 46.6 0.5 1.9 .. 32 Ghana 49.4 57 58 92 14 .. .. .. .. 11 Greece 50.6 77 82 .. .. 40.7 4.3 14.7 7 13 Guatemala 51.3 64 72 84 .. 38.8 21.3 24.5 7 8 Guinea 48.8 54 54 82 .. .. .. .. .. 19 Guinea-Bissau 50.6 44 47 62 .. .. .. .. 20 14 Haiti 50.7 52 53 79 18 .. .. .. .. 2 28 2007 World Development Indicators 1.5 WORLD VIEW 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 male % of female % of total Male Female % ages 15­19 % of total employment employment % of total seats 2005 2005 2005 2000­05a 2000­05a 2004 2000­05a 2000­05a 1990 2006 Honduras 49.6 67 71 83 .. 46.8 12.1 8.3 10 23 Hungary 52.4 69 77 .. .. 47.0 0.3 0.7 21 10 India 48.7 63 64 .. .. 17.3 .. .. 5 8 Indonesia 50.1 66 70 92 10 31.1 .. .. 12 11 Iran, Islamic Rep. 49.3 70 73 .. .. 13.7 .. .. 2 4 Iraq .. .. .. 77 .. .. .. .. 11 26 Ireland 50.3 77 82 .. .. 47.6 0.6 0.9 8 13 Israel 50.5 78 82 .. .. 49.6 0.2 0.5 7 14 Italy 51.5 78 83 .. .. 41.3 3.0 5.8 13 17 Jamaica 50.6 69 73 98 .. 47.0 0.4 2.5 5 12 Japan 51.1 79 86 .. .. 41.2 1.5 8.6 1 9 Jordan 48.0 71 74 99 4 25.0 .. .. 0 6 Kazakhstan 52.1 61 72 .. .. 49.4 1.0 1.3 .. 10 Kenya 49.9 50 48 88 23 38.7 .. .. 1 7 Korea, Dem. Rep. 50.0 61 67 .. .. .. .. .. 21 20 Korea, Rep. 49.9 74 81 .. .. 41.6 1.3 14.8 2 13 Kuwait 40.0 75 80 .. .. 25.2 .. .. .. 2 Kyrgyz Republic 50.8 65 72 .. .. 43.8 6.5 15.9 .. 0 Lao PDR 50.0 54 57 27 .. .. .. .. 6 25 Latvia 54.3 66 77 .. .. 53.2 2.5 2.1 .. 19 Lebanon 51.0 70 75 96 .. .. .. .. 0 5 Lesotho 53.5 34 36 90 .. .. .. .. .. 12 Liberia 50.1 42 43 85 .. .. .. .. .. 13 Libya 48.4 72 77 .. .. .. .. .. .. 8 Lithuania 53.4 65 77 .. .. 52.2 2.6 4.4 .. 22 Macedonia, FYR 50.1 71 76 81 .. 42.3 6.4 12.0 .. 28 Madagascar 50.3 55 57 80 34 .. 29.7 51.9 7 7 Malawi 50.3 41 40 92 33 12.4 .. .. 10 14 Malaysia 49.2 71 76 74 .. 36.9 2.2 9.6 5 9 Mali 50.2 48 49 57 40 .. .. .. .. 10 Mauritania 50.5 52 55 64 16 .. .. .. .. .. Mauritius 50.3 70 77 .. .. 37.5 0.8 4.7 7 17 Mexico 51.1 73 78 .. .. 37.4 5.5 11.0 12 .. Moldova 52.2 65 72 98 .. 54.6 0.5 1.5 .. 22 Mongolia 49.9 65 68 94 .. 50.3 18.4 31.7 25 7 Morocco 50.3 68 73 68 7 21.8 21.6 52.5 0 11 Mozambique 51.6 41 42 85 41 .. .. .. 16 35 Myanmar 50.3 58 64 76 .. .. .. .. .. .. Namibia 50.4 47 47 91 18 .. 12.8 22.0 7 27 Nepal 50.4 62 63 28 21 .. .. .. 6 6 Netherlands 50.4 77 82 .. .. 45.4 0.2 1.0 21 37 New Zealand 50.9 78 82 .. .. 50.5 0.4 0.9 14 32 Nicaragua 50.0 68 73 86 25 .. .. .. 15 21 Niger 48.9 45 45 41 .. 7.8 .. .. 5 12 Nigeria 49.4 44 44 58 25 .. .. .. .. 6 Norway 50.3 78 83 .. .. 49.2 0.2 0.3 36 38 Oman 43.8 73 76 100 .. 25.7 .. .. .. 2 Pakistan 48.5 64 65 36 .. 8.6 16.4 46.9 10 21 Panama 49.6 73 78 .. .. 43.5 2.8 3.7 8 17 Papua New Guinea 48.5 56 57 .. .. 35.4 .. .. 0 1 Paraguay 49.6 69 74 94 .. 43.9 .. .. 6 10 Peru 49.7 68 73 92 13 34.6 2.6 7.4 6 29 Philippines 49.7 69 73 88 8 40.4 8.2 17.4 9 16 Poland 51.5 71 79 .. .. 47.2 4.1 7.3 14 20 Portugal 51.7 75 81 .. .. 46.6 1.0 2.3 8 21 Puerto Rico 52.0 74 82 .. .. 39.3 0.1 0.9 .. .. 2007 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 male % of female % of total Male Female % ages 15­19 % of total employment employment % of total seats 2005 2005 2005 2000­05a 2000­05a 2004 2000­05a 2000­05a 1990 2006 Romania 51.3 68 75 94 .. 46.5 7.7 21.2 34 11 Russian Federation 53.6 59 72 .. .. 50.9 0.1 0.0 .. 10 Rwanda 51.5 43 46 94 4 .. .. .. 17 49 Saudi Arabia 46.0 71 75 .. .. 13.5 .. .. .. 0 Senegal 50.8 55 58 79 .. .. .. .. 13 19 Serbia and Montenegro 50.2 70 76 .. .. 45.4 .. .. .. .. Sierra Leone 50.7 40 43 68 .. .. .. .. .. 15 Singapore 49.7 78 82 .. .. 47.0 0.3 1.3 5 21 Slovak Republic 51.5 70 78 .. .. 52.0 0.1 0.2 .. 20 Slovenia 51.2 74 81 .. .. 47.6 4.3 6.9 .. 12 Somalia 50.4 47 49 .. .. .. .. .. 4 8 South Africa 50.9 47 49 92 .. 45.9 0.5 1.1 3 33 Spain 50.9 77 84 .. .. 42.0 0.8 2.4 15 36 Sri Lanka 49.2 72 77 100 .. 43.2 4.2 20.9 5 5 Sudan 49.7 55 58 60 .. 16.8 .. .. .. 15 Swaziland 51.8 42 41 90 .. 29.9 .. .. 4 11 Sweden 50.4 78 83 .. .. 50.9 0.3 0.3 38 47 Switzerland 51.6 79 84 .. .. 47.1 1.4 2.8 14 25 Syrian Arab Republic 49.7 72 76 71 .. 18.2 .. .. 9 12 Tajikistan 50.4 61 67 71 .. 53.3 .. .. .. 18 Tanzania 50.2 46 47 78 26 .. 3.0 4.6 .. 30 Thailand 50.9 68 74 92 .. 46.4 14.7 31.4 3 .. Togo 50.6 53 57 85 .. .. .. .. 5 9 Trinidad and Tobago 50.7 67 73 92 .. 41.1 0.5 1.9 17 19 Tunisia 49.6 72 76 92 .. 25.0 .. .. 4 23 Turkey 49.6 69 74 81 .. 19.9 8.9 49.8 1 4 Turkmenistan 50.8 59 67 98 4 .. .. .. 26 16 Uganda 50.0 49 51 92 31 .. 10.3 40.5 12 30 Ukraine 54.2 62 74 .. .. 55.1 0.6 0.6 .. 9 United Arab Emirates 31.9 77 82 .. .. 14.5 .. .. 0 0 United Kingdom 51.1 77 81 .. .. 49.4 0.3 0.5 6 20 United States 50.8 75 81 .. .. 48.5 0.1 0.1 7 15 Uruguay 51.5 72 79 .. .. 46.8 0.9 2.0 6 11 Uzbekistan 50.3 64 71 97 .. 39.5 .. .. .. 18 Venezuela, RB 49.7 71 77 94 .. 41.5 1.8 3.3 10 18 Vietnam 50.1 68 73 86 3 49.1 21.9 50.3 18 27 West Bank and Gaza 49.1 71 76 96 .. 17.9 6.4 32.2 .. .. Yemen, Rep. 49.3 60 63 41 .. .. .. .. 4 0c Zambia 49.9 39 38 93 32 .. .. .. 7 15 Zimbabwe 50.4 38 37 .. .. 21.8 10.4 13.6 11 16 World 49.7 w 66 w 70 w .. 38.1 w .. w .. w 13 w 17 w Low income 49.2 58 60 .. 23.4 .. .. 11 16 Middle income 49.8 68 73 .. 40.9 .. .. 14 16 Lower middle income 49.5 68 73 89 40.2 .. .. 14 16 Upper middle income 51.4 66 74 .. 44.2 3.5 7.5 .. 15 Low & middle income 49.6 64 67 .. 36.2 .. .. 13 16 East Asia & Pacific 49.1 69 73 90 40.6 .. .. 17 18 Europe & Central Asia 52.1 65 74 .. 47.6 3.0 7.1 .. 14 Latin America & Carib. 50.6 69 76 .. 43.3 4.4 7.1 12 .. Middle East & N. Africa 49.5 68 72 .. 17.7 .. .. 4 8 South Asia 48.8 63 64 .. 17.8 .. .. 6 13 Sub-Saharan Africa 50.1 46 47 .. .. .. .. .. 16 High income 50.7 76 82 .. 46.0 0.6 2.7 12 22 Europe EMU 51.1 77 83 .. 45.1 1.4 3.2 12 24 a. Data are for the most recent year available. b. Refers to women 15­49. c. Less than 0.5. 30 2007 World Development Indicators 1.5 WORLD VIEW Women in development 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 poor devel- workloads at home and in nonpaid domestic and prevailing patterns of mortality at the time of its birth oping countries. Gender inequalities in the alloca- market activities, and labor market discrimination were to stay the same throughout its life. · Pregnant tion of such resources as education, health care, often limit women's participation in paid economic women receiving prenatal care are the percentage nutrition, and political voice matter because of the activities, lower their productivity, and reduce their of women attended at least once during pregnancy strong association with well-being, productivity, and wages. When women are in salaried employment, by skilled health personnel for reasons related to economic growth. This pattern of inequality begins they tend to be concentrated in the nonagricultural pregnancy. · Teenage mothers are the percentage at an early age, with boys routinely receiving a larger sector. However, in many developing countries of women ages 15­19 who already have children share of education and health spending than do girls, women are a large part of agricultural employment, or are currently pregnant. · Women in nonagricul- for example. often as unpaid family workers. Among people who tural sector refers to women wage employees in the Because of biological differences girls are expected are unsalaried, women are more likely than men to nonagricultural sector as a percentage of total non- to experience lower infant and child mortality rates be unpaid family workers, while men are more likely agricultural employment. · Unpaid family workers and to have a longer life expectancy than boys. This than women to be self-employed or employers. There are those who work without pay in a market-oriented biological advantage, however, may be overshad- are several reasons for this. establishment or activity operated by a related per- owed by gender inequalities in nutrition and medical Few women have access to credit markets, capital, son living in the same household.· Women in parlia- interventions, and by inadequate care during preg- land, training, and education, which may be required ments are the percentage of parliamentary seats in nancy and delivery, so that female rates of illness to start up a business. Cultural norms may prevent a single or lower chamber held by women. and death sometimes exceed male rates, particularly women from working on their own or from super- during early childhood and the reproductive years. In vising other workers. Also, women may face time high-income countries women tend to outlive men by constraints due to their traditional family respon- four to eight years on average, while in low-income sibilities. Because of biases and misclassification countries the difference is narrower--about two to substantial numbers of employed women may be three years. The difference in child mortality rates underestimated or reported as unpaid family workers (table 2.20) is another good indicator of female even when they work in association or equally with social disadvantage because nutrition and medi- their husbands in the family enterprise. cal interventions are particularly important for the Women are vastly underrepresented in decision- 1­5 age group. Female child mortality rates that are making positions in government, although there is as high as or higher than male child mortality rates some evidence of recent improvement. Gender parity might be indicative of discrimination against girls. in parliamentary representation is still far from being Having a child during the teenage years limits girls' realized. In 2007 women represented 17 percent of opportunities for better education, jobs, and income parliamentarians worldwide, compared with 9 per- and increases the likelihood of divorce and separa- cent in 1987. Without representation at this level, it tion. Pregnancy is more likely to be unintended dur- is difficult for women to influence policy. ing the teenage years, and births are more likely to For information on other aspects of gender, see be premature and are associated with greater risks tables 1.2 (Millennium Development Goals: eradicat- of complications during delivery and of death. In ing poverty and improving lives), 2.3 (employment many countries maternal mortality (tables 1.2 and by economic activity), 2.4 (children at work), 2.5 Data sources 2.16) is a leading cause of death among women of (unemployment), 2.11 (education efficiency), 2.12 reproductive age. Most maternal deaths result from (education completion and outcomes), 2.16 (repro- Data on female population and life expectancy preventable causes--hemorrhage, infection, and ductive health), 2.18 (health risk factors and public are from the World Bank's population database. complications from unsafe abortions. Prenatal care health challenges), 2.19 (health gaps by income and Data on pregnant women receiving prenatal care is essential for recognizing, diagnosing, and promptly gender), and 2.20 (mortality). are from United Nations Children's Fund's State treating complications that arise during pregnancy. of the World's Children 2007. Data on teenage In high-income countries most women have access mothers are from Demographic and Health Sur- to health care during pregnancy, but in developing veys by Macro International. Data on labor force countries an estimated 8 million women suffer preg- and employment are from the International Labour nancy- related complications every year, and over Organization's Key Indicators of the Labour Market, half a million die (WHO 2004). This is reflected in fourth edition. Data on women in parliaments are the differences in maternal mortality ratios between from the Inter-Parliamentary Union. high- and low-income countries. 2007 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 2005 2005 2005 2005b 2005b 2005 2005 2004­05 2004­05 2005 2006c 2003 American Samoa 58 0.2 292 .. ..d .. .. .. .. .. .. 293 Andorra 66 0.5 141 .. ..e .. .. .. .. .. .. .. Antigua and Barbuda 83 0.4 188 855 10,500 969 11,700 7.2 5.4 .. .. 399 Aruba 100 0.2 528 .. ..e .. .. 1.6 .. .. 97 2,154 Bahamas, The 323 13.9 32 .. ..e .. .. .. .. 71 .. 1,868 Bahrain 727 0.7 1,023 10,288 14,370 15,470 21,290 6.9 5.3 75 87 21,872 Barbados 270 0.4 627 .. ..d .. .. .. .. 75 .. 1,190 Belize 292 23.0 13 1,042 3,570 1,967 6,740 3.1 ­0.2 72 .. 780 Bermuda 63 0.1 1,265 .. ..e .. .. .. .. 79 .. 498 Bhutan 637 47.0 14 798 1,250 f .. .. 6.1 2.6 64 60 385 Brunei Darussalam 374 5.8 71 .. ..e .. .. 1.7 ­0.5 77 93 4,549 Cape Verde 507 4.0 126 976 1,930 3,041 g 6,000 g 5.8 3.4 71 .. 143 Cayman Islands 45 0.3 173 .. ..e .. .. .. .. .. .. 304 Channel Islands 149 0.9 .. .. ..e .. .. .. .. 79 .. .. Comoros 600 2.2 269 389 650 1,201 g 2,000 g 4.2 2.1 63 .. 88 Cyprus 758 9.3 82 13,633 18,430 16,446 22,230 3.7 1.3 79 97 7,278 Djibouti 793 23.2 34 803 1,010 1,776g 2,240 g 3.2 1.4 53 .. 366 Dominica 72 0.8 96 271 3,800 400 5,560 6.4 6.0 .. .. 139 Equatorial Guinea 504 28.1 18 .. ..d 3,731g 7,580 g 10.0 7.5 42 87 165 Faeroe Islands 48 1.4 35 .. ..e .. .. .. .. .. .. 659 Fiji 848 18.3 46 2,684 3,170 5,052 5,960 0.7 ­0.1 68 .. 1,117 French Polynesia 257 4.0 70 .. ..e .. .. .. .. 74 .. 692 Greenland 57 410.5 0 .. ..e .. .. .. .. .. .. 568 Grenada 107 0.3 313 408 3,860 773 7,260 ­4.1 ­5.1 .. .. 220 Guam 170 0.6 308 .. ..e .. .. .. .. 75 .. 4,081 Guyana 751 215.0 4 770 1,020 3,178 g 4,230 g ­2.2 ­2.4 64 .. 1,630 Iceland 297 103.0 3 14,414 48,570 10,315 34,760 5.5 3.9 81 .. 2,187 Isle of Man 78 0.6 136 2,138 27,590 .. .. 6.3 6.0 .. .. .. About the data Definitions This table shows data for 55 economies--small · Population is based on the de facto definition of plus net receipts of primary income (compensation economies with populations between 30,000 and population, which counts all residents regardless of of employees and property income) from abroad. 1 million and smaller economies if they are members legal status or citizenship--except for refugees not Data are in current U.S. dollars converted using the of the World Bank. Where data on gross national permanently settled in the country of asylum, who World Bank Atlas method (see Statistical methods). income (GNI) per capita are not available, the esti- are generally considered part of the population of · GNI per capita is gross national income divided by mated range is given. For more information on the their country of origin. The values shown are midyear midyear population. GNI per capita in U.S. dollars is calculation of GNI (gross national product, or GNP, in estimates for 2005. See also table 2.1. · Surface converted using the World Bank Atlas method. · PPP the System of National Accounts 1968) and purchas- area is a country's total area, including areas under GNI is gross national income converted to interna- ing power parity (PPP) conversion factors, see About inland bodies of water and some coastal water- tional dollars using purchasing power parity rates. An the data for table 1.1. Since 2000 this table has ways. · Population density is midyear population international dollar has the same purchasing power excluded France's overseas departments--French divided by land area in square kilometers. · Gross over GNI as a U.S. dollar has in the United States. Guiana, Guadeloupe, Martinique, and Réunion--for national income (GNI) is the sum of value added by · Gross domestic product (GDP) is the sum of value which GNI and other economic measures are now all resident producers plus any product taxes (less added by all resident producers plus any product included in the French national accounts. subsidies) not included in the valuation of output taxes (less subsidies) not included in the valuation 32 2007 World Development Indicators 1.6 WORLD VIEW 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 2005 2005 2005 2005b 2005b 2005 2005 2004­05 2004­05 2005 2006c 2003 Kiribati 99 0.7 136 119 1,210 .. .. 0.3 ­0.9 .. .. 29 Liechtenstein 35 0.2 217 .. ..e .. .. .. .. .. .. .. Luxembourg 457 2.6 176 26,315 58,050 29,841 65,340 4.0 3.3 79 .. 9,927 Macao, China 460 0.0 16,318 .. ..e .. .. 6.7 6.0 80 91 1,864 Maldives 329 0.3 1,097 762 2,320 .. .. ­5.2 ­7.5 68 96 443 Malta 404 0.3 1,261 5,491 13,610 7,650 18,960 2.5 1.9 80 88 2,462 Marshall Islands 63 0.2 351 185 2,930 .. .. 3.5 0.1 .. .. .. Mayotte 180 0.4 481 .. ..d .. .. .. .. .. .. .. Micronesia, Fed. Sts. 110 0.7 158 254 2,300 .. .. 0.3 ­0.4 68 .. .. Monaco 33 0.0 17,128 .. ..e .. .. .. .. .. .. .. Netherlands Antilles 183 0.8 228 .. ..e .. .. .. .. 76 .. 4,051 New Caledonia 234 18.6 13 .. ..e .. .. .. .. 75 96 1,868 Northern Mariana Islands 79 0.5 166 .. ..d .. .. .. .. .. .. .. Palau 20 0.5 44 154 7,670 .. .. 5.5 5.0 .. .. 242 Qatar 813 11.0 74 .. ..e .. .. 6.1 1.4 74 89 46,172 Samoa 185 2.8 65 373 2,020 1,199 g 6,480 g 5.4 4.7 71 .. 150 São Tomé and Principe 157 1.0 163 68 440 .. .. 3.2 0.9 63 .. 92 Seychelles 84 0.5 184 691 8,180 1,347g 15,940 g ­2.3 ­3.3 .. 92 546 Solomon Islands 478 28.9 17 297 620 898 g 1,880 g 5.0 2.4 63 .. 179 San Marino 28 0.1 470 .. ..e .. .. .. .. .. .. .. St. Kitts and Nevis 48 0.4 133 369 7,840 600 12,500 8.8 8.2 .. .. 125 St. Lucia 165 0.6 270 744 4,580 985 5,980 5.8 4.6 74 .. 326 St. Vincent & Grenadines 119 0.4 305 421 3,530 769 6,460 2.2 1.7 72 .. 194 Suriname 449 163.3 3 1,141 2,540 .. .. 5.1 4.5 70 90 2,238 Timor-Leste 976 14.9 66 588 600 .. .. 2.5 ­2.8 57 .. 161 Tonga 102 0.8 142 178 1,750 823g 8,040 g 2.3 2.0 73 99 114 Vanuatu 211 12.2 17 331 1,560 670 g 3,170 g 2.8 0.8 69 74 88 Virgin Islands (U.S.) 109 0.4 311 .. ..e .. .. .. .. 79 .. 13,524 a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Actual reference year varies by country; for more information see the original source. d. Estimated to be upper middle-income ($3,466­$10,725). e. Estimated to be high-income ($10,726 or more). f. Included in the aggregates for low-income economies based on earlier data. g. Based on regression; others are extrapolated from the latest International Comparison Program benchmark estimates. of output. Growth is calculated from constant price GDP data in local currency. · 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 ages 15 and older who can, with understanding, read and The indicators here and throughout the rest of write a short, simple statement about their everyday the book are compiled by World Bank Group staff life. · Carbon dioxide emissions are those stemming from primary and secondary sources. More infor- from the burning of fossil fuels and the manufacture mation about the indicators and their sources can of cement. They include carbon dioxide produced be found in the About the data, Definitions, and during consumption of solid, liquid, and gas fuels Data sources entries that accompany each table and gas flaring. in subsequent sections. 2007 World Development Indicators 33 Text figures, tables, and boxes PEOPLE 2 ntroduction Introduction T he wide health divide Advances in technology and knowledge for health and hygiene have transformed life over the past 50 years. In 1960 more than 20 percent of children in developing countries died before reaching their fifth birthday; by 2005 this had fallen to just over 8 percent. The declines are large, even for the poorest countries (figure 2a). But this reassuring picture, painted by rising global averages, obscures substantial disparities among the world's regions and among the poor within countries. For millions of people health services and modern medicines are still out of reach, and many die prematurely from diseases that are easily prevented or cured. More than 25 years after the Health for All declaration, improving the health of the poorest people in developing countries remains a challenge. What can improve all this? There is no consensus on which determinants are most important across countries. But there is agreement on the need to reduce extreme income poverty, the major risk for poor health and premature death. The World Health Organization (WHO) con- curs, noting that a poverty-oriented health strategy requires complementary policies in other sectors (WHO 2003). These include improving access to education, enhancing the position of women and other marginalized groups, shaping development policies in agriculture and rural development, and promoting open and participatory governance. Priorities in healthcare are also clear: focus on health problems and diseases that affect the poor disproportionately. Health gains require directing program benefits toward the poor and increasing the quality and availability of health services, especially where they are least available. This section looks at the rich-poor health divide between and within countries--and at the factors behind that divide. Child mortality has fallen in the past 25 years for countries at all incomes 2a Under-five mortality (log) 1980 2005 2.75 1980 2005 2.25 1.75 1.25 0.75 0.25 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 GDP per capita (constant $, log) Source: World Bank estimates. 2007 World Development Indicators 35 The divide between rich and poor countries Differences in the health of rich and poor countries remain The differing patterns of mortality and well-being reflected large and in some cases are increasing. Under-five mortal- in the age distributions of death for developing and high- ity fell more than 36 percent in high-income countries from income countries show their impact on life expectancies at 1990 to 2005, but only 20 percent in developing countries, birth (figures 2d, 2e, and 2f). In developing countries, where as preventable diseases continue to take a toll on the world's deaths of children under age five are still the major health poorest people. But more important than the changes in pro- issue, average life expectancy at birth is 65 years. But several portions are the levels: under-five mortality is five times high- countries--such as Lesotho, Zambia, and Zimbabwe, with high er in middle-income countries than in high-income countries AIDS-related mortality--have life expectancies of less than 40 and 15 times higher in lower-income countries (figure 2b). years. In high-income countries, by contrast, noncommunica- What accounts for these disparities? Child mortality ble illnesses--such as cardiovascular diseases, diabetes, and from malaria doubled from 1990 to 2001, with the largest related conditions of high blood pressure, high cholesterol, and increase in Sub-Saharan Africa (Lopez and others 2006). excessive body weight--cluster deaths at older ages, and the Other increases in child mortality in developing countries average life expectancy at birth is 79 years. Indeed, in Canada, came from HIV/AIDS, again with the largest increase in Sub- France, Japan, Norway, Sweden, and Switzerland life expec- Saharan Africa, and problems in the first months of life, which tancies of 80 years and above are the norm. So any efforts depend strongly on the quality and availability of prenatal ser- to improve health and increase life expectancy in developing vices. Child deaths from these causes are far less common in countries will have to focus on diseases that kill children. high-income countries, just as they are from acute respiratory Why are there health gaps between rich and poor coun- infections, diarrheal diseases, and measles. But for develop- tries? Poverty makes people in developing countries more vul- ing countries, these diseases, along with malnutrition, remain nerable to disease. Nearly a third of the people in South Asia significant causes of avoidable child deaths (figure 2c). and half those in Sub-Saharan Africa lived on less than $1 Under-five mortality is 15 times higher in low- In Sierra Leone most income countries than in high-income countries 2b deaths occur before age 5 2d Age distribution of death, Sierra Leone around 2005 Male Female Under-five mortality (per 1,000) 1990 2005 160 95­99 90­94 85­89 140 80­84 75­79 120 70­74 65­69 60­64 Age group 100 55­59 50­54 80 45­49 40­44 35­39 60 30­34 25­29 40 20­24 15­19 10­14 20 5­9 0­4 0 Low-income All developing Middle-income High-income 60 50 40 30 20 10 0 10 20 30 40 50 60 Share of total deaths (%) Source: Harmonized estimates from WHO, UNICEF, and World Bank. Source: World Bank 2006f. Little reduction in A child born in Denmark risks for poor children 2c can expect to live to be 78 2e Risk of death by cause for Developing High income Age distribution of death, Denmark around 2005 Male Female children under five, per 1,000 1990 2001 1990 2001 95­99 20 90­94 85­89 80­84 75­79 70­74 15 65­69 60­64 Age group 55­59 50­54 10 45­49 40­44 35­39 30­34 5 25­29 20­24 15­19 10­14 5­9 0 0­4 Perinatal Acute Diarrheal Malaria Measles HIV/AIDS causes respiratory diseases 60 50 40 30 20 10 0 10 20 30 40 50 60 infection Share of total deaths (%) Source: Lopez and others 2006. Source: World Bank 2006f. 36 2007 World Development Indicators The health divide within countries: the rich-poor gap a day in 2002. The majority of them typically lack access to Inequalities in health within countries are pervasive. Even in safe drinking water and adequate sanitation, food, education, healthy countries such as Finland, the Netherlands, and the employment, health information, and professional healthcare. United Kingdom, the poor die 5­10 years before the rich (Carr Almost half the people in Sub-Saharan Africa cannot obtain 2004). But the inequalities are most apparent in poorer devel- essential drugs (Jamison and others 2006). Many developing oping countries. Studies from many developing countries show countries experienced little increase in immunization coverage that the poorest 20 percent of the population fares far worse between 1990 and 2005, and in 2005 only 75 percent of chil- than the richest 20 percent on a range of health outcomes, dren ages 12­23 months were vaccinated against measles including child mortality and nutritional status (box 2g, figures and diphtheria, pertussis, and whooping cough, compared 2h and 2i). On average a child in the poorest 20 percent is with almost 95 percent for high-income countries. twice as likely to die before age 5 as a child in the richest Several barriers beyond low income exclude people in 20 percent. The disparity is similar for maternal nutrition, with developing countries from getting appropriate care, and these women in the poorest 20 percent almost twice as likely to be can be related to services, clients, and institutions. Service malnourished as those in the richest 20 percent. factors include the high cost of care and transportation, poor Severe malnutrition among children reveals more pro- quality and inappropriate care, and negative staff attitudes. nounced inequality, with children in the poorest 20 percent Client factors include social and cultural constraints on wom- more than three times as likely to be underweight as children en's movements and limited information about their health in the richest 20 percent. The inequality is largest in South needs and availability of services. And institutional factors Asia, where 21 percent of children in the poorest 20 percent include men's control over decisionmaking and budgets, local were underweight, compared with 6 percent in the richest. perceptions about illness and treatment norms, and discrimi- Demographic and Health Surveys find that gaps in the nation in health settings. use of health services are closely related to economic A health gap Under-five mortality becomes a life gap 2f falls with rising income 2h Life expectancy at birth (years) 1990 2005 Under-five mortality (per 1,000, average for 56 countries) 80 150 70 120 60 50 90 40 30 60 20 30 10 0 0 Low-income All developing Middle-income High-income Poorest 20% Second Third Fourth Richest 20% Source: World Bank estimates. Source: Gwatkin and others 2007. Health inequalities by social, Health inequalities in cultural, and geographic factors Box 2g developing countries 2i Inequalities in health go beyond income to such sociocultural, demo- Ratio of poorest to richest 20% of the population (average for 56 countries) graphic, and geographic factors as sex, race, religion, ethnic group, 3.5 language, and residence. In parts of India and China infant girls are 3.0 more likely to die than infant boys because the cultural preference for male children puts girls at a disadvantage in nutrition and healthcare 2.5 early in life. Women and girls also face discrimination in healthcare 2.0 because cultural norms restrict them from traveling long distances, 1.5 especially alone. Poor communities--rural, remote, and in urban slums--typically 1.0 face multiple health risks related to gaps in infrastructure, services, 0.5 and trained personnel. For example, ethnic minorities, especially in isolated regions in Bangladesh, were less likely to be vaccinated for 0.0 childhood diseases. Severe child underweight Under-five mortality Women malnourished Source: Carr 2004; Ashford, Gwatkin, and Yazbeck 2006. Source: Gwatkin and others 2007. 2007 World Development Indicators 37 Main determinant of health status: health spending status (box 2j and figure 2k). On average, children ages Differences in health spending contribute to global dispari- 12­23 months in the richest 20 percent of the population ties in health outcomes (figure 2m). In rich countries, total are more than twice as likely as those in the poorest 20 per- health spending, at 6 percent of GDP, is almost twice that cent to have received basic immunizations. Inequality in of developing countries, and childhood vaccinations, skilled immunization is especially high in Sub-Saharan Africa: only attendants at birth, and access to effective health interven- 32 percent of children in the poorest 20 percent have been tions are almost universal. In developing countries, where fully immunized, compared with 60 percent in the richest access to free health services is seen as a basic human 20 percent. right, public spending on health is less than 3 percent of Use of professional healthcare during childbirth also var- GDP. In low-income countries the annual per capita spend- ies by income. Rich women are four times more likely to use ing on healthcare in 2004 was just $32, well below the $60 modern methods of birth control than their poorer counter- that the WHO deems sufficient for an adequately performing parts and nearly five times more likely to be attended by a health system (WHO, World Health Report 2000). By contrast, skilled health professional during childbirth. Several coun- annual per capita health spending in high-income countries tries, such as Benin, Morocco, Nicaragua, and Vietnam, have was $3,724. reduced inequalities and increased the coverage of trained The most obvious barrier to expanding health coverage medical staff attending childbirths for the poorest women (fig- in developing countries is the current low level of spending. ure 2l). Childbirths attended by trained staff among the poor- Expanding access to successful interventions will require more est 20 percent more than doubled in Nicaragua from 1997 funds, a situation made more difficult as HIV/AIDS spreads to 2001, from 33 percent to 78 percent. In a few countries, and more spending is allocated to the treatment of AIDS and such as Chad and Ghana, inequalities increased because of AIDS-related opportunistic infections, such as tuberculosis lack of progress in coverage among poor women. and pneumonia. As public funds for general health shrink, the Why do the poor receive and seek Rich people use health services less health care than the rich? Box 2j more than poor people 2k According to World Development Report 2006: Equity and Develop- Ratio of richest to poorest 20% of the population, 1992­2002 ment (World Bank 2005d), inequities occur when some groups of 5 people have less say and fewer opportunities to shape events and institutions around them, resulting in institutions that favor the privi- 4 leged, who are often the rich. In health this translates into a lower likelihood of the poor taking preventive measures and seeking and 3 using healthcare. Government actions affect the health of poor people. Public spend- 2 ing on health can influence the type and quality of services available to the poor. Governments may allocate high proportions of their health 1 budgets to urban hospitals, leaving rural residents without adequate health facilities. Income is another important constraint. In South 0 Use of oral Child Prenatal care Skilled assistance Africa people in the poorest 20 percent have to travel an average rehydration therapy vaccinations (three visits) at delivery of nearly two hours to obtain medical attention, compared with 34 Source: Ashford, Gwatkin, and Yazbeck 2006. minutes for those in the wealthiest 20 percent. Additional barriers that lower demand for health services include a lack of knowledge about hygiene, nutrition, and the availability of Some countries have reduced inequalities in treatment options, particularly among the uneducated. This can keep use of professional healthcare in childbirth 2l people from seeking care when they need it, even when price is not an Lowest Highest issue. In India immunization rates are low, even though immunization Childbirth attended by a medically trained person (%) 20 percent 20 percent is free: mothers cited lack of knowledge of the benefits of vaccination 100 and of the clinic location as the main reasons why their children had not been immunized. 80 Lack of knowledge can also lead people to pay for inappropriate healthcare. Unqualified providers can overprescribe treatment to 60 patients who do not know what is in their best interest. For example, instead of effective and inexpensive oral rehydration therapy, a poor 40 child in Indonesia gets more than four (often useless) drugs per diar- rhea episode. 20 Poorer members of a community often have less say in whether to seek care than wealthier members, and this can affect the level of 0 resources used in their interest. Similarly, within a family, women and 1997 2002 1997 2001 1996 2001 Vietnam Nicaragua Benin children have less voice than men and older family members. Source: Demographic and Health Surveys. 38 2007 World Development Indicators costs are borne more by households and the private sector. thus designed to give everyone equal access to care, and In 2004 more than 80 percent of the people in developing this rationale is typically invoked to justify direct government countries paid out of pocket for health services, compared involvement in service provision. In reality, equal access is with just 37 percent in high-income countries. elusive, and research confirms that publicly financed health- Greater public spending is not, however, always associ- care benefits the rich more than the poor (figure 2o). In 21 ated with better outcomes, and performance varies across countries the richest 20 percent received more than 26 per- countries based on the capabilities of government and health cent of government health spending, compared with 16 per- systems. In many countries staff ostensibly delivering ser- cent for the poorest 20 percent. Even health programs that vices do not, and absenteeism is high (figure 2n). Corrup- address illnesses affecting the poor tend to favor the rich. tion in the form of informal payments, coupled with the low In Sub- Saharan Africa the rich benefited more (53 percent) technical quality of service providers and the poor attitudes from prophylactic treatment for malaria than did the poor of health staff, especially to the poorer population, often (34 percent). discourage a second visit. According to World Development Primary healthcare is often free in the public health sys- Report 2006: Equity and Development (World Bank 2005d), tem, but treatment for major illnesses can be costly if pay- more than 70 percent of patients in Azerbaijan, Poland, and ment is required for drugs and services on top of transport the Russian Federation, and more than 90 percent in Arme- costs and time off from work. Indeed, health shocks can push nia, made "informal payments" for services. a high proportion of households into poverty because of out- To improve health conditions among the poor and vul- of-pocket health expenditures (figure 2p). This underscores nerable in developing countries, governments support free the need for policymakers to maintain and improve the health or subsidized health services, often as part of a national status of the poor through effective interventions--and to policy to reduce poverty. Government spending on health is protect households from falling into poverty. Differences in healthcare spending Public health spending contribute to global disparities 2m benefits the rich most 2o Health expenditure (% of GDP) High income Developing Share of health expenditure captured (%) 12 30 10 25 8 20 6 15 4 10 2 5 0 0 Total Public Poorest 20% Richest 20% Source: WHO and World Bank. Source: Filmer 2003. Where are healthcare Health shocks can push workers hiding? 2n households into poverty 2p Share of individuals experiencing catastrophic personal expenditure, Colombia 2003 (%) Inpatient Outpatient Absence rates among healthcare workers in primary health facilities 8 Country (%) 7 India (14 states) 43 6 5 Indonesia 42 4 Bangladesh 35 3 Uganda 35 2 Peru 26 1 Papua New Guinea 19 0 Poorest 20% Second Third Fourth Richest 20% Source: World Bank 2003c. Source: Baeza and Packard 2006. 2007 World Development Indicators 39 Tables 2.1 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as % proportion of working- Ages Ages Ages age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2005 2015 1990­2005 2005­15 2005 2005 2005 2005 2005 2005 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.3 3.1 3.2 ­0.3 0.4 27.0 64.7 8.3 0.4 0.1 6 13 Algeria 25.3 32.9 38.0 1.7 1.5 29.6 65.8 4.5 0.5 0.1 5 21 Angola 10.5 15.9 20.9 2.8 2.7 46.5 51.1 2.5 0.9 0.0a 22 48 Argentina 32.6 38.7 42.5 1.2 0.9 26.4 63.4 10.2 0.4 0.2 8 18 Armenia 3.5 3.0 3.0 ­1.1 ­0.2 20.8 67.1 12.1 0.3 0.2 8 12 Australia 17.1 20.3 22.3 1.2 0.9 19.6 67.7 12.7 0.3 0.2 6 13 Austria 7.7 8.2 8.3 0.4 0.1 15.5 67.8 16.7 0.2 0.2 9 10 Azerbaijan 7.2 8.4 9.2 1.1 0.9 25.8 67.1 7.1 0.4 0.1 6 17 Bangladesh 104.0 141.8 168.0 2.1 1.7 35.5 60.9 3.6 0.6 0.1 8 26 Belarus 10.2 9.8 9.2 ­0.3 ­0.6 15.2 70.2 14.7 0.2 0.2 15 9 Belgium 10.0 10.5 10.5 0.3 0.1 16.8 65.6 17.6 0.3 0.3 10 11 Benin 5.2 8.4 11.2 3.3 2.8 44.2 53.1 2.7 0.8 0.1 12 41 Bolivia 6.7 9.2 10.8 2.1 1.7 38.1 57.4 4.5 0.7 0.1 8 29 Bosnia and Herzegovina 4.3 3.9 3.9 ­0.7 ­0.1 16.5 69.5 14.0 0.2 0.2 9 9 Botswana 1.4 1.8 1.7 1.4 ­0.4 37.6 59.0 3.3 0.6 0.1 27 26 Brazil 149.4 186.4 208.8 1.5 1.1 27.9 66.0 6.1 0.4 0.1 7 20 Bulgaria 8.7 7.7 7.1 ­0.8 ­0.8 13.8 69.4 16.8 0.2 0.2 15 9 Burkina Faso 8.5 13.2 17.3 2.9 2.7 47.2 50.1 2.7 0.9 0.1 16 46 Burundi 5.7 7.5 10.6 1.9 3.4 45.0 52.3 2.7 0.9 0.1 18 45 Cambodia 9.7 14.1 17.1 2.5 1.9 37.1 59.5 3.4 0.6 0.1 10 30 Cameroon 11.7 16.3 20.2 2.2 2.1 41.2 55.1 3.7 0.7 0.1 17 34 Canada 27.8 32.3 34.9 1.0 0.8 17.6 69.3 13.1 0.3 0.2 7 11 Central African Republic 3.0 4.0 4.6 2.0 1.4 43.0 53.0 4.1 0.8 0.1 22 37 Chad 6.1 9.7 12.5 3.2 2.5 47.3 49.7 3.0 1.0 0.1 20 49 Chile 13.2 16.3 17.9 1.4 0.9 24.9 67.0 8.1 0.4 0.1 5 16 China 1,135.2 1,304.5 1,378.1 0.9 0.5 21.4 71.0 7.6 0.3 0.1 6 12 Hong Kong, China 5.7 6.9 7.6 1.3 0.9 14.4 73.6 12.0 0.2 0.2 6 8 Colombia 35.0 45.6 51.5 1.8 1.2 31.0 63.9 5.1 0.5 0.1 5 21 Congo, Dem. Rep. 37.8 57.5 77.9 2.8 3.0 47.3 50.1 2.7 0.9 0.1 20 50 Congo, Rep. 2.5 4.0 5.2 3.2 2.7 47.1 49.9 2.9 0.9 0.1 13 44 Costa Rica 3.1 4.3 5.0 2.3 1.4 28.4 65.8 5.8 0.4 0.1 4 17 Côte d'Ivoire 12.7 18.2 21.6 2.4 1.7 41.9 54.9 3.3 0.8 0.1 17 36 Croatia 4.8 4.4 4.3 ­0.5 ­0.2 15.5 67.3 17.2 0.2 0.3 11 9 Cuba 10.5 11.3 11.4 0.4 0.1 19.1 70.1 10.8 0.3 0.2 7 11 Czech Republic 10.4 10.2 10.1 ­0.1 ­0.2 14.6 71.2 14.2 0.2 0.2 11 10 Denmark 5.1 5.4 5.5 0.3 0.2 18.8 66.2 15.0 0.3 0.2 10 12 Dominican Republic 7.1 8.9 10.1 1.5 1.3 32.7 63.1 4.1 0.5 0.1 6 24 Ecuador 10.3 13.2 15.1 1.7 1.3 32.4 61.8 5.8 0.5 0.1 5 22 Egypt, Arab Rep. 55.7 74.0 88.1 1.9 1.7 33.6 61.7 4.8 0.5 0.1 6 26 El Salvador 5.1 6.9 8.0 2.0 1.5 34.0 60.7 5.4 0.6 0.1 6 24 Eritrea 3.0 4.4 5.8 2.5 2.8 44.8 52.9 2.3 0.8 0.0a 11 39 Estonia 1.6 1.3 1.3 ­1.0 ­0.3 15.2 68.3 16.5 0.2 0.2 13 11 Ethiopia 51.2 71.3 86.8 2.2 2.0 44.5 52.5 2.9 0.8 0.1 19 39 Finland 5.0 5.2 5.3 0.3 0.2 17.3 66.8 15.9 0.3 0.2 9 11 France 56.7 60.9 62.4 0.5 0.2 18.2 65.2 16.6 0.3 0.3 9 13 Gabon 1.0 1.4 1.6 2.5 1.5 40.0 55.6 4.4 0.7 0.1 13 30 Gambia, The 0.9 1.5 1.9 3.2 2.2 40.1 56.1 3.7 0.7 0.1 11 34 Georgia 5.5 4.5 4.2 ­1.3 ­0.7 18.9 66.8 14.3 0.3 0.2 10 11 Germany 79.4 82.5 81.8 0.3 ­0.1 14.3 66.9 18.8 0.2 0.3 10 8 Ghana 15.5 22.1 26.5 2.4 1.8 39.0 57.3 3.7 0.7 0.1 10 31 Greece 10.2 11.1 11.2 0.6 0.0 14.3 67.5 18.2 0.2 0.3 9 9 Guatemala 8.9 12.6 15.8 2.3 2.3 43.2 52.5 4.3 0.8 0.1 6 34 Guinea 6.2 9.4 11.8 2.8 2.3 43.7 52.7 3.5 0.8 0.1 13 41 Guinea-Bissau 1.0 1.6 2.1 3.0 2.9 47.5 49.4 3.1 1.0 0.1 19 50 Haiti 6.9 8.5 9.7 1.4 1.3 37.5 58.5 4.0 0.6 0.1 13 30 40 2007 World Development Indicators 2.1 PEOPLE Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as % proportion of working- Ages Ages Ages age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2005 2015 1990­2005 2005­15 2005 2005 2005 2005 2005 2005 2005 Honduras 4.9 7.2 8.8 2.6 2.0 39.2 56.9 3.9 0.7 0.1 6 28 Hungary 10.4 10.1 9.8 ­0.2 ­0.3 15.7 69.1 15.2 0.2 0.2 14 10 India 849.5 1,094.6 1,248.5 1.7 1.3 32.1 62.7 5.3 0.5 0.1 8 24 Indonesia 178.2 220.6 244.0 1.4 1.0 28.3 66.2 5.5 0.4 0.1 7 20 Iran, Islamic Rep. 54.4 68.3 78.4 1.5 1.4 28.7 66.8 4.5 0.4 0.1 4 15 Iraq 18.5 .. .. .. .. .. .. .. .. .. .. .. Ireland 3.5 4.2 4.7 1.1 1.1 20.2 68.9 10.9 0.3 0.2 7 15 Israel 4.7 6.9 8.1 2.6 1.5 27.8 62.1 10.1 0.4 0.2 6 21 Italy 56.7 58.6 58.0 0.2 ­0.1 14.0 66.0 20.0 0.2 0.3 10 10 Jamaica 2.4 2.7 2.7 0.7 0.3 31.2 61.2 7.6 0.5 0.1 6 16 Japan 123.5 127.8 124.9 0.2 ­0.2 14.0 66.3 19.7 0.2 0.3 9 8 Jordan 3.2 5.5 6.7 3.6 2.0 37.2 59.6 3.2 0.6 0.1 3 28 Kazakhstan 16.3 15.1 15.0 ­0.5 ­0.1 23.1 68.3 8.5 0.3 0.1 10 18 Kenya 23.4 34.3 44.1 2.5 2.5 42.8 54.4 2.8 0.8 0.1 14 39 Korea, Dem. Rep. 19.7 22.5 23.3 0.9 0.3 25.0 68.2 6.8 0.4 0.1 11 15 Korea, Rep. 42.9 48.3 49.2 0.8 0.2 18.6 72.0 9.4 0.3 0.1 5 9 Kuwait 2.1 2.5 3.4 1.2 2.8 24.3 73.9 1.8 0.3 0.0a 2 19 Kyrgyz Republic 4.4 5.1 5.7 1.0 1.0 31.5 62.4 6.1 0.5 0.1 7 21 Lao PDR 4.1 5.9 7.3 2.4 2.1 40.9 55.5 3.7 0.7 0.1 12 34 Latvia 2.7 2.3 2.2 ­1.0 ­0.6 14.7 68.4 16.9 0.2 0.2 14 9 Lebanon 2.7 3.6 4.0 1.8 1.0 28.6 64.0 7.3 0.4 0.1 7 18 Lesotho 1.6 1.8 1.7 0.8 ­0.3 38.6 56.2 5.3 0.7 0.1 25 28 Liberia 2.1 3.3 4.4 2.9 2.9 47.1 50.7 2.2 0.9 0.0a 20 50 Libya 4.3 5.9 7.0 2.0 1.8 30.1 65.9 4.1 0.5 0.1 4 23 Lithuania 3.7 3.4 3.3 ­0.5 ­0.5 16.7 67.8 15.5 0.2 0.2 13 9 Macedonia, FYR 1.9 2.0 2.1 0.4 0.2 19.6 69.3 11.1 0.3 0.2 9 11 Madagascar 12.0 18.6 23.8 2.9 2.5 44.0 52.9 3.1 0.8 0.1 12 38 Malawi 9.5 12.9 16.0 2.1 2.2 47.3 49.6 3.0 1.0 0.1 21 43 Malaysia 17.8 25.3 29.5 2.3 1.5 32.4 63.0 4.6 0.5 0.1 5 21 Mali 8.9 13.5 18.0 2.8 2.9 48.2 49.1 2.7 1.0 0.1 17 49 Mauritania 2.0 3.1 4.0 2.8 2.6 43.0 53.6 3.4 0.8 0.1 13 40 Mauritius 1.1 1.2 1.3 1.1 0.7 24.6 68.8 6.6 0.4 0.1 7 15 Mexico 83.2 103.1 114.3 1.4 1.0 31.0 63.7 5.3 0.5 0.1 4 18 Moldova 4.4 4.2 4.1 ­0.2 ­0.3 18.3 71.6 10.1 0.3 0.1 12 11 Mongolia 2.1 2.6 2.9 1.3 1.2 30.5 65.8 3.8 0.5 0.1 6 18 Morocco 23.9 30.2 34.2 1.5 1.3 31.1 64.1 4.8 0.5 0.1 6 23 Mozambique 13.4 19.8 23.5 2.6 1.7 44.0 52.7 3.3 0.8 0.1 20 39 Myanmar 40.8 50.5 54.9 1.4 0.8 29.5 65.6 4.9 0.4 0.1 9 19 Namibia 1.4 2.0 2.2 2.5 1.0 41.5 55.0 3.5 0.8 0.1 6 22 Nepal 19.1 27.1 32.7 2.3 1.9 39.0 57.3 3.7 0.7 0.1 8 29 Netherlands 15.0 16.3 16.8 0.6 0.3 18.2 67.7 14.1 0.3 0.2 8 12 New Zealand 3.4 4.1 4.4 1.2 0.6 21.3 66.4 12.3 0.3 0.2 7 14 Nicaragua 4.0 5.1 6.2 1.8 1.8 38.9 57.8 3.3 0.7 0.1 5 28 Niger 8.5 14.0 19.2 3.3 3.2 49.0 49.0 2.0 1.0 0.0a 20 53 Nigeria 90.6 131.5 160.8 2.5 2.0 44.3 52.7 3.0 0.8 0.1 19 41 Norway 4.2 4.6 4.8 0.6 0.5 19.6 65.4 15.0 0.3 0.2 9 12 Oman 1.8 2.6 3.2 2.2 2.1 34.5 63.0 2.6 0.5 0.0a 3 25 Pakistan 108.0 155.8 190.5 2.4 2.0 38.3 57.9 3.8 0.7 0.1 7 26 Panama 2.4 3.2 3.8 2.0 1.5 30.4 63.6 6.0 0.5 0.1 5 22 Papua New Guinea 4.1 5.9 7.0 2.4 1.7 40.3 57.3 2.4 0.7 0.0 a 10 29 Paraguay 4.2 5.9 7.1 2.2 1.8 37.6 58.7 3.7 0.6 0.1 5 29 Peru 21.8 28.0 32.1 1.7 1.4 32.2 62.5 5.3 0.5 0.1 6 22 Philippines 61.1 83.1 98.7 2.0 1.7 35.1 61.0 3.9 0.6 0.1 5 24 Poland 38.1 38.2 37.6 0.0a ­0.2 16.3 70.7 12.9 0.2 0.2 10 9 Portugal 9.9 10.5 10.9 0.4 0.3 15.9 67.0 17.1 0.2 0.3 10 11 Puerto Rico 3.5 3.9 4.1 0.7 0.4 22.3 65.7 12.1 0.3 0.2 8 13 2007 World Development Indicators 41 2.1 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as % proportion of working- Ages Ages Ages age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2005 2015 1990­2005 2005­15 2005 2005 2005 2005 2005 2005 2005 Romania 23.2 21.6 20.7 ­0.5 ­0.4 15.4 69.8 14.8 0.2 0.2 12 10 Russian Federation 148.3 143.1 136.0 ­0.2 ­0.5 15.3 70.9 13.8 0.2 0.2 16 10 Rwanda 7.1 9.0 11.3 1.6 2.2 43.5 54.0 2.5 0.8 0.0a 18 41 Saudi Arabia 16.4 23.1 28.9 2.3 2.2 37.3 59.8 2.9 0.6 0.0a 4 27 Senegal 8.0 11.7 14.5 2.5 2.2 42.6 54.3 3.1 0.8 0.1 11 36 Serbia and Montenegro 10.5b 8.1 8.0 c 0.1d ­0.1c 18.3 67.6 14.1 0.3 0.2 14 11 Sierra Leone 4.1 5.5 6.9 2.0 2.2 42.8 53.8 3.3 0.8 0.1 23 46 Singapore 3.0 4.3 4.8 2.4 1.1 19.5 72.0 8.5 0.3 0.1 4 10 Slovak Republic 5.3 5.4 5.4 0.1 ­0.1 16.7 71.5 11.8 0.2 0.2 10 10 Slovenia 2.0 2.0 2.0 0.0a ­0.2 13.9 70.5 15.6 0.2 0.2 9 9 Somalia 6.7 8.2 11.0 1.4 2.9 44.1 53.3 2.6 0.8 0.0a 17 44 South Africa 35.2 46.9 47.3 1.9 0.1 32.6 63.1 4.2 0.5 0.1 21 24 Spain 38.8 43.4 44.4 0.7 0.2 14.3 69.2 16.5 0.2 0.2 9 11 Sri Lanka 17.0 19.6 21.0 1.0 0.7 24.1 68.6 7.3 0.4 0.1 6 18 Sudan 26.1 36.2 44.1 2.2 2.0 39.2 57.2 3.6 0.7 0.1 11 32 Swaziland 0.8 1.1 1.1 2.6 ­0.4 41.0 55.5 3.5 0.7 0.1 20 34 Sweden 8.6 9.0 9.3 0.4 0.3 17.5 65.3 17.2 0.3 0.3 10 10 Switzerland 6.7 7.4 7.5 0.7 0.0a 16.5 67.6 16.0 0.2 0.2 8 10 Syrian Arab Republic 12.8 19.0 23.8 2.6 2.2 36.9 60.0 3.1 0.6 0.1 3 28 Tajikistan 5.3 6.5 7.6 1.4 1.5 39.0 57.2 3.9 0.7 0.1 7 28 Tanzania 26.2 38.3 47.1 2.5 2.1 42.6 54.2 3.2 0.8 0.1 16 36 Thailand 54.6 64.2 69.0 1.1 0.7 23.8 69.1 7.1 0.3 0.1 7 16 Togo 4.0 6.1 7.8 2.9 2.4 43.5 53.4 3.1 0.8 0.1 12 38 Trinidad and Tobago 1.2 1.3 1.3 0.5 0.2 21.5 71.1 7.4 0.3 0.1 8 14 Tunisia 8.2 10.0 11.0 1.4 1.0 25.9 67.8 6.3 0.4 0.1 6 17 Turkey 56.2 72.1 80.7 1.7 1.1 28.4 65.7 5.9 0.4 0.1 6 19 Turkmenistan 3.7 4.8 5.5 1.8 1.3 31.8 63.6 4.7 0.5 0.1 8 22 Uganda 17.8 28.8 41.8 3.2 3.7 50.5 47.1 2.5 1.1 0.1 15 51 Ukraine 51.9 47.1 42.3 ­0.6 ­1.1 14.9 69.0 16.1 0.2 0.2 17 9 United Arab Emirates 1.8 4.5 5.6 6.3 2.2 22.0 76.9 1.1 0.3 0.0a 1 16 United Kingdom 57.6 60.2 61.7 0.3 0.2 17.9 66.1 16.0 0.3 0.2 10 12 United States 249.6 296.4 322.5 1.1 0.8 20.8 66.9 12.3 0.3 0.2 8 14 Uruguay 3.1 3.5 3.7 0.7 0.5 24.3 62.5 13.2 0.4 0.2 9 15 Uzbekistan 20.5 26.2 30.1 1.6 1.4 33.2 62.1 4.7 0.5 0.1 6 20 Venezuela, RB 19.8 26.6 31.1 2.0 1.6 31.2 63.7 5.1 0.5 0.1 5 22 Vietnam 66.2 83.1 92.1 1.5 1.0 29.5 65.0 5.4 0.5 0.1 6 18 West Bank and Gaza 2.0 3.6 4.9 4.1 3.0 45.5 51.4 3.1 0.9 0.1 4 33 Yemen, Rep. 12.1 21.0 28.4 3.7 3.0 46.4 51.4 2.3 0.9 0.0a 8 40 Zambia 8.4 11.7 13.8 2.2 1.7 45.8 51.2 3.0 0.9 0.1 22 40 Zimbabwe 10.6 13.0 13.8 1.4 0.6 40.0 56.4 3.6 0.7 0.1 23 29 World 5,256.3 s 6,437.7 s 7,165.8 s 1.4 w 1.1 w 28.1 w 64.5 w 7.4 w 0.4 w 0.1 w 9w 20 w Low income 1,739.4 2,352.4 2,787.8 2.0 1.7 36.4 59.3 4.3 0.6 0.1 10 29 Middle income 2,613.4 3,074.5 3,322.7 1.1 0.8 25.0 67.7 7.3 0.4 0.1 8 16 Lower middle income 2,083.6 2,474.6 2,694.1 1.1 0.8 25.3 67.9 6.9 0.4 0.1 7 16 Upper middle income 529.8 599.8 628.6 0.8 0.5 24.2 66.7 9.1 0.4 0.1 10 16 Low & middle income 4,352.8 5,426.9 6,110.5 1.5 1.2 30.0 64.0 6.0 0.5 0.1 9 22 East Asia & Pacific 1,596.1 1,885.5 2,027.8 1.1 0.7 23.9 69.2 6.9 0.3 0.1 7 15 Europe & Central Asia 466.1 471.8 471.5 0.1 0.0a 19.7 68.6 11.8 0.3 0.2 12 13 Latin America & Carib. 437.6 550.8 620.1 1.5 1.2 30.0 63.9 6.1 0.5 0.1 6 20 Middle East & N. Africa 225.5 306.0 365.1 2.0 1.8 33.5 62.3 4.2 0.5 0.1 6 24 South Asia 1,113.1 1,469.8 1,703.4 1.9 1.5 33.4 61.7 4.9 0.5 0.1 8 25 Sub-Saharan Africa 514.4 743.1 922.6 2.5 2.2 43.5 53.4 3.1 0.8 0.1 17 40 High income 903.5 1010.8 1055.3 0.7 0.4 18.2 67.0 14.8 0.3 0.2 8 12 Europe EMU 295.3 313.9 316.7 0.4 0.1 15.5 66.8 17.7 0.2 0.3 9 10 a. Less than 0.05. b. Includes population of Kosovo and Metahia until 1999. c. Projections are based on data for Serbia and Montenegro before it separated into two independent states in 2006. d. Data are for 1990­99. 42 2007 World Development Indicators 2.1 PEOPLE Population dynamics About the data Definitions Population estimates are usually based on national Dependency ratios take into account variations in · Total population of an economy includes all resi- population censuses, but the frequency and quality the different age groups: the proportions of children, dents regardless of legal status or citizenship-- vary by country. Most countries conduct a complete elderly people, and working-age people in the popula- except for refugees not permanently settled in the enumeration no more than once a decade. Estimates tion. Separate calculations of young-age and old-age country of asylum, who are generally considered part for the years before and after the censuses are inter- dependency suggest the burden of dependency that of the population of their country of origin. The values polations or extrapolations based on demographic the working-age population must bear in relation to shown are midyear estimates for 1990 and 2005 models. Errors and undercounting occur even in children and the elderly. But dependency ratios show and projections for 2015. · Average annual popula- high-income countries; in developing countries such only the age composition of a population, not eco- tion growth rate is the exponential change for the errors may be substantial because of limits in the nomic dependency. Some children and elderly people period indicated. See Statistical methods for more transport, communications, and other resources are part of the labor force, and many working-age information. · Population age composition refers to required to conduct and analyze a full census. people are not. the percentage of the total population that is in spe- The quality and reliability of official demographic The vital rates shown in the table are based on cific age groups. · Dependency ratio is the ratio of data are also affected by the public trust in the gov- data derived from birth and death registration sys- dependents--people younger than 15 or older than ernment, the government's commitment to full and tems, censuses, and sample surveys conducted by 64--to the working-age population--those ages accurate enumeration, the confidentiality and protec- national statistical offices and other organizations, 15­64. · Crude death rate and crude birth rate are tion against misuse accorded to census data, and or on demographic analysis. The estimates for 2005 the number of deaths and the number of live births the independence of census agencies from undue for many countries are national projections based occurring during the year, per 1,000 population, esti- political influence. Moreover, the international com- on extrapolations of levels and trends measured in parability of population indicators is limited by dif- earlier years. mated at midyear. Subtracting the crude death rate ferences in the concepts, definitions, data collec- Vital registers are the preferred source of these from the crude birth rate provides the rate of natural tion procedures, and estimation methods used by data, but in many developing countries systems increase, which is equal to the population growth national statistical agencies and other organizations for registering births and deaths do not exist or are rate in the absence of migration. that collect population data. incomplete because of deficiencies in the coverage of Of the 152 economies listed in the table, 130 events or of geographic areas. Many developing coun- (about 86 percent) conducted a census between tries carry out special household surveys that esti- 1995 and 2005. The currentness of a census, along mate vital rates by asking respondents about births with the availability of complementary data from sur- and deaths in the recent past. Estimates derived in veys or registration systems, is one of many objective this way are subject to sampling errors as well as ways to judge the quality of demographic data. In errors due to inaccurate recall by the respondents. some European countries registration systems offer The United Nations Statistics Division monitors complete information on population in the absence the completeness of vital registration systems. The of a census. See Primary data documentation for the share of countries with at least 90 percent complete most recent census or survey year and for the com- vital registration increased from 45 percent in 1988 pleteness of registration. to 62 percent in 2005. Still, some of the most popu- Current population estimates for developing coun- lous developing countries--China, India, Indonesia, tries that lack recent census-based data, and pre- Brazil, Pakistan, Bangladesh, Nigeria--do not have Data sources and post-census estimates for countries with census complete vital registration systems. Between 2003 The World Bank's population estimates are com- data, are provided by the United Nations Population and 2005, 51 percent of births and deaths and 48 piled and produced by its Human Development Division and other agencies. The standard estimation percent of infant deaths worldwide were registered Network and Development Data Group in consulta- method requires fertility, mortality, and net migration and reported. tion with its operational staff and country offices. data, which are often collected from sample surveys, International migration is the only other factor Important inputs to the World Bank's demographic some of which may be small or limited in coverage. besides birth and death rates that directly determines work come from the United Nations Population The population estimates are the product of demo- a country's population growth. From 1990 to 2000 graphic modeling and so are susceptible to biases the number of migrants in high-income countries Division's World Population Prospects: The 2004 and errors because of shortcomings in the model as increased by 23 million. About 190 million people Revision; census reports and other statistical well as in the data. Population projections are made currently live outside their home country, accounting publications from national statistical offi ces; using the cohort component method. for about 3 percent of the world's population. Esti- household surveys conducted by national agen- The growth rate of the total population conceals mating international migration is difficult. At any time cies, Macro International, and the U.S. Centers for the fact that different age groups may grow at very many people are located outside their home country Disease Control and Prevention; Eurostat, Demo- different rates. In many developing countries the pop- as tourists, workers, or refugees or for other reasons. graphic Statistics (various years); Centro Latino- ulation under age 15 was previously growing rapidly Standards relating to the duration and purpose of americano de Demografía, Boletín Demográfico but is now starting to shrink. Previously high fertility international moves that qualify as migration vary, (various years); and U.S. Bureau of the Census, rates and declining mortality rates are now reflected and accurate estimates require information on flows International Database. in the larger share of the working-age population. into and out of countries that is difficult to collect. 2007 World Development Indicators 43 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15­64 Total average annual Female Male Female millions % growth % of labor force 1990 2005 1990 2005 1990 2005 1990­2005 1990 2005 Afghanistan .. .. .. .. .. .. .. .. .. Albania 86.3 75.7 63.3 54.7 1.6 1.4 ­0.9 40.2 42.1 Algeria 81.0 83.5 23.7 38.0 7.2 13.4 4.1 22.6 30.7 Angola 90.9 92.2 76.0 75.6 4.5 7.0 2.9 46.4 45.8 Argentina 84.7 82.4 43.5 61.1 13.0 18.4 2.3 34.4 42.9 Armenia 89.7 65.9 76.7 55.4 1.9 1.3 ­2.8 47.7 49.2 Australia 84.4 80.8 61.5 67.4 8.4 10.3 1.3 41.3 45.5 Austria 80.1 77.4 55.3 63.8 3.5 4.0 0.8 40.8 44.6 Azerbaijan 80.6 78.1 68.5 66.2 3.3 4.1 1.5 47.4 47.7 Bangladesh 89.8 88.1 64.5 55.2 46.9 63.9 2.1 40.2 36.9 Belarus 82.2 72.3 72.4 66.4 5.3 4.8 ­0.7 48.6 49.3 Belgium 71.3 72.5 46.2 57.3 3.9 4.5 0.9 39.0 43.5 Benin 90.0 86.5 59.2 54.8 2.0 3.3 3.3 40.8 38.3 Bolivia 80.9 84.3 49.9 64.5 2.5 4.2 3.4 39.2 43.6 Bosnia and Herzegovina 82.4 78.3 66.1 70.5 2.3 2.1 ­0.7 44.7 48.1 Botswana 76.0 68.2 58.9 46.7 0.5 0.6 1.2 45.2 41.8 Brazil 88.8 83.6 47.6 61.0 62.4 91.3 2.5 35.0 42.9 Bulgaria 77.8 62.6 72.3 52.4 4.4 3.1 ­2.4 48.0 46.0 Burkina Faso 92.1 90.2 79.3 79.5 3.8 5.8 2.9 46.3 46.6 Burundi 90.7 93.2 91.8 92.8 2.8 3.8 2.1 52.6 51.9 Cambodia 86.7 81.4 81.0 78.0 4.4 6.8 2.9 52.6 51.4 Cameroon 83.5 81.1 58.2 53.9 4.4 6.3 2.4 41.5 39.9 Canada 84.9 82.6 68.3 72.8 14.7 17.6 1.2 44.0 46.4 Central African Republic 89.4 89.4 71.7 70.8 1.4 1.8 2.0 47.0 46.1 Chad 79.0 77.0 64.7 66.0 2.3 3.7 3.0 46.0 46.9 Chile 80.9 76.0 35.2 40.9 5.0 6.5 1.8 30.5 35.1 China 88.9 87.8 79.1 75.8 650.1 776.0 1.2 44.8 44.5 Hong Kong, China 85.5 81.1 53.0 62.2 2.9 3.7 1.7 36.3 46.6 Colombia 85.0 85.2 48.5 65.9 14.1 22.3 3.1 36.9 44.3 Congo, Dem. Rep. 91.2 91.1 62.6 63.1 15.0 22.9 2.8 41.6 41.2 Congo, Rep. 86.3 86.6 57.7 56.1 1.0 1.5 3.0 41.5 40.3 Costa Rica 87.6 84.8 35.3 48.6 1.2 2.0 3.5 27.6 35.1 Côte d'Ivoire 90.3 89.1 44.5 40.1 4.6 6.8 2.6 30.2 29.3 Croatia 76.9 71.0 55.0 57.5 2.2 2.0 ­0.8 42.1 45.0 Cuba 79.5 82.3 43.5 50.8 4.5 5.4 1.1 34.8 37.4 Czech Republic 82.2 77.4 74.1 64.0 5.5 5.2 ­0.3 47.4 45.2 Denmark 87.1 82.6 77.6 74.2 2.9 2.8 ­0.2 46.1 46.8 Dominican Republic 85.6 84.0 37.8 48.5 2.6 3.8 2.5 29.6 35.9 Ecuador 85.9 85.4 33.6 64.1 3.7 6.4 3.6 27.8 42.4 Egypt, Arab Rep. 76.7 76.9 27.6 21.6 16.6 22.9 2.1 26.3 21.7 El Salvador 81.9 78.7 53.5 50.4 2.0 2.8 2.3 41.2 40.2 Eritrea 92.6 90.7 63.1 59.8 1.2 1.8 2.5 42.4 41.1 Estonia 83.0 73.6 76.0 64.4 0.9 0.7 ­1.7 49.9 49.4 Ethiopia 92.3 90.7 74.5 73.5 22.6 31.6 2.2 44.9 44.9 Finland 79.0 76.8 72.2 72.8 2.6 2.7 0.2 47.2 47.8 France 75.0 73.5 57.0 62.4 24.8 27.1 0.6 43.3 45.9 Gabon 85.5 83.9 65.5 64.1 0.4 0.6 2.7 43.9 43.3 Gambia, The 86.2 86.6 63.3 60.3 0.4 0.7 3.4 43.4 41.6 Georgia 78.2 76.1 79.1 52.4 2.9 2.3 ­1.7 52.3 43.4 Germany 81.4 79.3 56.8 67.4 38.3 41.0 0.4 40.4 45.2 Ghana 80.5 75.7 77.5 71.8 6.7 9.8 2.5 48.9 48.0 Greece 76.7 78.8 43.1 56.0 4.2 5.1 1.4 36.2 40.9 Guatemala 90.7 84.7 30.2 35.2 2.9 4.1 2.3 24.7 31.2 Guinea 90.8 88.6 82.8 82.6 3.0 4.4 2.6 46.2 46.6 Guinea-Bissau 91.4 93.0 60.5 63.1 0.4 0.6 2.9 40.3 40.9 Haiti 82.7 83.3 59.1 57.9 2.6 3.7 2.2 43.3 41.7 44 2007 World Development Indicators 2.2 PEOPLE Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15­64 Total average annual Female Male Female millions % growth % of labor force 1990 2005 1990 2005 1990 2005 1990­2005 1990 2005 Honduras 89.0 90.5 34.6 56.5 1.6 3.1 4.4 27.7 37.7 Hungary 74.4 66.8 57.3 53.5 4.5 4.2 ­0.5 44.5 45.1 India 86.6 84.3 40.3 36.0 335.1 435.0 1.7 29.9 28.4 Indonesia 82.9 87.1 52.1 53.0 75.3 107.2 2.4 38.4 37.9 Iran, Islamic Rep. 82.3 75.5 22.5 40.5 15.6 27.5 3.8 20.2 33.8 Iraq 77.8 .. 16.4 .. 4.7 .. .. 16.8 .. Ireland 77.9 80.4 42.3 62.2 1.3 2.1 3.0 34.3 43.0 Israel 68.1 65.6 46.8 58.7 1.6 2.7 3.4 40.5 47.0 Italy 76.7 74.3 44.6 50.1 23.9 24.4 0.1 37.1 40.1 Jamaica 83.0 78.0 71.3 59.3 1.1 1.2 0.3 46.8 43.6 Japan 83.1 84.8 57.1 60.5 63.9 66.6 0.3 40.6 41.1 Jordan 71.3 79.7 18.6 28.9 0.8 1.8 6.0 18.8 24.4 Kazakhstan 81.6 80.1 68.0 73.6 7.7 8.1 0.3 46.3 49.6 Kenya 90.6 89.6 76.2 71.3 9.8 15.5 3.0 46.0 43.8 Korea, Dem. Rep. 84.0 80.4 56.4 49.9 9.7 10.7 0.6 39.3 38.7 Korea, Rep. 75.3 77.3 49.7 54.2 19.1 24.4 1.6 39.3 40.8 Kuwait 83.1 86.4 35.6 50.4 0.9 1.4 3.2 21.8 25.4 Kyrgyz Republic 78.0 77.5 65.0 59.9 1.8 2.3 1.4 46.2 44.2 Lao PDR 81.6 82.3 56.3 56.4 1.5 2.4 2.8 41.3 40.6 Latvia 83.4 71.9 75.0 63.0 1.5 1.1 ­1.9 49.5 48.7 Lebanon 81.5 83.9 34.4 35.7 0.9 1.4 2.7 31.8 30.4 Lesotho 86.8 73.8 59.4 48.7 0.6 0.6 0.3 46.5 44.5 Liberia 85.2 83.8 55.9 55.7 0.8 1.2 2.8 39.4 39.9 Libya 81.4 82.8 19.9 33.9 1.3 2.3 4.1 17.3 27.1 Lithuania 81.7 72.4 70.4 65.9 1.9 1.6 ­1.1 48.1 49.2 Macedonia, FYR 77.5 73.2 52.8 47.9 0.9 0.9 0.1 40.0 39.2 Madagascar 83.6 86.3 79.5 79.8 5.4 8.6 3.1 49.2 48.4 Malawi 91.7 89.9 86.2 86.2 4.5 5.9 1.9 50.3 49.8 Malaysia 82.7 83.7 45.3 48.1 7.1 11.0 2.9 34.8 35.8 Mali 90.7 85.1 75.1 74.8 3.8 5.5 2.5 46.0 47.5 Mauritania 87.6 85.1 57.8 56.5 0.8 1.2 2.7 40.7 40.4 Mauritius 86.6 84.1 45.2 46.9 0.5 0.6 1.4 33.9 35.7 Mexico 85.4 83.0 36.2 42.6 29.5 42.3 2.4 30.6 35.2 Moldova 81.5 76.0 70.4 65.4 2.1 2.2 0.1 48.6 47.7 Mongolia 83.7 83.3 59.3 56.2 0.8 1.2 2.4 41.0 40.1 Morocco 83.9 83.8 25.6 28.7 7.5 11.1 2.6 23.7 25.5 Mozambique 88.0 82.7 88.1 84.9 6.3 9.3 2.6 54.0 53.5 Myanmar 89.2 87.7 71.2 70.0 20.0 27.4 2.1 44.6 45.0 Namibia 67.1 64.5 50.6 48.4 0.4 0.6 2.5 44.1 43.6 Nepal 82.5 80.6 50.4 52.5 7.1 10.5 2.6 37.9 40.5 Netherlands 80.0 84.5 53.1 69.5 6.9 8.6 1.4 39.1 44.2 New Zealand 83.0 83.3 63.2 71.2 1.7 2.2 1.7 43.1 46.6 Nicaragua 87.0 87.4 36.8 36.9 1.3 1.9 2.7 30.1 29.8 Niger 94.7 95.5 72.4 73.0 3.6 5.9 3.4 42.6 42.0 Nigeria 86.9 85.8 49.0 46.6 32.7 47.9 2.5 36.2 34.7 Norway 82.5 83.6 69.9 77.3 2.2 2.5 0.9 44.7 47.3 Oman 83.8 82.7 15.7 23.6 0.6 1.0 3.5 11.1 16.4 Pakistan 88.1 85.7 28.8 33.7 35.2 56.5 3.2 23.3 27.0 Panama 82.7 83.0 41.6 54.9 0.9 1.5 3.1 32.5 38.8 Papua New Guinea 75.9 75.2 72.3 72.8 1.8 2.6 2.5 46.4 47.6 Paraguay 85.7 86.9 54.4 68.6 1.6 2.8 3.4 38.3 43.5 Peru 82.0 83.5 48.6 61.2 8.5 13.3 3.0 37.0 42.0 Philippines 83.7 84.7 48.7 56.5 23.4 37.1 3.1 36.6 39.8 Poland 79.2 68.8 65.1 57.6 18.6 17.3 ­0.5 45.8 45.7 Portugal 82.6 79.7 59.2 67.8 4.8 5.6 1.0 42.7 46.5 Puerto Rico 67.4 67.7 35.0 44.4 1.2 1.5 1.6 35.8 41.5 2007 World Development Indicators 45 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15­64 Total average annual Female Male Female millions % growth % of labor force 1990 2005 1990 2005 1990 2005 1990­2005 1990 2005 Romania 77.2 69.5 61.1 55.3 11.0 10.3 ­0.4 44.3 46.2 Russian Federation 81.6 75.3 71.7 67.1 77.2 73.2 ­0.4 48.3 49.0 Rwanda 88.3 84.9 87.4 82.0 3.1 4.2 2.1 51.0 51.2 Saudi Arabia 81.3 80.4 15.6 18.5 5.1 7.5 2.5 11.4 15.2 Senegal 87.8 83.4 63.4 58.4 3.1 4.6 2.6 43.4 42.5 Serbia and Montenegro 77.0 76.0 54.9 54.7 4.9a 3.9 0.0 b 41.7 42.2 Sierra Leone 90.2 94.5 55.6 58.4 1.7 2.4 2.2 38.5 38.5 Singapore 83.9 82.8 54.2 56.7 1.6 2.2 2.4 38.8 39.9 Slovak Republic 82.5 76.4 70.6 62.4 2.6 2.7 0.1 46.3 45.1 Slovenia 76.9 75.5 63.3 66.6 1.0 1.0 0.4 45.5 46.2 Somalia 95.8 95.1 63.1 61.0 2.8 3.5 1.4 39.9 39.2 South Africa 81.6 81.9 57.4 49.3 14.4 19.6 2.1 41.6 38.2 Spain 80.3 80.7 41.9 57.2 16.0 20.9 1.8 34.3 41.0 Sri Lanka 82.9 81.9 48.2 38.5 7.3 8.4 1.0 34.8 30.4 Sudan 78.9 72.5 27.8 24.2 7.8 10.5 2.0 26.0 24.8 Swaziland 79.6 74.5 39.6 32.9 0.2 0.3 2.7 38.0 32.9 Sweden 86.0 79.0 81.9 74.9 4.7 4.7 ­0.1 47.7 47.4 Switzerland 90.2 87.6 62.8 75.3 3.7 4.2 0.9 40.4 46.6 Syrian Arab Republic 83.7 89.2 29.7 39.9 3.7 7.6 4.8 26.2 30.6 Tajikistan 77.6 65.8 56.2 49.5 1.9 2.1 0.8 42.2 43.8 Tanzania 92.1 90.7 90.2 88.2 12.8 19.3 2.7 50.2 49.4 Thailand 90.6 84.5 79.2 71.0 30.4 35.7 1.1 46.6 46.2 Togo 90.8 90.4 55.2 51.7 1.5 2.4 3.1 38.5 36.9 Trinidad and Tobago 79.7 82.5 45.9 51.4 0.5 0.6 1.9 36.1 38.9 Tunisia 79.2 78.4 22.1 31.1 2.4 3.8 3.0 21.5 27.6 Turkey 84.5 76.0 36.2 27.2 21.0 26.6 1.6 29.4 26.4 Turkmenistan 80.0 76.5 69.1 65.1 1.5 2.2 2.4 46.9 46.7 Uganda 92.4 87.3 82.0 81.2 7.8 11.9 2.8 47.5 48.3 Ukraine 79.7 72.4 70.7 62.9 26.3 22.3 ­1.1 49.2 49.1 United Arab Emirates 92.4 92.0 25.9 39.0 0.9 2.7 7.3 9.8 13.4 United Kingdom 87.9 81.9 67.2 69.3 29.4 30.6 0.3 44.0 46.0 United States 85.1 81.5 67.5 70.1 129.3 155.5 1.2 44.4 46.2 Uruguay 85.9 86.1 54.3 66.3 1.4 1.8 1.6 39.9 44.2 Uzbekistan 78.5 75.7 64.4 60.6 8.2 11.3 2.2 45.4 44.6 Venezuela, RB 82.4 85.7 39.8 61.9 7.3 12.9 3.8 31.8 40.9 Vietnam 85.5 82.4 79.4 77.4 31.3 44.0 2.3 48.3 48.5 West Bank and Gaza 67.0 68.8 9.5 10.9 0.4 0.8 4.5 11.9 13.1 Yemen, Rep. 76.1 77.5 28.6 30.8 3.0 5.9 4.6 27.3 27.9 Zambia 90.4 91.5 67.8 68.3 3.5 4.9 2.4 43.2 42.2 Zimbabwe 81.0 85.2 69.9 64.5 4.3 5.8 2.0 47.2 44.0 World 85.5 w 83.8 w 58.9 w 57.9 w 2,390.7 t 3,027.5 t 1.6 w 39.9 w 40.1 w Low income 87.0 85.0 50.6 47.8 699.6 965.3 2.1 35.7 35.0 Middle income 85.8 84.0 63.9 62.7 1,263.5 1,569.4 1.4 41.8 42.1 Lower middle income 86.7 85.4 66.2 65.1 1,031.9 1,301.1 1.5 42.0 42.3 Upper middle income 82.2 77.9 55.0 52.6 231.6 268.3 1.0 40.6 40.9 Low & middle income 86.3 84.4 59.0 56.7 1,963.1 2,534.7 1.7 39.6 39.4 East Asia & Pacific 87.8 87.0 74.3 71.4 856.8 1,063.4 1.4 44.1 43.8 Europe & Central Asia 80.8 74.0 65.1 57.9 223.8 219.1 ­0.1 45.6 44.9 Latin America & Carib. 85.9 83.5 43.8 56.0 171.0 252.9 2.6 34.0 40.7 Middle East & N. Africa 79.9 79.3 24.5 31.1 64.9 108.3 3.4 22.9 27.6 South Asia 86.9 84.8 41.7 38.1 437.0 585.0 1.9 30.6 29.4 Sub-Saharan Africa 87.8 86.3 65.1 62.6 209.7 306.0 2.5 43.0 42.1 High income 82.1 80.4 58.6 63.8 427.6 492.8 0.9 41.3 43.7 Europe EMU 78.5 77.4 51.7 61.1 131.5 147.2 0.8 39.6 43.7 a. Includes population of Kosovo and Metahia until 1999. b. Data are for 1990­99. 46 2007 World Development Indicators 2.2 PEOPLE Labor force structure About the data Definitions The labor force is the supply of labor available for methods to ensure comparability across countries · Labor force participation rate is the proportion the production of goods and services in an economy. and over time, including collection and tabulation of the population ages 15­64 that is economically It includes people who are currently employed and methodologies as well as for country-specific factors active: all people who supply labor for the produc- people who are unemployed but seeking work as well such as military service requirements. The estimates tion of goods and services during a specified period. as first-time job-seekers. Not everyone who works is are based mainly on labor force surveys. Some popu- · Total labor force comprises people ages 15 and included, however. Unpaid workers, family workers, lation census estimates are also included in the esti- older who meet the ILO definition of the economically and students are among those usually omitted, and mates, but only when no labor force survey data are active population. It includes both the employed and in some countries members of the military are not available. Data from official government estimates the unemployed. · Average annual growth rate of counted. The size of the labor force tends to vary dur- are not included as these methodologies can differ the labor force is calculated using the exponential ing the year as seasonal workers enter and leave it. significantly across countries and over time. Data Data on the labor force are from labor force sur- with limited age group and geographic coverage are endpoint method (see Statistical methods for more veys, censuses, establishment censuses and sur- also excluded. information). · Females as a percentage of the labor veys, and various types of administrative records The labor force participation rate of the population force show the extent to which women are active in such as employment exchange registers and unem- ages 15­64 provides an indication of the relative the labor force. ployment insurance schemes. For some countries size of the labor supply. But in many developing coun- a combination of these sources is used. While the tries children under age 15 work full or part time. resulting statistics may provide rough estimates of And in some high-income countries many workers the labor force, they are not comparable across coun- postpone retirement past age 65. As a result, labor tries or sometimes within countries because of the force participation rates calculated in this way may noncomparability of the original data, differences in systematically over- or under-estimate actual rates. concepts and methodologies, and the different ways For further information on the labor force participa- the original sources may be combined. tion rate, consult the original source. Labor force surveys are the most comprehensive The labor force estimates in the table were cal- source for internationally comparable labor force culated by World Bank staff by applying labor force data. They can be designed to cover all noninstitu- participation rates from the ILO database to World tionalized civilians, all branches and sectors of the Bank population estimates to create a series consis- economy, and all categories of workers, including tent with these population estimates. This procedure people who hold multiple jobs. By contrast, labor sometimes results in estimates of labor force size force data obtained from population censuses are that differ slightly from those in the ILO's Yearbook often based on a limited number of questions on the of Labour Statistics and its database Key Indicators economic characteristics of individuals, with little of the Labour Market. The labor force estimates in scope to probe. The resulting data are often contrary this year's World Development Indicators, as were last to labor force survey data and often vary consider- year's, are for the population ages 15 and older. In ably from country to country, depending on the scope previous editions the labor force included children and coverage of the census. Establishment censuses under age 15. For this reason, labor force estimates and surveys provide data only on the employed popu- are not comparable across editions. lation, leaving out unemployed workers, workers in In general, estimates of women in the labor force small establishments, and workers in the informal are lower than those of men and are not compa- sector (International Labour Organization, Key Indica- rable internationally, refl ecting the fact that for tors of the Labour Market 2001­2002). women demographic, social, legal, and cultural The reference period of the census or survey is trends and norms determine whether their activities another important source of differences: in some are regarded as economic. In many countries large countries data refer to people's status on the day numbers of women work on farms or in other fam- of the census or survey or during a specific period ily enterprises without pay, while others work in or before the inquiry date, while in others the data are near their homes, mixing work and family activities Data sources recorded without reference to any period. In devel- during the day. Countries differ in the criteria used The labor force participation rates are from the oping countries, where the household is often the to determine the extent to which such workers are ILO database Estimates and Projections of the basic unit of production and all members contribute to be counted as part of the labor force. In most Economically Active Population, 1980­2020, to output, but some at low intensity or irregular inter- economies the gap between male and female labor 5th edition. The ILO publishes estimates of the vals, the estimated labor force may be significantly force participation rates has been narrowing since smaller than the numbers actually working. 1980. This stems from both falling rates for men and economically active population in its Yearbook of The labor force participation rates presented in the rising rates for women. The largest gap between men Labour Statistics. Labor force numbers were cal- table are from the International Labour Organization's and women in labor force participation is observed in culated by World Bank staff, applying labor force (ILO) Estimates and Projections of the Economically the Middle East and North Africa, where low partici- participation rates from the ILO database to popu- Active Population, 5th edition. These new estimates pation of women in the work force also brings down lation estimates. used stricter data selection criteria and enhanced the overall labor force participation rate. 2007 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 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. 20 .. 22 .. 26 .. 28 .. 54 .. 49 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0 b, c 2c 0 b, c 1b, c 40 c 33c 18 c 11c 59 c 66c 81c 88 c Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 6 5c 4 3c 32 31c 12 9c 61 65c 84 88 c 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 3c 2c 1c 41c 35c 16c 11c 56c 62c 81c 82c Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 3c 6c 1c 3c 42c 39 c 17c 14 c 55c 55c 82c 82c Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 26 .. 19 .. 29 .. 13 .. 43 .. 58 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 .. 61 .. 59 .. 12 .. 13 .. 27 .. 27 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 .. 16 .. 19 .. 37 .. 18 .. 47 .. 63 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 23 3 2 23 24 21 15 52 53 76 83 Ecuador 10 c 11c 2c 4c 29 c 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 23 7 13 4 42 44 30 24 36 49 57 72 Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 11 7 6 3 38 38 15 12 51 56 78 84 France .. 5 .. 3 .. 35 .. 12 .. 60 .. 84 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 60 59 50 10 14 10 15 23 27 32 36 Greece 20 c 12c 26c 14 c 32c 30 c 17c 10 c 48 c 58 c 56c 76c Guatemala .. 50 .. 18 .. 18 .. 23 .. 27 .. 56 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 48 2007 World Development Indicators 2.3 PEOPLE 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 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a Honduras 53c 51c 6c 13c 18 c 20 c 25c 23c 29 c 29 c 69 c 63c Hungary .. 7c .. 3c .. 42c .. 21c .. 51c .. 76c India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 54 43 57 45 15 20 13 15 31 37 31 40 Iran, Islamic Rep. .. 23 .. 34 .. 31 .. 28 .. 46 .. 37 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 19 9 3 1 33 39 18 12 48 51 78 86 Israel 5 3 2 1 38 32 15 11 57 64 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 .. 13 .. 73 .. 83 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 .. 51 .. 55 .. 13 .. 7 .. 36 .. 38 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 15c .. 8c .. 35c .. 16c .. 49 c .. 75c Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 25 17 15 11 46 37 31 21 29 46 54 68 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 .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 15c 11 13c 9 36c 34 48 c 29 48 c 55 39 c 62 Mexico 33 21 10 5 25 30 19 19 41 49 62 76 Moldova .. 41 .. 40 .. 21 .. 12 .. 38 .. 48 Mongolia .. 43 .. 38 .. 19 .. 14 .. 39 .. 49 Morocco .. 41 .. 63 .. 23 .. 15 .. 36 .. 22 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 33 52 29 21 17 8 7 32 49 29 63 Nepal .. .. .. .. .. .. .. .. .. .. .. .. 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 .. 43 .. 10 .. 19 .. 17 .. 32 .. 52 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 7 5 3 2 34 32 10 8 58 63 86 90 Oman .. 7 .. 5 .. 11 .. 14 .. 82 .. 80 Pakistan 45 38 69 65 20 22 15 16 35 40 16 20 Panama 35 22 3 4 20 22 11 9 45 56 85 86 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 3c 39 0 b, c 20 33c 19 19 c 10 64 c 42 80 c 70 Peru 1c 1c 0 b, c 0c 30 c 31c 13c 13c 69 c 68 c 87c 86c Philippines 53 45 32 25 17 17 14 12 29 39 55 64 Poland .. 18 c .. 17c .. 39 c .. 17c .. 43c .. 66c Portugal 10 12 13 13 39 42 24 21 51 46 63 66 Puerto Rico 5 3 0b 0b 27 25 19 11 67 72 80 89 2007 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 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 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 .. 1 .. 24 .. 1 .. 71 .. 98 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 1 0b 0b 0b 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 49 c 52c 76c 84 c Sri Lanka .. 32c .. 40 c .. 40 c .. 35c .. 29 c .. 25c Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5 3 2 1 40 34 12 9 55 63 86 90 Switzerland 4 5c 4 3c 37 32c 15 12c 59 63c 81 85c Syrian Arab Republic .. 24 .. 58 .. 31 .. 7 .. 45 .. 35 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 78 c 80 c 90 c 84 c 7c 4c 1c 1c 15c 16c 8c 15c Thailand 59 44 60 41 16 22 14 19 25 34 25 41 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 10 6 2 34 37 14 14 51 53 80 84 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 22 72 52 26 28 11 15 41 50 17 33 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 91 60 91 77 4 11 6 5 5 28 3 17 Ukraine .. 21 .. 17 .. 38 .. 21 .. 41 .. 62 United Arab Emirates .. 9 .. 0b .. 36 .. 14 .. 55 .. 86 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 29 c 21c 13c 57c 64 c 78 c 86c Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 16c 2 2c 32 25c 16 11c 52 59 c 82 86c Vietnam .. 56 .. 60 .. 21 .. 14 .. 23 .. 26 West Bank and Gaza .. 12 .. 34 .. 28 .. 8 .. 59 .. 56 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. 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 .. 16 .. 12 .. 34 .. 19 .. 50 .. 69 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 17 .. 17 .. 35 .. 20 .. 47 .. 63 Latin America & Carib. 20 21 14 10 30 27 14 14 50 52 72 76 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 5 2 38 34 19 12 56 62 77 96 Europe EMU 7 5 7 3 42 39 20 15 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. 50 2007 World Development Indicators 2.3 PEOPLE Employment by economic activity About the data The International Labour Organization (ILO) classi- underreported. Caution should be also used where The distribution of economic activity by gender fies economic activity using the International Stan- the data refer only to urban areas, which record little reveals some clear patterns. Men still make up the dard Industrial Classification (ISIC) of All Economic or no agricultural work. Moreover, the age group and majority of people employed in all three sectors, but Activities, revision 2 (1968) and revision 3 (1990). area covered could differ by country or change over the gender gap is biggest in industry. Employment in Because this classification is based on where work time within a country. For detailed information on agriculture is also male-dominated, although not as is performed (industry) rather than on what type of breaks in series, consult the original source. much as industry. Segregating one sex in a narrow work is performed (occupation), all of an enterprise's Countries also take different approaches to the range of occupations significantly reduces economic employees are classified under the same industry, treatment of unemployed people. In most countries efficiency by reducing labor market flexibility and thus regardless of their trade or occupation. The catego- unemployed people with previous job experience are the economy's ability to adapt to change. This seg- ries should add up to 100 percent. Where they do classified according to their last job. But in some regation is particularly harmful for women, who have not, the differences arise because of workers who countries the unemployed and people seeking their a much narrower range of labor market choices and cannot be classified by economic activity. first job are not classifiable by economic activity. lower levels of pay than men (see box 2.3a). But Data on employment are drawn from labor force Because of these differences, the size and distribu- it is also detrimental to men when job losses are surveys, household surveys, official estimates, cen- tion of employment by economic activity may not be concentrated in industries dominated by men and suses and administrative records of social insurance fully comparable across countries. job growth is centered in service occupations, where schemes, and establishment surveys when no other The ILO's Yearbook of Labour Statistics and its data- women have better chances, as has been the recent information is available. The concept of employment base Key Indicators of the Labour Market report data experience in many countries. generally refers to people above a certain age who by major divisions of the ISIC revision 2 or revision There are several explanations for the rising impor- worked, or who held a job, during a reference period. 3. In this table the reported divisions or categories tance of service jobs for women. Many service jobs-- Employment data include both full-time and part-time are aggregated into three broad groups: agriculture, such as nursing and social and clerical work--are workers. industry, and services. Such broad classification may considered "feminine" because of a perceived simi- There are many differences in how countries define obscure fundamental shifts within countries' industrial larity to women's traditional roles. Women often do and measure employment status, particularly part- patterns. A slight majority of countries report economic not receive the training needed to take advantage of time workers, members of the armed forces, and activity according to the ISIC revision 2 instead of ISIC changing employment opportunities. And the greater household or contributing family workers. Where revision 3. The use of one classification or another availability of part-time work in service industries the armed forces are included, they are allocated to should not have a significant impact on the information may lure more women, although it is unclear whether the service sector, causing that sector to be some- for the three broad sectors presented in this table. this is a cause or an effect. what overstated relative to the service sector in The distribution of economic wealth in the world Definitions economies where they are excluded. Where data are remains strongly correlated with employment by obtained from establishment surveys, they cover only economic activity. The wealthier economies are · Agriculture corresponds to division 1 (ISIC revi- employees; thus self-employed and contributing fam- those with the largest share of total employment in sion 2) or tabulation categories A and B (ISIC revi- ily workers are excluded. In such cases the employ- services, whereas the poorer economies are largely sion 3) and includes hunting, forestry, and fishing. ment share of the agricultural sector is severely agriculture based. · Industry corresponds to divisions 2­5 (ISIC revi- sion 2) or tabulation categories C­F (ISIC revision Lower wages and less rewarding employment 3) and includes mining and quarrying (including oil opportunities mean higher risk of poverty for women Box 2.3a production), manufacturing, construction, and public utilities (electricity, gas, and water). · Services corre- Within any employment status, women's earnings in Egypt tend to be lower than men's (see table). A spond to divisions 6­9 (ISIC revision 2) or tabulation small- and micro-enterprise survey for Egypt found that while workers' wages increased with firm size, categories G­P (ISIC revision 3) and include whole- women accounted for a decreasing share of total employment. Taken together, less rewarding employ- sale and retail trade and restaurants and hotels; ment opportunities and lower wages mean that women face a higher risk of poverty. transport, storage, and communications; financing, Average wages per worker and women's share of employment by firm size in Egypt, 2003 insurance, real estate, and business services; and Women as share of community, social, and personal services. Average wages total employment Size of firm (2002 Egyptian pounds) (%) 1 worker 112.8 17.1 2­4 workers 172.1 9.4 5­9 workers 290.1 7.9 10­24 workers 1,073.4 5.9 Data sources Total (firms of all sizes) 160.1 14.3 Data on employment are from the ILO database Source: UNIFEM 2005. Key Indicators of the Labour Market, 4th edition. 2007 World Development Indicators 51 2.4 Children at work Survey Economically active children Employment by economic activitya year % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Agriculture Manufacturing Services Work and Total Male Female Work only study Male Female Male Female Male Female Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 2000 36.6 41.1 31.8 43.1 56.9 .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. Angolab 2001 30.1 30.0 30.3 26.6 73.4 .. .. .. .. .. .. Argentina 1997 20.7 25.4 16.0 8.6 91.4 .. .. .. .. .. .. Armenia .. .. .. .. .. .. .. .. .. .. Australia .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. Azerbaijan 2000 9.7 12.0 7.3 4.2 95.8 .. .. .. .. .. .. Bangladesh 2003 17.5 20.9 13.9 63.3 36.7 61.4 64.0 11.6 15.5 25.2 18.3 Belarus .. .. .. .. .. .. .. .. .. .. Belgium .. .. .. .. .. .. .. .. .. .. Benin .. .. .. .. .. .. .. .. .. .. Bolivia 2000 19.2 20.4 18.0 19.7 80.3 77.8 72.9 4.3 3.5 15.0 23.6 Bosnia and Herzegovina 2000 20.2 22.8 17.6 4.0 96.0 .. .. .. .. .. .. Botswana .. .. .. .. .. .. .. .. .. .. Brazil 2003 7.1 9.5 4.6 5.8 94.2 64.3 49.8 6.5 9.1 26.8 40.9 Bulgaria .. .. .. .. .. .. .. .. .. .. Burkina Fasoc 1998 66.5 65.4 67.7 95.9 4.1 98.0 98.2 0.6 0.5 1.3 1.2 Burundi 2000 37.0 38.4 35.7 48.3 51.7 .. .. .. .. .. .. Cambodia 2001 52.3 52.4 52.1 16.5 83.5 78.5 73.6 4.7 5.4 15.7 20.4 Cameroonc 2001 15.9 14.5 17.4 52.5 47.5 90.4 86.3 1.9 2.3 5.1 8.8 Canada .. .. .. .. .. .. .. .. .. .. Central African Republic 2000 67.0 66.5 67.6 54.9 45.1 .. .. .. .. .. .. Chad 2000 69.9 73.5 66.5 44.6 55.4 .. .. .. .. .. .. Chile 2003 8.8 10.5 6.9 4.0 96.0 31.5 11.9 7.6 5.8 58.5 80.6 China .. .. .. .. .. .. .. .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 2001 12.2 16.6 7.7 23.0 77.0 .. .. .. .. .. .. Congo, Dem. Rep. 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. Costa Rica 2002 6.7 9.7 3.5 20.8 79.2 56.5 55.2 8.7 2.7 28.0 42.1 Côte d'Ivoire 2000 40.7 40.9 40.5 46.4 53.6 .. .. .. .. .. .. Croatia .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. Dominican Republic 2000 12.5 16.7 8.1 7.2 92.8 .. .. .. .. .. .. Ecuador 2001 17.9 22.1 13.6 25.0 75.0 65.1 69.2 10.7 8.6 21.2 22.1 Egypt, Arab Rep. 1998 6.4 4.0 8.9 60.9 39.1 .. .. .. .. .. .. El Salvador 2003 12.7 17.1 8.1 19.5 80.5 66.4 17.6 10.8 16.1 21.2 66.3 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia 2001 57.1 67.9 45.9 63.5 36.5 96.5 88.7 0.5 2.8 2.5 6.2 Finland .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The 2000 25.3 25.4 25.3 41.6 58.4 .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. Germany .. .. .. .. .. .. .. .. .. .. Ghana 2000 28.5 28.5 28.4 36.4 63.6 81.0 59.1 4.5 7.6 13.8 32.0 Greece .. .. .. .. .. .. .. .. .. .. Guatemala 2000 20.1 25.9 13.9 38.5 61.5 74.5 39.8 5.9 20.1 14.7 40.0 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 .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 52 2007 World Development Indicators 2.4 PEOPLE Children at work Survey Economically active children Employment by economic activitya year % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Agriculture Manufacturing Services Work and Total Male Female Work only study Male Female Male Female Male Female Honduras 2002 11.4 16.5 6.1 41.9 58.1 73.6 19.8 5.9 24.4 18.6 55.7 Hungary .. .. .. .. .. .. .. .. .. .. India 2000 5.2 5.3 5.1 89.8 10.2 70.5 76.6 10.0 15.4 15.9 6.5 Indonesia .. .. .. .. .. .. .. .. .. .. Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. Iraq 2000 13.7 17.4 9.7 51.7 48.3 .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. Jamaica 2000 1.1 1.5 0.6 17.1 82.9 36.8 17.1 6.2 11.6 43.6 71.3 Japan .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. Kazakhstan 1996 29.7 30.3 29.1 4.4 95.6 .. .. .. .. .. .. Kenya 1999 6.7 6.9 6.4 44.8 55.2 87.3 74.4 2.5 0.3 8.8 25.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 1998 8.6 9.7 7.6 7.0 93.0 93.0 96.3 0.0 0.0 7.0 2.7 Lao PDR .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. .. .. .. Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho 2000 30.8 34.2 27.5 17.6 82.4 .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar 2001 25.6 26.1 25.1 85.1 14.9 94.1 93.9 0.6 1.4 2.0 2.9 Malawi 2000 10.6 9.4 11.6 17.1 82.9 .. .. .. .. .. .. Malaysia .. .. .. .. .. .. .. .. .. .. Mali 2001 25.3 32.3 18.6 68.7 31.3 .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. Mexicod 1996 14.7 20.0 9.5 45.6 54.4 61.3 38.3 11.4 12.9 22.6 48.2 Moldova 2000 33.5 34.1 32.8 3.8 96.2 .. .. .. .. .. .. Mongolia 2000 22.0 23.5 20.6 28.2 71.8 .. .. .. .. .. .. Morocco 1998­99 13.2 13.5 12.8 93.2 6.8 60.8 60.3 8.1 8.5 13.5 6.4 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. Namibia 1999 15.4 16.2 14.7 9.5 90.5 91.5 91.7 0.4 0.4 8.1 8.0 Nepal 1999 47.2 42.2 52.4 35.6 64.4 89.0 86.1 1.2 1.5 9.7 12.3 Netherlands .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. Nicaragua 2001 12.1 17.5 6.5 33.3 66.7 73.2 32.0 3.0 10.2 23.3 57.8 Niger .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. Pakistan .. .. .. .. .. .. .. .. .. .. Panama 2000 4.0 6.4 1.4 37.5 62.5 71.1 38.4 1.4 8.0 27.2 49.5 Papua New Guinea .. .. .. .. .. .. .. .. .. .. Paraguay 1999 8.1 11.7 4.4 24.2 75.7 61.2 30.9 3.8 4.6 33.1 64.5 Peru 1994 17.7 20.4 15.2 7.3 92.7 78.9 76.3 3.6 3.4 17.5 20.3 Philippines 2001 13.3 16.3 10.0 14.8 85.2 72.6 53.6 3.6 5.3 22.1 41.0 Poland .. .. .. .. .. .. .. .. .. .. Portugal 2001 3.6 4.6 2.6 3.6 96.4 52.7 40.7 11.4 10.7 25.6 47.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 53 2.4 Children at work Survey Economically active children Employment by economic activitya year % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Agriculture Manufacturing Services Work and Total Male Female Work only study Male Female Male Female Male Female Romania 2000 1.4 1.7 1.1 20.7 79.3 96.4 98.1 0.0 0.0 2.6 1.9 Russian Federation .. .. .. .. .. .. .. .. .. .. Rwanda 2000 33.1 36.1 30.3 27.5 72.5 .. .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegal 2000 35.4 43.2 27.7 56.2 43.8 .. .. .. .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 2000 74.0 24.7 72.7 53.8 46.2 .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. South Africa 1999 27.7 29.0 26.4 5.1 94.9 .. .. .. .. .. .. Spain .. .. .. .. .. .. .. .. .. .. Sri Lanka 1998 17.0 20.4 13.4 5.4 94.6 71.1 71.4 12.0 15.0 15.8 13.5 Sudan 2000 19.1 21.5 16.8 55.9 44.1 .. .. .. .. .. .. Swaziland 2000 11.2 11.4 10.9 14.0 86.0 .. .. .. .. .. .. Sweden .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania 2001 40.4 41.5 39.2 40.0 60.0 83.5 73.1 0.1 0.2 16.3 26.7 Thailand .. .. .. .. .. .. .. .. .. .. Togo 2000 72.5 73.4 71.6 28.4 71.6 .. .. .. .. .. .. Trinidad and Tobago 2000 3.9 5.2 2.8 12.8 87.2 .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. Turkey 1999 4.5 5.2 3.8 66.8 33.2 52.7 83.4 19.9 10.2 10.2 1.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 2002­03 13.1 15.0 11.3 18.3 81.7 94.3 92.3 1.5 1.3 3.2 6.0 Ukraine .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. .. .. .. .. Uzbekistan 2000 18.1 22.0 14.0 4.1 95.9 .. .. .. .. .. .. Venezuela, RBc 2003 9.1 11.4 6.6 17.6 82.4 35.2 9.2 7.3 9.5 53.9 81.0 Vietnam .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1999 13.1 12.4 14.0 64.3 35.7 87.2 96.6 1.2 0.8 10.8 1.8 Zambia 1999 14.4 15.0 13.9 72.8 27.2 92.7 88.1 0.3 0.8 6.6 11.0 Zimbabwe 1999 14.3 13.3 15.3 12.0 88.0 .. .. .. .. .. .. a. Shares by major industrial category do not sum to 100 percent because of a residual category not included in the table. b. The totals (urban and rural combined) represent what can be described as Angola-Secured Territory but not the nation as a whole. c. Data are for children ages 10­14. d. Data are for children ages 12­14. 54 2007 World Development Indicators 2.4 PEOPLE Children at work About the data Definitions The data in the table refer to children's economic activ- working for pay in cash or in kind or is involved in · Survey year is the year in which the underlying ity, a broader concept than child labor. According to a unpaid work, whether a child is working for someone data were collected. · Economically active children gradually emerging consensus, child labor is a subset who is not a member of the household, whether a refer to children involved in economic activity for at of children's economic activity or children's work that is child is involved in any type of family work (on the least one hour in the reference week of the survey. injurious and therefore targeted for elimination. There farm or in a business), and the like. The ages used in · Work only refers to children involved in economic is also growing recognition that there are certain country surveys to define child labor range from 5 to activity and not attending school. · Work and study intolerable, or "unconditionally worst," forms of child 14 years old. The data in the table have been recalcu- refer to children attending school in combination with labor that constitute especially serious violations of lated to present statistics for children ages 7­14. economic activity. · Employment by economic activ- children's rights, and these should be targeted as a Although efforts are made to harmonize the defini- ity refers to the distribution of economically active priority for immediate action. tion of employment and the questions on employment children by the major industrial categories (ISIC revi- In line with the international definition of employment, used in survey questionnaires, some differences sion 2 or revision 3). · Agriculture corresponds to the threshold for classifying a child as economically remain among the survey instruments used to col- division 1 (ISIC revision 2) or categories A and B (ISIC active is spending one hour on economic activity during lect the information on working children. Differences revision 3) and includes agriculture and hunting, for- the reference week. Economic activity is as defined by exist not only among different household surveys in estry and logging, and fishing. · Manufacturing cor- the 1993 United Nations System of National Accounts the same country, but also within the same type of responds to division 3 (ISIC revision 2) or category D (revision 3) and corresponds to the international defini- survey carried out in different countries. (ISIC revision 3). · Services correspond to divisions tion of employment adopted by the Thirteenth Interna- Because of the differences in the underlying sur- 6­9 (ISIC revision 2) or categories G­P (ISIC revision tional Conference of Labor Statisticians in 1982. Eco- vey instruments and in survey dates, estimates of 3) and include wholesale and retail trade, hotels and nomic activity covers all market production and certain the economically active child population are not fully restaurants, transport, financial intermediation, real types of nonmarket production, including production of comparable across countries. Caution should be estate, public administration, education, health and goods for own use. It excludes household chores per- exercised in drawing conclusions concerning relative social work, other community services, and private formed by children in their own household. But some levels of child economic activity across countries or household activity. forms of economic activity are not captured by house- regions based on the published estimates. hold surveys and so are not reflected in the estimates. The table aggregates the distribution of working These include unconditional forms of child labor, which children by the industrial categories of the Interna- require different data collection methodologies. tional Standard Industrial Classification (ISIC): agri- The data used to develop the indicators are from culture, industry, and services. The residual category, household surveys conducted by the International which includes mining and quarrying; electricity, gas, Labour Organization (ILO), the United Nations Chil- and water; construction; extraterritorial organization; dren's Fund (UNICEF), the World Bank, and national and other inadequately defined activities, is not pre- statistical offices. These surveys yield a variety of sented in the table, and so the broad groups do not data in education, employment, health, expenditure, add up to 100 percent. The use of either ISIC revision and consumption that relate to child work. But they 2 or revision 3 is strictly related to the codification do not provide information on unconditional forms applied by each country in describing the economic of children's work. activity. The use of two different classifications does Data sources Household survey data generally include informa- not affect the definition of the groups presented in tion on work type--for example, whether a child is the table. Estimates are produced by the Understanding Children's Work project based on household sur- Child labor is an obstacle vey datasets made available by the ILO's Inter- to education for all Box 2.4a national Programme on the Elimination of Child Labour under its Statistical Monitoring Programme There is broad consensus that the single most effective way to stem the flow of school-age children into on Child Labour, UNICEF under its Multiple Indi- work is to extend and improve access to school, so that families have the opportunity to invest in their cator Cluster Survey program, the World Bank children's education and it is worthwhile for them to do so. With no access to quality education, millions under its Living Standards Measurement Study of children are left to work. More than one in five children ages 5­17 is economically active (see table). program, and national statistical offices. Informa- Economically active children tion on how the data were collected and some Age group (% of age group) indication of their reliability can be found at www. 5­17 20.3 ilo.org/public/english/standards/ipec/simpoc/, 5­14 15.8 www.childinfo.org, and www.worldbank.org/lsms. 15­17 35.2 Detailed country statistics can be found at www. Source: ILO 2006. ucw-project.org. 2007 World Development Indicators 55 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Male Female Total % of total % of total % of male % of female % of total unemployment unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 2000­03a 2000­03a 2000­03a 2000­04a 2000­04a 2000­04a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 13.2 .. 18.3 .. 15.2 .. .. .. 56.4 38.4 3.4 Algeria .. 19.8 .. 21.3 .. 20.1 .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.4b 16.3b 7.0 b 14.7b 6.7b 15.6b .. .. .. 42.8 b 38.5b 17.7b Armenia .. .. .. .. .. .. 72.2 70.8 71.6 5.2 81.5 13.3 Australia 11.3 5.3b 9.5 5.5b 10.5 5.4b 27.1b 17.0 b 22.5b 48.3 32.7 19.0 Austria 3.5 4.5 3.8 5.4 3.6 4.9 25.0 23.9 24.5 37.3 55.7 7.0 Azerbaijan .. .. .. .. .. .. .. .. .. 4.6 31.4 64.1 Bangladesh 2.0 4.2 1.9 4.9 1.9 4.3 .. .. .. 54.3 22.7 8.4 Belarus .. .. .. .. .. .. .. .. .. 10.2 40.6 49.1 Belgium 4.8 6.6 9.5 8.3 6.7 7.4 44.8 48.2 46.3 43.7 38.1 18.2 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 5.5b 4.3 5.6b 6.9 5.5b 5.5 .. .. .. 60.2b 32.5b 4.4b Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 11.7 15.7 17.3 22.3 13.9 18.6 .. .. .. 63.8 23.8 .. Brazil 5.4b 7.8 b 7.9 b 12.3b 6.4b 9.7b .. .. .. .. .. .. Bulgaria .. 12.5 .. 11.5 .. 12.1 .. .. .. 37.8 50.9 11.4 Burkina Faso .. .. .. .. .. .. .. .. .. 46.8 19.3 5.6 Burundi 0.7 .. 0.3 .. 0.5 .. .. .. .. .. .. .. Cambodia .. 0.8 .. 0.9 .. 0.8 .. .. .. .. .. .. Cameroon .. 8.2 .. 6.7 .. 7.5 .. .. .. .. .. .. Canada 12.1b 7.5b 10.2b 6.8 b 11.2b 7.2b 11.4 8.4 10.1 29.0 b 30.8 b 40.2b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 3.9 6.9 5.3 9.5 4.4 7.8 .. .. .. 18.5 59.0 21.8 China .. .. .. .. 2.3b 4.2 .. .. .. .. .. .. Hong Kong, China 2.0 7.8 1.9 5.6 2.0 6.8 .. .. .. 48.6 39.4 10.1 Colombia 6.7b 10.6 13.0 b 17.8 9.4b 13.7 .. .. .. 26.9 52.9 16.5 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 3.4 5.4 5.4 8.5 4.0 6.4 8.9 13.3 10.9 62.2 24.1 9.9 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 11.7 .. 14.0 .. 12.7 52.9 c 56.3c 54.6c 21.5 68.4 9.8 Cuba .. .. .. .. 4.6 3.3 .. .. .. .. .. .. Czech Republic .. 7.0 .. 9.9 .. 8.3 47.4 51.9 49.9 24.6 71.8 3.5 Denmark 8.3 5.0 9.9 5.4 9.0 5.2 21.8 17.9 19.9 25.9 46.6 25.5 Dominican Republic 11.7 10.5 34.9 30.7 20.3 18.4 2.2 1.3 1.6 .. .. .. Ecuador 6.0 b 6.6b 13.2b 11.4b 8.9 b 8.6b .. .. .. 28.8 47.7 21.9 Egypt, Arab Rep. 6.4 7.3 17.0 23.2 9.0 11.0 .. .. .. .. .. .. El Salvador 3.9 b 8.7 4.9 b 3.9 4.3b 6.8 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.9 10.4 3.5 8.9 3.7 9.6 .. .. .. 20.9 62.1 16.8 Ethiopia .. 15.8b .. 31.2b .. 23.1b .. .. .. .. .. .. Finland 13.6 8.8 9.7 9.0 11.7 8.9 27.7 21.4 24.7 35.8 46.3 17.5 France 7.9 b 9.0 b 12.7b 11.1b 10.0 b 9.9 b 43.1b 42.8 b 42.9 b 40.6 39.9 17.7 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 13.4b .. 11.8 b .. 12.6b .. .. .. 5.8 57.6 36.5 Germany 5.3 10.2 8.4 9.3 6.6 9.8 48.3 52.3 50.0 27.1 60.5 12.4 Ghana .. 7.5 .. 8.7 .. 8.2 .. .. .. .. .. .. Greece 4.9 6.4 12.9 15.9 7.8 10.2 49.2 61.0 56.5 34.2 50.0 15.1 Guatemala 2.6b 2.2 4.6b 3.7 3.2b 2.8 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 11.2 .. 13.6 .. 12.2 .. .. .. .. .. .. .. 56 2007 World Development Indicators 2.5 PEOPLE Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Male Female Total % of total % of total % of male % of female % of total unemployment unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 2000­03a 2000­03a 2000­03a 2000­04a 2000­04a 2000­04a Honduras 3.3b 4.7b 3.0 b 8.3b 3.2b 5.9 b .. .. .. .. .. .. Hungary 11.0 6.1 8.7 6.1 9.9 6.1 42.2 42.2 42.2 33.5 61.2 5.4 India .. 4.9 b .. 5.3b .. 5.0 b .. .. .. 27.0 41.1 31.9 Indonesia 2.7 8.1 3.1 12.9 2.9 9.9 .. .. .. 46.0 36.6 6.7 Iran, Islamic Rep. 9.5 10.1 24.4 20.4 11.1 11.6 .. .. .. 38.3 37.1 19.3 Iraq .. 29.4 .. 15.0 .. 26.8 .. .. .. .. .. .. Ireland 15.2 4.9 15.2 3.7 15.2 4.4 40.9 26.0 35.4 48.2 24.9 24.0 Israel 9.2 10.2 13.9 11.3 11.2 10.7 .. .. .. 20.2 48.8 27.0 Italy 8.1 6.4 17.3 10.5 11.6 8.0 57.5 58.9 58.2 49.4 41.4 7.5 Jamaica 9.4 8.1 22.2 15.7 15.4 11.4 24.4 36.2 31.7 13.0 5.4 6.1 Japan 2.1b 4.9 b 2.2b 4.4b 2.2b 4.7b 38.9 24.6 33.5 70.8 53.4 29.2 Jordan .. 11.8 .. 16.5 .. 12.4 .. .. .. .. .. .. Kazakhstan .. 7.0 .. 9.8 .. 8.4 .. .. .. 7.9 53.2 38.9 Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.8 3.7 2.1 3.1 2.5 3.5 0.7 0.3 0.6 17.0 53.4 29.6 Kuwait .. .. .. .. .. 1.7 .. .. .. 27.5 39.9 6.1 Kyrgyz Republic .. 11.2 .. 14.3 .. 12.5 .. .. .. 13.7 67.8 18.5 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 9.0 .. 8.4 .. 8.7 .. .. .. 22.4 68.5 8.8 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. 8.3 .. .. 57.8 15.0 68.5 16.5 Macedonia, FYR .. 36.7 .. 37.8 .. 37.2 .. .. .. .. .. .. Madagascar .. 3.5 .. 5.6 .. 4.5 .. .. .. 42.7 18.8 6.1 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia .. 3.6 .. 3.6 3.7 3.5 .. .. .. 32.0 48.8 15.6 Mali .. 7.2 .. 10.9 .. 8.8 .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 3.2 5.8 3.1 13.5 3.1 8.5 .. .. .. 71.5b 28.2b .. Mexico 2.7 2.9 4.0 3.4 3.1 3.0 1.1 0.8 1.0 13.7 30.1 46.4 Moldova .. 10.0 .. 6.3 .. 8.1 .. .. .. .. .. .. Mongolia .. 14.3 .. 14.1 .. 14.2 .. .. .. 35.0 45.8 18.4 Morocco 13.0 b 11.0 25.3b 11.8 16.0 b 11.2 .. .. .. 51.5c 20.1c 19.8 c Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 4.7 .. 8.8 .. 6.0 .. .. .. .. .. .. .. Namibia 20.0 26.8 19.0 35.9 19.0 31.1 .. .. .. .. .. .. Nepal .. 7.4 .. 10.7 .. 8.8 .. .. .. .. .. .. Netherlands 4.3 4.1 7.3 4.4 5.5 4.3 30.1 28.1 29.2 46.3 35.1 17.4 New Zealand 11.0 b 3.5b 9.6b 4.4b 10.4b 3.9 b 15.5 11.0 13.3 1.0 48.8 16.0 Nicaragua 11.3 7.6 19.4 8.0 14.4 7.8 .. .. .. 50.8 b 24.8 b 19.7b Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 6.6 4.8 5.1 3.8 5.9 4.4 7.1 5.4 6.4 21.7 54.7 21.7 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 3.8 6.6 14.0 12.8 5.2 7.7 .. .. .. 14.7 12.3 24.1 Panama 10.8 9.4 22.3 17.2 14.7 12.3 24.0 35.7 29.3 35.9 37.3 26.0 Papua New Guinea 9.0 4.3 5.9 1.3 7.7 2.8 .. .. .. .. .. .. Paraguay 6.4b 6.7 3.8 b 10.1 5.3b 8.1 .. .. .. .. .. .. Peru 7.5b 9.4b 12.5b 12.0 b 9.4b 10.5b .. .. .. 9.4b 61.4b 28.6b Philippines 7.9 10.4 9.9 11.7 8.6 10.9 .. .. .. .. .. .. Poland 12.2 16.6 14.7 19.1 13.3 17.7 56.1c 59.3c 57.7c 18.0 75.4 6.7 Portugal 3.5b 5.8 5.0 b 7.6 4.1b 6.7b 31.2 32.7 32.0 70.7 14.6 8.8 Puerto Rico 19.2 11.7 13.3 9.1 17.0 10.6 .. .. .. .. .. .. 2007 World Development Indicators 57 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Male Female Total % of total % of total % of male % of female % of total unemployment unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1990­92a 2000­05a 1990­92a 2000­05a 1990­92a 2000­05a 2000­03a 2000­03a 2000­03a 2000­04a 2000­04a 2000­04a Romania .. 9.0 .. 6.9 .. 8.0 .. .. .. 26.0 66.9 5.4 Russian Federation 5.4 7.8 5.2 8.0 5.3 7.9 .. .. .. .. .. .. Rwanda .. .. .. .. .. .. .. .. .. 60.7 24.1 5.9 Saudi Arabia 7.4 4.7 4.9 14.7 .. .. .. .. .. 12.0 c 49.0 c 40.0 c Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro .. 14.4 .. 16.4 .. 15.2 .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.7 5.5 2.6 5.3 2.7 5.4 .. .. .. 22.4 25.0 38.8 Slovak Republic .. 17.3 .. 19.1 .. 18.1 60.2 62.1 61.1 24.1b 71.7b 4.3b Slovenia .. 5.7 .. 6.5 .. 6.1 .. .. .. 26.2 63.9 8.2 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 23.5b .. 31.6b .. 27.1b .. .. .. 50.2 41.0 5.1 Spain 13.9 8.2 25.8 15.0 18.1 11.0 34.3 43.9 39.8 56.0 20.4 22.7 Sri Lanka 10.1b 6.0 b 19.8 b 13.5b 13.3b 8.5b .. .. .. 47.2 .. 52.8 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 6.8 6.9 4.6 6.2 5.7 6.5 19.6 15.3 17.8 23.2 58.1 17.5 Switzerland 2.3 3.9 3.5 4.8 2.8 4.3 21.6 32.6 27.0 28.7 54.5 16.9 Syrian Arab Republic .. 9.0 .. 28.3 .. 12.3 .. .. .. 75.2 10.3 9.8 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 2.7b 4.4 4.2b 5.8 3.5b 5.1b .. .. .. .. .. .. Thailand 1.3 1.6 1.5 1.4 1.4 1.5 .. .. .. 40.0 47.2 0.2 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 17.0 b 7.8 b 23.9 b 14.5b 19.6b 10.4b 20.3 34.7 27.6 55.5 40.5 1.8 Tunisia .. .. .. .. .. 14.7 .. .. .. 43.4 37.4 10.0 Turkey 8.8 10.3 7.8 10.3 8.5 10.3 36.5c 46.9 c 39.2c 55.7c 28.1c 11.4 c Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 2.5 .. 3.9 .. 3.2 .. .. .. .. .. .. Ukraine .. 8.9 .. 8.3 .. 8.6 .. .. .. 13.5 54.3 32.2 United Arab Emirates .. 2.2 .. 2.6 .. 2.3 .. .. .. .. .. .. United Kingdom 11.5 5.0 7.3 4.2 9.7 4.6 26.5 17.1 23.0 30.3 44.4 14.6 United States 7.9 5.6 7.0 5.4 7.5 5.5 12.5 11.0 11.8 18.4 34.3 47.3 Uruguay 6.8 b 13.5b 11.8 b 20.8 b 9.0 b 16.8 b .. .. .. 54.8 b 31.3b 13.9 b Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 8.2 14.4b 6.8 20.3b 7.7 16.8 b .. .. .. .. .. .. Vietnam .. 1.9 .. 2.4 .. 2.1 .. .. .. .. .. .. West Bank and Gaza .. 28.1 .. 20.1 .. 26.8 .. .. .. 57.5 14.5 17.6 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 16.3 .. 22.4 .. 18.9 .. .. .. .. .. .. .. Zimbabwe .. 10.4 .. 6.1 .. 8.2 .. .. .. .. .. .. World .. w .. w .. w .. w .. w 6.4 w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. 3.9 6.6 .. .. .. .. .. .. Lower middle income .. .. .. .. 3.4 5.9 .. .. .. .. .. .. Upper middle income 6.3 9.6 6.8 10.8 6.4 9.8 .. .. .. 37.7 48.2 11.3 Low & middle income .. .. .. .. .. 6.4 .. .. .. .. .. .. East Asia & Pacific .. .. .. .. 2.5 4.2 .. .. .. .. .. .. Europe & Central Asia .. 9.9 .. 9.9 .. 9.9 .. .. .. .. .. .. Latin America & Carib. 5.4 8.1 8.5 12.0 6.6 9.6 .. .. .. .. .. .. Middle East & N. Africa .. 13.4 .. 21.3 .. 14.8 .. .. .. .. .. .. South Asia .. 5.1 .. 6.2 .. 5.4 .. .. .. 30.0 34.8 27.4 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.0 6.2 7.9 6.6 7.4 6.4 27.3 23.9 26.0 34.8 39.3 29.7 Europe EMU 7.5 8.2 12.6 10.6 9.5 9.2 44.1 46.4 45.5 40.3 42.5 16.3 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2005. 58 2007 World Development Indicators 2.5 PEOPLE Unemployment About the data Definitions Unemployment and total employment in an economy closely than that used by other sources and there- · Unemployment refers to the share of the labor are the broadest indicators of economic activity as fore generate statistics that are more comparable force without work but available for and seeking refl ected by the labor market. The International internationally. But the age group, geographic cover- employment. Definitions of labor force and unem- Labour Organization (ILO) defines the unemployed as age, and collection methods could differ by country ployment may differ by country (see About the data). members of the economically active population who or change over time within a country. For detailed · Long-term unemployment refers to the number of are without work but available for and seeking work, information on breaks in series, consult the original people with continuous periods of unemployment including people who have lost their jobs and those source. extending for a year or longer, expressed as a per- who have voluntarily left work. Some unemployment Women tend to be excluded from the unemploy- centage of the total unemployed. · Unemployment is unavoidable in all economies. At any time some ment count for various reasons. Women suffer more by educational attainment shows the unemployed workers are temporarily unemployed--between jobs from discrimination and from structural, social, and by level of educational attainment as a percent- as employers look for the right workers and workers cultural barriers that impede them from actively seek- age of the total unemployed. The levels of educa- search for better jobs. Such unemployment, often ing work. Also, women are often responsible for the tional attainment accord with the ISCED97 of the called frictional unemployment, results from the nor- care of children and the elderly or for other household United Nations Educational, Cultural, and Scientific mal operation of labor markets. affairs. They may not be available for work during Organization. Changes in unemployment over time may reflect the short reference period, as they need to make changes in the demand for and supply of labor, but arrangements before starting work. Furthermore, they may also reflect changes in reporting practices. women are considered to be employed when they are Ironically, low unemployment rates can often disguise working part-time or in temporary jobs in the informal substantial poverty in a country, while high unemploy- sector, despite the instability of these jobs or their ment rates can occur in countries with a high level of active searching for more secure employment. economic development and low incidence of poverty. Long-term unemployment is measured by the In countries without unemployment or welfare ben- length of time that an unemployed person has been efits, people eke out a living in the informal sector. without work and looking for a job. The data in this In countries with well-developed safety nets, workers table are from labor force surveys. The underlying can afford to wait for suitable or desirable jobs. But assumption is that shorter periods of joblessness high and sustained unemployment indicates serious are of less concern, especially when the unemployed inefficiencies in the allocation of resources. are covered by unemployment benefi ts or similar The ILO definition of unemployment notwithstand- forms of welfare support. The length of time that a ing, reference periods, the criteria for those consid- person has been unemployed is difficult to measure, ered to be seeking work, and the treatment of people because the ability to recall that time diminishes as temporarily laid off and those seeking work for the the period of joblessness extends. Women's long- first time vary across countries. In many developing term unemployment is likely to be lower in countries countries it is especially difficult to measure employ- where women constitute a large share of the unpaid ment and unemployment in agriculture. The timing of family workforce. Women in such countries have a survey, for example, can maximize the effects of more access than men to nonmarket work and are seasonal unemployment in agriculture. And informal more likely to drop out of the labor force and not be sector employment is difficult to quantify where infor- counted as unemployed. mal activities are not registered and tracked. Unemployment by level of educational attainment Data on unemployment are drawn from labor force provides insights into the relationship between the sample surveys and general household sample educational attainment of workers and unemploy- surveys, censuses, and offi cial estimates, which ment and may be used to draw inferences about are generally based on information from different changes in employment demand. Information on sources and can be combined in many ways. Admin- education attainment is the best available indicator istrative records, such as social insurance statistics of skill levels of the labor force. and employment office statistics, are not included Besides the limitations to comparability raised in this table because of their limitations in cover- for measuring unemployment, the different ways of age. Labor force surveys generally yield the most classifying the level of education across countries comprehensive data because they include groups may also cause inconsistency. The level of educa- Data sources not covered in other unemployment statistics, par- tion is supposed to be classified according to Inter- ticularly people seeking work for the first time. These national Standard Classification of Education 1997 Data on unemployment are from the ILO database surveys generally use a definition of unemployment (ISCED97). For more information on ISCED97, see Key Indicators of the Labour Market, 4th edition. that follows the international recommendations more About the data for table 2.9. 2007 World Development Indicators 59 2.6 Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Afganistan .. .. .. .. .. .. .. .. .. .. Albania 2002 29.6 19.8 25.4 .. .. .. 2004 a <2 <0.5 10.0 1.6 Algeria 1988 16.6 7.3 12.2 1995 30.3 14.7 22.6 1995a <2 <0.5 15.1 3.8 Angola .. .. .. .. .. .. .. .. .. .. Argentina 1995 .. 28.4 1998 29.9 2004b 6.6 2.1 17.4 7.1 Armenia 1998­99 50.8 58.3 55.1 2001 48.7 51.9 50.9 2003a <2 <0.5 31.1 7.1 Australia .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. Azerbaijan 1995 .. .. 68.1 2001 42.0 55.0 49.6 2001a 3.7 0.6 33.4 9.1 Bangladesh 1995­96 55.2 29.4 51.0 2000 53.0 36.6 49.8 2000a 41.3 10.3 84.0 38.3 Belarus 2000 .. .. 41.9 2002a <2 <0.5 <2 <0.5 Belgium .. .. .. .. .. .. .. .. .. .. Benin 1995 25.2 28.5 26.5 1999 33.0 23.3 29.0 2003a 30.9 8.2 73.7 31.7 Bolivia 1997 77.3 53.8 63.2 1999 81.7 50.6 62.7 2002b 23.2 13.6 42.2 23.2 Bosnia and Herzegovina 2001­02 19.9 13.8 19.5 .. .. .. .. .. .. .. Botswana 1993a 28.0 9.9 55.5 26.5 Brazil 1998 51.4 14.7 22.0 2002­03 41.0 17.5 21.5 2004b 7.5 3.4 21.2 8.5 Bulgaria 1997 .. .. 36.0 2001 .. .. 12.8 2003a <2 <0.5 6.1 1.5 Burkina Faso 1998 61.1 22.4 54.6 2003 52.4 19.2 46.4 2003a 27.2 7.3 71.8 30.4 Burundi 1990 36.0 43.0 36.4 .. .. .. 1998a 54.6 22.7 87.6 48.9 Cambodia 1997 40.1 21.1 36.1 2004 38.0 18.0 35.0 1997a 34.1 9.7 77.7 34.5 Cameroon 1996 59.6 41.4 53.3 2001 49.9 22.1 40.2 2001a 17.1 4.1 50.6 19.3 Canada .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. 1993a 66.6 38.1 84.0 58.4 Chad 1995­96 67.0 63.0 64.0 .. .. .. .. .. .. .. Chile 1996 .. .. 19.9 1998 .. .. 17.0 2003b <2 <0.5 5.6 1.3 China 1996 7.9 <2 6.0 1998 4.6 <2 4.6 2004 a 9.9 2.1 34.9 12.5 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 1995 79.0 48.0 60.0 1999 79.0 55.0 64.0 2003b 7.0 3.1 17.8 7.7 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. Costa Rica 1992 25.5 19.2 22.0 .. .. .. 2003b 3.3 1.6 9.8 4.0 Côte d'Ivoire .. .. .. .. .. .. 2002a 14.8 4.1 48.8 18.4 Croatia .. .. .. .. .. .. 2001a <2 <0.5 <2 <0.5 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. 1996b <2 <0.5 <2 <0.5 Denmark .. .. .. .. .. .. .. .. .. .. Dominican Republic 2000 45.3 18.2 27.7 2004 55.7 34.7 42.2 2004b 2.8 0.5 16.2 4.9 Ecuador 1995 56.0 19.0 34.0 1998 69.0 30.0 46.0 1998 b 17.7 7.1 40.8 17.7 Egypt, Arab Rep. 1995­96 23.3 22.5 22.9 1999­00 .. .. 16.7 1999­2000a 3.1 <0.5 43.9 11.3 El Salvador 1995 64.8 38.9 50.6 2002 49.8 28.5 37.2 2002b 19.0 9.3 40.6 17.7 Eritrea 1993­94 .. .. 53.0 .. .. .. .. .. .. .. Estonia 1995 14.7 6.8 8.9 .. .. .. 2003a <2 <0.5 7.5 1.9 Ethiopia 1995­96 47.0 33.3 45.5 1999­00 45.0 37.0 44.2 1999­2000a 23.0 4.8 77.8 29.6 Finland .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The 1992 .. .. 64.0 1998 61.0 48.0 57.6 1998 a 59.3 28.8 82.9 51.1 Georgia 2002 55.4 48.5 52.1 2003 52.7 56.2 54.5 2003a 6.5 2.1 25.3 8.6 Germany .. .. .. .. .. .. .. .. .. .. Ghana 1992 .. .. 50.0 1998­99 49.9 18.6 39.5 1998­99a 44.8 17.3 78.5 40.8 Greece .. .. .. .. .. .. .. .. .. .. Guatemala 1989 71.9 33.7 57.9 2000 74.5 27.1 56.2 2002b 13.5 5.5 31.9 13.8 Guinea 1994 .. .. 40.0 .. .. .. .. .. .. .. Guinea Bissau .. .. .. .. .. .. .. .. .. .. Haiti 1987 .. .. 65.0 1995 66.0 .. .. 2001b 53.9 26.6 78.0 47.4 60 2007 World Development Indicators 2.6 PEOPLE Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Honduras 1998­99 71.2 28.6 52.5 2004 70.4 29.5 50.7 2003b 14.9 4.4 35.7 15.1 Hungary 1993 .. .. 14.5 1997 .. .. 17.3 2002a <2 <0.5 <2 <0.5 India 1993­94 37.3 32.4 36.0 1999­00 30.2 24.7 28.6 2004­05a 33.5 7.6 80.0 34.6 Indonesia 1996 .. .. 15.7 1999 34.4 16.1 27.1 2002a 7.5 0.9 52.4 15.7 Iran, Islamic Rep. .. .. .. .. .. .. 1998a <2 <0.5 7.3 1.5 Iraq .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. Jamaica 1995 37.0 18.7 27.5 2000 25.1 12.8 18.7 2004 a <2 <0.5 14.4 3.3 Japan .. .. .. .. .. .. .. .. .. .. Jordan 1997 27.0 19.7 21.3 2002 18.7 12.9 14.2 2002­03a <2 <0.5 7.0 1.5 Kazakhstan 1996 39.0 30.0 34.6 .. .. .. 2003a <2 <.5 16.0 3.8 Kenya 1994 47.0 29.0 40.0 1997 53.0 49.0 52.0 1997a 22.8 5.9 58.3 23.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. 1998b <2 <0.5 <2 <0.5 Kuwait .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2001 51.0 41.2 47.6 2003 .. .. 41.0 2003a <2 <0.5 21.4 4.4 Lao PDR 1993 48.7 33.1 45.0 1997­98 41.0 26.9 38.6 2002a 27.0 6.1 74.1 30.2 Latvia .. .. .. .. .. .. 2003a <2 <0.5 4.7 1.2 Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho 1995a 36.4 19.0 56.1 33.1 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. 2003a <2 <0.5 7.8 1.8 Macedonia, FYR 2002 25.3 .. 21.4 2003 22.3 .. 21.7 2003a <2 <0.5 <2 <0.5 Madagascar 1997 76.0 63.2 73.3 1999 76.7 52.1 71.3 2001a 61.0 27.9 85.1 51.8 Malawi 1990­91 .. .. 54.0 1997­98 66.5 54.9 65.3 2004­05a 20.8 4.7 62.9 24.3 Malaysia 1989 .. .. 15.5 .. .. .. 1997b <2 <0.5 9.3 2.0 Mali 1998 75.9 30.1 63.8 .. .. .. 2001a 36.1 12.2 72.1 34.2 Mauritania 1996 65.5 30.1 50.0 2000 61.2 25.4 46.3 2000a 25.9 7.6 63.1 26.8 Mauritius .. .. .. .. .. .. .. .. .. .. Mexico 2000 42.4 12.6 24.2 2004 27.9 11.3 17.6 2004a 3.0 1.4 11.6 4.2 Moldova 2001 64.1 58.0 62.4 2002 67.2 42.6 48.5 2003a <2 <0.5 20.8 4.7 Mongolia 1998 32.6 39.4 35.6 2002 43.4 30.3 36.1 2002a 10.8 2.2 44.6 15.1 Morocco 1990­91 18.0 7.6 13.1 1998­99 27.2 12.0 19.0 1998­99 <2 <0.5 14.3 3.1 Mozambique 1996­97 71.3 62.0 69.4 .. .. .. 2002­03 36.2 11.6 74.1 34.9 Myanmar .. .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. 1993b 34.9 14.0 55.8 30.4 Nepal 1995­96 43.3 21.6 41.8 2003­04 34.6 9.6 30.9 2003­04 a 24.1 5.4 68.53 26.79 Netherlands .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. Nicaragua 1993 76.1 31.9 50.3 1998 68.5 30.5 47.9 2001a 45.1 16.7 79.9 41.2 Niger 1989­93 66.0 52.0 63.0 .. .. .. 1995a 60.6 34.0 85.8 54.6 Nigeria 1985 49.5 31.7 43.0 1992­93 36.4 30.4 34.1 2003a 70.8 34.5 92.4 59.5 Norway .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. Pakistan 1993 33.4 17.2 28.6 1998­99 35.9 24.2 32.6 2002a 17.0 3.1 73.6 26.1 Panama 1997 64.9 15.3 37.3 .. .. .. 2003b 7.4 2.1 18.0 7.5 Papua New Guinea 1996 41.3 16.1 37.5 .. .. .. .. .. .. .. Paraguay 1991 28.5 19.7 21.8 .. .. .. 2003b 13.6 5.6 29.8 13.8 Peru 2001 77.1 42.0 54.3 2004 72.1 42.9 53.1 2003b 10.5 2.9 30.6 11.9 Philippines 1994 53.1 28.0 40.6 1997 50.7 21.5 36.8 2002a 14.8 2.9 43.0 16.3 Poland 1993 .. .. 23.8 .. .. .. 2002a <2 <0.5 <2 <0.5 Portugal .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 61 2.6 Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Romania 1994 27.9 20.4 21.5 .. .. .. 2003a <2 0.5 12.9 3.0 Russian Federation 1994 .. .. 30.9 .. .. .. 2002a <2 <0.5 12.1 3.1 Rwanda 1993 .. .. 51.2 1999­00 65.7 14.3 60.3 2000a 60.3 25.6 87.8 51.5 Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegal 1992 40.4 23.7 33.4 .. .. .. 2001a 17.0 3.6 56.2 20.9 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 1989 .. .. 82.8 2003­04 79.0 56.4 70.2 1989a 57.0 39.5 74.5 51.8 Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. 1996b <2 <0.5 2.9 0.8 Slovenia .. .. .. .. .. .. 1998a <2 <0.5 <2 <0.5 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. .. .. .. .. .. 2000a 10.7 1.7 34.1 12.6 Spain .. .. .. .. .. .. .. .. .. .. Sri Lanka 1990­91 22.0 15.0 20.0 1995­96 27.0 15.0 25.0 2002a 5.6 0.8 41.6 11.9 Sudan .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. 2000­01a 47.7 19.4 77.8 42.4 Sweden .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. 2003a 7.4 1.3 42.8 13.0 Tanzania 1991 40.8 31.2 38.6 2000­01 38.7 29.5 35.7 2000­01a 57.8 20.7 89.9 49.3 Thailand 1994 .. .. 9.8 1998 .. .. 13.6 2002a <2 <0.5 25.2 6.2 Togo 1987­89 .. .. 32.3 .. .. .. .. .. .. .. Trinidad and Tobago 1992 20.0 24.0 21.0 .. .. .. 1992b 12.4 3.5 39.0 14.6 Tunisia 1990 13.1 3.5 7.4 1995 13.9 3.6 7.6 2000a <2 <0.5 6.6 1.3 Turkey 1994 .. .. 28.3 2002 34.5 22.0 27.0 2003a 3.4 0.8 18.7 5.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 1999­2000 37.4 9.6 33.8 2002­03 41.7 12.2 37.7 .. .. .. .. Ukraine 2000 34.9 .. 31.5 2003 28.4 .. 19.5 2003a <2 <0.5 4.9 0.9 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. Uruguay 1994 .. 20.2 .. 1998 .. 24.7 .. 2003b <2 <0.5 5.7 1.6 Uzbekistan 2000 30.5 22.5 27.5 .. .. .. 2003a <2 <0.5 <2 0.6 Venezuela, RB 1989 .. .. 31.3 .. .. .. 2003b 18.5 8.9 40.1 19.2 Vietnam 1998 45.5 9.2 37.4 2002 35.6 6.6 28.9 .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1998 45.0 30.8 41.8 .. .. .. 1998a 15.7 4.5 45.2 15.0 Zambia 1998 83.1 56.0 72.9 2004 78.0 53.0 68.0 2004 a 63.8 32.6 87.2 55.2 Zimbabwe 1990­91 35.8 3.4 25.8 1995­96 48.0 7.9 34.9 1995­96a 56.1 24.2 83.0 48.2 a. Expenditure base. b. Income base. 62 2007 World Development Indicators 2.6 PEOPLE Poverty Regional poverty estimates 2.6a Region 1981 1984 1987 1990 1993 1996 1999 2002 2004a People living on less than $1 a day (millions) East Asia & Pacific 796 564 429 476 420 279 277 227 169 China 634 425 310 374 334 211 223 177 128 Europe & Central Asia 3 2 2 2 17 21 18 6 4 Latin America & Caribbean 39 51 50 45 39 43 49 48 47 Middle East & North Africa 9 7 6 5 5 4 6 5 4 South Asia 473 457 469 479 440 459 475 485 462 Sub-Saharan Africa 168 200 223 240 252 286 296 296 298 Total 1,489 1,281 1,179 1,247 1,172 1,093 1,120 1,067 986 Excluding China 855 856 868 873 838 881 897 890 857 Share of people living on less than $1 a day (%) East Asia & Pacific 57.7 39.0 28.2 29.8 25.2 16.1 15.5 12.3 9.0 China 63.8 41.0 28.6 33.0 28.4 17.4 17.8 13.8 9.9 Europe & Central Asia 0.7 0.5 0.4 0.5 3.6 4.4 3.8 1.3 0.9 Latin America & Caribbean 10.8 13.1 12.1 10.2 8.4 8.9 9.7 9.1 8.6 Middle East & North Africa 5.1 3.8 3.1 2.3 1.9 1.7 2.1 1.7 1.5 South Asia 51.6 46.6 44.9 43.0 37.1 36.6 35.8 34.7 32.0 Sub-Saharan Africa 42.3 46.2 47.2 46.7 45.5 47.7 45.8 42.6 41.1 Total 40.6 33.0 28.7 28.7 25.6 22.7 22.3 20.4 18.4 Excluding China 32.0 30.1 28.7 27.1 24.6 24.6 23.8 22.6 21.1 People living on less than $2 a day (millions) East Asia & Pacific 1,170 1,116 1,041 1,113 1,083 908 883 766 684 China 876 819 744 819 803 649 628 524 452 Europe & Central Asia 20 17 14 20 78 85 88 61 46 Latin America & Caribbean 104 126 122 115 111 122 128 131 121 Middle East & North Africa 51 49 50 49 52 55 64 61 59 South Asia 818 853 904 954 976 1,035 1,073 1,124 1,124 Sub-Saharan Africa 295 333 365 396 422 458 491 513 522 Total 2,457 2,494 2,496 2,647 2,722 2,664 2,727 2,665 2,556 Excluding China 1,581 1,675 1,752 1,828 1,919 2,014 2,099 2,131 2,104 Share of people living on less than $2 a day (%) East Asia & Pacific 84.8 77.2 68.5 69.7 65.0 52.5 49.3 41.7 36.6 China 88.1 79.0 68.6 72.2 68.1 53.3 50.1 40.9 34.9 Europe & Central Asia 4.6 3.9 3.1 4.3 16.5 18.0 18.6 12.9 9.8 Latin America & Caribbean 28.4 32.2 29.6 26.2 24.1 25.2 25.3 24.8 22.2 Middle East & North Africa 29.2 25.6 24.2 21.7 21.4 21.4 23.6 21.1 19.7 South Asia 89.1 87.1 86.6 85.7 82.4 82.4 80.8 80.3 77.7 Sub-Saharan Africa 74.5 77.0 77.4 77.1 76.1 76.4 75.8 73.8 72.0 Total 67.1 64.3 60.7 60.8 59.4 55.5 54.4 50.8 47.7 Excluding China 59.3 58.9 57.9 56.8 56.4 56.2 55.8 54.1 51.8 a. Preliminary estimate. 2007 World Development Indicators 63 2.6 Poverty About the data The World Bank produced its first global poverty because of differences in timing or the quality and when making comparisons over time. The commonly estimates for developing countries for World Devel- training of survey enumerators. used $1 a day standard, measured in 1985 interna- opment Report 1990 using household survey data Comparisons of countries at different levels of tional prices and adjusted to local currency using for 22 countries (Ravallion, Datt, and van de Walle development also pose a potential problem because purchasing power parities (PPPs), was chosen for 1991). Incorporating survey data collected during of differences in the relative importance of consump- the World Bank's World Development Report 1990: the last 17 years, the database has expanded con- tion of nonmarket goods. The local market value of Poverty because it is typical of the poverty lines in siderably and now includes more than 550 surveys all consumption in kind (including own production, low-income countries. PPP exchange rates, such as representing about 100 developing countries. Some particularly important in underdeveloped rural econo- those from the Penn World Tables or the World Bank, 1.1 million randomly sampled households were inter- mies) should be included in total consumption expen- are used because they take into account the local viewed in these surveys, representing 93 percent of diture. Similarly, imputed profit from the production of prices of goods and services not traded internation- the population of developing countries. The surveys nonmarket goods should be included in income. This ally. But PPP rates were designed for comparing asked detailed questions on sources of income and is not always done, though such omissions were a far aggregates from national accounts, not for making how it was spent and on other household charac- bigger problem in surveys before the 1980s. Most international poverty comparisons. As a result, there teristics such as the number of people sharing that survey data now include valuations for consumption is no certainty that an international poverty line mea- income. Most interviews were conducted by staff of or income from own production. Nonetheless, valu- sures the same degree of need or deprivation across government statistics offices. Along with improve- ation methods vary. For example, some surveys use countries. ments in data coverage and quality, the underlying the price in the nearest market, while others use the Early editions of World Development Indicators used methodology has also improved, resulting in better average farmgate selling price. PPPs from the Penn World Tables. Recent editions and more comprehensive estimates. Whenever possible, the table uses consumption use 1993 consumption PPP estimates produced by data in deciding who is poor and income surveys only the World Bank. Recalculated in 1993 PPP terms, Data availability when consumption data are unavailable. In recent the original international poverty line of $1 a day in Since 1979 there has been considerable expansion editions there has been a change in how income 1985 PPP terms is now about $1.08 a day. The 2005 in the number of countries that field such surveys, surveys are used. In the past, average household round of the International Comparison Program will the frequency of the surveys, and the quality of their income was adjusted to accord with consumption provide new consumption PPPs in the coming year. data. The number of data sets rose dramatically from and income data from national accounts. But when Any revisions in the PPP of a country to incorporate a mere 10 between 1979 and 1981 to 162 between this approach was tested using data for some 20 better price indexes can produce dramatically differ- 2000 and 2004. The drop to 30 available surveys countries for which income and consumption expen- ent poverty lines in local currency. after 2002 reflects the lag between the time data diture data were both available from the same sur- Issues also arise when comparing poverty mea- are collected and the time they become available for veys, income was found to yield a higher mean than sures within countries. For example, the cost of living analysis, not a reduction in data collection. Data cov- consumption but also higher inequality. When pov- is typically higher in urban than in rural areas. One erage is improving in all regions, but the Middle East erty measures based on consumption and income reason is that food staples tend to be more expen- and North Africa continues to lag, with only three were compared, these two effects roughly cancelled sive in urban areas. So the urban monetary poverty countries having at least one data set available since each other out: statistically, there was no significant line should be higher than the rural poverty line. But it 2000. A complete overview of data availability by difference. So recent editions use income data to is not always clear that the difference between urban year and country can be obtained at http://iresearch. estimate poverty directly, without adjusting average and rural poverty lines found in practice reflects only worldbank.org/povcalnet/. income measures. differences in the cost of living. In some countries the urban poverty line in common use has a higher Data quality International poverty lines real value--meaning that it allows the purchase of The problems of estimating poverty and comparing International comparisons of poverty estimates more commodities for consumption--than does poverty rates do not end with data availability. Sev- entail both conceptual and practical problems. the rural poverty line. Sometimes the difference eral other issues, some related to data quality, also Countries have different definitions of poverty, and has been so large as to imply that the incidence of arise in measuring household living standards from consistent comparisons across countries can be poverty is greater in urban than in rural areas, even survey data. One relates to the choice of income difficult. Local poverty lines tend to have higher pur- though the reverse is found when adjustments are or consumption as a welfare indicator. Income is chasing power in rich countries, where more gen- made only for differences in the cost of living. As with generally more difficult to measure accurately, and erous standards are used, than in poor countries. international comparisons, when the real value of the consumption comes closer to the notion of stan- Is it reasonable to treat two people with the same poverty line varies it is not clear how meaningful such dard of living. And income can vary over time even standard of living--in terms of their command over urban-rural comparisons are. if the standard of living does not. But consumption commodities-- differently because one happens to By combining all this information, a team in the data are not always available. Another issue is that live in a better-off country? World Bank's Development Research Group cal- household surveys can differ widely, for example, in Poverty measures based on an international culates the number of people living below various the number of consumer goods they identify. And poverty line attempt to hold the real value of the international poverty lines, as well as other poverty even similar surveys may not be strictly comparable poverty line constant across countries, as is done and inequality measures that are published in World 64 2007 World Development Indicators 2.6 WORLD VIEW Poverty Definitions Development Indicators. The database is updated · Survey year is the year in which the underlying data annually as new survey data become available, and were collected. · Rural poverty rate is the percent- a major reassessment of progress against poverty age of the rural population living below the national is made about every three years. rural poverty line. · Urban poverty rate is the per- centage of the urban population living below the Do it yourself: PovcalNet national urban poverty line. · National poverty rate Recently, this research team developed PovcalNet, is the percentage of the population living below the an interactive Web-based computational tool that national poverty line. National estimates are based allows users to replicate the calculations by the on population-weighted subgroup estimates from World Bank's researchers in estimating the extent household surveys. · Population below $1 a day of absolute poverty in the world. PovcalNet is self and population below $2 a day are the percentages contained and powered by reliable built-in software of the population living on less than $1.08 a day and that performs the relevant calculations from a pri- $2.15 a day at 1993 international prices. As a result mary database. The underlying software can also of revisions in PPP exchange rates, poverty rates for be downloaded from the site and used with distribu- individual countries cannot be compared with poverty tional data of various formats. The PovcalNet primary rates reported in earlier editions. · Poverty gap is database consists of distributional data calculated the mean shortfall from the poverty line (counting directly from household survey data. Detailed infor- the nonpoor as having zero shortfall), expressed as a mation for each of these is also available from the percentage of the poverty line. This measure reflects site. the depth of poverty as well as its incidence. Estimation from distributional data requires an interpolation method. The method chosen was Lorenz curves with fl exible functional forms, which have proved reliable in past work. The Lorenz curve can be graphed as the cumulative percentages of total consumption or income against the cumulative num- ber of people, starting with the poorest individual. The empirical Lorenz curves estimated by PovcalNet are weighted by household size, so they are based on percentiles of population, not households. PovcalNet also allows users to calculate poverty measures under different assumptions. For example, instead of $1 a day, users can specify a different poverty line, say $1.50 or $3. Users can also spec- Data sources ify different PPP rates and aggregate the estimates using alternative country groupings (for example, UN The poverty measures are prepared by the World country groupings or groupings based on average Bank's Development Research Group. The national incomes) or a selected set of individual countries. poverty lines are based on the World Bank's PovcalNet is available online at http://iresearch. country poverty assessments. The international worldbank.org/povcalnet/ poverty lines are based on nationally represen- tative primary household surveys conducted by national statistical offices or by private agencies under the supervision of government or interna- tional agencies and obtained from government statistical offices and World Bank Group country departments. The World Bank Group has prepared an annual review of its poverty work since 1993. For details on data sources and methods used in deriving the World Bank's latest estimates, see Chen and Ravallion's "How Have the World's Poor- est Fared Since the Early 1980s?" (2004). 2007 World Development Indicators 65 2.7 Distribution of income or consumption Survey Gini Percentage share of year index income or consumption Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Afghanistan .. .. .. .. .. .. .. .. Albania 2004 a 31.1 3.4 8.2 12.6 17.0 22.6 39.5 24.4 Algeria 1995a 35.3 2.8 7.0 11.6 16.1 22.7 42.6 26.8 Angola .. .. .. .. .. .. .. .. Argentinab 2004 c 51.3 0.9 3.1 7.6 12.8 21.1 55.4 38.2 Armenia 2003a 33.8 3.6 8.5 12.3 15.7 20.6 42.8 29.0 Australia 1994c 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 2000 c 29.1 3.3 8.6 13.3 17.4 22.9 37.8 23.0 Azerbaijan 2001a 36.5 3.1 7.4 11.5 15.3 21.2 44.5 29.5 Bangladesh 2000a 33.4 3.7 8.6 12.1 15.6 21.0 42.7 27.9 Belarus 2002a 29.7 3.4 8.5 13.2 17.3 22.7 38.3 23.5 Belgium 2000 c 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1 Benin 2003a 36.5 3.1 7.4 11.3 15.4 21.5 44.5 29.0 Bolivia 2002c 60.1 0.3 1.5 5.9 10.9 18.7 63.0 47.2 Bosnia and Herzegovina 2001a 26.2 3.9 9.5 14.2 17.9 22.6 35.8 21.4 Botswana 1993a 60.5 1.2 3.2 6.0 9.7 16.0 65.1 51.0 Brazil 2004 c 57.0 0.9 2.8 6.4 11.0 18.7 61.1 44.8 Bulgaria 2003a 29.2 3.4 8.7 13.7 17.2 22.1 38.3 23.9 Burkina Faso 2003a 39.5 2.8 6.9 10.9 14.5 20.5 47.2 32.2 Burundi 1998 a 42.4 1.7 5.1 10.3 15.1 21.5 48.0 32.8 Cambodia 2004a 41.7 2.9 6.8 10.2 13.7 19.6 49.6 34.8 Cameroon 2001a 44.6 2.3 5.6 9.3 13.7 20.4 50.9 35.4 Canada 2000 c 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Central African Republic 1993a 61.3 0.7 2.0 4.9 9.6 18.5 65.0 47.7 Chad .. .. .. .. .. .. .. .. Chile 2003c 54.9 1.4 3.8 7.3 11.1 17.8 60.0 45.0 China 2004 c 46.9 1.6 4.3 8.5 13.7 21.7 51.9 34.9 Hong Kong, China 1996c 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2003c 58.6 0.74 2.48 6.20 10.60 18.05 62.67 46.90 Congo, Dem. Rep. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. Costa Rica 2003c 49.8 1.0 3.5 8.2 13.1 21.2 54.1 37.4 Côte d'Ivoire 2002a 44.6 2.0 5.2 9.1 13.7 21.3 50.7 34.0 Croatia 2001a 29.0 3.4 8.3 12.8 16.8 22.6 39.6 24.5 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996c 25.4 4.3 10.3 14.5 17.7 21.7 35.9 22.4 Denmark 1997c 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Dominican Republic 2004 c 51.6 1.4 4.0 7.8 12.1 19.3 56.7 41.1 Ecuador 1998c 53.6 0.9 3.3 7.5 11.7 19.4 58.0 41.6 Egypt, Arab Rep. 1999­2000a 34.4 3.7 8.6 12.1 15.4 20.4 43.6 29.5 El Salvador 2002c 52.4 0.7 2.7 7.5 12.8 21.2 55.9 38.8 Eritrea .. .. .. .. .. .. .. .. Estonia 2003a 35.8 2.5 6.7 11.8 16.3 22.4 42.8 27.6 Ethiopia 1999­2000a 30.0 3.9 9.1 13.2 16.8 21.5 39.4 25.5 Finland 2000 c 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 France 1995c 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon .. .. .. .. .. .. .. .. Gambia, The 1998a 50.2 1.8 4.8 8.7 12.8 20.3 53.4 37.0 Georgia 2003a 40.4 2.0 5.6 10.5 15.3 22.3 46.4 30.3 Germany 2000 c 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1 Ghana 1998­99a 40.8 2.1 5.6 10.1 14.9 22.9 46.6 30.0 Greece 2000 c 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 Guatemala 2002c 55.1 0.9 2.9 7.0 11.6 19.0 59.5 43.4 Guinea 2003a 38.6 2.9 7.0 10.8 14.7 21.4 46.1 30.7 Guinea-Bissau 1993a 47.0 2.1 5.2 8.8 13.1 19.4 53.4 39.3 Haiti 2001c 59.2 0.7 2.4 6.2 10.4 17.7 63.4 47.7 66 2007 World Development Indicators 2.7 PEOPLE Distribution of income or consumption Survey Gini Percentage share of year index income or consumption Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Honduras 2003c 53.8 1.2 3.4 7.1 11.6 19.6 58.3 42.2 Hungary 2002a 26.9 4.0 9.5 13.9 17.6 22.4 36.5 22.2 India 2004­05a 36.8 3.6 8.1 11.3 14.9 20.4 45.3 31.1 Indonesia 2002a 34.3 3.6 8.4 11.9 15.4 21.0 43.3 28.5 Iran, Islamic Rep. 1998a 43.0 2.0 5.1 9.4 14.1 21.5 49.9 33.7 Iraq .. .. .. .. .. .. .. .. Ireland 2000 c 34.3 2.9 7.4 12.3 16.3 21.9 42.0 27.2 Israel 2001c 39.2 2.1 5.7 10.5 15.9 23.0 44.9 28.8 Italy 2000 c 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8 Jamaica 2004 a 45.5 2.1 5.3 9.2 13.2 20.6 51.6 35.8 Japan 1993c 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2002­03a 38.8 2.7 6.7 10.8 14.9 21.3 46.3 30.6 Kazakhstan 2003a 33.9 3.0 7.4 11.9 16.4 22.8 41.5 25.9 Kenya 1997a 42.5 2.5 6.0 9.8 14.3 20.8 49.1 33.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998c 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2003a 30.3 3.8 8.9 12.8 16.4 22.5 39.4 24.3 Lao PDR 2002a 34.6 3.4 8.1 11.9 15.6 21.1 43.3 28.5 Latvia 2003a 37.7 2.5 6.6 11.2 15.5 22.0 44.7 29.1 Lebanon .. .. .. .. .. .. .. .. Lesotho 1995a 63.2 0.5 1.5 4.3 8.9 18.8 66.5 48.3 Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 2003a 36.0 2.7 6.8 11.6 16.0 22.3 43.2 27.7 Macedonia, FYR 2003a 39.0 2.4 6.1 10.8 15.5 22.2 45.5 29.6 Madagascar 2001a 47.5 1.9 4.9 8.5 12.7 20.4 53.5 36.6 Malawi 2004­05a 39.0 2.9 7.0 10.8 14.8 20.7 46.6 31.8 Malaysia 1997c 49.2 1.7 4.4 8.1 12.9 20.3 54.3 38.4 Mali 2001a 40.1 2.4 6.1 10.2 14.7 22.2 46.6 30.2 Mauritania 2000a 39.0 2.5 6.2 10.6 15.2 22.3 45.7 29.5 Mauritius .. .. .. .. .. .. .. .. Mexico 2004 a 46.1 1.6 4.3 8.3 12.6 19.7 55.1 39.4 Moldova 2003a 33.2 3.2 7.8 12.2 16.5 22.1 41.4 26.4 Mongolia 2002a 32.8 3.0 7.5 12.2 16.8 23.1 40.5 24.6 Morocco 1998­99a 39.5 2.6 6.5 10.6 14.8 21.3 46.6 30.9 Mozambique 2002­03a 47.3 2.1 5.4 9.3 13.0 18.7 53.6 39.4 Myanmar .. .. .. .. .. .. .. .. Namibia 1993c 74.3 0.5 1.4 3.0 5.4 11.5 78.7 64.5 Nepal 2003­04 a 47.2 2.6 6.0 9.0 12.4 18.0 54.6 40.6 Netherlands 1999c 30.9 2.5 7.6 13.2 17.2 23.3 38.7 22.9 New Zealand 1997c 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8 Nicaragua 2001a 43.1 2.2 5.6 9.8 14.2 21.1 49.3 33.8 Niger 1995a 50.5 0.8 2.6 7.1 13.9 23.1 53.3 35.4 Nigeria 2003a 43.7 1.9 5.0 9.6 14.5 21.7 49.2 33.2 Norway 2000 c 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 2002a 30.6 4.0 9.3 13.0 16.3 21.1 40.3 26.3 Panama 2003c 56.1 0.7 2.5 6.6 11.4 19.6 59.9 43.0 Papua New Guinea 1996a 50.9 1.7 4.5 7.9 11.9 19.2 56.5 40.5 Paraguay 2003c 58.4 0.7 2.4 6.3 10.8 18.6 61.9 46.1 Peru 2003c 52.0 1.3 3.7 7.7 12.2 19.7 56.7 40.9 Philippines 2003a 44.5 2.2 5.4 9.1 13.6 21.3 50.6 34.2 Poland 2002a 34.5 3.1 7.5 11.9 16.1 22.2 42.2 27.0 Portugal 1997c 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. 2007 World Development Indicators 67 2.7 Distribution of income or consumption Survey Gini Percentage share of year index income or consumption Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Romania 2003a 31.0 3.3 8.1 12.9 17.1 22.7 39.2 24.4 Russian Federation 2002a 39.9 2.4 6.1 10.5 14.9 21.8 46.6 30.6 Rwanda 2000a 46.8 2.1 5.3 9.1 13.2 19.4 53.0 38.2 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 2001a 41.3 2.7 6.6 10.3 14.2 20.6 48.4 33.4 Serbia and Montenegro 2003a 30.0 3.4 8.3 13.0 17.3 23.0 38.4 23.4 Sierra Leone 1989a 62.9 0.5 1.1 2.0 9.8 23.7 63.4 43.6 Singapore 1998c 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8 Slovak Republic 1996c 25.8 3.1 8.8 14.9 18.7 22.8 34.8 20.9 Slovenia 1998 a 28.4 3.6 9.1 14.2 18.1 22.9 35.7 21.4 Somalia .. .. .. .. .. .. .. .. South Africa 2000a 57.8 1.4 3.5 6.3 10.0 18.0 62.2 44.7 Spain 2000 c 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6 Sri Lanka 2002a 40.2 3.0 7.0 10.5 14.2 20.4 48.0 32.7 Sudan .. .. .. .. .. .. .. .. Swaziland 2000­01c 50.4 1.6 4.3 8.2 12.3 18.9 56.3 40.7 Sweden 2000 c 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2 Switzerland 2000 c 33.7 2.9 7.6 12.2 16.3 22.6 41.3 25.9 Syrian Arab Republic .. .. .. .. .. .. .. .. Tajikistan 2003a 32.6 3.3 7.9 12.3 16.5 22.4 40.8 25.6 Tanzania 2000­01a 34.6 2.9 7.3 12.0 16.1 22.3 42.4 26.9 Thailand 2002a 42.0 2.7 6.3 9.9 14.0 20.8 49.0 33.4 Togo .. .. .. .. .. .. .. .. Trinidad and Tobago 1992c 38.9 2.2 5.9 10.8 15.3 23.1 44.9 28.8 Tunisia 2000a 39.8 2.3 6.0 10.3 14.8 21.7 47.3 31.5 Turkey 2003a 43.6 2.0 5.3 9.7 14.2 21.0 49.7 34.1 Turkmenistan 1998a 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31.7 Uganda 2002a 45.7 2.3 5.7 9.4 13.2 19.1 52.5 37.7 Ukraine 2003a 28.1 3.9 9.2 13.6 17.3 22.4 37.5 23.0 United Arab Emirates .. .. .. .. .. .. .. ` United Kingdom 1999 c 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5 United States 2000 c 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9 Uruguay b 2003c 44.9 1.9 5.0 9.1 14.0 21.5 50.5 34.0 Uzbekistan 2003a 36.8 2.8 7.2 11.7 15.4 21.0 44.7 29.6 Venezuela, RB 2003c 48.2 0.7 3.3 8.7 13.9 22.0 52.1 35.2 Vietnam 2004 a 34.4 4.2 9.0 11.4 14.7 20.5 44.3 28.8 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 1998a 33.4 3.0 7.4 12.2 16.7 22.5 41.2 25.9 Zambia 2004 a 50.8 1.2 3.6 7.9 12.6 20.8 55.1 38.8 Zimbabwe 1995­96a 50.1 1.8 4.6 8.1 12.2 19.3 55.7 40.3 a. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. b. Urban data. c. Refers to income shares by percentiles of population, ranked by per capita income. 68 2007 World Development Indicators 2.7 PEOPLE Distribution of income or consumption About the data Definitions Inequality in the distribution of income is reflected countries in these respects may bias comparisons · Survey year is the year in which the underlying data in the percentage shares of income or consumption of distribution. were collected. · Gini index measures the extent accruing to portions of the population ranked by World Bank staff have made an effort to ensure to which the distribution of income (or consump- income or consumption levels. The portions ranked that the data are as comparable as possible. Wher- tion expenditure) among individuals or households lowest by personal income receive the smallest ever possible, consumption has been used rather within an economy deviates from a perfectly equal shares of total income. The Gini index provides a con- than income. Income distribution and Gini indexes distribution. A Lorenz curve plots the cumulative venient summary measure of the degree of inequal- for high-income countries are calculated directly from percentages of total income received against the ity. Data on the distribution of income or consump- the Luxembourg Income Study database, using an cumulative number of recipients, starting with the tion come from nationally representative household estimation method consistent with that applied for poorest individual. The Gini index measures the area surveys. Where the original data from the house- developing countries. between the Lorenz curve and a hypothetical line of hold survey were available, they have been used to absolute equality, expressed as a percentage of the directly calculate the income or consumption shares maximum area under the line. Thus a Gini index of by quintile. Otherwise, shares have been estimated 0 represents perfect equality, while an index of 100 from the best available grouped data. implies perfect inequality. · Percentage share of For most countries the income distribution indi- income or consumption is the share of total income cators are based on the same data used to derive or consumption that accrues to subgroups of popu- the $1 and $2 a day poverty estimates in table 2.6. lation indicated by deciles or quintiles. Percentage This table contains additional countries for which shares by quintile may not sum to 100 because of poverty estimates are not provided in table 2.6, rounding. either because no reasonable purchasing power par- ity estimates are available or because the interna- tional poverty lines are not relevant for high-income economies. The distribution data have been adjusted for household size, providing a more consistent mea- sure of per capita income or consumption. No adjust- ment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middle-income economies, see Ravallion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achiev- ing strict comparability is still impossible (see About the data for table 2.6). Two sources of noncomparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or con- sumption expenditure as the living standard indi- Data sources cator. The distribution of income is typically more unequal than the distribution of consumption. In Data on distribution are compiled by the World addition, the definitions of income used differ more Bank's Development Research Group using pri- often among surveys. Consumption is usually a mary household survey data obtained from govern- much better welfare indicator, particularly in devel- ment statistical agencies and World Bank country oping countries. Second, households differ in size departments. Data for high-income economies are (number of members) and in the extent of income estimated from the Luxembourg Income Study sharing among members. And individuals differ in database. age and consumption needs. Differences among 2007 World Development Indicators 69 2.8 Assessing vulnerability and security Urban informal Youth Female-headed Pension Public expenditure sector employment unemployment households contributors on pensions % of urban Male Female Average employment % of male % of female % of pension Male Female labor force labor force % of % of working- % of per 1998­ 1998­ ages 15­24 ages 15­24 total labor age % of capita 2001 a 2001 a 2000­05a 2000­05a 2000­05a Year force population Year GDP Year income Afghanistan .. .. .. .. .. .. .. 2005 0.5 .. Albania .. .. 42 27 .. 2004 48.8 33.0 2004 4.6 .. Algeria .. .. 43 46 .. 2002 36.7 22.0 2002 3.2 2002 89.1 Angola .. .. .. .. .. .. .. .. .. Argentina .. .. 22b 28 b .. 2004 34.9 25.8 1994 6.2 2002 73.7 Armenia .. .. .. .. 29 2002 64.4 48.3 2004 3.4 .. Australia .. .. 11 10 .. .. .. 2003 5.4 2002 52.4 Austria .. .. 11b 10 b .. 2004 80.8 58.8 2003 11.6 2002 93.2 Azerbaijan .. .. .. .. .. 1996 52.0 46.0 1996 2.5 .. Bangladesh .. .. 7 6 10 2004 2.8 2.1 1992 0.0 .. Belarus .. .. .. .. .. 1992 97.0 94.0 1997 7.7 .. Belgium .. .. 16 20 .. 1995 86.2 65.9 2003 8.5 2002 62.8 Benin 50 41 .. .. 21 1996 4.8 .. 1993 0.4 .. Bolivia .. .. .. .. 20 2002 10.1 7.8 2000 4.5 .. Bosnia and Herzegovina .. .. .. .. .. 2004 35.9 24.6 2004 8.8 .. Botswana .. .. 34 46 .. .. .. .. .. Brazil .. .. 14b 23b .. 2004 52.2 38.7 1997 9.8 .. Bulgaria .. .. 23 21 .. 1994 64.0 63.0 2005 8.9 2002 75.2 Burkina Faso .. .. .. .. 9 1993 3.1 3.0 1992 0.3 .. Burundi .. .. .. .. .. 1993 3.3 3.0 1991 0.2 .. Cambodia .. .. .. .. 25 .. .. .. .. Cameroon .. .. .. .. 24 1993 13.7 11.5 2001 0.8 .. Canada .. .. 14b 11b .. 2003 57.0 63.0 2003 5.4 2002 57.1 Central African Republic .. .. .. .. .. .. .. 1990 0.3 .. Chad .. .. .. .. 20 1990 1.1 1.0 1997 0.1 .. Chile .. .. 15 21 .. 2003 58.0 35.2 2001 2.9 2002 53.5 China .. .. .. .. .. 2005 20.5 17.2 1996 2.7 .. Hong Kong, China .. .. 14 8 .. .. .. .. .. Colombia .. .. 20 32 30 2000 19.0 14.0 1994 1.1 2002 54.4 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. 1992 5.8 5.6 1992 0.9 .. Costa Rica .. .. 11 22 .. 2004 55.2 37.6 1997 4.2 2002 103.1 Côte d'Ivoire .. .. .. .. .. 1997 9.3 9.1 1997 0.3 .. Croatia .. .. 30 36 .. 2004 71.0 46.0 2005 12.3 2002 61.6 Cuba .. .. .. .. .. .. .. 1992 12.6 .. Czech Republic .. .. 19 19 .. 2003 86.0 61.0 2003 10.5 2002 58.2 Denmark .. .. 9 9 .. 2003 92.0 74.0 2003 9.6 2002 54.1 Dominican Republic .. .. 16 34 28 2005 27.2 18.6 2000 0.8 2002 55.9 Ecuador .. .. 12b 21b .. 2004 27.0 20.7 2002 2.5 .. Egypt, Arab Rep. .. .. 21 40 12 2004 55.4 27.7 2004 4.1 2002 119.8 El Salvador .. .. 13 9 .. 2003 18.0 12.0 1997 1.3 2002 39.3 Eritrea .. .. .. .. 47 .. .. 2001 0.3 .. Estonia .. .. 16 15 .. 2000 91.0 66.0 2003 6.3 2002 60.9 Ethiopia 39 65 4 11 24 .. .. 1993 0.9 .. Finland .. .. 18 19 .. 2003 90.3 67.0 2003 9.0 2002 78.8 France .. .. 22b 24b .. 2003 90.0 62.0 2003 13.3 2002 65.0 Gabon .. .. .. .. 26 1995 15.0 14.0 .. .. Gambia, The .. .. .. .. .. .. .. .. .. Georgia 21 7 27 31 .. 2004 30.0 22.7 2004 3.0 .. Germany .. .. 16 14 .. 2003 88.0 64.0 2003 13.2 2002 71.8 Ghana .. .. 13 19 34 2003 9.1 7.1 2002 1.3 .. Greece .. .. 18 35 .. 2002 79.0 52.0 2003 12.8 2002 99.9 Guatemala .. .. .. .. .. 2000 19.0 11.7 1995 0.7 .. Guinea .. .. .. .. .. 1993 1.5 1.8 .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. 43 .. .. .. .. 70 2007 World Development Indicators 2.8 PEOPLE Assessing vulnerability and security Urban informal Youth Female-headed Pension Public expenditure sector employment unemployment households contributors on pensions % of urban Male Female Average employment % of male % of female % of pension Male Female labor force labor force % of % of working- % of per 1998­ 1998­ ages 15­24 ages 15­24 total labor age % of capita 2001 a 2001 a 2000­05a 2000­05a 2000­05a Year force population Year GDP Year income Honduras .. .. 5b 11b .. 1999 20.6 17.7 1994 0.6 .. Hungary .. .. 20 19 .. 1996 77.0 65.0 2003 11.0 2002 90.5 India 54 41 10 b 11b .. 2004 9.1 5.7 .. .. Indonesia .. .. .. .. 12 1995 8.0 7.0 .. .. Iran, Islamic Rep. .. .. 20 32 .. 2001 35.0 20.0 2000 1.1 2002 124.2 Iraq .. .. .. .. .. .. .. .. .. Ireland .. .. 9 7 .. 2002 93.0 64.7 2003 4.7 2002 36.6 Israel .. .. 17 19 .. 1992 82.0 63.0 1996 5.9 .. Italy .. .. 21 27 .. 2003 90.0 56.0 2003 15.5 2002 88.8 Jamaica .. .. 22 36 .. .. .. .. .. Japan .. .. 10 7 .. 2003 94.0 73.0 2003 8.9 2002 59.1 Jordan .. .. 28 43 12 2003 30.3 17.4 2001 2.2 2002 76.1 Kazakhstan .. .. 13 16 .. 2004 33.7 26.3 2004 4.9 .. Kenya .. .. .. .. 32 2005 8.0 6.2 1993 0.5 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. 12 9 .. 1996 58.0 43.0 2003 1.3 2002 43.3 Kuwait .. .. .. .. .. .. .. 1990 3.5 .. Kyrgyz Republic 33 25 19 21 .. 2004 40.1 28.3 1997 6.4 .. Lao PDR .. .. .. .. .. .. .. .. .. Latvia .. .. 12 14 .. 2003 90.0 64.0 2002 8.2 2002 81.8 Lebanon .. .. .. .. .. 2003 32.1 19.6 2003 2.1 .. Lesotho .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. 2003 65.5 37.1 2001 2.1 2002 91.2 Lithuania 50 27 16 15 .. 2004 79.7 58.9 2003 6.2 2002 71.3 Macedonia, FYR .. .. 63 62 .. 2000 63.8 38.8 1998 8.7 .. Madagascar .. .. .. .. 22 1993 5.4 4.8 1990 0.2 .. Malawi .. .. .. .. 27 .. .. .. .. Malaysia .. .. 8 8 .. 1993 48.7 37.8 1999 6.5 .. Mali .. .. .. .. 11 1990 2.5 2.0 1991 0.4 .. Mauritania .. .. .. .. 29 1995 5.0 4.0 1992 0.2 .. Mauritius .. .. 21 34 .. 2000 51.3 33.6 1999 4.4 .. Mexico 18 22 6 7 .. 2002 34.6 22.6 2003 1.3 2002 45.1 Moldova .. .. 19 18 .. 2000 60.0 43.0 2003 8.0 .. Mongolia .. .. 20 21 .. 2002 61.4 49.1 2002 5.8 .. Morocco .. .. 17 16 17 2003 22.4 12.3 2003 1.9 2002 74.1 Mozambique .. .. .. .. 26 1995 2.0 2.1 1996 0.0 .. Myanmar .. .. .. .. .. .. .. .. .. Namibia .. .. 40 49 42 .. .. .. .. Nepal 60 76 .. .. 16 2003 2.1 1.4 2003 0.3 .. Netherlands .. .. 10 10 .. 2002 94.0 72.0 2003 9.0 2002 84.1 New Zealand .. .. 9 10 .. .. .. 2003 7.4 2002 39.5 Nicaragua .. .. 11b 16b 31 2005 17.9 11.5 1996 2.5 .. Niger .. .. .. .. .. 1992 1.3 1.5 1992 0.1 .. Nigeria .. .. .. .. 17 2000 1.9 1.3 1991 0.1 .. Norway .. .. 13 12 .. 2003 92.0 75.0 2003 10.7 2002 65.1 Oman .. .. .. .. .. .. .. .. .. Pakistan 64 61 11 15 .. 2004 6.4 4.0 1993 0.9 .. Panama .. .. 19 30 .. 1998 51.6 40.7 1996 4.3 .. Papua New Guinea .. .. .. 5 .. .. .. .. .. Paraguay .. .. 12 17 .. 2004 11.6 9.1 2001 1.2 .. Peru .. .. 21b 21b 20 2003 16.3 12.3 2000 2.6 2002 43.9 Philippines .. .. 15 19 15 2000 27.0 18.6 1993 1.0 .. Poland .. .. 37 39 .. 2005 84.8 54.5 2003 15.8 2002 69.7 Portugal .. .. 14 19 .. 2003 92.0 71.0 2003 11.9 2002 79.8 Puerto Rico .. .. 25b 21b .. .. .. .. .. 2007 World Development Indicators 71 2.8 Assessing vulnerability and security Urban informal Youth Female-headed Pension Public expenditure sector employment unemployment households contributors on pensions % of urban Male Female Average employment % of male % of female % of pension Male Female labor force labor force % of % of working- % of per 1998­ 1998­ ages 15­24 ages 15­24 total labor age % of capita 2001 a 2001 a 2000­05a 2000­05a 2000­05a Year force population Year GDP Year income Romania .. .. 21 18 .. 2005 57.5 39.1 2003 6.9 .. Russian Federation 10 9 .. .. .. .. .. 2004 5.8 .. Rwanda .. .. .. .. 36 .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. Senegal .. .. .. .. .. 2003 5.2 3.8 2003 1.3 .. Serbia and Montenegro .. .. .. .. .. 2003 45.9 32.1 2003 12.4 .. Sierra Leone .. .. .. .. .. 2004 4.6 3.6 .. .. Singapore .. .. 4 6 .. 1995 73.0 56.0 1996 1.4 .. Slovak Republic .. .. 30 b 29 b .. 2003 58.8 55.0 2003 8.5 2002 60.2 Slovenia .. .. 11 12 .. 1995 86.0 68.7 2003 10.1 .. Somalia .. .. .. .. .. .. .. .. .. South Africa 16 28 56 65 .. .. .. .. .. Spain .. .. 17 24 .. 2003 92.0 63.0 2003 10.6 2002 88.3 Sri Lanka .. .. 20 37 .. 2004 35.6 22.2 1996 2.4 .. Sudan .. .. .. .. .. 1995 12.1 12.0 .. .. Swaziland .. .. .. .. .. .. .. .. .. Sweden .. .. 16 13 .. 2003 90.0 72.0 2003 14.0 2002 68.2 Switzerland .. .. 9 9 .. 2003 99.0 84.0 2003 12.1 2002 67.3 Syrian Arab Republic .. .. 21 39 .. .. .. 1991 0.5 .. Tajikistan .. .. .. .. .. .. .. 1996 3.0 .. Tanzania .. .. .. .. 25 1996 2.0 2.0 .. .. Thailand .. .. 5 5 .. 1999 18.0 17.0 .. .. Togo .. .. .. .. .. 1997 15.9 15.0 1997 0.6 .. Trinidad and Tobago .. .. 17b 26b .. .. .. 1996 0.6 .. Tunisia .. .. 31 29 .. 2003 54.9 30.0 2003 4.3 2002 72.7 Turkey 10 6 19 19 .. 2002 44.9 24.3 2002 7.1 2002 103.3 Turkmenistan .. .. .. .. 27 .. .. 1996 2.3 .. Uganda .. .. .. .. 28 2004 1.8 1.6 2003 0.3 .. Ukraine .. .. 16 17 .. 2005 76.0 52.0 2005 15.4 .. United Arab Emirates .. .. .. .. .. .. .. .. .. United Kingdom .. .. 13 10 .. 2003 94.0 73.0 2003 8.3 2002 47.6 United States .. .. 12 10 .. 2003 91.0 71.0 2003 7.5 2002 51.0 Uruguay .. .. 25b 35b .. 2001 57.7 44.3 1996 15.0 2002 125.4 Uzbekistan .. .. .. .. .. .. .. 1995 5.3 .. Venezuela, RB .. .. 24b 35b .. 2001 20.8 14.6 2001 2.7 .. Vietnam .. .. 4 5 27 1998 8.4 10.0 1998 1.6 .. West Bank and Gaza .. .. 39 45 .. 2000 19.0 6.4 2001 0.8 .. Yemen, Rep. .. .. .. .. .. 1999 15.0 7.0 1994 0.1 2002 106.3 Zambia .. .. .. .. 23 2000 5.9 4.9 1993 0.1 .. Zimbabwe .. .. 28 21 .. 1995 12.0 10.0 2002 2.3 .. World .. w .. w Low income .. .. Middle income .. .. Lower middle income .. .. Upper middle income 24 27 Low & middle income .. .. East Asia & Pacific .. .. Europe & Central Asia .. .. Latin America & Carib. 14 20 Middle East & N. Africa .. .. South Asia 11 12 Sub-Saharan Africa .. .. High income 13 12 Europe EMU 16 19 a. Data are for the most recent year available. b. Limited coverage. 72 2007 World Development Indicators 2.8 PEOPLE Assessing vulnerability and security About the data Definitions As traditionally defined and measured, poverty is a unemployment varies considerably over the year as a · Urban informal sector employment is all persons static concept, and vulnerability a dynamic one. Vul- result of different school opening and closing dates. who, during a given reference period, were employed nerability reflects a household's resilience in the face The youth unemployment rate shares similar limita- in at least one informal enterprise, irrespective of of shocks and the likelihood that a shock will lead to a tions on comparability as the general unemployment their status in employment and whether it was their decline in well-being. Thus, it depends primarily on the rate. For further information, see About the data for main or secondary job. · Youth unemployment refers household's asset endowment and insurance mecha- table 2.5 and the original source. to the share of the labor force ages 15­24 without nisms. Because poor people have fewer assets and The data on female-headed households are from work but available for and seeking employment. less diversified sources of income than the better-off, recent Demographic and Health Surveys. The defini- · Female-headed households refer to the percent- fluctuations in income affect them more. tion and concept of the female-headed household age of households with a female head. · Pension Enhancing security for poor people means reducing differ greatly across countries, making cross-country contributors refer to the share of the labor force their vulnerability to such risks as ill health, providing comparison difficult. In some cases it is assumed or working-age population (here defined as ages them the means to manage risk themselves, and that a woman cannot be the head of any household 15­64) covered by a pension scheme. · Public strengthening market or public institutions for man- in which an adult male is present, because of sex- expenditure on pensions includes all government aging risk. The tools include microfinance programs, biased stereotype. Users need to be cautious when expenditures on cash transfers to the elderly, the old age assistance and pensions, and public provi- interpreting the data. disabled, and survivors and the administrative costs sion of education and basic health care (see tables The data on pension contributors come from national of these programs. · Average pension is estimated 2.9 and 2.14). sources, the International Labour Organization (ILO), by dividing total pension expenditure by the number Poor households face many risks, and vulnerability and International Monetary Fund country reports. of pensioners. is thus multidimensional. The indicators in the table Coverage by pension schemes may be broad or even focus on individual risks--informal sector employ- universal where eligibility is determined by citizenship, ment, youth unemployment, female-headed house- residency, or income status. In contribution-related holds, income insecurity in old age, and the extent schemes, however, eligibility is usually restricted to to which publicly provided services may be capable individuals who have made contributions for a mini- of mitigating some of these risks. Poor people face mum number of years. Definitional issues--relating to labor market risks, often having to take up precari- the labor force, for example--may arise in comparing ous, low-quality jobs in the informal sector and to coverage by contribution-related schemes over time increase their household's labor market participation and across countries (for country-specific information, by sending their children to work (see table 2.4). see Palacios and Pallares-Miralles 2000). The share of Income security is a prime concern for the elderly. the labor force covered by a pension scheme may be For informal sector employment the data are from overstated in countries that do not attempt to count a variety of sources, including labor force and special informal sector workers as part of the labor force. informal sector surveys, household surveys, surveys Public interventions and institutions can provide of household industries or economic activities, sur- services directly to poor people, although whether veys of small enterprises and micro enterprises, and these interventions and institutions work well for the official estimates. The international comparability poor is debated. State action is often ineffective, Data sources of the data is affected by differences among coun- in part because governments can influence only a tries in definitions and coverage and in treatment of few of the many sources of well-being and in part Data on urban informal sector employment and domestic workers. The data in the table are based because of difficulties in delivering good and ser- youth unemployment are from the ILO database on national definitions of informal sector and urban vices. The effectiveness of public provision is further Key Indicators of the Labour Market, 4th edition. areas established by countries and therefore data constrained by the fiscal resources at governments' Data on female-headed household are from Demo- may not be comparable across countries. For details disposal and the fact that state institutions may not graphic and Health Surveys by Macro Interna- on these definitions, consult the original source. be responsive to the needs of poor people. tional. Data on pension contributors and pension Youth unemployment is an important policy issue The data on public pension spending are from spending are from Robert Palacios and Montser- for many economies. Experiencing unemployment national sources and cover all government expendi- rat Pallares-Miralles's "International Patterns of may permanently impair a young person's produc- tures, including the administrative costs of pension Pension Provision" (2000) and updates, Edward tive potential and future employment opportunities. programs. They cover noncontributory pensions or Whitehouse's Pensions Panorama (2007), and The table presents unemployment among youth ages social assistance targeted to the elderly and dis- the Organisation for Economic Co-operation and 15­24, but the lower age limit for young people in abled and spending by social insurance schemes for Development's Social Expenditure database (forth- a country could be determined by the minimum which contributions had previously been made. The coming). Further updates, notes, and sources will age for leaving school, so age groups could dif- pattern of spending in a country is correlated with be available in the World Bank's Progress Report fer across countries. Also, since this age group is its demographic structure--spending increases as of Pensions Indicators (forthcoming). likely to include school leavers, the level of youth the population ages. 2007 World Development Indicators 73 2.9 Education inputs Public expenditure Public expenditure Trained Primary per studenta on education teachers pupil- in primary teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2005b 1998 2005b 1998 2005b 2005b 2005b 2005b 2005b Afghanistan .. .. .. .. .. .. .. .. 36.5 83 Albania .. 7.8 .. 12.0 .. 36.6 2.9 8.4 .. 21 Algeria 26.5 11.3 .. 17.1 .. .. .. .. 98.5 25 Angola .. .. .. .. .. .. .. .. .. .. Argentina .. 10.9 13.6 14.3 20.2 10.3 3.5 12.0 .. 17 Armenia .. .. .. .. .. .. .. .. 77.5 21 Australia .. 16.4 14.5 14.4 27.0 22.5 4.8 .. .. .. Austria 18.2 23.2 29.9 28.6 51.6 45.7 5.5 10.8 .. 13 Azerbaijan .. 6.3 15.4 10.2 19.1 10.4 2.5 19.6 100.0 13 Bangladesh .. 7.0 12.4 14.7 46.3 49.7 2.5 14.2 48.0 51 Belarus .. 14.1 .. 25.3 .. 28.3 6.0 11.3 99.8 16 Belgium 16.3 20.2 .. 23.1 .. 37.1 6.2 11.8 .. 12 Benin .. 11.5 .. 21.2 .. .. 3.5 14.1 72.2 47 Bolivia .. 16.2 12.0 13.0 52.2 36.0 6.4 18.1 63.3 24 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 17.2 .. 44.0 .. 479.9 10.7 21.5 92.5 26 Brazil .. 10.8 10.4 11.2 85.7 48.9 4.1 10.9 .. 22 Bulgaria .. 19.0 .. 20.9 .. 28.3 4.2 .. 95.1 17 Burkina Faso .. 34.7 .. 21.6 .. 212.3 4.7 16.6 88.3 47 Burundi 13.4 19.1 .. 73.3 1051.9 348.8 5.1 17.7 87.5 49 Cambodia .. 6.1 11.4 .. .. 77.5 1.9 .. 97.7 53 Cameroon .. 10.3 18.2 16.0 69.7 67.9 1.8 8.6 62.7 48 Canada .. .. .. .. 49.0 44.6 5.2 .. .. .. Central African Republic 11.9 11.8 .. .. .. .. .. .. .. .. Chad 8.0 7.3 27.5 30.1 .. 359.9 2.1 10.1 26.8 63 Chile .. 12.8 13.8 14.2 21.0 15.5 3.7 18.5 96.4 27 China .. .. 11.5 .. 90.1 .. .. .. 84.7 21 Hong Kong, China .. 14.9 .. 19.9 .. 60.6 4.2 23.0 93.2 18 Colombia .. 19.5 14.9 18.4 35.6 24.6 4.8 11.1 .. 29 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. 34 Congo, Rep. .. 4.0 .. 18.3 404.9 245.9 2.2 8.1 62.2 83 Costa Rica 7.8 17.0 23.2 17.1 55.0 36.1 4.9 18.5 96.8 21 Côte d'Ivoire .. .. 54.5 .. 212.8 .. .. 21.6 100.0 42 Croatia .. 20.2 .. 26.0 41.5 31.5 4.7 10.0 100.0 15 Cuba .. 37.6 38.3 41.1 115.9 59.0 9.8 16.6 100.0 10 Czech Republic .. 12.9 22.1 23.4 34.4 33.6 4.5 8.5 .. 18 Denmark .. 25.5 38.3 35.0 66.2 67.2 8.4 15.1 .. .. Dominican Republic .. 8.1 .. 5.5 .. .. 1.8 9.7 88.3 24 Ecuador .. .. .. .. .. .. .. .. 70.9 23 Egypt, Arab Rep. .. .. .. .. .. .. .. .. .. 22 El Salvador .. 9.2 8.2 10.5 10.5 17.2 2.8 20.0 100.0 30 Eritrea .. 11.3 .. 15.4 .. 1,101.3 5.4 .. 83.6 48 Estonia .. 20.1 27.9 27.7 32.6 23.2 5.7 15.4 .. 14 Ethiopia 22.1 .. .. .. .. .. 5.0 19.4 97.1 72 Finland 21.9 18.7 26.4 28.1 41.3 37.4 6.5 12.8 .. 16 France 11.8 17.6 28.6 29.6 29.7 33.9 5.9 11.0 .. 19 Gabon .. .. .. .. .. .. .. .. 100.0 36 Gambia, The 13.2 7.4 .. 9.1 .. 238.0 2.0 8.9 57.8 35 Georgia .. .. .. .. .. .. 2.9 13.1 89.7 14 Germany .. 16.6 20.5 22.3 .. .. 4.7 9.7 .. 14 Ghana .. 12.8 .. 34.5 .. 209.8 5.4 .. 53.2c 35c Greece 7.6 16.1 15.5 22.5 30.7 24.3 4.0 8.0 .. 11 Guatemala .. 6.5 .. 3.5 .. .. .. .. .. 31 Guinea .. .. .. .. .. 244.1 2.0 .. 68.1 45 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 9.1 .. .. .. .. .. .. .. 40.5 .. 74 2007 World Development Indicators 2.9 PEOPLE Education inputs Public expenditure Public expenditure Trained Primary per studenta on education teachers pupil- in primary teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2005b 1998 2005b 1998 2005b 2005b 2005b 2005b 2005b Honduras .. .. .. .. .. .. .. .. 87.2 33 Hungary 21.2 21.9 18.6 26.8 36.0 31.9 5.9 10.3 .. 10 India .. 11.1 21.2 19.8 74.5 68.6 3.7 10.7 .. 40 Indonesia .. 2.6 .. 4.9 .. 13.3 0.9 9.0 92.9 20 Iran, Islamic Rep. .. 9.7 .. 11.0 .. 22.8 4.7 22.8 100.0 19 Iraq .. .. .. .. .. .. .. .. 100.0 21 Ireland 11.6 13.9 17.5 20.0 29.4 24.8 4.5 13.1 .. 18 Israel 12.6 22.8 23.3 23.4 32.9 30.0 7.3 13.7 .. 12 Italy 15.3 25.9 28.6 29.2 28.4 24.1 4.9 9.5 .. 11 Jamaica 9.9 11.5 .. 20.0 .. 40.7 4.5 9.5 100.0 28 Japan .. 22.6 19.9 22.3 13.2 19.6 3.7 10.7 .. 19 Jordan .. 14.0 15.7 16.9 .. .. .. .. .. 20 Kazakhstan .. 10.0 .. 7.9 .. 5.7 2.3 .. 97.2 17 Kenya 12.9 23.6 .. 23.5 .. 262.6 6.7 29.2 98.8 40 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 11.8 18.6 14.9 25.1 7.0 9.3 4.6 15.0 100.0 29 Kuwait 35.4 12.2 .. 18.1 .. 116.4 5.1 12.7 100.0 12 Kyrgyz Republic .. 7.6 11.9 14.3 27.7 20.8 4.4 .. 58.0 24 Lao PDR .. 8.6 4.3 4.0 66.9 22.4 2.3 11.7 83.4 31 Latvia .. 20.6 24.0 24.5 34.3 14.4 5.3 15.4 .. 13 Lebanon .. 7.2 .. 7.6 12.8 15.9 2.6 11.0 14.4 14 Lesotho .. 24.2 68.4 49.0 1237.4 1104.8 13.4 29.8 63.7 42 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. 23.8 .. .. .. .. .. Lithuania .. 14.4 .. 20.1 .. 20.6 5.2 15.7 .. 15 Macedonia, FYR .. 23.8 .. 7.5 .. 22.6 3.4 16.4 100.0 20 Madagascar .. 8.4 39.9 .. 180.9 175.0 3.2 25.3 36.5 54 Malawi 7.2 13.5 .. 28.6 .. .. 5.8 .. 85.8 64 Malaysia 10.1 18.6 .. 26.3 .. 93.7 8.0 28.0 .. 18 Mali .. .. 61.6 .. 265.0 .. 4.3 14.8 .. 54 Mauritania .. 9.8 38.7 24.7 85.0 39.9 2.3 8.3 100.0 40 Mauritius 10.1 11.8 .. 19.8 .. 37.1 4.5 14.3 100.0 22 Mexico 4.8 15.5 14.2 16.8 47.8 44.1 5.8 23.8 84.3 28 Moldova .. 16.6 .. 24.1 .. 12.9 4.3 21.1 .. 18 Mongolia .. 14.3 .. 13.2 .. 22.8 5.3 .. 96.4 34 Morocco 15.3 22.9 49.1 39.6 104.8 93.0 6.7 27.2 100.0 27 Mozambique .. 14.1 .. 48.4 .. 435.3 3.7 19.5 59.8 66 Myanmar .. 2.7 .. 2.9 .. .. .. .. 76.0 31 Namibia .. 20.1 36.4 24.1 157.6 106.6 6.9 .. 16.7 33 Nepal .. 12.4 13.1 10.5 .. 71.1 3.4 14.9 95.8 40 c Netherlands 12.6 18.7 21.8 23.6 44.2 43.0 5.3 10.8 .. .. New Zealand 17.3 19.4 24.5 22.7 42.0 34.1 6.8 20.9 .. 16 Nicaragua .. 8.8 .. 10.4 .. .. 3.1 15.0 76.9 34 Niger .. 19.0 .. 64.3 .. .. 2.3 .. 75.8 44 Nigeria .. .. .. .. .. .. .. .. 49.8 37 Norway 32.7 21.7 30.6 33.0 47.8 50.4 7.7 15.7 .. 11 Oman 10.5 16.3 22.2 15.5 .. 28.7 3.6 24.2 .. .. Pakistan .. 7.0 .. 11.0 .. .. 2.3 10.9 85.5 38 Panama 11.3 9.6 19.1 12.3 33.6 26.5 3.8 8.9 89.6 24 Papua New Guinea .. .. .. .. .. .. .. .. 100.0 35 Paraguay .. 12.6 .. 14.1 .. 30.1 4.3 10.8 67.0 28 Peru .. 6.7 10.8 8.9 .. 12.3 2.4 13.7 .. 22 Philippines .. 11.7 .. 10.1 .. 14.1 3.2 17.2 100.0 35 Poland 12.9 22.9 16.5 21.7 36.3 19.7 5.6 12.3 .. 13 Portugal 17.2 24.4 29.1 33.0 29.7 27.8 5.9 12.4 .. 12 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 75 2.9 Education inputs Public expenditure Public expenditure Trained Primary per studenta on education teachers pupil- in primary teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2005b 1998 2005b 1998 2005b 2005b 2005b 2005b 2005b Romania .. .. .. .. .. .. 3.6 .. 25.9 17 Russian Federation .. .. .. .. .. 12.1 3.7 12.3 99.0 17 Rwanda .. 11.3 .. 18.6 .. 408.8 3.8 12.2 81.7 62 Saudi Arabia .. .. .. .. .. .. 6.8 27.6 .. .. Senegal 18.9 18.7 .. 32.2 .. 267.6 5.4 18.9 100.0 47 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. 3.8 .. 61.5 67 Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic .. 13.0 18.5 17.8 33.0 29.3 4.4 11.2 .. 18 Slovenia 17.4 30.0 .. 25.7 .. 26.4 6.0 12.6 .. 15 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 20.2 14.2 21.2 17.6 64.3 49.6 5.4 17.9 78.7 36 Spain 11.2 18.6 24.4 23.8 19.6 22.7 4.3 11.2 .. 14 Sri Lanka .. .. .. .. .. .. .. .. .. 22 Sudan .. .. .. .. .. .. .. .. 55.1 28 Swaziland 6.5 12.4 26.1 30.9 388.4 341.5 6.2 .. 90.5 32 Sweden 46.2 24.0 26.3 26.8 53.3 46.9 7.5 12.8 .. 10 Switzerland 36.1 24.9 27.7 29.2 54.5 64.8 6.1 13.0 .. .. Syrian Arab Republic .. 14.2 22.1 26.3 .. .. .. .. 88.4 25 Tajikistan .. 8.7 .. 11.3 .. 14.1 3.5 18.0 84.1 21 Tanzania .. .. .. .. .. .. .. .. 100.0 56 Thailand 11.6 13.9 .. 13.1 .. 23.0 4.2 27.5 79.3 21 Togo .. 6.7 30.9 .. 317.9 .. 2.6 13.6 36.8 34 Trinidad and Tobago .. 15.7 12.2 .. 147.6 .. 4.2 .. 81.0 18 Tunisia .. 24.1 .. 24.1 .. 80.6 8.1 .. .. 21 Turkey 10.7 11.8 .. 14.8 .. 44.7 4.0 13.6 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 11.3 .. 34.0 .. 188.8 5.2 18.3 80.4 50 Ukraine .. 14.8 .. 23.9 .. 34.1 6.4 18.9 99.7 19 United Arab Emirates .. 7.1 11.5 9.3 .. 28.9 1.3 27.4 60.0 15 United Kingdom 15.0 18.4 26.6 28.4 32.8 28.1 5.5 11.9 .. 18 United States .. 21.5 22.5 25.8 27.5 26.7 5.9 15.2 .. 14 Uruguay 7.8 6.5 .. 7.2 .. 19.5 2.2 7.9 100.0 21 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB .. .. .. .. .. .. .. .. 84.0 19 Vietnam .. .. .. .. .. .. .. .. 93.4 22 West Bank and Gaza .. .. .. .. .. .. .. .. 100.0 25 Yemen, Rep. .. .. .. .. .. .. .. .. .. 26 Zambia .. 5.4 .. 8.2 .. .. 2.0 14.8 100.0 51 Zimbabwe 20.7 .. .. .. .. .. .. .. .. 39 World .. m 15.4 m .. m 20.3 m .. m 32.6 m 4.7 m .. m .. m 29 m Low income .. .. .. .. .. .. .. .. .. 42 Middle income .. 14.1 .. 17.4 .. 32.5 4.5 15.2 .. 22 Lower middle income .. 11.7 .. 16.5 .. 36.6 4.3 .. .. 22 Upper middle income .. 14.7 .. 20.1 33.6 26.3 4.6 15.4 .. 22 Low & middle income .. .. .. .. .. .. 4.3 .. .. 31 East Asia & Pacific .. 6.3 .. .. .. .. 2.7 .. 95.7 22 Europe & Central Asia .. 16.7 .. 20.5 .. 23.2 4.4 13.9 .. 17 Latin America & Carib. .. 12.3 .. 14.9 .. 31.3 4.3 15.0 .. 24 Middle East & N. Africa .. 14.3 .. 17.5 .. .. .. .. .. 23 South Asia .. 9.7 13.1 12.1 .. 68.6 2.9 12.8 .. 41 Sub-Saharan Africa .. .. .. .. .. .. 4.3 .. .. 48 High income 16.3 18.7 24.4 24.4 29.7 29.4 5.9 12.8 .. 16 Europe EMU 15.3 18.7 25.4 24.7 29.7 27.8 5.4 11.1 .. 14 a. Because of the change from International Standard Classification of Education (ISCED) 76 to ISCED 97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Provisional data. c. Data are for 2006. 76 2007 World Development Indicators 2.9 PEOPLE Education inputs About the data Definitions Data on education are compiled by the United contribute to the national economy (Hanushek · Public expenditure per student is public current Nations Educational, Scientific, and Cultural Organi- 2002). spending on education divided by the number of stu- zation (UNESCO) Institute for Statistics from official The share of trained teachers in primary educa- dents by level, as a percentage of gross domestic responses to surveys and from reports provided by tion measures the quality of the teaching staff. It product (GDP) per capita. · Public expenditure on education authorities in each country. Such data are does not take account of competencies acquired by education is current and capital public expenditure used for monitoring, policymaking, and resource allo- teachers through their professional experience or on education, as a percentage of GDP and as a per- cation. For a variety of reasons, however, education self-instruction or of such factors as work experi- centage of total government expenditure. · Trained statistics generally fail to provide a complete and ence, teaching methods and materials, or classroom teachers in primary education are the percentage accurate picture of a country's education system. conditions, which may affect the quality of teaching. of primary school teachers who have received the Statistics often lag by one to two years, though an Since the training teachers receive varies greatly minimum organized teacher training (pre-service effort is being made to shorten the delay. Moreover, (pre-service or in-service), care should be taken in or in-service) required for teaching in their country. coverage and data collection methods vary across comparing across countries. · Primary pupil-teacher ratio is the number of pupils countries and over time within countries, so compari- The primary pupil-teacher ratio reflects the average enrolled in primary school divided by the number of sons should be interpreted with caution. numbers of pupils per teacher. It is different from the primary school teachers (regardless of their teaching The data on education spending in the table average class size because of the different practices assignment). for the majority of the countries refer to public countries employ, such as part-time teaching, school spending--government spending on public educa- shifts, and multigrade classes. The comparability of tion plus subsidies for private education. The data pupil-teacher ratios across countries is affected by generally exclude foreign aid for education. They may the definition of teachers and by differences in class also exclude spending by religious schools, which size by grade and in the number of hours taught, play a significant role in many developing countries. as well as the different practices mentioned above. Data for some countries and for some years refer to Moreover, the underlying enrollment levels are sub- spending by the ministry of education only (exclud- ject to a variety of reporting errors (for further discus- ing education expenditures by other ministries and sion of enrollment data see About the data for table departments and local authorities). 2.10). While the pupil-teacher ratio is often used to Many developing countries have sought to supple- compare the quality of schooling across countries, it ment public funds for education. Some countries is often weakly related to the value added of school- have adopted tuition fees to recover part of the cost ing systems. of providing education services or to encourage devel- In 1998 UNESCO introduced the new International opment of private schools. Charging fees raises dif- Standard Classification of Education 1997. Thus the ficult questions relating to equity, efficiency, access, time-series data for the years through 1997 are not and taxation, however, and some governments have consistent with those for 1998 and later. Any time- used scholarships, vouchers, and other methods of series analysis should therefore be undertaken with public finance to counter criticism. For most coun- extreme caution. tries, the data reflect only public spending. Data for In 2006 the UNESCO Institute for Statistics also a few countries include private spending, although changed its convention for citing the reference year national practices vary with respect to whether par- of education data and indicators to the calendar year ents or schools pay for books, uniforms, and other in which the academic or financial year ends. Data supplies. For greater detail, see the country- and that used to be listed for 2004/05, for example, are indicator-specific notes in the source. now listed for 2005. This change was implemented The share of public expenditure devoted to educa- to present the most recent data available and to tion allows an assessment of the priority a govern- align the data reporting with that of other interna- ment assigns to education relative to other public tional organizations (in particular the Organisation investments, as well as a government's commit- for Economic Co-operation and Development and Data sources ment to investing in human capital development. It Eurostat). also reflects the development status of a country's Data on education inputs are from the UNESCO education system relative to that of others. How- Institute for Statistics, which compiles inter- ever, returns on investment to education, especially national data on education in cooperation with primary and lower secondary education, cannot be national commissions and national statistical understood simply by comparing current education services. Data for latest years are provisional, as indicators with national income. It takes a long time of January 2007. before currently enrolled children can productively 2007 World Development Indicators 77 2.10 Participation in education Gross enrollment Net enrollment Children out of ratio ratioa school thousand % of relevant age group % of relevant age group primary-school-age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female 2005b 2005b 2005b 2005b 1991 2005b 1991 2005b 2005b 2005b Afghanistan 1 87 16 1 .. .. .. .. .. .. Albania 49 106 78 19 95 94 .. 74 7 7 Algeria 6 112 83 20 89 97 53 66 0 39 Angola .. .. .. 1 50 .. .. .. .. .. Argentina 62 112 86 64 .. 99 .. 79 3 19 Armenia 33 94 88 28 .. 79 .. 84 11 7 Australia 102 103 149 72 99 96 79 85 42 35 Austria 89 106 101 50 88 .. .. .. .. .. Azerbaijan 29 96 83 15 89 85 .. 78 45 46 Bangladesh 11 109 46 6 .. 93 .. 43 .. .. Belarus 105 101 95 62 86 89 .. 89 17 21 Belgium 116 104 109 63 96 99 87 97 4 3 Benin 5 96 33 .. 41 78 .. .. .. .. Bolivia 50 113 88 41 .. 94 .. 73 28 19 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 105 75 5 83 83 35 55 26 25 Brazil 68 141 102 22 85 93 17 76 .. .. Bulgaria 78 105 102 41 86 95 63 88 5 5 Burkina Faso 2 58 14 2 29 45 .. 11 553 649 Burundi 2 85 13 2 53 60 .. .. 222 258 Cambodia 9 134 29 3 69 99 .. 24 .. .. Cameroon 25 117 44 6 74 .. .. .. .. .. Canada 68 100 109 60 98 .. 89 .. .. .. Central African Republic 2 56 12 2 52 .. .. .. .. .. Chad 1 77 16 1 35 61 .. 11 .. .. Chile 52 104 89 43 89 .. 55 .. .. .. China 36 118 73 19 97 .. .. .. .. .. Hong Kong, China 69 105 87 31 .. 93 .. 80 1 12 Colombia 39 113 79 28 69 87 34 55 276 257 Congo, Dem. Rep. 1 62 22 .. 54 .. .. .. .. .. Congo, Rep. 6 88 39 4 79 44 .. .. 203 173 Costa Rica 69 110 79 25 87 .. 38 .. .. .. Côte d'Ivoire 3 72 25 .. 45 56 .. 20 519 705 Croatia 48 96 88 42 79 87 63 85 7 7 Cuba 113 102 94 61 93 97 70 87 5 14 Czech Republic 107 102 96 43 87 .. .. .. .. .. Denmark 91 101 124 74 98 98 87 92 5 2 Dominican Republic 34 113 71 33 57 88 .. 53 67 53 Ecuador 77 117 61 .. 98 98 .. 52 11 0 Egypt, Arab Rep. 14 101 87 33 84 95 .. 79 58 161 El Salvador 51 113 63 19 .. 93 .. 53 26 22 Eritrea 12 64 31 1 16 47 .. 25 144 164 Estonia 114 100 98 65 99 94 .. 90 2 1 Ethiopia 2 93 31 3 22 61 .. 28 .. .. Finland 59 101 109 90 98 99 93 94 1 1 France 114 105 111 56 100 99 .. 96 11 4 Gabon 14 130 50 .. 85 .. .. .. .. .. Gambia, The 18 81 47 1 48 .. .. 45 .. .. Georgia 51 94 83 46 97 87 .. 72 26 22 Germany 97 100 100 .. 84 .. .. .. .. .. Ghana 56c 94 c 45c 5c 54 69 c .. 38 c 510 c 480 c Greece 66 102 96 79 95 99 83 87 0 3 Guatemala 28 114 51 10 .. 94 .. 34 .. .. Guinea 7 81 30 3 27 66 .. 24 .. .. Guinea-Bissau .. .. .. .. 38 .. .. .. .. .. Haiti .. .. .. .. 22 .. .. .. .. .. 78 2007 World Development Indicators 2.10 PEOPLE Participation in education Gross enrollment Net enrollment Children out of ratio ratioa school thousand % of relevant age group % of relevant age group primary-school-age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female 2005b 2005b 2005b 2005b 1991 2005b 1991 2005b 2005b 2005b Honduras 33 113 65 16 89 91 21 .. 43 27 Hungary 81 98 97 60 91 89 75 91 10 9 India 36 116 54 12 .. 90 .. .. .. .. Indonesia 22 117 64 17 97 94 39 57 0 246 Iran, Islamic Rep. 46 111 81 24 92 95 .. 77 307 0 Iraq 6 98 45 15 94 88 .. 38 .. .. Ireland .. 106 112 59 90 96 80 87 8 7 Israel 112 110 93 56 92 98 .. 89 9 7 Italy 103 101 99 63 100 99 .. 92 4 7 Jamaica 92 95 88 19 96 91 64 79 16 14 Japan 85 100 102 54 100 100 97 100 7 0 Jordan 30 98 87 39 94 91 .. 81 26 17 Kazakhstan 34 109 99 53 89 91 .. 92 4 6 Kenya 54 114 49 3 .. 80 .. 42 526 506 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 91 105 93 90 100 99 86 90 4 10 Kuwait 73 98 95 18 49 87 .. 78 14 14 Kyrgyz Republic 13 98 86 41 92 87 .. 80 13 11 Lao PDR 9 116 47 8 63 84 .. 38 55 71 Latvia 79 93 97 74 92 .. .. .. .. .. Lebanon 74 106 89 51 73 92 .. .. 12 12 Lesotho 34 132 39 3 71 87 15 25 25 16 Liberia .. .. .. .. .. .. .. .. .. .. Libya 8 107 104 56 96 .. .. .. .. .. Lithuania 64 97 102 73 .. 89 .. 94 7 6 Macedonia, FYR 32 98 84 28 94 92 .. 81 2 1 Madagascar 8 138 .. 3 64 92 .. .. 93 95 Malawi .. 122 28 0b 48 95 .. 24 83 30 Malaysia 108 93 76 32 .. 93 .. 76 112 110 Mali 3 66 24 3 21 51 5 .. 505 607 Mauritania 2 93 21 3 35 72 .. 15 65 65 Mauritius 95 102 89 17 91 95 .. 82 3 2 Mexico 84 109 80 23 98 98 44 64 22 7 Moldova 62 92 82 34 89 86 .. 76 12 12 Mongolia 40 118 94 41 90 89 .. 78 13 9 Morocco 54 105 50 11 56 86 .. 35 216 309 Mozambique .. 105 14 1 43 79 .. 7 331 468 Myanmar .. 100 40 11 98 90 .. 37 267 221 Namibia 29 99 61 6 .. 72 .. 38 62 50 Nepal 64 c 126c 43c 6 .. 78 .. .. .. .. Netherlands 89 107 119 59 95 99 84 89 3 11 New Zealand 92 102 118 86 98 99 85 91 1 1 Nicaragua 37 112 66 18 73 87 .. 43 27 27 Niger 1 47 9 1 22 40 5 8 634 737 Nigeria 15 103 34 10 58 91 .. 27 .. .. Norway 85 99 116 80 100 99 88 96 2 2 Oman 7 84 87 15 69 76 .. 75 41 38 Pakistan 50 87 27 5 33 68 .. 21 2,328 3,975 Panama 62 111 70 44 .. 98 .. 64 1 2 Papua New Guinea 59 75 26 .. .. .. .. .. .. .. Paraguay 31 106 63 24 94 .. 26 .. .. .. Peru 60 114 92 33 .. 97 .. 69 12 2 Philippines 40 112 86 29 96 94 .. 61 392 255 Poland 53 99 97 61 97 97 76 90 41 33 Portugal 76 116 97 57 98 98 .. 82 1 2 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 79 2.10 Participation in education Gross enrollment Net enrollment Children out of ratio ratioa school thousand % of relevant age group % of relevant age group primary-school-age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female 2005b 2005b 2005b 2005b 1991 2005b 1991 2005b 2005b 2005b Romania 76 107 85 40 81 92 .. 81 23 24 Russian Federation 85 123 93 68 99 91 .. .. 198 171 Rwanda 3 120 14 3 66 74 7 .. 196 177 Saudi Arabia 10 91 88 28 59 78 31 66 426 367 Senegal 8 88 26 5 43 76 .. 21 167 195 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 4 155 30 2 43 .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic 92 99 94 36 .. .. .. .. .. .. Slovenia 79 99 100 74 96 98 .. 95 0d 0 Somalia 2c 17 .. .. 9 19c .. .. .. .. South Africa 37 104 93 16 90 87 45 .. 321 248 Spain 111 108 119 66 100 99 .. 97 3 10 Sri Lanka .. 98 83 .. .. 97 .. .. 9 13 Sudan 25 60 34 .. 40 .. .. .. .. .. Swaziland 18 107 45 4 77 80 31 33 21 19 Sweden 85 99 103 84 100 99 85 98 4 5 Switzerland 95 102 93 47 84 94 80 83 5 4 Syrian Arab Republic 10 124 68 .. 91 95 43 62 0 70 Tajikistan 9 101 82 17 77 97 .. 80 2 15 Tanzania 29 106 .. 1 49 91 .. .. 273 331 Thailand 90 97 73 43 76 .. .. .. .. .. Togo 2 100 40 .. 64 78 15 .. .. .. Trinidad and Tobago 86 106 88 12 91 95 .. 75 0d 0 Tunisia 22 110 81 29 94 97 .. 67 11 6 Turkey 8 93 79 29 89 89 42 .. 354 546 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 2 118 16 3 .. .. .. 13 .. .. Ukraine 86 107 89 69 80 83 .. 79 152 144 United Arab Emirates 64 83 64 22 99 71 60 57 37 39 United Kingdom 59 107 105 60 98 99 81 95 1 0d United States 62 99 95 82 97 92 85 89 593 1,028 Uruguay 61 109 108 39 91 .. .. .. .. .. Uzbekistan 28 100 95 15 78 .. .. .. .. .. Venezuela, RB 58 105 74 41 87 91 18 63 130 106 Vietnam 60 95 76 16 90 88 .. 69 .. .. West Bank and Gaza 30 89 99 38 .. 80 .. 95 35 35 Yemen, Rep. 1 87 48 9 51 75 .. .. .. .. Zambia .. 111 28 .. .. 89 .. 26 119 109 Zimbabwe 43 96 36 4 .. 82 .. 34 224 206 World 38 w 107 w 65 w 24 w 83 w .. w .. w .. w Low income 27 102 45 9 .. 78 .. 37 Middle income 39 113 77 26 92 .. .. 69 Lower middle income 35 115 76 22 92 93 .. 65 Upper middle income 59 105 86 43 93 94 .. 75 Low & middle income 33 107 61 18 81 .. .. .. East Asia & Pacific 36 114 71 19 96 93 .. .. Europe & Central Asia 50 102 90 49 90 91 .. 84 Latin America & Carib. 62 118 86 28 85 95 30 67 Middle East & N. Africa 21 103 73 22 84 90 .. 65 South Asia 33 110 50 10 .. 86 .. .. Sub-Saharan Africa 16 92 30 5 50 66 .. 24 High income 76 100 100 67 95 94 .. 90 Europe EMU 101 104 106 62 95 99 .. 94 a. Because of the change from International Standard Classification of Education (ISCED) 76 to ISCED 97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Provisional data. c. Data are for 2006. d. Less than 0.5. 80 2007 World Development Indicators 2.10 PEOPLE Participation in education About the data Definitions School enrollment data are reported to the United the primary sources of data on school-age popula- · Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural Organi- tions, are commonly subject to underenumeration ment, regardless of age, to the population of the age zation (UNESCO) Institute for Statistics by national (especially of young children) aimed at circumvent- group that officially corresponds to the level of educa- education authorities and statistical offices. Enroll- ing laws or regulations. Errors are also introduced tion shown. · Preprimary education refers to the ini- ment ratios help to monitor two important issues when parents round up children's ages. While census tial stage of organized instruction, designed primarily for universal primary education: whether a country data are often adjusted for age bias, adjustments to introduce very young children to a school-type envi- is on track to achieve the Millennium Development are rarely made for inadequate vital registration ronment. · Primary education provides children with Goal of universal primary completion by 2015, which systems. Compounding these problems, pre- and basic reading, writing, and mathematics skills along implies achieving a net primary enrollment ratio of post-census estimates of school-age children are with an elementary understanding of such subjects 100 percent, and whether an education system has interpolations or projections based on models that as history, geography, natural science, social sci- sufficient capacity to meet the needs of universal may miss important demographic events (see the ence, art, and music. · Secondary education com- primary education, as indicated in part by its gross discussion of demographic data in About the data pletes the provision of basic education that began enrollment ratios. for table 2.1). at the primary level and aims at laying the founda- Enrollment ratios, while a useful measure of partici- Thus gross enrollment ratios indicate the capacity tions for lifelong learning and human development pation in education, also have some limitations. They of each level of the education system, but a high ratio by offering more subject- or skill-oriented instruction are based on data collected during annual school sur- does not necessarily mean a successful education using more specialized teachers. · Tertiary educa- veys, which are typically conducted at the beginning system. The net enrollment ratio excludes overage tion refers to a wide range of post-secondary educa- of the school year. They do not reflect actual rates and underage students in an attempt to capture tion institutions, including technical and vocational of attendance or dropouts during the school year. more accurately the system's coverage and internal education, colleges, and universities, whether or not And school administrators may report exaggerated efficiency. It does not solve the problem completely, leading to an advanced research qualification, that enrollments, especially if there is a financial incen- however, because some children fall outside the offi - normally require as a minimum condition of admis- tive to do so. Typically, the total number of teachers cial school age because of late or early entry rather sion the successful completion of education at the allocated to a given school is related to enrollment. than because of grade repetition. The difference secondary level. · Net enrollment ratio is the ratio This may create perverse incentives to inflate enroll- between gross and net enrollment ratios shows the of total enrollment of children of official school age ment levels, particularly when enrollment is closely incidence of overage and underage enrollments. based on the International Standard Classification linked to government school funding formulas, such In using enrollment data, it is also important to con- of Education 1997 to the population of the age group as student capitation grants. sider repetition rates. These rates are quite high in that officially corresponds to the level of education Also as international indicators, the gross and some developing countries, leading to a substantial shown. · Children out of school are the number of net primary enrollment ratios have an inherent number of overage children enrolled in each grade primary school age children not enrolled in primary weakness: the length of primary education differs and raising the gross enrollment ratio. or secondary school. significantly across countries, although the Interna- Children out of school are children in the primary tional Standard Classification of Education tries to school age group who are not enrolled in primary or minimize the difference. A relatively short duration secondary education. The data are calculated by the for primary education tends to increase the ratio, UNESCO Institute for Statistics using administrative whereas a relatively long duration tends to decrease data. Children out of school include dropouts and it (in part because there are more dropouts among children who never enrolled as well as children of older children). primary age enrolled in preprimary education. The Overage or underage enrollments frequently occur, large number of children out of school creates pres- particularly when parents prefer, for cultural or eco- sure for the education system to enroll children and nomic reasons, to have children start school at other to provide classrooms, teachers, and educational than the official age. Children's age at enrollment materials, a task made difficult in many developing may be inaccurately estimated or misstated, espe- countries by limited education budgets. However, cially in communities where registration of births getting these children into school is a high priority for is not strictly enforced. Parents who want to enroll countries and crucial for their prospects for achiev- an underage child in primary school may do so by ing the Millennium Development Goal of universal overstating the child's age. And in some education primary education. Data sources systems ages for children repeating a grade may be In 2006 the UNESCO Institute for Statistics underreported. changed its convention for citing the reference Data on gross and net enrollment ratios and out Other problems affecting cross-country compari- year. For more information, see About the data for of school children are from the UNESCO Institute sons of enrollment data stem from errors in esti- table 2.9. for Statistics. Data for latest years are provisional, mates of school-age populations. Age-sex struc- as of January 2007. tures from censuses or vital registration systems, 2007 World Development Indicators 81 2.11 Education efficiency Gross intake rate Share of cohort Repeaters in Transition to in grade 1 reaching grade 5a primary school secondary education % of relevant % of grade 1 % of % of enrollment in last age group students enrollment year of primary Male Female Male Female Male Female Male Female 2005b 2005b 1991 2004b 1991 2004b 2005b 2005b 2004b 2004b Afghanistan 96 67 .. .. .. .. 18 14 .. .. Albania 99 99 .. .. .. .. 3 2 100 99 Algeria 102 99 95 94 94 97 14 8 76 83 Angola .. .. .. .. .. .. .. .. .. .. Argentina 110 110 .. 84 .. 85 8 5 92 94 Armenia 98 102 .. .. .. .. 0c 0c 93 93 Australia 103 102 98 .. 99 .. .. .. 100 100 Austria 105 105 .. .. .. .. .. .. .. .. Azerbaijan 94 93 .. .. .. .. 0c 0c 99 99 Bangladesh 116 131 .. 63 .. 67 7 7 89d 96d Belarus 105 103 .. .. .. .. 0c 0c 99 100 Belgium 103 104 90 .. 92 .. .. .. .. .. Benin 109 97 54 53 56 50 17 17 51 51 Bolivia 119 119 .. 85 .. 85 2 1 90 90 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 108 102 81 89 87 92 6 4 97 98 Brazil 127 117 .. .. .. .. .. .. .. .. Bulgaria 107 104 91 .. 90 .. 3 2 96 96 Burkina Faso 81 69 71 75 68 76 12 12 47 44 Burundi 92 84 65 66 58 68 30 30 35 30 Cambodia 137 128 .. 62 .. 65 15 12 84 80 Cameroon 120 104 .. 64 .. 63 26 25 43 47 Canada 97 96 95 .. 98 .. .. .. .. .. Central African Republic 69 50 24 .. 22 .. 30 31 .. .. Chad 112 81 56 34 41 32 22 24 56 42 Chile 99 97 94 99 91 99 3 2 95 98 China 95 93 58 .. 78 .. 0c 0c .. .. Hong Kong, China 93 87 .. 99 .. 100 1 1 100 100 Colombia 126 119 .. 81 .. 86 5 4 100 100 Congo, Dem. Rep. 72 61 58 .. 50 .. 16 17 .. .. Congo, Rep. 62 62 56 65 65 67 25 23 58 58 Costa Rica 103 103 83 84 85 90 8 6 92 91 Côte d'Ivoire 75 68 75 .. 70 .. 17 18 42 36 Croatia 99 97 .. .. .. .. 0c 0c 100 100 Cuba 105 104 .. 96 .. 98 1 0c 98 99 Czech Republic 97 96 .. 98 .. 99 1 1 99 99 Denmark 98 99 94 .. 94 .. .. .. 100 100 Dominican Republic 118 108 .. .. .. .. 10 6 83 92 Ecuador 136 134 .. 75 .. 77 2 2 76 71 Egypt, Arab Rep. 99 99 .. 98 .. 99 5 3 83 89 El Salvador 129 123 56 67 60 72 7 5 93 93 Eritrea 55 45 .. 83 .. 74 13 13 91 85 Estonia 101 101 .. 99 .. 99 3 1 94 99 Ethiopia 148 135 16 .. 23 .. 8 6 84 84 Finland 98 97 100 100 100 100 1 0c 100 100 France .. .. 69 .. 95 .. .. .. .. .. Gabon 94 94 .. 68 .. 71 35 34 .. .. Gambia, The 87 99 .. .. .. .. 10 9 .. .. Georgia 103 105 .. 76 .. 83 0c 0c 98 99 Germany 105 105 .. .. .. .. 2 1 99 99 Ghana 107e 113e 81 62 79 65 6 6 87 87 Greece 103 103 100 .. 100 .. 0 0 .. .. Guatemala 125 122 .. 70 .. 66 13 12 97 95 Guinea 87 81 64 78 48 73 8 9 68 58 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 82 2007 World Development Indicators 2.11 PEOPLE Education efficiency Gross intake rate Share of cohort Repeaters in Transition to in grade 1 reaching grade 5a primary school secondary education % of relevant % of grade 1 % of % of enrollment in last age group students enrollment year of primary Male Female Male Female Male Female Male Female 2005b 2005b 1991 2004b 1991 2004b 2005b 2005b 2004b 2004b Honduras 129 127 .. .. .. .. 9 7 .. .. Hungary 96 94 77 .. 98 .. 3 2 98 99 India 139 130 .. 81 .. 76 3 3 87 82 Indonesia 121 116 34 88 78 90 3 3 84 84 Iran, Islamic Rep. 107 139 91 88 89 87 3 1 95 86 Iraq 110 103 .. 87 .. 73 9 7 73 66 Ireland 102 101 99 100 100 100 1 1 .. .. Israel 99 102 .. 100 .. 100 2 1 74 74 Italy 103 103 .. 96 .. 97 0c 0c 100 99 Jamaica 93 92 .. 86 .. 92 3 2 .. .. Japan 98 98 100 .. 100 .. .. .. .. .. Jordan 91 92 .. 99 .. 99 1 1 97 97 Kazakhstan 108 107 .. .. .. .. 0c 0c 100 100 Kenya 120 116 75 81 78 85 6 6 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 105 107 99 98 100 98 0 0 99 98 Kuwait 93 92 .. .. .. .. 2 2 93 97 Kyrgyz Republic 97 94 .. .. .. .. 0c 0c 98 100 Lao PDR 121 111 .. 64 .. 62 20 18 80 75 Latvia 90 89 .. .. .. .. 4 2 97 99 Lebanon 102 100 .. 91 .. 96 12 8 83 88 Lesotho 128 120 58 58 73 69 21 17 67 65 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania 101 102 .. .. .. .. 1 0c 99 99 Macedonia, FYR 98 97 .. .. .. .. 0c 0c 99 98 Madagascar 182 176 22 43 21 43 19 18 56 53 Malawi 177 188 71 40 57 37 9 8 77 72 Malaysia 94 94 97 99 97 98 .. .. .. .. Mali 70 59 71 78 67 70 18 19 53 48 Mauritania 112 113 76 51 75 55 10 10 48 43 Mauritius 102 102 97 97 98 97 5 4 60 69 Mexico 106 105 35 92 71 94 6 4 95 92 Moldova 93 91 .. .. .. .. 0c 0c 99 98 Mongolia 148 149 .. .. .. .. 0c 0c 96 99 Morocco 101 97 75 81 76 77 15 10 79 78 Mozambique 161 150 36 66 32 58 11 10 51 56 Myanmar 123 122 .. 68 .. 72 0c 0c 72 71 Namibia 93 94 60 84 65 85 15 12 90 93 Nepal 160 e 160 e 51 75d 51 83d 21e 20 e 79 74 Netherlands 100 99 .. 100 .. 99 .. .. 96 100 New Zealand 100 99 .. .. .. .. .. .. .. .. Nicaragua 147 137 11 51 37 56 11 9 .. .. Niger 65 51 61 66 65 64 5 6 63 53 Nigeria 124 107 .. 71 .. 75 2 3 .. .. Norway 98 98 99 99 100 100 .. .. 100 100 Oman 73 74 97 98 96 98 1 1 98 99 Pakistan 128 103 .. 68 .. 72 3 3 67 72 Panama 110 109 .. 85 .. 86 7 5 64 65 Papua New Guinea 101 90 70 68 68 68 0 0 77 77 Paraguay 109 106 73 80 75 83 9 6 91 91 Peru 105 106 .. 90 .. 90 8 7 96 94 Philippines 138 129 .. 71 .. 80 3 1 97 96 Poland 97 97 89 .. 96 .. 1 0c .. .. Portugal 105 106 .. .. .. .. 13 7 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 83 2.11 Education efficiency Gross intake rate Share of cohort Repeaters in Transition to in grade 1 reaching grade 5a primary school secondary education % of relevant % of grade 1 % of % of enrollment in last age group students enrollment year of primary Male Female Male Female Male Female Male Female 2005b 2005b 1991 2004b 1991 2004b 2005b 2005b 2004b 2004b Romania 126 126 .. .. .. .. 3 2 98 98 Russian Federation 98 97 .. .. .. .. .. .. .. .. Rwanda 178 177 61 43 59 49 19 19 .. .. Saudi Arabia 85 89 82 100 84 94 5 5 93 97 Senegal 101 105 .. 79 .. 77 13 13 63 59 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic 97 96 .. .. .. .. 3 2 98 99 Slovenia 145 144 .. .. .. .. 1 0c 100 99 Somalia .. .. .. 66 .. 52 .. .. .. .. South Africa 117 111 .. 82 .. 83 8 8 89 91 Spain 103 102 .. .. .. .. 3 2 .. .. Sri Lanka 99 97 92 .. 93 .. .. .. 96 98 Sudan 72 62 90 78 99 79 1 2 88 91 Swaziland 122 114 74 74 80 80 18 14 91 89 Sweden 92 93 100 .. 100 .. .. .. .. .. Switzerland 89 94 .. .. .. .. 2 1 100 100 Syrian Arab Republic 123 119 97 93 95 92 8 6 94 95 Tajikistan 101 97 .. .. .. .. 0c 0c 98 97 Tanzania 125 124 81 76 82 76 4 4 34 33 Thailand .. .. .. .. .. .. .. .. .. .. Togo 94 88 52 79 42 70 23 23 70 63 Trinidad and Tobago 99 98 .. 66 .. 76 6 4 95 97 Tunisia 94 96 94 96 77 97 9 6 86 90 Turkey 93 88 98 95 97 94 3 4 93 89 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 164 163 .. 63 .. 64 14 14 36 36 Ukraine 104 104 .. .. .. .. 0c 0c 99 100 United Arab Emirates 89 89 80 96 80 97 2 2 97 98 United Kingdom .. .. .. .. .. .. 0 0 .. .. United States 100 99 .. .. .. .. .. .. .. .. Uruguay 105 106 96 87 98 90 10 7 76 87 Uzbekistan 102 102 .. .. .. .. 0 0 100 99 Venezuela, RB 101 98 82 88 90 95 8 5 98 99 Vietnam 101 95 .. 87 .. 86 3 2 95 94 West Bank and Gaza 82 82 .. .. .. .. 1c 1c 100 100 Yemen, Rep. 122 97 .. 78 .. 67 5 4 .. .. Zambia 126 123 .. .. .. .. 7 6 54 57 Zimbabwe 122 118 72 68 81 71 .. .. 69 70 World 138 w 140 w .. w .. w .. w .. w 5w 4w .. w .. w Low income 138 140 .. 77 .. 75 6 6 81 77 Middle income 104 102 .. .. .. .. 3 3 .. .. Lower middle income 104 102 59 .. 79 .. 3 2 .. .. Upper middle income 103 100 .. .. .. .. .. .. .. .. Low & middle income 138 140 .. .. .. .. 5 4 .. .. East Asia & Pacific 104 101 55 .. 78 .. 1 1 .. .. Europe & Central Asia 99 97 .. .. .. .. .. .. .. .. Latin America & Carib. 120 115 .. .. .. .. .. .. .. .. Middle East & N. Africa 104 103 .. 90 .. 87 8 5 85 88 South Asia 160 160 .. 79 .. 75 4 4 85 82 Sub-Saharan Africa 120 110 .. .. .. .. 9 9 .. .. High income 100 100 .. .. .. .. .. .. .. .. Europe EMU 104 104 .. .. .. .. 2 1 .. .. a. Because of the change from International Standard Classification of Education (ISCED) 76 to ISCED 97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Provisional data. c. Less than 0.5. d. Data are for 2005. e. Data are for 2006. 84 2007 World Development Indicators 2.11 PEOPLE Education efficiency About the data Definitions Indicators of students' progress through school are indirectly reflects the quality of schooling, and a high · Gross intake rate in grade 1 is the number of estimated by the United Nations Educational, Scien- rate does not guarantee these learning outcomes. new entrants in the first grade of primary education tific, and Cultural Organization (UNESCO) Institute Measuring actual learning outcomes requires set- regardless of age, expressed as a percentage of the for Statistics. These indicators measure an educa- ting curriculum standards and measuring students' population of the official primary school entrance tion system's success in extending coverage to all learning progress against those standards through age. · Share of cohort reaching grade 5 is the per- students, maintaining the flow of students efficiently standardized assessments or tests. Currently, many centage of children enrolled in the first grade of pri- from one grade to the next, and imparting a particular countries do not systematically measure learning mary school who eventually reach grade 5. The esti- level of education. progress and outcomes. mate is based on the reconstructed cohort method Gross intake rate indicates the general level of The data on repeaters are often used to indicate (see About the data). · Repeaters in primary school access to primary education. It also indicates the the internal effi ciency of the education system. are the number of students enrolled in the same capacity of the education system to provide access Repeaters not only increase the cost of education grade as in the previous year, as a percentage of all to primary education. Low gross intake rates in grade for the family and for the school system, but also use students enrolled in primary school. · Transition to 1 reflect the fact that many children do not enter limited school resources. Countries have different secondary education refers to the number of new primary school even though school attendance, at policies on repetition and promotion; in some cases entrants to the first grade of secondary school in least through the primary level, is mandatory in all the number of repeaters is controlled because of a given year, as a percentage of the number of stu- countries. Because the gross intake rate includes all limited capacity. Care should be taken in interpret- dents enrolled in the final grade of primary school in new entrants regardless of age, it can be more than ing this indicator. the previous year. 100 percent. Once enrolled, students drop out for a The transition rate from primary school to second- variety of reasons, including low quality of schooling, ary school conveys the degree of access or transition relevance of curriculum (whether real or perceived by between the two levels of education. As completing parents or students), repetition and discouragement primary education is a prerequisite for participat- over poor performance, and the direct and indirect ing in lower secondary school, growing numbers of costs of schooling. Students' progress to higher primary completers will inevitably create pressures grades may also be limited by the availability of for expanding the number of places available at the teachers, classrooms, and educational materials. secondary level. A low transition rate can signal prob- The share of cohort reaching grade 5 (cohort lems such as an inadequate promotion and exami- survival rate) is estimated as the proportion of an nation system or insufficient capacity in secondary entering cohort of grade 1 students that eventually schools. The quality of data on the transition rate is reaches grade 5. It measures the holding power and affected when new entrants and repeaters are not internal efficiency of an education system. Cohort correctly distinguished in the first grade of secondary survival rates approaching 100 percent indicate a school. Students who interrupt their studies for one high level of retention and a low level of dropout. or more years after completing primary school could Cohort survival rates are typically estimated from also affect the quality of the data. data on enrollment and repetition by grade for two In 2006 the UNESCO Institute for Statistics consecutive years, in a procedure called the recon- changed its convention for citing the reference structed cohort method. This method makes three year. For more information, see About the data for simplifying assumptions: dropouts never return to table 2.9. school; promotion, repetition, and dropout rates remain constant over the entire period in which the cohort is enrolled in school; and the same rates apply to all pupils enrolled in a given grade, regardless of whether they previously repeated a grade (Fredrick- sen 1993). Given these assumptions, cross-country comparisons should be made with caution, because other flows--caused by new entrants, reentrants, grade skipping, migration, or school transfers during the school year--are not considered. Data sources The UNESCO Institute for Statistics measures the share of cohort reaching grade 5 because research Data on education efficiency are from the UNESCO suggests that five to six years of schooling is a critical Institute for Statistics. Data for latest years are threshold for the achievement of sustainable basic provisional, as of January 2007. literacy and numeracy skills. But the indicator only 2007 World Development Indicators 85 2.12 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Totala Malea Femalea Male Female Male Female 1991 2005b 1991 2005b 1991 2005b 1990 2006c 1990 2006c 2006c 2006c Afghanistan 25 32 37 46 13 18 .. 51 .. 18 43 13 Albania .. 97 .. 97 .. 97 97 99 92 99 99 98 Algeria 79 96 86 96 73 95 86 94 68 86 80 60 Angola 35 .. .. .. .. .. .. 84 .. 63 83 54 Argentina .. 100 .. 99 .. 105 98 99 98 99 97 97 Armenia 90 91 .. 89 .. 92 100 100 99 100 100 99 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan .. 94 .. 95 .. 93 .. 100 .. 100 99 98 Bangladesh 49 77 .. 74 .. 79 51 .. 33 .. .. .. Belarus 95 100 95 102 96 97 100 100 100 100 100 99 Belgium 79 .. 76 .. 82 .. .. .. .. .. .. .. Benin 21 65 28 78 13 52 57 59 25 33 48 23 Bolivia .. 101 .. 102 .. 99 96 99 89 96 93 81 Bosnia and Herzegovina .. .. .. .. .. .. .. 100 .. 100 99 94 Botswana 83 92 75 90 90 94 79 92 87 96 80 82 Brazil 93 108 .. .. .. .. 91 96 93 98 88 89 Bulgaria 85 98 87 99 83 97 100 98 99 98 99 98 Burkina Faso 21 31 26 35 16 27 .. 38 .. 25 29 15 Burundi 46 36 49 40 43 31 58 77 45 70 67 52 Cambodia .. 92 .. 94 .. 90 81 88 66 79 85 64 Cameroon 56 62 60 68 52 57 86 .. 76 .. 77 60 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 27 23 35 29 18 16 66 70 39 47 65 33 Chad 18 32 30 42 7 21 58 56 38 23 41 13 Chile .. 95 .. 96 .. 95 98 99 98 99 96 96 China 103 98 .. .. .. .. 97 99 93 99 95 87 Hong Kong, China 102 110 .. 112 .. 107 .. .. .. .. .. .. Colombia 70 98 67 96 73 100 94 98 96 98 93 93 Congo, Dem. Rep. 46 39 58 47 34 31 80 78 58 63 81 54 Congo, Rep. 54 57 59 60 49 55 95 .. 90 .. .. .. Costa Rica 79 92 77 91 81 93 97 97 98 98 95 95 Côte d'Ivoire 43 .. 55 .. 32 .. 65 71 40 52 61 39 Croatia 85 91 .. 92 .. 91 100 100 100 100 99 97 Cuba 96 94 .. 95 .. 93 99 100 99 100 100 100 Czech Republic .. 104 .. 104 .. 104 .. .. .. .. .. .. Denmark 98 99 98 99 98 100 .. .. .. .. .. .. Dominican Republic 61 92 .. 88 .. 96 87 93 88 95 87 87 Ecuador 91 101 91 100 92 101 96 96 95 96 92 90 Egypt, Arab Rep. .. 95 .. 96 .. 93 71 90 51 79 83 59 El Salvador 41 87 38 86 43 87 85 .. 83 .. .. .. Eritrea 19 51 22 58 17 44 73 .. 49 .. .. .. Estonia 93 101 93 103 94 100 100 100 100 100 100 100 Ethiopia 26 55 32 61 19 49 52 .. 34 .. .. .. Finland 97 100 98 99 97 100 .. .. .. .. .. .. France 104 .. .. .. .. .. .. .. .. .. .. .. Gabon 58 66 55 65 61 68 .. .. .. .. .. .. Gambia, The 44 .. 55 .. 33 .. 50 .. 34 .. .. .. Georgia .. 87 .. 86 .. 87 .. .. .. .. .. .. Germany 100 96 99 96 100 96 .. .. .. .. .. .. Ghana 63 72 70 75 55 69 88 76 75 65 66 50 Greece 99 102 99 103 98 101 99 99 100 99 98 94 Guatemala .. 74 .. 79 .. 69 80 86 66 78 75 63 Guinea 17 55 24 64 9 45 62 59 26 34 43 18 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 27 .. 29 .. 26 .. 56 .. 54 .. .. .. 86 2007 World Development Indicators 2.12 PEOPLE Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Totala Malea Femalea Male Female Male Female 1991 2005b 1991 2005b 1991 2005b 1990 2006c 1990 2006c 2006c 2006c Honduras 65 79 67 77 62 82 78 87 81 91 80 80 Hungary 93 95 88 95 90 96 100 .. 100 .. .. .. India 68 89 81 93 55 84 73 84 54 68 73 48 Indonesia 91 101 .. 101 .. 102 97 99 93 99 94 87 Iran, Islamic Rep. 91 96 97 91 85 100 92 .. 81 .. 84 70 Iraq 59 74 64 85 53 63 56 89 25 80 84 64 Ireland .. 101 .. 100 .. 102 .. .. .. .. .. .. Israel .. 105 .. 104 .. 105 99 100 98 100 98 96 Italy 104 101 104 101 104 101 .. 100 .. 100 99 98 Jamaica 90 84 86 83 94 86 87 .. 95 .. 74 86 Japan 101 .. 101 .. 102 .. .. .. .. .. .. .. Jordan 72 97 69 97 77 96 98 99 95 99 95 85 Kazakhstan .. 114 .. 115 .. 113 100 100 100 100 100 99 Kenya .. 95 .. 96 .. 94 93 80 87 81 78 70 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 98 104 98 104 98 104 .. .. .. .. .. .. Kuwait .. 100 .. 104 .. 97 88 100 87 100 94 91 Kyrgyz Republic .. 97 .. 97 .. 98 .. 100 .. 100 99 98 Lao PDR 43 76 48 80 38 72 79 83 61 75 77 61 Latvia .. 92 .. 93 .. 92 .. 100 .. 100 100 100 Lebanon .. 90 .. 88 .. 91 95 .. 89 .. .. .. Lesotho 59 67 42 55 76 79 77 .. 97 .. 74 90 Liberia .. .. .. .. .. .. 75 .. 39 .. .. .. Libya .. .. .. .. .. .. 99 .. 83 .. .. .. Lithuania 89 98 .. 99 .. 97 100 100 100 100 100 100 Macedonia, FYR 98 96 .. 96 .. 97 .. 99 .. 98 98 94 Madagascar 33 58 33 58 34 58 78 73 67 68 77 65 Malawi 28 61 36 62 21 59 76 82 51 71 75 54 Malaysia 91 94 91 91 91 91 95 97 94 97 92 85 Mali 11 38 13 45 9 31 .. 32 .. 17 27 12 Mauritania 33 45 40 46 26 43 56 68 36 55 60 43 Mauritius 107 97 107 97 107 98 91 94 91 95 88 81 Mexico 86 99 89 98 90 100 96 98 94 98 92 90 Moldova .. 92 .. 93 .. 91 100 99 100 100 99 98 Mongolia .. 97 .. 94 .. 99 .. 97 .. 98 98 98 Morocco 47 80 55 84 38 77 68 81 42 60 66 40 Mozambique 27 42 33 49 22 35 66 .. 32 .. .. .. Myanmar .. 79 .. 78 .. 80 90 96 86 93 94 86 Namibia 78 75 70 71 86 80 86 91 89 93 87 83 Nepal 51 76d 68 80 d 40 72d 67 81 27 60 63 35 Netherlands .. 100 .. 101 .. 99 .. .. .. .. .. .. New Zealand 100 .. 101 .. 99 .. .. .. .. .. .. .. Nicaragua 44 76 43 73 59 80 68 84 69 89 77 77 Niger 17 28 21 34 12 22 25 52 9 23 43 15 Nigeria .. 82 .. 89 .. 74 81 .. 66 .. .. .. Norway 100 101 100 101 100 101 .. .. .. .. .. .. Oman 74 93 78 94 70 93 95 98 75 97 87 74 Pakistan .. 63 .. 73 .. 52 63 76 31 55 63 36 Panama 86 97 86 97 86 97 96 97 95 96 93 91 Papua New Guinea 47 54 52 58 42 50 74 69 62 64 63 51 Paraguay 71 91 70 90 71 91 96 .. 95 .. .. .. Peru .. 100 .. 100 .. 99 97 98 92 96 93 82 Philippines 86 97 84 93 84 100 97 94 97 96 93 93 Poland 98 100 .. .. .. .. .. .. .. .. .. .. Portugal 95 104 94 102 95 107 .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 87 2.12 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Totala Malea Femalea Male Female Male Female 1991 2005b 1991 2005b 1991 2005b 1990 2006c 1990 2006c 2006c 2006c Romania 96 93 96 94 96 93 99 98 99 98 98 96 Russian Federation 93 94 92 .. 93 .. 100 100 100 100 100 99 Rwanda 33 39 36 40 30 38 78 79 67 77 71 60 Saudi Arabia 56 85 60 85 51 86 91 98 79 94 87 69 Senegal 39 52 47 56 30 49 50 58 30 41 51 29 Serbia and Montenegro 71 .. .. .. .. .. .. 99e .. 99e 99e 94 e Sierra Leone .. .. .. .. .. .. .. 59 .. 37 47 24 Singapore .. .. .. .. .. .. 99 99 99 100 97 89 Slovak Republic 96 99 95 99 96 100 .. 100 .. 100 100 100 Slovenia 95 102 .. 103 .. 102 100 .. 100 .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 75 99 71 99 80 99 89 93 88 94 84 81 Spain .. 109 .. 110 .. 109 .. .. .. .. .. .. Sri Lanka 97 .. 98 .. 96 .. 96 95 94 96 92 89 Sudan 41 50 46 53 37 46 76f 85f 54f 71f 71f 52f Swaziland 60 64 57 62 63 66 85 87 85 90 81 78 Sweden 96 .. 96 .. 96 .. .. .. .. .. .. .. Switzerland 53 97 53 96 54 98 .. .. .. .. .. .. Syrian Arab Republic 89 111 94 112 84 109 92 94 67 90 86 74 Tajikistan .. 102 .. 104 .. 100 100 100 100 100 100 99 Tanzania 61 54 60 55 62 53 89 81 77 76 78 62 Thailand .. 82 .. 83 .. 81 .. 98 .. 98 95 91 Togo 35 65 48 76 22 54 79 84 48 64 69 38 Trinidad and Tobago 100 99 97 97 102 100 100 .. 100 .. .. .. Tunisia 74 97 79 97 69 98 93 96 75 92 83 65 Turkey 90 88 93 93 86 82 97 98 88 93 95 80 Turkmenistan .. .. .. .. .. .. .. 100 .. 100 99 98 Uganda .. 57 .. 61 .. 53 80 83 60 71 77 58 Ukraine 94 114 98 .. 97 .. 100 100 100 100 100 99 United Arab Emirates 103 76 104 78 103 75 82 .. 89 .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 94 91 91 89 96 93 98 .. 99 .. .. .. Uzbekistan .. 97 .. 97 .. 96 100 .. 100 .. .. .. Venezuela, RB 43 92 37 89 49 95 95 96 97 98 93 93 Vietnam .. 94 .. 104 .. 98 94 94 94 94 94 87 West Bank and Gaza .. 98 .. 98 .. 98 .. 99 .. 99 97 88 Yemen, Rep. .. 62 .. 78 .. 46 74 .. 25 .. .. .. Zambia .. 78 .. 89 .. 66 86 73 76 66 76 60 Zimbabwe 99 80 100 82 97 79 97 .. 91 .. .. .. World .. w 85 w .. w 87 w .. w 83 w .. w 90 w .. w 84 w 87 w 77 w Low income 60 74 70 79 49 69 73 80 55 67 71 50 Middle income 93 96 96 96 90 95 95 97 91 95 93 87 Lower middle income 94 97 99 97 91 96 95 97 90 95 93 85 Upper middle income 87 95 86 95 87 95 97 98 95 98 96 93 Low & middle income 79 84 85 86 73 81 86 89 77 82 85 72 East Asia & Pacific 100 98 105 98 96 98 97 98 93 97 95 87 Europe & Central Asia 91 92 93 93 92 91 99 99 97 99 99 96 Latin America & Carib. 83 98 82 98 84 99 93 96 93 96 91 89 Middle East & N. Africa 77 89 83 92 71 86 80 89 59 77 81 61 South Asia 76 82 86 86 65 77 70 80 50 63 70 45 Sub-Saharan Africa 50 58 55 63 46 53 76 78 61 68 70 53 High income .. 97 .. 98 .. 97 .. 99 .. 99 99 98 Europe EMU .. .. .. .. .. .. .. .. .. .. .. .. a. Because of the change from International Standard Classification of Education (ISCED) 76 to ISCED 97 in 1998, data before 1998 are not fully comparable with data from 1998 onward. b. Provisional data. c. Actual reference year varies by country. For more information, see the original source. d. Data are for 2006. e. Data exclude Kosovo and Metohia. f. Data are for North Sudan only. 88 2007 World Development Indicators 2.12 PEOPLE Education completion and outcomes About the data Many governments collect and publish statistics that There are many reasons why the primary comple- The reported literacy data are compiled by the indicate how their education systems are working and tion rate can exceed 100 percent. The numerator UNESCO Institute for Statistics based on national developing--statistics on enrollment and on such may include late entrants and overage children who censuses and household surveys during 1995­2005. efficiency indicators as repetition rates, pupil-teacher have repeated one or more grades of primary school The data for 1991 are based on model estimations. ratios, and cohort progression through school. The but are now completing primary school as well as Therefore the data for 1991 and later years may not World Bank and the United Nations Educational, Sci- children who entered school early, while the denomi- be comparable. The estimation methodology can be entific, and Cultural Organization (UNESCO) Institute nator is the number of children of official completing reviewed at www.uis.unesco.org. for Statistics worked jointly to develop the primary age. There are other data limitations that contribute Literacy statistics for most countries cover the pop- completion rate indicator. Increasingly used as a core to completion rates exceeding 100 percent, such as ulation ages 15 and older, by five-year age groups, indicator of an education system's performance, it the use of estimates for the population with varying but some include younger ages or are confined to age reflects both the coverage of the education system reliability for some countries, the conduct of school ranges that tend to inflate literacy rates. The UNESCO and the educational attainment of students. The and population surveys at different times of year, Institute for Statistics has reported the narrower age indicator is vital as a key measure of educational and other discrepancies in the numbers used in the range of 15­24, which is better in capturing the abil- outcome at the primary level and of progress on the calculation. ity of participants in the formal education system Millennium Development Goals and the Education for Basic student outcomes include achievements and in reflecting recent progress in education. The All initiative. However, because curricula and stan- in reading and mathematics judged against estab- youth literacy rate reported in the table measures dards for school completion vary across countries, a lished standards. In many countries national learning the accumulated outcomes of primary education over high rate of primary completion does not necessarily assessments are enabling ministries of education to the previous 10 years or so by indicating the propor- mean high levels of student learning. monitor progress in these outcomes. Internationally, tion of people who have passed through the primary The primary completion rate reflects the primary the UNESCO Institute for Statistics has established education system and acquired basic literacy and cycle as defined by the International Standard Clas- literacy as an outcome indicator based on an inter- numeracy skills. sification of Education, ranging from three or four nationally agreed definition. Definitions years of primary education (in a very small number The literacy rate is defined as the percentage of of countries) to five or six years (in most countries) people who can, with understanding, both read and · Primary completion rate is the percentage of stu- and seven (in a small number of countries). write a short, simple statement about their every- dents completing the last year of primary school. It is The data in the table are for the proxy primary day life. In practice, literacy is difficult to measure. calculated by taking the total number of students in completion rate, calculated by taking the total num- To estimate literacy using such a definition requires the last grade of primary school, minus the number ber of students in the last grade of primary school, census or survey measurements under controlled of repeaters in that grade, divided by the total num- minus the number of repeaters in that grade, divided conditions. Many countries estimate the number of ber of children of official completing age. · Youth by the total number of children of official graduation literate people from self-reported data. Some use literacy rate is the percentage of people ages 15­24 age. Data limitations preclude adjusting this number educational attainment data as a proxy but apply that can, with understanding, both read and write a for students who drop out during the final year of different lengths of school attendance or levels of short, simple statement about their everyday life. primary school. Thus proxy rates should be taken completion. Because definition and methodologies · Adult literacy rate is the literacy rate among peo- as an upper-bound estimate of the actual primary of data collection differ across countries, data need ple ages 15 and older. completion rate. to be used with caution. Children from poorer families are less likely to complete their schooling 2.12a Share of children who have attained each grade, by wealth quintile, Zimbabwe, 1999 (%) 100 Richest 20% 80 60 Fourth 20% 40 Data sources Third 20% Second 20% Data on the primary completion rate for 1991 and 20 2005 are primarily from the UNESCO Institute for Poorest 20% 0 Statistics. The data for the latest years are provi- 1 2 3 4 5 6 7 8 9 >9 sional, as of January 2007. Data on literacy rates Source: Demographic and Health Survey. are from the UNESCO Institute for Statistics. 2007 World Development Indicators 89 2.13 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 children age group age group ages 15­24 % of relevant 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 About the data The data in the table describe basic information on section of the survey is not as robust and detailed but do have detailed information on households' own- school participation and attainment by individuals as the health section; however, it still provides useful ership of consumer goods and access to a variety in different socioeconomic groups within countries. micro-level information on education that cannot be of goods and services. Like income or consumption, The data are from Demographic and Health Surveys explained by aggregate national level data. the asset index defines disparities in primarily eco- conducted by Macro International with the support The table defines socioeconomic status in terms nomic terms. It therefore excludes other possibilities of the U.S. Agency for International Development. of a household's assets, including ownership of con- of disparities among groups, such as those based These large-scale household sample surveys, con- sumer items, features of the household's dwelling, on gender, education, ethnic background, or other ducted periodically in developing countries, collect and other characteristics related to wealth. Each facets of social exclusion. To that extent the index information on a large number of health, nutrition, household asset on which information was collected provides only a partial view of the multidimensional and population measures as well as on respondents' was assigned a weight generated through principal- concepts of poverty, inequality, and inequity. social, demographic, and economic characteristics component analysis. The resulting scores were stan- Creating one index that includes all asset indica- using a standard set of questionnaires. The data dardized in relation to a standard normal distribution tors limits the types of analysis that can be per- presented here draw on responses to individual and with a mean of zero and a standard deviation of one. formed. In particular, the use of a unified index does household questionnaires. The standardized scores were then used to create not permit a disaggregated analysis to examine Typically, Demographic and Health Surveys collect break-points defining wealth quintiles, expressed as which asset indicators have a more or less impor- basic information on educational attainment and quintiles of individuals in the population. tant association with health status or use of health enrollment levels from every household member The choice of the asset index for defining socio- services. In addition, some asset indicators may ages 5 or 6 and older as background characteris- economic status was based on pragmatic rather than reflect household wealth better in some countries tics. As the surveys are intended for the collection of conceptual considerations: Demographic and Health than in others--or reflect different degrees of wealth demographic and health information, the education Surveys do not provide income or consumption data in different countries. Taking such information into 90 2007 World Development Indicators 2.13 PEOPLE 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 children age group age group ages 15­24 % of relevant 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 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. Definitions account and creating country-specific asset indexes · Survey year is the year in which the underlying not in school. These data differ from those in table with country-specifi c choices of asset indicators data were collected. · Gross intake rate in grade 2.10 because the definition and methodology are might produce a more effective and accurate index 1 is the number of students in the first grade of different. for each country. The asset index used in the table primary education, regardless of age, expressed does not have this flexibility. as a percentage of the population of the official pri- The analysis was carried out for 48 countries. The mary school entrance age. These data may differ table shows the estimates for the poorest and rich- from those in table 2.11. · Gross primary partici- est quintiles only; the full set of estimates for 32 pation rate is the ratio of total students attending indicators is available in the country reports (see primary school, regardless of age, to the population Data sources). The data in this table will differ from of the age group that officially corresponds to pri- data for similar indicators in preceding tables either mary education. · Average years of schooling are because the indicator refers to a period a few years the years of formal schooling received, on average, preceding the survey date or because the indica- by adults ages 15­24. · Primary completion rate Data sources tor definition or methodology is different. Findings is the percentage of children of the official primary should be interpreted with caution because of mea- school completing age to the official primary school Data on education gaps by income and gender are surement error inherent in the use of survey data. completing age plus four, who have completed the from an analysis of Demographic and Health Sur- last year of primary school or higher. These data veys by Macro International and the World Bank. differ from those in table 2.12 as the definition and Country reports are available at http://devdata. methodology are different. · Children out of school worldbank.org/edstats/td16.asp. are the percentage of children ages 6­11 who are 2007 World Development Indicators 91 2.14 Health expenditure, services, and use Health Physicians Health Hospital beds expenditure worker density index Physicians, Out of nurses, and pocket External midwives Total Public % of resourcesa Per capita per 1,000 % of GDP % of GDP % of total private % of total $ per 1,000 people people per 1,000 people 2004 2004 2004 2004 2004 2004 1990 2000­05b 2000­03b 1990 2000­05b Afghanistan 4.4 0.7 16.9 97.7 6.1 14 0.1 0.2 0.4 0.2 0.4 Albania 6.7 3.0 44.1 99.8 2.4 157 1.4 1.3 5.4 4.0 3.1 Algeria 3.6 2.6 72.5 94.6 0.0 94 0.9 1.1 .. 2.5 .. Angola 1.9 1.5 79.4 100.0 9.1 26 0.0 c 0.1 .. 1.3 .. Argentina 9.6 4.3 45.3 48.7 0.2 383 2.7 .. .. 4.6 4.1 Armenia 5.4 1.4 26.2 89.2 7.2 63 3.9 3.6 8.8 9.1 4.4 Australia 9.6 6.5 67.5 61.6 0.0 3,123 2.2 2.5 10.8 9.2 7.4 Austria 10.3 7.8 75.6 67.9 0.0 3,683 2.2 3.4 9.3 10.2 8.3 Azerbaijan 3.6 0.9 25.0 93.6 1.6 37 3.9 3.5 12.0 10.1 8.3 Bangladesh 3.1 0.9 28.1 88.3 15.1 14 0.2 0.3 0.5 0.3 .. Belarus 6.2 4.6 74.9 72.7 0.1 147 3.6 4.6 17.5 13.2 11.3 Belgium 9.7 6.9 71.1 83.5 0.0 3,363 3.3 3.9 15.6 8.0 6.9 Benin 4.9 2.5 51.2 99.9 10.2 24 0.1 0.0 c .. 0.8 .. Bolivia 6.8 4.1 60.7 82.5 9.1 66 0.4 1.2 1.1 1.3 1.0 Bosnia and Herzegovina 8.3 4.1 49.4 100.0 1.3 198 1.6 1.3 5.7 4.5 3.1 Botswana 6.4 4.0 62.9 27.9 2.5 329 0.2 0.4 .. 1.6 .. Brazil 8.8 4.8 54.1 64.2 0.0 290 1.4 2.1 2.6 3.3 2.7 Bulgaria 8.0 4.6 57.6 98.0 1.0 251 3.2 3.6 8.3 9.8 6.3 Burkina Faso 6.1 3.3 54.8 97.9 26.8 24 0.0 c 0.1 0.3 0.3 .. Burundi 3.2 0.8 26.2 100.0 17.6 3 0.1 0.0 c 0.3 0.7 .. Cambodia 6.7 1.7 25.8 85.4 28.5 24 0.1 0.2 1.0 2.1 0.6 Cameroon 5.2 1.5 28.0 94.5 5.3 51 0.1 0.2 .. 2.6 .. Canada 9.8 6.8 69.8 49.4 0.0 3,038 2.1 2.1 12.2 6.0 3.7 Central African Republic 4.1 1.5 36.8 95.4 47.7 13 0.0 c 0.1 .. 0.9 .. Chad 4.2 1.5 36.9 95.8 7.0 20 0.0 c 0.0 c 0.2 0.7 .. Chile 6.1 2.9 47.0 45.9 0.1 359 1.1 1.1 1.7 3.2 2.6 China 4.7 1.8 38.0 86.5 0.1 71 1.5 1.5 2.7 2.6 2.5 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 7.8 6.7 86.0 49.0 0.1 168 1.1 1.4 1.9 1.4 1.1 Congo, Dem. Rep. 4.0 1.1 28.1 100.0 19.1 5 0.1 0.1 .. 1.4 .. Congo, Rep. 2.5 1.2 49.2 100.0 3.6 28 0.3 0.2 .. 3.3 .. Costa Rica 6.6 5.1 77.0 88.7 0.8 290 1.3 1.3 2.4 2.5 1.4 Côte d'Ivoire 3.8 0.9 23.2d 88.7 5.0 33 0.1 0.1 .. 0.8 .. Croatia 8.0 d 6.1d 81.0 93.8 0.4 609 2.1 2.4 7.7 7.4 5.5 Cuba 6.3 5.5 87.8 74.5 0.3 230 3.6 5.9 13.4 5.4 4.9 Czech Republic 7.3 6.5 89.2 95.5 0.0 771 2.7 3.5 13.4 11.3 8.8 Denmark 8.6 7.1 82.3 81.3 0.0 3,897 2.5 2.9 13.6 5.6 4.0 Dominican Republic 6.0 1.9 31.6 73.1 1.5 148 1.5 1.9 3.7 1.9 2.1 Ecuador 5.5 2.2 40.7 85.4 0.8 127 1.5 1.5 3.1 1.6 1.5 Egypt, Arab Rep. 5.9 2.2 37.0 99.0 0.8 64 0.8 0.5 4.9 2.1 2.2 El Salvador 7.9 3.5 44.4 94.2 1.2 184 0.8 1.2 2.0 1.5 .. Eritrea 4.5 1.8 39.2 100.0 59.6 10 .. 0.1 .. .. .. Estonia 5.3 4.0 76.0 88.8 0.5 463 3.5 3.2 9.8 11.6 6.0 Ethiopia 5.3 2.7 51.5 78.3 35.2 6 0.0 c 0.0 c 0.2 0.2 .. Finland 7.4 5.7 77.2 80.8 0.0 2,664 2.0 2.6 25.6 12.5 7.2 France 10.5 8.2 78.4 34.9 0.0 3,464 3.1 3.4 10.2 9.7 7.7 Gabon 4.5 3.1 68.8 100.0 1.3 231 0.5 0.3 .. 3.2 .. Gambia, The 6.8 1.8 27.1 68.2 23.0 19 .. 0.1 .. 0.6 .. Georgia 5.3 1.5 27.4 87.2 9.8 60 4.9 4.1 7.9 9.8 4.2 Germany 10.6 8.2 76.9 57.5 0.0 3,521 2.8 3.4 13.2 10.4 8.9 Ghana 6.7 2.8 42.2 78.2 29.9 27 0.0 c 0.2 0.9 1.5 .. Greece 7.9 4.2 52.8 95.7 .. 1,879 3.4 4.4 7.5 5.1 4.7 Guatemala 5.7 2.3 41.0 90.5 2.3 127 0.8 .. .. 1.1 0.5 Guinea 5.3 0.7 13.2 99.5 9.5 22 0.1 0.1 0.6 0.6 .. Guinea-Bissau 4.8 1.3 27.3 90.0 31.6 9 .. 0.1 .. 1.5 .. Haiti 7.6 2.9 38.5 69.6 14.2 33 0.1 .. .. 0.8 0.8 92 2007 World Development Indicators 2.14 PEOPLE Health expenditure, services, and use Health Physicians Health Hospital beds expenditure worker density index Physicians, Out of nurses, and pocket External midwives Total Public % of resourcesa Per capita per 1,000 % of GDP % of GDP % of total private % of total $ per 1,000 people people per 1,000 people 2004 2004 2004 2004 2004 2004 1990 2000­05b 2000­03b 1990 2000­05b Honduras 7.2 4.0 54.9 84.3 8.7 77 0.7 0.6 .. 1.0 1.0 Hungary 7.9 5.7 71.6 88.0 0.4 800 2.8 3.2 11.9 .. 7.8 India 5.0 0.9 17.3 93.8 0.5 31 0.5 0.6 .. 0.8 0.9 Indonesia 2.8 1.0 34.2 74.7 1.3 33 0.1 0.1 0.7 0.7 .. Iran, Islamic Rep. 6.6 3.2 47.8 94.8 0.2 158 0.3 0.4 .. 1.4 1.6 Iraq 5.3e 4.2e 78.5e 100.0 e 2.5e 58e 0.6 0.7 3.6 1.7 1.3 Ireland 7.2 5.7 79.5 65.9 0.0 3,234 2.0 2.8 19.0 6.1 4.3 Israel 8.7 6.1 70.0 75.0 3.2 1,534 3.2 3.8 10.3 6.2 6.1 Italy 8.7 6.5 75.1 84.4 0.0 2,580 .. 4.2 10.5 7.2 4.4 Jamaica 5.2 2.8 54.3 63.6 1.4 176 0.6 0.8 2.5 2.2 1.4 Japan 7.8 6.3 81.0 93.4 0.0 2,831 1.7 2.0 10.4 .. 14.3 Jordan 9.8 f 4.7f 48.4f 73.8 7.1f 200 f 1.3 2.0 4.8 1.8 1.7 Kazakhstan 3.8 2.3 59.8 100.0 0.9 109 4.0 3.5 9.5 13.7 7.7 Kenya 4.1 1.8 42.7 81.9 18.3 20 0.0 c 0.1 .. 1.6 .. Korea, Dem. Rep. 3.5 3.0 85.6 100.0 53.6 0g .. 3.3 .. .. .. Korea, Rep. 5.6 2.9 51.4 76.0 0.0 787 0.8 1.6 5.4 3.1 7.1 Kuwait 2.8 2.2 77.6 90.4 0.0 633 0.2 1.5 5.4 3 2.2 Kyrgyz Republic 5.6 2.3 40.9 94.3 15.1 24 3.4 2.5 10.1 12.0 5.3 Lao PDR 3.9 0.8 20.5 90.3 10.2 17 0.2 .. .. 2.6 1.2 Latvia 7.1 4.0 56.6 98.3 0.3 418 4.1 3.0 8.2 14.1 7.8 Lebanon 11.6 3.2 27.4 82.2 1.7 670 1.3 3.3 4.4 1.7 3.0 Lesotho 6.5 5.5 84.2 18.2 8.7 49 0.0 c 0.0 c .. .. .. Liberia 5.6 3.6 63.9 98.5 37.8 9 .. 0.0 c .. .. .. Libya 3.8 2.8 74.9 100.0 0.0 195 1.1 .. .. 4.2 3.9 Lithuania 6.5 4.9 75.0 96.8 3.1 424 4.0 4.0 12.4 12.5 8.7 Macedonia, FYR 8.0 5.7 71.0 100.0 1.4 212 2.2 2.2 8.1 5.9 4.8 Madagascar 3.0 1.8 59.1 52.5 45.5 7 0.1 0.3 0.4 0.9 0.4 Malawi 12.9 9.6 74.7 35.2 59.4 19 0.0 c 0.0 c 0.3 1.6 .. Malaysia 3.8 2.2 58.8 74.1 0.1 180 0.4 0.7 2.4 2.1 1.9 Mali 6.6 3.2 49.2 99.5 13.8 24 0.1 0.1 0.2 .. .. Mauritania 2.9 2.0 69.4 100.0 20.2 15 0.1 0.1 .. 0.7 .. Mauritius 4.3 2.4 54.7 80.8 1.4 222 0.8 1.1 .. 2.9 .. Mexico 6.5 3.0 46.4 94.4 0.3 424 1.0 1.5 3.9 1.0 1.0 Moldova 7.4 4.2 56.8 96.0 4.8 46 3.6 2.6 9.2 13.1 6.7 Mongolia 6.0 4.0 66.6 92.3 4.6 37 2.5 2.6 6.0 11.5 .. Morocco 5.1 1.7 34.3 76.0 0.9 82 0.2 0.5 1.5 1.3 0.8 Mozambique 4.0 2.7 68.4 38.5 55.9 12 0.0 c 0.0 c 0.3 0.9 .. Myanmar 2.2 0.3 12.9 99.4 13.1 5 0.1 0.4 0.8 0.6 0.6 Namibia 6.8 4.7 69.0 18.1 16.9 190 0.2 0.3 .. .. .. Nepal 5.6 1.5 26.3 88.1 17.6 14 0.1 0.2 0.3 0.2 .. Netherlands 9.2 5.7 62.4 20.6 0.0 3,442 2.5 3.1 16.7 5.8 4.7 New Zealand 8.4 6.5 77.4 76.1 0.0 2,040 1.9 2.2 10.9 8.5 6.1 Nicaragua 8.2 3.9 47.1 95.9 11.3 67 0.7 0.4 1.8 1.8 0.9 Niger 4.2 2.2 52.5 85.1 21.3 9 0.0 c 0.0 c 0.3 .. .. Nigeria 4.6 1.4 30.4 90.4 5.6 23 0.2 0.3 1.5 1.7 .. Norway 9.7 8.1 83.5 95.2 0.0 5,405 2.6 3.1 24.9 4.6 3.8 Oman 3.0 2.4 81.4 57.1 0.0 295 0.6 1.3 4.2 2.1 2.0 Pakistan 2.2 0.4 19.6 98.0 2.5 14 0.5 0.7 1.1 0.6 0.7 Panama 7.7 5.2 66.9 82.5 0.2 343 1.6 1.5 3.2 2.5 2.5 Papua New Guinea 3.6 3.0 84.3 46.4 26.5 30 0.1 0.1 0.6 4.0 .. Paraguay 7.7 2.6 33.7 72.2 1.9 88 0.6 1.1 1.4 0.9 1.2 Peru 4.1 1.9 46.9 79.2 1.3 104 1.1 .. .. 1.4 1.4 Philippines 3.4 1.4 39.8 77.9 3.6 36 0.1 1.2 7.4 1.4 1.0 Poland 6.2 4.3 68.6 89.6 0.1 411 2.1 2.5 7.7 5.7 5.6 Portugal 9.8 7.0 71.6 79.4 0.0 1,665 2.8 3.3 7.0 4.1 3.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 93 2.14 Health expenditure, services, and use Health Physicians Health Hospital beds expenditure worker density index Physicians, Out of nurses, and pocket External midwives Total Public % of resourcesa Per capita per 1,000 % of GDP % of GDP % of total private % of total $ per 1,000 people people per 1,000 people 2004 2004 2004 2004 2004 2004 1990 2000­05b 2000­03b 1990 2000­05b Romania 5.1 3.4 66.1 93.4 25.0 178 1.8 1.9 6.2 8.9 6.6 Russian Federation 6.0 3.7 61.3 76.7 0.1 245 4.1 4.3 12.5 13.1 10.5 Rwanda 7.5 4.3 56.8 36.9 37.1 16 0.0 c 0.0 c 0.2 1.7 .. Saudi Arabia 3.3 2.5 75.4 31.0 .. 348 1.4 1.4 4.4 2.5 2.2 Senegal 5.9 2.4 40.3 94.5 12.8 39 0.1 0.1 .. 0.7 .. Serbia and Montenegro 10.1h 7.3h 72.1h 88.2h 0.5h 219h 2.0 2.1 .. 5.9 6.0 Sierra Leone 3.3 1.9 59.0 100.0 35.4 7 .. 0.0 c .. .. .. Singapore 3.7 1.3 34.0 96.9 0.0 943 1.3 1.4 5.6 3.6 2.9 Slovak Republic 7.2 5.3 73.8 73.1 0.0 565 .. 3.1 10.6 7.4 7.2 Slovenia 8.7 6.6 75.6 39.5 0.1 1,438 2.0 2.3 9.4 6.0 5.0 Somalia .. .. .. .. .. .. .. .. .. 0.8 .. South Africa 8.6 3.5 40.4 17.2 0.5 390 0.6 0.8 4.6 .. .. Spain 8.1 5.7 70.9 81.0 0.0 1,971 .. 3.2 6.8 4.6 3.8 Sri Lanka 4.3 2.0 45.6 84.0 1.2 43 0.1 0.5 1.2 2.7 3.1 Sudan 4.1 1.5 35.4 98.1 5.1 25 .. 0.2 1.0 1.1 0.7 Swaziland 6.3 4.0 63.8 40.2 9.5 146 0.1 0.2 3.4 .. .. Sweden 9.1 7.7 84.9 92.0 0.0 3,532 2.9 3.3 13.5 12.4 3.6 Switzerland 11.5 6.7 58.5 76.7 0.0 5,572 3.0 3.6 12.1 19.9 6.0 Syrian Arab Republic 4.7 2.2 47.4 100.0 0.2 58 0.8 1.4 3.3 1.1 1.5 Tajikistan 4.4 1.0 21.6 97.3 9.1 14 2.6 2.0 7.2 10.7 6.1 Tanzania 4.0 1.7 43.6 83.2 27.1 12 .. 0.0 c 0.4 1.0 .. Thailand 3.5 2.3 64.7 74.7 0.3 88 0.2 0.4 .. 1.6 .. Togo 5.5 1.1 20.7 84.9 8.9 18 0.1 0.0 c 0.3 1.5 .. Trinidad and Tobago 3.5 1.4 38.9 88.5 0.2 329 0.7 .. .. 4.0 3.4 Tunisia 5.6 2.8 50.0 .. .. 126 0.5 1.3 .. 1.9 1.7 Turkey 7.7 5.2e 72.3 69.1 0.0 325 0.9 1.3 4.2 2.4 2.6 Turkmenistan 4.8 3.3 68.9 100.0 0.4 124 3.6 4.2 .. 11.5 .. Uganda 7.6 2.5 32.7 51.3 25.2 19 0.0 c 0.1 0.1 0.9 .. Ukraine 6.5 3.7 56.7 90.5 0.7 90 4.3 3.0 11.2 13.0 8.8 United Arab Emirates 2.9 2.0 69.9 71.0 0.0 711 0.8 2.0 6.2 2.6 2.2 United Kingdom 8.1 7.0 86.3 91.8 0.0 2,900 1.6 2.2 .. 5.9 4.2 United States 15.4 6.9 44.7 23.8 0.0 6,096 1.8 2.3 13.2 4.9 3.3 Uruguay 8.2 3.6 43.5 31.1 0.3 315 3.7 3.7 4.5 4.5 1.9 Uzbekistan 5.1 2.4 46.6 96.2 3.9 23 3.4 2.7 13.7 12.5 5.5 Venezuela, RB 4.7 2.0 42.0 88.3 0.0 196 1.6 1.9 2.6 2.7 0.8 Vietnam 5.5 1.5 27.1 88.0 2.0 30 0.4 0.5 1.3 3.8 2.4 West Bank and Gaza 13.0 7.8 60.0 100.0 42.0 .. .. 0.8 .. .. .. Yemen, Rep. 5.0 1.9 38.3 95.5 15.0 34 0.0 c 0.3 0.7 0.8 0.6 Zambia 6.3 3.4 54.7 71.4 36.3 30 0.1 0.1 .. .. .. Zimbabwe 7.5 3.5 46.1 48.7 13.1 27 0.1 0.2 0.6 0.5 .. World 10.1 w 5.9 w 59.1 w 44.4 w 0.1 w 649 w 1.4 w .. w .. w 3.7 w .. w Low income 4.7 1.1 23.8 94.0 5.4 24 0.5 0.5 .. .. .. Middle income 5.9 3.1 52.6 77.0 0.6 141 1.6 1.5 .. 3.6 .. Lower middle income 5.4 2.6 47.7 81.0 0.5 92 1.4 1.3 .. 2.8 .. Upper middle income 6.6 3.8 57.8 71.7 0.7 342 2.3 2.7 7.5 6.7 5.7 Low & middle income 5.8 2.8 49.3 80.8 1.2 90 1.3 .. .. 3.1 .. East Asia & Pacific 4.4 1.7 39.8 87.6 0.5 62 1.2 1.5 3.0 2.3 2.5 Europe & Central Asia 6.6 4.5 67.8 82.1 1.1 250 3.2 3.1 10.3 10.2 7.6 Latin America & Carib. 7.3 3.7 51.9 74.1 0.4 272 1.4 .. .. 2.5 .. Middle East & N. Africa 5.6 2.7 48.9 89.7 1.3 103 .. .. .. 1.8 .. South Asia 4.6 0.9 18.8 93.6 1.5 27 0.5 0.6 .. 0.7 0.9 Sub-Saharan Africa 6.3 2.6 41.8 44.8 6.8 45 .. .. .. 1.2 .. High income 11.2 6.7 60.4 37.8 0.0 3,727 1.9 2.6 .. 6.2 6.4 Europe EMU 9.6 7.2 74.7 59.9 0.0 2,969 2.9 3.5 12.2 8.1 6.6 a. 0 for category not applicable or less than 0.05. b. Data are for the most recent year available. c. Less than 0.05. d. Data are for 2005. e. Excludes northern Iraq. f. Includes contributions from the United Nations Relief and Works Agency for Palestine Refugees in the Near East to Palestinian refugees. g. Less than 0.5. h. Excludes Kosovo and Metahia. 94 2007 World Development Indicators 2.14 PEOPLE Health expenditure, services, and use About the data Definitions National health accounts track financial flows in the Organization (WHO), the Organisation for Economic · Total health expenditure is the sum of public and health sector, including public and private expendi- Co-operation and Development (OECD), and the private health expenditure. It covers the provision of tures, by source of funding. In contrast with high- World Bank to collect all available information on health services (preventive and curative), family plan- income countries, few developing countries have health expenditures from national and local govern- ning activities, nutrition activities, and emergency aid designated for health but does not include provision health accounts that are methodologically consistent ment budgets, national accounts, household sur- of water and sanitation. · Public health expendi- with national accounting approaches. The difficulties veys, insurance publications, international donors, ture consists of recurrent and capital spending from in creating national health accounts go beyond data and existing tabulations. government (central and local) budgets, external collection. To establish a national health accounting Indicators on health services (physicians, health borrowings and grants (including donations from system, a country needs to define the boundaries of worker density, and hospital beds per 1,000 people) international agencies and nongovernmental organi- the health care system and to define a taxonomy of come from a variety of sources (see Data sources). zations), and social (or compulsory) health insurance health care delivery institutions. The accounting sys- Data are lacking for many countries, and for oth- funds. · Out of pocket health expenditure is any tem should be comprehensive and standardized, pro- ers comparability is limited by differences in defini- direct outlay by households, including gratuities and viding not only accurate measures of financial flows tions. In estimates of health personnel, for example, in-kind payments, to health practitioners and suppli- but also information on the equity and efficiency of some countries incorrectly include retired physicians ers of pharmaceuticals, therapeutic appliances, and health financing to inform health policy. (because deletions to physician rosters are made other goods and services whose primary intent is to The absence of consistent national health account- only periodically) or physicians working outside the contribute to the restoration or enhancement of the ing systems in most developing countries makes health sector. There is no universally accepted defini- health status of individuals or population groups. It is a part of private health expenditure. · External cross-country comparisons of health spending dif- tion of hospital beds. Moreover, figures on physicians resources for health are funds or services in kind ficult. Compiling estimates of public health expen- and hospital beds are indicators of availability, not that are provided by entities not part of the country ditures is complicated in countries where state or of quality or use. They do not show how well trained in question. The resources may come from interna- provincial and local governments are involved in the physicians are or how well equipped the hospitals tional organizations, other countries through bilat- financing and delivering health care, because the or medical centers are. And physicians and hospital eral arrangements, or foreign nongovernmental orga- data on public spending often are not aggregated. beds tend to be concentrated in urban areas, so nizations. These resources are part of total health There are a number of potential data sources related these indicators give only a partial view of health expenditure. · Health expenditure per capita is total to external resources for health, including govern- services available to the entire population. health expenditure divided by number of people in the ment expenditure accounts, government records The WHO receives data on health professionals country. · Physicians are graduates of any faculty or on external assistance, routine surveys of external from ministries of health through its six regional school of medicine who are working in the country in financing assistance, and special surveys. Survey offices, often in cooperation with national statisti- any medical field (practice, teaching, or research). data are the major source of information about out cal offi ces. The data are scrutinized using such · Health worker density index reflects a combined density of physicians, nurses, and midwives per of pocket expenditure on health. The data in the additional resources as national and international 1,000 people. · Hospital beds include inpatient beds table are the product of an effort by the World Health employment surveys, records from professional available in public, private, general, and specialized associations, and other publications. Signifi cant hospitals and rehabilitation centers. In most cases inconsistencies are returned to national authorities Differences in healthcare beds for both acute and chronic care are included. expenditures contribute to global for validation and resubmission. disparities in health outcomes 2.14a The health worker density index indicates the over- Data sources all level of health workers (physicians, nurses, and Health expenditure as a Total Public Data on health expenditure come mostly from the share of GDP, 2004 (%) spending spending midwives) in the country. Dentists and pharmacists WHO's National Health Account database (www. 12 are not included. Comparability of the index across who.int/nha/en) and from the OECD for its mem- countries is affected by differences in the definition ber countries, supplemented by World Bank poverty 10 of health workers. Many countries continue to use assessments and country and sector studies. Data 8 national definitions and classifications for data col- are also drawn from World Bank public expenditure lection, and some countries provide information only reviews, the International Monetary Fund's Govern- 6 for public sector workers. ment Finance Statistics database, and other stud- ies. Data on physicians are from the WHO's World 4 Health Report 2006 and Global Atlas of the Health Workforce database, OECD, and TransMONEE, 2 supplemented by country data. Data for the health worker density index are from the Joint Learning 0 Initiative's Human Resources for Health. Data on Low-income Middle-income High-income hospital beds are from the WHO's World Health Source: World Health Organization, Organisation for Statistics 2006, OECD's Health Data 2006, and Economic Co-operation and Development, and World Bank. TransMONEE, supplemented by country data. 2007 World Development Indicators 95 2.15 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 continued bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of % of % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DPT with ARI with diarrhea under age 5 with fever cases cases 1990 2004 1990 2004 2005 2005 2000­05c 1998­2005c 2000­05c 2000­05c 2004 2005 Afghanistan 4 39 3 34 64 76 28 48 .. .. 89 44 Albania 96 96 .. 91 97 98 83 51 .. .. 78 25 Algeria 94 85 88 92 83 88 52 .. .. .. 91 106 Angola 36 53 29 31 45 47 58 32 2 63 68 85 Argentina 94 96 81 91 99 92 .. .. .. .. 58 67 Armenia .. 92 .. 83 94 90 28 48 .. .. 70 60 Australia 100 100 100 100 94 92 .. .. .. .. 85 42 Austria 100 100 100 100 75 86 .. .. .. .. 69 56 Azerbaijan 68 77 .. 54 98 93 36 40 1 1 60 55 Bangladesh 72 74 20 39 81 88 20 53 .. .. 90 59 Belarus 100 100 .. 84 99 99 .. .. .. .. 74 46 Belgium 100 100 100 100 88 97 .. .. .. .. 72 64 Benin 63 67 12 33 85 93 35 42 7 60 83 83 Bolivia 72 85 33 46 64 81 52 54 .. .. 80 72 Bosnia and Herzegovina 97 97 .. 95 90 93 80 23 .. .. 98 71 Botswana 93 95 38 42 90 97 14 7 .. .. 65 69 Brazil 83 90 71 75 99 96 .. .. .. .. 81 53 Bulgaria 99 99 99 99 96 96 .. .. .. .. 80 90 Burkina Faso 38 61 7 13 84 96 36 47 2 50 67 18 Burundi 69 79 44 36 75 74 40 16 1 31 78 30 Cambodia .. 41 .. 17 79 82 37 59 .. .. 91 66 Cameroon 50 66 48 51 68 80 40 43 1 53 71 106 Canada 100 100 100 100 94 94 .. .. .. .. 62 64 Central African Republic 52 75 23 27 35 40 32 47 2 69 91 40 Chad 19 42 7 9 23 20 12 27 1 44 69 22 Chile 90 95 84 91 90 91 .. .. .. .. 83 112 China 70 77 23 44 86 87 .. .. .. .. 94 80 Hong Kong, China .. .. .. .. 81 85 .. .. .. .. 80 53 Colombia 92 93 82 86 89 87 57 39 1 .. 85 26 Congo, Dem. Rep. 43 46 16 30 70 73 36 17 1 45 85 72 Congo, Rep. .. 58 .. 27 56 65 38 .. .. .. 63 57 Costa Rica .. 97 .. 92 89 91 .. .. .. .. 94 118 Côte d'Ivoire 69 84 21 37 51 56 38 34 4 58 71 38 Croatia 100 100 100 100 96 96 .. .. .. .. .. .. Cuba .. 91 98 98 98 99 .. .. .. .. 93 98 Czech Republic 100 100 99 98 97 97 .. .. .. .. 73 65 Denmark 100 100 100 100 95 93 .. .. .. .. 88 71 Dominican Republic 84 95 52 78 99 77 63 42 .. .. 80 76 Ecuador 73 94 63 89 93 94 .. .. .. .. 85 28 Egypt, Arab Rep. 94 98 54 70 98 98 73 29 .. .. 70 63 El Salvador 67 84 51 62 99 89 62 .. .. .. 90 67 Eritrea 43 60 7 9 84 83 44 54 4 4 85 13 Estonia 100 100 97 97 96 96 .. .. .. .. 71 64 Ethiopia 23 22 3 13 59 69 19 38 2 3 79 33 Finland 100 100 100 100 97 97 .. .. .. .. .. .. France 100 100 .. .. 87 98 .. .. .. .. .. .. Gabon .. 88 .. 36 55 38 48 44 .. .. 40 57 Gambia, The .. 82 .. 53 84 88 75 38 15 55 86 69 Georgia 80 82 97 94 92 84 99 .. .. .. 68 91 Germany 100 100 100 100 93 90 .. .. .. .. 68 52 Ghana 55 75 15 18 83 84 44 40 4 63 72 37 Greece .. .. .. .. 88 88 .. .. .. .. .. .. Guatemala 79 95 58 86 77 81 64 22 .. .. 85 55 Guinea 44 50 14 18 59 69 33 44 4 56 72 56 Guinea-Bissau .. 59 .. 35 80 80 64 23 7 58 75 79 Haiti 47 54 24 30 54 43 26 41 .. 12 80 57 96 2007 World Development Indicators 2.15 PEOPLE 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 continued bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of % of % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DPT with ARI with diarrhea under age 5 with fever cases cases 1990 2004 1990 2004 2005 2005 2000­05c 1998­2005c 2000­05c 2000­05c 2004 2005 Honduras 84 87 50 69 92 91 .. .. .. .. 85 82 Hungary 99 99 .. 95 99 99 .. .. .. .. 54 43 India 70 86 14 33 58 59 .. 22 .. 12 86 61 Indonesia 72 77 46 55 72 70 61 56 26 1 90 66 Iran, Islamic Rep. 92 94 83 .. 94 95 93 .. .. .. 84 64 Iraq 83 81 81 79 90 81 76 54 0d 1 85 43 Ireland .. .. .. .. 84 90 .. .. .. .. .. .. Israel 100 100 .. .. 95 95 .. .. .. .. 80 42 Italy .. .. .. .. 87 96 .. .. .. .. 95 72 Jamaica 92 93 75 80 84 88 39 21 .. .. 46 61 Japan 100 100 100 100 99 99 .. .. .. .. 57 57 Jordan 97 97 93 93 95 95 78 44 .. .. 85 63 Kazakhstan 87 86 72 72 99 98 .. 22 .. .. 72 72 Kenya 45 61 40 43 69 76 49 33 5 27 80 43 Korea, Dem. Rep. 100 100 .. 59 96 79 93 .. .. .. 89 99 Korea, Rep. .. 92 .. .. 99 96 .. .. .. .. 80 18 Kuwait .. .. .. .. 99 99 .. .. .. .. 63 66 Kyrgyz Republic 78 77 60 59 99 98 .. .. .. .. 85 67 Lao PDR .. 51 .. 30 41 49 36 37 18 9 86 68 Latvia 99 99 .. 78 95 99 .. .. .. .. 73 83 Lebanon 100 100 .. 98 96 92 74 .. .. .. 90 74 Lesotho .. 79 37 37 85 83 54 53 .. .. 69 85 Liberia 55 61 39 27 94 87 70 .. .. .. 70 50 Libya 71 .. 97 97 97 98 .. .. .. .. 64 178 Lithuania .. .. .. .. 97 94 .. .. .. .. 72 100 Macedonia, FYR .. .. .. .. 96 97 .. .. .. .. 84 66 Madagascar 40 46 14 32 59 61 48 47 .. 34 71 67 Malawi 40 73 47 61 82 93 27 51 15 28 71 39 Malaysia 98 99 .. 94 90 90 .. .. .. .. 56 73 Mali 34 50 36 46 86 85 36 45 8 38 71 21 Mauritania 38 53 31 34 61 71 41 28 2 33 22 28 Mauritius 100 100 .. 94 98 97 .. .. .. .. 89 32 Mexico 82 97 58 79 96 98 .. .. .. .. 82 110 Moldova .. 92 .. 68 97 98 54 52 .. .. 62 65 Mongolia 63 62 .. 59 99 99 78 66 .. .. 88 82 Morocco 75 81 56 73 97 98 38 46 .. .. 87 101 Mozambique 36 43 20 32 77 72 54 47 .. 15 77 49 Myanmar 57 78 24 77 72 73 66 48 .. .. 84 95 Namibia 57 87 24 25 73 86 53 39 3 14 68 90 Nepal 70 90 11 35 74 75 26 43 .. .. 87 67 Netherlands 100 100 100 100 96 98 .. .. .. .. 83 47 New Zealand 97 .. .. .. 82 89 .. .. .. .. 66 51 Nicaragua 70 79 45 47 96 86 57 49 .. 2 87 88 Niger 39 46 7 13 83 89 27 43 6 48 61 50 Nigeria 49 48 39 44 35 25 33 28 1 34 73 22 Norway 100 100 100 100 90 91 .. .. .. .. 89 44 Oman 80 .. 83 .. 98 99 .. .. .. .. 90 108 Pakistan 83 91 37 59 78 72 .. .. .. .. 82 37 Panama 90 90 71 73 99 85 .. .. .. .. 78 131 Papua New Guinea 39 39 44 44 60 61 .. .. .. .. 65 21 Paraguay 62 86 58 80 90 75 .. .. .. .. 83 33 Peru 74 83 52 63 80 84 68 57 .. .. 90 86 Philippines 87 85 57 72 80 79 55 76 .. .. 87 75 Poland .. .. .. .. 98 99 .. .. .. .. 79 62 Portugal .. .. .. .. 93 93 .. .. .. .. 84 85 Puerto Rico .. .. .. .. .. .. .. .. .. .. 71 74 2007 World Development Indicators 97 2.15 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 continued bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of % of % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DPT with ARI with diarrhea under age 5 with fever cases cases 1990 2004 1990 2004 2005 2005 2000­05c 1998­2005c 2000­05c 2000­05c 2004 2005 Romania .. 57 .. .. 97 97 .. .. 31 .. 82 82 Russian Federation 94 97 87 87 99 98 .. .. .. .. 59 30 Rwanda 59 74 37 42 89 95 27 16 13 12 77 29 Saudi Arabia 90 .. .. .. 96 96 .. .. .. .. 82 38 Senegal 65 76 33 57 74 84 27 33 14 29 74 51 Serbia and Montenegro 93 93 87 87 96 98 97 .. .. .. 89 31 Sierra Leone .. 57 .. 39 67 64 50 39 2 61 82 37 Singapore 100 100 100 100 96 96 .. .. .. .. 81 100 Slovak Republic 100 100 99 99 98 99 .. .. .. .. 88 39 Slovenia .. .. .. .. 94 96 .. .. .. .. 90 84 Somalia .. 29 .. 26 35 35 .. .. 9e .. 91 86 South Africa 83 88 69 65 82 94 .. 37 .. .. 70 103 Spain 100 100 100 100 97 96 .. .. .. .. .. .. Sri Lanka 68 79 69 91 99 99 .. .. .. .. 85 86 Sudan 64 70 33 34 60 59 57 38 0d 50 77 35 Swaziland .. 62 .. 48 60 71 60 24 0d 26 50 42 Sweden 100 100 100 100 94 99 .. .. .. .. 64 56 Switzerland 100 100 100 100 82 93 .. .. .. .. .. .. Syrian Arab Republic 80 93 73 90 98 99 66 .. .. .. 86 42 Tajikistan .. 59 .. 51 84 81 51 29 2 69 84 22 Tanzania 46 62 47 47 91 90 57 53 16 58 81 45 Thailand 95 99 80 99 96 98 .. .. .. .. 74 73 Togo 50 52 37 35 70 82 30 25 54 60 67 18 Trinidad and Tobago 92 91 100 100 93 95 74 31 .. .. .. .. Tunisia 81 93 75 85 96 98 43 .. .. .. 90 82 Turkey 85 96 85 88 91 90 41 19 .. .. 91 3 Turkmenistan .. 72 .. 62 99 99 51 .. .. .. 86 43 Uganda 44 60 42 43 86 84 67 28 0d .. 70 45 Ukraine 96 96 96 96 96 96 .. .. .. .. .. .. United Arab Emirates 100 100 97 98 92 94 .. .. .. .. 70 19 United Kingdom 100 100 .. .. 82 91 .. .. .. .. .. .. United States 100 100 100 100 93 96 .. .. .. .. 61 85 Uruguay 100 100 100 100 95 96 .. .. .. .. 86 83 Uzbekistan 94 82 51 67 99 99 57 33 .. .. 78 39 Venezuela, RB .. 83 .. 68 76 87 72 51 .. .. 81 73 Vietnam 65 85 36 61 95 95 71 39 16 7 93 84 West Bank and Gaza .. 92 .. 73 99 99 65 .. .. .. 50 2 Yemen, Rep. 71 67 32 43 76 86 47 .. .. .. 82 41 Zambia 50 58 44 55 84 80 69 48 7 52 83 52 Zimbabwe 78 81 50 53 85 90 .. 80 .. .. 54 41 World 77 w 83 w 45 w 57 w 77 w 78 w 84 w 60 w Low income 64 75 21 38 65 66 83 52 Middle income 78 84 48 62 87 88 85 74 Lower middle income 76 82 42 57 86 86 88 74 Upper middle income 90 94 79 84 93 94 70 73 Low & middle income 73 80 37 52 75 76 84 61 East Asia & Pacific 72 79 30 51 83 84 90 76 Europe & Central Asia 93 92 86 85 96 95 73 42 Latin America & Carib. 83 91 67 77 92 91 82 64 Middle East & N. Africa 88 89 70 76 92 93 84 71 South Asia 71 84 17 37 64 65 86 58 Sub-Saharan Africa 49 56 31 37 64 65 74 49 High income 100 100 100 100 93 95 66 40 Europe EMU 100 100 .. .. 90 95 .. 35 a. For malaria prevention only. b. Refers to children who were immunized before age 12 months or, in some cases, ages 12­23 months. c. Data are for the most recent year available. d. Less than 0.5. e. Data are for 2006. 98 2007 World Development Indicators 2.15 PEOPLE Disease prevention coverage and quality About the data People's health is influenced by the environment in over time based on scientific progress, so it is difficult to improved sanitation facilities refers to the percent- which they live. Lack of clean water and basic sani- to accurately compare use rates among countries. age of the population with at least adequate access tation is the main reason diseases transmitted by Until the current recommended method for home to excreta disposal facilities that can effectively pre- feces are so common in developing countries. The management of diarrhea is adopted and applied in vent human, animal, and insect contact with excreta. data on access to an improved water source mea- all countries, the data should be used with caution. Improved facilities range from simple but protected pit sure the percentage of the population using improved Also, the prevalence of diarrhea may vary by season. latrines to flush toilets with a sewerage connection. To drinking water sources or delivery points. Access to Since country surveys are administered at different be effective, facilities must be correctly constructed drinking water from an improved source and access to times, data comparability is further affected. and properly maintained. · Child immunization rate improved sanitation do not ensure safety or adequacy, Malaria is endemic to the poorest countries in the is the percentage of children ages 12­23 months who as these characteristics are not tested at the time of world, mainly in tropical and subtropical regions of received vaccinations before 12 months or at any time the surveys. But improved drinking water technologies Africa, Asia, and the Americas. An estimated 300­ before the survey for four diseases--measles and and improved sanitation facilities are more likely than 500 million clinical malaria cases and more than 1 diphtheria, pertussis (whooping cough), and tetanus those characterized as unimproved to provide safe million malaria deaths occur each year--the vast (DPT). A child is considered adequately immunized drinking water and to prevent contact with human majority in Sub-Saharan Africa and among children against measles after receiving one dose of vac- excreta. The data are derived by the Joint Monitoring under age five. Insecticide-treated bednets, if properly cine and against DPT after receiving three doses. Programme (JMP) of the WHO and United Nations used and maintained, are one of the most important · Children with acute respiratory infection taken Children's Fund (UNICEF) based on national censuses malaria-preventive strategies to limit human-mosquito to a health provider refer to the percentage of chil- and nationally representative household surveys. The contact. Studies have emphasized that mortality rates dren under age five with acute respiratory infection coverage rates for water and sanitation are based on could be reduced by about 25­30 percent if every in the two weeks prior to the survey who were taken information from service users on the facilities their child under age five in malaria-risk areas such as to an appropriate health provider, including hospital, households actually use rather than on information Africa slept under a treated bednet every night. health center, dispensary, village health worker, clinic, from service providers, who may include nonfunction- Prompt and effective treatment of malaria is a criti- and private physician · Children with diarrhea who ing systems. While the estimates are based on use, cal element of malaria control. It is vital that suffer- received oral rehydration and continued feeding refer the JMP reports use as access, because access is ers, especially children under age five, start treat- to the percentage of children under age five with diar- the term used in the Millennium Development Goal ment within 24 hours of the onset of symptoms, to rhea in the two weeks prior to the survey who received target for drinking water and sanitation. prevent progression--often rapid--to severe malaria either oral rehydration therapy or increased fluids, with Governments in developing countries usually and death. continued feeding. · Children sleeping under treated finance immunization against measles and diphthe- Data on the success rate of tuberculosis treatment bednets refer to the percentage of children under age ria, pertussis (whooping cough), and tetanus (DPT) are provided for countries that have implemented five who slept under an insecticide-treated bednet as part of the basic public health package. In many DOTS, the internationally recommended tuberculosis to prevent malaria. · Children with fever receiving developing countries, lack of precise information on control strategy. Countries that have not adopted antimalarial drugs refer to the percentage of children the size of the cohort of one-year-old children makes DOTS or have only recently done so are omitted under age five who were ill with fever in the last two immunization coverage diffi cult to estimate from because of lack of data or poor comparability or reli- weeks and received any appropriate (locally defined) program statistics. The data shown here are based ability of reported results. The treatment success antimalarial drugs. · Tuberculosis treatment success on an assessment of national immunization cover- rate for tuberculosis provides a useful indicator of rate is the percentage of new, registered smear-posi- age rates by the WHO and UNICEF. The assessment the quality of health services. A low rate or no suc- tive (infectious) cases that were cured or in which a full considered both administrative data from service cess suggests that infectious patients may not be course of treatment was completed. · DOTS detec- providers and household survey data on children's receiving adequate treatment. An essential comple- tion rate is the percentage of estimated new infec- immunization histories. Based on the data available, ment to the tuberculosis treatment success rate is tious tuberculosis cases detected under the directly consideration of potential biases, and contributions the DOTS detection rate, which indicates whether observed treatment, short course case detection and of local experts, the most likely true level of immuni- there is adequate coverage by the recommended treatment strategy. zation coverage was determined for each year. case detection and treatment strategy. A country Data sources Acute respiratory infection continues to be a lead- with a high treatment success rate may still face big ing cause of death among young children, killing challenges if its DOTS detection rate remains low. Data on water and sanitation are from the WHO about 2 million children under age five in developing and UNICEF's Meeting the MDG Drinking Water and Definitions Sanitation Target (www.who.int/water_sanitation_ countries in 2000. An estimated 60 percent of these deaths can be prevented by the selective use of anti- · Access to an improved water source refers to the health/monitoring/jmp2006). Data on immuni- biotics by appropriate health care providers. Data are percentage of the population with reasonable access zation are from WHO and UNICEF estimates of drawn mostly from household health surveys in which to an adequate amount of water from an improved national immunization coverage (www.who.int/ mothers report on number of episodes and treatment source, such as piped water into a dwelling, plot, or immunization_monitoring/en). Data on children for acute respiratory infection. yard; public tap or standpipe; tubewell or borehole; with acute respiratory infection, children with diar- Since 1990 diarrhea-related deaths among children protected dug well or spring; and rainwater collection. rhea, children sleeping under treated bednets, and have declined tremendously. Most diarrhea-related Unimproved sources include unprotected dug well or children receiving antimalarial drugs are from UNI- deaths are due to dehydration, and many of these spring, cart with small tank or drum, bottled water, CEF's State of the World's Children 2007, Childinfo, deaths can be prevented with the use of oral rehy- and tanker trucks. Reasonable access is defined as and Demographic and Health Surveys by Macro dration salts at home. However, recommendations the availability of at least 20 liters a person a day from International. Data on tuberculosis are from the for the use of oral rehydration therapy have changed a source within 1 kilometer of the dwelling. · Access WHO's Global Tuberculosis Control Report 2007. 2007 World Development Indicators 99 2.16 Reproductive health Total fertility Adolescent Unmet Contraceptive Tetanus Pregnant Births attended Maternal rate fertility rate need for prevalence rate vaccinations women by skilled mortality contraception receiving health staff ratio prenatal care births per % of married % of married per 100,000 live births 1,000 women women ages women ages % of pregnant National Modeled births per woman ages 15­19 15­49 15­49 women % % of total estimates estimates 1990 2005 2005 2000­05a 2000­05a 2005 2000­05a 1990­92 a 2000­05a 1990­2005a 2000 Afghanistan .. .. .. .. 10 55 16 .. 14 1,600 1,900 Albania 2.9 1.8 16 .. 75 .. 91 .. 98 16 55 Algeria 4.6 2.4 8 .. 57 .. 81 77 96 120 140 Angola 7.1 6.6 140 .. 6 75 66 .. 45 .. 1,700 Argentina 3.0 2.3 58 .. .. .. 98 96 95 39 82 Armenia 2.5 1.4 30 12 61 .. 93 .. 98 16 55 Australia 1.9 1.8 14 .. .. .. .. 100 99 .. 8 Austria 1.5 1.4 12 .. .. .. .. .. .. .. 4 Azerbaijan 2.7 2.3 31 .. 55 .. 70 .. 88 19 94 Bangladesh 4.3 3.0 118 11 58 89 49 .. 13 320 380 Belarus 1.9 1.2 26 .. .. .. .. .. 100 17 35 Belgium 1.6 1.7 8 .. .. .. .. .. .. .. 10 Benin 6.7 5.6 127 27 19 69 81 .. 75 500 850 Bolivia 4.9 3.7 81 23 58 .. 79 .. 67 30 420 Bosnia and Herzegovina 1.7 1.2 22 .. 48 .. 99 97 100 8 31 Botswana 4.4 3.0 74 .. 48 .. 97 .. 94 330 100 Brazil 2.8 2.3 89 .. .. .. 97 72 97 72 260 Bulgaria 1.8 1.3 43 .. .. .. .. .. 99 6 32 Burkina Faso 6.9 5.9 156 29 14 75 73 .. 38 480 1,000 Burundi 6.8 6.8 50 .. 16 45 78 .. 25 .. 1,000 Cambodia 5.5 3.9 46 30 24 53 38 .. 44 440 450 Cameroon 5.9 5.0 110 20 26 65 83 58 62 670 730 Canada 1.8 1.5 13 .. .. .. .. .. 98 .. 6 Central African Republic 5.6 4.7 122 .. 28 56 62 .. 44 1,100 1,100 Chad 6.7 6.3 191 21 3 39 39 .. 14 1,100 1,100 Chile 2.6 2.0 60 .. .. .. .. .. 100 17 31 China 2.1 1.8 5 .. 87 .. 90 .. 97 51 56 Hong Kong, China 1.3 1.0 5 .. .. .. .. .. 100 .. .. Colombia 3.1 2.4 75 6 78 86 94 82 96 84 130 Congo, Dem. Rep. 6.7 6.7 225 .. 31 66 68 .. 61 1,300 990 Congo, Rep. 6.3 5.6 144 .. 44 65 88 .. 86 .. 510 Costa Rica 3.2 2.0 74 .. .. .. 92 98 99 36 43 Côte d'Ivoire 6.5 4.7 117 .. .. 73 88 .. 68 600 690 Croatia 1.6 1.4 14 .. .. .. .. 100 100 8 8 Cuba 1.7 1.5 50 .. 73 .. 100 .. 100 37 33 Czech Republic 1.9 1.3 11 .. .. .. .. .. 100 4 9 Denmark 1.7 1.8 7 .. .. .. .. .. .. 10 5 Dominican Republic 3.3 2.7 91 11 70 .. 99 93 99 180 150 Ecuador 3.6 2.7 83 .. 73 .. 84 .. 75 80 130 Egypt, Arab Rep. 4.3 3.1 41 11 59 80 70 41 74 84 84 El Salvador 3.7 2.8 83 .. 67 .. 86 .. 92 170 150 Eritrea 6.2 5.2 92 27 8 62 70 .. 28 1,000 630 Estonia 2.0 1.5 23 .. .. .. .. .. 100 8 63 Ethiopia 6.9 5.3 87 35 15 45 28 .. 6 673 850 Finland 1.8 1.8 10 .. .. .. .. .. 100 6 6 France 1.8 1.9 7 .. .. .. .. .. .. 10 17 Gabon 5.3 3.7 102 28 33 60 94 .. 86 520 420 Gambia, The 5.8 4.4 116 .. 18 .. 91 44 55 730 540 Georgia 2.1 1.4 32 .. 47 .. 95 .. 92 52 32 Germany 1.5 1.4 10 .. .. .. .. .. .. 8 8 Ghana 5.7 4.1 61 34 25 84 92 .. 47 .. 540 Greece 1.4 1.3 9 .. .. .. .. .. .. 1 9 Guatemala 5.6 4.3 110 .. 43 .. 84 .. 41 150 240 Guinea 6.5 5.6 186 .. 7 76 82 31 56 530 740 Guinea-Bissau 7.1 7.1 192 .. 8 54 62 .. 35 910 1,100 Haiti 5.2 3.8 61 40 28 52 79 .. 24 520 680 100 2007 World Development Indicators 2.16 PEOPLE Reproductive health Total fertility Adolescent Unmet Contraceptive Tetanus Pregnant Births attended Maternal rate fertility rate need for prevalence rate vaccinations women by skilled mortality contraception receiving health staff ratio prenatal care births per % of married % of married per 100,000 live births 1,000 women women ages women ages % of pregnant National Modeled births per woman ages 15­19 15­49 15­49 women % % of total estimates estimates 1990 2005 2005 2000­05a 2000­05a 2005 2000­05a 1990­92 a 2000­05a 1990­2005a 2000 Honduras 5.1 3.5 97 .. 62 .. 83 45 56 110 110 Hungary 1.8 1.3 21 .. .. .. .. .. 100 7 16 India 3.8 2.8 70 .. 47 80 .. .. 43 540 540 Indonesia 3.1 2.3 53 9 57 70 92 32 72 310 230 Iran, Islamic Rep. 4.8 2.1 19 .. 74 .. .. .. 90 37 76 Iraq 5.9 .. .. .. 44 70 77 .. 72 290 250 Ireland 2.1 1.9 13 .. .. .. .. .. 100 6 5 Israel 2.8 2.8 14 .. .. .. .. .. .. 5 17 Italy 1.3 1.3 7 .. .. .. .. .. .. 7 5 Jamaica 2.9 2.4 78 .. 69 .. 98 .. 97 110 87 Japan 1.5 1.3 4 .. 56 .. .. 100 .. 8 10 Jordan 5.4 3.3 26 11 56 .. 99 87 100 41 41 Kazakhstan 2.7 1.7 29 .. .. .. .. .. .. 42 210 Kenya 5.8 5.0 95 25 .. 72 88 .. 42 410 1,000 Korea, Dem. Rep. 2.4 2.0 2 .. .. .. .. .. 97 110 67 Korea, Rep. 1.6 1.1 4 .. .. .. .. 98 100 20 20 Kuwait 3.5 2.4 23 .. .. .. .. .. 100 5 5 Kyrgyz Republic 3.7 2.4 32 .. .. .. .. .. 99 49 110 Lao PDR 6.0 4.5 88 .. 32 30 27 .. 19 410 650 Latvia 2.0 1.3 17 .. .. .. .. .. 100 14 42 Lebanon 3.1 2.3 26 .. 63 .. 96 .. 93 .. 150 Lesotho 4.8 3.4 36 .. 30 .. 90 .. 55 760 550 Liberia 6.9 6.8 222 .. 10 72 85 .. 51 .. 760 Libya 4.7 2.8 7 .. .. .. .. .. .. 77 97 Lithuania 2.0 1.3 21 .. .. .. .. .. 100 3 13 Macedonia, FYR 2.1 1.6 23 .. .. .. 81 .. 99 21 23 Madagascar 6.2 5.0 121 24 27 45 80 57 51 470 550 Malawi 7.0 5.8 155 30 31 70 92 55 56 980 1,800 Malaysia 3.8 2.7 18 .. .. .. 74 .. 97 30 41 Mali 7.4 6.7 197 29 8 75 57 .. 41 580 1,200 Mauritania 6.1 5.6 97 32 8 34 64 40 57 750 1,000 Mauritius 2.3 2.0 32 .. 76 .. .. 96 99 22 24 Mexico 3.3 2.1 66 .. 73 .. .. .. 83 63 83 Moldova 2.4 1.3 30 .. 68 .. 98 .. 100 22 36 Mongolia 4.0 2.3 53 .. 69 .. 94 .. 97 93 110 Morocco 4.0 2.4 42 10 63 .. 68 31 63 230 220 Mozambique 6.2 5.3 101 18 26 70 85 .. 48 410 1,000 Myanmar 4.0 2.2 18 .. 34 85 76 .. 57 230 360 Namibia 5.9 3.7 51 22 44 67 91 68 76 270 300 Nepal 5.1 3.5 110 28 38 42 28 7 15 540 740 Netherlands 1.6 1.7 5 .. .. .. .. .. .. 7 16 New Zealand 2.2 2.0 23 .. .. .. .. .. .. 15 7 Nicaragua 4.8 3.1 118 15 69 .. 86 .. 67 83 230 Niger 8.2 7.7 255 .. 14 54 41 15 16 590 1,600 Nigeria 6.7 5.5 137 17 13 51 58 31 35 .. 800 Norway 1.9 1.8 9 .. .. .. .. .. .. 6 16 Oman 6.5 3.4 45 .. 32 .. 100 .. 95 23 87 Pakistan 5.8 4.1 69 .. 28 57 36 19 31 530 500 Panama 3.0 2.6 85 .. .. .. .. .. 93 40 160 Papua New Guinea 5.1 3.8 57 .. .. 10 .. .. 41 .. 300 Paraguay 4.7 3.7 63 .. 73 .. 94 67 77 180 170 Peru 3.9 2.7 53 10 69 .. 92 .. 73 190 410 Philippines 4.3 3.2 35 17 49 70 88 .. 60 170 200 Poland 2.0 1.2 14 .. .. .. .. .. 100 4 13 Portugal 1.4 1.4 18 .. .. .. .. .. 100 8 5 Puerto Rico 2.2 1.8 53 .. .. .. .. .. 100 .. 25 2007 World Development Indicators 101 2.16 Reproductive health Total fertility Adolescent Unmet Contraceptive Tetanus Pregnant Births attended Maternal rate fertility rate need for prevalence rate vaccinations women by skilled mortality contraception receiving health staff ratio prenatal care births per % of married % of married per 100,000 live births 1,000 women women ages women ages % of pregnant National Modeled births per woman ages 15­19 15­49 15­49 women % % of total estimates estimates 1990 2005 2005 2000­05a 2000­05a 2005 2000­05a 1990­92 a 2000­05a 1990­2005a 2000 Romania 1.8 1.3 34 .. 70 .. 94 .. 99 17 49 Russian Federation 1.9 1.3 29 .. .. .. .. .. 99 .. 67 Rwanda 7.4 5.8 46 36 17 76 94 26 39 750 1,400 Saudi Arabia 5.9 3.8 32 .. .. .. .. .. 93 .. 23 Senegal 6.4 4.9 80 .. 11 85 79 .. 58 430 690 Serbia and Montenegro 2.1 1.6 23 .. 58 .. .. .. 92 7 11 Sierra Leone 6.5 6.5 172 .. 4 76 68 .. 42 1,800 2,000 Singapore 1.9 1.2 5 .. .. .. .. .. 100 6 30 Slovak Republic 2.1 1.3 20 .. .. .. .. .. 99 4 3 Slovenia 1.5 1.2 6 .. .. .. .. 100 100 17 17 Somalia 6.8 6.2 68 .. 15b 25 .. .. 33b 1,000 1,100 South Africa 3.3 2.8 65 .. 60 61 92 .. 92 150 230 Spain 1.3 1.3 9 .. .. .. .. .. .. 6 4 Sri Lanka 2.5 1.9 18 .. 70 76 100 .. 96 43 92 Sudan 5.6 4.1 50 .. 7 41 60 69 87 .. 590 Swaziland 5.3 3.9 35 .. 48 .. 90 .. 74 230 370 Sweden 2.1 1.8 7 .. .. .. .. .. .. 5 2 Switzerland 1.6 1.4 4 .. .. .. .. .. .. 5 7 Syrian Arab Republic 5.2 3.2 32 .. 48 .. 71 .. 70 65 160 Tajikistan 5.1 3.5 29 .. 34 .. 71 .. 71 37 100 Tanzania 6.1 5.2 106 22 26 90 78 44 43 578 1,500 Thailand 2.2 1.9 47 .. 72 .. 92 .. 99 24 44 Togo 6.4 5.0 95 .. 26 61 85 .. 61 480 570 Trinidad and Tobago 2.4 1.6 35 .. 38 .. 92 .. 96 45 160 Tunisia 3.5 2.0 7 .. 66 .. 92 .. 90 69 120 Turkey 3.0 2.2 40 .. 71 47 81 .. 83 .. 70 Turkmenistan 4.2 2.6 16 10 62 .. 98 .. 97 14 31 Uganda 7.2 7.1 207 35 23 56 92 .. 39 510 880 Ukraine 1.8 1.2 28 .. 89 .. .. .. 100 13 35 United Arab Emirates 4.3 2.4 19 .. .. .. .. .. 100 3 54 United Kingdom 1.8 1.8 25 .. 84 .. .. .. .. 7 13 United States 2.1 2.1 50 .. .. .. .. .. 99 8 17 Uruguay 2.5 2.0 69 .. .. .. .. .. 99 26 27 Uzbekistan 4.1 2.2 35 .. 68 .. 97 .. 96 30 24 Venezuela, RB 3.4 2.7 91 .. .. .. 94 .. 95 58 96 Vietnam 3.6 1.8 19 5 77 85 86 .. 90 170 130 West Bank and Gaza 6.3 4.6 .. .. 51 .. 96 .. 97 .. .. Yemen, Rep. 7.9 5.9 91 .. 23 24 41 16 27 370 570 Zambia 6.5 5.4 126 27 34 98 93 51 43 730 750 Zimbabwe 5.2 3.3 89 .. .. 70 .. .. .. 1,100 1,100 World 3.1 w 2.6 w 57 w 60 w .. w .. w .. w 63 w 410 w Low income 4.7 3.6 92 40 69 .. 33 41 684 Middle income 2.6 2.1 32 76 .. 89 .. 87 150 Lower middle income 2.7 2.1 29 77 .. 89 .. 86 163 Upper middle income 2.6 1.9 46 .. .. .. .. 92 91 Low & middle income 3.4 2.7 60 60 .. .. .. 61 450 East Asia & Pacific 2.5 2.0 16 79 .. 89 .. 87 117 Europe & Central Asia 2.3 1.6 29 .. .. .. .. 94 58 Latin America & Carib. 3.2 2.4 77 .. .. 95 .. 87 194 Middle East & N. Africa 4.8 3.0 32 59 .. .. .. 74 183 South Asia 4.1 3.1 76 46 77 .. 30 37 564 Sub-Saharan Africa 6.2 5.3 132 23 61 70 .. 45 921 High income 1.8 1.7 24 .. .. .. .. .. 14 Europe EMU 1.5 1.5 9 .. .. .. .. .. 10 a. Data are for the most recent year available. b. Data are for 2006. 102 2007 World Development Indicators 2.16 PEOPLE Reproductive health About the data Definitions Reproductive health is a state of physical and men- generally two tetanus shots during the first pregnancy · Total fertility rate is the number of children that tal well-being in relation to the reproductive system and one booster shot during each subsequent preg- would be born to a woman if she were to live to the and its functions and processes. Means of achieving nancy, with five doses considered adequate for lifetime end of her childbearing years and bear children in reproductive health include education and services protection. Information on tetanus shots during preg- accordance with current age-specific fertility rates. during pregnancy and childbirth, provision of safe and nancy is collected through surveys in which pregnant · Adolescent fertility rate is the number of births per 1,000 women ages 15­19. · Unmet need for effective contraception, and prevention and treatment respondents are asked to show antenatal cards on contraception is the percentage of fertile, mar- of sexually transmitted diseases. Complications of which tetanus shots have been recorded. Because ried women of reproductive age who do not want to pregnancy and childbirth are the leading cause of not all women have antenatal cards, respondents are become pregnant and are not using contraception. death and disability among women of reproductive age also asked about their receipt of these injections. Poor · Contraceptive prevalence rate is the percentage in developing countries. Reproductive health services recall may result in a downward bias in estimates of of women married or in-union ages 15­49 who are will need to expand rapidly over the next two decades, the share of births protected. But in settings where practicing, or whose sexual partners are practicing, when the number of women and men of reproductive receiving injections is common, respondents may erro- any form of contraception. · Tetanus vaccinations age is projected to increase by about 500 million. neously report having received tetanus shots. refer to the percentage of pregnant women who Total and adolescent fertility rates are based on The share of births attended by skilled health staff receive two tetanus toxoid injections during their data on registered live births from vital registration is an indicator of a health system's ability to provide first pregnancy and one booster shot during each systems or, in the absence of such systems, from adequate care for pregnant women. Good antena- subsequent pregnancy, with five doses considered censuses or sample surveys. As long as the surveys tal and postnatal care improve maternal health and adequate for a lifetime. · Pregnant women receiving are fairly recent, the estimated rates are generally reduce maternal and infant mortality. But data may prenatal care are the percentage of women attended considered reliable measures of fertility in the recent not reflect such improvements because health infor- at least once during pregnancy by skilled health per- past. Where no empirical information on age- specific mation systems are often weak, maternal deaths are sonnel for reasons related to pregnancy. · Births attended by skilled health staff are the percent- fertility rates is available, a model is used to estimate underreported, and rates of maternal mortality are age of deliveries attended by personnel trained to the share of births to adolescents. For countries with- difficult to measure. give the necessary supervision, care, and advice to out vital registration systems, fertility rates are gener- Maternal mortality ratios are generally of unknown women during pregnancy, labor, and the postpartum ally based on extrapolations from trends observed in reliability, as are many other cause-specific mortality period; to conduct deliveries on their own; and to censuses or surveys from earlier years. indicators. Household surveys such as the Demo- care for newborns. · Maternal mortality ratio is the An increasing number of couples in the develop- graphic and Health Surveys attempt to measure number of women who die from pregnancy-related ing world want to limit or postpone childbearing maternal mortality by asking respondents about sur- causes during pregnancy and childbirth, per 100,000 but are not using effective contraceptive methods. vivorship of sisters. The main disadvantage of this live births. These couples have an unmet need for contracep- method is that the estimates of maternal mortality tion, shown in the table as the percentage of mar- that it produces pertain to 12 years or so before the Data sources ried women of reproductive age who do not want survey, making them unsuitable for monitoring recent Data on fertility rates are compiled and esti- to become pregnant but are not using contracep- changes or observing the impact of interventions. mated by the World Bank's Development Data tion (Bulatao 1998). Information on this indicator is In addition, measurement of maternal mortality is Group. Important inputs come from the following collected through surveys and excludes women not subject to many types of errors. Even in high-income sources: the United Nations Population Division's exposed to the risk of unintended pregnancy because countries with vital registration systems, misclassi- World Population Prospects: The 2004 Revision; of menopause, infertility, or postpartum anovulation. fication of maternal deaths has been found to lead census reports and other statistical publications Common reasons for not using contraception are to serious underestimation. from national statistical offices; and household lack of knowledge about contraceptive methods and The maternal mortality ratios shown in the table as surveys such as Demographic and Health Surveys. concerns about possible health side-effects. national estimates are based on national surveys, Data on women with unmet need for contracep- Contraceptive prevalence reflects all methods-- vital registration records, and surveillance data or tion and contraceptive prevalence rates are from ineffective traditional methods as well as highly are derived from community and hospital records. household surveys, including Demographic and effective modern methods. Contraceptive prevalence The ratios shown as modeled estimates are based on Health Surveys by Macro International and Mul- rates are obtained mainly from household surveys, an exercise by the World Health Organization (WHO), tiple Indicator Cluster Surveys by UNICEF. Data on including Demographic and Health Surveys, Multiple United Nations Children's Fund (UNICEF), and the tetanus vaccinations, pregnant women receiving Indicator Cluster Surveys, and contraceptive preva- United Nations Population Fund (UNFPA). For coun- prenatal care, births attended by skilled health staff, and national estimates of maternal mortality lence surveys (see Primary data documentation for tries with national data, reported maternal mortal- ratios are from UNICEF's State of the World's Chil- the most recent survey year). Unmarried women are ity was adjusted by a factor of under- or over-enu- dren 2007 and Childinfo, and Demographic and often excluded from such surveys, which may bias meration and misclassification. For countries with no Health Surveys by Macro International. Modeled the estimates. national data, maternal mortality was estimated with estimates for maternal mortality ratios are from Neonatal tetanus is an important cause of infant a regression model using information on fertility, birth Carla AbouZahr and Tessa Wardlaw's "Maternal mortality in some developing countries. It can be attendants, and GDP. Neither set of ratios can be Mortality in 2000: Estimates Developed by WHO, prevented through immunization of the mother during assumed to provide an accurate estimate of maternal UNICEF, and UNFPA" (2003). pregnancy. Recommended doses for full protection are mortality for any of the countries in the table. 2007 World Development Indicators 103 2.17 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A undernourishment malnutrition of overweight birthweight breastfeeding of iodized supplemen- children babies salt tation % of children under age 5 % of children % of children % of % of children % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months 1990­92 2002­04a 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2004 Afghanistan .. .. 39.3 53.7 .. .. .. 28 96 Albania 5c 6 14.0 35.1 22.4 5 6 62 .. Algeria 5 4 10.4 19.1 10.1 7 13 69 .. Angola 58 35 30.5 45.2 .. 12 11 35 77 Argentina <2.5 3 3.8d 4.2d .. 8 .. .. .. Armenia 52c 24 2.6 12.9 10.4 7 33 97 .. Australia <2.5 <2.5 .. .. .. 7 .. .. .. Austria <2.5 <2.5 .. .. .. 7 .. .. .. Azerbaijan 34 c 7 6.8 13.3 2.6 12 7 26 14 Bangladesh 35 30 47.5 43.0 0.8 36 36 70 83 Belarus <2.5c 4 .. .. .. 5 .. 55 .. Belgium <2.5 <2.5 .. .. .. .. .. .. .. Benin 20 12 30.0 30.7 1.8 16 38 72 94 Bolivia 28 23 7.6 26.7 5.6 7 54 90 42 Bosnia and Herzegovina 9c 9 4.1 9.7 13.2 4 .. 62 .. Botswana 23 32 12.5 23.1 6.9 10 34 66 62e Brazil 12 7 .. .. .. 8 .. 88 .. Bulgaria 8c 8 .. .. .. 10 .. 98 .. Burkina Faso 21 15 37.7 38.7 2.9 19 19 45 95 Burundi 48 66 45.1 56.8 0.7 16 62 96 94 Cambodia 43 33 36.0 44.6 2.0 11 12 14 72 Cameroon 33 26 18.1 31.7 5.2 13 24 88 81 Canada <2.5 <2.5 .. .. .. 6 .. .. .. Central African Republic 50 44 24.3 38.9 .. 14 17 86 79 Chad 58 35 36.7 40.9 1.5 22 2 56 84 Chile 8 4 0.7 1.4 8.1 6 63 .. .. China 16 12 7.8 14.2 2.6 4 51 93 .. Hong Kong, China .. .. .. .. .. 5 .. .. .. Colombia 17 13 7.0 12.0 3.7 6 47 .. .. Congo, Dem. Rep. 31 74 31.0 38.1 3.9 12 24 72 81 Congo, Rep. 54 33 .. .. .. .. 19 .. 94 Costa Rica 6 5 .. .. .. 7 .. .. 60 Côte d'Ivoire 18 13 17.2 .. .. 17 5 84 .. Croatia 16c 7 .. .. .. 6 .. .. .. Cuba 7 <2.5 3.9 4.6 .. 5 41 88 .. Czech Republic <2.5c <2.5 .. .. .. 7 .. .. .. Denmark <2.5 <2.5 .. .. .. 5 .. .. .. Dominican Republic 27 29 5.3 8.9 6.5 11 10 18 .. Ecuador 8 6 11.6 .. .. .. .. .. .. Egypt, Arab Rep. 4 4 8.6 15.6 6.7 12 38 78 .. El Salvador 12 11 10.3 18.9 3.6 7 24 62 .. Eritrea 70 c 75 39.6 37.6 0.7 14 52 68 50 Estonia 9c <2.5 .. .. .. 4 .. .. .. Ethiopia 69 c 46 38.4 46.5 1.2 14 49 28 52 Finland <2.5 <2.5 .. .. .. 4 .. .. .. France <2.5 <2.5 .. .. .. .. .. .. .. Gabon 10 5 11.9 20.7 3.7 14 6 36 .. Gambia, The 22 29 17.2 19.2 1.5 17 26 8 27 Georgia 44 c 9 .. .. .. 7 .. 68 .. Germany <2.5 <2.5 .. .. .. .. .. .. .. Ghana 37 11 22.1 29.9 2.9 16 53 28 95 Greece <2.5 <2.5 .. .. .. .. .. .. .. Guatemala 16 22 22.7 49.3 5.4 12 51 67 18e Guinea 39 24 32.7 .. .. 16 27 68 95 Guinea-Bissau 24 39 25.0 30.5 3.3 22 37 2 64 Haiti 65 46 17.2 22.7 2.0 21 24 11 .. 104 2007 World Development Indicators 2.17 PEOPLE Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A undernourishment malnutrition of overweight birthweight breastfeeding of iodized supplemen- children babies salt tation % of children under age 5 % of children % of children % of % of children % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months 1990­92 2002­04a 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2004 Honduras 23 23 16.6 29.2 2.2 14 35 .. 40 Hungary <2.5c <2.5 .. .. .. 9 .. .. .. India 25 20 .. .. .. .. 37f 57 51e Indonesia 9 6 28.2 .. .. 9 40 73 73e Iran, Islamic Rep. 4 4 .. .. .. .. 44 .. .. Iraq .. .. 15.9 22.1 3.0 15 12 40 .. Ireland <2.5 <2.5 .. .. .. .. .. .. .. Israel <2.5 <2.5 .. .. .. 8 .. .. .. Italy <2.5 <2.5 .. .. .. .. .. .. .. Jamaica 14 9 3.6 .. .. 10 .. .. .. Japan <2.5 <2.5 .. .. .. 8 .. .. .. Jordan 4 6 4.4 8.5 3.5 12 27 88 .. Kazakhstan <2.5c 6 .. .. .. .. .. 83 .. Kenya 39 31 19.9 30.3 3.7 10 13 91 63 Korea, Dem. Rep. 18 33 23.9 38.6 0.6 7 65 40 95 Korea, Rep. <2.5 <2.5 .. .. .. 4 .. .. .. Kuwait 24 5 .. .. .. .. .. .. .. Kyrgyz Republic 21c 4 6.7 .. .. .. .. 42 95 Lao PDR 29 19 40.4 42.4 1.2 14 23 75 48 Latvia 3c 3 .. .. .. 5 .. .. .. Lebanon <2.5 3 3.9 11.0 .. 6 27f 92 .. Lesotho 17 13 18.0 46.1 12.1 13 36 91 71 Liberia 34 50 26.5 39.5 2.3 .. 35 .. 95 Libya <2.5 <2.5 .. .. .. .. .. .. .. Lithuania 4c <2.5 .. .. .. 4 .. .. .. Macedonia, FYR 15c 5 .. .. .. 6 99 94 .. Madagascar 35 38 41.9 47.7 .. 17 67 75 89 Malawi 50 35 21.9 49.0 4.3 16 53 49 57 Malaysia 3 3 10.6 .. .. 9 .. .. .. Mali 29 29 33.2 38.2 1.5 23 25 74 97 Mauritania 15 10 31.8 34.5 .. .. 20 2 95 Mauritius 6 5 .. .. .. 14 21f .. .. Mexico 5 5 .. .. .. 8 .. 91 .. Moldova 5c 11 4.3 8.4 .. 5 46 59 .. Mongolia 34 27 12.7 24.6 .. 7 51 75 93 Morocco 6 6 10.2 18.1 9.2 15 31 59 .. Mozambique 66 44 23.7 41.0 3.0 15 30 54 26 Myanmar 10 5 31.8 32.2 1.6 15 15f 60 96 Namibia 34 24 24.0 23.6 2.2 14 19 63 .. Nepal 20 17 45.0 g 43.0 g 0.2 21 68g 63 97 Netherlands <2.5 <2.5 .. .. .. .. .. .. .. New Zealand <2.5 <2.5 .. .. .. 6 .. .. .. Nicaragua 30 27 9.6 20.2 4.7 12 31 97 98 Niger 41 32 40.1 39.7 0.8 13 1 15 .. Nigeria 13 9 28.7 38.3 3.6 14 17 97 85 Norway <2.5 <2.5 .. .. .. 5 .. .. .. Oman .. .. .. .. .. 8 .. .. 95 Pakistan 24 24 37.8 36.8 2.1 .. .. 17 95 Panama 21 23 .. .. .. 10 .. .. .. Papua New Guinea .. .. .. .. .. .. .. .. 32 Paraguay 18 15 4.6 .. .. 9 22 88 .. Peru 42 12 7.1 25.4 7.6 11 64 91 .. Philippines 26 18 27.6 .. .. 20 34 56 85 Poland <2.5c <2.5 .. .. .. 6 .. .. .. Portugal <2.5 <2.5 .. .. .. 8 .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 105 2.17 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A undernourishment malnutrition of overweight birthweight breastfeeding of iodized supplemen- children babies salt tation % of children under age 5 % of children % of children % of % of children % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months 1990­92 2002­04a 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2004 Romania <2.5c <2.5 3.2 10.1 5.5 8 16 53 .. Russian Federation 4c 3 5.5 10.6 .. 6 .. 35 .. Rwanda 43 33 22.5 45.3 4.0 9 88 90 95 Saudi Arabia 4 4 .. .. .. .. .. .. .. Senegal 23 20 22.7 25.4 2.2 18 34 41 95 Serbia and Montenegro 5c 9 1.9 5.1 .. 4 11f 73 .. Sierra Leone 46 51 27.2 33.8 .. 23 4 23 95 Singapore .. .. 3.4 2.2 2.2 8 .. .. .. Slovak Republic 4c 7 .. .. .. 7 .. .. .. Slovenia 3c 3 .. .. .. 6 .. .. .. Somalia .. .. 33.0 g 23.3 .. .. .. .. 6 South Africa <2.5 <2.5 .. .. .. .. 7 .. 37 Spain <2.5 <2.5 .. .. .. .. .. .. .. Sri Lanka 28 22 29.4 13.5 .. 22 53 94 57e Sudan 31 26 40.7 43.3 3.4 .. 16 1 70 Swaziland 14 22 10.3 30.2 .. 9 24 59 86 Sweden <2.5 <2.5 .. .. .. .. .. .. .. Switzerland <2.5 <2.5 .. .. .. .. .. .. .. Syrian Arab Republic 5 4 6.9 18.8 .. 6 81f 79 .. Tajikistan 22c 56 .. 36.2 .. 15 50 28 98 Tanzania 37 44 21.8 37.7 .. 10 41 43 94 Thailand 30 22 .. .. .. 9 .. 63 .. Togo 33 24 .. .. .. 18 18 67 95 Trinidad and Tobago 13 10 5.9 3.6 .. 23 2 1 .. Tunisia <2.5 <2.5 4.0 12.3 .. 7 47 97 .. Turkey <2.5 3 3.9 .. .. .. 21 64 .. Turkmenistan 12c 7 12.0 22.3 .. 6 13 100 .. Uganda 24 19 22.9 39.1 2.6 12 63 95 68 Ukraine <2.5c <2.5 1.0 2.7 20.1 5 22 32 .. United Arab Emirates 4 3 .. .. .. .. .. .. .. United Kingdom <2.5 <2.5 .. .. .. 8 .. .. .. United States <2.5 <2.5 1.6 1.1 5.6 8 .. .. .. Uruguay 7 <2.5 .. .. .. 8 .. .. .. Uzbekistan 8c 25 7.9 21.1 .. 7 19 57 86 Venezuela, RB 11 18 4.4 12.8 3.2 9 .. .. .. Vietnam 31 16 28.4 36.5 2.7 9 15 83 95e West Bank and Gaza .. 16 4.9 9.9 .. 9 29 f 64 .. Yemen, Rep. 34 38 45.6 53.1 .. .. 12 30 20 Zambia 48 46 23.0 46.8 3.0 12 40 77 50 Zimbabwe 45 47 .. .. .. .. .. .. 20 World 17 w 14 w .. w .. w 11 w 37 w 69 w .. w Low income 27 24 .. .. .. 33 56 68 Middle income 14 10 11.5 15.6 8 42 80 .. Lower middle income 16 11 12.5 16.4 8 44 83 .. Upper middle income .. 4 .. .. 8 .. .. .. Low & middle income 20 16 21.7 .. 11 37 69 .. East Asia & Pacific 17 12 14.9 17.7 7 44 85 .. Europe & Central Asia 6c 6 4.9 .. 7 .. 49 .. Latin America & Carib. 13 10 .. .. 9 .. 84 .. Middle East & N. Africa 6 7 14.6 22.2 11 34 66 .. South Asia 26 21 .. .. .. 37 54 62 Sub-Saharan Africa 29 30 29.6 39.2 14 29 63 73 High income 3 3 .. .. .. .. .. .. Europe EMU 3 3 .. .. .. .. .. .. a. Preliminary data. b. Data are for the most recent year available. c. Data are for 1993­95. d. Data are for 2005­06. e. Country's vitamin A supplementation programs do not target children all the way up to 59 months of age. f. Refers to exclusive breastfeeding of children under four months. g. Data are for 2006. 106 2007 World Development Indicators 2.17 PEOPLE Nutrition About the data Data on undernourishment are produced by the Food good healthcare, and mothers who did not smoke. childhood ailments such as measles, malaria, and and Agriculture Organization (FAO) of the United Previously, the U.S. National Center for Health diarrhea. Improving the vitamin A status of pregnant Nations based on the calories available from local Statistics­WHO growth reference has been used to women helps reduce anemia, improves their resis- food production, trade, and stocks; the number of chart children's growth. This reference was based tance to infection, and may reduce their risk of dying calories needed by different age and gender groups; on data from a limited sample of a random mix of during pregnancy and childbirth. Giving vitamin A to the proportion of the population represented by each breastfed and artificially fed children from the United new mothers who are breastfeeding helps to protect age group; and a coefficient of distribution to take States only, and the growth reference describes only their children during the first months of life. Food for- account of inequality in access to food (FAO, State of how children grow in a particular region and time. tification with vitamin A is being introduced in many Food Insecurity in the World 2000). From a policy and Thus it does not provide a sound basis for evaluation developing countries. program standpoint, however, this measure has its against international standards and norms. Definitions limits. First, food insecurity exists even where food Adoption of the new standards will have important availability is not a problem because of inadequate implications for monitoring children's growth. A study · Prevalence of undernourishment is the percent- access of poor households to food. Second, food inse- based on the new standards shows that the under- age of the population that is undernourished--whose curity is an individual or household phenomenon, and weight rates increased during the first six months and dietary energy consumption is continuously below a the average food available to each person, even cor- decreased thereafter and that stunting and overweight minimum dietary energy requirement for maintaining rected for possible effects of low income, is not a good rates increased for all age groups (birth to five years). a healthy life and carrying out light physical activity. predictor of food insecurity among the population. And Differences are particularly important during infancy, · Prevalence of child malnutrition is the percent- third, nutrition security is determined not only by food likely due to the inclusion of only breast-fed infants in age of children under age five whose weight for age security but also by the quality of care of mothers the new standards (de Onis and others 2006). (underweight) or height for age (stunting) is more and children and the quality of the household's health The new standards are expected to be widely used than two standard deviations below the median for environment (Smith and Haddad 2000). as a tool for monitoring the nutritional status of com- the international reference population ages 0­59 Estimates of child malnutrition, based on weight munities and alerting practitioners and policymakers months. For children up to two years old height is for age (underweight) and height for age (stunting), to unhealthy trends in the population. They are also measured by recumbent length. For older children are from national survey data. The proportion of chil- expected to play a key role in measuring and monitor- height is measured by stature while standing. The dren who are underweight is the most common indi- ing health targets for the Millennium Development new Child Growth Standards were released by the cator of malnutrition. Being underweight, even mildly, Goals. Currently, national surveys are being reana- WHO in 2006, but the data using these standards increases the risk of death and inhibits cognitive lyzed with the new standards to update the global are not yet available · Prevalence of overweight development in children. Moreover, it perpetuates database, but the updated data are not yet available. children is the percentage of children under age five the problem from one generation to the next, as mal- The data on malnutrition and overweight presented in whose weight for height is more than two standard nourished women are more likely to have low-birth- the table are still based on the old standard. deviations above the median for the international weight babies. Height for age reflects linear growth Low birthweight, which is associated with maternal reference population of the corresponding age, achieved pre- and postnatally, and a deficit indicates malnutrition, raises the risk of infant mortality and established by the U.S. National Center for Health long-term, cumulative effects of inadequacies of stunts growth in infancy and childhood. There is also Statistics and the WHO. The new Child Growth Stan- health, diet, or care. It is often argued that stunting emerging evidence that low-birthweight babies are dards were released by WHO in 2006, but the data is a proxy for multifaceted deprivation and is a better more prone to noncommunicable diseases such as using these standards are not yet available. · Low- indicator of long-term changes in malnutrition. diabetes and cardiovascular diseases. Estimates of birthweight babies are the percentage of newborns Estimates of children who are overweight are also low-birthweight infants are drawn mostly from hos- weighing less than 2,500 grams, with the measure- from national survey data. Overweight in children has pital records and household surveys. Many births in ment taken within the first hours of life, before signifi - become a growing concern in developing countries. developing countries take place at home, and these cant postnatal weight loss has occurred. · Exclusive Researchers show an association between obesity in births are seldom recorded. A hospital birth may indi- breastfeeding refers to the percentage of children childhood and a high prevalence of diabetes, respira- cate higher income and therefore better nutrition, or less than six months old who are fed breast milk tory disease, high blood pressure, and psychosocial it could indicate a higher-risk birth, possibly skewing alone (no other liquids) in the past 24 hours. · Con- and orthopedic disorders (de Onis and Blössner the data on birthweights downward. The data should sumption of iodized salt refers to the percentage of 2000). The survey data were analyzed in a standard- therefore be treated with caution. households that use edible salt fortified with iodine. ized way by the World Health Organization (WHO) to It is estimated that improved breastfeeding prac- · Vitamin A supplementation refers to the percent- allow comparisons across countries. tice can save some 1.3 million children a year. Breast age of children ages 6­59 months old who received New international child growth standards for milk alone contains all the nutrients, antibodies, hor- at least one high-dose vitamin A capsule in the previ- infants and young children, called the Child Growth mones, and antioxidants an infant needs to thrive. ous six months. Standards, were released in 2006 by the WHO. The It protects babies from diarrhea and acute respira- new standards confirm that children born anywhere tory infections, stimulates their immune systems in the world, raised in healthy environments, and and response to vaccination, and according to some following recommended feeding practice have the studies confers cognitive benefits as well. The data potential to develop to within the same range of on breastfeeding are derived from national surveys. Data sources height and weight. Naturally, there are individual Iodine defi ciency is the single most important Data on undernourishment are from www.fao.org/ differences among children, but the differences in cause of preventable mental retardation, and it faostat/foodsecurity/index_en.htm. Data on mal- children's growth to age five are influenced more by contributes significantly to the risk of stillbirth and nutrition and overweight are from WHO's Global nutrition, feeding practices, environment, and health- miscarriage. Iodized salt is the best source of iodine, Database on Child Growth and Malnutrition. Data care than by genetics or ethnicity. The new standards and a global campaign to iodize edible salt is signifi - on low-birthweight babies, breastfeeding, iodized are the result of a community-based, multicountry cantly reducing the risks (UNICEF, State of the World's salt consumption, and vitamin A supplementation project involving more than 8,000 children from Children 1999). are from the WHO's World Health Report 2006 and Brazil, Ghana, India, Norway, Oman, and the United Vitamin A is essential for the functioning of the the United Nations Children's Fund's State of the States. The children were selected based on an opti- immune system. A child deficient in vitamin A faces World's Children 2007. mal environment for growth, including breastfeeding, a 23 percent greater risk of dying from a range of 2007 World Development Indicators 107 2.18 Health risk factors and public health challenges Prevalence Incidence of Prevalence Mortality Prevalence of HIV Cause of death of smoking tuberculosis of diabetes caused by road % of total deaths traffic Communicable injury diseases and Female maternal, per % of per Total % of perinatal, Non- % of adults 100,000 population 100,000 % of population population and nutrition communicable Male Female people ages 20­79 people ages 15­49 with HIV conditions diseases Injuries 2000­05a 2000­05a 2005 2007 1998­2003a 2003 2005 2003 2005 2002 2002 2002 Afghanistan .. .. 168 9.7 .. <0.1 <0.1 .. .. 65 29 6 Albania 60 18 20 4.5 11.1 .. .. .. .. 8 83 9 Algeria 32 0b 55 8.4 .. 0.1 0.1 20.6 21.6 33 54 13 Angola .. .. 269 3.3 .. 3.7 3.7 59.3 60.7 75 17 8 Argentina 32 25 41 5.6 .. 0.6 0.6 26.7 27.7 13 80 7 Armenia 62 2 71 7.7 5.6 0.1 0.1 .. .. 5 90 5 Australia 19 16 6 5.0 8.2 0.1 0.1 .. .. 4 89 6 Austria .. .. 11 7.9 11.5 0.3 0.3 19.2 19.2 3 92 6 Azerbaijan .. 1 76 7.3 6.9 <0.1 <0.1 .. .. 17 79 4 Bangladesh 55 27 227 5.3 .. <0.1 <0.1 .. 12.7 46 44 10 Belarus 53 7 62 7.6 14.3 0.3 0.3 24.4 25.5 3 85 12 Belgium 30 25 13 5.2 13.1 0.2 0.3 45.5 38.6 7 88 6 Benin .. .. 88 4.4 .. 2.0 1.8 59.3 58.4 69 23 7 Bolivia .. .. 211 5.8 .. 0.1 0.1 27.0 27.9 38 54 8 Bosnia and Herzegovina 49 30 52 7.0 .. .. 0.1 .. .. 3 92 5 Botswana .. .. 654 5.2 .. 24.0 24.1 56.0 53.8 87 10 2 Brazil 22 14 60 6.2 .. 0.5 0.5 34.5 36.1 19 70 11 Bulgaria 44 23 39 7.6 10.2 .. 0.1 .. .. 3 94 4 Burkina Faso .. .. 223 3.7 .. 1.8 c 2.0 59.2 57.1 78 16 6 Burundi .. .. 334 1.7 .. 3.3 3.3 60.8 60.8 71 17 12 Cambodia .. .. 506 5.0 .. 2.0 1.6 46.4 45.4 61 34 5 Cameroon .. .. 174 3.7 .. 5.5 5.5d 62.2 61.7 68 26 7 Canada 22 17 5 7.4 8.7 0.3 0.3 12.2 16.3 5 89 6 Central African Republic .. .. 314 4.4 .. 10.8 10.7 59.1 56.5 73 21 6 Chad .. .. 272 3.6 .. 3.4 3.5 54.7 56.3 74 19 6 Chile 48 37 15 5.6 10.7 0.3 0.3 26.4 27.1 12 79 9 China 67 4 100 4.1 19.0 0.1e 0.1e 24.5e 27.7e 12 77 11 Hong Kong, China 22 4 75 8.2 .. .. .. .. .. .. .. .. Colombia .. .. 45 5.0 24.2 0.5 0.6 26.4 28.1 16 60 24 Congo, Dem. Rep. .. .. 356 3.0 .. 3.2 3.2 59.0 58.4 73 17 11 Congo, Rep. .. .. 367 5.0 .. 5.4 5.3 58.6 61.0 67 23 9 Costa Rica 29 10 14 9.3 20.1 0.3 0.3 27.0 27.4 12 77 11 Côte d'Ivoire .. .. 382 4.6 .. 7.0 7.1 57.8 58.8 67 23 9 Croatia 34 27 41 7.1 11.4 .. 0.1 .. .. 3 91 5 Cuba .. .. 9 9.3 13.9 0.1 0.1 54.8 55.3 11 80 9 Czech Republic 31 20 10 7.6 14.2 <0.1 <0.1 .. .. 3 91 6 Denmark 31 25 7 5.5 8.0 0.2 0.2 24.0 23.6 4 90 6 Dominican Republic 16 11 91 8.7 41.1 1.0 f 1.1 49.2 50.0 35 58 8 Ecuador .. .. 131 5.7 16.9 0.3 0.3 52.4 54.5 24 63 13 Egypt, Arab Rep. 40 18 25 11.0 7.5 <0.1 <0.1 .. .. 18 78 4 El Salvador 42 15 51 9.0 41.7 0.9 0.9 27.1 28.3 29 57 14 Eritrea .. .. 282 2.3 .. 2.4 2.4 59.2 58.5 70 22 7 Estonia 45 18 43 7.6 14.8 1.1 1.3 22.1 24.0 3 84 12 Ethiopia 6 0b 344 2.3 .. .. .. .. .. 71 23 6 Finland 26 19 6 5.9 7.3 0.1 0.1 .. .. 6 86 8 France 30 21 13 5.9 10.2 0.4 0.4 33.3 34.6 6 85 8 Gabon .. .. 308 4.9 .. 7.7 7.9 59.6 58.9 53 39 7 Gambia, The .. .. 242 4.1 .. 2.2 2.4 58.8 57.9 59 32 8 Georgia 53 6 83 7.4 6.2 0.1 0.2 .. .. 4 93 2 Germany 37 28 7 7.9 8.0 0.1 0.1 29.5 30.6 4 92 4 Ghana 7 1 205 4.2 .. 2.2c 2.3 60.7 60.0 59 33 8 Greece 47 29 17 5.9 19.3 0.2 0.2 20.7 21.5 4 92 4 Guatemala 21 2 78 8.6 .. 0.9 0.9 26.4 27.1 50 40 10 Guinea .. .. 236 4.1 .. 1.6 1.5 68.9 67.9 68 24 9 Guinea-Bissau .. .. 206 3.8 .. 3.8 3.8 59.3 58.6 75 19 6 Haiti 15 6 305 9.0 .. 3.8 3.8 52.9 53.3 69 29 2 108 2007 World Development Indicators 2.18 PEOPLE Health risk factors and public health challenges Prevalence Incidence of Prevalence Mortality Prevalence of HIV Cause of death of smoking tuberculosis of diabetes caused by road % of total deaths traffic Communicable injury diseases and Female maternal, per % of per Total % of perinatal, Non- % of adults 100,000 population 100,000 % of population population and nutrition communicable Male Female people ages 20­79 people ages 15­49 with HIV conditions diseases Injuries 2000­05a 2000­05a 2005 2007 1998­2003a 2003 2005 2003 2005 2002 2002 2002 Honduras .. .. 78 9.1 .. 1.5 1.5 25.0 26.2 32 59 9 Hungary 41 28 22 7.6 13.1 0.1 0.1 .. .. 2 91 7 India 47 17 168 6.7 .. 0.9 0.9 28.8 28.6 41 49 10 Indonesia 58 3 239 2.3 .. 0.1 0.1 13.6 17.1 29 61 10 Iran, Islamic Rep. 22 2 23 7.8 .. 0.1 0.2 13.0 16.7 12 70 18 Iraq .. .. 56 10.0 8.4 .. .. .. .. 43 43 13 Ireland 28 26 12 5.1 10.1 0.2 0.2 32.0 36.0 10 85 5 Israel 32 18 8 6.9 5.9 .. .. .. .. 6 88 6 Italy 31 17 7 5.8 10.5 0.5 0.5 33.6 33.3 4 92 4 Jamaica .. .. 7 10.3 .. 1.5 1.5 27.1 27.6 14 84 2 Japan 47 15 28 4.9 7.0 <0.1 <0.1 56.5 58.2 12 81 8 Jordan 51 8 5 9.8 .. .. .. .. .. 18 65 16 Kazakhstan 65 9 144 5.6 .. 0.1 0.1 56.0 56.7 8 79 13 Kenya 21 1 641 3.3 .. 6.7c 6.1 64.2 61.7 72 22 6 Korea, Dem. Rep. .. .. 178 5.2 .. .. .. .. .. 32 61 7 Korea, Rep. .. .. 96 7.8 15.1 <0.1 <0.1 59.1 56.9 6 83 12 Kuwait .. .. 24 14.4 23.7 .. .. .. .. 13 72 15 Kyrgyz Republic 51 5 121 5.1 12.9 <0.1 <0.1 .. .. 17 74 9 Lao PDR 59 13 155 3.1 .. 0.1 0.1 .. .. 55 36 9 Latvia 51 19 63 7.6 22.7 0.6 0.8 20.3 22.0 4 86 11 Lebanon 42 31 11 7.7 .. 0.1 0.1 .. .. 10 77 13 Lesotho .. .. 696 3.8 .. 23.7 23.2 56.0 60.0 81 16 3 Liberia .. .. 301 4.6 .. .. .. .. .. 76 15 10 Libya .. .. 18 4.4 .. .. .. .. .. 17 73 10 Lithuania 44 13 63 7.6 19.3 0.1 0.2 .. .. 2 85 13 Macedonia, FYR .. .. 30 7.1 5.1 <0.1 <0.1 .. .. 3 89 8 Madagascar .. .. 234 3.0 .. 0.5 0.5 28.2 27.7 65 27 8 Malawi 21 5 409 2.1 .. 14.2 14.1 59.3 58.8 79 17 4 Malaysia 43 2 102 10.7 .. 0.4 0.5 25.0 25.4 20 71 9 Mali .. .. 278 4.1 .. 1.8g 1.7 57.3 60.0 78 16 6 Mauritania .. .. 298 4.6 .. 0.7 0.7 59.2 57.3 65 27 8 Mauritius 32 1 62 11.1 14.7 0.2 0.6 .. .. 7 86 6 Mexico 13 5 23 10.6 11.8 0.3 0.3 20.0 23.3 16 72 11 Moldova 34 2 138 7.6 14.1 0.9 1.1 56.5 57.1 5 86 9 Mongolia 68 26 191 1.9 .. <0.1 <0.1 .. .. 23 66 11 Morocco 29 0b 89 8.1 .. 0.1 0.1 18.2 21.1 23 69 8 Mozambique .. .. 447 3.7 .. 16.0 16.1 57.5 60.0 83 14 2 Myanmar 36 12 171 3.2 .. 1.4 1.3 31.6 31.4 45 47 9 Namibia 23 10 697 4.2 .. 19.5 19.6 60.0 61.9 71 24 5 Nepal 49 24 180 4.2 .. 0.5 0.5 20.3 21.6 49 42 9 Netherlands 36 28 7 5.2 6.4 0.2 0.2 33.8 34.7 8 89 4 New Zealand 24 22 9 6.4 11.5 0.1 0.1 .. .. 3 91 6 Nicaragua .. 5 58 10.1 20.1 0.2 0.2 22.4 23.6 30 58 12 Niger .. .. 164 3.7 .. 1.1 1.1 59.7 59.2 80 14 6 Nigeria .. 1 283 4.5 .. 3.7 3.9 58.3 61.5 71 22 7 Norway 27 25 5 3.6 6.1 0.1 0.1 .. .. 8 87 5 Oman .. .. 11 13.1 .. .. .. .. .. 13 75 12 Pakistan .. .. 181 9.6 .. 0.1 0.1 13.3 16.7 53 39 8 Panama .. .. 45 9.7 16.4 0.9 0.9 26.0 25.3 21 69 10 Papua New Guinea .. .. 250 2.9 .. 1.6 1.8 59.2 59.6 52 38 9 Paraguay 23 7 68 4.8 .. 0.4 0.4 27.3 26.9 28 62 10 Peru .. .. 172 6.0 17.6 0.5 0.6 26.8 28.6 32 58 9 Philippines 41 8 291 7.6 .. <0.1 <0.1 20.2 28.3 35 56 9 Poland 40 25 26 7.6 14.8 0.1 0.1 30.0 30.0 3 90 7 Portugal .. .. 33 5.7 14.8 0.4 0.4 3.9 4.1 9 86 4 Puerto Rico 17 10 5 10.7 .. .. .. .. .. .. .. .. 2007 World Development Indicators 109 2.18 Health risk factors and public health challenges Prevalence Incidence of Prevalence Mortality Prevalence of HIV Cause of death of smoking tuberculosis of diabetes caused by road % of total deaths traffic Communicable injury diseases and Female maternal, per % of per Total % of perinatal, Non- % of adults 100,000 population 100,000 % of population population and nutrition communicable Male Female people ages 20­79 people ages 15­49 with HIV conditions diseases Injuries 2000­05a 2000­05a 2005 2007 1998­2003a 2003 2005 2003 2005 2002 2002 2002 Romania 32 10 134 7.6 16.8 .. 0.1 .. .. 5 90 5 Russian Federation 60 16 119 7.6 19.4 0.9 1.1 21.1 22.3 4 81 15 Rwanda .. .. 361 1.5 .. 3.8 3.1 52.6 56.9 76 18 6 Saudi Arabia 19 8 41 16.7 .. .. .. .. .. 15 69 16 Senegal .. .. 255 4.6 .. 0.9 0.9 58.5 58.9 64 26 10 Serbia and Montenegro 48 34 33 7.1 .. 0.2 0.2 22.2 20.0 3 93 4 Sierra Leone .. .. 475 4.3 .. 1.6 1.6 60.0 60.5 78 14 8 Singapore 24 4 29 10.1 5.2 0.3 0.3 25.5 27.3 12 82 5 Slovak Republic .. .. 17 7.6 11.3 <0.1 <0.1 .. .. 4 90 6 Slovenia 28 20 15 7.6 12.1 <0.1 <0.1 .. .. 4 87 8 Somalia .. .. 224 2.8 .. 0.9 0.9h 60.5 57.5 66 23 10 South Africa 23 8 600 4.4 .. 15.6f 18.8 56.9 58.5 65 28 7 Spain 39 25 27 5.7 12.8 <0.1 <0.1 22.9 22.9 5 90 5 Sri Lanka 23 2 60 8.4 .. 0.1 0.1 .. .. 13 76 10 Sudan .. .. 228 4.0 .. 1.6 1.6 56.7 56.3 45 41 14 Swaziland 11 3 1,262 4.0 .. 32.4 33.4 63.2 57.1 84 13 3 Sweden 17 18 6 5.2 5.9 0.2 0.2 31.3 31.3 5 90 5 Switzerland 27 23 7 7.9 7.5 0.4 0.4 36.0 36.9 6 89 5 Syrian Arab Republic .. .. 37 10.6 .. .. .. .. .. 17 73 9 Tajikistan .. .. 198 4.9 5.6 <0.1 <0.1 .. .. 27 67 6 Tanzania .. .. 342 2.9 .. 6.6 7.0 d 52.3 54.6 77 17 6 Thailand 49 3 142 6.9 .. 1.4 1.4 38.6 39.3 31 58 11 Togo .. .. 373 4.1 .. 3.2 3.2 58.9 61.0 66 26 8 Trinidad and Tobago .. .. 9 11.5 .. 2.6 2.6 56.0 57.7 23 71 6 Tunisia 50 2 24 5.2 .. 0.1 0.1 .. 22.1 9 80 11 Turkey 49 18 29 7.8 .. .. .. .. .. 14 79 6 Turkmenistan .. .. 70 5.2 10.3 .. 0.1 .. .. 19 73 8 Uganda 25 3 369 2.0 .. 6.8 6.4i 57.6 57.8 75 18 7 Ukraine 53 11 99 7.6 10.8 1.3 1.4 47.4 48.8 4 87 9 United Arab Emirates 17 1 16 19.5 .. .. .. .. .. 12 67 21 United Kingdom 27 25 14 2.9 6.1 .. .. .. .. 12 85 3 United States 24 19 5 7.8 14.7 0.6 0.6 25.5 25.0 6 88 6 Uruguay 35 24 28 5.6 10.0 0.4 0.5 55.6 55.8 7 86 7 Uzbekistan 24 1 113 5.1 9.8 0.1 0.2 .. 13.2 14 80 7 Venezuela, RB .. .. 42 5.4 23.1 0.6 0.7 27.7 28.2 15 66 18 Vietnam 35 2 175 2.9 .. 0.4 0.5j 30.5 33.6 24 66 9 West Bank and Gaza .. .. 21 8.4 .. .. .. .. .. .. .. .. Yemen, Rep. .. .. 82 2.9 .. .. .. .. .. 48 43 10 Zambia 16 1 600 3.8 .. 15.6k 17.0 56.3 57.0 86 12 2 Zimbabwe 20 2 601 4.0 .. 22.1 20.1 58.1 59.3 83 14 3 World .. w .. w 136 w .. w 0.9 w 1.0 w 30.3 w 31.3 w 32 w 59 w 9w Low income .. 15 220 .. 1.7 1.7 35.6 34.2 54 37 9 Middle income .. .. 111 .. 0.6 0.6 26.1 28.7 18 72 11 Lower middle income .. .. 113 .. 0.3 0.3 25.9 28.7 18 71 11 Upper middle income .. .. 104 .. 2.2 2.2 27.1 28.6 15 74 11 Low & middle income .. .. 158 .. 1.1 1.1 29.8 31.0 36 54 10 East Asia & Pacific 67 4 136 19.0 0.2 0.2 24.3 27.4 19 71 10 Europe & Central Asia .. .. 84 .. 0.6 0.7 .. .. 6 84 11 Latin America & Carib. .. .. 61 .. 0.5 0.6 30.3 32.0 22 67 11 Middle East & N. Africa .. .. 43 .. 0.1 0.1 .. .. 24 65 11 South Asia 47 18 174 .. 0.7 0.7 26.9 25.6 43 47 10 Sub-Saharan Africa .. .. 348 .. 6.4 5.8 57.6 58.4 72 21 7 High income .. .. 17 10.9 0.4 0.4 33.1 33.2 7 87 6 Europe EMU .. .. 13 10.0 0.4 0.3 29.3 29.7 5 90 5 a. Data are for the most recent year available. b. Less than 0.5. c. Survey data, 2003. d. Survey data, 2004. e. Includes Hong Kong, China. f. Survey data, 2002. g. Survey data, 2001. h. Survey data, 2006. i. Survey data, 2004­05. j. Survey data, 2005. k. Survey data, 2001/02. 110 2007 World Development Indicators 2.18 PEOPLE Health risk factors and public health challenges About the data The limited availability of data on health status is a is considerable difference in completeness of the and Related Health Problems, 10th revision. Cause major constraint in assessing the health situation in vital registry data. In some countries the vital registry of death data have been carefully analyzed to take developing countries. Surveillance data are lacking system covers only part of the country. In some, not into account incomplete coverage of vital registration for many major public health concerns. Estimates all deaths are registered. In addition, mortality from and the likely differences in cause of death patterns of prevalence and incidence are available for some different causes is difficult to measure. For countries that would be expected in the uncovered and often diseases but are often unreliable and incomplete. with incomplete vital registry systems, the WHO has poorer subpopulations. Special attention has also National health authorities differ widely in their used demographic techniques to estimate deaths. been paid to problems of misattribution or miscoding capacity and willingness to collect or report infor- Adult HIV prevalence rates reflect the rate of HIV of causes of death in cardiovascular diseases, can- mation. To compensate for the paucity of data and infection in each country's population. Low national cer, injuries, and general ill-defined categories. For ensure reasonable reliability and international com- prevalence rates can be very misleading, however. further information, consult the original source. parability, the World Health Organization (WHO) pre- They often disguise serious epidemics that are ini- Definitions pares estimates in accordance with epidemiological tially concentrated in certain localities or among spe- models and statistical standards. cific population groups and threaten to spill over into · Prevalence of smoking is the percentage of men Smoking is the most common form of tobacco use the wider population. In many parts of the developing and women who smoke cigarettes. The age range var- in many countries, and the prevalence of smoking is world most new infections occur in young adults, with ies, but in most countries is 18 and older or 15 and therefore a good measure of the extent of the tobacco young women especially vulnerable. older. · Incidence of tuberculosis is the estimated epidemic (Corrao and others 2000). While the preva- Estimates from recent Demographic and Health number of new tuberculosis cases (pulmonary, smear lence of smoking has been declining in some high- Surveys that have collected data on HIV/AIDS differ positive, extrapulmonary). · Prevalence of diabetes income countries, it has been increasing in many from those of the Joint United Nations Programme on refers to the percentage of people ages 20­79 who developing countries. Tobacco use causes heart and HIV/AIDS (UNAIDS) and the WHO, which are based on have type 1 or type 2 diabetes. · Mortality caused other vascular diseases and cancers of the lung and surveillance systems that focus on pregnant women by road traffic injury refers to the number of deaths other organs. Given the long delay between starting who attend sentinel antenatal clinics. There are rea- per 100,000 people caused by road traffic injury. to smoke and the onset of disease, the health impact sons to be cautious about comparing the two sets · Prevalence of HIV is the percentage of people of smoking in developing countries will increase rap- of estimates. Demographic and Health Surveys are who are infected with HIV. · Cause of death refers idly in the next few decades. Because the data pres- household surveys that use a representative sample to the share of all deaths by underlying causes. ent a one-time estimate, with no information on the from the whole population, whereas surveillance · Communicable diseases and maternal, perina- intensity or duration of smoking, and because the data from antenatal clinics are limited to pregnant tal, and nutrition conditions include infectious and definition of adult varies across countries, the data women. Representative household surveys also fre- parasitic diseases, respiratory infections, and nutri- should be interpreted with caution. quently provide better coverage of rural populations. tional deficiencies such as underweight and stunt- Tuberculosis is one of the main causes of death from However, the fact that some respondents refuse to ing. · Noncommunicable diseases include cancer, a single infectious agent among adults in developing participate or are absent from the household adds diabetes mellitus, cardiovascular diseases, digestive countries. In high-income countries tuberculosis has considerable uncertainty to survey-based HIV esti- diseases, skin diseases, musculoskeletal diseases, reemerged largely as a result of cases among immi- mates, because the possible association of absence and congenital anomalies. · Injuries include uninten- grants. The estimates of tuberculosis incidence in the or refusal with higher HIV prevalence is unknown. tional and intentional injuries. table are based on a new approach in which reported UNAIDS and WHO estimates are generally based on Data sources cases are adjusted using the ratio of case notifications surveillance systems that focus on pregnant women to the estimated share of cases detected by panels of who attend sentinel antenatal clinics. UNAIDS and Data on smoking are from J. McCay, M. Erkson, 80 epidemiologists convened by the WHO. the WHO use a methodology to estimate HIV preva- and O. Shafey's Tobacco Atlas, 2nd edition (2006). Diabetes, an important cause of ill health and a lence for the adult population (ages 15­49) that Data on tuberculosis are from the WHO's Global risk factor for other diseases in developed countries, assumes that prevalence among pregnant women is Tuberculosis Control Report 2007. Data on diabe- is spreading rapidly in developing countries. While a good approximation of prevalence among men and tes are from the International Diabetes Federa- diabetes is most common among the elderly, preva- women. However, this assumption might not apply to tion's Diabetes Atlas, 3rd edition. Data on mortal- lence rates are rising among younger and productive all countries or over time. There are also other poten- ity caused by road traffic injury are from the WHO populations in developing countries. Economic devel- tial biases associated with the use of antenatal clinic and the World Bank's World Report on Road Traffic opment has led to the greater adoption of Western data, such as differences among women who attend Injury Prevention (2004) and the Organisation for lifestyles and diet in developing countries, resulting antenatal clinics and those who do not. Economic Co-operation and Development. Data on in a substantial increase in diabetes. Without effec- The data on cause of death are compiled by WHO, HIV are from UNAIDS and the WHO's 2006 Report tive prevention and control programs, diabetes will based mainly on data from national vital registry sys- on the Global AIDS Epidemic. Data on cause of likely continue to increase. Data are estimated based tems, as well as sample registration systems, popu- death are from the Disease Control Priorities on sample surveys. lation laboratories and epidemiological analyses of Project's (2006) Global Burden of Disease and Risk Data for mortality caused by road traffic injury are specific conditions. Data are classified based on the Factors (www.dcp2.org/pubs/GBD). collected by the WHO based on vital registries. There International Statistical Classification of Diseases 2007 World Development Indicators 111 2.19 Health gaps by income and gender Survey Prevalence of child Child Infant Under-five year malnutrition immunization rate mortality rate mortality rate Underweight % of children % of children ages 12­23 monthsa under age 5 Measles DPT per 1,000 live births per 1,000 Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile Armenia 2000 3 1 68 74b 89 84b 52 27 61 30 Bangladesh 2004 41 24 60 91 71 91 90 65 121 71 Benin 2001 22 9 57 83 63 89 112 50 198 93 Bolivia 2003 10 1 62 74 64 85 87 32 119 37 Brazil 1996 10 3 78 90 66 82 83 29 99 33 Burkina Faso 2003 26 16 48 71 45 73 97 78 206 144 Cambodia 2000 35 28 44 82 39 75 110 50 155 64 Cameroon 2004 22 5 57 86 55 86 101 52 189 88 Central African Republic 1994­95 25 15 31 80 27 76 132 54 193 98 Chad 2004 27 19 8 38 5 42 109 101 176 187 Colombia 2005 11 3 70 91 73 91 32 14 39 16 Comoros 1996 22 14 51 86 58 92 87 65 129 87b Côte d'Ivoire 1994 21 10 31 79 26 74 117 63 190 97 Dominican Republic 2002 9 1 83 94 46 66 50 20 66 22 Egypt, Arab Rep. 2000 5 2 95 99 94 93 76 30 98 34 Eritrea 1995 27 19 37 92 30 89 74 68 152 104 Ethiopia 2000 32 29 18 52 14 43 93 95 159 147 Gabon 2000 15 7 34 71 18 49 57 36 93 55 Ghana 2003 22 10 74 88 64 87 61 58 128 88 Guatemala 1998­99 26 10 80 91 74 76 58 39 78 39 Guinea 1999 22 13 33 73 30 69 119 70 230 133 Haiti 2000 18 6 43 63 31 58 100 97 164 109 India 1998­99 33 21 28 81 36 85 97 38 141 46 Indonesia 2002­03 .. .. 59 85 42 72 61 17 77 22 Jordan 1997 7 3 90 93 98 93 35 23 42 25 Kazakhstan 1999 5 6 74 76b 90 82b 68 42 82 45 Kenya 2003 22 7 54 88 56 73 96 62 149 91 Kyrgyz Republic 1997 10 7 82 81 82 87 83 46 96 49 Madagascar 1997 29 24 32 79 32 81 119 58 195 101 Malawi 2000 24 11 80 90 79 93 132 86 231 149 Mali 2001 26 13 40 77 28 71 137 90 248 148 Mauritania 2000­01 23 15 42 86 18 61 61 62 98 79 Morocco 2003­04 13 3 83 98 89 98 62 24 78 26 Mozambique 2003 21 7 61 96 52 96 143 71 196 108 Namibia 2000 22 10 76 86 76 83 36 23 55 31 Nepal 2001 40 26 61 83 62 85 86 53 130 68 Nicaragua 2001 13 2 76 94 77 83 50 16 64 19 Niger 1998 30 26 23 66 9 68 131 86 282 184 Nigeria 2003 24 10 16 71 7 61 133 52 257 79 Pakistan 1990­91 33 19 28 75 24 64 89 63 125 74 Paraguay 1990 5 1 48 69 40 69 43 16 57 20 Peru 2000 13 1 81 92 76 93 64 14 93 18 Philippines 2003 .. .. 70 89 64 92 42 19 66 21 Rwanda 2000 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 20 11 65 91 34 36 88 64 137 93 Togo 1998 23 10 35 63 29 68 84 66 168 97 Turkey 1998 13 3 64 89 45 81 68 30 85 33 Turkmenistan 2000 12 10 91 80 97 86 89 58 106 70 Uganda 2000­01 21 10 49 65 35 55 106 60 192 106 Uzbekistan 1996 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 24 17 81 88 74 89 115 57 192 92 Zimbabwe 1999 16 6 80 86 81 86 59 44 100 62 112 2007 World Development Indicators 2.19 PEOPLE Health gaps by income and gender Survey Prevalence of child Child Infant Under-five year malnutrition immunization rate mortality rate mortality rate Underweight % of children % of children ages 12­23 monthsa under age 5 Measles DPT 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 Comoros 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 2007 World Development Indicators 113 2.19 Health gaps by income and gender Survey Pregnant women Contraceptive Births attended by Total fertility Exclusive year receiving prevalence skilled health staffc rated breastfeeding prenatal care % 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 0e 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 55 32 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 18 b .. 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 0e 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 28 b 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 b .. 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. Refers to children who were immunized at any time before the survey. b. Data contain large sampling errors because of the small number of cases. c. Based on births in the fi ve years before the survey. d. Based on information in the three years before the survey. e. Less than 0.5. 114 2007 World Development Indicators 2.19 PEOPLE Health gaps by income and gender About the data The data in the table describe the health status and account and creating country-specific asset indexes sexual partners are practicing, any modern method use of health services by individuals in different with country-specifi c choices of asset indicators of contraception. These data may differ from those in socioeconomic groups within countries. The data are might produce a more effective and accurate index table 2.16. · Births attended by skilled health staff from Demographic and Health Surveys conducted for each country. The asset index used in the table are the percentage of deliveries attended by person- by Macro International with the support of the U.S. does not have this flexibility. nel trained to give the necessary supervision, care, Agency for International Development. These large- The analysis was carried out for 56 countries, and advice to women during pregnancy, labor, and scale household sample surveys, conducted peri- with the results issued in country reports. The table the postpartum period; to conduct deliveries on their odically in developing countries, collect information shows the estimates for the poorest and richest quin- own; and to care for newborns. Skilled health staff on a large number of health, nutrition, and popula- tiles and by sex only; the full set of estimates for up include doctors, nurses, and trained midwives, but tion measures as well as on respondents' social, to 117 indicators is available in the country reports exclude trained or untrained traditional birth atten- demographic, and economic characteristics using a (see Data sources). The data in this table will differ dants. Data in the tables are based on births in the standard set of questionnaires. The data presented from data for similar indicators in preceding tables five years preceding the survey and may therefore dif- here draw on responses to individual and household either because the indicator refers to a period a few fer from the estimates in table 2.16. · Total fertility questionnaires. years preceding the survey date or because the indi- rate is the number of children that would be born to The table defines socioeconomic status in terms cator definition or methodology is different. a woman if she were to live to the end of her child- of a household's assets, including ownership of con- bearing years and bear children in accordance with Definitions sumer items, features of the household's dwelling, current age-specific fertility rates. Data in the table and other characteristics related to wealth. Each · Survey year is the year in which the underlying are based on the information in the three years pre- household asset on which information was collected data were collected. · Prevalence of child malnu- ceding the survey and may therefore differ from the was assigned a weight generated through principal- trition is the percentage of children under age five estimates in table 2.16. · Exclusive breastfeeding component analysis. The resulting scores were stan- whose weight for age is between two and three refers to the percentage of children ages 0­3 months dardized in relation to a standard normal distribution standard deviations below the median reference who received only the breast milk in the 24 hours with a mean of zero and a standard deviation of one. standard for their age as established by the World preceding the survey. These data differ from those The standardized scores were then used to create Health Organization, the U.S. Centers for Disease in table 2.17 because the definition differs. break-points defining wealth quintiles, expressed as Control and Prevention, and the U.S. National Cen- quintiles of individuals in the population rather than ter for Health Statistics. These data may differ from quintiles of individuals at risk with respect to any those in table 2.17. · Child immunization rate is the one health indicator. percentage of children ages 12­23 months at the The choice of the asset index for defining socio- time of the survey who received vaccinations at any economic status was based on pragmatic rather than time before the survey for four diseases--measles conceptual considerations: Demographic and Health and diphtheria, pertussis (whooping cough), and Surveys do not provide income or consumption data tetanus (DPT). These data may differ from those in but do have detailed information on households' own- table 2.15. · Infant mortality rate is the number of ership of consumer goods and access to a variety infants dying before reaching one year of age, per of goods and services. Like income or consumption, 1,000 live births in a given year. Data in the table the asset index defines disparities in primarily eco- are based on births in the 10 years preceding the nomic terms. It therefore excludes other possibilities survey and may therefore differ from the estimates of disparities among groups, such as those based in table 2.20. · Under-five mortality rate is the prob- on gender, education, ethnic background, or other ability that a newborn baby will die before reaching facets of social exclusion. To that extent the index age fi ve, if subject to current age-specific mortal- provides only a partial view of the multidimensional ity rates. The probability is expressed as a rate per concepts of poverty, inequality, and inequity. 1,000. Data in the table are based on births in the Creating one index that includes all asset indica- 10 years preceding the survey and may therefore tors limits the types of analysis that can be per- differ from the estimates in table 2.20. · Pregnant formed. In particular, the use of a unified index does women receiving prenatal care are the percentage Data sources not permit a disaggregated analysis to examine of women with one or more births during the fi ve which asset indicators have a more or less impor- years preceding the survey, who were attended at Data on health gaps by income and gender are tant association with health status or use of health least once during pregnancy by skilled health per- from an analysis of Demographic and Health Sur- services. In addition, some asset indicators may sonnel for reasons related to pregnancy. These data veys by the World Bank and Macro International. reflect household wealth better in some countries may differ from those in table 2.16. · Contraceptive Country reports are available at www.worldbank. than in others--or reflect different degrees of wealth prevalence is the percentage of women married or org/povertyandhealth/countrydata. in different countries. Taking such information into in-union ages 15­49 who are practicing, or whose 2007 World Development Indicators 115 2.20 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 2005 1990 2005 1990 2005 1997­2005a 1997­2005a 2001­05a 2001­05a 2005 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 72 75 37 16 45 18 .. .. 96 55 81 88 Algeria 67 72 54 34 69 39 .. .. 132 112 76 80 Angola 40 41 154 154 260 260 .. .. 505 461 29 34 Argentina 72 75 26 15 29 18 .. .. 174 87 73 86 Armenia 68 73 46 26 54 29 5 3 204 92 67 82 Australia 77 81 8 5 10 6 .. .. 89 50 86 92 Austria 76 79 8 4 10 5 .. .. 120 59 83 91 Azerbaijan 71 72 84 74 105 89 .. .. 226 104 61 77 Bangladesh 55 64 100 54 149 73 24 29 238 205 61 66 Belarus 71 68 16 10 19 12 .. .. 357 128 52 80 Belgium 76 79 8 4 10 5 .. .. 125 67 83 91 Benin 53 55 111 89 185 150 72 79 309 277 50 55 Bolivia 59 65 89 52 125 65 25 29 253 192 61 69 Bosnia and Herzegovina 72 74 18 13 22 15 .. .. 152 78 75 86 Botswana 64 35 45 87 58 120 .. .. 841 853 11 11 Brazil 66 71 50 31 60 33 .. .. 252 132 65 79 Bulgaria 72 73 15 12 19 15 .. .. 216 91 70 85 Burkina Faso 48 48 113 96 210 191 110 113 407 386 40 43 Burundi 44 45 114 114 190 190 .. .. 512 492 32 35 Cambodia 54 57 80 68 115 87 34 30 372 208 47 63 Cameroon 52 46 85 87 139 149 73 72 508 499 34 36 Canada 77 80 7 5 8 6 .. .. 97 60 86 91 Central African Republic 48 39 102 115 168 193 .. .. 658 662 22 24 Chad 46 44 120 124 201 208 96 101 495 471 32 35 Chile 74 78 18 8 21 10 .. .. 131 65 80 89 China 69 72 38 23 49 27 .. .. 141 87 75 82 Hong Kong, China 77 82 .. .. .. .. .. .. 79 34 87 94 Colombia 68 73 26 17 35 21 4 3 182 103 72 82 Congo, Dem. Rep. 46 44 129 129 205 205 .. .. 486 460 32 36 Congo, Rep. 54 53 83 81 110 108 .. .. 450 424 40 46 Costa Rica 77 79 16 11 18 12 .. .. 117 66 82 89 Côte d'Ivoire 52 46 103 118 157 195 83 58 474 461 35 38 Croatia 72 76 11 6 12 7 .. .. 164 67 74 89 Cuba 75 77 11 6 13 7 .. .. 121 81 81 87 Czech Republic 71 76 11 3 13 4 .. .. 161 69 76 89 Denmark 75 78 8 4 9 5 .. .. 121 74 82 88 Dominican Republic 66 68 50 26 65 31 9 9 267 145 62 76 Ecuador 69 75 43 22 57 25 .. .. 184 105 73 83 Egypt, Arab Rep. 63 71 76 28 104 33 15 16 171 104 71 81 El Salvador 66 71 47 23 60 27 .. .. 221 137 68 79 Eritrea 48 55 88 50 147 78 55 50 455 384 39 48 Estonia 69 73 12 6 16 7 .. .. 288 94 60 85 Ethiopia 45 43 122 80 204 127 83 86 451 425 37 41 Finland 75 79 6 3 7 4 .. .. 136 61 82 92 France 77 80 7 4 9 5 .. .. 135 60 82 92 Gabon 60 54 60 60 92 91 32 33 438 432 44 46 Gambia, The 50 57 103 97 151 137 .. .. 320 281 51 56 Georgia 70 71 43 41 47 45 .. .. 214 82 67 83 Germany 75 79 7 4 9 5 .. .. 119 61 83 91 Ghana 56 57 75 68 122 112 44 52 344 330 51 54 Greece 77 79 10 4 11 5 .. .. 112 49 83 92 Guatemala 62 68 60 32 82 43 15 18 296 172 61 74 Guinea 47 54 139 97 234 160 101 98 324 303 49 52 Guinea-Bissau 42 45 153 124 253 200 .. .. 465 423 34 39 Haiti 49 53 102 84 150 120 52 54 454 447 41 43 116 2007 World Development Indicators 2.20 PEOPLE 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 2005 1990 2005 1990 2005 1997­2005a 1997­2005a 2001­05a 2001­05a 2005 2005 Honduras 65 69 44 31 59 40 .. .. 245 201 65 72 Hungary 69 73 15 7 17 8 .. .. 261 111 67 85 India 59 64 80 56 123 74 25 37 235 154 61 69 Indonesia 62 68 60 28 91 36 13 11 205 155 66 74 Iran, Islamic Rep. 65 71 54 31 72 36 .. .. 158 104 73 81 Iraq 62 .. 40 .. 50 .. .. .. .. .. .. .. Ireland 75 79 8 5 9 6 .. .. 94 56 84 90 Israel 77 80 10 5 12 6 .. .. 86 46 86 92 Italy 77 80 8 4 9 4 .. .. 92 48 85 92 Jamaica 71 71 17 17 20 20 .. .. 237 194 68 73 Japan 79 82 5 3 6 4 .. .. 92 45 86 94 Jordan 68 72 33 22 40 26 5 5 165 123 73 79 Kazakhstan 68 66 53 63 63 73 11 6 343 152 49 73 Kenya 58 49 64 79 97 120 42 39 479 551 38 35 Korea, Dem. Rep. 65 64 42 42 55 55 .. .. 305 208 53 67 Korea, Rep. 71 78 8 5 9 5 .. .. 138 54 79 91 Kuwait 75 78 14 9 16 11 .. .. 88 58 83 88 Kyrgyz Republic 68 68 68 58 80 67 10 11 264 124 60 77 Lao PDR 50 56 120 62 163 79 .. .. 318 269 50 55 Latvia 69 71 14 9 18 11 .. .. 300 116 62 83 Lebanon 69 73 32 27 37 30 .. .. 151 99 74 83 Lesotho 57 35 81 102 101 132 .. .. 853 817 10 14 Liberia 43 42 157 157 235 235 .. .. 535 500 28 32 Libya 68 74 35 18 41 19 .. .. 137 93 76 84 Lithuania 71 71 10 7 13 9 .. .. 303 106 62 86 Macedonia, FYR 72 74 33 15 38 17 .. .. 139 81 76 85 Madagascar 51 56 103 74 168 119 45 45 337 294 49 54 Malawi 46 41 131 79 221 125 101 102 635 653 25 25 Malaysia 70 74 16 10 22 12 .. .. 154 89 75 84 Mali 46 49 140 120 250 218 132 125 358 323 42 46 Mauritania 49 54 85 78 133 125 38 38 341 284 46 52 Mauritius 69 73 20 13 23 15 .. .. 207 110 68 82 Mexico 71 75 37 22 46 27 .. .. 155 86 76 85 Moldova 68 68 29 14 35 16 .. .. 276 137 60 77 Mongolia 63 67 78 39 108 49 .. .. 237 168 60 69 Morocco 64 70 69 36 89 40 9 11 162 109 72 80 Mozambique 43 42 158 100 235 145 61 64 600 593 26 28 Myanmar 56 61 91 75 130 105 .. .. 301 200 54 65 Namibia 62 47 60 46 86 62 22 20 620 625 30 31 Nepal 55 63 100 56 145 74 28 40 248 222 60 63 Netherlands 77 79 7 4 9 5 .. .. 90 64 84 90 New Zealand 75 80 8 5 11 6 .. .. 99 65 85 90 Nicaragua 64 70 52 30 68 37 10 9 219 146 68 76 Niger 40 45 191 150 320 256 184 202 368 339 39 41 Nigeria 46 44 120 100 230 194 120 123 499 495 32 33 Norway 77 80 7 3 9 4 .. .. 92 57 86 91 Oman 70 75 25 10 32 12 .. .. 114 85 80 85 Pakistan 59 65 100 79 130 99 .. .. 180 152 64 67 Panama 72 75 27 19 34 24 .. .. 152 83 77 86 Papua New Guinea 52 56 69 55 94 74 .. .. 388 349 44 49 Paraguay 68 71 33 20 41 23 .. .. 159 106 72 81 Peru 66 71 58 23 78 27 19 17 186 120 69 78 Philippines 66 71 41 25 62 33 14 9 169 116 72 80 Poland 71 75 19 6 18 7 .. .. 189 75 71 87 Portugal 74 78 11 4 14 5 .. .. 139 58 81 91 Puerto Rico 75 78 .. .. .. .. .. .. 186 69 74 89 2007 World Development Indicators 117 2.20 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 2005 1990 2005 1990 2005 1997­2005a 1997­2005a 2001­05a 2001­05a 2005 2005 Romania 70 72 27 16 31 19 .. .. 223 96 67 84 Russian Federation 69 65 21 14 27 18 .. .. 467 173 45 76 Rwanda 31 44 103 118 173 203 105 97 505 455 31 36 Saudi Arabia 68 73 35 21 44 26 3 4 148 101 77 82 Senegal 53 56 72 61 149 119 76 74 311 262 51 56 Serbia and Montenegro 72 73 24 12 28 15 .. .. 164 89 73 85 Sierra Leone 39 41 175 165 302 282 .. .. 432 379 32 37 Singapore 74 80 7 3 8 3 .. .. 85 50 85 91 Slovak Republic 71 74 12 7 14 8 .. .. 202 78 71 87 Slovenia 73 78 8 3 10 4 .. .. 141 63 78 89 Somalia 42 48 133 133 225 225 .. .. 395 341 39 44 South Africa 62 48 45 55 60 68 18 13 658 638 25 30 Spain 77 81 8 4 9 5 .. .. 113 46 83 93 Sri Lanka 71 75 26 12 32 14 .. .. 130 77 78 87 Sudan 53 57 74 62 120 90 .. .. 339 299 50 55 Swaziland 57 41 78 110 110 160 .. .. 885 893 8 8 Sweden 78 81 6 3 7 4 .. .. 82 51 87 92 Switzerland 77 81 7 4 9 5 .. .. 87 47 86 92 Syrian Arab Republic 68 74 31 14 39 15 .. .. 130 90 77 84 Tajikistan 63 64 91 59 115 71 .. .. 219 146 60 70 Tanzania 53 46 102 76 161 122 56 52 507 511 34 36 Thailand 68 71 31 18 37 21 .. .. 228 119 68 82 Togo 57 55 88 78 152 139 73 65 369 310 47 55 Trinidad and Tobago 71 70 28 17 33 19 .. .. 260 186 65 75 Tunisia 70 73 41 20 52 24 .. .. 134 77 77 86 Turkey 66 71 67 26 82 29 10 13 186 115 69 79 Turkmenistan 63 63 80 81 97 104 19 17 305 156 53 71 Uganda 46 50 93 79 160 136 78 70 459 447 39 41 Ukraine 70 68 19 13 26 17 .. .. 404 150 47 76 United Arab Emirates 73 79 13 8 15 9 .. .. 78 50 86 91 United Kingdom 76 79 8 5 10 6 .. .. 101 63 84 90 United States 75 78 9 6 11 7 .. .. 144 84 80 87 Uruguay 73 76 21 14 23 15 .. .. 161 83 74 87 Uzbekistan 69 67 65 57 79 68 .. .. 247 145 61 74 Venezuela, RB 71 74 27 18 33 21 .. .. 184 93 73 84 Vietnam 65 71 38 16 53 19 10 7 173 121 73 80 West Bank and Gaza 69 73 34 21 40 23 .. .. 138 101 76 83 Yemen, Rep. 55 62 98 76 139 102 33 36 267 222 57 63 Zambia 46 38 101 102 180 182 89 74 672 713 21 20 Zimbabwe 59 37 53 81 80 132 35 31 772 808 16 14 World 65 w 68 w 64 w 51 w 95 w 75 w 232 w 164 w 67 w 75 w Low income 56 59 94 75 147 114 290 237 55 61 Middle income 68 70 44 30 58 37 196 120 70 79 Lower middle income 67 71 46 31 62 39 176 111 71 80 Upper middle income 69 70 33 22 41 27 289 159 62 78 Low & middle income 63 65 69 56 103 82 235 167 64 72 East Asia & Pacific 67 71 43 26 59 33 162 105 73 80 Europe & Central Asia 69 69 39 27 48 32 320 136 58 79 Latin America & Carib. 68 72 43 26 54 31 208 118 70 81 Middle East & N. Africa 64 70 60 43 80 53 171 119 71 79 South Asia 59 63 86 62 129 83 230 161 61 68 Sub-Saharan Africa 49 47 109 96 185 163 483 470 35 38 High income 76 79 9 6 11 7 122 65 82 90 Europe EMU 76 80 8 4 9 5 118 57 83 92 a. Data are for the most recent year available. 118 2007 World Development Indicators 2.20 PEOPLE Mortality About the data Mortality rates for different age groups (infants, chil- based on outdated surveys may not be reliable for rent age-specific mortality rates. It shows that even dren, and adults) and overall indicators of mortality monitoring changes in health status or for compara- in countries where mortality is high, a certain share (life expectancy at birth or survival to a given age) tive analytical work. of the current birth cohort will live well beyond the life are important indicators of health status in a country. To produce harmonized estimates of infant and expectancy at birth, while in low-mortality countries Because data on the incidence and prevalence of under-five mortality rates that use all available infor- close to 90 percent will reach at least age 65. diseases (morbidity data) are frequently unavailable, mation in a transparent way, the United Nations Chil- Definitions mortality rates are often used to identify vulnerable dren's Fund (UNICEF) and the World Bank developed populations. And they are among the indicators most and adopted a methodology that fi ts a regression · Life expectancy at birth is the number of years frequently used to compare levels of socioeconomic line to the relationship between mortality rates and a newborn infant would live if prevailing patterns of development across countries. their reference dates using weighted least squares. mortality at the time of its birth were to stay the The main sources of mortality data are vital registra- (For further discussion of methodology for childhood same throughout its life. · Infant mortality rate is tion systems and direct or indirect estimates based mortality estimates, see Hill and others 1999.) the number of infants dying before reaching one year on sample surveys or censuses. A "complete" vital Infant and child mortality rates are higher for boys of age, per 1,000 live births in a given year. · Under- registration system--one covering at least 90 percent than for girls in countries in which parental gender five mortality rate is the probability that a newborn of vital events in the population--is the best source of preferences are insignificant. Child mortality cap- baby will die before reaching age five, if subject to age-specific mortality data. But such systems are fairly tures the effect of gender discrimination better than current age-specific mortality rates. The probability uncommon in developing countries. Thus estimates does infant mortality, as malnutrition and medical is expressed as a rate per 1,000. · Child mortality must be obtained from sample surveys or derived by interventions are more important in this age group. rate is the probability of dying between the ages of applying indirect estimation techniques to registra- Where female child mortality is higher, as in some one and five, if subject to current age-specific mortal- tion, census, or survey data. Survey data are subject countries in South Asia, girls probably have unequal ity rates. The probability is expressed as a rate per to recall error, and surveys estimating infant deaths access to resources. 1,000. · Adult mortality rate is the probability of require large samples because households in which a Adult mortality rates have increased in many coun- dying between the ages of 15 and 60--that is, the birth or an infant death has occurred during a given year tries in Sub-Saharan Africa and Europe and Central probability of a 15-year-old dying before reaching age cannot ordinarily be pre-selected for sampling. Indirect Asia. In Sub-Saharan Africa the increase stems 60--if subject to current age-specific mortality rates estimates rely on estimated actuarial "life" tables that from AIDS-related mortality and affects both men between those ages. · Survival to age 65 refers to may be inappropriate for the population concerned. and women. In Europe and Central Asia the causes the percentage of a cohort of newborn infants that Because life expectancy at birth is constructed using are more diverse and affect men more. They include would survive to age 65, if subject to current age- infant mortality data and model life tables, similar reli- a high prevalence of smoking, a high-fat diet, exces- specific mortality rates. ability issues arise for this indicator. sive alcohol use, and stressful conditions related to Life expectancy at birth and age-specific mortality the economic transition. Data sources rates are generally estimates based on vital registra- The percentage of a cohort surviving to age 65 tion or the most recent census or survey available reflects both child and adult mortality rates. Like life Data on infant and under-fi ve mortality are the (see Primary data documentation). Extrapolations expectancy, it is a synthetic measure based on cur- harmonized estimates of the World Health Organi- zation, UNICEF, and the World Bank, based mainly Under-five mortality rates improve on household surveys, censuses, and vital regis- as mothers' education levels rise 2.20a tration data, supplemented by the World Bank's No education Complete primary/ Complete secondary/ estimates based on household surveys and vital Under-five mortality rate (per 1,000) Some primary some secondary some higher registration data. Other estimates are compiled 200 and produced by the World Bank's Human Devel- opment Network and Development Data Group in 150 consultation with its operational staff and coun- try offices. Important inputs to the World Bank's demographic work come from the United Nations 100 Population Division's World Population Prospects: The 2004 Revision, census reports and other statistical publications from national statistical 50 offices, Demographic and Health Surveys by Macro International, and the Human Mortality Database 0 by the University of California, Berkeley, and the Tanzania Bolivia Bangladesh Philippines Egypt 2004­05 2003 2004 2003 2005 Max Planck Institute for Demographic Research Source: Demographic and Health Surveys. (www.mortality.org/). 2007 World Development Indicators 119 Text figures, tables, and boxes ENVIRONMENT 3 Introduction A griculture is environment For the 70 percent of the world's poor in rural areas, agriculture is the major source of income and employment. It takes up more than one-third of the world's area and more than two-thirds of the world's water withdrawals. Competition for these resources is increasing with growth of population, cities, and demand for food. And climate change is altering the patterns of rainfall and temperature that agriculture depends on. The depletion and degrada- tion of these resources thus pose serious challenges to the capacity of agriculture to produce enough food and other agricultural products to sustain the livelihoods of rural populations and accommodate the needs of urban populations. In the agriculture-based economies of Sub-Saharan Africa agriculture contributed a third to economic growth in 1990­2005. In the transforming economies of Asia, mainly China, India, and Indonesia, it contributed 8 percent to economic growth, while making up a fifth of the economy and employing half the labor force. And in the urbanizing economies of Latin America and some countries of Eastern Europe and Central Asia, it contributed 10 percent to the economy and to growth. Agriculture is a way of life throughout the world, with 2.5 billion of 3 billion rural people tied to agricultural activities, particularly to producing food. Producing food requires enormous amounts of water and cropland. In some parts of the world, the demand for water exceeds the supply. But in many places it appears that water scarcity is caused not by shortages of water but by its mismanagement. Not enough is known because data on the availability and productivity of water are limited. However, water is clearly central to the social, political, and economic affairs of a country and to cooperation or conflict across boundaries. Agricultural intensification--producing more crops on the same or smaller amounts of land-- along with irrigation and the conversion of forest lands to cropland have helped meet the increasing demand for food. Food production has thankfully outpaced population growth in most regions. But this has too often been at the expense of soil degradation, water pollu- tion, and added pressure on water resources. Turning forests into agricultural lands reduces biodiversity and contributes to global warming. Rising sea levels, warming temperatures, and changes in weather patterns affect millions of people. The impact is especially severe for those in developing countries, threatening their potential to move out of poverty. 2007 World Development Indicators 121 Agriculture, poverty reduction, and food security With economic growth the share of agriculture in the global Agriculture's changing role is underscored by rapid rural- economy declines. Even so, agriculture remains important in urban migration. The United Nations estimates that in 2007, many developing economies and the source of income for for the first time, the majority of the global population will be many poor people. In some African countries more than half urban (United Nations Population Division 2005, World Popu- the GDP is in agriculture--in Liberia 64 percent, in Guinea- lation Prospects 2004). And this will continue. Urban popula- Bissau 60 percent, and in Central African Republic 54 per- tion is expected to grow 1.8 percent a year through 2030, cent. On average agriculture contributes more than 20 per- almost twice as fast as the global population. Productivity cent to value added in low-income economies (figure 3a). must continue to rise, so that the shrinking rural population Globally, about 40 percent of the active labor force is in can provide more agricultural products for a rising urban pop- agriculture, but in Sub-Saharan Africa and Asia and the Pacific ulation with higher incomes. about 60 percent is in agriculture. Compare that with 18 per- In recent years the increases in demand for food have cent in Latin America and 4 percent in high-income econo- been met by higher productivity through agricultural intensifi - mies. Variations across countries are even greater, with agri- cation, technological advance, mechanization, and irrigation cultural employment's share ranging from less than 2 percent (figure 3b). However, continuing depletion and degradation in the United Kingdom and the United States to 44 percent of natural resources that constitute the agricultural sector's in China and 80 percent in Nepal. Agriculture is associated main inputs--water and land--could slow the growth of pro- with natural wealth-- particularly in developing economies. A ductivity and undermine food security. recent World Bank study estimates that roughly two-thirds of the natural wealth in low-income countries is embodied in cropland and pastureland (World Bank 2006e). Agriculture's share in GDP--declining, but Agricultural productivity has still more than a fifth in low-income economies 3a increased, yielding more output for all 3b Share of GDP by income (%) 1990 2005 Value added per capita (2000 $) 1970 1990 2005 35 500 30 400 25 20 300 15 200 10 100 5 0 0 Low-income Lower Upper High-income Low-income Lower Upper High-income middle-income middle-income middle-income middle-income Share of GDP by region (%) 1990 2005 Value added per agricultural worker (2000 $ thousands) 1990­92 2001­03 35 25 30 20 25 20 15 15 10 10 5 5 0 0 South Sub-Saharan East Asia Middle East Europe & Latin America Low-income Lower Upper High-income Asia Africa & Pacific & North Central & Caribbean middle-income middle-income Africa Asia Source: Table 4.2. Source: World Bank data files. 122 2007 World Development Indicators Water . . . water . . . Water is life. Water is health. Water is livelihood. But some est consumer of water worldwide. Water productivity is much 1.1 billion people in developing countries have inadequate ac- lower in agriculture than it is in industry (figure 3e). cess to water, and about 700 million people in 43 countries Globally, there is more than enough water for domestic live below the water-stress threshold of 1,700 cubic meters purposes, for agriculture, and for industry. But access to per person per year (figure 3c). One billion people live in areas water is very uneven across and within countries. Poor people of economic water scarcity--where human, institutional, and have limited access, not so much because of physical water financial capital limit access to water even though water in scarcity, but because of their lack of purchasing power and nature is available locally to meet human demands, a situa- because of inappropriate policies that limit their access to tion especially prevalent in much of Sub-Saharan Africa and infrastructure. South Asia (CAWMA 2007). Techniques to control soil moisture and intensify agri- Water is needed for most economic activities, but agricul- cultural production have been substantially improved in ture is the most water-intensive sector (figure 3d), using 70 the last 50 years in many parts of the world. Irrigation is percent of global water withdrawals (indicator table 3.5). Each increasing globally, in all income groups and all regions (fig- year some 7,100 cubic kilometers of water are consumed by ure 3f). While the world's cultivated land increased by about crops to meet global food demand, the equivalent of 90 times 13 percent from 1961 to 2003, the irrigated area almost the annual runoff of the Nile River, or more than 3,000 liters doubled, from 10 percent to 18 percent of cropland. About per person per day. Most of it (78 percent) comes directly 70 percent of the world's irrigated land and 30 percent of from rainfall, the remainder from irrigation (CAWMA 2007). cultivated land are in Asia. By contrast, there is very little Competition between water for food production and for other irrigation in Sub- Saharan Africa, where agriculture is almost sectors will intensify, but food production will remain the larg- exclusively rainfed. More people will experience . . . and the least water scarcity and water stress 3c productive user 3e GDP/water use (2000 $ per cubic meter) Industry Agriculture Billions of people 35 6 Water scarcity 30 5 25 4 20 3 Water stress 15 2 10 1 5 0 0 1900 2005 2025 2050 Low-income Lower Upper High-income World middle-income middle-income Source: UNDP 2006. Source: Table 3.5 and World Bank data files. Agriculture is the biggest Irrigation has increased, consumer of water . . . 3d demanding more water 3f Share of total water withdrawals, 2002 (%) Domestic Industry Agriculture Share of cropland (%) 1990­92 2001­03 100 40 35 80 30 60 25 20 40 15 10 20 5 0 0 East Asia Europe & Latin Middle East South Sub-Saharan High- South Middle East Latin America Europe & High-income Sub-Saharan & Pacific Central Asia America & & North Asia Africa income Asia & North Africa & Caribbean Central Asia Africa Caribbean Africa Source: Table 3.5. Source: Table 3.2. 2007 World Development Indicators 123 Land use and land loss Global demand for food is projected to double in the next 50 Perhaps more worrisome, productivity has declined sub- years, as urbanization proceeds and income rises (CAWMA stantially on approximately 16 percent of agricultural land in 2007). But arable land per capita is shrinking. In the last developing countries, especially in Africa and Central Amer- 12 years it has fallen from 2,100 square meters per person ica. One study estimates that global cropland production is to 1,700 in low-income countries, and from 2,300 to 2,100 12.7 percent lower and pastoral production 3.8 percent lower in high-income economies. than would have been the case without soil degradation. This Agricultural intensification has met global food demand. implies a total agricultural production loss of 4.8 percent. In Asia land under cereal production increased only 4 percent Another estimate puts the global loss at 8.9 percent (Scherr between 1970 and 1995, while cereal production doubled 1999, pp. 16­20). due to the green revolution (Rosengrant and Hazel 2000). In many countries soil degradation and the loss of agri- More recently, the high-income economies, already the cultural land combined with population growth have created most intensified producers, realized an almost 20 percent pressure that led to substantial deforestation. Global for- increase--from 4,260 kilograms per hectare in 1990­92 to ested area in 2005 was about 4 billion hectares, covering 5,040 in 2003­05 (figure 3g), substantially higher than their 30 percent of total land area (figure 3h). But deforestation rate of population increase. In contrast, cereal yields in water- continues at about 13 million hectares a year. Reforestation stressed Sub-Saharan Africa increased by 10 percent--far reduced the net loss of forest areas to 7.3 million hectares less than the region's population growth. The differences in a year in 2000­05--an improvement from losses of 8.9 mil- productivity are even starker among countries, ranging from lion hectares a year in 1990­2000. Africa and Latin America 296 kilograms per hectare in Eritrea to 8,710 in Belgium. continued to have the largest loss of forest after 1990. Cereal yields have increased in most regions-- East Asia has almost reached the high-income economies 3g Thousands of kilograms per hectare 1990­92 2003­05 6 5 4 3 2 1 0 High-income East Asia & Pacific Latin America South Asia Middle East Europe & Sub-Saharan Africa & Caribbean & North Africa Central Asia Source: Table 3.3. Forested areas are shrinking in Latin America and Sub-Saharan Africa--recovering in East Asia 3h Forested area (millions of hectares) 1990 2000 2005 10 8 6 4 2 0 High-income Latin America Europe & Central Asia Sub-Saharan Africa East Asia & Pacific South Asia Middle East & Caribbean & North Africa Source: Table 3.4. 124 2007 World Development Indicators Agriculture and climate change Agriculture and deforestation are estimated to be responsi- in South Africa reduced cereal harvests and exposed more ble for one-third of greenhouse gas emissions, which are the than 17 million to the risk of starvation (UNEP 2002). main contributors to climate change (figure 3i). In turn, climate Delay in addressing climate change could prove tremen- change affects agriculture more than any other sector, increas- dously costly, while efforts to mitigate may be less expensive ing risks of crop failures and livestock losses and threatening than commonly feared. A recent cost assessment argues that food security. The decline in crop yields, especially in Africa, tackling climate change is a pro-growth strategy--and that could leave hundreds of millions without the ability to produce ignoring it will ultimately undermine economic growth (Stern or purchase sufficient food. Warming may also induce sud- 2006). If action does not start now, the world may face far den shifts in regional weather patterns that would have severe higher costs later. Efforts to stabilize emissions must aim not consequences for water availability and flooding in tropical re- only at the energy sector, but also at reducing deforestation, gions. And the impact of sea level rise could be catastrophic encouraging reforestation, and fostering more sustainable for many developing countries (Dasgupta and others 2007). agricultural practices. Changes in climate patterns are already observed in While all countries will be affected, the poorest countries some parts of the world. Average rainfall has fallen in the and people will suffer earliest and most because they depend Sahel (figure 3j), with droughts in the 1970s and 1980s that heavily on agriculture, the most climate-sensitive of all eco- resulted in more than 100,000 deaths (UNEP 2002, p. 219). nomic sectors. The developing regions are also at a geo- Africa has had one major drought in each of the last three graphic disadvantage. They are already warmer, on average, decades (box 3k). Ethiopia's 1984 drought affected 8.7 mil- than developed regions. They suffer from high rainfall variabil- lion people--one million died and millions more faced mal- ity. And their low incomes and other vulnerabilities make their nourishment and famine (UNEP 2002). The 1991­92 drought adaptation to climate change particularly difficult. Agriculture accounts for a seventh Horn of Africa suffers floods of all greenhouse gas emissions 3i after parching drought Box 3k Greenhouse gas emissions by source, 2000 In November 2006 thousands of Somalis trekked from flooded Waste 3% Other energy-related 5% refugee camps to drier ground in northeast Kenya as UN agencies rushed emergency supplies to some 1.8 million people hit by the Buildings 8% worst floods in the Horn of Africa in 50 years. The floods, which also Power 24% affected Kenya and Ethiopia, began in late October. They worsened food insecurity caused by severe drought earlier this year. In some Agriculture areas the soil was so parched that it was not able to absorb the 14% rain, and the few crops that survived the drought were destroyed Deforestation 18% by floods. Industry 14% The flood displaced more than 100,000 of the estimated 160,000 Transport mainly Somali refugees in Dadaab, who had fled the increasing vio- 14% lence in their country. At least 80 people died in floods in southern Somalia. The rain also dislodged landmines seeded during Somalia's Source: Stern Review. long-standing conflict, posing additional hazards. Less rain is falling in the Sahel, with dire consequences 3j Mean normalized rainfall, 1950­2000, June­October 2.5 2.0 1.5 1.0 0.5 0.0 ­0.5 ­1.0 ­1.5 ­2.0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Note: The averages are standardized for the period 1950­2000 so that the mean of the series is zero and the standard deviation is one. Source: World Bank 2003c. 2007 World Development Indicators 125 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 2005 1990­2005 2005 1990 2005 1990 2005 1990 2005 1990­92 2003­05 Afghanistan .. .. .. 652.1 2.0 1.3 0.2 0.2 12.1 12.1 .. .. Albania 63.6 54.6 ­1.4 27.4 28.8 29.0 4.6 4.5 21.1 21.1 18.8 18.6 Algeria 47.9 36.7 ­0.1 2,381.7 0.8 1.0 0.2 0.3 3.0 3.2 24.6 23.7 Angola 62.9 46.7 0.7 1,246.7 48.9 47.4 0.4 0.2 2.3 2.6 21.3 21.9 Argentina 13.0 9.9 ­0.7 2,736.7 12.9 12.1 0.4 0.4 9.6 10.2 74.7 73.4 Armenia 32.5 35.9 ­0.4 28.2 12.3 10.0 4.3 2.1 18.2 17.6 16.2 16.4 Australia 14.6 11.8 ­0.3 7,682.3 21.9 21.3 0.0 0.0 6.2 6.4 249.0 241.1 Austria 34.2 34.0 0.3 82.5 45.8 46.8 1.0 0.8 17.3 16.8 17.3 17.0 Azerbaijan 46.3 48.5 1.4 82.7 11.2 11.3 6.0 2.7 21.1 22.3 22.6 22.2 Bangladesh 80.2 74.9 1.6 130.2 6.8 6.7 2.3 3.5 70.2 61.1 6.1 5.7 Belarus 33.6 27.8 ­1.6 207.5 35.6 38.0 1.4 0.6 30.1 26.3 58.4 56.2 Belgium 3.6 2.8 ­1.4 30.2 22.4 22.1 0.5 0.8 23.3 27.9 8.2 8.1 Benin 65.5 59.9 2.6 110.6 30.0 21.3 0.9 2.4 14.6 24.0 33.1 33.5 Bolivia 44.4 35.8 0.7 1,084.4 57.9 54.2 0.1 0.2 1.9 2.8 34.9 34.5 Bosnia and Herzegovina 60.8 54.3 ­0.8 51.2 43.2 42.7 2.0 1.9 23.6 19.5 25.8 25.9 Botswana 58.1 42.6 ­0.5 566.7 24.2 21.1 0.0 0.0 0.7 0.7 21.4 21.3 Brazil 25.2 15.8 ­1.7 8,459.4 61.5 56.5 0.8 0.9 6.0 7.0 33.2 32.5 Bulgaria 33.6 30.0 ­1.5 108.6 30.1 33.4 2.7 1.9 34.9 29.2 43.4 42.0 Burkina Faso 86.2 81.7 2.6 273.6 26.1 24.8 0.2 0.2 12.9 17.7 37.8 39.0 Burundi 93.7 90.0 1.4 25.7 11.3 5.9 14.0 14.2 36.2 38.6 14.7 14.1 Cambodia 87.4 80.3 1.8 176.5 73.3 59.2 0.6 0.9 20.9 21.0 28.5 26.8 Cameroon 59.3 45.4 0.5 465.4 52.7 45.6 2.6 2.6 12.8 12.8 39.3 37.8 Canada 23.4 19.9 ­0.2 9,093.5 34.1 34.1 0.7 0.7 5.0 5.0 147.4 144.4 Central African Republic 63.2 62.0 1.9 623.0 37.2 36.5 0.1 0.2 3.1 3.1 50.4 49.0 Chad 79.2 74.7 2.8 1,259.2 10.4 9.5 0.0 0.0 2.6 2.9 42.0 39.4 Chile 16.7 12.4 ­0.6 748.8 20.4 21.5 0.3 0.4 3.7 2.6 12.7 12.4 China 72.6 59.6 ­0.4 9,598.1a 16.8 21.2 0.8 1.3 11.1 11.1 8.1 8.0 Hong Kong, China 0.5 0.0 .. 1.0 .. .. .. .. .. .. .. .. Colombia 31.3 27.3 1.0 1,109.5 55.4 54.7 1.5 1.5 3.0 1.8 5.8 4.8 Congo, Dem. Rep. 72.2 67.9 2.3 2,267.1 62.0 58.9 0.5 0.5 2.9 3.0 13.1 12.4 Congo, Rep. 45.7 39.8 2.3 341.5 66.5 65.8 0.1 0.2 1.4 1.4 13.9 13.1 Costa Rica 49.3 38.3 0.7 51.1 50.2 46.8 4.9 5.9 5.1 4.4 5.6 5.4 Côte d'Ivoire 60.3 55.0 1.8 318.0 32.1 32.7 11.0 11.3 7.6 10.4 18.6 18.8 Croatia 46.0 43.5 ­0.7 55.9 37.8 38.2 1.4 2.1 30.8 19.8 32.7 27.6 Cuba 26.6 24.5 ­0.2 109.8 18.7 24.7 7.4 6.6 27.6 27.9 28.6 27.3 Czech Republic 24.8 26.5 0.3 77.3 34.0 34.3 3.1 3.1 41.1 39.4 30.1 29.9 Denmark 15.2 14.4 0.0 42.4 10.5 11.8 0.2 0.2 60.4 52.7 42.7 41.8 Dominican Republic 44.8 33.2 ­0.5 48.4 28.4 28.4 9.3 10.3 21.7 22.7 13.1 12.7 Ecuador 44.9 37.2 0.4 276.8 49.9 39.2 4.8 4.4 5.8 4.9 12.0 10.0 Egypt, Arab Rep. 56.5 57.2 2.0 995.5 0.0 0.1 0.4 0.5 2.3 3.0 4.2 4.1 El Salvador 50.8 40.2 0.4 20.7 18.1 14.4 12.5 12.1 26.5 31.9 10.2 9.9 Eritrea 84.2 80.6 2.3 101.0 .. 15.4 .. 0.0 .. 5.6 15.1 13.9 Estonia 28.9 30.9 ­0.6 42.4 51.0 53.9 0.5 0.3 27.0 13.9 52.1 40.9 Ethiopia 87.4 84.0 2.0 1,000.0 13.7 13.0 0.6 0.7 9.8 11.1 15.5 16.1 Finland 38.6 38.9 0.4 304.6 72.9 73.9 0.0 0.0 7.4 7.3 42.2 42.5 France 25.9 23.3 ­0.3 550.1 26.4 28.3 2.2 2.1 32.7 33.6 31.1 30.5 Gabon 30.9 16.4 ­1.7 257.7 85.1 84.5 0.6 0.7 1.1 1.3 25.1 24.2 Gambia, The 61.7 46.1 1.3 10.0 44.2 47.1 0.5 0.5 18.2 31.5 22.5 21.9 Georgia 44.8 47.8 ­0.9 69.5 39.7 39.7 7.8 3.8 11.8 11.5 17.1 17.8 Germany 26.6 24.8 ­0.2 348.8 30.8 31.8 1.3 0.6 34.3 34.1 14.3 14.4 Ghana 63.5 52.2 1.1 227.5 32.7 24.2 6.6 9.7 11.9 18.4 20.0 19.7 Greece 41.2 41.0 0.6 128.9 25.6 29.1 8.3 8.8 22.5 20.4 24.9 24.1 Guatemala 58.9 52.8 1.6 108.4 43.8 36.3 4.5 5.6 12.0 13.3 12.3 12.0 Guinea 72.0 67.0 2.2 245.7 30.1 27.4 2.0 2.6 3.0 4.5 11.7 12.2 Guinea-Bissau 71.9 70.4 2.9 28.1 78.8 73.7 4.2 8.9 10.7 10.7 21.3 20.1 Haiti 70.5 61.2 0.5 27.6 4.2 3.8 11.6 11.6 28.3 28.3 9.7 9.4 126 2007 World Development Indicators 3.1 ENVIRONMENT 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 2005 1990­2005 2005 1990 2005 1990 2005 1990 2005 1990­92 2003­05 Honduras 59.7 53.5 1.9 111.9 66.0 41.5 3.2 3.2 13.1 9.5 16.2 15.5 Hungary 34.2 33.7 ­0.2 89.6 20.0 22.1 2.6 2.3 56.2 51.3 45.2 45.5 India 74.5 71.3 1.4 2,973.2 21.5 22.8 2.2 3.4 54.8 53.7 15.5 14.8 Indonesia 69.4 51.9 ­0.6 1,811.6 64.3 48.8 6.5 7.5 11.2 12.7 10.3 10.6 Iran, Islamic Rep. 43.7 33.1 ­0.4 1,636.2 6.8 6.8 0.8 0.9 9.3 9.8 23.3 24.4 Iraq 30.3 .. .. 437.4 1.8 1.9 0.7 0.6 12.1 13.1 21.9 .. Ireland 43.1 39.5 0.5 68.9 6.4 9.7 0.0 0.0 15.1 17.6 29.7 29.5 Israel 9.6 8.4 1.6 21.6 7.1 7.9 4.1 3.5 15.9 14.6 5.3 4.8 Italy 33.3 32.4 0.0 294.1 28.5 33.9 10.1 8.6 30.6 26.3 14.7 13.6 Jamaica 50.6 46.9 0.2 10.8 31.9 31.3 9.2 10.2 11.0 16.1 6.7 6.6 Japan 36.9 34.2 ­0.2 364.5 68.4 68.2 1.3 0.9 13.1 12.0 3.5 3.4 Jordan 27.8 17.7 0.4 88.2 0.9 0.9 0.8 1.0 2.0 2.1 3.9 3.5 Kazakhstan 43.7 42.7 ­0.9 2,699.7 1.3 1.2 0.1 0.1 13.3 8.3 148.7 149.3 Kenya 81.8 79.3 2.3 569.1 6.5 6.2 0.9 1.0 7.4 8.2 14.6 14.2 Korea, Dem. Rep. 41.6 38.4 0.4 120.4 68.1 51.4 1.5 1.7 19.0 23.3 12.0 12.3 Korea, Rep. 26.2 19.2 ­1.1 98.7 64.5 63.5 1.6 2.0 19.8 16.6 3.6 3.4 Kuwait 2.0 1.7 1.7 17.8 0.2 0.3 0.1 0.2 0.2 0.8 0.6 0.6 Kyrgyz Republic 62.2 64.2 1.3 191.8 4.4 4.5 0.6 0.4 7.1 6.7 27.2 25.9 Lao PDR 84.6 79.4 2.0 230.8 75.0 69.9 0.3 0.4 3.5 4.3 16.7 17.2 Latvia 30.7 32.2 ­0.7 62.3 44.7 47.2 0.6 0.2 28.0 17.5 41.0 44.1 Lebanon 16.9 13.4 0.2 10.2 11.8 13.3 11.9 14.0 17.9 16.6 5.2 4.9 Lesotho 82.8 81.3 0.8 30.4 0.2 0.3 0.1 0.1 10.4 10.9 18.4 18.3 Liberia 54.7 41.9 2.1 96.3 42.1 32.7 2.2 2.3 4.2 4.0 12.1 11.9 Libya 21.4 15.2 ­0.3 1,759.5 0.1 0.1 0.2 0.2 1.0 1.0 33.5 32.3 Lithuania 32.4 33.4 ­0.4 62.7 31.0 33.5 1.1 0.6 47.3 30.4 58.8 49.0 Macedonia, FYR 42.2 31.1 ­1.6 25.4 35.6 35.6 1.5 1.8 33.9 22.3 27.9 27.9 Madagascar 76.4 73.2 2.7 581.5 23.5 22.1 1.0 1.0 4.7 5.1 17.6 16.7 Malawi 88.4 82.8 1.7 94.1 41.4 36.2 1.2 1.5 19.3 26.0 18.7 19.9 Malaysia 50.2 32.7 ­0.5 328.6 68.1 63.6 16.0 17.6 5.2 5.5 7.7 7.4 Mali 76.7 69.5 2.2 1,220.2 11.5 10.3 0.0 0.0 1.7 3.9 38.9 36.6 Mauritania 60.3 59.6 2.7 1,025.2 0.4 0.3 0.0 0.0 0.4 0.5 17.9 16.9 Mauritius 56.1 57.6 1.3 2.0 19.2 18.2 3.0 3.0 49.3 49.3 8.3 8.1 Mexico 27.5 24.0 0.5 1,908.7 36.2 33.7 1.0 1.3 12.6 13.0 25.1 24.6 Moldova 53.2 53.3 ­0.3 32.9 9.7 10.0 22.9 9.1 54.3 56.2 43.3 43.9 Mongolia 43.0 43.3 1.3 1,566.5 7.3 6.5 0.0 0.0 0.9 0.8 49.1 48.3 Morocco 51.6 41.3 0.0 446.3 9.6 9.8 1.6 2.0 19.5 19.0 30.4 29.4 Mozambique 78.9 65.5 1.4 784.1 25.5 24.6 0.3 0.3 4.4 5.5 22.0 22.8 Myanmar 75.1 69.4 0.9 657.6 59.6 49.0 0.8 1.4 14.5 15.3 20.5 20.4 Namibia 72.3 64.9 1.8 823.3 10.6 9.3 0.0 0.0 0.8 1.0 42.3 41.0 Nepal 91.1 84.2 1.8 143.0 33.7 25.4 0.5 0.9 16.0 16.5 9.4 8.9 Netherlands 31.3 19.8 ­2.4 33.9 10.2 10.8 0.9 1.0 25.9 26.8 5.7 5.6 New Zealand 15.3 13.8 0.5 268.0 28.8 31.0 5.1 7.0 9.4 5.6 38.5 37.4 Nicaragua 46.9 41.0 0.9 121.4 53.9 42.7 1.6 1.9 10.7 15.9 38.5 37.8 Niger 84.6 83.2 3.3 1,266.7 1.5 1.0 0.0 0.0 8.7 11.4 118.8 111.0 Nigeria 65.0 51.8 1.0 910.8 18.9 12.2 2.8 3.2 32.4 33.5 24.1 24.2 Norway 28.0 22.6 ­0.9 304.3 30.0 30.8 .. .. 2.8 2.8 19.6 18.9 Oman 34.6 28.5 1.1 309.5 0.0 0.0 0.1 0.1 0.1 0.1 1.5 1.5 Pakistan 69.4 65.1 2.0 770.9 3.3 2.5 0.6 1.0 26.6 27.6 15.2 14.1 Panama 46.1 29.2 ­1.1 74.4 58.8 57.7 2.1 2.0 6.7 7.4 18.1 17.6 Papua New Guinea 86.9 86.6 2.4 452.9 69.6 65.0 1.3 1.4 0.4 0.5 3.9 4.0 Paraguay 51.3 41.5 0.8 397.3 53.3 46.5 0.2 0.2 5.3 7.7 54.3 53.6 Peru 31.1 27.4 0.9 1,280.0 54.8 53.7 0.3 0.5 2.7 2.9 14.0 13.6 Philippines 51.2 37.3 ­0.1 298.2 35.5 24.0 14.8 16.8 18.4 19.1 7.4 7.1 Poland 38.7 37.9 ­0.2 306.3 29.2 30.0 1.1 1.2 47.3 39.6 35.3 32.6 Portugal 52.1 42.4 ­0.9 91.5 33.9 41.3 8.5 8.5 25.6 16.8 16.4 14.9 Puerto Rico 27.8 2.4 ­14.6 8.9 45.5 46.0 5.6 4.7 7.3 8.0 1.7 1.8 2007 World Development Indicators 127 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 2005 1990­2005 2005 1990 2005 1990 2005 1990 2005 1990­92 2003­05 Romania 45.7 46.3 ­0.4 230.0 27.8 27.7 2.6 2.3 41.2 40.4 42.4 43.2 Russian Federation 26.6 27.0 ­0.2 16,381.4 49.4 49.4 0.2 0.1 8.3 7.4 85.0 84.9 Rwanda 94.6 80.7 1.8 24.7 12.9 19.5 12.4 10.9 35.7 48.6 12.0 13.7 Saudi Arabia 23.4 19.0 1.0 2,000.0 b 1.4 1.4 0.0 0.1 1.7 1.8 17.0 16.3 Senegal 61.0 58.4 2.3 192.5 48.6 45.0 0.1 0.2 12.1 12.8 22.9 22.1 Serbia and Montenegro 49.1 47.8 ­2.4 102.0 25.1 26.4 2.4 3.1 51.9 34.4 41.9 42.4 Sierra Leone 69.9 59.3 0.9 71.6 42.5 38.5 0.8 1.0 6.8 8.0 10.8 11.1 Singapore 0.0 0.0 .. 0.7 3.0 2.9 1.5 0.3 1.5 0.9 0.0 0.0 Slovak Republic 43.5 43.8 0.2 48.1 40.0 40.1 1.0 0.5 32.5 28.9 27.1 26.0 Slovenia 49.6 49.0 ­0.1 20.1 59.0 62.8 1.2 1.3 14.1 8.7 8.6 8.7 Somalia 70.3 64.8 1.0 627.3 13.2 11.4 0.0 0.0 1.6 1.7 14.5 13.6 South Africa 48.0 40.7 0.9 1,214.5 7.6 7.6 0.7 0.8 11.1 12.1 33.0 32.2 Spain 24.6 23.3 0.3 499.2 27.0 35.9 9.7 9.9 30.7 27.4 32.2 32.0 Sri Lanka 82.8 84.9 1.1 64.6 36.4 29.9 15.9 15.5 13.5 14.2 4.7 4.8 Sudan 73.4 59.2 0.8 2,376.0 32.1 28.4 0.1 0.2 5.5 7.2 48.4 48.8 Swaziland 77.1 75.9 2.6 17.2 27.4 31.5 0.7 0.8 10.5 10.3 16.7 16.1 Sweden 16.9 15.8 ­0.1 410.3 66.7 67.1 0.0 0.0 6.9 6.6 30.3 29.8 Switzerland 31.6 24.8 ­1.0 40.0 28.9 30.5 0.5 0.6 9.8 10.3 5.7 5.5 Syrian Arab Republic 51.1 49.4 2.4 183.8 2.0 2.5 4.0 4.7 26.6 26.5 26.6 25.6 Tajikistan 68.5 75.3 2.0 140.0 2.9 2.9 1.4 0.9 6.3 6.6 14.9 14.6 Tanzania 81.1 75.8 2.1 883.6 46.9 39.9 1.0 1.2 4.0 4.5 11.3 10.8 Thailand 70.6 67.7 0.8 510.9 31.2 28.4 6.1 7.0 34.2 27.7 25.6 22.4 Togo 69.9 59.9 2.0 54.4 12.6 7.1 1.7 2.2 38.6 46.1 45.5 43.0 Trinidad and Tobago 91.5 87.8 0.2 5.1 45.8 44.1 9.0 9.2 14.4 14.6 5.8 5.8 Tunisia 40.4 34.7 0.3 155.4 4.1 6.8 12.5 13.8 18.7 18.0 29.0 28.4 Turkey 40.8 32.7 0.2 769.6 12.6 13.2 3.9 3.6 32.0 31.0 34.8 33.2 Turkmenistan 54.9 53.8 1.6 469.9 8.8 8.8 0.2 0.1 3.0 4.7 40.5 46.8 Uganda 88.9 87.4 3.1 197.1 25.0 18.4 9.4 10.9 25.4 26.4 20.3 19.4 Ukraine 33.2 32.2 ­0.9 579.4 16.0 16.5 3.1 1.6 59.2 56.0 66.9 68.5 United Arab Emirates 20.9 23.3 7.4 83.6 2.9 3.7 0.2 2.3 0.4 0.8 2.0 1.6 United Kingdom 11.3 10.3 ­0.4 241.9 10.8 11.8 0.3 0.2 27.4 23.7 9.7 9.6 United States 24.7 19.2 ­0.5 9,161.9 32.6 33.1 0.2 0.3 20.3 19.0 61.6 59.7 Uruguay 11.0 8.0 ­1.3 175.0 5.2 8.6 0.3 0.2 7.2 7.8 40.7 40.1 Uzbekistan 59.9 63.3 2.0 425.4 7.2 7.7 0.6 0.8 10.8 11.0 18.0 18.4 Venezuela, RB 16.0 6.6 ­3.9 882.1 59.0 54.1 0.9 0.9 3.2 2.9 10.5 10.1 Vietnam 79.7 73.6 1.0 310.1 28.8 41.7 3.2 7.6 16.4 21.3 8.2 8.0 West Bank and Gaza 32.1 28.4 3.4 6.0 1.5 1.5 19.1 19.1 18.4 17.8 3.4 3.1 Yemen, Rep. 79.1 72.7 3.1 528.0 1.0 1.0 0.2 0.3 2.9 2.9 8.2 7.8 Zambia 60.6 65.0 2.8 743.4 66.1 57.1 0.0 0.0 7.1 7.1 48.3 46.6 Zimbabwe 71.0 64.1 0.7 386.9 57.5 45.3 0.3 0.3 7.5 8.3 25.4 25.0 World 57.0 w 51.2 w 0.6 w 129,606.2 s 31.5 w 30.5 w 1.0 w 1.1 w 10.7 w 10.6 w 22.3 w 21.9 w Low income 74.6 70.0 1.6 28,184.8 26.3 23.9 1.0 1.3 12.9 13.6 17.3 16.9 Middle income 55.6 46.1 ­0.2 68,517.7 34.8 33.8 1.1 1.2 9.5 9.2 21.2 20.8 Lower middle income 61.7 50.5 ­0.2 39,305.8 32.6 31.2 1.4 1.6 9.5 9.3 15.3 15.1 Upper middle income 31.7 28.0 0.0 29,211.9 37.7 37.2 0.6 0.6 9.5 8.9 45.1 43.9 Low & middle income 63.2 56.5 0.7 96,702.5 32.3 30.9 1.0 1.2 10.5 10.5 19.5 19.1 East Asia & Pacific 71.2 58.5 ­0.2 15,869.9 28.8 28.4 2.2 2.8 10.8 11.0 9.5 9.4 Europe & Central Asia 36.9 36.3 ­0.1 23,367.1 38.2 38.3 0.6 0.4 13.0 11.5 56.8 58.1 Latin America & Carib. 29.1 22.8 ­0.1 20,126.9 48.9 45.5 0.9 1.0 6.5 7.1 27.3 26.6 Middle East & N. Africa 48.1 42.9 1.3 8,960.9 2.2 2.4 0.8 0.9 5.6 5.9 18.0 18.0 South Asia 75.1 71.5 1.5 4,781.3 16.5 16.8 1.8 2.8 42.6 47.1 14.6 13.8 Sub-Saharan Africa 72.1 64.8 1.8 23,596.5 29.2 26.5 0.8 0.9 6.4 7.5 25.1 24.8 High income 26.3 22.4 ­0.3 32,903.7 29.1 29.4 0.7 0.7 11.5 11.0 37.5 36.9 Europe EMU 29.1 26.7 ­0.2 2,455.3 33.5 37.3 4.7 4.4 27.0 25.6 20.5 20.2 a. Includes Taiwan, China; Macao, China; and Hong Kong, China. b. Provisional estimate. 128 2007 World Development Indicators 3.1 ENVIRONMENT Rural population and land use About the data Three billion people, including 70 percent of the (FAO), the primary compiler of these data, occasion- assessments every 5­10 years since 1946. Global world's poor people, live in rural areas. Therefore, ally adjusts its definitions of land use categories and Forest Resources Assessment 2005 was carried out adequate indicators to monitor progress in rural sometimes revises earlier data. Because the data between 2003 and 2005 and covered 229 countries areas are essential However, indicators of rural reflect changes in reporting procedures as well as and territories at three points in time: 1990, 2000, development are sparse, as few indicators are dis- actual changes in land use, apparent trends should and 2005. It is the most comprehensive assessment aggregated between rural and urban areas (for some be interpreted with caution. of forests and forestry to date both in scope and in that are, see tables 2.7, 3.5, and 3.10). This table Satellite images show land use that differs from number of countries and people involved. It exam- shows indicators of rural population and land use. that given by ground-based measures in both area ines current status and recent trends for about 40 Rural population is approximated as the midyear non- under cultivation and type of land use. Moreover, variables covering the extent, condition, uses, and urban population. While a practical means of identify- land use data in countries such as India are based values of forests and other wooded land with the aim ing the rural population, it is not precise (see box 3a on reporting systems that were designed for the col- of assessing all benefits from forest resources. for more discussion of the issue). lection of tax revenue. Because taxes on land are Definitions The data in the table show that land use patterns no longer a major source of government revenue, are changing. They also indicate major differences the quality and coverage of land use data (except · Rural population is calculated as the difference in resource endowments and uses among countries. for cropland) have declined. Data on forest area may between the total population and the urban popula- True comparability of the data is limited, however, by be particularly unreliable because of differences tion (see Definitions for tables 2.1 and 3.10). · Land variations in definitions, statistical methods, and the in definitions and irregular surveys (see About the area is a country's total area, excluding area under quality of data collection. Countries use different def- data for table 3.4). FAO's Global Forest Resources inland water bodies, national claims to the continental initions of rural and urban population and land use, Assessment 2005 aims to address this limitation. shelf, and exclusive economic zones. In most cases for example. The Food and Agriculture Organization FAO has been coordinating global forest resources the definitions of inland water bodies includes major rivers and lakes. (See table 1.1 for the total surface area of countries.) · Land use can be broken into sev- What is rural? Urban? Box 3.1a eral categories, three of which are presented in this The rural population identified in table 3.1 is approximated as the difference between total population table (not shown are land used as permanent pasture and the urban population, which is calculated on the basis of the urban share reported by the United and land under urban developments). · Forest area is Nations Population Division. However, there is no universal standard for distinguishing urban from rural land under natural or planted stands of trees, whether areas, and any urban-rural dichotomy is an oversimplification (see About the data for table 3.10). The productive or not. · Permanent cropland is land culti- two distinct images--isolated farm, thriving metropolis--represent poles on a continuum. Life changes vated with crops that occupy the land for long periods along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, and need not be replanted after each harvest, such past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near as cocoa, coffee, and rubber. This category includes large towns and small cities, eventually reaching the center of a cosmopolitan megacity. The changes land under flowering shrubs, fruit trees, nut trees, and may sometimes be abrupt, but more often they are gradual. Along the way access to infrastructure, vines, but excludes land under trees grown for wood social services, and nonfarm employment all increase, and with them population density and income. or timber. · Arable land includes land defined by the For policy purposes, therefore, one needs to go beyond the rural-urban dichotomy presented in tables FAO as under temporary crops (double-cropped areas 3.1 and 3.10, because rurality has many dimensions. are counted once), temporary meadows for mowing A recent World Bank Policy Research Paper proposes an operational definition of rurality based on two or for pasture, land under market or kitchen gardens, dimensions: population density and distance to large cities (population greater than 100,000; Chomitz, and land temporarily fallow. Land abandoned as a Buys, and Thomas 2005). The report argues that these criteria constitute important gradients along result of shifting cultivation is excluded. which economic behavior and appropriate development interventions vary substantially. Where popula- Data sources tion densities are low, markets of all kinds are thin, and the 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) Data on urban population shares used to estimate prices of outputs will be low and prices of inputs will be high, and it will be difficult to recruit skilled people rural population come from the United Nations to public service or private enterprises. Thus, remoteness and low population density together define a Population Division's World Urbanization Prospects: set of rural areas that face special challenges in development. The 2005 Revision. The total population figures are Based on these criteria, and using the Gridded Population of the World (CIESIN 2005), the authors World Bank estimates. Data on land area and land produced estimates of the rural population for Latin America and the Caribbean that differ substantially use are from the FAO's electronic files. The FAO from those presented in table 3.1. The range of these estimates are from 13 percent of the total popula- gathers these data from national agencies through tion, based on a population density of less than 20 people per square kilometer, to 64 percent, based annual questionnaires and by analyzing the results on a population density of more than 500 people per square kilometer. Taking remoteness into account, of national agricultural censuses. Data on forest the estimated rural population would be between 13 and 52 percent of the population. The estimate for area are from the FAO's Global Forest Resources Latin America and the Caribbean in table 3.1 is 23 percent. Assessment 2005. 2007 World Development Indicators 129 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 1990­92 2001­03 1990­92 2003­05 1990­92 2000­02 1990­92 2001­03 1990­92 2001­03 Afghanistan 58.3 58.3 33.9 33.8 2,283 2,788 59 19 .. .. 1 1 Albania 41.1 40.9 55.6 49.5 243 148 903 420 .. 62.7 177 141 Algeria 16.3 16.8 6.4 6.9 3,105 2,751 144 130 .. 21.1 128 129 Angola 46.1 46.2 2.3 2.3 893 1,306 29 5 .. .. 35 33 Argentina 46.6 47.0 5.6 5.4 8,510 9,391 73 295 0.4 1.1 103 108 Armenia 43.1 49.3 49.9 51.2 163 198 493 157 .. 45.7 277 289 Australia 60.5 57.5 4.2 5.2 12,814 19,905 275 479 5.5 4.4 67 65 Austria 42.5 40.0 0.3 0.3 903 810 1,995 1,540 7.5 5.6 2,367 2,380 Azerbaijan 51.5 57.5 68.0 70.5 628 788 432 61 32.5 40.1 184 164 Bangladesh 73.5 69.3 33.8 54.3 10,985 11,511 1,136 1,738 66.4 51.7 6 7 Belarus 43.6 42.7 2.1 2.3 2,581 2,148 2,251 1,325 21.7 .. 195 111 Belgium 43.6 46.0 .. 4.6 326 314 4,937 3,427 2.8 1.7 1,498 1,254 Benin 20.6 31.3 0.6 0.4 660 963 78 154 .. .. 1 1 Bolivia 32.9 34.2 5.5 4.1 633 792 42 37 1.7 .. 25 20 Bosnia and Herzegovina 15.0 42.1 0.2 0.3 333 326 .. 356 .. .. 235 289 Botswana 45.9 45.8 0.2 0.3 140 67 22 122 .. 16.8 143 159 Brazil 28.9 31.2 4.6 4.4 19,633 19,806 656 1,201 25.6 20.6 142 137 Bulgaria 55.7 48.5 29.6 16.5 2,174 1,721 1,194 500 19.7 18.0 128 95 Burkina Faso 34.9 39.8 0.6 0.5 2,725 3,249 60 30 .. .. 3 4 Burundi 82.9 91.3 1.2 1.6 219 210 34 33 .. .. 2 2 Cambodia 30.2 30.3 6.6 7.0 1,801 2,332 19 .. .. 70.2 3 7 Cameroon 19.7 19.7 0.3 0.4 816 1,077 34 75 60.6 .. 1 1 Canada 7.5 7.4 1.4 1.5 20,864 17,272 476 549 4.2 2.8 162 160 Central African Republic 8.0 8.3 0.0 0.1 104 187 5 3 .. .. 0 0 Chad 38.4 38.6 0.5 0.8 1,242 2,096 25 49 .. .. 1 0 Chile 21.0 20.4 57.1 82.4 742 687 1,215 2,386 18.8 13.6 144 272 China 57.0 59.5 43.6 47.5 93,430 79,896 2,777 3,519 53.5 44.7 77 89 Hong Kong, China .. .. .. .. .. .. .. .. 0.8 0.2 .. .. Colombia 40.5 38.2 14.3 23.3 1,598 1,265 1,822 2,670 1.4 21.2 98 91 Congo, Dem. Rep. 10.1 10.1 0.1 0.1 1,868 1,974 8 7 .. .. 4 4 Congo, Rep. 30.8 30.9 0.3 0.4 9 13 35 67 .. .. 15 14 Costa Rica 55.7 56.1 15.2 20.6 83 58 4,522 6,455 25.2 15.5 259 311 Côte d'Ivoire 59.8 62.6 1.1 1.1 1,434 953 151 256 .. .. 15 12 Croatia 15.0 50.8 0.2 0.4 647 646 1,514 1,303 .. 15.8 35 25 Cuba 61.5 60.6 22.6 22.5 235 309 1,288 476 25.1 21.7 250 249 Czech Republic 55.4 55.2 .. 0.7 .. 1,563 .. 1,186 10.1 4.7 .. 305 Denmark 65.4 62.0 16.9 19.6 1,581 1,495 2,249 1,393 5.4 3.2 625 540 Dominican Republic 74.7 76.4 14.9 17.2 134 150 860 848 19.5 15.4 22 17 Ecuador 28.6 26.9 27.9 33.0 861 892 508 1,679 7.0 8.5 67 106 Egypt, Arab Rep. 2.7 3.5 100.0 100.0 2,410 2,851 3,977 4,464 36.2 28.6 251 309 El Salvador 71.1 82.2 4.9 4.9 453 330 1,336 1,054 17.9 19.9 60 52 Eritrea .. 74.6 .. 3.7 .. 370 .. 119 .. .. .. 8 Estonia 31.2 19.1 0.5 0.6 454 268 993 432 19.5 6.7 395 889 Ethiopia 51.0 31.8 1.4 2.6 4,586 9,039 .. 145 .. .. .. 3 Finland 7.9 7.4 2.8 2.9 1,050 1,200 1,647 1,353 8.8 5.3 1,040 882 France 55.3 53.9 11.0 13.3 9,212 9,160 2,918 2,221 .. 4.3 784 685 Gabon 20.0 20.0 1.1 1.4 14 20 25 9 .. .. 50 46 Gambia, The 63.2 77.9 0.9 0.6 90 186 44 26 .. .. 2 1 Georgia 44.8 43.3 39.9 44.1 249 339 889 412 .. 53.8 279 254 Germany 49.8 48.8 4.0 4.0 6,673 6,875 2,616 2,245 4.0 2.5 1,253 801 Ghana 55.7 64.8 0.7 0.5 1,078 1,376 38 60 62.2 .. 15 9 Greece 71.3 65.2 31.1 37.4 1,455 1,272 2,289 1,580 22.7 15.6 774 939 Guatemala 39.5 42.9 6.8 6.4 768 666 1,072 1,427 .. 38.7 33 30 Guinea 48.9 50.7 7.0 5.6 627 778 16 32 .. .. 5 5 Guinea-Bissau 53.2 58.0 4.1 4.6 112 138 15 80 .. .. 1 1 Haiti 57.9 57.7 8.0 8.4 406 458 35 181 .. .. 2 2 130 2007 World Development Indicators 3.2 ENVIRONMENT 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 1990­92 2001­03 1990­92 2003­05 1990­92 2000­02 1990­92 2001­03 1990­92 2001­03 Honduras 29.8 26.2 3.8 5.6 502 391 203 1,193 42.1 35.1 31 49 Hungary 70.7 65.4 4.1 4.8 2,803 2,940 796 993 11.3 6.0 158 247 India 60.9 60.6 28.3 32.7 100,760 97,872 758 1,044 68.1 .. 65 141 Indonesia 23.5 26.3 14.5 12.7 13,861 15,140 1,330 1,259 54.9 44.8 18 41 Iran, Islamic Rep. 37.7 37.9 39.7 42.7 9,612 8,983 750 890 25.6 .. 136 161 Iraq 21.9 22.9 63.0 58.6 3,506 3,221 347 968 .. .. 72 80 Ireland 70.2 62.4 .. .. 298 294 6,591 5,172 14.1 6.8 1,667 1,324 Israel 26.7 24.4 44.4 45.4 108 92 2,836 2,575 3.7 2.0 763 714 Italy 55.4 50.7 22.9 24.9 4,347 4,142 2,195 1,819 8.4 5.0 1,619 2,031 Jamaica 44.0 47.4 11.0 5.9 3 1 1,737 1,258 27.3 20.4 242 177 Japan 15.5 12.9 54.3 54.7 2,439 2,008 3,779 3,066 6.8 4.7 4,297 4,588 Jordan 12.0 11.5 25.0 27.3 112 53 969 1,322 .. 3.9 352 308 Kazakhstan 79.1 76.9 9.8 15.7 22,174 13,697 133 23 .. 35.4 59 22 Kenya 45.7 .. 1.3 1.8 1,766 2,017 255 320 19.0 .. 24 28 Korea, Dem. Rep. 21.0 24.9 58.2 50.9 1,569 1,272 3,522 1,018 .. .. 297 241 Korea, Rep. 21.9 19.2 47.1 47.1 1,368 1,093 4,932 4,317 16.7 9.4 275 1,239 Kuwait 7.9 8.6 60.0 77.0 0 1 2,000 711 .. .. 215 69 Kyrgyz Republic 50.7 56.2 72.6 76.0 579 596 238 209 35.5 52.8 179 167 Lao PDR 7.2 8.5 16.2 17.2 630 758 31 94 .. .. 11 12 Latvia 39.3 26.5 1.1 2.1 699 451 977 572 .. 14.8 343 580 Lebanon 31.1 32.2 28.1 33.2 41 61 1,639 2,838 .. .. 188 488 Lesotho 76.7 76.9 0.6 0.9 178 177 167 309 .. .. 57 61 Liberia 27.1 27.0 0.5 0.5 135 120 8 .. .. .. 8 9 Libya 8.8 8.8 21.8 21.9 355 341 458 349 .. .. 187 219 Lithuania 52.1 42.5 0.5 0.4 1,135 899 531 903 18.8 17.7 242 641 Macedonia, FYR 17.9 48.8 12.1 9.0 257 195 .. 502 .. 23.0 730 954 Madagascar 47.0 47.4 30.7 30.6 1,321 1,457 34 31 .. 78.0 11 12 Malawi 40.2 47.2 1.2 2.3 1,443 1,544 351 400 .. .. 8 6 Malaysia 22.7 24.0 4.8 4.8 699 701 5,264 6,548 23.9 14.8 161 241 Mali 26.3 32.4 3.7 5.0 2,393 3,292 91 88 .. .. 11 6 Mauritania 38.7 38.8 11.8 6.5 133 176 132 59 .. .. 8 8 Mauritius 55.7 55.7 16.0 20.1 1 0 2,732 3,035 14.3 10.5 36 37 Mexico 54.5 56.2 22.4 23.2 10,075 10,126 696 727 24.2 17.2 129 131 Moldova 75.1 76.7 14.2 13.9 676 927 762 86 .. 47.9 293 221 Mongolia 79.9 83.3 5.8 7.0 620 180 111 31 .. 45.0 73 42 Morocco 68.2 68.1 13.2 15.5 5,374 5,565 353 440 .. 44.8 46 58 Mozambique 60.9 62.0 2.8 2.7 1,509 2,071 12 53 .. .. 16 14 Myanmar 15.8 17.2 10.1 17.9 5,283 7,215 79 146 69.4 .. 12 10 Namibia 47.0 47.2 0.7 1.0 215 244 .. 4 48.2 .. 47 39 Nepal 29.0 29.5 43.0 47.2 2,957 3,346 340 333 82.3 .. 23 24 Netherlands 58.9 56.8 61.0 60.0 185 217 6,298 4,286 4.3 2.8 2,056 1,645 New Zealand 65.0 64.3 7.6 8.5 153 121 1,892 5,704 10.7 8.7 322 507 Nicaragua 52.1 57.5 4.0 2.8 299 502 270 177 38.7 26.8 20 15 Niger 27.0 30.4 0.5 0.5 7,011 8,111 1 3 .. .. 0 0 Nigeria 79.4 79.7 0.7 0.8 16,417 17,799 142 66 .. .. 8 10 Norway 3.3 3.4 .. .. 361 326 2,362 2,086 5.9 3.8 1,723 1,486 Oman 3.5 3.5 71.6 88.4 2 2 2,441 2,491 .. .. 42 50 Pakistan 33.7 35.2 78.5 81.1 11,777 12,587 962 1,378 48.9 45.3 133 149 Panama 28.7 30.0 4.8 6.2 182 195 666 545 25.8 17.7 103 148 Papua New Guinea 2.0 2.3 .. .. 2 3 622 556 .. .. 59 53 Paraguay 59.6 62.5 2.9 2.1 455 782 92 319 1.7 32.2 72 55 Peru 17.1 16.6 29.9 27.9 683 1,116 246 759 1.0 3.5 36 36 Philippines 37.4 40.9 15.7 14.5 6,957 6,613 935 1,313 45.3 37.3 20 20 Poland 61.6 52.8 0.7 0.7 8,523 8,290 895 1,151 25.2 18.9 821 1,034 Portugal 42.8 41.7 20.5 27.2 780 435 1,123 1,258 15.6 12.5 569 1,044 Puerto Rico 47.5 25.1 36.8 37.1 0 0 .. .. 3.5 2.0 .. .. 2007 World Development Indicators 131 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 1990­92 2001­03 1990­92 2003­05 1990­92 2000­02 1990­92 2001­03 1990­92 2001­03 Romania 64.4 63.8 31.0 31.2 5,842 5,675 788 355 30.6 38.1 146 179 Russian Federation 13.0 13.2 4.2 3.7 59,600 39,471 410 121 14.5 11.4 92 52 Rwanda 75.6 78.4 0.3 0.7 258 332 20 48 .. .. 1 1 Saudi Arabia .. .. 44.2 42.7 1,062 669 1,446 1,067 .. 5.4 20 28 Senegal 41.9 42.4 3.3 4.6 1,154 1,202 65 140 .. .. 2 3 Serbia and Montenegro 21.1 54.8 1.9 0.8 2,618 1,987 265 732 .. .. 1,067 1,023 Sierra Leone 38.3 39.7 5.2 5.0 452 253 23 5 .. .. 3 2 Singapore 2.2 1.2 .. .. .. .. 54,333 25,920 0.3 0.3 637 794 Slovak Republic 50.9 42.3 .. 12.6 1 802 .. .. .. 6.0 .. .. Slovenia 9.8 25.0 0.8 1.5 130 99 3,168 4,239 .. 9.3 .. .. Somalia 70.2 70.3 19.2 18.7 531 682 26 5 .. .. 21 16 South Africa 80.2 82.0 8.3 9.5 5,736 4,429 549 558 .. 10.7 101 46 Spain 60.8 58.3 16.9 20.7 7,588 6,573 1,186 1,653 10.7 6.1 494 712 Sri Lanka 36.2 36.5 28.0 34.4 834 882 2,016 2,862 44.3 34.7 71 113 Sudan 52.0 56.6 13.9 11.0 6,267 10,005 51 40 .. .. 8 7 Swaziland 75.8 80.9 24.1 26.0 69 61 688 371 .. .. 251 222 Sweden 8.2 7.8 4.1 4.3 1,184 1,098 1,112 1,010 3.3 2.2 604 615 Switzerland 46.9 38.1 6.0 5.8 207 164 4,032 2,272 4.2 4.2 2,870 2,649 Syrian Arab Republic 73.7 75.6 14.3 24.0 3,812 3,195 621 718 .. 30.3 137 224 Tajikistan 30.9 30.4 72.9 68.2 266 393 1,461 175 .. .. 392 233 Tanzania 53.7 54.4 3.2 3.5 3,003 3,340 136 31 84.2 82.1 19 19 Thailand 41.9 36.2 21.0 26.6 10,594 11,134 598 1,039 61.5 45.7 39 144 Togo 58.7 66.7 0.3 0.3 610 767 56 74 .. .. 0 0 Trinidad and Tobago 25.7 25.9 3.3 3.3 6 2 1,111 631 11.8 7.4 354 360 Tunisia 58.4 63.0 7.3 8.0 1,525 1,450 330 372 .. .. 88 126 Turkey 51.8 53.3 14.8 19.5 13,760 13,817 757 768 46.5 35.5 287 410 Turkmenistan 66.1 70.2 .. 89.1 332 983 1,273 543 .. .. 439 256 Uganda 61.0 .. 0.1 0.1 1,098 1,550 1 14 91.5 69.1 9 9 Ukraine 69.8 71.4 7.6 6.8 12,555 13,082 792 154 .. 19.5 145 124 United Arab Emirates 3.7 6.7 .. 29.2 1 0 4,810 5,149 .. .. 50 55 United Kingdom 75.0 70.2 2.5 3.0 3,549 3,040 3,323 3,141 2.2 1.4 762 878 United States 46.6 45.3 11.3 12.5 64,547 57,163 1,015 1,096 2.9 2.2 245 270 Uruguay 84.7 85.4 10.2 14.3 509 528 610 849 4.5 4.3 259 241 Uzbekistan 62.8 64.1 87.3 87.4 1,227 1,692 1,602 1,614 .. .. 380 373 Venezuela, RB 24.7 24.5 13.9 16.9 799 1,042 1,388 1,132 12.6 10.0 176 189 Vietnam 21.0 30.8 44.6 33.9 6,726 8,392 1,299 3,172 73.8 61.9 60 247 West Bank and Gaza .. .. .. .. .. .. .. .. .. 14.1 .. .. Yemen, Rep. 33.4 33.6 24.3 31.4 738 635 127 95 .. .. 40 43 Zambia 47.4 47.5 0.7 2.8 813 717 131 84 .. .. 11 11 Zimbabwe 52.3 53.1 3.6 5.2 1,431 1,617 508 443 .. .. 61 75 World 37.7 w 38.3 w 17.7 w 18.4 w 704,675 s 677,585 s 925 w 1,020 w 41.8 w .. w 186 w 200 w Low income 44.3 45.2 21.8 24.4 211,290 230,781 541 686 .. .. 52 84 Middle income 34.6 35.5 20.1 18.5 350,107 310,863 970 1,110 44.2 35.8 127 137 Lower middle income 41.2 42.6 24.4 24.4 228,729 208,372 1,278 1,573 48.9 40.1 99 112 Upper middle income 25.7 25.9 11.8 9.5 121,378 102,492 553 471 20.8 15.5 164 173 Low & middle income 37.4 38.2 20.7 20.7 561,397 541,644 817 951 50.6 .. 100 117 East Asia & Pacific 48.4 50.7 .. .. 142,270 133,753 .. .. 54.2 44.9 63 89 Europe & Central Asia 28.4 27.6 10.3 11.2 140,517 114,042 581 347 22.4 20.9 172 185 Latin America & Carib. 34.7 35.8 11.3 11.4 47,720 49,696 587 896 18.1 16.7 123 123 Middle East & N. Africa 22.6 23.2 29.6 32.7 30,593 29,108 643 833 .. .. 115 142 South Asia 54.7 54.5 33.9 38.9 129,690 129,043 767 1,067 66.1 .. 67 129 Sub-Saharan Africa 43.4 44.1 3.4 3.6 70,608 86,002 136 125 .. .. 19 13 High income 38.5 38.6 10.7 11.8 143,278 135,941 1,213 1,212 5.7 3.8 417 431 Europe EMU 49.7 47.5 14.9 17.0 32,976 31,419 2,332 2,059 7.3 4.9 992 1,002 a. Agricultural land includes permanent pastures, arable land, and land under permanent crops. 132 2007 World Development Indicators 3.2 ENVIRONMENT Agricultural inputs About the data Definitions Agriculture is still a major sector in many econo- · Agricultural land refers to the share of land area Nearly 40 percent of land mies, and agricultural activities provide developing globally is used for agriculture 3.2a that is arable, under permanent crops, or under per- countries with food and revenue. But they also can manent pastures. Arable land includes land defined Total land: 130 million square kilometers degrade natural resources. Poor farming practices by the FAO as land under temporary crops (double- can cause soil erosion and loss of soil fertility. cropped areas are counted once), temporary mead- Efforts to increase productivity through the use of ows for mowing or for pasture, land under market Permanent chemical fertilizers, pesticides, and intensive irriga- Others pastures or kitchen gardens, and land temporarily fallow. 31.2% 26.4% tion have environmental costs and health impacts. Land abandoned as a result of shifting cultivation is Excessive use of chemical fertilizers can alter the excluded. Land under permanent crops is land culti- chemistry of soil. Pesticide poisoning is common in Arable land vated with crops that occupy the land for long periods 10.8% developing countries. And salinization of irrigated and need not be replanted after each harvest, such Forests land diminishes soil fertility. Thus inappropriate use 30.5% as cocoa, coffee, and rubber. This category includes of inputs for agricultural production has far-reach- Permanent crops land under flowering shrubs, fruit trees, nut trees, 1.1% ing effects. and vines, but excludes land under trees grown for This table provides indicators of major inputs to wood or timber. Permanent pasture is land used for Note: Agricultural land includes permanent pastures, agricultural production: land, fertilizer, labor, and arable land, and land under permanent crops. five or more years for forage, including natural and agricultural machinery. There is no single correct mix Source: Tables 3.1 and 3.2. cultivated crops. · Irrigated land refers to areas of inputs: appropriate levels and application rates purposely provided with water, including land irri- vary by country and over time, depending on the type gated by controlled flooding. · Cropland refers to of crops, the climate and soils, and the production arable land and permanent cropland (see table 3.1). process used. · Land under cereal production refers to harvested The data shown here and in table 3.3 are col- areas, although some countries report only sown lected by the Food and Agriculture Organization or cultivated area. · Fertilizer consumption is the (FAO) through annual questionnaires. The FAO tries to quantity of plant nutrients used per unit of arable impose standard definitions and reporting methods, land. Fertilizer products cover nitrogenous, potash, but exact consistency across countries and over time and phosphate fertilizers (including ground rock is not possible. For example, despite standard defini- phosphate). Traditional nutrients--animal and plant tions, data on agricultural land in different climates manures--are not included. The time reference for may not be comparable. For example, permanent fertilizer consumption is the crop year (July through pastures are quite different in nature and intensity June). · Agricultural employment refers to employ- in African countries and dry Middle Eastern coun- ment in agriculture, forestry, hunting, and fishing (see tries. Data on agricultural employment, in particular, table 2.3 for more detail). · Agricultural machinery should be used with caution. In many countries much refers to wheel and crawler tractors (excluding gar- agricultural employment is informal and unrecorded, den tractors) in use in agriculture at the end of the including substantial work performed by women and calendar year specified or during the first quarter of children. the following year. Fertilizer consumption measures the quantity of plant nutrients. Consumption is calculated as pro- duction plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. To smooth annual fluctuations in agricultural activ- ity, the indicators in the table have been averaged over three years. Data sources Data on agricultural inputs are from electronic files that the FAO makes available to the World Bank. 2007 World Development Indicators 133 3.3 Agricultural output and productivity Crop production Food production Livestock Cereal Agricultural index index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2002­04 1990­92 2002­04 1990­92 2002­04 1990­92 2003­05 1990­92 2001­03 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 86.2 100.6 74.2 105.9 66.6 108.9 2,372 3,491 773 1,314 Algeria 85.4 122.9 81.7 113.2 80.7 103.3 915 1,468 1,911 2,067 Angola 60.5 119.4 65.0 113.0 75.6 100.0 378 547 183 160 Argentina 67.2 106.4 73.6 101.4 89.2 92.0 2,652 3,771 6,764 9,272 Armenia 106.5 119.2 112.9 121.8 118.9 123.2 1,826 2,122 1,428 2,645 Australia 59.7 81.6 69.2 91.7 83.3 96.9 1,739 1,960 22,405 31,218 Austria 93.4 99.1 89.8 99.0 92.5 99.6 5,400 5,738 12,048 20,587 Azerbaijan 137.4 122.7 104.8 118.6 98.2 113.6 2,113 2,633 1,085 1,033 Bangladesh 75.4 104.7 73.8 104.6 73.8 103.2 2,567 3,533 246 308 Belarus 107.3 124.7 136.1 107.2 146.5 99.7 2,739 2,850 1,977 2,513 Belgium 77.6 106.0 91.3 101.3 94.3 99.7 6,122 8,710 21,356 36,043 Benin 57.7 125.4 62.7 126.3 89.1 109.2 880 1,147 368 578 Bolivia 63.6 116.4 70.0 111.6 77.2 107.9 1,385 1,857 670 746 Bosnia and Herzegovina 107.2 101.1 120.3 96.0 122.7 86.6 3,548 3,393 .. 5,696 Botswana 96.2 111.5 114.8 104.8 118.8 103.4 312 514 571 410 Brazil 77.2 119.6 70.4 118.0 65.5 116.8 1,916 3,149 1,700 3,002 Bulgaria 149.2 110.9 137.5 101.7 147.1 96.2 3,639 3,157 2,493 6,313 Burkina Faso 67.0 126.6 68.7 116.2 70.7 108.1 783 959 143 163 Burundi 112.4 107.0 112.1 106.3 135.1 100.2 1,370 1,336 110 80 Cambodia 65.2 105.8 65.1 106.3 65.7 103.5 1,356 2,062 .. 296 Cameroon 71.2 103.0 73.9 104.2 84.1 103.1 1,166 1,720 713 1,102 Canada 87.9 93.8 84.1 94.8 78.3 103.6 2,559 2,962 28,224 37,590 Central African Republic 74.4 97.7 69.9 106.9 68.1 113.5 884 1,046 290 407 Chad 69.0 110.9 72.5 110.2 84.5 105.4 636 711 179 225 Chile 78.2 110.5 74.0 108.6 68.0 107.0 3,949 5,621 3,618 4,795 China 69.6 110.6 60.1 113.2 49.4 116.1 4,307 5,057 254 368 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 98.4 107.4 83.9 106.8 80.6 107.1 2,492 3,567 3,406 2,951 Congo, Dem. Rep. 124.7 97.2 121.4 97.5 100.8 99.2 791 767 186 154 Congo, Rep. 80.2 105.1 79.0 107.0 76.0 114.5 688 806 314 329 Costa Rica 71.4 99.6 72.2 101.0 79.9 101.4 3,188 4,001 3,143 4,283 Côte d'Ivoire 73.0 96.2 72.9 100.0 74.9 110.9 869 1,266 605 798 Croatia 79.9 97.2 99.0 98.9 126.6 108.2 3,975 4,179 4,751 8,957 Cuba 112.1 112.6 111.5 108.1 130.0 92.7 2,092 3,076 .. .. Czech Republic .. 94.8 .. 96.6 .. 95.8 .. 4,816 .. 4,564 Denmark 103.5 97.7 97.6 100.6 89.0 102.8 5,448 6,080 15,157 35,696 Dominican Republic 119.1 110.0 104.0 106.1 79.5 103.7 4,078 4,177 2,254 4,108 Ecuador 80.1 95.9 72.4 106.5 65.1 115.3 1,724 2,485 1,686 1,486 Egypt, Arab Rep. 69.2 104.2 67.5 107.7 65.4 115.3 5,738 7,528 1,531 1,975 El Salvador 102.2 90.6 86.4 102.9 74.5 108.5 1,871 2,462 1,633 1,616 Eritrea .. 67.7 .. 83.2 .. 97.1 .. 296 .. 64 Estonia 121.4 89.9 181.3 100.4 193.3 101.7 1,304 2,274 3,002 3,168 Ethiopia .. 106.7 .. 110.6 .. 117.7 .. 1,261 .. 150 Finland 97.5 102.4 104.0 103.3 106.5 104.3 3,246 3,284 15,425 29,735 France 94.0 98.8 97.4 99.5 97.3 100.4 6,370 6,876 22,234 39,220 Gabon 87.2 101.9 89.1 101.6 86.5 100.5 1,712 1,641 1,566 1,696 Gambia, The 55.8 65.2 60.2 68.7 98.8 102.6 1,120 1,155 224 226 Georgia 120.6 91.9 102.7 100.9 78.9 110.3 1,998 2,152 2,388 1,404 Germany 83.7 95.1 98.0 97.5 107.5 101.0 5,578 6,497 14,025 23,475 Ghana 59.1 117.0 61.1 116.9 89.8 108.7 1,084 1,437 302 331 Greece 86.9 90.4 93.7 92.2 101.5 98.2 3,589 3,699 7,563 9,114 Guatemala 77.6 102.6 75.4 105.5 76.6 100.6 1,882 1,747 2,149 2,274 Guinea 73.7 107.5 72.9 110.7 60.5 111.8 1,064 1,476 172 225 Guinea-Bissau 71.1 104.9 73.1 105.1 81.2 106.6 1,529 1,149 205 225 Haiti 108.5 98.8 99.8 101.9 69.8 111.6 997 824 .. .. 134 2007 World Development Indicators 3.3 ENVIRONMENT Agricultural output and productivity Crop production Food production Livestock Cereal Agricultural index index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2002­04 1990­92 2002­04 1990­92 2002­04 1990­92 2003­05 1990­92 2001­03 Honduras 92.9 118.9 86.5 111.9 69.3 105.8 1,371 1,095 976 1,110 Hungary 114.0 99.7 117.0 100.9 125.5 101.9 4,551 4,718 3,268 4,120 India 79.6 100.0 75.9 102.5 69.4 110.5 1,947 2,391 332 381 Indonesia 82.8 112.7 83.8 113.1 85.8 127.3 3,826 4,278 483 556 Iran, Islamic Rep. 73.8 118.1 72.2 113.3 68.8 103.3 1,523 2,411 1,953 2,330 Iraq .. .. .. .. .. .. .. .. .. 2,256 Ireland 92.7 100.3 95.3 96.4 94.3 96.1 6,653 7,390 .. .. Israel 97.8 103.3 82.8 106.5 72.4 113.1 3,132 3,725 .. .. Italy 97.3 92.6 97.0 94.3 95.1 99.4 4,340 5,057 11,536 21,113 Jamaica 84.9 96.7 85.7 97.9 87.2 102.8 1,298 1,162 2,013 1,944 Japan 112.9 95.0 108.4 97.4 106.8 100.2 5,713 5,807 20,196 33,546 Jordan 100.1 136.6 85.4 124.1 71.2 94.1 1,167 1,354 1,892 1,099 Kazakhstan 163.8 108.4 163.0 106.4 178.5 111.6 1,338 994 1,745 1,389 Kenya 86.9 103.2 85.7 106.4 83.9 110.4 1,645 1,409 335 327 Korea, Dem. Rep. 126.2 108.4 119.6 109.1 145.1 114.2 5,073 3,408 .. .. Korea, Rep. 88.2 91.3 79.8 92.8 68.1 100.4 5,885 6,233 5,677 9,948 Kuwait 33.6 110.6 26.4 122.0 27.9 115.7 3,112 1,975 .. 13,048 Kyrgyz Republic 68.5 102.9 74.0 101.0 106.9 98.4 2,772 2,984 676 929 Lao PDR 62.2 115.3 59.1 115.9 60.6 107.5 2,344 3,180 360 459 Latvia 128.7 119.4 222.3 111.0 273.8 101.1 1,641 2,225 1,790 2,442 Lebanon 109.7 94.1 100.4 100.4 65.6 120.4 2,001 2,377 .. 24,436 Lesotho 67.5 100.8 87.8 100.4 115.0 100.0 703 906 476 503 Liberia 62.3 97.7 80.5 96.2 90.4 107.8 951 889 .. .. Libya 79.2 96.9 77.1 101.6 75.9 101.0 706 626 .. .. Lithuania 80.2 113.1 159.9 111.0 187.0 107.8 1,938 3,183 .. 4,117 Macedonia, FYR 107.4 93.3 107.8 96.2 105.1 103.3 2,652 3,053 2,256 2,964 Madagascar 93.6 103.5 90.4 101.8 93.3 97.1 1,935 2,321 185 177 Malawi 57.5 84.3 49.6 87.1 85.4 101.8 871 1,150 72 130 Malaysia 74.4 114.0 70.5 113.7 81.3 115.1 2,827 3,293 3,803 4,570 Mali 73.8 107.4 78.6 105.8 81.3 112.9 840 872 204 227 Mauritania 63.2 97.2 84.2 107.6 87.4 109.3 802 1,075 574 385 Mauritius 110.7 101.6 101.1 104.9 71.1 116.8 4,117 3,436 3,942 5,065 Mexico 82.8 103.8 77.7 105.5 71.4 107.8 2,520 2,872 2,247 2,708 Moldova 136.6 112.2 153.3 112.6 198.7 103.2 2,928 2,572 1,286 725 Mongolia 246.9 107.3 98.3 96.4 93.9 95.9 967 808 703 684 Morocco 101.1 133.4 94.3 122.6 81.3 102.0 1,095 1,282 1,438 1,515 Mozambique 64.7 106.1 70.5 103.0 94.8 100.9 330 921 108 137 Myanmar 61.5 114.7 62.3 115.2 65.0 115.1 2,739 3,420 .. .. Namibia 71.9 111.4 99.5 109.8 104.1 109.3 381 441 811 1,057 Nepal 73.5 111.2 75.2 109.4 80.1 107.3 1,831 2,284 196 208 Netherlands 93.7 97.9 105.5 94.8 105.3 92.6 7,145 8,036 24,056 37,337 New Zealand 78.9 101.9 77.8 112.1 80.7 112.1 5,257 7,360 20,180 26,310 Nicaragua 76.6 115.3 64.0 119.4 57.5 119.9 1,529 1,778 .. 1,901 Niger 71.4 119.5 75.4 116.3 82.0 104.7 323 394 170 172 Nigeria 68.9 103.4 69.1 103.7 76.9 106.6 1,135 1,057 592 843 Norway 120.7 103.4 104.1 98.6 98.2 97.3 3,744 4,121 20,055 32,649 Oman 62.8 87.3 60.2 89.9 65.7 94.0 2,145 2,332 1,005 1,128 Pakistan 80.6 102.5 70.6 106.0 67.6 109.1 1,818 2,438 589 690 Panama 110.9 104.2 94.8 101.8 76.3 101.1 1,862 1,958 2,363 3,557 Papua New Guinea 78.5 101.6 79.9 105.9 80.8 110.1 2,504 3,539 .. 614 Paraguay 85.8 120.7 77.4 110.3 87.3 98.2 1,905 2,245 1,596 1,939 Peru 52.6 108.1 57.1 110.7 68.3 114.1 2,463 3,399 930 1,428 Philippines 84.2 109.6 77.9 112.2 62.1 120.7 2,070 2,946 899 1,010 Poland 109.1 91.6 110.0 103.6 114.8 105.0 2,958 3,191 1,502 1,967 Portugal 103.1 98.6 98.7 99.1 85.7 98.2 1,939 2,683 4,640 5,925 Puerto Rico 167.7 114.6 127.6 97.8 118.4 94.1 1,100 1,731 .. .. 2007 World Development Indicators 135 3.3 Agricultural output and productivity Crop production Food production Livestock Cereal Agricultural index index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2002­04 1990­92 2002­04 1990­92 2002­04 1990­92 2003­05 1990­92 2001­03 Romania 92.2 112.2 97.7 110.7 114.5 107.6 2,777 3,255 2,196 3,477 Russian Federation 125.8 116.0 132.6 110.2 152.1 103.2 1,743 1,839 1,824 2,226 Rwanda 111.4 117.6 107.3 117.2 77.7 107.3 1,088 989 192 222 Saudi Arabia 120.7 114.8 105.2 116.0 67.8 104.9 4,212 4,430 7,867 13,964 Senegal 73.0 68.3 71.9 74.9 74.8 98.2 803 1,013 250 250 Serbia and Montenegro 97.6 110.0 109.2 106.0 103.8 94.9 2,926 4,056 .. 1,562 Sierra Leone 128.1 113.5 118.9 112.3 86.1 105.2 1,223 1,223 .. .. Singapore 157.1 100.0 352.1 69.3 396.3 74.2 .. .. 25,421 34,911 Slovak Republic .. .. .. .. .. .. .. .. .. 3,475 Slovenia 93.1 110.2 77.2 105.8 73.6 103.6 3,609 5,247 11,310 32,311 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 79.6 102.4 84.2 105.7 94.6 108.2 1,602 2,907 1,796 2,391 Spain 87.9 106.1 87.1 105.3 79.5 107.2 2,310 3,040 9,515 18,691 Sri Lanka 86.2 98.8 88.9 100.0 94.6 109.9 2,950 3,428 705 737 Sudan 68.9 110.8 66.7 107.6 67.6 106.3 596 481 346 707 Swaziland 106.6 100.1 108.9 105.3 130.3 111.9 1,299 1,114 1,239 1,149 Sweden 102.2 102.1 97.9 100.0 95.7 97.7 4,272 4,835 21,463 30,116 Switzerland 112.4 95.3 104.9 100.1 104.8 101.9 6,102 6,150 22,228 22,348 Syrian Arab Republic 73.6 117.1 75.1 122.2 75.0 115.6 947 1,786 2,247 3,248 Tajikistan 123.6 132.9 138.1 132.6 192.6 139.2 1,037 2,252 391 379 Tanzania 92.7 103.6 88.7 105.0 82.9 109.4 1,276 1,469 245 283 Thailand 82.0 106.1 84.1 106.0 86.8 105.5 2,186 2,725 501 586 Togo 73.4 110.3 74.1 104.2 87.9 106.7 791 1,040 354 404 Trinidad and Tobago 116.3 91.9 88.7 122.1 73.5 142.6 3,159 2,722 1,666 2,435 Tunisia 104.6 104.2 91.2 103.0 60.3 99.9 1,401 1,539 2,431 2,431 Turkey 88.0 104.0 89.5 103.2 92.2 101.6 2,192 2,399 1,788 1,764 Turkmenistan 111.4 116.5 57.1 125.2 64.0 121.7 2,210 3,011 1,222 .. Uganda 78.0 106.6 79.5 107.7 82.3 112.9 1,487 1,667 187 230 Ukraine 130.6 114.0 146.0 108.1 170.0 108.1 2,834 2,436 1,194 1,433 United Arab Emirates 23.4 56.0 26.5 62.2 57.5 116.9 2,042 3,119 9,390 34,155 United Kingdom 104.9 100.3 107.2 98.9 105.6 98.5 6,321 7,097 22,506 25,876 United States 88.4 101.5 84.8 102.7 83.4 102.6 4,875 6,444 20,797 36,216 Uruguay 70.4 112.7 76.7 104.3 84.2 98.3 2,445 4,279 5,714 6,743 Uzbekistan 107.8 109.0 91.3 107.9 99.7 104.7 1,777 3,461 1,274 1,524 Venezuela, RB 79.5 96.0 73.9 98.9 73.5 100.4 2,561 3,329 4,548 5,899 Vietnam 60.1 116.6 63.1 118.3 57.9 124.9 3,097 4,651 215 290 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 75.0 100.1 71.5 107.4 66.3 115.5 906 772 273 348 Zambia 80.7 102.4 84.3 104.1 80.1 99.2 1,251 1,584 161 205 Zimbabwe 69.2 69.3 77.3 85.7 90.1 100.1 1,123 676 244 266 World 82.5 w 105.7 w 82.0 w 106.2 w 83.4 w 107.0 w 2,868 w 3,247 w 756 w 875 w Low income 78.5 103.5 76.1 105.2 73.5 109.6 1,753 2,086 315 364 Middle income 80.9 110.2 79.8 110.5 81.2 111.0 2,987 3,312 535 717 Lower middle income 77.5 111.7 72.8 112.5 67.9 114.1 3,206 3,629 424 576 Upper middle income 93.1 104.9 101.8 104.2 115.8 102.7 2,453 2,673 2,378 2,731 Low & middle income 80.1 108.1 78.7 108.9 79.3 110.6 2,452 2,791 448 562 East Asia & Pacific 71.8 110.8 64.5 112.4 52.4 116.6 3,816 4,460 303 412 Europe & Central Asia 113.2 107.1 127.1 106.1 149.3 104.1 2,657 2,324 1,817 1,946 Latin America & Carib. 78.2 111.5 74.4 110.4 72.9 108.9 2,234 3,204 2,223 2,924 Middle East & N. Africa 78.8 113.7 75.7 112.5 70.4 107.7 1,632 2,405 1,575 1,919 South Asia 79.9 101.0 75.5 103.5 69.1 109.8 1,992 2,497 340 393 Sub-Saharan Africa 75.9 103.9 77.6 105.1 84.5 107.1 986 1,102 314 337 High income 89.9 98.2 89.7 99.9 90.1 101.2 4,263 5,041 15,048 25,144 Europe EMU 91.5 97.8 94.6 98.8 97.9 99.7 4,656 5,426 12,644 21,414 136 2007 World Development Indicators 3.3 ENVIRONMENT Agricultural output and productivity About the data The agricultural production indexes in the table are To ease cross-country comparisons, the FAO uses excluded. But millet and sorghum, which are grown prepared by the Food and Agriculture Organization international commodity prices to value production. as feed for livestock and poultry in Europe and North (FAO). The FAO obtains data from official and semiof- These prices, expressed in international dollars America, are used as food in Africa, Asia, and coun- ficial reports of crop yields, area under production, and (equivalent in purchasing power to the U.S. dollar), tries of the former Soviet Union. So some cereal livestock numbers. If data are not available, the FAO are derived using a Geary-Khamis formula applied crops are excluded from the data for some countries makes estimates. The indexes are calculated using to agricultural outputs (see Inter-Secretariat Work- and included elsewhere, depending on their use. the Laspeyres formula: production quantities of each ing Group on National Accounts 1993, sections commodity are weighted by average international com- 16.93­96). This method assigns a single price to Definitions modity prices in the base period and summed for each each commodity so that, for example, one metric ton year. Because the FAO's indexes are based on the con- of wheat has the same price regardless of where it · Crop production index shows agricultural pro- cept of agriculture as a single enterprise, estimates of was produced. The use of international prices elimi- duction for each period relative to the base period the amounts retained for seed and feed are subtracted nates fluctuations in the value of output due to transi- 1999­2001. It includes all crops except fodder from the production data to avoid double counting. The tory movements of nominal exchange rates unrelated crops. The regional and income group aggregates for resulting aggregate represents production available for to the purchasing power of the domestic currency. the FAO's production indexes are calculated from the any use except as seed and feed. The FAO's indexes Data on cereal yield may be affected by a variety underlying values in international dollars, normalized may differ from other sources because of differences of reporting and timing differences. Cereal crops to the base period 1999­2001. The data in this table in coverage, weights, concepts, time periods, calcula- harvested for hay or harvested green for food, feed, are three-year averages. · Food production index tion methods, and use of international prices. or silage, and those used for grazing, are generally covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded The five countries with the because, although edible, they have no nutritive highest agricultural productivity 3.3a value. · Livestock production index includes meat and milk from all sources, dairy products such as Agricultural value added per agricultural worker (2000 $ thousands) 1990­92 2001­03 cheese, and eggs, honey, raw silk, wool, and hides 40 and skins. · Cereal yield, measured in kilograms 35 per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, 30 and mixed grains. Production data on cereals refer to 25 crops harvested for dry grain only. Cereal crops har- vested for hay or harvested green for food, feed, or 20 silage, and those used for grazing, are excluded. The 15 FAO allocates production data to the calendar year in 10 which the bulk of the harvest took place. But most of a crop harvested near the end of a year will be 5 used in the following year.· Agricultural productivity 0 refers to the ratio of agricultural value added, mea- France Canada Netherlands United Belgium Denmark sured in 2000 U.S. dollars, to the number of workers States in agriculture. Agricultural productivity is measured Source: Table 3.3. by value added per unit of input. (For further discus- sion of the calculation of value added in national The 10 countries with the highest cereal yield accounts, see About the data for tables 4.1 and 4.2.) in 2003­05--and the 10 with the lowest 3.3b Agricultural value added includes that from forestry and fishing. Thus interpretations of land productiv- Kilograms Kilograms ity should be made with caution. To smooth annual Country per hectare Country per hectare fluctuations in agricultural activity, the indicators in Belgium 8,710 Eritrea 296 the table have been averaged over three years. Netherlands 8,036 Niger 394 Egypt, Arab Rep. 7,528 Namibia 441 Ireland 7,390 Sudan 481 Data sources New Zealand 7,360 Botswana 514 The agricultural production indexes are prepared United Kingdom 7,097 Angola 547 by the FAO. The FAO makes these data and the France 6,876 Libya 626 data on cereal yield and agricultural employment Germany 6,497 Zimbabwe 676 available to the World Bank in electronic files that United States 6,444 Chad 711 may contain more recent information than pub- Korea, Rep. 6,233 Congo, Dem. Rep. 767 lished versions. For sources of data on agricultural Source: Table 3.3. value added, see Data sources for table 4.2. 2007 World Development Indicators 137 3.4 Deforestation and biodiversity Forest Average Mammals Birds Higher GEF Nationally Marine area annual plantsb benefits protected areas protected deforestationa index for areas biodiversity % of % of thousand % Total known Threatened Total known Threatened Total known Threatened thousand total land thousand surface sq. km 1990­ species species species species species species sq. km area sq. km area 2005 2005 2004 2004 2004 2004 2002 2002 2005 2004 c 2004 c 2004 c 2004 c Afghanistan 9 2.3 144 12 434 17 4,000 1 3.6 2.2 0.3 .. .. Albania 8 0.0 73 1 303 9 3,031 0 0.2 0.7 2.7 0.3 1.0 Algeria 23 ­1.8 100 12 372 11 3,164 2 3.0 118.6 5.0 0.9 0.0 Angola 591 0.2 296 11 930 20 5,185 26 9.6 125.5 10.1 29.1 2.3 Argentina 330 0.4 375 32 1,038 55 9,372 42 18.5 174.5 6.4 7.8 0.3 Armenia 3 1.2 78 9 302 12 3,553 1 0.3 3.0 10.6 .. .. Australia 1,637 0.2 376 63 851 60 15,638 56 95.8 745.3 9.7 680.8 8.8 Austria 39 ­0.2 101 5 412 8 3,100 3 0.3 23.5 28.5 .. .. Azerbaijan 9 0.0 82 11 364 11 4,300 0 0.9 4.0 4.8 1.2 1.4 Bangladesh 9 0.1 131 22 604 23 5,000 12 1.6 0.7 0.5 0.3 0.2 Belarus 79 ­0.5 71 6 226 4 2,100 0 0.0 13.2 6.3 .. .. Belgiumd 7 0.1 92 9 427 10 1,550 0 0.0 1.0 3.5 0.0 0.0 Benin 24 1.9 159 6 485 2 2,500 14 0.2 26.4 23.9 .. .. Bolivia 587 0.4 361 26 1,414 30 17,367 70 13.8 211.0 19.5 .. .. Bosnia and Herzegovina 22 0.1 78 8 312 8 .. 1 0.4 0.3 0.5 .. .. Botswana 119 0.9 169 6 570 9 2,151 0 1.5 174.9 30.9 .. .. Brazil 4,777 0.5 578 74 1,712 120 56,215 381 100.0 1,532.6 18.1 47.4 0.6 Bulgaria 36 ­0.6 106 12 379 11 3,572 0 0.9 11.2 10.3 0.0 0.0 Burkina Faso 68 0.3 129 6 452 2 1,100 2 0.3 42.1 15.4 .. .. Burundi 2 3.2 116 7 597 9 2,500 2 0.5 1.5 5.7 .. .. Cambodia 104 1.3 127 23 521 24 .. 31 3.9 41.5 23.5 1.9 1.1 Cameroon 212 0.9 322 42 936 18 8,260 334 13.3 37.4 8.0 3.9 0.8 Canada 3,101 0.0 211 16 472 19 3,270 1 22.2 628.7 6.9 362.7 3.6 Central African Republic 228 0.1 187 11 663 3 3,602 15 1.7 103.3 16.6 .. .. Chad 119 0.6 104 12 531 5 1,600 2 2.1 119.8 9.5 .. .. Chile 161 ­0.4 159 22 445 32 5,284 40 16.2 26.9 3.6 114.5 15.1 China 1,973 ­1.7 580 80 1,221 82 32,200 443 64.8 1,100.7 11.8 16.0 0.2 Hong Kong, China .. .. 57 1 306 20 .. 6 .. 0.3 24.7 0.3 .. Colombia 607 0.1 467 39 1,821 86 51,220 222 57.3 825.3 74.4 8.1 0.7 Congo, Dem. Rep. 1,336 0.3 430 29 1,148 30 11,007 65 17.0 194.4 8.6 .. .. Congo, Rep. 225 0.1 166 14 597 4 6,000 35 3.4 61.3 18.0 .. .. Costa Rica 24 0.4 232 13 838 18 12,119 110 11.1 12.1 23.6 4.8 9.4 Côte d'Ivoire 104 ­0.1 229 23 702 11 3,660 105 3.9 54.5 17.1 0.3 0.1 Croatia 21 ­0.1 96 7 365 9 4,288 0 0.5 3.6 6.5 2.5 4.4 Cuba 27 ­2.1 65 11 358 18 6,522 163 13.5 1.5 1.4 31.7 28.6 Czech Republic 26 0.0 88 6 386 9 1,900 4 0.1 14.4 18.7 .. .. Denmark 5 ­0.8 81 4 427 10 1,450 3 0.2 10.9 25.7 5.1 11.8 Dominican Republic 14 0.0 36 5 224 16 5,657 30 6.8 11.9 24.6 8.6 17.6 Ecuador 109 1.4 341 34 1,515 69 19,362 1 30.0 67.2 24.3 141.0 49.7 Egypt, Arab Rep. 1 ­3.5 118 6 481 17 2,076 2 3.2 56.0 5.6 76.7 7.7 El Salvador 3 1.4 137 2 434 3 2,911 25 0.8 0.4 1.9 0.1 0.4 Eritrea 16 0.3 70 9 537 7 .. 3 0.9 5.0 5.0 .. .. Estonia 23 ­0.4 67 4 267 3 1,630 0 0.0 8.9 21.1 .. .. Ethiopia 130 0.9 288 35 839 20 6,603 22 8.5 186.2 18.6 .. .. Finland 225 ­0.1 80 3 421 10 1,102 1 0.2 29.5 9.7 1.1 0.3 France 156 ­0.5 148 16 517 15 4,630 2 3.9 16.2 3.0 0.5 0.1 Gabon 218 0.0 166 11 632 5 6,651 107 3.4 8.8 3.4 1.0 0.4 Gambia, The 5 ­0.4 133 3 535 2 974 4 0.1 0.3 3.5 0.2 1.9 Georgia 28 0.0 98 11 268 8 4,350 0 0.7 3.0 4.3 0.0 0.1 Germany 111 ­0.2 126 9 487 14 2,682 12 0.7 111.5 32.0 9.1 2.6 Ghana 55 1.7 249 15 729 8 3,725 117 2.0 36.9 16.2 .. .. Greece 38 ­0.9 118 11 412 14 4,992 2 3.0 4.3 3.3 2.5 1.9 Guatemala 39 1.1 193 7 684 10 8,681 85 8.9 25.4 23.4 0.1 0.1 Guinea 67 0.6 215 18 640 10 3,000 22 2.6 15.6 6.4 .. .. Guinea-Bissau 21 0.4 101 5 459 1 1,000 4 0.7 0.0 0.0 .. .. Haiti 1 0.6 41 4 271 15 5,242 28 5.8 0.1 0.3 .. .. 138 2007 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity Forest Average Mammals Birds Higher GEF Nationally Marine area annual plantsb benefits protected areas protected deforestationa index for areas biodiversity % of % of thousand % Total known Threatened Total known Threatened Total known Threatened thousand total land thousand surface sq. km 1990­ species species species species species species sq. km area sq. km area 2005 2005 2004 2004 2004 2004 2002 2002 2005 2004 c 2004 c 2004 c 2004 c Honduras 46 2.5 201 10 699 6 5,680 111 7.9 23.4 21.0 1.9 1.7 Hungary 20 ­0.6 88 7 367 9 2,214 1 0.2 8.3 9.3 .. .. India 677 ­0.4 422 85 1,180 79 18,664 246 43.9 156.3 5.3 16.1 0.5 Indonesia 885 1.6 667 146 1,604 121 29,375 383 90.0 259.9 14.3 130.1 6.8 Iran, Islamic Rep. 111 0.0 158 21 498 18 8,000 1 7.9 105.5 6.5 6.2 0.4 Iraq 8 ­0.1 102 9 396 18 .. 0 1.7 0.0 0.0 .. .. Ireland 7 ­3.4 63 4 408 8 950 1 0.7 0.8 1.1 0.0 0.0 Israel 2 ­0.7 115 13 534 18 2,317 0 0.9 4.6 21.3 0.1 0.6 Italy 100 ­1.3 132 12 478 15 5,599 3 4.4 32.4 11.0 1.5 0.5 Jamaica 3 0.1 35 5 298 12 3,308 208 4.9 1.8 16.2 8.2 74.5 Japan 249 0.0 171 37 592 53 5,565 12 41.4 52.2 14.3 10.6 2.8 Jordan 1 0.0 93 7 397 14 2,100 0 0.3 9.7 11.0 0.0 0.0 Kazakhstan 33 0.2 145 15 497 23 6,000 1 5.4 77.4 2.9 0.5 0.0 Kenya 35 0.3 407 33 1,103 28 6,506 103 9.9 71.9 12.6 3.1 0.5 Korea, Dem. Rep. 62 1.6 105 12 369 22 2,898 3 0.7 3.2 2.6 .. .. Korea, Rep. 63 0.1 89 12 423 34 2,898 0 1.8 3.5 3.6 3.5 3.5 Kuwait 0 ­6.7 23 1 358 12 234 0 0.1 0.0 0.0 0.3 1.5 Kyrgyz Republic 9 ­0.3 58 6 207 4 4,500 1 1.2 7.2 3.7 .. .. Lao PDR 161 0.5 215 30 704 21 8,286 19 5.4 37.4 16.2 .. .. Latvia 29 ­0.4 68 4 325 8 1,153 0 0.0 9.7 15.6 0.2 0.2 Lebanon 1 ­0.8 70 5 377 10 3,000 0 0.2 0.1 0.7 0.0 0.0 Lesotho 0 ­4.0 59 3 311 7 1,591 1 0.3 0.1 0.2 .. .. Liberia 32 1.5 183 20 576 11 2,200 46 2.9 15.2 15.8 0.6 0.5 Libya 2 0.0 87 5 326 7 1,825 1 1.7 1.2 0.1 0.5 0.0 Lithuania 21 ­0.5 71 5 227 4 1,796 0 0.0 5.9 9.5 0.5 0.8 Macedonia, FYR 9 0.0 89 9 291 9 3,500 0 0.2 2.0 7.9 .. .. Madagascar 128 0.4 165 49 262 34 9,505 276 31.4 18.3 3.1 0.2 0.0 Malawi 34 0.8 207 7 658 13 3,765 14 3.9 19.4 20.6 .. .. Malaysia 209 0.4 337 50 746 40 15,500 683 14.8 100.8 30.7 5.0 1.5 Mali 126 0.7 134 12 624 5 1,741 6 1.6 46.7 3.8 .. .. Mauritania 3 2.4 94 7 521 5 1,100 0 1.4 2.5 0.2 15.0 1.5 Mauritius 0 0.3 14 3 137 13 750 87 4.2 0.1 3.3 0.1 4.4 Mexico 642 0.5 544 72 1,026 57 26,071 261 75.8 99.0 5.2 82.1 4.2 Moldova 3 ­0.2 50 4 203 8 1,752 0 0.0 0.5 1.4 .. .. Mongolia 103 0.7 140 13 387 22 2,823 0 4.4 217.9 13.9 .. .. Morocco 44 ­0.1 129 12 430 13 3,675 2 4.0 4.7 1.1 0.5 0.1 Mozambique 193 0.2 228 12 685 23 5,692 46 8.2 45.3 5.8 22.5 2.8 Myanmar 322 1.2 288 39 1,047 41 7,000 38 10.6 35.3 5.4 0.2 0.0 Namibia 77 0.8 192 10 619 18 3,174 24 5.9 46.0 5.6 74.0 9.0 Nepal 36 1.6 203 29 274 31 6,973 7 2.2 26.6 18.6 .. .. Netherlands 4 ­0.4 95 9 444 11 1,221 0 0.1 9.5 28.0 0.8 1.9 New Zealand 83 ­0.5 73 8 351 74 2,382 21 22.3 64.7 24.1 22.7 8.4 Nicaragua 52 1.4 181 6 632 8 7,590 39 3.6 28.1 23.1 1.3 1.0 Niger 13 2.3 123 10 493 2 1,460 2 0.9 96.9 7.7 .. .. Nigeria 111 2.4 290 25 899 9 4,715 170 6.6 55.0 6.0 .. .. Norway 94 ­0.2 83 9 442 6 1,715 2 1.6 19.7 6.5 1.3 0.4 Oman 0 0.0 74 12 483 14 1,204 6 4.4 0.2 0.1 29.6 9.6 Pakistan 19 1.6 195 17 625 30 4,950 2 5.1 73.1 9.5 2.2 0.3 Panama 43 0.1 241 17 904 20 9,915 195 11.7 13.1 17.6 10.0 13.3 Papua New Guinea 294 0.4 260 58 720 33 11,544 142 27.7 7.3 1.6 3.5 0.8 Paraguay 185 0.8 168 11 696 27 7,851 10 3.3 16.6 4.2 .. .. Peru 687 0.1 441 46 1,781 94 17,144 274 36.3 216.1 16.9 3.4 0.3 Philippines 72 2.2 222 50 590 70 8,931 212 33.7 24.3 8.2 16.6 5.5 Poland 92 ­0.2 110 12 424 12 2,450 4 0.6 70.3 23.1 0.7 0.2 Portugal 38 ­1.5 105 15 501 15 5,050 15 3.8 4.7 5.1 2.0 2.2 Puerto Rico 4 ­0.1 38 2 310 12 2,493 52 3.8 0.3 3.5 1.7 19.1 2007 World Development Indicators 139 3.4 Deforestation and biodiversity Forest Average Mammals Birds Higher GEF Nationally Marine area annual plantsb benefits protected areas protected deforestationa index for areas biodiversity % of % of thousand % Total known Threatened Total known Threatened Total known Threatened thousand total land thousand surface sq. km 1990­ species species species species species species sq. km area sq. km area 2005 2005 2004 2004 2004 2004 2002 2002 2005 2004 c 2004 c 2004 c 2004 c Romania 64 0.0 101 15 365 13 3,400 1 .. 5.8 2.5 6.1 2.6 Russian Federation 8,088 0.0 296 43 645 47 11,400 7 37.1 1,287.0 7.9 301.8 1.8 Rwanda 5 ­3.4 206 13 665 9 2,288 3 1.1 1.9 7.9 .. .. Saudi Arabia 27 0.0 94 9 433 17 2,028 3 3.4 819.1 41.0 5.2 0.2 Senegal 87 0.5 191 11 612 5 2,086 7 1.3 21.6 11.2 0.9 0.4 Serbia and Montenegro 27 ­0.4 96 10 381 10 4,082 1 .. 3.8 3.7 0.1 0.1 Sierra Leone 28 0.6 197 12 626 10 2,090 47 1.5 3.2 4.5 .. .. Singapore 0 0.0 73 3 400 10 2,282 54 0.1 0.0 4.2 0.0 0.1 Slovak Republic 19 0.0 87 7 332 11 3,124 2 0.1 11.0 22.8 .. .. Slovenia 13 ­0.4 87 7 350 7 3,200 0 0.2 2.9 14.5 0.0 0.0 Somalia 71 0.9 182 15 642 13 3,028 17 6.7 1.9 0.3 3.3 0.5 South Africa 92 0.0 320 29 829 36 23,420 75 23.5 74.0 6.1 3.4 0.3 Spain 179 ­2.2 132 20 515 20 5,050 14 6.6 46.2 9.3 1.8 0.4 Sri Lanka 19 1.2 123 21 381 16 3,314 280 6.6 17.7 27.3 2.3 3.5 Sudan 675 0.8 302 16 952 10 3,137 17 5.5 123.0 5.2 0.3 0.0 Swaziland 5 ­1.0 124 6 490 6 2,715 11 0.1 0.6 3.5 .. .. Sweden 275 0.0 85 5 457 9 1,750 3 0.3 44.8 10.9 4.3 1.0 Switzerland 12 ­0.4 93 4 382 8 3,030 2 0.2 11.9 29.6 .. .. Syrian Arab Republic 5 ­1.6 82 3 350 11 3,000 0 0.9 2.7 1.5 .. .. Tajikistan 4 0.0 76 7 351 9 5,000 2 0.7 26.0 18.6 .. .. Tanzania 353 1.0 375 34 1,056 37 10,008 239 15.1 374.3 42.4 2.3 0.2 Thailand 145 0.6 300 36 971 42 11,625 84 8.0 80.3 15.7 5.8 1.1 Togo 4 2.9 175 7 565 2 3,085 10 0.4 6.5 11.9 .. .. Trinidad and Tobago 2 0.3 116 1 435 2 2,259 1 2.4 0.2 4.7 0.1 1.3 Tunisia 11 ­4.3 78 10 360 9 2,196 0 0.5 2.3 1.5 0.2 0.1 Turkey 102 ­0.3 145 15 436 14 8,650 3 6.0 20.3 2.6 4.5 0.6 Turkmenistan 41 0.0 103 12 318 13 .. 0 2.0 19.8 4.2 .. .. Uganda 36 1.8 360 29 1,015 15 4,900 38 3.3 64.3 32.6 .. .. Ukraine 96 ­0.2 120 14 325 13 5,100 1 0.4 19.4 3.3 3.1 0.5 United Arab Emirates 3 ­1.8 30 5 268 11 .. 0 0.2 0.2 0.2 .. .. United Kingdom 28 ­0.6 103 10 557 10 1,623 13 2.1 60.5 25.0 22.5 9.2 United States 3,031 ­0.1 468 40 888 71 19,473 240 90.3 1,490.1 16.3 909.5 9.4 Uruguay 15 ­4.4 118 6 414 24 2,278 1 1.4 0.7 0.4 0.1 0.0 Uzbekistan 33 ­0.5 91 7 343 16 4,800 1 1.2 20.5 4.8 .. .. Venezuela, RB 477 0.6 353 26 1,392 25 21,073 67 26.8 644.4 73.1 21.3 2.3 Vietnam 129 ­2.5 279 41 837 41 10,500 145 11.7 13.6 4.4 0.7 0.2 West Bank and Gaza 0 .. .. 1 .. 1 .. .. .. .. .. .. .. Yemen, Rep. 5 0.0 74 6 385 14 1,650 159 3.4 0.0 0.0 .. .. Zambia 425 0.9 255 11 770 12 4,747 8 5.0 312.3 42.0 .. .. Zimbabwe 175 1.4 222 8 661 10 4,440 17 2.1 57.5 14.9 .. .. World 39,426 s 0.1 w 15,048.4 s 11.6 w 4,348.9 s 3.8 w Low income 6,746 0.5 2,806.6 10.0 73.8 .. Middle income 23,132 0.1 7,988.4 11.7 1,233.1 1.9 Lower middle income 12,255 0.2 5,196.6 13.2 632.6 1.7 Upper middle income 10,878 0.1 2,791.8 9.6 600.6 2.1 Low & middle income 29,878 0.2 10,795.0 11.2 1,307.0 1.6 East Asia & Pacific 4,507 ­0.2 1,924.7 12.1 192.1 1.3 Europe & Central Asia 8,946 0.0 1,657.2 7.1 321.6 1.4 Latin America & Carib. 9,150 0.4 3,966.3 19.7 495.7 2.7 Middle East & N. Africa 211 ­0.5 301.1 3.4 114.7 1.5 South Asia 801 ­0.2 288.6 6.0 20.9 0.5 Sub-Saharan Africa 6,263 0.6 2,657.1 11.3 162.0 .. High income 9,548 ­0.1 4,253.5 12.9 3,042.0 8.8 Europe EMU 915 ­0.8 283.1 11.5 19.5 0.8 a. Negative numbers indicate an increase in forest area. b. Flowering plants only. c. Data may refer to earlier years. They are the most recent reported by the World Conservation Monitoring Centre in 2004. d. Includes Luxembourg. 140 2007 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity About the data Definitions Biological diversity is defined in terms of the variabil- With the support of the World Bank's Development · Forest area is land under natural or planted stands ity in genes, species, and ecosystems. Faced with Research Group and in close collaboration with scien- of trees, whether productive or not. · Average annual mounting threats to biodiversity, the international tific nongovernmental organizations, the Global Envi- deforestation refers to the permanent conversion of community has increasingly focused on conserving ronment Facility (GEF) developed the GEF benefits natural forest area to other uses, including shifting this diversity. Deforestation is a major cause of loss of index for biodiversity, a comprehensive indicator of cultivation, permanent agriculture, ranching, settle- biodiversity, and habitat conservation is vital for stem- national biodiversity status, to guide its biodiversity ments, and infrastructure development. Deforested ming this loss. Conservation efforts traditionally have priorities. This indicator incorporates information on areas do not include areas logged but intended for focused on protecting areas of high biodiversity. individual species range maps available from the regeneration or areas degraded by fuelwood gather- The estimates of forest area are from the Food IUCN for virtually all mammals (4,612), amphibians ing, acid precipitation, or forest fires. Negative num- and Agriculture Organization's (FAO) Global Forest (5,327), and endangered birds (1,098); country-level Resources Assessment 2005, which provides detailed data from the World Resources Institute (WRI) for bers indicate an increase in forest area. · Mammals information on forest cover in 2005 and adjusted esti- reptiles and vascular plants; country-level data from exclude whales and porpoises. · Birds are listed for mates of forest cover in 1990 and 2000. The current FishBase for 27,669 fish species; and the ecological countries included within their breeding or wintering survey is the latest global forest assessment and uses characteristics of 867 terrestrial ecoregions of the ranges. · Higher plants refer to native vascular plant a uniform global definition of forest. No breakdown of world from WWF International. For each country the species. · Threatened species are the number of forest cover between natural forest and plantation is biodiversity indicator incorporates the best available species classified by the IUCN as endangered, vul- shown in the table because of space limitations. (This and comparable information in four relevant dimen- nerable, rare, indeterminate, out of danger, or insuf- breakdown is provided by the FAO only for developing sions: represented species, threatened species, rep- ficiently known. · GEF benefits index for biodiversity countries.) For this reason the deforestation data in resented ecoregions, and threatened ecoregions. To is a composite index of relative biodiversity potential the table may underestimate the rate at which natural combine these dimensions into one measure, the for each country based on the species represented forest is disappearing in some countries. indicator uses dimensional weights that reflect the in each country, their threat status, and the diversity Measures of species richness are among the most consensus of conservation scientists in the GEF, of habitat types in each country. The index shown in straightforward ways to indicate the importance of an IUCN, WWF International, and other nongovernmental the table has been normalized so that values run area for biodiversity. The number of threatened spe- organizations. The index shown in the table has been cies is also an important measure of the immediate normalized so that values run from 0 (no biodiversity from 0 (no biodiversity potential) to 100 (maximum need for conservation efforts in a geographic area. potential) to 100 (maximum biodiversity potential). biodiversity potential). · Nationally protected areas Global analyses of the status of threatened species The table shows information on protected areas, are totally or partially protected areas of at least have been carried out for few groups of organisms. numbers of certain species, and numbers of those 1,000 hectares that are designated as scientifi c Only for mammals, birds, and amphibians has the species under threat. The World Conservation Moni- reserves with limited public access, national parks, status of virtually all known species been assessed. toring Centre (WCMC) compiles these data from a natural monuments, nature reserves or wildlife sanc- Threatened species are defined according to the World variety of sources. Because of differences in defi - tuaries, and protected landscapes. Marine areas, Conservation Union's (IUCN) classification categories: nitions and reporting practices, cross-country com- unclassified areas, and littoral (intertidal) areas are endangered (in danger of extinction and unlikely to sur- parability is limited. Compounding these problems, not included. The data also do not include sites pro- vive if causal factors continue operating), vulnerable available data cover different periods. tected under local or provincial law. Total land area (likely to move into the endangered category in the Nationally protected areas are areas of at least is used to calculate the percentage of total area pro- near future if causal factors continue operating), rare 1,000 hectares that fall into one of six IUCN manage- tected (see table 3.1). · Marine protected areas are (not endangered or vulnerable but at risk), indetermi- ment categories: nate (known to be endangered, vulnerable, or rare but · Scientific reserves and strict nature reserves with areas of intertidal or subtidal terrain--and overlying not enough information is available to say which), out limited public access. water and associated flora and fauna and historical of danger (formerly included in one of the above cat- · National parks of national or international significance and cultural features--that have been reserved by egories but now considered relatively secure because and not materially affected by human activity. law or other effective means to protect part or all of appropriate conservation measures are in effect), · Natural monuments and natural landscapes with the enclosed environment. and insufficiently known (suspected but not definitely unique aspects. known to belong to one of the above categories). · Managed nature reserves and wildlife sanctuaries. While the number of birds and mammals is fairly · Protected landscapes (which may include cultural Data sources well known, it is difficult to make an accurate count of landscapes). Data on forest area and deforestation are from the plants. The number of plant species is highly debated. · Areas managed mainly for the sustainable use of FAO's Global Forest Resources Assessment 2005. The IUCN's 2003 IUCN Red List of Threatened Plants natural systems to ensure long-term protection and Data on species are from the electronic files of the provides the most comprehensive list of threatened maintenance of biological diversity. United Nations Environmental Program and WCMC species on a global scale, the result of more than 20 Designating land as a protected area does not and 2003 IUCN Red List of Threatened Plants. For years' work by botanists from around the world. Only 5 necessarily mean that protection is in force. And for percent of plant species have been evaluated, and 70 small countries that may only have protected areas China the number of mammals is from Princeton percent of these are threatened with extinction. Plant smaller than 1,000 hectares, this size limit in the University Press Guide to the Mammals of China species data should be interpreted with caution since definition will result in an underestimate of the extent (forthcoming). The GEF benefi ts index for biodi- they are not necessarily comparable across countries and number of protected areas. versity is from Kiran Dev Pandey, Piet Buys, Ken because of differences in taxonomic concepts and Due to variations in consistency and methodology of Chomitz, and David Wheeler's, "Biodiversity Con- coverage. However, they do identify countries that are collection, the quality of the data are highly variable servation Indicators: New Tools for Priority Setting major sources of global biodiversity and that show across countries. Some countries update their infor- at the Global Environment Facility" (2006). Data national commitments to habitat protection. mation more frequently than others, some may have on protected areas are from the United Nations Setting priorities for conserving biodiversity requires more accurate data on extent of coverage, and many Environment Programme and WCMC. a broader set of information than species richness. underreport the number or extent of protected areas. 2007 World Development Indicators 141 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 2005 2005 2002b 2002b 2002b 2002b 2002b 2002 2004 2004 Afghanistan 55 .. 23.3 42.3 98 0 2 .. .. .. Albania 27 8,595 1.7 6.4 62 11 27 2.4 99 94 Algeria 11 341 6.1 54.2 65 13 22 9.7 88 80 Angola 148 9,284 0.4 0.2 60 17 23 30.8 75 40 Argentina 276 7,123 29.2 10.6 74 9 17 8.3 98 80 Armenia 9 3,017 3.0 32.4 66 4 30 0.8 99 80 Australia 492 24,202 23.9 4.9 75 10 15 17.9 100 100 Austria 55 6,680 2.1 3.8 1 64 35 93.5 100 100 Azerbaijan 8 966 17.3 213.0 68 28 5 0.4 95 59 Bangladesh 105 740 79.4 75.6 96 1 3 0.7 82 72 Belarus 37 3,805 2.8 7.5 30 47 23 5.0 100 100 Belgium 12 1,145 .. .. .. .. .. .. 100 100 Benin 10 1,221 0.1 1.3 45 23 32 19.0 78 57 Bolivia 304 33,054 1.4 0.5 81 7 13 6.1 95 68 Bosnia and Herzegovina 36 9,086 .. .. .. .. .. .. 99 96 Botswana 2 1,360 0.2 8.1 41 18 41 35.4 100 90 Brazil 5,418 29,066 59.3 1.1 62 18 20 10.5 96 57 Bulgaria 21 2,713 10.5 50.0 19 78 3 1.3 100 97 Burkina Faso 13 945 0.8 6.4 86 1 13 3.6 94 54 Burundi 10 1,338 0.3 2.9 77 6 17 2.6 92 77 Cambodia 121 8,571 4.1 3.4 98 0 1 1.0 64 35 Cameroon 273 16,726 1.0 0.4 74 8 18 11.1 86 44 Canada 2,850 88,238 46.0 1.6 12 69 20 16.3 100 99 Central African Republic 141 34,920 0.0 0.0 4 16 80 38.4 93 61 Chad 15 1,539 0.2 1.5 83 0 17 7.2 41 43 Chile 884 54,249 12.6 1.4 64 25 11 6.3 100 58 China 2,812 2,156 630.3 22.4 68 26 7 2.2 93 67 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 2,112 46,316 10.7 0.5 46 4 50 8.1 99 71 Congo, Dem. Rep. 900 15,639 0.4 0.0 31 17 53 12.1 82 29 Congo, Rep. 222 55,515 0.0 0.0 9 22 70 76.0 84 27 Costa Rica 112 25,975 2.7 2.4 53 17 29 6.2 100 92 Côte d'Ivoire 77 4,231 0.9 1.2 65 12 24 11.0 97 74 Croatia 38 8,485 .. .. .. .. .. .. 100 100 Cuba 38 3,381 8.2 21.5 69 12 19 .. 95 78 Czech Republic 13 1,290 2.6 19.5 2 57 41 23.0 100 100 Denmark 6 1,108 1.3 21.2 43 25 32 127.5 100 100 Dominican Republic 21 2,361 3.4 16.1 66 2 32 6.3 97 91 Ecuador 432 32,657 17.0 3.9 82 5 12 1.0 97 89 Egypt, Arab Rep. 2 24 68.3 3,794.4 86 6 8 1.6 99 97 El Salvador 18 2,587 1.3 7.2 59 16 25 10.7 94 70 Eritrea 3 636 0.3 10.7 97 0 3 2.3 74 57 Estonia 13 9,435 0.2 1.2 5 38 57 39.5 100 99 Ethiopia 122 1,712 5.6 4.6 94 0 6 1.5 81 11 Finland 107 20,396 2.5 2.3 3 84 14 50.3 100 100 France 179 2,932 40.0 22.4 10 74 16 34.2 100 100 Gabon 164 118,511 0.1 0.1 42 8 50 42.1 95 47 Gambia, The 3 1,977 0.0 1.0 65 12 23 14.1 95 77 Georgia 58 12,985 3.6 6.2 59 21 20 0.9 96 67 Germany 107 1,297 47.1 44.0 20 68 12 40.9 100 100 Ghana 30 1,370 1.0 3.2 66 10 24 5.5 88 64 Greece 58 5,223 7.8 13.4 80 3 16 16.1 .. .. Guatemala 109 8,667 2.0 1.8 80 13 6 10.0 99 92 Guinea 226 24,037 1.5 0.7 90 2 8 2.2 78 35 Guinea-Bissau 16 10,086 0.2 1.1 82 5 13 1.1 79 49 Haiti 13 1,524 1.0 7.6 94 1 5 3.8 52 56 142 2007 World Development Indicators 3.5 ENVIRONMENT 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 2005 2005 2002b 2002b 2002b 2002b 2002b 2002 2004 2004 Honduras 96 13,311 0.9 0.9 80 12 8 7.3 95 81 Hungary 6 595 7.6 127.3 32 59 9 6.7 100 98 India 1,261 1,152 645.8 51.2 86 5 8 0.8 95 83 Indonesia 2,838 12,867 82.8 2.9 91 1 8 2.2 87 69 Iran, Islamic Rep. 129 1,883 72.9 56.7 91 2 7 1.5 99 84 Iraq 35 .. 42.7 121.3 92 5 3 0.5 .. .. Ireland 49 11,781 1.1 2.3 0 77 23 95.9 100 .. Israel 1 116 2.1 256.3 62 7 31 55.5 100 100 Italy 183 3,114 44.4 24.3 45 37 18 25.3 100 .. Jamaica 9 3,541 0.4 4.4 49 17 34 20.2 98 88 Japan 430 3,365 88.4 20.6 62 18 20 52.9 100 100 Jordan 1 128 1.0 144.3 75 4 21 9.3 99 91 Kazakhstan 75 4,978 35.0 46.4 82 17 2 0.7 97 73 Kenya 21 604 1.6 7.6 64 6 30 8.4 83 46 Korea, Dem. Rep. 67 2,979 9.0 13.5 55 25 20 .. 100 100 Korea, Rep. 65 1,344 18.6 28.6 48 16 36 30.6 97 71 Kuwait 0 0 0.4 .. 52 2 45 90.8 .. .. Kyrgyz Republic 47 9,041 10.1 21.7 94 3 3 0.1 98 66 Lao PDR 190 32,140 3.0 1.6 90 6 4 0.6 79 43 Latvia 17 7,259 0.3 1.8 13 33 53 30.0 100 96 Lebanon 5 1,342 1.4 28.8 67 1 33 13.1 100 100 Lesotho 5 2,897 0.1 1.0 20 40 40 18.4 92 76 Liberia 200 60,915 0.1 0.1 55 18 27 5.4 72 52 Libya 1 103 4.3 711.3 83 3 14 8.7 .. .. Lithuania 16 4,569 0.3 1.7 7 15 78 48.2 .. .. Macedonia, FYR 5 2,655 .. .. .. .. .. .. .. .. Madagascar 337 18,113 15.0 4.4 96 2 3 0.2 77 35 Malawi 16 1,250 1.0 6.3 80 5 15 1.7 98 68 Malaysia 580 22,882 9.0 1.6 62 21 17 10.5 100 96 Mali 60 4,438 6.5 10.9 90 1 9 0.4 78 36 Mauritania 0 130 1.7 425.0 88 3 9 0.7 59 44 Mauritius 3 2,252 0.6 21.8 .. .. .. 7.9 100 100 Mexico 409 3,967 78.2 19.1 77 5 17 7.5 100 87 Moldova 1 238 2.3 231.0 33 58 10 0.6 97 88 Mongolia 35 13,626 0.4 1.3 52 27 20 2.3 87 30 Morocco 29 961 12.6 43.4 87 3 10 2.9 99 56 Mozambique 100 5,068 0.6 0.6 87 2 11 7.3 72 26 Myanmar 881 17,431 33.2 3.8 98 1 1 .. 80 77 Namibia 6 3,052 0.3 4.8 71 5 24 12.4 98 81 Nepal 198 7,305 10.2 5.1 96 1 3 0.6 96 89 Netherlands 11 674 7.9 72.2 34 60 6 49.4 100 100 New Zealand 327 79,778 2.1 0.6 42 9 48 27.2 100 .. Nicaragua 190 36,840 1.3 0.7 83 2 15 3.1 90 63 Niger 4 251 2.2 62.3 95 0 4 0.9 80 36 Nigeria 221 1,680 8.0 3.6 69 10 21 6.0 67 31 Norway 382 82,625 2.2 0.6 11 67 23 79.2 100 100 Oman 1 390 1.4 136.0 90 2 7 16.0 .. .. Pakistan 52 336 169.4 323.3 96 2 2 0.5 96 89 Panama 147 45,613 0.8 0.6 28 5 67 14.6 99 79 Papua New Guinea 801 136,059 .. .. .. .. .. .. 88 32 Paraguay 94 15,936 0.5 0.5 71 8 20 14.7 99 68 Peru 1,616 57,780 20.1 1.2 82 10 8 2.8 89 65 Philippines 479 5,767 28.5 6.0 74 9 17 2.8 87 82 Poland 54 1,404 16.2 30.2 8 79 13 10.8 100 .. Portugal 38 3,602 11.3 29.6 78 12 10 10.3 .. .. Puerto Rico 7 1,815 .. .. .. .. .. .. .. .. 2007 World Development Indicators 143 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 2005 2005 2002b 2002b 2002b 2002b 2002b 2002 2004 2004 Romania 42 1,955 23.2 54.8 57 34 9 1.8 91 16 Russian Federation 4,313 30,135 76.7 1.8 18 63 19 3.7 100 88 Rwanda 10 1,051 0.2 1.6 68 8 24 14.1 92 69 Saudi Arabia 2 104 17.3 721.7 89 1 10 11.0 97 .. Senegal 26 2,213 2.2 8.6 93 3 4 2.1 92 60 Serbia and Montenegro 44 5,456 .. .. .. .. .. .. 99 86 Sierra Leone 160 28,957 0.4 0.2 92 3 5 2.5 75 46 Singapore 1 138 .. .. .. .. .. .. 100 .. Slovak Republic 13 2,339 .. .. .. .. .. .. 100 99 Slovenia 19 9,348 .. .. .. .. .. .. .. .. Somalia 6 729 3.3 54.8 100 0 0 .. 32 27 South Africa 45 955 12.5 27.9 63 6 31 11.3 99 73 Spain 111 2,562 35.6 32.0 68 19 13 17.3 100 100 Sri Lanka 50 2,548 12.6 25.2 95 2 2 1.3 98 74 Sudan 30 828 37.3 124.4 97 1 3 0.4 78 64 Swaziland 3 2,299 1.0 40.1 97 1 2 1.4 87 54 Sweden 171 18,949 3.0 1.7 9 54 37 84.3 100 100 Switzerland 40 5,432 2.6 6.4 2 74 24 97.0 100 100 Syrian Arab Republic 7 368 20.0 285.0 95 2 3 1.0 98 87 Tajikistan 66 10,189 12.0 18.0 92 5 4 0.1 92 48 Tanzania 84 2,192 5.2 6.2 89 0 10 2.0 85 49 Thailand 210 3,269 87.1 41.5 95 2 2 1.5 98 100 Togo 12 1,871 0.2 1.5 45 2 53 8.2 80 36 Trinidad and Tobago 4 2,911 0.3 8.2 6 26 68 29.6 92 88 Tunisia 4 419 2.6 62.9 82 4 14 7.9 99 82 Turkey 227 3,150 37.5 16.5 74 11 15 5.3 98 93 Turkmenistan 1 290 24.7 1,760.7 98 1 2 .. 93 54 Uganda 39 1,353 0.3 0.8 40 17 43 22.0 87 56 Ukraine 53 1,128 37.5 70.7 52 35 12 1.0 99 91 United Arab Emirates 0 44 2.3 1,150.0 68 9 23 34.0 100 100 United Kingdom 145 2,408 9.5 6.6 3 75 22 157.7 100 100 United States 2,800 9,446 479.3 17.1 41 46 13 20.9 100 100 Uruguay 59 17,036 3.2 5.3 96 1 3 5.6 100 100 Uzbekistan 16 623 58.3 357.9 93 2 5 0.3 95 75 Venezuela, RB 723 27,185 8.4 1.2 47 7 46 13.2 85 70 Vietnam 367 4,409 71.4 19.5 68 24 8 0.5 99 80 West Bank and Gaza 0 0 .. .. .. .. .. .. 94 88 Yemen, Rep. 4 195 6.6 161.7 95 1 4 1.5 71 65 Zambia 80 6,873 1.7 2.2 76 7 17 2.0 90 40 Zimbabwe 12 945 4.2 34.2 79 7 14 1.6 98 72 World 43,507 s 6,794 w 3,807.4 s 9.1 w 70 w 20 w 10 w 8.6 w 95 w 72 w Low income 7,404 3,149 1,240.7 18.9 89 5 6 0.8 88 70 Middle income 26,662 8,677 1,667.0 6.3 71 19 10 3.3 95 92 Lower middle income 18,455 7,460 1,337.3 7.3 75 17 8 2.5 94 71 Upper middle income 8,207 13,701 329.6 4.0 54 29 18 6.8 98 82 Low & middle income 34,066 6,280 2,907.6 8.8 78 13 8 2.3 93 71 East Asia & Pacific 9,454 5,019 958.8 11.1 74 20 7 2.1 92 70 Europe & Central Asia 5,255 11,139 383.2 7.5 59 31 10 2.7 99 80 Latin America & Carib. 13,429 24,402 265.3 2.0 71 10 19 7.6 96 73 Middle East & N. Africa 228 746 239.8 105.0 89 4 7 2.0 96 81 South Asia 1,816 1,236 941.1 51.8 90 4 6 0.7 94 81 Sub-Saharan Africa 3,884 5,229 119.3 3.1 87 3 10 3.1 80 43 High income 9,441 9,640 899.7 10.2 42 42 15 28.2 100 99 Europe EMU 929 2,959 199.7 22.3 38 48 15 30.5 100 100 a. River flows from other countries are not included because of data unreliability. b. Data are for the most recent year available for 1987­2002 (see Primary data documentation). 144 2007 World Development Indicators 3.5 ENVIRONMENT Freshwater About the data Definitions The data on freshwater resources are based on esti- different times and at different levels of quality and · Renewable internal freshwater resources fl ows mates of runoff into rivers and recharge of groundwa- precision, requiring caution in interpreting the data, refer to internal renewable resources (internal river ter. These estimates are based on different sources particularly for water-short countries, notably in the flows and groundwater from rainfall) in the country. and refer to different years, so cross-country com- Middle East. · Renewable internal freshwater resources per cap- parisons should be made with caution. Because the The data on access to an improved water source ita are calculated using the World Bank's population data are collected intermittently, they may hide sig- measure the percentage of the population with ready estimates (see table 2.1). · Annual freshwater with- nificant variations in total renewable water resources access to water for domestic purposes. The data are drawals refer to total water withdrawals, not count- from one year to the next. The data also fail to distin- based on surveys and estimates provided by govern- ing evaporation losses from storage basins. With- guish between seasonal and geographic variations ments to the Joint Monitoring Program of the World drawals also include water from desalination plants in water availability within countries. Data for small Health Organization (WHO) and the United Nations in countries where they are a signifi cant source. countries and countries in arid and semiarid zones Children's Fund (UNICEF). The coverage rates are Withdrawals can exceed 100 percent of total renew- are less reliable than those for larger countries and based on information from service users on what able resources where extraction from nonrenewable countries with greater rainfall. their households actually use rather than on infor- aquifers or desalination plants is considerable or Caution is also needed in comparing data on mation from service providers, which may include where there is significant water reuse. Withdrawals annual freshwater withdrawals, which are subject nonfunctioning systems. Access to drinking water for agriculture and industry are total withdrawals to variations in collection and estimation methods. from an improved source does not ensure that the for irrigation and livestock production and for direct In addition, inflows and outflows are estimated at water is safe or adequate, as these characteristics industrial use (including withdrawals for cooling ther- are not tested at the time of surveys. While informa- moelectric plants). Withdrawals for domestic uses The rural-urban divide in access tion on access to an improved water source is widely include drinking water, municipal use or supply, and to an improved water source 3.5a used, it is extremely subjective, and such terms as use for public services, commercial establishments, safe, improved, adequate, and reasonable may have and homes. · Water productivity is calculated as Access to improved water source by income group, 2004 (percent) Rural Urban different meaning in different countries despite offi - GDP in constant prices divided by annual total water 100 cial WHO definitions (see Definitions). Even in high- withdrawal. · Access to an improved water source income countries treated water may not always be refers to the percentage of the population with rea- 80 safe to drink. While access to an improved water sonable access to an adequate amount of water from source is equated with connection to a supply sys- an improved source, such as piped water into a dwell- 60 tem, this does not take into account variations in ing, plot, or yard; public tap or standpipe; tubewell or the quality and cost (broadly defined) of the service borehole; protected dug well or spring; and rainwater 40 once connected. collection. Unimproved sources include unprotected Water productivity is an indication only of the dug wells or springs, cart with small tank or drum, 20 effi ciency by which each country uses its water bottled water, and tanker trucks. Reasonable access resources. Given the different economic struc- is defined as the availability of at least 20 liters a 0 ture of each country, these indicators should be person a day from a source within 1 kilometer of Low-income Lower Upper High-income middle- middle- used with proper caution, taking into account the the dwelling. income income countries' sectoral activities and natural resource endowments. Access to improved water source by developing region, 2004 (percent) 100 80 Data sources Data on freshwater resources and withdrawals are 60 compiled by the World Resources Institute from various sources and published in World Resources 40 2005 (produced in collaboration with the United Nations Environment Programme, United Nations 20 Development Programme, and World Bank). These data are supplemented by the Food and Agriculture 0 Organization's AQUASTAT data. Data on access to East Europe & Latin Middle South Sub- Asia & Central America & East & Asia Saharan water are from WHO and UNICEF's Meeting the Pacific Asia Caribbean North Africa Africa MDG Drinking Water and Sanitation Target (www. Source: Table 3.5. unicef.org/wes/mdgreport). 2007 World Development Indicators 145 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 2003a 1990 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a Afghanistan 5.9 0.2 0.16 0.21 .. 37.7 17.5 31.1 0.4 13.2 .. .. Albania 34.8 .. 0.14 .. .. .. .. .. .. .. .. .. Algeria 107.0 .. 0.25 .. .. .. .. .. .. .. .. .. Angola 4.5 .. 0.19 .. .. .. .. .. .. .. .. .. Argentina 186.7 164.3 0.20 0.23 5.6 14.6 8.6 58.9 0.1 7.6 1.1 3.5 Armenia 37.9 7.1 0.11 0.28 .. .. .. 77.6 .. 22.4 .. .. Australia 186.1 111.7 0.18 0.18 .. .. 5.6 77.1 0.2 5.1 5.3 .. Austria 94.1 36.9 0.15 0.08 14.6 14.8 15.4 34.9 0.6 0.9 12.3 3.5 Azerbaijan 53.3 15.5 0.15 0.16 20.2 5.5 15.9 39.2 0.3 11.0 0.9 6.9 Bangladesh 171.1 303.3 0.17 0.14 1.6 6.2 2.6 23.8 0.1 64.2 0.4 1.0 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 118.0 102.3 0.16 0.17 13.6 18.4 11.2 40.3 0.2 5.9 2.2 8.2 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 8.4 11.5 0.24 0.25 1.2 15.1 6.8 64.9 0.2 8.7 2.3 0.7 Bosnia and Herzegovina 50.7 .. 0.14 .. .. .. .. .. .. .. .. .. Botswana 4.5 5.5 0.19 0.19 1.9 10.8 1.6 56.3 0.3 25.0 1.7 2.5 Brazil 780.4 .. 0.19 .. .. .. .. .. .. .. .. .. Bulgaria 149.4 101.9 0.11 0.17 7.9 9.5 6.6 46.1 0.2 22.2 2.3 5.2 Burkina Faso .. 2.6 .. 0.22 3.5 1.1 5.4 73.8 0.1 4.1 10.1 1.9 Burundi 1.6 .. 0.24 .. .. .. .. .. .. .. .. .. Cambodia 11.8 .. 0.14 .. .. .. .. .. .. .. .. .. 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 321.5 312.5 0.17 0.16 9.6 22.1 8.6 39.5 0.1 5.8 5.4 8.9 Central African Republic 1.0 .. 0.18 .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 66.8 72.9 0.22 0.24 6.9 11.3 8.9 62.7 0.1 5.0 2.6 2.5 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 93.3 93.9 0.19 0.21 3.1 16.2 9.7 53.2 0.2 14.2 1.0 2.4 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 80.0 42.9 0.15 0.17 6.1 15.9 7.5 48.4 0.2 12.0 3.6 6.3 Cuba 173.0 .. 0.25 .. .. .. .. .. .. .. .. .. Czech Republic 205.1 158.5 0.13 0.14 15.6 7.0 7.9 43.6 0.3 10.4 3.9 11.4 Denmark 91.9 83.6 0.18 0.17 4.4 29.1 7.9 44.2 0.2 2.2 3.5 8.6 Dominican Republic 47.9 .. 0.36 .. .. .. .. .. .. .. .. .. Ecuador 25.6 40.2 0.23 0.28 2.2 11.2 5.9 72.3 0.1 5.8 1.3 1.3 Egypt, Arab Rep. 211.5 186.1 0.20 0.20 10.8 8.2 9.0 50.7 0.3 17.7 0.6 2.8 El Salvador 5.5 22.8 0.22 0.18 2.1 10.2 8.1 43.5 0.1 34.1 0.5 1.4 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 18.6 22.1 0.23 0.23 2.3 11.0 5.5 61.0 0.3 17.3 2.0 0.7 Finland 79.5 67.4 0.18 0.16 8.7 40.1 7.6 26.6 0.2 2.4 3.9 10.6 France 653.5 564.6 0.15 0.15 7.2 13.8 12.9 49.5 0.2 2.9 2.3 11.1 Gabon 2.0 .. 0.25 .. .. .. .. .. .. .. .. .. Gambia, The 0.8 .. 0.34 .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany 835.0 966.7 0.12 0.14 9.3 20.4 11.8 38.7 0.2 2.3 2.1 15.1 Ghana 16.5 .. 0.20 .. .. .. .. .. .. .. .. .. Greece 63.5 43.7 0.18 0.19 8.1 9.7 9.0 55.0 0.3 12.4 1.6 4.0 Guatemala 21.6 19.3 0.23 0.28 4.9 7.2 6.1 72.8 0.1 6.9 0.8 1.0 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 5.4 .. 0.20 .. .. .. .. .. .. .. .. .. 146 2007 World Development Indicators 3.6 ENVIRONMENT 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 2003a 1990 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a Honduras 17.8 .. 0.23 .. .. .. .. .. .. .. .. .. Hungary 178.0 60.7 0.16 0.10 11.8 .. 12.8 49.1 0.4 .. 5.5 9.8 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 495.6 733.0 0.19 0.18 2.5 8.2 9.2 53.7 0.1 19.4 4.5 2.4 Iran, Islamic Rep. 102.7 164.8 0.16 0.15 15.6 8.0 10.7 46.7 0.7 9.5 0.9 8.1 Iraq 26.7 .. 0.19 .. .. .. .. .. .. .. .. .. Ireland 34.6 11.6 0.18 0.21 1.9 .. 10.4 22.9 0.7 3.1 7.5 9.3 Israel 46.4 54.0 0.16 0.16 3.6 22.3 10.5 45.5 0.1 6.0 1.9 10.1 Italy 358.1 488.9 0.13 0.12 9.4 16.6 10.7 30.8 0.3 15.0 3.9 13.3 Jamaica 18.7 .. 0.29 .. .. .. .. .. .. .. .. .. Japan 1,556.6 1,184.7 0.14 0.15 7.1 19.0 9.4 45.7 0.2 4.8 1.6 12.3 Jordan 8.3 23.5 0.19 0.18 5.1 12.7 10.8 53.4 0.4 10.8 3.3 3.4 Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya 42.6 56.1 0.23 0.24 .. 11.5 5.4 66.8 0.1 12.8 1.7 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 369.2 315.2 0.12 0.12 11.4 18.9 13.0 25.8 0.2 13.6 1.5 15.7 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 30.9 19.1 0.12 0.21 7.3 7.8 3.5 65.4 0.4 11.0 0.9 3.7 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 39.9 29.2 0.12 0.19 4.1 15.4 3.6 53.8 0.1 9.6 9.7 3.7 Lebanon .. 14.9 .. 0.19 0.9 15.6 4.0 60.7 0.5 10.2 4.6 3.4 Lesotho 3.0 3.1 0.16 0.16 1.2 4.0 0.7 39.7 0.1 51.3 0.6 2.3 Liberia 0.6 .. 0.30 .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 53.8 45.3 0.13 0.17 0.8 10.6 4.9 54.0 0.2 18.1 7.1 4.3 Macedonia, FYR 32.4 .. 0.18 .. .. .. .. .. .. .. .. .. Madagascar 11.0 .. 0.27 .. .. .. .. .. .. .. .. .. Malawi 10.0 11.8 0.29 0.29 0.0 16.0 3.7 70.0 0.0 7.8 1.7 0.7 Malaysia 104.7 183.8 0.13 0.12 7.8 14.9 15.5 33.7 0.2 8.3 6.8 12.8 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 17.8 17.7 0.16 0.15 0.9 6.6 2.6 32.8 0.1 55.4 0.6 1.1 Mexico 174.3 296.1 0.18 0.20 7.8 12.5 10.4 55.6 0.2 7.5 0.9 5.1 Moldova 55.9 21.6 0.15 0.45 .. 2.2 .. 97.7 .. .. .. .. Mongolia 10.2 .. 0.18 .. .. .. .. .. .. .. .. .. Morocco 41.7 72.1 0.14 0.16 2.1 8.0 6.8 43.0 0.3 35.3 1.1 3.4 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 20.9 26.9 0.13 0.16 3.5 9.7 5.9 55.1 1.4 21.7 1.7 1.0 Netherlands 136.7 .. 0.18 .. .. .. .. .. .. .. .. .. New Zealand 50.2 46.1 0.22 0.22 3.2 21.7 5.2 57.3 0.1 4.6 3.6 4.2 Nicaragua 10.5 .. 0.27 .. .. .. .. .. .. .. .. .. Niger .. 0.4 .. 0.32 .. 17.0 4.4 76.9 0.3 .. 0.8 .. Nigeria 70.8 .. 0.22 .. .. .. .. .. .. .. .. .. Norway 55.0 51.7 0.20 0.20 9.0 31.3 4.7 42.8 0.1 1.4 3.1 7.5 Oman 0.4 5.8 0.11 0.17 7.3 13.3 10.1 54.3 0.9 8.3 2.4 3.4 Pakistan 104.1 .. 0.18 .. .. .. .. .. .. .. .. .. Panama 9.7 11.7 0.26 0.32 1.5 13.2 4.6 76.6 0.2 3.2 0.4 0.4 Papua New Guinea 5.7 .. 0.25 .. .. .. .. .. .. .. .. .. Paraguay 3.3 .. 0.28 .. .. .. .. .. .. .. .. .. Peru 56.1 .. 0.20 .. .. .. .. .. .. .. .. .. Philippines 228.3 .. 0.21 .. .. .. .. .. .. .. .. .. Poland 428.9 329.4 0.14 0.17 7.5 11.7 7.6 52.2 0.2 9.1 4.3 7.3 Portugal 147.9 127.5 0.15 0.15 3.1 16.4 4.9 37.8 0.4 26.1 5.3 6.0 Puerto Rico 19.0 9.2 0.15 0.18 1.9 14.9 36.4 .. 0.2 9.8 2.4 9.7 2007 World Development Indicators 147 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 2003a 1990 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a 2003a Romania 413.9 38.4 0.12 0.07 .. 17.6 .. 5.1 .. 28.7 12.5 .. Russian Federation 1,911.3 1,388.1 0.13 0.18 20.3 8.1 3.2 51.9 0.4 5.9 2.6 7.5 Rwanda 1.6 .. 0.25 .. .. .. .. .. .. .. .. .. Saudi Arabia 18.5 .. 0.15 .. .. .. .. .. .. .. .. .. Senegal 10.3 6.6 0.32 0.30 5.8 8.4 10.7 70.1 0.1 4.2 0.4 0.3 Serbia and Montenegro 137.8 98.7 0.15 0.16 9.9 11.8 8.2 47.4 0.3 12.7 2.2 7.6 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 77.2 43.3 0.13 0.14 2.9 16.9 8.4 43.7 0.3 12.2 4.0 11.6 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 261.6 221.3 0.17 0.18 15.1 18.0 10.5 36.0 0.1 10.9 3.9 5.5 Spain 320.3 352.9 0.17 0.15 7.5 20.6 9.5 39.6 0.4 8.6 4.3 9.6 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 0.7 2.5 3.1 88.6 0.4 3.2 0.6 1.1 Swaziland 6.6 .. 0.33 .. .. .. .. .. .. .. .. .. Sweden 109.6 103.9 0.15 0.14 11.3 35.0 7.8 26.6 0.1 1.3 3.0 14.9 Switzerland 146.0 .. 0.16 .. .. .. .. .. .. .. .. .. Syrian Arab Republic 21.7 15.1 0.22 0.20 4.1 1.5 3.9 69.8 0.9 19.4 0.2 0.2 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. 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 291.6 .. 0.17 .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 10.0 7.9 0.26 0.23 6.5 18.8 11.9 55.3 0.2 3.8 2.0 1.5 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 177.3 172.2 0.18 0.16 11.4 4.8 8.0 43.7 0.3 26.4 0.4 5.0 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 16.7 .. 0.30 .. .. .. .. .. .. .. .. .. Ukraine 692.4 445.8 0.14 0.18 28.1 4.2 7.0 46.8 0.4 5.4 1.1 7.0 United Arab Emirates 5.6 .. 0.14 .. .. .. .. .. .. .. .. .. United Kingdom 739.6 331.0 0.15 0.12 9.0 48.0 17.5 0.6 0.3 5.2 4.0 15.4 United States 2,565.2 1,805.9 0.15 0.13 9.6 10.6 14.0 42.1 0.2 5.4 4.2 13.9 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 94.2 0.21 0.21 13.7 10.4 10.2 53.1 0.3 7.5 1.5 3.3 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6.9 15.4 0.27 0.23 .. 7.7 6.8 74.6 0.4 7.6 0.9 .. Zambia 15.9 .. 0.23 .. .. .. .. .. .. .. .. .. Zimbabwe 37.1 .. 0.20 .. .. .. .. .. .. .. .. .. Note: Industry shares may not sum to 100 percent because data may be for different years. a. Data are for most recent year available for 1993­2003. 148 2007 World Development Indicators 3.6 ENVIRONMENT Water pollution About the data Definitions Emissions of organic pollutants from industrial activi- pollution control programs start by regulating emis- · Emissions of organic water pollutants are mea- ties are a major cause of degradation of water qual- sions of organic water pollutants. Such data are fairly sured in terms of biochemical oxygen demand, which ity. Water quality and pollution levels are generally reliable because sampling techniques for measur- refers to the amount of oxygen that bacteria in water measured in terms of concentration or load--the ing water pollution are more widely understood and will consume in breaking down waste. This is a stan- rate of occurrence of a substance in an aqueous much less expensive than those for air pollution. dard water treatment test for the presence of organic solution. Polluting substances include organic mat- Hettige, Mani, and Wheeler (1998) used plant- and pollutants. Emissions per worker are total emissions ter, metals, minerals, sediment, bacteria, and toxic sector-level information on emissions and employ- divided by the number of industrial workers. · Indus- chemicals. This table focuses on organic water pol- ment from 13 national environmental protection try shares of emissions of organic water pollutants lution resulting from industrial activities. Because agencies and sector-level information on output refer to emissions from manufacturing activities as water pollution tends to be sensitive to local condi- and employment from the United Nations Industrial defined by two-digit divisions of the International tions, the national-level data in the table may not Development Organization (UNIDO). Their economet- Standard Industrial Classification (ISIC) revision 2: reflect the quality of water in specific locations. ric analysis found that the ratio of BOD to employ- primary metals (ISIC division 37); paper and pulp The data in the table come from an international ment in each industrial sector is about the same (34); chemicals (35); food and beverages (31); stone, study of industrial emissions that may be the first across countries. This finding allowed the authors ceramics, and glass (36); textiles (32); wood (33); to include data from developing countries (Het- to estimate BOD loads across countries and over and other (38 and 39). tige, Mani, and Wheeler 1998). These data were time. The estimated BOD intensities per unit of updated through 2003 by the World Bank's Devel- employment were multiplied by sectoral employ- opment Research Group. Unlike estimates from ment numbers from UNIDO's industry database for earlier studies based on engineering or economic 1980­98. The estimates of sectoral emissions were models, these estimates are based on actual mea- then totaled to get daily emissions of organic water surements of plant-level water pollution. The focus is pollutants in kilograms per day for each country and on organic water pollution caused by organic waste, year. The data in the table were derived by updating measured in terms of biochemical oxygen demand these estimates through 2003. (BOD), because the data for this indicator are the most plentiful and the most reliable for cross-coun- try 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 treatment, by contrast, reduces BOD. Data on water pollution are more readily available than other emissions data because most industrial Emissions of organic water pollutants declined in most countries from 1990 to 2003, even among the top emitters 3.6a Countries with highest emissions of organic water pollutants (millions of kilograms per day) 1990 2003 8 7 6 Data sources 5 Data on water pollution come from a 1998 study 4 by Hemamala Hettige, Muthukumara Mani, and 3 David Wheeler, "Industrial Pollution in Economic 2 Development: Kuznets Revisited" (available at www.worldbank.org/nipr). The data were updated 1 through 2003 by the World Bank's Development 0 Research Group using the same methodology as China United India Russian Japan Germany States Federation the initial study. Sectoral employment numbers Source: Table 3.6. are from UNIDO's industry database. 2007 World Development Indicators 149 3.7 Energy production and use Total energy Energy Net energy production use importsa Total Per capita Combustible million million average average renewables metric tons of metric tons of annual kilograms of oil annual and waste % of oil equivalent oil equivalent % growth equivalent % growth % of total energy use 1990 2004 1990 2004 1990­2004 1990 2004 1990­2004 1990 2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 2.4 1.0 2.7 2.4 1.9 809 760 2.5 13.6 6.3 8 59 Algeria 104.4 165.7 23.9 32.9 2.2 943 1,017 0.5 0.1 0.2 ­338 ­404 Angola 28.7 57.4 6.3 9.5 2.9 597 613 0.2 68.8 64.7 ­356 ­505 Argentina 48.5 85.4 46.1 63.7 2.0 1,415 1,661 0.8 3.7 3.3 ­5 ­34 Armenia 0.3 0.7 4.3 2.1 ­1.5 1,246 704 ­0.5 0.0 0.0 94 65 Australia 157.5 261.8 87.5 115.8 2.2 5,130 5,762 1.1 4.5 4.3 ­80 ­126 Austria 8.1 9.9 25.0 33.2 1.9 3,246 4,060 1.6 9.9 11.3 68 70 Azerbaijan 18.2 20.1 16.7 12.9 ­2.4 2,259 1,559 ­3.3 0.0 0.0 ­9 ­55 Bangladesh 10.8 18.4 12.8 22.8 4.5 123 164 2.3 53.5 35.7 16 19 Belarus 3.5 3.6 38.9 26.8 ­1.9 3,810 2,725 ­1.5 0.6 4.2 91 86 Belgium 13.1 13.5 49.1 57.7 1.3 4,927 5,536 1.0 1.4 2.2 73 77 Benin 1.8 1.6 1.7 2.5 2.7 324 303 ­0.5 93.2 65.6 ­6 34 Bolivia 4.9 11.8 2.8 5.0 4.1 416 553 1.9 27.2 14.7 ­77 ­137 Bosnia and Herzegovina 3.6 3.2 4.5 4.7 5.8 1,130 1,203 5.0 3.6 3.9 19 31 Botswana 0.9 1.0 1.3 1.9 2.9 890 1,055 1.3 33.1 24.4 28 46 Brazil 98.1 176.3 134.0 204.8 3.2 897 1,114 1.7 31.1 26.5 27 14 Bulgaria 9.6 10.3 28.8 18.9 ­2.0 3,306 2,434 ­1.2 0.6 3.9 67 46 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 12.1 12.5 5.0 6.9 2.5 432 433 0.2 75.9 77.8 ­140 ­80 Canada 273.7 397.5 209.4 269.0 1.7 7,534 8,411 0.7 3.9 4.4 ­31 ­48 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 7.6 8.4 14.1 27.9 5.4 1,067 1,732 3.9 19.0 15.4 46 70 China 889.3 1,536.8 866.5 1,609.3 3.6 763 1,242 2.6 23.1 13.7 ­3 5 Hong Kong, China 0.0 0.0 10.7 17.1 3.1 1,869 2,488 1.6 0.5 0.3 100 100 Colombia 48.5 76.2 25.0 27.7 0.5 716 616 ­1.2 23.2 14.9 ­94 ­175 Congo, Dem. Rep. 12.0 17.0 11.9 16.6 2.4 315 297 ­0.3 84.0 92.5 ­1 ­3 Congo, Rep. 9.0 12.6 1.1 1.1 ­0.6 425 274 ­3.8 69.4 61.7 ­753 ­1,084 Costa Rica 1.0 1.7 2.0 3.7 4.7 658 870 2.3 36.6 8.2 49 53 Côte d'Ivoire 3.4 7.2 4.4 6.9 3.6 348 388 1.0 72.1 64.9 23 ­4 Croatia 4.3 3.9 6.7 8.8 2.2 1,502 1,985 2.5 3.8 4.3 35 56 Cuba 6.5 5.9 16.8 10.7 ­1.6 1,594 950 ­2.0 34.8 19.4 61 45 Czech Republic 40.1 34.2 49.0 45.5 ­0.3 4,728 4,460 ­0.2 1.2 3.3 18 25 Denmark 10.0 31.0 17.9 20.1 0.4 3,481 3,716 0.0 6.4 11.7 44 ­55 Dominican Republic 1.0 1.6 4.1 7.7 5.1 584 873 3.6 24.2 19.3 75 79 Ecuador 16.5 29.3 6.1 10.1 3.9 597 773 2.2 13.5 5.7 ­169 ­191 Egypt, Arab Rep. 54.9 64.7 31.9 56.9 4.4 573 783 2.5 3.3 2.5 ­72 ­14 El Salvador 1.7 2.4 2.5 4.5 4.1 496 664 2.0 48.2 32.5 32 46 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 4.1 3.6 6.3 5.2 ­1.2 4,091 3,835 ­0.2 2.9 11.7 34 31 Ethiopia 14.2 19.4 15.2 21.2 2.6 296 303 0.3 92.8 90.4 7 9 Finland 12.1 15.9 29.2 38.1 2.0 5,851 7,286 1.7 15.6 20.3 59 58 France 111.9 137.4 227.3 275.2 1.3 4,006 4,547 0.8 4.8 4.3 51 50 Gabon 14.6 12.1 1.2 1.7 2.2 1,298 1,243 ­0.4 59.8 58.8 ­1,077 ­615 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 1.5 1.3 8.8 2.8 ­7.9 1,642 626 ­6.6 7.7 22.8 83 54 Germany 186.2 136.0 356.2 348.0 0.0 4,485 4,218 ­0.2 1.3 3.0 48 61 Ghana 4.4 6.2 5.3 8.4 3.6 345 386 1.2 73.1 69.1 18 25 Greece 9.2 10.3 22.2 30.5 2.5 2,183 2,755 1.9 4.0 3.2 59 66 Guatemala 3.4 5.3 4.5 7.6 4.3 504 616 1.9 67.9 52.9 24 30 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1.3 1.7 1.6 2.2 3.2 231 262 1.8 76.5 74.0 21 25 150 2007 World Development Indicators 3.7 ENVIRONMENT Energy production and use Total energy Energy Net energy production use importsa Total Per capita Combustible million million average average renewables metric tons of metric tons of annual kilograms of oil annual and waste % of oil equivalent oil equivalent % growth equivalent % growth % of total energy use 1990 2004 1990 2004 1990­2004 1990 2004 1990­2004 1990 2004 1990 2004 Honduras 1.7 1.7 2.4 3.9 3.0 496 548 0.3 62.0 40.0 30 55 Hungary 14.3 10.2 28.6 26.4 ­0.2 2,753 2,608 0.0 1.3 3.3 50 61 India 333.4 466.9 361.6 572.9 3.3 426 531 1.5 48.6 37.4 8 19 Indonesia 164.7 258.0 97.6 174.0 4.0 548 800 2.6 40.4 27.1 ­69 ­48 Iran, Islamic Rep. 179.7 278.0 68.8 145.8 5.3 1,264 2,167 3.7 1.0 0.5 ­161 ­91 Iraq 104.9 103.4 19.1 29.7 3.5 1,029 .. .. 0.1 0.1 ­451 ­248 Ireland 3.5 1.9 10.4 15.2 3.5 2,969 3,738 2.5 1.0 1.4 67 87 Israel 0.4 1.7 12.1 20.7 4.0 2,599 3,049 1.3 0.0 0.0 96 92 Italy 25.3 30.1 148.0 184.5 1.6 2,610 3,171 1.5 0.6 3.3 83 84 Jamaica 0.5 0.5 2.9 4.1 2.6 1,231 1,541 1.8 16.2 11.7 84 88 Japan 76.8 96.8 446.0 533.2 1.2 3,610 4,173 1.0 1.1 1.2 83 82 Jordan 0.2 0.3 3.5 6.5 3.7 1,104 1,219 0.3 0.1 0.0 95 96 Kazakhstan 89.0 118.6 79.7 54.8 ­2.8 4,846 3,652 ­2.0 0.1 0.1 ­12 ­116 Kenya 10.3 13.7 12.5 16.9 2.1 533 506 ­0.4 78.4 74.1 18 19 Korea, Dem. Rep. 28.7 19.2 32.9 20.4 ­3.4 1,670 910 ­4.2 2.9 5.0 13 6 Korea, Rep. 21.9 38.0 92.7 213.0 6.0 2,161 4,431 5.1 0.3 0.8 76 82 Kuwait 50.4 132.8 8.5 25.1 8.4 3,985 10,212 6.4 0.1 .. ­495 ­429 Kyrgyz Republic 1.8 1.5 5.1 2.8 ­3.5 1,114 546 ­4.6 0.1 0.1 64 47 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 0.8 2.1 5.9 4.6 ­1.8 2,245 1,988 ­0.8 8.2 29.9 87 53 Lebanon 0.1 0.2 2.3 5.4 5.9 842 1,525 4.1 4.5 2.4 94 96 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 73.2 85.4 11.5 18.2 2.9 2,663 3,169 0.9 1.1 0.8 ­534 ­369 Lithuania 4.4 5.2 11.1 9.2 ­1.0 3,002 2,666 ­0.4 2.6 7.6 60 43 Macedonia, FYR 1.7 1.5 2.9 2.7 ­1.0 1,515 1,328 ­1.4 6.4 6.3 41 43 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 48.8 88.5 22.6 56.7 6.3 1,269 2,279 3.8 9.4 4.9 ­115 ­56 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico 194.8 253.9 124.3 165.5 1.9 1,494 1,622 0.4 5.9 5.0 ­57 ­53 Moldova 0.1 0.1 6.9 3.4 ­6.0 1,575 802 ­5.7 0.5 2.3 99 98 Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 0.8 0.7 6.7 11.5 3.8 281 384 2.3 4.7 3.9 89 94 Mozambique 6.8 8.2 7.2 8.6 1.1 536 441 ­1.6 94.4 84.1 5 4 Myanmar 10.7 19.0 10.7 14.1 2.0 262 283 0.5 84.4 73.4 0 ­34 Namibia 0.2 0.3 0.7 1.3 5.1 449 665 2.5 16.0 13.8 67 76 Nepal 5.5 8.1 5.8 9.1 3.3 304 341 0.9 93.4 86.8 5 11 Netherlands 60.5 67.9 66.7 82.1 1.3 4,464 5,045 0.6 1.4 2.6 9 17 New Zealand 12.0 13.0 13.8 17.6 1.9 3,990 4,344 0.7 4.0 5.0 13 26 Nicaragua 1.5 1.9 2.1 3.3 2.9 535 643 1.0 53.2 51.1 29 41 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 150.5 229.4 70.9 99.0 2.3 783 769 ­0.3 79.8 80.2 ­112 ­132 Norway 120.3 238.6 21.5 27.7 1.6 5,067 6,024 1.0 4.8 4.9 ­460 ­763 Oman 38.3 58.1 4.6 11.8 6.8 2,475 4,667 4.4 .. .. ­740 ­391 Pakistan 34.4 59.0 43.4 74.4 3.7 402 489 1.2 43.2 35.6 21 21 Panama 0.6 0.8 1.5 2.5 4.2 618 801 2.2 28.3 16.8 59 70 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 4.6 6.6 3.1 4.0 1.9 731 694 ­0.3 72.3 53.8 ­48 ­65 Peru 10.6 9.5 10.0 13.2 2.1 458 479 0.3 26.9 17.7 ­6 28 Philippines 13.7 23.4 26.2 44.3 4.3 428 542 2.1 29.2 23.9 48 47 Poland 99.4 78.8 99.9 91.7 ­0.9 2,620 2,403 ­0.8 2.2 5.0 1 14 Portugal 3.4 3.9 17.7 26.5 3.3 1,793 2,528 2.9 14.0 10.9 81 85 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 151 3.7 Energy production and use Total energy Energy Net energy production use importsa Total Per capita Combustible million million average average renewables metric tons of metric tons of annual kilograms of oil annual and waste % of oil equivalent oil equivalent % growth equivalent % growth % of total energy use 1990 2004 1990 2004 1990­2004 1990 2004 1990­2004 1990 2004 1990 2004 Romania 40.8 28.1 62.4 38.6 ­2.9 2,689 1,778 ­2.4 1.0 8.4 35 27 Russian Federation 1,118.7 1,158.5 774.8 641.5 ­1.2 5,211 4,460 ­0.9 1.6 1.1 ­44 ­81 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 376.9 556.2 67.4 140.4 4.3 4,114 6,233 1.9 0.0 0.0 ­459 ­296 Senegal 1.4 1.1 2.2 2.8 1.6 281 242 ­0.9 60.6 38.9 39 60 Serbia and Montenegro 13.2 11.5 21.5 16.2 ­1.1 2,044 2,004 1.1 1.8 4.9 38 29 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore .. 0.1 13.4 25.6 3.4 4,384 6,034 0.8 .. .. .. 99 Slovak Republic 5.3 6.5 21.3 18.3 ­0.3 4,035 3,407 ­0.4 0.8 2.2 75 65 Slovenia 2.8 3.4 5.0 7.2 2.7 2,508 3,591 2.7 5.3 6.7 45 52 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 114.5 156.0 91.2 131.1 2.4 2,592 2,829 0.3 11.4 10.0 ­26 ­19 Spain 34.6 32.5 91.1 142.2 3.4 2,345 3,331 2.7 4.5 3.4 62 77 Sri Lanka 4.2 5.2 5.5 9.4 3.8 324 485 2.9 71.0 52.0 24 45 Sudan 8.8 29.3 10.6 17.6 3.7 408 497 1.4 81.7 79.2 18 ­66 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 29.8 35.1 47.6 53.9 0.7 5,557 5,998 0.4 11.6 16.7 37 35 Switzerland 9.7 11.8 25.0 27.1 0.7 3,724 3,672 0.1 3.7 6.3 61 56 Syrian Arab Republic 22.3 29.5 11.7 18.4 3.2 909 993 0.5 0.0 0.0 ­91 ­60 Tajikistan 1.6 1.5 9.1 3.3 ­5.5 1,647 519 ­6.7 .. .. 83 55 Tanzania 9.1 17.5 9.8 18.7 4.6 374 498 1.9 91.0 91.6 8 7 Thailand 26.5 50.1 43.9 97.1 5.2 803 1,524 4.1 33.4 16.4 40 48 Togo 1.2 1.9 1.4 2.7 4.6 365 449 1.4 82.6 70.6 17 29 Trinidad and Tobago 12.6 29.4 6.0 11.3 5.4 4,968 8,675 4.9 0.8 0.2 ­109 ­160 Tunisia 6.1 6.8 5.5 8.7 3.4 679 876 2.0 18.7 12.4 ­11 22 Turkey 25.8 24.1 53.0 81.9 3.4 943 1,151 1.6 13.6 6.8 51 71 Turkmenistan 48.8 58.2 11.3 15.6 3.1 2,912 3,265 1.4 .. .. ­332 ­274 Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 101.3 76.3 210.0 140.3 ­3.0 4,027 2,958 ­2.1 0.1 0.2 52 46 United Arab Emirates 109.4 164.0 22.5 43.8 4.6 12,716 10,142 ­1.8 0.1 0.0 ­385 ­274 United Kingdom 208.0 225.2 212.2 233.7 0.6 3,686 3,906 0.3 0.3 1.3 2 4 United States 1,650.5 1,641.0 1,927.6 2,325.9 1.4 7,722 7,921 0.2 3.2 3.0 14 29 Uruguay 1.1 0.9 2.3 2.9 1.1 725 832 0.3 24.3 15.4 49 70 Uzbekistan 40.5 56.9 45.0 54.0 1.9 2,098 2,088 0.3 .. .. 10 ­5 Venezuela, RB 148.9 196.1 43.9 56.2 1.6 2,224 2,149 ­0.5 1.2 1.0 ­239 ­249 Vietnam 24.7 65.3 24.3 50.2 5.1 367 611 3.5 77.7 47.2 ­2 ­30 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 9.4 20.6 2.6 6.4 6.2 212 313 2.4 3.0 1.2 ­266 ­224 Zambia 4.9 6.4 5.5 6.9 1.6 653 605 ­0.7 73.4 79.1 10 8 Zimbabwe 8.6 8.6 9.4 9.3 ­0.1 888 719 ­1.5 50.4 63.8 9 8 World 8,798.3 t 11,171.2 t 8,609.9 t 11,026.3 t 1.7 w 1,685 w 1,793 w 0.3 w 10.8 w 10.3 w ­2 w ­2 w Low income 791.6 1,173.8 773.3 1,136.6 2.8 464 513 0.7 55.6 47.8 ­2 ­3 Middle income 4,386.6 5,604.9 3,502.9 4,431.1 1.4 1,349 1,451 0.2 11.8 10.5 ­25 ­27 Lower middle income 2,160.0 3,257.8 1,923.6 2,889.6 2.5 953 1,175 1.1 18.4 13.9 ­12 ­13 Upper middle income 2,226.9 2,347.3 1,579.4 1,541.6 ­0.2 2,980 2,583 ­1.1 3.7 4.0 ­41 ­52 Low & middle income 5,175.0 6,767.1 4,267.1 5,548.9 1.6 1,008 1,068 0.2 19.0 17.5 ­21 ­22 East Asia & Pacific 1,218.4 2,079.8 1,135.3 2,085.8 3.7 722 1,124 2.5 26.1 16.1 ­7 0 Europe & Central Asia 1,885.7 1,721.9 1,733.4 1,335.7 ­1.9 3,726 2,847 ­2.0 1.9 2.4 ­9 ­30 Latin America & Carib. 618.0 910.5 459.8 644.6 2.5 1,050 1,187 0.9 18.2 14.8 ­34 ­41 Middle East & N. Africa 601.9 823.7 194.4 356.7 4.3 861 1,189 2.3 1.8 1.2 ­210 ­131 South Asia 391.5 562.2 432.8 694.3 3.4 394 486 1.5 49.1 38.0 10 19 Sub-Saharan Africa 481.8 715.4 317.4 452.2 2.4 693 703 0.0 56.6 55.7 ­52 ­58 High income 3,657.9 4,450.0 4,369.4 5,512.8 1.7 4,842 5,511 0.9 2.9 3.1 16 19 Europe EMU 470.7 462.9 1,053.1 1,245.1 1.3 3,568 3,990 0.9 3.1 4.2 55 63 a. Negative value indicates that a country is a net exporter. 152 2007 World Development Indicators 3.7 ENVIRONMENT Energy production and use About the data In developing countries growth in energy use is closely waste--solid biomass and animal products, gas The IEA makes these estimates in consultation related to growth in the modern sectors--industry, and liquid from biomass, and industrial and munici- with national statistical offices, oil companies, elec- motorized transport, and urban areas--but energy pal waste. Biomass is defined as any plant matter tricity utilities, and national energy experts. The IEA use also reflects climatic, geographic, and economic used directly as fuel or converted into fuel, heat, occasionally revises its time series to reflect politi- factors (such as the relative price of energy). Energy or electricity. (The data series published in World cal changes. In addition, energy statistics for other use has been growing rapidly in low- and middle- Development Indicators 1998 and earlier editions did countries have undergone continuous changes in income countries, but high-income countries still not include energy from combustible renewables and coverage or methodology as more detailed energy use more than five times as much energy on a per waste.) Data for combustible renewables and waste accounts have become available in recent years. capita basis. are often based on small surveys or other incomplete Breaks in series are therefore unavoidable. Energy data are compiled by the International Energy information. Thus the data give only a broad impres- Definitions Agency (IEA). IEA data for countries that are not mem- sion of developments and are not strictly comparable bers of the Organisation for Economic Co-operation between countries. The IEA reports include country · Total energy production refers to forms of primary and Development (OECD) are based on national notes that explain some of these differences (see energy--petroleum (crude oil, natural gas liquids, energy data adjusted to conform to annual question- Data sources). All forms of energy--primary energy and oil from non-conventional sources), natural gas, naires completed by OECD member governments. and primary electricity--are converted into oil equiv- solid fuels (coal, lignite, and other derived fuels), and Total energy use refers to the use of primary energy alents. To convert nuclear electricity into oil equiva- combustible renewables and waste--and primary before transformation to other end-use fuels (such lents, a notional thermal efficiency of 33 percent electricity, all converted into oil equivalents (see as electricity and refined petroleum products). It is assumed; for hydroelectric power 100 percent About the data). · Energy use refers to use of primary includes energy from combustible renewables and efficiency is assumed. energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied Energy use per capita varies to ships and aircraft engaged in international trans- widely among the top energy users 3.7a port (see About the data). · Combustible renewables Countries with highest energy use (millions of metric tons of oil equivalent) 1990 2004 and waste comprise solid biomass, liquid biomass, 2,500 biogas, industrial waste, and municipal waste, mea- sured as a percentage of total energy use. · Net 2,000 energy imports are estimated as energy use less production, both measured in oil equivalents. The 1,500 deviations from zero of the world net imports are from statistical errors and changes in stock. 1,000 500 0 United China Russian India Japan Germany France States Federation Energy use per capita in countries with highest energy use (kilograms of oil equivalent per person) 8,000 7,000 6,000 5,000 4,000 Data sources 3,000 Data on energy production and use come from 2,000 IEA electronic files. The IEA's data are published 1,000 in its annual publications, Energy Statistics and 0 Balances of Non-OECD Countries, Energy Statistics United China Russian India Japan Germany France States Federation of OECD Countries, and Energy Balances of OECD Source: Table 3.7. Countries. 2007 World Development Indicators 153 3.8 Energy efficiency and emissions GDP per unit Carbon dioxide Methane Nitrous oxide of energy use emissions emissions emissions million million metric tons metric tons 2000 PPP $ average kilograms per of carbon average of carbon average per kilogram Total annual Per capita 2000 PPP $ dioxide annual dioxide annual of oil equivalent million metric tons % change metric tons of GDP equivalent % change equivalent % change 1990 2004 1990 2003 1990­2003 1990 2003 1990 2003 2000 1990­2000 2000 1990­2000 Afghanistan .. .. 2.6 0.7 ­9.0 0.2 .. .. .. 13.2 5.3 7.5 3.4 Albania 3.8 5.9 7.3 3.0 ­3.7 2.2 1.0 0.9 0.2 0.5 ­3.8 0.1 ­0.5 Algeria 5.7 6.0 77.0 163.6 7.8 3.0 5.1 0.7 0.8 28.5 4.0 9.2 1.4 Angola 3.7 3.3 4.6 8.6 4.3 0.4 0.6 0.3 0.3 15.8 1.6 6.1 2.0 Argentina 6.4 7.4 109.7 127.5 1.2 3.4 3.4 0.5 0.3 86.7 0.7 63.4 1.2 Armenia 1.6 5.6 4.2 3.4 ­0.9 1.2 1.1 0.5 0.3 2.8 ­2.0 0.3 ­4.1 Australia 4.0 4.8 272.2 354.1 2.7 15.9 17.8 1.0 0.6 113.2 0.1 27.0 3.4 Austria 7.1 7.3 57.7 70.3 1.2 7.5 8.7 0.4 0.3 9.7 ­1.6 2.8 1.0 Azerbaijan .. 2.5 53.7 29.2 ­5.1 7.5 3.5 1.9 1.0 11.9 ­2.4 0.8 ­4.2 Bangladesh 9.8 10.5 15.4 34.6 6.9 0.1 0.3 0.2 0.1 47.6 0.9 44.8 3.7 Belarus 1.2 2.4 107.8 62.5 ­4.4 10.6 6.3 2.4 1.0 21.6 ­1.1 8.3 ­3.4 Belgium 4.7 5.2 100.6 102.8 ­0.4 10.1 9.9 0.5 0.3 11.7 ­0.4 13.3 0.1 Benin 2.6 3.3 0.7 2.0 7.4 0.1 0.3 0.2 0.2 3.3 2.2 2.7 2.7 Bolivia 5.1 4.5 5.5 7.9 4.4 0.8 0.9 0.5 0.3 21.3 1.2 5.8 ­0.1 Bosnia and Herzegovina .. 5.3 6.9 19.1 15.3 1.6 4.9 .. .. 1.4 ­3.0 0.6 ­5.1 Botswana 6.2 8.6 2.2 4.1 4.2 1.5 2.3 0.3 0.2 7.0 1.3 4.8 1.0 Brazil 7.2 6.8 202.6 298.3 3.7 1.4 1.6 0.3 0.2 297.2 0.9 207.7 1.1 Bulgaria 2.1 3.0 75.3 44.0 ­3.6 8.6 5.6 1.6 0.7 10.0 ­6.3 18.5 ­2.2 Burkina Faso .. .. 1.0 1.0 1.2 0.1 0.1 0.2 0.1 8.8 2.1 11.7 2.3 Burundi .. .. 0.2 0.2 1.9 0.0 0.0 0.0 0.0 1.8 2.0 1.2 0.9 Cambodia .. .. 0.5 0.5 1.4 0.0 0.0 .. 0.0 68.0 1.0 0.1 3.6 Cameroon 4.7 4.5 1.6 3.5 4.7 0.1 0.2 0.1 0.1 11.8 1.2 9.8 1.9 Canada 3.0 3.4 415.7 565.5 2.2 15.0 17.9 0.8 0.6 123.4 5.8 57.5 0.9 Central African Republic .. .. 0.2 0.3 2.3 0.1 0.1 0.1 0.1 6.6 1.6 5.1 1.8 Chad .. .. 0.1 0.1 2.8 0.0 0.0 0.0 0.0 9.6 1.6 8.7 2.2 Chile 5.5 6.1 35.3 58.5 5.2 2.7 3.7 0.6 0.4 14.5 1.5 7.5 3.6 China 2.1 4.4 2,398.2 4,143.5 2.5 2.1 3.2 1.6 0.6 802.9 1.8 644.7 2.4 Hong Kong, China 10.8 11.5 26.2 37.8 2.5 4.6 5.6 0.3 0.2 .. .. .. .. Colombia 8.4 10.9 56.8 55.5 ­0.6 1.6 1.3 0.4 0.2 55.5 1.2 41.2 4.8 Congo, Dem. Rep. 5.0 2.2 4.0 1.8 ­6.5 0.1 0.0 0.1 0.1 32.9 0.6 17.2 0.0 Congo, Rep. 2.3 3.3 1.2 1.4 ­1.0 0.5 0.4 0.5 0.3 3.2 1.9 1.0 2.6 Costa Rica 9.7 10.0 2.9 6.3 5.1 0.9 1.5 0.2 0.2 3.6 ­0.3 3.6 ­1.0 Côte d'Ivoire 5.2 3.7 5.4 5.7 1.3 0.4 0.3 0.3 0.2 6.5 2.0 2.9 1.8 Croatia 5.0 5.6 24.6 23.8 1.6 5.1 5.4 0.6 0.5 3.8 ­0.5 3.4 ­1.2 Cuba .. .. 32.0 25.2 ­1.8 3.0 2.3 .. .. 9.1 ­0.8 9.3 ­3.3 Czech Republic 3.1 4.0 161.7 116.3 ­2.2 15.6 11.4 1.3 0.6 10.8 ­3.5 8.2 ­4.8 Denmark 6.9 7.9 49.7 54.5 ­1.2 9.7 10.1 0.5 0.3 6.0 ­0.3 9.3 ­1.5 Dominican Republic 7.1 7.6 9.6 21.3 6.8 1.3 2.5 0.4 0.3 5.9 1.1 4.3 0.4 Ecuador 5.8 4.8 16.6 23.2 2.4 1.6 1.8 0.6 0.5 16.2 1.8 2.9 ­0.3 Egypt, Arab Rep. 5.1 4.9 75.4 139.6 5.6 1.4 2.0 0.6 0.5 34.3 4.1 16.0 3.9 El Salvador 7.3 7.0 2.6 6.5 6.4 0.5 1.0 0.2 0.2 3.2 1.9 2.2 0.7 Eritrea .. .. .. 0.7 .. .. 0.2 .. 0.2 0.0 .. .. .. Estonia 1.6 3.5 28.3 18.2 ­3.9 18.1 13.5 2.5 1.1 2.4 ­4.4 0.4 ­5.8 Ethiopia 2.6 2.8 3.0 7.3 8.0 0.1 0.1 0.1 0.1 47.5 2.0 12.2 6.6 Finland 3.8 3.8 51.2 67.8 1.4 10.3 13.0 0.6 0.4 4.3 ­3.4 7.3 ­1.5 France 5.5 5.9 362.3 373.9 0.1 6.4 6.2 0.4 0.2 59.3 ­1.1 72.3 ­1.7 Gabon 4.8 4.9 6.0 1.2 ­9.1 6.3 0.9 1.2 0.1 3.8 2.3 1.8 0.0 Gambia, The .. .. 0.2 0.3 3.4 0.2 0.2 0.2 0.1 0.7 1.7 0.5 0.3 Georgia 1.2 4.1 17.3 3.7 ­11.3 3.2 0.8 0.8 0.3 4.4 ­1.9 1.1 ­4.4 Germany 4.7 6.2 980.3 805.0 ­1.1 12.3 9.8 0.7 0.4 62.7 ­4.4 60.5 ­3.2 Ghana 4.6 5.4 3.8 7.7 5.7 0.2 0.4 0.2 0.2 7.1 3.4 7.4 6.4 Greece 6.7 7.4 72.2 96.2 2.6 7.1 8.7 0.6 0.4 10.9 2.4 11.2 0.2 Guatemala 6.7 6.4 5.1 10.7 6.7 0.6 0.9 0.2 0.2 6.2 0.5 5.2 0.8 Guinea .. .. 1.0 1.3 2.3 0.2 0.1 0.1 0.1 5.7 1.9 2.4 2.9 Guinea-Bissau .. .. 0.2 0.3 2.0 0.2 0.2 0.3 0.2 0.9 0.0 0.8 2.4 Haiti 10.4 6.2 1.0 1.7 7.2 0.1 0.2 0.1 0.1 3.4 1.7 2.6 0.7 154 2007 World Development Indicators 3.8 ENVIRONMENT Energy efficiency and emissions GDP per unit Carbon dioxide Methane Nitrous oxide of energy use emissions emissions emissions million million metric tons metric tons 2000 PPP $ average kilograms per of carbon average of carbon average per kilogram Total annual Per capita 2000 PPP $ dioxide annual dioxide annual of oil equivalent million metric tons % change metric tons of GDP equivalent % change equivalent % change 1990 2004 1990 2003 1990­2003 1990 2003 1990 2003 2000 1990­2000 2000 1990­2000 Honduras 5.0 4.8 2.6 6.5 7.5 0.5 0.9 0.2 0.3 4.9 ­0.2 3.5 0.0 Hungary 4.2 5.9 60.1 58.2 ­0.4 5.8 5.7 0.6 0.4 11.3 ­2.5 12.9 13.6 India 4.0 5.5 677.7 1,273.2 4.9 0.8 1.2 0.6 0.4 445.3 1.6 399.0 2.6 Indonesia 4.1 4.1 149.3 295.0 4.6 0.8 1.4 0.5 0.4 169.2 1.4 38.7 1.0 Iran, Islamic Rep. 3.6 3.1 218.2 381.4 3.7 4.0 5.7 1.0 0.8 96.9 6.7 43.8 1.5 Iraq .. .. 48.5 72.9 3.8 2.6 .. .. .. 14.4 0.7 6.5 0.1 Ireland 5.2 9.5 30.6 41.4 2.9 8.7 10.4 0.7 0.3 12.9 ­0.1 9.8 0.6 Israel 7.0 7.3 33.1 68.3 5.8 7.1 10.2 0.5 0.4 11.4 3.3 1.7 2.0 Italy 8.4 8.2 389.5 445.5 1.0 6.9 7.7 0.4 0.3 37.0 ­0.7 43.5 0.6 Jamaica 3.0 2.5 8.0 10.7 2.4 3.3 4.1 1.3 1.0 1.3 0.8 1.3 0.3 Japan 6.5 6.4 1,070.4 1,231.3 1.0 8.7 9.6 0.4 0.3 21.8 ­1.7 37.0 ­0.6 Jordan 3.5 3.6 10.2 17.1 3.8 3.2 3.3 1.0 0.7 7.9 1.0 0.2 9.2 Kazakhstan 1.0 1.9 288.1 159.2 ­6.0 17.6 10.7 3.8 1.7 27.3 ­4.5 7.8 ­6.5 Kenya 2.2 2.1 5.8 8.8 5.0 0.2 0.3 0.3 0.2 21.5 1.1 22.6 0.3 Korea, Dem. Rep. .. .. 244.6 77.5 ­11.9 12.4 3.5 .. .. 33.5 0.3 6.5 ­4.0 Korea, Rep. 4.5 4.2 241.1 455.9 4.6 5.6 9.5 0.7 0.5 25.0 ­0.2 16.1 4.8 Kuwait 1.2 1.9 45.2 78.5 11.0 21.3 32.7 1.5 1.4 9.9 6.5 0.2 14.1 Kyrgyz Republic 1.7 3.3 12.6 5.3 ­7.5 2.8 1.1 1.4 0.6 2.2 ­2.4 0.1 1.2 Lao PDR .. .. 0.2 1.3 16.7 0.1 0.2 0.1 0.1 6.2 0.9 0.1 2.7 Latvia 2.5 5.6 14.5 6.7 ­6.9 5.4 2.9 0.7 0.3 2.6 ­4.0 1.2 ­6.6 Lebanon 2.7 3.5 9.1 19.0 5.1 3.3 5.4 1.5 1.1 1.3 8.6 1.1 5.5 Lesotho .. .. .. .. .. .. .. .. .. 1.2 2.0 1.5 0.5 Liberia .. .. 0.5 0.5 3.1 0.2 0.1 .. .. 1.2 ­0.8 0.8 0.9 Libya .. .. 37.8 50.2 2.3 8.7 8.9 .. .. 9.6 0.9 2.5 ­1.1 Lithuania 2.8 4.5 24.3 12.7 ­5.2 6.6 3.7 0.7 0.3 5.9 ­4.2 3.5 16.8 Macedonia, FYR 4.1 4.6 15.5 10.5 ­0.7 8.1 5.2 1.4 0.8 1.3 0.0 1.1 1.5 Madagascar .. .. 0.9 2.3 8.5 0.1 0.1 0.1 0.2 18.9 1.5 11.6 1.2 Malawi .. .. 0.6 0.9 2.8 0.1 0.1 0.2 0.1 3.6 1.6 2.3 1.3 Malaysia 4.4 4.1 55.3 156.4 6.8 3.1 6.4 0.7 0.7 30.4 4.3 13.3 1.5 Mali .. .. 0.4 0.6 2.3 0.0 0.0 0.1 0.0 12.0 0.9 13.8 2.4 Mauritania .. .. 2.6 2.5 ­1.2 1.3 0.9 0.9 0.4 4.4 1.3 6.4 1.3 Mauritius .. .. 1.5 3.1 6.4 1.4 2.6 0.3 0.2 0.3 5.0 0.9 1.7 Mexico 5.1 5.5 375.1 415.9 0.7 4.5 4.1 0.7 0.4 111.7 0.0 10.0 1.1 Moldova 1.4 2.0 23.8 7.2 ­10.1 5.5 1.7 1.9 1.0 2.6 ­4.1 1.6 ­6.0 Mongolia .. .. 10.0 8.0 ­2.6 4.7 3.2 3.3 1.8 8.2 1.7 12.1 3.7 Morocco 11.9 10.3 23.5 37.9 3.7 1.0 1.3 0.4 0.3 10.0 1.0 15.7 0.6 Mozambique 1.3 2.6 1.0 1.6 3.2 0.1 0.1 0.1 0.1 11.1 1.8 3.2 1.0 Myanmar .. .. 4.3 9.5 6.5 0.1 0.2 .. .. 61.1 2.4 12.5 3.2 Namibia 12.3 10.2 0.0 2.3 .. 0.0 1.2 0.0 0.2 4.5 0.5 4.2 ­0.2 Nepal 3.4 4.0 0.6 2.9 11.4 0.0 0.1 0.0 0.1 16.4 1.5 11.3 1.5 Netherlands 5.2 5.8 139.7 140.9 0.2 9.3 8.7 0.5 0.3 21.6 ­2.3 17.2 0.3 New Zealand 4.1 5.1 23.6 34.8 3.3 6.8 8.7 0.5 0.4 36.2 ­0.5 12.4 0.5 Nicaragua 5.3 5.5 2.6 3.9 5.0 0.7 0.8 0.3 0.2 5.3 1.3 4.0 0.8 Niger .. .. 1.0 1.2 1.1 0.1 0.1 0.2 0.1 6.5 2.5 5.0 2.8 Nigeria 1.1 1.4 45.3 52.2 ­0.1 0.5 0.4 0.7 0.4 72.5 4.2 41.6 1.9 Norway 5.1 5.9 35.3 45.0 2.0 8.3 9.9 0.4 0.3 7.1 0.6 5.1 0.0 Oman 4.3 3.0 10.3 32.2 8.7 5.6 12.8 0.6 0.9 3.7 8.5 1.0 1.9 Pakistan 3.9 4.2 68.0 114.1 4.3 0.6 0.8 0.5 0.4 94.7 2.5 84.6 3.4 Panama 7.4 8.4 3.1 6.0 5.5 1.3 1.9 0.4 0.3 3.3 1.0 2.7 0.7 Papua New Guinea .. .. 2.4 2.5 ­0.2 0.6 0.4 0.4 0.2 3.9 3.9 2.3 1.8 Paraguay 6.5 6.6 2.3 4.1 4.7 0.5 0.7 0.1 0.2 12.3 0.5 10.2 0.2 Peru 8.4 10.9 21.0 26.1 2.3 1.0 1.0 0.3 0.2 19.6 1.5 21.9 8.0 Philippines 9.1 7.9 43.9 76.9 5.0 0.7 1.0 0.2 0.2 34.2 0.7 20.8 3.3 Poland 2.9 5.1 347.5 304.5 ­1.3 9.1 8.0 1.5 0.7 47.2 ­2.2 23.9 ­2.2 Portugal 7.9 7.1 42.3 57.5 3.0 4.3 5.5 0.4 0.3 14.3 0.3 8.1 0.3 Puerto Rico .. .. 11.8 2.1 ­4.1 3.3 0.5 .. .. .. .. .. .. 2007 World Development Indicators 155 3.8 Energy efficiency and emissions GDP per unit Carbon dioxide Methane Nitrous oxide of energy use emissions emissions emissions million million metric tons metric tons 2000 PPP $ average kilograms per of carbon average of carbon average per kilogram Total annual Per capita 2000 PPP $ dioxide annual dioxide annual of oil equivalent million metric tons % change metric tons of GDP equivalent % change equivalent % change 1990 2004 1990 2003 1990­2003 1990 2003 1990 2003 2000 1990­2000 2000 1990­2000 Romania 2.5 4.5 155.0 91.1 ­4.0 6.7 4.2 1.2 0.6 36.1 ­1.7 7.2 ­6.6 Russian Federation 1.6 2.0 2,261.7 1,493.0 ­3.3 15.3 10.3 1.8 1.2 298.7 ­4.6 51.5 ­3.7 Rwanda .. .. 0.5 0.6 1.7 0.1 0.1 0.1 0.1 2.2 ­1.5 1.2 ­1.4 Saudi Arabia 2.8 2.2 197.4 302.3 0.5 12.1 13.7 1.1 0.9 54.4 5.7 8.7 1.5 Senegal 5.0 6.5 3.1 4.8 2.6 0.4 0.4 0.3 0.3 8.4 2.5 6.6 3.8 Serbia and Montenegro .. .. 65.4 49.9 ­0.4 6.2 6.2 .. .. 9.5 ­2.6 6.1 ­3.5 Sierra Leone .. .. 0.3 0.7 4.3 0.1 0.1 0.1 0.2 2.6 0.8 0.9 3.0 Singapore 3.4 4.4 45.1 47.8 1.2 14.8 11.4 1.2 0.5 1.2 7.1 0.9 46.1 Slovak Republic 2.7 3.9 51.4 37.5 ­1.8 9.7 7.0 1.1 0.5 4.2 ­3.1 3.2 ­4.9 Slovenia 4.9 5.4 18.0 15.4 1.1 9.0 7.7 0.8 0.4 2.5 ­0.7 2.0 2.3 Somalia .. .. 0.0 .. .. 0.0 .. .. .. .. .. .. .. South Africa 3.9 3.7 285.4 364.2 1.7 8.1 7.9 1.1 0.8 37.4 0.7 25.8 0.1 Spain 7.3 6.9 211.8 309.2 3.2 5.5 7.4 0.4 0.3 39.6 2.3 30.1 1.5 Sri Lanka 7.3 8.3 3.8 10.3 8.8 0.2 0.5 0.1 0.1 13.3 2.9 2.9 2.0 Sudan 2.7 3.7 5.4 9.0 5.0 0.2 0.3 0.2 0.1 46.6 1.7 47.1 2.0 Swaziland .. .. 0.4 1.0 12.0 0.6 0.9 0.2 0.2 1.1 1.0 1.2 1.2 Sweden 4.0 4.5 49.4 52.7 0.0 5.8 5.9 0.3 0.2 7.1 ­1.0 7.1 ­0.4 Switzerland 8.2 8.3 42.7 40.4 ­0.2 6.4 5.5 0.3 0.2 5.0 ­1.1 3.7 0.3 Syrian Arab Republic 2.9 3.4 35.8 48.9 1.9 2.8 2.7 1.4 0.8 9.7 6.7 9.4 2.0 Tajikistan 0.9 2.1 23.4 4.7 ­12.5 4.4 0.7 2.2 0.7 1.4 0.8 0.1 1.5 Tanzania 1.4 1.3 2.3 3.8 2.8 0.1 0.1 0.2 0.2 31.7 1.8 27.1 1.6 Thailand 5.7 4.9 95.7 245.9 6.4 1.8 3.9 0.5 0.5 75.9 0.4 13.1 0.7 Togo 4.3 3.1 0.8 2.2 7.8 0.2 0.4 0.2 0.3 2.1 1.7 2.3 1.5 Trinidad and Tobago 1.4 1.3 16.9 28.6 3.5 13.9 22.1 2.4 1.8 3.1 2.4 0.3 ­1.6 Tunisia 6.7 8.2 13.3 20.9 3.2 1.6 2.1 0.4 0.3 4.8 3.0 5.2 1.4 Turkey 5.8 6.2 146.2 220.0 3.6 2.6 3.1 0.6 0.4 97.4 2.1 40.6 ­1.2 Turkmenistan 1.6 .. 32.0 43.3 2.6 8.7 9.2 1.9 .. 27.1 1.7 0.6 ­1.4 Uganda .. .. 0.8 1.7 7.2 0.0 0.1 0.1 0.0 12.4 2.5 12.9 2.7 Ukraine 1.8 2.0 684.0 314.4 ­6.6 13.2 6.6 1.8 1.2 153.5 ­2.2 19.9 ­4.3 United Arab Emirates 1.9 2.2 54.7 135.0 7.4 30.8 33.4 1.5 1.5 35.2 7.1 0.1 3.2 United Kingdom 5.9 7.3 569.1 558.5 ­0.4 9.9 9.4 0.6 0.3 51.1 ­3.3 43.8 ­3.5 United States 3.7 4.6 4,816.2 5,788.2 1.7 19.3 19.9 0.8 0.5 613.4 ­0.5 430.0 0.8 Uruguay 9.9 10.4 3.9 4.4 0.7 1.3 1.3 0.2 0.2 18.3 2.0 0.7 3.0 Uzbekistan 0.7 0.8 129.2 123.6 0.1 6.3 4.8 4.2 2.8 46.2 1.5 13.5 1.9 Venezuela, RB 2.6 2.6 117.3 144.0 2.0 5.9 5.6 1.3 1.1 95.1 2.4 6.9 ­0.5 Vietnam 3.3 4.2 21.4 76.1 11.5 0.3 0.9 0.3 0.4 68.1 1.5 12.9 5.2 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 3.0 2.8 9.6 17.1 4.2 0.8 0.9 1.4 1.0 8.7 8.9 5.6 0.9 Zambia 1.5 1.5 2.4 2.2 ­2.0 0.3 0.2 0.4 0.2 11.2 1.4 5.5 1.3 Zimbabwe 3.0 2.6 16.6 11.5 ­2.7 1.6 0.9 0.7 0.4 11.0 0.2 8.6 ­0.5 World 3.9 w 4.8 w 22,501.8 t 26,750.9 t 1.2 w 4.3 w 4.3 w 0.7 w 0.5 w 5,893.6 t ­0.6 w 3,454.4 t 0.3 w Low income 3.5 4.4 1,336.9 1,893.3 2.4 0.8 0.8 0.5 0.4 1,344.6 1.8 910.8 2.7 Middle income 3.0 4.2 9,319.6 10,753.5 0.5 3.5 3.6 1.1 0.6 3,033.6 0.8 1,545.4 1.0 Lower middle income 3.1 4.5 4,965.3 6,943.3 1.7 2.4 2.9 1.0 0.5 2,080.8 1.5 1,242.3 1.7 Upper middle income 2.8 3.7 4,353.8 3,811.5 ­1.1 8.1 6.4 1.2 0.7 952.9 ­0.1 303.4 0.0 Low & middle income 3.0 4.3 10,656.6 12,646.8 0.8 2.4 2.4 1.0 0.5 4,377.7 1.0 2,455.6 1.2 East Asia & Pacific 2.6 4.4 3,030.6 5,100.6 2.5 1.9 2.7 1.2 0.6 1,365.5 1.7 780.0 2.2 Europe & Central Asia 2.1 2.8 4,821.9 3,265.3 ­3.1 10.2 6.9 1.6 0.9 844.1 ­2.0 236.3 ­1.9 Latin America & Carib. 6.0 6.2 1,037.3 1,299.9 2.0 2.4 2.4 0.5 0.3 800.9 0.8 418.8 1.4 Middle East & N. Africa 4.6 4.2 575.4 1,012.5 4.5 2.5 3.4 0.8 0.7 233.0 4.2 118.3 1.9 South Asia 4.2 5.5 768.4 1,436.7 4.9 0.7 1.0 0.5 0.4 631.7 1.7 550.3 2.8 Sub-Saharan Africa 2.8 2.8 418.3 531.9 1.6 0.8 0.8 0.6 0.4 504.6 1.5 353.8 1.2 High income 4.7 5.2 10,651.9 12,738.4 1.5 11.8 12.8 0.6 0.4 1,450.2 ­0.9 961.9 0.1 Europe EMU 5.8 6.5 2,466.1 2,535.8 0.4 8.3 8.2 0.5 0.3 287.0 ­1.8 278.2 ­1.1 156 2007 World Development Indicators 3.8 ENVIRONMENT Energy efficiency and emissions About the data Definitions The ratio of GDP to energy use provides a measure · GDP per unit of energy use is the PPP GDP per High-income countries contribute of energy effi ciency. To produce comparable and kilogram of oil equivalent of energy use. PPP GDP more than half of global carbon consistent estimates of real GDP across countries dioxide emissions 3.8a is gross domestic product converted to 2000 con- relative to physical inputs to GDP--that is, units of stant international dollars using purchasing power Share of carbon dioxide emissions, 2003 energy use--GDP is converted to 2000 constant parity rates. An international dollar has the same Other low-income 2% international dollars using purchasing power parity purchasing power over GDP as a U.S. dollar has in India 5% (PPP) rates. Differences in this ratio over time and the United States. · Carbon dioxide emissions are United across countries reflect in part structural changes Other States those stemming from the burning of fossil fuels and middle- 23% in the economy, changes in the energy efficiency of income the manufacture of cement. They include carbon 26% particular sectors, and differences in fuel mixes. dioxide produced during consumption of solid, liquid, Because commercial energy is widely traded, it is Other and gas fuels and gas flaring. · Methane emissions high-income China 28% necessary to distinguish between its production and 16% are those stemming from human activities such as its use. Net energy imports show the extent to which agriculture and from industrial methane production. an economy's use exceeds its domestic production. · Nitrous oxide emissions are those stemming from Source: Table 3.8. High-income countries are net energy importers; mid- agriculture, biomass burning, industrial activities, dle-income countries have been their main suppliers. and livestock management. Carbon dioxide emissions, largely byproducts of The five largest contributors to carbon dioxide emissions differ energy production and use (see table 3.7), account considerably in per capita emissions 3.8b for the largest share of greenhouse gases, which are associated with global warming. Anthropogenic car- Per capita carbon dioxide emissions of the five largest producers bon dioxide emissions result primarily from fossil fuel (metric tons) 1990 2003 20 combustion and cement manufacturing. In combus- tion, different fossil fuels release different amounts of carbon dioxide for the same level of energy use. 15 Burning oil releases about 50 percent more carbon dioxide than burning natural gas, and burning coal releases about twice as much. Cement manufactur- 10 ing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. 5 Methane emissions, largely the result of agricultural activities and industrial production of methane, are expressed in carbon dioxide equivalents using global 0 United China Russian India Japan warming potential, which allows different gases to States Federation be compared on the basis of their effective contribu- Source: Table 3.8. tions. A kilogram of methane is 23 times as effective at trapping heat in the earth's atmosphere as a kilo- dioxide by multiplying the carbon mass by 3.664 (the gram of carbon dioxide within a time horizon of 100 ratio of the mass of carbon to that of carbon dioxide). years. The global warming potential of a kilogram of Although the estimates of global carbon dioxide emis- nitrous oxide is nearly 300 times that of a kilogram of sions are probably within 10 percent of actual emis- carbon dioxide within the same time horizon. sions (as calculated from global average fuel chemis- The Carbon Dioxide Information Analysis Center try and use), country estimates may have larger error (CDIAC), sponsored by the U.S. Department of Energy, bounds. The world totals shown in the table include calculates annual anthropogenic emissions of carbon the carbon dioxide emissions not allocated to specific Data sources dioxide. These calculations are based on data on fos- countries. Trends estimated from a consistent time sil fuel consumption (from the World Energy Data Set series tend to be more accurate than individual val- The underlying data on energy use are from elec- maintained by the United Nations Statistics Division) ues. Each year the CDIAC recalculates the entire time tronic files of the International Energy Agency. Data and data on world cement manufacturing (from the series from 1950 to the present, incorporating its on carbon dioxide emissions are from the CDIAC, Cement Manufacturing Data Set maintained by the most recent findings and the latest corrections to its Environmental Sciences Division, Oak Ridge U.S. Bureau of Mines). Emissions of carbon dioxide database. Estimates do not include fuels supplied to National Laboratory, in the U.S. state of Tennes- are often calculated and reported in terms of their ships and aircraft engaged in international transport see. Data on methane and nitrous oxide emissions content of elemental carbon. For this table these because of the difficulty of apportioning these fuels are compiled by the World Resources Institute. values were converted to the actual mass of carbon among the countries benefiting from that transport. 2007 World Development Indicators 157 3.9 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.2 5.6 .. .. .. .. 10.9 1.7 89.1 98.3 .. .. Algeria 16.1 31.3 .. .. 93.7 97.0 5.4 2.2 0.8 0.8 .. .. Angola 0.8 2.2 .. .. .. .. 13.8 33.5 86.2 66.5 .. .. Argentina 51.0 100.3 1.3 1.7 39.0 54.8 9.7 4.0 35.6 30.4 14.3 7.8 Armenia 9.0 6.0 .. .. 22.9 30.4 43.3 .. 33.8 33.1 .. 36.5 Australia 154.3 239.3 77.1 79.3 10.6 12.3 2.7 0.7 9.2 6.8 .. .. Austria 49.3 61.6 14.2 14.8 15.7 17.8 3.8 3.0 63.9 59.1 .. .. Azerbaijan 19.7 21.6 .. .. 0.5 58.9 91.1 28.4 8.9 12.7 .. .. Bangladesh 7.7 21.5 .. .. 84.3 87.5 4.3 6.7 11.4 5.7 .. .. Belarus 37.6 31.2 .. 0.0 47.9 87.3 52.1 12.6 0.0 0.1 .. .. Belgium 70.3 84.4 28.2 13.6 7.7 25.5 1.9 2.0 0.4 0.4 60.8 56.1 Benin 0.0 0.1 .. .. .. .. 100.0 98.8 .. 1.2 .. .. Bolivia 2.1 4.4 .. .. 37.6 29.2 5.3 19.7 55.3 49.0 .. .. Bosnia and Herzegovina 6.5 12.6 47.8 52.1 .. .. .. 1.1 52.2 46.8 .. .. Botswana 0.9 1.3 88.1 95.7 .. .. 11.9 4.3 .. .. .. .. Brazil 222.8 387.5 2.0 2.7 0.0 5.0 2.2 3.2 92.8 82.8 1.0 3.0 Bulgaria 42.1 41.4 50.3 46.1 7.6 3.6 2.9 2.0 4.5 7.6 34.8 40.6 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 0.8 .. .. .. .. .. 49.3 .. 1.8 .. .. Cameroon 2.7 4.1 .. .. .. .. 1.5 4.6 98.5 95.4 .. .. Canada 481.9 598.4 17.1 17.2 2.0 5.4 3.4 3.6 61.6 57.0 15.1 15.1 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 18.4 52.0 34.3 16.1 1.3 34.0 7.6 1.5 55.3 45.4 .. .. China 621.2 2,199.6 71.2 77.9 0.5 0.4 7.9 3.3 20.4 16.1 0.2 2.3 Hong Kong, China 28.9 37.1 98.3 68.5 .. 30.9 1.7 0.6 .. .. .. .. Colombia 36.2 50.2 9.8 6.0 12.4 12.9 1.0 0.2 76.0 79.8 .. .. Congo, Dem. Rep. 5.7 6.9 .. .. .. .. 0.4 0.3 99.6 99.7 .. .. Congo, Rep. 0.5 0.4 .. .. .. .. 0.6 .. 99.4 100.0 .. .. Costa Rica 3.5 8.2 .. .. .. .. 2.5 1.8 97.5 79.0 .. .. Côte d'Ivoire 2.0 5.4 .. .. .. 67.5 33.3 0.1 66.7 32.4 .. .. Croatia 8.9 13.2 .. 16.2 15.4 18.6 35.8 12.4 48.8 52.7 .. .. Cuba 15.0 15.7 .. .. 0.2 0.0 91.5 95.3 0.6 0.6 .. .. Czech Republic 62.3 83.8 76.4 60.3 0.6 2.6 0.9 0.4 1.9 2.4 20.2 31.4 Denmark 26.0 40.5 90.7 46.1 2.7 24.7 3.4 4.0 0.1 0.1 .. .. Dominican Republic 3.7 13.8 1.2 15.2 .. 0.1 88.6 72.6 9.4 11.5 .. .. Ecuador 6.3 12.6 .. .. .. 8.5 21.5 32.6 78.5 58.9 .. .. Egypt, Arab Rep. 42.3 101.3 .. .. 39.6 70.8 36.9 16.2 23.5 12.5 .. .. El Salvador 2.2 4.4 .. .. .. .. 6.9 45.6 73.5 31.2 .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 11.8 10.3 90.0 92.4 5.5 4.7 4.5 0.3 0.0 0.2 .. .. Ethiopia 1.2 2.5 .. .. .. .. 11.6 0.7 88.4 99.3 .. .. Finland 54.4 85.8 33.0 27.5 8.6 14.9 3.1 0.7 20.0 17.6 35.3 26.5 France 417.2 567.1 8.5 5.0 0.7 3.2 2.1 1.0 12.9 10.5 75.3 79.0 Gabon 1.0 1.5 .. .. 16.4 16.7 11.2 24.8 72.1 58.1 .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 11.2 6.9 .. .. 36.6 12.0 5.0 0.6 58.3 87.4 .. .. Germany 547.7 610.0 58.8 50.5 7.4 10.1 1.9 1.7 3.2 3.5 27.8 27.4 Ghana 5.7 6.0 .. .. .. .. .. 12.6 100.0 87.4 .. .. Greece 34.8 58.8 72.4 60.2 0.3 15.3 22.3 14.3 5.1 7.9 .. .. Guatemala 2.3 7.0 .. 17.1 .. .. 9.0 35.7 76.0 34.7 .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 0.6 0.5 .. .. .. .. 20.6 52.5 76.5 47.5 .. .. 158 2007 World Development Indicators 3.9 ENVIRONMENT Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 2.3 4.9 .. .. .. .. 1.7 51.5 98.3 48.1 .. .. Hungary 28.4 33.7 30.5 24.7 15.7 34.8 4.8 2.3 0.6 0.6 48.3 35.3 India 289.4 667.8 65.3 69.1 3.4 9.5 4.3 5.4 24.8 12.7 2.1 2.5 Indonesia 33.3 120.2 31.5 40.1 2.3 16.1 42.7 30.2 20.2 8.1 .. .. Iran, Islamic Rep. 59.1 164.5 .. .. 52.5 76.2 37.3 17.3 10.3 6.5 .. .. Iraq 24.0 32.3 .. .. .. .. 89.2 98.5 10.8 1.5 .. .. Ireland 14.2 25.2 57.4 30.6 27.7 51.1 10.0 12.7 4.9 2.5 .. .. Israel 20.9 49.1 50.1 75.3 .. 8.8 49.9 15.8 0.0 0.1 .. .. Italy 213.1 293.0 16.8 17.4 18.6 44.3 48.2 15.7 14.8 13.5 0.1 .. Jamaica 2.5 7.2 .. .. .. .. 92.4 96.5 3.6 1.9 .. .. Japan 838.2 1,071.0 13.9 27.5 19.8 22.8 18.4 9.2 10.7 8.8 24.1 26.4 Jordan 3.6 9.0 .. .. 11.9 50.2 87.8 49.2 0.3 0.6 .. .. Kazakhstan 82.7 66.9 72.3 69.9 10.6 10.6 8.8 7.4 8.3 12.0 .. .. Kenya 3.0 5.6 .. .. .. .. 7.6 24.1 81.6 51.5 .. .. Korea, Dem. Rep. 27.7 22.0 40.1 38.6 .. .. 3.6 4.5 56.3 56.9 .. .. Korea, Rep. 105.4 366.6 16.8 38.8 9.1 16.2 17.9 7.6 6.0 1.2 50.2 35.7 Kuwait 18.5 41.3 .. .. 45.7 20.5 54.3 79.5 .. .. .. .. Kyrgyz Republic 11.9 15.1 9.1 3.5 13.6 3.5 .. .. 77.4 93.1 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 4.0 4.7 3.8 0.6 25.4 30.6 7.6 1.3 63.3 66.4 .. .. Lebanon 1.5 10.2 .. .. .. .. 66.7 89.0 33.3 11.0 .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 10.2 20.2 .. .. .. 19.3 100.0 80.7 .. .. .. .. Lithuania 18.7 18.8 .. .. 6.7 14.4 12.4 1.9 2.5 2.2 78.2 80.5 Macedonia, FYR 6.1 6.7 85.0 77.6 .. .. 1.0 0.2 14.0 22.2 .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23.0 82.9 12.3 27.9 20.4 61.8 50.0 3.3 17.3 7.0 .. .. Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico 124.1 224.1 6.3 10.7 11.6 38.8 56.7 31.1 18.9 11.2 2.4 4.1 Moldova 11.2 3.6 34.4 .. 36.9 97.9 26.4 0.4 2.3 1.6 .. .. Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 9.6 19.3 23.0 67.4 .. .. 64.4 23.2 12.7 8.4 .. .. Mozambique 0.5 11.7 13.9 .. 0.2 0.1 23.6 0.2 62.6 99.7 .. .. Myanmar 2.5 6.4 1.6 .. 39.3 57.0 10.9 6.8 48.1 36.2 .. .. Namibia 1.4 1.7 1.5 0.4 .. .. 3.3 2.7 95.2 96.9 .. .. Nepal 0.9 2.3 .. .. .. .. 0.1 0.2 99.9 99.8 .. .. Netherlands 71.9 100.8 38.3 26.0 50.9 60.5 4.3 2.8 0.1 0.1 4.9 3.8 New Zealand 32.1 41.8 1.5 9.9 17.7 16.7 0.0 0.1 72.6 64.6 .. .. Nicaragua 1.4 2.8 .. .. .. .. 39.8 75.2 28.8 11.4 .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 13.5 20.2 0.1 .. 53.7 62.7 13.7 3.1 32.6 34.2 .. .. Norway 121.6 110.1 0.1 0.1 0.0 0.3 0.0 0.0 99.6 98.8 .. .. Oman 4.5 11.5 .. .. 81.6 82.0 18.4 18.0 .. .. .. .. Pakistan 37.7 85.7 0.1 0.2 33.6 50.7 20.6 15.9 44.9 30.0 0.8 3.3 Panama 2.7 5.8 .. .. .. .. 14.7 34.0 83.2 65.6 .. .. Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 27.2 51.9 .. .. .. .. 0.0 .. 99.9 100.0 .. .. Peru 13.8 24.3 .. 3.8 1.7 8.2 21.5 15.1 75.8 72.3 .. .. Philippines 25.2 56.0 7.7 28.9 .. 22.1 46.7 15.2 24.0 15.4 .. .. Poland 134.4 152.6 97.5 94.1 0.1 2.1 1.2 1.6 1.1 1.4 .. .. Portugal 28.4 44.8 32.1 33.1 .. 26.1 33.1 12.7 32.3 22.0 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 159 3.9 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 1990 2004 Romania 64.3 56.5 28.8 38.5 35.1 18.5 18.4 3.9 17.7 29.2 .. 9.8 Russian Federation 1,008.5 929.9 15.3 17.3 45.7 45.3 9.9 2.7 17.0 18.9 11.9 15.6 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 69.2 159.9 .. .. 43.5 49.2 56.5 50.8 .. .. .. .. Senegal 0.9 2.4 .. .. 2.0 1.8 98.0 75.0 .. 12.5 .. .. Serbia and Montenegro 36.5 35.4 65.6 69.9 1.6 1.5 1.7 0.8 31.1 27.9 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 15.7 36.8 .. .. 11.8 68.8 100.0 31.2 .. .. .. .. Slovak Republic 25.5 30.5 31.9 20.0 7.1 7.9 6.4 2.4 7.4 13.5 47.2 55.9 Slovenia 12.1 15.3 36.2 34.0 0.2 2.3 2.5 0.3 28.2 26.8 32.9 35.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 165.4 242.2 94.3 93.2 .. 0.0 0.0 .. 0.6 0.9 5.1 5.5 Spain 151.2 277.1 40.1 29.0 1.0 20.0 5.7 8.6 16.8 11.4 35.9 23.0 Sri Lanka 3.2 8.0 .. .. .. .. 0.2 63.2 99.8 36.8 .. .. Sudan 1.5 3.9 .. .. .. .. 36.8 72.8 63.2 27.2 .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 146.0 151.7 1.1 1.7 0.3 0.5 0.9 1.3 49.7 39.6 46.7 51.1 Switzerland 54.9 63.6 0.1 .. 0.6 1.5 0.7 0.3 54.3 53.1 43.1 42.4 Syrian Arab Republic 11.6 32.1 .. .. 20.5 41.2 56.0 45.6 23.5 13.2 .. .. Tajikistan 16.8 17.3 .. .. 5.3 2.3 .. .. 94.7 97.7 .. .. Tanzania 1.6 2.5 .. 3.5 .. .. 4.9 1.5 95.1 95.1 .. .. Thailand 44.2 125.7 25.0 15.9 40.2 71.0 23.5 6.2 11.3 4.8 .. .. Togo 0.2 0.3 .. .. .. .. 39.9 38.9 60.1 61.1 .. .. Trinidad and Tobago 3.6 6.4 .. .. 99.0 99.5 0.1 0.1 .. .. .. .. Tunisia 5.8 13.1 .. .. 63.7 90.2 35.5 8.3 0.8 1.2 .. .. Turkey 57.5 150.7 35.1 22.9 17.7 41.3 6.9 5.1 40.2 30.6 .. .. Turkmenistan 13.2 11.5 .. .. 100.0 100.0 .. .. 0.0 0.0 .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 252.5 182.0 41.8 24.7 16.8 20.7 9.0 0.3 3.2 6.5 29.2 47.8 United Arab Emirates 17.1 52.4 .. .. 96.3 97.5 3.7 2.5 .. .. .. .. United Kingdom 317.8 393.2 65.0 34.1 1.6 40.6 10.9 1.3 1.6 1.3 20.7 20.3 United States 3,202.8 4,147.7 53.1 50.4 11.9 17.6 4.1 3.4 8.5 6.5 19.1 19.6 Uruguay 7.4 5.9 .. .. .. 0.0 5.1 18.3 94.2 81.0 .. .. Uzbekistan 50.9 51.0 4.9 3.9 75.9 74.0 6.9 9.2 12.3 12.8 .. .. Venezuela, RB 59.3 98.5 .. .. 26.2 16.9 11.5 12.1 62.3 71.0 .. .. Vietnam 8.7 46.0 23.1 15.3 0.1 42.7 15.0 3.7 61.8 38.4 .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.7 4.3 .. .. .. .. 100.0 100.0 .. .. .. .. Zambia 8.0 8.5 0.5 0.2 .. .. 0.3 0.4 99.2 99.4 .. .. Zimbabwe 9.4 9.7 53.3 43.0 .. .. .. 0.2 46.7 56.8 .. .. World 11,787.7 s 17,372.6 s 38.1 w 39.8 w 13.8 w 19.7 w 10.3 w 6.4 w 18.1 w 16.0 w 17.1 w 15.8 w Low income 518.1 1,026.4 40.9 47.1 15.0 19.9 6.6 7.0 34.8 23.4 1.2 1.9 Middle income 3,842.9 6,258.9 35.8 42.7 19.5 20.6 14.7 7.6 21.6 21.5 7.4 6.7 Lower middle income 1,828.6 3,903.4 41.5 50.0 10.4 13.3 16.0 8.0 27.5 23.4 5.0 4.3 Upper middle income 2,014.3 2,355.4 30.6 30.6 27.6 32.6 13.5 6.9 16.2 18.3 9.6 10.7 Low & middle income 4,361.0 7,285.3 36.4 43.3 18.9 20.5 13.7 7.5 23.1 21.8 6.7 6.0 East Asia & Pacific 785.8 2,659.5 61.3 69.1 3.5 7.6 12.7 4.9 21.7 15.6 0.2 1.9 Europe & Central Asia 2,213.4 1,964.1 33.0 28.6 28.4 33.8 12.1 3.2 13.3 17.4 12.0 16.6 Latin America & Carib. 608.5 1,088.3 3.8 4.7 9.7 19.6 18.8 14.0 63.5 56.3 2.0 2.6 Middle East & N. Africa 190.0 449.0 1.2 2.9 38.4 60.2 48.2 29.8 12.2 7.0 .. .. South Asia 338.9 785.3 55.8 58.7 8.6 16.0 6.1 7.1 27.6 14.9 1.9 2.5 Sub-Saharan Africa 224.4 339.0 72.1 68.2 3.3 4.9 2.2 2.7 18.4 19.5 3.8 3.9 High income 7,426.7 10,087.3 39.1 37.3 10.8 19.1 8.4 5.6 15.2 11.9 23.2 22.8 Europe EMU 1,667.3 2,227.2 34.5 27.0 8.6 18.3 9.4 4.9 11.1 10.0 35.4 34.0 a. Shares may not sum to 100 percent because some sources of generated electricty are not shown. 160 2007 World Development Indicators 3.9 ENVIRONMENT Sources of electricity About the data Use of energy is important in improving people's stan- IEA data for countries that are not members of the in coverage or methodology as more detailed energy dard of living. But electricity generation also can dam- Organisation for Economic Co-operation and Devel- accounts have become available in recent years. age the environment. Whether such damage occurs opment (OECD) are based on national energy data Breaks in series are therefore unavoidable. depends largely on how electricity is generated. For adjusted to conform to annual questionnaires com- Definitions example, burning coal releases twice as much carbon pleted by OECD member governments. In addition, dioxide--a major contributor to global warming--as estimates are sometimes made to complete major · Electricity production is measured at the termi- does burning an equivalent amount of natural gas aggregates from which key data are missing, and nals of all alternator sets in a station. In addition to (see About the data for table 3.8). Nuclear energy adjustments are made to compensate for differ- hydropower, coal, oil, gas, and nuclear power genera- does not generate carbon dioxide emissions, but it ences in definitions. The IEA makes these estimates tion, it covers generation by geothermal, solar, wind, produces other dangerous waste products. The table in consultation with national statistical offices, oil and tide and wave energy as well as that from com- provides information on electricity production by companies, electricity utilities, and national energy bustible renewables and waste. Production includes source. Shares may not sum to 100 percent because experts. It occasionally revises its time series to the output of electricity plants designed to produce some sources of generated electricity (such as wind, reflect political changes. Since 1990, for example, it electricity only, as well as that of combined heat and solar, and geothermal) are not shown. has constructed energy statistics for countries of the power plants. · Sources of electricity refer to the The International Energy Agency (IEA) compiles former Soviet Union. In addition, energy statistics for inputs used to generate electricity: coal, gas, oil, data on energy inputs used to generate electricity. other countries have undergone continuous changes hydropower, and nuclear power. · Coal refers to all coal and brown coal, both primary (including hard Coal is still the major source of electricity in all income groups, coal and lignite-brown coal) and derived fuels (includ- with low-income countries increasingly relying on this source 3.9a ing patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in 1990 2004 this category. · Gas refers to natural gas but not Other 1.5% Other 0.7% Nuclear power 1.2% Nuclear power 1.9% to natural gas liquids. · Oil refers to crude oil and petroleum products. · Hydropower refers to electric- Hydropower ity produced by hydroelectric power plants.· Nuclear 23% Hydropower Coal Coal power refers to electricity produced by nuclear power 35% 41% Low-income 47% countries plants. Oil 7% Gas Oil Gas 20% 7% 15% Other 1% Other 0.9% Nuclear power 7% Nuclear power 7% Hydropower Coal Hydropower Coal 22% 36% Middle-income 22% 43% countries Oil Oil 8% 15% Gas Gas 20% 21% Other 3% Other 3% Nuclear Nuclear power power 23% Coal Coal 23% 37% 39% High-income countries Data sources Hydropower Hydropower 15% 12% Data on electricity production are from the IEA's Gas Oil Gas Oil 11% 6% 19% electronic files and its annual publications Energy 8% Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Note: Components may not sum to 100 percent because of rounding. Source: Table 3.9. Balances of OECD Countries. 2007 World Development Indicators 161 3.10 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 2005 1990 2005 1990­2005 1990 2005 1990 2005 1990 2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. .. Albania 1.2 1.4 36 45 1.0 .. .. .. .. 99 99 .. 84 Algeria 13.2 20.8 52 63 3.0 8 10 14 15 99 99 77 82 Angola 3.9 8.5 37 53 5.2 15 17 40 33 61 56 18 16 Argentina 28.3 34.9 87 90 1.4 39 39 37 36 86 92 45 83 Armenia 2.4 1.9 68 64 ­1.4 33 37 49 57 96 96 .. 61 Australia 14.6 17.9 85 88 1.4 60 61 25 24 100 100 100 100 Austria 5.1 5.4 66 66 0.4 27 27 41 42 100 100 100 100 Azerbaijan 3.8 4.3 54 52 0.7 24 22 45 43 .. 73 .. 36 Bangladesh 20.6 35.6 20 25 3.7 9 13 32 35 55 51 12 35 Belarus 6.8 7.1 66 72 0.3 16 18 24 25 .. 93 .. 61 Belgium 9.6 10.2 96 97 0.4 10 10 10 10 100 100 100 100 Benin 1.8 3.4 35 40 4.3 .. .. .. .. 32 59 2 11 Bolivia 3.7 5.9 56 64 3.1 25 31 29 26 49 60 14 22 Bosnia and Herzegovina 1.7 1.8 39 46 1.0 .. .. .. .. 99 99 .. 92 Botswana 0.6 1.0 42 57 3.5 .. .. .. .. 61 57 21 25 Brazil 111.7 157.0 75 84 2.3 34 37 13 12 82 83 37 37 Bulgaria 5.8 5.4 66 70 ­0.5 14 14 21 20 100 100 96 96 Burkina Faso 1.2 2.4 14 18 4.9 .. .. 50 38 32 42 3 6 Burundi 0.4 0.8 6 10 4.9 .. .. .. .. 42 47 44 35 Cambodia 1.2 2.8 13 20 5.6 6 10 48 49 .. 53 .. 8 Cameroon 4.7 8.9 41 55 4.3 14 20 20 20 59 58 40 43 Canada 21.3 25.9 77 80 1.3 40 44 18 21 100 100 99 99 Central African Republic 1.1 1.5 37 38 2.2 .. .. .. .. 34 47 17 12 Chad 1.3 2.5 21 25 4.6 .. .. 38 36 28 24 2 4 Chile 11.0 14.3 83 88 1.8 35 35 42 40 91 95 52 62 China 311.0 527.0 27 40 3.6 13 18 3 3 64 69 7 28 Hong Kong, China 5.7 6.9 100 100 1.4 100 100 100 100 .. .. .. .. Colombia 24.0 33.2 69 73 2.1 30 36 20 23 95 96 52 54 Congo, Dem. Rep. 10.5 18.5 28 32 3.7 15 17 35 33 53 42 1 25 Congo, Rep. 1.3 2.4 54 60 4.0 28 29 52 49 .. 28 .. 25 Costa Rica 1.6 2.7 51 62 3.7 24 28 47 46 .. 89 97 97 Côte d'Ivoire 5.0 8.2 40 45 3.3 17 20 42 44 37 46 10 29 Croatia 2.6 2.5 54 57 0.0 .. .. .. .. 100 100 100 100 Cuba 7.7 8.5 73 76 0.7 20 19 27 26 99 99 95 95 Czech Republic 7.8 7.5 75 74 ­0.3 12 11 16 16 99 99 97 97 Denmark 4.4 4.6 85 86 0.4 26 20 31 23 100 100 100 100 Dominican Republic 3.9 5.9 55 67 2.9 21 23 39 34 60 81 43 73 Ecuador 5.7 8.3 55 63 2.5 26 29 28 29 77 94 45 82 Egypt, Arab Rep. 24.2 31.7 44 43 1.8 22 20 37 35 70 86 42 58 El Salvador 2.5 4.1 49 60 3.4 19 22 39 37 70 77 33 39 Eritrea 0.5 0.9 16 19 4.0 .. .. .. .. 44 32 0 3 Estonia 1.1 0.9 71 69 ­1.2 .. .. .. .. 97 97 96 96 Ethiopia 6.4 11.4 13 16 3.9 3 4 28 25 13 44 2 7 Finland 3.1 3.2 61 61 0.3 17 21 28 34 100 100 100 100 France 42.0 46.7 74 77 0.7 23 22 22 21 .. .. .. .. Gabon 0.7 1.2 69 84 3.8 .. .. .. .. .. 37 .. 30 Gambia, The 0.4 0.8 38 54 5.7 .. .. .. .. .. 72 .. 46 Georgia 3.0 2.3 55 52 ­1.7 22 23 41 45 99 96 94 91 Germany 58.3 62.0 73 75 0.4 8 8 6 5 100 100 100 100 Ghana 5.6 10.6 37 48 4.2 12 16 21 19 23 27 10 11 Greece 6.0 6.6 59 59 0.6 30 29 51 49 .. .. .. .. Guatemala 3.7 5.9 41 47 3.3 .. .. 22 17 73 90 47 82 Guinea 1.7 3.1 28 33 3.8 14 15 51 46 27 31 10 11 Guinea-Bissau 0.3 0.5 28 30 3.3 .. .. .. .. .. 57 .. 23 Haiti 2.0 3.3 30 39 3.3 17 25 56 64 25 57 23 14 162 2007 World Development Indicators 3.10 ENVIRONMENT 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 2005 1990 2005 1990­2005 1990 2005 1990 2005 1990 2004 1990 2004 Honduras 2.0 3.4 40 47 3.6 .. .. 29 28 77 87 31 54 Hungary 6.8 6.7 66 66 ­0.2 19 17 29 25 100 100 .. 85 India 216.6 314.1 26 29 2.5 10 12 6 6 45 59 3 22 Indonesia 54.5 106.1 31 48 4.6 9 12 14 12 65 73 37 40 Iran, Islamic Rep. 30.6 45.7 56 67 2.7 23 23 21 16 86 .. 78 .. Iraq 12.9 .. 70 .. .. 26 .. 32 .. 95 .. 48 .. Ireland 2.0 2.5 57 61 1.5 26 25 46 41 .. .. .. .. Israel 4.2 6.3 90 92 2.6 43 44 48 47 100 100 .. .. Italy 37.8 39.6 67 68 0.2 19 17 9 8 .. .. .. .. Jamaica 1.2 1.4 49 53 1.2 .. .. .. .. 86 91 64 69 Japan 78.0 84.1 63 66 0.5 46 48 42 42 100 100 100 100 Jordan 2.3 4.5 72 82 4.1 27 24 37 29 97 94 82 87 Kazakhstan 9.2 8.7 56 57 ­0.7 7 8 12 13 87 87 52 52 Kenya 4.3 7.1 18 21 3.4 6 8 32 39 48 46 37 41 Korea, Dem. Rep. 11.5 13.9 58 62 1.3 16 20 22 24 .. 58 .. 60 Korea, Rep. 31.6 39.0 74 81 1.4 51 51 33 25 .. .. .. .. Kuwait 2.1 2.5 98 98 2.9 65 71 67 73 .. .. .. .. Kyrgyz Republic 1.7 1.8 38 36 0.6 .. .. 38 43 75 75 51 51 Lao PDR 0.6 1.2 15 21 4.4 .. .. .. .. .. 67 .. 20 Latvia 1.9 1.6 69 68 ­1.2 .. .. .. .. .. 82 .. 71 Lebanon 2.3 3.1 83 87 2.0 47 50 57 57 100 100 .. 87 Lesotho 0.3 0.3 17 19 1.4 .. .. .. .. 61 61 32 32 Liberia 1.0 1.9 45 58 5.7 .. .. 55 49 59 49 24 7 Libya 3.4 5.0 79 85 2.5 49 55 44 42 97 97 96 96 Lithuania 2.5 2.3 68 67 ­0.7 .. .. .. .. .. .. .. .. Macedonia, FYR 1.1 1.4 58 69 1.6 .. .. .. .. .. .. .. .. Madagascar 2.8 5.0 24 27 3.8 8 9 33 32 27 48 10 26 Malawi 1.1 2.2 12 17 4.8 .. .. .. .. 64 62 45 61 Malaysia 8.9 17.1 50 67 4.5 6 6 13 8 95 95 .. 93 Mali 2.1 4.1 23 31 4.7 8 10 36 33 50 59 32 39 Mauritania 0.8 1.2 40 40 2.9 .. .. .. .. 42 49 22 8 Mauritius 0.5 0.5 44 42 0.9 .. .. .. .. 95 95 .. 94 Mexico 60.3 78.3 73 76 1.8 32 35 25 25 75 91 13 41 Moldova 2.0 2.0 47 47 ­0.3 .. .. .. .. .. 86 .. 52 Mongolia 1.2 1.4 57 57 1.2 .. .. 48 60 .. 75 .. 37 Morocco 11.6 17.7 48 59 2.7 16 16 23 18 87 88 27 52 Mozambique 2.8 6.8 21 35 6.0 6 7 27 19 49 53 12 19 Myanmar 10.1 15.5 25 31 2.9 7 8 29 27 48 88 16 72 Namibia 0.4 0.7 28 35 4.2 .. .. .. .. 70 50 8 13 Nepal 1.7 4.3 9 16 6.4 .. .. 23 19 48 62 7 30 Netherlands 10.3 13.1 69 80 1.6 14 14 10 9 100 100 100 100 New Zealand 2.9 3.5 85 86 1.2 25 28 30 33 .. .. 88 .. Nicaragua 2.1 3.0 53 59 2.6 19 23 35 38 64 56 24 34 Niger 1.3 2.3 15 17 4.0 .. .. 33 36 35 43 2 4 Nigeria 31.7 63.4 35 48 4.7 11 14 15 17 51 53 33 36 Norway 3.1 3.6 72 77 1.1 .. .. 22 22 100 100 100 100 Oman 1.2 1.8 65 72 2.7 .. .. .. .. 97 97 61 .. Pakistan 33.0 54.4 31 35 3.4 16 18 22 21 82 92 17 41 Panama 1.3 2.3 54 71 3.9 35 38 65 53 89 89 51 51 Papua New Guinea 0.5 0.8 13 13 2.6 .. .. .. .. 67 67 41 41 Paraguay 2.1 3.5 49 59 3.5 22 31 45 54 72 94 45 61 Peru 15.0 20.3 69 73 2.0 27 26 39 35 69 74 15 32 Philippines 29.8 52.1 49 63 3.8 14 14 27 21 66 80 48 59 Poland 23.4 23.7 61 62 0.1 4 4 7 7 .. .. .. .. Portugal 4.7 6.1 48 58 1.7 37 39 54 45 .. .. .. .. Puerto Rico 2.6 3.8 72 98 2.6 44 67 60 68 .. .. .. .. 2007 World Development Indicators 163 3.10 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 2005 1990 2005 1990­2005 1990 2005 1990 2005 1990 2004 1990 2004 Romania 12.6 11.6 54 54 ­0.5 8 9 14 17 .. 89 .. .. Russian Federation 108.8 104.5 73 73 ­0.3 18 19 8 10 93 93 70 70 Rwanda 0.4 1.7 5 19 12.4 .. .. 57 45 49 56 36 38 Saudi Arabia 12.5 18.7 77 81 2.7 30 36 19 22 100 100 .. .. Senegal 3.1 4.8 39 42 3.0 17 19 44 45 53 79 19 34 Serbia and Montenegro 5.4 4.2 51 52 ­2.1 11 14 22 26 97 97 77 77 Sierra Leone 1.2 2.2 30 41 4.0 .. .. 43 36 .. 53 .. 30 Singapore 3.0 4.3 100 100 2.4 99 100 99 100 100 100 .. .. Slovak Republic 3.0 3.0 57 56 0.1 .. .. .. .. 100 100 98 98 Slovenia 1.0 1.0 50 51 0.1 .. .. .. .. .. .. .. .. Somalia 2.0 2.9 30 35 2.7 14 16 48 46 .. 48 .. 14 South Africa 18.3 27.8 52 59 2.9 25 30 10 12 85 79 53 46 Spain 29.3 33.3 75 77 0.8 22 24 15 17 100 100 100 100 Sri Lanka 2.9 3.0 17 15 0.0 .. .. .. .. 89 98 64 89 Sudan 6.9 14.8 27 41 5.2 9 12 34 31 53 50 26 24 Swaziland 0.2 0.3 23 24 3.1 .. .. .. .. .. 59 .. 44 Sweden 7.1 7.6 83 84 0.4 17 19 21 22 100 100 100 100 Switzerland 4.6 5.6 68 75 1.2 14 15 20 20 100 100 100 100 Syrian Arab Republic 6.3 9.6 49 51 2.9 25 25 25 26 97 99 50 81 Tajikistan 1.7 1.6 32 25 ­0.3 .. .. .. .. .. 70 .. 45 Tanzania 5.0 9.3 19 24 4.2 5 7 27 29 52 53 45 43 Thailand 16.1 20.7 29 32 1.7 11 10 37 32 95 98 74 99 Togo 1.2 2.5 30 40 5.1 16 22 52 54 71 71 24 15 Trinidad and Tobago 0.1 0.2 9 12 2.9 .. .. .. .. 100 100 100 100 Tunisia 4.9 6.5 60 65 2.0 .. .. .. .. 95 96 47 65 Turkey 33.2 48.5 59 67 2.6 22 26 20 20 96 96 70 72 Turkmenistan 1.7 2.2 45 46 1.9 .. .. .. .. .. 77 .. 50 Uganda 2.0 3.6 11 13 4.1 4 5 38 36 54 54 41 41 Ukraine 34.7 31.9 67 68 ­0.7 12 13 7 8 98 98 92 93 United Arab Emirates 1.4 3.5 79 77 6.3 27 29 34 38 98 98 95 95 United Kingdom 51.1 54.0 89 90 0.4 26 26 15 16 .. .. .. .. United States 188.0 239.5 75 81 1.6 41 43 9 8 100 100 100 100 Uruguay 2.8 3.2 89 92 1.0 41 36 46 40 100 100 99 99 Uzbekistan 8.2 9.6 40 37 1.0 10 8 25 23 69 78 39 61 Venezuela, RB 16.6 24.8 84 93 2.7 34 37 17 12 .. 71 .. 48 Vietnam 13.4 21.9 20 26 3.3 13 13 30 23 58 92 30 50 West Bank and Gaza 1.3 2.6 68 72 4.6 .. .. .. .. .. 78 .. 61 Yemen, Rep. 2.5 5.7 21 27 5.4 5 9 26 31 82 86 19 28 Zambia 3.3 4.1 39 35 1.3 9 11 23 31 63 59 31 52 Zimbabwe 3.1 4.7 29 36 2.8 10 12 34 32 69 63 42 47 World 2,253.0 s 3,128.3 s 43 w 49 w 2.2 w 18 w 20 w 17 w 16 w 77 w 80 w 23 w 38 w Low income 442.0 704.7 25 30 3.2 10 12 17 18 50 61 12 28 Middle income 1,160.1 1,657.4 44 54 2.4 17 20 15 14 79 81 25 42 Lower middle income 798.0 1,225.8 38 50 2.9 16 19 14 12 75 77 22 39 Upper middle income 362.0 431.6 68 72 1.2 .. .. 18 19 89 91 58 66 Low & middle income 1,602.1 2,362.1 37 44 2.6 14 17 16 15 71 75 19 35 East Asia & Pacific 459.7 781.5 29 41 3.6 .. .. 9 8 65 72 15 36 Europe & Central Asia 294.0 300.5 63 64 0.1 15 16 13 15 94 93 72 71 Latin America & Carib. 310.1 425.4 71 77 2.1 32 34 24 22 81 86 36 49 Middle East & N. Africa 117.1 174.7 52 57 2.7 20 20 27 25 87 92 52 58 South Asia 277.7 418.4 25 28 2.8 10 12 10 11 50 63 6 27 Sub-Saharan Africa 143.5 261.7 28 35 4.1 .. .. 26 26 52 53 24 28 High income 650.9 766.2 74 78 1.1 .. .. 20 19 100 100 100 100 Europe EMU 209.5 230.1 71 73 0.6 18 18 15 15 .. .. .. .. 164 2007 World Development Indicators 3.10 ENVIRONMENT Urbanization About the data There is no consistent and universally accepted stan- Estimates of the world's urban population would effectively prevent human, animal, and insect con- dard for making the distinction between urban and change significantly if China, India, and a few other tact with excreta. The rural population with access rural. The wide variety of situations across countries populous nations were to change their definition of is included to allow comparison of rural and urban makes it difficult to adopt uniform criteria for distin- urban centers. According to China's State Statis- access. This definition and the definition of urban guishing urban and rural areas. Most countries have tical Bureau, by the end of 1996 urban residents areas vary, however, so comparisons between coun- adopted an urban classification related to the size or accounted for about 43 percent of China's popula- tries can be misleading. characteristics of settlements. Other countries have tion, while in 1994 only 20 percent of the population Definitions defined urban areas based on the presence of cer- was considered urban. In addition to the continuous tain infrastructure and services. And some countries migration of people from rural to urban areas, one of · Urban population is the midyear population of have designated urban areas based on administrative the main reasons for this shift was the rapid growth areas defined as urban in each country and reported arrangements. The population of a city or metropolitan in the hundreds of towns reclassified as cities in to the United Nations (see About the data). · Popula- area depends on the boundaries chosen. For example, recent years. Because the estimates in the table are tion in urban agglomerations of more than 1 million in 1990 Beijing, China, contained 2.3 million people in based on national definitions of what constitutes a is the percentage of a country's population living in 87 square kilometers of "inner city" and 5.4 million in city or metropolitan area, cross-country comparisons metropolitan areas that in 2005 had a population 158 square kilometers of "core city." The population should be made with caution. To estimate urban of more than 1 million. · Population in largest city of "inner city and inner suburban districts" was 6.3 populations, UN ratios of urban to total population is the percentage of a country's urban population million, and that of "inner city, inner and outer subur- were applied to the World Bank's estimates of total living in that country's largest metropolitan area. ban districts, and inner and outer counties" was 10.8 population (see table 2.1). · Access to improved sanitation facilities refers to million. (For most countries the last definition is used.) The urban population with access to improved sani- the percentage of the urban or rural population with For further discussion of urban-rural issues see box tation facilities is defined as people with access to access to at least adequate excreta disposal facili- 3.1a in About the data for table 3.1. at least adequate excreta disposal facilities that can ties (private or shared but not public) that can effec- tively prevent human, animal, and insect contact with excreta. Improved facilities range from simple but Population of the world's largest metropolitan protected pit latrines to flush toilets with a sewerage areas in 1000, 1900, 2000, and 2015 (millions) 3.10a connection. To be effective, facilities must be cor- 1000 1900 rectly constructed and properly maintained. City Population City Population Cordova 0.45 London 6.5 Kaifeng 0.40 New York 4.2 Constantinopole 0.30 Paris 3.3 Angkor 0.20 Berlin 2.7 Kyoto 0.18 Chicago 1.7 Cairo 0.14 Vienna 1.7 Baghdad 0.13 Tokyo 1.5 Nishapur 0.13 St. Petersburg 1.4 Hasa 0.11 Manchester 1.4 Anhivada 0.10 Philadelphia 1.4 2000 2015 City Population City Population Tokyo 34.5 Tokyo 35.5 Mexico City 18.1 Mumbai 21.9 New York­Newark 17.9 Mexico City 21.6 São Paulo 17.1 São Paulo 20.5 Data sources Mumbai 16.1 New York­Newark 19.9 Shanghai 13.2 Delhi 18.6 Data on urban population and the population in Kolkata 13.1 Shanghai 17.2 urban agglomerations and in the largest city are Delhi 12.4 Kolkata 17.0 from the United Nations Population Division's World Buenos Aires 11.9 Dhaka 16.8 Urbanization Prospects: The 2005 Revision. The Los Angeles­Long total population figures are World Bank estimates. Beach­Santa Ana 11.8 Jakarta 16.8 Data on access to sanitation in urban and rural Source: O'Meara 1999; United Nations Population Division, 2005, World Urbanization Prospects: The 2005 Revision. areas are from the World Health Organization. 2007 World Development Indicators 165 3.11 Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units People 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 88 b 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 .. 68 b .. 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 58 b 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 19 166 2007 World Development Indicators 3.11 ENVIRONMENT Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units People 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 .. .. .. 98 b .. 58 b .. 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 48 b .. .. .. 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 .. 28 b .. 88 98 b 80 66b 10 b 10 b 14 .. Papua New Guinea 1990 4.5b 6.5 .. .. .. .. .. 44 .. 8 .. .. Paraguay 2002 4.6 4.5 38 b .. 95b 98 b 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 .. 2007 World Development Indicators 167 3.11 Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units People 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 1992 6.1 .. .. .. 92 .. 42 .. .. .. .. .. 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 58 b 1 14b 13 1b Sudan 1993 5.8 6.0 .. .. .. .. 86b 58 b 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 .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 2000 3.7 .. 9b .. 98 b .. 74b .. 17b .. .. .. Tunisia 1994 8.0 .. .. .. 99 .. 71 89 b 6 10 b 15 12b Turkey 1990 5.0 .. .. .. .. .. 70 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 1991 4.9 4.0 b .. .. 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 .. .. 88 b 68 b 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. 168 2007 World Development Indicators 3.11 ENVIRONMENT Urban housing conditions About the data Definitions Urbanization can yield important social benefi ts, conditions thus requires an extensive set of indica- · Census year is the year in which the underlying improving access to public services and the job tors within a reasonable framework. data were collected. · Household size refers to the market. At the same time it also leads to signifi - There is a strong demand for quantitative indi- average number of people within a household. It is cant demands for services. Inadequate living quar- cators that can measure housing conditions on a calculated by dividing total population by the number ters and demand for housing and shelter are major regular basis to monitor progress. However, data of households in the country and in urban areas. concerns for policymakers. The unmet demand for deficiencies and lack of rigorous quantitative analy- · Overcrowding refers to the number of households affordable housing, along with urban poverty, has led sis hamper informed decision-making on desirable living in dwellings with two or more people per room to the emergence of slums in many poor countries. policies to improve housing conditions. The data as a percentage of the total number of households in Improving the shelter situation requires a better in the table are from housing and population cen- the country and in urban areas. · Durable dwelling understanding of the mechanisms governing hous- suses, collected using similar definitions. The table units refer to the number of housing units in struc- ing markets and the processes governing housing will incorporate household survey data in future edi- tures made of durable building materials (concrete, availability. That requires good data and adequate tions. The table focuses attention on urban areas, stone, cement, brick, asbestos, zinc, and stucco) policy-oriented analysis so that housing policy can be where housing conditions are typically most severe. expected to maintain their stability for 20 years or formulated in a global comparative perspective and Not all the compiled indicators are presented in the longer under local conditions with normal mainte- drawn from the lessons learned in other countries. table because of space limitations. Additional indica- nance and repair, taking into account location and Housing policies and outcomes affect such broad tors for more countries will be available in the World environmental hazards such as floods, mudslides, socioeconomic conditions as the infant mortality Bank's central database. and earthquakes as a percentage of total dwellings. rate, performance in school, household saving, pro- · Home ownership refers to the number of privately ductivity levels, capital formation, and government owned dwellings as a percentage of total dwellings budget deficits. A good understanding of housing or the number of households that own housing units as a percentage of total households. This category includes privately owned and owner-occupied units, depending on the definition used in the census data. Selected housing indicators for smaller economies 3.11a State- and community-owned units, rented, squat- ted, and rent-free units are not included. · Multiunit Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate dwellings refer to the number of multiunit dwellings, units such as apartments, flats, condominiums, barracks, People living Buildings Privately boarding houses, orphanages, retirement houses, in overcrowded with durable owned Unoccupied hostels, hotels, and collective dwellings, as a per- number of dwellingsa structure dwellings dwellings people % of total % of total % of total % of total % of total centage of total occupied dwellings. · Vacancy rate Antigua and Barbuda 2001 3.0 .. 99b 65b 3b 22 refers to the percentage of completed dwelling units Bahamas 1990 3.8 12 99 55 13 14 that 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. 2007 World Development Indicators 169 3.12 Traffic and congestion Motor Passenger Road Fuel Particulate matter vehicles cars density prices concentrations Urban-population- km. of road $ per liter weighted PM10 per 1,000 per kilometer per 1,000 per 100 sq. Super micrograms per people of road people km. of land gasoline Diesel fuel cubic meter 1990 2004a 1990 2004 a 1990 2004 a 2004a 2006 2006 1990 2004 Afghanistan 3 .. 3 .. 2 9 5 0.68 0.65 75 46 Albania 11 70 3 12 2 47 66 1.44 1.29 92 56 Algeria 55 .. 15 .. 26 .. 5 0.32 0.19 115 88 Angola 19 .. 3 .. 14 .. 4 0.50 0.36 142 91 Argentina 181 181 27 37 134 140 15 0.62 0.48 105 78 Armenia 5 .. 2 .. 1 .. 27 0.96 0.77 .. 69 Australia 530 .. 11 .. 450 .. 11 0.93 0.94 22 16 Austria 421 599 30 33 387 503 162 1.32 1.26 38 35 Azerbaijan 52 66 7 9 36 53 72 0.46 0.41 105 59 Bangladesh 1 1 0 1 0 0 184 0.79 0.45 223 140 Belarus 61 168 13 18 59 174 45 0.79 0.55 7 7 Belgium 423 529 30 37 385 468 498 1.63 1.34 32 25 Benin 3 .. 2 .. 2 .. 17 0.81 0.81 75 43 Bolivia 41 49 6 7 25 15 6 0.54 0.47 120 86 Bosnia and Herzegovina 114 .. 24 .. 101 .. 43 1.34 1.24 42 19 Botswana 18 105 3 8 10 42 4 0.78 0.74 119 69 Brazil 88 170 8 18 84 136 21 1.26 0.84 40 28 Bulgaria 163 360 39 63 146 314 40 1.05 1.08 111 55 Burkina Faso 4 .. 3 .. 2 .. 6 1.15 1.12 149 94 Burundi .. .. 3 .. 3 .. 48 1.20 1.22 56 39 Cambodia .. 30 0 31 .. 25 22 1.01 0.78 116 64 Cameroon 10 .. 3 .. 6 .. 11 1.14 1.07 119 64 Canada 605 577 20 34 468 561 15 0.84 0.78 25 19 Central African Republic 1 .. 0 .. 1 .. 4 1.37 1.27 61 48 Chad 2 .. 0 .. 1 .. 3 1.31 1.20 214 127 Chile 81 136 13 26 52 89 11 1.09 0.86 88 54 China 5 15 4 11 1 10 20 0.69 0.61 113b 72b Hong Kong, China 66 72 253 254 42 53 186 1.69 1.06 .. .. Colombia 39 51 13 19 21 43 10 0.98 0.57 38 23 Congo, Dem. Rep. .. .. 9 .. 17 .. 7 0.94 1.00 73 52 Congo, Rep. 18 .. 3 .. 12 .. 5 0.96 0.67 130 85 Costa Rica 87 198 7 24 55 146 69 0.98 0.67 45 39 Côte d'Ivoire 24 .. 6 .. 15 .. 25 1.20 1.06 94 38 Croatia .. 370 34 58 185 302 51 1.34 1.22 60 31 Cuba 37 .. 16 .. 18 .. 55 1.10 0.91 44 19 Czech Republic 246 391 46 31 228 358 165 1.30 1.29 73 23 Denmark 368 424 27 32 320 360 169 1.58 1.45 30 20 Dominican Republic 75 .. 48 .. 21 .. 26 1.03 0.75 44 30 Ecuador 35 55 8 17 31 32 16 0.47 0.39 38 25 Egypt, Arab Rep. 29 .. 33 .. 21 .. 9 0.30 0.12 221 135 El Salvador 33 .. 14 .. 17 .. 48 0.82 0.80 46 35 Eritrea 1 .. 1 .. 1 .. 4 1.90 0.81 122 85 Estonia 211 459 22 11 154 349 134 1.23 1.22 19 16 Ethiopia 1 2 2 4 1 1 4 0.93 0.62 134 76 Finland 441 515 29 34 386 446 26 1.55 1.26 24 19 France 494 597 32 38 405 495 173 1.48 1.33 18 14 Gabon 32 .. 4 .. 19 .. 4 0.64 0.39 9 6 Gambia, The 13 7 5 3 6 5 37 1.08 1.01 141 95 Georgia 107 63 27 16 89 50 29 0.86 0.89 .. 45 Germany 405 580 53 207 386 546 .. 1.55 1.38 27 19 Ghana 8 .. 4 .. 5 .. 21 0.86 0.84 39 35 Greece 248 476 22 46 171 368 89 1.16 1.19 69 41 Guatemala 21 57 16 45 11 52 13 0.78 0.64 63 67 Guinea 4 .. 1 .. 2 .. 18 0.79 0.82 105 71 Guinea-Bissau 7 .. 2 .. 4 .. 12 .. .. 117 78 Haiti 8 .. 14 .. 5 .. 15 0.88 0.60 70 42 170 2007 World Development Indicators 3.12 ENVIRONMENT Traffic and congestion Motor Passenger Road Fuel Particulate matter vehicles cars density prices concentrations Urban-population- km. of road $ per liter weighted PM10 per 1,000 per kilometer per 1,000 per 100 sq. Super micrograms per people of road people km. of land gasoline Diesel fuel cubic meter 1990 2004a 1990 2004 a 1990 2004 a 2004a 2006 2006 1990 2004 Honduras 22 61 10 28 5 52 12 0.89 0.73 45 47 Hungary 212 313 21 19 188 274 178 1.30 1.31 36 18 India 4 9 2 3 2 6 114 1.01 0.75 110 72 Indonesia 16 .. 10 .. 7 .. 20 0.57 0.44 139 102 Iran, Islamic Rep. 34 .. 14 .. 25 .. 11 0.09 0.03 86 58 Iraq 14 .. 6 .. 1 .. 10 .. .. 146 138 Ireland 270 447 10 11 227 382 140 1.34 1.35 26 19 Israel 210 288 74 112 174 234 81 1.47 1.27 71 38 Italy 529 610 99 73 476 590 165 1.56 1.49 42 27 Jamaica 52 .. 7 .. 43 135 194 0.82 0.75 58 42 Japan 469 586 52 63 283 441 323 1.09 0.90 43 31 Jordan 60 106 26 77 44 71 8 0.86 0.45 110 50 Kazakhstan 76 100 8 17 50 80 3 0.70 0.45 12 19 Kenya 12 18 5 10 10 9 11 1.12 0.98 66 39 Korea, Dem. Rep. .. .. .. .. .. .. 26 0.71 0.79 184 79 Korea, Rep. 79 302 60 145 48 218 102 1.65 1.33 82 38 Kuwait 474 422 165 181 368 349 32 0.22 0.21 82 108 Kyrgyz Republic 44 38 10 10 44 39 10 0.64 0.54 65 24 Lao PDR 9 .. 3 .. 6 .. 14 0.86 0.73 73 47 Latvia 135 348 6 12 106 297 112 1.20 1.15 40 16 Lebanon 321 .. 183 .. 300 .. 71 0.74 0.62 45 42 Lesotho 11 .. 4 .. 3 .. 20 0.89 0.88 85 54 Liberia 14 .. 4 .. 7 .. 11 0.79 0.85 59 44 Libya 165 .. 10 .. 96 .. 5 0.13 0.13 107 98 Lithuania 160 421 12 18 133 383 127 1.08 1.09 30 10 Macedonia, FYR 132 .. 30 .. 121 .. 34 1.23 1.09 38 20 Madagascar 6 .. 2 .. 4 .. 9 1.15 1.00 77 45 Malawi 4 .. 4 .. 2 .. 16 1.17 1.12 74 46 Malaysia 124 254 26 75 101 222 30 0.53 0.40 37 29 Mali 3 .. 2 .. 2 .. 2 1.22 1.04 264 165 Mauritania 10 .. 3 .. 7 .. 1 0.97 0.84 146 103 Mauritius 59 130 35 79 44 96 99 0.74 0.56 26 16 Mexico 119 211 41 93 82 142 18 0.74 0.52 69 39 Moldova 53 87 17 29 48 65 39 0.45 0.31 110 39 Mongolia 21 41 1 2 6 26 3 0.88 0.87 65 68 Morocco 37 45 15 23 28 45 13 1.22 0.87 32 20 Mozambique 4 .. 2 .. 3 .. 4 1.15 1.06 110 39 Myanmar 2 .. 3 .. 1 .. 4 0.66 0.75 116 69 Namibia 71 82 1 4 39 42 5 0.87 0.87 74 43 Nepal .. .. .. .. .. .. 12 0.94 0.73 67 39 Netherlands 405 427 58 58 368 429 372 1.70 1.32 45 34 New Zealand 524 701 19 31 436 592 35 0.98 0.70 16 15 Nicaragua 19 46 5 13 10 18 15 0.67 0.58 49 31 Niger 6 .. 4 .. 5 .. 1 1.14 1.11 216 144 Nigeria 30 .. 21 .. 12 17 21 0.51 0.66 179 67 Norway 458 527 22 26 380 424 30 1.80 1.66 24 12 Oman 130 .. 9 .. 83 .. 11 0.31 0.39 148 120 Pakistan 6 14 4 8 4 10 34 1.01 0.64 224 128 Panama 75 107 18 27 60 76 16 0.70 0.60 58 37 Papua New Guinea 27 .. 6 .. 7 .. 4 0.94 0.64 34 19 Paraguay 27 88 4 15 16 52 7 0.97 0.77 105 101 Peru 128 47 43 16 62 30 6 1.22 0.86 98 65 Philippines 10 34 4 13 7 9 67 0.76 0.67 55 32 Poland 168 354 18 33 138 294 138 1.30 1.30 59 38 Portugal 222 463 34 278 162 429 86 1.56 1.10 52 26 Puerto Rico 295 .. 79 .. 242 .. 289 0.65 0.78 27 20 2007 World Development Indicators 171 3.12 Traffic and congestion Motor Passenger Road Fuel Particulate matter vehicles cars density prices concentrations Urban-population- km. of road $ per liter weighted PM10 per 1,000 per kilometer per 1,000 per 100 sq. Super micrograms per people of road people km. of land gasoline Diesel fuel cubic meter 1990 2004a 1990 2004 a 1990 2004 a 2004a 2006 2006 1990 2004 Romania 72 185 11 20 56 149 86 1.26 1.24 36 16 Russian Federation 87 174 14 48 65 140 3 0.77 0.66 13 20 Rwanda 2 .. 1 .. 1 .. 57 1.11 1.08 49 37 Saudi Arabia 165 .. 19 .. 98 .. 8 0.16 0.07 163 133 Senegal 11 14 6 9 8 11 7 1.31 1.09 95 76 Serbia and Montenegro 137 199 31 102 133 181 44 1.48 1.31 28 13 Sierra Leone 10 4 4 2 7 2 16 0.98 0.98 91 56 Singapore 130 134 142 179 89 99 463 0.92 0.63 106 44 Slovak Republic 194 256 57 32 163 222 89 1.35 1.43 40 16 Slovenia 306 505 42 26 289 456 191 1.23 1.21 40 30 Somalia 2 .. 1 .. 1 .. 4 0.74 0.67 78 41 South Africa 139 144 26 24 97 92 30 0.85 0.84 34 26 Spain 360 558 43 34 309 455 133 1.15 1.10 41 33 Sri Lanka 21 34 4 .. 7 13 151 0.88 0.55 95 104 Sudan 9 .. 22 .. 8 .. 1 0.72 0.49 288 182 Swaziland 66 83 18 24 35 40 21 0.80 0.85 60 34 Sweden 464 504 29 11 426 457 104 1.46 1.44 15 12 Switzerland 491 559 46 58 449 516 178 1.27 1.36 37 24 Syrian Arab Republic 26 36 10 7 10 12 52 0.60 0.13 158 86 Tajikistan 3 .. 1 .. 0 .. 20 0.80 0.74 148 55 Tanzania 5 .. 2 .. 1 .. 9 1.04 0.99 57 28 Thailand 46 .. 36 .. 14 .. 11 0.70 0.65 88 73 Togo 24 .. 11 .. 16 .. 14 1.03 1.01 49 43 Trinidad and Tobago 117 .. 19 .. 98 .. 162 0.43 0.24 142 114 Tunisia 48 95 19 49 23 83 12 0.83 0.57 71 33 Turkey 50 108 8 18 34 75 55 1.88 1.62 75 48 Turkmenistan .. .. .. .. .. .. 5 .. .. 185 62 Uganda 2 5 .. 4 1 2 36 1.17 1.01 27 17 Ukraine 63 138 20 39 63 115 29 0.81 0.87 47 27 United Arab Emirates 121 .. 52 .. 97 .. 1 0.37 0.53 264 126 United Kingdom 400 510 64 79 341 451 160 1.63 1.73 25 15 United States 758 808 30 37 573 465 70 0.63 0.69 30 23 Uruguay 138 .. 45 .. 122 .. 34 1.23 0.94 237 134 Uzbekistan .. .. .. .. .. .. 19 0.85 0.54 79 76 Venezuela, RB 93 .. 25 .. 73 .. 11 0.03 0.02 22 7 Vietnam .. .. .. .. .. .. 72 0.67 0.53 124 65 West Bank and Gaza .. 35 .. 24 .. 27 83 1.29 0.98 .. .. Yemen, Rep. 34 .. 8 .. 14 .. 12 0.30 0.28 142 91 Zambia 14 .. 3 .. 8 .. 12 1.31 1.22 95 58 Zimbabwe 32 50 4 7 29 44 25 0.61 0.65 35 28 World 118 w 141 w .. .. 91 w 100 w 22 w 0.97 m 0.84 m 77 w 54 w Low income 5 8 .. .. 3 5 19 0.98 0.84 129 77 Middle income 37 69 .. .. 24 51 15 0.86 0.74 79 56 Lower middle income 22 38 .. .. 10 27 16 0.85 0.70 93 64 Upper middle income 119 186 .. .. 90 142 12 0.92 0.79 50 36 Low & middle income 25 47 .. .. 16 35 15 0.89 0.79 93 63 East Asia & Pacific 9 20 .. .. 4 14 18 0.53 0.40 112 72 Europe & Central Asia 97 170 .. .. 79 142 12 1.14 1.09 38 30 Latin America & Carib. 100 153 .. .. 72 108 17 0.82 0.67 59 38 Middle East & N. Africa 36 .. .. .. 24 .. 7 0.46 0.34 124 84 South Asia 4 10 .. .. 2 6 .. 0.91 0.65 131 84 Sub-Saharan Africa 21 .. .. .. 14 .. 6 1.03 0.98 114 64 High income 499 636 .. .. 390 457 41 1.33 1.24 38 28 Europe EMU 428 569 .. .. 379 522 139 1.52 1.29 33 24 a. Data are for 2004 or most recent year available. b. Includes Taiwan, China; Macao, China; and Hong Kong, China. 172 2007 World Development Indicators 3.12 ENVIRONMENT Traffic and congestion About the data Traffi c congestion in urban areas constrains eco- compiled data are of uneven quality. The coverage of pollution are emissions from traffic and industrial nomic productivity, damages people's health, and each indicator may differ across countries because sources, but nonanthropogenic sources such as dust degrades the quality of their lives. The particulate of differences in definitions. Comparability also is storms may also be a significant contributor for some air pollution emitted by motor vehicles--the dust and limited when time series data are reported. The IRF cities. Data on particulate matter for selected cities soot in exhaust--is proving to be far more damaging recently took steps to improve the quality of the data are in table 3.13. Estimates of economic damages to human health than was once believed. (For infor- published in its 2006 World Road Statistics. However, from death and illness due to particulate matter pol- mation on particulate matter and other air pollutants, this effort covers data only for 1999­2004. There- lution are shown in table 3.15. see table 3.13.) fore, the data shown in this table for 1990 and 2004 In recent years ownership of passenger cars has may not be comparable. Moreover, the data do not Definitions increased, and the expansion of economic activity capture the quality or age of vehicles. Road density has led to the transport by road of more goods and is a very rough indicator of accessibility and does · Motor vehicles include cars, buses, and freight services over greater distances (see table 5.9). not capture the condition, type, or width of roads. vehicles but not two-wheelers. Population figures These developments have increased demand for Thus comparisons over time and between countries refer to the midyear population in the year for which roads and vehicles, adding to urban congestion, air should be made with caution. data are available. Roads refer to motorways, pollution, health hazards, and traffic accidents and The data on fuel prices are compiled by the Ger- highways, main or national roads, and secondary injuries. Congestion, the most visible cost of expand- man Agency for Technical Cooperation (GTZ) from its or regional roads. A motorway is a road specially ing vehicle ownership, is reflected in the indicators in global network of regional offices and representa- designed and built for motor traffic that separates the table. Other relevant indicators--such as aver- tives and other sources, including the Allgemeiner the traffic flowing in opposite directions. · Passenger age vehicle speed in major cities or the cost of traffic Deutscher Automobile Club (for Europe) and a project cars refer to road motor vehicles, other than two- congestion, which takes a heavy toll on economic of the Latin American Energy Organization for Latin wheelers, intended for the carriage of passengers productivity--are not included because data are America. Local prices are converted to U.S. dollars and designed to seat no more than nine people incomplete or difficult to compare. using the exchange rate on the survey date listed (including the driver). · Road density refers to the The data in the table--except those on fuel prices in the international monetary table of the Financial ratio of the length of the country's total road network and particulate matter--are compiled by the Interna- Times. For countries with multiple exchange rates the to the country's land area. The road network includes tional Road Federation (IRF) through questionnaires market, parallel, or black market rate is used rather all roads in the country--motorways, highways, main sent to national organizations. The IRF uses a hierar- than the official exchange rate. or national roads, secondary or regional roads, and chy of sources to gather as much information as pos- Significant uncertainties exist around estimates other urban and rural roads. · Fuel prices refer to sible. The primary sources are national road associa- of particulate matter concentrations, and caution the pump prices of the most widely sold grade of tions. Where such an association lacks data or does should be used in interpreting them. But they do allow gasoline and of diesel fuel. Prices are converted not respond, other agencies are contacted, including for cross-country comparisons of the relative risk of from the local currency to U.S. dollars (see About the road directorates, ministries of transport or public particulate matter pollution that urban residents face. data). · Particulate matter concentrations refer to works, and central statistical offices. As a result, the Major sources of urban outdoor particulate matter fine suspended particulates less than 10 microns in diameter (PM10) that are capable of penetrating The 15 economies with the most expensive deep into the respiratory tract and causing significant gasoline--and the 15 with the cheapest, 2006 3.12a health damage. Data for countries and aggregates for regions and income groups are urban-population- Economy $ per liter Economy $ per liter weighted PM10 levels in residential areas of cities Eritrea 1.90 Venezuela, RB 0.03 with more than 100,000 residents. The estimates Turkey 1.88 Iran, Islamic Rep. 0.09 represent the average annual exposure level of the Norway 1.80 Libya 0.13 average urban resident to outdoor particulate mat- ter. The state of a country's technology and pollution Netherlands 1.70 Saudi Arabia 0.16 controls is an important determinant of particulate Hong Kong, China 1.69 Kuwait 0.22 matter concentrations. Korea, Rep. 1.65 Egypt, Arab Rep. 0.30 Data sources Belgium 1.63 Yemen, Rep. 0.30 United Kingdom 1.63 Oman 0.31 Data on vehicles and traffic are from the IRF's electronic files and its annual World Road Statis- Denmark 1.58 Algeria 0.32 tics. The data on fuel prices are from the GTZ's Italy 1.56 United Arab Emirates 0.37 electronic files. Data on particulate matter con- Portugal 1.56 Trinidad and Tobago 0.43 centrations are from Kiran Dev Pandey, David Finland 1.55 Azerbaijan 0.46 Wheeler, Bart Ostro, Uwe Deichmann, Kirk Ham- Germany 1.55 Ecuador 0.47 ilton, and Katie Bolt's "Ambient Particulate Mat- France 1.48 Angola 0.50 ter Concentrations in Residential and Pollution Serbia and Montenegro 1.48 Nigeria 0.51 Hotspot Areas of World Cities: New Estimates Based on the Global Model of Ambient Particu- Source: Table 3.12. lates (GMAPS)" (2006). 2007 World Development Indicators 173 3.13 Air pollution City City Particulate Sulfur Nitrogen About the data population matter dioxide dioxide Indoor and outdoor air pollution place a major bur- den on world health. More than half of the world's micrograms per micrograms per micrograms per population rely on dung, wood, crop waste, or coal to thousands cubic meter cubic meter cubic meter meet their basic energy needs. Cooking and heating 2005 2004 1995­2001 a 1995­2001 a with such solid fuels on open fires or stoves without Argentina Córdoba 1,423 58 .. 97 chimneys leads to indoor air pollution. Every year Melbourne 3,626 12 .. 30 indoor air pollution is responsible for the deaths of Perth 1,474 12 5 19 1.6 million people--one death every 20 seconds. Sydney 4,331 20 28 81 Austria Vienna 2,260 41 14 42 In many urban areas exposure to air pollution is the Belgium Brussels 1,012 28 20 48 main environmental threat to human health. Long- Brazil Rio de Janeiro 11,469 35 129 .. term exposure to high levels of soot and small par- São Paulo 18,333 40 43 83 ticles in the air contributes to a wide range of health Bulgaria Sofia 1,093 61 39 122 effects, including respiratory diseases, lung cancer, Canada Montréal 3,640 19 10 42 Toronto 5,312 22 17 43 and heart disease. Particulate pollution, on its own Vancouver 2,188 13 14 37 or in combination with sulfur dioxide, leads to an Chile Santiago 5,683 61 29 81 enormous burden of ill health. China Anshan 1,611 82 115 88 Emissions of sulfur dioxide and nitrogen oxides Beijing 10,717 89 90 122 lead to the deposition of acid rain and other acidic Changchun 3,046 74 21 64 Chengdu 4,065 86 77 74 compounds over long distances. Acid deposition Chongqing 6,363 123 340 70 changes the chemical balance of soils and can lead Dalian 3,073 50 61 100 to the leaching of trace minerals and nutrients criti- Guangzhou 8,425 63 57 136 cal to trees and plants. Guiyang 3,447 70 424 53 Where coal is the primary fuel for power plants, Harbin 3,695 77 23 30 Jinan 2,743 94 132 45 steel mills, industrial boilers, and domestic heat- Kunming 2,837 70 19 33 ing, the result is usually high levels of urban air Lanzhou 2,411 91 102 104 pollution--especially particulates and sometimes Liupanshui 1,149 59 102 .. sulfur dioxide--and, if the sulfur content of the coal Nanchang 2,188 78 69 29 is high, widespread acid deposition. Where coal is Pingxiang 905 67 75 .. Quingdao 2,817 68 190 64 not an important primary fuel or is used in plants Shanghai 14,503 73 53 73 with effective dust control, the worst emissions of air Shenyang 4,720 101 99 73 pollutants stem from the combustion of petroleum Taiyuan 2,794 88 211 55 products. Tianjin 7,040 125 82 50 The data on sulfur dioxide and nitrogen dioxide con- Wulumqi 2,025 57 60 70 Wuhan 7,093 79 40 43 centrations are based on reports from urban monitor- Zhengzhou 2,590 97 63 95 ing sites. Annual means (measured in micrograms per Zibo 2,982 74 198 43 cubic meter) are average concentrations observed at Colombia Bogotá 7,747 31 .. .. these sites. Coverage is not comprehensive because Croatia Zagreb 908 b 33 31 .. not all cities have monitoring systems. Cuba Havana 2,189 21 1 5 Czech Republic Prague 1,171 23 14 33 The data on concentrations of particulate matter Denmark Copenhagen 1,088 21 7 54 are estimates, for selected cities, of average annual Ecuador Guayaquil 2,387 23 15 .. concentrations in residential areas away from air pol- Quito 1,514 30 22 .. lution "hotspots," such as industrial districts and Egypt, Arab Rep. Cairo 11,128 169 69 .. transport corridors. The data are extracted from a Finland Helsinki 1,091 21 4 35 France Paris 9,820 11 14 57 complete set of estimates by the World Bank's Devel- Germany Berlin 3,389 22 18 26 opment Research Group and Environment Depart- Frankfurt 668 b 19 11 45 ment in a study of annual ambient concentrations Munich 1,263 20 8 53 of particulate matter in world cities with populations Ghana Accra 1,981 33 .. .. exceeding 100,000 (Pandey and others 2006). Greece Athens 3,230 43 34 64 Hungary Budapest 1,693 19 39 51 Pollutant concentrations are sensitive to local con- Iceland Reykjavik 164b 18 5 42 ditions, and even in the same city different moni- India Ahmadabad 5,120 83 30 21 toring sites may register different concentrations. Bangalore 6,462 45 .. .. Thus these data should be considered only a general 174 2007 World Development Indicators 3.13 ENVIRONMENT Air pollution City City Particulate Sulfur Nitrogen population matter dioxide dioxide indication of air quality in each city, and cross-coun- try comparisons should be made with caution. The current World Health Organization (WHO) air quality guidelines are annual mean concentrations of 20 micrograms per micrograms per micrograms per micrograms per cubic meter for particulate matter thousands cubic meter cubic meter cubic meter less than 10 microns in diameter (PM10) and 40 2005 2004 1995­2001 a 1995­2001 a micrograms for nitrogen dioxide and daily mean con- India Kolkata 14,277 128 49 34 centrations of 20 micrograms per cubic meter for Madras 6,916 37 15 17 sulfur dioxide. Delhi 15,048 150 24 41 Hyderabad 6,115 41 12 17 Definitions Kanpur 3,018 109 15 14 Lucknow 2,566 109 26 25 · City population is the number of residents of Mumbai 18,196 63 33 39 the city or metropolitan area as defined by national Nagpur 2,350 56 6 13 authorities and reported to the United Nations. Pune 4,409 47 .. .. · Particulate matter refers to fine suspended par- Indonesia Jakarta 13,215 104 .. .. Iran, Islamic Rep. Tehran 7,314 58 209 .. ticulates less than 10 microns in diameter (PM10) Ireland Dublin 1,037 19 20 .. that are capable of penetrating deep into the respi- Italy Milan 2,953 30 31 248 ratory tract and causing significant health damage. Rome 3,348 29 .. .. Data are extracted from a larger study of urban- Turin 1,660 44 .. .. population-weighted PM10 levels in residential areas Japan Osaka-Kobe 11,268 35 19 63 Tokyo 35,197 40 18 68 of cities with more than 100,000 residents. The Yokohama 3,366b 31 100 13 estimates represent the average annual exposure Kenya Nairobi 2,773 43 .. .. level of the average urban resident to outdoor par- Korea, Rep Pusan 3,554 44 60 51 ticulate matter. The state of a country's technology Seoul 9,645 41 44 60 and pollution controls is an important determinant Taegu 2,511 50 81 62 Malaysia Kuala Lumpur 1,405 29 24 .. of particulate matter concentrations. · Sulfur diox- Mexico Mexico City 19,411 51 74 130 ide is an air pollutant produced when fossil fuels Netherlands Amsterdam 1,147 34 10 58 containing sulfur are burned. It contributes to acid New Zealand Auckland 1,148 14 3 20 rain and can damage human health, particularly that Norway Oslo 802 14 8 43 of the young and the elderly. · Nitrogen dioxide is Philippines Manila 10,686 39 33 .. Poland Katowice 2,914b 39 83 79 a poisonous, pungent gas formed when nitric oxide Lódz 776 39 21 43 combines with hydrocarbons and sunlight, producing Warsaw 1,680 43 16 32 a photochemical reaction. These conditions occur in Portugal Lisbon 2,761 23 8 52 both natural and anthropogenic activities. Nitrogen Romania Bucharest 1,934 18 10 71 dioxide is emitted by bacteria, motor vehicles, indus- Russian Federation Moscow 10,654 21 109 .. Omsk 1,132 22 20 34 trial activities, nitrogenous fertilizers, combustion Singapore Singapore 4,326 44 20 30 of fuels and biomass, and aerobic decomposition of Slovak Republic Bratislava 456b 15 21 27 organic matter in soils and oceans. South Africa Cape Town 3,083 16 21 72 Durban 2,631 32 31 .. Data sources Johannesburg 3,254 33 19 31 Spain Barcelona 4,795 35 11 43 Data on city population are from the United Nations Madrid 5,608 30 24 66 Population Division. Data on particulate matter Sweden Stockholm 1,708 11 3 20 concentrations are from a recent World Bank Switzerland Zurich 1,144 23 11 39 study by Kiran D. Pandey, David Wheeler, Bart Thailand Bangkok 6,593 79 11 23 Ostro, Uwe Deichman, Kirk Hamilton, and Kath- Turkey Ankara 3,573 46 55 46 Istanbul 9,712 55 120 .. rine Bolt, "Ambient Particulate Matter Concentra- Ukraine Kiev 2,672 35 14 51 tion in Residential and Pollution Hotspot Areas of United Kingdom Birmingham 2,280 25 9 45 World Cities: New Estimates Based on the Global London 8,505 21 25 77 Model of Ambient Particulates (GMAPS)" (2006). Manchester 2,228 15 26 49 Data on sulfur dioxide and nitrogen dioxide con- United States Chicago 8,814 25 14 57 Los Angeles 12,298 34 9 74 centrations are from the WHO's Healthy Cities Air New York­Newark 18,718 21 26 79 Management Information System and the World Venezuela, RB Caracas 2,913 10 33 57 Resources Institute. a. Data are for the most recent year available. b. Data are for 2000. 2007 World Development Indicators 175 3.14 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of the Biological Kyoto Stockholm changeb layer control Seac diversity b Protocol CITES CCD Convention Afghanistan .. .. 2002 2004f 2004f .. 2002 1985f 1995f .. Albania 1993 .. 1995 1999 f 1999 f 2003f 1994f 2005f 2003f 2000 f 2004 Algeria 2001 .. 1994 1992f 1992f 1996 1995 2005f 1983f 1996 2006 Angola .. .. 2000 2000 f 2000 f 1994 1998 .. .. 1997 2006 Argentina 1992 .. 1994 1990 1990 1995 1994 2001 1981 1997 2005 Armenia .. .. 1994 1999 f 1999 f 2002f 1993d 2003f .. 1997 2003 Australia 1992 1994 1994 1987f 1989 1994 1993 .. 1976 2000 2004 Austria .. .. 1994 1987 1989 1995 1994 2002 1982f 1997f 2002 Azerbaijan 1998 .. 1995 1996f 1996f .. 2000 e 2000 f 1998f 1998f 2004f Bangladesh 1991 1990 1994 1990 f 1990 f 2001 1994 2001f 1981 1996 .. Belarus .. .. 2000 1986d 1988d 2006f 1993 .. 1995f 2001f 2004f Belgium .. .. 1996 1988 1988 1998 1996 2002 1983 1997f 2006 Benin 1993 .. 1994 1993f 1993f 1997 1994 2002f 1984f 1996 2004 Bolivia 1994 1988 1995 1994f 1994f 1995 1994 1999 1979 1996 2003 Bosnia and Herzegovina .. .. 2000 1992g 1992g 1994g 2002f .. 2002 2002f .. Botswana 1990 1991 1994 1991f 1991f 1994 1995 2003f 1977f 1996 2002f Brazil .. 1988 1994 1990 f 1990 f 1994 1994 2002 1975 1997 2004 Bulgaria .. 1994 1995 1990 f 1990 f 1996 1996 2002 1991f 2001f 2004 Burkina Faso 1993 .. 1994 1989 1989 2005 1993 2005f 1989 f 1996 2004 Burundi 1994 1989 1997 1997f 1997f .. 1997 2001f 1988f 1997 2005 Cambodia 1999 .. 1996 2001f 2001f .. 1995f 2002f 1997 1997 2006 Cameroon .. 1989 1995 1989 f 1989 f 1994 1994 2002f 1981f 1997 .. Canada 1990 1994 1994 1986 1988 2003 1992 2002 1975 1995 2001 Central African Republic .. .. 1995 1993f 1993f .. 1995 .. 1980 f 1996 .. Chad 1990 .. 1994 1989 f 1994 .. 1994 .. 1989 f 1996 2004 Chile .. 1993 1995 1990 1990 1997 1994 2002 1975 1997 2005 China 1994 1994 1994 1989 f 1991f 1996 1993 2002e 1981f 1997 2004 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 1998 1988 1995 1990 f 1993f 1994 2001f 1981 1999 .. Congo, Dem. Rep. .. 1990 1995 1994f 1994f 1995 1996 2005f 1976f 1997 2005f Congo, Rep. .. 1990 1997 1994f 1994f .. 1994 .. 1983f 1999 .. Costa Rica 1990 1992 1994 1991f 1991f 1994 1994 2002 1975 1998 .. Côte d'Ivoire 1994 1991 1995 1993f 1993f 1994 1994 .. 1994f 1997 2004 Croatia 2001 2000 1996 1991d 1991d 1994g 1996 .. 2000 f 2000 d .. Cuba .. .. 1994 1992f 1992f 1994 1994 2002 1990 f 1997 .. Czech Republic 1994 .. 1994 1993d 1993d 1996 1993e 2001e 993g 2000 f 2002 Denmark 1994 .. 1994 1988 1988 2004 1993 2002 1977 1995f 2003 Dominican Republic .. 1995 1999 1993f 1993f .. 1996 2002f 1986f 1997f .. Ecuador 1993 1995 1994 1990 f 1990 f .. 1993 2000 1975 1995 2004 Egypt, Arab Rep. 1992 1988 1995 1988 1988 1994 1994 2005f 1978 1995 2003 El Salvador 1994 1988 1996 1992 1992 .. 1994 1998 1987f 1997f .. Eritrea 1995 .. 1995 2005f 2005f .. 1996f 2005f 1994f 1996 2005f Estonia 1998 .. 1994 1996f 1996f 2005f 1994 2002 1992f .. .. Ethiopia 1994 1991 1994 1994f 1994f .. 1994 2005f 1989 f 1997 2003 Finland 1995 .. 1994 1986 1988 1996 1994d 2002 1976f 1995d 2002d France 1990 .. 1994 1987e 1988e 1996 1994 2002e 1978 1997 2004 e Gabon .. 1990 1998 1994f 1994f 1998 1997 .. 1989 f 1996f .. Gambia, The 1992 1989 1994 1990 f 1990 f 1994 1994 2001f 1977f 1996 2006 Georgia 1998 .. 1994 1996f 1996f 1996f 1994f 1999 f 1996f 1999 2006 Germany .. .. 1994 1988 1988 1994f 1993 2002 1976 1996 2002 Ghana 1992 1988 1995 1989 f 1989 1994 1994 2003f 1975 1996 2003 Greece .. .. 1994 1988 1988 1995 1994 2002 1992f 1997 2006 Guatemala 1994 1988 1996 1987f 1989 f 1997 1995 1999 1979 1998f .. Guinea 1994 1988 1994 1992f 1992f 1994 1993 2000 f 1981f 1997 .. Guinea-Bissau 1993 1991 1996 2002f 2002f 1994 1995 .. 1990 f 1995 .. Haiti 1999 .. 1996 2000 f 2000 f 1996 1996 2005f .. 1996 .. 176 2007 World Development Indicators 3.14 ENVIRONMENT Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of the Biological Kyoto Stockholm changeb layer control Seac diversity b Protocol CITES CCD Convention Honduras 1993 .. 1996 1993f 1993f 1994 1995 2000 1985f 1997 2005 Hungary 1995 .. 1994 1988f 1989 f 2002 1994 2002f 1985f 1999 f .. India 1993 1994 1994 1991f 1992f 1995 1994 2002f 1976 1996 2006 Indonesia 1993 1993 1994 1992f 1992 1994 1994 2004 1978f 1998 .. Iran, Islamic Rep. .. .. 1996 1990 f 1990 f .. 1996 2005f 1976 1997 2006 Iraq .. .. .. .. .. 1994 .. .. .. .. .. Ireland .. .. 1994 1988f 1988 1996 1996 2002 2002 1997 .. Israel .. .. 1996 1992f 1992 .. 1995 2004 1979 1996 .. Italy .. .. 1994 1988 1988 1995 1994 2002 1979 1997 .. Jamaica 1994 .. 1995 1993f 1993f 1994 1995 1999 f 1997f 1997f .. Japan .. .. 1994 1988f 1988 1996 1993d 2002d 1980 1998d 2002f Jordan 1991 .. 1994 1989 f 1989 f 1995f 1993 2003f 1978f 1996 2004 Kazakhstan .. .. 1995 1998f 1998f .. 1994 .. 2000 f 1997 .. Kenya 1994 1992 1994 1988f 1988 1994 1994 2005f 1978 1997 2004 Korea, Dem. Rep. .. .. 1995 1995f 1995f .. 1994 e 2005f .. 2003f 2002f Korea, Rep. .. .. 1994 1992 1992 1996 1994 2002 1993f 1999 2007 Kuwait .. .. 1995 1992f 1992f 1994 2002 2005f 2002 1997 2006 Kyrgyz Republic 1995 .. 2000 2000 f 2000 f .. 1996e 2003f .. 1997f 2006 Lao PDR 1995 .. 1995 1998f 1998f 1998 1996e 2003f 2004f 1996d 2006 Latvia .. .. 1995 1995f 1995f 2004f 1995 2002 1997f 2002f 2004 Lebanon .. .. 1995 1993f 1993f 1995 1994 .. .. 1996 2003 Lesotho 1989 .. 1995 1994f 1994f .. 1995 2000 f 2003 1995 2002 Liberia 2003 1996f 1996f .. 2000 2002f 2005f 1998f 2002f Libya .. .. 1999 1990 f 1990 f .. 2001 .. 2003f 1996 2005f Lithuania .. .. 1995 1995f 1995f 2003f 1996 2003 2001f 2003f 2006 Macedonia, FYR .. .. 1998 1994g 1994g 1994g 1997f 2004f 2000 f 2002f 2004 Madagascar 1988 1991 1999 1996f 1996f 2001 1996 2003f 1975 1997 .. Malawi 1994 .. 1994 1991f 1991f .. 1994 2001f 1982f 1996 .. Malaysia 1991 1988 1994 1989 f 1989 f 1996 1994 2002 1977f 1997 .. Mali .. 1989 1995 1994f 1994f 1994 1995 2002 1994f 1995 2003 Mauritania 1988 .. 1994 1994f 1994f 1996 1996 2005f 1998f 1996 2005 Mauritius 1990 .. 1994 1992f 1992f 1994 1992 2001f 1975 1996 2004 Mexico .. 1988 1994 1987 1988 1994 1993 2000 1991f 1995 2003 Moldova 2002 .. 1995 1996f 1996f .. 1995 2003f 2001f 1999 f 2004 Mongolia 1995 .. 1994 1996f 1996f 1996 1993 1999 f 1996f 1996 2004 Morocco .. 1988 1996 1995 1995 .. 1995 2002f 1975 1996 2004 Mozambique 1994 .. 1995 1994f 1994f 1997 1995 2005f 1981f 1997 2005 Myanmar .. 1989 1995 1993f 1993f 1996 1995 2003f 1997f 1997f 2004f Namibia 1992 .. 1995 1993f 1993f 1994 1997 2003f 1990 f 1997 2005f Nepal 1993 .. 1994 1994f 1994f 1998 1993 2005f 1975f 1996 .. Netherlands 1994 .. 1994 1988f 1988d 1996 1994 d 2002f 1984 1995d 2002d New Zealand 1994 .. 1994 1987 1988 1996 1993 2002 1989 f 2000 f 2004 Nicaragua 1994 .. 1996 1993f 1993f 2000 1995 1999 1977f 1998 .. Niger .. 1991 1995 1992f 1992f .. 1995 2004 1975 1996 2006 Nigeria 1990 1992 1994 1988f 1988f 1994 1994 2004f 1974 1997 2004 Norway .. 1994 1994 1986 1988 1996 1993 2002 1976 1996 2002 Oman .. .. 1995 1999 f 1999 f 1994 1995 2005f .. 1996f 2005 Pakistan 1994 1991 1994 1992f 1992f 1997 1994 2005f 1976f 1997 .. Panama 1990 .. 1995 1989 f 1989 1996 1995 1999 1978 1996 2003 Papua New Guinea 1992 1993 1994 1992f 1992f 1997 1993 2002 1975f 2000 f 2003 Paraguay .. .. 1994 1992f 1992f 1994 1994 1999 1976 1997 2004 Peru .. 1988 1994 1989 1993f .. 1993 2002 1975 1995 2005 Philippines 1989 1989 1994 1991f 1991 1994 1993 2003 1981 2000 2004 Poland 1993 1991 1994 1990 f 1990 f 1998 1996 2002 1989 2001f .. Portugal 1995 .. 1994 1988f 1988 1997 1993 2002e 1980 1996 2004 d Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 177 3.14 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of the Biological Kyoto Stockholm changeb layer control Seac diversity b Protocol CITES CCD Convention Romania 1995 .. 1994 1993f 1993f 1996 1994 2001 1994f 1998f 2004 Russian Federation 1999 1994 1995 1986d 1988d 1997 1995 2004 1992 2003f .. Rwanda 1991 .. 1998 2001f 2001f .. 1996 2004f 1980 f 1998 2002f Saudi Arabia .. .. 1995 1993f 1993f 1996 2001e 2005f 1996f 1997f .. Senegal 1984 1991 1995 1993f 1993 1994 1994 2001f 1977f 1995 2003 Serbia and Montenegro 2001 2001 g 2001 g 2001 g 2002 .. 2002 .. 2002 Sierra Leone 1994 .. 1995 2001f 2001f 1994 1994 e 2006f 1994f 1997 2003f Singapore 1993 1995 1997 1989 f 1989 f 1994 1995 2006f 1986f 1999 f 2005 Slovak Republic .. .. 1994 1993g 1993g 1996 1994 e 2002 1993 2001f 2002 Slovenia 1994 .. 1996 1992g 1992g 1995g 1996 2002 2000 f 2001f 2004 Somalia .. 2001f 2001f 1994 .. .. 1985f 2002f .. South Africa 1993 .. 1997 1990 f 1990 f 1997 1995 2002f 1975 1997 2002 Spain .. .. 1994 1988f 1988 1997 1995 2002 1986f 1996 2004 Sri Lanka 1994 1991 1994 1989 f 1989 f 1994 1994 2002f 1979 f 1998f .. Sudan .. .. 1994 1993f 1993f 1994 1995 2004f 1982 1995 2006 Swaziland 1997 1992f 1992f .. 1994 .. 1997f 1996 2006 Sweden .. .. 1994 1986 1988 1996 1993 2002 1974 1995 2002 Switzerland .. .. 1994 1987 1988 .. 1994 2006f 1974 1996 2003 Syrian Arab Republic 1999 .. 1996 1989 f 1989 f .. 1996 2006f 2003f 1997 2005 Tajikistan .. .. 1998 1996f 1998f .. 1997e .. .. 1997f .. Tanzania 1994 1988 1996 1993f 1993f 1994 1996 2002f 1979 1997 2004 Thailand .. .. 1995 1989 f 1989 .. 2004 2002 1983 2001f 2005 Togo 1991 .. 1995 1991f 1991 1994 1995d 2004f 1978 1995d 2004 Trinidad and Tobago .. .. 1994 1989 f 1989 f 1994 1996 1999 1984f 2000 f 2002f Tunisia 1994 1988 1994 1989 f 1989 f 1994 1993 2003f 1974 1995 2004 Turkey 1998 .. 2004 1991f 1991f .. 1997 .. 1996f 1998 .. Turkmenistan .. .. 1995 1993f 1993f .. 1996e 1999 .. 1996 .. Uganda 1994 1988 1994 1988f 1988 1994 1993 2002f 1991f 1997 2004f Ukraine 1999 .. 1997 1986d 1988d 1999 1995 2004 1999 f 2002f .. United Arab Emirates .. .. 1996 1989 f 1989 f .. 2000 2005f 1990 f 1998f 2002 United Kingdom 1995 1994 1994 1987 1988 1997f 1994 2002 1976 1996 2005 United States 1995 1995 1994 1986 1988 .. .. .. 1974 2000 .. Uruguay .. .. 1994 1989 f 1991f 1994 1993 2001 1975 1999 f 2004 Uzbekistan .. .. 1994 1993f 1993f .. 1995e 1999 1997f 1995 .. Venezuela .. .. 1995 1988f 1989 .. 1994 .. 1977 1998f 2005 Vietnam .. 1993 1995 1994f 1994f 2006f 1994 2002 1994f 1998f 2002 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1996 1992 1996 1996f 1996f 1994 1996 2004f 1997f 1997f 2004 Zambia 1994 .. 1994 1990 f 1990 f 1994 1993 2006f 1980 f 1996 2006 Zimbabwe 1987 .. 1994 1992f 1992f 1994 1994 .. 1981f 1997 .. a. Ratification of the treaty. b. Years shown refer to the year the treaty entered into force in that country. c. Convention became effective November 16, 1994. d. Acceptance. e. Approval. f. Accession. g. Succession. 178 2007 World Development Indicators 3.14 ENVIRONMENT Government commitment About the data Definitions National environmental strategies and participation · The Framework Convention on Climate Change · Environmental strategies or action plans provide in international treaties on environmental issues pro- aims to stabilize atmospheric concentrations of a comprehensive, cross-sectoral analysis of conser- vide some evidence of government commitment to greenhouse gases at levels that will prevent human vation and resource management issues to help inte- sound environmental management. But the signing activities from interfering dangerously with the grate environmental concerns with the development of these treaties does not always imply ratification, global climate. process. They include national conservation strate- nor does it guarantee that governments will comply · The Vienna Convention for the Protection of the gies, national environmental action plans, national with treaty obligations. Ozone Layer aims to protect human health and the environmental management strategies, and national In many countries efforts to halt environmental environment by promoting research on the effects sustainable development strategies. The year shown degradation have failed, primarily because govern- of changes in the ozone layer and on alternative for a country refers to the year in which a strategy ments have neglected to make this issue a pri- substances (such as substitutes for chlorofluoro- or action plan was adopted. · Biodiversity assess- ority, a refl ection of competing claims on scarce carbon) and technologies, monitoring the ozone ments, strategies, or action plans include biodiver- resources. To address this problem, many countries layer, and taking measures to control the activities sity profiles (see About the data). · Participation in are preparing national environmental strategies-- that produce adverse effects. treaties covers nine international treaties (see About some focusing narrowly on environmental issues, · The Montreal Protocol for Chlorofl uorocarbon the data). · Climate change refers to the Framework and others integrating environmental, economic, Control requires that countries help protect the Convention on Climate Change (signed in New York and social concerns. Among such initiatives are earth from excessive ultraviolet radiation by cut- in 1992). · Ozone layer refers to the Vienna Conven- conservation strategies and environmental action ting chlorofluorocarbon consumption by 20 percent tion for the Protection of the Ozone Layer (signed plans. Some countries have also prepared country over their 1986 level by 1994 and by 50 percent in 1985). · CFC control refers to the Montreal Pro- environmental profiles and biodiversity strategies over their 1986 level by 1999, with allowances tocol for Chlorofluorocarbon Control (formally, the and profiles. for increases in consumption by developing Protocol on Substances That Deplete the Ozone National conservation strategies--promoted by countries. Layer, signed in 1987). · Law of the Sea refers to the World Conservation Union (IUCN)--provide a · The United Nations Convention on the Law of the the United Nations Convention on the Law of the Sea comprehensive, cross-sectoral analysis of conser- Sea, which became effective in November 1994, (signed in Montego Bay, Jamaica, in 1982). · Bio- vation and resource management issues to help inte- establishes a comprehensive legal regime for seas logical diversity refers to the Convention on Biologi- grate environmental concerns with the development and oceans, establishes rules for environmental cal Diversity (signed at the Earth Summit in Rio de process. Such strategies discuss current and future standards and enforcement provisions, and devel- Janeiro in 1992). · Kyoto Protocol refers to the pro- needs, institutional capabilities, prevailing technical ops international rules and national legislation to tocol on climate change adopted at the third confer- conditions, and the status of natural resources in prevent and control marine pollution. ence of the parties to the United Nations Framework a country. · The Convention on Biological Diversity promotes Convention on Climate Change, held in Kyoto, Japan, National environmental action plans, supported conservation of biodiversity through scientifi c in December 1997. · CITES refers to the Conven- by the World Bank and other development agencies, and technological cooperation among countries, tion on International Trade in Endangered Species of describe a country's main environmental concerns, access to financial and genetic resources, and Wild Fauna and Flora, an agreement among govern- identify the principal causes of environmental prob- transfer of ecologically sound technologies. ments to ensure that the survival of wild animals and lems, and formulate policies and actions to deal with But 10 years after Rio the World Summit on Sus- plants is not threatened by uncontrolled exploitation. them. These plans are a continuing process in which tainable Development in Johannesburg recognized · CCD refers to the United Nations Convention to governments develop comprehensive environmental that many of the proposed actions have yet to mate- Combat Desertification, an international convention policies, recommend specific actions, and outline rialize. To help developing countries comply with dedicated to addressing the problems of land deg- the investment strategies, legislation, and institu- their obligations under these agreements, the Global radation in the world's drylands. Adopted in Paris on tional arrangements required to implement them. Environment Facility (GEF) was created to focus on June 17, 1994, it entered into force on December 26, Biodiversity profiles--prepared by the World Con- global improvement in biodiversity, climate change, 1996. · Stockholm Convention is an international servation Monitoring Centre and the IUCN--provide international waters, and ozone layer depletion. The legally binding instrument designed to protect human basic background on species diversity, protected UNEP, United Nations Development Programme, and health and the environment from persistent organic areas, major ecosystems and habitat types, and the World Bank manage the GEF according to the poli- pollutants. It was adopted on May 22, 2001, and legislative and administrative support. In an effort cies of its governing body of country representatives. entered into force May 17, 2004. to establish a scientific baseline for measuring prog- The World Bank is responsible for the GEF Trust Fund ress in biodiversity conservation, the United Nations and is chair of the GEF. Data sources Environment Programme (UNEP) coordinates global Data on environmental strategies and participa- biodiversity assessments. tion in international environmental treaties are To address global issues, many governments have from the Secretariat of the United Nations Frame- also signed international treaties and agreements launched in the wake of the 1972 United Nations work Convention on Climate Change, the Ozone Conference on Human Environment in Stockholm and Secretariat of the UNEP, the World Resources the 1992 United Nations Conference on Environment Institute, the UNEP, the Center for International and Development (the Earth Summit) in Rio de Earth Science Information Network, and the Janeiro, which produced Agenda 21--an array of United Nations Treaty Series. actions to address environmental challenges: 2007 World Development Indicators 179 3.15 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed savings expenditure depletion depletion forest dioxide emission net capital depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Afghanistan 23.8 7.6 16.2 .. 0.0 .. 1.0 0.1 0.7 .. Albania 15.6 10.7 4.9 2.8 1.9 0.0 0.0 0.2 0.2 5.4 Algeria 53.8 11.6 42.2 4.5 46.9 0.1 0.1 1.3 0.3 ­2.1 Angola 23.4 12.0 11.3 3.0 51.3 0.0 0.0 0.3 1.8 ­39.1 Argentina 24.8 12.1 12.8 4.1 10.4 0.4 0.0 0.6 1.6 3.9 Armenia 25.7 10.1 15.6 3.0 0.0 0.8 0.0 0.7 1.8 15.3 Australia 20.7a 15.0 5.8 4.8 3.1 3.1 0.0 0.4 0.1 3.9 Austria 24.6 14.3 10.3 5.6 0.2 0.0 0.0 0.2 0.3 15.2 Azerbaijan 34.5 11.6 22.9 3.5 60.4 0.0 0.0 2.8 1.1 ­37.9 Bangladesh 28.8 8.2 20.7 1.7 3.8 0.0 0.7 0.4 0.5 17.0 Belarus 30.9 11.0 19.9 5.5 2.4 0.0 0.0 1.8 .. 21.2b Belgium 23.4 15.4 8.0 3.0 0.0 0.0 0.0 0.2 0.2 10.6 Benin 10.7 8.7 2.0 2.4 0.0 0.0 0.9 0.3 0.4 2.7 Bolivia 20.4 10.0 10.4 6.3 33.7 1.0 0.0 0.7 1.3 ­20.0 Bosnia and Herzegovina ­1.9 10.3 ­12.2 .. 1.2 0.0 .. 1.5 0.1 .. Botswana 49.2 12.9 36.3 5.6 0.4 2.1 0.0 0.3 .. 39.1b Brazil 23.0 11.9 11.1 4.1 4.1 2.4 0.0 0.3 0.3 8.0 Bulgaria 17.0 11.1 5.9 3.5 1.2 1.0 0.0 1.3 1.4 4.7 Burkina Faso .. 8.3 .. 2.4 0.0 0.0 1.2 0.2 1.4 .. Burundi 8.7 6.7 2.0 3.9 0.0 0.1 11.3 0.2 0.1 ­5.8 Cambodia 15.0 8.8 6.2 1.8 0.0 0.0 0.3 0.1 0.4 7.3 Cameroon 18.1 9.9 8.2 3.2 13.8 0.0 0.0 0.2 0.8 ­3.4 Canada 21.7a 14.6 7.1 5.2 6.8 0.4 0.0 0.3 0.2 4.6 Central African Republic 14.0 8.1 5.9 1.6 0.0 0.0 0.0 0.1 0.4 6.9 Chad 25.4 10.8 14.6 1.4 73.3 0.0 0.0 0.0 1.1 ­58.4 Chile 19.1 13.4 5.6 3.9 0.4 13.6 0.0 0.4 0.6 ­5.5 China 50.4 10.2 40.2 2.0 6.8 0.8 0.0 1.4 1.4 31.8 Hong Kong, China 31.9 13.9 18.0 3.7 0.0 0.0 0.0 0.2 .. 21.6b Colombia 19.0 11.4 7.6 4.9 10.2 0.7 0.0 0.4 0.1 1.1 Congo, Dem. Rep. 14.1 7.0 7.1 0.9 4.3 1.6 0.0 0.2 0.6 1.2 Congo, Rep. 37.6 12.8 24.8 3.8 74.9 0.0 0.0 0.2 0.8 ­47.3 Costa Rica 19.2 6.1 13.0 4.0 0.0 0.0 0.2 0.2 0.3 16.3 Côte d'Ivoire 13.3 9.9 3.5 4.6 5.4 0.0 0.0 0.3 0.3 2.1 Croatia 24.0 12.9 11.1 4.1 1.6 0.0 0.2 0.5 0.4 12.5 Cuba .. .. .. 8.1 .. .. .. .. .. .. Czech Republic 25.6 13.6 12.0 4.2 0.7 0.0 0.0 0.8 0.1 14.6 Denmark 23.7 15.1 8.6 8.1 2.3 0.0 0.0 0.1 0.1 14.2 Dominican Republic 20.5 11.9 8.6 1.2 0.0 1.5 0.0 0.6 0.3 7.5 Ecuador 24.9 11.5 13.3 1.4 28.1 0.1 0.0 0.5 0.1 ­14.2 Egypt, Arab Rep. 21.4 9.8 11.6 4.4 17.5 0.2 0.2 1.2 0.9 ­4.0 El Salvador 11.2 11.1 0.0 2.8 0.0 0.0 0.5 0.3 0.2 1.8 Eritrea 10.3 7.6 2.8 2.7 0.0 0.0 1.2 0.5 0.7 3.2 Estonia 22.4 a 13.5 8.9 5.1 1.7 0.0 0.2 1.2 0.0 11.0 Ethiopia 17.3 7.1 10.2 3.0 0.0 0.0 0.0 0.5 0.3 12.3 Finland 22.6 16.1 6.5 6.0 0.0 0.0 0.0 0.2 0.1 12.2 France 18.0 12.5 5.5 5.2 0.0 0.0 0.0 0.1 0.0 10.5 Gabon 35.5 13.2 22.2 3.3 37.8 0.0 0.0 0.2 .. ­12.5b Gambia, The 15.6 8.2 7.4 2.0 0.0 0.0 0.6 0.5 0.8 7.5 Georgia 20.0 9.9 10.1 2.9 0.6 0.0 0.0 0.5 1.1 10.7 Germany 21.1 14.8 6.3 4.3 0.2 0.0 0.0 0.2 0.1 10.1 Ghana 22.2 8.7 13.6 2.8 0.1 0.5 1.8 0.5 0.1 13.3 Greece 14.9 8.7 6.2 3.1 0.3 0.1 0.0 0.3 0.9 7.7 Guatemala 14.8 10.9 3.8 1.6 1.5 0.0 0.7 0.2 0.5 2.5 Guinea 6.6 8.3 ­1.7 2.0 0.0 3.5 2.0 0.3 0.3 ­5.8 Guinea-Bissau 7.8 7.6 0.2 2.3 0.0 0.0 0.0 0.6 1.0 0.8 Haiti .. 8.6 .. 1.5 0.0 0.0 0.8 0.2 0.5 .. 180 2007 World Development Indicators 3.15 ENVIRONMENT Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed savings expenditure depletion depletion forest dioxide emission net capital depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Honduras 30.8 10.1 20.6 3.5 0.0 0.3 0.0 0.6 0.4 22.9 Hungary 16.5 13.6 2.9 5.8 0.8 0.0 0.0 0.4 0.1 7.4 India 32.2 9.2 23.0 4.0 4.8 1.0 0.6 1.3 0.7 18.6 Indonesia 24.7 10.2 14.4 0.9 13.7 2.0 0.0 0.8 1.1 ­2.3 Iran, Islamic Rep. 41.6 11.1 30.5 4.4 48.1 0.5 0.0 1.4 0.8 ­16.0 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 27.6a 10.9 16.7 4.8 0.0 0.1 0.0 0.2 0.1 21.1 Israel .. 17.6 .. 7.3 0.2 0.0 0.0 0.4 0.5 .. Italy 19.9 13.4 6.5 4.6 0.2 0.0 0.0 0.2 0.2 10.5 Jamaica 19.6a 7.6 12.0 5.0 0.0 1.7 0.0 0.8 0.3 14.3 Japan 26.2a 14.0 12.2 3.1 0.0 0.0 0.0 0.2 0.5 14.6 Jordan 6.5 10.4 ­3.9 5.6 0.5 0.1 0.0 1.0 0.7 ­0.5 Kazakhstan 28.5 12.5 16.0 4.4 53.6 1.7 0.0 2.5 0.3 ­37.6 Kenya 12.2 8.8 3.4 6.6 0.0 0.0 1.1 0.4 0.1 8.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 32.2 13.4 18.9 3.7 0.0 0.0 0.0 0.4 0.6 21.5 Kuwait .. 12.8 .. 6.9 52.1 0.0 0.0 0.7 1.3 .. Kyrgyz Republic 5.7 8.8 ­3.1 4.4 1.5 0.0 0.0 1.5 0.3 ­1.9 Lao PDR 1.7 9.5 ­7.8 1.4 0.0 0.0 0.0 0.4 0.7 ­7.5 Latvia 22.7 17.8 4.9 5.6 0.0 0.0 0.8 0.4 0.1 9.2 Lebanon ­0.9 12.3 ­13.2 2.4 0.0 0.0 0.0 0.6 1.1 ­12.5 Lesotho 21.7 7.6 14.1 6.7 0.0 0.0 1.4 0.0 0.3 19.1 Liberia 23.0 9.0 14.0 .. 0.0 0.0 6.1 0.6 0.5 .. Libya .. 12.4 .. .. 76.9 0.0 0.0 1.0 .. .. Lithuania 18.5 12.4 6.1 5.7 0.5 0.0 0.1 0.4 0.2 10.7 Macedonia, FYR 20.3 11.1 9.2 4.9 0.0 0.0 0.2 1.4 0.1 12.4 Madagascar 11.6 7.9 3.7 2.5 0.0 0.0 0.0 0.3 0.2 5.6 Malawi ­7.6 7.2 ­14.8 5.1 0.0 0.0 0.9 0.3 0.3 ­11.3 Malaysia 37.6 12.4 25.2 5.8 20.9 0.0 0.0 0.9 0.2 9.0 Mali 12.0 8.7 3.3 2.7 0.0 0.0 0.0 0.1 1.1 4.8 Mauritania ­5.2 8.6 ­13.8 3.2 0.0 28.1 0.6 1.0 2.5 ­42.7 Mauritius 19.8 11.8 8.0 3.9 0.0 0.0 0.0 0.4 .. 11.6b Mexico 21.5 12.5 9.0 5.3 9.6 0.2 0.0 0.4 0.4 3.6 Moldova 20.8 8.1 12.7 4.2 0.0 0.0 0.0 1.8 0.7 14.4 Mongolia 38.2 8.9 29.3 5.4 0.0 13.0 0.0 3.6 1.2 16.9 Morocco 29.1 10.3 18.8 6.0 0.0 0.3 0.0 0.5 0.1 23.9 Mozambique 4.7 8.6 ­3.9 1.8 0.1 0.0 0.5 0.2 0.3 ­3.2 Myanmar .. .. .. 0.8 .. .. .. .. .. .. Namibia 39.2 10.9 28.3 7.3 0.0 1.2 0.0 0.3 0.1 34.1 Nepal 31.0 7.8 23.2 2.6 0.0 0.0 2.5 0.3 0.1 23.0 Netherlands 26.5 15.0 11.5 4.9 1.6 0.0 0.0 0.2 0.6 14.1 New Zealand 23.0a 13.7 9.3 7.2 1.0 0.1 0.0 0.2 0.1 15.1 Nicaragua 12.9 9.6 3.3 2.9 0.0 0.1 0.0 0.6 0.1 5.4 Niger 10.3 7.7 2.7 2.3 0.0 0.0 2.6 0.3 0.8 1.3 Nigeria 34.1 10.5 23.6 0.9 54.4 0.0 0.1 0.5 0.8 ­31.4 Norway 37.1 13.4 23.7 7.0 16.0 0.0 0.0 0.1 0.1 14.6 Oman .. .. .. 4.2 .. .. .. .. .. .. Pakistan 18.4 8.9 9.5 1.6 7.5 0.0 0.4 0.8 1.5 0.9 Panama 10.5 12.6 ­2.1 4.4 0.0 0.0 0.0 0.3 0.2 1.8 Papua New Guinea .. .. .. .. .. .. .. .. .. .. Paraguay 16.4 9.8 6.6 4.2 0.0 0.0 0.0 0.4 0.7 9.7 Peru 19.9 11.7 8.2 2.9 2.2 3.4 0.0 0.3 0.7 4.5 Philippines 28.2 8.1 20.1 2.8 0.5 0.4 0.2 0.6 0.4 20.8 Poland 18.2 12.8 5.4 5.6 1.7 0.4 0.0 0.8 0.4 7.7 Portugal 13.1 17.1 ­4.1 5.7 0.0 0.0 0.0 0.2 0.4 1.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 181 3.15 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed savings expenditure depletion depletion forest dioxide emission net capital depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Romania 14.2 11.7 2.4 3.2 4.2 0.1 0.0 0.7 0.1 0.6 Russian Federation 32.7 7.0 25.7 3.5 36.8 0.8 0.0 1.6 0.4 ­10.4 Rwanda 19.5 7.7 11.8 3.5 0.0 0.0 2.6 0.2 0.1 12.3 Saudi Arabia .. 13.0 .. 7.2 61.4 0.0 0.0 0.7 1.2 .. Senegal 15.8 9.3 6.5 3.7 0.0 0.0 0.0 0.4 0.9 8.8 Serbia and Montenegro 9.8 11.3 ­1.5 .. 2.4 0.0 .. 1.4 0.0 .. Sierra Leone 7.0 7.7 ­0.6 1.0 0.0 0.0 1.9 0.5 1.1 ­3.1 Singapore .. 14.6 .. 2.7 0.0 0.0 0.0 0.4 0.9 .. Slovak Republic 21.2 22.5 ­1.3 4.1 0.1 0.0 0.5 0.7 0.0 1.5 Slovenia 25.2 13.5 11.7 5.4 0.1 0.0 0.2 0.3 0.2 16.3 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 14.4 12.0 2.4 5.3 4.8 1.2 0.2 1.1 0.1 0.3 Spain 22.6 14.6 8.1 4.1 0.0 0.0 0.0 0.2 0.4 11.6 Sri Lanka 20.5 9.9 10.6 2.6 0.0 0.0 0.3 0.3 0.4 12.1 Sudan 18.5 9.9 8.6 0.9 18.9 0.0 0.0 0.2 0.4 ­10.1 Swaziland 16.6 10.6 6.0 6.3 0.0 0.0 0.0 0.3 0.1 11.9 Sweden 23.1 12.1 11.0 8.0 0.0 0.2 0.1 0.1 0.0 18.6 Switzerland .. 13.7 .. 5.0 0.0 0.0 0.0 0.1 0.2 .. Syrian Arab Republic 14.6 10.3 4.3 2.6 43.8 0.1 0.0 1.4 0.9 ­39.4 Tajikistan 7.3 8.5 ­1.2 2.6 0.8 0.0 0.0 1.8 0.4 ­1.6 Tanzania 9.3 8.0 1.3 2.4 0.0 0.4 0.0 0.2 0.2 2.8 Thailand 30.1 11.2 18.9 4.7 3.8 0.0 0.3 1.0 0.4 18.2 Togo 9.9 8.3 1.6 2.6 0.0 0.1 2.8 0.6 0.3 0.4 Trinidad and Tobago .. 12.4 .. 4.0 57.9 0.0 0.0 1.7 0.2 .. Tunisia 21.9 11.6 10.3 5.9 6.3 0.2 0.1 0.6 0.3 8.7 Turkey 18.5 11.8 6.7 3.5 0.4 0.1 0.0 0.5 1.3 7.9 Turkmenistan 36.4 11.0 25.4 .. .. 0.0 .. 3.7 1.1 .. Uganda 10.1 8.1 2.0 4.0 0.0 0.0 4.6 0.2 0.0 1.1 Ukraine 22.7 10.4 12.2 4.4 9.0 0.0 0.0 3.2 0.7 3.7 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 13.9 10.3 3.6 5.3 1.6 0.0 0.0 0.2 0.0 7.1 United States 13.0a 12.2 0.8 4.8 1.9 0.0 0.0 0.3 0.3 3.0 Uruguay 13.1 12.1 1.0 2.6 0.0 0.0 0.3 0.2 1.8 1.3 Uzbekistan 35.1 8.7 26.4 9.4 75.4 0.0 0.0 7.1 1.1 ­47.9 Venezuela, RB 40.5 12.0 28.5 4.4 37.9 1.0 0.0 0.8 0.0 ­6.9 Vietnam 34.4 9.2 25.3 2.8 17.5 0.0 0.5 1.1 0.6 8.5 West Bank and Gaza 11.6 8.8 2.8 .. 0.0 0.0 .. .. .. .. Yemen, Rep. 35.4 10.2 25.2 .. 52.3 0.0 0.0 0.9 0.8 .. Zambia 10.9 9.5 1.4 2.9 0.1 7.9 0.0 0.2 1.0 ­4.9 Zimbabwe 2.9 8.1 ­5.2 6.9 5.2 2.7 0.0 2.2 0.1 ­8.6 World 20.8 w 12.6 w 8.2 w 4.4 w 4.1 w 0.3 w 0.0 w 0.4 w 0.4 w 7.4 w Low income 28.1 9.1 19.0 3.3 9.8 0.7 0.6 1.1 0.7 9.5 Middle income 30.0 11.0 19.0 3.6 12.1 0.9 0.0 1.0 0.7 7.8 Lower middle income 35.0 10.7 24.3 2.9 10.4 1.0 0.0 1.1 0.9 13.7 Upper middle income 23.4 11.4 12.0 4.5 14.4 0.8 0.0 0.8 0.5 ­0.1 Low & middle income 29.7 10.7 19.0 3.5 11.8 0.9 0.1 1.0 0.7 8.0 East Asia & Pacific 44.4 10.3 34.1 2.2 7.8 0.8 0.0 1.2 1.2 25.3 Europe & Central Asia 23.2 10.6 12.6 4.1 16.6 0.4 0.0 1.2 0.5 ­2.0 Latin America & Carib. 22.9 12.0 10.9 4.4 8.9 1.7 0.0 0.4 0.5 3.7 Middle East & N. Africa 30.9 11.0 19.9 4.5 35.2 0.2 0.1 1.2 0.6 ­13.0 South Asia 30.1 9.1 21.0 3.6 4.9 0.8 0.6 1.1 0.8 16.4 Sub-Saharan Africa 17.4 10.7 6.7 3.8 15.5 0.8 0.3 0.7 0.5 ­7.3 High income 18.7 13.1 5.7 4.6 2.0 0.1 0.0 0.3 0.3 7.7 Europe EMU 20.7 13.9 6.8 4.6 0.2 0.0 0.0 0.2 0.2 10.8 a. World Bank estimate based on preliminary data. b. Adjusted net savings do not include particulate emission damage. 182 2007 World Development Indicators 3.15 ENVIRONMENT Toward a broader measure of savings About the data Definitions Adjusted net savings measure the change in value of rents because they are not produced; in contrast, for · Gross savings are the difference between gross a specified set of assets, excluding capital gains. If produced goods and services competitive forces will national income and public and private consumption, a country's net savings are positive and the account- expand supply until economic profits are driven to plus net current transfers. · Consumption of fixed ing includes a sufficiently broad range of assets, zero. For each type of resource and each country, unit capital represents the replacement value of capital economic theory suggests that the present value resource rents are derived by taking the difference used up in the process of production. · Net savings of social welfare is increasing. Conversely, persis- between world prices and the average unit extrac- are gross savings minus the value of consumption of tently negative adjusted net savings indicate that an tion or harvest costs (including a "normal" return on fixed capital. · Education expenditure refers to public economy is on an unsustainable path. capital). Unit rents are then multiplied by the physi- current operating expenditures in education, including The table provides a test to check the extent cal quantity extracted or harvested in order to arrive wages and salaries and excluding capital investments to which today's rents from a number of natural at a depletion figure. This figure is one of a range in buildings and equipment. · Energy depletion is the resources and changes in human capital are bal- of depletion estimates that are possible, depending product of unit resource rents and the physical quanti- anced by net savings, that is, this generation's on the assumptions made about future quantities, ties of energy extracted. It covers coal, crude oil, and bequest to future generations. prices, and costs, and there is reason to believe that natural gas. · Mineral depletion is the product of unit Adjusted net savings are derived from standard it is at the high end of the range. World prices are resource rents and the physical quantities of miner- national accounting measures of gross savings by used in order to reflect the social opportunity cost als extracted. It refers to tin, gold, lead, zinc, iron, making four adjustments. First, estimates of capital of depleting minerals and energy. copper, nickel, silver, bauxite, and phosphate. · Net consumption of produced assets are deducted to A positive net depletion figure for forest resources forest depletion is the product of unit resource rents obtain net savings. Second, current public expen- implies that the harvest rate exceeds the rate of and the excess of roundwood harvest over natural ditures on education are added to net savings (in natural growth; this is not the same as deforesta- growth. · Carbon dioxide damage is estimated to be standard national accounting these expenditures tion, which represents a change in land use (see $20 per ton of carbon (the unit damage in 1995 U.S. are treated as consumption). Third, estimates of Definitions for table 3.4). In principle, there should dollars) times the number of tons of carbon emitted. the depletion of a variety of natural resources are be an addition to savings in countries where growth · Particulate emission damage is the willingness to deducted to reflect the decline in asset values asso- exceeds harvest, but empirical estimates suggest pay to avoid mortality and morbidity attributable to ciated with their extraction and harvest. And fourth, that most of this net growth is in forested areas particulate emissions.· Adjusted net savings are net deductions are made for damages from carbon diox- that cannot be exploited economically at present. savings plus education expenditure and minus energy ide and particulate emissions. Because the depletion estimates reflect only timber depletion, mineral depletion, net forest depletion, and The exercise treats public education expenditures values, they ignore all the external and nontimber carbon dioxide and particulate emissions damage. as an addition to savings effort. However, because benefits associated with standing forests. Data sources of the wide variability in the effectiveness of govern- Pollution damage from emissions of carbon dioxide ment education expenditures, these figures cannot is calculated as the marginal social cost per unit mul- Gross savings are derived from the World Bank's be construed as the value of investments in human tiplied by the increase in the stock of carbon dioxide. national accounts data files, described in the capital. The reader should bear in mind that current The unit damage figure represents the present value Economy section. Consumption of fi xed capital expenditure of $1 on education does not necessarily of global damage to economic assets and to human is from the United Nations Statistics Division's yield $1 of human capital. The calculation should also welfare over the time the unit of pollution remains National Accounts Statistics: Main Aggregates consider private education expenditure, but data are in the atmosphere. and Detailed Tables, 1997, extrapolated to 2005. Data on education expenditure are from the United not available for a large number of countries. Pollution damage from particulate emissions is Nations Statistics Division's Statistical Yearbook While extensive, the accounting of natural resource estimated by valuing the human health effects from 1997 and from the United Nations Educational, depletion and pollution costs still has some gaps. exposure to particulate matter pollution in urban Scientifi c, and Cultural Organization Institute Key estimates missing on the resource side include areas. The estimates are calculated as willingness to for Statistics online database. Missing data are the value of fossil water extracted from aquifers, net pay to avoid mortality and morbidity from cardiopul- estimated. The wide range of data sources and depletion of fish stocks, and depletion and degrada- monary disease and lung cancer in adults and acute estimation methods used to arrive at resource tion of soils. Important pollutants affecting human respiratory infections in children that is attributable depletion estimates are described in Kunte and health and economic assets are excluded because to particulate emissions. others' "Estimating National Wealth" (1998). The no internationally comparable data are widely avail- For a detailed note on methodology, see www. unit damage figure for carbon dioxide emissions able on damage from ground-level ozone or from worldbank.org/data. is from Frankhauser's "Fractales, tissues urbains sulfur oxides. et reseaux de transport" (1994). The estimates of damage from particulate emissions are from Pan- Estimates of resource depletion are based on dey and others' "The Human Costs of Air Pollution: the calculation of unit resource rents. An economic New Estimates for Developing Countries" (2006). rent represents an excess return to a given factor of The conceptual underpinnings of the savings mea- production--in this case the returns from resource sure appear in Hamilton and Clemens' "Genuine extraction or harvest are higher than the normal rate Savings Rates in Developing Countries" (1999). of return on capital. Natural resources give rise to 2007 World Development Indicators 183 Text figures, tables, and boxes ECONOMY 4 Introduction A portrait of the global economy A portrait of the global economy and the activity of more than 200 countries and territories that produce, trade, and consume the world's output--that is what the data in this section provide. Timely and reliable macroeconomic statistics are important for three reasons. First, they provide a measure of the wealth of economies, reflecting the welfare of their residents and prospects for future growth. Second, because the design of sound macroeconomic policies requires an understanding of historical patterns and trends, they provide guidance in shaping development policies. Third, they inform consumers, workers, investors, taxpayers, voters, and citizens on how their economy is man- aged so that they can make appropriate choices and exert control over their governments. Developing economies grew faster over the last decade (1995­2005) than in the two previous decades and faster than high-income countries. World output in 2005 amounted to about $61 tril- lion, measured in purchasing power parities. This was a 45 percent increase over 1995, when the world output was $42.3 trillion (figure 4a). The share of developing economies in global output increased from 39 percent to 46 percent. The developing economies in the East Asia and the Pacific region grew the most, doubling their output and increasing their share of global output from 13 percent to 19 percent. Further integration into world markets, better functioning internal markets, and rising demand for many commodities all contributed to the acceleration of growth in developing countries. Past periods of growth were often interrupted by financial or balance of payments crises. Indeed, from 1997 to 1998 some of the fastest growing economies experienced a major financial crisis, which started in Asia and spread to the transition economies of Europe and Central Asia. But recovery from this crisis has been widespread and durable. Developing economies are running lower fiscal and external deficits, accumulating larger reserves, and adopting more cautious monetary and financial policies. These policies make economies less vulnerable to shocks and less volatile, increasing the confidence of investors. The financial shocks of the period also revealed the importance of reliable, publicly available data for monitoring the actions of governments and private agents. Developing economies increase their share of global output 4a 1995 2005 $42.3 trillion $61.3 trillion East Asia & Pacific 13% East Asia & Pacific 19% Latin America & Caribbean 8% Latin America Europe & High-income & Caribbean 8% Central Asia 7% 54% High-income 60% South Asia 6% Middle East & Europe & North Africa 3% Central Asia 7% Sub-Saharan Africa 2% South Asia 8% Middle East & North Africa 3% Note: Global output is measured in 2005 international Sub-Saharan Africa 2% dollars (GDP in purchasing power parity terms). Source: World Bank staff estimates. 2007 World Development Indicators 185 Better policies to achieve Long-term trends macroeconomic stability Developing economies are expected to grow faster than high- The high growth experienced in the developing world was due income economies. The surprise is that they often don't. in part to expanding trade (section 6) and a better investment Labor surpluses and higher returns to physical capital in de- climate (section 5). The very rapid industrialization of large veloping countries, along with ready access to technology al- countries such as China and India also benefited the export- ready developed and amortized in high-income countries, are ers of primary commodities--oil, metals and minerals, and among the reasons that developing economies are expected agricultural produce. to grow faster and, in the long run, close the gap with richer Macroeconomic stability also helped. Since the high infla- economies. But until recently only a few developing econo- tion and the debt crises of the 1970s and 1980s, better fis- mies enjoyed sustained periods of high growth. And even cal, monetary, and exchange rate policies have brought infla- fewer have reached the average growth of the high-income tion rates down in most developing countries. And the very economies. Poverty traps, exclusion from global markets, rapid inflation in European and Central Asian countries after and government and market failures are some of the reasons the collapse of the Soviet Union came back to earth after put forward to explain the failure to converge. their transition from central planning to market economies The last decade brought a change, however. The average (figure 4d). growth of low- and middle-income economies surpassed that Macroeconomic stability, one factor in a favorable invest- of high-income economies (figure 4b). The most successful ment climate, promotes economic growth (figure 4e). But low are no longer counted as "developing." During this period 13 inflation does not always lead to high economic growth. In countries graduated from the World Bank's classification of general, developed economies have lower inflation and eco- low- and middle-income economies: Antigua and Barbuda, nomic growth rates. The median inflation rate was below 10 Bahrain, Greece, Guam, Isle of Man, Republic of Korea, percent in all developing regions, well below the median of Malta, New Caledonia, Northern Mariana Islands, Puerto around 15 percent or higher in 1990 in three regions. Rico, Saudi Arabia, San Marino, and Slovenia. But these are only a few, and they account for less than 2 percent of the world's population. Growth is still uneven (figure 4c). Global and regional averages are driven by a few large countries, which carry large weights in the aggregate measures. Growth is accelerating in Inflation is now less than the low-income economies 4b 10 percent in all developing regions 4d Average annual growth (%) 1985­95 1975­85 1995­2005 Average annual growth in GDP deflator (%) 6 50 Europe & Central Asia 5 40 4 30 Latin America 3 South Asia & Caribbean Sub-Saharan Africa 20 2 1 10 Middle East & North Africa East Asia & Pacific 0 0 Low-income Middle-income High-income 1970 1975 1980 1985 1990 1995 2000 2005 Source: World Bank data files. Source: World Bank data files. Patterns of regional Economies with high growth rates growth vary widely 4c generally have lower rates of inflation 4e Average annual growth (%) 1975­85 1985­95 1995­2005 Inflation, 1990­2005 (%) 10 200 Belarus 8 175 6 150 High-income average, 1975­2005 125 4 Brazil 100 2 Moldova 75 0 50 25 ­2 China Uzbekistan 0 ­4 ­25 ­6 ­5 0 5 10 15 20 East Asia Europe & Latin America Middle East & South Sub-Saharan GDP growth (%) & Pacific Central Asia & Caribbean North Africa Asia Africa Source: World Bank data files. Source: World Bank data files. 186 2007 World Development Indicators Rising reserves External public debt relief Trade surpluses and growing workers' remittances have al- Improvements in macroeconomic management of the poor- lowed many developing countries to accumulate large hold- est countries have also paved the way for more extensive ings of reserve assets over the past five years. One motive debt relief. may be the desire to maintain larger precautionary reserves Since 1996 developing countries have benefited from to protect against financial and balance of payments crises. debt writeoffs by official donors and will continue to do so. It Indeed, the globalization of financial transactions may have makes sense to relieve debt when the causes of excessive made countries with open capital accounts more vulnerable. indebtedness are being tackled at their roots and when the China, India, and the Russian Federation are now among benefits of debt reduction are directed toward more effective the top 10 economies with the largest reserves holdings poverty reduction programs. (table 4f). Together they accounted for 25 percent of the world Making debt sustainable for poor countries is one of reserves in 2005. In contrast, the United States holds only the Millennium Development Goals. Debt can bridge financ- 4 percent of the world reserves. With one exception in 1991, ing gaps and meet investment needs for projects with high the current account deficit of the United States increased social returns. But when unsustainable, it obliges countries steadily from around $12 billion in 1982 to $792 billion in to undertake policies that might be disruptive and harmful for 2005. The U.S. current account deficit is financed largely by growth and welfare, such as default, large fiscal adjustments, China's current account surplus and growing investments by and devaluation. major oil exporters. In 2005 the external debt of developing countries Large reserve holdings also make economies less vulner- amounted to $2,730 billion, and related debt service (prin- able to debt crises, reassuring lenders and lowering inter- cipal and interest) to $513 billion. The debt stock has been est rates. Economies with large reserves are less likely to declining in most regions and, accordingly, debt service require assistance from lenders of last resort, such as the declined. The ratio of debt service to exports in 2005 was World Bank or International Monetary Fund (IMF). Since 1995 13.8 percent, the lowest in the last 20 years. The ratio of the ratio of reserves to external debt has increased for many total external debt to GDP declined from nearly 6.6 percent in economies (figure 4g). 1999 to 5.4 percent in 2005. The debt crises of the 1980s and 1990s were the result Top 10 economies of excessive borrowing with overly optimistic expectations. with largest reserves 4f But cyclical global recessions, declining agricultural com- International modity prices, and conflicts also left many poor countries Share Increase Reserves reserves of world over (months unable to service their debt. Traditional debt relief, based on $ billions total 2004 of import Economy 2004 2005 (%) (%) coverage) rescheduling and restructuring of payments, proved ineffec- Japan 844.7 846.9 18 0.3 16 tive for them. China 622.9 831.4 18 33.5 14 Special programs to address the problems of the poor Taiwan, China 247.7 260.3 6 5.1 14 Korea, Rep. 199.2 210.6 5 5.7 8 countries with predominantly official creditors were started United States 190.5 188.3 4 ­1.2 1 in 1996, when the World Bank and the IMF launched the Russian Federation 126.3 182.3 4 44.4 11 Heavily Indebted Poor Countries (HIPC) Initiative. The initia- India 131.6 137.8 3 4.7 12 Hong Kong, China 123.6 124.3 3 0.6 4 tive aims to provide permanent relief from unsustainable Singapore 112.2 115.8 3 3.2 5 debt by redirecting the resources for debt service toward Germany 97.2 101.7 2 4.6 1 social expenditures aimed at poverty reduction. The initia- Source: International Monetary Fund and World Bank data files. tive relieved $61 billion in total nominal debt service for 29 More reserves countries, and another 11 countries are eligible for addi- to cover debt 4g tional debt relief. Number of countries 1995 2005 The debt stock of the 29 HIPCs was reduced by 90 per- 60 cent and their debt service by 2 percent between 1999 and 50 2005. And as a direct result of debt relief, public expendi- tures in education and health have increased by 3 percent in 40 these countries. 30 The International Development Association (IDA), the IMF, 20 and the African Development Fund have committed to cancel 10 an additional debt stock of $49 billion for all HIPCs under the new Multilateral Debt Relief Initiative in 2006. IDA has 0 Less than 10% 10%­20% 21%­50% 51%­100% More than 100% since canceled $27 billion and the IMF $3 billion for 19 coun- Foreign reserves as share of external debt tries that have made progress in their economic and social Source: World Bank and International Monetary Fund data files. reforms. 2007 World Development Indicators 187 Tables 4.a Recent economic performance Gross domestic Exports of goods Imports of goods GDP deflator Current account Total reservesa product and services and services balance months average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2004 2005 2004 2005 2004 2005 2004 2005 2004 2005 2005 2005 Algeria 5.3 5.2 5.8 5.1 7.8 23.1 16.1 17.8 .. 21.2 .. .. Argentina 9.2 8.0 13.5 11.6 20.1 12.3 8.8 9.3 3.2 3.0 .. .. Armenia 14.0 9.5 15.9 4.5 11.7 12.5 3.2 4.0 ­3.9 ­4.7 841 3.8 Azerbaijan 26.2 22.7 58.5 48.0 ­0.6 20.1 10.3 17.7 1.3 ­3.3 1,028 1.9 Bangladesh 6.0 6.7 15.6 15.7 19.1 14.1 5.1 5.2 ­0.2 0.9 3,488 2.5 Bolivia 4.1 4.1 9.6 4.5 13.5 15.2 4.7 4.6 5.3 .. 2,545 7.7 Bosnia and Herzegovina 5.0 5.7 20.8 12.0 13.2 3.9 1.6 ­5.5 ­21.7 ­15.4 2,457 5.0 Botswana 6.2 4.2 22.0 4.6 5.2 2.4 8.8 8.4 14.2 10.8 6,335 13.5 Brazil 2.3 3.5 11.6 ­3.1 9.5 ­1.8 7.2 5.3 1.8 1.3 60,357 5.0 Bulgaria 5.5 5.6 7.2 11.8 14.6 8.9 3.8 5.2 ­11.3 ­12.5 10,253 4.6 Cameroon 2.0 3.5 ­3.9 1.7 23.1 9.3 4.7 5.1 .. ­1.2 131 0.3 Chile 6.3 5.0 6.1 4.8 20.4 8.9 4.8 3.7 0.6 3.9 .. .. China 10.2 10.4 24.3 14.6 11.4 16.5 3.9 3.6 7.2 5.6 1,046,465 13.6 Colombia 5.1 4.7 4.6 4.4 25.2 2.4 6.2 5.8 ­1.6 ­1.3 13,659 5.0 Congo, Dem. Rep. 5.9 6.5 14.3 2.4 20.0 5.3 21.5 7.9 0.0 ­9.7 470 1.5 Congo, Rep. 9.2 5.7 14.9 4.2 16.4 6.5 7.2 ­5.6 17.7 1.4 .. .. Costa Rica 5.9 6.5 12.6 5.6 11.7 6.2 11.1 11.5 ­4.8 ­4.9 2,682 2.5 Côte d'Ivoire 1.8 1.9 1.5 9.0 7.3 2.9 3.6 3.3 ­0.1 1.8 .. .. Croatia 4.3 4.6 4.6 5.2 3.5 5.0 3.2 2.9 ­6.7 ­6.8 10,101 4.8 Dominican Republic 9.3 9.0 6.1 ­8.2 14.2 11.3 4.2 8.6 ­1.7 ­2.2 2,325 1.9 Ecuador 4.7 4.5 7.4 8.5 13.5 17.6 6.7 7.0 ­0.2 0.8 3,923 2.9 Egypt, Arab Rep. 4.9 5.8 22.5 14.9 23.8 21.2 5.4 4.5 2.4 1.7 26,660 8.4 El Salvador 2.8 3.8 0.4 4.4 0.8 8.4 4.4 3.9 ­4.6 ­4.6 1,922 2.5 Gabon 2.2 2.7 ­5.8 ­8.0 1.8 0.2 8.9 2.3 .. 1.8 .. .. Ghana 5.9 6.0 9.3 10.3 6.7 13.7 15.0 14.8 ­7.6 ­5.1 2,084 3.0 Honduras 4.0 5.1 6.0 8.9 9.3 15.7 10.3 5.5 ­1.0 ­0.6 2,776 4.9 India 9.2 8.3 21.9 5.2 22.1 1.1 4.4 4.4 .. ­0.9 172,635 8.2 Indonesia 5.6 5.5 8.6 8.9 12.3 8.6 13.7 14.5 0.3 .. 53,223 5.2 Iran, Islamic Rep. 4.4 5.8 ­13.2 0.8 ­13.2 13.7 16.0 23.5 .. 5.6 47,130 9.0 Jamaica 1.8 2.7 .. .. .. .. 9.6 13.0 ­11.3 ­9.6 1,728 2.7 Jordan 7.3 6.3 5.8 0.7 21.2 4.9 4.0 5.8 ­18.2 ­22.8 6,192 5.3 Kazakhstan 9.7 9.0 1.4 10.3 13.3 2.9 17.9 7.0 ­1.3 7.0 11,800 4.2 Kenya 5.8 5.7 4.7 6.4 14.3 1.5 4.2 4.0 ­2.6 ­7.6 2,654 4.3 Lesotho 1.2 2.8 ­2.6 2.4 ­1.5 7.8 3.2 5.0 ­3.0 1.7 455 3.9 188 2007 World Development Indicators ECONOMY Gross domestic Exports of goods Imports of goods GDP deflator Current account Total reservesa product and services and services balance months average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2004 2005 2004 2005 2004 2005 2004 2005 2004 2005 2005 2005 Macedonia, FYR 4.0 3.2 8.5 13.7 2.4 18.2 3.0 3.4 ­1.4 ­1.2 1,725 4.6 Malawi 2.6 9.2 20.2 2.1 11.0 ­1.5 15.5 10.8 .. ­16.1 .. .. Malaysia 5.2 5.5 8.6 5.5 8.0 5.0 4.6 1.4 15.3 15.6 86,827 6.6 Mauritius 4.6 3.5 5.7 7.0 4.8 7.7 4.8 4.1 ­5.4 ­5.2 1,244 3.3 Mexico 3.0 4.5 6.9 5.7 8.7 8.5 5.4 5.3 ­0.6 ­0.3 73,465 3.1 Moldova 7.1 3.0 20.2 ­0.1 21.2 ­2.0 7.3 11.9 ­8.3 ­21.2 603 2.1 Morocco 1.7 7.0 9.8 5.9 5.7 4.8 1.4 2.7 2.2 1.2 18,226 8.1 Nicaragua 4.0 3.7 5.3 13.9 6.2 9.8 10.3 10.7 ­16.3 ­15.6 899 2.8 Nigeria 6.9 6.3 ­1.8 4.4 21.3 16.0 26.9 12.8 24.5 18.1 .. .. Pakistan 7.8 6.3 7.6 13.4 44.1 20.0 9.8 8.2 ­3.1 ­3.9 11,374 3.8 Panama 6.4 7.0 13.8 7.3 14.2 7.0 2.4 2.4 ­5.1 ­4.6 1,358 1.2 Paraguay 2.9 3.5 2.7 14.2 4.6 33.3 5.9 6.0 ­0.3 ­4.5 1,370 2.7 Peru 6.4 6.6 14.9 5.4 10.6 9.9 3.3 8.6 1.3 1.1 17,627 8.1 Philippines 5.0 5.4 4.2 8.5 2.4 6.1 6.2 6.0 2.4 2.3 21,800 4.1 Poland 3.4 5.0 8.1 5.7 4.9 6.9 2.8 2.0 ­1.7 ­1.5 39,656 3.5 Romania 4.1 5.8 4.2 10.3 3.7 8.6 12.0 8.5 ­8.6 ­11.4 20,730 4.9 Russian Federation 6.4 6.5 6.3 4.3 17.3 20.2 19.7 14.5 10.9 10.8 276,803 14.0 Senegal 5.1 5.1 3.1 ­15.5 1.9 ­21.2 2.6 2.3 .. ­7.4 1,188 3.7 Serbia and Montenegro 4.7 6.1 10.0 41.7 ­4.0 28.9 17.3 5.4 .. ­9.6 6,149 4.5 Slovak Republic 6.0 6.7 10.9 10.2 11.2 11.6 2.4 6.3 .. ­7.2 18,750 4.7 South Africa 4.9 4.2 6.7 5.2 10.1 9.4 4.7 5.8 ­3.8 ­4.8 22,218 3.0 Sri Lanka 5.3 6.0 7.5 5.4 8.7 5.4 10.4 7.8 ­2.8 ­5.7 2,731 2.9 Swaziland 1.8 2.3 6.0 6.0 6.3 5.5 4.9 4.0 1.7 ­11.5 382 1.6 Syrian Arab Republic 5.1 4.0 3.9 ­1.5 17.9 1.9 5.8 4.0 ­4.0 ­2.5 3,064 2.8 Thailand 4.5 4.5 4.3 8.4 9.4 0.6 4.6 5.5 ­2.1 1.4 56,681 4.6 Tunisia 4.2 5.3 3.2 3.9 1.1 1.4 1.9 2.4 ­1.1 ­1.2 6,824 4.5 Turkey 7.4 5.0 8.6 14.3 11.6 5.5 5.4 9.6 ­6.4 ­6.8 .. .. Ukraine 2.6 5.2 ­11.2 ­1.3 2.1 8.4 20.0 12.3 3.1 0.3 16,934 4.2 Uruguay 6.6 5.0 16.8 8.5 8.8 15.0 1.7 5.1 .. ­3.5 3,329 5.7 Uzbekistan 7.0 6.0 7.1 2.0 7.3 1.2 15.9 22.0 .. 5.2 2,460 6.2 Venezuela, RB 9.3 8.5 5.2 8.5 30.0 28.2 29.1 15.0 18.1 17.2 31,033 8.3 Zambia 5.2 6.0 12.3 6.3 20.6 11.0 19.0 14.3 .. ­10.4 350 0.9 Zimbabwe ­6.5 ­5.1 ­4.3 3.5 ­3.1 3.6 237.7 1,216.0 .. ­8.0 .. .. Note: Data for 2006 are the latest preliminary estimates and may differ from those in earlier World Bank publications. a. International reserves including gold valued at the London gold price. Source: World Bank staff estimates. 2007 World Development Indicators 189 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 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 Afghanistan .. 12.0 .. 0.4 .. 21.1 .. 13.8 .. 21.9 Albania 3.5 5.3 4.3 1.4 ­0.5 3.4 .. ­0.2 6.9 8.3 Algeria 1.9 5.2 3.6 7.3 1.8 4.4 ­2.1 2.4 1.8 5.2 Angolaa 1.6 9.9 ­1.4 14.1 4.4 10.5 ­0.3 13.4 ­2.2 6.7 Argentina 4.3 2.2 3.5 2.5 3.8 3.8 2.7 3.7 4.5 0.9 Armenia ­1.9 12.4 0.5 8.4 ­7.8 16.8 ­4.3 9.2 6.4 12.2 Australia 4.0 3.2 3.8 ­0.5 2.8 3.0 2.1 1.6 4.5 3.6 Austria 2.4 1.5 1.6 ­0.2 2.7 2.1 2.7 1.4 2.3 1.4 Azerbaijan ­6.3 12.7 ­2.1 6.6 ­0.8 16.7 ­12.0 9.1 ­2.3 8.8 Bangladesh 4.8 5.4 2.9 2.5 7.3 7.3 7.2 6.7 4.5 5.6 Belarus ­1.7 7.5 ­4.0 6.0 ­1.8 11.1 ­0.7 11.5 ­0.4 5.3 Belgium 2.1 1.5 2.9 ­0.1 1.6 0.4 2.5 0.4 1.9 1.9 Benina 4.8 4.0 5.8 4.6 4.1 3.8 5.8 2.7 4.2 3.5 Bolivia 4.0 3.0 2.9 3.6 4.1 3.2 3.8 3.2 4.3 2.1 Bosnia and Herzegovina .. 5.0 .. 1.5 .. 4.9 .. 5.6 .. 5.2 Botswana 6.0 5.9 ­1.2 2.1 5.8 5.7 4.4 0.8 7.8 5.4 Brazil 2.9 2.2 3.3 4.5 2.6 2.3 1.6 1.8 3.0 1.7 Bulgaria ­1.8 5.0 3.0 0.4 ­5.0 5.7 .. 8.7 ­5.2 5.1 Burkina Fasoa 4.0 5.1 4.2 5.8 2.3 2.7 1.6 2.2 4.5 12.0 Burundi ­2.9 2.2 ­1.9 ­1.5 ­4.3 ­6.2 ­8.7 .. ­2.8 10.4 Cambodia 7.1 8.9 3.9 5.7 14.3 14.2 18.6 14.1 7.1 8.2 Cameroon 1.7 3.7 5.5 3.9 ­0.9 3.9 1.4 5.3 0.2 7.5 Canada 3.1 2.5 1.1 0.9 3.2 1.6 4.5 0.2 3.0 3.0 Central African Republic 2.0 ­1.4 3.8 2.6 0.7 4.2 ­0.2 4.0 ­0.3 ­12.9 Chad 2.2 14.5 4.9 2.2 0.6 45.9 .. .. 0.9 8.4 Chile 6.6 4.3 2.2 6.0 5.6 3.8 4.4 3.7 6.9 4.0 Chinaa,b 10.6 9.6 4.1 3.9 13.7 10.9 12.7 11.1 10.2 10.0 Hong Kong, China 4.1 4.3 .. ­0.2 .. ­3.4 .. ­5.7 .. 5.7 Colombia 2.8 3.5 ­2.2 1.9 1.8 4.9 ­2.2 4.1 4.2 2.8 Congo, Dem. Rep. ­4.9 4.4 1.4 0.4 ­8.0 9.0 ­8.7 4.8 ­12.3 5.5 Congo, Rep.a 1.2 3.9 1.0 5.6 3.2 1.4 ­3.0 12.7 ­0.6 4.2 Costa Rica 5.3 4.2 4.1 1.7 6.2 3.6 6.8 3.3 4.7 5.4 Côte d'Ivoirea 3.2 ­0.1 3.5 0.9 6.3 ­1.8 5.5 ­3.3 2.0 0.2 Croatia 0.6 4.7 ­2.6 ­0.2 ­2.4 5.3 ­3.5 4.5 1.9 5.1 Cubaa 4.2 3.4 .. .. .. .. .. .. .. .. Czech Republic 1.1 3.5 0.0 4.4 0.2 3.6 4.3 4.7 1.2 3.6 Denmark 2.7 1.2 4.6 ­0.1 2.5 ­0.9 2.2 ­2.5 2.7 1.8 Dominican Republica 6.0 2.8 3.9 2.8 7.0 ­0.6 4.9 0.7 6.0 4.6 Ecuadora 1.9 5.1 ­1.7 4.4 2.6 6.2 1.5 4.5 2.4 4.6 Egypt, Arab Rep. 4.4 3.7 3.1 3.4 5.1 4.3 6.4 2.8 4.0 3.4 El Salvador 4.8 2.2 1.2 1.4 5.1 2.1 5.2 2.2 4.0 2.4 Eritrea 5.7 3.5 1.5 0.8 15.0 4.1 10.6 6.6 5.7 3.5 Estonia 0.2 7.5 ­3.4 ­1.7 ­3.3 10.5 5.9 11.5 3.1 6.6 Ethiopia 3.5 4.2 2.2 3.1 4.0 5.8 3.8 2.4 4.5 3.9 Finland 2.5 2.4 1.8 ­0.2 3.9 1.7 7.6 1.9 2.2 2.6 France 1.9 1.5 2.0 ­0.9 1.0 1.4 .. 1.2 2.2 1.6 Gabona 2.8 1.7 ­1.4 4.9 2.5 2.8 0.6 .. 3.9 0.0 Gambia, The 3.0 3.7 3.3 1.4 1.0 5.9 0.9 4.2 3.7 5.4 Georgia ­7.1 7.4 ­11.0 3.1 ­8.1 12.6 ­7.0 6.3 ­0.3 7.8 Germany 1.8 0.7 ­0.1 1.6 ­0.1 0.6 0.2 1.0 2.9 1.0 Ghanaa 4.3 5.1 3.4 5.0 2.6 4.6 ­3.2 1.4 5.7 5.3 Greece 2.2 4.4 0.5 ­2.0 1.0 3.3 2.0 2.2 2.6 4.7 Guatemalaa 4.2 2.5 2.8 2.6 4.3 1.5 2.8 1.5 4.7 2.8 Guinea 4.4 2.9 4.6 4.0 4.7 3.1 4.1 1.9 3.6 1.9 Guinea-Bissau 1.2 ­0.5 3.9 4.0 ­3.1 3.6 ­2.0 3.5 ­0.6 0.7 Haiti ­1.5 ­0.5 .. .. .. .. .. .. .. .. 190 2007 World Development Indicators 4.1 ECONOMY 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 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 Honduras 3.2 3.6 2.2 3.4 3.6 3.6 4.0 4.2 3.8 4.3 Hungary 1.6 4.1 ­2.4 7.2 3.5 3.8 8.2 4.7 1.2 3.6 India 6.0 7.0 3.0 2.5 6.3 7.5 7.0 6.9 8.0 8.5 Indonesiaa 4.2 4.7 2.0 3.4 5.2 3.9 6.7 5.2 4.0 6.2 Iran, Islamic Rep. 3.1 5.8 3.2 5.5 2.6 7.0 5.1 10.2 3.8 5.1 Iraq .. ­11.4 .. ­3.6 .. ­17.0 .. ­12.8 .. 5.9 Ireland 7.5 5.2 .. .. .. .. .. .. .. .. Israel 5.3 1.9 .. .. .. .. .. .. .. .. Italy 1.5 0.6 2.1 0.2 0.8 ­0.2 1.4 ­1.2 1.7 0.9 Jamaica 1.8 1.8 ­0.3 ­2.7 ­1.0 2.0 ­2.2 0.1 2.3 1.7 Japan 1.1 1.4 ­1.6 ­0.9 ­0.3 0.0 .. 0.8 1.9 1.7 Jordan 5.0 6.1 ­3.0 12.2 5.2 9.3 5.6 11.6 5.0 4.9 Kazakhstan ­4.1 10.1 ­8.0 4.6 0.6 11.3 2.7 9.2 0.3 10.8 Kenya 2.2 3.4 1.9 2.8 1.2 4.5 1.3 3.3 3.2 3.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 5.8 4.6 1.6 ­0.1 6.0 6.3 7.3 7.0 5.6 3.7 Kuwaita 4.9 7.3 1.0 15.1 0.3 1.9 ­0.1 2.5 3.5 10.2 Kyrgyz Republic ­4.1 4.0 1.5 2.8 ­10.3 0.6 ­7.5 0.9 ­4.9 7.3 Lao PDR 6.5 6.2 4.8 2.8 11.1 12.1 11.7 10.4 6.6 6.7 Latvia ­1.5 7.9 ­5.5 2.8 ­8.5 8.4 ­7.6 7.4 2.7 8.1 Lebanon 6.0 4.0 2.3 1.8 ­0.9 4.5 ­5.0 5.1 3.2 4.3 Lesotho 3.9 2.9 2.0 ­1.1 5.0 3.7 6.6 4.1 4.4 3.4 Liberiaa 4.1 ­6.8 .. .. .. .. .. .. .. .. Libya .. 5.3 .. .. .. .. .. .. .. .. Lithuania ­2.7 7.8 ­0.8 3.0 3.3 10.5 5.7 10.2 5.5 7.0 Macedonia, FYR ­0.8 1.7 0.2 0.9 ­2.3 1.3 ­5.3 1.2 0.5 2.2 Madagascar 2.0 2.0 1.9 1.7 2.4 1.1 2.0 2.7 2.3 1.7 Malawi 3.7 3.4 8.6 0.5 2.0 3.8 0.5 1.7 1.6 3.2 Malaysiaa 7.0 4.8 0.3 3.4 8.6 4.6 9.5 5.2 7.3 5.3 Mali 4.1 5.9 2.6 4.9 6.4 5.1 ­1.4 5.7 3.0 6.2 Mauritania 2.9 4.0 ­0.2 ­2.4 3.4 3.1 5.8 ­4.1 4.9 6.8 Mauritius 5.2 4.0 ­0.5 2.0 5.5 1.9 5.3 0.6 6.4 5.9 Mexico 3.1 1.9 1.5 1.9 3.8 0.6 4.3 0.0 2.9 2.4 Moldova ­9.6 7.1 ­11.2 1.4 ­13.6 8.9 ­7.1 7.7 0.7 6.4 Mongolia 2.7 5.8 3.7 0.1 2.3 7.5 ­9.7 5.5 0.2 7.8 Moroccoa 2.3 4.3 ­0.8 6.9 3.2 4.0 2.7 3.4 2.8 3.8 Mozambique 5.9 8.6 4.9 8.3 12.8 10.3 10.2 14.5 3.6 7.8 Myanmara 6.9 9.2 5.7 .. 10.5 .. 7.9 .. 7.2 .. Namibia 4.0 4.6 3.8 1.4 2.4 7.3 2.6 6.2 4.5 4.3 Nepal 4.9 2.8 2.4 3.2 7.2 1.1 8.9 ­0.6 6.4 2.8 Netherlands 2.9 0.7 2.0 1.3 1.5 ­0.1 .. .. 3.3 1.1 New Zealand 3.2 3.7 2.9 0.3 2.4 4.0 2.2 3.1 3.5 4.3 Nicaragua 3.7 3.0 4.7 2.3 5.5 3.9 5.3 4.5 5.2 3.7 Niger a 2.4 3.7 3.0 6.4 2.0 3.1 2.6 3.9 1.9 4.3 Nigeria 2.5 5.9 3.4 5.8 1.0 5.5 1.1 8.8 3.3 6.3 Norway 4.0 2.0 2.6 2.1 3.8 0.7 1.6 0.7 4.0 2.5 Omana 4.5 3.0 5.0 2.2 3.9 ­0.5 6.0 9.3 5.0 5.9 Pakistan 3.8 4.8 4.4 2.3 4.1 6.5 3.8 9.1 4.4 5.4 Panama 4.7 4.3 3.1 4.8 6.0 1.6 2.7 ­1.4 4.5 4.8 Papua New Guinea 4.3 1.6 .. 2.2 .. ­3.6 .. ­1.1 .. 1.4 Paraguaya 2.2 2.6 3.3 5.5 0.6 1.3 1.4 0.9 2.5 1.9 Peru 4.7 4.3 5.5 3.1 5.4 5.3 3.8 4.9 4.0 3.9 Philippinesa 3.3 4.7 1.7 3.9 3.5 3.3 3.0 4.3 4.0 6.0 Poland 4.7 3.2 0.5 3.3 7.1 3.2 9.9 6.7 5.1 3.0 Portugal 2.8 0.5 ­0.3 ­1.8 3.1 ­1.1 3.6 0.0 2.4 1.4 Puerto Ricoa 4.2 .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 191 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 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 Romania ­0.6 5.8 ­1.9 8.8 ­1.2 5.6 .. .. 0.9 5.5 Russian Federation ­4.7 6.2 ­4.9 4.5 ­7.1 6.1 .. .. ­1.7 6.4 Rwandaa ­0.3 5.1 2.6 4.3 ­3.7 5.6 ­6.0 1.4 ­1.2 5.8 Saudi Arabiaa 2.1 4.2 1.6 1.1 2.2 3.6 5.6 5.5 2.2 3.6 Senegala 3.2 4.7 2.9 1.8 4.1 6.5 3.1 5.2 3.0 5.1 Serbia and Montenegro 1.4 5.1 .. ­2.9 .. 1.9 .. .. .. 7.3 Sierra Leone ­5.1 13.7 ­13.0 .. ­4.5 .. 6.1 .. ­2.9 .. Singapore 7.6 4.2 ­1.8 0.9 7.7 3.2 7.0 5.2 7.8 4.8 Slovak Republica 1.9 4.9 2.7 4.7 2.4 6.4 6.6 6.7 5.7 4.1 Slovenia 2.7 3.4 0.0 0.7 1.3 3.6 1.1 4.2 3.4 3.8 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2.1 3.7 1.0 0.8 1.1 2.7 1.6 2.3 2.7 4.4 Spain 2.7 3.1 3.1 ­0.5 2.3 2.6 .. 1.0 2.7 3.3 Sri Lanka 5.3 4.2 1.8 0.7 6.9 3.3 8.1 2.9 5.7 5.8 Sudan 5.4 6.1 9.2 .. 5.8 .. 4.4 .. 2.7 .. Swaziland 3.3 2.3 1.2 0.3 3.7 1.9 2.8 1.8 3.6 3.1 Sweden 2.1 2.3 ­1.1 0.2 4.2 3.7 8.6 2.7 1.9 1.7 Switzerland 1.0 0.9 ­2.0 ­5.3 0.4 1.1 1.2 0.9 1.2 0.2 Syrian Arab Republic 5.1 3.7 6.0 3.7 9.2 ­0.7 .. 29.6 1.5 5.9 Tajikistan ­10.4 9.6 ­6.8 10.3 ­10.8 11.6 ­10.0 10.3 ­12.7 7.0 Tanzaniac 2.9 6.9 3.2 5.1 3.1 9.7 2.7 8.0 2.7 6.0 Thailanda 4.2 5.4 1.0 1.9 5.7 6.9 6.9 7.2 3.7 4.5 Togoa 3.5 2.7 4.0 2.8 1.8 8.1 1.8 7.5 3.9 ­0.1 Trinidad and Tobago 3.1 8.3 2.7 ­7.3 3.2 12.7 4.9 6.7 3.2 3.8 Tunisiaa 4.7 4.5 2.3 3.6 4.6 3.1 5.5 3.0 5.3 5.4 Turkey 3.8 5.2 1.4 1.4 4.1 4.6 4.9 5.9 4.0 5.1 Turkmenistan ­4.8 .. ­5.7 .. ­3.4 .. .. .. ­5.4 .. Uganda 7.1 5.6 3.7 4.1 12.2 7.3 14.1 5.7 8.2 7.4 Ukraine ­9.3 8.0 ­5.6 3.3 ­12.9 9.4 ­11.2 14.0 ­8.1 7.7 United Arab Emirates 4.8 8.2 13.2 2.9 3.0 5.6 11.9 8.5 7.2 9.3 United Kingdom 2.7 2.4 ­0.2 0.7 1.5 ­0.1 .. ­0.9 3.4 3.1 United States 3.5 2.6 3.7 0.2 3.7 0.7 .. 1.3 3.4 2.7 Uruguay 3.4 0.9 2.8 5.6 1.1 0.7 ­0.1 2.3 3.7 ­0.5 Uzbekistan ­0.2 5.3 0.5 6.9 ­3.4 3.9 0.7 1.8 0.4 5.1 Venezuela, RB 1.6 1.3 1.2 4.7 1.2 ­0.2 4.5 0.8 ­0.1 3.1 Vietnama 7.9 7.5 4.3 3.8 11.9 10.2 11.2 11.5 7.5 6.9 West Bank and Gazaa 7.3 ­0.9 .. .. .. .. .. .. .. .. Yemen, Rep. 6.0 3.3 5.6 0.6 8.2 0.0 5.7 2.8 5.0 6.5 Zambia 0.5 4.7 4.2 1.4 ­4.2 9.6 0.8 5.3 2.5 4.0 Zimbabwe 2.1 ­5.9 4.3 ­8.5 0.4 ­10.0 0.4 ­12.0 2.9 ­8.7 World 2.9 w 2.8 w 2.0 w 2.2 w 2.4 w 2.0 w .. w 2.3 w 3.1 w 2.7 w Low income 4.8 6.1 3.2 3.0 4.9 6.9 5.9 6.9 6.0 7.2 Middle income 3.8 5.2 2.0 3.6 4.5 6.1 6.9 7.2 3.9 4.9 Lower middle income 5.3 6.3 2.7 3.8 7.1 7.7 8.5 9.4 5.0 6.2 Upper middle income 2.1 3.5 0.3 3.1 1.4 3.2 4.4 3.1 2.8 3.5 Low & middle income 3.9 5.3 2.4 3.4 4.6 6.2 6.8 7.2 4.1 5.2 East Asia & Pacific 8.5 8.4 3.4 3.7 11.0 9.4 10.8 9.8 8.1 8.7 Europe & Central Asia ­0.7 5.4 ­1.7 3.5 ­2.9 5.6 .. .. 0.9 5.2 Latin America & Carib. 3.3 2.3 2.0 3.3 3.2 2.2 3.4 1.7 3.3 2.2 Middle East & N. Africa 3.8 4.1 2.9 4.5 4.1 2.6 3.8 6.4 3.4 4.5 South Asia 5.6 6.5 3.1 2.4 6.1 7.2 6.6 7.0 7.1 7.8 Sub-Saharan Africa 2.5 4.3 3.3 3.8 1.9 4.7 1.9 2.6 2.5 4.3 High income 2.7 2.2 1.3 ­0.1 1.8 0.9 .. 1.1 2.9 2.3 Europe EMU 2.1 1.3 1.7 ­0.1 0.9 0.9 2.0 0.6 2.4 1.5 a. Components are at producer prices. b. China has revised its national accounts data from 1993 onwards. Data before 1993 are linked to the revised data on the basis of earlier growth rates. c. Data cover mainland Tanzania only. 192 2007 World Development Indicators 4.1 ECONOMY Growth of output 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 the current pattern of production subsidies) not included in the valuation of output. It indicators for calculating growth: the volume of gross or uses of output. The new base year should repre- is calculated without deducting for depreciation of domestic product (GDP), real gross domestic income, sent normal operation of the economy--that is, it fabricated capital assets or for depletion and degra- and real gross national income. The volume of GDP should be a year without major shocks or distortions. dation of natural resources. Value added is the net is the sum of value added, measured at constant Some developing countries have not rebased their output of an industry after adding up all outputs and prices, by households, government, and industries national accounts for many years. Using an old base subtracting intermediate inputs. The industrial origin operating in the economy. year can be misleading because implicit price and of value added is determined by the International Each industry's contribution to growth in the econ- volume weights become progressively less relevant Standard Industrial Classification (ISIC) revision 3. omy's output is measured by growth in the industry's and useful. value added. In principle, value added in constant To obtain comparable series of constant price · Agriculture corresponds to ISIC divisions 1­5 and prices can be estimated by measuring the quantity data, the World Bank rescales GDP and value added includes forestry and fishing. · Industry covers min- of goods and services produced in a period, valu- by industrial origin to a common reference year. This ing, manufacturing (also reported separately), con- ing them at an agreed set of base year prices, and year's World Development Indicators continues to struction, electricity, water, and gas (ISIC divisions subtracting the cost of intermediate inputs, also in use 2000 as the reference year. Because rescaling 10­45). · Manufacturing corresponds to industries constant prices. This double-deflation method, rec- changes the implicit weights used in forming regional belonging to ISIC divisions 15­37. · Services cor- ommended by the 1993 SNA and its predecessors, and income group aggregates, aggregate growth respond to ISIC divisions 50­99. This sector is requires detailed information on the structure of rates in this year's World Development Indicators are derived as a residual (from GDP less agriculture and prices of inputs and outputs. not comparable with those from earlier publications industry) and may not properly reflect the sum of In many industries, however, value added is with different base years. services output, including banking and financial ser- extrapolated from the base year using single vol- Rescaling may result in a discrepancy between vices. For some countries it includes product taxes ume indexes of outputs or, more rarely, inputs. Par- the rescaled GDP and the sum of the rescaled com- (minus subsidies) and may also include statistical ticularly in the services industries, including most of ponents. Because allocating the discrepancy would discrepancies. government, value added in constant prices is often cause distortions in the growth rates, the discrep- imputed from labor inputs, such as real wages or ancy is left unallocated. As a result, the weighted number of employees. In the absence of well-defined average of the growth rates of the components gen- measures of output, measuring the growth of ser- erally will not equal the GDP growth rate. vices remains difficult. Computing growth rates Moreover, technical progress can lead to improve- Growth rates of GDP and its components are calcu- ments in production processes and in the quality of lated using the least squares method and constant goods and services that, if not properly accounted price data in the local currency. Constant price U.S. for, can distort measures of value added and thus dollar series are used to calculate regional and of growth. When inputs are used to estimate output, Data sources income group growth rates. Local currency series as is the case for nonmarket services, unmeasured National accounts data for most developing are converted to constant price U.S. dollars using technical progress leads to underestimates of the vol- countries are collected from national statistical an exchange rate in the common reference year. ume of output. Similarly, unmeasured improvements The growth rates in the table are average annual organizations and central banks by visiting and in the quality of goods and services produced lead compound growth rates. Methods of computing resident World Bank missions. Data for high- to underestimates of the value of output and value growth rates and the alternative conversion factor income economies come from data files of the added. The result can be underestimates of growth are described in Statistical methods. Organisation for Economic Co-operation and and productivity improvement and overestimates of Development (for information on the OECD's inflation. These issues are highly complex, and only Changes in the System of National Accounts national accounts series, see its Annual National a few high-income countries have attempted to intro- World Development Indicators adopted the termi- duce any GDP adjustments for these factors. nology of the 1993 SNA in 2001. Although many Accounts for OECD Member Countries: Data from Informal economic activities pose a particular mea- countries continue to compile their national accounts 1970 Onwards). The World Bank rescales constant surement problem, especially in developing countries, according to the SNA version 3 (referred to as the price data to a common reference year. The com- where much economic activity may go unrecorded. 1968 SNA), more and more are adopting the 1993 plete national accounts time series is available on Obtaining a complete picture of the economy requires SNA. Some low-income countries still use concepts the World Development Indicators 2007 CD-ROM. estimating household outputs produced for home from the even older 1953 SNA guidelines, including The United Nations Statistics Division publishes use, sales in informal markets, barter exchanges, valuations such as factor cost, in describing major detailed national accounts for UN member coun- and illicit or deliberately unreported activities. The economic aggregates. Countries that use the 1993 tries in National Accounts Statistics: Main Aggre- consistency and completeness of such estimates SNA are identified in Primary data documentation. gates and Detailed Tables and publishes updates depend on the skill and methods of the compiling in the Monthly Bulletin of Statistics. statisticians and the resources available to them. 2007 World Development Indicators 193 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan .. 7,308 .. 36 .. 25 .. 15 .. 39 Albania 2,102 8,380 36 23 48 22 .. 12 16 56 Algeria 62,045 102,256 11 9 48 62 11 6 41 30 Angolaa 10,260 32,811 18 7 41 74 5 4 41 19 Argentina 141,352 183,193 8 9 36 36 27 23 56 55 Armenia 2,257 4,903 17 21 52 44 33 21 31 35 Australia 319,265 732,499 4 3 30 27 15 12 66 70 Austria 164,984 306,073 4 2 32 31 21 20 64 68 Azerbaijan 8,858 12,561 29 10 33 62 19 8 38 28 Bangladesh 30,129 60,034 30 20 22 27 13 17 48 53 Belarus 17,370 29,566 24 10 47 41 39 33 29 49 Belgium 202,691 370,824 2 1 31 24 .. 17 67 75 Benina 1,845 4,287 36 32 13 13 8 8 51 54 Bolivia 4,868 9,334 17 15 35 32 19 14 49 53 Bosnia and Herzegovina .. 9,949 .. 10 .. 25 .. 12 .. 65 Botswana 3,792 10,317 5 2 61 53 5 4 34 44 Brazil 461,952 796,055 8 8 39 38 .. .. 53 54 Bulgaria 20,726 26,648 17 10 49 32 .. 20 34 59 Burkina Fasoa 3,120 5,171 28 31 20 20 15 14 52 50 Burundi 1,132 800 56 35 19 20 13 9 25 45 Cambodia 1,115 6,187 .. 34 .. 27 .. 19 .. 39 Cameroon 11,152 16,875 25 41 30 14 15 7 46 45 Canada 574,192 1,113,810 3 .. 32 .. 17 .. 65 .. Central African Republic 1,488 1,369 48 54 20 21 11 .. 33 25 Chad 1,739 5,469 29 23 18 51 14 5 53 26 Chile 31,559 115,248 9 6 42 47 20 18 50 48 Chinaa,b 354,644 2,234,297 27 13 42 48 33 34 31 40 Hong Kong, China 76,887 177,703 0 0 24 10 17 4 75 90 Colombia 40,274 122,309 17 13 38 34 21 15 45 53 Congo, Dem. Rep. 9,350 7,103 31 46 29 25 11 6 40 29 Congo, Rep.a 2,799 5,091 13 6 41 46 8 6 47 48 Costa Rica 7,403 20,021 12 9 30 30 23 22 58 62 Côte d'Ivoirea 10,796 16,344 33 23 23 26 21 19 44 51 Croatia 18,156 38,506 11 7 36 31 29 20 53 62 Cubaa .. .. .. .. .. .. .. .. .. .. Czech Republic 34,880 124,365 6 3 49 37 .. 25 45 60 Denmark 135,838 258,714 4 2 26 25 17 14 70 74 Dominican Republica 7,074 29,502 13 12 31 26 18 15 55 62 Ecuadora 10,356 36,489 13 7 38 46 19 9 49 48 Egypt, Arab Rep. 43,130 89,369 19 15 29 36 18 17 52 49 El Salvador 4,801 16,974 17 10 27 30 22 23 55 60 Eritrea 477 970 31 23 12 23 8 8 57 55 Estonia 5,010 13,101 17 4 50 29 42 19 34 67 Ethiopia 12,083 11,174 52 48 12 13 5 5 36 39 Finland 138,231 193,160 6 3 33 30 .. 22 61 68 France 1,239,256 2,126,630 4 2 27 21 .. 13 70 77 Gabona 5,952 8,055 7 8 43 58 6 5 50 35 Gambia, The 317 461 29 33 13 13 7 5 58 54 Georgia 7,738 6,395 32 17 34 27 24 18 35 56 Germany 1,707,383 2,794,926 2 1 38 30 28 23 61 69 Ghanaa 5,886 10,720 45 38 17 23 10 8 38 39 Greece 85,929 225,206 10 5 26 21 .. 11 63 74 Guatemalaa 7,650 31,717 26 23 20 19 15 13 54 58 Guinea 2,818 3,289 24 25 33 36 5 5 43 39 Guinea-Bissau 244 301 61 60 19 12 8 9 21 28 Haiti 2,864 4,268 .. 28 .. 17 .. 8 .. 55 194 2007 World Development Indicators 4.2 ECONOMY Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 3,049 8,291 22 14 26 31 16 20 51 55 Hungary 33,056 109,239 15 4 39 31 23 23 46 65 India 316,937 805,714 31 18 28 27 17 16 41 54 Indonesiaa 114,426 287,217 19 13 39 46 21 28 42 41 Iran, Islamic Rep. 116,035 189,784 19 10 29 45 12 12 52 45 Iraq 48,422 12,602 .. 9 .. 70 .. 2 .. 21 Ireland 47,854 201,817 9 3 35 37 .. 27 57 60 Israel 52,490 123,434 .. .. .. .. .. .. .. .. Italy 1,133,407 1,762,519 4 2 32 27 23 18 64 71 Jamaica 4,592 9,574 .. 6 .. 33 .. 14 .. 61 Japan 3,018,112 4,533,965 3 2 40 30 .. 21 58 68 Jordan 4,020 12,712 8 3 28 30 15 19 64 68 Kazakhstan 26,933 57,124 27 7 45 40 9 15 29 54 Kenya 8,591 18,730 30 27 19 19 12 12 51 54 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 263,777 787,624 9 3 42 40 27 28 50 56 Kuwait a 18,428 80,781 1 1 52 51 12 2 47 49 Kyrgyz Republic 2,674 2,441 34 34 36 21 28 14 30 45 Lao PDR 866 2,875 61 45 15 30 10 21 24 26 Latvia 7,447 15,826 22 4 46 22 35 13 32 74 Lebanon 2,838 21,944 .. 7 .. 22 .. 14 .. 71 Lesotho 615 1,450 24 17 33 41 14 19 44 41 Liberiaa 384 548 54 64 17 15 .. 12 29 21 Libya 28,905 38,756 .. .. .. .. .. .. .. .. Lithuania 10,507 25,625 27 6 31 34 21 22 42 61 Macedonia, FYR 4,472 5,766 9 13 45 29 36 18 47 58 Madagascar 3,081 5,040 29 28 13 16 11 14 59 56 Malawi 1,881 2,072 45 35 29 19 20 13 26 46 Malaysiaa 44,024 130,326 15 9 42 52 24 31 43 40 Mali 2,421 5,305 46 37 16 24 9 3 39 39 Mauritania 1,020 1,850 30 24 29 29 10 5 42 47 Mauritius 2,383 6,290 13 6 33 28 25 20 54 66 Mexico 262,710 768,438 8 4 28 26 21 18 64 70 Moldova 3,593 2,917 36 17 37 25 .. 17 27 59 Mongolia 2,093 1,880 15 22 41 29 36 3 44 49 Moroccoa 25,821 51,621 18 14 32 30 18 17 50 56 Mozambique 2,463 6,636 37 22 18 30 10 14 45 48 Myanmar a .. .. 57 .. 11 .. 8 .. 32 .. Namibia 2,350 6,126 12 10 38 32 14 14 50 58 Nepal 3,628 7,391 51 38 16 21 6 8 34 41 Netherlands 307,384 624,202 4 2 28 24 .. 14 67 74 New Zealand 43,898 109,291 7 .. 28 .. 19 .. 65 .. Nicaragua 1,009 4,911 .. 19 .. 28 .. 18 .. 53 Niger a 2,481 3,405 35 40 16 17 7 7 49 43 Nigeria 28,472 98,951 33 23 41 57 6 4 26 20 Norway 116,108 295,513 4 2 36 43 13 11 61 55 Omana 11,685 24,284 3 2 54 56 3 8 43 42 Pakistan 40,010 110,732 26 22 25 25 17 18 49 53 Panama 5,313 15,467 10 8 15 16 10 8 75 76 Papua New Guinea 3,221 4,945 .. 42 .. 39 .. 6 .. 19 Paraguaya 5,265 7,328 28 22 25 19 17 12 47 59 Peru 26,294 79,379 9 7 27 35 18 16 64 58 Philippinesa 44,312 99,029 22 14 35 32 25 23 44 53 Poland 58,976 303,229 8 5 50 31 .. 18 42 65 Portugal 75,274 183,305 9 3 29 25 .. 16 62 73 Puerto Ricoa 30,604 .. 1 .. 42 .. 40 .. 57 .. 2007 World Development Indicators 195 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 38,299 98,565 24 10 50 35 34 24 26 55 Russian Federation 516,814 763,720 17 6 48 38 .. 18 35 56 Rwandaa 2,584 2,153 33 42 25 21 18 8 43 37 Saudi Arabiaa 116,778 309,778 6 4 49 59 9 10 46 37 Senegala 5,699 8,238 20 18 19 19 13 11 61 63 Serbia and Montenegro .. 26,215 .. 16 .. 33 .. 20 .. 51 Sierra Leone 650 1,193 47 46 19 24 5 4 34 30 Singapore 36,842 116,764 0 0 35 34 27 28 65 66 Slovak Republica 15,485 46,412 7 4 59 29 .. 19 34 67 Slovenia 17,382 34,354 6 3 42 34 34 25 52 63 Somalia 917 .. 66 .. .. .. 5 .. .. .. South Africa 112,014 239,543 5 3 40 30 24 19 55 67 Spain 520,969 1,124,640 6 3 34 30 .. 16 61 67 Sri Lanka 8,032 23,479 26 17 26 26 15 15 48 57 Sudan 13,167 27,542 .. 34 .. 30 .. 7 .. 37 Swaziland 882 2,731 13 12 42 48 35 37 45 41 Sweden 242,178 357,683 3 1 31 28 .. 20 66 71 Switzerland 235,808 367,029 3 1 33 28 22 20 64 70 Syrian Arab Republic 12,309 26,320 30 23 25 35 21 30 45 41 Tajikistan 2,629 2,312 33 24 38 32 25 24 29 44 Tanzaniac 4,259 12,111 46 45 18 18 9 8 36 38 Thailanda 85,345 176,634 13 10 37 44 27 35 50 46 Togoa 1,628 2,203 34 42 23 23 10 10 44 35 Trinidad and Tobago 5,068 14,358 3 1 47 60 14 6 50 40 Tunisiaa 12,291 28,683 16 12 30 29 17 18 55 60 Turkey 150,642 362,502 18 12 30 24 20 14 52 65 Turkmenistan 3,232 8,067 32 20 30 41 .. 22 38 39 Uganda 4,304 8,724 57 33 11 25 6 9 32 43 Ukraine 81,456 82,876 26 11 45 34 39 21 30 55 United Arab Emirates 33,653 129,702 2 2 64 56 8 14 35 42 United Kingdom 989,524 2,198,789 2 1 35 26 23 15 63 73 United States 5,757,200 12,416,505 2 1 28 22 19 14 70 77 Uruguay 9,287 16,791 9 9 33 31 27 22 58 60 Uzbekistan 13,361 13,951 33 28 33 29 22 11 34 43 Venezuela, RB 47,028 140,192 6 5 61 52 15 18 34 44 Vietnama 6,472 52,408 39 21 23 41 12 21 39 38 West Bank and Gazaa .. 4,014 .. .. .. .. .. .. .. .. Yemen, Rep. 4,828 15,066 24 13 27 41 9 5 49 45 Zambia 3,288 7,270 21 19 51 25 36 12 28 56 Zimbabwe 8,784 3,372 17 18 33 23 23 13 50 59 World 21,784,509 t 44,645,437 t 5w 4w 33 w 28 w 21 w 18 w 61 w 69 w Low income 595,576 1,416,212 32 22 26 28 15 15 41 50 Middle income 3,253,159 8,553,721 16 9 39 38 24 23 46 53 Lower middle income 1,636,824 4,879,773 19 12 38 42 27 27 43 47 Upper middle income 1,615,064 3,673,796 11 6 39 32 22 19 50 62 Low & middle income 3,849,735 9,969,591 18 11 37 37 23 22 45 52 East Asia & Pacific 667,503 3,039,976 25 13 40 46 30 32 35 41 Europe & Central Asia 1,101,711 2,201,159 16 8 43 32 .. 18 41 60 Latin America & Carib. 1,103,860 2,460,991 9 8 36 34 .. 12 55 59 Middle East & N. Africa 276,910 625,311 17 12 33 40 14 14 50 48 South Asia 401,938 1,016,267 31 19 27 27 17 16 43 54 Sub-Saharan Africa 302,890 621,879 20 17 34 32 17 14 47 52 High income 17,935,976 34,687,058 3 2 32 26 21 17 65 72 Europe EMU 5,653,415 9,984,125 4 2 32 26 .. 19 64 72 a. Components are at producer prices. b. China has revised its national accounts data from 1993 onwards. Data before 1993 are not comparable with the later revised data. c. Data cover mainland Tanzania only. 196 2007 World Development Indicators 4.2 ECONOMY Structure of output 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 firms. But is the sum of gross value added by all resident pro- that economy. Value added is the value of the gross in most developing countries such surveys are infre- ducers in the economy plus any product taxes (less output of producers less the value of intermediate quent, so earlier survey results must be extrapo- subsidies) not included in the valuation of output. It goods and services consumed in production, before lated using an appropriate indicator. The choice of is calculated without deducting for depreciation of taking account of the consumption of fixed capital sampling unit, which may be the enterprise (where fabricated assets or for depletion and degradation of in the production process. The United Nations Sys- responses may be based on financial records) or natural resources. Value added is the net output of tem of National Accounts calls for estimates of value the establishment (where production units may be an industry after adding up all outputs and subtract- added to be valued at either basic prices (excluding recorded separately), also affects the quality of the ing intermediate inputs. The industrial origin of value net taxes on products) or producer prices (including data. Moreover, much industrial production is orga- added is determined by the International Standard net taxes on products paid by producers but excluding nized in unincorporated or owner-operated ventures Industrial Classification (ISIC) revision 3. · Agricul- sales or value added taxes). Both valuations exclude that are not captured by surveys aimed at the formal ture corresponds to ISIC divisions 1­5 and includes transport charges that are invoiced separately by pro- sector. Even in large industries, where regular surveys forestry and fishing. · Industry covers mining, manu- ducers. Total GDP shown in the table and elsewhere are more likely, evasion of excise and other taxes and facturing (also reported separately), construction, in this book is measured at purchaser prices. Value nondisclosure of income lower the estimates of value electricity, water, and gas (ISIC divisions 10­45). added by industry is normally measured at basic added. Such problems become more acute as coun- · Manufacturing corresponds to industries belong- prices. When value added is measured at producer tries move from state control of industry to private ing to ISIC divisions 15­37. · Services correspond prices, this is noted in Primary data documentation. enterprise, because new firms enter business and to ISIC divisions 50­99. This sector is derived as a While GDP estimates based on the production growing numbers of established firms fail to report. residual (from GDP less agriculture and industry) and approach are generally more reliable than estimates In accordance with the System of National Accounts, may not properly reflect the sum of services output, compiled from the income or expenditure side, dif- output should include all such unreported activity as including banking and financial services. For some ferent countries use different definitions, methods, well as the value of illegal activities and other unre- countries it includes product taxes (minus subsidies) and reporting standards. World Bank staff review corded, informal, or small-scale operations. Data on and may also include statistical discrepancies. the quality of national accounts data and sometimes these activities need to be collected using techniques make adjustments to improve consistency with other than conventional surveys of firms. international guidelines. Nevertheless, significant In industries dominated by large organizations discrepancies remain between international stan- and enterprises, such as public utilities, data on dards and actual practice. Many statistical offices, output, employment, and wages are usually read- especially those in developing countries, face severe ily available and reasonably reliable. But in the limitations in the resources, time, training, and bud- services industry the many self-employed workers gets required to produce reliable and comprehensive and one-person businesses are sometimes difficult series of national accounts statistics. to locate, and they have little incentive to respond to surveys, let alone to report their full earnings. Data problems in measuring output Compounding these problems are the many forms Data sources Among the difficulties faced by compilers of national of economic activity that go unrecorded, including accounts is the extent of unreported economic activ- the work that women and children do for little or no National accounts data for most developing coun- ity in the informal or secondary economy. In develop- pay. For further discussion of the problems of using tries are collected from national statistical organi- ing countries a large share of agricultural output is national accounts data, see Srinivasan (1994) and zations and central banks by visiting and resident either not exchanged (because it is consumed within Heston (1994). World Bank missions. Data for high-income econ- the household) or not exchanged for money. omies come from data files of the Organisation Agricultural production often must be estimated Dollar conversion for Economic Co-operation and Development (for indirectly, using a combination of methods involv- To produce national accounts aggregates that are information on the OECD's national accounts ing estimates of inputs, yields, and area under cul- measured in the same standard monetary units, series, see its Annual National Accounts for OECD tivation. This approach sometimes leads to crude the value of output must be converted to a single Member Countries: Data from 1970 Onwards). The approximations that can differ from the true values common currency. The World Bank conventionally complete national accounts time series is avail- over time and across crops for reasons other than uses the U.S. dollar and applies the average official able on the World Development Indicators 2007 climatic conditions or farming techniques. Similarly, exchange rate reported by the International Monetary CD-ROM. The United Nations Statistics Division agricultural inputs that cannot easily be allocated to Fund for the year shown. An alternative conversion publishes detailed national accounts for UN mem- specific outputs are frequently "netted out" using factor is applied if the official exchange rate is judged ber countries in National Accounts Statistics: Main equally crude and ad hoc approximations. For further to diverge by an exceptionally large margin from the 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. 2007 World Development Indicators 197 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 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 Afghanistan .. 679 .. .. .. .. .. .. .. .. .. .. Albania .. 610 24 .. 33 .. .. .. .. .. 44 .. Algeria 6,452 4,458 13 10 17 19 .. 3 .. 4 70 64 Angola 513 542 .. .. .. .. .. .. .. .. .. .. Argentina 37,868 29,142 20 30 10 7 13 10 12 16 46 37 Armenia 681 541 .. .. .. .. .. .. .. .. .. .. Australia 42,564 62,976 18 14 6 4 20 19 7 7 49 56 Austria 31,439 44,672 15 .. 7 .. 28 .. 8 .. 43 .. Azerbaijan 1,561 628 .. .. .. .. .. .. .. .. .. .. Bangladesh 3,839 7,899 24 39 38 1 7 1 17 10 15 49 Belarus 6,630 4,751 .. .. .. .. .. .. .. .. .. .. Belgium .. 48,185 17 18 7 15 .. 23 13 7 62 37 Benin 145 301 .. .. .. .. .. .. .. .. .. .. Bolivia 826 1,036 28 39 5 4 1 1 3 5 63 52 Bosnia and Herzegovina .. 727 12 .. 15 .. 18 .. 7 .. 49 .. Botswana 181 323 51 20 12 5 .. .. .. .. 37 75 Brazil .. .. 14 .. 12 .. 27 .. .. .. 48 .. Bulgaria .. 3,184 .. 19 .. 20 .. 19 .. 10 .. 32 Burkina Faso 460 539 46 46 3 4 1 2 1 1 49 48 Burundi 134 45 83 16 9 5 .. .. 2 6 7 73 Cambodia 58 854 .. .. .. .. .. .. .. .. .. .. Cameroon 1,581 1,061 61 37 -13 6 1 1 5 10 46 46 Canada 91,671 119,900 15 17 6 9 26 21 10 7 44 46 Central African Republic 154 .. 58 6 6 53 2 0 6 2 28 39 Chad 239 211 .. .. .. .. .. .. .. .. .. .. Chile 5,613 13,268 25 21 8 14 5 12 10 9 52 45 Chinab 116,573 539,026 15 13 15 10 24 32 13 11 34 33 Hong Kong, China 12,357 5,702 8 11 36 23 21 21 2 4 33 41 Colombia 8,034 11,606 31 21 15 18 9 5 14 11 31 45 Congo, Dem. Rep. 1,029 302 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 234 227 58 .. 4 .. 3 .. .. .. 35 .. Costa Rica 1,514 3,361 47 45 8 5 7 6 9 11 30 34 Côte d'Ivoire 2,257 2,452 38 .. 7 .. 8 .. .. .. 47 .. Croatia 4,770 4,850 22 .. 15 .. 20 .. 8 .. 36 .. Cuba .. .. 67 .. 5 .. 1 .. .. .. 27 .. Czech Republic .. 20,466 .. .. .. .. .. .. .. .. .. .. Denmark 20,364 27,476 22 20 4 6 24 27 12 4 39 43 Dominican Republic 1,270 2,550 78 109 7 11 0 1 4 5 11 -25 Ecuador 1,988 2,735 22 27 10 4 5 3 8 4 56 61 Egypt, Arab Rep. 7,296 14,466 19 .. 16 .. 9 .. 14 .. 43 .. El Salvador 1,043 3,391 36 43 14 26 4 4 24 9 23 18 Eritrea 35 61 53 50 18 9 2 2 18 8 9 31 Estonia 1,985 1,458 .. 15 .. 13 .. 13 .. 4 .. 55 Ethiopia 601 410 12 4 5 3 0 0 0 1 82 93 Finland .. 31,856 13 17 4 12 24 18 8 6 52 48 France .. 227,906 13 15 6 3 31 32 9 14 41 37 Gabon 332 297 45 .. 2 .. 1 .. 7 .. 45 .. Gambia, The 18 18 .. .. .. .. .. .. .. .. .. .. Georgia 1,773 706 .. .. .. .. .. .. .. .. .. .. Germany 451,915 489,786 .. 9 .. 2 .. 43 .. .. .. 47 Ghana 575 684 .. 22 .. 3 .. 1 .. 4 .. 70 Greece .. 17,716 22 20 20 22 12 13 10 9 36 36 Guatemala 1,151 3,157 38 32 11 4 4 3 18 4 29 58 Guinea 126 159 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 19 23 .. .. .. .. .. .. .. .. .. .. Haiti .. 216 51 .. 9 .. .. .. .. .. 40 .. 198 2007 World Development Indicators 4.3 ECONOMY 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 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 Honduras 443 1,253 45 57 10 9 3 1 6 4 36 30 Hungary 6,613 15,887 14 12 9 15 26 19 12 3 39 51 India 48,808 84,971 12 12 15 0 26 4 14 5 34 79 Indonesia 23,643 68,794 28 23 15 13 12 18 9 10 37 36 Iran, Islamic Rep. 13,357 15,034 12 10 20 5 20 28 8 13 40 44 Iraq .. 263 20 22 16 17 4 8 11 3 49 51 Ireland .. 39,819 27 37 4 21 29 13 17 6 24 24 Israel .. .. 14 16 9 14 32 12 9 4 37 55 Italy 240,462 258,746 8 9 13 11 35 27 7 8 37 45 Jamaica 853 1,017 41 15 5 7 .. .. .. .. 54 78 Japan .. 886,172 9 8 5 2 40 19 10 7 37 65 Jordan 520 1,527 28 26 7 11 4 5 15 16 47 42 Kazakhstan 1,941 4,384 .. .. .. .. .. .. .. .. .. .. Kenya 864 1,448 39 39 10 7 10 17 9 8 33 29 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 64,605 141,947 11 32 12 20 32 8 9 10 36 31 Kuwait 2,142 1,087 4 10 3 5 2 5 3 3 88 76 Kyrgyz Republic 706 255 .. .. .. .. .. .. .. .. .. .. Lao PDR 85 405 .. .. .. .. .. .. .. .. .. .. Latvia 2,474 1,330 .. 24 .. 10 .. 11 .. 4 .. 51 Lebanon .. 2,328 .. .. .. .. .. .. .. .. .. .. Lesotho 71 189 .. .. .. .. .. .. .. .. .. .. Liberia .. 30 .. .. .. .. .. .. .. .. .. .. Libya .. .. 48 73 5 4 .. 4 1 15 47 5 Lithuania 2,164 3,211 .. 23 .. 15 .. 15 .. 4 .. 43 Macedonia, FYR 1,411 730 20 .. 26 .. 14 .. 9 .. 31 .. Madagascar 314 688 39 3 36 6 3 6 8 1 14 84 Malawi 313 175 38 262 10 9 1 6 18 50 33 -227 Malaysia 10,665 32,355 13 9 7 3 31 39 11 9 39 40 Mali 200 114 .. .. .. .. .. .. .. .. .. .. Mauritania 94 70 .. .. .. .. .. .. .. .. .. .. Mauritius 491 1,010 30 78 46 5 2 4 4 3 17 9 Mexico 49,992 104,107 22 .. 5 .. 24 .. 18 .. 32 .. Moldova .. 308 .. 57 .. 11 .. 7 .. .. .. 25 Mongolia 745 62 33 .. 37 .. 1 .. 1 .. 27 .. Morocco 4,753 7,319 22 33 17 18 8 8 12 13 41 28 Mozambique 230 605 .. 75 .. 19 .. 8 .. 4 .. -6 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 292 512 .. .. .. .. .. .. .. .. .. .. Nepal 209 441 37 45 31 19 1 2 5 10 26 23 Netherlands .. 67,292 21 3 3 1 25 3 17 2 35 92 New Zealand 7,613 8,097 28 19 8 7 13 12 7 4 44 58 Nicaragua .. 692 40 31 3 9 0 2 3 9 54 50 Niger 163 179 37 32 29 .. .. .. .. 25 34 43 Nigeria 1,562 2,268 15 .. 46 .. 13 .. 4 .. 22 .. Norway 13,450 21,702 18 3 2 11 25 24 9 8 46 55 Oman 343 1,782 .. 9 .. 2 .. 2 .. 4 .. 84 Pakistan 6,184 12,387 24 35 28 92 9 14 15 15 25 -56 Panama 502 924 51 48 8 7 2 2 8 3 31 39 Papua New Guinea .. 215 6 19 .. .. 7 10 .. 4 87 68 Paraguay 883 691 56 54 16 15 .. 1 .. 4 29 27 Peru 3,926 8,811 23 .. 11 .. 8 .. 9 .. 49 .. Philippines 11,003 18,824 39 38 11 10 13 8 12 12 26 33 Poland .. 33,786 21 9 9 3 26 7 7 4 37 78 Portugal .. 21,352 15 14 21 18 13 17 6 6 45 45 Puerto Rico 12,126 27,099 16 .. 5 .. 18 .. 44 .. 17 .. 2007 World Development Indicators 199 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 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 1990 2003 Romania 9,152 16,141 19 .. 18 .. 14 .. 4 .. 45 .. Russian Federation .. 64,391 .. .. .. .. .. .. .. .. .. .. Rwanda 473 149 .. 93 .. .. .. .. .. .. .. 7 Saudi Arabia 10,049 23,005 7 .. 1 .. 4 .. 39 .. 50 .. Senegal 747 752 60 .. 3 .. 5 .. 9 .. 23 .. Serbia and Montenegro .. 3,305 .. 36 .. 6 .. 14 .. 11 .. 33 Sierra Leone 28 34 .. .. .. .. .. .. .. .. .. .. Singapore 9,562 22,207 4 3 3 1 53 50 10 24 29 22 Slovak Republic .. 6,255 .. .. .. .. .. .. .. .. .. .. Slovenia 5,190 6,433 12 10 15 8 16 17 9 15 48 50 Somalia 41 .. 2 31 3 16 .. .. .. .. 95 54 South Africa 24,043 29,301 15 16 8 13 18 17 9 9 50 45 Spain .. 134,438 18 13 8 19 25 16 10 11 39 41 Sri Lanka 1,077 2,524 51 37 24 33 4 5 4 6 17 20 Sudan .. 1,205 .. 66 .. 4 .. 4 .. 4 .. 21 Swaziland 250 432 69 37 8 2 1 .. .. .. 22 60 Sweden .. 52,365 10 10 2 4 33 24 9 3 47 59 Switzerland 49,484 60,215 10 .. 4 .. 34 .. .. .. 53 .. Syrian Arab Republic 2,508 1,737 35 36 29 41 .. 2 .. 2 36 19 Tajikistan 653 436 .. .. .. .. .. .. .. .. .. .. Tanzaniac 361 685 51 39 3 23 7 3 11 5 29 29 Thailand 23,217 49,735 24 23 30 14 19 4 2 25 26 34 Togo 162 163 .. .. .. 7 .. .. .. 9 .. 84 Trinidad and Tobago 681 747 31 16 3 1 3 4 19 1 44 78 Tunisia 2,075 4,480 19 35 20 12 5 5 4 20 52 29 Turkey 26,882 26,753 16 29 15 22 16 6 10 2 43 42 Turkmenistan .. 1,045 .. .. .. .. .. .. .. .. .. .. Uganda 230 538 61 10 14 28 3 1 6 .. 16 62 Ukraine 31,517 9,320 .. .. .. .. .. .. .. .. .. .. United Arab Emirates 2,643 11,495 .. .. .. 2 .. .. .. 3 .. 95 United Kingdom 206,719 238,575 13 12 5 11 32 32 11 10 38 36 United States 1,040,600 1,488,100 12 12 5 8 31 30 12 10 40 39 Uruguay 2,597 2,078 31 .. 18 .. 9 .. 10 .. 32 .. Uzbekistan .. 807 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 6,921 14,289 17 25 5 8 5 14 9 9 64 44 Vietnam 793 8,115 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 449 517 .. 48 .. 7 .. 0 .. .. .. 44 Zambia 1,048 473 44 63 12 8 7 6 9 12 29 12 Zimbabwe 1,799 911 28 26 19 16 10 10 6 11 38 37 World 4,401,339 t 6,163,496 t Low income 81,598 136,860 Middle income 577,839 1,358,768 Lower middle income 312,003 913,666 Upper middle income 271,707 440,727 Low & middle income 665,884 1,495,945 East Asia & Pacific 188,030 722,097 Europe & Central Asia .. .. Latin America & Carib. 174,072 281,559 Middle East & N. Africa 32,128 56,299 South Asia 60,477 108,845 Sub-Saharan Africa 43,592 50,518 High income .. 4,693,358 Europe EMU .. 1,390,728 a. Includes unallocated data. b. China has revised its national accounts data from 1993 onwards. Data before 1993 are not comparable with the later revised data. c. Data cover mainland Tanzania only. 200 2007 World Development Indicators 4.3 ECONOMY Structure of manufacturing About the data The data on the distribution of manufacturing value Nations International Standard Industrial Classifica- painting as well as advertising, accounting, and many added by industry are provided by the United Nations tion (ISIC) revision 2. First published in 1948, the other service activities. In some cases the processes Industrial Development Organization (UNIDO). UNIDO ISIC has its roots in the work of the League of Nations may be carried out by different technical units within obtains data on manufacturing value added from a Committee of Statistical Experts. The committee's the larger enterprise, but collecting data at such a variety of national and international sources, includ- efforts, interrupted by the Second World War, were detailed level is not practical, nor would it be useful ing the United Nations Statistics Division, the World taken up by the United Nations Statistical Commis- to record production data at the very highest level of Bank, the Organisation for Economic Co-operation sion, which at its first session appointed a commit- a large, multiplant, multiproduct firm. The ISIC has and Development, and the International Monetary tee on industrial classification. The latest revision, therefore adopted as the definition of an establish- Fund. To improve comparability over time and across ISIC revision 3, was completed in 1989, and many ment "an enterprise or part of an enterprise which countries, UNIDO supplements these data with infor- countries have now switched to it. But revision 2 is independently engages in one, or predominantly one, mation from industrial censuses, statistics supplied still widely used for compiling cross-country data. kind of economic activity at or from one location . . . by national and international organizations, unpub- Concordances matching ISIC categories to national for which data are available . . ." (United Nations lished data that it collects in the field, and estimates systems of classifi cation and to related systems 1990, p. 25). By design, this definition matches the by the UNIDO Secretariat. Nevertheless, coverage such as the Standard International Trade Classifica- reporting unit required for the production accounts may be less than complete, particularly for the infor- tion are readily available. of the UN System of National Accounts. mal sector. To the extent that direct information on In establishing a classifi cation system, compil- Definitions inputs and outputs is not available, estimates may ers must define both the types of activities to be be used, which may result in errors in industry totals. described and the organizational units whose activi- · Manufacturing value added is the sum of gross Moreover, countries use different reference periods ties are to be reported. There are many possibili- output less the value of intermediate inputs used (calendar or fiscal year) and valuation methods (basic ties, and the choices made affect how the resulting in production for industries classified in ISIC major or producer prices) to estimate value added. (See statistics can be interpreted and how useful they division 3. · Food, beverages, and tobacco corre- also About the data for table 4.2.) are in analyzing economic behavior. The ISIC empha- spond to ISIC division 31. · Textiles and clothing The data on manufacturing value added in U.S. sizes commonalities in the production process and is correspond to ISIC division 32. · Machinery and dollars are from the World Bank's national accounts explicitly not intended to measure outputs (for which transport equipment correspond to ISIC groups files. These figures may differ from those used by there is a newly developed Central Product Classifi - 382­84. · Chemicals correspond to ISIC groups UNIDO to calculate the shares of value added by cation). Nevertheless, the ISIC views an activity as 351 and 352. · Other manufacturing covers wood industry, in part because of differences in exchange defined by "a process resulting in a homogeneous and related products (ISIC division 33), paper and rates. Thus estimates of value added in a particular set of products" (United Nations 1990 [ISIC, series related products (ISIC division 34), petroleum and industry calculated by applying the shares to total M, no. 4, rev. 3], p. 9). related products (ISIC groups 353­56), basic met- manufacturing value added will not match those Firms typically use a multitude of processes to als and mineral products (ISIC divisions 36 and 37), from UNIDO sources. The classification of manufac- produce a final product. For example, an automo- fabricated metal products and professional goods turing industries in the table accords with the United bile manufacturer engages in forging, welding, and (ISIC groups 381 and 385), and other industries (ISIC group 390). When data for textiles and clothing, machinery and transport equipment, or chemicals Manufacturing continues to show are shown in the table as not available, they are strong growth in East Asia 4.3a included in "other manufacturing." Value added in manufacturing (index, 1990 = 100) 500 East Asia & Pacific 450 Data sources 400 Data on value added in manufacturing in U.S. dol- 350 lars are from the World Bank's national accounts 300 South Asia files. Data used to calculate shares of value added 250 Middle East & North Africa by industry are provided to the World Bank in elec- 200 tronic files by UNIDO. The most recent published 150 Sub-Saharan Africa source is UNIDO's International Yearbook of Indus- 100 Latin America & Caribbean trial Statistics 2006. The ISIC system is described 50 in the United Nations' International Standard Indus- 1990 1995 2000 2005 trial Classification of All Economic Activities, Third Manufacturing continues to be the dominant sector in the East Asia and Pacific region, growing by an Revision (1990). The discussion of the ISIC draws average of about 10 percent a year between 1990 and 2005. on Jacob Ryten's "Fifty Years of ISIC: Historical Source: World Bank data files. Origins and Future Perspectives" (1998). 2007 World Development Indicators 201 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan 235 560 .. .. .. .. .. .. .. .. .. .. Albania 230 658 .. 6 .. 4 .. 3 .. 7 .. 80 Algeria 12,930 46,001 0 0 .. .. 96 97 0 0 3 2 Angola 3,910 23,400 0 .. 0 .. 93 .. 6 .. 0 .. Argentina 12,353 40,044 56 47 4 1 8 16 2 3 29 31 Armenia .. 950 .. 12 .. 1 .. 2 .. 13 .. 71 Australia 39,752 105,825 22 17 10 3 21 27 20 20 27 25 Austria 41,265 123,987 3 6 4 2 1 5 3 3 88 80 Azerbaijan .. 7,649 .. 7 .. 1 .. 77 .. 1 .. 13 Bangladesh 1,671 9,294 14 8 7 2 1 0 .. 0 77 90 Belarus .. 15,977 .. 8 .. 3 .. 35 .. 1 .. 52 Belgium 117,703a 334,298 9a 8 2a 1 3a 7 4a 3 77a 79 Benin 288 561 15 25 56 61 15 1 0 1 13 13 Bolivia 926 2,671 19 21 8 2 25 49 44 17 5 11 Bosnia and Herzegovina 276 2,402 .. .. .. .. .. .. .. .. .. .. Botswana 1,784 4,425 .. 2 .. 0 .. 0 .. 11 .. 86 Brazil 31,414 118,308 28 26 3 4 2 6 14 10 52 54 Bulgaria 5,030 11,725 .. 11 .. 2 .. 10 .. 14 .. 59 Burkina Faso 152 493 .. 16 .. 72 .. 3 .. 1 .. 8 Burundi 75 111 .. 87 .. 4 .. 0 .. 2 .. 6 Cambodia 86 3,100 .. 1 .. 2 .. .. .. 0 .. 97 Cameroon 2,002 2,829 20 17 14 13 50 50 7 6 9 3 Canada 127,629 359,399 9 7 9 5 10 20 9 6 59 58 Central African Republic 120 128 31 1 24 41 .. .. 1 17 44 36 Chad 188 3,065 .. .. .. .. .. .. .. .. .. .. Chile 8,372 40,574 24 19 9 7 1 2 55 56 11 14 China 62,091 761,954 13 3 3 1 8 2 2 2 72 92 Hong Kong, Chinab 82,390 292,119 4 1 1 1 1 0 1 1 92 96 Colombia 6,766 21,146 33 18 4 5 37 40 0 1 25 36 Congo, Dem. Rep. 2,326 2,050 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 981 5,000 .. .. .. .. .. .. .. .. .. .. Costa Rica 1,448 7,039 58 30 5 3 1 0 1 1 27 66 Côte d'Ivoire 3,072 7,610 .. 56 .. 9 .. 13 .. 0 .. 20 Croatia 4,597 8,809 13 10 6 3 9 14 5 4 68 68 Cuba 5,100 2,682 .. 40 .. 0 .. 2 .. 39 .. 19 Czech Republic 12,170 78,246 .. 4 .. 1 .. 3 .. 2 .. 88 Denmark 36,870 85,137 27 18 3 3 3 9 1 1 60 65 Dominican Republic 2,170 6,133 21 .. 0 .. 0 .. 0 .. 78 .. Ecuador 2,714 10,100 44 28 1 4 52 58 0 0 2 9 Egypt, Arab Rep. 3,477 10,654 10 10 10 7 29 43 9 4 42 31 El Salvador 582 3,390 57 32 1 1 2 4 3 3 38 60 Eritrea 16 10 .. .. .. .. .. .. .. .. .. .. Estonia .. 7,667 .. 7 .. 6 .. 7 .. 2 .. 69 Ethiopia 298 883 .. 62 .. 26 .. 0 .. 1 .. 11 Finland 26,571 66,016 2 2 10 5 1 4 4 3 83 84 France 216,588 460,157 16 11 2 1 2 4 3 2 77 80 Gabon 2,204 4,920 .. 1 .. 10 .. 76 .. 6 .. 7 Gambia, The 31 8 .. 78 .. 4 .. 1 .. 0 .. 17 Georgia .. 867 .. 36 .. 2 .. 3 .. 18 .. 40 Germany 421,100 969,858 5 4 1 1 1 2 3 2 89 83 Ghana 897 2,490 51 77 15 5 9 3 17 2 8 12 Greece 8,105 17,044 30 22 3 2 7 9 7 8 54 56 Guatemala 1,163 5,381 67 34 6 3 2 6 0 1 24 57 Guinea 671 890 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 19 101 .. .. .. .. .. .. .. .. .. .. Haiti 160 470 14 .. 1 .. 0 .. 0 .. 85 .. Data for Taiwan, China 67,245 197,776 4 1 2 1 1 5 1 2 93 91 202 2007 World Development Indicators 4.4 ECONOMY 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 831 1,695 82 55 4 3 1 0 4 5 9 36 Hungary 10,000 62,109 23 6 3 1 3 3 6 2 63 84 India 17,969 95,096 16 9 4 2 3 11 5 7 70 70 Indonesia 25,675 86,226 11 12 5 5 44 28 4 8 35 47 Iran, Islamic Rep. 19,305 56,252 .. 4 .. 0 .. 83 .. 1 .. 9 Iraq 12,380 24,096 .. .. .. .. .. .. .. .. .. .. Ireland 23,743 109,853 22 8 2 0 1 1 1 1 70 86 Israel 12,080 42,659 8 2 3 1 1 0 2 1 87 83 Italy 170,304 367,200 6 6 1 1 2 3 1 1 88 85 Jamaica 1,158 1,500 19 22 0 0 1 2 9 9 70 66 Japan 287,581 594,905 1 1 1 1 0 1 1 2 96 92 Jordan 1,064 4,302 10 15 1 0 0 0 33 12 56 72 Kazakhstan .. 27,849 .. 4 .. 1 .. 65 .. 14 .. 16 Kenya 1,031 3,293 49 40 6 12 13 23 3 4 30 21 Korea, Dem. Rep. 1,857 1,338 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 65,016 284,419 3 1 1 1 1 5 1 2 94 91 Kuwait 7,042 45,011 1 .. 0 .. 93 .. 0 .. 6 .. Kyrgyz Republic .. 672 .. 11 .. 8 .. 12 .. 4 .. 27 Lao PDR 79 510 .. .. .. .. .. .. .. .. .. .. Latvia .. 5,161 .. 11 .. 16 .. 9 .. 4 .. 57 Lebanon 494 2,337 .. 16 .. 1 .. 0 .. 12 .. 70 Lesotho 62 649 .. .. .. .. .. .. .. .. .. .. Liberia 868 200 .. .. .. .. .. .. .. .. .. .. Libya 13,225 30,110 1 .. 0 .. 95 .. .. .. 4 .. Lithuania .. 11,813 24 12 6 3 8 27 1 2 59 56 Macedonia, FYR 1,199 2,041 .. 16 .. 1 .. 8 .. 3 .. 72 Madagascar 319 760 73 61 4 6 1 4 8 5 14 22 Malawi 417 520 91 80 2 4 0 0 0 0 7 16 Malaysia 29,452 140,949 12 7 14 3 18 13 2 1 54 75 Mali 359 1,109 36 .. 62 .. .. .. 0 .. 2 .. Mauritania 469 565 .. .. .. .. .. .. .. .. .. .. Mauritius 1,194 2,144 32 28 1 0 1 0 0 1 66 70 Mexico 40,711 213,711 12 5 2 1 38 15 6 2 43 77 Moldova .. 1,091 .. 53 .. 6 .. 0 .. 2 .. 39 Mongolia 661 1,054 .. 2 .. 14 .. 5 .. 58 .. 21 Morocco 4,265 10,641 26 21 3 2 4 2 15 9 52 65 Mozambique 126 1,745 .. 12 .. 4 .. 15 .. 58 .. 7 Myanmar 325 2,925 51 .. 36 .. 0 .. 2 .. 11 .. Namibia 1,085 2,070 .. 48 .. 1 .. 1 .. 7 .. 41 Nepal 204 850 13 21 3 1 .. .. .. 4 83 74 Netherlands 131,775 402,407 20 14 4 3 10 12 3 3 59 68 New Zealand 9,394 21,729 45 50 18 10 4 2 5 4 26 31 Nicaragua 330 858 77 85 14 2 0 1 1 1 8 11 Niger 282 502 .. 30 .. 4 .. 2 .. 55 .. 8 Nigeria 13,596 42,277 1 0 1 0 97 98 0 .. 1 2 Norway 34,047 103,780 7 5 2 0 48 68 10 6 32 17 Oman 5,508 18,692 1 2 0 0 92 86 1 1 5 6 Pakistan 5,615 15,917 9 12 10 1 1 4 0 0 79 82 Panama 340 1,010 75 85 1 1 0 1 1 4 21 9 Papua New Guinea 1,177 3,192 22 21 9 3 0 22 58 49 10 6 Paraguay 959 1,688 52 75 38 12 0 0 0 1 10 13 Peru 3,230 17,206 21 21 3 2 10 11 47 49 18 17 Philippines 8,117 41,255 19 6 2 1 2 2 8 2 38 89 Poland 14,320 89,288 12 9 3 1 12 5 10 4 58 78 Portugal 16,417 38,133 7 8 6 2 3 4 3 3 80 75 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 203 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 4,960 27,730 1 3 3 2 18 11 4 4 73 80 Russian Federation .. 243,569 .. 2 .. 3 .. 49 .. 7 .. 19 Rwanda 110 125 .. 52 .. 7 .. 7 .. 23 .. 10 Saudi Arabia 44,417 181,440 1 1 0 0 90 89 1 0 8 9 Senegal 761 1,641 53 29 3 2 12 21 9 3 23 43 Serbia and Montenegro 2,539 5,065 19 21 3 4 6 2 10 11 62 61 Sierra Leone 138 158 .. .. .. .. .. .. .. .. .. .. Singaporeb 52,730 229,649 5 2 3 0 18 12 2 1 72 81 Slovak Republic 6,355 31,956 .. 4 .. 2 .. 7 .. 3 .. 84 Slovenia 6,681 18,633 7 3 2 2 3 2 3 4 86 88 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 23,549 51,876 8c 9 4c 2 7c 10 10 c 22 29c 57 Spain 55,642 187,182 15 14 2 1 4 4 2 2 75 77 Sri Lanka 1,912 6,347 34 22 6 2 1 0 2 4 54 70 Sudan 374 4,824 60 7 38 5 .. 87 0 0 2 0 Swaziland 556 2,020 .. .. .. .. .. .. .. .. .. .. Sweden 57,540 130,104 2 3 7 4 3 5 3 3 83 79 Switzerland 63,784 130,898 3 3 1 0 0 0 3 3 94 93 Syrian Arab Republic 4,212 5,760 14 15 5 4 45 68 1 1 36 11 Tajikistan .. 909 .. .. .. .. .. .. .. .. .. .. Tanzania 331 1,481 .. 57 .. 17 .. 0 .. 12 .. 14 Thailand 23,068 110,110 29 12 5 5 1 4 1 1 63 77 Togo 268 569 23 21 21 9 0 1 45 10 9 58 Trinidad and Tobago 1,960 9,035 5 3 0 0 67 70 1 0 27 26 Tunisia 3,526 10,494 11 11 1 1 17 10 2 1 69 78 Turkey 12,959 73,414 22 10 3 1 2 4 4 2 68 82 Turkmenistan .. 4,935 .. .. .. .. .. .. .. .. .. .. Uganda 152 853 .. 64 .. 12 .. 5 .. 2 .. 17 Ukraine .. 34,287 .. 12 .. 1 .. 10 .. 6 .. 69 United Arab Emirates 23,544 115,453 2 .. 0 .. 7 .. 78 .. 12 .. United Kingdom 185,172 382,761 7 5 1 1 8 9 3 3 79 77 United States 393,592 904,383 11 7 4 2 3 3 3 3 75 82 Uruguay 1,693 3,405 40 55 21 7 0 5 0 1 39 32 Uzbekistan .. 4,749 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17,497 55,487 2 0 0 0 80 89 7 2 10 9 Vietnam 2,404 31,625 .. 23 .. 2 .. 21 .. 1 .. 53 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 692 6,380 8 4 1 0 74 92 1 0 15 4 Zambia 1,309 1,720 .. 13 .. 5 .. 1 .. 72 .. 9 Zimbabwe 1,726 1,820 44 31 7 16 1 2 16 23 31 28 World 3,474,778 t 10,433,971 t 10 w 7w 3w 2w 9w 10 w 4w 3w 73 w 75 w Low income 67,127 261,853 15 15 4 3 27 28 5 3 49 50 Middle income 552,565 2,795,181 17 9 4 2 21 17 6 5 50 65 Lower middle income 272,538 1,520,827 18 10 4 2 16 12 5 4 54 71 Upper middle income 281,789 1,274,355 16 8 5 2 28 22 7 5 44 58 Low & middle income 620,808 3,057,040 17 9 4 2 20 17 6 5 51 64 East Asia & Pacific 155,928 1,185,572 15 6 6 2 13 8 3 3 60 81 Europe & Central Asia .. 761,588 .. 6 .. 2 .. 23 .. 5 .. 56 Latin America & Carib. 143,275 565,896 21 15 3 2 30 22 10 7 36 54 Middle East & N. Africa 81,103 225,759 .. 6 .. 1 .. 69 .. 2 .. 20 South Asia 27,754 128,475 16 11 5 2 2 9 4 6 71 72 Sub-Saharan Africa 68,368 189,745 .. 15 .. 5 .. 36 .. 10 .. 33 High income 2,850,034 7,376,990 8 6 3 2 6 8 3 3 77 78 Europe EMU 1,241,084 3,113,158 11 8 2 1 3 5 3 2 80 80 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). 204 2007 World Development Indicators 4.4 ECONOMY Structure of merchandise exports About the data Data on merchandise trade are from customs reports from customs storage; and (c) goods previously reported here have not been fully reconciled with of goods movement into or out of an economy or included as imports for domestic consumption but the estimates of exports of goods and services from from reports of the financial transactions related to subsequently exported without transformation. the national accounts or those from the balance of merchandise trade recorded in the balance of pay- Under the special system exports comprise catego- payments. ments. Because of differences in timing and defini- ries a and c. In some compilations categories b and The classification of commodity groups is based tions, estimates of trade flows from customs reports c are classified as re-exports. Because of differences on the Standard International Trade Classification are likely to differ from those based on the balance of in reporting practices, data on exports may not be (SITC) revision 1. Most countries now report using payments. Moreover, several international agencies fully comparable across economies. later revisions of the SITC or the Harmonized Sys- process trade data, each correcting unreported or The data on total exports of goods (merchandise) tem. Concordance tables are used to convert data misreported data, and this leads to other differences in this table come from the World Trade Organization reported in one system of nomenclature to another. in the available data. (WTO). The WTO uses two main sources, national The conversion process may introduce some errors The most detailed source of data on international statistical offices and the IMF's International Finan- of classification, but conversions from later to earlier trade in goods is the Commodity Trade (Comtrade) cial Statistics. It supplements these with the Com- systems are generally reliable. database maintained by the United Nations Statistics trade database and publications or databases of Definitions Division. In addition, the International Monetary Fund regional organizations, specialized agencies, eco- (IMF) collects customs-based data on exports and nomic groups, and private sources (such as Eurostat, · Merchandise exports are the f.o.b. value of goods imports of goods. The value of exports is recorded the Food and Agriculture Organization, and country provided to the rest of the world, valued in U.S. dol- as the cost of the goods delivered to the frontier of reports of the Economist Intelligence Unit). In recent lars. · Food corresponds to the commodities in the exporting country for shipment--the free on board years country websites and direct contacts through SITC sections 0 (food and live animals), 1 (bever- (f.o.b.) value. Many countries report trade data in U.S. email have helped to improve the collection of up- ages and tobacco), and 4 (animal and vegetable oils dollars. When countries report in local currency, the to-date statistics for many countries, reducing the and fats) and SITC division 22 (oil seeds, oil nuts, United Nations Statistics Division applies the average proportion of estimated figures. The WTO database and oil kernels). · Agricultural raw materials cor- official exchange rate for the period shown. now covers most of the major traders in Africa, Asia, respond to SITC section 2 (crude materials except Countries may report trade according to the gen- and Latin America, which together with the high- fuels) excluding divisions 22, 27 (crude fertilizers eral or special system of trade (see Primary data income countries account for nearly 95 percent of and minerals excluding coal, petroleum, and pre- documentation). Under the general system, exports total world trade. There has also been a remarkable cious stones), and 28 (metalliferous ores and scrap). comprise outward-moving goods that are (a) goods improvement in the availability of reliable figures for · Fuels correspond to SITC section 3 (mineral fuels). wholly or partly produced in the country; (b) foreign countries in Europe and Central Asia. · Ores and metals correspond to the commodities goods, neither transformed nor declared for domes- The shares of exports by major commodity group in SITC divisions 27, 28, and 68 (nonferrous met- tic consumption in the country, that move outward are from Comtrade. The values of total exports als). · Manufactures correspond to the commodities in SITC sections 5 (chemicals), 6 (basic manufac- Developing economies' share of world tures), 7 (machinery and transport equipment), and merchandise exports continues to expand 4.4a 8 (miscellaneous manufactured goods), excluding division 68. 1990 2005 East Asia & Pacific 4% Europe & Central Asia 4% East Asia & Pacific 11% Latin America & Caribbean 4% Middle East & North Africa 2% Europe & Sub-Saharan Africa 2% Central Asia 7% South Asia 1% Data sources Latin America & Caribbean 5% The WTO publishes data on world trade in its High-income 83% Annual Report. The IMF publishes estimates of Middle East & North Africa 2% total exports of goods in its International Financial Sub-Saharan Statistics and Direction of Trade Statistics, as does High-income 72% Africa 2% the United Nations Statistics Division in its Monthly South Asia 1% Bulletin of Statistics. And the United Nations Con- ference on Trade and Development publishes data on the structure of exports and imports in its Handbook of International Trade and Develop- Developing economies' share of world merchandise exports increased by 11 percentage points from 1990 ment Statistics. Tariff line records of exports and to 2005. East Asia and Pacific was the biggest gainer, capturing an additional 7 percentage points. imports are compiled in the United Nations Sta- Source: World Bank data files. tistics Division's Comtrade database. 2007 World Development Indicators 205 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan 936 3,200 .. .. .. .. .. .. .. .. .. .. Albania 380 2,614 .. 17 .. 1 .. 9 .. 2 .. 71 Algeria 9,780 20,357 24 22 5 2 1 1 2 1 68 74 Angola 1,578 8,150 .. .. .. .. .. .. .. .. .. .. Argentina 4,076 28,692 4 3 4 2 8 5 6 3 78 87 Armenia .. 1,768 .. 18 .. 1 .. 16 .. 3 .. 62 Australia 41,985 125,280 5 5 2 1 6 11 1 1 85 81 Austria 49,146 126,179 5 6 3 2 6 13 4 4 81 74 Azerbaijan .. 4,200 .. 10 .. 1 .. 12 .. 2 .. 74 Bangladesh 3,618 13,839 19 19 5 9 16 8 3 2 56 62 Belarus .. 16,699 .. 9 .. 2 .. 33 .. 3 .. 46 Belgium 119,702a 318,658 10a 8 2a 1 8a 12 6a 4 68a 75 Benin 265 894 38 30 4 4 1 20 1 1 56 44 Bolivia 687 2,341 12 10 2 1 1 10 1 1 85 77 Bosnia and Herzegovina 360 7,097 .. .. .. .. .. .. .. .. .. .. Botswana 1,946 3,272 .. 14 .. 1 .. 4 .. 1 .. 75 Brazil 22,524 77,585 9 5 3 2 27 19 5 4 56 71 Bulgaria 5,100 18,181 8 5 3 1 36 5 4 7 49 65 Burkina Faso 536 1,305 .. 12 .. 1 .. 24 .. 1 .. 62 Burundi 231 267 .. 6 .. 1 .. 8 .. 1 .. 82 Cambodia 164 3,700 .. 8 .. 2 .. 10 .. 0 .. 79 Cameroon 1,400 2,885 19 18 0 2 2 26 1 1 78 53 Canada 123,244 319,686 6 6 2 1 6 9 3 3 81 80 Central African Republic 154 151 19 17 1 27 7 17 2 2 71 37 Chad 285 770 .. .. .. .. .. .. .. .. .. .. Chile 7,742 32,542 4 6 2 1 16 22 1 4 75 67 China 53,345 660,003 9 3 6 4 2 10 3 8 80 75 Hong Kong, China 84,725 300,160 8 3 2 1 2 3 2 2 85 92 Colombia 5,590 21,204 7 9 4 2 6 3 3 3 77 83 Congo, Dem. Rep. 1,739 2,175 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 621 1,415 .. .. .. .. .. .. .. .. .. .. Costa Rica 1,990 9,798 8 6 2 1 10 11 2 1 66 80 Côte d'Ivoire 2,097 5,350 .. 22 .. 1 .. 17 .. 1 .. 48 Croatia 4,500 18,547 13 8 4 1 10 15 4 2 70 73 Cuba 4,600 7,125 12 22 3 1 32 22 1 1 46 54 Czech Republic 12,880 76,707 .. 5 .. 2 .. 7 .. 4 .. 79 Denmark 33,333 76,018 12 11 3 2 7 7 2 2 73 76 Dominican Republic 3,006 9,614 .. .. .. .. .. .. .. .. .. .. Ecuador 1,861 10,309 9 8 3 1 2 8 3 1 84 82 Egypt, Arab Rep. 12,412 19,819 32 22 7 5 3 8 2 4 56 50 El Salvador 1,263 6,766 14 18 3 2 16 14 4 1 63 65 Eritrea 351 495 .. .. .. .. .. .. .. .. .. .. Estonia .. 10,033 .. 8 .. 3 .. 9 .. 1 .. 71 Ethiopia 1,081 4,127 .. 21 .. 1 .. 12 .. 2 .. 64 Finland 27,001 58,999 5 5 2 3 12 14 4 6 76 70 France 234,436 497,853 10 8 3 1 10 13 4 3 74 75 Gabon 918 1,393 .. 24 .. 1 .. 3 .. 1 .. 70 Gambia, The 188 235 .. 38 .. 1 .. 16 .. 1 .. 43 Georgia .. 2,491 .. 17 .. 0 .. 20 .. 1 .. 61 Germany 355,686 773,804 10 7 3 1 8 11 4 4 72 68 Ghana 1,205 5,005 11 21 1 1 17 2 0 2 70 74 Greece 19,777 53,965 15 11 3 1 8 18 3 3 70 66 Guatemala 1,649 10,493 10 11 2 1 17 16 2 1 69 71 Guinea 723 820 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 86 119 .. .. .. .. .. .. .. .. .. .. Haiti 332 1,454 .. .. .. .. .. .. .. .. .. .. Data for Taiwan, China 54,782 182,569 7 4 5 2 11 16 6 6 69 72 206 2007 World Development Indicators 4.5 ECONOMY 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 935 4,484 10 16 1 1 16 20 1 1 71 63 Hungary 10,340 66,045 8 4 4 1 14 7 4 2 70 77 India 23,580 134,831 3 3 4 2 27 36 8 5 51 52 Indonesia 21,837 69,498 5 8 5 3 9 31 4 3 77 55 Iran, Islamic Rep. 20,322 35,859 .. 8 .. 2 .. 10 .. 2 .. 70 Iraq 7,660 23,430 .. .. .. .. .. .. .. .. .. .. Ireland 20,669 68,007 11 8 2 1 6 7 2 1 76 77 Israel 16,793 47,142 8 5 2 1 9 15 3 2 77 76 Italy 181,968 379,772 12 9 6 3 11 12 5 4 64 66 Jamaica 1,928 4,460 15 16 1 2 20 23 1 1 61 57 Japan 235,368 514,922 15 10 7 2 24 26 9 6 44 54 Jordan 2,600 10,506 26 14 1 1 18 23 1 2 52 58 Kazakhstan .. 17,353 .. 7 .. 1 .. 13 .. 2 .. 77 Kenya 2,223 6,149 9 10 3 2 20 24 2 2 66 61 Korea, Dem. Rep. 2,930 2,718 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 69,844 261,238 6 4 8 2 16 25 7 7 63 61 Kuwait 3,972 16,275 17 .. 1 .. 1 .. 2 .. 79 .. Kyrgyz Republic .. 1,108 .. 15 .. 2 .. 29 .. 2 .. 52 Lao PDR 185 745 .. .. .. .. .. .. .. .. .. .. Latvia .. 8,696 .. 11 .. 3 .. 15 .. 1 .. 66 Lebanon 2,529 9,633 .. 16 .. 1 .. 22 .. 2 .. 58 Lesotho 672 1,390 .. .. .. .. .. .. .. .. .. .. Liberia 570 1,190 .. .. .. .. .. .. .. .. .. .. Libya 5,336 7,000 24 17 2 1 0 1 1 1 73 81 Lithuania .. 15,453 12 8 5 2 44 24 2 2 35 62 Macedonia, FYR 1,206 3,228 .. 13 .. 1 .. 19 .. 3 .. 64 Madagascar 651 1,550 11 14 1 0 17 23 1 0 69 62 Malawi 575 1,165 9 18 1 1 11 11 1 1 78 68 Malaysia 29,258 114,602 7 5 1 1 5 8 4 4 82 80 Mali 602 1,612 26 .. 1 .. 19 .. 1 .. 53 .. Mauritania 388 750 .. .. .. .. .. .. .. .. .. .. Mauritius 1,618 3,160 12 17 3 2 8 17 1 1 76 64 Mexico 43,548 231,670 15 6 4 1 4 6 3 3 64 83 Moldova .. 2,312 .. 12 .. 4 .. 21 .. 1 .. 62 Mongolia 924 1,149 .. 13 .. 0 .. 27 .. 0 .. 59 Morocco 6,922 20,332 10 11 6 3 17 22 6 3 61 62 Mozambique 878 2,408 .. 14 .. 1 .. 2 .. 0 .. 49 Myanmar 270 2,250 13 .. 1 .. 5 .. 0 .. 81 .. Namibia 1,163 2,520 .. 15 .. 1 .. 10 .. 4 .. 69 Nepal 672 1,820 15 17 7 5 9 16 2 4 67 59 Netherlands 126,098 359,055 13 10 2 2 10 15 3 3 71 70 New Zealand 9,501 26,239 7 8 1 1 8 12 3 2 81 77 Nicaragua 638 2,595 19 13 1 1 19 18 1 0 59 65 Niger 388 871 .. 34 .. 4 .. 17 .. 1 .. 44 Nigeria 5,627 17,265 6 16 1 1 0 16 2 2 67 66 Norway 27,231 55,495 6 7 2 2 4 4 6 6 82 80 Oman 2,798 8,971 19 12 1 1 4 4 1 4 69 77 Pakistan 7,411 25,331 17 11 4 4 21 22 4 3 54 60 Panama 1,539 4,155 12 12 1 1 16 18 1 1 70 68 Papua New Guinea 1,193 1,729 18 16 0 1 7 13 1 0 73 69 Paraguay 1,352 3,700 8 9 0 1 14 16 1 1 77 74 Peru 2,634 12,502 24 11 2 2 12 20 1 1 61 66 Philippines 13,042 47,418 10 7 2 1 15 14 3 2 53 75 Poland 11,570 100,951 8 6 3 2 24 11 5 3 60 75 Portugal 25,263 61,126 12 11 4 2 11 14 2 3 71 65 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 207 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 7,600 40,463 12 6 4 1 38 14 6 3 39 76 Russian Federation .. 125,303 .. 16 .. 1 .. 2 .. 2 .. 73 Rwanda 288 403 .. 12 .. 4 .. 16 .. 2 .. 67 Saudi Arabia 24,069 59,409 15 15 1 1 0 0 3 4 81 80 Senegal 1,219 3,190 29 28 2 2 16 23 2 2 51 45 Serbia and Montenegro 3,859 11,635 9 8 3 1 23 15 3 4 62 71 Sierra Leone 149 345 .. .. .. .. .. .. .. .. .. .. Singapore 60,774 200,047 6 3 2 0 16 18 2 2 73 77 Slovak Republic 6,670 35,337 .. 6 .. 1 .. 14 .. 3 .. 75 Slovenia 6,142 20,090 9 6 4 3 11 10 4 5 67 75 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 18,399 62,304 8b 4 2b 1 1b 14 1b 2 75b 70 Spain 87,715 278,825 11 9 3 1 12 14 4 3 71 72 Sri Lanka 2,688 8,834 19 12 2 1 13 13 2 3 65 69 Sudan 618 6,757 13 13 1 1 20 1 0 1 66 83 Swaziland 663 2,080 .. .. .. .. .. .. .. .. .. .. Sweden 54,264 111,228 6 7 2 2 9 12 3 3 79 73 Switzerland 69,681 126,524 6 6 2 1 5 6 3 4 84 83 Syrian Arab Republic 2,400 8,106 31 17 2 4 3 7 1 3 62 64 Tajikistan .. 1,330 .. .. .. .. .. .. .. .. .. .. Tanzania 1,027 2,659 .. 12 .. 1 .. 10 .. 1 .. 76 Thailand 33,045 118,191 5 4 5 2 9 18 4 4 75 70 Togo 581 895 22 16 1 1 8 29 1 2 67 53 Trinidad and Tobago 1,109 5,674 19 9 1 1 11 35 6 4 62 51 Tunisia 5,513 13,177 11 9 4 3 9 10 4 3 72 76 Turkey 22,302 116,553 8 3 4 3 21 14 5 6 61 69 Turkmenistan .. 3,588 .. .. .. .. .. .. .. .. .. .. Uganda 288 1,779 .. 15 .. 2 .. 17 .. 1 .. 65 Ukraine .. 36,141 .. 7 .. 1 .. 30 .. 4 .. 57 United Arab Emirates 11,199 80,744 17 .. 0 .. 6 .. 4 .. 72 .. United Kingdom 222,977 510,237 10 9 3 1 6 8 4 2 75 72 United States 516,987 1,732,348 6 4 2 1 13 17 3 2 73 72 Uruguay 1,343 3,879 7 8 4 3 18 24 2 2 69 63 Uzbekistan .. 3,666 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 7,335 24,249 11 10 4 1 3 1 4 1 77 87 Vietnam 2,752 36,476 .. 6 .. 3 .. 11 .. 3 .. 77 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,571 4,260 27 24 1 1 40 21 1 1 31 53 Zambia 1,220 2,750 .. 6 .. 1 .. 12 .. 3 .. 78 Zimbabwe 1,847 2,330 4 19 3 2 16 14 2 10 73 54 World 3,549,585 t 10,684,930 t 9w 6w 3w 2w 11 w 14 w 4w 3w 71 w 72 w Low income 78,024 316,559 7 11 3 3 22 22 5 3 56 61 Middle income 512,919 2,552,089 10 6 4 2 10 11 3 4 70 74 Lower middle income 273,099 1,380,029 10 6 5 3 9 15 3 5 72 71 Upper middle income 237,015 1,172,061 10 6 3 1 10 9 4 3 68 78 Low & middle income 592,618 2,868,603 10 6 4 2 11 13 4 4 69 73 East Asia & Pacific 160,502 1,061,614 8 4 5 3 5 13 3 6 77 74 Europe & Central Asia 163,450 747,497 .. 7 .. 2 .. 11 .. 3 .. 72 Latin America & Carib. 120,119 520,640 12 6 3 1 10 10 3 3 66 79 Middle East & N. Africa 80,058 181,770 .. 15 .. 3 .. 10 .. 3 .. 66 South Asia 39,124 188,994 8 5 4 2 24 32 6 5 54 55 Sub-Saharan Africa 57,641 168,092 .. 12 .. 1 .. 14 .. 2 .. 67 High income 2,943,620 7,816,297 9 7 3 1 11 14 4 3 71 72 Europe EMU 1,261,194 3,018,041 11 8 3 2 9 13 4 3 71 71 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). 208 2007 World Development Indicators 4.5 ECONOMY Structure of merchandise imports About the data Definitions Data on imports of goods are derived from the same transformation and repair) and withdrawals for · Merchandise imports are the c.i.f. value of goods sources as data on exports. In principle, world domestic consumption from bonded warehouses purchased from the rest of the world valued in U.S. exports and imports should be identical. Similarly, and free trade zones. Goods transported through a dollars. · Food corresponds to the commodities in exports from an economy should equal the sum of country en route to another are excluded. SITC sections 0 (food and live animals), 1 (bever- imports by the rest of the world from that economy. The data on total imports of goods (merchandise) ages and tobacco), and 4 (animal and vegetable oils But differences in timing and definitions result in dis- in this table come from the World Trade Organization and fats) and SITC division 22 (oil seeds, oil nuts, crepancies in reported values at all levels. For further (WTO). For further discussion of the WTO's sources and oil kernels). · Agricultural raw materials cor- discussion of indicators of merchandise trade, see and methodology, see About the data for table 4.4. respond to SITC section 2 (crude materials except About the data for tables 4.4 and 6.2. The shares of imports by major commodity group fuels) excluding divisions 22, 27 (crude fertilizers The value of imports is generally recorded as the are from the United Nations Statistics Division's and minerals excluding coal, petroleum, and pre- cost of the goods when purchased by the importer Commodity Trade (Comtrade) database. The values cious stones), and 28 (metalliferous ores and scrap). plus the cost of transport and insurance to the fron- of total imports reported here have not been fully · Fuels correspond to SITC section 3 (mineral fuels). tier of the importing country--the cost, insurance, reconciled with the estimates of imports of goods · Ores and metals correspond to the commodities and freight (c.i.f.) value, corresponding to the landed and services from the national accounts (shown in in SITC divisions 27, 28, and 68 (nonferrous met- cost at the point of entry of foreign goods into the table 4.8) or those from the balance of payments als). · Manufactures correspond to the commodities country. A few countries, including Australia, Canada, (table 4.15). in SITC sections 5 (chemicals), 6 (basic manufac- and the United States, collect import data on a free The classification of commodity groups is based tures), 7 (machinery and transport equipment), and on board (f.o.b.) basis and adjust them for freight and on the Standard International Trade Classification 8 (miscellaneous manufactured goods), excluding insurance costs. Many countries collect and report (SITC) revision 1. Most countries now report using division 68. trade data in U.S. dollars. When countries report in later revisions of the SITC or the Harmonized Sys- local currency, the United Nations Statistics Division tem. Concordance tables are used to convert data applies the average official exchange rate for the reported in one system of nomenclature to another. period shown. The conversion process may introduce some errors Countries may report trade according to the gen- of classification, but conversions from later to earlier eral or special system of trade (see Primary data systems are generally reliable. Shares may not sum documentation). Under the general system imports to 100 percent because of unclassified trade. 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 Top 10 developing country exporters of merchandise in 2005 4.5a Merchandise exports ($ billions) 1990 2005 800 700 Data sources 600 The WTO publishes data on world trade in its 500 Annual Report. The International Monetary Fund 400 publishes estimates of total imports of goods in 300 its International Financial Statistics and Direction 200 of Trade Statistics, as does the United Nations Sta- tistics Division in its Monthly Bulletin of Statistics. 100 And the United Nations Conference on Trade and 0 Development publishes data on the structure of China Russian Mexico Malaysia Brazil Thailand India Poland Indonesia Turkey Federationa exports and imports in its Handbook of Interna- China continues to be the top developing country exporter. The Russian Federation has surpassed tional Trade and Development Statistics. Tariff line Mexico. records of exports and imports are compiled in the United Nations Statistics Division's Comtrade a. Data are for 1994 and 2005. Source: World Trade Organization data files. database. 2007 World Development Indicators 209 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan 1 .. .. .. .. .. .. .. .. .. Albania 32 1,154 20.0 10.9 11.1 74.0 2.2 1.9 66.7 13.2 Algeria 479 .. 41.7 .. 13.4 .. 5.9 .. 39.0 .. Angola 65 177 48.8 10.2 20.6 49.9 4.6 .. 26.1 39.9 Argentina 2,264 6,121 51.1 21.2 39.9 45.0 .. 0.1 9.0 33.8 Armenia 17 323 .. 28.5 .. 43.5 .. 4.7 .. 23.3 Australia 9,835 27,767 35.4 22.4 43.2 53.9 4.2 4.7 17.2 19.1 Austria 22,755 53,104 6.4 20.1 59.0 29.4 2.9 6.6 31.7 44.0 Azerbaijan .. 625 .. 38.3 .. 12.4 .. 1.3 .. 48.0 Bangladesh 296 472 13.0 23.8 6.4 14.8 0.1 4.8 80.6 56.6 Belarus 185 1,942 54.1 63.0 13.3 13.0 1.0 0.2 31.6 23.7 Belgium 26,646a 53,536 27.5a 26.0 14.0a 18.4 18.2a 7.9 40.3a 47.8 Benin 109 204 33.4 16.5 50.2 58.1 6.9 1.5 9.5 23.9 Bolivia 133 473 35.8 30.3 43.6 50.5 10.0 9.0 10.6 10.2 Bosnia and Herzegovina .. 1,010 .. 6.9 .. 56.0 .. 3.5 .. 33.6 Botswana 183 844 20.4 10.1 64.1 66.6 8.2 9.0 7.3 14.4 Brazil 3,706 14,901 36.4 21.4 37.3 25.9 3.1 4.3 23.2 48.4 Bulgaria 837 4,288 27.5 26.1 38.2 56.2 3.1 1.1 31.2 16.6 Burkina Faso 34 .. 37.1 .. 34.1 .. .. .. 28.9 .. Burundi 7 7 38.7 25.6 51.4 22.2 1.6 0.7 8.3 51.5 Cambodia 50 1,052 .. 12.0 .. 79.8 .. 0.1 .. 8.1 Cameroon 369 393 42.6 30.5 14.4 28.9 9.4 9.8 33.6 30.8 Canada 18,350 52,193 23.0 18.4 34.7 26.0 .. 9.3 42.3 46.3 Central African Republic 17 .. 50.9 .. 16.0 .. 18.8 .. 14.3 .. Chad 23 .. 18.4 .. 34.1 .. 0.2 .. 47.3 .. Chile 1,786 7,077 40.0 59.3 29.8 17.8 4.9 2.6 25.3 20.4 China 5,748 73,909 47.1 20.9 30.2 39.6 4.0 0.9 18.7 38.6 Hong Kong, China .. 62,175 .. 31.5 .. 16.3 .. 9.0 .. 43.2 Colombia 1,548 2,590 31.3 30.1 26.2 47.0 17.1 1.2 25.5 21.7 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 65 223 53.9 34.3 12.9 15.1 .. 15.9 33.2 50.6 Costa Rica 583 2,579 16.3 10.9 48.9 64.6 .. 0.4 34.8 24.1 Côte d'Ivoire 425 658 62.4 22.0 12.1 12.7 8.3 14.9 17.2 65.3 Croatia 2,216 9,920 29.2 11.0 59.1 74.3 1.4 0.6 10.3 14.1 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 4,679 10,729 26.5 30.6 33.3 43.1 9.6 4.1 30.6 22.3 Denmark 12,731 42,383 32.5 47.1 26.2 15.6 2.3 .. 39.0 37.4 Dominican Republic 1,086 3,840 5.6 3.4 66.9 91.4 0.2 0.4 27.3 4.9 Ecuador 508 940 47.6 35.6 37.0 51.7 9.3 0.1 6.1 12.6 Egypt, Arab Rep. 4,813 14,449 50.1 32.8 22.9 47.4 1.0 1.4 26.1 18.4 El Salvador 301 1,116 26.2 32.5 25.3 48.6 7.5 3.6 41.1 15.3 Eritrea 73 .. 85.7 .. 1.0 .. .. .. 13.3 .. Estonia 200 3,117 74.7 40.0 13.7 30.4 0.1 1.8 11.5 27.8 Ethiopia 261 789 80.7 59.0 2.1 21.3 0.7 4.0 16.6 15.7 Finland 4,562 16,895 38.4 14.4 25.8 12.9 0.1 0.9 35.6 71.8 France 74,948 114,955 21.7 23.6 27.1 36.7 14.8 2.7 36.4 37.0 Gabon 214 136 33.4 59.8 1.4 7.2 5.8 17.1 59.4 15.9 Gambia, The 53 80 8.8 19.4 87.9 70.8 0.1 0.5 3.3 9.4 Georgia .. 631 .. 49.9 .. 38.3 .. 5.0 .. 6.8 Germany 50,562 148,540 29.2 25.6 28.3 19.6 1.0 5.6 41.5 49.2 Ghana 79 1,043 49.2 14.0 5.6 76.3 2.7 0.8 42.6 8.9 Greece 6,514 34,051 4.9 50.8 39.7 39.9 0.1 1.1 55.2 8.3 Guatemala 313 1,138 7.4 8.6 37.6 74.3 2.0 6.3 53.1 10.8 Guinea 91 31 14.2 21.8 32.6 .. 0.1 0.4 53.2 77.8 Guinea-Bissau 4 6 5.4 22.9 .. 16.6 .. 19.5 94.6 41.0 Haiti 43 112 19.8 .. 78.9 98.0 1.3 .. .. 2.0 210 2007 World Development Indicators 4.6 ECONOMY Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 121 716 35.1 10.2 24.0 65.9 12.9 2.4 28.0 21.4 Hungary 2,677 12,294 1.6 14.9 36.8 34.8 0.2 2.8 61.4 47.5 India 4,610 56,094b 20.8 13.3 33.8 16.8 2.7 3.5 42.7 66.4 Indonesia 2,488 12,570 2.8 22.6 86.5 36.0 .. 3.0 10.7 38.4 Iran, Islamic Rep. 343 .. 10.5 .. 8.2 .. 6.4 .. 74.9 .. Iraq .. .. .. .. .. .. .. .. .. .. Ireland 3,286 56,768 31.1 4.7 44.4 8.3 .. 25.2 24.5 61.9 Israel 4,546 17,731 30.8 20.8 30.7 16.1 ­0.3 0.1 38.8 63.0 Italy 48,579 88,820 21.0 17.6 33.9 39.8 5.5 3.1 39.6 39.6 Jamaica 976 2,296 18.0 19.7 77.0 67.3 1.4 2.8 3.6 10.2 Japan 41,384 107,876 40.4 33.1 7.9 11.5 ­0.4 5.5 52.1 49.9 Jordan 1,430 2,188 26.0 21.5 35.8 65.8 .. .. 38.3 12.7 Kazakhstan .. 2,019 .. 51.6 .. 34.7 .. 1.1 .. 12.6 Kenya 774 1,523 32.1 48.4 60.2 38.0 0.7 0.7 7.1 12.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 9,155 43,927 34.7 54.4 34.5 12.9 0.1 4.6 30.7 28.2 Kuwait 1,054 3,790 87.5 58.4 12.5 4.4 .. 3.1 .. 34.2 Kyrgyz Republic 9 234 25.1 25.9 3.8 31.3 .. 1.7 71.1 41.1 Lao PDR 11 .. 74.8 .. 24.3 .. 0.9 .. .. .. Latvia 290 2,137 94.9 57.2 2.5 16.0 .. 6.5 2.6 20.3 Lebanon .. 10,740 .. 4.1 .. 50.6 .. 2.3 .. 43.0 Lesotho 34 47 14.1 1.3 51.2 64.9 .. ­4.9 34.7 38.7 Liberia 32 .. 84.6 .. 15.4 .. .. .. .. .. Libya 83 419 83.8 27.7 7.7 59.7 .. 10.3 8.5 2.4 Lithuania 198 3,075 83.6 51.6 10.9 30.0 .. 0.5 5.5 17.9 Macedonia, FYR .. 445 .. 30.0 .. 18.8 .. 1.9 .. 49.3 Madagascar 129 142 32.1 28.2 31.3 43.6 0.3 0.1 36.4 28.1 Malawi 37 49 46.1 32.7 42.6 67.3 0.1 .. 11.2 .. Malaysia 3,769 19,463 31.8 20.8 44.7 45.5 0.1 1.7 23.5 32.0 Mali 71 227 31.0 13.5 54.3 61.9 4.9 2.3 9.8 22.2 Mauritania 14 .. 35.3 .. 64.7 .. .. .. .. .. Mauritius 478 1,604 33.0 23.9 51.1 54.3 0.1 1.3 15.8 20.5 Mexico 7,222 16,098 12.4 10.9 76.5 73.3 4.6 9.6 6.5 6.2 Moldova .. 409 .. 41.5 .. 31.3 .. 0.9 .. 26.3 Mongolia 48 329 41.8 32.7 10.4 56.2 4.6 1.2 43.2 9.9 Morocco 1,871 7,570 9.6 17.2 68.4 60.9 0.8 1.0 21.2 21.0 Mozambique 103 316 61.3 28.3 .. 41.1 .. 0.4 38.7 30.2 Myanmar 94 232 10.3 36.8 20.9 36.2 0.5 .. 68.3 27.0 Namibia 106 463 .. 7.1 81.0 87.5 5.9 .. 13.1 5.5 Nepal 166 271 3.6 12.0 65.6 48.4 .. 1.4 30.8 38.2 Netherlands 28,478 78,183 45.4 27.4 14.6 13.4 0.8 1.7 39.2 57.5 New Zealand 2,415 8,408 43.4 19.5 42.7 59.3 ­0.3 1.4 14.2 19.9 Nicaragua 34 272 19.2 12.4 35.5 76.2 .. 1.0 45.4 10.4 Niger 22 88 5.2 8.5 59.5 35.6 13.5 1.4 21.8 54.4 Nigeria 965 4,164 3.9 17.5 2.5 0.4 0.3 0.2 93.3 81.8 Norway 12,452 28,457 68.7 54.7 12.6 11.5 0.4 1.6 18.3 32.2 Oman 68 822 15.3 36.4 84.7 58.5 .. 0.6 .. 4.4 Pakistan 1,218 2,042 59.3 52.7 12.0 8.9 1.4 3.9 27.3 34.6 Panama 907 3,106 64.9 57.2 18.9 25.1 3.8 7.5 12.4 10.3 Papua New Guinea 198 285 11.2 10.9 12.0 1.3 0.5 5.4 76.3 82.4 Paraguay 404 615 18.3 14.6 21.1 12.6 .. 4.6 60.5 68.2 Peru 714 2,057 43.4 21.8 30.4 60.3 11.2 5.8 15.0 12.1 Philippines 2,897 4,462 8.5 23.3 16.1 47.7 0.5 1.5 74.9 27.5 Poland 3,200 16,181 57.3 33.7 11.2 38.8 4.0 1.8 27.6 25.8 Portugal 5,054 14,940 15.6 21.4 70.4 52.8 0.7 2.1 13.3 23.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 211 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 610 5,056 50.5 29.1 17.4 20.8 5.6 2.6 26.6 47.5 Russian Federation .. 24,337 .. 37.4 .. 22.5 .. 2.9 .. 37.2 Rwanda 31 83 56.1 36.1 32.8 58.6 1.0 0.1 10.0 5.1 Saudi Arabia 3,027 5,916 .. .. .. .. .. .. .. .. Senegal 356 598 19.2 16.1 42.8 35.4 0.5 1.6 37.6 47.0 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 45 78 9.7 14.9 76.2 82.1 .. 3.0 14.1 0.1 Singapore 12,719 51,200 17.5 35.0 36.6 11.2 0.7 9.4 45.3 44.4 Slovak Republic 1,939 3,270 23.7 43.2 19.8 26.4 .. 2.3 56.5 28.1 Slovenia 1,219 3,969 22.6 28.9 55.0 45.2 1.2 0.9 21.2 25.0 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 3,291 10,898 21.6 14.1 55.8 67.3 10.8 6.1 11.9 12.6 Spain 27,649 92,730 17.2 16.6 67.2 51.4 4.3 3.8 11.3 28.2 Sri Lanka 425 1,519 39.7 44.3 30.2 28.3 4.2 4.8 25.9 22.6 Sudan 134 101 14.1 3.4 15.7 88.7 0.5 4.1 69.7 3.9 Swaziland 102 272 24.5 10.3 29.2 25.5 .. 56.0 46.3 8.2 Sweden 13,453 42,761 35.8 20.5 21.7 17.2 9.1 5.4 33.5 56.9 Switzerland 18,325 45,794 16.3 9.5 40.4 24.2 23.7 32.6 19.6 33.8 Syrian Arab Republic 740 2,827 29.8 8.6 43.3 76.9 .. 1.0 27.0 13.4 Tajikistan .. 103 .. 54.4 .. 1.5 .. 8.1 .. 36.0 Tanzania 131 1,181 19.9 17.2 36.4 69.7 0.5 3.4 43.1 9.7 Thailand 6,292 20,495 21.1 22.6 68.7 49.3 0.2 1.4 10.0 26.8 Togo 114 122 26.9 38.2 50.8 15.7 13.7 1.5 8.6 44.6 Trinidad and Tobago 322 838 50.7 35.2 29.4 40.8 .. 13.5 19.9 10.5 Tunisia 1,575 3,884 23.0 29.3 64.8 54.7 1.5 2.5 10.7 13.5 Turkey 7,882 25,552 11.7 15.8 40.9 71.0 .. 2.6 47.4 10.6 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 466 .. 2.3 .. 76.2 .. 4.6 .. 16.9 Ukraine .. 8,913 .. 50.3 .. 35.1 .. 0.7 .. 14.0 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 53,830 199,454 25.2 16.5 29.0 15.3 16.4 22.7 29.4 45.4 United States 132,880 354,020 28.1 17.9 37.9 28.8 3.5 10.2 30.5 43.1 Uruguay 460 1,304 36.9 33.8 51.8 45.5 1.0 5.3 10.3 15.4 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 1,121 1,240 40.9 31.1 44.3 51.7 0.2 0.1 14.7 17.1 Vietnam .. 4,176 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 82 285 27.2 16.1 48.8 63.3 .. .. 24.0 20.6 Zambia 95 .. 68.9 .. 13.5 .. 4.1 .. 13.4 .. Zimbabwe 253 .. 44.3 .. 25.3 .. 1.2 .. 29.2 .. World 815,799 t 2,459,852 t 28.4 w 24.1 w 34.9 w 28.4 w 4.7 w 6.8 w 38.5 w 41.5 w Low income 13,307 84,840 23.8 19.5 23.3 18.8 1.9 3.1 51.2 58.9 Middle income 97,390 412,960 29.7 23.6 44.9 45.7 3.1 3.2 22.4 27.5 Lower middle income 46,586 217,782 32.1 23.5 39.5 41.3 3.3 1.8 25.2 33.4 Upper middle income 51,500 195,671 26.9 23.7 51.4 49.8 2.8 4.4 19.0 22.1 Low & middle income 110,583 495,951 29.2 23.7 43.0 44.6 3.0 3.1 24.9 28.7 East Asia & Pacific 22,788 137,881 32.3 21.4 43.5 42.1 2.0 1.5 22.2 35.0 Europe & Central Asia 41,109 142,205 .. 33.1 .. 34.5 .. 2.5 .. 29.8 Latin America & Carib. 25,840 72,823 25.8 19.5 56.2 56.5 4.1 5.8 13.9 18.2 Middle East & N. Africa .. .. 31.1 .. 29.3 .. 3.4 .. 36.4 .. South Asia 6,847 60,989 26.8 22.1 28.2 17.4 2.3 3.7 42.7 56.8 Sub-Saharan Africa 9,580 29,946 25.7 16.7 31.6 42.1 5.4 3.8 37.9 37.7 High income 701,445 1,962,711 28.1 24.3 32.3 23.5 5.2 7.8 42.2 45.3 Europe EMU 312,162 796,772 27.1 22.8 30.3 26.5 5.9 5.1 36.7 45.7 a. Includes Luxembourg. b. World Trade Organization estimate. 212 2007 World Development Indicators 4.6 ECONOMY Structure of service exports About the data Definitions Balance of payments statistics, the main source of around the clock for data processing, exploiting · Commercial service exports are total service information on international trade in services, have time zone differences between their home country exports minus exports of government services not many weaknesses. Some large economies--such and the host countries of their affiliates. Another included elsewhere. International transactions in ser- as the former Soviet Union--did not report data on important dimension of service trade not captured vices are defined by the IMF's Balance of Payments trade in services until recently. Disaggregation of by conventional balance of payments statistics is Manual (1993) as the economic output of intangible important components may be limited, and it varies establishment trade--sales in the host country by commodities that may be produced, transferred, and significantly across countries. There are inconsisten- foreign affiliates. By contrast, cross-border intrafirm consumed at the same time. Definitions may vary cies in the methods used to report items. And the transactions in merchandise may be reported as among reporting economies. · Transport covers all recording of major flows as net items is common (for exports or imports in the balance of payments. transport services (sea, air, land, internal waterway, example, insurance transactions are often recorded The data on exports of services in this table and space, and pipeline) performed by residents of one as premiums less claims). These factors contribute on imports of services in table 4.7, unlike those in economy for those of another and involving the car- to a downward bias in the value of the service trade editions before 2000, include only commercial ser- riage of passengers, movement of goods (freight), reported in the balance of payments. vices and exclude the category "government services rental of carriers with crew, and related support and Efforts are being made to improve the coverage, not included elsewhere." The data are compiled by auxiliary services. Excluded are freight insurance, quality, and consistency of these data. Eurostat and the IMF based on returns from national sources. which is included in insurance services; goods pro- the Organisation for Economic Co-operation and Data on total trade in goods and services from the cured in ports by nonresident carriers and repairs of Development, for example, are working together IMF's Balance of Payments database are shown in transport equipment, which are included in goods; to improve the collection of statistics on trade in table 4.15. repairs of harbors, railway facilities, and airfield facil- services in member countries. In addition, the Inter- ities, which are included in construction services; national Monetary Fund (IMF) has implemented the and rental of carriers without crew, which is included new classification of trade in services introduced in in other services. · Travel covers goods and ser- the fifth edition of its Balance of Payments Manual vices acquired from an economy by travelers in that (1993). economy for their own use during visits of less than Still, difficulties in capturing all the dimensions of one year for business or personal purposes. Travel international trade in services mean that the record services include the goods and services consumed is likely to remain incomplete. Cross-border intrafirm by travelers, such as meals, lodging, and transport service transactions, which are usually not captured (within the economy visited), including car rental. in the balance of payments, have increased in recent · Insurance and fi nancial services cover freight years. One example of such transactions is trans- insurance on goods exported and other direct insur- national corporations' use of mainframe computers ance such as life insurance, financial intermediation services such as commissions, foreign exchange transactions, and brokerage services; and auxil- Top 10 developing country exporters of commercial services in 2005 4.6a iary services such as financial market operational and regulatory services. · Computer, information, Commercial service exports ($ billions) 1990 2005 communications, and other commercial services 80 include such activities as international telecommu- 70 nications and postal and courier services; computer 60 data; news-related service transactions between 50 residents and nonresidents; construction services; royalties and license fees; miscellaneous business, 40 professional, and technical services; and personal, 30 cultural, and recreational services. 20 10 0 China India Turkey Russian Thailand Malaysia Poland Mexico Brazil Egypt, Federationa Arab Rep. Data sources The top 10 developing country exporters of commercial services accounted for 60 percent of developing Data on exports of commercial services are from country commercial service exports and almost 12 percent of world commercial service exports. the IMF. The IMF publishes balance of payments data in its International Financial Statistics and Bal- a. Data are for 1994 and 2005. Source: International Monetary Fund and World Trade Organization data files. ance of Payments Statistics Yearbook. 2007 World Development Indicators 213 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan 97 .. 85.9 .. .. .. 9.5 .. 4.6 .. Albania 29 1,318 26.3 17.2 .. 59.7 2.9 3.6 70.8 19.5 Algeria 1,155 .. 58.1 .. 12.9 .. 9.8 .. 19.2 .. Angola 1,288 6,191 38.3 21.3 3.0 1.2 2.6 1.9 56.1 75.6 Argentina 2,876 7,353 32.6 26.8 40.7 38.3 ... 6.1 26.7 28.7 Armenia 40 377 89.2 50.6 0.9 31.1 9.9 6.8 0.0 11.5 Australia 13,388 28,753 33.9 35.5 31.5 39.2 4.8 3.9 29.8 21.3 Austria 14,104 49,002 8.4 14.9 54.9 22.4 4.6 6.7 32.1 56.1 Azerbaijan .. 2,625 .. 14.4 .. 6.3 .. 1.9 .. 77.4 Bangladesh 554 1,983 71.1 76.9 14.1 6.6 6.6 8.1 8.3 8.4 Belarus 125 1,213 34.0 25.5 44.6 49.8 12.3 2.0 9.2 22.8 Belgium 25,924a 50,518 23.3a 24.4 21.1a 29.3 14.8a 8.0 40.8a 38.3 Benin 113 273 46.9 63.4 12.8 10.6 5.7 11.3 34.6 14.7 Bolivia 291 664 61.7 35.2 20.6 28.1 10.0 18.5 7.6 18.3 Bosnia and Herzegovina .. 449 .. 44.7 .. 27.3 .. 12.8 .. 15.3 Botswana 371 840 57.5 40.5 15.0 33.6 5.5 3.7 22.0 22.2 Brazil 6,733 22,296 44.4 22.3 22.4 21.2 2.7 6.5 30.5 50.1 Bulgaria 600 3,457 40.5 34.7 31.5 37.4 4.5 3.6 23.5 24.3 Burkina Faso 196 .. 64.7 .. 16.6 .. 5.1 .. 13.6 .. Burundi 59 104 62.6 21.1 29.0 58.1 6.3 4.2 2.2 16.7 Cambodia 64 620 24.5 57.9 .. 15.6 .. 4.7 75.5 21.8 Cameroon 1,018 1,053 45.3 31.7 27.5 20.1 7.2 8.3 20.1 40.0 Canada 27,479 64,170 21.1 22.6 39.8 28.6 .. 10.9 39.2 37.9 Central African Republic 166 .. 49.7 .. 30.6 .. 8.9 .. 10.7 .. Chad 223 .. 45.1 .. 31.2 .. 4.4 .. 19.2 .. Chile 1,982 7,591 47.4 54.2 21.5 13.9 3.3 9.6 27.9 22.3 China 4,113 83,173 78.9 34.2 11.4 26.2 2.3 8.9 7.4 30.8 Hong Kong, China .. 32,384 .. 28.0 .. 42.8 .. 5.7 .. 23.4 Colombia 1,683 4,701 34.9 44.8 27.0 24.0 13.7 9.2 24.4 22.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 748 1,550 18.4 19.9 15.2 6.6 1.6 .. 64.9 73.5 Costa Rica 540 1,496 41.2 42.3 28.8 31.5 6.0 6.0 24.0 20.3 Côte d'Ivoire 1,518 1,942 32.1 51.7 11.1 17.8 4.7 .. 52.0 30.5 Croatia 1,088 3,349 30.5 18.7 34.4 22.5 3.7 4.1 31.4 54.8 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 3,701 9,870 19.8 18.2 14.2 24.4 14.0 11.9 52.0 45.5 Denmark 10,106 37,841 38.3 43.4 36.5 21.8 1.6 .. 23.6 34.8 Dominican Republic 435 1,409 40.0 61.0 33.1 25.0 4.1 7.3 22.9 6.6 Ecuador 755 2,049 41.6 50.8 23.2 19.6 8.1 7.1 27.2 22.6 Egypt, Arab Rep. 3,327 9,507 44.0 39.3 3.9 17.1 4.6 10.3 47.5 33.3 El Salvador 296 1,194 45.9 44.1 20.6 29.0 12.1 9.3 21.5 17.6 Eritrea 1 .. .. .. .. .. .. .. .. .. Estonia 123 2,132 76.3 44.4 15.4 21.0 0.3 1.9 8.0 32.7 Ethiopia 348 1,178 76.5 64.9 3.3 6.5 3.4 5.1 16.9 23.5 Finland 7,432 15,061 26.1 26.8 37.2 20.3 1.1 1.6 35.5 51.3 France 59,560 104,897 29.4 27.3 20.7 29.7 19.2 5.1 30.7 37.9 Gabon 984 921 23.2 33.5 13.9 23.2 5.3 5.8 57.6 37.5 Gambia, The 35 45 65.1 75.4 23.1 11.7 9.0 10.8 2.8 2.2 Georgia .. 578 .. 49.0 .. 29.2 .. 10.4 .. 11.4 Germany 83,264 200,944 20.5 21.7 46.9 36.1 1.0 4.5 31.6 37.7 Ghana 226 1,131 55.1 51.3 5.9 26.8 11.2 5.1 27.8 16.8 Greece 2,756 14,292 34.0 54.2 39.5 21.3 5.4 5.7 21.0 18.9 Guatemala 363 1,423 41.0 51.3 27.4 31.2 3.4 11.8 28.2 5.7 Guinea 243 195 57.5 47.3 12.2 12.8 5.5 12.7 24.9 27.2 Guinea-Bissau 17 42 54.5 53.5 19.8 30.9 5.6 0.4 20.0 15.1 Haiti 71 431 47.9 50.4 52.1 12.6 .. .. .. 37.0 214 2007 World Development Indicators 4.7 ECONOMY Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 213 831 45.4 49.8 17.6 29.9 15.0 .. 22.0 20.4 Hungary 2,264 11,626 8.8 15.3 25.9 25.2 1.0 5.4 64.4 54.1 India 5,943 52,211b 57.5 36.7 6.6 13.8 5.8 6.5 30.1 43.1 Indonesia 5,898 23,516 47.4 29.9 14.2 15.2 4.0 3.7 34.5 51.1 Iran, Islamic Rep. 3,703 .. 47.3 .. 9.2 .. 10.8 .. 32.8 .. Iraq .. .. .. .. .. .. .. .. .. .. Ireland 5,145 69,759 24.3 3.5 22.6 8.7 1.9 14.9 51.2 73.0 Israel 4,825 13,439 39.6 35.1 29.7 21.5 4.4 3.1 26.3 40.3 Italy 46,602 88,889 23.7 24.8 22.1 25.2 10.4 3.6 43.9 46.5 Jamaica 667 1,683 47.9 43.1 17.0 14.8 6.7 10.0 28.4 32.1 Japan 84,281 132,601 30.8 30.5 27.9 28.3 2.1 3.5 39.3 37.8 Jordan 1,118 2,465 52.0 54.4 30.1 23.7 5.2 8.5 12.7 13.3 Kazakhstan .. 7,404 .. 15.8 .. 10.2 .. 3.1 .. 71.0 Kenya 598 950 66.2 44.0 6.4 13.0 8.9 13.7 18.5 29.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 10,050 57,746 39.8 34.6 27.5 26.5 -0.1 1.4 32.8 37.5 Kuwait 2,805 7,571 31.9 39.8 65.5 56.5 1.2 1.9 1.4 1.8 Kyrgyz Republic 51 287 74.1 41.4 0.8 20.3 7.6 6.8 17.6 31.5 Lao PDR 25 .. 73.0 .. 13.3 .. 6.4 .. 20.6 .. Latvia 120 1,541 82.3 32.5 10.9 37.9 4.8 3.8 2.1 25.8 Lebanon .. 7,838 .. 17.0 .. 36.7 .. 3.1 .. 43.2 Lesotho 48 79 67.9 65.9 24.7 34.1 5.6 .. 1.7 .. Liberia 74 .. 60.8 .. 33.7 .. 5.6 .. .. .. Libya 926 2,128 41.9 47.7 45.8 32.0 4.1 8.5 8.3 11.8 Lithuania 177 1,989 90.7 45.1 6.9 37.4 .. 1.7 2.4 15.9 Macedonia, FYR .. 483 .. 41.2 .. 12.3 .. 4.3 .. 42.2 Madagascar 172 157 43.5 48.5 23.4 15.8 3.5 1.0 29.5 34.7 Malawi 268 222 81.8 50.1 5.9 35.2 8.7 0.0 3.7 14.7 Malaysia 5,394 21,750 46.9 38.6 26.9 17.1 .. 2.9 26.2 41.4 Mali 352 528 57.4 64.9 15.8 12.6 1.9 6.5 24.9 16.0 Mauritania 126 .. 76.9 .. 18.3 .. 3.1 .. 1.7 .. Mauritius 407 1,211 51.6 43.1 23.0 22.7 5.5 5.3 19.9 28.9 Mexico 10,063 20,915 25.0 13.0 54.9 36.3 6.2 44.3 14.0 6.4 Moldova .. 418 .. 35.2 .. 40.4 .. 1.6 .. 22.8 Mongolia 155 496 56.2 40.2 0.8 38.8 6.3 8.2 36.8 12.8 Morocco 940 3,103 58.3 50.9 19.9 19.7 6.0 2.6 15.9 26.8 Mozambique 206 627 57.7 36.6 .. 28.1 4.3 3.1 38.1 32.2 Myanmar 73 444 35.4 51.2 22.6 6.5 2.5 .. 39.5 42.4 Namibia 341 376 46.9 36.1 17.9 23.3 6.8 5.5 28.5 35.1 Nepal 159 424 40.8 38.0 28.5 38.5 3.2 6.2 27.5 17.3 Netherlands 28,995 72,414 37.7 20.7 25.4 22.3 0.6 2.5 36.3 54.6 New Zealand 3,251 8,135 40.6 33.8 29.5 32.7 2.5 4.0 27.5 29.5 Nicaragua 73 402 70.7 57.9 20.1 22.5 7.9 3.8 1.4 15.9 Niger 209 250 68.3 65.3 10.4 8.9 4.3 3.0 17.1 22.8 Nigeria 1,901 7,321 33.6 20.7 30.3 15.2 3.1 .. 32.9 64.2 Norway 12,247 27,209 44.6 33.7 30.0 35.9 1.7 2.9 23.7 27.6 Oman 719 3,052 36.6 34.4 6.5 21.1 4.1 9.8 52.8 34.7 Pakistan 1,863 7,179 67.0 36.2 23.1 17.8 1.4 3.4 8.6 42.6 Panama 666 1,673 66.6 56.3 14.8 16.2 10.2 12.6 8.4 14.9 Papua New Guinea 393 1,151 35.6 24.2 12.8 4.8 4.0 10.3 47.6 60.7 Paraguay 361 325 61.6 55.6 19.8 24.2 11.4 17.2 7.3 3.1 Peru 1,070 2,959 43.5 44.4 27.6 23.0 10.9 8.7 18.0 24.0 Philippines 1,721 5,790 56.9 54.0 6.5 22.1 3.4 5.1 33.2 18.8 Poland 2,847 14,104 52.4 23.5 14.9 30.8 1.0 5.6 31.8 40.1 Portugal 3,772 9,891 48.5 30.8 23.0 31.1 5.1 4.6 23.5 33.5 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 215 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of commercial services % of commercial services % of commercial services % of commercial services 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 787 5,425 65.5 36.2 13.1 17.1 7.3 5.4 14.1 41.3 Russian Federation .. 38,465 .. 13.4 .. 46.3 .. 5.4 .. 35.0 Rwanda 94 176 69.0 76.6 23.7 20.8 0.0 .. 7.3 2.5 Saudi Arabia 12,677 14,239 18.1 29.2 .. .. 2.2 3.2 79.7 67.6 Senegal 368 681 60.1 55.8 12.4 8.4 8.8 10.2 18.7 25.6 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 67 85 29.5 50.0 32.7 37.8 4.8 9.7 33.0 2.5 Singapore 8,575 54,076 41.0 36.8 21.0 18.2 9.1 5.6 29.0 39.4 Slovak Republic 1,666 3,012 17.3 29.8 13.1 19.0 .. 8.7 69.6 42.4 Slovenia 1,034 2,890 42.5 22.5 27.3 32.9 2.5 2.2 27.8 42.4 Somalia 122 .. 38.2 .. .. .. 4.2 .. 57.6 .. South Africa 3,594 11,863 40.2 44.9 31.5 28.4 11.6 5.6 16.7 21.1 Spain 15,197 65,159 30.9 27.1 28.0 23.1 6.3 5.4 34.9 44.4 Sri Lanka 620 2,051 64.2 61.8 11.9 15.3 6.8 6.0 17.1 16.9 Sudan 202 1,801 31.9 59.9 25.4 37.1 4.9 0.7 37.8 2.3 Swaziland 171 431 6.1 20.4 20.6 3.5 .. 30.8 73.4 45.3 Sweden 16,959 35,023 23.2 16.3 37.1 30.8 7.9 3.3 31.7 49.6 Switzerland 11,093 26,089 33.7 21.3 53.0 35.5 1.4 5.8 12.0 37.4 Syrian Arab Republic 702 2,136 54.5 64.6 35.5 25.8 4.4 3.4 5.7 6.3 Tajikistan .. 250 .. 71.3 .. 1.5 .. 7.2 .. 20.0 Tanzania 288 1,088 58.0 25.4 7.9 50.9 6.2 4.5 27.9 19.2 Thailand 6,160 27,458 58.1 51.0 23.3 18.2 5.5 6.0 13.3 24.8 Togo 217 237 56.9 72.7 18.4 3.6 9.1 11.4 15.6 12.4 Trinidad and Tobago 460 314 51.7 51.7 26.6 30.5 9.9 0.1 11.9 17.7 Tunisia 682 2,066 51.4 53.6 26.2 17.7 7.4 8.7 15.0 20.1 Turkey 2,794 10,697 32.2 49.8 18.6 26.9 .. 11.9 49.2 11.4 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 195 783 58.3 41.4 .. 17.0 6.5 6.4 35.2 35.2 Ukraine .. 6,962 .. 29.5 .. 40.3 .. 5.4 .. 24.9 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 44,713 155,861 33.2 23.5 41.0 38.2 2.4 6.7 23.4 31.5 United States 97,950 281,168 36.3 31.4 38.9 26.2 4.5 12.5 20.4 30.0 Uruguay 363 857 48.2 48.6 30.7 29.4 1.5 3.1 19.6 18.9 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 2,390 5,250 33.5 44.6 42.8 24.4 4.3 9.3 19.4 21.7 Vietnam .. 5,282 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 639 1,103 27.6 47.7 9.9 15.2 5.4 8.5 57.1 28.6 Zambia 370 .. 76.8 .. 14.6 .. 5.3 .. 3.3 .. Zimbabwe 460 .. 51.8 .. 14.4 .. 3.4 .. 30.4 .. World 834,571 t 2,346,205 t 34.9 w 28.5 w 32.5 w 28.1 w 5.0 w 8.2 w 32.3 w 35.4 w Low income 21,190 101,435 55.6 44.0 13.0 17.6 5.2 6.0 26.6 32.8 Middle income 107,026 449,275 48.7 32.5 25.3 26.8 4.1 12.6 22.1 28.0 Lower middle income 51,519 254,719 61.6 38.3 15.9 23.6 4.3 7.3 18.3 30.9 Upper middle income 56,695 196,106 35.2 27.3 35.1 29.8 3.9 17.5 26.1 25.5 Low & middle income 128,521 548,077 49.3 33.1 24.1 26.4 4.2 12.4 22.5 28.2 East Asia & Pacific 25,122 171,206 65.5 38.8 15.8 22.1 2.6 6.6 16.2 32.5 Europe & Central Asia .. 132,848 30.9 27.7 19.2 30.9 6.2 7.1 44.2 34.3 Latin America & Carib. 33,527 88,781 34.0 25.6 40.9 30.2 5.9 25.7 19.6 18.5 Middle East & N. Africa 18,677 43,583 49.2 .. 16.3 .. 6.9 .. 27.7 .. South Asia 9,262 64,639 60.5 44.9 10.7 16.3 5.3 6.3 23.5 32.5 Sub-Saharan Africa 18,237 50,365 45.1 40.8 22.6 23.4 7.6 5.9 25.5 31.0 High income 701,461 1,800,743 31.0 27.3 34.8 28.5 5.1 7.1 34.8 37.3 Europe EMU 301,701 768,538 26.3 23.8 31.1 28.2 7.6 5.0 35.0 43.0 a. Includes Luxembourg. b. World Trade Organization estimate. 216 2007 World Development Indicators 4.7 ECONOMY Structure of service imports 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. International transactions in ser- in the producing country (for example, use of a hotel vices are defined by the IMF's Balance of Payments room) but are classified as imports of the traveler's Manual (1993) as the economic output of intangible country. In other cases services may be supplied commodities that may be produced, transferred, and from a remote location; for example, insurance consumed at the same time. Definitions may vary services may be supplied from one location and among reporting economies. · Transport covers all consumed in another. For further discussion of the transport services (sea, air, land, internal waterway, problems of measuring trade in services, see About space, and pipeline) performed by residents of one the data for table 4.6. economy for those of another and involving the car- The data on exports of services in table 4.6 and on riage of passengers, movement of goods (freight), imports of services in this table, unlike those in edi- rental of carriers with crew, and related support and tions before 2000, include only commercial services auxiliary services. Excluded are freight insurance, and exclude the category "government services not which is included in insurance services; goods pro- included elsewhere." The data are compiled by the cured in ports by nonresident carriers and repairs of International Monetary Fund (IMF) based on returns transport equipment, which are included in goods; from national sources. repairs of harbors, railway facilities, and airfield facil- ities, which are included in construction services; and rental of carriers without crew, which is included in other services. · Travel covers goods and ser- vices acquired from an economy by travelers in that economy for their own use during visits of less than one year for business or personal purposes. Travel services include the goods and services consumed by travelers, such as meals, lodging, and transport (within the economy visited), including car rental. · Insurance and fi nancial services cover freight insurance on goods imported and other direct insur- ance such as life insurance, financial intermediation services such as commissions, foreign exchange transactions, and brokerage services; and auxil- The mix of commercial service imports iary services such as financial market operational by developing countries is changing 4.7a and regulatory services. · Computer, information, 1990 2005 communications, and other commercial services include such activities as international telecommu- nications, and postal and courier services; computer Other data; news-related service transactions between 22% residents and nonresidents; construction services; Insurance and Other finance 4% Transport 28% royalties and license fees; miscellaneous business, 50% Transport 34% professional, and technical services; and personal, Travel 24% cultural, and recreational services. Insurance and finance 12% Travel 26% Data sources Data on imports of commercial services are from Between 1990 and 2005 transport was displaced by travel and insurance and other services as the the IMF. The IMF publishes balance of payments most important services imported by developing economies. data in its International Financial Statistics and Bal- Source: International Monetary Fund data files. ance of Payments Statistics Yearbook. 2007 World Development Indicators 217 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan .. 110 .. 9 .. 25 .. 12 .. 56 .. 24 Albania 61 91 19 9 29 24 15 22 23 46 21 16 Algeria 57 34 16 12 29 30 23 48 25 24 26 51 Angola 36 67 35 ..a 12 8 39 74 21 48 9 21 Argentina 77 61 3 12 14 22 10 25 5 19 13 24 Armenia 46 73 18 11 47 30 35 27 46 40 .. 26 Australia 59 59 19 18 23 26 16 18 16 21 19 20 Austria 57 56 19 18 24 21 38 53 37 48 24 24 Azerbaijan 51 48 18 11 27 38 44 57 39 54 .. 30 Bangladesh 86 76 4 6 17 25 6 17 14 23 14 30 Belarus 47 50 24 20 27 30 46 61 44 60 29 31 Belgium 56 53 20 23 22 21 70 87 68 85 23 24 Benin 87 78 11 15 14 20 14 14 26 26 10 11 Bolivia 77 68 12 14 13 14 23 36 24 33 10 20 Bosnia and Herzegovina .. 99 .. 26 .. 19 .. 36 .. 81 .. ­2 Botswana 33 28 24 25 37 32 55 51 50 35 43 46 Brazil 59 56 19 20 20 21 8 17 7 12 19 22 Bulgaria 60 70 18 19 26 28 33 61 37 77 16 17 Burkina Faso 82 80 13 13 18 21 11 9 24 22 13 7 Burundi 95 87 11 28 15 12 8 9 28 36 ­5 9 Cambodia 91 85 7 4 8 20 6 65 13 74 6 14 Cameroon 67 71 13 10 18 21 20 23 17 25 16 18 Canada 56 55 23 20 21 21 26 39 26 34 18 23 Central African Republic 86 .. 15 .. 12 .. 15 .. 28 .. 0 14 Chad 98 58 10 5 7 17 14 59 28 39 ­3 21 Chile 61 57 10 12 25 23 34 42 31 34 24 17 Chinab 46c 37c 14 14 36 44 19 38 16 32 40 51 Hong Kong, China 57 58 7 9 27 21 131 198 122 185 33 32 Colombia 66 61 9 19 19 19 21 22 15 21 22 18 Congo, Dem. Rep. 79 85 12 8 9 14 30 32 29 39 1 14 Congo, Rep. 62 34 14 14 16 24 54 82 46 55 7 29 Costa Rica 73 66 15 14 19 26 30 49 36 54 10 19 Côte d'Ivoire 72 74 17 8 7 11 32 50 27 42 ­4 13 Croatia 75 58 24 20 11 31 78 47 86 56 ­16 23 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 49 49 23 22 25 27 45 72 43 70 28 24 Denmark 50 49 25 26 20 21 37 49 33 44 20 24 Dominican Republic 80 74 4 10 25 20 34 34 44 38 22 19 Ecuador 68 66 11 11 21 24 33 31 32 32 11 24 Egypt, Arab Rep. 73 71 11 13 29 18 20 31 33 33 21 21 El Salvador 89 93 10 10 14 15 19 27 31 45 6 11 Eritrea 101 82 22 45 11 20 11 9 45 56 13 10 Estonia 62 56 16 18 30 32 60 84 54 90 41 21 Ethiopia 77 82 13 14 13 26 6 16 9 39 12 17 Finland 52 54 22 23 29 20 22 39 24 35 24 23 France 57 57 22 24 22 20 21 26 23 27 21 18 Gabon 50 52 13 7 22 21 46 59 31 39 24 32 Gambia, The 76 96 14 ..a 22 25 60 45 72 65 5 15 Georgia 65 68 10 18 31 26 40 42 46 55 .. 20 Germany 57 59 20 19 24 17 25 40 25 35 24 21 Ghana 85 81 9 15 14 29 17 36 26 62 7 22 Greece 73 67 15 16 23 24 18 21 28 28 18 15 Guatemala 84 90 7 6 14 19 21 16 25 30 10 15 Guinea 73 86 9 5 18 12 31 26 31 30 11 7 Guinea-Bissau 87 85 10 18 30 15 10 38 37 55 15 8 Haiti 81 92 8 8 13 30 18 16 20 45 6 28 218 2007 World Development Indicators 4.8 ECONOMY 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 67 77 13 14 23 30 37 41 40 61 22 30 Hungary 61 68 11 10 25 24 31 66 29 69 26 16 India 66 59 12 11 24 33 7 21 9 24 22 32 Indonesia 59 65 9 8 31 22 25 34 24 29 28 24 Iran, Islamic Rep. 59 46 12 12 37 33 15 39 23 30 35 41 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 58 44 16 16 21 25 57 83 52 68 19 24 Israel 56 59 30 28 25 19 35 46 45 51 22 .. Italy 57 59 20 20 22 21 19 26 19 26 21 20 Jamaica 65 73 13 15 26 32 48 41 52 61 19 20 Japan 53 57 13 18 33 23 11 13 10 11 34 26 Jordan 74 103 25 15 32 24 62 52 93 93 22 7 Kazakhstan 52 53 18 11 32 27 74 54 75 45 .. 26 Kenya 63 74 19 17 24 17 26 27 31 35 19 12 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 52 53 12 14 38 30 28 43 29 40 37 32 Kuwait 57 28 39 15 18 20 45 68 58 30 .. .. Kyrgyz Republic 71 86 25 19 24 14 29 39 50 58 4 6 Lao PDR .. 72 .. ..a .. 32 12 27 25 31 ­4 2 Latvia 53 62 9 18 40 34 48 48 49 62 56 23 Lebanon 140 89 25 16 18 20 18 19 100 44 22 ­1 Lesotho 139 84 14 15 53 41 17 48 122 88 60 27 Liberia .. 87 .. 11 .. 16 .. 37 .. 50 .. 18 Libya 48 .. 24 .. 19 .. 40 .. 31 .. .. .. Lithuania 57 65 19 17 33 25 52 58 61 65 .. 18 Macedonia, FYR 72 78 19 19 19 20 26 45 36 63 9 20 Madagascar 86 84 8 8 17 22 17 26 28 40 9 11 Malawi 72 95 15 17 23 15 24 27 33 53 14 ­7 Malaysia 52 44 14 13 32 20 75 123 72 100 30 36 Mali 80 79 14 10 23 23 17 26 34 37 15 11 Mauritania 69 92 26 23 20 45 46 36 61 95 18 ­5 Mauritius 64 67 13 14 31 23 64 57 71 61 26 20 Mexico 70 68 8 12 23 22 19 30 20 32 20 21 Moldova 77 93 ..a 16 25 30 48 53 51 91 58 23 Mongolia 62 57 30 15 36 36 22 76 49 84 6 37 Morocco 65 58 16 23 25 26 27 36 32 43 25 29 Mozambique 92 79 14 10 22 20 8 33 36 42 2 4 Myanmar 89 .. ..a .. 13 .. 3 .. 5 .. 12 .. Namibia 51 50 31 23 34 26 52 46 67 45 35 40 Nepal 84 77 9 10 18 29 11 16 21 33 11 31 Netherlands 50 49 23 24 23 19 56 71 52 63 28 27 New Zealand 61 59 19 18 20 25 27 29 27 30 17 17 Nicaragua 59 89 44 11 19 29 25 28 46 58 ­4 13 Niger 84 79 15 12 8 19 15 15 22 24 ­2 10 Nigeria 56 40 15 21 15 21 43 53 29 35 19 30 Norway 49 42 21 20 23 21 40 45 34 28 26 37 Oman 46 45 22 23 12 18 47 57 28 43 .. .. Pakistan 74 80 15 8 19 17 16 15 23 20 22 18 Panama 57 73 18 12 17 19 87 69 79 73 24 10 Papua New Guinea 59 .. 25 .. 24 .. 41 .. 49 .. 9 .. Paraguay 77 75 6 10 23 22 33 47 40 54 20 16 Peru 74 66 8 10 17 19 16 25 14 19 .. 19 Philippines 72 80 10 10 24 15 28 47 33 52 20 31 Poland 48 62 19 19 26 19 29 37 22 37 27 18 Portugal 64 66 16 21 27 22 31 29 38 37 25 13 Puerto Rico 65 .. 15 .. 17 .. .. .. .. .. .. .. 2007 World Development Indicators 219 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 66 78 13 10 30 23 17 33 26 43 22 14 Russian Federation 49 49 21 17 30 21 18 35 18 22 36 32 Rwanda 84 85 10 13 15 22 6 11 14 31 11 19 Saudi Arabia 47 26 29 23 15 16 41 61 32 26 18 .. Senegal 76 77 15 14 14 23 25 27 30 42 6 16 Serbia and Montenegro .. 86 .. 18 .. 18 .. 27 .. 50 .. 10 Sierra Leone 84 90 8 13 10 15 22 24 24 43 3 7 Singapore 46 41 10 11 37 19 .. 243 .. 213 46 .. Slovak Republic 54 56 22 20 33 29 27 79 36 83 .. 20 Slovenia 53 55 17 20 17 26 91 65 79 65 24 25 Somalia .. .. .. .. 16 .. 10 .. 38 .. 17 .. South Africa 57 63 20 20 18 18 24 27 19 29 20 14 Spain 60 58 17 18 26 30 16 25 19 31 23 22 Sri Lanka 77 77 10 9 23 26 29 34 38 46 17 20 Sudan .. 70 .. 17 .. 23 .. 18 .. 28 .. 17 Swaziland 75 61 18 28 19 19 75 88 87 95 25 17 Sweden 49 48 27 27 23 17 30 49 30 41 21 23 Switzerland 57 60 11 12 31 20 36 46 34 39 34 33 Syrian Arab Republic 69 70 14 14 17 20 28 37 28 40 15 14 Tajikistan 74 95 9 9 25 14 28 54 35 73 24 7 Tanzaniad 81 77 18 14 26 19 13 17 38 26 8 9 Thailand 57 58 9 12 41 32 34 74 42 75 33 29 Togo 71 86 14 10 27 18 34 34 45 47 20 10 Trinidad and Tobago 59 58 12 9 13 20 45 58 29 46 21 29 Tunisia 64 64 16 16 27 23 44 48 51 51 22 21 Turkey 69 69 11 13 24 25 13 27 18 34 24 18 Turkmenistan 49 46 23 13 40 23 .. 65 .. 48 .. 34 Uganda 92 79 8 14 13 21 7 13 19 27 1 10 Ukraine 57 61 17 19 28 19 28 54 29 53 36 22 United Arab Emirates 38 46 16 11 21 24 66 94 41 76 .. .. United Kingdom 63 65 20 22 20 17 24 26 27 30 16 14 United States 67 70 17 16 18 19 10 10 11 15 15 13 Uruguay 70 74 12 11 12 13 24 30 18 28 14 13 Uzbekistan 61 51 25 16 32 23 29 40 48 30 3 35 Venezuela, RB 62 47 8 11 10 22 40 41 20 21 27 40 Vietnam 84 64 12 6 13 35 36 70 45 75 ­2 34 West Bank and Gaza .. 96 .. 33 .. 26 .. 14 .. 68 .. 13 Yemen, Rep. 74 50 18 16 15 27 14 46 20 38 28 32 Zambia 64 70 19 13 17 26 36 16 37 25 7 10 Zimbabwe 63 70 19 27 17 14 23 43 23 53 16 3 World 60 w 61 w 17 w 17 w 23 w 22 w 19 w 26 w 19 w 26 w 22 w 21 w Low income 70 64 12 11 21 29 13 25 16 29 18 28 Middle income 59 56 14 15 26 27 22 36 21 33 27 30 Lower middle income 56 51 15 15 29 31 19 34 19 31 30 35 Upper middle income 63 61 13 14 23 22 25 38 23 35 22 23 Low & middle income 61 57 14 14 26 27 20 34 20 32 26 29 East Asia & Pacific 50 44 13 13 35 38 24 46 23 41 36 45 Europe & Central Asia 56 60 17 16 27 23 24 41 24 41 25 23 Latin America & Carib. 67 62 12 15 19 21 17 26 15 23 20 22 Middle East & N. Africa 66 59 15 14 28 26 24 37 34 36 26 30 South Asia 69 64 11 10 23 31 9 20 12 25 21 30 Sub-Saharan Africa 64 65 17 18 18 19 27 33 26 35 16 17 High income 60 62 18 18 23 20 19 25 19 25 22 19 Europe EMU 57 58 20 21 23 20 27 37 28 36 23 21 a. Data on general government final consumption expenditure are not available separately; they are included in household final consumption expenditure. b. China has revised its national accounts data from 1993 onwards. However, data by expenditure are not available. Data shown here are based on earlier series. c. Includes the difference between the old and the new GDP series. d. Data cover mainland Tanzania only. 220 2007 World Development Indicators 4.8 ECONOMY Structure of demand About the data Gross domestic product (GDP) from the expenditure intangibles such as computer software and mineral payments and fees to governments to obtain permits side is made up of household final consumption exploration outlays. Data on capital formation may and licenses. Expenditures of nonprofit institutions expenditure, general government final consumption be estimated from direct surveys of enterprises and serving households are included, even when reported expenditure, gross capital formation (private and administrative records or based on the commodity separately by the country. In practice, household public investment in fixed assets, changes in inven- flow method using data from production, trade, and consumption expenditure may include any statisti- tories, and net acquisitions of valuables), and net construction activities. The quality of data on fixed cal discrepancy in the use of resources relative to exports (exports minus imports) of goods and ser- capital formation by government depends on the qual- the supply of resources. · General government final vices. Such expenditures are recorded in purchaser ity of government accounting systems (which tend to consumption expenditure includes all government prices and include net taxes on products. be weak in developing countries). Measures of fixed current expenditures for purchases of goods and Because policymakers have tended to focus on capital formation by households and corporations-- services (including compensation of employees). It fostering the growth of output, and because data on particularly capital outlays by small, unincorporated also includes most expenditures on national defense production are easier to collect than data on spend- enterprises--are usually unreliable. and security but excludes government military expen- ing, many countries generate their primary estimate Estimates of changes in inventories are rarely ditures that potentially have wider public use and of GDP using the production approach. Moreover, complete but usually include the most important are part of government capital formation. · Gross many countries do not estimate all the separate activities or commodities. In some countries these capital formation consists of outlays on additions components of national expenditures but instead estimates are derived as a composite residual along to the fixed assets of the economy, net changes derive some of the main aggregates indirectly using with household fi nal consumption expenditure. in the level of inventories, and net acquisitions of GDP (based on the production approach) as the con- According to national accounts conventions, adjust- valuables. Fixed assets include land improvements trol total. Household final consumption expenditure ments should be made for appreciation of the value (fences, ditches, drains, and so on); plant, machin- (private consumption in the 1968 System of National of inventory holdings due to price changes, but this ery, and equipment purchases; and the construction Accounts, or SNA) is often estimated as a residual, by is not always done. In highly inflationary economies of roads, railways, and the like, including schools, subtracting from GDP all other known expenditures. this element can be substantial. offices, hospitals, private residential dwellings, and The resulting aggregate may incorporate fairly large Data on exports and imports are compiled from commercial and industrial buildings. Inventories are discrepancies. When household consumption is cal- customs reports and balance of payments data. stocks of goods held by firms to meet temporary culated separately, many of the estimates are based Although the data from the payments side provide or unexpected fluctuations in production or sales, on household surveys, which tend to be one-year stud- reasonably reliable records of cross-border transac- and "work in progress." · Exports and imports ies with limited coverage. Thus the estimates quickly tions, they may not adhere strictly to the appropriate of goods and services are the value of all goods become outdated and must be supplemented by esti- definitions of valuation and timing used in the bal- and other market services provided to, or received mates using price- and quantity-based statistical pro- ance of payments or correspond to the change-of- from, the rest of the world. They include the value of cedures. Complicating the issue, in many developing ownership criterion. This issue has assumed greater merchandise, freight, insurance, transport, travel, countries the distinction between cash outlays for significance with the increasing globalization of inter- royalties, license fees, and other services, such as personal business and those for household use may national business. Neither customs nor balance of communication, construction, financial, information, be blurred. World Development Indicators includes in payments data usually capture the illegal transac- business, personal, and government services. They household consumption the expenditures of nonprofit tions that occur in many countries. Goods carried exclude compensation of employees and investment institutions serving households. by travelers across borders in legal but unreported income (factor services in the 1968 SNA) as well as General government final consumption expenditure shuttle trade may further distort trade statistics. transfer payments. · Gross savings are calculated (general government consumption in the 1968 SNA) Gross savings represent the difference between dis- as gross national income less total consumption, includes expenditures on goods and services for posable income and consumption and replace gross plus net transfers. individual consumption as well as those on services domestic savings, a concept used by the World Bank for collective consumption. Defense expenditures, and included in World Development Indicators editions Data sources including those on capital outlays (with certain excep- before 2006. The change was made to conform to the National accounts indicators for most developing tions), are treated as current spending. SNA concepts and definitions. For further discussion of countries are collected from national statistical Gross capital formation (gross domestic invest- the problems in compiling national accounts, see Srini- organizations and central banks by World Bank ment in the 1968 SNA) consists of outlays on vasan (1994), Heston (1994), and Ruggles (1994). missions. Data for high-income economies come additions to the economy's fixed assets plus net For an analysis of the reliability of foreign trade and from Organisation for Economic Co-operation and changes in the level of inventories. It is generally national income statistics, see Morgenstern (1963). Development data files (see the OECD's Annual obtained from reports by industry of acquisition and Definitions National Accounts for OECD Member Countries: distinguishes only the broad categories of capital Data from 1970 Onwards). The United Nations formation. The 1993 SNA recognizes a third cat- · Household final consumption expenditure is the Statistics Division publishes detailed national egory of capital formation: net acquisitions of valu- market value of all goods and services, including accounts for UN member countries in National ables. Included in gross capital formation under the durable products (such as cars, washing machines, Accounts Statistics: Main Aggregates and Detailed 1993 SNA guidelines are capital outlays on defense and home computers), purchased by households. It Tables and updates in the Monthly Bulletin of establishments that may be used by the general pub- excludes purchases of dwellings but includes imputed Statistics. lic, such as schools, airfields, and hospitals, and rent for owner-occupied dwellings. It also includes 2007 World Development Indicators 221 4.9 Growth of consumption investment, and trade Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure Per capita average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth Exports Imports 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 Afghanistan .. 12.6 .. .. .. 13.1 .. 5.6 .. ­17.6 .. 1.9 Albania 4.3 3.5 5.2 3.0 2.4 1.1 25.8 3.3 17.9 15.6 15.8 15.0 Algeria ­0.1 5.7 ­1.9 4.1 3.6 5.1 ­0.6 10.6 3.2 4.4 ­1.0 9.2 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2.7 0.6 1.5 ­0.4 2.2 0.4 7.4 5.3 8.7 6.4 15.6 0.5 Armenia ­0.5 8.4 1.1 8.8 ­1.6 8.7 ­1.9 22.9 ­18.4 20.6 ­12.7 12.5 Australia 3.6 4.2 2.4 2.9 3.0 3.4 5.7 9.7 7.4 0.6 8.1 10.4 Austria 1.9 1.0 1.5 0.5 2.5 1.0 .. .. 5.5 5.4 5.0 4.3 Azerbaijan 1.5 12.2 0.4 11.2 ­1.7 4.7 42.9 39.0 6.8 16.2 15.5 27.3 Bangladesh 2.6 4.0 0.4 2.0 4.7 11.9 9.2 8.3 13.1 8.3 9.7 5.6 Belarus ­0.5 11.0 ­0.3 11.5 ­1.9 0.3 ­7.5 15.5 ­4.8 8.8 ­8.7 11.7 Belgium 1.8 1.1 1.5 0.6 1.5 2.4 2.6 1.4 4.7 2.8 4.5 2.8 Benin 2.6 2.3 ­0.7 ­0.9 4.4 8.3 12.2 4.8 1.8 2.7 2.1 1.8 Bolivia 3.6 2.3 1.4 0.3 3.6 2.6 8.5 ­2.7 4.5 10.8 6.0 5.4 Bosnia and Herzegovina .. ­1.3 .. .. .. 7.0 .. 3.0 .. 12.7 .. 4.5 Botswana 2.5 4.9 0.4 4.8 7.1 .. 6.7 ­0.7 4.7 2.2 3.8 3.3 Brazila 4.8 0.5 3.3 ­0.9 ­0.4 1.0 3.4 ­0.1 6.1 11.4 11.1 1.1 Bulgaria ­3.7 5.5 ­3.0 6.3 ­8.4 4.5 ­5.0 15.1 3.9 9.1 2.7 12.4 Burkina Faso 4.2 3.6 1.3 0.3 ­0.5 2.6 7.0 8.2 0.0 6.6 1.4 11.3 Burundi ­4.9 .. .. .. ­2.6 .. ­0.5 .. ­1.2 .. ­1.6 .. Cambodiaa 6.0 8.3 3.4 6.2 7.2 4.0 10.9 12.1 21.7 17.0 14.8 15.3 Cameroon 3.1 4.2 0.7 2.3 0.7 4.6 0.4 8.1 3.2 1.0 5.1 6.8 Canada 2.6 3.2 1.6 2.2 0.3 3.0 4.5 4.6 8.7 0.0 7.2 2.2 Central African Republica .. .. .. .. .. .. .. .. .. .. .. .. Chada 1.5 5.0 ­1.6 1.4 ­8.3 3.8 4.0 5.0 2.3 55.6 ­1.7 16.8 Chile 7.3 4.6 5.6 3.4 3.7 3.8 9.3 8.8 9.5 6.6 11.7 10.4 Chinab 8.9 6.9 7.8 6.2 9.7 8.1 11.5 13.5 13.0 24.8 14.3 20.8 Hong Kong, China 3.9 1.9 2.1 1.1 3.3 1.6 5.6 0.3 8.1 10.2 8.4 8.7 Colombia 2.2 3.3 0.3 1.7 10.5 1.4 2.0 13.6 5.3 3.8 9.0 9.7 Congo, Dem. Rep.a ­4.5 .. ­7.1 .. ­17.4 .. ­0.7 .. ­0.5 7.8 ­2.4 25.2 Congo, Rep.a ­1.7 18.5 ­4.8 15.0 ­2.0 9.8 0.4 22.4 5.1 5.5 2.9 26.1 Costa Ricaa 5.1 3.0 2.5 1.1 2.0 1.4 5.1 8.9 10.9 6.0 9.2 5.7 Côte d'Ivoire 4.3 ­0.5 1.4 ­2.1 0.8 3.2 8.3 ­9.7 1.5 3.5 6.8 3.5 Croatia 2.8 4.8 3.2 5.0 1.3 0.8 5.6 14.5 5.9 6.2 4.6 8.8 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 3.0 3.3 3.0 3.3 ­0.9 3.2 4.8 3.9 8.7 10.1 12.0 9.8 Denmark 2.2 2.1 1.8 1.8 2.4 1.4 .. .. 5.1 2.8 6.1 4.5 Dominican Republica 5.3 0.7 3.7 ­0.7 5.3 5.6 10.4 ­2.9 9.1 2.7 9.4 ­3.1 Ecuadora 2.1 5.8 0.3 4.3 ­1.5 2.6 ­0.7 10.1 5.3 6.6 2.8 10.4 Egypt, Arab Rep. 3.7 3.0 1.8 1.0 4.4 3.1 5.8 1.3 3.3 10.5 3.1 5.6 El Salvador 5.3 2.3 3.1 0.5 2.8 1.3 7.1 1.3 13.4 4.1 11.6 3.3 Eritrea ­5.0 1.8 ­6.7 ­2.5 22.6 5.0 19.1 ­8.0 ­2.5 ­7.6 7.5 ­4.1 Estonia 0.6 7.0 2.2 7.4 4.9 5.8 0.2 11.6 11.2 8.1 12.0 9.6 Ethiopia 2.8 6.7 0.6 4.5 9.6 ­0.1 5.9 6.6 7.1 15.0 5.8 11.0 Finland 1.7 2.9 1.3 2.7 0.9 2.3 1.3 1.4 9.9 3.7 6.2 4.0 France 1.6 2.2 1.3 1.5 1.4 1.8 1.8 1.1 6.9 1.7 5.7 3.4 Gabona 1.2 4.5 ­1.7 2.8 5.4 0.2 3.8 1.4 1.5 1.7 0.7 2.9 Gambia, The 3.6 1.8 0.1 ­1.1 ­2.2 4.2 1.9 11.9 0.1 4.0 0.1 2.5 Georgia 6.1 7.1 7.6 8.3 12.0 1.0 ­12.5 18.8 12.2 4.1 11.2 4.9 Germany 1.9 0.3 1.6 0.3 1.8 0.1 .. .. 6.0 5.5 5.8 3.5 Ghana 4.4 7.6 1.9 5.3 4.8 ­7.0 1.3 10.7 10.1 4.1 10.4 5.0 Greece 2.2 4.1 1.4 3.7 2.1 2.1 .. .. 7.6 1.2 7.4 1.9 Guatemalaa 4.2 3.7 1.9 1.2 5.1 ­2.5 6.2 6.9 6.2 ­1.0 9.2 5.6 Guinea 3.7 5.2 0.5 2.9 5.0 0.3 2.8 ­3.6 4.6 0.7 1.3 ­2.4 Guinea­Bissau 2.6 7.3 ­0.4 4.1 1.9 ­3.7 ­6.5 ­4.5 15.4 4.1 ­0.5 ­3.5 Haiti .. .. .. .. .. .. .. .. .. .. .. .. 222 2007 World Development Indicators 4.9 ECONOMY Growth of consumption, investment, and trade Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure Per capita average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth Exports Imports 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 Hondurasa 3.0 5.1 0.2 2.7 2.0 4.1 6.9 2.2 1.6 6.0 3.8 7.5 Hungary ­0.2 6.0 0.0 6.3 0.9 3.6 10.6 0.1 9.9 8.7 11.4 8.6 India 4.9 5.4 3.0 3.8 5.9 4.1 6.3 14.9 11.0 15.4 12.8 18.4 Indonesia 6.6 4.1 5.0 2.7 0.1 8.7 ­0.6 4.9 5.9 5.5 5.7 7.4 Iran, Islamic Rep. 3.2 7.5 1.6 6.1 1.6 2.1 ­0.1 9.3 1.2 2.1 ­6.8 14.5 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 5.3 3.9 4.5 2.2 4.0 5.6 .. .. 15.7 4.7 14.5 3.0 Israel 4.5 2.6 1.9 0.7 3.0 0.8 1.5 ­3.9 10.6 4.1 7.4 1.6 Italy 1.5 0.5 1.5 ­0.1 ­0.4 1.8 .. .. 5.1 ­0.8 3.8 0.8 Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan 1.5 1.2 1.3 1.0 2.9 2.4 ­1.8 ­2.2 4.2 6.2 4.2 3.3 Jordan 5.9 8.4 1.9 5.8 1.7 4.5 1.1 10.2 2.5 11.5 2.0 13.0 Kazakhstana ­8.1 9.6 ­7.0 9.3 ­7.1 7.1 ­18.3 13.6 ­2.6 8.0 ­11.2 3.5 Kenya 3.6 3.0 0.8 0.8 6.9 2.2 6.1 2.0 1.1 7.4 9.4 5.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 4.9 2.5 3.9 2.0 4.7 4.5 3.4 3.2 16.0 12.1 10.0 9.4 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 10.6 ­7.4 9.6 ­8.8 1.0 ­3.9 ­5.3 ­1.6 4.5 ­8.2 10.1 Lao PDR .. .. .. .. .. .. .. 17.9 .. 7.5 .. 14.8 Latvia ­3.9 8.6 ­2.7 9.3 1.8 2.2 ­3.9 16.7 4.3 8.6 7.6 12.1 Lebanon 4.2 4.1 1.9 3.0 6.2 0.9 7.3 4.8 14.0 11.9 3.3 6.5 Lesotho 0.5 3.8 ­0.7 3.7 6.0 ­2.5 1.5 ­2.3 11.1 10.2 0.9 4.7 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuaniaa 5.2 8.8 6.0 9.3 1.9 3.9 11.1 17.0 4.9 12.1 7.5 14.8 Macedonia, FYR 2.2 2.6 1.7 2.4 ­0.4 ­2.0 3.6 2.2 4.2 ­1.2 7.6 ­0.6 Madagascar 2.3 5.3 ­0.7 2.4 0.0 3.6 3.4 13.3 3.9 ­2.5 4.3 10.3 Malawi 5.4 4.1 3.5 1.8 ­4.4 8.5 ­8.4 2.5 4.0 2.7 ­1.1 6.2 Malaysia 5.3 6.8 2.6 4.7 4.8 9.9 5.3 1.5 12.0 6.1 10.3 6.8 Mali 3.0 1.9 0.2 ­1.1 3.2 21.1 0.4 6.7 10.0 6.7 3.5 4.5 Mauritania .. 7.4 .. 4.3 .. 3.1 .. 23.8 ­1.4 ­2.1 0.6 14.1 Mauritius 5.1 4.6 3.9 3.7 4.8 4.6 4.7 4.2 5.4 1.7 5.2 1.3 Mexico 2.4 3.0 0.7 1.9 1.8 ­0.2 4.7 ­0.4 14.6 4.0 12.3 4.1 Moldovaa 9.9 9.6 10.2 9.9 ­12.4 7.0 ­15.5 8.4 0.7 16.3 5.6 15.8 Mongoliaa 5.4 3.3 4.3 2.1 11.8 2.2 7.6 4.0 30.9 8.4 29.3 7.8 Morocco 1.6 3.4 0.1 1.7 3.8 4.7 2.9 5.5 5.4 5.0 4.8 4.6 Mozambiquea 4.7 6.6 1.6 4.5 3.1 8.5 11.5 5.1 11.0 20.0 6.3 10.1 Myanmar 3.9 .. .. .. .. .. 15.3 .. 10.0 .. 5.8 .. Namibia 4.8 ­0.5 1.7 ­1.8 3.3 0.9 6.9 9.6 3.8 7.2 5.4 1.4 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 2.8 0.3 2.2 ­0.2 2.0 2.1 3.2 ­1.5 6.8 3.7 6.6 3.4 New Zealand 3.2 5.0 2.0 3.6 2.5 3.5 6.1 9.3 5.2 4.2 6.2 9.5 Nicaraguaa 6.1 3.5 3.9 2.6 ­1.5 1.1 11.3 ­0.9 9.3 6.8 12.2 3.7 Niger 1.8 .. .. .. 0.8 .. 4.0 .. 3.1 .. ­2.1 .. Nigeria 0.2 4.0 .. .. ­1.8 3.3 5.4 15.0 5.0 4.4 4.0 11.9 Norway 3.5 3.3 2.9 2.7 2.8 2.7 6.0 2.8 5.6 0.8 5.8 3.6 Oman 5.4 1.3 2.4 0.4 2.4 6.1 4.0 17.0 6.2 7.0 5.9 12.8 Pakistan 4.9 4.6 2.3 2.1 0.7 5.1 1.8 1.6 1.7 11.6 2.5 7.1 Panamaa 6.4 8.0 4.2 6.0 1.7 3.4 10.4 3.2 ­0.4 1.0 1.2 3.7 Papua New Guinea 5.6 .. .. .. 2.7 .. 0.5 .. 4.3 .. 2.8 .. Paraguay 2.6 1.0 0.2 ­1.0 2.5 ­0.2 0.7 2.9 3.1 3.9 2.9 0.6 Perua 4.0 3.5 2.2 2.0 5.2 3.2 7.4 4.1 8.6 9.7 9.0 5.8 Philippines 3.7 4.8 1.5 2.9 3.8 ­0.1 4.1 ­0.6 7.8 5.4 7.8 6.2 Polanda 5.2 2.8 5.1 3.0 3.7 3.3 10.6 0.3 11.3 9.5 16.7 6.2 Portugal 3.0 1.4 2.7 0.7 2.8 2.0 5.6 ­3.6 5.3 2.7 7.3 1.6 Puerto Rico .. .. .. .. .. .. .. .. 1.6 .. 4.5 .. 2007 World Development Indicators 223 4.9 Growth of consumption, investment, and trade Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure Per capita average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth Exports Imports 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 Romaniaa 1.4 7.1 1.7 7.9 0.8 4.0 ­5.1 9.4 8.1 11.4 6.0 13.4 Russian Federation ­0.9 9.3 ­0.8 9.8 ­2.2 1.8 ­19.1 9.2 0.8 9.7 ­6.1 18.0 Rwandaa 1.1 3.5 ­0.1 1.3 ­1.7 10.4 1.4 5.1 ­3.8 11.4 5.0 4.6 Saudi Arabia .. 1.9 .. ­0.3 .. 0.6 .. 6.2 .. 2.2 .. 2.2 Senegal 2.4 5.4 ­0.2 2.9 2.1 7.5 7.9 10.4 6.3 3.3 3.5 5.9 Serbia and Montenegro .. 7.8 .. 14.8 .. 5.7 .. 14.5 .. 15.3 .. 19.4 Sierra Leone ­4.4 .. .. .. 10.4 .. ­5.6 .. ­11.2 .. ­0.2 .. Singapore .. 3.3 .. .. .. 1.2 .. 0.3 .. .. .. .. Slovak Republic 4.7 3.4 4.5 3.4 2.9 3.1 7.9 5.2 9.0 11.9 11.7 10.8 Slovenia 3.9 2.6 3.9 2.5 2.2 2.8 10.9 5.2 1.7 7.5 5.2 7.3 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2.9 4.6 0.7 3.3 0.3 5.5 5.0 7.5 5.6 1.9 7.1 8.0 Spain 2.4 3.4 2.0 1.8 2.7 4.8 .. .. 10.5 2.8 9.4 6.1 Sri Lankaa 5.7 .. .. .. 7.5 .. 6.9 6.7 7.5 4.8 8.6 7.0 Sudan 6.2 .. 3.7 .. 0.2 .. 11.3 19.5 14.2 9.1 8.8 4.5 Swazilanda 3.8 1.9 0.6 0.3 5.5 ­1.1 2.7 4.3 3.8 2.1 4.5 1.4 Sweden 1.3 1.6 0.9 1.3 0.6 1.0 1.8 1.2 8.6 4.8 6.3 2.9 Switzerland 1.1 0.9 0.5 0.2 0.8 2.3 .. .. 4.0 1.4 4.2 1.7 Syrian Arab Republic 3.0 6.2 0.2 3.6 2.0 6.5 3.3 10.6 12.0 0.2 4.4 10.1 Tajikistan ­4.2 .. ­5.5 .. ­19.2 .. ­17.6 18.2 ­1.4 13.8 ­3.9 1.7 Tanzaniac 2.1 1.7 ­0.8 ­0.2 3.4 19.1 ­1.6 9.4 7.1 2.5 0.3 5.2 Thailand 3.7 5.5 2.5 4.5 5.1 3.9 ­4.0 10.1 9.5 6.6 4.6 8.8 Togo 5.0 0.5 1.8 ­2.2 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 5.9 9.9 9.5 Tunisia 4.3 5.0 2.6 4.0 4.1 4.5 3.6 0.9 5.1 2.9 3.8 1.8 Turkey 3.5 4.2 1.7 2.9 4.9 ­0.2 5.0 11.6 11.7 11.8 11.0 12.8 Turkmenistan .. .. .. .. .. .. 2.2 .. ­6.1 13.9 0.6 12.3 Uganda 6.7 4.1 3.4 0.6 7.1 5.1 8.9 8.8 14.7 8.7 10.0 8.0 Ukraine ­6.9 12.1 ­6.4 13.1 ­4.1 5.1 ­18.5 5.8 ­3.6 6.4 ­6.6 7.5 United Arab Emirates 7.1 12.9 0.7 5.1 6.9 0.8 5.5 5.5 5.5 12.2 6.4 13.6 United Kingdom 2.9 2.9 2.6 2.8 1.1 3.5 4.6 2.3 6.6 2.6 6.8 4.5 United States 3.6 3.0 2.4 2.0 0.7 3.5 7.4 1.2 7.3 0.3 9.8 3.9 Uruguaya 5.0 ­1.1 4.2 ­1.8 2.3 ­3.0 6.3 ­0.9 6.0 5.1 9.9 ­0.5 Uzbekistan .. .. .. .. .. .. ­2.6 4.7 2.4 5.3 ­1.2 4.9 Venezuela, RB 0.6 3.7 ­1.5 1.9 3.7 6.0 11.0 ­1.0 1.0 ­0.8 8.2 4.2 Vietnam 5.4 7.2 3.8 5.9 3.2 6.9 19.8 11.5 24.1 16.6 28.2 19.9 West Bank and Gaza 5.3 ­1.5 0.9 ­5.4 12.8 1.3 9.2 ­3.0 8.7 ­3.1 7.5 ­2.3 Yemen, Rep. 3.2 4.9 ­0.8 1.7 1.7 8.7 11.4 11.8 16.6 ­0.3 8.3 7.1 Zambia ­3.6 3.2 ­5.9 1.4 ­8.1 7.2 5.4 4.9 2.8 12.7 1.5 8.4 Zimbabwe 0.0 ­4.4 ­1.7 ­5.0 ­2.2 ­3.2 ­2.5 ­10.7 10.5 ­7.6 9.4 ­4.2 World 3.0 w 2.6 w 1.5 w 1.4 w 1.7 w 2.9 w 3.2 w 2.5 w 6.9 w 5.9 w 6.9 w 5.2 w Low income 4.2 5.0 2.1 3.1 4.4 4.6 5.9 12.4 8.5 11.5 9.2 13.9 Middle income 3.9 4.4 2.6 3.4 2.9 3.9 2.6 7.8 7.3 10.9 6.6 10.4 Lower middle income 5.1 4.7 3.8 3.7 3.9 5.0 4.8 9.8 6.9 14.7 5.8 12.5 Upper middle income 2.6 4.0 1.6 3.4 1.6 2.3 ­0.4 4.1 7.7 6.9 7.4 8.2 Low & middle income 3.9 4.5 2.3 3.1 3.0 4.0 2.9 8.4 7.4 11.0 6.9 10.8 East Asia & Pacific 7.5 6.3 6.1 5.4 8.1 7.6 8.2 12.0 11.0 16.5 10.4 15.0 Europe & Central Asia 1.1 6.1 0.9 6.2 0.1 2.4 ­7.2 7.6 3.6 9.8 2.0 11.5 Latin America & Carib. 3.5 2.1 1.8 0.7 1.4 1.1 5.1 1.6 8.5 5.4 10.7 4.0 Middle East & N. Africa 3.1 4.8 0.9 2.8 3.2 3.8 1.9 7.3 4.1 5.2 0.6 8.4 South Asia 4.6 5.2 2.6 3.4 5.3 4.4 6.0 13.2 9.5 13.8 10.6 15.4 Sub-Saharan Africa 2.8 4.1 0.3 1.7 0.5 5.1 4.1 6.8 5.0 3.7 5.4 7.8 High income 2.8 2.3 2.0 1.6 1.5 2.7 3.3 0.6 6.8 3.4 7.0 3.9 Europe EMU 1.9 1.3 1.6 0.7 1.4 1.7 .. .. 6.6 3.3 6.0 3.3 a. Household final consumption expenditure includes statistical discrepancy. b. China has revised its national accounts data from 1993 onwards. However, data by expenditure are not availble. Data shown here are based on earlier series. c. Data cover mainland Tanzania only. 224 2007 World Development Indicators 4.9 ECONOMY Growth of consumption, investment, and trade About the data Measures of growth in consumption and capital applying a wage (price) index or extrapolate from expenditures on national defense and security but formation are subject to two kinds of inaccuracy. The the change in government employment. Neither excludes government military expenditures that first stems from the difficulty of measuring expendi- technique captures improvements in productivity potentially have wider public use and are part of tures at current price levels, as described in About or changes in the quality of government services. government capital formation. · Gross capital for- the data for table 4.8. The second arises in deflat- Deflators for household consumption are usually cal- mation consists of outlays on additions to the fixed ing current price data to measure volume growth, culated on the basis of the consumer price index. assets of the economy, net changes in the level of where results depend on the relevance and reliability Many countries estimate household consumption inventories, and net acquisitions of valuables. Fixed of the price indexes and weights used. Measuring as a residual that includes statistical discrepancies assets include land improvements (fences, ditches, price changes is more difficult for investment goods associated with the estimation of other expenditure drains, and so on); plant, machinery, and equipment than for consumption goods because of the one-time items, including changes in inventories; thus these purchases; and the construction of roads, railways, nature of many investments and because the rate of estimates lack detailed breakdowns of household and the like, including schools, offices, hospitals, technological progress in capital goods makes cap- consumption expenditures. private residential dwellings, and commercial and turing change in quality difficult. (An example is com- industrial buildings. Inventories are stocks of goods Definitions puters--prices have fallen as quality has improved.) held by firms to meet temporary or unexpected fluctu- Several countries estimate capital formation from · Household final consumption expenditure is the ations in production or sales, and "work in progress." the supply side, identifying capital goods entering market value of all goods and services, including · Exports and imports of goods and services repre- an economy directly from detailed production and durable products (such as cars, washing machines, sent the value of all goods and other market services international trade statistics. This means that the and home computers), purchased by households. provided to, or received from, the rest of the world. price indexes used in deflating production and inter- It excludes purchases of dwellings but includes They include the value of merchandise, freight, insur- national trade, reflecting delivered or offered prices, imputed rent for owner-occupied dwellings. It also ance, transport, travel, royalties, license fees, and will determine the defl ator for capital formation includes payments and fees to governments to other services, such as communication, construc- expenditures on the demand side. obtain permits and licenses. World Development Indi- tion, financial, information, business, personal, and Growth rates of household final consumption expen- cators includes in household consumption expendi- government services. They exclude compensation of diture, household final consumption expenditure per ture the expenditures of nonprofit institutions serving employees and investment income (factor services in capita, general government final consumption expen- households, even when reported separately by the the 1968 SNA) as well as transfer payments. diture, gross capital formation, and exports and country. In practice, household consumption expen- imports of goods and services are estimated using diture may include any statistical discrepancy in the constant price data. (Consumption, capital forma- use of resources relative to the supply of resources. tion, and exports and imports of goods and services · General government final consumption expendi- as shares of GDP are shown in table 4.8.) ture includes all government current expenditures To obtain government consumption in constant for purchases of goods and services (including prices, countries may defl ate current values by compensation of employees). It also includes most Investment is rising rapidly in Asia 4.9a Gross capital formation (2000 $ billions) Data sources 1,000 East Asia & Pacific National accounts indicators for most developing countries are collected from national statistical 800 organizations and central banks by visiting and resident World Bank missions. Data for high- 600 Latin America & Caribbean income economies come from data files of the 400 Organisation for Economic Co-operation and South Asia Development (see the OECD's Annual National Middle East & North Africa 200 Accounts for OECD Member Countries: Data from Sub-Saharan Africa 1970 Onwards). The United Nations Statistics Divi- 0 sion publishes detailed national accounts for UN 1980 1985 1990 1995 2000 2005 member countries in National Accounts Statistics: Between 1980 and 2005 investment increased eightfold in East Asia & Pacific and fivefold in South Asia. Main Aggregates and Detailed Tables and updates Source: World Bank data files. in the Monthly Bulletin of Statistics. 2007 World Development Indicators 225 4.10 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest payments % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 2005 2005 Afghanistanb .. 6.2 .. 11.9 .. 0.9 .. 0.1 .. 1.4 9.4 0.3 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 35.3 24.2 24.1 ­1.3 1.2 ­7.4 1.8 8.6 ­1.6 47.1 8.6 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. 18.1 .. 18.3 .. ­0.5 .. 0.5 .. 1.5 .. 26.5 Armeniab .. 19.3 .. 18.1 .. ­1.0 .. 0.3 .. 0.6 .. 2.2 Australia .. 26.0 .. 24.8 .. 1.1 .. .. .. .. 21.4 3.7 Austria 38.9 39.9 44.2 42.1 ­5.4 ­2.0 .. .. .. .. 66.6 7.1 Azerbaijanb 18.0 .. 19.8 .. ­3.1 .. .. .. .. .. .. .. Bangladeshb .. 10.0 .. 8.8 .. ­0.7 .. 2.3 .. 0.9 36.2 16.4 Belarusb 30.0 33.3 28.7 29.8 ­2.7 0.2 2.2 1.3 0.4 0.3 12.3 1.1 Belgium 41.5 42.1 45.6 42.3 ­3.8 ­0.1 .. .. .. .. 88.7 9.5 Beninb .. 15.5 .. 24.7 .. ­10.3 .. 2.7 .. 3.0 .. 1.5 Bolivia .. 23.5 .. 26.6 .. ­3.8 .. 2.1 .. 1.7 .. 10.0 Bosnia and Herzegovina .. 39.1 .. 36.2 .. 2.3 .. ­0.1 .. 0.6 .. 1.5 Botswanab 40.5 .. 30.4 .. 4.9 .. 0.2 .. ­0.4 .. .. .. Brazilb 26.9 .. 32.9 .. ­2.7 .. .. .. .. .. .. .. Bulgariab 35.5 39.0 39.4 34.3 ­5.1 3.5 7.4 ­1.4 ­0.8 ­5.8 .. 4.0 Burkina Faso .. 13.2 .. 11.7 .. ­4.1 .. ­3.3 .. 6.4 .. 3.7 Burundib 19.3 .. 23.6 .. ­4.7 .. 3.1 .. 4.0 .. .. .. Cambodia .. 9.8 .. 7.7 .. 0.0 .. ­1.1 .. 1.6 .. 1.8 Cameroonb 13.0 .. 11.7 .. 0.3 .. ­0.3 .. 0.4 .. .. .. Canadab 20.6 20.0 24.6 18.1 ­4.4 1.7 5.0 ­1.0 0.0 0.3 48.7 7.5 Central African Republicb .. 8.1 .. 9.4 .. ­0.5 .. 1.2 .. 0.1 .. 8.0 Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 24.4 .. 18.7 .. 4.7 .. ­2.0 .. ­0.8 .. 3.5 Chinab 5.4 9.5 .. 11.1 .. ­2.1 1.6 .. .. .. .. 4.7 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 27.6 .. 31.4 .. 3.9 .. 5.2 .. ­1.7 56.7 21.6 Congo, Dem. Rep.b 5.3 7.9 8.2 .. 0.0 .. 0.0 .. 0.2 .. .. .. Congo, Rep. .. 30.9 .. 19.9 .. 6.4 .. .. .. .. 0.2 18.1 Costa Ricab 20.3 22.8 21.3 22.7 ­2.1 ­0.8 .. .. ­0.8 .. .. 18.0 Côte d'Ivoireb 20.1 17.1 .. 16.9 .. ­1.5 ­1.2 ­0.1 3.8 1.2 107.9 11.3 Croatiab 43.1 40.4 42.5 40.3 ­1.3 ­2.8 ­2.7 5.3 0.8 ­1.8 .. 5.4 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republicb 33.2 31.6 32.6 35.8 ­0.9 ­3.5 ­0.5 1.2 ­0.4 2.1 23.1 2.4 Denmark 39.1 36.0 38.2 34.7 1.5 1.8 .. .. .. .. 42.2 8.4 Dominican Republicb 16.0 16.7 10.2 16.2 0.8 ­0.7 0.0 1.0 ­1.0 2.3 .. 9.1 Ecuador b 14.1 .. 12.0 .. 0.0 .. .. .. .. .. .. .. Egypt, Arab Rep.b 25.9 19.5 23.8 22.6 ­1.1 ­5.7 .. .. .. .. .. 29.4 El Salvador .. 16.0 .. 17.7 .. ­4.4 .. 3.6 .. 2.9 48.7 13.6 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 32.6 .. 29.5 .. 2.7 .. .. .. .. 9.6 0.4 Ethiopiab .. 15.2 .. 22.0 .. ­8.0 .. 1.0 .. 7.6 .. 7.6 Finland 39.8 39.2 38.5 36.6 1.9 3.2 0.3 ­1.0 ­1.3 2.3 45.6 4.0 France 43.3 43.1 47.3 46.1 ­4.1 ­2.8 .. .. .. .. 71.9 5.8 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Theb 23.7 .. .. .. .. .. .. .. .. .. .. .. Georgiab 12.2 18.2 15.4 17.4 ­4.3 1.5 2.2 ­0.3 2.4 ­0.3 35.2 5.4 Germany 29.9 28.7 38.6 31.2 ­8.3 ­2.3 .. 1.7 .. 0.3 44.2 5.8 Ghanab 17.0 23.8 .. 20.9 .. ­2.9 .. .. .. 3.3 .. 14.4 Greece 38.6 41.7 47.9 44.2 ­10.0 ­5.1 .. .. .. .. 137.8 11.2 Guatemalab 8.4 10.1 7.6 11.0 ­0.5 ­1.5 .. 1.5 0.4 0.7 18.3 11.6 Guineab 11.2 .. 12.1 .. ­4.3 .. ­0.1 .. 4.5 .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 226 2007 World Development Indicators 4.10 ECONOMY Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest payments % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 2005 2005 Honduras .. .. .. .. .. .. .. .. .. .. .. .. Hungary .. 35.9 .. 43.0 .. ­7.4 .. 1.3 .. 4.2 67.2 11.3 Indiab 12.3 12.5 14.5 15.8 ­2.2 ­3.6 5.2 3.6 0.0 0.3 65.4 31.9 Indonesiab 17.7 18.5 9.7 17.0 3.0 ­1.1 ­0.6 0.0 ­0.4 ­0.4 29.0 14.8 Iran, Islamic Rep.b 24.2 35.6 15.8 20.5 1.1 7.4 .. ­0.6 0.1 ­0.9 .. 0.7 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 33.6 31.8 37.4 30.3 ­2.1 1.0 .. .. .. .. 29.6 3.1 Israel .. 41.8 .. 46.7 .. ­2.6 .. .. .. .. .. 12.1 Italy 40.3 35.7 48.0 39.4 ­7.5 ­3.5 .. .. .. .. 114.4 12.7 Jamaicab .. 35.1 33.3 33.8 .. ­1.2 .. .. .. .. 139.6 41.3 Japan 20.7 .. .. .. .. .. 1.5 .. .. .. .. .. Jordanb 28.2 28.4 26.1 35.3 0.9 ­4.7 ­2.5 3.1 6.1 ­3.0 89.1 6.8 Kazakhstanb 14.0 21.3 18.7 18.2 ­1.8 2.6 0.8 0.9 2.8 ­1.5 7.1 1.7 Kenyab 21.6 19.9 25.9 20.7 ­5.1 ­1.5 3.9 0.7 ­1.3 0.7 .. 10.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.b 17.8 23.4 14.3 21.4 2.4 0.8 ­0.3 ­0.1 ­0.1 ­0.3 .. 5.4 Kuwait .. 37.2 .. 26.2 .. 8.2 .. .. .. .. .. 0.1 Kyrgyz Republicb 16.7 .. 25.6 .. ­10.8 .. .. .. .. .. .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latviab 25.8 26.6 28.3 29.4 ­2.7 ­0.9 2.4 0.5 1.5 ­0.2 .. 1.9 Lebanon .. 21.4 .. 26.2 .. ­8.4 .. ­1.3 .. 12.3 .. 56.0 Lesothob 49.8 47.7 34.4 36.5 5.1 5.1 0.0 .. 6.2 .. .. 3.8 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 28.8 .. 28.3 .. ­0.4 .. ­0.1 .. 1.0 21.4 2.8 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. 60.4 .. 63.0 .. ­22.5 .. ­3.6 .. 31.8 .. 14.5 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiab 24.4 23.7 17.2 20.1 2.4 ­4.3 .. .. ­0.8 .. .. 10.5 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusb 21.6 21.1 19.9 20.9 ­1.3 ­2.1 3.1 2.9 ­0.6 0.3 43.8 12.0 Mexicob 15.3 .. 15.0 .. ­0.6 .. .. .. 5.5 .. .. .. Moldovab 28.4 32.5 38.4 30.0 ­6.3 1.9 3.0 0.2 2.7 ­0.1 33.2 3.8 Mongolia .. 37.9 .. 30.8 .. ­0.5 .. 11.3 .. ­6.8 119.8 3.1 Moroccob .. 28.8 .. 31.3 .. ­5.6 .. 7.6 .. ­0.7 63.2 13.0 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.4 5.0 .. .. .. .. .. .. .. .. .. .. Namibiab 31.7 28.1 35.7 31.1 ­5.0 ­6.8 .. ­20.0 .. ­0.1 .. 9.1 Nepalb 10.5 12.8 .. 16.6 .. ­1.2 0.6 0.3 2.5 0.6 57.2 7.5 Netherlands 40.3 40.2 49.2 40.0 ­8.9 0.0 .. .. .. .. 55.5 5.1 New Zealand .. 38.0 .. 32.5 .. 4.6 .. ­1.8 .. 2.8 46.4 4.5 Nicaraguab 15.0 22.4 16.3 21.0 0.6 ­0.7 .. .. 3.4 .. .. 7.4 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 50.7 .. 34.1 .. 16.3 .. 0.0 .. 1.1 37.0 1.7 Omanb 27.8 .. 32.4 .. ­8.9 .. ­0.1 .. 0.0 .. .. .. Pakistanb 17.2 12.9 19.1 14.5 ­5.3 ­3.2 .. .. .. .. .. 31.8 Panamab 26.1 .. 22.0 .. 1.5 .. .. .. .. .. .. .. Papua New Guineab 23.9 22.5 25.8 22.1 ­0.5 ­2.3 1.5 4.9 ­0.7 ­2.2 69.7 19.9 Paraguay b .. 21.2 .. 16.7 .. 1.1 .. 0.1 .. ­0.7 .. 5.6 Perub 17.4 17.6 17.4 17.3 ­1.3 ­0.8 .. 1.9 3.9 ­1.2 .. 10.6 Philippinesb 17.7 15.1 15.9 18.0 ­0.8 ­3.0 ­0.5 1.4 ­0.7 1.9 69.9 38.7 Poland .. 33.6 .. 36.3 .. ­2.3 .. ­0.3 .. 3.6 41.5 7.1 Portugal 35.3 37.9 37.8 42.4 ­3.0 ­5.8 ­3.5 0.3 4.1 6.3 73.7 6.8 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 227 4.10 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest payments % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 2005 2005 Romania .. 25.8 .. 25.9 .. ­2.0 .. 0.4 .. 1.7 .. 8.4 Russian Federation .. 30.7 .. 20.0 .. 9.9 .. 0.3 .. ­4.2 41.4 3.1 Rwandab 10.6 .. 15.0 .. ­5.6 .. 2.9 .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegalb 16.6 .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegrob .. 35.8 .. 39.9 .. ­3.0 .. .. .. .. .. 2.6 Sierra Leoneb 9.4 12.3 .. 23.8 .. ­2.5 0.3 .. .. .. .. 21.0 Singaporeb 26.7 20.1 12.4 15.4 19.8 4.1 10.3 9.2 0.0 .. 108.9 0.8 Slovak Republic .. 31.4 .. 34.9 .. ­3.4 .. ­0.6 .. ­4.3 37.2 5.4 Sloveniab 36.7 40.3 35.2 41.3 ­0.2 ­1.5 ­0.4 2.0 0.3 ­1.9 .. 4.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 30.2 .. 29.6 .. 0.2 .. 1.8 .. 0.1 .. 11.1 Spain 32.0 26.9 37.1 24.9 ­5.8 1.5 .. .. .. .. 44.4 5.5 Sri Lankab 20.4 16.1 26.0 21.0 ­7.6 ­7.3 5.2 5.6 3.2 2.0 94.2 29.1 Sudanb 7.2 .. 6.8 .. ­0.4 .. 0.3 .. .. .. .. .. Swazilandb .. 26.6 .. 24.4 .. ­2.6 .. .. .. .. .. 4.5 Sweden 40.4 38.9 39.0 36.1 2.2 2.1 .. ­1.3 .. .. 54.3 4.4 Switzerlandb 22.7 19.4 25.8 19.2 ­0.6 0.6 ­0.5 ­0.6 .. .. 28.6 4.5 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 .. 21.0 .. 16.3 .. 2.5 .. ­3.3 .. ­0.9 27.3 6.1 Togob .. 14.1 .. 15.4 .. ­5.5 .. .. .. .. .. 7.1 Trinidad and Tobagob 27.2 28.5 25.3 24.6 ­0.1 2.1 2.8 .. 2.6 .. .. 11.0 Tunisiab 30.0 29.7 28.4 29.5 ­2.5 ­3.0 0.9 ­0.3 2.9 0.9 59.0 9.6 Turkey b 17.7 .. 19.1 .. ­1.8 .. .. .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandab 10.6 12.1 .. 22.8 .. ­3.8 .. 0.5 .. 4.2 .. 6.5 Ukraineb .. 36.5 .. 37.5 .. ­1.4 .. 4.5 .. 0.2 .. 2.1 United Arab Emiratesb 10.1 .. 9.3 .. 0.5 .. .. .. .. .. .. .. United Kingdom 37.2 38.0 37.1 41.1 0.3 ­2.9 ­0.3 3.6 0.0 0.0 .. 5.6 United States .. 18.4 .. 21.2 .. ­2.9 .. 1.7 .. 1.2 47.2 11.1 Uruguay b 27.6 27.2 27.1 27.5 ­1.2 ­1.6 7.9 2.4 1.1 1.7 78.7 16.0 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBb 16.9 29.3 18.5 26.0 ­2.3 2.3 1.1 1.3 0.1 3.4 .. 10.4 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep.b 17.3 .. 19.1 .. ­3.9 .. .. .. .. .. .. .. Zambiab 20.0 .. 21.4 .. ­3.1 .. 28.0 .. 16.2 .. .. .. Zimbabweb 26.7 .. 32.1 .. ­5.4 .. ­1.4 .. 1.6 .. .. .. World .. w 26.6 w .. w 28.2 w .. w ­1.7 w .. m .. m .. m .. m .. m 7.9 m Low income 13.3 13.0 15.5 15.5 ­2.7 ­3.2 .. .. .. .. .. .. Middle income 17.2 .. .. .. .. .. .. 1.1 .. 0.6 .. 6.8 Lower middle income 16.2 15.0 .. 15.6 .. ­1.7 .. 1.1 .. 1.1 .. 6.8 Upper middle income .. .. .. .. .. .. .. 0.5 .. 1.7 .. 6.2 Low & middle income 16.6 .. .. .. .. .. .. .. .. .. .. 10.2 East Asia & Pacific 8.4 11.4 .. 12.5 .. ­1.9 .. .. .. .. .. 7.6 Europe & Central Asia .. 32.3 .. 29.7 .. 2.2 .. 0.3 .. 0.0 .. 2.9 Latin America & Carib. 21.0 .. 23.1 .. ­1.5 .. .. .. .. .. .. 11.1 Middle East & N. Africa 26.1 25.6 .. 23.2 .. ­2.8 .. .. .. .. .. 10.1 South Asia 13.2 12.5 15.4 15.2 ­2.7 ­3.2 3.8 0.3 1.1 1.4 57.2 18.3 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income .. 26.5 .. 28.4 .. ­2.0 .. .. .. .. 48.7 5.6 Europe EMU 34.8 35.2 42.3 37.2 ­7.4 ­2.0 .. .. .. .. 69.2 5.8 a. Excluding grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework. 228 2007 World Development Indicators 4.10 ECONOMY Central government finances About the data Definitions Tables 4.10­4.12 present an overview of the size borrowing for temporary periods can also be used. The · Revenue is cash receipts from taxes, social con- and role of central governments relative to national definition of government excludes public corporations tributions, and other revenues such as fines, fees, economies. The data in these tables are based on and quasi corporations (such as the central bank). rent, and income from property or sales. Grants are the concepts and recommendations of the second Units of government meeting this definition exist also considered as revenue but are excluded here. edition of the International Monetary Fund's (IMF) at many levels, from local administrative units to the · Expense is cash payments for operating activities Government Finance Statistics Manual 2001. Before highest level of national government, but inadequate of the government in providing goods and services. It 2005, World Development Indicators reported data statistical coverage precludes the presentation of derived on the basis of the 1986 manual. The 2001 subnational data. Although data for general govern- includes compensation of employees (such as wages manual, which is harmonized with the 1993 System of ment are available for a few countries under the 2001 and salaries), interest and subsidies, grants, social National Accounts, recommends an accrual account- manual, only data for the central government are benefits, and other expenses such as rent and divi- ing method instead of the cash-based method of the shown to minimize disparities. However, cross-coun- dends. · Cash surplus or deficit is revenue (includ- 1986 manual. The new manual focuses on all eco- try comparisons are potentially misleading due to dif- ing grants) minus expense, minus net acquisition of nomic events affecting assets, liabilities, revenues, ferent accounting concepts of central government. nonfinancial assets. In the earlier version nonfinancial and expenses, instead of only those represented by Central government can refer to one of two account- assets were included under revenue and expenditure cash transactions. The new manual takes all stocks ing concepts: consolidated or budgetary. For most in gross terms. This cash surplus or deficit is clos- into account, so that the stock data at the end of countries central government finance data have been est to the earlier overall budget balance (still missing an accounting period is equal to the stock data at consolidated into one account, but for others only the beginning of the period plus the flows during budgetary central government accounts are avail- is lending minus repayments, which are brought in the period. The 1986 manual considered only the able. Countries reporting budgetary data are noted below as a financing item under net acquisition of debt stock data. Further, the new manual does not in Primary data documentation. Because budgetary financial assets). · Net incurrence of government distinguish between current and capital revenue accounts do not necessarily include all central gov- liabilities includes domestic financing (obtained or expenditures, unlike the 1986 manual. The new ernment units (such as extrabudgetary accounts and from residents) and foreign financing (obtained from manual also introduces the concepts of nonfinancial social security funds), the picture they provide of nonresidents), or the means by which a government and financial assets. Most countries still follow the central government activities is usually incomplete. provides financial resources to cover a budget deficit previous manual, however. The IMF has reclassified Data on government revenues and expenditures or allocates financial resources arising from a bud- historical Government Finance Statistics Yearbook are collected by the IMF through questionnaires dis- data to conform to the format of the 2001 manual. tributed to member governments and by the Organ- get surplus. The net incurrence of liabilities should Because of differences in reporting, the reclassified isation for Economic Co-operation and Development. be offset by the net acquisition of financial assets data understate both revenue and expense. Despite the IMF's efforts to systematize and stan- (a third financing item). The difference between the Government Finance Statistics Manual 2001 dardize the collection of public finance data, statis- cash surplus or deficit and the three financing items describes the economic functions of a government as tics on public finance are often incomplete, untimely, is the net change in the stock of cash. · Total debt the provision of goods and services to the community and not comparable across countries. is the entire stock of direct government fixed-term on a nonmarket basis for collective or individual con- Government finance statistics are reported in local contractual obligations to others outstanding on a sumption, and the redistribution of income and wealth currency. The indicators here are shown as percent- particular date. It includes domestic and foreign liabil- through transfer payments. The activities of govern- ages of GDP. Many countries report government ities such as currency and money deposits, securities ment are financed mainly by taxation and other trans- finance data by fiscal year; see Primary data documen- fers of income, though other forms of financing such as tation for information on fiscal year end by country. other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the govern- Fourteen developing economies had a ment. Because debt is a stock rather than a flow, it cash deficit greater than 4 percent of GDP 4.10a is measured as of a given date, usually the last day of Central government cash deficit as a share of GDP, 2005 (%) the fiscal year. · Interest payments include interest 0 payments on government debt--including long-term bonds, long-term loans, and other debt instruments ­5 --to domestic and foreign residents. Burkina Faso Jordan El Salvador Malaysia Morocco Togo Taijikistan Namibia Sri Lanka Hungary Lebanon ­10 Data sources Benin Data on central government finances are from Maldives ­15 the IMF's Government Finance Statistics Yearbook Madagascar 2006 and IMF data files. Each country's accounts ­20 are reported using the system of common defi - nitions and classifications in the IMF's Govern- ­25 ment Finance Statistics Manual 2001. See these sources for complete and authoritative explana- Note: Data are for the most recent year available for 2003­05. Source: International Monetary Fund, Government Finance Statistics data files, and World Bank data files. tions of concepts, definitions, and data sources. 2007 World Development Indicators 229 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 2005 1995 2005 1995 2005 1995 2005 1995 2005 Afghanistana .. 45 .. 47 .. 0 .. 5 .. 2 Albaniaa 18 12 14 30 9 17 59 42 0 0 Algeriaa 6 6 39 32 13 12 34 50 8 .. Angola .. .. .. .. .. .. .. .. .. .. Argentina .. 5 .. 12 .. 26 .. 50 .. 7 Armeniaa .. 40 .. 20 .. 2 .. 36 .. 1 Australia .. 10 .. 10 .. 4 .. 71 .. 6 Austria 5 5 13 12 8 7 55 52 2 1 Azerbaijana 49 .. 10 .. 0 .. 41 .. 0 .. Bangladesha .. 17 .. 25 .. 20 .. 29 .. 9 Belarusa 39 11 5 13 1 1 55 67 0 8 Belgium 3 3 7 7 18 9 71 51 3 1 Benina .. 72 .. 25 .. 1 .. 1 .. .. Bolivia .. 14 .. 23 .. 10 .. 47 .. 5 Bosnia and Herzegovina .. 23 .. 28 .. 2 .. 43 .. 4 Botswanaa 32 .. 30 .. 2 .. 36 .. 2 .. Brazila 5 .. 8 .. 45 .. 45 .. 1 .. Bulgariaa 18 22 7 11 37 5 38 59 2 3 Burkina Faso .. 21 .. 41 .. 6 .. 32 .. 0 Burundia 20 .. 30 .. 6 .. 14 .. 10 .. Cambodia .. 40 .. 36 .. 3 .. 19 .. 2 Cameroona 17 .. 40 .. 26 .. 14 .. .. .. Canadaa 8 7 10 12 18 8 64 66 .. 6 Central African Republica .. 27 .. 53 .. 9 .. .. .. 11 Chad .. .. .. .. .. .. .. .. .. .. Chile .. 10 .. 21 .. 5 .. 57 .. 12 Chinaa .. .. .. .. .. 4 .. 64 .. 4 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia .. 10 .. 20 .. 19 .. 42 .. 9 Congo, Dem. Rep.a 37 .. 58 .. 1 .. 2 .. .. .. Congo, Rep. .. 29 .. 37 .. 29 .. 5 .. 0 Costa Ricaa 12 11 38 41 20 18 26 28 4 2 Côte d'Ivoirea .. 32 .. 39 .. 12 .. 17 .. 2 Croatiaa 35 8 27 26 3 5 32 54 3 6 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republica 7 6 9 9 3 2 75 68 5 15 Denmark 8 10 13 13 13 9 64 66 4 4 Dominican Republica 16 11 41 28 9 10 19 40 6 11 Ecuadora 6 .. 49 .. 26 .. .. .. .. .. Egypt, Arab Rep.a 21 10 26 33 31 27 7 18 .. 12 El Salvador .. 18 .. 39 .. 12 .. 25 .. 5 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia .. 17 .. 21 .. 0 .. 45 .. 3 Ethiopiaa .. 24 .. 14 .. 7 .. 42 .. 14 Finland 10 10 10 10 9 4 68 71 7 7 France 8 6 23 22 6 5 51 53 2 2 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, Thea .. .. .. .. .. .. .. .. .. .. Georgiaa 52 21 11 17 10 6 26 53 .. 3 Germany 4 5 5 5 6 5 67 82 20 3 Ghanaa .. .. .. 45 .. 21 .. 5 .. .. Greece 10 10 22 25 26 11 33 40 10 0 Guatemalaa 15 12 50 25 12 11 18 24 6 28 Guineaa 17 .. 34 .. 28 .. 9 .. 1 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 230 2007 World Development Indicators 4.11 ECONOMY 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 2005 1995 2005 1995 2005 1995 2005 1995 2005 Honduras .. .. .. .. .. .. .. .. .. .. Hungary .. 8 .. 14 .. 10 .. 61 .. 11 Indiaa 14 15 10 10 27 26 33 .. 0 .. Indonesiaa 21 8 20 13 16 16 41 63 2 0 Iran, Islamic Rep.a 21 13 56 47 0 1 .. 34 .. 5 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 5 14 15 24 14 3 33 36 1 1 Israel .. 27 .. 24 .. 11 .. 31 .. 8 Italy 4 4 14 16 24 12 54 46 6 2 Jamaicaa 22 20 24 32 32 43 1 2 21 3 Japan .. .. .. .. .. .. .. .. .. .. Jordana 7 5 67 58 11 7 12 3 4 11 Kazakhstana .. 18 .. 7 3 2 58 44 .. 28 Kenyaa 15 29 28 50 46 10 .. 9 2 2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 16 10 15 11 3 6 63 52 3 21 Kuwait .. 23 .. 31 .. 0 .. 32 .. 13 Kyrgyz Republica 32 .. 36 .. 5 .. 27 .. .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. Latviaa 20 13 20 16 3 2 56 39 0 31 Lebanon .. 3 .. 33 .. 46 .. 16 .. 2 Lesothoa 32 31 45 38 5 5 8 26 3 .. Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. 13 .. 20 .. 3 .. 58 .. 8 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar .. 14 .. 39 .. 23 .. 11 .. 13 Malawi .. .. .. .. .. .. .. .. .. .. Malaysiaa 23 26 34 30 17 12 27 31 1 1 Mali .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 13 45 39 12 12 28 33 2 3 Mexicoa 9 .. 19 .. 19 .. .. .. .. .. Moldovaa 10 18 8 14 11 4 71 53 1 11 Mongolia .. 36 .. 30 .. 4 .. 31 .. 0 Moroccoa .. 16 .. 43 .. 12 .. 24 .. 5 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. Namibiaa 28 28 53 49 1 8 .. 14 4 2 Nepala .. .. .. .. .. 7 .. .. .. .. Netherlands 5 7 8 8 9 5 42 48 2 2 New Zealand .. 28 .. 26 .. 5 .. 39 .. 6 Nicaraguaa 16 16 23 30 15 9 34 41 13 4 Niger .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. Norway .. 11 .. 16 .. 3 .. 67 .. 6 Omana 55 .. 30 .. 7 .. 8 .. 0 .. Pakistana .. 36 .. 4 28 29 2 30 .. .. Panamaa 16 .. 45 .. 8 .. 30 .. 1 .. Papua New Guineaa 19 35 36 28 20 21 26 16 1 .. Paraguaya .. 11 .. 52 .. 7 .. 26 .. 3 Perua 20 20 19 20 19 11 33 45 8 4 Philippinesa 15 18 34 30 33 32 15 17 .. 2 Poland .. 7 .. 12 .. 7 .. 69 .. 7 Portugal 7 6 30 30 10 6 43 47 11 1 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 231 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 2005 1995 2005 1995 2005 1995 2005 1995 2005 Romania .. 22 .. 16 .. 8 .. 43 .. 12 Russian Federation .. 15 .. 19 .. 5 .. 53 .. 9 Rwandaa 52 .. 36 .. 12 .. 5 .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegala .. .. .. .. .. .. .. .. .. .. Serbia and Montenegroa .. 10 .. 14 .. 2 .. 68 .. 6 Sierra Leonea .. 28 .. 26 .. 19 .. 9 .. 18 Singaporea 38 35 39 31 8 1 15 33 .. .. Slovak Republic .. 10 .. 13 .. 5 .. 63 .. 9 Sloveniaa 19 12 21 19 3 4 55 63 3 3 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. 12 .. 15 .. 11 .. 56 .. 5 Spain 5 4 14 9 11 6 42 51 2 2 Sri Lankaa 23 11 20 28 22 24 24 28 10 8 Sudana 44 .. 38 .. 8 .. 10 .. .. .. Swazilanda .. 29 .. 42 .. 5 .. 21 .. 2 Sweden 11 12 9 10 13 5 64 68 5 8 Switzerlanda 24 9 6 7 4 5 66 74 0 5 Syrian Arab Republica .. .. .. .. .. .. .. .. .. .. Tajikistana 47 29 8 9 12 5 33 27 .. 30 Tanzania .. .. .. .. .. .. .. .. .. .. Thailand .. 22 .. 35 .. 8 .. 33 .. 5 Togoa .. 48 .. 31 .. 6 .. 2 .. 13 Trinidad and Tobagoa 20 18 36 33 20 13 24 35 1 1 Tunisiaa 7 7 37 39 13 10 36 34 7 10 Turkeya 8 .. 28 .. 14 .. 33 .. 4 .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Ugandaa .. 36 .. 11 .. 6 .. 47 .. .. Ukrainea .. 12 .. 13 .. 2 .. 68 .. 4 United Arab Emiratesa 50 .. 37 .. .. .. .. .. .. .. United Kingdom 22 18 7 15 9 5 54 54 9 10 United States .. 15 .. 13 .. 10 .. 61 .. 2 Uruguaya 13 14 17 22 6 16 64 47 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 .. 35 .. 16 .. 19 .. 0 .. Zimbabwea 16 .. 34 .. 31 .. 19 .. .. .. World .. m 12 m .. m 21 m .. m 8m .. m 44 m .. m 5m Low income .. .. .. .. .. .. .. .. .. .. Middle income .. 13 .. 21 .. 7 .. 44 .. 5 Lower middle income .. 16 .. 28 .. 8 .. 36 .. 5 Upper middle income 14 10 29 18 10 6 .. 57 .. 5 Low & middle income .. .. .. 27 .. 11 .. .. .. .. East Asia & Pacific .. 27 .. 31 .. 7 .. 31 .. 0 Europe & Central Asia .. 13 .. 14 .. 4 .. 56 .. 8 Latin America & Carib. 16 13 24 24 19 11 .. 42 .. 4 Middle East & N. Africa 8 8 39 41 13 12 .. .. .. .. South Asia .. 36 .. 28 27 16 24 28 .. 9 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. High income 7 10 15 14 9 5 54 52 4 3 Europe EMU 5 6 14 14 11 6 54 51 3 2 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. 232 2007 World Development Indicators 4.11 ECONOMY Central government expenses About the data Definitions The term expense has replaced expenditure in this is reflected in compensation of employees, use of · Goods and services include all government pay- table since the 2005 edition of World Development goods and services, and consumption of fixed capi- ments in exchange for goods and services used for Indicators in accordance with use in the Interna- tal. Purchases from a third party and cash transfers the production of market and nonmarket goods and tional Monetary Fund's (IMF) Government Finance to households are shown as subsidies and other services. Own-account capital formation is excluded. Statistics Manual 2001. Government expenses transfers, and other expenses. The economic clas- · Compensation of employees consists of all pay- include all nonrepayable payments, whether current sification can be problematic. For example, the dis- ments in cash, as well as in kind (such as food and or capital, requited or unrequited. Total central gov- tinction between current and capital expense may housing), to employees in return for services ren- ernment expense as presented in the IMF's Govern- be arbitrary, and subsidies to public corporations or dered, and government contributions to social insur- ment Finance Statistics Yearbook is comparable to banks may be disguised as capital financing. Subsi- ance schemes such as social security and pensions the concept used in the 1993 System of National dies may also be hidden in special contractual pric- that provide benefits to employees. · Interest pay- Accounts. ing for goods and services. For further discussion of ments are payments made to nonresidents, to resi- Expenses can be measured either by function government finance statistics, see About the data for dents, and to other general government units for the (health, defense, education) or by economic type tables 4.10 and 4.12. use of borrowed money. (Repayment of principal is (interest payments, wages and salaries, purchases shown as a financing item, and commission charges of goods and services). Functional data are often are shown as purchases of services.) · Subsidies incomplete, and coverage varies by country because and other transfers include all unrequited, nonrepay- functional responsibilities stretch across levels of able transfers on current account to private and government for which no data are available. Defense public enterprises; grants to foreign governments, expenses, usually the central government's respon- international organizations, and other government sibility, are shown in table 5.7. For more information units; and social security, social assistance benefits, on education expenses, see table 2.9; for more on and employer social benefi ts in cash and in kind. health expenses, see table 2.14. · Other expense is spending on dividends, rent, and The classification of expenses by economic type in other miscellaneous expenses, including provision this table shows whether the government produces for consumption 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 Interest payments are a large part of government expenses for some developing countries 4.11a Central government interest payments as a share of total expense (%) 1995 2005 60 50 40 30 20 10 Data sources Data on central government expenses are from 0 the IMF's Government Finance Statistics Yearbook n ca es n ep a a ka a vis ar no ta in di an sc ai an in R Ne nt In is ba Gh m pp a o, iL k ge ag 2006 and IMF data files. Each country's accounts Ja Pa & Le ng ili Sr Ar ad Ph s Co itt M .K are reported using the system of common defi - St nitions and classifications in the IMF's Govern- Interest payments accounted for more than 20 percent of total expenses in 2005 for 11 countries. ment Finance Statistics Manual 2001. See these Note: Data are for the most recent year for 2003­05. For Lebanon, Madagascar, and Philippines, data for 1995 refer sources for complete and authoritative explana- to 2000. No data for 1995 are available for Argentina, Republic of Congo, Ghana, and St. Kitts and Nevis. Source: International Monetary Fund, Government Finance Statistics data files. tions of concepts, definitions, and data sources. 2007 World Development Indicators 233 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 2005 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Afghanistana .. 2 .. 3 .. 20 .. 1 .. 0 .. 74 Albaniaa 8 15 39 49 14 8 1 1 15 18 22 10 Algeriaa 65 66 10 9 18 13 1 1 .. .. 5 11 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. 19 .. 29 .. 16 .. 14 .. 17 .. 5 Armeniaa .. 16 .. 32 .. 3 .. 20 .. 14 .. 14 Australia .. 65 .. 24 .. 2 .. 0 .. .. .. 8 Austria 20 23 21 23 0 0 5 4 41 38 .. .. Azerbaijana 31 .. 34 .. 33 .. 2 .. 23 .. 0 .. Bangladesha .. 12 .. 29 .. 33 .. 4 .. .. .. 22 Belarusa 16 8 33 35 6 8 11 11 31 35 3 4 Belgium 36 37 23 25 .. .. 2 1 36 34 3 2 Benina .. 13 .. 39 .. 23 .. 7 .. .. .. 18 Bolivia .. 9 .. 41 .. 3 .. 9 .. 8 .. 30 Bosnia and Herzegovina .. 2 .. 19 .. 29 .. 5 .. 34 .. 11 Botswanaa 21 .. 4 .. 15 .. 0 .. .. .. 59 .. Brazila 14 .. 24 .. 2 .. 4 .. 31 .. 26 .. Bulgariaa 17 13 28 43 8 2 3 0 21 26 23 16 Burkina Faso .. 16 .. 38 .. 11 .. 3 .. .. .. 33 Burundia 14 .. 30 .. 20 .. 1 .. 5 .. 30 .. Cambodia .. 7 .. 37 .. 21 .. 0 .. .. .. 35 Cameroona 17 .. 25 .. 28 .. 3 .. 2 .. 25 .. Canadaa 50 53 17 17 2 1 .. .. 22 23 10 6 Central African Republica .. 14 .. 23 .. 19 .. 4 .. 6 .. 34 Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 30 .. 41 .. 2 .. 6 .. 6 .. 15 Chinaa 9 22 61 79 7 -12 0 1 .. .. 22 10 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 21 .. 29 .. 3 .. 1 .. 7 .. 39 Congo, Dem. Rep.a 21 25 12 24 21 27 5 1 1 .. 41 23 Congo, Rep. .. 4 .. 16 .. 7 .. 1 .. 4 .. 69 Costa Ricaa 11 15 32 38 15 5 1 2 28 32 12 8 Côte d'Ivoirea 15 21 14 16 58 41 3 2 5 7 5 13 Croatiaa 11 8 42 48 9 2 1 1 33 34 4 9 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republica 15 20 32 27 4 0 1 1 40 46 8 7 Denmark 34 38 40 44 .. .. 7 2 5 4 14 12 Dominican Republica 16 19 34 41 36 28 1 2 4 2 9 9 Ecuadora 50 .. 26 .. 11 .. 1 .. .. .. 12 .. Egypt, Arab Rep.a 22 25 17 26 13 12 13 3 .. .. 35 33 El Salvador .. 24 .. 44 .. 7 .. 3 .. 13 .. 10 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 14 .. 38 .. 0 .. 0 .. 34 .. .. Ethiopiaa .. 15 .. 12 .. 27 .. 0 .. 5 .. 41 Finland 21 21 34 35 0 .. 2 2 32 31 12 12 France 17 24 25 24 0 0 3 4 47 42 7 6 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Thea 14 .. 32 .. 42 .. 0 .. 0 .. 7 .. Georgiaa 7 2 48 58 10 6 .. 0 13 19 22 17 Germany 16 16 20 22 .. .. 0 .. 58 58 6 4 Ghanaa 15 22 31 22 24 29 .. 2 .. .. 9 26 Greece 17 21 31 28 0 0 3 3 31 35 18 14 Guatemalaa 19 26 46 52 23 15 3 1 2 2 6 4 Guineaa 8 .. 4 .. 62 .. 2 .. 1 .. 23 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 234 2007 World Development Indicators 4.12 ECONOMY 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 2005 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Honduras .. .. .. .. .. .. .. .. .. .. .. .. Hungary .. 19 .. 36 .. 0 .. 2 .. 35 .. 9 Indiaa 23 35 28 31 24 14 0 0 0 0 25 19 Indonesiaa 46 28 33 32 4 3 1 4 6 3 9 30 Iran, Islamic Rep.a 12 13 5 2 9 6 1 1 6 11 66 67 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 37 37 35 36 0 0 2 5 17 17 9 5 Israel .. 31 .. 29 .. 1 .. 6 .. 17 .. 17 Italy 32 33 21 22 .. .. 5 5 35 36 6 5 Jamaicaa .. 15 .. 33 .. 10 .. 20 .. 7 .. 15 Japan 35 .. 14 .. 1 .. 5 .. 26 .. 18 .. Jordana 10 9 23 36 22 11 9 14 .. 1 36 28 Kazakhstana 11 49 28 38 3 4 5 0 48 .. 6 9 Kenyaa 35 29 40 40 14 11 1 0 0 0 10 20 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 31 29 32 28 7 3 10 7 8 16 12 16 Kuwait .. 1 .. .. .. 2 .. 0 .. .. .. 97 Kyrgyz Republica 26 .. 56 .. 5 .. 1 .. .. .. 11 .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latviaa 7 12 41 39 3 1 0 0 35 29 13 20 Lebanon .. 11 .. 45 .. 8 .. 12 .. 1 .. 24 Lesothoa 15 20 12 17 49 45 1 0 .. .. 24 17 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 23 .. 36 .. 0 .. 0 .. 30 .. 11 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. 6 .. 16 .. 27 .. 4 .. .. .. 46 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiaa 37 47 26 21 12 6 5 0 1 .. 19 26 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 15 25 45 34 20 6 5 6 4 16 11 Mexicoa 27 .. 54 .. 4 .. 2 .. 14 .. 16 .. Moldovaa 6 2 38 47 5 6 1 1 38 24 2 20 Mongolia .. 16 .. 35 .. 6 .. 0 .. 16 .. 27 Moroccoa .. 32 .. 36 .. 11 .. 5 .. .. .. 15 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 20 16 26 22 12 2 .. .. .. .. 42 60 Namibiaa 27 38 32 20 28 32 2 2 .. 1 11 8 Nepala 10 11 33 32 26 19 4 4 .. .. 27 34 Netherlands 26 25 24 28 .. 1 2 3 40 34 .. .. New Zealand .. 55 .. 27 .. 2 .. 0 .. 0 .. 16 Nicaraguaa 8 19 46 42 6 4 0 0 10 16 29 19 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 34 .. 25 .. 0 .. 1 .. 18 .. 22 Omana 21 .. 1 .. 3 .. 2 .. .. .. 74 .. Pakistana 18 20 27 34 24 14 7 4 .. .. 24 28 Panamaa 20 .. 17 .. 11 .. 3 .. 16 .. 34 .. Papua New Guineaa 40 50 8 13 27 26 2 3 0 0 23 8 Paraguaya .. 10 .. 35 .. 8 .. 3 .. 15 .. 28 Perua 15 24 46 40 10 6 8 6 10 9 11 15 Philippinesa 33 40 26 23 29 18 4 6 .. .. 8 13 Poland .. 12 .. 36 .. 0 .. 0 .. 40 .. 12 Portugal 23 20 32 32 0 0 2 2 29 33 14 14 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 235 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 2005 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Romania .. 9 .. 33 .. 3 .. 1 .. 42 .. 13 Russian Federation .. 6 .. 24 .. 24 .. 0 .. 18 .. 29 Rwandaa 11 .. 25 .. 23 .. 3 .. 2 .. 36 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegala 17 .. 19 .. 36 .. 2 .. .. .. 26 .. Serbia and Montenegroa .. 13 .. 39 .. 7 .. 4 .. 29 .. 9 Sierra Leonea 15 16 34 9 39 27 0 .. .. .. 12 48 Singaporea 26 28 20 24 1 0 15 11 .. .. 38 38 Slovak Republic .. 9 .. 37 .. 0 .. 0 .. 40 .. 13 Sloveniaa 13 15 33 33 9 0 0 4 42 38 3 10 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 50 .. 33 .. 4 .. 4 .. 2 .. 7 Spain 28 27 21 18 0 .. 0 0 40 48 .. .. Sri Lankaa 12 13 49 55 17 14 4 0 1 1 18 17 Sudana 17 .. 41 .. 27 .. 1 .. .. .. 14 .. Swazilanda .. 28 .. 19 .. 48 .. 0 .. .. .. 5 Sweden 15 9 26 34 .. .. 12 11 35 37 13 10 Switzerlanda 11 16 21 30 1 1 2 3 49 39 17 11 Syrian Arab Republica 23 .. 37 .. 13 .. 8 .. 0 .. 19 .. Tajikistana 6 3 63 54 12 11 0 1 13 12 5 18 Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 33 .. 40 .. 7 .. 1 .. 5 .. 14 Togoa .. 19 .. 48 .. 22 .. 6 .. .. .. 5 Trinidad and Tobagoa 50 42 26 21 6 6 1 16 2 5 15 11 Tunisiaa 16 26 20 34 28 7 4 4 15 18 17 10 Turkeya 32 .. 40 .. 4 .. 3 .. .. .. 21 .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandaa 10 12 45 24 7 16 2 0 .. .. 37 48 Ukrainea .. 15 .. 28 .. 5 .. 0 .. 35 .. 16 United Arab Emiratesa .. .. 15 .. .. .. .. .. 1 .. 84 .. United Kingdom 39 37 31 31 .. .. 6 6 19 22 5 4 United States .. 55 .. 3 .. 1 .. 1 .. 37 .. 2 Uruguaya 10 11 32 49 4 5 10 3 31 20 8 12 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 .. 22 .. 36 .. 0 .. 0 .. 15 .. Zimbabwea 36 .. 22 .. 17 .. 3 .. 2 .. 19 .. World .. m 19 m .. m 33 m .. m 6m .. m 2m .. m .. m .. m 15 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income 17 15 29 36 12 5 3 1 .. 16 17 14 Lower middle income 17 17 31 36 12 7 3 1 .. 12 15 15 Upper middle income 20 15 29 37 8 3 2 1 .. 30 16 11 Low & middle income .. 16 .. 33 .. 8 .. 1 .. .. .. 16 East Asia & Pacific 35 29 26 32 12 6 .. 1 .. .. 20 25 Europe & Central Asia .. 12 .. 36 .. 3 .. 0 .. 34 .. 13 Latin America & Carib. 15 20 31 41 10 5 4 3 10 8 13 15 Middle East & N. Africa 19 22 14 29 16 12 4 3 .. .. 35 21 South Asia 15 12 31 33 24 16 4 4 .. 1 25 31 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 25 30 24 27 .. 1 4 4 32 34 12 9 Europe EMU 26 24 23 27 0 0 2 4 40 35 6 5 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. 236 2007 World Development Indicators 4.12 ECONOMY Central government revenues About the data Definitions The International Monetary Fund (IMF) classifi es significance except with respect to the capacity to · Taxes on income, profits, and capital gains are government revenues as taxes, grants, and property fi x tax rates. Direct taxes tend to be progressive, levied on the actual or presumptive net income income. Taxes are classified by the base on which whereas indirect taxes are proportional. of individuals, on the profi ts of corporations and the tax is levied, grants by the source, and property Social security taxes do not reflect compulsory pay- enterprises, and on capital gains, whether real- income by type (for example, interest, dividends, ments made by employers to provident funds or other ized or not, on land, securities, and other assets. or rent). The most important source of revenue is agencies with a like purpose. Similarly, expenditures Intragovernmental payments are eliminated in con- taxes. Grants are unrequited, nonrepayable, non- from such funds are not refl ected in government solidation. · Taxes on goods and services include compulsory receipts from other government units expenses (see table 4.11). For further discussion of general sales and turnover or value added taxes, and foreign governments or from international orga- taxes and tax policies, see About the data for table selective excises on goods, selective taxes on ser- nizations. Transactions are generally recorded on an 5.6. For further discussion of government revenues vices, taxes on the use of goods or property, taxes accrual basis. and expenditures, see About the data for tables 4.10 on extraction and production of minerals, and prof- The IMF's Government Finance Statistics Manual and 4.11. 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 taxes. However, contributions include social security contributions by the distinctions are not always clear. Taxes levied employees, employers, and self-employed individu- on the income and profits of individuals and corpora- als, and other contributions whose source cannot tions are classified as direct taxes, and taxes and be determined. They also include actual or imputed duties levied on goods and services are classified contributions to social insurance schemes operated as indirect taxes. This distinction may be a useful by governments. · Grants and other revenue include simplifi cation, but it has no particular analytical grants from other foreign governments, international organizations, and other government units; interest; dividends; rent; requited, nonrepayable receipts Rich countries rely for public purposes (such as fines, administrative more on direct taxes 4.12a fees, and entrepreneurial income from government Taxes on income and capital gains as a share of central government revenue, 2003­05 (%) ownership of property); and voluntary, unrequited, 70 nonrepayable receipts other than grants. 60 50 40 30 Data sources 20 Data on central government revenues are from the IMF's Government Finance Statistics Yearbook 10 2006 and IMF data files. Each country's accounts are reported using the system of common defini- 0 100 1,000 10,000 100,000 tions and classifications in the IMF's Government GNI per capita ($) Finance Statistics Manual 2001. The IMF receives additional information from the Organisation for Low-income economies Middle-income economies High-income economies Economic Co-operation and Development on the High-income economies prefer to tax income and property. Low-income economies tend to rely on indirect tax revenues of some of its members. See the IMF taxes on international trade and goods and services. But in all groups there are exceptions. sources for complete and authoritative explana- Source: International Monetary Fund, Government Finance Statistics data files, and World Bank data files. tions of concepts, definitions, and data sources. 2007 World Development Indicators 237 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 14.1 .. 10.5 .. 1.1 18.5 5.1 20.6 13.1 ­65.5 9.3 Algeria 11.4 10.3 12.2 5.7 3.2 ­25.0 8.0 1.8 .. 8.0 .. ­7.0 Angola .. 60.5 .. 20.0 .. ­27.1 .. 13.4 .. 67.7 .. 16.9 Argentina 1,113.3 21.5 1,444.7 10.6 1,573.2 ­10.5 1,517.9 3.8 .. 6.2 .. ­2.5 Armenia 1,076.8 27.8 92.0 16.5 583.8 8.0 .. 5.8 .. 18.0 .. 14.4 Australia 12.8 7.7 13.8 17.2 ­3.6 ­2.0 13.5 3.7 17.9 9.1 14.0 4.2 Austriaa .. .. .. .. .. .. 3.4 .. .. .. .. .. Azerbaijan 825.8 23.2 134.1 27.3 150.5 ­3.0 .. 8.5 .. 17.0 .. 6.1 Bangladesh 10.4 17.2 9.2 11.6 ­0.6 3.4 12.0 8.1 16.0 14.0 9.1 8.5 Belarus .. 42.5 .. 38.6 .. 0.3 65.1 9.2 71.6 11.4 ­85.1 ­4.5 Belgiuma .. .. .. .. .. .. 6.1 1.6 13.0 6.7 9.9 4.4 Benin 28.6 26.3 ­1.3 12.5 12.4 4.6 7.0 3.5 16.0 .. 14.2 .. Bolivia 52.9 16.8 43.7 2.9 ­8.8 ­0.3 23.8 4.9 41.8 16.6 22.0 11.5 Bosnia and Herzegovina .. 18.7 .. 22.4 .. 0.1 .. 3.6 .. 9.6 .. 7.8 Botswana ­14.0 10.6 12.6 6.1 ­53.1 ­27.5 6.1 9.3 7.9 15.7 1.5 6.4 Brazil 1,251.8 19.6 2,100.5 10.8 2,400.8 7.9 9,394.3 17.6 .. 55.4 .. 44.9 Bulgaria 51.7 24.5 37.5 22.0 43.1 ­0.1 39.5 3.0 52.5 7.9 ­53.3 3.8 Burkina Faso ­0.5 ­4.8 3.6 15.8 ­1.5 0.7 7.0 3.5 16.0 .. 14.4 .. Burundi 9.6 19.0 15.4 ­0.6 ­10.1 6.0 4.0 .. 12.3 19.1 6.0 2.1 Cambodia .. 8.9 .. 9.2 .. ­5.5 .. 1.9 .. 17.3 .. 11.0 Cameroon ­1.7 4.9 0.9 5.3 ­1.9 ­8.5 7.5 4.9 18.5 17.7 16.6 12.4 Canada 8.1 9.7 8.4 9.9 0.9 0.8 9.9 0.8 14.1 4.4 10.5 1.2 Central African Republic ­3.7 16.5 ­1.6 ­1.1 ­5.0 11.9 7.5 4.9 18.5 17.7 15.9 15.0 Chad ­2.4 31.3 1.3 8.0 ­6.0 0.8 7.5 4.9 18.5 17.7 9.7 ­2.0 Chile 24.2 19.3 21.7 21.3 9.0 ­7.9 40.4 3.9 48.9 6.7 21.6 1.8 China 28.9 17.9 26.5 6.9 1.5 ­0.2 8.6 2.3 9.4 5.6 3.5 1.6 Hong Kong, China 8.5 3.5 7.9 3.3 ­1.0 ­1.0 6.7 1.3 10.0 7.8 0.3 8.0 Colombia 33.0 19.2 8.7 14.0 ­0.7 5.9 36.4 7.0 45.2 14.6 15.2 7.9 Congo, Dem. Rep. 195.4 25.5 18.0 11.9 421.6 16.2 .. .. .. 66.8 .. 26.4 Congo, Rep. 18.4 37.1 5.3 1.2 ­9.4 ­70.2 7.5 4.9 18.5 17.7 19.7 9.8 Costa Rica 27.5 24.7 7.3 21.6 5.0 ­2.4 21.2 10.1 32.6 24.7 13.2 12.2 Côte d'Ivoire ­2.6 7.7 ­3.9 0.8 ­3.0 1.9 7.0 3.5 16.0 .. 21.5 .. Croatia .. 10.6 .. 14.4 .. 3.8 658.5 1.7 1,157.8 11.2 80.9 7.8 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 8.4 .. 9.6 .. ­7.5 7.0 1.2 14.1 5.8 ­3.6 4.8 Denmark 6.5 16.1 3.0 40.9 ­3.1 0.4 7.9 2.4 14.1 7.1 10.1 4.7 Dominican Republic 42.5 14.3 19.1 6.5 0.6 18.1 20.0 13.9 35.3 24.1 ­14.5 19.1 Ecuador 50.3 20.9 9.3 21.4 ­28.9 ­10.9 43.5 3.5 37.5 9.3 29.9 2.4 Egypt, Arab Rep. 28.7 11.5 6.3 2.8 15.2 ­1.6 12.0 7.2 19.0 13.1 0.5 7.4 El Salvador ­17.3 2.7 ­30.1 8.7 15.9 ­0.3 18.0 .. 21.2 .. 15.7 .. Eritrea .. 10.7 .. 2.7 .. 10.9 .. .. .. .. .. .. Estonia 76.5 41.9 27.6 66.9 1.7 ­2.9 .. 2.1 30.5 4.9 ­86.6 ­1.2 Ethiopia 19.9 18.6 0.3 14.7 23.1 12.8 3.6 3.5 6.0 7.0 2.0 1.0 Finlanda .. .. .. .. .. .. 7.5 1.0 11.6 3.7 4.9 3.2 Francea .. .. .. .. .. .. 4.5 2.1 10.6 6.6 8.2 4.9 Gabon 3.7 27.5 1.1 7.5 ­21.0 ­13.2 7.5 4.9 18.5 17.7 2.7 8.1 Gambia, The 8.4 13.1 7.8 4.9 ­35.4 7.1 11.3 17.3 26.5 34.9 13.0 29.4 Georgia .. 26.5 .. 49.5 .. ­8.0 .. 7.6 .. 21.6 .. 12.7 Germanya .. .. .. .. .. .. 7.1 2.7 11.6 9.7 8.1 8.1 Ghana 13.3 9.3 4.9 18.2 14.6 ­2.0 21.3 10.2 25.6 .. ­5.9 .. Greecea .. .. .. .. .. .. 19.5 2.2 27.6 6.8 5.7 3.2 Guatemala 22.2 14.1 19.8 15.2 13.5 3.7 18.2 4.3 23.3 13.0 ­12.3 4.9 Guinea ­17.4 33.4 13.1 19.8 3.0 18.1 21.0 14.4 21.2 .. ­2.2 .. Guinea-Bissau 574.6 21.3 90.5 2.5 460.7 3.4 32.7 3.5 45.8 .. 11.9 .. Haiti 2.5 17.9 ­0.6 7.9 2.2 2.2 .. 3.4 .. 27.4 .. 10.3 238 2007 World Development Indicators 4.13 ECONOMY 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 21.4 20.4 13.0 14.4 ­10.9 ­1.8 8.8 10.9 17.1 18.8 ­3.4 7.7 Hungary 29.2 13.3 23.0 18.4 69.4 ­1.3 24.7 5.2 28.8 8.5 2.5 5.9 India 15.1 15.6 5.9 14.7 10.5 ­0.6 .. .. 16.5 10.8 5.4 6.0 Indonesia 44.6 16.4 66.5 12.3 ­5.6 0.5 17.5 8.1 20.8 14.1 12.2 0.3 Iran, Islamic Rep. 18.0 22.8 14.7 21.1 5.8 ­4.1 .. 11.8 .. 16.0 .. 0.1 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Irelanda .. .. .. .. .. .. 6.3 0.0 11.3 2.6 12.1 ­0.9 Israel 19.4 11.2 18.5 10.8 4.9 ­4.5 14.4 3.2 26.4 6.4 9.1 5.8 Italya .. .. .. .. .. .. 6.8 0.9 14.9 5.3 6.0 3.2 Jamaica 21.6 10.1 8.3 6.4 ­2.3 2.2 23.9 7.5 30.5 17.4 4.3 7.0 Japan 6.9 0.2 8.5 2.7 0.7 0.9 3.6 0.3 7.0 1.7 4.4 3.6 Jordan 8.3 21.4 4.7 17.8 1.0 4.9 8.2 2.9 10.3 7.6 ­1.0 3.5 Kazakhstan .. 26.3 .. 70.6 .. ­23.8 .. .. .. .. .. .. Kenya 20.1 10.0 8.0 5.5 20.5 0.0 13.7 5.1 18.8 12.9 7.3 8.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 17.2 3.1 36.1 9.4 ­1.2 2.9 10.0 3.7 10.0 5.6 ­0.5 6.1 Kuwait 4.8 12.3 0.4 16.8 ­1.6 ­1.8 7.4 3.5 8.4 7.5 10.3 ­13.6 Kyrgyz Republic .. 10.0 .. 7.1 .. 0.9 .. 5.8 .. 26.6 .. 18.6 Lao PDR 7.8 7.9 3.6 9.0 ­0.5 0.4 30.0 4.8 26.0 26.8 11.4 17.4 Latvia .. 38.3 .. 70.5 .. 3.3 34.8 2.8 86.4 6.1 21.3 ­2.9 Lebanon 55.1 4.5 27.6 0.5 ­35.2 3.1 16.9 8.1 39.9 10.6 21.2 10.3 Lesotho 9.7 9.1 8.4 11.0 ­16.7 ­8.3 13.0 4.0 20.4 11.7 10.8 8.3 Liberia 21.1 34.1 19.0 8.7 33.2 ­11.1 6.8 3.4 13.8 17.0 10.2 7.4 Libya 19.0 29.0 2.0 0.9 9.4 ­128.9 5.5 2.1 7.0 6.1 0.4 ­14.7 Lithuania .. 32.9 .. 40.4 .. 1.2 88.3 1.2 91.8 5.7 ­52.8 2.9 Macedonia, FYR .. 15.2 .. 12.9 .. ­12.0 .. 6.6 .. 12.2 .. 8.8 Madagascar 4.5 2.2 23.8 9.2 ­14.8 ­5.6 20.5 18.8 25.8 27.0 12.9 7.3 Malawi 11.1 16.2 15.5 7.7 ­14.0 2.3 12.1 10.9 21.0 33.1 9.3 15.3 Malaysia ­43.7 6.3 ­13.2 8.1 ­28.5 ­1.0 5.7 3.0 8.8 6.0 4.8 1.3 Mali ­4.9 9.8 0.1 0.1 ­13.4 4.3 7.0 3.5 16.0 .. 10.6 .. Mauritania 11.5 10.5 20.2 18.7 1.5 ­15.8 5.0 8.0 10.0 23.1 7.2 3.6 Mauritius 21.0 19.5 9.9 5.0 0.7 1.4 12.6 7.3 18.0 21.0 6.6 15.5 Mexico 83.8 10.0 48.4 9.3 10.6 ­3.8 30.4 3.5 17.7 9.7 7.5 4.0 Moldova 358.0 34.4 53.3 17.8 300.3 ­9.1 .. 13.2 .. 19.3 .. 11.2 Mongolia 31.6 37.1 40.2 28.0 6.8 ­10.0 300.0 13.0 300.0 23.6 ­5.8 10.7 Morocco 21.5 14.0 12.4 7.9 ­4.9 0.1 8.5 3.5 9.0 11.5 3.3 9.9 Mozambique 37.2 31.0 22.0 15.9 ­8.0 ­9.4 .. 7.8 .. 19.5 .. 12.2 Myanmar 37.7 27.3 12.8 6.8 23.7 23.5 5.9 9.5 8.0 15.0 ­8.9 ­2.2 Namibia 30.3 9.8 15.4 25.3 ­7.8 3.4 12.8 6.2 23.4 10.6 17.9 8.4 Nepal 18.5 9.8 5.7 .. 6.0 2.6 11.9 2.3 14.4 8.1 3.2 3.4 Netherlandsa .. .. .. .. .. .. 3.3 2.3 11.8 2.8 9.3 1.2 New Zealand 12.5 12.2 4.2 20.6 ­1.6 ­0.8 11.7 6.7 16.0 11.5 13.2 8.8 Nicaragua 7,677.8 9.8 4,932.9 19.1 3,222.5 ­1.8 9.5 4.0 22.0 12.1 ­97.6 1.7 Niger ­4.1 7.0 ­5.1 8.5 1.4 ­5.8 7.0 3.5 16.0 .. 17.9 .. Nigeria 32.7 16.2 7.8 19.5 26.3 ­23.6 19.8 10.5 25.3 17.9 16.9 ­7.0 Norway 5.6 3.4 5.0 10.4 0.4 ­5.3 9.7 1.8 14.2 4.0 10.0 ­4.1 Oman 10.0 21.3 9.6 13.7 ­11.2 ­12.7 8.3 3.3 9.7 7.0 ­12.1 ­1.4 Pakistan 11.6 16.5 5.0 8.8 7.5 4.1 .. .. .. .. .. .. Panama 36.6 8.3 0.8 15.6 ­25.7 ­4.6 8.4 2.7 12.0 8.7 11.4 6.2 Papua New Guinea 4.3 29.5 ­1.1 10.5 6.4 ­4.6 8.7 0.9 15.5 11.5 10.9 ­5.0 Paraguay 53.9 9.8 37.4 10.3 ­5.2 ­2.4 22.9 1.7 31.0 29.9 ­3.9 22.7 Peru 6,384.9 16.8 2,123.7 10.9 2,127.1 ­2.6 2,439.6 3.4 4,774.5 15.0 ­29.7 11.2 Philippines 22.4 6.4 15.6 ­1.2 1.8 ­4.1 19.5 5.6 24.1 10.2 9.9 3.7 Poland 160.1 12.2 158.7 6.4 ­20.6 ­2.7 41.7 2.8 504.2 6.8 ­0.4 3.9 Portugala .. .. .. .. .. .. 14.0 .. 21.8 .. 7.6 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 239 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 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 30.1 20.2 .. 24.3 .. ­0.9 .. .. .. .. .. .. Russian Federation .. 36.3 .. 27.3 .. ­26.2 .. 4.0 .. 10.7 .. ­7.5 Rwanda 5.6 18.0 ­10.0 14.5 26.8 ­13.8 6.9 7.9 13.2 .. ­0.3 .. Saudi Arabia 4.6 13.2 ­4.5 25.1 4.2 ­33.2 8.0 3.8 .. .. .. .. Senegal ­4.8 8.2 ­8.4 12.7 ­5.3 ­4.1 7.0 3.5 16.0 .. 14.6 .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 74.0 31.3 4.9 4.4 228.6 ­5.7 40.5 11.1 52.5 24.6 ­10.6 10.2 Singapore 20.0 6.2 13.7 1.8 ­4.9 ­5.7 4.7 0.4 7.4 5.3 3.1 4.7 Slovak Republic .. 3.6 .. 13.1 .. 2.8 8.0 2.4 14.4 6.7 ­11.0 4.1 Slovenia 123.0 7.5 96.1 19.7 ­10.4 2.9 682.5 3.2 824.6 7.8 374.3 6.2 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 11.9 19.9 13.7 21.1 2.2 ­6.1 18.9 6.0 21.0 10.6 4.7 5.6 Spaina .. .. .. .. .. .. 10.7 2.5 16.0 4.3 8.1 ­0.1 Sri Lanka 19.9 19.0 16.2 15.9 4.4 3.1 19.4 10.2 13.0 7.0 ­5.9 ­3.1 Sudan 48.8 43.5 12.6 25.9 27.9 ­4.5 .. .. .. .. .. .. Swaziland 0.6 9.7 20.5 22.9 ­5.5 ­25.1 8.7 4.0 14.5 10.6 ­0.4 5.5 Sweden 10.7 11.2 13.6 23.7 ­12.2 4.6 9.9 0.8 16.7 3.3 7.3 2.1 Switzerland 0.8 6.8 11.7 7.0 1.0 ­1.0 8.3 0.8 7.4 3.1 2.9 2.5 Syrian Arab Republic 26.1 15.3 3.4 4.4 11.4 3.3 4.0 1.0 9.0 8.0 ­8.7 2.1 Tajikistan .. 25.9 .. 38.8 .. ­8.2 .. 9.7 .. 23.3 .. 13.4 Tanzania 41.9 38.2 22.6 12.8 80.6 16.2 17.0 4.7 31.0 15.1 8.6 11.0 Thailand 25.5 5.9 36.6 6.5 ­4.4 ­0.4 12.3 1.9 14.4 5.8 8.2 1.2 Togo 9.5 2.2 1.8 6.9 6.9 ­0.8 7.0 3.5 16.0 .. 12.6 .. Trinidad and Tobago 6.2 29.4 2.7 19.6 0.9 ­21.6 6.0 2.2 12.9 9.1 ­2.3 ­0.8 Tunisia 7.6 11.0 5.9 8.8 1.8 1.0 7.4 .. 4.8 .. ­3.7 .. Turkey 53.2 25.3 43.0 20.2 ­6.4 ­0.2 47.5 20.4 .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 60.2 16.5 23.3 4.3 0.8 ­6.1 31.3 8.8 38.7 19.6 ­4.0 11.0 Ukraine 1,809.2 53.9 78.3 44.1 109.3 ­16.1 148.6 8.6 184.3 16.2 ­91.7 ­3.2 United Arab Emirates ­8.2 30.5 1.3 34.4 ­4.8 ­1.9 .. .. .. .. .. .. United Kingdom 10.5 13.8 13.1 13.2 1.9 ­0.1 12.5 .. 14.8 4.6 6.6 2.6 United States 2.7 7.5 ­0.5 8.1 0.7 0.4 .. .. 10.0 6.2 5.9 3.1 Uruguay 118.5 0.3 56.2 ­2.1 3.0 ­14.7 147.5 2.8 163.8 13.6 27.5 11.7 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 64.9 54.1 17.6 35.7 44.3 ­1.4 27.8 11.6 35.5 16.8 ­4.4 ­9.5 Vietnam 12.3 30.9 19.6 26.8 23.7 6.9 22.0 7.1 32.2 11.0 12.6 2.4 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 11.3 14.4 1.4 4.3 8.3 ­3.1 .. 13.0 .. 18.0 .. 0.7 Zambia 47.9 1.9 22.8 6.7 185.8 ­43.2 25.7 11.2 35.1 28.2 ­34.5 7.7 Zimbabwe 15.1 532.7 13.5 154.0 7.4 421.5 8.8 91.1 11.7 235.7 ­2.6 ­0.6 a. As members of the European Monetary Union, these countries share a single currency, the euro. 240 2007 World Development Indicators 4.13 ECONOMY Monetary indicators About the data Definitions Money and the financial accounts that record the during the reporting period. The valuation of finan- · Money and quasi money comprise the sum of cur- supply of money lie at the heart of a country's cial derivatives and the net liabilities of the banking rency outside banks, demand deposits other than financial system. There are several commonly used system can also be difficult. The quality of commer- those of the central government, and the time, sav- defi nitions of the money supply. The narrowest, cial bank reporting also may be adversely affected ings, and foreign currency deposits of resident sec- M1, encompasses currency held by the public and by delays in reports from bank branches, especially tors other than the central government. This definition demand deposits with banks. M2 includes M1 plus in countries where branch accounts are not com- of the money supply, often called M2, corresponds to time and savings deposits with banks that require puterized. Thus the data in the balance sheets of lines 34 and 35 in the IMF's International Financial a notice for withdrawal. M3 includes M2 as well as commercial banks may be based on preliminary esti- Statistics (IFS). The change in money supply is mea- various money market instruments, such as cer- mates subject to constant revision. This problem is sured as the difference in end-of-year totals relative tificates of deposit issued by banks, bank deposits likely to be even more serious for nonbank financial to M2 in the preceding year. · Claims on private sec- denominated in foreign currency, and deposits with intermediaries. tor (IFS line 32d) include gross credit from the finan- fi nancial institutions other than banks. However Many interest rates coexist in an economy, reflect- cial system to individuals, enterprises, nonfinancial defined, money is a liability of the banking system, ing competitive conditions, the terms governing loans public entities not included under net domestic distinguished from other bank liabilities by the spe- and deposits, and differences in the position and credit, and financial institutions not included else- cial role it plays as a medium of exchange, a unit of status of creditors and debtors. In some economies where. · Claims on governments and other public account, and a store of value. interest rates are set by regulation or administra- entities (IFS line 32an + 32b + 32bx + 32c) usually The banking system's assets include its net for- tive fiat. In economies with imperfect markets, or comprise direct credit for specific purposes, such eign assets and net domestic credit. Net domestic where reported nominal rates are not indicative of as financing the government budget deficit; loans credit includes credit extended to the private sector effective rates, it may be difficult to obtain data on to state enterprises; advances against future credit and general government and credit extended to the interest rates that reflect actual market transactions. authorizations; and purchases of treasury bills and nonfinancial public sector in the form of investments Deposit and lending rates are collected by the Inter- bonds, net of deposits by the public sector. Public in short- and long-term government securities and national Monetary Fund (IMF) as representative inter- sector deposits with the banking system also include loans to state enterprises; liabilities to the public est rates offered by banks to resident customers. sinking funds for the service of debt and temporary and private sectors in the form of deposits with the The terms and conditions attached to these rates deposits of government revenues. · Deposit interest banking system are netted out. Net domestic credit differ by country, however, limiting their comparabil- rate is the rate paid by commercial or similar banks also includes credit to banking and nonbank financial ity. Real interest rates are calculated by adjusting for demand, time, or savings deposits. · Lending institutions. nominal rates by an estimate of the inflation rate in interest rate is the rate charged by banks on loans to Domestic credit is the main vehicle through which the economy. A negative real interest rate indicates prime customers. · Real interest rate is the lending changes in the money supply are regulated, with cen- a loss in the purchasing power of the principal. The interest rate adjusted for inflation as measured by tral bank lending to the government often playing the real interest rates in the table are calculated as the GDP deflator. most important role. The central bank can regulate (i ­ P) / (1 + P), where i is the nominal lending inter- lending to the private sector in several ways--for est rate and P is the inflation rate (as measured by example, by adjusting the cost of the refinancing the GDP deflator). 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 Monetary and financial data are published by the ceilings on the credit provided by banks to the pri- IMF in its monthly International Financial Statis- vate sector. tics and annual International Financial Statistics Monetary accounts are derived from the balance Yearbook. The IMF collects data on the financial sheets of financial institutions--the central bank, systems of its member countries. The World Bank commercial banks, and nonbank financial interme- receives data from the IMF in electronic files that diaries. Although these balance sheets are usually may contain more recent revisions than the pub- reliable, they are subject to errors of classification, lished sources. The discussion of monetary indi- valuation, and timing and to differences in account- cators draws from an IMF publication by Marcello ing practices. For example, whether interest income Caiola, A Manual for Country Economists (1995). is recorded on an accrual or a cash basis can make Also see the IMF's Monetary and Financial Statis- a substantial difference, as can the treatment of non- tics Manual (2000) for guidelines for the presen- performing assets. Valuation errors typically arise tation of monetary and financial statistics. World with respect to foreign exchange transactions, par- Bank data on the GDP deflator are used to derive ticularly in countries with flexible exchange rates or real interest rates. in those that have undergone a currency devaluation 2007 World Development Indicators 241 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 official 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 2005 2006 1990 2005 2005 2005 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 Afghanistan 49.50 .. .. .. .. .. .. 12.2 .. .. .. .. Albania 99.87 122.20a 2.1 50.3 0.5 .. 38.0 4.0 27.8 3.2 .. 5.4 Algeria 73.28 72.65 5.0 32.3 0.4 83.3 18.5 7.3 17.3 2.6 .. 3.8 Angola 87.16 80.37 .. 76.8 0.9 .. 739.4 82.2 711.0 79.2 .. .. Argentina 2.90 3.05 0.3 1.0 0.3 .. 5.2 12.4 8.9 11.2 0.1 22.6 Armenia 457.69 416.04 0.0 150.4 0.3 97.7 212.5 4.2 72.8 3.6 .. 0.9 Australia 1.31 1.33 1.5 1.5 1.1 125.9 1.5 3.4 2.1 2.9 1.1 2.3 Austriab 0.80 0.80 0.9 0.9 1.1 105.7 1.7 1.6 2.2 1.9 0.3 2.0 Azerbaijan 0.95 0.89 .. 0.3 0.3 .. 203.0 6.0 170.9 4.3 .. .. Bangladesh 64.33 68.93 9.9 12.7 0.2 .. 4.0 3.8 5.5 5.6 .. .. Belarus 2,153.82 2,140.43a .. 822.7 0.4 .. 355.1 35.8 271.3 30.4 267.8 35.3 Belgiumb 0.80 0.80 0.9 0.9 1.1 109.4 1.8 1.9 1.9 2.0 1.2 1.4 Benin 527.47 522.89 140.2 235.0 0.4 .. 8.7 2.9 8.7 2.5 .. .. Bolivia 8.07 7.99a 1.4 2.9 0.4 79.8 8.6 4.8 8.7 3.1 .. .. Bosnia and Herzegovina 1.57 1.56 .. .. .. .. 3.4 2.5 .. .. .. .. Botswana 5.11 6.05a 1.1 2.4 0.5 .. 9.7 4.2 10.4 2.1 .. .. Brazil 2.43 2.18 .. 1.2 0.5 .. 208.0 10.1 199.5 9.1 204.9 15.6 Bulgaria 1.57 1.56 0.0 0.6 0.4 120.5 103.3 4.0 117.5 5.1 85.7 4.4 Burkina Faso 527.47 522.89 138.9 170.0 0.3 .. 5.0 2.7 5.5 2.5 .. .. Burundi 1,081.58 1,051.61a 46.2 163.2 0.2 71.1 13.4 8.3 16.1 7.4 .. .. Cambodia 4,092.50 4,122.00a .. 660.6 0.2 88.0 4.4 2.8 6.3 2.7 .. .. Cameroon 527.47 522.89 168.8 237.3 0.5 109.6 6.1 2.1 6.5 1.8 .. .. Canada 1.21 1.13 1.3 1.3 1.0 119.7 1.5 2.4 1.7 2.3 2.7 0.7 Central African Republic 527.47 522.89 128.0 146.1 0.3 122.3 4.5 1.9 5.3 2.1 6.0 4.4 Chad 527.47 522.89 110.9 207.0 0.4 .. 7.1 7.6 6.9 2.2 .. .. Chile 560.09 530.29 155.8 329.4 0.6 92.3 7.9 5.3 8.9 2.5 7.0 5.7 China 8.19 7.97 1.2c 2.1c 0.3c 92.5 7.9 3.2 8.6 1.3 .. .. Hong Kong, China 7.78 7.77 6.5 5.7 0.7 .. 4.0 ­3.6 5.9 ­1.6 0.6 ­0.3 Colombia 2,320.83 2,361.14 126.9 852.3 0.4 105.3 21.7 6.6 20.3 6.5 16.4 6.5 Congo, Dem. Rep. 473.91 448.30a .. 81.9 0.2 27.8 965.0 43.6 932.8 41.1 .. .. Congo, Rep. 527.47 522.89 299.8 532.0 1.0 .. 8.9 ­0.6 9.3 2.4 .. .. Costa Rica 477.79 511.30 43.1 217.1 0.5 91.9 15.9 9.7 15.6 10.9 14.1 11.3 Côte d'Ivoire 527.47 522.89 170.7 288.2 0.5 116.4 9.2 2.7 7.2 3.1 .. .. Croatia 5.95 5.84 0.0 4.0 0.7 109.7 86.0 3.6 86.2 2.4 83.9 1.7 Cuba .. .. .. .. .. .. 3.0 2.6 .. .. .. .. Czech Republic 23.96 22.60 5.0 14.2 0.6 125.2 12.8 2.5 7.8 2.0 8.2 1.9 Denmark 6.00 5.95 8.7 8.4 1.4 108.4 1.6 2.3 2.1 2.0 1.1 1.4 Dominican Republic 30.41 33.37 2.4 12.1 0.4 104.6 9.4 20.6 8.7 20.6 .. .. Ecuador 1.00 1.00 0.4 0.6 0.6 147.9 4.3 11.5 37.1 10.7 .. 7.5 Egypt, Arab Rep. 5.78 5.71a 0.8 1.7 0.3 .. 8.7 5.9 8.8 5.3 6.1 9.8 El Salvador 8.75 8.75 0.3 0.5 0.5 .. 6.2 2.9 8.5 3.2 .. 3.1 Eritrea 15.37 15.38 1.0 3.1 0.2 .. 7.9 15.7 .. .. .. .. Estonia 12.58 12.47 0.1 7.9 0.6 .. 53.6 3.9 21.6 3.3 8.1 1.7 Ethiopia 8.67 8.75a 0.7 1.3 0.1 .. 6.3 4.2 5.5 5.8 .. .. Finlandb 0.80 0.80 1.0 0.9 1.1 104.8 2.1 0.9 1.5 1.1 1.0 0.5 Franceb 0.80 0.80 1.0 0.9 1.2 107.9 1.4 2.0 1.6 2.1 .. 1.3 Gabon 527.47 522.89 319.3 441.5 0.8 102.6 6.2 2.7 4.6 1.0 .. .. Gambia, The 28.58 28.04a 2.0 4.5 0.2 53.5 4.2 16.5 4.1 10.6 .. .. Georgia 1.81 1.78 .. 0.8 0.4 .. 356.7 6.0 24.7 5.6 .. .. Germany b 0.80 0.80 1.0 0.9 1.2 106.9 1.7 1.0 2.0 1.5 0.4 1.8 Ghana 9,072.54 9,225.15a 94.0 1,773.9 0.2 109.5 26.7 22.8 28.4 19.8 .. .. Greeceb 0.80 0.80 0.3 0.7 0.9 113.7 9.2 3.3 9.0 3.4 3.6 3.3 Guatemala 7.63 7.60 1.4 4.2 0.6 .. 10.4 7.2 10.1 7.2 .. .. Guinea 3,644.33 5,597.00a 201.7 550.6 0.2 .. 5.6 13.2 .. .. .. .. Guinea-Bissau 527.47 522.89 9.9 121.1 0.2 .. 32.5 0.7 34.0 1.0 .. .. Haiti 40.45 39.58a 1.2 12.2 0.3 .. 22.7 18.0 21.9 21.3 .. .. 242 2007 World Development Indicators 4.14 ECONOMY 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 official 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 2005 2006 1990 2005 2005 2005 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 Honduras 18.83 18.90a 1.2 6.4 0.3 .. 18.9 8.0 18.8 8.2 .. .. Hungary 199.58 210.39 21.3 120.8 0.6 132.6 19.4 6.1 20.3 5.8 16.8 2.3 India 44.10 45.31 5.0 9.4 0.2 .. 8.0 3.9 9.1 4.0 7.4 4.8 Indonesia 9,704.74 9,159.32 652.2 3,220.5 0.3 .. 15.8 8.2 13.7 8.9 15.4 7.5 Iran, Islamic Rep. 8,963.96 9,170.94 168.8 3,128.3 0.3 129.5 27.7 18.0 26.0 14.4 28.4 10.1 Iraq 1,472.00 .. .. .. .. .. .. 0.3 .. .. .. .. Irelandb 0.80 0.80 0.8 1.0 1.3 123.5 3.5 3.5 2.3 3.5 1.6 ­0.3 Israel 4.49 4.46 1.5 3.1 0.7 78.0 10.2 1.3 9.7 1.7 8.1 4.1 Italy b 0.80 0.80 0.7 0.8 1.1 111.0 3.8 2.9 3.7 2.4 2.9 1.8 Jamaica 62.28 66.14a 5.3 53.3 0.8 .. 23.0 10.5 23.5 10.5 .. .. Japan 110.22 116.30 183.0 125.1 1.1 79.4 0.0 ­1.5 0.8 ­0.4 ­1.0 ­0.5 Jordan 0.71 0.71 0.3 0.3 0.4 .. 3.2 2.1 3.5 2.4 .. 6.0 Kazakhstan 132.88 126.09 0.0 63.8 0.5 .. 204.7 12.0 67.8 6.8 16.3 9.4 Kenya 75.55 72.10 9.1 33.3 0.4 .. 16.6 4.4 15.6 7.9 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 1,024.12 954.85 543.8 758.2 0.7 .. 5.7 2.4 5.1 3.3 3.6 2.1 Kuwait 0.29 0.29 0.3 0.4 1.2 .. 1.5 8.3 2.0 1.5 1.4 2.2 Kyrgyz Republic 41.01 40.15 0.0 10.1 0.2 .. 110.6 4.6 23.3 3.8 35.6 6.4 Lao PDR 10,655.17 10,184.00a 173.9 2,539.9 0.2 .. 27.0 11.2 28.2 10.9 .. .. Latvia 0.57 0.56 0.0 0.3 0.5 .. 48.0 4.8 29.2 3.9 12.0 4.3 Lebanon 1,507.50 1,507.50 328.5 1,656.2 1.1 .. 17.9 2.0 .. .. .. .. Lesotho 6.36 6.77 0.6 1.5 0.2 132.8 10.0 6.3 9.8 8.8 .. .. Liberia 57.10 58.26a .. .. .. .. 51.8 11.6 .. .. .. .. Libya 1.31 1.31 .. .. .. .. .. 18.7 5.6 ­5.9 .. .. Lithuania 2.77 2.75 0.0 1.4 0.5 .. 75.0 1.2 32.6 0.6 24.7 2.0 Macedonia, FYR 49.28 47.47a 0.0 19.4 0.4 99.8 79.3 2.1 10.8 1.7 8.4 0.7 Madagascar 2,003.03 2,142.30 98.1 587.6 0.3 .. 19.1 11.0 18.7 9.9 .. .. Malawi 118.42 137.00a 1.3 28.5 0.2 75.2 33.6 14.7 33.8 13.8 .. .. Malaysia 3.79 3.63a 1.5 1.8 0.5 95.2 3.9 3.4 3.6 1.6 3.4 3.6 Mali 527.47 522.89 131.4 200.4 0.4 .. 7.0 4.0 5.2 1.7 .. .. Mauritania 265.53 268.60a 26.7 72.5 0.3 .. 8.7 8.7 6.1 6.9 .. .. Mauritius 29.50 31.71 6.5 11.5 0.4 .. 6.4 5.6 6.9 4.2 .. .. Mexico 10.90 10.90 1.4 7.6 0.7 .. 19.0 7.1 19.4 4.9 18.4 6.5 Moldova 12.60 13.17a 0.0 4.2 0.3 109.1 119.6 10.6 19.2 10.3 .. .. Mongolia 1,205.22 1,164.10a 3.5 421.1 0.3 .. 54.2 11.5 36.7 5.5 .. .. Morocco 8.87 8.80 3.3 3.3 0.4 91.8 2.9 0.9 3.8 1.5 2.9 ­0.6 Mozambique 23.06 25.40 331.2 6,224.4 0.3 .. 32.4 11.4 31.8 12.5 .. .. Myanmar 5.76 5.78 .. .. .. .. 25.5 21.1 25.9 26.3 .. .. Namibia 6.36 6.77 1.0 2.5 0.4 .. 10.4 5.3 .. 4.5 .. .. Nepal 71.37 72.76 6.4 12.7 0.2 .. 8.2 4.1 8.7 4.2 .. .. Netherlandsb 0.80 0.80 0.9 0.9 1.2 113.3 2.0 2.7 2.4 2.4 1.3 2.3 New Zealand 1.42 1.54 1.6 1.5 1.1 114.4 1.7 2.3 1.7 2.4 1.4 1.8 Nicaragua 16.73 17.57 0.0 4.1 0.2 87.7 42.4 6.9 30.8 6.5 .. .. Niger 527.47 522.89 123.7 164.8 0.3 .. 6.0 2.4 6.1 1.9 .. .. Nigeria 131.27 127.02a 4.2 87.6 0.7 123.8 29.5 15.9 32.4 15.6 .. .. Norway 6.44 6.41 8.3 9.9 1.5 111.1 2.7 2.9 2.2 1.7 1.6 3.7 Oman 0.39 0.39 0.3 0.2 0.6 .. 0.1 1.8 0.6 ­0.2 .. .. Pakistan 59.51 60.27 5.9 17.7 0.3 94.0 11.1 6.0 9.7 4.9 10.4 6.2 Panama 1.00 1.00 0.6 0.6 0.6 .. 3.6 1.6 1.1 0.9 1.0 1.0 Papua New Guinea 3.10 3.02a 0.5 1.0 0.3 101.0 7.1 7.1 9.3 8.4 .. .. Paraguay 6,177.96 5,411.40a 422.6 1,583.6 0.3 78.0 11.5 11.1 13.1 9.0 .. 14.5 Peru 3.30 3.27 0.1 1.5 0.5 .. 26.7 2.8 27.3 2.0 23.7 1.9 Philippines 55.09 51.31 5.8 12.7 0.2 92.3 8.3 5.2 7.7 5.0 6.3 8.4 Poland 3.24 3.10 0.2 1.9 0.6 107.4 24.7 2.4 25.3 2.5 19.8 2.9 Portugalb 0.80 0.80 0.5 0.7 0.9 110.7 5.2 3.1 4.5 3.1 .. 1.9 Puerto Rico 1.00 1.00 .. .. .. .. 3.0 .. .. .. .. .. 2007 World Development Indicators 243 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 official 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 2005 2006 1990 2005 2005 2005 1990­2000 2000­05 1990­2000 2000­05 1990­2000 2000­05 Romania 2.91 2.81 0.0 1.5 0.5 119.9 98.0 21.8 100.5 17.7 93.8 21.4 Russian Federation 28.28 26.59a 0.0 13.9 0.5 149.2 161.5 16.8 99.1 14.4 99.8 17.4 Rwanda 557.82 549.94a 35.6 109.8 0.2 .. 14.6 5.9 16.2 6.8 .. .. Saudi Arabia 3.75 3.75 2.5 3.0 0.8 82.3 1.6 6.2 1.0 0.2 1.3 1.3 Senegal 527.47 522.89 172.1 208.0 0.4 .. 5.0 2.0 5.4 1.3 .. .. Serbia and Montenegro 72.44 59.64a .. .. .. .. 55.0 25.2 .. .. .. .. Sierra Leone 2,889.59 2,961.91 35.9 790.0 0.3 70.9 32.1 6.8 29.3 6.3 .. .. Singapore 1.66 1.59 1.8 1.5 0.9 92.1 1.3 0.2 1.7 0.6 ­1.0 2.4 Slovak Republic 31.02 29.70 5.9 16.8 0.5 134.4 11.3 4.1 8.4 6.1 9.5 5.0 Slovenia 192.71 191.03 8.6 148.6 0.8 .. 28.7 5.5 12.0 5.5 9.1 4.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 6.36 6.77 1.1 2.9 0.5 108.5 9.9 6.6 8.7 5.2 7.4 5.3 Spainb 0.80 0.80 0.6 0.8 1.0 112.9 3.9 4.2 3.8 3.2 2.4 2.2 Sri Lanka 100.50 103.91 9.8 26.2 0.3 .. 9.1 8.7 9.9 9.2 8.1 9.3 Sudan 243.61 217.15 0.7 88.9 0.4 124.0 66.6 9.6 71.8 7.8 .. .. Swaziland 6.36 6.77 0.9 3.2 0.5 .. 12.5 10.6 9.4 6.9 .. .. Sweden 7.47 7.38 9.1 9.1 1.2 96.9 2.2 1.5 1.9 1.5 2.4 1.6 Switzerland 1.25 1.25 2.0 1.7 1.4 104.0 1.0 1.0 1.6 0.8 ­0.4 0.4 Syrian Arab Republic 11.23 11.23 10.2 19.2 0.4 .. 7.9 4.2 6.4 .. 4.7 .. Tajikistan 3.12 3.30 .. 0.8 0.3 .. 235.0 21.2 .. .. .. .. Tanzania 1,128.93 1,251.90 74.6 479.2 0.4 .. 21.6 6.3 20.9 3.1 .. .. Thailand 40.22 37.88 10.7 12.7 0.3 .. 4.2 2.3 4.9 2.1 3.8 4.6 Togo 527.47 522.89 92.6 125.6 0.2 113.6 7.0 1.2 8.5 2.0 .. .. Trinidad and Tobago 6.30 6.31 3.1 4.7 0.8 107.8 5.4 3.5 5.7 4.5 2.8 1.6 Tunisia 1.30 1.33 0.4 0.4 0.3 85.3 4.4 2.3 4.4 2.7 3.6 3.0 Turkey 1.34 d 1.43d 1,569.1 804,128.7 0.6 .. 76.1 25.5 79.9 26.5 .. .. Turkmenistan .. .. 0.0 .. .. .. 407.5 .. .. .. .. .. Uganda 1,780.67 1.81a 114.5 361.9 0.2 88.8 11.8 5.0 10.5 4.1 .. .. Ukraine 5.13 5.05 .. 1.3 0.3 106.0 271.0 10.9 155.7 7.1 161.6 10.8 United Arab Emirates 3.67 3.67 3.3 4.1 1.1 .. 2.2 4.9 .. .. .. .. United Kingdom 0.55 0.54 0.5 0.6 1.1 101.3 2.8 2.5 2.9 2.5 2.4 1.3 United States 1.00 1.00 1.0 1.0 1.0 92.8 2.0 2.3 2.7 2.4 1.2 3.4 Uruguay 24.48 24.48 0.6 11.9 0.5 76.6 31.1 11.5 33.9 11.4 27.2 20.3 Uzbekistan .. .. 0.0 281.8 0.3 .. 245.8 29.0 .. .. .. .. Venezuela, RB 2,089.75 2,147.00 23.8 1,662.2 0.8 69.0 45.3 28.6 49.0 22.0 44.1 32.9 Vietnam 15,858.92 15,921.00a 674.1 3,282.4 0.2 .. 15.2 5.9 4.1 4.5 .. .. West Bank and Gaza .. .. .. .. .. .. 5.7 3.4 .. .. .. .. Yemen, Rep. 191.51 198.08a 18.0 147.9 0.8 .. 22.4 10.3 26.3 11.7 .. .. Zambia 4,463.50 3,603.07 17.7 2,719.3 0.6 134.8 52.1 20.4 57.0 20.4 101.4 .. Zimbabwe 22.36 250.00a 0.9 2,844.9 0.1 .. 26.7 232.6 29.0 .. 25.9 .. Note: The inconsistencies in the growth rates of the GDP deflator and the consumer and wholesale price indexes are due mainly to uneven coverage of the time period. a. Latest quarterly or monthly data available. b. As members of the European Monetary Union, these countries share a single currency, the euro. c. Based on a 1986 bilateral comparison of China and the United States (Rouen and Kai 1995), employing a different methodology than that used for other countries. This interim methodology will be revised when the next round of PPP estimates are completed in 2007. d. New liras per dollar. 244 2007 World Development Indicators 4.14 ECONOMY Exchange rates and prices About the data In a market-based economy the choices that house- effective exchange rate index represents the ratio a country, consumer price indexes are of less value holds, producers, and governments make about the (expressed on the base 2000 = 100) of an index of a in making comparisons across countries. Food price allocation of resources are influenced by relative currency's period-average exchange rate to a weighted indexes, like consumer price indexes, should be inter- prices, including the real exchange rate, real wages, geometric average of exchange rates for currencies of preted with caution because of the high variability real interest rates, and a host of other prices in the selected countries and the euro area. For most high- across countries in the items covered. economy. Relative prices also reflect, to a large extent, income countries, weights are derived from trade in Wholesale price indexes are based on the prices of the choices of these agents. Thus relative prices con- manufactured goods among industrial countries. The commodities that have some significance in the output vey vital information about the interaction of economic data are compiled from the nominal effective exchange or consumption of the country at the first commercial agents in an economy and with the rest of the world. rate index and a cost indicator of relative normalized transaction. The prices are farm gate prices for agricul- The exchange rate is the price of one currency unit labor costs in manufacturing. For selected other tural commodities and ex-factory prices for industrial in terms of another. Offi cial exchange rates and countries the nominal effective exchange rate index is goods. Preference should be given to indexes that pro- exchange rate arrangements are established by gov- based on each country's trade in both manufactured vide the broadest coverage of the economy. ernments. (Other exchange rates fully recognized by goods and primary products with its partner or com- The least-squares method is used to calculate the governments include market rates, which are deter- petitor countries. For these countries the real effective growth rates of the GDP implicit deflator, consumer mined largely by legal market forces, and for coun- exchange rate index is derived from the nominal index price index, and wholesale price index. tries maintaining multiple exchange arrangements, adjusted for relative changes in consumer prices. An Definitions principal rates, secondary rates, and tertiary rates.) increase in the real effective exchange rate represents Also see Statistical methods for information on alter- an appreciation of the local currency. Because of con- · Official exchange rate is the exchange rate deter- native conversion factors used in the World Bank ceptual and data limitations, changes in real effective mined by national authorities or the rate determined Atlas method of calculating gross national income exchange rates should be interpreted with caution. in the legally sanctioned exchange market. It is cal- (GNI) per capita in U.S. dollars. Controlling inflation is one of the primary goals of culated as an annual average based on monthly aver- The official or market exchange rate is often used monetary policy and is intimately linked to the growth ages (local currency units relative to the U.S. dollar). to compare prices in different currencies. Since in money supply. Inflation is measured by the rate of · Purchasing power parity (PPP) conversion factor exchange rates reflect at best the relative prices of increase in a price index, but actual price change can is the number of units of a country's currency required tradable goods, the volume of goods and services be negative. Which index is used depends on which to buy the same amount of goods and services in the that a U.S. dollar buys in the United States may not set of prices in the economy is being examined. The domestic market as a U.S. dollar would buy in the correspond to what a U.S. dollar converted to another GDP deflator reflects changes in prices for total gross United States. · Ratio of PPP conversion factor to country's currency at the official exchange rate would domestic product. The most general measure of the official exchange rate is the result obtained by divid- buy in that country. Since identical volumes of goods overall price level, it takes into account changes in ing the PPP conversion factor by the official exchange and services in different countries correspond to dif- government consumption, capital formation (includ- rate. · Real effective exchange rate is the nominal ferent values (and vice versa) when official exchange ing inventory appreciation), international trade, and effective exchange rate (a measure of the value of a rates are used, an alternative method of comparing the main component, household final consumption currency against a weighted average of several for- prices across countries has been developed. In this expenditure. The GDP defl ator is usually derived eign currencies) divided by a price deflator or index method national currency estimates of GNI are con- implicitly as the ratio of current to constant price of costs. · GDP implicit deflator measures the aver- verted to a common unit of account by using conver- GDP, resulting in a Paasche index. It is defective as a age annual rate of price change in the economy as a sion factors that reflect equivalent purchasing power. general measure of inflation for use in policy because whole for the periods shown. · Consumer price index Purchasing power parity (PPP) conversion factors are of the long lags in deriving estimates and because it reflects changes in the cost to the average consumer based on price and expenditure surveys conducted by is often only an annual measure. of acquiring a basket of goods and services that may the International Comparison Program and represent Consumer price indexes are produced more fre- be fixed or may change at specified intervals, such the conversion factors applied to equalize price levels quently and so are more current. They are also con- as yearly. The Laspeyres formula is generally used. across countries. See About the data for table 1.1 for structed explicitly, based on surveys of the cost of a · Wholesale price index refers to a mix of agricultural further discussion of the PPP conversion factor. defined basket of consumer goods and services. Nev- and industrial goods at various stages of production The ratio of the PPP conversion factor to the official ertheless, consumer price indexes should be inter- and distribution, including import duties. The Laspey- exchange rate (also referred to as the national price preted with caution. The definition of a household, the res formula is generally used. level) makes it possible to compare the cost of the basket of goods chosen, and the geographic (urban or Data sources bundle of goods that make up gross domestic product rural) and income group coverage of consumer price (GDP) across countries. These national price levels surveys can all vary widely across countries. In addi- Data on official and real effective exchange rates vary systematically, rising with GNI per capita. Real tion, the weights are derived from household expen- and consumer and wholesale price indexes are effective exchange rates represent a nominal effective diture surveys, which, for budgetary reasons, tend to from the International Monetary Fund's International exchange rate index adjusted for relative movements be conducted infrequently in developing countries, Financial Statistics. PPP conversion factors and GDP in national price or cost indicators of the home coun- leading to poor comparability over time. Although deflators are from the World Bank's data files. try, selected countries, and the euro area. A nominal useful for measuring consumer price inflation within 2007 World Development Indicators 245 4.15 Balance of payments current account Goods and Net income Net current Current account Total services transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan 261 .. 727 .. 12 .. 311 .. ­143 .. 638 .. Albania 354 1,821 485 3,860 ­2 174 15 1,294 ­118 ­571 .. 1,440 Algeria 13,462 .. 10,106 .. ­2,268 .. 333 .. 1,420 .. 2,703 59,167 Angola 3,992 24,286 3,386 15,144 ­765 ­4,031 ­77 27 ­236 5,138 .. 3,197 Argentina 14,800 46,343 6,846 34,916 ­4,400 ­6,207 998 570 4,552 5,789 6,222 28,082 Armenia .. 1,337 .. 1,984 .. 45 .. 409 .. ­193 1 669 Australia 49,846 135,505 53,056 149,738 ­13,176 ­27,690 439 ­363 ­15,948 ­42,286 19,319 43,257 Austria 63,694 171,154 61,580 162,913 ­942 ­1,337 ­6 ­2,652 1,166 4,252 17,228 11,828 Azerbaijan .. 8,332 .. 7,003 .. ­1,646 .. 484 .. 167 .. 1,178 Bangladesh 2,064 10,432 3,960 14,456 ­116 ­798 1,613 4,691 ­398 ­132 660 2,825 Belarus .. 18,068 .. 17,859 .. 56 .. 169 .. 434 .. 1,342 Belgium 138,605b 318,775 135,098 b 308,430 2,316b 5,394 ­2,197b ­6,411 3,627b 9,328 23,789 11,996 Benin 364 784 454 1,129 ­25 ­37 97 93 ­18 ­288 69 657 Bolivia 977 3,160 1,086 2,872 ­249 ­373 159 584 ­199 498 511 1,795 Bosnia and Herzegovina .. 3,602 .. 8,004 .. 405 .. 1,841 .. ­2,156 .. 2,531 Botswana 2,005 5,285 1,987 3,683 ­106 ­812 69 678 ­19 1,469 3,331 6,309 Brazil 35,170 134,403 28,184 97,794 ­11,608 ­25,967 799 3,558 ­3,823 14,199 9,200 53,799 Bulgaria 6,950 16,057 8,027 20,600 ­758 310 125 1,229 ­1,710 ­3,004 670 8,697 Burkina Faso 349 .. 758 .. .. .. 332 .. ­77 .. 305 438 Burundi 89 92 318 353 ­15 ­18 174 23 ­69 ­256 112 101 Cambodia 314 4,017 507 4,559 ­21 ­254 120 440 ­93 ­356 .. 1,158 Cameroon 2,508 2,894 2,475 3,239 ­558 ­445 ­26 116 ­551 ­675 37 965 Canada 149,538 427,955 149,118 385,473 ­19,388 ­15,508 ­796 ­419 ­19,764 26,555 23,530 33,018 Central African Republic 220 .. 410 .. ­22 .. 123 .. ­89 .. 123 145 Chad 271 .. 488 .. ­21 .. 192 .. ­46 .. 132 231 Chile 10,221 47,746 9,166 38,154 ­1,737 ­10,624 198 1,735 ­485 703 6,784 16,933 China 57,374 836,888 46,706 712,090 1,055 10,635 274 25,385 11,997 160,818 34,476 831,410 Hong Kong, China .. 351,754 .. 329,590 .. 312 .. ­2,192 .. 20,284 24,656 124,278 Colombia 8,679 24,393 6,858 24,901 ­2,305 ­5,563 1,026 4,089 542 ­1,981 4,869 14,955 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 261 .. Congo, Rep. 1,488 4,964 1,282 2,917 ­460 ­1,122 3 ­22 ­251 903 10 738 Costa Rica 1,963 9,716 2,346 10,730 ­233 ­215 192 270 ­424 ­959 525 2,314 Côte d'Ivoire 3,503 8,289 3,445 7,174 ­1,091 ­662 ­181 ­465 ­1,214 ­12 21 1,322 Croatia .. 18,876 .. 21,702 .. ­1,235 .. 1,475 .. ­2,585 167 8,800 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 89,007 .. 86,461 .. ­5,929 .. 888 .. ­2,495 .. 29,554 Denmark 48,902 125,046 41,415 112,482 ­5,708 275 ­408 ­4,223 1,372 8,616 11,226 34,028 Dominican Republic 1,832 10,056 2,233 11,333 ­249 ­1,957 371 2,734 ­280 ­500 69 1,853 Ecuador 3,262 11,439 2,519 11,826 ­1,210 ­1,938 107 2,267 ­360 ­59 1,009 2,148 Egypt, Arab Rep. 9,895 30,716 14,091 34,326 ­1,022 ­35 7,545 5,748 2,327 2,103 3,620 21,857 El Salvador 973 4,573 1,624 7,652 ­132 ­571 631 2,865 ­152 ­786 595 1,890 Eritrea .. .. .. .. .. .. .. .. .. .. .. 28 Estonia 664 10,939 711 11,784 ­13 ­700 97 100 36 ­1,445 198 1,947 Ethiopia 597 1,929 1,271 4,895 ­69 ­5 449 1,402 ­294 ­1,568 55 1,121 Finland 31,180 82,457 33,456 71,091 ­3,735 ­278 ­952 ­1,572 ­6,962 9,517 10,415 11,332 France 285,389 555,204 283,238 577,463 ­3,896 16,314 ­8,199 ­27,344 ­9,944 ­33,289 68,291 74,360 Gabon 2,730 4,228 1,812 2,155 ­617 ­965 ­134 ­184 168 924 279 675 Gambia, The 168 181 192 261 ­11 ­32 59 69 23 ­44 55 98 Georgia .. 2,171 .. 3,312 .. 92 .. 359 .. ­690 .. 479 Germany 473,672 1,127,020 427,547 985,673 22,574 10,676 ­21,954 ­35,989 46,745 116,035 104,547 101,676 Ghana 983 3,869 1,506 6,610 ­111 ­187 411 2,117 ­223 ­812 309 1,897 Greece 13,018 51,790 19,564 66,626 ­1,709 ­7,030 4,718 3,987 ­3,537 ­17,879 4,721 2,287 Guatemala 1,568 4,939 1,812 9,547 ­196 ­337 227 3,558 ­213 ­1,387 362 3,777 Guinea 829 811 953 964 ­149 ­27 70 18 ­203 ­162 145 97 Guinea-Bissau 26 83 88 127 ­22 ­10 39 67 ­45 14 18 80 Haiti 318 593 515 1,756 ­18 ­37 193 1,254 ­22 54 10 134 Data for Taiwan, China 74,172 221,604 67,015 210,224 4,362 9,053 ­596 ­4,271 10,923 16,162 77,653 260,272 246 2007 World Development Indicators 4.15 ECONOMY Balance of payments current account Goods and Net income Net current Current account Total services transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 1,033 3,427 1,127 5,035 ­237 ­331 280 1,854 ­51 ­86 47 2,338 Hungary 12,035 74,168 11,017 75,596 ­1,427 ­6,915 787 237 379 ­8,106 1,185 18,590 India 22,911 82,735 29,527 93,918 ­3,257 ­4,451 2,837 22,488 ­7,036 6,853 5,637 137,825 Indonesia 29,295 99,104 27,511 87,584 ­5,190 ­11,849 418 1,258 ­2,988 929 8,657 34,579 Iran, Islamic Rep. 19,741 .. 22,292 .. 378 .. 2,500 .. 327 .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. 8,340 12,201 Ireland 26,786 161,366 24,576 137,081 ­4,955 ­30,307 2,384 691 ­361 ­5,331 5,362 869 Israel 17,312 57,874 20,228 57,525 ­1,981 ­2,622 5,061 6,029 163 3,756 6,598 28,059 Italy 219,971 462,709 218,573 463,295 ­14,712 ­17,078 ­3,164 ­10,061 ­16,479 ­27,724 88,595 65,954 Jamaica 2,217 3,994 2,390 5,975 ­430 ­676 291 1,578 ­312 ­1,079 168 2,170 Japan 323,692 677,782 297,306 607,869 22,492 103,444 ­4,800 ­7,573 44,078 165,783 87,828 846,896 Jordan 2,511 6,584 3,569 11,859 ­214 376 1,045 2,588 ­227 ­2,311 1,139 5,461 Kazakhstan .. 30,548 .. 25,503 .. ­5,357 .. ­412 .. ­724 .. 7,070 Kenya 2,228 5,126 2,705 6,540 ­418 ­108 368 1,028 ­527 ­495 236 1,799 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 73,297 334,370 76,373 313,989 ­88 ­1,320 1,150 ­2,502 ­2,014 16,559 14,916 210,552 Kuwait 8,268 51,574 7,169 24,513 7,738 8,834 ­4,951 ­3,261 3,886 32,634 2,929 10,165 Kyrgyz Republic .. 942 .. 1,397 .. ­81 .. 332 .. ­203 .. 612 Lao PDR 102 .. 212 .. ­1 .. 56 .. ­55 .. 8 309 Latvia 1,090 7,526 997 9,936 2 ­188 96 596 191 ­2,002 .. 2,360 Lebanon .. 13,037 .. 16,222 .. 247 .. 1,057 .. ­1,881 4,210 16,618 Lesotho 100 705 754 1,354 433 305 286 301 65 ­44 72 519 Liberia .. .. .. .. .. .. .. .. .. .. 1 25 Libya 11,468 29,383 8,960 13,523 174 ­281 ­481 ­634 2,201 14,945 7,225 41,880 Lithuania .. 14,879 .. 16,745 .. ­627 .. 662 .. ­1,831 107 3,816 Macedonia, FYR .. 2,511 .. 3,602 .. ­55 .. 1,065 .. ­81 .. 1,340 Madagascar 471 450 809 691 ­161 ­27 234 80 ­265 ­188 92 481 Malawi 443 .. 549 .. ­80 .. 99 .. ­86 .. 142 165 Malaysia 32,665 161,384 31,765 130,609 ­1,872 ­6,318 102 ­4,477 ­870 19,980 10,659 70,450 Mali 420 1,218 830 1,625 ­37 ­195 225 193 ­221 ­409 198 855 Mauritania 471 .. 520 .. ­46 .. 86 .. ­10 .. 59 420 Mauritius 1,722 3,762 1,916 4,154 ­23 ­8 97 61 ­119 ­340 761 1,372 Mexico 48,805 230,369 51,915 243,259 ­8,316 ­12,242 3,975 20,484 ­7,451 ­4,647 10,217 74,110 Moldova .. 1,528 .. 2,743 .. 403 .. 570 .. ­242 2 597 Mongolia 493 1,211 1,096 1,405 ­44 ­11 7 269 ­640 63 23 430 Morocco 6,239 18,788 7,783 22,739 ­988 ­314 2,336 5,375 ­196 1,110 2,338 16,551 Mozambique 229 2,087 996 2,891 ­97 ­360 448 403 ­415 ­761 232 1,103 Myanmar 319 3,181 603 2,458 ­192 ­745 39 134 ­436 112 410 889 Namibia 1,220 2,310 1,584 2,495 37 151 354 669 28 634 50 312 Nepal 422 1,283 834 2,711 14 48 109 1,533 ­289 153 354 1,565 Netherlands 159,304 427,949 147,652 374,710 ­620 6,194 ­2,943 ­10,497 8,089 48,936 34,401 20,448 New Zealand 11,683 30,467 11,699 32,921 ­1,576 ­7,626 138 459 ­1,453 ­9,622 4,129 8,893 Nicaragua 392 1,861 682 3,292 ­217 ­119 202 750 ­305 ­800 166 728 Niger 533 530 728 852 ­54 ­13 14 104 ­236 ­231 226 250 Nigeria 14,550 52,233 6,909 24,609 ­2,738 ­6,732 85 3,310 4,988 24,202 4,129 28,632 Norway 47,078 133,032 38,910 81,545 ­2,700 1,045 ­1,476 ­3,045 3,992 49,488 15,788 46,986 Oman 5,577 19,514 3,342 11,080 ­254 ­1,459 ­874 ­2,257 1,106 4,717 1,784 4,358 Pakistan 6,835 19,059 10,205 29,042 ­1,084 ­2,516 2,794 9,036 ­1,661 ­3,463 1,046 11,109 Panama 4,438 10,736 4,193 10,636 ­255 ­1,124 219 243 209 ­782 344 1,211 Papua New Guinea 1,381 3,580 1,509 2,692 ­103 ­538 156 291 ­76 640 427 750 Paraguay 2,514 3,927 2,169 4,098 2 ­74 43 223 390 ­22 675 1,297 Peru 4,120 19,426 4,087 15,176 ­1,733 ­5,011 281 1,791 ­1,419 1,030 1,891 14,171 Philippines 11,430 44,693 13,967 53,635 ­872 ­123 714 11,403 ­2,695 2,338 2,036 18,474 Poland 19,037 112,622 15,095 113,476 ­3,386 ­11,186 2,511 6,935 3,067 ­5,105 4,674 42,561 Portugal 21,554 53,272 27,146 69,078 ­96 ­3,932 5,507 2,731 ­181 ­17,007 20,579 10,364 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 247 4.15 Balance of payments current account Goods and Net income Net current Current account Total services transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 6,380 32,813 9,901 42,866 161 ­2,900 106 4,449 ­3,254 ­8,504 1,374 21,601 Russian Federation .. 268,136 .. 164,718 .. ­19,111 .. ­1,122 .. 83,184 .. 182,272 Rwanda 143 257 354 659 ­16 ­16 143 366 ­85 ­52 44 406 Saudi Arabia 47,381 180,551 43,880 79,274 7,968 272 ­15,616 ­14,418 ­4,147 87,131 13,437 28,888 Senegal 1,453 2,180 1,840 3,194 ­129 ­131 153 632 ­363 ­513 22 1,191 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 210 263 215 452 ­71 ­51 7 137 ­69 ­103 5 171 Singapore 67,489 283,565 64,953 248,627 1,006 ­541 ­421 ­1,184 3,122 33,212 27,748 115,794 Slovak Republic .. 25,241 .. 25,649 .. ­119 .. 245 .. ­282 .. 15,480 Slovenia 7,900 22,121 6,930 22,319 ­38 ­363 46 ­120 978 ­682 112 8,160 Somalia 68 .. 468 .. ­84 .. 328 .. ­157 .. 23 .. South Africa 27,160 66,437 21,017 68,639 ­4,271 ­4,929 ­321 ­2,011 1,552 ­9,142 2,583 20,624 Spain 83,595 288,042 100,870 345,642 ­3,533 ­21,452 2,799 ­4,084 ­18,009 ­83,136 57,238 17,227 Sri Lanka 2,293 7,887 2,965 10,066 ­167 ­297 541 1,828 ­298 ­647 447 2,736 Sudan 499 4,938 877 7,790 ­136 ­1,362 141 1,446 ­372 ­2,768 11 1,869 Swaziland 658 2,110 768 2,212 59 20 102 128 51 46 216 244 Sweden 70,560 178,072 70,490 150,358 ­4,473 545 ­1,936 ­4,616 ­6,339 23,643 20,324 24,868 Switzerland 97,033 197,159 96,389 171,456 7,878 37,132 ­2,398 ­8,977 6,124 53,859 61,284 57,575 Syrian Arab Republic 5,030 9,769 2,955 10,718 ­401 ­863 88 751 1,762 ­1,061 535 .. Tajikistan .. 1,254 .. 1,682 .. ­41 .. 450 .. ­19 .. 189 Tanzania 538 2,890 1,474 3,825 ­185 ­204 562 603 ­559 ­536 193 2,049 Thailand 29,229 129,847 35,870 133,599 ­853 ­2,921 213 3,004 ­7,281 ­3,670 14,258 52,076 Togo 663 751 847 1,093 ­32 ­33 132 169 ­84 ­206 358 195 Trinidad and Tobago 2,289 7,254 1,427 5,266 ­397 ­597 ­6 56 459 1,447 513 4,888 Tunisia 5,203 14,492 6,039 14,638 ­455 ­1,659 828 1,501 ­463 ­303 867 4,548 Turkey 21,042 102,806 25,524 121,766 ­2,508 ­5,663 4,365 1,468 ­2,625 ­23,155 7,626 52,494 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 178 1,343 686 2,584 ­48 ­157 293 1,139 ­263 ­259 44 1,344 Ukraine .. 44,378 .. 43,707 .. ­985 .. 2,845 .. 2,531 469 19,388 United Arab Emirates .. .. .. .. .. .. .. .. .. .. 4,891 21,010 United Kingdom 239,226 587,541 264,089 669,823 ­5,154 54,814 ­8,794 ­21,990 ­38,811 ­49,459 43,146 43,593 United States 535,260 1,275,245 616,120 1,991,975 28,560 11,294 ­26,660 ­86,073 ­78,960 ­791,509 173,094 188,259 Uruguay 2,158 5,087 1,659 4,626 ­321 ­585 8 121 186 ­2 1,446 3,078 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 18,806 56,821 9,451 29,371 ­774 ­1,984 ­302 ­107 8,279 25,359 12,733 29,803 Vietnam .. 36,618 .. 38,562 .. ­1,219 .. 3,380 .. 217 .. 9,051 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,490 6,752 2,170 5,285 ­372 ­1,657 1,790 1,406 739 1,215 441 6,141 Zambia 1,360 .. 1,897 .. ­437 .. 380 .. ­594 .. 201 560 Zimbabwe 2,012 .. 2,001 .. ­263 .. 112 .. ­140 .. 295 .. World 4,324,290 t 12,691,551 t 4,306,213 t 12,539,143 t Low income 79,141 217,936 97,905 245,934 Middle income 635,146 3,261,414 591,292 2,905,260 Lower middle income 302,331 1,793,100 300,012 1,586,704 Upper middle income 336,906 1,483,562 291,782 1,328,253 Low & middle income 714,951 3,596,835 689,821 3,263,799 East Asia & Pacific 167,506 1,327,062 166,319 1,178,084 Europe & Central Asia .. 926,132 .. 865,611 Latin America & Carib. 170,445 650,474 147,430 593,769 Middle East & N. Africa .. .. 105,814 222,757 South Asia 34,864 114,362 48,099 131,775 Sub-Saharan Africa 78,020 228,841 72,772 209,500 High income 3,594,026 9,151,976 3,592,287 9,318,222 Europe EMU 1,530,521 3,731,193 1,491,055 3,590,256 a. International reserves including gold valued at London gold price. b. Includes Luxembourg. 248 2007 World Development Indicators 4.15 ECONOMY Balance of payments current account About the data Definitions The balance of payments records an economy's by enterprises, surveys to estimate service transac- · Exports and imports of goods and services com- transactions with the rest of the world. Balance of tions, and foreign exchange records. Differences in prise all transactions between residents of an econ- payments accounts are divided into two groups: collection methods--such as in timing, definitions omy and the rest of the world involving a change in the current account, which records transactions in of residence and ownership, and the exchange rate ownership of general merchandise, goods sent for goods, services, income, and current transfers, and used to value transactions--contribute to net errors processing and repairs, nonmonetary gold, and ser- the capital and financial account, which records capi- and omissions. In addition, smuggling and other ille- vices. · Net income refers to receipts and payments tal transfers, acquisition or disposal of nonproduced, gal or quasi-legal transactions may be unrecorded or of employee compensation for nonresident workers, nonfinancial assets, and transactions in financial misrecorded. For further discussion of issues relat- and investment income (receipts and payments on assets and liabilities. The table presents data from ing to the recording of data on trade in goods and direct investment, portfolio investment, and other the current account with the addition of gross inter- services, see About the data for tables 4.4­4.7. investments and receipts on reserve assets). Income national reserves. The concepts and definitions underlying the data derived from the use of intangible assets is recorded The balance of payments is a double-entry account- in the table are based on the fi fth edition of the under business services. · Net current transfers ing system that shows all flows of goods and services International Monetary Fund's (IMF) Balance of Pay- are recorded in the balance of payments whenever into and out of an economy; all transfers that are the ments Manual (1993). That edition redefined as capi- an economy provides or receives goods, services, counterpart of real resources or financial claims pro- tal transfers some transactions previously included income, or financial items without a quid pro quo. All vided to or by the rest of the world without a quid pro in the current account, such as debt forgiveness, transfers not considered to be capital are current. quo, such as donations and grants; and all changes migrants' capital transfers, and foreign aid to acquire · Current account balance is the sum of net exports in residents' claims on and liabilities to nonresidents capital goods. Thus the current account balance now that arise from economic transactions. All transac- reflects more accurately net current transfer receipts of goods and services, net income, and net current tions are recorded twice--once as a credit and once in addition to transactions in goods, services (previ- transfers. · Total reserves comprise holdings of as a debit. In principle the net balance should be ously nonfactor services), and income (previously monetary gold, special drawing rights, reserves of zero, but in practice the accounts often do not bal- factor income). Many countries maintain their data IMF members held by the IMF, and holdings of foreign ance. In these cases a balancing item, net errors and collection systems according to the fourth edition. exchange under the control of monetary authorities. omissions, is included. Where necessary, the IMF converts such reported The gold component of these reserves is valued at Discrepancies may arise in the balance of pay- data to conform to the fifth edition (see Primary data year-end (31 December) London prices ($385.00 an ments because there is no single source for balance documentation). Values are in U.S. dollars converted ounce in 1990, and $438.00 an ounce in 2004). of payments data and therefore no way to ensure at market exchange rates. that the data are fully consistent. Sources include The data in this table come from the IMF's Balance customs data, monetary accounts of the banking of Payments and International Financial Statistics system, external debt records, information provided databases. Top 15 economies with the largest current account surplus--and top 15 economies with the largest current account deficit in 2005 4.15a Surplus Share of GDP Deficit Share of GDP Economy ($ billions) (%) Economy ($ billions) (%) Japan 165.8 3.7 United States ­791.5 ­6.4 China 160.8 7.2 Spain ­83.1 ­7.4 Data sources Germany 116.0 4.2 United Kingdom ­49.5 ­2.2 Saudi Arabia 87.1 28.1 Australia ­42.3 ­5.8 Data on the balance of payments are published in Russian Federation 83.2 10.9 France ­33.3 ­1.6 the IMF's Balance of Payments Statistics Yearbook Switzerland 53.9 14.7 Italy ­27.7 ­1.6 and International Financial Statistics. The World Bank exchanges data with the IMF through elec- Norway 49.5 16.7 Turkey ­23.2 ­6.4 tronic files that in most cases are more timely and Netherlands 48.9 7.8 Grece ­17.9 ­7.9 cover a longer period than the published sources. Singapore 33.2 28.4 Portugal ­17.0 ­9.3 More information about the design and compila- Kuwait 32.6 40.4 New Zealand ­9.6 ­8.8 tion of the balance of payments can be found in Canada 26.6 2.4 South Africa ­9.1 ­3.8 the IMF's Balance of Payments Manual, fifth edition Venezuela, RB 25.4 18.1 Romania ­8.5 ­8.6 (1993), Balance of Payments Textbook (1996), and Nigeria 24.2 24.5 Hungary ­8.1 ­7.4 Balance of Payments Compilation Guide (1995). Sweden 23.6 6.6 Ireland ­5.3 ­2.6 The IMF's International Financial Statistics and Hong Kong, China 20.3 11.4 Poland ­5.1 ­1.7 Balance of Payments databases are available on CD-ROM. Source: International Monetary Fund, balance of payments data files. 2006 World Development Indicators 249 4.16 External debt Total external Long-term Public and publicly Private Use of IMF debt debt guaranteed debt nonguaranteed credit external debt $ millions IBRD loans and $ millions $ millions Total IDA credits $ millions $ millions 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 1,839 .. 1,459 .. 1,375 .. .. .. 84 .. 92 Algeria 28,149 16,879 26,688 16,363 26,688 15,476 1,208 .. .. 887 670 0 Angola 8,592 11,755 7,603 9,428 7,603 9,428 .. .. 0 .. 0 .. Argentina 62,233 114,335 48,676 85,477 46,876 61,952 2,609 .. 1,800 23,525 3,083 9,513 Armenia .. 1,861 .. 1,386 .. 923 .. 752 .. 464 .. 176 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan .. 1,881 .. 1,531 .. 1,344 .. 501 .. 187 .. 164 Bangladesh 12,439 18,935 11,658 17,938 11,658 17,938 4,159 8,688 0 .. 626 308 Belarus .. 4,734 .. 1,231 .. 783 .. .. .. 448 .. 0 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 1,292 1,855 1,218 1,762 1,218 1,762 326 .. 0 .. 18 53 Bolivia 4,275 6,390 3,864 5,965 3,687 4,564 587 1,673 177 1,401 257 244 Bosnia and Herzegovina .. 5,564 .. 4,400 .. 2,560 .. 1,403 .. 1,840 .. 62 Botswana 553 473 547 438 547 438 169 9 0 .. 0 .. Brazil 119,964 187,994 94,427 164,001 87,756 94,497 8,427 .. 6,671 69,505 1,821 0 Bulgaria .. 16,786 .. 11,922 .. 4,587 .. .. .. 7,335 .. 660 Burkina Faso 832 2,045 748 1,920 748 1,920 282 .. 0 .. 0 104 Burundi 907 1,322 851 1,228 851 1,228 .. .. 0 .. 43 58 Cambodia 1,845 3,515 1,683 3,155 1,683 3,155 .. .. 0 .. 27 81 Cameroon 6,431 7,151 5,373 6,114 5,144 5,521 871 1,115 230 592 121 272 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 699 1,016 624 871 624 871 265 .. 0 .. 37 36 Chad 529 1,633 469 1,537 469 1,537 .. 899 0 .. 30 79 Chile 19,226 45,154 14,687 38,281 10,425 9,096 1,874 293 4,263 29,184 1,156 .. China 55,301 281,612 45,515 133,345 45,515 82,853 5,881 20,880 .. 50,492 469 .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 17,222 37,656 15,784 31,480 14,671 22,491 3,874 3,900 1,113 8,989 .. .. Congo, Dem. Rep. 10,259 10,600 8,994 9,412 8,994 9,412 1,161 .. 0 .. 521 791 Congo, Rep. 4,934 5,936 4,187 5,161 4,187 5,161 239 280 0 .. 11 26 Costa Rica 3,756 6,223 3,367 4,118 3,063 3,470 412 60 304 648 11 .. Côte d'Ivoire 17,251 10,735 13,223 9,854 10,665 9,007 1,920 2,185 2,558 847 431 198 Croatia .. 30,169 .. 25,848 .. 9,782 .. .. .. 16,066 .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 4,372 7,398 3,518 6,094 3,419 6,093 258 416 99 2 72 400 Ecuador 12,107 17,129 10,029 15,332 9,865 10,662 848 815 164 4,670 265 78 Egypt, Arab Rep. 33,017 34,114 28,439 28,132 27,439 24,892 2,401 1,912 1,000 3,240 125 .. El Salvador 2,149 7,088 1,938 5,513 1,913 4,760 164 448 26 754 0 .. Eritrea .. 736 .. 723 .. 723 .. .. .. .. .. .. Estonia .. 11,255 .. 7,256 .. 435 .. .. .. 6,821 .. .. Ethiopia 8,630 6,259 8,479 5,897 8,479 5,897 851 .. 0 .. 6 160 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 3,983 3,902 3,150 3,582 3,150 3,582 69 .. 0 .. 140 68 Gambia, The 369 672 308 626 308 626 102 .. 0 .. 45 21 Georgia .. 1,911 .. 1,626 .. 1,494 .. .. .. 132 .. 232 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 3,734 6,739 2,670 5,734 2,637 5,734 1,423 4,234 33 0 745 417 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 2,849 5,349 2,368 3,793 2,241 3,688 293 .. 127 105 67 .. Guinea 2,476 3,247 2,253 2,931 2,253 2,931 419 .. 0 .. 51 87 Guinea-Bissau 692 693 630 671 630 671 145 .. 0 .. 5 12 Haiti 917 1,323 778 1,276 778 1,276 324 .. 0 .. 38 21 250 2007 World Development Indicators 4.16 ECONOMY External debt Total external Long-term Public and publicly Private Use of IMF debt debt guaranteed debt nonguaranteed credit external debt $ millions IBRD loans and $ millions $ millions Total IDA credits $ millions $ millions 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 3,718 5,242 3,487 4,660 3,420 4,152 635 1,353 66 509 32 168 Hungary 21,201 66,119 17,931 53,725 17,931 21,216 1,512 .. .. 32,509 330 0 India 83,628 123,123 72,462 114,335 70,974 80,281 20,996 28,919 1,488 34,054 2,623 .. Indonesia 69,872 138,300 58,242 105,993 47,982 72,335 10,385 9,132 10,261 33,658 494 7,807 Iran, Islamic Rep. 9,020 21,260 1,797 10,574 1,797 10,493 86 .. .. 81 0 .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 4,752 6,511 4,049 5,897 4,015 5,508 672 .. 34 390 357 0 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 8,333 7,696 7,202 6,878 7,202 6,878 593 970 0 .. 94 236 Kazakhstan .. 43,354 .. 35,334 .. 2,184 .. .. .. 33,150 .. .. Kenya 7,055 6,169 5,639 5,520 4,759 5,520 2,056 2,663 880 0 482 159 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 2,032 .. 1,830 .. 1,670 .. .. .. 161 .. 178 Lao PDR 1,768 2,690 1,757 2,656 1,757 1,971 131 .. .. 685 8 29 Latvia .. 14,283 .. 6,791 .. 1,318 .. .. .. 5,473 .. 0 Lebanon 1,779 22,373 358 18,923 358 17,912 34 .. .. 1,011 0 .. Lesotho 396 690 378 647 378 647 .. 271 0 .. 15 35 Liberia 1,849 2,581 1,116 1,115 1,116 1,115 248 251 0 .. 322 320 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 11,201 .. 5,876 .. 1,511 .. .. .. 4,365 .. 0 Macedonia, FYR .. 2,243 .. 2,084 .. 1,613 .. 608 .. 471 .. 62 Madagascar 3,689 3,465 3,320 3,178 3,320 3,178 797 .. 0 .. 144 212 Malawi 1,558 3,155 1,385 3,040 1,382 3,040 854 1,940 2 .. 115 75 Malaysia 15,328 50,981 13,422 38,805 11,592 22,449 1,102 .. 1,830 16,356 .. .. Mali 2,468 2,969 2,337 2,843 2,337 2,843 498 .. 0 .. 69 109 Mauritania 2,113 2,281 1,806 2,043 1,806 2,043 264 .. 0 .. 70 69 Mauritius 984 2,160 910 797 762 731 195 79 147 66 22 .. Mexico 104,442 167,228 81,809 160,649 75,974 108,786 11,030 .. 5,835 51,863 6,551 0 Moldova .. 2,053 .. 1,240 .. 700 .. 371 .. 540 .. 95 Mongolia .. 1,327 .. 1,267 .. 1,267 .. .. .. .. .. 35 Morocco 25,004 16,846 23,847 16,164 23,647 13,113 3,138 2,278 200 3,051 750 0 Mozambique 4,650 5,121 4,231 4,419 4,211 3,727 .. 1,575 19 692 74 157 Myanmar 4,695 6,645 4,466 5,196 4,466 5,196 .. .. 0 .. 0 .. Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 1,640 3,285 1,572 3,217 1,572 3,217 667 .. 0 .. 44 20 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 10,745 5,144 8,313 4,405 8,313 4,113 299 .. .. 292 0 201 Niger 1,726 1,972 1,487 1,803 1,226 1,771 461 .. 261 33 85 128 Nigeria 33,439 22,178 31,935 20,342 31,545 20,342 3,321 1,859 391 .. 0 .. Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. 3,472 .. 1,805 .. 842 .. .. .. 963 0 .. Pakistan 20,663 33,675 16,643 30,953 16,506 29,490 3,922 9,104 138 1,463 835 1,492 Panama 6,493 9,765 3,842 9,256 3,842 7,514 462 .. .. 1,742 272 24 Papua New Guinea 2,594 1,849 2,461 1,654 1,523 1,266 349 327 938 387 61 0 Paraguay 2,105 3,120 1,732 2,607 1,713 2,264 320 244 19 343 0 .. Peru 20,044 28,653 13,959 25,387 13,629 22,222 1,188 .. 330 3,165 755 57 Philippines 30,580 61,527 25,241 54,743 24,040 35,233 4,044 3,082 1,201 19,510 912 389 Poland 49,364 98,821 39,261 81,118 39,261 35,094 55 .. .. 46,024 509 .. Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 251 4.16 External debt Total external Long-term Public and publicly Private Use of IMF debt debt guaranteed debt nonguaranteed credit external debt $ millions IBRD loans and $ millions $ millions Total IDA credits $ millions $ millions 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 1,140 38,694 230 31,199 223 13,341 0 .. 7 17,858 0 261 Russian Federation .. 229,042 .. 204,911 .. 75,359 .. .. .. 129,552 .. 0 Rwanda 712 1,518 664 1,420 664 1,420 340 .. 0 .. 0 77 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 3,744 3,793 3,008 3,609 2,948 3,467 835 .. 60 141 314 148 Serbia and Montenegro .. 16,295 .. 13,186 .. 7,972 .. 2,984 .. 5,214 .. 866 Sierra Leone 1,197 1,682 940 1,420 940 1,420 92 .. 0 .. 108 192 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. 23,654 .. 8,493 .. 3,340 .. .. .. 5,153 .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia 2,370 2,750 1,926 1,882 1,926 1,882 419 .. 0 .. 159 160 South Africa .. 30,632 .. 20,922 .. 11,662 .. .. .. 9,260 .. .. Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 5,863 11,444 5,049 10,055 4,947 9,812 946 2,095 102 243 410 381 Sudan 14,762 18,455 9,651 11,659 9,155 11,163 1,048 .. 496 496 956 518 Swaziland 298 532 294 451 294 451 44 27 0 .. 0 .. Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 17,259 6,508 15,108 5,640 15,108 5,640 523 22 0 .. .. .. Tajikistan .. 1,022 .. 811 .. 785 .. .. .. 26 .. 127 Tanzania 6,454 7,763 5,794 6,192 5,782 6,183 1,493 3,861 12 9 140 342 Thailand 28,094 52,266 19,771 36,252 12,460 13,483 2,530 459 7,311 22,769 1 .. Togo 1,281 1,708 1,081 1,469 1,081 1,469 398 .. 0 .. 87 14 Trinidad and Tobago 2,511 2,652 2,055 1,310 1,782 1,197 41 .. 273 113 329 .. Tunisia 7,688 17,789 6,878 14,723 6,660 12,982 1,406 1,594 218 1,741 176 0 Turkey 49,424 171,059 39,924 118,195 38,870 62,580 6,429 5,901 1,054 55,614 0 14,646 Turkmenistan .. 1,092 .. 945 .. 912 .. .. .. 33 .. .. Uganda 2,584 4,463 2,162 4,250 2,162 4,250 969 .. 0 .. 282 131 Ukraine .. 33,297 .. 20,047 .. 10,458 .. .. .. 9,588 .. 1,188 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 4,415 14,551 3,114 8,286 3,045 7,866 359 .. 69 421 101 2,304 Uzbekistan .. 4,226 .. 4,189 .. 3,639 .. 310 .. 551 .. 0 Venezuela, RB 33,171 44,201 28,159 33,984 24,509 29,317 974 .. 3,650 4,667 3,012 .. Vietnam 23,270 19,287 21,378 16,513 21,378 16,513 59 .. 0 .. 112 203 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6,352 5,363 5,160 4,717 5,160 4,717 602 .. 0 .. 0 292 Zambia 6,905 5,668 4,543 4,887 4,541 4,085 813 2,488 2 802 949 591 Zimbabwe 3,279 4,257 2,681 3,253 2,496 3,222 449 915 185 32 7 111 World .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s Low income 309,885 379,239 266,712 338,595 259,250 298,209 55,118 105,286 7,462 40,385 10,671 8,322 Middle income 1,020,176 2,363,139 827,982 1,808,585 775,449 1,063,425 82,201 116,307 52,533 745,160 23,981 40,857 Lower middle income 575,126 1,146,475 474,426 834,842 441,124 548,961 54,704 80,600 33,302 285,881 8,452 14,021 Upper middle income 445,050 1,216,664 353,557 973,742 334,325 514,464 27,497 35,708 19,232 459,278 15,529 26,836 Low & middle income 1,330,061 2,742,378 1,094,694 2,147,179 1,034,699 1,361,634 137,319 221,593 59,996 785,545 34,652 49,179 East Asia & Pacific 234,079 621,223 194,620 400,185 172,984 256,316 25,306 39,829 21,635 143,869 2,085 8,545 Europe & Central Asia 210,841 834,484 172,395 646,633 167,474 266,975 10,429 30,447 4,921 379,658 1,305 18,810 Latin America & Carib. 444,637 727,628 352,724 621,868 327,705 419,555 35,877 40,379 25,018 202,313 18,298 13,122 Middle East & N. Africa 139,541 152,724 118,031 124,308 116,613 113,334 10,074 10,102 1,418 10,974 1,815 547 South Asia 124,396 191,479 107,527 177,441 105,800 141,681 30,717 50,329 1,727 35,760 4,537 2,208 Sub-Saharan Africa 176,568 214,841 149,398 176,743 144,122 163,773 24,916 50,507 5,276 12,970 6,612 5,947 High income Europe EMU 252 2007 World Development Indicators 4.16 ECONOMY External debt About the data Definitions Data on the external debt of developing countries into U.S. dollars to produce summary tables. Stock · Total external debt is debt owed to nonresidents are gathered by the World Bank through its Debtor figures (amount of debt outstanding) are converted repayable in foreign currency, goods, or services. It Reporting System. World Bank staff calculate the using end-of-period exchange rates, as published in is the sum of public, publicly guaranteed, and private indebtedness of these countries using loan-by-loan the IMF's International Financial Statistics (line ae). nonguaranteed long-term debt, use of IMF credit, and reports submitted by them on long-term public and Flow fi gures are converted at annual average short-term debt. Short-term debt includes all debt publicly guaranteed borrowing, along with informa- exchange rates (line rf). Projected debt service is having an original maturity of one year or less and tion on short-term debt collected by the countries converted using end-of-period exchange rates. Debt interest in arrears on long-term debt. · Long-term or collected from creditors through the reporting repayable in multiple currencies, goods, or services debt is debt that has an original or extended maturity systems of the Bank for International Settlements and debt with a provision for maintenance of the of more than one year. It has three components: and the Organisation for Economic Co-operation and value of the currency of repayment are shown at public, publicly guaranteed, and private nonguaran- Development. These data are supplemented by infor- book value. teed debt. · Public and publicly guaranteed debt mation on loans and credits from major multilateral Because flow data are converted at annual aver- comprises the long-term external obligations of pub- banks, loan statements from official lending agen- age exchange rates and stock data at end-of-period lic debtors, including the national government and cies in major creditor countries, and estimates by exchange rates, year-to-year changes in debt out- political subdivisions (or an agency of either) and World Bank and International Monetary Fund (IMF) standing and disbursed are sometimes not equal autonomous public bodies, and the external obli- staff. In addition, the table includes data on private to net flows (disbursements less principal repay- gations of private debtors that are guaranteed for nonguaranteed debt for 77 countries either reported ments); similarly, changes in debt outstanding, repayment by a public entity. · IBRD loans and IDA to the World Bank or estimated by its staff. including undisbursed debt, differ from commitments The coverage, quality, and timeliness of debt data less repayments. Discrepancies are particularly credits are extended by the World Bank. The Inter- vary across countries. Coverage varies for both debt significant when exchange rates have moved sharply national Bank for Reconstruction and Development instruments and borrowers. With the widening spec- during the year. Cancellations and reschedulings of (IBRD) lends at market rates. The International Devel- trum of debt instruments and investors and the expan- other liabilities into long-term public debt also con- opment Association (IDA) provides credits at con- sion of private nonguaranteed borrowing, comprehen- tribute to the differences. cessional rates. · Private nonguaranteed external sive coverage of long-term external debt becomes more Variations in reporting rescheduled debt also affect debt consists of the long-term external obligations complex. Reporting countries differ in their capacity to cross-country comparability. For example, reschedul- of private debtors that are not guaranteed for repay- monitor debt, especially private nonguaranteed debt. ing under the auspices of the Paris Club of official ment by a public entity. · Use of IMF credit denotes Even data on public and publicly guaranteed debt are creditors may be subject to lags between the com- repurchase obligations to the IMF for all uses of IMF affected by coverage and accuracy in reporting-- pletion of the general rescheduling agreement and resources (excluding those resulting from drawings again because of monitoring capacity and sometimes the completion of the specific bilateral agreements on the reserve tranche). These obligations, shown for because of an unwillingness to provide information. A that define the terms of the rescheduled debt. Other the end of the year specified, comprise purchases key part often underreported is military debt. areas of inconsistency include country treatment of outstanding under the credit tranches (including Because debt data are normally reported in the arrears and of nonresident national deposits denomi- enlarged access resources) and all special facilities currency of repayment, they have to be converted nated in foreign currency. (the buffer stock, compensatory financing, extended fund, and oil facilities), trust fund loans, and opera- External debt started to decline in the Sub-Saharan African economies in 2005 4.16a tions under the structural adjustment and enhanced structural adjustment facilities. $ millions External debt Gross domestic product 700 600 500 400 Data sources 300 The main sources of external debt information 200 are reports to the World Bank through its Debtor 100 Reporting System from member countries that have received IBRD loans or IDA credits. Additional 0 information is from the files of the World Bank and 1980 1990 2000 2001 2002 2003 2004 2005 the IMF. Summary tables of the external debt of Because GDP has risen, the ratio of external debt to gross domestic product has declined for the last developing countries are published annually in the five years. World Bank's Global Development Finance and on Source: World Bank data files. its Global Development Finance CD-ROM. 2007 World Development Indicators 253 4.17 Debt ratios Present value Total debt Multilateral Short-term of debt service debt service debt % of exports of goods, sevices, and % of exports of goods, % of public and publicly % of exports of goods, % of GNI incomea % of GNI sevices, and incomea guaranteed debt % of total debt sevices, and income 2005b 2005b 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 19 51 .. 1.0 .. 2.5 .. 50.4 .. 15.7 .. 9.0 Algeria 21 45 14.7 6.1 63.4 .. 5.0 26.6 2.8 3.1 5.7 .. Angola 59 72 4.0 7.8 8.1 9.2 2.2 0.6 11.5 19.8 24.7 9.6 Argentina 73 245 4.6 6.0 37.0 20.8 16.2 68.3 16.8 16.9 62.9 38.1 Armenia 36 100 .. 2.8 .. 7.9 .. 87.0 .. 16.0 .. 17.0 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 18 27 .. 2.2 .. 2.6 .. 37.0 .. 9.9 .. 2.1 Bangladesh 22 102 2.4 1.3 25.8 5.4 22.8 56.2 1.3 3.6 5.4 4.7 Belarus 20 31 .. 2.3 .. 3.7 .. 21.8 .. 74.0 .. 19.1 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 23c 112c 2.1 1.6 8.2 .. 95.7 58.8 4.3 2.1 11.9 .. Bolivia 38 c 112c 8.3 5.9 38.6 14.8 67.6 92.8 3.6 2.8 15.5 5.1 Bosnia and Herzegovina 52 102 .. 2.6 .. 4.9 .. 73.6 .. 19.8 .. 19.9 Botswana 5 8 2.9 0.5 4.4 0.9 61.3 67.9 1.0 7.4 0.2 0.6 Brazil 34 183 1.8 8.1 22.2 44.8 43.5 15.9 19.8 12.8 64.4 17.1 Bulgaria 68 105 .. 21.5 .. 31.5 .. 13.8 .. 25.1 .. 22.7 Burkina Faso 22c 196c 1.1 0.9 6.8 .. 73.1 77.0 10.1 1.0 16.6 .. Burundi 110 c 1,072c 3.8 5.0 43.4 41.4 51.1 87.1 1.5 2.7 13.7 37.4 Cambodia 58 84 2.7 0.5 .. 0.7 .. 74.6 7.4 8.0 .. 6.6 Cameroon 14 c 61c 4.8 4.9 20.4 .. 43.9 21.8 14.6 10.7 36.9 .. Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 67c 715c 2.0 0.4 13.2 .. 50.3 99.8 5.4 10.7 17.1 .. Chad 31c 51c 0.7 1.4 4.4 .. 73.1 74.5 5.6 1.0 10.9 .. Chile 52 114 9.3 7.3 25.9 15.4 35.7 20.1 17.6 15.2 31.6 13.8 China 14 40 2.0 1.2 11.7 3.1 7.6 21.4 16.9 52.7 15.4 16.8 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 43 171 10.2 8.7 40.9 35.3 32.2 39.5 8.4 16.4 15.1 21.5 Congo, Dem. Rep. 123c 383c 4.1 3.1 .. .. 49.7 30.6 7.3 3.8 .. .. Congo, Rep. 124 c 106c 22.9 3.0 35.4 2.4 12.7 73.4 14.9 12.6 49.0 15.0 Costa Rica 36 69 7.0 3.1 23.9 5.9 36.1 67.7 10.1 33.8 18.0 20.3 Côte d'Ivoire 69 c 131c 13.7 3.0 35.4 5.5 77.5 12.3 20.9 6.4 101.0 8.1 Croatia 89 159 .. 13.2 .. 24.0 .. 9.8 .. 14.3 .. 21.0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 37 61 3.4 3.2 10.4 6.9 50.3 23.1 17.9 12.2 35.0 7.0 Ecuador 60 166 11.9 12.0 32.5 30.6 34.8 36.5 15.0 10.0 54.4 12.7 Egypt, Arab Rep. 36 99 7.3 2.9 20.4 6.8 18.7 23.8 13.5 17.5 29.6 16.1 El Salvador 48 105 4.4 4.0 15.3 8.6 60.2 49.7 9.8 22.2 15.5 20.8 Eritrea 57c 213c .. 2.1 .. .. .. 61.6 .. 1.7 .. .. Estonia 102 115 .. 12.8 .. 13.7 .. 14.6 .. 35.5 .. 34.4 Ethiopia 21c 111c 2.0 0.8 39.0 4.1 14.5 53.6 1.7 3.2 24.0 9.4 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 63 92 3.3 1.5 6.4 .. 32.6 77.5 17.4 6.5 25.2 .. Gambia, The 99 c 162c 13.0 6.5 22.2 12.1 25.3 62.0 4.3 3.7 9.3 10.2 Georgia 28 72 .. 2.9 .. 7.4 .. 20.1 .. 2.7 .. 2.1 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 26c 64 c 6.3 2.7 38.1 7.1 31.2 36.5 8.6 8.7 33.5 14.6 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 20 77 3.1 1.5 13.6 5.8 33.4 57.6 14.5 29.1 24.4 19.0 Guinea 35c 146c 6.3 5.0 20.1 .. 22.1 50.2 6.9 7.1 20.4 .. Guinea-Bissau 290 c 660 c 3.6 11.3 31.1 .. 70.4 12.2 8.2 1.4 208.3 .. Haiti 24 c 63c 1.3 1.4 11.1 3.7 70.5 81.3 11.0 1.9 31.0 1.6 254 2007 World Development Indicators 4.17 ECONOMY Debt ratios Present value Total debt Multilateral Short-term of debt service debt service debt % of exports of goods, sevices, and % of exports of goods, % of public and publicly % of exports of goods, % of GNI incomea % of GNI sevices, and incomea guaranteed debt % of total debt sevices, and income 2005b 2005b 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 37c 60 c 13.9 4.8 35.3 7.2 90.8 86.0 5.4 7.9 18.1 7.8 Hungary 69 96 13.4 22.9 34.3 31.0 8.0 6.8 13.9 18.8 23.9 16.3 India 16 73 2.6 3.0 31.9 .. 22.5 9.1 10.2 7.1 33.3 .. Indonesia 55 159 9.1 6.5 33.3 .. 22.5 39.2 15.9 17.7 37.3 23.7 Iran, Islamic Rep. 13 39 0.6 1.4 3.2 .. 30.5 9.1 80.1 50.3 35.8 .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 93 141 15.9 10.8 26.9 16.3 38.6 20.1 7.3 9.4 14.1 10.3 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 65 89 16.5 4.7 20.4 6.5 26.8 49.0 12.4 7.6 33.7 6.1 Kazakhstan 106 185 .. 25.5 .. 42.1 .. 80.4 .. 18.5 .. 25.6 Kenya 28 103 9.6 1.3 35.4 4.4 44.7 60.7 13.2 8.0 41.8 9.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 54c 106c .. 5.4 .. 10.0 .. 97.7 .. 1.2 .. 1.9 Lao PDR 63c 200 c 1.1 6.6 8.7 .. 54.3 72.2 0.1 0.2 2.1 .. Latvia 104 211 .. 19.8 .. 37.4 .. 59.1 .. 52.5 .. 90.3 Lebanon 114 127 2.9 16.5 .. 17.7 27.8 3.1 79.9 15.4 .. 17.3 Lesotho 32 50 2.3 3.1 4.2 5.0 44.5 46.5 0.7 1.2 0.5 0.7 Liberia 1,087c 3,514 c .. 0.2 .. .. 99.8 100.0 22.2 44.4 .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 52 90 .. 10.3 .. 16.5 .. 19.4 .. 47.5 .. 34.1 Macedonia, FYR 40 89 .. 4.2 .. 8.6 .. 41.7 .. 4.3 .. 3.5 Madagascar 37c 323c 7.5 1.6 45.5 17.0 23.7 66.4 6.1 2.2 46.0 16.4 Malawi 58 c 162c 7.2 4.7 29.3 .. 38.3 63.4 3.8 1.3 12.9 .. Malaysia 46 35 10.3 7.6 12.6 5.6 9.9 3.4 12.4 23.9 5.5 7.3 Mali 30 c 100 c 2.8 1.7 12.4 .. 54.3 77.8 2.5 0.6 11.3 .. Mauritania 117c 289 c 13.5 3.5 29.8 .. 73.7 63.6 11.2 7.4 48.7 .. Mauritius 37 60 6.6 4.5 8.8 7.2 51.6 18.4 5.3 63.1 2.9 34.9 Mexico 26 79 4.5 5.8 20.7 17.2 26.0 12.8 15.4 3.9 29.5 2.6 Moldova 70 97 .. 7.7 .. 10.2 .. 70.2 .. 35.0 .. 29.1 Mongolia 63 73 .. 2.5 .. .. .. 38.2 .. 1.9 .. .. Morocco 34 77 7.2 5.3 21.6 11.3 39.8 38.8 1.6 4.1 4.9 2.8 Mozambique 28 c 85c 3.4 1.5 26.2 4.3 30.6 63.0 7.4 10.6 115.2 24.9 Myanmar .. 148 .. .. 18.4 .. 43.6 2.7 4.9 21.8 69.8 .. Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 34 c 104 c 1.9 1.6 15.7 4.6 36.9 75.5 1.5 1.4 5.4 1.8 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 46c 94 c 1.6 3.6 3.9 6.9 21.4 45.0 22.6 10.5 602.0 21.7 Niger 25c 142c 4.1 1.1 17.4 .. 70.9 98.2 8.9 2.1 27.1 .. Nigeria 34 53 13.0 10.2 22.6 15.8 15.5 5.5 4.5 8.3 10.2 3.3 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman 14 22 .. .. .. 7.5 .. 45.9 .. 48.0 .. 8.4 Pakistan 30 134 4.6 2.3 21.3 10.2 40.3 58.5 15.4 3.7 35.6 5.1 Panama 90 118 6.8 14.5 6.2 17.5 90.6 10.6 36.6 5.0 42.5 4.1 Papua New Guinea 55 60 17.9 .. 37.2 10.8 23.0 46.3 2.8 10.6 4.8 5.4 Paraguay 54 84 6.0 6.7 12.4 11.4 35.9 49.6 17.7 16.4 14.2 12.0 Peru 49 198 1.9 7.5 10.8 26.0 28.8 21.6 26.6 11.2 121.1 14.9 Philippines 67 120 8.2 9.2 27.0 16.7 28.7 15.9 14.5 10.4 33.3 10.8 Poland 39 98 1.7 11.6 4.9 28.8 9.2 2.6 19.4 17.9 48.9 15.0 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 255 4.17 Debt ratios Present value Total debt Multilateral Short-term of debt service debt service debt % of exports of goods, sevices, and % of exports of goods, % of public and publicly % of exports of goods, % of GNI incomea % of GNI sevices, and incomea guaranteed debt % of total debt sevices, and income 2005b 2005b 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 51 137 0.1 7.1 0.3 18.3 .. 24.6 79.8 18.7 13.9 19.0 Russian Federation 40 104 .. 5.6 .. 14.6 .. 3.6 .. 10.5 .. 8.4 Rwanda 18 c 154 c 0.8 1.1 14.2 8.1 60.6 77.5 6.6 1.4 31.9 7.4 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 34 c 89 c 5.9 2.4 19.9 .. 40.0 60.2 11.3 0.9 25.9 .. Serbia and Montenegro 69 202 .. 4.9 .. .. .. 60.2 .. 13.8 .. .. Sierra Leone 40 c 178 c 3.7 2.2 10.1 9.2 26.0 37.5 12.4 4.1 70.5 25.6 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 61 73 .. 13.2 .. .. .. 11.6 .. 64.1 .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. 1,137 1.3 .. .. .. 100.0 .. 12.0 25.8 .. .. South Africa 14 47 .. 2.0 .. 6.9 .. 3.7 .. 31.7 .. 13.7 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 48 109 4.9 1.9 13.8 4.5 13.8 45.6 6.9 8.8 14.5 10.2 Sudan 88 c 358 c 0.4 1.5 8.7 6.5 100.0 15.0 28.2 34.0 724.8 104.7 Swaziland 24 26 4.9 1.6 5.7 1.9 73.0 57.7 1.5 15.3 0.6 3.6 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 27 69 10.0 0.8 21.8 1.9 3.5 31.2 12.5 13.3 39.4 7.9 Tajikistan 41 53 .. 3.5 .. 4.5 .. 37.1 .. 8.2 .. 4.9 Tanzania 22c, d 95c, d 4.4 d 1.1d 32.9d 4.3d 52.7 97.0 8.1 15.8 95.5d 41.3d Thailand 32 44 6.3 11.3 16.9 14.6 22.1 18.5 29.6 30.6 26.6 12.0 Togo 74 c 162c 5.4 0.8 11.9 .. 40.8 72.6 8.8 13.2 15.6 .. Trinidad and Tobago 24 36 9.6 2.8 19.3 .. 4.7 35.3 5.1 50.6 5.5 .. Tunisia 69 125 12.1 7.7 24.5 13.0 26.0 45.8 8.3 17.2 10.8 19.2 Turkey 59 195 4.9 11.6 29.4 39.1 23.3 10.6 19.2 22.3 37.7 35.6 Turkmenistan 16 23 .. 4.1 .. .. .. 3.2 .. 13.5 .. .. Uganda 29c 137c 3.4 2.0 81.4 9.2 37.4 49.7 5.4 1.8 78.8 4.4 Ukraine 53 89 .. 7.2 .. 13.0 .. 21.3 .. 36.2 .. 26.6 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 116 332 11.0 13.7 40.8 38.9 16.2 32.2 27.2 27.2 49.7 69.1 Uzbekistan 34 88 .. 5.7 .. .. .. 15.8 .. 0.9 .. .. Venezuela, RB 48 118 10.8 4.0 23.3 9.1 1.6 16.0 6.0 23.1 9.3 16.7 Vietnam 38 56 2.9 1.9 .. 2.6 3.4 10.7 7.7 13.3 .. 7.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 32 56 3.5 1.6 5.6 2.6 50.9 57.4 18.8 6.6 39.4 4.3 Zambia 29 c 80 c 6.7 3.5 14.7 .. 41.0 40.6 20.5 3.4 103.8 .. Zimbabwe 85 228 5.5 7.0 23.2 .. 24.0 30.9 18.0 21.0 29.0 .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income 3.8 3.2 23.9 .. 27.8 15.7 10.5 8.5 37.2 9.3 Middle income 4.7 5.8 19.6 14.2 18.3 17.6 16.5 21.8 29.8 15.5 Lower middle income 4.2 4.4 21.6 11.3 22.3 26.1 16.0 26.1 31.4 16.5 Upper middle income .. 7.5 17.5 17.8 13.7 11.2 17.1 17.8 27.7 14.3 Low & middle income 4.5 5.4 20.1 13.8 19.5 17.3 15.1 20.0 30.8 15.1 East Asia & Pacific 4.8 2.9 17.6 6.1 17.7 20.7 16.0 34.2 20.5 15.2 Europe & Central Asia .. 9.4 .. 22.0 10.2 9.4 17.6 20.3 36.0 18.3 Latin America & Carib. 4.2 6.8 23.8 22.5 27.6 22.9 16.6 12.8 39.7 12.9 Middle East & N. Africa 6.4 3.9 20.6 8.9 13.1 25.2 14.1 19.2 23.9 11.4 South Asia 2.9 2.8 27.6 .. 25.3 16.0 9.9 6.2 30.1 5.9 Sub-Saharan Africa .. 3.7 13.6 8.8 30.0 13.2 11.6 15.0 .. 13.0 High income Europe EMU a. Includes workers' remittances. b. The numerator refers to 2005, whereas the denominator is a three-year average of 2003­05 data. c. Data are from debt sustainability analyses undertaken as part of the Heavily Indebted Poor Countries (HIPC) Initiative. Present value estimates for these countries are for public and publicly guaranteed debt only. d. GNP and export data refer to mainland Tanzania only. 256 2007 World Development Indicators 4.17 ECONOMY Debt ratios About the data Definitions The indicators in the table measure the relative bur- using a special drawing rights reference rate, as are · Present value of debt is the sum of short-term den on developing countries of servicing external obligations to the International Monetary Fund (IMF). external debt plus the discounted sum of total debt debt. The present value of external debt provides a When the discount rate is greater than the interest service payments due on public, publicly guaranteed, measure of future debt service obligations that can rate of the loan, the present value is less than the and private nonguaranteed long-term external debt be compared with the current value of such indicators nominal sum of future debt service obligations. over the life of existing loans. · Exports of goods, as gross national income (GNI) and exports of goods The ratios in the table are used to assess the services, and income refer to international trans- and services. The table shows the present value of sustainability of a country's debt service obliga- actions involving a change in ownership of general total debt service both as a percentage of GNI in tions, but there are no absolute rules to determine merchandise, goods sent for processing and repairs, 2005 and as a percentage of exports in 2005. The what values are too high. Empirical analysis of the nonmonetary gold, services, receipts of employee ratios compare total debt service obligations with the experience of developing countries and their debt compensation for nonresident workers, investment size of the economy and its ability to obtain foreign service performance has shown that debt service income, and workers' remittances. · Total debt ser- exchange through exports. The ratios shown here difficulties become increasingly likely when the ratio vice is the sum of principal repayments and interest may differ from those published elsewhere because of the present value of debt to exports reaches 200 actually paid on total long-term debt (public and pub- estimates of exports and GNI have been revised to percent. Still, what constitutes a sustainable debt licly guaranteed and private nonguaranteed), use of incorporate data available as of February 1, 2007. burden varies from one country to another. Countries IMF credit, and interest on short-term debt. · Multi- Exports refer to exports of goods, services, income, with fast-growing economies and exports are likely to lateral debt service is the repayment of principal and workers' remittances. be able to sustain higher debt levels. and interest to the World Bank, regional development The present value of external debt is calculated by The most indebted low-income countries may be banks, and other multilateral and intergovernmental discounting the debt service (interest plus amortiza- eligible for debt relief under special programs, such agencies. · Short-term debt includes all debt having tion) due on long-term external debt over the life of as the Heavily Indebted Poor Countries Debt Initiative an original maturity of one year or less and interest existing loans. Short-term debt is included at its face and Multilateral Debt Relief Initiative. Indebted coun- in arrears on long-term debt. value. The data on debt are in U.S. dollars converted tries may also apply to the Paris and London Clubs at official exchange rates (see About the data for for renegotiation of obligations to public and private table 4.16). The discount rate applied to long-term creditors. The World Bank no longer classifies coun- debt is determined by the currency of repayment of tries by their level of indebtedness for the purposes the loan and is based on reference rates for com- of developing debt management strategies. mercial interest established by the Organisation for Economic Co-operation and Development. Loans from the International Bank for Reconstruction and Development (IBRD) and credits from the Interna- tional Development Association (IDA) are discounted The debt burden of Sub-Saharan Africa rose slightly in 2005, after falling 4.17a Percent Total debt service (share of exports) Total debt service (share of GNI) 20 15 Data sources The main sources of external debt information 10 are reports to the World Bank through its Debtor Reporting System from member countries that have received IBRD loans or IDA credits. Additional 5 information is from the files of the World Bank and the IMF. Data on GNI and exports of goods 0 and services are from the World Bank's national 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 accounts files and the IMF's Balance of Payments The debt burden of Sub-Saharan economies rose slightly in 2005 after falling to less than half its 1995 database. Summary tables of the external debt of level. developing countries are published annually in the World Bank's Global Development Finance and on a. Includes goods, services, income, and workers' remittances. Source: World Bank data files. its Global Development Finance CD-ROM. 2007 World Development Indicators 257 Text figures, tables, and boxes STATES & MARKETS 5 Introduction C ountry policies and governance matter for development Governance matters for economic development. Capable governments and high-quality institutions promote growth, raise incomes, and reduce poverty. Governance indicators are tools for assessing the performance of governments and the strengths and weaknesses of public institutions. Donors and governments use them to identify weaknesses and improve the management of development programs. And by providing feedback to policymakers and citizens, governance indicators can help to improve the quality of governance over time. This section--on states and markets--includes a broad range of indicators showing how effec- tive and accountable government, together with energetic private initiative, help to create opportunities for growth and development. The World Bank defines governance as the way public officials and public institutions acquire and exercise authority to provide public goods and services, including basic services, infra- structure, and a sound investment climate. Measuring governance and measuring corruption are not the same thing. While governance encompasses all of the state institutions and arrangements that shape the relations between the state and society, corruption is one aspect of poor governance--an outcome and a consequence of the failure of public account- ability. Measuring the quality of policies, institutions, and governance--and corruption--is difficult and often subject to margins of error, whether based on objective or subjective information. The World Bank has used assessments of government performance in allocating conces- sional resources since the mid-1970s. Focusing at first on macroeconomic management, the assessment criteria have expanded to include trade and financial policies, business regulation, social sector policies, the effectiveness of the public sector, and transparency, accountability, and corruption. Now called the Country Performance and Institutional Assess- ment (CPIA), the criteria are assessed annually for all World Bank borrowers. This edition of World Development Indicators includes a new indicator table--Table 5.8, Pub- lic policies and institutions--showing the most recent CPIA data for 76 countries eligible to receive grants or credits from the International Development Association (IDA), the World Bank's concessional lending arm. Indicator tables 5.2 and 5.3 continue to report on govern- ment policies and regulations affecting the investment climate. Improved infrastructure such as roads, ports, and rails (indicator table 5.9), power and telecommunications (indicator tables 5.10 and 5.11), and water supply and sanitation (indicator table 2.15) are crucial for citizens' health, economic growth, and competitiveness. And effective, accountable govern- ments are needed to complement an energetic private sector to deliver these services. 2007 World Development Indicators 259 Country Policy and Governance and growth Institutional Assessment The first major World Bank discussion of the role of governance The CPIA indicators measure the extent to which a country's was the 1991 World Bank Discussion Paper Managing De- policy and institutional framework supports sustainable velopment: The Governance Dimension (World Bank 1991). A growth and poverty reduction and, consequently, the effec- few years later World Development Report 1997: The State in a tive use of development assistance. Country performance is Changing World (World Bank 1997d) argued that a determining assessed against 16 criteria grouped in four clusters: eco- factor in development was the effectiveness of the state. The nomic management, structural policies, policies for social report noted that "an effective state is vital for the provision of inclusion and equity, and public sector management and in- the goods and services--and the rules and institutions--that stitutions (box 5b). allow markets to flourish and people to lead healthier, happier The overall score for each country, known as the IDA lives. . . ." The 1997 report presented systematic assessments Resource Allocation Index (IRAI), is a key element of a of the reliability of governmental institutions (predictability of country's IDA country performance rating. IDA resources are rulemaking, perceptions of political stability, crime against per- allocated in per capita terms on the basis of the country sons and property, and reliability of judicial enforcement) and of performance rating and, to a limited extent, per capita gross corruption from a 1996 World Bank­sponsored survey. Subse- national income. This ensures that good performers receive quent research suggests that the causality between growth and a higher IDA allocation, in per capita terms. The individual governance is two-way--that improvements in either income or CPIA criteria are also used to inform the World Bank's coun- governance can give momentum to development--but that cau- try policy dialogue with member governments and for other sation is stronger from governance to growth in income. operational and research purposes. Reflecting the IDA14 Although the links are complex, there is ample evidence funding agreement, the numerical IRAI scores and separate of the connection between governance and long-term growth. CPIA criteria were first publicly disclosed for IDA recipient Figure 5a shows the statistical relationship (controlling for countries in June 2006 to enhance transparency and exter- initial income and schooling levels) between the quality of nal scrutiny of these scores (see indicator table 5.8 and governance measured by an International Country Risk Guide figure 5c). (ICRG) index in 1982 and the growth of per capita income The scores depend on actual policies and performance, through 2002. The ICRG index comprises five elements of rather than on promises or intentions. In some cases the governance: corruption in government, rule of law, risk of passage of specific legislation can represent an important expropriation, repudiation of contracts by government, and action that deserves consideration. But it is implementa- quality of the bureaucracy in 71 developing countries. tion of legislation that determines its impact. The average Governance and Criteria for measuring economic and growth go together 5a sector policies and governance system Box 5b Per capita income growth, 1982­2002 (%, residual) Cluster A: Economic management 8 6 Macroeconomic management 4 Fiscal policy 2 Debt policy 0 ­2 Cluster B: Structural policies ­4 Trade ­6 Financial sector ­8 Business regulatory environment ­20 ­15 ­10 ­5 0 5 10 15 20 25 30 Quality of initial governance, 1982 (index) Cluster C: Policies for social inclusion and equity Source: Knack 2006. Gender equality Equity of public resource use Building human resources Social protection and labor Policies and institutions for environmental sustainability Cluster D: Public sector management and institutions Property rights and rule-based governance Quality of budgetary and financial management Efficiency of revenue mobilization Quality of public administration Transparency, accountability, and corruption in the public sector 260 2007 World Development Indicators The IDA Resource Allocation Index is a key element of a country's IDA performance rating 5c IDA Resource Allocation Index (IRAI), 1 (low) to 6 (high) Zimbabwe Bangladesh Central African Rep. Mongolia Comoros Mozambique Côte d'Ivoire Rwanda Togo Moldova Eritrea Lesotho Angola Kyrgyz Rep. Sudan Madagascar Guinea-Bissau Bosnia & Herzegovina Haiti Kenya Congo, Rep Sri Lanka Solomon Islands Azerbaijan Congo, Dem. Rep. Benin Chad Indonesia Tonga Pakistan Burundi Albania São Tomé and Principe Grenada Lao PDR Serbia & Montenegro Uzbekistan Mali Guinea Bolivia Gambia, The Nicaragua Cambodia Vietnam Papua New Guinea Senegal Sierra Leone Burkina Faso Djibouti India Nigeria Dominica Vanuatu Bhutan Kiribati Georgia Mauritania Maldives Niger Ghana Cameroon Uganda Yemen, Rep. Honduras Zambia St. Vincent & Grenadines Nepal Tanzania Tajikistan St. Lucia Guyana Samoa Malawi Cape Verde Ethiopia Armenia 1 2 3 4 5 6 1 2 3 4 5 6 Source: World Bank. 2007 World Development Indicators 261 Other World Bank sources of data for monitoring governance score on public sector management and institutions can The growing recognition of the link between good governance be used as an aggregate indicator of the country's gover- and successful development has stimulated efforts to moni- nance system (focused primarily on economic governance). tor the performance of governments and other public insti- It is a part of the "governance factor" that is given extra tutions by private commercial rating agencies, multilateral weight in the IDA country performance rating for determining development institutions, and nongovernmental agencies. In IDA resource allocations. (For more information see www. addition to the CPIA policy and governance measures, the worldbank.org/ida.) World Bank has several other governance and governance- Scores on the CPIA public sector management and insti- related measurement programs and indicators that are used tutions cluster are bunched around the mid-range, with no in monitoring governance. (See box 5g at the end of this in- countries scoring in either the lowest or highest ranges, and troduction for other selected organizations' governance mea- only one country in the 4.0­4.9 range (figure 5d). Although surement initiatives.) these measures give some indication of the quality of pub- · Worldwide Governance Indicators are the most com- lic sector management and institutions, for some countries prehensive publicly available governance indicators and they do not always match the strong performance on eco- among the most widely used by the media, academia, nomic management policies (macroeconomic management, and international organizations for assessing governance. fiscal policy, and debt policy). Armenia, Bangladesh, Kyrgyz Compiled since 1996, these data measure the quality of Republic, Tajikistan, and Uganda score relatively well on six dimensions of governance for 213 countries, based CPIA cluster A, economic management, but not so well on on 31 data sources produced by 25 organizations (box cluster D, public sector management and institutions (figure 5f). The underlying data are based on hundreds of vari- 5e). These patterns reveal the complexity of the relation- ables and reflect the perceptions and views of experts, ships between measures of the quality of public sector man- firm survey respondents, and citizens worldwide on vari- agement and institutions and economic outcomes, requir- ous dimensions of governance. The measures, also known ing better diagnostics and understanding of each country's as Kaufmann-Kraay, include the margins of error associ- situation to develop workable approaches to governance ated with each estimate, allowing users to identify a range reform. of statistically likely ratings for each country, not just a On public sector management, Worldwide Governance Indicators-- countries bunch around the middle 5d Six key dimensions of governance Box 5f Distribution of IDA recipient scores for CPIA cluster D, public sector management and institutions, 2005 The Worldwide Governance Indicators measure the qual- Number of IDA recipient countries ity of six dimensions of governance: 30 · Voice and accountability, the extent to which a coun- 25 try's citizens are able to participate in selecting their 20 government, as well as freedom of expression, free- 15 dom of association, and free media 10 · Political stability and absence of violence, perceptions of the likelihood that the government will be desta- 5 bilized or overthrown by unconstitutional or violent 0 means, including political violence and terrorism 1.0­2.0 2.0­2.4 2.5­2.9 3.0­3.4 3.5­3.9 4.0­4.9 5.0­6.0 · Government effectiveness, the quality of public ser- CPIA score, 1 (low) to 6 (high) vices, the quality of the civil service and the degree Source: World Bank. of its independence from political pressures, the Strong performance on economic management, quality of policy formulation and implementation, weaker on public sector management 5e and the credibility of the government's commitment to such policies CIPA score, Economic management Public sector management · Regulatory quality, the ability of the government to for- 1 (low) to 6 (high) (CPIA cluster A) and institutions (CPIA cluster D) 6 mulate and implement sound policies and regulations that permit and promote private sector development 5 · Rule of law, the extent to which agents have confi - 4 dence in and abide by the rules of society, and in particular the quality of contract enforcement, the 3 police, and the courts, as well as the likelihood of 2 crime and violence · Control of corruption, the extent to which public power 1 Nigeria Kyrgyz Bangla- Tajikistan Pakistan Indonesia Honduras Uganda Armenia is exercised for private gain, including both petty and Republic desh grand forms of corruption, as well as "capture" of the Source: World Bank. state by elites and private interests. 262 2007 World Development Indicators single rating. Margins of error are present in all efforts · HIPC (Highly Indebted Poor Countries) Public Expenditure to measure governance; some sources explicitly report Management Assessment and Action Plans use expendi- them, while others do not. See www.govindicators.org. ture tracking tools developed by the World Bank and the · Enterprise Surveys and the Business Environment and International Monetary Fund to monitor poverty-reducing Enterprise Performance Surveys capture business per- public expenditures in HIPCs. Data are collected on 15 ceptions of the biggest obstacles to enterprise growth, the indicators on budget formulation, execution, and reporting, relative importance of various constraints to increasing and 1 indicator on government procurement. A new pro- employment and productivity, and the effects of a coun- gram to measure public expenditure and management has try's investment climate on its international competitive- been developed and will be used for monitoring in HIPCs. ness. Surveys cover almost 58,000 firms in 97 countries. See www.worldbank.org/hipc. Although designed to monitor the investment climate, · Public Expenditure and Financial Accountability Program, which is a product of a number of governance-related fac- started by the World Bank in 2001, is now a partnership tors, these surveys include measures, such as a business with several multilateral and bilateral development institu- managers' perception of corruption as a constraint to doing tions that support an integrated and harmonized approach business, that can be directly linked to governance and are to assessment and reform in public expenditure, procure- therefore useful for governance monitoring at the country ment, and financial accountability. The public expenditure level. See indicator table 5.2 and www.enterprisesurveys. and financial accountability framework includes 28 indi- org and http://info.worldbank.org/governance/beeps. cators on budget credibility, transparency, auditing, and · Doing Business surveys cover key indicators on the procurement, and three indicators on donor practices that environment for doing business for 175 economies. The affect the country public financial management system. The indicators identify regulations that enhance or constrain program is being implemented in 70 countries; 8 countries business investment, productivity, and growth. Some have completed reviews and made them available publicly indicators, such as enforcing contracts, are useful in (in addition, one country has published data for the indica- monitoring governance. See indicator table 5.3 and www. tors). See www.pefa.org. doingbusiness.org. · The Joint Venture on Procurement of the World Bank and · Anticorruption Diagnostic Surveys are designed to facili- the Organisation for Economic Co-operation and Develop- tate governance monitoring by providing inputs to policy- ment's Development Assistance Committee has selected makers and civil society. The World Bank Institute's Gov- 22 pilot developing countries to test the Common Bench- ernance Diagnostic Capacity Building program aims to marking and Assessment Tool for Public Procurement, which strengthen the capacity of countries to conduct gover- developing countries and donors can use to assess the qual- nance diagnostic surveys through technical assistance for ity and effectiveness of national procurement systems. See the design of surveys and governance action plans, train- www.oecd.org and search for Joint Venture on Procurement. ing, and partnerships between the government and civil For an overview of the World Bank's framework for global society organizations. Agencies in several countries have monitoring of governance and in-depth discussion of the uses undertaken governance and anticorruption diagnostic sur- and limitations of governance measures, see the World Bank veys. See www.worldbank.org/wbi/governance and click and International Monetary Fund's (2006a) Global Monitoring on Diagnostics. Report 2006. Other selected sources of data for monitoring governance Box 5g · Freedom House, a private nonprofit advocacy organization founded in 1941, was among the earliest to systematically measure and publish governance ratings. Freedom House has published Freedom in the World since 1972; it now includes ratings of political rights and civil liberties in 192 countries and territories. See www.freedomhouse.org. · International Country Risk Guide is privately owned and has been assessing financial, economic, and political risks since 1980 for about 140 countries. See www.prsgroup.com. · Transparency International (TI), a newer entrant, has attracted media attention since 1995 with its Corruption Per- ceptions Index. The index is a compilation of surveys of perceptions of resident and nonresident business people and expert assessments of the degree of corruption in a country. See www.transparency.org. · Global Integrity, a Washington, D.C.­based nonprofit organization funded by private foundations and the World Bank, assesses the existence and effectiveness of anticorruption mechanisms that promote public integrity. More than 290 indicators are used to generate the Global Integrity Index for more than 40 countries. See www.globalintegrity.org. · The Open Budget Initiative, sponsored by the International Budget Project, tracks 122 indicators of budget trans- parency for almost 60 countries. The country reports give citizens, legislators, and civil society advocates compre- hensive and practical information that can be used to assess a government's commitment to budget transparency and accountability. The initiative is funded by private foundations and bilateral aid agencies such as the U.K. Depart- ment for International Development and the Swedish International Development Cooperation Agency. See www. openbudgetindex.org. 2007 World Development Indicators 263 Tables 5.1 Private sector in the economy Investment in infrastructure projects Domestic New busi- Micro, with private participationa credit to nesses small, and private sector registered medium-size enterprises % of total $ millions Water and businesses per 1,000 Telecommunications Energy Transport sanitation % of GDP registered Total people 1995­99 2000­05 1995­99 2000­05 1995­99 2000­05 1995­99 2000­05 1990 2005 2003 2000­05b 2000­05b Afghanistan .. 284.1 .. .. .. .. .. .. .. .. .. .. .. Albania .. 443.2 0.0 789.0 .. 308.0 .. 8.0 .. 14.9 6 38,331 12.3 Algeria .. 3,422.5 .. 962.0 .. .. .. 510.0 44.4 11.8 15 580,000 18.8 Angola .. 278.7 .. 45.0 .. 55.0 .. .. .. 4.8 .. .. .. Argentina 10,498.6 5,925.6 12,931.9 3,830.0 6,892.1 200.2 3,307.1 791.6 15.6 11.7 11 .. .. Armenia 1,680.5 243.4 0.0 47.0 .. 50.0 .. 0.0 40.4 8.0 7 99,805 33.1 Australia .. .. .. .. .. .. .. .. 59.9 104.6 11 1,269,000 63.2 Austria .. .. .. .. .. .. .. .. 89.7 112.9 7 252,399 30.9 Azerbaijan 122.0 355.6 .. 375.2 .. .. .. 0.0 10.8 10.0 7 49,527 6.0 Bangladesh 438.1 1,527.3 554.9 501.5 0.0 0.0 .. .. 16.7 31.5 .. 177,000 1.3 Belarus 20.0 735.4 500.0 .. .. .. .. .. .. 16.2 .. 25,108 2.5 Belgium .. .. .. .. .. .. .. .. 35.9 75.1 7 686,533 66.2 Benin .. 116.9 .. .. .. .. .. .. 20.3 16.6 .. .. .. Bolivia 528.0 520.5 2,777.3 886.0 168.7 16.6 682.0 .. 24.0 40.7 8 .. .. Bosnia and Herzegovina 0.0 0.0 .. 277.9 .. .. .. .. .. 47.9 6 14,986 3.8 Botswana 97.0 104.0 .. .. .. .. .. .. 9.4 19.0 .. 13,137 7.4 Brazil 45,135.2 41,018.7 34,196.8 28,204.0 17,195.0 4,271.3 2,137.0 1,587.6 39.0 34.8 .. 4,903,268 27.4 Bulgaria 202.5 2,179.1 .. 2,646.0 .. .. .. 152.0 82.8 44.5 .. 216,489 27.7 Burkina Faso .. 41.9 5.6 .. 63.3 .. .. .. 16.8 17.3 .. .. .. Burundi .. 53.6 .. .. .. .. .. .. 13.7 20.8 .. .. .. Cambodia 102.4 148.1 143.0 88.1 120.0 125.3 .. .. .. 9.4 .. .. .. Cameroon 12.7 394.4 .. 91.9 90.0 0.0 .. .. 26.7 9.4 .. .. .. Canada .. .. .. .. .. .. .. .. 92.2 181.4 14 2,245,245 69.5 Central African Republic 1.1 .. .. .. .. .. .. .. 7.2 6.7 .. .. .. Chad 2.0 11.0 .. 0.0 .. .. .. .. 7.3 3.1 .. .. .. Chile 3,489.0 3,714.6 6,808.6 1,528.3 3,104.1 4,768.6 4,190.3 1,495.2 50.6 82.3 .. 700,000 43.4 China 5,970.0 8,548.0 17,166.6 6,365.7 10,852.5 7,948.0 985.9 3,131.5 87.7 114.4 9 8,000,000 6.3 Hong Kong, China .. .. .. .. .. .. .. .. 160.6 146.2 13 263,959 38.4 Colombia 1,384.3 1,570.9 6,964.8 4,483.2 995.5 1,331.4 233.0 250.0 30.8 23.9 .. 664,000 15.0 Congo, Dem. Rep. 68.0 453.4 .. .. 0.0 .. .. .. 1.8 1.9 10 .. .. Congo, Rep. 12.2 61.8 325.0 .. .. .. .. .. 15.7 2.9 .. .. .. Costa Rica .. .. 301.2 80.0 .. 465.2 .. .. 12.2 35.8 .. 40,921 9.6 Côte d'Ivoire 752.3 134.9 260.6 0.0 241.3 140.0 .. .. 36.5 13.8 .. .. .. Croatia 978.0 1,181.9 368.5 7.1 672.2 451.0 .. 298.7 .. 61.2 .. 94,088 21.2 Cuba .. 60.0 165.0 .. .. 0.0 .. 600.0 .. .. .. .. .. Czech Republic 6,178.5 8,348.0 944.1 3,865.3 283.7 106.7 135.5 263.7 .. 37.0 4 .. .. Denmark .. .. .. .. .. .. .. .. 51.2 171.1 10 257,950 47.8 Dominican Republic 163.0 393.0 979.0 1,306.6 .. 898.9 .. .. 27.5 27.7 .. .. .. Ecuador 696.4 357.8 30.0 302.0 686.8 685.0 .. 550.0 13.6 23.0 14 1,043,440 83.6 Egypt, Arab Rep. 1,914.5 3,360.9 700.0 678.0 123.9 821.5 .. .. 30.6 52.4 .. .. .. El Salvador 610.5 821.2 900.2 85.0 .. .. .. .. 16.9 43.4 7 461,642 72.1 Eritrea .. 40.0 .. .. .. .. .. .. .. 31.0 .. .. .. Estonia 628.2 287.1 26.5 .. 1.0 298.4 .. 115.0 20.2 60.0 10 65,194 48.4 Ethiopia .. .. .. 300.0 .. .. .. .. 13.9 25.3 .. .. .. Finland .. .. .. .. .. .. .. .. 85.8 76.1 10 221,000 42.4 France .. .. .. .. .. .. .. .. 94.3 93.1 8 2,612,960 43.2 Gabon 8.4 26.6 294.0 0.0 46.7 177.4 .. .. 12.9 8.9 .. .. .. Gambia, The .. 6.6 .. .. .. .. .. .. 11.0 13.0 .. .. .. Georgia 61.0 168.8 159.0 13.0 .. .. .. .. .. 14.8 10 33,860 7.6 Germany .. .. .. .. .. .. .. .. 88.7 111.4 23 3,162,111 38.3 Ghana 491.1 156.5 376.0 184.0 .. 10.0 .. 0.0 4.9 15.5 .. 25,679 1.2 Greece .. .. .. .. .. .. .. .. 35.5 84.8 7 771,000 69.9 Guatemala 1,366.3 560.1 1,223.2 110.0 33.8 .. .. .. 14.2 25.2 .. .. .. Guinea 120.3 18.0 36.4 .. .. .. 0.0 .. 3.5 5.1 .. .. .. Guinea-Bissau .. 21.9 .. .. .. .. .. .. 22.0 2.1 .. .. .. Haiti 1.5 18.0 4.7 .. .. .. .. .. 12.6 15.4 .. .. .. 264 2007 World Development Indicators 5.1 STATES AND MARKETS Private sector in the economy Investment in infrastructure projects Domestic New busi- Micro, with private participationa credit to nesses small, and private sector registered medium-size enterprises % of total $ millions Water and businesses per 1,000 Telecommunications Energy Transport sanitation % of GDP registered Total people 1995­99 2000­05 1995­99 2000­05 1995­99 2000­05 1995­99 2000­05 1990 2005 2003 2000­05b 2000­05b Honduras 51.3 135.0 112.1 358.8 10.5 120.0 .. 220.0 31.1 42.7 .. 257,953 40.2 Hungary 6,430.2 5,234.8 3,812.1 260.6 135.0 3,297.5 205.8 0.0 46.6 51.7 8 .. .. India 7,456.8 20,642.0 7,182.7 8,882.1 1,275.1 3,941.1 .. 2.1 25.2 40.8 .. .. .. Indonesia 8,852.5 6,494.6 9,942.1 1,575.6 1,530.8 3.7 955.2 36.7 48.1 26.9 .. 41,362,315 195.3 Iran, Islamic Rep. 28.0 695.0 .. 650.0 .. .. .. .. 33.8 40.9 .. .. .. Iraq .. 984.0 .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. 47.1 160.7 10 97,000 24.3 Israel .. .. .. .. .. .. .. .. 57.6 97.5 .. 468,338 67.6 Italy .. .. .. .. .. .. .. .. 54.9 90.2 7 4,486,000 77.9 Jamaica 235.5 700.3 43.0 201.0 0.0 565.0 .. .. 31.6 17.9 4 .. .. Japan .. .. .. .. .. .. .. .. 197.4 186.9 .. 5,712,191 44.7 Jordan 39.9 1,589.0 .. .. 182.0 0.0 0.0 169.0 72.3 87.2 .. 141,327 26.4 Kazakhstan 1,633.5 1,078.2 1,825.0 300.0 .. .. .. 100.0 .. 35.7 12 .. .. Kenya 193.0 1,434.0 189.0 .. 53.4 .. .. .. 32.7 25.9 .. 2,800,000 87.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. 62.8 102.1 .. 2,998,223 62.4 Kuwait .. .. .. .. .. .. .. .. 52.1 63.1 .. .. .. Kyrgyz Republic 100.0 9.1 .. .. .. .. .. .. .. 8.0 .. 142,475 28.3 Lao PDR 157.1 87.8 535.5 1,250.0 0.0 0.0 .. .. 1.0 7.0 .. .. .. Latvia 600.9 708.9 106.0 71.1 75.0 .. .. .. .. 59.9 6 32,571 13.8 Lebanon 485.7 138.1 .. .. .. 153.0 .. 0.0 79.4 76.3 .. .. .. Lesotho 15.7 88.4 .. 0.0 .. .. .. .. 15.1 8.4 .. .. .. Liberia .. 61.3 .. .. .. .. .. .. 30.9 6.6 .. .. .. Libya .. .. .. .. .. .. .. .. 31.0 9.0 .. .. .. Lithuania 832.7 993.0 10.0 399.3 .. .. .. .. .. 34.9 3 56,428 16.5 Macedonia, FYR .. 706.6 .. .. .. .. .. .. .. 25.9 7 55,742 27.5 Madagascar 30.0 12.6 .. 0.0 .. 48.5 .. .. 16.9 9.9 4 .. .. Malawi 23.1 36.3 .. 0.0 6.0 .. .. .. 10.9 10.5 13 747,396 64.9 Malaysia 4,187.6 3,756.9 1,610.2 6,637.6 8,200.1 4,276.4 10.0 6,502.2 69.4 128.3 .. 518,996 20.5 Mali .. 82.6 .. 365.9 .. 55.4 .. .. 12.8 18.4 .. .. .. Mauritania .. 92.1 .. .. .. .. .. .. 43.5 27.0 .. .. .. Mauritius .. 413.0 109.3 0.0 42.6 .. .. .. 35.6 76.7 .. 75,267 62.2 Mexico 10,757.5 18,131.4 2,095.8 6,614.3 4,706.1 3,135.4 276.5 520.7 17.5 18.2 .. 2,891,300 28.3 Moldova 84.6 46.1 60.0 25.3 .. .. .. .. 5.9 24.2 5 25,667 6.1 Mongolia 21.9 22.1 .. .. .. .. .. .. 17.7 37.5 18 .. .. Morocco 1,240.0 5,993.5 5,978.0 1,049.0 .. .. 1,000.0 .. 34.0 62.2 46 450,000 15.8 Mozambique 29.0 123.0 .. 1,205.8 441.0 334.6 25.5 0.0 18.3 11.2 7 .. .. Myanmar 4.0 .. 394.0 .. 50.0 .. .. .. 4.7 5.6 .. .. .. Namibia 55.2 35.2 4.0 1.0 .. 450.0 .. 0.0 22.6 61.4 .. .. .. Nepal .. 97.3 98.2 39.0 .. .. .. .. 12.8 .. .. 3,040 0.1 Netherlands .. .. .. .. .. .. .. .. 76.6 173.4 6 735,160 45.0 New Zealand .. .. .. .. .. .. .. .. 75.5 133.8 18 334,031 82.2 Nicaragua 24.5 278.5 232.4 115.0 .. 104.0 .. .. 112.6 29.1 .. .. .. Niger .. 85.5 .. .. .. .. .. 3.4 12.3 6.8 .. .. .. Nigeria 69.0 6,950.7 .. 1,248.0 .. 22.8 .. .. 9.4 14.9 8 .. .. Norway .. .. .. .. .. .. .. .. 81.7 9.0 7 316,243 68.4 Oman .. 1,047.0 183.0 1,364.3 77.5 473.8 .. .. 20.6 34.9 5 7,373 2.9 Pakistan 75.5 5,572.2 4,298.3 598.6 421.3 71.0 .. .. 24.2 28.4 4 2,956,704 19.0 Panama 1,429.2 183.4 669.2 445.7 994.6 51.4 25.0 .. 46.7 91.7 .. .. .. Papua New Guinea .. .. 65.0 .. .. .. 71.0 .. 28.6 13.9 .. .. .. Paraguay 259.3 194.6 .. .. 58.0 .. .. .. 15.8 18.0 .. 548,000 95.5 Peru 4,774.5 2,238.0 3,004.9 2,478.3 86.3 561.5 .. 128.0 11.8 19.4 1 658,837 23.9 Philippines 5,358.3 4,570.9 6,998.0 3,783.4 1,364.0 1,060.5 7,567.2 0.0 22.3 30.5 .. 808,634 10.1 Poland 6,403.1 16,800.0 628.1 2,341.5 169.4 1,672.0 6.1 64.3 21.1 27.4 7 1,654,822 43.3 Portugal .. .. .. .. .. .. .. .. 46.6 147.3 3 693,000 66.4 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2,069 0.5 2007 World Development Indicators 265 5.1 Private sector in the economy Investment in infrastructure projects Domestic New busi- Micro, with private participationa credit to nesses small, and private sector registered medium-size enterprises % of total $ millions Water and businesses per 1,000 Telecommunications Energy Transport sanitation % of GDP registered Total people 1995­99 2000­05 1995­99 2000­05 1995­99 2000­05 1995­99 2000­05 1990 2005 2003 2000­05b 2000­05b Romania 2,072.8 3,073.9 100.0 1,240.8 23.4 .. .. 1,022.0 .. 20.0 11 392,544 18.1 Russian Federation 5,643.1 19,583.8 2,281.3 1,714.0 406.0 109.4 108.0 660.5 .. 25.7 .. 6,891,300 48.2 Rwanda 8.0 52.3 .. 0.0 .. .. .. .. 6.9 13.5 .. .. .. Saudi Arabia .. 8,537.0 .. .. 55.0 190.0 .. 52.0 54.7 53.9 .. .. .. Senegal 273.9 345.1 124.0 87.0 .. 55.4 20.0 .. 26.5 23.8 .. .. .. Serbia and Montenegro 1,590.0 830.6 .. .. .. .. .. 0.0 .. .. 9 68,220 8.4 Sierra Leone 7.0 48.8 .. .. .. .. .. .. 3.2 4.5 29 .. .. Singapore .. .. .. .. .. .. .. .. 97.0 101.7 13 136,363 32.2 Slovak Republic 488.5 2,709.9 .. 4,459.6 .. .. 0.0 .. .. 36.2 14 70,553 13.1 Slovenia .. .. .. .. .. .. .. .. 34.9 53.3 7 91,066 45.6 Somalia 0.0 13.4 .. .. .. .. .. .. .. .. .. .. .. South Africa 2,975.3 5,499.5 3.0 1,251.3 1,386.4 504.7 56.9 31.3 81.0 143.5 8 .. .. Spain .. .. .. .. .. .. .. .. 78.5 146.1 10 3,168,735 73.0 Sri Lanka 601.9 679.9 192.3 254.0 240.0 .. .. .. 19.6 32.9 8 121,426 6.3 Sudan 18.3 621.2 .. .. .. .. .. .. 4.8 10.0 .. 22,460 0.7 Swaziland 21.2 27.7 .. .. .. .. .. .. 20.7 20.0 .. .. .. Sweden .. .. .. .. .. .. .. .. 126.4 111.7 5 898,454 99.6 Switzerland .. .. .. .. .. .. .. .. 162.6 166.8 7 344,000 46.9 Syrian Arab Republic .. 583.2 .. .. .. .. .. .. 7.5 11.8 .. .. .. Tajikistan 1.2 8.5 .. 16.0 .. .. .. .. .. 17.2 .. 92,964 14.8 Tanzania 100.2 487.3 150.0 372.0 16.5 6.5 .. 8.5 13.9 10.4 .. 2,700,000 74.6 Thailand 2,735.2 5,470.7 6,550.4 4,693.3 2,001.1 939.0 246.3 245.6 83.4 93.1 10 842,360 13.5 Togo 5.0 0.0 0.0 67.7 0.0 .. .. .. 22.6 16.8 6 .. .. Trinidad and Tobago 146.7 .. 207.0 .. .. .. 0.0 120.0 44.7 38.5 .. 19,150 14.7 Tunisia .. 751.0 265.0 30.0 .. .. .. .. 55.1 65.6 .. .. .. Turkey 3,269.7 12,515.9 2,992.2 6,569.8 610.0 3,943.6 942.0 .. 16.7 26.1 .. 210,134 3.1 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. .. Uganda 119.3 387.6 .. 142.1 .. .. 0.0 0.0 4.0 6.7 .. 160,453 6.2 Ukraine 1,094.6 3,162.8 .. 160.0 .. .. .. .. 2.6 33.5 .. 343,786 7.3 United Arab Emirates .. .. .. .. .. .. .. .. 38.0 60.9 .. .. .. United Kingdom .. .. .. .. .. .. .. .. 115.8 165.5 19 4,415,260 73.8 United States .. .. .. .. .. .. .. .. 118.9 194.8 .. 5,868,737 20.0 Uruguay 63.7 114.2 86.0 330.0 20.0 196.1 .. 351.0 32.4 27.0 .. 143,035 42.8 Uzbekistan 513.8 277.6 .. .. .. .. .. 0.0 .. .. .. 212,424 8.3 Venezuela, RB 4,877.9 3,337.0 103.0 30.0 268.0 34.0 29.0 15.0 26.2 13.6 .. 11,314 0.5 Vietnam 256.0 430.0 435.5 2,279.0 85.0 30.0 38.8 174.0 2.5 66.0 .. 90,935 1.1 West Bank and Gaza 265.0 279.8 .. 150.0 .. .. 0.0 0.0 .. .. .. 97,194 27.7 Yemen, Rep. .. 376.8 .. .. 190.0 .. .. .. 6.1 7.7 7 310,000 16.2 Zambia 64.2 208.3 277.0 12.4 .. 15.6 .. .. 8.9 7.6 10 .. .. Zimbabwe 46.0 59.0 600.0 .. 85.0 .. .. .. 23.0 26.9 0 .. .. World .. s .. s .. s .. s .. s .. s .. s .. s 104.3 w 133.8 w 9 u 139,432,731 s Low income 11,549.1 41,659.1 15,730.4 17,689.2 3,047.9 4,856.7 155.3 188.0 21.3 33.8 10 4,222,837 Middle income 161,868.2 217,654.4 138,174.3 106,539.8 63,732.8 44,614.8 23,098.8 20,026.7 43.1 58.3 9 94,515,052 Lower middle income 88,980.5 103,357.9 101,392.0 63,499.2 35,618.9 20,239.4 13,806.6 7,688.4 51.3 73.1 10 78,159,635 Upper middle income 72,887.7 114,296.5 36,782.3 43,040.6 28,113.9 24,375.4 9,292.2 12,338.3 38.4 38.8 8 16,355,417 Low & middle income 173,417.3 259,313.5 153,904.7 124,229.0 66,780.7 49,471.5 23,254.1 20,214.7 39.2 54.9 9 98,737,889 East Asia & Pacific 27,681.5 29,637.6 43,840.3 26,679.7 24,203.5 14,382.9 9,874.4 10,090.0 73.9 101.1 12 68,387,110 Europe & Central Asia 40,629.4 81,682.2 13,812.8 25,578.5 2,375.7 10,236.6 1,397.4 2,684.2 .. 29.7 8 13,753,937 Latin America & Carib. 86,847.8 80,778.2 73,997.4 51,388.2 35,219.5 17,442.2 10,879.9 6,716.2 28.6 27.8 7 9,077,990 Middle East & N. Africa 3,973.1 19,220.8 7,126.0 4,883.3 573.4 1,498.3 1,000.0 679.0 35.0 39.9 18 3,669,844 South Asia 8,604.5 28,856.1 12,326.4 10,275.2 1,936.4 4,012.1 .. 2.1 24.2 38.7 6 177,000 Sub-Saharan Africa 5,681.0 19,138.6 2,801.8 5,424.1 2,472.2 1,899.4 102.4 43.2 41.0 64.8 10 3,672,008 High income .. .. .. .. .. .. .. .. 115.4 156.3 10 40,694,842 Europe EMU .. .. .. .. .. .. .. .. 77.9 110.5 9 16,589,542 a. Data refer to total for the period shown. Includes projects that became privatized during financial closure years 1990­2005. b. Data are for the most recent year available. 266 2007 World Development Indicators 5.1 STATES AND MARKETS Private sector in the economy About the data Definitions Private sector development and investment--that by the World Bank's Development Data Group. For · Investment in infrastructure projects with private is, tapping private sector initiative and investment more information, see http://ppi.worldbank.org/. participation refers to infrastructure projects in tele- for socially useful purposes--are critical for poverty Credit is an important link in the money transmis- communications, energy (electricity and natural gas reduction. In parallel with public sector efforts, pri- sion process; it finances production, consumption, transmission and distribution), transport, and water vate investment, especially in competitive markets, and capital formation, which in turn affect the level and sanitation that have reached financial closure and has tremendous potential to contribute to growth. of economic activity. The data on domestic credit to directly or indirectly serve the public. Incinerators, Private markets are the engine of productivity the private sector are taken from the banking survey movable assets, stand-alone solid waste projects, and small projects such as windmills are excluded. growth, creating productive jobs and higher incomes. of the International Monetary Fund's (IMF) Interna- Included are operation and management contracts, And with government playing a complementary role tional Financial Statistics or, when data are unavail- operation and management contracts with major capi- of regulation, funding, and service provision, private able, from its monetary survey. The monetary survey tal expenditure, greenfield projects (in which a private initiative and investment can help provide the basic includes monetary authorities (the central bank), entity or a public-private joint venture builds and oper- services and conditions that empower poor people-- deposit money banks, and other banking institutions, ates a new facility), and divestitures. Investment com- by improving health, education, and infrastructure. such as finance companies, development banks, and mitments are the sum of investments in facilities and Investment in infrastructure projects with private savings and loan institutions. In some cases credit to investments in government assets. Investments in participation has made important contributions to the private sector may include credit to state-owned facilities are the resources the project company com- easing fiscal constraints, improving the efficiency of or partially state-owned enterprises. mits to invest during the contract period either in new infrastructure services, and extending delivery to poor Entrepreneurship, the effort by individuals or facilities or in expansion and modernization of existing people. The privatization trend in infrastructure that groups to makes to initiate economic activity in the facilities. Investments in government assets are the began in the 1970s and 1980s took off in the 1990s, formal sector under a legal form of business, lends resources the project company spends on acquiring peaking in 1997. Developing countries have been at dynamism to an economy. Greater entry of new firms government assets such as state-owned enterprises, the head of this wave, pioneering better approaches can foster competition and economic growth. This rights to provide services in a specific area, or the to providing infrastructure services and reaping the edition of World Development Indicators introduces use of specific radio spectrums. · Domestic credit to benefits of greater competition and customer focus. a new indicator measuring entrepreneurship, new private sector refers to financial resources provided Between 1990 and 2005 more than 3,200 projects businesses registered as a percentage of total to the private sector--such as through loans, pur- in more than 139 developing countries introduced businesses. chases of nonequity securities, and trade credits and other accounts receivable--that establish a claim for private participation in at least one infrastructure Formal and informal micro, small, and medium-size repayment. For some countries these claims include sector, with $964 billion in investments. enterprises employ more than half the working popula- credit to public enterprises. ·New businesses regis- In 2005, investments in 160 new infrastructure tion in many market economies and account for about tered are the number of new firms, defined as firms projects with private participation valued at $40 bil- 90 percent of all firms. And they contribute signifi - registered in the current year of reporting, expressed lion were implemented. In addition, $56 billion cantly to innovation. If small businesses are allowed as a percentage of total registered firms. Data are col- in investment projects reached fi nancial closure to compete on an equal playing field, the good ones lected on firm entry and exit and total firms. Because between 1990 and 2005. Telecommunications can become larger, workers can earn higher wages, of underreporting of firms that have closed or exited, attracted $59 billion in investment in 2005, mostly and productivity will increase. A good investment cli- especially in developing countries, the data on total in standalone mobile operations. Transport also saw mate--one that provides opportunities and incentives registered firms may be biased upward. · Micro, an increase, from $7 billion in 2004 to $16 billion for firms, reduces legal and regulatory costs, lowers small, and medium-size enterprises are business in 2005. Energy experienced some recovery, from the costs of financial institutions in providing financial that may be defined by the number of employees. $16 billion to $19 billion. Water, down from about services, and facilitates the transfer of technology and There is no international standard definition of firm $4.8 billion in 2004 to about $1.5 billion in 2005, knowledge and the upgrading of capabilities in small size; however, many institutions that collect informa- was the only sector in which investment in infrastruc- and medium-size firms--is important for economic tion use the following size categories: micro enter- ture projects with private participation declined. progress, better jobs, and a more inclusive society. prises have 0­9 employees, small enterprises have The data on investment in infrastructure projects Data on the business registration of micro, small, 10­49 employees, and medium-size enterprises have with private participation refer to all investment (pub- and medium-size enterprises are collected by govern- 50­249 employees. lic and private) in projects in which a private company ments, international organizations, foundations, and assumes operating risk during the operating period or small business organizations. These data have been Data sources assumes development and operating risk during the collated by the International Finance Corporation (IFC) Data on investment in infrastructure projects with contract period. Investment refers to commitments and are available in two databases: Entrepreneurship private participation are from the World Bank's PPI not disbursements. Foreign state-owned companies Data and Micro, Small, and Medium Enterprises: A Project database (http://ppi.worldbank. org). Data are considered private entities for the purposes of Collection of Published Data. This IFC initiative is a on domestic credit are from the IMF's International this measure. The data are from the World Bank's work in progress, improved and updated as new data Private Participation in Infrastructure (PPI) Project become available. Because the concepts and defini- Financial Statistics. Data on business registration Database, which tracks more than 3,300 projects, tions of micro, small, and medium-size enterprises and micro, small, and medium-size enterprises newly owned or managed by private companies, vary by source, using these data for precise country are from the IFC's Micro, Small, and Medium that reached financial closure in low- and middle- rankings may be inappropriate. See www.ifc.org/ Enterprises database (www.ifc.org/ifcext/sme. income economies in 1990­2005. Aggregates for ifcext/sme.nsf/Content/Resources for additional nsf/Content/Resources). geographic regions and income groups are calculated information on sources and precise firm size. 2007 World Development Indicators 267 5.2 Investment climate: enterprise surveys Survey Policy Corruption Courts Crime Regulation and tax administration Finance Electricity Labor year uncertainty Lack confidence courts Time dealing Average uphold Tax rates with officials time to Major Major Major property Major as major % of clear Major Major Major constraint constraint constraint constraint rights constraint constraint management customs constraint constraint % % % % % % % time days % % Skills Regulation Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 2005 18.7 31.0 23.2 43.6 8.4 40.9 10.4 1.4 27.1 34.5 10.3 2.5 Algeria 2002 38.8 34.3 .. 27.3 .. 44.6 .. 8.6 62.3 11.4 25.4 12.7 Angola 2006 1.6 12.5 .. 51.0 6.2 3.0 7.1 16.5 11.6 34.5 1.1 .. Argentina 2006 16.5 4.3 2.8 64.0 1.6 14.5 12.3 4.5 15.7 2.5 6.0 15.4 Armenia 2005 14.0 14.5 9.0 46.2 5.5 21.0 2.3 5.5 28.0 3.0 2.0 1.5 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2005 2.5 19.5 6.5 34.7 4.0 24.5 5.2 1.7 12.5 6.0 1.0 1.5 Bangladesh 2002 44.3 57.6 .. 83.0 39.1 35.3 3.7 8.3 56.7 72.9 19.2 8.3 Belarus 2005 23.3 6.2 2.5 33.4 2.8 20.2 3.6 2.8 29.5 0.9 6.5 3.4 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 2004 61.4 81.7 48.7 65.3 47.2 86.8 6.5 6.3 82.7 68.5 25.4 35.0 Bolivia 2006 30.3 8.0 0.1 63.8 2.3 3.6 12.8 10.4 7.3 4.5 2.0 3.2 Bosnia and Herzegovina 2005 33.3 23.1 20.5 41.6 19.0 15.4 4.3 2.0 34.9 8.2 3.6 3.1 Botswana 2006 0.7 7.9 1.4 31.4 10.9 7.3 5.0 1.2 24.3 1.7 9.4 1.5 Brazil 2003 75.8 66.9 32.5 39.6 52.0 84.5 7.2 7.8 84.0 20.3 39.6 56.7 Bulgaria 2005 27.4 18.4 16.7 56.7 11.4 20.4 2.8 1.7 31.1 6.4 10.4 7.7 Burkina Faso 2006 .. 5.1 0.7 29.9 1.4 18.8 9.5 3.1 37.0 19.6 .. .. Burundi 2006 14.3 2.2 0.2 36.9 2.9 3.7 5.7 4.4 16.0 40.7 0.1 .. Cambodia 2003 37.9 55.1 28.9 61.0 41.3 17.8 8.6 6.2 12.2 12.6 6.4 5.6 Cameroon 2006 .. 5.2 1.2 37.3 2.9 32.6 12.8 4.3 13.4 15.1 .. 1.2 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 2004 15.3 12.9 11.9 22.9 14.7 22.8 5.8 4.0 27.1 17.8 23.8 25.4 China 2003 32.9 27.3 24.9 17.5 20.0 36.8 18.5 6.2 29.1 29.7 30.7 20.7 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2006 3.3 2.6 1.6 37.8 13.0 12.5 14.2 6.5 7.6 4.3 9.3 1.8 Congo, Dem. Rep. 2006 5.3 0.5 .. 56.5 1.8 9.6 6.3 3.6 14.5 45.5 1.0 .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2005 28.3 39.9 21.9 28.1 28.0 38.2 9.6 2.8 60.1 16.6 13.4 24.2 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia 2005 17.4 17.4 28.9 26.0 3.8 11.9 2.7 2.0 17.9 2.1 7.2 3.0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 2005 21.7 20.2 24.9 53.1 15.5 58.9 2.1 2.7 25.2 15.5 12.3 15.5 Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic .. .. .. .. .. .. .. .. .. .. .. .. Ecuador 2003 60.7 49.2 34.0 70.8 27.8 38.0 13.4 5.9 46.8 28.3 22.3 14.1 Egypt, Arab Rep. 2004 63.8 50.3 24.7 .. 9.4 80.0 2.1 4.8 36.7 26.5 29.7 28.1 El Salvador 2003 28.4 35.1 16.3 46.6 49.0 22.6 7.2 1.5 37.6 21.5 20.0 3.9 Eritrea 2002 29.1 2.5 11.6 29.0 1.3 29.1 3.8 3.2 57.0 36.7 40.5 5.1 Estonia 2005 5.1 4.2 1.9 29.6 1.9 2.8 2.3 1.7 8.8 3.3 7.0 18.6 Ethiopia 2002 38.2 38.4 4.6 18.2 9.4 72.2 2.1 4.2 50.0 42.5 17.7 4.5 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 2006 2.1 0.6 2.3 28.4 2.3 6.5 7.3 5.0 11.6 53.7 1.7 .. Georgia 2005 44.7 19.6 11.6 29.0 23.6 35.7 3.0 3.4 31.2 33.2 14.1 7.0 Germany 2005 5.8 3.8 2.3 10.3 1.9 29.4 4.5 4.0 23.2 1.0 6.9 9.5 Ghana .. .. .. .. .. .. .. .. .. .. .. .. Greece 2005 9.1 9.8 4.6 18.2 5.2 27.5 3.7 4.9 23.3 4.6 8.5 7.6 Guatemala 2003 66.4 80.9 31.2 71.3 80.4 56.5 12.4 1.9 47.5 26.6 31.4 16.7 Guinea 2006 1.4 2.7 0.4 59.0 1.7 3.1 2.6 4.1 8.3 61.0 .. .. Guinea-Bissau 2006 5.0 6.1 1.4 70.3 0.7 5.3 2.9 5.6 19.6 41.4 .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 268 2007 World Development Indicators 5.2 STATES AND MARKETS Investment climate: enterprise surveys Survey Policy Corruption Courts Crime Regulation and tax administration Finance Electricity Labor year uncertainty Lack confidence courts Time dealing Average uphold Tax rates with officials time to Major Major Major property Major as major % of clear Major Major Major constraint constraint constraint constraint rights constraint constraint management customs constraint constraint % % % % % % % time days % % Skills Regulation Honduras 2003 46.7 62.7 21.6 56.1 60.9 34.4 10.2 1.6 62.4 36.4 26.4 14.2 Hungary 2005 25.5 10.3 24.5 49.7 7.1 49.7 5.3 3.3 32.9 2.3 13.5 9.7 India 2006 9.2 25.0 2.7 25.3 11.8 27.5 6.7 13.6 19.4 32.0 7.9 8.6 Indonesia 2003 48.2 41.5 24.7 40.8 22.0 29.5 4.0 3.4 30.7 22.3 18.9 25.9 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 2005 5.6 3.0 2.8 28.3 4.8 17.4 2.3 2.6 13.8 6.4 15.6 9.6 Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 2005 41.1 45.6 20.0 26.7 54.4 60.0 6.3 4.3 72.2 45.6 41.1 18.9 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. .. .. Kazakhstan 2005 9.7 11.3 14.3 42.0 6.7 16.0 3.2 6.0 20.0 3.7 7.7 2.3 Kenya 2003 49.6 72.5 .. 51.3 69.6 67.8 11.7 4.3 72.5 47.1 27.5 22.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2005 40.4 8.3 3.4 37.2 3.4 14.9 3.2 6.0 15.7 8.3 6.8 4.1 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2005 32.2 32.2 16.3 50.8 19.3 31.2 6.1 4.1 32.7 4.0 18.8 2.5 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 2005 21.8 8.9 5.4 51.3 3.0 29.2 2.9 1.7 9.9 4.5 17.8 3.5 Lebanon 2006 54.1 64.9 56.1 69.8 22.9 61.2 12.0 6.4 65.4 61.2 38.0 38.2 Lesotho 2003 31.1 35.1 24.3 38.2 45.9 41.9 19.8 2.3 54.1 35.1 29.7 17.6 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 2005 22.1 13.2 14.2 49.7 9.3 40.7 5.1 1.8 15.2 3.9 15.2 8.8 Macedonia, FYR 2005 25.5 32.4 28.7 55.4 12.2 19.7 8.2 1.9 41.0 11.7 5.9 8.5 Madagascar 2005 41.0 46.1 33.4 44.6 37.2 44.7 20.8 2.9 68.3 41.3 30.4 14.7 Malawi 2005 5.8 3.2 1.3 28.9 5.1 9.6 5.8 3.5 27.6 19.2 5.8 0.6 Malaysia .. .. .. .. .. .. .. .. .. .. .. .. Mali 2003 20.8 48.7 16.9 33.1 22.1 36.4 7.5 5.8 63.6 24.0 20.8 3.9 Mauritania 2006 0.7 1.5 0.8 35.6 1.2 12.8 5.8 3.9 21.6 13.0 3.8 0.4 Mauritius 2005 23.9 36.1 22.9 29.4 25.4 27.8 9.6 3.3 52.7 12.7 42.9 27.8 Mexico 2006 8.5 17.8 0.1 63.6 7.3 10.6 20.2 4.5 8.7 8.2 3.1 1.1 Moldova 2005 32.2 19.6 23.6 64.7 17.6 44.2 3.5 2.6 40.7 3.5 12.1 9.5 Mongolia 2004 39.2 48.5 24.7 37.1 27.8 64.9 5.7 3.5 64.4 25.8 28.9 10.3 Morocco 2004 39.2 16.9 29.1 23.5 7.6 62.6 7.5 2.0 84.4 8.9 21.1 16.2 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 2006 0.8 9.3 0.6 24.9 20.6 17.2 2.9 1.3 11.8 3.1 9.4 4.4 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2003 58.2 65.7 33.2 60.4 39.2 34.7 13.0 1.5 65.9 34.7 17.0 6.9 Niger 2005 .. 11.2 1.6 50.0 .. 32.8 11.5 4.0 12.0 1.6 .. 0.8 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman 2003 20.5 11.9 14.8 12.9 8.6 20.5 6.9 3.4 38.0 10.1 34.4 34.7 Pakistan 2002 40.1 40.3 20.0 62.6 21.5 45.6 8.7 8.9 47.5 39.2 12.7 15.0 Panama 2006 4.2 10.8 1.1 51.6 7.3 14.6 10.2 5.0 6.6 30.6 3.8 4.4 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 2006 11.5 14.8 3.0 74.1 3.1 1.4 10.4 4.4 21.0 1.9 6.2 5.4 Peru 2006 17.0 4.3 0.6 75.2 5.5 7.7 8.2 4.1 9.3 2.2 2.4 4.0 Philippines 2003 29.5 35.2 13.0 33.8 26.5 30.4 6.9 5.4 25.0 33.4 11.9 24.7 Poland 2005 39.9 15.0 30.6 45.4 15.4 55.4 3.8 3.1 46.3 4.0 13.8 16.9 Portugal 2005 21.5 14.3 17.0 47.7 14.9 19.6 3.2 6.6 25.6 7.8 12.1 17.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 269 5.2 Investment climate: enterprise surveys Survey Policy Corruption Courts Crime Regulation and tax administration Finance Electricity Labor year uncertainty Lack confidence courts Time dealing Average uphold Tax rates with officials time to Major Major Major property Major as major % of clear Major Major Major constraint constraint constraint constraint rights constraint constraint management customs constraint constraint % % % % % % % time days % % Skills Regulation Romania 2005 35.3 27.6 31.1 44.0 15.1 34.0 1.5 2.6 31.7 6.4 12.5 15.1 Russian Federation 2005 25.8 15.4 8.6 63.9 9.0 21.6 6.3 7.2 21.8 5.1 12.8 3.0 Rwanda 2006 0.9 0.8 .. 34.6 .. 26.9 5.9 6.7 13.6 31.8 2.8 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2003 30.5 39.3 13.0 40.5 14.9 50.0 3.2 5.9 71.0 30.5 18.3 16.0 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 2005 12.7 10.0 12.3 44.4 5.0 8.2 3.0 5.8 10.9 2.7 8.2 4.5 Slovenia 2005 11.3 3.6 8.1 34.4 0.9 12.6 3.7 2.9 14.9 2.7 5.4 4.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2003 17.9 16.1 8.8 20.8 29.0 18.6 9.2 4.3 22.6 9.0 35.5 32.8 Spain 2005 10.1 7.6 7.8 16.6 9.6 18.7 0.8 3.7 19.7 8.3 13.8 11.8 Sri Lanka 2004 34.0 16.9 .. 31.2 14.0 19.1 3.5 3.1 28.0 41.3 21.3 25.6 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 2006 0.6 5.2 1.0 56.7 18.5 15.4 4.4 1.9 10.3 6.8 2.3 0.4 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 2003 26.4 57.1 8.6 .. 29.6 62.3 10.3 5.6 30.0 57.5 36.1 33.0 Tajikistan 2005 5.5 14.5 4.5 35.9 4.0 22.0 3.3 4.8 10.0 10.0 4.5 1.5 Tanzania 2006 0.5 0.5 .. 35.4 1.9 3.9 4.0 4.8 9.3 72.9 1.4 .. Thailand 2004 29.1 18.3 38.6 25.8 10.3 24.4 1.3 1.3 20.6 25.6 30.0 11.4 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 2005 31.1 16.7 25.5 28.5 18.2 37.6 .. 4.5 25.0 9.2 9.7 12.1 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 2006 0.3 2.4 0.1 35.6 0.2 11.0 5.2 2.9 6.7 63.3 0.4 .. Ukraine 2005 31.0 21.2 13.7 48.2 11.9 45.6 8.1 3.9 40.5 4.9 19.8 6.4 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 2006 5.8 2.4 1.5 37.9 5.3 20.8 6.8 1.7 11.8 5.2 1.8 7.2 Uzbekistan 2005 10.6 6.5 5.1 41.7 6.8 18.1 2.5 4.7 16.0 7.2 4.1 2.7 Venezuela, RB .. .. .. .. .. .. .. .. .. .. .. .. Vietnam 2005 14.0 11.2 4.9 23.1 3.7 13.6 5.8 2.5 40.5 15.7 22.2 10.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 2002 56.5 45.9 38.6 36.1 48.8 57.5 13.0 1.6 84.5 39.6 35.7 16.9 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. Note: Data are based on enterprise surveys conducted by the World Bank and its partners during 2002­06. While averages are reported, there are significant variations across firms. Surveys of Eastern Europe and Central Asia were conducted under the joint World Bank­European Bank for Reconstruction and Development Business Environment and Enterprise Performance Surveys Initiative. 270 2007 World Development Indicators 5.2 STATES AND MARKETS Investment climate: enterprise surveys About the data Definitions The World Bank Group's Enterprise Surveys capture infrastructure-- telecommunications, reliable elec- · Survey year is the year in which the underlying data business perceptions on the biggest obstacles to tricity supplies, and effi cient transportation--are were collected. · Policy uncertainty measures the enterprise growth, the relative importance of con- more productive, and improvements in infrastruc- percentage of senior managers who ranked economic straints to increasing employment and productivity, ture services also benefit households. Ill-considered and regulatory policy uncertainty as a major or very and the effects of a country's investment climate on labor regulations can discourage firms from creating severe constraint. · Corruption measures the per- its international competitiveness. These surveys cover more jobs, and while some employees may benefit, centage of senior managers who ranked corruption almost 58,000 firms in 97 countries for 2002­06. In the unemployed, the low skilled, and those working as a major or very severe constraint. · Courts mea- addition to these surveys, data from the Doing Busi- in the informal economy will not. sure the percentage of senior managers who ranked ness project, which benchmarks regulatory regimes in Data in this table for 27 countries in the Europe and courts and dispute resolution systems as a major or 175 countries, are presented in table 5.3. Central Asia region plus 7 comparators in Europe and very severe constraint. · Lack confi dence courts Improving government policies and behaviors is key Asia (Germany, Greece, Ireland, Republic of Korea, uphold property rights measures the percentage of to shaping the investment climate because they are Portugal, Spain, and Vietnam) are based on the joint managers who do not agree with the statement: "I influential in driving growth and poverty reduction. European Bank for Reconstruction and Development­ Firms evaluating alternative investment options, gov- World Bank Business Environment and Enterprise Per- am confident that the judicial system will enforce ernments interested in improving their investment formance Surveys (BEEPS). All other data are from the my contractual and property rights in business dis- climate, and economists seeking to understand the World Bank Group's Enterprise Surveys. Both surveys putes." · Crime measures the percentage of senior role of different factors in explaining economic per- sample the universe of registered businesses and managers who ranked crime, theft, and disorder as formance have all grappled with defining and measur- follow either a simple random sample or a stratified a major or very severe constraint. · Tax rates as ing the investment climate. random sample methodology, drawing from registered major constraint measure the percentage of senior The indicators in the table cover eight dimensions establishments. BEEPS use a simple random sample managers who ranked tax rates as a major or very of the investment climate: policy uncertainty, corrup- methodology based on population proportions and severe constraint. · Time dealing with officials is tion, courts, crime, regulation and tax administration, can be compared across countries. In the Enterprise the percentage of management time in a given week finance, infrastructure (electricity), and labor. Surveys a random sample across sectors is supple- spent on requirements imposed by government regu- Firms in developing countries rate access to and mented by an emphasis on firms from the same few lations (taxes, customs, labor regulations, licensing, cost of finance as their dominant concern among selected manufacturing industries plus the retail sec- and registration). · Average time to clear customs investment climate constraints. Another highly ranked tor as a means of providing measures of productivity is the number of days to clear an imported good constraint is policy uncertainty, which measures the that can be compared across economies. Because through customs. · Finance measures percentage credibility of governments and their policies and the the distribution of establishments in most countries is of senior managers who ranked access to finance or ability to deliver on promises. Corruption--the exploi- overwhelmingly populated by small and medium-size cost of finance as a major or very severe constraint. tation of public office for private gain--can harm the enterprises, Enterprise Surveys generally oversample investment climate in several ways. It can distort large establishments. Other differences include the · Electricity measures the percentage of senior policymaking, undermine government credibility, tax question related to "problems doing business," which managers who ranked electricity as a major or severe entrepreneurial activities, and divert resources from offers a similar but slightly different response scale constraint. · Labor skills measure the percentage public coffers. Better courts reduce the risks firms between the two surveys. As a result, the data are not of senior managers who ranked skills of available face, so that firms are willing to invest more. And the strictly comparable. In the two countries where the workers as a major or severe constraint. · Labor importance of courts grows as the number of large and two surveys overlapped (Turkey and Vietnam), BEEPS regulations measure the percentage of senior man- complex long-term transactions increases. Robbery, survey data were used for Turkey and Enterprise Sur- agers who ranked labor regulations as a major or fraud, and other crimes against property and against vey data were used for Vietnam (a BEEPS comparator severe constraint. the person undermine the investment climate. country). Sample sizes for recent surveys range from Most countries have room to improve regulation 200 to 1,500 businesses. and taxation without compromising broader social For the 2006 Enterprise Surveys in Africa and Latin interests. The investment climate is harmed when America and for the 2005 surveys for Malawi and governments impose unnecessary costs, by increas- Niger the indicators for severity of constraints reflect ing uncertainty and risk and by erecting unjustified the percentage of managers who identified a particu- barriers to competition. Improvements in the tax sys- lar constraint as their biggest. In any given country tem may include broadening the tax base, simplifying some constraints may not be identified by any firms tax structures, increasing the autonomy of tax agen- as the biggest constraint even if they would be consid- cies, and improving compliance through computeriza- ered major or severe. The 2006 Enterprise Surveys Data sources tion. When financial markets work well, they connect in Latin America and Africa, except those for Burkina firms to lenders and investors, which allow firms to Faso, Cameroon, and Cape Verde, have weights for Data on the investment climate are from the World seize business opportunities and grow their busi- the estimates of the aggregate indicators in order to Bank Group's Enterprise Surveys website (www. nesses. But too often government distortions intro- account for the random stratified sample design. enterprisesurveys.org/), which compiles data duced by state ownership or directed credit under- For more information on the investment climate from surveys undertaken by the World Bank and mine financial sector development, productivity, and and Enterprise Surveys, see www.worldbank.org/ other development partners. economic growth. Firms that have access to modern eca/econ and www.enterprisesurveys.org/. 2007 World Development Indicators 271 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property licenses workers contracts investors business Rigidity of Disclosure Time employment index Cost Number of required index 0 (less Time to Time % of per Time procedures to build a 0 (less rigid) Time disclosure) resolve Number of required capita Number of required to build a warehouse to 100 (more Number of required to 10 (more insolvency procedures days income procedures days warehouse days rigid) procedures days disclosure) years April April April April April April April April April April April April 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Afghanistan 3 8 67.4 11 252 .. .. 46 .. 1,642 0 .. Albania 11 39 22.4 7 47 22 344 38 39 390 0 4.0 Algeria 14 24 21.5 15 51 25 244 45 49 397 6 2.5 Angola 13 124 486.7 7 334 15 326 64 47 1,011 5 6.2 Argentina 15 32 12.1 5 44 23 288 41 33 520 6 2.8 Armenia 9 24 5.1 3 4 18 112 31 24 185 5 1.9 Australia 2 2 1.8 5 5 17 140 3 19 181 8 1.0 Austria 9 29 5.6 3 32 14 195 37 23 342 2 1.1 Azerbaijan 15 53 9.5 7 61 28 212 38 27 267 4 2.7 Bangladesh 8 37 87.6 8 425 13 185 30 50 1,442 6 4.0 Belarus 16 69 26.1 7 231 18 354 27 28 225 1 5.8 Belgium 4 27 5.8 7 132 15 184 20 27 328 8 0.9 Benin 7 31 173.3 3 50 16 333 46 49 720 5 4.0 Bolivia 15 50 140.6 7 92 14 183 74 47 591 1 1.8 Bosnia and Herzegovina 12 54 37.0 7 331 16 467 42 36 595 3 3.3 Botswana 11 108 10.6 4 30 24 169 20 26 501 8 1.3 Brazil 17 152 9.9 14 47 19 460 42 42 616 5 4.0 Bulgaria 9 32 7.9 9 19 22 226 47 34 440 10 3.3 Burkina Faso 8 34 120.8 8 107 32 226 64 41 446 6 4.0 Burundi 11 43 222.4 5 94 18 302 59 47 403 .. 4.0 Cambodia 10 86 236.4 7 56 28 181 49 31 401 5 .. Cameroon 12 37 152.2 5 93 15 444 56 58 800 8 3.2 Canada 2 3 0.9 6 10 15 77 4 17 346 8 0.8 Central African Republic 10 14 209.3 3 69 21 245 73 45 660 4 4.8 Chad 19 75 226.1 6 44 16 199 60 52 743 3 10.0 Chile 9 27 9.8 6 31 12 171 24 33 480 8 5.6 China 13 35 9.3 3 32 29 367 24 31 292 10 2.4 Hong Kong, China 5 11 3.3 5 54 22 160 0 16 211 10 1.1 Colombia 13 44 19.8 7 23 12 150 27 37 1,346 7 3.0 Congo, Dem. Rep. 13 155 481.1 8 57 14 306 78 51 685 3 5.2 Congo, Rep. 8 71 214.8 7 137 15 175 69 47 560 4 3.0 Costa Rica 11 77 23.5 6 21 19 119 32 34 615 2 3.5 Côte d'Ivoire 11 45 134.1 6 32 22 569 45 25 525 6 2.2 Croatia 10 45 12.2 5 399 28 278 50 22 561 2 3.1 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 10 24 8.9 4 123 31 271 28 21 820 2 9.2 Denmark 3 5 0.0 6 42 7 70 17 15 190 7 3.0 Dominican Republic 10 73 30.2 7 107 17 165 42 29 460 5 3.5 Ecuador 14 65 31.8 10 20 19 149 51 41 498 1 8.0 Egypt, Arab Rep. 10 19 68.8 7 193 30 263 53 55 1,010 5 4.2 El Salvador 10 26 75.6 6 33 22 144 24 41 626 6 4.0 Eritrea 13 76 115.9 12 101 .. .. 20 35 305 4 1.7 Estonia 6 35 5.1 3 51 13 117 58 25 275 8 3.0 Ethiopia 7 16 45.9 13 43 12 133 34 30 690 4 2.4 Finland 3 14 1.1 3 14 17 56 48 27 228 6 0.9 France 7 8 1.1 9 183 10 155 56 21 331 10 1.9 Gabon 10 60 162.8 8 60 13 268 59 32 880 5 5.0 Gambia, The 8 27 292.1 5 371 17 145 27 26 247 2 3.0 Georgia 7 16 10.9 6 9 17 137 7 24 285 4 3.3 Germany 9 24 5.1 4 40 11 133 44 30 394 5 1.2 Ghana 12 81 49.6 7 382 16 127 34 29 552 7 1.9 Greece 15 38 24.2 12 23 17 176 58 22 730 1 2.0 Guatemala 13 30 52.1 5 37 23 390 34 36 1,459 3 3.0 Guinea 13 49 186.5 6 104 29 278 41 44 276 5 3.8 Guinea-Bissau 17 233 261.2 9 211 11 161 77 40 1,140 0 .. Haiti 12 203 127.7 5 683 12 141 24 35 368 4 5.7 272 2007 World Development Indicators 5.3 STATES AND MARKETS Business environment: Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property licenses workers contracts investors business Rigidity of Disclosure Time employment index Cost Number of required index 0 (less Time to Time % of per Time procedures to build a 0 (less rigid) Time disclosure) resolve Number of required capita Number of required to build a warehouse to 100 (more Number of required to 10 (more insolvency procedures days income procedures days warehouse days rigid) procedures days disclosure) years April April April April April April April April April April April April 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Honduras 13 44 60.6 7 36 14 199 36 36 480 1 3.8 Hungary 6 38 20.9 4 78 25 212 34 21 335 2 2.0 India 11 35 73.7 6 62 20 270 41 56 1,420 7 10.0 Indonesia 12 97 86.7 7 42 19 224 44 34 570 8 5.5 Iran, Islamic Rep. 8 47 5.4 9 36 21 668 49 23 520 5 4.5 Iraq 11 77 67.6 5 8 14 216 59 65 520 4 .. Ireland 4 19 0.3 5 38 10 181 33 18 217 10 0.4 Israel 5 34 5.1 7 144 21 215 27 31 585 7 4.0 Italy 9 13 15.2 8 27 17 284 54 40 1,210 7 1.2 Jamaica 6 8 9.4 5 54 14 242 4 18 415 4 1.1 Japan 8 23 7.5 6 14 11 96 29 20 242 7 0.6 Jordan 11 18 73.0 8 22 16 122 27 43 342 5 4.3 Kazakhstan 7 20 7.0 8 52 32 248 23 37 183 7 3.3 Kenya 13 54 46.3 8 73 11 170 28 25 360 4 4.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 12 22 15.2 7 11 14 52 34 29 230 7 1.5 Kuwait 13 35 1.6 8 55 26 149 13 52 390 7 4.2 Kyrgyz Republic 8 21 9.8 7 8 20 218 38 44 140 8 4.0 Lao PDR 8 163 17.3 9 135 24 192 37 53 443 0 5.0 Latvia 5 16 3.5 8 54 22 152 59 21 240 5 3.0 Lebanon 6 46 105.4 8 25 16 275 24 39 721 9 4.0 Lesotho 8 73 39.9 6 101 14 265 35 58 695 2 2.6 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 7 26 2.8 3 3 14 151 48 24 166 6 1.7 Macedonia, FYR 10 18 7.4 6 98 18 222 54 27 385 5 3.7 Madagascar 10 21 35.0 8 134 19 297 57 29 591 5 .. Malawi 10 37 134.7 6 118 22 185 21 40 337 4 2.6 Malaysia 9 30 19.7 5 144 25 281 10 31 450 10 2.3 Mali 13 42 201.9 5 33 15 209 51 28 860 6 3.6 Mauritania 11 82 121.6 4 49 19 152 59 40 400 0 8.0 Mauritius 6 46 8.0 6 210 21 145 30 37 630 6 1.7 Mexico 8 27 14.2 5 74 12 142 38 37 415 8 1.8 Moldova 10 30 13.3 6 48 34 158 54 37 310 7 2.8 Mongolia 8 20 5.1 5 11 18 96 34 29 314 5 4.0 Morocco 6 12 12.7 4 46 21 217 63 42 615 6 1.8 Mozambique 13 113 85.7 8 42 13 364 54 38 1,010 7 5.0 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 10 95 18.0 9 23 11 105 27 31 270 5 1.5 Nepal 7 31 78.5 3 5 15 424 52 28 590 6 5.0 Netherlands 6 10 7.2 2 5 18 184 42 22 408 4 1.7 New Zealand 2 12 0.2 2 2 7 184 7 28 109 10 2.0 Nicaragua 6 39 131.6 8 124 12 192 24 20 486 4 2.2 Niger 11 24 416.8 5 49 19 148 77 33 360 4 5.0 Nigeria 9 43 54.4 16 80 16 465 21 23 457 6 1.5 Norway 4 13 2.5 1 1 13 104 54 14 277 7 0.9 Oman 9 34 4.5 2 16 16 242 35 41 598 8 4.0 Pakistan 11 24 21.3 6 50 12 218 43 55 880 6 2.8 Panama 7 19 23.9 7 44 22 121 56 45 686 3 2.5 Papua New Guinea 8 56 28.2 4 72 20 218 10 22 440 5 3.0 Paraguay 17 74 136.8 6 46 15 273 59 46 478 6 3.9 Peru 10 72 32.5 5 33 19 201 61 35 300 8 3.1 Philippines 11 48 18.7 8 33 23 197 39 25 600 1 5.7 Poland 10 31 21.4 6 197 25 322 33 41 980 7 3.0 Portugal 8 8 4.3 5 81 20 327 51 24 495 6 2.0 Puerto Rico 7 7 0.8 8 15 20 212 32 43 620 7 3.8 2007 World Development Indicators 273 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property licenses workers contracts investors business Rigidity of Disclosure Time employment index Cost Number of required index 0 (less Time to Time % of per Time procedures to build a 0 (less rigid) Time disclosure) resolve Number of required capita Number of required to build a warehouse to 100 (more Number of required to 10 (more insolvency procedures days income procedures days warehouse days rigid) procedures days disclosure) years April April April April April April April April April April April April 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Romania 5 11 4.4 8 150 17 242 51 43 335 9 4.6 Russian Federation 7 28 2.7 6 52 22 531 44 31 178 7 3.8 Rwanda 9 16 188.3 5 371 17 252 49 27 310 2 .. Saudi Arabia 13 39 58.6 4 4 18 125 7 44 360 8 2.8 Senegal 10 58 112.6 6 114 15 185 61 33 780 4 3.0 Serbia and Montenegroa 10 18 10.2 6 111 20 211 38 33 635 7 2.7 Sierra Leone 9 26 1,194.5 8 235 48 236 63 58 515 3 2.6 Singapore 6 6 0.8 3 9 11 129 0 29 120 10 0.8 Slovak Republic 9 25 4.8 3 17 13 272 39 27 565 2 4.0 Slovenia 9 60 9.4 6 391 14 207 57 25 1,350 3 2.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 9 35 6.9 6 23 17 174 41 26 600 8 2.0 Spain 10 47 16.2 3 17 11 277 63 23 515 5 1.0 Sri Lanka 8 50 9.2 8 63 17 167 27 20 837 4 2.2 Sudan 10 39 58.6 6 9 17 172 55 67 770 0 .. Swaziland 13 61 41.1 11 46 11 114 17 31 972 1 2.0 Sweden 3 16 0.7 1 2 8 116 43 19 208 6 2.0 Switzerland 6 20 2.2 4 16 15 152 23 22 215 0 3.0 Syrian Arab Republic 12 43 21.1 4 34 20 134 30 47 872 6 4.1 Tajikistan 14 67 75.1 6 37 18 187 31 46 257 0 3.0 Tanzania 13 30 91.6 10 123 26 313 67 21 393 3 3.0 Thailand 8 33 5.8 2 2 9 127 18 26 425 10 2.7 Togo 13 53 252.7 7 242 14 273 58 37 535 4 3.0 Trinidad and Tobago 9 43 1.1 8 162 19 292 7 37 1,340 4 .. Tunisia 10 11 9.3 5 57 24 79 46 21 481 0 1.3 Turkey 8 9 26.8 8 9 32 232 49 34 420 8 5.9 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 17 30 114.0 13 227 19 156 7 19 484 7 2.2 Ukraine 10 33 9.2 10 93 18 242 55 28 183 1 2.9 United Arab Emirates 12 63 36.4 3 6 21 125 20 34 607 4 5.1 United Kingdom 6 18 0.7 2 21 19 115 14 19 229 10 1.0 United States 5 5 0.7 4 12 18 69 0 17 300 7 1.5 Uruguay 10 43 44.2 8 66 17 156 31 39 655 3 2.1 Uzbekistan 8 29 14.1 12 97 19 287 34 35 195 4 4.0 Venezuela, RB 16 141 25.4 8 47 13 276 76 41 435 3 4.0 Vietnam 11 50 44.5 4 67 14 133 37 37 295 4 5.0 West Bank and Gaza 12 93 324.7 10 72 21 134 31 26 700 7 .. Yemen, Rep. 12 63 228.0 6 21 13 107 33 37 360 6 3.0 Zambia 6 35 29.9 6 70 16 196 23 21 404 3 3.1 Zimbabwe 10 96 35.6 4 30 21 481 34 33 410 8 3.3 World 9u 48 u 69.0 u 6u 86 u 18 u 205 u 37 u 35 u 540 u 5u 2.9 u Low income 10 59 146.3 7 125 18 231 44 39 572 4 3.1 Middle income 10 51 48.6 6 80 18 210 35 35 575 5 3.2 Lower middle income 10 57 62.9 7 85 18 215 35 35 576 4 3.2 Upper middle income 9 42 26.0 6 72 18 201 35 36 573 5 3.2 Low & middle income 10 54 83.5 7 96 18 217 38 37 574 5 3.2 East Asia & Pacific 9 50 48.8 5 112 18 152 24 33 507 5 2.6 Europe & Central Asia 9 31 14.7 6 91 22 248 40 31 357 5 3.7 Latin America & Carib. 10 77 51.0 7 81 15 200 32 39 655 4 3.3 Middle East & N. Africa 10 40 89.5 7 48 19 223 42 42 643 6 2.8 South Asia 8 33 46.6 7 136 16 227 35 39 969 4 3.6 Sub-Saharan Africa 11 62 162.9 7 110 18 236 47 38 581 4 3.0 High income 7 21 7.7 5 44 16 155 30 26 398 6 1.8 Europe EMU 8 22 7.8 6 54 15 196 46 25 473 6 1.3 a. Data are for Serbia only. 274 2007 World Development Indicators 5.3 STATES AND MARKETS Business environment: Doing Business indicators About the data Definitions The table presents key indicators on the environment and take values between 0 and 100, with higher val- · Number of procedures for starting a business for doing business. The indicators identify regula- ues indicating more rigid regulation. is the number of procedures required to start a tions that enhance or constrain business invest- Contract enforcement is critical to enable busi- business, including interactions required to obtain ment, productivity, and growth. The data are from nesses to engage with new borrowers or customers. necessary permits and licenses and to complete all the World Bank's Doing Business database, which Without good contract enforcement, trade and credit inscriptions, verifications, and notifications to start now includes data on 175 economies. will be restricted to a small community of people operations. Data are for businesses with specific A vibrant private sector is central to promoting who have developed relationships through repeated characteristics of ownership, size, and type of pro- growth and expanding opportunities for poor people. dealings or the security of assets. The institution duction. · Time required for starting a business is But encouraging firms to invest, improve productivity, that enforces contracts between debtors and credi- the number of calendar days needed to complete and create jobs requires a legal and regulatory envi- tors, and suppliers and customers, is the court. The the required procedures for legally operating a busi- ronment that fosters access to credit, protects prop- effi ciency of contract enforcement is refl ected in ness. If a procedure can be speeded up at additional erty rights, and supports efficient judicial, taxation, two indicators: the number of judicial procedures to cost, the fastest procedure, independent of cost, is and customs systems. The indicators in the table resolve a dispute and the time it takes to enforce a chosen. · Cost for starting a business is normalized point to the administrative and regulatory reforms commercial contract. by presenting it as a percentage of gross national and institutions needed to create a favorable environ- What companies disclose to the public has a large income (GNI) per capita. · Number of procedures ment for doing business. impact on investor protection. Both investors and entre- for registering property is the number of proce- When entrepreneurs start a business, the first preneurs benefit greatly from such legal protection. dures required for a business to secure rights to obstacles they face are the administrative and legal The disclosure index is based on several measures property · Time required for registering property is procedures required to register the new firm. Coun- that cover ownership disclosure measures that reduce the number of calendar days needed for a business tries differ widely in how they regulate the entry of new expropriation, and disclosures to help investors. to secure rights to property · Number of procedures businesses. In some countries the process is straight- Unviable businesses prevent assets and human to build a warehouse is the number of interactions forward and affordable. But in others the procedures capital from being allocated to more productive uses of a company's employees or managers with external are so burdensome that entrepreneurs may opt to in new companies or in viable companies that are parties, including government agency staff, public run their business informally. The data on starting a financially distressed. The time to close a business inspectors, notaries, land registry and cadastre business cover the number of start-up procedures, the (resolve an insolvency) captures the average time to staff, and technical experts apart from architects time required, and cost to complete them. complete a procedure, as estimated by insolvency and engineers · Time required to build a warehouse Property registries were first developed to help lawyers. Information is collected on the sequence of is the number of calendar days needed to complete raise tax revenue, but they have benefited entrepre- bankruptcy procedures, and on whether any proce- the required procedures for building a warehouse. If a neurs as well. Securing rights to land and buildings, dures can be carried out simultaneously. Delays due procedure can be speeded up at additional cost, the a major source of wealth in most countries, strength- to legal derailment tactics that parties to the insol- fastest procedure, independent of cost, is chosen. ens incentives to invest and facilitates trade. More vency may use, in particular extension of response · Rigidity of employment index measures the regu- complex procedures to register property are associ- periods or appeals, are taken into account. lation of employment, specifically the employing of ated with less perceived security of property rights, To ensure cross-country comparability, several workers and the rigidity of working hours. This index more informality, and more corruption. The data standard characteristics of a company are defined in is the average of three subindexes: a difficulty of cover the number procedures required and time all surveys, such as size, ownership, location, legal hiring index, a rigidity of hours index, and a difficulty required to secure rights to property. status, and type of activities undertaken. For exam- of firing index. The index ranges from 0 and 100, Lack of access to credit is one of the biggest bar- ple, for the starting a business data, these standard with higher values indicating more rigid regulations. riers entrepreneurs face in starting and operating a characteristics include that the business is a limited · Number of procedures for enforcing contracts is business. Indicators covering access to credit and liability company; operates in the country's most the number of independent actions, mandated by financial information are presented in table 5.5. populous city; is 100 percent domestically owned law or court regulation, that demand interaction There are many types of business licenses required, and has five owners, none of whom is a legal entity; between the parties to a contract or between them and striking the right balance between the ease of has start-up capital of 10 times income per capita at and the judge or court officer. · Time required for doing business and consumer safety requires continu- the end of 2005, has paid in cash; performs general enforcing contracts is the number of calendar days ous reform. Since construction is a large sector in industrial or commercial activities, such as produc- from the time of the filing of the lawsuit in court to most economies, the procedures required for a busi- tion or sale of products or services to the public; does the final determination and payment. · Disclosure ness in the construction industry to build a standard- not perform foreign trade activities or handle prod- index measures the degree to which investors are ized warehouse are recorded. These include obtain- ucts subject to a special tax regime; does not use protected through disclosure of ownership and finan- ing all necessary licenses and permits, completing all heavily polluting production processes; leases the cial information. The index ranges from 0 to 10, with required notifications and inspections, and submitting commercial plant and offices and is not a proprietor higher values indicating more disclosure. · Time to the relevant documents to the authorities. The data of real estate; does not qualify for investment incen- resolve insolvency is the number of years from the cover the number of procedures and time needed by tives or any special benefits; has up to 50 employees time of filing for insolvency in court until resolution the construction firm to complete all procedures. within one month of commencement of operations, of distressed assets. Every economy has a complex system of laws and all of them nationals; has turnover at least 100 times institutions to protect the interests of workers and income per capita; and has a company deed at least guarantee a minimum standard of living for its popu- 10 pages long. The data were collected through a lation. The rigidity of employment index focuses on study of laws and regulations in each country, surveys Data sources the regulation of employment, specifically employing of regulators or private sector professionals on each Data on the business environment are from the workers and the rigidity of working hours. This index topic, and cooperative arrangements with private con- World Bank's Doing Business project (www.doing- is the average of three subindexes: a difficulty of hir- sulting firms and business and law associations. ing index, a rigidity of hours index, and a difficulty of For more information on the methodology, see business.org). firing index. All subindexes have several components www.doingbusiness.org/. 2007 World Development Indicators 275 5.4 Stock markets Market Market Turnover Listed domestic S&P/EMDB capitalization liquidity ratio companies indexes Value of shares traded Value traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2006 2000 2005 2000 2005 2000 2006 2000 2006 2005 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 166,068 79,730 58.4 33.6 2.1 9.0 4.8 6.6 127 103 45.4 a 57.6a Armenia 2 43 0.1 0.9 0.0 0.0 4.6 3.7 105 198 .. .. Australia 372,794 804,074 93.3 109.8 56.6 84.1 56.5 78.0 1,330 1,643 .. .. Austria 29,935 126,324 15.4 41.3 4.8 15.0 29.8 43.3 97 92 .. .. Azerbaijan 4 .. 0.1 .. .. .. .. .. 2 .. .. .. Bangladesh 1,186 3,610 2.5 5.1 1.6 1.7 74.4 31.7 221 269 ­27.7b 12.9 b Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 182,481 327,065 78.7 88.2 16.4 30.7 20.7 20.8 174 145 .. .. Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 1,742 2,200 20.7 23.6 0.8 0.0 0.1 0.1 26 36 .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 978 3,947 15.8 23.6 0.8 0.4 4.8 2.4 16 18 ­3.2b 53.0 b Brazil 226,152 711,100 37.6 59.6 16.8 19.4 43.5 42.5 459 392 47.6a 43.1a Bulgaria 617 10,325 4.9 19.1 0.5 5.2 9.2 25.0 503 347 16.4b 31.4b Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 841,385 1,480,891 117.8 133.0 88.8 75.9 77.3 63.6 1,418 3,721 .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 60,401 174,556 80.3 118.4 8.1 16.4 9.4 19.2 258 244 14.7a 28.6a China 580,991 2,426,326 48.5 34.9 60.2 26.2 158.3 136.4 1,086 1,440 13.3a 80.7a Hong Kong, China 623,398 1,006,228 369.4 566.2 223.9 258.9 61.3 49.3 779 1,126 .. .. Colombia 9,560 56,204 11.4 37.6 0.5 5.2 3.8 22.3 126 114 108.1b 12.7b Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2,924 1,478 18.3 7.4 0.7 0.8 12.0 7.7 21 19 .. .. Côte d'Ivoire 1,185 4,155 11.4 14.2 0.3 0.2 2.6 3.7 41 40 16.9 b 35.6b Croatia 2,742 29,006 14.9 33.5 1.0 2.1 7.4 9.8 64 183 7.4b 85.2b Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 11,002 48,604 19.4 30.8 11.6 33.0 60.3 77.5 131 29 43.5a 30.9a Denmark 107,666 178,038 67.3 68.8 57.2 58.8 86.0 92.3 225 168 .. .. Dominican Republic 141 .. 0.8 .. .. .. .. .. 6 .. .. .. Ecuador 704 4,040 4.4 8.8 0.1 0.4 5.5 9.0 30 34 26.0 b 32.0 b Egypt, Arab Rep. 28,741 93,477 28.8 89.1 11.1 28.4 34.7 55.2 1,076 603 158.0 a 10.2a El Salvador 2,041 3,623 15.5 21.3 0.2 0.4 1.3 2.3 40 35 .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 1,846 5,963 33.7 26.7 6.0 18.9 18.9 27.5 23 16 22.8 b 30.3b Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 293,635 209,504 243.6 108.5 171.4 141.6 64.3 139.1 154 134 .. .. France 1,446,634 1,710,029 108.9 80.4 81.6 69.4 74.1 82.7 808 664 .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 24 355 0.8 5.5 0.1 0.6 .. 13.6 269 257 .. .. Germany 1,270,243 1,221,250 66.8 43.7 56.3 63.1 79.1 146.0 1,022 648 .. .. Ghana 502 1,729 10.1 12.8 0.2 0.4 1.5 3.4 22 32 ­33.9 b 9.7b Greece 110,839 145,013 96.7 64.4 83.0 29.0 63.7 48.3 329 307 .. .. Guatemala 240 .. 1.2 .. 0.0 .. 0.0 .. 44 .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 276 2007 World Development Indicators 5.4 STATES AND MARKETS Stock markets Market Market Turnover Listed domestic S&P/EMDB capitalization liquidity ratio companies indexes Value of shares traded Value traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2006 2000 2005 2000 2005 2000 2006 2000 2006 2005 2006 Honduras 458 .. 8.8 .. .. .. .. .. 46 .. .. .. Hungary 12,021 41,935 25.6 29.8 25.8 21.9 90.7 88.2 60 41 16.1a 31.4 a India 148,064 818,879 32.2 68.6 110.8 55.0 133.6 96.4 5,937 4,796 33.6a 46.7a Indonesia 26,834 138,886 16.3 28.4 8.7 14.6 32.9 45.5 290 344 9.1a 67.9a Iran, Islamic Rep. 7,350 38,724 7.3 20.4 1.1 4.3 12.7 19.1 304 420 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 81,882 114,134 85.1 56.6 15.0 32.1 19.2 56.7 76 53 .. .. Israel 64,081 173,306 55.5 97.3 20.3 48.5 36.3 60.7 654 612 24.1a ­6.3a Italy 768,364 798,167 70.0 45.3 70.9 63.3 104.0 140.5 291 275 .. .. Jamaica 3,582 12,277 44.6 136.1 0.9 4.5 2.5 3.0 46 41 ­14.1b ­1.5b Japan 3,157,222 4,736,513 67.9 104.5 57.9 110.2 69.9 118.8 2,561 3,279 21.7c 5.9 c Jordan 4,943 29,729 58.4 296.1 4.9 187.3 7.7 59.0 163 227 117.8 b ­36.0 b Kazakhstan 1,342 10,521 7.3 18.4 0.5 1.9 25.1 14.9 23 83 .. .. Kenya 1,283 11,378 10.1 34.1 0.4 2.7 3.6 15.8 57 51 60.0 b 60.3b Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 171,587 835,188 33.5 91.2 208.7 152.7 233.2 173.7 1,308 1,694 58.8a 13.3a Kuwait 20,772 128,940 55.1 161.0 11.2 116.4 21.3 45.1 77 163 .. ­4.6b Kyrgyz Republic 4 42 0.3 1.7 1.7 0.5 .. 34.1 80 8 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 563 2,705 7.2 16.0 2.9 0.6 48.6 4.9 64 40 32.8b 1.5b Lebanon 1,583 8,279 9.4 22.5 0.7 4.2 6.7 38.1 12 11 111.8b ­9.2b Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 1,588 10,191 13.9 31.9 1.8 2.9 14.8 23.5 54 44 6.2b 9.7b Macedonia, FYR 7 646 0.2 11.2 3.3 1.7 6.6 18.3 1 57 .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi 126 .. 7.2 .. 0.5 .. 13.8 .. 7 .. .. .. Malaysia 116,935 235,356 129.5 139.1 64.8 38.3 44.6 33.2 795 1,027 ­2.9a 34.6a Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania 1,090 .. 97.2 .. .. .. .. .. 40 .. .. .. Mauritius 1,331 3,598 29.8 41.6 1.7 2.4 5.0 6.0 40 41 10.5b 44.3b Mexico 125,204 348,345 21.5 31.1 7.8 6.9 32.3 29.7 179 131 43.9a 41.1a Moldova 392 574 30.4 22.1 1.9 0.6 5.8 5.9 36 23 .. .. Mongolia 37 46 3.9 2.4 0.8 0.1 7.3 6.1 410 392 .. .. Morocco 10,899 49,360 32.7 52.7 3.3 8.0 9.2 32.9 53 65 8.4a 78.5a Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 311 542 9.1 6.8 0.6 0.1 4.5 4.6 13 9 ­1.1b 12.8 b Nepal 790 963 14.4 13.0 0.6 0.3 6.9 2.4 110 105 .. .. Netherlands 640,456 727,515 165.7 116.6 175.2 121.3 101.4 112.2 234 170 .. .. New Zealand 18,866 40,620 35.8 37.2 20.5 15.9 45.9 41.3 142 154 .. .. Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 4,237 32,819 9.2 19.6 0.6 2.0 7.3 13.8 195 202 20.7b 34.0 b Norway 65,034 190,952 39.0 64.6 36.0 65.9 93.4 117.2 191 202 .. .. Oman 3,463 16,158 17.4 26.0 2.8 7.4 14.2 22.1 131 124 38.0 b 7.9 b Pakistan 6,581 45,518 9.0 41.5 45.0 127.3 475.5 251.4 762 652 58.5b 1.3b Panama 2,794 5,074 24.0 32.8 1.3 0.5 1.7 1.8 29 24 .. .. Papua New Guinea 1,520 4,863 49.6 98.3 0.0 0.3 .. 0.4 7 9 .. .. Paraguay 224 234 3.5 3.2 0.1 0.0 3.5 0.7 56 54 .. .. Peru 10,562 59,658 19.8 45.3 2.9 2.5 12.6 9.2 230 193 29.8 a 82.5a Philippines 25,957 68,382 34.4 40.5 10.9 7.0 15.8 22.1 228 238 21.3a 50.3a Poland 31,279 149,054 18.3 31.0 8.5 9.9 49.9 46.8 225 267 20.8a 38.1a Portugal 60,681 66,981 53.9 36.5 48.3 21.2 85.5 55.4 109 52 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 277 5.4 Stock markets Market Market Turnover Listed domestic S&P/EMDB capitalization liquidity ratio companies indexes Value of shares traded Value traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2006 2000 2005 2000 2005 2000 2006 2000 2006 2005 2006 Romania 1,069 32,784 2.9 20.9 0.6 3.4 23.1 16.9 5,555 2,478 58.7b 54.2b Russian Federation 38,922 1,321,833 15.0 71.8 7.8 20.9 36.9 65.7 249 309 64.9a 62.0a Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 67,171 326,869 35.6 208.6 9.2 356.2 27.1 269.1 75 86 111.0 b ­48.9 b Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro 734 5,409 4.7 20.6 0.1 2.5 0.0 15.3 6 864 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 152,827 208,300 164.8 178.4 98.7 102.6 52.1 63.1 418 557 .. .. Slovak Republic 1,217 5,574 6.0 9.5 4.4 0.1 129.8 1.9 493 173 16.6b 24.0 b Slovenia 2,547 15,182 13.2 23.0 2.4 2.3 20.7 10.3 38 100 ­6.9 b 74.3b Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 204,952 715,025 154.2 236.0 58.3 83.8 33.9 49.5 616 401 24.8a 17.2a Spain 504,219 960,024 86.8 85.4 169.8 138.5 210.7 163.9 1,019 3,300 .. .. Sri Lanka 1,074 7,769 6.6 24.4 0.9 4.8 11.0 14.8 239 237 29.3b 45.3b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 73 197 5.3 7.2 0.0 0.0 9.8 0.0 6 6 .. .. Sweden 328,339 403,948 135.7 112.9 161.2 129.7 111.2 118.9 292 252 .. .. Switzerland 792,316 938,624 322.0 255.7 247.6 240.7 82.0 100.1 252 263 .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 233 588 2.6 4.9 0.4 0.1 2.4 2.3 4 6 .. .. Thailand 29,489 139,564 24.0 69.9 19.0 50.6 53.2 67.6 381 476 3.8 a 6.2a Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 4,330 15,571 53.1 118.2 1.7 4.4 3.1 3.0 27 37 ­1.3b ­6.5b Tunisia 2,828 4,446 14.5 10.0 3.2 1.6 23.3 15.2 44 48 11.1b 47.9 b Turkey 69,659 162,399 35.0 44.6 89.9 55.5 206.2 143.0 315 314 49.5a ­4.0a Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 35 103 0.6 1.2 0.0 0.0 .. 3.1 2 5 .. .. Ukraine 1,881 42,870 6.0 30.1 0.9 0.8 19.6 4.6 139 249 52.8 b 48.6b United Arab Emirates 5,727 138,531 8.1 173.9 0.2 110.4 3.9 65.9 54 81 .. ­44.6b United Kingdom 2,576,992 3,058,182 178.6 139.1 127.2 189.5 66.6 141.9 1,904 2,759 4.4 d 26.2d United States 15,104,037 16,997,982 154.7 136.9 326.3 173.2 200.8 129.1 7,524 5,143 3.0 e 13.6e Uruguay 161 354 0.8 2.1 0.0 0.0 0.5 0.0 16 11 .. .. Uzbekistan 32 37 0.2 0.3 0.1 0.3 .. 184.7 5 114 .. .. Venezuela, RB 8,128 8,251 6.9 3.6 0.6 0.2 8.9 9.4 85 53 ­22.0 b 79.0 b Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 765 4,461 18.6 111.1 4.6 61.7 10.0 89.1 24 28 .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 236 989 7.3 13.6 0.2 0.2 20.8 2.0 9 12 .. .. Zimbabwe 2,432 26,557 32.9 71.2 3.8 9.8 10.8 7.9 69 80 36.6b 912.3b World 32,187,882 s 43,642,048 s 103.0 w 99.6 w 153.4 w 108.1 w 122.4 w 78.2 w 47,884 s 49,946 s .. .. Low income 166,928 944,645 23.9 54.2 78.0 49.7 151.6 96.6 7,929 6,122 .. .. Middle income 1,852,197 7,273,817 37.4 49.5 26.8 21.8 71.5 75.3 15,533 11,141 .. .. Lower middle income 979,347 3,854,955 35.9 41.1 32.4 21.3 92.3 95.2 5,931 5,057 .. .. Upper middle income 872,850 3,418,862 39.3 60.1 19.7 22.4 47.0 50.4 9,602 6,084 .. .. Low & middle income 2,019,125 8,218,463 35.8 50.1 33.1 25.3 81.3 78.2 23,462 17,263 .. .. East Asia & Pacific 780,487 3,008,514 47.2 41.3 50.0 26.4 125.2 123.1 3,190 3,525 .. .. Europe & Central Asia 176,208 1,863,241 19.2 45.8 25.4 21.9 81.9 68.5 8,295 4,490 .. .. Latin America & Carib. 626,283 1,469,731 32.6 44.6 8.6 10.7 26.9 29.2 1,806 1,342 .. .. Middle East & N. Africa 60,573 201,450 20.0 49.1 5.0 16.4 12.6 27.0 1,807 1,078 .. .. South Asia 157,695 875,775 26.2 60.4 90.3 58.2 167.6 108.7 7,269 5,954 .. .. Sub-Saharan Africa 217,880 799,751 89.3 137.0 32.1 46.2 22.2 32.6 1,095 874 .. .. High income 30,168,757 38,980,586 117.9 112.9 179.7 130.3 131.0 122.2 24,422 28,733 .. .. Europe EMU 5,425,933 6,465,158 87.5 64.8 80.8 72.8 90.6 116.8 4,405 5,995 .. .. a. Data refer to the S&P/IFC Investable index. b. Data refer to the S&P/IFC Global index. c. Data refer to the Nikkei 225 index. d. Data refer to the FT 100 index. e. Data refer to the S&P 500 index. 278 2007 World Development Indicators 5.4 STATES AND MARKETS Stock markets About the data Definitions The development of an economy's financial markets companies is another measure of market size. Mar- · Market capitalization (also known as market is closely related to its overall development. Well ket size is positively correlated with the ability to value) is the share price times the number of shares functioning financial systems provide good and eas- mobilize capital and diversify risk. outstanding. · Market liquidity is the total value ily accessible information. That lowers transaction Market liquidity, the ability to easily buy and traded divided by GDP. Value traded is the total costs, which in turn improves resource allocation sell securities, is measured by dividing the total value of shares traded during the period. This indi- and boosts economic growth. Both banking systems value traded by GDP. The turnover ratio--the cator complements the market capitalization ratio by and stock markets enhance growth, the main fac- value of shares traded as a percentage of market showing whether market size is matched by trading. tor in poverty reduction. At low levels of economic capitalization--is also a measure of liquidity as well · Turnover ratio is the total value of shares traded development commercial banks tend to dominate as of transaction costs. (High turnover indicates low during the period divided by the average market capi- the financial system, while at higher levels domestic transaction costs.) The turnover ratio complements talization for the period. Average market capitaliza- stock markets tend to become more active and effi - the ratio of value traded to GDP, because the turn- tion is calculated as the average of the end-of-period cient relative to domestic banks. over ratio is related to the size of the market and values for the current period and the previous period. Open economies with sound macroeconomic poli- the value traded ratio to the size of the economy. A · Listed domestic companies are the domestically cies, good legal systems, and shareholder protection small, liquid market will have a high turnover ratio incorporated companies listed on the country's stock attract capital and therefore have larger financial mar- but a low value traded ratio. Liquidity is an impor- exchanges at the end of the year. This indicator does kets. Recent research on stock market development tant attribute of stock markets because, in theory, not include investment companies, mutual funds, or shows that new communications technology and liquid markets improve the allocation of capital and other collective investment vehicles. · S&P/EMDB increased financial integration have resulted in more enhance prospects for long-term economic growth. indexes measure the U.S. dollar price change in the cross-border capital flows, a stronger presence of A more comprehensive measure of liquidity would stock markets covered by the S&P/IFCI country index financial firms around the world, and the migration of include trading costs and the time and uncertainty and S&P/IFCG indexes. stock exchange activities to international exchanges. in finding a counterpart in settling trades. Many firms in emerging markets now cross-list on The S&P/EMDB, the source for all the data in international exchanges, which provides them with the table, provides regular updates on 56 emerg- lower cost capital and more liquidity-traded shares. ing stock markets encompassing more than 2,200 However, this also means that exchanges in emerg- stocks. Standard & Poor's maintains a series of ing markets may not have enough financial activity indexes for investors interested in investing in stock to sustain them, putting pressure on them to rethink markets in developing countries. At the core of the their operations. S&P/EMDB indexes, the Global (S&P/IFCG) index The stock market indicators in the table include is intended to represent the most active stocks in measures of size (market capitalization, number the markets it covers and to be the broadest pos- of listed domestic companies) and liquidity (value sible indicator of market movements. The Investable traded as a percentage of gross domestic product, (S&P/IFCI) index, which applies the same calculation value of shares traded as a percentage of market methodology as the S&P/IFCG index, is designed capitalization). The comparability of such indicators to measure the returns that foreign portfolio inves- between countries may be limited by conceptual tors might receive from investing in emerging market and statistical weaknesses, such as inaccurate stocks that are legally and practically open to foreign reporting and differences in accounting standards. portfolio investment. These indexes are widely used The percentage change in stock market prices in benchmarks for international portfolio management. U.S. dollars, from the Standard & Poor's Emerg- See Standard & Poor's (2000) for further information ing Markets Data Base (S&P/EMDB) indexes, is an on the indexes. important measure of overall performance. Regula- Because markets included in Standard & Poor's Data sources tory and institutional factors that can affect investor emerging markets category vary widely in level of confidence, such as entry and exit restrictions, the development, it is best to look at the entire category Data on stock markets are from Standard & Poor's existence of a securities and exchange commission, to identify the most significant market trends. And Global Stock Markets Factbook 2006, which draws and the quality of laws to protect investors, may influ- it is useful to remember that stock market trends on the Emerging Markets Data Base, supple- ence the functioning of stock markets but are not may be distorted by currency conversions, espe- mented by other data from Standard & Poor's. included in the table. cially when a currency has registered a significant The firm collects data through an annual survey Stock market size can be measured in a number devaluation. of the world's stock exchanges, supplemented by of ways, and each may produce a different ranking About the data is based on Demirgüç-Kunt and information provided by its network of correspon- of countries. Market capitalization shows the overall Levine (1996), Beck and Levine (2001), and Claes- dents and by Reuters. Data on GDP are from the size of the stock market in U.S. dollars and as a sens, Klingebiel, and Schmukler (2002). World Bank's national accounts data files. percentage of GDP. The number of listed domestic 2007 World Development Indicators 279 5.5 Financial access, stability, and efficiency Getting credit Bank Bank non- Domestic Interest Risk premium capital to performing credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Credit Lending Prime lending Legal rights information % of adults rate minus rate minus index index Public Private deposit rate treasury bill rate 0 (weaker) to 0 (less) to credit registry credit bureau percentage percentage 10 (stronger) 10 (more) coverage coverage % % % of GDP points points April 2006 April 2006 April 2006 April 2006 2005 2005 2005 2005 2005 Afghanistan 0 0 0.0 0.0 .. .. .. .. .. Albania 9 0 0.0 0.0 .. .. 48.6 8.0 7.6 Algeria 3 2 0.2 0.0 .. .. 11.1 6.3 6.7 Angola 3 4 2.9 0.0 11.3 13.3 2.1 54.3 .. Argentina 3 6 25.4 100.0 13.0 5.2 38.3 2.4 .. Armenia 5 3 1.5 0.0 21.5 6.9 8.8 12.2 13.9 Australia 9 5 0.0 100.0 5.9 0.2 109.8 5.4 .. Austria 5 6 1.2 39.9 7.4 2.2 127.6 .. .. Azerbaijan 7 4 1.1 0.0 14.2 7.2 11.8 8.5 9.5 Bangladesh 7 2 0.6 0.0 3.8 15.3 43.9 5.9 .. Belarus 2 3 0.0 0.0 19.8 1.9 22.2 2.1 .. Belgium 5 4 56.2 0.0 2.7 2.0 105.2 5.2 4.7 Benin 4 1 10.3 0.0 .. .. 13.2 .. .. Bolivia 3 5 11.5 32.3 11.3 11.2 50.1 11.7 11.7 Bosnia and Herzegovina 8 5 0.0 22.9 15.0 5.3 47.7 6.0 .. Botswana 7 5 0.0 43.2 9.7 2.8 ­5.3 6.5 .. Brazil 2 5 9.2 43.0 9.2 4.4 82.5 37.8 36.6 Bulgaria 6 4 20.7 .. 10.5 1.7 43.6 4.8 6.1 Burkina Faso 4 1 2.4 0.0 .. .. 16.2 .. .. Burundi 2 1 0.1 0.0 .. .. 35.1 .. .. Cambodia 0 0 0.0 0.0 .. .. 7.6 15.4 .. Cameroon 3 2 3.4 0.0 .. .. 12.4 12.8 .. Canada 7 6 0.0 100.0 4.5 0.5 206.1 3.6 1.7 Central African Republic 3 2 1.1 0.0 .. .. 17.5 12.8 .. Chad 4 1 0.2 0.0 .. .. 7.5 12.8 .. Chile 4 6 31.3 19.3 6.8 0.9 85.8 2.7 .. China 2 4 10.2 0.0 3.8 10.5 135.7 3.3 .. Hong Kong, China 10 5 0.0 64.5 12.2 1.5 142.8 6.5 4.1 Colombia 3 4 0.0 28.3 12.3 2.7 35.1 7.5 .. Congo, Dem. Rep. 3 0 0.0 0.0 .. .. 2.7 .. .. Congo, Rep. 3 2 1.4 0.0 .. .. 1.5 12.8 .. Costa Rica 4 6 2.5 39.2 12.2 1.5 43.4 14.5 .. Côte d'Ivoire 3 1 3.1 0.0 .. .. 18.2 .. .. Croatia 5 0 0.0 0.0 8.7 4.0 74.1 9.5 .. Cuba .. .. .. .. .. .. .. .. .. Czech Republic 6 5 3.5 51.0 5.8 4.3 43.6 4.6 3.8 Denmark 8 4 0.0 11.5 5.7 0.7 177.5 .. .. Dominican Republic 4 6 11.9 57.1 9.4 5.9 40.4 10.2 .. Ecuador 3 5 15.2 43.7 9.6 4.9 16.8 5.8 .. Egypt, Arab Rep. 1 2 1.5 0.0 .. 25.0 105.5 5.9 4.6 El Salvador 4 6 30.5 79.6 7.6 12.0 47.5 .. .. Eritrea 3 0 0.0 0.0 .. .. 141.2 .. .. Estonia 4 5 0.0 18.2 8.6 0.2 71.3 2.8 .. Ethiopia 5 2 0.1 0.0 .. .. 54.9 3.5 6.8 Finland 6 5 0.0 14.9 8.8 0.3 78.5 2.7 .. France 5 4 12.3 0.0 4.4 3.5 109.5 4.4 4.3 Gabon 4 2 2.6 0.0 .. 15.8 10.4 12.8 .. Gambia, The 4 0 0.0 0.0 .. .. 23.8 17.6 .. Georgia 6 3 0.0 0.0 18.8 3.8 21.7 14.1 12.1 Germany 8 6 0.5 93.9 4.4 4.8 135.8 .. .. Ghana 5 0 0.0 0.0 12.0 13.9 29.7 .. .. Greece 3 4 0.0 37.5 5.0 5.5 110.9 4.3 4.4 Guatemala 4 5 16.1 9.2 .. .. 31.4 8.7 .. Guinea 4 1 0.0 0.0 .. .. 15.8 .. .. Guinea-Bissau 3 1 1.0 0.0 .. .. 9.1 .. .. Haiti 3 2 0.7 0.0 .. .. 29.3 24.0 19.1 280 2007 World Development Indicators 5.5 STATES AND MARKETS Financial access, stability, and efficiency Getting credit Bank Bank non- Domestic Interest Risk premium capital to performing credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Credit Lending Prime lending Legal rights information % of adults rate minus rate minus index index Public Private deposit rate treasury bill rate 0 (weaker) to 0 (less) to credit registry credit bureau percentage percentage 10 (stronger) 10 (more) coverage coverage % % % of GDP points points April 2006 April 2006 April 2006 April 2006 2005 2005 2005 2005 2005 Honduras 6 5 8.3 18.7 8.4 6.6 34.2 7.9 .. Hungary 6 5 0.0 5.9 9.1 2.1 62.9 3.4 1.6 India 5 3 0.0 6.1 6.3 5.2 60.4 .. .. Indonesia 5 2 8.4 0.2 10.5 15.6 47.0 6.0 .. Iran, Islamic Rep. 5 3 13.7 0.0 .. .. 46.2 4.2 .. Iraq 4 0 0.0 0.0 .. .. .. .. .. Ireland 8 5 0.0 100.0 4.7 0.7 160.2 2.6 .. Israel 8 5 0.0 100.0 6.7 10.3 84.8 3.2 2.1 Italy 3 5 7.0 67.8 7.3 6.3 108.9 4.9 3.1 Jamaica 6 0 0.0 0.0 8.7 2.9 53.2 9.9 4.0 Japan 6 6 0.0 .. 4.2 1.8 318.7 1.4 .. Jordan 5 2 0.7 0.0 7.2 13.6 111.6 4.7 .. Kazakhstan 5 4 0.0 5.5 8.7 9.6 24.7 .. .. Kenya 8 2 0.0 0.1 .. 5.2 38.4 7.8 4.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. 6 5 0.0 76.6 5.8 1.2 106.6 1.9 .. Kuwait 4 3 0.0 16.1 12.6 4.5 71.7 4.0 .. Kyrgyz Republic 5 3 0.0 0.4 .. .. 9.5 20.8 22.2 Lao PDR 2 0 0.0 0.0 .. .. 8.8 22.1 8.2 Latvia 8 4 1.9 0.0 7.6 0.7 72.8 3.3 2.1 Lebanon 4 5 4.3 0.0 .. 15.8 184.0 2.5 5.4 Lesotho 5 0 0.0 0.0 .. .. ­1.1 7.8 4.5 Liberia .. .. .. .. .. .. 188.9 13.6 .. Libya .. .. .. .. .. .. ­50.7 4.0 0.6 Lithuania 4 6 4.2 7.2 7.3 2.5 42.3 4.5 3.2 Macedonia, FYR 6 3 2.1 0.0 .. .. 20.9 5.6 .. Madagascar 2 1 0.3 0.0 6.2 10.1 12.9 8.3 8.2 Malawi 8 0 0.0 0.0 .. .. 22.1 22.2 8.7 Malaysia 8 6 42.2 .. 7.9 9.9 143.7 3.0 3.5 Mali 3 1 2.9 0.0 .. .. 17.5 .. .. Mauritania 5 1 0.2 0.0 .. .. ­6.1 15.1 11.2 Mauritius 6 1 10.2 0.0 .. .. 108.8 13.8 .. Mexico 2 6 0.0 69.5 12.0 1.8 35.3 6.2 0.5 Moldova 6 0 0.0 0.0 17.0 4.3 32.3 6.0 15.6 Mongolia 5 3 10.2 0.0 .. .. 37.1 10.6 .. Morocco 3 1 2.3 0.0 7.7 15.7 88.0 7.9 .. Mozambique 4 3 0.7 0.0 6.5 4.6 8.8 11.7 10.4 Myanmar .. .. .. .. .. .. 28.1 5.5 .. Namibia 5 5 0.0 35.2 7.8 2.0 65.9 4.4 3.5 Nepal 4 2 0.0 0.1 .. .. .. 5.9 5.9 Netherlands 7 5 0.0 68.9 4.0 1.2 184.8 0.4 .. New Zealand 9 5 0.0 100.0 .. .. 132.8 4.9 5.0 Nicaragua 4 5 12.5 3.4 8.8 8.0 79.4 8.1 .. Niger 3 1 1.2 0.0 .. .. 10.7 .. .. Nigeria 7 0 0.0 0.0 9.9 21.9 9.0 7.4 10.3 Norway 6 4 0.0 100.0 5.1 0.7 10.0 2.2 .. Oman 3 1 17.5 0.0 .. .. 34.9 3.7 .. Pakistan 4 4 0.3 1.1 7.7 10.6 43.6 .. .. Panama 6 6 0.0 59.8 12.8 1.8 88.2 6.0 .. Papua New Guinea 6 0 0.0 0.0 .. .. 21.9 10.6 7.7 Paraguay 3 6 10.6 52.2 11.0 3.2 20.1 28.2 .. Peru 4 6 19.2 28.6 7.7 2.1 17.6 11.5 .. Philippines 3 3 0.0 4.8 12.3 20.0 50.9 4.6 4.1 Poland 4 4 0.0 38.1 7.8 7.7 32.6 4.0 .. Portugal 4 4 72.0 9.1 5.2 1.6 150.7 .. .. Puerto Rico 6 5 0.0 63.6 .. .. .. .. .. 2007 World Development Indicators 281 5.5 Financial access, stability, and efficiency Getting credit Bank Bank non- Domestic Interest Risk premium capital to performing credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Credit Lending Prime lending Legal rights information % of adults rate minus rate minus index index Public Private deposit rate treasury bill rate 0 (weaker) to 0 (less) to credit registry credit bureau percentage percentage 10 (stronger) 10 (more) coverage coverage % % % of GDP points points April 2006 April 2006 April 2006 April 2006 2005 2005 2005 2005 2005 Romania 4 5 2.6 5.5 8.8 8.3 20.8 .. .. Russian Federation 3 0 0.0 0.0 13.5 3.2 20.7 6.7 7.6 Rwanda 1 2 0.2 0.0 .. 34.1 9.7 .. .. Saudi Arabia 3 5 0.2 12.5 8.8 3.0 46.9 .. .. Senegal 3 1 4.7 0.0 8.4 14.2 23.1 .. .. Serbia and Montenegro 5a 5a 0.1a 43.4 a 17.2 19.8 .. .. .. Sierra Leone 5 0 0.0 0.0 11.6 14.8 24.9 13.5 1.6 Singapore 9 4 0.0 38.6 10.5 3.8 70.8 4.9 3.3 Slovak Republic 9 3 1.0 45.3 7.6 2.0 49.6 4.2 .. Slovenia 6 3 2.9 0.0 7.4 4.9 64.8 4.6 4.1 Somalia .. .. .. .. .. .. .. .. .. South Africa 5 5 0.0 53.0 8.3 1.5 184.6 4.6 3.7 Spain 5 6 44.9 7.4 4.9 0.6 159.7 .. .. Sri Lanka 3 3 0.0 3.1 6.7 9.6 44.3 ­3.2 ­2.0 Sudan 4 0 0.0 0.0 .. .. 12.7 .. .. Swaziland 6 5 0.0 39.0 .. .. 16.3 6.6 3.6 Sweden 6 4 0.0 100.0 5.8 1.1 120.7 2.5 1.6 Switzerland 6 5 0.0 24.5 5.1 0.5 179.9 2.4 2.4 Syrian Arab Republic 5 0 0.0 0.0 .. .. 31.3 7.0 .. Tajikistan 4 0 0.0 0.0 .. .. 16.4 13.5 .. Tanzania 5 0 0.0 0.0 .. .. 14.0 10.4 4.4 Thailand 5 5 0.0 21.7 9.8 11.1 111.1 3.9 .. Togo 3 1 3.6 0.0 .. .. 17.2 .. .. Trinidad and Tobago 6 3 0.0 31.5 .. .. 25.7 6.9 4.2 Tunisia 3 3 11.6 0.0 7.7 20.9 71.5 .. .. Turkey 3 5 6.7 .. 13.5 4.8 56.6 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. Uganda 3 0 0.0 0.0 10.3 2.2 9.9 10.9 11.1 Ukraine 8 0 0.0 0.0 11.5 19.6 34.6 7.6 .. United Arab Emirates 3 2 1.7 0.0 8.3 8.3 59.5 .. .. United Kingdom 10 6 0.0 86.1 8.5 1.0 168.0 .. 0.1 United States 7 6 0.0 100.0 10.3 0.7 224.3 .. 3.0 Uruguay 4 6 13.2 85.3 8.6 2.7 40.5 10.8 9.5 Uzbekistan 3 0 0.0 0.0 .. .. .. .. .. Venezuela, RB 4 0 0.0 0.0 11.1 1.2 13.1 5.2 .. Vietnam 4 3 2.7 0.0 .. .. 69.6 3.9 4.9 West Bank and Gaza 5 3 0.7 0.0 .. .. .. .. .. Yemen, Rep. 3 2 0.1 0.0 .. .. 4.7 5.0 3.1 Zambia 7 0 0.0 0.0 .. 10.8 22.0 17.0 11.9 Zimbabwe 6 0 0.0 0.0 12.1 23.2 94.0 144.6 50.6 World 4.8 w 2.6 w 3.8 m 17.4 m 8.6 m 4.4 m 164.6 w 6.5 m .. Low income 3.9 1.0 0.9 0.2 .. .. 48.1 11.7 .. Middle income 4.7 2.8 4.6 14.2 9.3 4.6 76.0 6.5 .. Lower middle income 4.6 2.7 4.4 10.4 10.2 7.2 95.5 7.5 .. Upper middle income 5.0 3.1 4.9 20.4 8.7 2.5 50.6 6.0 .. Low & middle income 4.5 2.2 3.3 9.1 9.7 7.3 72.2 7.4 .. East Asia & Pacific 4.7 1.5 3.7 3.2 .. .. 121.4 5.5 .. Europe & Central Asia 5.4 3.0 1.8 8.7 10.5 4.2 35.5 6.0 .. Latin America & Carib. 4.4 3.4 7.5 27.6 9.5 3.1 52.0 7.8 .. Middle East & N. Africa 3.7 1.9 4.1 0.0 .. .. 53.6 4.8 .. South Asia 3.8 1.8 0.1 1.3 6.3 10.1 57.2 5.9 .. Sub-Saharan Africa 4.2 1.3 1.5 3.8 .. .. 81.8 12.2 .. High income 6.2 4.6 6.0 52.9 5.8 1.5 191.0 4.3 .. European Monetary Union 5.4 4.9 17.6 39.9 5.0 2.0 128.6 4.3 .. a. Data are for Serbia only. 282 2007 World Development Indicators 5.5 STATES AND MARKETS Financial access, stability, and efficiency About the data This year's table has been revised to include data comprise tier 2 and tier 3 capital). Total assets ability of more credit information, from either a pub- on getting credit from the World Bank Group's Doing include all nonfinancial and financial assets. Data lic registry or a private bureau, to facilitate lending Business database. are from internally consistent financial statements decisions. · Public credit registry coverage reports Financial sector development has positive impacts to enhance the quality and analytical usefulness of the number of individuals and firms listed in a pub- on economic growth and poverty. The size of the sec- the indicators. lic credit registry with current information on repay- tor determines the amount of resources mobilized for The ratio of bank nonperforming loans to total gross ment history, unpaid debts, or credit outstanding. investment. Access to finance can expand opportuni- loans is a measure of bank health and efficiency. It The number is expressed as a percentage of the ties for all--not just the rich and well connected-- helps to identify problems with asset quality in the adult population · Private credit bureau coverage with higher levels of access and use of banking loan portfolio. A high ratio may signal deterioration reports the number of individuals or firms listed by services associated with lower financing obstacles in the quality of the credit portfolio. International a private credit bureau with current information on for people and businesses. A stable financial sys- guidelines recommend that loans be classified as repayment history, unpaid debts, or credit outstand- tem that promotes efficient savings and investment nonperforming when payments of principal and inter- ing. The number is expressed as a percentage of the is also crucial for a thriving democracy and market est are past due by 90 days or more or when future adult population. · Bank capital to asset ratio is the economy. The banking system is the largest sector payments are not expected to be received in full. ratio of bank capital and reserves to total assets. in the financial system in most countries, so most See the International Monetary Fund's (IMF) Global Capital and reserves include funds contributed by indicators in the table cover the banking system. Financial Stability Report for detailed background owners, retained earnings, general and special There are several aspects of access to financial information. reserves, provisions, and valuation adjustments. services: availability, cost, and quality of services. Domestic credit provided by the banking sector as · Bank nonperforming loans to total gross loans The development and growth of credit markets a share of GDP is a measure of banking sector depth are the value of nonperforming loans divided by the depend on access to timely, reliable, and accurate and financial sector development in terms of size. total value of the loan portfolio (including nonper- data on borrowers' credit experiences. For secured In a few countries governments may hold interna- forming loans before the deduction of specific loan transactions, such as mortgages or vehicle loans, tional reserves as deposits in the banking system loss provisions). The loan amount recorded as non- having rapid access to information in property reg- rather than in the central bank. Since the claims on performing should be the gross value of the loan as istries is also vital, and for small business loans, the central government are a net item (claims on recorded on the balance sheet, not just the amount corporate registry data are needed. An effective way the central government minus central government that is overdue. · Domestic credit provided by bank- to improve access to credit is to increase informa- deposits), this net figure may be negative, resulting ing sector includes all credit to various sectors on a tion about potential borrowers' creditworthiness in a negative figure of domestic credit provided by gross basis, except credit to the central government, and make it easy to create and enforce collateral the banking sector. which is net. The banking sector includes monetary agreements. Lenders look at the borrower's credit The interest rate spread--the margin between authorities, deposit money banks, and other banking history and collateral when extending loans. Where the cost of mobilizing liabilities and the earnings on institutions for which data are available (including credit registries and effective collateral laws are assets--is a measure of the efficiency by which the institutions that do not accept transferable depos- absent--as in many developing countries--banks financial sector intermediates funds. A narrow inter- its but do incur such liabilities as time and savings make fewer loans. Indicators that cover financial est rate spread means low transaction costs, which deposits). · Interest rate spread is the interest access, or getting credit, include legal rights index, lowers the overall cost of funds for investment, cru- rate charged by banks on loans to prime customers credit information index, public registry coverage, cial to economic growth. The risk premium on lending minus the interest rate paid by commercial or similar and private bureau coverage. Other measures of is the spread between the lending rate to the private banks for demand, time, or savings deposits. · Risk access and use, such as number of bank branches sector and the "risk-free" government rate. A small premium on lending is the interest rate charged by per capita and number of bank deposits per capita spread indicates that the market considers its best banks on loans to prime private sector customers are not presented in the table this year since they corporate customers to be low risk. Interest rate minus the "risk free" treasury bill interest rate at are not collected or updated regularly. spreads are expressed as annual averages. In some which short-term government securities are issued The size and mobility of international capital flows countries this spread may be negative, indicating or traded in the market. have made it increasingly important to monitor the that the market considers its best corporate clients strength of financial systems. Robust financial sys- to be lower risk than the government. tems help to increase economic activity and welfare, but instability in the financial system can disrupt Definitions financial activity and impose huge and widespread costs on the economy. The ratio of bank capital to · Legal rights index measures the degree to which assets, a measure of bank solvency and resiliency, collateral and bankruptcy laws protect the rights of Data sources provides a measure of the extent to which banks borrowers and lenders and thus facilitate lending. Data on getting credit are from the World Bank's can deal with unexpected losses. Capital includes The index ranges from 0 to 10, with higher scores tier 1 capital (paid-up shares and common stock), indicating that these laws are better designed to Doing Business project (www.doingbusiness.org). which is a common feature in all countries' banking expand access to credit. · Credit information index Data on bank capital and nonperforming loans are systems, and total regulatory capital, which includes measures rules affecting the scope, accessibility, from the IMF's Global Financial Stability Report. several specified types of subordinated debt instru- and quality of credit information available through Data on credit and interest rates are from the ments that need not be repaid if the funds are public or private credit registries. The index ranges IMF's International Financial Statistics. required to maintain minimum capital levels (these from 0 to 6, with higher values indicating the avail- 2007 World Development Indicators 283 5.6 Tax policies Tax revenue collected Taxes payable Highest marginal by central government by businesses tax ratea Time to prepare, Number file, and pay taxes Total tax rate Individual of payments hours % of profi t On income Corporate % of GDP Fiscal year Fiscal year Fiscal year % over $ % 2000 2005 2006 2006 2006 2006 2006 2006 Afghanistanb .. 3.9 2 275 36.3 .. .. .. Albaniab 16.1 17.3 42 240 55.8 20 2,003 20 Algeriab 36.9 .. 61 504 76.4 .. .. .. Angola .. .. 42 272 64.4 .. .. .. Argentina 9.8 14.2 34 615 116.8 35 41,379 35 Armeniab .. 14.3 50 1,120 42.5 .. .. .. Australia 22.1 23.9 11 107 52.2 47 72,519 30 Austria 19.6 20.0 20 272 56.1 50 63,750 25 Azerbaijanb 12.7 .. 36 1,000 44.9 35 12,632 22 Bangladeshb 7.6 8.1 17 400 40.3 .. .. .. Belarusb 16.6 20.6 125 1,188 186.1 .. .. .. Belgium 27.4 26.5 10 160 70.1 50 39,625 34 Beninb .. 14.6 72 270 68.5 35c .. 38 c Bolivia 13.2 16.6 41 1,080 80.3 .. .. 25 Bosnia and Herzegovina .. 21.8 73 100 50.4 15 .. 30 Botswanab .. .. 24 140 53.3 25 19,569 15 Brazilb 12.2 .. 23 2,600 71.7 28 11,486 15 Bulgariab 18.3 23.4 27 616 40.7 24 4,586 15 Burkina Faso .. 12.1 45 270 51.1 .. .. .. Burundib 13.6 .. 40 140 286.7 .. .. .. Cambodia 8.2 8.0 27 121 22.3 20 36,652 20 Cameroonb 12.3 .. 39 1,300 46.2 .. .. .. Canadab 15.3 14.4 10 119 43.0 29 97,756 22 Central African Republicb .. 6.0 54 504 209.5 .. .. .. Chad .. .. 65 122 68.2 .. .. .. Chile 16.6 19.2 10 432 26.3 40 6,127 17 Chinab 6.8 8.8 c 48 872 77.1 45 8,637 .. Hong Kong, China .. .. 4 80 28.8 20 11,568 18 Colombia .. 15.1 68 456 82.8 22 43,154 39 Congo, Dem. Rep.b 0.0 .. 34 312 235.4 50 4,920 40 Congo, Rep. 9.2 8.5 94 576 57.3 .. .. .. Costa Ricab 12.1 13.7 41 402 83.0 25 19,414 30 Côte d'Ivoireb 14.6 14.5 71 270 45.7 10 4,550 35 Croatiab 26.2 23.3 39 196 37.1 45 3,765 20 Cuba .. .. .. .. .. .. .. .. Czech Republicb 15.4 15.6 14 930 49.0 32 13,823 24 Denmark 31.0 30.6 18 135 31.5 59 53,117 28 Dominican Republicb 14.7 15.1 87 178 67.9 30 29,596 30 Ecuador b .. .. 8 600 34.9 25 61,440 25 Egypt, Arab Rep.b 14.6 .. 41 536 50.4 20 6,920 .. El Salvador 10.7 12.6 66 224 27.4 .. .. .. Eritrea .. .. 18 216 86.3 .. .. .. Estonia 16.8 17.0 11 104 50.2 23 1,908 23 Ethiopiab 10.8 12.7c 20 212 32.8 35c .. 30 c Finland 24.9 22.9 19 264 47.9 33 72,750 26 France 23.4 22.7 33 128 68.2 48 60,673 33 Gabon .. .. 27 272 48.3 .. .. .. Gambia, Theb .. .. 47 376 291.4 .. .. .. Georgiab 7.7 12.1 35 423 37.8 12 .. 20 Germany 11.9 11.0 32 105 57.1 42 65,190 25 Ghanab 17.2 22.4 35 304 32.3 25 10,581 25 Greece 25.5 21.8 33 204 60.2 40 28,750 29 Guatemalab 10.1 9.6 50 294 40.9 31 38,663 31 Guineab 11.1 .. 55 416 49.4 .. .. .. Guinea-Bissau .. .. 47 208 47.5 .. .. .. Haiti .. .. 53 160 40.5 .. .. .. 284 2007 World Development Indicators 5.6 STATES AND MARKETS Tax policies Tax revenue collected Taxes payable Highest marginal by central government by businesses tax ratea Time to prepare, Number file, and pay taxes Total tax rate Individual of payments hours % of profi t On income Corporate % of GDP Fiscal year Fiscal year Fiscal year % over $ % 2000 2005 2006 2006 2006 2006 2006 2006 Honduras .. .. 48 424 51.4 25 26,553 25 Hungary 22.5 20.5 24 304 59.3 36 7,766 16 Indiab 9.0 10.2 59 264 81.1 30 5,669 34 Indonesiab 11.3 12.5 52 576 37.2 35 20,608 30 Iran, Islamic Rep.b 6.3 7.9 28 292 46.4 35 114,101 25 Iraq .. .. 13 312 38.7 .. .. .. Ireland 26.1 25.0 8 76 25.8 42 40,000 13 Israel 31.0 29.3 33 225 39.1 49 94,530 31 Italy 23.2 21.3 15 360 76.0 43 125,000 33 Jamaicab 24.7 27.3 72 414 52.3 25 1,993 33 Japanb .. .. 15 350 52.8 37 163,310 30 Jordanb 19.0 24.2 26 101 31.9 .. .. .. Kazakhstanb 10.2 20.6 34 156 45.0 20 55,810 30 Kenyab 16.8 16.9 17 432 74.2 30 5,841 30 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep.b 16.1 15.8 27 290 30.9 35 78,116 25 Kuwait 1.0 1.0 14 118 55.7 0 .. 0 Kyrgyz Republicb 11.7 .. 89 204 67.4 .. .. .. Lao PDR .. .. 31 180 32.5 .. .. .. Latviab 14.2 15.3 8 320 42.6 25 .. 15 Lebanon 12.2 16.1 21 208 37.3 .. .. .. Lesothob 32.4 41.7 21 352 25.6 .. .. .. Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 14.5 17.5 13 162 48.4 33 .. 15 Macedonia, FYRb .. .. 54 96 43.5 24 14,610 15 Madagascar 56.6 54.4 25 304 43.2 .. .. .. Malawi .. .. 29 878 32.6 .. .. .. Malaysiab 14.3 17.6 35 190 35.2 28 65,963 28 Mali .. .. 60 270 50.0 .. .. .. Mauritania .. .. 61 696 104.3 .. .. .. Mauritiusb 18.2 18.1 7 158 24.8 30 16,949 25 Mexicob 11.7 .. 49 552 37.1 29 9,470 29 Moldovab 14.7 18.9 44 250 48.8 20 1,667 15 Mongolia .. 22.6 42 204 32.2 .. .. .. Morocco 22.3 22.6 28 468 52.7 .. .. .. Mozambique .. .. 36 230 39.2 32 43,710 32 Myanmar b 3.0 .. .. .. .. .. .. .. Namibiab 30.0 25.9 34 .. 25.6 35 31,447 35 Nepalb 8.7 10.1 35 408 32.8 .. .. .. Netherlands 22.2 23.2 22 250 48.1 52 65,285 30 New Zealand 29.4 31.8 9 70 36.5 39 42,254 33 Nicaraguab 13.8 16.6 64 240 66.4 30 29,886 30 Niger .. .. 44 270 46.0 .. .. .. Nigeria .. .. 35 1,120 31.4 .. .. .. Norway 27.7 30.4 3 87 46.1 .. .. 28 Omanb 7.2 .. 14 52 20.2 0 .. 12 Pakistanb 10.2 9.5 47 560 43.4 35 11,763 37 Panamab 10.2 .. 59 560 52.4 30 200,000 30 Papua New Guineab 19.0 .. 44 198 44.3 .. .. .. Paraguay b .. 12.1 33 328 43.2 10 .. .. Perub 12.2 13.5 53 424 40.8 30 .. 30 Philippinesb 13.7 13.0 59 94 53.0 32 9,076 35 Poland 16.0 16.5 43 175 38.4 40 22,854 19 Portugal 21.5 21.6 7 328 47.0 42 75,000 25 Puerto Rico .. .. 17 140 40.9 33 50,000 20 2007 World Development Indicators 285 5.6 Tax policies Tax revenue collected Taxes payable Highest marginal by central government by businesses tax ratea Time to prepare, Number file, and pay taxes Total tax rate Individual of payments hours % of profi t On income Corporate % of GDP Fiscal year Fiscal year Fiscal year % over $ % 2000 2005 2006 2006 2006 2006 2006 2006 Romania 11.7 .. 89 198 48.9 16 .. 16 Russian Federation 13.6 16.6 70 256 54.2 13 .. 24 Rwandab .. .. 43 168 41.1 .. .. .. Saudi Arabia .. .. 14 75 14.9 0 .. 0 Senegalb 17.3 .. 59 696 47.7 0 .. .. Serbia and Montenegrob 23.0 .. 41d 168d 38.9d 10 .. 10 Sierra Leoneb 10.2 11.0 20 399 277 .. .. .. Singaporeb 15.4 12.4 16 30 28.8 21 192,771 20 Slovak Republic .. 15.1 30 344 48.9 19 .. 19 Sloveniab 21.2 21.4 34 272 39.4 50 .. 25 Somalia .. .. .. .. .. .. .. .. South Africa 24.0 27.5 23 350 38.3 40 47,170 29 Spain 16.2 12.6 7 602 59.1 29 58,524 35 Sri Lankab 14.5 14.3 61 256 74.9 35 4,975 35 Sudanb 6.4 7.0 c 66 180 37.1 .. .. .. Swazilandb .. 26.0 34 104 39.5 33 11,792 30 Sweden 19.7 20.8 5 122 57.0 25 61,673 28 Switzerlandb 11.3 .. 13 68 24.9 .. .. 9 Syrian Arab Republicb 17.4 .. 21 336 35.5 .. .. .. Tajikistanb 7.7 9.8 55 224 87.0 .. .. .. Tanzania .. .. 48 248 45.0 30 5,740 30 Thailand .. 17.1 46 104 40.2 37 99,453 30 Togob .. 13.3 51 270 48.3 .. .. .. Trinidad and Tobagob 22.1 24.0 28 114 37.2 25 .. 25 Tunisiab 21.3 21.3 45 268 58.8 .. .. .. Turkey b 22.1 .. 18 254 46.3 35 .. 30 Turkmenistan .. .. .. .. .. .. .. .. Ugandab 10.9 11.9 31 237 32.2 30 2,763 30 Ukraineb 14.1 17.8 98 2,185 60.3 13 .. 25 United Arab Emiratesb 1.7 .. 15 12 15.0 0 .. .. United Kingdom 29.0 28.3 7 105 35.4 40 60,545 30 United States 12.7 11.2 10 325 46.0 35 326,450 35 Uruguay b 16.7 18.5 41 300 27.6 0 .. 30 Uzbekistan .. .. 130 152 122.3 29 960 12 Venezuela, RBb 13.3 16.1 68 864 51.9 34 93,767 34 Vietnamb .. .. 32 1,050 41.6 40 5,044 28 West Bank and Gaza .. .. 50 154 31.5 .. .. .. Yemen, Rep.b 9.4 .. 32 248 48.0 .. .. .. Zambiab 18.4 .. 37 131 22.2 30 368 35 Zimbabweb .. .. 59 216 37.0 45 26,249 30 World 15.8 w 16.1 w 35 u 334 u 54.0 u Low income 9.8 10.6 43 331 70.5 Middle income 12.5 12.8 38 378 48.6 Lower middle income 9.7 10.7 43 442 50.0 Upper middle income .. .. 30 280 46.5 Low & middle income 12.0 12.4 40 361 56.4 East Asia & Pacific 7.7 9.8 32 273 43.9 Europe & Central Asia 15.5 17.3 48 448 58.2 Latin America & Carib. 11.8 .. 42 437 49.4 Middle East & N. Africa 15.7 .. 33 276 43.8 South Asia 9.3 10.1 30 305 45.1 Sub-Saharan Africa .. .. 41 336 71.2 High income 16.6 16.0 17 220 43.8 Europe EMU 19.2 18.1 19 250 56.0 a. These data are from PriceWaterhouseCoopers' Worldwide Tax Summaries online b. Data on central government taxes were reported on a cash basis and have been adjusted to the accrual framework of the Government Finance Statistics Manual 2001. c. World Bank staff estimate. d. Data are for Serbia only. 286 2007 World Development Indicators 5.6 STATES AND MARKETS Tax policies About the data Definitions Taxes are the main source of revenue for most activities, and they have a certain level of start-up · Tax revenue collected by central government governments. The sources of tax revenue and their capital, employees, and turnover. For details about refers to compulsory transfers to the central gov- relative contributions are determined by government the assumptions, see Doing Business 2007. ernment for public purposes. Certain compulsory policy choices about where and how to impose taxes A potentially important influence on both domestic transfers such as fines, penalties, and most social and by changes in the structure of the economy. Tax and international investors is a tax system's progres- security contributions are excluded. Refunds and policy may refl ect concerns about distributional sivity, as reflected in the highest marginal tax rate corrections of erroneously collected tax revenue are effects, economic efficiency (including corrections levied at the national level on individual and corpo- treated as negative revenue. The analytic framework for externalities), and the practical problems of rate income. Figures for individual marginal tax rates of the International Monetary Fund's (IMF) Govern- administering a tax system. There is no ideal level generally refer to employment income. In some coun- ment Finance Statistics Manual 2001 (GFSM 2001) of taxation. But taxes influence incentives and thus tries the highest marginal tax rate is also the basic is 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 Taxes are compulsory transfers to governments ent corporate tax rates may be levied, depending on GFSM 2001 accrual framework. These countries are from individuals, businesses, or institutions. Certain the type of business (mining, banking, insurance, footnoted in the table. · Number of tax payments compulsory transfers, such as fines, penalties, and agriculture, manufacturing), ownership (domestic by businesses is the total number of taxes paid by most social security contributions are excluded from or foreign), volume of sales, or whether surtaxes or businesses during one year. When electronic filing is tax revenue. exemptions are included. The corporate tax rates in available, the tax is counted as paid once a year even The level of taxation is typically measured by tax the table are mainly general rates applied to domes- if payments are more frequent. · Time to prepare, revenue as a share of gross domestic product (GDP). tic companies. For more detailed information, see file, and pay taxes is the time, in hours per year, it Comparing levels of taxation across countries pro- the country's laws, regulations, and tax treaties. takes to prepare, file, and pay (or withhold) three vides a quick overview of the fiscal obligations and major types of taxes: the corporate income tax, the incentives facing the private sector. The table shows value-added or sales tax, and labor taxes, includ- only central government data, which may significantly ing payroll taxes and social security contributions. understate the total tax burden, particularly in coun- · Total tax rate is the total amount of taxes payable tries where provincial and municipal governments are by businesses (except for labor taxes) after account- large or have considerable tax authority. ing for deductions and exemptions as a percentage Low ratios of tax revenue to GDP may reflect weak of profit. For further details on the method used for administration and large-scale tax avoidance or eva- assessing the total tax payable, see Doing Business sion. Low ratios may also reflect a sizable parallel 2007. · Highest marginal tax rate is the highest rate economy with unrecorded and undisclosed incomes. shown on the national level schedule of tax rates Tax revenue ratios tend to rise with income, with applied to the annual taxable income of individuals higher income countries relying on taxes to finance and corporations. Also presented are the income lev- a much broader range of social services and social els for individuals above which the highest marginal security than lower income countries are able to. tax rates levied at the national level apply. The new indicators covering taxes payable by busi- nesses go beyond the usual measures of tax rates, which capture only part of the taxpayer burden. In some countries tax systems are so complex that businesses must make more than 100 payments and spend up to 2,600 hours a year to prepare and pay taxes. Taxes are measured at all levels of government and Data sources include corporate income tax, personal income tax withheld by businesses, value-added or sales taxes, Data on central government tax revenues are from property transfer taxes, financial transactions taxes, print and electronic editions of the IMF's Govern- dividend taxes, waste collection taxes, and vehicle ment Finance Statistics Yearbook. Data on taxes and road taxes. To make the data comparable across payable by businesses are from Doing Business countries, several assumptions are made about 2007 (www.doingbusiness.org). Data on individ- the business. The main assumptions are that they ual and corporate tax rates are from Pricewater- are limited liability companies, they operate in the houseCoopers's Worldwide Tax Summaries online country's most populous city, they are domestically (www.pwc.com). owned, they perform general industrial or commercial 2007 World Development Indicators 287 5.7 Defense expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers $ millions % of central government % of 1990 prices % of GDP expenditure thousands labor force Exports Imports 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Afghanistan .. .. .. .. 383 27 5.5 0.3 0 .. 0 22 Albania 2.1 1.4 8.2 5.7 87 23 6.0 1.7 .. .. 24 31 Algeria 3.0 2.8 12.2 .. 163 319 1.8 2.4 .. .. 346 149 Angola 8.1 5.0 .. .. 122 118 2.3 1.7 0 0 1 22 Argentina 1.6 1.0 .. 5.9 99 102 0.7 0.6 3 0 70 67 Armenia 4.1 2.7 .. 15.0 61 49 4.2 3.8 .. .. 49 0 Australia 1.9 1.8 .. 7.2 57 53 0.6 0.5 28 50 147 396 Austria 0.9 0.7 2.0 1.7 56 40 1.4 1.0 0 3 23 21 Azerbaijan 2.3 2.1 11.7 .. 127 82 3.8 2.0 .. .. 0 0 Bangladesh 1.4 1.1 .. 13.6 171 252 0.3 0.4 .. .. 121 27 Belarus 1.6 1.2 5.5 4.2 106 183 2.1 3.8 8 0 0 0 Belgium 1.6 1.2 3.4 2.9 47 37 1.1 0.8 299 173 16 0 Benin .. .. .. .. 7 8 0.3 0.2 .. .. 0 0 Bolivia 1.9 1.9 .. 7.1 64 70 2.2 1.7 .. .. 1 9 Bosnia and Herzegovina .. 1.8 .. 4.8 92 12 5.2 0.6 0 0 0 0 Botswana 3.5 2.5 11.4 .. 9 11 1.4 1.8 .. .. 7 0 Brazil 2.1 1.6 4.8 .. 681 673 0.9 0.7 28 62 237 142 Bulgaria 2.6 2.4 6.6 7.0 136 85 3.5 2.7 2 0 0 158 Burkina Faso 1.5 1.5 .. 12.6 10 11 0.2 0.2 .. .. 0 19 Burundi 4.2 0.0 17.8 .. 15 82 0.5 2.1 .. .. 0 0 Cambodia 5.4 1.8 .. 23.1 309 191 6.2 2.8 0 0 0 0 Cameroon 1.4 1.3 11.8 .. 24 23 0.5 0.4 .. .. 0 0 Canada 1.6 1.1 6.4 6.3 76 71 0.5 0.4 369 365 339 112 CentralAfrican Republic 1.2 1.1 .. 12.3 5 3 0.3 0.2 .. .. 0 0 Chad 1.4 0.9 .. .. 35 35 1.3 1.0 0 0 1 0 Chile 3.1 3.8 .. 20.2 130 116 2.3 1.8 0 0 468 456 China 1.7a 2.0a ..a 18.2a 4,130 3,755 0.6 0.5 962 129 523 2,697 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2.6 3.7 .. 11.9 233 336 1.4 1.5 .. .. 37 11 Congo, Dem. Rep. 1.5 2.1 13.5 .. 65 65 0.4 0.3 .. .. 0 14 Congo, Rep. .. 1.4 .. 6.9 17 12 1.4 0.8 .. .. 0 0 Costa Rica .. .. .. .. 16 0 1.2 0.0 .. .. 0 0 Côte d'Ivoire 0.8 1.6 .. 8.9 15 19 0.3 0.3 .. .. 2 0 Croatia 9.4 1.6 22.2 4.0 150 31 7.2 1.6 0 0 22 0 Cuba .. .. .. .. 124 76 2.5 1.4 .. .. 0 0 Czech Republic 1.7 1.8 5.2 5.0 92 28 1.8 0.5 122 10 0 630 Denmark 1.7 1.4 .. 4.2 33 21 1.2 0.7 8 2 127 78 Dominican Republic 0.6 0.6 5.6 3.3 40 40 1.3 1.0 .. .. 0 0 Ecuador 2.4 2.4 9.2 .. 57 47 1.3 0.7 .. .. 10 33 Egypt, Arab Rep. 3.5 2.8 14.7 .. 610 799 3.5 3.5 7 0 1,700 596 El Salvador 1.0 0.6 .. 3.5 39 16 1.8 0.6 0 .. 3 0 Eritrea 20.8 19.3 .. .. 55 202 4.4 11.3 0 0 3 276 Estonia 1.0 1.6 .. 6.2 6 8 0.8 1.2 0 0 18 10 Ethiopia 1.6 3.1 .. .. 120 183 0.5 0.6 0 0 0 0 Finland 1.5 1.2 .. 3.3 35 31 1.4 1.2 20 22 159 77 France 3.0 2.5 6.2 5.4 502 359 2.0 1.3 681 2,399 43 3 Gabon .. 1.4 .. .. 10 7 2.0 1.2 .. .. 0 0 Gambia, The 0.8 0.3 .. .. 1 1 0.2 0.1 .. .. 0 0 Georgia 2.2 3.1 8.2 18.1 14 23 0.5 1.0 0 0 0 0 Germany 1.6 1.4 4.2 4.3 365 285 0.9 0.7 1,430 1,855 252 216 Ghana 0.8 0.7 .. 3.8 13 7 0.2 0.1 .. .. 0 0 Greece 4.2 4.5 8.8 10.1 202 168 4.5 3.3 0 0 870 1,114 Guatemala 1.0 0.4 13.1 3.8 57 48 1.8 1.2 .. .. 3 0 Guinea 1.4 .. .. .. 19 13 0.5 0.3 .. .. 0 0 Guinea-Bissau 0.9 .. .. .. 9 9 1.9 1.4 .. .. 0 0 Haiti 0.1 .. .. .. 7 0 0.2 0.0 .. .. .. .. 288 2007 World Development Indicators 5.7 STATES AND MARKETS Defense expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers $ millions % of central government % of 1990 prices % of GDP expenditure thousands labor force Exports Imports 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Honduras .. 0.6 .. .. 24 20 1.2 0.6 .. .. 0 0 Hungary 1.6 1.3 .. 3.1 73 44 1.7 1.0 6 70 24 12 India 2.7 2.9 18.4 18.6 2,150 3,047 0.6 0.7 2 0 943 1,471 Indonesia 1.6 0.9 16.2 6.5 461 582 0.5 0.5 25 8 339 19 Iran, Islamic Rep. 2.4 4.5 15.2 21.7 763 585 4.4 2.1 1 0 373 403 Iraq .. .. .. .. 407 227 7.0 2.7 0 0 0 290 Ireland 1.0 0.6 2.7 1.9 13 10 0.9 0.5 0 .. 0 4 Israel 9.0 7.9 .. 17.0 178 176 8.5 6.4 110 160 265 1,422 Italy 1.7 1.8 3.6 4.5 585 445 2.6 1.8 340 827 315 224 Jamaica 0.6 0.7 1.7 2.1 4 3 0.3 0.3 .. .. 0 0 Japan 1.0 1.0 .. .. 252 272 0.4 0.4 16 0 877 250 Jordan 12.4 7.7 47.5 21.7 129 111 10.2 6.0 0 15 19 23 Kazakhstan 1.1 1.1 5.7 5.8 75 101 1.0 1.2 24 0 99 68 Kenya 1.6 1.5 6.4 7.6 29 29 0.2 0.2 .. .. 0 25 Korea, Dem. Rep. .. .. .. .. 1,243 1,295 12.4 12.1 52 0 68 2 Korea, Rep. 2.8 2.6 19.4 12.1 641 693 3.0 2.8 21 38 1,674 544 Kuwait 13.6 5.7 .. 21.9 22 23 2.5 1.7 0 0 631 55 Kyrgyz Republic 1.6 2.8 6.1 .. 7 18 0.4 0.8 61 0 0 3 Lao PDR 2.9 .. .. .. 137 129 7.7 5.5 .. .. 0 0 Latvia 0.9 1.7 3.1 5.8 11 5 0.9 0.5 0 0 16 7 Lebanon 6.4 3.8 .. 14.4 63 85 5.5 6.0 0 0 34 1 Lesotho 3.7 2.4 10.7 6.8 2 2 0.3 0.3 .. .. 0 0 Liberia 31.2 .. .. .. 21 15 2.7 1.3 .. .. 0 0 Libya 4.1 1.9 .. .. 81 76 5.2 3.3 0 0 0 0 Lithuania 0.5 1.8 .. 6.4 9 29 0.5 1.8 0 0 4 9 Macedonia, FYR 3.0 2.2 .. .. 18 19 2.2 2.2 0 29 0 0 Madagascar 0.9 .. .. .. 29 22 0.5 0.3 .. .. 0 0 Malawi 0.8 0.7 .. .. 10 7 0.2 0.1 0 0 0 0 Malaysia 2.8 1.9 16.0 13.8 140 135 1.7 1.2 0 0 898 467 Mali 2.2 1.9 .. .. 15 12 0.4 0.2 .. .. 0 0 Mauritania 2.0 1.0 .. .. 21 21 2.3 1.7 .. .. 1 0 Mauritius 0.4 0.2 1.8 1.0 2 2 0.4 0.4 .. .. 0 0 Mexico 0.6 0.4 3.8 .. 189 204 0.5 0.5 .. .. 45 35 Moldova 0.9 0.3 2.4 1.0 15 10 0.8 0.5 0 4 6 0 Mongolia 1.7 1.7 .. 6.2 31 16 3.3 1.3 .. .. 0 0 Morocco 4.6 4.3 .. 13.7 238 251 2.7 2.3 .. .. 30 32 Mozambique 1.5 1.4 .. .. 12 11 0.2 0.1 .. .. 0 0 Myanmar 3.7 .. .. .. 371 483 1.7 1.8 .. .. 216 20 Namibia 1.9 3.0 .. 8.5 8 15 1.5 2.3 .. .. 4 0 Nepal 0.9 2.0 .. 11.8 63 131 0.8 1.2 .. .. 1 0 Netherlands 1.9 1.6 3.8 4.0 78 60 1.0 0.7 383 840 46 129 New Zealand 1.4 1.0 .. 3.1 10 9 0.6 0.4 0 0 7 8 Nicaragua 1.1 0.7 6.8 3.3 12 14 0.8 0.7 5 0 0 0 Niger 1.0 1.1 .. .. 11 10 0.3 0.2 .. .. 0 0 Nigeria 0.7 0.9 .. .. 89 161 0.2 0.3 0 0 2 0 Norway 2.4 1.6 .. 4.8 31 47 1.4 1.9 22 13 83 9 Oman 14.6 12.2 45.2 .. 48 46 6.2 4.8 0 0 157 98 Pakistan 6.0 3.4 31.4 23.1 846 921 2.2 1.6 0 9 .. .. Panama 1.2 .. 5.6 .. 12 12 1.1 0.8 .. .. 0 0 Papua New Guinea 1.0 0.5 3.9 .. 4 3 0.2 0.1 .. .. 0 0 Paraguay 1.4 0.8 .. 4.5 28 25 1.4 0.9 .. .. 0 1 Peru 1.9 1.2 10.7 7.2 178 157 1.8 1.2 0 0 32 368 Philippines 1.4 0.8 8.5 4.5 149 147 0.5 0.4 .. .. 36 38 Poland 2.0 1.8 .. 4.9 302 162 1.7 0.9 176 124 125 96 Portugal 2.4 2.1 5.7 5.1 104 93 2.1 1.7 0 0 18 406 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 289 5.7 Defense expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers $ millions % of central government % of 1990 prices % of GDP expenditure thousands labor force Exports Imports 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Romania 2.8 2.1 .. .. 297 177 2.4 1.7 6 17 0 579 Russian Federation 4.4 3.7 .. 18.8 1,800 1,452 2.5 2.0 3,273 5,771 40 0 Rwanda 4.4 2.2 .. .. 47 53 2.0 1.3 .. .. 0 0 Saudi Arabia 9.3 8.2 .. .. 178 216 3.0 2.7 0 36 975 470 Senegal 1.8 1.5 .. .. 17 19 0.5 0.4 .. .. 2 0 Serbia and Montenegro 5.3 2.7 .. .. 165 110 3.5 2.8 0 0 18 0 Sierra Leone 2.9 1.1 .. 5.1 7 13 0.4 0.6 .. .. 15 0 Singapore 4.4 4.7 35.1 30.5 66 167 3.7 7.5 0 3 237 423 Slovak Republic 3.2 1.8 .. 5.1 51 20 2.1 0.7 91 0 220 0 Slovenia 1.6 1.7 4.7 4.0 13 12 1.3 1.2 .. .. 19 2 Somalia .. .. .. .. 225 0 8.3 0.0 .. .. 0 0 South Africa 2.2 1.4 .. 4.8 277 56 1.7 0.3 15 39 38 606 Spain 1.4 1.0 3.9 4.2 282 220 1.7 1.1 82 113 363 281 Sri Lanka 5.3 2.7 20.3 12.7 236 200 3.3 2.4 .. .. 49 8 Sudan 2.7 2.3 .. .. 134 123 1.6 1.2 .. .. 3 0 Swaziland 2.4 .. .. .. 3 .. 1.1 .. .. .. 0 0 Sweden 2.3 1.6 .. 4.3 100 29 2.2 0.6 184 592 95 104 Switzerland 1.3 1.0 5.2 .. 31 109 0.8 2.6 38 74 93 144 Syrian Arab Republic 7.1 6.2 .. .. 531 416 11.2 5.5 0 0 43 0 Tajikistan 1.0 2.2 .. 15.8 18 13 0.9 0.6 .. .. 0 0 Tanzania 1.5 1.1 .. .. 36 28 0.2 0.1 .. .. 0 0 Thailand 2.3 1.1 .. 7.0 421 421 1.3 1.2 0 0 558 98 Togo 2.4 1.5 .. 9.8 8 10 0.4 0.4 .. .. 3 0 Trinidad and Tobago 0.5 .. 1.8 .. 7 3 1.3 0.5 .. .. 0 0 Tunisia 1.9 1.5 6.7 5.1 59 47 2.1 1.2 .. .. 42 156 Turkey 3.9 3.2 20.4 .. 690 617 3.0 2.3 0 28 1,562 746 Turkmenistan 2.3 .. .. .. 11 26 0.7 1.2 .. .. 0 0 Uganda 2.2 2.5 .. 11.1 52 47 0.6 0.4 .. .. 38 0 Ukraine 2.8 2.4 .. 6.5 519 273 2.0 1.2 242 188 0 29 United Arab Emirates 5.2 1.9 49.2 .. 71 51 5.5 1.9 27 10 426 2,381 United Kingdom 3.0 2.6 .. 6.3 233 217 0.8 0.7 1,206 791 633 94 United States 3.8 4.1 .. 19.3 1,636 1,546 1.2 1.0 10,689 7,101 415 387 Uruguay 2.1 1.4 7.9 5.0 27 25 1.8 1.4 0 0 8 18 Uzbekistan 1.1 0.5 .. .. 42 91 0.5 0.8 0 0 0 0 Venezuela, RB 1.6 1.1 8.7 4.4 80 82 0.9 0.6 0 0 0 7 Vietnam 2.6 .. .. .. 622 495 1.8 1.1 .. .. 270 291 West Bank and Gaza .. .. .. .. .. 56 .. 7.3 .. .. 1 0 Yemen, Rep. 6.4 5.6 33.4 .. 70 138 1.8 2.3 .. .. 124 289 Zambia 2.2 .. .. .. 23 16 0.6 0.3 0 0 0 0 Zimbabwe 3.6 3.4 11.2 .. 68 51 1.4 0.9 .. .. 0 0 World 2.5 w 2.5 w .. w 11.1 w 30,182 s 30,898 s 1.2 w 0.9 w 21,064 s 21,941 s 20,951 s 21,804 s Low income 2.7 2.6 19.6 18.5 7,698 8,536 1.0 0.9 115 9 1,813 2,459 Middle income 2.3 2.0 .. 13.2 16,128 16,527 1.2 0.9 4,996 6,465 8,354 9,196 Lower middle income 2.1 2.0 .. 15.4 11,456 12,989 1.0 0.8 1,304 406 4,598 5,352 Upper middle income 2.7 2.0 .. .. 4,672 3,538 1.9 1.3 3,692 6,059 3,756 3,844 Low & middle income 2.4 2.1 .. 13.8 23,826 25,063 1.1 0.9 5,111 6,474 10,167 11,655 East Asia & Pacific 1.8 1.8 .. 16.5 8,021 10,125 0.9 0.7 1,039 137 2,920 3,632 Europe & Central Asia 3.4 2.7 .. 11.2 4,971 3,581 2.3 1.7 4,011 6,212 2,227 2,349 Latin America & Carib. 1.7 1.3 5.1 .. 2,112 2,076 1.1 0.8 36 62 914 1,147 Middle East & N. Africa 4.1 3.7 18.1 .. 3,172 3,169 4.2 2.9 8 15 2,872 2,037 South Asia 3.0 2.8 20.4 19.0 3,852 4,578 0.8 0.8 2 9 1,114 1,528 Sub-Saharan Africa 2.1 1.6 .. .. 1,698 1,534 0.7 0.5 15 39 120 962 High income 2.5 2.6 .. 10.6 6,356 5,836 1.3 1.1 15,953 15,467 10,584 10,149 Europe EMU 2.0 1.7 3.9 4.5 2,270 1,750 1.7 1.2 3,235 6,232 2,105 2,475 Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2006). a. Estimates differ from official statistics of the govenment of China, which has published the following estimates: military expenditure as 1.1 percent of GDP in 1995 and 1.6 percent in 2004 and 9.3 percent of central government expenditure in 1995 and 7.7 percent in 2004 (see National Bureau of Statistics of China, www.stats.gov.cn). 290 2007 World Development Indicators 5.7 STATES AND MARKETS Defense expenditures and arms transfers About the data Definitions Although national defense is an important function Balance 2007. These data refer to military personnel · Military expenditures data from SIPRI are derived of government and security from external threats on active duty, including paramilitary forces. Reserve from the NATO definition, which includes all cur- contributes to economic development, high lev- forces, which are units that are not fully staffed or rent and capital expenditures on the armed forces, els of defense spending burden the economy and operational in peace time, are not included. These including peacekeeping forces; defense ministries may impede growth. Data on military expenditures data also exclude civilians in the defense establish- and other government agencies engaged in defense as a share of gross domestic product (GDP) are a ment and so are not consistent with the data on mili- projects; paramilitary forces, if these are judged to rough indicator of the portion of national resources tary spending on personnel. Moreover, because data be trained and equipped for military operations; and used for military activities and of the burden on the exclude personnel not on active duty, they underes- military space activities. Such expenditures include national economy. Comparisons of defense spend- timate the share of the labor force working for the military and civil personnel, including retirement ing between countries should take into account the defense establishment. Because governments rarely pensions of military personnel and social services many factors that influence perceptions of vulner- report the size of their armed forces, such data typi- for personnel; operation and maintenance; procure- ability and risk, including historical and cultural tradi- cally come from intelligence sources. ment; military research and development; and mili- tions, the length of borders that need defending, the The data on arms transfers are from SIPRI's Arms tary aid (in the military expenditures of the donor quality of relations with neighbors, and the role of Transfers Project, which reports on international country). Excluded are civil defense and current the armed forces in the body politic. As an "input" flows of conventional weapons. Data are collected expenditures for previous military activities, such measure, military spending is not directly related to from open sources, and since publicly available infor- as for veterans' benefits, demobilization, conver- the "output" of military activities, capabilities, or mation is inadequate for tracking all weapons and sion, and destruction of weapons. This definition military security. other military equipment, SIPRI covers only what it cannot be applied for all countries, however, since Data on defense spending reported by governments terms major conventional weapons. that would require much more detailed information are not compiled using standard definitions. They are SIPRI's data on arms transfers cover sales of than is available about what is included in military often incomplete and unreliable. Even in countries weapons, manufacturing licenses, aid, and gifts; budgets and off-budget military expenditure items. where the parliament vigilantly reviews budgets and therefore the term arms transfers rather than arms (For example, military budgets might or might not spending, defense spending and arms transfers rarely trade is used. The transferred weapons must be cover civil defense, reserves and auxiliary forces, receive close scrutiny and full, public disclosure (see transferred voluntarily by the supplier, must have police and paramilitary forces, dual-purpose forces Ball 1984 and Happe and Wakeman-Linn 1994). The a military purpose, and must be destined for the such as military and civilian police, military grants data on military expenditures as a share of GDP and armed forces, paramilitary forces, or intelligence in kind, pensions for military personnel, and social a share of central government expenditure are esti- agencies of another country. SIPRI data also cover security contributions paid by one part of govern- mated by the Stockholm International Peace Research weapons supplied to or from rebel forces in an armed ment to another.) · Armed forces personnel are Institute (SIPRI). Central government expenditures are conflict as well as arms deliveries for which neither active duty military personnel, including paramili- from the International Monetary Fund (IMF). Therefore the supplier nor the recipient can be identified with tary forces if the training, organization, equipment, the data shown in the table may differ from compa- an acceptable degree of certainty; these data are and control suggest they may be used to support rable data published by national governments. available in SIPRI's database. or replace regular military forces. · Arms transfers SIPRI's primary source of military expenditure data SIPRI's estimates of arms transfers, presented in cover the supply of military weapons through sales, is official data provided by national governments. 1990 constant price U.S. dollars, are designed as aid, gifts, and those made through manufacturing These data are derived from national budget docu- a trend-measuring device in which similar weapons licenses. Data cover major conventional weapons ments, defense white papers, and other public docu- have similar values, reflecting both the value and such as aircraft, armored vehicles, artillery, radar ments from official government agencies, including quality of weapons transferred. The trends presented systems, missiles, and ships designed for military governments' responses to questionnaires sent by in the tables are based on actual deliveries only. use. Excluded are transfers of other military equip- SIPRI, the United Nations, or the Organization for SIPRI cautions that these estimated values do not ment such as small arms and light weapons, trucks, Security and Co-operation in Europe. Secondary reflect financial value (payments for weapons trans- small artillery, ammunition, support equipment, tech- sources include international statistics, such as those ferred) for three reasons: reliable data on the value nology transfers, and other services. See About the of the North Atlantic Treaty Organization (NATO) and of the transfer are not available; even when the value data for more detail. the IMF's Government Finance Statistics Yearbook. of a transfer is known, it usually includes more than Other secondary sources include country reports of the actual conventional weapons such as spares, the Economist Intelligence Unit, country reports by support systems, and training; and even when the IMF staff, and specialist journals and newspapers. value of the transfer is known, details of the financial Lack of sufficiently detailed data makes it difficult arrangements such as credit and loan conditions and to apply a common definition of military expenditure discounts are usually not known. globally, so SIPRI has adopted a definition (derived Given these measurement issues, SIPRI's method from the NATO definition) as a guideline (see Defini- of estimating the transfer of military resources tions). This definition cannot be applied for all coun- includes an evaluation of the technical parameters of tries, however, since that would require much more the weapons. Weapons for which a price is not known detailed information than is available about what is are compared with the same weapons for which included in military budgets and off-budget military actual acquisition prices are available ("core weap- Data sources expenditure items. In the many cases where SIPRI ons") or for the closest match. These weapons are cannot make independent estimates, it uses the assigned a value in an index that reflects their mili- Data on military expenditures and arms transfers national data provided. Because of the differences in tary resource value in relation to the core weapons. are from SIPRI's Yearbook 2006: Armaments, Disar- definitions and the difficulty in verifying the accuracy These matches are based on such characteristics mament, and International Security. Data on armed and completeness of data, the data on military spend- as size, performance, and type of electronics, and ing are not strictly comparable across countries. adjustments are made for second-hand weapons. forces personnel are from the International Institute The data on armed forces are from the Interna- More information on SIPRI's arms transfers project is for Strategic Studies' The Military Balance 2007. tional Institute for Strategic Studies' The Military available at www.sipri.org/contents/armstrad/. 2007 World Development Indicators 291 5.8 Public policies and institutions IDA Economic management Structural policies Resource 1 (low) to 6 (high) 1 (low) to 6 (high) Allocation Index 1 (low) to 6 (high) Business Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2005 2005 2005 2005 2005 2005 2005 2005 2005 Albania 3.7 4.5 3.5 4.0 4.0 4.5 4.0 3.5 4.0 Angola 2.6 3.0 2.5 2.0 2.5 4.0 2.5 2.0 2.8 Armenia 4.3 5.5 5.0 5.5 5.3 4.5 3.5 4.0 4.0 Azerbaijan 3.7 4.5 4.5 4.5 4.5 4.0 3.0 3.5 3.5 Bangladesh 3.4 4.0 3.5 4.5 4.0 3.0 3.0 3.5 3.2 Benin 3.7 4.5 4.0 3.5 4.0 4.5 3.5 4.0 4.0 Bhutan 3.8 4.0 4.0 4.0 4.0 3.0 3.0 3.5 3.2 Bolivia 3.7 4.0 4.0 4.0 4.0 5.0 3.5 3.0 3.8 Bosnia and Herzegovina 3.6 4.0 3.5 4.0 3.8 4.0 4.0 3.5 3.8 Burkina Faso 3.8 4.5 4.5 4.5 4.5 4.0 3.0 3.0 3.3 Burundi 3.0 3.5 3.5 3.0 3.3 3.0 3.0 2.5 2.8 Cambodia 3.1 4.0 3.5 3.5 3.7 3.5 2.0 3.5 3.0 Cameroon 3.3 4.0 3.5 2.5 3.3 3.5 3.0 3.5 3.3 Cape Verde 4.1 4.5 4.0 4.0 4.2 4.0 4.0 4.0 4.0 Central African Republic 2.4 3.0 3.0 1.5 2.5 3.5 2.5 2.0 2.7 Chad 2.9 4.0 3.0 3.0 3.3 3.0 3.0 3.0 3.0 Comoros 2.4 3.0 2.5 1.5 2.3 2.0 2.5 2.5 2.3 Congo, Dem. Rep. 2.8 3.5 3.5 2.5 3.2 4.0 2.0 3.0 3.0 Congo, Rep. 2.8 3.5 3.0 2.5 3.0 3.0 2.5 2.5 2.7 Côte d'Ivoire 2.5 2.5 2.0 1.5 2.0 3.5 3.0 3.0 3.2 Djibouti 3.1 3.5 3.0 3.0 3.2 4.0 3.5 3.0 3.5 Dominica 3.8 4.0 4.0 3.0 3.7 4.0 4.0 4.5 4.2 Eritrea 2.5 2.0 2.0 2.5 2.2 1.5 2.0 2.0 1.8 Ethiopia 3.4 3.5 4.0 3.5 3.7 3.0 3.0 3.5 3.2 Gambia, The 3.1 3.5 3.0 2.5 3.0 4.0 3.0 3.0 3.3 Georgia 3.8 4.5 4.0 4.0 4.2 3.5 3.5 4.0 3.7 Ghana 3.9 4.0 4.5 4.0 4.2 4.0 3.5 4.0 3.8 Grenada 3.7 4.0 3.0 2.5 3.2 4.0 3.5 4.5 4.0 Guinea 3.0 2.5 3.0 2.5 2.7 4.5 3.0 3.0 3.5 Guinea-Bissau 2.7 2.5 2.5 2.0 2.3 3.5 2.5 3.0 3.0 Guyana 3.4 3.5 3.5 3.5 3.5 4.0 3.5 3.0 3.5 Haiti 2.8 3.5 3.0 2.5 3.0 4.0 3.0 2.5 3.2 Honduras 3.9 4.5 4.5 4.0 4.3 4.5 3.5 4.0 4.0 India 3.8 4.5 3.0 4.5 4.0 3.5 4.0 3.5 3.7 Indonesia 3.7 4.5 4.0 4.5 4.3 4.5 3.5 3.0 3.7 Kenya 3.6 4.5 4.0 4.0 4.2 4.0 3.5 4.0 3.8 Kiribati 3.2 2.5 2.5 5.0 3.3 3.0 3.0 3.0 3.0 Kyrgyz Republic 3.5 4.5 3.5 4.0 4.0 4.5 3.5 3.5 3.8 About the data The International Development Association (IDA) mid-1970s. Over time the criteria have been revised The CPIA exercise is intended to capture the qual- is the part of the World Bank Group that helps the from a largely macroeconomic focus to include gov- ity of a country's policies and institutional arrange- poorest countries reduce poverty by providing con- ernance aspects and a broader coverage of social ments, focusing on key elements that are within the cessional loans and grants for programs aimed at and structural dimensions. Country performance is country's control, rather than on outcomes (such boosting economic growth and improving living con- assessed against a set of 16 criteria grouped into four as economic growth rates) that are influenced by ditions. IDA funding helps these countries deal with clusters: economic management, structural policies, events beyond the country's control. More spe- the complex challenges they face in striving to meet policies for social inclusion and equity, and public sec- cifi cally, the CPIA measures the extent to which a the Millennium Development Goals. tor management and institutions. IDA resources are country's policy and institutional framework sup- The World Bank's IDA Resource Allocation Index allocated to a county on per capita terms on the basis ports sustainable growth and poverty reduction (IRAI), which is presented in the table, is based on the of its IDA country performance rating and, to a limited and, consequently, the effective use of develop- results of the annual Country Policy and Institutional extent, on the basis of its per capita gross national ment assistance. Assessment (CPIA) exercise, which covers the IDA- income. This ensures that good performers receive a All criteria within each cluster receive equal weight, eligible countries. Country assessments have been higher IDA allocation in per capita terms. The IRAI is a and each cluster has a 25 percent weight in the carried out annually by World Bank staff since the key element in the country performance rating. overall score, which is obtained by averaging the 292 2007 World Development Indicators 5.8 STATES AND MARKETS Public policies and institutions IDA Economic management Structural policies Resource 1 (low) to 6 (high) 1 (low) to 6 (high) Allocation Index 1 (low) to 6 (high) Business Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2005 2005 2005 2005 2005 2005 2005 2005 2005 Lao PDR 3.0 4.0 3.5 3.5 3.7 3.5 1.5 3.0 2.7 Lesotho 3.5 4.0 4.0 4.0 4.0 3.5 3.5 3.0 3.3 Madagascar 3.5 3.5 3.0 3.5 3.3 4.0 3.5 4.0 3.8 Malawi 3.4 3.0 3.0 3.0 3.0 4.0 3.0 3.5 3.5 Maldives 3.8 3.5 3.5 4.5 3.8 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 3.2 2.0 2.5 4.0 2.8 4.5 2.5 3.5 3.5 Moldova 3.5 3.5 3.5 3.0 3.3 3.5 3.5 4.0 3.7 Mongolia 3.4 4.0 3.5 3.0 3.5 4.5 3.0 3.5 3.7 Mozambique 3.5 4.0 4.0 4.5 4.2 4.0 2.5 3.0 3.2 Nepal 3.3 4.5 3.5 3.5 3.8 4.0 3.0 3.0 3.3 Nicaragua 3.7 3.5 4.0 4.5 4.0 4.5 3.0 3.5 3.7 Niger 3.3 3.5 3.0 3.5 3.3 4.0 3.0 3.5 3.5 Nigeria 3.1 4.0 4.0 3.5 3.8 2.5 3.0 3.0 2.8 Pakistan 3.7 4.5 3.5 4.5 4.2 4.0 4.5 4.0 4.2 Papua New Guinea 3.1 4.0 3.0 3.5 3.5 4.0 3.0 3.0 3.3 Rwanda 3.5 4.0 3.5 3.0 3.5 3.5 3.5 3.5 3.5 Samoa 4.0 4.0 3.5 4.0 3.8 4.5 4.0 4.0 4.2 São Tomé and Principe 3.0 3.0 3.0 2.5 2.8 4.0 2.5 3.0 3.2 Senegal 3.8 4.5 4.0 4.0 4.2 4.5 3.5 3.5 3.8 Serbia and Montenegro 3.7 3.5 4.5 3.5 3.8 4.5 3.0 3.5 3.7 Sierra Leone 3.1 4.0 3.5 3.5 3.7 3.5 3.0 2.5 3.0 Solomon Islands 2.8 3.5 3.5 2.5 3.2 3.0 3.0 2.5 2.8 Sri Lanka 3.6 3.5 3.0 3.5 3.3 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 3.9 4.0 4.0 4.0 4.0 4.0 4.0 4.5 4.2 Sudan 2.6 3.5 3.5 1.5 2.8 3.0 2.5 3.0 2.8 Tajikistan 3.3 4.5 4.0 4.0 4.2 4.0 3.0 3.5 3.5 Tanzania 3.9 5.0 4.5 4.0 4.5 4.0 3.5 3.5 3.7 Togo 2.5 2.5 2.0 1.5 2.0 4.0 2.5 3.0 3.2 Tonga 2.9 3.0 2.0 3.5 2.8 3.0 3.0 3.0 3.0 Uganda 3.9 4.5 4.5 4.5 4.5 4.0 3.5 4.0 3.8 Uzbekistan 3.0 3.0 3.5 4.0 3.5 2.5 2.5 2.5 2.5 Vanuatu 3.1 3.0 3.0 4.0 3.3 4.0 3.0 3.0 3.3 Vietnam 3.7 5.0 4.0 4.0 4.3 3.5 3.0 3.5 3.3 Yemen, Rep. 3.3 4.0 3.0 4.5 3.8 4.5 2.5 3.0 3.3 Zambia 3.3 3.5 3.5 3.0 3.3 4.0 3.0 3.0 3.3 Zimbabwe 1.8 1.0 1.0 1.0 1.0 2.0 2.5 2.0 2.2 average scores of the four clusters. For each of the developmentally neutral manner. Accordingly, higher process involves two key phases. In the bench- 16 criteria countries are rated on a scale of 1 (low) scores can be attained by a country that, given its marking phase a small representative sample of to 6 (high). The scores depend on the level of perfor- stage of development, has a policy and institutional countries drawn from all regions is rated. Country mance in a given year assessed against the criteria, framework that more strongly fosters growth and teams prepare proposals that are reviewed first at rather than on changes in performance compared poverty reduction. the regional level and then in a Bankwide review pro- with the previous year. All 16 CPIA criteria contain a The country teams that prepare the ratings are cess. A similar process is then followed to assess detailed description of each rating level. In assessing very familiar with the country, and their assess- the performance of the remaining countries, using country performance World Bank staff evaluate the ments are based on country diagnostic studies the benchmark countries' scores as guideposts. country's actual performance on each of the criteria prepared by the World Bank or other development The final ratings are determined following a Bank- and assign a rating. The ratings reflect a variety of organizations and on their own professional judg- wide review. The numerical IRAI overall score and indicators, observations, and judgments based on ment. An early consultation is conducted with coun- the separate criteria scores were first publicly dis- country knowledge and on relevant publicly available try authorities to make sure that the assessments closed in June 2006. indicators. In interpreting the assessment scores, it are informed by up-to-date information. To ensure See IDA's website at www.worldbank.org/ida for should be noted that the criteria are designed in a that scores are consistent across countries, the more information. 2007 World Development Indicators 293 5.8 Public policies and institutions Policies for social inclusion and equity Public sector management and institutions 1 (low) to 6 (high) 1 (low) to 6 (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 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Albania 4.0 3.5 3.0 3.5 3.0 3.4 3.0 4.0 3.5 3.0 3.0 3.3 Angola 3.0 2.5 2.5 2.5 2.5 2.6 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 4.0 4.0 3.5 3.8 Azerbaijan 4.0 3.5 3.0 3.5 3.0 3.4 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 2.5 2.9 Benin 3.0 3.0 3.5 3.0 3.5 3.2 3.0 4.0 3.5 3.0 3.5 3.4 Bhutan 4.5 4.0 4.5 3.5 4.5 4.2 3.5 3.5 4.0 4.0 4.0 3.8 Bolivia 3.5 4.0 4.0 3.5 3.5 3.7 2.5 3.5 4.0 3.5 3.0 3.3 Bosnia and Herzegovina 4.0 3.0 3.5 3.5 3.0 3.4 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 3.5 3.6 Burundi 3.5 3.0 3.0 3.0 2.5 3.0 2.5 2.5 3.0 2.5 3.0 2.7 Cambodia 3.5 3.0 3.5 3.0 2.5 3.1 2.5 2.5 3.0 2.5 2.5 2.6 Cameroon 3.5 3.0 3.5 3.0 4.0 3.4 2.5 3.5 4.0 3.0 2.5 3.1 Cape Verde 4.5 4.5 4.0 4.5 4.0 4.3 4.0 3.5 3.5 4.0 4.5 3.9 Central African Republic 2.5 2.0 2.0 2.0 2.5 2.2 2.0 2.0 2.5 2.0 2.5 2.2 Chad 2.5 3.0 3.0 3.0 2.5 2.8 2.0 3.0 2.5 2.5 2.0 2.4 Comoros 3.0 3.0 3.0 2.5 2.0 2.7 2.5 2.0 2.5 2.0 2.5 2.3 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 3.0 3.0 2.5 3.0 2.9 2.0 3.0 3.0 2.5 2.5 2.6 Côte d'Ivoire 2.5 1.5 2.0 2.5 3.0 2.3 2.0 2.5 4.0 2.0 2.0 2.5 Djibouti 3.0 3.0 3.5 3.0 3.0 3.1 2.5 3.0 3.5 2.5 2.5 2.8 Dominica 4.5 3.5 4.0 3.5 3.0 3.7 4.0 3.0 3.5 3.5 4.0 3.6 Eritrea 3.5 3.0 3.5 3.0 3.0 3.2 2.5 2.5 3.5 3.0 2.5 2.8 Ethiopia 3.0 4.5 3.5 3.5 3.5 3.6 2.5 3.5 4.0 3.0 2.5 3.1 Gambia, The 3.5 3.0 3.5 2.5 3.0 3.1 3.5 2.5 3.5 3.0 2.0 2.9 Georgia 4.5 4.0 4.0 3.5 3.5 3.9 3.5 3.5 4.0 3.5 3.5 3.6 Ghana 4.0 4.0 3.5 3.5 3.5 3.7 3.5 3.5 4.5 3.5 3.5 3.7 Grenada 4.5 3.5 4.0 3.5 4.0 3.9 4.0 3.5 3.5 3.5 4.0 3.7 Guinea 4.0 3.0 3.0 3.5 2.5 3.2 2.0 3.0 3.0 3.0 2.5 2.7 Guinea-Bissau 3.0 3.0 2.5 2.5 3.0 2.8 2.5 2.5 3.0 2.5 2.5 2.6 Guyana 3.5 3.5 3.5 3.0 3.0 3.3 3.0 3.5 3.5 2.5 3.0 3.1 Haiti 3.0 2.5 2.5 2.5 2.5 2.6 2.0 2.5 2.5 2.5 2.0 2.3 Honduras 4.0 4.0 4.0 4.0 3.0 3.8 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 Indonesia 3.5 4.0 3.5 3.5 2.5 3.4 2.5 3.5 3.5 3.5 3.0 3.2 Kenya 3.0 3.0 3.5 3.0 3.0 3.1 3.0 3.5 4.0 3.0 3.0 3.3 Kiribati 3.0 3.5 2.5 3.0 3.0 3.0 3.5 3.5 3.0 3.0 3.5 3.3 Kyrgyz Republic 4.0 3.5 3.5 3.5 3.0 3.5 2.5 3.0 3.0 2.5 2.5 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 294 2007 World Development Indicators 5.8 STATES AND MARKETS Public policies and institutions Policies for social inclusion and equity Public sector management and institutions 1 (low) to 6 (high) 1 (low) to 6 (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 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Lao PDR 3.5 3.5 3.0 2.0 3.5 3.1 3.0 2.5 2.5 2.5 2.0 2.5 Lesotho 4.0 3.0 3.5 3.0 3.0 3.3 3.5 3.0 4.0 3.0 3.5 3.4 Madagascar 3.5 3.5 3.5 3.5 4.0 3.6 3.5 3.0 3.5 3.5 3.5 3.4 Malawi 3.5 3.5 3.5 3.5 3.5 3.5 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 3.0 3.6 Mali 3.5 3.5 3.5 3.5 3.0 3.4 3.5 4.0 4.0 3.0 3.5 3.6 Mauritania 3.5 3.0 3.5 3.5 3.5 3.4 3.0 2.0 4.0 3.0 2.5 2.9 Moldova 4.5 3.5 4.0 3.5 3.5 3.8 3.5 3.5 3.0 3.0 3.0 3.2 Mongolia 3.5 3.5 3.5 3.5 2.5 3.3 3.0 4.0 3.5 3.5 2.5 3.3 Mozambique 3.5 3.5 3.5 3.0 3.0 3.3 3.0 3.5 3.5 3.0 3.0 3.2 Nepal 3.0 3.5 3.5 3.0 3.0 3.2 2.5 3.5 3.5 3.0 2.5 3.0 Nicaragua 4.0 4.0 3.5 3.5 3.5 3.7 3.0 3.5 4.0 3.5 3.5 3.5 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 3.0 3.0 3.1 2.5 3.0 3.0 2.5 3.0 2.8 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 3.0 2.5 3.0 1.5 2.5 2.5 3.5 3.5 3.0 3.0 3.1 Rwanda 3.5 4.0 4.0 3.5 3.0 3.6 3.0 3.5 3.5 3.5 3.0 3.3 Samoa 4.0 4.0 4.0 3.5 4.0 3.9 4.0 4.0 4.0 4.0 4.0 4.0 São Tomé and Principe 3.0 3.5 2.5 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 4.5 3.5 3.0 3.6 Serbia and Montenegro 4.5 4.0 3.5 4.0 3.5 3.9 3.0 3.5 3.5 4.0 3.0 3.4 Sierra Leone 3.0 3.0 3.0 3.0 2.5 2.9 2.5 3.5 3.0 3.0 2.5 2.9 Solomon Islands 3.0 3.0 3.0 2.5 2.0 2.7 2.5 3.0 2.5 2.0 3.0 2.6 Sri Lanka 4.0 3.5 4.5 3.5 3.5 3.8 3.5 4.0 3.5 3.0 3.5 3.5 St. Lucia 4.5 3.5 4.0 3.5 3.5 3.8 4.0 4.0 3.5 3.5 4.5 3.9 St. Vincent & Grenadines 4.5 3.5 4.0 3.5 3.5 3.8 4.0 3.5 3.5 3.5 4.0 3.7 Sudan 2.0 2.5 2.5 2.0 2.5 2.3 2.0 2.5 3.0 2.5 2.0 2.4 Tajikistan 3.5 3.0 3.0 3.5 2.5 3.1 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 4.5 4.0 3.5 3.5 3.8 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 2.5 2.0 2.7 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 3.5 3.5 4.0 3.5 3.5 3.6 2.0 3.0 3.0 2.5 1.5 2.4 Vanuatu 3.0 3.5 2.5 2.0 3.0 2.8 3.0 3.5 3.5 2.5 3.0 3.1 Vietnam 4.5 4.0 4.0 3.0 3.5 3.8 3.5 4.0 3.5 3.5 3.0 3.5 Yemen, Rep. 2.5 3.5 3.0 3.5 3.0 3.1 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 3.0 4.0 3.0 3.0 3.2 Zimbabwe 2.5 1.5 2.0 1.5 2.5 2.0 1.0 2.5 3.5 2.0 1.5 2.1 and institutions for environmental sustainability accurate accounting and fiscal reporting, including extent to which public employees within the executive assess the extent to which environmental policies timely and audited public accounts. · Effi ciency are 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 here are the account- sector management and institutions cluster: Prop- tax structure, but also revenue from all sources as ability of the executive to oversight institutions and erty rights and rule-based governance assess the actually collected. · Quality of public administration of public employees for their performance, access extent to which private economic activity is facili- assesses the extent to which civilian central govern- of civil society to information on public affairs, and tated by an effective legal system and rule-based ment staff is structured to design and implement state capture by narrow vested interests. governance structure in which property and contract government policy and deliver services effectively. Data sources rights are reliably respected and enforced. · Quality · Transparency, accountability, and corruption in 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 elec- at www.worldbank.org/ida. fi nancial management systems, and timely and torate and by the legislature and judiciary, and the 2007 World Development Indicators 295 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 2000­04a 2000­04a 2000­04a 2000­04a 2000­05a 2000­05a 2000­05a 2005 2005 2005 2005 Afghanistan 34,782 23.7 .. .. .. .. .. .. .. .. .. Albania 18,000 39.0 197 .. 447 73 26 .. 4 196 0 Algeria 108,302 70.2 .. .. 3,572 929 1,471 .. 46 3,037 32 Angola 51,429 10.4 166,045 4,709 2,761 .. .. .. 5 240 68 Argentina 400,000 30.0 .. .. 35,753 6,979 .. 1,196 81 6,938 133 Armenia 7,633 100.0 1,867 280 732 30 678 .. 6 556 7 Australia 810,200 .. .. 152,777 9,528 1,290 46,164 4,830 343 44,657 2,445 Austria 133,928 100.0 69,000 26,411 5,781 8,586 17,060 .. 142 8,038 537 Azerbaijan 59,141 49.4 10,279 6,965 2,122 789 7,551 .. 12 1,134 12 Bangladesh 239,226 9.5 .. .. 2,855 4,340 896 901 7 1,634 183 Belarus 93,310 87.0 9,382 13,969 5,498 13,568 43,559 .. 5 282 1 Belgium 150,567 78.0 126,680 54,856 3,542 9,150 8,130 7,890 152 3,341 705 Benin 19,000 9.5 .. .. 578 66 86 .. .. .. .. Bolivia 62,479 7.0 .. .. 3,698 286 1,057 .. 26 1,892 25 Bosnia and Herzegovina .. .. .. .. 1,000 53 1,173 .. 5 73 1 Botswana 24,455 36.5 .. .. 888 171 842 .. 8 230 0 Brazil 1,751,868 5.5 .. .. 29,314 .. 221,600 5,598 515 37,662 1,531 Bulgaria 44,033 99.0 14,401 6,840 4,163 2,389 5,166 .. 10 654 3 Burkina Faso 15,272 31.2 .. .. 622 .. .. .. 1 66 .. Burundi 12,322 10.4 .. .. .. .. .. .. .. .. .. Cambodia 38,257 6.3 .. .. 650 45 92 .. 3 169 1 Cameroon 50,000 10.0 .. .. 974 324 1,119 .. 11 384 24 Canada 1,408,900 .. .. 184,774 57,671 2,790 338,661 4,163 1,018 45,230 1,527 Central African Republic .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. Chile 79,604 20.2 .. .. 2,030 1,571 3,848 1,813 93 5,939 1,054 China 1,870,661 81.0 769,560 784,090 62,200 583,320 1,934,612 88,549b 1,349 136,722 7,579 Hong Kong, China 1,943 100.0 .. .. .. .. .. .. 123 20,230 7,764 Colombia .. .. .. .. 2,137 .. 7,751 1,165 162 9,984 1,092 Congo, Dem. Rep. 153,497 1.8 .. .. 3,641 140 444 .. .. .. .. Congo, Rep. 17,289 5.0 .. .. 795 135 231 .. 5 52 .. Costa Rica 35,330 24.4 .. .. 950 .. .. 779 36 953 10 Côte d'Ivoire 80,000 8.1 .. .. 639 10 675 710 .. .. .. Croatia 28,344 84.7 3,716 4,373 2,726 1,266 2,835 .. 22 1,361 2 Cuba .. .. .. .. 4,382 .. .. .. 11 813 31 Czech Republic 127,672 100.0 90,055 475 9,513 6,631 14,385 .. 75 4,706 39 Denmark 71,847 100.0 61,258 17,766 2,212 5,459 1,888 1,321 160 10,340 190 Dominican Republic .. .. .. .. 1,743 .. .. 537 .. .. .. Ecuador 43,197 15.0 10,641 5,453 966 .. .. 633 30 2,011 5 Egypt, Arab Rep. 92,370 81.0 .. .. 5,150 40,837 3,917 3,691 45 4,888 287 El Salvador .. .. .. .. 283 .. .. .. 24 2,541 21 Eritrea .. .. .. .. 306 .. .. .. .. .. .. Estonia 56,856 23.5 2,465 6,722 959 248 10,311 .. 8 578 1 Ethiopia 36,469 19.1 219,113 2,456 .. .. .. .. 31 1,667 133 Finland 78,158 64.7 69,400 28,100 5,732 3,478 9,706 1,313 107 7,075 354 France 951,220 100.0 781,000 197,000 29,286 77,219 41,263 3,840 728 52,477 5,802 Gabon 9,170 10.2 .. .. 810 95 2,219 .. 9 465 66 Gambia, The 3,742 19.3 16 .. .. .. .. .. .. .. .. Georgia 20,247 39.4 5,200 570 1,336 720 6,127 .. 5 249 3 Germany .. 100.0 1,062,700 232,296 34,228 72,568 88,022 13,507 1,024 90,789 7,722 Ghana 47,787 17.9 .. .. 977 85 242 .. 1 96 7 Greece 114,931 .. .. 18,360 2,576 1,854 613 1,779 131 9,452 64 Guatemala .. .. .. .. 886 .. .. 776 .. .. .. Guinea 44,348 9.8 .. .. 1,115 .. .. .. .. .. .. Guinea-Bissau 3,455 27.9 .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. 296 2007 World Development Indicators 5.9 STATES AND MARKETS 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 2000­04a 2000­04a 2000­04a 2000­04a 2000­05a 2000­05a 2000­05a 2005 2005 2005 2005 Honduras .. .. .. .. 699 .. .. 553 .. .. .. Hungary 159,568 43.9 13,300 12,505 7,950 7,135 9,005 .. 47 2,735 21 India 3,383,344 47.4 .. .. 63,465 575,702 407,398 4,938 327 27,528 773 Indonesia 368,360 58.0 .. .. .. 25,535 4,698 5,503 321 26,836 440 Iran, Islamic Rep. 179,388 67.4 .. .. 7,131 11,149 19,127 1,326 121 12,708 98 Iraq .. .. .. .. 1,963 570 1,682 .. .. .. .. Ireland 96,602 100.0 .. 6,500 1,919 1,781 303 980 304 42,873 107 Israel 17,446 100.0 .. .. 899 1,618 1,149 1,525 34 4,392 1,213 Italy 484,688 100.0 .. 184,756 16,751 47,368 21,045 9,856 446 36,116 1,365 Jamaica 20,996 73.3 .. .. 272 .. .. 1,671 22 1,574 16 Japan 1,177,278 77.7 947,562 327,632 20,052 145,957 22,632 16,777 652 102,279 8,549 Jordan 7,500 100.0 .. .. 293 .. 1,024 .. 20 1,737 224 Kazakhstan 90,018 93.4 85,240 43,910 14,204 12,129 171,855 .. 17 1,160 16 Kenya 63,265 14.1 .. 22 1,917 226 1,399 .. 28 2,424 253 Korea, Dem. Rep. .. .. .. .. 5,214 .. .. .. 2 101 2 Korea, Rep. 100,279 86.8 9,169 518 3,392 31,004 10,108 15,113 221 33,888 7,433 Kuwait 5,749 85.0 .. .. .. .. .. .. 19 2,433 242 Kyrgyz Republic 18,840 .. 5,624 847 424 50 561 .. 5 226 2 Lao PDR 31,210 14.4 .. .. .. .. .. .. 9 293 2 Latvia 69,532 100.0 2,779 2,330 2,375 894 17,921 .. 23 1,032 2 Lebanon .. .. .. .. 401 .. .. .. 12 1,076 87 Lesotho .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. 490 .. .. .. .. .. .. Libya .. .. .. .. 2,757 .. .. .. 8 918 0 Lithuania 79,331 91.3 23,184 12,279 1,772 428 12,457 .. 12 505 1 Macedonia, FYR .. .. .. .. 699 94 530 .. 2 192 0 Madagascar .. .. .. .. 732 10 12 .. 18 575 15 Malawi 15,451 45.0 .. .. 710 25 88 .. 5 132 1 Malaysia 98,721 81.3 .. .. 1,667 1,181 1,178 12,027 176 20,369 2,578 Mali 18,709 18.0 .. .. 733 196 189 .. .. .. .. Mauritania .. .. .. .. 717 .. .. .. 2 139 0 Mauritius 2,015 100.0 .. .. .. .. .. 382 15 1,146 212 Mexico 337,192 49.5 410,000 199,800 26,662 74 .. 2,145 331 21,858 390 Moldova 12,733 86.2 1,640 1,577 1,075 355 2,980 .. 4 232 1 Mongolia 49,250 3.5 381 1,889 1,810 1,128 8,857 .. 5 295 6 Morocco 57,493 56.9 .. 1,251 1,907 2,987 5,919 561 49 3,493 61 Mozambique .. .. .. .. 3,070 172 768 .. 10 347 5 Myanmar .. .. .. .. .. .. .. .. 26 1,504 3 Namibia 42,237 12.8 47 591 .. .. .. .. 6 306 60 Nepal 17,380 30.3 .. .. 59 .. .. .. 6 480 7 Netherlands 126,100 .. .. 45,700 2,813 14,730 4,026 9,521 241 26,133 4,894 New Zealand 92,931 64.3 .. .. 3,898 .. 3,853 1,614 209 11,952 781 Nicaragua 18,669 11.4 .. .. 6 .. .. .. .. .. .. Niger 14,565 25.0 .. .. .. .. .. .. .. .. .. Nigeria 193,200 15.0 .. .. 3,528 174 77 513 8 584 10 Norway 91,916 77.5 56,573 13,614 4,087 2,440 9,568 .. 234 11,568 182 Oman 34,965 27.7 .. .. .. .. .. 2,727 31 3,369 237 Pakistan 258,340 64.7 209,959 .. 7,791 23,045 4,796 1,391 49 5,364 408 Panama 11,643 .. .. .. 355 .. .. 3,068 30 1,796 37 Papua New Guinea .. .. .. .. .. .. .. .. 20 819 21 Paraguay .. .. .. .. 441 .. .. .. 10 446 .. Peru 78,829 14.4 .. .. 2,177 119 1,159 992 61 4,332 139 Philippines 200,037 21.6 .. .. .. .. .. 3,634 59 8,057 323 Poland 423,997 69.7 29,996 85,989 19,599 16,742 46,060 428 79 3,554 71 Portugal 78,470 .. .. 20,470 2,839 3,412 2,422 905 149 10,140 235 Puerto Rico 25,645 95.0 .. 10 96 .. .. 1,727 .. .. .. 2007 World Development Indicators 297 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 2000­04a 2000­04a 2000­04a 2000­04a 2000­05a 2000­05a 2000­05a 2005 2005 2005 2005 Romania 198,817 50.7 5,283 267 10,781 7,960 16,032 771 39 1,708 5 Russian Federation 537,289 .. 164 5,702 85,542 164,262 1,801,601 1,803 391 26,522 1,541 Rwanda 14,008 19.0 .. .. .. .. .. .. .. .. .. Saudi Arabia 152,044 29.9 .. .. 1,020 393 1,192 897 116 15,933 1,021 Senegal 13,576 29.3 .. .. 906 138 371 .. 6 450 .. Serbia and Montenegro 45,290 62.0 .. 452 3,809 .. .. .. 25 1,414 6 Sierra Leone 11,300 8.0 .. .. .. .. .. .. 0 17 8 Singapore 3,188 100.0 .. .. .. .. .. 23,192 77 17,744 7,571 Slovak Republic 43,000 87.3 32,214 18,517 3,659 2,166 9,326 .. 14 712 0 Slovenia 38,451 100.0 980 9,007 1,228 777 3,245 .. 18 758 3 Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 364,131 17.3 .. .. 20,047 991 108,513 2,868 148 11,845 923 Spain 666,292 99.0 397,117 132,868 14,484 21,047 11,586 9,170 586 49,855 1,022 Sri Lanka 97,286 81.0 21,067 .. .. 4,682 138 2,455 20 2,818 310 Sudan .. .. .. .. 5,478 40 766 .. 9 511 43 Swaziland 3,594 .. .. .. 301 .. 11,394 .. .. .. .. Sweden 424,947 .. 106,868 37,677 9,867 5,673 13,120 1,217 146 9,019 264 Switzerland 71,214 100.0 96,845 15,000 3,252 14,277 9,313 .. 135 9,663 1,110 Syrian Arab Republic 94,890 20.1 .. .. 2,702 571 2,075 .. 17 1,240 22 Tajikistan 27,767 .. .. .. 616 50 1,117 .. 7 479 6 Tanzania 78,891 8.6 .. .. 2,600 c 628 c 1,196c .. 7 263 2 Thailand 57,403 98.5 .. .. 4,044 9,195 4,037 5,115 124 18,903 2,002 Togo .. .. .. .. 568 .. .. .. .. .. .. Trinidad and Tobago .. .. .. .. .. .. .. 440 14 1,055 48 Tunisia 19,232 65.8 .. 16,611 1,909 1,294 2,082 .. 21 1,997 18 Turkey 426,906 41.6 174,312 156,853 8,697 5,036 8,939 3,170 146 16,944 383 Turkmenistan .. .. .. .. 2,529 1,286 8,670 .. 14 1,654 10 Uganda 70,746 23.0 .. .. 259 .. 218 .. 0 49 29 Ukraine 169,447 97.2 58,308 28,847 22,001 52,655 223,980 580 42 2,513 39 United Arab Emirates .. .. .. .. .. .. .. 9,846 96 16,210 4,417 United Kingdom 387,674 100.0 736,000 160,000 16,208 44,036 22,110 8,599 1,018 93,603 5,998 United States 6,433,272 64.5 7,780,158 2,034,915 228,999 8,869 2,717,513d 38,519 9,970 e 720,548e 37,358e Uruguay 60,000 .. .. .. 2,993 .. .. .. 9 586 4 Uzbekistan .. .. .. .. 4,014 2,012 18,007 .. 22 1,639 72 Venezuela, RB .. .. .. .. 682 .. 32 1,121 136 5,043 2 Vietnam 222,179 .. .. .. 2,671 4,558 2,928 2,694 54 5,454 230 West Bank and Gaza 4,996 100.0 .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. .. .. .. .. .. .. 17 1,083 67 Zambia 91,440 22.0 .. .. 1,273 186 554 .. 6 54 0 Zimbabwe 97,267 19.0 .. .. .. .. .. .. 4 243 22 World .. m .. m .. m .. s 2,278 m 5,543 m 369,847 s 24,878 s 2,020,604 s 142,571 s Low income .. .. .. .. .. .. 9,772 714 54,774 2,307 Middle income .. .. .. .. 1,290 5,919 155,727 5,297 440,245 22,500 Lower middle income .. .. .. .. 1,286 3,977 122,240 3,283 295,803 14,626 Upper middle income 50.5 .. .. .. 1,571 10,311 33,487 2,014 144,442 7,875 Low & middle income .. .. .. .. .. .. 166,361 6,011 495,019 24,807 East Asia & Pacific .. .. .. .. 4,558 4,037 117,522 2,220 220,887 13,285 Europe & Central Asia .. 9,815 6,602 207,077 1,649 9,005 5,530 1,014 71,522 2,240 Latin America & Carib. .. .. .. .. .. .. 21,509 1,596 105,739 4,566 Middle East & N. Africa .. .. .. .. 1,265 2,082 .. 387 35,547 1,132 South Asia 30.3 .. .. .. 13,864 2,846 9,685 417 37,955 1,682 Sub-Saharan Africa .. .. .. .. .. .. .. 379 23,368 1,903 High income 100.0 .. 29,960 .. 8,586 10,847 203,486 18,866 1,525,585 117,763 Europe EMU 100.0 126,680 51,147 119,711 8,868 9,706 58,759 4,070 337,896 27,960 a. Data are for the latest year available in the period shown. b. Includes Hong Kong, China. c. Excludes Tazara railway. d. Refers to Class 1 railways only. e. Data cover only carriers desIgnated by the U.S. Department of Transportation as major and national air carriers. 298 2007 World Development Indicators 5.9 STATES AND MARKETS Transport services About the data Definitions Transport infrastructure--highways, railways, ports either air transport for passengers and freight or · Total road network covers motorways, highways, and waterways, and airports and air traffic control sea transport for freight tend to be more competi- main or national roads, secondary or regional roads, systems--and the services that flow from it are cru- tive. The railways indicators in the table focus on and all other roads in a country. · Paved roads are cial to the activities of households, producers, and scale and output measures: total route-kilometers, roads surfaced with crushed stone (macadam) and governments. Because performance indicators vary passenger-kilometers, and goods (freight) hauled in hydrocarbon binder or bituminized agents, with con- significantly by transport mode and focus (whether ton-kilometers. crete, or with cobblestones. · Passengers carried physical infrastructure or the services flowing from Measures of port container traffi c, much of it by road are the number of passengers transported that infrastructure), highly specialized and carefully commodities of medium to high value added, give by road times kilometers traveled. · Goods hauled specified indicators are required. The table provides some indication of economic growth in a country. by road are the volume of goods transported by road selected indicators of the size, extent, and produc- But when traffic is merely transshipment, much of vehicles, measured in millions of metric tons times tivity of roads, railways, and air transport systems the economic benefit goes to the terminal operator kilometers traveled. · Rail lines are the length of rail- and of the volume of traffic in these modes as well and ancillary services for ships and containers rather way route available for train service, irrespective of as in ports. than to the country more broadly. In transshipment the number of parallel tracks. · Passengers carried Data for transport sectors are not always inter- centers empty containers may account for as much by railway are the number of passengers transported nationally comparable. Unlike for demographic sta- as 40 percent of traffic. by rail times kilometers traveled. · Goods hauled tistics, national income accounts, and international The air transport data represent the total (inter- by railway are the volume of goods transported by trade data, the collection of infrastructure data has national and domestic) scheduled traffic carried by railway, measured in metric tons times kilometers not been "internationalized." But data on roads are the air carriers registered in a country. Countries traveled. · Port container traffic measures the flow collected by the International Road Federation (IRF), submit air transport data to ICAO on the basis of of containers from land to sea transport modes and and data on air transport by the International Civil standard instructions and definitions issued by ICAO. vice versa in twenty-foot-equivalent units (TEUs), a Aviation Organization (ICAO). In many cases, however, the data include estimates standard-size container. Data cover coastal shipping National road associations are the primary source by ICAO for nonreporting carriers. Where possible, as well as international journeys. Transshipment of IRF data. In countries where such an association these estimates are based on previous submissions traffic is counted as two lifts at the intermediate is lacking or does not respond, other agencies are supplemented by information published by the air port (once to off-load and again as an outbound contacted, such as road directorates, ministries carriers, such as flight schedules. lift) and includes empty units. · Registered car- of transport or public works, or central statistical The data cover the air traffic carried on scheduled rier departures worldwide are domestic takeoffs offices. As a result, due to differing definitions and services, but changes in air transport regulations and takeoffs abroad of air carriers registered in the data collections methods and quality, the compiled in Europe have made it more diffi cult to classify country.· Passengers carried by air include both data are of uneven quality. Moreover, the quality of traffic as scheduled or nonscheduled. Thus recent domestic and international passengers of air car- transport service (reliability, transit time, and condi- increases shown for some European countries may riers registered in the country. · Air freight is the tion of goods delivered) is rarely measured, though be due to changes in the classification of air traffic volume of freight, express, and diplomatic bags car- it may be as important as quantity in assessing an rather than actual growth. For countries with few air ried on each flight stage (operation of an aircraft from economy's transport system. Several new initiatives carriers or only one, the addition or discontinuation takeoff to its next landing), measured in metric tons are under way to improve data availability and con- of a home-based air carrier may cause significant times kilometers traveled. sistency. The IRF is collaborating with national and changes in air traffic. international development agencies to improve the quality and coverage of road statistics. To improve measures of progress and performance, the World Bank is also working on better measures of access, Data sources affordability, efficiency, quality, and fiscal and insti- tutional aspects of infrastructure. Data on roads are from the IRF's World Road Unlike the road sector, where numerous qualified Statistics, supplemented by World Bank staff motor vehicle operators can operate anywhere on estimates. Data on railways are from a database the road network, railways are a restricted transport maintained by the World Bank's Transport and system with vehicles confined to a fixed guideway. Urban Development Department, Transport Divi- Considering their cost and service characteristics, sion, based on data from the International Union railways generally are best suited to carry--and can of Railways. Data on port container traffic are from effectively compete for--bulk commodities and con- Containerisation International's Containerisation tainerized freight for distances of 500­5,000 kilo- International Yearbook. Data on air transport are meters, and passengers for distances of 50­1,000 from the ICAO's Civil Aviation Statistics of the World kilometers. Below these limits road transport tends and ICAO staff estimates. to be more competitive, while above these limits 2007 World Development Indicators 299 5.10 Power and communications Electric power Telephones Access Quality Affordability and efficiency Transmission Population $ per month and covered International Price Cost of Total tele- Total tele- Consumption distribution per 1,000 people by mobile voice traffic Faults basket for call to U.S. communications phone sub- per capita losses Fixed Mobile telephonya minutes per per 100 residential Price basket $ per revenuea scribers per kWh % of output mainlinesa subscribersa % persona mainlinesa fi xed lineb for mobilea 3 minutesa % of GDP employeea 2004 2004 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Afghanistan .. .. 3 40 .. .. 25.0 11.3 10.8 0.39 5.1 1,576 Albania 1,200 36 88 405 90 .. .. 5.1 22.7 1.34 6.0 414 Algeria 812 16 78 416 75 52 0.8 6.3 7.5 2.08 3.4 302 Angola 124 14 6 69 .. .. .. .. 11.9 3.23 .. .. Argentina 2,301 15 227 570 .. .. .. 6.8 7.8 .. 3.1 969 Armenia 1,428 16 192 106 88 29 52.9 2.4 8.3 2.42 3.0 146 Australia 11,193 6 564 906 96 .. 11.2 30.5 18.3 .. 5.7 506 Austria 7,850 5 450 991 99 293 5.0 29.0 23.7 0.71 2.4 592 Azerbaijan 2,437 13 130 267 99 33 45.2 8.5 15.1 4.18 1.7 139 Bangladesh 140 9 8 63 80 5 .. 6.9 2.5 2.02 1.5 .. Belarus 3,144 11 336 419 88 64 23.1 2.4 11.8 1.90 1.3 203 Belgium 8,576 5 461 903 99 .. 5.9 33.1 18.9 0.75 2.1 586 Benin 67 .. 9 89 43 7 5.8 16.1 13.2 4.80 1.6 364 Bolivia 435 12 70 264 .. 49 .. 8.5 5.6 .. 5.8 680 Bosnia and Herzegovina 2,180 16 248 408 95 190 .. 4.9 6.5 3.62 7.1 337 Botswana 1,325 10 75 466 99 76 .. 10.4 8.6 2.88 3.0 665 Brazil 1,955 17 230 462 88 .. 1.6 15.6 26.5 0.71 3.7 .. Bulgaria 3,939 12 321 807 100 37 4.2 8.9 16.6 0.57 6.7 364 Burkina Faso .. .. 7 43 72 7 18.4 16.9 13.1 1.14 3.1 383 Burundi .. .. 4 20 .. .. 6.0 4.5 12.4 2.45 .. 234 Cambodia .. .. 3 75 88 2 .. 5.2 5.1 2.94 0.4 539 Cameroon 207 19 6 138 73 .. .. 9.3 16.5 .. 5.3 420 Canada 17,156 7 566 514 95 .. .. .. 6.9 .. 2.7 456 Central African Republic .. .. 2 25 .. .. 56.0 .. 12.6 1.99 1.1 134 Chad .. .. 1 22 .. 2 .. 16.9 13.3 .. .. 127 Chile 3,084 8 211 649 100 48 .. 9.7 11.4 .. .. .. China 1,585 6 269 302 .. 5 .. .. 2.9 2.90 3.2 928 Hong Kong, China 5,699 13 546 1,252 100 1,049 1.1 12.6 2.2 0.77 3.8 623 Colombia 866 19 168 479 80 52 30.6 8.0 10.2 .. 5.2 .. Congo, Dem. Rep. 93 3 0 48 .. .. .. .. 11.0 .. 6.6 513 Congo, Rep. 131 74 4 123 80 .. .. .. 11.0 5.39 2.9 .. Costa Rica 1,667 11 321 254 .. 82 4.0 6.0 1.9 .. 2.5 388 Côte d'Ivoire 176 16 14 121 55 17 81.0 28.2 22.2 2.25 4.3 678 Croatia 3,316 17 425 672 98 170 14.0 13.1 14.9 .. 2.6 407 Cuba 1,177 15 75 12 61 29 7.6 13.1 22.6 7.49 2.6 .. Czech Republic 6,224 6 314 1,151 100 73 6.5 24.1 17.7 1.06 4.1 699 Denmark 6,631 4 619 1,010 .. 338 9.0 30.7 6.1 0.89 2.6 439 Dominican Republic 1,071 32 101 407 .. .. .. 23.3 8.6 0.22 0.5 .. Ecuador 687 42 129 472 .. .. .. 9.0 18.9 .. 2.2 667 Egypt, Arab Rep. 1,215 12 140 184 98 23 0.1 4.0 5.8 1.45 3.5 312 El Salvador 629 13 141 350 95 397 1.7 12.8 8.5 2.40 36.1 1,182 Eritrea .. .. 9 9 0 10 54.3 6.2 .. 3.59 2.6 71 Estonia 5,484 11 328 1,074 99 109 .. 15.6 8.8 0.90 5.8 486 Ethiopia 33 10 9 6 .. 2 100.0 2.9 3.0 4.01 1.7 81 Finland 16,780 3 404 997 99 .. .. 28.7 6.8 1.80 3.0 420 France 7,900 6 586 789 99 177 .. 29.0 30.0 0.84 2.4 585 Gabon 928 18 28 470 78 61 45.0 32.4 14.7 2.77 1.8 244 Gambia, The .. .. 29 163 .. .. .. .. .. 1.81 .. .. Georgia 1,577 16 151 326 95 57 .. 4.7 44.1 .. 5.8 .. Germany 7,029 6 667 960 99 .. .. 26.5 17.3 0.43 3.0 559 Ghana 247 15 15 129 69 15 5.6 14.8 6.9 0.39 .. 557 Greece 5,148 9 568 904 100 .. 13.8 21.1 23.6 1.09 4.4 612 Guatemala 514 4 99 358 .. 129 .. 15.4 6.1 1.21 .. .. Guinea .. .. 3 20 .. .. .. .. 7.7 .. .. .. Guinea-Bissau .. .. 7 42 .. .. .. .. 21.9 .. .. .. Haiti 30 53 17 48 .. .. .. .. 4.6 2.15 .. 92 300 2007 World Development Indicators 5.10 STATES AND MARKETS Power and communications Electric power Telephones Access Quality Affordability and efficiency Transmission Population $ per month and covered International Price Cost of Total tele- Total tele- Consumption distribution per 1,000 people by mobile voice traffic Faults basket for call to U.S. communications phone sub- per capita losses Fixed Mobile telephonya minutes per per 100 residential Price basket $ per revenuea scribers per kWh % of output mainlinesa subscribersa % persona mainlinesa fi xed lineb for mobilea 3 minutesa % of GDP employeea 2004 2004 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Honduras 586 23 69 178 .. 82 .. 5.9 10.8 2.52 5.9 186 Hungary 3,680 12 333 924 99 45 8.2 28.5 11.7 1.01 4.7 670 India 457 26 45 82 .. .. .. 3.3 2.4 1.19 1.9 .. Indonesia 478 13 58 213 90 5 .. 5.8 4.3 2.79 2.2 1,084 Iran, Islamic Rep. 2,036 17 278 106 90 8 .. 2.4 2.6 0.55 1.3 407 Iraq 1,126 6 37 20 .. .. .. .. 2.6 .. .. .. Ireland 6,169 8 489 1,012 99 .. 5.6 39.5 19.7 0.71 2.5 401 Israel 6,803 3 424 1,120 99 .. .. 10.5 9.3 0.59 4.5 692 Italy 5,640 7 427 1,232 100 236 .. 26.6 14.4 0.79 3.0 948 Jamaica 2,455 10 129 1,017 95 233 31.0 9.1 7.5 0.87 4.1 686 Japan 8,072 5 460 742 99 43 .. 26.1 20.5 1.63 3.9 1,283 Jordan 1,602 13 119 304 99 128 10.0 10.0 6.7 1.44 8.3 444 Kazakhstan 3,621 16 167 327 .. .. .. .. 11.5 .. .. 108 Kenya 140 17 8 135 .. 5 130.4 13.9 16.5 3.00 4.1 141 Korea, Dem. Rep. 827 16 44 .. .. .. .. .. .. .. .. .. Korea, Rep. 7,391 3 492 794 99 81 1.0 8.3 14.2 0.76 4.8 567 Kuwait 14,955 11 201 939 100 .. 4.0 10.5 75.2 1.51 3.4 338 Kyrgyz Republic 1,421 30 85 105 90 17 .. 5.9 6.4 5.40 4.5 91 Lao PDR .. .. 13 108 .. 3 .. 5.6 3.8 1.11 1.7 130 Latvia 2,549 19 318 814 98 52 20.3 13.3 9.5 1.63 1.5 587 Lebanon 2,499 15 277 277 100 .. .. 15.0 20.1 2.19 7.2 .. Lesotho .. .. 27 137 80 .. 75.0 18.4 14.2 3.28 .. .. Liberia .. .. .. 49 16 .. .. .. .. .. .. .. Libya 2,519 28 133 41 .. .. .. .. 6.3 .. .. .. Lithuania 3,145 7 235 1,275 100 37 3.8 17.7 9.1 1.55 3.5 .. Macedonia, FYR 3,183 21 262 620 99 .. .. 11.4 14.8 .. 5.7 .. Madagascar .. .. 4 27 30 1 59.6 18.5 7.9 0.59 12.8 148 Malawi .. .. 8 33 .. .. .. 5.8 10.2 .. 4.5 .. Malaysia 3,166 5 172 771 .. .. 7.3 8.7 5.0 0.71 4.8 770 Mali .. .. 6 64 .. .. .. 16.1 13.8 .. 7.6 .. Mauritania .. .. 13 243 .. 20 .. 11.6 .. .. 19.5 533 Mauritius .. .. 289 574 100 92 .. 7.9 4.2 1.59 3.2 451 Mexico 1,838 16 189 460 100 119 1.8 16.1 14.0 0.83 2.7 617 Moldova 1,228 38 221 259 97 92 5.1 4.5 17.1 1.46 9.9 220 Mongolia .. .. 61 218 .. 4 20.6 2.4 5.5 .. 4.4 116 Morocco 595 16 44 411 98 55 25.0 23.0 16.3 1.69 5.4 821 Mozambique 367 10 4 62 95 17 66.0 17.6 10.3 1.17 1.8 392 Myanmar 104 20 9 4 .. 3 125.0 .. .. 0.17 0.6 66 Namibia 1,389 18 64 244 88 .. 40.4 12.3 14.0 .. 4.8 .. Nepal 69 19 17 9 .. 6 68.0 3.1 2.0 2.04 1.2 110 Netherlands 6,920 4 466 970 100 .. .. .. 23.4 0.32 .. .. New Zealand 8,937 13 422 861 98 363 .. 28.6 19.1 1.30 3.9 962 Nicaragua 417 24 43 217 60 65 4.8 9.2 15.1 3.15 3.7 334 Niger .. .. 2 21 15 .. .. 10.5 16.9 .. 2.2 .. Nigeria 104 34 9 141 58 .. 20.6 .. 10.6 1.49 3.5 256 Norway 24,645 8 460 1,028 .. 260 .. 37.9 20.2 .. 3.3 445 Oman 3,836 15 103 519 .. 185 89.7 12.1 5.5 1.87 2.3 583 Pakistan 425 25 34 82 .. .. .. 5.1 2.4 1.03 2.5 213 Panama 1,466 17 136 418 89 .. 13.9 10.3 16.7 .. 3.9 273 Papua New Guinea .. .. 11 4 .. .. .. 7.3 14.8 .. .. .. Paraguay 816 4 54 320 .. 28 8.2 6.4 3.3 0.90 4.2 .. Peru 794 10 80 200 .. 64 .. 19.6 22.9 1.80 0.8 472 Philippines 597 13 41 419 92 29 .. 11.6 5.3 1.20 3.9 1,555 Poland 3,418 9 309 764 99 61 .. 14.3 7.8 1.35 3.8 184 Portugal 4,526 9 401 1,085 100 137 9.7 31.8 23.6 1.04 5.1 988 Puerto Rico .. .. 285 689 100 .. .. 33.5 .. .. .. .. 2007 World Development Indicators 301 5.10 Power and communications Electric power Telephones Access Quality Affordability and efficiency Transmission Population $ per month and covered International Price Cost of Total tele- Total tele- Consumption distribution per 1,000 people by mobile voice traffic Faults basket for call to U.S. communications phone sub- per capita losses Fixed Mobile telephonya minutes per per 100 residential Price basket $ per revenuea scribers per kWh % of output mainlinesa subscribersa % persona mainlinesa fi xed lineb for mobilea 3 minutesa % of GDP employeea 2004 2004 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Romania 2,271 11 203 617 98 .. 10.4 10.1 10.5 0.82 3.8 263 Russian Federation 5,642 12 280 838 .. .. .. .. 6.0 2.03 2.9 334 Rwanda .. .. 3 32 75 .. .. 6.6 12.3 2.43 2.7 .. Saudi Arabia 6,571 7 164 575 .. .. .. 11.7 9.7 .. 3.2 .. Senegal 176 15 23 148 85 55 .. 15.4 9.6 1.02 6.9 408 Serbia and Montenegro 4,029 16 332 585 95 113 25.0 2.7 6.4 2.27 2.9 .. Sierra Leone .. .. .. 22 .. .. .. .. 71.9 .. .. .. Singapore 8,170 6 425 1,010 100 .. 0.3 6.7 6.1 0.69 3.4 .. Slovak Republic 5,088 4 222 843 99 88 9.5 19.8 12.3 1.06 3.8 508 Slovenia 6,835 6 408 879 99 .. 13.6 17.6 10.3 0.65 3.2 1,228 Somalia .. .. 12 61 .. .. .. .. 5.1 .. .. .. South Africa 4,885 6 101 724 96 .. .. 22.7 13.3 0.79 5.7 725 Spain 5,924 9 422 952 99 117 14.2 25.8 22.1 0.60 3.0 643 Sri Lanka 344 17 63 171 85 .. 8.1 8.2 1.2 2.11 2.2 313 Sudan 92 16 18 50 .. 11 .. 6.3 4.0 .. 3.8 651 Swaziland .. .. 31 177 .. 47 70.0 8.3 13.3 2.97 2.0 279 Sweden 15,424 7 717 935 99 .. .. 26.7 6.2 0.41 2.8 858 Switzerland 8,204 6 689 921 100 .. .. 29.5 28.4 0.32 3.5 525 Syrian Arab Republic 1,317 24 152 155 99 44 50.0 2.7 9.9 .. 2.8 221 Tajikistan 2,240 15 39 41 .. 10 144.0 0.8 23.3 7.84 0.6 57 Tanzania 53 23 4 52 25 .. .. 14.0 9.5 3.17 .. .. Thailand 1,865 8 110 430 .. 12 2.5 8.3 4.4 0.67 3.2 1,271 Togo 87 34 10 72 85 21 .. 15.4 12.3 3.98 5.7 363 Trinidad and Tobago 4,658 6 248 613 .. 381 .. 7.0 6.7 2.19 2.7 .. Tunisia 1,157 12 125 566 98 84 30.0 3.7 5.4 .. 4.4 740 Turkey 1,782 15 263 605 96 31 30.4 14.7 12.6 2.40 3.6 883 Turkmenistan 1,740 13 80 11 .. .. .. .. 17.2 .. .. .. Uganda .. .. 3 53 85 2 .. 16.8 9.3 3.21 4.2 750 Ukraine 3,152 15 256 366 96 36 .. .. 9.3 1.65 5.9 .. United Arab Emirates 11,331 7 273 1,000 100 .. 0.3 17.4 4.1 1.73 2.7 485 United Kingdom 6,206 8 528 1,088 99 .. .. 31.3 14.0 0.77 3.1 .. United States 13,351 6 606 680 99 279 13.2 25.0 5.2 .. 2.7 346 Uruguay 1,867 31 290 333 100 .. .. 10.7 16.1 0.52 .. .. Uzbekistan 1,796 9 67 28 .. .. .. 1.0 1.8 .. .. .. Venezuela, RB 2,760 27 136 470 .. .. .. .. 1.2 0.84 3.5 .. Vietnam 501 10 191 115 .. .. .. 3.7 6.2 1.95 0.0 79 West Bank and Gaza .. .. 96 302 95 66 69.4 7.5 9.8 1.17 0.7 871 Yemen, Rep. 165 23 39 95 68 .. .. 2.8 4.3 2.39 1.3 .. Zambia 692 4 8 81 51 7 108.0 6.0 14.2 1.41 2.7 175 Zimbabwe 795 15 25 54 .. 24 7.7 4.3 3.4 .. 4.4 243 World 2,606 w 9w 180 w 342 w .. w .. w .. m 11.7 m 10.5 m 1.44 m 3.6 w 479 m Low income 375 23 37 77 .. .. .. 8.7 9.6 1.99 0.7 141 Middle income 1,840 11 211 379 .. 22 .. 9.7 10.1 1.65 3.6 497 Lower middle income 1,448 10 205 306 .. 14 25.0 8.5 10.2 2.08 1.9 444 Upper middle income 3,454 12 230 671 .. .. .. 12.1 9.5 1.06 3.6 583 Low & middle income 1,243 13 135 247 .. .. .. 10.1 9.9 1.81 3.6 279 East Asia & Pacific 1,343 7 214 282 .. 6 .. 5.9 5.0 1.16 2.7 1,006 Europe & Central Asia 3,637 12 273 624 .. .. 15.7 9.5 11.8 1.51 3.6 364 Latin America & Carib. 1,674 17 177 439 90 .. .. 10.0 9.4 1.80 4.3 390 Middle East & N. Africa 1,289 16 160 229 90 30 25.0 7.3 6.3 1.66 1.3 501 South Asia 414 26 39 79 .. .. .. 5.1 2.4 2.02 2.0 125 Sub-Saharan Africa 550 9 17 125 .. .. .. 14.0 12.3 2.43 3.3 248 High income 9,609 6 503 835 99 171 5.8 27.6 17.8 0.76 4.5 586 European Monetary Union 6,869 6 531 980 99 .. 7.8 29.0 20.9 0.73 3.0 592 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. Calculated by the World Bank based on ITU data. 302 2007 World Development Indicators 5.10 STATES AND MARKETS Power and communications About the data Definitions The quality of an economy's infrastructure, includ- Operators are the main source of telecommunica- · Electric power consumption measures the produc- ing power and communications, is an important ele- tions data, so information on subscribers is widely tion of power plants and combined heat and power ment in investment decisions for both domestic and available for most countries. This gives a general plants less transmission, distribution, and trans- foreign investors. Government effort alone is not idea of access, but a more precise measure is the formation losses and own use by heat and power enough to meet the need for investments in modern penetration rate--the share of households with plants. · Electric power transmission and distri- infrastructure; public-private partnerships, especially access to telecommunications. Also important are bution losses are losses in transmission between those involving local providers and financiers, are data on actual use of the telecommunications equip- sources of supply and points of distribution and in critical for lowering costs and delivering value for ment. Ideally, statistics on telecommunications (and distribution to consumers, including pilferage.· Fixed money. In telecommunications, competition in the other information and communications technologies) telephone mainlines are telephone lines connecting marketplace, along with sound regulation, is lower- should be compiled for all three measures: subscrip- a subscriber to the telephone exchange equipment. ing costs and improving the quality of and access to tion and possession, access, and use. The quality · Mobile telephone subscribers are subscribers to services around the globe. of data varies among reporting countries as a result a public mobile telephone service using cellular tech- An economy's production and consumption of of differences in regulations covering the provision nology. · Population covered by mobile telephony electricity is a basic indicator of its size and level of data. is the percentage of people within range of a mobile of development. Although a few countries export Globally, there have been huge improvements in cellular signal regardless of whether they are sub- electric power, most production is for domestic access to telecommunications, driven mainly by scribers. · International voice traffic is the sum of consumption. Expanding the supply of electricity mobile telephony. By 2002 access to mobiles out- international incoming and outgoing telephone traffic to meet the growing demand of increasingly urban- paced access to fixed-line telephones in developing (in minutes) divided by total population. · Telephone ized and industrialized economies without incurring countries, and rural areas are catching up with urban mainline faults are the number of reported faults unacceptable social, economic, and environmental areas (although gaps are still large). By 2004 approxi- for the year divided by the number of telephone costs is one of the great challenges facing develop- mately 98 percent of the population in high income mainlines and multiplied by 100. · Price basket ing countries. countries and about 64 percent of the population in for residential fixed line is calculated as one-fifth Data on electric power production and consump- developing countries were covered by mobile tele- of the installation charge, the monthly subscription tion are collected from national energy agencies by phony (within range of a mobile cellular signal). charge, and the cost of local calls (15 peak and 15 the International Energy Agency (IEA) and adjusted Telephone mainline faults are a measure of tele- off-peak calls of three minutes each). · Price bas- by the IEA to meet international definitions (for data communications quality. The definition varies among ket for mobile is calculated as the pre-paid price on electricity production, see table 3.9). Electricity countries: some operators define faults as includ- for 25 calls per month spread over the same mobile consumption is equivalent to production less power ing malfunctioning customer equipment while others network, other mobile networks, and mobile to fixed plants' own use and transmission, distribution, and include only technical faults. calls and during peak, off-peak, and weekend times. transformation losses less exports plus imports. It Although access is the key to delivering telecom- It also includes 30 text messages per month. · Cost includes consumption by auxiliary stations, losses munications services to people, if that service is of call to U.S. is the cost of a three-minute, peak in transformers that are considered integral parts of not affordable to most people, then goals of univer- rate, fixed-line call from the country to the United those stations, and electricity produced by pumping sal usage will not be met. Three indicators of tele- States. · Total telecommunications revenue is the installations. Where data are available, it covers elec- communications affordability are presented in the revenue from the provision of telecommunications tricity generated by primary sources of energy--coal, table (price basket for fixed-line telephone service, services such as fixed-line, mobile, and data divided oil, gas, nuclear, hydro, geo-thermal, wind, tide and price basket for mobile service, and the cost of an by GDP. · Total telephone subscribers per employee wave, and combustible renewables. Neither produc- international call). Telecommunications efficiency is are telephone subscribers (fi xed-line plus mobile) tion nor consumption data capture the reliability of measured by total telecommunications revenue as divided by the total number of telecommunications supplies, including breakdowns, load factors, and percent of GDP and by total telephone subscribers employees. frequency of outages. per employee. Over the past decade new financing and technol- Data sources ogy, along with privatization and liberalization, have spurred dramatic growth in telecommunications Data on electricity consumption and losses are in many countries. With the rapid development of from the IEA's Energy Statistics and Balances of mobile telephony and the global expansion of the Non-OECD Countries 2003­2004, the IEA's Energy Internet, information and communication technolo- Statistics of OECD Countries 2003­2004, and the gies are increasingly recognized as essential tools of United Nations Statistics Division's Energy Statis- development, contributing to global integration and tics Yearbook. Data on telecommunications are enhancing public sector effectiveness, effi ciency, from the International Telecommunication Union's and transparency. The table presents telecommu- World Telecommunication Development Report nications indicators covering access, quality, and database and World Bank estimates. affordability and efficiency. 2007 World Development Indicators 303 5.11 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisiona technology expenditures Access Quality Application Affordability Schools Broadband Secure per 1,000 people connected to subscribers International Internet servers Price basket per 1,000 Personal Internet the Internet per 1,000 Internet bandwidth per million people for Internet Per capita people % computersa usersa % peoplea bits per capitaa October $ per montha % of GDP $ 2000 2005 2005 2005 2005 2005 2005 2006 2005 2005 2005 Afghanistan .. .. .. 1 .. 0.0 0 0 .. .. .. Albania .. 90 .. 60 .. 0.0 4 2 16.3 .. .. Algeria 27 88 11 58 53 5.9 .. 0 9.4 2.4 76 Angola 11 9 .. 11 .. .. .. 0 34.3 .. .. Argentina 40 .. 83 177 .. 21.7 316 12 14.4 7.1 337 Armenia .. 91 66 53 .. 0.3 12 3 52.5 .. .. Australia 161 96 683 698 97 103.4 5,903 581 22.8 6.2 2,247 Austria 309 95 607 486 .. 142.8 6,681 284 15.5 5.5 2,059 Azerbaijan 10 .. 23 81 .. 0.3 29 0 10.0 .. .. Bangladesh .. 23 12 3 .. 0.0 0 0 24.0 2.4 10 Belarus .. 97 .. 347 .. 0.2 48 1 10.5 .. .. Belgium 153 98 348 458 .. 191.3 11,279 146 37.2 5.8 2,061 Benin 5 20 4 50 .. 0.0 5 0 20.7 .. .. Bolivia 99 .. 23 52 .. 1.2 44 3 12.3 5.5 56 Bosnia and Herzegovina .. 87 .. 206 .. 3.5 40 4 7.8 .. .. Botswana 25 .. 45 34 4 .. .. 1 21.3 .. .. Brazil 46 91 105 195 50 17.7 149 16 26.0 7.8 333 Bulgaria 173 97 59 206 60 0.2 318 11 7.3 3.8 130 Burkina Faso 1 7 2 5 .. 0.0 6 0 90.6 .. .. Burundi 2 14 5 5 .. 0.0 .. 0 52.0 .. .. Cambodia .. 43 3 3 .. 0.0 1 0 33.1 .. .. Cameroon 6 18 10 15 .. .. .. 0 44.6 5.0 52 Canada 168 99 700 520 98 207.6 6,800 645 8.9 5.9 2,034 Central African Republic 2 2 3 3 .. 0.0 0 0 147.8 .. .. Chad 0 2 2 4 .. .. 0 .. 86.3 .. .. Chile .. 87 141 172 62 43.5 788 22 25.6 6.1 430 China 59 89 41 85 .. 28.7 104 0 9.8 5.3 90 Hong Kong, China 218 99 601 508 100 238.9 9,451 191 3.9 8.9 2,280 Colombia 26 92 41 104 .. 7.0 488 6 7.8 8.5 227 Congo, Dem. Rep. 3 2 .. 2 .. 0.0 0 0 93.2 .. .. Congo, Rep. 6 6 4 13 .. 0.0 0 0 84.5 .. .. Costa Rica 70 93 219 254 15 6.6 241 67 28.1 7.7 358 Côte d'Ivoire 16 35 15 11 .. 0.0 3 0 67.1 .. .. Croatia 134 94 190 327 100 20.2 1,074 48 16.1 .. .. Cuba 54 .. 33 17 .. 0.0 8 0 30.0 .. .. Czech Republic .. .. 240 269 95 43.7 .. 64 18.8 7.1 866 Denmark 283 97 656 527 100 249.3 34,891 614 23.2 6.0 2,849 Dominican Republic 28 76 .. 169 .. 7.4 .. 6 18.8 .. .. Ecuador 98 89 39 47 .. 2.0 48 5 37.0 3.2 87 Egypt, Arab Rep. 31 89 38 68 66 1.5 49 1 5.0 1.5 18 El Salvador 29 .. 51 93 .. 6.1 23 6 22.6 .. .. Eritrea .. 14 8 16 .. 0.0 2 .. 28.6 .. .. Estonia 192 93 483 513 75 133.1 3,566 163 10.8 .. .. Ethiopia 0 2 3 2 1 0.0 .. 0 23.3 .. .. Finland 445 94 481 534 .. 223.8 4,326 380 22.2 6.9 2,527 France 142 95 575 430 94 155.5 3,286 96 12.4 6.3 2,213 Gabon 29 54 33 48 .. 1.1 145 5 40.1 .. .. Gambia, The 2 12 16 33 13 0.0 6 1 17.8 .. .. Georgia 5 89 42 39 .. 0.3 .. 5 9.9 .. .. Germany 291 95 545 455 .. 129.7 6,860 349 7.4 6.1 2,059 Ghana 14 26 5 18 1 0.1 8 0 23.6 .. .. Greece .. 100 89 180 .. 14.4 589 40 16.4 4.1 822 Guatemala .. .. 19 79 .. 2.2 57 6 54.3 .. .. Guinea .. 9 5 5 .. 0.0 .. .. 24.7 .. .. Guinea-Bissau 5 26 .. 20 .. .. .. .. 75.0 .. .. Haiti .. 27 .. 70 .. .. .. 1 71.0 .. .. 304 2007 World Development Indicators 5.11 STATES AND MARKETS The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisiona technology expenditures Access Quality Application Affordability Schools Broadband Secure per 1,000 people connected to subscribers International Internet servers Price basket per 1,000 Personal Internet the Internet per 1,000 Internet bandwidth per million people for Internet Per capita people % computersa usersa % peoplea bits per capitaa October $ per montha % of GDP $ 2000 2005 2005 2005 2005 2005 2005 2006 2005 2005 2005 Honduras .. 58 16 36 .. 0.0 .. 4 33.4 4.6 53 Hungary 162 96 146 297 85 64.6 991 36 11.0 5.8 632 India 60 32 16 55 .. 1.2 18 1 6.8 5.8 42 Indonesia 23 65 14 73 .. .. 7 1 17.3 3.4 44 Iran, Islamic Rep. .. 77 109 103 .. 0.3 15 0 2.3 2.5 69 Iraq .. .. .. 1 .. .. .. 0 .. .. .. Ireland 148 95 494 276 .. 65.1 6,043 416 31.1 4.4 2,127 Israel .. 93 740 470 95 177.6 2,499 182 22.0 8.3 1,475 Italy 109 96 367 478 .. 115.7 2,080 53 24.8 4.3 1,308 Jamaica .. 70 63 404 .. .. .. 17 34.3 10.6 381 Japan 566 99 542 668 99 175.0 1,038 331 13.8 7.5 2,678 Jordan 74 96 56 118 18 1.9 58 4 11.1 8.4 195 Kazakhstan .. .. .. 27 .. 0.1 .. 1 15.8 .. .. Kenya 8 17 9 32 .. 0.0 3 0 75.9 2.8 15 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. 545 684 100 252.4 1,030 22 32.6 6.9 1,127 Kuwait .. 95 237 276 .. 8.1 348 35 22.2 1.4 437 Kyrgyz Republic .. .. 19 54 .. 0.5 15 1 12.0 .. .. Lao PDR .. 30 17 4 .. 0.0 3 0 27.6 .. .. Latvia 138 98 217 448 97 113.4 972 46 12.5 .. .. Lebanon 63 93 114 196 20 36.3 81 11 10.0 .. .. Lesotho 9 12 .. 24 .. 0.0 4 .. 38.6 .. .. Liberia 14 .. .. .. .. 0.0 .. .. .. .. .. Libya 14 .. .. 36 .. .. .. 0 22.0 .. .. Lithuania 31 98 155 358 56 68.6 1,460 26 7.2 .. .. Macedonia, FYR 54 95 222 79 100 6.1 17 2 25.3 .. .. Madagascar 5 8 5 5 .. 0.0 2 0 45.9 .. .. Malawi 2 3 2 4 1 0.0 2 0 41.9 .. .. Malaysia 95 89 197 435 .. 19.4 128 17 7.4 7.0 360 Mali 1 15 3 4 .. 0.0 2 0 28.4 .. .. Mauritania .. 21 14 7 .. 0.1 15 1 54.3 .. .. Mauritius 116 93 162 146 .. 2.2 50 18 17.5 .. .. Mexico 94 93 136 181 60 22.4 110 10 20.0 3.3 246 Moldova 153 .. 27 96 50 2.5 97 4 24.1 .. .. Mongolia 18 30 133 105 19 0.7 16 4 10.7 .. .. Morocco 29 76 25 152 .. 8.3 235 1 26.8 6.3 108 Mozambique 3 6 6 7 0 .. 1 0 32.9 .. .. Myanmar 9 3 8 2 .. 0.0 2 0 48.9 .. .. Namibia 17 39 109 37 13 0.0 4 8 48.7 .. .. Nepal .. .. 4 4 .. .. 2 1 8.1 .. .. Netherlands 279 99 682 739 .. 251.2 20,549 413 12.4 6.3 2,402 New Zealand 202 98 474 672 100 80.8 1,126 594 11.9 9.8 2,611 Nicaragua .. .. 40 26 .. 1.9 1 3 28.1 .. .. Niger 0 5 1 2 .. 0.0 2 0 101.8 .. .. Nigeria 25 26 7 38 .. 0.0 1 0 50.4 3.5 27 Norway 569 100 573 735 .. 214.4 9,368 389 29.8 5.1 3,252 Oman .. 79 47 111 .. 3.3 194 3 14.5 .. .. Pakistan 39 47 .. 67 .. 0.3 5 0 9.5 6.9 49 Panama .. 77 46 64 .. 5.4 292 57 38.4 8.4 403 Papua New Guinea .. 9 64 23 .. .. .. 1 25.0 .. .. Paraguay .. 76 75 32 .. 0.9 42 1 11.7 .. .. Peru 23 69 100 164 .. 12.5 358 6 23.6 6.6 187 Philippines .. 63 45 54 .. 0.7 39 3 1.8 7.0 83 Poland 102 91 193 262 90 32.6 560 38 11.3 4.2 331 Portugal 102 99 133 279 .. 114.9 833 65 37.8 4.4 758 Puerto Rico .. 97 .. 221 .. .. .. 33 .. .. .. 2007 World Development Indicators 305 5.11 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisiona technology expenditures Access Quality Application Affordability Schools Broadband Secure per 1,000 people connected to subscribers International Internet servers Price basket per 1,000 Personal Internet the Internet per 1,000 Internet bandwidth per million people for Internet Per capita people % computersa usersa % peoplea bits per capitaa October $ per montha % of GDP $ 2000 2005 2005 2005 2005 2005 2005 2006 2005 2005 2005 Romania .. 94 113 208 57 34.7 623 7 17.0 3.6 164 Russian Federation .. 98 122 152 43 11.1 100 3 12.7 3.6 191 Rwanda 0 2 .. 6 .. .. .. .. 30.1 .. .. Saudi Arabia .. 99 354 66 .. 0.8 31 4 21.3 2.3 285 Senegal .. 29 21 46 .. 1.6 66 0 25.6 8.3 59 Serbia and Montenegro .. 92 48 148 70 .. 87 2 13.2 .. .. Sierra Leone .. 7 .. 2 .. .. .. 0 10.6 .. .. Singapore 273 99 .. 571 100 153.3 5,826 291 20.5 9.4 2,537 Slovak Republic 131 99 358 464 65 25.7 2,636 28 18.9 5.6 486 Slovenia 168 96 404 545 96 85.0 1,258 95 18.6 3.1 532 Somalia .. 8 6 11 .. 0.0 0 .. .. .. .. South Africa 25 59 85 109 .. 3.5 19 23 63.2 9.9 504 Spain 98 99 277 348 .. 115.1 2,822 100 31.7 3.7 959 Sri Lanka 29 32 27 14 .. 0.7 25 2 4.6 5.5 66 Sudan .. 49 90 77 .. 0.0 6 .. 65.5 .. .. Swaziland .. 18 32 32 .. 0.0 .. 4 51.7 .. .. Sweden 410 94 763 764 .. 214.0 17,531 405 19.2 7.4 2,941 Switzerland 372 100 865 498 .. 232.0 9,671 577 7.9 7.5 3,691 Syrian Arab Republic .. 80 42 58 .. 0.0 .. 0 14.0 .. .. Tajikistan .. .. .. 1 .. 0.0 0 .. 12.3 .. .. Tanzania .. 6 7 9 .. 0.0 .. 0 93.6 .. .. Thailand 197 92 58 110 .. 0.7 106 6 6.9 4.1 112 Togo 2 51 30 49 .. 0.0 7 0 44.7 .. .. Trinidad and Tobago .. 88 79 123 15 8.3 375 28 13.4 .. .. Tunisia 19 92 57 95 25 1.6 75 2 12.4 5.8 167 Turkey .. 92 52 222 40 22.1 405 25 11.6 7.9 396 Turkmenistan 7 .. .. 8 .. .. .. .. 69.5 .. .. Uganda 3 5 9 17 1 .. 3 0 99.6 .. .. Ukraine 175 97 38 97 .. .. 17 2 7.7 8.0 141 United Arab Emirates .. 86 197 308 .. 28.3 923 54 13.1 3.6 1,027 United Kingdom 326 .. 600 473 99 163.8 13,062 560 27.3 7.3 2,683 United States 196 98 762 630 100 166.6 3,306 870 15.0 8.8 3,690 Uruguay .. 93 125 193 50 17.7 462 29 23.9 7.9 385 Uzbekistan .. .. .. 34 .. 0.1 1 0 5.7 .. .. Venezuela, RB .. 83 82 125 .. 13.4 51 5 42.6 3.9 205 Vietnam 6 83 13 129 .. 2.5 43 0 10.7 15.1 95 West Bank and Gaza .. 93 48 67 .. 2.1 66 1 15.6 .. .. Yemen, Rep. .. 43 15 9 .. .. .. 0 10.9 .. .. Zambia 22 26 10 20 .. 0.0 2 0 68.4 .. .. Zimbabwe .. 26 92 77 .. 0.8 4 0 24.6 7.7 33 World 90 w 79 m 130 w 137 w .. m 41.6 w 816 w 74 w 22.0 m 6.8 w 538 w Low income 45 15 11 44 .. 0.9 15 0 30.1 5.9 41 Middle income 55 88 58 115 .. 22.6 92 5 17.0 5.4 149 Lower middle income 61 84 45 95 .. 23.1 116 2 16.8 5.5 108 Upper middle income .. 91 113 196 60 21.0 218 17 17.0 5.2 312 Low & middle income 49 48 40 84 .. 13.4 59 3 23.4 5.4 109 East Asia & Pacific 60 36 38 89 .. 25.9 97 1 10.7 5.3 89 Europe & Central Asia .. 92 98 190 .. 20.9 211 13 12.2 5.1 274 Latin America & Carib. 61 87 88 156 .. 16.4 161 12 25.8 5.9 278 Middle East & N. Africa .. 84 48 89 .. 0.5 .. 1 11.8 3.1 66 South Asia 59 32 16 49 .. 1.0 18 1 8.1 5.7 40 Sub-Saharan Africa 12 14 15 29 .. .. .. 2 45.3 7.4 .. High income 263 97 579 527 .. 163.2 4,530 443 19.9 7.2 2,466 European Monetary Union 188 96 421 439 .. 134.7 5,784 184 20.4 5.4 1,726 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. 306 2007 World Development Indicators 5.11 STATES AND MARKETS The information age About the data Definitions The digital and information revolution has changed The number of secure Internet servers, from the · Daily newspapers refer to those published at the way the world learns, communicates, does busi- Netcraft Secure Server Survey, gives an indication least four times a week and calculated as average ness, and treats illnesses. New information and com- of how many companies are conducting encrypted circulation (or copies printed) per 1,000 people. munications technologies offer vast opportunities for transactions over the Internet. · Households with television are the percentage of progress in all walks of life in all countries--opportu- The data on information and communications households with a television set. Some countries nities for economic growth, improved health, better technology expenditures cover the world's 75 larg- report only the number of households with a color service delivery, learning through distance educa- est buyers of such technology among countries and television set, and therefore the true number may tion, and social and cultural advances. The table regions. be higher than reported. · Personal computers are presents indicators of the penetration of the infor- Ensuring universal access to information and com- self-contained computers designed for use by a mation economy (newspapers, televisions, personal munication technology is a goal of many countries, single individual. · Internet users are people with computers, and Internet use), quality (broadband but not all countries regularly track accessibility. access to the worldwide network. · Schools con- subscribers, international Internet bandwidth, and There is no common set of information and com- nected to the Internet are the share of primary and secure Internet servers), and some of the econom- munications technology indicators and definitions, secondary schools in the country that have access ics of the information age (Internet access charges and data are often drawn from administrative records to the Internet. · Broadband subscribers are the and spending on information and communications rather than from specific surveys. Access needs to number of broadband subscribers with a digital technology). be accurately measured in three major areas: indi- subscriber line, cable modem, or other high-speed The data on the number of daily newspapers in vidual, household, and community access. technologies. Reporting countries may have differ- circulation are from surveys by the United Nations ent definitions of broadband, so data are not strictly Educational, Scientifi c, and Cultural Organization comparable across countries. · International Inter- (UNESCO) Institute for Statistics. In some countries net bandwidth is the contracted capacity of inter- definitions, classifications, and methods of enumera- national connections between countries for trans- tion do not entirely conform to UNESCO standards. mitting Internet traffic. · Secure Internet servers For example, newspaper circulation data should refer are servers using encryption technology in Internet to the number of copies distributed, but in some transactions.· Price basket for Internet is calculated cases the figures reported are the number of copies based on the cheapest available tariff for accessing printed. The data for other electronic communica- the Internet 20 hours a month (10 hours peak and tions and information technology are from the Inter- 10 hours off-peak). The basket does not include the national Telecommunication Union (ITU), the Internet telephone line rental but does include telephone Software Consortium, Netcraft, the World Informa- use charges if applicable. Data are compiled in the tion Technology and Services Alliance (WITSA), national currency and converted to U.S. dollars using Global Insights, and World Bank staff estimates. the annual average exchange rate. · Information and Estimates of households with television are derived communications technology expenditures include from household surveys; data presented in the table computer hardware (computers, storage devices, are from the ITU and World Bank staff estimates. printers, and other peripherals); computer software The estimates of personal computers are derived (operating systems, programming tools, utilities, from an annual ITU questionnaire, supplemented by applications, and internal software development); other sources. In many countries mainframe comput- computer services (information technology consult- ers are used extensively. Since thousands of users ing, computer and network systems integration, Web can be connected to a single mainframe computer, hosting, data processing services, and other ser- the number of personal computers understates the vices); and communications services (voice and data total use of computers. communications services) and wired and wireless The data on Internet users and related Internet communications equipment. indicators are based on nationally reported data. Some countries derive these data from Internet Data sources surveys, but since survey questions and definitions Data on newspapers are compiled by the UNESCO differ across countries, the estimates may not be Institute for Statistics. Data on televisions, per- strictly comparable. For example, questions on the sonal computers, Internet users, broadband sub- age of Internet users and frequency of use vary by scribers, international Internet bandwidth, and country. Countries that do not have surveys generally price basket for Internet are from the ITU's World derive their estimates from reported Internet service Telecommunication Development Report data- provider subscriber counts, calculated by multiplying base. Data on schools connected to the Internet the number of subscribers by a selected multiplier. are World Bank staff estimates. Data on secure This method may undercount the actual number of Internet servers are from Netcraft (www.netcraft. people using the Internet, particularly in developing com/). Data on information and communications countries, where many commercial subscribers rent technology expenditures are from WITSA's Digital out computers connected to the Internet or prepaid Planet 2006: The Global Information Economy and cards are used to access the Internet. from Global Insight, Inc. 2007 World Development Indicators 307 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 filedb journal articles % of manu- per million per million factured Receipts Payments Non- Non- people people % of GDP $ millions exports $ millions $ millions Residents residents Residents residents 2000­04c 2000­04c 2003 2000­04c 2005 2005 2005 2005 2004 2004 2004 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. 5 1 1 4 0 .. .. .. Algeria .. .. 285 .. 7 1 .. .. 58 334 1,488 871 Angola .. .. .. .. .. .. 49 3 .. .. .. .. Argentina 720 316 3,086 0.41 809 7 54 635 786 3,816 61,953 19,139 Armenia .. .. 175 0.25 5 1 .. .. 151 6 1,598 256 Australia 3,759 .. 15,809 1.70 3,276 13 508 1,645 8,555 21,651 37,202 16,007 Austria 2,968 1,254 4,906 2.26 11,623 13 177 1,334 1,965 549 7,336 1,049 Azerbaijan .. .. 107 0.30 5 1 0 0 .. .. 144 339 Bangladesh .. .. 204 0.62 3 0 0 3 .. .. .. .. Belarus .. .. 532 0.62 216 3 3 20 1,065 382 2,410 1,027 Belgium 3,065 1,473 6,604 1.90 22,809 9 1,107 1,107 519 188 21,010d 10,695d Benin .. .. .. .. 0 0 0 2 .. .. .. .. Bolivia 120 6 .. 0.28 28 9 2 11 .. .. .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. 47 349 267 700 Botswana .. .. 76 .. .. .. 0 12 0 .. .. .. Brazil 344 332 8,684 0.98 8,007 13 102 1,404 3,892 14,800 81,036 13,218 Bulgaria 1,263 477 829 0.51 326 5 5 79 263 133 5,978 1,086 Burkina Faso .. .. .. .. 3 10 .. .. .. .. .. .. Burundi .. .. .. .. 0 6 0 0 .. .. 20 132 Cambodia .. .. .. .. 4 0 0 7 .. .. 409 1,638 Cameroon .. .. 123 .. 2 2 0 2 .. .. .. .. Canada 3,597 770 24,803 1.93 29,777 14 3,471 6,649 3,929 33,298 17,719 22,169 Central African Republic .. .. .. .. 0 0 .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 444 303 1,500 0.61 195 5 54 322 .. .. .. .. China 708 .. 29,186 1.44 214,246 31 157 5,321 65,586 64,798 527,591 52,788 Hong Kong, China 1,564 225 .. 0.60 94,808 34 218 1,111 127 9,878 7,374 11,208 Colombia 109 77 337 0.17 362 5 10 118 52 198 .. .. Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 30 32 .. .. .. .. .. .. .. .. .. .. Costa Rica .. .. 84 0.39 1,775 38 0 57 .. .. .. .. Côte d'Ivoire .. .. .. .. 93 8 0 22 .. .. .. .. Croatia 1,296 455 845 1.14 689 12 73 193 383 841 1,283 767 Cuba .. .. 264 0.65 .. .. .. .. .. .. 379 1,937 Czech Republic 1,594 923 2,950 1.28 7,662 13 63 216 619 633 9,365 1,042 Denmark 5,016 2,713 5,291 2.63 11,733 22 .. .. 1,843 172 4,185 944 Dominican Republic .. .. .. .. .. .. 0 31 .. .. .. .. Ecuador 50 .. .. 0.07 64 8 0 42 13 489 5,571 4,822 Egypt, Arab Rep. .. .. 1,720 0.19 15 1 136 182 157 537 .. .. El Salvador 47 .. .. .. 37 4 2 30 .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 2,523 427 368 0.91 931 18 5 25 27 97 1,241 583 Ethiopia .. .. 99 .. 0 0 0 1 .. .. .. .. Finland 7,832 .. 5,202 3.51 13,835 25 1,207 1,123 2,004 216 2,598 722 France 3,213 .. 31,971 2.16 69,673 20 5,924 3,230 14,230 3,060 57,784 2,935 Gabon .. .. .. .. 28 15 .. .. .. .. .. .. Gambia, The .. .. .. .. 0 6 .. .. 0 .. .. .. Georgia .. .. 117 0.29 76 23 9 5 248 210 148 132 Germany 3,261 1,089 44,305 2.49 137,547 17 6,828 6,589 48,329 10,905 62,576 3,342 Ghana .. .. 83 .. 19 9 0 0 .. .. .. .. Greece 1,413 895 3,770 0.58 994 10 60 442 487 27 5,290 1,143 Guatemala .. .. .. .. 98 3 0 0 .. .. .. .. Guinea .. .. .. .. .. .. 0 0 .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. 0 .. .. .. .. Haiti .. .. .. .. .. .. 0 0 .. .. .. .. 308 2007 World Development Indicators 5.12 STATES AND MARKETS 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 filedb journal articles % of manu- per million per million factured Receipts Payments Non- Non- people people % of GDP $ millions exports $ millions $ millions Residents residents Residents residents 2000­04c 2000­04c 2003 2000­04c 2005 2005 2005 2005 2004 2004 2004 2004 Honduras .. .. .. 0.05 6 2 0 22 7 161 1,149 3,388 Hungary 1,472 466 2,503 0.88 13,045 25 834 1,068 738 1,919 4,293 1,047 India .. .. 12,774 0.85 2,840 5 25 421 6,795 10,671 .. .. Indonesia 207 .. 178 0.05 6,571 16 263 961 226 3,441 .. .. Iran, Islamic Rep. 1,279 .. 1,806 0.67 98 3 .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 2,674 621 1,758 1.21 .. .. 589 19,426 787 58 1,285 1,142 Israel .. .. 6,941 4.46 4,937 14 610 537 1,329 8,929 215 5,022 Italy 1,213 .. 24,696 1.14 24,616 8 1,131 1,942 .. .. .. .. Jamaica .. .. .. 0.07 .. .. 13 11 15 54 663 1,433 Japan 5,287 528 60,067 3.15 122,680 22 17,655 14,653 362,342 60,739 110,270 18,573 Jordan .. .. 263 .. 160 5 .. .. .. .. .. .. Kazakhstan 629 92 128 0.22 72 2 0 31 1,696 102 2,908 1,070 Kenya .. .. 258 .. 18 3 18 37 .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 3,187 567 13,746 2.64 83,527 32 1,827 4,398 105,027 35,088 91,935 16,529 Kuwait .. .. 244 0.20 .. .. 0 0 .. .. .. .. Kyrgyz Republic .. .. .. 0.20 4 2 2 2 179 1 133 345 Lao PDR .. .. .. .. .. .. .. .. .. .. 25 656 Latvia 1,434 318 153 0.42 159 5 10 14 107 44 1,290 595 Lebanon .. .. 223 .. 26 2 0 0 .. .. .. .. Lesotho .. 0 .. 0.01 .. .. 18 .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 361 493 .. .. .. .. 0 0 .. .. .. .. Lithuania 2,136 427 322 0.76 410 6 2 21 69 45 1,929 570 Macedonia, FYR 504 69 74 0.26 16 1 3 10 37 415 478 515 Madagascar 15 45 .. 0.12 1 1 1 3 16 22 411 321 Malawi .. .. .. .. 6 7 .. .. .. .. 138 440 Malaysia 299 58 520 0.69 57,376 55 27 1,370 .. .. .. .. Mali .. .. .. .. .. .. 0 1 .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. 0.35 298 21 0 5 .. .. .. .. Mexico 268 96 3,747 0.40 32,262 20 70 111 531 12,667 41,813 20,775 Moldova .. .. 78 .. 11 3 2 2 297 9 1,574 372 Mongolia .. .. .. 0.28 0 0 .. .. 143 86 423 1,321 Morocco .. .. 428 0.62 702 10 13 45 .. .. .. .. Mozambique .. .. .. 0.59 9 8 2 5 .. .. .. .. Myanmar 17 133 .. 0.07 .. .. 0 0 .. .. .. .. Namibia .. .. .. .. 15 3 0 3 .. .. .. .. Nepal 59 137 .. 0.66 1 0 .. .. .. .. .. .. Netherlands 2,482 1,725 13,475 1.85 65,758 30 3,866 3,692 2,187 556 .. .. New Zealand 3,945 833 3,034 1.16 943 14 101 555 1,579 4,952 8,426 7,864 Nicaragua .. .. .. 0.05 4 5 0 0 .. .. .. .. Niger .. .. .. .. 1 3 .. 0 .. .. .. .. Nigeria .. .. 384 .. 9 2 .. 45 .. .. .. .. Norway 4,587 1,754 3,339 1.75 3,010 17 364 546 .. .. .. 6,981 Oman .. .. 114 .. 25 2 .. .. 0 .. .. .. Pakistan .. .. 368 0.22 211 2 15 110 .. 1,081 8,319 4,455 Panama 97 387 .. 0.34 1 1 0 42 .. .. .. .. Papua New Guinea .. .. .. .. 47 39 .. .. .. .. .. .. Paraguay 79 113 .. 0.10 14 7 196 1 12 173 .. .. Peru .. .. 129 0.10 64 3 2 69 38 785 8,227 5,661 Philippines 48 8 179 0.11 26,077 71 6 265 157 2,539 6,861 5,253 Poland 1,581 282 6,770 0.58 2,688 4 61 1,036 2,381 5,359 13,776 1,153 Portugal 1,949 307 2,625 0.78 2,639 9 60 328 121 66 8,123 1,012 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 309 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 filedb journal articles % of manu- per million per million factured Receipts Payments Non- Non- people people % of GDP $ millions exports $ millions $ millions Residents residents Residents residents 2000­04c 2000­04c 2003 2000­04c 2005 2005 2005 2005 2004 2004 2004 2004 Romania 976 249 988 0.40 758 3 48 173 937 163 10,298 1,193 Russian Federation 3,319 557 15,782 1.17 3,690 8 260 1,593 22,944 7,246 23,571 7,088 Rwanda .. .. .. .. 1 25 0 0 .. .. .. .. Saudi Arabia .. .. 573 .. 215 1 0 0 61 552 .. .. Senegal .. .. 79 .. 75 12 0 7 .. .. .. .. Serbia and Montenegro .. .. 600 1.17 .. .. .. .. 381 .. 1,085 647 Sierra Leone .. .. .. .. .. .. 1 0 .. .. .. .. Singapore 4,999 381 3,122 2.25 105,078 57 544 8,647 509 8,076 4,839 18,409 Slovak Republic 1,984 445 943 0.53 1,960 7 50 91 214 239 2,912 1,148 Slovenia 2,543 1,600 969 1.61 786 5 16 113 327 42 1,615 550 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 307 73 2,364 0.76 1,739 7 45 1,071 .. .. .. .. Spain 2,195 861 16,826 1.11 10,409 7 561 2,639 2,864 320 52,718 2,059 Sri Lanka 128 .. 141 0.14 60 1 .. .. 95 189 3,989 1,773 Sudan .. .. .. 0.34 0 0 0 0 .. .. .. .. Swaziland .. .. .. .. .. .. 0 88 .. .. .. .. Sweden 5,416 .. 10,237 3.74 17,070 17 3,324 1,498 2,752 478 .. .. Switzerland 3,601 2,319 8,542 2.57 25,544 22 .. .. 1,827 466 .. .. Syrian Arab Republic .. .. 67 .. 6 1 .. 12 .. .. .. .. Tajikistan .. .. .. .. .. .. 1 0 32 2 65 253 Tanzania .. .. 86 .. 1 1 0 0 .. .. .. .. Thailand 287 208 1,072 0.26 22,480 27 17 1,674 681 4,329 22,612 9,241 Togo .. .. .. .. 0 0 0 2 .. .. .. .. Trinidad and Tobago .. .. .. 0.12 34 1 .. .. 2 205 340 1,317 Tunisia 1,013 34 452 0.63 370 5 14 8 .. .. .. .. Turkey 341 37 6,224 0.66 906 2 0 439 465 383 30,136 2,101 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. 90 0.81 18 14 7 1 .. .. .. .. Ukraine .. .. 2,089 1.16 869 4 22 421 4,086 1,692 11,516 2,434 United Arab Emirates .. .. 193 .. .. .. .. .. .. .. .. .. United Kingdom .. .. 48,288 1.89 82,841 28 13,303 9,069 18,816 11,138 23,186 4,653 United States 4,605 .. 211,233 2.68 233,079 32 57,410 24,501 185,008 171,935 213,495 26,988 Uruguay 366 50 194 0.26 22 2 0 4 37 514 4,589 6,732 Uzbekistan .. .. 192 .. .. .. .. .. 273 205 438 494 Venezuela, RB .. .. 563 0.28 118 3 0 239 .. .. .. .. Vietnam 115 .. 216 0.19 594 6 .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. .. .. 11 5 .. 9 .. .. .. .. Zambia .. .. .. .. 2 1 .. .. .. .. .. .. Zimbabwe .. .. 96 .. 5 1 .. .. .. .. .. .. World .. w .. w 697,397 s 2.28 w 1,243,114 s 22 w 123,690 s 134,689 s 872,278 s 473,770 s 1,337,033 s 267,528 s Low income .. .. 14,929 0.73 .. 4 47 224 7,259 12,067 10,198 8,827 Middle income 725 .. 100,288 0.85 332,483 21 2,693 19,573 105,144 120,688 678,572 107,953 Lower middle income 504 .. 49,969 1.12 .. 26 1,083 10,892 76,157 90,921 566,801 71,992 Upper middle income 1,453 308 50,319 0.67 120,551 17 1,610 8,681 28,987 29,767 111,771 35,961 Low & middle income .. .. 115,217 0.83 270,664 21 2,740 19,797 112,403 132,755 688,770 116,780 East Asia & Pacific 708 .. 31,351 1.44 .. 34 471 9,599 66,112 70,866 535,284 61,115 Europe & Central Asia 1,993 384 42,695 0.94 26,767 8 1,405 5,353 34,767 19,989 96,993 23,355 Latin America & Carib. 256 .. 18,588 0.56 40,879 15 542 3,204 4,498 29,255 47,763 27,534 Middle East & N. Africa .. .. 5,358 .. 1,405 3 163 256 215 871 1,488 871 South Asia .. .. 13,487 0.73 .. 4 18 113 6,795 11,752 8,319 4,455 Sub-Saharan Africa .. .. 3,738 .. .. 4 141 1,272 16 22 411 321 High income 3,781 .. 582,180 2.45 1,156,714 22 120,950 114,892 759,875 341,015 648,263 150,748 Europe EMU 2,607 .. 156,184 1.92 358,491 16 21,814 42,096 72,974 15,757 133,351 8,184 Note: The original information on patent and trademark application 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. a. Excludes applications filed under the auspices of the European Patent Office (32,178 by residents, 91,523 by nonresidents) and the Eurasian Patent Organization (1,630 by nonresidents). b. Excludes applications filed under the auspices of the EU Office for Harmonization in the Internal Market (40,305 by residents, 18,540 by nonresidents). c. Data are for the most recent year available. d. Includes Luxembourg and the Netherlands. 310 2007 World Development Indicators 5.12 STATES AND MARKETS Science and technology About the data Definitions Technological innovation, often fueled by govern- sales) for groups of products from six countries (Ger- · Researchers in R&D are professionals engaged in ment-led research and development (R&D), has many, Italy, Japan, the Netherlands, Sweden, and the conceiving of or creating new knowledge, products, been the driving force for industrial growth around United States). Because industrial sectors charac- processes, methods, and systems, and also in man- the world. The best opportunities to improve living terized by a few high-technology products may also aging the projects concerned. Postgraduate students standards, including new ways of reducing poverty, produce many low-technology products, the product at the PhD level (International Standard Classifica- will come from science and technology. Science, approach is more appropriate for analyzing interna- tion of Education 1997 level 6) engaged in R&D are considered researchers. · Technicians in R&D and advancing rapidly in virtually all fields--particularly tional trade than is the sectoral approach. To con- equivalent staff are people whose main tasks require in biotechnology--is playing a growing economic struct a list of high-technology manufactured prod- technical knowledge and experience in fields of engi- role: countries able to access, generate, and apply ucts (services are excluded), the R&D intensity was neering, physical and life sciences (technicians), and relevant scientific knowledge will have a competitive calculated for products classified at the three-digit social sciences and humanities (equivalent staff). edge over those that cannot. And there is greater level of the Standard International Trade Classifica- They participate in R&D by performing scientific and appreciation of the need for high-quality scientific tion revision 3. The final list was determined at the technical tasks involving the application of concepts input into public policy issues such as regional and four- and five-digit levels. At these levels, since no and operational methods, normally under the supervi- global environmental concerns. R&D data were available, final selection was based sion of researchers. · Scientific and technical journal Science and technology cover a range of issues too on patent data and expert opinion. This method articles refer to published scientific and engineering complex and too broad to be quantified by any single takes only R&D intensity into account. Other charac- articles in physics, biology, chemistry, mathematics, set of indicators, but those in the table shed light teristics of high technology are also important, such clinical medicine, biomedical research, engineer- on countries' "technology base"--the availability of as know-how, scientific and technical personnel, and ing and technology, and earth and space sciences. skilled human resources, the number of scientific technology embodied in patents; considering these · Expenditures for R&D are current and capital and technical articles published, the competitive characteristics would result in a different list. (See expenditures on creative work undertaken system- atically to increase the stock of knowledge, including edge countries enjoy in high-technology exports, Hatzichronoglou 1997 for further details.) Moreover, knowledge of humanity, culture, and society, and the sales and purchases of technology through royal- the R&D for high-technology exports may not have use of this knowledge to devise new applications. The ties and licenses, and the number of patent and occurred in the reporting country. term R&D covers basic research, applied research, trademark applications filed. Most countries have adopted systems that protect and experimental development. · High-technology The United Nations Educational, Scientific, and Cul- patentable inventions. The Patent Cooperation Treaty exports are products with high R&D intensity, such tural Organization (UNESCO) Institute for Statistics provides an international system for filing patent appli- as in aerospace, computers, pharmaceuticals, scien- (UIS) collects data on researchers, technicians and cations. The procedure consists of an international tific instruments, and electrical machinery. · Royalty expenditure in R&D from countries and territories phase followed by a national or regional phase. In the and license fees are payments and receipts between around the World, through questionnaires and special international phase an applicant files an international residents and nonresidents for the authorized use surveys as well as from other international sources. application and designates the countries in which pat- of intangible, nonproduced, nonfinancial assets, Data on researchers and technicians are normally ent protection is eventually to be sought (since 2004 and proprietary rights (such as patents, copyrights, calculated in terms of full-time equivalents. all eligible countries are automatically designated in trademarks, franchises, and industrial processes) R&D expenditures include all expenditures for R&D every application under the treaty). The application is and for the use, through licensing agreements, of performed within a country, including both capital searched, published, and, optionally, an international produced originals of prototypes (such as films and expenditures and current costs (annual wages and preliminary examination is conducted. In the national manuscripts). · Patent applications filed are world- wide patent applications filed through the Patent salaries and all associated costs of researchers, (or regional) phase the applicant requests national Cooperation Treaty procedure or with a national pat- technicians and supporting staff, and other current processing of the application, pays additional fees, ent office. A patent is an exclusive right to an inven- costs, such as noncapital purchases of materials, and initiates the national search, examination, and tion (a product or process that provides a new way of supplies and R&D equipment such as utilities, granting procedure. International applications under doing something or offers a new technical solution books, journals, reference materials, subscriptions the treaty provide only for a national patent grant-- to a problem). It must be of practical use and display to libraries and scientific societies, and materials there is no international patent. The national phase a new characteristic unknown in the body of exist- for laboratories). filing represents an action on the part of the applicant ing knowledge in its technical field. A patent grants The information does not reflect the quality of train- to actively seek patent protection for a given territory, protection to the owner of the patent for a specified ing and education, which varies widely. Similarly, R&D whereas international filings and designations, while period, generally 20 years. · Trademark applications expenditures are no guarantee of progress; govern- they represent a legal right, do not accurately reflect filed are applications to register a trademark with a ments need to pay close attention to the practices where patent protection is eventually sought. Resident national or regional trademark office. Trademarks are that make R&D expenditures effective. filings are those from applicants who are a resident of distinctive signs identifying goods or services as pro- Article counts are from a set of journals classified the country or region concerned. Nonresident filings duced or provided by a specific person or enterprise. and covered by the Institute for Scientific Information's are from applicants outside the relevant country or Trademarks protect owners of the mark by ensuring exclusive right to use it to identify goods or services Science Citation Index (SCI) and Social Sciences Cita- region. In the case of regional offices such as the or to authorize its use in return for payment. tion Index (SSCI). Article counts are based on frac- European Patent Office, an application from a resident tional assignments; for example, an article with two of any member state of the regional patent convention Data sources authors from different countries is counted as one- is considered a resident filing. Some offices (nota- half of an article for each country (see Definitions for bly the U.S. Patent and Trademark Office) use the Data on R&D are provided by UIS. Data on sci- the fields covered). The SCI and SSCI databases cover residence of the inventor rather than the applicant to entifi c and technical journal articles are from the core set of scientific journals but may exclude classify resident and nonresident filings. the U.S. National Science Foundation's Science and Engineering Indicators 2006. Data on high- some of regional or local importance. They may also A trademark provides protection to its owner by technology exports are from the United Nations reflect some bias toward English-language journals. ensuring the exclusive right to use it to identify goods Statistics Division's Commodity Trade (Comtrade) The method used for determining a country's high or services or to authorize another to use it in return database. Data on royalty and license fees are technology exports was developed by the Organi- for payment. The period of protection varies, but a from the International Monetary Fund's Balance of sation for Economic Co-operation and Development trademark can be renewed indefinitely by paying addi- Payments Statistics Yearbook. Data on patents and in collaboration with Eurostat. Termed the "prod- tional fees. The trademark system helps consumers trademarks are from the World Intellectual Prop- uct approach" to distinguish it from a "sectoral identify and purchase a product or service whose erty Organization's WIPO Patent Report: Statistics approach," the method is based on the calculation nature and quality, indicated by its unique trademark, on Worldwide Patent Activity (2006 edition). of R&D intensity (R&D expenditure divided by total meet their needs. 2007 World Development Indicators 311 Text figures, tables, and boxes GLOBAL LINKS 6 Introduction G lobalization and global links Globalization--the integration of the world economy--has been a persistent theme of the past quarter century. The growth of cross-border economic activity has changed the structure of economies and the political and social organization of countries. Not all effects of globalization can be measured directly. But the scope and pace of change can be monitored along four important channels: trade in goods and services, financial flows, the movement of people, and the diffusion of technology and knowledge. · Exports and imports of goods and services exceeded $26 trillion in 2005, or 58 percent of total global output, up from 44 percent in 1980. Developing economies still account for less than one-third of global trade, but their share has been increasing steadily. · Gross private capital flows across national borders exceeded 32 percent of global output in 2005, up from 9 percent in 1980. Foreign direct investment and cross-border portfolio investment flows to developing economies have soared despite occasional setbacks. · People have become more mobile. More than 800 million people traveled to foreign desti- nations in 2005, nearly triple the number in 1980. Some 190 million people are estimated to reside outside their land of birth, nearly double the 1980 level. · Technology and knowledge are diffusing at unprecedented speed across countries. Inter- national phone traffic, measured in minutes, increased more than fourfold between 1995 and 2005 (see section 5). Many factors have accelerated the pace of globalization. Barriers to international trade and investment are coming down. Technological progress has dramatically cut transportation and communications costs, enabling production processes and distribution networks to move from local to global. Some previously nontradable services can now be traded easily around the world. Efficiency gains due to resource allocation at global scale have made globalization an increasingly powerful source of growth. Globalization has created opportunities and challenges for developing countries. While the experiences of China, India, Indonesia, Thailand, and some other countries have demon- strated that integration into the global economy is necessary for long-term growth and poverty reduction, concerns have emerged over equality of opportunity and the unequal distribution of benefits. Many poor countries and poor people in many countries have not been able to take full advantage of the opportunities brought by globalization or to participate in its benefits. Removing the obstacles to full participation by poor countries and poor people is essential to making globalization more inclusive. For example, subsidies to domestic farmers in high- income economies have created formidable barriers for developing economies trying to reach global markets for agricultural products. But there is much that developing countries need to do to make their economies more competitive. Scaling up and increasing the flexibility of official development assistance could assist low-income countries' efforts to attract invest- ment and improve their trade-facilitating infrastructure, whose limitations now constrict poor countries' capacity to take advantage of growing global opportunities. 2007 World Development Indicators 313 Expanding flows of private Expanding trade financial resources International trade is the hallmark of an integrated global International private financial flows have increased rapidly in economy. Between 1990 and 2005 growth in trade outpaced both gross and net terms. Between 1990 and 2005 total growth in the overall economy across the board (figure 6a). gross capital flows recorded in the balance of payments tri- Low- and middle-income economies gained market share in pled as a share of world GDP, and high-income economies still world merchandise exports--from about 16 percent in 1990 account for the lion's share (indicator table 6.1). All types of to almost 30 percent in 2005 (indicator table 6.3)--but the external financial flows to developing economies have soared Sub-Saharan share lagged at around 1.5 percent. during this period, but foreign direct investment (FDI) remains Trade between developing economies has expanded the largest (figure 6c). From a low initial level of less than considerably and now makes up about 8 percent of world $25 billion in 1990, net inflows of FDI to developing countries merchandise exports. Between 1990 and 2005 merchandise increased tenfold by 2005 (indicator table 6.8). exports between developing economies grew at an impres- Large differences in external financial inflows exist among sive average annual rate of 13 percent, compared with less developing economies. The top 10 receivers of FDI net inflows than 6 percent for exports between high-income economies accounted for about two-thirds of total FDI inflows among (figure 6b). But tariff barriers affecting exports to developing developing economies in 2005. FDI inflows are dominant in economies are still much higher than those affecting exports Latin America and Caribbean and East Asia and Pacific; port- to high-income economies. The simple mean tariff rate aver- folio investments are more important in South Asia (figure ages 9 percent in developing economies but less than 4 per- 6d). Meanwhile, some developing economies are increasingly cent in high-income economies (indicator table 6.7). investing overseas to expand their global operations. Trade growth Foreign direct investment leads outpaces GDP growth 6a resource flows to developing economies 6c Average annual growth, 1990­2005 (%) Trade GDP $ billions 10 300 Foreign direct investment 8 250 200 6 150 Remittances 4 Aid 100 2 Equity 50 Bond 0 0 High-income Middle-income Low-income Sub-Saharan Africa 1990 1995 2000 2005 Source: World Bank staff estimates. Source: World Bank staff estimates. Exports from developing Developing economies differ countries have grown fast 6b greatly in external resource flows 6d Merchandise exports ($ trillions) Foreign direct investment Equity Bond Lending 10 East Asia Developing-country to developing-country & Pacific 8 Europe & Central Asia Developing-country to high-income 6 Latin America & Caribbean High-income to developing-country Middle East & 4 North Africa South Asia 2 Sub-Saharan High-income to high-income Africa 0 1990 2005 ­2 ­1 0 1 2 3 4 5 6 7 8 Share of GDP (%) Source: World Bank staff estimates. Source: World Bank staff estimates. 314 2007 World Development Indicators Expanding aid and increasing emphasis on effective aid Expanding movements of people Developed economies have committed to providing more and The flow of people across national borders is another mark better aid, especially to the poorest economies that commit of integration. Important for many developing economies, in- themselves to poverty reduction and good governance. After ternational tourism has increased rapidly since its downturn a period of decline and stagnation, aid flows began to rise, in 2001. In 2005 international tourist arrivals worldwide ex- particularly after the Financing for Development conference ceeded 800 million, nearly double the 1990 level. Receipts in Monterrey, Mexico, in 2002. Total official development as- reached $680 billion (excluding air tickets), accounting for sistance (ODA) rose to a record high of $106.8 billion in 2005 6.5 percent of global exports of goods and services (indicator (indicator table 6.9). However, many donor economies still table 6.15). Developing economies, accounting for a third of need to scale up aid significantly to fulfill commitments made international tourist arrivals, are attracting new tourists at a at the Monterrey conference and at the Gleneagles Group of faster rate than the world as whole (figure 6g). Eight summit in 2005 (figure 6e). International migration is a major global development A large amount of aid is earmarked for special pur- issue, posing opportunities and challenges to both devel- poses. In 2005 more than half of ODA was used for special oped and developing economies. In 2005 recorded remit- purposes such as debt relief, technical cooperation and tance flows repatriated by developing economy migrants were administrative costs, and emergency relief and food aid $188 billion, close to 2 percent of GDP (indicator table 6.14). (indicator table 6.10). Excluding these "special purpose" While high-income economies are the most popular destina- items and setting aside aid to Afghanistan and Iraq, only tions, migration between developing economies accounts for 41 percent of ODA in 2005 was available to finance devel- nearly half the migrants from developing economies (figure opment projects and budget support for general financing 6h). Migration between developing economies occurs primar- needs (fi gure 6f). ily between neighbors, particularly in Europe and Central Asia and Sub-Saharan Africa. Aid flows Fast growth in tourism, especially are rising 6e for low-income countries 6g Official development assistance as share of GNI (%) Average annual growth in arrivals, 1990­2005 (%) 0.35 10 Total 0.30 8 0.25 Excluding special-purpose grants 0.20 6 0.15 4 To Sub-Saharan Africa 0.10 2 0.05 0.00 0 1990 1995 2000 2005 World 50 least Other low-income Upper High-income developed and lower middle-income Source: Organisation for Economic Co-operation and Development, Development countries middle-income Assistance Committee. Source: World Tourism Organization. Only 41 percent of aid finances development Migration to developing economies projects and general budget support 6f accounts for almost half of all migrants 6h 2004 $ billions Within own group Share of migrants from developing economies (%) To high-income Rest 120 Debt relief East Asia 100 & Pacific Europe & Emergency aid and food aid Central Asia 80 Latin America & Caribbean 60 Middle East & Technical cooperation and administrative costs North Africa 40 South Asia Aid to Afghanistan and Iraq 20 Sub-Saharan Africa Aid excluding special purpose grants and Afghanistan and Iraq All developing 0 countries 1999 2000 2001 2002 2003 2004 2005 0 20 40 60 80 100 Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. Source: Ratha and Shaw 2007. 2007 World Development Indicators 315 Tables 6.1 Integration with the global economy Merchandise Trade Growth in real Gross private Foreign direct trade in services trade less growth capital flows investment in real GDP % of GDP % of GDP % of GDP percentage points % of GDP Net inflows Net outflows 1990 2005 1990 2005 1990­2005 1990 2005 1990 2005 1990 2005 Afghanistan .. 51.4 .. .. .. .. .. .. .. .. .. Albania 29.0 39.0 2.9 30.4 12.5 18.0 7.3 2.8 3.1 0.0 0.0 Algeria 36.6 64.9 2.9 .. 0.1 2.6 .. 0.0 1.1 0.0 .. Angola 53.5 96.2 18.7 21.2 .. 10.1 29.5 ­3.3 ­4.0 0.0 0.2 Argentina 11.6 37.5 3.9 7.6 4.0 8.2 9.1 1.3 2.6 0.0 0.2 Armenia .. 55.4 .. 14.7 ­6.9 .. 10.5 81.4 5.3 .. 0.1 Australia 25.6 31.6 7.5 7.9 3.1 9.0 32.5 2.5 ­4.7 0.3 2.7 Austria 54.8 81.7 22.7 33.7 3.4 9.6 51.9 0.4 3.0 1.0 2.5 Azerbaijan .. 94.3 .. 26.6 13.8 .. 87.0 0.0 13.4 .. 13.9 Bangladesh 17.6 38.5 3.6 5.7 4.2 0.9 2.9 0.0 1.3 0.0 0.0 Belarus .. 110.5 .. 10.9 ­1.5 .. 5.4 0.0 1.0 .. 0.0 Belgium 110.2a 176.1 25.5a 28.9 2.2 78.8a 382.1a 3.7a 8.6 2.9a 5.1 Benin 30.0 33.9 13.9 12.4 ­2.4 10.7 8.1 3.4 0.5 0.0 0.1 Bolivia 33.1 53.7 9.4 12.5 1.7 3.1 14.1 0.6 ­3.0 0.0 0.0 Bosnia and Herzegovina .. 95.5 .. 14.8 ­2.7 .. 28.0 .. 3.0 .. 0.0 Botswana 98.4 74.6 15.4 16.6 ­2.3 9.0 12.3 2.5 2.7 0.2 2.5 Brazil 11.7 24.6 2.4 5.1 4.4 1.9 5.9 0.2 1.9 0.1 1.6 Bulgaria 48.9 112.2 6.9 29.2 5.9 39.2 34.9 0.0 9.8 0.0 ­0.9 Burkina Faso 22.0 34.8 9.1 .. ­1.2 1.0 .. 0.0 0.4 0.0 .. Burundi 27.0 47.2 12.9 18.4 .. 3.7 3.0 0.1 0.1 0.0 0.0 Cambodia 22.4 109.9 5.7 28.1 9.7 3.2 13.6 1.7 6.1 .. 0.2 Cameroon 30.5 33.9 12.8 11.3 1.8 15.5 14.4 ­1.0 0.1 0.1 .. Canada 43.7 61.0 8.3 10.6 3.1 8.1 14.3 1.3 3.1 0.9 4.8 Central African Republic 18.4 20.4 16.0 .. .. 2.2 .. 0.0 0.4 0.3 .. Chad 27.2 70.1 15.5 .. 3.3 5.6 .. 0.5 12.9 0.0 .. Chile 51.1 63.4 12.4 13.0 3.5 14.4 20.4 2.1 5.8 0.0 1.0 China 32.5 63.6 2.9 7.1 6.3 2.5 10.9 1.0 3.5 0.2 0.1 Hong Kong, China 217.4 333.3 .. 53.2 3.6 .. 78.4 .. 20.2 .. 24.0 Colombia 30.7 34.6 8.3 6.1 2.7 3.1 16.3 1.2 8.5 0.0 0.1 Congo, Dem. Rep. 43.5 59.5 .. .. 9.6 .. .. 0.2 5.7 .. .. Congo, Rep. 57.2 126.0 31.0 35.3 3.8 6.6 34.2 ­0.5 14.2 0.0 0.0 Costa Rica 46.4 84.1 15.7 20.6 3.2 5.4 16.9 2.2 4.3 0.0 0.3 Côte d'Ivoire 47.9 79.3 20.5 17.6 0.5 3.5 6.8 0.4 1.6 0.0 0.0 Croatia 88.8 71.0 .. 34.6 4.1 .. 23.1 .. 4.6 .. 1.0 Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic 83.6 124.6 .. 16.7 8.4 .. 22.0 0.0 4.1 .. 0.5 Denmark 51.7 62.3 17.0 31.0 3.2 14.8 27.9 0.8 2.0 1.1 ­4.1 Dominican Republic 73.2 53.4 21.7 18.2 0.2 5.0 6.6 1.9 3.5 0.0 0.0 Ecuador 44.2 55.9 13.0 8.6 2.1 11.0 14.3 1.2 4.5 0.0 0.0 Egypt, Arab Rep. 36.8 34.1 22.6 28.1 ­1.1 6.8 16.4 1.7 6.0 0.0 0.2 El Salvador 38.4 59.8 13.4 13.9 5.8 2.0 11.7 0.0 3.0 0.0 0.0 Eritrea 77.0 52.1 .. .. ­2.6 53.0 .. .. 1.2 .. .. Estonia .. 135.1 9.1 40.5 7.2 3.9 93.9 2.1 22.9 0.0 2.4 Ethiopia 11.4 44.8 5.5 19.7 4.1 1.1 5.4 0.1 2.4 0.0 .. Finland 38.8 64.7 8.9 16.7 4.3 17.2 39.2 0.6 2.1 2.0 ­0.8 France 36.4 45.0 11.1 10.4 3.8 20.2 32.9 1.1 3.3 2.8 2.3 Gabon 52.5 78.4 21.0 15.2 ­1.3 18.0 14.6 1.2 3.7 0.5 0.3 Gambia, The 69.1 52.7 34.5 27.1 ­3.2 0.9 15.5 4.5 11.3 0.0 .. Georgia .. 52.5 .. 20.7 8.9 .. 12.2 .. 7.0 .. 0.2 Germany 45.5 62.4 8.7 12.8 4.4 10.2 30.7 0.2 1.1 1.4 ­0.3 Ghana 35.7 69.9 6.6 21.7 2.9 2.9 5.2 0.3 1.0 0.0 0.0 Greece 32.4 31.5 11.1 21.7 3.3 3.8 38.0 1.2 0.3 0.0 0.3 Guatemala 36.8 50.0 9.7 8.6 2.7 2.9 9.1 0.6 0.7 0.0 0.0 Guinea 49.5 52.0 18.6 8.9 ­1.6 3.9 1.5 0.6 3.1 .. 0.0 Guinea-Bissau 43.0 73.1 11.0 19.3 4.1 23.0 9.1 0.8 3.3 0.0 0.2 Haiti 17.2 45.1 4.3 13.6 .. 1.1 2.6 0.3 0.2 ­0.3 0.0 316 2007 World Development Indicators 6.1 GLOBAL LINKS Integration with the global economy Merchandise Trade Growth in real Gross private Foreign direct trade in services trade less growth capital flows investment in real GDP % of GDP % of GDP % of GDP percentage points % of GDP Net inflows Net outflows 1990 2005 1990 2005 1990­2005 1990 2005 1990 2005 1990 2005 Honduras 57.9 74.5 11.7 19.6 0.3 7.2 7.5 1.4 5.6 0.0 0.0 Hungary 61.5 117.3 16.0 22.0 8.4 4.6 26.2 1.9 5.9 0.0 1.1 India 13.1 28.5 3.4 8.2 4.4 0.8 5.9 0.1 0.8 0.0 0.2 Indonesia 41.5 54.2 7.5 12.8 0.6 4.1 7.2 1.0 1.8 0.0 .. Iran, Islamic Rep. 34.2 48.5 3.8 .. ­2.9 2.7 .. ­0.3 0.0 0.0 .. Iraq 55.4 155.9 .. .. .. .. .. .. .. .. .. Ireland 92.8 88.1 18.0 63.0 6.1 21.9 355.6 1.3 ­14.7 0.8 8.6 Israel 55.0 72.8 18.1 25.5 1.2 6.5 23.1 0.3 4.5 0.4 2.7 Italy 31.1 42.4 8.5 10.2 1.7 10.3 28.3 0.6 1.1 0.7 1.1 Jamaica 67.2 62.3 37.5 42.4 .. 8.4 50.3 3.0 7.1 0.0 0.7 Japan 17.3 24.5 4.2 5.4 2.9 5.4 15.9 0.1 0.1 1.7 0.7 Jordan 91.1 116.5 67.5 38.0 ­0.7 6.3 20.7 0.9 12.1 ­0.8 0.0 Kazakhstan .. 79.1 .. 17.1 ­2.8 .. 39.7 0.4 3.5 .. ­3.0 Kenya 37.9 50.4 21.4 16.1 2.2 3.5 6.3 0.7 0.1 0.0 0.0 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 51.1 69.3 7.5 13.2 6.4 5.3 7.1 0.3 0.6 0.4 0.7 Kuwait 59.8 75.9 25.2 16.8 ­3.0 19.3 24.0 0.0 0.3 1.3 3.2 Kyrgyz Republic .. 72.9 .. 22.4 ­1.2 .. 9.4 .. 1.7 .. 2.0 Lao PDR 30.5 43.6 5.8 .. .. 3.7 .. 0.7 1.0 0.0 .. Latvia .. 87.6 9.2 23.5 4.8 2.3 36.3 0.6 4.6 0.0 0.7 Lebanon 106.5 54.5 .. 84.8 0.4 .. .. 0.2 11.7 .. .. Lesotho 119.3 140.6 19.8 10.3 ­0.2 9.6 6.7 2.8 6.3 0.0 0.0 Liberia 374.1 253.5 .. .. .. .. .. 58.6 35.4 .. .. Libya 64.2 95.8 5.2 7.4 .. 7.3 7.9 .. .. 0.4 ­0.7 Lithuania .. 106.4 .. 20.1 8.0 .. 29.1 0.1 4.0 .. 1.2 Macedonia, FYR 103.8 91.4 .. 16.9 4.1 .. 11.8 0.0 1.7 .. 0.0 Madagascar 31.5 45.8 12.8 7.5 2.3 1.8 0.8 0.7 0.6 0.0 0.0 Malawi 52.7 81.3 16.2 .. ­1.7 3.2 .. 1.2 0.1 0.0 .. Malaysia 133.4 196.1 21.2 31.9 2.9 10.3 24.1 5.3 3.0 0.0 1.3 Mali 39.7 51.3 19.0 15.9 1.8 2.0 8.2 0.2 3.0 0.0 0.0 Mauritania 84.1 71.1 16.0 .. ­1.0 48.8 .. 0.7 6.2 0.0 .. Mauritius 118.0 84.3 38.0 45.1 ­0.5 8.0 13.0 1.7 0.6 0.0 0.5 Mexico 32.1 58.0 7.0 4.9 7.7 9.2 7.7 1.0 2.4 0.0 0.5 Moldova .. 116.7 .. 29.9 11.2 .. 13.8 0.7 6.8 .. 0.1 Mongolia 75.7 117.1 9.7 52.2 15.7 26.0 26.1 0.6 9.7 0.0 0.0 Morocco 43.3 60.0 13.4 23.1 2.1 5.5 8.4 0.6 3.0 0.0 0.0 Mozambique 40.8 62.6 12.5 14.9 2.8 0.4 6.6 0.4 1.6 0.0 0.0 Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 95.6 74.9 20.7 15.2 0.0 16.5 23.5 .. .. 0.1 ­0.4 Nepal 24.1 36.1 10.2 11.0 .. 3.5 4.2 0.2 0.0 0.0 .. Netherlands 83.9 122.0 19.2 24.6 3.5 28.6 94.0 3.5 6.5 4.5 2.9 New Zealand 43.0 43.9 13.3 15.3 2.3 17.7 6.0 4.0 1.8 3.6 ­0.8 Nicaragua 95.9 70.3 17.0 15.0 5.2 9.0 7.8 0.1 4.9 0.0 0.0 Niger 27.0 40.3 10.9 11.6 .. 2.8 3.7 1.6 0.4 0.0 0.0 Nigeria 67.5 60.2 10.3 11.6 1.7 5.9 25.4 2.1 2.0 0.0 .. Norway 52.8 53.9 21.6 18.9 1.0 11.9 40.1 0.9 1.1 1.3 0.8 Oman 71.1 91.2 6.7 14.3 2.5 3.5 11.1 1.2 0.8 0.0 0.0 Pakistan 32.6 37.3 8.8 10.1 ­0.5 4.2 4.3 0.6 2.0 0.0 0.1 Panama 35.4 33.4 33.5 31.5 ­3.5 106.6 56.8 2.6 6.6 0.0 0.0 Papua New Guinea 73.6 99.5 18.9 29.7 .. 5.7 20.4 4.8 0.7 0.0 .. Paraguay 43.9 73.5 16.2 13.7 ­1.4 5.4 7.6 1.5 0.9 0.0 0.1 Peru 22.3 37.4 7.5 6.6 3.3 3.2 10.4 0.2 3.2 0.0 0.0 Philippines 47.8 89.5 11.3 10.4 2.4 4.4 17.8 1.2 1.1 0.0 0.5 Poland 43.9 62.7 10.3 10.1 7.3 11.0 13.6 0.2 3.2 0.0 0.3 Portugal 55.4 54.1 12.1 13.7 2.8 10.8 50.9 3.5 1.7 0.2 3.4 Puerto Rico .. .. .. .. ­0.5 .. .. .. .. .. .. 2007 World Development Indicators 317 6.1 Integration with the global economy Merchandise Trade Growth in real Gross private Foreign direct trade in services trade less growth capital flows investment in real GDP % of GDP % of GDP % of GDP percentage points % of GDP Net inflows Net outflows 1990 2005 1990 2005 1990­2005 1990 2005 1990 2005 1990 2005 Romania 32.8 69.2 3.6 10.8 8.5 2.9 16.7 0.0 6.7 0.0 0.1 Russian Federation .. 48.3 .. 8.4 3.5 .. 19.6 0.3 2.0 .. 1.8 Rwanda 15.4 24.5 6.6 20.1 0.5 2.8 1.6 0.3 0.4 0.0 0.0 Saudi Arabia 58.6 77.7 21.8 10.9 .. 8.8 31.6 .. .. 0.0 0.0 Senegal 34.7 58.6 20.9 17.9 1.6 4.8 6.2 1.0 0.7 ­0.2 0.0 Serbia and Montenegro .. 63.7 .. .. .. .. .. .. 5.6 .. .. Sierra Leone 44.2 42.2 20.9 14.1 .. 11.0 5.7 5.0 4.9 0.0 0.0 Singapore 308.1 368.0 58.2 90.4 .. 54.3 95.5 15.1 17.2 5.5 9.9 Slovak Republic 110.8 145.0 .. 19.4 6.8 .. 15.5 0.6 4.1 .. 0.1 Slovenia 102.4 112.7 18.0 20.1 2.0 3.4 33.3 0.9 1.6 0.0 1.7 Somalia .. .. 11.2 .. .. 21.3 .. 0.6 .. 0.0 .. South Africa 37.4 47.7 6.4 9.7 2.6 2.4 10.6 ­0.1 2.6 0.0 0.7 Spain 27.5 41.4 8.4 14.1 5.5 11.1 46.0 2.7 2.0 0.7 4.8 Sri Lanka 57.3 64.7 13.4 15.5 2.5 13.1 5.9 0.5 1.2 0.0 0.0 Sudan 7.5 42.0 3.0 7.1 4.7 0.3 14.6 ­0.2 8.4 0.0 0.0 Swaziland 138.2 150.2 32.4 27.1 1.0 10.7 7.4 3.4 ­0.6 0.9 0.1 Sweden 46.2 67.5 12.7 21.9 4.1 33.6 39.0 0.8 3.0 6.0 4.4 Switzerland 56.6 70.1 12.8 20.0 2.9 28.1 83.9 2.4 4.2 2.3 7.2 Syrian Arab Republic 53.7 52.7 14.3 20.8 2.7 18.0 2.6 0.6 1.6 0.0 0.0 Tajikistan .. 96.8 .. 17.2 3.3 .. 9.2 0.5 2.4 .. 0.0 Tanzania 31.9 34.2 9.8 19.7 ­1.2 0.2 5.7 0.0 3.9 0.0 .. Thailand 65.7 129.3 14.9 27.3 2.9 13.5 12.6 2.9 2.6 0.2 0.1 Togo 52.1 66.5 24.1 18.9 ­1.1 9.6 17.7 1.1 0.1 0.0 ­0.4 Trinidad and Tobago 60.6 102.4 15.9 10.0 3.2 11.4 17.1 2.2 7.7 0.0 ­2.1 Tunisia 73.5 82.5 20.6 21.6 ­0.4 9.5 3.2 0.6 2.5 0.0 0.0 Turkey 23.4 52.4 7.4 10.4 6.9 4.3 14.8 0.5 2.7 0.0 0.3 Turkmenistan .. 105.7 .. .. 12.1 .. .. .. 0.8 .. .. Uganda 10.2 30.2 4.5 14.6 2.8 1.1 5.2 ­0.1 2.9 0.0 0.0 Ukraine .. 85.0 .. 20.4 3.2 .. 31.4 0.3 9.4 .. 0.0 United Arab Emirates 103.2 151.3 .. .. 1.8 .. .. .. .. .. .. United Kingdom 41.2 40.6 10.6 16.5 3.2 35.3 122.8 3.4 7.2 2.0 3.8 United States 15.8 21.2 4.6 5.6 3.9 5.6 14.4 0.8 0.9 0.6 2.2 Uruguay 32.7 43.4 9.2 13.3 2.3 12.7 26.5 0.4 4.2 0.0 0.1 Uzbekistan .. 60.3 .. .. ­0.9 .. .. 0.1 0.3 .. .. Venezuela, RB 52.8 56.9 7.9 4.8 0.6 51.6 17.9 1.0 2.1 0.8 ­0.3 Vietnam 79.7 129.9 .. 18.0 14.2 .. 8.3 2.8 3.7 .. .. West Bank and Gaza .. .. .. .. ­1.2 .. .. .. .. .. .. Yemen, Rep. 46.9 70.6 16.3 10.2 2.4 16.2 2.1 ­2.7 ­1.8 .. 0.0 Zambia 76.9 61.5 15.0 .. 1.6 64.7 .. 6.2 3.6 0.0 .. Zimbabwe 40.7 123.1 8.6 .. 5.5 1.7 .. ­0.1 3.0 0.0 .. World 32.3 w 47.3 w 7.8 w 11.0 w .. 10.3 w 32.4 w 1.0 w 2.2 w 1.2 w 2.1 w Low income 23.6 41.1 6.2 9.8 .. 2.4 6.7 0.4 1.5 0.0 0.2 Middle income 34.5 62.1 7.1 10.5 .. 6.6 13.3 0.9 3.1 0.1 0.5 Lower middle income 31.6 58.9 6.4 10.0 .. 4.4 11.5 0.8 3.1 0.1 0.3 Upper middle income 38.3 66.4 8.0 11.1 .. 7.9 15.7 1.2 3.1 0.3 0.7 Low & middle income 32.5 59.2 7.0 10.6 .. 5.9 13.1 0.8 2.9 0.1 0.5 East Asia & Pacific 47.1 74.6 7.3 10.3 .. 5.0 11.4 1.6 3.2 0.2 0.1 Europe & Central Asia 49.7 68.6 7.1 12.6 .. 5.3 20.3 1.0 3.5 0.0 0.8 Latin America & Carib. 23.2 44.2 5.7 6.8 .. 7.9 9.8 0.8 2.9 0.1 0.7 Middle East & N. Africa 43.5 57.6 9.2 .. .. 5.0 .. 0.3 2.4 0.0 .. South Asia 16.5 31.2 4.2 8.2 .. 1.4 5.4 0.1 1.0 0.0 0.2 Sub-Saharan Africa 41.9 57.8 10.8 13.1 .. 5.1 14.2 0.4 2.7 0.0 0.3 High income 32.3 43.9 7.9 11.1 .. 11.0 37.2 1.0 2.1 1.4 2.4 Europe EMU 44.0 61.4 11.0 15.9 .. 13.5 58.7 1.1 3.2 1.7 2.7 a. includes Luxembourg. 318 2007 World Development Indicators 6.1 GLOBAL LINKS Integration with the global economy About the data Definitions The growing integration of societies and economies abundant labor to gain a competitive advantage in · Merchandise trade is the sum of merchandise has helped reduce poverty in many countries. One labor-intensive manufactures and services. exports and imports divided by the value of GDP, indication of increasing global economic integration This year the table includes net inflows and out- all in current U.S. dollars. · Trade in services is the is the growing importance of trade in the world econ- flows of foreign direct investment based on balance sum of services exports and imports divided by the omy. Another is the increasing size and importance of payments data reported by the International Mon- value of GDP, all in current U.S. dollars. · Growth of private capital flows to developing countries that etary Fund (IMF), supplemented by staff estimates in real trade less growth in real GDP is the differ- have liberalized their financial markets. using data reported by the United Nations Confer- ence between annual growth in trade of goods and The table presents standardized measures of ence on Trade and Development and official national services and annual growth in GDP. Growth rates are the size of trade and capital flows relative to gross sources. calculated using constant price series taken from domestic product (GDP). The numerators on trade The internationally accepted definition of foreign national accounts and are expressed as a percent- and private capital flows are based on gross flows direct investment is provided in the fifth edition of age. · Gross private capital flows are the sum of the that capture the two-way flow of goods, services, the IMF's Balance of Payments Manual (1993). For absolute values of direct, portfolio, and other invest- and capital. In conventional balance of payments a more detailed explanation of foreign direct invest- ment inflows and outflows recorded in the balance accounting exports are recorded as a credit and ment, see About the data for table 6.8. of payments financial account, excluding changes imports as a debit. And in the financial account Foreign direct investment may be understated in in the assets and liabilities of monetary authorities inward investment is a credit and outward investment many developing countries. Some countries fail to and general government. The indicator is calculated a debit. Thus net flows, the sum of credits and deb- report reinvested earnings, and the definition of long- as a ratio to GDP in U.S. dollars. · Foreign direct its, represent a balance in which many transactions term loans differs among countries. Underreporting investment net inflows are the net inflows of invest- are canceled out. Gross flows are a better measure of FDI outflows is more pervasive, particularly when ment to acquire a lasting management interest in an of integration because they show the total value of investors are attempting to avoid controls on capital enterprise operating in an economy other than that financial transactions during a given period. and foreign exchange or high taxes on investment of the investor. It is the sum of equity capital, rein- Merchandise trade and trade in services (exports income. Some countries do not identify FDI outflows vestment of earnings, and other short- and long-term and imports) are shown relative to total GDP. Merchan- in their balance of payments statistics. However, the capital, as shown in the balance of payments. This dise trade is an important part of global trade. Trade in quality and coverage of the data are improving as series shows net inflows in the reporting economy services (such as transport, travel, finance, insurance, a result of continuous efforts by international and and is divided by the value of GDP. · Foreign direct royalties, construction, communications, and cultural national statistics agencies. investment net outfl ows are the net outflows of services) is an increasingly important element of Trade and capital flows are converted to U.S. dollars investment from the reporting economy to the rest global integration. The difference between the growth at the IMF's average official exchange rate for the year of the world. of real trade in goods and services and the growth of shown. An alternative conversion factor is applied if GDP helps to identify economies that have integrated the official exchange rate diverges by an exceptionally with the global economy by liberalizing trade, lowering large margin from the rate effectively applied to trans- barriers to foreign investment, and harnessing their actions in foreign currencies and traded products. Data sources Private capital flows are rising, but Data on merchandise trade are from the World they remain below the peak of 2000 6.1a Trade Organization. Data on GDP are from the Gross private capital GPCF, low- and GPCF, Foreign direct investment World Bank's national accounts files, converted flows (GPCF) as a middle-income high-income (FDI) net inflows as from national currencies to U.S. dollars using the share of GDP (%) countries countries a share of GDP (%) 60 6 official exchange rate, supplemented by an alter- FDI, high-income countries native conversion factor if the official exchange 50 5 rate is judged to diverge by an exceptionally 40 4 large margin from the rate effectively applied to transactions in foreign currencies and traded FDI, low- and middle-income countries 30 3 products. Data on trade in services are from the IMF's Balance of Payments database. Data on real 20 2 trade and GDP growth are from the World Bank's 10 1 national accounts files. Gross private capital flows and foreign direct investment are reported 0 0 in the World Bank Debtor Reporting System and 1990 1995 2000 2005 are calculated using mainly the IMF's Balance of Source: World Bank staff estimates. Payments database. 2007 World Development Indicators 319 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 1980­90 1990­2005 1980­90 1990­2005 1980­90 1990­2005 1980­90 1990­2005 1990 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. Algeria 4.7 2.7 ­8.0 1.7 ­3.1 5.8 ­2.7 1.2 74 126 Angola 9.0 5.8 ­1.9 8.2 6.4 9.6 0.7 9.3 94 121 Argentinaa .. 7.8 .. 6.8 .. 7.9 .. 5.6 85 107 Armenia .. .. .. .. .. .. .. .. .. .. Australiaa 6.3 5.6 6.0 7.8 12.1 7.4 12.5 8.9 116 131 Austriaa .. .. .. .. .. .. .. .. 98 103 Azerbaijan .. .. .. .. .. .. .. .. .. .. Bangladesh 7.1 10.5 1.8 4.1 7.8 12.4 3.6 8.2 117 88 Belarus .. .. .. .. .. .. .. .. .. .. Belgiuma .. 5.8 .. 5.4 .. 8.0 .. 8.2 106 99 Benin 11.8 3.0 ­9.9 5.9 18.8 3.6 ­4.9 6.3 107 93 Bolivia 3.1 4.9 ­1.3 5.2 ­1.9 5.6 ­0.3 5.8 102 108 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 14.8 3.8 11.5 2.3 18.8 3.4 11.1 1.3 98 92 Brazila 4.6 5.9 ­1.2 2.9 4.9 7.5 ­1.9 8.0 138 101 Bulgaria .. .. .. .. .. .. .. .. .. .. Burkina Faso ­0.3 12.1 3.8 5.0 7.9 10.5 4.3 5.2 119 98 Burundi 3.5 8.0 1.0 6.3 2.5 ­6.3 2.2 ­3.9 128 84 Cambodia .. .. .. .. .. .. .. .. .. .. Cameroon 7.0 3.3 4.8 7.2 1.4 2.7 0.1 4.6 81 112 Canadaa 6.4 6.6 7.4 7.0 7.7 9.1 9.3 8.6 97 111 Central African Republic 0.0 14.7 4.2 1.6 3.5 1.7 7.9 ­1.3 238 99 Chad 8.6 7.7 11.0 13.0 9.4 7.8 12.6 13.6 112 101 Chile 9.2 9.4 ­3.0 6.8 8.1 8.2 2.8 7.1 114 116 China 13.6 15.6 11.9 15.1 12.8 15.5 13.5 15.4 102 92 Hong Kong, Chinaa 15.4 7.9 13.7 7.7 21.7 7.1 19.8 7.0 100 98 Colombia 7.9 4.4 ­2.1 6.2 7.7 5.9 0.0 6.6 81 93 Congo, Dem. Rep. 9.7 4.4 12.2 11.0 2.7 ­3.3 3.1 1.5 86 94 Congo, Rep. 7.4 4.5 3.3 6.7 2.1 8.6 5.3 6.1 63 121 Costa Rica 3.8 10.1 5.2 11.8 4.6 11.3 4.4 11.0 75 102 Côte d'Ivoire 2.6 4.6 ­2.1 0.7 1.7 6.0 ­1.5 3.0 143 121 Croatia .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. Denmarka 4.1 4.9 3.1 5.0 7.3 5.9 5.0 6.1 102 104 Dominican Republic ­0.9 3.5 2.9 8.3 ­2.1 4.2 5.4 8.6 96 96 Ecuador 7.1 4.7 ­1.8 7.6 ­0.4 6.2 ­1.3 8.8 114 109 Egypt, Arab Rep. 13.4 3.4 8.1 ­0.5 7.3 4.2 12.6 2.0 101 107 El Salvador ­4.6 3.2 4.6 6.6 ­4.6 6.9 2.4 9.4 84 91 Eritrea .. ­2.8 .. 8.1 .. ­4.2 .. 7.2 99 93 Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia ­1.0 9.7 4.0 9.6 ­1.1 7.6 4.3 10.2 121 91 Finlanda .. .. .. .. .. .. .. .. 111 86 Francea 3.6 3.3 3.7 3.5 .. 2.9 .. 2.7 103 111 Gabon 2.5 5.8 ­3.5 1.3 ­3.9 2.5 1.1 1.1 157 125 Gambia, The 2.2 ­9.3 ­6.0 ­0.8 6.6 ­9.1 2.5 ­1.3 100 115 Georgia .. .. .. .. .. .. .. .. .. .. Germanya, b .. .. .. .. .. .. .. .. 100 101 Ghana ­17.2 4.7 ­19.3 6.5 ­2.7 6.6 0.6 6.6 100 123 Greecea 5.0 8.9 6.4 9.3 21.4 16.4 26.0 16.4 97 95 Guatemala ­1.1 6.8 0.1 9.8 ­2.2 6.7 0.6 10.7 115 93 Guinea .. 3.2 .. 2.5 4.0 0.2 9.7 ­0.5 122 107 Guinea-Bissau ­2.0 14.0 ­0.3 ­4.1 4.2 11.7 5.2 ­3.1 146 94 Haiti ­0.4 11.6 ­4.6 10.8 ­1.2 11.2 ­2.9 12.4 132 87 Data for Taiwan, China 26.1 2.8 30.3 3.3 14.9 5.8 12.4 6.4 97 92 320 2007 World Development Indicators 6.2 GLOBAL LINKS 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 1980­90 1990­2005 1980­90 1990­2005 1980­90 1990­2005 1980­90 1990­2005 1990 2005 Honduras 4.1 2.7 1.6 10.5 1.6 4.9 0.6 11.6 78 90 Hungarya 3.4 11.8 1.3 11.8 8.3 23.4 7.0 23.8 111 97 India 4.2 11.5 4.7 10.8 7.3 9.7 4.2 10.3 86 76 Indonesia 7.6 5.9 0.3 3.3 ­1.3 6.5 2.6 3.2 95 104 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. Irelanda 9.3 12.1 4.8 8.7 13.5 13.2 7.8 10.1 106 99 Israela 6.9 8.4 5.8 6.3 9.3 8.8 7.1 6.4 90 95 Italya 4.3 2.9 5.3 3.4 10.5 7.4 8.7 7.5 94 101 Jamaicaa ­1.0 1.5 .. .. 15.4 13.7 .. .. .. .. Japana 5.0 2.8 6.6 4.8 1.8 0.8 ­1.3 4.3 105 83 Jordana 10.2 7.4 1.5 4.7 14.9 9.3 4.2 7.7 93 89 Kazakhstan .. .. .. .. .. .. .. .. .. .. Kenyaa .. .. 0.2 5.9 .. .. 11.2 18.2 85 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 12.4 14.8 11.9 8.6 17.1 15.4 14.8 13.1 126 77 Kuwait .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. Latviaa .. 7.5 .. .. .. 10.7 .. .. .. .. Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho 7.2 16.0 3.9 2.8 3.7 14.7 3.5 1.6 100 91 Liberia .. .. .. .. .. .. .. .. .. .. Libyaa 2.8 ­3.3 ­1.8 ­1.3 ­5.8 5.8 ­1.1 3.9 89 186 Lithuania .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar ­2.5 4.6 ­6.2 4.3 ­1.2 7.9 ­4.3 5.8 81 82 Malawi 2.4 3.0 ­0.1 ­0.3 2.0 0.8 3.3 1.0 148 82 Malaysia 4.8 11.4 8.5 8.5 8.6 9.3 7.7 7.2 103 99 Mali 4.4 10.9 3.0 6.2 6.0 8.6 2.7 5.7 135 113 Mauritania 3.9 1.2 ­3.1 3.9 8.0 ­3.0 ­2.1 0.3 97 95 Mauritiusa .. 3.6 13.6 3.3 .. 3.5 12.7 4.2 97 85 Mexico 15.3 12.4 0.9 11.1 5.9 12.9 6.4 11.6 102 98 Moldova .. .. .. .. .. .. .. .. .. .. Mongolia .. .. .. .. .. .. .. .. .. .. Moroccoa 6.0 3.4 3.4 5.2 12.4 4.3 10.4 5.5 97 100 Mozambique ­9.5 21.5 ­2.7 2.3 ­9.6 16.3 0.1 3.1 175 94 Myanmar ­8.4 18.5 ­18.1 8.6 ­7.6 16.7 ­4.7 14.8 252 102 Namibia .. 1.5 .. 4.7 .. 0.3 .. 1.7 93 97 Nepal .. .. .. .. .. .. .. .. .. .. Netherlandsa 4.4 5.9 4.3 5.4 3.7 6.6 3.2 6.3 101 100 New Zealanda 3.5 4.2 4.4 6.1 10.7 4.9 9.8 6.4 105 112 Nicaragua ­4.8 8.6 ­3.5 7.2 ­5.8 7.7 ­3.1 9.3 155 91 Niger ­5.2 1.4 ­5.2 ­0.9 ­5.4 0.8 ­3.5 1.4 165 131 Nigeria ­4.4 1.9 ­20.8 5.2 ­8.4 5.7 ­15.0 5.8 89 122 Norwaya 4.2 4.9 3.5 6.5 4.2 8.6 7.1 5.8 67 122 Oman 11.2 2.4 .. .. 3.3 7.4 0.7 6.5 .. .. Pakistana 9.1 3.4 2.9 4.4 18.3 11.6 11.8 14.9 109 75 Panama ­0.5 5.1 ­6.7 4.2 ­0.5 7.0 ­3.6 5.3 69 94 Papua New Guinea ­0.6 ­6.8 .. .. 4.9 1.8 0.7 ­0.7 .. .. Paraguay 12.8 1.9 10.4 1.6 11.6 3.6 4.2 2.7 103 112 Peru 2.7 10.0 ­2.0 5.4 ­1.5 8.9 1.3 5.9 114 110 Philippinesa .. 2.3 .. ­1.9 .. 3.9 .. ­1.5 88 89 Polanda 4.8 11.5 1.5 14.5 56.1 23.1 40.3 25.2 96 107 Portugala 11.9 0.1 15.1 ­0.2 22.7 0.1 21.8 0.0 104 102 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2007 World Development Indicators 321 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 1980­90 1990­2005 1980­90 1990­2005 1980­90 1990­2005 1980­90 1990­2005 1990 2005 Romania .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. Rwanda 2.6 ­2.6 1.8 ­0.2 ­0.9 ­0.5 2.7 ­0.9 40 89 Saudi Arabia ­8.3 1.1 .. .. ­12.7 5.6 ­6.1 2.3 .. .. Senegal 1.2 10.5 0.4 6.1 3.5 4.7 1.4 5.8 172 96 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. 79 Singaporea 12.1 10.3 8.6 7.2 8.6 8.7 6.7 6.7 116 87 Slovak Republic .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. South Africaa 2.2 4.5 ­0.2 5.7 15.3 13.8 12.6 15.5 112 109 Spaina 2.5 9.2 8.8 8.8 9.0 11.4 10.8 10.9 100 102 Sri Lankaa 4.2 5.5 1.6 6.9 13.5 15.1 10.7 15.2 75 101 Sudan .. .. .. .. .. .. .. .. .. 121 Swaziland 7.6 4.0 2.4 2.0 4.7 4.6 ­0.5 3.1 100 94 Swedena 4.4 7.6 5.0 5.4 10.7 9.3 9.1 8.3 108 90 Switzerland .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania .. 8.6 .. 2.0 ­5.1 8.7 ­0.5 2.7 107 100 Thailanda 13.8 9.2 11.2 2.6 16.5 15.7 15.1 10.6 118 93 Togo ­1.2 12.6 0.7 0.3 1.1 7.1 2.0 6.3 133 30 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. Tunisia 3.0 6.6 1.6 5.6 3.5 6.5 2.7 5.5 109 99 Turkeya .. 11.4 .. 10.6 .. 10.6 .. 10.8 109 101 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda ­13.5 14.4 ­6.8 13.2 ­4.0 9.8 4.5 12.7 146 88 Ukraine .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdoma 4.5 5.0 7.1 6.4 7.7 5.3 11.3 6.6 101 105 United Statesa 3.6 4.7 7.2 7.9 5.7 5.2 8.2 8.4 101 97 Uruguay 4.4 3.4 ­0.5 3.4 4.5 2.9 ­1.2 3.5 116 108 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 3.4 2.4 ­4.1 1.6 ­4.4 5.7 ­3.2 2.5 90 108 Vietnam .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. ­7.2 6.3 ­3.2 17.0 ­5.0 2.9 .. .. Zambia ­0.5 6.5 2.0 6.6 0.9 ­0.1 0.0 4.5 207 119 Zimbabwe 3.6 7.7 3.4 7.2 2.5 2.6 ­0.5 1.9 98 105 a. Data are from the International Monetary Fund's International Financial Statistics database. b. Data prior to 1990 refer to the Federal Republic of Germany before unification. 322 2007 World Development Indicators 6.2 GLOBAL LINKS Growth of merchandise trade About the data Definitions Data on international trade in goods are available unions may need to collect data through direct inquiry · Export and import volumes are average annual from each country's balance of payments and of companies. Economic or political concerns may lead growth rates calculated for low- and middle-income customs records. While the balance of payments some national authorities to suppress or misrepresent economies from UNCTAD's quantum index series focuses on the financial transactions that accom- data on certain trade flows, such as oil, military equip- and for high-income economies from export and pany trade, customs data record the direction of ment, or the exports of a dominant producer. In other import data deflated by the IMF's trade price defla- trade and the physical quantities and value of goods cases reported trade data may be distorted by deliber- tors. · Export and import values are average annual entering or leaving the customs area. Customs data ate under- or over-invoicing to effect capital transfers or growth rates calculated from UNCTAD's value indexes may differ from data recorded in the balance of pay- avoid taxes. And in some regions smuggling and black or from current values of merchandise exports and ments because of differences in valuation and time market trading result in unreported trade flows. imports. · Net barter terms of trade index is cal- of recording. The 1993 System of National Accounts By international agreement customs data are culated as the ratio of the export price index to the and the fifth edition of the International Monetary reported to the United Nations Statistics Division, corresponding import price index measured relative Fund's (IMF) Balance of Payments Manual (1993) which maintains the Commodity Trade (Comtrade) to the base year 2000. attempted to reconcile defi nitions and reporting database. The United Nations Conference on Trade standards for international trade statistics, but dif- and Development (UNCTAD) compiles international ferences in sources, timing, and national practices trade statistics, including price and volume indexes, limit comparability. Real growth rates derived from based on Comtrade data. The IMF also compiles data trade volume indexes and terms of trade based on on trade prices and volumes. The growth rates and unit price indexes may therefore differ from those terms of trade for most low- and middle-income econ- derived from national accounts aggregates. omies shown in the table were calculated from index Trade in goods, or merchandise trade, includes all numbers compiled by UNCTAD. The growth rates and goods that add to or subtract from an economy's terms of trade for high-income and selected develop- material resources. Trade data are collected on the ing countries were calculated from index numbers basis of a country's customs area, which in most compiled in the IMF's International Financial Statis- cases is the same as its geographic area. Goods tics. In some cases price and volume indexes from provided as part of foreign aid are included, but different sources may vary significantly as a result goods destined for extraterritorial agencies (such of differences in estimation procedures. All indexes as embassies) are not. are rescaled to a 2000 base year. Collecting and tabulating trade statistics are difficult. The terms of trade measures the relative prices of Some developing countries lack the capacity to report a country's exports and imports. There are several timely data, especially countries that are landlocked ways to calculate it. The most common is the net and those whose territorial boundaries are porous. barter (or commodity) terms of trade index, or the Their trade has to be estimated from the data reported ratio of the export price index to the import price by their partners. (For further discussion of the use index. When a country's net barter terms of trade of partner country reports, see About the data for index increases, its exports become more valuable table 6.3.) Countries that belong to common customs or its imports cheaper. Terms of trade are deteriorating for non-oil-exporting developing countries 6.2a Index (2000 = 100) 200 180 Oil-exporting developing countries 160 140 Other developing countries Data sources 120 100 The main source of trade indexes data for develop- High-income countries ing countries is UNCTAD's annual Handbook of 80 60 International Trade and Development Statistics. The IMF's International Financial Statistics provides 40 1980 1985 1990 1995 2000 2005 these data for high-income and selected develop- Source: United Nations Conference on Trade and Development and International Monetary Fund. ing economies. 2007 World Development Indicators 323 6.3 Direction and growth of merchandise trade Direction of trade High-income importers % of world trade, 2005 European United Other high- All high- Union Japan States income income Source of exports High-income economies 28.9 2.8 9.7 12.7 54.0 European Union 22.4 0.5 3.1 3.7 29.7 Japan 0.8 .. 1.4 1.9 4.0 United States 1.8 0.6 .. 3.6 6.0 Other high-income economies 3.8 1.7 5.3 3.5 14.3 Low- and middle-income economies 8.0 1.8 6.5 5.1 21.5 East Asia & Pacific 1.9 1.4 2.3 3.6 9.2 China 1.3 0.8 1.6 2.4 6.2 Europe & Central Asia 3.8 0.1 0.2 0.4 4.6 Russian Federation 1.1 0.0 0.1 0.2 1.4 Latin America & Caribbean 0.7 0.1 3.0 0.4 4.1 Brazil 0.2 0.0 0.2 0.1 0.6 Middle East & N. Africa 0.9 0.1 0.2 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 0.5 0.1 0.5 0.2 1.2 South Africa 0.2 0.1 0.0 0.1 0.3 World 36.9 4.6 16.2 17.8 75.5 Low- and middle-income importers % of world trade, 2005 Europe Latin Middle All low- East Asia & Central America East & South Sub-Saharan & middle- & Pacifi c Asia & Caribbean N. Africa Asia Africa income World Source of exports High-income economies 7.3 4.3 1.6 1.3 1.0 0.9 16.4 70.4 European Union 1.0 3.6 0.5 0.8 0.3 0.5 6.7 36.4 Japan 1.4 0.1 0.2 0.1 0.1 0.1 1.8 5.9 United States 0.7 0.2 0.7 0.1 0.1 0.1 1.8 7.8 Other high-income economies 4.3 0.4 0.3 0.3 0.5 0.2 6.0 20.3 Low- and middle-income economies 2.2 2.8 1.2 0.7 0.6 0.6 8.1 29.6 East Asia & Pacific 1.2 0.4 0.2 0.2 0.3 0.2 2.5 11.7 China 0.4 0.4 0.2 0.1 0.2 0.1 1.4 7.5 Europe & Central Asia 0.2 2.1 0.0 0.2 0.1 0.0 2.8 7.3 Russian Federation 0.2 0.8 0.0 0.1 0.0 0.0 1.0 2.4 Latin America & Caribbean 0.2 0.1 0.8 0.1 0.0 0.1 1.3 5.4 Brazil 0.1 0.1 0.2 0.0 0.0 0.0 0.5 1.1 Middle East & N. Africa 0.2 0.1 0.0 0.1 0.0 0.0 0.6 2.1 Algeria 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.4 South Asia 0.1 0.0 0.0 0.0 0.1 0.1 0.4 1.3 India 0.1 0.0 0.0 0.0 0.1 0.1 0.3 1.0 Sub-Saharan Africa 0.2 0.0 0.1 0.0 0.0 0.2 0.5 1.7 South Africa 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.5 World 9.6 7.1 2.8 1.9 1.5 1.5 24.5 100.0 324 2007 World Development Indicators 6.3 GLOBAL LINKS Direction and growth of merchandise trade Nominal growth of trade High-income importers annual % growth, 1995­2005 European United Other high- All high- Union Japan States income income Source of exports High-income economies 6.5 3.2 5.9 5.0 5.8 European Union 6.9 2.7 9.0 5.5 6.8 Japan 1.5 .. 1.1 3.1 2.0 United States 3.9 ­1.5 .. 4.3 3.5 Other high-income economies 6.8 5.6 6.0 6.7 6.3 Low- and middle-income economies 12.6 7.7 12.7 11.9 12.0 East Asia & Pacific 14.1 8.2 13.9 11.7 12.0 China 21.3 11.4 20.8 15.5 17.0 Europe & Central Asia 15.1 3.8 10.4 12.2 14.3 Russian Federation 14.9 1.7 3.9 10.1 12.5 Latin America & Caribbean 5.8 2.4 11.3 10.1 9.7 Brazil 6.2 1.1 10.0 12.0 8.0 Middle East & N. Africa 10.6 9.6 22.2 12.6 12.0 Algeria 14.4 ­5.1 20.2 22.0 16.2 South Asia 8.7 ­0.8 10.6 13.0 9.9 India 9.9 1.1 11.9 15.1 11.6 Sub-Saharan Africa 10.5 20.1 16.7 17.7 13.9 South Africa 5.1 5.1 2.5 4.6 4.5 World 7.5 4.7 8.1 6.6 7.2 Low- and middle-income importers annual % growth, 1995­2005 Europe Latin Middle All low- East Asia & Central America East & South Sub-Saharan & middle- & Pacifi c Asia & Caribbean N. Africa Asia Africa income World Source of exports High-income economies 8.7 11.9 2.4 8.2 9.1 6.2 8.4 6.4 European Union 6.7 12.4 1.5 7.8 9.0 6.3 8.9 7.2 Japan 6.0 15.2 0.5 5.7 2.5 0.6 5.4 3.0 United States 7.0 5.8 3.4 3.4 7.5 6.7 5.2 3.8 Other high-income economies 10.6 10.6 3.1 12.1 10.9 8.3 10.1 7.3 Low- and middle-income economies 16.7 12.2 8.8 13.8 14.4 15.8 13.1 12.3 East Asia & Pacific 16.0 21.4 15.2 16.1 16.5 18.7 16.9 12.9 China 18.3 25.9 19.5 23.1 20.3 22.4 21.1 17.6 Europe & Central Asia 12.3 11.1 6.0 13.6 12.0 17.7 11.4 13.1 Russian Federation 11.7 11.1 8.0 17.0 9.9 34.4 11.4 12.0 Latin America & Caribbean 16.8 15.5 6.9 9.2 15.4 13.9 9.2 9.6 Brazil 13.4 15.7 9.1 10.0 10.6 16.4 11.0 9.2 Middle East & N. Africa 21.5 10.3 16.6 14.3 9.2 10.5 14.6 12.7 Algeria 57.3 11.4 26.7 16.2 8.1 9.7 19.3 16.6 South Asia 17.7 6.4 18.6 15.1 14.9 15.2 14.7 11.1 India 19.6 6.7 22.7 20.4 13.0 15.5 15.9 12.8 Sub-Saharan Africa 29.3 15.6 22.6 10.8 8.6 15.6 18.9 15.2 South Africa 3.6 4.8 4.7 8.8 10.7 3.5 4.4 4.5 World 10.1 12.0 4.7 9.8 10.7 9.0 9.7 7.8 2007 World Development Indicators 325 6.3 Direction and growth of merchandise trade About the data Definitions The table provides estimates of the flow of trade in · Merchandise trade includes all trade in goods; Three regions account from more goods between groups of economies. The data are trade in services is excluded. · High-income econo- than 75 percent of exports to from the International Monetary Fund's (IMF) Direc- other developing regions, 2005 6.3a mies are those classified as such by the World Bank tion of Trade database. All developed and 23 devel- (see inside front cover). · European Union is defined South Asia 7% oping countries report trade on a timely basis, cover- as all high-income EU members: Austria, Belgium, Sub-Saharan Africa 7% ing about 80 percent of trade for recent years. Trade Cyprus, Denmark, Finland, France, Germany, Greece, by less timely reporters and by countries that do not Middle East & Ireland, Italy, Luxembourg, Malta, the Netherlands, North Africa 8% Europe & report is estimated using reports of trading partner Portugal, Slovenia, Spain, Sweden, and the United Central Asia countries. Because the largest exporting and import- 35% Kingdom. · Other high-income economies include ing countries are reliable reporters, a large portion Latin America all high-income economies (OECD and non-OECD) & Caribbean of the missing trade flows can be estimated from 15% except the European Union, Japan, and the United partner reports. Partner country data may introduce East Asia States. · Low- and middle-income regional group- & Pacific discrepancies due to smuggling, confidentiality, dif- 28% ings are based on World Bank classifications and ferent exchange rates, overreporting of transit trade, may differ from those used by other organizations. inclusion or exclusion of freight rates, and different Source: IMF Direction of Trade database. points of valuation and times of recording. In addition, estimates of trade within the Euro- pean Union (EU) have been significantly affected by changes in reporting methods following the creation of a customs union. The current system for collect- ing data on trade between EU members--Intrastat, introduced in 1993--has less exhaustive coverage than the previous 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 using the IMF's published period average exchange rates (series rf or rh, monthly averages of the market or official rates) for the reporting country or, if those are not available, monthly average rates in New York. Because imports are reported at cost, insurance, and freight (c.i.f.) valuations, and exports at free on board (f.o.b.) valuations, the IMF adjusts country reports of import values by dividing them by 1.10 to estimate equivalent export values. This approximation is more or less accurate, depending on the set of partners and the items traded. Other factors affecting the accuracy of trade data include lags in reporting, recording differences across countries, and whether the country reports trade according to the general or special system of trade. (For further discussion of the measurement of exports and imports, see About Data sources the data for tables 4.4 and 4.5.) The regional trade flows shown in the table were Data on the direction and growth of merchandise calculated from current price values. The growth rates trade were calculated using the IMF's Direction of are presented in nominal terms; that is, they include Trade database. the effects of changes in both volumes and prices. 326 2007 World Development Indicators 6.4 GLOBAL LINKS High-income economy trade with low- and middle-income economies Exports to low-income economies High-income countries European Union Japan United States 1995 2005 1995 2005 1995 2005 1995 2005 Total ($ billions) 67.6 134.9 33.1 58.3 9.3 12.5 7.9 16.8 % of total exports Food 8.6 6.0 8.5 6.4 0.5 0.4 20.3 12.1 Cereals 3.1 1.9 2.6 1.5 0.2 0.1 14.4 7.4 Agricultural raw materials 2.7 1.8 1.3 1.4 1.7 1.4 6.7 4.3 Ores and nonferrous metals 2.7 3.3 1.5 3.5 0.7 1.1 3.1 2.5 Fuels 4.3 7.6 2.2 4.3 0.9 0.9 1.5 2.2 Crude petroleum 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 3.5 5.1 2.2 4.2 0.8 0.8 1.4 1.3 Manufactured goods 79.5 77.4 84.4 81.4 95.1 92.9 64.9 71.7 Chemical products 12.4 11.0 12.2 11.7 6.6 7.2 14.4 10.2 Iron and steel 3.6 3.4 4.3 3.5 6.6 9.3 1.6 1.7 Machinery and transport equipment 45.0 41.0 44.2 40.2 69.2 62.2 38.3 44.3 Furniture 0.2 0.2 0.3 0.3 0.1 0.2 0.1 0.2 Textiles 4.4 3.5 1.7 1.3 3.0 3.6 1.5 2.0 Footwear 0.2 0.1 0.2 0.1 0.0 0.0 0.1 0.1 Other 13.6 18.1 21.4 24.2 9.5 10.4 9.0 13.1 Miscellaneous goods 2.3 3.9 2.0 2.9 1.0 3.3 3.5 7.4 Imports from low-income economies Total ($ billions) 68.0 178.4 34.2 66.3 8.4 13.0 16.0 66.7 % of total imports Food 20.3 10.8 24.8 15.2 25.4 16.5 8.3 5.3 Cereals 0.6 0.6 0.3 0.4 0.2 0.3 0.2 0.1 Agricultural raw materials 6.1 2.0 7.6 3.5 7.1 1.8 1.7 0.8 Ores and nonferrous metals 5.9 3.7 4.9 4.9 16.7 8.7 2.0 0.9 Fuels 19.4 29.8 11.4 16.2 15.2 35.6 33.0 43.6 Crude petroleum 17.9 24.6 10.8 12.0 12.0 28.6 31.6 38.7 Petroleum products 1.3 4.0 0.5 2.0 2.3 5.1 1.5 4.2 Manufactured goods 47.9 53.2 51.2 59.6 35.1 36.9 54.3 48.7 Chemical products 2.7 3.6 2.7 4.1 1.0 2.9 2.3 2.5 Iron and steel 1.3 1.7 0.7 1.9 2.4 1.2 1.3 1.4 Machinery and transport equipment 2.8 5.4 2.8 6.1 0.6 10.3 2.2 3.6 Furniture 0.2 1.5 0.2 1.6 0.5 1.7 0.2 1.8 Textiles 26.0 23.1 26.5 27.4 16.9 9.6 29.8 26.1 Footwear 1.3 3.1 2.1 5.8 0.3 2.3 0.6 1.4 Other 13.6 14.8 16.1 12.6 13.4 8.9 18.0 11.9 Miscellaneous goods 0.4 0.6 0.2 0.7 0.5 0.5 0.6 0.7 Simple applied tariff rates on imports from low-income economies (%) Food 9.7 7.1 11.6 6.8 12.9 12.8 3.8 3.4 Cereals 16.5 11.7 65.6 20.5 14.4 42.7 5.2 2.4 Agricultural raw materials 2.0 2.4 0.3 0.2 2.8 1.2 0.6 0.4 Ores and nonferrous metals 1.8 1.6 0.5 0.5 2.7 0.6 0.4 0.4 Fuels 4.7 1.7 0.1 0.1 1.3 1.0 2.5 0.7 Crude petroleum 7.6 1.1 0.0 0.0 0.0 0.5 0.0 0.1 Petroleum products 5.3 1.9 0.3 0.2 1.7 1.7 3.1 1.1 Manufactured goods 5.4 3.4 1.6 1.1 4.3 2.2 6.7 4.8 Chemical products 3.0 2.4 1.2 1.8 1.1 0.7 1.8 0.9 Iron and steel 3.3 2.1 0.5 0.3 0.3 0.2 3.6 0.2 Machinery and transport equipment 2.4 1.7 0.4 0.4 0.0 0.0 0.9 0.4 Furniture 4.1 2.6 0.2 0.0 0.2 0.0 5.6 1.4 Textiles 9.7 6.0 4.2 2.5 6.7 4.8 13.2 10.3 Footwear 10.3 6.7 4.5 2.5 11.0 9.1 19.0 10.7 Other 6.6 4.0 2.2 1.3 5.3 3.0 8.3 6.1 Miscellaneous goods 10.4 1.1 0.6 0.4 0.0 0.0 1.4 0.2 Average 4.9 3.5 2.8 1.7 2.8 2.4 6.0 4.4 2007 World Development Indicators 327 6.4 High-income economy trade with low- and middle-income economies Exports to middle-income economies High-income countries European Union Japan United States 1995 2005 1995 2005 1995 2005 1995 2005 Total ($ billions) 681.0 1,501.9 263.4 609.5 103.4 177.6 147.0 285.3 % of total exports Food 7.5 4.6 9.3 5.0 0.3 0.4 10.9 8.1 Cereals 2.0 0.9 1.5 0.7 0.0 0.0 4.8 2.2 Agricultural raw materials 2.3 1.8 1.4 1.4 1.0 0.9 3.9 3.4 Ores and nonferrous metals 2.1 2.9 1.6 2.0 1.3 2.6 1.9 3.1 Fuels 2.3 3.4 1.7 2.1 0.5 1.0 2.3 4.0 Crude petroleum 0.2 0.2 0.3 0.0 0.0 0.0 0.0 0.0 Petroleum products 1.7 2.7 1.2 1.9 0.5 0.9 1.5 3.4 Manufactured goods 83.3 84.3 82.7 86.0 95.7 91.6 77.4 77.6 Chemical products 11.0 12.1 11.8 13.3 6.6 9.0 11.4 11.7 Iron and steel 3.0 3.2 2.8 3.6 6.5 6.8 1.2 1.1 Machinery and transport equipment 47.4 50.5 45.7 48.3 68.1 61.3 46.1 47.9 Furniture 0.5 0.5 0.9 0.8 0.1 0.2 0.6 0.4 Textiles 6.4 4.2 5.7 4.5 2.7 2.1 4.5 3.5 Footwear 0.3 0.2 0.4 0.3 0.0 0.0 0.1 0.0 Other 14.7 13.7 15.4 15.2 11.6 12.2 13.5 13.0 Miscellaneous goods 2.5 3.0 3.2 3.4 1.2 3.6 3.7 3.8 Imports from middle-income economies Total ($ billions) 746.9 2,194.9 257.5 801.5 99.4 210.6 221.1 722.1 % of total imports Food 11.5 6.4 14.5 7.8 16.7 9.2 8.2 4.7 Cereals 0.3 0.3 0.2 0.3 0.2 0.3 0.2 0.1 Agricultural raw materials 3.7 1.5 4.9 2.0 5.9 2.1 1.9 1.0 Ores and nonferrous metals 6.0 4.1 7.7 4.5 11.0 8.4 3.5 2.3 Fuels 13.2 17.9 16.8 21.8 17.4 16.6 11.9 18.7 Crude petroleum 8.7 11.9 10.8 14.7 9.5 7.8 9.1 13.9 Petroleum products 2.2 3.4 2.9 4.1 1.2 1.6 2.5 3.6 Manufactured goods 63.8 68.6 53.4 62.1 47.9 62.5 72.3 71.4 Chemical products 3.8 3.3 5.2 3.3 2.8 3.6 2.7 2.8 Iron and steel 3.0 2.5 3.4 2.9 2.5 1.6 2.3 1.9 Machinery and transport equipment 22.3 32.9 14.1 28.0 11.8 27.6 31.5 35.1 Furniture 1.4 2.3 1.6 2.1 1.6 1.6 1.6 3.3 Textiles 13.9 9.5 13.9 9.5 14.6 10.9 12.4 8.8 Footwear 2.9 1.7 1.6 1.4 1.5 1.2 4.1 2.2 Other 16.5 16.4 13.6 14.8 13.0 15.9 17.7 17.3 Miscellaneous goods 1.8 1.5 2.7 1.8 1.2 1.2 2.1 2.0 Simple applied tariff rates on imports from low-income economies (%) Food 15.4 9.5 24.7 11.7 14.4 13.2 3.1 3.4 Cereals 21.6 15.3 63.3 28.8 22.8 34.2 2.7 2.0 Agricultural raw materials 2.0 1.9 1.0 0.3 1.5 1.6 0.6 0.4 Ores and nonferrous metals 1.7 1.1 1.8 0.7 0.5 0.1 0.5 0.5 Fuels 4.1 1.5 0.7 0.1 0.6 0.8 1.1 0.6 Crude petroleum 13.8 1.2 0.0 0.0 0.0 0.5 0.0 0.2 Petroleum products 4.5 1.9 1.1 0.2 1.1 1.6 1.7 1.0 Manufactured goods 5.6 3.2 4.1 1.1 1.8 2.2 3.9 3.0 Chemical products 3.6 2.2 3.6 1.8 1.4 0.8 1.4 1.0 Iron and steel 3.1 1.3 1.7 0.2 0.6 0.2 3.5 0.2 Machinery and transport equipment 3.2 1.8 2.0 0.4 0.0 0.0 0.6 0.3 Furniture 5.0 3.1 1.8 0.0 0.0 0.0 0.5 0.3 Textiles 11.0 6.6 8.9 2.9 4.7 6.7 11.0 8.8 Footwear 12.1 7.7 9.6 3.0 15.0 17.7 14.7 9.2 Other 6.8 3.9 5.0 1.4 2.5 3.2 5.3 4.2 Miscellaneous goods 7.6 1.1 2.0 0.4 0.0 0.0 1.0 0.4 Average 5.5 3.5 5.8 2.1 2.5 2.6 3.6 2.9 328 2007 World Development Indicators 6.4 GLOBAL LINKS High-income economy trade with low- and middle-income economies About the data Definitions Developing countries are becoming increasingly countries has grown. Moreover, trade between devel- The product groups in the table are defined in accor- important in the global trading system. Since the oping countries has grown substantially over the past dance with the SITC revision 1: food (0, 1, 22, and early 1990s trade between high-income countries decade. This growth has resulted from many factors, 4) and cereals (04); agricultural raw materials and low- and middle-income economies has grown including developing countries' increasing share of (2 excluding 22, 27, and 28); ores and nonferrous faster than trade among high-income economies. The world output and the liberalization of their trade. metals (27, 28, and 68); fuels (3), crude petroleum increased trade benefits consumers and producers. Yet trade barriers remain high. The table includes (331), and petroleum products (332); manufactured But as the World Trade Organization's (WTO) Minis- information about tariff rates by selected product goods (5­8 excluding 68), chemical products (5), iron terial Conferences in Doha, Qatar, in October 2001, groups. Applied tariff rates are the tariffs in effect Cancun, Mexico, in September 2003, and Hong for partners in preferential trade agreements such and steel (67), machinery and transport equipment Kong, China, in December 2005 showed, achieving a as the North American Free Trade Agreement. When (7), furniture (82), textiles (65 and 84), footwear more pro-development outcome from trade remains these are unavailable, most favored nation rates are (85), and other manufactured goods (6 and 8 exclud- a challenge. Meeting it will require strengthening used. The difference between most favored nation ing 65, 67, 68, 82, 84, and 85); and miscellaneous international consultation. Negotiations after the and applied rates can be substantial. Simple aver- goods (9). · Exports are all merchandise exports Doha meetings were launched on services, agricul- ages of applied rates are shown because they are by high-income countries to low-income and middle- ture, manufactures, WTO rules, the environment, generally a better indicator of tariff protection. income economies as recorded in the United Nations dispute settlement, intellectual property rights pro- The data are from the United Nations Conference Statistics Division's Comtrade database. · Imports tection, and disciplines on regional integration. At on Trade and Development (UNCTAD). Partner country are all merchandise imports by high-income countries the most recent negotiations in Hong Kong, China, reports by high-income countries were used for both from low-income and middle-income economies as trade ministers agreed to eliminate subsidies of agri- exports and imports. Exports are recorded free on cultural exports by 2013; to abolish cotton export board (f.o.b.); imports include insurance and freight recorded in the United Nations Statistics Division's subsidies in 2006 and grant unlimited export access charges (c.i.f.). Because of differences in sources Comtrade database. · High-, middle-, and low- to selected cotton-growing countries in Sub-Saha- of data, timing, and treatment of missing data, the income economies are those classified as such by ran Africa; to cut more domestic farm supports in numbers in the table may not be fully comparable the World Bank (see inside front cover). · European the European Union, Japan, and the United States; with those used to calculate the direction of trade Union is defined as all high-income EU members: and to offer more aid to developing countries to help statistics in table 6.3 or the aggregate flows in tables Austria, Belgium, Cyprus, Denmark, Finland, France, them compete in global trade. 4.4, 4.5, and 6.2. Data are classified using the Har- Germany, Greece, Ireland, Italy, Luxembourg, Malta, Trade flows between high-income countries and monized System of trade at the six- or eight-digit the Netherlands, Portugal, Slovenia, Spain, Sweden, low- and middle-income economies reflect the chang- level. Tariff line data were matched to Standard Inter- and the United Kingdom. ing mix of exports to and imports from developing national Trade Classification (SITC) revision 1 codes economies. While food and primary commodities to define commodity groups. For further discussion have continued to fall as a share of high-income of merchandise trade statistics, see About the data countries' imports, the share of manufactures in for tables 4.4, 4.5, 6.2, and 6.3, and for information goods imports from both low- and middle-income about tariff barriers, see table 6.7. Imports from low- and middle-income economies to high-income economies vary considerably 6.4a Share of total imports, 2005 (%) Imports from low-income Imports from middle-income 80 70 60 50 40 30 20 10 Data sources 0 Food Fuels Manufacturing Machinery and Textiles Trade values are from United Nations Statistics transport equipment Division's Comtrade database. Tariff data are The major manufactured imports of high-income economies from developing countries are manufac- from UNCTAD's Trade Analysis and Information tured textiles from low-income economies and machinery and transport equipment from middle-income System database and are calculated by World economies. Bank staff using the World Integrated Trade Solu- Source: United Nations Statistics Division's Comtrade database. tion system. 2007 World Development Indicators 329 6.5 Primary commodity prices 1970 1980 1990 1995 2000 2001 2002 2003 2004 2005 2006 World Bank commodity price index (1990 = 100) Nonenergy commodities 156 159 100 104 89 84 89 91 100 114 138 Agriculture 163 175 100 112 90 84 93 95 98 106 115 Beverages 203 230 100 129 91 76 91 87 88 109 111 Food 166 177 100 100 87 91 97 96 103 103 109 Raw materials 130 133 100 116 93 81 89 98 99 107 124 Fertilizers 108 164 100 88 109 105 108 106 118 126 130 Metals and minerals 144 120 100 87 85 80 78 82 105 133 195 Petroleum 19 204 100 64 127 113 117 126 154 218 254 Steel productsa 111 100 100 91 79 71 73 79 114 129 122 MUV G-5 index 28 79 100 117 97 94 93 100 107 107 110 Commodity prices (1990 prices) Agricultural raw materials Cotton (cents/kg) 225 260 182 182 134 112 109 140 128 114 115 Logs, Cameroon ($/cu. m)a 153 319 344 290 283 282 .. .. .. .. .. Logs, Malaysian ($/cu. m) 154 248 177 218 195 169 175 187 184 190 217 Rubber (cents/kg) 145 181 86 135 69 61 82 108 122 140 191 Sawnwood, Malaysian ($/cu. m) 625 503 533 632 612 510 565 550 543 616 678 Tobacco ($/mt) 3,836 2,887 3,392 2,258 3,063 3,185 2,947 2,643 2,560 2,606 2,672 Beverages (cents/kg) Cocoa 240 330 127 122 93 113 191 175 145 144 144 Coffee, robustas 330 411 118 237 94 64 71 81 74 104 135 Coffee, Arabica 409 440 197 285 198 146 146 141 166 237 228 Tea, avg., 3 auctions 298 211 206 127 193 169 162 151 157 154 170 Energy Coal, Australian ($/mt) .. 51 40 34 27 34 27 26 49 44 44 Coal, U.S. ($/mt) .. 55 42 33 34 48 43 .. .. .. .. Natural gas, Europe ($/mmbtu) .. 4 3 2 4 4 3 4 4 6 8 Natural gas, U.S. ($/mmbtu) 1 2 2 1 4 4 4 5 6 8 6 Petroleum ($/bbl) 4 47 23 15 29 26 27 29 35 50 58 About the data Primary commodities--raw or partially processed the prices paid by importers are used. Annual price indexes are compiled for petroleum and steel prod- materials that will be transformed into fi nished series are generally simple averages based on higher ucts, which are not included in the nonenergy com- goods--are often the most signifi cant exports of frequency data. The constant price series in the modity price index. developing countries, and revenues obtained from table is deflated using the manufactures unit value The MUV index is a composite index of prices them have an important effect on living standards. (MUV) index for the Group of Five (G-5) countries for manufactured exports from the fi ve major Price data for primary commodities are collected from (see below). (G-5) industrial countries (France, Germany, Japan, a variety of sources, including trade journals, inter- The commodity price indexes are calculated as the United Kingdom, and the United States) to low- national study groups, government market surveys, Laspeyres index numbers, in which the fixed weights and middle-income economies, valued in U.S. dol- newspaper and wire service reports, and commodity are the 1987­89 export values for low- and middle- lars. The index covers products in groups 5­8 of the exchange spot and near-term forward prices. income economies, rebased to 1990. Each index rep- Standard International Trade Classification revision The table is based on frequently updated price resents a fixed basket of primary commodity exports. 1. To construct the MUV G-5 index, unit value indexes reports. When possible, the prices received by The nonenergy commodity price index contains 37 for each country are combined using weights deter- exporters are used; if export prices are unavailable, price series for 31 nonenergy commodities. Separate mined by each country's export share. 330 2007 World Development Indicators 6.5 GLOBAL LINKS Primary commodity prices 1970 1980 1990 1995 2000 2001 2002 2003 2004 2005 2006 Commodity prices (continued) (1990 prices) Fertilizers ($/mt) Phosphate rock 39 59 41 30 45 44 43 38 38 39 40 TSP 152 229 132 128 142 135 143 149 174 188 186 Food Fats and oils ($/mt) Coconut oil 1,417 855 337 572 463 337 452 467 617 576 549 Groundnut oil 1,350 1,090 964 846 734 721 738 1,242 1,085 991 878 Palm oil 927 740 290 536 319 303 419 443 440 394 433 Soybeans 417 376 247 221 218 208 228 264 286 257 243 Soybean meal 367 332 200 168 195 192 188 211 225 200 189 Soybean oil 1,021 758 447 534 348 375 488 553 576 509 542 Grains ($/mt) Sorghum 185 164 104 102 91 101 109 106 103 90 111 Maize 208 159 109 105 91 95 107 105 104 92 110 Rice 450 521 271 274 208 183 206 197 222 267 276 Wheat 196 219 136 151 117 134 159 146 147 142 174 Other food Bananas ($/mt) 590 481 541 380 436 618 568 374 490 563 613 Beef (cents/kg) 465 350 256 163 199 226 226 198 235 245 231 Oranges ($/mt) 599 496 531 454 374 631 606 680 803 817 751 Sugar, EU domestic (cents/kg) 40 62 58 59 57 56 59 60 63 62 58 Sugar, U.S. domestic (cents/kg) 59 84 51 43 44 50 50 47 42 44 44 Sugar, world (cents/kg) 29 80 28 25 19 20 16 16 15 20 30 Metals and minerals Aluminum ($/mt) 1,982 1,847 1,639 1,542 1,594 1,531 1,449 1,430 1,603 1,774 2,327 Copper ($/mt) 5,038 2,768 2,662 2,508 1,866 1,673 1,674 1,777 2,678 3,437 6,086 Iron ore (cents/dmtu) 35 36 33 24 30 32 31 32 35 61 70 Lead (cents/kg) 108 115 81 54 47 50 49 51 83 91 117 Nickel ($/mt) 10,148 8,270 8,864 7,028 8,888 6,303 7,271 9,617 12,915 13,776 21,960 Tin (cents/kg) 1,310 2,128 609 531 559 475 436 489 795 690 795 Zinc (cents/kg) 105 97 151 88 116 94 84 83 98 129 297 a. Series not included in the nonenergy index. Definitions · Nonenergy commodity price index covers the iron ore, lead, nickel, tin, and zinc. · Petroleum Price Data" (also known as the "Pink Sheet") at the 31 nonenergy primary commodities that make up price index refers to the average spot price of Brent, Global Prospects Web site (www.worldbank.org/ the agriculture, fertilizer, and metals and minerals Dubai, and West Texas Intermediate crude oils, prospects, click on Products). indexes. · Agriculture includes beverages, food, equally weighted. · Steel products price index is the and agricultural raw materials. · Beverages include composite price index for eight steel products based cocoa, coffee, and tea. · Food includes rice, wheat, on quotations free on board (f.o.b.) Japan excluding Data sources maize, sorghum, soybeans, soybean oil, soybean shipments to China and the United States, weighted meal, palm oil, coconut oil, groundnut oil, bananas, by product shares of apparent combined consump- Data on commodity prices and the MUV G-5 index beef, oranges, and sugar. · Agricultural raw mate- tion (volume of deliveries) for Germany, Japan, and are compiled by the World Bank's Development rials include cotton, timber (logs and sawnwood), the United States. · MUV G-5 index is the manu- Prospects Group. Monthly updates of commodity natural rubber, and tobacco. · Fertilizers include factures unit value index for G-5 country exports to prices are available on the Web at www.worldbank. phosphate rock and triple superphosphate (TSP). low- and middle-income economies. · Commodity org/prospects. · Metals and minerals include aluminum, copper, prices--for definitions and sources, see "Commodity 2007 World Development Indicators 331 6.6 Regional trade blocs Merchandise exports within bloc $ millions Year of creation 1990 1995 1999 2000 2001 2002 2003 2004 2005 High-income and low- and middle-income economies APECa 1989 901,560 1,688,708 1,896,213 2,261,791 2,070,973 2,168,700 2,420,739 2,905,271 3,286,979 CEFTA 1992 4,235 12,118 13,226 15,123 17,054 19,180 25,309 37,541 48,726 CIS 1991 .. 29,943 20,842 27,043 22,262 28,029 36,540 40,446 55,521 EMFTA 1995 1,089,631 1,488,243 1,700,902 1,744,696 1,737,269 1,857,562 2,253,496 2,706,304 2,883,467 European Union 1957 1,011,019 1,385,805 1,579,070 1,608,174 1,612,634 1,721,082 2,087,311 2,482,418 2,642,578 FTAA 1994 300,700 525,346 734,848 855,659 810,360 787,232 826,281 967,653 1,110,730 NAFTA 1994 226,273 394,472 581,161 676,141 639,419 626,020 651,060 737,591 824,550 Latin America and the Caribbean ACS 1994 5,398 11,049 11,199 16,267 15,699 15,769 15,138 20,058 25,071 Andean Group 1969 1,312 4,812 3,929 5,300 5,609 5,065 5,036 7,261 9,453 CACM 1961 667 1,594 2,175 2,586 2,739 2,763 3,156 3,574 4,064 CARICOM 1973 448 867 1,136 1,050 1,420 1,184 1,410 1,734 2,078 Central American Group of Four 1993 399 1,026 1,369 1,765 1,886 1,906 2,036 2,315 2,631 Group of Three 1995 1,046 3,460 2,912 3,721 4,178 3,839 3,167 5,669 7,437 LAIA (ALADI) 1980 12,331 35,299 34,785 42,901 40,780 36,054 39,863 55,826 70,430 Mercosur 1991 4,127 14,199 15,313 17,829 15,156 10,228 12,732 17,354 21,118 OECS 1981 29 39 37 38 37 40 48 60 68 Middle East and Asia Arab Common Market 1964 911 1,368 951 1,312 1,728 1,998 1,797 6,303 7,138 ASEAN 1967 27,365 79,544 77,889 98,060 86,331 91,684 101,054 122,914 142,955 Bangkok Agreement 1975 4,476 12,066 14,463 16,844 16,733 17,957 21,808 24,925 29,506 EAEC 1990 281,067 634,606 612,415 772,423 698,552 779,384 940,950 1,177,286 1,335,003 ECO 1985 1,243 4,746 3,903 4,518 4,498 5,014 7,468 9,978 13,993 GAFTA 1997 13,313 13,129 13,752 16,238 17,528 19,195 21,511 35,554 44,777 GCC 1981 6,906 6,832 7,306 7,958 8,103 8,899 9,580 12,532 16,507 SAARC 1985 863 2,024 2,180 2,593 2,827 3,402 4,873 5,706 7,062 UMA 1989 958 1,109 919 1,094 1,137 1,202 1,338 1,375 1,926 Sub-Saharan Africa CEMAC 1994 139 120 127 97 118 136 148 176 201 CEPGL 1976 7 8 9 10 11 13 15 19 22 COMESA 1994 963 1,386 1,348 1,653 1,819 2,031 2,436 2,849 3,330 Cross Border Initiative 1992 613 1,002 964 1,166 1,070 1,373 1,536 1,705 1,913 EAC 1996 230 530 438 595 664 685 706 750 857 ECCAS 1983 163 163 179 191 203 199 198 238 272 ECOWAS 1975 1,557 1,936 2,364 2,835 2,371 3,229 3,140 4,499 5,673 Indian Ocean Commission 1984 73 127 91 106 134 105 179 155 159 MRU 1973 0 1 4 5 4 5 5 6 6 SADC 1992 1,630 3,373 4,224 4,282 3,771 4,316 5,377 6,384 6,384 UDEAC 1964 139 120 126 96 117 134 146 174 198 UEMOA 1994 621 560 805 741 775 857 1,076 1,233 1,390 Note: Regional bloc memberships are as follows: Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong (China), Indo- nesia, 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; Central European Free Trade Area (CEFTA), Bulgaria, the Czech Republic, Hungary, Poland, Romania, the Slovak Republic, and Slovenia; Commonwealth of Independent States (CIS), Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, the Kyrgyz Republic, Moldova, the Russian Federation, Tajikistan, Ukraine, and Uzbekistan; Euro-Mediterranean Free Trade Area (EMFTA), European Union, Algeria, Cyprus, Egypt, Israel, Jordan, Lebanon, Malta, Morocco, Syrian Arab Republic, Tunisia, Turkey, and West Bank and Gaza; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom; Free Trade Areas of the Americas (FTAA), Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Republica Bolivariana de Venezuela, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, Trinidad and Tobago, the United States, and Uruguay; North American Free Trade Agreement (NAFTA), Canada, Mexico, and the United States; Economic and Monetary Community of Central Africa (CEMAC), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Principe; Economic Community of the Countries of the Great Lakes (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; Common Market for Eastern and Southern Africa (COMESA), Angola, Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Tanzania, Zambia, and Zimbabwe; Cross Border Initiative, Burundi, Comoros, Ke- nya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe; East African Community (EAC), Kenya, Tanzania, and Uganda; 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, Rwanda, and São Tomé and Principe; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d'Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Indian Ocean Commis- sion, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Mano River Union (MRU), Guinea, Liberia, and Sierra Leone; Southern African Development Community a. No preferential trade agreement. 332 2007 World Development Indicators 6.6 GLOBAL LINKS Regional trade blocs Merchandise exports within bloc % of total bloc exports Year of creation 1990 1995 1999 2000 2001 2002 2003 2004 2005 High-income and low- and middle-income economies APECa 1989 68.3 71.7 71.8 73.1 72.6 73.3 72.6 72.0 70.7 CEFTA 1992 16.3 14.8 13.8 9.9 8.0 7.3 14.6 14.2 14.6 CIS 1991 .. 27.6 20.7 19.2 18.2 18.8 19.6 16.6 17.1 EMFTA 1995 69.6 68.7 70.4 69.3 68.3 68.5 69.4 69.2 68.3 FTAA 1994 46.6 52.5 59.7 60.7 60.5 60.8 60.0 60.0 60.3 European Union 1957 66.8 66.1 67.8 66.8 66.2 66.3 67.2 66.8 66.0 NAFTA 1994 41.4 46.2 54.6 55.7 55.5 56.6 56.1 55.9 55.8 Latin America and the Caribbean ACS 1994 8.4 8.5 5.6 6.7 6.9 6.9 6.3 6.9 7.2 Andean Group 1969 4.1 12.0 8.8 8.7 10.5 9.5 8.9 8.6 8.2 CACM 1961 15.3 21.8 13.6 19.1 22.8 19.5 20.2 20.9 18.9 CARICOM 1973 8.1 12.0 16.9 14.7 16.5 13.7 12.3 12.5 11.8 Central American Group of Four 1993 13.7 22.2 14.6 23.0 27.0 21.4 21.4 21.4 18.2 Group of Three 1995 2.0 3.2 1.7 1.7 2.1 1.9 1.5 2.3 2.5 LAIA (ALADI) 1980 10.8 17.1 12.7 12.8 12.8 11.2 11.4 12.6 13.2 Mercosur 1991 8.9 20.3 20.6 20.0 17.1 11.5 11.9 12.7 12.9 OECS 1981 8.1 12.6 13.1 10.0 6.0 4.0 7.6 11.7 11.3 Middle East and Asia Arab Common Market 1964 2.7 6.7 3.3 2.9 4.4 5.1 4.1 7.9 8.6 ASEAN 1967 18.9 24.5 21.7 23.0 22.4 22.7 22.1 22.3 22.7 Bangkok Agreement 1975 3.7 4.9 5.1 5.1 5.5 5.5 5.7 5.2 5.4 EAEC 39.7 47.9 43.8 46.6 46.6 48.1 49.4 49.8 49.2 ECO 1985 3.2 7.9 5.8 5.6 5.5 5.9 6.6 6.7 7.6 GAFTA 1997 10.3 9.9 8.9 7.2 8.4 9.3 8.5 10.0 9.8 GCC 1981 8.0 6.8 6.7 4.8 5.2 5.9 5.1 5.0 4.8 SAARC 1985 3.2 4.4 4.0 4.1 4.3 4.8 5.7 5.6 5.5 UMA 1989 2.9 3.8 2.5 2.3 2.6 2.8 2.4 1.9 2.0 Sub-Saharan Africa CEMAC 1994 2.3 2.1 1.7 1.1 1.4 1.5 1.4 1.3 0.9 CEPGL 1976 0.5 0.5 0.8 0.8 0.8 0.9 1.2 1.2 1.3 COMESA 1994 6.6 7.7 7.4 6.1 7.9 7.4 7.4 6.8 5.9 Cross Border Initiative 1992 10.3 11.9 12.1 11.8 11.5 14.5 13.0 13.8 14.0 EAC 1996 13.4 17.4 14.4 20.5 21.4 19.3 18.2 16.6 15.0 ECCAS 1983 1.4 1.5 1.3 1.1 1.3 1.1 1.0 0.9 0.6 ECOWAS 1975 7.9 9.0 10.4 7.9 8.5 10.9 8.6 9.4 9.5 Indian Ocean Commission 1984 4.1 6.0 4.8 4.4 5.6 4.3 6.2 4.3 4.6 MRU 1973 0.0 0.1 0.4 0.4 0.3 0.2 0.3 0.3 0.3 SADC 1992 17.0 31.6 11.9 9.3 8.6 9.5 9.8 9.5 7.7 UDEAC 1964 2.3 2.1 1.7 1.0 1.4 1.4 1.4 1.2 0.9 UEMOA 1994 13.0 10.3 13.1 13.1 12.7 12.2 13.3 12.9 13.4 (SADC; formerly Southern African Development Coordination Conference), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Central African Customs and Economic Union (UDEAC; formerly Union Douanière et Economique de l'Afrique Centrale), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; West African Economic and Mon- etary Union (UEMOA), Benin, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo; Association of Caribbean States (ACS), Antigua and Barbuda, the Bahamas, Barbados, Belize, Colombia, Costa Rica, Cuba, Dominica, the Dominican Republic, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, Trinidad and Tobago, and República Bolivariana de Venezuela; Andean Group, Bolivia, Colombia, Ecuador, Peru, and República Bolivariana de Venezuela; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas (part of the Caribbean Community but not of the Common Market), Barbados, Belize, Dominica, Grenada, Guyana, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Group of Four, El Salvador, Guatemala, Honduras, and Nicaragua; Group of Three, Colombia, Mexico, and República Bolivariana de Venezuela; Latin Ameri- can Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and República Bolivariana de Venezuela; Common Market of the South (Mercosur), Argentina, Brazil, Paraguay, and Uruguay; Organization of Eastern Caribbean States (OECS), Antigua and Barbuda, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Arab Common Market, the Arab Republic of Egypt, Iraq, Jordan, Libya, Mauritania, the Syrian Arab Republic, and the Republic of Yemen; Association of South-East Asian Nations (ASEAN), Brunei, Cambodia, Indonesia, the Lao People's Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Bangkok Agreement, Bangladesh, India, the Republic of Korea, the Lao People's Democratic Republic, the Philippines, Sri Lanka, and Thailand; East Asia Economic Caucus (EAEC; formerly East Asia Economic Group), Brunei, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, the Philippines, Singapore, Taiwan (China), and Thailand; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and Uzbekistan; Gulf Cooperation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; South Asian Association for Regional Cooperation (SAARC), Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; and Arab Maghreb Union (UMA), Algeria, Libya, Mauritania, Morocco, and Tunisia. 2007 World Development Indicators 333 6.6 Regional trade blocs Total merchandise exports by bloc % of world exports Year of creation 1990 1995 1999 2000 2001 2002 2003 2004 2005 High-income and low- and middle-income economies APECa 1989 39.0 46.3 46.6 48.5 46.5 46.0 44.5 44.2 45.0 CEFTA 1992 1.3 1.6 1.9 1.9 2.2 2.4 2.7 2.9 3.1 CIS 1991 .. 2.1 1.8 2.2 2.0 2.3 2.5 2.7 3.1 EMFTA 1995 46.3 42.7 42.7 39.4 41.4 42.2 43.3 42.9 40.8 FTAA 1994 19.1 19.7 21.7 22.1 21.8 20.2 18.4 17.7 17.8 European Union 1957 44.8 41.3 41.1 37.7 39.7 40.4 41.5 40.8 38.7 NAFTA 1994 16.2 16.8 18.8 19.0 18.7 17.2 15.5 14.5 14.3 Latin America and the Caribbean ACS 1994 1.9 2.6 3.5 3.8 3.7 3.6 3.2 3.2 3.4 Andean Group 1969 0.9 0.8 0.8 0.9 0.9 0.8 0.8 0.9 1.1 CACM 1961 0.1 0.1 0.3 0.2 0.2 0.2 0.2 0.2 0.2 CARICOM 1973 0.2 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 Central American Group of Four 1993 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 Group of Three 1995 1.5 2.1 3.0 3.3 3.2 3.1 2.7 2.7 2.9 LAIA (ALADI) 1980 3.4 4.1 4.8 5.3 5.2 5.0 4.7 4.8 5.1 Mercosur 1991 1.4 1.4 1.3 1.4 1.4 1.4 1.4 1.5 1.6 OECS 1981 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Middle East and Asia Arab Common Market 1964 1.0 0.4 0.5 0.7 0.6 0.6 0.6 0.9 0.8 ASEAN 1967 4.3 6.4 6.3 6.7 6.3 6.3 6.1 6.0 6.1 Bangkok Agreement 1975 3.6 4.8 5.0 5.2 4.9 5.1 5.1 5.3 5.3 EAEC 1990 20.9 26.1 24.7 26.0 24.4 25.2 25.4 25.9 26.2 ECO 1985 1.1 1.2 1.2 1.3 1.3 1.3 1.5 1.6 1.8 GAFTA 1997 3.8 2.6 2.7 3.5 3.4 3.2 3.4 3.9 4.4 GCC 1981 2.6 2.0 1.9 2.6 2.5 2.3 2.5 2.7 3.3 SAARC 1985 0.8 0.9 1.0 1.0 1.1 1.1 1.1 1.1 1.3 UMA 1989 1.0 0.6 0.6 0.8 0.7 0.7 0.7 0.8 0.9 Sub-Saharan Africa CEMAC 1994 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 CEPGL 1976 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 COMESA 1994 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.5 0.5 Cross Border Initiative 1992 0.2 0.2 0.1 0.2 0.2 0.1 0.2 0.1 0.1 EAC 1996 0.1 0.1 0.1 0.0 0.1 0.1 0.1 0.0 0.1 ECCAS 1983 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.4 ECOWAS 1975 0.6 0.4 0.4 0.6 0.5 0.5 0.5 0.5 0.6 Indian Ocean Commission 1984 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MRU 1973 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 SADC 1992 0.3 0.2 0.6 0.7 0.7 0.7 0.7 0.7 0.8 UDEAC 1964 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 UEMOA 1994 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 334 2007 World Development Indicators 6.6 GLOBAL LINKS Regional trade blocs About the data Trade blocs are groups of countries that have estab- partners. But making it work effectively is challenging international trade unit. The date of each trade bloc's lished special preferential arrangements governing for any government. All sectors of an economy may creation is also included. Although bloc exports have trade between members. Although in some cases be affected, and some sectors may expand while oth- been calculated back to 1990 on the basis of current the preferences--such as lower tariff duties or ers contract, so it is important to weigh the potential membership, several of the blocs came into exis- exemptions from quantitative restrictions--may be costs and benefits that membership may bring. tence in later years and their membership may have no greater than those available to other trading part- The table shows the value of merchandise intra- changed over time. For this reason, and because ners, such arrangements are intended to encourage trade for important regional trade blocs (service systems of preferences also change over time, intra- exports by bloc members to one another--some- exports are excluded) as well as the size of intra- trade in earlier years may not have been affected by times called intratrade. trade relative to each bloc's total exports of goods the same preferences as in recent years. In addi- Most countries are members of a regional trade and the share of the bloc's total exports in world tion, some countries belong to more than one trade bloc, and more than a third of the world's trade takes exports. Although the Asia Pacific Economic Coopera- bloc, so shares of world exports exceed 100 percent. place within such arrangements. While trade blocs tion (APEC) has no preferential arrangements, it is Exports of blocs include all commodity trade, which vary widely in structure, they all have the same main included in the table because of the volume of trade may include items not specified in trade bloc agree- objective: to reduce trade barriers between member between its members. ments. Differences from previously published esti- countries. But effective integration requires more than The data on country exports are drawn from the mates may be due to changes in bloc membership reducing tariffs and quotas. Economic gains from International Monetary Fund's (IMF) Direction of or to revisions in the underlying data. competition and scale may not be achieved unless Trade database and should be broadly consistent Definitions other barriers that divide markets and impede the free with those from other sources, such as the United flow of goods, services, and investments are lifted. Nations Statistics Division's Commodity Trade (Com- · Merchandise exports within bloc are the sum of For example, many regional trade blocs retain con- trade) database. However, trade flows between many merchandise exports by members of a trade bloc tingent protections or restrictions on intrabloc trade. developing countries, particularly in Sub-Saharan to other members of the bloc. They are shown both These include antidumping, countervailing duties, and Africa, are not well recorded. Thus the value of intra- in U.S. dollars and as a percentage of total mer- "emergency protection" to address balance of pay- trade for certain groups may be understated. Data on chandise exports by the bloc. · Total merchandise ments problems or to protect an industry from surges trade between developing and high-income countries exports by bloc as a share of world exports are the in imports. Other barriers include differing product are generally complete. ratio of the bloc's total merchandise exports (within standards, discrimination in public procurement, and Membership in the trade blocs shown is based on the bloc and to the rest of the world) to total mer- cumbersome and costly border formalities. the most recent information available, from the World chandise exports by all economies in the world. Membership in a regional trade bloc may reduce Bank Policy Research Report Trade Blocs (2000a), the frictional costs of trade, increase the credibility from the World Bank's Global Economic Prospects of reform initiatives, and strengthen security among 2005, and from consultation with the World Bank's Preferential regional trade agreements have a mixed impact on trade 6.6a Merchandise exports within bloc as share of total bloc exports (%) 1995 2000 2005 80 70 Data sources 60 Data on merchandise trade flows are published in 50 the IMF's Direction of Trade Statistics Yearbook and 40 Direction of Trade Statistics Quarterly; the data in 30 the table were calculated using the IMF's Direction 20 of Trade database. The United Nations Conference on Trade and Development (UNCTAD) publishes 10 data on intratrade in its Handbook of International 0 Trade and Development Statistics. The information Asia Pacific European North American Latin American Mercosur Association Economic Union Free Trade Integration of Southeast on trade bloc membership is from the World Bank Cooperation Agreement Association Asian Nations Policy Research Report Trade Blocs (2000a), the Regional trade agreements do not necessarily create net trade gains among bloc members. World Bank's Global Economic Prospects 2005, Source: International Monetary Fund's Direction of Trade database. and the World Bank's international trade unit. 2007 World Development Indicators 335 6.7 Tariff barriers All Primary Manufactured products products products % Share of Share of Most Simple Simple Weighted lines with lines with % % recent Binding mean mean mean international specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 2005a 100.0 7.0 6.3 7.1 0.0 0.0 7.3 6.5 6.1 7.2 Algeria 2005 .. .. 15.8 10.6 38.6 0.0 15.3 9.0 15.7 11.0 Angola 2005 100.0 59.2 7.6 6.0 10.3 0.0 12.0 13.1 6.8 4.4 Argentina 2005a 100.0 31.9 10.6 5.2 22.6 0.0 8.0 1.8 10.8 5.7 Armenia 2001 100.0 8.5 3.3 2.5 0.0 0.0 6.6 3.4 2.8 1.5 Australia 2005a 97.1 10.0 4.3 3.1 6.0 0.2 1.6 0.7 4.6 3.7 Azerbaijan 2005 .. .. 10.4 5.8 0.0 0.0 12.0 5.4 10.1 5.9 Bangladesh 2005 14.9 162.1 16.8 55.8 36.0 0.0 21.8 19.4 16.0 76.7 Belarus 2002 0.0 11.3 8.9 16.4 0.0 11.1 7.1 11.3 10.4 Benin 2005 39.1 28.6 14.4 12.4 57.6 0.0 15.4 12.0 14.2 12.8 Bolivia 2005a 100.0 40.0 7.2 5.5 0.0 0.0 7.5 5.2 7.2 5.6 Bosnia and Herzegovina 2001a .. .. 5.3 5.1 0.0 0.0 3.8 5.3 5.5 5.0 Botswana 2005a 96.3 19.0 9.9 11.2 23.6 0.2 5.1 1.0 10.1 12.9 Brazil 2005a 100.0 31.4 12.3 7.1 27.7 0.0 7.9 1.5 12.6 9.2 Bulgaria 2005a 100.0 24.7 10.7 9.1 26.8 1.9 15.9 10.0 10.0 8.8 Burkina Faso 2005 39.3 41.9 13.1 11.7 48.6 0.0 13.6 10.1 13.0 12.6 Burundi 2005 20.9 67.5 19.6 19.9 46.5 0.0 26.1 25.5 18.5 18.7 Cambodia 2003a .. .. 16.0 16.4 25.9 0.0 17.4 15.6 15.8 16.6 Cameroon 2005 12.6 79.9 18.4 16.5 52.6 0.0 20.9 19.5 18.0 15.5 Canada 2005a 99.7 5.1 4.5 1.5 7.7 3.5 6.4 3.4 4.1 1.0 Central African Republic 2005 .. .. 17.9 16.8 58.0 0.0 21.8 24.8 17.4 13.2 Chad 2005 .. .. 17.2 12.5 48.7 0.0 22.1 25.0 16.5 10.3 Chile 2005a 100.0 25.1 4.9 3.9 0.0 0.0 4.4 2.8 4.9 4.4 China 2005a 100.0 10.0 9.2 4.9 19.1 0.0 8.8 3.4 9.2 5.3 Colombia 2005a 100.0 42.8 11.9 9.6 21.5 0.0 11.5 9.5 11.9 9.5 Congo, Dem. Rep. 2003 .. .. 13.1 13.0 42.7 0.0 14.7 12.4 12.8 13.3 Congo, Rep. 2005 .. .. 19.1 17.7 56.4 0.0 22.9 22.1 18.5 16.2 Costa Rica 2005 100.0 42.9 7.0 4.1 0.5 0.0 10.4 6.1 6.6 3.6 Côte d'Ivoire 2005 33.2 11.2 12.6 10.3 44.3 0.0 14.9 11.2 12.2 9.9 Croatia 2005a 100.0 5.9 2.4 1.2 3.0 0.0 4.9 2.3 2.1 0.7 Cuba 2005a 31.0 21.3 10.5 9.6 11.0 0.0 10.8 8.6 10.4 10.1 Czech Republic 2003 100.0 5.0 5.0 4.4 4.8 0.0 5.6 4.1 4.9 4.3 Dominican Republic 2005a 100.0 34.9 9.0 8.5 27.2 0.0 12.5 7.6 8.5 8.8 Ecuador 2005a 99.9 21.8 11.8 8.7 23.8 0.0 11.0 6.6 11.8 9.1 Egypt, Arab Rep. 2005a 99.1 36.6 18.9 12.0 21.8 0.0 85.8 16.4 11.6 10.5 El Salvador 2005a 100.0 36.6 6.4 6.7 2.5 0.0 10.4 8.3 5.8 5.8 Estonia 2003 100.0 8.7 1.0 0.9 5.4 0.0 8.1 4.0 0.0 0.0 Ethiopia 2002 .. .. 19.7 13.5 52.9 0.0 22.1 6.7 19.4 15.7 European Unionb 2005a 100.0 4.2 2.7 2.0 6.7 9.0 7.9 2.3 1.7 1.8 Gabon 2005 100.0 21.4 19.9 16.8 60.6 0.0 22.9 19.4 19.3 15.8 Gambia, The 2003 13.0 101.8 Georgia 2004 100.0 7.2 7.5 9.5 5.4 0.7 11.8 13.2 6.8 7.1 Ghana 2004 13.5 92.1 13.2 11.0 45.3 0.0 17.4 17.1 12.3 8.8 Guatemala 2005a 100.0 42.2 6.7 5.8 1.0 0.0 8.8 5.5 6.4 5.9 Guinea 2005 39.0 20.1 14.2 12.7 58.6 0.0 16.3 14.3 13.9 11.2 Honduras 2005a 100.0 32.5 6.7 6.0 0.2 0.0 9.7 7.2 6.3 5.3 Hong Kong, China 2005 45.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hungary 2002 96.2 9.8 8.9 7.9 10.9 0.0 17.9 6.7 7.7 8.0 India 2005a 73.8 49.6 17.0 14.5 15.5 3.5 24.4 16.5 15.9 12.8 Indonesia 2005a 96.6 37.1 6.5 6.0 8.7 0.0 7.2 3.5 6.4 6.7 Iran, Islamic Rep. 2004 .. .. 18.7 13.8 43.4 0.0 14.9 11.2 18.9 14.5 Iraq .. .. .. .. .. .. .. .. .. .. .. Israel 2005a 76.3 20.9 2.7 1.7 1.5 1.1 6.9 3.9 2.1 0.9 Jamaica 2003 100.0 49.6 9.4 9.8 36.5 0.0 15.7 11.0 8.4 9.3 Japan 2005a 99.7 3.0 3.3 2.5 8.1 2.7 8.4 3.8 2.3 1.4 Jordan 2005a 100.0 16.3 12.4 7.6 34.5 0.0 15.6 4.0 11.9 9.9 Kazakhstan 2004 .. .. 2.3 1.9 0.0 0.0 3.3 3.1 2.2 1.6 Kenya 2005 14.0 95.1 12.1 7.5 36.4 0.0 15.9 8.6 11.6 6.7 336 2007 World Development Indicators 6.7 GLOBAL LINKS Tariff barriers All Primary Manufactured products products products % Share of Share of Most Simple Simple Weighted lines with lines with % % recent Binding mean mean mean international specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Korea, Rep. 2004 a 94.5 15.7 9.0 9.3 5.6 0.0 20.3 17.7 7.2 4.5 Kuwait 2005 .. .. 4.7 4.5 0.0 0.0 3.9 3.0 4.8 4.8 Kyrgyz Republic 2003 99.9 7.4 4.3 4.3 0.1 2.2 6.7 6.2 3.8 2.9 Lao PDR 2005a .. .. 9.2 14.0 23.5 0.0 18.5 29.7 8.5 10.3 Latvia 2001 100.0 12.8 3.3 2.6 3.0 0.0 8.1 5.4 2.5 1.5 Lebanon 2005a .. .. 7.2 6.3 12.2 0.0 14.2 6.1 6.1 6.3 Lesotho 2005a .. .. 9.9 16.8 24.1 0.8 7.4 3.3 10.0 17.6 Libya 2002 .. .. 20.2 25.2 46.6 0.0 19.2 15.1 20.1 28.5 Lithuania 2003a 100.0 9.2 1.3 0.7 3.3 0.0 3.5 1.3 1.0 0.4 Macedonia, FYR 2005 100.0 6.9 4.1 3.3 12.2 0.0 10.0 6.4 3.5 1.7 Madagascar 2005 29.7 27.4 11.6 5.2 37.1 0.0 16.9 4.1 11.0 5.9 Malawi 2001a 30.2 74.9 13.5 10.2 42.4 0.0 13.4 9.0 13.4 10.7 Malaysia 2005a 83.7 14.5 7.5 4.4 22.4 0.0 3.4 2.3 8.2 4.8 Mali 2005 40.7 28.8 12.4 10.7 43.7 0.0 14.4 11.7 12.2 10.4 Mauritania 2001 39.4 19.6 12.8 9.9 51.1 0.0 12.6 10.0 12.8 9.9 Mauritius 2005 18.0 94.0 8.5 4.7 19.7 0.0 9.0 5.3 8.3 4.2 Mexico 2005a 100.0 35.0 9.2 3.0 12.4 0.0 8.5 2.2 9.2 3.1 Moldova 2001 .. .. 4.8 2.9 0.2 0.4 8.4 2.7 4.1 3.0 Mongolia 2005 100.0 17.5 4.2 4.3 0.0 0.0 5.0 5.1 4.1 3.7 Morocco 2005 100.0 41.3 19.4 13.7 57.0 0.0 23.3 12.1 18.8 14.3 Mozambique 2005a .. .. 13.1 8.6 38.2 0.0 16.4 9.1 12.6 8.5 Myanmar 2005a 16.5 83.3 4.5 4.1 5.3 0.0 7.6 4.8 4.2 3.8 Namibia 2005a 96.3 19.4 5.6 1.3 15.2 0.0 3.7 0.5 5.9 1.6 Nepal 2005a .. .. 14.7 14.3 21.6 0.0 13.9 9.3 14.7 16.4 New Zealand 2005a 100.0 10.3 5.0 4.6 10.0 4.8 9.9 8.7 4.3 3.4 Nicaragua 2005a 100.0 41.7 6.8 5.4 0.5 0.0 10.6 5.4 6.3 5.4 Niger 2005 96.8 44.3 12.7 12.8 47.6 0.0 14.9 14.7 12.4 12.1 Nigeria 2005 18.2 117.8 11.6 10.8 41.0 0.0 14.9 14.9 11.3 9.3 Norway 2003a 100.0 3.0 2.6 1.9 4.2 5.7 17.8 8.6 0.5 0.2 Oman 2005a 100.0 13.8 3.8 3.2 0.1 0.0 4.1 2.9 3.8 3.3 Pakistan 2005a 44.8 52.2 14.6 12.4 42.6 0.0 13.8 8.6 14.6 14.5 Panama 2005a 99.9 23.4 7.4 6.9 1.8 0.0 11.2 7.9 7.0 6.4 Papua New Guinea 2005 100.0 31.7 6.1 2.2 25.3 0.3 14.9 3.1 4.8 1.7 Paraguay 2005a 100.0 33.6 8.3 5.8 16.8 0.0 6.3 1.5 8.5 7.2 Peru 2005a 100.0 30.1 9.2 8.3 11.1 0.0 10.7 9.8 9.1 7.6 Philippines 2005a 67.0 25.6 5.4 3.1 4.8 0.0 6.9 5.1 5.1 2.7 Poland 2003a 96.2 11.9 5.5 3.4 9.9 3.3 27.8 12.6 2.5 1.2 Romania 2005a 100.0 39.9 6.6 3.1 21.0 0.0 13.3 7.2 5.7 1.8 Russian Federation 2005 0.0 11.4 9.6 17.9 16.0 10.7 12.2 11.5 8.9 Rwanda 2005a 100.0 89.5 17.2 9.7 47.0 0.0 12.7 5.5 17.7 12.2 Saudi Arabia 2005a .. .. 4.1 4.1 0.0 0.0 3.2 2.7 4.3 4.4 Senegal 2005 100.0 30.0 14.0 9.2 53.8 0.0 14.9 8.2 13.8 10.4 Serbia and Montenegro 2005 .. .. 8.2 7.9 20.0 0.0 13.2 10.7 7.4 7.0 Sierra Leone 2004 100.0 47.4 Singapore 2005a 69.3 6.9 0.1 0.0 0.1 0.1 0.3 0.1 0.0 0.0 Slovak Republic 2002 100.0 5.0 22.1 21.2 50.8 0.0 19.2 12.8 22.3 23.5 Slovenia 2003a 100.0 23.7 4.4 1.8 11.4 0.0 7.0 3.9 3.9 1.2 South Africa 2005a 96.3 19.4 8.5 5.4 21.3 1.0 5.4 1.7 8.8 6.5 Sri Lanka 2005a 36.8 29.6 11.3 7.7 23.4 0.4 18.2 9.5 10.3 6.8 Sudan 2002 .. .. 21.1 19.6 43.8 0.0 28.2 24.0 20.5 18.9 Swaziland 2005a 96.3 19.4 10.8 10.5 26.6 0.0 10.3 4.3 10.8 10.8 Sweden 1989 .. .. 5.4 4.3 3.6 0.0 1.4 1.0 6.0 5.0 Switzerland 2005a 99.8 0.0 2.7 1.4 8.7 35.1 14.0 8.1 0.6 0.1 Syrian Arab Republic 2002 .. .. 14.7 15.5 23.3 0.0 14.4 11.7 14.5 16.6 Tajikistan 2002 .. .. 7.6 6.1 6.8 1.7 9.6 5.7 7.3 6.6 Tanzania 2005a 13.4 120.0 12.9 8.4 38.0 0.0 18.7 10.6 12.2 7.7 Thailand 2005a 74.8 25.8 10.6 4.9 22.1 0.9 13.1 2.3 10.0 5.7 Togo 2005 13.2 80.0 14.6 10.4 55.3 0.0 15.4 9.7 14.4 11.0 Trinidad and Tobago 2003 100.0 55.8 9.8 5.5 36.6 0.0 15.5 4.8 8.8 5.9 2007 World Development Indicators 337 6.7 Tariff barriers All Primary Manufactured products products products % Share of Share of Most Simple Simple Weighted lines with lines with % % recent Binding mean mean mean international specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Tunisia 2005a 57.9 57.7 13.4 9.1 31.0 0.0 27.4 13.8 12.0 7.5 Turkey 2005a 47.7 29.6 2.4 1.6 4.8 0.0 12.6 2.6 1.4 1.2 Turkmenistan 2002 .. .. 5.4 2.9 14.8 2.8 14.8 12.6 3.8 1.1 Uganda 2005a 14.9 73.5 12.4 9.0 38.3 0.0 16.7 10.1 11.9 8.4 Ukraine 2002 .. .. 7.6 3.9 11.2 0.0 6.9 1.5 7.6 6.4 United Arab Emirates 2005a .. .. 4.8 4.8 0.2 0.0 4.9 4.7 4.8 4.8 United States 2005a 100.0 3.6 3.2 1.6 6.1 5.9 2.8 0.8 3.3 1.8 Uruguay 2005a 100.0 31.6 9.9 3.5 26.1 0.0 6.3 1.2 10.1 4.8 Uzbekistan 2001 .. .. 10.4 5.8 26.7 0.0 10.5 4.2 10.5 6.2 Venezuela, RB 2005a 99.9 36.8 12.8 12.7 23.7 0.0 12.2 11.3 12.8 12.9 Vietnam 2005a .. .. 13.2 13.6 34.1 0.0 17.7 14.9 12.3 12.8 Yemen, Rep. 2000 .. .. 12.8 11.8 11.2 0.0 13.6 10.8 12.7 12.4 Zambia 2005a 15.9 105.7 14.6 9.4 34.5 0.0 14.9 9.3 14.5 9.4 Zimbabwe 2003 20.8 90.7 16.7 17.3 38.8 0.0 19.5 19.8 16.2 14.7 World .. 77.4 30.8 7.7 3.3 13.8 0.5 9.9 3.3 7.4 3.2 Low income .. 48.2 47.1 13.0 14.2 30.1 0.8 15.9 14.1 12.6 14.2 Middle income .. 88.5 31.0 8.8 5.3 16.5 0.7 11.8 4.6 8.3 5.5 Lower middle income .. 86.6 31.5 9.6 5.8 18.1 1.3 12.5 4.4 9.2 6.2 Upper middle income .. 90.8 30.4 7.9 4.7 15.0 0.8 11.2 5.0 7.4 4.6 Low & middle income .. 76.2 34.8 9.4 6.1 17.8 0.9 12.3 5.9 9.0 6.1 East Asia & Pacific .. 79.0 32.4 9.0 5.0 18.4 0.1 9.6 3.5 8.8 5.4 Europe & Central Asia .. 85.6 11.9 6.3 4.9 10.2 1.5 9.9 6.0 5.8 4.5 Latin America & the Carib. .. 97.1 42.8 9.6 5.3 17.4 0.0 11.9 3.9 9.3 5.6 Middle East & N. Africa .. 93.4 34.8 10.6 8.9 23.7 0.0 15.8 8.9 9.8 8.8 South Asia .. 61.1 42.6 15.2 16.1 31.4 1.6 18.4 15.1 14.6 16.8 Sub-Saharan Africa .. 48.7 43.0 12.2 8.1 33.7 0.0 14.3 8.1 11.9 8.1 High-income .. 83.0 13.3 3.4 1.9 4.1 0.2 4.6 2.1 3.2 1.8 OECD .. 98.6 7.4 3.1 2.0 3.7 0.0 3.7 2.1 3.0 1.9 Non-OECD .. 67.3 21.3 4.1 1.2 5.0 0.8 6.3 1.9 3.7 1.1 Note: Tariff rates include ad valorem equivalents of specific rates unavailable in previous years. a. Rates are either partially or fully recorded applied rates. All other simple and weighted tariff rates are most favored nation rates. b. Data refer to all 25 member states of the European Union. 338 2007 World Development Indicators 6.7 GLOBAL LINKS Tariff barriers About the data Poor people in developing countries work primar- Free Trade Agreement. The difference between and Development (UNCTAD) and the World Trade ily in agriculture and labor-intensive manufactures, most favored nation and applied rates can be sub- Organization (WTO). Data are classifi ed using the sectors that confront the greatest trade barriers. stantial. As more countries report their free trade Harmonized System of trade at the six- or eight-digit Removing barriers to merchandise trade could agreements, suspensions of tariffs, or other spe- level. Tariff line data were matched to Standard increase growth by about 0.8 percent a year in these cial preferences, World Development Indicators will International Trade Classifi cation (SITC) revision countries--even more if trade in services (retailing, include their effectively applied rates. All estimates 2 codes to defi ne commodity groups and import business, fi nancial, and telecommunications ser- are calculated using the most up-to-date information, weights. Import weights were calculated using the vices) were also liberalized. which is not necessarily updated every year. As a United Nations Statistics Division's Commodity In general, tariffs in high-income countries on result, data for the same year may differ from data Trade (Comtrade) database. Data are shown only for imports from developing countries, though low, are in last year's publication. the last year for which complete data are available. twice the size of those collected from other high- Three measures of average tariffs are shown: sim- To conserve space, data for the European Union are income countries. But protection is also an issue ple bound rates and the simple and the weighted shown instead of data for individual members. for developing countries, which maintain high tariffs mean tariffs. The most favored nation or applied Definitions on agricultural commodities, labor-intensive manu- rates are calculated using all traded items, while factures, and other products and services. In some bound rates are based on all products in a country's · Binding coverage is the percentage of product developing regions new trade policies could make tariff schedule. Weighted mean tariffs are weighted lines with an agreed bound rate. · Simple mean the difference between achieving important Millen- by the value of the country's trade with each trading bound rate is the unweighted average of all the lines nium Development Goals--reducing poverty, lower- partner. Simple averages are often a better indicator in the tariff schedule in which bound rates have been ing maternal and child mortality rates, improving of tariff protection than weighted averages, which are set. · Simple mean tariff is the unweighted average educational attainment--and falling far short. biased downward because higher tariffs discourage of effectively applied rates or most favored nation Countries use a combination of tariff and nontariff trade and reduce the weights applied to these tariffs. rates for all products subject to tariffs calculated measures to regulate imports. The most common Bound rates have resulted from trade negotiations for all traded goods. · Weighted mean tariff is the form of tariff is an ad valorem duty, based on the that are incorporated into a country's schedule of average of effectively applied rates or most favored value of the import, but tariffs may also be levied concessions and are thus enforceable. If a contract- nation rates weighted by the product import shares on a specific, or per unit, basis or may combine ad ing party raises a tariff to a higher level than its corresponding to each partner country. · Share of valorem and specific rates. Tariffs may be used to bound rate, beneficiaries of the earlier binding have lines with international peaks is the share of lines raise fiscal revenues or to protect domestic indus- a right to receive compensation, usually as reduced in the tariff schedule with tariff rates that exceed 15 tries from foreign competition--or both. Nontariff tariffs on other products they export to the country. percent. · Share of lines with specific rates is the barriers, which limit the quantity of imports of a par- If the beneficiaries are not compensated, they may share of lines in the tariff schedule that are set on ticular good, include quotas, prohibitions, licensing retaliate by raising their own tariffs against an equiv- a per unit basis or that combine ad valorem and per schemes, export restraint arrangements, and health alent value of the original country's exports. unit rates. · Primary products are commodities clas- and quarantine measures. Some countries set fairly uniform tariff rates across sified in SITC revision 2 sections 0­4 plus division Nontariff barriers are generally considered less all imports. Others are more selective, setting high tar- 68 (nonferrous metals). · Manufactured products desirable than tariffs because changes in an export- iffs to protect favored domestic industries. The share are commodities classified in SITC revision 2 sec- ing country's efficiency and costs no longer result in of tariff lines with international peaks (those for which tions 5­8 excluding division 68. changes in market share in the importing country. ad valorem tariff rates exceed 15 percent) provides an Further, the quotas or licenses that regulate trade indication of how selectively tariffs are applied. The become very valuable, and resources are often wasted effective rate of protection--the degree to which the in attempts to acquire these assets. A high percent- value added in an industry is protected--may exceed age of products subject to nontariff barriers suggests the nominal rate if the tariff system systematically a protectionist trade regime, but the frequency of differentiates among imports of raw materials, inter- nontariff barriers does not measure how much they mediate products, and finished goods. restrict trade. Moreover, a wide range of domestic The share of tariff lines with specific rates shows policies and regulations (such as health regulations) the extent to which countries use tariffs based on may act as nontariff barriers. Based on the difficulty of physical quantities or other, non­ad valorem mea- Data sources combining nontariff barriers into an aggregate indica- sures. Some countries apply only specific duties. tor, they are not included in this table. Specific duties are not included in the table, except All indicators in the table were calculated by World The tariff rates used in calculating the indicators in for Switzerland. Work is under way to complete the Bank staff using the World Integrated Trade Solution the table are most favored nation rates unless they estimations for ad valorem equivalents of specific system. Data on tariffs were provided by UNCTAD and are specified as applied rates. Effectively applied duties for all countries. the WTO. Data on global imports are from the United rates are those in effect for partners in preferen- The indicators were calculated from data sup- Nations Statistics Division's Comtrade database. tial trade agreements such as the North American plied by the United Nations Conference on Trade 2007 World Development Indicators 339 6.8 Global private financial flows Foreign direct Portfolio Bank and trade- investment investment flows related lending $ millions $ millions Bonds Equity $ millions 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan .. .. .. .. .. .. .. .. Albania .. 262 .. 0 0 .. .. 34 Algeria 0 1,081 ­15 0 0 .. ­409 ­821 Angola ­335 ­1,304 0 0 0 .. 570 1,550 Argentina 1,836 4,730 ­857 1,872 0 ­48 ­1,195 ­824 Armenia 1,836 258 .. 0 0 1 .. 83 Australia 8,111 ­34,420 .. .. .. .. .. .. Austria 653 9,057 .. .. .. .. .. .. Azerbaijan 4 1,680 .. 0 0 0 .. 9 Bangladesh 3 802 0 0 0 1 55 ­9 Belarus .. 305 .. 0 0 1 .. 42 Belgium 8,047a 31,959 .. .. .. .. .. .. Benin 62 21 0 0 0 .. .. ­4 Bolivia 27 ­277 0 0 0 .. ­24 314 Bosnia and Herzegovina .. 299 .. 0 .. .. .. 282 Botswana 96 279 0 0 0 62 ­18 ­2 Brazil 989 15,193 129 3,580 103 6,451 ­555 ­1,708 Bulgaria 4 2,614 .. ­1,257 0 92 .. 2,421 Burkina Faso 0 19 0 0 0 .. .. .. Burundi 1 1 0 0 0 0 ­6 ­5 Cambodia .. 379 0 0 .. .. .. .. Cameroon ­113 18 0 0 0 .. ­14 ­44 Canada 7,581 34,146 .. .. .. .. .. .. Central African Republic 1 6 0 0 0 .. ­1 .. Chad 9 705 0 0 0 .. ­1 ­1 Chile 661 6,667 ­7 584 367 1,635 1,194 2,593 China 3,487 79,127 ­48 2,702 0 20,346 4,668 2,442 Hong Kong, China .. 35,897 .. .. .. .. .. .. Colombia 500 10,375 ­4 496 0 86 ­151 ­768 Congo, Dem. Rep. 23 402 0 ­1 .. .. ­12 ­2 Congo, Rep. ­14 724 0 0 0 .. ­100 0 Costa Rica 163 861 ­42 ­32 0 0 ­99 287 Côte d'Ivoire 48 266 ­1 0 0 35 10 ­163 Croatia .. 1,761 .. ­785 .. 113 .. 2,429 Cuba .. .. .. .. .. .. .. .. Czech Republic 0 .. .. ­201 0 .. 669 ­4,524 Denmark 1,132 5,238 .. .. .. .. .. .. Dominican Republic 133 1,023 0 ­20 0 0 ­3 195 Ecuador 126 1,646 0 650 0 2 58 ­80 Egypt, Arab Rep. 734 5,376 ­1 1,554 0 729 ­65 2,936 El Salvador 2 517 0 375 0 .. 5 78 Eritrea .. 11 .. 0 .. .. .. .. Estonia .. 2,997 .. 0 .. ­1,349 .. 425 Ethiopia 12 265 0 0 0 0 ­57 116 Finland 812 3,978 .. .. .. .. .. .. France 13,183 70,686 .. .. .. .. .. .. Gabon 73 300 0 0 0 .. 29 6 Gambia, The 14 52 0 0 0 .. ­7 .. Georgia .. 450 .. 0 .. 3 .. 46 Germany 3,004 32,034 .. .. .. .. .. .. Ghana 15 107 0 0 0 0 ­23 13 Greece 1,005 640 .. .. .. .. .. .. Guatemala 48 208 ­11 0 0 .. 1 ­15 Guinea 18 102 0 0 .. .. ­19 .. Guinea-Bissau 2 10 0 0 0 .. .. .. Haiti 8 10 0 0 0 0 .. .. 340 2007 World Development Indicators 6.8 GLOBAL LINKS Global private financial flows Foreign direct Portfolio Bank and trade- investment investment flows related lending $ millions $ millions Bonds Equity $ millions 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 44 464 0 0 0 0 32 57 Hungary 623 6,436 921 2,978 0 ­16 ­1,379 2,124 India 237 6,598 147 ­3,959 0 11,968 1,458 4,338 Indonesia 1,093 5,260 26 3,791 0 ­165 1,804 ­2,306 Iran, Islamic Rep. ­362 30 0 0 0 .. ­30 644 Iraq .. .. .. .. .. .. .. .. Ireland 627 ­29,730 .. .. .. .. .. .. Israel 151 5,585 .. .. .. .. .. .. Italy 6,411 19,585 .. .. .. .. .. .. Jamaica 138 682 0 919 0 .. ­46 22 Japan 1,777 3,214 .. .. .. .. .. .. Jordan 38 1,532 0 134 0 60 214 11 Kazakhstan .. 1,975 .. 3,050 .. 170 .. 3,557 Kenya 57 21 0 0 0 3 65 ­8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 789 4,339 .. .. .. .. .. .. Kuwait 0 250 .. .. .. .. .. .. Kyrgyz Republic .. 43 .. 0 .. 0 .. .. Lao PDR 6 28 0 0 0 .. .. 228 Latvia .. 730 .. 125 .. 27 .. 2,352 Lebanon 6 2,573 0 1,070 .. 1,436 6 ­37 Lesotho 17 92 0 0 0 .. 0 ­8 Liberia 225 194 0 0 .. .. .. 0 Libya .. .. .. .. .. .. .. .. Lithuania .. 1,032 .. ­405 .. 130 .. 374 Macedonia, FYR .. 100 .. 187 .. 52 .. ­79 Madagascar 22 29 0 0 0 .. ­15 ­1 Malawi 23 3 0 0 1 .. 2 ­3 Malaysia 2,332 3,966 ­1,239 492 0 ­1,200 ­617 ­1,396 Mali 6 159 0 0 0 9 ­1 3 Mauritania 7 115 0 0 0 .. ­1 14 Mauritius 41 39 0 0 0 36 44 ­36 Mexico 2,549 18,772 661 ­839 1,995 3,353 4,396 1,705 Moldova .. 199 .. ­6 .. 1 .. 90 Mongolia .. 182 .. 0 0 .. .. 0 Morocco 165 1,552 0 ­41 0 64 318 115 Mozambique 9 108 0 0 0 .. 26 ­21 Myanmar 163 300 0 0 0 .. ­8 ­26 Namibia .. .. .. .. .. .. .. .. Nepal 6 2 0 0 0 .. ­14 .. Netherlands 10,676 40,416 .. .. .. .. .. .. New Zealand 1,735 1,979 .. .. .. .. .. .. Nicaragua 1 241 0 0 0 0 20 17 Niger 41 12 0 0 .. .. 10 ­7 Nigeria 588 2,013 0 0 0 .. ­121 ­171 Norway 1,003 3,285 .. .. .. .. .. .. Oman 142 715 0 0 0 10 .. ­524 Pakistan 245 2,183 0 1,092 0 451 ­63 ­158 Panama 136 1,027 ­2 529 ­1 0 ­4 ­148 Papua New Guinea 155 34 0 0 0 .. 49 ­164 Paraguay 77 64 0 0 0 .. ­9 2 Peru 41 2,519 0 2,640 0 766 18 ­981 Philippines 530 1,132 395 1,081 0 1,461 ­286 66 Poland 89 9,602 0 11,384 0 1,341 ­18 2,717 Portugal 2,610 3,200 .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. 2007 World Development Indicators 341 6.8 Global private financial flows Foreign direct Portfolio Bank and trade- investment investment flows related lending $ millions $ millions Bonds Equity $ millions 1990 2005 1990 2005 1990 2005 1990 2005 Romania 0 6,630 0 249 0 229 4 7,066 Russian Federation .. 15,151 .. 10,033 .. ­215 .. 33,290 Rwanda 8 8 0 .. 0 0 ­2 .. Saudi Arabia .. .. .. .. .. .. .. .. Senegal 57 54 0 0 1 .. ­15 18 Serbia and Montenegro .. 1,481 .. 0 .. .. .. 2,071 Sierra Leone 32 59 0 0 0 .. 4 .. Singapore 5,575 20,071 .. .. .. .. .. .. Slovak Republic 93 1,908 .. ­934 .. .. .. ­1,380 Slovenia .. 541 .. .. .. .. .. .. Somalia 6 24 0 0 .. .. .. .. South Africa ­76 6,257 .. 406 389 7,230 .. 587 Spain 13,984 22,789 .. .. .. .. .. .. Sri Lanka 43 272 0 0 0 ­216 10 ­89 Sudan ­31 2,305 0 0 0 0 .. 64 Swaziland 30 ­16 0 0 ­2 0 ­2 11 Sweden 1,982 10,679 .. .. .. .. .. .. Switzerland 5,545 15,420 .. .. .. .. .. .. Syrian Arab Republic 71 427 0 0 0 .. ­9 ­3 Tajikistan .. 54 .. 0 .. 0 .. ­3 Tanzania 0 473 0 0 0 3 5 3 Thailand 2,444 4,527 ­87 1,156 440 5,665 1,574 ­1,565 Togo 18 3 0 0 4 .. 0 .. Trinidad and Tobago 109 1,100 ­52 ­150 0 .. ­126 .. Tunisia 76 723 ­60 ­136 5 12 ­137 4 Turkey 684 9,805 597 3,212 89 5,669 466 14,588 Turkmenistan .. 62 .. .. .. .. .. ­85 Uganda ­6 257 0 0 0 2 16 3 Ukraine .. 7,808 .. 576 .. 82 .. 3,284 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 33,504 158,801 .. .. .. .. .. .. United States 48,490 109,754 .. .. .. .. .. .. Uruguay 42 711 ­16 573 0 20 ­176 ­234 Uzbekistan .. 45 .. 0 .. .. .. ­240 Venezuela, RB 451 2,957 345 5,365 0 91 ­922 ­512 Vietnam 180 1,954 0 724 .. .. .. ­43 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. ­131 ­266 0 0 .. .. 161 24 Zambia 203 259 0 0 0 .. ­9 127 Zimbabwe ­12 103 ­30 0 0 .. 127 ­16 World 203,236 s 974,283 s .. s .. s .. s .. s .. s .. s Low income 2,343 20,522 116 ­2,144 7 12,471 1,623 3,902 Middle income 22,237 260,273 966 57,254 3,383 54,209 13,172 77,231 Lower middle income 11,999 150,874 388 21,431 545 35,662 6,437 11,838 Upper middle income 10,238 109,399 577 35,823 2,838 18,547 6,735 65,393 Low & middle income 24,580 280,795 1,082 55,110 3,390 66,680 14,795 81,134 East Asia & Pacific 10,512 96,898 ­952 9,947 440 26,108 7,180 ­2,772 Europe & Central Asia 3,333 73,687 1,893 28,406 89 6,328 3,612 75,498 Latin America & Carib. 8,242 70,017 101 16,640 2,464 12,351 2,430 ­75 Middle East & N. Africa 741 13,765 ­76 2,581 5 2,311 ­350 2,350 South Asia 542 9,869 147 ­2,868 1 12,204 1,446 4,086 Sub-Saharan Africa 1,210 16,559 ­31 405 393 7,379 477 2,046 High income 178,656 693,488 .. .. .. .. .. .. Europe EMU 61,012 315,043 .. .. .. .. .. .. a. Includes Luxembourg. 342 2007 World Development Indicators 6.8 GLOBAL LINKS Global private financial flows About the data Definitions The data on foreign direct investment (FDI) are based and thus omit nonequity crossborder transactions · Foreign direct investment is net inflows of invest- on balance of payments data reported by the Interna- such as intrafirm flows of goods and services. For ment to acquire a lasting management interest tional Monetary Fund (IMF), supplemented by staff a detailed discussion of the data issues, see the in an enterprise operating in an economy other than estimates using data reported by the United Nations World Bank's World Debt Tables 1993­94 (vol. 1, that of the investor. It is the sum of equity capital, Conference on Trade and Development and official chap. 3). reinvestment of earnings, other long-term capital, national sources. Portfolio fl ow data are compiled from several and short-term capital, as shown in the balance The internationally accepted definition of FDI is market and official sources, including Euromoney of payments. · Portfolio investment flows are net provided in the fi fth edition of the IMF's Balance databases and publications; Micropal; Lipper Ana- and include portfolio debt flows (public and publicly of Payments Manual (1993). Under this definition lytical Services; published reports of private invest- guaranteed and private nonguaranteed bond issues FDI has three components: equity investment, ment houses, central banks, national securities purchased by foreign investors) and non-debt-creat- reinvested earnings, and short- and long-term inter- and exchange commissions, and national stock ing portfolio equity flows (the sum of country funds, company loans between parent firms and foreign exchanges; and the World Bank's Debtor Reporting depository receipts, and direct purchases of shares affiliates. Distinguished from other kinds of interna- System. by foreign investors). · Bank and trade-related tional investment, FDI is made to establish a lasting Gross statistics on international bond and equity lending covers commercial bank lending (public and interest in or effective management control over an issues are produced by aggregating individual trans- publicly guaranteed and private nonguaranteed) and enterprise in another country. As a guideline, the actions reported by market sources. Transactions of other private credits. IMF suggests that investments should account for at public and publicly guaranteed bonds are reported least 10 percent of voting stock to be counted as FDI. through the Debtor Reporting System by World Bank In practice, many countries set a higher threshold. member economies that have received either loans Also, many countries fail to report reinvested earn- from the International Bank for Reconstruction and ings, and the definition of long-term loans differs Development or credits from the International Devel- among countries. opment Association. Information on private nonguar- FDI data do not give a complete picture of inter- anteed bonds is collected from market sources, national investment in an economy. Balance of because official national sources reporting to the payments data on FDI do not include capital raised Debtor Reporting System are not asked to report the locally, which has become an important source of breakdown between private nonguaranteed bonds financing for investment projects in some developing and private nonguaranteed loans. Information on countries. In addition, FDI data capture only cross- transactions by nonresidents in local equity markets border investment flows involving equity participation is gathered from national authorities, investment positions of mutual funds, and market sources. The volume of portfolio investment reported by the Private capital flows to World Bank generally differs from that reported by developing countries are rising 6.8a other sources because of differences in sources, Bank Portfolio bond classifi cation of economies, and method used to Portfolio equity adjust and disaggregate reported information. Dif- Share of GDP (%) Foreign direct investment 5 ferences in reporting arise particularly for foreign investments in local equity markets because clar- 4 ity, adequate disaggregation, and comprehensive and periodic reporting are lacking in many develop- 3 ing economies. By contrast, capital flows through international debt and equity instruments are well 2 recorded, and for these the differences in reporting lie primarily in classification of economies, exchange 1 rates used, whether particular installments of the transactions are included, and treatment of certain 0 offshore issuances. Net private capital flows--calculated as the sum of Data sources ­1 1995 2000 2005 foreign direct investment, portfolio investment flows, Data are compiled from a variety of public and and bank and trade-related lending--are no longer Economic integration over the past decade has private sources, including the World Bank's Debtor included in the table because they are likely to be favored foreign direct investment infl ows to Reporting System, the IMF's International Finan- overestimated. The source of overestimation is the developing countries whose investment climate cial Statistics and Balance of Payments data- possible double counting of intercompany lending, has improved markedly. Other private capital which is a debt liability but may also be included in bases, and other sources mentioned in About the flows have also surged. foreign direct investment flows. There is currently data. These data are also published in the World no practical way to know when double counting has Bank's Global Development Finance 2007. Source: World Bank Debtor Reporting System. occurred and therefore to adjust for it. 2007 World Development Indicators 343 6.9 Financial flows from Development Assistance Committee members Net disbursements Official Other Private Net Total net development assistancea official flowsa grants by flowsa flowsa NGOsa Contributions Foreign Bilateral Multilateral Private Bilateral Bilateral to multilateral direct portfolio portfolio export Total grants loans institutions Total investment investment investment credits 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 $ millions Australia 1,680 1,449 .. 231 74 2,786 1,588 1,066 .. 132 825 5,366 Austria 1,573 1,244 ­12 341 310 2,192 2,090 0 .. 102 139 4,215 Belgium 1,963 1,328 ­20 655 391 539 1,422 .. .. ­884 249 3,142 Canada 3,756 2,853 ­20 923 ­534 9,178 6,647 1,744 .. 787 973 13,373 Denmark 2,109 1,384 ­27 751 ­8 33 33 .. .. .. 81 2,215 Finland 902 591 6 305 .. 723 149 736 .. ­161 16 1,642 France 10,026 7,707 ­468 2,787 ­1,390 7,107 6,856 1,163 .. ­911 .. 15,744 Germany 10,082 8,248 ­801 2,635 7,055 11,399 12,986 ­1,504 47 ­131 1,523 30,059 Greece 384 207 .. 178 .. 325 325 .. .. .. 1 709 Ireland 719 482 .. 237 .. 4,271 .. 4,271 .. .. 308 5,298 Italy 5,091 2,213 57 2,821 ­1,125 44 951 ­2,358 .. 1,451 94 4,103 Japan 13,147 9,195 1,212 2,740 ­2,421 12,278 14,472 1,158 81 ­3,433 255 23,259 Luxembourg 256 187 .. 69 .. .. .. .. .. .. 8 265 Netherlands 5,115 3,696 ­13 1,432 152 17,091 2,348 4,604 ­474 10,614 422 22,781 New Zealand 274 224 .. 50 7 26 26 .. .. .. 94 401 Norway 2,786 1,968 64 754 5 .. .. .. .. .. .. 2,791 Portugal 377 201 17 159 ­3 728 556 0 .. 172 6 1,109 Spain 3,018 2,020 ­157 1,155 67 3,716 4,158 0 .. ­442 .. 6,801 Sweden 3,362 2,247 9 1,106 ­4 159 430 0 .. ­271 29 3,545 Switzerland 1,767 1,380 20 367 .. 5,375 6,827 0 ­722 ­729 332 7,474 United Kingdom 10,767 8,244 ­80 2,603 ­99 34,924 29,865 5,683 .. ­625 726 46,318 United States 27,622 26,042 ­762 2,343 ­1,048 69,206 18,965 50,091 255 ­104 8,629 104,410 Total 106,777 83,109 ­976 24,644 1,430 182,100 110,695 66,652 ­814 5,567 14,712 305,019 Official development assistance Net Gross Commitmentsb Net disbursementsb disbursementsb disbursementsa % of general total per capita total total government $ millions $ $ millions $ millions % of GNI disbursement 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 Australia 1,424 1,557 74 77 1,424 1,557 1,653 1,907 0.27 0.25 0.73 0.68 Austria 632 1,539 78 187 635 1,553 823 1,586 0.23 0.52 0.44 1.04 Belgium 1,191 1,924 116 184 1,223 1,975 1,223 2,062 0.36 0.53 0.72 1.06 Canada 2,165 3,410 70 105 2,195 3,429 2,477 3,395 0.25 0.34 0.59 0.85 Denmark 2,441 2,076 457 382 2,467 2,140 2,313 2,446 1.06 0.81 1.93 1.54 Finland 522 883 101 168 532 888 497 1,117 0.31 0.46 0.63 0.92 France 5,931 9,893 101 163 7,223 11,377 6,774 11,970 0.30 0.47 0.60 0.88 Germany 7,089 10,013 86 121 8,182 11,515 8,061 12,435 0.27 0.36 0.59 0.77 Greece 348 372 32 34 348 372 348 372 0.20 0.17 0.38 0.37 Ireland 365 703 96 176 365 703 365 703 0.29 0.42 0.77 1.05 Italy 2,073 4,958 36 85 2,409 5,127 2,435 5,489 0.13 0.29 0.27 0.60 Japan 12,786 13,534 101 106 15,429 19,190 16,199 19,934 0.28 0.28 0.74 0.78 Luxembourg 179 248 406 552 179 248 179 248 0.71 0.82 1.61 1.63 Netherlands 4,774 5,036 300 308 4,914 5,120 5,240 4,367 0.84 0.82 1.86 1.79 New Zealand 184 251 48 61 184 251 195 340 0.25 0.27 0.54 0.62 Norway 1,766 2,494 393 538 1,775 2,494 1,572 2,535 0.76 0.94 1.77 2.20 Portugal 417 371 41 36 642 376 642 376 0.26 0.21 0.56 0.43 Spain 1,895 2,911 47 67 2,202 3,393 2,202 3,393 0.22 0.27 0.53 0.70 Sweden 2,407 3,377 271 373 2,408 3,377 1,925 3,749 0.80 0.94 1.31 1.67 Switzerland 1,258 1,757 175 236 1,261 1,764 1,281 1,707 0.34 0.44 1.07 1.32 United Kingdom 6,031 10,640 103 177 6,099 11,030 6,099 11,030 0.32 0.47 0.83 1.09 United States 10,861 26,888 39 91 11,851 27,682 13,757 27,926 0.10 0.22 0.30 0.60 Total 66,740 104,835 79 119 73,945 115,561 76,258 119,086 0.22 0.33 0.57 0.80 Note: Components may not sum to totals because of gaps in reporting. a. At current prices and exchange rates. b. At 2004 prices and exchange rates. 344 2007 World Development Indicators 6.9 GLOBAL LINKS Financial flows from Development Assistance Committee members About the data The flows of official and private financial resources Union, and certain advanced developing countries and than 25 percent. · Private flows consist of flows at from the members of the Development Assistance territories. This distinction has been dropped. ODA market terms financed from private sector resources Committee (DAC) of the Organisation for Economic recipients now comprise all low- and middle-income in donor countries. They include changes in hold- Co-operation and Development (OECD) to developing countries, except those that are members of the Group ings of private long-term assets by residents of the economies are compiled by DAC, based principally on of Eight or the European Union (including countries with reporting country. · Foreign direct investment is reporting by DAC members using standard question- a firm date for EU accession).The content and structure investment by residents of DAC member countries naires issued by the DAC Secretariat. of tables 6.9 through 6.12 have been revised to reflect to acquire a lasting management interest (at least DAC exists to help its members coordinate their this change. Because official aid flows are quite small 10 percent of voting stock) in an enterprise operating development assistance and to encourage the expan- relative to ODA, the net effect of these changes is in the recipient country. The data reflect changes in sion and improve the effectiveness of the aggregate believed to be minor. the net worth of subsidiaries in recipient countries resources flowing to recipient economies. In this capac- Flows are transfers of resources, either in cash or whose parent company is in the DAC source country. ity DAC monitors the flow of all financial resources, but in the form of commodities or services measured on · Bilateral portfolio investment covers bank lend- its main concern is official development assistance a cash basis. Short-term capital transactions (with ing and the purchase of bonds, shares, and real (ODA). Grants or loans to countries and territories on one year or less maturity) are not counted. Repay- estate by residents of DAC member countries in the DAC list of aid recipients have to meet three criteria ments of the principal (but not interest) of ODA loans recipient countries. · Multilateral portfolio invest- to be counted as ODA. They are undertaken by the offi - are recorded as negative flows. Proceeds from offi - ment records the transactions of private banks and cial sector. They promote economic development and cial equity investments in a developing country are nonbanks in DAC member countries in the securities welfare as the main objective. And they are provided reported as ODA, while proceeds from their later sale issued by multilateral institutions. · Private export at concessional financial terms (if a loan they have a are recorded as negative flows. credits are loans extended to recipient countries grant element of at least 25 percent, calculated at Because the table is based on donor country by the private sector in DAC member countries to a discount rate of 10 percent). The DAC Statistical reports, it does not provide a complete picture of the promote trade; they may be supported by an offi - Reporting Directives provide the most detailed expla- resources received by developing economies for two cial guarantee. · Net grants by nongovernmental nation of this definition and all ODA-related rules. reasons. First, flows from DAC members are only part organizations (NGOs) are private grants by nongov- This definition excludes nonconcessional flows of the aggregate resource flows to these economies. ernmental organizations, net of subsidies from the from official creditors, which are classified as "other Second, the data that record contributions to mul- official sector. · Total net fl ows comprise ODA or official flows," and aid for military purposes. Transfer tilateral institutions measure the flow of resources official aid flows, other official flows, private flows, payments to private individuals, such as pensions, made available to those institutions by DAC mem- and net grants by nongovernmental organizations. reparations, and insurance payouts, are in general bers, not the flow of resources from those institu- · Net disbursements are gross disbursements of not counted. In addition to financial flows, technical tions to developing and transition economies. grants and loans minus repayments of principal on cooperation is included in ODA. Most expenditures for Aid as a share of gross national income (GNI), aid earlier loans. · Gross disbursements are the actual peacekeeping under UN mandates and assistance to per capita, and ODA as a share of the general gov- international transfer of fi nancial resources and refugees are counted in ODA. Also included are contri- ernment disbursements of the donor are calculated goods and services (valued at the cost to the donor). butions to multilateral institutions, such as the United by the OECD. The denominators used in calculating · Commitments are firm obligations, expressed in Nations and its specialized agencies, and concessional these ratios may differ from corresponding values writing and backed by the necessary funds, under- funding to multilateral development banks. In 1999, elsewhere in this book because of differences in tim- taken by an official donor to provide specified assis- to avoid double counting of extrabudgetary expendi- ing or definitions. tance to a recipient country or a multilateral organi- tures reported by DAC countries and flows reported zation. · Aid as a percentage of GNI is the donor's Definitions by the United Nations, all UN agencies revised their contribution of ODA as a share of its gross national data to include only regular budgetary expenditures · Official development assistance comprises flows income. · Aid as a percentage of general govern- since 1990 (except for the World Food Programme and that meet the DAC definition of ODA and are made to ment disbursements is the donor's contribution of the United Nations High Commissioner for Refugees, countries and territories on the DAC list of aid recipi- ODA as a share of public spending. which revised their data from 1996 onward). ents. · Bilateral grants are transfers of money or in DAC has revised the list of countries and territories kind for which no repayment is required. · Bilateral Data sources that are counted as aid recipients. These revisions will loans are loans extended by governments or official govern aid reporting for three years, starting with 2005 agencies that have a grant element of at least 25 per- Data on financial flows are compiled by OECD-DAC flows. In the past DAC distinguished aid going to Part I cent (calculated at a rate of discount of 10 percent). and published in its annual statistical report, Geo- and Part II countries. Part I countries, the recipients of · Contributions to multilateral institutions are con- graphical Distribution of Financial Flows to Aid Recipi- ODA, comprised many of the countries classified by the cessional funding received by multilateral institutions ents and its annual Development Cooperation Report. World Bank as low- and middle-income economies. Part from DAC members in the form of grants or capital Data are available electronically on the OECD's II countries, whose assistance was designated official subscriptions. · Other official flows are transactions International Development Statistics CD-ROM and aid, included the more advanced countries of Central by the official sector whose main objective is other at www.oecd.org/dac/stats/idsonline. and Eastern Europe, countries of the former Soviet than development or whose grant element is less 2007 World Development Indicators 345 6.10 Allocation of bilateral aid from Development Assistance Committee members Aid by purpose and tying status Total Share of bilateral ODA commitment % Investment projects, Emergency assistance Technical cooperation program aid, and other and developmental and administrative $ millionsa resource provisions Debt-related aid food aid charges Untied aidb 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 Australia 758 1,449 14.9 7.3 1.1 1.4 14.2 22.4 60.0 56.3 77.4 71.9 Austria 378 1,260 18.8 4.2 32.9 69.4 8.5 8.0 26.7 15.7 59.2 88.7 Belgium 498 1,360 14.5 11.5 10.6 35.1 8.1 9.1 69.0 40.2 85.7 95.7 Canada 1,412 2,816 10.9 36.3 0.9 16.7 19.9 14.1 26.1 33.2 24.9 59.4 Denmark 940 1,739 67.1 50.0 0.0 3.8 10.8 2.2 13.3 9.0 80.5 86.5 Finland 200 693 15.8 33.1 .. 0.2 16.8 21.6 37.5 9.5 89.5 95.1 France 3,412 8,862 27.0 19.3 23.4 42.4 5.2 8.2 43.5 30.2 68.0 94.7 Germany 2,968 9,236 26.1 28.6 2.9 42.7 6.6 4.1 61.4 24.1 93.2 93.0 Greece 99 207 40.5 12.5 .. 0.0 7.7 13.5 22.6 51.7 23.5 73.6 Ireland 154 482 66.3 67.2 .. 0.1 12.7 17.6 5.1 9.1 .. 100.0 Italy 729 2,686 42.1 21.7 29.7 62.6 15.0 3.1 6.6 5.5 38.2 92.1 Japan 13,854 17,265 87.1 46.4 10.4 32.9 1.0 3.9 24.9 15.0 86.4 89.6 Luxembourg 93 187 80.1 68.7 .. 0.0 12.6 12.8 3.6 8.0 96.7 99.1 Netherlands 2,834 3,529 32.2 55.8 6.7 2.5 13.9 16.3 18.5 22.0 95.3 96.2 New Zealand 85 224 90.4 48.9 .. 0.0 3.4 29.3 8.8 25.0 .. 92.3 Norway 795 2,033 44.0 48.0 0.9 0.1 25.6 20.3 18.1 22.5 97.7 99.6 Portugal 320 224 1.5 26.3 53.6 1.5 1.1 5.7 29.5 58.1 98.2 60.7 Spain 913 2,362 17.9 21.6 6.6 38.7 4.6 6.1 17.1 26.1 47.2 86.6 Sweden 1,093 2,256 58.9 58.3 3.3 2.3 20.2 17.9 13.9 11.8 85.4 98.3 Switzerland 630 1,407 52.4 41.0 0.9 15.9 23.1 23.4 16.7 13.1 93.6 97.4 United Kingdom 2,759 8,509 42.5 16.8 5.6 41.5 12.5 7.4 31.4 14.9 91.5 100.0 United States 10,030 25,836 24.3 24.4 1.3 16.3 21.0 18.1 56.0 41.9 .. .. Total 44,954 94,623 47.7 30.8 7.8 27.5 10.5 11.0 35.5 26.5 81.1 91.8 Aid by sector Social infrastructure and services Economic Production sectors Multi- Total infrastructure sector or sector and services cross- allocable cutting Water Government supply and and civil Transport and Total Education Health Population sanitation society Total communications Total Agriculture Share of bilateral 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 ODA commitment (%) Australia 45.2 5.6 6.1 2.9 2.4 21.4 3.8 3.0 6.6 5.3 14.1 69.8 Austria 15.2 7.6 2.2 0.1 1.3 3.3 0.7 0.1 1.9 0.8 1.6 19.4 Belgium 32.9 8.9 6.2 1.4 2.8 7.5 6.2 2.7 4.8 3.8 3.3 47.1 Canada 39.8 8.8 10.3 1.5 1.5 15.6 4.3 0.8 5.5 4.4 6.0 55.6 Denmark 41.3 7.4 5.6 0.9 10.3 13.9 14.9 10.5 18.3 13.2 10.2 84.6 Finland 36.5 7.1 4.0 1.0 6.2 16.1 9.2 1.4 7.4 6.2 16.6 69.6 France 25.2 16.5 3.1 0.1 1.3 1.2 9.4 6.4 2.2 1.4 4.9 41.7 Germany 18.2 4.4 1.3 0.8 4.1 5.3 12.0 1.7 3.1 2.3 16.2 49.4 Greece 55.5 18.4 15.3 0.2 0.3 18.6 8.9 8.5 1.2 0.4 5.3 70.9 Ireland 54.0 12.0 20.0 2.0 3.5 14.9 1.5 1.1 3.8 3.4 4.2 63.6 Italy 10.5 2.0 3.8 0.4 2.6 1.4 10.9 0.3 1.3 0.7 4.4 27.1 Japan 20.0 4.9 1.2 0.0 12.3 0.6 23.4 17.1 7.7 5.8 3.1 54.3 Luxembourg 51.8 14.9 18.4 4.6 6.6 2.5 2.9 0.6 5.4 3.8 10.5 70.6 Netherlands 37.6 14.1 3.4 3.3 5.4 8.8 8.8 1.2 4.7 3.9 14.3 65.4 New Zealand 34.7 14.9 5.0 2.3 1.0 9.9 1.2 0.5 4.0 2.2 3.5 43.3 Norway 43.0 9.5 7.7 2.1 2.1 16.1 7.9 0.6 5.5 4.0 10.5 66.9 Portugal 55.8 28.6 4.4 0.0 1.1 11.1 13.1 12.2 2.7 1.3 8.4 80.0 Spain 26.8 9.2 4.9 1.2 2.5 4.5 8.5 3.2 4.4 3.0 8.2 47.9 Sweden 36.5 4.9 4.8 4.1 3.0 16.2 5.9 2.6 4.9 2.9 7.2 54.5 Switzerland 19.6 2.9 2.6 0.3 2.5 10.2 6.2 2.2 7.7 4.4 13.4 46.8 United Kingdom 25.3 3.9 3.3 3.6 0.5 12.8 2.7 1.7 3.2 1.9 3.9 35.1 United States 42.8 2.7 4.9 5.2 3.9 18.3 7.8 4.7 5.4 2.5 4.4 60.4 Average 30.5 6.1 3.8 2.3 4.8 9.7 10.6 5.2 5.2 3.3 6.5 52.8 a. At current prices and exchange rates. b. Excludes technical cooperation and administrative charges. 346 2007 World Development Indicators 6.10 GLOBAL LINKS Allocation of bilateral aid from Development Assistance Committee members About the data Aid can be used in many ways. The sectoral destina- food aid aim to provide humanitarian relief to lessen equipment). Contributions mainly take the form of tion to which aid goes, the form that aid takes, and the the adverse impact of sudden disasters and to sup- the supply of human resources from donors or action procurement restrictions attached to aid are among port food programs in nonemergency situations. directed to human resources (such as training or important factors that influence aid effectiveness. The These types of aid do not generally contribute to advice). Assistance provided specifically to facilitate data on allocation of official development assistance financing long-term development. implementation of a capital project is not included. (ODA) presented in this table are based principally on The proportion of untied aid is reported here · Administrative charges include the total current reporting by members of the Organisation for Economic because tying arrangements may prevent recipi- budget outlays of institutions responsible for the Co-operation and Development (OECD) Development ents from obtaining the best value for their money formulation and implementation of donor's aid pro- Assistance Committee (DAC). For more detailed expla- and so reduces the value of the aid received. Tying grams as well as other administrative costs incurred nation of ODA, see About the data for table 6.9. arrangements require recipients to purchase goods by donors in the process of aid delivery. · Untied aid The sector of destination for an ODA contribution and services from the donor country or from a speci- is the share of ODA that is not subject to restrictions is defined as the specific area of the recipient coun- fied group of countries. Such arrangements may be by donors on procurement sources. · Social infra- try's economic or social structure that the transfer justified on the grounds that they prevent a recipient structure and services refer to efforts to develop the is intended to foster. The DAC sector classification from misappropriating or mismanaging aid receipts, human resources potential of aid recipients. · Educa- comprises a hierarchy of three levels. The top level is but they may also be motivated by a desire to benefit tion includes general teaching and instruction at all grouped by themes, including social infrastructure and suppliers in the donor country. The same volume levels, as well as construction specifically to improve services, economic infrastructure and services, pro- of aid may have different purchasing power depend- or adapt educational establishments. Training in a duction sectors, and multisector cross-cutting areas. ing on the relative costs of suppliers in countries to particular fi eld, such as agriculture, is reported The second level includes six sectors under social which the aid is tied and the degree to which each against the sector concerned. · Health covers infrastructure and services (for example, education recipient's aid basket is untied. assistance to hospitals, clinics, other medical and and health), five sectors under economic infrastruc- Reporting on the sectoral destination and the form dental services, public health administration, and ture and services (for example, transport and stor- of aid by donors may not be complete. Furthermore, medical insurance programs. · Population covers age), and three production sectors (for example, agri- measures of aid allocation may differ from the per- all activities related to family planning and research culture). The third level comprises subsectors, such spectives of donors and recipients because of dif- into population problems. · Water supply and sani- as basic education and basic health. Some contribu- ference in classification, availability of information, tation cover assistance for water supply and use, tions are not susceptible to allocation by sectors and and time of recording. sanitation, and water resources development (includ- are reported as nonsector allocable aid. Examples ing rivers). · Government and civil society include Definitions include aid for general development purposes, bal- assistance to strengthen the administrative appa- ance of payment support, aid relating to debt, emer- · Bilateral (ODA) commitments are firm obligations, ratus and government planning, and activities pro- gency assistance, administrative costs of donors, and expressed in writing and backed by the necessary moting good governance and strengthening civil soci- support to nongovernmental organizations. funds, undertaken by official bilateral donors to pro- ety. · Economic infrastructure and services group The form in which an ODA contribution reaches the vide specified assistance to a recipient country or assistance for networks, utilities, and services that benefiting sector or the economy in general is also a multilateral organization. Bilateral commitments facilitate economic activity. · Transport and commu- important. A distinction is made between technical are recorded in the full amount of expected transfer, nications cover road, rail, water, and air transport and cooperation and resource provision. Aid in the form irrespective of the time required for completing dis- post and telecommunications, radio, television, and of technical cooperation includes grants to nationals bursements. · Investment projects, program aid, print media. · Production sectors refer to contribu- of aid recipient countries receiving education or train- and other resource provisions are aid contributions tions to all directly productive sectors. · Agriculture ing at home or abroad, and payments to consultants, in the form of cash transfers, aid in kind, and the includes agricultural sector policy, agricultural devel- advisers, and similar personnel as well as teachers financing of capital projects. Their aim is to increase opment and inputs, crop and livestock production, and administrators serving in recipient countries or improve the recipient's stock of physical capital and agricultural credit, cooperatives, and research. (including the cost of associated equipment). Because and to support recipient's development plans and · Multisector or cross-cutting includes support for technical cooperation is spent mostly in the donor other activities with finance and commodity supply. projects that straddle several sectors. · Total sector economy, it is combined with the administrative costs · Debt-related aid groups all actions relating to debt, allocable is the sum of aid that can be assigned to a of donor aid programs. Resource provision involves including forgiveness, swaps, buybacks, reschedul- specific sector or multisector. mainly cash or in-kind transfers and financing of capi- ing, and refinancing. · Emergency assistance and Data sources tal projects, with deliverables being financial support developmental food aid comprise emergency and and the provision of commodities and supplies. distress relief (including aid to refugees and assis- Data on aid flows are published by OECD-DAC in its Two other types of aid are presented because they tance for disaster preparedness) as well as all food annual statistical report, Geographical Distribution serve distinctive purposes. Debt-related aid aims to aid­related costs. · Technical cooperation refers to of Financial Flows to Aid Recipients, and its annual provide debt relief on liabilities that recipient coun- the provision of resources whose main aim is to aug- Development Cooperation Report. Data are avail- tries have difficulty servicing. Thus, this type of aid ment the stock of human intellectual capital, such able electronically on the OECD's International may not provide a full value of new resource flows for as the level of knowledge, skills, technical know-how, Development Statistics CD-ROM and at www.oecd. development, in particular for heavily indebted poor and productive aptitude of the population in the aid org/dac/stats/idsonline. countries. Emergency assistance and development recipient country (including the cost of associated 2007 World Development Indicators 347 6.11 Aid dependency Net official Aid per Aid dependency development capita ratios assistance Aid as % Aid as % of of central Aid as Aid as % of gross imports of goods government $ millions $ % of GNI capital formation and services expenditure 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 Afghanistan 136 2,775 .. .. .. 37.8 .. 151.6 .. .. .. 316.9 Albania 317 319 104 102 8.4 3.7 32.5 16.1 21.0 8.1 .. .. Algeria 201 371 7 11 0.4 0.4 1.5 1.2 .. .. 1.8 .. Angola 302 442 22 28 4.1 1.5 22.0 17.9 4.1 2.3 .. .. Argentina 53 100 1 3 0.0 0.1 0.1 0.3 0.1 0.2 .. .. Armenia 216 193 70 64 11.0 3.9 60.6 13.3 21.2 8.4 .. 21.7 Australia Austria Azerbaijan 139 223 17 27 2.8 2.0 12.8 4.7 5.8 2.5 .. .. Bangladesh 1,168 1,321 9 9 2.4 2.1 10.8 9.0 11.7 8.6 .. .. Belarus 40 54 4 5 0.3 0.2 1.2 0.6 0.5 0.3 1.5 0.6 Belgium Benin 238 349 33 41 10.6 8.2 55.9 41.6 31.7 .. .. 32.9 Bolivia 472 583 57 63 5.8 6.5 31.0 45.4 19.3 17.3 .. 23.5 Bosnia and Herzegovina 737 546 192 140 13.1 5.2 68.8 28.6 17.4 6.7 .. 15.2 Botswana 31 71 17 40 0.5 0.7 1.4 2.2 1.0 1.4 .. .. Brazil 232 192 1 1 0.0 0.0 0.2 0.1 0.2 0.2 .. .. Bulgariaa 311 .. 39 .. 2.5 .. 13.5 .. 3.7 .. 7.6 .. Burkina Faso 335 660 30 50 12.9 12.8 56.8 61.8 48.5 .. .. 108.9 Burundi 93 365 14 48 12.8 46.8 212.5 378.2 55.8 97.7 .. .. Cambodia 396 538 31 38 11.2 9.1 64.2 44.2 16.1 11.0 .. 112.6 Cameroon 379 414 26 25 4.0 2.5 22.5 11.6 12.8 .. .. .. Canada Central African Republic 75 95 20 24 8.0 7.0 72.8 .. .. .. .. .. Chad 130 380 16 39 9.5 8.6 40.4 39.8 .. .. .. .. Chile 49 152 3 9 0.1 0.1 0.3 0.6 0.2 0.3 .. 0.7 China 1,728 1,757 1 1 0.1 0.1 0.4 0.2 0.6 0.2 .. .. Hong Kong, Chinaa 4 .. 1 .. 0.0 .. 0.0 .. 0.0 .. .. .. Colombia 187 511 4 11 0.2 0.4 1.6 2.2 1.1 1.6 .. 1.3 Congo, Dem. Rep. 177 1,828 4 32 4.5 26.9 118.7 181.2 .. .. 15.2 .. Congo, Rep. 33 1,449 10 362 1.5 36.8 4.9 118.1 1.6 35.7 .. .. Costa Rica 11 30 3 7 0.1 0.2 0.4 0.6 0.1 0.3 0.3 0.7 Côte d'Ivoire 351 119 21 7 3.6 0.8 31.2 6.8 7.9 1.5 .. 4.3 Croatia 66 125 15 28 0.4 0.3 1.8 1.0 0.6 0.5 0.8 0.8 Cuba 44 88 4 8 .. .. .. .. .. .. .. .. Czech Republica 438 .. 43 .. 0.8 .. 2.6 .. 1.1 .. 2.3 .. Denmark Dominican Republic 56 77 7 9 0.3 0.3 1.2 1.3 0.5 0.6 2.1 .. Ecuador 146 210 12 16 1.0 0.6 4.6 2.4 2.3 1.5 .. .. Egypt, Arab Rep. 1,328 926 20 13 1.3 1.0 6.8 5.7 5.6 2.6 6.6 .. El Salvador 180 199 29 29 1.4 1.2 8.1 7.6 3.0 2.4 .. 58.0 Eritrea 176 355 49 81 27.7 36.9 86.9 182.2 34.5 .. .. .. Estoniaa 64 .. 47 .. 1.2 .. 4.2 .. 1.2 .. .. .. Ethiopia 686 1,937 11 27 8.8 17.4 42.7 66.0 41.0 39.2 .. .. Finland France Gabon 12 54 9 39 0.3 0.7 0.9 3.3 0.5 .. .. .. Gambia, The 49 58 37 38 12.2 13.0 66.9 50.4 .. 19.6 .. .. Georgia 169 310 36 69 5.3 4.8 25.7 18.4 13.6 8.9 47.9 27.9 Germany Ghana 600 1,120 30 51 12.4 10.6 50.5 36.0 17.3 16.4 .. .. Greece Guatemala 263 254 24 20 1.4 0.8 7.7 4.2 4.4 2.5 12.5 7.3 Guinea 153 182 18 19 5.0 5.6 22.4 46.1 15.7 .. .. .. Guinea-Bissau 80 79 59 50 39.5 27.4 329.8 180.0 .. .. .. .. Haiti 208 515 26 60 5.5 12.1 20.8 .. 15.1 28.7 .. .. 348 2007 World Development Indicators 6.11 GLOBAL LINKS Aid dependency Net official Aid per Aid dependency development capita ratios assistance Aid as % Aid as % of of central Aid as Aid as % of gross imports of goods government $ millions $ % of GNI capital formation and services expenditure 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 Honduras 449 681 70 94 7.7 8.6 24.5 27.5 12.8 12.4 .. .. Hungarya 252 .. 25 .. 0.6 .. 1.8 .. 0.6 .. 1.3 .. India 1,463 1,724 1 2 0.3 0.2 1.3 0.6 1.8 .. 2.0 .. Indonesia 1,654 2,524 8 11 1.1 0.9 4.5 4.0 2.5 2.5 .. .. Iran, Islamic Rep. 130 104 2 2 0.1 0.1 0.4 0.2 0.7 .. 0.2 0.3 Iraq 100 21,654 .. .. .. .. .. .. .. .. .. .. Ireland Israela 800 .. 127 .. 0.7 .. 3.2 .. 1.4 .. 1.5 .. Italy Jamaica 10 36 4 13 0.1 0.4 0.5 1.2 0.2 0.5 0.4 1.1 Japan Jordan 552 622 114 114 6.4 4.8 30.9 20.7 8.7 5.1 24.1 13.9 Kazakhstan 189 229 13 15 1.1 0.4 5.7 1.5 1.8 0.7 7.5 2.2 Kenya 510 768 17 22 4.1 4.1 23.0 24.4 12.9 11.4 23.9 .. Korea, Dem. Rep. 73 81 3 4 .. .. .. .. .. .. .. .. Korea, Rep.a ­198 .. ­4 .. 0.0 .. ­0.1 .. ­0.1 .. ­0.2 .. Kuwait a 3 .. 1 .. 0.0 .. 0.1 .. 0.0 .. .. .. Kyrgyz Republic 215 268 44 52 16.7 11.4 78.3 76.5 28.5 18.0 99.2 .. Lao PDR 282 296 53 50 17.0 11.4 77.7 32.2 44.1 .. .. .. Latviaa 91 .. 38 .. 1.2 .. 4.9 .. 2.3 .. 4.1 .. Lebanon 199 243 59 68 1.2 1.1 5.9 5.5 .. 1.3 3.8 .. Lesotho 37 69 21 38 3.4 3.9 10.1 11.7 4.4 4.8 .. .. Liberia 67 236 22 72 17.4 54.1 .. 270.9 .. .. .. .. Libya 14 24 3 4 .. 0.1 0.3 .. 0.2 0.2 .. .. Lithuaniaa 99 .. 28 .. 0.9 .. 4.4 .. 1.6 .. 3.2 .. Macedonia, FYR 251 230 125 113 7.1 4.0 31.5 20.0 10.6 6.1 .. .. Madagascar 322 929 20 50 8.4 18.7 55.1 82.4 20.3 127.9 15.6 .. Malawi 446 575 39 45 26.1 28.4 188.7 191.1 65.7 .. .. .. Malaysia 45 32 2 1 0.1 0.0 0.2 0.1 0.0 0.0 0.3 .. Mali 359 691 31 51 15.0 13.6 60.4 57.5 34.4 .. .. .. Mauritania 211 190 80 62 19.4 9.9 101.0 23.1 .. .. .. .. Mauritius 20 32 17 26 0.5 0.5 1.8 2.2 0.7 0.7 2.2 2.5 Mexico ­56 189 ­1 2 0.0 0.0 0.0 0.1 0.0 0.1 ­0.1 .. Moldova 123 192 29 46 9.4 5.9 39.7 22.1 11.3 6.7 32.9 21.9 Mongolia 217 212 91 83 23.1 11.6 63.5 31.8 27.5 .. .. .. Morocco 419 652 15 22 1.3 1.3 5.3 4.9 3.1 2.7 .. 4.0 Mozambique 876 1,286 49 65 24.7 20.7 69.1 95.2 49.7 38.4 .. .. Myanmar 106 145 2 3 .. .. .. .. 4.0 .. .. .. Namibia 152 123 80 61 4.4 2.0 22.8 7.9 8.2 .. 14.1 .. Nepal 387 428 16 16 7.0 5.8 29.0 20.0 21.2 15.3 .. 34.4 Netherlands New Zealand Nicaragua 561 740 114 144 15.0 15.4 47.2 51.3 23.6 21.6 74.2 71.7 Niger 208 515 18 37 11.7 15.2 101.4 81.8 43.0 .. .. .. Nigeria 174 6,437 1 49 0.4 7.4 1.9 31.2 1.1 20.1 .. .. Norway Oman 45 31 18 12 0.2 .. 1.9 .. 0.6 0.2 0.9 .. Pakistan 692 1,666 5 11 1.0 1.5 5.4 8.9 4.8 5.2 5.6 10.4 Panama 16 20 5 6 0.1 0.1 0.6 0.6 0.2 0.2 0.6 .. Papua New Guinea 275 266 52 45 8.2 .. .. .. 13.7 8.2 26.2 .. Paraguay 82 51 15 9 1.2 0.7 6.1 3.2 2.3 1.2 .. 4.2 Peru 398 398 15 14 0.8 0.5 3.7 2.7 3.4 1.9 4.2 2.9 Philippines 575 562 8 7 0.7 0.5 3.6 3.7 1.1 1.0 4.3 3.2 Polanda 1,396 .. 36 .. 0.8 .. 3.3 .. 2.3 .. .. .. Portugal Puerto Rico 2007 World Development Indicators 349 6.11 Aid dependency Net official Aid per Aid dependency development capita ratios assistance Aid as % Aid as % of of central Aid as Aid as % of gross imports of goods government $ millions $ % of GNI capital formation and services expenditure 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 2000 2005 Romaniaa 432 .. 19 .. 1.2 .. 6.0 .. 2.9 .. .. .. Russian Federationa 1,561 .. 11 .. 0.6 .. 3.2 .. 2.2 .. .. .. Rwanda 321 576 40 64 17.9 27.1 101.3 119.5 71.2 82.0 .. .. Saudi Arabia 22 26 1 1 0.0 0.0 0.1 0.1 0.0 0.0 .. .. Senegal 423 689 41 59 9.9 8.5 46.2 35.8 21.9 .. 70.9 .. Serbia and Montenegro 1,134 1,132 139 140 13.2 4.4 92.9 23.5 .. .. .. .. Sierra Leone 181 343 40 62 29.4 29.6 356.3 191.6 68.8 67.5 98.8 .. Singaporea 1 .. 0 .. 0.0 .. 0.0 .. 0.0 .. 0.0 .. Slovak Republica 113 .. 21 .. 0.6 .. 2.1 .. 0.7 .. .. .. Sloveniaa 61 .. 31 .. 0.3 .. 1.2 .. 0.5 .. 0.8 .. Somalia 101 236 14 29 .. .. .. .. .. .. .. .. South Africa 487 700 11 15 0.4 0.3 2.3 1.6 1.3 0.9 1.3 1.0 Spain Sri Lanka 276 1,189 14 61 1.8 5.1 6.0 19.3 3.2 11.4 7.3 24.1 Sudan 220 1,829 7 50 2.1 7.1 9.7 28.5 8.5 19.9 .. .. Swaziland 13 46 13 41 0.9 1.7 4.8 9.1 0.9 2.0 .. .. Sweden Switzerland Syrian Arab Republic 158 78 9 4 0.9 0.3 5.0 1.5 2.4 0.7 .. .. Tajikistan 124 241 20 37 13.1 10.9 109.9 73.0 .. 13.9 160.3 .. Tanzania 1,019 1,505 29 39 11.4 12.5 63.7 65.8 45.7 36.6 .. .. Thailand 698 ­171 11 ­3 0.6 ­0.1 2.5 ­0.3 0.9 ­0.1 .. ­0.6 Togo 70 87 13 14 5.4 4.0 29.4 22.4 10.5 .. .. 25.5 Trinidad and Tobago ­2 ­2 ­1 ­2 0.0 0.0 ­0.1 .. 0.0 .. .. .. Tunisia 222 376 23 38 1.2 1.4 4.2 5.6 2.1 2.3 4.1 4.5 Turkey 327 464 5 6 0.2 0.1 0.7 0.5 0.5 0.4 0.5 .. Turkmenistan 31 28 7 6 1.2 0.4 3.1 1.5 .. .. .. .. Uganda 817 1,198 34 42 14.1 14.0 69.1 64.8 51.9 42.9 92.4 .. Ukraine 541 410 11 9 1.8 0.5 8.5 2.6 2.8 0.9 6.4 1.3 United Arab Emiratesa 3 .. 1 .. 0.0 .. 0.0 .. .. .. .. .. United Kingdom United States Uruguay 17 15 5 4 0.1 0.1 0.6 0.7 0.3 0.3 0.3 0.3 Uzbekistan 186 172 8 7 1.4 1.2 8.3 5.4 .. .. .. .. Venezuela, RB 76 49 3 2 0.1 0.0 0.3 0.2 0.3 0.1 0.3 0.1 Vietnam 1,681 1,905 21 23 5.5 3.7 18.2 10.3 9.3 4.7 .. .. West Bank and Gaza 637 1,102 215 304 13.3 25.0 47.4 106.7 .. .. .. .. Yemen, Rep. 263 336 15 16 3.0 2.5 14.3 8.3 6.2 4.7 .. .. Zambia 795 945 74 81 25.8 13.9 131.4 50.3 53.6 .. .. .. Zimbabwe 176 368 14 28 2.5 11.4 17.5 78.6 .. .. .. .. World 57,760 s 106,372 s 10 w 17 w 0.2 w 0.2 w 0.8 w .. w 0.6 w 0.7 w .. w .. w Low income 18,718 40,353 9 17 2.3 2.9 9.8 9.9 9.2 .. .. .. Middle income 24,895 46,913 8 15 0.5 0.6 1.9 2.0 1.5 1.5 .. .. Lower middle income 17,560 43,146 7 17 0.6 0.9 2.2 2.7 2.0 2.5 .. .. Upper middle income 6,176 2,776 11 5 0.3 0.1 1.2 0.3 0.7 0.2 .. .. Low & middle income 55,970 106,338 11 20 0.9 1.1 3.7 3.8 2.9 2.9 .. .. East Asia & Pacific 8,589 9,497 5 5 0.5 0.3 1.6 0.8 1.4 0.8 .. .. Europe & Central Asia 11,203 5,731 23 11 1.1 0.2 5.0 1.0 2.6 0.5 .. .. Latin America & Carib. 4,841 6,309 9 11 0.3 0.3 1.2 1.2 0.9 0.9 .. .. Middle East & N. Africa 4,534 26,946 16 88 1.0 3.9 4.1 15.1 3.3 11.2 .. .. South Asia 4,194 9,260 3 6 0.7 0.9 2.9 3.0 3.6 .. .. .. Sub-Saharan Africa 13,194 32,620 20 44 4.1 5.5 21.6 27.3 10.9 13.4 .. .. High income Europe EMU Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. a. Starting with 2005 flows, official development assistance will not be reported for these countries. 350 2007 World Development Indicators 6.11 GLOBAL LINKS Aid dependency About the data Ratios of aid to gross national income (GNI), gross The table does not distinguish among different the balance of payments. Moreover, DAC statistics capital formation, imports, and government spend- types of aid (program, project, or food aid; emer- exclude purely military aid. ing provide a measure of the recipient country's gency assistance; postconflict peacekeeping assis- The nominal values used here may overstate the dependency on aid. But care must be taken in draw- tance; or technical cooperation), each of which may real value of aid to the recipient. Changes in interna- ing policy conclusions. For foreign policy reasons, have very different effects on the economy. Expendi- tional prices and in exchange rates can reduce the some countries have traditionally received large tures on technical cooperation do not always directly purchasing power of aid. The practice of tying aid, amounts of aid. Thus aid dependency ratios may benefit the economy to the extent that they defray still prevalent though declining in importance, also reveal as much about a donor's interest as they do costs incurred outside the country on the salaries tends to reduce its purchasing power (see About the about a recipient's needs. Ratios in Sub-Saharan and benefits of technical experts and the overhead data for table 6.10). Africa are generally much higher than those in other costs of firms supplying technical services. The values for population, GNI, gross capital for- regions, and they increased in the 1980s. These In 1999, to avoid double counting extrabudgetary mation, imports of goods and services, and central high ratios are due only in part to aid flows. Many expenditures reported by DAC countries and flows government expenditure used in computing the ratios African countries saw severe erosion in their terms reported by the United Nations, all UN agencies are taken from World Bank and International Mon- of trade in the 1980s, which, along with weak poli- revised their data since 1990 to include only regular etary Fund (IMF) databases. The aggregates also cies, contributed to falling incomes, imports, and budgetary expenditures (except for the World Food refer to World Bank definitions. Therefore the ratios investment. Thus the increase in aid dependency Programme and the United Nations Office of the High shown may differ somewhat from those computed ratios reflects events affecting both the numerator Commissioner for Refugees, which revised their data and published by the Organisation for Economic Co- and the denominator. from 1996 onward). These revisions have affected operation and Development (OECD). Aid not allocated As defined here, aid includes official development net ODA and official aid and, as a result, aid per by country or region--including administrative costs, assistance (ODA; see About the data for table 6.9). capita and aid dependency ratios. research on development issues, and aid to nongov- The data cover loans and grants from Development Because the table relies on information from ernmental organizations--is included in the world Assistance Committee (DAC) member countries, mul- donors, it is not necessarily consistent with infor- total. Thus regional and income group totals do not tilateral organizations, and non-DAC donors. They do mation recorded by recipients in the balance of pay- sum to the world total. not reflect aid given by recipient countries to other ments, which often excludes all or some technical Definitions developing countries. As a result, some countries assistance--particularly payments to expatriates that are net donors (such as Saudi Arabia) are shown made directly by the donor. Similarly, grant commod- · Net offi cial development assistance comprises in the table as aid recipients (see table 6.10a). ity aid may not always be recorded in trade data or in flows (net of repayment of principal) that meet the DAC definition of ODA and are made to countries Official development assistance and territories on the DAC list of aid recipients. See from non-DAC donors, 2001­05 6.11a About the data for table 6.9. · Aid per capita is ODA divided by population. · Aid dependency ratios are Net disbursements ($ millions) calculated using values in U.S. dollars converted at 2001 2002 2003 2004 2005 official exchange rates. For definitions of GNI, gross OECD members (non-DAC) capital formation, imports of goods and services, Czech Republic 26 45 91 108 135 and central government expenditure, see Definitions Hungary .. .. 21 70 100 for tables 1.1, 4.8, and 4.11. Iceland 10 13 18 21 27 Korea, Rep. 265 279 366 423 752 Poland 36 14 27 118 205 Slovak Republic 8 7 15 28 56 Turkey 64 73 67 339 601 Arab countries Data sources Kuwait 73 20 138 209 547 Data on fi nancial fl ows are compiled by DAC Saudi Arabia 490 2,478 2,391 1,734 .. and published in its annual statistical report, United Arab Emirates 127 156 188 181 141 Geographical Distribution of Financial Flows to Other donors Aid Recipients, and in its annual Development Israela 93 131 112 84 85 Cooperation Report. Data are available in elec- Other donors 2 3 4 22 87 tronic format on the OECD's International Devel- Total 1,194 3,218 3,436 3,759 3,231 opment Statistics CD-ROM and at www.oecd.org/ dac/stats/idsonline. Data on population, GNI, Note: China also provides aid, but does not disclose the amount. a. Includes $50.1 million in 2001, $87.8 million in 2002, $68.8 million in 2003, $47.9 million in 2004, and gross capital formation, imports of goods and $49.2 million in 2005 for first-year sustenance expenses for people arriving from developing countries (many of which services, and central government expenditure are are experiencing civil war or severe unrest) or people who have left their country for humanitarian or political reasons. Source: Organisation for Economic Co-operation and Development. from World Bank and IMF databases. 2007 World Development Indicators 351 6.12 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United United DAC donors $ millions States Japan Kingdom Germany France Netherlands Italy Canada Sweden Spain $ millions 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Afghanistan 2,191.7 1,341.8 71.1 219.9 99.2 19.5 79.1 27.4 89.5 44.2 19.0 181.1 Albania 190.0 42.6 17.6 3.8 30.5 12.6 9.1 8.6 0.6 8.6 7.8 48.1 Algeria 289.7 1.4 1.9 .. 2.6 255.0 0.1 9.5 1.9 2.2 ­4.0 19.2 Angola 258.2 64.1 26.3 14.1 12.2 23.6 12.8 11.6 4.0 11.2 16.1 62.3 Argentina 77.9 1.5 11.0 .. 13.0 12.3 0.3 21.6 3.5 0.4 12.3 2.0 Armenia 148.1 53.6 5.4 6.2 30.0 25.2 11.6 1.2 1.0 1.6 0.4 11.9 Australia Austria Azerbaijan 109.7 44.1 8.3 0.0 19.1 15.1 5.5 1.2 1.4 1.2 0.1 13.7 Bangladesh 562.9 49.7 ­1.0 203.3 46.1 12.2 60.7 1.9 50.8 23.9 0.2 115.3 Belarus 33.7 1.7 0.4 0.1 13.9 3.8 0.2 0.1 0.2 5.7 1.8 5.8 Belgium Benin 206.9 23.6 17.9 .. 27.6 42.9 22.7 0.0 10.8 1.4 0.5 59.6 Bolivia 388.3 90.6 40.6 ­24.3 51.4 16.5 46.7 4.6 14.9 20.8 66.7 59.9 Bosnia and Herzegovina 287.6 46.1 16.7 6.6 26.1 28.5 21.1 2.7 7.7 46.9 6.1 79.2 Botswana 51.9 39.8 ­0.9 0.3 3.5 1.4 1.0 .. 1.9 0.3 0.2 4.5 Brazil 170.9 ­29.6 30.8 6.5 77.0 28.7 15.4 1.5 8.6 2.4 10.2 19.4 Bulgaria Burkina Faso 338.5 20.0 18.9 2.6 29.7 79.6 53.8 1.5 16.5 15.2 2.9 97.9 Burundi 180.7 54.7 0.5 14.8 11.4 14.5 22.9 3.4 5.1 5.3 0.7 47.6 Cambodia 344.4 67.5 100.6 21.5 24.8 30.1 8.0 1.9 8.5 14.8 0.7 66.0 Cameroon 336.0 13.6 19.3 4.6 183.0 21.5 17.5 0.8 34.9 8.7 ­5.6 37.7 Canada Central African Republic 62.2 17.2 0.1 .. 3.0 35.0 0.4 0.4 1.6 1.3 0.6 2.7 Chad 166.6 61.8 2.1 ­0.7 24.0 44.9 1.6 0.1 6.2 2.5 1.6 22.7 Chile 75.6 ­0.1 10.6 1.1 35.2 14.4 0.8 ­1.3 3.8 2.4 4.1 4.7 China 1,689.4 19.6 1,064.3 55.5 255.1 153.6 27.8 ­12.8 30.0 10.1 8.4 78.0 Hong Kong, China Colombia 457.9 334.3 ­2.2 1.3 21.5 ­2.0 29.9 ­6.0 9.1 14.6 31.0 26.7 Congo, Dem. Rep. 1,034.3 141.4 376.3 77.6 51.1 88.0 46.2 1.0 24.8 23.7 9.2 195.1 Congo, Rep. 1,359.5 15.1 0.2 0.6 63.7 1,014.3 6.1 61.2 22.3 2.2 134.2 39.6 Costa Rica 25.0 ­12.1 ­1.4 5.9 5.4 4.9 3.3 ­0.4 3.0 1.0 2.3 13.2 Côte d'Ivoire 151.0 32.7 1.4 3.1 13.2 67.9 2.4 1.2 6.5 3.6 3.6 15.4 Croatia 61.3 21.2 0.5 1.8 7.1 3.5 0.3 ­1.5 0.4 5.6 0.3 22.0 Cuba 68.7 10.1 5.8 9.0 3.5 3.5 1.3 0.1 8.0 0.9 15.2 11.3 Czech Republic Denmark Dominican Republic 56.6 18.9 3.0 0.5 14.7 ­5.9 1.3 ­4.4 2.5 0.2 21.4 4.3 Ecuador 174.8 53.2 6.2 0.3 17.0 2.6 13.2 0.1 4.5 1.1 48.2 28.5 Egypt, Arab Rep. 658.8 397.4 ­36.1 6.2 109.2 80.4 7.8 ­2.8 15.7 1.6 28.5 51.0 El Salvador 162.4 46.8 22.7 0.0 8.9 3.4 6.2 0.2 4.4 6.0 42.6 21.3 Eritrea 226.4 141.5 7.2 3.1 4.9 1.7 5.8 25.0 4.2 3.1 0.3 29.4 Estonia Ethiopia 1,201.7 625.2 34.2 75.5 49.9 15.9 58.7 86.9 64.9 68.4 4.5 117.7 Finland France Gabon 29.8 1.8 6.1 .. 1.9 16.8 0.0 0.1 2.6 .. 0.2 0.2 Gambia, The 15.0 2.0 4.4 1.5 1.4 0.6 0.3 0.3 2.5 0.7 0.2 1.2 Georgia 198.4 73.3 7.3 3.3 51.1 17.5 12.0 1.3 3.5 4.2 0.1 24.8 Germany Ghana 602.7 66.8 44.2 119.7 66.4 39.2 70.5 3.5 51.7 26.7 38.9 75.1 Greece Guatemala 218.5 37.8 32.8 0.1 18.1 3.4 26.4 ­1.6 8.1 15.2 38.9 39.3 Guinea 127.8 42.8 12.0 1.5 19.3 32.4 1.0 .. 11.5 1.3 0.6 5.6 Guinea-Bissau 39.4 1.4 0.0 .. 0.7 15.6 2.6 0.2 2.0 .. 2.3 14.7 Haiti 354.4 154.0 0.9 1.4 3.7 82.0 3.3 .. 81.7 1.6 10.3 15.4 352 2007 World Development Indicators 6.12 GLOBAL LINKS Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United United DAC donors $ millions States Japan Kingdom Germany France Netherlands Italy Canada Sweden Spain $ millions 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Honduras 456.1 88.1 103.5 30.2 24.0 4.5 16.3 23.5 28.6 20.3 95.0 22.0 Hungary India 846.3 53.3 71.5 579.2 ­68.8 ­8.0 72.8 3.5 34.0 16.5 11.2 81.1 Indonesia 2,247.2 160.8 1,223.1 24.1 164.7 29.3 176.0 3.4 95.9 21.6 33.7 314.7 Iran, Islamic Rep. 78.2 3.8 ­2.5 0.7 40.6 14.8 6.8 0.6 .. 0.2 0.4 13.0 Iraq 21,426.6 10,829.7 3,502.9 1,317.5 2,019.7 635.8 120.5 953.7 385.5 11.3 191.8 1,458.2 Ireland Israel Italy Jamaica 11.2 17.5 ­17.9 23.1 ­13.8 ­1.3 ­4.0 ­3.4 7.6 0.3 0.2 2.9 Japan Jordan 440.8 353.9 23.6 6.1 21.9 1.1 0.8 14.4 7.9 0.6 3.2 7.4 Kazakhstan 153.3 57.1 66.2 1.7 14.1 4.1 2.4 .. 1.2 0.8 1.1 4.7 Kenya 494.6 137.8 60.9 86.3 49.6 8.1 28.3 ­10.5 21.6 42.1 1.5 69.1 Korea, Dem. Rep. 39.4 7.9 .. .. 5.2 ­0.4 0.7 0.8 1.6 5.5 .. 18.2 Korea, Rep. Kuwait Kyrgyz Republic 126.4 41.4 21.0 9.4 27.6 1.7 3.1 .. 0.7 2.5 0.1 19.0 Lao PDR 159.0 7.4 54.1 0.2 15.0 22.6 2.9 .. 3.7 15.0 .. 38.2 Latvia Lebanon 129.8 38.4 1.0 0.6 12.9 57.9 0.2 0.5 3.4 0.4 2.5 12.2 Lesotho 39.1 1.9 6.7 7.6 5.0 ­1.3 0.1 .. 3.7 0.0 .. 15.3 Liberia 148.6 90.0 .. 7.5 1.3 1.6 7.2 0.0 2.9 14.8 1.5 21.7 Libya 16.8 0.1 0.3 .. 3.7 2.4 0.2 9.3 .. .. 0.1 0.7 Lithuania Macedonia, FYR 167.1 45.3 11.3 2.8 28.9 3.0 29.7 2.6 0.3 11.2 0.9 31.2 Madagascar 500.5 80.4 39.6 13.5 11.0 91.2 1.0 51.0 2.2 0.0 135.4 75.2 Malawi 322.1 53.1 19.7 102.0 25.3 2.4 19.4 0.0 17.0 19.3 1.2 62.6 Malaysia 20.1 3.4 ­2.1 1.3 7.9 ­4.1 0.3 .. 1.5 0.7 0.5 10.7 Mali 378.2 58.0 23.2 1.3 29.0 90.0 65.8 .. 35.5 21.7 5.3 48.5 Mauritania 124.5 21.5 14.7 .. 12.5 47.5 0.6 1.9 3.4 0.6 15.7 6.1 Mauritius 22.2 1.2 16.6 ­0.8 ­0.9 3.6 0.0 .. 1.6 0.0 .. 1.0 Mexico 160.6 128.6 11.8 ­9.7 25.3 19.4 0.2 0.1 5.8 0.3 ­24.5 3.1 Moldova 106.1 30.5 3.7 3.0 7.8 25.8 8.3 .. 0.6 8.5 0.1 17.8 Mongolia 131.9 18.1 56.5 0.3 28.2 6.8 7.5 0.1 1.5 2.5 .. 10.5 Morocco 289.3 ­13.2 ­54.2 .. 61.8 197.6 1.6 39.4 4.5 0.6 29.0 22.2 Mozambique 770.8 96.0 14.8 80.8 42.6 13.7 64.5 21.6 56.2 79.3 29.4 272.1 Myanmar 77.8 4.1 25.5 10.6 4.4 1.6 0.7 0.3 0.5 4.5 .. 25.7 Namibia 98.8 39.5 0.4 1.3 21.4 3.4 3.2 0.0 1.5 5.4 7.6 15.0 Nepal 348.7 54.7 63.4 61.6 63.1 ­1.7 12.0 0.0 10.2 1.2 0.1 84.1 Netherlands New Zealand Nicaragua 509.5 102.4 49.2 6.1 24.5 1.9 33.9 81.0 9.0 40.9 60.1 100.4 Niger 255.7 30.6 23.7 8.0 24.8 70.2 7.6 0.8 17.0 1.6 16.2 55.3 Nigeria 5,966.3 120.5 69.2 2,200.9 1,180.9 1,436.1 202.0 529.6 19.2 0.6 1.9 205.5 Norway Oman 3.9 ­1.2 3.7 .. 0.2 0.9 .. 0.0 .. .. 0.0 0.2 Pakistan 832.2 362.4 73.8 63.1 34.1 26.0 43.1 ­0.8 51.1 9.1 4.6 165.6 Panama 17.3 7.5 2.1 0.1 1.1 0.3 0.1 .. 1.1 .. 4.5 0.4 Papua New Guinea 245.3 0.0 ­5.2 .. 2.4 0.1 2.5 .. 0.4 0.1 .. 244.9 Paraguay 55.3 9.4 27.5 ­0.2 2.5 0.4 1.9 0.1 3.2 1.9 7.1 1.6 Peru 310.2 76.4 43.5 3.3 39.0 6.8 13.5 1.2 15.4 3.6 65.5 42.1 Philippines 526.4 98.4 276.4 6.4 49.4 ­8.5 22.3 ­8.6 19.4 2.5 10.4 58.2 Poland Portugal Puerto Rico 2007 World Development Indicators 353 6.12 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United United DAC donors $ millions States Japan Kingdom Germany France Netherlands Italy Canada Sweden Spain $ millions 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Romania Russian Federation Rwanda 292.0 63.3 2.9 82.0 18.5 14.0 28.4 0.2 10.0 23.4 0.9 48.6 Saudi Arabia 13.8 1.2 5.2 .. 1.2 6.1 0.1 .. .. .. .. 0.1 Senegal 440.1 39.8 28.0 6.9 34.3 158.2 20.5 11.7 23.5 0.6 82.5 34.3 Serbia and Montenegro 808.2 181.5 121.6 93.0 67.8 57.5 10.8 16.1 9.2 35.5 16.3 199.0 Sierra Leone 130.4 21.0 2.1 60.6 6.4 4.2 7.2 0.7 6.9 2.1 2.4 16.9 Singapore Slovak Republic Slovenia Somalia 146.1 36.9 .. 10.7 5.1 1.7 14.2 11.1 6.0 12.9 0.1 47.3 South Africa 486.0 136.6 16.1 70.3 37.0 28.3 55.5 3.2 14.6 22.9 0.4 101.3 Spain Sri Lanka 857.1 58.9 312.9 13.7 75.2 40.7 56.2 20.8 45.7 51.7 3.5 177.7 Sudan 1,472.0 771.5 2.1 196.5 44.9 23.0 154.8 16.8 21.8 45.5 9.7 185.6 Swaziland 20.2 0.9 25.9 ­9.3 ­1.2 0.2 0.1 0.3 3.8 .. .. ­0.5 Sweden Switzerland Syrian Arab Republic 5.9 0.4 ­45.3 0.2 12.9 26.3 2.2 0.4 1.9 0.2 1.1 5.8 Tajikistan 105.9 57.6 9.9 4.4 8.3 0.7 0.9 .. 6.5 4.6 0.0 13.0 Tanzania 871.0 108.9 36.1 215.9 49.9 4.5 90.2 4.7 33.0 91.8 4.1 232.0 Thailand ­219.9 15.0 ­313.9 0.3 9.2 2.1 7.9 1.5 7.8 6.5 0.8 43.0 Togo 59.4 3.0 0.8 0.9 8.4 30.5 5.3 0.0 3.3 0.4 2.0 5.0 Trinidad and Tobago 6.1 0.5 2.0 0.1 0.4 1.2 0.0 .. 1.8 .. 0.1 0.0 Tunisia 269.1 ­15.2 51.1 21.2 29.0 182.3 ­1.9 ­9.2 1.0 0.5 5.6 4.7 Turkey 51.8 ­13.9 ­62.3 ­1.1 ­33.6 114.6 4.5 ­5.0 ­2.4 2.5 12.4 36.0 Turkmenistan 11.8 9.6 0.1 0.1 1.2 0.7 0.0 .. 0.1 .. .. 0.2 Uganda 704.3 242.3 14.4 55.6 51.4 7.7 80.1 3.9 12.8 47.9 ­0.6 188.5 Ukraine 252.1 113.4 2.5 10.8 53.2 15.5 0.6 0.0 18.6 10.6 0.5 26.5 United Arab Emirates United Kingdom United States Uruguay 2.8 ­1.5 2.2 .. 0.7 3.7 0.0 ­2.8 2.1 0.4 2.3 ­4.3 Uzbekistan 124.1 37.5 54.4 0.6 17.0 3.8 0.5 .. 0.9 1.0 0.1 8.4 Venezuela, RB 20.8 9.0 4.3 0.2 2.0 6.7 0.1 0.2 1.8 0.1 ­5.4 2.0 Vietnam 1,252.1 27.1 602.7 96.6 82.9 96.8 56.1 ­3.2 28.4 41.9 9.1 213.7 West Bank and Gaza 569.4 180.2 5.8 23.5 39.8 30.6 29.9 15.9 15.9 36.9 39.4 151.5 Yemen, Rep. 134.7 17.6 8.4 20.3 41.8 6.3 31.9 2.9 1.1 0.6 0.1 3.8 Zambia 835.9 124.2 131.9 165.7 118.2 15.8 55.9 0.2 49.7 34.2 0.2 139.8 Zimbabwe 178.8 33.4 4.1 45.5 13.5 3.8 13.6 1.1 13.5 15.1 0.8 34.4 World 82,133.3 s 25,279.5 s 10,406.1 s 8,164.0 s 7,446.8 s 7,239.2 s 3,682.7 s 2,269.5 s 2,832.8 s 2,255.9 s 1,863.0 s 10,693.7 s Low income 26,746.4 5,685.2 2,280.5 4,932.4 2,471.1 2,822.0 1,555.5 803.2 941.5 799.2 433.1 4,022.8 Middle income 39,752.9 14,537.6 6,862.5 1,851.5 4,011.0 3,657.2 989.5 1,268.7 989.2 497.1 1,110.2 3,978.5 Lower middle income 37,437.8 13,962.1 6,799.7 1,695.4 3,811.0 3,092.9 852.0 1,232.9 891.1 436.6 1,043.8 3,620.4 Upper middle income 1,646.6 376.5 43.5 138.6 122.8 504.1 67.2 24.8 68.5 37.1 36.6 227.0 Low & middle income 82,112.6 25,278.5 10,395.5 8,164.0 7,445.6 7,233.1 3,682.7 2,269.5 2,831.3 2,255.9 1,863.0 10,693.6 East Asia & Pacific 7,665.1 773.3 3,222.5 225.4 667.8 439.8 324.5 ­16.7 205.9 147.2 83.0 1,592.5 Europe & Central Asia 2,973.8 842.7 284.9 150.3 370.0 338.3 120.5 30.6 50.3 151.0 48.0 587.3 Latin America & Carib. 4,589.8 1,344.8 409.5 154.6 433.5 250.1 272.5 121.9 368.9 170.8 584.2 479.1 Middle East & N. Africa 24,468.5 11,801.1 3,468.4 1,398.7 2,418.8 1,528.4 218.7 1,037.7 445.8 66.3 319.2 1,765.5 South Asia 5,735.4 1,921.9 632.6 1,142.7 251.0 89.1 332.3 52.8 288.0 146.8 38.7 839.6 Sub-Saharan Africa 23,066.3 4,192.2 1,133.1 3,745.8 2,444.2 3,892.2 1,400.2 872.7 977.1 793.2 563.8 3,051.8 High income Europe EMU Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. 354 2007 World Development Indicators 6.12 GLOBAL LINKS Distribution of net aid by Development Assistance Committee members About the data The table shows net bilateral aid to low- and middle- been assigned to regional totals based on the World In 1999 all UN agencies revised their data since income economies from members of the Develop- Bank's regional classification system. Aid to unspeci- 1990 to include only regular budgetary expenditures ment Assistance Committee (DAC) of the Organi- fied economies has been included in regional totals (except for the World Food Programme and the United sation for Economic Co-operation and Development and, when possible, in income group totals. Aid not Nations Office of the High Commissioner for Refu- (OECD). The DAC compilation of the data includes allocated by country or region--including adminis- gees, which revised their data from 1996 onward). aid to some countries and territories not shown in trative costs, research on development issues, and They did so to avoid double counting extrabudgetary the table and aid to unspecified economies that is aid to nongovernmental organizations--is included expenditures reported by DAC countries and flows recorded only at the regional or global level. Aid to in the world total. Thus regional and income group reported by the United Nations. countries and territories not shown in the table has totals do not sum to the world total. The table is based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to The flow of bilateral aid from DAC members information on such aid expenditures as develop- reflects global events and priorities 6.12a ment-oriented research, stipends and tuition costs Total bilateral aid, 2005 for aid-financed students in donor countries, and All DAC members United States payment of experts hired by donor countries. More- over, a full accounting would include donor country contributions to multilateral institutions, the flow Iraq of resources from multilateral institutions to recipi- 26% ent countries, and flows from countries that are not Others Iraq 45% 43% members of DAC. Others 59% Nigeria 7% The expenditures that countries report as official development assistance (ODA) have changed. For Indonesia 3% example, some DAC members have reported as Afghanistan 3% China 2% ODA the aid provided to refugees during the first 12 Egypt, Arab Rep. 2% Afghanistan 5% Ethiopia 2% Sudan 3% months of their stay within the donor's borders. Some of the aid recipients shown in the table are Japan United Kingdom also aid donors. See table 6.10a for a summary of ODA from non-DAC countries. Nigeria Iraq 27% Definitions Others 34% 34% Others 44% · Net aid comprises net bilateral official develop- ment assistance to part I recipients and net bilateral Iraq 16% official aid to part II recipients (see About the data for Indonesia Congo, Dem. 12% table 6.9). · Other DAC donors are Australia, Aus- China India Rep. 4% 10% 7% tria, Belgium, Denmark, Finland, Greece, Ireland, Vietnam 6% Tanzania 3% Afghanistan 3% Luxembourg, New Zealand, Norway, Portugal, and Switzerland. Germany France Nigeria Iraq 20% 27% Others Others Congo, Rep. 50% 50% 14% Nigeria Data sources 16% Iraq 9% Data on financial flows are compiled by DAC and published in its annual statistical report, Geograph- China 3% Algeria 4% ical Distribution of Financial Flows to Aid Recipients, Indonesia 2% Morocco 3% Cameroon 2% and its annual Development Cooperation Report. The figure shows the distribution of aid from all DAC members and the top five donors to the top five Data are available electronically on the OECD's recipients in 2005. International Development Statistics CD-ROM and Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. at www.oecd.org/dac/stats/idsonline. 2007 World Development Indicators 355 6.13 Net financial flows from multilateral institutions International financial institutions United Nations Total $ millions Regional development IMF banks World Bank Conces- Non- Conces- Non- $ millions IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Afghanistan .. .. .. .. .. .. .. 7.1 4.9 17.1 .. 26.3 55.3 Albania 29.6 0.0 2.7 0.0 0.0 11.5 21.4 1.5 0.4 1.1 1.3 1.9 71.3 Algeria 0.0 ­125.3 0.0 ­611.6 0.0 ­211.6 ­493.3 1.0 0.7 1.1 3.7 5.6 ­1,429.7 Angola 25.3 0.0 0.0 0.0 0.4 ­1.6 1.8 5.9 1.9 7.8 9.1 15.6 66.2 Argentina 0.0 ­566.4 0.0 ­3,571.2 0.0 60.9 0.0 0.6 0.5 0.6 .. 3.2 ­4,071.8 Armenia 31.4 ­0.6 ­22.6 ­2.1 0.0 ­7.9 ­0.4 1.3 0.6 0.8 1.0 2.7 4.1 Australia Austria Azerbaijan 45.6 0.0 ­5.2 ­23.9 0.5 ­11.8 5.0 2.9 0.7 1.2 1.9 3.6 20.5 Bangladesh 394.5 0.0 97.4 0.0 57.3 86.1 ­13.8 16.0 5.4 11.6 18.2 10.2 682.7 Belarus 0.0 ­8.9 0.0 ­8.6 0.0 ­10.9 ­14.9 0.6 0.3 0.8 .. 0.8 ­41.0 Belgium Benin 42.8 0.0 ­6.2 0.0 34.0 ­0.3 ­13.0 3.2 2.7 3.1 2.7 4.5 73.4 Bolivia 62.2 0.0 ­22.5 ­3.5 98.1 ­60.8 76.5 0.9 1.5 1.8 3.2 2.2 159.5 Bosnia and Herzegovina 55.6 ­23.6 0.0 ­39.4 0.0 ­3.8 61.8 0.9 0.3 0.9 .. 9.0 61.7 Botswana ­0.5 ­1.1 0.0 0.0 ­1.8 17.6 ­13.8 0.6 0.9 1.0 .. 4.3 7.1 Brazil 0.0 ­255.4 0.0 ­23,809.8 0.0 722.8 87.7 0.7 1.1 2.7 .. 5.4 ­23,245.0 Bulgaria 0.0 114.8 0.0 ­443.4 0.0 1.0 ­9.1 .. .. .. .. .. ­336.8 Burkina Faso 102.0 0.0 11.5 0.0 55.6 0.0 15.7 5.9 2.7 6.8 2.1 3.5 205.8 Burundi 18.9 0.0 21.1 0.0 5.9 ­2.7 ­2.2 6.5 1.1 5.1 1.7 2.7 58.1 Cambodia 33.7 0.0 ­8.7 0.0 82.2 0.0 8.6 4.5 2.0 4.8 1.2 3.3 131.6 Cameroon 19.0 ­31.5 ­34.7 0.0 20.4 ­17.6 ­14.8 4.5 2.8 3.2 1.2 5.8 ­42.0 Canada Central African Republic 0.0 0.0 ­4.9 0.0 0.0 0.0 ­0.4 2.6 2.3 2.7 3.1 5.2 10.7 Chad 59.6 ­1.8 ­7.2 0.0 19.6 0.0 9.7 5.2 2.1 8.4 12.8 3.7 112.2 Chile ­0.7 ­151.2 0.0 0.0 ­1.4 ­29.8 0.0 0.7 0.2 0.5 .. 1.9 ­179.8 China ­146.7 273.7 0.0 0.0 0.0 688.3 ­0.3 9.4 4.7 14.4 8.0 12.4 863.8 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. .. Colombia ­0.7 486.9 0.0 0.0 ­20.7 ­1,145.9 75.7 1.3 1.1 1.4 2.9 2.8 ­595.3 Congo, Dem. Rep. 225.9 0.0 39.4 0.0 22.5 0.0 ­10.9 14.9 7.0 21.6 0.5 12.9 333.8 Congo, Rep. 29.7 0.0 7.5 ­7.8 9.9 ­30.5 ­2.3 1.4 0.6 1.1 2.0 6.5 18.1 Costa Rica ­0.2 ­12.1 0.0 0.0 ­11.2 ­156.0 ­25.5 0.4 0.6 0.7 .. 2.5 ­200.7 Côte d'Ivoire 0.0 0.0 ­91.1 0.0 0.1 ­0.3 ­14.5 3.5 1.6 4.4 2.9 9.0 ­84.5 Croatia 0.0 11.0 0.0 0.0 0.0 51.9 57.2 0.6 .. 0.2 .. 4.4 125.3 Cuba .. .. .. .. .. .. .. 1.0 0.7 0.7 3.9 2.0 8.3 Czech Republic 0.0 ­19.2 0.0 0.0 0.0 0.0 ­48.9 .. .. .. .. .. ­68.0 Denmark Dominican Republic ­0.7 26.4 0.0 219.9 ­21.0 86.1 0.1 0.7 0.8 1.1 0.0 2.0 315.4 Ecuador ­1.1 ­35.2 0.0 ­195.0 ­26.6 ­80.4 54.5 1.3 0.9 1.1 0.2 2.9 ­277.5 Egypt, Arab Rep. 27.8 19.8 0.0 0.0 ­0.4 ­91.4 125.5 1.2 1.8 2.7 4.1 5.1 96.2 El Salvador ­0.8 88.4 0.0 0.0 ­23.1 16.7 28.4 0.7 0.8 1.1 0.7 1.5 114.3 Eritrea 56.7 0.0 0.0 0.0 12.3 0.0 ­5.5 3.3 2.0 2.5 4.4 8.6 84.3 Estonia 0.0 ­5.1 0.0 0.0 0.0 0.0 ­3.8 .. .. .. .. .. ­8.9 Ethiopia 161.8 0.0 ­4.0 0.0 127.0 ­7.6 33.1 12.1 4.2 24.1 14.1 13.1 377.8 Finland France Gabon 0.0 ­7.0 0.0 ­24.7 ­0.2 ­35.5 17.4 0.6 0.1 0.6 .. 5.2 ­43.4 Gambia, The 15.6 0.0 ­2.0 0.0 7.2 0.0 21.0 2.2 0.6 1.1 1.7 2.9 50.2 Georgia 52.1 0.0 ­9.5 ­3.4 0.0 ­2.3 0.6 1.9 0.5 0.9 0.8 4.1 45.7 Germany Ghana 290.8 ­2.2 7.4 0.0 57.7 ­3.4 19.7 4.2 3.7 4.5 3.3 8.9 394.5 Greece Guatemala 0.0 1.4 0.0 0.0 ­17.6 ­7.0 8.9 1.0 1.5 1.0 3.6 2.5 ­4.7 Guinea 22.1 0.0 ­25.7 0.0 4.3 ­7.4 ­24.5 2.3 1.4 3.7 3.0 13.6 ­7.2 Guinea-Bissau 10.6 0.0 ­2.9 ­0.3 3.5 0.0 2.7 2.7 1.0 1.6 2.1 2.0 23.1 Haiti ­5.1 0.0 ­4.5 15.3 53.6 0.0 ­1.6 4.8 4.2 3.0 1.8 1.8 73.3 356 2007 World Development Indicators 6.13 GLOBAL LINKS Net financial flows from multilateral institutions International financial institutions United Nations Total $ millions Regional development IMF banks World Bank Conces- Non- Conces- Non- $ millions IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Honduras 142.9 ­69.7 ­0.4 0.0 52.1 ­21.1 31.4 1.1 2.7 1.3 1.1 1.2 142.7 Hungary 0.0 ­39.5 0.0 0.0 0.0 ­6.7 ­121.7 .. .. .. .. .. ­167.9 India 571.9 715.8 0.0 0.0 0.0 419.5 ­12.8 15.4 13.7 34.7 9.7 14.5 1,782.3 Indonesia 40.1 ­805.2 0.0 ­1,144.7 48.2 465.4 ­38.3 8.2 15.7 6.4 7.6 11.5 ­1,385.0 Iran, Islamic Rep. 0.0 102.1 0.0 0.0 0.0 0.0 1.0 0.6 1.5 2.1 0.4 9.8 117.5 Iraq .. .. .. .. .. .. .. .. 4.7 1.9 .. 2.1 8.7 Ireland Israel .. .. .. .. .. .. .. .. .. .. .. .. .. Italy Jamaica 0.0 ­22.0 0.0 ­0.9 ­5.3 ­35.0 11.0 0.7 .. 1.1 .. 1.7 ­48.7 Japan Jordan ­2.6 ­25.0 0.0 ­77.1 0.0 0.0 19.3 0.6 0.3 1.2 0.3 104.6 21.7 Kazakhstan 0.0 ­621.0 0.0 0.0 0.1 ­181.3 ­30.4 0.9 0.6 1.4 .. 2.2 ­827.5 Kenya ­20.1 ­1.1 66.5 0.0 19.0 ­5.7 ­14.8 5.7 3.8 4.9 11.9 26.9 97.0 Korea, Dem. Rep. .. .. .. .. .. .. .. 2.6 1.0 2.6 8.4 3.0 17.6 Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 29.5 0.0 ­13.0 0.0 29.6 ­8.8 3.8 2.4 0.8 1.1 .. 2.8 48.2 Lao PDR 26.1 0.0 ­6.1 0.0 57.4 11.1 ­2.7 4.3 1.2 2.2 2.9 2.2 98.6 Latvia 0.0 ­61.8 0.0 0.0 0.0 ­5.9 9.3 .. .. .. .. .. ­58.3 Lebanon 0.0 ­6.2 0.0 0.0 0.0 0.0 ­8.3 0.7 0.6 0.8 .. 68.6 56.3 Lesotho 8.1 ­3.5 0.0 0.0 ­2.1 ­1.7 ­3.1 0.9 0.1 1.3 4.9 2.1 7.0 Liberia 0.0 0.0 0.0 ­0.1 0.0 0.0 0.0 4.1 0.8 3.8 .. 16.6 25.2 Libya .. .. .. .. .. .. .. .. .. .. 1.1 1.8 3.0 Lithuania 0.0 ­97.4 0.0 ­24.8 0.0 ­2.6 ­7.1 .. .. .. .. .. ­132.0 Macedonia, FYR 5.6 43.2 ­8.1 12.8 0.0 6.4 ­0.6 1.2 0.0 1.1 .. 3.1 64.8 Madagascar 209.2 0.0 6.5 0.0 18.4 0.0 ­2.0 6.0 1.5 5.9 3.7 2.6 251.8 Malawi 32.6 ­0.2 ­3.3 ­3.2 25.9 ­1.9 1.7 7.7 3.7 6.1 5.5 4.4 78.9 Malaysia 0.0 ­92.3 0.0 0.0 0.0 ­39.4 13.2 0.6 0.5 0.4 .. 3.8 ­113.3 Mali 102.0 0.0 ­14.8 0.0 53.0 0.0 21.3 4.3 1.7 6.9 5.7 3.1 183.2 Mauritania 40.1 0.0 ­9.6 0.0 7.1 ­1.4 39.3 2.8 2.2 1.8 6.4 2.5 91.2 Mauritius ­0.6 ­6.3 0.0 0.0 ­0.1 ­8.2 14.5 0.2 0.0 .. .. 1.7 1.0 Mexico 0.0 ­381.8 0.0 0.0 0.0 346.4 0.0 1.0 2.2 1.2 .. 4.0 ­27.1 Moldova 23.6 ­13.4 0.0 ­21.5 0.0 ­13.5 ­7.4 1.8 0.4 1.1 .. 1.2 ­27.8 Mongolia 12.1 0.0 ­5.9 0.0 26.3 0.0 4.4 1.4 1.1 1.1 .. 3.3 43.7 Morocco ­1.4 ­46.3 0.0 0.0 0.9 292.8 196.8 1.0 2.8 1.6 0.0 3.0 451.2 Mozambique 221.6 0.0 ­10.5 0.0 76.6 12.7 31.9 7.4 5.9 8.7 6.4 5.3 366.0 Myanmar 0.0 0.0 0.0 0.0 0.0 0.0 ­1.9 11.5 4.0 8.6 1.1 6.8 30.2 Namibia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.7 1.1 0.6 5.0 8.1 Nepal 2.5 0.0 0.0 0.0 17.6 0.0 5.1 6.3 6.6 4.9 5.5 9.5 57.9 Netherlands New Zealand Nicaragua 63.1 0.0 ­8.9 0.0 120.9 ­8.0 17.7 3.3 2.5 1.2 1.8 1.5 195.0 Niger 64.6 0.0 12.1 0.0 14.7 ­2.5 41.7 6.5 3.3 8.2 15.8 3.0 167.4 Nigeria 245.9 ­223.6 0.0 0.0 9.1 ­82.7 0.0 8.0 7.9 23.7 .. 8.0 ­3.7 Norway Oman 0.0 0.0 0.0 0.0 0.0 0.0 ­182.8 .. 0.2 0.0 .. 1.1 ­181.5 Pakistan 513.0 ­94.4 ­76.8 ­160.5 158.0 187.5 ­10.8 11.6 9.5 14.0 10.7 22.6 584.5 Panama 0.0 ­30.5 0.0 ­9.8 ­8.9 12.8 ­8.4 0.7 0.5 0.5 .. 1.7 ­41.5 Papua New Guinea ­3.6 ­2.9 0.0 ­61.1 ­1.3 3.5 ­2.6 2.2 0.7 1.7 .. 3.6 ­59.8 Paraguay ­1.5 ­8.2 0.0 0.0 ­15.2 3.6 ­2.9 0.5 0.8 1.0 .. 1.0 ­21.0 Peru 0.0 ­17.3 0.0 ­39.5 ­9.5 268.3 ­1.0 0.7 22.0 1.7 3.5 2.2 231.0 Philippines ­6.8 ­246.6 0.0 ­317.4 ­18.4 5.0 ­4.3 2.2 5.7 2.9 .. 3.0 ­574.8 Poland 0.0 78.9 0.0 0.0 0.0 0.0 0.0 .. .. .. .. .. 78.9 Portugal Puerto Rico 2007 World Development Indicators 357 6.13 Net financial flows from multilateral institutions International financial institutions United Nations Total $ millions Regional development IMF banks World Bank Conces- Non- Conces- Non- $ millions IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Romania 0.0 76.6 0.0 ­151.9 18.8 ­43.9 461.2 .. .. .. .. .. 360.8 Russian Federation 0.0 ­528.3 0.0 ­3,388.7 0.0 86.9 0.0 .. .. .. .. .. ­3,830.0 Rwanda 46.7 0.0 ­2.0 0.0 35.1 0.0 ­3.2 4.0 1.9 4.3 4.2 5.9 96.7 Saudi Arabia .. .. .. .. .. .. .. .. .. 0.2 .. 1.8 2.0 Senegal 170.5 0.0 ­28.4 0.0 19.6 ­0.2 31.4 3.9 2.4 3.6 3.2 4.4 210.4 Serbia and Montenegro 88.4 ­19.6 0.0 ­21.7 0.0 49.8 137.2 .. .. 1.1 .. 20.7 255.8 Sierra Leone 18.6 0.0 18.2 0.0 19.0 0.0 11.9 5.0 1.7 4.6 5.4 14.3 98.7 Singapore .. .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 0.0 ­55.5 0.0 0.0 0.0 ­1.6 8.2 .. .. .. .. .. ­48.8 Slovenia .. .. .. .. .. .. .. .. .. .. .. .. .. Somalia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.3 0.3 7.9 5.2 5.2 24.9 South Africa 0.0 8.3 0.0 0.0 0.0 52.2 0.0 2.3 0.9 1.4 0.0 6.1 71.2 Spain Sri Lanka 73.6 ­1.0 0.0 114.5 129.1 37.1 24.9 2.6 2.9 0.7 6.7 6.0 397.0 Sudan ­1.3 0.0 0.0 ­28.3 0.0 ­2.2 100.4 11.6 8.1 13.0 43.8 6.8 151.8 Swaziland ­0.3 0.0 0.0 0.0 ­1.0 22.3 42.4 0.5 0.5 0.8 0.6 1.9 67.9 Sweden Switzerland Syrian Arab Republic ­1.5 0.0 0.0 0.0 0.0 0.0 ­40.8 1.5 2.0 1.0 1.8 38.4 2.4 Tajikistan 34.8 0.0 15.1 0.0 26.1 0.5 0.4 3.7 0.8 1.9 1.7 1.6 86.5 Tanzania 260.5 0.0 ­38.1 0.0 122.8 0.0 17.2 7.8 5.1 10.9 5.7 5.0 396.8 Thailand ­3.4 ­97.5 0.0 0.0 ­2.4 ­368.3 ­12.7 2.2 2.1 1.3 .. 12.1 ­466.6 Togo 0.0 0.0 ­11.2 0.0 0.0 ­1.4 9.2 1.7 0.6 1.7 0.3 2.1 2.9 Trinidad and Tobago 0.0 ­13.0 0.0 0.0 ­0.1 ­9.6 ­7.5 0.7 .. .. .. 0.9 ­28.5 Tunisia ­2.1 ­54.5 0.0 0.0 0.0 ­8.5 250.0 0.7 0.4 0.7 .. 2.2 189.0 Turkey ­5.9 ­294.0 0.0 ­5,319.9 0.0 0.0 286.9 0.7 1.1 1.8 .. 7.5 ­5,321.9 Turkmenistan 0.0 ­1.8 0.0 0.0 0.0 0.0 ­2.9 1.0 0.5 0.9 .. 1.2 ­1.0 Uganda 111.7 0.0 ­30.2 0.0 63.7 ­1.6 17.8 6.1 3.8 9.6 .. 9.6 190.6 Ukraine 0.0 316.0 0.0 ­299.6 0.0 22.0 ­65.8 2.8 0.7 1.4 .. 3.6 ­18.9 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom United States Uruguay 0.0 30.3 0.0 ­171.7 ­2.4 25.4 ­0.1 0.6 0.5 0.5 .. 0.8 ­116.2 Uzbekistan 7.1 4.3 0.0 ­18.4 0.0 64.3 0.0 3.1 0.9 2.3 .. 2.6 66.0 Venezuela, RB 0.0 ­92.9 0.0 0.0 0.0 ­182.3 ­75.5 0.5 0.8 0.9 .. 2.3 ­346.2 Vietnam 377.7 0.0 ­53.5 0.0 217.6 ­2.2 ­2.5 6.5 7.6 5.1 .. 5.6 561.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. 1.9 .. 307.4 309.3 Yemen, Rep. 102.2 0.0 ­44.3 ­11.4 0.0 0.0 56.2 5.8 3.6 5.3 7.1 6.8 131.4 Zambia 75.8 0.0 ­62.8 0.0 17.8 ­9.2 4.9 5.3 1.8 4.5 7.4 10.1 55.5 Zimbabwe 0.0 ­1.7 0.0 ­164.7 0.0 0.0 3.4 3.1 4.4 2.0 125.4 4.3 ­23.7 World .. s .. s .. s .. s .. s .. s .. s 398.9 s 386.4 s 710.8 s 554.5 s 1,410.2 s .. s Low income 4,692.4 392.1 ­271.9 ­432.7 1,589.8 643.7 374.6 291.5 165.3 348.1 380.8 366.1 8,539.7 Middle income 699.2 ­3,302.8 ­75.6 ­39,380.9 364.9 512.0 999.6 96.6 114.8 105.0 83.0 849.5 ­38,934.7 Lower middle income 698.4 ­1,062.8 ­79.1 ­26,717.8 373.0 385.7 576.4 81.6 96.2 91.3 81.9 658.7 ­24,816.4 Upper middle income 0.8 ­2,240.0 3.5 ­12,663.2 ­8.1 126.2 423.2 13.0 11.1 12.8 1.1 126.4 ­14,193.1 Low & middle income 5,391.6 ­2,910.6 ­347.5 ­39,813.6 1,594.6 1,155.7 1,374.2 398.9 386.4 710.5 554.5 1,408.3 ­29,737.0 East Asia & Pacific 340.1 ­973.1 ­74.2 ­1,523.2 410.5 774.3 ­40.1 61.0 49.5 57.1 29.3 117.4 ­771.6 Europe & Central Asia 397.4 ­1,125.8 ­40.5 ­9,754.7 75.2 ­6.6 788.9 29.2 10.4 19.9 6.6 100.4 ­9,499.7 Latin America & Carib. 267.6 ­1,024.1 ­13.1 ­27,566.7 206.3 ­202.5 292.5 25.6 50.8 30.6 23.5 84.6 ­27,824.9 Middle East & N. Africa 125.8 ­135.5 ­45.1 ­700.1 1.1 ­18.7 ­59.3 13.8 21.1 21.9 20.4 597.7 ­156.9 South Asia 1,566.8 620.4 20.6 ­39.9 377.5 730.1 ­2.3 62.2 46.8 84.7 54.9 93.0 3,614.9 Sub-Saharan Africa 2,693.9 -272.6 ­195.1 ­229.0 884.1 ­120.9 394.5 196.4 115.2 245.1 342.4 346.2 4,400.2 High income Europe EMU Note: The aggregates for the regional development banks, United Nations, and total net financial flows include amounts for economies not specified elsewhere. 358 2007 World Development Indicators 6.13 GLOBAL LINKS Net financial flows from multilateral institutions About the data Definitions The table shows concessional and nonconcessional (IDA). Eligibility for IDA resources is based on gross · Net financial flows are disbursements of public financial flows from the major multilateral institutions-- national income (GNI) per capita; countries must also or publicly guaranteed loans and credits, less repay- the World Bank, the International Monetary Fund meet performance standards assessed by World ments of principal. · IDA is the International Devel- (IMF), regional development banks, UN agencies, Bank staff. Since July 1, 2005, the GNI per capita opment Association, the concessional loan window and regional groups such as the Commission of the cutoff has been set at $825, measured in 2003 of the World Bank. · IBRD is the International Bank European Communities. Much of the data comes using the World Bank Atlas method (see Users guide). for Reconstruction and Development, the founding from the World Bank's Debtor Reporting System. In exceptional circumstances IDA extends eligibility and largest member of the World Bank Group. · IMF The multilateral development banks fund their temporarily to countries that are above the cutoff and nonconcessional lending operations primarily by are undertaking major adjustment efforts but are not is the International Monetary Fund. Its nonconces- selling low-interest, highly rated bonds (the World creditworthy for lending by the International Bank for sional lending consists of the credit it provides to its Bank, for example, has a AAA rating) backed by pru- Reconstruction and Development (IBRD). An excep- members, mainly to meet their balance of payments dent lending and financial policies and the strong tion has also been made for small island economies. needs. It provides concessional assistance through financial support of their members. These funds are Lending by the International Finance Corporation is the Poverty Reduction and Growth Facility and the IMF then on-lent at slightly higher interest rates and with not included in this table. Trust Fund. · Regional development banks include relatively long maturities (15­20 years) to developing The IMF makes concessional funds available the African Development Bank, in Tunis, Tunisia, countries. Lending terms vary with market conditions through its Poverty Reduction and Growth Facility, which lends to all of Africa, including North Africa; and the policies of the banks. which replaced the Enhanced Structural Adjustment the Asian Development Bank, in Manila, Philippines, Concessional fl ows from bilateral donors are Facility in 1999, and through the IMF Trust Fund. Eli- defined by the Development Assistance Committee gibility is based principally on a country's per capita which serves countries in South and Central Asia (DAC) of the Organisation for Economic Co-operation income and eligibility under IDA, the World Bank's and East Asia and Pacific; the European Bank for and Development (OECD) as financial flows contain- concessional window. Reconstruction and Development, in London, United ing a grant element of at least 25 percent. The grant Regional development banks also maintain con- Kingdom, which serves countries in Europe and Cen- element of loans is evaluated assuming a nominal cessional windows for funds. Loans from the major tral Asia; the European Development Fund, in Brus- market interest rate of 10 percent. The grant ele- regional development banks--the African Develop- sels, Belgium, which serves countries in Africa, the ment is nil for a loan carrying a 10 percent interest ment Bank, Asian Development Bank, and Inter- Caribbean, and the Pacific; and the Inter-American rate, and it is 100 percent for a grant, which requires American Development Bank--are recorded in the Development Bank, in Washington, D.C., which is no repayment. Concessional flows from multilateral table according to each institution's classification. the principal development bank of the Americas. development agencies are credits provided through In 1999 all UN agencies revised their data since Concessional financial flows cover disbursements their concessional lending facilities. The cost of 1990 to include only regular budgetary expenditures these loans is reduced through subsidies provided (except for the World Food Programme and the United made through concessional lending facilities. Non- by donors or drawn from other resources available to Nations Office of the High Commissioner for Refu- concessional financial flows cover all other disburse- the agencies. Grants provided by multilateral agen- gees, which revised their data from 1996 onward). ments. · Others is a residual category in the World cies are not included in the net flows. They did so to avoid double counting extrabudgetary Bank's Debtor Reporting System. It includes such All concessional lending by the World Bank is car- expenditures reported by DAC countries and flows institutions as the Caribbean Development Bank and ried out by the International Development Association reported by the United Nations. the European Investment Bank. · United Nations includes the United Nations Development Programme Maintaining financial flows from multilateral (UNDP), United Nations Population Fund (UNFPA), institutions to developing countries 6.13a United Nations Children's Fund (UNICEF), World Food World Regional International Other international Programme (WFP), and other UN agencies, such as $ billions Bank development banks Monetary Fund financial institutions 40 the Office of the High Commissioner for Refugees, United Nations Relief and Works Agency for Palestine 30 Refugees in the Near East, and United Nations Regu- 20 lar Programme for Technical Assistance. 10 0 Data sources ­10 Data on net fi nancial fl ows from international ­20 financial institutions are from the World Bank's ­30 Debtor Reporting System. These data are pub- ­40 lished in the World Bank's Global Development Finance 2007 and electronically as GDF Online. ­50 1970 1975 1980 1985 1990 1995 2000 2005 Data on aid from UN agencies are from the DAC annual Development Cooperation Report. Data are As developing countries pay off loans from international financial institutions, net disbursements from available in electronic format on the OECD's Inter- these institutions have fallen greatly in recent years. national Development Statistics CD-ROM and at Source: World Bank Debtor Reporting System. www.oecd.org/dac/stats/idsonline. 2007 World Development Indicators 359 6.14 Movement of people Net migration Migration stock Refugees Workers' remittances and compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 2000­05 1990 2005 1996 2005 1996 2005 1990 2005 1990 2005 Afghanistan 3,313 2,140 29 43 2,674.2 2,166.1 18.8 0.0 .. .. .. .. Albania ­423 ­100 66 83 5.8 12.7 4.9 0.1 0 1,290 .. 7 Algeria ­58 ­100 274 242 2.2 12.0 190.3 94.1 352 1,950 31 .. Angola 143 145 34 56 249.7 215.8 9.4 14.0 .. .. 150 215 Argentina 50 ­100 1,650 1,500 .. .. 10.4 3.1 15 413 21 279 Armenia ­500 ­100 659 235 203.2 14.0 219.0 219.6 .. 940 a .. 146 Australia 390 500 3,984 4,097 .. .. 67.3 65.0 2,370 2,858 674 1,358 Austria 262 100 473 1,234 .. .. 89.1 21.2 635 2,941 320 2,543 Azerbaijan ­116 ­100 361 182 236.1 233.7 233.7 3.0 .. 693 .. 268 Bangladesh ­260 ­350 882 1,032 58.0 7.3 30.7 21.1 779 4,251 .. 6 Belarus 15 ­10 1,271 1,191 0.5 8.9 30.5 0.7 .. 370 .. 94 Belgium 85 67 899 719 .. .. 36.1 15.3 3,583 7,155 2,310 2,758 Benin 105 99 76 175 .. .. 6.0 30.3 101 63a 21 7a Bolivia ­100 ­100 60 116 .. .. 0.7 0.5 5 338 8 66 Bosnia and Herzegovina ­1,000 40 56 41 993.9 109.9 40.0 10.6 .. 1,844 .. 40 Botswana ­7 ­6 27 80 .. .. 0.2 3.1 86 125 119 123 Brazil ­184 ­130 804 641 .. .. 2.2 3.5 573 3,540 12 498 Bulgaria ­309 ­50 22 104 .. .. 1.4 4.4 .. 2,130 .. 13 Burkina Faso ­128 100 345 773 .. .. 28.4 0.5 140 50a 81 44 a Burundi ­250 192 333 100 428.7 438.7 20.7 20.7 .. .. 6 1a Cambodia 194 ­10 38 304 62.2 17.8 0.0 0.1 9 200 14 144 Cameroon ­5 13 171 137 2.1 9.1 46.4 52.0 23 11a 111 63a Canada 643 1,050 4,319 6,106 .. .. 138.4 147.2 .. .. .. .. Central African Republic 37 ­45 63 76 0.2 42.9 36.6 24.6 .. .. 36 .. Chad 20 271 74 437 58.4 48.4 0.1 275.4 1 .. 39 .. Chile 90 30 108 231 12.8 0.9 0.3 0.8 1 3 7 6 China ­1,281 ­1,950 380 596 105.8 124.1 290.1 301.0 210 22,492a 5 2,602 Hong Kong, China 300 300 2,218 2,999 .. .. 1.3 1.9 .. 240 .. 335 Colombia ­200 ­200 102 123 2.2 60.5 0.2 0.2 495 3,346 44 56 Congo, Dem. Rep. 1,208 ­322 919 539 158.8 430.9 676.0 204.3 .. .. .. .. Congo, Rep. 14 ­14 130 288 0.2 24.4 20.5 66.1 4 11 55 45 Costa Rica 62 84 418 441 .. .. 23.2 11.3 12 421 .. 209 Côte d'Ivoire 200 ­371 1,953 2,371 0.3 18.3 327.7 41.6 44 160 471 592 Croatia 153 100 475 661 310.1 119.1 165.4 2.9 .. 1,222 .. 62 Cuba ­100 ­160 100 74 25.5 19.0 1.7 0.7 .. .. .. .. Czech Republic 38 50 424 453 1.0 3.6 2.3 1.8 .. 1,017 .. 2,135 Denmark 58 61 220 389 .. .. 66.4 44.4 464 1,075a 160 1,226a Dominican Republic ­220 ­140 103 156 .. .. 0.6 .. 315 2,717 .. 26 Ecuador ­50 ­250 78 114 .. .. 0.2 10.1 51 2,038 2 38 Egypt, Arab Rep. ­600 ­450 176 166 1.2 6.3 6.0 88.9 4,284 5,017 27 57 El Salvador ­57 ­38 47 24 19.6 4.3 0.2 0.0 366 2,842 3 24 Eritrea ­359 280 12 15 332.2 144.1 2.1 4.4 .. .. .. .. Estonia ­117 ­10 382 202 .. .. .. 0.0 .. 265 .. 50 Ethiopia 888 ­150 1,155 555 96.3 65.5 390.5 100.8 5 174 1 16 Finland 43 41 61 156 .. .. 11.4 11.8 63 695 16 249 France 424 300 5,907 6,471 .. .. 151.3 137.3 4,035 12,742 6,949 4,867 Gabon 20 ­15 128 245 .. .. 0.8 8.5 .. 6a 147 110a Gambia, The 45 31 118 232 .. .. 6.9 7.3 10 58 .. 1a Georgia ­560 ­248 338 191 48.5 7.3 0.1 2.5 .. 346 .. 29 Germany 2,688 1,100 5,936 10,144 .. .. 1,266.0 700.0 4,876 6,542 6,856 12,519 Ghana 40 12 717 1,669 15.1 18.4 35.6 53.5 6 99 4 6a Greece 470 179 412 974 .. .. 5.8 2.4 1,817 1,220 122 809 Guatemala ­360 ­300 264 53 40.3 3.4 1.6 0.4 119 3,033 14 33 Guinea 350 ­299 402 406 0.5 5.8 663.9 63.5 27 42a 20 48a Guinea-Bissau 20 1 14 19 0.9 1.1 15.4 7.6 1 28a 12 5a Haiti ­105 ­105 19 30 15.1 13.5 .. .. 61 985 63 59 360 2007 World Development Indicators 6.14 GLOBAL LINKS Movement of people Net migration Migration stock Refugees Workers' remittances and compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 2000­05 1990 2005 1996 2005 1996 2005 1990 2005 1990 2005 Honduras ­40 ­30 270 26 .. .. 0.1 0.0 63 1,796 .. 1 Hungary 101 50 348 316 .. .. 7.5 8.0 .. 300 .. 155 India ­1,407 ­1,400 7,493 5,700 7.6 16.3 233.4 139.3 2,384 23,725a 106 1,008a Indonesia ­725 ­1,000 466 160 11.4 34.4 0.1 0.1 166 1,883 .. 1,178 Iran, Islamic Rep. ­1,512 ­1,379 3,809 1,959 104.1 99.4 2,030.4 974.3 1,200 1,032a .. .. Iraq 170 240 84 28 714.7 262.3 113.0 50.2 .. .. .. .. Ireland ­1 194 230 585 .. .. 0.1 7.1 286 651 165 1,128 Israel 484 158 1,633 2,661 .. .. 0.0 0.6 812 851 850 2,349 Italy 573 600 1,346 2,519 .. .. 64.7 20.7 5,075 2,398 3,764 5,815 Jamaica ­100 ­100 21 18 .. .. 0.0 .. 229 1,783 27 394 Japan 248 270 877 2,048 .. .. 5.3 1.9 508 1,080 290 1,281 Jordanb 495 100 1,146 2,225 .. .. 0.9 1.0 499 2,500 71 349 Kazakhstan ­1,509 ­600 3,619 2,502 40.2 4.3 15.6 7.3 .. 178 .. 1,670 Kenya 222 ­212 146 345 9.4 4.6 223.6 251.3 139 524 7 56 Korea, Dem. Rep. 0 0 34 37 .. .. .. .. .. .. .. .. Korea, Rep. ­115 ­80 572 551 .. .. 0.0 0.1 1,037 808 364 3,336 Kuwait ­626 240 1,551 1,669 .. .. 3.8 1.5 .. .. 770 2,648 Kyrgyz Republic ­273 ­75 623 288 17.1 3.1 16.7 2.6 .. 322 .. 122 Lao PDR ­10 ­7 23 25 46.9 24.4 0.0 .. 11 1a .. 1a Latvia ­174 ­12 805 449 .. .. 0.0 0.0 .. 381 .. 20 Lebanonb 178 ­35 520 657 10.9 18.3 2.4 1.1 1,818 4,924 .. 4,233a Lesotho ­84 ­36 7 6 .. .. .. .. 428 327 .. 17 Liberia ­283 ­245 81 50 784.0 231.1 120.1 10.2 .. .. .. .. Libya 10 10 457 618 .. .. 7.7 12.2 .. 15 446 914 Lithuania ­100 ­20 349 165 .. .. 0.0 0.5 .. 534 .. 47 Macedonia, FYR ­27 ­10 95 121 13.0 8.6 5.1 1.3 .. 226 .. 16 Madagascar ­6 0 58 63 .. .. .. .. 8 3 18 8 Malawi ­835 ­20 1,157 279 .. .. 1.3 4.2 .. 1a .. 1a Malaysia 230 150 1,014 1,639 .. .. 5.3 33.7 325 1,281a 230 5,679 Mali ­260 ­134 60 46 55.2 0.5 18.2 11.2 107 155a 45 64 a Mauritania ­15 30 94 66 83.2 31.7 15.9 0.6 14 2a 31 .. Mauritius ­7 0 9 21 .. .. .. .. .. 215a 1 11a Mexico ­1,800 ­2,000 702 644 .. .. 34.6 3.2 3,098 21,772 .. .. Moldova ­121 ­40 579 440 5.8 12.1 .. 0.1 .. 920 .. 68 Mongolia ­60 ­50 7 9 .. .. .. .. .. 202a .. 49a Morocco ­300 ­400 85 132 .. .. 0.1 0.2 2,006 4,589 16 40 Mozambique 650 ­20 122 406 34.7 0.1 0.2 2.0 70 57 25 21 Myanmar ­126 70 101 117 143.0 164.9 .. .. 6 117a .. 25a Namibia 3 ­6 119 143 .. .. 2.2 5.3 13 16a 30 17a Nepal ­101 ­100 413 819 .. .. 126.8 126.4 0 1,211 .. 65 Netherlands 190 150 1,192 1,638 .. .. 102.6 118.2 709 2,227 1,393 5,678 New Zealand 79 78 529 642 .. .. 3.8 5.3 762 739 367 936 Nicaragua ­110 ­100 41 28 22.8 1.5 0.6 0.2 0 600 .. .. Niger 5 ­10 115 124 10.4 0.7 25.8 0.3 14 60a 66 25a Nigeria ­96 ­170 447 971 4.8 22.1 8.5 9.0 10 3,329 9 18 Norway 42 58 185 344 .. .. 48.4 43.0 158 429 295 953 Oman 25 ­160 452 628 .. .. .. 0.0 39 39 856 2,257 Pakistan ­2,611 ­1,810 6,556 3,254 7.5 29.9 1,202.7 1,084.7 2,006 4,280 1 3 Panama 8 8 62 102 .. .. 0.9 1.7 110 126 22 91 Papua New Guinea 0 0 33 25 .. .. 10.2 10.0 5 13 43 135 Paraguay ­25 ­25 183 168 .. .. 0.1 0.1 34 268 .. .. Peru ­450 ­300 56 42 6.7 4.9 0.7 0.8 87 1,440 75 164 Philippines ­900 ­900 164 374 0.6 0.5 0.7 0.1 1,465 13,566 5 15 Poland ­77 ­80 1,127 703 12.9 19.6 0.6 4.6 .. 3,549 .. 598 Portugal ­7 250 436 764 .. .. 0.3 0.4 4,479 3,017 77 1,151 Puerto Rico ­4 ­3 322 418 .. .. .. .. .. .. .. .. 2007 World Development Indicators 361 6.14 Movement of people Net migration Migration stock Refugees Workers' remittances and compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 2000­05 1990 2005 1996 2005 1996 2005 1990 2005 1990 2005 Romania ­529 ­150 143 133 11.9 11.5 0.3 2.1 .. 4,733 .. 34 Russian Federation 1,858 400 11,525 12,080 173.7 103.0 246.7 1.5 .. 3,117 .. 7,651 Rwanda ­1,714 45 73 121 469.1 100.3 25.3 45.2 3 21 21 35 Saudi Arabia ­325 250 4,743 6,361 .. .. 9.9 240.7 .. .. 11,221 14,318 Senegal ­100 ­100 293 326 17.6 8.7 65.0 20.7 142 633a 79 77a Serbia and Montenegro 200 ­100 130 512 104.0 190.0 563.2 148.3 .. 4,650a .. .. Sierra Leone ­380 438 112 119 375.1 40.5 13.5 60.0 .. 2 .. 2 Singapore 250 200 727 1,843 .. .. 0.0 0.0 .. .. .. .. Slovak Republic 9 5 41 124 .. .. 1.4 0.4 .. 424 a .. 16a Slovenia 38 10 178 167 .. .. 10.0 0.3 38 264 2 95 Somalia ­1,083 170 633 282 637.0 395.6 0.7 0.5 .. .. .. .. South Africa 1,125 50 1,225 1,106 .. .. 22.6 29.7 136 658 1,199 1,055 Spain 500 2,025 766 4,790 .. .. 5.7 5.4 2,186 7,927 254 7,733 Sri Lanka ­182 ­160 461 368 109.6 108.1 0.0 0.1 401 2,088 .. 257 Sudan ­158 ­519 1,273 639 475.3 693.6 393.9 147.3 62 1,016 2 2 Swaziland ­38 ­6 73 45 .. .. 0.6 0.8 113 81 4 11 Sweden 151 157 781 1,117 .. .. 191.2 74.9 153 630 654 611 Switzerland 80 40 1,376 1,660 .. .. 84.4 48.0 924 1,910 8,168 13,200 Syrian Arab Republicb ­30 ­30 711 985 8.6 16.4 27.8 26.1 385 823 .. 40 Tajikistan ­313 ­345 426 306 107.5 54.8 1.2 1.0 .. 466 .. 145 Tanzania 591 ­345 574 792 .. .. 498.7 548.8 .. 16 .. 41 Thailand ­88 ­50 391 1,050 .. .. 108.0 117.1 973 1,187 199 .. Togo ­122 ­4 163 183 25.6 51.1 12.6 9.3 27 148a 13 34 a Trinidad and Tobago ­24 ­20 51 38 .. .. .. .. 3 87a 22 .. Tunisia ­22 ­20 38 38 .. .. 0.2 0.1 551 1,393 13 15 Turkey 71 ­250 1,150 1,328 50.4 170.6 8.2 2.4 3,246 851 .. .. Turkmenistan 50 ­10 307 224 .. .. 15.6 12.0 .. .. .. .. Uganda 135 ­15 550 518 28.3 34.2 264.3 257.3 .. 476 .. 374 Ukraine 598 ­700 7,097 6,833 6.1 84.2 3.6 2.3 .. 595 .. 34 United Arab Emirates 340 960 1,330 3,212 .. .. 0.5 0.1 .. .. .. .. United Kingdom 381 686 3,753 5,408 .. .. 98.6 303.2 2,099 6,722 2,034 3,087 United States 5,200 5,800 23,251 38,355 .. .. 607.0 379.3 1,170 2,924 11,850 41,072 Uruguay ­20 ­10 98 84 .. .. 0.1 0.1 .. 78 .. 2 Uzbekistan ­340 ­300 1,653 1,268 69.7 8.3 2.9 44.0 .. .. .. .. Venezuela, RB 40 40 1,024 1,010 .. .. 1.6 0.4 1 148 701 211 Vietnam ­270 ­200 28 21 518.3 358.3 34.4 2.4 .. 4,000a .. .. West Bank and Gazab ­5 ­40 911 1,680 80.2 349.7 .. .. .. 436a .. .. Yemen, Rep. 650 ­100 107 265 .. .. 53.5 81.9 1,498 1,283 106 109 Zambia ­7 ­65 280 275 .. .. 131.1 155.7 .. .. 17 24 a Zimbabwe ­182 ­50 804 511 0.0 11.3 0.6 13.9 1 .. 16 .. World ..c ..c 154,688 s 190,206 s 11,701.6d s 8,300.6d s 13,357.1d, e s 8,662.0d, e s 68,584 s 262,489 s 66,295 s 178,677 s Low income ­3,286 ­4,000 31,745 27,120 7,935.2 5,811.7 5,762.5 3,895.6 7,664 48,188 1,305 3,379 Middle income ­9,673 ­11,987 51,290 50,804 3,766.4 2,488.9 4,523.9 2,364.2 23,474 144,716 4,770 34,784 Lower middle income ­10,872 ­10,086 26,469 24,999 3,182.8 2,042.2 3,972.8 2,230.4 14,370 97,779 959 8,759 Upper middle income 1,200 ­1,901 24,821 25,804 583.6 446.7 551.1 133.8 9,104 46,937 3,811 26,025 Low & middle income ­12,958 ­15,987 83,035 77,923 11,701.6 8,300.6 10,286.4 6,259.8 31,138 192,904 6,075 38,163 East Asia & Pacific ­3,072 ­3,939 2,748 4,432 888.3 724.6 450.7 464.5 3,263 45,053 527 9,918 Europe & Central Asia ­3,398 ­2,665 34,071 31,137 2,411.3 1,179.3 1,545.8 483.8 3,246 31,363 .. 13,420 Latin America & Carib. ­3,776 ­4,012 6,343 5,777 145.1 107.9 88.4 37.7 5,763 48,201 996 2,288 Middle East & N. Africa ­1,030 ­2,374 8,828 9,642 940.1 765.0 2,457.3 1,340.5 11,432 24,001 1,566 8,014 South Asia ­1,368 ­1,679 15,845 11,229 2,963.6 2,434.3 1,612.4 1,371.6 5,572 35,558 115 1,338 Sub-Saharan Africa ­314 ­1,318 15,200 15,706 4,353.1 3,089.5 4,131.9 2,561.6 1,862 8,728 2,871 3,185 High income 12,929 15,970 71,653 112,282 .. .. 3,070.7 2,402.2 37,446 69,585 60,220 140,514 Europe EMU 5,285 5,036 17,950 30,335 .. .. 1,743.7 1,041.8 27,744 48,981 22,226 51,944 a. World Bank staff estimates. b. Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East are not included in statistics from the United Nations Office of the High Commissioner for Refugees (UNHCR). c. 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. d. World totals include refugees without a specified country or region, which are classified by UNHCR in the category "various." e. World totals come from UNHCR. Thus regional and income group totals do not add up to the world total. 362 2007 World Development Indicators 6.14 GLOBAL LINKS Movement of people About the data Movement of people, most often through migration, interpolation or extrapolation was used to estimate their residency status. Some countries also report is a significant part of integration. Migrants contrib- the international migrant stock on July 1 of the refer- remittances entirely as workers' remittances or com- ute to the economies of both their host country and ence years. For countries with only one observation, pensation of employees. Following the fifth edition of their country of origin. Yet reliable statistics on migra- estimates for the reference years were derived using the Balance of Payments Manual in 1993, migrants' tion are difficult to collect and are often incomplete, rates of change in the migrant stock in the years pre- transfers are considered a capital transaction but making international comparisons a challenge. ceding or following the single observation available. in previous editions they were regarded as current The United Nations Population Division provides A model was used to estimate migration for countries transfers. For these reasons the figures presented in data on net migration and migration stock. To derive that had no data. the table take all three items into account. estimates of net migration, the organization takes Registration, together with other sources-- including Definitions into account the past migration history of a country estimates and surveys--are the main sources of or area, the migration policy of a country, and the refugee data. Yet there are difficulties in collecting · Net migration is the net total of migrants during influx of refugees in recent periods. The data to cal- accurate statistics. Although refugees are often regis- the period, that is, the total number of immigrants culate these official estimates come from a variety of tered individually, the accuracy of registrations varies less the total number of emigrants, including both sources, including border statistics, administrative greatly. Many refugees may not be aware of the need citizens and noncitizens. Data are five-year estimates. records, surveys, and censuses. When no official to register or may choose not to do so. And admin- · Migration stock is the number of people born in a estimates can be made due to insufficient data, net istrative records tend to overestimate the number country other than that in which they live. It includes migration is derived through the balance equation, of refugees because it is easier to register than to refugees. · Refugees are people who are recognized which is the difference between overall population de-register. Palestinian refugees under the mandate as refugees under the 1951 Convention Relating to growth and the natural increase during the 1990­ of the United Nations Relief and Works Agency for the Status of Refugees or its 1967 Protocol, the 1969 2000 intercensal period. Palestine Refugees in the Near East are not included Organization of African Unity Convention Governing The data used to estimate the international migrant in the statistics of the United Nations Office of the the Specific Aspects of Refugee Problems in Africa, stock at a particular point in time are obtained mainly High Commissioner for Refugees (UNHCR). people recognized as refugees in accordance with the from population censuses. The estimates are derived Workers' remittances and compensation of employ- UNHCR statute, people granted a refugee-like humani- from the data on foreign-born population--those who ees are World Bank staff estimates based on data tarian status, and people provided with temporary pro- have residence in one country but who were born from the International Monetary Fund's (IMF) Balance tection. Asylum seekers are people who have applied in another country. When data on the foreign-born of Payments Yearbook. The IMF data are supplemented for asylum or refugee status and who have not yet population are not available, data on foreign popula- by World Bank staff estimates for missing data for received a decision or who are otherwise registered tion are used as estimates. countries where workers' remittances are important. as asylum seekers. · Country of origin generally After the breakup of the Soviet Union in 1991, peo- The data reported here are the sum of three items refers to the nationality or country of citizenship of a ple living in one of the newly independent countries defined in the IMF Balance of Payments Manual (fifth claimant. · Country of asylum is the country where who were born in another of the countries were classi- edition). These are workers' remittances, compensa- an asylum claim was filed. · Workers' remittances fied as international migrants. Estimates of migration tion of employees, and migrants' transfers. and compensation of employees, received and paid stock in the newly independent states from 1990 on The distinction between these three items is not comprise current transfers by migrant workers and are based on the 1989 census of the Soviet Union. always consistent in the data reported by countries to wages and salaries earned by nonresident workers. For countries with information on the international the IMF. In some cases, countries compile data on the Workers' remittances are classified as current private migration stock for at least two points in time, basis of the citizenship of migrant workers rather than transfers from migrant workers who are residents of the host country to recipients in their country of origin. High-skill workers in developing countries are They include only transfers made by workers who have increasingly emigrating to high-income countries 6.14a been living in the host country for more than a year, irrespective of their immigration status. Compensa- Emigration rate as share of adult labor force (%) 1990 2000 tion of employees is the income of migrants who have 15 lived in the host country for less than a year. Migrants' transfers are defined as the net worth of migrants who 12 are expected to remain in the host country for more than one year that is transferred from one country to 9 another at the time of migration. Data sources 6 Data on net migration come from the United Nations Population Division's World Population 3 Prospects: The 2006 Revision. Data on migration stock come from the United Nations Population 0 Division's Trends in Total Migrant Stock: The 2005 China India Turkey Bulgaria Morocco Ecuador Poland Mexico Romania Philippines Revision. Data on refugees are from the United The increase in migration among high-skill workers is due partly to selective immigration policies in Organi- Nations Office of the High Commissioner for Refu- sation for Economic Co-operation and Development countries and partly to rising skill premiums in these gees' Statistical Yearbook 2005. Data on remit- labor markets. tances are World Bank staff estimates based on Source: Docquier and Marfouk 2004. IMF balance of payments data. 2007 World Development Indicators 363 6.15 Travel and tourism International tourists Tourism expenditure in the country Tourism expenditure in other countries thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Afghanistan .. .. .. .. .. .. .. .. 1 .. .. .. Albania 40 46 12 2,097 70 880 23.2 48.3 19 808 2.3 20.9 Algeria 520 1,443 1,090 1,513 32 178 .. .. 186 394 .. .. Angola 9 210 3 .. 27 82 0.7 0.6 113 86 3.2 0.8 Argentina 2,289 3,895 3,815 4,002 2,550 3,241 10.2 7.0 4,013 3,572 15.4 10.2 Armenia 12 319 .. 269 14 161 4.7 12.0 12 146 1.7 7.4 Australia 3,726 5,497 2,519 4,754 11,900 20,637 17.1 15.2 7,272 15,076 9.7 10.1 Austria 17,173 19,952 3,713 6,564 14,529 19,310 16.2 11.3 11,686 12,755 12.7 7.8 Azerbaijan 93 1,177 432 1,830 88 100 11.2 1.2 165 188 12.8 2.7 Bangladesh 156 208 830 1,767 .. 78 .. 0.7 251 371 3.4 2.6 Belarus 161 91 626 572 28 346 0.5 1.9 101 672 1.8 3.8 Belgium 5,560 6,747 5,645 9,318 .. 10,879 .. 3.4 .. 16,636 .. 5.4 Benin 138 174 .. .. 79 108 12.1 15.1 48 53 5.4 4.9 Bolivia 284 504 249 312 92 346 7.5 11.0 72 258 4.6 9.0 Bosnia and Herzegovina 115 190 .. .. .. 604 .. 16.8 .. 160 .. 2.0 Botswana 521 1,523 .. .. 176 550 7.3 12.4 153 280 7.5 7.7 Brazil 1,991 5,358 2,600 4,696 1,085 4,169 2.1 3.1 3,982 5,905 6.3 6.0 Bulgaria 3,466 4,837 3,524 4,235 662 3,026 9.8 18.8 312 1,836 4.8 8.9 Burkina Faso 124 222 .. .. .. .. .. .. .. .. .. .. Burundi 34 148 36 .. 2 2 1.9 2.1 .. 62 .. 17.6 Cambodia 220 1,422 31 239 71 927 7.3 23.1 22 138 1.6 3.0 Cameroon 100 190 .. .. 75 162 3.7 5.6 140 294 8.7 9.1 Canada 16,932 18,770 18,206 21,101 9,176 15,830 4.2 3.7 12,658 23,061 6.3 6.0 Central African Republic 26 8 .. 7 4 4 .. .. 43 32 .. .. Chad 19 29 .. .. 43 .. .. .. 38 .. .. .. Chile 1,540 2,027 1,070 2,343 1,186 1,779 6.1 3.7 934 1,381 5.1 3.6 China 20,034 46,809 4,520 31,026 12,626 31,842 6.1 3.8 9,220 24,715 5.6 3.5 Hong Kong, China 10,200 23,359 3,023 4,957 .. 13,586 .. 3.9 .. .. .. .. Colombia 1,433 933 1,057 1,553 887 1,570 7.2 6.4 1,162 1,562 7.3 6.3 Congo, Dem. Rep. 35 61 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 37 .. .. .. 15 23 1.1 0.6 69 176 5.1 8.9 Costa Rica 785 1,453 273 425 763 1,804 17.1 18.6 336 556 7.1 5.2 Côte d'Ivoire 188 .. .. .. 103 76 2.4 1.2 312 551 8.2 11.0 Croatia 1,485 8,467 .. .. .. 7,625 .. 40.4 .. 786 .. 3.6 Cuba 742 2,261 72 115 963 1,920 .. .. .. .. .. .. Czech Republic 3,381 6,336 44,873 36,650 .. 5,580 .. 6.3 .. 2,605 .. 3.0 Denmark 2,124 3,358 5,035 4,630 .. .. .. .. .. .. .. .. Dominican Republic 1,776 3,691 168 419 .. .. .. .. 267 494 4.4 4.4 Ecuador 440 861 271 661 315 488 6.1 4.3 331 616 5.8 5.2 Egypt, Arab Rep. 2,871 8,244 2,683 3,644 2,954 7,206 22.3 23.5 1,371 1,932 8.0 5.6 El Salvador 235 1,154 348 1,397 152 838 7.5 18.3 99 430 2.7 5.6 Eritrea 315 83 .. .. 58 66 .. .. .. .. .. .. Estonia 530 1,900 1,764 2,075 452 1,207 17.6 11.0 121 538 4.2 4.6 Ethiopia 103 227 120 .. 177 533 23.1 27.6 30 59 2.1 1.6 Finland 1,779 2,080 5,147 6,035 2,384 3,055 5.0 3.7 2,853 3,529 7.6 5.0 France 60,033 76,001 18,686 22,270 31,295 .. 8.6 .. 20,699 .. 6.2 .. Gabon 125 222 .. 236 94 74 3.2 1.8 183 275 10.6 12.8 Gambia, The 45 111 .. 387 67 57 30.5 31.6 16 7 6.9 2.7 Georgia 85 560 228 .. 75 288 13.1 13.3 171 238 12.1 7.2 Germany 14,847 21,500 55,800 77,400 24,052 38,381 4.0 3.4 66,527 80,276 11.2 8.1 Ghana 286 584 .. .. 30 827 1.9 21.4 74 472 3.5 7.1 Greece 10,130 14,276 .. .. 4,182 13,697 26.9 26.4 1,495 3,046 6.0 4.6 Guatemala 563 1,316 333 982 216 883 7.7 17.9 167 500 4.5 5.2 Guinea 12 45 .. .. 1 32 0.1 4.3 29 29 2.9 3.0 Guinea-Bissau .. 5 .. .. .. 2 .. 2.6 6 22 6.7 17.3 Haiti 145 96 .. .. .. .. .. .. .. 173 .. 9.9 364 2007 World Development Indicators 6.15 GLOBAL LINKS Travel and tourism International tourists Tourism expenditure in the country Tourism expenditure in other countries thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Honduras 271 749 149 301 85 476 5.2 13.9 99 315 5.3 6.3 Hungary 2,878 3,446 13,083 18,622 2,938 4,581 14.9 6.2 1,501 3,037 7.5 4.0 India 2,124 3,915 3,056 6,200 .. 4,128 .. 5.0 .. 4,758 .. 5.1 Indonesia 4,324 5,002 1,782 4,106 .. 5,092 .. 5.1 .. 4,741 .. 5.4 Iran, Islamic Rep. 489 1,659 1,000 .. 205 1,324 1.1 .. 247 4,353 1.6 .. Iraq 61 .. .. .. .. .. .. .. .. .. .. .. Ireland 4,818 7,333 2,547 6,113 2,698 6,722 5.5 4.2 .. 6,168 .. 4.5 Israel 2,215 1,903 2,259 3,687 3,491 3,414 12.7 5.9 2,626 3,780 7.4 6.6 Italy 31,052 36,513 18,173 23,349 30,426 38,264 10.3 8.3 17,219 26,459 6.9 5.7 Jamaica 1,147 1,479 .. .. 1,199 1,783 35.3 44.6 173 291 4.6 4.9 Japan 3,345 6,728 15,298 17,404 4,894 15,555 1.0 2.3 46,966 48,102 11.2 7.9 Jordan 1,075 2,987 1,128 1,523 973 1,759 28.0 26.7 719 653 14.7 5.5 Kazakhstan .. 3,073 523 3,915 155 793 2.6 2.6 296 854 4.9 3.3 Kenya 896 1,199 .. .. 590 969 20.0 18.9 183 .. 5.2 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 3,753 6,022 3,819 10,078 6,670 8,148 4.5 2.4 6,947 16,831 4.5 5.4 Kuwait 72 91 878 1,928 309 414 2.2 1.2 2,513 4,150 19.9 21.6 Kyrgyz Republic 36 315 42 239 .. 94 .. 10.0 7 71 0.7 5.1 Lao PDR 60 672 .. .. 52 .. 12.8 .. 34 .. 4.5 .. Latvia 539 1,116 1,812 2,959 37 446 1.8 5.9 62 655 2.8 6.6 Lebanon 450 1,140 .. .. 710 5,869 .. 45.0 .. 3,535 .. 21.8 Lesotho 87 .. .. .. 29 .. 14.6 .. 17 36 1.6 2.7 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 56 149 484 .. 4 261 0.1 1.5 98 789 1.7 7.4 Lithuania 650 2,000 1,925 3,502 102 975 3.2 6.6 107 757 2.7 4.5 Macedonia, FYR 147 197 .. .. 35 92 2.7 3.7 30 94 1.7 2.6 Madagascar 75 229 39 67 106 265 14.2 51.5 79 184 8.0 24.0 Malawi 192 471 .. .. 22 36 4.7 .. 53 58 8.0 .. Malaysia 7,469 16,431 20,642 30,761 5,044 10,389 6.1 6.4 2,722 4,339 3.1 3.3 Mali 42 143 .. .. 26 142 4.9 11.7 74 126 7.5 7.8 Mauritania .. .. .. .. .. .. .. .. 30 .. 5.9 .. Mauritius 422 761 107 183 616 1,189 26.2 31.6 184 295 7.5 7.1 Mexico 20,241 21,915 8,450 13,305 6,847 12,801 7.7 5.6 3,587 8,951 4.4 3.7 Moldova 32 23 71 57 71 163 8.0 10.7 73 197 7.3 7.2 Mongolia 108 301 .. .. 33 205 6.5 16.9 22 207 4.2 14.7 Morocco 2,602 5,843 1,317 1,746 1,469 5,426 16.2 28.9 356 999 3.2 4.4 Mozambique .. 470 .. .. .. 138 .. 6.6 .. 187 .. 6.5 Myanmar 117 232 .. .. 169 98 12.9 3.1 38 32 1.5 1.3 Namibia 272 695 .. .. .. 426 .. 18.4 .. .. .. .. Nepal 363 375 100 373 232 160 22.5 12.5 167 221 10.3 8.2 Netherlands 6,574 10,012 12,313 17,086 10,611 .. 4.4 .. 13,151 .. 6.1 .. New Zealand 1,409 2,365 920 1,872 .. .. .. .. .. .. .. .. Nicaragua 281 712 255 740 51 211 7.7 11.3 56 161 4.9 4.9 Niger 35 55 10 .. 26 29 7.1 7.0 26 39 5.7 5.7 Nigeria 656 962 .. .. 47 49 0.4 0.1 939 1,469 7.3 7.0 Norway 2,880 3,859 590 3,122 2,730 3,884 4.9 2.9 4,481 8,788 9.6 12.2 Oman 279 1,116 .. .. .. 679 .. 3.5 .. 838 .. 7.6 Pakistan 378 798 .. .. 582 827 5.7 4.3 654 1,748 4.6 6.0 Panama 345 702 185 285 372 1,108 4.9 10.3 181 388 2.3 3.6 Papua New Guinea 42 69 51 .. .. .. .. .. .. .. .. .. Paraguay 438 341 427 188 162 96 3.4 2.4 173 129 3.3 3.1 Peru 479 1,486 508 1,841 521 1,371 7.9 7.1 428 900 4.5 5.9 Philippines 1,760 2,623 1,615 2,144 1,141 2,620 4.3 5.9 551 1,547 1.7 2.9 Poland 19,215 15,200 36,387 40,841 6,927 7,127 19.4 6.3 5,865 4,686 17.3 4.1 Portugal 9,511 11,617 .. .. 5,646 9,222 17.5 17.3 2,540 3,763 6.4 5.4 Puerto Rico 3,131 3,686 1,237 1,410 1,828 3,239 .. .. 1,155 1,663 .. .. 2007 World Development Indicators 365 6.15 Travel and tourism International tourists Tourism expenditure in the country Tourism expenditure in other countries thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Romania 2,757 .. 5,737 7,140 689 1,310 7.3 4.0 749 1,022 6.6 2.4 Russian Federation 10,290 22,201 21,329 28,416 .. 7,402 .. 2.8 .. 18,795 .. 11.4 Rwanda .. .. .. .. 4 .. 5.4 .. 13 .. 3.5 .. Saudi Arabia 3,325 9,100 .. 3,811 .. 6,111 .. 3.4 .. 3,763 .. 4.7 Senegal 280 387 .. .. 168 269 11.2 14.7 154 129 8.5 4.9 Serbia and Montenegro 228 725 .. .. .. .. .. .. .. .. .. .. Sierra Leone 38 40 6 63 .. .. .. .. 51 34 19.4 7.4 Singapore 6,070 7,080 2,867 5,165 .. .. .. .. .. .. .. .. Slovak Republic 903 1,515 218 486 630 932 5.7 3.5 338 903 3.2 2.6 Slovenia 732 1,555 .. 2,800 1,128 1,894 10.9 8.6 606 1,019 5.6 4.6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 4,488 7,369 2,520 .. 2,655 8,448 7.7 12.7 2,414 4,813 7.2 7.0 Spain 34,920 55,577 3,648 5,121 27,369 52,960 20.4 18.4 5,826 18,440 4.3 5.3 Sri Lanka 403 549 504 727 367 729 7.9 9.2 279 553 4.7 5.5 Sudan 29 61 195 .. .. .. .. .. .. .. .. .. Swaziland 300 839 .. 1,082 54 109 5.3 4.9 45 54 3.5 2.4 Sweden 2,310 .. 10,127 12,598 4,390 8,584 4.6 4.8 6,816 11,847 8.4 7.9 Switzerland 6,946 7,229 11,148 .. 11,354 12,961 9.2 6.6 9,478 11,060 8.7 6.5 Syrian Arab Republic 815 .. 1,746 4,564 .. 2,283 .. 23.4 .. 593 .. 5.5 Tajikistan .. .. .. .. .. 10 .. 0.8 .. .. .. .. Tanzania 285 566 157 .. 344 836 28.4 28.9 424 577 21.6 15.1 Thailand 6,952 11,567 1,820 3,047 9,257 12,629 13.2 9.7 4,791 5,790 5.8 4.3 Togo 53 81 .. .. .. 25 .. 3.3 41 38 6.1 3.5 Trinidad and Tobago 260 463 261 .. 232 661 8.3 7.8 91 288 4.3 2.7 Tunisia 4,120 6,378 1,778 2,241 1,838 2,782 23.0 19.2 294 443 3.3 3.0 Turkey 7,083 20,273 3,981 8,246 .. .. .. .. .. .. .. .. Turkmenistan 218 12 21 33 13 .. 0.7 .. 74 .. 4.1 .. Uganda 160 468 148 189 .. 357 .. 26.6 .. 137 .. 5.3 Ukraine 3,716 15,629 6,552 15,488 448 3,542 2.2 8.0 405 3,078 1.9 7.0 United Arab Emirates 2,315 5,871 .. .. 632 2,200 .. .. .. 5,300 .. .. United Kingdom 23,537 29,971 41,345 66,494 27,577 39,573 8.6 6.7 30,749 73,786 9.4 11.0 United States 43,490 49,209 51,285 63,502 93,700 122,944 11.8 9.6 60,924 99,624 6.8 5.0 Uruguay 2,022 1,808 562 658 725 690 20.7 13.6 332 328 9.3 7.1 Uzbekistan 92 262 246 455 15 57 .. .. .. .. .. .. Venezuela, RB 700 706 534 1,067 995 713 4.8 1.3 1,852 1,837 11.0 6.3 Vietnam 1,351 3,468 .. .. .. 1,880 .. 5.1 .. .. .. .. West Bank and Gaza 220 88 .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 61 336 .. .. .. .. .. .. .. 224 .. 4.2 Zambia 163 515 .. .. 47 161 6.1 .. 83 .. 6.2 .. Zimbabwe 1,363 .. 256 .. 145 99 .. .. 106 .. .. .. World 524,060 t 736,109 t 427,305 t 568,830 t 497,633 t 787,293 t 8.0 w 6.5 w 383,191 t 621,415 t 7.9 w 6.3 w Low income 10,879 17,998 .. 9,317 .. 12,490 .. 5.9 .. 11,060 .. 6.2 Middle income 159,782 265,628 208,088 273,023 93,536 187,387 8.2 6.4 43,377 126,571 5.6 5.1 Lower middle income 66,091 123,098 38,567 88,101 49,702 104,938 7.6 6.1 31,176 62,312 5.4 4.3 Upper middle income 93,691 142,530 169,521 184,922 50,435 83,641 8.9 6.6 26,008 64,259 6.9 6.0 Low & middle income 170,661 279,291 213,011 282,340 101,738 205,221 8.1 6.4 46,653 131,051 5.7 5.1 East Asia & Pacific 44,254 91,295 36,006 81,084 35,094 66,121 7.1 5.0 20,679 39,432 4.9 3.7 Europe & Central Asia 58,037 90,756 142,185 161,107 .. .. .. 6.1 .. 41,223 .. 6.8 Latin America & Carib. 39,667 54,142 21,025 32,407 20,620 38,687 7.1 5.7 18,505 29,372 6.5 5.2 Middle East & N. Africa 13,420 27,605 11,226 14,092 11,096 25,288 12.3 23.0 3,287 9,217 4.3 7.1 South Asia 3,744 6,254 4,522 8,792 .. 6,343 .. 4.6 .. 6,951 .. 5.1 Sub-Saharan Africa 12,119 17,247 .. .. 6,385 17,893 6.8 9.2 5,739 8,670 7.0 6.7 High income 353,399 456,818 214,294 365,507 393,204 580,977 7.9 6.5 336,538 490,364 8.2 6.8 Europe EMU 197,165 250,904 124,665 174,788 175,291 253,742 8.2 7.1 141,996 171,072 8.2 6.6 366 2007 World Development Indicators 6.15 GLOBAL LINKS Travel and tourism About the data Definitions Tourism is defined as the activities of people trav- outbound tourists refer to the number of arrivals and · International inbound tourists (overnight visitors) eling to and staying in places outside their usual departures of visitors within the reference period, not are the number of tourists who travel to a country environment for no more than one year for leisure, to the number of people traveling. Thus a person who other than that in which they have their usual resi- business, and other purposes not related to an activ- makes several trips to a country during a given period dence, but outside their usual environment, for a ity remunerated from within the place visited. The is counted each time as a new arrival. International period not exceeding 12 months and whose main social and economic phenomenon of tourism has visitors include tourists (overnight visitors), same-day purpose in visiting is other than an activity remuner- grown substantially over the past quarter century. visitors, cruise passengers, and crew members. ated from within the country visited. · International Past descriptions of tourism focused on the char- The World Tourism Organization is improving its outbound tourists are the number of departures that acteristics of visitors, such as the purpose of their coverage of tourism expenditure data. It is now people make from their country of usual residence visit and the conditions in which they traveled and using balance of payments data from the Interna- to any other country for any purpose other than a stayed. Now, there is a growing awareness of the tional Monetary Fund (IMF), supplemented by data remunerated activity in the country visited. · Tour- direct, indirect, and induced effects of tourism on received from individual countries. The new data, ism expenditure in the country is expenditures by employment, value added, personal income, govern- shown in the table, now include travel and passenger international inbound visitors, including payments to ment income, and the like. transport items as defined in the IMF's Balance of national carriers for international transport. These Statistical information on tourism is based mainly Payments Manual. receipts include any other prepayment made for on data on arrivals and overnight stays along with Aggregates are based on the World Bank's classifi - goods or services received in the destination coun- balance of payments information. But these data cation of countries and differ from those in the World try. They also may include receipts from same-day do not completely capture the economic phenom- Tourism Organization's publications. Countries not visitors, except in cases where these are important enon of tourism or give governments, businesses, shown in the table but for which data are available enough to justify separate classification. Their share and citizens the information needed for effective are included in the regional and income group totals. in exports is calculated as a ratio to exports of goods public policies and efficient business operations. The aggregates in the table are calculated using the and services (for definition of exports of goods and Credible data are needed on the scale and signifi - World Bank's weighted aggregation methodology services see Definitions for table 4.8). · Tourism cance of tourism. Information on the role tourism (see Statistical methods) and differ from aggregates expenditure in other countries is expenditures of plays in national economies throughout the world provided by the World Tourism Organization. international outbound visitors in other countries, is particularly deficient. Although the World Tourism including payments to foreign carriers for interna- Organization reports that progress has been made tional transport. These expenditures may include in harmonizing definitions and measurement units, those by residents traveling abroad as same-day differences in national practices still prevent full visitors, except in cases where these are important international comparability. enough to justify separate classification. Their share The data in the table are from the World Tourism in imports is calculated as a ratio to imports of goods Organization, a specialized agency of the United and services (for definition of imports of goods and Nations. The data on international inbound and services see Definitions for table 4.8). International tourism generated more than $2 billion a day in 2005 6.15a $ billions 700 600 Middle East 500 Europe 400 300 Data sources 200 Asia and Pacific Data on visitors and tourism expenditure are avail- 100 America Africa able in the World Tourism Organization's Yearbook 0 of Tourism Statistics and Compendium of Tourism 1980 1985 1990 1995 2000 2005 Statistics 2007. Data in the table are updated International tourism has become an important pillar of some economies and accounted for 40 percent from electronic files provided by the World Tour- of global services trade in recent years. In 2005 Africa recorded the highest growth rate in international ism Organization. Data on exports and imports tourism receipts (8.5 percent), followed by Asia and Pacific (4.3 percent). are from the IMF's International Financial Statistics Source: World Tourism Organization. and World Bank staff estimates. 2007 World Development Indicators 367 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 investment climate 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 the primary data collectors--usually national statistical agencies, central banks, and customs services--and by international organizations, which compile the statistics that appear in the World Development Indicators 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 is central to implementing poverty reduction strategies and lies 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. 2007 World Development Indicators 369 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 B Albania Albanian lek a 1996 b VAB 1996 BPM5 Actual G C G Algeria Algerian dinar 1980 VAB BPM5 Actual S B Angola Angolan kwanza 1997 VAP 1991­96 BPM4 Preliminary S G Argentina Argentine peso 1993 b VAB 1971­84 1996 BPM5 Actual S C S Armenia Armenian dram a 1996 b VAB 1990­95 2000 BPM5 Actual S C S Australia Australian dollar a 2000 b VAB 2002 BPM5 G C S Austria Euro 2000 b VAB 2002 BPM5 S C S Azerbaijan New Azeri manat a 2003 b VAB 1992­95 2000 BPM5 Actual G C G Bangladesh Bangladesh taka 1995/96 b VAB 1996 BPM5 Actual G C G Belarus Belarusian rubel a 2000 b VAB 1990­95 2000 BPM5 Actual G C S Belgium Euro 2000 b VAB 2002 BPM5 S C S Benin CFA franc 1985 VAP 1992 1996 BPM5 Preliminary S B G Bolivia Boliviano 1990 b VAB 1960­85 1996 BPM5 Actual S C G Bosnia and Herzegovina Konvertible mark a 1996 b VAB BPM5 Actual C Botswana Botswana pula 1993/94 b VAB 1996 BPM5 Actual G B G Brazil Brazilian real 1990 b VAB 1996 BPM5 Actual S C S Bulgaria Bulgarian lev a 2002 b VAB 1978­89, 2002 BPM5 Actual G C S 1991­92 Burkina Faso CFA franc 1990 VAP 1992­93 BPM4 Actual G B G Burundi Burundi franc 1980 VAB BPM5 Actual S C Cambodia Cambodian riel 2000 VAB BPM5 Actual G C G Cameroon CFA franc 2000 b VAB 1996 BPM5 Preliminary S B G Canada Canadian dollar 2000 b VAB 2002 BPM5 G C S Central African Republic CFA franc 1987 VAB BPM4 Preliminary S B G Chad CFA franc 1995 VAB BPM5 Actual S C G Chile Chilean peso 1996 b VAB 1996 BPM5 Actual S C S China Chinese yuan 2000 1990 b VAP 1978­93 1986 BPM5 Preliminary S B G Hong Kong, China Hong Kong dollar 2000 b VAB 1996 BPM5 G C S Colombia Colombian peso 1994 b VAB 1992­94 BPM5 Actual S C S Congo, Dem. Rep. Congo franc 1987 VAB 1999­2001 BPM5 Preliminary S C G Congo, Rep. CFA Franc 1978 VAP 1996 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 1996 BPM5 Estimate S C G Croatia Croatian kuna a 1997 b VAB 2002 BPM5 Actual G C S Cuba Cuban peso 1984 VAP G Czech Republic Czech koruna 2000 1995 b VAB 2002 BPM5 G C S Denmark Danish krone 2000 b VAB 2002 BPM5 G C S Dominican Republic Dominican peso 1990 VAP BPM5 Actual G C G Ecuador U.S. dollar 2000 b VAB 1996 BPM5 Preliminary S B S Egypt, Arab Rep. Egyptian pound 1991/92 VAB 1996 BPM5 Actual S B S El Salvador U.S. dollar 1990 VAB 1982­90 BPM5 Actual S C S Eritrea Eritrean nakfa 1992 VAB BPM4 Actual Estonia Estonian kroon 2000 b VAB 1991­95 2002 BPM5 Actual G C S Ethiopia Ethiopian birr 1999/2000 b VAB BPM5 Actual G C G Finland Euro 2000 b VAB 2002 BPM5 G C S France Euro a 2000 b VAB 2002 BPM5 S C S Gabon CFA franc 1991 VAP 1993 1996 BPM5 Preliminary S B G Gambia, The Gambian dalasi 1987 VAB BPM5 Actual G B G Georgia Georgian lari a 1994 b VAB 1990­95 2000 BPM5 Actual G C G Germany Euro 2000 b VAB 2002 BPM5 S C S Ghana Ghanaian cedi 1975 VAP 1973­87 BPM5 Actual G B G Greece Euro a 2000 VAB 2002 BPM5 S C S Guatemala Guatemalan quetzal 1958 VAP 1980 BPM5 Actual S B G Guinea Guinean franc 1996 1994 VAB 1996 BPM5 Estimate S B G Guinea-Bissau CFA franc 1986 VAB BPM5 Estimate G G Haiti Haitian gourde 1975/76 VAB 1991 BPM5 Actual G 370 2007 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 1987 Albania 2001 RHS, 2002 LSMS, 2004 Yes 1998 1990 2005 1995 Algeria 1998 MICS, 2000 HLSS, 1995 2001 2004 2004 1995 Angola 1970 MICS, 2001 1964­65 1991 1987 Argentina 2001 EPH, 2003 Yes 2002 2001 2005 1995 Armenia 2001 DHS, 2000 ILCS, 2003 Yes 2005 1994 Australia 2001 SIHC, 1994 Yes 2001 2004 2005 1985 Austria 2001 Microcensus 2000 Yes 1999­2000 2004 2005 1991 Azerbaijan 1999 RHS, 2001 HBS, 2003 Yes 2005 1995 Bangladesh 2001 DHS, 2004 HES, 2000 1996 2004 2004 1990 Belarus 1999 IES, 2002 Yes 1994 2005 1990 Belgium 2001 ECHP, 2000 Yes 1999­2000c 2004 2005 Benin 2002 DHS, 2001 CWIQ, 2003 1992 1999 2005 1994 Bolivia 2001 DHS, 2003 MECOVI, 2002 1984­88 2001 2005 1987 Bosnia and Herzegovina 1991 MICS, 2000 LSMS, 2001 Yes 1991 2005 1995 Botswana 2001 MICS, 2000 HIES, 1993­94 1993 2003 2003 1992 Brazil 2000 DHS, 1996 PNAD, 2004 1996 1995 2005 1992 Bulgaria 2001 HBS, 2003 Yes 2003 2005 1988 Burkina Faso 1996 DHS, 2003 EVCBM, 2003 1993 2004 2004 1992 Burundi 1990 MICS, 2000 Priority survey, 1998 2004 2005 1987 Cambodia 1998 DHS, 2005 SES, 2004 2004 1987 Cameroon 1987 DHS, 2004 Priority survey, 2001 1984 2002 2005 1987 Canada 2001 EBC, 2001 Yes 1996/2001 2004 2005 1991 Central African Republic 2003 MICS, 2000 SLID, 2000 1985 2004 2005 1987 Chad 1993 DHS, 2004 EPI, 1993 1975 1995 1987 Chile 2002 CASEN, 2003 Yes 1996­97 2004 2005 1987 China 2000 Intercensal survey 1995 HHS (Rural/Urban), 2004 1997 2003 2005 1993 Hong Kong, China 2006 Yes 2002 2005 Colombia 2005 DHS, 2005 ECV, 2003 2001 2004 2005 1996 Congo, Dem. Rep. 1984 MICS, 2001 1990 1986 1990 Congo, Rep. 1996 DHS, 2005 1985­86 1988 1995 1987 Costa Rica 2000 RHS, 1993 EHPM, 2003 Yes 1973 2004 2005 1997 Côte d'Ivoire 1998 MICS, 2000; AIS, 2005 LSMS, 2002 2001 1997 2005 1987 Croatia 2001 HBS, 2001 Yes 2003 1992 2005 1996 Cuba 2002 MICS, 2000 Yes 1989 2004 1995 Czech Republic 2001 RHS, 1993 Microcensus 1996/97 Yes 2000 1998 2005 1991 Denmark 2001 Income Tax Register 1997 Yes 1999­2000 2004 2005 1990 Dominican Republic 2002 DHS, 2002 ENFT, 2004 1971 2004 2001 1994 Ecuador 2001 RHS, 2004 LSMS, 1998 1999­2000 2004 2005 1997 Egypt, Arab Rep. 1996 DHS, 2005 HECS, 2000 Yes 1999­2000 2004 1996 El Salvador 1992 RHS, 2002/03 EHPM, 2002 Yes 1970­71 2004 2004 1992 Eritrea 1984 DHS, 2002 2003 2003 Estonia 2000 HBS, 2003 Yes 2001 2003 2005 1995 Ethiopia 1994 DHS, 2005 ICES, 2000 2001­02 2002 2003 1987 Finland 2000 IDS, 2000 Yes 1990­2000 2004 2005 1991 France 1999 HBS, 1994/95 Yes 1999­2000 2004 2005 1999 Gabon 2003 DHS, 2000 1974­75 2004 1987 Gambia, The 2003 MICS, 2000 HHS, 1998 2001­02 1982 2005 1982 Georgia 2002 MICS, 1999; RHS, 1999 SGH, 2003 Yes 2005 1990 Germany 2004 GSOEP, 2000 Yes 1999­2000 2003 2005 1991 Ghana 2000 SPA, 2002; DHS, 2003 LSMS, 1998/99 1984 2004 2004 1997 Greece 2001 ECHP, 2000 Yes 1999­2000 2004 2005 1980 Guatemala 2002 RHS, 2002 ENEI-2, 2002 Yes 2003 2004 2005 1992 Guinea 1996 DHS, 2005 LSMS, 1994 2000 2002 1987 Guinea-Bissau 1991 MICS, 2000 IES, 1993 1988 1995 1991 Haiti 2003 DHS, 2000 ECVH, 2001 1971 1996 1997 1991 2007 World Development Indicators 371 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 Honduras Honduran lempira 1978 VAB 1988­89 BPM5 Actual S G Hungary Hungarian forint a 2000 b VAB 2002 BPM5 Actual S C S India Indian rupee 1999/2000 b VAB BPM5 Actual G C S Indonesia Indonesian rupiah 2000 VAP 1996 BPM5 Preliminary S C S Iran, Islamic Rep. Iranian rial 1997/98 VAB 1980­90 1996 BPM5 Actual G C Iraq Iraqi dinar 1997 VAB S Ireland Euro 2000 b VAB 2002 BPM5 G C S Israel Israeli new shekel 2000 b VAP 2002 BPM5 S C S Italy Euro 2000 b VAB 2002 BPM5 S C S Jamaica Jamaica dollar 1996 VAB 1996 BPM5 Preliminary G C G Japan Japanese yen 2000 VAB 2002 BPM5 G C S Jordan Jordan dinar 1994 VAB 1996 BPM5 Actual G B G Kazakhstan Kazakh tenge a 1995 b VAB 1987­95 2000 BPM5 Actual G C S Kenya Kenya shilling 2001 b VAB 1996 BPM5 Preliminary G B G Korea, Dem. Rep. Democratic Republic BPM5 of Korea won Korea, Rep. Korean won 2000 b VAB 2002 BPM5 S C S Kuwait Kuwaiti dinar 1995 VAP BPM5 S C G Kyrgyz Republic Kyrgyz som a 1995 b VAB 1990­95 2000 BPM5 Actual G B S Lao PDR Lao kip 1990 VAB 1993 BPM5 Preliminary G Latvia Latvian lat 2000 b VAB 1991­95 2002 BPM5 Actual S C S Lebanon Lebanese pound 2003 VAB 1996 BPM4 Actual G B G Lesotho Lesotho loti 1995 b VAB BPM5 Actual G C G Liberia Liberian dollar 1992 VAB Estimate G Libya Libyan dinar 1975 VAB 1986 BPM5 G Lithuania Lithuanian litas 2000 b VAB 1990­95 2002 BPM5 Actual G C S Macedonia, FYR Macedonian denar 1997 1995 b VAB 2002 BPM5 Actual G G Madagascar Malagasy ariary 1984 VAB 1996 BPM5 Actual S C G Malawi Malawi kwacha 1994 VAB 1996 BPM5 Actual G B G Malaysia Malaysian ringgit 1987 VAP 1993 BPM5 Estimate G C S Mali CFA franc 1987 VAB 1996 BPM4 Actual G G Mauritania Mauritanian ouguiya 1985 VAB BPM4 Actual G G Mauritius Mauritian rupee 1997/98 VAB 1996 BPM5 Actual G C G Mexico Mexican new peso 1993 b VAB 2002 BPM5 Actual G C S Moldova Moldovan leu a 1996 b VAB 1987­95 2000 BPM5 Actual G C S Mongolia Mongolian tugrik 2000 b VAB 2000 BPM5 Actual S C G Morocco Moroccan dirham 1980 VAP 1996 BPM5 Actual S C S Mozambique Mozambican metical 1995 VAB 1992­95 BPM5 Actual S G Myanmar Myanmar kyat 1985/86 VAP BPM5 Estimate G C Namibia Namibia dollar 1995/96 b VAB BPM5 B G Nepal Nepalese rupee 1994/95 VAB 1996 BPM5 Actual S C G Netherlands Euro a 2000 b VAB 2002 BPM5 S C S New Zealand New Zealand dollar 2000/01 VAB 2002 BPM5 G C Nicaragua Nicaraguan gold cordoba 1994 b VAB 1965­93 BPM5 Actual S C G Niger CFA franc 1987 VAP 1993 BPM5 Preliminary S G Nigeria Nigerian naira 1987 VAB 1971­98 1996 BPM5 Preliminary G G Norway Norwegian krone a 2000 b VAB 2002 BPM5 G C S Oman Rial Omani 1988 VAP 1996 BPM5 Actual G B G Pakistan Pakistan rupee 1999/2000 b VAB 1996 BPM5 Actual G C G Panama Panamanian balboa 1996 b VAB 1996 BPM5 Actual S C G Papua New Guinea Papua New Guinea kina 1983 VAB 1989 BPM5 Actual G B Paraguay Paraguayan guarani 1994 b VAP 1982­88 BPM5 Actual S B G Peru Peruvian new sol 1994 VAB 1985­91 1996 BPM5 Actual S C S Philippines Philippine peso 1985 VAP 1996 BPM5 Actual G B S Poland Polish zloty a 2002 b VAB 2002 BPM5 Actual S C S Portugal Euro 2000 b VAB 2002 BPM5 S C S Puerto Rico U.S. dollar 1954 VAP G 372 2007 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 Honduras 2001 DHS, 2005 EPHPM, 2003 1993 2004 2005 1992 Hungary 2001 FBS, 2002 Yes 2000 2004 2005 1991 India 2001 MICS, 2000 NSS, 2004/05 1995­96/ 2004 2005 1990 2000­01 Indonesia 2000 DHS, 2002 SUSENAS, 2002 2003 2003 2005 1990 Iran, Islamic Rep. 1996 DHS, 2000 SECH, 1998 Yes 2003 2004 2005 1993 Iraq 1997 MICS, 2000 1981 2004 1976 1990 Ireland 2006 ECHP, 2000 Yes 2000 2004 2005 1980 Israel 1995 HES, 2001 Yes 1981 2004 2005 1997 Italy 2001 SHIW, 2000 Yes 2000 2003 2005 1998 Jamaica 2001 RHS, 2002/03 LSMS, 2004 1978­79 2004 2004 1993 Japan 2005 Yes 2000 2004 2005 1992 Jordan 2004 DHS, 2002 HIES, 1997 1997 2004 2005 1993 Kazakhstan 1999 DHS, 1999 HBS, 2003 Yes 2005 1993 Kenya 1999 DHS, 2003; SPA, 2004 WMS II, 1997 1977­79 2004 2004 1990 Korea, Dem. Rep. 1993 MICS, 2000 1987 Korea, Rep. 2000 NSFIE, 1998/99 Yes 2000 2004 2005 1994 Kuwait 1995 FHS, 1996 Yes 1970 2001 2001 1994 Kyrgyz Republic 1999 DHS, 1997 HBS, 2003 Yes 2002 2005 1994 Lao PDR 2005 MICS, 2000 ECS I, 2002 1998­99 1974 1987 Latvia 2000 HBS, 2003 Yes 2001 2003 2005 1994 Lebanon 1970 MICS, 2000 1998­99 2004 1996 Lesotho 1996 DHS, 2004 HBS, 1995 1999­2000 1985 2002 1987 Liberia 1984 MICS, 1995 1984 1987 Libya 1995 MICS, 2000 2001 2004 2004 1999 Lithuania 2001 HBS, 2003 Yes 1994 2003 2005 1995 Macedonia, FYR 2002 HBS, 2003 Yes 1994 1996 2005 1996 Madagascar 1993 DHS, 2003/04 Priority survey, 2001 1984­85 2003 2004 1984 Malawi 1998 DHS, 2004 HHS, 2004/05 1993 2004 2005 1994 Malaysia 2000 HIBAS, 1997 Yes 2002 2005 1995 Mali 1998 DHS, 2001 EMCES, 2001 1984 2001 1987 Mauritania 2000 DHS, 2000/01 LSMS, 2000 1984­85 1978 1996 1985 Mauritius 2000 Yes 2004 2005 Mexico 2000 ENPF, 1995 ENIGH, 2004 1991 2000 2005 1998 Moldova 2004 DHS, 2005 HBS, 2003 Yes 2003 2005 1992 Mongolia 2000 MICS, 2000 LSMS/Integrated Survey, 2002 Yes 1995 2005 1993 Morocco 2004 DHS, 2003/04 LSMS, 1998/99 1996 2001 2005 1998 Mozambique 1997 DHS, 2003 NHS, 2002/03 1999­2000 2004 2005 1992 Myanmar 1983 MICS, 2000 2003 1993 1987 Namibia 2001 DHS, 2000 NHIES, 1993 1996­97 1994 2003 1991 Nepal 2001 DHS, 2001 LSMS, 2003/04 2002 2002 2003 1994 Netherlands 2001 ECHP, 1999 Yes 1999­2000c 2004 2005 1991 New Zealand 2006 Yes 2002 2004 2005 1991 Nicaragua 2005 DHS, 2001 LSMS, 2001 2001 2004 2005 1998 Niger 2001 MICS, 2000 1980 2002 2005 1988 Nigeria 2006 DHS, 2003 LSMS, 2003 1960 2003 1987 Norway 2001 IF 2000 Yes 1999 2004 2005 1985 Oman 2003 FHS, 1995 1978­79 2003 2005 1991 Pakistan 1998 RHS, 2000/01 PIHS, 2002 2000 2004 2005 1991 Panama 2000 LSMS, 2003 EH, 2003 2001 2004 2005 1990 Papua New Guinea 2000 DHS, 1996 HGS, 1996 2004 2003 1987 Paraguay 2002 RHS, 2004 EIH, 2003 1991 2004 2004 1987 Peru 2005 DHS, 2004 ENAHO, 2003 1994 1996 2005 1992 Philippines 2000 DHS, 2003 FIES, 2003 Yes 2002 2004 2005 1995 Poland 2002 HBS, 2002 Yes 1996/2002 2004 2005 1991 Portugal 2001 Yes 1999 2004 2005 1990 Puerto Rico 2000 RHS, 1995/96 Yes 1997/2002 2002 2007 World Development Indicators 373 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 Romania New Romanian leu a 1999 b VAB 1987­89, 2002 BPM5 Actual S C S 1992 Russian Federation Russian ruble 2000 b VAB 1987­95 2002 BPM5 Preliminary G C S Rwanda Rwanda franc 1995 VAP BPM5 Preliminary G C G Saudi Arabia Saudi Arabian riyal 1999 VAP BPM4 G Senegal CFA franc 1999 1987 b VAP 1996 BPM5 Preliminary S B G Serbia and Montenegro Yugoslav new dinar 1998 VAB Actual C Sierra Leone Sierra Leonean leone 2001 1990 b VAB 1996 BPM5 Actual G B G Singapore Singapore dollar 1995 b VAB 1996 BPM5 G C S Slovak Republic Slovak koruna 2000 1995 b VAP 2002 BPM5 Actual G C S Slovenia Slovenian tolar a 2000 b VAB 2002 BPM5 S C S Somalia Somali shilling 1985 VAB 1977­90 Estimate South Africa South African rand 2000 b VAB BPM5 Preliminary S C S Spain Euro 2000 b VAB 2002 BPM5 S C S Sri Lanka Sri Lankan rupee 1996 VAB 1996 BPM5 Actual G B G Sudan Sudanese dinar 1981/82d 1982 VAB BPM5 Actual G B G Swaziland Lilangeni 1985 VAB 1996 Actual B G Sweden Swedish krona a 2000 VAB 2002 BPM5 G C S Switzerland Swiss franc 2000 VAB 2002 BPM5 S C S Syrian Arab Republic Syrian pound 2000 VAB 1970­2005 1996 BPM5 Estimate S C Tajikistan Tajik somoni a 1997 b VAB 1990­95 2000 BPM5 Preliminary G C G Tanzania Tanzania shilling 1992 VAB 1996 BPM5 Estimate S G Thailand Thai baht 1988 VAP 1996 BPM5 Preliminary G C S Togo CFA franc 1978 VAP BPM5 Actual S B G Trinidad and Tobago Trinidad and 2000 b VAB 1996 BPM5 Actual S C G Tobago dollar Tunisia Tunisian dinar 1990 VAP 1996 BPM5 Actual G C S Turkey Turkish lira 1987 VAB 2002 BPM5 Actual S B S Turkmenistan Turkmen manat a 1987 b VAB 1987­95, 2000 BPM5 Actual G 1997­2005 Uganda Uganda shilling 1997/98 VAB BPM5 Actual G B G Ukraine Ukrainian hryvnia a 2003 b VAB 1990­95 2000 BPM5 Actual G C S United Arab Emirates U.A.E. dirham 1995 VAB BPM4 G C United Kingdom Pound sterling 2000 b VAB 2002 BPM5 G C S United States U.S. dollar a 2000 VAB 2002 BPM5 G C S Uruguay Uruguayan peso 1983 VAB 1996 BPM5 Actual S C S Uzbekistan Uzbek sum a 1997 b VAB 1990­95 2000 BPM5 Actual G Venezuela, RB Venezuelan bolivar 1984 VAB 1996 BPM5 Actual G C G Vietnam Vietnamese dong 1994 b VAP 1991 1996 BPM4 Actual G C G West Bank and Gaza Israeli new shekel 1997 VAB B G Yemen, Rep. Yemen rial 1990 VAP 1991­96 1996 BPM5 Actual G B G Zambia Zambian kwacha 1994 VAB 1990­92 1996 BPM5 Actual G B G Zimbabwe Zimbabwe dollar 1990 VAB 1991, 1998 1996 BPM5 Actual G C G 374 2007 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 Romania 2002 RHS, 1999 LSMS, 2003 Yes 2002 2005 1994 Russian Federation 2002 RHS, 1996 LMS, Round 9, 2002 Yes 1994­95 2000 2005 1994 Rwanda 2002 DHS, 2005 LSMS, 1999/2000 1984 2004 2003 1993 Saudi Arabia 2004 Demographic survey, 1999 1999 1989 2005 1992 Senegal 2002 DHS, 2005 ESASM 1995 1998­99 1997 2005 1987 Serbia and Montenegro Serbia 2002, MICS, 2000 Yes 2002 2004 Montenegro 2003 Sierra Leone 2004 MICS, 2000 SHEHEA, 1989­90 1984­85 1993 2002 1987 Singapore 2000 General household, 2005 Yes 2004 2005 1975 Slovak Republic 2001 Microcensus, 1996 Yes 2001 1999 2005 1991 Slovenia 2002 HBS, 1998 Yes 2000 2003 2005 1996 Somalia 1987 MICS, 1999 2003 1982 1987 South Africa 2001 DHS, 1998 IES, 2000 2003 2005 1990 Spain 2001 ECHP, 2000 Yes 1999 2004 2005 1997 Sri Lanka 2001 DHS, 1987 HIEs, 2002 Yes 2002 2001 2005 1990 Sudan 1993 MICS, 2000 2001 2005 1995 Swaziland 1997 MICS, 2000 SHIES, 2000/01 2000 2004 2002 Sweden 2005 HINK, 2000 Yes 1999­2000 2004 2005 1991 Switzerland 2000 EVE, 2000 Yes 2000 1997 2005 1991 Syrian Arab Republic 1994 MICS, 2000 1981 2004 2004 1995 Tajikistan 2000 MICS, 2000 LSMS, 2003 Yes 1994 2000 1994 Tanzania 2002 DHS, 2004 HIES, 2000/01 2003 2004 2005 1994 Thailand 2000 DHS, 1987 SES, 2002 2003 2002 2005 1990 Togo 1981 MICS, 2000 1996 2004 2005 1987 Trinidad and Tobago 2000 MICS, 2000 LSMS, 1992 Yes 2004 2004 2005 1997 Tunisia 2004 MICS, 2000 LSMS, 2000 2004 2004 2005 1996 Turkey 2000 DHS, 1998 LSMS, 2002 2001 2004 2005 1997 Turkmenistan 1995 DHS,2000 LSMS, 1998 Yes 2000 1994 Uganda 2002 DHS, 2000/01; AIS, 2004 NIHS III, 2002 1991 2004 2005 1970 Ukraine 2001 MICS, 2000 HBS, 2003 Yes 2005 1992 United Arab Emirates 2005 1998 2004 2001 1995 United Kingdom 2001 FRS, 1999 Yes 1999­2000c 2004 2005 1991 United States 2000 CPS (monthly) CPS, 2000 Yes 1997/2002 2004 2005 1990 Uruguay 1996 ECH, 2003 Yes 2000 1997 2005 1965 Uzbekistan 1989 MICS, 2000; FBS, 2003 Yes 1994 DHS special, 2002 Venezuela, RB 2001 MICS, 2000 EHM, 2003 Yes 1997 2003 2005 1970 Vietnam 1999 DHS 2002; AIS 2005 LSMS, 2004 2001 2000 2003 1990 West Bank and Gaza 1997 Health Survey, 2000 1971 Yemen, Rep. 2004 DHS, 1997 HBS, 1998 2002 2003 2005 1990 Zambia 2000 DHS, 2001/02; SPA, 2005 LCMS II, 2004 1990 2004 2005 1994 Zimbabwe 2002 DHS, 1999 LCMS III, 1995 1960 2004 2004 1987 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. 2007 World Development Indicators 375 Primary data documentation notes · Base year is the year used as the base or pricing IMF's Balance of Payments Manual (1977), and BPM5 reliable economic, financial, and sociodemographic period for constant price calculations in the country's to the 5th edition (1993). · External debt shows debt statistics. IMF member countries voluntarily elect to national accounts. Price indexes derived from national reporting status for 2005 data. Actual indicates that participate in either the SDDS or the GDDS. Both the accounts aggregates, such as the implicit deflator for data are as reported; preliminary indicates that data SDDS and the GDDS are expected to enhance the gross domestic product (GDP), express the price level are preliminary and include an element of staff esti- availability of timely and comprehensive data and relative to prices in the base year. · Reference year mation; and estimate indicates that data are World therefore contribute to the pursuit of sound macro- is the year in which the local currency, constant price Bank staff estimates. · System of trade refers to the economic policies. The SDDS is also expected to series of a country is valued. In most cases the refer- United Nations general trade system (G) or the spe- improve the functioning of financial markets. · Latest ence year is same as the base year used to report cial trade system (S). For imports under the general population census shows the most recent year in the constant price series. However, when the con- trade system both goods entering directly for domes- which a census was conducted and in which at least stant price data are chain linked, the base year is tic consumption and goods entered into customs preliminary results have been released. It includes changed annually, so the data are rescaled to a spe- storage are recorded as imports at the time of arrival; registration-based censuses. Some countries with cifi c reference year to provide a consistent time under the special trade system goods are recorded complete population registration systems produce series. In a few other cases, when the country has as imports when they are declared for domestic con- similar tables every 5 or 10 years instead of conduct- not rescaled following a change in base year, World sumption whether at the time of entry or on with- ing regular censuses. · Latest demographic, educa- Bank staff rescale the data to maintain a longer his- drawal from customs storage. Exports under the tion, or health household survey gives information torical series. To allow for cross-country comparison general system comprise outward-moving goods: on the household surveys used in compiling the and aggregation of the data, constant price data (a) national goods wholly or partly produced in the demographic, education, and health data in sec- reported in World Development Indicators are rescaled country; (b) foreign goods, neither transformed nor tion 2. AIS is the AIDS indicator Survey, CPS is Cur- to a common reference year (2000) and currency declared for domestic consumption in the country, rent Population Survey, DHS is Demographic and (U.S. dollars). · System of National Accounts identi- that move outward from customs storage; and Health Survey, ENPF is National Family Planning Sur- fies countries that use the 1993 System of National (c) nationalized goods that have been declared from vey (Encuesta Nacional de Planifi cacion Familiar), Accounts (1993 SNA), the terminology applied in domestic consumption and move outward without FHS is Family Health Survey, MICS is Multiple Indica- World Development Indicators since 2001, to compile having been transformed. Under the special system tor Cluster Survey, RHS is Reproductive Health Sur- their national accounts. Although more and more of trade exports comprise categories (a) and (c). In vey; and SPA is Service Provision Assessments. countries are adopting the 1993 SNA, many countries some compilations categories (b) and (c) are classi- Detailed information for AIS, DHS, and SPA are avail- continue to follow the 1968 SNA, and some low- fied as re-exports. Direct transit trade, consisting of able at www.measuredhs.com/aboutsurveys; for income countries still use concepts from the 1953 goods entering or leaving for transport purposes only, MICS at www.childinfo.org; and for RHS at www.cdc. SNA. · SNA price valuation shows whether value is excluded from both import and export statistics. gov/reproductivehealth/surveys. · Source of most added in the national accounts is reported at basic See About the data for tables 4.4, 4.5, and 6.2 for recent income and expenditure data shows house- prices (VAB) or at producer prices (VAP). Producer further discussion. · Government finance account- hold surveys that collect income and expenditure prices include the value of taxes paid by producers ing concept describes the accounting basis for data. HBS is Household Budget Survey; ICES is and thus tend to overstate the actual value added in reporting central government financial data. For most Income, Consumption, and Expenditure Survey; IES production. However, the VAB prices can be higher countries government finance data have been con- is Income and Expenditure Survey; LSMS is Living than VAP prices in countries that have high agricul- solidated (C) into one set of accounts capturing all Standards Measurement Study; and SES is Socio- tural subsidies. See About the data for tables 4.1 and the central government's fiscal activities. Budgetary Economic Survey. · Vital registration complete iden- 4.2 for further discussion of national accounts valu- central government accounts (B) exclude some cen- tifies countries judged to have at least 90 percent ation. · Alternative conversion factor identifies the tral government units. See About the data for tables complete registries of vital (birth and death) statistics countries and years for which a World Bank­esti- 4.10, 4.11, and 4.12 for further details. · IMF data by the United Nations Department of Economic and mated conversion factor has been used in place of dissemination standard shows the countries that Social Affairs Statistics Division and reported in the official exchange rate (line rf in the International subscribe to the IMF's Special Data Dissemination Population and Vital Statistics Reports. Countries with Monetary Fund's [IMF] International Financial Statis- Standard (SDDS) or General Data Dissemination Sys- complete vital statistics registries may have more tics). See Statistical methods for further discussion tem (GDDS). S refers to countries that subscribe to accurate and more timely demographic indicators of the use of alternative conversion factors . · Pur- the SDDS and have posted data on the Dissemination than other countries. · Latest agricultural census chasing power parity (PPP) survey year refers to the Standards Bulletin Board web site (posted data are shows the most recent year in which an agricultural latest available survey year for the International Com- at http://dsbb.imf.org). G refers to countries that census was conducted and reported to the Food and parison Program's estimates of PPPs. For a more subscribe to the GDDS. The SDDS was established Agriculture Organization of the United Nations. · Lat- detailed description of PPPs see About the data for by the IMF for member countries that have or that est industrial data refer to the most recent year for table 1.1. · Balance of Payments Manual in use might seek access to international capital markets which manufacturing value added data at the three- refers to the classification system used for compiling to guide them in providing their economic and finan- digit level of the International Standard Industrial and reporting data on balance of payments items in cial data to the public. The GDDS helps countries Classification (ISIC, revision 2 or revision 3) are avail- table 4.15. BPM4 refers to the 4th edition of the disseminate comprehensive, timely, accessible, and able in the United Nations Industrial Development 376 2007 World Development Indicators Primary data documentation notes Organization database. · Latest trade data show the Reporting most recent year for which structure of merchandise period for national trade data from the United Nations Statistical Divi- Fiscal accounts sion's Commodity Trade (Comtrade) database are year end data available. · Latest water withdrawal data show the Afghanistan Mar. 20 FY most recent year for which data on freshwater with- Australia Jun. 30 FY drawals have been compiled from a variety of sources. Bangladesh Jun. 30 FY See About the data for table 3.5 for more informa- Botswana Jun. 30 FY tion. Canada Mar. 31 CY Egypt, Arab Rep. Jun. 30 FY Exceptional reporting periods Ethiopia Jul. 7 FY In most economies the fi scal year is concurrent Gambia, The Jun. 30 CY with the calendar year. The exceptions are shown in Haiti Sep. 30 FY this table. The fiscal year ending date reported here India Mar. 31 FY refers to the fiscal year of the central government. Indonesia Mar. 31 CY Fiscal years for other levels of government and the Iran, Islamic Rep. Mar. 20 FY reporting years for statistical surveys may differ. Fur- Japan Mar. 31 CY ther, some countries that follow a fiscal year report their national accounts data on a calendar year basis Kenya Jun. 30 CY as shown in the reporting period column. Kuwait Jun. 30 CY The reporting period for national accounts data Lesotho Mar. 31 CY is designated as either calendar year basis (CY) or Malawi Mar. 31 CY fiscal year basis (FY). Most economies report their Mauritius Jun. 30 FY national accounts and balance of payments data Myanmar Mar. 31 FY using calendar years, but some use fiscal years Namibia Mar. 31 CY that straddle two calendar years. In World Devel- Nepal Jul. 14 FY opment Indicators fiscal year data are assigned to New Zealand Mar. 31 FY the calendar year that contains the larger share of Pakistan Jun. 30 FY the fiscal year. If a country's fiscal year ends before Puerto Rico Jun. 30 FY June 30, the data are shown in the first year of the Sierra Leone Jun. 30 CY fiscal period; if the fiscal year ends on or after June Singapore Mar. 31 CY 30, the data are shown in the second year of the South Africa Mar. 31 CY period. Balance of payments data are reported in Swaziland Mar. 31 CY World Development Indicators by calendar year and so are not comparable to the national accounts data Sweden Jun. 30 CY of the countries that report their national accounts Thailand Sep. 30 CY on a fiscal year basis. Uganda Jun. 30 FY United States Sep. 30 CY Zimbabwe Jun. 30 CY 2007 World Development Indicators 377 STATISTICAL METHODS This section describes some of the statistical procedures used in preparing the of the ratios (using the value of the denominator or, in some cases, another World Development Indicators. It covers the methods employed for calculating indicator as a weight) and denoted by a u when calculated as unweighted regional and income group aggregates and for calculating growth rates, and it averages. The aggregate ratios are based on available data, including data describes the World Bank Atlas method for deriving the conversion factor used for economies not shown in the main tables. Missing values are assumed to estimate gross national income (GNI) and GNI per capita in U.S. dollars. Other to have the same average value as the available data. No aggregate is cal- statistical procedures and calculations are described in the About the data sec- culated if missing data account for more than a third of the value of weights tions 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 · Aggregate growth rates are denoted by a w when calculated as a weighted economies appear at the end of most tables. The countries included in these average of growth rates. In a few cases growth rates may be computed from classifications are shown on the flaps on the front and back covers of the book. time series of group totals. Growth rates are not calculated if more than half Most tables also include the aggregate Europe EMU. This aggregate includes the the observations in a period are missing. For further discussion of methods member states of the Economic and Monetary Union (EMU) of the European Union of computing growth rates see below. that have adopted the euro as their currency: Austria, Belgium, Finland, France, · Aggregates denoted by an m are medians of the values shown in the table. Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Slovenia, No value is shown if more than half the observations for countries with a and Spain. Other classifications, such as the European Union and regional trade population of more than 1 million are missing. 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 152 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 fi tting 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 378 2007 World Development Indicators average annual growth rate, r, is obtained as [exp(b*) ­ 1] and is multiplied by 100 The inflation rate for Japan, the United Kingdom, the United States, and the for expression as a percentage. The calculated growth rate is an average rate that Euro Zone, representing international inflation, is measured by the change in the is representative of the available observations over the entire period. It does not SDR deflator. (Special drawing rights, or SDRs, are the International Monetary necessarily match the actual growth rate between any two periods. Fund's unit of account.) The SDR deflator is calculated as a weighted average of these countries' GDP deflators in SDR terms, the weights being the amount of Exponential growth rate. The growth rate between two points in time for cer- each country's currency in one SDR unit. Weights vary over time because both tain demographic indicators, notably labor force and population, is calculated the composition of the SDR and the relative exchange rates for each currency from the equation change. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conver- r = ln(pn/p 0)/n sion factor is then applied to a country's GNI. The resulting GNI in U.S. dollars is divided by the midyear population to derive GNI per capita. where pn and p 0 are the last and first observations in the period, n is the number When official exchange rates are deemed to be unreliable or unrepresenta- of years in the period, and ln is the natural logarithm operator. This growth rate is tive of the effective exchange rate during a period, an alternative estimate of the based on a model of continuous, exponential growth between two points in time. exchange rate is used in the Atlas formula (see below). It does not take into account the intermediate values of the series. Nor does it The following formulas describe the calculation of the Atlas conversion fac- correspond to the annual rate of change measured at a one-year interval, which tor for year t: is given by (pn ­ pn­1)/pn­1. 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 and the calculation of GNI per capita in U.S. dollars for year t: intervals, in which case the compound growth model is appropriate. The average 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 year t, et is the average annual exchange rate (national currency to the U.S. dollar) Like the exponential growth rate, it does not take into account intermediate for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. dollar values of the series. terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current GNI (local currency) for year t, and Nt is the midyear population for year t. World Bank Atlas method In calculating GNI and GNI per capita in U.S. dollars for certain operational Alternative conversion factors purposes, the World Bank uses the Atlas conversion factor. The purpose of the The World Bank systematically assesses the appropriateness of official exchange Atlas conversion factor is to reduce the impact of exchange rate fluctuations in rates as conversion factors. An alternative conversion factor is used when the the cross-country comparison of national incomes. official exchange rate is judged to diverge by an exceptionally large margin from The Atlas conversion factor for any year is the average of a country's exchange the rate effectively applied to domestic transactions of foreign currencies and rate (or alternative conversion factor) for that year and its exchange rates for the traded products. This applies to only a small number of countries, as shown two preceding years, adjusted for the difference between the rate of inflation in in Primary data documentation. Alternative conversion factors are used in the the country and that in Japan, the United Kingdom, the United States, and the Euro Atlas methodology and elsewhere in World Development Indicators as single-year Zone. A country's inflation rate is measured by the change in its GDP deflator. conversion factors. 2007 World Development Indicators 379 CREDITS Credits contributions were made by Edward Gillin and Carola Fabi of the Food and Agri- World Development Indicators draws on a wide range of World Bank reports and culture Organization of the United Nations; Ricardo Quercioli of the International numerous external sources, listed in the bibliography following this section. Many Energy Agency; Amay Cassara, Christian Layke, Daniel Prager, and Robin White people inside and outside the World Bank helped in writing and producing this book. of the World Resources Institute; Laura Battlebury of the World Conservation The team would like to particularly acknowledge the help and encouragement of Monitoring Centre; and Gerhard Metchies of German Technical Cooperation (GTZ). François Bourguignon, Senior Vice President and Chief Economist of the World Bank, The World Bank's Environment Department devoted substantial staff resources and Shaida Badiee, Director, Development Data Group. The team is also grateful to to the book, for which the team is very grateful. M.H. Saeed Ordoubadi wrote the people who provided valuable comments on the entire book. This note identi- the introduction with valuable comments from Sarwar Lateef, Jeffrey Lewis, and fies many of those who made specific contributions. Numerous others, too many to Eric Swanson. Other contributions were made by Kiran Pandey (biodiversity); acknowledge here, helped in many ways for which the team is extremely grateful. Susmita Dasgupta, Craig Meisner, Kiran Pandey, and David Wheeler (air and water pollution); Solly Angel, Augusto Clavijo, Maria Emilia Ferire, Mahyar Eshragh- 1. World view Tabary, Christine Kessides, and Micah Perlin (urban housing conditions); and Kirk The introduction to section 1 was prepared by Sebastien Dessus and Eric Hamilton, Beat Hintermann, and Giovanni Ruta (adjusted savings). Swanson. Alan Gelb, Sarwar Lateef, and Jeffrey Lewis provided valuable sug- gestions. Changqing Sun and Raymond Muhula provided the decomposition of 4. Economy poverty rates. K.M. Vijayalakshmi prepared tables 1.1 and 1.6. Changqing Sun Section 4 was prepared by K.M. Vijayalakshmi in close collaboration with the prepared the estimates of gross national income in purchasing power parity terms Macroeconomic Data Team of the World Bank's Development Data Group, led and table 1.4. Tables 1.2, 1.3, and 1.5 were prepared by Masako Hiraga. Dorte by Soong Sup Lee. K.M. Vijayalakshmi and Eric Swanson wrote the introduction Domeland-Narvaez of the World Bank's Economic Policy and Debt Department with valuable suggestions from Sarwar Lateef and Sebastien Dessus. Contribu- provided the estimates of debt relief for the Heavily Indebted Poor Countries Debt tions to the section were provided by Azita Amjadi (trade) and Ibrahim Levent Initiative and Multilateral Debt Relief Initiative. The team is grateful to Yasmin (external debt). The national accounts data for low- and middle-income economies Ahmad and Aimee Nichols at the Organisation for Economic Co-operation and were gathered by the World Bank's regional staff through the annual Unified Development for data and advice on official development assistance flows and Survey. Maja Bresslauer, Mahyar Eshragh-Tabary, Victor Gabor, and Soong Sup agricultural support estimates. Lee worked on updating, estimating, and validating the databases for national accounts. The team is grateful to the International Monetary Fund, World Trade 2. People Organization, United Nations Industrial Development Organization, and the Section 2 was prepared by Masako Hiraga and Sulekha Patel in partnership with Organisation for Economic Co-operation and Development for access to the the World Bank's Human Development Network and the Development Research databases. Group in the Development Economics Vice Presidency. Mehdi Akhlaghi and William Prince provided invaluable assistance in data and table preparation, and 5. States and markets Kiyomi Horiuchi prepared the demographic estimates and projections under the Section 5 was prepared by David Cieslikowski and Raymond Muhula, in partner- guidance of Eduard Bos. Sulekha Patel wrote the introduction with valuable com- ship with the World Bank's Financial and Private Sector Development Network, ments from Davidson Gwatkin, Sarwar Lateef, Jeffrey Lewis, and Eric Swanson. Sustainable Development Network, Poverty Reduction and Economic Manage- The poverty estimates were prepared by Shaohua Chen and and Prem Sangraula ment Network, the International Finance Corporation, and external partners. of the World Bank's Poverty Monitoring Group with help from Changquin Sun. Brian Pascual assisted in data and table preparation. David Cieslikowski wrote The data for table 2.19 on health gaps by income and gender were based on the introduction to the section with valuable comments from Rui Coutinho, data prepared by Darcy Gallucio and Davidson Gwatkin of the Human Develop- Steve Knack, Aart Kraay, Sarwar Lateef, Raymond Muhula, and Eric Swanson. ment Network. Other contributions were provided by Eduard Bos and Emi Suzuki Other contributors include Ada Karina Izaguirre (privatization and infrastructure (population, health, and nutrition); Montserrat Pallares-Miralles (vulnerability and projects); Michael Ingram (micro, small, and medium-size enterprises); David security); Raymond Muhula, Juan Cruz Perusia, and Lianqin Wang of the United Stewart (investment climate); Caralee McLeish (business environment); Alka Nations Educational, Scientific, and Cultural Organization Institute for Statistics Banerjee and Isilay Cabuk (Standard & Poor's global stock market indexes); (education); and Lucia Fort and Juan Carlos Guzman Roa (gender). Himmat Kalsi (financial); Rui Coutinho (public policies and institutions); Nigel Adderley of the International Institute for Strategic Studies (military person- 3. Environment nel); Bjorn Hagelin and Petter Stålenheim of the Stockholm International Section 3 was prepared by Mehdi Akhlaghi and M. H. Saeed Ordoubadi in part- Peace Research Institute (military expenditures and arms transfers); Henrich nership with the World Bank's Sustainable Development Network. Important Bofi nger, Tsukasa Hattori, and Peter Roberts (transport); Jane Degerlund of 380 2007 World Development Indicators Containerisation International (ports); Vanessa Grey and Esperanza Magpantay Amy Ditzel, Laura Peterson Nussbaum, and Zachary Schauf provided copyedit- of the International Telecommunication Union, and Mark Williams (communica- ing, proofreading, and production assistance. Communications Development's tions and information); Ernesto Fernandez Polcuch of the United Nations Educa- London partner, Peter Grundy of Peter Grundy Art & Design, provided art direc- tional, Scientific, and Cultural Organization Institute for Statistics (research and tion and design. Staff from External Affairs oversaw printing and dissemination development, researchers, and technicians); and Anders Halvorsen of the World of the book. Information Technology and Services Alliance (information and communication technology expenditures). Client services The Development Data Group's Client Services Team (Azita Amjadi, Uranbileg 6. Global links Batjargal, Richard Fix, and William Prince) contributed to the design and planning Section 6 was prepared by Changqing Sun and Azita Amjadi in partnership with of World Development Indicators and helped coordinate work with the Office of the World Bank's Development Research Group (trade), Prospects Group (com- the Publisher. modity prices), and external partners. Many thanks to Amy Heyman, Sarwar Lateef, Ibrahim Levent, and Eric Swanson for initial comments and feedback Administrative assistance and office technology support about possible revisions to the section. Substantial input for the data came Estela Zamora and Awatif Abuzeid provided administrative assistance. Jean- from Azita Amjadi, Jerzy Rozanski (tariffs), Gloria Moreno, and Ibrahim Levent Pierre Djomalieu, Gytis Kanchas, Nacer Megherbi, and Shahin Outadi provided (fi nancial data). Other contributors include David Cristallo and Henri Laurencin information technology support. of the United Nations Conference on Trade and Development, Francis Ng, and Dominique van der Mensbrugghe (trade); Betty Dow (commodity prices); Dilek Publishing and dissemination Aykut (foreign direct investment fl ows); Brian Hammond, Aimee Nichols, and The Office of the Publisher, under the direction of Dirk Koehler, provided valuable Yasmin Ahmad of the Organisation for Economic Co-operation and Develop- assistance throughout the production process. Stephen McGroarty, Randi Park, ment (aid); Khassoum Diallo and Henrik Pilgaard of the United Nations Offi ce and Nora Ridolfi coordinated printing and supervised marketing and distribution. of the High Commissioner for Refugees; Bela Hovy and Francois Pelletier of Merrell Tuck-Primdahl of the Development Economics Vice President's Office the United Nations Population Division (migration); K.M. Vijayalatshmi (remit- managed the communications strategy. tances); and John Kester and Teresa Ciller of the World Tourism Organiza- tion (tourism). Mehdi Akhlaghi and William Prince provided valuable technical World Development Indicators CD-ROM assistance. Programming and testing were carried out by Reza Farivari and his team: Azita Amjadi, Uranbileg Batjargal, Ying Chi, Ramgopal Erabelly, Nacer Megherbi, Shahin Other parts of the book Outadi, and William Prince. Masako Hiraga produced the social indicators tables. Jeff Lecksell of the World Bank's Map Design Unit coordinated preparation of William Prince coordinated user interface design and overall production and the maps on the inside covers. David Cieslikowski prepared the Users guide. Eric provided quality assurance. Photo credits: Curt Carnemark, Julio Etchart, Alan Swanson wrote Statistical methods. K.M. Vijayalakshmi coordinated preparation Gignoux, John Isaac, and Bill Lyons (World Bank). of Primary data documentation, and Uranbileg Batjargal assisted in updating the The interactive World Development Indicators 2007 was designed and pro- Primary data documentation table. Richard Fix prepared Partners and Index of grammed for this CD-ROM by Dohatec New Media. indicators. WDI Online Database management Design, programming, and testing were carried out by Reza Farivari and his Mehdi Akhlaghi coordinated management of the integrated World Development team: Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, Saurabh Gupta, Nacer Indicators database with assistance from William Prince. Operation of the data- Megherbi, Gonca Okur, and Shahin Outadi. William Prince coordinated production base management system was made possible by the Systems Upgrade team and provided quality assurance. Valentina Kalk and Triinu Tombak of the Office under the leadership of Reza Farivari. of the Publisher were responsible for implementation of WDI Online and manage- ment of the subscription service. Design, production, and editing Richard Fix and Azita Amjadi coordinated all stages of production with Commu- Client feedback nications Development Incorporated, which provided overall design direction, The team is grateful to the many people who have taken the time to provide com- editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, and ment on its publications. Their feedback and suggestions have helped improve Christopher Trott. Elaine Wilson created the graphics and typeset the book. this year's edition. 2007 World Development Indicators 381 BIBLIOGRAPHY AbouZahr, Carla, and Tessa Wardlaw. 2003. Maternal Mortality in 2000: Estimates CELADE (Centro Latinoamericano de Demografia). Various issues. Boletín Developed by WHO, UNICEF, and UNFPA. Geneva: World Health Organization. Demografico. African Union and UNECA (United Nations Economic Commission for Africa). Chen, Shaohua, and Martin Ravallion. 2004. "How Have the World's Poorest Fared 2005. 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Annual Report. Geneva. 388 2007 World Development Indicators INDEX OF INDICATORS References are to table numbers. A Agriculture livestock value added annual growth 3.3 4.1 agricultural raw materials as share of GDP 4.2 exports per worker 3.3 as share of total 4.4, 6.4 total 6.4 Aid imports by recipient as share of total 4.5, 6.4 aid dependency ratios 6.11 total 6.4 per capita 6.11 tariff rates applied by high-income countries 6.4 total 6.11 cereal net concessional flows area under production 3.2 from international financial institutions 6.13 exports, as share of total exports 6.4 from UN agencies 6.13 exports, total 6.4 net official development assistance by DAC members imports, as share of total imports 6.4 as share of general government disbursements 6.10 imports, total 6.4 as share of GNI of donor country 1.4, 6.10 tariff rates applied by high-income countries 6.4 average annual change in volume 6.10 yield 3.3 by type 6.9 employment, as share of total 3.2 for basic social services, as share of sector-allocable ODA fertilizer commitments 1.4 commodity prices 6.5 from major donors, by recipient 6.12 consumption, per hectare of arable land 3.2 per capita of donor country 6.10 food total 6.9, 6.10, 6.12 commodity prices 6.5 untied aid 6.10 exports, as share of total exports 4.4, 6.4 exports, total 6.4 AIDS--see HIV, prevalence imports, as share of total imports 4.5, 6.4 imports, total 6.4 Air pollution--see Pollution tariff rates applied by high-income countries 6.4 freshwater withdrawals for, as share of total 3.5 Air transport labor force, male and female as share of total 2.3 air freight 5.9 land passengers carried 5.9 agricultural, as share of land area 3.2 registered carrier departures 5.9 arable, as share of land area 3.1 arable, per 100 people 3.1 Asylum seekers--see Migration area under cereal production 3.2 irrigated, as share of cropland 3.2 Average years of schooling 2.13 permanent cropland, as share of land area 3.1 machinery tractors per 100 square kilometers of arable land production indexes 3.2 B Balance of payments crop 3.3 current account balance 4.15 food 3.3 exports and imports of goods and services 4.15 2007 World Development Indicators 389 INDEX OF INDICATORS net current transfers net income total reserves 4.15 4.15 4.15 C Carbon dioxide See also Exports; Imports; Investment; Private capital flows; Trade damage 3.15 emissions Bank and trade-related lending 6.8 per capita 1.3, 3.8 per 2000 PPP dollar of GDP 3.8 Biodiversity total 1.6, 3.8 GEF benefits index 3.4 Cause of death Biological diversity communicable diseases, maternal, perinatal, and nutritional conditions 2.18 assessment, date prepared, by country 3.14 noncommunicable diseases 2.18 benefits index 3.4 injury 2.18 treaty 3.14 Child labor Birds by economic activity 2.4 species 3.4 male and female 2.4 threatened species 3.4 study and work 2.4 total 2.4 Birth rate, crude 2.1 work only 2.4 Births attended by skilled health staff 1.2, 2.16, 2.19 Cities air pollution 3.13 Birthweight, low 2.17 population in largest city 3.10 Breastfeeding, exclusive 2.17, 2.19 in selected cities 3.13 in urban agglomerations of more than one million 3.10 Business environment urban population 3.10 closing a business See also Urban environment time to resolve insolvency 5.3 dealing with licenses Closing a business--see Business environment number of procedures to build a warehouse 5.3 time required to build a warehouse 5.3 Commodity prices and price indexes 6.5 employing workers rigidity of employment index 5.3 Communications--see Internet, users; Newspapers; Telephones; Television protecting investors disclosure, index 5.3 enforcing contracts Compensation of government employees 4.11 procedures to enforce a contract 5.3 time to enforce a contract 5.3 Computers per 1,000 people 5.11 new businesses registered 5.1 registering property Consumption number of procedures 5.3 distribution--see Income, distribution time to register 5.3 fixed capital 3.15 starting a business government, general cost to start a business 5.3 annual growth 4.9 number of start-up procedures 5.3 as share of GDP 4.8 time to start a business 5.3 household 390 2007 World Development Indicators average annual growth 4.9 total 4.17 per capita 4.9 IMF credit, use of 4.16 as share of GDP 4.8 long-term 4.16 See also Purchasing power parity (PPP) present value 4.17 private nonguaranteed 4.16 Corruption, major constraint, in investment climate 5.2 public and publicly guaranteed IBRD loans and IDA credits 4.16 Contraceptive prevalence rate 2.16, 2.19 Total 4.16 short-term 4.17 Contract enforcement total 4.16 number of procedures 5.3 time required for 5.3 Defense armed forces personnel Country Policy and Institutional Assessment (CPIA)--see Economic as share of labor force 5.7 management; Policies for social inclusion and equity; Public sector total 5.7 management and institutions; Structural policies arms transfers exports 5.7 Courts imports 5.7 lack confidence in courts to uphold property rights 5.2 military expenditure major constraint, in investment climate 5.2 as share of central government expenditure 5.7 as share of GDP 5.7 Credit getting credit Deforestation credit information index 5.5 average annual 3.4 legal rights index 5.5 private credit registry coverage 5.5 Density--see Population, density public credit registry coverage 5.5 provided by banking sector 5.5 Dependency ratio--See Population to private sector 5.1 Development assistance--see Aid Crime, major constraint, in investment climate 5.2 Disease--see Health risks Current account balance 4.15 See also Balance of payments Distribution of income or consumption--see Income, distribution Customs, average time to clear 5.2 E D DAC (Development Assistance Committee)--see Aid Economic management (Country Policy and Institutional Assessment) debt policy economic management cluster average 5.8 5.8 fiscal policy 5.8 Death rate, crude 2.1 macroeconomic management 5.8 See also Mortality rate Education Debt, external attainment debt service share of cohort reaching grade 5, male and female 2.11 multilateral 4.17 enrollment ratio 2007 World Development Indicators 391 INDEX OF INDICATORS female to male enrollment in primary and secondary schools 1.2 GDP per unit 3.8 gross, by level 2.10 per capita net, by level 2.10 average annual growth 3.7 gross intake rate, grade 1 2.11, 2.13 total 3.7 out of school children, male and female 2.10, 2.13 total 3.7 primary completion rate 1.2, 2.12, 2.13 See also Electricity; Fuels male and female 2.12, 2.13 public expenditure on Enforcing contracts--see Business environment as share of GDP 2.9 as share of total government expenditure 2.9 Entry regulations for business--see Business environment per student, as share of GDP per capita, by level 2.9 pupil-teacher ratio, primary level 2.9 Environmental strategy, year adopted 3.14 repeaters, primary level 2.11 teachers, primary, trained 2.9 Exchange rates transition to secondary school 2.12 official, local currency units to U.S. dollar 4.14 unemployment by level of educational attainment 2.5 ratio of PPP conversion factor to official exchange rate 4.14 real effective 4.14 Electricity See also Purchasing power parity (PPP) consumption 5.10 major constraint, in investment climate 5.2 Exports production arms 5.7 share of total 3.9 goods and services sources 3.9 as share of GDP 4.8 transmissions and distribution losses 5.10 average annual growth 4.9 total 4.15 Employment high-technology in agriculture, as share of total employment 3.2 share of manufactured exports 5.12 in agriculture, male and female 2.3 total 5.12 in industry, male and female 2.3 merchandise in informal sector, urban annual growth 6.3 male and female 2.8 by high-income OECD countries, by product 6.4 in services, male and female 2.3 by regional trade blocs 6.6 rigidity index 5.3 direction of trade 6.3 structure 4.4 Employing workers total 4.4 rigidity of employment index 5.3 value, average annual growth 6.2 volume, average annual growth 6.2 Endangered species--see Biological diversity; Birds; Mammals; Plants services structure 4.6 Energy total 4.6 depletion, as share of GNI 3.15 transport 4.6 emissions--see Pollution travel 4.6, 6.15 imports, net 3.7 See also Trade production 3.7 use average annual growth combustible renewables and waste 3.7 3.7 F Female-headed households 2.8 392 2007 World Development Indicators Fertility rate imports adolescent 2.16 as share of total imports 4.5, 6.4 total 2.16, 2.19 total 6.4 prices 3.12 Finance, major constraint, in investment climate 5.2 tariff rates applied by high-income countries 6.4 Financial access, stability, and efficiency bank capital to asset ratio bank nonperforming loans 5.5 5.5 G GEF benefits index for biodiversity 3.4 Financial flows, net Gender differences from DAC members 6.9 in child employment 2.4 from multilateral institutions in education international financial institutions 6.13 enrollment, primary and secondary 1.2 total 6.13 in employment 2.3 United Nations 6.13 in HIV prevalence 2.18 official development assistance and official aid in labor force participation 2.2 grants from NGOs 6.9 in life expectancy at birth 1.5 other official flows 6.9 in literacy private 6.9 adult 2.12 total 6.9 youth 2.12 See also Aid in mortality adult 2.20 Food--see Agriculture, production indexes; Commodity prices and price indexes child 2.20 in smoking 2.18 Foreign direct investment, net--see Investment; Private capital flows in survival to age 65 2.20 in youth employment 2.8 Forest unpaid family workers 1.5 area, as share of total land area 3.1 women in parliaments 1.5 deforestation, average annual 3.4 women in nonagricultural sector 1.5 net depletion 3.15 Gini index 2.7 Freshwater annual withdrawals Government, central as share of internal resources 3.5 cash surplus or deficit 4.10 for agriculture 3.5 debt for domestic use 3.5 as share of GDP 4.10 for industry 3.5 interest, as share of revenue 4.10 renewable internal resources interest, as share of total expenses 4.11 flows 3.5 expense per capita 3.5 as share of GDP 4.10 See also Water, access to improved source of by economic type 4.11 military 5.7 Fuels net incurrence of liabilities, as share of GDP exports domestic 4.10 as share of total exports 4.4, 6.4 foreign 4.10 total 6.4 revenues, current 2007 World Development Indicators 393 INDEX OF INDICATORS as share of GDP 4.10 pregnant women receiving prenatal care 1.5, 2.16, 2.19 grants and other 4.12 pregnant women receiving tetanus vaccinations 2.16 social contributions 4.12 reproductive tax, as share of GDP 5.6 births attended by skilled health staff 1.2, 2.16, 2.19 tax, by source 4.12 contraceptive prevalence rate 2.16, 2.19 fertility rate Gross capital formation adolescent 2.16 annual growth 4.9 total 2.16, 2.19 as share of GDP 4.8 low-birthweight babies 2.17 maternal mortality ratio 1.2, 2.16 Gross domestic product (GDP) tetanus vaccinations, share of pregnant women receiving 2.16 annual growth 1.1, 1.6, 4.1 unmet need for contraception 2.16 implicit deflator--see Prices tuberculosis per capita, annual growth 1.1, 1.6 DOTS detection rate 2.15 total 4.2 incidence 1.3, 2.18 treatment success rate 2.15 Gross foreign direct investment--see Investment Health expenditure Gross national income (GNI) as share of GDP 2.14 per capita external resources 2.14 PPP dollars 1.1, 1.6 out of pocket 2.14 rank 1.1 per capita 2.14 U.S. dollars 1.1, 1.6 public 2.14 rank total 2.14 PPP dollars 1.1 U.S. dollars 1.1 Health risks total child malnutrition, prevalence 1.2, 2.17, 2.19 PPP dollars 1.1, 1.6 diabetes, prevalence 2.18 U.S. dollars 1.1, 1.6 HIV, prevalence 1.3, 2.18 overweight children, prevalence 2.17 Gross savings, as share of GNI 3.15 road traffic injury, mortality caused by 2.18 smoking prevalence 2.18 Gross savings, as share of GDP 4.8 tuberculosis, incidence 1.3, 2.18 undernourishment, prevalence 2.17 H Health care Heavily indebted poor countries (HIPCs) assistance 1.4 child completion point 1.4 children sleeping under treated bednets 2.15 decision point 1.4 children with acute respiratory infection taken to health provider 2.15 Multilateral Debt Relief Initiative (MDRI) assistance 4.1 children with diarrhea who received oral rehydration and continued feeding 2.15 HIV, prevalence 1.3, 2.18 children with fever receiving antimalarial drugs 2.15 female 2.18 health worker density index 2.14 hospital beds per 1,000 people 2.14 Hospital beds--see Health care immunization 2.15, 2.19 physicians per 1,000 people 2.14 Housing conditions, national and urban 394 2007 World Development Indicators durable dwelling units 3.11 as share of GDP 4.2 home ownership 3.11 labor force, male and female as share of total 2.3 household size 3.11 multiunit dwellings 3.11 Inflation--see Prices overcrowding 3.11 vacancy rate 3.11 Information and communications technology expenditures as share of GDP 5.11 I IDA Resource Allocation Index (IRAI) 5.8 per capita Integration, global economic, indicators 5.11 6.1 Immunization rate Interest payments--see Government, central, debt child DPT, share of children ages 12­23 months 2.15, 2.19 Interest rates measles, share of children ages 12­23 months 2.15, 2.19 deposit 4.13 tetanus, share of pregnant women receiving 2.16 lending 4.13 real 4.13 Imports risk premium on lending 5.5 arms 5.7 spread 5.5 energy, as share of total energy use 3.7 goods and services International Bank for Reconstruction and Development (IBRD) as share of GDP 4.8 IBRD loans and IDA credits 4.16 average annual growth 4.9 net financial flows from 6.13 total 4.15 merchandise International Development Association (IDA) annual growth 6.3 IBRD loans and IDA credits 4.16 by high-income OECD countries, by product 6.4 net concessional flows from 6.13 direction of trade 6.3 structure 4.5 International Monetary Fund (IMF) tariffs 6.4, 6.7 net financial flows from 6.13 total 4.5 use of IMF credit 4.16 value, average annual growth 6.2 volume, average annual growth 6.2 Internet services broadband subscribers 5.11 structure 4.7 price basket 5.11 total 4.7 secure servers 5.11 transport 4.7 users 5.11 travel 4.7, 6.15 international bandwidth 5.11 See also Trade schools connected 5.11 Income Investment distribution climate 5.2 Gini index 2.7 foreign direct, net inflows percentage of 1.2, 2.7 as share of GDP 6.1 total 6.8 Industry foreign direct, net outflows annual growth 4.1 as share of GDP 6.1 2007 World Development Indicators 395 INDEX OF INDICATORS infrastructure, private participation in Literacy energy 5.1 adult, male and female 1.6, 2.12 telecommunications 5.1 youth, male and female 1.6, 2.12 transport 5.1 water and sanitation portfolio bonds 5.1 6.8 M Malnutrition, in children under age 5 1.2, 2.19 equity 6.8 See also Gross capital formation; Private capital flows Malaria children sleeping under treated bednets 2.15 Iodized salt, consumption of 2.17 children with fever receiving antimalarial drugs 2.15 L Labor force Mammals species threatened species 3.4 3.4 annual growth 2.2 armed forces 5.7 Management time dealing with officials 5.2 child labor 2.4 female 2.2 Manufacturing in agriculture, male and female as share of total 2.3 exports 4.4, 6.4 in industry, male and female as share of total 2.3 imports 4.5, 6.4 in services, male and female as share of total 2.3 structure 4.3 male 2.2 value added participation of population ages 15­64 2.2 annual growth 4.1 regulation, major constraint, in investment climate 5.2 as share of GDP 4.2 skills, major constraint, in investment climate 5.2 total 4.3 total 2.2 See also Merchandise See also Employment; Migration; Unemployment Market access to high-income countries Land area goods admitted free of tariffs 1.4 arable--see Agriculture, land, land use support to agriculture 1.4 See also Protected areas; Surface area tariffs on exports from low- and middle-income countries agricultural products 1.4 Land use textiles and clothing 1.4 arable land, as share of total land 3.1 area under cereal production 3.2 Merchandise by type 3.1 exports forest area, as share of total land 3.1 agricultural raw materials 4.4, 6.4 irrigated land 3.2 cereals 6.4 permanent cropland, as share of total land 3.1 chemicals 6.4 total area 3.1 food 4.4, 6.4 footwear 6.4 Life expectancy at birth fuels 4.4 male and female 1.5 furniture 6.4 total 1.6, 2.19 iron and steel 6.4 machinery and transport equipment 6.4 396 2007 World Development Indicators manufactures 4.4 as share of GNI of donor country 1.4 ores and metals 4.4 as share of total ODA commitments 1.4 textiles 6.4 access to improved water source 1.3, 2.15, 3.5 total 4.4 access to improved sanitation facilities 1.3, 2.15 value, average annual growth 6.2 births attended by skilled health staff 1.2, 2.16 volume, average annual growth 6.2 carbon dioxide emissions per capita 1.3, 3.8 imports children sleeping under treated bednets 2.15 agricultural raw materials 4.5 consumption, national share of poorest quintile 1.2, 2.7 cereals 6.4 female to male enrollments, primary and secondary 1.2 chemicals 6.4 heavily indebted poor countries (HIPCs) food 4.5 assistance 1.4 footwear 6.4 completion point 1.4 fuels 4.5 decision point 1.4 furniture 6.4 Multilateral Debt Relief Initiative (MDRI) assistance 1.4 iron and steel 6.4 malnutrition, prevalence 1.2, 2.17, 2.19 machinery and transport equipment 6.4 maternal mortality ratio 1.2, 2.16 manufactures 4.5 primary enrollment ratio, net 2.10 ores and metals 4.5 poverty gap 2.6 textiles 6.4 poverty, population below a $1 a day 2.6 total 4.5 telephone lines, fixed-line and mobile 1.3, 5.10 value, average annual growth 6.2 tuberculosis, incidence 1.3, 2.18 volume, average annual growth 6.2 under-five mortality rate 1.2, 2.20 trade undernourishment, prevalence 2.17 as share of GDP 6.1 youth unemployment 1.3, 2.8 direction 6.3 growth 6.3 Minerals, depletion of 3.15 regional trading blocs 6.6 Monetary indicators Methane claims on governments and other public entities 4.13 emissions claims on private sector 4.13 percentage change 3.8 total 3.8 Money and quasi money, annual growth 4.13 Micro, small, and medium-size enterprises Mortality rate employment per 1,000 people 5.1 adult, male and female 2.20 number of firms 5.1 caused by road traffic injury 2.18 child, male and female 2.20 Migration children under age 5 1.2, 2.20 net 6.14 infant 2.20 stock 6.14 maternal 1.2, 2.16 See also Refugees; Remittances Motor vehicles Military--see Defense passenger cars 3.12 per kilometer of road 3.12 Millennium Development Goals, indicators for per 1,000 people 3.12 aid See also Roads; Traffic 2007 World Development Indicators 397 INDEX OF INDICATORS N Nationally protected areas--see Protected areas Plants, higher species threatened species 3.4 3.4 Net national savings 3.15 Policy uncertainty, major constraint, in investment climate 5.2 Newspapers, daily 5.11 Pollution carbon dioxide damage, as share of GNI 3.15 Nitrous oxide carbon dioxide emissions emissions per capita 3.8 percentage change 3.8 per 2000 PPP dollar of GDP 3.8 total 3.8 total 3.8 methane Nutrition emissions breastfeeding 2.17, 2.19 percentage change 3.8 iodized salt consumption 2.17 total 3.8 malnutrition, child 1.2, 2.17, 2.19 nitrogen dioxide, selected cities 3.13 overweight children, prevalence 2.17 nitrous oxide undernourishment, prevalence 2.17 emissions vitamin A supplementation 2.17 percentage change 3.8 total 3.8 O Official development assistance--see Aid organic water pollutants, emissions by industry per day 3.6 3.6 per worker 3.6 Official flows, other 6.9 particulate matter, selected cities 3.13 sulfur dioxide, selected cities 3.13 P Passenger cars per 1,000 people 3.12 urban-population-weighted PM10 Population 3.12 age dependency ratio 2.1 Particulate matter annual growth 2.1 emission damage 3.15 by age group selected cities 3.13 0­14 2.1 urban-population-weighted PM10 3.12 15­64 2.1 65 and older 2.1 Patent applications filed 5.12 density 1.1, 1.6 female, as share of total 1.5 Pension rural average, as share of per capita income 2.8 annual growth 3.1 contributors, as share of labor force 2.8 as share of total 3.1 contributors, as share of working-age population 2.8 total 1.1, 1.6, 2.1 public expenditure on urban as share of GDP 2.8 as share of total 3.10 average annual growth 3.10 Physicians--see Health care in largest city 3.10 in selected cities 3.13 398 2007 World Development Indicators in urban agglomerations 3.10 Protected areas total 3.10 marine See also Migration as share of total surface area 3.4 total 3.4 Portfolio investment flows national bonds 6.8 as share of total land area 3.4 equity 6.8 total 3.4 Ports, container traffic in 5.9 Protecting investors disclosure, index 5.3 Poverty Public sector management and institutions (Country Policy and Institutional international poverty line Assessment) population below $1 a day 2.6 efficiency of revenue mobilization 5.8 population below $2 a day 2.6 property rights and rule-based governance 5.8 poverty gap at $1 a day 2.6 public sector management and institutions cluster average 5.8 poverty gap at $2 a day 2.6 quality of budgetary and financial management 5.8 national poverty line quality of public administration 5.8 population below 2.6 transparency, accountability, and corruption in the public sector 5.8 national 2.6 rural 2.6 Purchasing power parity (PPP) survey year 2.6 conversion factor 4.14 urban 2.6 gross national income 1.1, 1.6 Power--see Electricity, production Prenatal care, pregnant women receiving 1.5 R Railways goods hauled by 5.9 Prices lines, total 5.9 commodity prices and price indexes 6.5 passengers carried 5.9 consumer, annual growth 4.14 GDP implicit deflator, annual growth 4.14 Regulation and tax administration terms of trade 6.2 average days to clear customs 5.2 wholesale, annual growth 4.14 management time dealing with officials 5.2 tax rates, major constraint, in investment climate 5.2 Private capital flows bank and trade-related lending 6.8 Refugees foreign direct investment, net inflows 6.8 country of asylum 6.14 from DAC members 6.9 country of origin 6.14 gross, as share of GDP 6.1 portfolio investment 6.8 Regional development banks, net financial flows from 6.13 See also Investment Registering property Productivity number of procedures 5.3 in agriculture time to register 5.3 value added per worker 3.3 water productivity, total 3.5 Relative prices (PPP)--see Purchasing power parity (PPP) 2007 World Development Indicators 399 INDEX OF INDICATORS Remittances See also Research and development workers' remittances and compensation of employees, paid 6.14 workers' remittances and compensation of employees, received 6.14 Services exports Research and development structure 4.6 expenditures 5.12 total 4.6 researchers 5.12 imports technicians 5.12 structure 4.7 total 4.7 Reserves, gross international--see Balance of payments labor force by economic activity, male and female as share of total 2.3 trade, as share of GDP 6.1 Roads value added goods hauled by 5.9 annual growth 4.1 passengers carried 5.9 as share of GDP 4.2 paved, as share of total 5.9 total network 5.9 Smoking, prevalence, male and female 2.18 traffic 3.12 Social inclusion and equity policies (Country Policy and Institutional Royalty and license fees Assessment) payments 5.11 building human resources 5.8 receipts 5.11 equity of public resource use 5.8 gender equity 5.8 Rural environment policy and institutions for environmental sustainability 5.8 access to improved sanitation facilities 3.10 social inclusion and equity cluster average 5.8 population social protection and labor 5.8 annual growth 3.1 as share of total 3.1 Starting a business--see Business environment S S&P/EMDB Indices 5.4 Stock markets listed domestic companies market capitalization 5.4 as share of GDP 5.4 Sanitation total 5.4 access to improved facilities, population with market liquidity 5.4 rural 3.10 S&P/EMDB Indices 5.4 total 1.3, 2.15 turnover ratio 5.4 urban 3.10 Structural policies (Country Policy and Institutional Assessment) Savings business regulating environment 5.8 gross, as share of GDP 4.8 financial sector 5.8 gross, as share of GNI 3.15 structural policies cluster average 5.8 net 3.15 trade 5.8 Schooling--see Education Sulfur dioxide emissions--see Pollution Science and technology Surface area 1.1, 1.6 scientific and technical journal articles 5.11 See also Land area 400 2007 World Development Indicators Survival to age 65 price basket 5.10 male and female 2.20 mobile per 1,000 people 1.3, 5.10 Suspended particulate matter--see Pollution population covered 5.10 price basket 5.10 T Tariffs total revenue total subscribers per employee 5.10 5.10 all products Television, households with 5.11 binding coverage 6.7 simple mean board rate 6.7 Terms of trade, net barter 6.2 simple mean tariff 6.7 weighted mean tariff 6.7 Tetanus vaccinations, share of pregnant women receiving 2.16 manufactured products simple mean tariff 6.7 Threatened species--see Biological diversity weighted mean tariff 6.7 primary products Tourism, international simple mean tariff 6.7 expenditures 6.15 weighted mean tariff 6.7 inbound tourists, by country 6.15 See also Taxes and tax policies, duties outbound tourists, by country 6.15 receipts 6.15 Taxes and tax policies business taxes Trade number of payments 5.6 arms 5.7 time to prepare, file, and pay 5.6 merchandise total tax rate, share of profit 5.6 as share of GDP 6.1 goods and services taxes, domestic 4.12 direction of, by region 6.3 highest marginal tax rate nominal growth, by region 6.3 corporate 5.6 regional trading blocs 6.6 individual 5.6 OECD trade by commodity 6.4 income, profit, and capital gains taxes real growth in, less growth in real GDP 6.1 as share of revenue 4.12 services international trade taxes 4.12 as share of GDP 6.1 other taxes 4.12 computer, information, communications, and other 4.6, 4.7 rates, major constraint, in investment climate 5.2 insurance and financial 4.6, 4.7 social contributions 4.12 transport 4.6, 4.7 tax revenue, as share of GDP 5.6 travel 4.6, 4.7 See also Balance of payments; Exports; Imports; Manufacturing; Technology--see Computers; Exports, high-technology; Internet; Research and Merchandise; Terms of trade; Trade blocs development; Science and technology Trade blocs, regional Telephones exports within bloc 6.6 cost of call to U.S. 5.10 total exports, by bloc 6.6 international voice traffic 5.10 mainlines Trademark applications filed 5.12 faults per 100 5.10 per 1,000 people 5.10 Trade policies--see Tariffs 2007 World Development Indicators 401 INDEX OF INDICATORS Traffic in largest city 3.10 road traffic 3.12 in urban agglomerations 3.10 road traffic injury and mortality 2.18 total 3.10 See also Roads selected cities nitrogen dioxide 3.13 Transport--see Air transport; Railways; Roads; Traffic; Urban environment particulate matter 3.13 population 3.13 Treaties, participation in sulfur dioxide 3.13 biological diversity 3.14 See also Pollution; Population; Sanitation; Water, access to improved CFC control 3.14 source of climate change 3.14 Convention on International Trade on Endangered Species (CITES) Convention to Combat Desertification (CCD) Kyoto Protocol 3.14 3.14 3.14 V Value added Law of the Sea 3.14 as share of GDP ozone layer 3.14 in agriculture 4.2 Stockholm Convention on Persistent Organic Pollutants 3.14 in industry 4.2 in manufacturing 4.2 Tuberculosis, incidence 1.3, 2.18 in services 4.2 growth U UN agencies, net concessional flows from 6.13 in agriculture in industry in manufacturing 4.1 4.1 4.1 in services 4.1 Undernourishment, prevalence of 2.17 per worker in agriculture 3.3 UNDP, net concessional flows from 6.13 total, in manufacturing 4.3 Unemployment incidence of long-term total, male and female 2.5 W Water by level of educational attainment access to improved source of, population with 1.3, 2.15 primary, secondary, tertiary 2.5 pollution--see Pollution, organic water pollutants total, male and female 2.5 productivity 3.5 youth 1.3, 2.8 WFP, net concessional flows from 6.13 UNFPA, net concessional flows from 6.13 Women in development UNICEF, net concessional flows from 6.13 teenage mothers 1.5 women in nonagricultural sector 1.5 Urban environment women in parliaments 1.5 access to sanitation 3.10 employment, informal sector 2.8 World Bank, net financial flows from 6.13 population See also International Bank for Reconstruction and Development; as share of total 3.10 International Development Association average annual growth 3.10 402 2007 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 REGION MAP The world by region East Asia and Pacific Barbados Sub-Saharan Africa Greece* American Samoa Belize Angola Iceland Cambodia Bolivia Benin Ireland* China Brazil Botswana Italy* Fiji Chile Burkina Faso Japan Indonesia Colombia Burundi Korea, Rep. Kiribati Costa Rica Cameroon Luxembourg* Korea, Dem. Rep. Cuba Cape Verde Netherlands* Lao PDR Dominica Central African Republic New Zealand Malaysia Dominican Republic Chad Norway Marshall Islands Ecuador Comoros Portugal* Micronesia, Fed. Sts. El Salvador Congo, Dem. Rep. Spain* Mongolia Grenada Congo, Rep. Sweden Myanmar Guatemala Côte d'Ivoire Switzerland Northern Mariana Islands Guyana Equatorial Guinea United Kingdom Palau Haiti Eritrea United States Papua New Guinea Honduras Ethiopia Philippines Jamaica Gabon Other high income Samoa Mexico Gambia, The Andorra Solomon Islands Nicaragua Ghana Antigua and Barbuda Thailand Panama Guinea Aruba Timor-Leste Paraguay Guinea-Bissau Bahamas, The Tonga Peru Kenya Bahrain Vanuatu St. Kitts and Nevis Lesotho Bermuda Vietnam St. Lucia Liberia Brunei Darussalam St. Vincent and the Madagascar Cayman Islands Europe and Central Grenadines Malawi Channel Islands Asia Suriname Mali Cyprus Albania Trinidad and Tobago Mauritania Faeroe Islands Armenia Uruguay Mauritius French Polynesia Azerbaijan Venezuela, RB Mayotte Greenland Belarus Mozambique Guam Bosnia and Herzegovina Middle East and Namibia Hong Kong, China Bulgaria North Africa Niger Isle of Man Croatia Algeria Nigeria Israel Czech Republic Djibouti Rwanda Kuwait Estonia Egypt, Arab Rep. São Tomé and Principe Liechtenstein Georgia Iran, Islamic Rep. Senegal Macao, China Hungary Iraq Seychelles Malta Kazakhstan Jordan Sierra Leone Monaco Kyrgyz Republic Lebanon Somalia Netherlands Antilles Latvia Libya South Africa New Caledonia Lithuania Morocco Sudan Puerto Rico Macedonia, FYR Oman Swaziland Qatar Moldova Syrian Arab Republic Tanzania San Marino Poland Tunisia Togo Saudi Arabia Romania West Bank and Gaza Uganda Singapore Russian Federation Yemen, Rep. Zambia Slovenia* Serbia and Montenegro Zimbabwe United Arab Emirates Slovak Republic South Asia Virgin Islands (U.S.) Tajikistan Afghanistan High-income OECD Turkey Bangladesh Australia Turkmenistan Bhutan Austria* Ukraine India Belgium* Uzbekistan Maldives Canada Nepal Denmark Latin America and Pakistan Finland* the Caribbean Sri Lanka France* *Member of the European Argentina Germany* Monetary Union The World Bank 1818 H Street N.W. Washington, D.C. 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: www.worldbank.org ISBN 0-8213-6959-8 Email: feedback@worldbank.org The World Development Indicators · Includes more than 800 indicators for 152 economies · Provides definitions, sources, and other information about the data · Organizes the data into six thematic areas WORLD VIEW Progress toward the Millennium Development Goals PEOPLE Gender, health, and employment ENVIRONMENT Natural resources and environmental changes ECONOMY New opportunities for growth STATES & MARKETS Elements of a good investment climate GLOBAL LINKS Evidence on globalization Saved: 93 trees 4,354 pounds of solid waste 33,908 gallons of waste water 8,169 pounds of net greenhouse gases 65 million BTUs of total energy