54167 08 WORLD DEVELOPMENT INDICATORS WORLD VIEW ENVIRONMENT PEOPLE STATES & MARKETS GLOBAL LINKS ECONOMY INCOME MAP The world by income Low income Azerbaijan Costa Rica Greece Afghanistan Belarus Croatia Greenland Bangladesh Bhutan Dominica Guam Benin Bolivia Equatorial Guinea Hong Kong, China Burkina Faso Bosnia and Herzegovina Gabon Iceland Burundi Cameroon Grenada Ireland Cambodia Cape Verde Hungary Isle of Man Central African Republic China Kazakhstan Israel Chad Colombia Latvia Italy Comoros Congo, Rep. Lebanon Japan Congo, Dem. Rep. Cuba Libya Korea, Rep. Côte d'Ivoire Djibouti Lithuania Kuwait Eritrea Dominican Republic Malaysia Liechtenstein Ethiopia Ecuador Mauritius Luxembourg Gambia, The Egypt, Arab Rep. Mayotte Macao, China Ghana El Salvador Mexico Malta Guinea Fiji Montenegro Monaco Guinea-Bissau Georgia Northern Mariana Islands Netherlands Haiti Guatemala Oman Netherlands Antilles India Guyana Palau New Caledonia Kenya Honduras Panama New Zealand Korea, Dem. Rep. Indonesia Poland Norway Kyrgyz Republic Iran, Islamic Rep. Romania Portugal Lao PDR Iraq Russian Federation Puerto Rico Liberia Jamaica Serbia Qatar Madagascar Jordan Seychelles San Marino Malawi Kiribati Slovak Republic Saudi Arabia Mali Lesotho South Africa Singapore Mauritania Macedonia, FYR St. Kitts and Nevis Slovenia Mongolia Maldives St. Lucia Spain Mozambique Marshall Islands St. Vincent and the Sweden Myanmar Micronesia, Fed. Sts. Grenadines Switzerland Nepal Moldova Turkey Trinidad and Tobago Niger Morocco Uruguay United Arab Emirates Nigeria Namibia Venezuela, RB United Kingdom Pakistan Nicaragua United States Papua New Guinea Paraguay High income Virgin Islands (U.S.) Rwanda Peru Andorra São Tomé and Principe Philippines Antigua and Barbuda Senegal Samoa Aruba Sierra Leone Sri Lanka Australia Solomon Islands Suriname Austria Somalia Swaziland Bahamas, The Sudan Syrian Arab Republic Bahrain Tajikistan Thailand Barbados Tanzania Tonga Belgium Timor-Leste Tunisia Bermuda Togo Turkmenistan Brunei Darussalam Uganda Ukraine Canada Uzbekistan Vanuatu Cayman Islands Vietnam West Bank and Gaza Channel Islands Yemen, Rep. Cyprus Zambia Upper middle income Czech Republic Zimbabwe American Samoa Denmark Argentina Estonia Lower middle income Belize Faeroe Islands Albania Botswana Finland Algeria Brazil France Angola Bulgaria French Polynesia Armenia Chile Germany The world by income Low ($905 or less) Classified according to Lower middle ($906­$3,595) World Bank estimates of 2006 GNI per capita Upper middle ($3,596­$11,115) High ($11,116 or more) No data Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London 2008 WORLD DEVELOPMENT INDICATORS Copyright 2008 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 2008 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, Roobon/The Hunger Project, Curt Carnemark/World Bank, Curt Carnemark/World Bank, 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 978-0-8213-7386-6 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 2008 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: 70 trees 3,290 pounds of solid waste 25,621 gallons of waste water 6,172 pounds of net greenhouse gases 49 million BTUs of total energy 2008 WORLD DEVELOPMENT INDICATORS PREFACE Release of the final report of the International Comparison Program (ICP) and publication of new estimates of purchas- ing power parities (PPPs) in World Development Indicators 2008 are an important statistical milestone. The estimates offer a consistent and comprehensive set of data on the cost of living in developed and developing countries, the first since 1997, when the results of the previous ICP data collection were published in World Development Indicators. The 2005 data cover 146 countries and territories, 29 more than the last round in 1993--and many for the first time. Collecting data on thousands of products sold through a multitude of outlets, the 2005 ICP is the largest international statistical program ever undertaken. New methods were used to describe the products being priced, record the data, and analyze the results. Countries in Africa took the opportunity to review their national accounts and adopt new stan- dards and methods. In all regions regional coordinators worked closely with national statistical offices to collect and validate the data. The result is a genuine global effort, with an extensive capacity building component. More work will follow from the ICP. First is the revision of the international ($1 a day) poverty line and estimation of the corresponding poverty rates, certain to change our view of the absolute level of poverty in the world. PPPs have many applications in economic analysis. They are used to determine the relative size of countries and their obligations to international institutions. The publication of new estimates will inspire a new wave of academic studies. And as all of this work goes on, planning for the next round of the ICP will be getting under way. There is much of interest in this year's World Development Indicators besides the ICP results. The Millennium Develop- ment Goal targets have been expanded to include new ones for reproductive health, protection of biodiversity, access to treatment for HIV/AIDS, and full and productive employment and decent work for all. Measuring the associated indicators consistently and reporting on progress pose new challenges for statisticians. The World Development Indi- cators database includes as many of these indicators as possible. The introduction to the People section looks at the importance of reproductive health for the well-being of women and children. The Environment section considers today's great environmental challenge: climate change. Governance--the performance of public officials and the quality of government institutions--has long been recognized as an important determinant of development success. But to understand how governance, good or bad, affects devel- opment, it must be measured. And to provide guidance for improved performance, it must be measured in ways that are sensible to politicians, citizens, and others responsible for improving governance. The States and Markets section discusses how to measure governance and the problems frequently encountered in doing so. The tables provide a selection of governance indicators and other measures of the interaction of states and markets. World Development Indicators remains a rich source of information on the world's people, their economies, and the environment. To make it more useful, we have expanded the Primary data documentation section. As always, we could not bring it to you without the help of our many partners and the work of hundreds of thousands of statisticians and others in developed and developing countries who gather the primary data on which these statistics are based. Shaida Badiee Director Development Data Group 2008 World Development Indicators v ACKNOWLEDGMENTS This book and its companion volumes, The Little Data Book and The Little Green Data Book, are prepared by a team led by David Cieslikowski under the supervision of Eric Swanson and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, Sebastien Dessus, Richard Fix, Masako Hiraga, Kiyomi Horiuchi, Soong Sup Lee, Ibrahim Levent, Raymond Muhula, Kyoko Okamoto, M.H. Saeed Ordoubadi, Sulekha Patel, Beatriz Prieto-Oramas, Changqing Sun, and K.M. Vijayalakshmi, working closely with other teams in the Development Economics Vice Presidency's Development Data Group. The CD-ROM development team included Azita Amjadi, Ramgopal Erabelly, Reza Farivari, Buyant Erdene Khaltarkhuu, and William Prince. The work was carried out under the management of Shaida Badiee. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in the World Bank's four thematic networks--Financial and Private Sector Development, Human Development, Poverty Reduction and Economic Management, and Sustainable 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 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. Amye Kenall and Joseph Caponio provided proofreading and production assistance. Communications Development's London partner, Peter Grundy of Peter Grundy Art & Design, provided art direction and design. Staff from External Affairs oversaw printing and dissemination of the book. 2008 World Development Indicators vii TABLE OF CONTENTS FRONT 2. PEOPLE Introduction 35 Preface v Acknowledgments vii Tables Partners xii 2.1 Population dynamics 40 Users guide xx 2.2 Labor force structure 44 2.3 Employment by economic activity 48 2.4 Decent work and productive employment 52 1. WORLD VIEW Introduction 1 2.5 2.6 2.7 2.8 Unemployment Children at work Poverty Distribution of income or consumption 56 60 64 68 Tables 2.9 Assessing vulnerability and security 72 2.10 Education inputs 76 1.a New purchasing power parity estimates from the 2005 2.11 Participation in education 80 International Comparison Program 8 2.12 Education efficiency 84 1.1 Size of the economy 14 2.13 Education completion and outcomes 88 1.2 Millennium Development Goals: eradicating poverty and 2.14 Education gaps by income and gender 92 saving lives 18 2.15 Health expenditure, services, and use 94 1.3 Millennium Development Goals: protecting our common 2.16 Disease prevention coverage and quality 98 environment 22 2.17 Reproductive health 102 1.4 Millennium Development Goals: overcoming obstacles 26 2.18 Nutrition 106 1.5 Women in development 28 2.19 Health risk factors and public health challenges 110 1.6 Key indicators for other economies 32 2.20 Health gaps by income and gender 114 Text figures, tables, and boxes 2.21 Mortality 118 1a Participation in the International Comparison Program has been Text figures, tables, and boxes growing 2 2a Most maternal deaths occur in developing countries . . . 35 1b The 2005 International Comparison Program's population 2b . . . especially in Sub-Saharan Africa and South Asia 35 coverage was above 85 percent in every region 2 2c Women in developing countries are more likely to die of 1c Nontradable goods and services show wider variation in prices 2 pregnancy-related causes than women in high-income countries 36 1d Purchasing power parities transform the size of developing 2d The lifetime risk of dying from pregnancy-related causes is economies' GDP in 2005 . . . 3 unacceptably high in Sub-Saharan Africa and South Asia 36 1e . . . and their shares of world GDP 3 2e East Asia and Pacific leads in contraceptive use among 1f China and India's economies, revised downward, remain large 3 married women ages 15­49 36 1g Income disparities remain wide . . . 4 2f Women from the richest households are more likely to use 1h . . . and regional rankings change under purchasing power parities 4 contraception--but contraceptive prevalence rates remain low 36 1i Half the people in the world consumed less than 2g Meeting family planning needs remains a challenge--despite PPP $1,300 a year in 2005 4 benefits such as reduced fertility 37 1j The global distribution of consumption is highly uneven 4 2h Many women in developing countries have an unmet need for 1k Latin America and the Caribbean and Sub-Saharan Africa contraception 37 have the most unequal income distributions 5 2i High adolescent fertility rates mean young women and their 1l Inequality within countries is greatest in Latin America and children are at higher risk of death and disability 37 the Caribbean and lowest in Sub-Saharan Africa 5 2j Age-specific fertility for girls ages 15­17 37 1m For similar investment efforts poor countries grew faster 2k All regions have made progress in providing prenatal care to between 1996 and 2006 . . . 5 women at least once during their pregnancy 38 1n . . . but investment efforts in low-income countries were 2l In South Asia rich women are three times more likely to insufficient to match the growth of richer countries 5 receive prenatal care than are poor women 38 1o Regional differences in food consumption are less than 2m The proportion of births attended by skilled health staff differences in income 6 remains low in South Asia and Sub-Saharan Africa 38 1p For similar levels of food consumption, malnutrition is 2n Nearly all women in Europe and Central Asia have births attended particularly high in South Asia 6 by skilled health staff--but even there poor women lag behind 38 1q Health spending has less impact on life expectancy in 2o The importance of emergency obstetric care 39 Sub-Saharan Africa 6 2p Most unsafe abortions take place in developing countries, 1r For similar education spending youth literacy rates are much especially in Latin America and the Caribbean and Africa 39 lower in West Africa 6 2.6a In developing countries the majority of child workers ages 1s Fragile states spend more on collective goods 7 5­14 are involved in unpaid family work 63 1t The world economy is becoming more energy efficient, but too 2.8a The Gini coefficient and ratio of income or consumptionof the slowly to stabilize energy consumption 7 richest quintile to the poorest quintiles are closely correlated 71 1u Workers' remittances play a sizable role in the Middle East 2.11a In some countries close to 10 percent of primary-school-age and North Africa and Latin America and the Caribbean 7 children are enrolled in secondary school 83 1v Sub-Saharan Africa is the main recipient of programmable aid 7 2.12a In Lesotho more girls who enroll in primary school stay in and 1.2a Location of indicators for Millennium Development Goals 1­4 21 complete school than boys do 87 1.3a Location of indicators for Millennium Development Goals 5­7 25 2.13a In 2005 more than 770 million people were illiterate-- 1.4a Location of indicators for Millennium Development Goal 8 27 64 percent of them women, a share unchanged since 1990 91 viii 2008 World Development Indicators 3. ENVIRONMENT Introduction 123 3o Forested areas are shrinking in Latin America and Sub-Saharan Africa--recovering in East Asia 128 Tables 3.1 Rural population and land use 130 3p The vast majority of people without access to electricity in 2004 lived in developing countries 128 3.2 Agricultural inputs 134 3q China and India generate more than two-thirds of their 3.3 Agricultural output and productivity 138 electricity from coal 128 3.4 Deforestation and biodiversity 142 3r Greater coal efficiency can reduce carbon dioxide emissions 128 3.5 Freshwater 146 3s Social insurance spending is lower in developing countries, where 3.6 Water pollution 150 people are exposed to higher risk of climate change impact 129 3.7 Energy production and use 154 3t The climate information gap makes adaptation more difficult 129 3.8 Energy dependency and efficiency and carbon 3u Adaptation is expensive, and funding for developing dioxide emissions 158 countries is inadequate 129 3.9 Trends in greenhouse gas emissions 162 3.1a What is rural? Urban? 133 3.10 Sources of electricity 166 3.2a Nearly 40 percent of land globally is devoted to agriculture 137 3.11 Urbanization 170 3.2b Developing regions lag in agricultural machinery, which 3.12 Urban housing conditions 174 reduces their agricultural productivity 137 3.13 Traffic and congestion 178 3.3a Cereal yield in low-income countries was only 40 percent of 3.14 Air pollution 182 the yield in high-income countries 141 3.15 Government commitment 184 3.3b Sub-Saharan Africa had the lowest yield, while East Asia 3.16 Toward a broader measure of savings 188 and Pacific is closing the gap with high-income countries 141 Text figures, tables, and boxes 3.5a Agriculture is still the largest user of water, accounting for 3a Greenhouse gas emissions by sector and by activity 123 some 70 percent of global withdrawals 149 3b Use of ozone-depleting substances has dropped 3.5b The share of withdrawals for agriculture approaches substantially since 1990 124 90 percent in some developing regions 149 3c The United States and China lead the world in carbon 3.6a Emissions of organic water pollutants declined in most countries dioxide emissions 124 from 1990 to 2004, even in some of the top emitters 153 3d High-income countries produce far more carbon dioxide 3.7a A person in a high-income economy uses an average of emissions per capita than low- or middle-income countries 124 more than 11 times as much energy as a person in a 3e High-income economies emitted half the global carbon low-income economy 157 dioxide emissions in 2005 124 3.8a High-income economies depend on imported energy . . . 161 3f Power generation and land use change were the two largest 3.8b . . . mostly from middle-income countries in the Middle East sources of greenhouse gas emissions in 2000 125 and North Africa and Latin America and the Caribbean 161 3g Fossil fuels accounted for three-quarters of the fuel used in 3.9a The 10 largest contributors to methane emissions account the power sector in 2002 125 for about 62 percent of emissions 165 3h Coal was responsible for the majority of emissions from the 3.9b The 10 largest contributors to nitrous oxide emissions power sector in 2002 125 account for about 56 percent of emissions 165 3i Road transport accounted for more than three-quarters of 3.10a Sources of electricity generation have shifted since 1990 . . . 169 total transport carbon dioxide emissions in 2000 125 3.10b . . . with low-income countries relying more on coal 169 3j Climate change would hurt developing countries' 3.11a Developing economies had the largest increase in urban agricultural output 126 population between 1990 and 2006 173 3k Less rain is falling in the Sahel, with dire consequences 126 3.11b Latin America and the Caribbean had the same share of 3l The rise in global mean surface temperature is accelerating 127 urban population as high-income economies in 2006 173 3m Climate disasters are affecting more and more people, 3.12a Selected housing indicators for smaller economies 177 mostly in developing countries 127 3.13a Particulate matter concentration has fallen in all 3n Developing countries are exposed to higher risk of income groups, and the higher the income, the lower natural disaster 127 the concentration 181 2008 World Development Indicators ix TABLE OF CONTENTS 4. ECONOMY 5. STATES AND MARKETS Introduction 193 Introduction 259 Tables Tables 4.1 Growth of output 198 5.1 Private sector in the economy 268 4.2 Structure of output 202 5.2 Business environment: enterprise surveys 272 4.3 Structure of manufacturing 206 5.3 Business environment: Doing Business indicators 276 4.4 Structure of merchandise exports 210 5.4 Stock markets 280 4.5 Structure of merchandise imports 214 5.5 Financial access, stability, and efficiency 284 4.6 Structure of service exports 218 5.6 Tax policies 288 4.7 Structure of service imports 222 5.7 Military expenditures and arms transfers 292 4.8 Structure of demand 226 5.8 Public policies and institutions 296 4.9 Growth of consumption and investment 230 5.9 Transport services 300 4.10 Central government finances 234 5.10 Power and communications 304 4.11 Central government expenses 238 5.11 The information age 308 4.12 Central government revenues 242 5.12 Science and technology 312 4.13 Monetary indicators 246 Text figures, tables, and boxes 4.14 Exchange rates and prices 250 5a Governance and growth go together 259 4.15 Balance of payments current account 254 5b Who uses governance indicators? 260 Text figures, tables, and boxes 5c Not producing the desired results 261 4a Developing economies increased their share of world output 193 5d Governance in theory and in practice 261 4b Low- and lower middle-income economies have had the 5e Examples of governance outcome indicators 262 strongest growth 194 5f Selected actionable governance indicators 263 4c Patterns of regional growth vary widely 194 5g Drilling down: the Worldwide Governance Indicators 263 4d Inflation is now less than 9 percent in all developing regions 194 5h Experts generally agree on governance assessments at the 4e Real interest rates have fallen in many developing economies 194 aggregate level . . . 264 4f Oil, metal, and mineral prices have increased since 1990 195 5i . . . but experts can still disagree, even using a very specific 4g Oil-exporting economies have experienced gains 195 assessment protocol 264 4h Terms of trade, gross domestic product, and gross domestic 5j Comparing governance scores in the light of uncertainty 265 income growth for selected economies 195 5k The World Bank and governance indicators 267 4.3a Manufacturing continues to show strong growth in East Asia 209 4.4a Developing economies' share of world merchandise exports continues to expand 213 4.5a Top 10 developing country exporters of merchandise goods in 2006 217 4.6a Top 10 developing country exporters of commercial services in 2006 221 4.7a The mix of commercial service imports by developing countries is changing 225 4.9a Investment is rising rapidly in Asia 233 4.10a Fifteen developing economies had a total debt to GDP ratio of 50 percent or higher 237 4.11a Interest payments are a large part of government expenses for some developing countries 241 4.12a Rich countries rely more on direct taxes 245 4.15a Top 15 economies with the largest current account surplus--and top 15 economies with the largest current account deficit in 2006 257 x 2008 World Development Indicators 6. GLOBAL LINKS BACK Introduction 317 Primary data documentation 381 Statistical methods 390 Tables Credits 392 6.1 Integration with the global economy 320 Bibliography 394 6.2 Growth of merchandise trade 324 Index of indicators 403 6.3 Direction and growth of merchandise trade 328 6.5 Primary commodity prices 334 6.6 Regional trade blocs 336 6.7 Tariff barriers 340 6.8 External debt 344 6.9 Ratios for external debt 348 6.10 Global private financial flows 352 6.11 Net official financial flows 356 6.12 Financial flows from Development Assistance Committee members 360 6.13 Allocation of bilateral aid fromDevelopment Assistance Committee members 362 6.14 Aid dependency 364 6.15 Distribution of net aid by Development Assistance Committee members 368 6.16 Movement of people 372 6.17 Travel and tourism 376 Text figures, tables, and boxes 6a Developing countries' share of global trade is rising 318 6b Manufactured goods dominate the exports of developing countries 318 6c Rising reserves and falling debt make developing countries less vulnerable to crises 318 6d Private financing has long exceeded official development assistance to developing countries 318 6e More migrants in high-income economies . . . 319 6f . . . are sending more remittances to developing countries 319 6g Europe and Central Asia and Latin America and the Caribbean lead other developing regions in access to the Internet . . . 319 6h . . . and in international bandwith per capita 319 6.1a Trade and international finance are leading globalization 323 6.3a More than half of the world's merchandise trade takes place between high-income economies. But integration of low- and middle-income economies in global merchandise trade increased substantially during 1996­2006 330 6.4a The composition of high-income economies' imports from low- and middle-income economies has changed over the last decade 333 6.6a The number of trade agreements has increased rapidly since 1990, especially free trade agreements 339 6.8a Financial integration has complemented growth 347 6.9a Developing countries have reduced financial vulnerability 351 6.10a Financial integration of low-income economies remains marginal 355 6.11a While net financial flows to middle-income economies are falling, low-income economies are still borrowing from international financial institutions 359 6.14a Official development assistance from non-DAC donors, 2002­06 367 6.15a Debt relief and political interests have shaped the allocation of official development assistance 371 6.17a Developing countries are spending more on tourism in other countries 379 2008 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, 2008. Information about the World Bank is also provided. International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and infor- mation analysis center of the U.S. Department of Energy. The CDIAC's scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere; the role of the ter- restrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases; emissions of carbon dioxide to the atmosphere; long-term climate trends; the effects of elevated carbon dioxide on vegetation; and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.esd.ornl.gov/. Deutsche Gesellschaft für Technische Zusammenarbeit The Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH is a German government-owned corpora- tion for international cooperation with worldwide operations. GTZ's aim is to positively shape political, economic, eco- logical, and social development in partner countries, thereby improving people's living conditions and prospects. For more information, see www.gtz.de/. Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org/. xii 2008 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) is the world's central organization for international monetary coop- eration. Its 184 member countries work together to promote sustainable economic growth and rising living standards by ensuring the stability of the international monetary system--the system of exchange rates and international payments that enables countries (and their citizens) to buy goods and services from each other. The IMF reviews national, regional, and global economic and financial developments, provides finan- cial advice to member countries, and serves as a forum where they can discuss the national, regional, and global consequences of their policies. The IMF also makes financing temporarily available to member countries to help them address balance of payments problems and provides technical assistance and training to help countries build the expertise and institutions they need for economic stability and growth. For more information, see www.imf.org/. International Telecommunication Union The International Telecommunication Union (ITU) is the leading UN agency for information and com- munication technologies. ITU's mission is to enable the growth and sustained development of telecom- munications and information networks and to facilitate universal access so that people everywhere can participate in, and benefit from, the emerging information society and global economy. A key priority lies in bridging the so-called Digital Divide by building information and communication infrastructure, promot- ing adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster preven- tion and mitigation. For more information, see www.itu.int/. 2008 World Development Indicators xiii PARTNERS National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. NSF's goals--discovery, learning, research infrastructure, and stewardship--provide an integrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive science and engineering workforce, expand the scientific literacy of all citizens, build the nation's research capabil- ity through investments in advanced instrumentation and facilities, and support excellence in science and engineering research and education through a capable and responsive organization. For more information, see www.nsf.gov/. Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 30 member countries shar- ing a commitment to democratic government and the market economy to support sustainable economic growth, boost employment, raise living standards, maintain financial stability, assist other coun- tries' economic development, and contribute to growth in world trade. With active relationships with some 100 other countries it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac/) is one of the principal bodies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum of major bilateral donors, who work together to increase the effectiveness of their common efforts to sup- port sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.oecd.org/. Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an under- standing of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI's main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/. Understanding Children's Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labor Organization, the United Nations Children's Fund (UNICEF), and the World Bank initiated the joint interagency research program "Understanding Children's Work and Its Impact" in December 2000. The Understanding Children's Work (UCW) project was located at UNICEF's Innocenti Research Centre in Flor- ence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. xiv 2008 World Development Indicators The UCW project addresses the crucial need for more and better data on child labor. UCW's online data- base contains data by country on child labor and the status of children. For more information, see www.ucw-project.org/. United Nations The United Nations currently has 192 member states. The purposes of the United Nations, as set forth in the Charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promot- ing respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org/. United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/. United Nations Children's Fund The United Nations Children's Fund (UNICEF) works with other UN bodies and with governments and nongovern- mental organizations to improve children's lives in more than 190 countries through various programs in educa- tion and health. UNICEF focuses primarily on five areas: child survival and development, basic Education and gender equality (including girls' education), child protection, HIV/AIDS, and policy advocacy and partnerships. For more information, see www.unicef.org/. United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization is a specialized agency of the United Nations that promotes "collaboration among nations through education, science, and culture in order to 2008 World Development Indicators xv PARTNERS further universal respect for justice, for the rule of law, and for the human rights and fundamental free- doms . . . 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, national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/. The UN Refugee Agency The UN Refugee Agency (UNHCR) is mandated to lead and coordinate international action to protect refugees and resolve refugee problems worldwide. Its primary purpose is to safeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees. For more information, see www.unhcr.org World Bank Group The World Bank is one of the world's largest sources of funding and knowledge for developing countries. Its main focus is on helping the poorest people and the poorest countries. It uses its financial resources, staff, and extensive experience to help developing countries reduce poverty, increase economic growth, and improve their quality of life. The Bank brings a mix of money and knowledge to encourage economic and social develop- ment and help countries achieve the internationally agreed Millennium Development Goals. The World Bank supports projects that help countries to invest in many different areas: health and education, fighting corruption, boosting agricultural production, building roads and ports, and protecting the environment. Since resources are scarce, assessing the effect of projects the Bank supports is essential in developing countries and is part of its focus on actual results for poor people. 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; xvi 2008 World Development Indicators 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 a specialized agency of the United Nations dedicated to developing a balanced and accessible international intellectual property (IP) system, which rewards creativ- ity, stimulates innovation, and contributes to economic development while safeguarding the public interest. WIPO carries out a wide variety of tasks related to the protection of IP rights. These include assisting gov- ernments and organizations to develop the policies, structures and skills needed to harness the potential of IP for economic development; working with member states to develop international IP law; administering treaties; running global registration systems for trademarks, industrial designs, and appellations of origin and a filing system for patents; delivering dispute resolution services; and providing a forum for informed debate and for the exchange of expertise. For more information, see www.wipo.int/. World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.unwto.org/. World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as pos- sible. It does this by administering trade agreements, acting as a forum for trade negotiations, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues--through technical assistance and training programs--and cooperating with other international organizations. At the heart of the system--known as the multilateral trading system--are the WTO's agreements, negotiated and signed by a large majority of the world's trading nations and ratified by their parliaments. For more information, see www.wto.org/. Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk/. 2008 World Development Indicators xvii PARTNERS International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/. International Road Federation The International Road Federation (IRF) is a unique global platform that brings together public and private entities committed to road development. Working together with its members and associates, the IRF pro- motes social and economic benefits that flow from well planned and environmentally sound transportation networks. The IRF serves as a catalyst for public and private partnership to organize, promote, and develop international road programs. The main objectives include promoting the understanding of the social, eco- nomic, and environmental benefits derived from developing modern road networks, road transport systems, and road traffic control; improving road safety; planning and executing economically and environmentally sound programs for the improvement and extension of road networks; conducting educational and training programs relating to the development and maintenance of road and road transport systems; facilitating the exchange of experience with national, regional, and international institutions; and harmonizing standards, research, and dissemination of road related information. For more information, see www.irfnet.org/. Netcraft Netcraft is an Internet services company and a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic com- merce, scripting languages, and content technologies on the Internet. Netcraft provides Internet security services, including antifraud and antiphishing services, application testing, code reviews, and automated penetration testing as well as research data and analysis on many aspects of the Internet. For more information, see www.netcraft.com/. PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused assurance, tax, human resources, transactions, perfor- mance improvement, and crisis management services to help address client and stakeholder issues. For more information, see www.pwc.com/. Standard & Poor's Standard & Poor's is the world's foremost provider of independent credit ratings, indexes, risk evaluation, investment research, and data. S&P's Global Stock Markets Factbook draw on data from S&P's Emerging Markets Database (EMDB) and other sources covering data on more than 100 markets with comprehensive market profiles for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor's calculates indices to serve as benchmarks that are consistent across national boundaries. Standard & xviii 2008 World Development Indicators Poor's calculates one index, the S&P/IFCG (Global) index, that reflects the perspective of local investors and those interested 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 invest- ment 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 a consortium of more than 60 informa- tion technology (IT) industry associations from economies around the world. WITSA members represent over 90 percent of the world IT market. As the global voice of the IT industry, WITSA has an active role in international public policy issues affecting the creation of a robust global information infrastructure, includ- ing advocating policies that advance the industry's growth and development, facilitating international trade and investment in IT products and services, increasing competition through open markets and regulatory reform, strengthening national industry associations through the sharing of knowledge, protecting intel- lectual property, encouraging cross-industry and government cooperation to enhance information security, bridging the education and skills gap, and safeguarding the viability and continued growth of the Internet and electronic commerce. For more information, see www.witsa.org/. World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides--and helps other institutions provide--objec- tive 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 agriculture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/. 2008 World Development Indicators xix USERS GUIDE Tables Gap filling of amounts not allocated to countries may affecting the collection and reporting of data, such The tables are numbered by section and display the result in discrepancies between subgroup aggregates as problems stemming from conflicts. identifying icon of the section. Countries and econo- and overall totals. For further discussion of aggrega- For these reasons, although data are drawn from mies are listed alphabetically (except for Hong Kong, tion methods, see Statistical methods. the sources thought to be most authoritative, they China, which appears after China). Data are shown should be construed only as indicating trends and for 153 economies with populations of more than Aggregate measures for regions characterizing major differences among economies 1 million, as well as for Taiwan, China, in selected The aggregate measures for regions cover only low- rather than as offering precise quantitative mea- tables. Table 1.6 presents selected indicators for and middle-income economies, including econo- sures of those differences. Discrepancies in data 56 other economies--small economies with popu- mies with populations of less than 1 million listed presented in different editions of World Development lations between 30,000 and 1 million and smaller in table 1.6. Indicators reflect updates by countries as well as economies if they are members of the International The country composition of regions is based on revisions to historical series and changes in meth- Bank for Reconstruction and Development (IBRD) or, the World Bank's analytical regions and may differ odology. Thus readers are advised not to compare as it is commonly known, the World Bank. The term from common geographic usage. For regional clas- data series between editions of World Development country, used interchangeably with economy, does sifications, see the map on the inside back cover and Indicators or between different World Bank publica- not imply political independence, but refers to any the list on the back cover flap. For further discussion tions. Consistent time-series data for 1960­2006 territory for which authorities report separate social of aggregation methods, see Statistical methods. are available on the World Development Indicators or economic statistics. When available, aggregate CD-ROM and in WDI Online. measures for income and regional groups appear at Statistics Except where otherwise noted, growth rates are the end of each table. Data are shown for economies as they were con- in real terms. (See Statistical methods for information Indicators are shown for the most recent year stituted in 2006, and historical data are revised to on the methods used to calculate growth rates.) Data or period for which data are available and, in most reflect current political arrangements. Exceptions are for some economic indicators for some economies tables, for an earlier year or period (usually 1990 or noted throughout the tables. are presented in fiscal years rather than calendar 1995 in this edition). Time-series data are available Additional information about the data is provided years; see Primary data documentation. All dollar fig- on the World Development Indicators CD-ROM and in Primary data documentation. That section sum- ures are current U.S. dollars unless otherwise stated. in WDI Online. marizes national and international efforts to improve The methods used for converting national currencies Known deviations from standard definitions or basic data collection and gives country-level informa- are described in Statistical methods. breaks in comparability over time or across countries tion on primary sources, census years, fiscal years, are either footnoted in the tables or noted in About statistical methods and concepts used, and other Country notes the data. When available data are deemed to be background information. Statistical methods provides · Unless otherwise noted, data for China do not too weak to provide reliable measures of levels and technical information on some of the general calcula- include data for Hong Kong, China; Macao, China; trends or do not adequately adhere to international tions and formulas used throughout the book. or Taiwan, China. standards, the data are not shown. · Data for Indonesia include Timor-Leste through Data consistency, reliability, and comparability 1999 unless otherwise noted Aggregate measures for income groups Considerable effort has been made to standardize · Montenegro declared independence from Serbia The aggregate measures for income groups include the data, but full comparability cannot be assured, and Montenegro on June 3, 2006. When avail- 209 economies (the economies listed in the main and care must be taken in interpreting the indicators. able, data for each country are shown separately. tables plus those in table 1.6) whenever data are Many factors affect data availability, comparability, However, some indicators for Serbia continue to available. To maintain consistency in the aggregate and reliability: statistical systems in many develop- include data for Montenegro through 2005; these measures over time and between tables, missing ing economies are still weak; statistical methods, data are footnoted in the tables. Moreover, data data are imputed where possible. The aggregates coverage, practices, and definitions differ widely; and for most indicators from 1999 onward for Serbia are totals (designated by a t if the aggregates include cross-country and intertemporal comparisons involve exclude data for Kosovo, a territory within Serbia gap-filled estimates for missing data and by an s, complex technical and conceptual problems that can- that is currently under international administra- for simple totals, where they do not), median values not be resolved unequivocally. Data coverage may tion pursuant to UN Security Council Resolution (m), weighted averages (w), or simple averages (u). not be complete because of special circumstances 1244 (1999); any exceptions are noted. xx 2008 World Development Indicators Classification of economies Symbols For operational and analytical purposes the World .. Bank's main criterion for classifying economies is means that data are not available or that aggregates gross national income (GNI) per capita (calculated cannot be calculated because of missing data in the by the World Bank Atlas method). Every economy is years shown. classified as low income, middle income (subdivided into lower middle and upper middle), or high income. 0 or 0.0 For income classifications see the map on the inside means zero or small enough that the number would front cover and the list on the front cover fl ap. Low- round to zero at the displayed number of decimal and middle-income economies are sometimes places. referred to as developing economies. The term is used for convenience; it is not intended to imply / that all economies in the group are experiencing in dates, as in 2003/04, means that the period of time, similar development or that other economies have usually 12 months, straddles two calendar years and reached a preferred or final stage of development. refers to a crop year, a survey year, or a fiscal year. Note that classifi cation by income does not neces- sarily refl ect development status. Because GNI per $ capita changes over time, the country composition means current U.S. dollars unless otherwise noted. of income groups may change from one edition of World Development Indicators to the next. Once the > classifi cation is fi xed for an edition, based on GNI means more than. per capita in the most recent year for which data are available (2006 in this edition), all historical < data presented are based on the same country means less than. grouping. Low-income economies are those with a GNI Data presentation conventions per capita of $905 or less in 2006. Middle-income · A blank means not applicable or, for an aggre- economies are those with a GNI per capita of more gate, not analytically meaningful. than $905 but less than $11,116. Lower middle- · A billion is 1,000 million. income and upper middle-income economies are · A trillion is 1,000 billion. separated at a GNI per capita of $3,595. High- · Figures in italics refer to years or periods other income economies are those with a GNI per capita than those specified or to growth rates calculated of $11,116 or more. The 15 participating mem- for less than the full period specified. ber countries of the euro area are presented as a · Data for years that are more than three years subgroup under high-income economies. Note that from the range shown are footnoted. Cyprus and Malta joined the euro area on January 1, 2008. The cutoff date for data is February 1, 2008. 2008 World Development Indicators xxi WORLD VIEW 1 Introduction V iewing the world at purchasing power parity Comparable measures of economic activity and living standards are useful for many purposes. Foreign investors, traders, and potential immigrants want to know an economy's market size, productivity, and prices. The globalization of markets for goods, services, finance, labor, and ideas reinforces the interdependence of economies and the need to measure them on a common scale. Countries cannot share responsibilities for global public goods--the environment, security, development assistance, and global governance--without meaningful assessments of the real size of their economies and the well-being of their people. But comparing the real size of economies is not easy. Even in an integrated global economy large differences in the costs of goods and services persist. Exchange rates can be used to convert values in one currency to another, but since they do not fully reflect differences in price levels they cannot measure the real volume of output. Exchange rates are determined by the demand for and supply of currencies used in international transactions, ignoring domestic economic sectors where prices are set in relative isolation from the rest of the world. Thus the familiar experience of international travelers, who discover that they can buy more, or less, of the same goods in different countries when converting their money using the prevailing exchange rates. To measure the real size of the world's economy and to compare costs of living across coun- tries, we need to adjust for differences in purchasing power. Finding a way to adjust for those differences has given rise to the efforts to measure purchasing power parties (PPPs), which convert local currencies to a common currency, such as the U.S. dollar. Since 1970 the International Comparison Program (ICP) has conducted eight rounds of PPP estimates for the major components of countries' gross domestic product (GDP)--the most recent for 2005. The PPP process calls for the systematic collection of price data on hundreds of representative and carefully defined products and services consumed in each country, requir- ing the full cooperation of national statistical agencies and international organizations. High-income countries regularly take part in such programs, but 2005 was the first time since 1993 that comprehensive price surveys were carried out in developing economies. An unprecedented number, 101, took part. These new PPPs provide a better and more complete view of the world economy. They show that in 2005 developing country economies were on average 2.2 times larger when measured by PPPs than by exchange rates. They also reveal that past estimates of the real size of the economies of developing countries based on the 1993 ICP round were often too large. This section reports the major findings of the 2005 ICP round and explores some of the implications. In doing so, it aims to provide a better picture of today's important issues, highlighting the diversity--and the commonality--of development patterns and outcomes. 2008 World Development Indicators 1 Country participation and population coverage Measuring price differences The eighth round of the ICP included 146 economies--101 of Purchasing power parities are needed because similar goods them classified by the World Bank as low and middle income and services have widely varying prices across countries when based on gross national income per capita at market exchange converted to a common currency using market exchange rates. rates--covering more than 95 percent of the world's people Differences are greatest in sectors not commonly traded in- (figure 1a). This was the first global price collection since 1993, ternationally, such as housing, construction, and health and although some European economies have carried out regular education services (figure 1c). Price differences are smaller for price comparisons, the last in 2002. Some large economies, widely traded products, such as machinery and equipment, af- such as China, and many smaller ones in Africa, took part for ter allowing for taxes, distributor margins, and transport costs. the first time. India took part for the first time since 1985. PPPs include the prices of tradable and nontradable goods, us- Noteworthy is that the two poorest developing regions, ing weights that reflect their relative importance in total GDP. South Asia and Sub-Saharan Africa, have the best population Comparing prices across economies is complicated by ten- coverage--more than 98 percent (figure 1b). Latin America and sion between comparability and representativeness. Goods and the Caribbean and the Middle East and North Africa recorded services should have similar characteristics (comparable) and less coverage, both below 87 percent. Caribbean countries and be consumed everywhere (representative). To compensate for Algeria, Libya, and West Bank and Gaza did not participate in noncomparability of representative products, the ICP conducted the 2005 round. Many fragile and conflict-beset states were parallel programs: selecting items at the regional level, where underrepresented (with coverage around 50 percent), with weak consumption patterns are broadly similar across countries, and statistical capacity and conditions inimical to data collection. selecting items for global comparison among a few countries The new ICP round, with its expanded coverage, provides from each region. The results of the second program were used a more complete view of the world economy and, not surpris- to link the results of the first into a single set of global PPPs. For ingly, a different picture of its size and structure. details see the ICP Global Report (World Bank 2008). Participation in the International Nontradable goods and services Comparison Program has been growing 1a show wider variation in prices 1c Number of economies participating in the International Comparison Program Cross-country variations in price level indexes, by product groups (coefficient of variation) 150 Machinery and equipment Food 100 Household equipment Recreation and culture 50 Other household goods Clothing and footwear 0 Transportation 1970 1973 1975 1980 1985 1990 1993 2005 Source: World Bank 2008. Communications The 2005 International Comparison Program's population Restaurants and hotels coverage was above 85 percent in every region 1b Other goods and services Percent 100 Alcohol and tobacco Construction 75 Housing and utilities 50 Health Other government services 25 Education 0 East Asia Europe Latin Middle East South Sub-Saharan High- ­1.0 ­0.5 0.0 0.5 1.0 & Pacific & Central America & & North Asia Africa income Asia Caribbean Africa Source: World Bank staff estimates. Source: World Bank staff estimates. 2 2008 World Development Indicators The size of the global economy What has changed since the 1993 round? Converting GDP and its components to a common currency The PPPs previously published in World Development Indica- using PPPs leads to dramatic revisions in size and structure tors and used to estimate international poverty rates were of world economies. Generally, the poorer an economy, the extrapolated from the benchmark results of the 1993 ICP. greater the upward revision of estimates based on market Data for economies participating in the more recent price col- exchange rates. The GDPs of low-income economies are on lection by Eurostat were updated through 2002 and then ex- average revised upward 160 percent and those of middle- trapolated forward and backward. The extrapolation method income economies 120 percent (figure 1d). The GDPs of high- assumes that an economy's PPP conversion factor adjusts income economies are revised upward only 10 percent. But according to the different rates of inflation for its economy the results are not uniform. Within each group, particularly and the base economy, the United States. A good approxima- low-income economies, the diversity of patterns is great. tion in the short run, but over a longer period changes in the Viewed through PPPs, low-income economies produced relative prices of goods and services and in the structure of 7 percent of global GDP in 2005, compared with 3 percent at economies--what they produce and consume--distort this market exchange rates. Middle-income economies produced 33 relationship, and new measurements must be made. New percent, compared with 19 percent at market exchange rates. methods of data collection, differences in country participa- High-income economies produced 60 percent of world GDP at tion, and changes in analytical methods all add to the differ- PPPs, compared with 78 percent at market exchange rates. ences between new PPPs and old. East Asia and Pacific has the largest upward revision--from Under the new PPPs the aggregate GDP of developing 7 percent of world GDP to 13 percent (figure 1e). But South Asia economies in 2005 is 21 percent smaller than previously esti- and the Middle East and North Africa have the largest relative mated, corresponding to a 7 percentage point reduction in their increases. Sub-Saharan Africa produced 2 percent of world GDP share of world GDP--from 47 percent to 40 percent. at PPPs in 2005, twice that at market exchange rates. The largest revisions are for developing economies. Among the 20 economies with the largest revisions are 14 Sub-Saha- Purchasing power parities transform the size of developing economies' GDP in 2005 . . . 1d ran African countries, 10 fragile states, and 10 economies Unweighted average and standard deviation of GDP correction that did not participate in the 1993 ICP. In absolute terms from market exchange rates to purchasing power parities (%) 250 the largest changes were for China and India, which did not 200 participate in the 1993 ICP. China's estimated GDP in 2005 was revised downward 40 percent and India's 36 percent, 150 accounting for a large part of the net decrease in develop- 100 ing economy GDP (figure 1f). The smaller share of world GDP 50 attributed to developing economies increases high-income 0 economies' shares. The United States--as the base country, ­50 Low-income Middle-income High-income unaffected by any revision--increased its share from 20.6 Source: World Bank staff estimates. percent to 22.1 percent. . . . and their shares China and India's economies, of world GDP 1e revised downward, remain large 1f Shares of world GDP in purchasing power parities (and market exchange rates), 2005 Old estimate (1993) Sub-Saharan Africa 2% (1%) GDP, 2005 (PPP $ billions) New estimate (2005) Middle East & North Africa 3% (1%) South Asia 5% (2%) United States Europe & Central Asia China 7% (5%) Japan Latin America Germany & Caribbean India 8% (6%) High-income United Kingdom 60% (78%) France East Asia & Pacific Russian Federation 13% (7%) Italy Brazil 0 5,000 10,000 15,000 Source: World Development Indicators data files. Source: World Bank staff estimates. 2008 World Development Indicators 3 Combining inequalities within The global distribution of income and between countries From a global perspective income inequality has two sources: Inequality within countries is measured using household sur- inequalities within countries and inequalities between coun- vey data on income or consumption per capita. Common in- tries. PPPs provide a clearer picture of both. equality measures include the Gini coefficient and the ratio The distribution of income between economies can be of income or consumption of the richest 20 percent of the measured by differences in their average GDP per capita. population to that of the poorest 20 percent (table 2.7). At the Because PPPs tend to increase the value of output from low end of the inequality range the Gini may be 25­30 and the poorer economies, inequality between economies is less ratio of the richest to poorest less than 4 (many countries in when measured in PPPs. Eastern Europe). At the high end the Gini may be as high as In 2005 PPP GDP per capita in high-income economies 60 and the ratio of the richest to poorest more than 15 (many was more than five times higher than that in middle-income countries in Latin America and parts of Africa). economies and more than 19 times higher than that in low- Under PPPs both sources of inequalities--between and income economies (figure 1g). At market exchange rates the within countries--can be combined. PPPs are used to compare inequalities would have been greater. incomes of individuals from different countries and create a The use of PPPs also leads to a reordering of regions by global income distribution curve. Including inequalities within GDP per capita. South Asia, the poorest region at market countries widens already highly unequal income distribution exchange rates, surpasses Sub-Saharan Africa at PPPs (figure between countries. Based on countries with data (90 percent of 1h). Average incomes in Europe and Central Asia are higher the world's population), half the world's people consumed less than those in Latin America and the Caribbean at PPPs, and than PPP $1,300 a year and the bottom quarter less than PPP the gap between the Middle East and North Africa and East $660 in 2005 (figure 1i). The richest 20 percent of the world's Asia and Pacific widens under PPPs compared with the gap population spent more than 75 percent of the world total, while under market exchange rates. the poorest 20 percent spent less than 2 percent (figure 1j). Income disparities Half the people in the world consumed remain wide . . . 1g less than PPP $1,300 a year in 2005 1i GDP per capita, population-weighted Market exchange rates Share of world population (%) average, 2005 ($ thousands) Purchasing power parities 100 40 75 30 50 20 25 10 0 0 660 1,300 21,500 Low-income Middle-income High-income Private consumption per capita, 2005 (PPP $, log scale) Source: World Development Indicators data files. Source: World Bank staff estimates. . . . and regional rankings change The global distribution of under purchasing power parities 1h consumption is highly uneven 1j GDP per capita, population-weighted Market exchange rates Share of world private consumption (%) average, 2005 ($ thousands) Purchasing power parities 60 59.0 10 8 40 6 4 20 17.6 2 8.1 3.3 4.8 1.0 1.4 1.9 2.4 0 0.5 0 East Asia Europe Latin Middle East South Sub-Saharan 1 2 3 4 5 6 7 8 9 10 & Pacific & Central America & & North Asia Africa Asia Caribbean Africa World population decile Source: World Development Indicators data files. Source: World Bank staff estimates. 4 2008 World Development Indicators Regional inequalities Convergence in incomes? Inequalities between individuals are high in Latin America Have income inequalities across countries declined? Although and the Caribbean and Sub-Saharan Africa, where the income developing economies have grown faster than high-income share of the richest 20 percent of the population is at least economies, PPP data show that economies starting from a lower 18 times that of the poorest 20 percent, and lower in South GDP per capita did not systematically grow more rapidly between Asia and Europe and Central Asia, where the ratio falls below 7 1996 and 2006. The reason: large, high-performing economies, (figure 1k). East Asia and Pacific and the Middle East and North such as China and India, raise their group averages. Africa stand in between, but the estimate for the Middle East But after controlling for investment in 1996 (PPP per and North Africa is less reliable because many countries have capita expenditure in education and gross fi xed capital no household surveys for estimating income distribution. formation), initial GDP per capita had a substantial effect Half of Sub-Saharan Africa's inequalities can be attrib- on future growth: for the same investment poorer countries uted to differences in average incomes between countries, grew faster than richer ones over the decade (figure 1m). This reflecting the region's low economic integration. Its average emphasizes the importance of improving the investment cli- per capita private consumption is the lowest of all regions, mate in developing economies; an effectively invested dollar but there are large differences across countries. By contrast, generates much higher growth in poor countries. less than 20 percent of inequality in South Asia, East Asia Yet low-income countries did not systematically catch up and Pacific, and Latin American and the Caribbean can be with richer ones, as their investments in human and physical attributed to different country patterns (figure 1l). There are capital were on average much smaller. From 1996 to 2006 different reasons for similar patterns. South Asia and East the average yield of these expenditures is about 2 percent- Asia and Pacific are each dominated by one large economy. In age points of annual per capita GDP growth in low-income contrast, Latin America and the Caribbean has more equally countries, compared with more than 3 percentage points in sized economies with similar consumption per capita. middle-income countries (figure 1n). Latin America and the Caribbean and Sub-Saharan For similar investment efforts poor countries Africa have the most unequal income distributions 1k grew faster between 1996 and 2006 . . . 1m Share of income (%) Poorest 20% Richest 20% Per capita GDP growth conditional on investment effort, 1996­2006 (%) 75 10 64.5 8 57.1 48.5 49.8 6 50 45.6 41.1 41.7 4 2 25 0 ­2 8.7 5.2 6.6 5.1 5.9 50 400 3,000 22,000 50,000 2.9 3.6 0 GDP per capita, 1996 (2005 $, log scale) East Asia Europe Latin Middle East South Sub-Saharan High- & Pacific & Central America & & North Asia Africa income Note: In line with Mankiw, Romer, and Weil (1992), per capita GDP growth rates are Asia Caribbean Africa regressed on the logarithms of initial per capita GDP, initial per capita investment expenditure, initial per capita education expenditure, and population growth rate. Source: World Bank staff estimates. Source: World Bank staff estimates. Inequality within countries is greatest in Latin America . . . but investment efforts in low-income countries were and the Caribbean and lowest in Sub-Saharan Africa 1l insufficient to match the growth of richer countries 1n Share of inequality (%) Between-country Within-country Estimated contribution of investment efforts 100 to countries' annual per capita GDP growth, 1996­2006 (%) 3.5 3.0 75 2.5 50 2.0 1.5 25 1.0 0.5 0 East Asia Europe Latin Middle East South Sub-Saharan High- 0.0 & Pacific & Central America & & North Asia Africa income Low-income Lower Upper High-income Asia Caribbean Africa middle-income middle-income Source: World Bank staff estimates. Source: World Bank staff estimates. 2008 World Development Indicators 5 Comparing standards of living Health and education The 2005 ICP estimated PPPs for subcomponents of GDP, Similar cross-country comparisons can be made for the rela- including expenditures on food, health, and education. As tive impact of health and education expenditures on selected has long been observed, differences in spending on food are outcomes, such as life expectancy at birth and the youth lit- smaller than differences in income or overall consumption. eracy rate. Both public and private expenditures contribute to South Asia's GDP per capita is one-sixteenth that of high-in- the improvement of these and of many other indicators. And come economies; per capita food consumption, only one-fifth. many factors other than spending affect life expectancy and And despite wide differences in income per capita, food ex- literacy outcomes. But it is still interesting to observe that penditures in South Asia and East Asia and Pacific are almost among countries with similar expenditures per capita, there the same (figure 1o). These two regions also have the small- is a large range of outcomes. est range between maximum and minimum average food. Among developing economies with similar per capita Within developing countries per capita food consumption health spending, Southern African countries have much lower is strongly correlated with malnutrition, accounting for more life expectancy, which must to some extent be the conse- than half the differences across countries. But even at similar quence of high HIV/AIDS prevalence (figure 1q). In contrast, average food per capita consumption, differences in malnutri- most developing regions have some countries that record tion rates remain significant. Average expenditures conceal above-average life expectancies. inequalities in the food consumption measure, specific diets, Compared with developing countries at similar per capita geographic conditions, and the absence of complementary education expenditures, West African countries record par- factors that can prevent malnutrition (micronutrients, health ticularly low literacy rates for youth ages 15­24 (figure 1r). care, education). In South Asia five of seven countries have Again, while worst performers are concentrated geographi- malnutrition rates much above the average of developing cally, best performers are from diverse regions, including economies at similar food consumption levels. Sub-Saharan Africa. Regional differences in food consumption Health spending has less impact on are less than differences in income 1o life expectancy in Sub-Saharan Africa 1q Per capita food consumption, unweighted average, Actual Developing country average maximum, and minimum, 2005 (PPP $ per day) Life expectancy at birth, 2005 (years) for similar health spending 6 80 5 4 40 3 2 0 da aa oa ea ia a ea a aa aa a ia la law on fric th bi an n mb go uin ila mi so Le tsw Ma An hA 1 az Za Le lG Na ra Bo Sw ut ria er So Si to 0 ua Eq East Asia Europe Latin Middle East South Sub-Saharan & Pacific & Central America & & North Asia Africa Note: Calculations based on countries that took part in the 2005 International Asia Caribbean Africa Comparison Program. The relationship between life expectancy and health spending is estimated for a sample of 105 developing countries with data. Source: World Bank staff estimates. a. Economy deviates significantly from the sample average. Source: World Bank staff estimates. For similar levels of food consumption, For similar education spending youth malnutrition is particularly high in South Asia 1p literacy rates are much lower in West Africa 1r Underweight children Actual Developing country average Youth literacy rate Actual Developing country average under age 5 (%) at similar food consumption levels (% ages 15­24) for similar education spending 50 100 75 25 50 25 0 a al a oa a a na la ra 0 an e ad i ne ea qu ga ge s ni aa a ha t a a M Fa Ch o in is l es n ka R ne bi Be Ni pa n an n Le ija PD k Gu di da es ta am an in a Pa Se Ne Om ba In in is ra ad pp Su iL o oz k rk er er La gl ili Pa Sr M Bu Az Si Ph n Ba Note: Calculations based on countries that took part in the 2005 International Note: Calculations based on countries that took part in the 2005 International Com- Comparison Program. The relationship between malnutrition and food consumption parison Program. The relationship between youth literacy and education spending is is estimated for a sample of 77 developing countries with data. estimated for a sample of 86 developing countries with data. a. Economy deviates significantly from the sample average. a. Economy deviates significantly from the sample average. Source: World Bank staff estimates. Source: World Bank staff estimates. 6 2008 World Development Indicators Public goods Foreign resources Governments finance the provision of services destined to Developing economies receive large financial flows from of- individuals, such as public health and education, and the ficial development assistance (ODA) and the remittances of provision of public goods, such as security, justice, and the workers abroad. Because prices in developing economies are environment. Countries at similar levels of development de- lower, the purchasing power of aid or remittances spent in vote different amounts to collective consumption, most to the local economy is greater than the purchasing power of the financing public institutions through recurrent administrative same amount spent in the sending country. Adjusting ODA expenditures. While fragile states spend relatively more on and remittances by the PPP price level index provides better collective goods than do nonfragile states at similar levels measures of their relative impact. of development (figure 1s), interpreting this result is difficult. In 2006 developing countries received PPP $15 per It might reflect a response to the poor quality and prior un- capita in net programmable assistance (net ODA excluding derfunding of general administration, poor governance that debt relief, humanitarian assistance, and technical cooper- yields less value for money, or the diversion of resources into ation). Low-income countries received PPP $25 per capita, conflict-related expenditures, such as security and defense. and middle-income countries received PPP $7. Fragile states Energy consumption has a strong impact on the local and received PPP $50. global environment. Regions differ in energy efficiency (PPP Developing countries received 2006 PPP $62 per cap- GDP per unit of energy consumed), but all increased energy ita in net workers' remittances. Middle-income countries efficiency between 1995 and 2005, except the Middle East received PPP $67, low-income countries PPP $55, and fragile and North Africa (figure 1t). In 2005 $1 of GDP was produced states PPP $16. The Middle East and North Africa is the main with 13 percent less energy than in 1995. But the world's recipient of remittances. At the other end Sub-Saharan Africa GDP grew 42 percent in that same period, for a net increase received PPP $22 in remittances in 2006 (figure 1u), half what of 24 percent in global energy consumption. it received in programmable aid (figure 1v). Fragile states spend Workers' remittances play a sizable role in the Middle East more on collective goods 1s and North Africa and Latin America and the Caribbean 1u Individual public consumption Net workers' remittances per capita, 2006 (PPP $) Per capita public consumption (PPP$, 2005) Collective public consumption 150 1,500 100 1,000 500 50 0 0 Other low-income Low-income Other middle-income Middle-income East Asia Europe Latin Middle East South Sub-Saharan countries fragile states countries fragile states & Pacific & Central America & & North Asia Africa Asia Caribbean Africa Source: World Bank staff estimates. Source: World Bank staff estimates. The world economy is becoming more energy efficient, Sub-Saharan Africa is the main but too slowly to stabilize energy consumption 1t recipient of programmable aid 1v GDP per unit of energy use, weighted average, 2005 Programmable aid per capita, 2006 (PPP $) (PPP $ per kilogram of oil equivalent) 1995 2005 50 8 40 6 30 4 20 2 10 0 East Asia Europe Latin Middle East South Sub-Saharan High- 0 & Pacific & Central America & & North Asia Africa income East Asia Europe Latin Middle East South Sub-Saharan Asia Caribbean Africa & Pacific & Central America & & North Asia Africa Asia Caribbean Africa Source: World Development Indicators data files. Source: World Bank staff estimates. 2008 World Development Indicators 7 Tables 1.a New purchasing power parity estimates from the 2005 International Comparison Program Purchasing Market Ratio Gross domestic Fixed Collective Consumption expenditure power exchange of PPP product capital government parity (PPP) rate conversion formation consumption conversion factor to factor market per capita exchange PPP $ local currency Individual units to rate Individual international local currency per capita per capita by household Actual $ units to $ PPP $ billions PPP $ PPP $ Final individual Food Education Health 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Albania 48.56 99.87 0.49 17.2 5,465 1,374 639 3,241 4,280 650 681 855 Angola 44.49 87.16 0.51 60.0 3,729 850 712 541 692 132 122 75 Argentina 1.269 2.904 0.44 419.0 10,815 1,775 1,120 6,226 7,463 1,192 779 1,641 Armenia 178.6 457.7 0.39 12.6 4,162 750 423 2,855 3,925 1,380 1,237 510 Australia 1.388 1.309 1.06 695.8 34,106 8,133 3,297 17,487 21,915 1,613 3,421 3,449 Austria 0.8736 0.8041 1.09 280.6 34,075 6,254 2,424 18,163 23,443 1,813 2,568 3,499 Azerbaijana 0.3263 0.9454 0.35 38.4 4,573 1,073 334 1,795 2,669 903 1,127 385 Bahrain 0.2488 0.376 0.66 24.2 33,451 6,926 2,441 10,170 12,822 2,268 2,632 2,376 Bangladesh 22.64 61.75 0.37 163.7 1,068 254 71 764 903 290 238 112 Belarus 779.3 2154 0.36 83.5 8,541 1,351 829 4,438 6,733 1,422 2,435 1,453 Belgium 0.8988 0.8041 1.12 332.2 31,699 6,512 2,427 16,077 21,647 1,958 2,759 3,957 Benin 219.6 527.5 0.42 10.3 1,213 184 232 758 948 197 168 73 Bhutan 15.74 44.1 0.36 2.3 3,649 1,715 868 1,277 1,924 417 446 906 Bolivia 2.232 8.066 0.28 34.1 3,715 298 557 2,151 2,972 481 1,129 519 Bosnia and Herzegovina 0.7268 1.573 0.46 23.3 5,949 1,157 923 4,859 6,320 1,163 1,075 963 Botswana 2.421 5.110 0.47 22.0 12,010 1,981 3,491 2,228 2,895 352 1,428 307 Brazil 1.357 2.434 0.56 1,583.2 8,474 1,218 1,640 4,416 5,639 712 851 1,306 Brunei Darussalam 0.9031 1.664 0.54 17.6 46,991 4,825 14,595 9,283 12,672 1,489 6,086 1,653 Bulgaria 0.5928 1.574 0.38 72.2 9,328 1,418 1,563 5,234 7,285 925 1,822 1,306 Burkina Faso 200.2 527.5 0.38 14.8 1,061 136 414 624 778 170 135 51 Burundi 343.0 1082 0.32 2.5 319 .. .. .. .. .. .. .. Cambodia 1,279 4097 0.31 20.1 1,440 146 202 926 1,197 324 594 430 Cameroon 251.0 527.5 0.48 35.5 1,993 210 268 1,211 1,499 335 233 72 Canada 1.214 1.212 1.00 1,130.0 34,972 7,265 2,695 18,233 23,526 1,465 2,743 3,269 Cape Verde 69.36 88.67 0.78 1.3 2,521 936 421 1,964 2,449 480 766 239 Central African Republic 263.7 527.5 0.50 2.7 654 36 85 496 607 168 96 22 Chad 208.0 527.5 0.39 14.9 1,471 166 576 548 781 169 469 62 Chile 333.7 560.1 0.60 199.6 12,248 2,372 995 6,143 7,430 917 1,084 1,323 Chinab 3.448 8.194 0.42 5,333.2 4,088 1,581 823 1,310 1,751 265 582 549 Hong Kong, China 5.688 7.777 0.73 243.2 35,690 8,326 3,078 16,320 19,622 1,266 2,923 3,632 Macao, China 5.270 7.987 0.66 17.4 36,869 8,520 2,735 8,266 10,525 963 2,181 2,164 Taiwan, China 19.34 32.18 0.60 592.3 26,057 5,303 4,257 13,645 16,836 1,407 4,727 4,803 Colombia 1,082 2135 0.51 263.7 5,867 962 1,002 3,266 4,098 610 678 914 Comoros 226.2 395.6 0.57 0.7 1,127 98 406 762 918 330 171 39 Congo, Dem. Rep. 214.3 473.9 0.45 15.7 267 52 77 125 151 45 20 16 Congo, Rep. 268.8 527.5 0.51 11.7 3,246 252 549 679 943 166 478 135 Côte d'Ivoire 287.5 527.5 0.55 30.0 1,614 63 279 991 1,216 271 118 90 Croatia 3.935 5.949 0.66 58.8 13,231 3,161 1,695 6,641 9,076 1,423 1,740 1,805 Cyprus 0.424 0.4636 0.91 18.6 24,534 4,647 2,601 14,709 17,859 2,213 2,420 1,725 Czech Republic 14.40 23.96 0.60 207.6 20,280 3,770 2,897 9,278 13,145 1,322 2,145 2,756 Denmark 8.517 5.997 1.42 182.2 33,645 6,955 2,960 15,082 21,490 1,583 2,895 3,283 Djibouti 84.69 177.7 0.48 1.5 1,850 240 762 864 1,135 187 366 104 Ecuador 0.4226 1 0.42 88.0 6,737 1,329 690 3,680 4,577 781 781 785 Egypt, Arab Rep. 1.616 6.004 0.27 333.2 4,574 570 887 2,835 3,662 856 1,230 665 Equatorial Guineac 287.4 527.5 0.54 13.8 13,610 2,019 860 2,359 2,912 558 731 612 Estonia 7.813 12.59 0.62 22.2 16,456 3,694 2,008 7,811 11,291 1,306 2,605 1,731 Ethiopia 2.254 8.652 0.26 43.7 581 70 121 373 457 139 .. 29 Fiji 1.430 1.691 0.85 3.5 4,282 1,116 731 2,996 3,768 750 1,016 691 Finland 0.9834 0.8041 1.22 159.8 30,462 5,969 2,475 13,761 19,501 1,672 2,473 3,234 France 0.9225 0.8041 1.15 1,862.2 30,591 5,654 2,260 16,724 23,027 2,263 2,567 4,059 Gabon 256.2 527.5 0.49 17.8 13,821 2,428 2,304 2,641 3,620 594 1,691 595 Gambia, The 7.560 28.58 0.26 1.7 1,078 62 409 405 550 75 .. 121 Georgia 0.7380 1.812 0.41 15.7 3,520 650 366 2,200 3,063 564 820 836 Germany 0.8926 0.8041 1.11 2,510.7 30,445 4,963 2,325 17,278 21,742 1,780 1,436 4,123 Ghana 3,721 9073 0.41 26.1 1,160 254 118 745 912 189 241 140 Greece 0.7022 0.8041 0.87 324.9 29,261 5,523 3,313 15,481 18,545 2,168 2,170 2,557 8 2008 World Development Indicators 1.a WORLD VIEW New purchasing power parity estimates from the 2005 International Comparison Program Purchasing Market Ratio Gross domestic Fixed Collective Consumption expenditure power exchange of PPP product capital government parity (PPP) rate conversion formation consumption conversion factor to factor market per capita exchange PPP $ local currency Individual units to rate Individual international local currency per capita per capita by household Actual $ units to $ PPP $ billions PPP $ PPP $ Final individual Food Education Health 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Guinea 1,219 3640 0.33 9.9 1,105 167 95 548 682 123 241 143 Guinea-Bissau 217.3 527.5 0.41 0.7 458 57 266 295 361 96 49 25 Hungary 128.5 199.6 0.64 171.6 17,014 2,804 2,129 8,481 12,365 1,242 2,189 2,434 Iceland 97.06 62.98 1.54 10.5 35,465 12,207 3,245 19,100 26,816 1,808 4,118 4,394 India 14.67 44.27 0.33 2,431.9 2,222 504 233 1,183 1,464 317 391 485 Indonesia 3,934 9705 0.41 707.9 3,209 615 248 1,934 2,326 607 658 144 Iran, Islamic Rep. 2,675 8964 0.30 643.5 9,314 1,646 1,489 5,275 6,645 655 1,257 2,119 Iraq 558.7 .. .. .. .. 269 1,643 1,297 1,862 394 543 877 Ireland 1.023 0.8041 1.27 157.6 37,886 8,864 2,183 15,560 20,997 867 3,177 2,998 Israel 3.717 4.488 0.83 156.7 22,627 3,775 3,602 11,096 15,278 1,681 3,385 2,248 Italy 0.8750 0.8041 1.09 1,626.3 27,750 6,016 2,165 15,678 19,667 2,032 1,865 2,914 Japan 129.6 110.2 1.18 3,870.3 30,290 6,656 2,615 15,342 20,438 1,348 1,767 4,653 Jordan 0.3805 0.709 0.54 23.5 4,342 1,552 875 2,947 3,843 898 1,202 724 Kazakhstan 57.61 132.9 0.43 131.8 8,699 1,632 811 3,746 5,426 735 2,768 1,728 Kenya 29.52 75.55 0.39 49.0 1,375 145 177 948 1,196 221 351 259 Korea, Rep. 788.9 1024 0.77 1,027.4 21,273 6,376 2,046 9,829 12,157 874 2,124 2,240 Kuwait 0.2136 0.292 0.73 110.4 43,551 9,288 5,292 10,978 13,683 2,316 2,437 1,365 Kyrgyz Republic 11.35 41.02 0.28 8.9 1,728 138 251 1,249 1,901 403 841 282 Lao PDR 2,988 10636 0.28 10.3 1,814 476 678 859 1,109 268 575 165 Latvia 0.2980 0.5647 0.53 30.4 13,215 2,663 2,007 6,985 9,745 1,277 2,464 1,498 Lebanon 847.5 1508 0.56 38.3 9,545 2,814 1,715 6,265 7,639 1,842 3,260 1,390 Lesotho 3.490 6.359 0.55 2.6 1,311 274 219 1,319 1,686 309 738 446 Liberiad 0.4926 1 0.49 1.1 312 59 60 200 248 31 216 37 Lithuania 1.484 2.776 0.53 48.1 14,084 2,030 1,551 8,169 11,402 1,888 2,478 1,944 Luxembourg 0.9225 0.8041 1.15 31.9 69,776 14,390 3,898 27,061 34,295 1,849 2,853 4,345 Macedonia, FYR 19.06 49.29 0.39 15.0 7,394 905 1,276 4,623 6,123 1,181 991 1,007 Madagascar 649.6 2003 0.32 15.5 834 119 249 557 702 189 383 66 Malawi 39.46 118.4 0.33 8.6 648 121 124 400 482 53 161 139 Malaysia 1.734 3.8 0.46 299.6 11,678 2,483 1,642 4,302 5,669 649 1,728 779 Maldives 8.134 12.8 0.64 1.2 3,995 1,965 1,497 1,496 2,190 355 2,095 932 Mali 240.1 527.5 0.46 11.7 1,004 98 290 616 772 180 176 76 Malta 0.2474 0.346 0.71 8.3 20,483 3,462 2,471 11,778 15,662 1,887 2,164 2,457 Mauritania 98.84 268.6 0.37 5.0 1,684 647 556 906 1,150 336 222 124 Mauritius 14.68 28.94 0.51 12.4 9,975 1,524 1,768 5,837 7,621 1,158 1,778 889 Mexico 7.127 10.90 0.65 1,173.9 11,387 1,631 798 7,189 8,924 1,658 2,007 910 Moldova 4.434 12.60 0.35 8.5 2,190 305 237 1,854 2,688 374 1,345 364 Mongolia 417.2 1205 0.35 6.7 2,609 714 402 1,159 1,618 353 1,137 421 Montenegro 0.3659 0.8027 0.46 4.5 7,450 980 3,144 4,201 5,739 1,112 885 975 Morocco 4.8782 8.865 0.55 107.1 3,554 851 540 1,801 2,254 494 372 191 Mozambique 10,909 23061 0.47 13.9 677 104 108 455 574 180 117 53 Namibia 4.265 6.359 0.67 9.3 4,599 979 1,233 2,068 2,769 483 1,046 589 Nepal 22.65 72.06 0.31 26.0 960 179 98 706 850 277 183 303 Netherlands 0.8983 0.8041 1.12 562.9 34,492 5,711 3,468 16,477 22,587 1,974 2,515 3,680 New Zealand 1.535 1.420 1.08 101.6 24,566 4,842 2,114 13,620 17,750 1,670 2,180 2,698 Niger 226.7 527.5 0.43 8.0 602 80 164 370 453 103 51 43 Nigeria 60.23 131.3 0.46 214.8 1,520 150 207 937 1,172 269 280 97 Norway 8.840 6.443 1.37 219.8 47,538 8,600 3,358 17,357 24,603 1,885 2,832 4,502 Oman 0.2324 0.3845 0.60 51.0 20,350 4,800 4,385 5,814 7,402 1,515 1,446 723 Pakistan 19.10 59.36 0.32 340.3 2,184 329 266 1,663 2,026 525 491 511 Paraguay 2,007 6178 0.32 22.6 3,824 480 353 2,763 3,350 761 505 348 Peru 1.487 3.296 0.45 176.0 6,452 1,070 536 3,834 4,564 854 799 559 Philippines 21.75 55.09 0.39 250.0 2,956 382 308 1,845 2,218 612 811 175 Poland 1.898 3.235 0.59 516.6 13,535 1,945 1,504 7,421 10,271 1,423 1,985 1,858 Portugal 0.7074 0.8041 0.88 210.5 19,956 4,337 1,940 11,920 15,288 1,851 1,681 2,778 Qatar 2.745 3.64 0.75 56.3 70,716 29,906 7,576 9,476 12,893 2,072 3,756 2,503 Romania 1.421 2.914 0.49 202.7 9,368 1,499 1,483 5,280 7,311 1,165 1,350 1,438 2008 World Development Indicators 9 1.a New purchasing power parity estimates from the 2005 International Comparison Program Purchasing Market Ratio Gross domestic Fixed Collective Consumption expenditure power exchange of PPP product capital government parity (PPP) rate conversion formation consumption conversion factor to factor market per capita exchange PPP $ local currency Individual units to rate Individual international local currency per capita per capita by household Actual $ units to $ PPP $ billions PPP $ PPP $ Final individual Food Education Health 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Russian Federation 12.736 28.28 0.45 1,697.5 11,858 1,377 1,333 5,545 7,916 1,298 1,723 1,394 Rwanda 186.2 557.8 0.33 6.4 696 109 243 464 592 148 202 59 São Tomé and Principe 5,558 10558 0.53 0.2 1,401 199 418 1,167 1,446 388 300 176 Saudi Arabia 2.410 3.747 0.64 490.6 21,220 4,657 3,376 5,037 6,976 1,108 1,924 1,229 Senegal 251.7 527.5 0.48 18.1 1,541 262 250 988 1,239 300 181 144 Serbia 27.21 66.71 0.41 64.3 8,644 1,139 1,050 4,726 6,712 1,015 1,109 1,209 Sierra Leone 1,074 2890 0.37 3.3 584 62 254 523 667 118 240 278 Singapore 1.079 1.665 0.65 180.1 41,479 10,352 5,534 12,636 15,564 929 3,159 3,043 Slovak Republic 17.20 31.02 0.55 85.6 15,881 2,856 2,561 8,181 11,077 1,227 1,916 1,990 Slovenia 147.0 192.7 0.76 45.0 22,506 5,638 2,094 11,305 14,970 1,457 2,075 2,628 South Africa 3.872 6.359 0.61 397.5 8,478 1,214 1,587 4,582 5,886 764 1,228 1,062 Spain 0.7676 0.8041 0.95 1,179.6 27,180 7,020 2,265 14,826 19,232 2,117 2,156 3,280 Sri Lanka 35.17 100.5 0.35 67.3 3,420 658 499 2,126 2,735 568 393 341 Sudan 107.7 243.6 0.44 63.1 1,711 257 234 1,493 1,799 489 77 69 Swaziland 3.293 6.359 0.52 5.0 4,461 678 752 2,537 3,157 746 625 1,057 Sweden 9.243 7.473 1.24 288.9 32,016 4,784 2,752 14,381 21,833 1,631 3,339 3,635 Switzerland 1.741 1.245 1.40 261.7 35,182 7,609 1,779 19,472 23,235 1,871 2,413 4,294 Syrian Arab Republic 19.72 52.86 0.37 75.6 4,002 909 542 2,210 2,881 861 878 664 Tajikistan 0.7444 3.117 0.24 9.7 1,478 67 209 948 1,560 363 1,161 236 Tanzania 395.6 1129 0.35 35.9 933 132 126 618 750 261 .. 40 Thailand 15.93 40.22 0.40 444.9 7,061 1,908 747 3,638 4,616 448 1,451 1,072 Togo 240.4 527.5 0.46 4.6 742 75 170 618 767 174 168 41 Tunisia 0.5813 1.297 0.45 64.0 6,382 1,149 894 3,463 4,371 697 553 519 Turkey 0.8683 1.341 0.65 561.1 7,786 1,192 1,057 4,612 5,715 888 913 346 Uganda 619.6 1737 0.36 24.5 848 115 181 583 748 155 .. 98 Ukraine 1.678 5.125 0.33 263.0 5,583 732 512 3,138 4,657 953 2,081 922 United Kingdom 0.6489 0.5493 1.18 1,889.4 31,371 4,937 2,841 19,187 25,155 1,586 1,955 3,665 United States 1 1 1.00 12,397.9 41,813 8,018 3,962 29,368 32,045 1,998 2,725 5,853 Uruguay 13.28 24.48 0.54 30.6 9,266 1,111 933 5,886 7,074 1,071 716 1,506 Venezuela, RB 1,153 2090 0.55 262.5 9,877 1,287 985 4,290 5,364 844 1,026 866 Vietnam 4,713 15804 0.30 178.1 2,143 634 367 990 1,310 238 1,009 466 Yemen, Rep. 69.49 191.5 0.36 46.2 2,188 472 386 1,073 1,405 376 454 190 Zambia 2,415 4464 0.54 13.4 1,171 211 275 672 894 59 .. 233 Zimbabwe 33,068 22364 1.48 2.3 176 45 169 284 381 90 159 9 a. Original data collected in old manat are converted to new manat at 1 new manat = 5,000 old manat. b. Results for China were based on national average prices extrapolated by the World Bank and Asian Development Bank using price data for 11 cities submitted by the National Bureau of Statistics for China. The data for China do not include Hong Kong, China; Macao, China; and Taiwan, China. c. Per capita figures derived using population from the International Comparison Program. d. Data in U.S. dollars. 10 2008 World Development Indicators 1.a WORLD VIEW New purchasing power parity estimates from the 2005 International Comparison Program About the data The International Comparison Program (ICP) is a methodology, such as how basic heading PPPs were compensation of employees). Data are converted to worldwide statistical initiative to collect comparative computed and aggregated. Annex F of the 2005 ICP U.S. dollars using PPP rates and divided by midyear price data and estimate purchasing power parities report (available at www.worldbank.org/data/ICP) population. · PPP individual by household final con- (PPPs) of the world's economies. Using PPPs instead provides a review of the methods used. sumption expenditure per capita is the market value of market exchange rates to convert currencies For the 2005 ICP GDP data were compiled using of all goods and services, including durable products, allows the output of economies and the welfare of the expenditure approach, with its components purchased by households. It excludes purchases of their inhabitants to be compared in real terms--that allocated to 155 basic headings for the year 2005. dwellings but includes imputed rent for owner-occupied is, controlling for differences in price levels. PPPs The detailed breakdown of GDP expenditure used dwellings. Data are converted to U.S. dollars using are the preferred means of converting gross domes- by the ICP may differ from other national accounts PPP rates and divided by midyear population. · PPP tic product (GDP) and its components to a common data presented in World Development Indicators actual individual consumption expenditure per cap- currency. They enable cross-country comparison of 2008 because of the timing of data collection and ita is household final consumption expenditure plus the size of economies, average consumption levels, differences in methodology. In table 1.a gross fixed the individual component of government consumption poverty rates, productivity, and use of resources. capital formation and consumption data are from the expenditure and the final consumption expenditure The ratio of the PPP conversion factor to the market ICP, and GDP data are collected by World Bank staff by nonprofit institutions serving households. The exchange rate (also referred to as the price level from national and international sources and in some individual component of government consumption index) allows the cost of the goods and services that cases differ from ICP data. All per capita figures are expenditure relates to services provided to specific make up GDP to be compared across countries. estimated using the World Bank's population data, individuals, such as health and education. Data are The new estimates of PPP, published for the first except where otherwise noted. converted to U.S. dollars using PPP rates and divided time in World Development Indicators, are the result by midyear population. · PPP individual consumption Definitions of a global program of price surveys carried out using expenditure on food per capita is expenditure on food similar methods in 146 countries. New methods of · Purchasing power parity (PPP) conversion factor is products and nonalcoholic beverages purchased for data collection and analysis were used to overcome the number of units of a country's currency required consumption at home. It excludes food products and problems encountered in previous rounds of the to buy the same amount of goods and services in beverages sold for immediate consumption away from ICP. Teams in each region identified characteristic the domestic market as a U.S. dollar would buy in home, cooked dishes prepared by restaurants and goods and services to be priced. Surveys conducted the United States. · Market exchange rate is the catering contractors, and products sold as pet foods. by each country in 2005 and 2006 yielded prices for exchange rate determined by national authorities or Data are converted to U.S. dollars using PPP rates more than 1,000 goods and services. Many coun- the rate determined in the legally sanctioned exchange and divided by midyear population. · PPP individual tries participated for the first time, including China. market. When the official exchange rate diverges by consumption expenditure on education per capita is (Previous estimates of China's PPPs came from a an exceptionally large margin from the rate effectively expenditures by households on pre-primary, primary, research study using data for 1986.) India partici- applied to domestic transactions of foreign currencies secondary, post-secondary, and tertiary education. pated for the first time since 1985. and traded products, the market exchange rate is an Data are converted to U.S. dollars using PPP rates The ICP Global Office within the World Bank coordi- estimated alternative conversion factor. It is calcu- and divided by midyear population. · PPP individual nated the collection of data and calculation of PPPs lated as an annual average based on monthly aver- consumption expenditure on health per capita is in more than 100 (mostly developing) economies. ages (local currency units relative to the U.S. dollar). expenditures by households on medical products, The program was organized in five geographic areas: · Ratio of PPP conversion factor to market exchange appliances and equipment, outpatient services, and Africa, Asia-Pacific, Commonwealth of Independent rate, also known as the price level index, is obtained hospital services. Data are converted to U.S. dollars States, South America, and Western Asia. Regional by dividing the PPP conversion factor by the market using PPP rates and divided by midyear population. agencies coordinated the work in the five regions. In exchange rate. · PPP gross domestic product (GDP) parallel the Statistical Office of the European Commu- is GDP converted to U.S. dollars using PPP rates. GDP Data sources nities (Eurostat) and the Organisation for Economic is the sum of value added by all resident producers PPP conversion factors are estimates by World Co-operation and Development (OECD) conducted its plus any product taxes (less subsidies) not included Bank staff based on data collected by the Interna- 2005 PPP program, which included 46 countries. in the valuation of output. · PPP GDP per capita is tional Comparison Program (www.worldbank.org/ Each region and the Eurostat-OECD group differ in PPP GDP divided by midyear population. Population is data/ICP). Data on GDP are estimated by World the size and structure of their economies and their based on the de facto definition of population, which Bank staff based on national accounts data col- statistical capacity. To ensure the most consistent counts all residents regardless of legal status or citi- lected by World Bank staff during economic mis- comparisons of countries within regions, different zenship, except refugees not permanently settled in sions or reported to other international organiza- methods were used in each region. Three methods the country of asylum, who are generally considered tions such as the OECD. Population estimates were used to compute housing PPPs. Asia and Africa part of the population of their country of origin. · PPP are prepared by World Bank staff from a variety used reference volumes, Eurostat and West Asia gross fixed capital formation per capita is outlays on of sources (see Data sources for table 2.1). Data used a combination of rentals and quantities, and additions to the fixed assets of an economy converted on gross fixed capital formation, government con- the CIS and Latin America used the quantity method. to U.S. dollars using PPP rates and divided by midyear sumption, and household consumption expendi- In Africa, Asia-Pacific, and Western Asia government population. · PPP collective government consump- tures are based on data collected by the Interna- expenditures were adjusted to account for produc- tion per capita is all government current expendi- tional Comparison Program. tivity differences. There were other differences in tures for purchases of goods and services (including 2008 World Development Indicators 11 Millennium Development Goals Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of 1.1 Proportion of population below $1 purchasing power people whose income is less than $1 a day parity (PPP) a day1 1.2 Poverty gap ratio [incidence × depth of poverty] 1.3 Share of poorest quintile in national consumption Target 1.B Achieve full and productive employment and decent 1.4 Growth rate of GDP per person employed work for all, including women and young people 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 (PPP) a day 1.7 Proportion of own-account and contributing family workers in total employment Target 1.C Halve, between 1990 and 2015, the proportion of 1.8 Prevalence of underweight children under-five years of age people who suffer from hunger 1.9 Proportion of population below minimum level of dietary energy consumption Goal 2 Achieve universal primary education Target 2.A Ensure that by 2015 children everywhere, boys and 2.1 Net enrolment ratio in primary education girls alike, will be able to complete a full course of 2.2 Proportion of pupils starting grade 1 who reach last primary schooling grade of primary education 2.3 Literacy rate of 15- to 24-year-olds, women and men Goal 3 Promote gender equality and empower women Target 3.A Eliminate gender disparity in primary and secondary 3.1 Ratios of girls to boys in primary, secondary, and tertiary education, preferably by 2005, and in all levels of education education no later than 2015 3.2 Share of women in wage employment in the nonagricultural sector 3.3 Proportion of seats held by women in national parliament Goal 4 Reduce child mortality Target 4.A Reduce by two-thirds, between 1990 and 2015, the 4.1 Under-five mortality rate under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Target 5.A Reduce by three-quarters, between 1990 and 2015, 5.1 Maternal mortality ratio the maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel Target 5.B Achieve by 2015 universal access to reproductive 5.3 Contraceptive prevalence rate health 5.4 Adolescent birth rate 5.5 Antenatal care coverage (at least one visit and at least four visits) 5.6 Unmet need for family planning Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 6.A Have halted by 2015 and begun to reverse the 6.1 HIV prevalence among population ages 15­24 years spread of HIV/AIDS 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15­24 years with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10­14 years Target 6.B Achieve by 2010 universal access to treatment for 6.5 Proportion of population with advanced HIV infection with HIV/AIDS for all those who need it access to antiretroviral drugs Target 6.C Have halted by 2015 and begun to reverse the 6.6 Incidence and death rates associated with malaria incidence of malaria and other major diseases 6.7 Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www. un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on 14 November 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries "to create an environment--at the national and global levels alike--which is conducive to development and the elimination of poverty." All indicators should be disaggregated by sex and urban-rural location as far as possible. 12 2008 World Development Indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development 7.1 Proportion of land area covered by forest into country policies and programs and reverse the 7.2 Carbon dioxide emissions, total, per capita and loss of environmental resources per $1 GDP (PPP) 7.3 Consumption of ozone-depleting substances Target 7.B Reduce biodiversity loss, achieving, by 2010, a 7.4 Proportion of fish stocks within safe biological limits significant reduction in the rate of loss 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction Target 7.C Halve by 2015 the proportion of people without 7.8 Proportion of population using an improved drinking water sustainable access to safe drinking water and basic source sanitation 7.9 Proportion of population using an improved sanitation facility Target 7.D Achieve by 2020 a significant improvement in the 7.10 Proportion of urban population living in slums2 lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 8.A Develop further an open, rule-based, predictable, Some of the indicators listed below are monitored separately nondiscriminatory trading and financial system for the least developed countries (LDCs), Africa, landlocked developing countries, and small island developing states. (Includes a commitment to good governance, development, and poverty reduction--both Official development assistance (ODA) nationally and internationally.) 8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donors' gross national income 8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic Target 8.B Address the special needs of the least developed education, primary health care, nutrition, safe water, and countries sanitation) 8.3 Proportion of bilateral official development assistance of (Includes tariff and quota-free access for the least OECD/DAC donors that is untied developed countries' exports; enhanced program of 8.4 ODA received in landlocked developing countries as a debt relief for heavily indebted poor countries (HIPC) proportion of their gross national incomes and cancellation of official bilateral debt; and more 8.5 ODA received in small island developing states as a generous ODA for countries committed to poverty proportion of their gross national incomes reduction.) Market access Target 8.C Address the special needs of landlocked 8.6 Proportion of total developed country imports (by value developing countries and small island developing and excluding arms) from developing countries and least states (through the Programme of Action for developed countries, admitted free of duty the Sustainable Development of Small Island 8.7 Average tariffs imposed by developed countries on Developing States and the outcome of the 22nd agricultural products and textiles and clothing from special session of the General Assembly) developing countries 8.8 Agricultural support estimate for OECD countries as a percentage of their GDP 8.9 Proportion of ODA provided to help build trade capacity Target 8.D Deal comprehensively with the debt problems of developing countries through national and Debt sustainability international measures in order to make debt 8.10 Total number of countries that have reached their HIPC sustainable in the long term decision points and number that have reached their HIPC completion points (cumulative) 8.11 Debt relief committed under HIPC Initiative and Multilateral Debt Relief Initiative (MDRI) 8.12 Debt service as a percentage of exports of goods and services Target 8.E In cooperation with pharmaceutical companies, 8.13 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 8.F In cooperation with the private sector, make 8.14 Telephone lines per 100 population available the benefits of new technologies, 8.15 Cellular subscribers per 100 population especially information and communications 8.16 Internet users per 100 population 1. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. 2. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material. 2008 World Development Indicators 13 1.1 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2006 2006 2006 2006b 2005 2006b 2006 2006 2006 2006 2005­06 2005­06 Afghanistan .. 652 .. 8.1 117 ..c .. 23.9d ..d .. 5.3 .. Albania 3 29 116 9.3 109 2,930 116 19.0 6,000 118 5.0 4.4 Algeria 33 2,382 14 101.2 49 3,030 111 198.0 d 5,940 d 119 3.0 1.5 Angola 17 1,247 13 32.7 69 1,970 131 64.5 3,890 139 18.6 15.3 Argentina 39 2,780 14 201.4 31 5,150 88 456.8 11,670 78 8.5 7.4 Armenia 3 30 107 5.8 132 1,920 133 14.9 4,950 127 13.3 13.6 Australia 21 7,741 3 742.3 15 35,860 25 702.5 33,940 26 2.5 1.0 Austria 8 84 100 329.2 22 39,750 18 298.4 36,040 18 3.1 2.5 Azerbaijan 8 87 103 15.6 95 1,840 134 46.1 5,430 123 34.5 33.0 Bangladesh 156 144 1,198 70.5 55 450 182 191.9 1,230 180 6.6 4.8 Belarus 10 208 47 33.8 66 3,470 105 94.4 9,700 88 9.9 10.4 Belgium 11 31 349 405.4 18 38,460 20 356.9 33,860 27 3.2 2.6 Benin 9 113 79 4.7 138 530 176 10.9 1,250 178 4.1 0.9 Bolivia 9 1,099 9 10.3 105 1,100 149 35.6 3,810 142 4.6 2.7 Bosnia and Herzegovina 4 51 77 12.7 102 3,230 106 26.6 6,780 109 6.0 5.7 Botswana 2 582 3 10.4 104 5,570 81 21.8 11,730 77 2.1 0.9 Brazil 189 8,515 22 892.6 11 4,710 93 1,647.5 8,700 96 3.7 2.4 Bulgaria 8 111 71 30.7 71 3,990 98 79.0 10,270 84 6.1 6.7 Burkina Faso 14 274 52 6.3 129 440 184 16.2 1,130 184 6.4 3.2 Burundi 8 28 318 0.8 189 100 209 2.6 320 206 5.1 1.1 Cambodia 14 181 80 7.0 123 490 180 22.1 1,550 174 10.8 9.0 Cameroon 18 475 39 18.1 87 990 154 37.4 2,060 163 3.8 1.6 Canada 33 9,985 4 1,196.6 9 36,650 22 1,184.4 36,280 16 2.8 1.7 Central African Republic 4 623 7 1.5 173 350 188 2.9 690 196 4.1 2.3 Chad 10 1,284 8 4.7 137 450 182 12.3 1,170 181 0.5 ­2.6 Chile 16 757 22 111.9 46 6,810 76 185.6 11,300 80 4.0 3.1 China 1,312 9,635e 141 2,621.0 4 2,000 130 6,119.1 4,660 133 10.7 10.1 Hong Kong, China 7 1 6,581 199.1 32 29,040 31 268.8 39,200 12 6.8 6.1 Colombia 46 1,142 41 142.0 39 3,120 108 279.2 6,130 114 6.8 5.3 Congo, Dem. Rep. 61 2,345 27 7.7 119 130 207 16.2 270 207 5.1 1.8 Congo, Rep. 4 342 11 3.8 .. 1,050 .. 8.7 2,420 .. 6.4 4.1 Costa Rica 4 51 86 21.9 82 4,980 90 40.6d 9,220 d 91 8.2 6.4 Côte d'Ivoire 19 322 59 16.6 91 880 158 29.8 1,580 171 0.9 ­0.9 Croatia 4 57 79 41.4 62 9,310 65 61.5 13,850 72 4.8 4.8 Cuba 11 111 103 .. .. ..f .. .. .. .. 5.4 5.2 Czech Republic 10 79 133 131.4 40 12,790 56 214.9 20,920 55 6.1 5.7 Denmark 5 43 128 283.3 27 52,110 7 196.7 36,190 17 3.2 2.8 Dominican Republic 10 49 199 28.0 77 2,910 118 53.3d 5,550 d 121 10.7 9.0 Ecuador 13 284 48 38.5 63 2,910 118 89.9 6,810 108 3.9 2.8 Egypt, Arab Rep. 74 1,001 75 100.9 50 1,360 143 366.5 4,940 128 6.8 4.9 El Salvador 7 21 326 18.1 86 2,680 121 37.9 d 5,610 d 120 4.2 2.7 Eritrea 5 118 46 0.9 183 190 202 3.2d 680 d 198 ­1.0 ­4.5 Estonia 1 45 32 15.3 96 11,400 60 24.3 18,090 58 11.4 11.7 Ethiopia 77 1,104 77 12.9 101 170 204 49.0 630 200 9.0 6.2 Finland 5 338 17 217.8 29 41,360 16 174.7 33,170 30 5.5 5.1 France 61 552 111 2,306.7g 6 36,560 g 24 1,974.9 32,240 34 2.0 1.4 Gabon 1 268 5 7.0 121 5,360 85 14.7 11,180 81 1.2 ­0.4 Gambia, The 2 11 166 0.5 194 290 196 1.8 1,110 186 4.5 1.6 Georgia 4 70 64 7.0 122 1,580 137 17.2 3,880 140 9.4 10.4 Germany 82 357 236 3,032.6 3 36,810 21 2,692.3 32,680 32 2.8 2.9 Ghana 23 239 101 11.8 103 510 177 28.4 1,240 179 6.2 4.0 Greece 11 132 86 305.3 26 27,390 34 344.1 30,870 36 4.3 3.9 Guatemala 13 109 120 33.7 67 2,590 123 66.7d 5,120 d 124 4.5 1.9 Guinea 9 246 37 3.7 147 400 186 10.4 1,130 184 2.8 0.8 Guinea-Bissau 2 36 59 0.3 203 190 202 0.8 460 205 4.2 1.1 Haiti 9 28 343 4.0 144 430 185 10.1d 1,070 d 187 2.3 0.7 14 2008 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 2006 2006 2006 2006b 2005 2006b 2006 2006 2006 2006 2005­06 2005­06 Honduras 7 112 62 8.8 113 1,270 146 23.9d 3,420 d 147 6.0 4.0 Hungary 10 93 112 109.5 47 10,870 62 170.8 16,970 61 3.9 4.1 India 1,110 3,287 373 909.1 10 820 161 2,726.3 2,460 155 9.2 7.7 Indonesia 223 1,905 123 315.9 24 1,420 140 737.2 3,310 149 5.5 4.3 Iran, Islamic Rep. 70 1,745 43 205.0 30 2,930 116 686.9 9,800 87 4.6 3.1 Iraq .. 438 .. .. .. ..f .. .. .. .. 46.5 .. Ireland 4 70 62 191.3 34 44,830 10 148.2 34,730 19 5.7 3.0 Israel 7 22 326 142.2 38 20,170 44 168.1 23,840 49 5.1 3.2 Italy 59 301 200 1,882.5 7 31,990 28 1,704.9 28,970 38 1.9 1.5 Jamaica 3 11 246 9.5 107 3,560 104 18.8d 7,050 d 107 2.5 2.0 Japan 128 378 350 4,934.7 2 38,630 19 4,195.9 32,840 31 2.2 2.2 Jordan 6 89 63 14.7 99 2,650 122 26.7 4,820 129 5.7 3.3 Kazakhstan 15 2,725 6 59.2 57 3,870 99 133.2 8,700 96 10.7 9.5 Kenya 37 580 64 21.3 83 580 175 53.8 1,470 176 6.1 3.3 Korea, Dem. Rep. 24 121 197 .. .. ..c .. .. .. .. .. .. Korea, Rep. 48 99 490 856.6 12 17,690 51 1,113.0 22,990 50 5.0 4.7 Kuwait 3 18 146 77.7 .. 30,630 .. 122.5 48,310 .. 8.5 5.3 Kyrgyz Republic 5 200 27 2.6 157 500 178 9.3 1,790 167 2.7 1.7 Lao PDR 6 237 25 2.9 155 500 178 10.0 1,740 169 7.6 5.8 Latvia 2 65 37 18.5 85 8,100 71 33.9 14,840 67 11.9 12.6 Lebanon 4 10 396 22.6 81 5,580 80 38.9 9,600 89 0.0 ­1.1 Lesotho 2 30 66 2.0 167 980 155 3.6 1,810 166 7.2 6.4 Liberia 4 111 37 0.5 195 130 207 0.9 260 208 7.8 3.7 Libya 6 1,760 3 44.0 61 7,290 75 70.2d 11,630 d 79 5.6 3.5 Lithuania 3 65 54 26.9 78 7,930 73 49.4 14,550 68 7.7 8.3 Macedonia, FYR 2 26 80 6.3 128 3,070 109 16.0 7,850 102 3.0 2.9 Madagascar 19 587 33 5.3 134 280 197 16.6 870 193 4.9 2.1 Malawi 14 118 144 3.1 152 230 201 9.4 690 196 7.4 4.7 Malaysia 26 330 79 146.8 37 5,620 79 317.4 12,160 75 5.9 4.0 Mali 12 1,240 10 5.6 133 460 181 11.9 1,000 189 5.3 2.2 Mauritania 3 1,031 3 2.3 163 760 165 6.0 1,970 164 11.7 8.7 Mauritius 1 2 617 6.8 124 5,430 82 13.3 10,640 83 3.5 2.7 Mexico 104 1,964 54 815.7 14 7,830 74 1,249.2 11,990 76 4.8 3.6 Moldova 4 34 117 3.7h 149 1,080h 151 10.2 2,660 152 4.0 5.2 Mongolia 3 1,567 2 2.6 158 1,000i 153 7.3 2,810 150 8.6 7.3 Morocco 30 447 68 65.8 56 2,160 128 117.7 3,860 141 8.0 6.7 Mozambique 21 799 27 6.5 126 310 193 13.9 660 199 8.0 5.7 Myanmar 48 677 74 .. .. ..c .. .. .. .. 5.0 4.1 Namibia 2 824 2 6.6 125 3,210 107 9.8 4,770 130 2.9 1.6 Nepal 28 147 193 8.8 114 320 192 27.8 1,010 188 2.8 0.8 Netherlands 16 42 482 703.5 16 43,050 13 620.0 37,940 15 2.9 2.7 New Zealand 4 268 16 112.0 45 26,750 37 107.7 25,750 44 1.9 0.7 Nicaragua 6 130 46 5.2 135 930 156 15.1d 2,720 d 151 3.7 2.4 Niger 14 1,267 11 3.7 148 270 198 8.6 630 200 4.8 1.2 Nigeria 145 924 159 90.0 52 620 173 203.7 1,410 177 5.2 2.8 Norway 5 324 15 318.9 23 68,440 2 233.3 50,070 4 2.9 2.1 Oman 3 310 8 27.9 .. 11,120 j .. 49.5 19,740 .. 5.8 4.6 Pakistan 159 796 206 126.7 42 800 162 382.8 2,410 156 6.9 4.7 Panama 3 76 44 16.4 93 5,000 89 28.6d 8,690 d 98 8.1 6.3 Papua New Guinea 6 463 14 4.6 141 740 168 10.1d 1,630 d 170 2.6 0.4 Paraguay 6 407 15 8.5 115 1,410 141 24.3 4,040 137 4.3 2.2 Peru 28 1,285 22 82.2 54 2,980 113 179.2 6,490 110 7.7 6.5 Philippines 86 300 289 120.2 44 1,390 142 296.2 3,430 146 5.4 3.4 Poland 38 313 124 313.0 25 8,210 70 543.4 14,250 71 6.1 6.2 Portugal 11 92 116 189.0 35 17,850 50 211.3 19,960 57 1.3 0.9 Puerto Rico 4 9 443 .. .. ..k .. .. .. .. .. .. 2008 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 2006 2006 2006 2006b 2005 2006b 2006 2006 2006 2006 2005­06 2005­06 Romania 22 238 94 104.4 48 4,830 91 219.2 10,150 85 7.7 7.9 Russian Federation 143 17,098 9 822.3 13 5,770 78 1,814.9 12,740 74 6.7 7.2 Rwanda 9 26 384 2.3 162 250 199 6.9 730 195 5.3 2.7 Saudi Arabia 24 2,000l 12 331.0 21 13,980 55 528.0 22,300 52 4.3 1.8 Senegal 12 197 63 9.1 110 760 165 18.8 1,560 172 2.3 ­0.3 Serbia 7m 88 96m 30.0m 75 4,030m 97 69.3 9,320 90 5.7 5.8 Sierra Leone 6 72 80 1.4 175 240 200 3.5 610 202 7.4 4.4 Singapore 4 1 6,508 128.8 41 28,730 33 194.1 43,300 9 7.9 4.5 Slovak Republic 5 49 112 51.8 60 9,610 64 91.9 17,060 60 8.3 8.2 Slovenia 2 20 100 37.4 64 18,660 49 48.1 23,970 48 5.2 4.9 Somalia 8 638 13 .. .. ..c .. .. .. .. .. .. South Africa 47 1,219 39 255.4 28 5,390 84 421.7 8,900 94 5.0 3.9 Spain 44 505 88 1,206.2 8 27,340 35 1,244.2 28,200 39 3.9 2.2 Sri Lanka 20 66 308 26.0 79 1,310 144 74.2 3,730 143 7.4 6.2 Sudan 38 2,506 16 30.1 74 800 162 67.2 1,780 168 11.8 9.4 Swaziland 1 17 66 2.7 156 2,400 124 5.3 4,700 132 2.1 1.5 Sweden 9 450 22 395.4 19 43,530 12 311.7 34,310 20 4.2 3.5 Switzerland 7 41 187 434.8 17 58,050 6 305.9 40,840 11 3.2 2.5 Syrian Arab Republic 19 185 106 30.3 72 1,560 138 79.7 4,110 136 5.1 2.3 Tajikistan 7 143 47 2.6 159 390 187 10.3 1,560 172 7.0 5.6 Tanzania 39 947 45 13.4n 100 350n 188 38.8 980 190 5.9 3.3 Thailand 63 513 124 193.7 33 3,050 110 472.2 7,440 104 5.0 4.3 Timor-Leste 1 15 69 0.9 185 840 160 5.2d 5,100 d 125 ­1.6 ­6.7 Togo 6 57 118 2.3 165 350 188 4.9 770 194 4.1 1.3 Trinidad and Tobago 1 5 259 16.6 90 12,500 57 22.3d 16,800 d 62 12.0 11.6 Tunisia 10 164 65 30.1 73 2,970 115 65.7 6,490 110 5.2 4.2 Turkey 73 784 95 393.9 20 5,400 83 613.7 8,410 99 6.1 4.8 Turkmenistan 5 488 10 .. .. ..f .. 19.3d 3,990d .. .. .. Uganda 30 241 152 9.0 112 300 195 26.3 880 192 5.4 2.1 Ukraine 47 604 81 90.7 51 1,940 132 286.0 6,110 115 7.1 7.8 United Arab Emirates 4 84 51 103.5 .. 26,210 .. 123.1d 31,190d .. 8.5 4.3 United Kingdom 61 244 250 2,455.7 5 40,560 17 2,037.2 33,650 29 2.8 2.2 United States 299 9,632 33 13,386.9 1 44,710 11 13,195.7 44,070 8 2.9 1.9 Uruguay 3 176 19 17.6 89 5,310 86 32.9 9,940 86 7.0 6.7 Uzbekistan 27 447 62 16.2 94 610 174 58.1d 2,190 d 159 7.3 5.8 Venezuela, RB 27 912 31 164.0 36 6,070 77 296.4 10,970 82 10.3 8.5 Vietnam 84 329 271 58.5 58 700 169 194.4 2,310 157 8.2 6.9 West Bank and Gaza 4 6 627 4.5 .. 1,230 .. 14.0 d 3,720 d 144 1.4 ­2.6 Yemen, Rep. 22 528 41 16.4 92 760 165 45.5 2,090 162 3.3 0.3 Zambia 12 753 16 7.4 120 630 172 13.4 1,140 182 6.2 4.2 Zimbabwe 13 391 34 4.5 .. 340 .. 2.2 170 .. ­5.3 ­6.0 World 6,538 s 133,946 s 50 w 48,694.1 t 7,448 w 60,210 t 9,209 w 3.8 w 2.6 w Low income 2,420 29,220 86 1,570.8 649 4,501 1,860 8.0 6.1 Middle income 3,088 70,112 45 9,426.9 3,053 19,920 6,451 7.2 6.3 Lower middle income 2,276 28,646 81 4,639.8 2,038 11,152 4,899 8.8 7.9 Upper middle income 811 41,466 20 4,797.3 5,913 8,826 10,879 5.7 4.9 Low & middle income 5,507 99,332 57 10,997.7 1,997 24,430 4,436 7.3 6.0 East Asia & Pacific 1,899 16,300 120 3,524.7 1,856 8,277 4,359 9.4 8.6 Europe & Central Asia 461 24,114 20 2,217.1 4,815 4,509 9,791 6.8 6.7 Latin America & Carib. 556 20,421 28 2,661.2 4,785 4,828 8,682 5.5 4.2 Middle East & N. Africa 311 9,087 35 778.8 2,507 2,084 6,710 5.1 3.3 South Asia 1,499 5,140 314 1,151.3 768 3,432 2,289 8.7 7.0 Sub-Saharan Africa 782 24,270 33 647.9 829 1,314 1,681 5.6 3.0 High income 1,031 34,614 31 37,731.7 36,608 36,005 34,933 2.9 2.2 Euro area 317 2,536 128 10,864.1 34,307 9,874 31,181 2.7 2.2 a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be low income ($905 or less). d. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. e. Includes Taiwan, China; Macao, China; and Hong Kong, China. f. Estimated to be lower middle income ($906­$3,595). g. Includes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. h. Excludes Transnistria. i. Included in the aggregates for low-income economies based on earlier data. j. Included in the aggregates for upper middle-income economies based on earlier data. k. Estimated to be high income ($11,116 or more). l. Provisional estimate. m. Excludes Kosovo and Metohija. n. Covers mainland Tanzania only. 16 2008 World Development Indicators 1.1 WORLD VIEW Size of the economy About the data Definitions Population, land area, income, output, and growth in allowing comparison of real levels of expenditure · Population is based on the de facto definition of output are basic measures of the size of an economy. between countries, just as conventional price population, which counts all residents regardless of They also provide a broad indication of actual and indexes allow comparison of real values over time. legal status or citizenship--except for refugees not potential resources. Population, land area, income The PPP conversion factors used are derived from the permanently settled in the country of asylum, who (as measured by gross national income, GNI) and out- 2005 round of price surveys covering 146 economies are generally considered part of the population of put (as measured by gross domestic product, GDP) conducted by the International Comparison Program. their country of origin. The values shown are midyear are therefore used throughout World Development For Organisation for Economic Co-operation and estimates. See also table 2.1. · Surface area is Indicators to normalize other indicators. Development (OECD) countries data come from the a country's total area, including areas under inland Population estimates are generally based on most recent round of surveys, completed in 2005. bodies of water and some coastal waterways. · Pop- extrapolations from the most recent national cen- Estimates for economies not included in the surveys ulation density is midyear population divided by land sus. For further discussion of the measurement of are derived from statistical models using available area in square kilometers. · Gross national income population and population growth, see About the data data. (GNI) is the sum of value added by all resident pro- for table 2.1 and Statistical methods. For more information on the results of the 2005 ducers plus any product taxes (less subsidies) not The surface area of an economy includes inland International Comparison Program, see the introduc- included in the valuation of output plus net receipts bodies of water and some coastal waterways. Sur- tion to World View. The final report of the program is of primary income (compensation of employees and face area thus differs from land area, which excludes available at www.worldbank.org/data/icp. property income) from abroad. Data are in current bodies of water, and from gross area, which may All 209 economies shown in World Development U.S. dollars converted using the World Bank Atlas include offshore territorial waters. Land area is par- Indicators are ranked by size, including those that method (see Statistical methods). · GNI per capita is ticularly important for understanding an economy's appear in table 1.6. The ranks are shown only in GNI divided by midyear population. GNI per capita in agricultural capacity and the environmental effects table 1.1. No rank is shown for economies for which U.S. dollars is converted using the World Bank Atlas of human activity. (For measures of land area and numerical estimates of GNI per capita are not pub- method. · Purchasing power parity (PPP) GNI is GNI data on rural population density, land use, and agri- lished. Economies with missing data are included in converted to international dollars using PPP rates. An cultural productivity, see tables 3.1­3.3.) Innova- the ranking at their approximate level, so that the rel- international dollar has the same purchasing power tions in satellite mapping and computer databases ative order of other economies remains consistent. over GNI that a U.S. dollar has in the United States. have resulted in more precise measurements of land · Gross domestic product (GDP) is the sum of value and water areas. added by all resident producers plus any product GNI measures total domestic and foreign value taxes (less subsidies) not included in the valuation added claimed by residents. GNI comprises GDP of output. Growth is calculated from constant price plus net receipts of primary income (compensation GDP data in local currency. · GDP per capita is GDP of employees and property income) from nonresident divided by midyear population. sources. The World Bank uses GNI per capita in U.S. dollars to classify countries for analytical purposes and to determine borrowing eligibility. For definitions of the income groups in World Development Indica- tors, see Users guide. For discussion of the useful- ness of national income and output as measures of Data sources productivity or welfare, see About the data for tables 4.1 and 4.2. Population estimates are prepared by World Bank When calculating GNI in U.S. dollars from GNI staff from a variety of sources (see Data sources reported in national currencies, the World Bank fol- for table 2.1). Data on surface and land area lows the World Bank Atlas conversion method, using are from the Food and Agriculture Organization a three-year average of exchange rates to smooth (see Data sources for table 3.1). GNI, GNI per the effects of transitory fluctuations in exchange capita, GDP growth, and GDP per capita growth rates. (For further discussion of the World Bank Atlas are estimated by World Bank staff based on method, see Statistical methods.) GDP and GDP per national accounts data collected by World Bank capita growth rates are calculated from data in con- staff during economic missions or reported by stant prices and national currency units. national statistical offices to other international Because exchange rates do not always reflect dif- organizations such as the OECD. PPP conversion ferences in price levels between countries, the table factors are estimates by World Bank staff based also converts GNI and GNI per capita estimates into on data collected by the International Comparison international dollars using purchasing power parity Program. (PPP) rates. PPP rates provide a standard measure 2008 World Development Indicators 17 1.2 Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Prevalence of in national Vulnerable malnutrition Ratio of girls to boys consumption employment Underweight Primary enrollments in primary Under-fi ve or income Unpaid family workers % of children completion ratea and secondary schoola mortality rate % 1992­ % of total employment under age 5 % % per 1,000 2005b,c 1990 2005 1990 2000­06b 1991 2006d 1991 2006d 1990 2006 Afghanistan .. .. .. .. .. .. .. 54 56 .. .. Albania 8.2 .. .. .. 17.0 .. 96 96 97 45 17 Algeria 7.0 .. 29 .. 10.2 80 85 83 99 69 38 Angola .. .. .. .. 27.5 35 .. .. .. 260 260 Argentina 3.1e .. 21 .. 2.3 .. 99 .. 104 29 16 Armenia 8.5 .. .. .. 4.2 90 91 .. 104 56 24 Australia 5.9 10 10 .. .. .. .. 101 97 10 6 Austria 8.6 .. 9 .. .. .. 103 95 97 10 5 Azerbaijan 7.4 .. .. .. 14.0 .. 92 100 96 105 88 Bangladesh 8.8 .. 63 .. 39.2 49 72 .. 103 149 69 Belarus 8.8 .. .. .. .. 94 95 .. 101 24 13 Belgium 8.5 .. 11 .. .. 79 .. 101 98 10 4 Benin 7.4 .. .. .. 21.5 21 65 49 73 185 148 Bolivia 1.5 40 62 8.9 5.9 .. 101 .. 98 125 61 Bosnia and Herzegovina 7.0 .. .. .. 4.2 .. .. .. .. 22 15 Botswana 3.2 .. 12 .. 10.7 89 95 109 100 58 124 Brazil 2.9 29 29 .. 3.7 93 105 .. 102 57 20 Bulgaria 8.7 .. 10 .. 1.6 84 99 99 97 19 14 Burkina Faso 6.9 .. .. .. 35.2 20 31 62 80 206 204 Burundi 5.1 .. .. .. 38.9 46 36 82 89 190 181 Cambodia 6.8 .. 87 .. 28.4 .. 87 73 89 116 82 Cameroon 5.6 .. .. .. 15.1 53 58 83 84 139 149 Canada 7.2 .. .. .. .. .. .. 99 98 8 6 Central African Republic 2.0 .. .. .. 21.8 27 24 60 .. 173 175 Chad .. 94 .. .. 33.9 18 31 42 61 201 209 Chile 3.8 .. 27 .. .. .. 123 100 98 21 9 China 4.3 .. .. .. 6.8 105 .. 87 100 45 24 Hong Kong, China 5.3 5 8 .. .. 102 .. 103 .. .. .. Colombia 2.9 28 44 .. 5.1 70 105 108 104 35 21 Congo, Dem. Rep. .. .. .. .. 33.6 46 38 .. 73 205 205 Congo, Rep. .. .. .. .. 11.8 54 73 85 90 103 126 Costa Rica 4.1 25 21 .. .. 79 89 101 102 18 12 Côte d'Ivoire 5.2 .. .. .. .. 43 43 65 .. 153 127 Croatia 8.8 .. 19 .. .. 85 92 102 101 12 6 Cuba .. .. .. .. .. 99 92 106 100 13 7 Czech Republic 10.3 7 12 .. 2.1 .. 102 98 101 13 4 Denmark 8.3 .. .. .. .. 98 99 101 102 9 5 Dominican Republic 4.1 39 43 8.4 4.2 61 83 .. 104 65 29 Ecuador 3.3 36 33 .. 6.2 91 106 .. 100 57 24 Egypt, Arab Rep. 8.9 28 26 .. 5.4 .. 98 81 93 91 35 El Salvador 2.7 35 36 7.2 6.1 41 88 102 99 60 25 Eritrea .. .. .. .. 34.5 19 48 .. 72 147 74 Estonia 6.8 2 5 .. .. 93 106 103 100 16 7 Ethiopia 9.1 .. 91 .. 34.6 26 49 68 81 204 123 Finland 9.6 .. .. .. .. 97 100 109 102 7 4 France 7.2 .. 7 .. .. 104 .. 102 100 9 4 Gabon .. 48 .. .. 8.8 58 75 .. .. 92 91 Gambia, The 4.8 .. .. .. 15.4 44 63 66 102 153 113 Georgia 5.4 .. 64 .. .. .. 85 98 103 46 32 Germany 8.5 .. 6 .. .. 100 95 99 99 9 4 Ghana 5.6 .. .. .. 18.8 61 71 79 95f 120 120 Greece 6.7 40 28 .. .. 99 100 99 99 11 4 Guatemala 3.9 .. 55 27.8 17.7 .. 77 .. 92 82 41 Guinea 7.0 .. .. .. 22.5 17 64 45 74 235 161 Guinea-Bissau 5.2 .. .. .. 21.9 .. .. .. .. 240 200 Haiti 2.4 .. .. .. 18.9 27 .. 94 .. 152 80 18 2008 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Prevalence of in national Vulnerable malnutrition Ratio of girls to boys consumption employment Underweight Primary enrollments in primary Under-fi ve or income Unpaid family workers % of children completion ratea and secondary schoola mortality rate % 1992­ % of total employment under age 5 % % per 1,000 2005b,c 1990 2005 1990 2000­06b 1991 2006d 1991 2006d 1990 2006 Honduras 3.4 49 49 .. 8.6 64 89 106 109 58 27 Hungary 8.6 7 8 2.3 .. 93 94 100 99 17 7 India 8.1 .. .. .. 43.5 64 85 70 91 115 76 Indonesia 7.1 .. .. 31.0 24.4 91 99 93 97 91 34 Iran, Islamic Rep. 6.5 .. .. .. .. 91 101 85 105 72 34 Iraq .. .. .. .. .. 59 .. 78 78 53 .. Ireland 7.4 20 12 .. .. .. 97 104 103 9 5 Israel 5.7 .. 8 .. .. .. 101 105 100 12 5 Italy 6.5 16 13 .. .. 104 100 100 99 9 4 Jamaica 5.3 42 34 .. 3.1 90 82 102 101 33 31 Japan 10.6 19 12 .. .. 101 .. 101 100 6 4 Jordan 6.7 .. .. .. 3.6 72 100 101 102 40 25 Kazakhstan 7.4 .. 36 .. .. .. 101f 102 99 f 60 29 Kenya 6.0 .. .. 20.1 16.5 .. 93 94 96 97 121 Korea, Dem. Rep. .. .. .. .. 17.8 .. .. .. .. 55 55 Korea, Rep. 7.9 .. 26 .. .. 98 101 99 96 9 5 Kuwait .. .. .. .. .. .. 91 97 102 16 11 Kyrgyz Republic 8.9 .. 50 .. .. .. 99 .. 100 75 41 Lao PDR 8.1 .. .. .. 36.4 43 75 76 85 163 75 Latvia 6.8 .. 8 .. .. .. 92 101 99 18 9 Lebanon .. .. .. .. .. .. 80 .. 103 37 30 Lesotho 1.5 38 .. .. 16.6 59 78 123 104 101 132 Liberia .. .. .. .. 22.8 .. 63 .. .. 235 235 Libya .. .. .. .. .. .. .. .. 105 41 18 Lithuania 6.8 .. .. .. .. 89 91 .. 100 13 8 Macedonia, FYR 6.1 .. 22 .. 1.2 98 97 99 99 38 17 Madagascar 4.9 .. 82 35.5 36.8 33 57 98 96 168 115 Malawi 7.0 .. .. 24.4 18.4 29 55 81 100 221 120 Malaysia 4.4 .. 20 .. .. 91 95 101 105 22 12 Mali 6.1 .. .. 29.0 30.1 13 49 57 74 250 217 Mauritania 6.2 .. .. .. 30.4 34 47 71 102 133 125 Mauritius .. .. 17 .. .. 107 92 102 103 23 14 Mexico 4.3 37 31 13.9 3.4 88 103 97 99 53 35 Moldova 7.8 .. 36 .. 3.2 .. 90 106 102 37 19 Mongolia 7.5 .. 60 .. 4.8 .. 109 109 108 109 43 Morocco 6.5 .. 58 8.1 9.9 48 84 70 87 89 37 Mozambique 5.4 .. .. .. 21.2 26 42 71 85 235 138 Myanmar .. .. .. .. 29.6 .. 95 97 101 130 104 Namibia 1.4 .. .. .. 20.3 78 76 106 104 86 61 Nepal 6.0 .. .. .. 38.8 51 76 59 93 142 59 Netherlands 7.6 .. .. .. .. .. 100 97 98 9 5 New Zealand 6.4 13 12 .. .. 100 .. 100 104 11 6 Nicaragua 5.6 .. 38 .. 7.8 42 73 109 102 68 36 Niger 2.6 .. .. 41.0 39.9 18 33 53 70 320 253 Nigeria 5.0 .. .. 35.1 27.2 .. 76 77 83 230 191 Norway 9.6 .. .. .. .. 100 99 102 101 9 4 Oman .. .. .. .. .. 74 94 89 98 32 12 Pakistan 9.1 .. 61 39.0 31.3 .. 62 .. 78 130 97 Panama 2.5 34 32 .. .. 86 94 .. 101 34 23 Papua New Guinea 4.5 .. .. .. .. 46 .. 80 .. 94 73 Paraguay 2.4 23 50 2.8 .. 68 94 98 99 41 22 Peru 3.7 36 36 8.8 5.2 .. 100 96 101 78 25 Philippines 5.4 .. 45 .. 20.7 86 96 100 103 62 32 Poland 7.4 28 22 .. .. 98 97 101 99 18 7 Portugal 5.8 19 19 .. .. 95 104 103 102 14 5 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 19 1.2 Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Prevalence of in national Vulnerable malnutrition Ratio of girls to boys consumption employment Underweight Primary enrollments in primary Under-fi ve or income Unpaid family workers % of children completion ratea and secondary schoola mortality rate % 1992­ % of total employment under age 5 % % per 1,000 2005b,c 1990 2005 1990 2000­06b 1991 2006d 1991 2006d 1990 2006 Romania 8.2 27 33 .. 3.5 96 99 99 100 31 18 Russian Federation 6.1 1 6 .. .. 93 94 104 99 27 16 Rwanda 5.3 .. .. 24.3 18.0 35 35 92 102 176 160 Saudi Arabia .. .. .. .. .. 55 85 84 95 44 25 Senegal 6.6 83 .. 21.9 14.5 39 49 69 91 149 116 Serbia 8.3g .. .. .. .. .. .. .. .. .. 8 Sierra Leone 6.5 .. .. .. 24.7 .. 81f 67 86f 290 270 Singapore 5.0 8 9 .. 3.3 .. .. 95 101 8 3 Slovak Republic 8.8 .. 9 .. .. 96 94 .. 100 14 8 Slovenia 8.3 12 11 .. .. 95 99 .. 100 10 4 Somalia .. .. .. .. .. .. .. .. .. 203 145 South Africa 3.5 .. 19 .. .. 76 100 104 100 60 69 Spain 7.0 22 13 .. .. .. 103 104 103 9 4 Sri Lanka 7.0 .. 39 29.3 22.8 102 108 102 104 32 13 Sudan .. .. .. .. 38.4 42 47 77 89 120 89 Swaziland 4.3 .. .. .. 9.1 60 67 98 95 110 164 Sweden 9.1 .. .. .. .. 96 .. 102 100 7 3 Switzerland 7.6 9 10 .. .. 53 91 97 97 9 5 Syrian Arab Republic .. .. .. .. .. 89 115 85 95 38 14 Tajikistan 7.8 .. .. .. .. .. 106 .. 88 115 68 Tanzania 7.3 .. .. 25.1 16.7 62 85f 97 .. 161 118 Thailand 6.3 70 53 17.4 .. .. .. 97 104 31 8 Timor-Leste .. .. .. .. 40.6 .. .. .. 95 177 55 Togo .. .. .. 21.2 .. 35 67 59 73 149 108 Trinidad and Tobago 5.9 22 16 4.7 4.4 101 88 101 101 34 38 Tunisia 6.0 .. .. 8.5 .. 74 99 86 104 52 23 Turkey 5.3 .. 41 8.7 .. 90 86 81 89 82 26 Turkmenistan 6.1 .. .. .. .. .. .. .. .. 99 51 Uganda 5.7 .. 85 19.7 19.0 .. 54 82 98 160 134 Ukraine 9.0 .. .. .. 4.1 94 105 .. 99 25 24 United Arab Emirates .. .. .. .. .. 103 100 104 101 15 8 United Kingdom 6.1 .. .. .. .. .. .. 102 101 10 6 United States 5.4 .. .. .. 1.1 .. .. 100 100 11 8 Uruguay 5.0e .. 25 .. 6.0 94 93 .. 106 23 12 Uzbekistan 7.2 .. .. .. .. .. 98 94 98f 74 43 Venezuela, RB 3.3 .. 35 .. .. 43 96 105 103 33 21 Vietnam 7.1 .. 74 36.9 26.7 .. 92 .. 97 53 17 West Bank and Gaza .. .. 38 .. .. .. 89 .. 104 40 22 Yemen, Rep. 7.2 .. .. .. .. .. 60 .. 66 139 100 Zambia 3.6 65 79 21.2 23.3 .. 84 .. 96 180 182 Zimbabwe 4.6 .. 62 8.0 14.0 97 81 92 96 76 105 World .. w .. w .. w 23.5 w 79 w 86 w 86 w 95 w 92 w 73 w Low income .. .. .. 35.3 57 73 73 89 143 112 Middle income .. .. .. 9.5 93 97 91 99 56 33 Lower middle income .. .. .. 10.7 95 97 89 98 60 36 Upper middle income .. 24 .. .. 88 99 99 100 47 26 Low & middle income .. .. .. 24.5 78 85 84 94 101 79 East Asia & Pacific .. .. .. 12.9 101 98 89 99 56 29 Europe & Central Asia .. 18 .. .. 93 95 98 96 49 26 Latin America & Carib. 36 32 .. 5.1 82 99 99 101 55 26 Middle East & N. Africa .. .. .. .. 77 91 82 94 77 42 South Asia .. .. .. 41.0 62 80 70 90 123 83 Sub-Saharan Africa .. .. .. 27.0 51 60 79 86 184 157 High income .. .. .. .. .. 97 100 100 12 7 Euro area .. 12 .. .. 100 .. 101 .. 9 4 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 1999 onward. b. Data are for the most recent year available. c. See table 2.8 for survey year and whether share is based on income or consumption expenditure. d. Provisional data. e. Urban data. f. Data are for 2007. g. Includes Montenegro. 20 2008 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives About the data Definitions This table and the two following present indicators for undernourished mothers who give birth to under- · Share of poorest quintile in national consumption 17 of the 21 targets specified by the Millennium Devel- weight children. or income is the share of the poorest 20 percent of opment Goals. Each of the eight goals includes one or Progress toward universal primary education is the population in consumption or, in some cases, more targets, and each target has several associated measured by the primary completion rate. Because income. · Vulnerable employment is the sum of indicators for monitoring progress toward the target. many school systems do not record school comple- unpaid family workers and own-account workers as Most of the targets are set as a value of a specific indi- tion on a consistent basis, it is estimated from the a percentage of total employment. · Prevalence of cator to be attained by a certain date. In some cases gross enrollment rate in the final grade of primary malnutrition is the percentage of children under age the target value is set relative to a level in 1990. In oth- school, adjusted for repetition. Official enrollments five whose weight for age is more than two standard ers it is set at an absolute level. Some of the targets sometimes differ significantly from attendance, and deviations below the median for the international for goals 7 and 8 have not yet been quantified. even school systems with high average enrollment reference population ages 0­59 months. The data The indicators in this table relate to goals 1­4. ratios may have poor completion rates. are based on the new international child growth stan- Goal 1 has three targets between 1990 and 2015: Eliminating gender disparities in education would dards for infants and young children, called the Child to reduce by half the proportion of people whose help to increase the status and capabilities of women. Growth Standards, released in 2006 by the World income is less than $1 a day, to achieve full and The ratio of female to male enrollments in primary and Health Organization. · Primary completion rate is productive employment and decent work for all, secondary school provides an imperfect measure of the percentage of students completing the last year and to reduce by half the proportion of people who the relative accessibility of schooling for girls. of primary school. It is calculated as the total num- suffer from hunger. Estimates of poverty rates are The targets for reducing under-five mortality rates ber of students in the last grade of primary school, in table 2.7. The indicator shown here, the share are among the most challenging. Under-five mortal- minus the number of repeaters in that grade, divided of the poorest quintile in national consumption, ity rates are harmonized estimates produced by a by the total number of children of official graduation is a distributional measure. Countries with more weighted least squares regression model and are age. · Ratio of girls to boys enrollments in primary unequal distributions of consumption (or income) available at regular intervals for most countries. and secondary school is the ratio of the female to have a higher rate of poverty for a given average Most of the 60 indicators relating to the Millennium male gross enrollment rate in primary and secondary income. Vulnerable employment measures the Development Goals can be found in World Develop- school. · Under-five mortality rate is the probability portion of the labor force that receives the low- ment Indicators. Table 1.2a shows where to find the that a newborn baby will die before reaching age five, est wages and least security in employment. No indicators for the first four goals. For more informa- if subject to current age-specific mortality rates. The single indicator captures the concept of suffering tion about data collection methods and limitations, probability is expressed as a rate per 1,000. from hunger. Child malnutrition is a symptom of see About the data for the tables listed there. For inadequate food supply, lack of essential nutri- information about the indicators for goals 5, 6, 7, and ents, illnesses that deplete these nutrients, and 8, see About the data for tables 1.3 and 1.4. Location of indicators for Millennium Development Goals 1­4 1.2a Goal 1. Eradicate extreme poverty and hunger 1.1 Proportion of population below $1 a day 2.7* 1.2 Poverty gap ratio 2.7 1.3 Share of poorest quintile in national consumption 1.2, 2.8 1.4 Growth rate of GDP per person employed 2.4* 1.5 Employment to population ratio 2.4 1.6 Proportion of employed people living below $1 per day -- 1.7 Proportion of own-account and unpaid family workers in total employment 1.2, 2.4 Data sources 1.8 Prevalence of underweight in children under age five 1.2, 2.18, 2.20 1.9 Proportion of population below minimum level of dietary energy consumption 2.18 The indicators here and throughout this book have Goal 2. Achieve universal primary education been compiled by World Bank staff from primary 2.1 Net enrollment ratio in primary education 2.11 and secondary sources. Data on primary school 2.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.12 completion rates are provided by the United 2.3 Literacy rate of 15- to 24-year-olds 2.13 Goal 3. Promote gender equality and empower women Nations Educational, Scientifi c, and Cultural 3.1 Ratio of girls to boys in primary, secondary, and tertiary education 1.2, 2.11* Organization Institute of Statistics and national 3.2 Share of women in wage employment in the nonagricultural sector 1.5, 2.3* sources. Efforts have been made to harmonize 3.3 Proportion of seats held by women in national parliament 1.5 the data series used to compile this table with Goal 4. Reduce child mortality those published on the United Nations Millen- 4.1 Under-five mortality rate 1.2, 2.20, 2.21 4.2 Infant mortality rate 2.20, 2.21 nium Development Goals Web site (www.un.org/ 4.3 Proportion of one-year-old children immunized against measles 2.16, 2.20 millenniumgoals), but some differences in timing, -- No data are available in the World Development Indicators database. * Table shows information on related indicators. sources, and definitions remain. 2008 World Development Indicators 21 1.3 Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened Fixed-line and estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved mobile phone per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities subscribers live births ages 15­49 ages 15­49 people metric tons % % of population per 100 peoplea 2005 1990 2000­06b 2005 2006 1990 2004 2007 1990 2004 2006 Afghanistan .. .. .. .. .. .. .. 0.8 .. .. 10 Albania 92 .. 60 0.2 19 2.2 1.2 1.3 .. 91 60 Algeria 180 47 61 0.1 56 3.0 6.0 2.0 88 92 71 Angola 1,400 .. 6 3.7 285 0.4 0.5 1.4 29 31 14 Argentina 77 .. .. 0.6 39 3.4 3.7 1.8 81 91 105 Armenia 76 .. 53 0.1 72 1.2 1.2 0.9 .. 83 30 Australia 4 .. .. 0.1 6 16.3 16.2 3.7 100 100 143 Austria 4 .. .. 0.3 13 7.5 8.5 1.8 100 100 155 Azerbaijan 82 .. 55 0.1 77 7.5 3.8 0.8 .. 54 53 Bangladesh 570 40 58 <0.1 225 0.1 0.2 1.8 20 39 13 Belarus 18 .. 73 0.3 61 10.6 6.6 .. .. 84 96 Belgium 8 78 .. 0.3 13 10.1 9.7 1.4 .. .. 136 Benin 840 .. 17 1.8 90 0.1 0.3 1.5 12 33 13 Bolivia 290 30 58 0.1 198 0.8 0.8 0.8 33 46 36 Bosnia and Herzegovina 3 .. 36 <0.1 51 1.6 4.0 14.4 .. 95 73 Botswana 380 33 44 24.1 551 1.6 2.4 0.6 38 42 60 Brazil 110 59 .. 0.5 50 1.4 1.8 1.2 71 75 73 Bulgaria 11 .. .. <0.1 40 8.6 5.5 1.2 99 99 138 Burkina Faso 700 .. 17 2.0 248 0.1 0.1 0.9 7 13 8 Burundi 1,100 .. 9 3.3 367 0.0 0.0 1.6 44 36 2 Cambodia 540 .. 40 1.6 500 0.0 0.0 17.4 .. 17 8 Cameroon 1,000 16 29 5.5c 192 0.1 0.2 5.4 48 51 13 Canada 7 .. .. 0.3 5 15.0 20.0 2.0 100 100 117 Central African Republic 980 .. 19 10.7 345 0.1 0.1 0.7 23 27 3 Chad 1,500 .. 3 3.5 299 0.0 0.0 1.0 7 9 5 Chile 16 56 .. 0.3 15 2.7 3.9 2.3 84 91 96 China 45 85 87 0.1d 99 2.1 3.9 2.3 23 44 63 Hong Kong, China .. 86 .. .. 62 4.6 5.5 11.8 .. .. 193 Colombia 130 66 78 0.6 45 1.7 1.2 1.1 82 86 83 Congo, Dem. Rep. 1,100 8 21e 3.2 392 0.1 0.0 2.5 16 30 7 Congo, Rep. 740 .. 44 5.3 403 0.5 1.0 1.1 .. 27 14 Costa Rica 30 .. 96 0.3 14 0.9 1.5 1.8 .. 92 64 Côte d'Ivoire 810 .. 13 7.1 420 0.4 0.3 3.9 21 37 23 Croatia 7 .. 69 <0.1 40 5.1 5.3 1.7 100 100 142 Cuba 45 .. 73 0.1 9 3.0 2.3 4.0 98 98 10 Czech Republic 4 78 .. 0.1 10 15.6 11.5 1.8 99 98 147 Denmark 3 78 .. 0.2 8 9.7 9.8 1.6 .. .. 164 Dominican Republic 150 56 61 1.1 89 1.3 2.1 1.9 52 78 57 Ecuador 210 53 73 0.3 128 1.6 2.3 10.3 63 89 78 Egypt, Arab Rep. 130 47 59 <0.1 24 1.4 2.2 2.3 54 70 39 El Salvador 170 47 67 0.9 50 0.5 0.9 1.6 51 62 72 Eritrea 450 .. 8 2.4 94 .. 0.2 6.8 7 9 2 Estonia 25 .. .. 1.3 39 18.1 14.0 0.7 97 97 164 Ethiopia 720 4 15 1.4f 378 0.1 0.1 1.4 3 13 2 Finland 7 77 .. 0.1 5 10.3 12.6 1.2 100 100 144 France 8 81 .. 0.4 14 6.4 6.2 2.3 .. .. 140 Gabon 520 .. 33 7.9 354 6.5 1.1 2.0 .. 36 61 Gambia, The 690 12 18 2.4 257 0.2 0.2 2.1 .. 53 27 Georgia 66 .. 47 0.2 84 3.2 0.9 1.0 97 94 51 Germany 4 75 .. 0.1 6 12.3 9.8 2.2 100 100 168 Ghana 560 13 17 2.3 203 0.2 0.3 3.7 15 18 24 Greece 3 .. .. 0.2 18 7.1 8.7 1.9 .. .. 155 Guatemala 290 .. 43 0.9 79 0.6 1.0 2.3 58 86 65 Guinea 910 .. 9 1.5 265 0.2 0.2 2.2 14 18 2 Guinea-Bissau 1,100 .. 10 3.8 219 0.2 0.2 2.1 .. 35 7 Haiti 670 10 32 2.2g 299 0.1 0.2 2.2 24 30 7 22 2008 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened Fixed-line and estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved mobile phone per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities subscribers live births ages 15­49 ages 15­49 people metric tons % % of population per 100 peoplea 2005 1990 2000­06b 2005 2006 1990 2004 2007 1990 2004 2006 Honduras 280 47 65 1.5 76 0.5 1.1 3.2 50 69 42 Hungary 6 .. .. 0.1 19 5.8 5.7 2.1 .. 95 132 India 450 43 56 0.9 168 0.8 1.2 2.8 14 33 19 Indonesia 420 50 57 0.1 234 1.2 1.7 2.7 46 55 35 Iran, Islamic Rep. 140 49 74 0.2 22 4.0 6.4 0.9 83 .. 51 Iraq .. 14 .. .. .. 2.6 .. 8.0 81 .. 6 Ireland 1 60 .. 0.2 13 8.7 10.4 1.1 .. .. 159 Israel 4 68 .. 0.2 8 7.1 10.5 2.7 .. .. 162 Italy 3 .. .. 0.5 7 6.9 7.7 2.2 .. .. 165 Jamaica 170 55 69 1.5 7 3.3 4.0 7.4 75 80 118 Japan 6 58 56 <0.1 22 8.7 9.8 3.2 100 100 123 Jordan 62 40 56 0.2 5 3.2 3.1 1.7 93 93 90 Kazakhstan 140 .. 51 0.1 130 17.6 13.3 1.1 72 72 70 Kenya 560 27 39 6.1 384 0.2 0.3 3.4 40 43 19 Korea, Dem. Rep. 370 62 .. 0.2 178 12.1 3.4 1.4 .. 59 .. Korea, Rep. 14 79 .. <0.1 88 5.6 9.7 1.6 .. .. 139 Kuwait 4 .. .. 0.2 24 20.4 40.4 .. .. .. 114 Kyrgyz Republic 150 .. 48 0.1 123 2.8 1.1 0.8 60 59 19 Lao PDR 660 .. 32 0.1 152 0.1 0.2 1.1 .. 30 13 Latvia 10 .. .. 0.8 57 5.4 3.1 1.5 .. 78 124 Lebanon 150 .. 58 0.1 11 3.1 4.1 1.1 .. 98 44 Lesotho 960 23 37 23.4 c 635 .. .. 0.6 37 37 15 Liberia 1,200 .. 10 .. 331 0.2 0.1 3.6 39 27 .. Libya 97 .. .. 0.2 18 8.7 10.3 1.4 97 97 73 Lithuania 11 .. .. 0.2 62 6.6 3.9 .. .. .. 162 Macedonia, FYR 10 .. 14 <0.1 29 8.1 5.1 0.9 .. .. 94 Madagascar 510 17 27 0.5 248 0.1 0.2 5.5 14 32 6 Malawi 1,100 13 42 14.1 377 0.1 0.1 3.3 47 61 4 Malaysia 62 50 .. 0.5 103 3.1 7.0 5.5 .. 94 91 Mali 970 .. 8 1.7 280 0.1 0.1 1.1 36 46 13 Mauritania 820 3 8 0.7 316 1.4 0.9 .. 31 34 36 Mauritius 15 75 76 0.6 23 1.4 2.6 17.0 .. 94 90 Mexico 60 .. 71 0.3 21 5.0 4.3 3.0 58 79 74 Moldova 22 .. 68 1.1 141 5.4 2.0 1.4 .. 68 62 Mongolia 46 .. 66 <0.1 188 4.7 3.4 1.1 .. 59 28 Morocco 240 42 63 0.1 93 1.0 1.4 1.8 56 73 57 Mozambique 520 .. 17 16.1 443 0.1 0.1 2.1 20 32 11 Myanmar 380 17 34 1.3 171 0.1 0.2 1.9 24 77 1 Namibia 210 29 44 19.6 767 0.0 1.2 2.0 24 25 31 Nepal 830 23 48 0.5 176 0.0 0.1 1.1 11 35 6 Netherlands 6 76 .. 0.2 8 9.4 8.7 1.5 100 100 144 New Zealand 9 .. .. 0.1 9 6.6 7.7 5.2 .. .. 127 Nicaragua 170 .. 69 0.2 58 0.6 0.7 1.2 45 47 38 Niger 1,800 4 11 1.1 174 0.1 0.1 1.1 7 13 3 Nigeria 1,100 6 13 3.9 311 0.5 0.8 4.2 39 44 24 Norway 7 74 .. 0.1 6 7.8 19.1 1.5 .. .. 152 Oman 64 9 32 0.2 13 5.6 12.5 3.2 83 .. 82 Pakistan 320 15 28 0.1 181 0.6 0.8 1.4 37 59 25 Panama 130 .. .. 0.9 45 1.3 1.8 2.8 71 73 67 Papua New Guinea 470 .. .. 1.8 250 0.6 0.4 2.4 44 44 2 Paraguay 150 48 73 0.4 71 0.5 0.7 0.6 58 80 59 Peru 240 59 46 0.6 162 1.0 1.2 2.6 52 63 39 Philippines 230 36 49 <0.1 287 0.7 1.0 4.8 57 72 54 Poland 8 49 .. 0.1 25 9.1 8.0 1.4 .. .. 126 Portugal 11 .. .. 0.4 32 4.3 5.6 2.9 .. .. 155 Puerto Rico 18 .. .. .. 5 3.3 0.5 3.5 .. .. 112 2008 World Development Indicators 23 1.3 Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened Fixed-line and estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved mobile phone per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities subscribers live births ages 15­49 ages 15­49 people metric tons % % of population per 100 peoplea 2005 1990 2000­06b 2005 2006 1990 2004 2007 1990 2004 2006 Romania 24 .. 70 <0.1 128 6.7 4.2 1.7 .. .. 100 Russian Federation 28 34 .. 1.1 107 15.3 10.6 1.3 87 87 112 Rwanda 1,300 21 17 3.0 f 397 0.1 0.1 1.6 37 42 3 Saudi Arabia 18 .. .. 0.2 44 15.6 13.7 1.9 91 99 100 Senegal 980 .. 12 0.7f 270 0.4 0.4 2.1 33 57 27 Serbia 14h .. 41 0.2h 32h 12.4 6.6 2.0h 87h 87h 99 Sierra Leone 2,100 .. 5 1.6 517 0.1 0.2 3.3 .. 39 .. Singapore 14 65 .. 0.3 26 14.8 12.3 3.6 100 100 148 Slovak Republic 6 74 .. <0.1 15 9.7 6.7 1.3 99 99 112 Slovenia 6 .. .. <0.1 13 9.0 8.1 .. .. .. 132 Somalia 1,400 1 15 0.9 218 0.0 .. 1.9 .. 26 7 South Africa 400 57 60 18.8 940 9.4 9.4 1.6 69 65 83 Spain 4 .. .. 0.6 30 5.5 7.7 3.8 100 100 146 Sri Lanka 58 .. 70 <0.1 60 0.2 0.6 12.0 69 91 37 Sudan 450 9 8 1.6 242 0.2 0.3 1.5 33 34 14 Swaziland 390 20 48 33.4 1,155 0.6 0.9 0.8 .. 48 26 Sweden 3 .. .. 0.2 6 5.8 5.9 1.4 100 100 165 Switzerland 5 .. .. 0.4 7 6.4 5.5 1.3 100 100 166 Syrian Arab Republic 130 .. 58 0.2 32 2.8 3.7 1.7 73 90 41 Tajikistan 170 .. 38 0.1 204 4.4 0.8 0.8 .. 51 8 Tanzania 950 10 26 6.5 312 0.1 0.1 4.7 47 47 15 Thailand 110 .. 77 1.4 142 1.8 4.3 1.9 80 99 75 Timor-Leste 380 .. 10 0.2 556 .. 0.2 .. .. 36 .. Togo 510 34 17 3.2 389 0.2 0.4 1.1 37 35 12 Trinidad and Tobago 45 .. 43 2.6 8 13.8 24.7 1.4 100 100 149 Tunisia 100 50 63 0.1 25 1.6 2.3 2.0 75 85 85 Turkey 44 63 71 0.2 29 2.6 3.2 1.3 85 88 98 Turkmenistan 130 .. 48 <0.1 65 8.7 8.7 11.2 .. 62 10 Uganda 550 5 24 6.4i 355 0.0 0.1 2.7 42 43 7 Ukraine 18 .. 66 1.4 106 13.2 6.9 1.1 .. 96 131 United Arab Emirates 37 .. .. 0.2 16 30.8 37.8 .. 97 98 161 United Kingdom 8 .. 84 0.2 15 10.1 9.8 2.2 .. .. 171 United States 11 71 .. 0.6 4 19.3 20.6 5.7 100 100 135 Uruguay 20 .. .. 0.5 27 1.3 1.7 2.4 100 100 100 Uzbekistan 24 .. 65 0.2 121 6.3 5.3 0.9 51 67 10 Venezuela, RB 57 .. .. 0.7 41 5.9 6.6 1.0 .. 68 85 Vietnam 150 53 76 0.5f 173 0.3 1.2 2.6 36 61 31 West Bank and Gaza .. .. 50 .. 20 .. .. .. .. 73 31 Yemen, Rep. 430 10 23 0.2 78 0.8 1.0 9.8 32 43 14 Zambia 830 15 34 17.0 553 0.3 0.2 0.8 44 55 15 Zimbabwe 880 43 60 18.1g 557 1.6 0.8 1.0 50 53 9 World 400 w 57 w 60 w 1.0 w 139 w 4.3 w 4.5 w 45 w 57 w 59 w Low income 650 33 44 1.7 221 0.8 0.9 21 38 17 Middle income 160 68 75 0.7 114 3.6 4.0 47 62 66 Lower middle income 180 73 76 0.3 116 2.3 3.4 37 55 60 Upper middle income 97 51 .. 1.7 109 6.9 5.6 77 81 88 Low & middle income 440 54 60 1.1 161 2.4 2.6 36 51 44 East Asia & Pacific 150 75 79 0.2 135 1.9 3.3 30 51 58 Europe & Central Asia 43 46 63 0.6 82 10.3 7.1 84 85 88 Latin America & Carib. 130 57 69 0.6 57 2.4 2.5 67 77 73 Middle East & N. Africa 200 41 60 0.1 42 2.5 3.9 70 76 53 South Asia 500 40 53 0.7 174 0.7 1.0 17 37 19 Sub-Saharan Africa 900 15 22 5.8 368 0.9 0.9 31 37 15 High income 9 71 .. 0.4 16 11.9 13.2 100 100 143 Euro area 5 .. .. 0.3 13 8.4 8.2 100 100 153 a. Data are from the International Telecommunication Union's World Telecommunication Development Report database. b. Data are for the most recent year available. c. Survey data, 2004. d. Includes Hong Kong, China. e. Data are for 2007. f. Survey data, 2005. g. Survey data, 2005­06. h. Includes Montenegro. i. Survey data, 2004­05. 24 2008 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment About the data Definitions The Millennium Development Goals address con- HIV/AIDS, which has a long latency between contrac- · Maternal mortality ratio is the number of women cerns common to all economies. Diseases and envi- tion of the virus and the appearance of symptoms, who die from pregnancy-related causes during preg- ronmental degradation do not respect national bound- or malaria, which has periods of dormancy, can be nancy and childbirth, per 100,000 live births. Data aries. Epidemic diseases, wherever they occur, pose particularly difficult. The table shows the estimated are from various years and adjusted to a common a threat to people everywhere. And environmental prevalence of HIV among adults ages 15­49. Preva- 2000 base year. The values are modeled estimates damage in one location may affect the well-being of lence among older populations can be affected by (see About the data for table 2.17). · Contracep- plants, animals, and humans far away. The indicators life-prolonging treatment. The incidence of tubercu- tive prevalence rate is the percentage of women in the table relate to goals 5, 6, and 7 and the targets losis is based on case notifications and estimates ages 15­49 married or in-union who are practicing, of goal 8 that address access to new technologies. of cases detected in the population. or whose sexual partners are practicing, any form of For the other targets of goal 8, see table 1.4. Carbon dioxide emissions are the primary source contraception. · HIV prevalence is the percentage The target of achieving universal access to repro- of greenhouse gases, which contribute to global of people ages 15­49 who are infected with HIV. ductive health has been added to goal 5 to address warming, threatening human and natural habitats. · Incidence of tuberculosis is the estimated number the importance of family planning and health service In recognition of the vulnerability of animal and plant of new tuberculosis cases (pulmonary, smear posi- in improving maternal health and preventing maternal species, a new target of reducing biodiversity loss tive, and extrapulmonary). · Carbon dioxide emis- death. Women with multiple pregnancies are more has been added to goal 7. sions are those stemming from the burning of fossil likely to die in childbirth. Access to contraception is Access to reliable supplies of safe drinking water and fuels and the manufacture of cement. They include an important way to limit and space births. sanitary disposal of excreta are two of the most impor- emissions produced during consumption of solid, Measuring the prevalence or incidence of a dis- tant means of improving human health and protecting liquid, and gas fuels and gas flaring (see table 3.8). ease can be difficult. Most developing economies the environment. Improved sanitation facilities prevent · Proportion of species threatened with extinction lack reporting systems for monitoring diseases. Esti- human, animal, and insect contact with excreta. is the total number of threatened mammal (exclud- mates are often derived from surveys and reports Fixed telephone lines and mobile phones are ing whales and porpoises), bird, and higher native, from sentinel sites that must be extrapolated to among the telecommunications technologies that vascular plant species as a percentage of the total the general population. Tracking diseases such as are changing the way the global economy works. number of known species of the same categories. · Access to improved sanitation facilities is the Location of indicators for Millennium Development Goals 5­7 1.3a percentage of the population with at least adequate access to excreta disposal facilities (private or Goal 5. Improve maternal health shared, but not public) that can effectively prevent 5.1 Maternal mortality ratio 1.3, 2.17 human, animal, and insect contact with excreta 5.2 Proportion of births attended by skilled health personnel 2.17, 2.20 (facilities do not have to include treatment to ren- 5.3 Contraceptive prevalence rate 1.3, 2.17, 2.20 5.4 Adolescent fertility rate 2.17 der sewage outflows innocuous). Improved facilities 5.5 Antenatal care coverage 1.5, 2.17, 2.20 range from simple but protected pit latrines to flush 5.6 Unmet need for family planning 2.17 toilets with a sewerage connection. To be effective, Goal 6. Combat HIV/AIDS, malaria, and other diseases facilities must be correctly constructed and properly 6.1 HIV prevalence among pregnant women ages 15­24 1.3*, 2.19* maintained. · Fixed-line and mobile phone subscrib- 6.2 Condom use at last high-risk sex 2.19* ers are telephone mainlines connecting a customer's 6.3 Proportion of population ages 15­24 with comprehensive correct -- equipment to the public switched telephone network knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of -- and users of portable telephones subscribing to an nonorphans ages 10­14 automatic public mobile telephone service using cel- 6.5 Proportion of population with advanced HIV infection with access -- lular technology that provides access to the public to antiretroviral drugs switched telephone network. 6.6 Incidence and death rates associated with malaria -- 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets and proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 2.16 6.8 Incidence, prevalence, and death rates associated with tuberculosis 1.3, 2.19 6.9 Proportion of tuberculosis cases detected and cured under directly observed treatment short course 2.16 Goal 7. Ensure environmental sustainability Data sources 7.1 Proportion of land area covered by forest 3.1 7.2 Carbon dioxide emissions, total, per capita, and per $1 GDP, and consumption The indicators here and throughout this book have of ozone-depleting substances 3.8 7.3 Proportion of fish stocks within safe biological limits -- been compiled by World Bank staff from primary 7.4 Proportion of total water resources used 3.5 and secondary sources. Efforts have been made 7.5 Proportion of terrestrial and marine areas protected 3.4 to harmonize the data series used to compile this 7.6 Proportion of species threatened with extinction 1.3 table with those published on the United Nations 7.7 Proportion of population using and improved drinking water source 1.3, 2.16, 3.5 Millennium Development Goals Web site (www. 7.8 Proportion of population using an improved sanitation facility 1.3, 2.16, 3.11 7.9 Proportion of urban population living in slums un.org/millenniumgoals), but some differences in -- No data are available in the World Development Indicators database. * Table shows information on related indicators. timing, sources, and definitions remain. 2008 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 2006 2006 1999 2005 1999 2005 1999 2005 1999 2005 2006b Australia 0.30 15.4 96.3 100.0 13.7 0.0 6.3 0.0 25.5 0.0 0.22 Canada 0.29 24.3 45.7 99.7 9.3 0.7 7.5 0.2 19.8 1.7 0.80 European Union 96.9 97.8 1.0 1.2 0.0 0.1 0.0 1.2 1.10 Austria 0.47 14.9 Belgium 0.50 18.5 Denmark 0.80 26.8 Finland 0.40 15.7 France 0.47 11.1 Germany 0.36 13.3 Greece 0.17 16.4 Ireland 0.54 22.8 Italy 0.20 11.6 Luxembourg 0.89 26.3 Netherlands 0.81 42.6 Portugal 0.21 4.8 Spain 0.32 13.4 Sweden 1.02 13.6 United Kingdom 0.21 12.9 Japan 0.25 18.6 58.0 23.2 3.7 2.5 5.1 2.8 0.4 0.1 1.11 New Zealandc 0.27 21.0 93.8 99.2 0.0 6.7 9.6 0.0 13.0 0.0 0.25 Norway 0.89 11.9 97.5 99.1 3.3 0.4 4.8 0.0 1.5 1.0 0.99 Switzerland 0.39 8.8 99.9 96.7 1.5 0.9 0.0 0.0 0.0 0.0 1.46 United States 0.18 13.5 53.4 76.7 9.4 7.9 7.1 5.7 14.3 11.7 0.73 Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC MDRI HIPC HIPC HIPC MDRI decision completion Initiative assistancef decision completion Initiative assistancef pointd pointd assistancee pointd pointd assistancee $ millions $ millions $ millions $ millions Afghanistan Jul. 2007 Floating 546 .. Haiti Nov. 2006 Floating 140 .. Benin Jul. 2000 Mar. 2003 344 570 Honduras Jul. 2000 Apr. 2005 729 1,474 Boliviag Feb. 2000 Jun. 2001 1,752 1,526 Madagascar Dec. 2000 Oct. 2004 1,096 1,205 Burkina Fasog,h Jul. 2000 Apr. 2002 725 564 Malawih Dec. 2000 Aug. 2006 1,278 662 Burundi Aug. 2005 Floating 864 .. Malig Sep. 2000 Mar. 2003 707 982 Cameroon Oct. 2000 Apr. 2006 1,662 687 Mauritania Feb. 2000 Jun. 2002 816 422 Central African Republic Sep. 2007 Floating 583 .. Mozambiqueg Apr. 2000 Sep. 2001 2,758 1,004 Chad May 2001 Floating 214 .. Nicaragua Dec. 2000 Jan. 2004 4,340 900 Congo, Dem. Rep. Jul. 2003 Floating 7,229 .. Nigerh Dec. 2000 Apr.2004 853 477 Congo, Rep. Apr. 2006 Floating 1,757 .. Rwandah Dec. 2000 Apr. 2005 872 200 Ethiopiah Nov. 2001 Apr. 2004 2,446 1,366 São Tomé & Principeh Dec. 2000 Mar. 2007 156 22 Gambia, The Dec. 2000 Dec. 2007 81 201 Senegal Jun. 2000 Apr. 2004 641 1,298 Ghana Feb. 2002 Jul. 2004 2,742 1,938 Sierra Leone Mar. 2002 Dec. 2006 809 316 Guinea Dec. 2000 Floating 716 .. Tanzania Apr. 2000 Nov. 2001 2,658 1,907 Guinea-Bissau Dec. 2000 Floating 546 .. Ugandag Feb. 2000 May 2000 1,349 1,713 Guyanag Nov. 2002 Dec. 2003 824 382 Zambia Dec. 2000 Apr. 2005 3,279 1,437 a. Includes basic health, education, nutrition, and water and sanitation services. b. Preliminary. c. Estimates of market access 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 Systems database. d. Refers to the Enhanced HIPC Initiative. e. Total HIPC assistance (committed debt relief) assuming full participation of creditors, in end-2006 net present value terms. Topping-up assistance and assistance provided under the original HIPC Initiative were committed in net present value terms as of the decision point and are converted to end-2006 terms. f. Multilateral Debt Relief Initiative (MDRI) assistance has been delivered in full to all post-completion point countries, shown in end-2006 net present value terms. g. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. h. Assistance includes topping up at completion point. 26 2008 World Development Indicators 1.4 WORLD VIEW Millennium Development Goals: overcoming obstacles About the data Definitions Achieving the Millennium Development Goals requires falling, averages may disguise high tariffs on specific · Net offi cial development assistance (ODA) is an open, rule-based global economy in which all goods (see table 6.7 for each country's share of tariff grants and loans (net of repayments of principal) countries, rich and poor, participate. Many poor lines with "international peaks"). The averages in the that meet the DAC definition of ODA and are made countries, lacking the resources to finance develop- table include ad valorem duties and equivalents. to countries and territories on the DAC list of recipi- ment, burdened by unsustainable debt, and unable Subsidies to agricultural producers and exporters ent countries. · ODA for basic social services is to compete globally, need assistance from rich coun- in OECD countries are another barrier to developing aid reported by DAC donors for basic health, educa- tries. For goal 8--develop a global partnership for economies' exports. The table shows the total sup- tion, nutrition, and water and sanitation services. development--many indicators therefore monitor the port to agriculture as a share of the economy's gross · Goods admitted free of tariffs are exports of goods actions of members of the Organisation for Economic domestic product (GDP). Agricultural subsidies in OECD (excluding arms) from least developed countries Co-operation and Development's (OECD) Develop- economies are estimated at $372 billion in 2006. admitted without tariff as a share of total exports ment Assistance Committee (DAC). The Debt Initiative for Heavily Indebted Poor Coun- from least developed countries. · Average tariff is Official development assistance (ODA) has risen tries (HIPCs), an important step in placing debt relief the unweighted average of the effectively applied in recent years as a share of donor countries' gross within the framework of poverty reduction, is the first rates for all products subject to tariffs. · Agricultural national income (GNI), but the poorest countries comprehensive approach to reducing the external products are plant and animal products, including need additional assistance to achieve the Millen- debt of the world's poorest, most heavily indebted tree crops but excluding timber and fish products. nium Development Goals. After rising to a record countries. A 1999 review led to an enhancement of · Textiles and clothing are natural and synthetic $106 billion in 2005, ODA fell 4.5 percent in 2006 the framework. In 2005, to further reduce the debt fi bers and fabrics and articles of clothing made to $104 billion in nominal terms. of HIPCs and provide resources for meeting the Mil- from them. · Support to agriculture is the value of One important action that high-income economies lennium Development Goals, the Multilateral Debt gross transfers from taxpayers and consumers aris- can take is to reduce barriers to low- and middle- Relief Initiative (MDRI), proposed by the Group of ing from policy measures that support agriculture, income economy exports. The European Union has Eight countries, was launched. Under the MDRI the net of associated budgetary receipts, regardless of begun to eliminate tariffs on developing country International Development Association (IDA), Interna- their objectives and impacts on farm production and exports of "everything but arms," and the United tional Monetary Fund (IMF), and African Development income or consumption of farm products. · HIPC States offers special concessions to Sub-Saharan Fund (AfDF) provide 100 percent debt relief on eligible decision point is the date when a heavily indebted African exports. However, these programs still have debts due to them from countries that completed poor country with an established track record of many restrictions. the HIPC Initiative process. Debt relief under the two good performance under adjustment programs sup- Average tariffs in the table reflect high-income OECD initiatives is expected to reduce the debt stocks of ported by the IMF and the World Bank commits to member tariff schedules for exports of countries the 32 HIPCs that have reached the decision point additional reforms and a poverty reduction strategy. designated least developed countries by the United by almost 90 percent. Twenty-two countries have · HIPC completion point is the date when a country Nations. Agricultural commodities, textiles, and cloth- reached the completion point and have received successfully completes the key structural reforms ing are three of the most important exports of devel- nearly $45 billion in HIPC Initiative assistance and agreed on at the decision point, including developing oping economies. Although average tariffs have been $42 billion in MDRI assistance in nominal terms. and implementing a poverty reduction strategy. The country then receives the bulk of debt relief under Location of indicators for Millennium Development Goal 8 1.4a the HIPC Initiative without further policy conditions. · HIPC Initiative assistance is the net present value Goal8. Develop a global partnership for development Table of debt relief committed as of the decision point and 8.1 Net ODA as a percentage of DAC donors' gross national income 1.4, 6.12 converted to end-2006 values. · MDRI assistance is 8.2 Proportion of ODA for basic social services 1.4, 6.13b* the net present value of debt relief from IDA, IMF, and 8.3 Proportion of ODA that is untied 6.13b 8.4 Proportion of ODA received in landlocked countries as a percentage of GNI -- AfDF, delivered to countries having reached the HIPC 8.5 Proportion of ODA received in small island developing states as a percentage of GNI -- completion point converted to end-2006 values. 8.6 Proportion of total developed country imports (by value, excluding arms) from least developed countries admitted free of duty 1.4 8.7 Average tariffs imposed by developed countries on agricultural products and Data sources textiles and clothing from least developed countries 1.4, 6.7* Data on ODA are from the OECD. Data on goods 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 1.4 admitted free of tariffs and average tariffs are from 8.9 Proportion of ODA provided to help build trade capacity -- the World Trade Organization, in collaboration with 8.10 Number of countries reaching HIPC decision and completion points 1.4 8.11 Debt relief committed under new HIPC initiative 1.4 the United Nations Conference on Trade and Devel- 8.12 Debt services as a percentage of exports of goods and services 6.9* opment and the International Trade Centre. These 8.13 Proportion of population with access to affordable, essential drugs on a data are available electronically at www.mdg-trade. sustainable basis -- org. Data on subsidies to agriculture are from the 8.14 Telephone lines per 100 people 1.3*, 5.10 OECD's Producer and Consumer Support Estimates, 8.15 Cellular subscribers per 100 people 1.3*, 5.10 OECD Database 1986­2006. Data on the HIPC Ini- 8.16 Internet users per 100 people 5.11 tiative and MDRI are from the World Bank's Eco- -- No data are available in the World Development Indicators database. * Table shows information on related indicators. nomic Policy and Debt Department. 2008 World Development Indicators 27 1.5 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of nonagricultural % of male % of female % of total Male Female % ages 15­19 wage employment employment employment % of total seats 2006 2006 2006 2000­06a 2000­06a 2005 2000­05a 2000­05a 1990 2007 Afghanistan .. .. .. 16 .. .. .. .. 4 27 Albania 50.0 73 80 97 .. 33 .. .. 29 7 Algeria 49.4 71 73 89 .. 14 7.2 7.2 2 8 Angola 50.7 41 44 66 .. .. .. .. 15 15 Argentina 50.8 71 79 99 .. 45 0.7b 1.9b 6 35 Armenia 53.2 68 75 93 5 .. 1.1 0.8 36 9 Australia 49.7 79 83 .. .. 49 0.2 0.4 6 25 Austria 50.5 77 83 .. .. 47 1.0 1.9 12 32 Azerbaijan 51.3 70 75 70 .. 49 .. .. .. 11 Bangladesh 48.8 63 65 48 33 .. 9.9 48.0 10 15 Belarus 53.2 63 74 99 .. 53 .. .. .. 29 Belgium 50.5 77 82 .. .. 45 0.4 3.4 9 35 Benin 49.6 55 57 84 21 .. .. .. 3 8 Bolivia 50.1 63 67 79 16 32 12.6 34.8 9 17 Bosnia and Herzegovina 51.2 72 77 99 .. .. .. .. .. 14 Botswana 50.3 50 50 97 .. 40 2.3 2.2 5 11 Brazil 50.5 69 76 97 .. .. 5.4b 9.1b 5 9 Bulgaria 51.0 69 76 .. .. 53 0.9 2.2 21 22 Burkina Faso 49.9 50 53 85 23 .. .. .. .. 15 Burundi 51.1 48 50 92 .. .. .. .. .. 31 Cambodia 51.2 57 61 69 8 52 31.6 53.3 .. 10 Cameroon 50.0 50 51 82 28 .. 9.5 27.2 14 14 Canada 50.0 78 83 .. .. 49 0.1 0.2 13 21 Central African Republic 51.2 43 46 69 .. .. .. .. 4 11 Chad 50.3 49 52 39 37 .. .. .. .. 7 Chile 50.3 75 81 .. .. 38 1.4 3.2 .. 15 China 48.2 70 74 90 .. .. .. .. 21 20 Hong Kong, China 51.6 79 85 .. .. 48 0.2 1.4 .. .. Colombia 50.6 69 76 94 21 48 3.5 7.7 5 8 Congo, Dem. Rep. 50.5 45 47 85c .. .. .. .. 5 8 Congo, Rep. 50.4 54 56 86 27 .. .. .. 14 7 Costa Rica 49.0 76 81 92 .. 40 1.7 3.5 11 39 Côte d'Ivoire 49.2 47 49 85 .. .. .. .. 6 9 Croatia 51.5 73 79 100 4 44 1.1d 3.6d .. 19 Cuba 49.5 76 80 100 .. 43 .. .. 34 36 Czech Republic 50.8 73 80 .. .. 47 0.3 1.3 .. 16 Denmark 50.0 76 80 .. .. 49 0.2 1.3 31 37 Dominican Republic 49.6 69 75 99 23 38 2.8 4.9 8 20 Ecuador 49.7 72 78 84 .. 42 3.0 b 9.4b 5 25 Egypt, Arab Rep. 49.8 69 73 70 9 20 9.4 32.2 4 2 El Salvador 50.8 69 75 86 .. 35 7.7 7.7 12 17 Eritrea 50.9 55 60 70 14 .. .. .. .. 22 Estonia 53.6 67 78 .. .. 53 0.3 0.2 .. 22 Ethiopia 50.2 51 54 28 17 41 34.6 68.5 .. 22 Finland 50.6 76 83 .. .. 51 0.6 0.4 32 42 France 50.7 77 84 .. .. 48 0.5 1.6 7 19 Gabon 49.9 56 57 94 33 .. .. .. 13 13 Gambia, The 49.8 58 60 98 .. .. .. .. 8 9 Georgia 52.5 67 75 94 .. 49 19.0 39.0 .. 9 Germany 50.7 76 82 .. .. 47 0.5 1.9 .. 32 Ghana 49.3 59 60 92 14 .. .. .. .. 11 Greece 49.9 77 82 .. .. 41 3.3 11.2 7 16 Guatemala 51.1 66 74 84 .. .. 21.3 24.5 7 12 Guinea 49.5 54 57 82 32 .. .. .. .. 19 Guinea-Bissau 50.5 45 48 78 .. .. .. .. 20 14 Haiti 50.4 59 62 85 14 .. .. .. .. 4 28 2008 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 nonagricultural % of male % of female % of total Male Female % ages 15­19 wage employment employment employment % of total seats 2006 2006 2006 2000­06a 2000­06a 2005 2000­05a 2000­05a 1990 2007 Honduras 50.2 66 73 92 22 45 12.1b 8.3b 10 23 Hungary 52.0 69 77 .. .. 49 0.3 0.7 21 10 India 48.1 63 66 74 .. 18 .. .. 5 8 Indonesia 49.9 66 70 92 10 .. .. .. 12 11 Iran, Islamic Rep. 49.2 69 72 .. .. .. .. .. 2 4 Iraq .. .. .. 84 .. .. .. .. 11 26 Ireland 49.8 77 82 .. .. 48 0.6 0.9 8 13 Israel 50.1 78 82 .. .. 49 0.2 0.5 7 14 Italy 50.7 78 84 .. .. 43 1.2 2.8 13 17 Jamaica 50.3 70 73 91 .. 47 0.4 2.5 5 13 Japan 50.5 79 86 .. .. 41 1.5 8.6 1 9 Jordan 48.5 71 74 99 4 .. .. .. 0 6 Kazakhstan 52.1 61 72 100 7 49 1.0 1.3 .. 16 Kenya 50.1 52 55 88 23 .. .. .. 1 7 Korea, Dem. Rep. 50.6 65 69 .. .. .. .. .. 21 20 Korea, Rep. 49.8 75 82 .. .. 42 1.3 14.0 2 13 Kuwait 39.8 76 80 .. .. .. .. .. .. 2 Kyrgyz Republic 50.6 64 72 97 .. 52 9.6 21.8 .. 0 Lao PDR 50.1 63 65 27 .. .. .. .. 6 25 Latvia 53.6 65 77 .. .. 53 2.5 2.1 .. 19 Lebanon 50.8 70 74 96 .. .. .. .. 0 5 Lesotho 52.9 43 43 90 20 .. .. .. .. 24 Liberia 50.0 44 46 85 .. .. .. .. .. 13 Libya 48.1 71 77 .. .. .. .. .. .. 8 Lithuania 53.1 65 77 .. .. 51 2.1 3.9 .. 25 Macedonia, FYR 49.9 72 76 98 .. 44 6.4 16.7 .. 28 Madagascar 50.2 57 61 80 34 46 29.7 51.9 7 8 Malawi 50.3 47 48 92 31 .. .. .. 10 14 Malaysia 49.1 72 76 79 .. 38 2.2 9.6 5 9 Mali 51.2 52 56 57 40 50 18.4 10.2 .. 10 Mauritania 49.3 62 66 64 16 .. .. .. .. 18 Mauritius 50.2 70 77 .. .. 37 0.9 4.7 7 17 Mexico 51.0 72 77 .. .. 39 5.5 11.0 12 23 Moldova 52.0 65 72 98 6 55 0.8 1.4 .. 22 Mongolia 50.0 66 69 99 .. 53 18.4 31.7 25 7 Morocco 50.7 69 73 68 7 22 22.8 55.7 0 11 Mozambique 51.5 42 43 85 41 .. .. .. 16 35 Myanmar 50.3 59 65 76 .. .. .. .. .. .. Namibia 50.6 52 53 91 18 .. 12.8 22.0 7 27 Nepal 50.4 63 64 44 19 .. .. .. 6 17 Netherlands 50.1 78 82 .. .. 47 0.2 1.0 21 37 New Zealand 50.3 78 82 .. .. 47 0.4 0.9 14 32 Nicaragua 50.0 70 76 86 25 .. 3.1 4.2 15 19 Niger 49.2 57 56 46 39 .. .. .. 5 12 Nigeria 50.0 46 47 58 25 21 .. .. .. 7 Norway 49.7 78 83 .. .. 49 0.2 0.3 36 38 Oman 44.0 74 77 100 .. .. .. .. .. 0 Pakistan 48.5 65 66 36 .. 10 18.3 52.8 10 21 Panama 49.4 73 78 .. .. 43 2.8 5.5 8 17 Papua New Guinea 49.2 55 60 .. .. .. .. .. 0 1 Paraguay 49.3 69 74 94 .. .. 10.9b 8.7b 6 10 Peru 49.8 69 74 92 26 38 1.6b 7.0 b 6 29 Philippines 49.6 69 74 88 8 42 8.9 18.7 9 22 Poland 51.4 71 80 .. .. 47 3.8 7.0 14 20 Portugal 51.1 75 82 .. .. 47 0.9 2.1 8 21 Puerto Rico 51.6 74 83 .. .. 40 0.1 0.9 .. .. 2008 World Development Indicators 29 1.5 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of nonagricultural % of male % of female % of total Male Female % ages 15­19 wage employment employment employment % of total seats 2006 2006 2006 2000­06a 2000­06a 2005 2000­05a 2000­05a 1990 2007 Romania 51.0 69 76 94 .. 46 7.8 21.2 34 11 Russian Federation 53.5 59 73 .. .. 51 0.1 0.1 .. 10 Rwanda 51.8 44 47 94 4 .. .. .. 17 49 Saudi Arabia 44.8 71 75 .. .. .. .. .. .. 0 Senegal 50.0 61 65 87 19 .. .. .. 13 22 Serbia 50.2 70 76 98 .. .. .. .. .. 20 Sierra Leone 50.7 41 44 81 .. 23 .. .. .. 13 Singapore 49.4 78 82 .. .. 48 0.3 1.2 5 25 Slovak Republic 51.2 70 78 .. .. 51 0.0 b 0.1b .. 19 Slovenia 50.9 74 81 .. .. 47 3.1 6.4 .. 12 Somalia 50.3 47 49 26 .. .. .. .. 4 8 South Africa 50.8 49 53 92 .. 43 0.4 1.1 3 33 Spain 50.1 78 84 .. .. 42 1.1 2.4 15 36 Sri Lanka 50.4 72 78 100 .. 40 4.2b 20.9b 5 5 Sudan 49.6 57 60 70 .. .. .. .. .. 18 Swaziland 51.6 42 40 90 .. .. .. .. 4 11 Sweden 49.6 79 83 .. .. 51 0.2 0.2 38 47 Switzerland 50.7 79 84 .. .. 47 1.3 2.9 14 30 Syrian Arab Republic 49.4 72 76 84 .. .. 10.8 44.2 9 12 Tajikistan 50.3 64 69 77 .. .. .. .. .. 18 Tanzania 50.2 51 53 78 26 .. .. .. .. 30 Thailand 51.1 66 75 98 .. 48 14.7 31.4 3 9 Timor-Leste 49.2 56 58 61 .. .. .. .. .. 28 Togo 50.5 56 60 89 .. .. .. .. 5 7 Trinidad and Tobago 50.6 68 72 96 .. 44 0.3 1.7 17 19 Tunisia 49.5 72 76 92 .. 25 .. .. 4 23 Turkey 49.5 69 74 81 .. 20 7.0 41.7 1 9 Turkmenistan 50.7 59 67 99 4 .. .. .. 26 16 Uganda 49.9 50 51 94 25 39 10.3b 40.5b 12 30 Ukraine 53.6 62 74 99 .. 55 0.5 0.5 .. 9 United Arab Emirates 32.2 77 82 .. .. .. .. .. 0 23 United Kingdom 50.4 77 81 .. .. 49 0.3 0.5 6 20 United States 50.3 75 81 .. .. 48 0.1 0.1 7 16 Uruguay 51.3 72 80 .. .. 48 0.7b 2.2b 6 11 Uzbekistan 50.2 64 71 99 .. .. .. .. .. 18 Venezuela, RB 49.6 72 77 94 .. .. 2.0 3.9 10 19 Vietnam 49.8 68 73 91 3 46 18.9 47.2 18 26 West Bank and Gaza 49.1 71 74 99 .. 18 6.4 32.2 .. .. Yemen, Rep. 49.4 61 64 41 .. .. .. .. 4 0e Zambia 50.1 41 42 93 32 .. .. .. 7 15 Zimbabwe 50.2 43 42 94 21 .. 10.4 13.6 11 17 World 49.4 w 66 w 70 w 80 w .. w .. w .. w 13 w 18 w Low income 49.0 59 62 69 24 .. .. 11 16 Middle income 49.6 68 73 90 .. .. .. 14 16 Lower middle income 49.0 69 73 89 .. .. .. 14 16 Upper middle income 51.0 67 74 .. 44 3.8 7.9 12 15 Low & middle income 49.3 64 68 80 .. .. .. 13 16 East Asia & Pacific 48.7 69 73 89 .. .. .. 17 18 Europe & Central Asia 51.9 65 74 91 48 2.8 6.9 .. 15 Latin America & Carib. 50.4 70 76 95 .. 4.6 8.4 12 20 Middle East & N. Africa 49.5 68 72 76 .. .. .. 4 9 South Asia 48.3 63 66 66 17 .. .. 6 14 Sub-Saharan Africa 50.2 49 52 72 .. .. .. .. 17 High income 50.1 76 82 .. 46 0.6 2.6 12 23 Euro area 50.5 77 83 .. 46 0.8 2.3 12 25 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2007. d. Data are for 2006. e. Less than 0.5. 30 2008 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 developing workloads at home and in nonpaid domestic and prevailing patterns of mortality at the time of its birth countries. Gender inequalities in the allocation of market activities, and labor market discrimination were to stay the same throughout its life. · Pregnant such resources as education, health care, nutrition, often limit women's participation in paid economic women receiving prenatal care are the percentage and political voice matter because of the strong activities, lower their productivity, and reduce their of women attended at least once during pregnancy association with well-being, productivity, and eco- wages. When women are in salaried employment, by skilled health personnel for reasons related to nomic growth. These patterns of inequality begin at they tend to be concentrated in the nonagricultural pregnancy. · Teenage mothers are the percentage of an early age, with boys routinely receiving a larger sector. However, in many developing countries women ages 15­19 who already have children or are share of education and health spending than do girls, women are a large part of agricultural employment, currently pregnant. · Women in nonagricultural sec- for example. often as unpaid family workers. Among people who tor are female wage employees in the nonagricultural Because of biological differences girls are are unsalaried, women are more likely than men to sector as a percentage of total nonagricultural wage expected to experience lower infant and child mor- be unpaid family workers, while men are more likely employment. · Unpaid family workers are those who tality rates and to have a longer life expectancy than women to be self-employed or employers. There work without pay in a market-oriented establishment than boys. This biological advantage, however, may are several reasons for this. or activity operated by a related person living in the be overshadowed by gender inequalities in nutri- Few women have access to credit markets, capital, same household. · Women in parliaments are the tion and medical interventions and by inadequate land, training, and education, which may be required percentage of parliamentary seats in a single or care during pregnancy and delivery, so that female to start a business. Cultural norms may prevent lower chamber held by women. rates of illness and death sometimes exceed male women from working on their own or from super- rates, particularly during early childhood and the vising other workers. Also, women may face time reproductive years. In high-income countries women constraints due to their traditional family respon- tend to outlive men by four to eight years on aver- sibilities. Because of biases and misclassification age, while in low-income countries the difference is substantial numbers of employed women may be narrower--about two to three years. The difference underestimated or reported as unpaid family workers in child mortality rates (table 2.21) is another good even when they work in association or equally with indicator of female social disadvantage because their husbands in the family enterprise. nutrition and medical interventions are particularly Women are vastly underrepresented in decision- important for the 1­4 age group. Female child mor- making positions in government, although there is tality rates that are as high as or higher than male some evidence of recent improvement. Gender parity child mortality rates may indicate discrimination in parliamentary representation is still far from being against girls. realized. In 2007 women accounted for 18 percent Having a child during the teenage years limits of parliamentarians worldwide, compared with 9 per- girls' opportunities for better education, jobs, and cent in 1987. Without representation at this level, it income. Pregnancy is more likely to be unintended is difficult for women to influence policy. during the teenage years, and births are more likely For information on other aspects of gender, see Data sources to be premature and are associated with greater tables 1.2 (Millennium Development Goals: eradi- risks of complications during delivery and of death. cating poverty and saving lives), 2.3 (Employment Data on female population and life expectancy are In many countries maternal mortality (tables 1.3 and by economic activity), 2.4 (Decent work and produc- from the World Bank's population database. Data 2.17) is a leading cause of death among women of tive employment), 2.5 (Unemployment), 2.6 (Children on pregnant women receiving prenatal care are reproductive age. Most maternal deaths result from at work), 2.9 (Assessing vulnerability and security), from household surveys, including Demographic preventable causes--hemorrhage, infection, and 2.12 (Education efficiency), 2.13 (Education comple- and Health Surveys by Macro International and complications from unsafe abortions. Prenatal care tion and outcomes), 2.14 (Education gaps by income Multiple Indicator Cluster Surveys by the United is essential for recognizing, diagnosing, and promptly and gender), 2.17 (Reproductive health), 2.19 Nations Children's Fund (UNICEF), and UNICEF's treating complications that arise during pregnancy. (Health risk factors and public health challenges), State of the World's Children 2008. Data on teen- In high-income countries most women have access 2.20 (Health gaps by income and gender), and 2.21 age mothers are from Demographic and Health to health care during pregnancy, but in developing (Mortality). Surveys by Macro International. Data on labor countries an estimated 200 million women suffer force and employment are from the International pregnancy-related complications, and over half a mil- Labour Organization's Key Indicators of the Labour lion die every year (Glasier and others 2006). This Market, fi fth edition. Data on women in parlia- is reflected in the differences in maternal mortality ments are from the Inter-Parliamentary Union. ratios between high- and low-income countries. 2008 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 2006 2006 2006 2006b 2006b 2006 2006 2005­06 2005­06 2006 2005 2004 American Samoa 60 0.2 298 .. ..c .. .. .. .. .. .. 41 Andorra 67 0.5 142 .. ..d .. .. .. .. .. .. .. Antigua and Barbuda 84 0.4 191 929 11,050e 1,273f 15,130 f 11.5 10.1 .. .. 414 Aruba 101 0.2 562 .. ..d .. .. .. .. .. .. 2,154 Bahamas, The 327 13.9 33 .. ..d ..f ..f .. .. 73 .. 2,007 Bahrain 739 0.7 1,041 14,022 19,350 24,869 34,310 7.8 5.6 76 .. 16,934 Barbados 293 0.4 681 .. ..d 4,422 f 15,150 f .. .. 77 .. 1,267 Belize 298 23.0 13 1,114 3,740 2,108f 7,080 f 5.6 3.5 72 .. 791 Bermuda 64 0.1 1,276 .. ..d .. .. .. .. 79 .. 549 Bhutan 649 47.0 14 928 1,430 2,596 4,000 8.5 6.5 65 60 414 Brunei Darussalam 382 5.8 72 10,287 26,930 19,059 49,900 5.1 2.9 77 .. 8,802 Cape Verde 519 4.0 129 1,105 2,130 1,344 2,590 6.1 3.7 71 81 275 Cayman Islands 46 0.3 177 .. ..d .. .. .. .. .. .. 311 Channel Islands 149 0.2 784 .. ..d .. .. .. .. 79 .. .. Comoros 614 1.9 330 406 660 698 1,140 0.5 ­1.6 63 .. 88 Cyprus 771 9.3 83 17,948 23,270 19,328 25,060 4.0 2.2 79 .. 6,744 Djibouti 819 23.2 35 864 1,060 1,787 2,180 4.9 3.0 54 .. 366 Dominica 72 0.8 97 300 4,160 566f 7,870 f 4.0 3.4 .. .. 106 Equatorial Guinea 496 28.1 18 4,216 8,510 8,238 16,620 ­5.6 ­7.8 51 .. 5,421 Faeroe Islands 48 1.4 35 .. ..d .. .. .. .. 79 .. 659 Fiji 833 18.3 46 3,098 3,720 g 3,707 4,450 3.6 2.9 69 .. 1,070 French Polynesia 259 4.0 71 .. ..d .. .. .. .. 74 .. 670 Greenland 57 410.5 0h .. ..d .. .. .. .. .. .. 571 Grenada 108 0.3 318 495 4,650 934f 8,770 f 0.7 ­0.8 .. .. 216 Guam 171 0.5 317 .. ..d .. .. .. .. 75 .. 4,081 Guyana 739 215.0 4 849 1,150 2,522f 3,410 f 4.8 4.9 66 .. 1,443 Iceland 302 103.0 3 15,078 49,960 10,181 33,740 2.6 0.9 81 .. 2,227 Isle of Man 77 0.6 135 3,088 40,600 .. .. 5.9 4.9 .. .. .. About the data Definitions The table shows data for 56 economies with popu- · Population is based on the de facto definition of net receipts of primary income (compensation of lations between 30,000 and 1 million and smaller population, which counts all residents regardless employees and property income) from abroad. Data economies if they are members of the World Bank. of legal status or citizenship--except for refugees are in current U.S. dollars converted using the World Where data on gross national income (GNI) per capita not permanently settled in the country of asylum, Bank Atlas method (see Statistical methods). · GNI are not available, the estimated range is given. For who are generally considered part of the popula- per capita is GNI divided by midyear population. more information on the calculation of GNI (gross tion of their country of origin. The values shown are GNI per capita in U.S. dollars is converted using national product, or GNP, in the System of National midyear estimates. See also table 2.1. · Surface the World Bank Atlas method. · Purchasing power Accounts 1968) and purchasing power parity (PPP) area is a country's total area, including areas under parity (PPP) GNI is GNI converted to international conversion factors, see About the data for table 1.1. inland bodies of water and some coastal waterways. dollars using PPP rates. An international dollar has Since 2000 the table has excluded France's over- · Population density is midyear population divided the same purchasing power over GNI that a U.S. seas departments--French Guiana, Guadeloupe, by land area in square kilometers. · Gross national dollar has in the United States. · Gross domestic Martinique, and Réunion--for which GNI and other income (GNI) is the sum of value added by all resi- product (GDP) is the sum of value added by all economic measures are now included in the French dent producers plus any product taxes (less sub- resident producers plus any product taxes (less national accounts. sidies) not included in the valuation of output plus subsidies) not included in the valuation of output. 32 2008 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 2006 2006 2006 2006b 2006b 2006 2006 2005­06 2005­06 2006 2005 2004 Kiribati 100 0.8 124 124 1,240 624f 6,230 f 5.8 4.5 .. .. 29 Liechtenstein 35 0.2 218 .. ..d .. .. .. .. .. .. .. Luxembourg 462 2.6 178 32,904 71,240 28,117 60,870 6.2 5.0 79 .. 11,267 Macao, China 478 0.0 16,934 .. ..d .. .. 16.6 15.5 80 .. 2,205 Maldives 300 0.3 1,001 903 3,010 1,424 4,740 23.5 21.5 68 .. 725 Malta 406 0.3 1,269 6,216 15,310 8,523 20,990 3.4 2.8 79 .. 2,451 Marshall Islands 65 0.2 363 195 2,980 525f 8,040 f 3.0 ­0.3 .. .. .. Mayotte 187 0.4 499 .. ..c .. .. .. .. .. .. .. Micronesia, Fed. Sts. 111 0.7 158 264 2,390 672f 6,070 f ­0.7 ­1.2 68 .. .. Monaco 33 0.0 16,718 .. ..d .. .. .. .. .. .. .. Montenegro 601 14.0 44 2,481 4,130 5,366 8,930 16.2 17.5 74 .. .. Netherlands Antilles 189 0.8 236 .. ..d .. .. .. .. 75 96 4,084 New Caledonia 238 18.6 13 .. ..d .. .. .. .. 75 .. 2,575 Northern Mariana Islands 82 0.5 178 .. ..c .. .. .. .. .. .. .. Palau 20 0.5 44 161 7,990 290 f 14,340 f 5.7 5.2 .. .. 238 Qatar 821 11.0 75 .. ..d .. .. 6.1 1.8 75 89 52,857 Samoa 185 2.8 66 421 2,270 943f 5,090 f 2.3 1.5 71 99 150 San Marino 29 0.1 477 1,291 45,130 .. .. 5.0 3.5 82 .. .. São Tomé and Principe 155 1.0 162 124 800 231 1,490 7.0 5.3 65 .. 92 Seychelles 85 0.5 184 751 8,870 1,215f 14,360 f 5.3 3.2 72 .. 546 Solomon Islands 484 28.9 17 333 690 896f 1,850 f 6.1 3.6 63 .. 176 St. Kitts and Nevis 48 0.3 186 406 8,460 597f 12,440 f 5.8 4.9 .. .. 125 St. Lucia 166 0.6 272 833 5,060 1,400 f 8,500 f 4.5 3.7 74 .. 366 St. Vincent & Grenadines 120 0.4 307 395 3,320 741f 6,220 f 1.5 1.0 71 .. 198 Suriname 455 163.3 3 1,918 4,210g 3,514f 7,720 f 5.8 5.1 70 90 2,282 Tonga 100 0.8 139 225 2,250 546f 5,470 f 1.4 0.9 73 .. 117 Vanuatu 221 12.2 18 373 1,690 768f 3,480 f 7.2 4.6 70 .. 88 Virgin Islands (U.S.) 109 0.4 310 .. ..d .. .. .. .. 79 .. 13,524 a. PPP is purchasing power parity, see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be upper middle income ($3,596­$11,115). d. Estimated to be high income ($11,116 or more). e. Included in the aggregates for high-income economies based on earlier data. f. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. g. Included in the aggregates for lower middle-income economies based on earlier data. h. Less than 0.5. Growth is calculated from constant price GDP data in local currency. · GDP per capita is GDP divided by midyear population. · Life expectancy at birth is the number of years a newborn infant would live if prevailing patterns of mortality at the time of Data sources its birth were to stay the same throughout its life. · Adult literacy rate is the percentage of adults The indicators here and throughout the book ages 15 and older who can, with understanding, are compiled by World Bank staff from primary read and write a short, simple statement about their and secondary sources. More information about everyday life. · Carbon dioxide emissions are those the indicators and their sources can be found in stemming from the burning of fossil fuels and the the About the data, Definitions, and Data sources manufacture of cement. They include carbon dioxide entries that accompany each table in subsequent produced during consumption of solid, liquid, and sections. gas fuels and gas fl aring. 2008 World Development Indicators 33 Text figures, tables, and boxes PEOPLE 2 Introduction Introduction R eproductive health Keeping mothers alive and healthy is good for women, their families, and society. Prioritizing women's health will help countries meet many of the Millennium Development Goals--first improved maternal and child health, then reduced poverty, universal education, and gender equality. Poor people tend to have large families, suffer disproportionately from illness, and use fewer health services during pregnancy and childbirth. Reproductive health care can enhance poor people's overall health care and help families escape the poverty impact of having many children. When financial resources are divided among fewer family members, more is left for education, health care, and savings, decreasing vulnerability and insecurity (UN Millennium Project 2005a). This important link between reproductive health and development outcomes was first articu- lated in 1994 at the International Conference on Population and Development in Cairo. But as fertility declined in many countries and new priorities arose, reproductive health and fam- ily planning fell steadily in international priority. Complicating this was the lack of sectoral ownership of reproductive health and the requirement for multisectoral action. The targets for the Millennium Development Goals, drafted in 2000, ignored the overarching Cairo goal of universal access to sexual and reproductive health services, instead focusing on the target of reducing maternal mortality, a problem of immense magnitude in poor coun- tries (figures 2a and 2b). Millennium Development Goal 5 in 2000 identified two indicators to measure progress: maternal mortality ratios and the proportion of births attended by skilled staff. At an analytical level, however, it is impossible to disentangle maternal health from reproductive health, of which maternal health is just one facet. Most maternal deaths . . . especially in Sub-Saharan occur in developing countries . . . 2a Africa and South Asia 2b Maternal mortality ratio, by income group (per 100,000 live births) 1990 2005 Maternal mortality ratio, by region (per 100,000 live births) 1990 2005 800 1,000 800 600 600 400 400 200 200 0 0 Low-income Lower Upper High-income East Asia Europe & Latin Middle South Sub-Saharan middle-income middle-income & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Source: Estimates from the World Health Organization, United Nations Children's Source: Estimates from the World Health Organization, United Nations Children's Fund, United Nations Population Fund, and the World Bank. Fund, United Nations Population Fund, and the World Bank. 2008 World Development Indicators 35 Why reproductive health now? Pregnancy and childbirth are leading causes of death and Poor women disproportionately bear the burden of disability disability for women of reproductive age in developing coun- and loss of productive life. Women in low-income countries tries. In 2005 more than half a million women died from face a 1 in 40 risk of a pregnancy-related death; those in high- pregnancy-related causes, and about 200 million women suf- income countries, a 1 in 6,700 risk (figure 2c). The contrast fered life-threatening complications and disabilities (Glasier is also large within countries. In Peru the poorest women are and others 2006). As a result of reproductive health prob- about 7 times more likely than the richest to die of pregnancy- lems an estimated 250 million years of productive life are related causes (Ronsman and Graham 2006). Even though lost every year (UNFPA 2005). Over 99 percent of all mater- cheap and easy methods to prevent unintended or unwanted nal deaths occur in developing countries, the majority in Sub- pregnancies are available, 120 million couples hoping to avoid Saharan Africa and South Asia (Glasier and others 2006). pregnancy did not use contraception. As a result, 80 million In 2005 Millennium Development Goal 5--improved women became pregnant against their will, and 45 million maternal health--was expanded to include family planning sought abortions, about 20 million of them unsafe, performed and reproductive health services. Reproductive health care by untrained providers (Glasier and others 2006). was recognized as important for improving maternal health Progress in maternal and reproductive health in recent and preventing maternal deaths, but also as essential for years has been mixed in developing countries. Several mid- achieving all the Millennium Development Goals. A new dle-income countries have made rapid progress in reducing target was introduced for universal access to reproductive maternal deaths, but maternal mortality ratios and the lifetime health by 2015, along with indicators measuring adolescent risk of dying in childbirth remain unacceptably high in Sub- fertility, prenatal care, unmet need for contraception, and Saharan Africa and South Asia (figure 2d). Within countries, contraceptive prevalence. poorer women are more vulnerable than wealthier women. Women in developing countries are more likely to die of pregnancy- East Asia and Pacific leads in contraceptive related causes than women in high-income countries 2c use among married women ages 15­49 2e Lifetime risk of dying from pregnancy-related causes, by income group, 2005 (%) Contraceptive prevalence rate, by region 3 (% of married women ages 15­49) 1990 2006 100 1 in 40 2 75 50 1 25 1 in 270 1 in 540 1 in 6,700 0 0 Low-income Lower Upper High-income East Asia Europe & Latin Middle South Sub-Saharan middle-income middle-income & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Source: Estimates from the World Health Organization, United Nations Children's Fund, United Nations Population Fund, and the World Bank. Source: Household surveys. The lifetime risk of dying from pregnancy-related causes is Women from the richest households are more likely to use unacceptably high in Sub-Saharan Africa and South Asia 2d contraception--but contraceptive prevalence rates remain low 2f Lifetime risk of dying from pregnancy-related causes, by region, 2005 (%) Contraceptive prevalence rate, by region and wealth 5 quintile (% of married women ages 15­49) Poorest 20% Richest 20% 1 in 22 100 4 75 3 2 1 in 59 50 1 1 in 160 25 1 in 340 1 in 280 1 in 1,400 0 East Asia Europe & Latin Middle South Sub-Saharan 0 & Pacific Central Asia America & East & Asia Africa East Asia Europe & Latin Middle South Sub-Saharan Caribbean North Africa & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Source: Estimates from the World Health Organization, United Nations Children's Fund, United Nations Population Fund, and the World Bank. Source: Gwatkin and others 2007. 36 2008 World Development Indicators Maternal and reproductive health: current status The vast majority of maternal deaths and disabilities can be Despite the benefits, many countries continue to face prevented through appropriate reproductive health services major challenges in meeting their family planning needs before, during, and after pregnancy. Key among them is ex- (figure 2g), and rates of unmet need for family planning in panding family planning to allow women to space or limit developing countries remain high (figure 2h). According to their births. surveys, one married woman in seven in these countries has Contraceptive use among women in developing countries an unmet need for contraception, and in Sub-Saharan Africa has increased steadily, from about 14 percent of married nearly one in four does. Regional aggregates mask wide dif- women ages 15­49 in 1965 to 60 percent in 2006. But ferences: in Asia only 5 percent of women in Vietnam have use is uneven across and within countries. In Sub-Saharan an unmet need, compared with 28 percent in Nepal (Sedgh Africa only 22 percent of married women use contraception, and others 2007b). Preventing unplanned pregnancies alone compared with 63 percent in Europe and Central Asia, about could avert around one-quarter of maternal deaths, including 70 percent in Latin America and the Caribbean, and about those from unsafe abortions (Sedgh and others 2007b). 80 percent in East Asia and the Pacific (figure 2e). Young girls are particularly vulnerable to maternal death. Contraceptive use follows the distribution of wealth, and They have limited information, means, and access to contra- the poorest women come up short. Differences are especially ception and even less access to good quality maternal health stark in South Asia and Sub-Saharan Africa (figure 2f). In Sub- care, especially if they are not married. In regions where the Saharan Africa women from richer households are three times adolescent fertility rate is high (figure 2i), many young women more likely to use contraception, but prevalence is still less and their children, particularly very young women, face higher than 30 percent of eligible women. In South Asia richer women risks of death and disability (box 2j). Young girls either are twice as likely as poorer women to use contraception. continue unintended pregnancies, giving up opportunities Meeting family planning needs remains a challenge-- High adolescent fertility rates mean young women and despite benefits such as reduced fertility 2g their children are at higher risk of death and disability 2i Contraceptive prevalence rate, 2006 Total fertility rate, 2006 Adolescent fertility rate, by region (per 1,000 women ages 15­19) 1997 2006 (% of married women ages 15­49) (births per woman) 150 100 8 75 6 100 50 4 50 25 2 0 0 0 East Asia Europe & Latin Middle South Sub-Saharan East Asia Europe & Latin Middle South Sub-Saharan & Pacific Central Asia America & East & Asia Africa & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Caribbean North Africa Source: Household surveys and World Development Indicators data files. Source: United Nations Population Division 2007. Many women in developing countries Age-specific fertility have an unmet need for contraception 2h for girls ages 15­17 Box 2j Married women with an unmet need for contraception (%) 1990­95 2000­05 The age below which giving birth is physically risky for a woman var- 30 ies depending on general health conditions and access to prenatal care. Although the physical risk of giving birth during adolescence 20 is not high for women in countries with good nutritional levels and extensive access to prenatal care, the risk rises in societies where anemia and malnutrition are prevalent and where access to health care is generally poor. The adolescent fertility rate for ages 15­19 is 10 now included as a Millennium Development Goal indicator. However, the fertility rate of girls ages 15­17 is argued to be a better indica- 0 tor, as this age group is at higher risk of suffering pregnancy-related Latin America North Africa South and Sub-Saharan complications and having very low birthweight babies. Even when and the and West Asia Southeast Asia Africa Caribbean very young adolescents deliver their babies in health facilities, they suffer higher rates of mortality than older women do. Note: Regions are Guttmacher Institute regions, which differ from World Bank regions. Source: Sedgh and others 2007b. Source: Lule and others 2005. 2008 World Development Indicators 37 An improvement, but is it enough? for education and employment, or seek unsafe abortions. Both preventive and strategic interventions are needed to Forty percent of all the abortions are performed on women treat the many factors that contribute to maternal mortality. under age 25 (Glasier and others 2006). The expanded Millennium Development Goal 5 indicators are Prenatal care, long at the core of maternal health services, mainly process indicators to assess reproductive health and identifies risks, helps plan for safe delivery, and provides entry address preventive interventions: preparing for birth, including into the health care system. All regions but Sub-Saharan Africa timing and spacing of births for both adults and adolescents; have made progress in providing prenatal care to women at recognizing danger signs in the prenatal period and respond- least once during pregnancy (figure 2k). In South Asia, with the ing appropriately; and having skilled health staff at delivery. slowest progress, 66 percent of pregnant women have at least Equally important are the strategic interventions, espe- one prenatal care visit. But rich women are three times more cially during labor and delivery. Among these are obstetric care, likely to get prenatal care than are poor women (figure 2l). including timely and safe transfers of mothers to a hospital or A key factor in lowering maternal mortality is the pres- health care center with the necessary staff, equipment, drugs, ence of a skilled attendant during childbirth. Nearly half of and other supplies. The World Health Organization (WHO) has maternal deaths in developing countries occur during labor proposed that national public health administrators monitor and delivery or just after delivery (Lule and others 2005). The the availability of essential obstetric care and access to emer- proportion of attended births remains low in South Asia and gency obstetric care at the country level (box 2o). An estimated Sub-Saharan Africa (figure 2m) and is even lower in the poorer 15 percent of pregnancies result in complications (Nanda, segments of these countries (figure 2n). Other regions have Switlick, and Lule 2005). But data on complications are col- made impressive gains, with countries in Europe and Central lected only by ad hoc studies, usually in limited areas of coun- Asia providing skilled care to nearly all women giving birth. tries, and no standard definition or methodology is followed. All regions have made progress in providing prenatal The proportion of births attended by skilled health staff care to women at least once during their pregnancy 2k remains low in South Asia and Sub-Saharan Africa 2m Pregnant women receiving prenatal care, by region (%) 1990 2006 Births attended by skilled health staff, by region (%) 1990 2006 100 100 75 75 50 50 25 25 0 0 East Asia Europe & Latin Middle South Sub-Saharan East Asia Europe & Latin Middle South Sub-Saharan & Pacific Central Asia America & East & Asia Africa & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Caribbean North Africa Source: Household surveys. Source: Household surveys. In South Asia rich women are three times more Nearly all women in Europe and Central Asia have births attended likely to receive prenatal care than are poor women 2l by skilled health staff--but even there poor women lag behind 2n Pregnant women receiving prenatal care, Births attended by skilled health staff, various years, by region and wealth quintile (%) Poorest 20% Richest 20% various years, by wealth quintile (%) Poorest 20% Richest 20% 100 100 75 75 50 50 25 25 0 0 East Asia Europe & Latin Middle South Sub-Saharan East Asia Europe & Latin Middle South Sub-Saharan & Pacific Central Asia America & East & Asia Africa & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Caribbean North Africa Source: Gwatkin and others 2007. Source: Gwatkin and others 2007. 38 2008 World Development Indicators Challenges ahead Complications from abortion are also now recognized as The interventions to prevent the vast majority of conditions a major public and reproductive health problem, especially that kill women of reproductive age--and to enable health in developing countries. Abortions, especially unsafe ones, systems to protect and promote women's health--have al- account for 13 percent of maternal deaths, and good qual- ready been identified. Some are simple, low-tech, and cost- ity post-abortion services and family planning services to effective, such as the provision and use of contraception. avoid unwanted pregnancies are essential. Of an estimated Yet many people in developing countries, especially in South 20 million unsafe abortions worldwide each year, the major- Asia and Sub-Saharan Africa, do not benefit. Behind the ity are in developing countries (Nanda, Switlick, and Lule failure of these health systems are weak commitments to 2005) (figure 2p). Abortion information is particularly difficult improving maternal health, poor management systems, inad- to gather because abortion is restricted and stigmatized in equate human and medical resources and equipment, and, many countries, leading to false reporting by women and for most of the poor, the inability to pay for services. service providers. Regional estimates of abortion rates are Underlying the failures of the health system is the lack of available from the WHO, UN agencies, national authorities, reliable data for monitoring progress in maternal and repro- and nongovernmental organizations. But reliable country ductive health and in other safe motherhood indicators. And data are not routinely collected. most developing countries have inadequate health informa- In addition to definitional gaps, data collection for these tion systems or lack them altogether. So, providing timely two indicators faces additional hurdles because the infrastruc- and reliable information often depends on local, one-off data ture for collecting data is weak or because there is political, collection, such as household surveys, which are both costly cultural, or moral hesitation. Obtaining accurate values also and unsustainable because they do not establish permanent requires significant clinical resources and technical skills. health information structures. Ideally, there would be vital registration systems, hospital and health service data, and The importance of household surveys. emergency obstetric care Box 2o Least available are data on maternal deaths, needed to Emergency obstetric care encompasses a set of functions performed monitor the Millennium Development Goal target of cutting at health facilities that can prevent the death of women experiencing obstetric complications. Basic emergency obstetric care, usually pro- maternal mortality ratios by 75 percent. While vital registra- vided at health centers and small maternity homes, includes admin- tion systems are a rich and valuable source of health data in istering certain drugs and performing lifesaving procedures, such as for preeclampsia and eclampsia. Comprehensive emergency obstet- developed countries, they are incomplete in developing coun- ric care, usually provided at subdistrict or district hospitals, also includes providing Caesarean sections and blood transfusions. tries. For example, the share of developing countries with at More maternal health programs now recognize that emergency least 90 percent complete vital registration increased from obstetric care is critical to reducing maternal death and disability. Much can be accomplished by upgrading existing facilities. In pro- 45 percent in 1988 to 62 percent in 2006. Still, some of gramming for emergency obstetric care, bottlenecks in accessing services are often assessed using the "three delays" model: delays the most populous countries--China, India, Indonesia, Bra- in the decision to seek care, delays in arrival at a health facility, and zil, Pakistan, Bangladesh, Nigeria--do not have complete delays in the provision of adequate care at the facility. Source: Nanda, Switlick, and Lule 2005. vital registration systems. Hospital or other health service records are sometimes a source of information. But these Most unsafe abortions take place in developing countries, especially in Latin America and the Caribbean and Africa 2p record only women who have access to health services, and Incidence of unsafe abortion, 2003 (per 1,000 women) a large number of women, especially in rural areas, do not. 30 Household surveys for estimating maternal mortality ratios are costly and yield unreliable estimates. 20 The evidence base should be strong enough to provide 10 crucial information on who dies and why--and to generate insights about interventions that are available, accessible, 0 World Developed Developing Africa Asia Europe Latin Oceania appropriate, and affordable. countries countries America & the Caribbean Note: Regions are World Health Organization regions, which differ from World Bank regions. Source: WHO 2007. 2008 World Development Indicators 39 Tables 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate dependents as % proportion of Ages Ages Ages working-age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2006 2015 1990­2006 2006­15 2006 2006 2006 2006 2006 2006 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.3 3.2 3.3 ­0.2 0.4 25.5 65.8 8.7 0.4 0.1 6 16 Algeria 25.3 33.4 38.0 1.7 1.5 28.9 66.5 4.6 0.4 0.1 5 21 Angola 10.5 16.6 21.2 2.8 2.8 46.3 51.3 2.4 0.9 0.0a 21 48 Argentina 32.6 39.1 42.5 1.1 0.9 26.1 63.6 10.3 0.4 0.2 8 18 Armenia 3.5 3.0 3.0 ­1.0 ­0.2 20.0 67.9 12.1 0.3 0.2 9 12 Australia 17.1 20.7 22.4 1.2 0.9 19.3 67.4 13.3 0.3 0.2 7 13 Austria 7.7 8.3 8.4 0.4 0.2 15.6 68.0 16.4 0.2 0.2 9 9 Azerbaijan 7.2 8.5 9.2 1.1 0.9 24.2 68.5 7.2 0.4 0.1 6 18 Bangladesh 113.0 156.0 180.0 2.0 1.6 34.7 61.7 3.6 0.6 0.1 8 25 Belarus 10.2 9.7 9.2 ­0.3 ­0.6 15.3 70.4 14.3 0.2 0.2 15 9 Belgium 10.0 10.5 10.7 0.3 0.1 16.9 65.8 17.3 0.3 0.3 10 12 Benin 5.2 8.8 11.3 3.3 2.9 44.0 53.3 2.7 0.8 0.1 11 41 Bolivia 6.7 9.4 10.9 2.1 1.6 37.7 57.7 4.6 0.7 0.1 8 28 Bosnia and Herzegovina 4.3 3.9 3.9 ­0.6 ­0.2 17.3 68.6 14.1 0.3 0.2 9 9 Botswana 1.4 1.9 2.1 1.9 1.2 35.1 61.5 3.4 0.6 0.1 15 25 Brazil 149.5 189.3 209.4 1.5 1.1 27.6 66.2 6.3 0.4 0.1 6 19 Bulgaria 8.7 7.7 7.1 ­0.8 ­0.8 13.6 69.2 17.3 0.2 0.2 15 9 Burkina Faso 8.9 14.4 18.6 3.0 2.9 46.0 51.0 3.1 0.9 0.1 15 44 Burundi 5.7 8.2 11.2 2.3 3.5 44.7 52.7 2.6 0.8 0.0a 16 47 Cambodia 9.7 14.2 16.6 2.4 1.8 36.7 60.1 3.2 0.6 0.1 9 27 Cameroon 12.2 18.2 21.5 2.5 1.9 41.5 55.0 3.5 0.8 0.1 15 35 Canada 27.8 32.6 35.1 1.0 0.8 17.3 69.4 13.3 0.2 0.2 7 11 Central African Republic 3.0 4.3 5.0 2.2 1.8 42.5 53.7 3.9 0.8 0.1 18 37 Chad 6.1 10.5 13.4 3.4 2.7 46.2 50.9 2.9 0.9 0.1 16 46 Chile 13.2 16.4 17.8 1.4 0.9 24.3 67.4 8.3 0.4 0.1 5 15 China 1,135.2 1,311.8 1,382.5 0.9 0.6 21.1 71.1 7.8 0.3 0.1 7 12 Hong Kong, China 5.7 6.9 7.4 1.2 0.9 14.8 73.2 12.1 0.2 0.2 5 10 Colombia 34.9 45.6 50.6 1.7 1.2 29.8 65.0 5.2 0.5 0.1 6 19 Congo, Dem. Rep. 37.9 60.6 78.5 2.9 2.9 47.3 50.1 2.6 0.9 0.1 18 44 Congo, Rep. 2.4 3.7 4.5 2.6 2.1 41.9 54.9 3.2 0.8 0.1 12 36 Costa Rica 3.1 4.4 5.0 2.2 1.4 27.8 66.3 5.9 0.4 0.1 4 18 Côte d'Ivoire 12.8 18.9 22.3 2.5 1.9 41.4 55.4 3.2 0.7 0.1 16 36 Croatia 4.8 4.4 4.3 ­0.5 ­0.3 15.3 67.4 17.3 0.2 0.3 11 9 Cuba 10.6 11.3 11.2 0.4 ­0.1 18.9 69.7 11.4 0.3 0.2 8 11 Czech Republic 10.4 10.3 10.2 ­0.1 ­0.1 14.5 71.2 14.3 0.2 0.2 10 10 Denmark 5.1 5.4 5.5 0.4 0.1 18.7 66.0 15.4 0.3 0.2 10 12 Dominican Republic 7.3 9.6 10.9 1.7 1.4 33.2 61.1 5.7 0.5 0.1 6 24 Ecuador 10.3 13.2 14.6 1.6 1.1 32.2 61.7 6.0 0.5 0.1 5 21 Egypt, Arab Rep. 55.1 74.2 86.2 1.9 1.7 33.0 62.1 4.9 0.5 0.1 6 24 El Salvador 5.1 6.8 7.6 1.8 1.3 33.7 60.7 5.6 0.6 0.1 6 23 Eritrea 3.2 4.7 6.2 2.5 3.0 42.9 54.8 2.3 0.8 0.0a 9 40 Estonia 1.6 1.3 1.3 ­1.0 ­0.4 14.9 68.4 16.7 0.2 0.2 13 11 Ethiopia 51.2 77.2 96.0 2.6 2.4 44.2 52.9 2.9 0.8 0.1 13 39 Finland 5.0 5.3 5.4 0.3 0.2 17.2 66.7 16.1 0.3 0.2 9 11 France 56.7 61.3 63.1 0.5 0.3 18.3 65.4 16.3 0.3 0.2 9 13 Gabon 0.9 1.3 1.5 2.2 1.5 35.4 60.0 4.6 0.6 0.1 12 26 Gambia, The 1.0 1.7 2.1 3.4 2.5 41.0 55.2 3.8 0.7 0.1 11 36 Georgia 5.5 4.4 4.2 ­1.3 ­0.7 18.4 67.3 14.4 0.3 0.2 12 11 Germany 79.4 82.4 81.1 0.2 ­0.2 14.1 66.6 19.2 0.2 0.3 10 8 Ghana 15.6 23.0 27.3 2.4 1.9 38.6 57.7 3.7 0.7 0.1 9 30 Greece 10.2 11.1 11.2 0.6 0.0a 14.2 67.4 18.4 0.2 0.3 9 10 Guatemala 8.9 13.0 16.2 2.4 2.4 42.9 52.8 4.3 0.8 0.1 6 34 Guinea 6.0 9.2 11.4 2.6 2.4 43.3 53.7 3.1 0.8 0.1 12 40 Guinea-Bissau 1.0 1.6 2.2 3.0 3.0 47.6 49.4 3.0 1.0 0.1 19 50 Haiti 7.1 9.4 11.0 1.8 1.7 37.5 58.3 4.2 0.6 0.1 9 28 40 2008 World Development Indicators 2.1 PEOPLE Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate dependents as % proportion of Ages Ages Ages working-age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2006 2015 1990­2006 2006­15 2006 2006 2006 2006 2006 2006 2006 Honduras 4.9 7.0 8.2 2.2 1.8 39.4 56.4 4.2 0.7 0.1 6 28 Hungary 10.4 10.1 9.7 ­0.2 ­0.4 15.5 69.1 15.4 0.2 0.2 13 10 India 849.5 1,109.8 1,233.2 1.7 1.2 32.5 62.4 5.0 0.5 0.1 8 24 Indonesia 178.2 223.0 245.1 1.4 1.0 28.0 66.3 5.6 0.4 0.1 7 20 Iran, Islamic Rep. 54.4 70.1 78.9 1.6 1.3 27.8 67.8 4.5 0.4 0.1 5 18 Iraq 18.5 .. .. .. .. .. .. .. .. .. .. .. Ireland 3.5 4.3 4.8 1.2 1.3 20.7 68.2 11.1 0.3 0.2 6 15 Israel 4.7 7.0 8.0 2.6 1.5 27.9 62.0 10.1 0.4 0.2 6 21 Italy 56.7 58.8 58.4 0.2 ­0.1 13.9 66.1 19.9 0.2 0.3 9 10 Jamaica 2.4 2.7 2.8 0.7 0.4 31.3 61.2 7.5 0.5 0.1 6 17 Japan 123.5 127.8 124.5 0.2 ­0.3 13.8 66.0 20.3 0.2 0.3 9 9 Jordan 3.2 5.5 6.8 3.5 2.2 36.5 60.2 3.3 0.6 0.1 4 29 Kazakhstan 16.3 15.3 16.4 ­0.4 0.8 23.9 68.2 8.0 0.4 0.1 10 20 Kenya 23.4 36.6 46.1 2.8 2.6 42.6 54.7 2.7 0.8 0.0a 12 39 Korea, Dem. Rep. 20.1 23.7 24.4 1.0 0.3 23.6 67.5 8.8 0.4 0.1 10 14 Korea, Rep. 42.9 48.4 49.2 0.8 0.2 18.1 72.0 9.8 0.3 0.1 5 9 Kuwait 2.1 2.6 3.2 1.3 2.2 23.6 74.6 1.9 0.3 0.0a 2 21 Kyrgyz Republic 4.4 5.2 5.7 1.0 1.0 30.4 63.8 5.8 0.5 0.1 7 23 Lao PDR 4.1 5.8 6.7 2.2 1.7 38.9 57.5 3.5 0.7 0.1 7 27 Latvia 2.7 2.3 2.2 ­1.0 ­0.6 14.0 69.2 16.8 0.2 0.2 15 10 Lebanon 3.0 4.1 4.4 1.9 1.0 28.2 64.5 7.3 0.4 0.1 7 18 Lesotho 1.6 2.0 2.1 1.4 0.6 40.1 55.1 4.7 0.7 0.1 19 29 Liberia 2.1 3.6 5.1 3.2 3.9 47.0 50.8 2.2 0.9 0.0a 19 50 Libya 4.4 6.0 7.1 2.0 1.8 30.2 65.9 3.9 0.5 0.1 4 24 Lithuania 3.7 3.4 3.2 ­0.5 ­0.5 16.2 68.3 15.5 0.2 0.2 13 9 Macedonia, FYR 1.9 2.0 2.0 0.4 ­0.0 b 19.2 69.5 11.3 0.3 0.2 9 11 Madagascar 12.0 19.2 24.1 2.9 2.5 43.6 53.3 3.2 0.8 0.1 10 37 Malawi 9.4 13.6 17.0 2.3 2.5 47.0 49.9 3.0 0.9 0.1 15 41 Malaysia 18.1 26.1 30.0 2.3 1.5 31.0 64.6 4.4 0.5 0.1 4 21 Mali 7.7 12.0 15.7 2.8 3.0 47.6 48.8 3.6 1.0 0.1 15 48 Mauritania 1.9 3.0 3.8 2.8 2.4 40.1 56.3 3.6 0.7 0.1 8 33 Mauritius 1.1 1.3 1.3 1.1 0.7 24.0 69.3 6.7 0.3 0.1 8 15 Mexico 83.2 104.2 113.7 1.4 1.0 30.2 63.8 6.0 0.5 0.1 5 19 Moldova 4.4 3.8 3.6 ­0.8 ­0.8 19.4 69.5 11.1 0.3 0.2 12 11 Mongolia 2.1 2.6 2.9 1.3 1.1 28.0 68.1 4.0 0.4 0.1 6 18 Morocco 24.2 30.5 33.9 1.5 1.2 29.7 65.0 5.3 0.5 0.1 6 22 Mozambique 13.5 21.0 24.7 2.7 1.8 44.3 52.5 3.2 0.8 0.1 20 40 Myanmar 40.1 48.4 51.9 1.2 0.8 26.7 67.7 5.6 0.4 0.1 10 18 Namibia 1.4 2.0 2.3 2.3 1.2 38.3 58.2 3.5 0.7 0.1 13 26 Nepal 19.1 27.6 32.2 2.3 1.7 38.5 57.8 3.7 0.7 0.1 8 29 Netherlands 15.0 16.3 16.5 0.6 0.1 18.3 67.4 14.3 0.3 0.2 8 11 New Zealand 3.4 4.2 4.5 1.2 0.8 21.2 66.5 12.3 0.3 0.2 7 14 Nicaragua 4.1 5.5 6.3 1.8 1.4 37.2 58.7 4.0 0.6 0.1 5 25 Niger 7.8 13.7 18.5 3.5 3.3 48.0 48.8 3.2 1.0 0.1 14 49 Nigeria 94.5 144.7 175.6 2.7 2.1 44.1 53.0 2.9 0.8 0.1 17 40 Norway 4.2 4.7 4.9 0.6 0.6 19.4 65.9 14.7 0.3 0.2 9 12 Oman 1.8 2.5 3.0 2.0 2.0 33.1 64.1 2.7 0.5 0.0a 3 22 Pakistan 108.0 159.0 191.9 2.4 2.1 36.4 59.7 3.9 0.6 0.1 7 26 Panama 2.4 3.3 3.8 1.9 1.5 30.1 63.8 6.1 0.5 0.1 5 21 Papua New Guinea 4.1 6.2 7.3 2.5 1.8 40.3 57.3 2.4 0.7 0.0a 10 30 Paraguay 4.2 6.0 7.0 2.2 1.7 35.4 59.8 4.8 0.6 0.1 6 25 Peru 21.8 27.6 30.7 1.5 1.2 31.2 63.1 5.7 0.5 0.1 6 21 Philippines 61.2 86.3 101.0 2.1 1.8 35.8 60.3 3.9 0.6 0.1 5 26 Poland 38.1 38.1 37.4 0.0a ­0.2 15.9 70.8 13.3 0.2 0.2 10 10 Portugal 9.9 10.6 10.8 0.4 0.2 15.6 67.4 17.0 0.2 0.3 10 10 Puerto Rico 3.5 3.9 4.1 0.7 0.5 21.6 65.7 12.7 0.3 0.2 8 13 2008 World Development Indicators 41 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate dependents as % proportion of Ages Ages Ages working-age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2006 2015 1990­2006 2006­15 2006 2006 2006 2006 2006 2006 2006 Romania 23.2 21.6 20.5 ­0.5 ­0.6 15.4 69.8 14.9 0.2 0.2 12 10 Russian Federation 148.3 142.5 135.2 ­0.2 ­0.6 14.9 71.4 13.7 0.2 0.2 15 10 Rwanda 7.3 9.5 12.1 1.6 2.8 43.1 54.5 2.5 0.8 0.0a 17 44 Saudi Arabia 16.4 23.7 28.5 2.3 2.1 34.0 63.2 2.8 0.5 0.0a 4 25 Senegal 7.9 12.1 15.4 2.7 2.7 41.9 53.8 4.3 0.8 0.1 9 36 Serbia 7.5c 7.4 c 7.3c ­0.1c ­0.2c 18.4 d 66.9d 14.7d 0.3d 0.2d 14 c 10 c Sierra Leone 4.1 5.7 6.9 2.1 2.1 42.8 53.9 3.3 0.8 0.1 22 46 Singapore 3.0 4.5 4.8 2.4 0.8 18.8 72.4 8.8 0.3 0.1 4 10 Slovak Republic 5.3 5.4 5.4 0.1 ­0.0 b 16.3 71.8 11.8 0.2 0.2 10 10 Slovenia 2.0 2.0 2.0 0.0a ­0.1 13.9 70.3 15.8 0.2 0.2 9 9 Somalia 6.7 8.4 10.9 1.4 2.8 44.2 53.2 2.6 0.8 0.0a 17 43 South Africa 35.2 47.4 49.1 1.9 0.4 31.9 63.7 4.4 0.5 0.1 21 23 Spain 38.8 44.1 45.7 0.8 0.4 14.5 68.7 16.9 0.2 0.2 9 11 Sri Lanka 17.0 19.9 20.5 1.0 0.3 23.7 69.7 6.6 0.3 0.1 6 19 Sudan 25.9 37.7 45.6 2.3 2.1 40.3 56.1 3.6 0.7 0.1 10 32 Swaziland 0.8 1.1 1.2 2.4 0.5 39.2 57.5 3.3 0.7 0.1 22 33 Sweden 8.6 9.1 9.4 0.4 0.4 17.1 65.5 17.4 0.3 0.3 10 12 Switzerland 6.7 7.5 7.6 0.7 0.2 16.5 67.9 15.7 0.2 0.2 8 10 Syrian Arab Republic 12.7 19.4 23.5 2.6 2.1 36.0 60.8 3.2 0.6 0.1 3 27 Tajikistan 5.3 6.6 7.7 1.4 1.6 38.7 57.4 3.9 0.7 0.1 6 28 Tanzania 25.5 39.5 48.9 2.7 2.4 44.4 52.6 3.0 0.8 0.1 13 40 Thailand 54.3 63.4 66.6 1.0 0.5 21.4 70.6 8.0 0.3 0.1 8 15 Timor-Leste 0.7 1.0 1.4 2.0 3.7 44.7 52.6 2.7 0.8 0.1 15 51 Togo 4.0 6.4 8.0 3.0 2.5 43.0 53.9 3.1 0.8 0.1 10 37 Trinidad and Tobago 1.2 1.3 1.4 0.5 0.4 21.7 71.7 6.6 0.3 0.1 8 15 Tunisia 8.2 10.1 11.2 1.4 1.1 25.4 68.3 6.3 0.4 0.1 6 17 Turkey 56.2 73.0 81.0 1.6 1.2 27.9 66.5 5.7 0.4 0.1 6 19 Turkmenistan 3.7 4.9 5.5 1.8 1.3 30.9 64.5 4.6 0.5 0.1 8 22 Uganda 17.8 29.9 40.7 3.2 3.4 49.3 48.3 2.5 1.0 0.1 14 47 Ukraine 51.9 46.8 43.4 ­0.6 ­0.8 14.3 69.5 16.2 0.2 0.2 16 10 United Arab Emirates 1.8 4.2 5.3 5.5 2.4 19.6 79.3 1.1 0.2 0.0a 1 15 United Kingdom 57.6 60.6 62.4 0.3 0.3 17.8 66.1 16.1 0.3 0.2 10 12 United States 249.6 299.4 323.9 1.1 0.9 20.7 67.0 12.3 0.3 0.2 8 14 Uruguay 3.1 3.3 3.4 0.4 0.2 23.6 62.8 13.6 0.4 0.2 9 15 Uzbekistan 20.5 26.5 29.6 1.6 1.2 32.4 62.9 4.7 0.5 0.1 6 19 Venezuela, RB 19.8 27.0 31.1 2.0 1.5 30.9 64.0 5.1 0.5 0.1 5 22 Vietnam 66.2 84.1 93.7 1.5 1.2 28.9 65.6 5.6 0.4 0.1 5 17 West Bank and Gaza 2.0 3.8 4.7 4.1 2.5 45.6 51.4 3.0 0.9 0.1 3 32 Yemen, Rep. 12.3 21.7 28.2 3.6 2.9 45.4 52.2 2.3 0.9 0.0a 8 38 Zambia 8.1 11.7 13.8 2.3 1.9 45.6 51.4 2.9 0.9 0.1 19 40 Zimbabwe 10.5 13.2 14.8 1.5 1.3 39.0 57.5 3.5 0.7 0.1 18 28 World 5,263.9 s 6,538.1 s 7,200.7 s 1.4 w 1.1 w 28.0 w 64.6 w 7.4 w 0.4 w 0.1 w 8w 20 w Low income 1,747.9 2,419.7 2,815.3 2.0 1.7 36.3 59.4 4.3 0.6 0.1 10 29 Middle income 2,599.1 3,087.7 3,313.9 1.1 0.8 24.7 67.9 7.4 0.4 0.1 8 16 Lower middle income 1,899.6 2,276.5 2,456.3 1.1 0.8 24.7 68.3 7.0 0.4 0.1 7 16 Upper middle income 699.5 811.3 857.7 0.9 0.6 24.6 66.9 8.6 0.4 0.1 9 17 Low & middle income 4,347.0 5,507.4 6,129.2 1.5 1.2 29.8 64.2 6.0 0.5 0.1 8 22 East Asia & Pacific 1,595.9 1,898.9 2,032.7 1.1 0.8 23.5 69.4 7.1 0.3 0.1 7 14 Europe & Central Asia 451.8 460.5 460.7 0.1 0.0a 19.4 68.9 11.6 0.3 0.2 12 13 Latin America & Carib. 436.9 556.1 616.5 1.5 1.1 29.6 64.1 6.3 0.5 0.1 6 20 Middle East & N. Africa 225.6 310.7 361.9 2.0 1.7 32.7 63.0 4.3 0.5 0.1 6 24 South Asia 1,120.1 1,499.4 1,694.9 1.8 1.4 33.4 61.9 4.7 0.5 0.1 8 24 Sub-Saharan Africa 516.7 781.8 962.6 2.6 2.3 43.3 53.6 3.1 0.8 0.1 15 39 High income 916.9 1,030.7 1,071.5 0.7 0.4 17.9 67.1 14.9 0.3 0.2 8 12 Euro area 296.2 316.7 319.7 0.4 0.1 15.5 66.7 17.8 0.2 0.3 9 10 a. Less than 0.05. b. More than ­0.05. c. Excludes Kosovo and Metohija. d. Includes Kosovo and Metohija. 42 2008 World Development Indicators 2.1 PEOPLE Population dynamics About the data Definitions Population estimates are usually based on national mortality rates are now reflected in the larger share · Population is based on the de facto definition of population censuses, but their frequency and quality of the working-age population. population, which counts all residents regardless of vary by country. Most countries conduct a complete Dependency ratios account for variations in the legal status or citizenship--except for refugees not enumeration no more than once a decade. Estimates proportions of children, elderly people, and working- permanently settled in the country of asylum, who for the years before and after the census are inter- age people in the population. Calculations of young are generally considered part of the population of polations or extrapolations based on demographic and old-age dependency suggest the dependency their country of origin. The values shown are mid- models. Errors and undercounting occur even in high- burden that the working-age population must bear in year estimates for 1990 and 2006 and projections income countries; in developing countries errors may relation to children and the elderly. But dependency for 2015. · Average annual population growth is be substantial because of limits in the transport, ratios show only the age composition of a population, the exponential change for the period indicated. See communications, and other resources required to not economic dependency. Some children and elderly Statistical methods for more information. · Popula- conduct and analyze a full census. people are part of the labor force; many working-age tion age composition is the percentage of the total The quality and reliability of official demographic people are not. population that is in specific age groups. · Depen- data are also affected by public trust in the govern- The vital rates in the table are based on data from dency ratio is the ratio of dependents--people ment, government commitment to accurate enumera- birth and death registration systems, censuses, and younger than 15 or older than 64--to the working- tion, confidentiality and protection against misuse of sample surveys by national statistical offices and other age population--those ages 15­64. · Crude death census data, and census agencies' independence organizations, or on demographic analysis. The 2006 rate and crude birth rate are the number of deaths from political influence. Moreover, comparability of estimates for many countries are projections based on and the number of live births occurring during the population indicators is limited by differences in the extrapolations of levels and trends from earlier years or year, per 1,000 population, estimated at midyear. concepts, definitions, collection procedures, and esti- interpolations of population estimates and projections Subtracting the crude death rate from the crude birth mation methods used by national statistical agencies from the United Nations Population Division. rate provides the rate of natural increase, which is and other organizations that collect the data. Vital registers are the preferred source for these equal to the population growth rate in the absence Of the 153 economies in the table, 131 (about data, but in many developing countries systems for of migration. 86 percent) conducted a census between 1995 and registering births and deaths are absent or incom- 2006. The currentness of censuses and the availabil- plete because of defi ciencies in the coverage of ity of complementary data from surveys or registra- events or geographic areas. Many developing coun- tion systems are objective ways to judge demographic tries carry out special household surveys that ask data quality. Some European countries' registration respondents about births and deaths in the recent systems offer complete information on population past. Estimates derived in this way are subject to in the absence of a census. See Primary data docu- sampling errors and errors due to inaccurate recall. mentation for the most recent census or survey year The United Nations Statistics Division monitors and for the completeness of registration. the completeness of vital registration systems. The Current population estimates for developing coun- share of countries with at least 90 percent complete tries that lack recent census-based data and pre- and vital registration rose from 45 percent in 1988 to 62 post-census estimates for countries with census data percent in 2006. Still, some of the most populous Data sources are provided by the United Nations Population Division developing countries--China, India, Indonesia, Brazil, and other agencies. The standard estimation method Pakistan, Bangladesh, Nigeria--lack complete vital The World Bank's population estimates are com- requires fertility, mortality, and net migration data, registration systems. From 2003 to 2006, 51 percent piled and produced by its Human Development often collected from sample surveys, which can be of births and deaths and 48 percent of infant deaths Network and Development Data Group in consulta- small or limited in coverage. Population estimates are worldwide were registered and reported. tion with its operational staff and country offices. from demographic modeling and so are susceptible International migration is the only other factor Important inputs to the World Bank's demographic to biases and errors from shortcomings in the model besides birth and death rates that directly determines work come from the United Nations Population as well as in the data. Population projections use a country's population growth. From 1990 to 2005 the Division's World Population Prospects: The 2006 the cohort component method. Because of a drastic number of immigrants in high-income countries rose Revision; census reports and other statistical reduction in estimated mortality due partly to revised by 40 million. About 190 million people (3 percent publications from national statistical offi ces; lower estimates of HIV prevalence, populations of of the world's population) currently live outside their household surveys conducted by national agen- several countries, notably in Sub-Saharan Africa, home country. Estimating international migration is cies, Macro International, and the U.S. Centers for have been revised upward from previous estimates. difficult. At any time many people are located outside Disease Control and Prevention; Eurostat, Demo- The growth rate of the total population conceals their home country as tourists, workers, or refugees graphic Statistics (various years); Centro Latino- the fact that different age groups may grow at differ- or for other reasons. Standards for the duration and americano de Demografía, Boletín Demográfico ent rates. In many developing countries the under-15 purpose of international moves that qualify as migra- (various years); and U.S. Bureau of the Census, population was growing rapidly but has begun to tion vary, and estimates require information on flows International Database. shrink. Previously high fertility rates and declining into and out of countries that is difficult to collect. 2008 World Development Indicators 43 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2006 1990 2006 1990 2006 1990­2006 1990 2006 Afghanistan .. .. .. .. .. .. .. .. .. Albania 83 70 58 49 1.6 1.4 ­0.7 40.2 41.8 Algeria 78 80 23 37 7.2 13.9 4.1 22.6 31.0 Angola 90 92 74 74 4.5 7.3 3.0 46.4 45.8 Argentina 78 76 38 54 13.0 18.8 2.3 34.4 43.1 Armenia 87 60 72 48 1.9 1.3 ­2.6 47.7 48.9 Australia 75 70 52 56 8.4 10.5 1.4 41.3 44.8 Austria 70 66 43 50 3.5 4.0 0.8 40.8 44.4 Azerbaijan 78 73 64 61 3.3 4.3 1.6 47.4 47.7 Bangladesh 89 86 63 52 51.2 71.0 2.0 40.2 36.7 Belarus 76 64 61 53 5.3 4.8 ­0.7 48.8 49.1 Belgium 61 60 37 44 3.9 4.5 0.9 39.1 43.6 Benin 90 86 58 54 2.0 3.4 3.3 40.8 38.3 Bolivia 80 84 49 63 2.5 4.3 3.3 39.2 43.5 Bosnia and Herzegovina 78 68 60 59 2.2 2.0 ­0.6 44.4 48.4 Botswana 77 70 57 46 0.5 0.7 2.1 44.5 40.3 Brazil 85 79 45 57 62.5 93.1 2.5 35.0 42.9 Bulgaria 68 52 60 40 4.4 3.1 ­2.3 48.0 45.0 Burkina Faso 91 89 77 78 3.9 6.5 3.2 47.5 47.1 Burundi 90 93 91 92 2.8 4.2 2.5 52.5 51.4 Cambodia 85 80 78 75 4.4 6.9 2.9 52.4 50.7 Cameroon 82 80 56 52 4.6 7.0 2.6 41.3 39.6 Canada 76 72 58 61 14.7 17.9 1.2 44.0 46.1 Central African Republic 89 89 71 71 1.4 2.0 2.3 47.0 46.0 Chad 80 78 64 66 2.4 4.0 3.3 45.7 46.8 Chile 77 70 32 37 5.0 6.6 1.7 30.5 35.4 China 85 82 73 69 650.6 780.5 1.1 44.8 44.1 Hong Kong, China 80 70 47 54 2.9 3.6 1.5 36.3 45.5 Colombia 81 81 46 62 14.0 22.8 3.0 37.0 44.8 Congo, Dem. Rep. 91 91 61 61 15.2 24.2 2.9 41.6 41.3 Congo, Rep. 86 88 58 57 1.0 1.5 2.9 41.3 40.1 Costa Rica 84 81 33 46 1.2 2.0 3.5 27.6 35.6 Côte d'Ivoire 90 89 44 39 4.7 7.1 2.6 30.0 29.3 Croatia 71 60 47 45 2.2 1.9 ­0.8 42.1 44.8 Cuba 73 73 39 44 4.6 5.3 0.9 34.6 37.3 Czech Republic 73 67 61 52 5.4 5.2 ­0.3 47.5 44.9 Denmark 75 69 62 59 2.9 2.8 ­0.2 46.1 46.4 Dominican Republic 84 82 36 47 2.7 4.1 2.5 29.6 36.4 Ecuador 85 82 33 61 3.7 6.4 3.5 27.8 42.7 Egypt, Arab Rep. 75 73 27 20 16.5 23.1 2.1 26.3 21.7 El Salvador 80 75 51 48 2.0 2.7 2.1 41.2 40.7 Eritrea 92 90 61 58 1.3 2.0 2.7 41.8 41.0 Estonia 77 65 64 52 0.9 0.7 ­1.6 49.8 48.9 Ethiopia 91 89 72 71 22.6 34.4 2.6 44.9 44.9 Finland 70 66 58 57 2.6 2.7 0.2 47.2 47.4 France 65 61 46 48 24.8 27.3 0.6 43.3 45.5 Gabon 84 83 63 62 0.4 0.6 2.7 43.8 42.7 Gambia, The 86 86 63 59 0.4 0.7 3.5 42.6 40.8 Georgia 72 76 69 49 2.9 2.2 ­1.6 52.3 42.7 Germany 72 65 44 51 38.3 41.0 0.4 40.4 45.1 Ghana 80 75 76 70 6.7 10.3 2.6 48.8 47.8 Greece 67 65 36 44 4.2 5.2 1.4 36.2 40.7 Guatemala 89 83 29 34 2.9 4.2 2.4 24.7 31.3 Guinea 90 87 80 80 2.8 4.4 2.7 47.3 47.5 Guinea-Bissau 91 93 58 61 0.4 0.7 2.9 40.3 40.8 Haiti 83 84 58 56 2.8 4.1 2.4 42.7 41.3 44 2008 World Development Indicators 2.2 PEOPLE Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2006 1990 2006 1990 2006 1990­2006 1990 2006 Honduras 87 89 33 55 1.6 3.0 4.0 27.9 39.4 Hungary 64 58 46 42 4.5 4.2 ­0.5 44.5 45.0 India 85 82 37 34 325.6 438.0 1.9 28.4 28.1 Indonesia 81 85 50 51 75.3 109.2 2.3 38.4 37.9 Iran, Islamic Rep. 81 74 22 40 15.6 29.1 3.9 20.2 34.3 Iraq 76 .. 16 .. 4.7 .. .. 16.8 .. Ireland 70 72 36 54 1.3 2.1 2.9 34.3 43.0 Israel 62 59 41 51 1.6 2.8 3.3 40.5 47.0 Italy 66 61 36 38 23.9 24.8 0.2 37.1 39.9 Jamaica 80 74 66 54 1.1 1.2 0.2 46.8 43.3 Japan 77 73 50 48 63.9 66.2 0.2 40.6 40.8 Jordan 69 77 18 28 0.8 1.9 5.7 18.8 25.4 Kazakhstan 78 75 61 65 7.7 8.1 0.3 46.3 49.4 Kenya 90 90 75 70 9.8 16.7 3.3 45.9 44.2 Korea, Dem. Rep. 82 78 52 48 9.9 11.4 0.9 40.6 39.3 Korea, Rep. 73 74 47 50 19.1 24.5 1.6 39.3 40.8 Kuwait 82 85 35 50 0.9 1.4 3.2 21.8 25.7 Kyrgyz Republic 74 74 59 55 1.8 2.3 1.5 46.2 44.0 Lao PDR 80 80 53 54 1.5 2.4 2.8 40.6 41.0 Latvia 77 64 63 49 1.5 1.1 ­1.8 49.7 48.0 Lebanon 78 80 32 34 1.0 1.6 2.9 31.2 31.0 Lesotho 86 74 57 46 0.6 0.7 0.8 46.5 43.5 Liberia 85 83 55 55 0.8 1.3 3.2 39.4 39.7 Libya 79 82 19 35 1.3 2.5 4.2 16.9 27.8 Lithuania 75 64 59 52 1.9 1.6 ­1.0 48.1 49.0 Macedonia, FYR 73 65 48 41 0.9 0.9 0.1 40.0 39.0 Madagascar 83 86 79 79 5.4 8.9 3.2 49.2 48.3 Malawi 91 90 85 86 4.4 6.3 2.2 50.2 50.0 Malaysia 81 81 44 47 7.1 11.6 3.0 34.8 36.0 Mali 89 82 72 72 3.2 4.8 2.5 46.9 49.2 Mauritania 86 84 56 54 0.8 1.3 3.1 40.2 39.2 Mauritius 82 79 42 43 0.5 0.6 1.4 33.9 35.7 Mexico 84 80 34 40 29.9 43.1 2.3 30.0 35.2 Moldova 75 68 61 54 2.1 1.9 ­0.8 48.5 46.8 Mongolia 82 82 56 54 0.8 1.3 2.5 41.0 40.1 Morocco 81 80 24 27 7.6 11.3 2.5 23.7 26.1 Mozambique 88 83 88 85 6.4 9.8 2.7 54.0 53.4 Myanmar 88 86 69 68 20.2 27.3 1.9 44.7 44.9 Namibia 65 63 49 47 0.4 0.7 2.7 45.0 43.8 Nepal 80 78 48 50 7.1 10.8 2.6 37.9 40.5 Netherlands 71 73 44 57 6.9 8.6 1.3 39.1 44.4 New Zealand 74 74 53 61 1.7 2.2 1.8 43.1 46.1 Nicaragua 86 86 35 36 1.3 2.1 2.8 29.7 30.0 Niger 95 95 71 71 3.3 5.9 3.6 43.7 42.4 Nigeria 86 85 48 46 33.9 52.7 2.8 36.5 35.3 Norway 73 73 57 64 2.2 2.6 0.9 44.7 46.8 Oman 83 81 15 24 0.6 1.0 3.3 11.1 17.3 Pakistan 86 83 28 33 35.0 59.6 3.3 23.3 27.3 Panama 79 79 39 52 0.9 1.5 3.0 32.5 39.2 Papua New Guinea 75 75 72 72 1.8 2.7 2.7 46.7 48.7 Paraguay 83 84 52 65 1.7 2.9 3.5 38.1 43.3 Peru 80 82 47 60 8.5 13.4 2.8 37.0 42.5 Philippines 83 83 47 56 23.5 38.4 3.1 36.5 40.2 Poland 74 61 57 47 18.6 17.2 ­0.5 45.8 45.7 Portugal 73 70 50 56 4.8 5.6 1.0 42.7 46.2 Puerto Rico 61 59 31 38 1.2 1.5 1.5 35.8 41.4 2008 World Development Indicators 45 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2006 1990 2006 1990 2006 1990­2006 1990 2006 Romania 71 62 54 50 11.0 10.1 ­0.5 44.3 45.9 Russian Federation 77 68 60 55 77.3 73.5 ­0.3 48.4 48.8 Rwanda 87 84 86 80 3.1 4.4 2.1 51.8 51.4 Saudi Arabia 80 80 15 18 5.1 8.4 3.2 11.4 14.2 Senegal 87 81 61 56 3.2 4.8 2.5 40.8 41.2 Serbia 72a 70a 50a 51a 3.5b 3.6b 0.2b 41.8b 42.9b Sierra Leone 90 94 53 56 1.7 2.5 2.3 38.5 38.5 Singapore 80 76 50 50 1.6 2.3 2.4 38.8 39.9 Slovak Republic 75 68 60 52 2.6 2.7 0.1 46.3 44.9 Slovenia 70 67 54 54 1.0 1.0 0.4 45.5 46.0 Somalia 96 95 61 59 2.9 3.6 1.5 39.9 39.2 South Africa 79 79 54 46 14.4 20.0 2.1 41.6 37.9 Spain 69 67 34 45 15.9 21.1 1.8 34.4 40.6 Sri Lanka 79 76 45 35 7.2 8.4 1.0 36.0 32.3 Sudan 79 71 27 24 7.7 10.7 2.0 26.0 24.9 Swaziland 78 75 38 32 0.2 0.4 2.9 38.0 32.3 Sweden 72 67 63 59 4.7 4.7 0.0 47.7 46.6 Switzerland 80 75 52 61 3.7 4.2 0.9 40.4 46.1 Syrian Arab Republic 82 88 29 39 3.6 7.9 4.9 26.0 30.5 Tajikistan 74 62 52 46 1.9 2.2 0.9 42.2 43.7 Tanzania 91 90 88 86 12.4 19.3 2.8 50.2 49.7 Thailand 88 81 75 66 31.4 36.5 0.9 46.9 46.7 Timor-Leste 79 83 50 56 0.3 0.4 1.9 37.5 39.5 Togo 90 90 54 50 1.5 2.5 3.2 38.5 36.7 Trinidad and Tobago 75 77 42 47 0.5 0.6 2.0 37.0 38.9 Tunisia 76 75 21 29 2.4 3.9 3.0 21.6 27.9 Turkey 82 76 34 28 21.0 27.4 1.7 29.4 26.5 Turkmenistan 77 73 64 61 1.5 2.3 2.4 46.9 46.5 Uganda 92 86 80 80 8.0 12.6 2.9 47.2 48.4 Ukraine 73 64 58 50 26.3 22.5 ­1.0 49.3 48.1 United Arab Emirates 92 93 25 41 0.9 2.7 6.7 9.8 14.6 United Kingdom 75 69 53 55 29.7 30.8 0.2 43.3 45.4 United States 76 73 57 60 129.3 157.0 1.2 44.3 45.9 Uruguay 76 78 46 57 1.4 1.7 1.3 39.9 44.4 Uzbekistan 76 73 60 57 8.2 11.6 2.2 45.4 44.6 Venezuela, RB 81 84 38 59 7.3 13.3 3.8 31.8 41.3 Vietnam 81 78 74 72 31.3 44.8 2.2 48.4 48.2 West Bank and Gaza 64 66 9 10 0.4 0.8 4.4 11.9 13.2 Yemen, Rep. 74 75 28 30 3.0 6.3 4.6 27.5 28.2 Zambia 90 91 66 66 3.4 5.0 2.3 43.1 42.6 Zimbabwe 80 85 70 64 4.2 6.0 2.2 47.0 43.6 World 81 w 79 w 54 w 53 w 2,386.6 t 3,081.8 t 1.6 w 39.7 w 39.9 w Low income 85 83 48 46 694.0 995.4 2.3 35.1 35.0 Middle income 82 79 59 57 1,258.0 1,582.6 1.4 41.7 41.9 Lower middle income 83 81 63 60 954.4 1,208.6 1.5 42.4 42.0 Upper middle income 79 74 48 49 303.7 374.0 1.3 39.5 41.5 Low & middle income 83 81 55 53 1,952.1 2,578.0 1.7 39.4 39.2 East Asia & Pacific 85 82 69 66 858.7 1,074.1 1.4 44.1 43.5 Europe & Central Asia 75 68 56 49 216.4 214.6 ­0.1 45.7 44.7 Latin America & Carib. 83 80 41 53 171.1 257.4 2.6 33.9 40.8 Middle East & N. Africa 78 77 23 30 64.9 111.8 3.4 22.9 28.0 South Asia 85 82 39 36 430.6 597.1 2.0 29.7 29.3 Sub-Saharan Africa 87 85 63 61 210.3 323.0 2.7 43.0 42.2 High income 73 70 49 52 434.5 503.8 0.9 41.4 43.4 Euro area 68 64 41 47 131.8 148.8 0.8 39.6 43.4 a. Includes Montenegro. b. Excludes Kosovo and Metohija. 46 2008 World Development Indicators 2.2 PEOPLE Labor force structure About the data Definitions The labor force is the supply of labor available for pro- The labor force participation rates in the table are · Labor force participation rate is the proportion ducing goods and services in an economy. It includes from Key Indicators of the Labour Market, 5th edition. of the population ages 15 and older that is eco- people who are currently employed and people who These harmonized estimates use strict data selec- nomically active: all people who supply labor for the are unemployed but seeking work as well as first-time tion criteria and enhanced methods to ensure compa- production of goods and services during a specified job-seekers. Not everyone who works is included, rability across countries and over time, including col- period. · Total labor force comprises people ages however. Unpaid workers, family workers, and stu- lection and tabulation methodologies and methods 15 and older who meet the ILO definition of the dents are often omitted, and some countries do not applied to such country-specific factors as military economically active population. It includes both the count members of the armed forces. Labor force size service requirements. Estimates are based mainly on employed and the unemployed. · Average annual tends to vary during the year as seasonal workers labor force surveys, with other sources (population percentage growth of the labor force is calculated enter and leave. censuses and nationally reported estimates) used using the exponential endpoint method (see Statisti- Data on the labor force are collected from labor only when no survey data are available. cal methods for more information). · Females as a force surveys, censuses, establishment censuses Participation rates indicate the relative size of the percentage of the labor force show the extent to and surveys, and administrative records such as labor supply. The indicator in this edition is for the which women are active in the labor force. employment exchange registers and unemployment population ages 15 and older, to include people who insurance schemes. For some countries a combina- continue working past age 65. In previous editions tion of these sources is used. Labor force surveys the indicator was for the population ages 15­64, are the most comprehensive source for internation- so participation rates are not comparable across ally comparable labor force data. They can cover all editions. noninstitutionalized civilians, all branches and sec- The labor force estimates in the table were cal- tors of the economy, and all categories of workers, culated by applying labor force participation rates including people holding multiple jobs. By contrast, from the International Labour Organization (ILO) data- labor force data from population censuses are often base to World Bank population estimates to create a based on a limited number of questions on the eco- series consistent with these population estimates. nomic characteristics of individuals, with little scope This procedure sometimes results in labor force to probe. The resulting data often differ from labor estimates that differ slightly from those in the ILO's force survey data and vary considerably by country, Yearbook of Labour Statistics and its database Key depending on the census scope and coverage. Estab- Indicators of the Labour Market. lishment censuses and surveys provide data only on Estimates of women in the labor force and employ- the employed population, not unemployed workers, ment are generally lower than those of men and are workers in small establishments, or workers in the not comparable internationally, reflecting that demo- informal sector (International Labour Organization, graphic, social, legal, and cultural trends and norms Key Indicators of the Labour Market 2001­2002). determine whether women's activities are regarded The reference period of a census or survey is as economic. In many countries many women work another important source of differences: in some on farms or in other family enterprises without pay, countries data refer to people's status on the day and others work in or near their homes, mixing work of the census or survey or during a specific period and family activities during the day. before the inquiry date, while in others data are recorded without reference to any period. In devel- oping countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Differing definitions of employment age also affect Data sources comparability. For most countries the working age is 15 and older, but in some developing countries chil- Data on labor force participation rates are from dren younger than 15 work full- or part-time and are the ILO database Key Indicators of the Labour included in the estimates. Similarly, some countries Market, 5th edition. Labor force numbers were have an upper age limit. As a result, calculations may calculated by World Bank staff, applying labor systematically over- or underestimate actual rates. force participation rates from the ILO database For further information on source, reference period, to population estimates. or definition, consult the original source. 2008 World Development Indicators 47 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. 23 .. 11 .. 24 .. 25 .. 53 .. 64 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0 b,c 2c 0 b,c 1c 40 c 33c 18 c 11c 59c 66c 81c 88 c Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 6 5 4 3 32 31 12 9 61 65 84 88 Austria 6 6c 8 6c 47 40 c 20 13c 46 55c 72 81c Azerbaijan .. 41 .. 37 .. 15 .. 9 .. 44 .. 54 Bangladesh 54 50 85 59 16 12 9 18 25 38 2 23 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 3c 2c 2c 2c 41c 35c 16c 11c 56c 62c 81c 86c Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 3c .. 1c .. 42c .. 17c .. 55c .. 82c .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 29 .. 13 .. 28 .. 17 .. 43 .. 71 Brazil 31c 25c 25c 16c 27c 27c 10 c 13c 43c 48 c 65c 71c Bulgaria .. 11 .. 7 .. 39 .. 29 .. 50 .. 64 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 53 .. 68 .. 14 .. 4 .. 26 .. 23 .. Canada 6c 4c 2c 2c 31c 32c 11c 11c 64 c 64 c 87c 88 c Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 24 17 6 6 32 29 15 12 45 54 79 83 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China 1 0b 0b 0b 37 22 27 7 63 77 73 93 Colombia 2 32b 1b,c 8b,c 35 21 25 16 63 48 74 76 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 32 21 5 5 27 26 25 13 41 52 69 82 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 16c .. 19c .. 37c .. 18 c .. 47c .. 63c Cuba .. 28 .. 10 .. 23 .. 14 .. 50 .. 76 Czech Republic 9 5 7 3 55 49 33 27 36 46 61 71 Denmark 7 4 3 2 37 34 16 12 56 62 81 86 Dominican Republic 26 21 3 3 23 26 21 15 52 53 76 82 Ecuador 10 c 11c 2c 4c 29c 27c 17c 12c 62c 62c 81c 84 c Egypt, Arab Rep. 35 28 52 39 25 23 10 6 41 49 37 55 El Salvador 48 c 28 15c 5 23c 25 23c 22 29c 45 63c 75 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 23 7 13 4 42 44 30 24 36 49 57 72 Ethiopia .. 84 c .. 76c .. 5c .. 8c .. 10 c .. 16c Finland 11 7 6 3 38 38 15 12 51 56 78 84 France .. 5 .. 2 .. 35 .. 12 .. 60 .. 85 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 52 .. 57 .. 14 .. 4 .. 34 .. 38 Germany 4 3 4 2 50 41 24 16 47 56 72 82 Ghana 66 .. 59 .. 10 .. 10 .. 23 .. 32 .. Greece 20 c 12c 26c 14 c 32c 30 c 17c 10 c 48 c 58 c 56c 76c Guatemala .. .. .. .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 48 2008 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 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a Honduras 53 51 6 13 18 20 25 23 29 29 69 63 Hungary .. 7c .. 3c .. 42c .. 21c .. 51c .. 76c India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 54 43 57 41 15 20 13 15 31 37 31 44 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 31 15 11 57 65 83 88 Italy 8 5 9 3 37 39 22 18 55 56 70 79 Jamaica 36 25 16 9 25 27 12 5 39 48 72 86 Japan 6 4 7 5 40 35 27 18 54 59 65 77 Jordan .. 4 .. 2 .. 23 .. 12 .. 73 .. 84 Kazakhstan .. 33 .. 30 .. 25 .. 12 .. 42 .. 58 Kenya 19 c .. 20 c .. 23c .. 9c .. 58 c .. 71c .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 14 7 18 9 40 34 28 17 46 59 54 74 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 39 .. 39 .. 23 .. 11 .. 38 .. 50 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 15c .. 8c .. 35c .. 16c .. 49c .. 75c Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 25 17c 15 11c 46 37c 31 21c 29 46c 54 68 c Macedonia, FYR .. 20 .. 19 .. 34 .. 30 .. 46 .. 51 Madagascar .. 77 .. 79 .. 7 .. 6 .. 16 .. 15 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23 16 20 11 31 35 32 27 46 49 48 62 Mali .. 50 .. 30 .. 18 .. 15 .. 32 .. 55 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 15 11 13 9 36 34 48 29 48 55 39 62 Mexico 34 21 11 5 25 30 19 19 41 49 70 76 Moldova .. 41 .. 40 .. 21 .. 12 .. 38 .. 48 Mongolia .. 43 .. 37 .. 19 .. 15 .. 38 .. 48 Morocco .. 38 .. 63 .. 22 .. 14 .. 40 .. 23 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 .. 52 .. 21 .. 8 .. 34 .. 40 .. Nepal 75 .. 91 .. 4 .. 1 .. 20 .. 8 .. Netherlands 5 4 3 2 33 30 10 8 60 62 82 86 New Zealand 13 9 8 5 31 32 13 11 56 59 80 84 Nicaragua .. 41 .. 10 .. 19 .. 17 .. 33 .. 52 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 7 5 3 2 34 32 10 8 58 63 86 90 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 45 38 69 67 20 21 15 15 35 41 16 18 Panama 35 22 3 4 20 22 11 9 45 56 85 86 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 3c 39c 0 b,c 20 c 33c 19c 19c 10 c 64 c 42c 80 c 70 c Peru 1c 1c 0 b,c 0 b,c 30 c 31c 13c 13c 69c 68 c 87c 86c Philippines 53c 45 32c 25 17c 17 14 c 12 29c 39 55c 64 Poland .. 18 c .. 17c .. 39c .. 17c .. 43c .. 66c Portugal 10 c 11c 13c 13c 39c 41c 24 c 19c 51c 48 c 63c 68 c Puerto Rico 5 3 0b 0b 27 25 19 11 67 72 80 89 2008 World Development Indicators 49 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a Romania 29 c 31 38 c 33 44 c 35 30 c 25 28 c 34 33c 42 Russian Federation .. 12 .. 8 .. 38 .. 21 .. 50 .. 71 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5 .. 0b .. 11 .. 1 .. 85 .. 99 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 1 0 0b 0 36 36 32 21 63 63 68 79 Slovak Republic .. 6c .. 3c .. 50 c .. 25c .. 44 c .. 72c Slovenia .. 9 .. 9 .. 47 .. 25 .. 43 .. 65 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 13 .. 7 .. 33 .. 14 .. 54 .. 79 Spain 11c 6c 8c 4c 41c 41c 16c 12c 49c 52c 76c 84 c Sri Lanka .. .. .. .. .. .. .. .. .. .. .. .. Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5c 3c 2c 1c 40 c 34 c 12c 9c 55c 63c 86c 90 c Switzerland 4c 5c 4c 3c 37c 32c 15c 11c 59c 63c 81c 86c Syrian Arab Republic 23 23 54 49 28 29 8 8 49 48 38 43 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 78 c .. 90 c .. 7c .. 1c .. 15c .. 8c .. Thailand 60 44 62 41 18 22 13 19 22 34 25 41 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 6 6 2 34 41 14 16 51 52 80 82 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 22 72 52 26 28 11 15 41 50 17 33 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 91 60c 91 77c 4 11c 6 5c 5 29c 3 18 c Ukraine .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 3 2 1 1 41 33 16 9 55 65 82 90 United States 4 2 1 1 34 30 14 10 62 68 85 90 Uruguay 7c 7c 1c 2c 36c 29c 21c 13c 57c 64 c 78 c 86c Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 16c 2 2c 32 25c 16 11c 52 59c 82 86c Vietnam .. 56 .. 60 .. 21 .. 14 .. 23 .. 26 West Bank and Gaza .. 12 .. 34 .. 28 .. 8 .. 59 .. 56 Yemen, Rep. 44 .. 83 .. 14 .. 2 .. 38 .. 13 .. Zambia .. .. .. .. .. .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 20 .. 14 .. 31 .. 17 .. 49 .. 68 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 19 .. 18 .. 34 .. 19 .. 47 .. 62 Latin America & Carib. 20 21 14 10 30 27 14 15 50 52 72 76 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 5 3 38 34 19 13 56 62 76 85 Euro area 7 5 7 3 42 38 20 14 50 56 72 82 Note: Data across sectors may not sum to 100 percent because of workers not classified by sectors. a. Data are for the most recent year available. b. Less than 0.5. c. Limited coverage. 50 2008 World Development Indicators 2.3 PEOPLE Employment by economic activity About the data Definitions The International Labour Organization (ILO) classi- aggregated into three broad groups: agriculture, · Agriculture corresponds to division 1 (ISIC revi- fies economic activity using the International Stan- industry, and services. Such broad classification may sion 2) or tabulation categories A and B (ISIC revi- dard Industrial Classification (ISIC) of All Economic obscure fundamental shifts within countries' indus- sion 3) and includes hunting, forestry, and fishing. Activities, revision 2 (1968) and revision 3 (1990). trial patterns. A slight majority of countries report · Industry corresponds to divisions 2­5 (ISIC revi- Because this classification is based on where work economic activity according to the ISIC revision 2 sion 2) or tabulation categories C­F (ISIC revision is performed (industry) rather than type of work per- instead of revision 3. The use of one classification or 3) and includes mining and quarrying (including oil formed (occupation), all of an enterprise's employees the other should not have a significant impact on the production), manufacturing, construction, and public are classified under the same industry, regardless information for the three broad sectors presented utilities (electricity, gas, and water). · Services corre- of their trade or occupation. The categories should in the table. spond to divisions 6­9 (ISIC revision 2) or tabulation sum to 100 percent. Where they do not, the differ- The distribution of economic wealth in the world categories G­P (ISIC revision 3) and include whole- ences are due to workers who cannot be classified remains strongly correlated with employment by sale and retail trade and restaurants and hotels; by economic activity. economic activity. The wealthier economies are transport, storage, and communications; financing, Data on employment are drawn from labor force those with the largest share of total employment in insurance, real estate, and business services; and surveys, household surveys, official estimates, cen- services, whereas the poorer economies are largely community, social, and personal services. suses and administrative records of social insurance agriculture based. schemes, and establishment surveys when no other The distribution of economic activity by gender information is available. The concept of employment reveals some clear patterns. Men still make up the generally refers to people above a certain age who majority of people employed in all three sectors, but worked, or who held a job, during a reference period. the gender gap is biggest in industry. Employment in Employment data include both full-time and part-time agriculture is also male-dominated, although not as workers. much as industry. Segregating one sex in a narrow There are many differences in how countries define range of occupations significantly reduces economic and measure employment status, particularly, mem- efficiency by reducing labor market flexibility and thus bers of the armed forces, self-employed workers, and the economy's ability to adapt to change. This seg- unpaid family workers. Where members of the armed regation is particularly harmful for women, who have forces are included, they are allocated to the service a much narrower range of labor market choices and sector, causing that sector to be somewhat over- lower levels of pay than men. But it is also detri- stated relative to the service sector in economies mental to men when job losses are concentrated where they are excluded. Where data are obtained in industries dominated by men and job growth is from establishment surveys, data cover only employ- centered in service occupations, where women have ees; thus self-employed and unpaid family workers better chances, as has been the recent experience are excluded. In such cases the employment share in many countries. of the agricultural sector is severely underreported. There are several explanations for the rising impor- Caution should be also used where the data refer tance of service jobs for women. Many service jobs-- only to urban areas, which record little or no agricul- such as nursing and social and clerical work--are tural work. Moreover, the age group and area covered considered "feminine" because of a perceived simi- could differ by country or change over time within a larity to women's traditional roles. Women often do country. For detailed information on breaks in series, not receive the training needed to take advantage of consult the original source. changing employment opportunities. And the greater Countries also take different approaches to the availability of part-time work in service industries may treatment of unemployed people. In most countries lure more women, although it is unclear whether this unemployed people with previous job experience are is a cause or an effect. classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribu- tion of employment by economic activity may not be fully comparable across countries. Data sources The ILO's Yearbook of Labour Statistics and its data- base Key Indicators of the Labour Market report data Data on employment are from the ILO database by major divisions of the ISIC revision 2 or revision 3. Key Indicators of the Labour Market, 5th edition. In the table the reported divisions or categories are 2008 World Development Indicators 51 2.4 Decent work and productive employment Employment to Vulnerable Labor population ratio employment productivity Unpaid family workers and own-account workers GDP per person employed Male Female Index % ages 15 and older % ages 15­24 % of male employment % of female employment 1990 PPP $a 1980 = 100 1991 2006 1991 2006 1990 2005 1990 2005 1990 2006 1990 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 62 51 45 37 .. .. .. .. 2,499 3,502 107 149 Algeria 37 50 22 33 .. 29 .. 32 2,946 3,401 94 108 Angola 74 75 64 66 .. .. .. .. 869 1,143 90 119 Argentina 54 60 45 46 .. 23b .. 19b 6,436 8,915 78 109 Armenia 68 49 51 19 .. .. .. .. 6,066 8,428 .. .. Australia 57 60 56 63 12 12 8 7 17,106 24,603 119 171 Austria 54 55 61 50 .. 9 .. 8 16,895 22,708 123 165 Azerbaijan 59 61 39 41 .. .. .. .. 4,639 5,954 .. .. Bangladesh 73 67 64 57 .. 60 .. 73 640 1,014 117 185 Belarus 59 52 40 36 .. .. .. .. 7,184 9,491 .. .. Belgium 46 48 32 28 .. 11 .. 10 17,197 22,582 119 156 Benin 67 64 55 49 .. .. .. .. .. .. .. .. Bolivia 61 70 44 51 32b .. 50 b .. 2,197 2,764 85 107 Bosnia and Herzegovina 58 55 37 37 .. .. .. .. 3,737 6,469 .. .. Botswana 57 44 38 21 .. 7 .. 17 .. .. .. .. Brazil 60 61 54 49 29b 34b 30 b 32b 4,923 5,812 95 112 Bulgaria 50 41 31 20 .. 11 .. 9 5,597 7,780 93 129 Burkina Faso 81 82 74 73 .. .. .. .. 810 1,135 111 155 Burundi 83 84 67 71 .. .. .. .. .. .. .. .. Cambodia 79 76 69 63 .. .. .. .. 880 1,827 106 220 Cameroon 63 61 48 44 .. .. .. .. 1,222 1,155 102 97 Canada 59 62 57 59 .. .. .. .. 18,872 24,633 117 152 Central African Republic 73 72 56 57 .. .. .. .. .. .. .. .. Chad 66 65 44 45 .. .. .. .. .. .. .. .. Chile 51 49 34 22 .. 29 .. 24 6,402 12,207 113 215 China 76 73 73 65 .. .. .. .. 1,871 6,352 176 599 Hong Kong, China 63 58 54 39 .. 10 .. 5 17,541 27,769 167 264 Colombia 54 63 41 46 30 b 44 26 b 44 4,840 5,767 114 135 Congo, Dem. Rep. 67 68 56 58 .. .. .. .. 510 224 85 38 Congo, Rep. 66 66 49 48 .. .. .. .. .. .. .. .. Costa Rica 55 60 48 44 26 20 20 23 4,747 7,321 97 149 Côte d'Ivoire 62 58 47 45 .. .. .. .. 1,363 1,310 65 63 Croatia 52 45 34 27 .. 19 .. 21 7,351 8,326 .. .. Cuba 54 58 39 37 .. .. .. .. 2,948 3,008 112 114 Czech Republic 62 55 51 30 .. 15 .. 8 8,895 11,688 .. .. Denmark 62 61 65 61 .. .. .. .. 18,452 24,816 121 163 Dominican Republic 49 53 32 32 42 49 30 31 2,473 4,344 104 183 Ecuador 55 66 43 48 33b 30 b 41b 39b 3,903 4,831 95 117 Egypt, Arab Rep. 43 42 22 20 .. 21 .. 46 2,522 3,386 122 164 El Salvador 58 57 41 37 .. 29 .. 45 .. .. .. .. Eritrea 68 66 60 56 .. .. .. .. .. .. .. .. Estonia 68 54 51 29 2 7 3 4 10,820 20,795 .. .. Ethiopia 77 76 74 71 .. 89 .. 93 578 702 89 108 Finland 59 56 45 43 .. .. .. .. 16,866 23,358 130 180 France 50 49 28 23 .. 8 .. 5 18,093 22,402 120 148 Gabon 60 59 42 39 .. .. .. .. .. .. .. .. Gambia, The 68 66 52 51 .. .. .. .. .. .. .. .. Georgia 60 53 37 24 .. 64 .. 65 7,616 4,721 .. .. Germany 56 52 58 41 .. 7 .. 6 16,306 20,018 .. .. Ghana 72 66 51 42 .. .. .. .. 1,063 1,485 92 128 Greece 46 50 31 28 .. 29 .. 28 10,015 15,440 112 172 Guatemala 58 55 52 49 .. .. .. .. 3,631 4,554 83 104 Guinea 82 81 72 70 .. .. .. .. .. .. .. .. Guinea-Bissau 67 69 56 60 .. .. .. .. .. .. .. .. Haiti 60 65 39 50 .. .. .. .. .. .. .. .. 52 2008 World Development Indicators 2.4 PEOPLE Decent work and productive employment Employment to Vulnerable Labor population ratio employment productivity Unpaid family workers and own-account workers GDP per person employed Male Female Index % ages 15 and older % ages 15­24 % of male employment % of female employment 1990 PPP $a 1980 = 100 1991 2006 1991 2006 1990 2005 1990 2005 1990 2006 1990 2006 Honduras 57 69 48 60 48b 48b 50 b 51b .. .. .. .. Hungary 49 46 39 24 8 9 7 6 6,459 9,291 102 147 India 59 56 46 40 .. .. .. .. 1,309 2,611 140 278 Indonesia 63 61 45 37 .. .. .. .. 2,526 4,126 135 220 Iran, Islamic Rep. 46 51 33 34 .. .. .. .. 3,503 5,786 89 146 Iraq 33 .. 20 .. .. .. .. .. 2,458 .. 39 .. Ireland 45 60 38 48 25 17 9 5 11,818 27,768 138 325 Israel 46 50 24 25 .. 9 .. 5 12,968 17,548 118 160 Italy 44 46 30 26 .. 15 .. 11 16,313 19,653 124 150 Jamaica 61 57 39 30 46 37 37 31 3,786 3,751 121 120 Japan 63 58 43 41 15 11 26 14 18,789 22,461 140 167 Jordan 39 47 26 31 .. .. .. .. 3,792 4,591 85 103 Kazakhstan 63 65 45 44 .. 33 .. 39 7,458 8,954 .. .. Kenya 64 63 44 43 .. .. .. .. 1,117 1,060 106 101 Korea, Dem. Rep. 64 60 49 33 .. .. .. .. .. .. .. .. Korea, Rep. 59 60 36 34 .. 24 .. 29 8,704 18,086 212 440 Kuwait 65 71 34 38 .. .. .. .. 6,121 11,806 46 89 Kyrgyz Republic 59 59 41 41 .. 50 .. 50 3,602 2,464 .. .. Lao PDR 65 66 53 54 .. .. .. .. .. .. .. .. Latvia 61 51 46 31 .. 9 .. 7 9,916 13,514 .. .. Lebanon 47 51 32 32 .. .. .. .. .. .. .. .. Lesotho 54 37 40 25 .. .. .. .. .. .. .. .. Liberia 63 63 48 47 .. .. .. .. .. .. .. .. Libya 47 54 30 33 .. .. .. .. .. .. .. .. Lithuania 55 53 35 24 .. .. .. .. 8,663 10,309 .. .. Macedonia, FYR 40 33 20 13 .. 23 .. 21 3,972 3,538 .. .. Madagascar 77 78 61 63 .. 79 .. 86 799 675 76 64 Malawi 80 80 66 66 .. .. .. .. 554 620 86 96 Malaysia 61 62 47 44 .. 20 .. 21 5,132 9,782 140 268 Mali 75 70 67 58 .. .. .. .. 747 1,026 102 140 Mauritania 64 64 49 48 .. .. .. .. .. .. .. .. Mauritius 53 55 39 35 .. 18 .. 15 .. .. .. .. Mexico 57 57 50 40 37 30 36 34 6,085 7,816 96 124 Moldova 58 56 36 36 .. 37 .. 36 6,165 3,057 .. .. Mongolia 50 59 39 44 .. 62 .. 57 .. .. .. .. Morocco 46 47 39 36 .. 54 .. 67 2,596 2,998 114 132 Mozambique 80 77 62 55 .. .. .. .. 1,115 1,783 91 146 Myanmar 75 75 63 58 .. .. .. .. 778 2,387 95 291 Namibia 46 38 23 18 .. .. .. .. .. .. .. .. Nepal 59 58 48 44 .. .. .. .. .. .. .. .. Netherlands 53 61 55 69 .. .. .. .. 17,262 23,385 117 159 New Zealand 57 65 54 58 15 15 10 9 13,909 18,306 113 148 Nicaragua 56 56 45 44 .. .. .. .. .. .. .. .. Niger 78 79 68 71 .. .. .. .. 540 514 67 64 Nigeria 60 59 44 43 .. .. .. .. 1,214 1,329 85 93 Norway 60 66 49 60 .. .. .. .. 18,466 28,044 123 186 Oman 52 52 28 28 .. .. .. .. 6,479 7,528 159 185 Pakistan 54 55 44 44 .. 60 .. 69 1,589 2,278 137 196 Panama 50 59 34 36 44 35 19 26 .. .. .. .. Papua New Guinea 71 71 58 58 .. .. .. .. .. .. .. .. Paraguay 62 69 51 58 17b 50 b 31b 52b .. .. .. .. Peru 56 64 40 43 30 b 34b 45b 39b 3,021 4,272 71 100 Philippines 59 64 42 44 .. 43 .. 48 2,224 2,734 94 115 Poland 55 46 35 22 .. 23 .. 20 5,113 8,999 89 157 Portugal 59 58 53 38 18b 18 21b 20 10,826 14,174 135 176 Puerto Rico 38 43 21 30 .. .. .. .. 10,539 15,026 129 184 2008 World Development Indicators 53 2.4 Decent work and productive employment Employment to Vulnerable Labor population ratio employment productivity Unpaid family workers and own-account workers GDP per person employed Male Female Index % ages 15 and older % ages 15­24 % of male employment % of female employment 1990 PPP $a 1980 = 100 1991 2006 1991 2006 1990 2005 1990 2005 1990 2006 1990 2006 Romania 58 52 47 22 7b 33 10 b 34 3,511 4,305 85 104 Russian Federation 58 56 36 33 1 6 1 6 7,779 7,297 .. .. Rwanda 79 73 63 58 .. .. .. .. .. .. .. .. Saudi Arabia 51 51 26 25 .. .. .. .. 8,993 8,691 68 66 Senegal 67 62 55 47 77 .. 91 .. 1,279 1,433 101 113 Serbia 49c 51c 28 c 33c .. .. .. .. 5,160 c 2,935c .. .. Sierra Leone 64 68 51 60 .. .. .. .. .. .. .. .. Singapore 64 60 56 41 10 12 6 6 14,220 24,688 157 273 Slovak Republic 56 52 41 30 .. 13b .. 5b 7,763 11,057 .. .. Slovenia 55 57 37 33 .. 12 .. 10 10,860 16,136 .. .. Somalia 70 69 64 63 .. .. .. .. .. .. .. .. South Africa 48 45 31 27 .. 18 .. 20 3,842 4,821 88 110 Spain 43 51 37 36 20 14 24 11 12,055 17,110 131 186 Sri Lanka 52 52 32 37 .. 39 b .. 39 b 2,448 4,193 132 227 Sudan 47 43 33 26 .. .. .. .. 743 947 80 102 Swaziland 42 39 26 22 .. .. .. .. .. .. .. .. Sweden 65 59 59 44 .. .. .. .. 17,609 23,831 118 160 Switzerland 65 65 68 63 8 9 11 10 21,487 23,475 114 125 Syrian Arab Republic 51 56 40 43 .. .. .. .. 5,701 7,015 88 108 Tajikistan 54 48 37 28 .. .. .. .. 2,979 1,318 .. .. Tanzania 87 84 77 72 .. .. .. .. 551 690 92 115 Thailand 77 72 70 46 67 51 74 55 4,633 7,888 181 309 Timor-Leste 62 67 46 57 .. .. .. .. .. .. .. .. Togo 65 63 53 51 .. .. .. .. .. .. .. .. Trinidad and Tobago 48 58 33 46 22 17 21 13 9,272 23,233 75 188 Tunisia 41 45 29 29 .. .. .. .. 3,337 5,362 113 182 Turkey 53 47 48 39 .. 36 .. 55 5,445 8,080 136 201 Turkmenistan 58 60 36 37 .. .. .. .. 3,626 2,609 .. .. Uganda 83 81 74 71 .. 77b .. 92b 598 889 104 154 Ukraine 60 52 43 34 .. .. .. .. 6,027 4,154 .. .. United Arab Emirates 72 76 43 47 .. .. .. .. 13,070 22,700 47 82 United Kingdom 58 59 66 59 .. .. .. .. 16,430 22,967 127 178 United States 61 63 56 55 .. .. .. .. 23,201 31,245 125 168 Uruguay 55 62 49 50 .. 27b .. 22b 6,474 8,313 98 126 Uzbekistan 56 58 36 37 .. .. .. .. 4,241 4,202 .. .. Venezuela, RB 55 60 38 41 .. 33 .. 40 8,313 8,815 82 87 Vietnam 75 73 75 66 .. 70 .. 79 1,025 2,458 135 325 West Bank and Gaza 29 28 18 15 .. 37 .. 43 .. .. .. .. Yemen, Rep. 44 47 32 32 .. .. .. .. 2,272 2,861 99 125 Zambia 63 70 48 61 56 .. 81 .. 810 719 89 79 Zimbabwe 71 70 50 52 .. .. .. .. 1,356 910 105 70 World 63 w 62 w 53 w 47 w .. w .. w .. w .. w 5,408 w 7,629 w 106 m 146 m Low income 63 61 51 47 .. .. .. .. 1,175 1,937 95 115 Middle income 66 64 57 48 .. .. .. .. 3,208 5,775 97 120 Lower middle income 69 67 61 52 .. .. .. .. 2,353 5,348 103 120 Upper middle income 57 55 44 38 .. 26 .. 24 6,099 7,245 96 123 Low & middle income 65 63 54 48 .. .. .. .. 2,507 4,356 96 120 East Asia & Pacific 74 71 68 58 .. .. .. .. 2,006 6,352 135 279 Europe & Central Asia 57 53 40 33 .. 19 .. 17 6,359 6,704 .. .. Latin America & Carib. 57 60 47 45 .. 33 .. 34 5,186 6,452 96 117 Middle East & N. Africa 43 46 29 30 .. .. .. .. 3,110 4,253 96 125 South Asia 60 57 47 43 .. .. .. .. 1,266 2,611 135 212 Sub-Saharan Africa 67 66 54 52 .. .. .. .. 1,061 1,192 90 102 High income 57 57 47 45 .. .. .. .. 18,145 24,534 123 167 Euro area 50 51 41 35 .. 13 .. 10 15,772 20,101 123 169 a. Based on extrapolated PPPs from the 1993 ICP. b. Limited coverage. c. Includes Montenegro. 54 2008 World Development Indicators 2.4 PEOPLE Decent work and productive employment About the data Definitions At the 2005 World Summit four targets were added within a country. Information from labor force surveys · Employment to population ratio is the proportion to the UN Millennium Declaration. One was full and is not always consistent in terms of what is included of a country's population that is employed. Ages 15 productive employment and decent work for all, in employment. For example, information provided and older are generally considered the working-age which is seen as the main route for people to escape by the Organisation for Economic Co-operation and population. Ages 15­24 are generally considered poverty. The four indicators for this target have an Development relates only to civilian employment, the youth population. · Vulnerable employment is economic focus, and three of them are presented which can result in an underestimation of "employ- unpaid family workers and own-account workers as in the table. ees" and "workers not classified by status," espe- a percentage of total employment · Labor productiv- The employment to population ratio indicates how cially in countries with large armed forces. While ity is gross domestic product (GDP) divided by total efficiently an economy provides jobs for people who the categories of unpaid family workers and self- employment in the economy. Purchasing power parity want to work. A high ratio means that a large propor- employed workers, which include own-account work- (PPP) GDP is GDP converted to 1990 constant inter- tion of the population is employed. But this indicator ers, would not be affected, their relative shares would national dollars using PPP rates. An international dol- has a gender bias because women who do not con- be. Geographic coverage is another factor that can lar has the same purchasing power over GDP that a sider their work employment or who are not perceived limit cross-country comparisons. The employment by U.S. dollar has in the United States. as working tend to be undercounted. This bias has status data for most Latin American countries covers different effects across countries. urban areas only. Similarly, in some countries in Sub- Comparability of employment ratios across coun- Saharan Africa, where limited information is available tries is also affected by variations in definitions of anyway, the members of producer cooperatives are employment and population (see About the data for usually excluded from the self-employed category. table 2.3). The biggest difference results from the For detailed information on definitions and coverage, age range used to define labor force activity. The consult the original source. population base for employment ratios can also Labor productivity, measured as output per per- vary (see table 2.1). Most countries use the resi- son employed, can be used to assess a country's dent, noninstitutionalized population of working age economic ability to create and sustain decent living in private households, excluding members of employment opportunities with fair and equitable the armed forces and individuals residing in mental, remuneration. For comparability of individual sectors penal, or other types of institutions. But some coun- labor productivity is estimated according to national tries include members of the armed forces in the accounts conventions. However, there are still signifi - population base of their employment ratio while still cant limitations on the availability of reliable data, as excluding them from employment data (International the information on consistent series of output in both Labour Organization, Key Indicators of the Labour national currencies and purchasing power parity U.S. Market, 5th edition). dollars is not easily available, especially in devel- The proportion of unpaid family workers and own- oping countries, because the definition, coverage, account workers in total employment is derived from and methodology are not always consistent across information on status in employment. Each status countries. For example, countries employ different group faces different economic risks, and unpaid methodologies for estimating the missing values family workers and own-account workers are the for the nonmarket service sectors and use different most vulnerable--and therefore the most likely to definitions of the informal sector (see About the data fall into poverty. They are the least likely to have for- for tables 4.1 and 4.14). mal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of gen- erating sufficient savings to offset these shocks. A high proportion of unpaid family workers in a country indicates weak development, little job growth, and often a large rural economy. Data on employment by status are drawn from labor Data sources force surveys and household surveys, supplemented by official estimates and censuses for a small group Data on decent work and productive employment of countries. The labor force survey is the most are from the International Labour Organization comprehensive source for international comparable database Key Indicators of the Labour Market, employment, but there are still some limitations for 5th edition. comparing data across countries and over time even 2008 World Development Indicators 55 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990­92a 2003­05a 1990­92a 2003­05a 1990­92a 2003­05a 2000­05a 2000­05a 2000­05a 2003­05a 2003­05a 2003­05a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 14.4 .. 12.4 .. 17.5 .. .. .. 98.3 .. 1.7 Algeria .. 15.3 .. 14.9 .. 17.5 .. .. .. 59.3 23.0 11.4 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.6b 10.2b 6.8b 9.2b 6.3b 12.5b .. .. .. 40.3b 39.8b 18.4b Armenia .. .. .. .. .. .. 71.6b 72.2b 70.8b 6.2 79.8 14.0 Australia 10.8 5.1 11.4 4.9 10.0 5.3 17.7b 20.2b 14.9b 51.4 29.1 19.3 Austria 3.6 5.2 3.5 4.9 3.8 5.5 25.3 25.6 24.9 35.2b 55.0 b 9.6b Azerbaijan .. 8.6 .. 7.6 .. 9.5 .. .. .. 4.4 30.2 65.4 Bangladesh 1.9 4.3 2.0 4.2 1.9 4.9 .. .. .. .. .. .. Belarus .. .. .. .. .. .. .. .. .. 10.2 40.6 49.1 Belgium 6.7 8.1 4.8 7.4 9.5 9.0 51.6 50.4 52.7 42.1 38.4 19.6 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 5.5b .. 5.5b .. 5.6b .. .. .. .. .. .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 14.2 23.8 12.1 21.4 17.5 26.3 .. .. .. 65.5 27.3 .. Brazil 6.4b 8.9b 5.4b 6.8b 7.9b 11.7b .. .. .. 53.4b 30.4b 3.0 b Bulgaria .. 10.1 .. 10.3 .. 9.9 .. .. .. 38.6 51.0 10.3 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 11.2b 6.8b 12.0 b 7.0 b 10.2b 6.5b 9.6b 10.1b 9.1b 27.1b 31.2b 41.7b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 4.4 6.9 3.9 6.1 5.3 8.5 .. .. .. 16.1 58.9 24.5 China 2.3b 4.2b .. .. .. .. .. .. .. .. .. .. Hong Kong, China 2.0 5.6 2.0 6.5 1.9 4.4 .. .. .. 46.3b 39.7b 12.6b Colombia 9.4b 9.5 6.7b 7.4 13.0 b 12.3 .. .. .. 58.4 .. 15.6 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 4.0 6.6 3.4 5.0 5.4 9.6 10.9 8.9 13.3 64.0 20.5 12.0 Côte d'Ivoire 6.7 .. .. .. .. .. .. .. .. .. .. .. Croatia .. 11.2c .. 10.1c .. 13.2c 53.7c 52.7c 54.5c 22.0 c 69.1c 9.8 c Cuba .. 1.9 .. 1.7 .. 2.2 .. .. .. 50.6 44.7 4.7 Czech Republic .. 7.9 .. 6.5 .. 9.8 53.6 52.9 54.2 24.1 72.0 4.1 Denmark 9.0 4.8 8.3 4.1 9.9 5.6 25.9 29.7 22.7 27.7 44.8 27.5 Dominican Republic 20.7 17.9 12.0 11.3 35.2 28.8 1.6 2.2 1.3 .. .. .. Ecuador 8.9b 7.7b 6.0 b 5.6b 13.2b 10.8b .. .. .. 76.0 b .. 22.5b Egypt, Arab Rep. 9.1 10.7 6.5 6.8 17.3 24.4 .. .. .. .. .. .. El Salvador 7.9b 6.6 8.4b 8.5 7.2b 3.9 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.7 7.9 3.9 8.8 3.5 7.1 .. .. .. 15.7 64.4 19.9 Ethiopia .. 5.4 .. 2.7 .. 8.2 24.4 24.3 24.4 35.9 13.3 3.2 Finland 11.7 8.4 13.6 8.2 9.7 8.7 24.9 27.9 21.9 35.5 46.8 17.7 France 10.0 b 9.8b 7.9b 9.0 b 12.7b 10.8b 42.5 41.8 43.2 40.6 39.4 18.7 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 13.8 .. 14.8 .. 12.7 .. .. .. 4.8 56.0 38.8 Germany 6.6 11.1 5.3 11.3 8.4 10.9 54.0 53.8 54.4 27.1 60.5 12.4 Ghana .. .. .. .. .. .. .. .. .. .. .. .. Greece 7.8 9.6 4.9 5.8 12.9 15.2 53.7 43.1 59.6 30.8 49.7 19.1 Guatemala 3.2b 3.4 2.6b 2.5 4.6b 4.9 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 12.2 .. 11.2 .. 13.6 .. .. .. .. .. .. .. 56 2008 World Development Indicators 2.5 PEOPLE Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990­92a 2003­05a 1990­92a 2003­05a 1990­92a 2003­05a 2000­05a 2000­05a 2000­05a 2003­05a 2003­05a 2003­05a Honduras 3.2b 4.2b 3.3b 3.2b 3.0 b 6.2b .. .. .. .. .. .. Hungary 9.9 7.2 11.0 7.0 8.7 7.5 46.1 47.9 44.2 30.2 62.2 7.6 India .. 5.0 b .. 4.9b .. 5.3b .. .. .. 27.0 41.1 31.9 Indonesia 2.8 10.3c 2.7 8.5c 3.0 13.4 c .. .. .. 48.7c 38.0 c 6.2c Iran, Islamic Rep. 11.1 11.5 9.5 10.1 24.4 17.1 .. .. .. 41.8 34.7 19.6 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 15.2 4.3 15.2 4.6 15.2 3.8 34.3 42.4 21.1 45.0 25.6 26.1 Israel 11.2b 9.0 b 9.2b 8.5b 13.9 b 9.5b .. .. .. 20.6 48.7 25.9 Italy 11.5 7.7 8.1 6.2 17.3 10.1 52.2 50.5 53.8 48.1 39.4 10.7 Jamaica 15.7 10.9 9.5 7.4 22.8 15.3 31.7 24.4 36.2 12.9 4.2 9.2 Japan 2.2 4.4 2.1 4.6 2.2 4.2 33.3 40.3 22.6 67.7 .. 29.9 Jordan .. 12.4 .. 11.8 .. 16.5 .. .. .. .. .. .. Kazakhstan .. 7.8 c .. 6.4 c .. 9.2c .. .. .. 7.1c 49.0 c 43.9c Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.5 3.7 2.8 4.0 2.1 3.4 0.8 1.0 0.4 17.4 53.2 29.4 Kuwait .. 1.7 .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 8.5 .. 8.0 .. 9.3 .. .. .. 9.9 79.5 10.7 Lao PDR .. 1.4 .. 1.3 .. 1.4 .. .. .. .. .. .. Latvia .. 8.7 .. 9.0 .. 8.4 .. .. .. 23.6 65.6 10.7 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 8.3 .. 8.2 .. 8.3 57.8 .. .. 16.4 69.5 14.1 Macedonia, FYR .. 37.3 .. 36.5 .. 38.4 .. .. .. .. .. .. Madagascar .. 5.0 .. 3.8 .. 6.2 .. .. .. 61.5 .. 6.1 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 3.7 3.5 .. 3.6 .. 3.6 .. .. .. 32.0 48.8 15.6 Mali .. 8.8 .. 7.2 .. 10.9 .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 3.1 9.6 3.2 5.8 3.1 16.5 .. .. .. 48.6 44.9 5.4 Mexico 3.1 3.5 2.7 3.4 4.0 3.6 2.4b 2.3b 2.6b 51.7 24.4 21.5 Moldova .. 7.3 .. 8.7 .. 6.0 .. .. .. .. .. .. Mongolia .. 14.2 .. 14.3 .. 14.1 .. .. .. 35.1 45.8 18.5 Morocco 16.0 b 9.7c 13.0 b 9.7c 25.3b 9.7c .. .. .. 51.1b 22.4b 21.6b Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.0 .. 4.7 .. 8.8 .. .. .. .. .. .. .. Namibia 19.1 .. 19.6 .. 18.6 .. .. .. .. .. .. .. Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 5.5 5.2 4.3 4.9 7.3 5.6 40.1 44.7 35.0 40.7 39.1 17.9 New Zealand 10.4b 3.7b 11.0 b 3.4b 9.6b 4.0 b 9.4b 12.6b 6.2b 0.0 52.7 14.4 Nicaragua 14.4 8.0 11.3 7.9 19.4 8.1 .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 5.9 4.6 6.6 4.8 5.1 4.4 9.5 10.4 8.5 24.3 54.1 18.9 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 5.2 7.7 3.8 6.6 14.0 12.8 .. .. .. 13.1 12.3 29.1 Panama 14.7 10.3 10.8 8.1 22.3 14.0 29.3 24.0 35.7 31.7 38.4 29.1 Papua New Guinea 7.7 .. 9.0 .. 5.9 .. .. .. .. .. .. .. Paraguay 5.0 b 7.9b 6.0 b 6.6b 3.7b 10.0 b .. .. .. .. .. .. Peru 9.4b 11.4b 7.5b 9.7b 12.5b 13.7b .. .. .. 69.6b .. 30.0 b Philippines 8.6 7.4 7.9 7.4 9.9 7.3 .. .. .. 15.2 45.2 38.9 Poland 13.3 17.7 12.2 16.6 14.7 19.1 52.2 51.3 53.1 17.7 74.8 7.6 Portugal 4.1b 7.6 3.5b 6.7 5.0 b 8.7 48.6 47.1 49.9 70.2 15.3 10.9 Puerto Rico 17.0 11.3 19.3 12.2 13.3 10.2 .. .. .. .. .. .. 2008 World Development Indicators 57 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990­92a 2003­05a 1990­92a 2003­05a 1990­92a 2003­05a 2000­05a 2000­05a 2000­05a 2003­05a 2003­05a 2003­05a Romania .. 7.2 .. 7.7 .. 6.4 .. .. .. 23.1 69.1 6.6 Russian Federation 5.3 7.9 5.4 7.8 5.2 8.0 .. .. .. .. .. .. Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 6.2 .. 4.7 .. 14.7 .. .. .. 12.3 43.9 40.0 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia .. 15.2d .. 14.4 d .. 16.4 d .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.7 4.2 2.7 3.7 2.6 5.0 .. .. .. 20.2 25.7 59.2 Slovak Republic .. 16.2 .. 15.4 .. 17.2 68.1 68.7 67.4 27.1b 68.3b 4.5b Slovenia .. 5.8 .. 5.5 .. 6.0 .. .. .. 22.4 69.0 8.6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 26.7 .. 26.8 .. 26.6 .. .. .. 50.2 41.0 5.1 Spain 18.1 9.2 13.9 7.0 25.8 12.2 32.6 28.2 36.0 53.9 22.1 23.1 Sri Lanka 13.3b 7.6b 10.1b 5.5b 19.9b 11.9 b .. .. .. 41.7b .. 58.3b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5.7 7.7 6.7 7.8 4.6 7.6 18.9 20.9 16.4 25.9 54.4 17.8 Switzerland 2.8 4.4 2.3 3.9 3.5 5.1 38.8 37.1 40.4 28.6 53.5 17.3 Syrian Arab Republic .. 12.3 .. 9.0 .. 28.3 .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 3.6b .. 2.8b .. 4.3b .. .. .. .. .. .. .. Thailand 1.4 1.3 1.3 1.5 1.5 1.2 .. .. .. 39.7 46.3 0.2 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 19.6 8.0 17.0 5.8 23.9 11.0 27.6 20.3 34.7 .. .. .. Tunisia .. 14.2 .. 13.1 .. 17.3 .. .. .. 79.1 .. 13.6 Turkey 8.5 10.3 8.8 10.3 7.8 10.3 39.6 36.9 47.4 54.3 28.1 11.4 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 3.2 .. 2.5 .. 3.9 .. .. .. .. .. .. Ukraine .. 7.2 .. 7.5 .. 6.8 .. .. .. 10.9 53.2 35.8 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 9.7 4.6 11.5 5.0 7.3 4.1 22.4 26.2 16.9 36.7 46.1 16.2 United States 7.5b 5.1b 7.9b 5.1b 7.0 b 5.1b 11.8b 12.6b 10.8b 19.1b 35.5b 45.4b Uruguay 9.0 b 12.2b 6.8b 9.5b 11.8b 15.3b .. .. .. .. .. .. Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 7.7 15.0 8.2 13.4 6.8 17.6 .. .. .. .. .. .. Vietnam .. 2.1 .. 1.9 .. 2.4 .. .. .. .. .. .. West Bank and Gaza .. 26.8 .. 28.1 .. 20.1 .. .. .. 58.5 13.1 18.9 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 18.9 .. 16.3 .. 22.4 .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w 6.7 w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income 3.9 6.4 .. .. .. .. .. .. .. .. .. .. Lower middle income 3.2 5.7 .. .. .. .. .. .. .. .. .. .. Upper middle income 6.3 9.8 6.0 9.0 7.0 11.4 .. .. .. 44.0 41.2 8.7 Low & middle income .. 6.8 .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 2.5 4.9 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 10.0 .. 10.0 .. 9.9 .. .. .. .. .. .. Latin America & Carib. 6.7 8.9 5.5 7.1 8.4 11.5 .. .. .. 56.6 31.9 12.7 Middle East & N. Africa .. 13.8 .. 12.8 .. 18.7 .. .. .. .. .. .. South Asia .. 5.3 .. 5.1 .. 6.3 .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.4 6.2 7.0 6.0 7.9 6.6 26.4 28.0 24.0 36.3 38.1 29.1 Euro area 9.5 9.0 7.5 8.1 12.5 10.3 45.8 44.6 46.5 45.8 35.5 17.2 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2006. d. Includes Montenegro and excludes Kosovo and Metohija. 58 2008 World Development Indicators 2.5 PEOPLE Unemployment About the data Definitions Unemployment and total employment are the broad- generate statistics that are more comparable inter- · Unemployment is the share of the labor force with- est indicators of economic activity as reflected by nationally. But the age group, geographic coverage, out work but available for and seeking employment. the labor market. The International Labour Organiza- and collection methods could differ by country or Definitions of labor force and unemployment may tion (ILO) defines the unemployed as members of the change over time within a country. For detailed infor- differ by country (see About the data). · Long-term economically active population who are without work mation, consult the original source. unemployment is the number of people with continu- but available for and seeking work, including people Women tend to be excluded from the unemploy- ous periods of unemployment extending for a year or who have lost their jobs or who have voluntarily left ment count for various reasons. Women suffer more longer, expressed as a percentage of the total unem- work. Some unemployment is unavoidable. At any from discrimination and from structural, social, and ployed. · Unemployment by educational attainment time some workers are temporarily unemployed-- cultural barriers that impede them from seeking is the unemployed by level of educational attainment between jobs as employers look for the right workers work. Also, women are often responsible for the as a percentage of the total unemployed. The levels and workers search for better jobs. Such unemploy- care of children and the elderly and for household of educational attainment accord with the ISCED97 ment, often called frictional unemployment, results affairs. They may not be available for work during of the United Nations Educational, Scientific, and from the normal operation of labor markets. the short reference period, as they need to make Cultural Organization. Changes in unemployment over time may reflect arrangements before starting work. Furthermore, changes in the demand for and supply of labor; they women are considered to be employed when they are may also refl ect changes in reporting practices. working part-time or in temporary jobs in the informal Paradoxically, low unemployment rates can disguise sector, despite the instability of these jobs or their substantial poverty in a country, while high unemploy- active search for more secure employment. ment rates can occur in countries with a high level Long-term unemployment is measured by the of economic development and low rates of poverty. length of time that an unemployed person has been In countries without unemployment or welfare ben- without work and looking for a job. The data in the efits people eke out a living in the informal sector. table are from labor force surveys. The underlying In countries with well developed safety nets workers assumption is that shorter periods of joblessness can afford to wait for suitable or desirable jobs. But are of less concern, especially when the unem- high and sustained unemployment indicates serious ployed are covered by unemployment benefi ts or inefficiencies in resource allocation. similar forms of support. The length of time that a The ILO definition of unemployment notwithstand- person has been unemployed is difficult to measure, ing, reference periods, the criteria for people consid- because the ability to recall that time diminishes as ered to be seeking work, and the treatment of people the period of joblessness extends. Women's long- temporarily laid off or seeking work for the first time term unemployment is likely to be lower in countries vary across countries. In many developing countries where women constitute a large share of the unpaid it is especially difficult to measure employment and family workforce. unemployment in agriculture. The timing of a survey, Unemployment by level of educational attainment for example, can maximize the effects of seasonal provides insights into the relation between the edu- unemployment in agriculture. And informal sector cational attainment of workers and unemployment employment is difficult to quantify where informal and may be used to draw inferences about changes activities are not tracked. in employment demand. Information on educational Data on unemployment are drawn from labor force attainment is the best available indicator of skill sample surveys and general household sample levels of the labor force. Besides the limitations to surveys, censuses, and offi cial estimates, which comparability raised for measuring unemployment, are generally based on information from different the different ways of classifying the education level sources and can be combined in many ways. Admin- may also cause inconsistency. Education level is istrative records, such as social insurance statistics supposed to be classifi ed according to Interna- and employment office statistics, are not included tional Standard Classifi cation of Education 1997 in the table because of their limitations in cover- (ISCED97). For more information on ISCED97, see age. Labor force surveys generally yield the most About the data for table 2.10. comprehensive data because they include groups not covered in other unemployment statistics, par- Data sources ticularly people seeking work for the first time. These surveys generally use a definition of unemployment Data on unemployment are from the ILO database that follows the international recommendations more Key Indicators of the Labour Market, 5th edition. closely than that used by other sources and therefore 2008 World Development Indicators 59 2.6 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 Work Agriculture Manufacturing Services Study Total Male Female only and work 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 30.1 26.6 73.4 .. .. .. .. .. .. Argentina 2004 15.1 18.0 12.0 4.1 95.9 .. .. .. .. .. .. 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 2002 23.2 24.0 22.5 15.2 84.8 78.8 73.4 4.5 3.8 15.5 22.6 Bosnia and Herzegovina 2000 20.2 22.8 17.6 4.0 96.0 .. .. .. .. .. .. Botswana .. .. .. .. .. .. .. .. .. .. .. Brazil 2004 7.0 9.4 4.6 7.2 92.8 66.2 48.9 5.2 9.7 26.4 40.8 Bulgaria .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 2004 50.0 49.0 51.0 98.1 1.9 98.4 96.1 0.2 0.5 1.3 3.1 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 2004 60.4 64.4 56.2 59.0 41.0 .. .. .. .. .. .. Chile 2003 4.1 5.1 3.1 3.2 96.8 31.0 12.2 8.2 4.5 57.8 81.5 China .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 2005 4.0 6.2 1.8 32.8 67.2 .. .. .. .. .. .. Congo, Dem. Rep. 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. Costa Ricac 2004 5.7 8.1 3.5 44.6 55.4 48.0 19.4 9.5 9.6 40.8 71.1 Côte d'Ivoire 2000 40.7 40.9 40.5 46.4 53.6 .. .. .. .. .. .. Croatia .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. Dominican Republicc 2002 3.5 5.9 0.9 11.4 88.6 .. .. .. .. .. .. Ecuador 2004 12.0 14.6 9.3 27.0 73.0 71.2 68.0 5.0 4.1 21.1 27.8 Egypt, Arab Rep. 2005 7.9 11.5 4.3 21.0 79.0 .. .. .. .. .. .. 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 2005 56.0 64.3 47.1 69.4 30.6 96.8 91.4 0.6 2.8 2.4 5.6 Finland .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. Gambia, The 2000 25.3 25.4 25.3 41.6 58.4 .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. Germany .. .. .. .. .. .. .. .. .. .. .. Ghana 2003 6.0 6.0 5.9 71.2 28.8 89.0 67.9 1.5 4.1 7.5 23.5 Greece .. .. .. .. .. .. .. .. .. .. .. Guatemala 2003 21.1 26.2 16.0 33.8 66.2 74.2 43.0 6.0 20.1 16.5 36.9 Guinea 1994 48.3 47.2 49.5 98.6 1.4 .. .. .. .. .. .. Guinea-Bissau 2000 67.5 67.4 67.5 63.7 36.3 .. .. .. .. .. .. Haiti 2005 33.4 37.3 29.6 17.7 82.3 .. .. .. .. .. .. 60 2008 World Development Indicators 2.6 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 Work Agriculture Manufacturing Services Study Total Male Female only and work Male Female Male Female Male Female Honduras 2004 6.8 10.4 3.2 48.6 51.4 76.9 20.2 5.3 17.9 13.9 59.4 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 2000 8.9 8.8 9.1 24.9 75.1 .. .. .. .. .. .. Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. Jamaica 2002 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 2004 42.6 45.0 40.3 13.9 86.1 .. .. .. .. .. .. Malaysia .. .. .. .. .. .. .. .. .. .. .. Mali 2005 70.9 71.2 70.7 53.3 46.7 78.4 41.8 1.4 3.2 19.6 54.6 Mauritania .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. Mexicod 2004 8.9 12.2 5.6 34.1 65.9 46.4 20.6 12.6 11.5 38.6 68.0 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 .. .. .. .. .. .. .. .. .. .. .. Panamac 2003 5.1 7.7 2.2 38.4 61.6 62.0 41.3 2.5 5.2 34.0 53.5 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. Paraguayc 2005 15.3 22.6 7.7 24.2 75.7 69.8 33.9 6.0 6.9 34.0 59.3 Peru 2000 24.1 25.7 22.3 4.8 95.2 75.4 69.1 3.1 2.5 21.2 28.4 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 .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 61 2.6 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 Work Agriculture Manufacturing Services Study Total Male Female only and work 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 2005 18.5 24.4 12.6 61.9 38.1 85.2 67.0 6.5 2.3 6.7 28.5 Serbia .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 2000 65.0 64.7 65.4 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 Sudane 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 .. .. .. .. .. .. .. .. .. .. .. Tajikistanf 1999 7.3 7.9 6.8 11.2 88.8 23.8 35.3 .. .. 76.2 64.7 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 .. .. .. .. .. .. .. .. .. .. .. Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 2006 39.6 40.5 38.5 30.2 69.8 89.7 77.2 0.9 1.5 8.3 20.8 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 2005­06 38.2 39.8 36.5 7.7 92.3 96.0 94.9 1.0 1.7 2.7 3.3 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 2005 47.9 48.9 46.8 25.9 74.1 96.5 95.3 0.7 0.5 2.8 4.2 Zimbabwe 1999 14.3 15.3 13.3 12.0 88.0 .. .. .. .. .. .. a. Shares by major industrial category may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Covers children ages 10­14. d. Covers children ages 12­14. e. Covers northern Sudan only. f. Covers children ages 11­14. 62 2008 World Development Indicators 2.6 PEOPLE Children at work About the data Definitions The indicators in the table refer to children's eco- recalculated to present statistics for children ages · Survey year is the year in which the underlying data nomic activity, a broader concept than child labor. 7­14. were collected. · Economically active children are According to a gradually emerging consensus, child Although efforts are made to harmonize the defini- children involved in economic activity for at least one labor is a subset of children's economic activity. tion of employment and the questions on employ- hour in the reference week of the survey. · Work only Based on International Labour Organization (ILO) ment used in survey questionnaires, substantial dif- refers to children involved in economic activity and Conventions 138 and 182, child labor is work that ferences remain among the survey instruments used not attending school. · Study and work refer to chil- is damaging to the child and therefore targeted for to collect data on working children and the sampling dren attending school in combination with economic elimination. design underlying these surveys. Differences exist activity. · Employment by economic activity is the In line with the defi nition of economic activity not only among different household surveys in the distribution of economically active children by the adopted by the Thirteenth International Conference same country, but also within the same type of sur- major industrial categories (ISIC revision 2 or revi- of Labour Statisticians and set by the 1993 United vey carried out in different countries. sion 3). · Agriculture corresponds to division 1 (ISIC Nations System of National Accounts, the threshold Because of differences in the underlying survey revision 2) or categories A and B (ISIC revision 3) for classifying a person as employed is spending at instruments and survey dates, estimates of working and includes agriculture and hunting, forestry and least one hour during the reference period in the children are not fully comparable across countries. logging, and fishing. · Manufacturing corresponds production of goods and services. Economic activity Great caution should be exercised in drawing conclu- to division 3 (ISIC revision 2) or category D (ISIC covers all market production and certain types of sions concerning relative levels of child economic revision 3). · Services correspond to divisions 6­9 nonmarket production, including the production of activity across countries or regions based on the (ISIC revision 2) or categories G­P (ISIC revision 3) goods for own use. It excludes household chores published data. and include wholesale and retail trade, hotels and performed in one's own household. The table aggregates the distribution of working restaurants, transport, financial intermediation, real The data used to develop the indicators are from children by the industrial categories of the Interna- estate, public administration, education, health and household surveys conducted by the ILO, the United tional Standard Industrial Classification (ISIC): agri- social work, other community services, and private Nations Children's Fund (UNICEF), the World Bank, culture, industry, and services. A residual category, household activity. and national statistical offices. These surveys yield which includes mining and quarrying; electricity, gas, data on education, employment, health, expenditure, and water; construction; extraterritorial organization; and consumption that relate to child work. and other inadequately defined activities, is not pre- Household survey data generally include informa- sented in the table, and so the broad groups do not tion on work type--for example, whether a child is add up to 100 percent. The use of either ISIC revision working for pay in cash or in kind or is involved in 2 or revision 3 is strictly related to the codification unpaid work, whether a child is working for someone applied by each country in describing the economic who is not a member of the household, whether a activity. The use of two different classifications does child is involved in any type of family work (on the not affect the definition of the groups presented in farm or in a business), and the like. The age used the table. in country surveys to define child labor ranges from 5 to 17 years old. The data in the table have been In developing countries the majority of child workers Data sources ages 5­14 are involved in unpaid family work 2.6a Data on children at work are estimates produced Share of child workers (%) Unpaid Self-employed Wage and Unclassified by the Understanding Children's Work project family workers workers salary workers based on household survey data sets made avail- 100 able by the ILO's International Programme on the 80 Elimination of Child Labour under its Statistical Monitoring Programme on Child Labour, UNICEF 60 under its Multiple Indicator Cluster Survey pro- 40 gram, the World Bank under its Living Standards Measurement Study program, and national sta- 20 tistical offices. Information on how the data were 0 collected and some indication of their reliability Argentina Cambodia Ethiopia Philippines Turkey Yemen, Rep. can be found at www.ilo.org/public/english/ The incidence of child work varies substantially by country, as does status in employment for working chil- standards/ipec/simpoc/, www.childinfo.org, and dren. A majority of children are unpaid family workers, with self-employed workers the next largest group. www.worldbank.org/lsms. Detailed country statis- Source: Understanding Children's Work. tics can be found at www.ucw-project.org. 2008 World Development Indicators 63 2.7 Poverty Population below national poverty line Poverty gap at national poverty line Survey % Survey % Survey % year Rural Urban National year Rural Urban National year Rural Urban National Afghanistan .. .. .. .. .. .. .. .. .. Albania 2002 29.6 19.8 25.4 .. .. .. 2002 6.6 .. 5.7 Algeria 1988 16.6 7.3 12.2 1995 30.3 14.7 22.6 1995 4.5 1.8 3.2 Angola .. .. .. .. .. .. .. .. .. Argentina 1995 .. 28.4 .. 1998 .. 29.9 .. 1998 .. 11.6 .. Armenia 1998­99 50.8 58.3 55.1 2001 48.7 51.9 50.9 2001 .. .. 15.1 Australia .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. Azerbaijan 1995 .. .. 68.1 2001 42.0 55.0 49.6 2001 .. .. 15.5 Bangladesh 1995­96 55.2 29.4 51.0 2000 53.0 36.6 49.8 2000 13.8 9.5 12.9 Belarus 2000 .. .. 41.9 2002 .. .. 18.5 2002 .. .. 20.0 Belgium .. .. .. .. .. .. .. .. .. Benin 1995 25.2 28.5 26.5 1999 33.0 23.3 29.0 1999 9.4 6.9 .. Bolivia 1999 84.0 51.4 63.5 2002 83.5 53.9 65.2 2002 43.4 23.8 31.2 Bosnia and Herzegovina 2001­02 19.9 13.8 19.5 .. .. .. 2001­02 4.9 2.8 4.6 Botswana .. .. .. .. .. .. .. .. .. Brazil 1998 51.4 14.7 22.0 2002­03 41.0 17.5 21.5 2002­03 28.4 17.8 19.6 Bulgaria 1997 .. .. 36.0 2001 .. .. 12.8 2001 .. .. 4.2 Burkina Faso 1998 61.1 22.4 54.6 2003 52.4 19.2 46.4 2003 17.6 5.1 15.3 Burundi 1998 64.6 66.5 68.0 .. .. .. .. .. .. Cambodia 1994 .. .. 47.0 2004 38.0 18.0 35.0 2004 7.8 1.2 6.7 Cameroon 1996 59.6 41.4 53.3 2001 49.9 22.1 40.2 .. .. .. Canada .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. Chad 1995­96 48.6 .. 43.4 .. .. .. 1995­96 26.3 .. 27.5 Chile 1996 .. .. 19.9 1998 .. .. 17.0 1998 .. .. 5.7 China 1998 4.6 .. 4.6 2004 .. .. 2.8 .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. Colombia 1995 79.0 48.0 60.0 1999 79.0 55.0 64.0 1999 44.0 26.0 34.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. Costa Rica 1992 25.5 19.2 22.0 2004 28.3 20.8 23.9 2004 10.8 7.0 8.6 Côte d'Ivoire .. .. .. .. .. .. .. .. .. Croatia .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. Dominican Republic 2000 45.3 18.2 27.7 2004 55.7 34.7 42.2 2004 24.0 12.9 16.8 Ecuador 1995 56.0 19.0 34.0 1998 69.0 30.0 46.0 1998 29.0 9.0 18.0 Egypt, Arab Rep. 1995­96 23.3 22.5 22.9 1999­2000 .. .. 16.7 1999­2000 .. .. 3.0 El Salvador 1995 64.8 38.9 50.6 2002 49.8 28.5 37.2 2002 24.2 11.1 16.5 Eritrea 1993­94 .. .. 53.0 .. .. .. .. .. .. Estonia 1995 14.7 6.8 8.9 .. .. .. 1995 6.6 1.8 3.1 Ethiopia 1995­96 47.0 33.3 45.5 1999­2000 45.0 37.0 44.2 1999­2000 12.0 10.0 12.0 Finland .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. Gambia, The 1998 61.0 48.0 57.6 2003 63.0 57.0 61.3 2003 .. .. 25.9 Georgia 2002 55.4 48.5 52.1 2003 52.7 56.2 54.5 .. .. .. Germany .. .. .. .. .. .. .. .. .. Ghana 1998­99 49.6 19.4 39.5 2005­06 39.2 10.8 28.5 2005­06 13.5 3.1 9.6 Greece .. .. .. .. .. .. .. .. .. Guatemala 1989 71.9 33.7 57.9 2000 74.5 27.1 56.2 2000 .. .. 22.6 Guinea 1994 .. .. 40.0 .. .. .. .. .. .. Guinea-Bissau 2002 .. 52.6 65.7 .. .. .. 2000 .. 17.5 25.7 Haiti 1987 .. .. 65.0 1995 66.0 .. .. .. .. .. 64 2008 World Development Indicators 2.7 PEOPLE Poverty Population below national poverty line Poverty gap at national poverty line Survey % Survey % Survey % year Rural Urban National year Rural Urban National year Rural Urban National Honduras 1998­99 71.2 28.6 52.5 2004 70.4 29.5 50.7 2004 34.5 9.1 22.3 Hungary 1993 .. .. 14.5 1997 .. .. 17.3 1997 4.1 .. .. India 1993­94 37.3 32.4 36.0 1999­2000 30.2 24.7 28.6 1999­2000 5.6 6.9 .. Indonesia 1996 .. .. 17.5 2004 .. .. 16.7 2004 .. .. 2.9 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. Jamaica 1995 37.0 18.7 27.5 2000 25.1 12.8 18.7 .. .. .. Japan .. .. .. .. .. .. .. .. .. Jordan 1997 27.0 19.7 21.3 2002 18.7 12.9 14.2 2002 4.7 2.9 3.3 Kazakhstan 2001 .. .. 17.6 2002 .. .. 15.4 2002 4.5 2.0 3.1 Kenya 1994 47.0 29.0 40.0 1997 53.0 49.0 52.0 .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2003 57.5 35.7 49.9 2005 50.8 29.8 43.1 2005 12.0 7.0 10.0 Lao PDR 1997­98 41.0 26.9 38.6 2002­03 .. .. 33.0 2002­03 .. .. 8.0 Latvia 2002 11.6 .. 7.5 2004 12.7 .. 5.9 2004 .. .. 1.2 Lebanon .. .. .. .. .. .. .. .. .. Lesotho 1993 53.9 27.8 49.2 1999 .. .. 68.0 .. .. .. Liberia .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. Macedonia, FYR 2002 25.3 .. 21.4 2003 22.3 .. 21.7 2003 6.5 .. 6.7 Madagascar 1997 76.0 63.2 73.3 1999 76.7 52.1 71.3 1999 36.1 21.4 32.8 Malawi 1990­91 .. .. 54.0 1997­98 66.5 54.9 65.3 .. .. .. Malaysia 1989 .. .. 15.5 .. .. .. .. .. .. Mali 1998 75.9 30.1 63.8 .. .. .. .. .. .. Mauritania 1996 65.5 30.1 50.0 2000 61.2 25.4 46.3 .. .. .. Mauritius .. .. .. .. .. .. .. .. .. Mexico 2002 34.8 11.4 20.3 2004 27.9 11.3 17.6 2002 12.2 2.8 6.3 Moldova 2001 64.1 58.0 62.4 2002 67.2 42.6 48.5 2002 .. .. 16.5 Mongolia 1998 32.6 39.4 35.6 2002 43.4 30.3 36.1 2002 13.2 9.2 11.0 Morocco 1990­91 18.0 7.6 13.1 1998­99 27.2 12.0 19.0 1998­99 6.7 2.5 4.4 Mozambique 1996­97 71.3 62.0 69.4 2002­03 55.3 51.5 54.1 2002­03 20.9 19.7 20.5 Myanmar .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. .. .. .. Nepal 1995­96 43.3 21.6 41.8 2003­04 34.6 9.6 30.9 2003­04 8.5 2.2 7.5 Netherlands .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. Nicaragua 1998 68.5 30.5 47.9 2001 64.3 28.7 45.8 2001 25.9 8.7 17.0 Niger 1989­93 66.0 52.0 63.0 .. .. .. .. .. .. Nigeria 1985 49.5 31.7 43.0 1992­93 36.4 30.4 34.1 .. .. .. Norway .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. Pakistan 1993 33.4 17.2 28.6 1998­99 35.9 24.2 32.6 1998­99 7.9 5.0 7.0 Panama 1997 64.9 15.3 37.3 .. .. .. 1997 32.1 3.9 16.4 Papua New Guinea 1996 41.3 16.1 37.5 .. .. .. 1996 13.8 4.3 12.4 Paraguaya 1990 28.5 19.7 20.5 .. .. .. 1990 10.5 5.6 6.0 Peru 2001 77.1 42.0 54.3 2004 72.1 42.9 53.1 2004 28.3 12.4 18.0 Philippines 1994 45.4 18.6 32.1 1997 36.9 11.9 25.1 1997 10.0 2.6 6.4 Poland 1996 .. .. 14.6 2001 .. .. 14.8 .. .. .. Portugal .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 65 2.7 Poverty Population below national poverty line Poverty gap at national poverty line Survey % Survey % Survey % year Rural Urban National year Rural Urban National year Rural Urban National Romania 1995 .. .. 25.4 2002 .. .. 28.9 2002 .. .. 7.6 Russian Federation 1998 .. .. 31.4 2002 .. .. 19.6 2002 .. .. 5.1 Rwanda 1993 .. .. 51.2 1999­2000 65.7 14.3 60.3 .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. Senegal 1992 40.4 23.7 33.4 .. .. .. 1992 16.4 3.1 13.9 Serbia .. .. .. .. .. .. .. .. .. Sierra Leone 1989 .. .. 82.8 2003­04 79.0 56.4 70.2 2003­04 34.0 .. 29.0 Singapore .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. South Africa .. .. .. .. .. .. .. .. .. Spain .. .. .. .. .. .. .. .. .. Sri Lanka 1995­96 27.0 15.0 25.0 2002 7.9 24.7 22.7 2002 .. .. 5.1 Sudan .. .. .. .. .. .. .. .. .. Swaziland 2000­01 75.0 49.0 69.2 .. .. .. 2000­01 .. .. 32.9 Sweden .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. Tajikistan 1999 .. .. 74.9 2003 .. .. 44.4 2003 .. .. 12.7 Tanzania 1991 40.8 31.2 38.6 2000­01 38.7 29.5 35.7 .. .. .. Thailand 1994 .. .. 9.8 1998 .. .. 13.6 1998 .. .. 3.0 Timor-Leste 2001 .. .. 39.7 .. .. .. 2001 .. .. 11.9 Togo 1987­89 .. .. 32.3 .. .. .. 1987­89 .. .. 10.0 Trinidad and Tobago 1992 20.0 24.0 21.0 .. .. .. 1992 6.2 7.4 7.3 Tunisia 1990 13.1 3.5 7.4 1995 13.9 3.6 7.6 1990 3.3 0.9 1.7 Turkey 1994 .. .. 28.3 2002 34.5 22.0 27.0 2002 .. .. 0.3 Turkmenistan .. .. .. .. .. .. .. .. .. Uganda 1999­2000 37.4 9.6 33.8 2002­03 41.7 12.2 37.7 2002­03 12.6 3.0 11.3 Ukraine 2000 34.9 .. 31.5 2003 28.4 .. 19.5 .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. Uruguay 1994 .. 20.2 .. 1998 .. 24.7 .. 1998 .. 8.6 .. Uzbekistan 2000 30.5 22.5 27.5 .. .. .. .. .. .. Venezuela, RB 1989 .. .. 31.3 .. .. .. 1989 .. 24.0 .. Vietnam 1998 45.5 9.2 37.4 2002 35.6 6.6 28.9 2002 8.7 1.3 6.9 West Bank and Gaza .. .. .. .. .. .. .. .. .. Yemen, Rep. 1998 45.0 30.8 41.8 .. .. .. 1998 14.7 8.2 13.2 Zambia 1998 83.1 56.0 72.9 2004 78.0 53.0 68.0 2004 44.0 22.0 36.0 Zimbabwe 1990­91 35.8 3.4 25.8 1995­96 48.0 7.9 34.9 .. .. .. a. Covers Asunción metropolitan area only. 66 2008 World Development Indicators 2.7 PEOPLE Poverty About the data The World Bank periodically prepares poverty surveys can differ widely. Even similar surveys may from household survey data. Detailed information is assessments for member countries in which it has not be strictly comparable because of differences in available from the site. an active program in close collaboration with national timing or in the quality and training of enumerators. Estimation from distributional data requires an institutions, other development agencies, and civil Comparisons of countries at different levels of interpolation method. The method chosen was Lorenz society groups, including poor people's organiza- development also pose a potential problem because curves with fl exible functional forms, which have tions. Poverty assessments assess the extent and of differences in the relative importance of consump- proved reliable in past work. The Lorenz curve can causes of poverty and propose strategies to reduce tion of nonmarket goods. The local market value of be graphed as the cumulative percentages of total it. Since 1992 the World Bank has conducted about all consumption in kind (including own production, consumption or income against the cumulative num- 180 poverty assessments, which are the source of particularly important in underdeveloped rural econo- ber of people, starting with the poorest individual. all poverty estimates based on national poverty lines mies) should be included in total consumption expen- The empirical Lorenz curves estimated by PovcalNet presented in the table. diture. Similarly, imputed profit from the production are weighted by household size, so they are based on The World Bank published its first systematic review of nonmarket goods should be included in income. percentiles of population, not households. of poverty for developing countries in World Devel- This is not always done, though such omissions were PovcalNet also allows users to calculate poverty opment Report 1990 using household survey data a far bigger problem in surveys before the 1980s. measures under different assumptions. For exam- for 22 countries (Ravallion, Datt, and van de Walle Most survey data now include valuations for con- ple, users can specify different poverty lines and 1991). Since then the number of countries that field sumption or income from own production, but valu- aggregate the estimates using alternative country such surveys has increased considerably, as have ation methods vary. groupings (for example, UN groupings or groupings the frequency of the surveys and the quality of the The statistics reported here are based on con- based on average incomes) or a selected set of data. Household survey data sets rose dramatically sumption data or, when unavailable, on income individual countries. PovcalNet is available online at from 10 between 1979 and 1981 to 111 between surveys. Analysis of some 20 countries for which http://iresearch.worldbank.org/povcalnet/. It will be 2000 and 2002. Fewer surveys are available after income and consumption expenditure data were both updated using the 2005 PPP results along with the 2002, reflecting the lag between data collection and available from the same surveys found income to World Development Indicators supplemental publica- availability for analysis, not a reduction in collection yield a higher mean than consumption but also found tion later this year. effort. Coverage is improving in all regions, but Sub- higher inequality. When poverty measures based on Definitions Saharan Africa continues to lag, with only 21 of 48 consumption and income were compared, the two countries having at least one data set available since effects roughly cancelled each other out: there was · Survey year is the year in which the underlying data 2000. Overall more than 550 surveys representing no significant statistical difference. were collected. · Rural population below national about 100 developing countries are now included poverty line is the percentage of the rural population in the World Bank's data sets. Some 1.1 million International poverty lines and the 2005 living below the national rural poverty line. · Urban randomly sampled households were interviewed in International Comparison Project population below national poverty line is the per- these surveys, representing 93 percent of the popu- This year's table does not include poverty estimates centage of the urban population living below the lation of developing countries. A complete overview using the international poverty lines of $1 a day and national urban poverty line. · National population of data availability by year and country is available at $2 a day, which were based on 1993 purchasing below national poverty line is the percentage of the http://iresearch.worldbank.org/povcalnet/. power parities (PPPs). The International Comparison country's population living below the national poverty These household surveys ask detailed questions Program recently released new PPP estimates bench- line. National estimates are based on population- on sources of income and how income was spent and marked to 2005 (see introduction to World View). weighted subgroup estimates from household sur- on household characteristics such as the number Poverty estimates using new international poverty veys. · Poverty gap at national poverty line is the of people sharing that income. Most interviews are lines based on PPPs will be published later as a mean shortfall from the poverty line (counting the conducted by staff of government statistics offices. supplement to World Development Indicators. nonpoor as having zero shortfall) as a percentage of As data coverage and quality have improved, so has the poverty line. This measure reflects the depth of the underlying methodology, resulting in more com- Do it yourself: PovcalNet poverty as well as its incidence. prehensive estimates. The World Bank's Development Research Group Data sources Estimating poverty and comparing poverty rates developed PovcalNet, an interactive Web-based tool is difficult. In addition to survey data availability are that allows users to replicate the calculations by The poverty measures are prepared by the data quality issues that arise in measuring household the World Bank's researchers in estimating abso- World Bank's Development Research Group. The living standards. One concerns the choice of income lute poverty in the world. PovcalNet is self-contained national poverty lines are based on the World or consumption as a welfare indicator. Income is and powered by built-in software that performs the Bank's country poverty assessments. For details generally more difficult to measure accurately, and calculations from a primary database. The under- on data sources and methods used in deriving consumption comes closer to the notion of living lying software can also be downloaded from the the World Bank's latest estimates, see Chen and standards. And income can vary over time even if PovcalNet site and used with distributional data of Ravallion's "How Have the World's Poorest Fared living standards do not. But consumption data are various formats. The PovcalNet primary database Since the Early 1980s?" not always available. Another issue is that household consists of distributional data calculated directly 2008 World Development Indicators 67 2.8 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Afghanistan .. .. .. .. .. .. .. .. Albania 2004b 31.1 3.4 8.2 12.6 17.0 22.6 39.5 24.4 Algeria 1995b 35.3 2.8 7.0 11.6 16.1 22.7 42.6 26.8 Angola .. .. .. .. .. .. .. .. Argentinac 2004 d 51.3 0.9 3.1 7.6 12.8 21.1 55.4 38.2 Armenia 2003b 33.8 3.6 8.5 12.3 15.7 20.6 42.8 29.0 Australia 1994 d 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 2000 d 29.1 3.3 8.6 13.3 17.4 22.9 37.8 23.0 Azerbaijan 2001b 36.5 3.1 7.4 11.5 15.3 21.2 44.5 29.5 Bangladesh 2005b 33.2 3.8 8.8 12.2 15.6 20.9 42.5 28.0 Belarus 2005b 28.0 3.6 8.8 13.7 17.7 23.0 36.8 22.1 Belgium 2000 d 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1 Benin 2003b 36.5 3.1 7.4 11.3 15.4 21.5 44.5 29.0 Bolivia 2002d 60.1 0.3 1.5 5.9 10.9 18.7 63.0 47.2 Bosnia and Herzegovina 2005b 35.8 2.7 7.0 11.6 15.9 22.3 43.2 27.5 Botswana 1993b 60.5 1.2 3.2 6.0 9.7 16.0 65.1 51.0 Brazil 2005d 56.6 0.9 2.9 6.5 11.1 18.7 60.8 44.9 Bulgaria 2003b 29.2 3.4 8.7 13.7 17.2 22.1 38.3 23.9 Burkina Faso 2003b 39.5 2.8 6.9 10.9 14.5 20.5 47.2 32.2 Burundi 1998 b 42.4 1.7 5.1 10.3 15.1 21.5 48.0 32.8 Cambodia 2004b 41.7 2.9 6.8 10.2 13.7 19.6 49.6 34.8 Cameroon 2001b 44.6 2.3 5.6 9.3 13.7 20.4 50.9 35.4 Canada 2000 d 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Central African Republic 1993b 61.3 0.7 2.0 4.9 9.6 18.5 65.0 47.7 Chad .. .. .. .. .. .. .. .. Chile 2003d 54.9 1.4 3.8 7.3 11.1 17.8 60.0 45.0 China 2004 d 46.9 1.6 4.3 8.5 13.7 21.7 51.9 34.9 Hong Kong, China 1996d 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2004 d 56.2 0.8 2.9 6.9 11.0 18.3 60.9 45.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. Costa Rica 2004 d 48.2 1.4 4.1 8.5 13.2 20.9 53.3 36.7 Côte d'Ivoire 2002b 44.6 2.0 5.2 9.1 13.7 21.3 50.7 34.0 Croatia 2005b 29.0 3.6 8.8 13.3 17.3 22.7 37.9 23.1 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996d 25.4 4.3 10.3 14.5 17.7 21.7 35.9 22.4 Denmark 1997d 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Dominican Republic 2005d 49.9 1.5 4.1 8.1 12.6 19.9 55.3 39.0 Ecuador 1998b 53.6 0.9 3.3 7.5 11.7 19.4 58.0 41.6 Egypt, Arab Rep. 2004­05b 34.4 3.8 8.9 12.7 16.0 20.8 41.5 27.6 El Salvador 2002d 52.4 0.7 2.7 7.5 12.8 21.2 55.9 38.8 Eritrea .. .. .. .. .. .. .. .. Estonia 2004b 36.0 2.6 6.8 11.7 16.2 22.0 43.3 27.8 Ethiopia 1999­2000 b 30.0 3.9 9.1 13.2 16.8 21.5 39.4 25.5 Finland 2000 d 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 France 1995d 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon .. .. .. .. .. .. .. .. Gambia, The 2003­04b 47.4 1.8 4.8 8.7 13.0 20.7 52.9 36.9 Georgia 2005b 40.8 1.9 5.4 10.5 15.3 22.2 46.7 30.6 Germany 2000 d 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1 Ghana 1998­99b 40.8 2.1 5.6 10.1 14.9 22.9 46.6 30.0 Greece 2000 d 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 Guatemala 2004 d 49.4 1.3 3.9 8.2 13.1 20.6 54.1 38.0 Guinea 2003b 38.6 2.9 7.0 10.8 14.7 21.4 46.1 30.7 Guinea-Bissau 1993b 47.0 2.1 5.2 8.8 13.1 19.4 53.4 39.3 Haiti 2001d 59.2 0.7 2.4 6.2 10.4 17.7 63.4 47.7 68 2008 World Development Indicators 2.8 PEOPLE Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Honduras 2003d 53.8 1.2 3.4 7.1 11.6 19.6 58.3 42.2 Hungary 2004b 30.1 3.5 8.6 13.1 17.1 22.3 38.9 24.2 India 2004­05b 36.8 3.6 8.1 11.3 14.9 20.4 45.3 31.1 Indonesia 2005b 39.4 3.0 7.1 10.7 14.4 20.5 47.3 32.3 Iran, Islamic Rep. 2005b 38.4 2.5 6.5 10.9 15.4 22.1 45.1 29.6 Iraq .. .. .. .. .. .. .. .. Ireland 2000 d 34.3 2.9 7.4 12.3 16.3 21.9 42.0 27.2 Israel 2001d 39.2 2.1 5.7 10.5 15.9 23.0 44.9 28.8 Italy 2000 d 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8 Jamaica 2004b 45.5 2.1 5.3 9.2 13.2 20.6 51.6 35.8 Japan 1993d 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2002­03b 38.8 2.7 6.7 10.8 14.9 21.3 46.3 30.6 Kazakhstan 2003b 33.9 3.0 7.4 11.9 16.4 22.8 41.5 25.9 Kenya 1997b 42.5 2.5 6.0 9.8 14.3 20.8 49.1 33.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998d 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2003b 30.3 3.8 8.9 12.8 16.4 22.5 39.4 24.3 Lao PDR 2002b 34.6 3.4 8.1 11.9 15.6 21.1 43.3 28.5 Latvia 2004b 35.8 2.6 6.8 11.7 16.2 22.3 42.9 27.5 Lebanon .. .. .. .. .. .. .. .. Lesotho 1995b 63.2 0.5 1.5 4.3 8.9 18.8 66.5 48.3 Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 2004b 35.8 2.6 6.8 11.7 16.1 22.4 43.0 27.5 Macedonia, FYR 2003b 39.0 2.4 6.1 10.8 15.5 22.2 45.5 29.6 Madagascar 2001b 47.5 1.9 4.9 8.5 12.7 20.4 53.5 36.6 Malawi 2004­05b 39.0 2.9 7.0 10.8 14.8 20.7 46.6 31.8 Malaysia 1997d 49.2 1.7 4.4 8.1 12.9 20.3 54.3 38.4 Mali 2001b 40.1 2.4 6.1 10.2 14.7 22.2 46.6 30.2 Mauritania 2000 b 39.0 2.5 6.2 10.6 15.2 22.3 45.7 29.5 Mauritius .. .. .. .. .. .. .. .. Mexico 2004b 46.1 1.6 4.3 8.3 12.6 19.7 55.1 39.4 Moldova 2003b 33.2 3.2 7.8 12.2 16.5 22.1 41.4 26.4 Mongolia 2002b 32.8 3.0 7.5 12.2 16.8 23.1 40.5 24.6 Morocco 1998­99b 39.5 2.6 6.5 10.6 14.8 21.3 46.6 30.9 Mozambique 2002­03b 47.3 2.1 5.4 9.3 13.0 18.7 53.6 39.4 Myanmar .. .. .. .. .. .. .. .. Namibia 1993d 74.3 0.5 1.4 3.0 5.4 11.5 78.7 64.5 Nepal 2003­04b 47.2 2.6 6.0 9.0 12.4 18.0 54.6 40.6 Netherlands 1999d 30.9 2.5 7.6 13.2 17.2 23.3 38.7 22.9 New Zealand 1997d 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8 Nicaragua 2001b 43.1 2.2 5.6 9.8 14.2 21.1 49.3 33.8 Niger 1995b 50.5 0.8 2.6 7.1 13.9 23.1 53.3 35.4 Nigeria 2003b 43.7 1.9 5.0 9.6 14.5 21.7 49.2 33.2 Norway 2000 d 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 2005b 31.2 3.9 9.1 12.9 16.1 21.1 40.8 26.5 Panama 2003d 56.1 0.7 2.5 6.6 11.4 19.6 59.9 43.0 Papua New Guinea 1996b 50.9 1.7 4.5 7.9 11.9 19.2 56.5 40.5 Paraguay 2003d 58.4 0.7 2.4 6.3 10.8 18.6 61.9 46.1 Peru 2003d 52.0 1.3 3.7 7.7 12.2 19.7 56.7 40.9 Philippines 2003b 44.5 2.2 5.4 9.1 13.6 21.3 50.6 34.2 Poland 2005b 34.9 3.0 7.4 11.7 16.1 22.3 42.5 27.2 Portugal 1997d 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. 2008 World Development Indicators 69 2.8 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Romania 2005b 31.5 3.3 8.2 12.8 16.9 22.1 40.0 25.4 Russian Federation 2002b 39.9 2.4 6.1 10.5 14.9 21.8 46.6 30.6 Rwanda 2000 b 46.8 2.1 5.3 9.1 13.2 19.4 53.0 38.2 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 2001b 41.3 2.7 6.6 10.3 14.2 20.6 48.4 33.4 Serbiae 2003b 30.0 3.4 8.3 13.0 17.3 23.0 38.4 23.4 Sierra Leone 2003b 40.0 2.6 6.5 10.5 14.5 21.2 47.3 31.2 Singapore 1998d 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8 Slovak Republic 1996d 25.8 3.1 8.8 14.9 18.7 22.8 34.8 20.9 Slovenia 2004b 30.9 3.4 8.3 12.8 16.7 22.6 39.6 24.6 Somalia .. .. .. .. .. .. .. .. South Africa 2000 b 57.8 1.4 3.5 6.3 10.0 18.0 62.2 44.7 Spain 2000 d 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6 Sri Lanka 2002b 40.2 3.0 7.0 10.5 14.2 20.4 48.0 32.7 Sudan .. .. .. .. .. .. .. .. Swaziland 2000­01d 50.4 1.6 4.3 8.2 12.3 18.9 56.3 40.7 Sweden 2000 d 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2 Switzerland 2000 d 33.7 2.9 7.6 12.2 16.3 22.6 41.3 25.9 Syrian Arab Republic .. .. .. .. .. .. .. .. Tajikistan 2004b 33.6 3.2 7.8 12.0 16.4 21.9 41.9 26.6 Tanzania 2000­01b 34.6 2.9 7.3 12.0 16.1 22.3 42.4 26.9 Thailand 2002b 42.0 2.7 6.3 9.9 14.0 20.8 49.0 33.4 Timor-Leste .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. Trinidad and Tobago 1992d 38.9 2.2 5.9 10.8 15.3 23.1 44.9 28.8 Tunisia 2000 b 39.8 2.3 6.0 10.3 14.8 21.7 47.3 31.5 Turkey 2003b 43.6 2.0 5.3 9.7 14.2 21.0 49.7 34.1 Turkmenistan 1998b 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31.7 Uganda 2002b 45.7 2.3 5.7 9.4 13.2 19.1 52.5 37.7 Ukraine 2005b 28.2 3.8 9.0 13.5 17.4 22.7 37.4 22.6 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 1999d 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5 United States 2000d 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9 Uruguayc 2003d 44.9 1.9 5.0 9.1 14.0 21.5 50.5 34.0 Uzbekistan 2003b 36.8 2.8 7.2 11.7 15.4 21.0 44.7 29.6 Venezuela, RB 2003d 48.2 0.7 3.3 8.7 13.9 22.0 52.1 35.2 Vietnam 2004b 37.0 2.9 7.1 11.1 15.1 21.8 44.8 28.9 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 2005b 37.7 2.9 7.2 11.4 15.3 20.8 45.3 30.9 Zambia 2004b 50.8 1.2 3.6 7.9 12.6 20.8 55.1 38.8 Zimbabwe 1995b 50.1 1.8 4.6 8.1 12.2 19.3 55.7 40.3 a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Urban data. d. Refers to income shares by percentiles of population, ranked by per capita income. e. Includes Montenegro. 70 2008 World Development Indicators 2.8 PEOPLE Distribution of income or consumption About the data Definitions Inequality in the distribution of income is reflected but achieving strict comparability is still impossible · Survey year is the year in which the underlying in the percentage shares of income or consumption (see About the data for table 2.7). data were collected. · Gini index measures the accruing to portions of the population ranked by Two sources of noncomparability should be noted extent to which the distribution of income (or con- income or consumption levels. The portions ranked in particular. First, the surveys can differ in many sumption expenditure) among individuals or house- lowest by personal income receive the smallest respects, including whether they use income or con- holds within an economy deviates from a perfectly shares of total income. The Gini index provides a con- sumption expenditure as the living standard indi- equal distribution. A Lorenz curve plots the cumula- venient summary measure of the degree of inequal- cator. The distribution of income is typically more tive percentages of total income received against the ity. Data on the distribution of income or consump- unequal than the distribution of consumption. In cumulative number of recipients, starting with the tion come from nationally representative household addition, the definitions of income used differ more poorest individual. The Gini index measures the area surveys. Where the original data from the house- often among surveys. Consumption is usually a between the Lorenz curve and a hypothetical line hold survey were available, they have been used to much better welfare indicator, particularly in devel- of absolute equality, expressed as a percentage of directly calculate the income or consumption shares oping countries. Second, households differ in size the maximum area under the line. Thus a Gini index by quintile. Otherwise, shares have been estimated (number of members) and in the extent of income of 0 represents perfect equality, while an index of from the best available grouped data. sharing among members. And individuals differ in 100 implies perfect inequality. · Percentage share The distribution data have been adjusted for age and consumption needs. Differences among of income or consumption is the share of total household size, providing a more consistent mea- countries in these respects may bias comparisons income or consumption that accrues to subgroups of sure of per capita income or consumption. No adjust- of distribution. population indicated by deciles or quintiles. ment has been made for spatial differences in cost World Bank staff have made an effort to ensure of living within countries, because the data needed that the data are as comparable as possible. Wher- for such calculations are generally unavailable. For ever possible, consumption has been used rather further details on the estimation method for low- and than income. Income distribution and Gini indexes for middle-income economies, see Ravallion and Chen high-income economies are calculated directly from (1996). the Luxembourg Income Study database, using an Because the underlying household surveys differ estimation method consistent with that applied for in method and type of data collected, the distribu- developing countries. tion data are not strictly comparable across coun- tries. These problems are diminishing as survey methods improve and become more standardized, The Gini coefficient and ratio of income or consumption of the richest quintile to the poorest quintiles are closely correlated 2.8a Gini coefficient (%) 80 70 60 50 40 30 20 0 10 20 30 40 50 60 Ratio of income or consumption of richest quintile to poorest quintile Data sources There are many ways to measure income or consumption inequality. The Gini coefficient shows inequal- Data on distribution are compiled by the World ity over the entire population; the ratio of income or consumption of the richest quintile to the poorest Bank's Development Research Group using pri- quintiles shows differences only at the tails of the population distribution. Both measures are closely mary household survey data obtained from govern- correlated and provide similar information. At low levels of inequality the Gini coefficient is a more sensi- ment statistical agencies and World Bank country tive measure, but above a Gini value of 45­55 percent the inequality ratio rises faster. departments. Data for high-income economies are Source: World Development Indicators data files. from the Luxembourg Income Study database. 2008 World Development Indicators 71 2.9 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 ages 15­24 ages 15­24 total labor age % of capita 1998­ 2005a 2005a 2003­05a 2003­05a 2003­05a Year force population Year GDP Year income Afghanistan .. .. .. .. .. .. .. 2005 0.5 .. Albania .. .. .. .. .. 2004 48.9 33.0 2004 4.6 .. Algeria .. .. 43 46 .. 2002 36.7 22.1 2002 3.2 2002 89.1 Angola .. .. .. .. .. .. .. .. .. Argentina .. .. 22b 28b .. 2004 35.0 25.9 1994 6.2 2002 73.7 Armenia .. .. .. .. 36 2002 64.4 48.3 2004 3.4 .. Australia .. .. 11b 11b .. 2005 92.6 69.6 2003 5.4 2002 52.4 Austria .. .. 11 10 .. 2005 96.4 68.7 2003 14.6 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 .. .. .. .. 54 1992 97.0 94.0 1997 7.7 .. Belgium .. .. 21 19 .. 2005 94.2 61.6 2003 11.3 2002 62.8 Benin 50 b 41b .. .. 23 1996 4.8 .. 1993 0.4 .. Bolivia .. .. .. .. 20 2002 10.1 7.8 2000 4.5 .. Bosnia and Herzegovina .. .. .. .. .. 2004 36.0 27.0 2004 8.8 .. Botswana .. .. .. .. .. .. .. .. .. Brazil .. .. 14b 23b .. 2004 52.6 39.1 2004 12.6 .. Bulgaria .. .. 23 21 .. 1994 64.0 63.0 2005 8.9 2002 75.2 Burkina Faso .. .. .. .. 9 1993 3.1 3.0 1992 0.3 .. Burundi .. .. .. .. .. 1993 3.3 3.0 1991 0.2 .. Cambodia .. .. .. .. 24 .. .. .. .. Cameroon .. .. .. .. 24 1993 13.7 11.5 2001 0.8 .. Canada .. .. 14b 11b .. 2005 90.5 71.4 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 .. .. 12 19 30 2000 19.0 14.0 1994 1.1 2002 54.4 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. 23 1992 5.8 5.6 1992 0.9 .. Costa Rica .. .. 11 22 .. 2004 55.3 37.6 1997 4.2 2002 103.1 Côte d'Ivoire .. .. .. .. .. 1997 9.3 9.1 1997 0.3 .. Croatia .. .. 27c 31c .. 2005 77.0 50.0 2005 12.3 2002 61.6 Cuba .. .. .. .. 46 .. .. 1992 12.6 .. Czech Republic .. .. 19 19 .. 2003 86.3 61.5 2003 8.7 2002 58.2 Denmark .. .. 6 10 .. 2005 94.6 75.0 2003 11.0 2002 54.1 Dominican Republic .. .. .. .. 28 2005 27.2 18.6 2000 0.8 2002 55.9 Ecuador 32b 42b 12b 21b .. 2004 27.0 20.8 2002 2.5 .. Egypt, Arab Rep. .. .. .. .. 12 2004 55.5 27.7 2004 4.1 2002 119.8 El Salvador 43c 55c 13c 10 c .. 2005 29.8 19.7 1997 1.3 2002 39.3 Eritrea .. .. .. .. 47 .. .. 2001 0.3 .. Estonia .. .. 16 15 .. 2004 95.2 68.6 2003 6.0 2002 60.9 Ethiopia 33b 46b 4 11 23 .. .. 1993 0.9 .. Finland .. .. 21 19 .. 2005 88.7 67.2 2003 11.2 2002 78.8 France .. .. 21 25b .. 2005 89.9 61.4 2003 13.1 2002 65.0 Gabon .. .. .. .. 26 1995 15.0 14.0 .. .. Gambia, The .. .. .. .. .. 2003 3.8 2.9 .. .. Georgia 21b 7b 27 31 .. 2004 29.9 22.7 2004 3.0 .. Germany .. .. 16 14 .. 2005 88.2 65.5 2003 13.3 2002 71.8 Ghana .. .. .. .. 34 2003 9.1 7.1 2002 1.3 .. Greece .. .. 18 35 .. 2005 85.2 58.5 2003 12.8 2002 99.9 Guatemala .. .. .. .. .. 2000 19.0 11.7 1995 0.7 .. Guinea .. .. .. .. 17 1993 1.5 1.8 .. .. Guinea-Bissau .. .. .. .. .. 2004 1.9 1.5 2005 2.1 .. Haiti .. .. .. .. 44 .. .. .. .. 72 2008 World Development Indicators 2.9 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 ages 15­24 ages 15­24 total labor age % of capita 1998­ 2005a 2005a 2003­05a 2003­05a 2003­05a Year force population Year GDP Year income Honduras .. .. 5b 11b 26 1999 20.6 17.7 1994 0.6 .. Hungary .. .. 20 19 .. 2002 56.3 34.0 2003 9.1 2002 90.5 India 54b 41b 10 b 11b .. 2004 9.0 5.7 .. .. Indonesia .. .. 25 34 12 2002 15.5 11.3 .. .. Iran, Islamic Rep. .. .. 20 32 .. 2001 35.0 20.0 2000 1.1 2002 124.2 Iraq .. .. .. .. 11 .. .. .. .. Ireland .. .. 9 7 .. 2005 88.0 63.9 2003 4.1 2002 36.6 Israel .. .. 17 19 .. 1992 82.0 63.0 1996 5.9 .. Italy .. .. 22 27 .. 2005 92.4 58.4 2003 14.7 2002 88.8 Jamaica .. .. 22 36 .. 2004 17.4 12.6 .. .. Japan .. .. 10 b 7b .. 2005 95.3 75.0 2003 8.9 2002 59.1 Jordan .. .. .. .. 12 2003 30.3 17.4 2001 2.2 2002 76.1 Kazakhstan .. .. 10 c 15c .. 2004 33.8 26.4 2004 4.9 .. Kenya .. .. .. .. 32 2005 8.0 6.7 1993 0.5 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. 12 9 .. 2005 74.3 52.0 2003 1.3 2002 43.3 Kuwait .. .. .. .. .. .. .. 1990 3.5 .. Kyrgyz Republic 33b 25b 14 18 .. 2006 42.2 28.9 2006 4.8 .. Lao PDR .. .. .. .. .. .. .. .. .. Latvia .. .. 12 14 .. 2003 92.4 66.5 2002 7.5 2002 81.8 Lebanon .. .. .. .. .. 2003 33.1 19.9 2003 2.1 .. Lesotho .. .. .. .. 37 2005 5.7 3.6 .. .. Liberia .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. 2003 65.5 38.1 2001 2.1 2002 91.2 Lithuania 50b 27b 16 15 .. 2004 79.7 56.0 2003 6.2 2002 71.3 Macedonia, FYR .. .. 63 62 8 2000 63.8 38.9 1998 8.7 .. Madagascar .. .. 7 7 22 1993 5.4 4.8 1990 0.2 .. Malawi .. .. .. .. 25 .. .. .. .. Malaysia .. .. .. .. .. 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 51.4 33.6 1999 4.4 .. Mexico 18b 22b 6 7 .. 2002 34.5 22.7 2003 1.3 2002 45.1 Moldova .. .. 19 18 .. 2000 60.6 43.1 2003 8.0 .. Mongolia .. .. 20 21 .. 2002 61.4 49.1 2002 5.8 .. Morocco .. .. 18c 14 c 17 2003 22.4 12.8 2003 1.9 2002 74.1 Mozambique .. .. .. .. 26 1995 2.0 2.1 1996 0.0 .. Myanmar .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. 42 .. .. .. .. Nepal 60 b 76b .. .. 23 2003 2.1 1.4 2003 0.3 .. Netherlands .. .. 10 10 .. 2005 90.3 70.4 2003 12.8 2002 84.1 New Zealand .. .. 9b 10 b .. .. .. 2003 7.4 2002 39.5 Nicaragua .. .. 11 16 31 2005 17.9 11.5 1996 2.5 .. Niger .. .. .. .. 19 1992 1.3 1.5 2005 0.2 .. Nigeria .. .. .. .. 17 2005 1.7 1.2 1991 0.1 .. Norway .. .. 13 12 .. 2005 90.8 75.7 2003 10.7 2002 65.1 Oman .. .. .. .. .. .. .. .. .. Pakistan 44b 22b 11 15 .. 2004 6.4 4.0 1993 0.9 .. Panama .. .. 19 30 .. 1998 51.6 40.7 1996 4.3 .. Papua New Guinea .. .. .. .. .. .. .. .. .. Paraguay .. .. 12b 21b .. 2004 11.6 9.1 2001 1.2 .. Peru 56b 55b 21b 21b 22 2003 16.3 12.3 2000 2.6 2002 43.9 Philippines .. .. 15 19 15 2000 27.1 18.7 1993 1.0 .. Poland .. .. 37 39 .. 2005 84.9 54.5 2003 13.9 2002 69.7 Portugal .. .. 14 19 .. 2005 91.4 71.9 2003 11.9 2002 79.8 Puerto Rico .. .. 25b 21b .. .. .. .. .. 2008 World Development Indicators 73 2.9 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 ages 15­24 ages 15­24 total labor age % of capita 1998­ 2005a 2005a 2003­05a 2003­05a 2003­05a Year force population Year GDP Year income Romania .. .. 21 18 .. 2005 57.6 39.1 2003 6.9 .. Russian Federation .. .. .. .. .. .. 2004 5.8 .. .. Rwanda .. .. .. .. 34 2004 4.8 4.1 .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. Senegal .. .. .. .. 23 2003 5.3 3.9 2003 1.3 .. Serbia .. .. .. .. 26 2003 46.0 d 32.2d 2003 12.4 d .. Sierra Leone .. .. .. .. .. 2004 4.6 3.6 .. .. Singapore .. .. 4 6 .. 1995 73.0 56.0 1996 1.4 .. Slovak Republic .. .. 31 29 .. 2003 78.5 55.3 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 .. 2005 91.0 63.2 2003 9.2 2002 88.3 Sri Lanka .. .. 20 b 37b .. 2004 35.6 22.2 1996 2.4 .. Sudan .. .. .. .. 19 1995 12.1 12.0 .. .. Swaziland .. .. .. .. .. .. .. .. .. Sweden .. .. 23 22 .. 2005 91.0 72.3 2003 12.7 2002 68.2 Switzerland .. .. 9 9 .. 2005 100.0 79.1 2003 12.1 2002 67.3 Syrian Arab Republic .. .. .. .. .. 2004 17.4 11.4 2004 1.3 .. Tajikistan .. .. .. .. .. .. .. 1996 3.0 .. Tanzania .. .. .. .. 25 1996 2.0 2.0 .. .. Thailand .. .. 5 5 30 2003 22.5 18.0 .. .. Timor-Leste .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. 1997 15.9 15.0 1997 0.6 .. Trinidad and Tobago .. .. .. .. .. 2004 55.6 .. 1996 0.6 .. Tunisia .. .. 31 29 .. 2004 45.3 25.4 2003 4.3 2002 72.7 Turkey 10 b 6b 19 19 .. 2002 45.0 24.3 2002 7.1 2002 103.3 Turkmenistan .. .. .. .. 27 .. .. 1996 2.3 .. Uganda .. .. .. .. 30 2004 10.7 9.3 2003 0.3 .. Ukraine 3b 3b 15 14 .. 2005 76.0 52.3 2005 15.4 .. United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom .. .. 13 10 .. 2005 92.7 71.4 2003 10.9 2002 47.6 United States .. .. 12b 10 b .. 2005 92.5 72.5 2003 7.5 2002 51.0 Uruguay .. .. 25 35 .. 2004 55.0 44.3 1996 15.0 2002 125.4 Uzbekistan .. .. .. .. .. .. .. 1995 5.3 .. Venezuela, RB .. .. 24 35 .. 2004 31.8 23.8 2001 2.7 .. Vietnam .. .. 4 5 27 2005 13.2 10.8 1998 1.6 .. West Bank and Gaza .. .. 39 45 .. 2000 18.8 7.8 2001 0.8 .. Yemen, Rep. .. .. .. .. .. 2005 10.0 5.5 1994 0.1 2002 106.3 Zambia .. .. .. .. 23 2000 5.9 4.9 1993 0.1 .. Zimbabwe .. .. .. .. 38 1995 12.0 10.0 2002 2.3 .. World .. w .. w Low income .. .. Middle income .. .. Lower middle income .. .. Upper middle income 21 26 Low & middle income .. .. East Asia & Pacific .. .. Europe & Central Asia .. .. Latin America & Carib. 14 20 Middle East & N. Africa .. .. South Asia 11 12 Sub-Saharan Africa .. .. High income 14 13 Euro area 18 20 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2006. d. Includes Montenegro. 74 2008 World Development Indicators 2.9 PEOPLE Assessing vulnerability and security About the data Definitions As traditionally measured, poverty is a static con- likely to include school leavers, the level of youth · Urban informal sector employment is all people cept, and vulnerability a dynamic one. Vulnerabil- unemployment varies considerably over the year as a who, during a given reference period, were employed ity reflects a household's resilience in the face of result of different school opening and closing dates. in at least one informal enterprise, irrespective of shocks and the likelihood that a shock will lead to a The youth unemployment rate shares similar limita- their status in employment and whether it was their decline in well-being. Thus, it depends primarily on tions on comparability as the general unemployment main or secondary job. · Youth unemployment is the the household's assets and insurance mechanisms. rate. For further information, see About the data for share of the labor force ages 15­24 without work but Because poor people have fewer assets and less table 2.5 and the original source. available for and seeking employment. · Female- diversified sources of income than do the better-off, The definition of female-headed household differs headed households are the percentage of house- fluctuations in income affect them more. greatly across countries, making cross-country com- holds with a female head. · Pension contributors are Enhancing security for poor people means reduc- parison difficult. In some cases it is assumed that a the share of the labor force or working-age population ing their vulnerability to such risks as ill health, pro- woman cannot be the head of any household with an (here defined as ages 15­64) covered by a pension viding them the means to manage risk themselves, adult male, because of sex-biased stereotype. Cau- scheme. · Public expenditure on pensions is all and strengthening market or public institutions for tion should be exercised in interpreting the data. government expenditures on cash transfers to the managing risk. Tools include microfinance programs, Pension scheme coverage may be broad or even elderly, the disabled, and survivors and the adminis- public provision of education and basic health care, universal where eligibility is determined by citizen- trative costs of these programs. · Average pension and old age assistance (see tables 2.10 and 2.15). ship, residency, or income status. In contribution- is estimated by dividing total pension expenditure by Poor households face many risks, and vulnerability related schemes, however, eligibility is usually the number of pensioners. is thus multidimensional. The indicators in the table restricted to individuals who have contributed for focus on individual risks--informal sector employ- a minimum number of years. Definitional issues-- ment, youth unemployment, female-headed house- relating to the labor force, for example--may arise in holds, income insecurity in old age--and the extent comparing coverage by contribution-related schemes to which publicly provided services may be capable over time and across countries (for country-specific of mitigating some of these risks. Poor people face information, see Palacios and Pallares-Miralles labor market risks, often having to take up precari- 2000). The share of the labor force covered by a ous, low-quality jobs in the informal sector and to pension scheme may be overstated in countries that increase their household's labor market participa- do not try to count informal sector workers as part tion by sending their children to work (see table 2.6). of the labor force. Income security is a prime concern for the elderly. Public interventions and institutions can provide Data on informal sector employment are from a services directly to poor people, although whether variety of sources, including labor force and special these interventions and institutions work well for the informal sector surveys, household surveys, surveys poor is debated. State action is often ineffective, of household industries or economic activities, sur- in part because governments can influence only a veys of small enterprises and microenterprises, and few of the many sources of well-being and in part official estimates. In most countries data on the infor- because of difficulties in delivering goods and ser- mal economy are collected on an ad hoc basis or less vices. The effectiveness of public provision is further frequently than annually. The international compara- constrained by the fiscal resources at governments' bility of the data is affected by differences among disposal and the fact that state institutions may not countries in definitions and coverage and in treatment be responsive to the needs of poor people. of domestic workers. The data in the table are based The data on public pension spending cover non- on national definitions of informal sector and urban contributory pensions or social assistance targeted areas established by countries, and therefore data to the elderly and disabled and spending by social may not be comparable across countries. For details insurance schemes for which contributions had previ- Data sources on these definitions, consult the original source. ously been made. A country's pattern of spending is Youth unemployment is an important policy issue correlated with its demographic structure--spending Data on urban informal sector employment and for many economies. Experiencing unemployment increases as the population ages. youth unemployment are from the ILO database may permanently impair a young person's produc- Key Indicators of the Labour Market, 5th edi- tive potential and future employment opportunities. tion. Data on female-headed household are from The table presents unemployment among youth ages Demographic and Health Surveys by Macro Inter- 15­24, but the lower age limit for young people in national. Data on pension contributors and pen- a country could be determined by the minimum sion spending are from the World Bank Pensions age for leaving school, so age groups could dif- Database (available June 2008). fer across countries. Also, since this age group is 2008 World Development Indicators 75 2.10 Education inputs Public expenditure Public expenditure Trained Primary per studenta on education teachers school in primary pupil-teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2006b 1999 2006b 1999 2006b 2006b 2006b 2006b 2006b Afghanistan .. .. .. .. .. .. .. .. 36.5 83 Albania .. .. .. .. .. .. .. .. .. 21 Algeria 26.5 .. .. .. .. .. .. .. 99.3 24 Angola .. .. .. .. .. 65.5 2.4 .. .. .. Argentina .. 11.3 16.4 15.7 17.7 11.8 3.8 13.1 .. 17 Armenia .. .. 12.4 .. 29.1 .. .. .. 77.5 21 Australia .. 15.9 14.5 14.5 25.7 22.5 4.6 .. .. .. Austria 18.2 22.5 29.9 27.2 51.6 48.5 5.4 10.8 .. 12 Azerbaijan .. 5.5 17.0 8.5 19.1 9.4 2.1 17.4 100.0 13 Bangladesh .. 7.6 12.4 14.6 46.3 49.4 2.5 14.2 48.3 51 Belarus .. 14.3 .. 27.0 .. 29.0 6.1 12.9 99.6 16 Belgium 15.8 20.0 23.7 33.5 38.3 35.1 6.0 12.2 .. 11 Benin .. 11.5 26.1 .. 202.9 .. 4.4 17.1 72.2 47 Bolivia .. .. 11.7 .. 44.1 .. .. .. .. 24 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 15.7 .. 40.2 .. 438.4 8.7c 21.0 c 96.7 25 Brazil .. 12.8 9.5 11.5 57.0 32.6 4.0 .. .. 21 Bulgaria .. 11.9 18.8 10.8 17.9 17.8 2.5 .. .. 16 Burkina Faso .. 27.4 .. 20.5 .. 208.1 4.2 15.4 86.9 46 Burundi 13.4 19.1 .. 74.5 1,051.9 348.8 5.1 17.7 87.5 54 Cambodia .. 5.6 11.4 .. 43.8 .. 1.7 .. 98.3 50 Cameroon .. 6.3 16.5 22.8 63.0 94.1 3.3 16.8 61.8 44 Canada .. .. .. .. 47.9 .. .. .. .. .. Central African Republic 11.9 10.5 .. .. .. 291.3 1.4 .. 49.7 .. Chad 8.0 6.8 27.5 28.0 .. 333.9 1.9 10.1 26.8 63 Chile .. 11.9 14.8 13.1 19.4 11.6 3.4 18.5 .. 26 China .. .. 11.5 .. 90.1 .. .. .. .. 18 Hong Kong, China .. 14.1 17.7 18.2 .. 58.3 3.9 23.9 94.8 18 Colombia .. 19.2 16.9 18.0 39.6 23.6 4.7 11.1 .. 28 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. 3.4 .. .. 404.9 .. 1.9 8.1 89.0 55 Costa Rica 7.8 17.0 23.2 17.1 55.0 35.9 4.7 29.8 88.0 20 Côte d'Ivoire .. .. 54.5 .. 212.8 .. .. .. .. 46 Croatia .. 23.7 .. 22.5 41.5 27.9 4.4 9.1 100.0 15 Cuba 21.6 33.8 41.3 43.0 86.4 34.5 9.1 14.2 100.0 10 Czech Republic .. 12.8 21.7 23.3 33.7 30.4 4.4 10.0 .. 16 Denmark .. 24.8 38.1 35.3 65.9 62.5 8.4 15.3 .. .. Dominican Republic .. 8.2 .. 5.9 .. .. 3.6 16.8 88.3 23 Ecuador .. .. 9.7 .. .. .. .. .. 71.1 23 Egypt, Arab Rep. .. .. .. .. .. .. .. .. .. 26 El Salvador .. 10.0 7.9 9.3 9.4 16.6 3.1 20.0 94.0 40 Eritrea .. 9.3 37.3 9.3 429.4 1,082.5 5.3 .. 87.5 47 Estonia .. 19.2 27.9 25.5 32.6 18.2 5.1 14.9 .. .. Ethiopia 22.1 14.1 .. 13.7 .. 747.7 6.0 17.5 .. 59 Finland 21.7 18.8 26.2 32.9 40.9 36.7 6.5 12.8 .. 16 France 11.8 17.8 28.6 29.0 29.7 34.0 5.8 10.9 .. 19 Gabon .. .. .. .. .. .. .. .. .. 36 Gambia, The 13.2 7.4 .. 9.1 .. 238.0 2.0 .. 76.3 35 Georgia .. .. .. .. .. .. 3.1 9.3 .. 15 Germany .. 16.3 20.5 21.7 .. .. 4.6 9.8 .. 14 Ghana .. 17.8 .. 28.0 .. 209.4 5.4 .. 53.0 c 32c Greece 7.5 16.5 17.0 22.6 28.7 27.1 4.2 8.5 .. 11 Guatemala .. 9.2 4.2 4.1 .. 34.9 2.6 .. .. 31 Guinea .. .. .. .. .. 188.8 1.6 .. 67.7 44 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 9.1 .. .. .. .. .. .. .. .. .. 76 2008 World Development Indicators 2.10 PEOPLE Education inputs Public expenditure Public expenditure Trained Primary per studenta on education teachers school in primary pupil-teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2006b 1999 2006b 1999 2006b 2006b 2006b 2006b 2006b Honduras .. .. .. .. .. .. .. .. 87.2 28 Hungary 21.2 23.3 19.1 23.5 34.2 24.3 5.4 11.1 .. 10 India .. 9.2 24.9 27.0 90.8 61.0 3.8 .. .. 40 Indonesia .. .. 7.3 .. 21.3 .. .. .. .. 20 Iran, Islamic Rep. .. 13.6 9.8 11.1 34.6 30.0 5.1 18.6 70.4 19 Iraq .. .. .. .. .. .. .. .. 100.0 17 Ireland 11.5 14.3 16.8 21.1 28.5 23.9 4.7 14.0 .. 18 Israel 12.6 22.3 23.3 22.7 32.9 25.6 6.9 .. .. 13 Italy 14.9 24.9 27.7 27.2 27.6 22.7 4.6 9.6 .. 10 Jamaica 9.9 14.6 23.6 21.5 79.0 .. 5.3 8.8 .. 28 Japan .. 22.7 21.0 22.7 15.2 20.8 3.7 9.8 .. 19 Jordan .. 14.6 15.8 17.6 .. .. .. .. .. 20 Kazakhstan .. 9.8 .. 7.7 .. 5.6 3.2 15.8 .. 17c Kenya 12.9 21.0 14.8 20.7 204.8 284.5 6.9 17.9 98.8 40 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 11.8 19.2 15.7 25.0 8.4 8.9 4.6 16.5 .. 28 Kuwait 35.4 9.6 .. 13.9 .. 80.5 3.8 12.9 100.0 10 Kyrgyz Republic .. .. 11.9 .. 27.7 21.8 4.9 .. 61.3 24 Lao PDR .. 9.1 4.3 4.7 66.5 25.2 3.0 14.0 85.8 31 Latvia .. 20.7 23.7 24.0 27.9 12.4 5.1 14.2 .. 12 Lebanon .. 8.3 .. 8.8 14.2 17.2 2.7 11.0 12.6 14 Lesotho .. 22.2 69.0 44.2 1,247.8 1,012.0 13.0 29.8 66.1 40 Liberia .. .. .. .. .. .. .. .. .. 19 Libya .. .. .. .. 23.8 .. .. .. .. .. Lithuania .. 15.0 .. 21.2 34.2 20.0 5.2 15.6 .. 14 Macedonia, FYR .. .. .. .. .. .. .. .. .. 19 Madagascar .. 8.1 39.9 15.3 180.9 187.8 3.1 25.3 36.5 48 Malawi 7.2 .. .. .. .. .. .. .. .. .. Malaysia 10.1 14.5 22.3 21.1 83.3 71.0 6.2 25.2 .. 17 Mali .. 24.5 61.6 36.4 265.0 .. 4.5 16.8 .. 56 Mauritania .. 10.0 36.4 25.1 80.1 40.6 2.9 10.1 100.0 41 Mauritius 10.1 10.3 15.3 17.4 40.4 29.8 3.9 12.7 100.0 22 Mexico 4.8 14.9 14.2 15.7 47.8 41.3 5.4 25.6 .. 28 Moldova .. .. .. .. .. 43.8 7.6 20.2 .. 17 Mongolia .. 14.0 .. 13.0 .. 22.4 5.2 .. .. 33 Morocco 15.4 22.9 50.1 39.7 107.0 84.3 6.8 27.2 100.0 27 Mozambique .. 15.0 .. 94.8 .. 361.2 5.0 19.5 64.6 67 Myanmar .. .. 7.0 .. 28.6 .. .. .. 98.3 30 Namibia .. 20.0 36.4 19.9 157.6 .. .. .. 92.4 31 Nepal .. .. 13.1 .. 141.7 .. .. .. 30.5 40 Netherlands 12.1 17.9 20.9 24.0 42.3 40.6 5.2 11.2 .. .. New Zealand 17.2 19.3 24.3 22.5 41.6 25.2 6.5 .. .. 16 Nicaragua .. 9.2 .. 4.2 .. .. .. .. 73.6 33 Niger .. 32.4 64.4 49.1 .. 384.9 3.6 15.0 91.9 40 Nigeria .. .. .. .. .. .. .. .. 49.8 37 Norway 32.7 20.3 27.0 30.5 46.1 52.2 7.6 16.6 .. 11 Oman 10.5 15.4 22.2 12.9 .. 14.2 4.7 31.1 100.0 14 Pakistan .. .. .. .. .. .. 2.6 12.2 84.6 39 Panama 11.3 9.7 19.1 12.3 33.6 26.5 3.8 8.9 91.1 25 Papua New Guinea .. .. .. .. .. .. .. .. .. 36 Paraguay .. .. 18.4 .. 58.9 .. .. .. .. 28 Peru .. 6.6 10.8 8.9 21.2 9.0 2.7 17.0 .. 23 Philippines .. 9.2 10.7 9.0 15.0 12.4 2.7 16.4 .. 35 Poland 12.9 22.8 16.5 21.6 21.1 21.5 5.4 12.7 .. 12 Portugal 16.3 23.2 27.5 34.9 28.1 23.5 5.4 11.5 .. 11 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 77 2.10 Education inputs Public expenditure Public expenditure Trained Primary per studenta on education teachers school in primary pupil-teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2006b 1999 2006b 1999 2006b 2006b 2006b 2006b 2006b Romania .. 9.9 16.0 14.7 32.6 22.1 3.3 8.6 .. 17 Russian Federation .. .. .. .. .. 10.8 3.5 12.9 .. 17 Rwanda .. 10.4 28.4 18.4 657.6 404.5 3.8 19.0 c 98.3 66 Saudi Arabia .. .. .. .. .. .. 6.8 27.6 .. 15 Senegal 18.9 18.3 .. 35.0 .. 235.3 5.0 26.3 100.0 39 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. 3.8 .. 50.1c 44 c Singapore .. .. 17.9 .. .. .. .. .. .. 24 Slovak Republic .. 11.9 18.3 16.7 32.6 32.2 4.2 10.8 .. 18 Slovenia 17.4 25.9 26.5 30.6 28.8 25.8 6.0 .. .. 15 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 20.2 14.3 20.0 17.6 60.7 50.1 5.4 17.6 .. 36 Spain 11.3 19.0 24.4 23.8 19.6 22.7 4.3 11.0 .. 14 Sri Lanka .. .. .. .. .. .. .. .. .. 22 Sudan .. .. .. .. .. .. .. .. 58.7 34 Swaziland 6.7 14.2 26.1 38.8 388.4 320.6 7.0 .. 90.8 33 Sweden 45.8 25.7 26.1 34.5 52.7 43.7 7.3 12.9 .. 10 Switzerland 36.1 25.0 27.7 28.0 54.5 63.1 6.0 .. .. 13 Syrian Arab Republic .. .. 22.1 .. .. .. .. .. .. .. Tajikistan .. 8.8 .. 11.4 .. 11.2 3.4 19.0 93.0 22 Tanzania .. .. .. .. .. .. .. .. 100.0c 53c Thailand 11.6 14.1 15.7 15.5 35.5 25.0 4.2 25.0 .. 18 Timor-Leste .. .. .. .. .. .. .. .. .. 34 Togo .. .. 30.9 .. 317.9 .. .. .. 36.8 38 Trinidad and Tobago .. .. 12.2 .. 147.6 .. .. .. 81.0 17 Tunisia .. 21.1 27.1 24.4 89.4 56.4 7.3 20.8 .. 20 Turkey 10.7 14.1 14.3 17.8 45.5 40.7 4.0 .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 11.3 .. 34.0 .. 188.9 5.2 18.3 84.8 49 Ukraine .. 16.0 11.2 24.5 36.5 31.5 6.3 19.3 99.6 17 United Arab Emirates .. 7.1 11.5 9.2 41.5 .. 1.3 27.4 60.0 15 United Kingdom 15.0 18.0 24.4 27.0 26.2 27.6 5.4 11.7 .. 17 United States .. 22.0 22.5 24.7 27.0 23.5 5.6 14.4 .. 14 Uruguay 7.8 7.6 11.3 8.7 19.1 20.1 2.6 14.1 .. 21 Uzbekistan .. .. .. .. .. .. .. .. 100.0 c 18 c Venezuela, RB .. 8.0 .. 8.3 .. 34.3 3.7 .. 83.1 17 Vietnam .. .. .. .. .. .. .. .. 95.6 21 West Bank and Gaza .. .. .. .. .. .. .. .. 100.0 32 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. Zambia .. 5.4 19.9 8.2 168.2 .. 2.0 14.8 .. 51 Zimbabwe 20.7 .. 19.5 .. 195.2 .. .. .. .. .. World .. m 14.5 m .. m 21.1 m .. m .. m 4.6 m .. m 30 w Low income .. .. .. .. .. .. .. .. 41 Middle income .. 13.0 16.6 17.1 37.2 25.9 4.3 .. .. Lower middle income .. .. .. .. .. .. .. .. 19 Upper middle income .. 13.2 17.1 16.7 31.8 23.3 4.1 14.1 21 Low & middle income .. .. .. .. .. .. 4.1 .. 33 East Asia & Pacific .. .. 7.0 .. 32.2 .. 3.5 .. 19 Europe & Central Asia .. 13.6 .. 18.2 .. 21.8 4.2 13.1 16 Latin America & Carib. .. 11.4 14.8 14.1 37.1 .. 4.0 .. 24 Middle East & N. Africa .. .. .. .. .. .. .. .. 23 South Asia .. .. 13.1 .. 90.8 .. 2.2 .. 41 Sub-Saharan Africa .. 11.8 .. .. .. .. 4.2 .. 47 High income 15.8 19.2 24.3 24.8 32.8 29.0 5.4 12.5 16 Euro area 14.9 18.9 25.3 27.2 28.7 27.1 5.3 11.0 14 a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data for 1991 are not fully comparable with data from 1999 onward. b. Provisional data. c. Data are for 2007. 78 2008 World Development Indicators 2.10 PEOPLE Education inputs About the data Definitions Data on education are compiled by the United The general quality of the data on education finance · Public expenditure per student is public current Nations Educational, Scientific, and Cultural Organi- is poor. This is partly because ministries of educa- and capital spending on education divided by the zation (UNESCO) Institute for Statistics from official tion, from which the UNESCO Institute for Statistics number of students by level as a percentage of gross responses to surveys and from reports provided by collects data, are not necessarily the best source for domestic product (GDP) per capita. · Public expen- education authorities in each country. The data are education finance data. Other agencies, particularly diture on education is current and capital public used for monitoring, policymaking, and resource allo- ministries of finance, need to be consulted, but coor- expenditure on education as a percentage of GDP cation. For a variety of reasons, however, education dination is not easy. It is also difficult to track actual and as a percentage of total government expendi- statistics generally fail to provide a complete and spending from the central government to local institu- ture. · Trained teachers in primary education are accurate picture of a country's education system. tions. And private spending adds to the complexity of the percentage of primary school teachers who have Statistics often lag by one to two years, though collecting accurate data on public spending. received the minimum organized teacher training efforts have been made to shorten the delay. More- The share of trained teachers in primary educa- (pre-service or in-service) required for teaching in over, coverage and data collection methods vary tion measures the quality of the teaching staff. It their country. · Primary school pupil-teacher ratio across countries and over time within countries, so does not take account of competencies acquired by is the number of pupils enrolled in primary school comparisons should be interpreted with caution. teachers through their professional experience or divided by the number of primary school teachers For most countries the data on education spending self-instruction or of such factors as work experi- (regardless of their teaching assignment). in the table refer to public spending--government ence, teaching methods and materials, or classroom spending on public education plus subsidies for pri- conditions, which may affect the quality of teaching. vate education--and generally exclude foreign aid for Since the training teachers receive varies greatly education. They may also exclude spending by reli- (pre-service or in-service), care should be taken in gious schools, which play a significant role in many making comparisons across countries. developing countries. Data for some countries and The primary school pupil-teacher ratio refl ects some years refer to ministry of education spending the average number of pupils per teacher. It differs only and exclude education expenditures by other from the average class size because of the differ- ministries and local authorities. ent practices countries employ, such as part-time Many developing countries seek to supplement teachers, school shifts, and multigrade classes. The public funds for education, some with tuition fees comparability of pupil-teacher ratios across coun- to recover part of the cost of providing education tries is affected by the definition of teachers and by services or to encourage development of private differences in class size by grade and in the number schools. Fees raise diffi cult questions of equity, of hours taught, as well as the different practices efficiency, access, and taxation, however, and some mentioned above. Moreover, the underlying enroll- governments have used scholarships, vouchers, and ment levels are subject to a variety of reporting errors other public finance methods to counter criticism. For (for further discussion of enrollment data, see About most countries the data reflect only public spend- the data for table 2.11). While the pupil-teacher ratio ing. Data for a few countries include private spend- is often used to compare the quality of schooling ing, although countries vary on whether parents or across countries, it is often weakly related to the schools pay for books, uniforms, and other supplies. value added of schooling systems. For greater detail, consult the country- and indicator- In 1998 UNESCO introduced the new International specific notes in the original source. Standard Classification of Education 1997. Thus the The share of public expenditure devoted to edu- time-series data for the years through 1997 are not cation allows an assessment of the priority a gov- comparable with those for 1999 onward. Any time- ernment assigns to education relative to other series analysis should therefore be undertaken with public investments, as well as a government's extreme caution. commitment to investing in human capital develop- In 2006 the UNESCO Institute for Statistics also ment. It also reflects the development status of a changed its convention for citing the reference year country's education system relative to that of oth- of education data and indicators to the calendar year Data sources ers. However, returns on investment to education, in which the academic or financial year ends. Data especially primary and lower secondary education, that used to be listed for 2005/06, for example, are Data on education inputs are from the UNESCO cannot be understood simply by comparing current now listed for 2006. This change was implemented Institute for Statistics, which compiles inter- education indicators with national income. It takes to present the most recent data available and to align national data on education in cooperation with a long time before currently enrolled children can the data reporting with that of other international orga- national commissions and national statistical productively contribute to the national economy nizations (in particular the Organisation for Economic services. (Hanushek 2002). Co-operation and Development and Eurostat). 2008 World Development Indicators 79 2.11 Participation in education Gross enrollment Net enrollment Total net enrollment Children out of ratio ratioa ratio, primary school thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2006b 2006b 2006b 2006b 1991 2006b 1991 2006b 2006b 2006b 2006b 2006b Afghanistan .. .. .. . .. .. .. .. .. .. .. .. Albania 49 105 77 19 95 94 .. 73 94 93 8 8 Algeria 14 110 83 22 89 95 53 66 100 98 26 62 Angola .. .. .. 3 50 .. .. .. .. .. .. .. Argentina 64 113 86 65 .. 99 .. 79 .. .. .. .. Armenia 36 98 90 32 .. 82 .. 86 84 88 7 4 Australia 104 104 149 73 99 96 80 86 96 97 35 27 Austria 88 102 102 49 88 97 .. .. 96 98 8 4 Azerbaijan 32 96 83 15 89 85 .. 78 87 84 38 43 Bangladesh 10 103 44 6 .. 89 .. 41 91 94 842 529 Belarus 103 96 96 66 85 89 .. 88 91 88 18 21 Belgium 120 102 109 62 96 98 86 97 98 98 9 7 Benin 5 96 32 .. 41 78 .. .. 89 71 79 198 Bolivia 50 109 82 41 .. 95 .. 71 96 97 30 22 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 108 75 5 88 86 39 61 88 89 19 17 Brazil 63 140 106 24 85 95 17 78 95 97 336 224 Bulgaria 80 102 105 44 85 93 63 89 95 94 8 8 Burkina Faso 2 60 15 2 27 47 .. 12 49 39 562 653 Burundi 2 103 14 2 53 75 .. .. 61 56 154 170 Cambodia 11 122 38 5 72 90 .. 24 97 96 98 114 Cameroon 22 106 41 7 69 .. .. .. .. .. .. .. Canada 68 100 117 62 98 .. 89 .. .. .. 0 .. Central African Republic 2 61 .. 1 52 45 .. .. .. .. 160 212 Chad 1 76 15 1 34 .. .. .. .. .. .. .. Chile 55 104 91 48 89 .. 55 .. 95 94 44 53 China 39 111 76 22 98 .. .. .. .. .. .. .. Hong Kong, China .. .. 85 33 .. .. .. 78 97 93 .. .. Colombia 40 116 82 31 68 88 34 65 92 92 193 174 Congo, Dem. Rep. .. .. .. .. 54 .. .. .. .. .. .. .. Congo, Rep. 9 108 43 .. 82 55 .. .. 49 60 116 133 Costa Rica 70 111 86 25 87 .. 38 .. .. .. .. .. Côte d'Ivoire 3 71 .. .. 45 .. .. .. .. .. .. .. Croatia 53 93 89 46 79 .. 63 .. .. .. .. .. Cuba 113 101 94 88 94 97 73 87 97 97 15 12 Czech Republic 114 102 96 48 87 93 .. .. 91 94 22 15 Denmark 94 99 124 81 98 96 87 91 96 97 9 6 Dominican Republic 32 98 69 35 56 77 .. 52 78 81 139 116 Ecuador 80 117 65 .. 98 97 .. 55 99 100 12 0 Egypt, Arab Rep. 17 102 86 35 86 94 .. 83 100 94 10 256 El Salvador 51 114 64 21 .. 94 .. 54 96 97 21 18 Eritrea 14 62 31 1 15 47 .. 25 53 45 145 163 Estonia 116 100 100 66 100 95 .. 91 97 97 1 1 Ethiopia 2 83 27 2 22 65 .. 24 70 65 2,047 2,426 Finland 59 100 111 92 98 99 93 95 99 99 3 2 France 117 110 114 56 100 99 .. 99 99 99 19 9 Gabon .. 152 .. .. 94 .. .. .. .. .. .. .. Gambia, The 17 74 45 1 46 62 .. 38 .. .. 49 41 Georgia 55 96 85 38 97 89 .. 79 87 88 19 14 Germany 97 101 100 .. 84 .. .. .. .. .. .. .. Ghana 55 98 c 47c 5 54 66c .. 38 64 65 572c 569c Greece 68 102 102 90 95 100 83 91 100 100 0 1 Guatemala 29 114 53 9 .. 94 .. 38 97 93 21 62 Guinea 7 88 35 3 27 72 .. 28 76 64 159 230 Guinea-Bissau .. .. .. .. 38 .. .. .. .. .. .. .. Haiti .. .. .. .. 21 .. .. .. .. .. .. .. 80 2008 World Development Indicators 2.11 PEOPLE Participation in education Gross enrollment Net enrollment Total net enrollment Children out of ratio ratioa ratio, primary school thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2006b 2006b 2006b 2006b 1991 2006b 1991 2006b 2006b 2006b 2006b 2006b Honduras 38 118 76 17 88 96 21 .. 96 97 21 11 Hungary 84 98 96 65 91 89 75 90 96 96 10 9 India 39 115 54 11 .. 88 .. .. 96 92 2,780 4,713 Indonesia 33 115 62 17 96 95 39 57 99 96 142 544 Iran, Islamic Rep. 53 118 81 27 92 94 .. 77 91 100 305 0 Iraq .. .. .. . 94 .. .. .. .. .. .. .. Ireland .. 104 112 58 90 95 80 87 94 95 13 11 Israel 93 110 93 58 92 97 .. 89 97 98 11 7 Italy 104 102 99 65 100 99 .. 92 100 99 4 12 Jamaica 92 95 87 .. 96 90 64 78 91 91 16 15 Japan 85 100 102 55 100 100 97 100 100 100 12 0 Jordan 32 97 89 40 94 91 .. 79 95 96 23 17 Kazakhstan 36 105c 93c 51c 88 90 c .. 86c 98 99 6c 3c Kenya 50 108 48 3 .. 76 .. 42 76 77 670 649 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 96 105 96 91 100 98 86 94 .. .. .. .. Kuwait 75 96 89 18 49 83 .. .. 89 88 12 12 Kyrgyz Republic 14 97 86 43 92 86 .. 80 94 93 14 14 Lao PDR 11 116 43 9 62 84 .. 35 85 80 54 71 Latvia 87 95 99 75 94 90 .. .. 90 94 4 3 Lebanon 64 94 81 48 66 82 .. 73 83 83 40 40 Lesotho 18 114 37 4 72 72 15 24 73 78 55 48 Liberia 100 91 .. .. .. 39 .. .. .. .. 177 179 Libya 9 110 111 .. 93 .. .. .. .. .. .. .. Lithuania 65 94 100 76 .. 88 .. 94 90 91 8 7 Macedonia, FYR 33 98 84 30 94 92 .. 81 97 97 2 1 Madagascar 8 139 24 3 64 96 .. .. 93 93 54 52 Malawi .. 119 29 0d 49 91 .. 24 91 96 136 66 Malaysia 122 100 72 31 .. 99 .. 72 99 99 11 15 Mali 3 80 28 3 25 61 6 .. 67 52 328 466 Mauritania 2 102 22 4 36 79 .. 16 75 79 52 40 Mauritius 101 102 86 17 91 95 .. 79 94 96 3 2 Mexico 96 112 85 25 98 98 45 69 100 99 15 52 Moldova 68 91 82 36 88 83 .. 75 85 85 14 13 Mongolia 54 101 89 47 90 91 .. 82 95 99 6 1 Morocco 59 106 52 12 56 88 .. .. 90 85 168 261 Mozambique .. 105 16 1 42 69 .. 4 80 73 568 662 Myanmar 6 114 49 .. 99 100 .. 46 98 100 16 0 Namibia 31 107 57 6 .. 76 .. 35 74 79 49 40 Nepal 27 126 43 6 .. 79 .. .. 85 75 267 436 Netherlands 90 107 118 59 95 98 84 87 99 97 8 15 New Zealand 93 102 121 82 98 99 85 .. 99 99 1 1 Nicaragua 52 116 66 .. 70 90 .. 43 93 94 38 34 Niger 2 51 11 1 24 43 6 9 49 36 565 680 Nigeria 14 96 32 10 55 63 .. 26 70 60 3,550 4,547 Norway 88 98 113 78 100 98 88 96 98 98 5 4 Oman 8 82 89 25 69 74 .. 77 76 77 44 38 Pakistan 52 84 30 5 33 66 .. 30 76 58 2,705 4,116 Panama 67 112 70 45 .. 98 .. 64 99 99 1 2 Papua New Guinea .. 55 .. .. .. .. .. .. .. .. .. .. Paraguay 34 112 67 25 94 94 26 .. 94 95 24 21 Peru 66 116 92 34 .. 96 .. 70 98 100 30 2 Philippines 40 111 85 28 96 93 .. 60 92 95 463 315 Poland 55 98 100 64 97 97 76 93 97 97 50 38 Portugal 79 116 97 55 98 98 .. 82 100 99 1 3 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 81 2.11 Participation in education Gross enrollment Net enrollment Total net enrollment Children out of ratio ratioa ratio, primary school thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2006b 2006b 2006b 2006b 1991 2006b 1991 2006b 2006b 2006b 2006b 2006b Romania 74 105 86 45 81 91 .. 81 95 95 24 24 Russian Federation 88 129 91 70 98 92 .. .. 92 93 170 140 Rwanda .. 140 13 3 67 91 8 .. 72 75 78 45 Saudi Arabia 12 108 96 27 87 93 39 60 87 87 110 108 Senegal 9 80 22 6 45 71 .. 17 75 71 250 262 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. 145c 32c .. 43 .. .. 23c .. .. .. .. Singapore .. 78 63 .. .. .. .. .. .. .. .. .. Slovak Republic 95 99 96 41 .. 92 .. .. 92 92 10 9 Slovenia 78 98 96 79 96 96 .. 91 97 97 1 1 Somalia .. .. .. .. 9 19 .. .. .. .. .. .. South Africa 38 106 95 15 90 88 45 .. 93 94 262 207 Spain 119 105 118 66 100 100 .. 94 100 99 3 6 Sri Lanka .. 108 87 .. .. 97 .. .. .. .. .. .. Sudan 24 66 34 .. 40 54 .. 19 .. .. .. .. Swaziland 17 106 47 4 75 78 30 32 76 77 23 22 Sweden 93 98 103 82 100 97 85 99 97 97 10 10 Switzerland 96 98 93 45 84 90 80 82 94 94 16 14 Syrian Arab Republic 11 126 70 .. 91 .. 43 63 .. .. .. .. Tajikistan 9 100 83 19 77 97 .. 80 99 95 2 17 Tanzania 28 112c .. 1 51 100 c .. .. 99 97 0c 10 c Thailand 92 108 78 46 88 94 .. 71 100 100 0 1 Timor-Leste 10 99 53 .. .. 68 .. .. 70 67 28 29 Togo 2 102 40 .. 64 80 15 .. 87 74 58 120 Trinidad and Tobago 85 95 76 11 89 85 .. 65 89 90 8 7 Tunisia .. 110 83 30 93 97 .. .. 98 99 12 6 Turkey 10 94 74 31 89 90 42 66 92 88 329 499 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 3 117 18 3 .. .. .. 16 .. .. .. .. Ukraine 90 102 93 73 81 90 .. 84 91 91 81 79 United Arab Emirates 78 104 90 .. 99 88 60 79 93 92 7 6 United Kingdom 71 107 105 59 98 99 80 95 100 100 3 0d United States 61 98 94 82 97 92 84 88 93 94 954 750 Uruguay 67 113 107 42 91 94 .. .. 97 98 5 4 Uzbekistan 27 95c 102c 10 c 78 .. .. .. .. .. .. .. Venezuela, RB 60 104 78 52 87 91 18 67 91 91 123 103 Vietnam 60 90 76 16 90 84 .. 69 .. .. .. .. West Bank and Gaza 30 83 94 41 .. 76 .. 90 80 80 48 45 Yemen, Rep. 1 87 46 9 50 75 .. 37 86 62 275 632 Zambia .. 117 36 .. .. 92 .. 28 92 94 96 54 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World 40 w 106 w 65 w 24 w 84 w 86 w .. w 58 w 90 w 87 w Low income 34 102 45 9 .. 78 .. 39 84 78 Middle income .. 112 78 27 93 93 .. 70 95 94 Lower middle income 41 112 73 23 93 93 .. 68 94 94 Upper middle income 63 113 92 40 91 94 .. 76 96 95 Low & middle income 34 107 61 19 82 85 .. 54 89 86 East Asia & Pacific 41 111 72 20 96 93 .. 68 94 94 Europe & Central Asia 54 103 89 51 90 91 .. 81 93 91 Latin America & Carib. 62 119 89 30 85 94 31 69 96 96 Middle East & N. Africa 23 104 74 24 82 91 .. 67 94 90 South Asia 41 110 49 9 .. 85 .. .. 92 87 Sub-Saharan Africa 16 93 31 5 49 68 .. 25 72 66 High income 78 101 101 67 95 95 85 91 96 96 Euro area 103 .. .. .. 95 .. .. .. .. .. a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data for 1991 are not fully comparable with data from 1999 onward. b. Provisional data. c. Data are for 2007. d. Less than 0.5. 82 2008 World Development Indicators 2.11 PEOPLE Participation in education About the data Definitions School enrollment data are reported to the United Overage or underage enrollments are frequent, par- · Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural Organiza- ticularly when, for cultural or economic reasons, par- ment, regardless of age, to the population of the age tion (UNESCO) Institute for Statistics by national edu- ents prefer children to start school at other than the group that officially corresponds to the level of educa- cation authorities and statistical offices. Enrollment official age. Age at enrollment may be inaccurately tion shown. · Preprimary education refers to the ini- ratios help monitor whether a country is on track to estimated or misstated, especially in communities tial stage of organized instruction, designed primarily achieve the Millennium Development Goal of univer- where registration of births is not strictly enforced. to introduce very young children to a school-type envi- sal primary education by 2015, which implies achiev- Other problems of cross-country comparison of ronment. · Primary education provides children with ing a net primary enrollment ratio of 100 percent, enrollment data stem from errors in school-age popu- basic reading, writing, and mathematics skills along and whether an education system has the capacity lation estimates. Age-sex structures drawn from cen- with an elementary understanding of such subjects to meet the needs of universal primary education, as suses or vital registrations, the primary data sources as history, geography, natural science, social sci- indicated in part by its gross enrollment ratios. on school-age population, commonly underenumer- ence, art, and music. · Secondary education com- Enrollment ratios, while a useful measure of par- ate (especially young children) to circumvent laws or pletes the provision of basic education that began ticipation in education, have limitations. They are regulations. Errors are also introduced when parents at the primary level and aims at laying the founda- based on data from annual school surveys, which round children's ages. While census data are often tions for lifelong learning and human development are typically conducted at the beginning of the school adjusted for age bias, adjustments are rarely made by offering more subject- or skill-oriented instruction year. They do not reflect actual attendance or drop- for inadequate vital registration systems. Compound- using more specialized teachers. · Tertiary educa- out rates during the year. And school administrators ing these problems, pre- and postcensus estimates tion refers to a wide range of post-secondary educa- may exaggerate enrollments, especially if there is a of school-age children are model interpolations or tion institutions, including technical and vocational financial incentive to do so. projections that may miss important demographic education, colleges, and universities, whether or not Also, as international indicators, the gross and net events (see discussion of demographic data in About leading to an advanced research qualification, that primary enrollment ratios have an inherent weakness: the data for table 2.1). normally require as a minimum condition of admis- the length of primary education differs across coun- Gross enrollment ratios indicate the capacity of sion the successful completion of education at the tries, although the International Standard Classifica- each level of the education system, but a high ratio secondary level. · Net enrollment ratio is the ratio tion of Education tries to minimize the difference. A may reflect a substantial number of overage children of total enrollment of children of official school age relatively short duration for primary education tends enrolled in each grade because of repetition rather based on the International Standard Classification of to increase the ratio; a relatively long one to decrease than a successful education system. The net enroll- Education 1997 to the population of the age group it (in part because more older children drop out). ment ratio excludes overage and underage students that officially corresponds to the level of education to capture more accurately the system's coverage and shown. · Total net enrollment ratio, primary, is the internal efficiency but does not account for children ratio of total enrollment of children of official school In some countries close to 10 percent of primary-school-age children are who fall outside the official school age because of age for primary education who are enrolled in primary enrolled in secondary school 2.11a late or early entry rather than grade repetition. Differ- or secondary education to the total primary-school- ences between gross and net enrollment ratios show age population. · Children out of school are the Net enrollment ratio, primary the incidence of overage and underage enrollments. number of primary-school-age children not enrolled Percent Total net enrollment ratio, primary 100 Total net primary enrollment was recently added in primary or secondary school. as a Millennium Development Goal indicator. It cap- tures the children of primary-school age who have progressed to secondary education, which the tradi- tional net enrollment ratio excludes. Children out of school are primary-school-age chil- 50 dren not enrolled in primary or secondary education. The data are calculated by the UNESCO Institute for Statistics using administrative data. Children out of school include dropouts, children never enrolled, and children of primary age enrolled in preprimary educa- 0 tion. Large numbers of children out of school create Hungary Kazakhstan Kyrgyz Republic pressure to enroll children and provide classrooms, The difference between net enrollment and total teachers, and educational materials, a task made primary net enrollment is small in most coun- difficult in many developing countries by limited edu- tries. But it is larger in some countries because cation budgets. However, getting children into school many children start primary school earlier than is a high priority for countries and crucial for achiev- Data sources the official entrance age and are younger than the ing the Millennium Development Goal of universal official age when they reach secondary school. primary education. Data on gross and net enrollment ratios and out In 2006 the UNESCO Institute for Statistics changed of school children are from the UNESCO Institute Source: United Nations Educational, Scientific, and Cultural Organization Institute for Statistics. its convention for citing the reference year. For more for Statistics. information, see About the data for table 2.10. 2008 World Development Indicators 83 2.12 Education efficiency Gross intake rate Cohort Repeaters in Transition to in grade 1 survival rate primary school secondary school % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5a primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2006b 2006b 1991 2005b 1991 2005b 2005b 2005b 2006b 2006b 2005b 2005b Afghanistan .. .. .. .. .. .. .. .. 18 14 .. .. Albania 100 99 .. .. .. .. 89 91 3 2 100 99 Algeria 99 97 95 95 94 96 90 92 14 9 74 79 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 110 109 .. 96 .. 98 94 97 8 5 93 96 Armenia 102 106 .. .. .. .. 100 99 0c 0c 100 99 Australia 106 105 98 .. 99 .. .. .. .. .. .. .. Austria 102 100 .. .. .. .. 97 100 1 1 .. .. Azerbaijan 99 97 .. .. .. .. 100 94 0c 0c 100 98 Bangladesh 122 124 .. 63 .. 67 63 67 7 7 86 92 Belarus 102 100 .. .. .. .. 99 100 0c 0c 99 100 Belgium 97 99 90 .. 92 .. .. .. 3 3 .. .. Benin 109 96 54 53 56 50 48 44 17 17 .. .. Bolivia 122 122 .. 85 .. 85 83 81 1 1 90 90 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 111 104 81 89 87 92 83 88 5 4 95 95 Brazil 106 97 .. .. .. .. .. .. 20 20 .. .. Bulgaria 97 94 91 .. 90 .. 91 93 3 2 95 96 Burkina Faso 79 67 71 71 68 74 63 66 12 12 45 43 Burundi 164 164 65 66 58 68 57 61 29 28 37 31 Cambodia 135 127 .. 61 .. 64 54 57 14 11 83 80 Cameroon 111 97 .. .. .. .. .. .. 28 23 43 47 Canada 97 95 95 .. 98 .. .. .. .. .. .. .. Central African Republic 73 55 24 .. 22 .. .. .. 29 30 46 52 Chad 109 79 56 34 41 32 27 23 22 24 56 42 Chile 101 99 94 100 91 99 98 98 3 2 96 98 China 88 87 58 .. 78 .. .. .. 0c 0c .. .. Hong Kong, China .. .. .. 99 .. 100 99 100 1 1 100 100 Colombia 127 123 .. 78 .. 86 78 86 4 3 99 100 Congo, Dem. Rep. .. .. 58 .. 50 .. .. .. .. .. .. .. Congo, Rep. 78 78 56 .. 65 .. .. .. 21 21 58 58 Costa Rica 108 108 83 93 85 95 89 92 8 6 100 97 Côte d'Ivoire 73 61 75 .. 70 .. .. .. 23 24 .. .. Croatia .. .. .. .. .. .. .. .. 0c 1 100 d 100 d Cuba 102 104 .. 96 .. 98 96 98 1 0c 98 99 Czech Republic 102 103 .. 98 .. 99 98 99 1 1 99 100 Denmark 98 97 94 93 94 93 92 92 .. .. 100 99 Dominican Republic 102 100 .. 66 .. 71 58 65 10 6 81 87 Ecuador 133 131 .. 75 .. 77 75 77 2 1 81 76 Egypt, Arab Rep. 106 102 .. 98 .. 99 98 99 3 2 72 82 El Salvador 121 116 56 70 60 74 65 70 9 6 91 92 Eritrea 53 45 .. 77 .. 69 77 69 15 15 86 79 Estonia 100 97 .. 98 .. 99 99 99 2 1 96 99 Ethiopia 125 113 16 57d 23 59d 62 63 6 5 91 91 Finland 98 98 100 99 100 100 99 100 1 0c 100 100 France .. .. 69 .. 95 .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 65 71 .. .. .. .. .. .. 6 6 .. .. Georgia 97 103 .. 86 .. 90 83 89 0c 0c 98 100 Germany 104 103 .. .. .. .. 99 100 1 1 99 99 Ghana 105 110 81 .. 79 .. .. .. 6 6 .. .. Greece 100 100 100 98 100 100 98 100 1 0c 99 100 Guatemala 125 122 .. 70 .. 68 65 62 13 11 92 90 Guinea 94 87 64 83 48 78 79 72 8 9 75 66 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 84 2008 World Development Indicators 2.12 PEOPLE Education efficiency Gross intake rate Cohort Repeaters in Transition to in grade 1 survival rate primary school secondary school % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5a primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2006b 2006b 1991 2005b 1991 2005b 2005b 2005b 2006b 2006b 2005b 2005b Honduras 139 134 .. 80 .. 87 77 85 8 6 68 74 Hungary 97 95 77 .. 98 .. 98 98 3 2 99 99 India 132 125 .. 73 .. 73 73 73 3 3 87 83 Indonesia 120 116 34 92 78 87 88 83 6 4 79 78 Iran, Islamic Rep. 112 150 91 .. 89 .. .. .. 3 1 93 83 Iraq .. .. .. 87 .. 73 78 61 9 7 73 66 Ireland 99 99 99 100 100 100 .. .. 1 1 .. .. Israel 96 99 .. 100 .. 100 100 100 2 1 74 73 Italy 104 102 .. 100 .. 100 100 100 0c 0c 100 99 Jamaica 94 92 .. .. .. .. .. .. 3 2 100 97 Japan 98 99 100 .. 100 .. .. .. .. .. .. .. Jordan 92 92 .. 97 .. 96 96 95 1 1 96 97 Kazakhstan 107 107 .. .. .. .. 100 d 100 d 0 c,e 0 c,e 100 d 100 d Kenya 112 108 75 81 78 85 74 71 6 6 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 105 106 99 99 100 99 99 99 0c 0c 99 99 Kuwait 96 93 .. 95 .. 97 95 97 2 2 95 100 Kyrgyz Republic 98 97 .. .. .. .. 97 100 0c 0c 100 100 Lao PDR 129 120 .. 62 .. 62 62 62 19 17 79 75 Latvia 94 93 .. .. .. .. 99 98 4 2 97 98 Lebanon 86 86 .. 88 .. 94 83 91 11 8 83 88 Lesotho 105 99 58 68 73 80 53 71 21 16 67 65 Liberia 109 106 .. .. .. .. .. .. 6 6 .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 94 93 .. .. .. .. 98 98 1 0c 98 99 Macedonia, FYR 99 99 .. .. .. .. 98 99 0c 0c 100 99 Madagascar 181 176 22 35 21 37 35 37 20 19 56 54 Malawi 145 156 71 44 57 44 36 36 21 20 74 71 Malaysia 102 101 97 .. 97 .. .. .. .. .. .. .. Mali 89 76 71 83 67 79 75 70 17 17 63 48 Mauritania 124 129 76 59 75 56 46 43 10 10 51 45 Mauritius 104 104 97 98 98 100 97 100 5 4 61 72 Mexico 111 109 35 93 71 94 91 92 6 4 95 93 Moldova 90 87 .. .. .. .. 96 98 0c 0c 98 99 Mongolia 117 119 .. .. .. .. 91 91 0c 0c 95 99 Morocco 104 100 75 82 76 79 76 72 15 10 78 77 Mozambique 153 143 36 60 32 55 41 39 5 5 52 56 Myanmar 139 136 .. 71 .. 72 71 72 1 0c 76 72 Namibia 104 105 60 84 65 90 73 80 19 14 72 77 Nepal 160 160 51 75 51 83 75 83 21 20 79 74 Netherlands 101 100 .. 99 .. 100 .. .. .. .. 96 100 New Zealand 105 104 .. .. .. .. .. .. .. .. .. .. Nicaragua 173 163 11 50 37 57 46 55 11 8 .. .. Niger 76 59 61 58 65 54 55 50 5 5 61 58 Nigeria 116 99 .. 71 .. 75 61 64 3 3 .. .. Norway 97 97 99 100 100 100 100 100 .. .. 100 100 Oman 76 76 97 100 96 100 100 99 0c 1 99 98 Pakistan 125 100 .. 68 .. 72 68 72 2 2 69 75 Panama 116 114 .. 87 .. 89 84 86 7 5 92 95 Papua New Guinea .. .. 70 .. 68 .. .. .. .. .. .. .. Paraguay 117 114 73 79 75 83 74 79 8 5 90 90 Peru 110 112 .. 91 .. 90 86 85 9 9 96 94 Philippines 137 128 .. 71 .. 80 66 77 3 2 91 92 Poland 97 98 89 .. 96 .. .. .. 1 0c .. .. Portugal 106 106 .. .. .. .. .. .. 13 7 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 85 2.12 Education efficiency Gross intake rate Cohort Repeaters in Transition to in grade 1 survival rate primary school secondary school % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5a primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2006b 2006b 1991 2005b 1991 2005b 2005b 2005b 2006b 2006b 2005b 2005b Romania 97 96 .. .. .. .. 94 95 3 2 98 98 Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 209 206 61 43 59 49 30 32 15 15 .. .. Saudi Arabia 102 105 82 100 84 93 100 94 6 4 93 97 Senegal 95 98 .. 65 .. 65 54 53 11 10 52 48 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. 10e 10e .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 100 98 .. .. .. .. 97 98 3 2 98 99 Slovenia 98 96 .. .. .. .. .. .. 1 0c .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 118 112 .. 82 .. 83 75 79 8 8 89 91 Spain 103 101 .. 100 .. 100 100 100 3 2 .. .. Sri Lanka 109 109 92 .. 93 .. .. .. 1 1 .. .. Sudan 67 58 90 78 99 79 73 75 1 2 94 100 Swaziland 111 103 74 81 80 87 66 75 19 15 88 89 Sweden 96 95 100 .. 100 .. .. .. .. .. .. .. Switzerland 86 91 .. .. .. .. .. .. 2 1 99 100 Syrian Arab Republic 125 122 97 .. 95 .. 92 93 7 5 95 97 Tajikistan 103 99 .. .. .. .. 100 97 0c 0c 98 97 Tanzania 105 104 81 85d 82 89d 81d 85d 4e 4e 47 45 Thailand .. .. .. .. .. .. .. .. .. .. .. .. Timor-Leste 118 105 .. .. .. .. .. .. .. .. .. .. Togo 101 95 52 79 42 70 74 62 23 23 68 61 Trinidad and Tobago 96 92 .. 90 .. 92 80 87 6 4 94 92 Tunisia 100 101 94 97 77 97 93 95 10 7 86 90 Turkey 97 93 98 97 97 97 95 93 3 3 93 90 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 145 147 .. 49 .. 49 26 25 30 29 42 43 Ukraine 99 99 .. .. .. .. .. .. 0c 0c 100 100 United Arab Emirates 103 101 80 98 80 100 98 100 2 2 99 100 United Kingdom .. .. .. .. .. .. .. .. 0 0 .. .. United States 102 100 .. .. .. .. .. .. .. .. .. .. Uruguay 107 105 96 90 98 93 88 91 9 6 75 87 Uzbekistan 97 94 .. .. .. .. .. .. 0e 0e .. .. Venezuela, RB 102 99 82 90 90 94 87 93 8 5 99 99 Vietnam 99 94 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 78 78 .. .. .. .. 97 100 1 1 98 99 Yemen, Rep. 122 102 .. 67 .. 65 61 57 5 4 83 82 Zambia 119 125 .. 92 .. 87 79 73 7 6 49 60 Zimbabwe .. .. 72 .. 81 .. .. .. .. .. .. .. World 116 w 111 w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income 126 116 .. 71 .. 71 69 69 6 6 79 77 Middle income .. .. 61 .. 80 .. .. .. .. .. .. .. Lower middle income 94 95 59 .. 79 .. .. .. 3 2 .. .. Upper middle income 105 101 .. .. .. .. .. .. 10 9 .. .. Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 91 90 55 .. 78 .. .. .. 1 1 .. .. Europe & Central Asia .. .. .. .. .. .. .. .. .. .. .. .. Latin America & Carib. 112 108 .. .. .. .. .. .. 10 9 .. .. Middle East & N. Africa 108 110 .. 90 .. 87 87 84 7 4 82 83 South Asia 130 120 .. 72 .. 73 72 73 4 4 84 82 Sub-Saharan Africa 117 108 .. .. .. .. .. .. 9 9 .. .. High income 101 101 .. .. .. .. .. .. .. .. .. .. Euro area 103 102 .. .. .. .. .. .. 1 1 .. .. a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data for 1991 are not fully comparable with data from 1999 onward. b. Provisional data. c. Less than 0.5. d. Data are for 2006. e. Data are for 2007. 86 2008 World Development Indicators 2.12 PEOPLE Education efficiency About the data Definitions The United Nations Educational, Scientific, and Cul- power and internal effi ciency. Rates approaching · Gross intake rate in grade 1 is the number of tural Organization (UNESCO) Institute for Statistics 100 percent indicate high retention and low dropout new entrants in the first grade of primary education estimates indicators of students' progress through levels. Cohort survival rates are typically estimated regardless of age as a percentage of the population school. These indicators measure an education sys- from data on enrollment and repetition by grade for of the official primary school entrance age. · Cohort tem's success in reaching all students, efficiently two consecutive years. This procedure, called the survival rate is the percentage of children enrolled moving students from one grade to the next, and reconstructed cohort method, makes three simplify- in the first grade of primary school who eventually imparting a particular level of education. ing assumptions: dropouts never return to school; reach grade 5 or the last grade of primary educa- The gross intake rate indicates the level of access promotion, repetition, and dropout rates remain con- tion. The estimate is based on the reconstructed to primary education and the education system's stant over the period in which the cohort is enrolled cohort method (see About the data). · Repeaters in capacity to provide access to primary education. in school; and the same rates apply to all pupils primary school are the number of students enrolled Low gross intake rates in grade 1 reflect the fact enrolled in a grade, regardless of whether they previ- in the same grade as in the previous year as a per- that many children do not enter primary school even ously repeated a grade (Fredricksen 1993). Cross- centage of all students enrolled in primary school. though school attendance, at least through the pri- country comparisons should thus be made with cau- · Transition to secondary school is the number of mary level, is mandatory in all countries. Because tion, because other flows--caused by new entrants, new entrants to the first grade of secondary school the gross intake rate includes all new entrants reentrants, grade skipping, migration, or transfers in a given year as a percentage of the number of regardless of age, it can exceed 100 percent. Once during the school year--are not considered. students enrolled in the final grade of primary school enrolled, students drop out for a variety of reasons, Research suggests that five to six years of school- in the previous year. including low quality schooling, relevance of cur- ing, which is how long primary education lasts in most riculum (real or perceived by parents or students), countries, is a critical threshold for achieving sus- repetition, discouragement over poor performance, tainable basic literacy and numeracy skills. But the and direct and indirect schooling costs. Students' indicator only indirectly reflects the quality of school- progress to higher grades may also be limited by the ing, and a high rate does not guarantee these learn- availability of teachers, classrooms, and materials. ing outcomes. Measuring actual learning outcomes The cohort survival rate is the estimated proportion requires setting curriculum standards and measuring of an entering cohort of grade 1 students that eventu- students' learning progress against those standards ally reaches grade 5 or the last grade of primary edu- through standardized assessments, actions that cation. It measures an education system's holding many countries do not systematically undertake. Data on repeaters are often used to indicate an In Lesotho more girls who enroll education system's internal efficiency. Repeaters not in primary school stay in and only increase the cost of education for the family complete school than boys do 2.12a and the school system, but also use limited school Percent Girls Boys resources. Country policies on repetition and promo- 120 tion differ; in some cases the number of repeaters is controlled because of limited capacity. Care should be taken in interpreting this indicator. The transition rate from primary to secondary 80 school conveys the degree of access or transition between the two levels. As completing primary edu- cation is a prerequisite for participating in lower 40 secondary school, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low transi- 0 tion rate can signal such problems as an inadequate Gross intake Primary school Primary examination and promotion system or insufficient rate in repeaters completion grade 1 rate secondary school capacity. The quality of data on In many developing countries, especially in the transition rate is affected when new entrants and Sub-Saharan Africa, fewer girls than boys enroll repeaters are not correctly distinguished in the first and stay in school. But in Lesotho more girls com- grade of secondary school. Students who interrupt plete primary school because they repeat grades their studies after completing primary school could also affect data quality. Data sources less often and are less likely to drop out. In 2006 the UNESCO Institute for Statistics changed Data on education efficiency are from the UNESCO Source: United Nations Educational, Scientific, and its convention for citing the reference year. For more Institute for Statistics. Cultural Organization Institute for Statistics. information, see About the data for table 2.10. 2008 World Development Indicators 87 2.13 Education completion and outcomes Primary completion Youth literacy Adult literacy ratea rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2006b 1991 2006b 1991 2006b 1990 2005 1990 2005 2005 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 96 .. 97 .. 96 .. 99 .. 99 99 98 Algeria 80 85 86 86 73 84 86 94 62 86 80 60 Angola 35 .. .. .. .. .. .. 84 .. 63 83 54 Argentina .. 99 .. 97 .. 102 98 99 99 99 97 97 Armenia 90 91 .. 90 .. 93 100 100 100 100 100 99 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. 103 .. 103 .. 102 .. .. .. .. .. .. Azerbaijan .. 92 .. 94 .. 90 .. .. .. .. .. .. Bangladesh 49 72 .. 70 .. 74 52 67 38 60 54 41 Belarus 94 95 95 96 96 93 100 .. 100 .. .. .. Belgium 79 .. 76 .. 82 .. .. .. .. .. .. .. Benin 21 65 28 78 13 51 55 59 27 33 48 23 Bolivia .. 101 .. 102 .. 100 96 99 92 96 93 81 Bosnia and Herzegovina .. .. .. .. .. .. .. 100 .. 100 99 94 Botswana 89 95 82 75 97 115 86 92 92 96 80 82 Brazil 93 105 .. .. .. .. .. 96 .. 98 88 89 Bulgaria 84 99 86 98 83 99 .. 98 .. 98 99 98 Burkina Faso 20 31 24 35 15 28 27 40 14 26 31 17 Burundi 46 36 49 40 43 32 59 77 48 70 67 52 Cambodia .. 87 .. 87 .. 86 .. 88 .. 79 85 64 Cameroon 53 58 57 65 49 51 .. .. .. .. 77 60 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 27 24 35 31 18 18 63 70 35 47 65 33 Chad 18 31 29 41 7 21 .. 56 .. 23 41 13 Chile .. 123 .. 130 .. 116 98 99 99 99 96 96 China 105 .. .. .. .. .. 97 99 91 99 95 87 Hong Kong, China 102 .. .. .. .. .. .. .. .. .. .. .. Colombia 70 105 67 103 73 107 89 98 92 98 93 93 Congo, Dem. Rep. 46 38 58 46 34 31 .. 78 .. 63 81 54 Congo, Rep. 54 73 59 77 49 69 .. 98 .. 97 91 79 Costa Rica 79 89 77 87 81 91 .. 97 .. 98 95 95 Côte d'Ivoire 43 43 55 53 32 33 60 71 38 52 61 39 Croatia 85 92 .. 93 .. 92 100 100 100 100 99 97 Cuba 99 92 .. 92 .. 91 .. 100 .. 100 100 100 Czech Republic .. 102 .. 102 .. 102 .. .. .. .. .. .. Denmark 98 99 98 99 98 99 .. .. .. .. .. .. Dominican Republic 61 83 .. 80 .. 87 .. 93 .. 95 87 87 Ecuador 91 106 91 105 92 106 97 96 96 96 92 90 Egypt, Arab Rep. .. 98 .. 102 .. 94 .. 90 .. 79 83 59 El Salvador 41 88 38 88 43 88 85 87 85 90 82 79 Eritrea 19 48 22 56 17 41 .. .. .. .. .. .. Estonia 93 106 93 107 94 104 100 100 100 100 100 100 Ethiopia 26 49 32 55 19 42 .. 62 .. 39 50 23 Finland 97 100 98 101 97 99 .. .. .. .. .. .. France 104 .. .. .. .. .. .. .. .. .. .. .. Gabon 58 75 55 73 61 76 94 97 92 95 88 80 Gambia, The 44 63 55 62 33 64 .. .. .. .. .. .. Georgia .. 85 .. 83 .. 86 .. .. .. .. .. .. Germany 100 95 99 94 100 95 .. .. .. .. .. .. Ghana 61 71 69 73 54 68 .. 76 .. 65 66 50 Greece 99 100 99 100 98 100 99 99 99 99 98 94 Guatemala .. 77 .. 80 .. 73 .. 86 .. 78 75 63 Guinea 17 64 25 74 9 53 .. 59 .. 34 43 18 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 27 .. 29 .. 26 .. .. .. .. .. .. .. 88 2008 World Development Indicators 2.13 PEOPLE Education completion and outcomes Primary completion Youth literacy Adult literacy ratea rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2006b 1991 2006b 1991 2006b 1990 2005 1990 2005 2005 2005 Honduras 64 89 67 86 61 91 .. 87 .. 91 80 80 Hungary 93 94 88 94 90 94 .. .. .. .. .. .. India 64 85 75 87 52 82 74 84c 49 68c 73c 48c Indonesia 91 99 .. 99 .. 100 97 99 95 99 94 87 Iran, Islamic Rep. 91 101 97 95 85 108 92 98 81 97 88 77 Iraq 59 .. 64 .. 53 .. .. .. .. .. .. .. Ireland .. 97 .. 96 .. 97 .. .. .. .. .. .. Israel .. 101 .. 101 .. 101 .. .. .. .. .. .. Italy 104 100 104 100 104 99 .. 100 .. 100 99 98 Jamaica 90 82 86 81 94 84 .. .. .. .. .. .. Japan 101 .. 101 .. 102 .. .. .. .. .. .. .. Jordan 72 100 69 100 77 101 .. 99 .. 99 95 87 Kazakhstan .. 101d .. 100 d .. 101d 100 .. 100 .. .. .. Kenya .. 93 .. 94 .. 92 .. 80 .. 81 78 70 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 98 101 98 107 98 95 .. .. .. .. .. .. Kuwait .. 91 .. 90 .. 92 .. 100 .. 100 94 91 Kyrgyz Republic .. 99 .. 99 .. 100 .. .. .. .. .. .. Lao PDR 43 75 48 80 38 70 .. 83 .. 75 77 61 Latvia .. 92 .. 93 .. 92 100 100 100 100 100 100 Lebanon .. 80 .. 79 .. 82 .. .. .. .. .. .. Lesotho 59 78 42 65 76 92 .. .. .. .. 74 90 Liberia .. 63 .. 69 .. 58 .. 65 .. 69 58 46 Libya .. .. .. .. .. .. .. 100 .. 96 93 75 Lithuania 89 91 .. 91 .. 91 100 100 100 100 100 100 Macedonia, FYR 98 97 .. 96 .. 98 .. 99 .. 98 98 94 Madagascar 33 57 33 57 34 57 .. 73 .. 68 77 65 Malawi 29 55 36 55 21 55 70 .. 49 .. .. .. Malaysia 91 95 91 95 91 95 96 97 95 97 92 85 Mali 13 49 15 59 10 40 .. .. .. .. 33 16 Mauritania 34 47 41 47 27 47 .. 68 .. 55 60 43 Mauritius 107 92 107 91 107 94 91 94 92 95 88 81 Mexico 88 103 89 102 90 103 96 98 95 98 93 90 Moldova .. 90 .. 90 .. 91 100 100 100 100 100 99 Mongolia .. 109 .. 108 .. 110 .. 97 .. 98 98 98 Morocco 48 84 57 88 39 80 .. 81 .. 60 66 40 Mozambique 26 42 32 49 21 35 .. .. .. .. .. .. Myanmar .. 95 .. 93 .. 98 .. 96 .. 93 94 86 Namibia 78 76 70 73 86 80 86 91 90 93 87 83 Nepal 51 76 68 80 40 72 68 81 33 60 63 35 Netherlands .. 100 .. 101 .. 99 .. .. .. .. .. .. New Zealand 100 .. 101 .. 99 .. .. .. .. .. .. .. Nicaragua 42 73 43 70 59 77 .. 84 .. 89 77 77 Niger 18 33 22 39 13 26 .. 52 .. 23 43 15 Nigeria .. 76 .. 83 .. 68 81 87 62 81 78 60 Norway 100 99 100 99 100 98 .. .. .. .. .. .. Oman 74 94 78 95 70 92 .. 98 .. 97 87 74 Pakistan .. 62 .. 70 .. 53 .. 77 .. 53 64 35 Panama 86 94 86 94 86 95 95 97 95 96 93 91 Papua New Guinea 46 .. 51 .. 42 .. .. 69 .. 64 63 51 Paraguay 68 94 68 94 69 95 96 96 95 96 94 93 Peru .. 100 .. 100 .. 100 97 98 94 96 94 82 Philippines 86 96 84 92 84 100 96 94 97 97 92 94 Poland 98 97 .. .. .. .. .. .. .. .. .. .. Portugal 95 104 94 102 95 107 99 100 99 100 96 92 Puerto Rico .. .. .. .. .. .. 92 86 94 86 90 90 2008 World Development Indicators 89 2.13 Education completion and outcomes Primary completion Youth literacy Adult literacy ratea rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2006b 1991 2006b 1991 2006b 1990 2005 1990 2005 2005 2005 Romania 96 99 96 99 96 98 99 98 99 98 98 96 Russian Federation 93 94 92 .. 93 .. 100 100 100 100 100 99 Rwanda 35 35 40 36 31 35 .. 79 .. 77 71 60 Saudi Arabia 55 85 60 .. 51 .. 94 97 81 95 88 78 Senegal 39 49 47 51 30 47 49 58 28 41 51 29 Serbia .. .. .. .. .. .. .. 99e .. 99e 99e 94 e Sierra Leone .. 81d .. 92d .. 70 d .. 60 .. 37 47 24 Singapore .. .. .. .. .. .. 99 99 99 100 97 89 Slovak Republic 96 94 95 95 96 94 .. .. .. .. .. .. Slovenia 95 99 .. 100 .. 99 100 100 100 100 100 100 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 76 100 71 100 80 100 .. .. .. .. .. .. Spain .. 103 .. 103 .. 103 100 .. 100 .. .. .. Sri Lanka 102 108 103 107 102 108 .. 95f .. 96f 92 f 89 f Sudan 42 47 47 50 37 43 .. 85g .. 71g 71g 52g Swaziland 60 67 57 64 63 69 .. 87 .. 90 81 78 Sweden 96 .. 96 .. 96 .. .. .. .. .. .. .. Switzerland 53 91 53 91 54 92 .. .. .. .. .. .. Syrian Arab Republic 89 115 94 116 84 113 .. 95 .. 90 88 74 Tajikistan .. 106 .. 108 .. 104 100 100 100 100 100 99 Tanzania 62 85d 62 87d 63 83d 86 81 78 76 78 62 Thailand .. .. .. .. .. .. .. 98 .. 98 95 91 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 35 67 48 78 22 56 .. 84 .. 64 69 38 Trinidad and Tobago 101 88 98 86 104 90 .. 99 .. 99 99 98 Tunisia 74 99 79 98 70 100 .. 96 .. 92 83 65 Turkey 90 86 93 90 86 82 97 98 88 93 95 80 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 54 .. 57 .. 51 77 83 63 71 77 58 Ukraine 94 105 98 105 97 105 .. 100 .. 100 100 99 United Arab Emirates 103 100 104 101 103 100 .. 98 .. 95 89 88 United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 94 93 91 92 96 93 .. .. .. .. .. .. Uzbekistan .. 98 .. 98 .. 98 .. .. .. .. .. .. Venezuela, RB 43 96 37 93 49 98 95 96 96 98 93 93 Vietnam .. 92 .. 103 .. 97 94 .. 93 .. .. .. West Bank and Gaza .. 89 .. 89 .. 89 .. 99 .. 99 97 88 Yemen, Rep. .. 60 .. 74 .. 46 .. 91 .. 59 73 35 Zambia .. 84 .. 89 .. 79 67 .. 66 .. .. .. Zimbabwe 97 81 99 83 96 79 97 97 94 98 93 86 World 79 w 86 w 85 w 88 w 74 w 84 w 88 w 91 w 79 w 84 w 87 w 77 w Low income 57 73 68 77 48 69 72 80 54 66 72 50 Middle income 93 97 96 98 90 97 95 97 91 96 93 87 Lower middle income 95 97 98 97 90 96 95 97 90 95 93 85 Upper middle income 88 99 88 99 88 99 97 98 96 98 94 92 Low & middle income 78 85 84 87 72 83 86 90 76 82 85 73 East Asia & Pacific 101 98 103 98 95 98 97 98 92 98 95 87 Europe & Central Asia 93 95 94 96 91 94 99 99 98 98 99 96 Latin America & Carib. 82 99 82 98 83 100 93 96 94 96 91 89 Middle East & N. Africa 77 91 83 93 71 88 84 93 68 84 83 63 South Asia 62 80 75 83 52 76 71 81 48 65 70 46 Sub-Saharan Africa 51 60 56 65 46 55 71 76 58 64 69 50 High income .. 97 .. 99 .. 96 99 99 99 99 99 98 Euro area 100 .. .. .. .. .. .. .. .. .. .. .. a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data for 1991 are not fully comparable with data from 1999 onward. b. Provisional data. c. Excludes Mao Maram, Paomata, and Purul of Senapati district of Manipur. d. Data are for 2007. e. Includes Montenegro and excludes Kosovo and Metohija. f. Covers 18 of 25 districts. g. Covers northern Sudan only. 90 2008 World Development Indicators 2.13 PEOPLE Education completion and outcomes About the data Definitions Many governments publish statistics that indi- proxy rates should be taken as an upper estimate of · Primary completion rate is the percentage of stu- cate how their education systems are working and the actual primary completion rate. dents completing the last year of primary school. It is developing--statistics on enrollment and such effi - There are many reasons why the primary comple- calculated by taking the total number of students in ciency indicators as repetition rates, pupil-teacher tion rate can exceed 100 percent. The numerator the last grade of primary school, minus the number of ratios, and cohort progression. The World Bank may include late entrants and overage children who repeaters in that grade, divided by the total number and the United Nations Educational, Scientific, and have repeated one or more grades of primary school of children of official completing age. · Youth literacy Cultural Organization (UNESCO) Institute for Statis- as well as children who entered school early, while rate is the percentage of people ages 15­24 that tics jointly developed the primary completion rate the denominator is the number of children of official can, with understanding, both read and write a short, indicator. Increasingly used as a core indicator of completing age. Other data limitations contribute to simple statement about their everyday life. · Adult an education system's performance, it reflects an completion rates exceeding 100 percent, such as the literacy rate is the literacy rate among people ages education system's coverage and the educational use of estimates for the population of varying reli- 15 and older. attainment of students. The indicator is a key mea- ability, the conduct of school and population surveys sure of educational outcome at the primary level at different times of year, and other discrepancies in and of progress on the Millennium Development the numbers used in the calculation. Goals and the Education for All initiative. However, Basic student outcomes include achievements in because curricula and standards for school comple- reading and mathematics judged against established tion vary across countries, a high primary comple- standards. National assessments are enabling many tion rate does not necessarily mean high levels of countries' ministries of education to monitor progress student learning. in these outcomes. International comparable assess- The primary completion rate reflects the primary ments are not yet available, although a few exist for cycle as defined by the International Standard Clas- some countries. The UNESCO Institute for Statistics sification of Education, ranging from three or four has established literacy as an outcome indicator years of primary education (in a very small number based on an internationally agreed definition. of countries) to five or six years (in most countries) The literacy rate is the percentage of people who and seven (in a small number of countries). can, with understanding, both read and write a short, The table shows the proxy primary completion rate, simple statement about their everyday life. In prac- calculated by subtracting the number of repeaters tice, literacy is difficult to measure. To estimate lit- in the last grade of primary school from the total eracy using such a definition requires census or sur- number of students in that grade and dividing by the vey measurements under controlled conditions. Many total number of children of official graduation age. countries estimate the number of literate people from Data limitations preclude adjusting for students who self-reported data. Some use educational attainment drop out during the final year of primary school. Thus data as a proxy but apply different lengths of school attendance or levels of completion. Because defini- In 2005 more than 770 million tions and methodologies of data collection differ people were illiterate--64 percent across countries, data should be used cautiously. of them women, a share unchanged since 1990 2.13a The reported literacy data are compiled by the UNESCO Institute for Statistics based on national Millions Female Male censuses and household surveys during 1985­2005. 900 For detailed information on sources and definitions, consult the original source. Literacy statistics for most countries cover the pop- 600 ulation ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The literacy data in the narrower age 300 range of 15­24 captures the ability of participants in the formal education system better and reflects recent progress in education. The youth literacy rate reported in the table measures the accumulated out- Data sources 0 1990 2005 comes of primary education over the previous 10 Data on primary completion rates and lit- years or so by indicating the proportion of people who Source: United Nations Educational, Scientific, and eracy rates are from the UNESCO Institute for Cultural Organization Institute for Statistics. have passed through the primary education system Statistics. and acquired basic literacy and numeracy skills. 2008 World Development Indicators 91 2.14 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 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. 92 2008 World Development Indicators 2.14 PEOPLE Education gaps by income and gender About the data Definitions The data in the table describe basic information on performed. In particular, the use of a unified index · Survey year is the year in which the underlying data school participation and attainment by individuals does not permit a disaggregated analysis to examine were collected. · Gross intake rate in grade 1 is in different socioeconomic groups within countries. which asset indicators have a more or less important the number of students in the first grade of primary The data are from Demographic and Health Surveys association with education status. In addition, some education regardless of age as a percentage of the conducted by Macro International with the support asset indicators may reflect household wealth better population of the official primary school entrance of the U.S. Agency for International Development. in some countries than in others--or reflect differ- age. These data may differ from those in table 2.12. These large-scale household sample surveys, con- ent degrees of wealth in different countries. Taking · Gross primary participation rate is the ratio of ducted periodically in developing countries, collect such information into account and creating country- total students attending primary school regardless information on a large number of health, nutrition, specific asset indexes with country-specific choices of age to the population of the age group that offi - and population measures as well as on respondents' of asset indicators might produce a more effective cially corresponds to primary education. · Average social, demographic, and economic characteristics and accurate index for each country. The asset index years of schooling are the years of formal school- using a standard set of questionnaires. The data used in the table does not have this flexibility. ing received, on average, by youths and adults ages presented here draw on responses to individual and The analysis was carried out for 48 countries. The 15­24. · Primary completion rate is the percentage household questionnaires. table shows the estimates for the poorest and rich- of children of the official primary school completing Typically, Demographic and Health Surveys collect est quintiles only; the full set of estimates for 32 indi- age to the official primary school completing age plus basic information on educational attainment and cators is available in the country reports (see Data four who have completed the last year of primary enrollment levels from every household member sources). The data in the table differ from data for school or higher. These data differ from those in ages 5 or 6 and older as background characteris- similar indicators in preceding tables either because table 2.13 because the definition and methodology tics. As the surveys are intended for the collection of the indicator refers to a period a few years preceding are different. · Children out of school are the per- demographic and health information, the education the survey date or because the indicator definition centage of children ages 6­11 who are not in school. section of the survey is not as robust and detailed or methodology is different. Findings should be inter- These data differ from those in table 2.11 because as the health section; however, it still provides useful preted with caution because of measurement error the definition and methodology are different. micro-level information on education that cannot be inherent in the use of survey data. explained by aggregate national-level data. Socioeconomic status as displayed in the table is based on a household's assets, including ownership of consumer items, features of the household's dwell- ing, and other characteristics related to wealth. Each household asset on which information was collected was assigned a weight generated through principal- component analysis. The resulting scores were stan- dardized in relation to a standard normal distribution with a mean of zero and a standard deviation of one. The standardized scores were then used to create break-points defining wealth quintiles, expressed as quintiles of individuals in the population. The choice of the asset index for defining socio- economic status was based on pragmatic rather than conceptual considerations: Demographic and Health Surveys do not collect income or consumption data but do have detailed information on households' own- ership of consumer goods and access to a variety of goods and services. Like income or consumption, the asset index defines disparities primarily in eco- nomic terms. It therefore excludes other possibilities Data sources of disparities among groups, such as those based on gender, education, ethnic background, or other Data on education gaps by income and gender are facets of social exclusion. To that extent the index from an analysis of Demographic and Health Sur- provides only a partial view of the multidimensional veys by Macro International and the World Bank. concepts of poverty, inequality, and inequity. Country reports are available at www.worldbank. Creating one index that includes all asset indi- org/education/edstats/. cators limits the types of analysis that can be 2008 World Development Indicators 93 2.15 Health expenditure, services, and use Health Health workers Hospital expenditure beds Public per 1,000 people % of Out of External Community Total government pocket resourcesa Per capita Nurses and health per 1,000 % of GDP % of GDP % of total expenditure % of private % of total $ Physicians midwives workers people 2005 2005 2005 2005 2005 2005 2005 2000­06b 2000­06b 2000­06b 2000­06b Afghanistan 5.2 1.0 20.0 3.3 97.4 13.1 .. .. .. .. .. Albania 6.5 2.6 40.3 8.6 97.0 1.9 169 1.2 4.7 .. 3.0 Algeria 3.5 2.6 75.3 9.5 94.6 0.1 108 1.1 2.2 0.0 c 1.7 Angola 1.8 1.5 81.5 4.7 100.0 7.3 36 0.1 1.4 .. .. Argentina 10.2 4.5 43.9 14.2 43.4 0.0 484 .. .. .. 4.1 Armenia 5.4 1.8 32.9 8.2 89.2 12.7 88 3.7 4.9 .. 4.5 Australia 8.8 5.9 67.0 17.0 55.2 0.0 3,181 2.5 9.7 0.2 4.0 Austria 10.2 7.7 75.7 15.5 67.4 0.0 3,788 3.7 6.6 .. 7.7 Azerbaijan 3.9 1.0 24.8 3.8 84.6 0.4 62 3.6 8.4 .. 8.2 Bangladesh 2.8 0.8 29.1 5.5 88.3 12.2 12 0.3 0.3 0.2 0.3 Belarus 6.6 5.0 75.8 10.5 69.0 .. 204 4.8 12.5 .. 11.1 Belgium 9.6 6.9 71.4 13.9 78.7 0.0 3,451 4.2 14.2 .. 5.3 Benin 5.4 3.0 55.6 13.5 99.9 19.7 28 0.0 c 0.8 0.0 c 0.5 Bolivia 6.9 4.3 61.6 12.4 81.4 6.8 71 1.2 2.1 0.1 1.0 Bosnia and Herzegovina 8.8 5.2 58.7 14.0 100.0 0.6 243 1.4 4.7 .. 3.0 Botswana 7.0 4.5 63.6 12.4 26.2 4.7 362 0.4 2.7 .. 2.2 Brazil 7.9 3.5 44.1 6.7 54.6 0.0 371 1.2 3.8 .. 2.6 Bulgaria 7.7 4.7 60.6 12.1 96.3 1.1 272 0.3 4.6 .. 6.4 Burkina Faso 6.7 4.0 59.5 18.4 94.2 29.5 27 0.1 0.5 0.1 .. Burundi 3.4 1.0 28.6 2.3 100.0 50.9 3 0.0 c 0.2 0.1 0.7 Cambodia 6.4 1.5 24.2 12.0 79.3 25.7 29 0.2 0.9 .. 0.6 Cameroon 5.2 1.5 28.0 11.0 94.6 5.3 49 0.2 1.6 .. .. Canada 9.7 6.8 70.3 17.5 48.7 0.0 3,430 1.9 10.1 .. 3.6 Central African Republic 4.0 1.5 37.5 10.9 95.3 38.5 13 0.1 0.4 0.1 .. Chad 3.7 1.5 39.8 9.5 96.2 12.5 22 0.0 c 0.3 0.0 c 0.4 Chile 5.4 2.8 51.4 13.2 54.3 0.1 397 1.1 0.6 .. 2.4 China 4.7 1.8 38.8 1.0 85.3 0.1 81 1.5 1.0 0.1 2.5 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 7.3 6.2 84.8 17.7 45.1 0.0 201 1.4 0.6 .. 1.2 Congo, Dem. Rep. 4.2 1.5 34.6 7.2 100.0 23.6 5 0.1 0.5 .. .. Congo, Rep. 1.9 0.9 47.1 4.0 100.0 4.7 31 0.2 1.0 0.0 c .. Costa Rica 7.1 5.4 76.0 21.0 79.4 0.2 327 1.3 0.9 1.3 1.4 Cote d'Ivoire 3.9 0.8 21.5 4.2 87.8 6.6 34 0.1 0.6 .. .. Croatia 8.4 d 6.3d 75.5d 13.1d 93.6 0.0 812d 2.5 5.5 .. 5.5 Cuba 7.6 6.9 90.8 11.7 93.2 0.3 310 5.9 7.4 .. 4.9 Czech Republic 7.1 6.3 88.6 14.4 95.3 0.0 868 3.6 8.9 .. 8.4 Denmark 9.1 7.7 84.1 14.4 90.1 0.0 4,350 3.6 10.1 .. 3.8 Dominican Republic 5.4 1.7 31.1 9.3 86.4 2.5 197 1.9 1.8 .. 2.2 Ecuador 5.3 2.1 40.0 8.0 85.0 0.4 147 1.5 1.7 .. 1.4 Egypt, Arab Rep. 6.1 2.3 38.0 7.3 94.9 0.9 78 2.4 3.4 .. 2.2 El Salvador 8.1d 4.0 d 50.0 d 22.0 d 94.0 d 2.7d 220 d 1.5 0.8 .. 0.9 Eritrea 3.7 1.7 44.9 4.2 100.0 50.5 8 0.1 0.6 .. .. Estonia 5.0 3.8 76.9 11.5 88.7 0.3 516 3.3 7.0 0.0 c 5.8 Ethiopia 4.9 3.0 61.0 10.8 80.6 37.9 6 0.0 c 0.2 0.3 0.2 Finland 7.5 5.8 77.8 11.6 80.0 0.0 2,824 3.3 8.9 .. 7.0 France 11.1 8.9 79.8 16.5 34.2 0.0 3,807 3.4 8.0 .. 7.5 Gabon 4.1 3.0 74.0 13.9 100.0 1.5 276 0.3 5.0 .. .. Gambia, The 5.2 3.4 65.4 11.2 70.3 29.3 15 0.1 1.3 0.7 0.8 Georgia 8.6 1.7 19.5 6.7 95.7 5.1 123 4.7 4.0 .. 3.8 Germany 10.7 8.2 76.9 17.6 56.8 0.0 3,628 3.4 8.0 .. 8.4 Ghana 6.2 2.1 34.1 6.9 79.1 26.0 30 0.2 0.9 .. 0.9 Greece 10.1 4.3 42.8 11.5 62.0 .. 2,580 5.0 3.6 .. 4.7 Guatemala 5.2 2.0 37.9 15.7 92.2 1.1 132 .. .. .. 0.7 Guinea 5.6 0.7 11.9 4.7 99.5 12.2 21 0.1 0.5 0.0 c .. Guinea-Bissau 5.2 1.7 31.9 4.0 85.7 31.8 10 0.1 0.7 2.9 .. Haiti 6.2 3.2 51.3 27.7 90.1 18.9 28 .. .. .. 0.8 94 2008 World Development Indicators 2.15 PEOPLE Health expenditure, services, and use Health Health workers Hospital expenditure beds Public per 1,000 people % of Out of External Community Total government pocket resourcesa Per capita Nurses and health per 1,000 % of GDP % of GDP % of total expenditure % of private % of total $ Physicians midwives workers people 2005 2005 2005 2005 2005 2005 2005 2000­06b 2000­06b 2000­06b 2000­06b Honduras 7.5 3.8 50.6 16.1 87.0 6.8 91 0.6 1.3 .. 1.0 Hungary 7.8 5.5 70.8 11.1 86.8 .. 855 3.0 9.2 .. 7.9 India 5.0 1.0 19.0 3.5 94.0 0.4 36 0.6 1.3 0.1 0.9 Indonesia 2.1 1.0 46.6 5.1 66.4 4.6 26 0.1 0.8 0.0 .. Iran, Islamic Rep. 7.8 4.4 55.8 9.2 94.8 0.1 212 0.9 1.6 0.4 1.7 Iraq 4.1e 3.1e 74.4 e 3.4 e 100.0e 4.9e .. .. .. .. .. Ireland 8.2 6.5 79.5 19.0 59.3 0.0 3,993 2.9 19.5 .. 5.7 Israel 7.9 4.8 61.3 10.4 61.0 0.0 1,533 3.7 6.2 .. 6.3 Italy 8.9 6.8 76.6 14.1 86.6 0.0 2,692 3.7 7.2 .. 4.0 Jamaica 4.7 2.3 48.8 3.5 63.6 1.8 170 0.9 1.7 .. 1.7 Japan 8.2 6.7 82.2 17.8 83.5 0.0 2,936 2.1 9.5 .. 14.3 Jordan 10.5f 4.8f 45.3f 9.5f 76.1 4.5 241 2.4 3.2 0.2 1.7 Kazakhstan 4.1d 2.2d 67.4 d 10.8d 100.0 d 0.3d 204 d 3.9 7.6 .. 7.7 Kenya 4.5 2.1 46.6 6.1 80.0 18.1 24 0.1 1.2 .. 1.9 Korea, Dem. Rep. 3.5 3.0 85.6 6.0 100.0 36.6 0g 3.3 4.1 .. 13.2 Korea, Rep. 5.9 3.1 53.0 10.9 80.1 0.0 973 1.6 1.9 .. 7.1 Kuwait 2.2 1.7 77.2 6.2 91.6 0.0 687 1.8 3.7 .. 1.9 Kyrgyz Republic 6.1 2.5 40.3 8.6 95.0 7.5 29 2.4 5.8 .. 5.1 Lao PDR 3.6 0.7 20.6 4.1 92.7 11.3 18 0.4 1.0 .. 0.9 Latvia 6.4 3.9 60.5 10.8 97.7 0.3 443 3.1 5.6 .. 7.7 Lebanon 8.7 3.8 43.5 11.9 74.7 2.3 460 2.4 1.3 .. 3.6 Lesotho 9.4 8.5 90.1 18.2 18.3 10.7 69 0.1 0.6 .. .. Liberia 6.4 4.4 68.2 36.3 98.7 41.2 10 0.0 c 0.3 0.0 c .. Libya 3.2 2.2 69.5 6.5 100.0 0.0 223 1.3 4.8 .. 3.4 Lithuania 5.9 4.0 67.3 11.9 98.6 0.0 448 4.0 7.7 .. 8.1 Macedonia, FYR 7.8 5.5 70.4 15.8 100.0 1.0 224 2.6 4.3 .. 4.7 Madagascar 3.2 2.0 62.5 9.6 52.6 46.1 9 0.3 0.3 0.0c 0.4 Malawi 12.2 8.7 71.3 16.6 30.6 61.2 19 0.0 c 0.6 .. .. Malaysia 4.2 1.9 44.8 7.0 75.7 0.0 222 0.7 1.8 .. 1.8 Mali 5.8 2.9 50.6 12.0 99.5 15.6 28 0.1 0.6 0.0c .. Mauritania 2.7 1.7 63.2 5.0 100.0 26.1 17 0.1 0.6 0.1 0.6 Mauritius 4.3 2.2 51.5 9.2 81.4 1.1 218 1.1 3.7 0.2 3.0 Mexico 6.4 2.9 45.5 12.5 93.9 0.0 474 1.5 0.9 .. 1.0 Moldova 7.5 4.2 55.5 11.3 96.4 2.6 58 2.7 6.2 .. 6.4 Mongolia 4.3 3.3 77.5 11.0 86.5 1.5 35 2.6 3.5 1.5 7.5 Morocco 5.3 1.9 36.6 5.5 76.0 1.0 89 0.5 0.8 .. 0.9 Mozambique 4.3 2.7 63.6 12.6 40.5 66.5 14 0.0 c 0.3 .. .. Myanmar 2.2 0.3 11.6 1.2 99.4 12.7 4 0.4 1.0 1.0 0.6 Namibia 5.3 3.5 65.2 10.1 15.5 13.5 165 0.3 3.1 .. .. Nepal 5.8 1.6 28.1 8.4 87.0 16.4 16 0.2 0.5 0.6 0.2 Netherlands 9.2 6.0 64.9 13.2 21.9 0.0 3,560 3.7 14.6 .. 5.0 New Zealand 8.9 6.9 77.4 18.0 74.4 0.0 2,403 2.2 8.9 1.4 6.0 Nicaragua 8.3 4.1 49.6 13.7 96.2 9.2 75 0.4 1.1 .. 0.9 Niger 3.8 1.9 50.5 10.2 85.2 17.0 9 0.0 c 0.2 .. .. Nigeria 3.9 1.2 30.9 3.5 90.4 4.8 27 0.3 1.7 0.9 1.2 Norway 9.0 7.5 83.6 17.9 95.3 0.0 5,910 3.8 16.2 .. 4.2 Oman 2.5 2.1 85.0 6.1 64.4 0.0 312 1.7 3.7 .. 2.1 Pakistan 2.1 0.4 17.5 1.5 98.0 3.6 15 0.8 0.5 0.4 0.7 Panama 7.3 5.0 68.9 12.3 80.8 0.2 351 1.5 2.8 0.5 2.4 Papua New Guinea 4.2 3.6 86.2 9.6 42.5 37.0 34 0.1 0.5 .. .. Paraguay 7.3 2.7 36.5 15.3 87.7 0.6 92 1.1 1.8 1.2 1.2 Peru 4.3 2.1 49.0 8.4 80.0 1.7 125 .. .. .. 1.1 Philippines 3.2 1.2 36.6 5.5 80.3 5.1 37 1.2 6.1 .. 1.2 Poland 6.2 4.3 69.3 9.9 85.1 0.1 495 2.0 5.2 .. 5.3 Portugal 10.2 7.4 72.3 15.5 79.8 0.0 1,800 3.4 4.7 .. 3.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 95 2.15 Health expenditure, services, and use Health Health workers Hospital expenditure beds Public per 1,000 people % of Out of External Community Total government pocket resourcesa Per capita Nurses and health per 1,000 % of GDP % of GDP % of total expenditure % of private % of total $ Physicians midwives workers people 2005 2005 2005 2005 2005 2005 2005 2000­06b 2000­06b 2000­06b 2000­06b Romania 5.5 3.9 70.3 12.4 85.0 0.8 250 1.9 4.2 .. 6.6 Russian Federation 5.2 3.2 62.0 10.1 82.4 0.0 277 4.3 8.5 3.0 9.7 Rwanda 7.2 4.1 56.9 16.9 36.9 43.9 19 0.1 0.4 1.4 1.7 Saudi Arabia 3.4 2.6 76.2 8.7 16.5 0.0 448 1.7 3.0 .. 2.3 Senegal 5.4 1.7 31.7 6.7 90.3 13.0 38 0.1 0.3 .. .. Serbia 8.0h 5.8h 71.9h 15.1h 86.7h 0.5h 212h 2.0 4.3 .. 5.9 Sierra Leone 3.7 1.9 51.5 7.8 100.0 41.0 8 0.0 c 0.5 0.1 0.4 Singapore 3.5 1.1 31.9 5.6 93.8 0.0 944 1.5 4.5 .. 2.8 Slovak Republic 7.0 5.2 74.4 13.9 88.1 0.0 626 3.1 6.6 .. 6.9 Slovenia 8.5 6.2 72.4 13.4 45.0 0.1 1,495 2.4 8.0 .. 4.8 Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 8.7 3.6 41.7 9.9 17.4 0.5 437 0.8 4.1 0.2 .. Spain 8.2 5.9 71.4 15.4 73.1 0.0 2,152 3.3 7.6 .. 3.5 Sri Lanka 4.1 1.9 46.2 7.8 86.0 1.2 51 0.6 1.7 .. 3.1 Sudan 3.8 1.4 37.6 7.0 98.3 6.8 29 0.3 0.9 0.2 0.7 Swaziland 6.3 4.0 64.1 10.9 41.7 5.6 146 0.2 6.3 4.3 .. Sweden 8.9 7.5 84.6 13.6 89.6 0.0 3,598 3.3 10.9 .. 3.6 Switzerland 11.4 6.8 59.7 18.7 75.7 0.0 5,694 4.0 11.0 .. 5.7 Syrian Arab Republic 4.2 2.1 50.5 6.8 100.0 0.5 61 0.5 1.4 .. 1.3 Tajikistan 5.0 1.1 22.8 5.0 96.6 11.8 18 2.0 5.0 .. 6.2 Tanzania 5.1 2.9 56.9 12.6 83.4 27.8 17 0.0 c 0.4 .. .. Thailand 3.5 2.2 63.9 11.3 76.6 0.2 98 0.4 2.8 0.1 2.2 Timor-Leste 13.7 11.9 86.6 19.1 37.2 57.2 45 0.1 2.2 2.0 .. Togo 5.3 1.4 25.5 6.9 84.7 13.3 18 0.0 c 0.4 0.1 0.9 Trinidad and Tobago 4.5 2.4 53.7 8.3 87.8 2.4 513 .. .. .. 3.3 Tunisia 5.5 2.4 44.3 6.5 82.2 0.8 158 1.3 2.9 .. 1.8 Turkey 7.6 5.4 71.4 13.9 69.5 0.0 383 1.6 2.9 .. 2.6 Turkmenistan 4.8 3.2 66.7 14.9 100.0 0.3 156 2.5 4.7 .. 4.9 Uganda 7.0 2.0 28.6 10.0 51.8 33.1 22 0.1 0.7 .. 0.7 Ukraine 7.0 3.7 52.8 8.4 84.8 0.6 128 3.1 8.5 .. 8.7 United Arab Emirates 2.6 1.9 71.6 8.6 77.9 0.0 833 1.7 3.5 .. 2.2 United Kingdom 8.2 7.1 87.1 16.2 92.1 0.0 3,064 2.2 .. .. 3.9 United States 15.9 7.2 45.4 0.7 23.9 0.0 6,657 2.3 9.4 .. 3.3 Uruguay 8.1 3.4 42.5 10.1 31.1 0.6 404 3.7 0.9 .. 2.4 Uzbekistan 5.0 2.4 47.7 7.4 97.1 3.5 26 2.7 10.9 .. 5.2 Venezuela, RB 4.7 2.1 45.3 7.9 88.2 0.1 247 1.9 1.1 .. 0.9 Vietnam 6.0 1.5 25.7 5.1 86.1 2.0 37 0.6 0.8 .. 1.4 West Bank and Gaza .. .. .. .. .. .. .. 0.8 .. .. .. Yemen, Rep. 5.1 2.1 41.8 5.6 95.2 15.0 39 0.3 0.7 0.3 0.6 Zambia 5.6 2.7 49.0 10.7 71.5 40.5 36 0.1 2.0 .. 2.0 Zimbabwe 8.1 3.6 44.8 8.9 52.0 20.6 21 0.2 0.7 0.0c .. World 10.1 w 6.0 w 59.3 w 10.4 w 43.5 w 0.1 w 703 w .. w .. w .. w .. w Low income 4.6 1.2 24.9 6.9 92.0 5.6 27 0.5 .. 0.2 .. Middle income 5.8 2.9 51.1 8.2 74.5 0.4 162 1.6 .. .. 3.1 Lower middle income 4.8 2.2 46.9 5.9 84.9 0.8 86 1.3 1.0 .. 2.7 Upper middle income 6.7 3.6 53.8 .. 66.8 0.1 374 2.3 .. .. .. Low & middle income 5.6 2.7 48.1 7.3 77.4 1.0 104 .. .. .. .. East Asia & Pacific 4.3 1.8 40.3 2.1 83.8 0.7 70 1.5 1.0 0.1 2.5 Europe & Central Asia 6.2 4.1 66.2 10.5 82.8 0.2 279 3.1 6.8 .. 7.2 Latin America & Carib. 7.1 3.3 47.9 .. 68.0 0.2 329 .. .. .. .. Middle East & N. Africa 5.8 3.0 53.4 8.2 90.5 1.1 123 .. .. .. .. South Asia 4.5 0.9 20.2 3.5 93.9 1.3 31 0.6 1.3 0.1 0.9 Sub-Saharan Africa 6.1 2.6 42.9 .. 45.7 7.4 49 .. .. .. .. High income 11.4 7.0 60.9 10.9 36.8 0.0 3,979 2.6 .. .. 6.2 Euro area 9.9 7.4 75.1 15.6 58.2 0.0 3,155 3.5 .. .. 6.6 a. 0.0 is 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 2006. e. Excludes northern Iraq. f. Includes contributions from the United Nations Relief and Works Agency for Palestine Refugees. g. Less than 0.5. h. Excludes Kosovo and Metohija. 96 2008 World Development Indicators 2.15 PEOPLE Health expenditure, services, and use About the data Definitions National health accounts track financial flows in the the data for nurses and midwives, because for some · Total health expenditure is the sum of public and health sector, including public and private expendi- countries the available information does not clearly private health expenditure. It covers the provision tures, by source of funding. In contrast with high- distinguish between the two groups. There is no uni- of health services (preventive and curative), family income countries, few developing countries have versally accepted definition of hospital beds. More- planning and nutrition activities, and emergency aid health accounts that are methodologically consis- over, fi gures on physicians and hospital beds are for health but excludes provision of water and sani- tent with national accounting approaches. Efforts indicators of availability, not of quality or use. They tation. · Public health expenditure is recurrent and are needed to standardize and harmonize the various do not show how well trained the physicians are or capital spending from central and local governments, competing national health account methodologies. how well equipped the hospitals or medical centers external borrowing and grants (including donations The difficulties in creating national health accounts are. And physicians and hospital beds tend to be from international agencies and nongovernmental go beyond data collection. To establish a national concentrated in urban areas, so these indicators give organizations), and social (or compulsory) health health accounting system, a country needs to define only a partial view of health services available to the insurance funds. · Out of pocket health expendi- the boundaries of the health care system and to entire population. ture, part of private health expenditure, is direct define a taxonomy of health care delivery institutions. Meeting the minimum of 2.5 physicians, nurses, household outlays including gratuities and in-kind The accounting system should be comprehensive and midwives per 1,000 people is critical for coun- payments to health practitioners and pharmaceutical and standardized, providing not only accurate mea- tries to provide the adequate primary health care suppliers, therapeutic appliances, and other goods sures of financial flows but also information on the interventions needed to achieve the health-related and services whose primary intent is to contribute equity and efficiency of health financing to inform Millennium Development Goals (WHO, World Health to health restoration or enhancement. · External health policy. Report 2006). resources for health, part of total health expendi- The absence of consistent national health account- ture, are funds or services in kind provided by enti- ing systems in most developing countries makes ties not part of the country. Resources may come cross-country comparisons of health spending dif- from international organizations, other countries, ficult. Compiling estimates of public health expen- or foreign nongovernmental organizations. · Health ditures is complicated in countries where state or expenditure per capita is total health expenditure provincial and local governments are involved in divided by population. · Physicians are graduates financing and delivering health care, often because of any faculty or school of medicine working in the the data on public spending are not aggregated. country in any medical field (practice, teaching, or There are a number of potential data sources related research). · Nurses and midwives are professional to external resources for health, including govern- nurses, auxiliary nurses, enrolled nurses, and other ment expenditure accounts, government records nurses, such as dental nurses and primary care on external assistance, routine surveys of external nurses, and professional midwives, auxiliary mid- financing assistance, and special surveys. Survey wives, and enrolled midwives. · Community health data are the major source of information about out workers are traditional medicine practitioners, faith of pocket expenditure on health. The data in the healers, assistant and community health education table are the product of an effort by the World Health workers, community health officers, family health Organization (WHO), the Organisation for Economic workers, lady health visitors, health extension pack- Co-operation and Development (OECD), and the age workers, community midwives, and traditional World Bank to collect all available information on birth attendants. · Hospital beds are inpatient beds health expenditures from national and local govern- for both acute and chronic care available in public, ment budgets, national accounts, household sur- private, general, and specialized hospitals and reha- veys, insurance publications, international donors, bilitation centers. and existing tabulations. Indicators on health services (physicians, nurses and midwives, community health workers, and Data sources hospital beds) are compiled by the WHO based on Data on health expenditure come mostly from the household and labor force surveys, censuses, and WHO's National Health Account database (www. professional and administrative records. Data com- who.int/nha/en) and from the OECD for its mem- parability is limited by differences in definitions. In ber countries, supplemented by country data. estimates of health personnel, for example, some Data on physicians, nurses and midwives, com- countries incorrectly include retired physicians munity health workers, and hospital beds are from (because deletions to physician rosters are made the WHO, OECD, and TransMONEE, supplemented only periodically) or physicians working outside the by country data. health sector. Caution must be exercised in using 2008 World Development Indicators 97 2.16 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS an improved improved immunization with acute diarrhea who sleeping with fever treatment detection water source sanitation rate respiratory received oral under receiving success rate facilities infection rehydration treated antimalarial rate taken to and continuous bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of new % of new % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2004 1990 2004 2006 2006 2000­06c 2000­06c 2000­06c 2000­06c 2005 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. 90 66 Albania 96 96 .. 91 97 98 45 50 .. .. 77 37 Algeria 94 85 88 92 91 95 53 24 .. .. 87 102 Angola 36 53 29 31 48 44 58 32 2.3 63.0 72 76 Argentina 94 96 81 91 97 91 .. .. .. .. 53 71 Armenia .. 92 .. 83 92 87 36 59 .. .. 72 59 Australia 100 100 100 100 94 92 .. .. .. .. 80 40 Austria 100 100 100 100 80 83 .. .. .. .. 75 46 Azerbaijan 68 77 .. 54 96 95 36 40 1.4 0.8 59 50 Bangladesh 72 74 20 39 81 88 30 49 .. .. 91 65 Belarus 100 100 .. 84 97 99 90 54 .. .. 73 40 Belgium .. .. .. .. 88 97 .. 42 .. .. 66 55 Benin 63 67 12 33 89 93 36 42 20.1 54.0 87 86 Bolivia 72 85 33 46 81 81 52 54 .. .. 78 69 Bosnia and Herzegovina 97 97 .. 95 90 87 91 53 .. .. 97 62 Botswana 93 95 38 42 90 97 14 7 .. .. 70 80 Brazil 83 90 71 75 99 99 .. .. .. .. 77 55 Bulgaria 99 99 99 99 96 95 .. .. .. .. 86 94 Burkina Faso 38 61 7 13 88 95 39 42 9.6 48.0 71 17 Burundi 69 79 44 36 75 74 38 23 8.3 30.0 79 24 Cambodia .. 41 .. 17 78 80 48 59 4.2 0.2 93 62 Cameroon 50 66 48 51 73 81 35 22 13.1 57.8 74 91 Canada 100 100 100 100 94 94 .. .. .. .. 68 55 Central African Republic 52 75 23 27 35 40 32 47 15.1 57.0 65 69 Chad 19 42 7 9 23 20 7 27 0.6 44.0 69 19 Chile 90 95 84 91 91 94 .. .. .. .. 78 141 China 70 77 23 44 93 93 .. .. .. .. 94 79 Hong Kong, China .. .. .. .. .. .. .. .. .. .. 77 56 Colombia 92 93 82 86 88 86 62 39 0.7 .. 71 83 Congo, Dem. Rep. 43 46 16 30 73 77 36 17 5.8d 29.8d 85 61 Congo, Rep. .. 58 .. 27 66 79 48 39 6.1 48.0 28 51 Costa Rica .. 97 .. 92 89 91 .. .. .. .. 89 102 Côte d'Ivoire 69 84 21 37 73 77 35 45 5.9 36.0 75 37 Croatia 100 100 100 100 96 96 .. .. .. .. .. .. Cuba .. 91 98 98 96 89 .. .. .. .. 91 94 Czech Republic 100 100 99 98 97 98 .. .. .. .. 72 57 Denmark 100 100 .. .. 99 93 .. .. .. .. 83 62 Dominican Republic 84 95 52 78 99 81 64 42 .. .. 85 66 Ecuador 73 94 63 89 97 98 .. .. .. .. 83 34 Egypt, Arab Rep. 94 98 54 70 98 98 63 27 .. .. 79 59 El Salvador 67 84 51 62 98 96 62 .. .. .. 91 61 Eritrea 43 60 7 9 95 97 44 54 4.2 3.6 88 35 Estonia 100 100 97 97 96 95 .. .. .. .. 72 66 Ethiopia 23 22 3 13 63 72 19 15 1.5 3.0 78 27 Finland 100 100 100 100 97 97 .. .. .. .. .. .. France 100 100 .. .. 87 98 .. .. .. .. .. .. Gabon .. 88 .. 36 55 38 48 44 .. .. 46 58 Gambia, The .. 82 .. 53 95 95 69 38 49.0 62.6 87 64 Georgia 80 82 97 94 95 87 99 .. .. .. 73 109 Germany 100 100 100 100 94 90 .. .. .. .. 71 54 Ghana 55 75 15 18 85 84 59 29 21.8 60.8 73 38 Greece .. .. .. .. 88 88 .. .. .. .. .. .. Guatemala 79 95 58 86 95 80 64 .. .. .. 85 56 Guinea 44 50 14 18 67 71 42 38 0.3 43.5 72 55 Guinea-Bissau .. 59 .. 35 60 77 57 25 39.0 45.7 69 64 Haiti 47 54 24 30 58 53 35 43 .. 5.1 81 55 98 2008 World Development Indicators 2.16 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 continuous bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of new % of new % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2004 1990 2004 2006 2006 2000­06c 2000­06c 2000­06c 2000­06c 2005 2006 Honduras 84 87 50 69 91 87 56 49 .. 0.5 88 85 Hungary 99 99 .. 95 99 99 .. .. .. .. 45 49 India 70 86 14 33 59 55 69 32 .. 12.0 86 64 Indonesia 72 77 46 55 72 70 61 56 0.1 0.7 91 73 Iran, Islamic Rep. 92 94 83 .. 99 99 93 .. .. .. 83 69 Iraq 83 .. 81 .. .. .. .. .. .. .. 86 40 Ireland .. .. .. .. 86 91 .. .. .. .. .. .. Israel 100 100 .. .. 95 95 .. .. .. .. 78 31 Italy .. .. .. .. 87 96 .. .. .. .. 74 71 Jamaica 92 93 75 80 87 85 75 39 .. .. 57 73 Japan 100 100 100 100 99 99 .. .. .. .. 60 79 Jordan 97 97 93 93 99 98 72 44 .. .. 83 76 Kazakhstan 87 86 72 72 99 99 71 48 .. .. 74 e 69 Kenya 45 61 40 43 77 80 49 33 4.6 26.5 82 70 Korea, Dem. Rep. 100 100 .. 59 96 89 93 .. .. .. 89 97 Korea, Rep. .. 92 .. .. 99 98 .. .. .. .. 83 18 Kuwait .. .. .. .. 99 99 .. .. .. .. 63 95 Kyrgyz Republic 78 77 60 59 97 92 62 22 .. .. 85 63 Lao PDR .. 51 .. 30 48 57 36 37 17.7 8.7 90 77 Latvia 99 99 .. 78 95 98 .. .. .. .. 74 85 Lebanon 100 100 .. 98 96 92 74 .. .. .. 92 55 Lesotho .. 79 37 37 85 83 59 53 .. .. 73 79 Liberia 55 61 39 27 94 88 70 .. 2.6 .. 76 55 Libya 71 .. 97 97 98 98 .. .. .. .. 69 156 Lithuania .. .. .. .. 97 94 .. .. .. .. 70 109 Macedonia, FYR .. .. .. .. 94 93 93 45 .. .. 84 66 Madagascar 40 46 14 32 59 61 48 47 0.2 34.2 74 73 Malawi 40 73 47 61 85 99 51 26 23.0 23.9 73 42 Malaysia 98 99 .. 94 90 96 .. .. .. .. 70 80 Mali 34 50 36 46 86 85 43 45 8.4 38.0 75 26 Mauritania 38 53 31 34 62 68 41 9 2.1 33.4 55 34 Mauritius 100 100 .. 94 99 97 .. .. .. .. 86 67 Mexico 82 97 58 79 96 98 .. .. .. .. 77 118 Moldova .. 92 .. 68 96 97 60 48 .. .. 62 69 Mongolia 63 62 .. 59 99 99 63 47 .. .. 88 97 Morocco 75 81 56 73 95 97 38 46 .. .. 81 95 Mozambique 36 43 20 32 77 72 55 47 .. 15.0 79 47 Myanmar 57 78 24 77 78 82 66 65 .. .. 85 109 Namibia 57 87 24 25 63 74 53 39 3.4 14.4 75 83 Nepal 70 90 11 35 85 89 43 43 .. .. 88 64 Netherlands 100 100 100 100 96 98 .. .. .. .. 84 36 New Zealand 97 .. .. .. 82 89 .. .. .. .. 60 61 Nicaragua 70 79 45 47 99 87 57 49 .. 1.8 85 89 Niger 39 46 7 13 47 39 47 43 7.4 33.0 74 49 Nigeria 49 48 39 44 62 54 33 28 1.2 33.9 75 20 Norway 100 100 .. .. 91 93 .. .. .. .. 91 39 Oman 80 .. 83 .. 96 98 .. .. .. .. 90 122 Pakistan 83 91 37 59 80 83 .. .. .. .. 83 50 Panama 90 90 71 73 94 99 .. .. .. .. 80 134 Papua New Guinea 39 39 44 44 65 75 .. .. .. .. 71 21 Paraguay 62 86 58 80 88 73 .. .. .. .. 91 48 Peru 74 83 52 63 99 94 68 71 .. .. 91 96 Philippines 87 85 57 72 92 88 55 76 .. .. 89 77 Poland .. .. .. .. 99 99 .. .. .. .. 77 67 Portugal .. .. .. .. 93 93 .. .. .. .. 89 88 Puerto Rico .. .. .. .. .. .. .. .. .. .. 75 82 2008 World Development Indicators 99 2.16 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS an improved improved immunization with acute diarrhea who sleeping with fever treatment detection water source sanitation rate respiratory received oral under receiving success rate facilities infection rehydration treated antimalarial rate taken to and continuous bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of new % of new % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2004 1990 2004 2006 2006 2000­06c 2000­06c 2000­06c 2000­06c 2005 2006 Romania .. 57 .. .. 95 97 .. .. .. .. 82 79 Russian Federation 94 97 87 87 99 99 .. .. 13.0 .. 58 44 Rwanda 59 74 37 42 95 99 28 24 5.0 12.3 83 27 Saudi Arabia 94 96 91 99 95 96 .. .. .. .. 65 40 Senegal 65 76 33 57 80 89 47 43 7.1 26.8 74 48 Serbia 93f 93f 87f 87f 88 92 93 31 .. .. 85 79 Sierra Leone .. 57 .. 39 67 64 48 31 5.3 51.9 86 35 Singapore 100 100 100 100 93 95 .. .. .. .. 83 107 Slovak Republic 100 100 99 99 98 99 .. .. .. .. 92 43 Slovenia .. .. .. .. 96 97 .. .. .. .. 84 71 Somalia .. 29 .. 26 35 35 13 7 9.2 7.9 89 83 South Africa 83 88 69 65 85 99 .. .. .. .. 71 71 Spain 100 100 100 100 97 98 .. .. .. .. .. .. Sri Lanka 68 79 69 91 99 99 .. .. .. .. 86 85 Sudan 64 70 33 34 73 78 57 56 27.6 54.2 82 30 Swaziland .. 62 .. 48 57 68 60 24 0.1 25.5 42 49 Sweden 100 100 100 100 95 99 .. .. .. .. 64 58 Switzerland 100 100 100 100 86 95 .. .. .. .. .. .. Syrian Arab Republic 80 93 73 90 98 99 77 34 .. .. 89 48 Tajikistan .. 59 .. 51 87 86 64 22 1.3 1.2 86 33 Tanzania 46 62 47 47 93 90 57 53 16.0 58.2 82 46 Thailand 95 99 80 99 96 98 84 46 .. .. 75 73 Timor-Leste .. 58 .. 36 64 67 24 .. 8.0 47.4 82 33 Togo 50 52 37 35 83 87 23 22 38.4 47.7 71 19 Trinidad and Tobago 92 91 100 100 89 92 74 32 .. .. .. .. Tunisia 81 93 75 85 98 99 43 .. .. .. 90 81 Turkey 85 96 85 88 98 90 41 .. .. .. 89 80 Turkmenistan .. 72 .. 62 99 98 83 25 .. .. 81 58 Uganda 44 60 42 43 89 80 74 28 9.7 61.8 73 44 Ukraine .. 96 .. 96 98 98 .. .. .. .. .. 65 United Arab Emirates 100 100 97 98 92 94 .. .. .. .. 73 17 United Kingdom 100 100 .. .. 85 92 .. .. .. .. .. .. United States 100 100 100 100 93 96 .. .. .. .. 64 88 Uruguay 100 100 100 100 94 95 .. .. .. .. 84 77 Uzbekistan 94 82 51 67 95 95 68 28 .. .. 81 48 Venezuela, RB .. 83 .. 68 55 71 72 51 .. .. 83 71 Vietnam 65 85 36 61 93 94 71 65 5.1 2.6 92 85 West Bank and Gaza .. 92 .. 73 .. .. 65 .. .. .. 100 5 Yemen, Rep. 71 67 32 43 80 85 47 18 .. .. 80 43 Zambia 50 58 44 55 84 80 69 48 22.8 57.9 84 53 Zimbabwe 78 81 50 53 90 90 26 .. 2.9 4.7 68 42 World 76 w 83 w 45 w 57 w 80 w 80 w .. w 85 w 62 w Low income 64 75 21 38 69 68 21.1 84 54 Middle income 78 84 47 62 91 91 .. 86 74 Lower middle income 74 81 37 55 90 89 ..