06 WORLD 54165 DEVELOPMENT YEARS INDICATORS WORLD VIEW ECONOMY STATES & MARKETS PEOPLE ENVIRONMENT GLOBAL LINKS INCOME MAP The world by income Low-income Lower-middle-income Barbados Greenland Afghanistan Albania Belize Guam Bangladesh Algeria Botswana Hong Kong, China Benin Angola Chile Iceland Bhutan Armenia Costa Rica Ireland Burkina Faso Azerbaijan Croatia Isle of Man Burundi Belarus Czech Republic Israel Cambodia Bolivia Dominica Italy Cameroon Bosnia and Herzegovina Equatorial Guinea Japan Central African Republic Brazil Estonia Korea, Rep. Chad Bulgaria Gabon Kuwait Comoros Cape Verde Grenada Liechtenstein Congo, Dem. Rep. China Hungary Luxembourg Congo, Rep. Colombia Latvia Macao, China Côte d'Ivoire Cuba Lebanon Malta Eritrea Djibouti Libya Monaco Ethiopia Dominican Republic Lithuania Netherlands Gambia, The Ecuador Malaysia Netherlands Antilles Ghana Egypt, Arab Rep. Mauritius New Caledonia Guinea El Salvador Mayotte New Zealand Guinea-Bissau Fiji Mexico Norway Haiti Georgia Northern Mariana Islands Portugal India Guatemala Oman Puerto Rico Kenya Guyana Palau Qatar Korea, Dem. Rep. Honduras Panama San Marino Kyrgyz Republic Indonesia Poland Saudi Arabia Lao PDR Iran, Islamic Rep. Russian Federation Singapore Lesotho Iraq Seychelles Slovenia Liberia Jamaica Slovak Republic Spain Madagascar Jordan South Africa Sweden Malawi Kazakhstan St. Kitts and Nevis Switzerland Mali Kiribati St. Lucia United Arab Emirates Mauritania Macedonia, FYR St. Vincent and the United Kingdom Moldova Maldives Grenadines United States Mongolia Marshall Islands Trinidad and Tobago Virgin Islands (U.S.) Mozambique Micronesia, Fed. Sts. Turkey Myanmar Morocco Uruguay Nepal Namibia Venezuela, RB Nicaragua Paraguay Niger Peru High-income Nigeria Philippines Andorra Pakistan Romania Aruba Papua New Guinea Samoa Australia Rwanda Serbia and Montenegro Austria São Tomé and Principe Sri Lanka Bahamas, The Senegal Suriname Bahrain Sierra Leone Swaziland Belgium Solomon Islands Syrian Arab Republic Bermuda Somalia Thailand Brunei Darussalam Sudan Tonga Canada Tajikistan Tunisia Cayman Islands Tanzania Turkmenistan Channel Islands Timor-Leste Ukraine Cyprus Togo Vanuatu Denmark Uganda West Bank and Gaza Faeroe Islands Uzbekistan Finland Vietnam Upper-middle-income France Yemen, Rep. American Samoa French Polynesia Zambia Antigua and Barbuda Germany Zimbabwe Argentina Greece The world by income Low ($825 or less) Classified according to Lower middle ($826­$3,255) World Bank estimates of 2004 GNI per capita Upper middle ($3,256­$10,065) High ($10,066 or more) No data Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Grundy & Northedge, London Copyright 2006 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 2006 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, from top to bottom, Shaida Badiee/World Bank, Mark Edwards/Still Pictures, World Bank photo library, and Digital Vision. If you have questions or comments about this product, please contact: Development Data Center The World Bank 1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: data@worldbank.org Web site: www.worldbank.org or www.worldbank.org/data ISBN 0-8213-6470-7 The developing world has made remarkable progress. The number of people living in extreme poverty on less than $1 a day has fallen by about 400 million in the last 25 years. Many more children, particularly girls, are completing primary school. Illiteracy rates have fallen by half in 30 years. And life expectancy is nearly 15 years longer, on aver- age, than it was 40 years ago. These often spectacular achievements have put many countries securely on track to meet the Millennium Development Goals by 2015. But many others are being left behind, and for them progress in eradicating poverty and improving living standards remains stubbornly slow. In Sub-Saharan Africa the number of people living on less than $1 a day has nearly doubled since 1981. Every day thousands of people, many of them children, still die from preventable diseases. AIDS, malaria, and simple dehydration ravage the developing world. Reaching the Millennium Development Goals is a challenge that depends on having access to the best information available. In designing policies and targeting resources, we need to know how many people are poor and where they live. We need vital information about them, such as their gender, age, and the nature of their work or, indeed, if they have work. We also need to know whether they have access to health care, schools, and safe water. And because economic growth is essential to poverty reduction, we need to know more about the economy, the business environ- ment, the expected demographic trends, the scale of environmental degradation, and the infrastructure services available, among many other statistics. Since 1978 World Development Indicators has compiled statistics to provide an annual snapshot of progress in the developing world and the challenges that remain. It is the product of intensive collaboration with numerous international organizations, government agencies, and private and nongovernmental organizations. Our collective efforts have greatly improved the coverage and reliability of statistics on poverty and development. But more is needed. Better statistics are of value to us all. They allow us to assess the scope of the problems we face and measure progress in solving them. They make politicians and policymakers more accountable. They discourage arbitrariness, corruption, and reliance on anecdotal evidence. But they are costly to produce. Improving our knowledge base will require sustained investment, backed by a sustained commitment by national governments and international agencies. To achieve the ambitious targets we have set ourselves, we must scale up our efforts to produce reliable statistics that will inform public policy, guide debate, and strengthen the effectiveness of development efforts. Paul D. Wolfowitz President The World Bank Group 2006 World Development Indicators v This book and its companion volumes, Little Data Book and The Little Green Data Book, are prepared by a team led by Eric Swanson and comprising Awatif Abuzeid, Mehdi Akhlaghi, David Cieslikowski, Mahyar Eshragh-Tabary, Richard Fix, Amy Heyman, Masako Hiraga, Raymond Muhula, M. H. Saeed Ordoubadi, Sulekha Patel, Juan Carlos Rodriguez, Changqing Sun, K. M. Vijayalakshmi, and Vivienne Wang, 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, Saurabh Gupta, Reza Farivari, and William Prince. The work was carried out under the management of Shaida Badiee. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in five of the World Bank's thematic networks--Environmentally and Socially Sustainable Development, Human Development, Poverty Reduction and Economic Management, Private Sector Development, and Infrastructure--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 contri- butions to the book's content, please see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont and Bruce Ross-Larson, with the assistance of Christopher Trott. The editing and production team consisted of Jodi Baxter, Brendon Boyle, Michael Diavolikis, Timothy Walker, and Elaine Wilson. Communications Development's London partner, Grundy & Northedge, provided art direction and design. Staff from External Affairs oversaw publication and dissemination of the book. vi 2006 World Development Indicators In the 10 years that we have been producing the World Development Indicators, the world of development statistics has grown larger and deeper. It has also become better integrated. The demand for statistics to measure progress and demonstrate the effectiveness of development programs has stimulated growing interest in the production and dissemination of statistics. And not just in the traditional domains of debt, demographics, and national accounts, but in new areas such as biodiversity, information, communications, technology, and measures of government and busi- ness performance. In response World Development Indicators has continued to grow and change. In 1999 members of the statistical community, recognizing that the production of sound statistics for measuring progress is a global responsibility, established the Partnership in Statistics for Development in the Twenty-first Cen- tury (PARIS21) to strengthen statistical capacity at all levels. In 2000 the United Nations Millennium Summit called on all countries to work toward a quantified, time-bound set of development targets, which became the Millennium Development Goals. In the five years since the Millennium Summit, the idea of working toward specific goals has evolved into a general strategy of managing for development results. Countries are reporting on progress toward the Millennium Development Goals and monitoring their own results using a variety of economic and social indicators. Bilateral and multilateral development agencies are incorporating results into their own management planning and evaluation systems and using new indicators to set targets for harmonizing their joint work programs. All of these efforts depend on statistics. So, what has been done to improve the quality and availability of statistics? A lot. Supported by five donors, the Trust Fund for Statistical Capacity Building has provided $20 million in grants for 86 projects, many to create national sta- tistical development strategies. Several countries, recognizing the need for large-scale investments in their statistical systems, have taken out loans or credits to finance them. PARIS21 has conducted advocacy and training workshops around the world to strengthen national statistical systems. The International Comparison Program has more than 100 countries participating in the largest ever global collection of price data. The Health Metrics Network, sponsored by the World Health Organization and the Bill & Melinda Gates Foundation, is now under way. The United Nations Children's Fund launched a new round of data collection through its Multiple Indicators Cluster Surveys. And the program of Demographic and Health Surveys, funded largely by the United States, continues to operate in many countries. To accelerate global cooperation in statistical capacity building, the World Bank will provide $7.5 million a year toward implementing the Marrakech Action Plan for Statistics (MAPS), a grant-funded program. In its first year MAPS will fund the International Household Survey Network to harmonize, document, and provide technical support to survey programs everywhere. It is also funding work by the United Nations Statistics Division to prepare for the 2010 round of censuses; work on education by the United Nations Educational, Scientific, and Cultural Organization's Institute for Statistics; a project on migration by the International Labour Organization; and work on measuring slums by the United Nations Human Settlements Programme. And through PARIS21 it is supporting a pilot program to accelerate the production of key development indicators in low-income countries. National statistical offices and international and regional agencies now find themselves at the center of attention. The challenge is to maintain the momentum in producing more and better quality data. The fruits of today's efforts will be harvested in the years to come. When they are, you will see them here in the tables of World Development Indicators. Shaida Badiee Director Development Data Group 2006 World Development Indicators vii Foreword v Introduction 1 Acknowledgments vi Millennium Development Goals, targets, and indicators 18 Preface vii Tables Partners xii Size of the economy 20 Users guide xx Millennium Development Goals: eradicating poverty and improving lives 24 Millennium Development Goals: protecting our common environment 28 Millennium Development Goals: overcoming obstacles 32 Women in development 34 Key indicators for other economies 38 Text figures, tables, and boxes Poverty rates are falling, but progress has been uneven 2 Country by country progress on poverty 3 Malnutrition rates are predicted to fall everywhere--except in Sub-Saharan Africa 3 Malnutrition--a persistent problem 3 More children everywhere are completing primary school 4 Country by country progress toward universal primary education 5 A long march to literacy 5 Patterns of school attendance 5 More girls in school, but many countries have missed the 2005 target 6 Country by country progress toward equal enrollment 7 Degrees of difference 7 Wealth, gender, and location make a difference 7 Improving the odds for children 8 Country by country progress toward reduced child mortality 9 Prevention comes first 9 Cruel differences 9 Mothers at risk in Africa and South Asia 10 Country by country progress in providing skilled care at births 11 Decreasing risk of young motherhood 11 Poor women need reproductive health services 11 As the HIV/AIDS epidemic matures, the death toll keeps rising 12 The HIV epidemic can be reversed 13 Tuberculosis rates on the rise or falling slowly 13 Malaria is a leading killer in Africa 13 Poor children bear the burden of malaria 13 Water and sanitation--basic services needed by all 14 Country by country progress toward access to water . . . 15 . . . and to sanitation 15 Forests falling 15 Fuel for climate change--high carbon dioxide emitters 15 Many sources and many patterns 16 Official development assistance is rising, but still too little 17 Tariffs remain high on poor countries' exports 17 Debt service is falling, but more relief is needed 17 New technologies are spreading quickly 17 Developing countries produce slightly less than half the world's output 23 Location of indicators for Millennium Development Goals 1­5 27 Location of indicators for Millennium Development Goals 6­7 31 Location of indicators for Millennium Development Goal 8 33 viii 2006 World Development Indicators Introduction 41 Introduction 125 Tables Tables Population dynamics 46 Rural population and land use 130 Labor force structure 50 Agricultural inputs 134 Employment by economic activity 54 Agricultural output and productivity 138 Child labor 58 Deforestation and biodiversity 142 Unemployment 62 Freshwater 146 Wages and productivity 66 Water pollution 150 Poverty 70 Energy production and use 154 Distribution of income or consumption 76 Energy efficiency and emissions 158 Assessing vulnerability and security 80 Sources of electricity 162 Education inputs 84 Urbanization 166 Participation in education 88 Urban housing conditions 170 Education efficiency 92 Traffic and congestion 174 Education completion and outcomes 96 Air pollution 178 Health expenditure, services, and use 100 Government commitment 180 Disease prevention coverage and quality 104 Toward a broader measure of savings 184 Reproductive health 108 Text figures, tables, and boxes Nutrition 112 More than three-fourths of the 1.4 billion people living on Health risk factors and public health challenges 116 fragile lands are in Asia and Africa 126 Mortality 120 Water withdrawal is skewed toward agriculture in every Text figures, tables, and boxes developing region 126 Total fertility rates by region, 1970, 1980, and 2004 42 Many more people lack access to an improved water source Family planning and the fertility transition 42 in rural than in urban areas 127 Population growth rates by region (%) 42 Sustainable management of forests is spreading 128 Total fertility rates in selected Sub-Saharan countries, 2004 42 Use of fossil fuels continues to rise faster than that of other Desired family size in selected countries in Sub-Saharan Africa sources of energy 128 and South Asia, latest year available 43 High-income countries are the leading source of carbon Contraceptive method mix, selected countries, 2000­04 43 dioxide emissions 128 Sub-Saharan Africa's delayed demographic transition 44 Sub-Sarahan Africa has the highest death rate from Projected fertility rates in selected African regions 44 respiratory disease 129 Population projections--trends and uncertainty 45 More efficient use of traditional biomass is improving the The demographic divide: Nigeria and Japan 45 lives of women 129 Of children who work, some combine work and schooling 61 Use of renewable sources of energy is growing, but is still small 129 Regional poverty estimates 73 Ten countries with the largest forest area, 2005 133 Estimated impact of HIV/AIDS on education in three Five countries had more than half the world's forest in 2005 133 Sub-Saharan countries, 2005 87 Irrigated lands have increased in all income groups and most In Uganda most births in rural areas take place at home 103 regions, putting further pressure on water resources 137 Deaths from diarrhea can be sharply reduced with The 10 countries with the highest cereal yield in improvements in drinking water and sanitation 107 2002­04--and the 10 with the lowest 141 Agriculture uses 70 percent of freshwater globally 149 Emission of organic water pollutants declined in most countries from 1990 to 2003 153 In 2003 high-income economies, with 15 percent of world population, used 52 percent of world energy--and produced 41 percent 157 The five largest producers of carbon dioxide . . . 161 . . . differ significantly in per capita emissions 161 Electricity sources have shifted since 1990 . . . 165 . . . with a more profound shift in low-income countries 165 The urban population in developing countries has increased substantially since 1990 169 Selected housing indicators for smaller economies 173 The 15 countries with the fewest passenger cars per 1,000 people in 2003--and the 15 with the most 177 2006 World Development Indicators ix Introduction 189 Introduction 263 Tables Tables Recent economic performance 192 Private sector in the economy 266 Growth of output 194 Investment climate 270 Structure of output 198 Business environment 274 Structure of manufacturing 202 Stock markets 278 Structure of merchandise exports 206 Financial access, stability, and efficiency 282 Structure of merchandise imports 210 Tax policies 286 Structure of service exports 214 Defense expenditures and arms transfers 290 Structure of service imports 218 Transport services 294 Structure of demand 222 Power and communications 298 Growth of consumption investment, and trade 226 The information age 302 Central government finances 230 Science and technology 306 Central government expenses 234 Text figures, tables, and boxes Central government revenues 238 Africa had the lowest business environment reform intensity Monetary indicators 242 in 2004 264 Exchange rates and prices 246 Rural access index for selected low-income countries (% of Balance of payments current account 250 rural population) 265 External debt 254 Excessive paperwork adds to the time that businesses Debt ratios 258 spend complying with taxes 289 Text figures, tables, and boxes Europe and Central Asia had the highest Internet use Fast growing--and backsliding--economies in 2004 190 among developing country regions in 2004 305 Inflation, median annual growth of GDP deflator (%) 190 Real interest rates (%) 190 Accelerating regional growth 190 Raising demand for energy supplies 191 China's data revision 191 Manufacturing growth trends for selected Sub-Saharan countries 205 Developing economies' share of world merchandise exports continues to increase 209 Top 10 exporters in Sub-Saharan Africa in 2004 213 Top 10 developing country exporters of commercial services in 2004 217 The mix of commerical service imports is changing 221 Gross capital formation and government consumption are both on the rise in Sub-Saharan Africa 229 Selected developing countries with large cash deficits 233 Interest payments are a large part of government expenditure for some developing economies 237 Rich countries rely more on direct taxes 241 Top 15 countries with the largest current account surplus, and top 15 countries with the largest current account deficit in 2003 253 GDP is outpacing external debt in Sub-Saharan countries 257 The debt burden of Sub-Saharan countries has been falling since 1995 261 x 2006 World Development Indicators Introduction 311 Primary data documentation 369 Statistical methods 378 Tables Credits 380 Integration with the global economy 316 Bibliography 382 Growth of merchandise trade 320 Index of indicators 389 Direction and growth of merchandise trade 324 High-income trade with low- and middle-income economies 327 Primary commodity prices 330 Regional trade blocs 332 Tariff barriers 336 Global private financial flows 340 Net financial flows from Development Assistance Committee members 344 Aid flows from Development Assistance Committee members 346 Aid dependency 348 Distribution of net aid by Development Assistance Committee members 352 Net financial flows from multilateral institutions 356 Movement of people 360 Travel and tourism 364 Text figures, tables, and boxes Trade spurs growth and growth spurs trade 312 Foreign direct investment is the largest source of external finance for developing countries 313 Aid is the largest source of external finance for Sub-Saharan Africa 313 New promises of aid and debt relief 314 Immigrant populations are expanding in high-income economies 315 Immigrants in OECD countries are better educated 315 Trade in services is becoming increasingly important 319 Exports are growing in developing countries 323 Triangular trade in manufactures between China, selected other large East Asian economies, and the United States and Japan 326 Growing trade between developing countries 329 Regional trade agreements are proliferating 335 Which developing countries received the most net inflows of foreign direct investment in 2004? 343 Who were the largest donors in 2004? 345 Official development assistance from non-DAC donors, 2000­04 ($ millions) 347 More aid flows to developing countries 351 The flow of bilateral aid from DAC members reflects global events and priorities 355 Maintaining financial flows from the World Bank to developing countries 359 Officially recorded remittance flows are surging 363 International tourist arrivals reached an all-time high in 2004 367 2006 World Development Indicators xi Defining, gathering, and disseminating international statistics is a collective effort of many people and orga- nizations. 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 standards 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, 2006. Information about the World Bank is also provided. International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and infor- mation analysis center of the U.S. Department of Energy. The CDIAC's scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere; the role of the ter- restrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases; emissions of carbon dioxide to the atmosphere; long-term climate trends; the effects of elevated carbon dioxide on vegetation; and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.esd.ornl.gov/. Deutsche Gesellschaft für Technische Zusammenarbeit The Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GTZ's aim is to positively shape political, economic, ecologi- cal, and social development in partner countries, thereby improving people's living conditions and prospects. For more information, see www.gtz.de/. Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org/. xii 2006 World Development Indicators International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is respon- sible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. ICAO's strategic objectives include enhancing global aviation safety and security and the efficiency of aviation operations, minimizing the adverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations, and strengthening laws governing international civil aviation. For more information, see www.icao.int/. International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promo- tion of social justice and internationally recognized human and labor rights. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org/. International Monetary Fund The International Monetary Fund (IMF) was established to promote international monetary cooperation, facilitate the expansion and balanced growth of international trade, promote exchange rate stability, help establish a multilateral payments system, make the general resources of the IMF temporarily available to its members under adequate safeguards, and shorten the duration and lessen the degree of disequilibrium in the international balance of payments of members. For more information, see www.imf.org/. International Telecommunication Union The International Telecommunication Union (ITU), a specialized agency of the United Nations, covers all aspects of telecommunication, from setting standards that facilitate seamless interworking of equip- ment and systems on a global basis to adopting operational procedures for the vast and growing array of wireless services and designing programs to improve telecommunication infrastructure in the devel- oping world. The ITU is also a catalyst for forging development partnerships between government and private industry. For more information, see www.itu.int/. National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. It is responsible for promoting science and engineering through almost 20,000 research and education projects. In addition, the NSF fosters the exchange of scientific information among scien- tists and engineers in the United States and other countries, supports programs to strengthen scientific and engineering research potential, and evaluates the impact of research on industrial development and general welfare. For more information, see www.nsf.gov/. 2006 World Development Indicators xiii Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 30 member countries shar- ing a commitment to democratic government and the market economy. With active relationships with some 70 other countries, nongovernmental organizations, and civil society, it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac/) is one of the principal bod- ies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum of major bilateral donors, who work together to increase the effectiveness of their common efforts to support sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.new.oecd.org/. Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an under- standing of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI's main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/. Understanding Children's Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labor Organization, the United Nations Children's Fund (UNICEF), and the World Bank initiated the joint interagency research program "Understanding Children's Work and Its Impact" in December 2000. The Understanding Children's Work (UCW) project was located at UNICEF's Innocenti Research Centre in Flor- ence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. The UCW project addresses the crucial need for more and better data on child labor. UCW's online data- base contains data by country on child labor and the status of children. For more information, see www.ucw-project.org/. United Nations The United Nations currently has 191 member states. The purposes of the United Nations, as set forth in the Charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promot- ing respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org/. xiv 2006 World Development Indicators United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/. United Nations Children's Fund The United Nations Children's Fund works with other UN bodies and with governments and nongovernmental organizations to improve children's lives in more than 140 developing countries through community-based services in primary health care, basic education, and safe water and sanitation. For more information, see www.unicef.org/. United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization is a specialized agency of the United Nations that promotes "collaboration among nations through education, science, and culture in order to further universal respect for justice, for the rule of law, and for the human rights and fundamental freedoms . . . for the peoples of the world, without distinction of race, sex, language, or religion." For more information, see www.uis.unesco.org/. United Nations Environment Programme The mandate of the United Nations Environment Programme is to provide leadership and encourage partner- ship in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org/. United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, 2006 World Development Indicators xv national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/. World Bank Group The World Bank Group is the world's largest source of development assistance. Its mission is to fight poverty and improve the living standards of people in the developing world. It is a development bank, providing loans, policy advice, technical assistance, and knowledge sharing services to low- and middle-income countries to reduce poverty. The Bank promotes growth to create jobs and to empower poor people to take advantage of these opportunities. It uses its financial resources, trained staff, and extensive knowledge base to help each developing country onto a path of stable, sustainable, and equitable growth in the fight against poverty. The World Bank Group has 184 member countries. For more information, see www.worldbank.org/data/. World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. The WHO carries out a wide range of func- tions, including coordinating international health work; helping governments strengthen health services; providing technical assistance and emergency aid; working for the prevention and control of disease; pro- moting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promoting and coordinating biomedical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international standards for biological, pharmaceuti- cal, and similar products; and standardizing diagnostic procedures. For more information, see www.who.int/. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is an international organization dedicated to helping to ensure that the rights of creators and owners of intellectual property are protected worldwide and that inventors and authors are thus recognized and rewarded for their ingenuity. WIPO's main tasks include harmonizing national intellectual property legislation and procedures, providing services for international applications for industrial property rights, exchanging intellectual property information, providing legal and technical assistance to developing and other countries facilitating the resolution of private intellectual property disputes, and marshalling information technology as a tool for storing, accessing, and using valuable intellectual property information. A substantial part of its activities and resources is devoted to development cooperation with developing countries. For more information, see www.wipo.int/. World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.world-tourism.org/. xvi 2006 World Development Indicators World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as possible. It does this by administering trade agreements, acting as a forum for trade negotia- tions, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues--through technical assistance and training programs--and cooperating with other inter- national organizations. At the heart of the system--known as the multilateral trading system--are the WTO's agreements, negotiated and signed by a large majority of the world's trading nations and ratified by their parliaments. For more information, see www.wto.org/. Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the con- tainer industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk/. International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/. International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization with a mission to encourage and promote development and maintenance of better and safer roads and road networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF has a major role to play in all aspects of road policy and development worldwide. For govern- ments and financial institutions, the IRF provides a wide base of expertise for planning road develop- ment strategy and policy. For its members, the IRF is a business network, a link to external institutions and agencies and a business card of introduction to government officials and decisionmakers. For the community of road professionals, the IRF is a source of support and information for national road associations, advocacy groups, companies, and institutions dedicated to the development of road infrastructure. For more information, see www.irfnet.org/. 2006 World Development Indicators xvii Netcraft Netcraft's work includes the provision of network security services and research data and analysis of the Internet. It is an authority on the market share of Web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scripting languages, and content technologies on the Internet. For more information, see www.netcraft.com/. PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused assurance, tax, and advisory services for public and private clients in corporate accountability, risk management, structuring and mergers and acquisitions, and performance and process improvement. For more information, see www.pwcglobal.com/. Standard & Poor's Emerging Markets Data Base Standard & Poor's Emerging Markets Data Base (EMDB) is the world's leading source for information and indices on stock markets in developing countries. It currently covers 53 markets and more than 2,600 stocks. Drawing a sample of stocks in each EMDB market, Standard & Poor's calculates indices to serve as benchmarks that are consistent across national boundaries. Standard & Poor's calculates one index, the S&P/IFCG (Global) index, that reflects the perspective of local investors and those inter- ested in broad trends in emerging markets and another, the S&P/IFCI (Investable) index, that provides a broad, neutral, and historically consistent benchmark for the growing emerging market investment community. For more information, see www.standardandpoors.com/. World Conservation Monitoring Centre The World Conservation Monitoring Centre provides information on the conservation and sustainable use of the world's living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the information needed for wise management of the world's living resources. For more information, see www.unep-wcmc.org/. World Information Technology and Services Alliance The World Information Technology and Services Alliance (WITSA) is the global voice of the information tech- nology industry. It is dedicated to advocating policies that advance the industry's growth and development; facilitating international trade and investment in information technology products and services; strengthening WITSA's national industry associations; and providing members with a broad network of contacts. WITSA also hosts the World Congress on Information Technology and other worldwide events. For more information, see www.witsa.org/. xviii 2006 World Development Indicators World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides--and helps other institutions provide--objective information and practical proposals for policy and institutional change that will foster environmentally sound, socially equitable development. The institute's current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agricul- ture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/. 2006 World Development Indicators xix Tables are totals (designated by a t if the aggregates include and reliability. Statistical systems in many develop- The tables are numbered by section and display the gap-filled estimates for missing data and by an s, ing economies are still weak; statistical methods, identifying icon of the section. Tables 1.1, 1.2, 1.3, for simple totals, where they do not), median values coverage, practices, and definitions differ widely; and and 1.5 are presented by World Bank region, with the (m), weighted averages (w), or simple averages (u). cross-country and intertemporal comparisons involve economies for each region listed alphabetically within Gap filling of amounts not allocated to countries may complex technical and conceptual problems that can- the region. For other tables countries and econo- result in discrepancies between subgroup aggregates not be unequivocally resolved. Data coverage may not mies are listed alphabetically (except for Hong Kong, and overall totals. For further discussion of aggrega- be complete because of special circumstances or for China, which appears after China). Data are shown tion methods, see Statistical methods. economies experiencing problems (such as those for 152 economies with populations of more than stemming from conflicts) affecting the collection 1 million, as well as for Taiwan, China, in selected and reporting of data. For these reasons, although tables. Table 1.6 presents selected indicators for Aggregate measures for regions data are drawn from the sources thought to be most 56 other economies--small economies with popu- The aggregate measures for regions include only authoritative, they should be construed only as indi- lations between 30,000 and 1 million and smaller low- and middle-income economies (note that these cating trends and characterizing major differences economies if they are members of the International measures include developing economies with popu- among economies rather than offering precise quan- Bank for Reconstruction and Development (IBRD) or, lations of less than 1 million, including those listed titative measures of those differences. as it is commonly known, the World Bank. The term in table 1.6). Discrepancies in data presented in different edi- country, used interchangeably with economy, does The country composition of regions is based on tions of World Development Indicators reflect updates not imply political independence, but refers to any the World Bank's analytical regions and may differ by countries as well as revisions to historical series territory for which authorities report separate social from common geographic usage. For regional clas- and changes in methodology. Thus readers are or economic statistics. When available, aggregate sifications, see the map on the inside back cover and advised not to compare data series between editions measures for income and regional groups appear at the list on the back cover flap. For further discussion of World Development Indicators or between different the end of each table. of aggregation methods, see Statistical methods. World Bank publications. Consistent time-series data Indicators are shown for the most recent year for 1960­2004 are available on the World Develop- or period for which data are available and, in most ment Indicators CD-ROM and in WDI Online. tables, for an earlier year or period (usually 1990 Statistics Except where otherwise noted, growth rates are in this edition). Time-series data are available on Data are shown for economies as they were con- in real terms. (See Statistical methods for information the World Development Indicators CD-ROM and in stituted in 2004, and historical data are revised to on the methods used to calculate growth rates.) Data WDI Online. reflect current political arrangements. Exceptions are for some economic indicators for some economies Known deviations from standard definitions or noted throughout the tables. are presented in fiscal years rather than calendar breaks in comparability over time or across countries Additional information about the data is provided years; see Primary data documentation. All dollar fig- are either footnoted in the tables or noted in About in Primary data documentation. That section sum- ures are current U.S. dollars unless otherwise stated. the data. When available data are deemed to be marizes national and international efforts to improve The methods used for converting national currencies too weak to provide reliable measures of levels and basic data collection and gives information on pri- are described in Statistical methods. trends or do not adequately adhere to international mary sources, census years, fiscal years, and other standards, the data are not shown. background. Statistical methods provides technical information on some of the general calculations and Country notes formulas used throughout the book. · Unless otherwise noted, data for China do not Aggregate measures for income groups include data for Hong Kong, China; Macao, China; The aggregate measures for income groups include or Taiwan, China. 208 economies (the economies listed in the main Data consistency and reliability · Data for Indonesia include Timor-Leste through tables plus those in table 1.6) wherever data are Considerable effort has been made to standardize 1999 unless otherwise noted. available. To maintain consistency in the aggregate the data, but full comparability cannot be assured, · External debt data presented for the Russian Feder- measures over time and between tables, missing and care must be taken in interpreting the indicators. ation prior to 1992 are for the former Soviet Union. data are imputed where possible. The aggregates Many factors affect data availability, comparability, See About the data for table 4.16 for details. xx 2006 World Development Indicators Changes in the System of National Accounts at a GNI per capita of $3,255. High-income econo- World Development Indicators uses terminology in mies are those with a GNI per capita of $10,066 or line with the 1993 United Nations System of National more. The 12 participating member countries of the Accounts (SNA). For example, in the 1993 SNA gross European Monetary Union (EMU) are presented as a national income (GNI) replaces gross national product subgroup under high-income economies. (GNP). See About the data for tables 1.1 and 4.8. Most economies continue to compile their national accounts according to the 1968 SNA, but Symbols more and more are adopting the 1993 SNA. Econo- .. mies that use the 1993 SNA are identified in Primary means that data are not available or that aggregates data documentation. A few low-income economies cannot be calculated because of missing data in the still use concepts from older SNA guidelines, includ- years shown. ing valuations such as factor cost, in describing major economic aggregates. 0 or 0.0 means zero or less than half the unit shown. Classification of economies / For operational and analytical purposes the World in dates, as in 2003/04, means that the period of Bank's main criterion for classifying economies is time, usually 12 months, straddles two calendar GNI per capita. Each economy is classified as low years and refers to a crop year, a survey year, an income, middle income (subdivided into lower middle academic year, or a fiscal year. and upper middle), or high income. For income classi- fications see the map on the inside front cover and the $ list on the front cover flap. Low- and middle-income means current U.S. dollars unless otherwise noted. economies are sometimes referred to as developing economies. The use of the term is convenient; it is > not intended to imply that all economies in the group means more than. are experiencing similar development or that other economies have reached a preferred or final stage of < development. Note that classification by income does means less than. not necessarily reflect development status. Because GNI per capita changes over time, the country composition of income groups may change Data presentation conventions from one edition of World Development Indicators to · A blank means not applicable or, for an aggre- the next. Once the classification is fixed for an edi- gate, not analytically meaningful. tion, based on GNI per capita in the most recent year · A billion is 1,000 million. for which data are available (2004 in this edition), · A trillion is 1,000 billion. all historical data presented are based on the same · Figures in italics refer to years or periods other country grouping. than those specified or to growth rates calculated Low-income economies are those with a GNI per for less than the full period specified. capita of $825 or less in 2004. Middle-income econ- · Data for years that are more than three years omies are those with a GNI per capita of more than from the range shown are footnoted. $825 but less than $10,066. Lower-middle-income and upper-middle-income economies are separated The cutoff date for data is February 1, 2006. 2006 World Development Indicators xxi he Millennium Development Goals have become the principal global scorecard for develop- ment. In September 2005 the United Nations World Summit reaffirmed the principles in the 2000 Millennium Declaration and recognized the need for ambitious national development strategies backed by increased international support. Financing the needed investments. Financing the investments needed to achieve the Goals remains a challenge for the domestic resources of developing countries and the aid budgets of developed countries. Developing countries need to pursue good governance and sound macro- economic policies, and rich countries need to increase their support for developing countries able to absorb more aid. Some developed countries have adopted timetables to increase official development assistance to 0.7 percent of gross national income by 2015 and to reach at least 0.5 percent by 2010, while ensuring that at least 0.2 percent goes to the least developed coun- tries. The World Summit also called for increased debt relief or restructuring for countries with unsustainable debt burdens that are not part of the Heavily Indebted Poor Countries Initiative. The challenge of measurement. Many of these strengthened goals and targets are not eas- ily measured. Reliable, direct measures of the incidence or prevalence of many diseases are unavailable. And because models and data sources are still evolving, estimates may not be comparable over time or across countries. Gaps remain even for the well established measures of poverty, education, mortality, and health care, and major investments in statistical systems will be needed to fill them, by developing countries themselves and international agencies. Expanding targets to support the goals. The World Summit resolution draws attention to four issues that should receive greater prominence over the next five years: · Reproductive health, integrating reproductive health into strategies for achieving the goals of improving maternal health, reducing child mortality, promoting gender equality, combating HIV/AIDS, and eradicating poverty. · Combating disease, intensifying the fight against HIV/AIDS by "providing sufficient health workers, infrastructure, management systems, and supplies to achieve the health-related [goals] by 2015" and calling for renewed efforts to come "as close as possible to the goal of universal access to HIV treatment by 2010." · Employment, strengthening the focus of the goals on employment by making it "a central objective of our relevant national and international policies as well as our national develop- ment strategies. . . ." · Environment, extending the areas of concern in at least three dimensions: biodiversity, development of indigenous people, and protection from natural and human-caused hazards. The resolution calls on all states to "significantly reduce the loss of biodiversity by 2010." The next five years. When the Millennium Development Goals were promulgated in 2000, the international community reached back a decade to establish a baseline. Nothing could be done to alter the course of those preceding 10 years. In the succeeding five years the world took stock of its commitments and took the first steps to accelerate progress toward the goals. But without measures that accelerate change, many countries may fall short of the targets set for 2015. That is why the next five years are so important. By 2010 we will know whether the goals can be achieved. If by then we have not committed the necessary resources, adopted reforms, and implemented effective new programs, it will be difficult to make further course corrections. 2006 World Development Indicators 1 · Halve, between 1990 and · Halve, between 1990 and 2015, the proportion of people 2015, the proportion of people living on less than $1 a day who suffer from hunger Reducing poverty and hunger Poverty exists everywhere, but there has been But more than 600 million people will still be progress. trapped in poverty in 2015, most of them in Sub- Extreme poverty in developing countries Saharan Africa and South Asia and wherever poor fell from 28 percent in 1990 to 19 percent in health and lack of education deprive people of 2002. Over the same period the number of productive employment; environmental resourc- people in developing countries grew 20 per- es have been depleted or spoiled; and corrup- cent, to more than 5 billion, leaving 1 billion tion, conflict, and misgovernance waste public people in extreme poverty. If economic growth resources and discourage private investment. rates in developing countries are sustained, Even as the first target of the Millennium global poverty will fall to 10 percent by 2015-- Development Goals appears in sight, the ef- a striking success. forts to eliminate poverty must be renewed. Poverty rates are falling, but progress has been uneven Share of people living on less than $1 or $2 a day (%) Sub-Saharan Africa South Asia In Sub-Saharan Africa the number 50 44.0 50 of poor people has increased 38.1 40 44.6 40 41.3 31.2 by a third, but accelerating 30 30 22.3 20.7 growth in India has put South 20 20 Asia on track to meet the goal 10 10 13.8 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 East Asia & Pacific Latin America & Caribbean East Asia has experienced a 50 50 sustained period of economic 40 40 growth, led by China, while 29.6 28.4 30 30 23.4 growth and poverty reduction 14.8 17.2 20 20 11.3 have been slower in Latin 11.6 8.9 5.7 10 10 0.7 America and the Caribbean. 4.7 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Europe & Central Asia Middle East & North Africa The transition economies of Europe 50 50 and Central Asia saw poverty 40 40 rates rise in the 1990s and then 30 30 21.4 fall. There and in the Middle East 16.1 19.9 20 20 10.4 and North Africa consumption of 8.2 10 4.9 10 2.1 2.3 1.6 1.2 $2 a day may be a more realistic 0.5 0.9 0.7 0 0.3 0 limit of extreme poverty. 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Poverty rate at $1 a day Actual Projected Goal Poverty rate at $2 a day Actual Projected Source: World Bank staff estimates. 2 2006 World Development Indicators Country by country progress on poverty Malnutrition--a persistent problem Share of countries on track to achieve the poverty reduction target (%) Moderately and severely malnourished children (millions under age 5) Insufficient data Seriously off track Off track On track Reached target In 2020 the number 100 1997 2020 East Asia of malnourished 0 8 & Pacific (24 countries) children will have fallen 6 0 Europe & everywhere, except in Central Asia Sub-Saharan Africa, (27 countries) 0 4 where there are likely to Latin America 20 & Caribbean be more than in 1997. (32 countries) 0 Middle East South Sub- Southeast West Asia Latin & North Africa Asia Saharan Asia & North America (14 countries) Africa Africa Source: Tarmann 2002. South Asia Sub-Saharan Africa (8 countries) Share of children under age 5 (%) Sub-Saharan Malnutrition rates will 40 1990 Africa fall too slowly in most 2015 (48 countries) of Africa to meet the 30 100 50 0 50 100 Millennium Development Goal target, and they may 20 Source: World Bank staff estimates. rise in eastern Africa. The Millennium Development Goals are Those shown as on track could reach 10 intended to be met by all countries. This the 2015 target if they maintain their figure shows the share of countries in current progress. But those shown as off each region that are on track to achieve track or seriously off track are reducing 0 the poverty reduction target, based on poverty too slowly--or have even seen it Eastern Western Central Southern available survey estimates. Some coun- increase--to achieve the first of the Mil- Africa Africa Africa Africa tries have already achieved the target. lennium Development Goals. Source: de Onis and others 2004. Malawi Share of children under age 5, by wealth quintile (%) Malnutrition rates are predicted to fall everywhere-- except in Sub-Saharan Africa Child malnutrition 30 1992 Prevalence of moderate to severe malnutrition (% of children under age 5) remained unchanged in 2000 Malawi during the 1990s, 1990 2015 20 with improvements in South Central some groups offset by & East Asia increases in others. 10 Sub-Saharan Africa 0 Lowest Second Third Fourth Highest East quintile quintile quintile quintile quintile Asia Source: Demographic and Health Surveys. Mali West Asia & Share of children under age 5, by wealth quintile (%) North Africa In Mali average child 30 1995 malnutrition rates fell, but 2001 Caribbean most of the improvement 20 Latin was among the wealthier America part of the population. 0 10 2 0 0 3 0 4 50 10 Note: Regions differ from the World Bank's operational classification. Source: de Onis and others 2004. 0 Lowest Second Third Fourth Highest Malnutrition in children often begins at nium Development Goal target. Faster quintile quintile quintile quintile quintile birth. Malnourished children develop progress is possible. Programs to con- Source: Demographic and Health Surveys. more slowly, enter school later, and tinue breastfeeding and to improve the perform less well. The proportion of se- diets of pregnant and lactating moth- verely underweight children is falling, ers help. So do appropriate care and but fewer than 40 percent of the 77 feeding of sick children, oral rehydration countries with adequate data to monitor therapy, control of parasitic diseases, trends are on track to reach the Millen- and vitamin A supplementation. 2006 World Development Indicators 3 · Ensure that by 2015 children everywhere, boys and girls alike, will be able to complete a full course of primary schooling Educating all children Since 1990 the world has called for all chil- address parents' concern for the safety of dren to be able to complete primary school. their children. But more than 100 million primary school age Education is the foundation of all societ- children remain out of school. ies and globally competitive economies. It is To reach the target of universal primary the basis for reducing poverty and inequality, education by 2015, school systems with low improving health, enabling the use of new tech- completion rates will need to start now to train nologies, and creating and spreading knowl- teachers, build classrooms, and improve the edge. In an increasingly complex, knowledge- quality of education. Most important, they dependent world, primary education, as the will have to remove such barriers to atten- gateway to higher levels of education, must dance as fees and lack of transportation, and be the first priority. More children everywhere are completing primary school Primary completion rate, total (% of relevant age group) Sub-Saharan Africa South Asia Neither Sub-Saharan Africa nor South 100 100 Asia are on track to achieve the goal, 100 100 90 90 but in both regions some countries 80 80 73.2 82.0 have shown that it can be done. 70 61.7 70 60 60 50.8 50 50 40 40 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 East Asia & Pacific Latin America & Caribbean East Asia and Pacific and Latin 100 100 96.7 America and the Caribbean are 97.0 99.0 100 100 90 90 86.0 close to achieving universal 80 80 primary education. However, high 70 70 60 60 regional averages disguise some 50 50 countries that lag behind. 40 40 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Europe & Central Asia Middle East & North Africa Slow progress in Europe and Central 100 100 Asia reflects the dislocations of the 94.4 100 100 92.0 90 90 87.8 transition period. In the Middle East 80 80 77.5 and North Africa there has been a 70 70 60 60 decline in completion rates for boys. 50 50 40 40 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Actual Goal Source: World Bank staff estimates. 4 2006 World Development Indicators Country by country progress toward universal primary education Patterns of school attendance Share of countries on track to achieve the primary education target (%) Share of children ages 6­11 enrolled in primary school, by gender (%) Gabon, by wealth quintile, 2000 Insufficient data Seriously off track Off track On track Reached target Male Gabon has high levels Female 100 East Asia of primary school & Pacific (24 countries) attendance across 80 Europe & all income groups, Central Asia 60 (27 countries) although completion rates are still low. 40 Latin America & Caribbean (32 countries) 20 Middle East & North Africa 0 (14 countries) Poorest Second Third Fourth Richest quintile quintile quintile quintile quintile South Asia Nigeria, by wealth quintile, 2003 (8 countries) Male In Nigeria only the Female Sub-Saharan 100 wealthiest families Africa (48 countries) are able to provide 80 50 0 50 100 primary education for 60 all their children. Source: World Bank staff estimates. 40 In many developing countries children are completion rates by about 10 percentage already able to complete a full course of points to achieve the target. But those 20 primary education, but in all regions at that are seriously off track have much least a few countries remain off track farther to go. Unless they accelerate 0 and unlikely to reach the target of edu- progress, they will not reach the target Poorest Second Third Fourth Richest quintile quintile quintile quintile quintile cation for all by 2015. Countries that before 2040, depriving several more gen- are off track typically need to raise their erations of the benefits of education. Gabon, by urban and rural area, 2000 In Gabon attendance Male Female A long march to literacy rates are equally high 100 Youth literacy rate (% of youths ages 15­24) for boys and girls and in 80 urban and rural settings. 1970 1980 1990 2000­04 60 Sub-Saharan Africa 40 South & 20 West Asia 0 Arab Urban Rural states Nigeria, by urban and rural area, 2003 Latin America & Caribbean But in Nigeria rural Male Female children have fewer 100 East Asia & Pacific opportunities to 80 attend and complete Developing countries primary school. 60 Developed & transition 40 economies 0 20 40 60 80 100 20 Source: UNESCO 2005. 0 Urban Rural Literacy rates among young people Throughout the developing world liter- ages 15­24 are the only widely reported acy rates are higher among youth than Source: World Bank staff estimates based on Demographic and Helth Surveys. measure of educational outcomes. As among adults, a sign of progress. Ef- more children have entered school and forts are under way to develop better stayed in school longer, the global youth measures of literacy and more direct literacy rate has risen from 75 percent measures of the quality of educational in 1970 to 88 percent in 2000­04. outcomes. 2006 World Development Indicators 5 · Eliminate gender disparity in primary and secondary education, preferably by 2005, and at all levels by 2015. Empowering women When a country educates its girls, its mortality responsibilities for household work prevent rates usually fall, fertility rates decline, and the women from finding productive employment health and education prospects of the next or participating in public decisionmaking. generation improve. What will it take to improve girls' enroll- Unequal treatment of women--by the ments? Mainly, overcoming the social and eco- state, in the market, and by their communi- nomic obstacles that stop parents from send- ty and family--puts them at a disadvantage ing their daughters to school. For many poor throughout their lives and stifles the develop- families the economic value of girls' work at ment prospects of their societies. Illiterate and home exceeds the perceived returns to school- poorly educated mothers are less able to care ing. Improving the accessibility of schools and for their children. Low education levels and their quality and affordability is a first step. More girls in school, but many countries have missed the 2005 target Ratio of girls to boys in primary and secondary education (%) Sub-Saharan Africa South Asia The differences between boys' 120 120 100 100 and girls' schooling are greatest 100 100 in regions with the lowest primary 80 80 87.0 79.7 83.6 70.6 school completion rates and 60 60 40 40 the lowest average incomes. 20 20 0 0 1990 1995 2000 2005 1990 1995 2000 2005 East Asia & Pacific Latin America & Caribbean East Asia and Pacific has almost 120 120 100 100 achieved the 2005 target. In 100 100 a 100.9 101.5 98.1 some Latin American countries 80 89.3 80 girls' enrollments exceed boys'. 60 60 40 40 20 20 0 0 1990 1995 2000 2005 1990 1995 2000 2005 Europe & Central Asia Middle East & North Africa In Europe and Central Asia a 120 120 100 100 strong tradition of educating 100 100 97.1 96.3 girls needs to be sustained. In 80 80 90.2 81.5 the Middle East and North Africa 60 60 40 40 more girls are overcoming a 20 20 bias against educating them. 0 0 1990 1995 2000 2005 1990 1995 2000 2005 a. Based on 40 percent of the eligible population. Actual Goal Source: World Bank staff estimates. 6 2006 World Development Indicators Country by country progress toward equal enrollment Wealth, gender, and location make a difference Share of countries on track to achieve equal enrollment of Share of children 15­19 who have completed primary school, by gender (%) girls and boys in primary and secondary school (%) Benin, by wealth quintile, 2002 Insufficient data Seriously off track Off track On track Reached target School attendance 80 Male rates are low in Benin, Female 70 East Asia & Pacific except among the rich. 0 6 (24 countries) Poor children rarely 0 5 Europe & 40 Central Asia complete school, (27 countries) 30 and even among rich Latin America families girls have few 20 & Caribbean (32 countries) opportunities to complete 10 0 Middle East primary education. Poorest Second Third Fourth Richest & North Africa quintile quintile quintile quintile quintile (14 countries) Malawi, by wealth quintile, 2000 South Asia (8 countries) A recent survey in Malawi 100 Male found almost equal Female Sub-Saharan 80 Africa completion rates for (48 countries) boys and girls, although 60 50 0 50 100 children of the poorest 40 Source: World Bank staff estimates. families are still less likely to attend school. 20 The first target of the Millennium Devel- achieved the target on average, such as opment Goals to fall due calls for enroll- Europe and Central Asia and Latin Amer- 0 ing equal numbers of boys and girls in ican and the Caribbean, some countries Poorest Second Third Fourth Richest primary and secondary school by 2005, still fall short. And in South Asia and quintile quintile quintile quintile quintile an important stepping stone on the way Sub-Saharan Africa, where large num- to full gender equality at all levels of bers of children are out of school, girls Benin, by urban and rural area, 2002 education. But even in regions that have are at a severe disadvantage. In Benin efforts 60 Degrees of difference to increase girls' Urban Rural Ratio of girls' to boys' gross enrollment rates (%) 50 attendance will have to improve the accessibility 40 Primary level Secondary level of schools and overcome 30 East Asia the reluctance of rural & Pacific 20 families to send their 10 Europe & daughters to school. Central Asia 0 Male Female Latin America & Caribbean Malawi, by urban and rural area, 2000 In Malawi, where Middle East 100 & North Africa Urban completion rates have Rural risen in recent years, 8 0 South Asia rural areas still lag, 60 but boys and girls are Sub-Saharan 40 represented equally Africa among those who 20 0 20 40 60 80 100 120 complete primary school. 0 Note: A value of more than 100 means that enrollment rates of girls exceed those of boys. Male Female Source: World Bank staff estimates. In a competitive world economy both primary school or drop out of secondary boys and girls need to be educated to school faster than girls. In other regions Source: World Bank staff estimates based on Demographic and Health Surveys. higher levels. Girls are underrepresented the familiar pattern is repeated: fewer in primary education in all regions, but girls are enrolled in primary schools and in some they are overrepresented at their share falls even farther at higher the secondary level. This may happen levels. Whatever the cause, the result because boys take longer to complete is not equitable. 2006 World Development Indicators 7 · Reduce by two thirds, between 1990 and 2015, the under-five mortality rate. Saving children Rapid improvements before 1990 gave hope Only two regions, Latin America and the that mortality rates for infants and children Caribbean and Europe and Central Asia, are under five could be cut by two-thirds in the fol- close to achieving the target on average. But lowing 25 years. But progress slowed almost even there, more than half the countries are everywhere in the 1990s. off track. Progress has been particularly slow Every year almost 11 million children in in Sub-Saharan Africa, where civil disturbances developing countries still die before the age and the HIV/AIDS epidemic have driven up rates of five. Most die from causes that are read- of infant and child mortality. By the most recent ily preventable in rich countries: acute re- data available, only 35 countries are making spiratory infections, diarrhea, measles, and enough progress to reduce under-five mortality malaria. rates to one-third of their 1990 level by 2015. Improving the odds for children Under-five mortality rate (deaths per 1,000 children) Sub-Saharan Africa South Asia The gap between goal and reality 200 200 168 is greatest in Sub-Saharan Africa, 185 150 150 but millions of children are also 129 92 at risk in populous South Asia. 100 100 62 43 50 50 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 East Asia & Pacific Latin America & Caribbean The pace of mortality reduction in 200 200 East Asia and Pacific is slowing. 150 150 The regional average in Latin America and the Caribbean 100 100 59 54 disguises wide variations. 50 50 37 20 18 31 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Europe & Central Asia Middle East & North Africa In Europe and Central Asia questions 200 200 remain about the quality and 150 150 comparability of data over time. More than half the countries in 100 100 55 49 81 the Middle East and North Africa 50 50 34 16 are on track to reach the target. 27 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Actual Goal Source: World Bank staff estimates. 8 2006 World Development Indicators Country by country progress toward reduced child mortality Cruel differences Share of countries on track to achieve the child mortality target (%) Under-five mortality and immunization rates by wealth quintiles Mali Insufficient data Seriously off track Off track On track Reached target Mortality rate (per 1,000 children), 2000 East Asia Child mortality rates 0 30 & Pacific in Mali are high even (24 countries) 0 25 for comparatively Europe & 200 Central Asia wealthy families. (27 countries) 0 15 Latin America & Caribbean 100 (32 countries) 5 0 Middle East & North Africa 0 (14 countries) Lowest Second Third Fourth Highest quintile quintile quintile quintile quintile South Asia (8 countries) South Africa Mortality rate (per 1,000 children), 1998 Sub-Saharan Africa In South Africa the 300 (48 countries) disparity between rich 250 50 0 50 100 and poor is greater, but 200 the average is much Source: World Bank staff estimates. lower than in Mali. 150 A concerted effort to improve the mea- on track to achieve a two-thirds reduc- surement of infant and child mortality tion in mortality rates. Every country in 100 has filled many gaps in the international Sub-Saharan Africa is off track, and in 50 data set, revealing that many countries some countries mortality rates have in- still fall short of achieving the target, creased since 1990. The good news is 0 even where regional averages have that recent surveys have found rapidly Lowest Second Third Fourth Highest been improving. Based on estimates falling mortality rates. These could be quintile quintile quintile quintile quintile through 2004, only 35 countries are the first signs of faster progress. Mali Immunization rate, 2000 (%) Prevention comes first Mali has low 100 Measles Measles immunization rate (% of children ages 12­23 months) DPT immunization rates, 80 especially for its poorest 1990 2004 children. Diphtheria, 60 Europe & pertussis, and tetanus Central Asiaa 40 (DPT) immunization, Latin America which is harder to deliver, 20 & Caribbean lags behind measles 0 for all income groups. Lowest Second Third Fourth Highest Middle East quintile quintile quintile quintile quintile & North Africa South Africa East Asia Immunization rate, 1998 (%) & Pacific In South Africa 100 Measles Sub-Saharan immunization programs DPT Africa 80 reach most children in all South income groups, and DPT 60 Asia immunization rates are 40 0 20 40 60 80 100 almost equal to those for a. Data are for 1992 and 2004. measles immunization. 20 Source: World Health Organization and United Nations Children's Fund estimates. 0 Many causes of early childhood deaths are Measles immunization now reaches more Lowest Second Third Fourth Highest preventable through the basic elements infants, and measles deaths are falling. quintile quintile quintile quintile quintile of public health: immunization programs, Developing regions with more than 90 hand washing, access to safe water and percent immunization rates are on par sanitation facilities, and good nutrition. with the high-income economies. Source: World Bank staff estimates based on Demographic and Health Surveys. 2006 World Development Indicators 9 · Reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio Caring for mothers Death in childbirth is a rare event in rich coun- More than 500,000 women die each year tries, where there are typically fewer than in childbirth, most of them in developing coun- 10 maternal deaths for every 100,000 live tries. What makes maternal mortality such a births. But in the poorest countries of Africa compelling problem is that it strikes young and Asia the ratio may be 100 times higher. women experiencing a natural function of life. And because women in poor countries have They die because they are poor. Malnourished. more children, their lifetime risk of maternal Weakened by disease. Exposed to multiple death may be more than 200 times greater pregnancies. And they die because they lack than for women in Western Europe and North access to trained health care workers and America. modern medical facilities. Mothers at risk in Africa and South Asia Left axis (line): total fertility rate (births per woman); right axis (bar): maternal mortality ratio (deaths per 100,000 live births) Sub-Saharan Africa South Asia Maternal mortality ratios are 8 921 1,000 8 1,000 still unacceptably high in many 6 750 6 750 developing countries as a result 564 of high fertility rates and a 4 500 4 500 high risk of dying each time a 2 250 2 250 woman becomes pregnant. 0 0 0 0 1990 1995 2000 2004 1990 1995 2000 2004 East Asia & Pacific Latin America & Caribbean Some developing countries 8 1,000 8 1,000 have substantially improved 6 750 6 750 maternal health through better services in hospitals and 4 500 4 500 increased numbers of trained 2 250 2 194 250 117 birth attendants and midwives. 0 0 0 0 1990 1995 2000 2004 1990 1995 2000 2004 Europe & Central Asia Middle East & North Africa Still others not only improved 8 1,000 8 1,000 maternal health, but significantly 6 750 6 750 lowered fertility rates directly through use of contraceptives and indirectly 4 500 4 500 through increased female education. 2 250 2 183 250 58 0 0 0 0 1990 1995 2000 2004 1990 1995 2000 2004 Source: World Bank staff estimates. 10 2006 World Development Indicators Country by country progress in providing skilled care at births Poor women need reproductive health services Share of countries on track to achieve adequate coverage Zimbabwe, 1999 of births by skilled health personnel (%) Total fertility rate by wealth quintile (births per woman) In Zimbabwe total fertility 5 Insufficient data Seriously off track Off track On track Reached target rates are high except East Asia 4 & Pacific for women from the (24 countries) highest income group. 3 Europe & Central Asia 2 (27 countries) Latin America 1 & Caribbean (32 countries) 0 Lowest Second Third Fourth Highest Middle East quintile quintile quintile quintile quintile & North Africa (14 countries) Zimbabwe, 1999 South Asia Contraceptive prevalence rate by wealth quintile (%) (8 countries) Wealthier men and 80 Women Sub-Saharan women are more likely Men Africa (48 countries) to use contraception. 60 50 0 50 100 40 Source: World Bank staff estimates. 20 Because few countries are able to mea- tries in each region that provide skilled sure maternal mortality over time, other health personnel for 90 percent of births indicators are often used to measure or could do so by 2015 based on current 0 progress toward this goal. Skilled health trends. Countries that are off track may Lowest Second Third Fourth Highest quintile quintile quintile quintile quintile personnel and modern medical facilities be able to achieve 75 percent coverage by are needed to deal with the complications 2015, while seriously off-track countries Zimbabwe, 1999 of childbirth that can claim mothers' lives. will not reach even that level unless they Source of contraception by wealth quintiles (%) This figure shows the proportion of coun- make rapid progress in the next decade. In Zimbabwe both poor 10 0 Decreasing risk of young motherhood Public sources and rich rely heavily Private sources Adolescent fertility rate (births per 1,000 women ages 15­19) 80 on public sources for contraception. 60 150 40 Sub-Saharan Africa 120 20 0 Lowest Second Third Fourth Highest 90 quintile quintile quintile quintile quintile South Asia Contraceptive prevalence rates Latin America & Caribbean Share of women in union (%) 60 Where contraceptive 60 Any contraceptive method Middle East & North Africa prevalence rates are Modern contraceptive method Europe & Central Asia 50 higher, men and women 30 40 High-income are more likely to be East Asia & Pacific using modern methods. 30 0 20 1997 1998 1999 2000 2001 2002 2003 2004 10 Source: World Bank staff estimates. 0 Southern Central Eastern Western Fertility rates among young women babies. They are also likely to have Africa Africa Africa Africa have been falling, but they remain high more births over their lifetime, increas- in Sub-Saharan Africa, South Asia, and ing their lifetime risk of maternal death. Source: World Bank staff estimates based on Latin America and the Caribbean. Young Education and access to reproductive Demographic and Health Surveys; UNFPA 2005. mothers run higher risks of complica- health services help to lower fertility tions in childbirth and lower birthweight rates. 2006 World Development Indicators 11 · Have halted by 2015 and begun to · Have halted by 2015 and begun to reverse the incidence of malaria reverse the spread of HIV/AIDS. and other major diseases. Combating disease Epidemic diseases exact a huge toll in human deaths. Nearly all the cases and more than 95 suffering and lost opportunities for develop- percent of the deaths occur in Sub-Saharan ment. Poverty, armed conflict, and natural Africa. Most deaths from malaria are among disasters contribute to the spread of disease children younger than five years old. and are worsened by it. Tuberculosis kills some 2 million people a In Africa the spread of HIV/AIDS has re- year, most of them 15­45 years old. The dis- versed decades of improvements in life expec- ease is spreading more rapidly because of the tancy and left millions of children orphaned. It emergence of drug-resistant strains of tuber- is draining the supply of teachers and eroding culosis; the spread of HIV/AIDS, which reduces the quality of education. resistance to tuberculosis; and the growing There are 300­500 million cases of ma- number of refugees and displaced people. laria each year, leading to more than 1 million As the HIV/AIDS epidemic matures, the death toll keeps rising Left axis: adult (ages 15­49) HIV prevalence rate (%); right axis: number of deaths due to AIDS (millions) Worldwide, 40 million adults and 8 4.0 children are living with HIV/AIDS and almost 5 million new infections Sub-Saharan Africa occurred in 2005. The adult 7 3.5 prevalence rate has stabilized in Sub-Saharan Africa and other 6 3.0 developing regions, not because the epidemic has been halted but because the death rate now 5 2.5 equals the rate of new infections. Sub-Saharan Africa 4 2.0 Although prevalence rates are lower outside of Sub-Saharan Africa, the number of people infected is 3 1.5 increasing and so is the death rate. There were almost a million 2 1.0 new cases in South and East Asia, where more than 7 million people Other developing regions are now living with HIV/AIDS. 1 0.5 Other developing regions 0 0.0 1990 1995 2000 2005 HIV prevalence Deaths due to AIDS Source: UNAIDS/WHO 2005. 12 2006 World Development Indicators The HIV epidemic can be reversed Malaria is a leading killer in Africa Model projections of HIV infections in Sub-Saharan Africa (millions) Malaria deaths (per 100,000 people) Sub-Saharan Africa Rest of the world 4 Baseline Treatment-centered response (optimal) 1970 3 Combined response (pessimistic) 2 1990 Prevention-centered response 1 Combined response (optimistic) 1997 0 2003 2005 2007 2009 2011 2013 2015 2017 2020 0 25 50 75 100 125 150 175 Source: Salomon and others 2005. What will it take to halt and reverse to alter behavior. Computer simula- Source: WHO 1999. the HIV epidemic? A combination of ef- tions of the epidemic suggest that a Malaria, once widespread, is now large- from repeated infections, leaving them fective treatment and prevention pro- combination of intensive treatment ly a disease of the tropics. It takes its unable to work for weeks at a time. grams. Antiretroviral therapy is start- and prevention programs would be greatest toll in Sub-Saharan Africa, The World Bank estimates that the ing to reach people in poor countries, most effective in reducing new infec- where more than 1 million people die disease has slowed economic growth although not yet at the levels needed, tions and averting 10 million deaths each year, most of them children un- in Africa by 1.3 percent a year (World and prevention programs have begun between now and 2020. der the age of five. Millions more suffer Bank 2001). Tuberculosis rates on the rise or falling slowly Poor children bear the burden of malaria Incidence of tuberculosis (per 100,000 people) Children under age 5 receiving antimalarial treatment (%) 1990 2004 Children in poorest quintile Children in richest quintile Sub-Saharan Africa Niger South Asia East Asia & Pacific Senegal Europe & Central Asia Latin America Chad & Caribbean Middle East & North Africa Rwanda High- income 0 50 100 150 200 250 300 350 400 0 10 20 30 40 50 60 70 Source: World Bank staff estimates. Source: World Bank 2005e. Each year there are 8 million new cases programs allow drug-resistant strains Malaria is a disease of poverty and a can save lives and reduce the burden of tuberculosis--3 million in South and to spread. And tuberculosis is often cause of poverty. Although adults may of disease, but in many countries chil- East Asia, 2 million in Sub-Saharan Af- associated with HIV infections, which experience repeated bouts of the debil- dren in the poorest families do not rica, and more than a quarter million in compromise the body's immune sys- itating disease, children are most likely receive treatment. Prevention is also countries of the former Soviet Union. tem. Positive diagnosis, effective treat- to die--more than 2,000 children die important. The use of insecticide- The disease has spread fastest in ment, and follow-up care can achieve each day because of malaria in Sub- treated bednets has been shown to poor countries with ineffective health high cure rates, but many cases go Saharan Africa. Effective treatment protect children. systems. Poorly managed tuberculosis undetected. 2006 World Development Indicators 13 · Integrate the principles of sustain- able development into country · Reduce by half the proportion · Achieve significant improvement policies and programs and reverse of people without sustainable in the lives of at least 100 million the loss of environmental resources. access to safe drinking water. slum dwellers by 2020. Using resources wisely Sustainable development can be ensured only But good intentions are not enough. Around by protecting the environment and using its re- the world land is being degraded. Forests are sources wisely. Poor people, often dependent being lost. Fisheries are being overused. Plant on environmental resources for their livelihood, and animal species are becoming extinct. And are the most affected by environmental deg- carbon dioxide emissions are driving changes radation and natural disasters (fires, storms, in global climate. earthquakes) whose effects are worsened by Rich countries are major consumers of environmental mismanagement. products and services from the environment. Most countries have adopted principles of Thus rich countries and poor countries alike sustainable development and agreed to inter- have a stake in using environmental resources national accords on protecting the environment. wisely. Water and sanitation--basic services needed by all Population without access to an improved water source or sanitation facilities (%) Sub-Saharan Africa South Asia In Sub-Saharan Africa 300 million 90 90 people lack access to improved 68 84 64 65 water sources and 450 million lack 60 60 42 adequate sanitation services. South 51 42 34 30 30 Asia has made excellent progress 30 26 16 in providing water, but progress has 15 0 0 been slower in providing sanitation. 5 0 2 2 1 0 1 2 5 0 1990 1995 2000 2005 2010 2015 East Asia & Pacific Latin America & Caribbean In East Asia rapid urbanization is 90 90 posing a challenge for the provision 70 of water and other public utilities. 60 51 60 Latin America and the Caribbean, 35 32 29 25 30 22 30 the most urban developing region, 15 16 has made slow progress in 18 11 9 0 0 providing sanitation services. 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Europe & Central Asia Middle East & North Africa Many countries in Europe 90 90 and Central Asia lack reliable benchmarks for the early 1990s. In 60 60 the Middle East and North Africa, 31 25 30 18 30 Egypt, Tunisia, and Morocco have 14 15 9 7 7 made the fastest progress. 9 5 13 12 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Without access to an improved water source Actual Goal Without access to improved sanitation facilities Actual Goal Source: World Bank staff estimates. 14 2006 World Development Indicators Country by country progress toward access to water . . . Forests falling Share of countries on track to achieve the target Forest area (thousands of square kilometers) for access to improved water source (%) Change in forest area, 1990­2005 Forest area, 1990 Insufficient data Seriously off track Off track On track Reached target Middle East 200 & North Africa East Asia & Pacific (24 countries) South 789 Asia Europe & Central Asia East Asia (27 countries) 4,581 & Pacific Latin America & Caribbean Sub-Saharan 6,913 (32 countries) Africa Middle East Europe & & North Africa 8,919 Central Asia (14 countries) High-income South Asia 9,441 economies (8 countries) Latin America Sub-Saharan 9,836 & Caribbean Africa (48 countries) ­750 ­500 ­250 0 250 100 50 0 50 100 Note: Positive values indicate an increase in forest area. Source: World Bank staff estimates. Source: FAO data and World Bank staff estimates. Lack of clean water and basic sanitation sources that are not protected from con- Since 1990 the world has lost about 1.3 ber, are important sources of livelihood is the main reason diseases transmit- tamination. Even the modest target of million square kilometers of forest-- for people in developing countries, and ted by feces are so common in develop- reducing by half the number of people almost 100,000 square kilometers each forests provide habitat for many plant ing countries. Water is a daily need that without access to an improved water year. The losses have been greatest in and animal species. To ensure sustain- must be met, but in some places people source will not be met in many countries the great tropical forests of Sub-Saha- able development, forests must be man- spend many hours to obtain water from at the current rate of progress. ran Africa and Latin America and the Ca- aged wisely to continue to benefit future ribbean. Forest products, including tim- generations. . . . and to sanitation Fuel for climate change--high carbon dioxide emitters Share of countries on track to achieve the target Emissions of carbon dioxide, 2002 (billions of metric tons) for access to improved sanitation source (%) Insufficient data Seriously off track Off track On track Reached target Rest of Rest of developing East Asia South Asia economies & Pacific 0.2 2.7 (24 countries) India Europe & 1.2 Central Asia Rest of United (27 countries) States Europe & Central Asia 5.8 Latin America & Caribbean 1.7 (32 countries) Russian Middle East Federation EU-15 & North Africa 1.4 3.2 (14 countries) China South Asia Rest of 3.5 (8 countries) East Asia Japan & Pacific 1.2 1.0 Sub-Saharan Rest of Africa high-income (48 countries) 2.5 100 50 0 50 100 Source: World Bank staff estimates. Source: CDIAC data and World Bank staff estimates. An improved sanitation system pro- substances before they are released Carbon dioxide, which is produced by economies are the largest emitters of vides disposal facilities that can into the environment. Large popula- burning fossil fuels and manufactur- carbon dioxide, and their share has in- effectively prevent human, animal, tions in Africa and Asia still lack ad- ing cement, is a greenhouse gas that creased. However, China is the world's and insect contact with excreta. It equate sanitation facilities, and few contributes to global climate change. second largest emitter, next to the does not, however, ensure treat- countries are currently on track to Emissions rose by 3 billion metric tons United States. Emissions by India are ment of effluents to remove harmful reach the target. between 1990 and 2002. High-income also increasing. 2006 World Development Indicators 15 · Develop further an open trading and landlocked and small island work for youth. financial system that is rule-based, developing states. · Provide access to affordable essential predictable, and nondiscriminatory. · Deal comprehensively with developing drugs in developing countries. · Address the special needs of the countries' debt problems to make · Make available the benefits of new least developed countries. debt sustainable in the long term. technologies--especially information · Address the special needs of · Develop decent and productive and communications technologies. Working together The eighth and final goal complements the responsibilities for achieving the Millennium others. In partnership, wealthy countries work Development Goals. The consensus calls for with developing countries to create an environ- developing countries to improve governance ment in which rapid, sustainable development and policies aimed at increasing economic is possible. Important steps toward global growth and reducing poverty and for high-in- partnership were taken at international meet- come countries to provide more and better aid ings in 2001 in Doha, which launched a new and greater access to their markets. "development round" of trade negotiations, Goal 8 also reminds us that the develop- and in 2002 at the International Conference ment challenges differ for large countries and on Financing for Development in Monterrey, small countries. And that developing countries Mexico, where high-income and develop- need access to new technologies to increase ing countries reached consensus on mutual productivity and improve people's lives. Many sources and many patterns Selected net flows, 2004 ($ billions) Sub-Saharan Africa South Asia Aid plays an important role in Multilateral Multilateral development, especially in low- finance 4.3 finance 1.8 income countries. The extremely poor Aid 26.0 Aid 6.8 countries of Sub-Saharan Africa and Private Private Asia still need substantial increases capital flows 5.9 capital flows 13.8 in aid to reach their development Trade ­8.6 Trade ­31.3 goals. Countries in all regions borrow from multilateral institutions, such Remittances 4.3 Remittances 30.3 as the World Bank, but some are ­40 ­20 0 20 40 60 ­40 ­20 0 20 40 60 repaying more than they borrow. East Asia & Pacific Latin America & Caribbean In addition to aid, developing Multilateral Multilateral countries meet part of their financing finance ­3.3 finance ­7.5 needs through private capital 6.9 Aid 6.9 Aid flows. Rapidly growing economies Private Private 9.3 need and attract large flows of capital flows 106.2 capital flows direct and portfolio investment, ­24.4 Trade 59.6 Trade which have been particularly important in East Asia and Pacific. Remittances 34.5 Remittances 39.2 ­40 ­20 0 20 40 60 ­40 ­20 0 20 40 60 Europe & Central Asia Middle East & North Africa Export demand can be an Multilateral Multilateral important source of growth, finance ­4.0 finance ­0.9 and trade surpluses can also Aid Aid 11.9 10.5 provide substantial foreign Private Private exchange earnings. Remittances capital flows 65.8 capital flows 4.1 from people living and working Trade Trade ­19.1 7.3 abroad are a growing source of income for households in Remittances 9.7 Remittances 17.2 some developing economies. ­40 ­20 0 20 40 60 -40 -20 0 20 40 60 Source: World Bank staff estimates. 16 2006 World Development Indicators Official development assistance is rising, but still too little Debt service is falling, but more relief is needed Left axis (bars): official development assistance (2003 $ billions); Ratio of external debt service to exports of goods and right axis (line): net disbursements as a share of 2003 donors' GNI (%) services including workers' remittances (%) 80 0.35 30 0.30 25 60 0.25 Middle income 0.20 20 40 0.15 Low income, 15 excluding HIPCs 0.10 20 10 0.05 Heavily indebted poor countries (HIPCs) 0 0.00 1990 1992 1994 1996 1998 2000 2002 2004 5 1990 1992 1994 1996 1998 2000 2002 2004 Source: OECD Development Assistance Committee. Source: World Bank staff estimates. Official development assistance below 0.26 percent. Since 2002 do- Low-income countries paid $26 bil- slowly, reducing debt burdens for (ODA) is the aid provided by the rich- nors have pledged to increase aid by lion in debt service on public debt in many countries. But for extremely est countries to the poorest. Through $20 billion a year in 2006 and to 2004. Middle-income countries paid poor countries debt service repre- much of the 1990s ODA levels fell provide nearly $130 billion a year by $173 billion. sents a crucial loss of potential de- while ODA as a proportion of donors' 2010. But large increases in aid have, Developing country export earnings, velopment resources. Since 1998 GNI fell even faster. Many donors so far, gone to only a few countries needed to acquire the currencies to the Heavily Indebted Poor Countries pledged to provide at least 0.7 per- such as Iraq, Afghanistan, and the pay their creditors, have been rising Initiative has provided $57 billion in cent of GNI, but the average remains Democratic Republic of Congo. while debt service has grown more debt relief. Tariffs remain high on poor countries' exports New technologies are spreading quickly Average tariffs imposed by developed countries Information and communications technology users in low- and on developing country imports (%) middle-income economies (per 1,000 people) 12 200 Clothing Mobile phones 10 Agricultural products 150 8 Textiles 6 100 Telephone mainlines 4 Internet users 50 2 Personal computers 0 0 1996 1997 1998 1999 2000 2001 2002 2003 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Note: Based on UN definitions of developed and developing countries, Source: World Bank staff estimates and data from the International Telecommunication Union. which may differ slightly from those of the World Bank. Source: International Trade Centre, World Trade Organization, and United Nations Conference on Trade and Development. New technologies bring new opportu- which reduce barriers of time, space, and nities to developing countries. Mobile culture. Developing countries also need Creating opportunities for develop- recent dropping of quotas on textiles phones help to eliminate the bottlenecks access to new medicines to reduce the ing countries to sell their products has created new opportunities for effi- of fixed, mainline phone service. Personal terrible burden of disease. Bringing these in wealthier markets is an important cient producers. But high-income coun- computers are more widely available, and and other life-saving technologies to poor complement to aid. Many high-income tries' tariffs on goods important to de- the Internet is expanding rapidly. These people will require willing cooperation be- countries allow selected exports of veloping countries, such as textiles and are examples of integrating technologies, tween the public and private sectors. poor countries to enter duty-free. The agricultural products, remain high. 2006 World Development Indicators 17 Goals, targets, and indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1 Halve, between 1990 and 2015, the proportion of 1 Proportion of population below $1 (PPP) a day a people whose income is less than $1 a day 1a Poverty headcount ratio (percentage of population below the national poverty line) 2 Poverty gap ratio [incidence x depth of poverty] 3 Share of poorest quintile in national consumption Target 2 Halve, between 1990 and 2015, the proportion of 4 Prevalence of underweight children under five years people who suf fer from hunger of age 5 Proportion of population below minimum level of dietary energy consumption Goal 2 Achieve universal primary education Target 3 Ensure that, by 2015, children everywhere, boys and 6 Net enrollment ratio in primary education girls alike, will be able to complete a full course of 7 Proportion of pupils starting grade 1 who reach grade 5 b primar y schooling 8 Literacy rate of 15- to 24-year-olds Goal 3 Promote gender equality and empower women Target 4 Eliminate gender disparity in primary and secondary 9 Ratios of girls to boys in primary, secondary, and education, preferably by 2005, and in all levels of tertiary education education no later than 2015 10 Ratio of literate women to men ages 15­24 11 Share of women in wage employment in the nonagricultural sector 12 Proportion of seats held by women in national parliaments Goal 4 Reduce child mortality Target 5 Reduce by two-thirds, between 1990 and 2015, 13 Under-five mortality rate the under-five mor tality rate 14 Infant mor tality rate 15 Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Target 6 Reduce by three-quarters, between 1990 and 2015, 16 Maternal mortality ratio the maternal mor tality ratio 17 Propor tion of bir ths attended by skilled health personnel Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 7 Have halted by 2015 and begun to reverse the spread 18 HIV prevalence among pregnant women ages 15­24 of HIV/AIDS 19 Condom use rate of the contraceptive prevalence rate c 19a Condom use at last high-risk sex 19b Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDS d 19c Contraceptive prevalence rate 20 Ratio of school attendance of orphans to school attendance of nonorphans ages 10­14 Target 8 Have halted by 2015 and begun to reverse the 21 Prevalence and death rates associated with malaria incidence of malaria and other major diseases 22 Proportion of population in malaria-risk areas using effective malaria prevention and treatment measures e 23 Prevalence and death rates associated with tuberculosis 24 Proportion of tuberculosis cases detected and cured under directly observed treatment, short course (DOTS) Goal 7 Ensure environmental sustainability Target 9 Integrate the principles of sustainable development 25 Proportion of land area covered by forest into country policies and programs and reverse the 26 Ratio of area protected to maintain biological diversity to loss of environmental resources sur face area 27 Energy use (kilograms of oil equivalent) per $1 GDP (PPP) 28 Carbon dioxide emissions per capita and consumption of ozone-depleting chlorofluorocarbons (ODP tons) 29 Proportion of population using solid fuels Target 10 Halve, by 2015, the proportion of people without 30 Proportion of population with sustainable access sustainable access to safe drinking water and basic to an improved water source, urban and rural sanitation 31 Propor tion of population with access to improved sanitation, urban and rural 18 2006 World Development Indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Target 11 By 2020, to have achieved a significant improvement 32 Proportion of households with access to secure tenure in the lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 12 Develop further an open, rule-based, predictable, Some of the indicators listed below are monitored nondiscriminatory trading and financial system separately for the least developed countries (LDCs), Africa, landlocked countries and small island developing states. Includes a commitment to good governance, development and poverty reduction--both nationally Official development assistance (ODA) and internationally 33 Net ODA, total and to the least developed countries, as a percentage of OECD/DAC donors' gross national income 34 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic Target 13 Address the special needs of the least developed education, primary health care, nutrition, safe water countries and sanitation) Includes tariff and quota free access for the least 35 Proportion of bilateral official development assistance of OECD/DAC donors that is untied developed countries' exports; enhanced programme of debt relief for heavily indebted poor countries 36 ODA received in landlocked countries as a proportion of their gross national incomes (HIPC) and cancellation of official bilateral debt; and more generous ODA for countries committed to 37 ODA received in small island developing states as proportion of their gross national incomes poverty reduction Market access 38 Proportion of total developed country imports (by value Target 14 Address the special needs of landlocked countries and excluding arms) from developing countries and from and small island developing states (through the the least developed countries, admitted free of duty Programme of Action for the Sustainable 39 Average tariffs imposed by developed countries on Development of Small Island Developing States agricultural products and textiles and clothing from and the outcome of the 22nd special session of the developing countries General Assembly) 40 Agricultural support estimate for OECD countries as a percentage of their gross domestic product 41 Proportion of ODA provided to help build trade capacity Target 15 Deal comprehensively with the debt problems of Debt sustainability developing countries through national and 42 Total number of countries that have reached their international measures in order to make debt HIPC decision points and number that have reached sustainable in the long term their HIPC completion points (cumulative) 43 Debt relief committed under HIPC Debt Initiative 44 Debt service as a percentage of exports of goods and services Target 16 In cooperation with developing countries, develop 45 Unemployment rate of 15- to 24-year-olds, male and and implement strategies for decent and productive female and total f work for youth Target 17 In cooperation with pharmaceutical companies, 46 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 18 In cooperation with the private sector, make 47 Telephone lines and cellular subscribers per 100 people available the benefits of new technologies, especially 48a Personal computers in use per 100 people infor mation and communications 48b Internet users per 100 people Note: Goals, targets, and indicators effective September 8, 2003. a. For monitoring country poverty trends, indicators based on national poverty lines should be used, where available. b. An alternative indicator under development is "primary completion rate." c. Among contraceptive methods, only condoms are effective in preventing HIV transmission. Since the condom use rate is only measured among women in union, it is supplemented by an indicator on condom use in high-risk situations (indicator 19a) and an indicator on HIV/AIDS knowledge (indicator 19b). Indicator 19c (contraceptive prevalence rate) is also useful in track- ing progress in other health, gender, and poverty goals. d. This indicator is defined as the percentage of 15- to 24-year-olds who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can transmit HIV. However, since there are currently not a sufficient number of surveys to be able to calculate the indicator as defined above, UNICEF, in collaboration with UNAIDS and WHO, produced two proxy indicators that represent two components of the actual indicator. They are the percentage of women and men ages 15­24 who know that a person can protect herself from HIV infection by "consistent use of condom," and the percentage of women and men ages 15­24 who know a healthy-looking person can transmit HIV. e. Prevention to be measured by the percentage of children under age five sleeping under insecticide-treated bednets; treatment to be measured by percentage of children under age five who are appropriately treated. f. An improved measure of the target for future years is under development by the International Labour Organization. 2006 World Development Indicators 19 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 2004 2004 2004 2004b 2004 2004b 2004 2004 2004 2004 2003­04 2003­04 East Asia & Pacificc 1,870 s 16,301 s 118 w 2,647.2 t 1,416 w 9,968 t 5,332 w 9.0 w 8.1 w Cambodia 14 181 78 4.8 128 350 183 32d 2,310 d 154 7.7 5.6 China 1,296 9,598e 139 1,938.0 5 1,500 129 7,634f 5,890 f 108 10.1 9.4 Hong Kong, China 7 .. .. 183.5 28 26,660 27 217 31,560 12 8.1 6.9 Indonesia 218 1,905 120 248.0 22 1,140 137 757 3,480 140 5.1 3.7 Korea, Dem. Rep. 22 121 186 .. .. ..g .. .. .. .. .. .. Lao PDR 6 237 25 2.3 154 390 180 11 1,880 171 6.3 3.9 Malaysia 25 330 76 112.6 37 4,520 79 242 9,720 78 7.1 5.2 Mongolia 3 1,567 2 1.5 164 600 161 5 2,040 162 10.7 9.2 Myanmar 50 677 76 .. .. ..g .. .. .. .. .. .. Papua New Guinea 6 463 13 3.3 142 560 164 13d 2,280 d 155 2.5 0.4 Philippines 82 300 274 95.1 41 1,170 136 404 4,950 125 6.1 4.2 Thailand 64 513 125 158.4 31 2,490 104 505 7,930 88 6.2 5.3 Vietnam 82 332 252 44.6 58 540 168 222 2,700 149 7.7 6.6 Europe & Central Asia 472 s 24,238 s 20 w 1,557.1 t 3,295 w 3,945 t 8,350 w 7.2 w 7.1 w Albania 3 29 114 6.6 114 2,120 120 16 5,070 124 5.9 5.3 Armenia 3 30 107 3.2 143 1,060 139 13 4,160 135 7.0 7.4 Azerbaijan 8 87 101 7.8 105 940 147 32 3,810 137 10.2 9.2 Belarus 10 208 47 21.0 75 2,140 119 68 6,970 97 11.0 11.6 Bosnia and Herzegovina 4 51 76 8.0 104 2,040 122 28 7,230 95 6.2 6.4 Bulgaria 8 111 70 21.3 73 2,750 99 62 7,940 86 5.6 6.4 Croatia 4 57 79 30.3 61 6,820 69 53 11,920 69 3.8 3.8 Czech Republic 10 79 132 93.3 42 9,130 59 188 18,420 51 4.4 4.3 Estonia 1 45 32 9.5 99 7,080 67 18 13,630 62 7.8 8.2 Georgia 5 70 65 4.8 129 1,060 139 13d 2,900 d 146 6.2 7.3 Hungary 10 93 110 84.6 46 8,370 62 160 15,800 57 4.6 4.9 Kazakhstan 15 2,725 6 33.8 60 2,250 114 104 6,930 99 9.4 8.8 Kyrgyz Republic 5 200 27 2.1 155 400 178 9 1,860 172 7.1 5.9 Latvia 2 65 37 12.9 91 5,580 75 27 11,820 70 8.3 8.9 Lithuania 3 65 55 19.7 77 5,740 74 44 12,690 65 6.7 7.2 Macedonia, FYR 2 26 80 4.9 126 2,420 105 13 6,560 102 2.9 2.7 Moldova 4 34 128 2.6h 147 720 h 157 8 1,950 165 7.3 7.6 Poland 38 313 125 232.9 25 6,100 72 486 12,730 64 5.4 5.5 Romania 22 238 94 64.2 51 2,960 98 181 8,330 85 8.3 8.6 Russian Federation 144 17,098 9 488.5 16 3,400 94 1,392 9,680 79 7.1 7.7 Serbia and Montenegro 8 102 80 21.8i 72 2,680i 101 .. .. 106 8.2 8.3 Slovak Republic 5 49 112 34.9 59 6,480 71 78 14,480 59 5.5 5.4 Tajikistan 6 143 46 1.8 162 280 190 7 1,160 186 10.6 9.4 Turkey 72 784 93 269.0 20 3,750 89 554 7,720 90 8.9 7.4 Turkmenistan 5 488 10 .. .. .. .. .. .. .. .. .. Ukraine 47 604 82 60.2 53 1,270 132 300 6,330 104 12.1 13.0 Uzbekistan 26 447 62 11.9 93 450 172 49 1,860 172 7.7 6.1 Latin America & Carib. 546 s 20,418 s 27 w 1,952.1 t 3,576 w 4,183 t 7,661 w 5.9 w 4.4 w Argentina 38 2,780 14 137.3 35 3,580 93 481 12,530 66 9.0 7.9 Bolivia 9 1,099 8 8.6 101 960 145 23 2,600 151 3.6 1.6 Brazil 184 8,515 22 551.6 13 3,000 97 1,460 7,940 86 4.9 3.5 Chile 16 757 22 84.2 47 5,220 76 171 10,610 77 6.1 4.9 Colombia 45 1,139 43 90.9 43 2,020 123 312d 6,940 d 98 4.1 2.5 Costa Rica 4 51 83 19.0 79 4,470 80 39d 9,220 d 82 4.2 2.3 Cuba 11 111 102 .. .. ..j .. .. .. .. 1.1 0.8 Dominican Republic 9 49 181 18.4 81 2,100 121 60d 6,860 d 100 2.0 0.5 Ecuador 13 284 47 28.9 63 2,210 116 49 3,770 138 6.9 5.4 El Salvador 7 21 326 15.7 84 2,320 109 33d 4,890 d 126 1.5 ­0.2 Guatemala 12 109 113 26.9 65 2,190 117 52d 4,260 d 130 2.7 0.2 Haiti 8 28 305 3.3 140 400 175 .. .. 166 0.4 ­1.0 Honduras 7 112 63 7.3 109 1,040 141 19d 2,760 d 148 4.6 2.3 20 2006 World Development Indicators 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 2004 2004 2004 2004b 2004 2004b 2004 2004 2004 2004 2003­04 2003­04 Jamaica 3 11 244 8.7 100 3,300k 96 10 3,950 136 0.9 0.4 Mexico 104 1,958 54 704.9 10 6,790 70 1,001 9,640 80 4.4 2.9 Nicaragua 5 130 44 4.5 132 830l 149 19 3,480 140 5.1 3.0 Panama 3 76 43 13.4 89 4,210 83 21d 6,730 d 101 6.2 4.4 Paraguay 6 407 15 6.9 113 1,140 137 29d 4,820 d 127 4.0 1.6 Peru 28 1,285 22 65.0 50 2,360 108 149 5,400 118 4.8 3.3 Trinidad and Tobago 1 5 254 11.4 95 8,730 61 15 11,430 73 6.2 5.9 Uruguay 3 176 20 13.4 88 3,900 88 31 9,030 83 11.9 11.1 Venezuela, RB 26 912 30 105.3 38 4,030 86 152 5,830 110 17.9 15.8 Middle East & N. Africa 300 s 8,984 s 34 w 592.0 t 1,972 w 1,722 t 5,734 w 5.9 w 3.8 w Algeria 32 2,382 14 73.3 49 2,270 113 204d 6,320 d 105 5.2 3.6 Egypt, Arab Rep. 73 1,001 73 90.6 45 1,250 133 305 4,200 134 4.2 2.2 Iran, Islamic Rep. 67 1,648 41 155.3 32 2,320 109 505 7,530 92 5.6 4.6 Iraq .. 438 .. .. .. ..j .. .. .. .. 46.5 .. Jordan 5 89 62 11.9 92 2,190 117 26 4,770 128 7.7 5.1 Lebanon 4 10 346 21.3 74 6,010 73 20 5,550 117 6.3 5.2 Libya 6 1,760 3 25.3 68 4,400 81 .. .. 84 4.5 2.5 Morocco 30 447 67 46.9 56 1,570 128 127 4,250 131 4.2 0.7 Oman 3 310 8 23.0 70 9,070 60 37 14,680 58 3.1 2.2 Syrian Arab Republic 19 185 101 22.8 71 1,230 134 65 3,500 139 2.0 ­0.4 Tunisia 10 164 64 26.3 66 2,650 102 74 7,430 94 5.8 4.9 West Bank and Gaza 4 .. .. 3.8 135 1,120 135 .. .. 142 ­1.7 ­5.6 Yemen, Rep. 20 528 39 11.2 96 550 167 16 810 197 2.7 ­0.5 South Asia 1,447 s 5,140 s 303 w 859.0 t 594 w 4,129 t 2,854 w 6.7 w 5.0 w Afghanistan .. 652 .. 5.5 .. ..g .. .. .. .. 7.5 .. Bangladesh 139 144 1,069 61.3 52 440 174 274 1,970 164 6.3 4.3 India 1,080 3,287 363 673.2 11 620 159 3,369d 3,120 d 144 6.9 5.4 Nepal 27 147 186 6.6 115 250 193 39 1,480 178 3.5 1.4 Pakistan 152 796 197 90.7 44 600 161 330 2,170 157 6.4 3.9 Sri Lanka 19 66 300 19.5 78 1,010 143 82 4,210 133 5.4 4.5 Sub-Saharan Africa 726 s 24,265 s 31 w 436.5 t 601 w 1,337 t 1,842 w 4.8 w 2.6 w Angola 15 1,247 12 14.4 85 930 148 30d 1,930 d 167 11.1 7.9 Benin 8 113 74 3.7 139 450 172 9 1,090 189 2.7 ­0.5 Botswana 2 582 3 7.7 106 4,360 82 17 9,580 81 4.9 5.0 Burkina Faso 13 274 47 4.4 133 350 183 15d 1,170 d 184 3.9 0.6 Burundi 7 28 284 0.7 189 90 208 5d 660 d 206 5.5 1.9 Cameroon 16 475 34 13.0 90 810 151 34 2,120 160 4.3 2.4 Central African Republic 4 623 6 1.2 169 310 187 4d 1,100 d 188 1.3 0.0 Chad 9 1,284 8 2.3 152 250 193 13 1,340 182 29.8 25.5 Congo, Dem. Rep. 56 2,345 25 6.4 116 110 206 38d 680 d 203 6.3 3.2 Congo, Rep. 4 342 11 2.9 145 760 152 3 740 201 3.6 0.6 Côte d'Ivoire 18 322 56 13.6 87 760 152 26 1,470 180 1.6 0.1 Eritrea 4 118 42 0.8 180 190 199 4d 960 d 191 1.8 ­2.5 Ethiopia 70 1,104 70 7.6 107 110 206 52d 750 d 200 13.1 10.9 Gabon 1 268 5 5.6 119 4,080 85 8 5,700 112 1.4 ­0.2 Gambia, The 1 11 148 0.4 192 280 190 3d 1,890 d 170 8.3 5.4 Ghana 22 239 95 8.3 102 380 182 48d 2,220 d 156 5.8 3.6 Guinea 9 246 37 3.8 138 410 177 20 2,160 158 2.6 0.4 Guinea-Bissau 2 36 55 0.3 203 160 201 1d 690 d 202 4.3 1.2 Kenya 33 580 59 16.1 83 480 171 38 1,130 187 4.3 2.0 Lesotho 2 30 59 1.3 166 730 156 6d 3,250 d 143 2.3 2.5 Liberia 3 111 34 0.4 195 120 205 .. .. 203 2.4 1.8 Madagascar 18 587 31 5.2 124 290 189 15 840 195 5.2 2.4 Malawi 13 118 134 2.0 156 160 201 8 630 207 6.7 4.4 Mali 13 1,240 11 4.3 134 330 184 12 950 193 2.2 ­0.8 Mauritania 3 1,026 3 1.6 163 530 169 6d 2,050 d 161 6.9 3.7 2006 World Development Indicators 21 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 2004 2004 2004 2004b 2004 2004b 2004 2004 2004 2004 2003­04 2003­04 Mauritius 1 2 608 5.7 118 4,640 78 15 11,950 68 4.2 3.2 Mozambique 19 802 25 5.3 122 270 192 23d 1,170 d 184 7.2 5.1 Namibia 2 824 2 4.8 130 2,380 107 15d 7,520 d 93 6.0 4.7 Niger 13 1,267 11 2.8 146 210 196 11d 780 d 199 0.9 ­2.4 Nigeria 129 924 141 55.3 54 430 175 125d 970 d 190 6.0 3.7 Rwanda 9 26 360 1.9 158 210 196 11 1,240 183 4.0 2.5 Senegal 11 197 59 7.2 110 630 158 19d 1,660 d 176 6.2 3.7 Sierra Leone 5 72 75 1.1 175 210 196 3 550 208 7.4 3.0 Somalia 8 638 13 .. .. ..g .. .. .. .. .. .. South Africa 46 1,219 37 165.3 30 3,630 92 499d 10,960 d 75 3.7 4.4 Sudan 36 2,506 15 18.7 80 530 169 64d 1,810 d 174 6.0 4.0 Swaziland 1 17 65 1.9 160 1,660 127 6 5,650 114 2.1 0.8 Tanzania 38 945 43 11.6m 94 320 m 185 25 670 205 6.3 4.3 Togo 6 57 110 1.9 159 310 187 9d 1,510 d 177 3.0 0.4 Uganda 28 241 141 6.9 112 250 193 40 d 1,450 d 181 5.7 2.1 Zambia 11 753 15 4.6 131 400 178 10 890 194 4.6 2.9 Zimbabwe 13 391 33 8.0 103 620 159 26 2,040 162 ­4.2 ­4.7 High income 1,004 s 34,595 s 30 w 32,245.3 t 32,112 w 31,138 t 31,009 w 3.4 w 2.6 w Australia 20 7,741 3 544.3 14 27,070 25 590 29,340 22 3.0 1.8 Austria 8 84 99 263.9 21 32,280 15 260 31,800 10 2.2 1.5 Belgium 10 33 318 326.0 18 31,280 17 329 31,530 13 2.9 2.5 Canada 32 9,985 4 905.0 9 28,310 21 984 30,760 16 2.9 1.8 Denmark 5 43 127 220.2 26 40,750 6 172 31,770 11 2.4 2.1 Finland 5 338 17 171.9 29 32,880 14 156 29,800 19 3.7 3.4 France 60 552 110 1,888.4n 6 30,370 n 19 1,779 29,460 20 2.3 1.7 Germany 83 357 236 2,532.3 3 30,690 18 2,324 28,170 27 1.6 1.6 Greece 11 132 86 185.0 27 16,730 42 246 22,230 41 4.2 3.9 Ireland 4 70 59 139.6 34 34,310 12 134 32,930 8 4.9 3.0 Israel 7 22 313 118.0 36 17,360 39 162 23,770 37 4.4 2.8 Italy 58 301 196 1,513.1 7 26,280 28 1,613 28,020 28 1.2 1.4 Japan 128 378 351 4,734.3 2 37,050 9 3,809 29,810 18 2.7 2.5 Korea, Rep. 48 99 487 673.1 12 14,000 50 987 20,530 46 4.6 4.1 Kuwait 2 18 138 55.3 55 22,470 33 53d 21,610 d 43 7.2 4.5 Netherlands 16 42 481 523.1 15 32,130 16 511 31,360 15 1.4 1.1 New Zealand 4 271 15 81.2 48 19,990 37 90 22,260 40 4.4 3.1 Norway 5 324 15 237.8 24 51,810 2 178 38,680 4 2.9 2.6 Portugal 11 92 115 149.3 33 14,220 49 202 19,240 49 1.0 0.4 Puerto Rico 4 9 439 .. .. ..o .. .. .. .. .. .. Saudi Arabia 24 2,150 11 242.9 23 10,140 55 331d 13,810 d 61 5.2 2.5 Singapore 4 1 6,329 105.0 39 24,760 29 116 27,370 29 8.4 7.0 Slovenia 2 20 99 29.5 62 14,770 47 42 20,830 45 4.6 4.5 Spain 43 505 86 919.1 8 21,530 34 1,057 24,750 33 3.1 1.4 Sweden 9 450 22 322.3 19 35,840 10 269 29,880 17 3.6 3.2 Switzerland 7 41 185 366.5 17 49,600 3 264 35,660 6 2.1 1.4 United Arab Emirates 4 84 52 102.7 40 23,770 31 104 24,090 34 8.5 1.5 United Kingdom 60 244 247 2,013.4 4 33,630 13 1,882 31,430 14 3.1 2.6 United States 294 9,629 32 12,168.5 1 41,440 5 11,693 39,820 3 4.2 3.2 World 6,365 s 133,941 s 49 w 40,282.3 t 6,329 w 56,289 t 8,844 w 4.1 w 2.9 w Low income 2,343 30,276 80 1,187.7 507 5,291 2,258 6.5 4.6 Middle income 3,018 69,070 45 6,862.7 2,274 20,051 6,644 7.2 6.3 Lower middle income 2,442 39,173 63 4,116.0 1,686 14,233 5,829 7.6 6.6 Upper middle income 576 29,897 20 2,748.2 4,769 5,859 10,168 6.6 6.0 Low & middle income 5,361 99,346 55 8,050.1 1,502 25,334 4,726 7.1 5.8 High income 1,004 34,595 30 32,245.3 32,112 31,138 31,009 3.4 2.6 a. PPP is purchasing power parity. b. Calculated by the World Bank Atlas method. c. Hong Kong, China, a high-income economy, is not included in this aggregate. d. Based on regression; others are extrapolated from International Comparison Program benchmark estimates. e. Includes Hong Kong, China; Macao, China; and Taiwan, China. f. Based on a 1986 bilateral comparison of China and the United States (Rouen and Kai 1995) employing a different methodology than that used for other countries. This interim methodology will be revised in the next few years. g. Estimated to be low income. h. Excludes data for Transnistria. i. Excludes data for Kosovo. j. Estimated to be lower middle income. k. Included in the aggregates for lower-middle-income economies based on earlier data. l. Included in the aggregates for low-income economies based on earlier data. m. Data refers to mainland Tanzania only. n. Includes French Guiana, Guadeloupe, Martinique, and Réunion. o. Estimated to be high income. 22 2006 World Development Indicators Size of the economy About the data Definitions Population, land area, income, output, and growth in productivity or welfare, see About the data for tables · Population is based on the de facto definition of output are basic measures of the size of an economy. 4.1 and 4.2. population, which counts all residents regardless of They also provide a broad indication of actual and When calculating GNI in U.S. dollars from GNI legal status or citizenship--except for refugees not potential resources. Population, land area, income reported in national currencies, the World Bank follows permanently settled in the country of asylum, who (as measured by gross national income, GNI) and out- its Atlas conversion method, using a three-year average are generally considered part of the population of put (as measured by gross domestic product, GDP) of exchange rates to smooth the effects of transitory their country of origin. The values shown are midyear are therefore used throughout World Development fluctuations in exchange rates. (For further discussion estimates for 2004. See also table 2.1. · Surface Indicators to normalize other indicators. of the Atlas method, see Statistical methods.) GDP and area is a country's total area, including areas under Population estimates are generally based on GDP per capita growth rates are calculated from data in inland bodies of water and some coastal water- extrapolations from the most recent national census. constant prices and national currency units. ways. · Population density is midyear population For further discussion of the measurement of popula- Because exchange rates do not always reflect dif- divided by land area in square kilometers. · Gross tion and population growth, see About the data for ferences in price levels between countries, this table national income (GNI) is the sum of value added by table 2.1 and Statistical methods. also converts GNI and GNI per capita estimates into all resident producers plus any product taxes (less The surface area of an economy includes inland international dollars using purchasing power parity subsidies) not included in the valuation of output bodies of water and some coastal waterways. Sur- (PPP) rates. PPP rates provide a standard measure plus net receipts of primary income (compensation face area thus differs from land area, which excludes allowing comparison of real levels of expenditure of employees and property income) from abroad. bodies of water, and from gross area, which may between countries, just as conventional price indexes Data are in current U.S. dollars converted using the include offshore territorial waters. Land area is par- allow comparison of real values over time. The PPP World Bank Atlas method (see Statistical methods). ticularly important for understanding an economy's conversion factors used here are derived from price · GNI per capita is gross national income divided by agricultural capacity and the environmental effects surveys covering 118 countries conducted by the midyear population. GNI per capita in U.S. dollars is of human activity. (For measures of land area and International Comparison Program. For Organisation converted using the World Bank Atlas method. · PPP data on rural population density, land use, and agri- for Economic Co-operation and Development (OECD) GNI is gross national income converted to interna- cultural productivity, see tables 3.1­3.3.) Innova- countries data come from the most recent round of tional dollars using purchasing power parity rates. An tions in satellite mapping and computer databases surveys, completed in 2002; the rest are from either international dollar has the same purchasing power have resulted in more precise measurements of land the 1996 or the 1993 survey or earlier round and over GNI as a U.S. dollar has in the United States. and water areas. extrapolated to the 1996 benchmark. Estimates for · Gross domestic product (GDP) is the sum of value GNI measures the total domestic and foreign value countries not included in the surveys are derived added by all resident producers plus any product added claimed by residents. GNI comprises GDP from statistical models using available data. taxes (less subsidies) not included in the valuation plus net receipts of primary income (compensation All economies shown in World Development Indica- of output. Growth is calculated from constant price of employees and property income) from nonresident tors are ranked by size, including those that appear in GDP data in local currency. · GDP per capita is gross sources. The World Bank uses GNI per capita in U.S. table 1.6. The ranks are shown only in table 1.1. No domestic product divided by midyear population. dollars to classify countries for analytical purposes rank is shown for economies for which numerical esti- and to determine borrowing eligibility. For definitions mates of GNI per capita are not published. Economies of the income groups in World Development Indica- with missing data are included in the ranking at their tors, see Users guide. For discussion of the useful- approximate level, so that the relative order of other ness of national income and output as measures of economies remains consistent. Developing countries produce slightly less than half the world's output Data sources Share of PPP GNI, 2004 East Asia & Pacific 18% Population estimates are prepared by World Bank staff from a variety of sources (see Data sources for table 2.1). Data on surface and land area are Latin America & Caribbean 7% from the Food and Agriculture Organization (see Data sources for table 3.1). GNI, GNI per capita, High-income 56% South Asia 7% GDP growth, and GDP per capita growth are esti- mated by World Bank staff based on national Europe & Central Asia 7% accounts data collected by World Bank staff dur- Middle East & North Africa 3% ing economic missions or reported by national Sub-Saharan Africa 2% statistical offices to other international organiza- When measured by purchasing power parities (PPPs), which take into account national differences in the cost of tions such as the OECD. Purchasing power parity living, developing countries produce a large part of the world's output. Much of this is in the form of nontradable conversion factors are estimates by World Bank goods and services, which are undervalued at market exchange rates. For this reason PPPs are used in international comparisons of well-being such as $1 and $2 a day measures of absolute poverty. staff based on data collected by the International Source: World Bank staff estimates. Comparison Program. 2006 World Development Indicators 23 Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme Achieve universal Promote gender Reduce child Improve maternal poverty and hunger primary education equality mortality health Share of poorest Maternal quintile in mortality national Prevalence of child ratio consumption malnutrition Ratio of female to male Modeled or income Underweight Primary completion enrollments in primary estimates Births attended by % % of children ratea and secondary schoola Under-five mortality rate per 100,000 skilled health staff under age 5 % % per 1,000 live births live births % of total 1992­ 2004b,c 1989­94b 2000­04b 1991 2004 1991 2004 1990 2004 2000 1989­94b 2000­04b East Asia & Pacificd 19 w 12 w 97 w 99 w 89 w 98 w 59 w 37 w 117 w .. w 86 w Cambodia 6.9 .. 45 .. 82 73 85 115 141 450 .. 32 China 4.7 17 8 103 99 87 98 49 31 56 .. 96 Hong Kong, China 5.3 .. .. 102 111 107 104 .. .. .. .. .. Indonesia 8.4 .. 28 91 101 93 98 91 38 230 37 72 Korea, Dem. Rep. .. .. 24 .. .. .. .. 55 55 67 .. 97 Lao PDR 8.1 40 40 43 74 75 84 163 83 650 .. 19 Malaysia 4.4 22 11 90 95 101 105 22 12 41 .. 97 Mongolia 5.6 12 13 .. 95 109 108 108 52 110 .. 99 Myanmar .. 31 32 .. 72 96 99 130 106 360 .. 57 Papua New Guinea 4.5 .. .. 50 55 80 87 101 93 300 .. 41 Philippines 5.4 30 28 86 98 100 102 62 34 200 53 60 Thailand 6.3 19 .. .. .. 95 98 37 21 44 .. 99 Vietnam 7.5 45 28 .. 101 .. 94 53 23 130 .. 90 Europe & Central Asia .. w .. w 92 w 94 w 97 w 96 w 49 w 34 w 58 w .. w 94 w Albania 9.1 .. 14 .. 99 96 97 45 19 55 .. 98 Armenia 8.5 .. 3 90 107 .. 103 60 32 55 .. 97 Azerbaijan 12.2 .. 7 .. 96 100 97 105 90 94 .. 84 Belarus 8.5 .. .. 95 101 .. 100 17 11 35 .. 100 Bosnia and Herzegovina 9.5 .. 4 .. .. .. .. 22 15 31 97 100 Bulgaria 8.7 .. .. 90 97 99 97 19 15 32 .. 99 Croatia 8.3 1 .. 85 91 106 98 12 7 8 .. 100 Czech Republic 10.3 1 .. .. 102 101 103 13 4 9 .. 100 Estonia 6.7 .. .. 93 103 68 73 16 8 63 .. 100 Georgia 5.6 .. .. .. 86 99 99 47 45 32 .. .. Hungary 9.5 .. .. 82 96 100 100 17 8 16 .. 100 Kazakhstan 7.4 .. .. .. 110 102 98 63 73 210 .. .. Kyrgyz Republic 8.9 .. 7 .. 93 .. 101 80 68 110 .. 99 Latvia 6.6 .. .. .. 98 100 99 18 12 42 .. .. Lithuania 6.8 .. .. 89 105 .. 98 13 8 13 .. 100 Macedonia, FYR 6.1 .. .. 98 97 99 99 38 14 23 .. 99 Moldova 7.8 .. .. .. 83 105 102 40 28 36 .. .. Poland 7.5 .. .. 96 100 101 97 18 8 13 .. 100 Romania 8.1 6 3 96 90 99 100 31 20 49 99 99 Russian Federation 6.1 4 6 93 .. 104 99 29 21 67 .. 99 Serbia and Montenegro .. .. 2 71 96 103 101 28 15 11 .. 93 Slovak Republic 8.8 .. .. 96 101 .. 100 14 9 3 .. 99 Tajikistan 7.9 .. .. .. 92 .. 88 119 93 100 .. 71 Turkey 5.3 10 4 90 .. 81 85 82 32 70 76 83 Turkmenistan 6.1 .. 12 .. .. .. .. 97 103 31 .. 97 Ukraine 9.2 .. 1 92 91 .. 99 26 18 35 .. 100 Uzbekistan 9.2 .. 8 .. 98 94 98 79 69 24 .. 96 Latin America & Carib. .. w .. w 86 w 97 w .. w 102 w 54 w 31 w 194 w 77 w 87 w Argentina 3.2e 2 .. .. 102 .. 103 29 18 82 96 99 Bolivia 1.5 15 8 71 100 .. 98 125 69 420 47 67 Brazil 2.6 7 .. 93 111 .. 103 60 34 260 72 96 Chile 3.3 1 1 .. 97 100 99 21 8 31 100 100 Colombia 2.5 10 7 71 94 .. .. 36 21 130 82 86 Costa Rica 3.9 2 .. 74 92 65 68 18 13 43 98 98 Cuba .. .. 4 96 93 98 101 13 7 33 100 100 Dominican Republic 3.9 10 5 61 91 .. 100 65 32 150 93 98 Ecuador 3.3 .. 12 91 101 81 94 57 26 130 .. .. El Salvador 2.7 11 10 41 84 .. 73 60 28 150 51 92 Guatemala 2.9 .. 23 .. 70 46 72 82 45 240 .. 41 Haiti 2.4 27 17 27 .. 108 .. 150 117 680 23 24 Honduras 3.4 18 17 65 79 103 95 59 41 110 45 56 24 2006 World Development Indicators Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme Achieve universal Promote gender Reduce child Improve maternal poverty and hunger primary education equality mortality health Share of poorest Maternal quintile in mortality national Prevalence of child ratio consumption malnutrition Ratio of female to male Modeled or income Underweight Primary completion enrollments in primary estimates Births attended by % % of children ratea and secondary schoola Under-five mortality rate per 100,000 skilled health staff under age 5 % % per 1,000 live births live births % of total 1992­ 2004b,c 1989­94b 2000­04b 1991 2004 1991 2004 1990 2004 2000 1989­94b 2000­04b Jamaica 6.7 5 4 90 84 102 101 20 20 87 79 97 Mexico 4.3 17 .. 86 97 98 102 46 28 83 .. 95 Nicaragua 5.6 11 10 41 73 109 103 68 38 230 .. 67 Panama 2.5 6 .. 86 97 .. 101 34 24 160 86 93 Paraguay 2.2 4 5 65 89 99 98 41 24 170 67 77 Peru 3.2 11 7 .. 96 96 97 80 29 410 .. 59 Trinidad and Tobago 5.5 .. 6 100 94 101 101 33 20 160 .. 96 Uruguay 5.0e 4 .. 95 94 .. 105 25 17 27 .. .. Venezuela, RB 4.7 5 4 81 89 105 103 27 19 96 .. 94 Middle East & N. Africa .. w .. w 78 w 88 w 81 w 90 w 81 w 55 w 183 w 42 w 72 w Algeria 7.0 9 10 79 94 83 99 69 40 140 77 96 Egypt, Arab Rep. 8.6 10 9 .. 93 102 98 104 36 84 41 69 Iran, Islamic Rep. 5.1 .. .. 91 95 85 100 72 38 76 .. 90 Iraq .. 12 16 59 74 78 78 50 .. 250 54 72 Jordan 6.7 6 4 101 97 101 101 40 27 41 87 100 Lebanon .. .. .. .. 94 .. 102 37 31 150 .. .. Libya .. .. .. .. .. .. 103 41 20 97 .. .. Morocco 6.5 10 10 46 67 70 88 89 43 220 31 63 Oman .. 24 .. 74 91 89 98 32 13 87 .. 95 Syrian Arab Republic .. 12 7 89 107 85 94 44 16 160 77 .. Tunisia 6.0 .. 4 74 94 86 102 52 25 120 .. 90 West Bank and Gaza .. .. .. .. 98 .. 103 .. .. .. .. 97 Yemen, Rep. 7.4 39 46 .. 62 .. 63 142 111 570 16 27 South Asia 53 w .. w 73 w 82 w 71 w 87 w 129 w 92 w 564 w .. w 36 w Afghanistan .. .. 39 25 .. 54 34 260 .. 1,900 .. 14 Bangladesh 9.0 68 48 49 73 .. 106 149 77 380 10 13 India 8.9 53 .. .. 84 70 88 123 85 540 34 43 Nepal 6.0 .. 48 51 71 59 90 145 76 740 7 15 Pakistan 9.3 40 38 .. .. .. 73 130 101 500 19 23 Sri Lanka 8.3 38 30 94 .. 102 102 32 14 92 94 96 Sub-Saharan Africa .. w .. w 51 w 62 w 80 w 84 w 185 w 168 w 921 w 41 w 42 w Angola .. 20 31 35 .. .. .. 260 260 1,700 .. 45 Benin 7.4 .. 23 21 49 49 71 185 152 850 .. 66 Botswana 2.2 .. 13 79 92 109 102 58 116 100 .. 94 Burkina Faso 6.9 33 38 21 29 62 76 210 192 1,000 42 38 Burundi 5.1 .. 45 46 33 82 82 190 190 1,000 .. 25 Cameroon 5.6 15 18 56 72 83 87 139 149 730 58 62 Central African Republic 2.0 .. 24 27 .. 60 .. 168 193 1,100 .. 44 Chad .. .. 37 18 29 41 58 203 200 1,100 .. 14 Congo, Dem. Rep. .. .. 31 46 .. 85 87 205 205 990 .. 61 Congo, Rep. .. .. .. 54 66 101 101 110 108 510 .. .. Côte d'Ivoire 5.2 24 17 43 43 102 101 157 194 690 45 68 Eritrea .. 41 40 19 44 104 100 147 82 630 .. 28 Ethiopia 9.1 48 47 21 51 109 106 204 166 850 .. 6 Gabon .. .. 12 58 66 64 85 92 91 420 .. 86 Gambia, The 4.8 .. 17 44 .. 98 99 154 122 540 44 55 Ghana 5.6 27 22 63 65 99 101 122 112 540 44 47 Guinea 6.4 27 33 17 48 .. 65 240 155 740 31 56 Guinea-Bissau 5.2 .. 25 .. 27 95 .. 253 203 1,100 .. 35 Kenya 6.0 23 20 .. 89 94 94 97 120 1,000 45 42 Lesotho 1.5 21 18 58 71 124 104 104 112 550 50 60 Liberia .. .. 27 .. .. .. .. 235 235 760 .. 51 Madagascar 4.9 45 42 33 45 98 .. 168 123 550 57 51 Malawi 4.9 28 22 31 58 81 98 241 175 1,800 55 61 Mali 4.6 .. 33 11 44 59 74 250 219 1,200 .. 41 Mauritania 6.2 48 32 33 43 67 96 133 125 1,000 40 57 2006 World Development Indicators 25 Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme Achieve universal Promote gender Reduce child Improve maternal poverty and hunger primary education equality mortality health Share of poorest Maternal quintile in mortality national Prevalence of child ratio consumption malnutrition Ratio of female to male Modeled or income Underweight Primary completion enrollments in primary estimates Births attended by % % of children ratea and secondary schoola Under-five mortality rate per 100,000 skilled health staff under age 5 % % per 1,000 live births live births % of total 1992­ 2004b,c 1989­94b 2000­04b 1991 2004 1991 2004 1990 2004 2000 1989­94b 2000­04b Mauritius .. .. .. 102 100 102 103 23 15 24 97 99 Mozambique 6.5 .. 24 26 29 72 82 235 152 1,000 .. 48 Namibia 1.4 26 24 78 81 108 105 86 63 300 68 76 Niger 2.6 43 40 17 25 57 71 320 259 1,600, 15 16 Nigeria 5.0 39 29 .. 76 79 84 230 197 800 31 35 Rwanda .. 29 24 47 37 96 100 173 203 1,400 26 31 Senegal 6.4 22 23 39 45 69 90 148 137 690 47 58 Sierra Leone .. 29 27 .. .. 67 71 302 283 2,000 .. 42 Somalia .. .. 26 .. .. .. .. 225 225 1,100 .. 25 South Africa 3.5 .. .. 75 96 104 101 60 67 230 .. .. Sudan .. 34 41 40 49 78 88 120 91 590 86 87 Swaziland 2.7 .. 10 62 61 98 96 110 156 370 56 74 Tanzania 7.3 29 .. 61 57 97 .. 161 126 1,500 44 46 Togo .. .. .. 35 66 59 73 152 140 570 .. 61 Uganda 5.9 23 23 .. 57 82 97 160 138 880 38 39 Zambia 6.1 25 23 .. 66 .. 93 180 182 750 51 43 Zimbabwe 4.6 16 .. 91 80 92 96 80 129 1,100 69 .. High income .. w .. w .. w .. w 100 w 101 w 11 w 7w 14 w .. w .. w Australia 5.9 .. .. .. 100 101 98 10 6 8 100 .. Austria 8.6 .. .. .. .. 95 96 10 5 4 100 .. Belgium 8.5 .. .. 79 .. 101 106 10 5 10 .. .. Canada 7.2 .. .. .. .. 99 100 8 6 6 .. 98 Denmark 8.3 .. .. 98 103 .. 105 9 5 5 .. .. Finland 9.6 .. .. 97 102 102 100 7 4 6 100 100 France 7.2 .. .. 104 .. .. .. 9 5 17 99 .. Germany 8.5 .. .. 100 97 79 91 9 5 8 .. .. Greece 6.7 .. .. 99 .. .. 91 11 5 9 .. .. Ireland 7.4 .. .. .. 101 104 103 9 6 5 .. 100 Israel 5.7 .. .. .. 101 105 99 12 6 17 .. .. Italy 6.5 .. .. 104 103 100 99 9 5 5 .. .. Japan 10.6 .. .. 101 .. 101 100 6 4 10 100 .. Korea, Rep. 7.9 .. .. 98 105 99 100 9 6 20 98 .. Kuwait .. .. .. 57 91 97 104 16 12 5 .. .. Netherlands 7.6 .. .. .. 100 97 98 9 6 16 .. .. New Zealand 6.4 .. .. 100 .. 100 107 11 7 7 95 .. Norway 9.6 .. .. 100 103 102 101 9 4 16 .. .. Portugal 5.8 .. .. 95 .. 103 102 14 5 5 98 100 Puerto Rico .. .. .. .. .. .. .. .. .. 25 .. .. Saudi Arabia .. 15 .. 56 62 84 92 44 27 23 .. .. Singapore 5.0 .. 3 .. .. 95 .. 8 3 30 .. .. Slovenia 9.1 .. .. 95 102 .. 99 10 4 17 100 100 Spain 7.0 .. .. .. .. 104 102 9 5 4 .. .. Sweden 9.1 .. .. 96 .. 102 111 7 4 2 .. .. Switzerland 7.6 .. .. 53 96 97 96 9 5 7 .. .. United Arab Emirates .. .. .. 95 75 104 102 14 8 54 .. .. United Kingdom 6.1 .. .. .. .. 98 116 10 6 13 .. .. United States 5.4 1 2 .. .. 100 100 11 8 17 99 .. World .. w .. w .. w .. w 86 w 93 w 95 w 79 w 410 w .. w 60 w Low income .. .. 66 74 74 86 147 122 682 .. 40 Middle income .. 11 92 97 91 98 57 37 142 .. 87 Lower middle income .. 11 93 98 89 98 61 40 153 .. 86 Upper middle income .. .. 88 96 98 98 42 28 92 .. 95 Low & middle income .. .. 81 86 84 92 103 86 450 .. 60 High income .. .. .. .. 100 101 11 7 14 .. .. a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. 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. Hong Kong, China, is classified as a high-income economy and is not included in the East Asia and Pacific aggregate. e. Urban data. 26 2006 World Development Indicators Millennium Development Goals: eradicating poverty and improving lives About the data This table and the following two present indicators Progress toward achieving universal primary education indicators for the first five goals. For more informa- for 17 of the 18 targets specified by the Millennium is measured by primary school completion rates. Before tion about data collection methods and limitations, Development Goals. Each of the eight goals com- World Development Indicators 2003, progress was mea- see About the data for the tables listed there. For prises one or more targets, and each target has sured by net enrollment ratios. But official enrollments information about the indicators for goals 6, 7, and associated with it several indicators for monitoring sometimes differ significantly from actual attendance, 8, see About the data for tables 1.3 and 1.4. progress toward the target. Most of the targets are and even school systems with high average enrollment set as a value of a specific indicator to be attained ratios may have poor completion rates. Estimates of pri- Definitions by a certain date. In some cases the target value is mary school completion rates were calculated by World · Share of poorest quintile in national consumption or set relative to a level in 1990. In others it is set at Bank staff using data provided by the United Nations income is the share of consumption or, in some cases, an absolute level. Some of the targets for goals 7 Educational, Scientific, and Cultural Organization Insti- income that accrues to the poorest 20 percent of the and 8 have not yet been quantified tute of Statistics and national sources. population. · Prevalence of child malnutrition is the The indicators in this table relate to goals 1­5. Eliminating gender disparities in education would percentage of children under age five whose weight Goal 1 has two targets between 1990 and 2015: help to increase the status and capabilities of for age is more than two standard deviations below to reduce by half the proportion of people whose women. The ratio of girls' to boys' enrollments in the median for the international reference population income is less than $1 a day and to reduce by half primary and secondary school provides an imperfect ages 0­59 months. The reference population, adopted the proportion of people who suffer from hunger. measure of the relative accessibility of schooling for by the World Health Organization in 1983, is based on Estimates of poverty rates can be found in table 2.7. girls. With a target date of 2005, this is the first of children from the United States, who are assumed to The indicator shown here, the share of the poorest the goals to fall due. be well nourished. · Primary completion rate is the quintile in national consumption, is a distributional The targets for reducing under-five and maternal percentage of students completing the last year of measure. Countries with more unequal distributions mortality are among the most challenging. Although primary school. It is calculated as the total number of of consumption (or income) will have a higher rate of estimates of under-five mortality rates are available students in the last grade of primary school, minus the poverty for a given average income. No single indica- at regular intervals for most countries, maternal number of repeaters in that grade, divided by the total tor captures the concept of suffering from hunger. mortality is difficult to measure, in part because it number of children of official graduation age. · Ratio of Child malnutrition is a symptom of inadequate food is relatively rare. female to male enrollments in primary and secondary supply, lack of essential nutrients, illnesses that Most of the 48 indicators relating to the Millennium school is the ratio of female to male gross enrollment deplete these nutrients, and undernourished moth- Development Goals can be found in World Develop- rate in primary and secondary school. · Under-five ers who give birth to underweight children. ment Indicators. Table 1.2a shows where to find the mortality rate is the probability that a newborn baby will die before reaching age five, if subject to current age- specific mortality rates. The probability is expressed Location of indicators for Millennium Development Goals 1­5 as a rate per 1,000. · Maternal mortality ratio is the number of women who die from pregnancy-related Goal 1. Eradicate extreme poverty and hunger Table 1. Proportion of population below $1 a day 2.7 causes during pregnancy and childbirth, per 100,000 2. Poverty gap ratio 2.7 live births. The data shown here have been collected 3. Share of poorest quintile in national consumption 1.2, 2.8 in various years and adjusted to a common 2000 base 4. Prevalence of underweight in children under age five 1.2, 2.17 year. The values are modeled estimates (see About 5. Proportion of population below minimum level of dietary energy consumption 2.17 the data for table 2.16). · Births attended by skilled Goal 2. Achieve universal primary education health staff are the percentage of deliveries attended 6. Net enrollment ratio 2.11 by personnel trained to give the necessary supervision, 7. Proportion of pupils starting grade 1 who reach grade 5 2.12 care, and advice to women during pregnancy, labor, and 8. Literacy rate of 15- to 24-year-olds 2.13 Goal 3. Promote gender equality and empower women the postpartum period; to conduct deliveries on their 9. Ratio of girls to boys in primary, secondary, and tertiary education 1.2* own; and to care for newborns. 10. Ratio of literate females to males among 15- to 24-year-olds 2.13* 11. Share of women in wage employment in the nonagricultural sector 1.5, 2.2* Data sources 12. Proportion of seats held by women in national parliament 1.5 The indicators here and throughout this book have Goal 4. Reduce child mortality been compiled by World Bank staff from primary 13. Under-five mortality rate 1.2, 2.19 and secondary sources. Efforts have been made to 14. Infant mortality rate 2.19 harmonize these data series with those published 15. Proportion of one-year-old children immunized against measles 2.15 Goal 4. Improve maternal health on the United Nations Millennium Development 16. Maternal mortality ratio 1.2, 2.16 Goals Web site (www.un.org/millenniumgoals), but 17. Proportion of births attended by skilled health personnel 1.2, 2.16 some differences in timing, sources, and defini- tions remain. * Table shows information on related indicators. 2006 World Development Indicators 27 Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Fixed-line and Incidence of Youth mobile phone HIV prevalence tuberculosis Carbon dioxide emissions Access to an improved Access to improved unemployment subscribers % of population per 100,000 per capita water source sanitation facilities % ages per 1,000 ages 15­49 people metric tons % of population % of population 15­24 peoplea 2003 2004 1990 2002 1990 2002 1990 2002 2004 2004 East Asia & Pacificb 0.2 w 138 w 1.9 w 2.4 w 71 w 78 w 30 w 49 w .. w 435 w Cambodia 2.6 510 0.0 0.0 .. 34 .. 16 .. 40 China 0.1 101 2.1 2.7 70 77 23 44 .. 499 Hong Kong, China 0.1 75 4.6 5.2 .. .. .. .. 15 1,733 Indonesia 0.1 245 0.9 1.4 71 78 46 52 .. 184 Korea, Dem. Rep. .. 178 12.4 6.5 100 100 .. 59 .. 41 Lao PDR 0.1 156 0.1 0.2 .. 43 .. 24 .. 48 Malaysia 0.4 103 3.1 6.3 .. 95 96 .. .. 766 Mongolia <0.1 192 4.7 3.4 62 62 .. 59 20 184 Myanmar 1.2 171 0.1 0.2 48 80 21 73 .. 10 Papua New Guinea 0.6 233 0.6 0.4 39 39 45 45 .. 14 Philippines <0.1 293 0.7 0.9 87 85 54 73 26 446 Thailand 1.5 142 1.8 3.7 81 85 80 99 5 537 Vietnam 0.4 176 0.3 0.8 72 73 22 41 5 184 Europe & Central Asia 0.7 w 83 w 10.2 w 6.7 w .. w 91 w 86 w 82 w .. w 536 w Albania .. 22 2.2 0.8 97 97 .. 89 36 438 Armenia 0.1 78 1.1 1.0 .. 92 .. 84 .. 260 Azerbaijan <0.1 75 6.4 3.4 66 77 .. 55 .. 333 Belarus .. 60 9.3 6.0 100 100 .. .. .. 424 Bosnia and Herzegovina <0.1 53 1.2 4.7 98 98 .. 93 .. 507 Bulgaria 0.1 36 8.6 5.3 100 100 100 100 28 966 Croatia <0.1 41 3.8 4.7 .. .. .. .. 37 996 Czech Republic 0.1 11 13.1 11.2 .. .. .. .. 20 1,392 Estonia 1.1 46 16.2 11.7 .. .. .. .. 21 1,260 Georgia 0.1 82 2.8 0.7 .. 76 .. 83 25 337 Hungary 0.1 26 5.8 5.6 99 99 .. 95 16 1,217 Kazakhstan 0.2 151 15.4 9.9 86 86 72 72 15 351 Kyrgyz Republic 0.1 122 2.4 1.0 .. 76 .. 60 20 106 Latvia 0.6 68 4.8 2.7 .. .. .. .. 19 937 Lithuania 0.1 63 5.8 3.6 .. .. .. .. 25 1,235 Macedonia, FYR <0.1 30 5.5 5.1 .. .. .. .. 66 642 Moldova 0.2 138 4.8 1.6 .. 92 .. 68 15 391 Poland 0.1 29 9.1 7.7 .. .. .. .. 41 777 Romania <0.1 146 6.7 4.0 .. 57 .. 51 19 673 Russian Federation 1.1 115 13.3 9.8 94 96 87 87 .. 508 Serbia and Montenegro 0.2 33 .. 3.7 93 93 87 87 .. 910 Slovak Republic <0.1 19 8.1 6.8 100 100 100 100 33 1,027 Tajikistan <0.1 177 3.7 0.7 .. 58 .. 53 .. 46 Turkey .. 28 2.6 3.0 81 93 84 83 20 751 Turkmenistan <0.1 65 7.2 9.1 .. 71 .. 62 .. 82 Ukraine 1.4 101 11.5 6.4 .. 98 99 99 17 545 Uzbekistan 0.1 117 5.3 4.8 89 89 58 57 .. 79 Latin America & Carib. 0.7 w 64 w 2.4 w 2.4 w 82 w 89 w 68 w 75 w 14 w 499 w Argentina 0.7 43 3.4 3.5 94 .. 82 .. 34 579 Bolivia 0.1 217 0.8 1.2 72 85 33 45 .. 269 Brazil 0.7 60 1.4 1.8 83 89 70 75 18 587 Chile 0.3 16 2.7 3.6 90 95 85 92 19 799 Colombia 0.7 50 1.6 1.3 92 92 82 86 .. 427 Costa Rica 0.6 14 0.9 1.4 .. 97 .. 92 15 533 Cuba 0.1 10 3.0 2.1 .. 91 98 98 .. 75 Dominican Republic 1.0c 91 1.3 2.5 86 93 48 57 .. 396 Ecuador 0.3 131 1.6 2.0 69 86 56 72 22 472 El Salvador 0.7 54 0.5 1.0 67 82 51 63 11 402 Guatemala 1.1 77 0.6 0.9 77 95 50 61 .. 350 Haiti 5.6 306 0.1 0.2 53 71 15 34 .. 64 Honduras 1.8 77 0.5 0.9 83 90 49 68 8 153 28 2006 World Development Indicators Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Fixed-line and Incidence of Youth mobile phone HIV prevalence tuberculosis Carbon dioxide emissions Access to an improved Access to improved unemployment subscribers % of population per 100,000 per capita water source sanitation facilities % ages per 1,000 ages 15­49 people metric tons % of population % of population 15­24 peoplea 2003 2004 1990 2002 1990 2002 1990 2002 2004 2004 Jamaica 1.2 7 3.3 4.1 92 93 75 80 26 1,021 Mexico 0.3 32 4.5 3.8 80 91 66 77 6 545 Nicaragua 0.2 63 0.7 0.7 69 81 47 66 13 177 Panama 0.9 45 1.3 2.0 .. 91 .. 72 29 388 Paraguay 0.5 71 0.5 0.7 62 83 58 78 14 349 Peru 0.5 178 1.0 1.0 74 81 52 62 19 223 Trinidad and Tobago 3.2 9 13.9 31.8 92 91 100 100 21 745 Uruguay 0.3 28 1.3 1.2 .. 98 .. 94 38 465 Venezuela, RB 0.7 42 5.9 4.3 .. 83 .. 68 28 450 Middle East & N. Africa 0.1 w 54 w 2.5 w 3.2 w 87 w 88 w 69 w 75 w .. w 219 w Algeria 0.1 54 3.0 2.9 95 87 88 92 .. 215 Egypt, Arab Rep. <0.1 27 1.4 2.1 94 98 54 68 28 235 Iran, Islamic Rep. 0.1 27 4.0 5.5 91 93 83 84 .. 270 Iraq <0.1 132 2.6 .. 83 81 81 80 .. 57 Jordan <0.1 5 3.2 3.2 98 91 .. 93 .. 407 Lebanon 0.1 11 3.3 4.7 100 100 .. 98 .. 429 Libya 0.3 20 8.7 9.1 71 72 97 97 .. 156 Morocco 0.1 110 1.0 1.5 75 80 57 61 17 357 Oman 0.1 11 6.0 12.1 77 79 83 89 .. 413 Syrian Arab Republic <0.1 41 2.8 2.8 79 79 76 77 26 269 Tunisia <0.1 22 1.6 2.3 77 82 75 80 .. 480 West Bank and Gaza .. 23 .. .. .. 94 .. 76 43 380 Yemen, Rep. 0.1 89 0.8 0.7 69 69 21 30 .. 92 South Asia 0.8 w 177 w 0.7 w 1.0 w 70 w 84 w 16 w 35 w .. w 76 w Afghanistan .. 333 0.2 .. .. 13 .. 8 .. 23 Bangladesh .. 229 0.1 0.3 71 75 23 48 .. 37 India 0.9 168 0.8 1.2 68 86 12 30 .. 85 Nepal 0.5 184 0.0 0.2 69 84 12 27 .. 22 Pakistan 0.1 181 0.6 0.7 83 90 38 54 13 63 Sri Lanka <0.1 60 0.2 0.5 68 78 70 91 27 165 Sub-Saharan Africa 7.2 w 363 w 0.8 w 0.7 w 49 w 58 w 32 w 36 w .. w 65 w Angola 3.9 259 0.4 0.5 32 50 30 30 .. 29 Benin 1.9 87 0.1 0.3 60 68 11 32 .. 38 Botswana 37.3 670 1.5 2.3 93 95 38 41 40 396 Burkina Faso 1.8 d 191 0.1 0.1 39 51 13 12 .. 37 Burundi 6.0 343 0.0 0.0 69 79 44 36 .. 12 Cameroon 5.5e 179 0.1 0.2 50 63 21 48 .. 74 Central African Republic 13.5 322 0.1 0.1 48 75 23 27 .. 18 Chad 4.8 279 0.0 0.0 20 34 6 8 .. 14 Congo, Dem. Rep. 4.2 366 0.1 0.0 43 46 18 29 .. 11 Congo, Rep. 4.9 377 0.5 0.6 .. 46 .. 9 .. 102 Côte d'Ivoire 7.0 393 0.4 0.4 69 84 31 40 .. 86 Eritrea 2.7 271 0.0 0.2 40 57 8 9 .. 14 Ethiopia 4.4 353 0.1 0.1 25 22 4 6 .. 8 Gabon 8.1 280 6.3 2.6 .. 87 .. 36 .. 388 Gambia, The 1.2 233 0.2 0.2 .. 82 .. 53 .. 99 Ghana 2.2d 206 0.2 0.4 54 79 43 58 .. 93 Guinea 3.2 240 0.2 0.1 42 51 17 13 .. 15 Guinea-Bissau .. 199 0.2 0.2 .. 59 .. 34 .. 8 Kenya 6.7d 619 0.2 0.2 45 62 42 48 .. 85 Lesotho 28.9 696 .. .. .. 76 37 37 .. 109 Liberia 5.9 310 0.2 0.1 56 62 38 26 .. 3 Madagascar 1.7 218 0.1 0.1 40 45 12 33 .. 19 Malawi 14.2 413 0.1 0.1 41 67 36 46 .. 25 Mali 1.9 281 0.0 0.0 34 48 36 45 .. 36 Mauritania 0.6 287 1.3 1.1 41 56 28 42 .. 135 2006 World Development Indicators 29 Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Fixed-line and Incidence of Youth mobile phone HIV prevalence tuberculosis Carbon dioxide emissions Access to an improved Access to improved unemployment subscribers % of population per 100,000 per capita water source sanitation facilities % ages per 1,000 ages 15­49 people metric tons % of population % of population 15­24 peoplea 2003 2004 1990 2002 1990 2002 1990 2002 2004 2004 Mauritius .. 64 1.4 2.6 100 100 99 99 .. 700 Mozambique 12.2 460 0.1 0.1 .. 42 .. 27 .. 27 Namibia 21.3 717 0.0 1.1 58 80 24 30 45 206 Niger 1.2 157 0.1 0.1 40 46 7 12 .. 13 Nigeria 5.4 290 0.5 0.4 49 60 39 38 .. 79 Rwanda 5.1 371 0.1 0.1 58 73 37 41 .. 18 Senegal 0.8 245 0.4 0.4 66 72 35 52 .. 72 Sierra Leone .. 443 0.1 0.1 .. 57 .. 39 .. 19 Somalia .. 411 0.0 .. .. 29 .. 25 .. 88 South Africa 15.6c 718 8.1 7.6 83 87 63 67 60 473 Sudan 2.3 220 0.2 0.3 64 69 33 34 .. 58 Swaziland 38.8 1,226 0.6 0.9 .. 52 .. 52 .. 119 Tanzania 7.0 e 347 0.1 0.1 38 73 47 46 .. 32 Togo 4.1 355 0.2 0.3 49 51 37 34 .. 48 Uganda 4.1 402 0.0 0.1 44 56 43 41 .. 44 Zambia 15.6f 680 0.3 0.2 50 55 41 45 .. 29 Zimbabwe 24.6 674 1.6 1.0 77 83 49 57 25 55 High income 0.4 w 17 w 11.8 w 12.8 w .. w 99 w .. w .. w 13 w 1,306 w Australia 0.1 6 16.0 18.1 100 100 100 100 12 1,359 Austria 0.3 14 7.5 7.9 100 100 100 100 10 1,438 Belgium 0.2 13 10.1 8.9 .. .. .. .. 18 1,333 Canada 0.3 5 15.0 16.5 100 100 100 100 13 1,053 Denmark 0.2 8 9.7 8.8 100 100 .. .. 8 1,599 Finland 0.1 9 10.3 12.0 100 100 100 100 21 1,407 France 0.4 12 6.4 6.2 .. .. .. .. 23 1,299 Germany 0.1 8 12.3 10.3 100 100 .. .. 12 1,525 Greece 0.2 19 7.1 8.5 .. .. .. .. 27 1,465 Ireland 0.1 11 8.7 11.0 .. .. .. .. 8 1,425 Israel 0.1 9 7.1 10.6 100 100 .. .. 22 1,499 Italy 0.5 7 6.9 7.5 .. .. .. .. 24 1,541 Japan <0.1 30 8.7 9.4 100 100 100 100 10 1,176 Korea, Rep. <0.1 90 5.6 9.4 .. 92 .. .. 10 1,303 Kuwait .. 26 21.3 25.6 .. .. .. .. .. 1,015 Netherlands 0.2 8 9.3 9.3 100 100 100 100 8 1,393 New Zealand 0.1 11 6.8 8.6 97 .. .. .. 9 1,189 Norway 0.1 5 11.1 13.9 100 100 .. .. 12 1,396 Portugal 0.4 42 4.3 6.0 .. .. .. .. 15 1,384 Puerto Rico .. 5 3.3 3.5 .. .. .. .. 24 974 Saudi Arabia .. 40 11.0 15.0 90 .. .. .. .. 537 Singapore 0.2 40 14.8 13.7 .. .. .. .. 8 1,350 Slovenia <0.1 15 6.2 7.7 .. .. .. .. 15 1,278 Spain 0.7 25 5.5 7.4 .. .. .. .. 22 1,321 Sweden 0.1 4 5.8 5.8 100 100 100 100 17 1,750 Switzerland 0.4 7 6.4 5.6 100 100 100 100 8 1,560 United Arab Emirates .. 17 34.2 25.0 .. .. 100 100 .. 1,128 United Kingdom 0.2 12 9.9 9.2 .. .. .. .. 11 1,584 United States 0.6 5 19.3 20.2 100 100 100 100 12 1,223 World 1.1 w 139 w 4.0 w 3.9 w 75 w 82 w 43 w 54 w .. w 476 w Low income 2.1 224 0.8 0.8 64 75 20 36 .. 76 Middle income 0.7 114 3.5 3.3 77 83 48 61 .. 486 Lower middle income 0.3 114 2.4 2.6 75 81 42 57 .. 438 Upper middle income 2.6 112 8.1 6.2 88 93 80 81 .. 564 Low & middle income 1.2 162 2.4 2.2 71 79 37 50 .. 312 High income 0.4 17 11.8 12.8 .. 99 .. .. 13 1,306 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. Hong Kong, China, is classified as a high-income economy and is not included in the East Asia and Pacific aggregate. c. Survey data, 2002. d. Survey data, 2003. e. Survey data, 2004. f. Survey data, 2001/02. 30 2006 World Development Indicators Millennium Development Goals: protecting our common environment About the data The Millennium Development Goals address issues from sentinel sites or through targeted surveys. In among the telecommunications technologies that of common concern to all nations. Diseases and older, generalized epidemics antenatal clinics are a key are changing the way the global economy works. For environmental degradation do not respect national site for monitoring HIV and other sexually transmitted more information on goal 8, see table 1.4. boundaries. Epidemic diseases, wherever they per- diseases. Recently, household surveys have been used sist, pose a threat to people everywhere. And damage to track the disease. The table shows the estimated Definitions to the environment in one location may affect the prevalence among adults ages 15­49. Prevalence well-being of plants, animals, and humans far away. rates in the older population can be affected by life- · HIV prevalence is the percentage of people ages The indicators in the table relate to goals 6 and 7 prolonging treatment. The incidence of tuberculosis is 15­49 who are infected with HIV. · Incidence of tuber- and the targets of goal 8 that address youth employ- based on data on case notifications and estimates of culosis is the estimated number of new tuberculosis ment and access to new technologies. For the other the proportion of cases detected in the population. cases (pulmonary, smear positive, extrapulmonary). targets of goal 8, see table 1.4. Carbon dioxide emissions are the primary source · Carbon dioxide emissions are those stemming from Measuring the prevalence or incidence of a disease of greenhouse gases, which are believed to contrib- the burning of fossil fuels and the manufacture of can be difficult. Much of the developing world lacks ute to global warming. cement. They include carbon dioxide produced during reporting systems for monitoring diseases. Estimates Access to reliable supplies of safe drinking water consumption of solid, liquid, and gas fuels and gas are often derived from surveys and reports from sen- and sanitary disposal of excreta are two of the most flaring. · Access to an improved water source refers tinel sites that must be extrapolated to the general important means of improving human health and to the percentage of the population with reasonable population. Tracking diseases such as HIV/AIDS, protecting the environment. There is no widespread access to an adequate amount of water from an which has a long latency between contraction of the program for testing the quality of water. The indicator improved source, such as a household connection, virus and the appearance of symptoms, or malaria, shown here measures the proportion of households public standpipe, borehole, protected well or spring, or which has periods of dormancy, can be particularly with access to an improved source, such as piped rainwater collection. Unimproved sources include ven- difficult. For some of the most serious illnesses inter- water or protected wells. Improved sanitation facili- dors, tanker trucks, and unprotected wells and springs. national organizations have formed coalitions such ties prevent human, animal, and insect contact with Reasonable access is defined as the availability of at as the Joint United Nations Programme on HIV/AIDS excreta but do not include treatment to render sew- least 20 liters a person a day from a source within and the Roll Back Malaria campaign to gather infor- age outflows innocuous. 1 kilometer of the dwelling. · Access to improved mation and coordinate global efforts to treat victims The eighth goal--to develop a global partnership sanitation facilities refers to the percentage of the and prevent the spread of disease. for development--takes note of the need for decent population with access to at least adequate excreta The models and data used to estimate HIV preva- and productive work for youth. Labor market informa- disposal facilities (private or shared but not public) lence depend on the nature of the epidemic in each tion, such as unemployment rates, is still generally that can effectively prevent human, animal, and insect country. In early stages infections are usually concen- unavailable for most low- and middle-income econo- contact with excreta. Improved facilities range from trated in high risk groups for which data are collected mies. Fixed telephone lines and mobile phones are simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained. · Youth unemployment refers to the share of the labor force Location of indicators for Millennium Development Goals 6­7 ages 15­24 without work but available for and seeking Goal 6. Combat HIV/AIDS, malaria, and other diseases Table employment. Definitions of labor force and unemploy- 18. HIV prevalence among pregnant women ages 15­24 1.3*, 2.18* ment differ by country. · Fixed-line and mobile phone 19. Condom use rate of the contraceptive prevalence rate -- subscribers are telephone mainlines connecting a 19a. Condom use at last high-risk sex -- customer's equipment to the public switched telephone 19b. Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDS -- network, and users of portable telephones subscribing 19c. Contraceptive prevalence rate 2.16 to an automatic public mobile telephone service using 20. Ratio of school attendance of orphans to school attendance of nonorphans ages cellular technology that provides access to the public 10­14 -- switched telephone network. 21. Prevalence and death rates associated with malaria -- 22. Proportion of population in malaria-risk areas using effective malaria prevention and treatment measures 2.15* 23. Prevalence and death rates associated with tuberculosis 1.3*, 2.18* 24. Proportion of tuberculosis cases detected and cured under DOTS 2.15 Goal 7. Ensure environmental sustainability 25. Proportion of land area covered by forest 3.4 26. Ratio of area protected to maintain biological diversity to surface area 3.4 Data sources 27. Energy use (kilograms of oil equivalent) per $1 of GDP (PPP) 3.8 The indicators here and throughout this book have 28. Carbon dioxide emissions per capita and consumption of ozone-depleting chloro- been compiled by World Bank staff from primary fluorocarbons 3.8* and secondary sources. Efforts have been made to 29. Proportion of population using solid fuels 3.7* harmonize these data series with those published 30. Proportion of population with sustainable access to an improved water source, on the United Nations Millennium Development urban and rural 2.15, 3.5 Goals Web site (www.un.org/millenniumgoals), but 31. Proportion of population with access to improved sanitation, urban and rural 2.15, 3.10 some differences in timing, sources, and defini- 32. Proportion of population with access to secure tenure 3.11 tions remain. -- No data are available in the World Development Indicators database. * Table shows information on related indicators. 2006 World Development Indicators 31 Millennium Development Goals: overcoming obstacles Development Assistance Committee members Official development Least developed countries' access Support to assistances (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 2004 2003­04 1997 2004 1997 2004 1997 2004 1997 2004 2004 b Australia 0.25 15.8 96.6 97.3 0.2 0.4 10.0 0.9 28.3 0.0 0.3 Canada 0.27 29.0 65.9 98.6 0.5 0.2 11.4 0.3 21.8 1.4 0.7 European Union 97.3 95.9 3.4 2.8 0.0 0.2 0.0 1.0 1.2 Austria 0.23 12.6 Belgium 0.41 14.7 Denmark 0.85 23.6 Finland 0.35 15.3 France 0.41 10.0 Germany 0.28 12.7 Greece 0.23 20.6 Ireland 0.39 28.9 Italy 0.15 18.4 Luxembourg 0.83 20.7 Netherlands 0.73 18.1 Portugal 0.63 2.8 Spain 0.24 13.8 Sweden 0.78 16.0 United Kingdom 0.36 31.8 Japan 0.19 5.4 67.9 37.9 7.4 6.6 3.9 1.7 0.5 0.1 1.3 New Zealand 0.23 19.1 0.4 Norway 0.87 18.0 1.3 Switzerland 0.41 8.4 72.8 99.4 7.2 6.7 0.0 0.0 0.0 0.0 1.7 United States 0.17 19.1 22.5 67.0 4.9 3.5 6.9 5.7 14.6 12.3 0.9 Heavily indebted poor countries (HIPCs) HIPC HIPC Estimated HIPC HIPC Estimated decision completion total nominal decision completion total nominal pointc pointd debt service pointc pointd debt service relief e relief e $ millions $ millions Benin Jul. 2000 Mar. 2003 460 Madagascar Dec. 2000 Oct. 2004 1,900 Bolivia Feb. 2000 Jun. 2001 2,060 Malawi Dec. 2000 Floating 1,000 Burkina Faso Jul. 2000 Apr. 2002 930 Mali Sep. 2000 Mar. 2003 895 Cameroon Aug. 2005 Floating 1,472 Mauritania Feb. 2000 Jun. 2002 1,100 Burundi Oct. 2000 Floating 2,800 Mozambique Apr. 2000 Sep. 2001 4,300 Chad May 2001 Floating 260 Nicaragua Dec. 2000 Jan. 2004 4,500 Congo, Dem. Republic Jul. 2003 Floating 10,389 Niger Dec. 2000 Apr. 2004 1,190 Côte d'Ivoire Mar. 1998 .. 800 Rwanda Dec. 2000 Apr. 2005 1,400 Ethiopia Nov. 2001 Apr. 2004 3,275 São Tomé & Principe Dec. 2000 Floating 200 Gambia, The Dec. 2000 Floating 90 Senegal Jun. 2000 Apr. 2004 850 Ghana Feb. 2002 Jul. 2004 3,500 Sierra Leone Mar. 2002 Floating 950 Guinea Dec. 2000 Floating 800 Tanzania Apr. 2000 Nov. 2001 3,000 Guinea-Bissau Dec. 2000 Floating 790 Uganda Feb. 2000 May 2000 1,950 Guyana Nov. 2000 Dec. 2003 1,353 Zambia Dec. 2000 Apr. 2005 3,900 Honduras Jul. 2000 Apr. 2005 1,053 a. Includes basic health, education, nutrition, and water and sanitation services. b. Preliminary. c. Except for Côte d'Ivoire the date refers to the Enhanced Heavily Indebted Poor Countries (HIPC) Initiative. The following countries also reached their decision point under the original HIPC framework: Bolivia in September 1997, Burkina Faso in September 1997, Côte d'Ivoire in March 1998, Guyana in December 1997, Mali in September 1998, Mozambique in April 1998, and Uganda in April 1997. d. The date refers to the Enhanced HIPC Initiative. The following countries also reached completion points under the original framework: Bolivia in September 1998, Burkina Faso in July 2000, Guyana in May 1999, Mali in September 2000, Mozambique in July 1999, and Uganda in April 1998. e. Includes estimated total nominal debt service relief under original and enhanced HIPC, as well as a topping-up of HIPC debt relief at completion point for Burkina Faso, Ethiopia, and Niger. 32 2006 World Development Indicators Millennium Development Goals: overcoming obstacles About the data Definitions Achieving the Millennium Development Goals will to exports of countries designated least developed · Net official development assistance (ODA) com- require an open, rule-based global economy in which countries by the United Nations. Agricultural com- prises grants and loans (net of repayments of prin- all countries, rich and poor, participate. Many poor modities, textiles, and clothing are three of the most cipal) that meet the DAC definition of ODA and are countries, lacking the resources to finance their devel- important categories of goods exported by develop- made to countries and territories on part I of the opment, burdened by unsustainable levels of debt, ing economies. Although average tariffs have been DAC list of recipient countries. · ODA for basic and unable to compete in the global marketplace, falling, averages may disguise high tariffs targeted social services is aid reported by DAC donors for need assistance from rich countries. For goal 8-- at specific goods (see table 6.7 for estimates of basic health, education, nutrition, and water and develop a global partnership for development--many the share of tariff lines with "international peaks" sanitation services. · Goods admitted free of tar- of the indicators therefore monitor the actions of in each country's tariff schedule). The averages in iffs refer to the value of exports of goods (exclud- members of the Development Assistance Committee the table include ad valorem duties and ad valorem ing arms) from least developed countries admitted (DAC) of the Organisation for Economic Co-operation equivalents of non-ad valorem duties. Subsidies to without tariff, as a share of total exports from least and Development (OECD). agricultural producers and exporters in OECD coun- developed countries. · Average tariff is the simple Official development assistance (ODA) has risen tries are another form of barrier to developing econo- mean tariff, the unweighted average of the effec- in recent years as a share of donor countries' gross mies' exports. The table shows the value of total tively applied rates for all products subject to tariffs. national income (GNI), but the poorest countries will support to agriculture as a share of the economy's · Agricultural products comprise plant and animal need additional assistance to achieve the Millennium gross domestic product (GDP). Agricultural subsi- products, including tree crops but excluding timber Development Goals. Official aid rose to a record high dies in OECD economies are estimated at $378 and fish products. · Textiles and clothing include of $79 billion in 2004, and donor countries have billion in 2004. natural and synthetic fibers and fabrics and articles pledged to increase ODA by $20 billion by 2006 and The Debt Initiative for Heavily Indebted Poor Coun- of clothing made from them. · Support to agriculture to a total of more than $100 billion by 2010. However, tries (HIPCs) is the first comprehensive approach is the annual monetary value of all gross transfers this would still fall short of levels considered neces- to reducing the external debt of the world's poor- from taxpayers and consumers arising from policy sary to achieve the Millennium Development Goals. est, most heavily indebted countries. It represents measures that support agriculture, net of the asso- One of the most important actions that high-income an important step forward in placing debt relief ciated budgetary receipts, regardless of their objec- economies can take to help is to reduce barriers to within an overall framework of poverty reduction. tives and impacts on farm production and income, the exports of low- and middle-income economies. A major review in 1999 led to an enhancement or consumption of farm products. · HIPC decision The European Union has launched a program to of the original framework. Through the HIPC Initia- point is the date at which a heavily indebted poor eliminate tariffs on developing country exports of tive nominal debt service relief of more than $56 country with an established track record of good per- "everything but arms," and the United States offers billion has been approved for 28 countries, reduc- formance under adjustment programs supported by special concessions to exports from Sub-Saharan ing the net present value of their external debt by the International Monetary Fund and the World Bank Africa. However, there are still many restrictions built approximately two-thirds. Of these countries, 19 commits to undertake additional reforms and to into these programs. have reached the completion point and have been develop and implement a poverty reduction strategy. The average tariffs in the table reflect the tariff granted unconditional debt service relief of more · HIPC completion point is the date at which the schedules applied by high-income OECD members than $37 billion. country successfully completes the key structural reforms agreed on at the decision point, including developing and implementing its poverty reduction Location of indicators for Millennium Development Goal 8 strategy. The country then receives the bulk of debt Goal 8. Develop a global partnership for development Table relief under the HIPC Initiative without further policy 33. Net ODA as a percentage of DAC donors' gross national income 6.9 conditions. · Estimated total nominal debt service 34. Proportion of ODA for basic social services 1.4 relief is the amount of debt service relief, calculated 35. Proportion of ODA that is untied 6.9 at the decision point, that will allow the country to 36. Proportion of ODA received in landlocked countries as a percentage of GNI -- achieve debt sustainability at the completion point. 37. Proportion of ODA received in small island developing states as a percentage of GNI -- 38. Proportion of total developed country imports (by value, excluding arms) from developing countries admitted free of duty 1.4 39. Average tariffs imposed by developed countries on agricultural products and textiles and clothing from developing countries 1.4, 6.7* 40. Agricultural support estimate for OECD countries as a percentage of GDP 1.4 Data sources 41. Proportion of ODA provided to help build trade capacity -- The indicators here, and where they appear 42. Number of countries reaching HIPC decision and completion points 1.4 throughout the rest of the book, have been com- 43. Debt relief committed under new HIPC initiative 1.4 piled by World Bank staff from primary and sec- 44. Debt services as a percentage of exports of goods and services 4.17 ondary sources. The World Trade Organization, in 45. Unemployment rate of 15- to 24-year-olds 1.3, 2.9 collaboration with the UN Conference on Trade and 46. Proportion of population with access to affordable, essential drugs on a sustainable basis -- Development and the International Trade Centre, 47. Telephone lines and cellular subscribers per 100 people 1.3, 5.10 provided the estimates of goods admitted free 48a. Personal computers in use per 100 people 5.11 of tariffs and average tariffs. Subsidies to agri- 48b. Internet users per 100 people 5.11 culture are compiled by the OECD. Data on the HIPC Initiative are from the August 2005 "HIPC -- No data are available in the World Development Indicators database. * Table shows information on related indicators. Status Report." 2006 World Development Indicators 33 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of male % of female % of total Male Female % ages 15­19 % of total employment employment % of total seats 2004 2004 2004 2000­04a 1995­2004a 2003 2000­04a 2000­04a 1990 2006 East Asia & Pacificb 49.1 w 68 w 72 w 39.7 w .. w .. w 19 w 17 w Cambodia 51.7 53 60 38 8 52.6 31.6 53.3 .. 10 China 48.6 70 73 89 .. 39.5 .. .. 21 20 Hong Kong, China 52.7 79 85 .. .. 46.9 0.2 1.4 .. .. Indonesia 50.1 66 69 92 10 30.8 .. .. 12 11 Korea, Dem. Rep. 50.0 61 67 .. .. .. .. .. 21 20 Lao PDR 50.0 54 57 27 .. .. .. .. 6 23 Malaysia 49.2 71 76 74 .. 38.0 2.2 9.6 5 9 Mongolia 49.9 62 68 94 .. 49.4 18.4 31.7 25 7 Myanmar 50.3 58 64 76 .. .. .. .. .. .. Papua New Guinea 48.4 55 57 .. .. 35.4 .. .. 0 1 Philippines 49.7 69 73 88 8 41.1 .. .. 9 16 Thailand 50.8 67 74 92 .. 46.9 16.0 35.2 3 11 Vietnam 50.1 68 73 86 3 51.8 21.9 50.3 18 27 Europe & Central Asia 52.1 w 64 w 73 w 47.3 w 2.9 w 7.3 w .. w 13 w Albania 50.4 71 77 91 .. 40.3 .. .. 29 7 Armenia 53.3 68 75 92 6 47.0 1.1 0.8 36 5 Azerbaijan 51.4 70 75 66 .. 48.5 .. .. .. 12 Belarus 53.2 63 74 .. .. 55.9 .. .. .. 29 Bosnia and Herzegovina 51.4 72 77 99 .. .. .. .. .. 17 Bulgaria 51.5 69 76 .. .. 52.2 1.3 2.6 21 22 Croatia 51.9 72 79 .. .. 46.3 1.8 6.3 .. 22 Czech Republic 51.3 73 79 .. .. 45.8 0.3 1.2 .. 17 Estonia 54.0 66 77 .. .. 51.5 0.3 0.4 .. 19 Georgia 52.7 67 74 .. .. 45.2 19.9 38.8 .. 9 Hungary 52.4 69 77 .. .. 47.1 0.4 0.7 21 9 Kazakhstan 52.0 60 71 .. 7 48.7 0.8 1.2 .. 10 Kyrgyz Republic 50.8 64 72 .. 9 44.0 6.5 15.9 .. 0 Latvia 54.2 66 78 .. .. 53.4 3.5 3.9 .. 21 Lithuania 53.3 66 78 .. .. 50.0 2.8 4.3 .. 22 Macedonia, FYR 50.1 71 76 81 .. 42.2 7.0 18.1 .. 19 Moldova 52.2 65 72 .. .. 54.6 1.3 3.4 .. 22 Poland 51.5 70 79 .. .. 47.7 4.0 7.2 14 20 Romania 51.2 68 75 .. .. 45.3 7.8 23.4 34 11 Russian Federation 53.6 59 72 .. .. 50.1 0.1 0.1 .. 10 Serbia and Montenegro 50.3 71 76 .. .. 44.9 .. .. .. 8 Slovak Republic 51.5 70 78 .. .. 52.1 .. .. .. 17 Tajikistan 50.3 61 67 71 .. 52.3 .. .. .. 18 Turkey 49.6 69 71 81 10 20.6 8.2 49.0 1 4 Turkmenistan 50.7 59 67 98 4 .. .. .. 26 16 Ukraine 54.1 63 74 .. .. 53.6 1.1 2.0 .. 5 Uzbekistan 50.3 64 70 97 10 41.5 .. .. .. 18 Latin America & Carib. 50.6 w 69 w 75 w 43.7 w .. w .. w 8w 20 w Argentina 51.1 71 78 98 .. 47.6 0.8 1.6 6 35 Bolivia 50.2 62 67 79 16 36.5 5.2 11.1 9 17 Brazil 50.7 67 75 .. 18 46.9 .. .. 5 9 Chile 50.5 75 81 .. .. 37.3 1.4 3.3 .. 15 Colombia 50.6 70 76 91 19 48.8 4.3 8.5 5 12 Costa Rica 49.2 76 81 .. .. 39.5 2.1 3.6 11 35 Cuba 50.0 75 79 100 .. 37.7 .. .. 34 36 Dominican Republic 49.5 64 71 99 23 34.9 .. .. 8 17 Ecuador 49.8 72 78 .. .. 41.1 3.0 8.6 5 16 El Salvador 50.9 68 74 86 .. 31.1 8.5 9.0 12 11 Guatemala 51.2 64 71 84 22 38.7 21.3 24.5 7 8 Haiti 50.8 51 53 79 18 .. .. .. .. 4 Honduras 49.6 66 70 83 .. 50.5 12.9 11.0 10 23 34 2006 World Development Indicators Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of male % of female % of total Male Female % ages 15­19 % of total employment employment % of total seats 2004 2004 2004 2000­04a 1995­2004a 2003 2000­04a 2000­04a 1990 2006 Jamaica 50.6 69 73 .. .. 48.0 0.7 2.0 5 12 Mexico 51.1 73 78 .. .. 37.4 5.5 11.3 12 24 Nicaragua 50.0 68 73 86 25 .. .. .. 15 21 Panama 49.5 73 78 .. .. 44.0 3.1 4.0 8 17 Paraguay 49.6 69 74 94 .. 42.0 .. .. 6 10 Peru 49.7 68 73 84 13 37.2 2.0 6.2 6 18 Trinidad and Tobago 50.6 67 73 92 .. 41.3 0.5 1.9 17 19 Uruguay 51.5 72 79 .. .. 46.3 0.9 2.0 6 11 Venezuela, RB 49.7 71 77 94 .. 41.5 1.8 3.3 10 17 Middle East & N. Africa 49.5 w 68 w 71 w .. w .. w .. w 4w 8w Algeria 49.6 70 73 81 .. 15.5 .. .. 2 6 Egypt, Arab Rep. 49.8 68 72 69 9 21.6 8.4 19.5 4 2 Iran, Islamic Rep. 49.3 69 72 .. .. .. .. .. 2 4 Iraq .. .. .. 77 .. .. .. .. 11 26 Jordan 48.0 70 73 99 4 24.9 .. .. 0 6 Lebanon 51.0 70 75 .. .. .. .. .. 0 5 Libya 48.4 72 77 .. .. .. .. .. .. 5 Morocco 50.3 68 72 68 7 26.2 21.6 52.5 0 11 Oman 43.4 73 76 100 .. 25.6 .. .. .. 2 Syrian Arab Republic 49.7 72 75 71 .. 18.2 .. .. 9 12 Tunisia 49.6 71 75 92 .. 25.3 .. .. 4 23 West Bank and Gaza 49.1 71 75 .. .. .. 7.0 32.5 .. .. Yemen, Rep. 49.3 60 63 41 16 6.1 .. .. 4 0c South Asia 48.7 w 63 w 64 w 18.1 w .. w .. w 6w 14 w Afghanistan .. .. .. 16 .. .. .. .. 4 .. Bangladesh 48.9 63 64 49 33 d 24.2 10.1 73.2 10 15 India 48.7 63 64 .. 21 17.5 .. .. 5 8 Nepal 50.4 62 63 28 21 .. .. .. 6 0 Pakistan 48.5 64 66 43 .. 8.7 16.4 46.9 10 21 Sri Lanka 49.2 72 77 95 .. 43.2 4.2 20.9 5 5 Sub-Saharan Africa 50.1 w 46 w 47 w .. w .. w .. w .. w 16 w Angola 50.7 40 43 66 .. .. .. .. 15 15 Benin 49.7 54 55 81 22 .. .. .. 3 7 Botswana 50.9 36 35 97 .. 47.0 1.4 1.2 5 11 Burkina Faso 49.8 47 49 73 23 15.2 .. .. .. 12 Burundi 51.3 43 45 78 .. .. .. .. .. 31 Cameroon 50.3 45 47 83 28 .. 9.5 27.2 14 9 Central African Republic 51.3 39 40 62 36 .. .. .. 4 11 Chad 50.5 43 45 42 39 .. .. .. .. 7 Congo, Dem. Rep. 50.4 43 45 68 .. .. .. .. 5 12 Congo, Rep. 50.4 51 54 .. .. .. .. .. 14 9 Côte d'Ivoire 49.1 45 47 88 31 20.2 .. .. 6 9 Eritrea 51.0 53 56 70 14 35.0 .. .. .. 22 Ethiopia 50.3 42 43 27 16 .. .. .. .. 21 Gabon 50.2 54 55 94 33 .. .. .. 13 9 Gambia, The 50.4 55 58 91 .. .. .. .. 8 13 Ghana 49.4 57 58 92 14 .. .. .. .. 11 Guinea 48.8 54 54 84 37 .. .. .. .. 19 Guinea-Bissau 50.6 44 46 62 .. .. .. .. 20 14 Kenya 50.0 49 47 88 23 38.5 .. .. 1 7 Lesotho 53.5 35 37 85 .. .. .. .. .. 12 Liberia 50.1 42 43 85 .. .. .. .. .. 13 Madagascar 50.3 54 57 80 34 .. 29.7 51.9 7 7 Malawi 50.4 40 40 94 33 12.5 .. .. 10 14 Mali 50.2 48 49 57 40 .. .. .. .. 10 Mauritania 50.6 52 55 64 16 .. .. .. .. .. 2006 World Development Indicators 35 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving prenatal care Male Female years % of women % of male % of female % of total Male Female % ages 15­19 % of total employment employment % of total seats 2004 2004 2004 2000­04a 1995­2004a 2003 2000­04a 2000­04a 1990 2006 Mauritius 50.3 69 76 .. .. 38.5 .. .. 7 17 Mozambique 51.7 41 42 85 41 .. .. .. 16 35 Namibia 50.4 47 48 91 18 50.8 12.8 22.0 7 27 Niger 48.9 45 45 41 43 .. .. .. 5 12 Nigeria 49.4 43 44 58 25 .. .. .. .. 6 Rwanda 51.6 42 46 92 7 .. .. .. 17 49 Senegal 50.8 55 57 79 22 .. .. .. 13 19 Sierra Leone 50.7 40 43 68 .. .. .. .. .. 15 Somalia 50.4 46 48 .. .. .. .. .. 4 8 South Africa 50.9 44 45 .. 16 .. 0.5 1.1 3 33 Sudan 49.7 55 58 60 .. 18.9 .. .. .. 15 Swaziland 51.9 43 42 90 .. 31.3 .. .. 4 11 Tanzania 50.3 46 47 94 25 .. 3.0 4.6 .. 30 Togo 50.6 53 57 85 19 .. .. .. 5 7 Uganda 50.0 48 49 92 31 .. 10.3 40.5 12 24 Zambia 50.0 39 38 93 32 .. .. .. 7 13 Zimbabwe 50.5 38 37 .. 21 21.8 10.4 13.6 11 16 High income 50.7 w 76 w 82 w 46.0 w .. w 3.1 w 8w 22 w Australia 50.6 77 83 .. .. 48.9 0.3 0.4 6 25 Austria 51.1 76 82 .. .. 44.5 1.4 3.0 12 34 Belgium 50.9 76 82 .. .. 44.4 .. .. 9 35 Canada 50.4 77 83 .. .. 49.2 0.2 0.3 13 21 Denmark 50.5 75 80 .. .. 48.3 0.3 1.1 31 37 Finland 51.1 75 82 .. .. 50.6 0.4 0.4 32 38 France 51.3 77 84 .. .. 47.0 .. .. 7 12 Germany 51.2 76 81 .. .. 46.4 0.5 1.9 .. 32 Greece 50.6 77 81 .. .. 41.1 3.9 14.2 7 13 Ireland 50.3 76 81 .. .. 47.4 0.8 1.3 8 13 Israel 50.5 77 81 .. .. 48.9 0.2 0.7 7 15 Italy 51.5 77 83 .. .. 41.2 3.1 5.8 13 12 Japan 51.1 78 85 .. .. 40.8 1.6 9.2 1 9 Korea, Rep. 49.8 74 81 .. .. 41.2 1.3 16.7 2 13 Kuwait 39.8 75 79 .. .. 24.1 .. .. .. 2 Netherlands 50.4 76 81 .. .. 45.7 0.2 1.1 21 37 New Zealand 50.9 77 81 .. .. 51.3 0.5 0.9 14 32 Norway 50.4 78 82 .. .. 49.1 0.3 0.4 36 38 Portugal 51.7 74 81 .. .. 46.9 1.0 2.3 8 21 Puerto Rico 52.0 73 82 .. .. 40.1 0.1 1.1 .. .. Saudi Arabia 46.0 70 74 .. .. 14.5 .. .. .. 0 Singapore 49.7 77 81 .. .. 47.8 0.3 1.3 5 16 Slovenia 51.2 73 80 .. .. 47.4 3.1 5.6 .. 12 Spain 50.9 77 84 .. .. 40.7 0.9 2.7 15 36 Sweden 50.4 78 83 .. .. 50.9 .. .. 38 45 Switzerland 51.5 79 84 .. .. 46.9 1.6 3.0 14 25 United Arab Emirates 32.0 77 81 .. .. 14.4 .. .. 0 0 United Kingdom 51.2 76 81 .. .. 49.9 0.6 0.7 6 20 United States 50.8 75 80 .. .. 48.8 .. 0.1 7 15 World 49.7 w 65 w 69 w 38.1 w .. w .. w 11 w 17 w Low income 49.3 58 60 23.3 .. .. 7 16 Middle income 49.8 68 73 40.5 .. .. 15 15 Lower middle income 49.4 68 73 39.9 .. .. 16 15 Upper middle income 51.4 66 73 44.1 2.8 6.7 .. 15 Low & middle income 49.6 63 67 36.1 .. .. 12 15 High income 50.7 76 82 46.0 .. 3.1 8 22 a. Data are for the most recent year available. b. Hong Kong, China, is classified as a high-income economy and is not included in the East Asia and Pacific aggregate. c. Less than 0.5. d. Refers to women ages 15­49. 36 2006 World Development Indicators Women in development About the data Definitions Despite much progress in recent decades, gender Women's wage work is important for economic · Female population is the percentage of the popu- inequalities remain pervasive in many dimensions of growth and the well-being of families. But restricted lation that is female. · Life expectancy at birth is life--worldwide. But while disparities exist through- access to education and vocational training, heavy the number of years a newborn infant would live if out the world, they are most prevalent in poor devel- workloads at home and in nonpaid domestic and prevailing patterns of mortality at the time of its birth oping countries. Gender inequalities in the alloca- market activities, and labor market discrimination were to stay the same throughout its life. · Pregnant tion of such resources as education, health care, often limit women's participation in paid economic women receiving prenatal care are the percentage nutrition, and political voice matter because of the activities, lower their productivity, and reduce their of women attended at least once during pregnancy strong association with well-being, productivity, and wages. When women are in salaried employment, by skilled health personnel for reasons related to economic growth. This pattern of inequality begins they tend to be concentrated in the nonagricultural pregnancy. · Teenage mothers are the percentage at an early age, with boys routinely receiving a larger sector. However, in many developing countries of women ages 15­19 who already have children share of education and health spending than do girls, women are a large part of agricultural employment, or are currently pregnant. · Women in nonagricul- for example. often as unpaid family workers. Among people who tural sector refers to women wage employees in the Because of biological differences girls are expected are unsalaried, women are more likely than men to nonagricultural sector as a percentage of total non- to experience lower infant and child mortality rates be unpaid family workers, while men are more likely agricultural employment. · Unpaid family workers and to have a longer life expectancy than boys. This than women to be self-employed or employers. There are those who work without pay in a market-oriented biological advantage, however, may be overshad- are several reasons for this. establishment or activity operated by a related per- owed by gender inequalities in nutrition and medical Few women have access to credit markets, capital, son living in the same household. · Women in parlia- interventions, and by inadequate care during preg- land, training, and education, which may be required ments are the percentage of parliamentary seats in a nancy and delivery, so that female rates of illness to start up a business. Cultural norms may prevent single or lower chamber occupied by women. and death sometimes exceed male rates, particularly women from working on their own or from super- during early childhood and the reproductive years. In vising other workers. Also, women may face time high-income countries women tend to outlive men by constraints due to their traditional family respon- four to eight years on average, while in low-income sibilities. Because of biases and misclassification countries the difference is narrower--about two to substantial numbers of employed women may be three years. The difference in child mortality rates underestimated or reported as unpaid family workers (table 2.19) is another good indicator of female even when they work in association or equally with social disadvantage because nutrition and medi- their husbands in the family enterprise. cal interventions are particularly important for the Women are vastly underrepresented in decision- 1­5 age group. Female child mortality rates that are making positions in government, although there is as high as or higher than male child mortality rates some evidence of recent improvement. Gender parity might be indicative of discrimination against girls. in parliamentary representation is still far from being Having a child during the teenage years limits girls' realized. In 2005 women represented 16 percent of opportunities for better education, jobs, and income parliamentarians worldwide, compared with 9 per- and increases the likelihood of divorce and separa- cent in 1987. Without representation at this level, it tion. Pregnancy is more likely to be unintended dur- is difficult for women to influence policy. ing the teenage years, and births are more likely to For information on other aspects of gender, see be premature and are associated with greater risks tables 1.2 (Millennium Development Goals: eradicat- of complications during delivery and of death. In ing poverty and improving lives), 2.3 (employment many countries maternal mortality (tables 1.2 and by economic activity), 2.4 (child labor), 2.5 (unem- 2.16) is a leading cause of death among women of ployment), 2.12 (education efficiency), 2.13 (educa- Data sources reproductive age. Most maternal deaths result from tion completion and outcomes), 2.16 (reproductive Data on female population and life expectancy preventable causes--hemorrhage, infection, and health), 2.18 (health risk factors and future chal- are from the World Bank's population database. complications from unsafe abortions. Prenatal care lenges), and 2.19 (mortality). Data on pregnant women receiving prenatal is essential for recognizing, diagnosing, and promptly care are from United Nations Children's Fund's treating complications that arise during pregnancy. State of the World's Children 2006. Data on teen- In high-income countries most women have access age mothers are from Demographic and Health to health care during pregnancy, but in developing Surveys by Macro International. Data on labor countries an estimated 8 million women suffer preg- force and employment are from the ILO's Key nancy-related complications every year, and over half Indicators of the Labour Market, fourth edition. a million die (WHO 2004). This is reflected in the Data on women in parliaments are from the Inter- differences in maternal mortality ratios between Parliamentary Union. high- and low-income countries. 2006 World Development Indicators 37 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 2004 2004 2004 2004b 2004 2004 2004 2003­04 2003­04 2004 2004 2002 American Samoa 57 0.2 285 .. ..c .. .. .. .. .. .. 286 Andorra 66 0.5 140 .. ..d .. .. .. .. .. .. .. Antigua and Barbuda 80 0.4 182 759 9,480 889 11,100 4.1 2.9 75 .. 370 Aruba 99 0.2 521 .. ..d .. .. .. .. .. .. 1,982 Bahamas, The 319 13.9 32 4,684 15,100 5,071 16,350 0.7 ­0.7 70 .. 2,081 Bahrain 716 0.7 1,008 10,288 14,370 14,080 19,670 5.4 3.9 75 .. 21,292 Barbados 269 0.4 625 2,831e 10,530e .. .. .. .. 75 100 1,220 Belize 283 23.0 12 1,115 3,940 1,851 6,550 4.2 0.9 72 .. 788 Bermuda 64 0.1 1,280 .. ..d .. .. .. .. .. .. 498 Bhutan 896 47.0 19 677 760 .. .. 4.9 2.3 64 .. 399 Brunei Darussalam 366 5.8 69 .. ..d .. .. .. .. 77 .. 6,174 Cape Verde 495 4.0 123 852 1,720 2,803f 5,660 f 5.5 3.1 70 76 147 Cayman Islands 44 0.3 169 .. ..d .. .. .. .. .. .. 289 Channel Islands 149 0.2 745 .. ..d .. .. .. .. 79 .. .. Comoros 588 2.2 264 328 560 1,135f 1,930 f 1.9 ­0.2 63 56 84 Cyprus 826 9.3 89 13,633 16,510 18,360 f 22,230 f 3.7 2.5 79 .. 6,661 Djibouti 779 23.2 34 739 950 1,675f 2,150 f 3.0 1.1 53 .. 359 Dominica 71 0.8 95 262 3,670 378 5,290 2.0 1.6 77 .. 121 Equatorial Guinea 492 28.1 18 .. ..c 3,731f 7,580 f 10.0 7.5 43 .. 169 Faeroe Islands 48 1.4 34 .. ..d .. .. .. .. .. .. 652 Fiji 841 18.3 46 2,286 2,720 4,835 5,750 4.1 3.2 68 .. 1,352 French Polynesia 253 4.0 69 .. ..d .. .. .. .. 74 .. 700 Greenland 57 410.5 0 .. ..d .. .. .. .. 69 .. 564 Grenada 106 0.3 311 397 3,750 746 7,050 ­2.8 ­3.8 73 .. 231 Guam 167 0.6 303 .. ..d .. .. .. .. 75 .. 4,089 Guyana 750 215.0 4 765 1,020 3,181f 4,240 f 1.6 1.4 64 .. 1,608 Iceland 292 103.0 3 11,077 37,920 9,455 32,370 5.2 4.3 80 .. 2,213 Isle of Man 77 0.6 135 2,138 23,750 .. .. 6.3 .. .. .. .. About the data Definitions This table shows data for 55 economies--small · Population is based on the de facto definition of of output plus net receipts of primary income (com- economies with populations between 30,000 and population, which counts all residents regardless of pensation of employees and property income) from 1 million and smaller economies if they are members legal status or citizenship--except for refugees not abroad. Data are in current U.S. dollars converted of the World Bank. Where data on gross national permanently settled in the country of asylum, who using the World Bank Atlas method (see Statisti- income (GNI) per capita are not available, the esti- are generally considered part of the population of cal methods). · GNI per capita is gross national mated range is given. For more information on the their country of origin. The values shown are mid- income divided by midyear population. GNI per cap- calculation of GNI (gross national product, or GNP, in year estimates for 2004. See also table 2.1. · Sur- ita in U.S. dollars is converted using the World Bank the System of National Accounts 1968) and purchas- face area is a country's total area, including areas Atlas method. · PPP GNI is gross national income ing power parity (PPP) conversion factors, see About under inland bodies of water and some coastal converted to international dollars using purchasing the data for table 1.1. Since 2000 this table has waterways. · Population density is midyear popu- power parity rates. An international dollar has the excluded France's overseas departments--French lation divided by land area in square kilometers. same purchasing power over GNI as a U.S. dollar Guiana, Guadeloupe, Martinique, and Réunion--for · Gross national income (GNI) is the sum of value has in the United States. · Gross domestic product which GNI and other economic measures are now added by all resident producers plus any product (GDP) is the sum of value added by all resident included in the French national accounts. taxes (less subsidies) not included in the valuation producers plus any product taxes (less subsidies) 38 2006 World Development Indicators 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 2004 2004 2004 2004b 2004 2004 2004 2003­04 2003­04 2004 2004 2002 Kiribati 98 0.7 134 95 970 .. .. 1.8 0.3 63 .. 29 Liechtenstein 34 0.2 213 .. ..d .. .. .. .. .. .. .. Luxembourg 453 2.6 174 25,559 56,380 27,928 61,610 4.5 3.8 78 .. 9,427 Macao, China 457 .. .. .. ..d .. .. 10.1 9.3 80 .. 1,806 Maldives 321 0.3 1,071 773 2,410 .. .. 10.8 8.1 67 .. 1,030 Malta 401 0.3 1,254 4,834 12,050 7,460 18,590 0.4 ­0.2 79 .. 2,953 Marshall Islands 61 0.2 340 142 2,320 .. .. 1.5 ­2.2 .. .. .. Mayotte 172 0.4 430 .. ..c .. .. .. .. .. .. .. Micronesia, Fed. Sts. 110 0.7 157 252 2,300 .. .. ­3.8 ­4.6 68 .. .. Monaco 33 0.0 16,923 .. ..d .. .. .. .. .. .. .. Netherlands Antilles 181 0.8 226 .. ..d .. .. .. .. 76 97 4,928 New Caledonia 230 18.6 13 .. ..d .. .. .. .. 75 .. 1,821 Northern Mariana Islands 77 0.5 161 .. ..c .. .. .. .. .. .. .. Palau 20 0.5 43 137 6,870 .. .. 2.0 0.5 .. .. 234 Qatar 777 11.0 71 .. ..d .. .. .. .. 74 89 36,391 Samoa 184 2.8 65 338 1,840 1,031f 5,610 f 3.1 2.3 70 99 143 São Tomé and Principe 153 1.0 159 60 390 .. .. 4.5 2.1 63 .. 92 Seychelles 84 0.5 182 685 8,190 1,328 f 15,880 f ­2.0 ­3.0 73 92 535 Solomon Islands 466 28.9 17 263 560 838 1,800 5.5 2.8 63 .. 172 San Marino 28 0.1 463 653 ..d .. .. 2.3 .. .. .. .. St. Kitts and Nevis 47 0.4 131 326 6,980 510 10,910 2.1 2.1 71 .. 114 St. Lucia 164 0.6 268 684 4,180 915 5,590 3.5 1.6 73 .. 377 St. Vincent & Grenadines 118 0.4 304 403 3,400 714 6,030 6.0 5.4 71 .. 183 Suriname 446 163.3 3 997 2,230 .. .. 4.6 3.9 69 .. 2,250 Timor-Leste 887 14.9 60 506 570 .. .. 1.8 ­5.0 56 .. .. Tonga 102 0.8 142 190 1,860 801f 7,850 f 4.3 3.9 72 .. 106 Vanuatu 207 12.2 17 287 1,390 612 f 2,950 f 3.0 1.0 69 .. 84 Virgin Islands (U.S.) 113 0.4 323 .. ..d .. .. .. .. 79 .. 10,241 a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be upper middle income ($3,256­$10,065). d. Estimated to be high income ($10,066 or more). e. Refers to GDP and GDP per capita at factor cost. f. The estimate is based on regression; others are extrapolated from the latest International Comparison Program benchmark estimates. not included in the valuation of output. Growth is calculated from constant price GDP data in local currency. · Life expectancy at birth is the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to Data sources stay the same throughout its life. · Adult illiteracy rate is the percentage of adults ages 15 and older The indicators here and throughout the rest of the who cannot, with understanding, read and write a book have been compiled by World Bank Group short, simple statement about their everyday life. staff from primary and secondary sources. More · Carbon dioxide emissions are those stemming information about the indicators and their sources from the burning of fossil fuels and the manufacture can be found in the About the data, Definitions, of cement. They include carbon dioxide produced and Data sources entries that accompany each during consumption of solid, liquid, and gas fuels table in subsequent sections. and gas flaring. 2006 World Development Indicators 39 he world is in the middle of a major demographic transition. Its population continues to grow every year, but the pace of growth has slowed as fertility rates decline. As population growth slows, the age structure of the population is changing, with the share of the young declining and that of the elderly growing. This changing age structure has important implications for economic and social policies and hence for sustainable development. But different countries and regions are at varying stages of this transition, depending on their fertility, mortality, and migration trends, creating a "demographic divide" between countries (Kent and Haub 2005). In much of the industrial world increasing life expectancy and aging populations have coincided with income growth, healthier lifestyles, and fertility rates that are below population replacement levels. For these countries there will be little change in future population size in the absence of inmigration. In fact, large increases in inmigration or in the retirement age would be needed to stabilize the labor force and maintain current labor force to population ratios. In developing countries fertility rates have also declined but remain much higher than in industrial countries, and fertility rates vary considerably across regions: high in Sub-Saharan Africa and the Middle East, but low in East Asia. Except in the transition economies of Eastern Europe, where fertility rates are near or below replacement levels, the population in developing countries will continue to grow well into the twenty-first century, and outmigration will only modestly reduce the population growth rate. Technology, consumption patterns, unequal distribution of wealth, and the choices people and governments make all affect demographic trends. These, in turn, affect social and economic outcomes, and, consequently, what place these countries will take on the world stage in the future. Sub-Saharan African countries are trailing most others in their progress through the demographic transition. And if economic growth continues to lag behind population growth, as was the case in the early 1990s, it will exacerbate poverty in the region. Rapid population growth in Sub-Saharan Africa The challenges facing Sub-Saharan Africa as it strives to meet its development objectives are more daunting than those facing other regions. Its efforts to alleviate poverty, empower women, reduce child mortality, and improve maternal health have been undercut by the AIDS epidemic, by conflict, and by human displacement in the wake of natural disasters. In the past three decades its population has grown faster than that of any other region, doubling between 1975 and 2000 and now growing at 2.5 percent a year. Roughly 47 percent of the Sub-Saharan population is between the ages of 5 and 24, indicating that the population will continue to increase well into the twenty-first century. This large cohort will require substantial increases in future spending on health, education, and care for dependents. 2006 World Development Indicators 41 Has success bred complacency? ductive health (table 2a). Globally, contraceptive prevalence Too little is being said about the challenge of continuing rapid increased from 54 percent in 1990 to 59 percent in 1995 population growth to African development. One possible rea- and to more than 60 percent in 2003 (box 2b). son for this may be that the success of fertility reductions The slowdown in population growth (table 2c) can be traced in other regions and in some African countries has left the to these fertility declines. In Europe and Central Asia women impression that the population problem has been solved now have on average only 1.6 births--too few to replace (Cleland and Sinding 2005). Fertility rates have declined today's population. At the other extreme is Sub-Saharan dramatically in the past 25 years where governments have Africa, with average fertility remaining very high. increased investments in education and in women's repro- Even in Sub-Saharan Africa regional figures mask huge differences across countries (table 2d). In South Africa, Botswana, Zimbabwe, and Lesotho fertility continues to Total fertility rates by region, 1970, 1980, and 2004 decline as a result of successful family planning programs. Region 1970 1980 2004 Of women ages 15­49, 54 percent were using contracep- East Asia & Pacific 5.4 3.0 2.1 tion in Zimbabwe and 48 percent in Botswana, compared Europe & Central Asia 2.5 2.2 1.6 with 14 percent in Niger and 8 percent in Chad in the past Latin America & Caribbean 5.3 4.2 2.4 decade. Even in countries with high fertility, the rates vary Middle East & North Africa 6.7 6.2 3.1 South Asia 6.0 5.2 3.1 by socioeconomic status. In Benin the fertility rate was 7.3 Sub-Saharan Africa 6.8 6.7 5.4 births for women in the lowest asset quintile and 3.8 for High-income 2.5 1.9 1.7 women in the richest quintile. World 4.8 3.7 2.6 Source: World Bank database. Why is fertility still high? Sub-Saharan Africa is becoming fragmented in its fertility declines. There are several reasons for this. The logistical and cultural challenge of delivering family planning programs, Family planning and the fertility transition the often poor quality of health services, ignorance about reproductive health issues, differences in economic status, The use of family planning among married women worldwide rose from and continuing gender inequality all contribute to high fertility 10 percent in 1960 to more than 60 percent in 2003. Due in part to mod- rates. Desired family size, though decreasing slowly over past ern contraception, the decline in fertility and the shift to smaller families decades, remains high--as high as eight children in some occurred faster in developing countries--in only a few decades--than had occurred in industrial countries, where the transition to low fertility began in the 1830s. Crude birth rates were about 37 per 1,000 people in pre-Revolutionary France and 42­43 in the 1850s in the United States, Population growth rates by region (%) before gradually commencing a decline to their current levels of 8 per Region 1950­80 1980­90 1990­2004 1,000 people. East Asia & Pacific 2.0 1.6 1.2 What contributed to smaller families? Organized family planning Europe & Central Asia 1.3 0.9 0.1 programs bringing contraceptive supplies and services to the people, Latin America & Caribbean 2.6 2.0 1.6 along with information campaigns promoting smaller, healthier families. Middle East & North Africa 2.6 3.0 2.1 Studies in the 1990s showed that these programs were responsible South Asia 2.2 2.2 1.8 for about half the fertility decline of developing countries since the Sub-Saharan Africa 2.6 2.9 2.5 1960s. Even couples in remote rural communities in Bangladesh and High-income 1.1 0.7 0.8 Vietnam gained access to modern contraceptives through nationwide World 1.9 1.7 1.4 family planning programs. Contraceptive prevalence is a key determinant of declining fertility. Source: World Bank database. Based on the current use of family planning services, contraceptive rates are not expected to increase rapidly because of Africa's widespread poverty, high rates of illiteracy, largely rural populations, and strong Total fertility rates in selected Sub-Saharan countries, 2004 traditional preferences for large families. However, there is an emerg- Country Fertility rate Country Fertility rate ing preference for spacing and limiting births among married women Niger 7.7 Lesotho 3.5 of reproductive age in African countries, ranging from 10 percent to Uganda 7.1 Zimbabwe 3.4 35 percent. The increased availability of contraception has reduced Guinea-Bissau 7.1 Botswana 3.1 the gap between the number of women who want to limit births and Mali 6.9 South Africa 2.7 those who can in most countries. But in some countries unmet need Burundi 6.8 Mauritius 2.0 remains high. Source: World Bank database. 42 2006 World Development Indicators African countries (table 2e). By contrast, the desired family bers of old and very young and a relatively small working-age size in South Asia is typically fewer than three children. population. But recent data indicate that prevalence among High desired family sizes are associated with high infant pregnant women attending antenatal clinics in Zimbabwe is mortality rates. But when birth rates began to drop in Ban- declining in all age groups. In South Africa, with the largest gladesh and Nepal in the 1980s their infant mortality rates number of infected people, rates of HIV infection among were higher than those in many western and central African pregnant women ages 15­24 have stabilized since 2000. countries (Cleland and Sinding 2005). HIV prevalence among pregnant women has declined coun- Another reason for high fertility rates is that contraceptive trywide in Kenya and Uganda (UNAIDS and WHO 2005). But prevalence rates remain low. For 9 of 20 African countries in western and central Africa there is no consistent evidence that conducted Demographic and Health Surveys between of declining prevalence among pregnant women in recent 1999 and 2005, contraceptive use, including traditional years. And overall in Sub-Saharan Africa the prevalence of methods, was less than 10 percent for women ages 15­49. HIV infections in people ages 15­49 has remained at about Compare that with other regions, where on average 40 per- 7 percent since 2000. So while life expectancy has fallen cent of women were using a method of contraception. In in some cases, fertility remains stubbornly high for many addition to contraceptive use, the method of contraception Sub-Saharan African countries, and high fertility remains the is also important for sustained fertility declines. In countries dominant influence on current and future population growth with low contraceptive prevalence, fewer women use modern and size. methods, further diluting the effect of low contraceptive use In many West African countries, where HIV prevalence on fertility (table 2f). Of 17 African countries that conducted has remained lower than in other regions in Africa, more Demographic and Health Surveys between 2000 and 2004, women die from unsafe abortions than as a result of AIDS in 8 of them use of modern methods was estimated at less (Population Action International 2006). If African nations can than 10 percent. expand the capacity and quality of family planning sevices, Finally, HIV/AIDS has affected fertility and mortality trends that will bring about much needed declines in fertility rates in Sub-Saharan Africa. AIDS-related deaths among working- while strengthening the status of women. Until this happens, age adults in the seven worst AIDS-affected countries will continuing high fertility rates and rapid population growth produce an age structure not seen before, with large num- may prove a more serious obstacle to poverty reduction than will AIDS. What will high fertility mean for Desired family size in selected countries in Sub-Saharan Africa and Sub-Saharan Africa's future population? South Asia, latest year available The population of Sub-Saharan Africa has grown from Desired Desired number of number of 225 million in 1960 to 733 million in 2004. The World Sub-Saharan Africa children South Asia children Bank projects a doubling of the population to 1.4 bil- Cameroon (2004) 5.7 Bangladesh (1999/2000) 2.5 lion by 2050, increasing the region's share of the world Chad (1996/97) 8.3 India (1998/99) 2.6 population from 13 percent today to 20 percent. Fertility Eritrea (2002) 5.8 Nepal (2001) 2.6 Niger (1998) 8.2 rates will remain over 3.5 births per woman until 2025, producing a youthful age structure, with a large proportion Source: Demographic and Health Surveys. of children under 15 years old. Comparisons with South Asia, another region with high fertility, show that the fertil- ity transition in Sub-Saharan Africa lags one generation behind (figure 2g). Contraceptive method mix, selected countries, 2000­04 Very rapid population growth is expected to continue in Contraceptive use several African countries, with the population likely to triple in Country Any method Any modern Burkina Faso, Burundi, Chad, Democratic Republic of Congo, Kenya 39.3 31.5 Republic of Congo, Guinea-Bissau, Liberia, Mali, Niger, and Madagascar 27.1 18.3 Uganda (United Nations 2005). Among the nine countries Benin 18.6 7.2 expected by the United Nations to account for half the world's Burkina Faso 13.8 8.8 projected population increase between 2005 and 2050, four Nigeria 12.6 8.2 are in Sub-Saharan Africa: Democratic Republic of Congo, Bangladesh 58.5 47.6 Ethiopia, Nigeria, and Uganda. Haiti 28.1 22.8 Although fertility rates have started to decline in many Cambodia 23.8 18.8 Sub-Saharan countries, the rates of decline are expected to be more modest and to be achieved over a longer period Source: Demographic and Health Surveys. of time. And they will occur at different paces. For several 2006 World Development Indicators 43 Sub-Saharan Africa's delayed demographic transition Share of deaths in each age group (%) Male Female Sub-Saharan Africa South Asia 75+ 75+ 70­74 70­74 65­69 65­69 60­64 60­64 55­59 55­59 50­54 2000 50­54 45­49 45­49 40­44 40­44 35­39 35­39 30­34 30­34 25­29 25­29 20­24 20­24 15­19 15­19 10­14 10­14 5­9 5­9 0­4 0­4 10 5 0 0 5 10 10 5 0 0 5 10 75+ 75+ 70­74 70­74 65­69 65­69 60­64 60­64 55­59 55­59 50­54 2020 50­54 45­49 45­49 40­44 40­44 35­39 35­39 30­34 30-34 25­29 25-29 20­24 20­24 15­19 15­19 10­14 10­14 5­9 5­9 0­4 0­4 10 5 0 0 5 10 10 5 0 0 5 10 75+ 75+ 70­74 70­74 65­69 65­69 60­64 60­64 55­59 55­59 50­54 2040 50­54 45­49 45­49 40­44 40­44 35­39 35­39 30­34 30­34 25­29 25­29 20­24 20­24 15­19 15­19 10­14 10­14 5­9 5­9 0­4 0­4 10 5 0 0 5 10 10 5 0 0 5 10 Source: World Bank staff estimates. Projected fertility rates in selected African regions Republic of Korea, and Thailand. Other countries with mod- 2005­ 2010­ 2015­ 2020­ 2025­ 2030­ 2035­ erate growth rates--such as Bangladesh, Brazil, India, and Region 10 15 20 25 30 35 40 Indonesia, which have had impressive fertility declines--still Western Africa 5.4 4.8 4.4 3.9 3.5 3.2 3.0 Central Africa 6.1 5.8 5.4 5.0 4.5 4.1 3.6 have considerable momentum for future growth due to a Southern Africa 2.7 2.5 2.4 2.3 2.2 2.1 2.0 young age structure. Each demographic situation is associated with its own Source: United Nations 2005. social, economic, environmental, and political challenge (box 2i). What is of concern about the demographic divide is not the differences in population growth rates, but the dis- decades fertility declines in western and central Africa are parities in living standards, personal well-being, and future expected to lag behind those that have already taken place prospects associated with these trends. in southern Africa (table 2h). People in Japan and Nigeria, with populations of similar size in 2004 but at opposing ends of the divide, have starkly differ- What does high fertility mean for ent lives today--and they face very different futures (table 2j). Africa's development? In Japan the elderly dependency ratio is expected to increase As average population growth slowed globally over the past dramatically, straining government budgets because of higher half century, the range of national and regional demographic spending on pensions, health care, and long-term residential experiences widened. Growth rates remained high in many care. Econometric models suggest that the projected decline African countries such as Burkina Faso and Chad, while they in the working-age population could result in lower savings plummeted in countries in other regions, including Italy, the and investment rates and slower GDP growth (IMF 2004). 44 2006 World Development Indicators By contrast, in Nigeria, a microcosm of Sub-Saharan Africa, per skills and find productive employment. Its inability to deal capita growth could be boosted by the increase in the working- with a higher burden of infectious diseases, lower education age population. With 36 percent of its population under age levels, and limited investment in health infrastructure could 15 in 2025, Nigeria has a considerable momentum for future result in very different economic outcomes. Without invest- growth well into the twenty-first century. This growth depends, ments in physical stocks and human capital, Nigeria's popu- however, on the country pursuing sound economic and social lation growth will exert an unsustainable demand for public- policies to enable the large wave of potential workers to acquire sector-provided health, education, and other services. Population projections--trends and uncertainty The demographic divide: Nigeria and Japan Nigeria Japan Future trends in population size, age structure, births, deaths, and other 2004 2025 2004 2025 demographic variables are of interest to policymakers, government plan- Population (millions) 137 205 128 120 ners, and industry strategists. The reason: population forecasts can Total fertility rate per woman 5.6 3.3 1.3 1.8 Population ages 0­14 (percent) 45.1 36.2 14.8 12.5 imply a wide range of consequences for society and its environment. Population ages 65 and older (percent) 2.7 3.4 16.5 28.1 Country projections became more accurate over the 1950s and 1960s, Life expectancy at birth (years) 45 52 82 84 as demographic data improved, but since then there have been few Infant mortality rate (per 1,000 live births) 98 72 3 3 significant improvements. Adults with HIV/AIDS (percent ages 15­49) 5.4 .. 0.1 .. Fertility, mortality, and migration are the components of population Health expenditure per capita 60 .. 2,476 .. GNI per capita 430 .. 37,060 .. growth. While broad trends can be discerned and projected into the future with reasonable confidence, substantial uncertainty is attached Source: World Bank database. to the specific trend for any country or region. Uncertainty arises in part because the present demographic situation in any country is not known perfectly. But the main cause of uncertainty is that future trends in fer- tility, mortality, and migration are subject to unpredictable influences. Future economic development; societal, cultural, epidemiological, and environmental changes; or progress in science and technology cannot be predicted. Uncertainty also arises from the fact that humans can influ- ence the future through deliberate policy intervention, such as investing heavily in family planning and reproductive services. Some demographers argue that population forecasts should not go beyond a horizon of 30­35 years, due to the rapid increase in uncer- tainty beyond this point. Others note, however, that if the forecast carries an appropriate indication of the range of uncertainty, users can decide when the informational content of the forecast ceases to be useful. Source: NRC 2000. 2006 World Development Indicators 45 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as % proportion of working- Ages Ages Ages age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2004 2020 1990­2004 2004­20 2004 2004 2004 2004 2004 2004 2004 Afghanistan 14.6 .. .. .. .. .. .. .. .. .. .. .. Albania 3.3 3.1 3.4 ­0.4 0.6 27.6 64.3 8.1 0.4 0.1 6 17 Algeria 25.3 32.4 40.6 1.8 1.4 30.4 65.1 4.5 0.5 0.1 5 21 Angola 10.5 15.5 23.8 2.8 2.7 46.6 51.0 2.5 0.9 0.0a 22 48 Argentina 32.6 38.4 44.5 1.2 0.9 26.7 63.1 10.1 0.4 0.2 8 18 Armenia 3.5 3.0 3.0 ­1.1 ­0.2 21.7 66.4 11.9 0.3 0.2 9 12 Australia 17.1 20.1 23.3 1.2 0.9 20.0 67.5 12.6 0.3 0.2 7 13 Austria 7.7 8.2 8.3 0.4 0.1 15.8 67.8 16.4 0.2 0.2 9 10 Azerbaijan 7.2 8.3 9.4 1.1 0.8 26.8 66.2 6.9 0.4 0.1 6 16 Bangladesh 104.0 139.2 181.2 2.1 1.6 35.9 60.5 3.6 0.6 0.1 8 27 Belarus 10.2 9.8 8.9 ­0.3 ­0.6 15.8 69.7 14.6 0.2 0.2 15 9 Belgium 10.0 10.4 10.6 0.3 0.1 16.9 65.6 17.5 0.3 0.3 10 11 Benin 5.2 8.2 12.7 3.3 2.8 44.5 52.8 2.7 0.8 0.1 12 41 Bolivia 6.7 9.0 11.6 2.1 1.6 38.5 57.0 4.5 0.7 0.1 8 29 Bosnia and Herzegovina 4.3 3.9 3.8 ­0.7 ­0.1 16.9 69.6 13.5 0.2 0.2 9 9 Botswana 1.4 1.8 1.7 1.5 ­0.4 37.9 58.9 3.2 0.6 0.1 26 26 Brazil 149.4 183.9 219.2 1.5 1.1 28.1 65.9 6.0 0.4 0.1 7 20 Bulgaria 8.7 7.8 6.9 ­0.8 ­0.8 14.1 69.2 16.8 0.2 0.2 14 9 Burkina Faso 8.5 12.8 20.3 2.9 2.9 47.4 49.8 2.8 1.0 0.1 17 47 Burundi 5.7 7.3 12.3 1.8 3.3 45.5 51.7 2.8 0.9 0.1 18 45 Cambodia 9.7 13.8 18.6 2.5 1.9 37.7 59.0 3.4 0.6 0.1 11 30 Cameroon 11.7 16.0 20.4 2.3 1.5 41.6 54.7 3.7 0.8 0.1 17 35 Canada 27.8 32.0 36.4 1.0 0.8 17.9 69.1 13.0 0.3 0.2 7 10 Central African Republic 3.0 4.0 5.0 2.0 1.4 43.1 52.9 4.0 0.8 0.1 22 37 Chad 6.1 9.4 14.9 3.2 2.8 47.2 49.7 3.1 0.9 0.1 20 48 Chile 13.2 16.1 18.6 1.4 0.9 25.5 66.6 7.9 0.4 0.1 5 16 China 1,135.2 1,296.2 1,423.9 0.9 0.6 22.0 70.5 7.5 0.3 0.1 6 12 Hong Kong, China 5.7 6.9 8.1 1.3 1.0 14.8 73.4 11.8 0.2 0.2 5 7 Colombia 35.0 44.9 55.0 1.8 1.3 31.4 63.6 5.0 0.5 0.1 5 21 Congo, Dem. Rep. 37.8 55.9 90.0 2.8 3.0 47.2 50.1 2.7 0.9 0.1 20 50 Congo, Rep. 2.5 3.9 6.4 3.2 3.1 47.0 50.1 2.9 0.9 0.1 13 44 Costa Rica 3.1 4.3 5.3 2.3 1.3 29.0 65.3 5.7 0.4 0.1 4 17 Côte d'Ivoire 12.7 17.9 23.3 2.5 1.7 42.1 54.6 3.2 0.8 0.1 17 37 Croatia 4.8 4.4 4.4 ­0.5 ­0.1 15.8 67.2 17.0 0.2 0.3 11 9 Cuba 10.5 11.2 11.4 0.5 0.1 19.5 70.0 10.5 0.3 0.2 7 11 Czech Republic 10.4 10.2 9.9 ­0.1 ­0.2 15.0 71.0 14.1 0.2 0.2 11 10 Denmark 5.1 5.4 5.6 0.4 0.2 18.8 66.3 14.9 0.3 0.2 10 12 Dominican Republic 7.1 8.8 10.7 1.5 1.2 33.1 62.8 4.1 0.5 0.1 6 24 Ecuador 10.3 13.0 16.0 1.7 1.3 32.8 61.5 5.7 0.5 0.1 5 23 Egypt, Arab Rep. 55.7 72.6 94.8 1.9 1.7 33.9 61.4 4.7 0.6 0.1 6 26 El Salvador 5.1 6.8 8.5 2.0 1.5 34.3 60.4 5.3 0.6 0.1 6 24 Eritrea 3.0 4.2 6.6 2.4 2.8 44.8 52.9 2.3 0.8 0.0a 11 39 Estonia 1.6 1.3 1.3 ­1.1 ­0.4 15.6 68.1 16.3 0.2 0.2 13 10 Ethiopia 51.2 70.0 107.7 2.2 2.7 44.8 52.3 2.9 0.9 0.1 19 40 Finland 5.0 5.2 5.4 0.3 0.2 17.5 66.8 15.7 0.3 0.2 9 11 France 56.7 60.4 63.0 0.4 0.3 18.2 65.2 16.6 0.3 0.3 8 13 Gabon 1.0 1.4 1.7 2.5 1.4 40.5 55.1 4.4 0.7 0.1 13 30 Gambia, The 0.9 1.5 2.1 3.3 2.1 40.3 56.0 3.7 0.7 0.1 12 35 Georgia 5.5 4.5 4.1 ­1.4 ­0.7 19.5 66.5 14.1 0.3 0.2 11 11 Germany 79.4 82.5 82.3 0.3 0.0a 14.6 67.2 18.3 0.2 0.3 10 9 Ghana 15.5 21.7 28.8 2.4 1.8 39.5 56.9 3.6 0.7 0.1 11 31 Greece 10.2 11.1 11.2 0.6 0.1 14.4 67.6 18.0 0.2 0.3 9 9 Guatemala 8.9 12.3 17.5 2.3 2.2 43.5 52.3 4.3 0.8 0.1 6 35 Guinea 6.2 9.2 13.4 2.8 2.3 43.8 52.7 3.5 0.8 0.1 13 41 Guinea-Bissau 1.0 1.5 2.5 3.0 3.0 47.4 49.5 3.1 1.0 0.1 20 50 Haiti 6.9 8.4 10.3 1.4 1.3 38.0 58.1 4.0 0.7 0.1 13 30 46 2006 World Development Indicators Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as % proportion of working- Ages Ages Ages age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2004 2020 1990­2004 2004­20 2004 2004 2004 2004 2004 2004 2004 Honduras 4.9 7.0 9.5 2.6 1.9 39.7 56.5 3.8 0.7 0.1 6 29 Hungary 10.4 10.1 9.6 ­0.2 ­0.3 16.0 68.9 15.1 0.2 0.2 13 9 India 849.5 1,079.7 1,332.0 1.7 1.3 32.5 62.3 5.2 0.5 0.1 8 24 Indonesia 178.2 217.6 255.9 1.4 1.0 28.6 66.0 5.4 0.4 0.1 7 20 Iran, Islamic Rep. 54.4 67.0 85.0 1.5 1.5 29.8 65.7 4.5 0.5 0.1 5 19 Iraq 18.5 .. .. .. .. .. .. .. .. .. .. .. Ireland 3.5 4.1 4.9 1.1 1.2 20.3 68.7 10.9 0.3 0.2 7 16 Israel 4.7 6.8 8.3 2.7 1.2 27.9 62.0 10.1 0.4 0.2 6 21 Italy 56.7 57.6 57.1 0.1 0.0a 14.1 66.3 19.7 0.2 0.3 9 10 Jamaica 2.4 2.6 2.8 0.7 0.3 31.7 60.8 7.6 0.5 0.1 6 18 Japan 123.5 127.8 126.7 0.2 ­0.1 14.1 66.7 19.2 0.2 0.3 9 9 Jordan 3.2 5.4 7.6 3.9 2.1 37.6 59.3 3.1 0.6 0.1 4 27 Kazakhstan 16.3 15.0 14.9 ­0.6 0.0a 23.9 67.8 8.3 0.4 0.1 10 15 Kenya 23.4 33.5 49.6 2.5 2.5 42.9 54.2 2.8 0.8 0.1 15 39 Korea, Dem. Rep. 19.7 22.4 23.7 0.9 0.4 25.4 68.0 6.5 0.4 0.1 11 16 Korea, Rep. 42.9 48.1 49.4 0.8 0.2 19.1 71.9 9.0 0.3 0.1 5 9 Kuwait 2.1 2.5 3.7 1.0 2.5 24.5 73.8 1.7 0.3 0.0a 2 19 Kyrgyz Republic 4.4 5.1 6.1 1.0 1.1 32.1 61.8 6.1 0.5 0.1 7 22 Lao PDR 4.1 5.8 8.0 2.4 2.0 41.2 55.1 3.6 0.7 0.1 12 35 Latvia 2.7 2.3 2.1 ­1.0 ­0.5 15.2 68.1 16.6 0.2 0.2 14 9 Lebanon 2.7 3.5 4.1 1.8 1.0 29.1 63.6 7.3 0.5 0.1 7 19 Lesotho 1.6 1.8 1.7 0.9 ­0.3 39.0 55.8 5.2 0.7 0.1 25 28 Liberia 2.1 3.2 5.0 3.0 2.8 47.0 50.8 2.2 0.9 0.0a 21 50 Libya 4.3 5.7 7.5 2.0 1.7 30.4 65.7 4.0 0.5 0.1 4 23 Lithuania 3.7 3.4 3.2 ­0.5 ­0.4 17.4 67.4 15.2 0.3 0.2 12 9 Macedonia, FYR 1.9 2.0 2.1 0.4 0.1 20.1 69.0 10.9 0.3 0.2 9 12 Madagascar 12.0 18.1 26.6 2.9 2.4 44.2 52.7 3.1 0.8 0.1 12 39 Malawi 9.5 12.6 17.8 2.1 2.2 47.3 49.7 3.0 1.0 0.1 21 43 Malaysia 17.8 24.9 31.5 2.4 1.5 32.8 62.8 4.5 0.5 0.1 5 22 Mali 8.9 13.1 20.9 2.8 2.9 48.3 49.0 2.7 1.0 0.1 17 49 Mauritania 2.0 3.0 4.5 2.7 2.5 43.1 53.5 3.4 0.8 0.1 14 41 Mauritius 1.1 1.2 1.4 1.1 0.7 24.9 68.6 6.5 0.4 0.1 7 16 Mexico 83.2 103.8 124.7 1.6 1.1 31.6 63.2 5.2 0.5 0.1 5 19 Moldova 4.4 4.2 4.1 ­0.2 ­0.2 19.1 70.9 10.0 0.3 0.1 12 10 Mongolia 2.1 2.5 3.1 1.3 1.4 31.3 65.0 3.8 0.5 0.1 6 22 Morocco 23.9 29.8 38.3 1.6 1.6 31.5 63.8 4.8 0.5 0.1 6 23 Mozambique 13.4 19.4 25.5 2.6 1.7 44.1 52.6 3.3 0.8 0.1 20 39 Myanmar 40.8 50.0 57.1 1.5 0.8 30.1 65.0 4.9 0.5 0.1 10 20 Namibia 1.4 2.0 2.4 2.6 1.1 42.1 54.4 3.4 0.8 0.1 6 23 Nepal 19.1 26.6 35.7 2.4 1.8 39.5 56.9 3.6 0.7 0.1 8 29 Netherlands 15.0 16.3 17.0 0.6 0.3 18.3 67.7 14.0 0.3 0.2 8 12 New Zealand 3.4 4.1 4.4 1.2 0.5 21.7 66.1 12.2 0.3 0.2 7 14 Nicaragua 4.0 5.4 7.2 2.2 1.8 39.5 57.2 3.3 0.7 0.1 5 28 Niger 8.5 13.5 22.6 3.3 3.2 49.0 49.0 2.0 1.0 0.0a 21 54 Nigeria 90.6 128.7 175.8 2.5 1.9 44.5 52.5 3.0 0.8 0.1 19 41 Norway 4.2 4.6 5.0 0.6 0.5 19.7 65.3 15.0 0.3 0.2 9 12 Oman 1.8 2.5 3.5 2.3 2.0 34.9 62.7 2.5 0.6 0.0a 3 25 Pakistan 108.0 152.1 211.7 2.4 2.1 38.9 57.3 3.8 0.7 0.1 7 27 Panama 2.4 3.2 4.0 2.0 1.5 30.6 63.5 5.9 0.5 0.1 5 22 Papua New Guinea 4.1 5.8 7.6 2.4 1.7 40.7 56.9 2.4 0.7 0.0a 10 30 Paraguay 4.2 6.0 8.3 2.5 2.0 38.0 58.3 3.7 0.7 0.1 5 29 Peru 21.8 27.6 34.2 1.7 1.4 32.7 62.1 5.2 0.5 0.1 6 23 Philippines 61.1 81.6 103.3 2.1 1.5 35.7 60.5 3.8 0.6 0.1 5 25 Poland 38.1 38.2 37.7 0.0a ­0.1 16.8 70.3 12.8 0.2 0.2 10 9 Portugal 9.9 10.5 10.9 0.4 0.2 15.9 67.2 16.9 0.2 0.3 10 10 Puerto Rico 3.5 3.9 4.2 0.7 0.5 22.5 65.6 11.9 0.3 0.2 8 14 2006 World Development Indicators 47 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as % proportion of working- Ages Ages Ages age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2004 2020 1990­2004 2004­20 2004 2004 2004 2004 2004 2004 2004 Romania 23.2 21.7 20.4 ­0.5 ­0.4 15.9 69.5 14.6 0.2 0.2 12 10 Russian Federation 148.3 143.8 133.1 ­0.2 ­0.5 15.7 70.7 13.6 0.2 0.2 16 11 Rwanda 7.1 8.9 12.4 1.6 2.1 44.1 53.5 2.4 0.8 0.0a 18 41 Saudi Arabia 16.4 24.0 34.0 2.7 2.2 37.8 59.4 2.9 0.6 0.0a 4 27 Senegal 8.0 11.4 16.0 2.5 2.1 43.0 53.9 3.1 0.8 0.1 11 36 Serbia and Montenegro 10.5b 8.1 10.3 0.1c 1.5 18.6 67.4 14.0 0.3 0.2 14 11 Sierra Leone 4.1 5.3 7.7 1.9 2.3 42.8 53.9 3.3 0.8 0.1 23 46 Singapore 3.0 4.2 5.0 2.4 1.0 20.2 71.6 8.2 0.3 0.1 4 10 Slovak Republic 5.3 5.4 5.4 0.1 0.0a 17.2 71.1 11.7 0.2 0.2 10 10 Slovenia 2.0 2.0 1.9 0.0a ­0.3 14.2 70.4 15.4 0.2 0.2 9 9 Somalia 6.7 8.0 12.3 1.3 2.7 44.1 53.3 2.6 0.8 0.0a 17 45 South Africa 35.2 45.5 48.1 1.8 0.3 32.8 63.1 4.1 0.5 0.1 22 24 Spain 38.8 42.7 44.4 0.7 0.2 14.3 69.2 16.5 0.2 0.2 9 11 Sri Lanka 17.0 19.4 22.9 0.9 1.0 24.5 68.4 7.1 0.4 0.1 6 19 Sudan 26.1 35.5 47.5 2.2 1.8 39.5 56.9 3.6 0.7 0.1 11 32 Swaziland 0.8 1.1 1.0 2.7 ­0.8 41.6 55.0 3.4 0.8 0.1 20 34 Sweden 8.6 9.0 9.5 0.4 0.3 17.7 65.1 17.1 0.3 0.3 10 11 Switzerland 6.7 7.4 7.4 0.7 0.0a 16.8 67.6 15.7 0.2 0.2 8 10 Syrian Arab Republic 12.8 18.6 26.0 2.6 2.1 37.4 59.5 3.1 0.6 0.1 3 28 Tajikistan 5.3 6.4 8.2 1.4 1.5 39.7 56.5 3.8 0.7 0.1 7 29 Tanzania 26.2 37.6 49.3 2.6 1.7 42.9 53.9 3.2 0.8 0.1 17 37 Thailand 54.6 63.7 71.0 1.1 0.7 24.1 69.0 6.9 0.3 0.1 7 16 Togo 4.0 6.0 8.7 3.0 2.4 43.7 53.2 3.1 0.8 0.1 12 38 Trinidad and Tobago 1.2 1.3 1.3 0.5 0.2 22.0 70.7 7.2 0.3 0.1 8 14 Tunisia 8.2 9.9 11.6 1.4 1.0 26.7 67.1 6.2 0.4 0.1 6 17 Turkey 56.2 71.7 86.8 1.7 1.2 29.5 65.1 5.4 0.5 0.1 6 19 Turkmenistan 3.7 4.8 5.8 1.9 1.2 32.7 62.7 4.7 0.5 0.1 8 22 Uganda 17.8 27.8 50.6 3.2 3.7 50.4 47.1 2.5 1.1 0.1 15 50 Ukraine 51.9 47.5 39.6 ­0.6 ­1.1 15.4 68.8 15.8 0.2 0.2 16 9 United Arab Emirates 1.8 4.3 6.1 6.4 2.2 22.4 76.5 1.1 0.3 0.0a 1 16 United Kingdom 57.6 59.9 62.5 0.3 0.3 18.2 65.9 15.9 0.3 0.2 10 12 United States 249.6 293.7 338.4 1.2 0.9 20.9 66.8 12.3 0.3 0.2 8 14 Uruguay 3.1 3.4 3.8 0.7 0.6 24.4 62.4 13.2 0.4 0.2 9 15 Uzbekistan 20.5 26.2 32.5 1.7 1.3 34.0 61.3 4.7 0.6 0.1 7 21 Venezuela, RB 19.8 26.1 33.5 2.0 1.5 31.7 63.4 4.9 0.5 0.1 5 22 Vietnam 66.2 82.2 99.9 1.5 1.2 30.3 64.2 5.5 0.5 0.1 6 18 West Bank and Gaza 2.0 3.5 5.7 4.1 3.0 45.7 51.1 3.1 0.9 0.1 4 35 Yemen, Rep. 12.1 20.3 32.7 3.7 3.0 46.7 51.0 2.3 0.9 0.0a 8 40 Zambia 8.4 11.5 15.1 2.3 1.7 46.0 51.0 3.0 0.9 0.1 22 41 Zimbabwe 10.6 12.9 14.1 1.4 0.6 40.5 55.9 3.6 0.7 0.1 23 30 World 5,256.3 s 6,365.0 s 7,573.5 s 1.4 w 1.1 w 28.5 w 64.2 w 7.3 w 0.4 w 0.1 w 9w 20 w Low income 1,763.4 2,343.0 3,084.4 2.0 1.7 36.8 58.9 4.3 0.6 0.1 11 29 Middle income 2,589.4 3,017.8 3,427.1 1.1 0.8 25.4 67.4 7.2 0.4 0.1 7 16 Lower middle income 2,082.8 2,441.6 2,796.9 1.1 0.8 25.5 67.6 6.8 0.4 0.1 7 16 Upper middle income 506.6 576.2 630.2 0.9 0.6 24.9 66.3 8.8 0.4 0.1 10 16 Low & middle income 4,352.8 5,360.8 6,511.5 1.5 1.2 30.4 63.7 5.9 0.5 0.1 9 22 East Asia & Pacific 1,596.1 1,869.5 2,107.6 1.1 0.7 24.5 68.7 6.8 0.4 0.1 7 15 Europe & Central Asia 466.1 472.5 476.9 0.1 0.1 20.2 68.2 11.6 0.3 0.2 12 13 Latin America & Carib. 437.6 545.9 660.3 1.6 1.2 30.4 63.6 5.9 0.5 0.1 6 21 Middle East & N. Africa 225.5 300.3 399.1 2.0 1.8 34.0 61.8 4.2 0.6 0.1 6 25 South Asia 1,113.1 1,446.8 1,834.9 1.9 1.5 33.8 61.4 4.8 0.6 0.1 8 25 Sub-Saharan Africa 514.4 725.8 1,032.7 2.5 2.2 43.7 53.2 3.1 0.8 0.1 18 40 High income 903.5 1,004.2 1,062.0 0.8 0.4 18.4 67.0 14.6 0.3 0.2 8 12 Europe EMU 293.3 309.3 315.7 0.4 0.1 15.6 66.9 17.5 0.2 0.3 9 10 a. Less than 0.05. b. Includes population of Kosovo until 1999. c. Data are for 1990­99. 48 2006 World Development Indicators Population dynamics About the data Definitions Population estimates are usually based on national Dependency ratios take into account variations in · Total population of an economy includes all resi- population censuses, but the frequency and quality the different age groups: the proportions of children, dents regardless of legal status or citizenship-- of these vary by country. Most countries conduct a elderly people, and working-age people in the popula- except for refugees not permanently settled in the complete enumeration no more than once a decade. tion. Separate calculations of young-age and old-age country of asylum, who are generally considered part Estimates for the years before and after the cen- dependency suggest the burden of dependency that of the population of their country of origin. The values suses are interpolations or extrapolations based the working-age population must bear in relation to shown are midyear estimates for 1990 and 2004 on demographic models. Errors and undercounting children and the elderly. But dependency ratios show and projections for 2020. · Average annual popula- occur even in high-income countries; in developing only the age composition of a population, not eco- tion growth rate is the exponential change for the countries such errors may be substantial because nomic dependency. Some children and elderly people period indicated. See Statistical methods for more of limits in the transport, communications, and are part of the labor force, and many working-age information. · Population age composition refers other resources required to conduct and analyze a people are not. to the percentage of the total population that is in full census. The vital rates shown in the table are based on specific age groups. · Dependency ratio is the ratio The quality and reliability of official demographic data derived from birth and death registration sys- of dependents--people younger than 15 or older data are also affected by the public trust in the gov- tems, censuses, and sample surveys conducted by than 64--to the working-age population--those ernment, the government's commitment to full and national statistical offices and other organizations, ages 15­64. · Crude death rate and crude birth accurate enumeration, the confidentiality and protec- or on demographic analysis. The estimates for 2004 rate are the number of deaths and the number of live tion against misuse accorded to census data, and for many countries are national projections based births occurring during the year, per 1,000 popula- the independence of census agencies from undue on extrapolations of levels and trends measured in tion, estimated at midyear. Subtracting the crude political influence. Moreover, the international com- earlier years. death rate from the crude birth rate provides the rate parability of population indicators is limited by dif- Vital registers are the preferred source of these of natural increase, which is equal to the population ferences in the concepts, definitions, data collec- data, but in many developing countries systems for growth rate in the absence of migration. tion procedures, and estimation methods used by registering births and deaths do not exist or are national statistical agencies and other organizations incomplete because of deficiencies in the coverage that collect population data. of events or of geographic areas. Many developing Of the 152 economies listed in the table, 119 countries carry out special household surveys that (about 78 percent) conducted a census between estimate vital rates by asking respondents about 1995 and 2005. The currentness of a census, along births and deaths in the recent past. Estimates with the availability of complementary data from sur- derived in this way are subject to sampling errors veys or registration systems, is one of many objective as well as errors due to inaccurate recall by the ways to judge the quality of demographic data. In respondents. some European countries registration systems offer The United Nations Statistics Division monitors complete information on population in the absence the completeness of vital registration systems. The of a census. See Primary data documentation for share of countries with at least 90 percent complete the most recent census or survey year and for the vital registration increased from 45 percent in 1988 completeness of registration. to 54 percent in 2004. Still, some of the most popu- Current population estimates for developing coun- lous developing countries--China, India, Indonesia, tries that lack recent census-based data, and pre- Brazil, Pakistan, Bangladesh, Nigeria--do not have and post-census estimates for countries with census complete vital registration systems. Fewer than 30 Data sources data, are provided by the United Nations Population percent of births and deaths and fewer than 40 per- Division, national statistical offices, and other agen- cent of infant deaths worldwide are thought to be The World Bank's population estimates are com- cies. The standard estimation method requires fer- registered and reported. piled and produced by its Human Development tility, mortality, and net migration data, which are International migration is the only other factor Network and Development Data Group in consulta- often collected from sample surveys, some of which besides birth and death rates that directly deter- tion with its operational staff and country offices. may be small or limited in coverage. The population mines a country's population growth. From 1990 Important inputs to the World Bank's demographic estimates are the product of demographic modeling to 2000 the number of migrants in high-income work come from the United Nations Population and so are susceptible to biases and errors because countries increased by 23 million. About 175 mil- Division's World Population Prospects: The 2004 of shortcomings in the model as well as in the data. lion people currently live outside their home country, Revision; census reports and other statistical Population projections are made using the cohort accounting for about 3 percent of the world's popula- publications from national statistical offices; component method. tion. Estimating international migration is difficult. household surveys conducted by national agen- The growth rate of the total population conceals At any time many people are located outside their cies, Macro International, and the U.S. Centers for the fact that different age groups may grow at very home country as tourists, workers, or refugees or Disease Control and Prevention; Eurostat, Demo- different rates. In many developing countries the pop- for other reasons. Standards relating to the dura- graphic Statistics (various years); Centro Latino- ulation under age 15 was previously growing rapidly tion and purpose of international moves that qualify americano de Demografía, Boletín Demográfico but is now starting to shrink. Previously high fertility as migration vary, and accurate estimates require (various years); and U.S. Bureau of the Census, rates and declining mortality rates are now reflected information on flows into and out of countries that International Database. in the larger share of the working-age population. is difficult to collect. 2006 World Development Indicators 49 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15­64 Total average annual Female Male Female millions % growth % of labor force 1990 2004 1990 2004 1990 2004 1990­2004 1990 2004 Afghanistan 88.7 .. 38.2 .. 5.0 .. .. 28.4 .. Albania 86.3 76.3 63.3 55.1 1.6 1.4 ­1.0 40.2 42.1 Algeria 81.0 83.3 23.7 37.0 7.2 12.9 4.2 22.6 30.2 Angola 90.9 92.2 76.0 75.7 4.5 6.8 2.9 46.4 45.8 Argentina 84.7 82.4 43.5 59.9 13.0 17.9 2.3 34.4 42.4 Armenia 89.7 66.4 76.7 55.6 1.9 1.3 ­3.0 47.7 48.9 Australia 84.4 81.0 61.5 67.0 8.4 10.2 1.3 41.3 45.3 Austria 80.1 77.6 55.3 63.3 3.5 3.9 0.8 40.8 44.4 Azerbaijan 80.6 78.0 68.5 65.5 3.3 4.0 1.4 47.4 47.4 Bangladesh 89.8 88.1 64.5 55.4 46.9 62.4 2.0 40.2 37.0 Belarus 82.2 72.5 72.4 66.5 5.3 4.8 ­0.7 48.6 49.2 Belgium 71.3 72.7 46.2 56.9 3.9 4.5 0.9 39.0 43.2 Benin 90.0 86.7 59.2 55.0 2.0 3.2 3.3 40.8 38.5 Bolivia 80.9 84.0 49.9 63.9 2.5 4.0 3.4 39.2 43.5 Bosnia and Herzegovina 82.4 78.3 66.1 69.4 2.3 2.0 ­0.7 44.7 47.8 Botswana 76.0 68.6 58.9 47.2 0.5 0.6 1.3 45.2 41.9 Brazil 88.8 84.1 47.6 60.6 62.4 89.9 2.6 35.0 42.5 Bulgaria 77.8 63.0 72.3 53.3 4.4 3.1 ­2.5 48.0 46.2 Burkina Faso 92.1 90.4 79.3 79.5 3.8 5.6 2.8 46.3 46.6 Burundi 90.7 93.0 91.8 92.8 2.8 3.7 1.9 52.6 52.2 Cambodia 86.7 81.3 81.0 78.0 4.4 6.6 2.9 52.6 51.5 Cameroon 83.5 81.6 58.2 54.1 4.4 6.2 2.4 41.5 39.8 Canada 84.9 82.5 68.3 72.4 14.7 17.4 1.2 44.0 46.2 Central African Republic 89.4 89.4 71.7 70.9 1.4 1.8 2.1 47.0 46.2 Chad 79.0 77.4 64.7 66.0 2.3 3.6 3.0 46.0 46.8 Chile 80.9 76.7 35.2 40.6 5.0 6.4 1.8 30.5 34.6 China 88.9 88.0 79.1 76.2 650.1 768.0 1.2 44.8 44.6 Hong Kong, China 85.5 81.4 53.0 61.2 2.9 3.6 1.7 36.3 45.8 Colombia 85.0 85.3 48.5 65.0 14.1 21.8 3.1 36.9 44.0 Congo, Dem. Rep. 91.2 91.1 62.6 63.1 15.0 22.3 2.8 41.6 41.2 Congo, Rep. 86.3 86.6 57.7 56.2 1.0 1.5 3.0 41.5 40.3 Costa Rica 87.6 84.9 35.3 47.2 1.2 1.9 3.5 27.6 34.5 Côte d'Ivoire 90.3 89.1 44.5 40.2 4.6 6.7 2.6 30.2 29.3 Croatia 76.9 71.4 55.0 57.4 2.2 2.0 ­0.9 42.1 44.8 Cuba 79.5 82.4 43.5 50.5 4.5 5.3 1.2 34.8 37.3 Czech Republic 82.2 77.7 74.1 63.7 5.5 5.2 ­0.4 47.4 44.9 Denmark 87.1 82.9 77.6 74.3 2.9 2.8 ­0.2 46.1 46.6 Dominican Republic 85.6 84.1 37.8 47.5 2.6 3.8 2.5 29.6 35.3 Ecuador 85.9 85.2 33.6 62.7 3.7 6.2 3.7 27.8 42.0 Egypt, Arab Rep. 76.7 76.6 27.6 21.6 16.6 22.3 2.1 26.3 21.8 El Salvador 81.9 79.5 53.5 49.7 2.0 2.7 2.3 41.2 39.6 Eritrea 92.6 90.7 63.1 59.9 1.2 1.7 2.4 42.4 41.1 Estonia 83.0 73.7 76.0 64.3 0.9 0.7 ­1.8 49.9 49.3 Ethiopia 92.3 90.9 74.5 73.5 22.6 30.9 2.2 44.9 44.9 Finland 79.0 76.8 72.2 72.7 2.6 2.6 0.2 47.2 47.8 France 75.0 73.8 57.0 62.4 24.8 26.9 0.6 43.3 45.9 Gabon 85.5 84.0 65.5 64.2 0.4 0.6 2.7 43.9 43.4 Gambia, The 86.2 86.7 63.3 60.4 0.4 0.6 3.5 43.4 41.7 Georgia 78.2 76.2 79.1 53.7 2.9 2.3 ­1.7 52.3 43.9 Germany 81.4 79.2 56.8 66.5 38.3 40.8 0.5 40.4 44.9 Ghana 80.5 75.8 77.5 72.0 6.7 9.6 2.5 48.9 48.1 Greece 76.7 78.6 43.1 54.8 4.2 5.1 1.4 36.2 40.5 Guatemala 90.7 84.8 30.2 35.1 2.9 4.0 2.3 24.7 31.1 Guinea 90.8 88.9 82.8 82.7 3.0 4.3 2.6 46.2 46.5 Guinea-Bissau 91.4 93.0 60.5 62.9 0.4 0.6 2.9 40.3 40.9 Haiti 82.7 83.1 59.1 57.4 2.6 3.6 2.2 43.3 41.7 50 2006 World Development Indicators Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15­64 Total average annual Female Male Female millions % growth % of labor force 1990 2004 1990 2004 1990 2004 1990­2004 1990 2004 Honduras 89.0 90.5 34.6 54.6 1.6 3.0 4.4 27.7 36.9 Hungary 74.4 66.9 57.3 53.4 4.5 4.2 ­0.5 44.5 45.0 India 86.6 84.4 40.3 36.1 335.1 427.2 1.7 29.9 28.3 Indonesia 82.9 87.0 52.1 52.8 75.3 105.1 2.4 38.4 37.8 Iran, Islamic Rep. 82.3 75.4 22.5 39.1 15.6 26.2 3.7 20.2 33.0 Iraq 77.8 .. 16.4 .. 4.7 .. .. 16.8 .. Ireland 77.9 80.1 42.3 60.7 1.3 2.0 2.9 34.3 42.5 Israel 68.1 65.9 46.8 58.1 1.6 2.7 3.4 40.5 46.8 Italy 76.7 74.5 44.6 49.3 23.9 24.0 0.0 37.1 39.6 Jamaica 83.0 78.4 71.3 60.0 1.1 1.2 0.3 46.8 43.7 Japan 83.1 85.0 57.1 60.4 63.9 67.0 0.3 40.6 41.0 Jordan 71.3 79.5 18.6 28.3 0.8 1.8 6.3 18.8 24.1 Kazakhstan 81.6 80.1 68.0 73.1 7.7 8.0 0.2 46.3 49.4 Kenya 90.6 89.6 76.2 71.4 9.8 15.1 3.1 46.0 44.0 Korea, Dem. Rep. 84.0 80.7 56.4 50.0 9.7 10.6 0.6 39.3 38.6 Korea, Rep. 75.3 77.3 49.7 54.0 19.1 24.1 1.7 39.3 40.7 Kuwait 83.1 86.5 35.6 49.3 0.9 1.3 3.2 21.8 24.8 Kyrgyz Republic 78.0 77.6 65.0 60.0 1.8 2.2 1.4 46.2 44.3 Lao PDR 81.6 82.3 56.3 56.4 1.5 2.3 2.8 41.3 40.7 Latvia 83.4 71.9 75.0 62.8 1.5 1.1 ­2.0 49.5 48.6 Lebanon 81.5 83.7 34.4 35.0 0.9 1.4 2.7 31.8 30.0 Lesotho 86.8 74.3 59.4 49.2 0.6 0.6 0.4 46.5 44.7 Liberia 85.2 84.0 55.9 55.7 0.8 1.2 2.9 39.4 39.9 Libya 81.4 82.3 19.9 32.3 1.3 2.2 4.2 17.3 26.3 Lithuania 81.7 72.8 70.4 65.9 1.9 1.6 ­1.1 48.1 49.1 Macedonia, FYR 77.5 73.2 52.8 47.8 0.9 0.9 0.1 40.0 39.1 Madagascar 83.6 86.3 79.5 79.8 5.4 8.3 3.1 49.2 48.4 Malawi 91.7 90.0 86.2 86.0 4.5 5.8 1.9 50.3 49.8 Malaysia 82.7 83.8 45.3 47.6 7.1 10.7 2.9 34.8 35.6 Mali 90.7 85.9 75.1 74.7 3.8 5.3 2.5 46.0 47.3 Mauritania 87.6 85.0 57.8 56.5 0.8 1.2 2.7 40.7 40.4 Mauritius 86.6 84.4 45.2 46.4 0.5 0.6 1.4 33.9 35.3 Mexico 85.4 83.8 36.2 42.2 29.5 42.4 2.6 30.6 34.7 Moldova 81.5 75.8 70.4 65.4 2.1 2.1 0.0 48.6 47.8 Mongolia 83.7 83.2 59.3 56.3 0.8 1.2 2.4 41.0 40.2 Morocco 83.9 83.8 25.6 28.5 7.5 10.9 2.7 23.7 25.4 Mozambique 88.0 83.1 88.1 85.1 6.3 9.1 2.6 54.0 53.5 Myanmar 89.2 87.7 71.2 70.0 20.0 26.9 2.1 44.6 44.9 Namibia 67.1 65.0 50.6 48.8 0.4 0.6 2.6 44.1 43.6 Nepal 82.5 80.6 50.4 52.3 7.1 10.2 2.6 37.9 40.3 Netherlands 80.0 84.5 53.1 69.0 6.9 8.6 1.5 39.1 44.0 New Zealand 83.0 83.4 63.2 70.5 1.7 2.1 1.7 43.1 46.3 Nicaragua 87.0 87.2 36.8 36.7 1.3 2.0 3.1 30.1 29.7 Niger 94.7 95.5 72.4 72.9 3.6 5.7 3.4 42.6 41.9 Nigeria 86.9 85.9 49.0 46.8 32.7 46.7 2.5 36.2 34.8 Norway 82.5 83.7 69.9 77.2 2.2 2.5 0.9 44.7 47.2 Oman 83.8 82.8 15.7 22.7 0.6 0.9 3.6 11.1 15.6 Pakistan 88.1 85.7 28.8 33.0 35.2 54.5 3.1 23.3 26.5 Panama 82.7 83.4 41.6 53.8 0.9 1.4 3.1 32.5 38.3 Papua New Guinea 75.9 75.1 72.3 72.7 1.8 2.5 2.5 46.4 47.6 Paraguay 85.7 86.9 54.4 67.7 1.6 2.8 3.7 38.3 43.1 Peru 82.0 83.5 48.6 60.3 8.5 13.0 3.0 37.0 41.6 Philippines 83.7 84.6 48.7 55.5 23.4 35.9 3.1 36.6 39.4 Poland 79.2 69.2 65.1 57.8 18.6 17.3 ­0.5 45.8 45.7 Portugal 82.6 79.6 59.2 67.1 4.8 5.5 1.0 42.7 46.2 Puerto Rico 67.4 67.8 35.0 44.0 1.2 1.4 1.6 35.8 41.2 2006 World Development Indicators 51 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15­64 Total average annual Female Male Female millions % growth % of labor force 1990 2004 1990 2004 1990 2004 1990­2004 1990 2004 Romania 77.2 68.8 61.1 55.6 11.0 10.4 ­0.4 44.3 46.1 Russian Federation 81.6 75.2 71.7 66.9 77.2 73.1 ­0.4 48.3 49.0 Rwanda 88.3 85.1 87.4 82.4 3.1 4.1 2.0 51.0 51.4 Saudi Arabia 81.3 80.5 15.6 18.2 5.1 7.7 2.9 11.4 14.8 Senegal 87.8 83.9 63.4 58.7 3.1 4.5 2.6 43.4 42.4 Serbia and Montenegro 77.0 75.9 54.9 53.3 4.9a 3.9 0.0 b 41.7 41.7 Sierra Leone 90.2 94.4 55.6 58.3 1.7 2.3 2.1 38.5 38.4 Singapore 83.9 83.2 54.2 56.7 1.6 2.2 2.3 38.8 39.8 Slovak Republic 82.5 76.5 70.6 62.4 2.6 2.7 0.1 46.3 45.2 Slovenia 76.9 75.2 63.3 66.1 1.0 1.0 0.3 45.5 46.2 Somalia 95.8 95.1 63.1 61.0 2.8 3.4 1.3 39.9 39.3 South Africa 81.6 82.1 57.4 49.7 14.4 19.1 2.0 41.6 38.4 Spain 80.3 80.5 41.9 56.3 16.0 20.3 1.7 34.3 40.8 Sri Lanka 82.9 81.8 48.2 38.6 7.3 8.3 0.9 34.8 30.5 Sudan 78.9 72.6 27.8 24.2 7.8 10.3 2.0 26.0 24.8 Swaziland 79.6 74.9 39.6 33.2 0.2 0.3 2.7 38.0 33.1 Sweden 86.0 79.2 81.9 75.1 4.7 4.7 ­0.1 47.7 47.4 Switzerland 90.2 88.0 62.8 74.7 3.7 4.2 0.9 40.4 46.3 Syrian Arab Republic 83.7 88.8 29.7 39.3 3.7 7.3 4.9 26.2 30.4 Tajikistan 77.6 66.1 56.2 49.7 1.9 2.1 0.8 42.2 43.6 Tanzania 92.1 90.8 90.2 88.4 12.8 18.9 2.8 50.2 49.5 Thailand 90.6 84.6 79.2 70.7 30.4 35.3 1.1 46.6 46.0 Togo 90.8 90.4 55.2 51.9 1.5 2.4 3.1 38.5 37.0 Trinidad and Tobago 79.7 82.2 45.9 51.2 0.5 0.6 2.0 36.1 38.9 Tunisia 79.2 78.3 22.1 30.4 2.4 3.7 3.0 21.5 27.1 Turkey 84.5 80.3 36.2 29.0 21.0 26.5 1.7 29.4 26.4 Turkmenistan 80.0 76.5 69.1 65.0 1.5 2.1 2.4 46.9 46.6 Uganda 92.4 87.8 82.0 81.2 7.8 11.5 2.8 47.5 48.2 Ukraine 79.7 72.3 70.7 63.1 26.3 22.4 ­1.1 49.2 49.1 United Arab Emirates 92.4 92.1 25.9 38.2 0.9 2.6 7.5 9.8 13.2 United Kingdom 87.9 82.2 67.2 69.2 29.4 30.4 0.2 44.0 45.9 United States 85.1 81.7 67.5 70.1 129.3 153.7 1.2 44.4 46.2 Uruguay 85.9 86.1 54.3 65.7 1.4 1.7 1.6 39.9 43.9 Uzbekistan 78.5 75.6 64.4 60.2 8.2 11.1 2.2 45.4 44.5 Venezuela, RB 82.4 85.5 39.8 60.1 7.3 12.4 3.8 31.8 40.3 Vietnam 85.5 82.6 79.4 77.6 31.3 43.1 2.3 48.3 48.5 West Bank and Gaza 67.0 69.0 9.5 10.9 0.4 0.7 4.5 11.9 13.1 Yemen, Rep. 76.1 77.4 28.6 30.4 3.0 5.7 4.7 27.3 27.7 Zambia 90.4 91.5 67.8 68.3 3.5 4.9 2.4 43.2 42.4 Zimbabwe 81.0 85.1 69.9 64.8 4.3 5.7 2.1 47.2 44.2 World 85.5 w 83.9 w 58.9 w 57.8 w 2,390.7 t 2,981.1 t 1.6 w 39.9 w 40.0 w Low income 87.0 85.0 50.7 47.8 709.0 956.4 2.1 35.7 35.0 Middle income 85.8 84.3 64.0 62.7 1,254.1 1,536.1 1.4 41.8 42.1 Lower middle income 86.6 85.5 66.3 65.2 1,033.5 1,280.1 1.5 42.0 42.4 Upper middle income 82.5 78.9 54.7 52.4 220.6 256.0 1.1 40.5 40.5 Low & middle income 86.3 84.6 59.0 56.7 1,963.1 2,492.5 1.7 39.6 39.3 East Asia & Pacific 87.8 87.2 74.3 71.6 856.8 1,049.4 1.4 44.1 43.8 Europe & Central Asia 80.8 74.6 65.1 58.1 223.8 218.5 ­0.2 45.6 44.9 Latin America & Carib. 85.9 83.8 43.8 55.3 171.0 248.8 2.7 34.0 40.2 Middle East & N. Africa 79.9 79.1 24.5 30.5 64.9 104.6 3.4 22.9 27.2 South Asia 86.9 84.9 41.7 38.0 437.0 572.7 1.9 30.6 29.3 Sub-Saharan Africa 87.8 86.4 65.1 62.7 209.7 298.5 2.5 43.0 42.2 High income 82.1 80.6 58.6 63.5 427.6 488.5 1.0 41.3 43.6 Europe EMU 78.5 77.5 51.6 60.4 130.5 144.5 0.7 39.5 43.4 a. Includes population of Kosovo until 1999. b. Data are for 1990­99. 52 2006 World Development Indicators Labor force structure About the data Definitions The labor force is the supply of labor available for oping countries, where the household is often the · Labor force participation rate is the proportion the production of goods and services in an economy. basic unit of production and all members contribute of the population ages 15­64 that is economically It includes people who are currently employed and to output, but some at low intensity or irregular inter- active: all people who supply labor for the produc- people who are unemployed but seeking work as well vals, the estimated labor force may be significantly tion of goods and services during a specified period. as first-time job-seekers. Not everyone who works smaller than the numbers actually working. · Total labor force comprises people ages 15 and is included, however. Unpaid workers, family work- The labor force estimates in the table were cal- older who meet the ILO definition of the economically ers, and students are among those usually omitted, culated by World Bank staff by applying labor force active population. It includes both the employed and and in some countries members of the military are participation rates from the ILO database to popu- the unemployed. While national practices vary in the not counted. The size of the labor force tends to lation estimates to create a series consistent with treatment of such groups as the armed forces and vary during the year as seasonal workers enter and these population estimates. This procedure some- seasonal or part-time workers, the labor force gener- leave it. times results in estimates of labor force size that ally includes the armed forces, the unemployed, and Data on the labor force are compiled by the Inter- differ slightly from those in the ILO's Yearbook of first-time job-seekers, but excludes homemakers and national Labour Organization (ILO) from labor force Labour Statistics. The labor force estimates in this other unpaid caregivers and workers in the informal surveys, censuses, establishment censuses and sur- year's World Development Indicators are for the pop- sector. · Average annual growth rate of the labor veys, and various types of administrative records ulation ages 15 and older. In previous editions the force is calculated using the exponential endpoint such as employment exchange registers and unem- labor force included children under age 15. For this method (see Statistical methods for more informa- ployment insurance schemes. For some countries reason, labor force estimates are not comparable tion). · Females as a percentage of the labor force a combination of these sources is used. While the across editions. The labor force participation rate of show the extent to which women are active in the resulting statistics may provide rough estimates of the population ages 15­64 provides an indication of labor force. the labor force, they are not comparable across coun- the relative size of the supply of labor. But in many tries because of the noncomparability of the original developing countries children under age 15 work data and the different ways the original sources may full or part time. And in some high-income countries be combined. many workers postpone retirement past age 65. As For international comparisons the most compre- a result, labor force participation rates calculated in hensive source is labor force surveys, which can be this way may systematically over- or under-estimate designed to cover all noninstitutionalized civilians, actual rates. all branches and sectors of the economy, and all In general, estimates of women in the labor force categories of workers, including people who hold mul- are lower than those of men and are not compa- tiple jobs. Despite the ILO's efforts to encourage the rable internationally, reflecting the fact that for use of international standards, labor force data are women demographic, social, legal, and cultural not fully comparable because of differences among trends and norms determine whether their activities countries, and sometimes within countries, in both are regarded as economic. In many countries large concepts and methodologies. Most important to numbers of women work on farms or in other fam- data comparability is the nature of the data source. ily enterprises without pay, while others work in or Labor force data obtained from population censuses near their homes, mixing work and family activities are often based on a limited number of questions on during the day. Countries differ in the criteria used the economic characteristics of individuals, with little to determine the extent to which such workers are scope to probe. The resulting data are often contrary to be counted as part of the labor force. In most to labor force survey data and often vary consider- economies the gap between male and female labor ably from economy to economy, depending on the force participation rates has been narrowing since scope and coverage of the census. Establishment 1980. This stems from both falling rates for men and censuses and surveys on the other hand provide rising rates for women. The largest gap between men Data sources data only on the employed population, leaving out and women in labor force participation is observed in The labor force participation rates are from the unemployed workers, workers in small establish- the Middle East and North Africa, where low partici- ILO database Estimates and Projections of the ments, and workers in the informal sector (ILO, Key pation of women in the work force also brings down Economically Active Population, 1980­2020, Indicators of the Labour Market 2001­2002). the overall labor force participation rate. fifth edition. The ILO publishes estimates of the The reference period of the census or survey is economically active population in its Yearbook of another important source of differences: in some Labour Statistics. Labor force numbers were cal- countries data refer to people's status on the day culated by World Bank staff, applying labor force of the census or survey or during a specific period participation rates from the ILO database to popu- before the inquiry date, while in others the data are lation estimates. recorded without reference to any period. In devel- 2006 World Development Indicators 53 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 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0 b, c 2c 0 b, c 1b, c 40 c 28 c 18 c 9c 59 c 70 c 81c 90 c Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 6 5c 4 3c 32 30 c 12 10 c 62 65c 85 87c Austria 6 5 8 6 47 43 20 13 46 51 72 81 Azerbaijan .. 41 .. 39 .. 15 .. 8 .. 44 .. 54 Bangladesh 54 53 85 77 16 8 9 9 26 30 2 12 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 3 2 2 1 38 35 13 12 57 63 84 87 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 3c 6 1c 3 42c 39 17c 14 55c 55 82c 82 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 17 .. 6 .. 31 .. 17 .. 51 .. 76 Brazil 31c 23 c 25c 16c 27c 28 c 10 c 13c 43 c 49 c 65c 71c Bulgaria .. 12 .. 8 .. 37 .. 29 .. 51 .. 64 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 72 .. 75 .. 7 .. 10 .. 20 .. 15 Cameroon 53 .. 68 .. 14 .. 4 .. 26 .. 23 .. Canada 6c 4c 3c 2c 31c 32c 11c 11c 63 c 64 c 86c 87c Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 24 18 6 5 32 29 15 12 45 53 79 83 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China 1 0b 0b 0b 37 25 27 8 63 75 73 92 Colombia 2c 31c 1c 8c 35c 21c 25c 17c 63 c 49 c 74 c 75c Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 32 21 5 4 27 26 25 14 41 52 69 81 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 16 .. 18 .. 39 .. 19 .. 45 .. 64 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 9 6 7 3 55 50 33 27 36 45 61 70 Denmark 7 5c 3 2c 37 34 c 16 12c 56 61c 81 86c Dominican Republic 26 23 3 2 23 24 21 15 52 53 76 83 Ecuador 10 c 11c 2c 5c 29 c 28 c 17c 14 c 62c 61c 81c 81c Egypt, Arab Rep. 35 28 52 28 25 23 10 10 41 50 37 62 El Salvador 48 29 15 4 23 27 23 22 29 45 63 74 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 23 9 13 4 42 42 30 23 36 50 57 73 Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 12 7 6 3 39 39 15 13 49 54 78 84 France .. .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 53 .. 57 .. 12 .. 4 .. 35 .. 39 Germany 4 3 4 2 51 44 24 17 45 53 72 81 Ghana 66 60 59 50 10 14 10 15 23 27 32 36 Greece 20 c 15c 26c 18 c 32c 30 c 17c 11c 48 c 56c 56c 71c Guatemala .. 50 .. 18 .. 18 .. 23 .. 27 .. 56 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 54 2006 World Development Indicators 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 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a Honduras 53c 52c 6c 9c 18 c 19 c 25c 26c 29 c 29 c 69 c 66c Hungary 15 8 8 3 42 42 29 24 44 50 64 74 India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 55 43 56 45 10 13 12 14 35 45 32 42 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 19 10 3 2 33 39 18 13 48 51 78 85 Israel 5 3 2 1 38 33 15 11 57 64 83 88 Italy 8 6 9 4 38 40 22 20 54 55 70 76 Jamaica 36 28 16 8 25 26 12 6 39 46 72 84 Japan 6 5 7 5 40 36 27 19 54 59 65 75 Jordan .. .. .. .. .. .. .. .. .. .. .. .. Kazakhstan .. 36 .. 34 .. 23 .. 10 .. 40 .. 56 Kenya 19 c .. 20 c .. 23 c .. 9c .. 58 c .. 71c .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 12c 8c 17c 10 c 41c 34 c 28 c 18 c 47c 58 c 55c 72c Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 43 .. 43 .. 19 .. 10 .. 38 .. 47 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 25 17 14 10 37 35 26 18 38 47 59 71 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 21 .. 15 .. 35 .. 22 .. 44 .. 64 Macedonia, FYR .. 22 .. 22 .. 36 .. 31 .. 42 .. 47 Madagascar .. 77 .. 79 .. 7 .. 6 .. 16 .. 15 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23 16 20 11 31 35 32 27 46 49 48 62 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 17 11 11 6 32 36 64 40 48 53 24 54 Mexico 33 22 10 5 25 28 19 20 41 50 71 75 Moldova .. 44 .. 42 .. 20 .. 12 .. 35 .. 46 Mongolia .. 44 .. 40 .. 17 .. 14 .. 39 .. 46 Morocco .. 39 .. 57 .. 21 .. 19 .. 40 .. 25 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 33 52 29 21 17 8 7 34 49 40 63 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 5 4 3 2 33 29 10 9 60 64 82 87 New Zealand 13c 10 c 8c 6c 31c 32c 13c 11c 56c 58 c 80 c 83 c Nicaragua .. 43 .. 10 .. 19 .. 17 .. 32 .. 52 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 8 6 3 2 35 35 10 9 57 60 86 89 Oman .. 7 .. 5 .. 11 .. 14 .. 82 .. 80 Pakistan 45 38 69 65 20 22 15 16 35 40 16 20 Panama 35 25 3 4 20 22 11 9 45 53 85 88 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 3c 39 0 b, c 20 33 c 19 19 c 10 64 c 42 80 c 70 Peru 1c 1c 0 b, c 0c 30 c 28 c 13c 11c 69 c 71c 87c 89 c Philippines 53 45 32 25 17 18 14 12 29 37 55 63 Poland .. 19 .. 18 .. 38 .. 17 .. 43 .. 65 Portugal 11c 12 13c 14 40 c 43 24 c 20 49 c 45 63 c 66 Puerto Rico 5 3 0b 0b 27 26 19 12 67 71 80 88 2006 World Development Indicators 55 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 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a Romania 29 34 38 37 44 34 30 25 28 32 33 38 Russian Federation .. 12 .. 8 .. 39 .. 23 .. 48 .. 70 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5 .. 1 .. 24 .. 1 .. 71 .. 98 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 1 0b 0b 0b 36 29 32 18 63 70 68 82 Slovak Republic .. 8c .. 4c .. 49 c .. 26c .. 43 c .. 71c Slovenia .. 8 .. 8 .. 46 .. 26 .. 45 .. 65 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 13 .. 7 .. 33 .. 14 .. 54 .. 79 Spain 11 7 8 4 41 42 16 14 48 52 76 82 Sri Lanka .. 32c .. 40 c .. 40 c .. 35c .. 29 c .. 25c Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5 3 2 1 40 35 12 10 55 62 86 89 Switzerland 5 5 4 3 39 33 15 12 57 62 81 85 Syrian Arab Republic .. 24 .. 58 .. 31 .. 7 .. 45 .. 35 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 78 c 80 c 90 c 84 c 7c 4c 1c 1c 15c 16c 8c 15c Thailand 59 47 62 43 17 21 13 19 24 33 25 39 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 10 6 2 34 37 14 14 51 53 80 84 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 24 72 59 26 26 11 13 41 49 17 28 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 91 60 91 77 4 11 6 5 5 29 3 18 Ukraine .. 21 .. 17 .. 38 .. 21 .. 41 .. 62 United Arab Emirates .. 9 .. 0b .. 36 .. 14 .. 55 .. 86 United Kingdom 3 2 1 1 41 35 16 10 55 64 82 89 United States 4c 4c 1c 1c 33 c 31c 14 c 11c 62c 65c 85c 88 c Uruguay 7c 7c 1c 2c 36c 29 c 21c 12c 57c 65c 78 c 86c Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 16 2 2 32 25 16 11 52 59 82 86 Vietnam .. 58 .. 62 .. 20 .. 13 .. 23 .. 25 West Bank and Gaza .. 12 .. 34 .. 30 .. 8 .. 57 .. 57 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia .. .. .. .. .. .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 16 .. 11 .. 33 .. 19 .. 51 .. 71 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 20 .. 19 .. 35 .. 20 .. 45 .. 61 Latin America & Carib. 20 21 14 9 30 27 14 14 50 52 72 76 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 4 3 38 35 19 14 56 60 77 83 Europe EMU 7 5 7 4 43 42 21 17 49 53 72 80 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. 56 2006 World Development Indicators Employment by economic activity About the data Definitions The International Labour Organization (ILO) classi- patterns. Most economies report economic activity · Agriculture corresponds to division 1 (ISIC revi- fies economic activity using the International Stan- according to the ISIC revision 2, although a group of sion 2) or tabulation categories A and B (ISIC revi- dard Industrial Classification (ISIC) of All Economic economies moved to ISIC revision 3. The use of one sion 3) and includes hunting, forestry, and fishing. Activities, revision 2 (1968) and revision 3 (1990). classification or another should not have a significant · Industry corresponds to divisions 2­5 (ISIC revi- Because this classification is based on where work impact on the information for the three broad sectors sion 2) or tabulation categories C­F (ISIC revision is performed (industry) rather than on what type of presented in this table. 3) and includes mining and quarrying (including oil work is performed (occupation), all of an enterprise's The distribution of economic wealth in the world production), manufacturing, construction, and public employees are classified under the same industry, remains strongly correlated with employment by utilities (electricity, gas, and water). · Services corre- regardless of their trade or occupation. The catego- economic activity. The wealthier economies are spond to divisions 6­9 (ISIC revision 2) or tabulation ries should add up to 100 percent. Where they do those with the largest share of total employment in categories G­P (ISIC revision 3) and include whole- not, the differences arise because of workers who services, whereas the poorer economies are largely sale and retail trade and restaurants and hotels; cannot be classified by economic activity. agriculture based. transport, storage, and communications; financing, Data on employment are drawn from labor force The distribution of economic activity by gender insurance, real estate, and business services; and surveys, household surveys, establishment cen- reveals some clear patterns. Industry accounts community, social, and personal services. suses and surveys, administrative records of social for a larger share of male employment than female insurance schemes, and official national estimates. employment worldwide, whereas a higher proportion The concept of employment generally refers to peo- of women work in the services sector. Employment ple above a certain age who worked, or who held in agriculture is also male-dominated, although not a job, during a reference period. Employment data as much as industry. Segregating one sex in a nar- include both full-time and part-time workers. row range of occupations significantly reduces eco- There are many differences in how countries define nomic efficiency by reducing labor market flexibility and measure employment status, particularly for and thus the economy's ability to adapt to change. students, part-time workers, members of the armed This segregation is particularly harmful for women, forces, and household or contributing family work- who have a much narrower range of labor market ers. Where the armed forces are included, they are choices and lower levels of pay than men. But it is allocated to the service sector, causing that sector also detrimental to men when job losses are concen- to be somewhat overstated relative to the service trated in industries dominated by men and job growth sector in economies where they are excluded. Where is centered in service occupations, where women data are obtained from establishment surveys, they often dominate, as has been the recent experience cover only employees; thus self-employed and con- in many countries. tributing family workers are excluded. In such cases There are several explanations for the rising impor- the employment share of the agricultural sector is tance of service jobs for women. Many service jobs-- severely underreported. Moreover, the age group and such as nursing and social and clerical work--are area covered could differ by country or change over considered "feminine" because of a perceived simi- time within a country. For detailed information on larity to women's traditional roles. Women often do breaks in series, consult the original source. not receive the training needed to take advantage of Countries also take different approaches to the changing employment opportunities. And the greater treatment of unemployed people. In most countries availability of part-time work in service industries unemployed people with previous job experience are may lure more women, although it is unclear whether classified according to their last job. But in some this is a cause or an effect. 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. The ILO's Yearbook of Labour Statistics and its data- base Key Indicators of the Labour Market report data by major divisions of the ISIC revision 2 or revision 3. In this table the reported divisions or categories Data sources are aggregated into three broad groups: agriculture, Data on employment are from the ILO database Key industry, and services. Such broad classification may Indicators of the Labour Market, fourth edition. obscure fundamental shifts within countries' industrial 2006 World Development Indicators 57 Child labor Economically active children Employment by economic activitya % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Agriculture Manufacturing Services Survey Work and year Total Male Female Work only study Male Female Male Female Male Female Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 2000 36.6 41.1 31.8 43.1 56.9 .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. Angola 1995 5.2 4.9 5.6 77.6 22.4 5.7 7.9 6.2 8.4 82.4 79.9 Argentina 1997 20.7 25.4 16.0 8.6 91.4 .. .. .. .. .. .. Armenia .. .. .. .. .. .. .. .. .. .. .. Australia .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2000 9.7 12.0 7.3 4.2 95.8 .. .. .. .. .. .. Bangladesh 2003 17.5 20.9 13.9 63.3 36.7 61.4 64.0 11.6 15.5 25.2 18.3 Belarus .. .. .. .. .. .. .. .. .. .. .. Belgium .. .. .. .. .. .. .. .. .. .. .. Benin .. .. .. .. .. .. .. .. .. .. .. Bolivia 2000 19.2 20.4 18.0 19.7 80.3 77.8 72.9 4.3 3.5 15.0 23.6 Bosnia and Herzegovina 2000 20.2 22.8 17.6 4.0 96.0 .. .. .. .. .. .. Botswana .. .. .. .. .. .. .. .. .. .. .. Brazil 2003 7.1 9.5 4.6 5.8 94.2 64.3 49.8 6.5 9.1 26.8 40.9 Bulgaria .. .. .. .. .. .. .. .. .. .. .. Burkina Fasob 1998 66.5 65.4 67.7 95.9 4.1 98.0 98.2 0.6 0.5 1.3 1.2 Burundi 2000 37.0 38.4 35.7 48.3 51.7 .. .. .. .. .. .. Cambodia 2001 52.3 52.4 52.1 16.5 83.5 78.5 73.6 4.7 5.4 15.7 20.4 Cameroonb 2001 15.9 14.5 17.4 52.5 47.5 90.4 86.3 1.9 2.3 5.1 8.8 Canada .. .. .. .. .. .. .. .. .. .. .. Central African Republic 2000 67.0 66.5 67.6 54.9 45.1 .. .. .. .. .. .. Chad 2000 69.9 73.5 66.5 44.6 55.4 .. .. .. .. .. .. Chile 2003 8.8 10.5 6.9 4.0 96.0 31.5 11.9 7.6 5.8 58.5 80.6 China .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 2001 12.2 16.6 7.7 23.0 77.0 .. .. .. .. .. .. Congo, Dem. Rep. 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2002 6.7 9.7 3.5 20.8 79.2 56.5 55.2 8.7 2.7 28.0 42.1 Côte d'Ivoire 2000 40.7 40.9 40.5 46.4 53.6 .. .. .. .. .. .. Croatia .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 2000 12.5 16.7 8.1 7.2 92.8 .. .. .. .. .. .. Ecuador 2001 17.9 22.1 13.6 25.0 75.0 65.1 69.2 10.7 8.6 21.2 22.1 Egypt, Arab Rep. 1998 6.4 4.0 8.9 60.9 39.1 .. .. .. .. .. .. El Salvador 2003 12.7 17.1 8.1 19.5 80.5 66.4 17.6 10.8 16.1 21.2 66.3 Eritrea .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. Ethiopia 2001 57.1 67.9 45.9 63.5 36.5 96.5 88.7 0.5 2.8 2.5 6.2 Finland .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. Gambia, The 2000 25.3 25.4 25.3 41.6 58.4 .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. Germany .. .. .. .. .. .. .. .. .. .. .. Ghana 2000 28.5 28.5 28.4 36.4 63.6 81.0 59.1 4.5 7.6 13.8 32.0 Greece .. .. .. .. .. .. .. .. .. .. .. Guatemala 2000 20.1 25.9 13.9 38.5 61.5 74.5 39.8 5.9 20.1 14.7 40.0 Guinea 1994 48.3 47.2 49.5 98.6 1.4 .. .. .. .. .. .. Guinea-Bissau 2000 67.5 67.4 67.5 63.7 36.3 .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. 58 2006 World Development Indicators Child labor Economically active children Employment by economic activitya % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Agriculture Manufacturing Services Survey Work and year Total Male Female Work only study Male Female Male Female Male Female Honduras 2002 11.4 16.5 6.1 41.9 58.1 73.6 19.8 5.9 24.4 18.6 55.7 Hungary .. .. .. .. .. .. .. .. .. .. .. India 2000 5.2 5.3 5.1 89.8 10.2 70.5 76.6 10.0 15.4 15.9 6.5 Indonesia .. .. .. .. .. .. .. .. .. .. .. Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. Iraq 2000 13.7 17.4 9.7 51.7 48.3 .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. Jamaica .. .. .. .. .. .. .. .. .. .. .. Japan .. .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. .. Kazakhstan 1996 29.7 30.3 29.1 4.4 95.6 .. .. .. .. .. .. Kenya 1999 6.7 6.9 6.4 44.8 55.2 87.3 74.4 2.5 0.3 8.8 25.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 1998 8.6 9.7 7.6 7.0 93.0 93.0 96.3 0.0 0.0 7.0 2.7 Lao PDR .. .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. .. .. .. .. Lebanon .. .. .. .. .. .. .. .. .. .. .. Lesotho 2000 30.8 34.2 27.5 17.6 82.4 .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. Madagascar 2001 25.6 26.1 25.1 85.1 14.9 94.1 93.9 0.6 1.4 2.0 2.9 Malawi 2000 10.6 9.4 11.6 17.1 82.9 .. .. .. .. .. .. Malaysia .. .. .. .. .. .. .. .. .. .. .. Mali 2001 25.3 32.3 18.6 68.7 31.3 .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. Mexicoc 1996 14.7 20.0 9.5 45.6 54.4 61.3 38.3 11.4 12.9 22.6 48.2 Moldova 2000 33.5 34.1 32.8 3.8 96.2 .. .. .. .. .. .. Mongolia 2000 22.0 23.5 20.6 28.2 71.8 .. .. .. .. .. .. Morocco 1998-99 13.2 13.5 12.8 93.2 6.8 60.8 60.3 8.1 8.5 13.5 6.4 Mozambique .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 1999 15.4 16.2 14.7 9.5 90.5 91.5 91.7 0.4 0.4 8.1 8.0 Nepal 1999 47.2 42.2 52.4 35.6 64.4 89.0 86.1 1.2 1.5 9.7 12.3 Netherlands .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2001 12.1 17.5 6.5 33.3 66.7 73.2 32.0 3.0 10.2 23.3 57.8 Niger .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. Pakistan .. .. .. .. .. .. .. .. .. .. .. Panama 2000 4.0 6.4 1.4 37.5 62.5 71.1 38.4 1.4 8.0 27.2 49.5 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. Paraguay 1999 8.1 11.7 4.4 24.2 75.7 61.2 30.9 3.8 4.6 33.1 64.5 Peru 1994 17.7 20.4 15.2 7.3 92.7 78.9 76.3 3.6 3.4 17.5 20.3 Philippines 2001 13.3 16.3 10.0 14.8 85.2 72.6 53.6 3.6 5.3 22.1 41.0 Poland .. .. .. .. .. .. .. .. .. .. .. Portugal 2001 3.6 4.6 2.6 3.6 96.4 52.7 40.7 11.4 10.7 25.6 47.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 59 Child labor Economically active children Employment by economic activitya % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Agriculture Manufacturing Services Survey Work and year Total Male Female Work only study Male Female Male Female Male Female Romania .. .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. .. Rwanda 2000 33.1 36.1 30.3 27.5 72.5 .. .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. Senegal 2000 35.4 43.2 27.7 56.2 43.8 .. .. .. .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 2000 74.0 24.7 72.7 53.8 46.2 .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 1999 27.7 29.0 26.4 5.1 94.9 .. .. .. .. .. .. Spain .. .. .. .. .. .. .. .. .. .. .. Sri Lanka .. .. .. .. .. .. .. .. .. .. .. Sudan 2000 19.1 21.5 16.8 55.9 44.1 .. .. .. .. .. .. Swaziland 2000 11.2 11.4 10.9 14.0 86.0 .. .. .. .. .. .. Sweden .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. Tanzania 2001 40.4 41.5 39.2 40.0 60.0 83.5 73.1 0.1 0.2 16.3 26.7 Thailand .. .. .. .. .. .. .. .. .. .. .. Togo 2000 72.5 73.4 71.6 28.4 71.6 .. .. .. .. .. .. Trinidad and Tobago 2000 3.9 5.2 2.8 12.8 87.2 .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. .. Turkey 1999 4.5 5.2 3.8 66.8 33.2 52.7 83.4 19.9 10.2 10.2 1.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. Uganda 2002­03 13.1 15.0 11.3 18.3 81.7 94.3 92.3 1.5 1.3 3.2 6.0 Ukraine .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. .. .. .. .. .. Uzbekistan 2000 18.1 22.0 14.0 4.1 95.9 .. .. .. .. .. .. Venezuela, RB 2003 9.1 11.4 6.6 17.6 82.4 35.2 9.2 7.3 9.5 53.9 81.0 Vietnam .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1999 13.1 12.4 14.0 64.3 35.7 87.2 96.6 1.2 0.8 10.8 1.8 Zambia 1999 14.4 15.0 13.9 72.8 27.2 92.7 88.1 0.3 0.8 6.6 11.0 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. a. Shares by major industrial category do not sum to 100 percent because of a residual category not included in the table. b. Data are for children ages 10­14. c. Data are for children ages 12­14. 60 2006 World Development Indicators Child labor About the data Definitions The data in the table refer to children's economic activ- who is not a member of the household, whether a · Economically active children refer to children ity, a broader concept than child labor. According to a child is involved in any type of family work (on the involved in economic activity for at least one hour gradually emerging consensus, child labor is a subset farm or in a business), and the like. The ages used in in the reference week of the survey. · Work only of children's economic activity or children's work that country surveys to define child labor range from 5 to refers to children involved in economic activity and is injurious and therefore targeted for elimination. 14 years old. The data in the table have been recalcu- not attending school. · Work and study refer to chil- In line with the international definition of employ- lated to present statistics for children ages 7­14. dren attending school in combination with economic ment, the threshold for classifying a child as eco- Although efforts are made to harmonize the defini- activity. · Employment by economic activity refers nomically active is spending one hour on economic tion of employment and the questions on employment to the distribution of economically active children by activity during the reference week. Economic activity used in survey questionnaires, some differences the major industrial categories (ISIC revision 2 or is as defined by the 1993 United Nations System of remain among the survey instruments used to col- revision 3). · Agriculture corresponds to division 1 National Accounts (revision 3) and corresponds to the lect the information on working children. Differences (ISIC revision 2) or categories A and B (ISIC revision international definition of employment adopted by the exist not only among different household surveys in 3) and includes agriculture and hunting, forestry and Thirteenth International Conference of Labor Statisti- the same country, but also within the same type of logging, and fishing. · Manufacturing corresponds cians in 1982. Economic activity covers all market survey carried out in different countries. to division 3 (ISIC revision 2) or category D (ISIC production and certain types of nonmarket produc- Because of the differences in the underlying sur- revision 3). · Services correspond to divisions 6­9 tion, including production of goods for own use. It vey instruments and in survey dates, estimates of (ISIC revision 2) or categories G­P (ISIC revision 3) excludes household chores performed by children in the economically active child population are not fully and include wholesale and retail trade, hotels and their own household. But some forms of economic comparable across countries. Caution should be restaurants, transport, financial intermediation, real activity are not captured by household surveys and exercised in drawing conclusions concerning relative estate, public administration, education, health and so are not reflected in the estimates. These include levels of child economic activity across countries or social work, other community services, and private unconditional forms of child labor, such as child com- regions based on the published estimates. household activity. mercial sexual exploitation and child slavery, which The table aggregates the distribution of working require different data collection methodologies. children by the industrial categories of the Inter- The data used to develop the indicators are from national Standard Industrial Classification (ISIC): household surveys conducted by the International agriculture, industry, and services. The residual Labor Organization (ILO), the United Nations Children's category, which includes mining and quarrying; elec- Fund (UNICEF), the World Bank, and national statistical tricity, gas, and water; construction; extraterritorial offices. These surveys yield a variety of data in educa- organization; and other inadequately defined activi- tion, employment, health, expenditure, and consump- ties, is not presented in the table, and so the broad tion that relate to child work. But they do not provide groups do not add up to 100 percent. The use of information on unconditional forms of children's work. either ISIC revision 2 or revision 3 is strictly related Household survey data generally include informa- to the codification applied by each country in describ- tion on work type--for example, whether a child is ing the economic activity. The use of two different working for pay in cash or in kind or is involved in classifications does not affect the definition of the unpaid work, whether a child is working for someone groups presented in the table. Data sources Estimates are produced by the Understanding Of children who work, some combine work and schooling Children's Work project based on household Share of children (%) Work only Work and study survey datasets made available by the ILO's Ethiopia, 2001 India, 2000 Philippines, 2001 International Programme on the Elimination of 100 100 100 Child Labour under its Statistical Monitoring Pro- gramme on Child Labour, UNICEF under its Mul- 80 80 80 tiple Indicator Cluster Survey program, the World 60 60 60 Bank under its Living Standards Measurement 40 40 40 Study program, and national statistical offices. Information on how the data were collected and 20 20 20 some indication of their reliability can be found 0 0 0 at www.ilo.org/public/english/standards/ipec/ Girls Boys Girls Boys Girls Boys simpoc/, www.childinfo.org, and www.worldbank. A little light work that does not interfere with education is not necessarily bad, but long working hours are likely to have org/lsms. Detailed country statistics can be found serious health and developmental consequences for children. Studies suggest more children who work are working long hours. at www.ucw-project.org. Source: Understanding Children's Work project. 2006 World Development Indicators 61 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Male Female Total % of total % of total % of male % of female % of total unemployment unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 2000­03a 2000­03a 2000­03a 2000­04a 2000­04a 2000­04a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 13.1 .. 18.3 .. 15.2 .. .. .. 56.4 38.4 3.4 Algeria 24.2 26.6 20.3 31.4 23.0 27.3 .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.4b 16.3 b 7.0 b 14.7b 6.7b 15.6b .. .. .. 42.8 b 38.5b 17.7b Armenia .. .. .. .. .. .. 72.2b 70.8 b 71.6b 5.2 81.5 13.3 Australia 11.3 5.3 b 9.5 5.5b 10.5 5.4b 27.1b 17.0 b 22.5b 48.3 32.7 19.0 Austria 3.5 4.5 3.8 5.4 3.6 4.9 25.0 23.9 24.5 37.3 55.7 7.0 Azerbaijan .. .. .. .. .. .. .. .. .. 4.6 31.4 64.1 Bangladesh .. 3.2 .. 3.3 .. 3.3 .. .. .. 54.3 22.7 8.4 Belarus .. .. .. .. .. .. .. .. .. 10.2 40.6 49.1 Belgium 4.8 6.6 9.5 8.3 6.7 7.4 44.8 48.2 46.3 43.7 38.1 18.2 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 5.5b 4.3 5.6b 6.9 5.5b 5.5 .. .. .. 60.2b 32.5b 4.4b Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 11.7 15.7 17.3 22.3 13.9 18.6 .. .. .. 63.8 23.8 .. Brazil 5.4b 7.8 b 7.9 b 12.3 b 6.4b 9.7b .. .. .. .. .. .. Bulgaria .. 14.1 .. 13.2 .. 13.7 .. .. .. 37.8 50.9 11.4 Burkina Faso .. .. .. .. .. .. .. .. .. 46.8 19.3 5.6 Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 1.4 .. 2.0 .. 1.8 .. .. .. .. .. .. Cameroon .. 8.2 .. 6.7 .. 7.5 .. .. .. .. .. .. Canada 12.1b 7.5b 10.2b 6.8 b 11.2b 7.2b 11.4b 8.4b 10.1b 29.0 b 30.8 b 40.2b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 3.9 6.9 5.3 8.3 4.4 7.4 .. .. .. 18.5 59.0 21.8 China .. .. .. .. 2.3 b 4.0 b .. .. .. .. .. .. Hong Kong, China 2.0 b 9.3 b 1.9 b 6.2b 2.0 b 7.9 b .. .. .. 48.6 39.4 10.1 Colombia 6.7 11.0 13.0 18.5 9.4 14.2 .. .. .. 26.9 52.9 16.5 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 3.4 5.8 5.4 8.2 4.0 6.7 8.9 13.3 10.9 62.2 24.1 9.9 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 13.1 .. 15.7 .. 14.3 .. .. 56.4 21.5 68.4 9.8 Cuba .. .. .. .. 4.6 3.3 .. .. .. .. .. .. Czech Republic .. 7.0 .. 9.9 .. 8.3 47.4 51.9 49.9 24.6 71.8 3.5 Denmark 8.3 5.0 9.9 5.4 9.0 5.2 21.8 17.9 19.9 25.9 46.6 25.5 Dominican Republic 11.7 9.4 34.9 26.0 20.3 15.6 2.2 1.3 1.6 .. .. .. Ecuador 6.0 b 9.0 b 13.2b 15.0 b 8.9 b 11.4b .. .. .. 28.8 47.7 21.9 Egypt, Arab Rep. 6.4 6.3 17.0 23.9 9.0 11.0 .. .. .. .. .. .. El Salvador 8.4b 9.3 7.2b 3.5 7.9 b 6.9 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.9 10.2 3.5 9.9 3.7 10.0 .. .. .. 20.9 62.1 16.8 Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 13.6 8.8 9.7 9.0 11.7 8.9 27.7 21.4 24.7 35.8 46.3 17.5 France 7.9 b 9.0 b 12.7b 11.1b 10.0 b 9.9 b 43.1b 42.8 b 42.9 b 40.6 39.9 17.7 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 11.5 .. 11.5 .. 11.5 .. .. .. 5.8 57.6 36.5 Germany 5.3 10.2 8.4 9.3 6.6 9.8 48.3 52.3 50.0 27.1 60.5 12.4 Ghana .. 7.5 .. 8.7 .. 8.2 .. .. .. .. .. .. Greece 4.9 6.4 12.9 15.9 7.8 10.2 49.2 61.0 56.5 34.2 50.0 15.1 Guatemala 2.6b 2.2 4.6b 3.7 3.2b 2.8 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 11.2 .. 13.6 .. 12.2 .. .. .. .. .. .. .. 62 2006 World Development Indicators Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Male Female Total % of total % of total % of male % of female % of total unemployment unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 2000­03a 2000­03a 2000­03a 2000­04a 2000­04a 2000­04a Honduras 3.3 b .. 3.0 b .. 3.2b 5.1b .. .. .. .. .. .. Hungary 11.0 6.1 8.7 6.1 9.9 6.1 42.2 42.2 42.2 33.5 61.2 5.4 India .. 4.4b .. 4.1b .. 4.3 b .. .. .. 27.0 41.1 31.9 Indonesia 3.5 8.1 4.5 12.9 3.9 9.9 .. .. .. 26.3 52.8 6.7 Iran, Islamic Rep. .. 10.1 .. 20.4 .. 11.6 .. .. .. 38.3 37.1 19.3 Iraq .. 30.2 .. 16.0 .. 28.1 .. .. .. .. .. .. Ireland 15.2 4.9 15.2 3.7 15.2 4.4 40.9 26.0 35.4 48.2 24.9 24.0 Israel 9.2 10.2 13.9 11.3 11.2 10.7 .. .. .. 20.2 48.8 27.0 Italy 8.1 6.4 17.3 10.5 11.6 8.0 57.5 58.9 58.2 49.4 41.4 7.5 Jamaica 9.4 8.1 22.2 15.7 15.4 11.4 24.4 36.2 31.7 13.0 5.4 6.1 Japan 2.1b 4.9 b 2.2b 4.4b 2.2b 4.7b 38.9 b 24.6b 33.5b 70.8 0.0 29.2 Jordan .. 11.8 .. 20.7 .. 13.2 .. .. .. .. .. .. Kazakhstan .. 7.2 .. 10.4 .. 8.8 .. .. .. 7.9 53.2 38.9 Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.8 3.7 2.1 3.1 2.5 3.5 0.7 0.3 0.6 17.0 53.4 29.6 Kuwait .. .. .. .. .. .. .. .. .. 27.5 39.9 6.1 Kyrgyz Republic .. 9.4 .. 10.5 .. 9.9 47.8 54.3 50.9 7.7 77.7 18.5 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 10.7 .. 10.5 .. 10.6 .. .. .. 22.4 68.5 8.8 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 12.7 .. 12.2 .. 12.4 .. .. 57.8 15.0 68.5 16.5 Macedonia, FYR .. 37.0 .. 36.3 .. 36.7 .. .. .. .. .. .. Madagascar .. 3.5 .. 5.6 .. 4.5 .. .. .. 42.7 18.8 6.1 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia .. .. .. .. 3.7 3.5 .. .. .. 32.0 48.8 15.6 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 3.2 9.0 3.6 12.6 3.3 10.2 .. .. .. 71.5b 28.2b .. Mexico 2.7 2.9 4.0 3.4 3.1 3.0 1.1 0.8 1.0 13.7 30.1 46.4 Moldova .. 9.6 .. 6.4 .. 7.9 .. .. .. .. .. .. Mongolia .. 14.3 .. 14.1 .. 14.2 .. .. .. 35.0 45.8 18.4 Morocco 13.0 b 10.6 25.3 b 11.4 16.0 b 10.8 .. .. .. 50.9b 20.6b 19.3 b Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 20.0 26.8 19.0 35.9 19.0 31.1 .. .. .. .. .. .. Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 4.3 4.1 7.3 4.4 5.5 4.3 30.1 28.1 29.2 46.3 35.1 17.4 New Zealand 11.0 b 3.5b 9.6b 4.4b 10.4b 3.9 b 15.5b 11.0 b 13.3 b 1.0 48.8 16.0 Nicaragua 11.3 7.6 19.4 8.0 14.4 7.8 .. .. .. 50.8 b 24.8 b 19.7b Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 6.6 4.8 5.1 3.8 5.9 4.4 7.1 5.4 6.4 21.7 54.7 21.7 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 4.3 6.2 14.2 16.4 5.8 7.8 .. .. .. 14.7 12.3 24.1 Panama 10.8 10.5 22.3 18.8 14.7 13.6 24.0 35.7 29.3 35.9 37.3 26.0 Papua New Guinea 9.0 4.3 5.9 1.3 7.7 2.8 .. .. .. .. .. .. Paraguay 6.3 b 6.7 3.8b 8.9 5.2b 7.6 .. .. .. .. .. .. Peru 7.5b 9.0 b 12.5b 11.9 b 9.4b 10.3 b .. .. .. 9.4b 61.4b 28.6b Philippines 7.9 9.4 9.9 10.3 8.6 9.8 .. .. .. .. .. .. Poland 12.2 18.2 14.7 19.9 13.3 19.0 48.6 50.8 49.7 18.0 75.4 6.7 Portugal 3.5b 5.8 5.0 b 7.6 4.1b 6.7 31.2 32.7 32.0 70.7 14.6 8.8 Puerto Rico 19.1b 12.8b 13.3 b 10.9 b 16.9 b 12.0 b .. .. .. .. .. .. 2006 World Development Indicators 63 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Male Female Total % of total % of total % of male % of female % of total unemployment unemployment labor force labor force labor force Male Female Total Primary Secondary Tertiary 1990­92a 2000­04a 1990­92a 2000­04a 1990­92a 2000­04a 2000­03a 2000­03a 2000­03a 2000­04a 2000­04a 2000­04a Romania .. 7.5 .. 6.4 .. 7.0 .. .. .. 26.0 66.9 5.4 Russian Federation 5.2 9.9 5.2 8.8 5.2 8.6 .. .. .. .. .. .. Rwanda 0.6 .. 0.2 .. 0.3 .. .. .. .. 60.7 24.1 5.9 Saudi Arabia .. 4.2 .. 11.5 .. 5.2 .. .. .. 38.3 34.7 20.1 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro .. 14.4b .. 16.4b .. 15.2b .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.7 5.5 2.6 5.3 2.7 5.4 .. .. .. 22.4 25.0 38.8 Slovak Republic .. 17.3 .. 19.1 .. 18.1 60.2 62.1 61.1 24.1 71.7 4.3 Slovenia .. 6.2 .. 6.6 .. 6.6 .. .. .. 26.2 63.9 8.2 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 25.5b .. 31.7b .. 28.4b .. .. .. 50.2 41.0 5.1 Spain 13.9 8.2 25.8 15.0 18.1 11.0 34.3 43.9 39.8 56.0 20.4 22.7 Sri Lanka 10.1b 6.2b 19.9 b 14.7b 13.3 b 9.0 b .. .. .. 47.2 0.0 52.8 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 6.8 6.9 4.6 6.2 5.7 6.5 19.6 15.3 17.8 23.2 58.1 17.5 Switzerland 2.3 3.9 3.5 4.8 2.8 4.3 21.6 32.6 27.0 28.7 54.5 16.9 Syrian Arab Republic .. 8.3 .. 24.1 .. 11.7 .. .. .. 75.2 10.3 9.8 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 2.7b 4.4 4.2b 5.8 3.5b 5.1 .. .. .. .. .. .. Thailand 1.3 1.6 1.5 1.4 1.4 1.5 .. .. .. 40.0 47.2 0.2 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 17.0 7.8 23.9 14.5 19.6 10.4 20.3 34.7 27.6 55.5 40.5 1.8 Tunisia .. .. .. .. .. 14.3 .. .. .. 43.4 37.4 10.0 Turkey 8.8 10.5 7.8 9.7 8.5 10.3 22.1 30.9 24.4 53.5 29.2 12.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 2.5 .. 3.9 .. 3.2 .. .. .. .. .. .. Ukraine .. 8.9 .. 8.3 .. 8.6 .. .. .. 13.5 54.3 32.2 United Arab Emirates .. 2.2 .. 2.6 .. 2.3 .. .. .. .. .. .. United Kingdom 11.5b 5.0 b 7.3 b 4.2b 9.7b 4.6b 26.5 17.1 23.0 30.3 44.4 14.6 United States 7.9 b 5.6b 7.0 b 5.4b 7.5b 5.5b 12.5b 11.0 b 11.8 b 18.4 34.3 47.3 Uruguay 6.8b 13.5b 11.8 b 20.8 b 9.0 b 16.8 b .. .. .. 54.8 b 31.3 b 13.9 b Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 8.1 14.4 6.8 20.3 b 7.7 16.8 b .. .. .. .. .. .. Vietnam .. 1.9 .. 2.4 .. 2.1 .. .. .. .. .. .. West Bank and Gaza .. 26.9 .. 18.6 .. 25.6 .. .. .. 57.5 14.5 17.6 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 16.3 .. 22.4 .. 18.9 .. .. .. .. .. .. .. Zimbabwe .. 10.4 .. 6.1 .. 8.2 .. .. .. .. .. .. World .. w .. w .. w .. w .. w 6.5 w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. 4.1 6.8 .. .. .. .. .. .. Lower middle income .. .. .. .. 3.6 5.9 .. .. .. .. .. .. Upper middle income 6.2 11.0 6.8 13.4 6.3 12.0 .. .. .. 38.2 47.3 11.5 Low & middle income .. .. .. .. .. 5.6 .. .. .. .. .. .. East Asia & Pacific .. .. .. .. 2.5 4.4 .. .. .. .. .. .. Europe & Central Asia .. 11.1 .. 10.7 .. 10.6 .. .. .. .. .. .. Latin America & Carib. 5.5 8.0 8.5 11.8 6.7 9.5 .. .. .. .. .. .. Middle East & N. Africa .. 12.7 .. 21.9 .. 13.6 .. .. .. .. .. .. South Asia .. 4.4 .. 5.0 .. 4.5 .. .. .. 30.0 33.8 27.4 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.0 6.2 7.9 6.6 7.4 6.4 27.3 23.9 26.0 34.8 34.4 29.7 Europe EMU 7.5 8.2 12.6 10.6 9.5 9.2 44.2 46.4 45.5 40.4 42.3 16.4 a. Data are for the most recent year available. b. Limited coverage. 64 2006 World Development Indicators Unemployment About the data Unemployment and total employment in an economy more comparable internationally. But the age group educational attainment of workers and unemploy- are the broadest indicators of economic activity as and area covered could differ by country or change ment and may be used to draw inferences about reflected by the labor market. The International over time within a country. For detailed information changes in employment demand. Information on Labour Organization (ILO) defines the unemployed as on breaks in series, consult the original source. education attainment is the best available indicator members of the economically active population who In contrast, the quality and completeness of data of skill levels of the labor force. are without work but available for and seeking work, from employment offices and social insurance pro- Besides the limitations to comparability raised including people who have lost their jobs and those grams vary widely. Where employment offices work for measuring unemployment, the different ways of who have voluntarily left work. Some unemployment closely with social insurance schemes and registration classifying the level of education across countries is unavoidable in all economies. At any time some with such offices is a prerequisite for receipt of unem- may also cause inconsistency. The level of educa- workers are temporarily unemployed--between jobs ployment benefits, the two sets of unemployment tion is supposed to be classified according to Inter- as employers look for the right workers and workers estimates tend to be comparable. Where registration national Standard Classification of Education 1997 search for better jobs. Such unemployment, often is voluntary and where employment offices function (ISCED97). For more information on ISCED97, see called frictional unemployment, results from the nor- only in more populous areas, employment office statis- About the data for table 2.10. mal operation of labor markets. tics do not give a reliable indication of unemployment. Changes in unemployment over time may reflect Most commonly excluded from both these sources Definitions changes in the demand for and supply of labor, but are discouraged workers who have given up their job they may also reflect changes in reporting practices. search because they believe that no employment · Unemployment refers to the share of the labor Ironically, low unemployment rates can often disguise opportunities exist or do not register as unemployed force without work but available for and seeking substantial poverty in a country, while high unemploy- after their benefits have been exhausted. Thus mea- employment. Definitions of labor force and unemploy- ment rates can occur in countries with a high level of sured unemployment may be higher in countries that ment differ by country (see About the data). · Long- economic development and low incidence of poverty. offer more or longer unemployment benefits. term unemployment refers to the number of people In countries without unemployment or welfare ben- Women tend to be excluded from the unemploy- with continuous periods of unemployment extending efits, people eke out a living in the informal sector. ment count for various reasons. Women suffer more for a year or longer, expressed as a percentage of In countries with well-developed safety nets, workers from discrimination and from structural, social, and the total unemployed. · Unemployment by educa- can afford to wait for suitable or desirable jobs. But cultural barriers that impede them from actively seek- tional attainment shows the unemployed by level of high and sustained unemployment indicates serious ing work. Also, women are often responsible for the educational attainment, as a percentage of the total inefficiencies in the allocation of resources. care of children and the elderly or for other household unemployed. The levels of educational attainment The ILO definition of unemployment notwithstand- affairs. They may not be available for work during accord with the International Standard Classification ing, reference periods, the criteria for those consid- the short reference period, as they need to make of Education 1997 of the United Nations Educational, ered to be seeking work, and the treatment of people arrangements before starting work. Furthermore, Cultural, and Scientific Organization. temporarily laid off and those seeking work for the women are considered to be employed when they are first time vary across countries. In many developing working part-time or in temporary jobs in the informal countries it is especially difficult to measure employ- sector, despite the instability of these jobs or their ment and unemployment in agriculture. The timing of active searching for more secure employment. a survey, for example, can maximize the effects of Long-term unemployment is measured by the seasonal unemployment in agriculture. And informal length of time that an unemployed person has been sector employment is difficult to quantify where infor- without work and looking for a job. The underlying mal activities are not registered and tracked. assumption is that shorter periods of joblessness Data on unemployment are drawn from labor force are of less concern, especially when the unemployed sample surveys and general household sample sur- are covered by unemployment benefits or similar veys, censuses, and other administrative records forms of welfare support. The length of time that a such as social insurance statistics, employment person has been unemployed is difficult to measure, office statistics, and official estimates, which are because the ability to recall that time diminishes as usually based on information drawn from one or more the period of joblessness extends. Women's long- of the above sources. Labor force surveys generally term unemployment is likely to be lower in countries yield the most comprehensive data because they where women constitute a large share of the unpaid include groups not covered in other unemployment family workforce. Women in such countries have statistics, particularly people seeking work for the more access than men to nonmarket work and are Data sources first time. These surveys generally use a definition more likely to drop out of the labor force and not be Data on unemployment are from the ILO data- of unemployment that follows the international rec- counted as unemployed. base Key Indicators of the Labour Market, fourth ommendations more closely than that used by other Unemployment by level of educational attainment edition. sources and therefore generate statistics that are provides insights into the relationship between the 2006 World Development Indicators 65 Wages and productivity Hours worked Minimum wage Agricultural wage Labor cost per worker Value added per worker in manufacturing in manufacturing average per week $ per year $ per year $ per year $ per year 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a Afghanistan .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. 1,340 .. .. 6,242 2,638 11,306 .. Angola .. .. .. .. .. .. .. .. .. .. Argentina 41 40 .. 2,400 .. .. 6,768 7,338 33,694 37,480 Armenia .. .. .. .. .. .. .. .. .. .. Australia 37 39 .. 12,712 11,212 15,124 14,749 26,087 27,801 57,857 Austria 33 32 .. b .. .. 11,949 28,342 20,956 53,061 Azerbaijan .. .. .. .. .. .. .. .. .. .. Bangladesh .. 52 .. 492 192 360 556 671 1,820 1,711 Belarus .. .. .. .. 1,641 410 2,233 754 .. .. Belgium .. 38 7,661 15,882 6,399 .. 12,805 24,132 25,579 58,678 Benin .. .. .. .. .. .. .. .. .. .. Bolivia .. 46 .. 529 .. .. 4,432 2,343 21,519 26,282 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 45 .. 894 961 650 1,223 3,250 2,884 7,791 .. Brazil .. .. 1,690 1,308 .. .. 10,080 14,134 43,232 61,595 Bulgaria .. .. .. 573 .. 1,372 2,485 1,179 .. .. Burkina Faso .. .. 695 585 .. .. 3,282 .. 15,886 .. Burundi .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. Canada 38 38 4,974 7,897 20,429 30,625 17,710 28,424 36,903 60,712 Central African Republic .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile 43 45 663 1,781 .. .. 6,234 5,822 32,805 32,977 China .. .. .. .. 349 325 472 729 3,061 2,885 Hong Kong, China 48 46 .. .. .. .. 4,127 10,353 7,886 32,611 Colombia .. .. .. 1,128 .. .. 2,988 2,507 15,096 17,061 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. Costa Rica .. 47 1,042 1,638 982 1,697 2,433 2,829 7,185 7,184 Côte d'Ivoire .. .. 1,246 871 .. .. 5,132 9,995 16,158 .. Croatia .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 43 43 .. 942 2,277 3,090 2,306 3,815 5,782 5,094 Denmark .. 37 9,170 19,933 .. .. 16,169 29,235 27,919 49,273 Dominican Republic 44 44 .. 1,439 .. .. 2,191 1,806 8,603 .. Ecuador .. .. 1,637 492 .. .. 5,065 3,738 12,197 9,747 Egypt, Arab Rep. 58 .. 343 415 .. .. 2,210 1,863 3,691 5,976 El Salvador .. .. .. 790 .. .. 3,654 .. 14,423 .. Eritrea .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia .. .. .. .. .. .. .. 1,596 .. 7,094 Finland .. 38 .. b .. .. 11,522 26,615 25,945 55,037 France 40 39 6,053 12,072 .. .. 18,488 .. 26,751 61,019 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. Germany 41 40 .. b .. .. 15,708 33,226 34,945 79,616 Ghana .. .. .. .. 1,470 .. 2,306 .. 12,130 .. Greece .. 41 .. 6,057 .. .. 6,461 12,296 14,561 30,429 Guatemala .. .. .. 459 .. .. 2,605 1,802 11,144 9,235 Guinea 40 .. .. .. .. .. .. .. .. .. Guinea-Bissau 48 .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 66 2006 World Development Indicators Wages and productivity Hours worked Minimum wage Agricultural wage Labor cost per worker Value added per worker in manufacturing in manufacturing average per week $ per year $ per year $ per year $ per year 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a Honduras .. 44 .. .. 1,623 .. 2,949 2,658 7,458 7,427 Hungary 35 33 1,186 1,132 1,186 2,676 1,410 3,755 4,307 10,918 India 46 .. .. 408 205 245 1,035 1,192 2,108 3,118 Indonesia 40 43 .. 241 .. .. 898 3,054 3,807 5,139 Iran, Islamic Rep. .. .. .. .. .. .. 9,737 30,562 17,679 89,787 Iraq .. .. .. .. .. .. 4,624 13,288 13,599 34,316 Ireland 41 41 5,556 12,087 .. .. 10,190 22,681 26,510 86,036 Israel 36 36 .. 5,861 4,582 7,906 13,541 21,150 23,459 35,526 Italy .. 32 .. b .. .. 9,955 34,859 24,580 50,760 Jamaica .. 39 782 692 .. .. 5,218 3,655 12,056 11,091 Japan 47 47 3,920 12,265 .. .. 12,306 31,687 34,456 92,582 Jordan .. 50 b b .. .. 4,643 2,082 16,337 11,906 Kazakhstan .. .. .. .. .. .. .. .. .. .. Kenya 41 39 .. 551 508 568 1,043 810 2,345 1,489 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 52 48 .. 3,903 .. .. 3,153 10,743 11,617 40,916 Kuwait .. .. .. 8,244 .. .. 10,281 .. 30,341 .. Kyrgyz Republic .. .. .. 65 1,695 168 2,287 687 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. 366 .. .. Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho .. 45 .. .. .. .. 1,442 .. 6,047 .. Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. 8,648 .. 21,119 .. Lithuania .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar .. 40 .. .. .. .. 1,575 .. 3,542 .. Malawi .. .. .. .. .. .. .. .. .. .. Malaysia .. .. .. b 1,435 .. 2,519 3,429 8,454 12,661 Mali .. .. 321 459 .. .. 2,983 .. 10,477 .. Mauritania .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. 1,465 1,973 2,969 4,217 Mexico 43 45 1,343 768 1,031 908 3,772 7,607 17,448 25,931 Moldova .. .. .. .. .. .. .. .. .. .. Mongolia .. .. .. .. .. .. .. .. .. .. Morocco .. .. .. 1,672 .. .. 2,583 3,391 6,328 9,089 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. .. .. .. .. Nepal .. .. .. .. .. .. 371 .. 1,523 .. Netherlands 40 40 9,074 15,170 .. .. 18,891 34,326 27,491 56,801 New Zealand 39 39 3,309 9,091 .. .. 10,605 18,419 16,835 32,723 Nicaragua .. 44 .. .. .. .. .. .. .. .. Niger 40 .. .. .. .. .. 4,074 .. 22,477 .. Nigeria .. .. .. 300 .. .. 4,812 .. 20,000 .. Norway 35 35 .. b .. .. 14,935 38,415 24,905 51,510 Oman .. .. .. .. .. .. .. 3,099 .. 61,422 Pakistan 48 .. .. 600 427 416 1,264 .. 6,214 .. Panama .. .. .. .. .. .. 4,768 6,351 15,327 17,320 Papua New Guinea 44 .. .. .. .. .. 4,825 .. 13,563 .. Paraguay 36 39 .. .. 1,606 1,210 2,509 3,241 .. 14,873 Peru 48 .. .. .. .. 944 2,988 .. 15,962 .. Philippines 47 43 915 1,472 382 .. 1,240 2,450 5,266 10,781 Poland 36 33 320 1,584 1,726 1,301 1,682 1,714 6,242 7,637 Portugal 39 40 1,606 4,086 .. .. 3,115 6,237 7,161 17,273 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 67 Wages and productivity Hours worked Minimum wage Agricultural wage Labor cost per worker Value added per worker in manufacturing in manufacturing average per week $ per year $ per year $ per year $ per year 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a 1980­84 1995­99a Romania 34 34 .. 531 1,669 1,864 1,757 1,190 .. 3,482 Russian Federation .. .. 863 297 2,417 659 2,524 1,528 .. .. Rwanda .. .. .. .. .. .. 1,871 .. 9,835 .. Saudi Arabia .. .. .. .. .. .. 9,814 .. .. .. Senegal .. .. 993 848 .. .. 2,828 7,754 6,415 .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 44 .. .. .. .. .. 1,624 .. 7,807 .. Singapore 46 47 .. .. .. 4,856 5,576 21,317 16,442 40,674 Slovak Republic 43 40 .. .. 2,277 1,885 2,306 1,876 5,782 5,094 Slovenia .. .. .. .. .. .. .. 9,632 .. 12,536 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 42 41 .. b 888 .. 6,261 8,475 12,705 16,612 Spain 38 37 3,058 5,778 .. .. 8,276 19,329 18,936 47,016 Sri Lanka 50 53 .. .. 198 264 447 604 2,057 3,405 Sudan .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. Sweden 36 37 .. .. 9,576 27,098 13,038 26,601 32,308 56,675 Switzerland 44 42 .. b .. .. .. .. 61,848 Syrian Arab Republic .. .. .. .. .. .. 2,844 4,338 9,607 9,918 Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania .. .. .. .. .. .. 1,123 .. 3,339 .. Thailand 50 47 749 1,159 .. .. 2,305 3,868 11,072 19,946 Togo .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. 40 .. 2,974 .. .. .. .. 14,008 .. Tunisia .. .. 1,381 1,525 668 968 3,344 3,599 7,111 .. Turkey .. 48 594 1,254 1,015 2,896 3,582 7,958 13,994 32,961 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 43 .. .. .. .. .. 253 .. .. .. Ukraine .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. 6,968 .. 20,344 .. United Kingdom 42 40 .. b .. .. 11,406 23,843 24,716 55,060 United States 40 41 6,006 8,056 .. .. 19,103 28,907 47,276 81,353 Uruguay 48 42 1,262 1,027 1,289 .. 4,128 3,738 13,722 16,028 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 41 .. 1,869 1,463 .. .. 11,188 4,667 37,063 24,867 Vietnam .. 47 .. 134 .. 442 .. 711 .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. .. .. .. .. 4,492 1,291 17,935 5,782 Zambia .. 45 .. .. .. .. 3,183 4,292 11,753 16,615 Zimbabwe .. .. .. .. 1,065 .. 4,097 3,422 9,625 11,944 Note: Data are period averages. a. Figures in italics refer to 1990­94. b. Country has sectoral minimum wage but no minimum wage policy. 68 2006 World Development Indicators Wages and productivity About the data Definitions Much of the data on labor markets are collected In many developing countries agricultural workers · Hours worked are average hours per week actu- through national reporting systems that depend on are hired on a casual or daily basis and lack any ally worked, hours paid for, or statutory hours of plant-level surveys. Even when these data are com- social security benefits. International comparisons of work in a normal workweek for all workers (male and piled and reported by international agencies such as agricultural wages should be subject to more caution female) in nonagricultural activities or, if unavailable, the International Labour Organization or the United than those of wages in other activities. The nature in manufacturing. · Minimum wage corresponds to Nations Industrial Development Organization, differ- of the work carried out by different categories of the most general regime for nonagricultural activi- ences in definitions, coverage, and units of account agricultural workers and the length of the workday ties. When rates vary across sectors, only that for limit their comparability across countries. The indica- and workweek vary considerably from one country to manufacturing (or commerce, if the manufacturing tors in this table are the result of a research project another. Seasonal fluctuations in agricultural wages wage is unavailable) is reported. · Agricultural wage at the World Bank that has compiled results from are more important in some countries than in others. is the daily wage in agriculture. To ensure comparabil- more than 300 national and international sources And the methods followed in different countries for ity with the other wage series, full employment over to provide a set of uniform and representative labor estimating the monetary value of payments in kind the year is assumed, although many wage earners market indicators. Nevertheless, many differences are not uniform. in agriculture are employed seasonally. · Labor cost in reporting practices persist, some of which are Labor cost per worker in manufacturing is some- per worker in manufacturing is the total payroll of described below. The purpose of the table is to times used as a measure of international com- manufacturing establishments divided by the number explore the relationship between labor markets and petitiveness. The indicator reported in the table of people employed or engaged in those establish- economic growth in the long run, not to follow labor is the ratio of total compensation to the number ments. · Value added per worker in manufactur- market developments in the short run. of workers in the manufacturing sector. Com- ing is the value added of manufacturing establish- Analyses of labor force participation, employment, pensation includes direct wages, salaries, other ments divided by the number of people employed or and underemployment often rely on the number of remuneration paid directly by employers, and all engaged in those establishments. hours worked per week, which is the time spent at contributions by employers to social security pro- the workplace working, preparing for work, or waiting grams on behalf of their employees. But there are for work to be supplied or for a machine to be fixed. It unavoidable differences in concepts and reference also includes the time spent at the workplace when periods and in reporting practices. Remuneration no work is being performed but for which payment for time not worked, bonuses and gratuities, and is made under a guaranteed work contract and time housing and family allowances should be consid- spent on short periods of rest. Hours paid for but ered part of the compensation costs, along with not spent at the workplace--such as paid annual severance and termination pay. These indirect and sick leave, paid holidays, paid meal breaks, and labor costs can vary substantially from country to time spent commuting--are not included. When this country, depending on labor laws and collective information is not available, the number of hours bargaining agreements. paid for--the hours actually worked plus the hours International competitiveness also depends on paid for but not spent in the workplace--is reported. productivity, which is often measured by value added Data on hours worked are influenced by differences per worker in manufacturing. The indicator reported in methods of compilation and coverage and by in the table is the ratio of total value added in manu- national practices relating to number of days worked facturing to the number of employees engaged in and overtime, making comparisons across countries that sector. Total value added is estimated as the difficult. difference between the value of industrial output and Wages refer to remuneration in cash and in kind the value of materials and supplies for production paid to employees at regular intervals. They exclude (including fuel and purchased electricity) and cost employer contributions to social security and pen- of industrial services received. sion schemes as well as other benefits received by Observations on labor costs and value added per employees under these schemes. In some countries worker are from plant surveys covering relatively large the national minimum wage represents a "floor," establishments, usually employing 10 or more work- with higher minimum wages for particular occupa- ers and mostly in the formal sector. In high-income tions and skills sets through collective bargaining. In countries the coverage of these surveys tends to be those countries the agreements reached by employ- quite good. In developing countries there is often a ers associations and trade unions are extended by substantial bias toward very large establishments Data sources the government to all firms in the sector or at least to in the formal sector. As a result, the data may not Data on wages and productivity are drawn from Mar- large firms. Changes in the national minimum wage be strictly comparable across countries. The data tin Rama and Raquel Artecona's "Database of Labor are generally associated with parallel changes in the are converted into U.S. dollars using the average Market Indicators across Countries" (2002). minimum wages set through collective bargaining. exchange rate for each year. 2006 World Development Indicators 69 Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 2002 29.6 19.8 25.4 .. .. .. 2002a <2 <0.5 11.8 2.0 Algeria 1988 16.6 7.3 12.2 1995 30.3 14.7 22.6 1995a <2 <0.5 15.1 3.8 Angola .. .. .. .. .. .. .. .. .. .. Argentina 1995 .. 28.4 1998 .. 29.9 .. 2003 b 7.0 2.0 23.0 8.4 Armenia 1998­99 50.8 58.3 55.1 2001 48.7 51.9 50.9 2003a <2 <0.5 31.1 7.1 Australia .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. Azerbaijan 1995 .. .. 68.1 2001 42.0 55.0 49.0 2002a <2 <0.5 <2 <0.5 Bangladesh 1995­96 55.2 29.4 51.0 2000 53.0 36.6 49.8 2000a 36.0 8.1 82.8 36.3 Belarus 2000 .. .. 41.9 .. .. .. 2002a <2 <0.5 <2 <0.5 Belgium .. .. .. .. .. .. .. .. .. .. Benin 1995 25.2 28.5 26.5 1999 33.0 23.3 29.0 2003a 30.9 8.2 73.7 31.7 Bolivia 1997 77.3 53.8 63.2 1999 81.7 50.6 62.7 2002b 23.2 13.6 42.2 23.2 Bosnia and Herzegovina 2001­02 19.9 13.8 19.5 .. .. .. .. .. .. .. Botswana .. .. .. .. .. .. 1993a 23.5 7.7 50.1 22.8 Brazil 1996 54.0 15.4 23.9 1998 51.4 14.7 22.0 2003 b 7.5 3.4 21.2 8.5 Bulgaria 1997 .. .. 36.0 2001 .. .. 12.8 2003a <2 <0.5 6.1 1.5 Burkina Faso 1998 61.1 22.4 54.6 2003 52.4 19.2 46.4 2003a 27.2 7.3 71.8 30.4 Burundi 1990 36.0 43.0 36.4 .. .. .. 1998a 54.6 22.7 87.6 48.9 Cambodia 1997 40.1 21.1 36.1 1999 40.1 13.9 35.9 1997a 34.1 9.7 77.7 34.5 Cameroon 1996 59.6 41.4 53.3 2001 49.9 22.1 40.2 2001a 17.1 4.1 50.6 19.3 Canada .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. 1993a 66.6 38.1 84.0 58.4 Chad 1995­96 67.0 63.0 64.0 .. .. .. .. .. .. .. Chile 1996 .. .. 19.9 1998 .. .. 17.0 2000 b <2 <0.5 9.6 2.5 China 1996 7.9 <2 6.0 1998 4.6 <2 4.6 2001a 16.6 3.9 46.7 18.4 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 1995 79.0 48.0 60.0 1999 79.0 55.0 64.0 2003 b 7.0 3.1 17.8 7.7 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. Costa Rica 1992 25.5 19.2 22.0 .. .. .. 2001b 2.2 0.8 7.5 2.8 Côte d'Ivoire .. .. .. .. .. .. 2002a 14.8 4.1 48.8 18.4 Croatia .. .. .. .. .. .. 2001a <2 <0.5 <2 <0.5 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. 1996b <2 <0.5 <2 <0.5 Denmark .. .. .. .. .. .. .. .. .. .. Dominican Republic 1992 49.0 19.3 33.9 1998 42.1 20.5 28.6 2003b 2.5 0.8 11.0 3.6 Ecuador 1995 56.0 19.0 34.0 1998 69.0 30.0 46.0 1998b 15.8 6.3 37.2 15.8 Egypt, Arab Rep. 1995­96 23.3 22.5 22.9 1999­2000 .. .. 16.7 1999­2000a 3.1 <0.5 43.9 11.3 El Salvador 1992 55.7 43.1 48.3 .. .. .. 2002b 19.0 9.3 40.6 17.7 Eritrea 1993­94 .. .. 53.0 .. .. .. .. .. .. .. Estonia 1995 14.7 6.8 8.9 .. .. .. 2003a <2 <0.5 7.5 1.9 Ethiopia 1995­96 47.0 33.3 45.5 1999­2000 45.0 37.0 44.2 1999­2000a 23.0 4.8 77.8 29.6 Finland .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The 1992 .. .. 64.0 1998 61.0 48.0 57.6 1998a 59.3 28.8 82.9 51.1 Georgia 2002 55.4 48.5 52.1 2003 52.7 56.2 54.5 2003a 6.5 2.1 25.3 8.6 Germany .. .. .. .. .. .. .. .. .. .. Ghana 1992 .. .. 50.0 1998­99 49.9 18.6 39.5 1998­99a 44.8 17.3 78.5 40.8 Greece .. .. .. .. .. .. .. .. .. .. Guatemala 1989 71.9 33.7 57.9 2000 74.5 27.1 56.2 2002b 13.5 5.5 31.9 13.8 Guinea 1994 .. .. 40.0 .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 1987 .. .. 65.0 1995 66.0 .. .. 2001b 53.9 26.6 78.0 47.4 70 2006 World Development Indicators Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Honduras 1997 58.0 35.0 47.0 1999 58.0 37.0 48.0 1999 b 20.7 7.5 44.0 20.2 Hungary 1993 .. .. 14.5 1997 .. .. 17.3 2002a <2 <0.5 <2 <0.5 India 1993­94 37.3 32.4 36.0 1999­2000 30.2 24.7 28.6 1999­2000a 34.7 8.2 52.4 15.7 Indonesia 1996 .. .. 15.7 1999 34.4 16.1 27.1 2002a 7.5 0.9 52.4 15.7 Iran, Islamic Rep. .. .. .. .. .. .. 1998a <2 <0.5 7.3 1.5 Iraq .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. Jamaica 1995 37.0 18.7 27.5 2000 25.1 12.8 18.7 2000a <2 <0.5 13.3 2.7 Japan .. .. .. .. .. .. .. .. .. .. Jordan 1991 .. .. 15.0 1997 .. .. 11.7 2002­03a <2 <0.5 7.0 1.5 Kazakhstan 1996 39.0 30.0 34.6 .. .. .. 2003a <2 <.5 16.0 3.8 Kenya 1994 47.0 29.0 40.0 1997 53.0 49.0 52.0 1997a 22.8 5.9 58.3 23.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. 1998b <2 <0.5 <2 <0.5 Kuwait .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2000 56.4 43.9 52.0 2001 51.0 41.2 47.6 2003a <2 <0.5 21.4 4.4 Lao PDR 1993 48.7 33.1 45.0 1997­98 41.0 26.9 38.6 2002a 27.0 6.1 74.1 30.2 Latvia .. .. .. .. .. .. 2003a <2 <0.5 4.7 1.2 Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. 1995a 36.4 19.0 56.1 33.1 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. 2003a <2 <0.5 7.8 1.8 Macedonia, FYR .. .. .. .. .. .. 2003a <2 <0.5 <2 <0.5 Madagascar 1997 76.0 63.2 73.3 1999 76.7 52.1 71.3 2001a 61.0 27.9 85.1 51.8 Malawi 1990­91 .. .. 54.0 1997­98 66.5 54.9 65.3 1997­98a 41.7 14.8 76.1 38.3 Malaysia 1989 .. .. 15.5 .. .. .. 1997b <2 <0.5 9.3 2.0 Mali 1998 75.9 30.1 63.8 .. .. .. 1994a 72.3 37.4 90.6 60.5 Mauritania 1996 65.5 30.1 50.0 2000 61.2 25.4 46.3 2000a 25.9 7.6 63.1 26.8 Mauritius .. .. .. .. .. .. .. .. .. .. Mexico 1996 52.4 26.5 37.1 2002 34.8 11.4 20.3 2002a 4.5 1.2 20.4 6.5 Moldova 2001 64.1 58.0 62.4 2002 67.2 42.6 48.5 2001a 22.0 5.8 63.7 25.1 Mongolia 1995 33.1 38.5 36.3 1998 32.6 39.4 35.6 1998a 27.0 8.1 74.9 30.6 Morocco 1990­91 18.0 7.6 13.1 1998­99 27.2 12.0 19.0 1999a <2 <0.5 14.3 3.1 Mozambique 1996­97 71.3 62.0 69.4 .. .. .. 1996a 37.9 12.0 78.4 36.8 Myanmar .. .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. 1993 b 34.9 14.0 55.8 30.4 Nepal 1995­96 43.3 21.6 41.8 2003­04 34.6 9.6 30.9 2003­04a 24.1 5.4 68.5 26.8 Netherlands .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. Nicaragua 1993 76.1 31.9 50.3 1998 68.5 30.5 47.9 2001a 45.1 16.7 79.9 41.2 Niger 1989­93 66.0 52.0 63.0 .. .. .. 1995a 60.6 34.0 85.8 54.6 Nigeria 1985 49.5 31.7 43.0 1992­93 36.4 30.4 34.1 2003a 70.8 34.5 92.4 59.5 Norway .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. Pakistan 1993 33.4 17.2 28.6 1998­99 35.9 24.2 32.6 2002a 17.0 3.1 73.6 26.1 Panama 1997 64.9 15.3 37.3 .. .. .. 2002b 6.5 2.3 17.1 6.9 Papua New Guinea 1996 41.3 16.1 37.5 .. .. .. .. .. .. .. Paraguay 1991 28.5 19.7 21.8 .. .. .. 2002b 16.4 7.4 33.2 16.2 Peru 1994 67.0 46.1 53.5 1997 64.7 40.4 49.0 2002b 12.5 4.4 31.8 13.4 Philippines 1994 53.1 28.0 40.6 1997 50.7 21.5 36.8 2000a 15.5 3.0 47.5 17.8 Poland 1993 .. .. 23.8 .. .. .. 2002a <2 <0.5 <2 <0.5 Portugal .. .. .. .. .. .. 1994b <2 <0.5 <2 <0.5 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 71 Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Romania 1994 27.9 20.4 21.5 .. .. .. 2003a <2 0.5 12.9 3.0 Russian Federation 1994 .. .. 30.9 .. .. .. 2002a <2 <0.5 12.1 3.1 Rwanda 1993 .. .. 51.2 1999­2000 65.7 14.3 60.3 1999­2000a 51.7 20.0 83.7 45.5 Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegal 1992 40.4 23.7 33.4 .. .. .. 1995a 22.3 5.7 63.0 25.2 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 1989 .. .. 82.8 2003­04 79.0 56.4 70.2 1989a 57.0 39.5 74.5 51.8 Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. 1996b <2 <0.5 2.9 0.8 Slovenia .. .. .. .. .. .. 1998a <2 <0.5 <2 <0.5 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. .. .. .. .. .. 2000a 10.7 1.7 34.1 12.6 Spain .. .. .. .. .. .. .. .. .. .. Sri Lanka 1990­91 22.0 15.0 20.0 1995­96 27.0 15.0 25.0 2002a 5.6 0.8 41.6 11.9 Sudan .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. Sweden .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. 2003a 7.4 1.3 42.8 13.0 Tanzania 1991 40.8 31.2 38.6 2000­01 38.7 29.5 35.7 2000­01a 57.8 20.7 89.9 49.3 Thailand 1990 .. .. 18.0 1992 15.5 10.2 13.1 2002a <2 <0.5 25.2 6.2 Togo 1987­89 .. .. 32.3 .. .. .. .. .. .. .. Trinidad and Tobago 1992 20.0 24.0 21.0 .. .. .. 1992b 12.4 3.5 39.0 14.6 Tunisia 1990 13.1 3.5 7.4 1995 13.9 3.6 7.6 2000a <2 <0.5 6.6 1.3 Turkey 1994 .. .. 28.3 2002 34.5 22.0 27.0 2003a 3.4 0.8 18.7 5.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 1999­2000 37.4 9.6 33.8 2002­03 41.7 12.2 37.7 .. .. .. .. Ukraine 2000 34.9 31.5 2003 28.4 .. 19.5 2003 b <2 <0.5 4.9 0.9 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. Uruguay 1994 .. 20.2 .. 1998 .. 24.7 .. 2003 b <2 <0.5 5.7 1.6 Uzbekistan 2000 30.5 22.5 27.5 .. .. .. .. .. .. .. Venezuela, RB 1989 .. .. 31.3 .. .. .. 2000 b 8.3 2.8 27.6 10.2 Vietnam 1998 45.5 9.2 37.4 2002 35.6 6.6 28.9 .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1998 45.0 30.8 41.8 .. .. .. 1998a 15.7 4.5 45.2 15.0 Zambia 1996 82.8 46.0 69.2 1998 83.1 56.0 72.9 2002­03a 75.8 36.4 94.1 62.2 Zimbabwe 1990­91 35.8 3.4 25.8 1995­96 48.0 7.9 34.9 1995­96a 56.1 24.2 83.0 48.2 a. Expenditure base. b. Income base. 72 2006 World Development Indicators Poverty Regional poverty estimates Region 1981 1984 1987 1990 1993 1996 1999 2002a People living on less than $1 a day (millions) East Asia & Pacific 796 562 426 472 415 287 282 214 China 634 425 308 375 334 212 223 180 Europe & Central Asia 3 2 2 2 17 20 30 10 Latin America & Caribbean 36 46 45 49 52 52 54 47 Middle East & North Africa 9 8 7 6 4 5 8 5 South Asia 475 460 473 462 476 461 429 437 Sub-Saharan Africa 164 198 219 227 242 271 294 303 Total 1,482 1,277 1,171 1,218 1,208 1,097 1,096 1,015 Excluding China 848 852 863 844 873 886 873 835 Share of people living on less than $1 a day (%) East Asia & Pacific 57.7 38.9 28.0 29.6 24.9 16.6 15.7 11.6 China 63.8 41.0 28.5 33.0 28.4 17.4 17.8 14.0 Europe & Central Asia 0.7 0.5 0.4 0.5 3.7 4.3 6.3 2.1 Latin America & Caribbean 9.7 11.8 10.9 11.3 11.3 10.7 10.5 8.9 Middle East & North Africa 5.1 3.8 3.2 2.3 1.6 2.0 2.6 1.6 South Asia 51.5 46.8 45.0 41.3 40.1 36.6 32.2 31.2 Sub-Saharan Africa 41.6 46.3 46.8 44.6 44.0 45.6 45.7 44.0 Total 40.4 32.8 28.4 27.9 26.3 22.8 21.8 19.4 Excluding China 31.7 29.8 28.4 26.1 25.6 24.6 23.1 21.1 People living on less than $2 a day (millions) East Asia & Pacific 1,170 1,109 1,028 1,116 1,079 922 900 748 China 876 814 731 825 803 650 627 533 Europe & Central Asia 20 18 15 23 81 98 113 76 Latin America & Caribbean 99 119 115 125 136 117 127 123 Middle East & North Africa 52 50 53 51 52 61 70 61 South Asia 821 859 911 958 1,005 1,029 1,039 1,091 Sub-Saharan Africa 288 326 355 382 410 447 489 516 Total 2,450 2,480 2,478 2,654 2,764 2,674 2,739 2,614 Excluding China 1,574 1,666 1,747 1,829 1,961 2,024 2,111 2,082 Share of people living on less than $2 a day (%) East Asia & Pacific 84.8 76.6 67.7 69.9 64.8 53.3 50.3 40.7 China 88.1 78.5 67.4 72.6 68.1 53.4 50.1 41.6 Europe & Central Asia 4.7 4.1 3.3 4.9 17.2 20.7 23.8 16.1 Latin America & Caribbean 26.9 30.4 27.8 28.4 29.5 24.1 25.1 23.4 Middle East & North Africa 28.9 25.2 24.2 21.4 20.2 22.3 24.3 19.8 South Asia 89.1 87.2 86.7 85.5 84.5 81.7 78.1 77.8 Sub-Saharan Africa 73.3 76.1 76.1 75.0 74.6 75.1 76.1 74.9 Total 66.7 63.7 60.1 60.8 60.2 55.5 54.4 50.0 Excluding China 58.8 58.4 57.5 56.6 57.4 56.3 55.8 52.7 Note: Estimates are computed based on population data from World Development Indicators 2005. a. Preliminary estimates not strictly comparable with earlier estimates. See About the data for more information. 2006 World Development Indicators 73 Poverty About the data The World Bank produced its first global poverty because of differences in timing or the quality and when making comparisons over time. The commonly estimates for developing countries for World Devel- training of survey enumerators. used $1 a day standard, measured in 1985 interna- opment Report 1990 using household survey data Comparisons of countries at different levels of tional prices and adjusted to local currency using for 22 countries (Ravallion, Datt, and van de Walle development also pose a potential problem because purchasing power parities (PPPs), was chosen for 1991). Incorporating survey data collected during the of differences in the relative importance of consump- the World Bank's World Development Report 1990: last 15 years, the database has expanded consid- tion of nonmarket goods. The local market value of Poverty because it is typical of the poverty lines in erably and now includes 440 surveys representing all consumption in kind (including own production, low-income countries. PPP exchange rates, such as almost 100 developing countries. Some 1.1 million particularly important in underdeveloped rural econo- those from the Penn World Tables or the World Bank, randomly sampled households were interviewed in mies) should be included in total consumption expen- are used because they take into account the local these surveys, representing 93 percent of the popu- diture. Similarly, imputed profit from the production of prices of goods and services not traded internation- lation of developing countries. The surveys asked nonmarket goods should be included in income. This ally. But PPP rates were designed for comparing detailed questions on sources of income and how it is not always done, though such omissions were a far aggregates from national accounts, not for making was spent and on other household characteristics bigger problem in surveys before the 1980s. Most international poverty comparisons. As a result, there such as the number of people sharing that income. survey data now include valuations for consumption is no certainty that an international poverty line mea- Most interviewas were conducted by staff of govern- or income from own production. Nonetheless, valu- sures the same degree of need or deprivation across ment statistics offices. Along with improvements in ation methods vary. For example, some surveys use countries. data coverage and quality, the underlying methodol- the price in the nearest market, while others use the Early editions of World Development Indicators ogy has also improved, resulting in better and more average farmgate selling price. used PPPs from the Penn World Tables. Recent edi- comprehensive estimates. Whenever possible, the table uses consumption tions use 1993 consumption PPP estimates pro- data for deciding who is poor and income surveys only duced by the World Bank. Recalculated in 1993 PPP Data availability when consumption data are unavailable. In recent terms, the original international poverty line of $1 a Since 1979 there has been considerable expansion editions there has been a change in how income day in 1985 PPP terms is now about $1.08 a day. in the number of countries that field such surveys, surveys are used. In the past, average household Any revisions in the PPP of a country to incorporate the frequency of the surveys, and the quality of their income was adjusted to accord with consumption better price indexes can produce dramatically differ- data. The number of data sets rose dramatically from and income data from national accounts. But in test- ent poverty lines in local currency. a mere 13 between 1979 and 1981 to 100 between ing this approach using data for some 20 countries Issues also arise when comparing poverty mea- 1997 and 1999. The drop to 41 available surveys for which income and consumption expenditure data sures within countries. For example, the cost of living after 1999 reflects the lag between the time data were both available from the same surveys, income is typically higher in urban than in rural areas. One are collected and the time they become available was found to yield a higher mean than consumption reason is that food staples tend to be more expen- for analysis, not a reduction in data collection. Data but also higher inequality. When poverty measures sive in urban areas. So the urban monetary poverty coverage is improving in all regions, but Sub-Saharan based on consumption and income were compared, line should be higher than the rural poverty line. But it Africa continues to lag, with only 28 of 48 countries these two effects roughly cancelled each other out: is not always clear that the difference between urban having at least one data set available. A complete statistically, there was no significant difference. and rural poverty lines found in practice reflects only overview of data availability by year and country So recent editions use income data to estimate differences in the cost of living. In some countries can be obtained at http://iresearch.worldbank.org/ poverty directly, without adjusting average income the urban poverty line in common use has a higher povcalnet/. measures. real value--meaning that it allows the purchase of more commodities for consumption--than does Data quality International poverty lines the rural poverty line. Sometimes the difference The problems of estimating poverty and comparing International comparisons of poverty estimates has been so large as to imply that the incidence of poverty rates do not end with data availability. Sev- entail both conceptual and practical problems. poverty is greater in urban than in rural areas, even eral other issues, some related to data quality, also Countries have different definitions of poverty, and though the reverse is found when adjustments are arise in measuring household living standards from consistent comparisons across countries can be made only for differences in the cost of living. As with survey data. One relates to the choice of income difficult. Local poverty lines tend to have higher international comparisons, when the real value of the or consumption as a welfare indicator. Income is purchasing power in rich countries, where more gen- poverty line varies it is not clear how meaningful such generally more difficult to measure accurately, and erous standards are used, than in poor countries. urban-rural comparisons are. consumption comes closer to the notion of stan- Is it reasonable to treat two people with the same By combining all this information, a team in the dard of living. And income can vary over time even standard of living--in terms of their command over World Bank's Development Research Group cal- if the standard of living does not. But consumption commodities--differently because one happens to culates the number of people living below various data are not always available. Another issue is that live in a better-off country? international poverty lines, as well as other poverty household surveys can differ widely, for example, in Poverty measures based on an international and inequality measures that are published in World the number of consumer goods they identify. And poverty line attempt to hold the real value of the Development Indicators. The database is updated even similar surveys may not be strictly comparable poverty line constant across countries, as is done annually as new survey data become available, and 74 2006 World Development Indicators Poverty Definitions a major reassessment of progress against poverty Note on the 2002 estimates · Survey year is the year in which the underlying data is made about every three years. The 2002 estimates are adapted from Global Eco- were collected. · Rural poverty rate is the percent- nomic Prospects 2006 (page 9, table 1.3). Note age of the rural population living below the national Do it yourself: PovcalNet that a typesetting error occurred in the printed edi- rural poverty line. · Urban poverty rate is the per- Recently, this research team developed PovcalNet, tion of Global Economic Prospects 2006; the 2002 centage of the urban population living below the an interactive Web-based computational tool that poverty rate estimates reported in table 2.7a are national urban poverty line. · National poverty rate allows users to replicate the calculations by the the correct estimates. is the percentage of the population living below the World Bank's researchers in estimating the extent national poverty line. National estimates are based of absolute poverty in the world. PovcalNet is self- on population-weighted subgroup estimates from contained and powered by reliable built-in software household surveys. · Population below $1 a day that performs the relevant calculations from a pri- and population below $2 a day are the percentages mary database. The underlying software can also of the population living on less than $1.08 a day and be downloaded from the site and used with distri- $2.15 a day at 1993 international prices. As a result butional data of various formats. The PovcalNet of revisions in PPP exchange rates, poverty rates for primary database consists of distributional data individual countries cannot be compared with poverty calculated directly from household survey data. rates reported in earlier editions. · Poverty gap is Detailed information for each of these is also avail- the mean shortfall from the poverty line (counting able from the site. the nonpoor as having zero shortfall), expressed as a Estimation from distributional data requires an percentage of the poverty line. This measure reflects interpolation method. The method chosen was Lorenz the depth of poverty as well as its incidence. curves with flexible functional forms, which have proved reliable in past work. The Lorenz curve can be graphed as the cumulative percentages of total consumption or income against the cumulative num- ber of people, starting with the poorest individual. The empirical Lorenz curves estimated by PovcalNet are weighted by household size, so they are based on percentiles of population, not households. PovcalNet also allows users to calculate poverty measures under different assumptions. For example, instead of $1 a day, users can specify a different poverty line, say $1.50 or $3. Users can also spec- ify different PPP rates and aggregate the estimates using alternative country groupings (for example, UN country groupings or groupings based on average Data sources incomes) or a selected set of individual countries. The poverty measures are prepared by the World PovcalNet is available online at http://iresearch. Bank's Development Research Group. The national worldbank.org/povcalnet/. poverty lines are based on the World Bank's country poverty assessments. The international poverty lines are based on nationally represen- tative primary household surveys conducted by national statistical offices or by private agencies under the supervision of government or interna- tional agencies and obtained from government statistical offices and World Bank Group country departments. The World Bank Group has prepared an annual review of its poverty work since 1993. For details on data sources and methods used in deriving the World Bank's latest estimates, see Chen and Ravallion (2004), "How Have the World's Poorest Fared Since the Early 1980s?" 2006 World Development Indicators 75 Distribution of income or consumption Gini Percentage share of index income or consumption Survey year Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Afghanistan .. .. .. .. .. .. .. .. Albania 2002a 28.2 3.8 9.1 13.5 17.3 22.8 37.4 22.4 Algeria 1995a 35.3 2.8 7.0 11.6 16.1 22.7 42.6 26.8 Angola .. .. .. .. .. .. .. .. Argentina b 2003 c 52.8 1.1 3.2 7.0 12.1 20.7 56.8 39.6 Armenia 2003a 33.8 3.6 8.5 12.3 15.7 20.6 42.8 29.0 Australia 1994 c 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 2000 c 29.1 3.3 8.6 13.3 17.4 22.9 37.8 23.0 Azerbaijan 2002a 19.0 5.4 12.2 15.8 18.7 22.2 31.1 18.0 Bangladesh 2000a 31.8 3.9 9.0 12.5 15.9 21.2 41.3 26.7 Belarus 2002a 29.7 3.4 8.5 13.2 17.3 22.7 38.3 23.5 Belgium 2000 c 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1 Benin 2003a 36.5 3.1 7.4 11.3 15.4 21.5 44.5 29.0 Bolivia 2002c 60.1 0.3 1.5 5.9 10.9 18.7 63.0 47.2 Bosnia and Herzegovina 2001a 26.2 3.9 9.5 14.2 17.9 22.6 35.8 21.4 Botswana 1993a 63.0 0.7 2.2 4.9 8.2 14.4 70.3 56.6 Brazil 2003c 58.0 0.8 2.6 6.2 10.7 18.4 62.1 45.8 Bulgaria 2003a 29.2 3.4 8.7 13.7 17.2 22.1 38.3 23.9 Burkina Faso 2003a 39.5 2.8 6.9 10.9 14.5 20.5 47.2 32.2 Burundi 1998a 42.4 1.7 5.1 10.3 15.1 21.5 48.0 32.8 Cambodia 1997a 40.4 2.9 6.9 10.7 14.7 20.1 47.6 33.8 Cameroon 2001a 44.6 2.3 5.6 9.3 13.7 20.4 50.9 35.4 Canada 2000 c 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Central African Republic 1993a 61.3 0.7 2.0 4.9 9.6 18.5 65.0 47.7 Chad .. .. .. .. .. .. .. .. Chile 2000 c 57.1 1.2 3.3 6.6 10.5 17.4 62.2 47.0 China 2001a 44.7 1.8 4.7 9.0 14.2 22.1 50.0 33.1 Hong Kong, China 1996c 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2003 c 58.6 0.7 2.5 6.2 10.6 18.0 62.7 46.9 Congo, Dem. Rep. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. Costa Rica 2001c 49.9 1.3 3.9 8.1 12.8 20.4 54.8 38.4 Côte d'Ivoire 2002a 44.6 2.0 5.2 9.1 13.7 21.3 50.7 34.0 Croatia 2001a 29.0 3.4 8.3 12.8 16.8 22.6 39.6 24.5 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996 c 25.4 4.3 10.3 14.5 17.7 21.7 35.9 22.4 Denmark 1997c 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Dominican Republic 2003c 51.7 1.4 3.9 7.8 12.1 19.4 56.8 41.3 Ecuador 1998a 43.7 0.9 3.3 7.5 11.7 19.4 58.0 41.6 Egypt, Arab Rep. 1999­2000a 34.4 3.7 8.6 12.1 15.4 20.4 43.6 29.5 El Salvador 2002c 52.4 0.7 2.7 7.5 12.8 21.2 55.9 38.8 Eritrea .. .. .. .. .. .. .. .. Estonia 2003a 35.8 2.5 6.7 11.8 16.3 22.4 42.8 27.6 Ethiopia 1999­00a 30.0 3.9 9.1 13.2 16.8 21.5 39.4 25.5 Finland 2000 c 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 France 1995c 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon .. .. .. .. .. .. .. .. Gambia, The 1998a 50.2 1.8 4.8 8.7 12.8 20.3 53.4 37.0 Georgia 2003a 40.4 2.0 5.6 10.5 15.3 22.3 46.4 30.3 Germany 2000 c 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1 Ghana 1998­99a 40.8 2.1 5.6 10.1 14.9 22.9 46.6 30.0 Greece 2000 c 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 Guatemala 2002c 55.1 0.9 2.9 7.0 11.6 19.0 59.5 43.4 Guinea 1994a 40.3 2.6 6.4 10.4 14.8 21.2 47.2 32.0 Guinea-Bissau 1993a 47.0 2.1 5.2 8.8 13.1 19.4 53.4 39.3 Haiti 2001c 59.2 0.7 2.4 6.2 10.4 17.7 63.4 47.7 76 2006 World Development Indicators Distribution of income or consumption Gini Percentage share of index income or consumption Survey year Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Honduras 2003 c 53.8 1.2 3.4 7.1 11.6 19.6 58.3 42.2 Hungary 2002a 26.9 4.0 9.5 13.9 17.6 22.4 36.5 22.2 India 1999­2000a 32.5 3.9 8.9 12.3 16.0 21.2 43.3 28.5 Indonesia 2002a 34.3 3.6 8.4 11.9 15.4 21.0 43.3 28.5 Iran, Islamic Rep. 1998a 43.0 2.0 5.1 9.4 14.1 21.5 49.9 33.7 Iraq .. .. .. .. .. .. .. .. Ireland 2000 c 34.3 2.9 7.4 12.3 16.3 21.9 42.0 27.2 Israel 2001c 39.2 2.1 5.7 10.5 15.9 23.0 44.9 28.8 Italy 2000 c 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8 Jamaica 2000a 37.9 2.7 6.7 10.7 15.0 21.7 46.0 30.3 Japan 1993 c 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2002­03a 38.8 2.7 6.7 10.8 14.9 21.3 46.3 30.6 Kazakhstan 2003a 33.9 3.0 7.4 11.9 16.4 22.8 41.5 25.9 Kenya 1997a 42.5 2.5 6.0 9.8 14.3 20.8 49.1 33.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998c 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2003a 30.3 3.8 8.9 12.8 16.4 22.5 39.4 24.3 Lao PDR 2002a 34.6 3.4 8.1 11.9 15.6 21.1 43.3 28.5 Latvia 2003a 37.7 2.5 6.6 11.2 15.5 22.0 44.7 29.1 Lebanon .. .. .. .. .. .. .. .. Lesotho 1995a 63.2 0.5 1.5 4.3 8.9 18.8 66.5 48.3 Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 2003a 36.0 2.7 6.8 11.6 16.0 22.3 43.2 27.7 Macedonia, FYR 2003a 39.0 2.4 6.1 10.8 15.5 22.2 45.5 29.6 Madagascar 2001a 47.5 1.9 4.9 8.5 12.7 20.4 53.5 36.6 Malawi 1997a 50.3 1.9 4.9 8.5 12.3 18.3 56.1 42.2 Malaysia 1997c 49.2 1.7 4.4 8.1 12.9 20.3 54.3 38.4 Mali 1994a 50.5 1.8 4.6 8.0 11.9 19.3 56.2 40.4 Mauritania 2000a 39.0 2.5 6.2 10.6 15.2 22.3 45.7 29.5 Mauritius .. .. .. .. .. .. .. .. Mexico 2002a 49.5 1.6 4.3 8.3 12.6 19.7 55.1 39.4 Moldova 2003a 33.2 3.2 7.8 12.2 16.5 22.1 41.4 26.4 Mongolia 1998a 30.3 2.1 5.6 10.0 13.8 19.4 51.2 37.0 Morocco 1998­99a 39.5 2.6 6.5 10.6 14.8 21.3 46.6 30.9 Mozambique 1996­97a 39.6 2.5 6.5 10.8 15.1 21.1 46.5 31.7 Myanmar .. .. .. .. .. .. .. .. Namibia 1993c 74.3 0.5 1.4 3.0 5.4 11.5 78.7 64.5 Nepal 2003­04a 47.2 2.6 6.0 9.0 12.4 18.0 54.6 40.6 Netherlands 1999c 30.9 2.5 7.6 13.2 17.2 23.3 38.7 22.9 New Zealand 1997c 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8 Nicaragua 2001a 43.1 2.2 5.6 9.8 14.2 21.1 49.3 33.8 Niger 1995a 50.5 0.8 2.6 7.1 13.9 23.1 53.3 35.4 Nigeria 2003a 43.7 1.9 5.0 9.6 14.5 21.7 49.2 33.2 Norway 2000 c 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 2002a 30.6 4.0 9.3 13.0 16.3 21.1 40.3 26.3 Panama 2002c 56.4 0.8 2.5 6.4 11.2 19.6 60.3 43.6 Papua New Guinea 1996a 50.9 1.7 4.5 7.9 11.9 19.2 56.5 40.5 Paraguay 2002c 57.8 0.6 2.2 6.3 11.3 18.8 61.3 45.4 Peru 2002c 54.6 1.1 3.2 7.1 11.8 19.3 58.7 43.2 Philippines 2000a 46.1 2.2 5.4 8.8 13.1 20.5 52.3 36.3 Poland 2002a 34.5 3.1 7.5 11.9 16.1 22.2 42.2 27.0 Portugal 1997c 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. 2006 World Development Indicators 77 Distribution of income or consumption Gini Percentage share of index income or consumption Survey year Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Romania 2003a 31.0 3.3 8.1 12.9 17.1 22.7 39.2 24.4 Russian Federation 2002a 39.9 2.4 6.1 10.5 14.9 21.8 46.6 30.6 Rwanda 1983­85a 28.9 4.2 9.7 13.2 16.5 21.6 39.1 24.2 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 1995a 41.3 2.6 6.4 10.3 14.5 20.6 48.2 33.5 Serbia and Montenegro .. .. .. .. .. .. .. .. Sierra Leone 1989a 62.9 0.5 1.1 2.0 9.8 23.7 63.4 43.6 Singapore 1998c 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8 Slovak Republic 1996c 25.8 3.1 8.8 14.9 18.7 22.8 34.8 20.9 Slovenia 1998­99c 28.4 3.6 9.1 14.2 18.1 22.9 35.7 21.4 Somalia .. .. .. .. .. .. .. .. South Africa 2000a 57.8 1.4 3.5 6.3 10.0 18.0 62.2 44.7 Spain 2000 c 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6 Sri Lanka 1999­2000a 33.2 3.4 8.3 12.5 16.0 21.0 42.2 27.8 Sudan .. .. .. .. .. .. .. .. Swaziland 1994 c 60.9 1.0 2.7 5.8 10.0 17.1 64.4 50.2 Sweden 2000 c 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2 Switzerland 2000 c 33.7 2.9 7.6 12.2 16.3 22.6 41.3 25.9 Syrian Arab Republic .. .. .. .. .. .. .. .. Tajikistan 2003a 32.6 3.3 7.9 12.3 16.5 22.4 40.8 25.6 Tanzania 2000­01a 34.6 2.9 7.3 12.0 16.1 22.3 42.4 26.9 Thailand 2002a 42.0 2.7 6.3 9.9 14.0 20.8 49.0 33.4 Togo .. .. .. .. .. .. .. .. Trinidad and Tobago 1992c 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 Tunisia 2000a 39.8 2.3 6.0 10.3 14.8 21.7 47.3 31.5 Turkey 2003a 43.6 2.0 5.3 9.7 14.2 21.0 49.7 34.1 Turkmenistan 1998a 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31.7 Uganda 1999a 43.0 2.3 5.9 10.0 14.0 20.3 49.7 34.9 Ukraine 2003a 28.1 3.9 9.2 13.6 17.3 22.4 37.5 23.0 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 1999 c 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5 United States 2000 c 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9 Uruguay b 2003 c 44.9 1.9 5.0 9.1 14.0 21.5 50.5 34.0 Uzbekistan 2000a 26.8 3.6 9.2 14.1 17.9 22.6 36.3 22.0 Venezuela, RB 2000 c 44.1 1.6 4.7 9.4 14.5 22.1 49.3 32.8 Vietnam 2002a 37.0 3.2 7.5 11.2 14.8 21.1 45.4 29.9 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 1998a 33.4 3.0 7.4 12.2 16.7 22.5 41.2 25.9 Zambia 2002­03a 42.1 2.4 6.1 10.2 14.2 20.7 48.8 33.7 Zimbabwe 1995a 50.1 1.8 4.6 8.1 12.2 19.3 55.7 40.3 a. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. b. Urban data. c. Refers to income shares by percentiles of population, ranked by per capita income. 78 2006 World Development Indicators Distribution of income or consumption About the data Definitions Inequality in the distribution of income is reflected World Bank staff have made an effort to ensure · Survey year is the year in which the underlying data in the percentage shares of income or consumption that the data are as comparable as possible. Wher- were collected. · Gini index measures the extent to accruing to portions of the population ranked by ever possible, consumption has been used rather which the distribution of income (or consumption income or consumption levels. The portions ranked than income. Income distribution and Gini indexes expenditure) among individuals or households within lowest by personal income receive the smallest shares for high-income countries are calculated directly from an economy deviates from a perfectly equal distribu- of total income. The Gini index provides a convenient the Luxembourg Income Study database, using an tion. A Lorenz curve plots the cumulative percent- summary measure of the degree of inequality. estimation method consistent with that applied for ages of total income received against the cumula- Data on the distribution of income or consump- developing countries. tive number of recipients, starting with the poorest tion come from nationally representative household individual. The Gini index measures the area between surveys. Where the original data from the house- the Lorenz curve and a hypothetical line of absolute hold survey were available, they have been used to equality, expressed as a percentage of the maximum directly calculate the income or consumption shares area under the line. Thus a Gini index of 0 represents by quintile. Otherwise, shares have been estimated perfect equality, while an index of 100 implies per- from the best available grouped data. fect inequality. · Percentage share of income or con- For most countries the income distribution indica- sumption is the share of total income or consumption tors are based on the same data used to derive the that accrues to subgroups of population indicated by $1 and $2 a day poverty estimates in table 2.7. This deciles or quintiles. Percentage shares by quintile table contains additional countries for which poverty may not sum to 100 because of rounding. estimates are not provided in table 2.7, either because no reasonable purchasing power parity estimates are available or because the international poverty lines are not relevant for high-income economies. The distribution data have been adjusted for household size, providing a more consistent mea- sure of per capita income or consumption. No adjustment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavail- able. For further details on the estimation method for low- and middle-income economies, see Raval- lion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achiev- ing strict comparability is still impossible (see About the data for table 2.7). Two sources of noncomparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or con- sumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the Data sources definitions of income used differ more often among surveys. Consumption is usually a much better wel- Data on distribution are compiled by the World fare indicator, particularly in developing countries. Bank's Development Research Group using pri- Second, households differ in size (number of mem- mary household survey data obtained from govern- bers) and in the extent of income sharing among ment statistical agencies and World Bank country members. And individuals differ in age and consump- departments. Data for high-income economies are tion needs. Differences among countries in these from the Luxembourg Income Study database. respects may bias comparisons of distribution. 2006 World Development Indicators 79 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 pension Male Female labor force labor force % of % of % of per 1995­ 1995­ ages 15­24 ages 15­24 total labor % of capita 2003 a 2003a 2000­04a 2000­04a 1990­2004a Year force Year GDP Year income Afghanistan .. .. .. .. .. .. .. .. Albania .. .. 42 27 .. 2004 40.7 2004 4.6 1995 36.4 Algeria .. .. .. .. .. 2002 38.9 1997 2.1 1991 75.0 Angola .. .. .. .. .. .. .. .. Argentina .. .. 34 34 .. 2004 34.9 1994 6.2 .. Armenia .. .. .. .. 29 1995 66.6 2004 3.4 1996 18.7 Australia .. .. 12 11 .. 2003 92.6 1997 5.9 1989 37.3 Austria .. .. 9 10 .. 2004 86.8 1995 14.9 1993 69.3 Azerbaijan .. .. .. .. .. 1996 52.0 2003 3.0 1996 51.4 Bangladesh .. .. 11 10 10 1993 3.5 1992 0.0 .. Belarus .. .. .. .. .. 1992 97.0 2004 10.6 1995 31.2 Belgium .. .. 16 20 .. 1995 86.2 1997 12.9 .. Benin 50 41 .. .. 21 1996 4.8 2003 1.3 1993 189.7 Bolivia .. .. 7 10 20 2002 10.8 2000 4.5 .. Bosnia and Herzegovina .. .. .. .. .. 2004 37.7 2003 7.4 .. Botswana .. .. 34 46 .. .. 1996 2.7 .. Brazil 27 27 15 22 20 2004 56.4 1997 9.8 .. Bulgaria .. .. 31 25 .. 1994 64.0 2005 8.9 1995 39.3 Burkina Faso .. .. .. .. 9 1993 3.1 1992 0.3 1992 207.3 Burundi .. .. .. .. .. 1993 3.3 1991 0.2 1991 57.4 Cambodia .. .. .. .. 25 .. .. .. Cameroon .. .. .. .. 24 1993 13.7 2004 0.1 .. Canada .. .. 15 12 .. 2003 68.3 1997 5.4 1994 54.3 Central African Republic .. .. .. .. 21 .. 1990 0.3 .. Chad .. .. .. .. 22 1990 1.1 1997 0.1 .. Chile .. .. 17 23 .. 2003 56.2 2001 2.9 1993 56.1 China .. .. .. .. .. 1994 17.6 1996 2.7 .. Hong Kong, China .. .. 19 11 .. .. .. .. Colombia .. .. .. .. 28 1999 20.7 1994 1.1 1989 72.2 Congo, Dem. Rep. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. 1992 5.8 1992 0.9 .. Costa Rica .. .. 13 18 .. 2004 62.5 1997 4.2 1993 76.1 Côte d'Ivoire .. .. .. .. 14 1997 9.3 1997 0.3 .. Croatia .. .. 35 40 .. 2004 100.0 2005 12.3 .. Cuba .. .. .. .. .. .. 1992 12.6 .. Czech Republic .. .. 21 20 .. 1995 85.0 2003 8.5 1996 37.0 Denmark .. .. 9 7 .. 2003 91.4 1997 8.8 1994 46.7 Dominican Republic .. .. 16 34 28 2001 26.8 2000 0.8 2000 42.0 Ecuador .. .. 19 25 .. 2004 30.9 2002 1.4 2002 55.3 Egypt, Arab Rep. .. .. 19 51 12 2004 65.0 1994 2.5 1994 45.0 El Salvador .. .. 14 8 .. 2003 25.1 1997 1.3 .. Eritrea .. .. .. .. 47 .. 2001 0.3 .. Estonia .. .. 17 26 .. 1995 76.0 2003 6.1 1995 56.7 Ethiopia 39 65 .. .. 24 .. 2003 0.4 .. .. Finland .. .. 22 19 .. 2003 91.2 1997 12.1 1994 57.4 France .. .. 22 24 .. 2003 90.1 1997 13.4 .. Gabon .. .. .. .. 26 1995 15.0 .. .. Gambia, The .. .. .. .. .. .. .. .. Georgia 21 7 20 32 .. 2004 25.9 2004 3.0 1996 12.6 Germany .. .. 13 10 .. 2003 87.9 1997 12.1 1995 62.8 Ghana .. .. 13 19 34 2003 7.4 2004 0.6 .. Greece .. .. 19 36 .. 2002 81.9 1993 11.9 1990 85.6 Guatemala .. .. .. .. 20 2000 16.4 1995 0.7 1995 27.6 Guinea .. .. .. .. 13 1993 1.5 .. .. Guinea-Bissau .. .. .. .. .. .. .. .. Haiti .. .. .. .. 43 .. .. .. 80 2006 World Development Indicators 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 pension Male Female labor force labor force % of % of % of per 1995­ 1995­ ages 15­24 ages 15­24 total labor % of capita 2003 a 2003a 2000­04a 2000­04a 1990­2004a Year force Year GDP Year income Honduras .. .. 6 12 .. 1999 20.6 1994 0.6 .. Hungary .. .. 16 14 .. 1996 77.0 2001 11.0 1996 33.6 India 54 41 10 10 10 1992 10.6 .. .. Indonesia .. .. .. .. 12 1995 8.0 .. .. Iran, Islamic Rep. .. .. .. .. .. 2000 35.1 2001 1.5 .. Iraq .. .. .. .. .. 2004 18.4 .. .. Ireland .. .. 9 7 .. 2002 100.0 1997 4.6 1993 77.9 Israel .. .. 22 22 .. 1992 82.0 1996 5.9 1992 48.1 Italy .. .. 21 27 .. 2003 86.0 1997 17.6 .. Jamaica .. .. 22 32 .. 1999 44.4 1996 .. 1989 25.9 Japan .. .. 11 8 .. 2003 92.8 1997 6.9 1989 33.9 Jordan .. .. .. .. 12 2001 36.0 2003 1.9 1995 144.0 Kazakhstan .. .. 13 16 33 2004 35.4 2004 4.9 2001 23.0 Kenya .. .. .. .. 32 1995 18.0 2003 6.4 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. .. .. 12 9 .. 2003 88.7 1997 1.3 .. Kuwait .. .. .. .. .. .. 1990 3.5 .. Kyrgyz Republic 52 48 15 16 33 1997 44.0 1997 6.4 2001 45.0 Lao PDR .. .. .. .. .. .. .. .. Latvia .. .. 17 20 .. 1995 60.5 2002 8.2 1994 47.6 Lebanon .. .. .. .. .. 2003 26.1 .. .. Lesotho .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. 2003 72.8 .. .. Lithuania 50 27 23 28 .. 2004 70.7 2003 6.2 1995 21.3 Macedonia, FYR .. .. 65 67 .. 1995 49.0 1998 8.7 1996 91.6 Madagascar .. .. .. .. 22 1993 5.4 1990 0.2 .. Malawi .. .. .. .. 27 .. .. .. Malaysia .. .. 8 8 .. 1993 48.7 2004 0.7 .. Mali .. .. .. .. 11 1990 2.5 1991 0.4 .. Mauritania .. .. .. .. 29 1995 5.0 1996 0.2 .. Mauritius .. .. .. .. .. 1995 60.0 2002 4.5 .. Mexico 18 22 6 8 .. 2002 25.1 2001 7.8 .. Moldova .. .. 17 13 .. .. 2003 8.0 .. Mongolia .. .. 20 21 .. .. 2004 8.3 .. Morocco .. .. 17 16 17 2003 19.2 2002 2.5 1994 118.0 Mozambique .. .. .. .. 26 1995 2.0 2004 1.4 .. Myanmar .. .. .. .. .. .. .. .. Namibia .. .. 40 49 42 .. .. .. Nepal 60 76 .. .. 16 .. .. .. Netherlands .. .. 8 8 .. 2002 100.0 1997 11.1 1989 48.5 New Zealand .. .. 9 10 .. 2003 95.7 1997 6.5 .. Nicaragua .. .. 11 16 31 2001 14.9 1996 2.5 .. Niger .. .. .. .. 13 1992 1.3 1992 0.1 .. Nigeria .. .. .. .. 17 1993 1.3 2002 1.5 1991 40.5 Norway .. .. 13 11 .. 2003 95.3 1997 8.2 1994 49.9 Oman .. .. .. .. .. .. .. .. Pakistan 64 61 12 21 7 1993 3.5 2004 0.9 .. Panama .. .. 24 38 .. 1998 51.6 1996 4.3 .. Papua New Guinea .. .. .. .. .. .. .. .. Paraguay .. .. 12 17 17 2004 14.3 2001 0.7b .. Peru .. .. 18 21 20 2003 20.8 2000 2.6 .. Philippines 16 19 24 31 15 1996 28.3 1993 1.0 .. Poland .. .. 39 43 .. 1996 68.0 2003 13.9 1995 61.2 Portugal .. .. 14 18 .. 2003 94.7 1997 10.0 1989 44.6 Puerto Rico .. .. 24 25 .. .. .. .. 2006 World Development Indicators 81 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 pension Male Female labor force labor force % of % of % of per 1995­ 1995­ ages 15­24 ages 15­24 total labor % of capita 2003 a 2003a 2000­04a 2000­04a 1990­2004 a Year force Year GDP Year income Romania .. .. 18 19 .. 1994 55.0 2002 7.1 1994 34.1 Russian Federation 10 9 .. .. .. .. 2004 5.8 1995 18.3 Rwanda .. .. .. .. 36 1993 9.3 .. .. Saudi Arabia .. .. .. .. .. .. .. .. Senegal .. .. .. .. 18 2003 4.1 1998 1.5 1997 85.0 b Serbia and Montenegro .. .. .. .. .. .. 2004 10.3 .. Sierra Leone .. .. .. .. .. 2004 3.5 .. .. Singapore .. .. 6 10 .. 1995 73.0 1999 0.5 .. Slovak Republic .. .. 34 31 .. 2000 70.9 2003 7.4 1994 44.5 Slovenia .. .. 13 15 .. 1995 86.0 2003 10.1 1996 49.3 Somalia .. .. .. .. .. .. .. .. South Africa 16 28 56 65 42 .. .. .. Spain .. .. 19 26 .. 2003 91.2 1997 10.9 1995 54.1 Sri Lanka .. .. 22 36 .. 1992 28.8 2005 1.8 .. Sudan .. .. .. .. .. 1995 12.1 .. .. Swaziland .. .. .. .. .. .. .. .. Sweden .. .. 18 16 .. 2003 87.1 1997 11.1 1994 78.0 Switzerland .. .. 8 7 .. 2003 100.0 1997 13.4 1993 44.4 Syrian Arab Republic .. .. 21 39 .. .. 1991 0.5 .. Tajikistan .. .. .. .. .. .. 1996 3.0 .. Tanzania 60 85 .. .. 23 1996 2.0 .. .. Thailand .. .. 5 4 .. 1999 18.0 .. .. Togo .. .. .. .. 24 1997 15.9 1997 0.6 1993 178.8 Trinidad and Tobago .. .. 17 26 .. .. 1996 0.6 .. .. Tunisia .. .. .. .. .. 2003 48.2 2000 4.2 1991 89.5 Turkey 10 6 20 19 10 2002 33.2 2002 7.1 1993 56.0 Turkmenistan .. .. .. .. 27 .. 1996 2.3 .. Uganda .. .. .. .. 28 1994 8.2 2003 0.3 .. Ukraine 5 5 16 17 .. 2005 67.5 2005 15.4 1995 30.9 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom .. .. 12 10 .. 2003 96.2 1997 10.3 .. United States .. .. 13 11 .. 2003 92.2 1997 7.5 1989 33.0 Uruguay .. .. 34 44 .. 2004 37.1 1996 15.0 1996 64.1 Uzbekistan .. .. .. .. 22 .. 2004 0.1 1995 45.8 Venezuela, RB .. .. 24 35 .. 2004 29.3 2001 2.7 .. Vietnam .. .. 4 5 26 1998 8.4 1998 1.6 .. West Bank and Gaza .. .. 43 37 .. 2000 18.6 .. .. Yemen, Rep. .. .. .. .. 9 1999 13.5 1994 0.1 .. Zambia .. .. .. .. 23 1994 10.2 1993 0.1 .. Zimbabwe .. .. 28 21 34 1995 12.0 .. .. World .. w .. w Low income .. .. Middle income .. .. Lower middle income .. .. Upper middle income 19 20 Low & middle income .. .. East Asia & Pacific .. .. Europe & Central Asia .. .. Latin America & Carib. 12 17 Middle East & N. Africa .. .. South Asia 10 13 Sub-Saharan Africa .. .. High income 14 13 Europe EMU 18 20 a. Data are for the most recent year available. b. Refers to system covering private sector workers. 82 2006 World Development Indicators Assessing vulnerability and security About the data Definitions As traditionally defined and measured, poverty is the year as a result of different school opening and · Urban informal sector employment is broadly char- a static concept, and vulnerability a dynamic one. closing dates. The youth unemployment rate shares acterized as employment in urban areas in units that Vulnerability reflects a household's resilience in similar limitations on comparability as the general produce goods or services on a small scale with the the face of shocks and the likelihood that a shock unemployment rate. For further information, see About primary objective of generating employment and will lead to a decline in well-being. Thus, it depends the data for table 2.5. income for those concerned. These units typically primarily on the household's asset endowment and The data on female-headed households are operate at a low level of organization, with little or insurance mechanisms. Because poor people have from recent Demographic and Health Surveys. no division between labor and capital as factors of fewer assets and less diversified sources of income The definition and concept of the female-headed production. Labor relations are based on casual than the better-off, fluctuations in income affect household differ greatly across economies, making employment, kinship, or social relationships rather them more. cross-country comparison difficult. In some cases than contractual arrangements. · Youth unemploy- Enhancing security for poor people means reduc- it is assumed that a woman cannot be the head of ment refers to the share of the labor force ages ing their vulnerability to such risks as ill health, pro- any household in which an adult male is present, 15­24 without work but available for and seeking viding them the means to manage risk themselves, because of sex-biased stereotype. Users need to employment. Definitions of labor force and unem- and strengthening market or public institutions for be cautious when interpreting the data. ployment may differ by country (see About the data). managing risk. The tools include microfinance pro- The data on pension contributors come from · Female-headed households refer to the percent- grams, old age assistance and pensions, and public national sources, the International Labour Organiza- age of households with a female head. · Pension provision of education and basic health care (see tion (ILO), and International Monetary Fund country contributors refer to the share of the labor force tables 2.10 and 2.14). reports. Coverage by pension schemes may be broad covered by a pension scheme. · Public expenditure Poor households face many risks, and vulnerability or even universal where eligibility is determined by on pensions includes all government expenditures on is thus multidimensional. The indicators in the table citizenship, residency, or income status. In contribu- cash transfers to the elderly, the disabled, and survi- focus on individual risks--informal sector employ- tion-related schemes, however, eligibility is usually vors and the administrative costs of these programs. ment, youth unemployment, female-headed house- restricted to individuals who have made contribu- · Average pension is estimated by dividing total pen- holds, income insecurity in old age, and the extent tions for a minimum number of years. Definitional sion expenditure by the number of pensioners. to which publicly provided services may be capable issues--relating to the labor force, for example-- of mitigating some of these risks. Poor people face may arise in comparing coverage by contribution- labor market risks, often having to take up precari- related schemes over time and across countries ous, low-quality jobs in the informal sector and to (for country-specific information, see Palacios and increase their household's labor market participation Pallares-Miralles 2000). The share of the labor force by sending their children to work. Income security is covered by a pension scheme may be overstated in a prime concern for the elderly. countries that do not attempt to count informal sec- For informal sector employment, the data are from tor workers as part of the labor force. labor force and special informal sector surveys, Public interventions and institutions can provide various household surveys, surveys of household services directly to poor people, although whether industries or economic activities, surveys of small these work well for the poor is debated. State action and micro enterprises, and official estimates. The is often ineffective, in part because governments international comparability of the data is affected can influence only a few of the many sources of well- by differences among countries in definitions and being and in part because of difficulties in delivering coverage and in the treatment of domestic workers good and services. The effectiveness of public provi- and those who have a secondary job in the informal sion is further constrained by the fiscal resources Data sources sector. The data in the table are based on national at governments' disposal and the fact that state Data on urban informal sector employment and definitions of urban areas established by countries. institutions may not be responsive to the needs of youth unemployment are from the ILO database Key For details on these definitions, see Data sources. poor people. Indicators of the Labour Market, fourth edition. Data Youth unemployment is an important policy issue for The data on public pension spending are from on female-headed household are from Demographic many economies. Experiencing unemployment may per- national sources and cover all government expendi- and Health Surveys by Macro International. Data on manently impair a young person's productive potential tures, including the administrative costs of pension pension contributors and pension spending are from and future employment opportunities. The table pres- programs. They cover noncontributory pensions or Robert Palacios and Montserrat Pallares-Miralles's ents unemployment among youth ages 15­24, but the social assistance targeted to the elderly and dis- "International Patterns of Pension Provision" (2000) lower age limit for young people in a country could be abled and spending by social insurance schemes for and updates. Further updates, notes, and sources determined by the minimum age for leaving school, so which contributions had previously been made. The will be available under "Knowledge and information" age groups could differ across countries. Also, since pattern of spending in a country is correlated with on the World Bank's Web site on pensions (www. this age group is likely to include school leavers, the its demographic structure--spending increases as worldbank.org/pensions). level of youth unemployment varies considerably over the population ages. 2006 World Development Indicators 83 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers pupil- in primary teacher education ratio % of total % of GDP per capita government pupils per Primarya Secondary Tertiary % of GDP expenditure % of total teacher 1991 2004b 1999 2004b 1999 2004b 2004b 2004b 2004b 2004b Afghanistan .. .. .. .. .. .. .. .. .. 65 Albania .. 7.7 .. 11.9 .. 36.3 2.8 .. .. 21 Algeria .. 11.3 .. 17.1 .. .. .. .. 98.3 27 Angola .. .. .. .. .. .. .. .. .. .. Argentina .. 10.9 16.4 14.9 17.7 13.1 4.0 13.8 .. 17 Armenia .. 8.9 12.4 11.1 29.1 38.3 3.2 .. 66.7 22 Australia .. 16.4 14.9 14.6 26.3 22.6 4.9 .. .. .. Austria 18.6 23.9 30.7 28.2 53.0 47.0 5.7 .. .. 13 Azerbaijan .. 7.6 17.2 13.4 19.3 12.8 3.3 19.2 99.8 14 Bangladesh .. 7.2 12.7 13.7 47.2 33.8 2.2 15.5 51.2 54 Belarus .. 13.7 .. 22.9 .. 27.6 5.8 13.0 98.5 15 Belgium 16.3 19.0 24.4 25.2 46.0 38.6 6.3 .. .. 12 Benin .. 12.2 26.1 22.1 202.9 .. 3.3 .. 72.2 52 Bolivia .. 16.4 11.7 13.0 44.1 35.9 6.4 18.1 .. 24 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. .. 5.6 .. 92.6 .. .. .. 89.7 26 Brazil .. .. 10.5 .. 63.2 .. .. .. .. 24 Bulgaria 22.4 16.2 18.8 19.0 18.0 18.7 3.6 .. .. 17 Burkina Faso .. .. .. .. .. .. .. .. 89.5 49 Burundi 13.4 19.9 .. 75.8 1,190.1 442.1 5.2 13.0 .. 51 Cambodia .. 6.5 6.8 .. 46.8 .. 2.0 .. 96.5 55 Cameroon .. .. 18.6 .. 71.3 75.7 3.8 17.2 68.5 53 Canada .. .. .. .. 49.3 .. .. .. .. 17 Central African Republic 11.9 .. .. .. .. .. .. .. .. .. Chad 8.0 .. 33.6 .. .. .. .. .. .. 69 Chile .. 15.3 14.9 16.3 19.4 15.3 4.1 19.1 .. 34 China .. .. 12.7 .. 99.2 .. .. .. 96.8 21 Hong Kong, China .. 16.0 18.2 22.0 62.9 67.9 4.7 23.3 91.8 19 Colombia .. 16.7 17.0 16.0 39.9 26.3 4.9 11.7 .. 28 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. 7.9 .. 18.3 404.9 245.9 3.2 .. 62.2 83 Costa Rica 7.8 17.1 23.2 19.7 55.0 36.3 4.9 18.5 97.4 22 Côte d'Ivoire .. .. 54.5 .. 212.8 .. .. .. 100.0 42 Croatia .. 24.0 .. 23.5 41.3 34.5 4.5 10.0 100.0 18 Cuba 21.4 .. 41.3 .. 86.4 .. .. 19.4 100.0 10 Czech Republic .. 12.0 22.1 23.0 34.4 31.8 4.4 .. .. 17 Denmark .. 24.9 38.3 36.1 66.2 74.6 8.5 .. .. .. Dominican Republic .. 5.0 2.5 1.3 .. .. 1.1 6.3 79.4 21 Ecuador .. .. 9.6 .. .. .. .. .. 70.9 23 Egypt, Arab Rep. .. .. .. .. .. .. .. .. .. 22 El Salvador .. 9.4 7.9 9.0 9.3 11.1 2.8 20.0 .. .. Eritrea .. 9.8 37.2 17.4 428.9 855.5 3.8 .. 83.1 47 Estonia .. 19.8 28.0 25.5 32.7 24.9 5.7 .. .. 14 Ethiopia 32.0 .. .. .. .. .. 6.1 20.4 .. 65 Finland 21.9 18.3 26.4 27.4 41.3 38.1 6.4 .. .. 16 France 12.0 17.8 29.0 28.6 30.2 29.3 5.6 .. .. 19 Gabon .. .. .. .. .. .. .. .. 100.0 36 Gambia, The 13.7 7.1 .. 8.7 .. 229.7 1.9 8.9 .. .. Georgia .. .. .. .. .. .. 2.9 13.1 97.4 22 Germany .. 16.7 20.8 22.6 41.1 43.0 4.8 .. .. 14 Ghana .. .. .. .. .. .. .. .. 60.7 32 Greece 7.6 15.6 17.4 .. 29.5 26.8 4.0 .. .. 12 Guatemala .. 4.7 4.2 3.7 .. .. .. .. .. 31 Guinea .. .. .. .. .. .. .. .. .. 45 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 9.1 .. .. .. .. .. .. .. .. .. 84 2006 World Development Indicators Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers pupil- in primary teacher education ratio % of total % of GDP per capita government pupils per Primarya Secondary Tertiary % of GDP expenditure % of total teacher 1991 2004b 1999 2004b 1999 2004b 2004b 2004b 2004b 2004b Honduras .. .. .. .. .. .. .. .. 87.2 34 Hungary 21.1 20.8 19.1 21.4 34.3 36.1 5.5 .. .. 10 India .. .. 21.6 .. 75.7 .. .. .. .. 41 Indonesia .. 2.9 8.7 5.6 21.9 15.6 1.1 9.0 .. 20 Iran, Islamic Rep. .. 10.5 10.9 11.5 38.4 26.5 4.8 17.7 100.0 20 Iraq .. .. .. .. .. .. .. .. 100.0 21 Ireland 11.6 12.4 16.9 18.1 28.8 26.6 4.3 .. .. 19 Israel 11.9 23.0 22.7 23.5 32.1 26.6 7.5 13.7 .. 15 Italy 15.3 25.4 26.8 28.1 25.6 27.4 4.7 .. .. 11 Jamaica 9.9 15.5 25.6 24.8 85.9 44.6 5.3 9.5 .. 30 Japan .. 22.2 20.5 21.7 14.9 17.1 3.6 .. .. 20 Jordan .. 15.2 16.4 18.0 .. .. .. .. .. 20 Kazakhstan .. 10.1 .. 7.9 .. 6.2 2.4 .. .. 18 Kenya 13.4 25.2 18.4 25.1 255.5 409.2 7.0 29.2 98.8 40 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 11.8 16.3 15.6 23.7 8.3 5.0 4.2 15.5 .. 30 Kuwait 34.8 25.9 .. 28.3 .. 178.1 8.2 17.4 100.0 13 Kyrgyz Republic .. 7.7 11.9 14.5 27.7 21.2 4.6 23.0 57.9 24 Lao PDR .. 6.7 4.3 8.9 66.9 82.4 2.3 11.0 79.4 31 Latvia .. 22.4 23.5 25.9 23.0 19.0 5.8 .. .. 14 Lebanon .. .. 6.2 .. 12.9 14.7 2.6 12.7 13.5 14 Lesotho .. 20.8 69.1 48.6 1,249.9 603.3 9.0 .. 66.8 44 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. 23.8 .. .. .. .. .. Lithuania .. .. .. .. 34.4 32.9 5.9 .. .. 16 Macedonia, FYR .. 23.6 .. 7.7 .. 23.7 3.5 .. .. 20 Madagascar .. .. 39.9 .. 180.9 184.2 3.3 18.2 .. 52 Malawi 7.4 14.4 .. 30.3 .. .. 6.0 .. .. .. Malaysia 10.1 20.2 22.3 28.3 83.3 102.4 8.1 20.3 .. 19 Mali .. .. 61.6 .. 265.0 .. .. .. .. 52 Mauritania .. .. 45.4 .. 99.9 .. 3.4 .. 100.0 45 Mauritius 10.3 13.6 15.6 20.3 41.2 45.3 4.7 15.7 100.0 22 Mexico 4.9 14.4 14.5 16.2 48.8 49.8 5.3 .. .. 27 Moldova .. 17.1 21.2 26.2 19.0 20.7 4.9 21.4 .. 19 Mongolia .. 15.7 19.5 14.6 36.4 25.0 7.5 19.1 .. 35 Morocco 15.8 19.3 51.4 46.9 109.6 87.2 6.3 27.8 .. 28 Mozambique .. .. .. .. .. .. .. .. .. 65 Myanmar .. .. 7.1 .. 29.0 .. .. .. 65.0 33 Namibia .. 21.3 37.4 25.5 161.7 112.6 7.2 .. 49.5 28 Nepal .. 12.7 13.5 10.7 144.9 72.7 3.4 14.9 30.5 40 Netherlands 12.6 18.0 21.8 22.9 44.2 39.8 5.1 .. .. .. New Zealand 17.2 18.7 24.3 22.2 41.6 35.6 6.7 15.1 .. 18 Nicaragua .. 9.1 .. 10.7 .. .. 3.1 15.0 74.6 35 Niger .. 19.0 88.4 64.3 344.8 345.1 2.3 .. 75.6 44 Nigeria .. .. .. .. .. .. .. .. 50.7 36 Norway 32.8 20.5 27.0 30.7 46.3 48.5 7.6 .. .. 10 Oman 11.7 13.1 22.2 20.5 47.5 53.8 4.6 26.1 99.8 19 Pakistan .. .. .. .. .. .. 2.0 .. .. 47 Panama 11.3 9.9 19.1 12.6 33.6 27.0 3.9 8.9 74.3 24 Papua New Guinea .. .. .. .. .. .. .. .. .. 35 Paraguay .. 12.3 17.3 13.7 55.2 28.2 4.4 11.4 .. 27 Peru .. 6.4 9.2 8.7 21.3 14.0 3.0 17.1 .. 25 Philippines .. 11.1 10.7 9.2 15.0 14.5 3.2 17.8 .. 35 Poland .. 23.5 11.6 20.8 21.5 22.1 5.6 12.8 .. 13 Portugal 17.2 24.0 29.1 31.6 29.7 26.0 5.8 .. .. 11 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 85 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers pupil- in primary teacher education ratio % of total % of GDP per capita government pupils per Primarya Secondary Tertiary % of GDP expenditure % of total teacher 1991 2004b 1999 2004b 1999 2004b 2004b 2004b 2004b 2004b Romania .. 9.9 15.9 15.1 32.4 26.5 3.5 .. .. 17 Russian Federation .. .. .. .. .. .. 3.8 10.7 99.0 17 Rwanda .. .. 28.4 .. .. .. .. .. 81.7 62 Saudi Arabia .. .. 31.4 .. .. .. .. .. .. 12 Senegal 19.4 .. .. .. .. .. 4.0 .. 50.9 43 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic .. 11.3 18.5 18.8 33.0 31.1 4.3 .. .. 18 Slovenia 16.8 .. .. .. 28.7 26.3 6.0 .. .. 13 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. 13.7 21.5 20.3 65.2 47.1 5.4 18.1 78.7 34 Spain 11.7 19.2 25.3 24.7 20.4 23.1 4.5 .. .. 14 Sri Lanka .. .. .. .. .. .. .. .. .. 23 Sudan .. .. .. .. .. .. .. .. .. 29 Swaziland 7.6 11.0 26.7 47.4 397.3 260.5 6.2 .. 90.6 31 Sweden 46.5 24.4 26.4 26.7 53.3 50.6 7.7 .. .. 11 Switzerland 36.5 24.3 27.7 29.2 54.5 59.9 5.8 .. .. .. Syrian Arab Republic .. 14.5 22.1 26.8 .. .. .. .. .. 18 Tajikistan .. 6.7 .. 9.2 .. 8.8 2.8 16.9 84.1 22 Tanzania .. .. .. .. .. .. .. .. 100.0 58 Thailand 11.6 13.8 11.5 13.0 35.5 22.7 4.2 27.5 .. 21 Togo .. 6.7 30.9 .. 317.9 .. 2.6 13.6 45.0 44 Trinidad and Tobago .. 16.0 12.2 .. 147.6 .. 4.3 .. 81.0 18 Tunisia .. 15.5 26.7 23.6 78.7 62.8 6.4 .. .. 22 Turkey 10.9 13.9 14.4 9.4 46.0 50.3 3.6 .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 11.6 .. 34.9 .. 194.1 5.2 18.3 80.4 50 Ukraine .. 10.4 11.2 15.7 36.4 27.1 4.6 18.3 99.7 19 United Arab Emirates .. 7.7 11.6 13.3 .. 2.2 1.6 22.5 60.9 15 United Kingdom 14.9 16.4 15.7 15.5 26.2 28.9 5.3 11.5 .. 17 United States .. 21.8 22.7 25.2 27.2 26.2 5.7 .. .. 15 Uruguay 7.8 7.9 11.4 9.0 19.3 19.0 2.6 9.6 .. 21 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB .. .. .. .. .. .. .. .. .. 20 Vietnam .. .. .. .. .. .. 4.4 17.1 87.0 23 West Bank and Gaza .. .. .. .. .. .. .. .. .. 27 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. Zambia .. 9.3 19.9 11.9 168.2 .. 2.8 14.8 100.0 49 Zimbabwe 21.3 .. 20.0 .. 200.4 .. .. .. .. 39 World .. m 15.2 m 20.0 m 18.9 m 38.5 m 34.5 m 4.4 m .. m .. m 29 m Low income .. .. .. .. .. .. .. .. .. 43 Middle income .. 13.1 16.3 16.3 37.8 32.5 4.4 .. .. 22 Lower middle income .. 11.3 14.1 13.6 .. 29.9 3.5 .. .. 22 Upper middle income .. 14.4 17.8 19.6 33.9 32.7 4.5 .. .. 22 Low & middle income .. 12.4 .. .. .. .. 4.1 .. .. 31 East Asia & Pacific .. 9.5 8.4 .. 34.3 .. 3.2 .. 95.4 22 Europe & Central Asia .. 12.0 16.4 16.4 31.1 25.7 4.1 .. .. 17 Latin America & Carib. .. 12.3 14.9 14.3 37.5 29.0 4.3 15.3 .. 25 Middle East & N. Africa .. 14.5 .. 20.5 .. .. .. .. .. 24 South Asia .. .. 13.5 .. 86.7 .. 2.4 .. .. 42 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. 49 High income 16.3 19.1 24.3 24.7 32.1 28.9 5.6 .. .. 16 Europe EMU 14.0 18.3 25.3 26.3 30.2 29.3 5.1 .. .. 14 a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. b. Provisional data. 86 2006 World Development Indicators Education inputs About the data Data on education are compiled by the United national practices vary with respect to whether par- a variety of reporting errors (for further discussion of Nations Educational, Scientific, and Cultural Organi- ents or schools pay for books, uniforms, and other enrollment data, see About the data for table 2.11). zation (UNESCO) Institute for Statistics from official supplies. For greater detail, see the country- and While the pupil-teacher ratio is often used to compare responses to surveys and from reports provided by indicator-specific notes in the source. the quality of schooling across countries, it is often education authorities in each country. Such data are The share of public expenditure devoted to educa- weakly related to the value added of schooling sys- used for monitoring, policymaking, and resource allo- tion allows an assessment of the priority a government tems (Behrman and Rosenzweig 1994). cation. For a variety of reasons, however, education assigns to education relative to other public invest- In 1998 UNESCO introduced the new International statistics generally fail to provide a complete and ments. It also reflects a government's commitment Standard Classification of Education (ISCED) 1997. accurate picture of a country's education system. to investing in human capital development. However, Thus the time-series data for the years through 1997 Statistics often lag by two to three years, though an returns on investment to education, especially primary are not consistent with those for 1998 and later. Any effort is being made to shorten the delay. Moreover, and lower secondary education, cannot be understood time-series analysis should therefore be undertaken coverage and data collection methods vary across simply by comparing current education indicators with with extreme caution. countries and over time within countries, so compari- national income. It takes a long time before currently And in 2006 the UNESCO Institute for Statistics sons should be interpreted with caution. enrolled children can productively contribute to the changed its convention for citing the reference year The data on education spending in the table refer national economy (Hanushek 2002). of education data and indicators to the calendar year solely to public spending--government spending on The share of trained teachers in primary educa- in which the academic or financial year ends. Data public education plus subsidies for private education. tion measures the quality of the teaching staff. It that used to be listed for 2003/04, for example, is The data generally exclude foreign aid for education. does not take account of competencies acquired by now listed for 2004. This change was implemented They may also exclude spending by religious schools, teachers through their professional experience or to present the most recent data available and to which play a significant role in many developing coun- self-instruction or of such factors as work experi- align the data reporting with that of other interna- tries. Data for some countries and for some years ence, teaching methods and materials, or classroom tional organizations (in particular the Organisation refer to spending by the ministry of education only conditions, which may affect the quality of teaching. for Economic Co-operation and Development and (excluding education expenditures by other minis- Since the training teachers receive varies greatly Eurostat). tries and departments and local authorities). (pre-service or in-service), care should be taken in Many developing countries have sought to supple- comparing across countries. Definitions ment public funds for education. Some countries The primary pupil-teacher ratio reflects the average · Public expenditure per student is public current have adopted tuition fees to recover part of the cost numbers of pupils per teacher. It is different from the spending on education divided by the number of stu- of providing education services or to encourage devel- average class size because of the different practices dents by level, as a percentage of gross domestic opment of private schools. Charging fees raises dif- countries employ, such as part-time teaching, school product (GDP) per capita. · Public expenditure on ficult questions relating to equity, efficiency, access, shifts, and multigrade classes. The comparability of education is current and capital public expenditure and taxation, however, and some governments have pupil-teacher ratios across countries is affected by on education, as a percentage of GDP and as a per- used scholarships, vouchers, and other methods of the definition of teachers and by differences in class centage of total government expenditure. · Trained public finance to counter criticism. For most coun- size by grade and in the number of hours taught, as teachers in primary education are the percentage of tries, the data reflect only public spending. Data for well as the different practices mentioned above. More- primary school teachers who have received the mini- a few countries include private spending, although over, the underlying enrollment levels are subject to mum organized teacher training (pre-service or in-ser- vice) required for teaching in their country. · Primary pupil-teacher ratio is the number of pupils enrolled in primary school divided by the number of primary school Estimated impact of HIV/AIDS on education in three Sub-Saharan countries, 2005 teachers (regardless of their teaching assignment). Kenya Tanzania Zambia Low Medium High Low Medium High Low Medium High Teacher deaths due to AIDS 700 1,620 3,020 605 1,290 2,010 580 1,030 1,500 Share of teacher attrition (%) 7.5 18 29.6 9.3 19.9 31 23.3 40.4 48.8 Teacher-years of absenteeism due to AIDS 690 1,590 2,930 610 1,290 2,200 605 1,090 1,580 Share of total teacher-years (%) 0.4 0.8 1.8 0.5 1.1 2.3 1.3 2.2 3.9 Data sources In the best-case scenario (low estimate) Kenya, Tanzania, and Zambia will lose 600­700 teacher-years through absentee- Data on education inputs are from the UNESCO ism caused by HIV/AIDS. In the worst-case scenario (high estimate) they will lose 1,200­3,000 teacher-years. Institute for Statistics, which compiles international Note: The teacher infection rate is half the general population infection rate for the low estimate, the same as the general data on education in cooperation with national com- population infection rate for the medium estimate, and twice the general population infection rate for the high estimate. All missions and national statistical services. Data for estimates are modeled estimates. latest years are provisional, as of January 2006. Source: Desai and Jukes 2005 cited in UNESCO 2006 (p.88). 2006 World Development Indicators 87 Participation in education Gross enrollment Net enrollment Children out of ratio ratio school thousand % of relevant age group % of relevant age group primary-school-age children Preprimary Primary Secondary Tertiary Primarya Secondarya Male Female 2004b 2004b 2004b 2004b 1991 2004b 1991 2004b 2004 2004 Afghanistan 1 90 13 1 .. .. .. .. .. .. Albania 49 104 78 16 95 96 .. 74 5 6 Algeria 5 112 81 20 89 97 53 66 0 41 Angola .. .. .. 1 50 .. .. .. .. .. Argentina 62 118 99 61 .. .. .. 81 3 11 Armenia 31 101 91 26 .. 97 .. 89 3 0 Australia 100 102 154 74 99 95 79 85 51 44 Austria 88 105 100 49 88 .. .. 89 .. .. Azerbaijan 28 97 83 15 89 84 .. 77 50 51 Bangladesh 12 106 51 7 .. .. .. 48 .. .. Belarus 100 101 93 61 86 95 .. 87 13 19 Belgium 116 105 160 61 96 100 87 97 4 4 Benin 4 99 26 .. 41 83 .. .. .. .. Bolivia 48 113 89 41 .. 95 .. 74 25 18 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 104 74 6 83 82 35 60 29 22 Brazil 55 145 110 20 85 97 17 75 .. .. Bulgaria 77 105 99 41 86 94 63 88 6 7 Burkina Faso 1 53 12 1 29 40 .. 10 590 681 Burundi 1 80 12 2 53 57 .. 8 240 278 Cambodia 9 137 26 3 69 98 .. 25 .. .. Cameroon 20 114 44 5 74 .. .. .. .. .. Canada 65 101 105 57 98 .. 89 .. .. .. Central African Republic 3 64 12 .. 52 .. .. .. .. .. Chad .. 71 15 .. 35 57 .. 11 243 413 Chile 50 99 88 43 89 86 55 78 119 124 China 36 115 70 15 97 .. .. .. .. .. Hong Kong, China 70 108 85 32 .. 97 .. 78 1 12 Colombia 38 111 75 27 69 83 34 55 379 334 Congo, Dem. Rep. 1 .. .. .. 54 .. .. .. .. .. Congo, Rep. 6 89 32 4 79 .. .. .. .. .. Costa Rica 64 112 68 19 87 92 38 50 22 18 Côte d'Ivoire 3 72 25 .. 45 56 .. 20 519 705 Croatia 47 94 88 39 79 87 63 85 7 7 Cuba 116 100 93 54 93 96 70 87 9 20 Czech Republic 104 102 97 37 87 87 .. 90 38 36 Denmark 90 103 127 67 98 100 87 95 0c 0 Dominican Republic 32 112 68 33 57 86 .. 49 77 63 Ecuador 77 117 61 .. 98 99 .. 52 11 0 Egypt, Arab Rep. 14 100 87 29 84 94 .. 79 78 218 El Salvador 50 113 60 18 .. 91 .. 48 35 32 Eritrea 7 66 28 1 16 48 .. 19 135 156 Estonia 109 100 96 64 99 95 .. 88 1 1 Ethiopia 2 77 28 2 22 46 .. 25 .. .. Finland 57 102 127 87 98 100 93 94 1 1 France 113 105 110 55 100 100 .. 95 7 3 Gabon 14 130 50 .. 85 .. .. .. .. .. Gambia, The 18 .. .. 1 48 .. .. 33 .. .. Georgia 49 95 82 41 97 93 .. 69 14 14 Germany 99 99 100 50 84 .. .. .. .. .. Ghana 46 81 42 3 54 58 .. 36 624 597 Greece 67 100 96 72 95 98 83 84 2 2 Guatemala 28 113 49 10 .. 93 .. 34 32 80 Guinea 6 79 26 2 27 64 .. 21 228 291 Guinea-Bissau .. .. .. .. 38 .. .. .. .. .. Haiti .. .. .. .. 22 .. .. .. .. .. 88 2006 World Development Indicators Participation in education Gross enrollment Net enrollment Children out of ratio ratio school thousand % of relevant age group % of relevant age group primary-school-age children Preprimary Primary Secondary Tertiary Primarya Secondarya Male Female 2004b 2004b 2004b 2004b 1991 2004b 1991 2004b 2004 2004 Honduras 33 118 .. 16 89 91 21 .. 57 45 Hungary 79 99 103 52 91 89 75 92 9 8 India 34 107 52 11 .. 87 .. .. .. .. Indonesia 22 116 62 16 97 96 39 55 0 215 Iran, Islamic Rep. 37 103 82 22 92 89 .. 78 400 402 Iraq 6 98 45 15 94 88 .. 38 .. .. Ireland .. 106 109 55 90 96 80 85 9 8 Israel 110 112 93 57 92 99 .. 89 7 6 Italy 101 101 99 59 100 99 .. 91 4 9 Jamaica 81 93 84 19 96 88 64 75 20 18 Japan 84 100 102 52 100 100 97 100 6 0 Jordan 30 100 88 35 94 93 .. 82 18 11 Kazakhstan 31 109 98 48 89 98 .. 92 4 6 Kenya 53 111 48 .. .. 76 .. .. 618 607 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 87 105 91 89 100 100 86 88 0c 9 Kuwait 71 96 90 22 49 86 .. 78 9 6 Kyrgyz Republic 12 98 88 40 92 90 .. .. 10 9 Lao PDR 8 116 46 6 63 84 .. 37 50 68 Latvia 75 95 95 71 92 87 .. 87 6 6 Lebanon 74 107 89 48 73 93 .. .. 10 10 Lesotho 31 131 36 3 71 86 15 23 27 18 Liberia .. .. .. .. .. .. .. .. .. .. Libya 8 112 104 56 96 .. .. .. .. .. Lithuania 62 100 103 69 .. 92 .. 94 5 4 Macedonia, FYR 31 98 84 27 94 92 .. 81 2 1 Madagascar 10 134 .. 3 64 89 .. .. 136 136 Malawi .. 125 29 0c 48 95 .. 25 71 19 Malaysia 99 93 70 29 .. 93 .. 70 113 107 Mali 3 64 22 2 21 46 5 .. 557 615 Mauritania 2 94 20 3 35 74 .. 14 58 60 Mauritius 95 103 80 17 91 95 .. 75 4 2 Mexico 81 109 79 22 98 100 44 62 25 8 Moldova 50 85 74 32 89 78 .. 69 23 22 Mongolia 33 102 93 39 90 84 .. 82 13 11 Morocco 53 106 47 11 56 87 .. 35 204 302 Mozambique .. 95 11 1 43 71 .. 4 475 614 Myanmar .. 93 38 11 98 85 .. 34 408 374 Namibia 29 101 58 6 .. 74 .. 37 59 47 Nepal 17 114 43 6 .. .. .. .. .. .. Netherlands 87 108 122 58 95 99 84 89 0c 8 New Zealand 90 102 119 72 98 100 85 92 1 1 Nicaragua 35 112 64 18 73 88 .. 41 25 23 Niger 1 45 8 1 22 39 5 7 609 717 Nigeria 15 99 35 10 .. 88 .. 28 .. .. Norway 82 99 114 80 100 99 88 95 1 1 Oman 6 87 86 13 69 78 .. 75 38 33 Pakistan 45 82 27 3 33 66 .. .. 2,294 3,834 Panama 55 112 70 46 .. 100 .. 64 2 3 Papua New Guinea 59 75 26 .. .. .. .. .. .. .. Paraguay 30 110 65 26 94 89 26 51 46 42 Peru 58 118 90 .. .. 100 .. 69 3 0 Philippines 39 113 84 29 96 94 .. 59 385 269 Poland 51 100 105 59 97 98 76 91 33 25 Portugal 75 118 109 56 98 .. .. 82 2 4 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 89 Participation in education Gross enrollment Net enrollment Children out of ratio ratio school thousand % of relevant age group % of relevant age group primary-school-age children Preprimary Primary Secondary Tertiary Primarya Secondarya Male Female 2004b 2004b 2004b 2004b 1991 2004b 1991 2004b 2004 2004 Romania 76 100 85 36 81 90 .. 81 33 34 Russian Federation 67 118 93 65 99 .. .. .. .. .. Rwanda 3 119 14 3 66 73 7 .. 205 185 Saudi Arabia 5 67 68 28 59 53 31 52 824 806 Senegal 6 76 19 5 43 66 .. 15 296 320 Serbia and Montenegro .. .. .. .. 69 .. 62 .. .. .. Sierra Leone .. .. .. 2 43 .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic 88 100 92 34 .. 85 .. 88 21 19 Slovenia 68 111 112 70 96 96 .. 95 1 1 Somalia .. .. .. .. 9 .. .. .. .. .. South Africa 33 105 90 15 90 89 45 .. 287 200 Spain 109 107 117 64 100 100 .. 95 2 10 Sri Lanka .. 102 81 .. .. 99 .. .. 9 13 Sudan 23 60 33 .. 40 .. .. .. .. .. Swaziland .. 101 42 4 75 77 30 29 24 23 Sweden 80 109 137 82 100 100 85 98 1 3 Switzerland 93 103 93 45 84 94 80 83 5 3 Syrian Arab Republic 10 123 63 .. 91 98 43 58 0 32 Tajikistan 9 100 82 16 77 98 .. 79 3 17 Tanzania 25 101 .. 1 49 86 .. .. 465 518 Thailand 92 99 77 41 76 87 .. .. 365 433 Togo 2 101 39 .. 64 79 15 .. .. .. Trinidad and Tobago 86 102 84 12 91 92 .. 72 2 2 Tunisia 22 111 77 26 94 97 .. 64 10 9 Turkey 7 95 85 28 89 89 42 .. 332 548 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 3 125 19 3 .. 98 .. 15 .. .. Ukraine 82 95 93 66 80 86 .. 84 162 155 United Arab Emirates 64 84 66 22 99 71 60 62 41 42 United Kingdom 77 101 170 63 98 100 81 95 0c 1 United States 60 100 95 83 97 94 85 89 740 584 Uruguay 64 109 106 38 91 90 .. 73 16 15 Uzbekistan 28 100 95 15 78 .. .. .. .. .. Venezuela, RB 55 105 72 39 87 92 18 61 109 89 Vietnam 47 98 73 10 90 93 .. .. .. .. West Bank and Gaza 30 93 94 38 .. 86 .. 89 22 19 Yemen, Rep. 1 87 48 9 51 75 .. .. .. .. Zambia .. 99 26 .. .. 80 .. 24 221 214 Zimbabwe 43 96 36 4 .. 82 .. 34 224 206 World 37 w 104 w 66 w 24 w 84 w .. w .. w .. w 53,784 s 61,590 s Low income 27 100 46 9 .. 79 .. .. .. .. Middle income 38 111 75 24 92 .. .. .. .. .. Lower middle income 35 112 72 20 92 .. .. .. .. .. Upper middle income 53 106 87 40 94 .. .. .. .. .. Low & middle income 32 105 61 17 82 .. .. .. 52,451d 60,508d East Asia & Pacific 36 113 69 17 96 .. .. .. 5,158d 4,870 d Europe & Central Asia 45 102 92 47 90 .. .. .. 1,439 d 1,669 d Latin America & Carib. 57 121 87 26 85 96 30 65 1,789d 1,497d Middle East & N. Africa 23 104 67 23 84 89 .. .. 2,585d 3,807d South Asia 33 103 49 10 .. 88 .. .. 18,742d 23,552d Sub-Saharan Africa 16 93 30 5 47 64 .. .. 22,738d 25,112d High income 76 100 105 67 95 95 85 90 1,333d 1,083d Europe EMU 101 104 108 57 95 99 .. 92 .. .. a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. b. Provisional data. c. Less than 0.5. d. Data are for 2001/02. 90 2006 World Development Indicators Participation in education About the data Definitions School enrollment data are reported to the United ing laws or regulations. Errors are also introduced · Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural Organi- when parents round up children's ages. While census ment, regardless of age, to the population of the zation (UNESCO) Institute for Statistics by national data are often adjusted for age bias, adjustments age group that officially corresponds to the level of education authorities and statistical offices. Enroll- are rarely made for inadequate vital registration education shown. · Preprimary education refers to ment ratios help to monitor two important issues systems. Compounding these problems, pre- and the initial stage of organized instruction, designed for universal primary education: whether a country post-census estimates of school-age children are primarily to introduce very young children to a school- is on track to achieve the Millennium Development interpolations or projections based on models that type environment. · Primary education provides Goal of universal primary completion by 2015, which may miss important demographic events (see the children with basic reading, writing, and mathemat- implies achieving a net primary enrollment ratio of discussion of demographic data in About the data ics skills along with an elementary understanding 100 percent, and whether an education system has for table 2.1). of such subjects as history, geography, natural sci- sufficient capacity to meet the needs of universal In using enrollment data, it is also important to ence, social science, art, and music. · Secondary primary education, as indicated in part by its gross consider repetition rates. These rates are quite education completes the provision of basic educa- enrollment ratios. high in some developing countries, leading to a sub- tion that began at the primary level and aims at lay- Enrollment ratios, while a useful measure of partici- stantial number of overage children enrolled in each ing the foundations for lifelong learning and human pation in education, also have some limitations. They grade and raising the gross enrollment ratio. development by offering more subject- or skill-ori- are based on data collected during annual school Thus gross enrollment ratios indicate the capacity ented instruction using more specialized teachers. surveys, which are typically conducted at the begin- of each level of the education system, but a high ratio · Tertiary education, whether or not leading to an ning of the school year. They do not reflect actual does not necessarily mean a successful education advanced research qualification, normally requires, rates of attendance or dropouts during the school system. The net enrollment ratio excludes overage as a minimum condition of admission, the success- year. And school administrators may report exagger- students in an attempt to capture more accurately ful completion of education at the secondary level. ated enrollments, especially if there is a financial the system's coverage and internal efficiency. It does · Net enrollment ratio is the ratio of children of offi- incentive to do so. Often the number of teachers not solve the problem completely, however, because cial school age based on the International Standard paid by the government is related to the number of some children fall outside the official school age Classification of Education 1997 who are enrolled in pupils enrolled. because of late or early entry rather than because school to the population of the corresponding official Also as international indicators, the gross and of grade repetition. The difference between gross school age. · Children out of school are the number net primary enrollment ratios have an inherent and net enrollment ratios shows the incidence of of primary-school-age children not enrolled in primary weakness: the length of primary education differs overage and underage enrollments. or secondary school. significantly across countries, although the Interna- Out of school children are children in the primary tional Standard Classification of Education tries to school age group who are not enrolled in primary or in minimize the difference. A relatively short duration secondary education. The data are calculated by the for primary education tends to increase the ratio, UNESCO Institute for Statistics using administrative whereas a relatively long duration tends to decrease data. Children out of school include dropouts and it (in part because there are more dropouts among children who never enrolled as well as children of older children). primary age enrolled in pre-primary education. The Overage or underage enrollments frequently occur, large number of children out of school creates pres- particularly when parents prefer, for cultural or eco- sure for the education system to enroll children and nomic reasons, to have children start school at other to provide classrooms, teachers, and educational than the official age. Children's age at enrollment materials, a task made difficult in many developing may be inaccurately estimated or misstated, espe- countries by limited education budgets. cially in communities where registration of births In 2006 the UNESCO Institiute for Statistics changed is not strictly enforced. Parents who want to enroll its convention for citing the reference year. For more an underage child in primary school may do so by information, see About the data for table 2.10. overstating the child's age. And in some education systems ages for children repeating a grade may be underreported. Other problems affecting cross-country compari- Data sources sons of enrollment data stem from errors in esti- Data on gross and net enrollment ratios and out mates of school-age populations. Age-gender struc- of school children are from the UNESCO Institute tures from censuses or vital registration systems, for Statistics. Data on gross and net enrollment the primary sources of data on school-age popula- ratios for latest years are provisional, as of tions, are commonly subject to underenumeration January 2006. (especially of young children) aimed at circumvent- 2006 World Development Indicators 91 Education efficiency Gross intake rate Share of cohort Repeaters in Transition to in grade 1 reaching grade 5 primary school secondary education % of relevant % of grade 1 % of % of enrollment in last age group students enrollment year of primary Male Female Malea Femalea Male Female Male Female 2004b 2004b 1991 2003b 1991 2003b 2004b 2004b 2004b 2004b Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 103 102 .. .. .. .. 3 2 98 100 Algeria 103 100 95 95 94 97 14 9 76 83 Angola .. .. .. .. .. .. .. .. .. .. Argentina 112 112 .. 91 .. 93 7 5 .. .. Armenia 96 101 .. .. .. .. 0c 0c 99 97 Australia 106 104 98 98 99 100 0 0 100 100 Austria .. .. .. .. .. .. .. .. .. .. Azerbaijan 96 93 .. .. .. .. 0c 0c 99 99 Bangladesh 134 128 .. 51 .. 54 7 7 92 99 Belarus 103 102 .. .. .. .. 0c 0c 100 97 Belgium 101 101 90 .. 92 .. .. .. .. .. Benin 112 94 54 70 56 69 23 23 51 51 Bolivia 119 120 .. 87 .. 86 2 1 92 91 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 110 107 81 87 87 92 6 4 97 97 Brazil 127 117 .. .. .. .. .. .. .. .. Bulgaria 106 106 91 .. 90 .. 3 2 96 95 Burkina Faso 76 66 71 74 68 78 13 13 42 37 Burundi 95 86 65 64 58 62 28 31 35 33 Cambodia 154 143 .. 58 .. 61 12 9 85 80 Cameroon 115 100 .. 64 .. 63 26 25 47 49 Canada .. .. 95 .. 98 .. .. .. .. .. Central African Republic 75 52 24 .. 22 .. .. .. .. .. Chad 98 70 56 51 41 39 24 25 60 46 Chile 98 96 94 100 91 98 2 1 95 98 China 100 99 58 100 78 98 0c 0c 92 92 Hong Kong, China 101 95 .. 100 .. 100 1 1 100 100 Colombia 126 120 .. 75 .. 80 5 4 100 100 Congo, Dem. Rep. .. .. 58 .. 50 .. .. .. .. .. Congo, Rep. 66 63 56 65 65 67 25 24 78 78 Costa Rica 108 107 83 92 85 93 8 6 93 90 Côte d'Ivoire 75 68 75 .. 70 .. 17 18 .. .. Croatia 99 97 .. .. .. .. 0c 0c 100 100 Cuba 105 103 .. 98 .. 97 1 0 98 99 Czech Republic 94 93 .. 97 .. 98 1 1 99 99 Denmark 98 98 94 100 94 99 .. .. 100 99 Dominican Republic 118 104 .. 52 .. 70 9 6 92 84 Ecuador 136 134 .. 75 .. 77 2 2 76 71 Egypt, Arab Rep. 100 98 .. 96 .. 100 6 3 83 86 El Salvador 134 130 56 67 60 70 8 6 94 94 Eritrea 63 52 .. 86 .. 73 21 22 85 76 Estonia 97 98 .. 98 .. 99 3 1 93 98 Ethiopia 106 93 16 59 23 54 12 11 90 88 Finland 100 98 100 100 100 100 1 0c 100 100 France .. .. 69 .. 95 .. .. .. .. .. Gabon 94 94 .. 68 .. 71 35 34 .. .. Gambia, The 95 101 .. .. .. .. .. .. .. .. Georgia 104 102 .. .. .. .. 0c 0c 98 99 Germany 101 100 .. .. .. .. 2 2 .. .. Ghana 87 89 81 62 79 65 6 6 95 100 Greece .. .. 100 .. 100 .. .. .. .. .. Guatemala 129 125 .. 79 .. 76 14 13 97 95 Guinea 87 79 64 87 48 76 10 11 49 45 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 92 2006 World Development Indicators Education efficiency Gross intake rate Share of cohort Repeaters in Transition to in grade 1 reaching grade 5 primary school secondary education % of relevant % of grade 1 % of % of enrollment in last age group students enrollment year of primary Male Female Malea Femalea Male Female Male Female 2004b 2004b 1991 2003b 1991 2003b 2004b 2004b 2004b 2004b Honduras 129 127 .. 63 .. 69 9 7 .. .. Hungary 99 98 77 .. 98 .. 3 2 99 99 India 135 129 .. 60 .. 64 4 4 85 89 Indonesia 120 120 34 88 78 90 4 4 80 83 Iran, Islamic Rep. 102 118 91 94 89 94 3 2 97 95 Iraq 110 103 .. .. .. .. 9 7 .. .. Ireland 104 104 99 98 100 100 1 1 .. .. Israel 97 98 .. 100 .. 99 2 1 72 73 Italy 98 97 .. 96 .. 97 0c 0c 100 100 Jamaica 90 88 .. 88 .. 93 4 3 .. .. Japan .. .. 100 .. 100 .. .. .. .. .. Jordan 99 100 .. 97 .. 98 0c 0c 97 98 Kazakhstan 106 105 .. .. .. .. 0c 0c 100 100 Kenya 122 119 75 78 78 73 11 10 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 103 103 99 100 100 100 0 0 99 99 Kuwait 96 97 .. .. .. .. 3 2 95 95 Kyrgyz Republic 99 97 .. .. .. .. 0c 0c 98 100 Lao PDR 123 114 .. 62 .. 63 21 18 80 76 Latvia 92 90 .. .. .. .. 2 1 99 99 Lebanon 100 99 .. 95 .. 100 12 9 83 89 Lesotho 144 131 58 58 73 69 21 16 64 62 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania 97 97 .. .. .. .. 1 0c 99 99 Macedonia, FYR 98 96 .. .. .. .. 0c 0c 98 97 Madagascar 168 164 22 56 21 58 31 29 56 55 Malawi 164 178 71 50 57 38 18 18 .. .. Malaysia 93 92 97 87 97 87 .. .. .. .. Mali 69 58 71 78 67 70 19 19 62 57 Mauritania 106 105 76 81 75 83 14 15 47 44 Mauritius 95 95 97 98 98 100 6 4 .. .. Mexico 108 107 35 92 71 94 6 4 94 92 Moldova 89 88 .. .. .. .. 0c 0c 97 99 Mongolia 113 115 .. .. .. .. 1 1 99 99 Morocco 100 96 75 82 76 80 16 11 78 81 Mozambique 138 129 36 53 32 45 21 21 .. .. Myanmar 124 125 .. 64 .. 66 1 1 74 66 Namibia 99 99 60 87 65 90 15 12 87 88 Nepal 115 105 51 63 51 67 22 22 80 76 Netherlands 98 97 .. 100 .. 100 .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. Nicaragua 144 135 11 52 37 61 12 9 .. .. Niger 68 51 61 75 65 72 5 5 43 41 Nigeria 120 103 .. 33 .. 38 3 3 .. .. Norway 99 99 99 100 100 99 0 0 .. .. Oman 74 75 97 97 96 98 1 1 99 99 Pakistan 126 95 .. .. .. .. .. .. .. .. Panama 121 118 .. 82 .. 87 6 5 63 65 Papua New Guinea 101 90 70 72 68 66 0 0 77 77 Paraguay 109 106 73 68 75 71 9 6 .. .. Peru 113 114 .. 85 .. 83 10 10 .. .. Philippines 140 130 .. 72 .. 80 3 1 98 97 Poland 97 98 89 .. 96 .. .. .. .. .. Portugal .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 93 Education efficiency Gross intake rate Share of cohort Repeaters in Transition to in grade 1 reaching grade 5 primary school secondary education % of relevant % of grade 1 % of % of enrollment in last age group students enrollment year of primary Male Female Malea Femalea Male Female Male Female 2004b 2004b 1991 2003b 1991 2003b 2004b 2004b 2004b 2004b Romania 107 106 .. .. .. .. 3 2 98 98 Russian Federation .. .. .. .. .. .. .. .. .. .. Rwanda 183 183 61 43 59 49 19 19 .. .. Saudi Arabia 66 66 82 94 84 93 5 3 100 93 Senegal 89 91 .. 79 .. 77 13 13 49 45 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic 96 94 .. .. .. .. 3 2 98 98 Slovenia 122 120 .. .. .. .. 1 0c 100 99 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 118 112 .. 82 .. 87 6 4 94 96 Spain .. .. .. .. .. .. .. .. .. .. Sri Lanka .. .. 92 .. 93 .. .. .. 96 98 Sudan 73 62 90 92 99 92 1 4 88 92 Swaziland 110 104 74 74 80 80 19 14 76 78 Sweden 97 97 100 .. 100 .. .. .. .. .. Switzerland 89 92 .. .. .. .. 2 2 100 100 Syrian Arab Republic 122 118 97 93 95 92 8 7 93 95 Tajikistan 98 94 .. .. .. .. 0c 0c 98 97 Tanzania 118 114 81 86 82 90 5 5 34 33 Thailand .. .. .. .. .. .. .. .. .. .. Togo 90 82 52 79 42 73 24 24 67 61 Trinidad and Tobago 97 96 .. 66 .. 76 6 4 96 99 Tunisia 98 100 94 96 77 97 11 7 86 90 Turkey .. .. 98 .. 97 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 164 163 .. 63 .. 64 14 14 .. .. Ukraine 105 105 .. .. .. .. 0c 0c .. .. United Arab Emirates 89 88 80 94 80 95 3 2 96 96 United Kingdom .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. Uruguay 109 107 96 91 98 95 10 7 .. .. Uzbekistan 102 102 .. .. .. .. 0 0 100 99 Venezuela, RB 103 100 82 89 90 94 9 6 97 100 Vietnam 101 95 .. 90 .. 88 3 2 99 100 West Bank and Gaza 85 84 .. .. .. .. 0 0 100 100 Yemen, Rep. 122 97 .. 78 .. 67 5 4 .. .. Zambia 110 110 .. .. .. .. 7 7 .. .. Zimbabwe 122 118 72 68 81 71 .. .. 69 70 World 116 w 110 w .. w .. w .. w .. w 4w 4w .. w .. w Low income 125 115 .. 63 .. 65 6 6 82 83 Middle income 107 106 61 93 81 93 3 2 91 91 Lower middle income 107 106 59 94 80 93 3 2 90 91 Upper middle income .. .. .. .. .. .. .. .. .. .. Low & middle income 117 111 .. .. .. .. 4 4 87 87 East Asia & Pacific 107 106 55 94 78 93 1 1 90 90 Europe & Central Asia .. .. .. .. .. .. .. .. .. .. Latin America & Carib. 120 115 .. .. .. .. .. .. .. .. Middle East & N. Africa 106 105 .. 92 .. 92 8 5 87 89 South Asia 132 122 .. 59 .. 63 4 4 86 90 Sub-Saharan Africa 113 104 .. .. .. .. 11 11 .. .. High income .. .. .. .. .. .. .. .. .. .. Europe EMU .. .. .. .. .. .. .. .. .. .. a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. b. Provisional data. c. Less than 0.5. 94 2006 World Development Indicators Education efficiency About the data Definitions Indicators of students' progress through school are Measuring actual learning outcomes requires set- · Gross intake rate in grade 1 is the number of estimated by the United Nations Educational, Scien- ting curriculum standards and measuring students' new entrants in the first grade of primary education tific, and Cultural Organization (UNESCO) Institute learning progress against those standards through regardless of age, expressed as a percentage of the for Statistics and the World Bank. These indicators standardized assessments or tests. population of the official primary school entrance measure an education system's success in extend- The data on repeaters are often used to indicate age. · Share of cohort reaching grade 5 is the per- ing coverage to all students, maintaining the flow of the internal efficiency of the education system. centage of children enrolled in the first grade of pri- students from one grade to the next, and imparting Repeaters not only increase the cost of education mary school who eventually reach grade 5. The esti- a particular level of education. for the family and for the school system, but also use mate is based on the reconstructed cohort method Gross intake rate indicates the general level of limited school resources. Countries have different (see About the data). · Repeaters in primary school access to primary education. It also indicates the policies on repetition and promotion; in some cases are the number of students enrolled in the same capacity of the education system to provide access the number of repeaters is controlled because of grade as in the previous year, as a percentage of all to primary education. Low gross intake rates in grade limited capacity. Care should be taken in interpreting students enrolled in primary school. · Transition to 1 reflect the fact that many children do not enter this indicator. secondary education refers to the number of new primary school even though school attendance, at The transition rate from primary school to second- entrants to the first grade of secondary school in least through the primary level, is mandatory in all ary school conveys the degree of access or transition a given year, as a percentage of the number of stu- countries. Because the gross intake rate includes all between the two levels of education. A low transi- dents enrolled in the final grade of primary school in new entrants regardless of age, it can be more than tion rate can signal problems such as an inadequate the previous year. 100 percent. Once enrolled, students drop out for a promotion and examination system or insufficient variety of reasons, including low quality of schooling, capacity in secondary schools. The quality of data on discouragement over poor performance, and the direct the transition rate is affected when new entrants and and indirect costs of schooling. Students' progress to repeaters are not correctly distinguished in the first higher grades may also be limited by the availability of grade of secondary school. Students who interrupt teachers, classrooms, and educational materials. their studies for one or more years after complet- The share of cohort reaching grade 5 (cohort ing primary school could also affect the quality of survival rate) is estimated as the proportion of an the data. entering cohort of grade 1 students that eventually In 2006 the UNESCO Institiute for Statistics changed reaches grade 5. It measures the holding power and its convention for citing the reference year. For more internal efficiency of an education system. Cohort information, see About the data for table 2.10. survival rates approaching 100 percent indicate a high level of retention and a low level of dropout. Cohort survival rates are typically estimated from data on enrollment and repetition by grade for two consecutive years, in a procedure called the recon- structed cohort method. This method makes three simplifying assumptions: dropouts never return to school; promotion, repetition, and dropout rates remain constant over the entire period in which the cohort is enrolled in school; and the same rates apply to all pupils enrolled in a given grade, regardless of whether they previously repeated a grade (Fredrick- sen 1993). Given these assumptions, cross-country comparisons should be made with caution, because other flows--caused by new entrants, reentrants, grade skipping, migration, or school transfers during the school year--are not considered. The UNESCO Institute for Statistics measures the share of cohort reaching grade 5 because research suggests that five to six years of schooling is a critical Data sources threshold for the achievement of sustainable basic Data on education efficiency are from the UNESCO literacy and numeracy skills. But the indicator only Institute for Statistics. Data for latest years are indirectly reflects the quality of schooling, and a high provisional, as of January 2006. rate does not guarantee these learning outcomes. 2006 World Development Indicators 95 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Totala Malea Femalea Male Female Male Female 1991 2004 1991 2004 1991 2004 1990 2002 1990 2002 2002 2002 Afghanistan 25 .. 37 .. 13 .. .. .. .. .. .. .. Albania .. 99 .. 99 .. 99 97 99 b 92 99 b 99 b 98 b Algeria 79 94 86 94 73 94 86 94 c 68 86c 79 c 60 c Angola 35 .. .. .. .. .. .. 83 c .. 63 c 82c 54 c Argentina .. 102 .. 100 .. 105 98 99 b 98 99 b 97b 97b Armenia 90 107 .. 106 .. 108 100 100 b 99 100 b 100 b 99 b Australia .. 100 .. 100 .. 100 .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan .. 96 .. 97 .. 95 .. .. .. .. .. .. Bangladesh 49 73 .. 70 .. 75 51 58 33 41 50 31 Belarus 95 101 95 103 96 99 100 .. 100 .. .. .. Belgium 79 .. 76 .. 82 .. .. .. .. .. .. .. Benin 21 49 28 59 13 38 57 58 b 25 33 b 46b 23 b Bolivia 71 100 78 102 64 98 96 99 b 89 96 b 93 b 80 b Bosnia and Herzegovina .. .. .. .. .. .. .. 100 c .. 100 c 98 c 91c Botswana 79 92 71 89 87 94 79 85 87 93 76 82 Brazil 93 111 .. 110 .. 111 91 96 c 93 98 c 88 c 89 c Bulgaria 90 97 89 98 92 96 100 98 b 99 98 b 99 b 98 b Burkina Faso 21 29 26 34 16 25 .. .. .. .. .. .. Burundi 46 33 49 39 43 27 58 76 c 45 69 c 67c 52c Cambodia .. 82 .. 85 .. 78 81 88 c 66 79 c 85 c 64 c Cameroon 56 72 60 77 52 66 86 .. 76 .. 77c 60 c Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 27 .. 35 .. 18 .. 66 70 c 39 47c 65 c 33 c Chad 18 29 30 41 7 18 58 55 c 38 23 c 41c 13 c Chile .. 97 .. 98 .. 97 98 99 b 98 99 b 96b 96b China 103 99 .. 99 .. 100 97 99 b 93 99 b 95 b 87b Hong Kong, China 102 111 .. 113 .. 108 .. .. .. .. .. .. Colombia 71 94 60 92 82 96 94 97c 96 98 c 94 c 95 c Congo, Dem. Rep. 46 .. 58 .. 34 .. 80 77c 58 61c 80 c 52c Congo, Rep. 54 66 59 70 49 63 95 98 90 97 89 77 Costa Rica 74 92 77 91 81 94 97 98 98 99 96 96 Côte d'Ivoire 43 43 55 52 32 34 65 70 c 40 51c 60 c 38 c Croatia 85 91 .. 92 .. 91 100 100 b 100 100 b 99 b 97b Cuba 96 93 .. 93 .. 92 99 100 b 99 100 b 100 b 100 b Czech Republic .. 102 .. 103 .. 101 .. .. .. .. .. .. Denmark 98 103 98 103 98 104 .. .. .. .. .. .. Dominican Republic 61 91 .. 88 .. 93 87 93 b 88 95b 87b 87b Ecuador 91 101 91 100 92 101 96 96 b 95 96 b 92b 90 b Egypt, Arab Rep. .. 93 .. 95 .. 91 71 .. 51 .. .. .. El Salvador 41 84 38 84 43 85 85 90 83 88 82 77 Eritrea 19 44 22 53 17 36 73 .. 49 .. .. .. Estonia 93 103 93 105 94 101 100 100 b 100 100 b 100 b 100 b Ethiopia 21 51 26 58 16 43 52 63 34 52 49 34 Finland 97 102 98 102 97 102 .. .. .. .. .. .. France 104 .. .. .. .. .. .. .. .. .. .. .. Gabon 58 66 55 65 61 68 .. .. .. .. .. .. Gambia, The 44 .. 55 .. 33 .. 50 .. 34 .. .. .. Georgia .. 86 .. 84 .. 87 .. .. .. .. .. .. Germany 100 97 99 97 100 97 .. .. .. .. .. .. Ghana 63 65 70 65 55 67 88 .. 75 .. 63 b 46 b Greece 99 .. 99 .. 98 .. 99 99 c 100 100 c 94 c 88 c Guatemala .. 70 .. 75 .. 65 80 86b 66 78 b 75b 63 b Guinea 17 48 24 58 9 39 62 .. 26 .. .. .. Guinea-Bissau .. 27 .. 35 .. 19 .. .. .. .. .. .. Haiti 27 .. 29 .. 26 .. 56 66 54 67 54 50 96 2006 World Development Indicators Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Totala Malea Femalea Male Female Male Female 1991 2004 1991 2004 1991 2004 1990 2002 1990 2002 2002 2002 Honduras 65 79 67 77 62 82 78 87b 81 91b 80 b 80 b Hungary 82 96 88 97 90 96 100 99 b 100 100 b 99 b 99 b India .. 84 .. 88 .. 79 73 84 b 54 68 b 73 b 48 b Indonesia 91 101 .. 101 .. 101 97 99 93 98 92 83 Iran, Islamic Rep. 91 95 97 92 85 97 92 .. 81 .. 84c 70 c Iraq 59 74 64 85 53 63 56 .. 25 .. .. .. Ireland .. 101 .. 101 .. 100 .. .. .. .. .. .. Israel .. 101 .. 101 .. 101 99 100 c 98 99 c 98 c 96 c Italy 104 103 104 103 104 102 .. .. .. .. .. .. Jamaica 90 84 86 84 94 89 87 91 95 98 84 91 Japan 101 .. 101 .. 102 .. .. .. .. .. .. .. Jordan 101 97 101 97 101 96 98 99 95 99 95 85 Kazakhstan .. 110 .. 110 .. 109 100 .. 100 .. .. .. Kenya .. 89 .. 90 .. 89 93 80 87 81 78 70 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 98 105 98 104 98 106 .. .. .. .. .. .. Kuwait 57 91 58 91 56 92 88 92 87 94 85 81 Kyrgyz Republic .. 93 .. 93 .. 93 .. .. .. .. .. .. Lao PDR 43 74 48 78 38 70 79 83 c 61 75 c 77c 61c Latvia .. 98 .. 99 .. 97 .. 100 b .. 100 b 100 b 100 b Lebanon .. 94 .. 92 .. 96 95 .. 89 .. .. .. Lesotho 58 71 41 60 75 82 77 .. 97 .. 74 c 90 c Liberia .. .. .. .. .. .. 75 86 39 55 72 39 Libya .. .. .. .. .. .. 99 100 83 94 92 71 Lithuania 89 105 .. 105 .. 105 100 100 b 100 100 b 100 b 100 b Macedonia, FYR 98 97 .. 97 .. 97 .. 99 b .. 98 b 98 b 94b Madagascar 33 45 33 45 34 46 78 72c 67 68 c 76 c 65 c Malawi 31 58 35 60 28 57 76 .. 51 .. .. .. Malaysia 90 95 90 95 90 95 95 97b 94 97b 92b 85 b Mali 11 44 13 58 9 30 .. .. .. .. .. .. Mauritania 33 43 40 45 26 41 56 68 b 36 55 b 60 b 31b Mauritius 102 100 103 98 102 102 91 94 b 91 95 b 88 b 81b Mexico 86 97 89 97 90 98 96 98 c 94 97c 92c 89 c Moldova .. 83 .. 82 .. 83 100 98 b 100 99 b 97b 95 b Mongolia .. 95 .. 95 .. 96 .. 97b .. 98 b 98 b 98 b Morocco 46 67 54 71 37 63 68 77 42 61 63 38 Mozambique 26 29 32 35 21 23 66 77 32 49 62 31 Myanmar .. 72 .. 72 .. 73 90 96 c 86 93 c 94 c 86 c Namibia 78 81 70 76 86 85 86 87b 89 92b 81b 81b Nepal 51 71 70 76 41 65 67 81b 27 60 b 63 b 35 b Netherlands .. 100 .. 101 .. 99 .. .. .. .. .. .. New Zealand 100 .. 100 .. 99 .. .. .. .. .. .. .. Nicaragua 41 73 43 70 59 77 68 84 c 69 89 c 77c 77c Niger 17 25 21 30 12 20 25 26 b 9 14 b 20 b 9b Nigeria .. 76 .. 83 .. 69 81 91 66 87 74 59 Norway 100 103 100 102 100 103 .. .. .. .. .. .. Oman 74 91 78 93 70 90 95 100 75 97 82 65 Pakistan .. .. .. .. .. .. 63 75 c 31 54 c 62c 35 c Panama 86 97 86 96 86 97 96 97b 95 96 b 93 b 91b Papua New Guinea 50 55 56 59 44 51 74 69 b 62 64 b 63 b 51b Paraguay 65 89 64 88 65 90 96 96 c 95 96 c 93 c 90 c Peru .. 96 .. 97 .. 95 97 98 c 92 96 c 93 c 82c Philippines 86 98 84 94 84 102 97 94 b 97 96 b 93 b 93 b Poland 96 100 .. .. .. .. .. .. .. .. .. .. Portugal 95 .. 94 .. 95 .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 97 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Totala Malea Femalea Male Female Male Female 1991 2004 1991 2004 1991 2004 1990 2002 1990 2002 2002 2002 Romania 96 90 96 90 96 89 99 98 b 99 98 b 98 b 96b Russian Federation 93 .. 92 .. 93 .. 100 100 b 100 100 b 100 b 99 b Rwanda 47 37 47 38 46 37 78 77c 67 76 c 70 c 59 c Saudi Arabia 56 62 60 62 51 61 91 98 c 79 94 c 87c 69 c Senegal 39 45 47 49 30 42 50 58 c 30 41c 51c 29 c Serbia and Montenegro 71 96 .. 97 .. 96 .. 99 b .. 99 b 99 b 94b Sierra Leone .. .. .. .. .. .. .. 47c .. 30 c 40 c 21c Singapore .. .. .. .. .. .. 99 99 b 99 100 b 97b 89 b Slovak Republic 96 101 95 102 96 100 .. 100 b .. 100 b 100 b 100 b Slovenia 95 102 .. 103 .. 102 100 100 100 100 100 100 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 75 96 71 94 80 98 89 .. 88 .. .. .. Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 94 .. 94 .. 94 .. 96 95 b 94 96 b 92b 89 b Sudan 40 49 45 53 36 44 76 82c 54 69 c 69 c 50 c Swaziland 62 61 59 58 65 64 85 87c 85 89 c 80 c 78 c Sweden 96 .. 96 .. 96 .. .. .. .. .. .. .. Switzerland 53 96 53 95 54 97 .. .. .. .. .. .. Syrian Arab Republic 89 107 94 109 84 104 92 97c 67 93 c 91c 74 c Tajikistan .. 92 .. 94 .. 90 100 100 b 100 100 b 100 b 99 b Tanzania 61 57 60 57 62 56 89 81b 77 76b 78 b 62b Thailand .. .. .. .. .. .. .. 98 b .. 98 b 95 b 91b Togo 35 66 48 78 22 55 79 83 c 48 63 c 68 c 38 c Trinidad and Tobago 100 94 97 93 102 95 100 100 100 100 99 98 Tunisia 74 94 79 94 69 94 93 96 b 75 92b 83 b 65 b Turkey 90 .. 93 .. 86 .. 97 98 c 88 95 c 96 c 81c Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 57 .. 61 .. 53 80 86 60 74 79 59 Ukraine 92 91 98 .. 97 .. 100 100 b 100 100 b 100 b 99 b United Arab Emirates 95 75 95 77 94 74 82 88 89 95 76 81 United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 95 94 92 92 97 97 98 99 99 99 97 98 Uzbekistan .. 98 .. 98 .. 98 100 100 100 100 100 99 Venezuela, RB 81 89 76 87 86 92 95 96 b 97 98 b 93 b 93 b Vietnam .. 101 .. 104 .. 98 94 .. 94 .. .. .. West Bank and Gaza .. 98 .. 98 .. 99 .. 99 c .. 99 c 96c 87c Yemen, Rep. .. 62 .. 78 .. 46 74 84 25 51 69 29 Zambia .. 66 .. 71 .. 61 86 .. 76 .. .. .. Zimbabwe 91 80 94 82 89 79 97 99 91 96 94 86 World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income 66 74 75 78 59 70 73 81 55 67 73 50 Middle income 92 97 93 97 92 96 95 98 91 97 94 87 Lower middle income 93 98 94 98 92 96 95 98 90 97 93 86 Upper middle income 88 96 88 95 90 96 97 98 95 97 95 94 Low & middle income 81 86 86 88 78 84 86 90 77 83 86 74 East Asia & Pacific 97 99 98 99 96 97 97 98 93 97 95 87 Europe & Central Asia 92 94 92 95 93 94 99 99 97 98 99 99 Latin America & Carib. 86 97 85 96 89 97 93 96 93 97 91 90 Middle East & N. Africa 78 88 83 89 71 86 80 .. 59 .. .. .. South Asia 73 82 87 85 65 78 70 82 50 65 72 46 Sub-Saharan Africa 51 62 55 66 47 56 76 .. 61 .. .. .. High income .. .. .. .. .. .. .. .. .. .. .. .. Europe EMU .. .. .. .. .. .. .. .. .. .. .. .. a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education (ISCED) 1976 to ISCED97. b. Based on census data. c. Based on survey data. 98 2006 World Development Indicators Education completion and outcomes About the data Definitions Many governments collect and publish statistics that lished standards. In many countries national learning · Primary completion rate is the percentage of stu- indicate how their education systems are working and assessments are enabling ministries of education to dents completing the last year of primary school. It developing--statistics on enrollment and on such monitor progress in these outcomes. Internationally, is calculated by taking the total number of students efficiency indicators as repetition rates, pupil-teacher the UNESCO Institute for Statistics has established in the last grade of primary school, minus the num- ratios, and cohort progression through school. literacy as an outcome indicator based on an inter- ber of repeaters in that grade, divided by the total The World Bank and the United Nations Educa- nationally agreed definition. number of children of official graduation age. · Youth tional, Scientific, and Cultural Organization (UNESCO) The literacy rate is defined as the percentage of literacy rate is the percentage of people ages 15­24 Institute for Statistics worked jointly to develop the people who can, with understanding, both read and who can, with understanding, both read and write a primary completion rate indicator. Increasingly used write a short, simple statement about their every- short, simple statement about their everyday life. as a core indicator of an education system's perfor- day life. In practice, literacy is difficult to measure. · Adult literacy rate is the literacy rate among peo- mance, it reflects both the coverage of the education To estimate literacy using such a definition requires ple ages 15 and older. system and the educational attainment of students. census or survey measurements under controlled The indicator is vital as a key measure of educational conditions. Many countries estimate the number of outcome at the primary level and of progress on the literate people from self-reported data. Some use Millennium Development Goals and the Education for educational attainment data as a proxy but apply All initiative. However, because curricula and stan- different lengths of school attendance or levels of dards for school completion vary across countries, a completion. Because definition and methodologies high rate of primary completion does not necessarily of data collection differ across countries, data need mean high levels of student learning. to be used with caution. The primary completion rate reflects the primary The reported literacy data are compiled by the cycle as defined by the International Standard Clas- UNESCO Institute for Statistics based on national sification of Education (ISCED), ranging from three censuses and household surveys during 1995­2004. or four years of primary education (in a very small When countries did not report data, the estimates number of countries) to five or six years (in most generated in July 2002 by UNESCO Institute for Sta- countries) and seven (in a small number of coun- tistics are used. The data for 1990 are also from the tries). For the countries that changed the primary model estimation. The estimation methodology can cycle, the most recent ISCED primary cycle is applied be reviewed at www.uis.unesco.org. consistently to the whole series. Literacy statistics for most countries cover the pop- The data in the table are for the proxy primary com- ulation ages 15 and older, by five-year age groups, pletion rate, calculated by subtracting the number of but some include younger ages or are confined to age students who repeat the final primary grade from the ranges that tend to inflate literacy rates. As an alter- number of students in that grade and dividing the native, the UNESCO Institute for Statistics has pro- result by the number of children of official gradua- posed the narrower age range of 15­24, which bet- tion age in the population. Data limitations preclude ter captures the ability of participants in the formal adjusting this number for students who drop out dur- education system. The youth literacy rate reported ing the final year of primary school. Thus proxy rates in the table measures the accumulated outcomes of should be taken as an upper-bound estimate of the primary education over the previous 10 years or so by actual primary completion rate. indicating the proportion of people who have passed The numerator may include late entrants and over- through the primary education system and acquired age children who have repeated one or more grades basic literacy and numeracy skills. of primary school but are now graduating as well as children who entered school early. The denominator is the number of children of official graduation age, which could cause the primary completion rate to exceed 100 percent. There are other data limitations that contribute to completion rates exceeding 100 Data sources percent, such as the use of estimates for the popula- Data on the primary completion rate for 1991 and tion, the conduct of school and population surveys 2004 are primarily from the UNESCO Institiute for at different times of year, and other discrepancies Statistics. The data for the latest years are provi- in the numbers used in the calculation. sional, as of January 2006. Data on literacy rates Basic student outcomes include achievements are from the UNESCO Institiute for Statistics. in reading and mathematics judged against estab- 2006 World Development Indicators 99 Health expenditure, services, and use Health Physicians Health Hospital beds expenditure worker density index physicians, Out of nurses, and pocket External midwives Total Public % of resources Per capita per 1,000 % of GDP % of GDP % of total private % of total $ per 1,000 people people per 1,000 people 2003 2003 2003 2003 2003 2003 1990 1997­2004a 2000­03a 1990 2000­03a Afghanistan 6.5 2.6 39.5 76.5 45.6 11 0.1 0.2 0.4 0.2 0.4 Albania 6.5 2.7 41.7 99.8 3.4 118 1.4 1.3 5.4 4.0 3.1 Algeria 4.1 3.3 80.8 95.3 0.0 89 0.9 1.1 .. 2.5 .. Angola 2.8 2.4 84.2 100.0 6.7 26 0.0 b 0.1 .. 1.3 .. Argentina 8.9 4.3 48.6 55.6 0.2 305 2.7 3.0 .. 4.6 4.1 Armenia 6.0 1.2 20.2 80.6 17.2 55 3.9 3.6 8.8 9.1 4.4 Australia 9.5 6.4 67.5 67.8 0.0 2,519 2.3 2.5 10.8 9.2 7.4 Austria 7.5 5.1 67.6 59.2 0.0 2,358 2.2 3.4 9.3 10.2 8.3 Azerbaijan 3.6 0.9 23.8 96.8 1.9 32 3.9 3.5 12.0 10.1 8.3 Bangladesh 3.4 1.1 31.3 85.8 12.4 14 0.2 0.3 0.5 0.3 .. Belarus 6.4 4.9 75.9 80.5 0.1 116 3.6 4.6 17.5 13.2 11.3 Belgium 9.4 6.3 67.2 66.6 0.0 2,796 3.3 3.9 15.6 8.0 6.9 Benin 4.4 1.9 43.1 90.3 11.5 20 0.1 0.0 b .. 0.8 .. Bolivia 6.7 4.3 64.0 79.3 7.3 61 0.4 1.2 1.1 1.3 1.0 Bosnia and Herzegovina 9.5 4.8 50.7 100.0 1.5 168 1.6 1.3 5.7 4.5 3.1 Botswana 5.6 3.3 58.2 28.8 2.9 232 0.2 0.4 .. 1.6 .. Brazil 7.6 3.4 45.3 64.2 0.3 212 1.4 2.1 2.6 3.3 2.7 Bulgaria 7.5 4.1 54.5 98.4 1.0 191 3.2 3.6 8.3 9.8 6.3 Burkina Faso 5.6 2.6 46.8 98.1 7.4 19 0.0 b 0.1 0.3 0.3 .. Burundi 3.1 0.7 23.3 100.0 14.1 3 0.1 0.0 b 0.3 0.7 .. Cambodia 10.9 2.1 19.3 86.2 18.5 33 0.1 0.2 1.0 2.1 0.5 Cameroon 4.2 1.2 28.9 98.3 3.2 37 0.1 0.2 .. 2.6 .. Canada 9.9 6.9 69.9 49.6 0.0 2,669 2.1 2.1 12.2 6.0 3.7 Central African Republic 4.0 1.5 38.6 95.3 2.9 12 0.0b 0.1 .. 0.9 .. Chad 6.5 2.6 39.9 96.3 11.8 16 0.0 b 0.0 b 0.2 0.7 .. Chile 6.1 3.0 48.8 46.2 0.0 282 1.1 1.1 1.7 3.2 2.6 China 5.6 2.0 36.2 87.6 0.1 61 1.5 1.6 2.7 2.6 2.5 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 7.6 6.4 84.1 47.2 0.0 138 1.1 1.4 1.9 1.4 1.1 Congo, Dem. Rep. 4.0 0.7 18.3 100.0 15.1 4 0.1 0.1 .. 1.4 .. Congo, Rep. 2.0 1.3 64.2 100.0 2.2 19 0.3 0.2 .. 3.3 .. Costa Rica 7.3 5.8 78.8 88.7 2.7 305 1.3 1.3 2.4 2.5 1.4 Côte d'Ivoire 3.6 1.0 27.6 90.5 3.4 28 0.1 0.1 .. 0.8 .. Croatia 7.8 6.5 83.6 100.0 0.6 494 2.1 2.4 7.7 7.4 5.6 Cuba 7.3 6.3 86.8 75.2 0.2 211 3.6 5.9 13.4 5.4 4.9 Czech Republic 7.5 6.8 90.0 83.9 0.0 667 2.8 3.5 13.4 11.3 8.8 Denmark 9.0 7.5 83.0 92.5 0.0 3,534 3.1 2.9 13.6 5.6 4.0 Dominican Republic 7.0 2.3 33.2 70.8 1.5 132 1.5 1.9 3.7 1.9 2.1 Ecuador 5.1 2.0 38.6 88.1 0.9 109 1.5 1.5 3.1 1.6 1.5 Egypt, Arab Rep. 5.9 2.2 37.0 99.0 0.8 64 0.8 0.5 4.9 2.1 2.2 El Salvador 8.1 3.7 46.1 93.5 1.0 183 0.8 1.2 2.0 1.5 .. Eritrea 4.4 2.0 45.5 100.0 19.6 8 .. 0.1 .. .. .. Estonia 5.3 4.1 77.1 88.3 0.1 366 3.5 3.2 9.8 11.6 6.0 Ethiopia 5.9 3.4 58.4 78.7 26.0 5 0.0 b 0.0 b 0.2 0.2 .. Finland 7.4 5.7 76.5 81.2 0.0 2,307 2.4 2.6 25.6 12.5 7.2 France 10.1 7.7 76.3 42.2 0.0 2,981 2.6 3.4 10.2 9.7 7.7 Gabon 4.4 2.9 66.6 100.0 0.7 196 0.5 0.3 .. 3.2 .. Gambia, The 8.1 3.2 40.0 67.0 21.8 21 .. 0.1b .. 0.6 .. Georgia 4.0 1.0 23.9 98.2 5.3 35 4.9 4.1 7.9 9.8 4.2 Germany 11.1 8.7 78.2 47.9 0.0 3,204 3.1 3.4 13.2 10.4 8.9 Ghana 4.5 1.4 31.8 100.0 15.8 16 0.0 b 0.2 0.9 1.5 .. Greece 9.9 5.1 51.3 95.4 .. 1,556 3.4 4.4 7.5 5.1 4.7 Guatemala 5.4 2.1 39.7 91.9 3.8 112 0.8 0.9 .. 1.1 0.5 Guinea 5.4 0.9 16.6 99.4 7.3 22 0.1 0.1 0.6 0.6 .. Guinea-Bissau 5.6 2.6 45.8 80.2 26.8 9 .. 0.1 .. 1.5 .. Haiti 7.5 2.9 38.1 69.5 12.4 26 0.1 0.2 .. 0.8 0.8 100 2006 World Development Indicators Health expenditure, services, and use Health Physicians Health Hospital beds expenditure worker density index physicians, Out of nurses, and pocket External midwives Total Public % of resources Per capita per 1,000 % of GDP % of GDP % of total private % of total $ per 1,000 people people per 1,000 people 2003 2003 2003 2003 2003 2003 1990 1997­2004a 2000­03a 1990 2000­03a Honduras 7.1 4.0 56.5 85.8 9.3 72 0.7 0.6 .. 1.0 1.0 Hungary 8.4 6.1 72.4 88.9 0.4 684 2.9 3.2 11.9 .. 7.8 India 4.8 1.2 24.8 97.0 1.6 27 0.5 0.6 .. 0.8 0.9 Indonesia 3.1 1.1 35.9 74.3 1.4 30 0.1 0.1 0.7 0.7 .. Iran, Islamic Rep. 6.5 3.1 47.3 94.8 0.1 131 0.3 0.4 .. 1.4 1.6 Iraq 2.7 1.4 51.8 100.0 3.8 23 0.6 0.7 3.6 1.7 1.3 Ireland 7.3 5.8 78.9 61.9 0.0 2,860 1.6 2.8 19.0 6.1 4.3 Israel 8.9 6.1 68.2 89.1 3.4 1,514 3.2 3.8 10.3 6.2 6.1 Italy 8.4 6.3 75.1 83.3 0.0 2,139 4.7 4.2 10.5 7.2 4.4 Jamaica 5.3 2.7 50.6 64.7 1.2 164 0.6 0.8 2.5 2.2 1.4 Japan 7.9 6.4 81.0 90.1 0.0 2,662 1.7 2.0 10.4 .. 14.3 Jordan 9.4 4.2 45.2 74.0 4.2 177 1.3 2.0 4.8 1.8 1.7 Kazakhstan 3.5 2.0 57.3 100.0 1.2 73 4.0 3.5 9.5 13.7 7.7 Kenya 4.3 1.7 38.7 82.6 15.3 20 0.0 b 0.1 .. 1.6 .. Korea, Dem. Rep. 5.8 5.3 91.2 100.0 19.1 1 .. 3.3 .. .. .. Korea, Rep. 5.6 2.8 49.4 82.8 0.0 705 0.8 1.6 5.4 3.1 7.1 Kuwait 3.5 2.7 77.5 91.2 0.0 580 0.2 1.5 5.4 3 2.2 Kyrgyz Republic 5.0 2.2 43.6 .. .. .. 3.4 2.5 10.1 12.0 5.3 Lao PDR 3.2 1.2 38.5 75.5 30.0 11 0.2 .. .. 2.6 1.2 Latvia 6.4 3.3 51.3 94.3 0.4 301 4.1 3.0 8.2 14.1 7.8 Lebanon 10.2 3.0 29.3 79.4 0.1 573 1.3 3.3 4.4 1.7 3.0 Lesotho 5.2 4.1 79.7 18.2 8.2 31 0.0 b 0.0 b .. .. .. Liberia 4.7 2.7 56.7 98.5 32.3 6 .. 0.0 b .. .. .. Libya 4.1 2.6 62.9 100.0 0.0 171 1.1 1.3 .. 4.2 3.9 Lithuania 6.6 5.0 76.0 96.6 1.3 351 4.0 4.0 12.4 12.5 8.7 Macedonia, FYR 7.1 6.0 84.5 100.0 1.7 161 2.2 2.2 8.1 5.9 4.8 Madagascar 2.7 1.7 63.4 91.7 22.0 8 0.1 0.3 0.4 0.9 0.4 Malawi 9.3 3.3 35.2 42.7 25.1 13 0.0 b 0.0 b 0.3 1.6 .. Malaysia 3.8 2.2 58.2 73.8 0.1 163 0.4 0.7 2.4 2.1 1.9 Mali 4.8 2.8 57.4 89.3 13.7 16 0.1 0.1 0.2 .. .. Mauritania 4.2 3.2 76.8 100.0 4.7 17 0.1 0.1 .. 0.7 .. Mauritius 3.7 2.2 60.8 100.0 1.0 172 0.8 1.1 .. 2.9 .. Mexico 6.2 2.9 46.4 94.2 0.4 372 1.1 1.5 3.9 1.0 1.0 Moldova 7.2 3.9 54.5 96.1 2.5 34 3.6 2.6 9.2 13.1 6.7 Mongolia 6.7 4.3 63.8 91.1 3.2 33 2.5 2.6 6.0 11.5 .. Morocco 5.1 1.7 33.1 76.1 1.0 72 0.2 0.5 1.5 1.3 0.8 Mozambique 4.7 2.9 61.7 38.8 40.8 12 0.0 b 0.0 b 0.3 0.9 .. Myanmar 2.8 0.5 19.4 99.7 2.2 394 0.1 0.4 0.8 0.6 0.6 Namibia 6.7 4.7 70.0 19.2 5.3 145 0.2 0.3 .. .. .. Nepal 5.3 1.5 27.8 92.2 9.9 12 0.1 0.2 0.3 0.2 .. Netherlands 9.8 6.1 62.4 20.8 0.0 3,088 2.5 3.1 16.7 5.8 4.7 New Zealand 8.1 6.3 78.3 72.1 0.0 1,618 1.9 2.2 10.9 8.5 6.1 Nicaragua 7.7 3.7 48.4 95.7 11.2 60 0.7 0.4 1.8 1.8 0.9 Niger 4.7 2.5 53.0 89.2 32.8 9 0.0 b 0.0 b 0.3 .. .. Nigeria 5.0 1.3 25.5 91.2 5.3 22 0.2 0.3 1.5 1.7 .. Norway 10.3 8.6 83.7 95.4 0.0 4,976 2.6 3.1 24.9 4.6 3.8 Oman 3.2 2.7 83.0 56.1 0.0 278 0.6 1.3 4.2 2.1 2.0 Pakistan 2.4 0.7 27.7 98.0 2.5 13 0.5 0.7 1.1 0.6 0.7 Panama 7.6 5.0 66.4 82.2 0.2 315 1.6 1.5 3.2 2.5 2.5 Papua New Guinea 3.4 3.0 88.9 87.2 28.3 23 0.1 0.1 0.6 4.0 .. Paraguay 7.3 2.3 31.5 74.6 1.8 75 0.6 1.1 1.4 0.9 1.2 Peru 4.4 2.1 48.3 79.0 3.2 98 1.1 1.2 .. 1.4 1.4 Philippines 3.2 1.4 43.7 78.2 3.8 31 0.1 1.2 7.4 1.4 1.0 Poland 6.5 4.5 69.9 87.8 0.0 354 2.1 2.5 7.7 5.7 5.6 Portugal 9.6 6.7 69.7 95.7 0.0 1,348 2.8 3.3 7.0 4.1 3.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2006 World Development Indicators 101 Health expenditure, services, and use Health Physicians Health Hospital beds expenditure worker density index physicians, Out of nurses, and pocket External midwives Total Public % of resources Per capita per 1,000 % of GDP % of GDP % of total private % of total $ per 1,000 people people per 1,000 people 2003 2003 2003 2003 2003 2003 1990 1997­2004a 2000­03a 1990 2000­03a Romania 6.1 3.8 62.9 90.4 3.8 159 1.8 1.9 6.2 8.9 6.6 Russian Federation 5.6 3.3 59.0 71.1 0.2 167 4.1 4.3 12.5 13.1 10.5 Rwanda 3.7 1.6 43.5 41.7 54.5 7 0.0 b 0.0 b 0.2 1.7 .. Saudi Arabia 3.3 2.5 75.4 31.0 .. 348 1.4 1.4 4.4 2.5 2.2 Senegal 5.1 2.1 41.8 95.3 15.4 29 0.1 0.1 .. 0.7 .. Serbia and Montenegro 9.6 7.2 75.5 85.3 0.5 181 2.0 2.1 .. 5.9 6.0 Sierra Leone 3.5 2.0 58.3 100.0 15.5 7 .. 0.0 b .. .. .. Singapore 4.5 1.6 36.1 97.1 0.0 964 1.3 1.4 5.6 3.6 2.9 Slovak Republic 5.9 5.2 88.3 100.0 0.0 360 2.9 3.1 10.6 7.4 7.2 Slovenia 8.8 6.7 76.3 41.1 0.1 1,218 2.0 2.3 9.4 6.0 5.0 Somalia 2.6 1.2 44.6 100.0 9.3 8 .. 0.0 b .. 0.8 .. South Africa 8.4 3.2 38.6 17.1 0.5 295 0.6 0.8 4.6 .. .. Spain 7.7 5.5 71.3 82.0 0.0 1,541 2.3 3.2 6.8 4.6 3.8 Sri Lanka 3.5 1.6 45.0 88.9 2.3 31 0.1 0.5 1.2 2.7 .. Sudan 4.3 1.9 43.2 96.3 2.2 21 .. 0.2 1.0 1.1 0.7 Swaziland 5.8 3.3 57.3 42.4 5.5 107 0.1 0.2 3.4 .. .. Sweden 9.4 8.0 85.2 92.1 0.0 3,149 2.9 3.3 13.5 12.4 3.6 Switzerland 11.5 6.7 58.5 76.0 0.0 5,035 3.0 3.6 12.1 19.9 6.0 Syrian Arab Republic 5.1 2.5 48.2 100.0 0.2 59 0.8 1.4 3.3 1.1 1.5 Tajikistan 4.4 0.9 20.8 100.0 14.9 11 2.6 2.0 7.2 10.7 6.1 Tanzania 4.3 2.4 55.4 81.1 21.9 12 .. 0.0 b 0.4 1.0 .. Thailand 3.3 2.0 61.6 74.8 0.3 76 0.2 0.4 .. 1.6 .. Togo 5.6 1.4 24.8 88.0 2.3 16 0.1 0.0 b 0.3 1.5 .. Trinidad and Tobago 3.9 1.5 37.8 88.6 1.4 316 0.7 0.8 .. 4.0 3.4 Tunisia 5.6 2.8 50.0 .. .. 126 0.5 1.3 .. 1.9 1.7 Turkey 7.6 5.4 71.6 69.9 0.0 257 0.9 1.4 4.2 2.4 2.6 Turkmenistan 3.9 2.6 67.4 100.0 0.4 89 3.6 4.2 .. 11.5 .. Uganda 7.3 2.2 30.4 52.8 28.5 18 0.0 b 0.1 0.1 0.9 .. Ukraine 5.7 3.8 65.9 78.6 0.1 60 4.3 3.0 11.2 13.0 8.8 United Arab Emirates 3.3 2.5 74.7 70.4 0.0 661 0.8 2.0 6.2 2.6 2.2 United Kingdom 8.0 6.9 85.7 76.7 0.0 2,428 1.4 2.2 .. 5.9 4.2 United States 15.2 6.8 44.6 24.3 0.0 5,711 2.4 2.3 13.2 4.9 3.3 Uruguay 9.8 2.7 27.2 25.0 0.4 323 3.7 3.7 4.5 4.5 1.9 Uzbekistan 5.5 2.4 43.0 95.5 3.0 21 3.4 2.7 13.7 12.5 5.5 Venezuela, RB 4.5 2.0 44.3 95.5 0.1 146 1.6 1.9 2.6 2.7 0.8 Vietnam 5.4 1.5 27.8 74.2 2.6 26 0.4 0.5 1.3 3.8 2.4 West Bank and Gaza .. .. .. .. .. .. .. 0.8 .. .. .. Yemen, Rep. 5.5 2.2 40.9 95.5 8.8 32 0.0 b 0.3 0.7 0.8 0.6 Zambia 5.4 2.8 51.4 68.2 44.7 21 0.1 0.1 .. .. .. Zimbabwe 7.9 2.8 35.9 56.7 6.8 40 0.1 0.2 0.6 0.5 .. World 10.2 w 5.9 w 60.4 w 43.4 w 0.1 w 588 w 1.6 w 1.5 w .. w 3.7 w .. w Low income 4.6 1.3 29.1 95.1 4.4 30 0.5 0.4 .. .. .. Middle income 6.0 3.0 49.5 77.5 0.5 116 1.6 1.7 .. 3.5 .. Lower middle income 5.6 2.5 43.7 81.4 0.6 77 1.4 1.5 .. 2.9 .. Upper middle income 6.5 3.7 57.6 72.0 0.3 280 2.4 2.4 7.6 6.6 5.6 Low & middle income 5.8 2.8 46.4 81.6 1.2 79 1.3 1.1 .. 3.1 .. East Asia & Pacific 5.0 1.9 39.0 88.3 0.8 64 1.2 1.3 3.0 2.3 2.4 Europe & Central Asia 6.5 4.5 67.3 79.9 0.5 194 3.2 3.0 10.3 10.2 7.6 Latin America & Carib. 6.8 3.3 48.2 75.3 0.6 222 1.4 1.9 .. 2.5 .. Middle East & N. Africa 5.6 2.7 50.9 89.2 0.8 92 .. 1.2 .. 1.8 .. South Asia 4.4 1.1 26.3 96.2 2.7 24 0.5 0.5 .. 0.7 0.9 Sub-Saharan Africa 6.1 2.4 41.2 47.4 5.5 36 .. 0.1 .. 1.2 .. High income 11.2 6.7 63.9 37.0 0.0 b 3,449 2.3 3.7 .. 6.2 6.4 Europe EMU 9.6 7.1 74.1 57.6 0.0 2,552 3.1 3.9 12.2 8.1 6.6 a. Data are for the most recent year available. b. Less than 0.05. 102 2006 World Development Indicators Health expenditure, services, and use About the data Definitions National health accounts track financial flows in the offices. The data are scrutinized using such additional · Total health expenditure is the sum of public and health sector, including public and private expendi- recources as national and international employment private health expenditure. It covers the provision of tures, by source of funding. In contrast with high- surveys, records from professional associations, health services (preventive and curative), family plan- income countries, few developing countries have and other publications. Significant inconsistenties ning activities, nutrition activities, and emergency aid health accounts that are methodologically consistent are returned to national authorities for validation and designated for health but does not include provision with national accounting approaches. The difficulties resubmission. of water and sanitation. · Public health expendi- in creating national health accounts go beyond data The health worker density index indicates the over- ture consists of recurrent and capital spending from collection. To establish a national health accounting all level of health workers (physicians, nurses, and government (central and local) budgets, external system, a country needs to define the boundaries of midwives) in the country. Dentists and pharmacists borrowings and grants (including donations from the health care system and to define a taxonomy of are not included. Comparability of the index across international agencies and nongovernmental organi- health care delivery institutions. The accounting sys- countries is affected by differences in the definition zations), and social (or compulsory) health insurance tem should be comprehensive and standardized, pro- of health workers. Many countries continue to use funds. · Out of pocket health expenditure is any viding not only accurate measures of financial flows national definitions and classifications for data col- direct outlay by households, including gratuities and but also information on the equity and efficiency of lection, and some countries provide information only in-kind payments, to health practitioners and suppli- health financing to inform health policy. for public sector workers. ers of pharmaceuticals, therapeutic appliances, and The absence of consistent national health account- other goods and services whose primary intent is to ing systems in most developing countries makes contribute to the restoration or enhancement of the cross-country comparisons of health spending dif- health status of individuals or population groups. It ficult. Compiling estimates of public health expen- is a part of private health expenditure. · External ditures is complicated in countries where state or resources for health are funds or services in kind provincial and local governments are involved in that are provided by entities not part of the country financing and delivering health care, because the in question. The resources may come from interna- data on public spending often are not aggregated. tional organizations, other countries through bilat- There are a number of potential data sources related eral arrangements, or foreign nongovernmental orga- to external resources for health, including govern- nizations. These resources are part of total health ment expenditure accounts, government records expenditure. · Health expenditure per capita is total on external assistance, routine surveys of external health expenditure divided by number of people in the financing assistance, and special surveys. Survey country. · Physicians are graduates of any faculty or data are the major source of information about out school of medicine who are working in the country in of pocket expenditure on health. The data in the table any medical field (practice, teaching, or research). are the product of an effort by the World Health Orga- · Health worker density index reflects a combined nization (WHO), the Organisation for Economic Co- density of physicians, nurses, and midwives per operation and Development (OECD), and the World 1,000 people. · Hospital beds include inpatient beds Bank to collect all available information on health available in public, private, general, and specialized expenditures from national and local government hospitals and rehabilitation centers. In most cases budgets, national accounts, household surveys, beds for both acute and chronic care are included. insurance publications, international donors, and existing tabulations. Data sources Indicators on health services (physicians, health worker density, and hospital beds per 1,000 people) Data on health expenditure come mostly from the come from a variety of sources (see Data sources). WHO's World Health Report 2006 and from the In Uganda most births in rural areas take Data are lacking for many countries, and for oth- OECD for its member countries, supplemented by place at home ers comparability is limited by differences in defini- World Bank poverty assessments and country and Location of delivery, 2000 (% of births) Urban tions. In estimates of health personnel, for example, sector studies. Data are also drawn from World Rural some countries incorrectly include retired physicians 80 Bank public expenditure reviews, the International (because deletions to physician rosters are made Monetary Fund's Government Finance Statistics only periodically) or those working outside the health 60 database, and other studies. Data on out of pocket sector. There is no universally accepted definition of expenditure in developing countries are drawn hospital beds. Moreover, figures on physicians and largely from household surveys conducted by gov- 40 hospital beds are indicators of availability, not of ernments or by statistical or international organiza- quality or use. They do not show how well trained the tions. Data on physicians are from the WHO's World physicians are or how well equipped the hospitals 20 Health Report 2006 and Global Atlas of the Health or medical centers are. And physicians and hospital Workforce database, OECD, and TransMONEE, beds tend to be concentrated in urban areas, so 0 supplemented by country data. Data for the health these indicators give only a partial view of health In a public In a private At home worker density index are from the Joint Learning health facility health facility services available to the entire population. Initiative's Human Resources for Health. Data on The WHO receives data on health professionals Rural areas lack accessibile medical facilities, as shown by hospital beds are from the WHO's World Health the low share of births in public or private health facilities Statistics 2005, OECD's Health Data 2005, and from ministries of health through its six regional offices, often in cooperation with national statistical Source: Demographic and Health Survey. TransMONEE, supplemented by country data. 2006 World Development Indicators 103 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS detection an improved improved immunization with acute diarrhea who sleeping with fever treatment rate water source sanitation rate respiratory received oral under receiving success facilities infection rehydration treated antimalarial rate taken to and continued bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of % of % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DPT with ARI with diarrhea under age 5 with fever cases cases 1990 2002 1990 2002 2004 2004 2000­04 c 1998­2004c 2000­04 c 2000­04 c 2003 2004 Afghanistan .. 13 .. 8 61 66 28 48 .. .. 86 19 Albania 97 97 .. 89 96 97 83 51 .. .. 91 34 Algeria 95 87 88 92 81 86 52 .. .. .. 90 105 Angola 32 50 30 30 64 59 58 32 2.3 63.0 68 94 Argentina 94 .. 82 .. 95 90 .. .. .. .. 66 65 Armenia .. 92 .. 84 92 91 25 48 .. .. 77 44 Australia 100 100 100 100 93 92 .. .. .. .. 82 33 Austria 100 100 100 100 74 83 .. .. .. .. 68 42 Azerbaijan 66 77 .. 55 98 96 36 40 1.4 1.0 70 47 Bangladesh 71 75 23 48 77 85 20 35 .. .. 85 44 Belarus 100 100 .. .. 99 99 .. .. .. .. 73 42 Belgium .. .. .. .. 82 95 .. .. .. .. 73 65 Benin 60 68 11 32 85 83 29 42 7.4 60.0 81 82 Bolivia 72 85 33 45 64 81 52 54 .. .. 81 71 Bosnia and Herzegovina 98 98 .. 93 88 84 80 23 .. .. 94 96 Botswana 93 95 38 41 90 97 14 7 .. .. 77 67 Brazil 83 89 70 75 99 96 .. .. .. .. 83 47 Bulgaria 100 100 100 100 81 90 .. .. .. .. 91 104 Burkina Faso 39 51 13 12 78 88 36 .. 2.0 50.0 66 18 Burundi 69 79 44 36 75 74 40 16 1.3 31.0 79 29 Cambodia .. 34 .. 16 80 85 35 59 .. .. 93 61 Cameroon 50 63 21 48 64 73 40 33 1.3 66.1 70 91 Canada 100 100 100 100 95 91 .. .. .. .. 35 58 Central African Republic 48 75 23 27 35 40 32 47 1.5 69.0 59 4 Chad 20 34 6 8 56 50 12 50 0.6 32.0 78 16 Chile 90 95 85 92 95 94 .. .. .. .. 85 114 China 70 77 23 44 84 91 .. .. .. .. 94 63 Hong Kong, China .. .. .. .. .. .. .. .. .. .. 78 55 Colombia 92 92 82 86 92 89 51 44 0.7 .. 83 17 Congo, Dem. Rep. 43 46 18 29 64 64 36 17 0.7 45.0 83 70 Congo, Rep. .. 46 .. 9 65 67 38 .. .. .. 69 65 Costa Rica .. 97 .. 92 88 90 .. .. .. .. 94 153 Côte d'Ivoire 69 84 31 40 49 50 38 34 4.0 57.5 72 38 Croatia .. .. .. .. 96 96 .. .. .. .. .. .. Cuba .. 91 98 98 99 88 .. .. .. .. 93 90 Czech Republic .. .. .. .. 97 98 .. .. .. .. 79 61 Denmark 100 100 .. .. 96 95 .. .. .. .. 84 78 Dominican Republic 86 93 48 57 79 71 61 53 .. .. 81 71 Ecuador 69 86 56 72 99 90 .. .. .. .. 84 42 Egypt, Arab Rep. 94 98 54 68 97 97 70 29 .. .. 80 61 El Salvador 67 82 51 63 93 90 62 .. .. .. 88 57 Eritrea 40 57 8 9 84 83 44 54 4.2 4.0 85 14 Estonia .. .. .. .. 96 94 .. .. .. .. 70 74 Ethiopia 25 22 4 6 71 80 16 38 .. 3.0 70 36 Finland 100 100 100 100 97 98 .. .. .. .. .. .. France .. .. .. .. 86 97 .. .. .. .. .. .. Gabon .. 87 .. 36 55 38 48 44 .. .. 34 81 Gambia, The .. 82 .. 53 90 92 75 38 14.7 55.0 75 66 Georgia .. 76 .. 83 86 78 99 .. .. .. 66 79 Germany 100 100 .. .. 92 97 .. .. .. .. 71 51 Ghana 54 79 43 58 83 80 44 40 4.0 63.0 66 37 Greece .. .. .. .. 88 88 .. .. .. .. .. .. Guatemala 77 95 50 61 75 84 64 22 .. .. 91 55 Guinea 42 51 17 13 73 69 33 44 4.0 56.0 75 52 Guinea-Bissau .. 59 .. 34 80 80 64 23 7.4 58.0 80 75 Haiti 53 71 15 34 54 43 26 41 .. 12.0 78 49 104 2006 World Development Indicators Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS detection an improved improved immunization with acute diarrhea who sleeping with fever treatment rate water source sanitation rate respiratory received oral under receiving success facilities infection rehydration treated antimalarial rate taken to and continued bednetsa drugs health feeding provider % of children ages % of children % of children % of % of children % of % of % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DPT with ARI with diarrhea under age 5 with fever cases cases 1990 2002 1990 2002 2004 2004 2000­04 c 1998­2004c 2000­04 c 2000­04 c 2003 2004 Honduras 83 90 49 68 92 89 .. .. .. .. 87 83 Hungary 99 99 .. 95 99 99 .. .. .. .. 48 47 India 68 86 12 30 56 64 .. 22 .. .. 86 57 Indonesia 71 78 46 52 72 70 61 61 0.1 1.0 87 53 Iran, Islamic Rep. 91 93 83 84 96 99 93 .. .. .. 84 58 Iraq 83 81 81 80 90 81 76 .. .. .. 85 20 Ireland .. .. .. .. 81 89 .. .. .. .. .. .. Israel 100 100 .. .. 96 96 .. .. .. .. 80 34 Italy .. .. .. .. 84 96 .. .. .. .. 95 58 Jamaica 92 93 75 80 80 77 39 21 .. .. 53 79 Japan 100 100 100 100 99 99 .. .. .. .. 76 45 Jordan 98 91 .. 93 99 95 72 44 .. .. 87 79 Kazakhstan 86 86 72 72 99 82 .. 22 .. .. 75 79 Kenya 45 62 42 48 73 73 46 33 5.0 27.0 80 46 Korea, Dem. Rep. 100 100 .. 59 95 72 93 .. .. .. 88 103 Korea, Rep. .. 92 .. .. 99 88 .. .. .. .. 82 21 Kuwait .. .. .. .. 97 98 .. .. .. .. 62 83 Kyrgyz Republic .. 76 .. 60 99 99 .. .. .. .. 84 62 Lao PDR .. 43 .. 24 36 45 36 37 18.0 9.0 79 55 Latvia .. .. .. .. 99 98 .. .. .. .. 74 83 Lebanon 100 100 .. 98 96 92 74 .. .. .. 92 82 Lesotho .. 76 37 37 70 78 .. 29 .. .. 70 86 Liberia 56 62 38 26 42 31 70 .. .. .. 73 58 Libya 71 72 97 97 99 97 .. .. .. .. 62 169 Lithuania .. .. .. .. 98 94 .. .. .. .. 74 89 Macedonia, FYR .. .. .. .. 96 94 .. .. .. .. 84 73 Madagascar 40 45 12 33 59 61 48 47 0.2 34.0 71 74 Malawi 41 67 36 46 80 89 27 51 2.9 27.0 73 40 Malaysia .. 95 96 .. 95 99 .. .. .. .. 72 69 Mali 34 48 36 45 75 76 43 45 8.4 38.0 65 19 Mauritania 41 56 28 42 64 70 39 28 .. 33.4 58 43 Mauritius 100 100 99 99 98 98 .. .. .. .. 87 33 Mexico 80 91 66 77 96 98 .. .. .. .. 83 71 Moldova .. 92 .. 68 96 98 78 52 .. .. 65 59 Mongolia 62 62 .. 59 96 99 78 66 .. .. 87 80 Morocco 75 80 57 61 95 97 35 50 .. .. 86 80 Mozambique .. 42 .. 27 77 72 51 .. .. .. 76 46 Myanmar 48 80 21 73 78 82 66 48 .. .. 81 83 Namibia 58 80 24 30 70 81 53 39 3.4 14.0 63 88 Nepal 69 84 12 27 73 80 24 43 .. .. 87 67 Netherlands 100 100 100 100 96 98 .. .. .. .. 86 61 New Zealand 97 .. .. .. 85 90 .. .. .. .. 36 59 Nicaragua 69 81 47 66 84 79 57 49 .. 2.0 84 87 Niger 40 46 7 12 74 62 27 43 5.8 48.0 70 46 Nigeria 49 60 39 38 35 25 31 28 1.0 34.0 59 21 Norway 100 100