410 The World Bank World @,-1 Development 0 w@I Indicators 20372 March 2000 I I~~~~~~~~~~~~~. Emil, U~~~~~~ :(if/v . + .X0 The world by income Low income Vietnaiy Uzbekistan France Afghanistan Yemen, Rep .Vanuatu French Polvnesia Angola Zambia West Batik and Gaza Germany Armenia Z mbabwe Yugoslav a, FR Greece Azerbaijan (Serbia 'Montenegro) Greenland Bangladesh Lower middle income Guaar) Ben n Aloarria Upper middle income Hong Koirg Ch na Bliutan Algeria American Samnoa Ice and Bkama Faso Belarus Antigua and Barbuda re and B Lroin d Re ize Argentina Israel Cambodia BoRvia Bahrain Italy Cameroon Bosnia and Herzegovina Barbados Japan Centra Africac Republic Bulgaria Botswana Kuwait Chad Cape Verde Brazi Liechtenstein China Colontbha Chi e Luxembourg Comoros Costa Rica Croatia Macao China Congo, Dem. Rep. Cuba Czech Repub IC Ma ta Congo. Rep. Djibout Estonia Monaco Cote Ovloire Dom nica Gabon Netherlands Eritiea Domin can Repaiblic Grenada Nether ands Ant lies Eth opia Ecuador Hungarv New Ca edonia Gamb a, The Egypt. Arab Rep Isle of Man New Zealand Ghana El Salvadoa Korea. Rep. Noitherin Mar ana Gumnea Equatorial Guinea Lebanon Islands Gu nea Bissau Fiji Libya Norway Ha ti Georgia Malaysia PDrtugal Honduras Guatemala Mauritius Qatar India Guyana Mayotte Singapore ndonesia Iran, Is acnic Rep Mexico S ovenia Kenya Iraq Onman Spain Korea. Dem. Rep. Janrarca Pa an Sweden Kyrgyz Repijbl c Jordan Parlnaird Switzerlarnd Lao PDR Kazakhlstan Po anid United Arab Emi rates Lesotho Kiribati Puierto Rica United Kingcdoin Liberia Latvia Saudi Arabsa United States Madagascar Lithuania Seychelles Virg n Islands (U.S. Malawi Macedonia. FYR Slovak Republc Mali Maldives St Kitts and Nev s Mauritania Marshal Islands St. Lucia Moldova Micronesia, Fed. Sts Tr nidad and Tobago Mongolia Morocco Trirkey Mozambique Nanniiba UrLnguay Myanniar Papua New Guinea Venezuela. RB Nepal Paraguay Nicaragua Per., High income Niger Philippines Andorra Nigeria Romania Aruba Pakistan Russian Federation Austral a Rwanda Samoa Austria Sao Tome and Principe South Africa Bahanras, The Senegal Sri Lanha Belg um Sierra Leone St. V ncent and the Bermnuda Solomon Is ands Grenadines Brunei Sorialra Surinanre Canada Sudan Swaziland Cayiman Islands Tajhkistani Syr ani Alan Republic Channel Islands Tanzan a Thailand Cyprus Togo Tonga Deniriark Ttirkmenistan Tunisia Faemoe Isrands Uganda Uhraine Finland Th. -cId bycca Low i9730' rs> e < > Clsiie corigt L-wOrm d: e,$761-3 330)Cl.fd...digt World Bank estimates of Uppo oidd e J53,631-9,362, C< 1998 GNP per capita. High [$e2361 .r more. Q Xe earI O ;t --t ;>i -ei/ ; s - 1 sn -I.. Ja \ --a . _ . 4 0~~,¢,,,z 'do| . ; r ; . *t e, ,i w N , ; @ ' , - * tS > .1 1 . t _ e~~~~~~1 ,~r> r'i4* I'~~ ~~~ * I.ztS ,.;-.>.z--s~~~~~ 5-.- 'I)~~~~~~~~'O TW-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~U ,-, - t - -i-- t, _ I rsr Coh m.r G- tl es N' oerld o L-do, DrIO6o.ie1tc &G Krororged euor World _________________r Development JWllll InIndicators Copyright 2000 by the Inmernational Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street, NW, Washington, DC 20433, USA All rights reserved Manufactured in the United States of America First printing March 2000 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 Executive Direc- tors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publica-ion and accepts no responsibility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not iinply on the part of the World Banik aniy judginent on tie legal status of any territory or toe endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which rep- resents both area and shape reasonably well for most of the earth's surface. NevertheJess, 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 dissemination of its work and will normally give permission prompty and, when reproduction is for noncommercial purposes, wihout ask- ing a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive. Danvers, Massachusetts 01923. USA. Photo credits: Curt Carnemark/World Bank. Jan Pakulski/World Bank. If you have questions and comments aoout this product, please contact: Development Data Center The World Bank 1818 H Street, NW. Room MC2-812, Washington, DC 20433, USA Hotline: (800) 590 1906 or (202) 473 7824; fax 1202) 522 1498 Email: info@worldbank.org Website: wwwv.worldbank.org or www.worldbank.org/data ISBN 0-8213-4553-2 World _ f]I Development I |s1 Indicators The VWorld Bank Foreword The past year brought the first sigigs of recovery from the financial crisis that swept the globe in 1997. It also brought a stronger and more focused comiittiment to reducing poverty in the world. These are both encouraging signs. Economic growth provides the resources needed to improve peo- ple's lives-creating new jobs, increasing productivity, and producing goods and services. But only growth with equLity-growth that reaches the poor can close the gap betweeii the rici anid the poor. Low income is just one of poverty's many dimensions. The poor lack material goods, education, medical care, and information. They also lack security and the means to protect their families. And they suffer the indignity of being displaced and dispossessed. even in their own comnimuni- ties. So we cannot look for a single solution to poverty. Nor can we measure poverty by just one indicator. We must look at a range of indicators. That is why the Development Assistance Commiiittee of the Organisation for Economic Co-operation and Development (OECD) in 1996 selected seven international development goals from the res- olutions of UN conferences. Then, in 1998 a joint meeting of the UN, the OECD, and the World Bank proposed 21 indicators to track progress toward those goals. This year's World Developmnent Indicators tells us that achieving those goals will be difficult but still attainable in many countries. Progress in reducing poverty rates stalled, especially in Asia, as a consequence of the financial crisis. and in Europe and Central Asia income distributions wors ened. Even so. the goal of reducing poverty rates to half of their 1990 levels can still be achieved in most regions. if growth resumes without further increases in inequality. Looking at other social indicators, we find that many countries will achieve equal school enroll- ments for girls and boys in the next five years. Overall, we may fall short of the goal, but the progress toward it will bring benefits that extend beyond the classroom to all society. Reaching full priiiary school enrolmenit in the next 15 years will be more difficult. It now appears that 75 millioni cliil- dren will be out of school in 2015, two-thirds of them in Sub-Saharan Africa. Even harder will be reducing child mortality to two-thirds of its 1990 level by 2015. Only 13 countries are on track. Some are falling back. But many more could achieve this goal by increasing health services for the poor and stemmirig the HIV/AIDS epidemic. These are only some of the enormous challenges we face in eliminating poverty-challenges we can begin to address only with knowledge. with energy, and with resolve. The beginning point is knowledge- knowledge of how far we have come and how far we have to go. And that is the purpose of the World Development Indicators. which we are pleased to offer now in its fourth year of publication. James D. Wolfensohn President The World Bank Groulp Acknowledgments This book and its companion volumes, the World Bank Atlas and the Little Data Book, were pre- pared by a team led by Eric Swanson. The team consisted of Swaminathan Aiyar, Mehdi Akhlaghi, David Cieslikowski, Richard Fix, Amy Heyman, Masako Hiraga, M. H. Saeed Ordoubadi, Sulekha Patel, K. M. Vijayalakshmi, Amy Wilson, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency's Development Data Group. The CD-ROM development team included Azita Amjadi, Elizabeth Crayford, Reza Farivari, Angelo Kostopoulos, and William Prince. K. Sarwar Lateef served as adviser to the team and provided substantial inputs. The work was car- ried out under the management of Shaida Badiee. The choice of indicators and textual content was shaped through close consultation with and substantial contributions from staff in the World Bank's four thematic networks-Environmentally and Socially Sustainable Development; Finance, Private Sector, and Infrastructure; Human Devel- opment; and Poverty Reduction and Economic Management-and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, we received sub- stantial help, guidance, and data from our external partners. For individual acknowledgments of contributions to the book's content, please see the Credits section. For a listing of our key part- ners, see the Partners section. Bruce Ross-Larson was the principal editor, and Peter Grundy, the art director. The cover and page design and the layout were done by Communications Development Incorporated with Grundy & Northedge of London. Staff from External Affairs oversaw publication and dissemination of the book. vi 2000 World Development Indicators Preface S Statistics are thought to be dry and narrow. The aim of the World Development Indicators is to make them lively and comprehensive-to paint a picture of the world and its peoples. The num- bers in this book tell us that people in most places are living longer, but that in countries gripped by the HIV/AIDS epidemic, life expectancies are going down. Average incomes continue to rise for most developing countries, but some of the poorest have slipped backward, subjecting millions more to extreme poverty. We can also see that the developing countries are becoming more impor- tant participants in the global economy. But the picture is flawed, and those flaws arise from the data: too often they are incomplete or unreliable or entirely unavailable. Why do statistics matter? Put sirnply, they are the evidence on which policies are built. They help to identify needs, set goals, and mor itor progress. Without good statistics, the development process is blind-policymakers cannot learn from their mistakes, and the public cannot hold them accountable. The World Bank's new poverty reductior strategy process calls for a country-driven, outcome-based approach to reducing poverty, and that approach depends on publicly accessible and reliable statistics. Why are good statistics so hard to obtain? A cyc e of underfunding has left many statistical agencies without the resources to carry out their tasks. Often the value of good statistics goes unappreciated. In some cases politicians, government agencies, and civil society-failing to understand their role in policymaking and democratic decisionmaking-fail to ask for the information they need. In other cases the demand for immediate answers leads to short-term responses- quick surveys and guesstimates-that take resources away from long-term development of the statistical system. Added to this, statisticians in developing countries need better training, better equipment, and better treatment tc carry out the important work they are charged with. Recognizing the many flaws in the statistical system and the need to address them directly by improving the capacity of countries tco produce statistics, a consortium of more than 90 countries and international organizations has come! together under the banner of Partnership in Statistics for Devel- opment in the 21st Century, or Paris2l. The aim of Paris2l is to raise awareness of the value of good statistics-and to increase the resources for statistical capacity buLilding in developing countries. The World Bank, as a member of Paris2:L, has pledged to work closely with all its development partners in this effort. Although the process of building strong statistical systems is slow, we hope that future editions of the World Development Indicators will reflect the new work that we embark on today. As we work together toward im )rovements, we continue to be grateful for the support and co- operation of our many partners-the international organizations, statistical offices, nongovernmental organizations, and private firms that have provided their data and contributed to this product. We also appreciate the comments and responses from users-helping us measure how we are doing in continuing to make the World Development Indicators a useful tool. So please write to us at info@worldbank.org. And for more information on the World Bank's statistical publications, please visit our website at www.worldbank.org and select data from the menu. Shaida Badiee Director Development Data Group 2000 World Development Indicators vii Contents Front matter Introduction 3 Foreword v International developmnent goals 8 Acknowledgments vi 1.1 Size of the economy 10 Preface ..i" 1.2 Development progress 14 Partners xi 1.3 Gender differences 18 Users guide ..~~~~. .......1.4 Trends in long-term economic development 22 1.5 Long-term structural change 26 1.6 Key indicators for other economies 30 Box la The international development goals 5 Figuies is The poorest have least access to maternal end child health services 6 lb Some developing regions are well on their way to meeting the enrolment target 7 Text tables is Poverty in developing ahd transition economies, selected years, 1987-98 4 lb Under-five mortality rate in poorest and richest quintiles 5 Vill 2000 World Development Indicators Introduction 33 Intrcductjon11 2.1 Population 38 3.1 RurulI environment and land use 114 2.2 Populati on dynamics 42 3.2 Agricultural inputs 118 2.3 Labor force structure 46 3.3 Agric:ultural output and productivity 122 2.4 Employment by economic activity 50 3.4 Deft restation and biodiversity 126 2.5 Unemployment 54 3.5 Freshwater 130 2.6 Wages and productivty 58 3.6 Water pollution 134 2.7 Poverty 62 3.7 Ereigy production and use 138 2.8 Distribution of income or consumption 66 3.8 Energy efficiency and emissions 142 2.9 Education inputs 70 3.9 Sou 'ces of electricity 146 2.10 Participat on in education 74 3.10 Urbainization 150 2.11 Education efficiency 78 3.11 Urbain environment 154 2.12 Education outcomes 82 3.12 Traf:ic and congestion 158 2.13 Gender and education 86 3.13 Air rollution 162 2.14 Health expenditure, services, and use 90 3.14 Government commitment 164 A~~~~~~~~~~~ ... ........ 2.15 Disease prevention: coverage and quality.94 3.15 Toward a measure of genuine savings 168 2.16 Reproductive health 98 2.17 Health: risk factors and future challenges 102 Boxes 2.18 Mortality 106 3a Moritoring progress in rural developmnent 113 3b Inte national goal for environmental sustainability Boxes and regeneration 113 2a Projecting the future 34 2b Population and development 36 Figures 3a The world has shifted toward cleaner energy ... 112 Figures 3b ...and the trend is expected to continue 112 2a The interval for adding another billion in world 3.l-a Ruanl areas hold a shrinking share population has become shorter and shorter 34 of the population everywhere . .. 117 2b Where the nest billion will come from 34 3.lb . .. but in low-income countries rural dwellers continue 2c Most of the next billion will be born in low-income countries 35 to g,ow in number 117 2d Rapid growth in the working-age population in low-income 3.2a Fertilizer consumption has more than doubled countries mill add to population momentum 35 in 1cw-income countries . .. 121 2.1 The rate of population growth is slowing faster 3.2h b ano in East Asia and the Pacific 12.1 than absolute growth is 41 3a3s Tire world's food production has outpaced 2.2 Growth in the working-age and elderly populations its population growth ... 125 has accelerated in developing countries 45 3.3b . .. except in Sub-Saharan Africa, where food 2.4 The informal sector is a vital source of employment 53 production has barely kept up with population growth -125 2.9 Households account for much of the spending on education 73 3.5 Agriculture accounted for most freshwater withdrawals 2.10 Millions of the world's children still are not in school 77 in developing economies n the past two decaces.. 2.15 Poor children are much less likely to be fully immunized 97 . nd fo oto h rwh in withdrawals in the past century 133 2.17 Developing countries will see a rapidly growing 3.6 As per capita income rises, pollution intensity falls 137 health impact from smoking 105 3.7a Accuse to energy is uneven 141 2.18 Under-five mortality is dramatically higher 3.7b Wealthy countries consume a disproportionate among the poorest 109 share of the wvorld's energy 141 3.8a Carnon dioxide emissions vary widely across countries 145 Text tables 3.8b Induistrial countries account for most of 2.3a The gap between men's and women's labor force the world's carbon dioxide emissions 145 participation is narrowing 49 3.9 The world's electricity sources are shifting- 2.5a Unemployment rate by level of educational aftainment 57 bat coal still dominates 149 2.13a Male and female unemployment rate by education level, 1994-97 89 3.10 The world's largest cities continue to boom 153 2.14a Health expenditure by aggregation method, 1990-98 93 3.12 Growvth in passenger cars accelerates 161 2.16a Total fertility and access to reproductive health care 3.14 Climiate change and biodiversity at the fore 166 among the poorest and richest, various years, 1990s 101 Text table's 3.lla Population of the world'sl10largest metropolitan areas in 1000, 1800, 1900, and 2000 157 3.12a The top 10 vehicle-owning countries, 1998 161 3.14a Status of national environmental action plans 164 3.14b Stales that have signed the Convention on Climate Change 165 2000 maria Deveiopment Indicators Ix Introduction 173 Introduction 257 4.1 Growth of output 182 5.1 Credit, investment, and expenditure 260 4.2 Structure of output 186 5.2 Stock markets 264 4.3 Structure of manufacturing 190 5.3 Portfolio investment regulation and risk 268 4.4 Growth of merchandise trade 194 5.4 Financial depth and efficiency 272 4.5 Structure of merchandise exports 198 5.5 Tax policies 276 4.6 Structure of merchandise imports 202 5.6 Relative prices and exchange rates 280 4.7 Structure of service exports 206 5.7 Defense expenditures and trade in arms 284 4.8 Structure of service imports 210 5.8 State-owned enterprises 288 4.9 Structure of demand 214 5.9 Transport infrastructure 292 4.10 Growth of consumption and investment 218 5.10 Power and communications 296 4.11 Structure of consumnption i nPPP terms 222 5.11 The information age 300 4.12 Relat ve prices in PPP terms 225 5.12 Science and technology 304 4.13 Central government finances 228 4.14 Central government expend tures 232 Boxes 4.15 Central government revenies 236 5e What can biotechnology do? 258 4.16 Monetary indicators and prices 240 Sb Bridging knowledge end policy 259 4.17 Balance of payments current account 244 4.18 External debt 248 Figures 4.19 External debt management 252 5.1 Foreign direct investment has remained resilient 263 Boxes 5.9 Air traffic is concentrated in high-income economies 295 4a An enhanced framework for poverty reduction 176 1<' 1.i.i. ccr .:.,s ...ur. ; i .1::, 5.11 The information technology revolution has not reached all shiores 303 Figures 4a HIPCs have seen their incomes decline- while those of other poor countries have risen 174 4h HIPCs have made less progress in reducing illiteracy ... 174 4c ... in lowering infant mortality ... 174 Ii F. *.. 1.-il r 4e HIPCs are also falling further behind in paving roads. ... 175 4g HIPCs have not made the sh ft from agriculture to industry and services .. . 175 41r - ... but they have maintained a higher share of exports 175 4i HIPCs' higher aid per capita ... 176 4j ... has translated into higher debt per capita 176 4.7 Exports of commercial services stalled in 1998 209 4.10 Private consumption has accelerated in East Asia and the Pacific 221 4.13 Worsening fiscal balances in Asia 231 4.14 High public interest payments strain national budgets in many developing and transition economies 235 4.15 High-income countries draw a large share of current revenue from income taees ... ..while many developing countries rely on duties and excise taxes 239 4.17 On the road to recovery? CLirrent accounts turn posrcve in East Asia 247 4.18 World Bank and International Monetary Fund lending expanded in the regions most at risk of financial crisis in 1998 251 Text tables 4 a Recent economic performance 178 45 Key macroeconomic indicators 179 x 2000 Worid Developmnent Ind cators Introduct'ion 309 Back matter 6.1 Integration with the glblecnm 314 Statistical mTethods 361 6.2 Direction and growth of merchandise trade 318 Primary datai documentation 363 6.3 OECD trade with low- and middle-income economies 321 Acronyms anid abbreviations 371- 6.4 Primary commodity prices 324 Credits 372 6.5 Regional trade blocs 326 Bibliograph 374 6.6 Tariff barriers 330 Index of indicators 381 6.7 Global financial flows 334 6.8 Net financial flows from Development Assistance Committee members 338 6.9 Aid flows from Development Assistance Committee members 340 6.10 Aid dependency 342 6.11 Distribution of net aid by Development Assistance Committee members 346 6.12 Net financial flows from multilateral institutions 350 6.13 Foreign labor and population in OECD countries 354 6.14 Travel and tourism 356 Figures. 6a Agriculture employs the majority of workers in developing countries ... ... .. .................... ... ... .... .... . 310 6b Developing countries are exporting more and more manufactures to high-income OECD countri'es 310 6c Children work less as incomes rise 311 6d High-income countries are net exporters of goods from the six most polluting industries-and low- and middle-income countries net importers 312 6.1 The importance of trade continues to grow 317 6.2 Developing economies make their own market 320 6.3 High-income economies' imports of manufactures from low- and middle-income economies have surged 323 6.9 Aid fell as a share of GNP for almost all donors between 1993 and 1998 341 6.10 The regional distribution of aid from DAC members has remained much the same 345 6.11 The flow of aid from DAC members in 1998 tended to reflect rgional itrssand relationships 349- 6.12 The top 10 recipients of financial flows from United Nations agencies 353 6.13 The nationalities of the foreign population in OECD countries in 1997 reflected traditional ties and recent events 355 6.14 More and more tourists are from developing economies 359 Text table . ......... ........ 6.8a Official development assistance from non-DAC donors 339 2000 World Development Indicators xi Partners Defining, gathering, and disseminating international statistics is a collective effort of many peo- ple and organizations. The indicators presented in the World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and house- hold surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an inter- national 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 car- rying 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 the World Development Indicators possible by shar- ing 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 com- prehensive, quantitative information about the world and its people. For your easy reference we have included URLs (web addresses) for organizations that main- tain websites. The addresses shown were active on 1 March 2000. Information about the World Bank is also provided. International and government agencies Bureau of Arms Control, U.S. Department of State The Bureau of Arms Control, U.S. Department of State, is responsible for international agree- ments on conventional, chemical and biological weapons, and strategic forces; treaty verifi- cation and compliance: and support to ongoing negotiations, policymaking, and interagency implementation efforts. For information contact the Public Affairs Officer, Bureau of Arms Control, U S. Department of State, 2201 C Street NW, Washington, DC 20520, USA; telephone: (202) 647 6946; web- site: www.state.gov/www/global/arms/bureauac. html. Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global change data and information analysis center of the U.S. Department of Energy. The CDIAC's scope includes potentially anything that would 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 terrestrial 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 information contact the CDIAC, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831-6335, USA; telephone: (423) 574 0390; fax: (423) 574 2232; email: cdiac@ornl.gov: website: cdiac.esd.ornl.gov. xii 2000 World Development Indicators Food and Agriculture Organization The Food and Agriculture Organizatior (FAO), a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agri- cultural 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; anc serves as an international forum for debate on food and agri- cultural issues. Statistical publications of the FAD include the Production Yearbook, Trade Yearbook, and Fertil- izer Yearbook. The FAO makes much of its data available on diskette through its Agrostat PC system. FAO publications can be ordered from national sales agents or directly from the FAO Sales and Marketing Group, Viale delle Terme di Caracalla, 00100 Rome, Italy; telephone: (39 06) 57051; fax: (39 06) 5705/3152; email: Publications-sales@fao.crg; website: www.fao.org. International Civil Aviation Orgainization The International Civil Aviation OrgEnization (ICAO), a specialized agency of the United Nations, was founded on 7 December 1944. It is responsible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects ______ ,of international civil aviation operal ions. The ICAO promotes the adoption of safety measures, establishes visual and instrument flight rules for pilots and crews, develops aeronautical charts, coordinates aircraft radio frequencies, and sets uniform regulations for the operation of air services and customs procedures To obtain ICAO publications contact the ICAO, Document Sales Unit, 999 University Street, Montreal, Quebec H3C 5H7, Canada; telephone: (514) 954 8022: fax: (514) 954 6769; email: sales_unit@icao.org; website: www.icao.int. International Labour Organization The International Labour Organization (ILO). a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. Founded in 1919, it is the only surviving major creation :f the Treaty of Versailles, which brought the League of Nations into being. It became the first specialized agency of the United Nations in 1946. Unique within the United Nations system, the ILO's tripartite structure has workers and employers participating as equal partners with governmentE in the work of its governing organs. As part of its mandate, the ILO maintains an extensive statistical publication program. The Yearbook of Labour Statistics is its -nost comprehensive collection of labor force data. Publications can be ordered fro n the International Labour Office, 4 route des Morillons, CH- 1211 Geneva 22. Switzerland, or from sales agents and major booksellers throughout the world and ILO offices in many countries. Telephone: (41 22) 799 78 66; fax: (41 22) 799 61 17; email: publns@ilo.org; website: www.ilo.org. International Monetary Fund vsNAsN The International Monetary Fund (IMF) was established at a conference in Bretton Woods, New Hampshire, United States, on 1-22.luly 1944. (The conference also established the World Bank.) The IMF came into official existence on 27 December 1945 and commenced financial operations on 1 March 1947. It currently has 182 member countries. 2000 World Development Indicators xill The statutory purposes of the IMF are to promote international monetary cooperation, facilitate the expansion and balanced growth of international trade, promote exchange rate stability, help estab- lish 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 balances of payments of members. The IMF maintains an extensive program for the development and compilation of international sta- tistics and is responsible for collecting and reporting statistics on international financial transactions and the balance of payments. In April 1996 it undertook an important initiative aimed at improving the quality of international statistics, establishing the Special Data Dissemination Standard (SDDS) to guide members that have or seek access to international capital markets in providing economic and financial data to the public. In 1997 the IMF established the General Data Dissemination System (GDDS) to guide countries in providingthe public with comprehensive, timely, accessible, and reliable economic, financial, and sociodemographic data. The IMF's major statistical publications include Intemational Financial Statistics, Balance ofPayments Statistics Yearbook, Govemment Finance Statistics Yearbook, and Direction of Trade Statistics Yearbook. For more information on IMF statistical publications contact the International Monetary Fund, Pub- lications Services, Catalog Orders, 700 19th Street NW, Washington, DC 20431, USA; telephone: (202) 623 7430; fax: (202) 623 7201; telex: RCA 248331 IMF UR; email: pub-web@imf.org; website: www.imf.org; SDDS and GDDS bulletin board: dsbb.imf.org. International Telecommunication Union Founded in Paris in 1865 as the International Telegraph Union, the International Telecommunication Union (ITU) took its current name in 1934 and became a specialized agency of the United Nations in 1947. The ITU is an intergovernmental organization in which the public and private sectors cooperate for the development of telecommunications. The ITU adopts international regulations and treaties gov- erning all terrestrial and space uses of the frequency spectrum and the use of the geostationary satel- lite orbit. It also develops standards forthe interconnection of telecommunications systems worldwide. The ITU fosters the development of telecommunications in developing countries by estab- lishing medium-term development policies and strategies in consultation with other partners in the sector and providing specialized technical assistance in management, telecommunications policy, human resource management, research and development, technology choice and trans- fer, network installation and maintenance, and investment financing and resource mobilization. The Telecommunications Yearbook is the ITU's main statistical publication. Publications can be ordered from ITU Sales and Marketing Service, Place ces Nations, CH- 1211 Geneva 20, Switzerland; telephone: (41 22) 730 6141 (English), (41 22) 730 6142 (French), and (41 22) 730 6143 (Spanish); fax: (41 22) 730 5194; email: sales.online@itu.int; telex: 421 000 uit ch; telegram: ITU GENEVE; website: www.itu.ch. National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mis- sion is to promote the progress of science: to advance the national health, prosperity, and wel- fare; 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 scientists and engineers in the United States and other xiv 2000 World Development Indicators countries, supports programs to st engthen scientific and engineering research potential, and evaluates the impact of research on industrial development and general welfare. As part of its mandate, the NSF biennially publishes Science and Engineering Indicators, which tracks national and international treids in science and engineering research and education. Electronic copies of NSF documents can be obtained from the NSF's Online Document Sys- tem (www.nsf.gov/pubsys/index.htm) or requested by email from its automated mailserver (getpub@nsf.gov). Documents can ilso be requested from the NSF Publications Clearinghouse by mail. at PO Box 218, Jessup, MD 20794-0218, or by telephone, at (301) 947 2722. For more information contact the National Science Foundation, 4201 Wilson Boulevard, Arling- ton, VA 22230, USA; telephone: (703) 306 1234; website: www.nsf.gov. Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) was set up in 1948 as OECD the Organisation for European EconDmic Co-operation (OEEC) to administer Marshall Plan fund- ing in Europe. In 1960, when the Marshall Plan had completed its task, the OEEC's member countries agreed to bring in Canada and the United States to form an organization to coordinate OCDE policy among industrial countries. The OECD is the international organization of the indus- trialized, market economy countries. Representatives of member countries meet at the OECD to exchange information and har- monize policy with a view to maxiriizing economic growth in member countries and helping nonmember countries develop mo e rapidly. The OECD has set up a number of specialized committees to further its aims. One of these is the Development Assistance Committee (DAC), whose members have agreed to coordinate their policies on assistance to developing and tran- sition economies. Also associated with the OECD) are several agencies or bodies that have their own gov- erning statutes, including the Interrational Energy Agency and the Centre for Co-operation with Economies in Transition. The OECD's main statistical publications include Geographical Distribution of Financial Flows to Developing Countries, National Accounts of OECD Countries, Labour Force Statistics, Revenue Statistics of OECD Member Countries, International Direct Investment Statistics Yearbook, Basic Science and Technology Statistics, Industrial Structure Statistics, and Services: Statistics on Inter- national Transactions. For information on OECD publications contact the OECD, 2, rue Andre-Pascal, 75775 Paris Cedex 16, France; telephone: (_3 1) 45 24 82 00; fax: (33 1) 49 10 42 76; email: sales@oecd.org; websites: www.oecd.org and www.oecdwash.org. United Nations The United Nations and its specialize!d agencies maintain a number of programs for the collection of international statistics, some of which are described elsewhere in this book. At United Nations ITi a headquarters the Statistics Division provides a wide range of statistical outputs and services for Li' - producers and users of statistics wcrldwide. The Statistics Division publishes statistics on international trade, national accounts, demog- raphy and population, gender, indus:ry, energy, environment, human settlements, and disability. Its major statistical publications include the International Trade Statistics Yearbook, Yearbook of 2000 World Development Indicators xv National Accounts, and Monthly Bulletin of Statistics, along with general statistics compendiums such as the Statistical Yearbook and World Statistics Pocketbook. For publications contact United Nations Publications, Room DC2 853, 2 UN Plaza, New York, NY 10017, USA; telephone: (212) 963 8302 or (800) 253 9646 (toll free); fax: (212) 963 3489; email: publications@un.org; website: www.un.org. United Nations Centre for Human Settlements (Habitat), Global Urban Observatory The Urban Indicators Programme of UNCHS (Habitat) was established to adcress the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to urban development at the city level. In 1997 the Urban Indicators Programme was integrated into the Global Urban Observatcry, the principal United Nations program for monitoring urban conditions and trends and for tracking progress in implementing the goals of the Habitat Agenda. With the Urban Indicators and Best Practices pro- grams, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity building network to help governments, local authorities, the private sector, and non- governmental and other civil society organizations. Contact Christine Auclair (guo@unchs.org), Urban Indicators Programme, Glooal Urban Obser- vatory, UNCHS (Habitat), PO Box 30030, Nairobi, Kenya; telephone: (2542) 623694: fax: (2542) 624266/7; website: www.urbanobservatory.org. United Nations Children's Fund The United Nations Children's Fund (UNICEF), the only organization of the United Nations dedi- cated exclusively to children, works with other United Nations bodies and with governments and nongovernmental organizations to improve children's lives in more than 140 ieveloping coun- tries through community-based services in primary health care, basic education, and safe water unicef and sanitation. UNICEF's major publications include The State of the World's Children and The Progress of Nations. For information on UNICEF publications contact UNICEF House, 3 United Nations Plaza, New York, NY 10017, USA; telephone: (212) 326 7000; fax: (212) 888 7465 or 7454; telex: RCA-239521; email: publications@un.org; website: 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 gV United Nations General Assembly in the field of trade and development. It was established as a permanent intergovernmental body in 1964 in Geneva with a view to accelerating economic -__4k growth and development, particularly in developing countries. UNCTAD discharges its mandate through UNCTAD policy analysis; intergovernmental deliberations, consensus building, and negotietion; monitoring, implementation, and follow-up; and technical cooperation. UNCTAD produces a number of publications containing trade and economic statistics, includ- ing the Handbook of International Trade and Development Statistics. For information contact UNCTAD. Palais des Nations, CH-1211 Geneva 10, Switzerland; tele- phone: (41 22) 907 12 34 or 917 12 34; fax: (41 22) 907 00 57; telex: 42962; email: reference .service@unctad .org; website: www. unctad .org. xvi 2000 World Development Indicators United Nations Educational, Scientific, and Cultural Organization The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized __________ agency of the United Nations established in 1945 to promote "collaboration among nations through education, science, and culture in order to further universal respect for justice, for the W HOLll\IU 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...." UNESCO's principal statistical publications are the Statistical Yearbook, World Education Report (biennial), and Basic Education and Literacy: World Statistical Indicators. For publications contact UNESC:O Publishing, Promotion, and Sales Division, 1, rue Miollis F, 75732 Paris Cedex 15, France; fax: :33 1) 45 68 57 41; email: publishing.promotion@unesco.org; website: www.unesco.org. United Nations Environment Programme The mandate of the United Nations Environment Programme (UNEP) is to provide leadership and encour- age partnership in caring for the env ronment by inspiring, informing, and enabling nations and peo- pie to improve their quality of life without compromising that of future generations. UNEP publications include Global Environment Outlookand OurPlanet(a bimonthly magazine). For information contact the UNEP, PO Box 30552, Nairobi, Kenya; telephone: (254 2) 62 1234 or 3292; fax: (254 2) 62 3927 or 3E92; email: oedinfo@unep.org; website: www.unep.org. United Nations Industrial Development Organization The United Nations Industrial DevelDpment Organization (UNIDO) was established in 1966 to act , , A'I as the central coordinating body for ndustrial activities and to promote industrial development and UNIDO cooperation at the global, regional, national, and sectoral levels. In 1985 UNIDO became the six- %w. - i Wteenth specialized agency of the Uniled Nations, with a mandate to help develop scientific and tech- nological plans and programs for industrialization in the public, cooperative, and private sectors. UNIDO's databases and infornation services include the Industrial Statistics Database (INDSTAT), Commodity Balance Stalistics Database (COMBAL), Industrial Development Abstracts (IDA), and the International Referral System on Sources of Information. Among its publications is the International Yearbook of Indus trial Statistics. For information contact UNIDO Flublic Information Section, Vienna International Centre, PO Box 300, A-1400 Vienna, Austria; telephDne: (43 1) 260 26 5031; fax: (43 1) 213 46 5031 or 260 26 6843; email: publications@unido.org; website: www.unido.org. World Bank Group The World Bank Group is made up of five organizations: the International Bank for Reconstruction and Development (IBRD), the International Development Association (IDA), the International Finance Corporation (IFC), the Multilateral Investment Guarantee Agency (MIGA), and the International Cen- tre for Settlement of Investment DisDutes (ICSID). Established in 1944 at a conference of world leaders in Bretton Woods, New Hampshire, United States, the World Bank is a lending institution whose aim is to help integrate developing and tran- sition economies with the global economy, and reduce poverty by promoting economic growth. The Bank lends for policy reforms and development projects and provides policy advice, technical assis- tance, and nonlending services to its 181 member countries. 2000 World Development Indicators xvii For information about the World Bank visit its website at www.worldbank.org. For more information about development data contact the Development Data Center, World Bank, 1818 H Street NW, Wash- ington, DC 20433, USA; telephone: (800) 590 1906 or (202) 473 7824; fax: (202) 522 1498; email: info@worldbank.org; website: www.worldbank.org/data. World Health Organization The constitution of tne World Health Organization (WHO) was adopted on 22 July 1946 by the Inter- national Health Conference, convened in New York by the Economic and Social Co incil. The objec- tive of the WHO, a specialized agency of the United Nations, is the attainment by all people of the i k" - 7 highest possible level of health. The WHO carries out a wide range of functions, including coordinating international health work; helping governments strengthen health services; providing technical assistance and emergency aid; working for the prevention and control of disease; promoting improved nutrition, housing, san- itation, 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, pharmaceutical, and similar products; and standardizing diagnostic procedures. The WHO publishes the World Health Statistics Annual and many other technical and statis- tical publications. For publications contact Distribution and Sales, Division of Publishing, Language, and Library Ser- vices, World Health Organization Headquarters, CH-1211 Geneva 27, Switzerland; telephone: (41 22) 791 2476 or 2477; fax: (41 22) 791 4857; email: publications@who.ch; websi-e: www.who.ch. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations based --- in Geneva, Switzerland. The objectives of WIPO are to promote the protection of inte lectual property (g 01po throughout the world through cooperation among states and, where appropriate, in collaboration with ws M .y ~~~~~other i nternational organizations and to ensure admi nistrative cooperation among the ntel lectual prop- erty unions-that is, the "unions" created by the Paris and Berne Conventions and several subtreaties concluded by members of the Paris Union. WIPO is responsible for administering var ous multilateral treaties dealing with the legal and administrative aspects of intellectual property. A substantial part of its activities and resources is devoted to development cooperation with developing countries. For information contact the World Intellectual Property Organization, 34, chemnin des Colom- bettes, Geneva, Switzerland; mailing address: PO Box 18, CH-1211 Geneva 20, Sivitzerland; tele- phone: (41 22) 338 9111; fax: (41 22) 733 5428; telex: 412912 ompi ch; email: publications.mail@wipo.int; website: www.wipo.int. World Tourism Organization The World Tourism Organization is an intergovernmental body charged by the United 'lations with pro- moting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. The organization began as the International Union of Official Tourist Publicity Organizations, set up in 1925 in The Hague. Renamed the World Tourism Organization, it held its first general assembly in Madrid in May 1975. Its membership includes 138 countries and territo- ries and more than 350 affiliate members representing local governments, tourism associations, xviii 2000 World Development Indicators and private companies, including airlines, hotel groups, and tour operators. The World Tourism Orga- nization publishes the Yearbook of Tourism Statistics, the Compendium of Tourism Statistics, and the triannual Travel and Tourism Barometer. For information contact the World Tourism Organization Capitan Haya, 42,28020 Madrid, Spain; telephone: (34) 91 567 81 00; fax: (34) 91 567 82 18; email: omtweb@world-tourism.org; website: www.world-tourism.org. World Trade Organization The World Trade Organization (WTO), established on 1 January 1995, is the successor to the Gen- WOBLD eral Agreement on Tariffs and Trade (GATT). The WTO provides the legal and institutional foun- )I l. E dation of the multilateral trading system and embodies the results of the Uruguay Round of trade / I hi1 , N\. .N' . . 1 I ON negotiations, which ended with the Marrakesh Declaration of 15 April 1994. The WTO is man- dated with administering and implementing multilateral trade agreements, serving as a forum for multilateral trade negotiations, seeking to resolve trade disputes, overseeing national trade policies, and cooperating with other international institutions involved in global economic policymaking. The WTO's Statistics and Information Systems Divisions compile statistics on world trade and maintain the Integrated Database, wiich contains the basic records of the outcome of the Uruguay Round. Its Annual Report includes a statistical appendix. For publications contact the World Trade Organization, Publications Services, Centre William Rappard, 154 rue de Lausanne, CH-1211, Geneva, Switzerland; telephone: (41 22) 739 5208 or 5308; fax: (41 22) 739 5792; email: publications@wto.org; website: www.wto.org. Private and nongovernmental orilanizations Currency Data & Intelligence, Inc. Currency Data & Intelligence, Inc. is a research and publishing firm that produces currency-related products and undertakes research lbr international agencies and universities worldwide. Its flag- ship product, the World Currency Yearbook, is the most comprehensive source of information on Currency Data & Intelligence,Inc. currency. It includes official and unofficial exchange rates and discussions of economic, social, and political issues that affect the value of currencies in world markets. A second publication, the monthly Global Currency Report, comers devaluations and other critical developments in exchange rate restrictions and valuations and provides parallel market exchange rates. For information contact Currency Data & Intelligence, Inc., 45 Northcote Drive, Melville, NY 11747, USA; telephone: (631) 643 2506; fax: (631) 643 2761; email: curncydata@aol.com; web- site: pacific.commerce.ubc.ca/xr/cdi. Euromoney Publications PLC Euromoney Publications PLC provides a wide range of financial, legal, and general business infor- JJAIhujjJI1Im7 mation. The monthly Euromoneymagazine carries a semiannual rating of country creditworthiness. For information contact Euromoney Publications PLC, Nestor House, Playhouse Yard, London EC4V 5EX, UK; telephone: (44 171) 779 8888; fax: (44 171) 779 8656; telex: 2907002; email: hotline@euromoneypic.com; websitE: www.euromoney.com. 2000 World Development Indicators xlx Institutional Investor, Inc. Institutional Investormagazine is published monthly by Institutional Investor, Inc., which develops country credit ratings every six months based on information provided by leading international banks. For information contact Institutional Investor, Inc., 488 Madison Avenue, New York, NY 10022, USA; telephone: (212) 224 3300; email: info@iimagazine.com; website: www.iirnagazine.com. International Road Federation The International Road Federation (IRF) is a not-for-profit, nonpolitical service organization. Its purpose is to encourage better road and transport systems worldwide and to help apply tech- S11 2 nology and management practices that will maximize economic and social returns from national road investments. The IRF has led global road infrastructure developments and is the interna- tional point of affiliation for about 600 member companies, associations, and governments. The IRF's mission is to promote road development as a key factor in economic and social growth, to provide governments and financial institutions with professional ideas and expertise, to facilitate business exchange among members, to establish links between IRF members and external institu- tions and agencies, to support national road federations, and to give information to professional groups. The IRF publishes World Road Statistics. Contact the Geneva office at 2 chemin de Blandonnet, CH-1214 Vernier, Geneva, Switzer- land; telephone: (41 22) 306 0260; fax: (41 22) 306 0270; or the Washingtcn, DC, office at 1010 Massachusetts Avenue NW, Suite 410, Washington, DC 20001, USA; telephone: (202) 371-5544; fax: (202) 371-5565; email: info@irfnet.com; website: www.irfnet.org. Moody's Investors Service _____ Moody's Investors Service is a global credit analysis and financial opinion firm. It provides the w Moodys Investors Service international investment community with globally consistent credit ratings on debt and other secu- rities issued by North American state and regional government entities, by coroorations world- wide, and by some sovereign issuers. It also publishes extensive financial data in both print and electronic form. Its clients include investment banks, brokerage firms, insurance compa- nies, public utilities, research libraries, manufacturers, and government agencies and departments. Moody's publishes Sovereign, Subnational and Sovereign-Guaranteed Issuers. For information contact Moody's Investors Service, 99 Church Street, New York, NY 10007, USA; telephone: (212) 553 1658; website: www.moodys.com. PricewaterhouseCoopers Drawing on the talents of 150.000 people in more than 150 countries, PricewaterhouseCoopers provides a full range of business advisory services to leading global, national, and local compa- nies and public institutions. Its service offerings have been organized into six lines of service, each staffed with highly qualified, experienced professionals and leaders. These services include audit, assurance, and business advisory services; business process outsourcing; financial advisory ser- vices; global human resource solutions; management consulting services; and gloDal tax services. PricewaterhouseCoopers publishes Corporate Taxes: Worldwide Summaries and Individual Taxes: Worldwide Summaries. For information contact PricewaterhouseCoopers, 1301 Avenue of the Americas, New York, NY 10019, USA; telephone: (212) 596 7000; fax: (212) 259 5324; website: www.pwcglobal.com. xx 2000 World Development Indicators The PRS Group Political Risk Services is a global lEader in political and economic risk forecasting and market vV AW ,ir analysis and has served internatior al companies large and small for about 20 years. The data P r S it contributed to this year's World Development Indicators come from the Intemational Country Risk Guide, a monthly publication that monitors and rates political, financial, and economic risk in 140 countries. The guide's data series and commitment to independent and unbiased analy- sis make it the standard for any organization practicing effective risk management. For information contact The PRS Group, 6320 Fly Road, Suite 102, PO Box 248, East Syra- cuse, NY 13057-0248, USA; telephone: (315) 431 0511; fax: (315) 431 0200; email: custserv@PRSgroup.com; website: \vww.prsgroup.com. Standard & Poor's Rating ServiciDs Standard & Poor's Sovereign Ratings provides issuer and local and foreign currency debt ratings for sovereign governments and for soveraign-supported and supranational issuers worldwide. Standard & Poor's Rating Services monitors the! credit quality of $1.5 trillion worth of bonds and other financial instruments and offers investors global coverage of debt issuers. Standard & Poor's also has ratings on commercial paper, mutual funds, and the financial condition of insurance companies worldwide. For information contact The McGraw-Hill Companies, Inc., Executive Offices, 1221 Avenue of the Americas, New York, NY 10020, UJSA; telephone: (212) 512 4105 or (800) 352 3566 (toll free); fax: (212) 512 4105; email: ratings '&mcgraw-hill.com; website: www.ratings.standardpoor.com. World Conservation Monitoring Centre The World Conservation Monitoring Ce:ntre (WCMC) provides information on the conservation and sus- tainable 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 organizations and people to increase access to the information needed for wise management of the world's living resources. Committed to the principle of data exchange wilh other centers and noncommercial users, the WCMC, when- WORLD CONSERVATION MONITORING CENTRE ever possible, places the data it manages in the public domain. For information contact the World Conservation Monitoring Centre, 219 Huntingdon Road, Cam- bridge CB3 ODL, UK, telephone: (44 12) 2327 7314; fax: (44 12) 2327 7136; email: info@wcmc.org.uk; website: www.wcmc.org.uk. World Resources Institute The World Resources Institute is ar independent center for policy research and technical assis- tance on global environmental and development issues. The institute provides-and helps other institutions provide-objective info'mation and practical proposals for policy and institutional change that will foster environmentailly sound, socially equitable development. The institute's cur- rent areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strate- gies for environmental and resource management. For information contact the Worl i Resources Institute, Suite 800, 10 G Street NE, Washington, DC 20002, USA; telephone: (202) 7.29 7600; fax: (202) 729 7610; telex 64414 WRIWASH; email: lauralee@wri.org: website: www.wri.crg. 2000 World Development Indicators xxi Users guide I Principal sections Are signposted by these icons: 4D 2.4 Employment by economic activity Section 1 World view .dl9 0228091 9800029 Section 2 People 190 Dr4 2 8 D6d 9880 9999 90' a8b D; 9920 1862-991 198990 f Al0n X 27 0 _1 0020' 2 3 4 102 2 65 Section 3 Environment 1 ___ a 6 4 4 30 020, , as 8 42 4 0 52 30 Ba g aa0fl 67 54 81 lo ; 20 04 5 leR 8egum 0 3 2 41 i 69 38 _ - Section 4 Economy 0 | 8o si2 26a 1a 27 78 . 1000757 05 02.722 6 1 00 4 a 3 4 90 Bud_l a Fro 925033 02 5 Section 5 States and markets 0 63 1 S O r - I 2 ~~~~~~~~~~~~2 0409052.0850.- 0. rad - 14 6 63 43 1 51 63 g1 Section 6 Global links C H o,- a 1 G 4 1 66 02 06 45 0 0g p 2 614 20 12 0070 . l1 0260 21 10 2 40 2 04 The tables 0 00 72 6 22 4 43 Tables are numbered by section and display the 4 60 identifying icons of each section. Countries and 22 2 20 le B4 economies are listed alphabetically (except for Hong_2_ l- 40 0 3 ° 20 1 25 147 6 Kong, China, which appears after China). Data are 090008 12 656 0 974 31 4 2 shown for 148 economies with populations of more 70100 0 6 3 2 than 1 million people and for which data are regu- G3 74 4 4 larly reported by the relevant authority, as well as woe3 7040970~e 4 for Taiwan, China, in selected tables. Selected indi- I3542 30 67 ' 1 26 cators for 58 other economies-small economies 000008 04 07 17 21 16 GUlrDa6672 1 12 3 with populations between 30,000 and 1 million, 921 61 6 3 0 1 smaller economies if they are members of the World 00420401 24 63 27 11 Bank, and larger economies for which data are not regularly reported-are shown in table 1.6. The term country, used interchangeably with economy, does not imply political independence or official recognition by the World Bank, but refers to any ter- ritory for which authorities report separate social or l,. icators economic statistics. When available, aggregate Indicators are shown for the most recent year or period measures for income and regional groups appear at for which data are available and, in most tables, for an the end of each table. earlier year or period. Time-series data are available on the World Development Indicators CD-ROM. xxii 2000 World Development Indicators On 25 October 1999 the United Nations Transitional Administration for East Timor (UNTAET) assumed responsibility for the administration of East 2.4 0 Timor. Data for Indonesia include East Timor. Data are shown for Eritrea whenever possible, but 0g,Iss7so9 7n3997,7 G9,9799. in most cases before 1992 Eritrea is included in the data for Ethiopia. Data for Germany refer to the unified Germany 199 9995297- 19990 999907- 9999 1992-9 9990 1999 -r *969 19992-97 12m0 1992-9,' unless otherwise noted. !7074 63 24 rs 17 45 40 915 25 51 2 0 G Data for Jordan refer to the East Bank only unless 4007019s 574 413 53 4 14 22 la Is 5 29 41 42 mn 71095 Pe 35 S 2 ' 33 3s otherwise noted. 777 2_ 504 91 55 40 19 3 34 15 49 79 In 1991 the Union of Soviet Socialist Republics was .59513 7 15 7 04 3G 52 22 4 SS SG 72 dissolved into 15 countries (Armenia, Azerbaijan, Belarus, .977471' 47 3 I 11 24 27 8 12 33 42 79 73 . dan7 13 6 4 29 12 51 I 8G G Estonia. Georgia, Kazakhstan, Kyrgyz Republic, Latvia, 79741-029 29 20 40 25 34 S Lithuania, Moldova. Russian Federation, Tajikistan, Turk- 6snm 24259440724 1 2L "i I I 3 - I 7: Rp 3 15 44 3 324 4 24 5 menistan, Ukraine, and Uzbekistan). Whenever possible, K#Gzz R2pa 47 44 40 34 42 2 7 44 1 0 data are shown for the individual countries. 190757 17 23 42 78 4G 33 36 1G 6 9 Data for the Republic of Yemen refer to that country Lstanon 13 ~~~20 29 21 Ese S 907577 2 94452 S 22 3: from 1990 onward; data for previous years refer to 141a 16 54 39 3 SS data'for4former Democratic 9thu2nn 26 52 24 18 4 27 30 41 27 42 41 61 aggregated dataforthe formerPeople'sDemocrabc M2:sdon0900 FYR 80 15 47 9 4 Es 43 41 51 42 77 51 749777799 74 93 9 2 19 eRepublic of Yemen and the former Yemen Arab Republic Malg2 79 50 99 73 10 I 25 1I 7 12 25 3 2G 5700499t 40 19 07 14 45 07 21 41 4007 ss s5 unless otherwise noted. 0-r00-7 75 71 11 2 24 10 In December 1999 the official name of Venezuela MDrmus ~~23 5 0 13 a. Is 40 43 47 49 31 45 09955: 40 - 13 44 19 25 70 was changed to Republica Bolivariana de Venezuela ^'21do;d ~ ~ ~ ~ s 49 3 2 21 19 4 - 43 51 21 21 35 - 4 (Venezuela, RB, in the table listings). bSoroc:e ~ `i 'i 72 3 21 II 1 6 4s 29 93 _ Ž40075q0 72 97 :40 1 14 2 Whenever possible, data are shown for the individ- 10040 04 14 372 Is 2G 2 ual countries formed from the former Socialist Federal 54,4zdGnds 14732 - °8 G2 G 3Republic of Yugoslavia-Bosnia and Herzegovina, 9907 -- 5 7 5 SI d4 49 2 51 4177 71 Croatia, the former Yugoslav Republic of Macedonia, 99074s2 52 67 10 3 -6 - 54575v G 7 6 3 40i 3' 14 10 01 39 G . Slovenia, and the Federal Republic of Yugoslavia. All om2n 52 24 ~~ ~ ~~~21 3' 27 43 79742m 57 17 ; 1 01 42 references to the Federal Republic of Yugoslavia in the 207247.7011.702 4I 7 7 2 I I tables are to the Federal Republic of Yugoslavia 79797174 567 6 6 20 37 13 13 225 '7 70 u l sn e 071u 45 4d d 20 27114 72 35 53 51 44 (Serb a/Montenegro) unless otherwise noted. P1 00 5029 1616 1551'3 45 33 45 56provide 54973d a 'O 47 On 3A 54 Additional information about the data is provided 777t214 4 47 14 5Tht722 2 3 6 :4 40 S 27 1 4aG 7177757106 24 40 10 1753 97 75 O in Primary data documentation. Thatsectionsumma R0797 ;sowo6e:3tJ so Is Gs 31 52 rizes national and international efforts to improve basic data collection and gives information on primary sources, census years, fiscal years, and other back- ground. Statistical methods provides technical infor- mation on some of the general calculations and formulas used throughout the book. Statistics Discrepancies in data presented in different edi- Data are shown for economies as they were tions of the World Development Ind/cators reflect constituted in 1998, and historical data are revised to updates by countr es as well as revisions to historical reflect current political arrangements. Exceptions are series and changes in methodology. Thus readers are noted throughout the tables. advised not to compare data series between editions On 1 July 1997 China resumed its exercise of sover- of the World Development Indicators or between differ- eignty over Hong Kong. On 20 December 1999 China ent World Bank publications. Consistent time-series resumed its exercise of sovereignty over Macao. Unless data for 1960-98 are available on the World Develop otherwise noted. data for China do not include data for ment Indicators CD-ROM. Except where noted, growth Hong Kong, China: Taiwan, China; or Macso. China. rates are in real terms. (See Statistical methods for Data for the Democratic Republic of the Congo information on the methods used to calculate growth (Congo, Dem. Rep., in the table listings) refer to the former rates.) Data for some economic indicators for some Zaire. For clarity, this edition also uses the formal name of economies are presented in fiscal years rather than the Repubic of Congo (Congo, Rep., in the table listings). calendar years; see Primary data documentation. All Data are shown whenever possible for the individ- dollar figures are current U.S. dollars unless otherwise ual countries formed from the former Czechoslovakia- stated. The methods used for converting national cur- the Czech Republic and the Slovak Republic. rencies are described in Stat/stical methods. 2000 World DeveJopment Indicators zxxII lhe World Bank's classification of economies For operational and analytical purposes the World Bank's main criterion for classifying economies is 2.4 gross national product (GNP) per capita. Every econ- omy is classified as low income, middle income (sub- wLIt,, I,,,,,'3 divided into lower middle and upper middle), or high income. For income classifications see the map on FZ the inside front cover and the list on the front cover ,s,o 198- 849O g332-33 80 8348-S2 1927 flap. Note that classification by income does not nec- sanda 9 . 88 17 8 393 70 essarily reflect development status. Because GNP per capita changes over time, the country composition of 9 -3 Is 2 o 8s ° 3 28 48 4 E S898817 2ms 14 1 17 1 49 493 38 33 39 8 38 3 1. 37 income groups may change from one edition of the E-f- : 28 Is World Development Indicators to the next. Once the 9,183, 8 8 2 8 33 30 31 33 39 classification is fixed for an edition, using the most E88 84 398 I 8 83 1 54 89 93 recent year for which GNP per capita data are avail- 38 28 8 4 3 able (1998 in this edition), all historical data E9 32 presented are based on the same country grouping. 1 12 2 3 3 89 Low-income economies are those with a GNP per T - 3 3j 98 283 S3 22 8 33 1e9 3 capita of $760 or less in 1998. Middle-income 3G 133 38 economies are those with a GNP per capita of more U,888,9888,,,3e8 87 than $760 but less than $9,360. Lower-middle- : 83 88 2 ro 8E income and upper-middle-income economies are sepa- 1,34- 2 3 Is Is .2 M ENel RG2 s1 2 3 29 19 14 4G 33 79s rated at a GNP per capita of $3.030. High-income s1 0 71 83 10 9 1 L 15 30 isst 3skscG- economies are those with a GNP per capita of $9,361 33. or more. The 11 participating member countries of 38933 19 13 7gmbabE3 ~29 23 0 3E1 3 10 0 43 41 5 the European Monetary Union (EMU) are presented as a subgroup under high-income economies. EW. 3. MhMls ~ ~ ~ E srmm 33 . 3E3 25 . 34 . 5 Aggregate measures for Income groups . ; 42 30 tS Is The aggregate measures for income groups include 206 81,3 26 29 3 38 3: 13 economies (the economies listed in the main tables plus _________ 22 19 233 19 5 0 I those in table 1.6) wherever data are available. Note that 82b8,h3433n33 82 2 13 22 83g3,I,,3m, ---- 9 48 0 22 87 1 58 Is El in this edition table 1.6 does not include France's over- seas departments-French Guiana, Guadeloupe, Mar- tinique, and Reunion-which are now included in the national accounts (GNP and other economic measures) of France. To maintain consistency in the aggregate mea sures over time and between tables, missing data are imputed where possible. Most aggregates are totals (designated by a t if the aggregates include gap-filled estimates for missing data; otherwise totals are desig nated by an s for simple totals), median values (m), or Footnotes weighted averages (w). Gap filling of amounts not allo- Known deviations from standard definitions or cated to countries may result in discrepancies between breaks in comparability over time or across subgroup aggregates and overall totals. See Statistical countries are either footnoted in the tables or noted methods for further discussion of aggregation methods. in About the data. When available data are deemed to be too weak to provide reliable measures of lev- Aggregate neasures for regions els and trends or do not adequately adhere to inter- The aggregate measures for regions include only low- national standards, the data are not shown. and middle-income economies (note that these mea sures include developing economies with populations of less than 1 million, including those listed in table 1.6). The country composition of regions is based on the World Bank's analytical regions and may differ from common geographic usage. For regional classifications see the map on the inside back cover and the list on the back cover flap. See Statistical methods for further discussion of aggregation methods. xxiv 2000 World Development Indicators Notes about data About the data provides a general discussion of inter- Awk, national data standards, data collection methods, 2.4 and sources of potential errors and inconsistencies. Readers are urged to read these notes to gain an understanding of the reliability and limitations of the rbmb 3aLaboo 0 ga 000000l 01c0as3Ites fggfeed0ne1neb0 00nallzmng texC31 1 0 0 0llo 0 , sndYshng3 data presented. For a full discussion of data collec- 3003300 of bOl 31300 agO 1000, nlooOto.,obo-o0Cl3c,oiglo-oJ,oaIIlSCo.ol203 I0 dusl-al CleAsrlfIa nI SIC) 3T All LfDmc ISb 000 3003 foa dgef onComll 300yo 00030 3000n 0 A and 3 (ISI Coslolo tion methods and definitions readers should consult s 301 LuUSlrh O hOI nOha t p03~ 103f y 0r0 hohasmc3 0 all03 11013000 3l30011000003 oll 0100030031,l00,03o10033. 30C 0031cty 3as snd the technical documentation provided by the original 314303 . c333oICad nder 3he sams nd0st0y olamnMIPmon ot1 b 3l0303,..0111103331. lox,ra3 d n sons 2 33l110300 3 0l33olgoo compilers cited in Data sources. yncesar-sebecause -MU1 nshoEntcathabentebe ex rxemay'ntss stlaDefinitions llCaaCa000.oo CalllOll 0-aOm g 11- Cab ,I-an, Pelona 0I I C0.1301110, I Definitions provide short descriptions of the main m COlS 0ssnd13. -nI3- I s.cha,n,,Cneoal lndCllal0-331000- looodownB ,ISICoaaa )o00ab3lat-olloe indicators in each table. Nellop p eb e eltm 3whaoSod01 h2 ammgneseP tae adsnte o cUsg 3'{r E ISources ,1310100313Cb d CAP2bneep m mn msmnoprD 25100003a00l lOOfllC00 Aa01033' el330l30933 C-CA laoy3, lwa'll00033300AA3330 r 30-'o:1e20x 01' 33in2A2:3mrm eawm 01C31C0300303C03 .0331310 33033130300013 33 IaC Partners are identified in the Data sources section S~ ~ ~ ~~~~013 031 wome arhc 3ItntCIC0I33oCOa oCerti 0330s brIdestlosa II 03a. 0110231 100 3. 0 IIw *lalal C,ans-f5 ,Cl. dNation5 following each table, and key publications of the 01CC CCCald olcnlra11n1df toaS Wmeled t U!| partners drawn on for the table are identified. For a 0101n Ctom esbab'shme t Survges. thef pvel ||| zl *as description of our partners and information on their 3 a01330930a IeIeesclO333d-lcl 330 oo gdata publications see the Partnets section. 1333033 nsmpl?yedf ope 033033113C0 30033 11 1cD Figures 10306003 poCylo OCt0 3003300 IOb 0.13013013 310 0 03030033100303001ha00aa0103 030 11011013131 3Lt n When appropriate, tables are accompanied by fig- C300030310C003033100333100000333131C00 I? TY ; T _ ures highlighting particular trends or issues. beas ILO misn dat iro th years shwn is not2 analyicall da?a°ningful. 30330300031C3,001 lC03aa3000 1100l30l00313 li. 0 orShex td..meanMs zeo o les thnhl hgnt sh ow.RA ilo i , mlin 30100C00l103100 in00 dates,00C0 as31 in 190/1 Ien htteprido ie saly1 rlin*s100blin yer orm Xb yfiscal er.3 peid otherSI the dan those specfie. hs ab l30l333x333301d3ng o n03313 10103d 1tnS Ip 300m o Ct 3 Ax nd2 meanson curen U.S. dolr unes otews noed ms Dat for year thate area mor than three year from * meansb moren than. thebPrange shown2 xare TneoL2bted. *bAdfaeacex meansa less thoan.eo mty=t1 nee4 IBg b tpn os ponomic x vf by gender leveals susUsa1 malxThe cuoff dae for dtanis . Februay 2000 31333130030 0330001nderepissenx n3 0 S s y.303110333 032 0DI3 0 or 30130.0310 me30bans3 zero3 oraless than.30 halfC. the30 untshw.31ilin s100 ilin 11/0333 0310 6131 in301 dates, 3as C 0in 0000/: meSansaa that.30 01 eo ftm,usal 2*Atiio s100bl 3030year,1or3a1fiscal3year.3periods30other3than0thosedspecified. Symbols en .. olrsulssohrws otd Data poresent tatio conentiorthnstreyasfo > means that dthan are norvial rta eae antb acltdAbankgean nhown appicbl fotorthaend.eg o r . means zero orlst hnhafteuitsonan.blini ,00mlin / in dates, salt 1990/91, means that the period of time3 usuallytoff1dateAftrillioniis11,ebrubillion. months. ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~20 stade w aedryas n eest rpya3 uvy*Fgrsi tlic idiae data tht are faor yearsxo , r:s . . r,,- m 0 A sixth of the world's people produce 78 percent of its goods and services and receive 78 percent of world income-an average of $70 a day. Three-fifths of the world's people in the poorest 61 countries receive 6 percent of the world's income-less than $2 a day. But their poverty goes beyond income. While 7 of every 1,000 children die before age five in high-income countries, more than 90 die in low-income countries. How do we bridge these huge and grow- ing income gaps, matched by similar gaps in social living standards? Can the nations of the world work together to reduce the numbers in extreme poverty? This is the fundamental challenge of the 21st century. Recent trends in poverty New data from the World Bank suggest that the number of people in extreme poverty (living on less than $1 a day) has been relatively stable in the past decade, rising in the early 1990s to a peak of 1.3 billion and then falling slightly to 1.2 billion in 1998-roughly the same as in 1987 (table 1 a) .' But the regional picture is varied. In East Asia and the Pacific the number in poverty fell sharply from 452 million in 1990 to 278 million in 1998, mainly because of progress in China, with thc rest of East Asia cutting its numbers by a third. Almost all other regions had their number in poverty increase. South Asia's rose from 495 mil- lion to 522 million, and Sub-Saharan Africa's from 242 million to 291 million. The proportion of people living in extreme poverty-the poverty rate-went down modestly from 29 percent in 1990 to 24 percent in 1998. Here again East Asia took the lead, reducing its rate from 28 percent to 15 percent. South Asia, home to the largest number of the world's poor, saw a modest decline of four points to 40 percent over the same period. Sub-Saharan Africa (46 percent) and Latin America and the Caribbean (16 percent) had barely discernible reductions. Some regional trends: * While the overall trend in East Asia and the Pacific is impressive, much of this gain was made before 1997. The financial crisis of 1997-98 checked the strong momentum of growth. Data from national surveys, based on national poverty lines, suggest that Indonesia, the Republic of Korea, and Thailand had sharp increases in poverty. VTietnam seemed the exception-its poverty continued to decline. In China, where growth slowed but was still high, the pace of poverty reduction slowed sharply after 1996, and the numbers in poverty may even have increased. Poverty in developing and transition economies, selected years, 1987-98 Population covered by at least People living on less than PPP $1 a day Headcount index one survey millions % % 1987 1990 1993 1996 j9988 1987 1990 1993 1996 1998' East Asia and the Pacific 90.8 417.5 452.4 431.9 265.1 278.3 26.6 27.6 25.2 14.9 15.3 Excluding China 71.1 114.1 92.0 83.5 55.1 65.1 23.9 18.5 15.9 10.0 11.3 Europe and Central Asia 81.7 1.1 7.1 18.3 23.8 24.0 0.2 1.6 4.0 5.1 5.1 Latin America and the Caribbean 88.0 63.7 73.8 70.8 76.0 78.2 15.3 16.8 15.3 15.6 15.6 Middle East and North Africa 52.5 9.3 5.7 5.0 5.0 5.5 4.3 2.4 1.9 1.8 1.9 South Asia 97.9 474.4 495.1 505.1 531.7 522.0 44.9 44.0 42.4 42.3 40.0 SuabSaharan Africa 72.9 217.2 242.3 273.3 289.0 290.9 46.6 47.7 49.7 48.5 46.3 Total 88.1 1183.2 1,276.4 1,304.3 1,190.6 1,198.9 28.3 29.0 28.1 24.5 24.0 Excluding China 84.2 879.8 915.9 955.9 980.5 985.7 28.5 28.1 27.7 27.0 26.2 Note: Theestimates in thetable are based on datafromthecountries in each regionforwhich at leastonesurveywas available in 1985-98. Where surveyyears do rotcoincidewith the years in the table, the survey estimates were adjusted usingthe closest available survey for each country and applyingthe consumption growth rate from national accounts. The number of poor in each region wasthenestimated usingthe assumption thatthe sample ofcountries covered by surveys is representative ofthe region as awhole. This assumption is obviously less robust in the regions with the lowest surveycoverage. The headcount index is the percentage of the population below the poverty line. Formore details on the data and methodology see Chen and Ravallion forthcoming. a. Estimated. Source: Chen and Ravallion forthcoming. * South Asia continued to record solid per capita GDP growth. * The economic depression in most transition economnies in Europe But the pace of poverty reduction slowed considerably, par- and Central Asia through much of the 1990s may have hit bot- ticularly in India, reflecting the drag on its overall perfor- tom. But the combination of falling output and rising inequal- mance from the populous and poorest states of north India itvled to large increases in the numbers in poverty, including those (Bihar, Madhva Pradesh, and Uttar Pradesh). Bangladesh's per- in extreme poverty. In 1990 very few in this region lived on less formance has been much better, while Pakistan's low growth than $1 a day. Today there may be more than 24 million, 5 per- rates throughout the 1990s made poverty worse. cent of the population-and as many as 93 million, or 20 percent * The gains in growth recorded in Sub-Saharan Africa in the of the population, now live on less than $2 a day. mid-1990s were reversed by lower commodity prices and * In the Middle EastandNorthAfricaonly2 percent of the pop- reduced export demand, reflecting both the slackening of ulation live on less than $1 a day, and some 22 percent on less growth in world trade and increased competition from coun- than $2. Poverty has declined in the 1990s, helped in recent tries that had sharp exchange rate depreciations. Africa's years by rising oil prices and stronger growth. aggregate performance conceals wide variations between the handful of steady reformers (C6te d'Jvoire, Ghana, Mauritania, - t £Jt-tJ' 2riM Tanzania, and Uganda) and the countries in severe conflict What are the prospects for attaining the international development (Burundi, the Democratic Republic of the Congo, Rwanda, goal of halving the proportion of people in extreme poverty Sierra Leone, and countries in the horn ofAfrica). In between between 1990 and 2015? lie a large number of cointries haxing difficulty in making Much depends on the pace and quality of growth, according the transition to a path of sustained economic reform. Those to the World Bank's Global Economic Prospects 2000. With slow enjoying good growth have seen poverty decline; the others growth and rising inequality there likely will be little progress in have seen worsening income poverty and social indicators. reducing the total number of poor-much like the experience of * While extreme poverty is confined to a relatively small share the last decade. In the next decade the number of people living of the people in Latin America and the Caribbean (15 percent in poverty would remain virtually unchanged, wAith more than a living on less than $1 a day, 36 percent on less than $2 a day), billion people still living on less than $1 a day. Only with inclusive both the share and the numbers in povertv remain stubbornly growth-only if the right combination of policies and interven- stagnant, apparently immune to the growth in the 1990s tions leads to sustained growth without increases ir inequality- because of high levels of inequality. There are exceptioils. can we stay on track to reach the target. Data for Brazil suggest that the Real Plan helped poverty drop This brighter picture requires policies that encourage ecoonomic 30 percent in two years after the 1994 launch. But the global stability and direct new resources toward poverty reduction, so that financial crisis wiped out a third of these gains. countries can grow out of extreme poverty. That should make it 4 2000 World Development Indicators only with even better growth effort and stronger reductions in inequality will Latin America and Sub-Saharan Africa be likely to The international development goals attain it. An OECD-United Nations-World Bank conference (held in Paris on 16-17 February 1998) identified 6 social goals and 16 complementary rWhjCat z the social deveOopmiant goals? indicators to be monitored by the development community as part of a new international development strategy. (The table numbers show where Social dexelopment indicators generally improve as incomes rise. these indicators appear.) And most. indicators continued to improve between 1990 and Reduce poverty by half 1998. But progress does not warrant confidence that the inter- * Headcount index (table 2.7) national Oevelopment goals can be attained (box 1 a). Moreover, * Poverty gap index (table 2.7) health gains are being eroded in countries suffering the AIDS epi- * Income inequality: share of income accruing to poorest 20 percent demic, many of which are experiencing sharp reductions in life (table 2.8) * Child malnutrition (table 2.17) expectancy (Tanzania, Uganda, Zambia, and Zimbabwe). And the countrywide averages that are the focus of the international Provide universal primary education development goals conceal considerable differences in health * Net pnmary enrollment ratio (table 2.10) * Progression to grade 5 (table 2.11) and education-with the poor systematically haNing higher mor- * nIliteracy rate of 15- to 24-year-clds (table 2.12) tality rate:s and lower enrollmenit ratios. Improve gender equality in education * Gender differences in education and literacy (tables 1.3 and 2.13) l C L . ales The inter national development goals call for a two-thirds reduc- Reduce infant and child mortality tion in infant and child mortality rates and a three-fourths reduc- * Infant mortality rate (table 2.18) * Under-five mortality rate (table 2.18) tion in maternal mortality ratios from 1990. Neither is likely on current trends. Infant mortality rates fell by 13 percent in South Reduce maternal mortality Asia, 9 pe rcent in Sub-Saharan Africa-and 10 percent in devel- * Maternal mortality ratio (table 2.16) * Births attended by skilled health staff (table 2.16) oping coIuntries as a group. Under-five mortality rates declined by 3 percent for Sub-Saharan Africa and 10 percent for all devel- Expand access to reproductive health services oping countries. To be on track for attaining the goals, mortal- * Contraceptive prevalence rate (table 2.16) * Total fertility rate (table 2.16) ity rates should have coie dowii by rougily 30 perceiit. * HIV prevalence in pregnant 15- to 24-year-olds (table 2.17)1 These national averages conceal wide disparities between rich and poor families in some countries. Generally, children born 1. These data are not yet available, but table 2.17 shows comparable indicators. into poor Families have a higher chance of dying before their fifth birthday than children born into better-off families, but inequal- ity varies by country. In Ghana and Pakistan the rates for the top and bottom fifths vary only slightly, with the poor having 1.1-1.2 possible to reduce the number of people living on less than $1 a times the rate of under-five mortality. But in South Africa the poor day to about 700 million by decade's end. have twice the rate of the rich-and in Northeast Brazil 10 times The fundamental message is that only with substantial policy (table lb,'. change will the world achieve the goal. Now only East Asia and the Equally difficult for the majority of developing countries is Pacific is poised to meet the goal. With more inclusive growth the achieving the maternal mortality reduction target. This is espe- poverty reduction goal is attainable in South Asia as well. But cially so lor countries with levels above 300 per 100,000 live Under-five mortality rate in poorest and richest quintiles Per 1.000 live births Ratio of Poorest Rlchest poorest to Period Average quintile quintile richest t. .l 1987-92 63 116 11 10.4 Ghana 1978-89 142 155 130 1.2 Pakistan 1981-90 147 16o 145 1.1 South Africa 1985-89 113 las 71 2.2 a. Data refer to the Northeast and Southeast. source: Wagstaff 1999. 2000 World Development Indicators S births in 1990 or later.2 Countries can make a dent in maternal cry, all regions other than South Asia and Sub-Saharan Africa deaths with safe motherhood initiatives, such as those prevent- could still reach the target. But if HIV/AIDS continues to spread ing and managing unwanted pregnancies. The most effective as projected by various epidemiological models, no regions intervention is having personnel trained in midwvifery attend the except East Asia and the Pacific would meet the goal of a two- delivery, which can substantially reduce the number of women thirds reduction. who remain at risk. For under-five mortality, all regions other than Sub-Saharan But the poor have a smaller percentage of births in the pres- Africa could reach the target if HIV/AIDS is contained and health ence of trained health professionals. Evidence from 10 develop- services continue to improve. Without improved services, East Asia ing countries studied between 1992 and 1997 shows that only 22 and the Pacific, Europe and Central Asia, and Latin America and percent of births were attended by medically trained health staff the Caribbean could still meet the target. In the AIDS pandemic for the poorest fifth of the population, while for the richest fifth scenario East Asia and the Pacific and Europe and Central Asia 76 percent of births were attended by trained staff (figure la). would still make the target, but the developing world as a whole There were undoubtedly large variations in the quality of trained would see only a 23 percent reduction in under-five mortality, far staff for the two groups. short of two-thirds. A big factor in mortality in many developing countries: None of the regions would achieve the maternal mortality tar- HIV/AIDS. In some countries in Sub-Saharan Africa infant and get, even assuming that 80 percent of births are attended by med- child mortality, after years of steady decline, has begun to rise again. ically trained staff. Starting from its high current maternal mortality And analysis by Hanmer and Naschold (1999) indicates that ratios, Sub-SaharanAfrica could get close to the targetunder the HRT/AIDS is strongly and positively correlated with maternal better health services scenario, but the scenario's assumptions may mortality. be too optimistic for the region. Does this mean that the international development goals for health outcomes are unlikely to be attained? Much depends on Education outcomnes containing the HIR/AIDS epidemic, improving delivery of health The international development goals aim at 100 percent net services to those in need, and realizing the benefits of continu- enrollment in primarv school by 2015, and equal enrollment for ing technological progress. Hanmer and Naschold's study sug- boys and girls in primary and secondary school by 2005. Neither gests that in the best case the infant mortality target could be met is likely. The exhaustive Oxfam study Education Now (Watkins for developing countries as a whole, with South Asia almost 1999) estimates that 125 million children of primary school age making the target and Sub-Saharan Africa reducing its rate by were out of school in 1995. Of these, two-thirds were girls. In 44 percent. Even without improvements in health service deliv- 1995 girls made up only 43 percent of those enrolled in primary school in low-income countries. By 2005 they are expected to make up only 47 percent of primary enrollment. While girls' enrollment in secondary school is rising faster than boys', they will The poorest have least access to maternal and child health serviCes make up only 47 percent of secondary enrollment by 2005. That last 3 percent is difficult to achieve. % of households The United Nations Educational, Scientific, and Cultural 100 Organization (UNESCO) is more optimistic, suggesting that East Asia and the Pacific will meet the target, and that Eulrope and Cen- 50 Lr tral Asia and Latin America and the Caribbean are- likely to (fig- 60 ure Ib). Progress will be slower in South Asia, Sub-Saharan Africa, 4 and the Middle East and North Africa. Oxfam projects gradual 40 = _ ~ progress, with 96 million still out of school in 2005 and 75 million _ in 2015, two-thirds of them in Sub-Saharan Africa. 20 Within countries the poor are systematically worse off than the 0 rich. In many countries few children from poor hcuseholds have Poorest 2nd 3rd 4th Richest quintile quintle quintile quintile quintile schooling. In Benin, India, Mali, and Pakistan the majority of 15- to 19-year-olds from the poorest 40 percent of households have -Births attended by medicallytrained health staff zero years of schooling. In India, by contrast, 15- to 19-vear-olds Diarrhea treated in health facilitY from the richest 20 percent of households have an average of 10 -Acute respiratory infections treated in health facility Children aged 12-23 months immunized (all vaccinations) years of schooling. In Brazil, where almost all children from the Note: The data are from 10 developing countries (Bolivia, Chad, Cote d'lvoire, India, poorest households attend some school, only 15 percent actually Malawi, Morocco, Peru, the Philippines, Tanzania. and Vietnam) for years between complete primary school. 1992 and 1997. Households are grouped into quintiles by assets. Source: Analysis of demographic and health surveys conducted by the World Bank and Macro International. Responding to the Slow progress has led to growing consensus on what is needed to scale up and accelerate the efforts to attain the ambitious targets. 6 2000 World Developmen: Indicators '-'L. rIM WAl 43AS most of them in Sub-Saharan Africa, the region with the farthest to go. The underlying premise of this new approach is that gov- Some developing regions are well on their way to meeting the enrollment target ernments can measurably increase the efficiency of their poverty reduction efforts by improving the policy and institutional envi- Net primary enrollment ratio (%) ronment. MvIuch research now shows this. As policies and institu- 100 tions improve, the cost of poverty reduction falls, so that for a given _= - ,_-' volume of resources more people can be lifted out of poverty. The sane research shows that donors can double the poverty reduction efficiency of their aid by targeting poorer countries, par- 75 ,-' ticularly those pursuing good policies and institutional environ- ments (see Collier and Dollar 1999). The new approach to poverty-supporting countries willing to fight poverty-raises hopes that the international development goals can be reached. 50 1990 1998 2010 2015 - East Asia & Pacific 1. These estimates are based on purchasing power parities (PPPs), which take into account -Europe & Central Asia' differences in the relative prices of goods and services between countries. The poverty line -Latin Amenca & Caribbean for extreme poverty was estimated as the average of the 10 lowest poverty lines of 33 coun- Middle East & North Africa tries for which poverty lines were available in 1990. That average in 1993 dollars-converted -South Asia 2using PPPs-is $1.08 a day. In the tet this poverty line is loosely referred to as $1 a day. -South Asia ~~~~~~~~~~~~~~~~~2. Achieving thE maternal mortality goa nasa different implications for countries with different * Sub-Saharan Africa mortality levels, By 2005 countries with intermediate levels of mortality should aim to lower All developing countnes the maternal murtality ratio to less than 100 per 100,000 live births, and by 2015 to less than a. Data for 1998 refer to 1997. 60. Countries with the highest levels of mortality should aim to achieve a maternal mortality Source: UNESCO and World Bank staff estimates. ratio of less than 125 per 100,000 live births by 2005, and less than 75 by 2015. This consensus is based on general agreement that: • Multidimensional societal transformation is the ultimate goal of development. * We need to ensure that such transformation is country led rather than donor led. • We need to work together through strategic partnerships to support countries anxious to move ahead. * We need to focus such efforts on a clear set of monitorable development outcomes. Building on this consensus, the annual meetings of the World Bank and the International Monetary Fund in Washington, D.C., in September 1999 set two priorities for action. First, greatly expand the debt relief granted to reforming heavily indebted poor countries and link such relief to their efforts to reduce poverty. Second, help indebted countries and all other recipients of concessional aid develop clearly articulated poverty reduction strategies in close consultation with civil society and their devel- opment partners. Such strategies would: * Aim at a better understanding of the nature and locus of poverty. * Identify and implement public policies that have the greatest impact on poverty. * Set clear goals for progress in poverty reduction, tracked in a participatory manner through carefully selected intermediate and outcome indicators. Early candidates for this approach would be the heavily indebted poor countries pursuing policy and institutional reforms, 2000 World Development Indicators 7 to reduce infant and child mortality rates by G O A L : to reduce income poverty by half by 2015 G O two-thirds by 2015 1990 * 1998 * 2015 (target) 1990 0 1998 * 2015 (target) Headcount index Deaths per 1,000 S0 40 30 20 10 (percent)a 110 100 80 60 40 20 live births 28 East Asia and 40 East Asia and the Pacific 35 -{ _ the Pacific 'A t. Europe and 28 hA.. Europe and 2 _0 Central Asia 22 45iF Central Asia 16 1 Latin America and 41 Latin America and theCaribbean 41 _ the Caribbean 1 2 > Middle East and 60 _ Middle East and 2 2 . North Africa - North Africa 44h -South Asia 87 South Asia 4S Sub-Saharan 10 Sub Saharan Afritca 101 Africa Source: World sank staff estimates, a. People livmg on less than PPP$1 a day. Source: World Bank staff ost mates _ =. G A I primary enrollment by 215 to achieve gender equality in primary and G O A L: - to achieve universal primaryenrollmentby2015 G A secondary education by 2005 1990 0 1998 * 2015 (target) 1990 | 1996 * 2005 (target) Percent 50 60 70 80 90 100 Percent 50 60 70 80 90 100 East Asia and East Asia and the Pacific 99 the Pacific Europe and 6 Europe and Central Asia 9 9 Central Asia _ Latin America and S8 Latin America and 100 the Caribbean as the Caribbean 98 Middle East and 7 Middle East and 82 North Africa 7 North Africa 82 South Asia 73 > South Asia 75 6 Sub-Saharan s _ s Sub-Saharan 82 Africa 4wAfrica Source: UNESCO estimates. 1999. So-ree: UNESCO. 8 2000 World Development Indicators The challenge of meeting our goals I believe that the greal:est moral challenge we face is the fact that one in four of the people with whom we share this small and beautiful planet live in abject poverty. I also believe that we live at a time when it is possible to inake massive reductions in poverty. But to do so, we must turn the development efforts of the international community from an obsession with inputs and generalized rhetoric about poverty to a clear focus on outputs and year-on-year effectiveness in reducing poverty measured against our agreed goals in each and every country. - The Hon. Clare Short, U.K. Secretary of State for International Development, Paris, 1999 *~~ U. V Ue G O A L . to provide access to reproductive health services G O x L to reduce maternal mortality ratios by * for all who need them by 2015 G a tnree-cquar Ters by 2015 * Country level, most recent year available in 1990-98 * Co'urfr. rn,o Wt re.Jenf ,.,r ;.a,atCi is l ew - Deaths per 100,000 Percent 0 20 40 60 s0 100 1,200 1,000 800 600 400 200 live births Lao PDR Indo-esi. China Leo PDR Mongolia Rpublic of Korea the Pacific $ S S the Pacific P.Iaud Russian Federation Kazakhstan Moldova i _ Po urea Europe and F -'--_ Europe and Central Asia - Central Asia Haiti Nicaragua Pu-rto Rico Bolivia Jama:o Latin America And Latin America and _________________________ LaiAmrcan the Caribbean the Caribbean Oman Republic of Yemen Jordan Isiamic Republic of Iran Republic of Yemen Tunisia> M e t Middle East and eo Middle East and North Africa W W North Africa Pak,Stan ledfa Bangladesh Nepal Bhutan St Lanka South Asia 3 * 9 O South Asia Sub-Saharan Guinec Cameroon Mauritius Central African Republio Tanzania Mauritius Sub-Saharan Africa 4 * 0 0 Africa Note: Use of contraceptives S affected by many factors, ftcluding access to reproductive health services. Note: The data shomn are for the lowest mediaR and highest value in each region for the most recent year available There Is no established goal. The data showy are terthe lowest, median, aed highest value in each region for the in 1985-19S8. most recent year available mn 1990-98. Source: National estimates. Source: National estimates. 2000 World Development Indicators 9 S ~1.1 Size of the economy Population Surface Population GNP GNP per PPP GNP' area denLsity capita Average Average annual annual Per thousand people growth growth capita milliono op. km per sq. km $ billions Rank % $ Rank % $ billiono $ Rank ±.998 1.998 1.998 1998b ±.998 ±.997-98 1998b ±.998 ±997-98 1998 ±.998 ±.998 Albania 3 29 122 2.7 135 7.9 810 139 6.8 10 2.864 137 Algeria 30 2.382 13 46.4 51 5.8 1,550 113 3.6 137'c 4,5950 101 Angola 12 1.247 10 4.6 115 19.8 380 165 16.3 120 9990 183 Argentina 36 2,780 13 290.3 17 3.9 8,030 55 26.6.. .424 11,728 53 Armenia 4 30 135 1.7 155 3.4 460 160 3.1 8 2,074 150 Australia 19 7,741 2 387.0 14 5.6 20,640 23 4.4 409 21,795 20 Austria 8 84 98 216.7 21 3.3 26,830 12 3.2 187 23,145 15 Azerba'ijan 8 87 91 3.8 123 9.9 480 156 8.9 17 2.168 149 Bangladesh 126 144 965 44.2 53 5.9 350 173 4.2 177 1.407 168 Belarus 10 208 49 22.3 62 10.5 2,180 99 10.8 65 6.314 81 Belgium 10 33 311 259.0 19 30. 25,380 15 2.8 241 23,622 13 Benin 6 113 54 2.3 141 4.7 380 165 1.9 5 857 189 Bolivia 8 1,099 7 8.0 93 5.1 .9010 134 2.7 18 2,205 146 Bosnia and Herzegovina 4 51 74 d. . 0~ Botswana 2 582 3 4.8 111 3.7 3,070 87 1.8 9 5.796 86 Brazil 166 8,547 20 767.6 8 0.0 4,630 68 -1.4 1,070 6,460 80 Bulgaria 8 111. 75. .10.1 84 4.4 1.220 125 5.1. 39 4.683 100 Burkina Faso 11 274 39 2.6 138 6.3 240 191 3.8 90 866' 188 Burund'i 7 28 255 0.9 170 4.7 140 202 2.6 40 561' 203 Cambodia 11 181 65 2.9 132 -0.1 260 187 -2.3 14' 1.2460 175 Cameroon 14 475 31 8.7 89 6.7 610 152 3.8 20 1,395 170 Canada 30 9,971 3 580.9 9 2.9 19.170 26 2.0 691 22,814 17 Central African Republic 3 623 8 1.1 166 4.5 300 181 2.6 4' 1,0980 179 Chad 7 1,284 6 1.7 157 8.4 230 192 5.5 60 8430 191 Chil 15 757 20 73.9 42 8.7 4,990 66 7.2 126 8,507 68 China 1,239 9,597 a 133 923.6 7 7.4 750 145 6.4 3,779 3,051 132 - t 1~~~~~~~... ... ............. . -7 I Colombia 41 1,139 39 100.7 35 -0.6 2,470 93 -24 39 5,8610 84 Congo, Dem. Rep. 48 2,345 21 5.4 104 4.0 110 205 0.7 350 7330 195 Congo, Rep. 3 342 8 1.9 149 11.4 680 148 8.4 2 846 190 Costa Rica 4 51 69 9.8 85 4.7 2,770 89 2.9 200 5,8120 85 COte dIlvoire 14 322 46 10.2 83 5.9 700 147 3.9 21 1.484 164 Croatia 5 67 80 20.8 64 1.8 4,620 69 2.6 30 6,698 78 Cuba 11 111 101 .. . 9 Czech Republic 10 79 133 53.0 48 -2.2 5.150 65 -2.1 126 12,197 52 Denmark..... 5.......43 125 175.2 23 27.7 33.040 6 2.4 126 23,855 12 Dominican Republic 8 49 171 14.6 77 6.8 1,770 105 4.9 360 4,337 c 104 Ecuador 12 284 44 18.4 70 4.2 1,520 116 2.2 37 3,003. 133 Egy.t,Arab Rep. 61 1,001 62 79.2 40 8.3 1,290 121 4.5 193 3,148 129 El Salvador 6 21 292 11.2 80 3.3 1,850 103 1.2. 240 4008' 114 Eritrea 4 118 38 0.8 174 -4.0 200 198 -6.7 40 9840 184 Estonia 1 45 34 4.9 110 5.7 3,360 82 6.4 11 7,563 73 Ethiopia 61 1,104 61 6.2 101 -1.8 100 206 -4.2 350 566' 202 Finland 5 338 17 125.1 31 6.7 24,280 19 6.5 106 20,641 24 France 59 552 107 1,465.4 h 4 3.2 24,210'1 20 2.8 1,248 21,214 22 Gabon 1 268 5 4.9 107 5.7 4,170 72 3.2 7 5,615 89 Gambia, The 1 .... ... .. ....... 11 122 0.4 189 5.0 340 176 2.0 2' 1,4281 0 167 Georgia 5 70 78 5.3 105 2..7 ..........970 136 2.5 19 3,429 124 Germany 82 357 235 2,179.8 3 2.8 26,570 13 2.8 1,807 22,026 19 Ghana 18 239 81 7.3 96 4.6 390 164 1.9 32' 1,735' 157 Greece 11 132 82 123.4 32 3.3 11,740 46 3.1 147 13,994 49 Guatemala 11 109 100 17.8 71 5.5 1,640 Ill 2.8 38' 3,4740 122 Guinea 7 246 29 3.8 125 3.9 530 154 1.5 12 1,722 158 Guinea-Bissau 1 36 41 0.2 200 -28.9 160 201 -30.4 1'. ..... 573'... 201 Haiti 8 28 277 3.2 131 3.2 410 162 1.1 11l 1,379' 171 Honduras 6 112 55 4.6 116 4.0 740 146 1.1 14' 2,338' 142 ±0 2000 World Development Indicators Population Surface Populartion GNP GNP per PPP GNP' area density capita Average Average annual annual Per thousand people growth growth capita millions sq. km per sq. km S billions Rank % $ Rank % $ b[Ilions $ Rank 1998 1998 1998 19Sb~ 1998 1997-98 1998b 1998 1997-98 1998 1998 1998 Hungary 10 93 110 45.7 52 4.2 4,510 71 4.6 99 9,832 63 India98 3,8 330 427.4 11 6.2 440 161 4.3 2,0180 2.060c 151 Indonesia 204 1,905 112 130.6 30 -16.7 640 149 -18.0 490 2,407 141 Iran, Islamic Rep. 62 1,633 38 102.2 34 1.5 1,650 110 -0.2 317 5.121 95 Iraq 22 438 51 .. . . .5 . Ireland 4 7 54 69.3 43 9.2 18,710 27 7.9 67 17,991 33 Israel 6 21 289 96.5 36 3.4 16,180 32 1.2 101 16,861 38 Italy 58 391 196 1,157.0 6 1.4 20,090 25 1.3 1,173 20,365 25 Jamaica 3 11 238 4.5 117 0.9 1,740 108 0.1 9 3,344 126 Japan 126 378 336 4,089.1 2 -2.7 32,350 7 -2.9 2,982 23,592 14 Jordan 5 89 51 5.3 106 3.3 1,150 128 0.5 12 2615 139 Kazakhsta 16 2,717 6 20.9 63 -2.2 1,340 120 -1.2 67 4,317 105 Kenya 29 580 51 10.2 82 2.7 350 173 0.3 28 964 186 Korea, Dem. Rep. 23 121 192 d. Korea, Rep 46 99 470 398.8 12 -6.6 8,600 51 -7.5 616 13,286 51 Kuwait 2 18 105 . ... ... ....... ... .. ..... ... ... .. . . ............1 ..... ...... ... . -.. 1%4 . . . ... .................. ..... .... ... ... 7 1.......... ................ Lao PDR 5 237 22 1.6 159 4.0 320 179 1.4 8 1,683 160 Latvia 2 65 39 5.9 102 3.4 2,420 95 4.3 14 5,777 87 Lebanon 4 10 412 15.0 76 3.0 3,560 80 1.4 17 4,144 111 Lesotho 2 30 68 1.2 164 -3.1 570 153 -5.3 5 2,194 148 Libya 5 1,760 3 .. Lithuania 4 65 57 9.4 86 4.8 2540 92 4.8 23 6.283 82 Macedonia, FYR 2 26 79 2.6 137 3.1 1,290 121 2.4 8 4,224 107 Madagascar 15 587 25 3.7 126 4.9 260 187 1.7 11 741 192 Malawi 11 118 112 2.2 142 1.5 210 195 -1.0 6 551 204 Malaysia 22 330 68 81.3 39 -5.8 3,670 78 -.8.0 171 7,699 72 Mali 11 1,240 9 2.6 136 4.3 250 189 1.3 7 673 199 Mauritania 3 1,026 2 1.0 167 4.3 410 162 1.5 4c 1,5000c 163 Mauritius 1 2 571 4.3 118 5.1 3,730 76 4.0 10 8.236 70 MexCico 96 1,958 50 368.1 15 4.7 3,840 75 3.0 714 7,450 75 Moldova 4 34 130 1.7 158 -9.5 380 165 -9.2 9 1,995 153 Mongolia 3 1.567 2 1.0 168 3.6 380 165 1.9 4 1.463 165 Morocco 28 447 62 34.4 56 7.0 1,240 124 5.3 89 3,188 128 Mozambique 17 802 22 3.5 127 11.8 210 195 9.7 130 740 c 193 Myanmar 44 677 68 . ..d Namibia 2 824 2 3.2 129 1.2 1,940 102 -1.2 9C1 5,2800 93 Nepal 23 147 160 4.9 109 2.7 210 195 0.3 27 1,181 177 Netherlands16 4 463 389.1 13 3.3 24.780 1 7 2.7 350 22,325 18 New Zealand 4 271 14 55.4 46 -0.6 14.600 36 -1.5 61 16,084 41 Nicaragua 5 130 39 1.8 153 6.1 370 170 3.3 90 1,8960c 156 Niger 10 1,267 8 2.0 146 8.4 200 198 4.8 70 7290c 196 Nigeria 121 924 133 36.4 55 1.1 300 181 -1.5 89 740 194 Norway 4 324 14 152.0 25 2.3 34,310 4 1.7 116 26,196 7 Oman 2 212 _ Pakistan 132 796 171 61.5 44 3.0 470 158 0.5 217 1,652 161 Panama 3 76 37 8 3 90 2.5 2,990 88 0.9 14 4,925 96 Papua New Guinea 5 463 10 4.1 120 2.3 890 138 0.0 lOC 2,2050c 147 Paraguay 5 407 13 9.2 87 -0.5 1,760 106 -3.0 230 4,3120 106 Peru 25 1,285 19 60.5 45 -1.6 2,440 94 .-.3 14 4,180 110 Philippines 75 300 252 78.9 41 0.1 1,050 132 -2.1 280 3.725 118 Poland 39 323 127 151.3 26 4.4 3,910 74 4.4 292 7.543 74 Portugal 10 92 109 106.4 33 3.9 1-0,670 48 3.7 145 14,569 46 Puerto Ric 4 9 435 . . . . . Romania 23 238 98 30.63 59 -8.3 1,360 119 -8.1 125 5,572 90 Russian Federation 147 17,075 9 331.8 16 -6.6 2,260 97 -6.4 907 6,180 83 2000 morld Development Indicators 11 Population Surface Population GNP GNP per PPP GNP' area denisity capita Average Average annual annual Per thousand people growth growth capita milliona sq. Km per sq. km $ billions Rank % $ Rank % $ billions $ Rank 1998 1999 1998 1998k 1998 1997-98 ±998k 1.998 1997-98 1.998 1998 ±9S98 Rwanda 8 26 329 1.9 150 9.9 230 192 7.1 Saudi Arabia 21 2,150. .14.4 . .7 2.3 6,910 60 -1.0 218C 10,4980 60 Senegal 9 197 47 4.7 112 6.7 520 155 3.8 12 1,297 173 SIerr-a Leone 5 72 68 0.7 175 -0.7 140 202 -2.9 2 445 206 Singapore 3 . 1 5,186 95.5 37 1.5 30,170 9 -0.4 80 25,295 8 Slovak Republic 5 49 112 19.9 66 4.2 3,700 77 4.1 52 9,624 66 Slovenia 2 20 99 19.4 67 3.9 9,780 50 4.1 29 14,400 48 South Africa 41 1,221 34 136.9 28 0.5 3,310 83 13 3430 8,2960 69 Spain 39 506 79 555.2 10 3.7 14,100 39 3.6 628 15,960 43 Sri Lanka 19 66 291 15.2 75 4.6 810 139 3.3 55 2,945 134 Sudan 28 2,506 12 8.2 91 5.0 290 183 2.7 35 0 1,2400 176 Sweden 9 450 22 226.5 20 2.8 25,580 14 2.8 176 19,848 27 Switzerland 7 41 180 284.1 18 1.8 39,980 3 1.5 191 26,876 6 Syrian Arab Republic 15 185 83 15.5 74 0.2 1,020 133 -2.3 41 2,702 138 Tajikistan 6 143 43 2.3 140 15.2 370 170 13.3 6 1,041 181 Tanzania 32 945 36 7.2 k 98 6.5 220 k 194 3.8 16 483 205 Thailand 61 513 120 131.9 29 -7.7 2,160 100 -8.6 338 5,524 92 Thgo ~~ ~~~ ~~~ ~~~~ ~~~~ ~~~~ ~~4 57 82 1.5 160 -1.0 330 177 -3.5 60 1,3520 172 Trinidad and Tobago 1 5 251 5.8 103 6.2 4.520 70 5.6 9 7,208 76 Tunisia 9 164 60 19.2 69 5.5 2,060 101 4.1 48 5,169 94 Turkey 63 775 82 200.5 22 3.9 3,160 85 2.3 419 6,594 79 Turkmenistan 5 488 10 d. . . . . . Uganda 21 241 105 6.6 99 5.7 310 180 2.8 22C 1,072 0 180 Ukraine 50 604 87 49.2 49 -2.4 980 135 -1.6 157 3.130 131 United Arab Emirates 3 84 33 48.7 50 -5.7 17,870 28 -10.6 51 18,871 31 United Kingdom 59 245 244 1,264.3 5 2.1 21,410 22 2.0 1,200 20.314 26 United States 270 9,364 30 7,903.0 1 2.5 29,240 10 1.5 7,904 29.240 4 Uruguay 3 177 19 20.0 65 3.9 6,070 64 3.2 28 8.541 67 Uzbekistan 24 447 58 22.9 61 5.2 950 137 3.6 49 2 044 152 Venezuela, RB 23 912 26 82.1 38 -0.4 3.530 81 -2.4 133 5 706 88 Vietnam 77 332 235 26.5 60 5.8 350 173 4.3 129 1 689 159 West Bank and Gaza 3 .. . 4.3 119 7.0 1,560 112 3.0 Yemen, Rep. 17 528 31 4.6 114 7.3 280 185 4.3 11 658 200 Yugoslavia, MR (Serb./Mont.). 11 102 104 .. . Zambia 10 753 13 3.2 128 -1.9 330 177 -4.1 7 678 198 Zimbabwe 12 391 30 7.2 97 0.5 620 150 -1.4 29 2.489 140 Low income 3,536 42,815 85 1,842 3.5 520 1.8 7,678 2:170 ExclI China & India 1,295 29810 45 491 -4.5 370 -. 1.759 1 360 Middle income 1,474 58,669 25 4,401 -0.1 2,990 -1.3 8,834 5.990 Lower middle income 886 36,609 25 1,541 -1.2 1,740 -2.3 4,164 4.700 Upper middle income 588 22,061 27 2,860 0.5 4,870 -0.8 4,714 8.020 Low& middle income 5,011 101,485 50 6.243 1.0 1,250 -0.5 16,541 3 300 East Asia & Pacific 1,817 16,384.114 1,802 -1.5 990 -2.6 5,959 3:280 Europe & Central Asia 475 24,208 20 1,044 -0.4 2,200 -0.5 2,617 5:510 Latin America & Carib. ...502 20,462 25 1,933 2.1 3,860 0.5 3.182 6 340 Middle East & N. Africa 286 11,023 26 581 3.7 2,030 1.6 1,324 4,630 South Asia 1,305 5,140 273 560 5.7 430 3.7 2,531 1.940 Sub-Saharan Africa 627 24,267 27 323 2.2 510 -0.4 902 1.440 High Income 886 32,082 29 22,592 1.4 25,480 0.9 20,745 23 420 Europe EU 291 2,374 126 6,542 3.0 22,350 2.8 5,985 20 440 a. PPP is purchasing power parity; see Definitions. b. Calculated usnig the Wodd Sank Atlas method. c. The estimate is based on regression; others are extrapolated from the latest International Comparison Pro- grarmms benchmark estimates. d. Estimated to be low sncome $760 or lens). a. Includes Taiwan, China. f. GNP data refer to GDP. g. Etsimated to be lower middle income l$761-3,030). h. GNP and GNP per capita eshimates include the French overseas departments of French Guiana, Guadeloupe, Martinique. and Rbunion. i. Estimated to be high income ($9.361 or more).). Estimated to be upper middle income l$3,031-9,360). k. Data refer to mainland Tanzania only. 1.2 2000 Wtorld Development indicators , i ___ ~ L 1.10o Population, land area, and output are basic measures of real values over time. The PPP conversion factors * Population is based on the de facto definition of pop- of the size of an economy. They also provide a broad used here are derived from price surveys covering 118 ulation, which counts all residents regardless of legal indication of actual and potential resources. Therefore, countries conducted by the International Comparison status or citizenship-except for refugees not perma- population, land area, and output-as measured by Programme (ICP). For 62 countries data come from the nently settled in the country of asylum, who are gen- gross national product (GNP) or gross domestic prod- most recent round of surveys, completed in 1996; the erally considered part of the population of their country uct (GDP)-are used throughout the World Development rest are from the 1993 round and have )een extrap- of origin. The values shown are midyear estimates for Indicators to normalize other indicators. olated to the 1996 benchmark. Estimates for coun- 1998. See also table 2.1. * Surface area is a coun- Population estimates are generally based on extrap- tries not included in the surveys are c erived from try's total area, including areas under inland bodies of olations from the most recent national census. See statistical models using available data See About water and some coastal waterways. * Population About the data for tables 2.1 and 2.2 for further dis- the data for tables 4.11 and 4.12 for more informa- density is midyear population divided by land area in cussion of the measurement of population and pop- tion on the ICP and the calculation of PFPs. square kilometers. * Gross national product (GNP) ulation growth. All economies shown in the World Deve'opment Indi- is the sum of value added by all resident producers plus The surface area of a country or economy includes cators are ranked by size, including those that appear any taxes (less subsidies) not included in the valua- inland bodies of water and some coastal waterways. in table 1.6. Ranks are shown only in table 1.1. (The tion of output plus net receipts of primary income Surface area thus differs from land area, which World Bank Atlas includes a table compa-ing the GNP (compensation of employees and property income) excludes bodies ot water, and from gross area, which percapitarankingsbasedontheAtlasmethDdwiththose from abroad. Data are in current U.S. dollars con- may include offshore territorial waters. Land area is based on the PPP method for all economies with avail- verted using the World Bank Atlas method (see Sta- particularly important for understandingthe agncultural able data.) No rank is shown for econom es for which tisticat methods). Growth is calculated from constant capacity of an economy and the effects of human activ- numercal estimates of GNP per capita are rot published. price GNP in national currency units. * GNP per capita ity on the environment. (See tables 3.1-3.3 for mea- Economies with missing data are included in the rank- is gross national product divided by midyear population. sures of land area and data on rural population density, ing process at their approximate level, sc that the rel- GNP per capita in U.S. dollars is converted using the land use, and agricultural productivity.) Recent inno- ative order of other economies remains consistent. In World Bank Atlas method. Growth is calculated from vations in satellite mappingtechniques and computer 1998 LuxembourgwasjudgedtohavethehighestGNP constant price GNP per capita in national currency databases have resulted in more precise measure- per capita in the world. units. * PPP GNP is gross national product con- ments of land and water areas. verted to international dollars using purchasing power GNP, the broadest measure of national income, parity rates. An international dollar has the same pur- measures the total domestic and foreign value added chasing power over GNP as a U.S. dollar has in the claimed by residents. GNP comprises GDP plus net United States. receipts of primary income from nonresident sources. The World Bank uses GNP per capita in U.S. dollars to Daa sources classify countries for analytical purposes and to deter- mine borrowing eligibility. See the Users guide for def- Population estimates are prepared by World Bank initions of the income groups used in the Wor/d staff from a variety of sources (see Data sources for Development Indicators. See About the data for tables table 2.1). The data on surface and land area are from 4.1 and 4.2 for further discussion of the usefulness of the Food and Agriculture Organization (see Data sources national income as a measure of productivity or welfare. for table 3.1). GNP and GNP per capita are estimated When calculating GNP in U.S. dollars from GNP by World Bank staff based on national accounts data reported in national currencies, the World Bank follows collected by Bank staff during economic missions or its Atlas conversion method. This involves using a three- reported by national statistical offices to other inter- year average of exchange rates to smooth the effects national organizations such as the Organisation for Eco- of transitory exchange rate fluctuations. See Statis- nomic Co-operation and Development. Purchasing tical methods for further discussion of the Atlas power parity conversion factors are estimates by World method. Note that growth rates are calculated from Bank staff based on data collected by the International data in constant prices and national currency units, Comparison Programme. not from the Atlas estimates. Because exchange rates do not always reflect inter- national differences in relative prices, this table also shows GNP and GNP per capita estimates converted into international dollars using purchasing power par- ities (PPPs). PPPs provide a standard measure allow- ing comparison of real price levels between countries, just as conventional price indexes allow comparison 2000 World Development Indicators 13 S ~1.2 Development progress Private Net primary enrollmernt Infant Under-five Maternal Access consumption ratio' mortality fate mortality rate mortaity to safe per capita ratio water average annual % growth Male Female per 1980-98 % of re avant % of relevant per 1.000 100.000 % of diatribution age group age group live births per 1,000 live blrths population corrected 1980 1997 1980 1.997 1970 1998 1970 1998 1.99O.95b j990-96b Albania .. . . . . 66 25 82 31 . 76 Algeria -2.3 -1.5 92 99 71 93 139 35 192 40 An ..l -6.5.87 35 80 34 178 124 301 204 . 32 Argentina 97 100 98 100 52 19 71 22 380 65 Armenia.. . .. . .. . 15 . 18 350 Australia 1.7 1.1 100 100 100 100 18 5 20 6 99~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...... . ..... .... Austraia 2.0 1.5 100 100 100 100 26 5 33 69 Azerbaijan .. .. .. .. .. .. 17 .. 21 37C~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...... ... -.....- ..... . Bangladesh 2.1 1.5 74 80.. 45 70 140 73 239 96 440' 84 Belarus -2.7 -2.1 .. 87 .. 84 .. 11 14 220 Belgium 1.6 1.2 100 100 100 100. 21 6 29 6 Benin -0.4 .. 72 ~~~ ~~~ ~~~ ~~~~ ~~~ ~ ~~85 34 50 146 87 .. 140 5000 50 Bolivia 0.1 0.1 85 100 74 95 153 60 243 78 3900 55 Bosnia and Herzegovina .. . . . . 59 13b Botswana 3.0 69_69 78 83... 83 95 62 139 105 33Q0 70 Brazil 0.7 0.3 82 100 79 94 95 33 135 40 1600 72 Bulgaria -0.8 -0.5 98 97 98 99 27 14 32 15 150 Burkina Faso 0.4 . 18 39 11 25 141 104 278 210 Burundi -9 . 23 38 16 33 138 118 28 165 Cambodia 100 100 100 100 161 102 244 143 . 13 Cameroon -1.3 .. 77 ~~~ ~~~ ~~~ ~~~~ ~~~ ~~~~~64 66 59 126 77 215 150 43Q0d 4 Canada 1.4 0.9 100 100 100 100 19 5.. 23 72.. 799 Central African Republic -1.7 . 73 55 41 38 139 98 248 162 1,1000 19 Chad . 38 61 15 35 171 99 252 172 5300 24 Chile 4.0 1.7 93 92 93 89 77 10 96 12 230 85 China 7.2 4.2 89 100 80 100 69 31 120 36 650 90 Hong Kong, China . 4.9 . 97 90 98 93 19 3 .. Colombia 1.2 0.5 72_ 89. 75 89 70 23 113 28 800 78 Congo, Dem. Rep. -4.5 . 82 69 59 48 131 90 245 141 . 27 Congo, Rep. -0.1 . 100 81 94 76 101 90 160 143 . 47 Costa Rica 0.8 0.4 89 89 90 89 62 13 77 15 290 92 C6te dIlvoire -2.2 -1.4 65 66 45 50 135 88 240 143 65000 72 Croatia 100 100 100 100 10 120 63 Cuba 97 100 97 100 39 7 43 9 270 93 Czech Republic 95 100 95 100 21 5 24 6 90 Denmark 1.8 1.4 9 6 100 96 100 14 5 19 6 100 Domninican Republic 0.0 0.0 98 89 100 94 98 40 128... 71 Ecuador -02.2 -0.1 92 100 91 100 100 32 140 37 1600 70 Egypt, Arab.Rep.2.0 1.4 83 100 61 91 158 49 235 59 1700 64 El Salvador 3.0 1.5 69 89 70 89 107 31 160 36 . 55 Eritrea . .. . 31 . 28 . 61 . 90 1,0D0Od 7 Estonia -. -0.6 100 100 100 100 20 9 27 12 5oe Ethiopia -0.4 . 35 44 22 27 158 107 239 173 . 27 Finland 1.4 1.1 100 100 100 100 13 4 16 5 60 98 France 1.6 1.1 100 100 100 100 18 5 24 5 100 100 Gabon -2.6 .. . . . . 138 86 232 13 . 67 Gambia, The -2.3 70 74 36 58 185 76 319... 76 Georgia 93 89 92 89 Is15. 20 700 Germany 100 100 100 100 23 5 26 6 80 Ghana 0.3 0.2 ... .. ...... 112. 65 186.. 96 . 56 Greece 1.9 . 100 100 100 100 30 6 54 8l1e Guatemala 02_.2 ... 0.1 63 77 55 70 107 42 168 52 19001 67 Guinea 1.4 0.8 39 58 20 33 181 118 345 184 . 62 Guinea-Bissau -0.3 -0.1 63 66 31 39 185 128 316 205 9100 53 Haiti . .. . .. . .. 141 71 221 116 . 28 Honduras -0.1 -0.1 79 86 79 89 110 36 170 46 2200 65 14 2000 World Development Indicators 1. 2 0 Private Net primary enrollment Infant Under-five Maternal Access consumption ratio' mortality rate mortality rate mortality to safe per capita ratio water average annual % growth Male Female per 1980-98 % of relevant % of relevant per 1,000 100,000 % Of distribution age group age group live births per 1,000 live births population corrected 1950 1.997 1980 1997 1970 1.998 ±.970 1,998 J9g0_981, 1990.96b Hungary -0.1 -0. 1 94 98 9 5 9 7 36 10 3 9 12 j5 India 2.7 1.9 75 83 53 71 137 70 206 83 410d 81 Indonesia 4.6 3.0 93 100 84 99 118 43 172 52 4500 62 Iran, Islamic Rep. 0.5 83 91 61 89 131 26 208 33 370 83 I raq ..... ....... ' .. ....... .100... 80 ...... 94 70. 102 103 127 125 .. 44 Ireland 2.9 1.9 100 100 100) 100 20 6 27 760 Israel 3.3 2.1 .. . .. . 25 6 27 850 99 Italy 2.1 1.5 100 100 100 100 30 5 33 67 0 Jamaica 1.3 0.8 97 96 99 96 48 21 62 24 . 70 Japan 2.8 . 100 100 100 100 13 4 21 580 96 Jordan -1.5 -0.9 73 67 73 68 60 27 . 31 410 89 Kazakhstan .. . . . . . . 22 2970 Kenya 0.4 0-2 92 63 89 67 102 76 156 124 590u 53 Korea, Dem. Rep . . 51 54 70 68 10 Korea, Rep. 6.5 100 100 100 100 46 9 54 11 20. 83 Kuwait . ...... ....... .......... .. -89 66 80 64 48 12 59 13 50 100 Kyrgyz Republic. 100 100 100 99 . 26 . 41 650 81 Lao PDR 75 77 69 69 146 96 218 . 6501 39 Latvia 100 10 10 1021 15 27 19 450 Lebanon. . . . . . 50 27 50 30 100u 100 Lesotho -2.7 -1.2 55 63 80 74 134 93 190 144 . 52 Libya10 10 10 100 122 23 160 27 751 90 Lithuania. . . . 24 9 30 12 180 M acedonia, FYR .. .. .. 96. . ... .. . ... 95. .. . .. 16.. ....... .. 18 110. ............ Madagascar -2.2 -0.1 . 60 . 62 153 92 285 146 490d 29 Malawi 0.8 . 48 9 38 100 193 134 330 229 620d 45 Malaysia 2-9 1.5 93 100 92 100 45 8 63 12 390 89 Mali -1.0 . 26 45 15 31 204 117 391 218 580d 37 Mauritania -0 -0.3 . 61 . 53 148 90 250 140 6. 4 Mauritius 5.1 80 96 79 97 56 19 86 22 50C 98 Mexico 0.2 0.1 97 100 99 100 73 3 0 110 35 48d 83 Moldova. . . . . . 18 . 22 420 56 M.ng.. i 100.......... . .. 83....100 88 102. 50 150 60 1500 Morocco 1.9 1.2 76 86 48 67 128 49 187 61 230u 52 Mozambique -0.9 . 37 45 32 34 171 134 281 213 . 32 Myanmar.. . 72 10 7 99 128 78 179 118 2300 38 Namibia .-3.0 .. 8 89 92 94. 118 67 155 112 23Od 57 Nepal 2.0 1.3 90. 93 39 63 166 77 234 107 5400 44 Netherlands 1.6 1.1 100 100 100 100 13 5 157 70 100 New Zealand 0.8 . 100 100 100 100 17 5 20 7150 Nicaragua -2.2 -1.1 70 77 72 80 104 36 168 42 1500 81 Niger -2.2 -1.4 28 30 15 19 170 118 320 250 5900 53 Nijgeria -4.2 -2.3 . . . . 139 76 201 119 . 39 Norway ~~~~1.6 1. 98 100 99 10 1 4 15 6 6 0 Oman 54 69 31 67 119 18 200 25 190 68 Pakistan 2.0 1.4 . . . 12 91 183 120 . 60 Panama 2.4 1.0 89 90 90 90 47 21 71 25 850 84 Papua New Guinea -0.6 -0.3 112 59 130 76 . 28 Paraguay 1.7 0.7 91 96 90 97 55 24 76 27 190d 39 Peru -0.4 -0.2 88 94 87 93 108 40 178 47 2700 80 Philippines 0.8 0.5 97 100 94 100 67 32 90 40 1700 83 Poland.. . 99 100 99 99 37 10 36 11 80 Portugal 3.1 . 97 100 100 100 56 8 62 880 82 Puerto Rico.. . .. . .. . 29 10..... 9 Romania 0.4 .0.3 93 100 90 100 49 21 .. ........25410 62 Russian Federation 92 100 92 100 . 17 .. 205Q 2000 World Development Indicators 18 1.2 Private Net primary enrollment Infant Under-five Maternal Access consumption ratio' mortality rate mortality rate mortality to safe per capita ratio water average annual % growth Male Female oer 1980-98 % of relevant % of relevant per 1,000 100,000 % Of distribution age groap age group line birthn per 1,000 line births population coroected 1980 1997 1980 1997 1970 1998 1970 1998 1990-981 1990-961 Rwanda -1.0 -0.7 62 76 57 75 142 123 210 205 Saudi. Arabia.. . ..... . . .. ... . ..61 62 .37 58 119 20 185 26 .. 93 Senegal -. -03 45 65 30 54 135 69 279 121 560d 5t5 Sierra Leone -3.4 -1.3 55 .. 39 .. 197 169 363 283 .. 34 Singapore 4.8 . 100 92 99 91 20 4 27 6 6. 100 Slovak Republic 25 9 2 9 10 9l Slovenia .. . . 95 .. 94 24 5 29 7 ill 98 South Africa -0.1 0.00...... 67 100 68 100 79, 51 108 83 .. 70 Spain 2.2 1.5 100 100 100 100 28 5 34 76 Sri Lanka 2.9 2.0 99 100 94 100 53 16 100 18 600 46 S- da .............. .. .. .. ........11 69. 177 105 .. 50 Sweden 0.7 0.5 100 100 100 100 11 4 15 5 5 Switzerland 0.5 0.3 100 100. 100 100 15 4 18 5 50 100 Syrian Arab Republic 0.9 . 9 99 80 91 96 28 129 32 .. 85 Tajikistan .. . . . . . . 23 . 33. 650 69 Tanzania 0.0 0.0 68 48 65 49 129 85 218 136 530d 49 Thailand 5.0 2.7 93 87 91 89 7 3 29 102 33 440 89 Togo -01.1 . 9 94 64 70 134. 78 216 144 480d 63 Trinidad and Tobago -1.5 .. 91 100 92 100 52 16 57 18. .. 82 Tunisia 1.1 0.7 93 100 72 100 121 28 201 32 700 99 Turkeye 2.6 85 100 78 98 144 38 201 42 Turkmenistan .. . . . . . . 33 .. 44 lion 6o Uganda 1.9 1.1 43 .. 35 .. 109 101 185 170 5100 34 Ukraine . .. . .. 2 2 14.720 5 5 United Arab Emirates 74 83 76 81 87 8 90 10 30 98 United Kingdom 2.6 1.7 100 100 100 100 19 6 23 7 70 100 United States 1.9 1.1 89 100 90 100 20 7 26 ..8c Uruguay 2.6 .. 87 94 87 95 46 16 57 19 210 89 Uzbekistan 5.5 .. .. . . .... .. 22... 29 210 57 Venezuela, RB -0.8 -0.4 81 81 85 84 53 21 61 25 650 79 Vietnam . .. 98 100 93 100 10 4 17 4 60 36 West Bank and Gaza .. . . . . . . 24 .. 26 Yemen, Rep. . .. 16 8 3096 350 9 Yugoslavia, FR (Serb./Mont.)6 . 7 54 13 .. 16 ioe Zambia -3.6 -2.0 81 73 73 72 106 114 181 192 6500 43 Zimbabwe 0.4 ~~~~~~~~~~~~~~.....7 ...94 68 92 96. 73 ...138 .125 A00I ..77 Low income 3.5 81 89 67 82 113 68 178 92 Excl. China & India 1.1 75 78 61 71 .137 83 213 125 Middle income 2.0 89 96 85 94 87 31 130 39 Lower middle income ..88 95 82 93 93 35 .. 44 Upper middle income 2.4 90 97 88 95 80 26 113 31...... Low & middle income 1.9 83 91 72 85 107 59 167 79 East Asia & Pacific 5.6 90 99 82 99 78 35 126 43 84 Europe & Central Asia 93 100 91 99 .. 22 .. 25 Latin America & Carlo. 0.6 86 95 85 93 84 31 123 36 Middle East & N. Africa ..84 91 64 84 134 45 200 55 South Asia 2.6 75 83 52 70 139 75 209 89 7 Sub-Saharan Africa -1.3 59 .. 49 .. 137 92 222 151 High income 2.2 96 100 97 100 21 6 26 6 Europe EMU ..100 100 100 100 25 5 29 6 a. UNESCO enrollment estimates and projections as assessed in 1999. b. Oats are for the most recent year available. c. Official estimate. d. Estimate based on survey data. a. Estimate by the World Health Organization and Eurostat. f. Estimate by UNICEF. 16 2000 Wtorld Development Indicators -~~~~S ~ 1.2 The indicators in this table are intended to measure * Growth of private consumption per capita is the progress toward the international development goals. average annual rate of change in private consumption The net enrollment ratio, infant and under-five mortality divided by the midyear population. For the definition of rates, maternal mortality ratio, and access to safe private consumption see Definitions for table 4.10. water are included in the set of 28 social and envi- * Distribution-corrected growth of private consumption ronmental indicators selected for monitoring devel- per capita is 1 minus the Gini index multiplied by the opment progress by the Organisation for Economic annual rate of growth in private consumption percapita. Co-operation and Development, the World Bank, and * Net primary enrollment ratio is the ratio of the num- the United Nations in consultation with countries that ber of children of official school age (as defined by the provide and those that receive development assistance. education system) enrolled in school to the number of The growth of private consumption per capita is childrenofofficialschoolageinthepopulation. * Infant included here as an indicator of the effect of economic mortality rate is the number of deaths of infants under development on income poverty. Positive growth rates one year of age during the indicated year per 1,000 live are generally associated with a reduction in poverty, births in the same year. * Under-five mortality rate is but where the distribution of income or consumption the probability of a child born in the indicated year dying is highly unequal, the poor may not share equally in before reaching the age of five, if subject to current age- the improvement. The relationship between the rate specific mortality rates. The probability is expressed as of poverty reduction and the distribution of income or a rate per 1,000. * Matenal mortality ratio is the num- consumption, as measured by an index such as the ber of women who die during pregnancy and childbirth, Gini index, is complicated. But Ravallion and Chen per1OO,000livebirths. * Accesstosafewateristhe (1997) have found that the rate of poverty reduction percentage of the population with reasonable access to is directly proportional to the distribution-corrected rate an adequate amount of safe water (including treated sur- of growth of private consumption per capita. The face water and untreated but uncontaminated water, such distribution-corrected rate of growth is calculated as as from springs, sanitary wells, and protected bore- (1- G)r. where G is the Gini index (O = perfect equal- holes). In urban areas the source may be a public foun- ity, 1 = perfect inequality) and r is the rate of growth tain or standpipe located not more than 200 meters away. in mean private consumption. The distribution-cor- In rural areas the definition implies that members ofthe rected growth rate may be thought of as the rate of household do not have to spend a disproportionate growth in consumption that would produce the same part of the day fetching water. An adequate amount of rate of poverty reduction as the observed growth in safe water is that needed to satisfy metabolic, hygienic, consumption, if consumption were evenly distributed. and domestic requirements-usually about 20 liters a It is not necessarily the rate of growth experienced by person a day. The definition of safe water has changed the poor or any other group in the economy. over time. In empirical tests covering 23 developing coun- tries, Ravallion and Chen estimated that factor of Data sources proportionality to be 4.4, implying a growth elasticity of poverty reduction of between 3.3 for a low Gini index The indicators here and throughout the rest of the book of 0.25 and 1.8 for a high Gini index of 0.60. This have been compiled by World Bank staff from pri- implies that a country such as China-with average mary and secondary sources. More information about annual growth in private consumption per capita of 7.2 the indicators and their sources can be found in the percent and a Gini index of 0.4-could reduce its About the data, Definitions, and Data sources entries poverty rate by 1.8 percentage points a year on aver- that accompany each table in subsequent sections. age. China's actual experience may have been different because the distribution of income or consumption may change over time. Estimates of the share of people living in poverty appear in table 2.7. Discussions of the other indica- tors can be found in About the data for tables 2.10 (net enrollment ratio), 2.16 (maternal mortality ratio), 2.18 (infant and under-five mortality rates), and 2.15 (access to safe water). 2000 World Development Indicators 17 0~~1.3 Gender differences Female Female advantage population Child mortality rate Labor farce Adult Net pr.mary Life female- partIcIpatIon illiterecy rate enrollment ratio expectancy at birth male % of totel ratio of female to male female-male difference female-male difference female-male differerce difference 1998 1.970 1.998 1.970 1.998 1.980 1.997 1970 1998 1.988-98- Albania 48.7 0.7 0.7 29 14 ..3 3 6 0 Algeria 49.4 0.3 0.4 27 22 -20 -7 2 3 Angoia 50.6 0.9 0.9 .. . 8 -1 3 3 Argentina 50.9 0.3 0.5 1. 0 1 0 7 7 Armenia 51.4 0.9 0.9 8 267 A ustralia ......... .. ..... ...... ...... 5 .0.5...... .. ...... ...... .... ................ 0 .8.. 0 0 7............... 6.... . - Austraia 50.9 0.6 0.7 0 0 7 6 Azerbaijan 51 .0 0.8 0.8 ......8 7 Bangladesh 49.4 0.7 0.7 24 23 -29 -11 -2 0 10 Belarus 53.0 1.0 1.0 0 ...8 12 Belgium 51.0 0.4 0.7 ...0 0 7 6 Benin 50.7 0.9 0.9 10 31 -38 -34 2 4 1 Bolivia 50 .3 0.5 0.6 25 14 -10 - Bosnia and Herzegovina 50.4 0.6 0.6 46 Botswana 51.0 1.2 0.8 -2 -5 14 5 4 2 -2 Brazil 50.6 0.3 0.5 7 0 -4 -64 8 1 Bulgaria 5 1.2 0.8 0.9 7 1 0 3 5 7 Burkina Faso 50.6 1.0 091.0 19 -7. -14 4 2 3 Burundi 51.0 1.0 1.0 2 7 1 7 -8 -5 3 3 13 Camoodia 51.6 1.0 1.1 25 3 8 0 0 3 3 Cameroon 50.3 0.8 0.6 ~ 26 13 -11 -5 3 3 6 Canada 50.4 0.5 0.8 ...0076 Central African Republic 51.4 20 26 -32 -17 5 4 1 Chad 50.5 0.7 0.8 11 18 -23 -25 3 3 -7 Chile 50 .5 0.3 0.5 2 0 1 -2 6 6 -1 China 48.4 0.7 0.8 31 16 -9 0 1 3 1 Hong Kong, China 49.9 0.5 0.6 27 7 1 4 6 6 Colombia 50.6 0.3 06.6 3.. 0 2 0 4 6 0 Congo, Dem. Rep. 50.5 . 0.8 . 0.8 ..424 . -23.... 21 3. 3. Congo, Rep. 51.1 0.7 0.8 28 14 -6 -5 5 Costa Rica 49 .3 0.2 0.4 1 0 1 1 4 C6te dlvoire 49.1 0.5 0.5 ............18 17 . . . -20 -131 -3 Croatia 51.6 0.6 0.8 12 2 0 0 Cuba 49.9 0.3 0.6 0 0 0 0 3 Czech Republic 51.3 0.8 0.9 0 0 Denmark 50.4 0.6 0. 9_. .. ... .. ... 0 0 5 Domninican Republic 49.2 0.3 0.4 5 0 2 5 4 L. 0 Ecuador 49.8 0.2 0.4 10 4 0 0 3 5 - Egypt, Arab Rep. 49.1 0.3 0.4 29 24 -22 -9 3 .n 6 El Salvador 50.9 0.3 0.6 11 6 0 0 4 6 3 Eritrea 50.4 0.9 0.9 29. 27 ..-3 3 3 -11 Estonia 53.2 1.0 1.0 0 0 9 II1 Ethiopia 49.8 0.7 0.7 13 12 -13 -17 3 2 Finland 51.2 0.6 0.9 -..0 07 France 51.3 0.6 0.8 0 0 8 8 Gabon 50.6 0.8 0.8 3 Gambia, The 50.6 0.8 0.8 6 14 -34 -15 3 L .4 Georgia 52.3 0.9 0.9 ... 1-1 ............. Germany 51.1 0.6 0.7 0 0 6 Ghana 50.3 1.0 1.0 26 19 -..3 .- Greece ..0.. 06 16 3 0 0 4 E Guatemala 49.6 0.2 0. 16 15 -8 -7 2 C6 2 Guinea 49.7 0.9 0.9 . .19 -25 1 1 -10 Guinea-Bissau 50.8 0.7 0.7 18 40 -.32 -27 2 3 Haiti 50.8 0.9 0.8 8 5 . .3 5 - Honduras 49.6 0.3 0.4 6 0 124 18 2000 World Development Indicators 1.3 Female Female advantage population Child mortality rate Labor force Adult Net primary Life fe male- participation illiteracy rate enrollment ratio expectancy at birth male % of total ratio of female to male female-male difference female-male difference female-male difference difference ±998 1970 1998 1.970 1998 1980 1.997 1970 1L998 ±.988-98, Hungary *52.1 0.7 0.8 1 01 -2 6 9 India 48.4 0.5 0.5 28 24 -22 -12 -2 2 13 Indonesia 60.1 0.4 0.7 25 11 -10 -1 24 1 Iran, Islamic Rep. 49.8 0.2 0.4 23 14 -22 -2-1 2 Iraq 49.1 0.2 0.2 25 21 -6 -102 2 Ireland 50.1 0.4 0.5 0055 Israel 50.3 0.4 0.7 10 4 3. 4. Italy 51.4 0.4 0.6 3 10 06 7 Jamaica 50.4 0.8 0.9 -7 -2 03 4 Japan 51.0 0.6 0.7 .0 0 57 Jordan 48.2 0.2 0.3 38 12 -1 I . Kazakhstan 51.5 0.9 0.9 .. .- 11 -5 Kenya 49.9 0.8 0.9 30 14 -4 3 42 2 Korea, Dem. Rep. 49.8 0.8 0.8...... 4 4 Koe,Rep. 49.6 0.5 0.7 14 30 04 7 Kuwait 47.5 0.1 0.5 20 5-9 -2 46 Kyrgyz Republic 51.0 0.9 0.9 .0 0..8... 1 Lao PDR 50.5 .... 24 32 -7 -73 3 Latvia 54.0 1.0 1.00 0 0 91I Lebanon 50.9 0.2 0.4 25 12...4 4 Lesotho 50.8 0.7 0.6 -26 -22 25 11 4 2 Libya 48.1 0.2 0.3 43 24 0 0 34 -1 Lithuania 52.8 1.0 0.9 10 8 10 Macedonia. FYR 50.0 0.4 0.7_ .... .. ..-2. .. 4....... :...... - Madagascar 50.2 0.8 0.8 21 14. 2 33 -7 Malawi 50.6 1.0 1.0 39 29' -10 2 10 -1 Malaysia 49.3 0.4 0.0 24 9 1 0 35 0 Mali 50.7 0.9 09 7 15 -1 -14 34 2 Mauritania 50.4 0.9 0.8 19 21 - 3 3 Mauritius 50.1 0.2 0.5 19 7-1 04 8 Mexico 50.5 0.2 0.5 10 43 0 562 Moldova 52.2 1.1 0.9 10 2. . ..7...... Mongolia 49 80.9 210 5.3 3 Morocco 500. 0 .5 0.5 24 26 -28 -19 3 4 Mozambique 51.5 1.0 0.9 21 31 -6 -11 33 -2 Myanmar 50.2 0.8 0.8 27 9 -2 -1 3 3 Namibia 50.2 0.7 0.7 13 2 12 5 324 Neap 49.4 0.6 0.7 25 35 -51 -31 -1 0 Netherlands 50.5 0.3 070 0 66 New Zealand 50.8 0.4 0.80 0 6 ... 5.. Nicaragua 50.3 0.3 0.5 2 -3 2 3 35 -1 Niger 50.6 0.8 08 9 15 -13 -12 34 18 Nigeria 50.7 0.6 0.6 20 18... 3 3 84 Norway 50.2 0.4 0.9I. 6 6 Oman 46.7 0.1 0.2 27 21 -23 -2 2 3 Pakistan 48.2 0.3 0.4 23 29... 0 2 15 Panama 49.5 0.3 0.5 2 11 1 3 5 Papua New Guinea 48.5 0.7 0.7 22 16 .. 02 - Paraguay 49.6 0.4 0.4 11 3 -2 1 45 2 Peru 50.3 0.3 0.4 22 10 -1 -.13 51 Philippines 49.6 0.5 0.6 4 1-3 0 34 -2 Poland 51.3 0.8 09 1 0 0 0 7 8 Portugal 52.:. 0.3 0.8 12 5.3 0 7 ..7 Puerto Ric 51.8 0.4 0.6 20 6 9 Romanfa 50.9 0.8 0. 7 2 ... -3. 0 48 -.2 Russian Federation 53.3 1.0 1.0 2 00 0..12 -1 2000 World Development Indicators 19 1.3- Female Female advantage population Child mortality rate Labor force Adult Net primary Life female- participation illiteracy rate enrollment ratio expectancy at birth male th of total ratio of female to male female-male difference female-male difference female-male difference difference ±998 1970 1998 1970 1998 1980 1997 1970 1998 1988-98, Rwanda 50.6 1.0 1.0 24 15 -5 . 3 2 -14 Saudi Arabia 44.7 0.1 0.2 35 18 -24 -4 3 4 Senegal 50.1 0.7 0.7 17 20 -14 -12 5 4 -2 Sierra Leone 50.9 0.6 0.6 ...3 3 Singapore 49.7 0.3 0.6 26 8 -1 -2.. 5 4 Slovakt Republic . 51.2 . 0.7 . 0-9.9 . ....... ..... ..... ..... ...... .... ... .. .... ... ........ Slovenia 51.4 0.6 0.9 ......... . 0 0 ..0 7 8 South Africa 51.9 0.5 0.6 4 2 2 0 6 5 ... Spain 51.1 0.3 0.6 7 2 0 0 5 7. Sri Lanka 49.1 0.3 0.6 1 7 6 -5 0 2 4--1 Sudan 49.6 0.4 0.4 31 25 3 3 1 Sweden 50.5 0. 0.9 0 0 5 Switzerland 50.4 0. 0.7 0 0 6. Syrian Arab Republic 49.4 0.3 0.4 40 29 -19 -8 3 .5 . .... ... lajikiatan .50.2 0.8 0.8 9 1 5 6 Tanzania 50.5 1.0 1.0 35 19 1 32 -7 Thailand 50.0 0.9 0.9 15 4 -1 2 45 0 Togo..-- . 50.4 0.6 0.7 28 34 -31. -24 3 2 15 Trinidad and Tobago..... 5004 0.5 12 4 1 0 5 5 -1 Tuniaia 49.5 0.3 0.5 26 22 -21 0 1 4 0 Turkey 49.5 0.6 0.6 33 18 -7 -24 5 2 Turkmeniatan 50.5 0.8 0.8 7...7 Uganda ~~~50.2 0.9 0.9 30 22 2- Ukraine 53.5 1.0 0.9 1 0 8 11 United Arab Emirates 33.6 0.0 0.2 23 -4 3 -1 4 3 United Kingdom 50.9 0.6 0.8 0 0 6 5 United States 507 0.6 0.8 1 0 8 6 Uruguay 51.6 0.4 0.7 -1 -1 -1 1 78 Uzbekistan 50.4 0.9 0.9 19 9 ...6 - Venezuela, RB 49.7 0.3 0.5 7 1 4 2 56 Vietnam 51.1 0.9 1.0 19 5 -5 0 350 West Bank and Gaza 49.2 ...3 Yemen. Rep. 48.9 0.4 0.4 25 43 1 1 3 Yugoslavia, FR (Serb./Mont.) 50.2 0.6 0.7 4...5 Zambia 50.4 0.8 0.8 32 15 -8 -13 0 -3 Zimbabwe 50.4 0.8 0.8 17 9 -9 -2 3 30 Low Income 49.0.....0.O6 0.7 28 19 -14. -6 1 3.. Exci. China & India 50.0 0.6 0.7 22 18 -14 8 23 Middle income 50.5 0.6 0.6 10 6 -4 -2 4 6 Lower middle income 50.8 0.7 0.7 9 7 -6 -2 4 6 Upper mniddle income 50.2 0.4 0.6 11 4 -2 -2 5 7 Low & middle income 49.4 0.63 0.7 22 15 -11 -5 2. 4...... East Asia & Pacific 48.9 0.7 0.8 28 14 -8- 0 2. 4.. Europe.& Central Asia 51.9 0.9 0.9 .......... 6 4 -1. 0.. 6 9..... Latin Amnerica & Carib: 504.4 0. 3 0.5 7 2 -1 ......... -2 4. 6... Middle East & N. Aflica 49.0 0.3 0.4 27 22 -20 -7 2 3 South Asia 48.5 0.5 0.5 27 24 -23 -12 -1 1 Sub-Saharan Africa 50.5 0.7 0.7 20 17.... ..-11 -9 3... 3 HihIncome 50.7 0.5 0.8 .. O..0 0 6 6 Europe EMU 512.2 0.5 0.7 67 a. Data are for the moat recent year mvailable. 20 2000 World Denelopment Indicators 1.3 This table contrasts male and female outcomes for (labor force), 2.10 (net primary enrollment), 2.12 * Female population is the percentage of the popu- selected social indicators: labor force participation, (illiteracy), and 2.18 (child mortality and life expectancy lation that is female. * Labor force comprises peo- adult illiteracy, net primary school enrollment, life at birth). For other gender-related indicators see ple who meet the International Labour Organization expectancy at birth, and child mortality. A labor force tables 1.2 (maternal mortality), 2.1 (wornen per 100 definition of the economically active population: all peo- participation ratio of 1.0 indicates gender equality in men aged 65 and older), 2.4 and 2.5 (smployment ple who supply labor for the production of goods and labor force participation in the formal sector, while and unemployment), 2.12 (education outcomes), services during a specified period. It includes both the a lower ratio shows that women's participation is lower 2.13 (pupils and teachers), 2.16 (reprodu, tive health), employed and the unemployed. While national prac- than men's. For net primary enrollment, a positive 2.17 (prevalence of anemia and smoking), and 2.18 tices vary in the treatment of such groups as the value means that the enrollment ratio for girls is (adult mortality). armed forces and seasonal or part-time workers, in higher than that for boys, and a negative number general the labor force includes the armed forces, the that girls are falling behind. Conversely, for adult illit- unemployed, and first-time job-seekers, but excludes eracy and child mortality, a positive value indicates homemakers and other unpaid caregivers and work- female disadvantage. A positive value for life ers in the informal sector. * Adult Illiteracy rate is expectancy represents female advantage. the percentage of adults aged 15 and above who Differences in outcome are the consequence of dif- cannot, with understanding, read and write a short, ferences in the opportunities and resources available simple statement about their everyday life. * Net pr- to men and women. Such disparities exist through- mary enrollment ratio is the ratio of the number of chil- out the world, but they are most prevalent in poor dren of official school age (as defined by the education developing countries. Inequalities in the allocation of system) enrolled in school to the number of children such resources as education, health care, and nutri- of official school age in the population. * Life tion matter because of the strong association of expectancy at birth is the number of years a newborn these resources with well-being, productivity, and would live if prevailing patterns of mortality at the time growth. This pattern of inequality begins at an early of its birth were to stay the same throughout its life. age, with boys routinely receiving a larger share of edu- * Child mortality rate is the probability of dying cation and health spending than girls do, for exam- between the ages of one and five, if subject to current ple. Girls in many developing countries are allowed age-specific mortality rates. less education by their families than boys are-a disparity reflected in lower female primary school Data sources enrollment and higher female illiteracy. As a result women have fewer employment opportunities, espe- The calculations of gender ratios and differences cially in the formal sector. Women who do work out- were carried out by World Bank staff. For the sources side the home often also bear a disproportionate of the underlying indicators see Data sources for the share of the responsibility for household chores and tables referred to in About the data. child-rearing. Life expectancy has increased for both men and women in all regions, but female morbidity and mor- tality rates sometimes exceed male rates, particularly during early childhood and the reproductive years. In high-income countries women tend to outlive men by four to eight years on average, while in low-income coun- tries the difference is narrower-about two to three years. The female disadvantage is best reflected in dif- ferences in child mortality rates in some countries. Child mortality captures the effect of preferences for boys because adequate nutrition and medical inter- ventions are particularly important for the age group 1-5. Because of the natural female biological advan- tage, when female child mortality is as high as or higher than male child mortality, there is good reason to believe that girls are discriminated against. For more information on the underlying indicators see About the data for tables 2.1 (population), 2.3 2000 World Development Indicators 21 1.4 Trends in long-term economic development Gross national Population Value added Private Gross Exports product consumption domestic of goods fixed and investment services average annual average annual average annual % grow' h % growth% growth average average average Per Labor annual annual annual Total capita Total force Agriculture Industry Services % growth % growth % growth 1988-98 1965-98 1965-98 1965-98 1965-98 1965-98 1965-98 1965-98 1965-98 1965-98 Albania.. . 1.8 2.2 3.0 -5.3 -1. 1 Algeria 3.9 1.0 2.8 3.3 4.8 2.8 4.1 4.7 2.2 ........... 2.6 Angola.. . 2.5 2.1...... -1.3 4.3 Argentina 1.9 0.4 1.5 1.5 1.6 1.1 2.6 2.4 1.0 5.2 Armenia . .. 1.6 2.3 Australia 3.2 1.7 1.5 2.1 1.8 2.2 3.5 3.4 2.6 5.7 Austria 2.9 2.6 0.3 0.5 0.8 2 0 2.6 2.9 2.9 6.2 Azerbaijan 1. 7. 1. 2.1 ... .... .................. .. ... .. Bangladesh 3.9 1.4 2-3 2.3 2.1 4.1 4.7 3.7 3.7 7.6 Belarus 0.5 0.6 . .. Belgium 2.5 2.3 0.2 0.5 1. 9 2.0 2.2 2.7 .18 4.7 Benin 3.0 0.1 2.8 2.3 40 4. ..2.4 2.6 3.3 Bolivia 2.3 2.4 . 2.4 2.1 3.0 Bosnia and Herzegovina 0 2 0.6 . Botswana 11.4 7.7 3.2 29 3.3 13.4 11.0 Brazil 4.3 2.2 2.0 2.9 3.4 4.5 4.9 4.4 ..1.7 8.3 Bulgaria -0.7 -0.3 0.0 -0.1 -2.5 -1.3 1.7 -1.2 -5.0 -11.8 Burkina Faso 3.3 0.9 2.3 1.26 2.5 5.5 3.0 5.9 3.3 Burundi 3.2 0.9 2.2 2.0 2.6 3.7 3.7 3.2 -27 2.6 Cambodia ... 1.9 1.9 . Cameroon 4.1 1.3 2.7 2.3 3.3 6.4 3.6 3.6 0 16.0 Canada 3.1 1.8 1.3 2.3 . .. 3.2 4.1 5.9 Central African Republic 1.1 .-1.2 2.2 . 1.6 2.1 0.3 2.0 2.4 3.0 Chad 1.8 -0.6 2.4 2.2 1.7 1.7 2.3 2.6 1.6 Chile 3.6 1.9 1.7 2.3 3.5 3.2 4.9 3.3 4.8 8.3 China 8.6 6.8 1.7 2. 4.1 10.9 93 7.4 9.9 13..5 Hong Kong, China 7.41 5.51 1 .8 2.6 . 7.9 7.7 11.8 Colombia 4.2 2.0 2.2 3.2 2.7 4.5 5.0 4.1 4.6 5.7 Congo, Demn. Rep. -0.8 -3.8 3.1 2.7 2.0 -3.0 -2.2 0.1 -0.5 2.4 Congo Rep. 4.3 1.4 2.8 2.6 2.8 7.3 4.3 3.9 6.5 Costa Rica 4.0 1.2 2.7 3.5 3.2 4.7 4.1 3.2 4.8 7.0 CMe dIlvoire 2.8 -0.8 3.5 3.3 2.2 6.1 2.9 2.6 0.2 5.2 Croatia ..0.1 0 1 ....... Cuba .. 1.1 2.1 Czech Republic .. 0.2 0.4 !. Denmark 2.2 1.9 0.3 0~9 2.3 1.9 2.6 17 0.9 4.5 Dominican Republic 4.7 2.3 2.3 3.2 3O.0 5.7 5.1 4.2 6.0 6.2 Ecuador 4.6 1.8 2.6 3.0. 35.5 6.1 4.6 4.2 3.1 7.2 Egypt, Arab Rep. 5.9 3.5 2.2 2.4 2.8 6.6 7.9 5.2 5.9 5.5 El Salvador 1.5 -0.4 2 .1 2.8 0.6 0.7 2.2 1.9 2.6 1.3 Estonia . 0 .4 0.5 Ethiopia 2.2 -0.5 2.7 2.4 1.9 0.4 3.5 2.3 3.9 1.2 Finland 2.8 2.4 0.4 0.6 0.2 3.0 3.4 2.8 1.1 4.9 France 2.6 2.1 0.6 0.7 1.7 0 9 2.6 2.8 1.9 5.6 Gabon 3.4 0.4 2.6 2.10 ....... -0.4 2.6 2.4 3.5 -2.5. ...5.6 Gambia, The 3.9 0.4 3.3 3.2. 1.8 .4.1 4.2 1.6 9.4 3.2 Georgia -0.6 -1.2 0 .6 0.8 Germany .. . 0.2 0.4 Ghana 1.9 -0.8 2.6 2.6 1.3 0.8 3.2 1.5 0.6 -0.5 Greece 3.1 2.4 0.6 0.8 1.3 3.2 4.0 3.4 1.3 7.4 Guatemala 3.4 0.7 2.6 2.8 2.8 3.6 3.5 3.3 2.5 2.4 G uinea ...2 11.8 .... ... ...:7... ... .... .. .... Guinea-Bissau 2.7 -0.1 2.4 2.1 1.5 2.3 6.0 11. . Haiti 1.1 -0.8 1.9 1.2 0.2 1.3 1.6 1.8 8.6 4.3 Honduras 3.8 0.6 3.1 3.3 2.6 4.4 4.4 3.7 4.0 2.6 ...... 22 2000 World Development indicators CZ sJoleoipuI lueLwdOIGAGeC PIJOM 00017 TO 17'0 uo92jeopaA ueissnlH 00 . 9 t06TO !UeWO8 T ~~~~~Z, ~~~PueIOd 017 917 017~~~~~~~~6- 17 . 17z LT 670- T17, wadO 88 6OL 89,~ 89s 89 Ci'i' .6~6 V.L 8,6 tl'E~~ 6'9 £17 T17 917 90 817 e,3u!nl8 MON end8d t, J~~ t-9 Z-9 L'9 T89 69 T9 uew0 .. ....... 8... 08 T9T -L17 617 00.8 69 . . 69 91'. 5~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. . 9<0 . . TT **9.I..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...... 917 1717 t'0 9Z~~~~~~~~~~~~~~; 6 0:17 9d* 1717 91 0 8 26i 8:9 . 5:17 . i'.9 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~*8 .. 60 ST 99 iewueA~~~~~~~~~~~~~~~.. ........ .... 09o 99 9 -E6T T1i 90 6nb qwe--- u 9...0. 89 ... 1717 L17 1717 T:0~~~~~~~~~-, "' o. 9""' L 0 eiueune1' - .. .v .............T r - - - i- 6di L-c 11. . .... 1717-v 9fO1 ft oT 9 9 E' ""T ........~~ ... .. 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S~~J0dX~~ SSOJ~~ aWA!Jd poppe enie~~~~~~ uoI~~eIndod inuoiwu sso~§.~ T 0 17T~~~~~9,-97..... 1. 4 Gross national Population Value added Private Gross Exports product consumption domestic of goods fixed and investment service average annual average annual average annual S growth % growth % growth average average average Per Labor annual annual annual Total capita Total force Agriculture Induatry Services % growth % growth % growth 1965-98 1.965-98 1.969-98 1965-98 1.969-98 1965-98 1965-98 1965-98 1965-98 1965-98 Rwanda 2.7 0.0 2.8 2.8 2.3 2.5 4.3 3.3 6.4 2.9 Saudi Arabia 5.4 0.5 4.4 4.9 7.4 3.2 6.9 Senegal 2.4 -0.4 ...2.8 2.6 1.1 3.7 2.4 2.4 3.1 1.6 Sierra Leone ......... 0.5 -1.6 2.1 1.7 3.2 -. 0.6 -. . -. Singapore 8.4 .6.4 1.9 3.1 -1.4 8.5 8:5 ...... .. 6.6 96. Slovak Republic . 0:6 1.3 Sloveria 0 .6 0.8 Spain 3.0 2.3 0.6 1.1 .. . .. 2.9 287.3 Sri Lanka 4. 301.6 2.2 2.7 5.1 5.2 4.1 7.6 4.4 Sudan 2.5 -0.2 2.5 2.6 3.0 3.7 3.5 4.0 . -2.1 Sweden 1.7 1.4 0.4 1.0 0.5 1.4 2.4 1.4 0.9 4.5 Switzerland 1.6 1.2 0.6 1.0.. 1.7 1.9 3.7 Syrian Arab Republic 5.3 2.0 3.2 3.3 4.4 8.4 6.3 4.6 0.6 6.3 Tajikiatan . 2.7 2.7 . .. Tanzania .. 3.0 2.9 . .. Thailand 7.3 5.0 2.1 26 4.0 9.5 7.3 6.1 9.0 11.3 Togo 2.4 -0.6 3.1 2.7 3.5 2.9 1.4 3.2 -0.9 3.4 Trinidad and Tobago 3.7 2.6 1.1 2.0 -2.0 0.1 2.4 3.3 . 4.0 Tunisia 5.1 2.7 2.1 2.8... 3.9 6. . ...0 5.0 5.7 4.4 6.8 Turkey ..4.3 2.1 2.2 2.1 J.. 3 .5 6 5 0..... ... Turkmnenistan .. 2.8 3. Uganda .. 2.9 2. Ukraine ..0.3 0.3 United Arab Emirates 3.7 -3.6 95 10.5 11.5 1.2 6.4 United Kingdom 2.1 1.9 0.3 0.5 - 2.5 1.8 4.1 United States 2.6 1.6 1.0 1.6 .. 3.0 ..2.4 .5.7 Uruguay.1.8 1.2 0.6 1.0 1.5 1.2 . ... 2.4 1.7 2.0 5.9 Uzbekistan 26 2 Venezuela. RB 2.0 -0.8 2.8 3.7 2.7 1.6 2.7 2.4 1.2 2.0 Vietnam . 2.1 2.1........- West Bank and Gaza . .. .. Yemen, Rep. . 3.2 2.8 Yugoslavia, FR (Serb./Mont. 07 . Zambia 1.1 -2.0 3.0 2.7 0.8 0.0 2.2 0.6 -5.3 -0.8 Zimbabwe 3.5 0.5 2.9 2.9 2.1 1.3 4.5 4.1 2.6 6.7 Low Income 5.9 3.7 2.1 2.2 3.3 7.8 6.5 5.3 7.0 7.0 Excl. China & India 4.3 1.7 2.5 2.5 2.8 5.7 5.1 4.2 4.0 4.3 Middle Income 3.7 1.9 1.7 1.9 2.3 2.9 3.9 . 2.6 6.1 Lower middle income.. . 1.6 1.7 ..- Upper middle income 4.2 2.2 1.9 2.3 2.4 3.6 4.2 . 3.6 8.4 Low & middie Income 4.2 2.2 2.0 2.1 2.9 4.3 4.6 4.1 3.7 5 6 East Asia & Pacific 7.5 5.7 1.8 2.2 3.6 9.9 7.9 6.7 9.8 10 5 Europe & Central Asia 0.8 0.9 Latin America & Carib. 3.5 1.3 2.1 2.8 2.6 3.2 3.9 3.5 1.8 5.9 Middle East & N. Africa 3.1 0.2 2.8 2.8 4.2 1.3 4.1 South Asia 4.9 2.7 2.2 2.1 2.9 5.5 5.7 4.5 5.2 ............7.2 Sub-Saharan Africa 2.6 -0.3 2.7 2.5 1.9 2.5 3.1 2.7 0.0 2.5 High Income 3.0 2.3 0.7 1.1 . 3.2 3.0 5-.7 Europe EMU .0 4 0.7 ... . 5.4 a. Data refer to GDP. 24 2000 World Development Indicators 1~~~~Hl . 4 lX II 1.4 The long-term trends shown in this table provide a view * Gross national product (GNP) is the sum of value of the relative rates of change of key social and eco- added by all resident producers plus any taxes (less nomic indicators over the period 1965-98. In viewing subsidies) not included in the valuation of output plus these growth rates, it may be helpful to keep in mind net receipts of primary income (compensation of that a quantity growing at 2.3 percent a year will dou- employees and property income) from abroad. ble in 30 years, while a quantity growing at 7 percent * GNP per capita is gross national product divided a year will double in 10 years. But like all averages, by midyear population. * Average annual growth of the rates reflect the general tendency and may disguise total population and labor force is calculated using considerable year-to-year variation, especially for eco- the exponential endpoint method. * Labor force nomic indicators. comprises all people who meet the International Average annual growth rates of gross national prod- Labour Organization's definition of the economically uct, value added, private consumption, gross domes- active population. * Value added is the net output tic fixed investment, and exports of goods and services of a sector after adding up all outputs and subtract- are calculated from data in 1995 constant prices using ing intermediate inputs. It is calculated without mak- the least-squares method. See Statistical methods ing deductions for depreciation of fabricated assets for more information on the calculation of growth rates. or depletion and degradation of natural resources. The All the indicators shown here appear elsewhere in the industrial origin of value added is determined by the World Development Indicators. For more information International Standard Industrial Classification (ISIC) about them see About the datafortables 1.1 (GNP and revision 3. * Agriculture corresponds to ISIC major GNP per capita), 2.1 (population), 2.3 (labor force), 4.2 divisions 1-5. * Industry comprises SIC divisions (value added by industrial origin), 4.9 (exports of goods 10-45. * Services correspond to ISIC divisions and services), and 4.10 (private consumption and 50-99. * Private consumption is the market value gross domestic investment). of all goods and services, including durable products, purchased or received as income in kind by house- holds and nonprofit institutions. It excludes pur- chases of dwellings but includes imputed rent for owner-occupied dwellings. * Gross domestic fixed investment consists of outlays on additions to the fixed assets of the economy. * Exports of goods and services are the value of all goods and market ser- vices provided to the rest of the world. Data sources The indicators here and throughout the rest of the book have been compiled by World Bank staff from primary and secondary sources. More information about the indicators and their sources can be found in the About the data, Definitions, and Data sources entries that accompany each table in subsequent sections. 2000 World Development Indicators 25 1.5 Long-term structural change Agriculture Labor force in Urban Trade Central Money value added agriculture population government and quasi revenue money % of total % of total % of GDP labor force population % of GDP % of GDP % of GDP 1.970 1998 1.970 1.990 1.970 1.998 1970 1.998 1970 1998 1970 1998 Albania 54 66 55 32 40 42 19 4 Algeria 11 12 47 26 40 59 51 47 32 51 43 Angola 12 78 75 15 33 93 17 Argentina 10 6 16 12 78 89 10 23 14 21 27 ArmTenia 33 27 18 59 69 .. 71 . Australia 6 3 8 6 85 85 29 42 21 24 44 64 Austria 1 15 8 65 65 60 85 28 37 Azerbaijan 20 35 31 5 5783 19 1 Bangladesh..a. ... . .. 42 2.2 81 65 8 23.. .17 33 29 Belarus 13 35 20 44 71 130 31 21 Belgium 3 1 5 3 94 97 100 141 35 44 Benin 36 39 81 64 17 41 50 55 10 21 Bolivia 15 5 47 4 612 4917 16 45 Botswana 28 4 82 46 8 49 71 69 17 44 . 24 Brazil 12 8 45 23 56 80 14 18... 17 30 Bulgaria 19 35 13 52 69 .. 91 .. 34 . 28 Burkina Faso 35 33 92 92 6 17 23 44 8 23 Burundi 71 54 94 92 2 8 22 28 14 9 19 Cambodia 51 79 74 12 15 14 78 . 11 Camneroon 31 42 85 70 20 47 51 51 14 14 Canada 5 . 8 3 76 77 43 80 19 36 64 Central African Republic 35 53 89 80 30 40 74 41 15 17 Chad 40 40 92 83 12 23 38 51 8 . 7 11 Chile 7 7 24 19 75 85 29 56 29 22 12 42 China 35 18 78 72 17 31 4 39 . 6 .. 124 H,:r, P - r*:: r.,r. I~ mm -11 Colombia 29 13 41 27 57 73 31 34 9 12 15 21 Congo,.De.m. Rep. 15 58 75 68 30 30 35 46 11 5 8 Congo, Rep..18 12 66 49 33 61 92 135 22 30 17 16 Costa Rica 23 15 43 26 40 47 63 100 15 26 19 39 Ci5te dIlvoire 32 26 76 60 27 45 65 82 . 21 25 26 Croatia 9 50 16D 40 57 .. 89 .. 45 .. 39 Cuba .. 30 18 60 75 . . . Czech Republic 4 17 11 52 75 .. 121 . 33 .. 66 Denmark 6 11 6 80 85 60 69 34 . 44 58 Dominican Republic 23 12 48 25 40 64 42 70 18 17 18 28 Ecuador 24 13 51 33 40 63 33 62... 20 33 Egy.t,Arab.Rep. . 29 . 17 52 40 42 45 33 40 ...26 34 75 El Salvador 40 12 57 36 39 46 49 59 11 . 20 44 Eritrea .. 9 86 80 11 18 .. 110 . Estonia .. 6 18 14 65 69 .. 169 .. 32 .. 28 Ethiopia .. 50 91 86 9 17 .. 43 .. . . 40 Finland 12 4 20 8 50 66 53 71 26 32 France . 2 14 5 71 75 31 49 33 42 Gabon 19 7 79 52 31 79 88 91 ... 15 17 Gambia, The 34 27 87 82 15 31 79 113 16 . 17 28 Georgia .. 26 37 26 48 60 .. 42 .. 6 ..5 Germany .. 1 9 4 80 87 . 52 .. 31 Ghana 47 10 60 59 29 37 44 63 15 . 18 17 Greece 15 . 42 23 53 60 23 40 22 23 34 45 Guatemala 27 23 62 52 36 39 36 46 9 17 21 Guinea .. 22 92 87 14 31 .. 45 .. 10 ..9 Guinea-Bissau 50 62 89 85 15 23 34 50 . .. 13 Haiti 30 74 68 20 34 31 41 .......... .... 12 28 Honduras 32 20 65 41 29 51 62 98 1 19 35 26 2000 World Developranen Indicators 1.5 Agriculture Labor force i'n Urban Trade Central Money value added agriculture population government and quasi revenue money % of total % of total % of GDP labor forca population % of GOP % of GOP % of GDP 1.970 1.998 1970 1990 1970 1.998 1970 ±.998 1970 1998 1970 1.998 Hungary . 6 25 15 49 64 63 102 . 36 India 45 29 71 64 20 28 8 25 . 12 20 44 Indonesal 45 20 66 55 17 39 28 98 13 17 8 49 Iran, Ialamic Rap. 25 44 39 42 61 28 . 27 . 39 Iraq ' ......... 47 16 56 71 ...... 22, Ireland 26 14 52 59 79 142 30 33 Israel.. . 10 4 84 91 79 75 33 43 47 84 Italy 8 3 19 9 64 67 33 50 . 41 Jamaica 7 8 33 25 42 55 71 112 ... 30 49 Japan 6 2 20 7 71 79 20 21 11 9 119 Jordan 12 3 28 15 51 73 . 120 . 27 54 101 Kazakhstan . 9 27 22 50 56 . 66 . .. 9 Kenya 33 26 86 80 10 31 60 57 17 27 27 40 K.ra,Dem. Rep . . 55 38 54 60 . . . Korea,Rep 26 5 49 18 41 80 37 85 15 20 29 51 Kuwait 0 . 2 1 78 97 84 92 42 . 36 99 Kyrgyz Republic 46 3 2 3 4. 7. 1 Lao PDR 53 81 78 10 22 9. 15 Latvia . 5 19 16 62 69 109 . 32 . 25 Lebanon .. 12 20 7 59 89 62 .. 17 . 143 Lesotho 35 .11 43 40 9 26 65 158 20 49 . 36 Libya 2 . 29 11 45 87 89 . . . 20 Lithuania 10 31 168 50 68 . 16 .. 27 . 18 Macedonia, FYR . 11 50 22 47 61 . 98 . .. 14 Madagascar 24 31 84 78 14 28 41 50 14 9 17 19 Malawi 44 36 91 87 6 22 63 74 16 . 18 15 Malaysia 29 13 54 27 34 56 80 207 20 23 31 96 Mali 66 47 93 86 14 29 31 58 ... 13 22 Mauritana 29 25 84 55 14 55 57 95 . . 8 15 Mauritius 16 9 34 17 42 41 85 130 . 21 3 75 Mexico 12 5 44 28 59 74 17 64 10 15 15 26 Moldova . 29 54 33 32 46 . 122. 21 Mongolia . 33 48 32 45 62 . 105 . 20 . 19 Morocco ... ... 20. 17 58 45 35 55 38 44 19 . 28 70 Mozambique. 34 66 83 6 38 .. 42 .. . 20 Myanmar 38 53 78 73 23 27 14 2 . 8 24 24 Namibia 10 64 49 19 30 . 126 40 Nepal.6 7 40 94 83 4 11 13 58 5 11 11 41 Netherlanda 7 5 86 89 89 105 . 46 New Zealand 12 . 12 10 81 86 48 57 28 34 20 91 Nicaraglua 25 3 4 5 0 28 47 55 5 6 111 12 . 14 5 Niger 65 41 93 90 9 20 29 40 ... 5 7 Nigeria 41 32 71 43 20 42 20 55 10..9 1 Norway . 2 12 6 65 75 74 75 32 43 49 53 Oman 16 .. 57 45 ~~~ ~~~ ~~~ ~~~~~~ ~~~ ~~~~~~~~~~~~~~~11 81 93 . 38 25 . 3 Pakiatan 37 26 59 52 25 36 22 36 . 16 41 44 Panama . 8 42 26 48 56 . 7 . 25 22 74 Papua New Guinea 37 24 86 79 10 17 72 138 . .. 35 Paraguay 32 25 53 39 37 55 31 94 11 . 17 28 Peru 19 7 48 36 57 72 34 29 14 16 18 26 Philippines 30 17 58 46 3 3 57 43 116 13 19 23 58 Poland . 5 39 27 52 65 . 56 . 36 . 36 Portugal.. . 32 18 26 01 s0 72 . 36 Puerto Ric 3 . 14 4 58 74 107 . . . Romania . 16 49 24 42 56 . 60 . 26 . 23 Ruasian Federation 7 19 14 63 77 . 58 . .. 20 2000 World Development Indicators 27 I.5~ Agriculture Labor force in Urban Trade Central Money value added agriculture population governmenit and quasi revenue money 0/of total % of total % of GDP labor force population % of GDP % of GOP % of GDP 1970 1998 1970 1990 ±970 1998 1970 1998 1970 1998 1970 1998 Rwanda 66 47 94 92 3 6 27 28. 11 14 Saudi Arabia 4 7 64 19 49 85 89 67 13 57 Senegai 24 17 83 77 33 46 56 71 16 14 22 Sierra Leone 30 44 76 67 18 35 48 53 11 13 13 Singapore 2 0 3 0 100 100 232 287 21 24 62 101 Slovak Republic 4 17 12 41 57 139 64 Slovenia 4 50 6 37 50 115 ..42 South Africa 7 4 31 14 48 53 46 50 21 26 58 53 Spain .. 3 26 12 66 77 27 56 18 30 Sri Lanka 28 21 55 48 22 23 54 78 20 17 22 30 Sudan 44 39 77 69 16 34 33 17 17 8 Sweden 81 83 48 81 29 40 Switzerland 8 6 55 68 64 7 4 2 0 4 Syrian Arab Republic 20 .. 50 33 43 54 39 69 25 24 34 34 Tajikistan .. 6 46 41 37 28 Tanzania .. 460 90 84 7 31 .. 43 .... 19 Thailand 26 11 80 64 13 21 34 101 12 16 27 99 Togo 34 42 74 66 13 32 88 74 17 22 Tr nidad and Tobago 5 2 19 11 63 73 84 98 ... 27 47 Tunisia 17 12 42 28 45 64 47 88 23 30 32 47 Turkey 40 18 71 53 38 73 10 53 14 22 20 30 Turkmenistan .. 25 3 37 48 45 . Uganda 54 45 90 85 8 14 44 30 14 17 11 Ukraine .. 14 31 20 55 68 .. 83 .. 13 United Arab Emirates . . 9 8 57 85 ...3 .. 56 -.1-.,-,, -~~ ~ ~~~~~ ~~ . ........ .. .. United States 3 2 4 3 74 77 11 26 18 22 63 61 Uruguay 18 8 19 14 82 91 29 44 24 32 20 41 Uzbekistan .. 31 44 35 37 38 .. 45 . Venezuela, RB 6 5 26 12 72 86 38 40 17 17 19 18 Vietnam .. 26 77 71 18 20 .. 95 .. 18 .. 20 West Bank and Gaza .. 7 .. . . . . 90 Yemen, Rep. .. 18 70 61 13 24 .. 86 41 .. 43 Yu~goslavia, FR (Serb./Mont.) . .. 50 30 39 52 . Zambia 11 17 79 75 30 39 90 68 22 .. 25 16 Zimnbabwe 17 19 77 68 17 34 .. 94 .. 29 .. 24 Low income .39 23 .75 68 18 .30 12 46 .. ..10 Exci. China & India 41 26 76 65 17 31 30 74 .. 18 Middle income 17 9 40 28 49 65 30 56 9 Lower middle income .. 11 39 30 46 58 .. 65 Upper middle income 16 8 42 25 54 77 27 52 5 Low & middle income 24 13 66 57 28 41 25 53 East Asia & Pacific 33 15 .76 68 19 34 24. 75 .. 14 Europe & Central Asia .. 12 33 23 52 66 .. 71 Latin America & Carib. 13 8 41 25 57 75 20 32 1 Middle East & N. Africa 13 14 50 35 41 57 .. 53 South Asia 43 28 71 63 19 28 12 29 .. 2 Sub-Saharan Africa 21 17 78 68 19 33 47 59 18 High income 5 2 11 5 73 77 29 44 19 30 Europe EMU .. 2 15 7 71 78 .. 61 .. 39 a. The data for GDP and its cornponents refer to rmainland Tanzania only. 28 2000 World Development Indicators 1.5 I Over a period of 25 years or longer cumulative * Agriculture value added is the sum of outputs of processes of change reshape an economy and the the agricultural sector (International Standard Indus- social order built on that economy. This table highlights trial Classification major divisions 1-5) less the cost some of the notable trends at work for much of the of intermediate inputs, measured as a share of gross past century: the shift of production from agriculture domestic product (GDP). * Labor force in agricul- to manufacturing and services; the reduction of the ture is the percentage of the total labor force recorded agricultural labor force and the growth of urban cen- as working in agriculture, hunting, forestry, and fish- ters; the expansion of trade; the increasing size of the ing (ISIC major divisions 1-5). * Urban population is central government in most countries-and the rever- the share of the total population living in areas defined sal of this trend in some; and the monetization of as urban in each country. * Trade is the sum of economies that have achieved stable macroeconomic exports and imports of goods and services, measured management. All the indicators shown here appear as a share of GDP. * Central government revenue elsewhere in the World Development Indicators. For includes all revenue to the central government from more information about them see tables 2.4 (labor taxes and nonrepayable receipts (other than grants), force employed in agriculture), 3.10 (urban population), measured as a share of GDP. * Money and quasi 4.2 (agriculture value added), 4.13 (central government money comprise the sum of currency outside banks, revenue), 4.16 (money and quasi money), and 6.1 demand deposits other than those of the central gov- (trade). ernment, and the time, savings, and foreign currency deposits of resident sectors other than the central gov- ernment. This measure of the money supply is com- monly called M2. Data sources The indicators here and throughout the rest of the book have been compiled by World Bank staff from pri- mary and secondary sources. More information about the indicators and their sources can be found in the About the data, Definitions, and Data sources entries that accompany each table in subsequent sections. 2000 World Development Indicators 29 1.6 Key indicators for other economies Population Surface Population Gross national product Life Adult Carbon area denisity expectancy illiteracy dioxide at rate emissions birth Per capita Average Average ppp O/ of thousand people annual growth annual growth PPP per capita people 15 thousand thousands asq.km per sq. km $ milliona % $ % $ mill ova $ yearn anc above metric tons 1.998 1998 1998 1998, 1997-98 1995a 1997-98 1995b lg99S 1998 1998 1996 Afghanistan 25.051 652.1 38 ..v.. ... 46 65 1,176 American Samoa 63 0.2 315 .. -d ..... .. _282 Andorra 65 0.5 144 . . ... ......... ... .-..... Antigua and Barbuda 67 0.4 152 565 3.7 8,450 29 594 8.890 75 .. 322 Aruba 94 0.2 495 . *,v1,517 Bahamas, The 294 13.9 29 .. 3.0 v 1.2 4,113 13,990 74 5 1,707 Bahrain 643 0.7 932 4,909 2.1 7,640 -1.5 7.430 11,556 73 14 10.578 Barbados 266 0.4 618 .. 4.4 . 4.1 ... 76 . 835 Belize 239 23.0 10 635 3.0 2,660 -0.9 1,042 4,367 7 5 .. 355 Bermuda 63 0.1 1,260 . *, * .. 1,458 23,302 ... 462 Bhutan 759 47.0 16 354 5.5 470 2.4 1,092r 1,4381 61 .. 260 Brunei 315 5.8 60 7. , * , 836f 24,886f 76 9 5,071 Cape Verde 416 4.0 103 499 5.2 1,200 2.2 1,327v 3,192f 68 27 121 Cayman Islands 36 0.3 138 ,,,e, . . . 282 Channel Islands 149 0.3 479 ..e79 Comoros 531.. 2.2. 238 197 0.0 370 -2.5 7431 1,400f 60 42 55 Cyprus 753 9.3 82 8,983 5.0 11,920 4,11 13,25ttf 17,g99f 78 3 5,379 Djibouti 636 23.2 27 .. . .. .. 50 .. 366 Dominica 73 0.8 97 230 4.1 3.150 4.1 349 4,77 76 8 Equatorial Guinea .431 28.1 15 478 34.7 1,110 31.2 ... 50 19 143 Faeroe Islands 44 1.4 31 . .. ... ... . .. 630 Fij I 790 18.3 43. 1,748 -4.2 2,210 -5.2 3,236 4,094 73 8 762 French Polynesia 227 4.0 62 ..v4,6081 20,586f 72 .. 561 Greenland 56 341.7 0 .*v , ... 68 .. 509 Grenada 96 0.3 283 313 5.3 3,250 4.5 535 5,557 72 .. 161 Guam 149 0.6 271 . ,v.. 77 .. 4,078 Guyana 849 215.0 4 661 0.8 780 0.1 2,6651 3,1391 64 2 953 Iceland 274 103.0 3 7,626 5.9 27,830 5.1 6,788 24,774 79 .. 2,195 Isle of Man 76 0.6 129 d... ., This table shows data for 58 economies-small *Population is based on the de facto definition of economies with populations between 30.000 and 1 population, which counts all residents regardless of million, smaller economies if they are members of the legal status or citizenship-except for refugees not per- World Bank, and larger economies for which data are manently settled in the country of asyrlum, who are gen- not regularly reported. Where data on GNP per capita erally considered part of the populati on of their country are not available, the estimated range is given. In this of origin. The values shown are midyear estimates for year's edition this table excludes France's overseas 1998. See also table 2.1. * Surface area is a coun- departmnents-French Guiana, Guadeloupe. Martinique, try's total area, including areas unJer inland bodies and R6union. The national accounts (GNP and other of water and some coastal waterNays. * Popula- economnic measures) of France now include these tion density is midyear populatio' divided by land French overseas departments. area in square kilometers. * Gross national product (GNP) is the sum of value added by ali resident pro- ducers plus any taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property 30 2000 World Development Indicatora 1.6 Population Surface Population Gross national product Life Adult Carbon area density expectancy illiteracy dioxide at rate emissions birth Per capita Average Average PPP % of thousand people annusi growth aninusi growth PPP per capita people 1.5 thousand thousands sq. km per sq. km $ millions % $ % $ millions $ years and shove metric tons 1L998 1.998 1998 19981 1997-98 1.99w, 1997-98 1998, 1998b 1998 1998 1996 Kiribati 86 0.7 118 101 15.3 1,170 11.8 334f 3,880f 61 . 22 Liberia 2,962 111.4 31 .. ... . ... 47 49 326 Liechtenstein 32 0.2 200 . .. Luxembourg 427 2.6 165 19,239 5.1 45,100 3.9 15,658 36,703 77 8,281 Macso, China 459 0.0 22,950 .. ... . ... 78 .. 140. Maldives 253 0.3 875 296 7.1 1.130 4.4 902' 3,436' 67 4 297 Malta 377 0.3 1,178 3,807 4.1 10.100 3.5 8,634' 22,901' 779 ,5 Marshall Islands 62 0.2 342 96 -4.3 1,540 -7.4 ... Mayotte 128 0.4 341 . . Micronesia, Fed. Sts. 113 0.7 162 204 -3.1 1,800 -5.0 ........ . .....67 Monaco 3 2 0.0 16,410 Netherlands Antilles 213 0.8 266 . 76 4 6,430 New Caledonia 207 18.6 11 . 73 . 1,751 Northern Mariana Islands 588 0.5 143 ... ... Palau ~~~~19 0. 40 71 .. 245 Qatar 742 11.0 67 . . .. 74 20 29,121 Samoa 169 2.8 60 181 1.3 1,070 0.9 6S2f 3,854f 69 . 132 Sio Tom&6 and Principe 142 1.0 148 38 -1.4 270 -0.9 -1831 -1,289f 64 . 7 7 Seychelles 79 0.5 175 505 -1.7 6,420 -3.0 8Of, lO,l8S, 72 . 169 Solomon Islands 416 28.9 15 315 -7.0 760 -9.8 793Y 1,904f 71 161 Somalia 9,076 637.7 14 ..C. 48 1 St. Kitts and Nevis 41 0.4 113 253 3.6 6,190 3.6 400 9,790 70 103 St. Lads' 152 0.6 249 556 3.0 3,660 1.4 744 4,897 72 . 191 St. Vincent and the Grenadines 113 0.4 290 290 5.2 2,560 4.4 508 4,484 73 . 125 Suriname 412 163.3 3 684 2.8 1,680 2.5 ... 70 . 2,099 Swaziland 989 17.4 57 1,84 1. P140 -1.3 4,147 4,195 56 22 341 Tongs 99 0.8 137 173 -1.5 1,750 -2.3 413 4,187 71 . 117 Vanuatu 183 12.2 15 231 21 120 -. 31 282 56 Virgin Islands (U.S.) 118 0.3 348 .. ... . ... 77 . 12,912 a. Calculated using the World Bank Atlas method. b. PPP is purchasing power parity. See Definitions. c. Estimated to he low income 1S760 or less). d. Estimated to he upper middle income 1$3,031-9,360). e. Estimated to he high income l$9,361 or more). f. The estimate is hased on regression: othetrs are extrapolated from the latest International Comparison Programme hench- mark estimates. g. Estimated to he lower mniddle income 15761-3,0301. Data sources income) from abroad. Data are in current U.S. dollars to stay the same throughout its life. * Adult illiter- The indicators here and throughout the rest of the hook converted usingthe World Bank Atlas method (see Sta- acy rate is the percentage of adults aged [S and above have been compiled by World Bank staff from pri- tistical methods). Growth is calculated from constant who cannot, with understanding, read and write a mary and secondary sources. More information about price GNP in national currency units. * GNP per short, simnple statement about their E~veryday life. the indlicators and their sources can be found in the capita is gross national product divided by midyear pop- * Carbon dioxide emissions are those stemming About the dafa, Definitions, and Data sources entries ulation. GNP per capita in U.S. dollars is converted from the burning of fossil fuels and the manufacture that accompany each table in subsequent sections. using the World Bank Atlas method. Growth is calcu- of cement. They include carbon dioxide produced dur- lated from constant price GNP per capita in national ing consumption of solid, liquid, and gas fuels and gas currency units. * PPP GNP is gross national product flaring. converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNP as a U.S. dollar has in the United States. * Ufe expectancy at birth is the number of years a newborn infant would live if pre- vailing patterns of mortality at the time of its birth were 2000 World Development Indicators 31 ,r. 'EU 'A- ' '' *? '4 (A.' ' - A '.'-VCl. ' ti ... The next billion people: who? where? No social phenomenon has attracted more attention in the past half century than the "population explos-ion"-that surge from about 2.5 billion people in 1950 to more than 6 billion in 1999, making the 20th century one of unprece- dented population growth. As the number of people grew, the interval for adding another billion people became shorter and shorter, with the increase from 5 billion to 6 billion occurring in only 12 years (figure 2a). According to recent projections,l the 7 billion mark will be exceeded in 2014-the first time since reaching one billion that adding the next billion peo- ple is expected to take longer than for the previous billion (box 2a). More than half of the next billion will come from South Asia (310 million) and Sub- Saharan Africa (240 million'. East Asia and the Pacific will add about 220 mil- lion, and the remaining 230 million will be divided mostly between the Middle East and lNorth Africa and Latin America and the Caribbean. Europe and Cen- tral Asia will add 9 million people-just 1 percent-and the world's high- income countries will add 30 million (figure 2b). Why the differences? Because of different rates of population growth and different base populations. Regions with the same growth rate will add more people when the rate is applied to a larger base. For example, Sub-Saharan Africa is growing at a much faster rawe than South Asia, but South Asia will claim a larger share of the next billion pecple because of its larger population base. The next billion people will also be born into less favorable economic cir- cumstances. The majority-just under 600 million-are projected to be in low- income countries (as defined in 1999). Middle-income countries will add 375 million people, most of theni in the lower-middle-income group. Today's high- income countries will add a scant 30 million, or 3 percent of the total, in the next 15 years (figure 2c). And the next billion people will be predominantly urban, concentrated in cities and areas of current population settlement, particularly environmentally stressed seacoasts and river valleys. During these 15 years the urban population will increase from about 47 percent of the total to 54 percent, a net gain of 925 million, mostly due to migration from rural tD urban areas and to the urbanization of rural areas. Population's momentum in developing countries The population growth rate is a key demographic characteristic of a country, but the composition of the population by age can have more important con- sequences. The age structure determines not only the allocation of resources Projecting the future By far the most common methodology for projecting future populations is recent past. The result: countries that had a fast fertility decline over the the cohort component method, used in the projections by the World Bank past 10 years are projected to maintain a faster than average decline. A and by many other international organizations and national statistical similar assumption is made for changes in mortality: countries in which agencies. health conditions have been improving rapidly are assumed to have The cohort component method involves first compiling information on continued mortality decline in the near future. Models-such as model the characteristics of a country's population in the starting, or base, year life tables and model fertility and migration schedules-are used to for the projection. The necessary pieces of information consist of supplement and adjust empirical data. estimates of the population by age and sex in the projection's base Future patterns of vital rates and migration thus play a major role in year-and estimates of fertility, mortality, and net migration by age and projections, and misspecified patterns are an important source of error. sex for the base year or the period immediately preceding it. The projections in the tables here use assumptions based on an The sources for these base year estimates vary. For population, they analysis of observed past patterns, in which it was determined that are usually recent censuses or estimates from national statistical offices preceding trends are the best predictor for following trends in vital based on registration data. For vital rates, vital registration systems are rates. the preferred source, but demographic surveys are frequently the only Only two added variables are included in the projection of future source available. trends in vital rates: urbanization and female enrollment in secondary In many developing countries no recent census is available, and education, both of which are fairly stable in the short term. Other current estimates may be extrapolations by the country, United Nations variables, such as income, are subject to rapid changes and are agencies, or others. The lack or poor quality of base year data is an therefore unsuitable for use in predicting changes in vital rates. important source of error in projections, which often becomes magnified Complications in determining future trends in the components arise as the projections extend into the future. from the fact that not all future patterns reflect the past. In countries Few countries collect reliable data on net migration, as movements in, with high HIV prevalence, as in some Sub-Saharan countries, mortality and especially out of, countries often are not monitored. Data from trends are not likely to resemble past trends. but are more likely to turn censuses, in countries that receive more migrants as well as in countries upward as mortality from AIDS becomes more frequent. Projections for from which more migrants depart, are often used to obtain a picture of countries with measurable levels of HIV infection have been modified by migratory movements. separate projections of the impact of AIDS on mortality. In the most The cohort component method for projecting national populations is severely affected countries this has resulted in substantial declines in based on assumptions on future trends in the three components of life expectancy and other mortality measures. Migration trends are population growth: particularly difficult to predict, as recent migration flows often reflect * Fertility-the distribution over age at which women bear children. short-term causes of population movements, such as political violence, * Mortality-the distribution over age at which people die. economic differences, or natural disasters. * Migration-the number of people who move from one country to Once estimates of a population's current size and composition have another. been made-and a method to estimate future levels of fertility, mortaliy, In the World Bank's projections these future trends in vital rates and and migration has been established-the cohort component projection is migration are derived from recent country-specific trends, in combination carried out by applying age- and sex-specific estimates of the components with a set of assumptions and demographic models. to the age and sex distribution of the base population. The results consist For example, future fertility in countries with declining family size is of future estimates of population by age and sex, which in turn can be assumed to follow a pattern in the near future similar to that in the used as a base to which subsequent component estimates are applied. The interval for adding another billion in world population has Where the next billion will come from become shorter and shorter Millions. 1999-2014 Billions 1,000 - 7 *-Middle East & North Africa - Europe & Central Asia 6 .5800 -Latin America & Caribbean _ High-income countries edo00 East Asia & Pacific 4 400 3 2 ,. ,200 2 I1 ', I .' 200 | South Asia 0 .~~~~~~~~~~~~~~~~~~~~~~ 1804 1927 1960 1974 1987 1999 2014 Source: World Bank staff estmates. Source: World Bank staff estimates. 34 2000 World Development Indicators Most of the next billion will be born in low-income countries Rapid growth in the working-age population in low-income countries will add to population momentum Milions, 1999-2014 Age distriution Low-income countries, 1999 Low-income countries, 2014 _ *~~~~~~~ Low income 5.1% 69o 6 p Lower middle income Upper middie income . % 594 0 High income .3.1% Source: World Bank staff estimates. High income countries, 1999 High-income countries, 201 4 to education, health, and social security, but also birth and death rates. In 1999 a tliird of the people in the poorest countries were 14.5% 18.0% 10.2% in the young-age dependent group (under 15 years old), but only a small fraction were aged 65 or older (figure 2d). By contrast, the high-income countries have a much smaller percentage under 15 (about 18 percent in 1999), but much greater old-age dependency. By 2014 young-age dependency is expected to decline to 28 per- cent in the low-income countries as a result of projected fertility declines, and only a small increase is expected in the percentage at older ages. As a result the proportion of people of working age 0 hge 0-14 0 Age 15-64 Age 65+ will increase to 66 percent. The rapid increase in the number of young working-age people will contribute to "population momen- Source: World Bank staff estimates. tum." Although fertility rates will decline, the number of births will remain high because the number of couples entering reproduc- tive ages is outpaciilg thle declicie in fer-tility rates. Such population momentum is becoming more important in South Asia. Bangladesh tal development and allow the labor force to absorb new entrants. and India can expect to grow by 30-40 percent even as they reach The declining dependency projected for low-income countries thus replacement-level fertility, expected in the next 10-15 years. provides an opportunity as well as a challenge. A demographic bonus? New demnands for services Wrhy are these changes in age structure important? Because when Countries that experienced earlier fertility declines, such as fertilitydeclines,young-agedependencyratiosquicklyfollowsuit. those in E.urope, face rapid aging of their populations. In 2014 The ratio of working-age people to dependents rises as the young the proportion aged 65 or older in high-income countries will population increases more slowly than the working-age populatioii. reach 18 percent. These shifts change the demands on health This happened first in East Asian countries, followed by Latin Amer- care systems and other social services, many of which may be ica and South Asia. unsustairable when the full effects of the new age structure are The long period of economic growth in East Asia occurred felt. To the extent that better management of chronic conditions as young-age dependency dropped rapidly, but before the rise increases life expectancy at older ages, resources for old-age in old-age dependency, providing a demographic window of support and health care may have to increase beyond current opportunity. Bloom and Williamson (1998) estimate that a third expectations. of the per capita GNP growth in some East Asian countries is due Although developing countries have more time before their to this "demographic bonus." Pressures on education systems were populations reach a mature age structure, the population aging reduced, allowing greater coverage and improvements in qual- will be fa ter than in Europe because of the rapid increase in the ity. These shifts brought tranisitory rises in savings rates-which, availabili by of techlnologies for reducing fertility. For countries with increases in productive employment, gave an added boost now moving through these transitions, the required reforms in to the East Asian economies. the financing and delivery of social services need to be enacted But this demographic bonus does not come automatically. It well in advance of the time when larger beneficiary populations requires a combination of policies that strengthen human capi- will be using them. The timing of these reforms is critical. 2000 World Development Indicators 35 populations will be born, but may also lengthen the interval between the six billionth child and the seven billionth. Population and development The 1994 International Conference on Population and Development (ICPD) held in Cairo adopted a program of action calling for new 1. For summary statistics see tables 2.1 and 2.2. The full set of demog'aphic projections by approaches to address the relationships between population and country is available on the World Development Indicators CD-ROM. sustainable development. Human development issues-women's reproductive health, gender equality, adolescence-are at the core of the agreed action plan. The conference endorsed an approach to population that deemphasizes demographic targets and instead stresses individuals' reproductive health rights, such as access to family planning, safe pregnancy and delivery, and prevention and treatment of sexually transmitted diseases. Sustainable population growth is seen as best achieved through individual reproductive choices freely made by women and men. The ICPD action plan led to the formulation of several indicators and targets that are now core international development goals, such as maternal mortality ratios (to be reduced by 75 percent by 2015), or that inform other goals, such as access to reproductive health. This second group includes universal access to safe and effective contraceptive methods, by 2015; a 50 percent reduction in the number of people who want to space or limit births but are not using family planning, by 2005; and an increase in the presence of skilled attendants to 90 percent of all births, by 2015. In 1999, five years after the Cairo conference, the United Nations reviewed progress by countries in implementing the action program, at an ICPD + 5' intergovernmental meeting. Among the achievements noted at ICPD + 5 was the widespread acceptance of viewing population as more than a demographic concept: population has become recognized as part of the development agenda, with governments and nongovernmental organizations jointly implementing reproductive health programs. Nevertheless, some parts of the action plan were seen as lagging, among them the capacity for data collection and analysis. Inadequate capacity in many countries is making it difficult to monitor ICPD goals for improving reproductive health. fd@Xf258pf,%an'LJ-3~.,--, nd Most of the increase in the global population over the past five decades has occurred in developing countries, and future increases are projected to occur in the poorest of them, mainly in South Asia and Sub-Saharan Africa. Has this rapid population growth been good or bad for the economic prospects of these countries? The links between population growth and poverty are com- plex. Evidence suggests that high fertility is as much a symptom of poverty as a cause. The poor continue to experience unac- ceptably poor reproductive health, including unwanted fertility, malnutrition, and high child and maternal mortality rates (box 2b). While poverty affects all, many of the burdens of poverty weigh more heavily on girls and women. In most parts of the develop- ing world fewer girls than boys enroll, stay, and learn in school- with negative implications for future reductions in fertility and child mortality. Some of the factors that affect fertility, and thus population growth, can be addressed by ensuring that programs in health and education are more focused, taking into account the different sit- uations and needs of women and men. Since investment in health and education is the most widely accepted way of improving the asset base of the poor, gender-sensitive investment in human cap- ital now not only will improve the environment in which future 36 2000 World Development Indicators 2.1 Population Total Average annual population Age dependency Population aged Women population growth rate ratio 65 and above aged 65 and above dependents as proportion of working- millions age population % of tota per 100 men 1980 1998 2015 1980-98 1998-2015 1980 1998 1998 2015 1998 2015 Albania 2.7 3.3 3.9 1.2 1.0 0.7 0.6 6.5 8.6 128 123 Algeria 18.7 29.9 39.8 2.6 1.7 1. 0 0.7 3.8 4.7 115 117 Angola 7.0 12.0 19.4 3.0 2.8 0.9 1.0 2.9 2.5 124 123 Argentina 28.1 36 .1 42.8 1.4 1.0 0.6 0.6 9 .6 10.6 144 142 Armenia 3.1 3 .8 4.1 1.1 0 .4 0.6 0.5 8.4 10.9 150 157 Australia 14.7 18.8 21.5 1.4 0.8 0.5 0.5 11.8 15.2 129 120 Aulstri'a 7 .6 8.1 8.0 0.4 -0O.1 0.6 0.5 14.9 19.2 166 136 Azerbaijan 6.2 7.9 9.3 1.4 0.9 0.7 0.6 6.4 7.3 159 158 Bangladesh 86.7 125.6 161.8 2.1 1.5 1.0 0.8 3.3 4.0 81 94 Belarus 9.6 10.2 9.4 0.3 -0.5 0 .5 0.5 13.0 13.4 202 181 Belgiuom .. ... 9.8 10..2 10.2 0. 0.0 0.5 0.5 16.2 19.5 147 133 Benin 3 .5 5 .9 9.1 3.0 25.5 1.0 1.0 2.9 2.6 102 118 Bolivia 5.4 7.9 10.:9 2.2 1.9 0.9 0.8 3.9 4.6 123. 128 Bosnia and Henzegovina 4.1 3.8 4 .3 -0.5 08.8 -0.5 0.4 8.1 12.9 ~147 146 Botawana 0.9 1.6 1.8 3.0 0.9 1.0 0.8 2.3 1.7 173 ...146 Brazil 121.7 165.9 200.0 1.7 1.1 0.7 0.5 4.9 6.5 130.. 142 Bulgari'a 8.9 8.3 7.3 -0.4 -0 .7 0.5 0 .5 15.5 18.9 134 148 Burkina Faso 7.0 1-0.7 15.9 2,4 2.3 1.0 1.0 2.8 2.2 110 143 Burundi 4.1 6.5 9.2 2.6 2.0 09.9. 0.9'.. 2.6 1.9 155 153 Cambodia 6.8 11.5 14.8 2.9 1.5 0.7 0.8 3.0 3.8 179 159 Camenoon 8.7 14.3 20.3 2.8 2.1 0.9 0:.9 3.5 3.4 120 119 Canada 24.6 30 .3 33 .7 1.2 0.6 0.5 0:.5 12.3 15.9 134 126 Central African Republic 2.3 3.5 4.6 2.3. 1.6 0.8 0.9. 3.6 2.6 138 139 Chad 4.5 7.3 11.6 2.7 2.7 0.8 1.2 3.1 2.2 89 150 Chile 11.1 14.8 17.7 1.6 1.1 0.O.6 0.6 6.9 9.7 143 136 China 981.2 1,238.6 1,388.5 1.3 0 .7 0 .7 0.5 6.7 8.9 105 104 Hong Kong, Chine 5.0 6.7 7.9 1.6 1.0 0.5 0.4 10.0 12.8 125 113 Colombi'a 28.4 40.8 51.4 2.0 1.4 0.8 0.6 4.5 5.7 130 138 Congo. Dem. Rep. 27.0 48.2 79.1 3.2 2.9 .1.0 1.0 2.7 2.6 134 125 Congo, Rep 1.7 2.8 4.3 2.. 2.6 0.9 1.0 3.1 2.4 131 140 Costa Rica 2 .3 3.5 4.4 2.4 1.3 0.7 0.6 5.0 7.7 115 118 Cote dIlvoire 8 .2 14.5 19.1 3.2 1.6 1 .0 0.9 2.7 2.3 93 86 Croatia 4.6 4.5 4.3 -0.1 -0.3 0.5 0.5 14.1 18.1 168 156 Cuba 9.7 11.1 11.6 0.7 0.3 0.7 0.4 9.2 14.0 110 120 Czech Republic 10.2 10.3 9.9 0.0 -0.2 0 .6 0.4 13.5 18.6 160 141 Denmark 5.1 5.3 5.3 0.2 0.0 0.5 0.5 14.7 19.0 140 122 Dominican Republic 5.7 8.3 10.4 2.1 1.3 0.8 0.6 43.3 5.9 106 113 Ecuacor 8.0 12.2 15.6 2.4 1.5 0..9 0 .6 4 .4 5.7 119. 124 Egypt. Arab Rep. 40.9 61.4 78.7 2.3 15.5.. 0 .8 0.7 4.4 5.5 120 115 El Salvador 4.6 6.1 8.0 1.5 1.6 0.9 0 .7 4.7 5.1 131 135 Eritrea 2.4 3.9 5.7 2.7 2.3 .. 0 .9 2.7 2.8 130 121 Estonia 1.5 1.4 1.3 -0.1 -0.5 0.5 0.5 13.6 16.7 210 200 Ethiopia 37.7 61.3 87.6 2.7 2.1 0.9 1.0 2.7 2.0 127 105 Finland 4.8 5.2 5.3 0.4 0.1 0.5 0.5 14.5 20.2 165 138 France 53.9 58.8 61.1 0.5 0.2 06.6 0.5 15.5 18.1 150 139. Gabon 0.7 ...1.2 1.7 3.0 2.2 0.7 0.8 5.7 4.9 123 118 Gambia, The 0.6 1.2 1.8 3.6 2.2 0.8 0.8 3.0 3.4 121 118 Georgia 5.1 5.4 5.3 0.4 -0.1 0.5 0.5 12.2 14.7 170 172 Germany 78.3 82.0 78.7 0.3 -0.2 0.5 0.5 151.7 .20.3 167 133 Ghana 10.7 18.5 26.8. 3.0. 2.2 0o.9 0.9 . ..3..1 3.5 120 119 Greece 96 10.5 10.3 0.5 -0.1 0.6 0.5 16.9 21.0 127 132 Guatemnala 6.8 10.8 15.5 2.6 2.1 1.0 0.9 3.4 3.4 110 126 Guinea 4.5 7.1 10.0 2.6 2.0 0.9 0.9 2.6 2.7 110 105 Guinea-Bissau 0.8 1.2 1.6 2.1 1.8 0.8 0.9 4.0 3.5 124 120 Haiti 5.4 7.6 10.0 2.0 1.6 0.9 0.8 3.6 3.6 126 142 Honduras 3.6 6.2 8.8 3.0 2.1 1.0 0 .8 3.2 3.6 118 121 38 2000 Wor d Deve[opment Indicators 62 Si olo!3pu I luawudoIAG20 PIJOM 0006 66T E6 vET . EttT - 6 iT- 9 ... :L 69 . 65TUO2JPY U!sfl Kr I rT I ., ~ T K I.: /6T T96T YC9T 1796 90o 90' T'O TO 9 .T t96""-;- '''' 1 biGlSJnd ........ .. 06.........80 . ....VL .d9.S ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. . ....... ~...T.E... EfL.OT.9 9 60 0 *. 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I .. dd A..pe........ ....O ieuuee ..Ay.. .6~~~~~~~~T ' Z ' ' ' C i6' C6, * 2.1 Total Average annual population Age dependency Population aged Women population growth rate ratio 65 and above aged 65 and above dependents as proportion of working- mnillions %age population i % of total per 100 men 1980 1998 2015 1980-98 1998-2015 1980 1998 1.998 2015 1998 2015 Rwanda 5.2 8.1 11.8 2.5 2.2 1.0 0.9 2.0 ...1.9 137 116 Saudi Arabia 9.4 20.7 33.7 4.4 2.9. 0.9 0.8 2.8 4.4 97 71 Senegal 5. . 33 2.7 2.3 0.9 0.9 2.6 2.6 123 122 Sierra Leone 3 .2 4.9 6.7 2.3 1.9 0.9 0.9 2.6 2.8 134 134 Singapore 2.3 3.2 3.7 1.8 1.0 0.5 0.4 6.6 11.4 122 114 Slovak Republic 5.0 5 .4 5.5 0.4 0.1 0. 0.5. 11.1 13.4 159 158 Slovenia 1.9 2.0 1.9 0.2-02 0.5 . 31 1. 179 14 South Africa 27.6 41.4 49.4 2.3 1.0 0..7 0.6 4.8 4.7 164 124 Spain 37.4 39.4 38.1 0.3 -0.2. 0.6 0.5 16.3 18B.8 140 142 Sri Lanka 14.7 18.8 22.6 1.3 1.1 0.7 0.5 6.3 9.2 103 .126 Sudan ..1-8.7 28..3 40.6 2.3 2I.1 0.9 0.7 3.1 3.4 119 118 Sweden 8.3 8 .9 8.6 0.4 -0. 1 0.6 0.6 17.2 22.0 135 124 Switzerland 6.3 7.1 7.0 0.7 -0.1I 0.5 0.5 14.9 20.8 147 130 Syrian Arab Republic 8.7 15.3 21.8 3.1 2.1 1.1 0.8 3.0 3.5 111 128 Tajikistan 4.0 6.1 7.9 2.4 1.5 0.9 0.8 4.4 4.1 141 128 Tanzania 18.6 32.1 44.8 3.0 2.0 1.0 0.9 2.4 2.0 123. 112 Thailand 46.7 61.2. 71.0 1.5 0.9 0.8 05 5.3 7.9 130 130 Togo 2.6 4.5 6.3 3.0 2.0 0.9 1.0 3.0 2.5 124 120 Trinidad and Tobago 1.1 1.3 1.5 1.0 0.7 0.7 0.5 6.2 8.5 118 121 Tunisia 6 .4 9.3 11.5 2.1 1.2 0.8 0.6 5.6 6.5 97 117 Turkey 44.5 63.5 77.9 2.0 1.2 0.8B 0.5 5.5 6.9 119 122 Turkmenistan 2.9 4.7 6.0 2.8 1 .5 0 .8 0.7 4 .2 4.3 157 142 Uganda 12.8 20.9 30.7 2.7 2.3 1.0 1.0 2.1 1.3 115 96 Ukrai'ne 50.0 50.3 44.0 0 .0 -0 .8 0.5 0.5 13.9 14.9 205 184 United Arab Emirates 1.0 2.7 3.7 5.3 1.9 0.4 0.4 2.1 8.3 48 28 United Kingdom 56.3 59.1 59.2 0.3 0.0 0. 6 0.5 15.8 18.9 138 125 United States .......227.2 270.3 304.9 1.0 0.7 0.5 0.5 12.3 15.1 142 132 Uruguay 2.9 3.3 3.6 0.7 0.6 0.6 0.6 12.5 12.8 148. 161 Uzbekistan 16.0 24.1 30.3 2.3 1.4 0.9 0.8 4.4 4.6 152 139 Venezuela, RB 15.1 23.2 302.2 2.4 1.5. 0.8 0.6 4.3 63.3 122 123 Vietnam 53.7 76 .5 94.4 2.0 1.2 0.9 0.7 4.9 4.9 14.2 146 West Bank and Gaza .. 2.7 '5.0 .. .3.5 .. 1.0 35.5 2.8. 126 140 Yemen, Rep.. 8.5 .16.6 26.6 3.7 2.8 1.1 1.1 3.0 2.4 91 105 Yugoslavia, FR (Setb./Mont.) 9.8 10.6 10.7 0.5 0.0 0.5 0.5 12.9 14.6 129 130 Zambia 5.7 9.7 13.0 2.9 1.7 1.1 0.9. 2.2 1.9 97 94 Zimbabwe 7.0 11.7 14.1 2.8 1.1 1.0 0.8 2.8 2.3 117 108 Low income 2,526.6 3.536.4 4,436.2 1.9 1.3 .0.8 0.6 5.0 5.9 109 108 Excl. China & India 840.4 1,295.0 1,797.1 2.4 1.9 0.9 0.8 3.4 3.7 116 118 Middle income 1,114.4 1,474.4 1,748.3 1.6 1.0 0.7 06.6 6 .6 7.7 151 141 Lower middle income 677.4 886.5 1,039. 1.5 0.9 0.7 0.6 7.0 7.6 158 144 Upper middle income 437.1 587.9 708.5 1.6 1.1 0.7 0.6 6.1 7.9 139 136 Low & middle income 3,641.0 5,010.8 6,184.5 1.8 1 .2 0.8. 0.6 5.5 6.4 122 118 East Asia & Pacific 1,397.8 1,817.1 2,098.6 1.5 0.8 0.7 0.5 6.1 8.0 110 109 Europe & Central Asia 425.8 474.6 482.8 0.6 0.1 0.6 0.5 10.6 11.7 181 165 Latin America & Carib. 360.3 501.7 623.3 1.8 1. 3 0.8 0..6 5.2 6.6 129 136 Middle East & N. Africa 174.0 285.7 390.2 2.8 1.8 0.9 0.7 4.0 4.7 110 110 South Asia 902.6 1,304.6 1,676.2 2.0 1.5 0.8 0.7 4 .4 5.2 104_ 106 Sub-Saharan Africa 380.5 627.1 913.5 2.8 2 .2 0.9 0.9 2.9 2.7 126 .120 High income 789.1 885.8 928.4 0.6 0.3 0.5 05.S 14.1 18.0 143 .131 Europe EMU 275.9 291.1 286.5 0.3 -01 0.5 0.5 15.8 19.6 152 138 40 2000 Wenld Development Indicators Knowing the size, growth rate, and age distribution of * Total population of an economy includes all resi- a country's population is important for evaluating the dents regardless of legal status or citizenship- welfare of the country's citizens, assessing the pro- faster than absolute growth Is except for refugees not permanently settled in the ductive capacity of its economy, and estimatingthe quan- country of asylum, who are generally considered part tity of goods and services that will be needed to meet Average annual Average an eual of the population of their country of origin. The indi- its future needs. Thus governments, businesses, and population growth population growth cators shown are midyear estimates for 1980 and (mill ions) rate (Vol) anyone interested in analyzing economic performance ( 1998 and projections for 2015. * Average annual must have accurate population estimates. 100 -2 population growth rate is the exponential change for Population estimates are usually based on national the period indicated. See Statistical methods for population censuses, but the frequency and quality of 80 more information. * Age dependency ratio is the these vary by country. Most countries conduct a com- 60 J ratio of dependents-people younger than 15 and plete enumeration no more than once a decade. Pre- 1 older than 64-to the working-age population-those census and postcensus estimates are interpolations or 40 aged 15-64. * Population aged 65 and above is the extrapolations based on demographic models. Errors and percentage of the total population that is 65 or older. - 20U undercounting occur even in high-income countries; in * Women aged 65 and above is the ratio of women developing countries such errors may be substantial 6 to men in that age group. because oflimits on thetransport, communications, and 1970-79 1980-89 1990-99 2000-09 other resources required to conduct a full census. More- Source: World Bank staff estimates.o s over, the international comparability of population indi- cators is limited by differences in the concepts, The global populatlon growth rate has dleclined The World Bank's population estimates are produced by rapidly since the 1.970s, but the number of definitions, data collection procedures, and estimation people added each year started to declne- its Human Development Network and Development Data methods used by national statistical agencies and other gradually-only In the early S990s. In ithe first Group in consultation with its operational staff and res- organizations that collect population data. decade of the 21st century the world's ident missions. Important inputs to the World Bank's population Is projected to grow LIr pe rcent a Of the 148 economies listed in the tab e, 125 (about year, adding 70 mllicon people annually. demographic work come from the following sources: 85 percent) conducted a census between 1989 and census reports and other statistical publications from 1999. The currentness of a census, along with the avail- country statistical offices; demographic and health sur- ability of complementary data from surveys or registraion veys conducted by national agencies, Macro Intemational. systems, is one of many objective ways tojudge the qual- and the U.S. Centers for Disease Control and Preven- ity of demographic data. In some European countries tion; United Nations Statistics Division, Population and registration systems offer complete information on Vital Statistics Report (quarterly); United Nations Pop- population in the absence of a census. See Primary data ulation Division, WorldPopulation Prospects: The 1998 documentation fur the most recent census or survey year Revision; Eurostat, Demographic Statistics (various and for registration completeness. years); South Pacific Commission, Pacific Island Popu- Current population estimates for developing countries lations Data Sheet 1999; Centro Latinoamericano de that lack recent censusbased data, and pre- and post- Demografla. Boletin Demografico (vanous years); and U.S. census estimates for countries with census data, are pro- Bureau of the Census, International Database. vided by national statistical offices, the United Nations Population Division, or other agencies. The standard esti- mation method requires fertility, mortality, and net migra- tion data, which are often collected from sample surveys, some of which may be small or limited in coverage. These estimates are the product of demographic modeling and so are also susceptible to biases and errors because of shortcomings of the model as well as the data. Popula- tion projections are made using the cohort component method (see box 2a in the introduction to this section). The quality and reliability of official demographic data are also affected by the public trust in the government, the government's commitment to full and accurate enu- meration, the confidentiality and protection against mis- use accorded to census data, and the independence of census agencies from undue political influence. 2000 World Development Indicators 41 sjole3ipul juawjdo1eAeo PIJOM 0O06 z* 26 65 ~~~ ~~~ ~~TS 9 9,0 96E VT TT Es S £ 9 OT seinlPUOH I. 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T 6-E 6-0 E'T TE-SZ!LUeUAp, T O!WCZd, 2.2 Crude death Crude birth Projected Population Average annual population rate rate population momentuin growth rates by 2030 Age Age Age per 1.000 per 1,000 0-14 15-64 65+ people people % 1980 1998 1980 1998 millions 1998 1980-98 1998-2015 1980-98 1998-2015 1980-98 1998-2015 Hungary 14 14 14 10 9 0.9 -1.6 -1.6 0.0 -0.4 0.0 0.6 India 13 9 34 27 1.398 1.4 1.3 0.0 2.3 1.9 2.8 2.4 Indonesia 12 8 34 23 285 1.4 0.3 -0.2 2.5 1.7 3.4 2.9 Iran, Islamic Rep. 11 5 44 22 98 1.7 1.4 0.0 3.2 2.5 4.3 2.2 Iraq 9 10 41 32 38 1.6 2.4 0.5 3.5 2.8 3.8 3.8 Ireland 10 9 22 15 4 1.3 -1.2 -0.2 1.1 0.7 0.8. 1.6 Israel 7 6 24 22 9 1.5 1.6 0.2 2.7 1.8 2.8 2.3 Italy 1 10 11 9 50 0.9 -2.2 -1.6 0.4 -0.6 1.5 ~1.3 Jamaica 7 6 28 23 3 1.5 -0.2 -0.8 1.9 1.5 0.7 1.6 Japan 6 7 14 10 117 0~.7 -2.0 -0.9....... 0 5 -0.7 3..6 2.5 Jordan.. 4 318 1.7 2.9 1.0 5:.1 ...........2..9 3.7 4.4 Kazakhstan8 10 24 14 17 1.2 -0.5 -0.8 0.5 0.6 1.1 1.1 Key 13 12 51 35 47 1.4 2.5 0.4 3.8 2.6 2.1 0.0 Korea, Dem. Rep.6 9 21 20 29 1.2 -0.6 -0.8 2.5 1.1 3..4 2.9 Korea, Rep.6 6 22 14 53 1.2 -1.3 -0.9 1.9 0.6 3.9 3.8 Kuwait 4 2 3 7 2 3 3 1.7 1.0 0.4 2.1 3.2 3.7 8.1 Kyrgyz Republic 9 7 30 22 7 1.5 1.2 -0.9 1.6 2.0 1.5 0.7 Lao PDR 20 13 45 38 9 1.5 2.6 1.3 2.2 2.8 3.8 1.7 Latvi a13 14 15 8 2 0.9 -0.7 -3.0 -0.2 -. 0.2 0.6 Lebanon 9 6 30 21 6 1.5 0.8 -0.5 2.5 2.0 2.3 1.3 Lesotho 15 13 40 35 3 1.4 2.1 0.4 2.6 2.3 2.1 2.4 Liy 12 4 46 29 9 1.7 2.1 0.5 3.8 2.6 4.7 4.9 Lithuania 10 11 16 10 4 1.0 -0.4 .-1.8 0.6 0.0. 1.2 1.1 Macedonia, FYR 7 8 21 15 2 1.2 -0.8 -0.8 .... ..0.6 0.6 2.1 2.1 Madagascar 16 11 47 41 30 1.7 2.9 1.6 2.8 3.3 1.2 3.6 Malawi 23 23 55 47 20 1.3 2.8 1.6 3.1 2.7 3.5 2.1 Malaysia 6 5 31 25 34 1.5 2.0 0.0 3.0 2.2 3.2 4.0 Mali 22 16 49 . .7 23 1.6 2.7 2.2 2.5 3.2 4.0 1.6 Mauritania 19 13 43 40 5 1.5 2.7 1.4 2.7 2.9 3.0 2.5 Mauritius 6 7 24 17 1 1.3 -0.8 -0.1 1.6 1.1 3.8 2.9 Mexico7 5 34 28 141 1.6 0.4 -0.2 3.0 1.9 2.8 3.1 Moldova 10 9 20 10 4 1.1 -0.1 -1.6 0.4 0.2 1.6 0.4 Mongolia 11 7 38 214 1i.6 1.5 -0.5 3.0 2.3 4.1 2.2 Morocco 12 7 38 25 41 1.5 0.6 0.0 2.9 2.0 2.3 2.6 Mozambique 20 20 46 41 30 1.4 2.0 1.2 1.7 2.7 3.1 0.8 Myanmar 14 10 36 26 59 1.3 -0.1 0.8 2.4 1.2 2.4 1.9 Namibia 14 13 41 35 3 1.3 2.5 0.9 2.8 2.4 2.6 1.0 Nepal 17 11 43 34 40 1.5 2.4 1.1 2.6 2.7 3.5 2.6 Netherlands8 9 13 12 16 1.0 -0.5 -0.9 0.7 0.1 1.5 1.9 New Zealand9 7 16 15 4 1.2 0.2 -0.5 1.3 0.5 1.9 1.9 Nicaragua I11 5 46 3 19 1.7 2.2 0.5 3.2 3.2 3.7 3.1 Nige 23 18 51 52 24 1.5 3.5 2:8 3.1 3.3... ...3.2 2.2 Nigeria 18 12 50 40 252 1.5 2.7 2.0 3.1 2.9 2.7 3.2 Norway 10 10 12 13 5 0.9 -0.3 -0.6 0.6 0.3 0.7 1.2 Oman 10 3 45 29 4 1.8 4.0 0.3 4.2 3.2 .4.1 5.6 Pakistan 15 8 47 35 244 1.7 2.3 1.1 2.8 3.0 3.1 3.3 Panama 6 5 30 22 4 1.5 0.7 -0.5 2.6 1.8 2.9 3.1 Papua New Guinea 14 10 36 327 i1.4 1. 0.8 2.5 2.3 6.0 3.3 Paraguay 8 5 37 30 9 1.6 2.8 0.2 3.2 2.8 1.4 3.0 Peru 10 6 35 25 37 1.5 1.0 0.1 2.6 2.0 3.2 2.8 Philippines9 6 35 28 119 1.6 1.8 0.1 28 2.3 3.8 3.8 Poland 10 10 20 10 38 1.1 -0.4 -1.6 0.6 0.2 1.2 1.3 Portugal 10 11 16 12 10 1.0 -2.1 -0.9 0.4 0.1 2.5 0.2 Puerto Rico 6 7 23 15 5 1.3 -0.4 -0.3 1.5 0.9 2.3 2.2 Romania 10 12 18 11 20 1.0 -1.8 -1.8 0.5 -0.1 1.2 0.6 Russian Federation 11 14 16 9 129 0.9 -0.3 -1.9 0.3 -0.1 1.3 0.2 2000 World Development Indicators 43 2.2 Crude death Crude birth Projected Population Average annual population rate rate population momentum growth rates by 2030 Age Age Age per 1,000 per 1,000 0-14 15-64 65+ people people%% S 1.980 1998 1950 1998 millions 1998 1980-98 1998-2015 1980-98 1998-2015 1980-98 1998-2015 Rwanda 19 21 Si. 46 15 1.3 2.1 1.7 3.0 2.6 1.6 2.0 Saudi Arabia 9 4 43 34 46 1.6 4.0 2.3 4.7 3.1 4.4 5.5 5, r.,~~~~ 15 13 17 1.41 27 1 25 Sierra Leone 29 25 49 45 9 1.4 2.6 1.1 2.0. 2.5 1.2 2.4 Singapore 5 5 17 13 4 1.0 0.8 -1.0 2.0 1.1 3.6 4.2 Slovak Republic 10 10 19 11 5 1.1 -0.8 -1.4 0.8 0.3 0.8 1.2 Slovenia 10 9 15 9 2 0.9 -1.6 -1.7 0.6 -0.3 1.0 1.7 South Africa 12.........9 36 25 ....56 1.3 1.3 0.0 2.7 1.5 4.2 1.0 Spain 8 9 15 9 36 1.0 -2.8 -1.0 0.8 -0.2 2.6 0.7 Sri Lanka 6 6 28 18 25 1.4 0.0 0.2 1.9 1.2 3.4 3.4 Sudan 17 11 45 33 50 1.4 1.6 1.8 2.8 2.3 3.1 2.6 Sweden 11 11 12 10 8 0.8 0.1 -1.9 0.3 -0. 1 0.7 1.3 Switzerland 9 9 12 II. 7 0.9 -0.1 -. . -0.2 1.0 1.9 Syrian Arab Republic 9 5 46 29 27 1.7 2.4 0.3 3.8 3.1 2.8 3.0 Tajikistan 8 5 37 21 10 1.6 2.1 -0.8 2J.7 2.8.. 2..2 1.1 Tanzania 15 16 47 41 56 1.3 2.8 1.2 3.2 2.6 3.3 0.8 Thailand 8 7 28 17 7 7 7 1.3 -0.8 -0.3 2.6 1.1 3.8 3.2 Togo 16 16 45 40 8 1.4 3.1 1.1 2.8 2.8 2.6 0.9 Trinidad and Tobago 7 6 29 15 2 1.3 -0.3 -0.9 1.5 1.1 1.6 2.6 Tunisia 9 6 35 18 13 1.5 0.7 -0.5 2.8 1.9 4.4 2.1 Turkey 10 6 32 21 88 1.4 0.4 0.1 2.8 1.5 2.8 2.6 Turkmenistan 8 6 34 20 7 1.5 2.3 -0.6 3.2 2.5 2.7 .1.6 Uganda 18 20 49 47 41 1.3 2.8 1.7 2.6 2.9 1.8 -0.4 Ukraine 11 15 15 9 40 0.9 -0.7 -2.2 0.1 -0.5 --0.9 ..-0.4 United Arab Emirates 5 3 30 17 4 1.2 5.2 0.2 5.3 1.9 8.1 9.9 United Kingdom 12 11 13 12 59 1.1 -0.3 -1.0 0.4 0.0 0.5 1.0 United States 9 9 16 14 327 1.2 0.8 -0.1 0.9 0.7 1.5 1.9 Uru ua 10 9 19 17 4 1.2 0.2 -0.2 0.7 0.8 1.7 0.7 Uzbekistan 8 6 34 2 36 1.6 1.9 -1. . . 1..5 1.6 Venezuela. RB 6 4 33 25 36 1.6 1.5 -0.2 2.9 2.2 3.9 3.8 Vietnam 8 6 36 21 110 1.5 0.8 -0.8 2.7 2.1 2.1 1.1 West Bank and Gaza .. 5 . 42 7 1.9 . 4.3 2.2 Yemen, Rep. 19 12 53 40 36 1.6 3.5 2.0 39 3.5 4.6 1.3 Yugoslavia, FR (Serb./Mont.) 9 10 18 11 11 .0.. . -0.3 -0.8 0.5 0.2 2.0 0.8 Zambia 15 19 50 42 16 1.3 2.5 0 7 3.3 2.5 2.4 0.8 Zimbabwe 12 13 43 31 16 1.3 2.1 -0.5 3.5 2.1 3.1 0.1 ........ . ... . .. .... ...... .......'....... .. .... . . . . ......... . Low income 11 9 31 26 5,149 1.4 1.0 0.3 2.3 1.8 3.0 2.3 Exci. China & India 16 12 43 34 2,246 1.5 2.0 1.1 2.7 2.5 2.7 2.3 Middle Income 9 8 28 20 1,957 1.4 0.7 -0.2 2.0 1.4 2.1 1.9 Lower middle income 10 9 28 20 1,158 1.3 0.8 -0.4 1.8 1.4 2.0 1.5 Upper middle income 8 7 28 21 798 1.4 0.5 -0. 1 2.2 1.4 2.3 2.6 Low & middle Income 11 9 30 24 7,106 1.4 0.9 0.1 2.2 17 2..7 2.2 East Asia & Pacific 7 7 22 18 2,284 1.3 -0.2 -0.6 2.1 1.2 3.2 2.5 Europe & Central Asia. 10.1 19 12 489 1.1 -0.1 -1.3 0.8 0.4 1.3 0.7 Latin America & Carib. 8 6 31 23 715 1.S 0.7 -0. 1 2.5 1.8 2.7 2.7 Middle East & N. Africa 12 7 41 27 475 1.6 1.9 0.4 3.3 2.5 3.4 2.8 South Asia 14 9 37 28 1,951 1.5 1.5 0.2 ..2.4 2.1 2.8 2.5 Sub-Saharan Africa 18 iS 47 40 1,191 1.4 2.7 1.6 2.9. 2.7 .8 1.8 High income 9 9 14 12 937 1.0 -0.2 -0.6. 0.9 0,.2... 1. 7 1.7 Europe EMU 10 10 13 10 276 1.0 -1.2 -1.1 0.6 -02.2. 1.2 1.2 44 2000 World Development Indicators 2.2 The vital rates shown in the table are based on data the youthful age structures typical of developing coun- * Crude death rate and crude birth rate are the num- derved from birth and death registration systems, cen- try populations. It occurs because large cchorts born ber of deaths and the number of live births occurring dur- suses, and sample surveys conducted by national in previous years move through the reprodt ctive ages, ing the year, per 1,000 population estimated at midyear. statistical offices, United Nations agencies, and other generating more births than are offset bv deaths in Subtracting the crude death rate from the crude birth organizations. The estimates for 1998 for many coun- the smaller, older cohorts. rate provides the rate of natural increase, which is equal tries are based on extrapolations of levels and trends The growth rate of the total population (see table to the population growth rate in the absence of net measured in earlier years. 2.1) conceals the fact that different age groups may migration. * Projected population by 2030 is the total Vital registers are the preferred soujrce of these grow at very different rates. In many developing coun- number of people expected to be alive in 2030, based data, but in many developing countries systems for reg- tries the population under 15 was earlier growing on a cohort component projection in which assumed istering births and deaths do not exist or are incom- rapidly, but is now starting to shrink. Previously high future patterns in fertility, mortality, and international plete because of deficiencies in geographic coverage fertility rates and declining mortality are now reflected migration are applied to the current age structure. or coverage of events. Many developing countries in rapid growth of the working-age population. * Population momentum isthe ratio of the population carry out specialized household surveys that esti- when zero growth has been achieved to the population mate vital rates by asking respondents about births in year t(in this case 2000), given the assumption that and deaths in the recent past. Estimates derived in fertility remains at replacement level from year tonward. Growth in the woeking-age and eldarly this way are subject to sampling errors due to inac- Growincos lhae accelerat de * Average annual population growth rates are calcu- curate recall by the respondents. In developing countries lated using the exponential endpoint method (see Sta- The United Nations Statistics Division monitors the tistical methods for more information). completeness of vital registration systems. It com- Average annual % growth, 1995-99 2.5- piles quarterly reports of the latest birth and death Data s,ources rates, as well as an indication of their completeness, 2.0 in the Population and Vital Statistics Report The share 1.5 The World Bank's population estimates are produced of countries with at least 90 percent complete vita reg- by its Human Development Network and Development istration increased from 45 percent in 1988 to 54 per- 1.0 Data Group in consultation with its operational staff and cent in 1999. Still, some of the most populous 0.5 resident missions. Important inputs to the World Bank's developing countries-China, India, Indonesia, Brazil, 0 demographic work come from many sources: census Pakistan, Nigeria, Bangladesh-do not have complete : reports and other statistical publications from country vital registration systems. Fewer than 25 percent of vital -05 Midle High World statistical offices; demographic and health surveys events worldwide are thought to be recorded. income income income conducted by national sources, Macro International, and International migration is the only other factor the U.S. Centers for Disease Control and Prevention; * hge 0-14 besides birth and death rates that directly determines 1 Age 15-64 United Nations Statistics Division, Population and Vital a country's population growth. In the industrial world Age 65+ - Statistics Report (quarterly); United Nations Population about 40 percent of annual population growth in Source: World Bank staff estimates. Division, World Population Prospects: The 1998 Revi- 1990-95 was due to migration, while in the develop- Population dynamics are reflected In thie growth sion; Eurostat, Demographic Statistics (various years); ing world migration reduced the population growth rate rates of different age groups. Changes In the South Pacific Commission, Pacific Island Populations by about 3 percent. Estimating international migra- size of the youth population (age 0-14), once Data Sheet 1999; Centro Latinoamericano de the driving force behind totai population growth, tion is difficult. At any time many people are located are no longer an Important factor. Lowver birth Demografia, Boletin Demograf/co (various years); and outside their home country as tourists, workers, or rates combined with an Increasing nmmber of U.S. Bureau of the Census, International Database. refugees or for other reasons. Standards relating to the women of childbearing age Indicate that the slze of the youth population will remain almost duration and purpose of intemational moves that qual- constant In the near future. In contiast, the ify as migration vary, and accurate estimates require working-age (i5-64) and elderly populations are information on flows into and out of countries that is both Increasing rapidly In many low- and middle- Income countries, while In high-income difficult to collect. countries only the elderly population Is growing. Over the next several decades the population of low- and middle-income countries will continue to grow. The rate of growth will decline, but the absolute increases will be large-and accompanied by substantial shifts in the age structure. Even when fertility reaches the replacement level of about two children per couple, the number of births will remain high-and population growth will not stop for several decades. This phe- nomenon, called population momentum, is a facet of 2000 World Development Indicators 45 2.3 Labor force structure Population aged Labor force 15-64 Average annual Total growth rate Female Children 10-14 millions millions % of l abor force % of age group 1980 1.998 1980 1998 2010 1980-98 1998-2010 1980 1998 1980 1998 Albania 2 2 1 2 2 1.7 1.6 38.8 41.1 4 1 Algeria 9 18 5 10 15 3.9 3.4 21.4 26.4 7 1 Angola 4 6 3 6 8 2.6 3.1 47.0 46.3 30 26 Argentina 17 23 11 14 18 1.7 2.0 27.6 32.3 8 3 Armenia 2 2 1 2 2 1.4 1.4 47.9 48.4 0 0 Australi'a 10 13 7 10 11 1.9 0.9 36.8 43.3 0 0 Austria 5 5 3 4 4 0.6 0.0 40.5 40.3 0 0 Azerbaij.an 4 5 3 3 4 1.4 2.0 47.5 44.2 0 0 Bangladesh 44 71 41 64 83 2.5 2.2 42.3 42.3 35 29 Belarus 6 7 5 5 5 0.2 0.0 49.9... 48.8 0... 0 Belgium 6 7 4 4 4 0.3 0.1 33.9 40.6 0 0 Benin 2 3 2 3 4 2.6 2.9 47.0 48.3 30 2 7 Bolivia 3 4 2 3 4 2.5 2.6 33.3 37.6 19 13 Bosnia and Herzegovina 3 3 2 2 2 0.5 1.1 32.8 38.1 1 Botswana 0 1 0 1 1 3.0 1.3 50:.1 45..5 26 15 Brazil1 70 108 47 76 90 2.6 1.3 28.4 35.4 19 15 Bulgaria 6 6 5 4 4 -0.5 -0.7 45.3 48.2 0 0 Burktina Faso 3 5 4' 5 7 1.9 2.0 47.6 46.6 71 47 Burundi2 3 2 ..4 5 2.5 2.4 50.2 48.9 50 49 Cambodia 4 6 4 6 8 2.7 2.2 55.4 51.9 27 24 Cameroon 5 7 4 6 8 2.7 2.3 36.8 37.8 34 24 Canada 17 21 12 16 18 1.6 0.6 39.5 45.4 0 0 Central African Republic 1 2 . Chad 2 3 2 3 5 2.5 2.9 43.4 44.6 42 37 Chile 7 10 4 6 8 2. 2.1 26.3 32.9 0 0 China 586 837 540 743 822 1.8 0.8 43.2 45.2 30 9 .Hong.Kong, C.hinfa 3 3. 3 4 1.9. 1.4 34.3 36.9 6 0 Colombia 16 25 9 18 23 3.5 2.2 26.2 38.2 12 6 Congo, Dem. Rep. 14 24 12 20 29 3.0 2.9 44.5 43.5 33 29 Congo Rep. 1 1 1 1 2 2.7 2.8 42.4 43.4 27 26 Costa Rica 1 2 1 1 2 3.2 1.7 20.8 30.5 10 5 CMe dIlvoire 4 8 3 6 7 3.2 2.0 32.2 33.1 28 19 Croatia3 3 2 2 2 -0O.1 -0.2 40.2 43.9 0 0 Cuba 6 8 4 5 6 2.2 0.6 31.4 38.9 0 0 Czech Republic 6 7 5 6 6 0.4 -0.4 47.1 47.4 0 0 Denmark3 4 3 3 3 0.5 -0.5 44.0 46.4 0 0 Dominican Republic 3 5 2 4 5 2.9 2.3 24.7 30.1 25 14 Ecuador 4 7 3 5 6 3.3 2.8 20.1 27.4 9 5 Egypt, A.ab Rep. . 23 37 14 23 32 2.6 2.8 26.5 29.7 18 10 El Salvador2 4 2 3 4 2.7 2.9 26.5 35.5 17 14 Eritrea 2 1 2 3 2.6 2.7 47.4 47.4 44 39 Estonia 1 1 1 1 1 -0.1 -0.1 50.6 49.0 0 0 Ethiopia 20 31 17 26 34 2.4 2.1 42.3 40.9 46 42 Finland 3 3 2 3 2 0.5 -0.5 46.5 47.9 0 0 France 34 39 24 26 27 0.6 0.3 40.1 44.8 0 0 Gabon 0 1 0 1 1 2.3 2.0 45O.0. 44.5 29 16 Gambia. The 0 1 0 1 1 3.6 2.3 44.8 45.0 44 35 Georgia 3 4 3 3 3 0.3 0.2 49.3 46.6 0 0 Germany 52 56 38 41 40 0.5 -0.2 40.1 42.1 0 0 Ghana 6 10 5 9 12 3.0 2.7 51.0 50.6 16 13 Greece 6 7 4 5 5 1.0 0.2 27.9 37.4 5 0 Guatemalia 3 6 2 4 6 2.9 3.4 22.4 27.8 19.. 15 Guinea 2 4 2 3 4 2.2 2.2 47..1 47.2 41. 32 Guinea-Bissau 0 1 0 1 1 1.8 1.9 39.9 40.4 43 37 Haiti 3 4 3 3 4 1.6 1.6 44.6 43.0 33 24 Honduras 2 3 1 2 3 3.5 3.5 25.2 31.0 14 8 46 2000 World Development Indicators L* sjoje3Ipul iu9wdoij'Ae PIJOM 000Z 6 .~~~~~~~~-r.~.9.g:. . . ........ 6 6 . 2i *- . -- ... .. .......... .... ......... .. .. ............ . .......... - . .~~~~~~~~~~~~~~~~~~~~~~~~~~~ . .~~~~~~~~~~~~~~~~~...~~........ ... 6 .~~~~9 . 6:6.2. t..i" i ...... -Z-, ST 90 6. U 6 2 o 6 . 670 g;2 to- 9~~~~~~~~~~~~~~~~~~~~~~~~~~~~6. 1...... 217 99.0~~~C17t 996.6EE ' 17T T. 2i 6i: 172~. .. 01.....v 6:... .......e 176 ~~~~~~t7 L 9.~. . 6.tT.i: . 66 ...191.........6 .. 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Z --~~~~~~~de ui6i~~ o o.1:0 17 L p (~uoLAJ/q1e . d.... o 17.1:9~~~~ ~~~ ~~6-~ . 66f" un . 9*98 o917 £Z 9 Li: 0-1: 9 . 81: 6 UP150jPPZfl~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. ....... .... ... .. . ...... - . 6 . i:~~~~~~~T7 .T T9- T .ITqo6' PllZ 6, 6,,Y 609~ 6 . di Z'UlPL..j..fl64 88 6t' 91:1'~Z-c.. 6kLt 6£6.OT1 ' "' ,s,un 9~~~~~~~~~~~~~~9 . d t' £6 8 6 9 9T" -1:T -6- 'it pP!Ui 96 £98 8Lt7~~1`6, V' C1 L£. -4 9£ 6.86 666 , .*~£ . 6 9t 6 9T,Tu pnS 0 a 917Z0u 6 ... L d SP1r 6 t7 96'0 L 6i*6. 8E Iqnd ad j.p OI 4o& 6t : ... T ... jd 5 ! O., £2 066 696 O6 9 T Ti: . 9i: dT!qj!p 066 096 996' 891T OTOZ-9T8-8T OO 866T 0:.T 866JOO6T 0njla? j oo 0oqe8L8 £91 %* 60 £ 8£ . 6ii sdoIIi2A .9 .VUOPILi TewaA 6£e 6Vmj, OT 2 .6 . 92!2t'pl Inuue ae6jaAV p,9-SgT goioj Joqel Pae~ uoIzeIndod 2.3 _-0. =-Ki17W4F1F-M The labor force is the supply of labor available for the as economic. In many countries large r umbers of * Population aged 15-64 is the number of people who production of goods and services in an economy. It women work on farms or in other family ente prises with- could potentially be economically active. * Total labor includes people who are currently employed and peo- out pay, while others work in or near their romes, mix- force compnses people who meet the ILO definition of the ple who are unemployed but seeking work, as well as ing work and personal activities duringthe day. Countnes economically active population: all people who supply first-time job-seekers. Not everyone who works is differ in the criteria used to determine the extent to labor for the production of goods and services during a included, however. Unpaid workers, family workers, which such workers are to be counted as part of the specified period. It includes both the employed and the and students are among those usually omitted, and in labor force. unemployed. While national practicesvary in thetreatment some countries members of the military are not Reliable estimates of child labor are hard to obtain. of such groups as the armed forces and seasonal or part- counted. The size of the labor force tends to vary dur- In many countries child labor is officially presumed not time workers, in general the labor force includes the ing the year as seasonal workers enter and leave it. to exist and so is not included in surveys or in official armed forces, the unemployed, and first-time job-seekers, Data on the labor force are compiled by the Inter- data. Underreporting also occurs because cata exclude but excludes homemakers and other unpaid caregivers national Labour Organization (ILO) from census or children engaged in agricultural or househcId activities and workers in the informal sector. * Average annual labor force surveys. For international comparisons, with theirfamilies. Most child workers are in Asia. But gowfi rate ofthelaborforce is calculated usingthe expo- the most comprehensive source is labor force sur- the share of children working is highest in Africa, nential endpoint method (see Statistical methods for veys. Despite the ILO's efforts to encourage the use where, on average, one in three children ciged 10-14 more information). * Females as a percentage of the of international standards, labor force data are not fully is engaged in some form of economic act vity, mostly labor force show the extent to which women are active comparable because of differences among countries, in agriculture (Fallon and Tzannatos 199E). Available in the labor force. * Children 1O-4 in the labor force and sometimes within countries, in their scope and cov- statistics suggest that more boys than gir s work. But are the share of that age group active in the labor force. erage. In some countries data on the labor force refer the number of girls working is often underestimated to people above a specific age, while in others there because surveys exclude those working as tnregistered Da" sous . is no specific age provision. The reference period of domestic help or doing full-time househald work to the census or survey is another important source of enable their parents to work outside the home. Population estimates are from the World Bank's pop- differences: in some countries data refer to a per- ulation database. Labor force activity rates are from son's status on the day of the census or survey or dur- the ILO database Estimates and Projections of the Eco- ing a specific period before the inquiry date, while in nomically Active Population, 1950-2010. The ILO pub- others the data are recorded without reference to any lishes estimates of the economically active population period. In developing countries, where the household in its Yearbook of Labour Statistics. is often the basic unit of production and all members contribute to output, but some at low intensity or irreg- ular intervals, the estimated labor force may be sig- The gap between men's and women's labor force partbicpation Is narrowing nificantly smaller than the numbers actually working (ILO, Labor force participation rate (% ) Yearbook of Labour Statistics 1997). 1sso 1997 The labor force estimates in the table were calcu- Male Female Male Female lated by World Bank staff by applying labor force activ- colcanibs 7e 4 2 .4 78.4 52 0 ity rates from the ILO database to World Bank population Egypt. Arab Rep. 72.4 7.4 73.4 21.6 estimates to create a seres consistent with these pop. Fiace 70.7 4 i 62 3 4. 2 Indonesia 84.7 44.3 82.3 52.8 ulation estimates. This procedure sometimes results Pal, alan s. 2 i o) d2.3 12 . in estimates of laborforce size that differ slightlyfrom Senegal 8r.0 61.3 85.7 61.2 those published in the ILO's Yearbook of Labour Sta- tistics. The population aged 15-64 is often used to pro- Source: international LabourOrganization, Key Indicators of the Labour Market. vide a rough estimate of the potential labor force. But in many developing countries children under 15 work In almost all countries for which data are a allable, women are less iikely than men to partcpate In the Labor force. sut the rates at which women do particIpate vary wdely. Female labor force partIcIpatIon tends to be lowest In the Midde full or part time. And in some high-income countries East and North Africa-and hhest In Subtaharan Aicta and the transiton eonoms of Europe and central Asia. many workers postpone retirement past age 65. As a Where women's labor force participation Is low, there are often cultural reasons. In the Middle East and North Africa result labor force participation rates calculated in this st gender segreon, stemmnng from ellglous strlctres and concemns about nmnargeanlt, discourage schoolIng and work outside tie home for girls and wcmen. Where women's particIpation is high, as In several Sub-Saharan African way may systematically over- or underestimate actual countries, It often reflects their large role Ir agricultural work. rates. In marny countres the gap between mef's and women's partlpaton nanrowed In 1980-97, reftg wonen's risIng In general, estimates of women in the labor force educatlon levels, the expandIng empi In services (oecupatns typically dorn_ited by women), end changng nonis and laws relating to women's economic role. are lower than those of men and are not comparable internationally, reflecting the fact that for women, demographic, social, legal, and cultural trends and norms determine whether their activities are regarded 2000 World Development Indicators 49 C ~2.4 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female %~ of male %of femlale % of male % of female % of male % of female labor force labor force labor force labor force labor force labor force ±980 1992-97o 1980 1992-97a 1980 1992-97a 1980 1992-97a 1980 1992-971 1980 1992-97o Albania 54 22 62 27 28 45 17 45 18 34 21 28 Algeria 27 69 33 6 40 25 Angola 67 87 13 120 11 Argentina 17 2 3 0 40 33 18 12 44 65 79 88 Armenia 21 21 48 38 31 41 Australia 8 6 4 4 39 31 16 11 53 63 80 85 Austria 6 8 42 14 52 78 L,b 4 Z~~~~~Ii--- Bangladesh 67 54 81 78 5 11 14 8 29 34 5 11 Belarus 29 23 44 33 28 44 Belgium 3 2 41 16 56 81 Benin 66 69 10 4 24 27 Bolivia 52 2 28 2 21 40 19 16 27 58 53 82 Bosnia and Herzegovina 26 38 45 24 30 39 Botswana 6 3 3 2 41 38 8 18 53 60 89 80 Brazil 34 28 20 23 30 26 13 9 36 45 6 7 68 Bulgaria . Burkina Faso 92 93 3 2 5 5 BLjrundi 88 9 Cambodia 70 71 80 79 7 6 7 3 23 23 14 18 Camneroon 65 87 112 24 11 Canada 7 5 3 2 38 32 16 12 58 63 84 86 Central African Republic 79 90. 5 1 15 9.... ..... Chad 82 95 6 0 12 4 Chile 22 19 3 4 27 34 16 14 51 47 81 81 China . Hong Kong, China 2 0 1 0 47 31 56 15 52 69 43 85 Colombia 2 1 1 0 39 32 26 21 59 66 74 76 Congo, Dern.Rep. 62 84 18 4 20 12 Congo,Rep. 42 81 20 2 38 17 Costa Rica 34 27 6 6 25 26 20 17 40 46 74 76 C5ted'lvoire 60........ . ......75 10 5 30 20 Croatia 7 3 50 34 43 63 Cuba 30 10 32 22 39 68 Czech Republic 13 7 11 4 57 50 39 29 30 43 50 66 Denmark 11 5 4 2 41 36 16 15 48 58 80 83 Domninican Republic 40 11 26 16 34 73 Ecuador 44 10 22 2 21 27 15 16 34 64 63 83 Egypt, Arab Rep. 46 . 32 . . .4 1 51 4. . . 34 . .3 76 48 El Salvador 51 38 10 7 21 25 21 21 28 37 69 72 Eritrea 79 88 7 2 14 1 Estonia 19 16 12 8 50 39 36 27 31 44 52 65 Ethiopia 90 89 89 88 2 2 2 2 8 9 10 11 Finland 15 9 12 5 45 39 23 14 39 52 63 81 France 9 6 7 4 44 37 22 15 47 57 71 81 Gabon 59 74 18 6 24 21 Gambia,The 78 93 10 3 135 Georgia 31 34 33 21 37 45 Germany 3 3 46 19 51 .. 79 Ghana 66 57 12 14 22 29 Greece 18 23 28 13 54 64 Guatemala 64 17 17 27 19 56 Guinea 86 97 2 I12 3 Guinea-Bissau 81 98 3 0 17 3 .. ......... Haiti 81 53 8 8 11 39 ........ Honduras 63 53 40 7 17 19 9 27 20 28 51 66 50 2000 World Development Indicatora TgS jsojeopul juewidol9Aa( PIJOM OOOE .69 ½ . . £ . . 69~~~~~~~~~~LEO CT : uoi ejapa_ ueissnH 69 8£. .;6. .~~~~~~~~~i d 4. :.. UIO 66 99~ ~ ~ ~~ " - -..;... ..... .............. 6; 9 ~~si 69 6 6 ............... .... -.. 6 171 T 171 L6 6L O~fl Z6 UedoA -,T r T ~ ~ ~ ~ I jr L . . . . . . ..C........66 ... ........... . 99 . 9........ ..... ....... ... 0..... ..... 9£.966 . O 9 ~~~~~~~~6 6l£6 N ........ Z .. ... .- . ... .. 99. 8£... 9 L.......... . . .O......0£... .. ..... . .. .......... . .. .. ..6 t0d.(0 f ~~~~~i i6 zz~~~~~~~~ qweoMflN 2 6 6 9 £~~~~~....... . .. . . - I- ...... 9 £ 0 ....... ........ .. ....... .. .. . ....... ..1 ........... 69 89IN 6 6 O' 9 9 t'8 08 89 69 t~~~~~~~~~~~~~~~~~~~~~~.......;. 9;... 8£ . ... ... . . . . . 8 ........ ....s... 62 617 9; ..~~~~~~ ~ ~~ ~~~~ . 19 T 9; p3 eueiajni 86 99~~~Z ; 6 9½bile o 8c66 6Y91 98......9Z EL 917 09 19 96 99~~~~~~~~~~~~~~~~~~~~~C i~S R 01 .. 6; 1 Li6 T oqjeeoJoiwe iej Iea SOOIAJOS ~ ~ ~ ~ ~ ....... .S.......U. . .... ................. . ... .......... .6~~~~~~~~~~~~~~z 2.4 Agriculture Industry Services Male Female Male Ferma e Male Female % of male % of female % of male % of female % of male % of female labor force labor force labor force labor farce labor force labor force 1980 .1992-97e 1980 1992-971 1980 1992-971 1980 1992-971 1980 1992-971 1980 1992-971 Rwanda 8. . 98 5171 Seudi Arabia 45 25 .. 17 .. 5 39 70 Senegal 74 90 .. 9 .17 8 Sierra Leone 63 .. 82 20 4 17 .. 14 Singapore 2 0 0 33 34 40 25 65 66 59 75 Slovak R.public 15 11 13 6 38 49 34 28 48 41 54 67 Slovenia 14 12 17 13 49 49 37 31 38 38 46 57 South Africa 18 16 45 .. 16 37 68 Spain 20 10 18 6 42 39 21 14 39 52 60 80 Sri Lanka 44 33 51 40 19 22 18 24 30 41 28 34 Sudan 66 .. 88 9 4 .. 24 8 Sweden 8 4 3 1 45 39 16 12 47 57 81 87 Switzerland 8 5 5 4 47 35 23 15 46 59 72 82 Syrian Arab Republic .. 23 .. 54 . 28 . 8 .. 49 .. 38 Tajikistan 36 .. 54 29 .. 16 .. 35 .. 30 Tanzania 8.. 92 .. 7 .2 13 ..7 Thailand 68 49 74 52 13 22 8 17 20 29 18 32 Togo 70 .. 67 127 .. 19 .. 26 Trinidad and Tobago 11 14 9 5 44 33 21 13 45 54 70 82 Tunisia 33 22 53 20 30 32 32 40 37 44 16 38 Turkey 45 30 88 65 22 29 5 13 33 41 8 21 Turkmenistan 33 .. 46 .. 32 . 16 36 .. 38 Uganda 84 .. 91 6 .. 2 .. 10 .. 8 Ukraine 26 24 .. 46 .. 33 28 44 United Arab Emirates 5 .. 0 . 40 .. 7 55 93 United Kingdom 4 3 1 1 48 38 23 13 49 59 76 86 United States 5 4 2 2 40 34 19 13 55 63 80 85 Uruguay. 7 .. 2 . 34 . 17 .. 59 . 8 2 Uzbekistan 35 .. 46 .. 34 . 19 32 .. 36 Venezuela, RB 20 19 2 2 31 28 18 14 49 53 79 84 Vietnam 71 70 75 71 16 12 10 9 13 18 15 20 West Bank and Gaza.. . .. .... Yemen, Rep. 60 98 I. 1 . 1 . 21 . Yugoslavia, FR (Serb./Mont. 49 . .. 19 . ..32 Zambia 69 85 13 13 19 13 Zimbabwe 29 23 50 38 31 32 8 10 40 46 42 52 ExlC In&ndia 64 .. 73 .. 12 . 8. 24 ......19. Middle Income 33 .. 31 .. 33 .. 25 .. 34 .. 45 Lower middle income 34 .. 29 .. 34 26 32 .. 45 UJpper middle income .. 24 .. 22 30 15 .. 46 62 Low & middle Income East Asia & Pacific.. . Europe.& Central Asia 26 .. 26 43 .. 31 .. 31 .. 43 Latin America & Carib. 22 .. 13 .. 28 13 .. 50 74 Middle East & N. Africa 39 47 .. 25 .. 14 . 37 .. 40 South Asia 64 83 .. 14 .. 10 .. 23 .. 8 Sub-Saharan Africa 62 .. 74 14. 5 .. 24 .. 22 High Income 8 5 7 3 41 37 22 16 51 58 71 81 Europe EMU . 6 -. 41 .. 18 .. 53 77 a. Data are for the moat recant fear available. 52 2000 World Development Indicators 2.4 The International Labour Organization (ILO) classifies Segregating one sex in a narrow range of occupations * Agriculture includes hunting, forestry, and fishing, economic activityonthe basis of the International Stan- significantly reduces economic efficiency by reducing corresponding to major division 1 (ISIC revision 2)or dard Industrial Classification (ISIC) of All Economic labor marketflexibilityand theeconomy's atilityto adapt tabulation categories A and B (ISIC revision 3). Activities. Because this classification is based on where to change. This segregation is particularly harmful for * Industry includes mining and quarrying (including work is performed (industry) rather than on the type of women, who have a much narrower range of labor mar- oil production), manufacturing, electricity, gas and work performed (occupation), all of an enterprise's ketchoicesandlowerlevelsofpaythanmen.Butitisalso water, and construction, correspondingto major divi- employees are classified under the same industry, detrimental to men when job losses are cor centrated in sions 2-5 (ISIC revision 2) or tabulation categories C-F regardless of their trade or occupation. The categories industries dominated by men and job growvth is centered (ISIC revision 3). * Services include wholesale and should add up to 100 percent. Where they do not, the in service occupations, where women often dominate, as retail trade and restaurants and hotels; transport, differences arise because of people who are not clas- has been the recent expeience in many countnes. storage, and communications; financing, insurance, sifiable by economic activity. There are several explanations for the rising impor- real estate, and business services; and community, Data on employment are drawn from labor force sur- tance of service jobs for women. Many service jobs- social, and personal services-corresponding to major veys, establishment censuses and surveys, adminis- such as nursing and social and clerical wo*k-are con- divisions 6-9 (ISIC revision 2) or tabulation categories trative records of social insurance schemes, and official sidered "feminine" because of a perceived s imilarity with G-P (ISIC revision 3). national estimates. The concept of employment gener- women's traditional roles. Women often do not receive ally refers to people above a certain age who worked, or the training needed to take advantage nf changing Date sources who held a job, during a reference period. Employment employment opportunities. And the greater availability data include both full-time and part-time workers. There of part-time work in service industries maiy lure more The employment data are from the ILO database Key are, however, many differences in how countries define women, although it is not clear whether this is a cause Indicators of the Labour Market (1999 issue). and measure employment status, particularlyfor part-time or an effect (United Nations Statistics Division 1991). workers, students, members of the armed forces, and household, or contributing family, workers. Where data - are obtained from establishment surveys, they cover The informal sector is a vital sour, e only employees; thus self-employed and contributing of employment family workers are excluded. In such cases the employ- ment share of the agrcultural sector is underreported. informal sector employment as % Countries also take very different approaches to the 100of urban employment, 1996 treatment of unemployed people. In most countries unemployed people with previous job experience are 80 classified according to their last job. But in some coun- tres the unemployed and people seeking their first job I are not classifiable by economic activity. Because of 40 these differences, the size and distribution of employment by economic activity may not be fully comparable across 20 countries (lLO, Yearbook of Labour Statisfics 1996, p. 64). The LO's Yearbook of Labour Statistics reports data Total Male 0 mate by major divisions of the [SIC revision 2 or ISIC revision 3. In this table the reported divisions or categories are * Slovak Rep. A Pakistan aggregated into three broad groups: agriculture, indus- * Myanmar try, and services. An increasing number of countries Jamaica report economic activity accordingto the ISIC. Where data Tanzania Note: Data for Pakistan refer to 1992 and those for are supplied according to national classifications, how- Tanzania to 1995. ever, industry definitions and descriptions may differ. In Source: International Labour Organizat on, Key addition, classification into broad groups may obscure Indicators of the Labour Market. fundamental differences in countries' industrial patterns. informal sector employment Is an essential survival The distribution ofeconomic activitybygender reveals strategy In countres lacking social salety nets some interesting pattems. Agriculture accounts for the such as unemployment Insurance or where pensions and wages--especially In the public sector-are largest share offemale employment in much ofAfca and low. The Informal sector Is a vital paut of the Asia. Services account for much of the increase in economy and of the iabor market In many women's labor force participation in North Africa, Latin developing countries, especially In Asia and Sub- Saharan Africa. Amerca and the Carbbean, and high-income economies. Worldwide, women are underrepresented in industry. 2000 World Development Indicators 53 sio;eoipuj iuE)wdoIeAaj pIJOM CooC I ~9 Z'C ~~~~~~~~S2fnPUOH r r 7 V rC ~ rT . ........... . .......... .. . . . ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~j 9£T 7869 T£6 L17" 'L'T9 9t't `6, 6OT .... 68 X" ' unL' wG ..~~~~ :':'~~~~~ rU r rrT rT T r . L 9 CZ 6-O~~TT 9 Tt6 6 9.eq 96~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. ..... ~~~~~~.Th ~~~~~~~~~~~~~~~~~~~~64 T . 986s 964~i L7T O'LC8 3 i~i 4 Ci 9 t .69. I. ....9~ 69 . . .da ueu.o L-6 -i~6 C6 ..LO 176-~ 0 £. .. ........ ...... .. 86T ££~~~~~~~~~~~~~~ 90£ .9.6:0 . CT.~~~~~~~~~~~~~. . . . .... I.£ £t'9~~~~~~~~~~~~~~~~~~~~~~~~9o .....9.. ..£... ...........0 ....... 6.. .T .T.9 . 66..... ...66 ..-8.66 69.... . uioAj,P959 . ...... . .. ...... . .. . .. . ......... ..~~~~~e poqw ~ 66~~~~~~~~~~~~~~~~~~~ §:66 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~Pf~l .. ....... . .. . ..... .... .- . . .. .. ....... . . .... .~~~~~~OeA 2U X ifl £ I. L 69 t709 LT9 869.L:£T T t'T 6 8T 2118~~~~~~~~~~~~~~~~~~~~~~~ 'ad o~uo m v ;69 86.. 88 6................. 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SIAlel~~~~~~~~~~. ....... a . ds~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. ....ao~ t7 t' .2s ....~~~~t' 2~ V69 8t'T 609 .. uep~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ...... orol 996 9:66.9:9 . 9T 916 09~~~~~~ ~~~~~6 99 6 99uenLjswe .99 9*99 96T 91 69T 69T L6~~~~~~~~~~~~~~~~~~~~~. 817.. . .6.6tie 619 . 956.LI 817~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. .. .. . .. 88... 09 8.... ... .. 96 99 19 ~~~~~~~~~~~917 999 SOT 9OT~....... ... SO ......... .O ... . ............I . ....... ..-0.: 6. c6.. - t:E6.~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~ei ... .... . . .. .. . .. . . . . .. ... .... ... ... .. . . . .. ... ... . 91 . 9. 6:t, .~~~~~~~~I79 £ ~ ~ ~ k ............ .... .. .... ...... ..... ............ .... . .. . ........... 86 T9 -T17 91 6617 99dL8 I 96 ,j28n ~L6-t'6T d6-'66T ~6-t~66 ~L6-166T ~L-1766I~L6-t'6T ~L6t'66r S-O8.......... .-86 . .6t..6......6 A J 'J .L ...... ..pu o~ .......... ......... . .ie .... ~ Jo ......S J. . O R . ........... ..... ..... .......... ............ V.wAjosu SJo VT % JOwAJdOUrJJ~4%JO O%SES O%S 0 ISJOI SISWOJ~.. ..... ........... . lUG WUIG1W IBuolleonpa JO IUOWAOIdWOUfl~ ~ ~ ~ ~~~~~~~ .. ... .......... ...... ......... ...ue Aod..nw1;......wA id eu C . . ..... .... . .. ... sJo;eo!pul luawdoIeAea] PIJOM OOOZ so eIjqejeAe JeaA JuDoeJ ;sow eql JOJ ale EWeG 2 f6~~~~~~~ 1769 TIP e~~~~~~~~~~~~~~~~~~~mq2qwiZ weqweZ 901.916 99 .' TB.~~~~~~~~~~~~~~~~~~~~~~~~da uaa lenzeueA>n . ...... ..SIP 16719 P 010 096 09O 06LeSczrIn L t6:6 OniUl . . ..s. ...L1...669 - I6 691 oa .6T. PT 69-9 9e-jueppiU!j- .da6j~~~~~~~~~~~~.......... .... ... .. .. . ... . . ....... ... ... .. . 906 911 .. 60 90 60 20 60 01 PU2I12P 9 9 6 99 901 26 66 P6 . . . ......... ....ue~ soiuaw 2i T ~~~~~~~~~~~~~~I 1 *24epun' 991 BL1 699 P0 . 6P.906 .11~~~~~~~~~~~~~~~~~~~~V6 9"66.6t oR.991 .01 .. 9 .......... JfolOSZ- Aaepnj~~~~~~~~~~~~~~~~~~ Ueuoe9qndud iioa9202 e qeeoojvqe ue0p491 0oJIs2 ..w..deu 2..4...0wA iwan120 .0 6.2444 --- -IW--0-6-1tH40 21± [W0 Ie' ~~uewuiene ~~~~~~~~~euo~~~~;eonps jo ;uowAoid....w..e.u.n ........ lOAm Aq ;uowAojdwau~~~~~ wao;-Juo, ;ueuJAoIdweun6-L . ..... .. ......... .. ... .... . ... ...........9 .. . 2.5 Unemployment and total employment in a country are the to be comparable. Where registration is voluntary, and * Unemployment refers to the share of the labor force broadest indicators of economic activity as reflected by the where employment offices function only in more populous without work but available for and seeking employment. labor market. The International Labour Organization (ILO) areas, employment office statistics do not give a reliable Defintions of labor force and unemployment differ by coun- defines the unemployed as members of the economically indicaton of unemployment. Most commonly excluded from try (see About the data). * Long-term unemployment active population who are without work but available for and both these sources are discouraged workers who have refers to the number of people with continuous periods seeking work, including people who have lost their jobs given up their job search because they believe that no of unemployment extending for a year or longer, expressed and those who have voluntarily left work. Some unemploy- employment opportunities exist or do not regi eter as unem- as a percentage of total unemployment. * Unemployment ment is unavoidable in all economies. At anytime some work- ployed after their benefits have been exhausted. Thus mea- by level of educational attainment shows the unem- ers are temporarily unemployed-betweenjobs as employers sured unemployment may be higher in economies that offer ployed by level of educational attainment. as a percent- look forthe rightworkers and workers search for betterjobs. more or longer unemployment benefits. age of total unemployed. The levels of educational Such unemployment, often called frictional unemployment, Long-term unemployment is measured in terms of dura- attainment accord with the United Nations Educational, results from the normal operation of labor markets. tion, that is, the length of time that an unemployed person Cultural, and Scientific Organization's (UNESCO) Inter- Changes in unemployment over time may reflect changes has been without work and looking for a job The underly- national Standard Classification of Education. in the demand for and supply of labor, but they may also ing assumption is that shorter periods of jot lessness are reflect changes in reporting practices. Ironically, low unem- of less concern, especially when the unemployed are cov- Data sources ploynment rates can often disguise substantial poverty in a ered by unemployment benefits or similar for ns of welfare country, while high unemployment rates can occur in coun- support. The length of time a person has been unemployed The unemployment data ame from the ILO database Key Indi- tries with a high level of economic development and low inci- is difficult to measure, because the ability to recall the cators of the Labour Market (1999 issue). dence of poverty. In countries without unemployment or length of that time diminishes as the period ofjoblessness welfare benefits, people eke out a living in the informal sec- extends. Women's long-term unemployment is likely to be tor In countries with welldeveloped safety nets, workers can lower in countries where women constitute a large share of afford to wait for suitable or desirable jobs. But high and the unpaid family workforce. Such women have more access sustained unemployment indicates serious inefficiencies in than men to nonmarket work and are more likely to drop out the allocation of resources. of the labor force and not be counted as unemployed. The ILO definition of unemployment notwithstanding, Economies for which unemployment dato are not con- reference periods, criteria for seeking work, and the treat- sistently available or were deemed unreliable have been omit- ment of people temporarily laid off and those seeking work ted from the table. for the first time vary across countries. In many developing countries it is especially difficult to measure employment _ and unemployment in agriculture. The timing of a survey, for Unemployment rate by level of educanal attainment example, can maximize the seasonal effects of agricultural % unemployment. And informal sector employment is difficult Less thmin prlmary Primary Secondary Tertiary to quantify in the absence of regulation for registering and utrra i . 37.2 57.8 5.0 tracking informal activities. Canada 1997 .. 33.9 30.5 35.6 Data on unemployment are drawn from labor force sam- , l i. 5 23.7 ple surveys and general household sample surveys, social Jordan 1996 2.5 50.2 14.8 32.4 199i.- Ir 15.4 57.1 25.7 insurance statistics, employment office statistics, and offi- Poland 1997 23.9 71.5 4.6 cial estimates, which are usually based on combined infor- Cuu;'i. F.j)lr3a.r. 199 1 ii4 72.3 8.3 motion drawn from one or more ofthe above sources. Labor Thailand 1997 i.7 63.3 11.3 20.5 force surveys generally yield the most comprehensive data '0 r,e rue.i.Fs 19'W - J 1 e.(' 22.6 13.1 because they include groups-particularly people seeking Zimbabwe 1997 '3.0 41.1 52.7 0.1 work for the first time-not covered in other unemployment Not available. statistics. These surveys generally use a definition of unem- Source: International Labour Organization. Key Indicators of the Labour Market. ploymenrtthat follows the international recommendations more The distribution of unemployed workers across education levels varies among countries, largely reflecting ecoe nomic conditions and labor market Insi Itutions. Information about this distribution can aid both employment erate statistics that are more comparable nternationally. and educatlon policy. By contrast, the quality and completeness of data Knowing the education and skill levels of the unemployed can help In Improving training programs for the obtained from employment offices and social insurance jobless or designing job creation progralms. And data on unemployment at different education levels can help In developing education and training strategies that improve education outcomes for workers. programs vary widely. Where employment offices work closely with social insurance schemes, and registration with such offices is a prerequisite for receipt of unemploy- ment benefits, the two sets of unemploymert estimates tend 2000 World Development Indicators 57 Cq 2.6 Wages and productivity Average hours Minimumi wage Agricultural wage La:bor cost Value added worked per week pr worker per worker in manufacturing in manufacturing $ per year $ per year $ per year $ per year 1980484 1995-99a 1980-84 ±995-99, 1980-84 1995-99, 1980484 1995-99, 1980-84 1995-99a Albania Algeria 1,340 6,242 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 Bangladesh 52 492 192 360 556 671 1,820 1,7.11 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,C61 2,885 Hr: I'.,, *r,,r, ~ -lz J, I 7 I Colombia -1,128 2,988 2,507 15,C96 .17,061 Congo, Dem.Rep. CongoRp Cosat Rica 47 1,042 1,638 982 1,697 2,433 2,829 7,A85 7,184 C6te dilvoire .. 1,246 871 5,132 9,995 16,158 Croatia Czech Republic 43 40 . 2,277 1,885 2,306 1,876 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 .3738. 1219 . 9747 El Salvador .. ......... . . . .. ... ..... .. . . 790 __79 3,654 .. 14,423 Erntrea 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 G e o r gi a .......... ......... .. ... . .. ................... ... ........... ....................... Germany 41 40 b.. 15,708 33,226 34,945 79,616 Ghana .. 1,470 .. 2,306 .. 12,130 Greece .. 41 5,246 .. 6,461 15,899 14,551 30,429 Guatemala ..459 ... 2,605 1,802 11,144 9,235 Guinea 40........... Guinea-Bissau 48 Honduras .. 44 .1,623 2,949 2,658 7,458 7,427 58 2000 World Development Indicators OB SJo1eoIpuI lUaWdOIaAAaQ PIJOM 000?: s86 7oqi . 699 8t'? L6C E:91 uoowujpaj u8!ssnl OIL '1UBo - . rT- T Cr- t rw; - C '; o -- -r,. - ;- Ir Tit- -rr Z96'4T ~88667 1716 w.88 VO O~9'Z17 L?:9T T'E9 89L't' - 7BE 6600: . LL8'66 . ;.... .. 88 8.IS~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~......... ... ......... 962'?:9 99891 L9L'96 90901 0*;6. 6099 69~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. .jewusk* .nqwezo9J .~~~~~~~~~~~~~~~~~~~2~I6~jOLojAJ 2A0PIOLJ L 776't' 696'?: 9261 998"1 Sfl!1Pfl~~~~~~6'ickif S, 6ixaJ L t8'OT 986t? ........... T99'6T 879878 6eP"O 619'Z: q . ..... ................ ............ L8 9 ......... .. . ........... . W 4 ...... 9... .... .. ..... . ........... . ...... ... Uod OBa ... ............... ....... . ........I......... ... ...... m. ........ ........ .............. ..... . .. ............... . ..... ..... . L89 £8:': 8 . 9 6O LT9'TT Etl .'0. .69........... .......o. ~~~96 . 86 . frOT 899 80 69 Tt7.eAOIJSNT' .......... .... .. .. .. ..... . . .......... . . . . . ..... ........... ... .... ....... .... .. .... .. .. .... ............... ....M.. ..... ............ .... ...... . .. ...~~ ~ ~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~W j~ > -6z 696'?: 9 '9 8T "906'9' 979Z;Z Z68£ 6179 9760'99 686 99 19 9066 6896 Ieisiwe 629'LT 9269T qa3wisU 6977 '9 L08'9 900'77 868 BiSeUOpUf~~~~~~~~~~~~~~~~~~~~~~~~~~~.......... ...... ......... . . ............ ................ 87777'9 80T6 77677'T 990'T 9877 906 808 98 E!PUI~~~~~~~~~~~~~~~~~~~~~~~~~9 9 9077/9 £098 222Z 0181 99277 9911 ?:9T'T 99 99 IJE8Ufli-i~~~~~~~~~~~~~~............ ...... ....... .. ........... . ..... ..... ~66-S66T ~9-S6r ~66-S66 t'S-086T ~6-S66T t'8-06T ~B6-S66T 'B-086~ ~6...........6 ...A .... .... .. ........ ss.... ... .......... . .d......J .... i c..d...... . ...... ... ......$ .... . . .. . ~~~~u!ln;oE ;nuB w Iii ~ .......... .... ... .... ......I.... ..U.!............ . .....Jfl...U..... .......W.. U....I JO)IIOM jed AGjJOM ~~~~~~~~~~~~~ed NOOM led PO)IJOM ~ ~ ~ ~ wesi ej peppe OflBA ... ..... 6 ........ . .. ..... .... ....J~ ........ .n . u I) s'.-..... ...... . ........ G 9s~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.aou 2.6 ' Average hours Minimum wage Agricultural wage Labor cost Value added worked per week per worker per worker in manufacturing in manufacturing $ per year $ per year $ per year $ per year 1980484 l995-99, 1980-84 1995-99a 1980-84 1995-99, 1.98084 1995-991 1980484 1995-99, Rwanda ... .. .1.871 .. 9,835 Saudi Arabia . .. .*. ,1 Senega ........9....3. 848 .. ... .2.828 7,754 6.415 Sierra Leone 44. . . . 1.624 .. 7,807 Singapore 46 4,856 5,576 21,534 16,442 40,674 Slovak Republic Slovenia . .... 9,632 .. 12,536 South Africa 42 * b . 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,.... Sweden 36 37 9,576 27,098 13,038 29,043 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 Thailand 48 1,083... 2,305 2,705 11.D72 19,946 Tog. Trinidad and Tobago 40 2,974. .. 14.308 Tunisaa1,8 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 Emirateas. 6,968 20,344 LJnited Kingdom 42 40 b ,. 11.406 23,843 24,716 55,060 United Stateas4 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 Yugoslavia, FR (Serb./Mont. Zambia . 45...... 3,183 4,292 11,753 16,615 Zimbabwe ...... 1,065 .. 4,097 3,422 9,625 11,944 a. Figures in italics refer te 1990-94. b. Country has sectoral m nimum wages bat no minimum wage policy. 60 2000 World Development Indicators 2.6 Much of the available data on labor markets are col- security benefits. International comparis.ons of agri- * Average hours worked per week refer to all work- lected thrDugh national reporting systems that depend cultural wages are subject to greater reservations ers (male and female) in nonagricultural activities or, on plant-level surveys. Even when these data are com- than those of wages in other activities. The nature of if unavailable, in manufacturing. The data correspond piled and reported by international agencies such as the work carned out by different categories of agricultural to hours actually worked, to hours paid for, or to statu- the International Labour Organization or the United workers and the length of the workday arid workweek tory hours of work in a normal workweek. * Minimum Nations Industrial Development Organization, differ- vary considerably from one country to another. Seasonal wage corresponds to the most general regime for ences in definitions, coverage, and units of account limit fluctuations in agricultural wages are mo e important nonagricultural activities. When rates vary across sec- their comparability across countries. The indicators in in some countries than in others. And the methods fol- tors, only that for manufacturing (or commerce, if the this table are the result of a research project at the lowed in different countries for estimatirg the mone- manufacturing wage is unavailable) is reported. World Bank that has compiled results from more than tary value of payments in kind are not uniform. * Agricultural wage is based on daily wages in agri- 300 national and international sources in an effort to Labor cost per worker in manufacturing is sometimes culture. * Labor cost perworker in manufacturing is provide a set of uniform and representative labor mar- used as a measure of international competitiveness. obtained by dividing the total payroll by the number of ket indicators. Nevertheless, many differences in The indicator reported in the table is the ratio of total employees, or the number of people engaged, in man- reporting practices persist, some of which are described compensation to the number of workers in the manu- ufacturing establishments. * Value added per worker below. facturing sector. Compensation includes dlirect wages, In manufacturing is obtained by dividing the value Analyses of labor force participation, employment, salaries, and other remuneration paidi directly by added of manufacturing establishments by the num- and underemployment often rely on the number of employers plus all contributions by emplovers to socia ber of employees, or the number of people engaged, hours of work per week. The indicator reported in the security programs on behalf of their employees. But in those establishments. table is the time spent at the workplace working, there are unavoidable differences in concepts and ref- preparing for work, or waiting for work to be supplied erence periods and in reporting practices. Remunera- Data sources or for a machine to be fixed. It also includes the time tion for time not worked, bonuses and gratuities, and spent at the workplace when no work is being performed housing and family allowances should be considered The data in the table are drawn from Martin Rama and butforwhichpaymentismadeunderaguaranteedwork partof thecompensation costs, alongwith severance Raquel Artecona's "Database of Labor Market Indi- contract or time spent on short periods of rest. Hours and termination pay. These indirect labor CDsts can vary cators across Countries" (1999). paid for but not spent at the place of work, such as substantially from country to country, depending on the paid annual and sick leave, paid holidays, paid meal labor laws and collective bargaining aE,reements in breaks, and time spent in commuting between home force. Figures are converted into U.S. dollars using the and workplace, are not included, however. When this average exchange rate for each year. information is not available, the table reports the num- International competitiveness also depends on pro- ber of hours paid for, comprising the hours actually ductivity. Value added per worker in manufacturing is a worked plus the hours paid for but not spent in the work- frequently cited measure of productivity. The indicator place. Data on hours worked are influenced by differ- reported in the table is the ratio of total value added ences in methods of compilation and coverage as well in manufacturing to the number of employees engaged as by national practices relating to the number of days in that sector. Total value added is estimated as the dif- worked and overtime, making comparisons across ference between the value of industrial output and the countries difficult. value of materials and supplies for production (includ- Wages refer to remuneration in cash and in kind paid ing fuel and purchased electricity) and cost of industrial to employees at regular intervals.They exclude employ- services received. Figures are converted into U.S. dol- ers' contributions to social security and pension lars using the average exchange rate for each year. schemes as well as other benefits received by employ- Observations on labor costs and val Ae added per ees under these schemes. In some countries the worker are from plant-level surveys covering relatively national minimum wage represents a "floor, with large establishments, usually employing 10 or more higher minimum wages for particular occupations and workers and mostly in the formal sector. Ii high-income skills set through collective bargaining. In those coun- countries the coverage of these surveys tends to be tries the agreements reached by employers associa- quite good. In developing countries there s often a sub- tions and trade unions are extended by the government stantial bias toward very large establishments in the to all firms in a specific sector, or at least to large firms. formal sector. As a result figures may not be strictly In general, changes in the national minimum wage comparable across countries. are associated with parallel changes in the minimum The data in the table are period averEges and refer wages set through collective bargaining. to workers of both sexes. In many developing countries agricultural workers are hired on a casual or daily basis and lack any social 2000 World Development tndicators 01 2.7 Poverty National poverty line International poverty line Population be ow 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 % % Albania . Algeria 1988 16.6 7.3 12.2 1995 30.3 14.7 22.6 1995 <2 <0.5 15.1 3.6 Angola Argentina 1991 25.5 1993 17.6 Armenia A u stral .. ... .ia .............. ....................... . ........ . Austraia Azerba'ijan 1995 68.1 Bangladesh 1991-92 46.0 23.3 42.7 1995-96 39.8 14.3 -35.6 1996 29.1 5.9 77.8 31.8 Belarus 1995 22.5 1998 < 05 < . Belgium Benin 1995 33.0 . ...... Bolivia 1993 29.3 1995 79.1 1990 11.3 2.2 38.6 13.5 Bosnia and Herzegovina . Botswana 1985-86 33.3 12.5 61.4 30.7 Brazil 1990 32.6 13.1 17.4 . ....... .... 1997... 5.1 1.3 17.4 6.3 Bulgaria 1995 < 2 <0.5 7.8 1.6 Burkina Faso 1994 61.2 25.5 85.8 50.9 Burundi 1990 36.2 . Cambodia 1993-94 43.1 24.8 39.0 1997 40.1 21.1 36.1 Canada .. Central African Republic 1993 666 38.1 84.0 58.4 Chad 1995-96 67.0 63.0 6. Chile 1992 21.6 1994 ........ 20.5 1994 4.2 0.7 20.3 5.9 China 1996 7.9 <2 6.0 1998 4.6 <2 4.6 1998 18.5 4.2 53.7 21.0 Ho.gKong. China. Colombia 1991 29.0 7.8 ...16.9 1992 31.2'....8.0 17.7 1996 11.0 3.2 28.7 11.6 Congo. Dam. Rep.. Congo Rep.. Costa Rica 1996 9.6 3.2 26.3 10.1 C8te dIvoire ~~~~~~~......... ... ........ 1995 12.3 2..4 49.4 16.8 Croatia Cuba Czech Republic 1993 <2 <0.5 <2 <0.5 Denmark Dominican Republic 1989 27.4 23.3 24.5 1992 29.8 10.9 20.6 1996 3.2 0.7 16.0 5.0 Ecuador 1994 47.0 25!0 35.0 1995 20.2 5.8 52.3 21.2 Egypt, Arab Rep. .1995-96 23.3 2. 22.9 1995 3.1 0.3 52.7 11.4 El Salvador 1992 .55.7 43.1 48.3.. 1996 25.3 10.4 5. 24.7 Eritrea Estonia 1995. 14.7 6.8 8 9 1995 4.9... 1 .2 17.7 6.0 Ethiopia 1995 3. . 54 3. Finland France . Gabon . Gambia. The 1992 64.0 1992 53.7 23.3 84.0 47.5 Georgia 1997 9.9 12.1 11.1 German. Ghana 1992 34.3 26.7 31.4 Greece . Guatemala 1989 71.9 33.7 57.9 1989 39.8 19.8 64.3 36.6 Guinea 1994 40.0 Haiti 1987 65.0 1995 66.0 Honduras 1992 .. .46.0 56.0 50.0 .1993 51.0 57.0 ..53.0 1996 40.5 17.5 68.8 36.9 62 2000 World Development Indicators £9 SJOI2elIPUI lUaWdOIaA9n PIJOM OOC)Z 1.9 196 9IL 9661 606 ~~~~~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~i~66~. uoia~pe- uL!ssnd 4U 1~ 0.9661......... .6-sr.996 69 1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~6T 9661 916 £61 996 1661...~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. .. ... . 601 196 66 601~~~'~ 1.f 66 t. i,266f 6L6 69 6 699 1.661 PLUeUd 0...L ..... ... 9...01.. 9661......0.. 096 69.6. 166...... S!. . .uenN .... ...........eM Jo 9L6 96 1.6 u1.6 9661 69 06 0. 96-966 . .. .......... ..... .. ... .... .. .... uewo~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~u4 896 991 061~~. .... . . 6.6... 9661 eflbIqweZ 61 9L 90>~6,v 6> 1-066 061.:61.6L 6.6.6 9 ..L 08.... 16-66166.6N 91.1 009 16 ff61 9661 696....... . ........... ... .... 99...966 601 616 61 L 6661 ~~~~~~~ ~~~~~~~~~~6: . 6 1.6 2OgOA .91.96~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.9961 OIX8 . 6-f- . .*...6661 . .......... . ... ........... . . ...... .. .. . .. .... ... . . .. .. w .. . . ..... A . ...... ~~~~~~~~.. 6 . T . 96.9661.l06-6 61 . . ........... . . . . .. ..... .. ...... . . . .. . .. . .... .... .. ........... ... . . . . .J 906 961. 9661~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~PelaI9 . . ................ ....9 .1 6......9 6.1.... 186 L99 606 169 6661 669 91.6 669 6661 . .. 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'. 6£.09 66: 89 069~ o09L 6861 auo21 ejjqS Z.86.Z 9.0~i£96 9661: m. ei~~~~~~~~~~~~~~~~~~~~~~~~~~qew ipn6S L9£ 9t9 98 TS:1~ £66f 2pueNd % % % ~~~~% ~2A % % % iai% j A2P 2? 6$ AeP 2 6$ ALP 2 TS$ AeP e T$ lauAns 2uoIleN Leqifn j12n2d 2AAjnS e2u04eN ueqJfn lInaf AeAJn9 ;e de8 moleq We deS mojeq GUll ~JeGAod aul A;JaAod A419AOd uo2!fledod Avje,Aod uo!lelndod eq; molq uoilejndod eL4 mol2q uoileflndOd Bull A4jaAod leuoileujBlul QUII A4ZBOAd I9UOIION 2.7 .70 Internationa comparisons of poverty data entail both con- The problems of making poverty comparisons do not * Survey year is the year in which the underlying data ceptual and practical prob ems. Different countries have end there. Further issues arise in measuring household were collected. * Rural poverty rate is the percentage different definitions of poverty, and consistent comparisons living standards. The choice between inco ne and con- of the rural population living below the national rural between countries can be difficult. Local poverty lines tend sumption as a welfare indicator is one issue. Income is poverty line. * Urban poverty rate is the percentage of to have higher purchasing power in rich countries, where generally more difficult to measure accurately. and con- the urban population living below the national urban more generous standards are used than in poor countries. sumption accords better with the idea of the standard of poverty line. * National poverty rate is the percent- Is it reasonable to treat two people with the same living than does income, which can vary over time even age of the population living below the national poverty standard of living-in terms of their command over if the standard of living does not. But consumption data line. National estimates are based on population-weighted commodities-differently because one happens to live in are not always available, and when they are not there is subgroup estimates from household surveys. * Popu- a better-off country? Can we hold the real value of the little choice but to use income. There are st 11 other prob- lation below $1 a day and $2 a day are the percentages poverty line constant between countries, just as we do when lems. Household survey questionnaires can differ widely, of the population living on less than $1.08 a day and making comparisons over time? for example, in the number of distinct categ;ories of con- $2.15 a day at 1993 international prices (equivalent to Poverty measures based on an international poverty sumer goods they identify. Survey quality varies, and $1 and $2 in 1985 prices, adjusted for purchasing power line attemptto do this. The commonly used $1 a day stan- even similar surveys may not be strictly comparable. parity using rates from the Penn World Tables). Poverty dard, measured in 1985 international prices and adjusted Comparisons across countries at different levels of rates are comparable across countries, but as a result to local currency using purchasing power parities (PPPs), development also pose a potential problen', because of of revisions in PPP exchange rates, they cannot be com- was choser for the World Bank's Wond Development differences in the relative importance of co isumption of pared with poverty rates reported in previous editions for Reportl990:Povertybecause it is typical of the poverty nonmarket goods. The local market valus of all con- individualcountries. * Povertygapisthemeanshort- lines in low-income countries. PPP exchange rates, such sumption in kind (including consumption fiom own pro- fall below the poverty line (counting the nonpoor as hav- as those from the Penn World Tables or the World Bank, duction, particularly important in underdeveloped rural ing zero shortfall), expressed as a percentage of the are used because they take into accountthe local prices economies) should be included in the measure of total poverty line. This measure reflects the depth of poverty of goods and services not traded internationally. But consumption expenditure. Similarly, the impuled profit from as well as its incidence. PPP rates were designed not for making international production of nonmarket goods should be included in poverty comparisons but for comparing dggregates from income. This is not always done, though su :h omissions Data sources national accounts. As a result there is no certainty that were a far bigger prob em in surveys beforo the 1980s. an international poverty line measures the same degree Most survey data now include valuations for consumption Poverty measures are prepared by the World Bank's of need or deprivation across countries. or income from own production. Nonetheless, valuation Development Research Group. National poverty lines are Past editions of the World Development Indicators methods vary-for example, some surveys use the price based on the Bank's country poverty assessments. Inter- used PPPs from the Penn World Tables. Because the at the nearest market, while others use the average farm national poverty lines are based on nationally represen- Penn World Tables updated to 1993 are not yet available, gate selling price. tative primary household surveys conducted by national this year's edition uses 1993 consumption PPP esti- The international poverty measures sh wn here are statistical offices or by private agencies under government mates produced by the World Bank. The international based on the most recent consumption PPF estimates in or international agency supervision and obtained from gov- poverty line, set at $1 a day in 1985 PPP terms, has been 1993 prices from the World Bank. Any revisions in the PPP ernment statistical offices and World Bank country depart- recalculated in 1993 PPP terms at about $1.08 a day, of a country to incorporate better price indexes can pro- ments. The World Bank has prepared an annual review Problems also exist in comparing poverty measures duce dramatically different poverty lines in lDcal currency. of poverty work in the Bank since 1993. The most recent within countries. For example, the cost of living is typically Whenever possible, consumption has been used as is Poverty Reduction and the World Bank: Progress in Fis- higher in urban than in rural areas. (Food staples, for exam- the welfare indicator for deciding who is poor. When only cal 1999 (forthcoming a). ple, tend to be more expensive in urban areas.) So the household income is available, average income has been urban monetary poverty line should be higher than the rural adjusted to accord with either a survey-besed estimate povertyline. Butitis notalwaysclearthatthedifference of mean consumption (when available) oi an estimate between urban and rural poverty lines found in practice based on consumption data from national accounts. This properly reflects the difference in the cost of living. For procedure adjusts only the mean, howeve'; nothing can some countries the urban poverty line in common use has be done to correct for the difference in Lorenz (income a higher real value-meaning that it allows poor people distribution) curves between consumption and income. to buy more commodities for consumption-than does the Empirical Lorenz curves were weighted by household rural poverty line. Sometimes the difference has been so size, so they are based on percentiles of population, not large as to imply that the incidence of poverty is greater households. In all cases the measures ol poverty have in urban than in rural areas, even though the reverse is been calculated from primary data sources (tabulations found when adjustments are made only for differences or household data) rather than existing estimates. Esti- in the cost of living. As with international comparisons, mation from tabulations requires an interpo ation method; when the real value of the poverty line varies, it is not clear the method chosen was Lorenz curves with flexible func- how meaningful such urban-rural comparisons are. tional forms, which have proved reliable in past work. 2000 World Development Indicators 65 C ~2.8 Distribution of income or consumption Survey year Gin! index Percentage share of income or consumption Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20Y 10% Albania......... Algeria 1995a.b 35.3 2.8 7.0 11.6 16.1 22.7 42.6 26.8 Angola Argentina Armnenia Austral a 1994c, 35.2 2.0... 5.9 12.0O 17.2 23.6 41.~3 25.4 Austria 1987c.d 23.1 4.4 10.4 14.8 18.5 22.9 33.3 19.3 Azerbaijan Bangladesh 1995-96a,s 33.6 3.9 8.7 12.0 15.7 20.8 42.8 28.6 Belarus 1998a,l 21.7 5.1 11.4 152.2 18.2 21.9 333.3 20.0 Belgium 19920.0 25.0 3.7 9.5 14.6 18.4 23.0 34.5 20.2 Benin Bolivia 19900.0 42.0 2.3 .5.6 9.7 14.5 22.0 48.2 31.7 Bosnia and Herzegovina Botswana Brazil 1996c.d 60.0 0.9 2.5 5..5. .100 18.3 638 47.6 Bulgaria 1995a,b 28.3 3.4 8.5 13.8. 17.9 22.7 37.0. 22.5 Burkina Faso 1994a, 48.2 2.2 5.5 8.7 12.0 18.7 55.0 39.5 Burundi 1992u,b 33.3 3.4 7.9 12.1 16.3 22.1 41.6 26.6 Cambodia 1997a, 40.4 2.9 6.9 10.7 14.7 20.1 47.6 33.8 Cameroon Canada 1994C,d 31.5 2.8 7.5. .12.9 17.2 23.0 39.3 23.8 Central African Republic 19930,b 61.3 0.7 2.0 4.9 9.6 18 .5 65.0 47.7 Chad Chile 1994c,d 56.5 1.4 3.5 6.6 10.9 18.1. 61.0 46.1 China 1998c,d 40.3 2.4 5.9 10.2 15.1 22.2 46.6 30.4 Hong Kong. China...... ... Colombia 19960.01 57.1 1.1 3.0 6.6 11.1 18.4 609 46.1 Congo, Rep.. Costa Rica ..19960, 47.9 1.3 4.0 8.8 13.7 21.7 51.6 34.7 Cbte d Ivoire 19950.l5 36.7 3.1 7.1 11.2 15.6 21.9 44 C 28.8 Croatia 1998 .5 26.8 4.0 9.3 13.6 17.8 22.9 36.2 21.6 Cuba Czech Republic 19960.0 25.4 4.3 10.3 14.5 17.7 21.7 35.9 22.4 Denmark 199200" 24.7 3.6 9.6 14.9 18.3 22.7 34. 5 20.5 Dominican Republic 1996c,s 48.7 1.7 4.3 8.3 13.1 20.6 53.7 37.8 Ecuador 1995a,b 43.7 2.2 5.4 9.4 14.2 21.3 49.7 33.8 Egypt, Arab Rep. 19950.0 28.9 4.4 9.8 13.2 16.6 21.4 39.C 25.0 El Salvador 19965,d .52.3 1.2 3.4 7.5 12.5 20.2 56.5. 40.5 Eri.trea Estonia 1995c,d 35.4 2.2 6.2 12.0 17.0 23.1 41.8 26.2 Ethiopia 19950.b 40.0 3.0 7.1 10.9 14.5 19.8 47.7 33.7 Finland 19910.d 25.6. 4.2 10.0 14.2 17.6 22.3 35.8 21.6 France 19,d32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon Gambia, The 19920,b 47.8 1.5 4.4 9.0 13.5 20.4 52.8 37.6 Georgia Germany 1994c,d 30.0 3.3 8.2 13.2 17.5 22.7 38.5 23.7 Ghana 1997a,b 32.7 3.6 8.4 12.2 15.8 21.9 41.7 26.1 Greece 199300 32.7 3.9 7.5 12.4 16.9 22.8 40.3 25.3 Guatemala 1989001 59.6 0.6 2.1 5.8 10.5.. 18.6 63.0 46.6'.. Guinea 19940,10 40.3 2.6 6.4 10..4 .14.8 21.2 47.2 320.0..... Guinea-Bissau 1991a,b 56.2 0.5 2.1 6.5 12.0 20.6 58.9 42.4....... Guyana 19930,0 40.2 2.4 6.3 19.7 ..15.0 21.2 46.9 32.0 H aiti.......... Honduras 19960,0 53.7 1.2 3.4 7.1 11.7 19.7 58.0 42.1 66 2000 Worid Deveiopment Indicators 2.8 Survey year Gini index Percentage share of income or consumption Lowest Lovwest Second Third Fourth Highest Highest 10% 2i)% 20%/ 20% 20% 20% 10% Hungary 1996c,d 30.8 3.9 8.8 12.5 16.6 22.3 39.9 24'8 India 1997a,b 37.8 3.5 8.1 11.6 15.0 19.3 46.1 33.5 Indonesia 1996c,d 36.5 3.6 8.0 11.3 15.1 20.8 44.9 30.3 Iran, Islamic Rep. Iraq Ireland 19870,d 35.9 2.5 6.7 11.6 16.4 22.4 42.9 27.4 Israel 19920,d1 35.5 2.8 6.9 11.4 16.3 22.9 42.5 26.9 Italy 19950,d 27.3 3.5 8.7 14.0 18.1 22.9 36.3 21.8 Jamaica 1996a,b 36.4 2.9 7.0 11.5 15.8 21.8 43.9 28 9 Japan 19930,d 24.9 4.8 1C.6 14.2 17.6 22.0 35.7 21.7 Jordan 1997a,b 36.4 3.3 7.6 11.4 15.5 21.1 44.4 29.8 Kazakhstan 1996a,b ....... 35.4 2.7 E.:7. 11.5 16.4 23.1 42.3 26.3 Kenya ~~~1994a,b 44.5 1.8 E.0 9.7 14.2 20.9 50.2 34.9 Korea, Dem. Rep. Korea, Rap. 1993abh1 62.9 7 5 12.9 17.4 22.9 39.3 24.3 Kuwait Kyrgyz Republic 19970.d 40.5 2.7 8.3 10.2 14.7 21.4 47.4 31.7 Lao PDR 1992 a,b 30.4 4.2 9.6 12.9 16.3 21.0 40.2 26.4 Latvia 1998c,d 32 4 2.9 !6 12.9 17.1 22.1 40.3 25.9 Lebanon Lesotho 19687, 56.0 0.9 118 6.5 11.2 19.4 60.1 43.4 Libya ' Lithuania 1996a,b 32.4 3.1 7.8 12.6 16.8 22.4 40.3 25.6 Luxembourg 1994 c,d 26.9 4.0 31.4 13.8 17.7 22. ...36.5 22.0 Macedonia, FYR Madagascar 1993a,b 46.0 1.9 5.1 9.4 13.3 20.1 52.1 36.7 Malawi Malaysia 1995c,d 48.5 1.8 4 83 13.0 204 53.8 37.9 Mali 1994a,h 50.5 1.8 4.6 8.0 11.9 19.3 56.2 40.4 Mauritania 1L995a,b 38.9 2.3 0.2 10.8 15.4 22.0 45.6 29.9 Mauritius Mexico 1995c,d 53.7 1.4 6 7.2 11.8 19.2 58.2 42.8 Moldova 19920,d 34.4 2.7 63.9 11.9 16.7 23.1 41.5 25.8 Mongolia 1995 ~ 33.2 2.9 '.3 12.2 16.6 23.0 40.9 24.5 Morocco 1998_99a,b 39.5 2.6 (3.5 10.6 14.8 21.3 46.6 30.9 Mozambique 1996-97a,b 39.6 2.5 61.5 10.8 15.1 21.1 46.5 31.7 Myanmar Namibia Nepal 1995-96a,b 36.7 3.2 7.6 11.5 15.1 21.0 44.8 29.8 Netherlands 19940.0 32.6 2.8. .7.3 12.7 17.2 22.8 40.1 25.1 New Zealand 1991c.0! 43.9 0.3 :2.7 10.0 16.3 24.1 46.9 29.8 Nicaragua 1993a,b 50.3 1.6 4.2 8.0 12.6 20.0 55.2 39.8 Niger 1995a,b 50.5 0.8 2.6 7.1 13.9 23.1 53.3 35.4 Nigeria 1996-97a,b 50.6 1.6 4.4 8.2 12.5 19.3 55.7 40.8 Norway 1995c.d 25.8 41.1.. ).7 14.3 17.9 22.2 35..8 21.8 Oman Pakistan 1996-97a.1 31.2 4.1 2.5 12.9 16.0 20.5 41.1 27.6 Panama 1997a.b 48.5 ..1.2 36.6.. -8.1 13.6 21.9 52.8 35.7 Papua New Guinea 1996 a, 50.9 1.7 4.5 7.9 11.9 19.2 56.5 40.5 Praguay 19950,d 59.1 0.7 2.3 5.9 10.7 18.7 62.4 46.6 Peru 1996 c,d 46.2 1.6 4.4 9.1 14.1 21.3 51.2 35.4 Philippines 1997a.b 46.2 2.3 5.4 8.8 13.2 20.3 52.3 36.6 Poland 19960.0 32.9 3.0 7.7 12.6 16.7 22.1 40..9 26.3 Portugl1994-95c,d 35.6 3.1 7.3 11.6 15.9 21.8 43.4 28.4 Puerto Rico Romania 19940.d 28.2 3.7 8.9 13.6 17.6 22.6 37.3 22.7 Russian Federation 1998a,b 48.7 1.7 4.4 8.6 13.3 20.1 53.7 38.7 2000 WVorld Development Indicators 67 2.8 Survey year Gini index Percentage share of income or consumption Lowest Lowest Second Third Fourth Highest Highest .10% 20% 20% 20% 20% 20% 10% Rwanda 1983-85e51 28.9 4.2 9.7 13.2 16.5 21.6 39.1 24.2 Saudi Arabia .. ... .... . .... . ... ... . Senegal 1995a,b 41.3 2.6 6.4 10.3 14.5 20.6 48 2 33.5 Sierra Leone 1989"b 62.9 0.5 1.1 2.0 9.8 23.7 63.4 43.6 Singapore Slovak Republic 1992c. 19.5 5.1 11 9 15.8 18.8 22.2 31.4 18.2 Slovenia 1.995c,d 26.8 3.2 8.4 14.3 18.5 23.4 35.4 20.7 South Africa 1993-94a,l 59.3 1.1 2.9 5.5 9.2 17.7 64.8 45.9 Spain 1990ccd 32.5 2.8 7.5 12.6 17.0 22.6 40.3 25.2 Sri Lanka 1995a,b 34.4 3.5 8.0 11.8 15.8 21.5 42.8 28.0 St.Lucia 1995c,d 42.6 2.0 52.2 9.9 14.8 21.8 48.3 32.5 S udan........ -.... ...... Swaziland 1994c." 60.9 1.0 2.7 5.8 10.0 17.1 64.4 50.2 Sweden 1L992c,d 25.0 3.7 9.6 14.5 18.1 23.2 34.5 20.1 Switzerland 1992c,d 33.1 2.6 6.9 12.7 17.3 22.9 40.3 25.2 Syrian Arab Republic ' Tajikistan Tanzania 1993 a'~ 38.2 2.8 6.8. 11.0 15.1 21.6 45.5 30.1 Thailand 1998a,b 41.4 2.8 6.4 9.8 14.2 21.2 48.4 32.4 Tog. Trinidad and Tobago 1992C,d 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 Tunisia 1L990a.b 40.2 2.3 5.9 10.4 15.3 22.1 46.3 30.7 Turkey 1994a," 41.5 2.3 5.8 10.2 14.8 21.6 47.7 32.3 Turkmenistan 1998a,b 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31..7 Uganda 1992-93a,b 39.2 2.6 6.6 10.9 15.2 21.3 46.1 31.2 Ukraine 1996a,b 32.5 3.9 8.6 12.0 16.2 22.0 41.2 26.4 United Arab Emirates United Kingdom 1991c,d 36.1 2.6 6.6 11.5 16.3 22.7 43.10 27.3 UJnited States 1997C,d 40.8 1.8 5.2 10.5 15:.6 ..22.4 46.4 30.5 Uruguay 98,d42.3 2.1 5.4 10.0 14.8 21.5 48.3 32.7 Uzbekistan 1993c,d 33.3 3.1 7.4 12.0 16.7 23.0 40.9 25.2 Venezuela, RB 48.896 1 l.3 3.7 8.4.. 13.6... 21.2 53." 37.0 Vietnam 1998~" 36.1 3.6 8.0 11.4 15.2 20.9 44:.5 29.9 West Bank and Gaza Yemen, Rep. 921b39.5 2.3 6.1 10.9 15.3 21.6 46.' 30.8 Yugoslavia, FR (Serb./Mont.) Zambia 1996a.b 49.8 1.6 4.2.. 82.2 -12.8 20.1 54.8 39.2 Zimbabwe 1990)-9ja, 56.8 1.8 4.0 6.3 10.0 17.4 62.3 46.9 a. Refers to espenditure shares by percentiles of population. b. Ranked by per capita expenditure. c. Refers to income shares by percentiles of population. d. Ranked by pyr capita income. 68 2000 World Development Indicators 2.8 Inequality in the distribution of income is reflected in sible, consumption has been used rather Ihan income. * Survey year is the year in which the underlying the percentage shares of either income or consump- The income distribution and Gini indexes for high- data were collected. * Gini index measures the tion accruing to segments of the population ranked by income countries are calculated directly from the Lux- extent to which the distribution of income (or, in some income or consumption levels. The segments ranked embourg Income Study database, using an estimation cases. consumption expenditures) among individu- lowest by personal income receive the smallest share method consistent with that applied fo developing als or households within an economy deviates from of total income. The Gini index provides a convenient countries. a perfectly equal distribution. A Lorenz curve p ots the summary measure of the degree of inequality. cumulative percentages of total income received Data on personal or household income or con- against the cumulative number of recipients, starting sumption come from nationally representative house- with the poorest individual or household. The Gini hold surveys. The data in the table referto differentyears index measures the area between the Lorenz curve between 1985 and 1999. Footnotes to the survey year and a hypothetical line of absolute equality, expressed indicate whether the rankings are based on per capita as a percentage of the maximum area under the line. income or consumption. Each distribution (including Thus a Gini index of zero represents perfect equality, for high-income economies) is based on percentiles of while an index of 100 implies perfect inequality. population-rather than of households-with households * Percentage share of income or consumption is ranked by income or expenditure per person. the share that accrues to subgroups of population indi- Where the original data from the household survey cated by deciles or quintiles. Percentage shares by quin- were available, they have been used to directly calcu- tile may not sum to 100 because of rounding. late the income (or consumption) shares by quintile. Otherwise, shares have been estimated from the best Data sources available grouped data. The distribution indicators have been adjusted for Data on distribution are compiled by the World Bank's household size, providing a more consistent measure Development Research Group using primary household of per capita income or consumption. No adjustment survey data obtained from government statistical has been made for spatial differences in cost of living agencies and World Bank country departments. Data within countries, because the data needed for such cal- for high-income economies are from the Luxembourg culations are generally unavailable. Forfurther details Income Study database. on the estimation method for low- and middle-income economies see Ravallion and Chen (1996). Because the underlying household surveys differ in method and in the type of data collected, the distrib- ution indicators are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achieving strict comparability is still impossible (see About the data for table 2.7). The following sources of noncomparability should be noted. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The dis- tribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used usually differ among surveys. Con- sumption is usually a much better welfare indicator, particularly in developing countries. Second, house- hold units differ in size (number of members) and in extent of income sharing among members. And indi- viduals differ in age and consumption needs. Differ- ences between countries in these respects may bias comparisons of distribution. World Bank staff have made an effort to ensure that the data are as comparable as possible. Whenever pos- 2000 World Development indicators 69 2.9 Education inputs Public expenditure Expenditure per student Expenditure Primary Duration on education on teachers' pupil- of compensation teacher primary ratio education Primary Secondary Tertiary % of total % of % Of % of % of current education pupils per GNP GNP per capita GNP per capita GNP per capita expenditure teacher years 1980 1997 1980 1997 1980 1997 1980 1997 1.980 1997 1997 1997 Albania 3.1 . 9. .. ...18 8 Algeria 7.8 5.1 8.9 26.10 23.9.. . 63.6 74.2 b 27 9 Angola ......... ...... ........62.2 ..8 Argent na 2.7 3.5 6.5 8.3 293 19.9 84.1 17 10 Armenia .2.0 . . 5 . . 19.. ...11.. Australia 5.5 5.4.. 17 44.5 16.8 51.1 29.7 54.2 i8 10d Austria 5.5 5.4 15.7 21.7 . 24.7 37.4 35.3 53.0 e 61.7 12 9d Azerbaijan 3.0 .. 17.9 .. . . 15.3 ...20 11 Bangladesh 1.1 2.2 3~6c 10.4 34.9 335 ..5 Belarus 5.9 1 c18.7 ..20 d Belgium 680 3.1 17.4 c 33.8 13.4 51.0 17.5 73.0 f 73.6. 12 Benin 3.2. 1.,.8 . .. 2490 .. 52 6 Bolivia 4.4 4.9 13.7 c 15.2 .. 75 .7 48.5 8 Bosnia and Herzegovina Botsaw na 6.0. .. 8.6 12.5 .. .611.7 .. 54.9.. .... ........... 25 Brazil 3.6 5.1 8.7 c 11.0 58.7 ...23 8 Bulgaria 4.5 3.2 17.5 30.8 . .. 51.3 17.4 ...1 7 8 Burkina Faso 2.2 1.5 23.1 c 21.2 87.5 . 2,957.4 590.6 61.0 67.8 53 7 Burundi 3.4 4.0 24.2 23.0 222.,2 .1,47.9.8 . 74.3 5.0 6 Cambodia 2.9. . . . . .. 46 6 Cameroon 3.8 .. 11.0 .. . . 401.2 . 65.4 ......... 6 Canada 6.9 6.9.. . 48.3 50.1 38.7 39.8 55.4 62.0 16 . 10d Contra African Republic..22.1 22.. 936.1 ......6 Chad 1.7.....6.3 .. .7 64.4 67 6 Chile 4.6 3.6 9.6 -11.1 16.8 11.8 112.0 21.1 76.8 30 8 China 2.5 2.3 3.8 6.6 . .. 246.2 662.2 . 249 ... . ........ .. . .. ... .. .. ... . .. .. ... . .. .. ... . Colombia 1.9 4.1 5.3 10.30 . 11.7 43.8 35 4 93.4 81.9 25 5 Congo, Dam. Rep. 2.6 ... . 7479.9 ... 45 Congo, Rep. 7.0 6.1 10.10 15.4 . .. 369.4 .. 70.8 .. ...70 10 Costa Rica ~~7.8 5.4 13.0 13.60 25.7 23.2 75.8 . 502.2 .......... 29 10..... CMe dIlvoire 7.2 5.0 22.6 0 16.90 . . 375.7 210.1 ........_41 6 Croatia 5.3 . .. . ..198 Cuba 7.2 6.7 10.4 16.3 .. . 28.5 98.1 388 12 9 Czech Republic 5.1I. 152 . 21.9 . 49. 441 Denmark 6.7 8.1 37.2 0 24.3 11.2 34.5 50.0 49.6 49.3 43.1 10 9 Dominican Republic 2.2 2.3 3.10c 3.3 . 4.4 . 9.7 62.2... 10 Ecuador 5.6 3.5 5.60 7.60 . 24.2 . 77.4 25 10 Egypt. Arab Rep. 5.7 4.8 .. 25.9 a 57.8 ...23 8 El Salvador 3.9 2.5 12.5 0 7.1 . 5.5 141.6 7.7 . 33 9 Entree 1.8 92 99~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. . .. . . 4 .7 Estonia 7.2 454 . 3. .1 Ethiopia 3.1 4.0 17.6 26.7 .. . 1,119.5 868.9 68.3 . 43 6 Finland 5.3 7.5 20.70c 23.0 . 27.5 37.3 45.6 52.6 47.7 1 9 France 5.0 6.0 12.0 15.8 20.2 26.9 29.3 28.2 6. 19 10 Gabon 2.7 2.9.......... ........... 56.7 5-1 10 Gambia. The 3.2 4.9 18.6 13.7 269,1 31) Georgia 5~~~~~~~~~~~~~~~~~.. ...2. .. . ... 25.0 25:5 18 9 German y.4.8 .. 30.8 9 . 37.8 17 12 Ghana 3.1 4.2 370 .. . 26.7 . 60.0 . Greece 2.0 3.1 7.0 1 7.50 9.5 15.1 30.1 22.3 84.8 149d Guatemala 1.8 -1.7 4.8 6.2 10.4 . .. 31.1.. 62.8 35 6 Guinea .. 1.9 7.9 c. . 444.7 ....49. 6 Guinea-Bissau .. . 09 698 .. . 73.5 .... Haiti 1.5 . 5.90 . .. 130.0 . 66.9 6.. Honduras 3.2 3.6 10 7 0 0 . . 774.4 .68.7 71.1 67.8 35 6 70 2000 World Development Indicators T L 0iole!PULJ OuOWdoIOAGO PI'OM OOOZ P6 99 99 64 OT7 00!8i O4JGnd 8 977 7779. . .317.77Y77 778 92 PU2IOd~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~n ...... 6 -mm~~~~~~~~~~~~91.7 8 t9 f7 GUdiI~ 9 877.969~~~~~~~~~~~~.. ... .. ...... . .... ...... 6 9 .79 ~~~~~~~~~~~~:~~~~j77 . 9.9 eM.......O.. N..... . 9 L9 ... 7 . . 2o .. 2UQ~~~~~~~~~~~~~~~~~~~-~bq: ' O`~-:''!N- 8 717 7789~~~~~~~~~~~~~~~~.. .............. .... ... . .........7 ...79.. b- .......: 7 ........ .... ........ 9 99 L69 8777.79 . 9 . 9.~~~~~~~~~~~~~~~~~~~~~~~) GNene 6677.... ......... 124N -, - - .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :.~~.. ... . . . 7~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~77 .79~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....... ....... ..... ... L..86 Sfl9Jfl2L~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....... - ,-- '-I- - r - .F.IPL'J~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. ........ .. . 9 CT97 97t 77 .~7 12 172 C09 6 .. .... . .... .. . . ...... 5 . ....6677 7... . 9~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~977 9777.~~~~~.. . ... ............ ~ 677 .7 .9.99 ...-.7.-. Old ZA8i~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..... pT~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~p0 6.~~~~~~~~~~~~~~~6i . . . .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~97]9~~~~~~~~~~~~~~~~~~~~~~~~17 . . . . 9:9~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. .... p6 .. 6'69 .~~~~~~~~~~~~~~~~~~~~~~Vt ;9797.... . ....6.....67.£67 897 99 .noeI d. 92 . ....... .. ..... w.. .I S £2.617 . R -s 2 z.9(---77.57Tc . 8.I p6 677 991, 9L9:z -999.09.k 7iT.67 1T ....767.7.7.09..*9.PUele4i .677 . . 7~~~~~~~~~~~~~~~~~~~~~~r 9L8 9 r 307.6i j . 129~~~~~~~~~~~~ ...... ........ ...... 9 .... 07777.......... .. 6 7777 67777 9*9.~~~~~~~~~~M.Mt.. 1777 f7 eseuopu -i~aid . ... 9 .966 ..8 .....7.77.7 39677.......... .............. .. uofleoulpe oi~~~~~ E 'lupe1v A.wd se ~ e uoies... ... .... .. .... 6O 9I!fl sJLoe c oweU .Ow J l . A. ........ . .......u.. .. .... ..~ I ua ..~! u x . . . .. . ..... .......q..... ..n....... O~~~~~~~~~~~~~~~~~~~~~~. m .m o .... .0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..... .... ............ ...... 2.9 Public expenditure Expenditure per student Expenditure Primary Duration on education on teachers' pupil- Of compensation teacher primary ratio education PrKmary Secondary Tertiary % of total % of % of % of S of current education pupils per GNP GNP per capita GNP per capita GNP per capita expenditure teacher years 1990 1997 1980 1.997 1980 1997 1980 1997 1980 1997 1997 1997 Rwanda 2.7 .. 11.1 .. . . 901.7 . 748...6 Saudi Arabia 41 7.5 18.90a 33.8 a . 109.2 60.0 13 Senegal .. 3.7 24.9 c 1730 c. 447.5 .. .56 6d Sierra Leone 3.5 .... .- Singapore 2.8 3.0 6.8 0 780 c 40.6 .28.0 47.5. 22d Slovak Republic 5:0 .. 23.6 30.8 .. 37.9 1.9 9 Slovenia .. 5.7 .. 2. 7 . 62.2 148 South Africa .. 7.9 .. 25.1 .. 64.5 3 Spain 2.3 5.0 .. 17.4 .. 17.8 .17 10 Sri Lanka 2.7 3.4 . .. . .. 65.6 85.0 28 9 Sudan 4.3 0.9 24.1 c 4.9 .. 528.1 ......... 29 8 Sweden 9.0 8.3 43.0 284.6 15.8 34.1 35.0 72.4 46.3 . 1 9 Switzerland 4.7 6.4 . 1 9.2 29..9 29.0 585 45.4 61.0 59.9 ..9 Syrian Arab Republic 4.6 3.1 8.0 r 8.20c 15.1 16.5 74.7 .. 85.9 ..23 6 Tajiktiatan .. 2.2 .. . . . . . 49 Tanzania .. .. .. .. .. .. ~~~~~~~~~~~~~~~~~~~~~~~...... . .. 37.... 7. Thailand 3.4 4.8 8.8 12.5 . .. 60.1 26.7 80.3 56.8 ..6 Togo 5.6 4.5 8.3 7.7 891.5 333.8 68.4 74.2 51 6 Trinidad and Tobago 4.0 3.6 9.2 0 70.8 20.4 59.4 .. 73.2 ..25 7 Tunisia 5.4 7.7 11.80c 75.2 0 37.7 .. 194.6 79.1 81.3 77.0 24 9 Turkey 2.2 2.2 6.40 7 3.3 .. 9.2 95.0 57.7 . . . . 28 8d Turkmenistan ~ ~ ~ ~~~~.. . .. ... .. ....... . . .. ......... Uganda 1.3 2.6 4.3 1. . . ,034.8 ... 69.9 b 35 Ukraine 5.6 7.3 21.2 _. .. .. 20.2 22.7 9. d United Arab Emirates 1.3 7.8 .. . . . . . . 32.4 i66 United Kingdom 5.6 5.3 . .. 22.1 20.5 79.8 40.7 52.1 ..11 United States 6.7 5.4 27.1 C 18.5 .. 23.8 48.2 24. 6 7..... .. .16 10d Uruguay 2.3 3.3 11.1 930 .. 28.1 24.2 56.90e 41.5 20 6 U zbekistan. . -. ~ . .. 7.7.... .. .. .. .... . ... ...2 Venezuela. RB .4.4 5.2 ..57.7 2.1 23.7 4.8 71.1 . 60. 7 27.4 27.. 10 Vietnam .. 3.0 .. 87.8 . 66.0 ..5 West Bank and Gaza Yemen, Rep. 7.0 9 Yugoslavia,F .(Serb.Mon~t.~) . . Zambia 4.5 2.2 -10.6 5.0 .. . 605.4 358.2 52.6 e9 7 Zimbabwe 5.3 .. 19.4 19.4 .. .. 324.8 340.3 75.2 91.1 39 8~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. 24834. . 9113 Low Income 3.2 3.2 . ..39 .7 Exci. China & India 3.3 32 .. . .7 Middle Income 4.0 4.9 9.4 ..... 58.7 ...9 Lower middle incoime 4.2 4.9 . 8 Upper middleincm 4.0 5.0 9.4 .. . . 58.7 39.8 24 9 Low & middle income 3.5 4.7 .. _.....36 ..8 East Asia & Pacific 2.5 2.9.. ... 41.7 24 8 Europe & Central Asia .. 5.7 39.8 9 Latin America & Carib. 3.8 3.6 8.9 ..51.3 .24. .9 Middle East & N. Africa 5.0 5.2 . 5.2 81.1 ..26 9 South Asia 2.0 3.1 10.5 ..88.2 85.0 433 6 Sulb-Saharan Africa 3.8 4.1 13.6 7.90.7 .7 .7 High income 5.6 5.4 1. 7.9 22.5 22.2 44.4 37.5 1710 Europe EMU 5.4 5.3 13.8 27.7 .. 25.8 37.4 35.8 ... 5 9 a. Includes expenditure on preprimary and secondary. b. Excludes expenditure on tertiary. c. Includes expenditure an preprimary. d. The education system allows other alternatives, a. Includes administration other than personnel.t. Refers only to ministry of education expenditure. g. Includes expenditure on preprimary and primary. h. Includes enpenditure on primary.I. Includes aspen- diture on secondary. 72 2000 World Development Indicators _2.90 Data on education are compiled bythe United Nations Edu- The comparability of pupil-teacher ratios is affected * Public expenditure on education is the percentage cational, Scientific, and Cultural Organization (UNESCO) by the definition of teachers, by whether teachers are of GNP accounted for by public spending on public edu- from official responses to surveys and from reports pro- assigned nonteaching duties, and by differences in class cation plus subsidies to private education at the primary, vided by education authorities in each country. Such data size by grade and in the number of hours l aught. More- secondary, and tertiary levels. * Expenditure per stu- are used for monitoring, policymaking, and resource allo- over, the underlying enrollment levels are su )ject to a van- dent is the public current spending on education divided cation. For a varety of reasons education statistics gen- ety of reporting errors. (See About the datatortable 2.10 by the total number of students by level, as a percent- erally fail to provide a complete and accurate picture of for further discussion of enrollment data.) While the ageofGNPpercapita. * Expenditureonteachers'com- a country's education system and should be interpreted pupil-teacher ratio is often used to compae the quality pensation is the public expenditure on teachers' gross with caution. Statistics often are out of date byt'o to three of schooling across countries, it is often only weakly salaries and other benefits as a percentage of the total years. The information collected focuses more on inputs related to the value added of schooling systens (Behrman public current spending on education. * Primary pupil- than on outcomes. And coverage, definitions. and data and Rosenzweig 1994). teacher ratio is the number of pupils enrolled in prmary collection methods vary across countries and over time Years of compulsory education show the level of school divided by the number of primary school teach- within countres. (For further discussion of the reliability development of the country's education system and ers (regardless of their teaching assignment). * Dura- of education data see Behrman and Rosenzweig 1994.) education policy. The actual length of conr pulsory edu- tion of compulsory education is the number of years of The data on education spending in the table refer cation is influenced by the length of the school day and compulsory school attendance a child must complete, solely to public spending-that is, government spend- school year. within a stipulated age range. ing on public education plus subsidies for private edu- cation. The data generally exclude foreign aid for Data sources education. They also may exclude spending by religious Households account for much of thne schools, which play a significant role in many develop- spending onaeducation International data on education are compiled by ing countres. Data for some countries and for some years UNESCO's Institute for Statistics in cooperation with refer to spending by the ministry of education at the cen- Prvate expenditure on education, all Irvels, national commnissions and national statistical ser- ter only (excluding education expenditures by other min- 1995 (% of total education expenditum) vices. The data in the table were compiled using a 100~ ----- -- - - istries and departments, local authorities, and so on). UNESCO electronic database corresponding to tables Many developing countres have sought to supplement 80 in UNESCO's Statistical Yearbook 1999. public funds for education. Some countries have adopted tuition fees to recover part of the cost of providing edu- 60 cation services or to encourage development of private 40~ schools. Charging fees raises difficult questions relating to equity, efficiency, access, and taxation, however, and 20 some governments have used scholarships, vouchers, O and other methods of public finance to counter this crit- °t 45 Kt \r5 Cel icism. Data for a few countries include private spending, s e ' t? r- ' although national practices vary with respect to whether .? '9 parents or schools pay for books, uniforms, and other supplies. For greater detail see the country- and indicator- Note: Data for Uganda refer to 1990. data for specific notes in the source cited below. Indonesia to 1991, data for Japan, the Republic of The percentage of GNP devoted to education can be Korea, and the United States to 1994, anal data for Chile to 1996. interpreted as reflecting a country's effort in education. Source: World Bank, EDSTATS. Often it bears a weak relationship to measures of out- put of the education system, as reflected in educational Private spending on education is sizable In many countries, averaging 25 percent of all education attainment. The pattern suggests wide variations across expenditures In developing countries and 12 countries in the efficiency with which the government's percent in high-Income countries. 3lobally, households contribute close to 20 percent of resources are translated into education outcomes. eholon trites education expenditures. Well-trained and motivated teachers are a critical input to education, but they come at a cost. Typically, two- thirds of education spending goes to teachers' com- pensation (gross salaries and other benefits). Teachers are defined here as including both ful- and part-time teach- ing staff and teachers assigned to nonteaching duties, but country reporting varies. Comparisons should thus be made with caution. 2000 World Development Indicators 73 2.10 Participation in education Gross enrollment Net enrollment ratio ratlca Preprimary Primary Secondary Tertiary Primary Secondary % of relevant % of relevant % of relevant % of relevant % of relevant % of relevant age group age group age group age group age group age group 1997 1980 1997 1980 1997 1.980 1997 1980 1997 1980 1997 Albani'a 39 113 107 67 38 5 11 Algeria 2 95 108 33 63 6 13 82 96 43 69 Angola 62 175 21 0 1 83 35 81 31 Argent'ina 54 106 III 56 73 22 42 97 100 59 77 A rm enia 2687 9012........ ........................................ Australia 80 112 101 71 153 b 25 80 100 100 81 96 Austria 80 99 100 93 103 22 48 100 100 91 97 Azerbaijan 19 115 106 95 77 24 18 Bangladesh 75 61 183 6 60 75 18 22 Belarus .82 104 98 98 93 39 44 85 Belgium 121 104 103 91 146"b 26 57 100 100 96 100 Benin 3 67 78 16 18 1 3 53 68 25 28 Bolivia 42 87 3716 24 79 97 34 40 Bosnia and Herzegovina Botawana 91~~..- .-711-...... . 108 19 65I 6 76 80. 40 89 Brazil 58 98 125 34 62 11 15 80 9 7 46 66 Bulgaria 63 98 99 85 77 16 41 98 98 75 78 Burkina Faso 2 18 40 3 0 1 15 32 5 13 Burundi 26 51 3 7 1 1 20 36 8 17 Cambodia 5 139 113 24 2 1 100 100 15 39 Cameroon 11 98 85 18 27 2 4 71 62 40 40 Canada 64 99 102 88 105 57 90 100 100 84 95 Central African Republic 6 71 14 1 1 57 46 27 19 Chad I 58 10 0 1 26 48 13 18 Chile 98 109 101 53 75 12 31 93 90 70 85 China 28 113 123 46 70 2 6 84 100 63 7 0 *Hong Kong, China 85 107 94 64 73 10 28 98 91 67 69 Colombia 33 112 113 39 67 9 1 7 73 89 60 76 Congo, Dem. Rep. 1 92 72 24 26 1 2 71 58 44 37 Congo, Rep. 2 141 114 74 53 5 8 97 78 98 84 Costa Rica 74 105 104 48 48 21 33 89 89 39 40 CMe dIlvoire 375 71 19 25 3 5 55 58 39 34 Croatia 40 87 77 82 19 28 100 100 80 72 Cuab 88 106 106 81 81 17 12 97 100 80 70 Czech Republic 91 96 104 99 99 18 24 95 100 93 100 Denmark 83 96 102 105 121 28 45 96 100 89 95 Dominican Republic 33 118 94 42 54 10 23 99 91 50 79 Ecuador 56 118 127 53 50 35 26 92 100 66 51 Egypt, Arab Rep. 9 73 101 51 78 16 23 72 95 43 75 El Salvador 40 75 97 24 37 13 18 70 89 23 36 Eritrea 4 53 20I 29 38 Estonia 75 103 94 127 104 25 45 100 100 00 86 Ethiopia .1 37 43 9 12 0 1 28 35 19 25 Finland 45 96 99 100 118 32 74 100 100 87 95 France 83 111 105 85 111 25 51 100 100 94 99 Gabon 174 162 34 56 4 8 Gambia, The 28 53 77 11 25 2 53 66 10 33 Georgia 34 93 88 109 77 30 41 93 89 97 76 Germany 89 104 -104 27 47 100 100 82 95 Ghana 36 79 79 41 2 1 Greece 64 103 93 81 95 17 47 100 100 75 91 Guatemala 35 71 88 19 26 8 8 59 74 28 35 Guinea 4 36 54 17 14 5 1 30 46 20 15 Guinea-Bissau 1 68 6 7 5 9 2 H aiti 3 7 77 14 1 1.... ... .............. . ....... .... Honduras 15 98 11l 30 8 11 79 88 44 36 74 2000 World Development Indicators 2.100 Gross enrollment Net enrollment ratio ratio' Preprimary Primary Secondary Tertiary Primary Secondary % of relevant % of relevant % of relevant % of relevant % of relevant Sof relevant age group age group age group age group age group age group 1997 1980 1997 ±980 1997 1980 1997 1980 1997 1980 1.997 Hungary 112 9 6 103 70 98 14 25 95 98 71 9 7 India 5 83 100 30 49 5 7 65 77 41 60 Indonesia 19 107 113 29 56 4 11 89 99 42 56 Iran, Ialamic Rep. 11 87 98 42 77 . 18 72 90 50 81 Irap 7 113 85 57 42 9 11 9 7 75 66 43 Ireland 114 100 105 90 118 18 41 100 100 90 100 Israel 70 95 98 73 88 29 44 Italy ~~~95 100 101 72 95 27 47 100 100 70 95 Jamaica 83 103 100 67 .7 8 98 96 71 70 Japan 49 101 101 93 103 31 43 100 100 93 100 Jordan 82 71 59 57 13 19 73 68 53 41 Kazakhstan 30 85 98 93 8 7 34 32 Kenya 36 115 85 20 24 1 2 91 65 55 61 Korea, Dem. Rep. Koea Rep 88 110 94 78 102 15 68 100 100 76 100 Kuwait 63 102 77 80 65 11 19 85 65 81 63 Lao PDR 8 114 112 21. 29 0 3 72 73 53 63 Latvia 47 102 96 99 84 24 33 100 100 90 81 Lebanon 75 111 111 59 81 30 27 . 76 Lesotho ...... :. ..................- 104 ....108 18 31 1 2 67 69 69 73 Libya 5 125 .. 76 .8 20 100 100 83 100 Lithuania 40 79 98 114 86 35 31. .. . 81 Macedonia, FYR 26 100 99 61 63 28 20 95 56 Madagascar 4 130 92 .. 16 3 2 61 Malawi ..60 134 5 17 1 1 43 99 39 73 Malaysia 42 93 101 48 64 4 11 92 100 48 64 Mali 2 26 49 8 13 1 1 20 38 10 18 Mauritania 1 37 79 11 16 1 4 . 5 7 Mauritius 104 93 106 50 65 1 6 79 97 56 68 Mexico 73 120 114 49 64 14 16 98 100 67 66 Moldova 45 83 97 78 81 30 27 Mongolia 27 107 88 92 56 22 19 100 85 89 56 Morocco 98 83 86 26 39 6 11 62 77 36 38 Mozambique ..99 60 5 7 0 1 35 40 40 22 Myanmar . 91 121 22 30 5 6 71 99 38 54 Namibia 9 .. 131 .. 62 .. 9 86 91 67 81 Nepal 1 86 113 22 42 3 5 66 78 26 55 Netherlands 100 100 108 93 132b 29 47 100 100 93 100 New Zealand 76 ill 101 83 113 27 63 100 100 85 93 Nicaragua 23 94 102 41 55 12 12 71 79 51 51 Niger 1 25 29 5 7 0 1 22 24 7 9 Nigeria . 109 98 18 33 3 4 Norway 103 100 100 94 119 26 62 99 100 84 98 Oman 5 51 76 12 67 . 8 43 68 20 67 Pakistan 16 4.. 1 ..2 4.. . Panama 76 107 106 61 69 21 32 89 90 65 71 Papua New Guinea 1 59 80 12 14 2 3.. . Paraguay 61 106 ill 27 47 9 10 91 96 37 61 Peru 40 114 123 59 73 17 26 87 94 80 84 Philippines 11 112 117 64 78 24 35 95 100 72 78 Poland 48 100 96 77 98 18 24 99 99 73 87 Portugal 59 123 128 37 11.1b 11 38 99 100 45 90 Puerto Rico ... . . . 42 42 . . . Romania 53 104 104 94 78 12 23 91 100 100 76 Russian Federation 74 102 107 96 . 46 41 92 100 98 88 2000 World Developmnent Indicators 75 2.10 Gross enrollment Net enroillment ratio ratio. Preprimary Primary Secondary Tertiary Primary Secondary S of relevant %of relevant % of relevant % of relevant % of relevant % of relevant age group age group age group age group age group age group 1997 1980 1.997 1.980 1.997 1.980 1.997 1980 1997 1980 1997 Rwanda 2 63 .. 3 0 1 59 Saudi Arabia 8 6 1 76 30 61 7 1 6 49 60 37 59 Senegal 2 46 7 1 11 16 3 3 37 60 19 20 Si erra Leone 252 ,. 14 1 2 Singapore 19 108 94 60 74 8 39 100 91 66 76 Slovak Republic 76 .. 102 94 18 22 Slovenia 61 98 98 92 20 36 .. 95 South Africa 35 90 133 95 5 1 7 68 100 62 95 Spain 72 109 107 87 120 23 53 100 100 79 92 Sri Lanka 60 103 109 55 75 3 5 96 100 59 76 Sudan 24 50 51 16 21 2 4 . Sweden 73 97 107 88 140 b 31 50 100 100 83 100 Switzerland 95 8 4 9 7 94 100 1 8 34 100 100 80 84 Syrian Arab Republic 7 100 101 46 43 17 15 90 95 48 42 Tajikistan 10 .. 95 .. 78 24 20 Tanzania 0 93 67 3 6 0 1 68 48 Thailand 62 99 89 2 9 5 9 15 21 9 2 88 25 48 Togo.3 118 120 3 3 2 7 2 4 79 82 65 5 8 Trinidad and Tobago 12 99 99 69 74 4 8 92 100 73 72 Tunisia 11 102 118 27 64 5 14 83 100 40 74 Turkey 8 96 107 35 58 5 21 81 100 42 58 Turkmenistan 37 ....... .............. .. 23.. .20 Uganda ..50 74 5 12 1 2 Ukraine 61 102 .. 94 .42 42 United Arab Emirates 57 89 89 52 80 3 12 75 82 63 78 United Kingdom 30 103 116 84 129u 19 52 100 100 88 92 United States 70 99 102 91 97 56 81 90 100 94 96 Uruguay 45 107 109 62 85 17 30 87 94 70 84 Uzbekistan 50 81 78 106 94 29 36 . Venezuela, RB 44 93 91 21 40 21 25 83 83 24 49 Vietnam 40 109 114 42 57 2 7 96 100 47 55 West Bank and Gaza... ... Yemenl, Rep. I .. 70 .. 34 4 4 Yugoslavia, FR (Serb./Mont.) 32 .. 69 .. 62 18 22 Zambia I90 89 16 27 2 3 77 72 35 42 Zimbabwe 85 112 8 50 1 7 72 93 20 59 .... .... .... .. ....... .. ... ...... .. . . . .... ........ .. .... Low income 20 94 107 34 56 3 6 74 86 49 59 Exci. China & India 22 81 90 22 32 3 5 68 75 34 41 Middle income 48 100 106 60 66 20 25 87 95 62 72 Lower middle income 42 98 .103 67 67 24 27 85 94 66 73 Upper middle income 56 103 109 50 65 13 23 89 96 57 71 Low & middle income 28 96 107 42 59 8 12 78 88 53 63 East Asia & Pacific 30 il1 119 44 69 4 8 86 99 59 67 Europe & Cenitral Asia 53 99 100 86 ..30 32 92 100 84 81 Latin America & Carib. 56 105 113 42 60 14 20 85 94 55 66 Middle East & N. Africa 18 87 95 42 64 11 16 74 87 46 66 South Asia 14 77 100 27 49 5 6 64 77 38 85 Sub-Saharan Africa 12 81 78 15 27 2 2 . High income 70 102 103 87 106 35 59 97 100 87 96 Europe EMU 87 106 104 81 108 25 49 100 .100 .82 96 a. UNESCO enrollment estimnates and pwojections as sassessed in 1999. b. Includes training for the unemployed. 76 2000 World Development Indicators 2.10 0 School enrollment data are reported to the United Nations of overage children enrolled in each grade and raising * Gross enrollment ratio is the ratio of total enrollment, Educational, Scientific, and Cultural Organizaton (UNESCO) the gross enrollment ratio. A common error :hat may also regardless of age, to the population of the age group by national education authorities. Enrollment ratios are distort enrollment ratios is the lack of distinction between that officially corresponds to the level of education a useful measure of participation in education, but they new entrants and repeaters, which, other :hings equal, shown. * Net enrollment ratio is the ratio of the may also have significant limitations. Enrollment ratios leads to underreporting of repeaters and overestimation number of children of official school age (as defined are based on data collected durng annual school surveys, of dropouts. Thus gross enrollment ratios provide an indi- by the national education system) who are enrolled in which are typically conducted at the beginning of the school cation of the capacity of each level of the e iucation sys- school to the population of the corresponding official year and therefore do not reflect actual rates of atten- tem, but a high ratio does not necessarily indicate a school age. Based on the International Standard Clas- dance or dropouts during the school year. And school successful education system. The net enrollment ratio sification of Education (ISCED), * Preprimary edu- administrators may report exaggerated enrollments, excludes overage students in an attempt to capture cation refers to the initial stage of organized instruction, especially if there is a financial incentive to do so. Often more accurately the system's coverage and internal designed primarily to introduce very young children to the number ofteachers paid bythe government is related efficiency. It does not solve the problem completely, how- a school-type environment. * Primary education pro- to the number of pupils enrolled. Behrman and Rosen- ever, because some children fall outside the official vides children with basic reading, writing, and mathe- zweig (1994), comparing official school enrollment data school age simply because of late or earl entry rather matics skills along with an elementary understanding for Malaysia in 1988 with gross school attendance rates than because of grade repetition. The difference between of such subjects as history, geography, natural science, from a household survey, found that the official statis- gross and net enrollment ratios shows the incidence of social science, art, and music. * Secondary educa- tics systematically overstated enrollment. overage and underage enrollments. tion completes the provision of basic education that Overage or underage enrollments frequently occur, par- began at the primary level, and aims at laying the ticularlywhen parents prefer, forcultural oreconomic rea- foundations for lifelong learning and human develop- sons, to have children start school at otherthan the official ment, by offering more subject- or skill-oriented instruc- MilSions of the world's children still age. Children'sageatenrollmentmaybe inaccuratelyesti- are not in school tion using more specialized teachers. * Tertiary mated or misstated, especially in communites where reg- education, whether or not leading to an advanced istrabon ofbirths is not strctlyenforced. Parents who want Miliions research qualificaton, normally requires, as a minimum to enroll their underage children in pnmary school may 200 condition of admission, the successful completion of do so by overstating the age of the children. And in education at the secondary level. some education systems ages for children repeating a 150 grade may be deliberately or inadvertently underreported. Data sources As an intemational indicator, the gross primary enro 1- 100 ment ratio has been used to indicate broad levels of par- The gross enrollment ratios are from UNESCO's Statis- ticipation as well as school capacity. It has an inherent 50 tical Yearbook 1999, and the net enrollment ratios are weakness: the length of primary education differs signif- the results of UNESCO's 1999 enrollment estimates icantly across countres. A short duration tends to increase o and projections. 1995 201r( the ratio and a long duration to decrease it (in part because there are more dropouts among older children). * Males aged 6-11 Other problems affecting cross-country comparisons 1 Females aged 6-11 of enrollment data stem from errors in estimates of F Males aged 12-17 Females aged 12-17 school-age populations. Age-gender structures from cen- Source: UNESCO 1998. suses or vital registration systems, the primary sources Education for All 2000 efforts over the past of data on school-age populations, are commonly sub- decade have boosted enrollment, espocially at ject to underenumeration (especially of young children) the primary level. Yet millions of priniary- and aimed at circumventing laws or regulations; errors are secondary-school-age children remaiin out of school, and their numbers are projected to grow. also introduced when parents round up children's ages. Where access remalns a problem, especially While census data are often adjusted for age bias, for the poor and disadvantaged, several i:trategles adjustments are rarely made for inadequate vital regis- to Increase access are being successfully Implemented, Including multiple shifts, niultigrade tration systems. Compounding these problems, pro- classrooms, and nontraditional schooling. and postcensus estimates of school-age children are inter- But to encourage girls' attendance, polations or projections based on models that may miss strategies will need to go beyond lricreasing supply. Measures wlil be needed that lower important demographic events (see the discussion of barriers to their enrollment-by providing demographic data in About the data for table 2.1). Incentives, Improving the relevance of educatlon, In using enrollment data, it is also important to con- and estabilshing supportive national polilces. sider repetition rates, which are quite high in some developing countries, leading to a substantial number 2000 World Development Indicators 77 O ~2411 Education efficiency Percentage of cohort Repeaters Children out of school reaching grade 5 Primary Secondary % of total % of total Primary Secondary Male Female enrollment enrollment thousands throusands ±980 ±.996 1.980 1996 ±.980 1997 ±.980 1997 1980 1997 1980 1997 Albani'a .. 81 .. 83 5.3 Algeria 90 94 85 95 11.7 10.5 8.5 19.6 608 176 1,765 1,319 Angola 29.2 124 931 178 1,367 Argentina .......................... 5.3 985 972 776 Armenia 02 A u stra la ........... ...... . .. ... ... ... ... . . . ... ..... ...... .... . . ... . Austraia Azerbaijan . . Bangladesh 18 26 . 17.8 . 5,464 3,896 12,524 18,130 Belarus 0.3 0.9 Belgium 19.4 Benin 59 64 62 5 7 19.6 25.1 269 30 427 686 Bolivia . ... .............. .. . .. .... ......... 233 . 40 309 402 Bosnia and Herzegovina Botswana 80 87 84 93 2.9 3.3 . 2.8 46 60 57 21 Brazil.. . .. . 20.2 18.4 7.3 10.8 4,514 74 4,56 3,5 Bulgaria . 93 . 90 1.7 3.4 0.1 2.0 23 9 93 211 Burkina Faso 77 74 74 77 17.1 16.0 14.3 . 985 1,271 971 1,499 Burundi 100 . 96 . 30.2 4.3. 535 699 615 807 Cambodia . 51 . 46 . 26.3 1 2 876 789 Cameroon 70 . 70 30.0 13.7 . 406 882 771 1,347 Canada . . .. . . Central African Republic 63 . 50 35.1.. . 151 304 242 440 Chad. 62 . 53 ..30 .. 18.4 510 635 8 903 Chile 94 100 97 100 5.4 4.3 145 217 312 148 China . 93 . 94 1 6 2.9 114 5336 6,2 Colombia 36 70 39 76 13.2 7.2 992 471 1,781 1,180 Congo, Dem. Rep. 56 . 59 . 18.8 . 1,326 3,520 2,010 4,113 Congo, Rep. 81 40 83 78 25.7 33.2 9 0 6 Coi. o Rep ~. 1.9- 101........... ... ... .......... ..... .... 5 68 . Costa Rica 77 86 82 89 7.9 10.1 7.5 9.6 33 42 122 177 C6te dIlvoire 86 77 79 71 19.6 ..24.2 .. . 610 1,022 733 1,614 Croatia 9 98 0.5 0.3 0 0 51 140 Cuba 5.7 3.1 381 2833 27 Czech Republic.. . .. .07 Denmark 99 100 99 99 Dominican Republic . . . . 1. .1 127 427 143 Ecuador 84 . 86 9.7 3.5 ..1..................... il2 383 782 Egypt, Arab Rep. 88 .. 7.9 6.5 1.785 378 3,291 2,297 El Salvador 46 76 48 77 8.8 4.309 340 133 232 263 Eritreea. 73 67 20 5 . 333. 28 Estonia . 96 97 2.8 3.4 0 0 0 15 Ethiopia 50 51 51 50 12.2 7.8 4.085 6,264 3,732 5,667 Finland .. 100 .. 100 0.4 France . .. . .. . .. 9.3 8.1 Gabon 57 58 56 61 34.8 34.9 Gambia, The 74 78 71 83 12.4 12.7 2.1 42 59 76 93 Georgia . 0.4 0.5 25 36 21 138 Germany 172.2 Ghana 2.1 Greece 99 98 1.1 . 3.9....... .............. Guatemala . 52 .. 47 15.0 15.3 .2.5, 462 460 656. ...976 Guinea 59 85 41 68 21.9 27.9 498 675 463 985 Guinea-Bissau ... 28.9 .. 14.5 5884 56 ......... 91 .. ... Haiti 33 34 . 15.5 . 517 1,065 433 776 Honduras.. . 16.2 12.0 129. 122 238 451 78 2000 World Development Indicators 2.11 Percentage of cohort Repeaters Children out of school reaching grade 5 Primary Secondary % of total % of total Primary Secondary Male Female enrollment enrollment thousands thousands 1980 1996 1980 1998 ±980 1997 1980 1997 1980 1997 1980 1997 Hungary 96 . 97 .. 2.1 India .. 62 .. 55 .. 3.7 . .. 31,412 25,434 64,986 57,216 Indonesia .. 88 .. 88 8.3 5.8 .. 0.7 2,718 192 11,399 11,211 Iran, Islamic Rep. . 92 89 5.9 .. . 1,536 927 3,271 2,237 Iraq 77 . 64 - 23.2 . . . 70 891 624 1,696 Ireland 99 .. 100 .. 1.722. Israel Italy 99 98 99 99 1.2 0.4 Jamaica 91 . 91 . 39 .. 2.1 . 8 14 10 10 Japan 100 100 100 100 . . . . Jordan 100 98 . 3.2 1.3 44 Kazakhstan . 0 6 Kenya 60 . 62 . 12.9 . . . 320 2,350 983 1,125 Korea, Dem. Rep.. . . . . . . Korea Rep. 94 98 94 99 . 0.0 .. Kuwait.. . 6.2 3.4 7.0 5.4 22 64 42 128 Lao PDR 5. 7 54 . 23.4 .. .5 11 196 205 252 Latvia .. 2.5 13~~~~~~~~~~~~~~~~~~~~~~~~~! 0 0 28 56 Lebanon .. . . 13.4 Lesotho 50 72 68 87 20.7 20.1 78 i1l 44 61 Libya 92 127I 1 670 Lithuania 1-3 .. 1.2 . Macedonia, FYR 95 95 0.5 0.2 Madagascar 49 .. 33 33.8 . Maiawi 48 . 40 . 17.4 15.1 767 34 327 250 Malaysia 97 98 97 100. 166 9 1,188 1,090 Mali 48 92 42 70 29.6 16.2 899 1,094 833 1,224 Mauritania 61 68 14.0 15.8 Mauritius 94 98 94 99 4.5 29 4 72 46 Mexico 85 .. 86 9.8 6.9 2.1 Moldova 1 M ongolia ........ ......- . ... ........ ...... .. .. ... .. 1. 1 0.7 . 0.2 0 40 30. . 159 Morocco 79 76 78 74 29.5 12.3 14.9 . 991 853 1,966 2,302 Mozambique 52 39 28.7 25.7 27.1 866 1,520 1,189 2,189 Myanmar.. . .. . 1,338 34 2,992 2,662 Namibia 7 82 11. 11.2 25 25 35 34 Nepal.. .. . 430 669 1,760 1,244 New Zealand 97 . 97 .. 3.5 . 2.7 0.8 Nicaragua 40 52 47 57 16.9 12.6 146 164 167 283 Niger 74 72 72 74 14.3 13.0 6.6 20.4 709 1,243 779 1,335 Nigeria Norway 100 100 100 100 . Oman 96 96 87 96 12.4 9.2 .. . 103 133 116 108 Pakistan Panama 74 . 9 . 12..7 . 10.3 . 34 36 97 94 Papua New Guinea . . . . . . . Paraguaa 59 77 58 80 13.6 9.1 . 3.0 47 30 274 272 Peru 78 74 . 18.8 16.2 .101 9.0 354 212 411 437 Philippines 68 73 . 2.4 352 10 1,301 1,427 Poland 2.2 1.3 0.4 Portugal 19.5 Puerto Rico Romania 2.8 .. 1.4 273 1 1 677 Russian Federation... 1 9 . .. 459 6 246 2,086 2000 World Development Indicators 79 O ~2. 11 Percentage of cohort Repeaters Children out of school reaching grade 5 Primnary S'econdary %, of total %of total Pr mary Secondary Male Female enrollment enrollmert thousands thousands 1980 1996 1980 1996 1.980 1997 i980 1997 1980 1997 1980 1997 Rwarida 69 .. ~~~ ~~~ ~~~~~ ~~~~~74 .. 5.7... .. . . .. Saudi Arabia 82 87 86 92 1_5.7 7.6 14.8 9.2 770 1,222. 749 1,077 Senegal 89 89 82 85 15.6 13.3 568 583 685 1,092 Sierra Leone ... . .. 14 8 . Singapore 100 -........ 100 . 6 63 1 26 106 73 Slovak Republic .. 2.1 .. Slovenia.. 11. South Africa 72 79 . ..1,574 6 1,184 209 Spain 95 .. 94 .. 6.4A 8.8........ Sri Lanka 92 .. 91 .. 10.4 2.3 74 2 943 735 Sudan 68 .. 71.. .... Sweden 98 97 99 97 . .. Switzerland ... . .. 2.0 . . .. Syrian Arab Republic 93 93 88 94 8.1 7.3 13.9 .. 155 141 679 1,358 Tajikistan . . . . . . Tanzania 89 78 90 84 1.2 2.1 Thailand . .. . .. 8.3 ..608 798 5,010 3.631 Togo.59 79 45 60 35.5 24.2 89 132 142 283 Trinidad and Tobago 85 97 87 97 3.9 5.6 14 0 35 41 Tunisia 89 90 84 92 20. 16.1 7 4 180 1 647 371 Turkey .. 93 .. 96 .. 4.9 Turkmenistan . . . Uganda 82 . 7 3 .. 103 Ukraine .. .. .. .. 0~~~ ~~~ ~~~ ~~~ ~~~~~~~~~~~~~~~~~~~~~~~~.3 . ... ........... Uni:ted Arab Emirates 100 98 100 98 9.0 4.2 .. 7.8 25 52 23 54 United Kingdom United States Uug ay 97 99 1. 9.5 .. . 1 18 87 51 Uzbekistan .. 0.2 Venezuela, RB 86 9 107 10.3 68 7 29 79 46 Vietnam . ..329 9 5,039 5,399 West Bank and Gaza Yemen, Rep.. Yugoslavia. FR (Serb./Mont. 1 . Zambia 88 82 .. 1.9 2.8 265 489 412 611 Zimbabwe 82 78 76 79 . ..405 154 777 654 Low income .. 4.8 .. .. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~79,944 58,289 1200165,899 Excl. China & India . .. . .. 28.133 32,741 53,908 72 ,261 Middle income . ..17,988 9,218 35.442 33,683 Lower middle income . ..11,446 5,898 26.260 26,014 Upper middle income .. 11.7 6,542 3,320 9.182 7,669 Low & middle income... .. . . . . 6.2 . ..97,932 67,508 207,641 199,582 East Asia & Pacific .. 93 .. 93 .. 2.8 . ..26,037 1,413 81,374 63,075 Europe &Central Asia .. 2.4 782 55 4 ,7 Latin America & Carib. . .. . .. 15.3 .12.9 8,846 4,800 12,731 11,714 Middle East & N. Africa 88 .. 84 . 122.2 8.16,4 479 1326 25 South Asia .. 62 .. 55 .. 3.7 39,725 31,457 81,356 79,216 Sub-Saharan Africa . .. . .. . ..16,300 24,991 18,523 29,247 High income 71 213 43 50 Europe EML .... ... ..... . .. .... 80 2000 World Development Indicators 2.11 0 Indicators of students' progress through school, esti- school system, but also use up limited school resources. * Percentage of cohort reaching grade 5is the share mated by the United Nations Educational, Scientific, Countries have different policies on repetition and pro- of children enrolled in the firstgrade of primary school and Cultural Organization (UNESCO). provide a mea- motion of students; in some cases the number of who eventually reach grade 5. The estimate is based sure of an education system's success in maintain- repeaters is controlled because of limited aapacity. on the reconstructed cohort method (see About the ing a flow of students from one grade to the next and Children out of school include dropouts and chil- data), a Repeaters are the total number of students thus in imparting a particular level of education. dren who never enrolled. The large backlog of children enrolled in the same grade as in the previous year, as Although school attendance is mandatory in most out of school creates pressure for the ecucation sys- a percentage of all students enrolled in that grade. countries, at least through the primary level, students tem to encourage children to enroll, an i to provide * Children out of school are the number of school- drop out of school for a variety of reasons, including classrooms, teachers, and educational materials to age children not enrolled in school. discouragement over poor performance, the cost of accommodate them, a task made difficult in many schooling, and the opportunity cost of time spent in developing countries by limited education budgets. Data sources school. In addition, students' progress to higher grades may be limited by the availability of teachers, The data in the table were compiled by UNESCO and classrooms, and educational materials. published in its World Education Report 1998 and The rate of progression-sometimes called the rate Statistical Yearbook 1999. of persistence or survival-is estimated as the pro- portion of a single-year cohort of stucents that even- tually reaches a particular grade of school. It measures the holding power and internal efficiency of an education system. Progression rates approaching 100 percent indi- cate a high level of retention and a low level of dropout. Because tracking data for individual students gen- erally are not available, aggregate student flows from one grade to the next are estmated using data on enroll- ment and repetition by grade for two consecutive years. This procedure, called the reconstructed cohort method (Fredricksen 1993), makes three simplifying assump- tions: dropouts never return to school; promotion, rep- etition, 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. Given these assumptions, cross-country com- parisons 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 percentage of the cohort reaching grade 5, rather than some other grade, is shown because it is generally agreed that children who reach grade 5 should have acquired the basic literacy and numeracy skills that would enable them to continue learning. This indicator provides no information on learning out- comes, however, and only indirectly reflects the qual- ity of schooling. Assessing learning outcomes requires setting standards and measuring the attainment of those standards. In general, national assessments are concerned with the performance not of individual stu- dents, but of all or part of the education system. The repetition rate is often used to indicate the inter- nal efficiency of the education system. Repeaters not only increase the cost of education for the family and for the 2000 World Development Indicators |1 C ~2.12 Education outcomes Adult illiteracy rate Youth illiteracy rate Expected years of schooling Male Female Male Female % aged 15 and over aged 15 and over % aged 15-24 % aged 15-24 Males Females 1980 1.998 1980 1998 1980 1998 1980 1998 1980 1997 1980 1997 Albania 21 9 46 24 6 2 15 3 A geri a 46 24 76 46 24 8 54 18 10 12 7 10 Arngola -8 977 Argentina 5 3 6 3 3 2 3 1 Armenia 2 1 6 3 1 0 1 0 Australia -12 17 12 1 Austria 11 15 11 14 Azerbaijan Bangladesh 5 9 49 83 7 1 5 2 4 0 7 4 61 5 3 Belarus 0 0 2 1 0 0 0 0 Belgium 14 17 13 17 Benin 7 3 46 90 7 7 54 25 83 65 Bolivia 20 9 42 22 7 2 20 7. Bosnia and Herzegovinaa Botswana 44 27 41 22 32 17 25 87 12 8 12 Brazil1 23 16 27 16 14 10 12 6 Bulgaria 3 1 7 2 1 0 1 1 11 12 11 12 Burkina Faso 62 68 96 87 73 56 92 79 2 3 12 Burundi 57 45 83 63 48 37 71 42 3 52 4 Cambodia 61 43 92 80 47 26 86 61 Cameroon 41 20 65 33 18 7 33 8B8 ..6 Canada 15 17 15 17 Central African Republic 64 43 89 68 44 26 76 45 Chad 75 51 91 69 58 29 80 45 Chile B 4 9 5 3 2 3 1 13 13 China 22 9 48 25 4 1 16 5 Hong Kong, China .. 4 24 11 3 1 4 0 12 12 11 12 Colombia 15 9 17 9 8 4 7 3 Congo, Dem. Rep. 52 29 79 53 31 12 62 28 74 Congo, Rep. 37 14 62 29 12 2 27 4 Costa Rica 8 5 9 5 4 2 3 2 10 10 CMe dIlvoire 66 47 87 64 51 32 76 44 Croatia 2 1 9 3 0 0 1 0 11 12 Cuba 7 4 8 4 2 0 2 0 Czech Republic 13 13 Denmark 14 15 13 15 Dominican Republic 25 17 27 17 18 10 17 9 11 11 Ecuador 15 8 22 11 6 3 9 4 Egypt, Arab Rep. . . . . ......3 2 61 4012 10 E Salvador 29 19 39 25 19 12 24 13 10 10 Eritrea 51 34 82 62 37 21 69 4154 Estonia.. . .. .... 12 13 Ethiopia 72 58 89 70 59 47 78 50 F'nland 15 1 France .. ........ 15 16 G abon .......................... Gambia,The 79 58 88 73 64 37 79 54 6 4 Georgia . 11 Germany...... 16 16 Ghana 43 22 70 40 21 7 46 14 Greece 4 2 14 5 1 0 1 0 13 14 12 14 Guatemala 39 25 55 40 26 15 43 28 g u in e a ................................ .......... Guinea-Bissau 67 43 93 83 44 21 87 70 Haiti 66 50 73 54 53 38 57 38 Honduras 37 27 40 27 27 19 27 16 82 2000 World Development Indicators 2.12 0 Adult Illiteracy rate Youth illiteracy rate Expected years of schooling Male Female Male Female % aged 15 and over % aged 15 and over % aged 15-24 % aged 15-24 Males Females 1980 1998 1980 1998 1980 1998 1980 1998 1980 1997 1980 1997 Hungary 1 1 2 1 0 0 1 0 .. 13 .. 13 India 45 33 74 57 33 22 58 37 Indonesia 21 9 40 20 7 2 15 4 10 . 10 Iran, Islamic Rep. 38 18 61 33 16 4 35 10 . 12 .. 11 Iraq 53 36 78 57 39 23 64 36 12 . 9 Ireland.. ... . 11 14 12 14 Israel 5 2 13 6 1 0 3 1 Italy 3 1 5 2 0 0 0 0 Jamaica 28 18 20 10 17 10 8 3 . 11 .. 11 Japan . .. . .. ... 14 .. 13 Jordan 18 6 46 17 4 1 14 1 12 . 12 Kazakhstan.. . ... Kenya 30 12 57 27 13 5 32 7 Korea, Dem. Rep.. . . . . Korea,Rep. 3 1 11 4 0 0 0 0 12 15 11 14 Kuwait 26 17 39 22 17 9 22 8 12 9 12 9 Kyrgyz Republic Lao PDR 59 38 90 70 40 19 79 46 Latvia 0 0 0 0 0 0 0 0 12 .. 13 Lebanon 17 9 37 21 7 3 18 8 Lesotho 42 29 17 7 30 18 5 2 7 9 10 10 Libya .29 10 70 35 5 0 39 8 13.. 1 Lithuania 1 0 2 1 0 0 0 0 Macedonia, FYR 1 Madagascar 43 28 61 42 29 18 45 25 Malawi 36 27 73 56 29 20 60 41 Malaysia 20 9 37 18 7 3 12 3 Mali 81 54 92 69 66 31 82 44 Mauritania 59 48 79 69 51 40 72 60 Mauritius 19 13 33 20 11 7 15 6 Mexico 14 7 22 11 6 3 10 4 Moldova 2 1 8 2 0 0 0 0 Mongolia 44 28 72 49 30 16 54 28 T.7 Morocco 58 40 85 66 43 25 72 45 8 . 5 Mozambique 62 42 89 73 43 26 80 57 5 4 4 3 Myanmar 15 11 34 21 11 9 19 10 Namibia 29 18 38 20 18 11 20 7 Nepal 62 43 93 78 48 26 86 61 Netherlands.. . 13 16 13 16 New Zealand.. ... . 14 16 13 17 Nicaragua 39 34 39 31 35 30 32 24 . 9 . Niger 87 78 97 93 82 69 95 88 3 2 Nigeria 55 30 78 48 32 12 58 19 . Norway.. . .. . 13 15 13 16 Oman 49 22 84 43 18 1 65 6 5 9 2 9 Pakistan 59 42 86 71 48 25 78 53... Panama 14 8 16 9 6 3 8 4 11 . 12 Papua New Guinea 41 29 61 45 30 20 47 30 Paraguay 11 6 18 9 6 3 7 3 10 1 Peru 12 6 29 16 4 2 13 5 11 . 10 Philippines 10 5 12 5 5 2 5 1 11 . 11 Poland 1 0 1 0 0 0 0 0 12 13 12 13 Portugal 13 6 23 11 2 0 2 0 . 14 .. 15 Puerto Rico 11 7 12 7 6 3 4 2 . .. R om ania 2~~~~ .... ..I... 1..... 7................. 3..1 .......1.. 0.. ...... ...... 12. ......... 12 .. Russian Federation 1 0 2 1 0 0 0 0 . .. 2000 World Development Indicators 83 O ~2.12 Adult illiteracy rate Youith Illiteracy rate [ Expected years of schooling Male Female Male Female % aged 15 and over % aged 15 and over % aged 15-24 Idaged 15-24 Males Females 1.980 1998 1980 1998 1980 1998 1980 1998 1980 1997 1980 1997 Rwanda 48 29 71 43 32 16 51 21 Saudi Arebia 33 17 67 36 15 5 40 11 7 10 5 9 Senegal 70 55 88 74 59 42 79 60 Sierra Leone Singapore 9 4 2 6 1 2 2 1 3 0 Slovak Republic South Africa 22 15 25 16 15 9 15 9 14 14 Spain 3 2 8 4 1 0 1 0 13 13 Sri Lanka 9 6 21 12 6 3 9 4 Sudan 49 32 81 57 34 18 65 32 Sweden 12 14. 13 15 Switzerland .- 13 15 12 14 Syrian Arab Republic 28 13 66 42 13 5 47 23 11 10 8 9 Tajikistan 2 1 7 1 0 0 0 0 Tanzania 33 17 66 36 18 7 43 13 107 Thailand 8 3 17 7 3 1 4 2 Togo 48 28 81 62 29 14 68 44 Trinidad and Tobago 7. 5. 16 8 4 2 7 3 11 11 Tunisia 42 21 69 42 14 3 42 13 10 7 Turkey 17 7 46 25 4 2 20 7 11 9 Turkmnenistan . Uganda 39 24 69 46 27 15 53 30 M ....... . Ukraine . 0 0 1 1 0 0 0 0 Uni.ted Arab Emirates 33 27 42 23 26 15 22 6 8 10 7 11 United Kingdom 13 6 13 17 United States... 14 16 15 16 Uruguay 6 3 5 2 2 1 1 1 Uzbekistan 17 7 33 17 6 2 14 5 Venezuela, RB 14 7 18 9 6 3 6 2 .. 10 .. 11 Vietnam 7 5 19 9 4 3 7 3 West Bank and Gaza. .. . .. . Yemen, Rep. 62 34 95 77 45 18 89 58 Yugoslavia, FR ~Serb./Mont.) . . . . . Zambia 29 16 53 31 18 10 35 16 8 7 Zimbabwe 22 8 38 17 8 2 20 5 Low income 35 22 60 41 21 14 39 24 Exci. China & India 43 29 65 46 31 18 49 30 Middle income 15 10 22 15 _9 5 14 8 Lower middle income 15 11 22 17 10 6 17 10 Upper middle income 14 9 21 13 7 4 10 4 Low & middle income 29 18 48 33 17 11 31 19 East Asia & Pacific 20 9 43 22 5 2 15 5 Europe &Central Asia 3 2 8 5 1 1 4 2 Latin America & Carib. 18 11 23 13 10 7 11 6 Middle East & N. Africa 44 26 72 48 27 13 53 25 South Asia 48 35 75 59 36 24 62 42 Sub-Saharan Africa 51 32 72 49 34 19 56 28 High income 84 2000 World Development Inodikators 2.12 0 Many governments have recently collected and pub- Because the calculation of this indicator assumes that * Adult illiteracy rate is the percentage of people aged lished statistics that indicate how their education sys- the probability of a child's being enrolled in school at 15 and overwho cannot, with understanding, read and tems are working and developing-statistics on student any future age is equal to the current enrollment ratio write a short, simple statement about their everyday enrollments and on such efficiency indicators as pupil- for that age, it does not account for changE s and trends life. D Youth illiteracy rate is the illiteracy rate among teacher ratios, repetition rates, and cohort progression in future enrollment ratios. The expected number of years people aged 15-24. * Expected years of schooling through school. But despite an obvious interest in and the expected number of grades completed are not are the average number of years of formal schooling what education achieves, few systems in high-income necessarily consistent, because the first ircludes years that a child is expected to receive, including univer- or developing countries have until recently systemati- spent in repetition. Comparability across countries and sity education and years spent in repetition. They are cally collected information on outcomes of education. over time may be affected by differences in the length the sum of the underlying age-specific enrollment Basic student outcomes include achievements in of the school year or changes in policies on automatic ratios for primary, secondary, and tertiary education. reading and mathematics judged against established promotions and grade repetition. standards. In many oountries national learning assess- Data sources ments are enabling ministries of education to moni- tor progress in these outcomes. Internationally, the The data shown in the table were compiled by UNESCO United Nations Educational, Scientific, and Cultural and published in its World Education Report 1998 and Organization (UNESCO) has established literacy as an Statistical Yearbook 1999. The data on illiteracy are based outcome indicator based on an internationally agreed on the results of UNESCO's 1999 literacy estimates and definition. The illiteracy rate is defined as the per projections. centage of people who cannot, with understanding, read and write a short, simple statement about their every- day life. In practice, illiteracy is difficult to measure. To estimate illiteracy using such a definition requires census or survey measurements under controlled conditions. Many countries estimate the number of illit erate people from self-reported data, or by taking people with no schooling as illiterate. Literacy statistics for most countries cover the pop- ulation aged 15 and above, by five-year age groups, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. As an alter native, UNESCO has proposed a narrower age range of 15-24, which better captures the ability of partici- pants in the formal education system. The youth illit- eracy rate reported in the table measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of people who have passed through the primary edu- cation system (or never entered it) without acquiring basic literacy and numeracy skills. Reasons for this may include difficulties in attending school or dropping out before reaching grade 5 (see About the data for table 2.11) and thereby failing to achieve basic learning competencies. The indicator expected years of schooling is an esti- mate of the total years of schooling that an average child at the age of school entry will receive, including years spent on repetition, given the current patterns of enroll- ment across cycles of education, It may also be inter- preted as an indicator of the total education resources, measured in school years, that a child will acquire over his or her "lifetime" in school-or as an indicator of an education system's overall level of development. 2000 Wored Deveiopment Indicators 85 C ~2.13 Gender and education Female teachers Female pupils Girls out of school Primary Secondary Primary Secondary Primary Secondary % of total % of total % of total % of total % of total % of total out of achool oat of school 1980 1996 1.980 1.996 1980 1.996 1980 1998 1L980 1997 1980 1.997 Albania 50 60 35 51 47 48 Algeria 37 45 45 42 46 39 48 77 90 58 56 Angola 47 33 .62 50 88 52 Argontina 92 89 66 49 49 37 49 47 43 Armenia 9 7 Australia 70 76 45 49 49 50 49............... Austria 75 84 49 56 49 49 48 Azerbaijan 80 48 Bangladesh 7 37 19 .67 60 53 52 Belarus 48 Belgium 59 49 50 50 Benin 23 24 32 36 . 70 7 60 57 Bolivia 48 4 43 62 98 54 52 Bosnia and Herzegovina Botswana 72 77 37 43 55 50 55 52 36 44 46 39 Brazil 85 . 53 ... 54 55 98 49 48 Bulgaria 72 89 53 72 49 48 48 50 46 1.9 50 53 Burkina Faso 20 24 .. . 37 39 34 52 55, 51 52 Burundi 47 50 20 39 45 37 52 52 52 52 Cambodia . 36 . 27 . 4 50 49 54 56 Cameroon 20 32 20 . 45 .. 35 60 53 56 54 Canada 66 67 44 67 49 48 49 49 Central African Republic 25 16 37 70 58 59 55 Chad . 8 . 4 34 58 62 54 55 Chile 72 52 49 49 52 51 48 55 50 42 China 37 47 25 36 4 63 48 63 56 Hong Kong, China 73 76 49 50 48 49 49 49 41 38 47 43 Colombia 79 77 42 48 50 49 . 53 47 49 48 45 Congo, Demn. Rep. 22 .. . 42 41 . 38 70 62 62 - 57 Congo.Rep, 25 36 .. 16 *48 48 95 56 97 82 Costa Rica 79 78 54 59 49 49 53 46 44 47 48 Cbte dIlvoire 15 21 40 42 61 59 60 58 Croatia 73 89 .. 64 .. 49 .. 51 4 49 43 48 Cuba 7 5 8 1 46 5 7 48 48 . 4 7 49 5 3 44 Czech Republic 93 6.. 48 .. 50 Denmark 58 . 52 49 49 49 49 Dominican Republic ......... .. .. ..49 . 49 .. 57 4 36 49 41 Ecuador 65 67 38 49 49 .. . 50 4951 4 Egypt, Arab Rep. 47 49 31 39 40 45 . 45 68 99 57 58 El Salvador 65 . 27 49 49 . 52 499 509 Eritrea 36 .. 14 .45 .. 42 51 53 Estonia 8.. 80 48 .. 52 49 49 48 4 Ethiopia 22 28 10 35 36 5 57 53 55 Finland 69 49 49 . 52 France 68 79 55 59 48 . 52 49 Gabon 27 39 24 18 49 50 Gambia, The 32 29 25 17 35 44 30 68 61 53 56 Georgia 89 95 54 71 48 . 49 53 50 99 50 Germany .. 81 49 .. 49 .. 48 Ghan 42 34 21...... ..... .44. Greece 48 57 49 56 48 48 46 49 Guatemala 62 . 36 . 45 46 54 56 52 51 Guinea 14 25 10 . 33 37 56 61 54 54 Guinea-Bissau 24 21 . 32 20 66 64 60 56 Haiti 49 . . . 46 ..50 4 54 50 Honduras 74 48 50 5048 8 49 48 86 2000 World Development Indicators 2.13 Female teachers Female pupils Girls out of school Primary Secondary Primary Secondary Primary Secondary % of total % of tot I % of total % of total % of total % oftotal out of schooI out of school 1980 1996 1980 1996 1980 1996 1980 1996 1980 9.997 1980 1997 .. .. .. .. ..... .. ... . .... .. .... ...... .... .. . . . .. . - Hungary 80 92 .... ...... 66 49. 48 50 India 26 33 39 43 63 6 1 58 62 ......................... ............ ....... .... ....... ....... ............ I. ....... .. ............ ..... ............ ....... .... ....... .... .......... . ....... ....... .... . .. .. .I. .... .. ......... .... ....... .... Indonesia 33 52 39 46 48 45 71 93 56 52 .......... ......I.................................. ............ .................. ... .......... ....... .......... Iran, Islamic Rep. 5 7 55 30 44 40 47 46 68 5 3 6 1 63 .......... . . . .... .... .... ....... .... - .- .................... ............ ....... ................. . .... ....... ..........I. ....... .... ....... .... ....... ..... .... .... ....... ............ .......... . ............. ....... .... .. .... .... ....... ............... . . .. Iraq 48 7J 40 56 46 45 32 98 59 74 56 .. .... .. .. .. ..... .. .. .... ....... .... ....... ....... .......... .............. ............ .................... ............ .. ...... Ireland 74 78 50 55 49 49 52 51 .. . .. .. .. .. ....... ...I....... ...... .. .. ..... .. .... ....... ..... ................................... ....... .... ....... ........... .. ............I. ....... Israel 57 49 ....... .... ....... ............ ............ ....... .... ....... .. .... ..... ................................... ......................... .............. . ......... .............. .............. . ..........I...... .... .. .... ..... . ..... Italy 87 94 58 64 49 48 ... ............ ....... ............ ....... .... . .. .... .... .... .. . .. ....... .... .... .. .... .. ... ... .. .... .... .. .. .... ....... . .. .... .. ........ Jamaica 90 50 53 2 748 45 46 ... ............... ..........I.................................... .... .. .... .. .... .. . ..... .... . .... . . .. . .. .... . .... .... ......... .. ............ ............ .. .... .... ............ .... .......... .... . Japan 57 62 26 33 49 49 49 ... ..........I............I............... ... . .... .. .. .. ..... .. . .. . ... .. .... ..... ........... ..... ... ... ... ................ .... .... ....... .. . .... .. .... ....... ............ ..... ...... .......... Jordan 59 61 43 48 48 49 45 50 I. ......... .. ..... .. ..... .. .. .. ..... .. .... .. .. . ... .. . .. ....-...... .......... ......... .... ....... .... .. .... ......... .................. ...................... .... ............... .......... ... .. .... .. .... ....... .... .... ...... .... ...... Kazakhstan 49 ............ ....... .................... ............ ............... .... .... .. .... ....... .. - . Kenya 31 40 47 49 59 47 57 54 .. . .. .. ............................... .I............ .. .... .... .. ................... ..... ..... Korea, Dem. Rep. .......... ... .... . .... . .. .... . ............ ........ .... ....... ............. ....... ........ ... .............:..... .... .. Korea, Rep. 37 61 26 39 49 48 45 48 .. .. .. . . ... .. .. .. ....... ....................... .......I. ................................. .. .. .. .. .. . .. .... . . . . . . . . Kuwait 56 63 50 55 48 49 46 49 63 51 53 49 . ..................................................................... . . . . . .. ...... .. . . . .. . . .. ...... ............ .... . Kyrgyz Republic 88 83 67 49 50 .65 50 47 .... . .... ....... ............ ....... .. ......... .................... ..............I..... ..... .... .... .. .... .... ....... ....... .... ....... .... ....... .. . I Lao PDR 30 42 38 45 44 39 55 56 58 64 .......... .......... . ..... . . . . . . ..... ..... ...... ..... I. .....I...... ....... ..... Latvia 95 79 48 51 .... .. .... .. . .... .... .. .............. . .. ......... .I..... .... .. .... ....... .... ....... .... .. .. . .- 49 5O.. .51 50 Lebanon .... .... ....... ............................ ............ ...................... .... . ..... .. .. .. .. ....... ..... .. .... .. .. .. . .. ..... .. .. .. ...... ....... .... ............. ...... ............ .. . .... . Lesotho 75 79 53 59 52 60 59 31 41 29 36 ...... .. .... ....... ....... .... .. .. . . .. . . .. . . . . . . . .. . . .... ....... ....... .... .................... ............ ............ .. .. . .. . . .. . . .......... .... ..................I. .......... . .... .... Libya 47 24 47 40 49 49 80 49 I......................... ....... ....... .... .. . .. . ....... .... ....... .. .... .. ......... .... .. .... ....... .......... . ......... .............. ............ Lithuania 97 91 77 48 50 .. . .. . . .. ..... .. .. .. ..... .. . .. .. . . .... .. . .. ....... .... .. .... .................. .......... . .. .. . .............. I..... ... ... .. . .. .. .. .... . . . . . . Macedonia, FYR 54 51 48 48 .. . . .... ....... ....... .. . ....... ......................... ...................... . . . .. .. .. ....... .. .... ...... .. .. .. ...... .. M adagascar 51 49 49 .... .... .... .. .... ...... :: .. ..... .. .. .. ...... .. .. .. .. ..... .... .. .. .. .... .. .. .. .. Malawi 32 39 41 47 55 10 66 83 : "' ' " ' ' ' ' " "" "' ' ' " " ' "' ' " ' ' ' "' ' ' - - - . .- -- . .. .... ....... .. Malaysia 44 60 45 49 49 .52 16 52 .43 . .. .. .. ... .. . .... . ..... .. . .. .......... .... ......................... ....... ....... .......... ........... .. .... ....... ... Mali 20 23 36 39 54 56 53 53 . ..... .. . .... .. ............... . ....... ....... ....... .... ............ .......... . ....... .. .I....... .. . .. .-. . . . . . Mauritani'a 9 20 8 35 47 .... ...... . . . . .. . . ................ . .... ..... ...................... ........ ... .. . .... ... .. .... .... .. .... ..... ....... ...................:............ .... .. .... . Mauritius 43 51 45 49 49 51 48 50 46 ...... .... ....... ............ .. .... ............ .... ...... .................... ..... ... .. .. .. .. .... '- - .. . .. . M exico .... .. -- .49 ..48 ... .. . .............. ... .. .. ....... . .. .... . .... .. .... ....... .. . .. .... ....... . .. .. ........ Moldova 96 97 73 49 .. ............... ............ ............ ...... ..... .......... .... ..........................I. .. . .............. . .......... ... ..... ......... .. . . . ... .. .... ........... ...... Mongolia 87 90 66 50 51 57 49 41 33 41 ... .......... ...I............ .... ..... ..... ....... .. Morocco 30 38 26 32 37 42 38 67 69 58 54 Mozambique 22 23 22 17 43 42 28 39 52 54 58 54 Myanmar 54 67 73 51 94 52 51 I.......... ...I......................................... ............ .... ..... .......... ........ ..... ............. .......... ............ .. .... .... .................... ............ ....... .... .... . . .. . .. . .... .... ....... ............ .. ........... ..... ....... .... .. . .. .... . ......- Namibia 50 28 35 44 41 .... I I . ....... .. . .. . .... . . . . . . . . . . Nepal 10 9 28 85 83 57 .... .. ......... ..... . .... .. ... .... .. ... ........ ..... ... .. .. .. .... .. .... .... .. .6 3 Netherlands 46 60 29 49 48 48 ................................ .................... .................... .... ....... .... . ............. .. . .. . ..... ....... .... ....... .... ....... .... ....... .... ... .. .... . ..... .... ....... .... ....... .... ....... .......... . ............ .....I. .... ..... . . .. ............ .......... 1: .... ...... New Zealand 66 82 57 49 49 49 50 . . . . . .. ............ ...... ............ ....... .... Nicaragua 78 84 51 50 53 48 46 46 48 .......... ....... ............ ............... .... .... ....... ............ ... .. .... .................... . .......... .... ....... .... .. .... ..... . ..... .............. ...... .....-.. .. .. .. .. .. .. . .. .... ....... .... .. .... . ..... .. .... ....... ... ...... Niger 30 32 21 21 35 38 29 35 54 54 52 52 . .... ....... ... . . .. .. .. ..... .. .. .. ..... .. .. .. . . . . . . . . ..- . .. .... ....... .... .... . ..... Nigeria 34 46 29 36 43 44 . . - .1 11 .. .. I. .... ...... . . .... ..... I.............. .... .I..... . .. ....... ............ ....... ......................... .... ....... .... ....... ....... "..... ....... .... ............ ....... ..... ...... ... ...... . ..... Norway 56 49 49 50 48 .......... .. ......... .................... . ............................... ...... .............. ............ ......................... ............ .....I. .... ............ ....... .......... ............I. .... .. .... ....... .... .. .... .... ....... .... .. .. .. .... .. . .. . .. . .... ........ Oman 34 50 27 48 34 48 24 60 51 56 52 ..... ............ .... .. .. .. .... . ....... .... . .. .. .... ....... ............ ....... .... .. .... .... .. .... .... .. .. Pakistan 32 30 33 . ...... .............I.... ....... .... .. ........... .... ....... .. . Panama 80 -53 48 52 48 47 48 48 I. ... ............................................................................ .. . . .... ....... .. . .. .... .... .. .... .... . .... . . ....... .... ..- Papua New Guinea 27 36 32 42 45 .... ......................... .... ..... ............ .... ....... ............ ............ .... .. .... .... ....... ............ ....... ........ ... ............ .... .. .... ..- Paraguay 48 8 ........................................ .4 53 40 51 51 Peru 60 58 46 39 48 49 46 48 51 53 64 58 I.............. .............................. .. .. . . .. .. .....I. .... ......................... ............ .... ....... ....................... . ....................................... .................... .... ..............I..... ......................... ... ..... ...... . ............ .. . .. ... Philippines 80 49 53 66 49 45 48 ............I............. .............. . .......... .... ....... .... .. . . .. .... ....... .... ....... .... . . Poland 48 50 49 . ...... ..... ........................................... . ..... .... .... .... ...... .... ....... .... ....... .... .. .. . .... . ...-......-. .. .. . ... .. .... .................... ....... .... ....... ....... ........:.. ..... ..... .... .. .... .. Portugal 59 48 51 ...... . ............................ .. . . . . . . ............ ....... ....... .... ....... ..... Puerto Rico I...... ............ ..... .. .. .. .... ....... .... .......... . ...... ..... .. .... ...... .. ..... ... .. .. .. ..... .. ... .. ........ .... ...... .. ...... .. .. .. ...... .. ..... .. .. .. ... .. .. .. .. . .... ....... ... .. .... .... Romania 70 85 43 63 49 49 56 49 49 48 Russian Federation 98 49 50 49 33 37 ......................... .... .... .... .. .... .......... . ................... 2000 World Development Indicators 97 2.13 Female teachers Female pupils Girls out of school Primary Secondary Primary Secondary Primary Secondary % of total % of total % of total % of total % of total % of total out of school out of school Rwanda 38 16 48 . Saudi Arabia 39 52 34 50 39 48 38 46 61 51 53 56 Senegal .24 26 40 45 55 57 53 52 Sierra Leone 22 . Singapore 66 77 48 48. 71 53 49 50 SlovaK Republic 91 70 .. 49 Slovenia .. 92 70 .. 49 South Africa 74 . . 495 49 0 51 3 Spain 67 66 40 52 49 48 50 .. . Sri Lanka . 96 62 48 48 81 49 47 42 Sudan 31 62 45 40 45 . Sweden . 73 59 49 490 52 52 Switzerland . 69 .. . 49 49 46 4 7 - Syrian Arab Republic 54 65 22 44 43 47 37 46 97 88 59 52 Tajikistan - 54-... 49.- Tanzania 3 7 44 28 26 47 49 ... Thailand ..48 53 44 50. 50 Togo 21 14 12 3 187 8 5 7 Trinidad and Tobago 66 74 50 49 50 46 49 51 48 Tunisia 29 49 29 34 42 47 37 . 78 49 57 53 Turkey 41 44 35 41 45 47 . 40 Turkmenistan . -, -~~.. .. . ... . . ......... . ... ... . .. . .. .. .. .... . Ukraine 97.. . United Arab Emirates 54 70 46 54 48 48 50 45 51 55 44 United Kingdom 78 81 .. 55 50 52 United States 86 56 49 49 ... .... .49 ....... Uruguay 48 4 .. . 50 5 49 34 Uzbekistan 78 82 .. 49 . .. . Venezuela, RB 83 75 51 50 57 58 43 46 47 44 Vietnam 6 5 77 4 7 ..74 49 51 50 West Bank and Gaza . .. .. Yemnen, Rep.2 17 . .. Yugoslavia, FR (Serb./Mont.) ... 49 . Zamnbia 40 43 47 48 60 51 60 56 Zimbabwe 38 44 36 48 49 58 57 52 54 Low income 32 41 45 63 5 9 57 Exci. China & ndia . ..43 65 67.. 55 54 Middle income . ... ..56 61 49 48 Lower middle income . ... .58 56. 48 48 Low & middle income 42 44 .. . . 46 61 58 57 55 East Asia & Pacific 40 48 25 37 .. 48 63 54 60 54 Europe & Central Asia 84 . . 48 Latin America & Carib. .... .. ......... 49 70 50 47 Middle East & N. Africa 46 48 32 42 42 46 73 72 60 57 South Asia 24 34 .. . 38 43 64 62 57 60 Sub-Saharan Africa 30 38 .. . 44 45.. . High Income .. 78 .. 55 49 49 . 49. Europe EMLI 72 80 . 54 49 48 . 49 SB 2000 World Developmenit Indicators 2.13 0 Data on female enrollment suffer from the same prob- of the first professions open to women, and the num- * Female teachers as a percentage of total teach- lems affecting data on general enrollment discussed ber of female teachers is a revealing indicalor of employ- ers include full-tme and part-time teachers. * Female in About the data for table 2.10. But female enrollment ment opportunities for women in the modern sector. In pupils as a percentage of total pupils include enroll- as a share of total enrollment is a relatively simp e addition, female teachers are important ro e modelsfor ments in public and private schools. * Girls out of indicator raising no serious problems of cross-country girls, particularly where female education i s not encour- school as a percentage of all children out of school comparability. aged or men are forbidden to teach girls. Over the past are the number of girls not enrolled in school as a share Because gender disparities in enrollment are not cor- decade the share of female primary school teachers has of all children not enrolled. related with overal standard of living-as measured by increased everywhere. But data on teacters may not GNP per capita, for example-countries can achieve gen- reflect the functions they perform. Schools may employ Data sources der parity in primary and secondary schooling if public teachers in many capacities outside the clE ssroom, and policies and education strategies address constraints the responsibilities assigned to male and female teach- The estimates in this table were compiled using the that inhibit girls' attendance. Providing segregated ers may differ systematically. United Nations Educational, Scientific, and Cultural schools and separate sanitation facilities, recruiting Organization's (UNESCO) electronic database on insti- female teachers. and reducing the direct and opportu- tutions, teachers, and pupils and UNESCO's World nity costs of educating girls are among the strategies Education Report 1998. that have worked in some countries. But disparities remain, and female enrollment ratios tend to be posi- -' tively correlated with other indicators of development, Male and female unemployment rate by education level, 1994-97 such as maternal and child health, and negatively with % total fertility rates (UNRISD 1977). Primery Secondary Tertiary Girls' enrollments have caught up with boys' in Male Female Male Female Male Female most high-income, Latin American and Caribbean, 6&r.IlEior, 46.0 t' 2 2 2C.. and Eastern European countries. But they lag behind Colombia 26.3 16.7 53.6 62.6 18.5 18.9 IC'.3.a ~371 I 26.0 3 IO '3 25.6 33 1 in South Asia and the Middle East. And regional aggre- Jordan 60.9 16.8 14.8 14.7 21.2 67.5 gates mask large disparities between countries. In Polad 26.5 A 7 69.7 72.9 3 7 5.4 Africa, for example, Mauritius and South Africa have Russian Federation 23.3 14.6 69.6 75.6 7.1 9.8 achieved nearly universal primary enrollment, but many other countries still have primary enrollment ..Not available. ratios for girls that are less than 50 percent (see a. Less than primary education. Source: International Labour Organization, Key Indicators of the Labour Market. table 1.2). In low-income and lower-middle-income countries dropout rates at the primary level are higher Among those with only a primary education, men are more likely than women to be unemployed. But among for girls than for boys, indicating that the gap in actual those with secondary and higher educaitlon, women are more likely to be unemployed. enrollment in these countries is wider than is reflected The explanatlon for this pattern? Women with only a primary education are more likely to leave the labor force-or to never enter It-than women with a higher education. More educated women generally have more by enrollment ratios. One reason for this in many of labor market opportunitles, and It Is also more costlyforthemtowlthdraw from the labor force. these countries is early childbearing, which is clearly incompatible with schooling. Many girls, especially in South Asia. still remain outside the formal education system. And girls who attend school tend to be directed away from science, mathematics, and other technical subjects in high demand in the labor market, and toward vocations considered "feminine," such as nursing, teaching, and clerical work. Limited employment opportunities and lower market returns for women discourage parents of girls from investing in education. Consequently, female partici- pation in the labor market is limited, with many women concentrated in the informal sector and those in the mod- ern sector relegated to the low end of the hierarchy. Ensuring that the market is competitive, making labor laws gender-neutral, and strengthening the machinery that enforces labor laws can improve women's employ- ment prospects. Traditionally, teaching has been one 2000 World Development Indicators S9 O ~2.14 Health expenditure, services, and use Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission iength visits per capita rate of stay per capita Public Private Total PPP per 1,000 per 1,000 %of % of GOP % of GDP % of GDP $ $ people people pouain dy 19O9~ 199O-95, jgg90-98,b 1990-95, 1990-95, 1980 1990-.981 1980 1990-981 1990-981 1990-951 1990-S81 Albania 2.7 7.8 10.5 282 73 1.4 . 3.2 . 13 2 Algeria 3.3 1.3 4.6 217 68 0.8 .. 2.1 Angola 3.9 ...0.0 C1.3 Argentina 40 5. 96 1.147 792 2.7 3.3 Armenia 3.1 4.2 7.8 147 27 3.5 3.0 8:4 7.6 8 15... 3 Australia 5.5 2.8 8.4 1,866 1,842 1.8 2.5 . 8.5 17 16 7 Austria 6.0 2.2 8.3 1896 2.108 2.3 2.8 11.2 9.2 25 11 6 Azerbaijan 1.2 5.9 7.2 146 36 3.4 3.8 9.7 9.7 6 18 1 Bangladesh 1.6 2.0 3.5 45 12 0.1 0.2 0.2 0.3 Belarus 4.9 1.1 6.0 303 82 3.4 4.3 12.5 12.2 2 6 18 1 1 Belgium 6.8 0.9 7.6 1,759 1,812 2.5 . ..3..4 9.4 7.2 20 11 .... 8 Benin 1.6... 0.4 2.0O. 19. 8 0.1 0.1. 1.5 .. 0 2 ....... ..6... ... Bolivia .1.1..1.6 2.6 60. 28 0.5 1.3 .I 7..... .......... Bosnia and Herzegovina ..0.5 .. 1815 Botswana 2.7 1.6 4.3 310 133 0.1 0.2 2.4 1 6 Brazil 3.4 4.0 7.3 503 359 0.8 1.3 . 3.1 d Bulgaria 3.2 0.8 4.0 193 59 2.5 3.5 11.1 10.6 18 14 5 BurKina Faso-........ ......... .12.2 2. .3.9 34 9 0.0 C 0.0' . 1.4 2 30 a Burundi 0.6 3.0 3.6 21 5 0.1 .. 0.7 Cambodia 06.6.. 63.3.. 6.9.. 87 17. 0.1_ . 2.1 Cameroon 1.0 4.0 5.0 83 3 1 0.1 .. 2.6 Canada 6.4 2.8 9.2 2,158 1,855 1.8 2.1 . 4.2 13 12 7 Central African Republic 1.9 0.9 2.8 31 0.0 C 0.1 1.6 0.9 Chad 2.4 0.6 3.1 26 7 0.0' C 0.7 Chile 2.4 1.5 3.9 344 201 1.1 3.4 2.7 China 2.0 2.6 4.5 142 33 0.9 2.0 2.0 2.9 4 13 lHon. Kong. China 2.1 2.8 5.0 1,121 1,134 0.8 1.3 9.1 . 2 1 Colombia 4.9 2.4 9.4 594 256 1.1 1.6 1.5 Congo,D.em. Rep.1.2 1.3 2.5 .. 0.1 .. 1. Congo Re.~5 1.8 3.2 5.0.. 62 42 0.1 0..3 . 3.4 .......... .. Costa Rica 6.9 2.1 9.0 542 268 1.4 3.3 1.9 C6te dIlvoire 1.4 2.6 3.7 66 27 0.1 .. 0.8 Croatia 8.1 1.6 9.6 643 431 2.0 . 5.9 12 Cuba 8.2 ..1.4 5.3 .. 5.1 Czech Republic 6.4 0.6 7.0 865 384 2.9 . 9.2 22 12 15 Denmark 6.7 1.3 8.0 1,931 2,576 2.4 2.9 . . 207 5 Domninican Republic 1.6 3.6 5.2 234 97.. 2.2 I. .5 Ecuador 2.5 2.4 4.9 146 74 1.7 1.9 1.6 ... .... Egyp, Arab.Rep. . . 2.0 . 3.8 124 48 1.1 2.1 2.0 2.0 3 6 4 El Salvador 2.6 4.4 7.0 282 136 0.3 1.0 . 1.6 En.trea 2.9 0.9 2.0 15 0.0 ' Estonia 5.1 1.4 6.4 492 230 4.2 3.1. 12.4 7.4 18 .9 5 Ethiopia 1.7 2.4 4.1 24 4 0.0 c 0.0 c 0.3 0.2............ Finland 5.7 1.8 7.4 1520 1,736 L 2.8 15.5 9.2 26 12 4 France 7.1 2.5 9.6 2.026 2.287 2.2 2.9 .. 8.7 23 11 6 Gabon 0.6 . ... .. 0.5 0.2 .. 3.2 Gamnbia, The 1.4 1.7 3.1 46 11 0.0 0.6 Georgia 0.7 4.0 4.7 156 46 4.8 3.8 10. 4.8 5.. 13 .2 Germany 8.3 2.5 10.7 2,364 2,727 2.2 3.4 . 9.6 21 14 6 Ghana 1.8 2.9 4J.7 82 ... 19 .. . . 1.5 Greece 5.3 3.6 ...9 1,226 1,016 2.4 3.9 6.2 5.0 15 8 Guatemala 1.5 0.9 2.4 83 41 .. 0.9 .. 1.0 Guinea 1.2 1.0 2.2 43 13 0.0'c 0.2 .. 0.6. .... ..... ........ Guinea-Bissau 1.1 .. .. .. ... ... .. 0.1 0.2 1.8 1.5 Haiti 1.3 2.1 3.4 47 17 0.1 0.2 0.7 0.7 Honduras 2.7 5.6 8.3 202 72 0.3 0.8 1.3 1.1 90 2000 World Development Indicators 2.14 0 Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita Public Private Total PPP per 1,000 per 1.000 % of % of GDP % of GDP % of GDP $ $people people population days 199O-95a 1990-95, 199g-95.1, 199O-9S. 199o-98a 1980 1990-98- 2.980 1990-95a ±99O-95, 199O-95, 1990-98a Hungary 4.1 2.0 6.4 638 290 2.5 3.4 9.1 9.1 24 11 15 India 0.6 4.1 5.2 73 ..18 0.4 0.4 0.8 0.8 Indonesia 0.6 0.7 1.3 3 86 0.1 0.2 . 0.7 Iran, Islamic Rep~. 1.7 2.5 4.3 216 93 0.3 0.9 1.5 1.6 I raq .. ... 0.6 0.6 1.9 1.5 Ireland 4.9 1. 5 6. 1,293 1,333 1.3 2.1 9.7 3.7 167 Israel 7.0 3.4 10.4 1,801 1,701 2.5 4.6 5.1 6.0................................ Italy 5.3 2.3 7.6 1,539 1,511 1.3 5.5 . 6.5 16 10 Jamaica 2.3 2.4 4.7 158 116 0.4 1.3 . 2.1 Japan 5.9 1.4 7.1 1,757 2,379 1.4 1.8 11.3 16.2 9 44 16 Jordan 3.7 4.2 7.9 215 123 0.8 1.7 1.3 1.8 113 3 Kazakhstan 2.1 2.5 4.8 217... 68 3. 3.5 13.2 8.5 15 16... 1 Kenya 2.2 1.0 1.0 10 3 0.1 0.00 .. 1.6 Korea, Dem. Rep. .. . .. . 2.5 . . . Korea, Rep. 2.5 3.0 5.6 824 578 0.6 1.1 1.7 4.6 6 13 10 Kuwait 2.9 0.4 3.3 . 551 1.7 1.9 4.1 2.8 Kyrgyz Republic 2.7 0.4 3.1 71 11 2.9 3.1 12.0 9.5 21 15 1 Lao PDR 1.2 1.3 2.6 34 6.. 0.2 2.6 Latvia 4.0 2.4 6.4 366 168 4.1 3.4 13.7 10.3 21 14 4 Lebanon 3.0 7.0 10.0 594 361 1.7 2.8 . 2.7 14 4 Lesotho 3.7 2.4.. . 0.1 Libya .. .. .. .. .. 1.3 1.3 4.8 4.3~~~~~~~~.. ........ ... . .................. 13 . 4 8 . Lithuania 7.2 1.0 8.3 533 240 3.9 3.9 12.1 9.6 24 12 7 Macedonia, FYR 7.8 0.8 7.5 . 171 . 2.3 5.2 10 15 3 Madagascar 1.1 1.0 2.1 . 5 0.1 0.3 0.9 Malawi 2.8 0.4 3.3 20 5 0.0 C0.0 C 1.32 Malaysia 1.3 1.0 2.4 180 78 0.3 0.5 2.3 2.0 Mali 2.0 1.8 3.8 28 10 0.00 c0.1 . 0.2 17d Mauritania 1.8 4.1 5.2 68 28 . 0 .1 . 0.7 Mauritius 1.9 1.6 3.5 361 120 0.5 0.9 3.1 3.1 d4 Mexico 2.8 1.9 4.7 369 201 0.9 1.2 .. .2 6 4 2 Moldova 4.8 1.9 6.7 145 30 3.1 3.6 12.0 12.1 19 18 6 Mongolia 4.3 0.4 4.7 68 23 9.9 2.6 11.2 11.5 Morocco 1.3 2.7 4.0 140 49 0.1 0.5 1.2 1.0 3 3 Mozambique 2.1.. .... 0.0 0C1.1 0.9 Myanmar 0.2 0.8 1.0 . 58 0.2 0.3 0.9 0 6 Namibia 3.8 3.6 7.4 399 .......150 ....0. ..2 Nepal 1.3 4.2 5.5 58 11 0.0 0.00 0.2 0.2 Netherlands 6.1 2.3 6.5 1,874 1,988 2.1 2.6 12.5 11.3 11 33 5 New Zealand 5.9 1.7 7.6 1,357 1,310 1.6 2.1 6.1 14.. Nicaragua 4.4 5.3 9.7 209 43 0.4 0.8 1.5 Niger 1.3 .. . .0.00 .. 0.1 285d Nigeria 0.2 0.5 0.7 6 9 01 0.2 0.9 1.7 .. Norway 6.2 1.3 7.5 1,996 2,616 1.9 2.5 15.0 15.0 15 10 4 Oman 2.1 . .... 0.5 1.3 1. 22 94 4 Pakistan 0.9 3.0 3.9 65 18 0.3 0.6 0.6 0.7 . 3 Panama 6.0 1.7 7.6 402 253 1.0 1.7 . 2.2 Papua New Guinea 2.6 0.6 . 77 34 0.1 0.1 5.5 4.0 Paraguay 2.6 4.8 7.4 348 122 0.6 1.1 . 1.3 Peru 2.2 3.4 5.6 240 141 0.7 0.9 . 1.5 16 2 Philippines 1.7 0.1 3.7 124 32 0.1 0.1 1.7 1.1 . Poland 4.2 1.7 5.9 449 242 1.8 2.3 5. 5.4 14 11 5 Puerto Rico .. 6.5 ... . . . . 3.3 ... Romania 2.9 1.8 4.2 192 65 1.5 1.8 8.8 7.6 18 10 4 Russian Federationi 4.5 1.2 5.7 404 130 4-0 4.6 13.0 12.1 22 17 8 2000 World Development Indicators 91 O ~2.14 Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita Public Private Total ppp per 1,000 per 1,000 % of Pt of GDP Pu of GDP %uofGDP $ $people people population taps 1990-98, 19W0981 1990-951,b 1.990-98, 1990-98, 1.980 1990-98. 1980 199G-98a 1990-98a 1990-98a 1990-98, Rwarnda 2.1 .. .. 0.0 C 0.0 C 1.5 1.7 Saudi Arabia 6.4 1.6 8.0 844 584 0.5 1.7 1.5 2.3 11 11 1 Senegal 2.6 2.1 4.7 66 23 0.1 0.1 . 0.4 22 10 1 Sierra Leone 1.7 6.2 7.9 39 14 0.1 . 1.2 Singapore 1.1 2.0. 3.2 744 841 0.9 1.4 4.2 3.6 1 2 Slovak Republic 5.2 1.6. 6.8 655. 255 .. 3.0 . 7.5 . ....20 11 12 Slovenia 6.8 1.0 7.8 1,115 768 -2.1 7.0 5.7 16 11 South Africa 3.2 3.5 7.1 571 246 0.6 Spain 5:6 1.8 7.4 1,182 1,001 2,8 4.2 3.9 10 11 Sri Lanka 1.4 1.2 2.672 22 0.1 0.2 2.9 2.7 Sudan . 1.9 . . . 0.1 0.1 0.9 1.1 Sweden 7.2 1.4 8.6 1,773 2220 2.2 3.1 14.8 5.6 18. 8 3 Switzerland 7.1 3.0 10.0 2,573 3,616 3.2 .. 20.8 15s . 11 Syrian Arab Republic . . . . . 0.4 1.4 1.1 1.5 Tajikistan 6.6 0.1 5.9 6 7.8 2.4 2.1 ~10.0 8.8 16 15 Tanzania 1.3 .. . . .O.. 0.0C 1.4 0.9 Thailand 1.7 4.5 6.2 329 112 0.1 0.4 1.5 2:0 1 Togo 1.1 2:.1 ...... 3.2 .46 11 0.1 0.1 . 1.5 0 Trinidad and Tobago 2.8 1.6 4.3 334 215 0.7 0.8 . 5.1 Tunisia 3.0 2.9 5.9 32 118. 0.3 0.7 2.1 1.7 8 Turkey 2.9 2.9 5.8 377 177 0.6 1.1 2.2 2..5 6 . 6 1 Turkmenistan 3.5.. ... . 2.9 0.2 10.6 11.5 17 15 Uganda 1.8 2.9 4.7 50 14 0.00 0.0 C 1.5 0.9 Ukraine 4.1 1.4 5.4 179 54 3.7 4.5 12.5 11.8 20 17 10 United Arab Emirates 4.5 0.4 2.4 446 396 1.1 1.8 2.8 2.6 11 5 United Kingdom 5.9 1.0 6.8 1,391 1,480 1.6 1.6 9.3 4.5 23 10 6 United States 6.5 7.5 139 4121 4,080 1.8 2.6 5.9 4.0 12 8 6 Uruguay 1.9 6.5 8.4 719 529 2.0 3.7 . 4.4 Uzbekistan 3.3.. ... . 2.9 3.3 11.5 8.3 19 .-4 Venezuela, RB 3.0 4.5 7.5 426 205 0.8 2.4 0.3 1.5 Vietnam 0.4 3.9 4.3 . 16 0.2 0.4 3.5 3.8 7 8 3 West Bank and Gaza 4.9 3.7 8.6 . 81 . 0.5 .. 1.2 9 3 4 Yemen, Rep. 2.1 3.0 5.0 38 18 0.1 0.2 .. .7. Yugoslavia, FR (Serb./Mont.) ... ... 2.0 . 5.3 8 2 2 Zambia 2.3 1.8 4.1 33 1 0.1 0.1 3.5 Zimbabwe 3.1 3.3 6.4 191 31 0.2 0.1 3.1 0.5 Low income 1.3 2.8 4.2 93 23 0.6 1.0 1.5 1.8 5 .3 3 Exci. China & India. 1.2 2.0 3.1 46 14 0.3 1.3 10 9.. 3 Middle Income 3.1 2.6 5.7 384 199 1.6 1.8 . 4.3 10 1 5 Lower middle income 3.0 2.2 5.3 275 102 2.2 2.0 7.2 5.1 15 3 6 Upper mniddle income 3.3 3.0 6.3 535 335 0.9 1.5 32 6 8 4 Low & middle Income 1.9 2.7 4.6 182 75 0.9 1.2 2.7 2.5 7 12 4 East Asia & Pacific 1.7 2.4 4.1 154 47 0.8 1 .5 2.0 2.6 4 13. 4 Europe & Central Asia 4.0 1.8 5.8 -355 138 3.0 3.3. 10.4 8.9 17. 14 6 Latin America & Carib. 3.3 3.3 6.6 461 284 0.8 1.5 . 2.3 2 4 2 Middle East & N. Africa 2.4 2.3 4.8 237 117 0.7 1.2 1.7 1.7 5 6 3 South Asia 0.8 3.7 4.8 69 17 0.3 0.4 0.7 0.7 .. . 3 Sub-Saharan Africa 1. 5.. 1..-J8 3.2 8 .33 0.1 .. 1.1 12 6 1 HighlIncome 6.2 3.7 9.8 2.505 2,585 1.9 2.8 . 7.4 .15 16 8 Europe EMU 6.6 2.3 8.9 1,842 1,974 2.1 3.7 . 7.8 18 1.3 6 a. Data are for the most recent year available. b. Data may not sum to totals because of roinding and because of differences in the year for which the most recent data are available. c. Less than 0.05. d. Less than 0.5. 92 2000 World Development Indicators 2.14 0 National health accounts track resource inputs to the country variations in average length of stay may result * Public health expenditure consists of recurrent health sector, including both public and private expen- from differences in the role of hospitals. MIany develop- and capital spending from government (central and ditures. In contrast with high-income countries, few ing countries do not have separate extenc ed care facil- local) budgets, external borrowings and grants (includ- developing countries have health accounts that are ities, so hospitals become the source of both long-term ing donations from international agencies and non- methodologically consistent with national accounting and acute care. Other factors may also explain the van- governmental organizations), and social (or compulsory) approaches. The difficulties in creating national health ations. Data for some countries may not include all pub- health insurance funds. * Private health expenditure accounts go beyond data collection. Before beginning lic and private hospitals. Admission rates may be includes direct household (out-of-pocket) spending, pri- to establish a national health accounting system, a overstated in some countries if outpatient surgeries are vate insurance, charitable donations, and direct ser- country needs to define the boundaries of the health counted as hospital admissions. And in many countries vice payments by private corporations. * Total health care system and ataxonomy of health care delivery inst[ outpatient visits, especially emergency visits, may result expenditure is the sum of public and private health tutions. The accounting system should be compre- in double counting if a patient receives treatment in expenditure. It covers the provision of health services hensive and standardized, providing not only accurate more than one department. (preventive and curative), family planning activities, bookkeeping but also critical information on the equity nutrition activities, and emergency aid designated for and efficiency oF health financing to inform health pol- . . ,health but does not include provision of water and san- icymaking and health system reform. itation. * Physicians are defined as graduates of Health expenditure by aregte rniethed, The absence of consistent national health account- 1990-98 any faculty or school of medicine who are working in ing systems in most developing countries makes cross- %ofGDP the country in any medical field (practice, teaching, country compansons of health spending difficult. Records research). * Hospital beds include inpatient beds of private out-of-pocket expenditures are often lacking. Weighted Weighted available in public, private, general, and specialized Unweighted by by And compiling estimates of public health expenditures average populathon GDP hospitals and rehabilitation centers. In most cases is complicated in countries where state or provincial and k!rd f beds for both acute and chronic care are included. local governments are involved in health care financing Low income 4.0 4.2 4.2 * Inpatient admission rate is the percentage of the and delivery because the data on public spending often MAccle .ncc.e 5 a 5 9. - i population admitted to hospitals during a year. * Aver- are not aggregated. The data in the table are the prod- High income 6.8 9.8 9.4 age length of stay is the average duration of inpatient uct of an effort to collect all available information on health hospital admissions. * Outpatient visits percapita Source: World sank staff estimates. expenditures from national and local govemment budgets, are the number of visits to health care facilities per national accounts, household surveys, insurance publi- Health expenditures for the world and for capita, including repeat visits. cations, international donors, and existing tabulations. country Income groups will vary-often substantially-depending on how tihey are Health service indicators (physicians and hospital aggregated. A population-weighted average of Dat sources beds per 1,000 people) and health utilization indicators global health expenditures, which gives (inpatient admission rates, average length of stay, and relatively large weights to such countries as Estimates of health expenditure come from the World outpatientvisits) come from avanety ofsources (see Data little on health-a far sman i aof aliGchspend Health Organization's (WHO) World Health Report 2000 sources). Data are lacking for many countries, and for weighted average, which gives the greatest and from the Organisation for Economic Co-operation otherscomparabilrtyis limited by differences in definitions. weights to the largest economies. and Development for its member countries, supple- In estimates of health personnel, for exampie, some coun- the purpose To show the pt guse dfgpendt on mented by World Bank country and sector studies, tries incorrectly include retired physicians (because dele- GDP spent on health, country data nmed to be including the Human Development Network's Sector tions are made only penodically) or those working outside weighted by GDP. To show the average Strategy: Health, Nutrition, anci Population (World Bank the health sector. There is no universally accepted defi- percentage of GDP spent per country taking 1997f). Data were also drawn from World Bank public account of differences In population slza, country nition of hospital beds. Moreover, figures on physicians data would need to be weighted by population. expenditure reviews, the International Monetary Fund's and hospital beds are indicators of availability, not of qual- Unweighted averages would show tho average Government Finance Statistics database, and other ity or use. They do not show how well trained the physi- percentage of GOP spent on health iriespective studies. The data on private expenditure are largely from of the size of populations or economies. cians are or how well equipped the hospitals or medical household surveys and World Bank poverty assess- centers are. And physicians and hospital beds tend to ments and sector studies. The data on physicians, hos- be concentrated in urban areas, so these indicators give pital beds, and utilization of health services are from only a partial view of health services available to the entire the WHO and OECD, supplemented by country data. population. Average length of stay in hospitals is an indicator of the efficiency of resource use. Longer stays may reflect a waste of resources if patients are kept in hospitals beyond the time medically required, inflating demand for hospital beds and increasing hospital costs. Aside from differences in cases and financing methods, cross- 2000 World Development Indicators 93 C ~2.15 Disease prevention: coverage and quality Access to Access to Tetanus Child Immunization Access Tuberculosis DOTS safe water sanitation vaccinations to essential treatment detection drugs success rate rate % of % of otildren % of ft of pregnant under 12 months ft of % of ft of population population women Measles OPT population cases oases 1982-85. 1990-961 1982-85o 1990-96o 1996-97 1995-98o 1995-98, 1997 1990-971 1996-971 Albani'a 92 76 58 65 95 99 60 Algeria 52 74 79 95 86 97 Angola 28 32 18 16 24 78 41 20 70 Argentina 55 65 69 75 98 85 70 4 Armenia 92 87 40 77 4 Australia 99 99 99 86 87 86 . . .100....... ... Austria 99 100 90 90 100 Azerbaijan 36 99 95 86 7 Bangladesh 40 84 4 35 86 97 98 65 72 19 Belarus 98 9770 Belgium 98 100642 Benin 14 50 10 20 66 82 78 72 35 Bolivia 53 55 36 41 27 98 82 70 62 80 Bosnia and Herzegovina 41 85 79 Botswana 70 36 55 54 79 76 90 70 80 Brazil 75 72 24 67 30 99 79 40 Bulgaria 85 99 93 94 Burkina Faso 35 .... .... ... 18.. 54 68. 70 60 29 16 Burundi 23 52 51 9 50 63 20 45 25 Cambodia 13 31.... .'68 .70 30 94 50 Cameroon 38 41 36 40 49 4344 Canada 100 99 85 95 100 Central African Republic 19 19 46 37 46 53 50 37 65 Chad 24 14 21 27 30 24 46 47 _ 15 Chile 86 85 67 92 91 80 80 China 90 2 13 96 96 85 96 23 *Hong Korng, China 82 88 Colombia 78 6883 89 81 Congo, Dem. Rep. 27 . 9 . 20 18 80 46 Congo, Rep 47 9 30 18 23 61 69 70 Costa Rica 92 95 97 99 91 100 CSte dIlvoire 20 72 17 54 44 68 70 80 56 55 Croatia 63 67 61 93 92 100 Cuba 82 93 . 88 70 99 99 100 92 87 Czech Republic 100..9 98 ~66 53 Denmark 100 100 84 90 Dominican Republic 49 71 6678 77 80 80 77 Ecuador 58 70 57 64 3 75 76 40 40 1 Egypt, Arab Rep. . 90 64 11 61 92 94 81 10 El Salvador 51 55 62 68 97 97 80 45 Eritrea 7 34 53 60 57 3 Estonia 88 8510 Ethiopia 27 8 30 52 63 72 24 Finland 95 98 100 100 98 100 98 France 98 100 96 83 83 97 Gabon 67 5076 4 57 41 3 Gamble, The 45 76 37 96 91 96 90 80 75 Georgia 95 92 30 5 82 9 Germany 8075 45 100 ..... Ghana 56 26 42 . 59 60 51 33 Greece 85 ..96 90.90 85 100 Guatemala 58 67 54 67 38 74 78 50 81 52 Guinea 20 62 12 14 . 56 53 93 75 52 Guinea-Bissau 31 53 20 46 51 63 Haiti 28 19 2438 32 34 30 2 Honduras 50 65 32 65 89 94 4 94 2000 World Development Indicators Access to Access to Tetanus Child Immunization Access Tuberculosis DOTS safe water sanitation vaccinations to essential treatment detection drugs success rate rate % of % of children % Of % of pregnant under 12 months % of % of % of population population women Measles DPT population cases cases 1982-155' 1990-961 1982-851 1990-96' 1996-97 1.995-98, 1995-98, 1997 1990-97- 1995-97' Hungary 87... 94 100 100 100 India 54 81 816 80 81 90 35 79 Indonesia 39 62 30 51 53 92 91 80 81 7 Iran, aslamic Rep. 71 83 65 67 75 96 100 85 87 7 Iraq 74 44 . 36 45 98 92 85 Ireland 97 . .100 Israel 100 99 . 100 . 94 92 100 Italy 99 . .100 .7560 . 82 9 Jamaica 96 70 91 74 52 88 90 95 72 81 Japa 99 96 99 100 . 94 100 100 Jordan 89 89 91 95 22 95 93 100 Kazakhatan . .. .. 97 96 Kenya 27 53 44 77 51 32 36 35 77 55 Korea, Dem. Rep. . . 100 ..5100 100 Korea, Rep. 83 83 100 100 . 85 80 7 56 Kuwait 100 100 100 100 8 95 96 Lao PDR 39 . 24 32 67 60 . 55 32 Latvi a . .. 97 75 90 6 4 69 Lebanon 92 100 75 100 89 92 ..89 56 Lesotho 18 52 12 6 53 57 80 71 65 Libya 90 90 70 86 92 96 100 Lithuania ... .. ... ...... .. .... ..96 . 90 Macedonia, FYR 91 98 97 Madaga car 31 29 . 15 30 68 73 65 55 60 Malawi 32 45 60 53 81 87 95 . 68 50 Malaysai 71 89 75 94 71 83 91 70 69 70 Mali . 37 21 31 62 56 52 60 65 17 Mauritana 37 64 . 32 63 20 28 100 96 40 Mauritius 99 98 97 100 78 85 89 Mexico 82 83 57 66 70 97 83 92 75 15 Moldova . 56 . 50 . 99 97 25 Mongotia. .. 8 26 83 Morocco 32 52 50 40 33 92 95-88 94 Mozambique 932 10 21 41 70 61 50 54 57 Myanmar 27 38 24 41 78 88 90 60 79 25 Namibia . 57 . 34 70 57 63 80 54 74 Nepal 24 44 16 65 85 78 20 85 11 Netherlands 100 10..10 96 95 100 81 45 New Zealand 100 . 88. 114 86100 Nicaragua 50 81 27 31 42 94 94 46 79 90 Niger 37 53 915 19 42 28 . 57 21 Nigeria 36 39 . 36 29 69 45 10 32 10 Norway 99 100 .. 10 ..... 10 80 90 Oman 58 68 39 85 96 98 99 90 87 83 Pakiatan 38 60 1630 58 74 74 65 70 2 Panama 82 84 81 90 . 92 95 80 Papua New Guinea . 28 ..22 11 40 45 90 60 4 Paraguay 23 39 49 32 32 61 82 . 51 55 Peru 53 80 48 44 57 94 98 60 89 95 Philippines 65 83 57' 77 38 83 83 95 82 3 Poland 82 . .100 . .. Portugal 66 82 . 100 . 99 95 100 74 67 Puerto Rico .. 97....... 681 Romania 71 62 . 44 97 97 85 Russian Fecteration 95 87 621 2000 World Development Indinoaors 95 C ~2.15 Access to Access to Tetanus Child immunization Access Tuberculosis DOTS safe water sanitation vaccinations to essential treatment detection drugs success rate rate % of % of children % of % Of pregnant under 12 months % of % &' % of population population women Measles DPT population cases cases 1982-85a 1990-961 1.982-85a 1990-961 1996-97 ±.995-98, 1995-98 1997 1.990-971 1995-97e Rwand-a . .43 66776614 Saudi Arabia 91 93 86 86 66 92 92 -. Senegal 44 50 58 34 65 65 41 62 Sierra Leone 24 34 13 11 42 28 26 74 37 Singapore 100 100 85 100 ..89 93 100 86 28 Slovak Republic 46 51 98 98 100 73 34 Slovenia 98. ...80. . .98 82 91 100 87 60 South Africa 70 46 26 76 73 80 69 6 Spain .99 100 ..100 Sri Lanka 37 46 52 78 94 97 95 80 71 Sudan 50 522 55 92 79 15 ..1 Sweden 100 100 96 99 Switzerland 100 100 100 100 Syrian Arab Republic .71 85 4556 53.. 93.. 95 80 92 5 Tajikistan 69 62955 Tanzania 52 49 86 27 69 74 ..76 55 Thailand 66 89 47 96 88 92 96 95 78 5 Togo 35 63 -14 26 41 38 33 70 60 15 Trinidad and Toago 98 82 96 88 90 Tunisia 89 99 52 96 80 92 96 51 Turkey 69 94 32 76 79 Turkmenistan 60 ..6 009 Uganda 16 34 13 57 38 60 58 70 33 65 Ukraine 55 49 ..97 96 United Arab Emirates 100 98 86 95 95 92 United Kingdom 100 100 100 95 95 United States ..98 89 94 71 86 Uruguay 83 89 5 9 61 80 88 80 95 Uzbekistan .. 57 ..18 88 96 Venezuela, RB 84 79 45 58 68 60 90 80 75 Vietnam .. 36 21 92 96 9585 90 77 West Bank and Gaza ......96 96 Yemen, Rep. .. 39 19 26 51 57 50 76 30 Yugoslavia, FR (Serb./Mont.) . .. 91 94 80 Zambia 48 43 47 23 ..69 70 Zimbabwe 52 77 26 66 58 73 78 70 Low income 24 80 82 Ecld China & India ..71 68 Middle Income 90 86 Lower middle income 89 89 Upper mniddle income 77 .. 51 92 82 Low & middle Income .. . 29 83 83 East Asia & Pacific ..84 29 93 93 Europe & Central Asia 91 89 Latin America & Carib. 72 .. 46 93 82 Middle East & N. Africa 68 ..88 90 South Asia 52 77 7 1681 87 Sub-Saniaran Africa ...58 53 High Income Europe EMU 97 ..99 75 69 a. Data are for the most recent fear available. 96 2000 World Development Indicators 2.15 II The indicators in the table are based on data provided immunization coverage difficult to measure (WHO 1996). *Percentage of population with access to safe water to the World Health Organization (WHO) by member states Essential drugs are pharmaceut cal proc ucts included is the share of the population with reasonable access to as part of their efforts to monitor and evaluate progress by the WHO on a periodically updated list of n afe and effeo- an adequate amount of safe water (including treated sur- in implementing national health-for-all strategies. Because tive treatments for both communicable and noncom- face water and untreated but uncontaminated water, such reliable, observation-based statistical data forthese indi- municable diseases. They are cost-effectivs elements of as from springs, sanitary wells, and protected boreholes). cators do not exist in some developing countries, the data a health system that can treat many comriron diseases In urban areas the source may be a public fountain or stand- are estimated, and conditions, including, among many others, anemia, pipe located not more than 200 meters away from the People's health is influenced by the environment in hypertension, tuberculosis, and malaria, dwelling. In rural areas the definition implies that main- whiGh they live. Lack of clean water and basic sanitation Date on the success rate of tuberculosis treatmnent are bers of the household do not have to spend a dispropor- is the main reason diseases transmitted by feces are so provided for countries that have implemented the recorin- tionate part of the day fetching water. An adequate amount common in developing countries. Drinking water conta- mended control strategy: directly observed treatment, of safe water is that needed to satisfy metabolic, hygienic. minated by feces deposited near homes and an inade- short-course (DOTS). Countries that have not adopted and domestc requirements-usually about 20 liters a per- quate supply of water cause diseases accounting for 10 DOTS or have only recently done so are omitted because son a day. The definition of safe water has changed over percent of the disease burden in developing countries of lack of data or poor comparability or reliabi ity of reported time. * Percentage orf population with access to sani- (World Bank 1993c). The data on access to safe water results. The treatment success rate for tut erculosis pro- tation is the share of the population With at least adequarte measure the share of the population served by improved vides a useful indicator of the quality of health services. excreta disposal facilities that can effectively prevent sources of water. An improved source can be any form A low rate or no success suggests that infectious patients human, animal, and insect contact with excreta. Suitable of collection or piping used to make water regularly avail- may not be receiving adequate treatment. An essential com- facilities range fromn simple but protected pit latrines to able. The reported data are based on surveys and esti- plementto the tuberculosis treatment succass rate is the flush toilets with a sewerage connection. To be effective, mates provided by governments. The underlying definitions DOTS detection rate, which indicates whether there is ade- all facilities must be correctly constructed and property main- vary from country to country and among locations within quate coverage by the recommended case detection and tained. * Pregnanrt women receiving tetanus vaccina- countries. They have also changed overtime. Moreover, treatment strategy. A country with a high treatment suc- ttons are the percentage of pregnant women who receive water quality generally is not tested during the surveys cess rate may still face big challenges if itt3 DOTS detec- two tetanus toxoid injections during their first pregnancy on which these date are based. Similar reservations tion rate remains low. and one booster shot during each subsequent pregnancy. apply to the data on access to sanitation. * Child immunizationilathe rate of vaccination coverage Neonatal tetanus is an important cause of infant mor- -of children under one year of age for four diseases- tality in some developing countries end can be prevented Poof children are much less likely to be measles and diphtheria, pertussis (or whooping cough), through immunization of the mother during pregnancy. Ree- fully Immunized and tetanus (OPT). A child is considered adequately immu- ommended doses for full protection are generally two nized against measles after receiving one dose of vaccine, tetanus shots during the first pregnancy and one booster % of one-year-olde tally immunized and against DPT after receiving two or three doses of vac- shot during each subsequent pregnancy, with five doses con- 10cine, depending on the immunization scheme. * Per- sidered adequate for lifetime protection. Informartion on 80centage of population with access to essential drugs is tetanus shots during pregnancy is collected through surveys the share of the population for which a minimum of 20 of in which pregnant respondents are asked to ashow antena- 60 '.0 the most essential drugs are continuously available and tal cards on which tetanus shots have been recorded. affordable at public or private health facilities or drug out- Because not all women have antenatal cards, respondents ijJ~s ets within onehour's walk. * Tubercuoesistreatment sue- are also askedaebouttheirreceiptof these injections. Poor 20 0"cess rate refers to the percentage of new, registered recall may result in a downward bias in estimates ofthe share smear-positive (infectiousi cases that were cured or in which of births protected. But in settiags where receiviag injections 0 ---a full-course treatment was completed. * DOTS detoe- Poorest 2nd 3rd 4th Richest is common, respondents may erroneously report having quintile quintle quintile quintiler quintile tion rate is the percentage of estimnated new infectious received tetanus toxoid. -agdehtuberculosis cases detected under the directly observed Governments in developing countries usually finance -Burkina Faso treatment, short-course (DOTS) case detection and treat- immunization against measles and diphtheria, pertussis -Dominican Republic ment strategy. (whooping cough), and tetanus (OPT) as part of the basic Indonesia publi heath pakage thogh thy ofen rey onper- Note: Households are grouped into quirtitles by assets. Data souwces publc helth ackae, toughtheyofte rel on er- Source: Analyss of demographic and health surreys sonnel with limited training to provide the vaccines, conducted by the World Bank and Macro Intermational. According to the World Bank's World Development Report The table was produced using information provided to the 1993:, Investing in H-ealth, these diseases accounted ChfrnI poo tiuexf ar kfiadess "t WHO by countries, the WHO's EPI Information System: for about 10 percent of the disease burden among chil- and do*-b, pert-al (whooephig cm"), md tetsas Global Summary, September 1998, its Essential Dregs and dren under five in 1990. compared with an expected 23 (DFT) thufin flieei wmeris~. Medicine Policy, and its Global Tuberculosis Control Report percent at 1970 levels of vaccination. In many develop- 1999 and the United Nations Children's Fund's (UNICEF) ing counitries, however, data recording practices make State of the World's Children 2000. 2000 Wenld Development Indicators 97 C ~2.16 Reproductive health Total fertility Adolescent Women at risk Contraceptive Births attended Maternal rate fertility of unintended prevalence by skilled mortality rate pregnancy rate health staff ratio births %Of per 1,000 married % of per births women womnen women 100,000 per woman aged 15-19 aged 15-49 aged 15-49 % of total I ven births 1980 1998 1998 1990-98, 199o-98a 1982 1996-98- 1990-98o Albania 3.6 2.5 12 ... . 99 Algeria 6.7 3.5 21 ..51 .. 77 Angola 6.9 6.7 217 34 17 Argentina 3.3 2.6 64 97 38b Armenia 2.3 1.3 46 .. 95 3 Australia 19.830 99 100 Austria 1.6 1.3 22 .. o10 AzerbaUjan 3.2 2.0 23 99 37b Bargladesh 6.1 3.1 140 14928 4405 Belarus 2.0 1.3 21 22d Belgium 1.7 1.6 11 .. 100 Benin 7.0 5.7 111 21 16 34 60 5oon Bolivia 5.5 4.1 78 24 49 46390r Bosnia and Herzegovina 2.1 1.6 33 ..0 Botsmana 6.1 4.2 76 77 30 Brazil 3.9 2.3 72 777 98 92 1600 Bulgaria 2.0 1.1 44 .99.. 9 Burkina Faso 7.5 6.7 145 31212 42 Burundi 6.8 6.2 54 24 Cambodia 4.7 4.5 14 ...31 Cameroon 6.4 5.0 137 22 19 55 430 c Canada 1.7 1.6 24 100 Central African Republic 5.8 4.8 130 1 46 l,100C Chad 6.9 6.4 188 9 4 24 15 83Q c Chile 2.8 2.2 47 . 92 99 23b China 2.5 1.9 15 85 ..65c H-ong K-ong. China 2.0 O 89 100 Colombia 3.9 2.7 86 ..7 285 SO5 Congo.ODem. Rep. 6.6 6.3 214 45 Congo, Rep. 6.3 6.0 140 .. 50 Costa Rica 3.6 2.6 82 93 97 290 CSte dIlvoi re 7.4 5.0 130 43 11 4560 Croatia ..1.5 19 ..12b Cuba2. 1.5 65 .. 99 27~ Czech Republic 21.1 1.2 23 69 .. 10 Denmark 1.5 1.8 9 .. ..l10 Dominican Republic 4.2... 2.9 12 1364 .. 96 Ecuador 5.0 2.9 68 ..57 62 64 60c .gpt, Arab Rep. . 5.1 3250 16 48. 6170c El Salvador 4.9 3.3 107 ..60 .. 87 Eritrea 5.7 118 28 8 21 1,0000 Estonia 2.0 1.2 36 .00 50d Ethiopia 666.4 154 B Finland 1.6 1.8 11... 100 60- France 1.9 1.8 9 71 99.o Gabon 4.5 5.1 164 .. 80 Gambia, The 6.5 56.6 . ... 170 ..... .. .. ........ 41 . .... 44. Georgia 2.3 1.3 35 . 100 70b Germnany 1.4 1.4 14 99 .. g Ghana 6.5 4..8 104 33 20 .. 44. Greece 2.2 1.3 17 971 Guatemala 6.3 4.4 106 24 32 .. 29 10 Guinea 615.4 8625 2 .. 31 Guinea-Bissau 5.8 5..6 187 ..............................................25 9l0 d Haiti 5.9 4.3 68 48 18 34 21... ..... ..... Honduras 6.5 4.2 1ll 50 . 47 20 98 2000 morld Development Indicators 2.160 Total fertility Adolescent women at risk Contraceptive Births attended Maternal rate fertility Of unintended prevalence by skilled mortality rate pregnancy rate health staff ratio births % of per 1,000 married % of per births women women women 100,000 per woman aged 15-19 aged 15-4 9 aged 15-49 % of total live births 1L980 1998 1998 199O-95, 1990-98, 1952 1996-98w 1990-98a Hungary 1.9 1.3 28 . 73 99 96 5 India 5.0 3.2 115 20 41 2 3 35 4100 Indonesia 4.3 2.7 59 11 57 2 7 36 4500 Iran, Islamic Rep. 6.7 2.7 49 . 73 . 74 370 Iraq 6.4 4.6 39 . 54 Ireland 3.2 1.9 14 60 .. 100 Israel 3.2 2.7 19 . 99 5 Italy 1.6128 . 1007 Jamaica 3.7 2.6 100 6 86 92 Japan .. 143100 8d Jordan 6.8 4.1 41 22 50.. 9 4i)b Kazakhstan 2.9 2.0 46 11 59... 70e Kenya 7.8 4.6 109 36 39 45 590C Korea, Dam. Rep. 28.8. 2.0 2 ..100 ll0d Korea, Rep. 2.6 1.64 70 98 20d Kuwait 5.3 2.8 33... 98 98 5 Kyrgyz Republic 4.1 2.8 34 12 60 . 98 5 Lao PDR 6.7 5.5 42 25 30 60 Latvia 2.0 1.1 32 . 100 5 Lebanon 4.0 2.4 25 . 89 1000 Lesotho 5.5 4.6 83 23 . 50 Libya 7.3 3.7 54 45 68 94 750 Lithuania 2.0 1.4 35... 100 18d Macedonia, FYR 2.5 1.8 38 .. . 95 l Madagascar 6.6 5.7 171 26 19 . 57 4900 Malawi 7.6 6.4 152 36 22 . 55 6200c Malaysia 4.2 3.1 25 . 88 98 9 Mali 7.1 6.5 178 267 14 24 5800 Mauritania 6.3 5.4 132 . 40 Mauritius 2.7 2.0 38 75 97 b Mexico 4.7 2.8 69 65 68 48 Moldova 2.4L17 50 74 42" Mongolia 5.3 2.5 46 . 99 5d Morocco 5.4 3.0 48 16 5 20 31 2300c Mozambique 6.5 5.2 161 7 6 29 44 Myanmar 4.9 3.1 19 . 57 20 Namibia 5.9 4.8 103 22 29 . 68 2300c Nepal 6.1 4.4 118 28 29 . 9 540c Netherlands 1.6 1.6 4 75 1010 New Zealand 2.0 1.9 52 . 15" Nicaragua 6.3 3.7 133 60 .. 65 Niger 7..4 ... 7.3 213198. 26. 15 5900 Nigeria 6.9 5. 3115 22 6 . 31 Norway 1.7 1.8 16 . 100 100 6d Oman 9.0 4.6 63 91 il9b Pakistan 7.0 4.9 102 32 24 . 18 Panama 3.7 2.6 80... 80 84 85d Papua New Guinea 5.8 4.2 67 29 26 53 Paraguay 5.2 3.9 73 15 59 61 1900 Peru 4.5 .."3.1 66 12 64 30 5 2700 Philippines 4.8 3.6 44 26 47. 53 170' Poland 2.3 1.4 23 d 88 Puerto Rico 2.6 1.9 68 78 90 Romania 2.4 1.3 41 57 . 99 1 Russian Federation 1.9 1.2 46349 b 2000 World Development Indicators 99 C ~2.16 Total fertility Adolescent Women at risk Contraceptive Births attended Maternal rate fertility of unintended prevalence by skilled mortality rate pregnancy rate health staff ratio births % of per 1,000 married % of per births women women womnen 100,000 per woman aged 15-19 aged 15-49 aged 15-49 % of total line births 1980 1998 1998 1990-98a 1990-98, 1982 1996-951 1990-98, Rwanda 8.3 6.1 55 37 21 20 26 Saudi Arabia 7.3 5.7 112 ... . 90 Senega 6.8 5.5 114 33 13 47. 50 Sierra Leone 6.5 6.0 199 .. 25 Singapore 1.7 1.5 10 100 100 6 Slovak Republic 2.3 1.4 33 100 9 Slovenia 2.1 .1.2 16 ..00 . Ild South Africa 4.6 2.8 42 69 82 Spain 2.2 1.2 8 96 6d Sri Lanka 3.5 .2.1 -21 .........85 ..d Sudan 6.5 4.6 54 25 10 23 69 Sweden 1.7 1..5 10 99 5 Switzerland 1.5 1.5 4 . 99 Syrian Arab Republic 7.4 3.9 43 40 .43 67 Tajikistan 5.6 3.4 29 92 650 Tanzania 6.7 5.4 123 24 18 .. 38 5300 Thailand 3.5 1.9 71 ..72 40 78 440 Togo 6.8 5.1 110 24 50 4800 Trinidad and Tobago 3.3 1.8 42 .. 98 Tunisia 5.2 2.2 13 ..60 50 81 70b Turkey 4.3 2.4 43 11 70 70 76 Turkmenistan 4.9 2.9 17 96 ll0b .........-.. Ukraine 2.0 1.3 34 25b United Arab Emirates. 5.4 3.4 57 ........ .. .. .. ..... ... 94 86 3 United Kingdom 1.9 1.7 28 .. 100 75 United States 18. .2-.0 51 76 100 99 . 8b- Uruguay 2.7 2.4 69 .. 96 210 Uzbekristan 4.8 2.8 48 56 98. 98 Venezuela. RB 4.2 2.9 96 ..82 97 650 Vietnam 5.0 2.3 34 75 100 79 1600 West Bank and Gaza 5.5.9 .98 42 Yemen), Rep. 7.9 6.3 103 21 43 3500 Yugoslavia, FR (Serb./Mont.) 2.3 1.7 34 ...... .............99lo Zambia -'..7.0 5.5 141 27 26.... 47 6500 Zimbabwe 0.4 3.7 87 15 48 49 69 400 d Low income 4.3 3.1 84 24 35 Exci. China & India 6 .0 4.3 108 24 34 Middle income 3 .7 2.5 53 53 77 Lower middle income 3.7 2.5 50 53 69 Upper middle income 3.7 2.4 58 65 87 86 Low & middle income 4.1 2.9 74 48 47 East Asia & Pacific 3 .0 2.1 26 52 Europe & Central Asia 2.5 1.6 39 67 92 Latin America & Carib. 4.1 2.7 74 59 85 78 Middle East & N. Africa 6.2 3.5 51 55 .. 62 South Asia 5.3 3.4 116 49 21 29 Sub-Saharan Africa 6.6 5.4 132 21 .. 38 High income .8 1.7 25 75 99 Europe EMU 1.8 1.4 11 75 99 a. Data are for most recent year available. b. Official estimate. c. Estimate based on survey data. d. Estimate by the World Health Organization and Eurostat. e. Estimate by UNICEF. 100 2000 World Development Indicators 2.16 ' Reproductive health is a state of physical and mental not reflect such improvements because health infor- * Total fertility rate is the number of children that well-being in relation to the reproductive system and mation systems are often weak, maternEI deaths are would be born to a woman if she were to live to the its functions and processes. Means of achieving repro- underreported, and rates of maternal mortality are dif- end of her childbearing years and bear children in ductive health include education and services during ficult to measure. accordance with current age-specific fertility rates. pregnancy and childbirth, provision of safe and effec- Household surveys such as the demographic and * Adolescent fertility rate is the number of births per tive contraception, and prevention and treatment of sex- health surveys attempt to measure maternal mortal- 1,000 women aged 15-19. * Women at risk of ually transmitted diseases. Health conditions related ity by asking respondents about survivorsh ip of sisters. unintended pregnancy are fertile. married women of to sex and reproduction have been estimated to account The main disadvantage of this method is ;hat the esti- reproductive age who do not want to become pregnant for 25 percent of the global disease burden in adult mates of maternal mortality that it produces pertain and are not using contraception. * Contraceptive women (Murray and Lopez 1998). Reproductive health to 12 years or so before the survey, making them prevalence rate is the percentage of women who are services will need to expand rapidly over the next two unsuitable for monitoring recent changes or observing practicing, or whose sexual partners are practicing, any decades, when the number of women and men of the impact of interventions. In addition, rr easurement form of contraception. It is usually measured for mar- reproductive age is projected to increase by more than of materna mortality is subject to many types of ried women aged 15-49 only. * Births attended by 300 million. errors. Even in high-income countries with vital regis- skilled health staff are the percentage of deliveries Total and adolescent fertility rates are based on data tration systems, misclassification of mat srnal deaths attended by personnel trained to give the necessary on registered live births from vital registration systems has been found to lead to serious underes:imation. The supervision, care, and advice to women during preg- or. in the absence of such systems, from censuses data in the table are official estimates based on nancy, labor, and the postpartum period, to conduct or sample surveys. As long as the surveys are fairly national surveys or derived from official community and deliveries on their own, and to care for newborns. recent, the estimated rates are generally considered hospital records. Some reflect only birthE in hospitals * Maternal mortality ratio is the number of women reliable measures of fertility in the recent past. In cases and other medical institutions. In some cases smaller who die during pregnancy and childbirth, per 100,000 where no empirical information on age-specific fertil- private and rural hospitals are excluded, and sometimes live births. ity rates is available, a model is used to estimate the even primitive local facilities are included. rhus the cov- share of births to adolescents. For countries without erage is not always comprehensive, and cross-country Data sources vital registration systems, fertility rates for 1998 are comparisons should be made with extreme caution. generally based on extrapolations from trends observed The data on reproductive health come from demo- in censuses or surveys from earlier years. graphic and health surveys, the World Health Organi- An increasing number of couples in the developing zation's Coverage of Maternity Care (1997a), and world want to limit or postpone childbearing but are not national statistical offices. using effective contraceptive methods. These couples face the risk of unintended pregnancy, shown in the table as the percentage of married women of repro- Total fertility and access to reproductive health care among the poorest and richest, ductive age who do not want to become pregnant but various years, 990s are not using contraception (Bulatao 1998). Informa- tion on this indicator is collected through surveys and Total fertility rate Antenate care received Births attended by skilled staff excludes women not exposed to the risk of pregnancy births per woeran % of pregnant women % of delivenes Poorest Richest Po.7rest Rtichest Poorest Richest because of postpartum anovulation, menopause, or quintile quintile Average quintile quintlie Average quintile qulntile Average infertility. Common reasons for not using contraception 7 -. _ I' 5 65 20 2S s7 are lack of knowledge about contraceptive methods and Cameroon 6.2 4.8 5.8 53 99 79 32 95 64 concerns about their possible health side-effects. - al.i 80 2 -4 5 1 3= 90 52 t 92 35 Contraceptive prevalence reflects all methods- India 4.1 2.1 3.4 25 89 49 12 79 34 ineffective traditional methods as well as highly effec- lnd; .,d 3 3 2 0 2 S 74 J- 59 J5 tive modern methods. Contraceptive prevalence rates Morocco 6.7 2.3 4.0 8 74 32 5 78 31 .,;A m ;nr. 3; .1 1e 2.3 7.2 i2 71 49 99 7 are obtained mainly from demographic and health surveys and contraceptive prevalence surveys (see Pri- Note: Households are grouped into quintile s by assets. marydata documentation for the most recent survey Source: Analysis of demographicand health surveys conducted bythe World Bank and Macro International. year). Unmarried women are often excluded from such In all regions reproductive health conltinues to be worst among the poor. Women In the poorest households surveys, which may bias the estimates. have much higher fertility rates than those In the wealthiest-and far fewer births In the presence of skilled The share of births attended by skilled health staff health professionals, contributing to higher maternal mortality ratios. Indicators of reproductive health by In- is an indicator of a health system's ability to provide come level can help focus Intervention,i where they are needed most. adequate care for pregnant women. Good antenatal and postnatal care improves maternal health and reduces maternal and infant mortality. But data may 2000 World Development Indicators 101 C4 2.17 Health: risk factors and future challenges Prevalence Low- Prevalence Consump- Prevalence Cigarette Tuberculosis Prevalence of anemia birthwelght of child tion of of smoking consump- Of HIV babies malnutrition iodized tion salt Weight for age Height for age Incidence % of % of % of per per Prevalence People pregnant children children % of Males Females smoker 100,000 thousands % oft nfecgted women % of births under 5 under 6 households % of adults % of adults per year people of cases adult lull aes) i.95-_99a iL992-981 iL992-98' i.992-98' 1992-98' 1.9855-98 j.9855.98 1988-.98 1.997 1997 1.997 1997 Albania 7 8 15 . 50 8 . 28 2 0.01 <100 Algeria 42 9 13 18 92 . 44 14 0.07 Angola 29 .. . . 10 .. 23 56 2.12 110,000 Argentina 26 7 2 5 90 40 23 2,771 56 30 0.69 120,000 Armenia 3 12 . ..... 44 2 0.1 <0 Australia 6 0 0 . 29 21 4,951 8 2 0.14 11,000 Austria .. 4262..3.042 19 2 0.18 7,500 Azerbaijan 36 6 10 22 ... . 58 7 0.1 <0 Bangladesh 53 50 56 55 78 60 15 351 246 620 0.03 21,000 Belarus 5 37 . 65 10 0.17 9,000 Belgium 6 31. 19. 5,300 16 2 0.14 7.500 Beni'n 41 2 9 2 5 7 9 220 21 2.06 54,000 Bolivia 54 10 8 27 90 50 21 . 253 27 0.07 2,600 Bosnia and Herzegovina . 1 5 00 Botswana 8 27 .. 503 9 25.10 190,000 Brazil 33. 6 11 95 40 25 . 78 194 0.63 580,000 Bulgaria 7 49 17 3,058 43 6 0.01 Burkina Faso 24 21 33 33 23 . 155 19 7.17 370,000 Burundi 68 16 80 . 252 16 8.30 260,000 C .mbodia 18..... . ..... ...... 7..... . 70 10 912 539... .101 2.40 130,000 Cameroon 44 13 22 29 82 . 3 35 4.8 320,000 Canada 631 2 3,8 7 2 0.33 44,000 Central African Republic 67 15 23 28 65 . 237 9 107 1800 Chad 37 39 40 55 . 205 22 2.72 87,000 Chile 13 7 1 2 97 38 25 1,718 29 5 0.20 16,000 China 52 6 16 31 83 . 113 2,721 0.06 400,000 Hong Kong, China 5 29 3 2,679 95 6 0.08 3,100 Colombia 24 9 8 15 92 35 19 1,684 55 31 0.36 72,000 Congo, Dem. Rep. 3 45 90o 263 188 4.35 950,000 Congo, Rep. 16 45 . 277 11 7.78 100,000 Costa Rica 27 7 5 6 89 35 20 . 18 1 0.55 10,000 C6te dIlvoire 34 14 24 24 . 29 48 10.06 700,000 Croatia 8 1 1 7.. 64 5 0.01 Cuba 47 8 45 49 25 2,566 18 2 D0.02 1,400 Czech Republic 2361 1 . 3p187 20 2 D0.04 200 Denmark ~ ~ ~~5 .. .. 3 7 3 7 2,532 11 1 0.12 3,100 Dominican Republic 116 11 13 66 14 1,303 114 14 1.89 83,000 Ecuador 17 13.. . 97 ... 165 32 0.28 18,000 Egypt, Arab.Rep.24 12 12 25 0 36 35 0.03 El Salvador 14 9 11 23 91 38 12 74 7 0.58 18.000 Eritreea. . 44 38 80 227 15 3.17 Estonia .. .. .. .. .. 52 ~~~ ~~~~~~ ~~~ ~~ ~~~~~ ~~~~~~~~~24 1,819 52 1 0.1 <100 Ethiopia 42 16 48 64 0 ... 251 213 9.31 2,00000 Finland . 5 .. 27 19 2,906 13 1 3.02 500 France .. 6 40 27 3,088.131 .37 19.9.9 Gabon . 10 . .. 174 4 4.25 23,000 Gambia, The .80 . 26.. 30 0 . 211 4 2.24 13,000 Georgia ~ ~ ~~~~~~~~~~~~~~~~~%..... .. .... .... .. 67 5 .01 <100 Germany 37 ~~~ ~~~~~~ ~~~ ~~~ ~~~~~ ~~~~~~~~~~~~~~~~~22 3,927 15 2 008 35,000 Ghana 64 17 27 26 10 . 214 67 2.38 210,000 Greece . 9 . .. 46 28 4,877 29 3 0.14. 7,500* Guatemala 45 14 27 50 64 38 18 646 85 13 3.52 27,000 Guinea . 13.. . 37 . 171 22 2.09 74,000 Guinea-Bissau 74 20 . ..... 181 4 2.5 1,0 Haiti 64 15 28 32 10 .. 35 36 5.17 190,000 Honduras 14 9 25 39 85 36 11 1,978 96 9 1.46 43,000 102 2000 World Development Indicators COT Sioleo!pui luawdolaAaG PIJOM OOOZ 00001', 90,0 Tt'9 90T 9gz'z . 0s L9 09 CT 9 09: UO!Tjeapaed Ue!ssnl C T :.o~ol? and '60 E 6-" a 9Tnj0 ~~~~~~~o 90:0 . 969 TI, E6 UOIT E dto6-L .... 99 9 TT 9--fli9d 9.9. ............1.. 9.. . . .....6... .. . ......... ......... .............. ......... . .. 66"6600 tv1 N: 96 BE 6 T 99 Leua9VN ...~ ~ ~ ~~~~.... ....... . ... ..T ............... .. OOT,, 6TO 9 96.:. .99 e9weiN 6d6 0 Puele9Z mO ~~~oo6t' . . . CT 69T9~~~~~~~~~~~~~........ .. .... .1'.. . ... 6666 R .' Zv btT 091' T 6 9 6 99...... ... .... .. ......... 99 . ed............... "666'k .t'66T.- 66 'L99 6S ' 69 69 E L 99 jTedPwN ~~66oCo9T LT~~~t'T 99 L 999 9 9i 99 09 99)euev 06T> -TOO 6 909 L 01' 89 9T 6 CT §V !I06Lio 0099~ TTC6 9" £11 0 eoj '66&at 90 0 . t 66 .. 9 . ~ , -i 6ti. 66T'9 990 ST9117 9 19eueu 000998 990 09.-- -& 0 L 99eiAi 009846 9TO 9196' 061'uo ...N. COT TOO...COT.... .... !uope0e7. 6J 900OL -V E T0 T Ar 66C99 959 ST.6t'~~~~ ~~~~~~~~ ~~~..L 1717 9T TT o......-1 6 0 0 T 99. . .. .. 6 . ...... .6.....6..... ............. o66t.> TOO6 .... 1 66 196 99" 9 f-----deiuznql!l COS . oO6"-~'' d 6 S 06 1 d eAq'!lo TC0 T6 . ~~~ ~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~.9 ..99.. .. w....jo~ "6o 00; 99TTSC 0T 66 ..T...9..9 069 `6O 19.66 T 6 009 TOO69' 6L 99T't 100,6 0LZ TS'9T0 8 Ieeia 0O' T60 TZ,9T1 9 ;pui TOT C.: 6 6.. .....6649 6 CT .jT d 980WeS ueGiIo 00099 906~~~.. 9C9'. 99 ............ . ..... .. . ..... ..... .0 ....... 660669t 1700 1.167.676.L- 07 6 L66T L66T L6BT L66~~~~... .,.96.......... .........-S9 .86.i.. ....66 ............. .S -6 T ~ 6 ~ 6 6 -8 oM034U- 40: % P2fO4 C'CT JfOS siw ~ SeeJ 4 epio epIoie~ .. doed ....IA J .... ....... .. . ........ ....... 40 O LT7 ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....... C ~2.17 Prevalence Low- Prevalence Consump- Prevalence Cigarette Tuberculosis Prevalence of anemia birthwelght of child tion of of smoking consump- of HIV babies malnutrition iodized tion salt Weight for age Height for age Incidence %Of % Of % of per per Prevalence People pregnant children children % of Melee Females smoker 100,000 thousande ft of infected women ft of births under 5 coder5 h ouseholds Y. of adults % of adults per year people ofcoases adults (all agee) 1.985-991 .±992-981 1992-98- ±.992-98a 1.992-98- 1985-~98a 1985--981 1988-981 1997 1997 1997 1997 Rwanda . 17 29 49 96 . .. 276 1 127 370,0 Saudi Arabia .. . . . . 53 .. 3,800 46 14. 0.01 26 .. 22 23~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ . .... Senegal ..22 239223 33 1.77 75,000 Sierra Leone 31 . 75 . .. 315 23 3.17 68,000 Singapore .. . . 32 3 4,250 48 2 0.15 3,0 Slovak Republic 6 .. 43 2 2,973 35 2 0.01 <100 Slovenia .. 6 .. . . 35 23 .. 30 1 0.01 mlOO South Africa 37 . 9 23 40 52 17 2,276 394 266 12.91 2,900,000 Spain I. 1. .. 48 25 3,384 61 23 0.57 120,000 Sri Lanka 39 18 38 24 47 55 1 786 48 14 0.07 6900 Sudan 36 15 34 34 0 .:.............. .. ..... 180 112 .0.99 Sweden . 5 . .. 22 24 2,641 5 0 0.07 3,000 Switzerland . 5 . .. 36 26 4,618 11 1 0.32 12,000 Syrian Arab Republic . 7 13 21 40 ... . 75 17 0.01 Tajikistan 50 8. 20.. 8 9 0.01 <100 Tanzania 59 14 31 43 74 . .. 307 124 9.42 1,400,000 Thailand 57 7 . .. 50 49 4 2,140 142 180 2.23 780,000 To o 48 20 25 22 73 65 14 . 353 19 8.52 170,000 Trinidad and Tobago 53 10 . ... 11 0 0.94 6, 800 Tunisia .38 ..16 9 2 3 98 .. 40 6 0.04 Turkey 7 4 8 10 2 1 18 63 24 2,319 41 42 0.01 Turkmenistan . 5 . 0 27 1 . 74 5 0.01 <100 Uganda 30 .. 26 38 69 .. 312 94 9.51 930,000 Ukraine .. 8 . ..4 57 22 2,471 61 49 0.43 110.000 United Arab Emirates .. 8 7 ... .. . 21 1 0.18 United Kingdom 6 .. 28 26 3,706 18 11 0.09 25,000 United States .. 8 10 . 2 3 498.76 820,000 Uruguay 20 8 4 10 .. 41 27 .. 31 1 0.33 5,200 Uzbekistan . 6 19 31 0 . .. 81 29 0.01 <100 Venezuela, RB 29 9 5 15 65 29 12 1,599 42 11 0.69 Vietnam 52 17 40 36 65 73 4 730 189 221 0.22 88,000 West Bank and Gaza . .. . . ..... 26 1 Yemen, Re p..19 46 52 39 ill11 31 0.01 Yugoslavia, .. 51 8 0.10~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .......... ...... .uolva R(Serb./Mont.) 2 7 70 5 .1 Zamnbia 34 13 24 42 78 . 576 61 19.07 770,000 Zimbabwe 14 16 21 80 36 15 .. 543 74 25.84 1,500,000~ 0 * I.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..... Low income 62 21 36 42 66 180 1.22 Exci. China & India 51 6 37 42 68 241 2.98 Middle income 37 8 12 19 58 50 21 101 3.71 Lower middle income 35 9 14 22 59 ...126 9.92 Upper middle income 40 8 ..7 .. 12 46 46 22 64 9.41 Low & middle intcome 55 18 31 37 82 157 1.06 East Asia & Pacific 54 6 22 33 66 151 9.20 Europe & Central Asia 39 7 8 18 72 60 27 75 9.08 Latin Amnerica & Carib. 34 8 8 16 23 39 20 81 9.59 Middle East & N. Africa 29 10 15 24 88... 67 1-03 South Asia 79 33 51 s0 50 193 9.66 Sub-Saharan Africa 45 .. 33 40 64 ...267 7.28 High income .. 7 . .. 60 37 2 3 18 D. 36 Europe EMU . 5 . .. 39 24 292 a. Date are for the most recent year available. 104 2000 World Development Indicators 2.17 The limited availability of data on health status is a salt is the best source of iodine, and a glot al campaign * Prevalence of anemia, or iron deficiency, refers to the major constraint in assessingthe health situation in devel- to iodize edible salt is significantly reducing the risks percentage of pregnant women with hemoglobin levels opingcountries. Surveillance data are lacking fora num- (UNICEF, The State of the World's Children 1999). less than 11 grams per deciliter. * Low-birthwelght ber of major public health concerns. Estimates of Data on smoking are obtained through surveys. babies are newborns weighing less than 2,500 grams, prevalence and incidence are available forsome diseases Because they give a one-time estimate cf the preva- with the measurementtaken within the first hours of life, but are often unreliable and incomplete. National health lence of smoking with no information on intensity or before significant postnatal weight loss has occurred. authorities differ widely in their capacity and willingness duration, they should be interpreted with caution. * Prevalence of child malnutrition is the percentage of to collect or report information. Even when intentions are Tuberculosis is the major cause of death from a sin- children under five whose weight for age and height for good, reporting is based on definitions that may vary gle infectious agent among adults in developing coun- age are less than minus two standard deviations from widely across countries or overtime. To compensate for tries (WHO 1999a). In industrial countries tuberculosis the median forthe intemational reference population aged the paucity of data and ensure reasonable reliability and has reemerged largely as a result of cases among immi- 0-59 months. For children up to two years of age, height international comparability. the World Health Organiza- grants. The estimates of tuberculosis inci dence in the is measured by recumbent length. Fbr older children, height tion (WHO) prepares estimates in accordance with epi- table are based on a new approach in wh ch reported is measured by stature while standing. The reference pop- demiological and statistical standards. cases are adjusted using the ratio of case notifications ulation, adopted by the WHO in 1983, is based on chil- Adequate quantities of micronutrients (vitamins and to the estimated share of cases detected by panels dren from the United States, who are assumed to be well minerals) are essential for healthy growth and develop- of 80 epidemiologists convened by the WHO. nourished. * Consumption of iodized salt refers to the ment. Studies indicate that more people are deficient in Adult HIV prevalence rates reflect the rate of HIV percentage of households that use edible salt fortified iron (anemic) than any other micronutrient, and most are infection for each country's population. Estimates of with iodine. * Prevalence of smokingis the percentage women of reprnductive age. Anemia during pregnancy can HIV prevalence among adults and of the total number of men and women over 15 who smoke cigarettes. * Cig harm both the mother and the fetus, causing loss of the of people currently infected are based on plausible arette consumption shows the number of cigarettes baby, premature birth, or low birthweight. Estimates of extrapolations from surveys of smalle, nonrepre- consumed persmoker in a year. * Incidence of tuber- the prevalence of anemia among pregnant women are sentative groups. culosis is the estimated numberof new tuberculosis cases generally drawn from clinical data. which suffer from two (pulmonary, smear positive, extrapulmonary). * Preva- weaknesses: the sampe is based on those who seek lence of tuberculosis refers to the number of people suf- care and is therefore not random, and private clinics or fering from tuberculosis in 1997. * Prevalence of HIV hospitals may not be part of the reporting network. Developing countries will see a ripidly refers to the percentage of people aged 15-49 who are growing health impact from smoking Low birthweight, which is associated with maternal infected with HIV. * People Infected with HIV include malnutrition. raises the risk of infant mortality and stunts Annual tobacco-related deaths (millions) all estimated cases, regardless of age. growth in infancy and childhood. Estimates of low- and middle-income U Low- admdl-n birthweight infants are drawn mostly from hospital countries Date sourees : records. But many births in developing countries take U High-iencome place at home, and these births are seldom recorded. a The data presented here are drawn from a variety of A hospital birth may indicate higher income and there- sources, including the United Nations Administrative fore better nutrition, or it could indicate a higher-risk birth, Committee on Coordination, Subcommittee on Nutrition's possibly skewing the data on birthweights downward. The 2 Update on the Nutrition Situation; the WHO's World data should therefore be treated with caution. Health Statistics Annual, Global Tuberculosis Control Estimates of child malnutrition, based on both weight ° Report 1999, and Tobacco or Health: A Global Status 1990 2030 for age (underweight) and height for age (stunting), are Report, 1997: UNICEF's State of the World's Children from national survey data. The proportion of children Source: WHO e999b. 1999; the WHO and UNICEF's Low Birth Weight A Tab- underweight is the most common indicator of malnu- ulation ofAvailable Information (1992); and UNAIDS and By the mld-±1990s one In three adults wfer trition. Being underweight, even mildly, increases the smokers (1.i billion worldwide). The p,evalence the WHO's Report on the Global HIV/AIDS Epidemic risk of death and inhibits cognitive development in chil- of smoking has been declining In hIgh-lncome (1998). dren. Moreover, it perpetuates the problem from one countries, but it has been increasing In many low- and middle-income countrles. generation to the next, as malnourished women are more Tobacco use causes heart and other vascuiar likely to have low-birthweight babies. Height for age diseases and cancers of the lung and other reflects linear growth achieved pre- and postnatally, organs. Given the long delay betweer, starting to smoke and developing a fatal dismese, the and a deficit indicates long-term, cumulative effects of health impact In developing couniries will inadequacies of health, diet, or care. It is often argued Increase rapidly In the next few decades. that stunting is a proxy for multifaceted deprivation. Iodine deficiency is the single most important cause of preventable mental retardation, and it contributes sig- nificantly to the risk of stillbirth and miscarriage. Iodized 2000 World Development Indicators 105 C ~2.18 iMortality Life expectancy Infant mortality Under-five Child mortality : Adult mortality Survival at birth rate mortality rate rate to age 65 rate Male Female per 1,000 Male Female Male Female % ef % of years leve births per 1,000 per 1,000 per 1,000 per 1,0OOC per 1,000 ~ohort ceohort 1980 1998 1980 1998 1980 ±998 198S-98, 1988-98, 1998 1.998 1997 ±997 Albania 69 7 2 47 25 57. 31 15 15 171 95 7 2 83. Algeria 59 7 1 98 35 139 40 158 123 7 2 79 Ango'a" 4 1 4 7 154 124 261 204. 415 358 36 42 Argentina 70 73 35 19 38 22 163 79 73 86 Armenia 73 74 26 15 18 .16.2 .79 74 .8.6.. Australia 74 79 11 5 13 6 11 6 839 Austria 73 78 14 5 17 6 122 60. 81 90 Azerbaijan 68 71 30. 17 .. 21 209 99 68 83 Bangladesh 48 59 132 73 211 96. 37. 47 283 306 54 54 Belarus 71 68 16 11 .. 14 . .. 332 116 55 81 Belgium 73 78 12. 6 15 6 130 60 80 90 Benin 48 53 116 87 214 140 89 90 367 308 44 51 Bolivia 52 62 118 60 170 78 26 26 265 215 57 64 Bosnia and Herzegovina 70 73 31 13 165 93 74 85 Botswana 58 46 71 62 94 105 18 16 617 576 25 29 Brazil 6 3 6 7 7 0 33 80 40 8 9 279 139 5 9 76 Bulgaria 71 71 20 14 25 15 222 107 67 82 Burkina Faso 44 44 121 104 .. 210 107 110 547 522 28 31 Burundi 47 42 122 118 193 196 101 114 554 496 26 31 Cambodia 39 54 201 102 330 143 ... 357 309 45 5 1 Cameroon 50 54 103 77 173 150 69 75 336 303 47 51 Canada 75 79 10 5 13 7 ... 106 52 83 91 Central African Republic 46 44 117 98 .. 162 63 64 576 488 26 34 Chad 42 46 123 99 235 172 106 99 454 388 36 42 Chile 69 75 32 10 35 12 3 2 142 73 77 87 China .70 .42 31 65 36 10 11 171 135 71 77 Hong Kong, China 74 79 11 3 . . . .. 109 56 83 91 Colombia 66 70 41 23 58 28 7 7 211 115 67 80 Congo, Dem, Rep. 49 .51 .112 90 210. 141 ... 422. 367. 39 ~ 45 Congo, Rep. 50 48 89 90 125 143 ... 503 408 32 42 Costa Rica 73 77 19 13 29 15 ... 115 69 81 88 C6te dIlvoire 49 46 108 88 170 143 71 58 526 513 31 32 Croatia 70 73 21 8 23 10 ... 216 87 66 86 Cuba 74 76 20 7 22 9 ... 124. 79 80 87 Czech Republic 70 75 16 5 19 6 ... 177 84 73 87 DenmarK 74 76 8 5 10 .. . . 138 78 78 87 Dominican Republic 64 71 76 40 92 47 13 13 153 96 73 81 Ecuador 63 70 74 32 101 37 12 9 182 105 70 81- Egypt, Arab Rep. ..6 67 120 49 175 59 22 28 195 171 66 71 El Salvador 57 69 84 31 120 36 17 20 207 119 67 79 Eritrea 44 51 .. 61 .. 90 89 78 511 447 34 41 Estonia 69 70 17 9 25 12 ... 300 95 59 84 Ethi'opia 42 43 iSS 107 213 173 ... 562 529 26 29 Finland 73 77 8 4 9 5 ... 139 60 78 90 France 74 78 10 6 13 5 127 81 80 92 Gabon 48 53 116 86 194 132 ... 384 342 43 48 Gambia, The 40 53 159 76 .216 .. 83 79 408 ~ 344 42 49 Georgia 71 73 25 15 ,. 20 194 82 70 86 Germany 73 77 12 5 16 6 . .. 132 66 79 89 Ghana 53 60 94 65 157 96 63 62 262 230 55 62 Greece 74 78 18 6 23 8 114 61 82 90 Guatemala 57 64 84 42 .. 52 22 24 297 195 56 69 Guinea 40 47 185 118 299 184 122 112 404 404 38 39 Guinea-Bissau 39 44 169 128 290 205 471 419 31 36 Haiti 51 54 123 71 200 116 59 58 432 339 40 50 Honduras 60 69 70 36 103 46 ... 196 121 68 78 100 2000 World Developmest indicators 2.18 10- Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival at birth rate mortality rate rate to age 65 rate Male Female per 1,000 Male Female Male Female ft of % Of years live births per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 cohort cohort 1930 1998 1980 1998 1980 1998 j983-938 j988~931 1998 1998 1997 1997 India 54 63 115 70 177 83 29 42 215 204 62 65 Indonesia 55 65 90 43 125 52 19 20 237 186 62 70 Iran, Ialamic Rep. 60 71 87 26 126 33 ... 161 150 72 76 Iraq 62 59 80 103 95 125 197 17 59 63 Ireland 73 76 11 6 14 7 135 73 79 88 Israel 73 78 16 6 19 8 110 68 83 89 Italy 74 78 15 5 17 6 117 53 81 91 Jamaica 71 75 33 21 39 24 140 86 77 86 Japan 76 81 8 4 11 5 98 45 85 93 Jordan . 71 41 27 . 31 4 7 158 119 73 80 Kazakhsta 67 65 33 22 . 29 10 5 382 167 49 74 Kenya 55 51 75 76 115 124 36 38 442 418 39 42 Korea, Dem. Rep. 67 63 32 54 43 68 ... 267 200 58 67 Korea, Rep 67 73 26 9 27 11 204 94 69 85 Kuwait 71 77 27 12 35 13 .. . 125. 65 80 89 Kyrgyz Republic 65 67 43 26 . 41 10 11 303 140 57 77 Lao PDR 45 54 127 96 200 . . . 376 320 43 50 Latvia 69 70 20 15 26 19 ... 301 102 58 83 Lebanon 65 70 48 27 . 30 176 132 71 78 Lesotho 53 55 119 93 168 144 ... 320 28 854 Libya 60 70 70 23 80 27 6 5 185 129 70 79 Lithuania 71 72 20 9 24 12 .. . 6 7 63 86 Macedonia, FYR .. 73 54 16 69 18la. 162 . .104 74 83 Madagascar 51 56 119 92 216 146 75 66 273 231 53 59 Malawi 44 42 169 134 265 229 126 114 464 483 31 31 Malaysia 67 72 30 8 42 12 4 4 186 113 71 82 Mali 42 50 184 117 . 218 136 138 404 325 39 47 Mauritani 47 54 120 90 175 140 .. . 345 294 46 52 Mauritius 66 71 32 19 40 22 ... 202 96 69 84 Mexico 67 72 51 30 74 35 15 17 165 84 72 84 Moldova 66 67 35 18 . 22 .. . 315 176 57 74 Mongolia 58 66 82 50 . 60 ... 201 165 65 71 Morocco 58 67 99 49 152 61 21 19 203 147 65 74 Mozambique 44 45 145 134 .. 213 84 82 408 364 36 40 Myanmar 52 60 109 78 134 118 ... 270 223 55 62 Namibia 53 54 90 67 114 112 30 34 383 364 45 48 Nepal 48 58 132 77 180 107 .. . 273 309 54 53 Netherlands 76 78 9 5 11 7 ... 11 62 81 90 New Zealand 73 77 13 5 16 7 ... 120 65 81 89 Nicaragua 59 68 84 36 143 42 12 11 208 139 66 76 Niger 42 46 135 118 317 250 184 202 453 352 34 43 Nigeria 46 53 99 76 196 119 118 202 401 339 43 50 Norway 76 78 8 4 11 6 112 58 82 91 Oman 60 73 41 18 95 25 141 106 76 82 Pakistan 55 62 127 91 161 120 22 37 172 152 64 68 Panama 70 74 32 21 36 25 ... 139 82 77 85 Papua New Guinea 51 56 78 59.. 76 28 21 348 331 49 53 P~araiguay ........... 67 ...70 50 24.. 61 27 10 12 203 129 68 79 Peru 60 69 81 40 126 47 19 20 199 123 67 78 Philippines 61 69 52 32 81 40 21 19 197 149 68 75 Poland 70 73 26 10 11 208 85 69 86 Romani.a 69 69 29 21 36 25 7 5 256 122 62 80 Russian Federation 67 67 22 17 - 20 3 2 364 128 52 79 2000 World Development Indicators 107 C~2.18~ Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival at birth rate mortality rate rate to age 65 late Male Female per 1,000 Male Female Male Female 3 of %' of years live births per 1,000 per 1,000 per 1,000 per 1.000 per 1,000 -overt conort 1980 1998 1.980 1998 1980 1998 j988-98a 1988-98o 1998 1998 1997 1997 Rwanda 46 41 128 123 205 87 73 578 527 24 28 Saudi Arabia 61 72 65 20 85 26 165 138 73 78 Senegal 45 52 117 69 . 121 76 74 456 385 38 46~ Sierra Leone 35 37 190 169 336 283 544 483 23 28 Sinapoire 7 1 77. 1 2 4 13. .6 131 75 80 88 Slovak Republic 70 73 21 9 23 10 207 90 69 85 Slovenia 70 75 15 5 18 7 169 75 74 88 South Africa 57 63 67 51 91 83 282 194 57 68 Spain 76 78 12 5 1 6 7 124. 56. 8 1 9 1 Sri Lanka 68 73 34 16 48 18 10 9 153 97 75 84 Sudan 48 55 94 69 145 105 62 63 378 333 46 51 Sweden 76 79 7 4 9 5 104 54 84 91 Switzerland 78 79 9 4 11 5 106 50 83 92 Syrian Arab Republic 62 69 56 28 73 32 203 138 67 77 Tajikistan 66 69 58 23 . 33 233 142 64 77 Tanzania 50 47 108 85 176 136 59 52 521 482 31 35 Thailand 64 72 49 29 58 33 11 11 206 116 67 79 Togo 49 49 100 78 188 144 75 90 488 444 34 39 Trinidad and Tobago 68 73 35 16 . 40 18 4 . 3 . 161 101 74 83 Tunisia 62 72 69 28 100 32 19 19 166 142 72 77 TurKey 61 69 109 38 133 42 12 14 186 122 68 78 Turkmenistan 64 66 54 33 . 44 .. . 282 159 58 74 Uganda 48 42 116 101 180 170 82 72 579 615 25 23 Ukraine 69 67 17 14 . 17 3.51, 35 135 53 79 United Arab Emirates 68 75 55 8 . 10 . . 127 92 80 85 United Kingdom 74 77 12 6 14 7 122 66 81 89 United States 74 77 13 7 15 . 133 68 79 89 Uruguay 70 74 37 16 42 19 171 76 73 87 Uzbekistan 67 69 47 22 . 29 15 9 229 126 65 79 Venezuela, RB 68 73 36 21 42 25 157 89 74 84 Vietnam63 68 57 34 105 42 . .. 225 153 65 75 West Bank and Gaza.....71 .. 24 .. 26 10 7 167 109 72 81 Yemen, Rap. 49 56 141 82 198 96 33 36 335 333 48 50 Yugoslavia, FR (Serb./Mont.l 70 72 33 13 . 16 .. . 178 17 72 82 Zamnbia 50 43 90 114 149 192 96 93 521 545 29 28 Zimbabwe 55 51 80 73 108 125 26 26 470 417 37 43 Low Income .. 63 97 68 150 92 37 48 235 208 64 69 Excl. China & India 51 57 114 83 177 125 62 78 329 292 52 58 Middle Income 66 69 60 31 89 3 . . 230 126 63 80 Lower middle income 64 68 62 35 . 44 15 15 244 137 61 78 Upper middle income 66 71 57 26 72 31 . . 210 110 68 82 Low & middle Income 58 65 87 59 135 79 32 41 234 183 64 73 East Asia & Pacific .. 69 55 35 82 43 12 13 188 145 69 76 Europe & Central Asia 68 69 41 22 . 26 283 120 59 80 Latin America & Carib. 65 70 61 31 78 38 13 14 216 116 67 81 Middle East & N. Africa 59 68 95 45 136 55 .. . 187 159 68 73 South Asia 54 62 119 75 180 89 29 42 220 213 62 65 Sub-Saharan Africa 48 50 115 92 188 151 92 114 432 383 40 46 NihIncome 74 78 12 6 15 6 123 61 81 90 Europe EMU 74 78 12 5 16 6 127 59 80 90 a. Oats are for the most recent year available. 1OS 2000 World Development ndicators 2.18 0 Mortality rates for different age groups-infants, children, able (see Primary data documentation). E>trapolations * Life expectancy at birth is the number of years a or adults-and overall indicators of mortality-life based on outdated surveys maynot be reliable formon- newborn infant would live if prevailing patterns of expectancyatbirthorsurvivaltoagiven age-areimpor- itoring changes in health status or for comparative mortality at the time of its birth were to stay the tant indicators ofthe health status in a country. Because analytical work. same throughout its life. * Infant mortality rate is data on the incidence or prevalence of diseases (morbidity Infant and child mortality rates are higher for boys the number of infants who die before reaching one year data) frequently are unavailable, mortality rates are often than forgirls in countries in which parental gender pref- of age, per 1,000 live births in a given year. * Under- used to identify vulnerable populations. And they are erences are absent. Child mortality captures the effect five mortality rate is the probability that a newborn among the indicators most frequently used to compare of gender discrimination better than does infant mor- babywill die before reaching age five, if subject to cur- levels of socioeconomic development across countries. tality, as malnutrition and medical interventions are more rent age-specific mortality rates. * Child mortality rate The main sources of mortality data are vital regstration important in this age group. Where fema e child mor- is the probability of dying between the ages of one and systems and direct or indirect estimates based on sam- tality is higher, as in some countries in South Asia, it five, if subject to current age-specific mortality rates. ple surveys or censuses. A complete vital registration is likely that girls have unequal access to resources. * Adult mortality rate is the probability of dying system-that is. a system covering at east 90 percent Adult mortality rates have increased ir many coun- between the ages of 15 and 60-that is, the proba- of the population-is the best source of age-specific mor- tries in Sub-Saharan Africa as well as in Eastern Europe bility of a 15-year-old dying before reaching age 60, if tality data. But such systems are fairly uncommon in and the countries of the former Soviet Union. In Sub- subject to current age-specific mortality rates between developing countries. Thus estimates must be obtained Saharan Africa the increase stems from AIDS-related ages 15 and 60. * Survival to age 65 refers to the from sample surveys or derived by applying indirect mortality and affects both men and womrn. In Europe percentage of a cohort of newborn infants who would estimation techniques to registration, census, or survey and Central Asia the causes are more diverse and affect survive to age 65, if subject to current age-specific mor- data. Survey data are subject to recall error, and surveys men more. They include a high prevalence of smoking, tality rates. estimating infant deaths require large samples, because a highfat diet, excessive alcohol use, and ;tressful con- households in which a birth or an infant death has ditions related to the economic transition. Deta sources occurred during a given year cannot ordinarily be pre- The percentage of a cohort surviving to age 65 selected for sampling. Indirect estimates rely on esti- combines child and adult mortality rates. Like life The data in the table are from the United Nations Sta- mated actuarial (life")tablesthatmaybe inappropriate expectancy, it is a synthetic measuretha: is based on tistics Division's Populationand VitalStatistics Report; for the population concerned. Because life expectancy current age-specific mortality rates and us 3d in the con- demographic and health surveys from national sources at birth is constructed using infant mortality data and struction of life tables. It shows that in countries where and Macro International; and UNICEF's State of the life tables, similar reliability issues arise for this indicator. mortality is high, a certain share of the current birth World's Children 2000. Life expectancy at birth and age-specific mortality cohort will live well beyond the life expectancy at birth, rates for 1998 are generally estimates based on vital while in low-mortality countries close to 9) percent will registration or the most recent census or survey avail- reach at least age 65. Under-five mortality is dramatically higher among the poorest Under-five mortality rate, various years, L99Os (per 1,000) 200 'so ~~ ~~~I I _ 100; lii i i I 50 1 1 1 1 11 .1 1 1 Brazil C6te d'lvoire India Indonesia Morocco TArkey * Poorest quintile * 2nd quintile U 3rd quintile 4th quintile Riciest quintile Note: Households are grouped into quintiles by assets. Source: Analysis of demographic and health surveys conducted by the World Sank and Macro International. The national-level mortalityindicatorsintable2.1 o8bscurethelargedifferences amongwealthstrati thatexist lnmostcountrles. The under-fivemortality rate inthepoorest qulntilelsoften atleasttwicethatInthe vealthlest. 2000 World Development Indicators 109 WQ.X~~~~~~~P i ~~~~~~i Rural and urban development can bring relief to more than 1.2 billion poor. But only with more attention to the links between development and the envi- ronment, including the environment's impact on health and the productive capac- ity of natural resources, can development be sustainable. More people are using more natural resources than ever, and demand will only increase. Food supply needs to double in the next 35 years to satisfy the growth of populations and economies. This will happen, to a large extent, at the expense of forests, wetlands, and biodiversity. More than a fifth of the world's tropical forests have been cleared since 1960, and at least 484 animal species and 654 plant species have become extinct since 1600 (Watson and oth- ers 1998). Water stress and water scarcity affect almost half a billion people; in 25 years that number will rise to 3 billion. Without efficient management, existing freshwater supply cannot meet the needs of growing populations in many countries. Millions of people die every year from contaminated water-almost all of them in lowv- and middle-income countries. And the irony is that the poor ' pay more than the rich for potable water (World Bank 1999d). To balance demand for grDAth and the use of resources and to monitor their environmental impact. we need information on how the environment is chang- ing and how its degradation affects the poor-in both rural and urban areas. But lack of meaningful data with meaningful breakdowns constrains the efforts to address the consequences of rural and urban development. Rural development should preserve the environment Poverty is overwhelmingly rural, with about 70 percent of the poorest people in developing countries in rural areas. Although the number and proportion of poor people in cities are expected to grow rapidly in the next decades, the majority of the poor will continue to live in the countryside. Reducing poverty and ending hunger thus requi res more attention to the rural economy and rural development. Environmental problems affect the poor for several reasons. Dirty water and dirty air are major causes of diarrhea and respiratory infections, the two biggest killers of poor children. And standing water and accumulated solid waste pro- ; mote the transmission of malaria and dengue fever. ..'- .-.-. U Poor people are often mere vulnerable to environmental changes because - ~. they use natural resources directly and because they have fewer alternative The world has shifted toward cleaner energy ... ... and the trend is expected to continue World consumption (millions of tons of oil equivalent) World consumption (exajoules) 3,500 800 3,000 m 2,500 600 2,000 400 - 1,500 z 1,000 200 5000 0 122 ! 1950 1960 1970 1980 1990 1998 0 1990 2025 2050 - Coal * Coal - Natural gas U Oil -Oil U Natural gas Source: Brown and others 1999. Nuclear * Hydropower Biomass Renewables E1 Solar Note: One exajoule is 10 (joules. ways to earn income, fewer alternative places to live, and fewer mech- Source: Intergovernmental Panel on Climate Change 1996. anisms for coping with shocks. And the rural poor are vulnerable because they often live on marginal land and in unstable housing- places most susceptible to natural disasters and extreme weather. Agricultural production-now keeping pace with population health are even higher: the annual price of dust and lead pollution growth in developing countries-contributes to environmental in Bangkok, Jakarta, and Kuala Lumpur has been estimated at $5 degradation and suffers from it. Unsustainable farming methods- billion, or about 10 percent of city income (WVVorld Bank 1996a). such as the excessive use of pesticides and fertilizer-reduce biodi- Just as the rural poor suffer more from pollution than their versity, degrade soil, and pollute water. In some parts of the world wealthy neighbors, so the urban poor bear the brun: of urban pol- poor farming techniques are the leading cause of deforestation, as lution. In Indonesia researchers found that factories in municipal- farmers continually seek to expand their landholdings and improve ities in the bottom quartile of income and education have organic their economic condition. pollution 15 times as intense as plants in communities in the top quar- Environmental damage can also harm agriculture. The destruc- tile. Rio dejaneiro and Sao Paulo also show that pollution-intensive tion of watersheds dries up sources of irrigation, while pollution industry dominates in poorer municipalities (World Bank 1999b). destroys fisheries and reduces crop yields. These lead to increased In China the density of suspended particulate pc Ilution rises as use of marginal land, reducing production and perpetuating poverty wages fall. Why this tragic association between poverty and pollution? for those whose livelihood depends on agriculture. Industrial production in richer areas is cleaner because citizen feed- back is strong and regulation tight. Industrial facilities in areas with Urban development brings ?r i -, 2 ad - -i - unskilled workers generally operate at lower efficiency and create in its wake, affecting the poor most vvf VA more waste. Another cause of the disparity is the poor's lack of More than 2.7 billion people (almost half the world's population) access to cleaner sources of energy. live in urban areas, a number projected to reach 5.1 billion by 2030, No country has developed much beyond a subsistence economy with 98 percent of the increase taking place in developing countries. without ensuring access to energy services for a large segment of With increasing inequality between the north and the south, grow- its population. At the same time, providing ene:-gy services- ing urbanization will have far-reaching consequences. Already close especially through combustion of fossil fuels and biomass-can to 30 percent of the developing world's urban population lives harm the environment. And this harms the poor, who must rely on below the poverty line. inefficient and polluting sources of energy for lack of better alter- The ability of cities to reap the benefits of economic growth and natives (tables 3.7, 3.8, and 3.9). In cities, burning coal and other sustainable development will depend largely on their success in dirty fuels for household heating and small-scale cc mmercial and improving the quality of life-and the quality of the environment- industrial activity causes smog and acid rain. And in rural areas, burn- for this growing number of urban poor. Traffic congestion in urban ing traditional fuels in ill-designed stoves or hearths causes indoor areas affects health, economic productivity, and quality of life. In air pollution, which damages the health of women and children. Bangkok about half a billion dollars a year could be saved just by The World Energy Council (1995) forecasts that energy use making peak hour traffic move 10 percent faster. The costs to will grow 1.4 percent a year until 2020, 2.6 percent a year in devel- 112 2000 World Development Indicators Monitoring progress in rural development International goal for environmental sustainability and regeneration As economies develop and incomes rise, people use a smaller share of their income for food and raw materials, and the share of agricultural and The international community has set a goal of implementing national other natural resource-based activities in the economy declines. strategies for sustainable development by 2005 to reverse the loss of Although not the only economic activity in rural areas, agriculture is the environrnental resources globally and nationally by 2015. To monitor backbone of all but the most advanced economies. Its relative decline is progress toward this goal, a joint OECD-United Nations-World Bank the primary reason for the decline in the rural population share and the working group has suggested the following set of indicators: high incidence of rural poverty in most countries. * Exisl ence of a national strategy for sustainable development. Rural development is the outcome of all productive activities in rural * Population with access to safe water. areas-agricultural and nonagricultural. It improves the livelihood and well- * Lant area protected. being of rural people. To understand the link between rural development * GDP per unit of energy use. and rural well-being, a comprehensive view reflecting both the process of * Per capita carbon dioxide emissions. rural development and the progress toward rural well-being must be * Fore st area. articulated. The World Bank is developing a framework for monitoring progress in rural development and rural well-being that focuses on three key development goals: an improved rural economy, a sustainable natural resource base, and sound institutions and governance. Progress toward each goal will be monitored using a set of indicators, with poverty reduction a proxy for rural well-being. Poverty must be Another policy-relevant issue is how to present national accounts tackled not only by increasing incomes but also by enhancing equity and and thus economic growth. Because the standard national account improving access to basic services. The framework emphasizes the estimates d(o not reflect environmental depletion and degradation, following tasks: * Reduce the proportion of the rural population with incomes below the they often send false policy signals to nations aiming for environ- poverty level. mentally s astainable development. "Green GNP," which integrates * Improve social and physical well-being environmental depletion and degradation, is one indicator gaining * Foster gender equity. currency. 'hile a greener measure of GNP would have some pol- * Enhance food security. icy use, a related measure-genuine savings (table 3.15)-gets The work on this monitoring framework has brought to the fore the immense problems in the availability, quality, and reliabilityof rural data direetlyto hequestionofwhetheracountryisonasustaiablepath, in most developing countries. making the data more useful for policymakers. The genuine savings measure links environment and economy by accounting for deple- tion and degradation of natural resources. To examine the links between growth, environment, and poverty oping.This growth has major environmental implica- and the ro le of rural development in reducing poverty and improv- oping countries. ings ruralh wel-eig newo approachesta to moiorn rua devlop tions, particularly for the level of pollution and for future emis- mg rural well-beig, new approaches to monitoring rural develop sions of greenhouse gases and their likely impact on climate ment, resource use, and environmental sustainability are being developed (boxes 3a and 3b). World Bank publications contribute change. Fortunately, the recent shift toward cleaner energy sources t is expected to conitinue (figures 3a and 3b). But even in scenar- to this work. RuralDeoeloment From Vision to Actiona broad strat ios with fairly optimistic assumptions about the growth of egy to develop rural economies-identifies four goals that a coun- hydropower and other forms of renewable energy, carbon emis- try can use to assess its rural development (World Bank 1997e). Fuel sions from burning fossil fuels are predicted to double by 2050. for Thought: Environmental Strateg for the Energ Sectr attempts to improve understanding of the nexus of energy and the environment Strike a balance between growth and resource use (World Bank 1999a). And a new environmental strategy emphasizes by measuring anid monitoring understandling the contribution of environmental activities to poverty Successful rural and urban development requires close monitoring reduction. of the impact of policy. Monitoring requires meaningful data bro- ken down along rural and urban lines, reflecting the different char- acteristics of rural and urban development. But today's coverage of rural and environmental indicators is sparse. Another problem: many environmental indicators have little meaning at the national level. Some national activities have transna- tional consequences, and some environmental issues are highly localized and location specific. So in many cases global, regional, or rural and ciy indicators are more meaningful than national aggre- gates (tables 3.11 and 3.13). Moreover, even on a national level many relevant indicators cannot be compiled because adequate or comparable data are lacking. And many do not capture depletion of natural resources-a serious constraint on measuring the state of the environment and designing sound policies. 2000 World Development Indicators 113 mw ~ 3.1 Rural environment and land use Rural population Rural Land area Land use population density people per Permanent average u mof thousand Arable land cropland Other % fttl annual % grwt arl land sq. km % of land area % of land area te of lend area 1.980 ±1998 1990-98 1.997 1.997 1.980 1997 ±9910 1997 1980 1997 Albania 66 60 0.6 344 27 21.~4 21.1 4.3 4.6 74.4 74.4 Algria 57 41 0.8 163 2,382 2.9 3.2 0.3 0.2 96.8 96.6 Angola 79 67 2.1 263 1,247 2.3 2.4 0.4 0.4 97.3 97.2 Argentina 17 11 -1.2 16 2,737 9.1 9.1 0.8 0.8 .90.1 90. Armenia 34 31 0.6 236 28 . 17.5 . 2.3 . 80.2 Australia 14 15 1.8 5 7,682 5.7 6.9 0.0 0.0 94.2 93.1 Austria 35 35 0.6 205 83 18.6 16.9 1.2 1.0 80.2 82.1 Azerbai'jan 47 43 1.0 205 87 . 19.3.. 30. 77.7 Bangladesh 86 77 1.5 1,204 130 68.3 60.8 2.0 2.5 29.6 36.7 Belarus 44 29 -1.7 49 207 . 29.8 .. 0.7 69.5 Belgium 5 3 -2.5 33' 23.201 23.4a 0.4 ~ 0.5 1 734 76.10 Benfn 73 59 1.9 240 11.1 12.2 13.1 0.8 1.4 87.0 85.6 Bolivia 55 39 0.3 163 1,004 1.7 1.7 0.2 0.2 90.1 98.1 Bosnia and Herzegovina 65 58 -1.5 425 51 9.8 .. 2.9 . 87.3 Botewana 85 51 ~~~~~~~~~ ~~~~~-0.1 229 567 0.7 0.6 00.0 0...0 993.9. Brazil 34 20 -1.3 63 8,457 4.6 6.3 1.2 .1.4 94.2 92.3 Bulgaria 39 31 -1.7 60 111 34.6 39.0 3.2 1.8 62.2 59.2 Burkina Faso ..... 92 83 .1.9 257 274 10.0 12.4 0.1 0.2 89.8 87.4 Burundi 96 92 2.4 766 26 35.8 30.0 10.1 12.9 54.0 57.2 Cambodia 88 85 2.9 259 177 11.3 21.0 0.4 0.6 88.3 78.4 Camneroon 69 53 1.3 125 465 12.7 12.8 2.2 2.6 85.1 84.6 Canada 24 23 1.0 15 9,221 4.9 4.9 0.0 0.0 95.0 95.0 Central African Republic 65 60 1.9 106 623 3.0 3.1 0.1 0.1 96.9 96.8 Chad 81 77 2.4 169 .1259 2.5 26 0.0 0.0 97.5 97.4 Chile 19 15 0.4 111 749 5.1 2.6 0.3 0.4 94.6 96.9 China' 80 69 0.5 685 9,326 10.4 13.3 0.4 1.2 89.3 85.5 Hong Kong, China 9 0 -37.2 0 1 7.0 5.1 1.0 1.0 92.0 93.9 Colombia 36 27 0.4 568 1,039 3.6 1.9 1.4 2.4 95.0 95.7 Congo, Dam. Rep. 71 70 3.2 493 2,267 2.9 3.0 0.4 0.5 96.6 96.5 Congo, Rep 59 39 0.5 773 342 0.4 0.4. 0.1 0.1 99.5 99.5 Costa Rica 57 53 2.0 813 51 5.5 4.4 4.4 5.5 90.1 90.1 COte dIlvoi re ....65 55. 2.3 267 318 6.1 9.3 7.2 13.8 86.6 76.9 C.roatia .....50 43 -.0.9 150 56 . 23.5 . 2.2 . .. 74.2 Cuba 32 25 -0.6 75 110 23.9 33.7 6.4 6.8 69.7 59.5 Czech Republic 25 25 0.1 85 77 . 40.0 .. 3.1. 56.9 Denmark 16 15 -0.4 33 42 62.3 55.7 0.3 0.2 327.4 44.1 Dominican Republic 50 36 0.3 293 48 22.1 21.1 7.2 9.9 70.6 69.0 Ecuador .... ... 53 37 .0.3 286 277 5.6 5.7 3.3 5.2 9-1.1 89.2. Egypt,.Arab Rep. 56 55 2.2 1,177 995. 2.3 2.8 0.2 0.5 -75 96.7 El Salvador .58 54 .1.1 570 21 26.9 27.3 11.7 12.1 Etl.4 60.6 Eritree 87 82 2.5 794 101 . 3.9 .. 0.0 . 96.1 Estoni'a 30 31 0.0 40 42 . 26.7 . 0.4 . 73.0 Ethiopia 90 83 2.3 508 1.000 . 9.9 .. 0.6 89.5 Finland 40 34 -06 83 305 7. . . 0.0.. 90 France 27 25 0.1 80 550 31.8 33.3 2.5 2.1 65.7 64.6 Gabon 50 21 -2.0 78 258 1.1 1.3 0.6 0.7 98.2 98.1 Gambia. The 80 69 2.9 421 10 15.5 19.5 0.4 0.5 84.1 80.0 Georgia 48 40 -0.6 283 70 . 11.2 . 4.1 . 84.7 Germany 17 13 -1.3 91 349 34.4 33.9 1.4 0.7 64. 1 65.5 Ghana 69 63 2.6 398 228 8.4 12.5 7.5 7.5 84.2 80.0 Greece 42 40 0.3 150 129 22.5 21.9.. 7.9 8.5 596.6 69.6 Guatemala 63 61 2.4 471 108 11.7 12.5 4.4. 5.0. 83.9 82.4 Guinea 81 69 1.8 542 246 2.9 ..3.6 1.8 2.4 95.4 94.0 Guinea-Bissau 83 77 1.7 294 28 9.1 10.7 1.1 1.8 89.9 87.6 Haiti' 76 66 1.1 885 28 19.8 20.3 12.5 12.7 67.7 67.0 Honduras 65 49 1.5 178 112 13.9 .15.1 1.8 3.1. 84.3 ..81.7 114 2000 World Development Indicators 3.10 Rural population Rural Land area Land use population density peopia per Permanent average sq. km of theLsand Arabia land cropland Other % of total annual % growtv 'arele land sq. km % of land area % of land area % of land area 1980 :1998 1980-98 1997 1997 1.980 1997 1980 1997 1980 1997 Hungary 43 36 -1.3 77 9 2 54.4 52.2 3.3 2.5 42.2 45.3 India 77 72 1.6 431 2,973 54.8 54.5 1.8 2.7 43.4 42.9 Indonesia 78 61 0.4 696 1,812 9. 9.9 4.4 7.2 85.6 82.9 Iran, Islamic Rep. 5 0 39 1.2 137 1,622 8.0 10.9 0.5 1.0 91.5 88.0 Iraq 35 29 1.9 134 437 12.0 11.9 0.4 0.8 87.6 87.3 Ireland 45 41 -0.1 114 69 1s.1 19.5 0.0 0.0 83.9 80.5 Israel 11 9 1.3 15 1 21 15.8 17.0 4.3 4.2 80.0 78..8 Italy 33 33 0.1 231 294 32.2 28.2 10.0 9.0 57.7 62.8 Jamaica 53 45 0.1 666 11 1 2.5 16.1 9.7 9.2 77.8 74.7 Japan 24 21 -0.2 696 377 11.4 10.4 1.6 1.0 87.0 88.6 Jordan 40 27 2.1 478 89 3.4 2.9 0.4 1.5 96.2 95.6 Kazakhstan 46 .44 0.1 23 2,671 11..2......... 1 . .. 88.7 Kenya 84 69 2.1 498 569 13.7 7.0 0.8 0.9 92.5 92.1 Korea, Daem. Rep 43 40 1.2 545.. 120 13.4.. 14.1 2.4 2.5 84.2 83.4 Korea. Rep. 43 20 -3.5 542 99 21).9 17.5 1.4 2.0 77.8 80.5 Kuwait 10 3 -6.7 832 18 0.1 0.3 .. 0.1 .. 99.6 Kyrgyz Republic 62 66 1.8 226 192 . 7.0 0.4 . 92.6 Lao PDR 87 78 1.9 474 231 2.9 3.5 0.1 0.2 97.0 96.3 Latvia 32 .31 -0.3 42 62 .. 29.0.. .......0.5...... 70..5 Lebanon 26 11 -2..9 268 10 20.5 17.6 8.9 12.5 70.6 69.9 Lesotho 87 74 1.5 461 30 9.6 10.7 Libya 31 13 -1.7 39 1,760 10' 1.0 0.2 0.2 98.8 98.8 Lithuania 39 32 -0.6 40 65 .. 45.5 .. 0.9 .. 53.6 Macedonia, FYR 47 39 -0.8 129 25 .. 23.9 . 1.9 . 74.1 Madagascar 82 72 2.0 399 582 4.3 4.4 0.9 0.9 94.8 94.7 Malawi 91 78 2.2 512 94 12.3 16.8 0.9 1.3 85.8 81.8 Malaysia 58 44 1.2 534 329 Cl;.0 5.5 11.6 17.6 85.4 76.9 Mali 82 71 1.9 161 1,220 1.6 3.8 0.0 0.0 98.3 96.2 Mauritania 73 45 0.0 233 1,025 0.2 0.5 0.0 0.0 99.8 99.5 Mauritius ~ 58 59 1.1 679 2 49.3 49.3 3.4 3.0 47.3 47.8 Mexico 34 26 0.5 98 1,909 121 13.2 0.8 1.1 87.1 85.7 Moldova 60 54 -0.2 130 33 .. 54.1 .. 12.1 .. 33.8 Mongolia 48 38 1.2 73 1,567 C'.8 0.8 . 0.0 . 99.2 Morocco 59 46 0.6 145 446 16.6 19.6 1.1 1.9 82.3 78.5 Mozambiqu 87 62 '-0.2 359 784 3.6 3.8 0.3 0.3 96.1 95.9 Myanmar 76 73 1.3 338 658 14.6 14.5 0.7 0.9 84.8 84.6 Namibia 77 70 2.2 140 823 C-8 1.0 0.0 0.0 99.2 99.0 Nepal 94 89 2.3 686 143 16.0 20.3 0.2 0.5 83.8 79.2 Netherlands 12 11 0.2 188 34 23.3 26.5 0.9 1.0 75.8 72.4 New Zealand 17 14 0.2 35 268 9.3 5.8 3.7 6.4 86.9 87.8 Nicaragua 50 45 2.1 85 121 9.5 202.2 1.5 2.4.. 89.1 77.4 Niger 87 80 2.9 159 1,267 2.8 3.9 0.0 0.0 97.2 96.1 Nigeria 73 58 1.6 245 911 30.6 31.0 2.8 2.8 66.6 66.3 Norway 30 25 -0.4 125 307 2.7 2.9 Oman 69 19 -3.0 2,967 212 0.1 0.1 0.1 0.2 99.8 99.7 Pakistan 72 64 2.0 395 771 25.9 27.3 0.4 0.7 73.7 72.0 Panama 50 44 1.3 242 74 5.8 6.7 1.6 2.1 92.5 91.2 Papua New Guinea 87 83 2.0 6,260 453 0.0 0.1 1.1 1.3 98.9 98.5 Paraguay 58 45 1.5 107 397 4.1 5.5 0.3 0.2 95.6 94.2 Peru 35 28 0.7 187 1,280 25 2.9 0..3 0.4 972.2 -96.7 Philippines 63 43 0.4 634 298 14 5 17.2 14.8 14.8 70.8 68.1 Poland 42 35 -0.5 98 304 48 0 46.2 1.1 1.2 50.9 52.6 Portugal 71 39 -3.3 187 92 26 5 23.5 7.8 8.2 65 .7 68.3 Puerto Rico 33 26 -0.4 3,008 9 5 6 3.7 5.6 5.1 88.7 91.2 Romania 51 44 -0.9 108 230 42 7 40.4 2.9 2.6 54.4 57.0 Russian Federation 30 23 -1.2 27 16,889 . 7.5 .. 0.1 92.4 2000 World Development Indicators 115 3.1 Rural population Rural Land area Land use population density people per Permnanent average sq. km of thoosand Arable land cropland Other % of totel annual % growth arable land sq. km % of land area % of land area % of land area 1.980 1998 1980-98 1997 199 1980 1997 1980 1.997 1980 1.997 Rwanda 95 94 1.9 874 25 30.8 34.5 10. 12.2 53.9 53.4 Saudi Arabia 34 15 -0. 1 87 2,150 0.9 1.7 0.0 0.1 939.1 98.2 Senegal 64 54 1.7 216. 193 12.2 11.6 0.0 0.2 87.8 88.2 Sierra Leone 76 65 1.4 639 72... 6.3 6.8. 0.7 0:.8 . 93.0 92.4 Singapore 0 0 .. 0 1 3.3 1.6 9.8 86.9 Slovak Republic 48 43 -0.3 156 48 .. 30.7 .. 2.6 .. 66.6 Slovenia 52 50 0.0 428 20 .. 11.5 .. 2.7 .. 85.8 South Africa 52 47 1.7 121 1,221 10.2 12.6 0.7 0.8 89.1 86.7 Spain 27 23 -0. 63 499 31.1 28.7 9.9 9.7 59. 61..6 Sri Lanka 78 77 1.2 1.652 65 13.2 13.4 15..9.. 15.8 70.9 70.8 Sudan 80 66 1.1 111 2,376 5.2 7.0 0.0 0.1.. 94..8 92.9 Sweden 17 17 0.4 53 412 7.2 6. Switzerland 43 32 -1.1 545 40 9.9 10.6) 0.5 0.6 89.6 88.8 Syrian Arab Republic 53 46 2.4 146 184 28.5 26.0 2.5 4.1 69.1 70.0 Tajikistan 66 73 3.2 574 141 .. 5.4 .. 0.9 .. 93.7 Tanzania 85 70 2.0 714 884 2.5 3..5 1.0 1.0 96.5 95..5 Thailand 83 79 1.3 281 511 32.3 33.4 3.5 6.6 64.2 60.0 Togo 77 68 2.4 143 54 36.8 38.1 6.6 6.6 E56.6 55.3 Trinidad and Tobago 327 -.9 466 136 14.6 9.0 9.2 .7.4 76.2 Tunisia 49 36 0 .4 116 155 20.5 18.7 9.7 12.9 E9 .7 68.5 Turkey 56 27 -2.1 67 770 32.9 34.5 4.1 3.4 E3.0 62.1 Turkmenistan 53 55 3.3 158 470 .. 3.5 .. 0.1 . 96.4 Uganda 91 86 2.6 349 200 20.4 25.3 8.0 8.8 71.6 65.9 Ukraine 38 32 -0.7 50 579 .. 57.1 .. 1.7 .. 41.2 United Arab Emirates 29 15 1.7 991 84 0.2 0.5 0.1 0.5 99.7 99.0 .ntd igdom 11 11 0.0 99 242. 28.7.. 26.4 0..3 0.2. 71.1 73.4 United States 26 23 0.3 35 9,159 20.6 19.3 0.2 0.2 79 .2 80.5 Uruguay -~~15 ...9 .-1.9 25 175 8.0 7.2 0.3 0.3 91.7 92.5 Uzbekistan 59 62 2.7 329 414 .. _10.8 .. 0.9 .. 88.3 Venezuela, RD 21 14 0.1 120 882 3.2 3.0 0.9 1.0 9)5.9 96.0 Vietnam 81 80 2.1 1,071 325 18.2 17.4 1.9 4.7 79.8 77.9 West Bank and Gaza.. . . .. . . . Yemen. Rep. ~ 81 76 3.7 850 528 2.6 2.7 0.2 0.2 97.2 97.1 Yugoslavia, FR (Serb./Mont.) 54 48 .-0.2 138 102 .. 36.3 . 3.4 .. 60.2 Zambia 60 61 3.0 109 743 6.9 7.1 0.0 0.0 93.1 92.9 Zimbabwe 78 66 2.0 249 387. 6..4 8.0 0.3 0.3 934.4 917.7 . 1 . ...... . Low income 78 70 1.2 573 41,383 11.5 12.4 0.9 1.4 87.7 86.2 Excl. China & India 78 69 1.7 576 28,963 7.0 7.8 1.0 1.3 92.0 90.9 Middle income 44 35 0.2 378 57.873 7.1 8.7 1.2 1.0 91.7 90.3 Lower middle income 49 42 0.7 449 36.096 7.5 9.2 1.3 0.8 91 .2 90.0 Upper middle income 37 23 -0.9 188 21.777 6.9 7.9 1.1 1.3 32.0 90.8 Low & middle income 68 59 10.0 539 99,257 9.4 10.3 1..0 1.2 39.5 88.6 East Asia & Pacific .78 66 ..0.6 .688 15.968. .10.0 12.0 1.5. 2.6 38.5 85.4 Europe & Central Asia 41 34 -0S 123 23,844 38.6 11.9 3.1 0.4 58.3 87.7 Latin America & Carib. 35 25 .0.1 253 20,064 5.8 6.7 1.1 1.3 33.1 92.0 Middle East & N. Africa 52 -43 1.5 522 10.995 4.4 5.2 0.4.. 0.7 35.1 94.1 South Asia 78 72 1.7 531 4.781 425 42.4 1.5 2.1 56.1 55.5 Sub-Saharan Africa 77 67 2.0 378 23,605 5.4 6.4 0.7 0.9 33.9 92.7 High income 25 .23 .--0.2 190 30,925 12.0 11.7. 0.5 0.5 857.5 .87.8 Europe EMU 26 22 -05 139 2,307 27.6 26.7 4.7. 4-.3 67.7 639.0 a. ncludes Luxembourg. b. includes Taiwan, China. 116 2000 World Development Indicators 3.1 Indicators of rural development are sparse, as few indi- * Rural population is calculated as the difference cators are disaggregated by a rural-urban breakdown between the total population and the urban population Rural areas; hold a shrinkling sharce (for some of these indicators see tables 2.7, 3.5, and of the population everywhere ... (see Definitions for tables 2.1 and 3.10). * Rural 3.10). This table shows indicators of rural population population density is the rural population divided by and land use. Rural population is approximated as the Rural population as % of total the arable land area. * Land area is a country's total midyear nonurban population. 100 * 1980 area, excluding area under inland water bodies, national The data in the table show that land use patterns 80 * 1998 claims to continental shelf, and exclusive economic are changing. They also indicate major differences in zones. In most cases the definition of inland water bod- resource endowments and uses among countries. 60 ies includes major rivers and lakes. (See table 1.1 for True comparability is limited, however, by variations the total surface area of countres.) * Land use is bro- in definitions, statistical methods, and the quality of 40*ken into three categories. * Arable land includes data collection. For example, countries use different 20 i I fl land defined by the FAO as land under temporary crops definitions of rural population and land use. The Food I 1 (double-cropped areas are counted once), temporary and Agriculture Organization (FAO), the primary com- - meadows for mowing or for pasture, land under mar- piler of these data, occasionally adjusts its defini- income income income ket or kitchen gardens, and land temporarily fallow. Land tions of land use categories and sometimes revises abandoned as a result of shifting cultivation is excluded. earlier data. (In 1985, for example, the FAO began to Source: Table 3.1. * Permanent cropland is land cultivated with crops that exclude from cropland land used for shifting cultiva- occupy the land for long periods and need not be tion but currently lying fallow.) And following FAO prac- replanted after each harvest, such as cocoa, coffee, tice, this year's edition of the Worlo Development and rubber. This category includes land under flower Indicators, like last year's, breaks down the category : ing shrubs. fruit trees, nut trees, and vines, but cropland, used in previous editions, into arable land excludes land under trees grown for wood or timber. and permanent cropland. Because the data reflect dweilers continue to grow in numnber * Other land includes forest and woodland as well as changes in data reporting procedures as well as logged-over areas to be forested in the near future. Also actual changes in land use, apparent trends should Billions included are uncultivated land, grassland not used for be interpreted with caution. 3 . pasture, wetlands, wastelands, and built-up areas- Satellite images show land use that differs from that 3.0 * 1998 residential, recreational, and industrial lands and areas given by ground-based measures in both area under 2.5 covered by roads and other fabricated infrastructure. cultivation and type of land use. Furthermore, land use 2.0 data in countries such as India are based on report- 15 Data sources ing systems that were geared to the collection of tax 1.0 revenue. Because taxes on land are no longer a major The data on urban population shares used to estimate source of government revenue, the quality and coverage 0. rural population come from the United Nations Popula- of land use data (except for cropland) have declined. 0 L M f W tion Division's World Urbanization Prospects: The 1998 Low Middle High Wuorld Data on forest area, aggregated in the category other, income income income Revision. The total population figures are World Bank esti- may be particularly unreliable because of differences mates. The data on land area and land use are from the in definitions and irregular surveys (see About the data Source: Tables 2.1 and 3.1. FAO's electronic files and are published in its Production for table 3.4). Yearbook. The FAO gathers these data from national agencies through annual questionnaires and by analyz- ing the results of national agricultural censuses. 2000 World Development Indicators; 117 3.2 Agricultural inputs Arable land Irrigated land Land under Fertilizer Agricultural machinery ..real consumption production Tractors Tractors hundreds of grams per 1,000 per 100 hectares % of thousand per hectare agricu tural hectares of per capita cropland hectares of arable and workers arable land 1979-8i1±995-97 1979-8±. 1995-97 1979-81. 1996-98 1979-al. 1995-97 I ±979-81 1.995-97 1979-81. 1995-97 Albania 0.22 0.18 53.0 48.4 367 225 1,556 133 15 11 173 147 Algeria 0.37 0.26 3.4 6.9 2,968 2,785 277 80 27 41 68 122 Angola 0.41 0.26 2.2 2.1 705 837 49 16 4 3 35 34 Argentina 0.89 0.71 5.8 6.3 11,099 10,455 4 295 12 190 73 112 Armenia .. 0.13 .. 5. . 190 155 68291 Australia 2.97 2.79 3.5 5.1 15,986 16,356 269 406.751 . 700 75 64 Austria 0.20 0.17 0.2 0.3 1,062 838 2,615 1,688 945 1,567 2 084 2,507 Azerbaij.a.n..... . 0.21 .. 74.9 .. 618 178 33 181 Bangladesh 0.10 0.06 17.1 43.4 10,823 10,983 459 1,453 0 0 5 6 Belarus .. 0.6 . 18 .. 2,412 10112417 Belgium' . . 1.7 3.8 426 323 5,323.4150 91 1,156 146 1,496. Benin 0.39 0.25 0.3 0.8 525 754 12 240 0 0 1 1 Bolivia 0.35 0.23 8.6 4.1 559 750 23 54 4 4 21 28 Bosnia and Herzegovina .. 0.14 .. 0.3 .. 194 140 257 580 Botswana 0.44 0.23 0.5 0.3 153 167 32 91 9 20 54 173 Brazil 0.32 0.33 3.3 4.8 20,612 18,116 915 927 31 57 139 137 Bulgaria 0.43 0.51 28.3 18.0 2,110 1,869 2,334 441 66 63 161 62 Burkina Faso 0.39 0.33 0.4 0.7 2,026 3014 26 .90 0 0 0 6 Burundi 0.22 0.12 0.7 1.3 203 209 11 45 0 0 1 2 Cambodia 0.29 0.33 5.8 7.1 1.241 1.959 68 23 0 0 6 3 Cameroon 0.68 0.43 0.2 0.3 1.021 1,036 56 ..55 0 0 1 1 Canada 1.86 1.53 1.3 1.6 19,561 19,360 416 587 824 1,642 144 163 Central African Republic 0.81 0.57 . .. 194 148 5 2 0 0 1 1 Chad 0.70 0.46 0.2 0.6 907 1,758 9 33 0 0 1 1 Chile 0.34 0.14 31.1 54.3, 820 633 338. 2,082. 43 49 86 124 China -0.10 0 .10. 45. 1 37.7 94.647 92,911 1,494 2,882 2 1 76. 55 Hong Kong, China 0.00 0.00 37.5 302 0 0 0.....O. 0 10 7 Colombia 0.13 0.05 7.7 23.7 1,361 1,208 812 2,783 8 6 77 114 Congo, Dem. Rep. 0.25 0.15 0.1 0.1 1,115 2,088 12 10 0 0 3 4 Congo, Rep. 0.08 0.05 0.6 0.5 19 5 28. 238 2 1 55 52 Costa Rica 0.12 0.07 12.1 24.7. 136 71.2650 7,021 22 22 210. 246 C6te dIlvoire 0.24 0.21 1.0 1.0 1,008 1,605 261 280 1 1 16 13 Croatia .. 0.29 .. 0.2 G. 44 1,050 14 28 Cuba 0.27 0.34 22.9 20.3 224 254 2,024 593 78 95 259 207 Czech Republic .. 0.30 .. 0.7 ,. 1,636 1,082 164 276 Denmark 0.52 0.44 14.5 20.5 1,818 1,525 2.453. 1,890 93 .1,16 708 626 Dominican Republic 0.19 0.13 11.7 17.2 149 142 572 922 3 4 20 17.. Ecuador 0.20 0.13 19.4 8.1 419 1,059 471 814 6 7 40 57 Egypt, Arab Rep. 0.06 0.05 100.0 99.8 2.007 2,581.2664.3,899 .4 11 . 158. 31 El Salvador 0.12 0.10 13.7 14.5 422 426 1,376 1,450 5 5 59 54 Eritrea .. 01 . 7.0 .. 313 . 109 .. 0 . 19 Estonia .. 0.77 .. 0.3 .. 323 .. 244 .. 495 .. 443 Ethiopia .. 0.17 .. 1.8 .. 7,181 .. 150 .. 0 .. 3 Finland 0.50 0.42 .. 3.0 1,190 1106.2,022 1,483 721 1147 824 923 France 0.32 0.31 4.6 8.5 9.804 9,095 3,260 2,742 737 1,236 836 717 Gabon 0.42 0.29 0.9 1.4 6 18 20 8 5 7 43 46 Gambia, The 0.26 0.16 0.6 1.0 54 103 136 51. 0 0 3 3 Georgia .. 0.14 .. 43.3 .. 384 .. 458 .. 29 .. 214 Germany 0P.1.5 0.14 3.7 3.9 7,692 6,921 4,249 2,394 624 991 1,340 1,027 Ghana 0.18 0.16 0.2 0.2 902 1,292 104 57 1 1 18 15. Greece 0.30 0.27 24.2 34.5 1,600 1,307 1,927 1,777 120 277 485 808 Guatemala 0.19 0.13 5.0 6.6 716 613 726 1,394 3 2 32 32 Guinea 0.18 0.13 7.9 6.4 708 719 10 45 0 0 3 Guinea-Bissau 0.32 0.27 6.0 4.9 142 128 24 10 0 0 1 1 Haiti 0.10 0.08 7.9 9.9 416 455 62 143 0 0 3 4 Honduras 0.44 0.29 4.1 3.6 421 507 163 598 5 7 21 29 118 2000 World Development indicators GTTY Sjo0E!!PuI 4USWdOIGAOO] P[.JOM 000Z 88 ~~~~~~901 081 E£1T9 a't' 98'0 uoi;rnapej uerssnu 6.rGr r toT-0r Orr C. 66;.Ž9.Pbs. .V.+6 . Ž90.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..... .... IM. SZ -t~ TT oft EEi ~z "-o i~6 n:... ft . Ponea2d 669 9 . .... V ~ ET 4L && ".'"'C`ij` 66 oz it Ei4 ... se ... s~6 '"'u bt~ to.ida~v n.MNn'de .t' .. 916 65+4 Žds~~~~~~~~~~~ .. ... . - El . [Ž2 ..i..to .... Ž6;.1Ž . Pal . Ž9 . P 6+ . . 9~~~~~~~~~~~~~~~~~~~ 1'6 1770.ZSo 2eLuned v 6 . . 9 . 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TSi.66.96:1.8 1f -66 6Ž; .0Ž.id 900 1+;.Ž16 . 565Ž.sŽŽ6.~~~~~~~~~88, 61:8.66:56+f6.EL681.Wa.160I0 Tt.t.dS.ŽZ .Ž6.666.+8Ž6 . Ž1.668 . 6a.80 88.+6 . 9 -i 6t 616 989 9 9t T 26:6.Ž.+1 . 10.t7f. T9 T99 69 128 906: 8286.6+6.56.26 28.0 ' aw ±6-SBST 18-6t6T t6.-........-...6.. ..- t .6-66 .B6 6 ....... . B-A. ......B-.B ...6 T B-A uoilciwnSuoo 190160~~~Tffbei 0. ~~~~~~~~~~~~~~~~......... ... sioleo!pul 1,adoja~aCg plIOM GOOF OZ1 ,~jnoqwaxn1 sapnLoul e -- - Z Trr r T -c'- .~J r T T ~ ~ - Tr T' T;'Tr: ~ - :' OTT 06 00 06 Tt' L. eg80 . 09`6t'8826t'.OLST.6TT.L6'O . 600 . 'qljj~ 'e F3FwJV' uflPlj T -T r r :.r -Fr _ ri F T' T-. FT ETT T%: r - --~ ~ ~ ~ ~ ~ ~~~----TT rTr:TTT : -1: I r Fi F: FT r . . . , I - -, - F :'~~ ''V '.F.': '.'V'>F :1 ~ ....1.. I.' '.~.' ... ... . .. .. ......... . R V tOT 2.9LE988 Ft7S'CL 8)L'TL t LZT EOT . 0' t0E0 aLWooui alpp!w jGddn 06 96L 6CV2.6 t2.6 P7T S'T 060o 66z' 9UJO~U ipfij E . . 6T66 00 tO 6T0t6 0,06 Z,9T V 60 £~~~~~~~~~ OSLTT '69s YocL:9L 9L176z &66 906.6ST*o. Siwooui''6 mol * I ~~~ ~~I I ' TO 99 L . 900.s-609.096,T.009gt.Lo.£.2T60.900.-6qqw TT 6 6 EOT.00T . 662.Z 060 6-0 tVo 900 6 eiqwJe7 82.T 8£ , T 990£ . 66L2.£9'26-.TC T064 L6aO TTO WFL LOT.LET 60 0o 9TT'T.T6½.7L t78 LO.9L.EZT'O.6T'O . 2 9fZeG ELL £06 . 66t,VT O:Zi.T . 66T 6ESI OE9P'L . 6: BOT£90 8.0 ae~is Pa!unl & oT £*8 06£ 0L'L £S'FT 664 § :, TO .,W q plu 6TT 68 ' 86.2..TZ TL q69:0 " aulJejfn 6S TE. ;' 0 0 LLz O ,T 92. 9T £ 86K 96r 90oe peppp .698.ILT.~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~LPTTT 9OT.£6.9F. ;r.oo 990 OL 000 Pdo . . 2.62 . . LT~~~~~~~~~~~~~~~~. ' ,_ . ... . .... 99T C 0 go66.66 .666.66"o. .606.66 . 690 9ST t966 169 060, 0817z £ Fe 6~ ~ e6 0- 5666 PPTaLT .. P& u"IezpiM's £60.iz zvt 806 . . P .. 66zEnt, 6S ZT .:; il 6. 6- UFPela!MS Z 9 9 ~~~~~~T k o1-. -660 . T . 6P .... . . .... .. P6T . . . .: . 12.2 . 6~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ..P~~~~~~~~~~d 3IIqnde~~~~~~~~~~~~~~~~.. .. ..... 6t, 6 0. 9TT OT . 62.T' .PTI . 6 TI8OUG &6S66 TS-Z6 ~ 6S66 S66 696 B66 6 6T T8-6L6T L6-S66T......6. 260S66 OID-6odLuT peel E)Iqeie FiFiJom pue a qeje lo seJeloeL] puejd=J eFpdeo :8d ~o sae:eoaq IeJljno~Iji aejeoe] :8d pUFeeoql 100% so:ejoaq 001 :ed oToT'a swemi lo spa:puny sJicle.:I SiOjoeJI uoD,ifnpoJd uo!ldwnlsuoo og fAJeu!LIO u jonIBflTift!J IOZ!I!4ja= jepun puel pu9l PawowJI puel eiqeiy 3.2 Agricultural activities provide developing countries with - _ * Arable land includes land defined by the FAO as land food and revenue, but they also can degrade natural under temporary crops (double-cropped areas are Fertilizer constttmption has more than resources. Poor farming practices can cause soil ero- doubled in low-inome countries .. . counted once), temporary meadows for mowing or for sion and loss of fertility. Efforts to increase pro- pasture, land under market or kitchen gardens, and land ductivity through the use of chemical fertilizers. Hundreds of grams per hectare of arable land temporarilyfallow. Land abandoned as a result of shift- pesticides, and intensive irrigation have environ- 1,500 - - - ingcultivationisexcluded. * Irrigatedlandrefersto U 1979-8l mental costs and health impacts. Excessive use of * 1995-97 areas purposely provided with water, including land irri- chemical fertilizers can alter the chemistry of soil. , gated bycontrolled flooding. Cropland refers to arable Pesticide poisoning is common in developing coun- land and land used for permanent crops (see table 3.1). tries. And salinization of irrigated land diminishes soil * Land under cereal production refers to harvested fertility. Thus inappropriate use of inputs for agri- 500 areas, although some countries report only sown or cul- cultural production has far-reaching effects. tivated area. * Fertilizer consumlytion measures the This table provides indicators of major inputs to . quantity of plant nutrients used per unit of arable land. agricultural production: land, fertilizers, and agri- 0 - . r Fertilizer products cover nitrogenous, potash, and Low Lower Upper High Wodd cultural machinery. There is no single correct mix of income middle middle income phosphate fertilizers (including ground rock phos- inputs: appropriate levels and application rates vary income income phate). The time reference for fertilizer consumption by country and over time, depending on the type of Source: Table 3.2. is the crop year (July through June). * Agricultural crops, the climate and soils, and the production machinery refers to wheel and crawler tractors (exclud- process used. The data shown here and in table 3.3 ing garden tractors) in use in agriculture at the end of are collected by the Food and Agriculture Organiza- the calendar year specified or during the first quarter tion (FAO) through annual questionnaires. The FAO - of the following year. tries to impose standard definitions and reporting methods, but exact consistency across countries Data soures and over time is not possible. Data on agricultural Hundreds of grams per hectare of arable land employment in particular should be used with cau- 2,29-The data in the table are from electronic files that the :2,329 * 1979-81 tion. In many countries much agricultural employment ' X 995-97 FAO makes available to the World Bank. Data on is informal and unrecorded, including substantial 1,500 arable land, irrigated land, and land under cereal pro- work performed by women and children. duction are published in the FAO's Production Yearbook. Fertilizer consumption measures the quantity of 1,000 plant nutrients in the form of nitrogen, potassium, and phosphorous compounds available for direct 500 application. Consumption is calculated as production I i I plus imports minus exports. Traditional nutrients- 0 _ i . . .I Ii animal and plant manures-are not included. 9P 4 ,ffy Because some chemical compounds used for fer- s cf -G0 e tilizers have other industrial applicatons, the con- . , sumption data may overstate the quantity available for crops. source: rable 3.2. To smooth annual fluctuations in agricultural activ- ity, the indicators in the table have been averaged over three years. 2000 World Development Indicators 1 21 s~ojeoipuj IuawdoleA~GC PJiOM OOOZ z 97 91£ 0~WT V1.11 LIT 89 9911T T ~~~ .TL - L .~~~~~~~~~~~~ 1- 2..'L I rTT 1: 7H 69t'66 :~~~~~~~~~~~~~~~~~~~~~~~~~~~IT .'~~~~~~~~~~~~~~~~~~ : - I 'Z .i... .. .. .... .... I... ~T rT . 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L6si:6 iE89g 031,668 ol1Jafnd 19L T L86l 9891~~~~~~~Jvi Ot'S* 096i 198 6L9~ T6 98 U~ ~~~~~~~~~.6:d~~~~~~~~~~~~~~~~~~~~~t;T .~~~~~~~~~~~~~~-:14 .16...... 61 ~ 6a86 9 m9 encFe 009'19 8t70'L1 ;9~~~~~~~~8 ~ ~ ~~:9O~~~ ...... ... 9*7weu.6.d L96V1 696 l99'6 9186 I2TMj9ONT -0 i'll'~~~~~~~~~~~~~~~~~~~~~~~~~~~Li ........ . .W'e 991 kl- W~e '`6 8:60 61.9Y T6 JO8N . .... ; . 6 i . ...El '.. . to..-6ZS V8T. ~nii ~....... . ..2)~ E66,t~ ;11. 66 99 V9 VL Vfl 09.ueN ... . . - ... ....fT Eli 9 896 L99 ... 6oo .6it'; 9*0`~biweo T91'T lTE6 L46 EL9 896 i196 9~88 1-88 V19 9887t .!IofU0IA ........ . ..... . ...1 .... .... .. 9........ ......9 ..., .6sg'q L90'9 E' 0909 9991 LO.86 OTT 0'68 E'O '6snlqpneV 811 .. 6~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ . 69T..*~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~T.t'L9.~~~~~~~~~~~~~~~~~~~~~ ...... 98~~~~ L6 ~~~~~96~~~~ 990o 9-69 9808 O-. f6- i6 ... .. ... . ..........6.. . ... .. ... . . . .. . . . - . . ...... .. .. . . 9119 8tL86 '1 'T 0996 69'tT 6L 98T 0O1 2iUfl1i 60VL1 998~~~~~~~~~~~iv 90'ET 9*0.§i i 6 f69 L9:1.6*9.udue- 6eT~ . . i ~ 6 '6 .64 i16£ 2!AW T1 98L'9 0989~~~`- 9968 69'8Z l191TOC 9LT 690. LLeA! C6~, --46 ~ 0911 t'69 -d66- -i fyt80 1t7 Z08911 9'T 0f9T 066£ do9; 099 LlT 996 1900l £666 98.6 990; 096 ITO; 810 800 90~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ . . ................... ........ 69t1. 88.1 9;;t 08 OLZOT 898 . 8&t 186nd,gj z&M ....... . . ..... ...... '8........9.9 9.....9.. . .6 60... . 6..96... .e!6 ii 86-966T T8-6L6T 86-966~~~ TS-8L6T 86-966T t8-6L6~~~ 86-966T TS-6~L6T 86-96T 8B-6L 6ti~ peppe eCIRA~~~~~~~~~~~~~~~~~~~~~~r~- 6spz~ . . . . .. . ... .. . ... .... .. .... . .. ......... . .. . . .... . .. ....... .. .... ..I..... . .. .. .. .... .. .. ... . . ... .... . .... .... ... . .. .. . .. . . .. .... . . .. .. . .... . . ... ...... . ...... . . .. .... .-V . .... . t, 6LT'T CC6 TZ9 9,99T Z'TSX9pLg XOP L XG0PU9!9 epo z~iAIbc s6`4i PielA uo6 r old uI-1npojuedi"epr' .I......If6l ....... ieeJa 6 O0Vi AI 6p0011 do-i W . S&E E& kE '6si '-~ ~Tt 1;6 -6 . ... 3.3 Crop Food Livestock Cereal Agricultural production production production yield productivity index index index Agriculture value added kilograms per worker 1989-91 = 100 1989-91 100 1989-91 = 100 per hectere 1995 $ 1979-8:1 1996-98 1979-81. 1996-98 1979-81 1996-98 1979-81 1996-98 11979-9 1996-98 Rwanda 88.9 75.5 89.7 79.1 81.0 95.1 1,134 1,218 316 212 Saudi Arabia 27.2 90.7 26.7 78.8 32.8 129.3 820 3,880 2,167 10.742 Senegal 77.6 88.7 74.2 100.4 65.1 134.3 690 719 341 320 Si:erra Leone 80.3 99.2 84.5 99.5 84.1 108.1 1,249 1,223 368 411 Singapore 595.0 50.6 154.3 31.8 173.7 34.1 ... 13,937 42,851 Slovak Republic 74.7 ... . 4.209 .. 3,379 Slovenlia .. 105.5 .. 100.3 .. 101.7 .. 5,435 .. 26,521 South Africa 95.0 104.6 92.6 100.8 89.7 90.9 2,105 2,261 2,819 3,884 Spain 83.0..' 108.7 82.1 110.1 84.2.. .116.2 1,986 3,173 .. 13,499 Sri Lanka ..99.3 ..108.2 98.3 109.1 93.2 131.8 2.462 3,103 648 726 Sudan 131.1 174.8 105.4 156.0 89.3 140.8 645 569 Sweden 92.0 96.9 100.1 100.8 103.8 103.3 3,595 4,687 Switzerland 95.5 99.9 95.8 95.8 98.8 93.8 4,883 6,709 Syrian Arab Republic 100.4 162.1 94.2 148.7 72.2 120.2 1,156 1,586 Tajikistan ...... . . 64.5 .. 59.0 .. 41.5 .. 1,682 .. 396 Tanzania 82.2 96.8 76.9 100.0 69.3 113.9 1,063 1,288 .. 174 Thailand 79.0 111.8 80.4 112.6 65.4 130.1 1,911 2,466 630 92 Togo 70.4 140.0 77.0 135.9 51.9 125.4 729 876 345 539 Trinidad and Tobago 119.9 96.9 101.9 99.9 84.3 96.7 3,167 3.292 2,887 2,102 . . . .. I . .. . ........... . ........ . -7 .. - ... . . - .... ......... . ..... . - ........ Tunisia 68.5 116.4 67.6 121.4 63.7 129.6 828 1,241 1,743 2,959 Turkey 76.6 113.7 75.8 111.3 80.4 10 .3 1,869 2.196 1,852.1,851 Turkmenistan .. 56.7 .. 101.7 .. 113.8 .. 1,292 Uganda.67.5 110.6 70.4 107.1 84.8 115.7 1,555 1,280 .. 345 Ukraine . .. .. .... .63.8 .. 52.3 .. 50.9 .. 2,211 .. 2,544 United Arab Emirates 38.9 232.3 49.2 222.7 46.0 158.5 2,224 1,360 United Kingdom 80.1 104.2 92.0 99.7 98.1 97.1 4,792 6,891 United States 98.9 119.1 94.5 117.9 88.8 115.1 4.151 5,380 .. 39,523 Uruguay.86.6 148.0 86.9 130.8 85.-5 119.6 1,644 3,257 6.521 9,826 Uzbekistan 83.6 .. 109.8 .. 108..7 .. 2,292 .. 2,128 Venezuela, RB 76.5 109.0 80.3 114.4 84.9 112.4 1,904 2.959 4.D41 5.036 Vietnam 66.7 143.7 63.8 140.5 52.9 145.8 2,049 3,754 West Bank and Gaza . . . .. .. Yemen, Rep. . 82.3 116.2 75.0 120.7 68.9 129.0 1.038.. .902 .. 302 Yugoslavia, FR (Serb./Mont.) .. 93.3 .. 102.4 .. 112.9 .. 3,799 Zambia 65.7 95.5 74.0 104.5 86.2 116.3 1.676 1,584 328 209 Zimbabwe 77.9 118.7 81.9 101.9 84.5 99.2 1,359 1,283 307 347 .. . ......... . ......... . .... .~ Low income 733 121.0 68.7 138.7 71.9 135.5 1,097 1,267 .. 348 ExcI. China & India ....... 73.8 127.1 ... 1,069 1,251 Middle Income 74.4 134.7 80.2 123.1 69.4 168.5 1789 2358 Lower middle income 71.7 144.8 .. 126.1 57.8 208.2 1.724 2,084 Upper middle income 80.8 111.4 78.5 119.9 84.3 117.5 1,984 2,692 Low & middle income 74.1 130.3 71.7 134.7 70.0 160.7 1,419 1,828 568 East Asia & Pacific.. 69.6 132.1 67.0 152.1 496.6 .185.8 2,11 2729 .,.......... Europe & Central Asia 192.7 ..263.3 2,677 2,383 .. 2,186 Latin America & Carib. 82.1 118.5 80.5 123.9 82.2 121.6 1,842 243 ...... ...... .... ...... .... Middle East & N. Africa 68.5 126.8 70.1 137.9 65.0 125.0 925 1,412 South Asia 72.5 117.4 70.4 122.1 63.9 131-.1 .1.510 . 2099 265 356 Sub-Saharan Africa 77.1 114.1 78.8 124.3 92.6 121.8 895 1,143 418 379 High income 92.7 112.5 93.1 107.4 91.S 107.4 3,170 4,051. Europe EMU 90.1 ......... S. 150 91.9.. 100.9 95.0 100.2 4,035 ... ,673 a. Includes Luxembourg. 124 2000 World Development Indlcators _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ I 3 .3 The agricultural production indexes in the table are pre- tables 4.1 and 4.2 for further discussic n of the cal- * Crop production index shows agricultural production pared by the Food and Agriculture Organization (FAO). culation of value added in national accounts.) Agri- for each period relative to the base period 1989-91. The FAO obtains data from official and semiofficial cultural value added includes that from forestry and It includes all crops except fodder crops. The regional reports of crop yields, area under production. and live- fishing. Thus interpretations of land productivity and income group aggregates for the FAO's production stock numbers. If data are not available, the FAO should be made with caution. indexes are calculated from the underlying values in makesestimates. The indexes are calculated usingthe To smooth annual fluctuations in agricultural activ- international dollars, normalized to the base period Laspeyres formula: production quantities of each com- ity, the indicators in the table have been averaged 1989-91. The data in this table are three-year aver- modity are weighted by average international com- over three years. ages. However, missing observations have not been modity prices in the base period and summed for each estimated or imputed. * Food production index cov- year. Because the FAO's indexes are based on the con- ers food crops that are considered edible and that con- cept of agriculture as a single enterprise, estimates of The world's food production has tain nutrients. Coffee and tea are excluded because, the amounts retained for seed and feed are subtracted outpaced its population growth.. although edible, they have no nutritive value. * Live- from the production data to avoid double counting. The stock production index includes meat and milk from resulting aggregate represents production available index (1980 100) all sources, dairy products such as cheese, and eggs, 200 -- - for any use except as seed and feed. The FAO's indexes honey, raw silk, wool, and hides and skins. * Cereal may differ from other sources because of differences - yield, measured in kilograms per hectare of harvested in coverage, weights, concepts, time perods, calculation 150 land, includes wheat, rice, maize, barley, oats, rye, mil- methods, and use of international prices. 10 let. sorghum, buckwheat, and mixed grains. Production To ease cross-country comparisons, the FAO uses 100 data on cereals refer to crops harvested for dry grain international commodity prices to value production. only. Cereal crops harvested for hay or harvested These prices. expressed in international dollars (equiv- 50 green for food, feed, or silage and those used for graz- alent in purchasing power to the U.S. dollar), are ing are excluded. * Agricultural productivity refers derived using a Geary-Khamis formula applied to agri- 0°- to the ratio of agricultural value added, measured in cultural outputs (see Inter-Secretariat Working Group sg '. s S's l t constant 1995 U.S. dollars, tothe numberofworkers on National Accounts 1993, sections 16.93-96). This in agriculture. method assigns a single price to each commodity so -Food production that, for example, one metric ton of wheat has the same urce: abies 21 and 3.3. Data soures price regardless of where it was produced. The use of international prices eliminates fluctuations in the value The agricultural production indexes are prepared by the of output due to transitory movements of nominal FAO and published annually in its Production Yearbook. exchange rates unrelated to the purchasing power of : The FAO makesthese data andthe data on cereal yields the domestic currency. Unlike the International Com- and agricultural employment available to the World Bank . .. except in Sub-Saharan Afirica, whore parison Programme (ICP), the FAO calculates interna- food production has barely kept up with in electronic files that may contain more recent information tional prices only for agricultural products. Substantial populatiion growth than the published versions. For sources of agricultural differences may arise between the implicit exchange value added see table 4.2. rate derived by the ICP and that of the FAO. (For fur- Index (1980 1001 ther discussion of the FAO's methods see FAO 1986. For a discussion of the ICP see About the data for tables 150 4.11 and 4.12.) Data on cereal yields may be affected by a variety of reporting and timing differences. The FAO allocates pro- duction data to the calendar year in which the bulk of the harvest took place. But most of a crop harvested near the end of a yearwill be used in the following year. In gen- 0 - -- eral, cereal crops harvested for hay or harvested green e e 9e5 a 9 for food, feed, or silage and those used for grazing are s s s s s s s s e s excluded. But millet and sorghum, which are grown as feed for livestock and poultry in Europe and North Amer- =Population ica, are used as food in Africa, Asia, and countries of the Source: Tables 2.1 and 3.3. former Soviet Union. Agricultural productivity is measured by value added per unit of input. (See About the data for 2000 World Development Indicators 125 S ~3.4 Deforestation and biodiversity Forest area Average Mammals Birds Higher plants0 Nationally annual protected deforestation areas ft of ft of thousand total Threatened Threatened Threatened thoisand total sq. km land area sq. km ft change Speciea species Speciea species Species species sq. km land area 1995 1995b 1990-95 1-990-95 1996b :199gb 1996b 1996b 1997b 1997b 1995b 1995b Albania 10 38.2 0 0.0 68 2 230 7 3.031 79 0.8 2.9 Algeria 19 0.8 234 1.2 92 15 192 8 3,164 141 58.9 2.5 Angola 222 17.8 2,370 1.0 276 17 765 13 5,185 30 81.8 6.6 Argentina 339 124.4. 894 0.3. .320 27 897 41 9,372 247 46.6 1.7 Armenia 3 11.8 -84 -2.7 . 4 .. 53 . 7.4 Australia 409 5.3 -170 0.0 252 58 649 45 15,638 2,245 563.9 7.3 Austria 3 9 46.9 0 0.0 83 7 213 5 3,100 23 23.4 28.3 Azerbaijan 10 11.4 0 0.0 .. 11 8 28 4.8 5.5 Bangladesh 10 7.8 88 0.8 109 18 295 30 5,000 24 1.0 0.8 Belarus 74 35.5 -688 -1.0 . 4 221 4 . 1 8.6 4.1 Belgium ... . 58 6 180 1,550 2 0.8 Benin 46 41.8 596 1.2 188 9 307 1 2,201 4 7.8 7.1 Bolivia 483 44.6 5,814 1.2 316 24 1,274 27 17,367 227 156d.0 14.4 Bosnia and Herzegovina 27 531 0 0.0 .. 10 . 2 64 0.2 0.4 Botswana 139 24.6 708 0.5 164 5 386 7 2,151 7 105.0 18.5 Brazil 5,511 65.2 25,544 0.5 394 71 1,492 1-03 56,215 1.358 355.5 4.2 Bulgaria 32 29.3 -6 0.0 81 13 240 12 3,572 106 4.9 4.4 Burkina Faso 43 15.6 320 0.7 147 6 335 1 1,100 0 28.6 1. Burundi 3 12.3 14 0.4 107 5 451 6 2,500 1 1.4 5.5 Cambodia 98 55.7 1,638 1.6 123 23 307 18 5 28.6 16.2 Cameroon 196 42.1 1,292 0.6 297 32 690 14 8,260 89 21.0 4.5 Canada 2,446 26.5.-1,764 -0.1 193 7 426 5 3,270 278 921.0 10.0 Central Atrican Republic 299 48.0 1,282 0.4 209 11 537 2 3,602 1 51.1 8.2 Chad 110 8.8 942 0.8 134 14.. 370 3 1,600. 12 11L.9 9.1 Chile 79 10.5 292 0.4 91 16 296 18 5.284 329 141.3 18.9 China 1,333 143 866 0.1 394 75 1,100 90 32,200 312.... 598.1 6.4 Hong Kong, China 24 0 76 14 1,984 9 01.4 40.4 Colombia 530 51.0 2.622 0.5 359 35 1,695 64 51,220.712.93.6 9.0 Cogo D.. ... Rep ....... . . .. ..... .415 38 929 26 11,007 .78 101.9 4.5 Congo, Rep. . 195 57.2 416 0.2 200 10 449 3 6,000 3 15.4 4.5 Costa Rica 12 24.4 414 3.0 205 14 600 13 12,119 527 7.0 13.7 Cdte dIlvoire 55 17.2 308 0.6 230 16 535 12 3,660 94 19.9 6.3 Croatia lB 32.6 0 0.0 .. 10 224 4 6 3.7 6.6 Cuba 18 16.8 236 1.2 31 9 137 13 6,522 888 19.1 17.4 Czech Republic 26 34.0 -2 0.0 .. 7. 199. 6 81 12.2 15.8 Denmark 4 9.8 0 0.0 43 3 196 2 1,450 2 13.7 32.3 Dominican Republic 16 32.7 264 1.6 20 4 136 11 5,657 136 12.2 25.2 Ecuador ill 40.2 1,890 1.6 302 28 .1388 53 19,362.824 119.3 43.1 0 0.0 0 0.0 98 iS ....-. 153 11 206 8 . .. Egypt, Arab Rep. 2,07 . 82 ~~~- ........ .......... .. 08 . El Salvador 1 5.1 38 3.3 135 2 251 0 2,911 42 0.1 0 Eritree 3 2.8 0 0.0 112 6 319 3 . 0 5.0 5.0 Estonia 20 47.6 -196 -1.0 65 4 213 2 . 2 5.1 12.1 Ethiopia 136 13.6 624 0.5 255 35 626 20 6,603 163 55.2 5.5 Finland 200 65.8 166 0.1 60 4 248 4 1,102 6 18.2 6.0 France 150 27.3 -1.608 -1.1 93 13 269 7 4,630 195 58.8 10.7 Gabon 179 69.3 910 0.5 190 12 466 4 6,651 91 7.2 2.8 Gambia, The 1 9.1 8 0.9 108 4 280 1 974 1 0.2 2.0 Georgia 30 42.9 0 0.0 .. 10 .. 5 .. 29 1.9 2.7 Germany 107 30.7 0 0.0 76 8 239 5 2,682 14 94.2 27.0 Ghana 90 39.7 1,172 1.3 222 13 529 10 3,725 103 11.0 4.8 Greece 65 50.5 -1,408 -2.3 95 13 251 10 4,992 571 :3.1 2.4 Guatemala 38 35.4 824 2.0 250 8 458 4 8,681 355 18.2 16.8 Guinea 64 25.9 748 1.1 190 11 409 12 3,000 39 1.6 0.7 Guinea-Bissau 23 82.1 104 0.4 108 4 243 1 1,000 0 0.0 0.0 Haiti 0 0.8 8 3.4 3 4 75 11 5.242 100 0.1 0. Honduras 41 36.8 1.022 2.3 173 7 422 4 5,680 98 11.1 9.99 126 2000 World Development Indicators 3.40 Forest area Average Mammals Birds Higher plants"' Nationally annual protected deforestation areas % Of % of thousand total Threatenn-d Threatened Threatened thousand total sq. km land area sq. km % change Species species Species species Species species sq. km land area 1995 1995b 1990-95 ±.990-95 1L996b 19968 1996b 1996b 1997b j997b 1996b 1996k Hun ary 17 18.6... -88 -0.5 72 8 205 10 2,214 30 6.3 6.8 India 650 21.9 -72 0.0 316 75 923 73 16,000 1,236 142.9 4.8 Indonesia 1,098 60.6 10,844 1.0 436 128 1,519 104 29,375 264 192.3 10.6 Iran, Islamic Rep. 15 1.0 284 1.7 140 20 323 14 8,000 2 83.0 5.1 Iraq 1 0.2 0 0.0 81 7 172 12 .. 2 9.0 0.0 Ireland6 8.3 -140 -2.7 25 2 142 1 950 1 0.6 ......0.9 Israel 1 4.9 0 0.0 92 13 180 8 2,317 32 3.1 15.0 Italy 65 22.1 -58 -0.1 90 10 234 7 5,599 311 21.5 7.3 Jamaica 2 16.2 158 7.2 24 4 113 7 3,308 744 0.0 0.0 Japan 251 66.8 132 0.1 132 29 250 33 5,565 707 25.5 6.8 Jordan 0 0.5 12 2.5 71 7 141 4.2,100 9 3.0 3.4 Kazakhstan 105 3.9 -1,928 -1.9 . 15 .. 15 71 73.4 2.7 Kenya 1 . 34 0.3 359 43 844 2 4 6,506 240 35.0 6.1 Korea, Dem. Rep. 62 51.2 0 0.0 . 7 115 19 2,898 4 341 2.6 Korea, Rep. .. 76 .77.2 13 0.2 49 6 112 19 2,898 66 6..8 6..9 Kuwait 0 0.3 0 0.0 21 1 20 3 234 0 0.3 1.7 Lao PDR . 172 30 487 27 2 00 0.0 Latvia 29 46.4 -250 -0.9 83 4 217 6 1,153 0 7.8 12.6 Lebanon 1 5.1 52 7.8 54 5 154 5 3,000( 0.0 Lesotho 0 0.2 0 0.0 33 2 58 5 1,591 21 0.1 0.3 Libya 4 0.2 0 0.0 76 11 91 2 1,825 57 1.7 0.1 Lithuania 20 30.5 -112 -0.6 68 5 202 4 .. 1 6.5 10.0 Macedonia, FYR 10 38.9 2 0.0 10 . 3 .. 0 1.8 7.1 Madagascar 151 26.0 1,300 - 0.8 105 46 202 2 9,505 306 11.2. 1. Malawi 33 35.5 546 1.6 195 7 521 9 3.765 61 10.6 11.3 Malaysia 155 47.1 4,002 2.4 286 42 501 34 15,500 490 14.8 4.5 Mali 116 9.5 1,138 1.0 137 13 397 6 1.741 15 45.3 3.7 Mauritania6 0.5 0 0.0 61 14 273 3 1,100 3 17.5 17 Mauritius 0 5.9 0 0.0 4 4 27 10 750 294 0.1 4.9 Mexico 554 29.0 5.080 0.9 450 64 769 36 26,071 1,593 71.0 3.7 Moldova 4 10.8 0 0.0 68 2 177 7 .. 5 0.4 1.2 Mongolia 94 6.0 0 0.0 134 12 390 14 2,272 0 161.3 10.3 Morocco 38 8.6 118 0.3 105 18 210 11 3,675 186 3.2 0.7 Mozambique 169 21.5 1,162 0.7 179 13 498 14 5,692 89 47.8 6.1 Myanmar 272 41.3.3,7~4 14 21 3 867 44 7,000 32 1.7 0.3 Namibia -124 15.0 .420 0.3 154 11 469 8 3.174 75 1062.2. 12.9 Nepal -48... 33.7 548 1--.1.... .. 167 28 611 27 6,973 20 11.1 7.8 Netherlands 3 9.8 0 0.0 55 6 191 3 1,221 1 2.4 7.1 New Zealand 79 29.4 -434 --0.6 10 3 150 4 2,382 211 63.3 23.6 Nicaragua 56 45.8 1,508. 2.5 200 4 . 8 750 9 . : .. .. ..... .............. ..... .. .. .... .... . -.482...3.. 7,5..0...8. 9.0..4. Niger 26 2.0 0 0.0 131 11 299 2 1,170 0 96.9 7.6 Nigeria 138 15.1 1,214 ..0.9 274 26 681 9 4,715 37 30.2 3.3 Norway 81 26.3 -180 -0.2 54 4 23 3 1.5 12 93.7 30.5 Oman 0 90 .0....... .0 .. 56 9 107 5 ,1,204 30 343.3. 16.1 Pakistan 17 2.3 550 2.9 151 13 375 25 490 1 37:2 4.8 ....... . ..... .......... .. . ..... .A O-R1 I .n.-. ... .. . .' 1. . - .'.... Panama 28 37.6 636 2.1 218 17 732 10 9,915 1,302 14.2 19.1 Papua New Guinea 369 81.6.1,332 0.4 214 57 644 31 11,544 9 0.1 0. Paraguay 115 29.0 3,266 2.6 305 10 556 26 7,851 129 14.0 3.5 Peru 676 52.8 2,168 0.3 344 46 1538 64 18,245 906 34.6 2.7 Philippines 68 22.7 2,624 3.5 153 49 395 86 8,931 360 14.5 4.9 Poland 87 28.7 -120 -0.1 84 10 227 6 2.450 27 29.1 9.6 Potugal ~ 29 31.4 -240 -0.9 63 13 207 7 5.050 269 5.9 6..4 Puerto Rico3 31.0 24 0.9 16 3 105 11 2,493 223 0.1 1.1 Romania 62 27.1 12 0.0 84 16 247 11 3,400 99 10.7 4.6 Russian Federation 7,635 45.2 0 0.0 269 31 626 38 . 214 518.7 3.1 2000 World Development Indicators 127 3.4 Forest area Average Mammals Birds Higher plants' Nationally annual protected deforestation areas % of % of thousand total Threatened Threatened Threatened thousand total sq. km land area sq. km 8 change Species species Species species Species species sq km land area 1995 19951 1990-95 1990-95 1.996k 19961 1996b 1996" 1.997k 1997b 19965 1996b Rwanda 3 10.1 4 0.2 151 9 513 6 2,288 0 3.6 14..6 Saudi Arabia 2 0.1 18 0.8 77 9 155 11 2,028 7 49.6 2.3 Senegal 74 38.3 496.. 0.7 155 13 384 6 2,086 31 21.8 11.3 Sierra Leone 13 18.3 426 3.0 147 9. 466 12 2,090 29 0.8 1.1 Singapore 0) 6.6 0. 0..0- ...45 6 118 9 2,168 29 0.0 0.0 Slovak Republic 20 41.4 ..-24 -0.1 8 209 4 65... 10.5 21.8 - ....... .........- .. .. - ... .. .. .... ........Slovenia 11 53.5 0 0.0 69 10 207 3 13 1.1 5.5 South Africa 85 7.0 150 0.2 247 33... . 596 16 23.420 2,215 65.8 5.4 Spain 84 16.8 0 0.0 82 19 278 10 5,050 985 42.2 8.4 Sri Lanka 18 27.8 202 1.1 88 14 250 11 3,314 455 8.6 13.3 Sudan 416 17.5 3,526 0.8 267 21 680 9 3,137 10 86.4 3.6 Sweden 244 59.3 24 0.0 60 5 249 4 1,750 13 36.2 8.8 Switzerland 11 28.6 0 0.0 75 6 193 4 3,030 30 7.1 18.0 Syrian Arab Republic 2 1.2 52 2.2. 63 4 204 7 3,000 8 0.0 0.0 Tejikistan 4 2.9 0 0.0 .. 5 9 50 5.9 4.2 Tanzania 325 368 3226 0 316 33 822 30 10,008 436 138.2 15.6 Thailand 116 22.8 3,294 2.6 265 34 616 45 11.625 385 707.7 13.8 Togo 12 22.9 .....186 ..1.4 196 8 391 1 2,201 4 4.3 7.9 Trinidad an obg 2 1.4 26 1.5 100 1 60 3 2,25 . 21. 0.2 3.9 Tunisia 6 3.6 30 0.5 78 11 173 6 2,196 24 0.4 0.3 Turkey 89 11.5 0 0.0 116 15 302 14 8,650 1876 10.7 1.4 Turkmenistan 38 8.0 0 0.0 .. 1 . 12 .. 17 19.8 4.2 Uganda 61 30.6 592 0.9 338 18 830 10 5,406 iS 19.1 9.6 Ukraine 92 15.9 -4 -0.1 .. 15 263 10 .. 52 9.0 1.6 United Arab Emirates 1 0.7 0 0.0 25 3 67 4 .. 0 C.0 0.0 United Kingdom 24 9.9 -128 -0.5 50 4 230 2 1,623 18 5C.6 20.9 United Staten 2,125 23.2 -5,886 ..-0.3 428 35 650 50 19,473 4669 1,226:.7 13.4 Uruguay 8 4.7 4 0.0 81 5 237 11 2,278 15 -C..5. .0.3 Uzbekistan 91 22.0 -2,260 -2.7 .. 7 . 11 . 41. 8. 2. 2.0 Venezuela, RB 440 49.9 5,034 1.1 305 24 1,181 22 21.073 426 312.8 36.3 Vietnam 91 28.0 1,352 1.4 213 38 535 47 10.500 341 2.9 3.0 W est Bank and Gaza ~~~~~~~~~~... . ... ...... ..... ... .... Yemen, Rep.. ...0 m ........ ... 0.0 66 5 ...143 13. .. 149 C.0 0..0 Yugoslavia, FR (Serb./Mont.). 18 Zambia 314 42.2 2,644 0.8 229 11 605 10 4,747 12 6S.6 8.6 Zimbabwe 87 22.5 500 0.6 270 9 532 9 4,440 100 30.7 7.9 .. . . . ....... . . ... ....... . . . . .. . ..... ..... . . . .. .. . Low income 7,379 17.8 49.332 0.7 . ..2,442.5 5.9 ExcI. China & India 5,334.... I.-. . .. . . 18.4 48,538 . 0.9.. .......1,698.4....5.9. Middle income 18,898 32.7 64,086 0.3 . ..2,806.8 4.9 Lower middle income 111,101 30.8 21.162 0.2 . ..1,560.5 4.3 Upper middle income 7,797 35.8 42,924 .0.5 1,246.3 5.7 Low & middle income 26,277 26.5 113,418 0.4 . ..5,249.3 5.3 East Asia & Pacific 3,832 24.0 29,956 08 . . ......... 1,102.2 6.9 Europe & Central Asia 8,579 36.1 -5,798 -0.1 768.0 3.2 Latin America & Carib. 9,064 45.2 57,766 0.6 1,456.3 7.3 Middle East &N. Africa 89 0.8 800 0.9 242.1 2.2 South Asia 744 15.6 1,318 0.2 21-3.0 4. Sub-Saharan Africa 3,969 16.8 29,378 0.7 . ..1,46'.7 6.2 High income 6,436 20..8 -11.694 -0.2.. . 3,294.2 10.8 Europe EMU 683 29.6 -1,880 -0.3 . ... .. . .. 268.4 1. a. Flowering plants only. h. Oata may refer to earlier years. They are the most recent reported by the World Conservation monitoring Centre in 1997. 128 2000 World Deveelopment indicators ___ i- 3.4. The estimates of forest area are from the Food and Agri- from around the world. Nearly 34,000 plant s pecies, 12.5 * Forest area is land under natural or planted stands culture Organization's (FAO) State of the World's Forests percent of the total, are threatened with extinction, of trees, whether productive or not (see About the 1999, which provides information on forest cover as of The table shows information on protected areas. data). * Average annual deforestation refers to the 1995 and a revised estimate of forest cover in 1990. For- numbers of certain species, and numbers of those permanent conversion of natural forest area to other est cover data for developing countres are based on coun- species under threat. The World Conservation Monitor- uses, including shifting cultivation, permanent agricul- try assessments that were prepared at different times ing Centre (WCMC) compiles these data from a variety ture, ranching, settlements, and infrastructure devel- and that, for reporting purposes, had to be adapted to of sources. Because of differences in delinitions and opment. Deforested areas do not include areas logged the standard reference years of 1990 and 1995. This reporting practices, cross-country companrbility is lim- but intended for regeneration or areas degraded by adjustment was made with a deforestation model designed ited. Compounding these problems, available data cover fuelwood gathering, acid precipitation, or forest fires. to correlate forest cover change over time with ancillary different periods. Negative numbers indicate an increase in forest area. variables, including population change and density, ini- Nationally protected areas are areas of at least * Mammals exclude whales and porpoises. * Birds tial forest cover, and the ecological zone of the forest area 1,000 hectares that fall into one of five management are listed for countries included within their breeding under consideration. Although the same model was used categories defined by the WCMC: or wintering ranges. * Higher plants refer to native vas- toestimateforestcoverforthe1990forestassessment, * Scientific reserves and strict nature reserves with lim- cular plant species. * Threatened species are the the inputs to State of the World's Forests 1999 had more ited public access. number of species classified by the IUCN as endangered, recent and accurate information on boundaries of eco- * National parks of national or international signifi- vulnerable, rare. indeterminate, out of danger, or insuf- logical zones and, for some countries, new national for- cance (not materially affected by human activity). ficiently known. D Nationally protected areas are est cover assessments. For the calculation of forest D Natural monuments and natural lands.capes with totally or partially protected areas of at least 1,000 cover for 1995 and the recalculation of the 1990 esti- unique aspects. hectares that are designated as national parks, natural mates, new forest inventory information was used for * Managed nature reserves and wildlife sanctuaries. monuments, nature reserves or wildlife sanctuaries, pro- Bolivia, Brazil, Cambodia, Cote d'lvoire, Guinea-Bissau, * Protected landscapes and seascapes (which may tected landscapes and seascapes, or scientific reserves Mexico, Papua New Guinea, the Philippines, and Sierra include cultural landscapes). with limited public access. The data do not include Leone. New information on global totals raised esti- Designating land as a protected area does not nec- sites protected under local or provincial law. Total land mates of forest cover. For high-income countries, the essarily mean that protection is in force, however. For area is used to calculate the percentage of total area United Nations Economic Commission for Europe and the small countries that may only have prote cted areas protected (see table 3.1). FAO use a detailed questonnaireto surveythe forestcover smaller than 1,000 hectares, this size limil in the defi- in each country. nition will result in an underestimate of the extent and Data sources No breakdown of forest cover between natural forest number of protected areas. and plantation is shown in the table because of space Threatened species are defined accorJing to the The forestry data are from the FAO's State of the limitations. (This breakdown is provided by the PAO only IUCN's classification categories: endangerei (in danger World's Forests 1999. The data on species are from for developing countries.) For this reason the defor- of extinction and unlikelyto survive ifcausal factors con- the WCMC's BiodiversityData Sourcebook(1994) and estation data in the table may underestimate the rate tinueoperating), vulnerable (likelyto move into the endan- the IUCN's 1996 IUCN Red List of ThreatenedAnimals at which naturai forest is disappearing in some countries, gered category in the near future if causal factors continue and 1997 IUCN Red List of Threatened Plants. The data Deforestation is a major cause of loss of biodiversity. operating), rare (not endangered or vulnerable, but at risk), on protected areas are from the WCMC's Protected and habitat conservation is vital for stemming this loss. indeterminate (known to be endangered, vulnerable, or Areas Data Unit. Conservation efforts traditionally have focused on pro- rare but not enough information is available tc say which), tected areas, which have grown substantially in recent out of danger (formerly included in one of the above cat- decades. Measures of species richness are one of the egories but now considered relatively secu e because most straightforward ways to indicate the importance of appropriate conservation measures are in effect), and an area for biodiversity. The number of small plants and insufficiently known (suspected but not defir itely known animals is usually estimated by sampling of plots. It is to belong to one of the above categores). also important to know which aspects are underthe most Figures on species are not necessarily comparable immediate threat. This, however, requires a large amount across countries because taxonomic concerits and cov- of data and time-consuming analysis. Forthis meason global erage vary. And while the number of birds an(i mammals analyses of the status of threatened species have been is fairly well known, it is difficult to make zn accurate carried out for few groups of organisms. Only for birds count of plants. Although the data in the table should has the status of all species been assessed. An estimated be interpreted with caution, especially for numbers of 45 percent of mammal species remain to be assessed. threatened species (where our knowledge is Jery incom- For plants the World Conservation Union's (IUCN) 1997 plete), they do identify countries that are ma or sources IUCN Red List of Threatened Plants provides the first-ever of global biodiversity and show national cornmitments comprehensive listing of threatened species on a global to habitat protection. scale, the result of more than 20 years' work by botanists 2000 World Develonment Indicators 129 3.5 Freshwater Freshwater Annual freshwater withdrawals Access to safe water resources cubic meters Urban Rural per capita billion % of total % for % for % for % of population % of population 1998 cu. ml resources- agriculture' industry' domestic' 1982-855 1990-961 1.982-85 1990-960 Albai 278 . 3 71 0 29 100 97 88 70 Algeria 47830 4.5 31.5 1 6Oe ise 250 Angola 15,783 0.5 0.3 760 100 140 80 69 15 15 Argentina 27,865 0 28.6 2.80d 75 9 16 63 71 17 24 Armenia 2,767 d 2.9 27.90d 66 4 30 . Australia 18,772 15.1 4.3 33 2 65. .. ...................................... Austria 10,399 d 2.2 2.70 9 60 31 100 .. 98 Azerba'ijan 3,8310d 16.5 54.60d 70 25 5 ..... ... ... Bangladesh 9,636 d 14.6 1.20d 86 2 12 29 47 43 85 Belarus 5,6650 2.7 4.7 0 35 43 22 Belgium 1,2280 9.0 72.201 4 85 11 100 .. 91 Ben in 4,337 0.2 0.601 670 100 230 45 41 9 53 Bolivia 38,625_1. 0.4 48 20 32 81 ..27 Bosnia and Herzegovina 9,952 Botswana 9.413 d 0.1 0.70 48l 200 320 . 100 .. 53 Brazil 42.4590d 54.9 0.50d 61 18 21 . ..52 Bulgaria 2,6 13.9 6.80d 22 76 3 95 .. 67 Burkina Faso .1,671 0.4 2.22 810 Qo 190 50 .. 26. Burundi' 561 0.1 2.8 640 00 360 33 .. 22 Cambodia 41,407 0.5 0.1 94 1 5 .. 2 . 12 Cameroon 18,737 0.4 0.1 351946 46 7 0 2 Canada ~~~~~~ ~ ~~92,142 45.1.. 1.6 9 80 11 100 .. 10 Central African Republic 41,250 0.1 0.0 730 60 210... ... ... .... ...... ... Chad 5,9940 0.2 040 82 20 1601 27 48 30 17 Chile ~~~~~~ ~ ~~32,007 21.4 3.6 84 11. 5 97 .. 22 China 2,285 525.5 18.6 77 15.. 93 8 Hong Kong, China... .... Colombia ~~~~~~ ~~26,72 2. 8.9 0.5 .. ....37 4 59 .. 88 48 Co.go, Dern. Re . ~~~~21,134 0.4. 0.0.. 230 160 610 43 ..5 Congo, Rep. . 298,9630 0. 0.010 270 620 42 50 7 8 Costa Rica 27,425 5.8 1.4 80. 7. 13.... CSte dIvoire ~~~~~~5,362 0.7 0.9 670 1 220 30 5 0 8 Croatia -15.863. 0.1 0.1 50 50 . 54 Cuba ~~~~~~~~~~3,120 5.2 23.5 51 0 49 .. 96 85 Czech Republic 1,554 2.5 15.8 2 57 41 100 . 0 Denm ark 2.4600d 0.9 9.2 d.0 43 ....... ...27.... ..... 30... .......100... .. .............. 99 ... ... Dominican Republic 2,467 8.3 14.9 89 1 11 72 7 4 6 Ecuador 26,305 17.0 1.8 82 6.28 23 55..... .. . Egypt. Arab Rep. 9490 55.1 94.50 860 80 60 93 82 61 50 El Salvador 3,19 ~~~~~~~~~~~07 .3 46 20 34 76 78 47 37 Eritrea 2. 6.. ... ...... ... .... Estonia 8.829 0.2 1.05d95 . .... . .......... Ethiopia 1,795 2.2 2.0 860 30 110.......90 .. 20. . .. Finland ~~~~~~ ~~21,347 .2.4 2.20d 3 85 12 98 .10086 85 France 3,2461 40.6 21.30d 12 73 15 100 100 95 100-- Gabon 136.942 ~~~~ ~~~~~~~ ~~0.1 0.0 60 220 720 75 80 34 30 Gambia, The ~~~~~~~6.579d 0.0 0.40 910i 20 70 100 .. 33 Georgia 11,6320d 3.5 5.5 d 92 1. Germany 2,169d 46.3 26.001 0 86...14..... Ghana 2,8820 ~~~~~~~~~~~~~~~~. ...... 520 130 350 57 70 40 49 Greece ~~~~~~~ ~~6,5620d 7.0 10.20d 81 3 16 91 .. 73 Guatemala 11,030 1.2 0.6 74 1 9 9 9 4 Guinea ~~~~~~ ~ ~~31.910 .0.7 0.3 870 30 100 . 6i.2 62 Guinea-Bissau 23.249 0001 6 0 0 1 3 7 5 Haiti ~~~~~~~~~~1'468 1.0 0.4 915.. 3.. 23 Honduras 9 258 1.5 2.7 91 5 4 51 81 49 53 130 2000 World Development Indicators TgT SJoP20!pul pluWd0laA90 PIJOM OOOZ 677 777 07 p L'T 77LL p6779 O uOI7jaJpGj ue!ssnH 09... .. 69.. ..... .76 .. ...69.p.977.....7......7..6....... 9 9 . . . 977 . ~ ~~~ ~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.......0.. 9..7. 077 . 99 . 99 . 677 . 777~ ~ ~~~~~~~~~~ ~~~~~~~~ 69. 00..770 06' .L . ........... ..fl ....... . 2.......d........ 9L 99 66 0077 977 77 OL 60 T96'~~~~~~~~~~~~~~~~~~~~~~~~~~~69. ~~~~W17 Iuddipd ~~~*779 . ~ .... .. 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S.~~~~~~~~~~~~~~~~TpTg OTp8Tupo 3.5 Freshwater Annual freshwater withdrawals Access to safe water resouJrces cubic meters Urban Rurai per capita billion % of total % for % for % for % of population % of population 1998 cu. ml resources- agriculture' industry' domestic' i1982-85' 1.990-96' 1982-85' 1.990-96' Rwanda 798 0.8 12.2 94' le 5e 55 60 44 Saudi Arabia 116 17.0 708.3 90 1 9 92 .. 87 Senegal 4,359 d 1.5 3.8'1 92' 3e 6' 63 82 27 28 Sierra Leone 32,957 0.4 0.2 89' 4' 70 58. 58 8 21 Singapore 193 0.2 31.7 4 51 45 100 100. Slovak Republic 15.396 1.4 1.7...... Slovenia 9.334 0.5 2.7 ..50 50 .. 100 .. 97 South Africa 1,206'd 13.3 26.6 d 72' 11' 17' . Spain 2,847d 35.5 31.7'1 62 26 12 100 . 9.5 Sri Lanka 2,329 9.8 14.6 96 2 2 76 .. 26 Sudan 5,433 17.8 11.6 94' 1' 5' 49 66 45 45 Sweden 20.109' 2.7 15'd 9 55 36 100 .. 98 Switzerland 7.458 d 2.8 4.9 d 0 58.. 42 100 ..100 100 100 Syrian Arab Republic 2,926 14.4 32.2 94 2 4 77 92 65 78 Tajikistan 13,0170 11.9 14.9 ' 92 4 4 .. 86 .. 32 Tanzania 2,770 1.2 1.8 9' 2' 9 85 65 47 45 Thailand 6,698'd 33.1 8.1Id 91 4 5 .. 94 .. 88 Togo ~~~~2.692'd 0.1 0.8'd 25' 13' 62' 68 .. 26 Trinidad and Tobago 3.991 0.2. 2.9 ..35 38 _ 27 _ 100 _83 93 80 Tunisia 439'd 2.8 69.00d 86' 2' 13' 98 .. 79 Turkey 3,209'd 35.5 17.401 73' II' 16e 73 .. 65 Turkmenistan 9.644'd 23.8 52.30d 98 1 1 .. 80 .. Uganda 3,158' 0.2 0.3'd 60 8 32 45 47 12 32 Ukrai ne 2,7760d 26.01 18.6'd 30 52 18 .. 77 .. 12 United Arab Emirates 73 2.1 1,055.0 67 9 24 100 98 100 98 United Kingdom 2,489 9.3 . 6.4 377 20 100 100 100 100 United States 9,1680d 447.7 18.1' 27' 65' 8' . Uruguay 37,971' 4.2. 0.5'd 91 3 6.. 95 99 ..27 Uzbekistan 5,476'd 58.1 63.4'1 94 2 4 .. 72 .. 46 Venezuela, RB 57,821' 4.1 0.3'd 46 10 44 88 .. 65 Vietnam 11,647 54.3 6.1 86 10 4 .. 53 .. 32 West Bank and Gaza...... Yemen, Re p. 254 2: 15 92. 1. .7 74 14 Yugoslavia, FR (Serb./Mont.) ... .. Zambia 12,001' 1.7 1.50 771 7' 16' 70 64 32 27 Zimbabwe 1.711' 1.2 6.1 79' 7' 14' 100 99 10 64 Low income 4,330 87 85 Exci. China & India 9,187 92 4 4 Middle income 15,145 74 13 12 Lower middle income 11,805 75 15 10 Upper middle income 73 10 17 58 Low & middle income 8,113 82 10 7 East Asia & Pacific .8014 689. 82. Europe & Central Asia 14,339 63 26 11 Latin America & Carib. 27,393 74 9 18 . .. 44 Middle East & N. Africa 1044 89 4 6 82 .. 42 South Asia 4.088 93 2 4 76 83 46 75 Sub-Saharan Africa 8,441 87 4 9 61 .. 26 High income 30 59 11 Europe EMU 3,771 . . 21 63 .16 100 .. 90. .. a. Data refer to any yeurfrom 1980 to 1998, unless otherwise noted. b. Unless otherwise noted, sectoral withdrawal shares are estimated for 1.987. c. Outs refer to the most recent year avail- able in the period. d. Totai muter resources include river flows from other countries. e. Data refer to pears other than 1987 (see Primary data documentation). f. Data refer to estimates for years before 1980 (see Primary data documentation). 132 2000 World Development Indicators 3.5 0 The data on freshwater resources are based on esti- * Freshwater resources refer to total renewable mates of runoff into rivers and recharge of ground- resources, which include flows of rivers and groundwater Agriculture accounted for most freshwrater water. These estimates are based on different sources withdrawals in developing economles In from rainfall in the country, and river flows from other and refer to different years, so cross-country compar- the past two decades . . countries. Freshwater resources per capita are calcu- isons should be made with caution. Because they are lated using the World Bank's population estimates collected intermittently, the data may hide significant (see table 2.1). * Annual freshwater withdrawals Low-income countries variations in total renewable water resources from one refer to total water withdrawal, not counting evapora- year to the next. The data also fail to distinguish , 50 tion losses from storage basins. Withdrawals also between seasonal and geographic variations in water include water from desalination plants in countries availability within countries. Data for small countries and 4 where they are a significant source. Withdrawal data countries in arid and semiarid zones are less reliable are for single years between 1980 and 1998 unless than those for larger countries and countries with higher otherwise indicated. Withdrawals can exceed 100 per- rainfall. Finally, caution is also needed in comparing data 87 cent of total renewable resources where extraction on annual freshwater withdrawals, which are subject to from nonrenewable aquifers or desalination plants is variations in collection and estimation methods. considerable or where there is significant water reuse. Middle-ncome countries This year's edition of the World Development Indi- Withdrawals for agriculture and industry are total with- cators and last year's define freshwater resources as 125 drawals for irrigation and livestock production and for including river flows arising outside the country. The data i direct industrial use (including withdrawals for cooling in these editions therefore are not comparable with thermoelectric plants). Withdrawals for domestic uses those published in previous years, which exclude exter include drnking water, municipal use or supply. and use nal sources. Because the definition includes river flows 75,. for public services, commercial establishments, and entering a country but does not deduct river flows out homes. For most countries sectoral withdrawal data are of countries, it double counts the availability of water estimated for 1987. * Access to safe water refers from international river ways. This can be important in to the percentage of people with reasonable access water-short countries, notably in the Middle East. HIgh-income countries to an adequate amount of safe water in a dwelling or Accesstosafewatermeasurestheshareofthepop- t1r K within a convenient distance of their dwelling (see ulation served by improved sources of water. An About the data). improved source can be any form of collection or pip- ing used to make water regularly available. While infor- Data sources mation on access to safe water is widely used, it is extremely subjective, and such terms as safe and ade- The data on freshwater resources and withdrawals quate amount may have very different meanings in dif- are compiled by the World Resources Institute from var- ferent countries despite official Word Health Organization ious sources and published in World Resources (WHO) definitions (see Definitionsfortable 2.15). Even 1998-99 and World Resources 2000-01 (produced in in high-income countries treated water may not always ... and for most of the growth In collaboration with the United Nations Environment Pro- withdrawals In the past century be safe to drink. While access to safe water is equated gramme, United Nations Development Programme, with connection to a public supply system, this does not Cubic kilometers and World Bank). The data on access to safe water take account of variations in the quality and cost 5,000 come from the WHO. (broadly defined) of the service once connected. Thus cross-country comparisons must be made cautiously. Changes over time within countries may result from 3,000 changes in definitions or measurements. 2,000 1,000, 1900 1920 1940 1960 19110 2000 * Agriculture U Industry )omestic Source: Table 3.5 and Shiklovanov 1993. 2000 World Development Indicators 133 S ~~3.6 Water pollution Emissions Industry shares of emissions of organic water pollutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics, klolgrams per day metals and pulp Chemicals beverages and glass Textiles Wood Other per day per worker % S%% % 1980 19971 ±980 19971 19971 19971 19971 1.9971 1997, 19971 1997, 19971 Albania . 5,844 . 0.24 22.9 1.5 6.2 62.0 0.4 4..7 0J. 1.5 Algeria .60,290 102,969 0.19 0.25 44.6 . 3.8 40.8 0.4 8.0 2.5 Angola . 1,472 . 0.20 7.6 3.0 9.1 65.9 0.3 5.5 4.4 4.1 Argentina 244,711 186,844 0.18 0.21 6.3. 12.6 8.1 59.4 0.2 7.4 1.5 4.6 Armenia . 12,858 . 0.23 . 0.0 66.5 33.5 Australia 204,333 173,269 0.18 0.19 12.4 22.8 6.7 43.5 0.2 5.3 2.8 6.3 Austria 108,416 78,040 0.16 0.14 13.1 19.5 9.1 36.1 0.3 6.7 4.3 10.9 Azerbaijan . 45,025 . 0.17 11.6 2.5 12.0 49.0 0.2 18.1 1.0 5.6 Bangladesh 66.713 186,852 0.16 0.16 2.8 6.8 3.5 34.2 0.1 50'9 0.6 1.1 Belarus .. .. ~~~~~~~~~~~~~~~~~~~... ..... ........ Belgium 136,452 113,460 0.16 0.16 14.4 17.7 11.6 36.8 0.2 8.8 2.0 8.4 Benin 1,646 .. 028 ... Bolivia 9,343 10,251 0.22 0.23 4.7 13.8 6.5 61 8 0 3 90 2.6 1.2 Bosnia and Herzegovina . 8,903 . 0.18 20.5 13.1 6.6 33.3 0.2 17.6 5.8 2.8 Botsanora 1,307 4,386 0.24 0.18 0.0 11.5 .2.8 67.5 0.0 12.5 2.1 3.7 Brazil 866,790 6,90,876 0.16 0.19 19.0 12.6 9.3 41.6 0.2 10.9 1.6 4.8 Bulgaria 152,125 88,729 0.13 0.15 14.6 8.6 11.0 38.8 0.3 15.2 2.1 9.3 Burkina Faso 2,385 . 0-29 Burundi 769 1,644 0.22 0.24 00 .0... 8.3 4.7 67.8 0.1 16.7 1.6 0.8 Cambodia . 12,078 . 0.16 0.0 3.4 3.3 59.2 0.6 24.7 5.8 3.1 Cameroon 14,569 12,796 0.29 0.24 3.0 5.7 20.8 63.4 0.0 2.9 3.8 0.3 Canada 330,241 295,525 0.18 0.17 9.6 29.8 9.1 34.0 0.1 5.8 3.9 7.6 Central African Republic 861 Chad Chile 44,371 77,111 0.21. 0..,23 7.2 11.8 8.6 . .... . 59f.6 0...'..1 7.2 2.6.. 2.9 China 3,377,105 7,396,000 .~14 0.14 20.6..11.9 14.2 28.9 0.4 14.1I 1.0 8.9 .ong.Kong, C.ina . 902.0 5,7 0.11 0.15 1.4 37.2 3.9 20.5 0.1 29.0 0.2 7.6 Colombia 96,055 111,139 0.19 0.20 3.6 15.2 10.6 51.3 0.2 14.8 0.9 3.3 I.n g D a .. .. . .. ...... .. .. Congo, Rep.1,039 0.2-1 Costa Rica . 32,301 0.22 12.2 10.2 6.6 62.3 0.1 15.8 1.5 2.3 C6te dIlvoire 15,414 0.23 .. . . . .. . Croatia . 50,014 01 4.7 14.2 8.9 45.8 0.2 16.2. 3.6. 6.4 Cuiba 120,703 172,973 0.24 0.25 50 4.-6.. 2.3 78.4 0.3 6.1 0. 27 Czech Republic ..162,615 . 014 24.6 9.2. .6.8 32. 7 0.4 123.3 2.4 11.7 Denmark 65,465 91,815 0.17 0.18 2.1 28 9 7.7 46.6 0.2 3.5 2.9 8.3 Dominican Republic .54,935 0.38 .. . .. . Ecuador 25,297 28,969 0.23 0.25. .2.4 13.2 7.5 66.1 .0.2 7 2 1.7 1.6 Egypt, ArbRp 169,146 216,060 0.19 0.19 12.4 5.3 9.5 50 -.1 ...0.3 18.2 ).6 3.7 El Salvador 9,390 16,385 0.24 0.18 1.0 10.6 8.6 46.5 0.1 30.9 3.5 1.7 Eritrea 16,754 22_175 . 1.4 8.9 4.4 58.5 0.1 24.8 1.4 0.5 Ethiopia . 19,390 0.22 0.22 2.0 1.1.3 3.4 56.6 0.2 24.2 L.7 0.6 Finland 0.25 6423 O17 0.18 9.1 39.8 7.0 30.0 0.1 2.6 :3.5 7.9 France 729,776 585,382 0.14 .0.15 11.6 21.2 10.8 37.7 02.2 6.1. 1.8 10.8 Gabon 2,661 1,886 0.15 0.26 0.0 6.0 4.9 79.7 0.1 1.2 6.9 1.2 Gambia, The 549 832 0.30 0.34 0.0 15.3 .1.9 77.8 0..1 2.6 I.9 0..........4 Germany . 811,315 .. 012 12.7 16.8 15.5 30.6 0.3 4.8 2.2 1~7.2 Ghana 15,868 14,449 0.20 0.17 9.8 16.9 10.5 39.5 0.2 9.1 12.4 1.7 Greece 65,304 58,229 0.17 0.19 6.1 12..1 .8.6 53.3 0.3 14.7 1.5 3.5 Guatemala 20,856 19,052 0.25 0.28 5.3 8.0 6.2 71.4 0.1 6.9 1.1 1.0 Guinea Guinea-Bissau H aiti 4 ~~~~~~~~~~~~~~~~. .. .. 0.1 .... . . . .................... Honduras 13,067 34,036 0.23 0.20 1.1 7.8 3.9 55.5 0.1. 26.8 4.0 0.8 134 2000 World Development Indicators SET SJOI83!PUI lUaWdOIGAOC] PIIOM 0006: 006T 96~ 08R t70 Z88 6 8,9 "'ST 960 98£'969'7r uo!lelpa.j ue!ssn8 88 £96 80...:. .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...~~~~~~~~~~~~~~~ .~~~~~~~.~~~~~~~~ 1901~~~~~~~~. ..... 9~~~9.66 . 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T f.0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~eunfh 4- S 66 ~ Sb k - ..0.................... 60. 0 60e - 06 0 6 0£0 6.6 £6oCtONsa gr~~~~~~i -6'' ~so99'1 oe qe~3OL 29o 69 ~ O 0 . : . 89 98 966 96o 860o e9.62AOPe- ....... .. . ... - .... .. .. ... - . . ...... . . .. .. .... .. ..... . . . ..... . . .... . .... . . .... .... .. ... . . .... .. - .... . ...... . . ... .. . .. ....... .. 8: l ,8 ' 6.. 6 0Z66T£8960 910 69t2'Z t8 ' s!!n . . . ... . . . ..... . ........... .... f.... 961 6T 9:9 T' 6 Z0 69£.9 6 9'eT 0 9610 VF'O .9t'90'6' 9t0i'9T MLA 86 T 6-~ f 6T £16 ITt' -6 60 66`£b96.. 6'8 . 0.9.j:~~~~~~~~~~.£*6£.191.6.86.9*9.9f9 610 269'66 6966£.WI~~~~~~~~~~~~~~~~~~~~, - ... ... 89 LO 62.~o :9.68 906 26 910 00 '66 e9'6 .!uenIlI UW £6.6 . 0 . £09 99 68.: . :~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. 66~~~~~~~~~~~262 010816 2!seuopuI~....... . .. ........... . ....... ....... ... sb 66 9T9 68 92 991 610 T60~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 09 d9' s96V eA!PU do . 6:. 0.~~~~~~~~~~~ £9 6*0. :~~~~~~~~~~6T T0 £9661 990 . ......... ..L6 T.......~L 6 ~ 6 1 L6 1 ~ L 6 .- L66T 0861 . . -......L6...... ...0....... J2~~~~~~~~~~~~~~O M.i..... ............... ................. ............... 2et:I -OOM SaIiTxp SSei pu :P8WA3 GIVWaI SWd u size 8fnp md swm8o .so. .....u. poo. . .d ................p r u: is S4UE~~~~~~~~~~~~nIIOd J0~~~~~~~~~~~~~~~~.. ....... i.9 6f 6-2 T q~~~~6 6:6~~ 9:~~'t -P, T'S 6~~iue0 J 1!on 9 " e' I od JO B o u e a J S S :W . . ...... . .... lPU .... .......u ..... .. ............ ...... .. . ..... ........ ......... .. .. ... ..... .... .. .. ....... ... .. .. g.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~T ZT 'T~,, T E'P 016d? eio O ~3.6 Emissions Industry shafes of emissions of organic water pollutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics, kilograms per day metals and pulp Chemicals beverages and glass Textiles Wood Other per day per worker % % % % % % % 1980 19971 1980 1997k .19971 1997a 1997w 19971 19970 19971 19971 19971 Rwanda ~~~~~~ ~~~~~~ ~~~ ~ ~ ~ ~~. . ........ ....... Saudi Arabia 184181. 24 .436 ..0.12 _0.14 4.4 15.9 21.1 45.1 1.0 3.8 2.0 6.8 Senegal 9,865 10,223 0.31 0.33 0.0 . 7.4 7.4 81.6 0.0 3.0 0.1 0.6 Sierra Leone 1,612 4,170 0.24 0.32 9.6 3.0 82.3 0.1 2.0 2.2 0.8 Singapore 28558 34267.0.10 0.09 2.4 27.9 14.2 18.7 0.1 6.2 1.5 29.0 Slovak Republic 6.4,293 0.14 15.5 13.8 9.6 34.9 0.3 14.0 1.6 103 Slovenia .. 40,148 .. 0.16 29.2 16.8 8.3 24.2 0.2 13.2 2.2 5.9 South Africa 237,599 241.756 0.17, .0.17 11.6 16.4.. 9..7 41.8 0.2 10.8. 3.3. 6.2 Spain 376,253 335,240 0.16 0.16 7.3 17.7 8.8 46.3 0.3 8.6 3.4 7.6 Sri Lanka 30,086 55,665 0.18 0.17 1.2 8.9 7.2 42.2 0.2 38.3 0.7 1.3 Sweden 130,439 91,981 0.15 0.16 10.9 37.0 7.6 27.8 0.1 1.6 3.3 11.7 Switzerland . . 123,752 .. 0.17 24.9 23.6 10.4 25.0 0.2 3.2 4.2 8.7 Syrian Arab Republic 36.262 21421 0.19 0.22 2.9 1.5 8.4 68.3 0.4 17.2 0.3 1.1 T a..ista ..... ........ .... . Tanzania 21,084 32,508 0.21 0.26 4.7 10.8 50.0 65.2 0..1 11.8 1.4 1.2 Thailand 213,271 355,819 0.22 0.16 6.1 5.3 ..5.3 42.2 0.2. 35.4 1.5 3.9 Togo 963 .. 0.27 .. 10.4 38.7 5.8 41.8 0.2 2.1 0.8 Trinidad and Tobago 7,835 11,787 0.18 0.28 ..4.4. 10.9 .6.7 72.6 0.1 2.9 1.3 1.2 Tunis:ia 20,294 45,806 0.16 ..0.16 ..6.4 ..79.9 6.0 40.4 0.4. .34.0 1.7 3.2. Turkey 160,173 177,161 0.20 0.17 12.7 7.6 7.3 43.8 0.3 22.5 0.9 4.9 Turkmenistan ~~~~~~~~~. . ... .... .. . ...... .. . Uganda .. 16,728 .. 0.30 1.6 5.2 1.0 81.6 0.1 8.0 1.5 1.0 Ukraine ..539,490 0.16 20.5 3.7 7.5 50 7 0.4 6.7 1:6 8.9 United Arab Emirates 4,524 0 15 ..5 UnitedKingdm 964510 62,362 0.15 0.15 7.4 26.3 10.6 35.7 0.2 7.5 2.0 10.4 United States 2,742,993 2.584,818 0.14 0.15 8.8 32.8 10.1 27.3 0.2 7.3 2.7 10.9 Uruuay. 34270.27727 0.21.0.25.. 14 56.6'7 . . 0.1 11.1 0.8 1.9 U z b e kista n .......... ......... ...- ....... . . .. Venezuela, RB 84,797 92,026 0.20 0.21 14.1 -11.5 9.9. 51.8 0.2 73.3 1.7 3.4 West Bank and Gaza . . . . . . . Yemnen. Rep.. 7,823 .. 0.25 0.0 9.1 12.9 71.1 0.3 4.9 1.0 0.9 Yugoslavia FR (Serb./Mont.) 2,4 l .9. 26 76 4. 03 1. . : Zamnbia 13,605 11,433 0.23 0.22 3.4 10.8 7.3 63.6 0.2 9.3 3.0 2.4 Zimbabwe 32.681 33,223 0.20 0.19 14.0 11.4 5.6 47.3 0.2 14.9 3.4 3.2 Note: Industry shares may flot sam to 100 percent because data may be from different years. a. Oats refer to mast recent year between 1993 and 1997. 136 2000 World Development Indicators - ~~~~~~~~- 3.6 60 Emissions of organic pollutants from industrial activ- -' * Emissions of organic water pollutants are rneasured ities are a major cause of degradation of water qual- in terms of biochemical oxygen demand, which refers As per capiita inicome rises, poliuticn ity. Water quality and pollution levels are generally iietensity falls to the amount of oxygen that bacteria in water wi Il con- measured in terms of concentration, or load-the sume in breaking down waste. This is a standard rate of occurrence of a substance in an aqueous solu- Water pollution intensity index water treatment test for the presence of organic pol- tion. Polluting substances include organic matter, 0 0 lutants. Emissions per worker are total emissions metals, minerals, sediment, bacteria, and toxic chem- 8 divided by the number of industrial workers. * Indus- icals. This table focuses on organic water pollution try shares of emissions of organic water pollutants resulting from industrial activities. Because water pol- 60 refer to emissions from manufacturing activities as lution tends to be sensitive to local conditions, the defined by two-digit divisions of the International Stan- 40i national-level data in the table may not reflect the qual- 4 dard Industrial Classification (ISIC) revision 2: pri- ity of water in specific locations. 201 , mary metals (ISIC division 37), paper and pulp (34). The data in the table come from an international chemicals (35), food and beverages (31), stone, study of industrial emissions that may be the first to o ceramics, and glass (36), textiles (32), wood (33), and include data from developing countries (Hettige, Mani, c) <4 other 138 and 39). and Wheeler 1998). Unlike estimates from earlier Percapta income $1 stud es based on engineering or economic models, Date sources Note: The water pollution intensity index meassures these estimates are based on actual measurements the organic poltutant per unit of industrial output. of plant-level water pollution. The focus is on organic Source: Henige, Mani, and Wheeler 1998. Indicators for 1980-93 were drawn from a 1998 water pollution measured in terms of biochemical study by Hemamala Hettige, Muthukumara Mani, and A recent World Bank study shows e ocrntinuous oxygen demand (BOD) because the data for this indi- refationship between per capita Income and the David Wheeler, "Industrial Pollution in Economic Devel- cator are the most plentiful and reliable for cross- Intensity of organic water pollution. For each 2. opment: Kuznets Revisited" (available on the World country comparisons of emissions. BOD measures the percent Increase In per capita Income, there Is Wide Web at www.worldbank.org/nipr). These indica- a I percent decline In pollution Intensity. The strength of an organic waste in terms of the amount fastest decline occurs before countries reach tors were then updated through 1997 by the World of oxygen consumed in breaking it down. A sewage over- middle-Income status. Bank's Development Research Group usingthe same load in natural waters exhausts the water's dissolved methodology as the initial study. Sectoral employ- oxygen content. Wastewater treatment, by contrast, ment numbers are from UNIDO's industry database. reduces BOD. Data on water pollution are more readily available than other emissions data because most industrial pol- lution control programs start by regulating emissions of organic water pollutants. Such data are fairly reli- able because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution. In their study Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental pro- tection agencies and sector-level information on out- put and employmentfrom the United Nations Industrial Development Organizaton (UNIDO). Their economet- ric analysis found that the ratio of BOD to employment in each industral sector is about the same across coun- tries. This finding allowed the authors to estimate BOD loads across countries and over t me. The esti- mated BOD intensities per unit of employment were multiplied by sectorai employment numbers from UNIDO's industry database for 1980-97. The sectoral emissions estimates were then totaled to get daily BOD emissions in kilograms per day for each country and year. 2000 World Development Indicators 137 "**==>This document did not complete OCR process. <==**"