MAP The world by income Low income Vietnam Upper middle income Hong Kong, China Afghanistan Yemen, Rep. American Samoa Iceland Angola Zambia Argentina Ireland Azerbaijan Zimbabwe Belize Isle of Man INCOME Bangladesh Botswana Israel Benin Lower middle income Chile Italy Bhutan Albania Costa Rica Japan Burkina Faso Algeria Croatia Korea, Rep. Burundi Armenia Czech Republic Kuwait Cambodia Belarus Dominica Liechtenstein Cameroon Bolivia Estonia Luxembourg Central African Republic Bosnia and Herzegovina Gabon Macao, China Chad Brazil Grenada Malta Comoros Bulgaria Hungary Monaco Congo, Dem. Rep. Cape Verde Latvia Netherlands Congo, Rep. China Lebanon Netherlands Antilles Côte d'Ivoire Colombia Libya New Caledonia Equatorial Guinea Cuba Lithuania New Zealand Eritrea Djibouti Malaysia Norway Ethiopia Dominican Republic Mauritius Portugal Gambia, The Ecuador Mayotte Puerto Rico Georgia Egypt, Arab Rep. Mexico Qatar Ghana El Salvador Northern Mariana Islands San Marino Guinea Fiji Oman Singapore Guinea-Bissau Guatemala Palau Slovenia Haiti Guyana Panama Spain India Honduras Poland Sweden Indonesia Iran, Islamic Rep. Saudi Arabia Switzerland Kenya Iraq Seychelles United Arab Emirates Korea, Dem. Rep. Jamaica Slovak Republic United Kingdom Kyrgyz Republic Jordan St. Kitts and Nevis United States Lao PDR Kazakhstan St. Lucia Virgin Islands (U.S.) Lesotho Kiribati Trinidad and Tobago Liberia Macedonia, FYR Uruguay Madagascar Maldives Venezuela, RB Malawi Marshall Islands Mali Micronesia, Fed. Sts. High income Mauritania Morocco Andorra Moldova Namibia Antigua and Barbuda Mongolia Paraguay Aruba Mozambique Peru Australia Myanmar Philippines Austria Nepal Romania Bahamas, The Nicaragua Russian Federation Bahrain Niger Samoa Barbados Nigeria Serbia and Montenegro Belgium Pakistan South Africa Bermuda Papua New Guinea Sri Lanka Brunei Rwanda St. Vincent and the Canada São Tomé and Principe Grenadines Cayman Islands Senegal Suriname Channel Islands Sierra Leone Swaziland Cyprus Solomon Islands Syrian Arab Republic Denmark Somalia Thailand Faeroe Islands Sudan Tonga Finland Tajikistan Tunisia France Tanzania Turkey French Polynesia Timor-Leste Turkmenistan Germany Togo Ukraine Greece Uganda Vanuatu Greenland Uzbekistan West Bank and Gaza Guam The world by income Low ($735 or less) Classified according to Lower middle ($736­2,935) World Bank estimates of Upper middle ($2,936­9,075) 2002 GNI per capita High ($9,076 or more) No data Designed, edited, and produced by Communications Development Incorporated, Washington, DC, with Grundy & Northedge, London 2004 WORLD DEVELOPMENT INDICATORS Copyright 2004 by the International 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 2004 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 Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibil- ity whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any terri- tory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth's surface. Nevertheless, some distor- tions 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 promptly and, when reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, Massachusetts 01923, USA. Photo credits: Front cover, from top to bottom and left to right, Mark Hakansson/Panos Pictures, Photodisc, Photodisc, Photodisc, Alex Baluyut/World Bank; Back cover, Curt Carnemark/World Bank; Page 251, Curt Carnemark/World Bank. If you have questions or comments about this product, please contact: Development Data Center The World Bank 1818 H Street NW, Room MC2-812, Washington, DC 20433, USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: data@worldbank.org Web site: www.worldbank.org or www.worldbank.org/data ISBN 0-8213-5729-8 2004 WORLD DEVELOPMENT INDICATORS The World Bank "When we read statistics, we must see real people. When we confront problems, we must cast them as oppor tunities. When we doubt our energy or question our faith in development, we must take fresh resolve from the reality that on our work depends the fate of millions." Barber Conable, 1922­2003 President, World Bank, 1986­91 FOREWORD Development is about people. But to measure development and see its effect on people, we need good statistics. Statistics that tell us that life expectancy in the last 40 years has gone up 20 years in developing countries, more than in all the time before that. That litera- cy has improved. That infant mortality and maternal mortality have decreased. And that fewer people are living in extreme poverty. However, the statistics also tell us that malnutrition and disease still claim the lives of millions of young children. That millions more never receive a primary education. And that in countries at the center of the HIV/AIDS epidemic, life expectancy has been falling. Of the 6 billion people on the planet today, 5 billion live in developing countries. But in the next 30 years the world's population will grow by 2 billion--from 6 billion to 8 billion--and all but 50 million of them will live in today's developing countries. What will their lives be like? We hope, much better than today. The Millennium Development Goals set specific targets for improving people's lives. They were proposed and adopted by the General Assembly of the United Nations--not as vague and lofty statements of our good intentions, but as a practical guide to what can and should be accomplished by the international community in the opening quarter of the 21st century. That is why they were presented so clearly, with precise, quantified targets, based on widely accepted statistical indicators. Setting goals and measuring progress toward them in a transparent process is a proven management technique for holding our focus, avoiding wasteful diversions of effort, and encouraging robust public discussion of both means and ends. Since the adoption of the Millennium Development Goals another important step in deepening the international consensus on develop- ment was the Monterrey Conference on Financing for Development. At Monterrey developing countries recognized the need to put reduc- ing poverty and achieving the human and environmental goals of the Millennium Declaration at the center of their development programs. Developed countries accepted an obligation to uphold their share of a partnership for development by providing resources, opening trade, and relieving the burden of debt on the poorest countries. That consensus requires monitoring not only the outcomes in developing countries--but also the policies and actions of rich countries and development agencies to meet their commitments. A comprehensive development strategy calls for a comprehensive set of statistics. Any user of World Development Indicators recognizes the many gaps in sound, available information. At the World Bank we are committed to working with our partners to improve the quality and availability of development statistics. This effort starts with strengthening national statistical systems in developing countries. But it must be matched by a commitment of the international community to provide the necessary technical and financial support. The World Bank's mission statement asks us to "fight poverty with passion and professionalism for lasting results." Ultimately, it is the results that count. If we act now with realism and foresight based on good information, if we think globally and allocate our resources accordingly, we can make a lasting difference in people's lives. James D. Wolfensohn President The World Bank Group 2004 World Development Indicators v ACKNOWLEDGMENTS This book and its companion volumes, The World Bank Atlas and The Little Data Book, are prepared by a team coordinated by David Cieslikowski. Team members are Mehdi Akhlaghi, Mahyar Eshragh-Tabary, Richard Fix, Amy Heyman, Masako Hiraga, M. H. Saeed Ordoubadi, Sulekha Patel, Eric Swanson, K. M. Vijayalakshmi, Vivienne Wang, 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, Ramgopal Erobelly, Reza Farivari, and William Prince. The work was carried out under the management of Shaida Badiee. The choice of indicators and text content was shaped through close consultation with and substantial contri- butions from staff in the World Bank's four thematic networks--Environmentally and Socially Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Private Sector Development and Infrastructure--and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book's content, please see the Credits section. For a listing of our key partners, see the Partners section. Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont and Bruce Ross-Larson. The editing and production team consisted of Joseph Costello, Elizabeth McCrocklin, Christopher Trott, and Elaine Wilson. Communications Development's London partner, Grundy & Northedge, provided art direction and design. Staff from External Affairs oversaw publication and dissemination of the book. vi 2004 World Development Indicators PREFACE Four years have passed since the Millennium Development Goals sharpened the focus on measuring the results of development--not the number of projects undertaken or the dollars spent, but the improvements in people's lives. The emphasis on quantitative targets and the requirement for monitoring progress on country poverty reduction strategies have increased the demand for statistics. And that showed us how deficient the statistical systems are in many parts of the developing world. Good statistics are not just a technical issue-- they are a development issue, requiring concerted action by the entire global community. As Trevor Manuel, South Africa's minister of finance, has put it, "If you can't measure it, you can't manage it." That is why data, statistics, and indicators are at the heart of the results agenda. Governments need them. Politicians need them. Managers of development programs need them. And citizens need them--to hold governments accountable for their actions and their results. The global effort to improve the quality of development statistics has three pillars: · Strengthening the capacity of developing countries to produce, analyze, and use reliable statistics. · Providing financial support to countries expanding their statistical capacity. · Improving the quality and availability of international statistics for monitoring global progress. Much is already happening. Around the world, 37 developing countries have prepared strategic plans to guide their statistical devel- opment. The African Development Bank is systematically carrying out statistical assessments in 47 countries in that region, a key step in identifying shortcomings and constraints and in better targeting support. The Trust Fund for Statistical Capacity Building, managed by the World Bank, has provided grants to support statistical projects in more than 60 countries. Interagency cooperation is much stronger than it was even two years ago. Joint efforts have improved the measurement of such indi- cators as child mortality and immunizations. And the International Comparison Program is proceeding with an ambitious plan to measure purchasing power parities in more than 100 countries. Much has been achieved, but much remains to be done. The Second Roundtable on Development Results--held at Marrakech, Morocco, and sponsored by the multilateral development banks and the Development Assistance Committee of the Organisation for Economic Co-operation and Development--identified six broad sets of actions to improve national and international statistics: · Mainstream the strategic planning of statistical systems and help all low-income countries prepare national statistical develop- ment strategies by 2006. · Strengthen preparations for the 2010 censuses. A core source of development statistics, censuses underpin the ability to moni- tor progress toward the Millennium Development Goals. · Increase financial support for statistical capacity building. Countries that adopt good policies for their statistical systems should receive the financial support they need for their statistics. · Set up an international household survey network to coordinate and improve the effectiveness of international survey programs. · Undertake urgent improvements needed to monitor the Millennium Development Goals for 2005. · Increase the accountability of the international statistical system. Good quality information is not produced overnight. We plan today for better information tomorrow. In doing so, we must be careful not to overburden fragile national systems. We must also recognize that the cost of making mistakes and allocating resources inefficiently can dwarf the cost of producing good statistics. World Development Indicators reflects the strengths and weaknesses of the international statistical system. As development statis- tics improve, the results will appear here--as we continue striving to meet the needs of policymakers, researchers, commentators, and interested citizens. You can find out more about our products at http://www.worldbank.org/data. And you can send queries and comments to data@worldbank.org. Shaida Badiee, Director Development Data Group 2004 World Development Indicators vii TABLE OF CONTENTS FRONT 1. WORLD VIEW Foreword v Introduction 1 Acknowledgments vi Millennium Development Goals, targets, and indicators 12 Preface vii Tables Partners xiii 1.1 Size of the economy 14 Users guide xxvi 1.2 Millennium Development Goals: eradicating poverty and improving lives 18 1.3 Millennium Development Goals: protecting our common environment 22 1.4 Millennium Development Goals: overcoming obstacles 26 1.5 Women in development 28 1.6 Key indicators for other economies 32 Text figures and boxes 1a Poverty rates have been falling in all regions except Sub-Saharan Africa 1 1b But more than 1.1 billion people remain in extreme poverty 1 1c Most regions are on a path to cut extreme poverty by half by 2015 2 1d With continuing growth the number of people living in extreme poverty will fall 3 1e And the proportion of people in extreme poverty will reach an all-time low 3 1f But more than 2 billion people will live on less than $2 a day 3 1g And more than half the population of South Asia and Sub-Saharan Africa will be very poor 3 1h The undernourished are everywhere 4 1i Malnourished children are among the most vulnerable 4 1j 5 1k 5 1l Many girls still do not have equal access to education 6 1m Literacy rates have been rising as more children remain in school, but girls lag behind boys 6 1n Few countries are on track to meet the child mortality target 7 1o To reduce early childhood deaths, immunization programs must be extended and sustained 7 1p Extreme risks of dying from pregnancy or childbirth in some regions 8 1q The presence of skilled health staff lowers the risk of maternal death 8 1r HIV strikes at youth--and women are particularly vulnerable 9 1s Treated bednets are a proven way to combat malaria, but they are still not widely used 9 1t Greenhouse gas emissions rise with income 10 1u Access to water and sanitation services will require large investments 10 1v Slums are growing in newly urbanized areas 10 1w Aid has increased, but not by as much as domestic subsidies to agriculture 11 1x New commitments by donors, the first major increase in more than a decade, will still meet only a fraction of the need 11 1.2a Location of indicators for Millennium Development Goals 1­5 21 1.3a Location of indicators for Millennium Development Goals 6­7 25 1.4a Location of indicators for Millennium Development Goal 8 27 1.5a Income and gender affect children's access to basic health care 31 viii 2004 World Development Indicators 2. PEOPLE 3. ENVIRONMENT Introduction 35 Introduction 113 Tables Tables 2.1 Population dynamics 38 3.1 Rural environment and land use 116 2.2 Labor force structure 42 3.2 Agricultural inputs 120 2.3 Employment by economic activity 46 3.3 Agricultural output and productivity 124 2.4 Unemployment 50 3.4 Deforestation and biodiversity 128 2.5 Poverty 54 3.5 Freshwater 132 2.6 Social indicators of poverty 58 3.6 Water pollution 136 2.7 Distribution of income or consumption 60 3.7 Energy production and use 140 2.8 Assessing vulnerability 64 3.8 Energy efficiency, dependency, and emissions 144 2.9 Enhancing security 68 3.9 Sources of electricity 148 2.10 Education inputs 72 3.10 Urbanization 152 2.11 Participation in education 76 3.11 Urban environment 156 2.12 Education efficiency 80 3.12 Traffic and congestion 160 2.13 Education outcomes 84 3.13 Air pollution 164 2.14 Health expenditure, services, and use 88 3.14 Government commitment 166 2.15 Disease prevention: coverage and quality 92 3.15 Toward a broader measure of savings 170 2.16 Reproductive health 96 2.17 Text figures and boxes Nutrition 100 2.18 3a High-income countries use more than half the world's energy 114 Health risk factors and future challenges 104 2.19 3b Emissions of carbon dioxide vary widely, even among the Mortality 108 five largest producers of emissions 115 Text figures and boxes 3c Emissions of some greenhouse and ozone-depleting gases 2a Poverty and illiteracy are related 35 have begun to fall or slow since Rio 115 2b Defining income poverty 36 3.1a All regions are becoming less rural 119 2c Why public services fail poor people 37 3.2a The 10 countries with the most arable land per person in 2d Poor women are much less likely to receive expert care 1999­2001--and the 10 with the least 123 in childbirth 37 3.3a The 15 countries with the highest cereal yield in 2.3a Women tend to suffer disproportionately from 2001­03--and the 15 with the lowest 127 underemployment 49 3.5a The distribution of freshwater resources is uneven 135 2.6a Education lowers birth rates dramatically for rich women, 3.5b Latin America and the Caribbean has more than 20 times but not for poor ones 59 the freshwater resources per capita as the Middle East 2.10a Education suffers in primary schools with high and North Africa 135 teacher absence rates 75 3.6a High- and middle-income countries account for most 2.11a Girls from rural areas and poor households have the water pollution from organic waste 139 lowest attendance rates in Guinea 79 3.7a Energy use varies by country, even among the five 2.13a There is a strong positive relationship between primary largest energy users 143 school enrollment ratios and literacy among youth 87 3.7b People in high-income countries use more than five times 2.14a High health personnel absence rates lower the quality as much energy as do people in low-income countries 143 of health care 91 3.8a Per capita emissions of carbon dioxide vary, even among 2.15a Children in rural households are less likely to use bednets 95 the five largest producers of emissions 147 2.16a Does household wealth affect antenatal care? 99 3.9a Sources of electricity generation have shifted differently 2.18a HIV prevalence rates vary by method of data collection 107 in different income groups 151 2.18b In some countries men know more about preventing 3.10a More people now live in urban areas in low-income HIV than women do 107 countries than in high-income countries . . . 155 2.19a Under-five mortality rates are higher in poor households 3.10b Latin America was as urban as the average high-income than in rich ones 111 country in 2002 155 3.11a The use of public transportation for work trips varied widely across cities in 1998 159 3.12a The 10 countries with the most vehicles per 1,000 people in 2001--and the 10 with the fewest 163 3.14a The Kyoto Protocol on climate change 166 3.14b Global atmospheric concentrations of chlorofluorocarbons have leveled off 167 3.14c Global focus on biodiversity and climate change 168 2004 World Development Indicators ix TABLE OF CONTENTS 4. ECONOMY 5. STATES AND MARKETS Introduction 175 Introduction 251 Tables Tables 4.1 Growth of output 182 5.1 Private sector investment 254 4.2 Structure of output 186 5.2 Investment climate 258 4.3 Structure of manufacturing 190 5.3 Business environment 262 4.4 Growth of merchandise trade 194 5.4 Stock markets 266 4.5 Structure of merchandise exports 198 5.5 Financial depth and efficiency 270 4.6 Structure of merchandise imports 202 5.6 Tax policies 274 4.7 Structure of service exports 206 5.7 Relative prices and exchange rates 278 4.8 Structure of service imports 210 5.8 Defense expenditures and arms transfers 282 4.9 Structure of demand 214 5.9 Transport infrastructure 286 4.10 Growth of consumption and investment 218 5.10 Power and communications 290 4.11 Central government finances 222 5.11 The information age 294 4.12 Central government expenditures 226 5.12 Science and technology 298 4.13 Central government revenues 230 4.14 Monetary indicators and prices 234 Text figures and boxes 4.15 Balance of payments current account 238 5a Higher income economies often have less regulated labor 4.16 External debt 242 markets than lower income economies 253 4.17 External debt management 246 5.1a Foreign direct investment has expanded rapidly in many developing countries, contributing to increased productivity 257 Text figures and boxes 5.10a Mobile phone subscribers are approaching (or surpassing) 4a Economic growth varies by region 175 500 per 1,000 people in some developing and transition 4b With two decades of rapid growth, East Asia and Pacific has economies 293 caught up with Latin America and the Caribbean 176 4.a Recent economic performance 178 4.b Key macroeconomic indicators 179 4.3a Manufacturing continues to show strong growth in East Asia 193 4.5a Some developing country regions are increasing their share of merchandise exports 201 4.6a Top 10 developing country exporters in 2002 205 4.7a Top 10 developing country exporters of commercial services in 2002 209 4.8a Developing economies are consuming less transport services 213 4.10a Per capita consumption has risen in Asia, fallen in Africa 221 4.11a Some developing economies spend a large part of their current revenue on interest payments 225 4.12a Interest payments are a large part of government expenditure for some developing economies 229 4.13a Poor countries rely more on indirect taxes 233 4.15a Worker remittances are an important source of income for many developing economies 241 4.16a Since 2000, GDP has been larger than external debt for the heavily indebted poor countries 245 4.17a When the present value of a country's external debt exceeds 220 percent of exports or 80 percent of GNI the World Bank classifies it as severely indebted 249 x 2004 World Development Indicators 6. GLOBAL LINKS BACK Introduction 303 Primary data documentation 353 Acronyms and abbreviations 361 Tables Statistical methods 362 6.1 Integration with the global economy 306 Credits 364 6.2 Direction and growth of merchandise trade 310 Bibliography 366 6.3 OECD trade with low- and middle-income economies 313 Index of indicators 374 6.4 Primary commodity prices 316 6.5 Regional trade blocs 318 6.6 Tariff barriers 322 6.7 Global private financial flows 326 6.8 Net financial flows from Development Assistance Committee members 330 6.9 Aid flows from Development Assistance Committee members 332 6.10 Aid dependency 334 6.11 Distribution of net aid by Development Assistance Committee members 338 6.12 Net financial flows from multilateral institutions 342 6.13 Foreign labor and population in selected OECD countries 346 6.14 Travel and tourism 348 Text figures and boxes 6a More than half of world output is globally traded 303 6b Aid after Monterrey 304 6c Immigrant labor plays an important role in some high-income economies 305 6.2a Rich markets for developing country exports 312 6.3a Manufactured goods from developing countries dominated imports by OECD countries in 2002 315 6.8a Who were the largest donors in 2002? 331 6.9a Official development assistance from selected non-DAC donors, 1998­2002 333 6.10a Where did aid go in 2002? 337 6.11a Top aid recipients from top DAC donors reflect historical alliances and geopolitical events 341 6.13a Migration to OECD countries is growing 347 6.14a Tourism is highest in high-income countries 351 2004 World Development Indicators xi PARTNERS Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an international statistical system. Nongovernmental organ- izations 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 develop- ing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statis- tical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank's efforts, and to those of many others, to improve the quality of life of the world's people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, this section includes Web addresses for organizations that maintain Web sites. The addresses shown were active on 1 March 2004. Information about the World Bank is also provided. International and government agencies Bureau of Verification and Compliance, U.S. Department of State The Bureau of Verification and Compliance, U.S. Department of State, is responsible for international agree- ments on conventional, chemical, and biological weapons and on strategic forces; treaty verification and compliance; and support to ongoing negotiations, policymaking, and interagency implementation efforts. For information, contact the Public Affairs Officer, Bureau of Verification and Compliance, U.S. Department of State, 2201 C Street NW, Washington, DC 20520, USA; telephone: 202 647 6946; Web site: www.state.gov/t/vc. Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and infor- mation analysis center of the U.S. Department of Energy. The CDIAC's scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere; the role of the ter- restrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases; emissions of carbon dioxide to the atmosphere; long-term climate trends; the effects of elevated carbon dioxide on vegetation; and the vulnerability of coastal areas to rising sea levels. For information, contact the CDIAC, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831- 6335, USA; telephone: 865 574 0390; fax: 865 574 2232; email: cdiac@ornl.gov; Web site: http://cdiac.esd.ornl.gov. 2004 World Development Indicators xiii Deutsche Gesellschaft für Technische Zusammenarbeit The Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH is a German government­owned corporation for international cooperation with worldwide operations. GTZ's aim is to positively shape politi- cal, economic, ecological, and social development in partner countries, thereby improving people's living conditions and prospects. The organization has more than 10,000 employees in some 130 countries of Africa, Asia, Latin America, and Eastern Europe. For publications, contact Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH Corporate Communications, Dag-Hammarskjöld-Weg 1-5, 65760 Eschborn, Germany; telephone: 49 0 6196 79 1174; fax: 49 0 6196 79 6196; email: presse@gtz.de; Web site: www.gtz.de. Food and Agriculture Organization The Food and Agriculture Organization (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 agricultural pro- ductivity, 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 govern- ments; and serves as an international forum for debate on food and agricultural issues. Statistical publications of the FAO include the Production Yearbook, Trade Yearbook, and Fertilizer Yearbook. The FAO makes much of its data available online through its FAOSTAT and AQUASTAT systems. 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 5705 5727; fax: 39 06 5705 3360; email: Publications-sales@fao.org; Web site: www.fao.org. International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, was found- ed on December 7, 1944. It is responsible for establishing international standards and recommended prac- tices and procedures for the technical, economic, and legal aspects of international civil aviation opera- tions. The ICAO works to achieve the highest practicable degree of uniformity worldwide in civil aviation issues whenever this will facilitate and improve air safety, efficiency, and regularity. 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.int; Web site: 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 of the Treaty of Versailles, which brought the League of Nations into being. It became the first special- ized 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 governments 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 most comprehensive collection of labor force data. Publications can be ordered from sales agents and major booksellers throughout the world and ILO offices in many countries or from ILO Publications, 4 route des Morillons, CH-1211 Geneva 22, Switzerland; tele- phone: 41 22 799 6111; fax: 41 22 798 8685; email: publns@ilo.org; Web site: www.ilo.org. xiv 2004 World Development Indicators International Monetary Fund The International Monetary Fund (IMF) was established at a conference in Bretton Woods, New Hampshire, United States, on July 1­22, 1944. (The conference also established the World Bank.) The IMF came into official existence on December 27, 1945, and commenced financial operations on March 1, 1947. It cur- rently has 184 member countries. 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 to establish a multilateral payments system, make the general resources of the IMF temporarily available to its members under adequate safeguards, and shorten the duration and lessen the degree of disequilibrium in the inter- national balance of payments of members. The IMF maintains an extensive program for developing and compiling international statistics and is respon- sible for collecting and reporting statistics on international financial transactions and the balance of payments. In April 1996 it undertook an important initiative to improve the quality of international statistics, establishing the Special Data Dissemination Standard (SDDS) to guide members that have, or seek, access to international cap- ital 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 providing the public with comprehensive, timely, accessible, and reliable economic, financial, and sociodemographic data. Building on this work, the IMF established the Data Quality Assessment Framework (DQAF) to assess data quality in subject areas such as debt and poverty. The DQAF comprises dimensions of data quality such as methodological soundness, accuracy, serviceability, and accessibility. In 1999 work began on Reports on the Observance of Standards and Codes (ROSC), which sum- marize the extent to which countries observe certain internationally recognized standards and codes in areas including data, monetary and financial policy transparency, fiscal transparency, banking supervision, securities, insurance, payments systems, corporate governance, accounting, auditing, and insolvency and creditor rights. The IMF's major statistical publications include International Financial Statistics, Balance of Payments Statistics Yearbook, Government Finance Statistics Yearbook, and Direction of Trade Statistics Yearbook. For more information on IMF statistical publications, contact the International Monetary Fund, Publications 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; Web site: www.imf.org; SDDS and GDDS bulletin board: http://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 unique among international organizations in that it was founded on the principle of cooperation between governments and the private sector. With a membership encompassing telecommunication poli- cymakers and regulators, network operators, equipment manufacturers, hardware and software developers, regional standards-making organizations, and financing institutions, ITU's activities, policies, and strategic direction are determined and shaped by the industry it serves. The ITU's standardization activities, which have already helped foster the growth of new technologies such as mobile telephony and the Internet, are now being put to use in defining the building blocks of the emerging global information infrastructure and in designing advanced multimedia systems that deftly handle a mix of voice, data, audio, and video signals. ITU's continuing role in managing the radio-frequency spectrum ensures that radio-based systems such as cellular phones and pagers, aircraft and maritime navigation systems, scien- tific research stations, satellite communication systems, and radio and television broadcasting continue to 2004 World Development Indicators xv function smoothly and provide reliable wireless services to the world's inhabitants. And ITU's increasingly impor- tant role as a catalyst for forging development partnerships between government and private industry is help- ing bring about rapid improvements in telecommunication infrastructure in the world's developing economies. The ITU's main statistical publications are the ITU Yearbook of Statistics and the World Telecom- munication Development Report. Publications can be ordered from ITU Sales and Marketing Service, Web site: www.itu.int/ITU-D/ict/ publications/index.htm; telephone: 41 22 730 6141 (English), 41 22 730 6142 (French), and 41 22 730 6143 (Spanish); fax: 41 22 730 5194; email: sales@itu.int; telex: 421 000 uit ch; telegram: ITU GENEVE; Web site: www.itu.int. National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to pro- mote the progress of science; to advance the national health, prosperity, and welfare; and to secure the nation- al defense. It is responsible for promoting science and engineering through almost 20,000 research and edu- cation projects. In addition, the NSF fosters the exchange of scientific information among scientists and engineers in the United States and other countries, supports programs to strengthen scientific and engineer- ing 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 trends in science and engineering research and education. Electronic copies of NSF documents can be obtained from the NSF's online document system (www.nsf.gov/ pubsys/ods/index.html) or requested by email from its automated mailserver (getpub@nsf.gov). Documents can also be requested from the NSF Publications Clearinghouse by mail, at PO Box 218, Jessup, MD 20794-0218, USA, or by telephone, at 301 947 2722. For more information, con- tact the National Science Foundation, 4201 Wilson Boulevard, Arlington, VA 22230, USA; telephone: 703 292 5111; Web site: 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 the Organisation for European Economic Co-operation (OEEC) to administer Marshall Plan funding 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 policy among industrial countries. The OECD is the international organization of the industrialized, market economy countries. Representatives of member countries meet at the OECD to exchange information and harmonize policy with a view to maxi- mizing economic growth in member countries and helping nonmember countries develop more 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 transition economies. Also associated with the OECD are several agencies or bodies that have their own governing statutes, including the International 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 Aid Recipients, 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, Trends in International Migration, and Services: Statistics on International Transactions. xvi 2004 World Development Indicators For information on OECD publications, contact the OECD, 2, rue André Pascal, F-75775 Paris Cedex 16, France; telephone: 33 1 45 24 81 67; fax: 33 1 45 24 19 50; email: sales@oecd.org; Web sites: www.oecd.org and www.oecd.org/bookshop. Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) was established by the Swedish Parliament as an independent foundation in July 1966. SIPRI conducts research on questions of conflict and cooper- ation of importance for international peace and security, with the aim of contributing to an understanding of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI's research work is disseminated through books and reports as well as through symposia and sem- inars. SIPRI's main publication, SIPRI Yearbook, serves as a single authoritative and independent source on armaments and arms control, armed conflicts and conflict resolution, security arrangements, and dis- armament. SIPRI Yearbook provides an overview of developments in international security, weapons and technology, military expenditure, the arms trade and arms production, and armed conflicts, along with efforts to control conventional, nuclear, chemical, and biological armaments. For more information on SIPRI publications contact SIPRI at Signalistgatan 9, SE-169 70 Solna, Sweden; telephone: 46 8 655 97 00; fax:46 8 655 97 33; email: sipri@sipri.org; for book orders: http://home.sipri.se/publications.html; Web site: www.sipri.org. United Nations The United Nations and its specialized agencies maintain a number of programs for the collection of inter- national statistics, some of which are described elsewhere in this book. At United Nations headquarters the Statistics Division provides a wide range of statistical outputs and services for producers and users of statistics worldwide. The Statistics Division publishes statistics on international trade, national accounts, demography and pop- ulation, gender, industry, energy, environment, human settlements, and disability. Its major statistical publi- cations include the International Trade Statistics Yearbook, Yearbook of 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, Department I004, 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; Web site: www.un.org. United Nations Centre for Human Settlements (Habitat), Global Urban Observatory The Urban Indicators Programme of the United Nations Centre for Human Settlements (Habitat) was estab- lished to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. In 1997 the Urban Indicators Programme was integrated into the Global Urban Observatory, the prin- cipal 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 programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. 2004 World Development Indicators xvii Contact the Co-ordinator, Global Urban Observatory and Statistics, Urban Secretariat, UN-HABITAT, PO Box 30030, Nairobi, Kenya; telephone: 254 2 623119; fax: 254 2 623080; email: habitat.publications@ unhabitat.org or guo@unhabitat.org; Web site: www.unhabitat.org. United Nations Children's Fund The United Nations Children's Fund (UNICEF), the only organization of the United Nations dedicated exclu- sively to children, works with other United Nations bodies and with governments and nongovernmental organizations to improve children's lives in more than 140 developing countries through community-based services in primary health care, basic education, and safe water and sanitation. UNICEF's major publications include The State of the World's Children and The Progress of Nations. For information on UNICEF publications contact the Chief, EPS, Division of Communication, UNICEF, 3 United Nations Plaza, New York, NY 10017, USA; telephone: 212 326 7000; fax: 212 303 7985; email: pubdoc@unicef.org; Web site: www.unicef.org and www.un.org/Publications. United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. It was established as a permanent inter- governmental body in 1964 in Geneva with a view to accelerating economic growth and development, par- ticularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmen- tal deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. UNCTAD produces a number of publications containing trade and economic statistics, including the Handbook of International Trade and Development Statistics. For information, contact UNCTAD, Palais des Nations, 8-14, Avenue de la Paix, 1211 Geneva 10, Switzerland; telephone: 41 22 907 1234; fax: 41 22 907 0043; email: info@unctad.org; Web site: www.unctad.org. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations established in 1945 to promote "collaboration among nations through education, science, and culture in order to further universal respect for justice, for the rule of law, and for the human rights and fun- damental freedoms . . . for the peoples of the world, without distinction of race, sex, language, or religion." The UNESCO Institute for Statistics' principal statistical publications are the Global Education Digest (GED) and regional statistical reports, as well as the on-line database. For publications, contact the UNESCO Institute for Statistics, C.P. 6128, Succursale Centre-ville, Montreal, Quebec, H3C 3J7, Canada; telephone: 514 343 6880; fax: 514 343 6882; email: uis@unesco.org; Web site: www.unesco.org; and for the Institute for Statistics: www.uis.unesco.org. United Nations Environment Programme The mandate of the United Nations Environment Programme (UNEP) is to provide leadership and encourage partnership in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. UNEP publications include Global Environment Outlook and Our Planet (a bimonthly magazine). For information, contact the UNEP, PO Box 30552, Nairobi, Kenya; telephone: 254 2 621234; fax: 254 2 624489/90; email: eisinfo@unep.org; Web site: www.unep.org. xviii 2004 World Development Indicators United Nations Industrial Development Organization The United Nations Industrial Development Organization (UNIDO) was established in 1966 to act as the cen- tral coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels. In 1985 UNIDO became the 16th specialized agency of the United Nations, with a mandate to help develop scientific and technological plans and programs for indus- trialization in the public, cooperative, and private sectors. UNIDO's databases and information services include the Industrial Statistics Database (INDSTAT), Commodity Balance Statistics Database (COMBAL), Industrial Development Abstracts (IDA), and the International Referral System on Sources of Information. Among its publications is the International Yearbook of Industrial Statistics. For information, contact UNIDO Public Information Section, Vienna International Centre, PO Box 300, A-1400 Vienna, Austria; telephone: 43 1 26026 5031; fax: 43 1 21346 5031 or 26026 6843; email: pub- lications@unido.org; Web site: 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 Centre for Settlement of Investment Disputes (ICSID). Established in 1944 at a conference of world leaders in Bretton Woods, New Hampshire, United States, the World Bank is the world's largest source of development assistance. In 2003 the World Bank provided $18.5 billion in development assistance and worked in more than 100 devel- oping countries, bringing finance and technical expertise to help them reduce poverty. The World Bank Group's mission is to fight poverty and improve the living standards of people in the devel- oping world. It is a development bank, providing loans, policy advice, technical assistance, and knowledge sharing services to low- and middle-income countries to reduce poverty. The Bank promotes growth to create jobs and to empower poor people to take advantage of these opportunities. It uses its financial resources, trained staff, and extensive knowledge base to help each developing country onto a path of stable, sustain- able, and equitable growth in the fight against poverty. The World Bank Group has 184 member countries. For information about the World Bank, visit its Web site at www.worldbank.org. For more information about development data, contact the Development Data Group, World Bank, 1818 H Street NW, Washington, DC 20433, USA; telephone: 800 590 1906 or 202 473 7824; fax: 202 522 1498; email: data@worldbank.org; Web site: www.worldbank.org/data. World Health Organization The constitution of the World Health Organization (WHO) was adopted on July 22, 1946, by the International Health Conference, convened in New York by the Economic and Social Council of the United Nations. The objective of the WHO, a specialized agency of the United Nations, is the attainment by all people of the high- est possible level of health. The WHO carries out a wide range of functions, including coordinating international health work; helping gov- ernments strengthen health services; providing technical assistance and emergency aid; working for the pre- vention and control of disease; promoting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promoting and coordinating biomedical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international standards for biological, pharmaceutical, and similar products; and standardizing diagnostic procedures. 2004 World Development Indicators xix The WHO publishes the World Health Statistics Annual and many other technical and statistical publications. For publications, contact the World Health Organization, Marketing and Dissemination, CH-1211 Geneva 27, Switzerland; telephone: 41 22 791 2476; fax: 41 22 791 4857; email: publications@who.int; Web site: www.who.int. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is an international organization dedicated to helping to ensure that the rights of creators and owners of intellectual property are protected worldwide and that inventors and authors are thus recognized and rewarded for their ingenuity. This international protection acts as a spur to human creativity, pushing forward the boundaries of science and technology and enrich- ing the world of literature and the arts. By providing a stable environment for the marketing of intellectual property products, WIPO also oils the wheels of international trade. WIPO's main tasks include harmonizing national intellectual property legislation and procedures, pro- viding services for international applications for industrial property rights, exchanging intellectual property information, providing legal and technical assistance to developing and other countries facilitating the res- olution of private intellectual property disputes, and marshalling information technology as a tool for stor- ing, accessing, and using valuable intellectual property information. 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, chemin des Colombettes, CH-1211 Geneva 20, Switzerland; telephone: 41 22 338 9734; fax: 41 22 740 1812; email: ebookshop@wipo.int; Web site: www.wipo.int. World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know- how. 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 141 countries, seven territories, and some 350 Affiliate Members rep- resenting the private sector, educational institutions, tourism associations, and local tourism authorities. The World Tourism Organization publishes the Yearbook of Tourism Statistics, Compendium of Tourism Statistics, and Travel and Tourism Barometer (triannual). For information, contact the World Tourism Organization, Calle Capitán Haya, 42, 28020 Madrid, Spain; telephone: 34 91 567 8100; fax: 34 91 571 3733; email: infoshop@world-tourism.org; Web site: www.world-tourism.org. World Trade Organization The World Trade Organization (WTO), established on January 1, 1995, is the successor to the General Agreement on Tariffs and Trade (GATT). The WTO has 144 member countries and is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as possible. It does this by administering trade agreements, act- ing as a forum for trade negotiations, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues--through technical assistance and training programs--and cooperating with other international organizations. At the heart of the system--known as the multilateral xx 2004 World Development Indicators trading system--are WTO's agreements, negotiated and signed by a large majority of the world's trading nations and ratified by their parliaments. The WTO's International Trade Statistics is its main statistical publication, providing comprehensive, comparable, and up-to-date statistics on trade. For publications, contact the World Trade Organization, Publications Services, Centre William Rappard, rue de Lausanne 154, CH-1211, Geneva 21, Switzerland; telephone: 41 22 739 5208 or 5308; fax: 41 22 739 5792; email: publications@wto.org; Web site: www.wto.org. Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. It has more than 850 pages of data, including detailed information on more than 560 container ports in more than 150 countries and a review section that features two-year rankings for 350 ports. The information can be accessed on the Web at www.ci-online.co.uk, which also provides a comprehensive online daily business news and information service for the container industry. For more information, contact Informa UK at 69-77 Paul Street, London, EC2A 4LQ, UK; telephone: 44 1206 772061; fax: 44 1206 772563; email: webtechhelp@informa.com. Euromoney Publications PLC Euromoney Publications PLC provides a wide range of financial, legal, and general business information. The monthly magazine Euromoney is an authoritative source of detailed yet concise information on the trends and developments in international banking and capital markets and carries a semiannual rating of country creditworthiness. For information, contact Euromoney Publications PLC, Nestor House, Playhouse Yard, London EC4V 5EX, UK; telephone: 44 870 90 62 600; email: customerservice@euromoney.com; Web site: www.euromoney.com. Institutional Investor, Inc. Institutional Investor, Inc., develops country credit ratings every six months based on information provided by leading international banks. It publishes the monthly magazine Institutional Investor, and InstitutionalInvestor.com strives to be the gateway to all Institutional Investor publications online, offering selected articles from its 40 publications. For information, contact Institutional Investor, Inc., 225 Park Avenue South, New York, NY 10003, USA; telephone: 212 224 3800; email: info@iiplatinum.com; Web site: www.institutionalinvestor.com. International Data Corporation International Data Corporation (IDC) is a premier global market intelligence and advisory firm in the information technology and telecommunications industries. IDC analyzes and predicts technology trends to enable clients to make strategic, fact-based decisions on information technology purchases and business strategy. More than 700 IDC analysts in 50 countries have provided local expertise and insights on technology markets for 40 years. For further information on IDC's products and services, contact IDC, Corporate Headquarters, 5 Speen Street, Framingham, MA 01701 USA; telephone: 508 872 8200; Web site: www.idc.com. 2004 World Development Indicators xxi International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization with public and private sector members in some 70 countries. The IRF's mission is to encourage and promote development and main- tenance of better and safer roads and road networks. It helps put in place technological solutions and man- agement practices that provide maximum economic and social returns from national road investments. The IRF believes that rationally planned, efficiently managed and well-maintained road networks offer high levels of user safety and have a significant impact on sustainable economic growth, prosperity, social well-being, and human development. The IRF has a major role to play in all aspects of road policy and development worldwide. For govern- ments and financial institutions, the IRF provides a wide base of expertise for planning road development strategy and policy. For its members, the IRF is a business network, a link to external institutions and agen- cies and a business card of introduction to government officials and decisionmakers. For the community of road professionals, the IRF is a source of support and information for national road associations, advoca- cy groups, companies, and institutions dedicated to the development of road infrastructure. The IRF publishes World Road Statistics. Contact the Geneva office at chemin de Blandonnet 2, CH-1214 Vernier, Geneva, Switzerland; telephone: 41 22 306 0260; fax: 41 22 306 0270; or the Washington, 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; Web site: www.irfnet.org. Moody's Investors Service Moody's Investors Service is a global credit analysis and financial opinion firm. It provides the international investment community with globally consistent credit ratings on debt and other securities issued by North American state and regional government entities, by corporations worldwide, 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 companies, public utilities, research libraries, manufacturers, and gov- ernment 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; tele- phone: 212 553 0377; fax: 212 553 0882; Web site: www.moodys.com. Netcraft Netcraft is an Internet services company based in Bath, United Kingdom. Netcraft's work includes the pro- vision of network security services and research data and analysis of the Internet. It is an authority on the market share of Web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scripting languages, and content technologies on the Internet. For information, visit www.netcraft.com. PricewaterhouseCoopers Drawing on the talents of 120,000 people in 139 countries, PricewaterhouseCoopers provides industry- focused assurance, tax, and advisory services for public and private clients in corporate accountability, risk management, structuring and mergers and acquisitions, and performance and process improvement. PricewaterhouseCoopers publishes Corporate Taxes: Worldwide Summaries and Individual Taxes: Worldwide Summaries. xxii 2004 World Development Indicators For information, contact PricewaterhouseCoopers, 1177 Avenue of the Americas, New York, NY 10036, USA; telephone: 646 471 4000; fax: 646 471 3188; Web site: www.pwcglobal.com. The PRS Group, Inc. The PRS Group, Inc., is a global leader in political and economic risk forecasting and market analysis and has served international companies large and small for more than 20 years. The data it contributed to this year's World Development Indicators come from the International 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 analysis make it the standard for any organization practicing effective risk management. For information, contact The PRS Group, Inc., 6320 Fly Road, Suite 102, East Syracuse, NY 13057-9358, USA; telephone: 315 431 0511; fax: 315 431 0200; email: custserv@PRSgroup.com; Web site: www.prsgroup.com or www.ICRGOnline.com. Standard & Poor's Equity Indexes and Rating Services For more than 140 years Standard & Poor's, a division of the McGraw-Hill Corporation, has been a preeminent global provider of independent highly valued investment data, valuation, analysis, and opinions and is still deliv- ering on that original mission. The S&P 500 index, one of its most popular products, is calculated and maintained by Standard & Poor's Index Services, a leading provider of equity indexes. Standard & Poor's indexes are used by investors around the world for measuring investment performance and as the basis for a wide range of financial instruments. Standard & Poor's Sovereign Ratings provides issuer and local and foreign currency debt ratings for sov- ereign governments and for sovereign-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 on equity indexes, contact Standard & Poor's Index Services, 55 Water Street, New York, NY 10041, USA; telephone: 212 438 1000; email: index_services@sandp.com; Web site: www.spglobal.com. For information on ratings contact the McGraw-Hill Companies, Inc., Executive Offices, 1221 Avenue of the Americas, New York, NY 10020, USA; telephone: 212 512 4105 or 800 352 3566 (toll free); fax: 212 512 4105; email: ratingsdirect@standardandpoors.com; Web site: www.ratingsdirect.com. Standard & Poor's Emerging Markets Data Base Standard & Poor's Emerging Markets Data Base (EMDB) is the world's leading source for information and indices on stock markets in developing countries. The EMDB was the first database to track emerging stock markets. It currently covers 53 markets and more than 2,200 stocks. Drawing a sample of stocks in each EMDB market, Standard & Poor's calculates indices to serve as benchmarks that are consistent across national boundaries. Standard & Poor's calculates one index, the S&P/IFCG (Global) index, that reflects the perspective of local investors and those interested in broad trends in emerging markets and another, the S&P/IFCI (Investable) index, that provides a broad, neutral, and historically consistent benchmark for the growing emerging market investment community. For information on subscription rates, contact S&P Emerging Markets Data Base, 55 Water Street, 42nd Floor, New York, NY, 10041-0003; Telephone: 212 438 2046; Fax: 212 438 3429; Email: indexservices@sandp.com; Web site: www.standardandpoors.com. 2004 World Development Indicators xxiii World Conservation Monitoring Centre The World Conservation Monitoring Centre (WCMC) provides information on the conservation and sustain- able use of the world's living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the infor- mation needed for wise management of the world's living resources. Committed to the principle of data exchange with other centers and noncommercial users, the WCMC, whenever possible, places the data it manages in the public domain. For information, contact the World Conservation Monitoring Centre, 219 Huntington Road, Cambridge CB3 0DL, UK; telephone: 44 12 2327 7314; fax: 44 12 2327 7136; email: info@unep-wcmc.org; Web site: www.unep-wcmc.org. World Information Technology and Services Alliance The World Information Technology and Services Alliance (WITSA) is a consortium of 53 information tech- nology industry associations from around the world. WITSA members represent more than 90 percent of the world information technology market. As the global voice of the information technology industry, WITSA is dedicated to advocating policies that advance the industry's growth and development; facilitating inter- national trade and investment in information technology products and services; strengthening WITSA's national industry associations by sharing knowledge, experience, and information; providing members with a network of contacts in nearly every region; and hosting the World Congress on Information Technology. WITSA's publication, Digital Planet 2002: The Global Information Economy, uses data provided by the International Data Corporation. For information, contact WITSA, 1401 Wilson Boulevard, Suite 1100, Arlington, VA 22209, USA; tele- phone: 703 284 5333; fax: 617 687 6590; email: ahalvorsen@itaa.org; Web site: www.witsa.org. World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on glob- al environmental and development issues. The institute provides--and helps other institutions provide-- objective information and practical proposals for policy and institutional change that will foster environmen- tally sound, socially equitable development. The institute's current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For information, contact the World Resources Institute, Suite 800, 10 G Street NE, Washington, DC 20002, USA; telephone: 202 729 7600; fax: 202 729 7610; email: front@wri.org; Web site: www.wri.org. xxiv 2004 World Development Indicators USERS GUIDE Tables result in discrepancies between subgroup aggre- affecting the collection and reporting of data. For The tables are numbered by section and display the gates and overall totals. For further discussion of these reasons, although data are drawn from the identifying icon of the section. Countries and aggregation methods, see Statistical methods. sources thought to be most authoritative, they economies are listed alphabetically (except for Hong should be construed only as indicating trends and Kong, China, which appears after China). Data are Aggregate measures for regions characterizing major differences among economies shown for 152 economies with populations of more The aggregate measures for regions include only low- rather than offering precise quantitative measures than 1 million, as well as for Taiwan, China, in select- and middle-income economies (note that these of those differences. Discrepancies in data present- ed tables. Table 1.6 presents selected indicators for measures include developing economies with popu- ed in different editions of the World Development 56 other economies--small economies with popula- lations of less than 1 million, including those listed Indicators reflect updates by countries as well as tions between 30,000 and 1 million and smaller in table 1.6). revisions to historical series and changes in economies if they are members of the International The country composition of regions is based on methodology. Thus readers are advised not to com- Bank for Reconstruction and Development (IBRD) or, the World Bank's analytical regions and may differ pare data series between editions of the World as it is commonly known, the World Bank. The term from common geographic usage. For regional classi- Development Indicators or between different World country, used interchangeably with economy, does fications, see the map on the inside back cover and Bank publications. Consistent time-series data for not imply political independence, but refers to any the list on the back cover flap. For further discussion 1960­2002 are available on the World Development territory for which authorities report separate social of aggregation methods, see Statistical methods. Indicators CD-ROM and in WDI Online. or economic statistics. When available, aggregate Except where otherwise noted, growth rates measures for income and regional groups appear at Statistics are in real terms. (See Statistical methods for infor- the end of each table. Data are shown for economies as they were consti- mation on the methods used to calculate growth Indicators are shown for the most recent year or tuted in 2002, and historical data are revised to rates.) Data for some economic indicators for some period for which data are available and, in most reflect current political arrangements. Exceptions are economies are presented in fiscal years rather than tables, for an earlier year or period (usually 1990 in noted throughout the tables. calendar years; see Primary data documentation. All this edition). Time-series data are available on the Additional information about the data is provided dollar figures are current U.S. dollars unless other- World Development Indicators CD-ROM and in WDI in Primary data documentation. That section sum- wise stated. The methods used for converting nation- Online. marizes national and international efforts to improve al currencies are described in Statistical methods. Known deviations from standard definitions or basic data collection and gives information on pri- breaks in comparability over time or across countries mary sources, census years, fiscal years, and other Country notes are either footnoted in the tables or noted in About background. Statistical methods provides technical the data. When available data are deemed to be too information on some of the general calculations and China. On July 1, 1997, China resumed its exercise weak to provide reliable measures of levels and formulas used throughout the book. of sovereignty over Hong Kong, and on December 20, trends or do not adequately adhere to international 1999, it resumed its exercise of sovereignty over standards, the data are not shown. Data consistency and reliability Macao. Unless otherwise noted, data for China do Considerable effort has been made to standardize not include data for Hong Kong, China; Taiwan, Aggregate measures for income groups the data, but full comparability cannot be assured, China; or Macao, China. The aggregate measures for income groups include and care must be taken in interpreting the indica- 208 economies (the economies listed in the main tors. Many factors affect data availability, compara- Democratic Republic of Congo. Data for the tables plus those in table 1.6) wherever data are bility, and reliability: statistical systems in many Democratic Republic of Congo (Congo, Dem. Rep., in available. To maintain consistency in the aggregate developing economies are still weak; statistical the table listings) refer to the former Zaire. (The measures over time and between tables, missing methods, coverage, practices, and definitions differ Republic of Congo is referred to as Congo, Rep., in data are imputed where possible. The aggregates widely; and cross-country and intertemporal com- the table listings.) are totals (designated by a t if the aggregates include parisons involve complex technical and conceptual gap-filled estimates for missing data and by an s, for problems that cannot be unequivocally resolved. Czech Republic and Slovak Republic. Data are simple totals, where they do not), median values (m), Data coverage may not be complete because of spe- shown whenever possible for the individual countries weighted averages (w), or simple averages (u). Gap cial circumstances or for economies experiencing formed from the former Czechoslovakia--the Czech filling of amounts not allocated to countries may problems (such as those stemming from conflicts) Republic and the Slovak Republic. xxvi 2004 World Development Indicators Eritrea. Data are shown for Eritrea whenever possi- previous years refer to aggregated data for the for- economies are those with a GNI per capita of more ble, but in most cases before 1992 Eritrea is includ- mer People's Democratic Republic of Yemen and the than $735 but less than $9,076. Lower-middle- ed in the data for Ethiopia. former Yemen Arab Republic unless otherwise noted. income and upper-middle-income economies are separated at a GNI per capita of $2,935. High- Germany. Data for Germany refer to the unified Changes in the System of National Accounts income economies are those with a GNI per capita Germany unless otherwise noted. World Development Indicators uses terminology in of $9,076 or more. The 12 participating member line with the 1993 United Nations System of countries of the European Monetary Union (EMU) Jordan. Data for Jordan refer to the East Bank only National Accounts (SNA). For example, in the 1993 are presented as a subgroup under high-income unless otherwise noted. SNA gross national income (GNI) replaces gross economies. national product (GNP). See About the data for Serbia and Montenegro. On February 4, 2003, the tables 1.1 and 4.9. Symbols Federal Republic of Yugoslavia changed its name to Most economies continue to compile their .. Serbia and Montenegro. national accounts according to the 1968 SNA, but means that data are not available or that aggregates more and more are adopting the 1993 SNA. cannot be calculated because of missing data in the Timor-Leste. On May 20, 2002, Timor-Leste became Economies that use the 1993 SNA are identified in years shown. an independent country. Data for Indonesia include Primary data documentation. A few low-income Timor-Leste through 1999 unless otherwise noted. economies still use concepts from older SNA guide- 0 or 0.0 lines, including valuations such as factor cost, in means zero or less than half the unit shown. Union of Soviet Socialist Republics. In 1991 the describing major economic aggregates. Union of Soviet Socialist Republics came to an end. / Available data are shown for the individual countries Classification of economies in dates, as in 1990/91, means that the period of now existing on its former territory (Armenia, For operational and analytical purposes the World time, usually 12 months, straddles two calendar Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Bank's main criterion for classifying economies is years and refers to a crop year, a survey year, an aca- Kyrgyz Republic, Latvia, Lithuania, Moldova, Russian GNI per capita. Every economy is classified as low demic year, or a fiscal year. Federation, Tajikistan, Turkmenistan, Ukraine, and income, middle income (subdivided into lower middle Uzbekistan). External debt data presented for the and upper middle), or high income. For income $ Russian Federation prior to 1992 are for the former classifications see the map on the inside front cover means current U.S. dollars unless otherwise noted. Soviet Union. The debt of the former Soviet Union is and the list on the front cover flap. Low- and middle- included in the Russian Federation data after 1992 income economies are sometimes referred to as > on the assumption that 100 percent of all outstand- developing economies. The use of the term is con- means more than. ing external debt as of December 1991 has become venient; it is not intended to imply that all economies a liability of the Russian Federation. Beginning in in the group are experiencing similar development or < 1993 the data for the Russian Federation have been that other economies have reached a preferred or means less than. revised to include obligations to members of the for- final stage of development. Note that classification mer Council for Mutual Economic Assistance and by income does not necessarily reflect development Data presentation conventions other countries in the form of trade-related credits status. Because GNI per capita changes over time, · A blank means not applicable or, for an aggre- amounting to $15.4 billion as of the end of 1996. the country composition of income groups may gate, not analytically meaningful. change from one edition of World Development · A billion is 1,000 million. República Bolivariana de Venezuela. In December Indicators to the next. Once the classification is fixed · A trillion is 1,000 billion. 1999 the official name of Venezuela was changed to for an edition, based on GNI per capita in the most · Figures in italics refer to years or periods other República Bolivariana de Venezuela (Venezuela, RB, recent year for which data are available (2002 in this than those specified. in the table listings). edition), all historical data presented are based on · Data for years that are more than three years the same country grouping. from the range shown are footnoted. Republic of Yemen. Data for the Republic of Yemen Low-income economies are those with a GNI per refer to that country from 1990 onward; data for capita of $735 or less in 2002. Middle-income The cutoff date for data is February 1, 2004. 2004 World Development Indicators xxvii 1 WORLDVIEW T he Millennium Development Goals put the world community on a time table. When 189 member states of the United Nations adopted the Millennium Declaration in September 2000, they looked backwards to 1990 and ahead to 2015 and gave themselves 25 years to produce substantial improvements in the lives of people. At the time, it was clear that in many places development progress had slowed and would have to be accelerated if the ambitious targets of the Millennium Development Goals were to be achieved. As in the past four editions, this section of World Development Indicators reviews progress toward the major development goals. Until recently we have been gauging progress toward the Millennium Development Goals based on the record of the 1990s. Now, we are closer to 2015 than to 1990, and we are getting our first look at the record of the 21st century. There are hopeful signs. Global poverty rates continue to fall. Fewer people are living in extreme poverty, after an increase in the late 1990s. In countries that have laid a good foundation for growth, indicators of social development are also improving. But progress is uneven. Slow growth, low educational achievement, poor health, and civil disturbances remain obstacles for many. It is still too early to conclude that the world as a whole is on track to achieve the Millennium Development Goals--or that it is not. What is clear is that the goals remain a great challenge and that hard work lies ahead. 1a 1b Poverty rates have been falling in all regions except But more than 1.1 billion people remain in extreme Sub-Saharan Africa poverty Share of people living on less than $1 a day (%) Number of people living on less than $1 a day (millions) 70 1,500 60 1,200 50 Sub-Saharan Africa 900 40 South Asia 30 600 East Asia and Pacific 20 China Latin America and Caribbean 300 10 Middle East and North Africa Europe and Central Asia 0 0 1981 1984 1987 1990 1993 1996 1999 2001 1981 1984 1987 1990 1993 1996 1999 2001 South Asia Sub-Saharan Africa East Asia and Pacific Latin America & Caribbean Middle East & North Africa Europe & Central Asia Source: World Bank staff estimates. Source: World Bank staff estimates. 2004 World Development Indicators 1 1 Eradicate extreme poverty . . . extreme poverty. Other regions have seen little or no change. In The first Millennium Development Goal calls for cutting in half the early 1990s the transition economies of Europe and Central the proportion of people living in extreme poverty--and those Asia experienced a sharp drop in income. Poverty rates rose to suffering from hunger--between 1990 and 2015. A poverty 6 percent at the end of the decade before beginning to recede. line of $1 a day ($1.08 in 1993 purchasing power parity terms) Continued progress in poverty reduction depends on eco- has been accepted as the working definition of extreme pover- nomic growth and the distribution of income. Growth without ty in low-income countries. In middle-income countries a pover- poverty reduction is at least a theoretical possibility, and in ty line of $2 a day ($2.15 in 1993 purchasing power parity regions such as Latin America, where the distribution of income terms) is closer to a practical minimum, and national poverty is less equitable, the poverty reducing effects of growth are lines may be set even higher. weaker. In looking ahead, income distribution is assumed to In 1990, 1,219 million people, 28 percent of the population remain unchanged on average. If projected growth remains on of low- and middle-income countries, lived on less than $1 a day. track through 2015, global poverty rates measured at $1 a day Over the next 11 years gross domestic product (GDP) in low- and will fall to 12.7 percent--less than half the 1990 level of middle-income countries grew 31 percent, and by 2001 the 28 percent--and 363 million fewer people will live in extreme poverty rate had fallen to 21 percent. During the same period poverty than at the beginning of the 21st century. population in those countries grew by 15 percent to 5 billion, Poverty rates will fall fastest in East Asia and Pacific outside leaving about 1,100 million people in extreme poverty. of China, but the huge reduction in the number of people below New estimates of poverty rates, based on reexamination of the $1 a day poverty line in China will dominate global totals. In household survey data back to 1981, show that global trends in Europe and Central Asia and in the Middle East and North Africa, poverty reduction have been dominated by rapid growth in China where poverty rates measured at $1 a day are low, a continua- and the East Asia and Pacific region. GDP per capita more than tion of current trends will cut poverty rates to half their current tripled while the proportion of people in extreme poverty fell levels. South Asia, led by continuing growth in India, is likely to from 56 percent to 16 percent. Poverty also fell in South Asia reach or exceed the target. But growth and poverty reduction are over the past 20 years, and while the decline was not as rapid, proceeding more slowly in Latin America and the Caribbean, almost 50 million fewer people were living in extreme poverty by which will not reach the target unless growth picks up. The most 2001. But in Sub-Saharan Africa, where GDP per capita shrank difficult case is Sub-Saharan Africa, where poverty has increased 14 percent, poverty rose from 41 percent in 1981 to 46 percent since 1990 and will, on present trends, fall very slowly in the next in 2001, and an additional 140 million people were living in 11 years, unless there is a major change in prospects. 1c Most regions are on a path to cut extreme poverty by half by 2015 Share of people living on less than $1 (or $2) a day (%) East Asia & Pacific Europe & Central Asia Latin America & the Caribbean 50 40 29.6 28.4 30 24.5 19.7 20.5 20 14.8 12.3 10.3 11.3 9.5 15.6 7.6 10 3.7 1.3 2.3 0.5 0.3 5.6 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Middle East & North Africa South Asia Sub-Saharan Africa 50 44.6 41.3 42.3 46.5 40 30 23.2 31.1 22.3 21.4 20.6 20 10.2 16.4 10 1.2 2.3 2.4 1.2 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Goal Poverty rate at $1 a day Actual Poverty rate at $2 a day Actual 1990 2001 2015 Projected Projected Source: World Bank staff estimates. Path to goal 2 2004 World Development Indicators New poverty estimates trace the decline of global poverty levels over the last two decades 1d With continuing growth the number of people living in extreme poverty will fall People living on less than $1 a day (millions) Region 1981 1984 1987 1990 1993 1996 1999 2001 East Asia & Pacific 796 562 426 472 415 287 282 284 China 634 425 308 375 334 212 223 212 Europe & Central Asia 1 1 2 2 17 20 30 18 Latin America & Caribbean 36 46 45 49 52 52 54 50 Middle East & North Africa 9 8 7 6 4 6 8 7 South Asia 475 460 473 462 476 461 453 428 India 382 374 370 357 380 400 352 359 Sub-Saharan Africa 164 198 219 227 241 270 292 314 Total 1,480 1,276 1,171 1,219 1,206 1,095 1,117 1,101 1e And the proportion of people in extreme poverty will reach an all-time low in 2015 Share of people living on less than $1 a day (%) Region 1981 1984 1987 1990 1993 1996 1999 2001 East Asia & Pacific 57.7 38.9 28 29.6 24.9 16.6 15.7 15.6 China 63.8 41.0 28.5 33.0 28.4 17.4 17.8 16.6 Europe & Central Asia 0.3 0.3 0.4 0.5 3.7 4.2 6.2 3.7 Latin America & Caribbean 9.7 11.8 10.9 11.3 11.3 10.7 10.5 9.5 Middle East & North Africa 5.1 3.8 3.2 2.3 1.6 2.0 2.6 2.4 South Asia 51.5 46.8 45 41.3 40.1 36.6 34.0 31.1 India 54.4 49.8 46.3 42.1 42.3 42.2 35.3 34.7 Sub-Saharan Africa 41.6 46.3 46.8 44.6 43.7 45.3 45.4 46.5 Total 40.3 32.8 28.4 27.9 26.2 22.7 22.2 21.3 1f But more than 2 billion people will live on less than $2 a day People living on less than $2 a day (millions) Region 1981 1984 1987 1990 1993 1996 1999 2001 East Asia & Pacific 1,170 1,109 1,028 1,116 1,079 922 900 868 China 876 814 731 825 803 650 628 594 Europe & Central Asia 8 9 8 58 78 97 112 94 Latin America & Caribbean 99 119 115 125 136 117 127 128 Middle East & North Africa 52 50 53 51 52 61 70 70 South Asia 821 859 911 958 1,005 1,029 1,034 1,059 India 630 661 697 731 770 806 804 826 Sub-Saharan Africa 288 326 355 382 409 445 487 514 Total 2,438 2,471 2,470 2,689 2,759 2,672 2,730 2,733 1g And more than half the population of South Asia and Sub-Saharan Africa will be very poor Share of people living on less than $2 a day (%) Region 1981 1984 1987 1990 1993 1996 1999 2001 East Asia & Pacific 84.8 76.6 67.7 69.9 64.8 53.3 50.3 47.6 China 88.1 78.5 67.4 72.6 68.1 53.4 50.1 46.7 Europe & Central Asia 1.9 2.0 1.7 12.3 16.6 20.6 23.5 19.7 Latin America & Caribbean 26.9 30.4 27.8 28.4 29.5 24.1 25.1 24.5 Middle East & North Africa 28.9 25.2 24.2 21.4 20.2 22.3 24.3 23.2 South Asia 89.1 87.2 86.7 85.5 84.5 81.7 77.7 76.9 India 89.6 88.2 87.3 86.1 85.7 85.2 80.6 79.9 Sub-Saharan Africa 73.3 76.1 76.1 75.0 74.3 74.8 75.7 76.3 Total 66.4 63.5 59.9 61.6 60.0 55.5 54.2 52.8 Source: World Bank staff estimates. 2004 World Development Indicators 3 1 . . . and reduce hunger and malnutrition fewer undernourished people than 10 years earlier. Countries that The world produces enough food to feed everyone, but hunger have succeeded in reducing hunger had higher economic growth, remains a persistent problem. Although famines and droughts especially in their agricultural sector and rural regions. They have cause terrible short-term crises and grab most of the head- also had lower population growth and lower rates of HIV infection. lines, the root cause of hunger is poverty. The Food and Malnutrition in children often begins at birth, when poorly nour- Agriculture Organization (FAO) estimates that worldwide there ished mothers give birth to underweight babies. Improper feed- are more than 840 million people who are chronically under- ing and child care practices contribute to the harm done by an nourished, most of them living in low-income countries. But inadequate diet, putting poor children at a permanent disadvan- there are hungry people everywhere, including 10 million under- tage. Malnourished children develop more slowly, enter school nourished people living in industrial countries. later, and perform less well. And malnutrition is an underlying fac- Undernourishment means consuming too little food to main- tor in more than half the deaths of children under age five. tain normal levels of activity. The FAO sets the average require- Progress in reducing child malnutrition has been fastest in ment at 1,900 calories a day. Among the less severely affected East Asia and Pacific. where child malnutrition rates declined the average daily shortfall is less than 200 calories a person. In by 33 percent, and South Asia, where rates declined 25 per- the FAO's estimation extreme hunger occurs with a shortfall of cent. But many countries, especially in Sub-Saharan Africa, lag more than 300 calories, but the needs of individuals vary with far behind. In many others data are inadequate for tracking age, sex, and height. Adding to the problems of undernourish- progress. In the 74 countries with two or more observations ment are diets that lack essential nutrients and illnesses that since 1988, only 29 are currently on track to achieve the tar- deplete nutrients. get by 2015. But faster progress is possible. Programs to The Millennium Development Goals call for cutting the preva- encourage breastfeeding and to improve the diets of pregnant lence of hunger to half of its 1990 levels by 2015. Prevalence and lactating mothers along with micronutrient supplementa- rates have been falling in most regions, but too slowly to tion help to prevent malnutrition. Appropriate care and feeding achieve the 2015 target, and in many regions the number of of sick children, oral rehydration therapy, control and treatment hungry people continues to grow. By 2001 only the East Asia of parasitic diseases, and programs to treat vitamin A defi- and Pacific and Latin America and the Caribbean regions had ciency have all been shown to reduce malnutrition rates. 1h 1i The undernourished are everywhere Malnourished children are among the most vulnerable Prevalence of undernourishment (%) Prevalence of underweight children (%) 40 60 1990­92 Around 1990 1995­97 Around 1992 50 1999­2001 Millennium Development 30 Goal target 40 20 30 20 10 10 95­ 1993 0 0 Sub-Saharan South East Asia Latin America Europe & Middle East South Sub-Saharan East Asia Latin America Middle East & Low- and Africa Asia & Pacific & Caribbean Central Asia & North Africa Asia Africa & Pacific & Caribbean North Africa middle-income economies Source: FAO 2003, The State of Food Insecurity in the World. Source: WHO and World Bank staff estimates. 4 2004 World Development Indicators 2 Achieve universal primary education goal. Many countries in these regions have already reached the Education is the foundation of democratic societies and globally target. China, Mexico, and Russia are at or near full enrollment. competitive economies. It is the basis for reducing poverty and Others, such as Brazil, Bulgaria, and Laos made rapid progress in inequality, increasing productivity, enabling the use of new tech- the 1990s and are likely to reach the target by 2015. But three nologies, and creating and spreading knowledge. In an increasingly regions, with 150 million primary school-age children, are in dan- complex, knowledge-dependent world, primary education, as the ger of falling short. Sub-Saharan Africa lags farthest behind, with gateway to higher levels of education, must be the first priority. The little progress since 1990. South Asia has chronically low enroll- Millennium Development Goals call on the world to ensure that by ment and completion rates. And completion rates in the Middle 2015 all children are able to complete a course of primary educa- East and North Africa stagnated in the 1990s. But even in these tion. This target can be achieved--and it must be, if all developing regions some countries have made large gains. Removing impedi- countries are to compete in the global economy. ments and reducing costs can boost enrollments. Malawi and Progress toward this target is commonly measured by the net Uganda lowered school fees but could not provide spaces for all enrollment ratio--the ratio of enrolled children of official school the new students. Many countries face the challenge of improving age to the number of children of that age in the population. Ratios school quality while attracting and keeping more children in school. at or near 100 percent imply that all children will receive a full pri- If current trends persist, children in more than half of devel- mary education. But lower ratios are ambiguous. Schools may fail oping countries will not complete a full course of primary educa- to enroll all students in the first grade, or many students may drop tion in 2015. But faster progress is possible, and successful out in later grades. Chad, for example, reports a net enrollment countries have set an example by: rate of almost 60 percent, but barely 20 percent complete the final · Committing a higher share of their budgets to public education. year of primary education. Primary completion rates--the propor- · Managing to efficiently control costs. tion of each age group finishing primary school--directly measure · Providing an adequate level of complementary inputs. progress toward the Millennium Development Goal. To achieve 100 · Keeping pupil-teacher ratios around 40 and repetition rates percent completion rates, school systems must enroll all children below 10 percent. in first grade and keep them in school throughout the primary cycle. Many poor countries cannot afford the cost of expanding their To reach the target of universal primary education by 2015, school education systems to reach the goal. They will need help from systems with low completion rates will need to start now to train donors that are prepared to make long-term commitments to teachers, build classrooms, and improve the quality of education. supporting education. The World Bank estimates the financing Three regions--East Asia and Pacific, Europe and Central Asia, gap in low-income countries at $2.4­3.7 billion a year (Bruns, and Latin America and the Caribbean--are on track to achieve the Mingat, and Rakotomalala 2003, p. 13). 1j 1k To reach the goal, all children need to complete primary school Schools need to do more to lower costs and attract students Average primary school completion rate, 2000­02 (%) Reasons for leaving primary school 100 Upper-middle-income average Mali 1995/96 Lower-middle-income 80 average Other 15% Low-income average Pregnancy Did not or marriage like school 60 11% Zambia 1996 42% Failed exams 12% Family Other 40 needed labor 16% or money 20% Pregnancy School fees or marriage 37% 20 12% Did not like school Failed 16% 0 exams East Asia Europe & Latin America Middle East South Sub-Saharan 19% & Pacific Central Asia & Caribbean & North Africa Asia Africa Source: World Bank staff estimates. Source: Demographic and Health Survey EdData Education Profiles (www.dhseddata.com). 2004 World Development Indicators 5 3 Promote gender equality and empower women South Asia, Sub-Saharan Africa, and the Middle East and North Gender disparities exist everywhere in the world. Women are Africa had closed the gender gap in schooling between 1960 and underrepresented in local and national decisionmaking bodies. 1992 as quickly as East Asia did, their income would have grown They earn less than men and are less likely to participate in by an additional 0.5 to 0.9 percentage point per year. In Africa this wage employment. And in many low-income countries girls are would have meant close to doubling per capita income growth. less likely to attend school. What does improving girls' enrollments require? Mainly over- Evidence from around the world shows that eliminating gender coming the social and economic obstacles that stop parents disparities in education is one of the most effective develop- from sending their daughters to school. For many poor families ment actions a country can take. When a country educates both the economic value of girls' work at home exceeds the perceived its boys and its girls, economic productivity tends to rise, mater- returns to schooling. Improving the quality and affordability of nal and infant mortality rates usually fall, fertility rates decline, schools is a first step. The World Bank's Girls' Education and the health and educational prospects of the next generation Initiative outlines many gender-sensitive strategies and inter- improve. With this in mind, the Millennium Development Goals ventions, including construction of toilet blocks and water call for eliminating gender disparities in primary and secondary sources in schools, provision of nursery and preschool centers school by 2005 and at all levels by 2015. But all regions except where girls can leave younger siblings, abolition of school fees Latin America are still short of the first target. and uniforms, and provision of free or subsidized textbooks. The differences between boys' and girls' schooling are greatest Overcoming women's disadvantages in the labor force and in regions with the lowest primary school completion rates and the increasing their representation in public life will also help lowest average incomes. In South Asia girls' enrollment in primary encourage girls to attend and stay in school. Progress is possi- schools is 12 points lower than boys', and only 61 percent of girls ble. Over the past decade gender differences at the primary complete primary school compared with 86 percent of boys. One level have been eliminated or greatly reduced in Algeria, Angola, consequence is that illiteracy rates among young women ages Bangladesh, China, the Arab Republic of Egypt, and The Gambia. 15­24 are almost 40 percent in South Asia and 26 percent in Because the Millennium Development Goals are mutually rein- Sub-Saharan Africa, and in both regions they are more than half forcing, progress toward one goal affects progress toward all the again as high as those of young men. The disparities are even others. Success in many of the goals will have positive impacts greater in the Middle East and North Africa, a region of higher aver- on gender equality, just as progress toward gender equality will fur- age incomes but a long history of neglecting female education. ther other goals. Increasing opportunities for women will also con- The failure to educate women has consequences for develop- tribute toward the goal of reducing poverty, educating children, ment. A recent study (Klasen 1999) estimates that if countries in improving health, and better managing environmental resources. 1l 1m Many girls still do not have equal access to education Literacy rates have been rising as more children remain in school, but girls lag behind boys Ratio of girls to boys in primary and secondary education (%) Youth literacy rate, ages 15­24 (%) Female 1990 120 100 Male 1990 1990 Female 2002 2000 Male 2002 100 80 80 60 60 40 40 20 20 1997 1998 1998 0 0 East Asia Europe & Latin America Middle East South Sub-Saharan East Asia Europe & Latin America Middle East South Sub-Saharan & Pacific Central Asia & Caribbean & North Africa Asia Africa & Pacific Central Asia & Caribbean & North Africa Asia Africa Source: United Nations Economic, Scientific and Cultural Organization and World Bank staff Source: United Nations Economic, Scientific and Cultural Organization and World Bank staff estimates. estimates. 6 2004 World Development Indicators 4 Reduce child mortality falls short of that needed to reach the target. There is evidence Every year more than 10 million children in developing coun- that improvements in child mortality have been greatest among tries die before the age of five. Rapid improvements before the better-off. In 20 developing countries with disaggregated 1990 gave hope that mortality rates for infants and children data, child mortality rates fell only half as fast for the poorest under five could be cut by two-thirds in the following 25 years. 20 percent of the population as for the whole population. In But progress slowed almost everywhere in the 1990s. And no Bolivia, which is nearly on track to achieve the target, under-five region, except possibly Latin America and the Caribbean, is on mortality rates fell 34 percent among the wealthiest 20 percent track to achieve the target. Progress has been particularly slow but only 8 percent among the poorest. In Vietnam mortality in Sub-Saharan Africa, where civil disturbances and the rates also fell among the better-off but scarcely changed for the HIV/AIDS epidemic have driven up rates of infant and child poor. But in Egypt in the late 1990s under-five mortality fell mortality in several countries. For the region the under-five mor- faster among the poor than among the general population. In tality rate stands at 171 deaths per 1,000 live births. the effort to reach the Millennium Development Goals, the poor Child mortality is closely linked to poverty. In 2002 the aver- do not need to be left behind. age under-five mortality rate was 122 deaths per 1,000 live Just as child deaths are the result of many causes, reducing births in low-income countries, 42 in lower-middle-income coun- child mortality will require multiple, complementary interventions. tries, and 21 in upper-middle-income countries. In high-income Raising incomes will help. So will increasing public spending on countries the rate was less than 7. For 70 percent of the deaths health services. But a greater effort is needed to ensure that the cause is a disease or a combination of diseases and mal- health care and other public services reach the poor. Access to nutrition that would be preventable in a high-income country: safe water, better sanitation facilities, and improvements in edu- acute respiratory infections, diarrhea, measles, and malaria. cation, especially for girls and mothers, are closely linked to Improvements in infant and child mortality have come slowly reduced mortality. Also needed are roads to improve access to in low-income countries, where mortality rates have fallen by health facilities and modern forms of energy to reduce dependence only 12 percent since 1990. Upper-middle-income countries on traditional fuels, which cause damaging indoor air pollution. The have made the greatest improvement, reducing average mor- Millennium Development Goals remind us of the need to look at tality rates by 36 percent. But even this rate of improvement health and health care from the broadest possible perspective. 1n 1o Few countries are on track to meet the child mortality target To reduce early childhood deaths, immunization programs must be extended and sustained Under-five mortality rate, 2001 (deaths per 1,000 live births) Measles immunization, 2001 (% of children under 12 months) 350 100 Middle-income economies average 300 80 250 Countries above the line are progressing too slowly Low-income to meet the target economies average 60 200 150 Iraq 40 100 Bhutan 20 Countries below the line are 50 on track to achieve a two-thirds Egypt reduction in mortality rates 0 0 0 50 100 150 200 250 300 350 East Asia Europe & Latin America Middle East South Sub-Saharan & Pacific Central Asia & Caribbean & North Africa Asia Africa Under-five mortality rate, 1990 Source: World Bank staff estimates. Source: WHO, UNICEF, and World Bank staff estimates. 2004 World Development Indicators 7 5 Improve the health of mothers nancies are exposed more often to the risk of maternal death In rich countries 13 women die in childbirth for every 100,000 and thus face a higher lifetime risk of death due to pregnancy or live births. In some poor countries 100 times more women die. childbirth. The greatest number of maternal deaths each year Overall, more than 500,000 women die each year in childbirth, occur in populous India, which has a maternal mortality ratio of most of them in developing countries. What makes maternal mor- 540 per 100,000 and a lifetime risk of maternal death of 1 in tality such a compelling problem is that it strikes young women 48. But in little Togo, with a similar maternal mortality ratio but undergoing what should be a normal function. They die because higher fertility rate, women are exposed to almost twice the risk they are poor. Malnourished. Weakened by disease. Exposed to of death (AbouZhar and Wardlaw 2003). multiple pregnancies. And they die because they lack access to New estimates of trends in maternal mortality suggest that all trained health care workers and modern medical facilities. regions, except possibly the Middle East and North Africa, will fall The Millennium Development Goals call for reducing the mater- short of the 2015 target (World Bank 2003). Across the developing nal mortality ratio by three-quarters between 1990 and 2015, or world 17 percent of countries, with almost a third of the population an average of 5.4 percent a year. Maternal mortality is difficult of developing countries, are on track to achieve the maternal mor- to measure accurately. Deaths from pregnancy or childbirth are tality target. In Sub-Saharan Africa, where maternal mortality ratios relatively rare and may not be captured in general-purpose sur- are on average the highest, the rate of improvement is expected veys or surveys with small sample sizes. Maternal deaths may to be less than in any region except Europe and Central Asia. be underreported in countries that lack good administrative sta- Significant progress in reducing maternal mortality will require tistics or where many women give birth outside the formal health a comprehensive approach to providing health services: deaths system. For these reasons, efforts to monitor maternal mortali- in childbirth often involve complications, such as hemorrhaging, ty often rely on proxy indicators or statistical models. that require fully equipped medical facilities, accessible roads, The share of births attended by skilled health staff is frequent- and emergency transportation. Causes of complications during ly used to identify where the need for intervention is greatest. Only pregnancy and childbirth include inadequate nutrition, unsafe 56 percent of women in developing countries are attended in child- sex, and poor health care. Gender inequality in controlling birth by a trained midwife or doctor. In Latin America, where the household resources and making decisions also contributes to share of births attended by skilled health personnel is high, mater- poor maternal health. Early childbearing and closely spaced nal mortality is relatively low. But in Africa, where skilled atten- pregnancies increase the risks for mothers and children. Access dants and health facilities are not readily available, it is very high. to family planning services helps women plan whether and The maternal mortality ratio measures the risk of a woman when to have children. Fewer pregnancies means a lower life- dying once she becomes pregnant. Women who have more preg- time exposure to the risk of maternal mortality. 1p 1q Extreme risks of dying from pregnancy or The presence of skilled health staff childbirth in some regions lowers the risk of maternal death Lifetime risk of maternal death, 2000 Births attended by skilled health staff, 1995­2000 (% of total) 100 1 in 16 Middle-income economies average 80 60 Low-income economies average 1 in 46 40 1 in 120 1 in 140 1 in 160 1 in 210 1 in 840 1 in 2,800 20 Sub-Saharan South- Western South- Latin North Eastern Developed Africa Central Asia Eastern America & Africa Asia countries Asia Asia Caribbean 0 The lifetime risk of maternal death is the risk of an individual woman dying from pregnancy or East Asia Europe & Latin America Middle East South Sub-Saharan childbirth during her lifetime. A 1 in 3,000 lifetime risk represents a low risk of dying from & Pacific Central Asia & Caribbean & North Africa Asia Africa pregnancy or childbirth, while a 1 in 100 lifetime risk is a high risk of dying. a. Excludes Australia, Japan, and New Zealand. Source: AbouZhar and Wardlaw 2003. Source: World Bank staff estimates. 8 2004 World Development Indicators 6 Combat HIV/AIDS, malaria, and other diseases deaths occur among the poorest 20 percent of the population. Epidemic diseases exact a huge toll in human suffering and The disease is estimated to have slowed economic growth in lost opportunities for development. Poverty, civil disturbances, African countries by 1.3 percent a year (World Bank 2001). and natural disasters all contribute to, and are made worse by, Because children bear the greatest burden of the disease, the the spread of disease. In Africa the spread of HIV/AIDS has Millennium Development Goals call for monitoring efforts reversed decades of improvements in life expectancy and left focusing on children under five. An effective means of prevent- millions of children orphaned. It is draining the supply of teach- ing new infections is the use of insecticide-treated bednets. ers and eroding the quality of education. Vietnam, where 16 percent of children sleep under treated bed- HIV has infected more than 60 million people worldwide. Each nets, has made significant strides in controlling malaria. But in day 14,000 people are newly infected, more than half of them Africa only 7 of 27 countries with survey data reported rates of below age 25. The Millennium Development Goals have set the bednet use of 5 percent or more. The emergence of drug- target of reducing prevalence among 15­24 year olds by 25 per- resistant strains of malaria has increased the urgency of find- cent by 2005 in the most severely affected countries and by 2010 ing new means of treatment and prevention. globally. At the end of 2002, 42 million adults and 5 million chil- Tuberculosis kills some 2 million people a year, most of them dren were living with HIV/AIDS--more than 95 percent of them in 15­45 years old. The emergence of drug-resistant strains of developing countries and 70 percent in Sub-Saharan Africa. There tuberculosis; the spread of HIV/AIDS, which reduces resistance were almost a million new cases in South and East Asia, where to tuberculosis; and the growing number of refugees and dis- more than 7 million people now live with HIV/AIDS. Projections placed persons have allowed the disease to spread more rapidly. suggest that by 2010, 45 million more people in low- and middle- Each year there are 8 million new cases--2 million in Sub- income countries will become infected unless the world mounts Saharan Africa, 3 million in Southeast Asia, and more than a quar- an effective campaign to halt the disease's spread. But there are ter million in Eastern Europe and the former Soviet Union. Poorly success stories: Brazil, Thailand, and Uganda are controlling the managed tuberculosis programs allow drug-resistant strains to spread of HIV/AIDS. Thailand has reduced the number of new spread. WHO has developed a treatment strategy--directly infections from 140,000 a decade ago to 30,000 in 2001. observed treatment, short course (DOTS)--that emphasizes pos- The World Health Organization (WHO 2002) estimates that itive diagnosis followed by a course of treatment and follow-up 300­500 million cases of malaria occur each year, leading to care. DOTS produces cure rates of up to 95 percent, even in poor 1.1 million deaths. Almost 90 percent of cases occur in Sub- countries. While some countries have made rapid progress in Saharan Africa, and most deaths are among children younger DOTS detection rates, those with high tuberculosis burdens are than five. Malaria is a disease of poverty: almost 60 percent of not increasing detection rates toward the 70 percent target. 1r 1s HIV strikes at youth--and women are particularly vulnerable Treated bednets are a proven way to combat malaria, but they are still not widely used Youth ages 15­24 living with HIV/AIDS, end 2001 (%) Children under age five sleeping under insecticide-treated bednets, 2000 (%) 15 25 Women Men 12 20 9 15 6 10 3 5 0 0 Eastern & Sub- West & Europe Latin South East Middle Swaziland Sudan Niger Burundi Tanzania Rwanda The Vietnam São Tomé Southern Saharan Central & Central America & Asia Asia & East & Gambia & Principe Africa Africa Africa Asia Caribbean Pacific North Africa Source: Joint United Nations Programme on HIV/AIDS. Source: World Health Organization. 2004 World Development Indicators 9 7 Ensure environmental sustainability protected from contamination, 40 percent of them in East Asia Sustainable development can be ensured only by protecting the and Pacific and 25 percent in Sub-Saharan Africa. Improved sani- environment and using its resources wisely. Because poor people tation services and good hygiene practices are also needed to are often dependent on environmental resources for their liveli- reduce the risk of disease. A basic sanitation system provides dis- hood, they are most affected by environmental degradation and posal facilities that can effectively prevent human, animal, and by natural disasters, such as fires, storms, and earthquakes, insect contact with excreta. Such systems do not, however, whose effects are worsened by environmental mismanagement. ensure that effluents are treated to remove harmful substances The Millennium Development Goals draw attention to some before they are released into the environment. Meeting the of the environmental conditions that need to be closely Millennium Development Goals will require providing about 1.5 bil- monitored--changes in forest coverage and biological diversity, lion people with access to safe water and 2 billion with access to energy use and the emission of greenhouse gases, the avail- basic sanitation facilities between 2000 and 2015. ability of adequate water and sanitation services, and the plight The world is rapidly urbanizing. While the movement of people of slum dwellers in rapidly growing cities. to cities may reduce immediate pressure on the rural environ- As a result of economic and demographic growth most devel- ment, it increases people's exposure to other environmental haz- oping regions have increased their carbon dioxide emissions, ards. The United Nations Human Settlements Programme (UN primarily due to the burning of fossil fuels such as coal, oil, and Habitat 2003) estimates that in 2001, 924 million people lived in natural gas, and land-use practices. In the last decade carbon slums, where they lack basic services, live in overcrowded and dioxide emissions have increased by 25 percent in low-income substandard housing, and are exposed to unhealthy living condi- countries, though from a significantly lower level than in other tions and hazardous locations. The Millennium Development Goals income groups. Globally, the increase in carbon dioxide emis- call for improving the lives of at least 100 million slum dwellers by sions has slowed in the last decade, and annual emissions per 2020. Polluted air is one of many hazards faced by urban dwellers. capita have declined from 4.1 metric tons to 3.8 a year. Still, Poor people, who live in crowded neighborhoods close to traffic greenhouse gases accumulate and increase the risk of climate corridors and industrial plants, are likely to suffer the most. Every changes, which will affect all of us for generations to come. year an estimated 0.5­1.0 million people die prematurely from Lack of clean water and basic sanitation is the main reason that respiratory and other illnesses associated with urban air pollution diseases transmitted by feces are so common in developing (World Bank 2002i). Much can be done to improve the lives of countries. In 1990 diarrhea resulted in 3 million deaths, 85 per- slum dwellers by improving basic infrastructure, mitigating environ- cent of them among children. In 2000, 1.2 billion people still mental hazards, increasing access to education and health serv- lacked access to a reliable source of water that was reasonably ices, and empowering them to control and manage their own lives. 1t 1u 1v Greenhouse gas emissions rise with Access to water and sanitation services Slums are growing in newly urbanized income will require large investments areas Per capita emissions of carbon dioxide (metric tons) Share of population with access to an improved Number of urban residents (millions) source, 2000 (%) 15 1990 100 1,000 Water Non-slum population 2000 Sanitation Slum dwellers 12 80 800 9 60 600 6 40 400 3 20 200 0 0 0 n n n ht Low Lower Upper High East Europe & Latin Middle South Sub- Asia Asia Asia Asia income middle middle income Asia & Central America East & Asia Saharan Africa stere Nor Africa Easter W income income Pacific Asia & North Africa American Developed economies Caribbean Caribbean Africa & South-Central Sub-Saharan Latin South-Easter Source: Carbon Dioxide Information Analysis Centre and Source: World Health Organization, UNICEF, and World Bank Note: United Nations­defined regions. World Bank staff estimates. staff estimates. Source: UN Habitat 2003. 10 2004 World Development Indicators 8 Develop a global partnership for development number of people living in poverty in 2015 by 140 million more The eighth and final goal complements the first seven. It commits than in current projections. But progress on trade issues has wealthy countries to work with developing countries to create an slowed since the Doha meetings, and the subsequent World Trade environment in which rapid, sustainable development is possible. Organization meetings at Cancun failed to reach agreement on It calls for an open, rule-based trading and financial system, outstanding issues, particularly the agricultural policies of high- more generous aid to countries committed to poverty reduction, income economies. Subsidies to agriculture by Organisation for and relief for the debt problems of developing countries. It draws Economic Co-operation and Development members were greater attention to the problems of the least developed countries and of than $300 billion in 2002. By distorting world prices and restrict- landlocked countries and small island developing states, which ing access to markets, subsidies hurt growth in the agricultural have greater difficulty competing in the global economy. And it sector, where many of the poorest people work. Trade in manu- calls for cooperation with the private sector to address youth factured goods faces fewer barriers. But tariff peaks are used unemployment, ensure access to affordable, essential drugs, selectively to keep out exports of developing countries. and make available the benefits of new technologies. The force of the Monterrey Consensus is that more aid should Important steps toward implementing the global partnership go to countries with good track records and to support reform envisioned in the Millennium Declaration were taken at inter- programs that produce results. After falling throughout most of national meetings held in 2001 in Doha, which launched a new the last decade, aid levels rose in 2002, and commitments "development round" of trade negotiations, and in 2002 at the made during or following the Monterrey Conference would International Conference on Financing for Development held in increase the real level of aid by $18.6 billion dollars more in Monterrey, Mexico, where developed and developing countries 2006. This is a substantial increase, but it will fall short of the reached a new consensus stressing mutual responsibilities $30­50 billion extra needed to meet the identified needs of the for reaching the Millennium Development Goals. The Monterrey poorest countries to set them on the path to achieving the Consensus calls for developing countries to improve their poli- Millennium Development Goals. The quality of aid is important cies and governance aimed at increasing economic growth and as well. Aid is most effective in reducing poverty when it goes reducing poverty and for developed countries to increase their to poor countries with good economic policies and sound gover- support, especially by providing more and better aid and nance and advances country-owned poverty reduction pro- greater access to their markets. grams. But about a third of official development assistance What is at stake? Greater access to markets in rich countries goes to middle-income economies. And when aid flows are for the exports of developing country goods and services could affected by geopolitical considerations, donors may overlook generate substantial gains in real incomes and reduce the weaknesses in the recipient country's policies and institutions. 1w 1x Aid has increased, but not by as much as New commitments by donors, the first major increase in more domestic subsidies to agriculture than a decade, will still meet only a fraction of the need $ billions Net official development assistance ($ billions) 120 Total agricultural support 1986­88 120 Total agricultural support 2000­02 Net official development 100 assistance 1986­88 With an additional $50 billion, aid would be equal to 0.35 percent of donors' GNI, about what it was in the early 1990s Net official development 100 assistance 2000­02 80 60 80 40 Monterrey commitments of $18.6 billion 60 20 0 40 European United Japan 1990 1995 2000 2006 Union States Source: Organisation for Economic Co-operation and Development, Development Assistance Source: Organisation for Economic Co-operation and Development, Development Assistance Committee, and World Bank staff estimates. Committee, and World Bank staff estimates. 2004 World Development Indicators 11 Goals, targets, and indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1 Halve, between 1990 and 2015, the proportion of 1 Proportion of population below $1 (PPP) a day a people whose income is less than $1 a day 1a Poverty headcount ratio (percentage of population below the national poverty line) 2 Poverty gap ratio [incidence x depth of poverty] 3 Share of poorest quintile in national consumption Target 2 Halve, between 1990 and 2015, the proportion of 4 Prevalence of underweight children under five years people who suffer from hunger of age 5 Proportion of population below minimum level of dietary energy consumption Goal 2 Achieve universal primary education Target 3 Ensure that, by 2015, children everywhere, boys and 6 Net enrollment ratio in primary education girls alike, will be able to complete a full course of 7 Proportion of pupils starting grade 1 who reach grade 5 b primary schooling 8 Literacy rate of 15- to 24-year-olds Goal 3 Promote gender equality and empower women Target 4 Eliminate gender disparity in primary and secondary 9 Ratios of girls to boys in primary, secondary, and education, preferably by 2005, and in all levels of tertiary education education no later than 2015 10 Ratio of literate women to men ages 15­24 11 Share of women in wage employment in the nonagricultural sector 12 Proportion of seats held by women in national parliaments Goal 4 Reduce child mortality Target 5 Reduce by two-thirds, between 1990 and 2015, 13 Under-five mortality rate the under-five mortality rate 14 Infant mortality rate 15 Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Target 6 Reduce by three-quarters, between 1990 and 2015, 16 Maternal mortality ratio the maternal mortality ratio 17 Proportion of births attended by skilled health personnel Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 7 Have halted by 2015 and begun to reverse the spread 18 HIV prevalence among pregnant women ages 15­24 of HIV/AIDS 19 Condom use rate of the contraceptive prevalence rate c 19a Condom use at last high-risk sex 19b Percentage of 15- to 24-year-olds with comprehensive correct knowledge of HIV/AIDS d 19c Contraceptive prevalence rate 20 Ratio of school attendance of orphans to school attendance of nonorphans ages 10­14 Target 8 Have halted by 2015 and begun to reverse the 21 Prevalence and death rates associated with malaria incidence of malaria and other major diseases 22 Proportion of population in malaria-risk areas using effective malaria prevention and treatment measures e 23 Prevalence and death rates associated with tuberculosis 24 Proportion of tuberculosis cases detected and cured under directly observed treatment, short course (DOTS) Goal 7 Ensure environmental sustainability Target 9 Integrate the principles of sustainable development 25 Proportion of land area covered by forest into country policies and programs and reverse the 26 Ratio of area protected to maintain biological diversity to loss of environmental resources surface area 27 Energy use (kilograms of oil equivalent) per $1 GDP (PPP) 28 Carbon dioxide emissions per capita and consumption of ozone-depleting chlorofluorocarbons (ODP tons) 29 Proportion of population using solid fuels Target 10 Halve, by 2015, the proportion of people without 30 Proportion of population with sustainable access sustainable access to safe drinking water and basic to an improved water source, urban and rural sanitation 31 Proportion of population with access to improved sanitation, urban and rural 12 2004 World Development Indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Target 11 By 2020, to have achieved a significant improvement 32 Proportion of households with access to secure tenure in the lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 12 Develop further an open, rule-based, predictable, Some of the indicators listed below are monitored nondiscriminatory trading and financial system separately for the least developed countries (LDCs), Africa, landlocked countries and small island developing states. Includes a commitment to good governance, development and poverty reduction--both nationally Official development assistance (ODA) and internationally 33 Net ODA, total and to the least developed countries, as a percentage of OECD/DAC donors' gross national income 34 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic Target 13 Address the special needs of the least developed education, primary health care, nutrition, safe water countries and sanitation) 35 Proportion of bilateral official development assistance Includes tariff and quota free access for the least of OECD/DAC donors that is untied developed countries' exports; enhanced programme 36 ODA received in landlocked countries as a proportion of debt relief for heavily indebted poor countries of their gross national incomes (HIPC) and cancellation of official bilateral debt; and 37 ODA received in small island developing states as more generous ODA for countries committed to proportion of their gross national incomes poverty reduction Market access 38 Proportion of total developed country imports (by value Target 14 Address the special needs of landlocked countries and excluding arms) from developing countries and from and small island developing states (through the the least developed countries, admitted free of duty Programme of Action for the Sustainable 39 Average tariffs imposed by developed countries on Development of Small Island Developing States agricultural products and textiles and clothing from and the outcome of the 22nd special session of the developing countries General Assembly) 40 Agricultural support estimate for OECD countries as a percentage of their gross domestic product 41 Proportion of ODA provided to help build trade capacity Target 15 Deal comprehensively with the debt problems of Debt sustainability developing countries through national and 42 Total number of countries that have reached their international measures in order to make debt HIPC decision points and number that have reached sustainable in the long term their HIPC completion points (cumulative) 43 Debt relief committed under HIPC Debt Initiative 44 Debt service as a percentage of exports of goods and services Target 16 In cooperation with developing countries, develop 45 Unemployment rate of 15- to 24-year-olds, male and and implement strategies for decent and productive female and total f work for youth Target 17 In cooperation with pharmaceutical companies, 46 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 18 In cooperation with the private sector, make 47 Telephone lines and cellular subscribers per 100 people available the benefits of new technologies, especially 48a Personal computers in use per 100 people information and communications 48b Internet users per 100 people Note: Goals, targets, and indicators effective September 8, 2003. a. For monitoring country poverty trends, indicators based on national poverty lines should be used, where available. b. An alternative indicator under development is "primary completion rate." c. Among contraceptive methods, only condoms are effective in preventing HIV transmission. Since the condom use rate is only measured among women in union, it is supplemented by an indicator on condom use in high-risk situations (indicator 19a) and an indicator on HIV/AIDS knowledge (indicator 19b). Indicator 19c (contraceptive prevalence rate) is also useful in track- ing progress in other health, gender, and poverty goals. d. This indicator is defined as the percentage of 15- to 24-year-olds who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can transmit HIV. However, since there are currently not a sufficient number of surveys to be able to calculate the indicator as defined above, UNICEF, in collaboration with UNAIDS and WHO, produced two proxy indicators that represent two components of the actual indicator. They are the percentage of women and men ages 15­24 who know that a person can protect herself from HIV infection by "consistent use of condom," and the percentage of women and men ages 15­24 who know a healthy-looking person can transmit HIV. e. Prevention to be measured by the percentage of children under age five sleeping under insecticide-treated bednets; treatment to be measured by percentage of children under age five who are appropri- ately treated. f. An improved measure of the target for future years is under development by the International Labour Organization. 2004 World Development Indicators 13 1.1 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross area density income income per capita income a domestic product Per Per thousand people capita capita millions sq. km per sq. km $ billions rank $ rank $ billions $ rank % growth % growth 2002 2002 2002 2002 b 2002 2002 b 2002 2002 2002 2002 2001­02 2001­02 Afghanistan 28 c 652 43 .. .. .. d .. .. .. .. .. .. Albania 3 29 115 4.6 120 1,450 120 16 4,960 112 4.7 4.1 Algeria 31 2,382 13 53.8 48 1,720 114 173 e 5,530 e 103 4.1 2.5 Angola 13 1,247 11 9.3 89 710 146 24 e 1,840 e 163 15.3 12.0 Argentina 36 2,780 13 154.0 27 4,220 74 387 10,190 72 ­10.9 ­12.0 Armenia 3 30 109 2.4 145 790 144 10 3,230 139 12.9 13.6 Australia 20 7,741 3 384.1 14 19,530 29 539 27,440 19 2.7 1.4 Austria 8 84 97 192.1 20 23,860 18 233 28,910 12 1.0 0.8 Azerbaijan 8 87 94 5.8 108 710 146 25 3,010 142 10.6 9.8 Bangladesh 136 144 1,042 51.1 51 380 171 241 1,770 165 4.4 2.6 Belarus 10 208 48 13.5 80 1,360 124 55 5,500 105 4.7 5.2 Belgium 10 31 315 237.1 18 22,940 21 291 28,130 16 0.7 0.2 Benin 7 113 59 2.5 144 380 171 7 1,060 185 6.0 3.3 Bolivia 9 1,099 8 7.9 96 900 140 21 2,390 149 2.8 0.5 Bosnia and Herzegovina 4 51 81 5.4 112 1,310 125 .. .. .. 3.9 2.5 Botswana 2 582 3 5.1 114 3,010 88 13 7,740 84 3.1 2.1 Brazil 174 8,547 21 494.5 12 2,830 91 1,300 7,450 86 1.5 0.3 Bulgaria 8 111 72 14.1 78 1,770 111 56 7,030 87 4.8 5.5 Burkina Faso 12 274 43 2.9 139 250 187 13 e 1,090 e 184 4.6 2.1 Burundi 7 28 275 0.7 179 100 206 4 e 630 e 204 3.6 1.7 Cambodia 12 181 71 3.8 126 300 178 25 e 1,970 e 159 5.5 3.6 Cameroon 16 475 34 8.7 94 550 156 30 1,910 162 4.4 2.3 Canada 31 9,971 3 702.0 8 22,390 23 907 28,930 11 3.3 2.3 Central African Republic 4 623 6 1.0 171 250 187 4 e 1,170 e 183 ­0.8 ­2.2 Chad 8 1,284 7 1.8 151 210 194 8 1,010 187 9.9 6.7 Chile 16 757 21 66.3 43 4,250 73 147 9,420 76 2.1 0.9 China 1,280 9,598 f 137 1,234.2 6 960 136 5,792 g 4,520 g 125 8.0 7.3 Hong Kong, China 7 .. .. 167.6 25 24,690 16 187 27,490 18 2.3 1.3 Colombia 44 1,139 42 79.6 42 1,820 109 269 e 6,150 e 98 1.6 0.0 Congo, Dem. Rep. 52 2,345 23 5.0 115 100 206 32 e 630 e 204 3.0 0.0 Congo, Rep. 4 342 11 2.2 147 610 153 3 710 202 3.5 0.6 Costa Rica 4 51 77 16.1 75 4,070 77 34 e 8,560 e 81 3.0 1.2 Côte d'Ivoire 17 322 52 10.2 87 620 152 24 1,450 177 ­1.8 ­3.8 Croatia 4 57 80 20.3 66 4,540 71 45 10,000 74 5.2 5.2 Cuba 11 111 103 .. .. .. h .. .. .. .. .. .. Czech Republic 10 79 132 56.0 46 5,480 68 152 14,920 55 2.0 2.1 Denmark 5 43 127 162.6 26 30,260 9 164 30,600 8 2.1 1.8 Dominican Republic 9 49 178 .. .. .. h .. 54 e 6,270 e 97 4.1 2.5 Ecuador 13 284 46 19.1 70 1,490 118 43 3,340 138 3.4 1.8 Egypt, Arab Rep. 66 1,001 67 97.6 37 1,470 119 253 3,810 132 3.0 1.1 El Salvador 6 21 310 13.6 79 2,110 101 31 e 4,790 e 120 2.1 0.4 Eritrea 4 118 43 0.8 173 190 196 4 e 1,040 e 186 1.8 ­0.5 Estonia 1 45 32 5.7 109 4,190 75 16 11,630 63 6.0 6.5 Ethiopia 67 1,104 67 6.5 102 100 206 52 e 780 e 200 2.7 0.5 Finland 5 338 17 124.2 29 23,890 17 136 26,160 25 1.6 1.4 France 59 552 108 1,362.1 i 5 22,240 i 24 1,609 27,040 21 1.2 0.7 Gabon 1 268 5 4.0 123 3,060 87 7 5,530 103 3.0 0.8 Gambia, The 1 11 139 0.4 193 270 184 2 e 1,660 e 169 ­3.1 ­5.7 Georgia 5 70 74 3.4 135 650 151 12 e 2,270 e 152 5.6 6.6 Germany 82 357 236 1,876.3 3 22,740 22 2,226 26,980 22 0.2 0.0 Ghana 20 239 89 5.5 111 270 184 42 e 2,080 e 156 4.5 2.7 Greece 11 132 82 123.9 30 11,660 48 200 18,770 43 4.0 3.6 Guatemala 12 109 111 21.0 64 1,760 112 48 e 4,030 e 129 2.2 ­0.4 Guinea 8 246 32 3.2 137 410 169 16 2,060 157 4.2 2.0 Guinea-Bissau 1 36 51 0.2 203 130 205 1 e 680 e 203 ­7.2 ­9.8 Haiti 8 28 301 3.6 129 440 165 13 e 1,610 e 172 ­0.9 ­2.7 14 2004 World Development Indicators WORLD 1.1 VIEW Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross area density income income per capita income a domestic product Per Per thousand people capita capita millions sq. km per sq. km $ billions rank $ rank $ billions $ rank % growth % growth 2002 2002 2002 2002 b 2002 2002 b 2002 2002 2002 2002 2001­02 2001­02 Honduras 7 112 61 6.3 105 930 138 17 e 2,540 e 147 2.5 0.0 Hungary 10 93 110 53.7 49 5,290 69 133 13,070 58 3.3 3.6 India 1,049 3,287 353 494.8 11 470 161 2,778 e 2,650 e 146 4.6 3.0 Indonesia 212 1,905 117 149.9 28 710 146 650 3,070 141 3.7 2.3 Iran, Islamic Rep. 66 1,648 40 112.9 33 1,720 114 438 6,690 91 6.7 5.1 Iraq 24 438 55 .. .. .. h .. .. .. .. .. .. Ireland 4 70 57 90.3 38 23,030 20 116 29,570 9 6.9 5.4 Israel 7 21 318 105.2 35 16,020 37 125 19,000 41 ­0.8 ­2.7 Italy 58 301 196 1,100.7 7 19,080 30 1,510 26,170 24 0.4 0.4 Jamaica 3 11 242 7.0 100 2,690 93 10 3,680 134 1.1 0.3 Japan 127 378 349 4,323.9 2 34,010 7 3,481 27,380 20 0.3 0.2 Jordan 5 89 58 9.1 92 1,760 112 22 4,180 127 4.9 2.0 Kazakhstan 15 2,725 6 22.6 62 1,520 117 84 5,630 101 9.8 10.1 Kenya 31 580 55 11.2 85 360 174 32 1,010 187 1.0 ­0.9 Korea, Dem. Rep. 22 121 187 .. .. .. d .. .. .. .. .. .. Korea, Rep. 48 99 483 473.0 13 9,930 53 808 16,960 51 6.3 5.7 Kuwait 2 18 131 38.0 55 16,340 36 41 e 17,780 e 47 ­1.0 ­3.3 Kyrgyz Republic 5 200 26 1.4 158 290 181 8 1,560 175 ­0.5 ­1.5 Lao PDR 6 237 24 1.7 153 310 176 9 1,660 169 5.0 2.6 Latvia 2 65 38 8.1 95 3,480 86 21 9,190 77 6.1 7.0 Lebanon 4 10 434 17.7 72 3,990 79 20 4,600 123 1.0 ­0.3 Lesotho 2 30 59 1.0 170 550 156 5 e 2,970 e 143 3.8 2.8 Liberia 3 111 34 0.5 190 140 201 .. .. .. 3.3 0.8 Libya 5 1,760 3 .. .. .. j .. .. .. .. .. .. Lithuania 3 65 54 12.7 81 3,670 83 35 10,190 72 6.7 7.1 Macedonia, FYR 2 26 80 3.5 132 1,710 116 13 6,420 95 0.7 0.6 Madagascar 16 587 28 3.8 124 230 191 12 730 201 ­12.7 ­15.2 Malawi 11 118 114 1.7 154 160 200 6 570 207 1.8 ­0.2 Malaysia 24 330 74 86.1 40 3,540 84 207 8,500 82 4.1 1.9 Mali 11 1,240 9 2.7 142 240 189 10 860 192 4.4 1.9 Mauritania 3 1,026 3 0.8 175 280 183 5 e 1,790 e 164 3.3 0.8 Mauritius 1 2 597 4.7 118 3,860 81 13 10,820 67 4.4 3.4 Mexico 101 1,958 53 597.0 9 5,920 66 887 8,800 80 0.9 ­0.5 Moldova 4 34 129 1.7 155 460 164 7 1,600 173 7.2 7.6 Mongolia 2 1,567 2 1.1 167 430 166 4 1,710 167 4.0 2.8 Morocco 30 447 66 34.7 58 1,170 128 111 3,730 133 3.2 1.6 Mozambique 18 802 24 3.6 128 200 195 18 e 990 e 189 7.7 5.6 Myanmar 49 677 74 .. .. .. d .. .. .. .. .. .. Namibia 2 824 2 3.5 131 1,790 110 14 e 6,880 e 89 2.7 0.6 Nepal 24 147 169 5.5 110 230 191 33 1,370 179 ­0.5 ­2.7 Netherlands 16 42 477 377.6 15 23,390 19 458 28,350 15 0.2 ­0.4 New Zealand 4 271 15 52.2 50 13,260 44 81 20,550 39 4.3 2.8 Nicaragua 5 130 44 3.8 125 710 146 13 e 2,350 e 150 1.0 ­1.6 Niger 11 1,267 9 2.0 149 180 197 9 e 800 e 195 3.0 ­0.1 Nigeria 133 924 146 39.5 54 300 178 106 800 195 ­0.9 ­3.1 Norway 5 324 15 175.8 23 38,730 3 166 36,690 3 1.0 0.4 Oman 3 310 8 19.9 67 7,830 59 33 13,000 59 0.0 ­2.3 Pakistan 145 796 188 60.9 45 420 168 284 1,960 160 2.8 0.4 Panama 3 76 40 11.8 83 4,020 78 18 e 6,060 e 99 0.8 ­0.7 Papua New Guinea 5 463 12 2.8 140 530 158 12 e 2,180 e 153 ­0.5 ­2.8 Paraguay 6 407 14 6.4 103 1,170 128 25 e 4,590 e 124 ­2.3 ­4.4 Peru 27 1,285 21 54.0 47 2,020 103 130 4,880 117 4.9 3.3 Philippines 80 300 268 82.4 41 1,030 134 356 4,450 126 4.4 2.3 Poland 39 313 127 176.6 22 4,570 70 404 10,450 70 1.4 1.4 Portugal 10 92 111 109.1 34 10,720 50 181 17,820 46 0.4 0.2 Puerto Rico 4 9 436 .. .. .. k .. .. .. .. .. .. 2004 World Development Indicators 15 1.1 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross area density income income per capita income a domestic product Per Per thousand people capita capita millions sq. km per sq. km $ billions rank $ rank $ billions $ rank % growth % growth 2002 2002 2002 2002 b 2002 2002 b 2002 2002 2002 2002 2001­02 2001­02 Romania 22 238 97 41.7 53 1,870 108 145 6,490 93 4.3 4.8 Russian Federation 144 17,075 9 306.6 16 2,130 99 1,165 8,080 83 4.3 4.8 Rwanda 8 26 331 1.8 150 230 191 10 e 1,260 e 182 9.4 6.3 Saudi Arabia 22 2,150 10 186.8 21 8,530 57 277 e 12,660 e 60 1.0 ­1.8 Senegal 10 197 52 4.6 119 470 161 15 e 1,540 e 176 1.1 ­1.2 Serbia and Montenegro 8 l 102 .. 11.6 l 84 1,400 l 123 .. .. .. 4.0 35.7 Sierra Leone 5 72 73 0.7 177 140 201 3 500 208 6.3 4.2 Singapore 4 1 6,826 86.1 39 20,690 27 99 23,730 31 2.2 1.4 Slovak Republic 5 49 .. 21.3 63 3,970 80 68 12,590 61 4.4 4.4 Slovenia 2 20 98 20.4 65 10,370 52 36 18,480 45 2.9 3.6 Somalia 9 638 15 .. .. .. d .. .. .. .. .. .. South Africa 45 1,221 37 113.4 32 2,500 94 445 e 9,810 e 75 3.0 1.8 Spain 41 506 82 596.5 10 14,580 40 868 21,210 36 2.0 1.6 Sri Lanka 19 66 293 16.1 74 850 142 67 3,510 135 4.0 2.7 Sudan 33 2,506 14 12.2 82 370 173 57 e 1,740 e 166 5.5 3.3 Swaziland 1 17 63 1.4 159 1,240 127 5 4,730 122 3.6 1.7 Sweden 9 450 22 231.8 19 25,970 12 230 25,820 26 1.9 1.5 Switzerland 7 41 184 263.7 17 36,170 4 232 31,840 7 0.1 ­0.7 Syrian Arab Republic 17 185 92 19.1 69 1,130 130 59 3,470 136 2.7 0.3 Tajikistan 6 143 45 1.1 164 180 197 6 930 191 9.1 8.5 Tanzania 35 945 40 9.7 m 88 290 m 181 20 580 206 6.3 4.1 Thailand 62 513 121 123.3 31 2,000 104 425 6,890 88 5.4 4.7 Togo 5 57 88 1.3 161 270 184 7 e 1,450 e 177 4.6 2.4 Trinidad and Tobago 1 5 254 8.8 93 6,750 63 12 9,000 79 2.7 2.1 Tunisia 10 164 63 19.5 68 1,990 105 63 6,440 94 1.7 0.6 Turkey 70 775 90 173.3 24 2,490 95 438 6,300 96 7.8 6.1 Turkmenistan 5 488 10 .. .. .. h .. 23 4,780 121 14.9 13.1 Uganda 25 241 125 5.9 107 240 189 33 e 1,360 e 180 6.7 3.8 Ukraine 49 604 84 37.9 56 780 145 234 4,800 119 4.8 5.6 United Arab Emirates 3 84 38 .. .. .. k .. 77 e 24,030 e 30 1.8 ­5.0 United Kingdom 59 243 246 1,510.8 4 25,510 13 1,574 26,580 23 1.8 1.5 United States 288 9,629 31 10,207.0 1 35,400 6 10,414 36,110 4 2.4 1.4 Uruguay 3 176 19 14.6 77 4,340 72 26 7,710 85 ­10.8 ­11.3 Uzbekistan 25 447 61 7.8 98 310 176 41 1,640 171 4.2 2.9 Venezuela, RB 25 912 28 102.3 36 4,080 76 131 5,220 110 ­8.9 ­10.5 Vietnam 80 332 247 34.8 57 430 166 185 2,300 151 7.0 5.8 West Bank and Gaza 3 .. .. 3.6 130 1,110 131 .. .. .. ­19.1 ­22.5 Yemen, Rep. 19 528 35 9.1 91 490 160 15 800 195 3.6 0.5 Zambia 10 753 14 3.5 133 340 175 8 800 195 3.3 1.6 Zimbabwe 13 391 34 .. .. .. d .. 28 2,180 153 ­5.6 ­6.7 World 6,199 s 133,895 s 48 w 31,720 t 5,120 w 48,462 t 7,820 w 1.9 w 0.7 w Low income 2,495 33,612 77 1,070 430 5,269 2,110 4.0 2.1 Middle income 2,738 67,886 41 5,056 1,850 15,884 5,800 3.1 2.3 Lower middle income 2,408 54,969 45 3,372 1,400 12,749 5,290 4.9 4.1 Upper middle income 329 12,917 26 1,682 5,110 3,145 9,550 ­1.2 ­2.4 Low & middle income 5,232 101,498 53 6,123 1,170 21,105 4,030 3.3 2.0 East Asia & Pacific 1,838 16,301 116 1,768 960 7,874 4,280 6.7 5.8 Europe & Central Asia 473 24,206 20 1,023 2,160 3,263 6,900 4.6 5.1 Latin America & Carib. 525 20,450 26 1,721 3,280 3,650 6,950 ­0.8 ­2.2 Middle East & N. Africa 306 11,135 28 685 2,240 1,733 5,670 3.0 1.0 South Asia 1,401 5,140 293 638 460 3,453 2,460 4.3 2.6 Sub-Saharan Africa 689 24,267 29 311 450 1,174 1,700 2.8 0.5 High income 966 32,397 31 25,596 26,490 27,516 28,480 1.6 1.0 Europe EMU 305 2,474 125 6,207 20,320 7,850 25,700 0.8 0.5 a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimate does not account for recent refugee flows. d. Estimated to be low income ($735 or less). e. The estimate is based on regression; others are extrapolated from the latest International Comparison Programme benchmark estimates. f. Includes Taiwan, China; Macao, China; and Hong Kong, China. g. Estimate based on bilateral comparison between China and the United States (Ruoen and Kai, 1995). h. Estimated to be lower middle income ($736­$2,935). i. GNI and GNI per capita estimates include the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. j. Estimated to be upper middle income ($2,936­$9,075). k. Estimated to be high income ($9,076 or more). l. Excludes data for Kosovo. m. Data refer to mainland Tanzania only. 16 2004 World Development Indicators WORLD 1.1 VIEW Size of the economy About the data Definitions Population, land area, income, and output are basic shows GNI and GNI per capita estimates converted · Population is based on the de facto definition of measures of the size of an economy. They also pro- into international dollars using purchasing power pari- population, which counts all residents regardless of vide a broad indication of actual and potential ty (PPP) rates. PPP rates provide a standard measure legal status or citizenship--except for refugees not resources. Population, land area, income--as meas- allowing comparison of real price levels between coun- permanently settled in the country of asylum, who are ured by gross national income (GNI)--and output-- tries, just as conventional price indexes allow compar- generally considered part of the population of their as measured by gross domestic product (GDP)--are ison of real values over time. The PPP conversion country of origin. The values shown are midyear esti- therefore used throughout World Development factors used here are derived from price surveys cov- mates for 2002. See also table 2.1. · Surface area Indicators to normalize other indicators. ering 118 countries conducted by the International is a country's total area, including areas under inland Population estimates are generally based on Comparison Program. For Organisation for Economic bodies of water and some coastal waterways. extrapolations from the most recent national census. Co-operation and Development (OECD) countries data · Population density is midyear population divided by For further discussion of the measurement of popu- come from the most recent round of surveys, com- land area in square kilometers. · Gross national lation and population growth, see About the data for pleted in 1999; the rest are either from the 1996 sur- income (GNI) is the sum of value added by all resi- table 2.1 and Statistical methods. vey, or data from the 1993 or earlier round and dent producers plus any product taxes (less subsi- The surface area of a country or economy includes extrapolated to the 1996 benchmark. Estimates for dies) not included in the valuation of output plus net inland bodies of water and some coastal waterways. countries not included in the surveys are derived from receipts of primary income (compensation of employ- Surface area thus differs from land area, which statistical models using available data. ees and property income) from abroad. Data are in excludes bodies of water, and from gross area, which All economies shown in World Development current U.S. dollars converted using the World Bank may include offshore territorial waters. Land area is Indicators are ranked by size, including those that Atlas method (see Statistical methods). · GNI per particularly important for understanding the agricul- appear in table 1.6. The ranks are shown only in table capita is gross national income divided by midyear tural capacity of an economy and the effects of 1.1. (World Bank Atlas includes a table comparing the population. GNI per capita in U.S. dollars is convert- human activity on the environment. (For measures of GNI per capita rankings based on the Atlas method ed using the World Bank Atlas method. · PPP GNI is land area and data on rural population density, land with those based on the PPP method for all economies gross national income converted to international dol- use, and agricultural productivity, see tables with available data.) No rank is shown for economies lars using purchasing power parity rates. An interna- 3.1­3.3.) Recent innovations in satellite mapping for which numerical estimates of GNI per capita are tional dollar has the same purchasing power over GNI techniques and computer databases have resulted in not published. Economies with missing data are as a U.S. dollar has in the United States. · Gross more precise measurements of land and water areas. included in the ranking process at their approximate domestic product (GDP) is the sum of value added GNI (or gross national product in the terminology of level, so that the relative order of other economies by all resident producers plus any product taxes (less the 1968 United Nations System of National remains consistent. Where available, rankings for subsidies) not included in the valuation of output. Accounts) measures the total domestic and foreign small economies are shown in World Bank Atlas. Growth is calculated from constant price GDP data in value added claimed by residents. GNI comprises Growth in GDP and growth in GDP per capita are local currency. · GDP per capita is gross domestic GDP plus net receipts of primary income (compensa- based on GDP measured in constant prices. Growth product divided by midyear population. tion of employees and property income) from non- in GDP is considered a broad measure of the growth resident sources. of an economy, as GDP in constant prices can be The World Bank uses GNI per capita in U.S. dollars estimated by measuring the total quantity of goods to classify countries for analytical purposes and to and services produced in a period, valuing them at determine borrowing eligibility. See the Users guide an agreed set of base year prices, and subtracting for definitions of the income groups used in World the cost of intermediate inputs, also in constant Data sources Development Indicators. For further discussion of prices. For further discussion of the measurement of Population estimates are prepared by World Bank the usefulness of national income as a measure of economic growth, see About the data for table 4.1. staff from a variety of sources (see Data sources productivity or welfare, see About the data for tables for table 2.1). The data on surface and land area 4.1 and 4.2. are from the Food and Agriculture Organization When calculating GNI in U.S. dollars from GNI (see Data sources for table 3.1). GNI, GNI per reported in national currencies, the World Bank fol- capita, GDP growth, and GDP per capita growth lows its Atlas conversion method. This involves using are estimated by World Bank staff based on a three-year average of exchange rates to smooth the national accounts data collected by Bank staff effects of transitory exchange rate fluctuations. (For during economic missions or reported by national further discussion of the Atlas method, see statistical offices to other international organiza- Statistical methods.) Note that growth rates are cal- tions such as the OECD. Purchasing power parity culated from data in constant prices and national conversion factors are estimates by World Bank currency units, not from the Atlas estimates. staff based on data collected by the International Because exchange rates do not always reflect inter- Comparison Program. national differences in relative prices, this table also 2004 World Development Indicators 17 1.2 Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme poverty Achieve Promote Reduce child Improve maternal health and hunger universal gender mortality Share of primary equality Maternal poorest quintile Prevalence of education Ratio of female to mortality ratio in national child malnutrition Primary male enrollments Under-five per 100,000 Births attended consumption Weight for age completion in primary and mortality rate live births by skilled or income % of children rate secondary school a per 1,000 Modeled health staff % under age 5 % % live births estimates % of total 1990­2002 b, c 1990 2002 2000/01­2002/03 b,d 1990/91 2001/02 d 1990 2002 2000 1990 1995­2000 b Afghanistan .. .. .. .. 50 .. 260 257 1,900 .. 12 Albania 9.1 .. 14 100 90 102 42 24 55 .. 99 Algeria 7.0 9 6 96 80 99 69 49 140 77 92 Angola .. 20 31 .. .. .. 260 260 1,700 .. 45 Argentina 3.1 e .. .. 100 .. 103 28 19 82 96 98 Armenia 6.7 .. 3 74 .. 104 60 35 55 .. 97 Australia 5.9 .. .. .. 96 99 10 6 8 100 100 Austria 8.1 .. .. .. 90 97 9 5 4 .. 100 f Azerbaijan 7.4 .. 17 100 94 98 106 96 94 .. 84 Bangladesh 9.0 66 48 77 72 105 144 73 380 .. 12 Belarus 8.4 .. .. 131 .. 102 21 20 35 .. 100 Belgium 8.3 .. .. .. 97 106 9 6 10 .. 100 f Benin .. .. 23 45 .. 65 185 151 850 .. 66 Bolivia 4.0 11 .. 89 89 98 122 71 420 38 69 Bosnia and Herzegovina 9.5 .. 4 77 .. .. 22 18 31 97 100 Botswana 2.2 .. 13 91 107 102 58 110 100 .. 94 Brazil 2.0 7 .. 82 .. 103 60 37 260 76 88 Bulgaria 6.7 .. .. 94 94 98 16 16 32 .. .. Burkina Faso 4.5 .. .. 29 61 70 210 207 1,000 .. 31 Burundi 5.1 .. 45 27 82 78 184 208 1,000 .. 25 Cambodia 6.9 .. 45 71 .. 84 115 138 450 .. 32 Cameroon 5.6 15 .. 57 82 85 139 166 730 58 60 Canada 7.0 .. .. .. 94 100 9 7 6 .. 98 Central African Republic 2.0 .. .. .. 61 .. 180 180 1,100 .. 44 Chad .. .. 28 22 .. 55 203 200 1,100 .. 16 Chile 3.3 .. 1 96 98 100 19 12 31 .. 100 China 4.7 17 10 102 81 .. 49 38 56 50 76 Hong Kong, China 5.3 .. .. .. .. .. .. .. .. .. .. Colombia 2.7 10 7 90 104 103 36 23 130 76 86 Congo, Dem. Rep. .. .. 31 .. 69 .. 205 205 990 .. 61 Congo, Rep. .. .. .. 58 88 87 110 108 510 .. .. Costa Rica 4.2 3 .. 90 96 101 17 11 43 98 98 Côte d'Ivoire 5.5 .. .. 48 .. 69 157 191 690 45 63 Croatia 8.3 .. .. 90 97 101 13 8 8 .. 100 Cuba .. .. 4 100 101 97 13 9 33 .. 100 Czech Republic 10.3 1 .. .. 94 101 11 5 9 .. 99 Denmark 8.3 .. .. .. 96 102 9 4 5 .. 100 f Dominican Republic 5.1 10 5 95 .. 109 65 38 150 92 98 Ecuador 3.3 .. .. 99 97 100 57 29 130 66 69 Egypt, Arab Rep. 8.6 10 4 91 78 93 104 39 84 37 61 El Salvador 2.9 15 .. 86 100 97 60 39 150 52 90 Eritrea .. .. 40 33 82 75 147 80 630 .. 21 Estonia 6.1 .. .. 103 99 99 17 12 63 .. .. Ethiopia 9.1 48 47 18 68 69 204 171 850 .. 6 Finland 9.6 .. .. .. 105 106 7 5 6 .. 100 f France 7.2 .. .. .. 98 100 9 6 17 .. 99 f Gabon .. .. 12 92 .. 96 96 85 420 .. 86 Gambia, The 4.0 .. 17 69 64 86 154 126 540 44 55 Georgia 6.4 .. .. 92 94 105 29 29 32 .. 96 Germany 8.5 .. .. .. 94 99 9 5 8 .. 100 f Ghana 5.6 30 .. 59 .. 89 125 97 540 40 44 Greece 7.1 .. .. .. 93 101 11 5 9 .. .. Guatemala 2.6 .. .. 59 .. 93 82 49 240 .. 41 Guinea 6.4 .. 33 .. 43 .. 240 165 740 31 35 Guinea-Bissau 5.2 .. 25 .. .. 65 253 211 1,100 .. 35 Haiti .. 27 17 .. .. .. 150 123 680 23 24 18 2004 World Development Indicators WORLD 1.2 Millennium Development Goals: VIEW eradicating poverty and improving lives Eradicate extreme poverty Achieve Promote Reduce child Improve maternal health and hunger universal gender mortality Share of primary equality Maternal poorest quintile Prevalence of education Ratio of female to mortality ratio in national child malnutrition Primary male enrollments Under-five per 100,000 Births attended consumption Weight for age completion in primary and mortality rate live births by skilled or income % of children rate secondary school a per 1,000 Modeled health staff % under age 5 % % live births estimates % of total 1990­2002 b, c 1990 2002 2000/01­2002/03 b,d 1990/91 2001/02 d 1990 2002 2000 1990 1995­2000 b Honduras 2.7 18 17 70 103 .. 61 42 110 45 56 Hungary 7.7 2 .. .. 96 100 16 9 16 .. .. India 8.9 64 .. 77 68 79 123 90 540 .. 43 Indonesia 8.4 .. 25 107 91 98 91 43 230 32 64 Iran, Islamic Rep. 5.1 .. .. 123 80 96 72 41 76 .. 90 Iraq .. 12 16 .. 75 76 50 125 250 54 72 Ireland 7.1 .. .. .. 99 .. 9 6 5 .. 100 Israel 6.9 .. .. .. 99 100 12 6 17 .. 99 f Italy 6.5 .. .. .. 95 98 10 6 5 .. .. Jamaica 6.7 5 .. 90 97 101 20 20 87 79 95 Japan 10.6 .. .. .. 96 100 6 5 10 100 100 Jordan 7.6 6 .. 98 93 101 43 33 41 87 97 Kazakhstan 8.2 .. .. 99 .. 98 52 99 210 .. 99 Kenya 5.6 .. .. 56 .. 97 97 122 1,000 50 44 Korea, Dem. Rep. .. .. 28 .. .. .. 55 55 67 .. 97 Korea, Rep. 7.9 .. .. .. 93 .. 9 5 20 98 100 Kuwait .. .. .. .. 97 104 16 10 5 .. 98 Kyrgyz Republic 9.1 .. 6 94 100 99 83 61 110 .. 98 Lao PDR 7.6 .. 40 73 75 83 163 100 650 .. 19 Latvia 7.6 .. .. 90 96 101 20 21 42 .. 100 Lebanon .. .. .. 68 .. 102 37 32 150 .. 89 Lesotho 1.5 16 18 65 124 105 148 132 550 .. 60 Liberia .. .. 27 .. .. .. 235 235 760 .. 51 Libya .. .. .. .. .. 103 42 19 97 .. 94 Lithuania 7.9 .. .. 106 93 99 13 9 13 .. .. Macedonia, FYR 8.4 .. .. 95 94 98 41 26 23 .. 97 Madagascar 4.9 41 33 41 .. .. 168 135 550 57 46 Malawi 4.9 28 25 55 79 .. 241 182 1,800 55 56 Malaysia 4.4 25 .. .. 98 104 21 8 41 .. 97 Mali 4.6 .. 33 39 57 .. 250 222 1,200 .. 41 Mauritania 6.2 48 32 46 67 92 183 183 1,000 40 57 Mauritius .. .. .. 108 98 98 25 19 24 .. 99 Mexico 3.1 17 .. 96 96 101 46 29 83 .. 86 Moldova 7.1 .. .. 80 103 .. 37 32 36 .. 99 Mongolia 5.6 12 .. 107 107 112 107 71 110 .. 97 Morocco 6.5 10 .. 68 67 85 85 43 220 31 40 Mozambique 6.5 .. .. 22 73 77 240 205 1,000 .. 44 Myanmar .. 32 .. 71 95 98 130 108 360 .. 56 Namibia 1.4 26 .. 95 111 104 84 67 300 68 78 Nepal 7.6 .. 48 73 53 83 143 83 740 7 11 Netherlands 7.3 .. .. .. 93 97 8 5 16 .. 100 New Zealand 6.4 .. .. .. 96 104 11 6 7 .. 100 Nicaragua 3.6 .. 10 75 .. 105 66 41 230 .. 67 Niger 2.6 43 40 21 54 67 320 264 1,600 15 16 Nigeria 4.4 35 .. .. 76 .. 235 201 800 31 42 Norway 9.6 .. .. .. 97 101 9 4 16 100 100 f Oman .. 24 .. 72 86 98 30 13 87 .. 95 Pakistan 8.8 40 .. .. 47 .. 138 101 500 19 20 Panama 2.4 6 .. 86 96 101 34 25 160 .. 90 Papua New Guinea 4.5 .. .. 59 77 97 101 94 300 .. 53 Paraguay 2.2 4 .. 89 95 98 37 30 170 53 71 Peru 2.9 11 7 98 93 .. 80 39 410 46 59 Philippines 5.4 34 .. 90 .. 102 63 37 200 .. 58 Poland 7.3 .. .. 95 96 98 19 9 13 .. 99 f Portugal 5.8 .. .. .. 99 102 15 6 5 98 100 Puerto Rico .. .. .. .. .. .. .. .. 25 .. .. 2004 World Development Indicators 19 1.2 Millennium Development Goals: eradicating poverty and improving lives Eradicate extreme poverty Achieve Promote Reduce child Improve maternal health and hunger universal gender mortality Share of primary equality Maternal poorest quintile Prevalence of education Ratio of female to mortality ratio in national child malnutrition Primary male enrollments Under-five per 100,000 Births attended consumption Weight for age completion in primary and mortality rate live births by skilled or income % of children rate secondary school a per 1,000 Modeled health staff % under age 5 % % live births estimates % of total 1990­2002 b, c 1990 2002 2000/01­2002/03 b, d 1990/91 2001/02 d 1990 2002 2000 1990 1995­2000 b Romania 8.2 6 3 94 95 100 32 21 49 .. 98 Russian Federation 4.9 .. 6 99 .. 100 21 21 67 .. 99 Rwanda .. 29 24 25 98 94 173 203 1,400 26 31 Saudi Arabia .. .. .. 66 82 94 44 28 23 .. 91 Senegal 6.4 22 23 49 69 85 148 138 690 .. 58 Serbia and Montenegro .. .. 2 .. 96 101 30 19 11 .. 99 Sierra Leone .. 29 27 .. 67 .. 302 284 2,000 .. 42 Singapore 5.0 .. .. .. 89 .. 8 4 30 .. 100 Slovak Republic 8.8 .. .. .. 98 101 15 9 3 .. .. Slovenia 9.1 .. .. 96 97 101 9 5 17 100 100 f Somalia .. .. 26 .. .. .. 225 225 1,100 .. 34 South Africa 2.0 .. .. 90 103 101 60 65 230 .. 84 Spain 7.5 .. .. .. 99 102 9 6 4 .. .. Sri Lanka 8.0 .. 33 108 99 .. 26 19 92 .. 97 Sudan .. .. 11 .. 75 86 120 94 590 .. 86 f Swaziland 2.7 .. 10 74 .. 93 110 149 370 .. 70 Sweden 9.1 .. .. .. 97 115 6 3 2 .. 100 f Switzerland 6.9 .. .. .. 92 96 8 6 7 .. .. Syrian Arab Republic .. .. 7 89 82 92 44 28 160 .. 76 f Tajikistan 8.0 .. .. 101 .. 88 127 116 100 .. 71 Tanzania 6.8 29 .. 58 97 100 163 165 1,500 44 36 Thailand 6.1 .. .. 91 94 95 40 28 44 .. 99 Togo .. 25 .. 84 59 69 152 140 570 31 49 Trinidad and Tobago 5.5 .. 6 108 98 101 24 20 160 .. 96 Tunisia 6.0 10 4 98 82 100 52 26 120 69 90 Turkey 6.1 .. .. 95 77 85 78 41 70 .. 81 Turkmenistan 6.1 .. 12 .. .. .. 98 86 31 .. 97 Uganda 5.9 23 23 67 .. .. 160 141 880 38 39 Ukraine 8.8 .. 3 98 .. 100 22 20 35 .. 100 United Arab Emirates .. .. .. .. 96 100 14 9 54 .. 96 United Kingdom 6.1 .. .. .. 97 110 10 7 13 .. 99 United States 5.4 .. .. .. 95 100 10 8 17 99 99 Uruguay 4.8 e 6 .. 95 .. 105 24 15 27 .. 100 Uzbekistan 9.2 .. .. 98 .. 98 65 65 24 .. 96 Venezuela, RB 3.0 8 4 58 101 104 27 22 96 .. 94 Vietnam 8.0 45 34 104 .. 93 53 26 130 .. 70 West Bank and Gaza .. .. .. 66 .. .. .. .. .. .. .. Yemen, Rep. 7.4 30 .. 68 .. 56 142 114 570 16 22 Zambia 3.3 25 28 59 .. .. 180 182 750 51 43 Zimbabwe 4.6 12 .. .. 96 95 80 123 1,100 70 73 World .. w .. w .. w 84 w .. w 95 w 81 w 403 w .. w 60 w Low income .. .. 74 74 84 144 121 657 .. 41 Middle income .. .. 98 84 .. 51 37 106 .. 80 Lower middle income .. 9 97 82 .. 54 40 112 .. 78 Upper middle income .. .. 89 96 101 34 22 67 .. 92 Low & middle income .. .. 86 80 .. 103 88 440 .. 56 East Asia & Pacific 19 15 100 83 .. 59 42 115 .. 72 Europe & Central Asia .. .. 97 .. 97 44 37 58 .. 93 Latin America & Carib. .. .. 87 .. 102 53 34 193 .. 82 Middle East & N. Africa .. .. 91 79 91 77 54 165 .. 70 South Asia 64 .. 78 68 81 130 95 566 .. 35 Sub-Saharan Africa .. .. 48 g 79 .. 187 174 917 .. 44 High income .. .. .. 96 101 9 7 13 .. 99 Europe EMU .. .. .. 97 100 9 6 10 .. .. a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education 1976 (ISCED76) to ISCED97. For information on ISCED, see About the data for table 2.10. b. Data are for the most recent year available. c. See table 2.7 for survey year and whether share is based on income or consumption expenditure. d. Data are preliminary. e. Urban data. f. Data refer to period other than specified, differ from the standard definition, or refer to only part of a country. g. Represent only 60% of the population. 20 2004 World Development Indicators WORLD 1.2 Millennium Development Goals: VIEW eradicating poverty and improving lives About the data Definitions This table and the following two present indicators Indicators, progress was measured by net enroll- · Share of poorest quintile in national consumption for 17 of the 18 targets specified by the Millennium ment ratios. But official enrollments sometimes dif- or income is the share of consumption or, in some Development Goals. Each of the eight goals com- fer significantly from actual attendance, and even cases, income that accrues to the poorest 20 per- prises one or more targets, and each target has school systems with high average enrollment ratios cent of the population. · Prevalence of child mal- associated with it several indicators for monitoring may have poor completion rates. Estimates of pri- nutrition is the percentage of children under age five progress toward the target. Most of the targets are mary school completion rates have been calculated whose weight for age is more than two standard set as a value of a specific indicator to be attained by World Bank staff using data provided by the deviations below the median for the international by a certain date. In some cases the target value is United Nations Educational, Scientific, and Cultural reference population ages 0­59 months. The refer- set relative to a level in 1990. In others it is set at Organization (UNESCO) and national sources. ence population, adopted by the World Health an absolute level. Some of the targets for goals 7 Eliminating gender disparities in education would Organization in 1983, is based on children from the and 8 have not yet been quantified. help to increase the status and capabilities of United States, who are assumed to be well nour- The indicators in this table relate to goals 1­5. Goal women. The ratio of girls' to boys' enrollments in pri- ished. · Primary completion rate is the number of 1 has two targets between 1990 and 2015: to reduce mary and secondary school provides an imperfect students successfully completing (or graduating by half the proportion of people whose income is less measure of the relative accessibility of schooling for from) the last year of primary school in a given year, than $1 a day and to reduce by half the proportion of girls. With a target date of 2005, this is the first of divided by the number of children of official gradua- people who suffer from hunger. Estimates of poverty the goals to fall due. The targets for reducing under- tion age in the population. · Ratio of female to male rates can be found in table 2.5. The indicator shown five and maternal mortality are among the most chal- enrollments in primary and secondary school is the here, the share of the poorest quintile in national con- lenging. Although estimates of under-five mortality ratio of female students enrolled in primary and sec- sumption, is a distributional measure. Countries with rates are available at regular intervals for most coun- ondary school to male students. · Under-five mor- less equal distributions of consumption (or income) tries, maternal mortality is difficult to measure, in tality rate is the probability that a newborn baby will will have a higher rate of poverty for a given average part because it is relatively rare. die before reaching age five, if subject to current income. No single indicator captures the concept of Most of the 48 indicators relating to the Millennium age-specific mortality rates. The probability is suffering from hunger. Child malnutrition is a symptom Development Goals can be found in the World expressed as a rate per 1,000. · Maternal mortal- of inadequate food supply, lack of essential nutrients, Development Indicators. Table 1.2a shows where to ity ratio is the number of women who die from preg- illnesses that deplete these nutrients, and undernour- find the indicators for the first five goals. For more nancy-related causes during pregnancy and ished mothers who give birth to underweight children. information about data collection methods and limi- childbirth, per 100,000 live births. The data shown Progress toward achieving universal primary edu- tations, see About the data for the tables listed there. here have been collected in various years and cation is measured by primary school completion For information about the indicators for goals 6, 7, adjusted to a common 1995 base year. The values rates. Before last year's World Development and 8, see About the data for tables 1.3 and 1.4. are modeled estimates (see About the data for table 2.17). · Births attended by skilled health staff are 1.2a the percentage of deliveries attended by personnel Location of indicators for Millennium Development Goals 1­5 trained to give the necessary supervision, care, and Goal 1. Eradicate extreme poverty and hunger advice to women during pregnancy, labor, and the 1. Proportion of population below $1 a day (table 2.5) postpartum period; to conduct deliveries on their own; and to care for newborns. 2. Poverty gap ratio (table 2.5) 3. Share of poorest quintile in national consumption (tables 1.2 and 2.7) 4. Prevalence of underweight in children under five (tables 1.2 and 2.17) 5. Proportion of population below minimum level of dietary energy consumption (table 2.17) Goal 2. Achieve universal primary education 6. Net enrollment ratio (table 2.11) 7. Proportion of pupils starting grade 1 who reach grade 5 (table 2.12) 8. Literacy rate of 15- to 24-year-olds (table 2.13) Goal 3. Promote gender equality and empower women 9. Ratio of girls to boys in primary, secondary, and tertiary education (see ratio of girls to boys in primary and secondary education in table 1.2) 10. Ratio of literate females to males among 15- to 24-year-olds (tables 1.5 and 2.12) Data sources 11. Share of women in wage employment in the nonagricultural sector (table 1.5) The indicators here and throughout this book 12. Proportion of seats held by women in national parliament (table 1.5) have been compiled by World Bank staff from pri- Goal 4. Reduce child mortality mary and secondary sources. Efforts have been 13. Under-five mortality rate (tables 1.2 and 2.19) made to harmonize these data series with those 14. Infant mortality rate (table 2.19) published by the United Nations Millennium 15. Proportion of one-year-old children immunized against measles (table 2.15) Development Goals Web site (www.un.org/millen- Goal 5. Improve maternal health niumgoals), but some differences in timing, 16. Maternal mortality ratio (tables 1.2 and 2.16) sources, and definitions remain. 17. Proportion of births attended by skilled health personnel (tables 1.2 and 2.16) 2004 World Development Indicators 21 1.3 Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Access to Fixed line Incidence of Carbon dioxide Access to an improved and mobile HIV prevalence tuberculosis emissions improved water sanitation Unemployment phone % ages 15­24 a per 100,000 per capita source facilities % ages subscribers per Male Female people metric tons % of population % of population 15­24 1,000 people b 2001 2001 2002 1990 2000 1990 2000 1990 2000 2002 2002 Afghanistan .. .. 333 0.1 0.0 .. 13 .. 12 .. 2 Albania .. .. 27 2.2 0.9 .. 97 .. 91 .. 348 Algeria .. .. 52 3.2 2.9 .. 89 .. 92 .. 74 Angola 2.2 5.7 335 0.5 0.5 .. 38 .. 44 .. 15 Argentina 0.9 0.3 46 3.4 3.9 94 .. 82 .. 32 396 Armenia 0.2 0.1 77 1.1 1.1 .. .. .. .. .. 162 Australia 0.1 0.0 c 6 15.6 18.0 100 100 100 100 12 1,178 Austria 0.2 0.1 15 7.4 7.6 100 100 100 100 5 1,275 Azerbaijan 0.1 0.0 c 82 6.4 3.6 .. 78 .. 81 .. 220 Bangladesh 0.0 c 0.0 c 221 0.1 0.2 94 97 41 48 11 13 Belarus 0.6 0.2 83 9.3 5.9 .. 100 .. .. .. 346 Belgium 0.1 0.1 14 10.1 10.0 .. .. .. .. 16 1,280 Benin 1.2 3.7 86 0.1 0.3 .. 63 20 23 .. 41 Bolivia 0.1 0.1 234 0.8 1.3 71 83 52 70 9 172 Bosnia and Herzegovina .. .. 60 1.1 4.8 .. .. .. .. .. 433 Botswana 16.1 37.5 657 1.7 2.3 93 95 60 66 .. 328 Brazil 0.6 0.5 62 1.4 1.8 83 87 71 76 18 424 Bulgaria .. .. 48 8.6 5.2 .. 100 .. 100 38 701 Burkina Faso 4.0 9.7 157 0.1 0.1 .. 42 .. 29 .. 13 Burundi 5.0 11.0 359 0.0 0.0 69 78 87 88 .. 11 Cambodia 1.0 2.5 549 0.0 0.0 .. 30 .. 17 .. 30 Cameroon 5.4 12.7 188 0.1 0.4 51 58 77 79 .. 50 Canada 0.3 0.2 6 15.4 14.2 100 100 100 100 14 1,013 Central African Republic 5.8 13.5 338 0.1 0.1 48 70 24 25 .. 5 Chad 2.4 4.3 222 0.0 0.0 .. 27 18 29 .. 6 Chile 0.4 0.1 18 2.7 3.9 90 93 97 96 19 659 China 0.2 0.1 113 2.1 2.2 71 75 29 40 3 328 Hong Kong, China 0.0 0.0 93 4.6 5.0 .. .. .. .. 11 1,507 Colombia 0.9 0.2 45 1.6 1.4 94 91 83 86 36 286 Congo, Dem. Rep. 2.9 5.9 383 0.1 0.1 .. 45 .. 21 .. 11 Congo, Rep. 3.3 7.8 395 0.8 0.5 .. 51 .. .. .. 74 Costa Rica 0.6 0.3 15 1.0 1.4 .. 95 .. 93 13 362 Côte d'Ivoire 2.9 8.3 412 1.0 0.7 80 81 46 52 .. 83 Croatia 0.0 0.0 47 3.5 4.4 .. .. .. .. 37 952 Cuba 0.1 0.0 c 12 3.0 2.8 .. 91 .. 98 .. 52 Czech Republic 0.0 0.0 13 13.4 11.6 .. .. .. .. 16 1,211 Denmark 0.1 0.1 13 9.9 8.4 .. 100 .. .. 7 1,522 Dominican Republic 2.1 2.8 95 1.3 3.0 83 86 66 67 23 317 Ecuador 0.3 0.2 137 1.6 2.0 71 85 70 86 15 231 Egypt, Arab Rep. .. .. 29 1.4 2.2 94 97 87 98 .. 177 El Salvador 0.8 0.4 60 0.5 1.1 66 77 73 82 11 241 Eritrea 2.8 4.3 268 .. 0.1 .. 46 .. 13 .. 9 Estonia 2.5 0.6 55 16.2 11.7 .. .. .. .. 22 1,001 Ethiopia 4.4 7.8 370 0.1 0.1 25 24 8 12 .. 6 Finland 0.0 c 0.0 c 10 10.6 10.3 100 100 100 100 21 1,391 France 0.3 0.2 14 6.3 6.2 .. .. .. .. 20 1,216 Gabon 2.3 4.7 248 7.0 2.8 .. 86 .. 53 .. 240 Gambia, The 0.5 1.4 230 0.2 0.2 .. 62 .. 37 .. 101 Georgia 0.1 0.0 c 85 2.8 1.2 .. 79 .. 100 20 234 Germany 0.1 0.0 c 10 11.1 9.6 .. .. .. .. 10 1,378 Ghana 1.4 3.0 211 0.2 0.3 53 73 61 72 .. 33 Greece 0.1 0.1 20 7.1 8.5 .. .. .. .. 26 1,337 Guatemala 0.9 0.8 77 0.6 0.9 76 92 70 81 .. 202 Guinea 0.6 1.4 215 0.2 0.2 45 48 55 58 .. 15 Guinea-Bissau 1.1 3.0 196 0.8 0.2 .. 56 44 56 .. 9 Haiti 4.1 5.0 319 0.2 0.2 53 46 23 28 .. 33 22 2004 World Development Indicators WORLD 1.3 Millennium Development Goals: VIEW protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Access to Fixed line Incidence of Carbon dioxide Access to an improved and mobile HIV prevalence tuberculosis emissions improved water sanitation Unemployment phone % ages 15­24 a per 100,000 per capita source facilities % ages subscribers per Male Female people metric tons % of population % of population 15­24 1,000 people b 2001 2001 2002 1990 2000 1990 2000 1990 2000 2002 2002 Honduras 1.2 1.5 86 0.5 0.7 83 88 61 75 6 97 Hungary 0.1 0.0 c 32 5.6 5.4 99 99 99 99 13 1,037 India 0.3 0.7 168 0.8 1.1 68 84 16 28 .. .. Indonesia 0.1 0.1 256 0.9 1.3 71 78 47 55 .. 92 Iran, Islamic Rep. 0.0 c 0.0 c 29 3.9 4.9 .. 92 .. 83 .. 220 Iraq .. .. 167 2.7 3.3 .. 85 .. 79 .. 29 Ireland 0.1 0.1 13 8.5 11.1 .. .. .. .. 8 1,266 Israel 0.1 0.1 10 7.4 10.0 .. .. .. .. 19 1,422 Italy 0.3 0.3 8 7.0 7.4 .. .. .. .. 26 1,419 Jamaica 0.8 0.9 8 3.3 4.2 93 92 99 99 .. 704 Japan 0.0 c 0.0 c 33 8.7 9.3 .. .. .. .. 10 1,195 Jordan .. .. 5 3.2 3.2 97 96 98 99 .. 355 Kazakhstan 0.1 0.0 c 146 15.3 8.1 .. 91 .. 99 .. 195 Kenya 6.0 15.6 540 0.2 0.3 45 57 80 87 .. 52 Korea, Dem. Rep. .. .. 160 12.3 8.5 .. 100 .. 99 .. 21 Korea, Rep. 0.0 c 0.0 c 91 5.6 9.1 .. 92 .. 63 8 1,168 Kuwait .. .. 26 19.9 21.9 .. .. .. .. .. 723 Kyrgyz Republic 0.0 0.0 142 2.4 0.9 .. 77 .. 100 .. 88 Lao PDR 0.0 c 0.0 c 170 0.1 0.1 .. 37 .. 30 .. 21 Latvia 0.9 0.2 78 4.8 2.5 .. .. .. .. 21 695 Lebanon .. .. 14 2.5 3.5 .. 100 .. 99 .. 426 Lesotho 17.4 38.1 726 .. .. .. 78 .. 49 .. 56 Liberia .. .. 247 0.2 0.1 .. .. .. .. .. 3 Libya .. .. 21 8.8 10.9 71 72 97 97 .. 127 Lithuania 0.2 0.0 c 66 5.8 3.4 .. 67 .. 67 29 746 Macedonia, FYR 0.0 0.0 41 5.5 5.5 .. .. .. .. .. 448 Madagascar 0.1 0.2 234 0.1 0.1 44 47 36 42 .. 14 Malawi 6.3 14.9 431 0.1 0.1 49 57 73 76 .. 15 Malaysia 0.7 0.1 95 3.0 6.2 .. .. .. .. .. 567 Mali 1.4 2.1 334 0.0 0.1 55 65 70 69 .. 10 Mauritania 0.4 0.6 188 1.3 1.2 37 37 30 33 .. 104 Mauritius 0.0 c 0.0 c 64 1.1 2.4 100 100 100 99 .. 559 Mexico 0.4 0.1 33 3.7 4.3 80 88 70 74 5 401 Moldova 0.5 0.1 154 4.8 1.5 .. 92 .. 99 .. 238 Mongolia .. .. 209 4.7 3.1 .. 60 .. 30 .. 142 Morocco .. .. 114 1.0 1.3 75 80 58 68 .. 247 Mozambique 6.1 14.7 436 0.1 0.1 .. 57 .. 43 .. 19 Myanmar 1.0 1.7 154 0.1 0.2 .. 72 .. 64 .. 8 Namibia 11.1 24.3 751 0.0 1.0 72 77 33 41 11 145 Nepal 0.3 0.3 190 0.0 0.1 67 88 20 28 .. 15 Netherlands 0.2 0.1 8 10.0 8.7 100 100 100 100 6 1,362 New Zealand 0.1 0.0 c 11 6.8 8.3 .. .. .. .. 11 1,070 Nicaragua 0.2 0.1 64 0.7 0.7 70 77 76 85 20 70 Niger 0.9 1.5 193 0.1 0.1 53 59 15 20 .. 3 Nigeria 3.0 5.8 304 0.9 0.3 53 62 53 54 .. 19 Norway 0.1 0.0 c 6 7.5 11.1 100 100 .. .. 11 1,578 Oman .. .. 11 7.1 8.2 37 39 84 92 .. 255 Pakistan 0.1 0.1 181 0.6 0.8 83 90 36 62 13 34 Panama 1.9 1.3 47 1.3 2.2 .. 90 .. 92 29 311 Papua New Guinea 0.3 0.4 254 0.6 0.5 40 42 82 82 .. 14 Paraguay 0.1 0.0 c 70 0.5 0.7 63 78 93 94 14 336 Peru 0.4 0.2 202 1.0 1.1 74 80 60 71 15 152 Philippines 0.0 c 0.0 c 320 0.7 1.0 87 86 74 83 19 233 Poland 0.1 0.0 c 32 9.1 7.8 .. .. .. .. 44 554 Portugal 0.4 0.2 47 4.3 5.9 .. .. .. .. 12 1,247 Puerto Rico .. .. 7 3.3 2.3 .. .. .. .. 21 662 2004 World Development Indicators 23 1.3 Millennium Development Goals: protecting our common environment Combat HIV/AIDS Ensure environmental Develop a global and other diseases sustainability partnership for development Access to Fixed line Incidence of Carbon dioxide Access to an improved and mobile HIV prevalence tuberculosis emissions improved water sanitation Unemployment phone % ages 15­24 a per 100,000 per capita source facilities % ages subscribers per Male Female people metric tons % of population % of population 15­24 1,000 people b 2001 2001 2002 1990 2000 1990 2000 1990 2000 2002 2002 Romania 0.0 c 0.0 c 148 6.7 3.8 .. 58 .. 53 18 430 Russian Federation 1.9 0.7 126 13.3 9.9 .. 99 .. .. .. 362 Rwanda 4.9 11.2 389 0.1 0.1 .. 41 .. 8 .. 16 Saudi Arabia .. .. 42 11.3 18.1 .. 95 .. 100 .. 361 Senegal 0.2 0.5 242 0.4 0.4 72 78 57 70 .. 77 Serbia and Montenegro .. .. 38 12.4 3.7 .. 98 .. 100 .. 489 Sierra Leone 2.5 7.5 405 0.1 0.1 .. 57 .. 66 .. 18 Singapore 0.1 0.2 43 13.8 14.7 100 100 100 100 5 1,258 Slovak Republic 0.0 0.0 24 8.4 6.6 .. 100 .. 100 37 812 Slovenia 0.0 0.0 21 6.2 7.3 100 100 .. .. 16 1,341 Somalia .. .. 405 0.0 .. .. .. .. .. .. 13 South Africa 10.7 25.6 558 8.3 7.4 86 86 86 87 56 410 Spain 0.5 0.2 30 5.5 7.0 .. .. .. .. 22 1,330 Sri Lanka 0.0 c 0.0 c 54 0.2 0.6 68 77 85 94 24 96 Sudan 1.1 3.1 217 0.1 0.2 67 75 58 62 .. 27 Swaziland 15.2 39.5 1,067 0.6 0.4 .. .. .. .. .. 95 Sweden 0.1 0.0 c 5 5.7 5.3 100 100 100 100 13 1,625 Switzerland 0.5 0.4 8 6.4 5.4 100 100 100 100 6 1,534 Syrian Arab Republic .. .. 44 3.0 3.3 .. 80 .. 90 .. 147 Tajikistan 0.0 0.0 109 3.7 0.6 .. 60 .. 90 .. 39 Tanzania 3.5 8.1 363 0.1 0.1 38 68 84 90 .. 24 Thailand 1.1 1.7 128 1.7 3.3 80 84 79 96 6 365 Togo 2.0 5.9 361 0.2 0.4 51 54 37 34 .. 45 Trinidad and Tobago 2.4 3.2 13 13.9 20.5 91 90 99 99 .. 528 Tunisia .. .. 23 1.6 1.9 75 80 76 84 .. 169 Turkey .. .. 32 2.6 3.3 79 82 87 90 20 629 Turkmenistan 0.0 0.0 94 7.2 7.5 .. .. .. .. .. 79 Uganda 2.0 4.6 377 0.0 0.1 45 52 .. 79 .. 18 Ukraine 2.0 0.9 95 11.5 6.9 .. 98 .. 99 24 300 United Arab Emirates .. .. 18 33.0 21.0 .. .. .. .. .. 1,010 United Kingdom 0.1 0.1 12 9.9 9.6 100 100 100 100 11 1,431 United States 0.5 0.2 5 19.3 19.8 100 100 100 100 12 1,134 Uruguay 0.5 0.2 29 1.3 1.6 .. 98 .. 94 34 472 Uzbekistan 0.0 c 0.0 101 5.3 4.8 .. 85 .. 89 .. 74 Venezuela, RB 0.7 0.1 42 5.8 6.5 .. 83 .. 68 23 369 Vietnam 0.3 0.2 192 0.3 0.7 55 77 29 47 .. 72 West Bank and Gaza .. .. 27 .. .. .. .. .. .. .. 180 Yemen, Rep. .. .. 92 0.7 0.5 .. 69 32 38 .. 49 Zambia 8.1 21.0 668 0.3 0.2 52 64 63 78 .. 21 Zimbabwe 12.4 33.0 683 1.6 1.2 78 83 56 62 .. 55 World 0.8 w 1.3 w 142 w 4.1 w 3.8 w 74 w 81 w 45 w 56 w 286 w Low income 1.1 2.4 226 0.8 0.9 66 76 30 43 40 Middle income 0.6 0.8 108 3.8 3.4 76 82 47 61 316 Lower middle income 0.6 0.8 116 3.6 3.0 75 81 45 59 263 Upper middle income 0.6 0.4 43 5.7 6.2 .. .. .. .. 431 Low & middle income 0.9 1.6 164 2.5 2.2 71 79 39 52 162 East Asia & Pacific 0.2 0.2 147 1.9 2.1 71 76 35 47 155 Europe & Central Asia 1.1 0.4 88 10.3 6.7 .. 91 .. .. 424 Latin America & Carib. 0.7 0.5 67 2.2 2.7 82 86 72 77 294 Middle East & N. Africa .. .. 57 3.3 4.2 .. 88 .. 85 159 South Asia 0.3 0.6 176 0.7 0.9 72 84 22 34 42 Sub-Saharan Africa 4.1 9.3 358 0.9 0.7 53 58 54 54 31 High income 0.3 0.1 18 11.8 12.4 .. .. .. .. 1,283 Europe EMU 0.2 0.1 15 8.4 8.0 .. .. .. .. 1,360 a. Data are an average of high and low estimates. b. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report 2003. Please cite the ITU for third-party use of these data. c. Less than 0.05. 24 2004 World Development Indicators WORLD 1.3 Millennium Development Goals: VIEW protecting our common environment About the data The Millennium Development Goals address issues Antenatal care clinics are a key site for monitoring economies. Fixed telephone lines and mobile phones of common concern to people of all nations. sexually transmitted diseases such as HIV and are among the telecommunications technologies Diseases and environmental degradation do not syphilis. The prevalence of HIV in young people pro- that are changing the way the global economy works. respect national boundaries. Epidemic diseases, vides an indicator of the spread of the epidemic. For more information on goal 8, see table 1.4. wherever they persist, pose a threat to people every- Prevalence rates in the older population can be where. And damage done to the environment in one affected by life-prolonging treatment. The table shows Definitions location may affect the well-being of plants, animals, the estimated prevalence among men and women and human beings in distant locations. ages 15­24. The incidence of tuberculosis is based · Prevalence of HIV is the percentage of people The indicators in the table relate to goals 6 and 7 on data on case notifications and estimates of the ages 15­24 who are infected with HIV. · Incidence and the targets of goal 8 that address youth employ- proportion of cases detected in the population. of tuberculosis is the estimated number of new ment and access to new technologies. For the other Carbon dioxide emissions are the primary source tuberculosis cases (pulmonary, smear positive, targets of goal 8, see table 1.4. of greenhouse gases, which are believed to con- extrapulmonary). · Carbon dioxide emissions are Measuring the prevalence or incidence of a dis- tribute to global warming. those stemming from the burning of fossil fuels and ease can be difficult. Much of the developing world Access to reliable supplies of safe drinking water and the manufacture of cement. They include carbon lacks reporting systems needed for monitoring the sanitary disposal of excreta are two of the most impor- dioxide produced during consumption of solid, liquid, course of a disease. Estimates are often derived tant means of improving human health and protecting and gas fuels and gas flaring. · Access to an from surveys and reports from sentinel sites that the environment. There is no widespread program for improved water source refers to the percentage of must be extrapolated to the general population. testing the quality of water. The indicator shown here the population with reasonable access to an ade- Tracking diseases such as HIV/AIDS, which has a measures the proportion of households with access to quate amount of water from an improved source, long latency between contraction of the virus and the an improved source, such as piped water or protected such as a household connection, public standpipe, appearance of outward symptoms, or malaria, which wells. Improved sanitation facilities prevent human, ani- borehole, protected well or spring, or rainwater col- has periods of dormancy, can be particularly difficult. mal, and insect contact with excreta but do not include lection. Unimproved sources include vendors, tanker For some of the most serious illnesses international treatment to render sewage outflows innocuous. trucks, and unprotected wells and springs. organizations have formed coalitions such as the The eighth goal--to develop a global partnership Reasonable access is defined as the availability of at Joint United Nations Programme on HIV/AIDS for development--takes note of the need for decent least 20 liters a person a day from a source within 1 (UNAIDS) and the Roll Back Malaria campaign to and productive work for youth. Labor market infor- kilometer of the dwelling. · Access to improved san- gather information and coordinate global efforts to mation, such as unemployment rates, is still gener- itation facilities refers to the percentage of the pop- treat victims and prevent the spread of disease. ally unavailable for most low- and middle-income ulation with access to at least adequate excreta disposal facilities (private or shared but not public) 1.3a that can effectively prevent human, animal, and insect contact with excreta. Improved facilities range Location of indicators for Millennium Development Goals 6­7 from simple but protected pit latrines to flush toilets Goal 6. Combat HIV/AIDS, malaria, and other diseases with a sewerage connection. To be effective, facili- 18. HIV prevalence among 15- to 24-year-old pregnant women (tables 1.3 and 2.18) ties must be correctly constructed and properly main- 19. Knowledge and use of methods to prevent HIV transmission* tained. · Unemployment refers to the share of the 20. School attendance of orphans and nonorphans* labor force without work but available for and seek- 21. Prevalence and death rates associated with malaria* ing employment. Definitions of labor force and unem- 22. Proportion of population in malaria-risk areas using effective malaria prevention and treatment ployment differ by country. · Fixed line and mobile measures* (see children sleeping under treated bednets in table 2.15) phone subscribers are telephone mainlines connect- 23. Tuberculosis prevalence and death rates (see incidence of tuberculosis in tables 1.3 and 2.18) ing a customer's equipment to the public switched 24. Proportion of tuberculosis cases detected and cured under directly observed treatment, short- telephone network, and users of portable telephones course (table 2.15) subscribing to an automatic public mobile telephone Goal 7. Ensure environmental sustainability service using cellular technology that provides 25. Proportion of land area covered by forest (table 3.4) access to the public switched telephone network. 26. Ratio of area protected to maintain biological diversity to surface area (table 3.4) 27. Energy use (kilograms of oil equivalent) per $1 of GDP (PPP) (see GDP per unit of energy use in Data sources table 3.8) The indicators here throughout this book have 28. Carbon dioxide emissions per capita (table 3.8) and consumption of ozone-depleting been compiled by World Bank staff from primary chlorofluorocarbons* and secondary sources. Efforts have been made 29. Proportion of population using solid fuels (see combustible renewables and waste as a percentage to harmonize these data series with those pub- of total energy use in table 3.7) lished on the United Nations Millennium 30. Proportion of population with sustainable access to an improved water source (tables 2.15 and 3.5) Development Goals Web site (http://www.un.org/ 31. Proportion of urban population with access to improved sanitation (table 2.15) millenniumgoals), but some differences in timing, 32. Proportion of population with access to secure tenure (table 3.11) sources, and definitions remain. * No data available in the World Development Indicators database. 2004 World Development Indicators 25 1.4 Millennium Development Goals: overcoming obstacles Development Assistance Committee members Official development Market access to high-income countries Support to assistance (ODA) by donor agriculture ODA for basic social services a Goods Average tariff on exports of least developed countries % of total (excluding arms) Agricultural Net ODA sector-allocatable admitted free of tariffs products Textiles Clothing % of donor GNI ODA % % % % % of GDP 2002 2000­02 1996 2002 1996 2002 1996 2002 1996 2002 2002 Australia 0.26 17.7 98.3 96.1 0.5 0.2 10.0 6.2 31.2 19.6 0.4 Canada 0.28 22.4 78.3 64.5 3.5 2.9 10.9 7.4 22.4 17.9 0.8 European Union 94.4 99.8 3.3 0.8 0.0 0.2 0.0 0.9 1.3 Austria 0.26 14.7 .. .. .. .. .. .. .. Belgium 0.43 20.4 .. .. .. .. .. .. .. Denmark 0.96 7.8 .. .. .. .. .. .. .. Finland 0.35 14.3 .. .. .. .. .. .. .. France 0.38 .. .. .. .. .. .. .. .. Germany 0.27 10.3 .. .. .. .. .. .. .. Greece 0.21 3.9 .. .. .. .. .. .. .. Ireland 0.40 30.8 .. .. .. .. .. .. .. Italy 0.20 10.7 .. .. .. .. .. .. .. Luxembourg 0.77 19.8 .. .. .. .. .. .. .. Netherlands 0.81 26.7 .. .. .. .. .. .. .. Portugal 0.27 3.1 .. .. .. .. .. .. .. Spain 0.26 11.5 .. .. .. .. .. .. .. Sweden 0.83 11.8 .. .. .. .. .. .. .. United Kingdom 0.31 29.9 .. .. .. .. .. .. .. Japan 0.23 4.8 57.0 85.7 10.1 12.0 1.7 0.7 0.0 0.0 1.4 New Zealand 0.22 8.3 .. .. .. .. .. .. 0.3 Norway 0.89 15.1 .. .. .. .. .. .. 1.5 Switzerland 0.32 19.8 50.8 93.3 8.5 5.8 0.0 0.0 0.0 0.0 2.0 United States 0.13 27.0 22.6 51.2 5.3 3.1 7.2 6.3 15.5 14.6 0.9 Heavily indebted poor countries (HIPCs) HIPC HIPC Estimated total HIPC HIPC Estimated total decision completion nominal debt decision completion nominal debt point b point c service relief point b point c service relief $ millions $ millions Benin Jul. 2000 Mar. 2003 460 Madagascar Dec. 2000 Floating 1,500 Bolivia Feb. 2000 Jun. 2001 2,060 Malawi Dec. 2000 Floating 1,000 Burkina Faso Jul. 2000 Apr. 2002 930 Mali Sep. 2000 Mar. 2003 895 Cameroon Oct. 2000 Floating 2,000 Mauritania Feb. 2000 Jun. 2002 1,100 Chad May 2001 Floating 260 Mozambique Apr. 2000 Sep. 2001 4,300 Congo, Dem. Republic Jul. 2003 Floating 10,389 Nicaragua Dec. 2000 Jan. 2004 4,500 Côte d'Ivoire Mar. 1998 .. 800 Niger Dec. 2000 Floating 900 Ethiopia Nov. 2001 Floating 1,930 Rwanda Dec. 2000 Floating 800 Gambia Dec. 2000 Floating 90 São Tomé & Principe Dec. 2000 Floating 200 Ghana Feb. 2002 Floating 3,700 Senegal Jun. 2000 Floating 850 Guinea Dec. 2000 Floating 800 Sierra Leone Mar. 2002 Floating 950 Guinea-Bissau Dec. 2000 Floating 790 Tanzania Apr. 2000 Nov. 2001 3,000 Guyana Nov. 2000 Dec. 2003 877 Uganda Feb. 2000 May. 2000 1,950 Honduras Jul. 2000 Floating 900 Zambia Dec. 2000 Floating 3,850 Madagascar Dec. 2000 Floating 1,500 a. Includes basic health, education, nutrition, and water and sanitation services. b. Except for Côte d'Ivoire the date refers to the HIPC enhanced framework. The following countries also reached decision points under the original framework: Bolivia in September 1997, Burkina Faso in September 1997, Côte d'Ivoire in March 1998, Guyana in December 1997, Mali in September 1998, Mozambique in April 1998, and Uganda in April 1997. c. Except for Côte d'Ivoire the date refers to the HIPC enhanced framework. The following countries also reached com- pletion points under the original framework: Bolivia in September 1998, Burkina Faso in July 2000, Guyana in May 1999, Mali in September 2000, Mozambique in July 1999, and Uganda in April 1998. 26 2004 World Development Indicators WORLD 1.4 Millennium Development Goals: VIEW overcoming obstacles About the data Achieving the Millennium Development Goals will launched a special program of concessions to initiative yielded significant early progress, multilateral require an open, rule-based global economy in which exports from Sub-Saharan Africa. organizations, bilateral creditors, HIPC governments, all countries, rich and poor, participate. Many poor The average tariffs in the table were calculated by and civil society have engaged in an intensive dialogue countries, lacking the resources to finance their devel- the World Trade Organization (WTO). They reflect the about its strengths and weaknesses. A major review in opment, burdened by unsustainable levels of debt, tariff schedules applied by high-income OECD mem- 1999 led to an enhancement of the original framework. and unable to compete in the global marketplace, bers to exports of countries designated "least devel- need assistance from rich countries. For goal 8-- oped countries" (LDCs) by the United Nations. Definitions develop a global partnership for development--many Agricultural commodities and textiles and clothing of the indicators therefore monitor the actions of mem- are three of the most important categories of goods · Net official development assistance (ODA) com- bers of the Development Assistance Committee (DAC) exported by developing economies. Although average prises grants and loans (net of repayments of princi- of the Organisation for Economic Co-operation and tariffs have been falling, averages may disguise high pal) that meet the DAC definition of ODA and are made Development (OECD). tariffs targeted at specific goods (see table 6.6 for to countries and territories in part I of the DAC list of Official development assistance (ODA) has declined estimates of the share of tariff lines with "interna- recipient countries. · ODA for basic social services is in recent years as a share of donor countries' gross tional peaks" in each country's tariff schedule). The aid reported by DAC donors for basic health, educa- national income (GNI). The poorest countries will averages in the table include ad valorem duties and tion, nutrition, and water and sanitation services. need additional assistance to achieve the Millennium ad valorem equivalents of non-ad valorem duties. · Goods admitted free of tariffs are the value of Development Goals. Recent estimates suggest that Subsidies to agricultural producers and exporters exports of goods (excluding arms) from least devel- $30­60 billion more a year would allow most of them in OECD countries are another form of barrier to oped countries admitted without tariff, as a share of to achieve the goals, if the aid goes to countries with developing economies' exports. The table shows total exports from LDCs. · Average tariff is the simple good policies. At the United Nations International the value of total support to agriculture as a share mean tariff, the unweighted average of the effectively Conference on Financing for Development in 2002 of the economy's gross domestic product (GDP). applied rates for all products subject to tariffs. many donor countries made new commitments that, Agricultural subsidies in OECD economies are esti- · Agricultural products comprise plant and animal if fulfilled, would add $18.6 billion to ODA. mated at $318 billion in 2002. products, including tree crops but excluding timber One of the most important actions that high-income The Debt Initiative for Heavily Indebted Poor and fish products. · Textiles and clothing include nat- economies can take to help is to reduce barriers to Countries (HIPCs) is the first comprehensive approach ural and synthetic fibers and fabrics and articles of the exports of low- and middle-income economies. to reducing the external debt of the world's poorest, clothing made from them. · Support to agriculture is The European Union has announced a program to most heavily indebted countries. It represents an the value of subsidies to the agricultural sector. eliminate tariffs on developing country exports of important step forward in placing debt relief within an · HIPC decision point is the date at which a heavily "everything but arms," and the United States has overall framework of poverty reduction. While the indebted poor country with an established track record 1.4a of good performance under adjustment programs sup- ported by the International Monetary Fund and the Location of indicators for Millennium Development Goal 8 World Bank commits to undertake additional reforms Goal 8. Develop a global partnership for development and to develop and implement a poverty reduction 33. Net ODA as a percentage of DAC donors' gross national income (table 6.9) strategy. · HIPC completion point is the date at which 34. Proportion of ODA for basic social services (table 1.4) the country successfully completes the key structural 35. Proportion of ODA that is untied (table 6.9) reforms agreed on at the decision point, including 36. Proportion of ODA received in landlocked countries as a percentage of GNI* developing and implementing its poverty reduction 37. Proportion of ODA received in small island developing states as a percentage of GNI* strategy. The country then receives the bulk of debt 38. Proportion of total developed country imports (by value, excluding arms) from developing relief under the HIPC Debt Initiative without further pol- countries admitted free of duty (table 1.4) icy conditions. · Estimated total nominal debt service 39. Average tariffs imposed by developed countries on agricultural products and textiles and clothing relief is the amount of debt service relief, calculated at the decision point, that will allow the country to from developing countries (see related indicators in table 6.6) achieve debt sustainability at the completion point. 40. Agricultural support estimate for OECD countries as a percentage of GDP (table 1.4) 41. Proportion of ODA provided to help build trade capacity* 42. Number of countries reaching HIPC decision and completion points (table 1.4) Data sources 43. Debt relief committed under new HIPC initiative (table 1.4) The indicators here, and where they appear through- 44. Debt service as a percentage of exports of goods and services (table 4.17) out the rest of the book, have been compiled by 45. Unemployment rate of 15- to 24-year-olds (see tables 2.4 and 2.8 for related indicators) World Bank staff from primary and secondary 46. Proportion of population with access to affordable, essential drugs on a sustainable basis* sources. The WTO, in collaboration with the UN 47. Telephone lines and cellular subscribers per 100 people (tables 1.3 and 5.10) Conference on Trade and Development and the 48a. Personal computers in use per 100 people (table 5.10) International Trade Centre, provided the estimates 48b. Internet users per 100 people (table 5.10) of goods admitted free of tariffs and average tariffs. Subsidies to agriculture are compiled by the OECD. * No data available in the World Development Indicators database. 2004 World Development Indicators 27 1.5 Women in development Female Life expectancy Pregnant Teenage Literacy Labor force Women Unpaid Women in population at birth women mothers gender gender parity in non- family parliaments receiving parity index agricultural workers prenatal index sector care Male Female years % of women ages % of male % of female % of total % of total Male Female % ages 15­19 15­24 % of total employment employment seats 2002 2002 2002 1995­2002 a 1995­2002 a 2002 1990 2002 2000­02 a 2000­02 a 2000­02 a 2003 Afghanistan 49.0 43 44 37 .. .. 0.5 0.6 .. .. .. .. Albania 48.9 72 76 95 .. 1.0 0.7 0.7 41.1 .. .. 6 Algeria 49.4 69 72 79 .. 0.9 0.3 0.4 12.2 .. .. 6 Angola 50.5 45 48 66 .. .. 0.9 0.9 .. .. .. 16 Argentina 50.9 71 78 95 b .. 1.0 0.4 0.5 42.9 0.7 1.8 31 Armenia 51.4 71 79 92 6 1.0 0.9 0.9 .. 1.1 0.8 5 Australia 50.1 76 82 100 b .. .. 0.7 0.8 48.1 0.4 0.7 25 Austria 51.3 76 82 100 b .. .. 0.7 0.7 43.5 1.4 3.7 34 Azerbaijan 50.9 62 69 66 .. .. 0.8 0.8 45.4 .. .. 11 Bangladesh 49.7 62 63 40 35 0.7 0.7 0.7 22.9 10.1 73.2 2 Belarus 53.1 63 74 100 .. 1.0 1.0 1.0 56.0 .. .. 10 Belgium 50.9 75 82 .. .. .. 0.7 0.7 44.8 .. .. 35 Benin 50.7 51 55 81 22 0.5 0.9 0.9 .. .. .. 6 Bolivia 50.2 62 65 83 14 1.0 0.6 0.6 36.4 5.2 11.1 19 Bosnia and Herzegovina 50.5 71 77 99 .. 1.0 0.6 0.6 .. .. .. 17 Botswana 50.2 38 38 91 .. 1.1 0.9 0.8 44.8 16.9 17.4 17 Brazil 50.7 65 73 86 18 1.0 0.5 0.6 45.7 .. .. 9 Bulgaria 51.4 69 75 .. .. 1.0 0.9 0.9 50.2 .. .. 26 Burkina Faso 50.4 42 44 61 25 0.5 0.9 0.9 .. .. .. 12 Burundi 51.0 42 42 78 .. 1.0 1.0 0.9 .. .. .. 18 Cambodia 51.2 53 56 38 8 0.9 1.2 1.1 51.7 31.6 53.3 7 Cameroon 50.0 48 49 75 31 .. 0.6 0.6 .. .. .. 9 Canada 50.5 76 82 .. .. .. 0.8 0.9 48.8 0.1 0.3 21 Central African Republic 51.2 42 43 62 36 0.7 .. .. .. .. .. 7 Chad 50.5 47 50 42 39 0.8 0.8 0.8 .. .. .. 6 Chile 50.5 73 79 95 b .. 1.0 0.4 0.5 36.6 .. .. 13 China 48.4 69 72 90 .. 1.0 0.8 0.8 39.2 .. .. 22 Hong Kong, China 50.9 78 83 .. .. .. 0.6 0.6 45.5 .. .. .. Colombia 50.5 69 75 91 19 1.0 0.6 0.6 49.1 5.1 7.1 12 Congo, Dem. Rep. 50.4 45 46 68 .. .. 0.8 0.8 .. .. .. .. Congo, Rep. 51.0 50 54 .. .. 1.0 0.8 0.8 .. .. .. 9 Costa Rica 50.1 75 80 70 .. 1.0 0.4 0.5 40.1 2.5 3.6 35 Côte d'Ivoire 49.2 45 46 88 31 0.7 0.5 0.5 .. .. .. 9 Croatia 51.7 70 78 .. .. 1.0 0.7 0.8 45.9 2.4 7.8 21 Cuba 50.0 75 79 100 .. 1.0 0.6 0.7 37.8 .. .. 36 Czech Republic 51.2 72 79 99 b .. .. 0.9 0.9 46.6 0.2 1.1 17 Denmark 50.5 75 79 .. .. .. 0.9 0.9 48.9 .. .. 38 Dominican Republic 49.3 64 70 98 21 1.0 0.4 0.5 34.3 .. .. 17 Ecuador 49.8 69 72 69 .. 1.0 0.3 0.4 41.4 4.4 10.2 16 Egypt, Arab Rep. 49.1 67 71 53 9 0.8 0.4 0.4 19.6 8.2 26.0 2 El Salvador 50.9 67 73 76 .. 1.0 0.5 0.6 31.2 .. .. 11 Eritrea 50.4 50 52 49 23 .. 0.9 0.9 .. .. .. 22 Estonia 53.5 65 77 .. .. 1.0 1.0 1.0 51.7 0.8 0.9 19 Ethiopia 49.8 41 43 27 16 0.8 0.7 0.7 .. .. .. 8 Finland 51.2 75 82 100 b .. .. 0.9 0.9 50.2 0.6 0.4 38 France 51.4 76 83 99 b .. .. 0.8 0.8 46.3 .. .. 12 Gabon 50.4 52 54 94 33 .. 0.8 0.8 .. .. .. 9 Gambia, The 50.5 52 55 91 .. .. 0.8 0.8 .. .. .. 13 Georgia 52.5 69 78 95 .. .. 0.9 0.9 48.6 23.2 40.2 7 Germany 50.9 75 81 .. .. .. 0.7 0.7 45.5 0.5 2.1 32 Ghana 50.2 54 56 88 14 1.0 1.0 1.0 .. .. .. 9 Greece 50.8 75 81 .. .. 1.0 0.5 0.6 40.5 4.2 14.7 9 Guatemala 49.6 63 69 60 22 0.9 0.3 0.4 39.2 .. .. 9 Guinea 49.7 46 47 71 37 .. 0.9 0.9 .. .. .. 19 Guinea-Bissau 50.6 44 47 62 .. .. 0.7 0.7 .. .. .. 8 Haiti 50.9 50 54 79 18 1.0 0.8 0.7 .. .. .. 4 28 2004 World Development Indicators WORLD 1.5 VIEW Women in development Female Life expectancy Pregnant Teenage Literacy Labor force Women Unpaid Women in population at birth women mothers gender gender parity in non- family parliaments receiving parity index agricultural workers prenatal index sector care Male Female years % of women ages % of male % of female % of total % of total Male Female % ages 15­19 15­24 % of total employment employment seats 2002 2002 2002 1995­2002 a 1995­2002 a 2002 1990 2002 2000­02 a 2000­02 a 2000­02 a 2003 Honduras 49.7 63 69 83 .. 1.0 0.4 0.5 51.7 .. .. 6 Hungary 52.3 68 77 .. .. 1.0 0.8 0.8 46.1 0.4 1.0 10 India 48.4 63 64 60 21 .. 0.5 0.5 17.1 .. .. 9 Indonesia 50.1 65 69 89 12 1.0 0.6 0.7 29.7 .. .. 8 Iran, Islamic Rep. 49.8 68 70 77 .. .. 0.3 0.4 .. .. .. 4 Iraq 49.2 61 64 77 .. .. 0.2 0.3 .. .. .. 8 Ireland 50.5 74 80 .. .. .. 0.5 0.5 46.5 0.8 1.5 13 Israel 50.3 77 81 .. .. 1.0 0.6 0.7 48.5 0.2 0.7 15 Italy 51.5 75 82 .. .. 1.0 0.6 0.6 40.6 3.0 6.0 12 Jamaica 50.8 74 78 99 .. 1.1 0.9 0.9 45.8 .. .. 12 Japan 51.1 78 85 .. .. .. 0.7 0.7 40.4 1.6 10.1 7 Jordan 48.3 70 74 96 6 1.0 0.2 0.3 20.8 .. .. 6 Kazakhstan 51.6 57 67 91 7 1.0 0.9 0.9 .. .. .. 10 Kenya 49.8 45 46 76 21 1.0 0.8 0.9 37.8 .. .. 7 Korea, Dem. Rep. 49.7 61 64 .. .. .. 0.8 0.8 .. .. .. 20 Korea, Rep. 49.7 71 78 .. .. .. 0.6 0.7 41.5 1.8 19.5 6 Kuwait 46.7 75 79 95 .. 1.0 0.3 0.5 .. .. .. 0 Kyrgyz Republic 51.1 61 70 97 9 .. 0.9 0.9 44.8 .. .. 10 Lao PDR 50.0 53 56 27 .. 0.8 .. .. .. .. .. 23 Latvia 54.1 65 76 .. .. 1.0 1.0 1.0 52.7 4.2 4.9 21 Lebanon 50.8 69 73 87 .. .. 0.4 0.4 .. .. .. 2 Lesotho 50.3 37 39 85 .. 0.6 0.6 .. .. .. 12 Liberia 49.7 46 48 85 .. 0.6 0.6 0.7 .. .. .. 8 Libya 48.3 70 75 81 .. 0.9 0.2 0.3 .. .. .. .. Lithuania 52.9 68 78 .. .. 1.0 0.9 0.9 51.3 2.8 3.5 11 Macedonia, FYR 50.0 71 76 100 .. .. 0.7 0.7 41.9 .. .. .. Madagascar 50.1 54 57 71 36 .. 0.8 0.8 .. .. .. 4 Malawi 50.8 37 38 91 33 0.8 1.0 0.9 12.2 .. .. 9 Malaysia 49.4 70 75 .. .. 1.0 0.6 0.6 36.5 .. .. 10 Mali 50.9 40 42 57 40 0.5 0.9 0.9 .. .. .. 10 Mauritania 50.4 49 53 64 16 0.7 0.8 0.8 .. .. .. 4 Mauritius 50.5 69 76 .. .. 1.0 0.4 0.5 39.0 .. .. 6 Mexico 51.4 71 77 86 .. 1.0 0.4 0.5 37.2 6.8 12.5 23 Moldova 52.4 63 71 99 .. 1.0 0.9 0.9 52.7 4.7 10.7 13 Mongolia 50.3 64 67 97 .. 1.0 0.9 0.9 .. .. .. 11 Morocco 50.0 66 70 42 .. 0.8 0.5 0.5 26.6 .. .. 11 Mozambique 51.4 40 42 76 40 0.6 0.9 0.9 .. .. .. 30 Myanmar 50.3 55 60 76 .. 1.0 0.8 0.8 .. .. .. .. Namibia 50.5 42 41 91 .. 1.0 0.7 0.7 48.8 .. .. 26 Nepal 48.7 60 60 28 21 0.6 0.7 0.7 .. .. .. 6 Netherlands 50.5 76 81 .. .. .. 0.6 0.7 44.3 0.2 1.1 37 New Zealand 51.1 76 81 95 b .. .. 0.8 0.8 50.9 0.6 1.2 28 Nicaragua 50.2 67 71 86 27 1.1 0.5 0.6 .. .. .. 21 Niger 50.6 46 47 41 43 0.4 0.8 0.8 .. .. .. 1 Nigeria 50.6 45 46 64 22 1.0 0.5 0.6 .. .. .. 5 Norway 50.4 76 82 .. .. .. 0.8 0.9 48.3 0.2 0.5 36 Oman 47.4 73 76 100 .. 1.0 0.1 0.2 25.3 .. .. .. Pakistan 48.3 63 65 43 .. 0.6 0.3 0.4 7.9 16.7 50.1 22 Panama 49.6 73 77 72 .. 1.0 0.5 0.6 41.7 .. .. 10 Papua New Guinea 48.5 56 58 78 .. .. 0.7 0.7 .. .. .. 1 Paraguay 49.6 69 73 89 .. 1.0 0.4 0.4 38.4 .. .. 9 Peru 49.7 68 72 84 13 1.0 0.4 0.5 34.6 4.7 11.5 18 Philippines 49.6 68 72 86 7 1.0 0.6 0.6 42.2 .. .. 18 Poland 51.4 70 78 .. .. .. 0.8 0.9 46.9 4.0 6.8 20 Portugal 52.0 73 79 .. .. 1.0 0.7 0.8 46.3 1.1 3.2 19 Puerto Rico 51.9 72 81 .. .. 1.0 0.5 0.6 39.0 0.2 1.0 .. 2004 World Development Indicators 29 1.5 Women in development Female Life expectancy Pregnant Teenage Literacy Labor force Women Unpaid Women in population at birth women mothers gender gender parity in non- family parliaments receiving parity index agricultural workers prenatal index sector care Male Female years % of women ages % of male % of female % of total % of total Male Female % ages 15­19 15­24 % of total employment employment seats 2002 2002 2002 1995­2002 a 1995­2002 a 2002 1990 2002 2000­02 a 2000­02 a 2000­02 a 2003 Romania 51.1 66 74 .. .. 1.0 0.8 0.8 45.7 10.4 29.1 11 Russian Federation 53.3 60 72 .. .. 1.0 0.9 1.0 49.7 .. .. 8 Rwanda 50.4 39 40 92 7 1.0 1.0 1.0 .. .. .. 49 Saudi Arabia 45.9 71 75 90 .. 1.0 0.1 0.2 14.2 .. .. 0 Senegal 50.2 51 54 79 22 0.7 0.7 0.7 .. .. .. 19 Serbia and Montenegro 50.2 70 75 .. .. .. 0.7 0.8 .. .. .. 8 Sierra Leone 50.9 36 39 68 .. .. 0.6 0.6 .. .. .. 15 Singapore 48.7 76 80 .. .. 1.0 0.6 0.6 46.9 0.3 1.7 16 Slovak Republic 51.4 69 77 98 b .. 1.0 0.9 0.9 51.9 0.1 0.2 19 Slovenia 51.3 72 80 98 b .. 1.0 0.9 0.9 47.7 3.8 7.0 12 Somalia 50.4 46 49 32 .. .. 0.8 0.8 .. .. .. .. South Africa 51.7 46 48 94 16 1.0 0.6 0.6 .. 0.7 1.4 30 Spain 51.1 75 82 .. .. 1.0 0.5 0.6 39.3 1.0 3.3 28 Sri Lanka 50.6 72 76 98 .. 1.0 0.5 0.6 46.6 .. .. 4 Sudan 49.7 57 60 60 .. 0.9 0.4 0.4 .. .. .. 10 Swaziland 51.7 44 44 87 .. 1.0 0.6 0.6 29.6 .. .. 3 Sweden 50.3 78 82 .. .. .. 0.9 0.9 50.7 0.3 0.4 45 Switzerland 50.4 77 83 .. .. .. 0.6 0.7 47.2 .. .. 27 Syrian Arab Republic 49.5 68 73 71 .. 1.0 0.3 0.4 17.4 .. .. 12 Tajikistan 50.1 64 70 71 .. 1.0 0.7 0.8 51.6 .. .. 13 Tanzania 50.4 43 44 49 25 1.0 1.0 1.0 .. .. .. 22 Thailand 50.8 67 72 92 .. 1.0 0.9 0.9 46.8 16.4 39.8 9 Togo 50.3 49 51 73 19 0.8 0.7 0.7 .. .. .. 7 Trinidad and Tobago 50.1 70 75 92 .. 1.0 0.5 0.5 39.9 1.0 0.6 19 Tunisia 49.5 71 75 92 .. 0.9 0.4 0.5 .. .. .. 12 Turkey 49.5 68 73 68 10 1.0 0.5 0.6 18.9 10.2 51.3 4 Turkmenistan 50.5 61 68 98 4 1.0 0.8 0.8 .. .. .. 26 Uganda 50.0 43 44 92 31 0.9 0.9 0.9 .. .. .. 25 Ukraine 53.5 63 74 .. .. 1.0 1.0 1.0 53.0 0.8 1.7 5 United Arab Emirates 34.4 74 77 97 .. 1.1 0.1 0.2 13.8 .. .. 0 United Kingdom 50.8 75 80 .. .. .. 0.7 0.8 49.7 0.2 0.5 18 United States 51.1 75 80 99 b .. .. 0.8 0.9 48.4 0.1 0.1 14 Uruguay 51.5 71 79 94 .. 1.0 0.6 0.7 46.5 .. .. 12 Uzbekistan 50.3 64 70 97 10 1.0 0.8 0.9 37.9 .. .. 7 Venezuela, RB 49.7 71 77 94 .. 1.0 0.5 0.5 39.6 .. .. 10 Vietnam 50.6 67 72 68 6 1.0 1.0 1.0 .. .. .. 27 West Bank and Gaza 49.3 71 75 .. .. .. .. .. .. 6.0 27.3 .. Yemen, Rep. 49.0 57 58 34 16 0.6 0.4 0.4 .. .. .. 0 Zambia 50.2 37 37 93 32 0.9 0.8 0.8 .. .. .. 12 Zimbabwe 49.9 39 39 93 21 1.0 0.8 0.8 20.2 .. .. 10 World 49.6 w 65 w 69 w .. 0.9 w .. .. .. .. .. Low income 49.2 58 60 .. 0.9 .. .. .. .. .. Middle income 49.6 68 72 .. 1.0 .. .. .. .. .. Lower middle income 49.5 67 72 .. 1.0 .. .. .. .. .. Upper middle income 50.8 70 77 .. 1.0 .. .. .. .. .. Low & middle income 49.5 63 66 .. 0.9 .. .. .. .. .. East Asia & Pacific 48.9 68 71 .. 1.0 .. .. .. .. .. Europe & Central Asia 52.0 64 73 .. 1.0 .. .. .. .. .. Latin America & Carib. 50.7 68 74 .. 1.0 .. .. .. .. .. Middle East & N. Africa 49.2 67 70 .. 0.9 .. .. .. .. .. South Asia 48.5 62 64 .. 0.8 .. .. .. .. .. Sub-Saharan Africa 50.2 45 47 .. 0.9 .. .. .. .. .. High income 50.6 75 81 .. .. .. .. .. .. .. Europe EMU 51.0 75 82 .. .. .. .. 42.8 .. .. a. Data are for the most recent year available. b. Data refer to a period other than specified, differ from the standard definition, or refer to only part of a country. 30 2004 World Development Indicators WORLD 1.5 VIEW Women in development About the data Despite much progress in recent decades, gender complications that arise during pregnancy. In high- For information on other aspects of gender, see tables inequalities remain pervasive in many dimensions of income countries most women have access to health 1.2 (Millennium Development Goals: eradicating poverty life--worldwide. But while disparities exist throughout care during pregnancy, but in developing countries an and improving lives), 2.3 (employment by economic the world, they are most prevalent in poor developing estimated 35 percent of pregnant women--some 45 mil- activity), 2.4 (unemployment), 2.12 (education efficien- countries. Gender inequalities in the allocation of such lion each year--receive no care at all (United Nations cy), 2.13 (education outcomes), 2.16 (reproductive resources as education, health care, nutrition, and polit- 2000b). This is reflected in the differences in maternal health), 2.18 (health risk factors and future challenges), ical voice matter because of the strong association with mortality ratios between high- and low-income countries. and 2.19 (mortality). well-being, productivity, and economic growth. This pat- Women's wage work is important for economic growth tern of inequality begins at an early age, with boys rou- and the well-being of families. But restricted access to Definitions tinely receiving a larger share of education and health education and vocational training, heavy workloads at spending than do girls, for example. home and in nonpaid domestic activities, and labor market · Female population is the percentage of the population Because of biological differences girls are expected to discrimination often limit women's participation in paid that is female. · Life expectancy at birth is the number experience lower infant and child mortality rates and to economic activities, lower their productivity, and reduce of years a newborn infant would live if prevailing patterns have a longer life expectancy than boys. This biological their wages. A gender labor force parity index of less than of mortality at the time of its birth were to stay the same advantage, however, may be overshadowed by gender 1.0 shows that women have lower activity rates than men. throughout its life. ·Teenage mothers are the percentage inequalities in nutrition and medical interventions, and However, a gender labor force parity index of 1.0 or more of women ages 15­19 who already have children or are by inadequate care during pregnancy and delivery, so does not necessarily imply equality in employment oppor- currently pregnant. · Pregnant women receiving prenatal that female rates of illness and death sometimes tunities. Women's unemployment rates tend to be higher care are the percentage of women attended at least once exceed male rates, particularly during early childhood than men's (table 2.4), and in many countries a large pro- during pregnancy by skilled health personnel for reasons and the reproductive years. In high-income countries portion of women who are reported as employed are related to pregnancy. · Literacy gender parity index is women tend to outlive men by four to eight years on unpaid family workers. Women's wage employment also the ratio of the female literacy rate to the male rate for average, while in low-income countries the difference is tends to be concentrated in the agricultural sector. ages 15­24. · Labor force gender parity index is the narrower--about two to three years. The difference in Nonsalaried men tend to be self-employed, while non- ratio of the percentage of women who are economically child mortality rates (table 2.19) is another good indica- salaried women tend to be unpaid family workers. There active to the percentage of men who are. According to the tor of female social disadvantage because nutrition and are several reasons for this. Most women have less International Labour Organization's (ILO) definition, the medical interventions are particularly important for the access to credit markets, capital, land, training, and economically active population is all those who supply 1­5 age group. Female child mortality rates that are as education, which may be required to start up a busi- labor for the production of goods and services during a high as or higher than male child mortality rates might ness. Cultural norms may prevent women from working specified period. It includes both the employed and the be indicative of discrimination against girls. on their own or from supervising other workers. Also, unemployed. While national practices vary in the treat- Having a child during the teenage years limits girls' women may face time constraints due to their tradition- ment of such groups as the armed forces and seasonal opportunities for better education, jobs, and income and al family responsibilities. Because of biases and mis- or part-time workers, in general the labor force includes increases the likelihood of divorce and separation. classification substantial numbers of employed women the armed forces, the unemployed, and first-time job Pregnancy is more likely to be unintended during the may be underestimated or reported as unpaid family seekers, but excludes homemakers and other unpaid teenage years, and births are more likely to be prema- workers even when they work in association or equally caregivers and workers in the informal sector. · Women ture and are associated with greater risks of complica- with their husbands in the family enterprise. in nonagricultural sector refer to women wage employees tions during delivery and of death. Women are vastly underrepresented in decisionmaking in the nonagricultural sector as a percentage of total In many countries maternal mortality (tables 1.2 and positions in government, although there is some evidence nonagricultural employment. · Unpaid family workers are 2.16) is a leading cause of death among women of of recent improvement. Gender parity in parliamentary rep- those who work without pay in a market-oriented estab- reproductive age. Most maternal deaths result from pre- resentation is still far from being realized. In 2003 women lishment or activity operated by a related person living in ventable causes--hemorrhage, infection, and complica- represented 15 percent of parliamentarians worldwide, the same household. · Women in parliaments are the tions from unsafe abortions. Prenatal care is essential compared with 9 percent in 1987. Without representation percentage of parliamentary seats in a single or lower for recognizing, diagnosing, and promptly treating at this level, it is difficult for women to influence policy. chamber occupied by women. 1.5a Income and gender affect children's access to basic health care Data sources Medical treatment of fever, by income quintile and gender (%) The data on female population and life expectancy are from the World Bank's population database. The 100 Male Female data on pregnant women receiving prenatal care are 80 from United Nations Children's Fund's (UNICEF) State of the World's Children 2004. The data on teenage 60 mothers are from Demographic and Health Surveys by Macro International. The data on the literacy gen- 40 der parity index are from the UNESCO Institute for Statistics. The data on the labor force gender parity 20 index are from the ILO database Estimates and 0 Projections of the Economically Active Population, Poorest Richest Poorest Richest Poorest Richest 1950­2010. The data on unpaid family workers are Indonesia, 1997 Haiti, 2000 Ethiopia, 2000 from the ILO database Key Indicators of the Labour Boys are more likely to receive treatment for fever than girls. But poverty has a larger impact than gender on access to Market, third edition. The data on women in parlia- basic health care. ments are from the United Nations' World's Women: Source: Demographic and Health Survey data. Trends and Statistics 2000. 2004 World Development Indicators 31 1.6 Key indicators for other economies Population Surface Population Gross national income Gross domestic Life Adult Carbon area density product expectancy literacy dioxide at rate emissions birth PPP a Per Per Per % thousand people capita capita capita ages 15 thousand thousands sq. km per sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2002 2002 2002 2002 b 2002 b 2002 2002 2001­02 2001­02 2002 2002 2000 American Samoa 69 0.2 344 .. .. c .. .. .. .. .. .. 286 Andorra 68 0.5 136 .. .. d .. .. .. .. .. .. .. Antigua and Barbuda 69 0.4 157 671 9,720 717 10,390 2.9 1.5 75 .. 352 Aruba 97 0.2 511 .. .. d .. .. .. .. .. .. 1,924 Bahamas, The 314 13.9 31 .. .. d .. .. .. .. 70 .. 1,795 Bahrain 698 0.7 983 7,326 10,500 11,298 16,190 3.5 1.4 73 89 19,500 Barbados 269 0.4 626 2,365 8,790 e 3,943 14,660 ­2.1 ­2.4 75 100 1,176 Belize 253 23.0 11 750 2,970 1,390 5,490 3.7 1.3 74 77 f 780 Bermuda 63 0.1 1,260 .. .. d .. .. .. .. .. .. 462 Bhutan 851 47.0 18 512 600 .. .. 7.7 4.8 63 .. 396 Brunei 351 5.8 67 .. .. d .. .. .. .. 77 94 f 4,668 Cape Verde 458 4.0 114 572 1,250 2,252 g 4,920 g 4.6 1.9 69 76 139 Cayman Islands 39 0.3 150 .. .. d .. .. .. .. .. .. 286 Channel Islands 149 0.2 745 .. .. d .. .. .. .. 79 .. .. Comoros 586 2.2 263 228 390 990 1,690 g 3.0 0.5 61 56 81 Cyprus 765 9.3 83 9,372 12,320 14,201 g 18,560 g 2.0 1.5 78 97 f 6,423 Djibouti 693 23.2 30 590 850 1,412 2,040 g 1.6 ­0.3 44 .. 385 Dominica 72 0.8 96 216 3,000 357 4,960 ­5.2 ­5.2 77 .. 103 Equatorial Guinea 482 28.1 17 437 930 h 4,390 9,110 g 16.2 13.3 52 .. 205 Faeroe Islands 46 1.4 33 .. .. d .. .. .. .. .. .. 649 Fiji 823 18.3 45 1,750 2,130 4,385 5,330 g 4.1 3.3 70 93 f 725 French Polynesia 240 4.0 66 .. .. d .. .. .. .. 74 .. 542 Greenland 57 410.5 0 .. .. d .. .. .. .. 69 .. 557 Grenada 102 0.3 300 361 3,530 673 6,600 1.2 ­0.8 73 .. 213 Guam 159 0.6 289 .. .. d .. .. .. .. 78 .. 4,071 Guyana 766 215.0 4 656 860 3,020 3,940 g ­1.1 ­1.6 62 .. 1,598 Iceland 284 103.0 3 7,940 27,960 8,305 29,240 ­0.5 ­1.2 80 .. 2,158 Isle of Man 75 0.6 125 .. .. d .. .. .. .. .. .. .. About the data Definitions The table shows data for 56 economies with popula- · Population is based on the de facto definition of from abroad. Data are in current U.S. dollars con- tions from 30,000 to 1 million and smaller population, which counts all residents regardless of verted using the World Bank Atlas method (see economies if they are members of the World Bank. legal status or citizenship--except for refugees not Statistical methods). · GNI per capita is gross Where data on gross national income (GNI) per capi- permanently settled in the country of asylum, who national income divided by midyear population. GNI ta are not available, an estimated range is given. For are generally considered part of the population of per capita in U.S. dollars is converted using the more information on the calculation of GNI (or gross their country of origin. The values shown are World Bank Atlas method. · PPP GNI is gross national product in the 1968 System of National midyear estimates for 2002. See also table 2.1. national income converted to international dollars Accounts) and purchasing power parity (PPP) conver- · Surface area is a country's total area, including using purchasing power parity rates. An internation- sion factors, see About the data for table 1.1. Since areas under inland bodies of water and some al dollar has the same purchasing power over GNI 2000 this table has excluded France's overseas coastal waterways. · Population density is midyear as a U.S. dollar has in the United States. · Gross departments--French Guiana, Guadeloupe, population divided by land area in square kilome- domestic product (GDP) is the sum of value added Martinique, and Réunion--for which GNI and other ters. · Gross national income (GNI) is the sum of by all resident producers plus any product taxes economic measures are now included in the French value added by all resident producers plus any prod- (less subsidies) not included in the valuation of out- national accounts. uct taxes (less subsidies) not included in the valua- put. Growth is calculated from constant price GDP tion of output plus net receipts of primary income data in local currency. · Life expectancy at birth is (compensation of employees and property income) the number of years a newborn infant would live if 32 2004 World Development Indicators WORLD 1.6 VIEW Key indicators for other economies Population Surface Population Gross national income Gross domestic Life Adult Carbon area density product expectancy literacy dioxide at rate emissions birth PPP a Per Per Per % thousand people capita capita capita ages 15 thousand thousands sq. km per sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2002 2002 2002 2002 b 2002 b 2002 2002 2001­02 2001­02 2002 2002 2000 Kiribati 95 0.7 130 91 960 .. .. 2.8 0.6 63 .. 26 Liechtenstein 33 0.2 205 .. .. d .. .. .. .. .. .. .. Luxembourg 444 2.6 171 17,523 39,470 23,659 53,290 1.1 0.2 78 .. 8,482 Macao, China 439 .. .. 6,335 i 14,600 i 9,618 21,910 g 10.1 8.9 79 91 f 1,634 Maldives 287 0.3 957 622 2,170 .. .. 5.6 3.0 69 97 498 Malta 397 0.3 1,241 3,678 9,260 7,030 17,710 1.5 1.0 78 93 2,814 Marshall Islands 53 0.2 265 126 2,380 .. .. 4.0 4.0 .. .. .. Mayotte 160 0.4 400 .. .. c .. .. .. .. 60 .. .. Micronesia, Fed. Sts. 122 0.7 174 240 1,970 .. .. 0.8 ­0.8 69 .. .. Monaco 32 0.0 16,842 .. .. d .. .. .. .. .. .. .. Netherlands Antilles 219 0.8 274 .. .. d .. .. .. .. 76 97 9,929 New Caledonia 220 18.6 12 .. .. d .. .. .. .. 74 97 f 1,667 Northern Mariana Islands 76 0.5 159 .. .. c .. .. .. .. .. .. .. Palau 20 0.5 43 136 6,820 .. .. 3.0 3.0 .. .. 242 Qatar 610 11.0 55 .. .. d .. .. .. .. 75 84 f 40,685 Samoa 176 2.8 62 251 1,430 981 5,570 g 1.9 0.7 69 99 139 São Tomé and Principe 154 1.0 160 46 300 .. .. 4.1 2.1 66 .. 88 Seychelles 84 0.5 187 569 6,780 .. .. 0.3 ­2.1 73 92 f 227 Solomon Islands 443 28.9 16 256 580 705 g 1,590 g ­2.7 ­5.3 69 .. 165 San Marino 28 0.1 277 .. .. d .. .. .. .. .. .. .. St. Kitts and Nevis 46 0.4 128 301 6,540 494 10,750 2.1 ­0.1 71 .. 103 St. Lucia 160 0.6 262 600 3,750 793 4,950 0.0 ­1.2 74 .. 322 St. Vincent and the Grenadines 117 0.4 300 330 2,820 607 5,190 1.1 0.2 73 .. 161 Suriname 433 163.3 3 841 1,940 .. .. 3.0 2.1 70 .. 2,118 Timor-Leste 780 14.9 52 402 520 .. .. .. .. .. .. .. Tonga 101 0.8 140 146 1,440 689 6,820 g 1.6 1.6 71 99 f 121 Vanuatu 206 12.2 17 221 1,070 587 2,850 g ­0.3 ­2.7 69 .. 81 Virgin Islands (U.S.) 110 0.3 324 .. .. d .. .. .. .. 78 .. 13,106 a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be upper middle income ($2,936­$9,075). d. Estimated to be high income ($9,076 or more). e. Included in the aggregates for high-income economies on the basis of earlier data. f. Census data. g. The estimate is based on regression; others are extrapolat- ed from the latest International Comparison Programme benchmark estimates. h. Included in the aggregates for low-income economies on the basis of earlier data. i. Refers to GDP and GDP per capita. prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. · Adult literacy rate is the percentage of adults ages 15 and older who can, with understanding, read and write a short, simple statement about their everyday life. · Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide Data sources produced during consumption of solid, liquid, and The indicators here and throughout the book were gas fuels and gas flaring. compiled by World Bank Group 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. 2004 World Development Indicators 33 2 PEOPLE T he ultimate aim of development is to improve human welfare in a substantial way. But development has often bypassed the poor, and so attacking poverty directly through its many dimensions has become an urgent global priority. To accelerate progress in human development, economic growth is, of course, necessary. But it is not enough. Because the most significant asset of people poor is their labor, the most effective way to improve their welfare is to increase their employment opportunities and the productivity of their labor through investments in human capital--the product of education and improvements in health and nutrition. Thus freedom from illiteracy (figure 2a) and freedom from illness are two of the most important ways that poor people can escape poverty. But although developing countries have made large investments in human capital, assisted by the private sector and official development agencies, good health and basic education remain elusive to many. To reinforce this paramount task of development, the Millennium Development Goals set specific targets for poverty reduction, education, status of women, and health, among others, in order to measure improvements in people's 2a lives (see section 1 for a fuller Poverty and illiteracy are related discussion of the Millennium Devel- opment Goals). Population under $1 a day, latest available data (%) 100 The challenge for governments is 80 formidable. They need to provide not only services that are linked 60 to human development, but also effective mechanisms that reduce 40 vulnerability to economic shocks, ill health, and disability. This 20 section tracks the progress countries have made in devel- 0 0 20 40 60 80 100 oping their human capital and in Illiteracy rate (%) reducing the vulnerability of their people. Source: World Bank data files. 2004 World Development Indicators 35 Population in sustainable development 2b In the second half of the 20th century the world population Defining income poverty underwent unprecedented growth--from 2.5 billion in 1950 to 6 billion in 1999--even as the population growth rate The most familiar definition of poverty uses a composite measure of was declining. The decline was triggered largely by a drop in total household consumption per member (with adjustments for house- fertility rates. Between 1950­55 and 2002 fertility hold size and composition), derived from household surveys. Poor peo- rates halved, from 5.1 to 2.6 births per woman. Thus while ple are then defined as living in households below a particular threshold the world population grew at 1.5 percent a year during of this measure of consumption. But many surveys do not include con- 1980­2002, it is expected to grow more slowly, at 1 percent sumption data, which are difficult to collect. Another approach, used by a year, during 2002­15, benefiting from continuing fertility the World Bank, is to aggregate indicators of a household's asset own- declines (table 2.1). But most developing countries will not ership and housing characteristics into an index and then rank house- benefit from this decline. Between 2002 and 2015 roughly holds into quintiles according to this index. These are typically referred 1 billion people will be added to the world, and most (95 to as asset or wealth quintiles. percent) will be born in low- and middle-income countries. Source: World Bank data files. Despite the increase in mortality rates brought on by AIDS, the fastest growing region will be Sub-Saharan Africa, and the largest number of people will be added in Asia. And the in social indicators between the rich and poor, confirming the populations of some high-income and Eastern European persistence of deprivation (box 2b; table 2.6). Globally, much countries will continue their decline. of the decline in income poverty took place in East Asia, Research shows that changes in population growth, age where sustained growth in China has lifted more than 150 structure, and spatial distribution interact closely with devel- million people out of poverty since 1990. And faster growth opment. Fertility decline in high-fertility countries, by slowing in India has led to a modest decline in the number of poor population growth, can have important economic benefits people in South Asia. But in other regions the number of poor by reducing the number of children relative to the working people has increased even as their share in the population age population. This can create a unique opportunity to has declined--and in Europe and Central Asia both the num- increase investments in health, education, and infrastruc- ber and the share of poor people have risen. Unemployment ture. Unfortunately, in many of the poorest countries that is high in many of the formerly centrally planned economies, most need such a break, high levels of unwanted fertility with long-term unemployment hovering around 40­50 per- and the pervasive HIV/AIDS pandemic are prematurely cur- cent of total unemployment in Croatia, the Czech Republic, tailing such opportunity. Together, the continuing dependen- and Hungary in 2002 (table 2.4). cy of youthful populations and the premature deaths of young adults prevent countries from benefiting from their Enhancing security for poor people demographic transition. Poor people face many risks. They face labor market risks, often having to take precarious jobs in the informal sector Enabling poverty reduction and to put their children to work to increase household In many developing countries agriculture is still the main income. In Sub-Saharan Africa one in four children ages economic activity for both men and women (table 2.3). But 10­14 was in the labor force in 2002 (table 2.8). Poor peo- as economies grow, more people work for wages. The ple also face health risks, with illness and injury having enlarged proportion of working-age populations in countries both direct and opportunity costs. In South Asia nearly undergoing fertility decline provides for increased labor 80 percent of all spending on health comes from private force participation (table 2.2). This contributes to economic sources, much of it out of pocket, exposing many poor growth, especially when it occurs in the formal sector. In households to the impoverishing effects of needed health developing countries gross domestic product (GDP) grew care (table 2.8). 4.3 percent a year in the 1990s, and the share of people Enhancing security for poor people means reducing their living on less than $1 a day fell from 28.3 percent to 21.6 vulnerability to ill health and economic shocks. Market- percent. By 2000, 137 million fewer people were living in based insurance and pension schemes can reduce risk sig- extreme poverty (table 2.5). And if projected growth remains nificantly, but they play only a minor role in many developing on track, global poverty rates will fall to 12.5 percent by countries. In 16 developing countries public spending on 2015, meeting the global Millennium Development Goal tar- pensions amounted to less than 0.5 percent of GDP in the get of halving the 1990 poverty rate. 1990s (table 2.9). To increase the security of poor people, Progress in reducing poverty has been uneven. But national poverty reduction strategies must support their because many poor people continue to be excluded from all immediate consumption needs and protect their assets by but the lowest level of economic activity, there are large gaps ensuring access to basic services. Education, health, and 36 2004 World Development Indicators 2c immunization, sanitation, access to safe drinking water, and safe motherhood initiatives (tables 2.15 and 2.16). But the Why public services fail poor people Millennium Development Goals remain unattainable for Public services are failing the poor in four ways: many countries. Some 20 countries have rates of child mal- · Public spending on health and education is typically enjoyed by people nutrition greater than 30 percent (table 2.17). An estimated who are not poor. In Nepal the richest 20 percent of the population 40 million people are living with HIV/AIDS, an unprecedent- benefits from 46 percent of education spending, while the poorest ed public health challenge (table 2.18), and more than half 20 percent gets just 11 percent. a million women in developing countries die each year dur- · Even when public spending is reallocated to the poor, very little of it reach- ing childbirth, often because births are not attended by es frontline public service providers. In Uganda in the early 1990s primary trained personnel (figure 2d). And the reemergence of old schools received an average of just 13 percent of nonsalary spending allo- diseases such as tuberculosis in Europe and Central Asia cations intended for them, and poorer schools received much less. and parts of South and East Asia has put severe strains on · There is a high degree of absenteeism among teachers, doctors, and health budgets. A high prevalence of disease puts a brake nurses in public sector facilities. A survey of primary health care in on poverty reduction. Beyond its direct impact on a house- Bangladesh found a 74 percent absentee rate among doctors. hold's living standards through out-of-pocket spending, ill- · The poor quality of service, opportunity costs of travel time to schools health has an indirect impact on labor productivity and the or health facilities, and cultural factors create lack of demand or weak number of hours people can work. demand for services. Source: World Bank, World Development Report 2004. * * * There are many ways to measure poverty and its effects on nutrition services are often the most needed and most val- people's lives. The indicators reported here suffer from ued by poor people. Yet many governments fail the poor in many shortcomings, noted in About the data for each table. the provision of these services (box 2c). But taken together, the indicators provide a broad picture of how well different economies are doing in reducing poverty, Remaining and emerging challenges in enhancing human security, and building human capital--and building human capital how large a task still lies ahead. Investments in education widen horizons, making it easier for people to take advantage of new opportunities and help- 2d ing them to participate in social and economic life. But Poor women are much less likely to receive expert care in despite increased spending on education, particularly pri- childbirth mary education (table 2.10), enrollment rates remain low in Births attended by medically trained personnel, by income group (%) many countries (table 2.11), and primary completion rates 100 are even lower (table 2.12), hampering achievement of the Millennium Development Goal target of universal primary education by 2015. Most children who do not attend pri- 80 mary school, or who drop out early, live in poor households and in poor countries (table 2.12). But in many poor coun- tries there is also a gender dimension to school attendance, 60 reflecting traditional biases against girls' education and reliance on girls' contributions to the household. One con- sequence of this imbalance: higher rates of illiteracy among women. In 2002, 33 developing countries had female liter- 40 acy rates of 60 percent or lower (table 2.13). And in pre- dominantly illiterate societies there is likely to be less pres- sure for those who cannot read or write to achieve literacy. 20 The Millennium Development Goals for health cover health status, nutritional status, illness, mortality rates, and reproductive health. The public sector is the main 0 provider of health care in developing countries--training Brazil Egypt Ghana India Indonesia Jordan medical personnel, investing in hospitals, and directly pro- Richest group Poorest group viding medical care (table 2.14). To reduce inequities, many countries have emphasized primary health care, including Source: World Bank data files. 2004 World Development Indicators 37 2.1 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as proportion of % working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1980 2002 2015 1980­2002 2002­15 2002 2002 2002 2002 2002 2002 2002 Afghanistan 16.0 28.0 a 38.8 2.6 2.5 43.8 53.4 2.8 0.8 0.1 21 49 Albania 2.7 3.2 3.5 0.7 0.8 28.0 64.9 7.1 0.4 0.1 6 17 Algeria 18.7 31.3 38.3 2.4 1.5 34.6 61.4 4.0 0.6 0.1 5 22 Angola 7.0 13.1 18.9 2.8 2.8 47.6 49.5 2.9 1.0 0.1 19 50 Argentina 28.1 36.5 42.9 1.2 1.2 27.3 63.0 9.8 0.4 0.2 8 19 Armenia 3.1 3.1 3.0 0.0 ­0.1 21.6 68.7 9.7 0.3 0.1 8 9 Australia 14.7 19.7 21.7 1.3 0.8 20.2 67.4 12.4 0.3 0.2 7 13 Austria 7.6 8.0 8.0 0.3 ­0.1 16.2 67.9 15.9 0.2 0.2 10 9 Azerbaijan 6.2 8.2 9.0 1.3 0.7 27.7 65.0 7.3 0.4 0.1 7 16 Bangladesh 85.4 135.7 166 2.1 1.5 36.2 60.5 3.3 0.6 0.1 8 28 Belarus 9.6 9.9 9.3 0.1 ­0.5 17.4 68.8 13.8 0.3 0.2 14 9 Belgium 9.8 10.3 10.4 0.2 0.1 17.1 66.2 16.7 0.3 0.3 10 11 Benin 3.5 6.6 9.0 2.9 2.4 45.4 51.9 2.7 0.9 0.1 13 38 Bolivia 5.4 8.8 10.9 2.3 1.7 38.7 56.9 4.4 0.7 0.1 8 29 Bosnia and Herzegovina 4.1 4.1 4.2 0.0 0.2 17.8 71.7 10.6 0.2 0.1 8 12 Botswana 0.9 1.7 1.8 2.9 0.4 41.8 56.0 2.2 0.7 0.0 23 30 Brazil 121.6 174.5 201 1.6 1.1 27.9 66.8 5.3 0.4 0.1 7 19 Bulgaria 8.9 8.0 7.3 ­0.5 ­0.7 14.8 68.9 16.3 0.2 0.2 14 9 Burkina Faso 7.0 11.8 15.6 2.4 2.1 47.0 50.3 2.7 0.9 0.1 19 43 Burundi 4.1 7.1 8.8 2.4 1.7 45.7 51.8 2.6 0.9 0.0 20 39 Cambodia 6.8 12.5 15.1 2.8 1.5 42.0 55.1 2.8 0.8 0.1 12 27 Cameroon 8.8 15.8 19.7 2.7 1.7 41.3 55.0 3.7 0.8 0.1 16 36 Canada 24.6 31.4 33.5 1.1 0.5 18.4 68.8 12.8 0.3 0.2 7 11 Central African Republic 2.3 3.8 4.6 2.3 1.5 42.1 54.4 3.5 0.8 0.1 20 36 Chad 4.5 8.3 12.1 2.8 2.8 48.8 48.3 2.9 1.0 0.1 16 45 Chile 11.1 15.6 17.8 1.5 1.0 27.4 65.3 7.3 0.4 0.1 5 17 China 981.2 1,280.4 1,389.5 1.2 0.6 24.2 68.6 7.2 0.4 0.1 8 15 Hong Kong, China 5.0 6.8 7.0 1.4 0.2 16.2 72.3 11.4 0.2 0.2 5 7 Colombia 28.4 43.7 51.4 2.0 1.2 31.9 63.3 4.8 0.5 0.1 6 21 Congo, Dem. Rep. 27.9 51.6 75.2 2.8 2.9 47.8 49.6 2.6 1.0 0.1 18 45 Congo, Rep. 1.8 3.7 5.2 3.2 2.8 46.7 50.2 3.2 0.9 0.1 14 44 Costa Rica 2.3 3.9 4.7 2.5 1.4 30.5 63.8 5.8 0.5 0.1 4 20 Côte d'Ivoire 8.2 16.5 20.2 3.2 1.6 41.8 55.6 2.6 0.8 0.0 17 37 Croatia 4.6 4.5 4.3 ­0.1 ­0.3 16.4 68.1 15.5 0.2 0.2 12 10 Cuba 9.7 11.3 11.7 0.7 0.3 20.7 69.0 10.3 0.3 0.1 8 12 Czech Republic 10.2 10.2 9.9 0.0 ­0.2 15.8 70.4 13.8 0.2 0.2 11 9 Denmark 5.1 5.4 5.4 0.2 0.1 18.5 66.6 14.9 0.3 0.2 11 12 Dominican Republic 5.7 8.6 10.1 1.9 1.2 32.5 63.0 4.5 0.5 0.1 7 23 Ecuador 8.0 12.8 15.4 2.2 1.4 33.2 62.0 4.8 0.5 0.1 6 23 Egypt, Arab Rep. 40.9 66.4 80.9 2.2 1.5 34.1 61.6 4.2 0.6 0.1 6 24 El Salvador 4.6 6.4 7.9 1.5 1.6 35.0 60.1 5.0 0.6 0.1 6 26 Eritrea 2.4 4.3 5.6 2.7 2.0 44.7 52.7 2.6 0.8 0.1 13 38 Estonia 1.5 1.4 1.3 ­0.4 ­0.6 16.5 68.4 15.1 0.2 0.2 14 9 Ethiopia 37.7 67.2 87.3 2.6 2.0 45.7 51.5 2.8 0.9 0.1 20 40 Finland 4.8 5.2 5.3 0.4 0.1 17.8 67.0 15.2 0.3 0.2 10 11 France 53.9 59.5 61.8 0.4 0.3 18.7 65.2 16.1 0.3 0.2 10 13 Gabon 0.7 1.3 1.7 2.9 2.2 40.4 54.1 5.6 0.7 0.1 15 35 Gambia, The 0.6 1.4 1.8 3.5 1.9 40.4 56.3 3.3 0.7 0.1 14 37 Georgia 5.1 5.2 4.7 0.1 ­0.8 19.2 67.1 13.8 0.3 0.2 10 8 Germany 78.3 82.5 80.3 0.2 ­0.2 15.1 68.1 16.9 0.2 0.2 10 9 Ghana 11.0 20.3 25.2 2.8 1.7 42.5 53.0 4.5 0.8 0.1 13 29 Greece 9.6 10.6 11 0.4 0.3 14.8 66.8 18.4 0.2 0.3 11 9 Guatemala 6.8 12 16.3 2.6 2.3 42.9 53.7 3.5 0.8 0.1 7 33 Guinea 4.5 7.7 9.8 2.5 1.8 44.0 53.4 2.6 0.8 0.0 17 38 Guinea-Bissau 0.8 1.4 2.0 2.7 2.6 44.2 52.3 3.5 0.8 0.1 20 49 Haiti 5.4 8.3 10.3 2.0 1.7 39.6 56.9 3.5 0.7 0.1 14 32 38 2004 World Development Indicators PEOPLE 2.1 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as proportion of % working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1980 2002 2015 1980­2002 2002­15 2002 2002 2002 2002 2002 2002 2002 Honduras 3.6 6.8 8.9 2.9 2.1 41.1 55.5 3.4 0.7 0.1 6 30 Hungary 10.7 10.2 9.6 ­0.2 ­0.4 16.5 68.8 14.6 0.2 0.2 13 10 India 687.3 1,048.6 1,231.6 1.9 1.2 32.8 62.2 5.0 0.5 0.1 9 24 Indonesia 148.3 211.7 245.5 1.6 1.1 29.8 65.4 4.8 0.5 0.1 7 20 Iran, Islamic Rep. 39.1 65.5 77.5 2.3 1.3 30.8 64.4 4.7 0.5 0.1 6 18 Iraq 13.0 24.2 31.1 2.8 1.9 40.1 56.9 3.0 0.7 0.1 8 29 Ireland 3.4 3.9 4.3 0.6 0.8 21.4 67.4 11.2 0.3 0.2 8 15 Israel 3.9 6.6 7.9 2.4 1.4 27.5 62.8 9.7 0.4 0.2 6 20 Italy 56.4 57.7 55.1 0.1 ­0.3 14.1 67.2 18.7 0.2 0.3 11 9 Jamaica 2.1 2.6 3.0 0.9 1.0 30.1 62.9 6.9 0.5 0.1 6 20 Japan 116.8 127.2 124.6 0.4 ­0.2 14.3 67.6 18.1 0.2 0.3 8 9 Jordan 2.2 5.2 6.8 3.9 2.2 37.8 59.1 3.1 0.6 0.1 4 28 Kazakhstan 14.9 14.9 15.5 0.0 0.3 25.3 67.0 7.7 0.4 0.1 12 15 Kenya 16.6 31.3 37.5 2.9 1.4 42.6 54.8 2.7 0.8 0.0 16 35 Korea, Dem. Rep. 17.2 22.5 24.0 1.2 0.5 26.0 67.7 6.4 0.4 0.1 11 17 Korea, Rep. 38.1 47.6 50.0 1.0 0.4 21.0 71.8 7.2 0.3 0.1 7 12 Kuwait 1.4 2.3 3.0 2.4 1.9 25.1 73.1 1.7 0.3 0.0 3 20 Kyrgyz Republic 3.6 5.0 5.8 1.5 1.1 32.5 61.4 6.1 0.5 0.1 7 20 Lao PDR 3.2 5.5 7.3 2.5 2.1 42.1 54.4 3.5 0.8 0.1 12 36 Latvia 2.5 2.3 2.1 ­0.4 ­0.7 15.8 69.1 15.2 0.2 0.2 14 8 Lebanon 3.0 4.4 5.2 1.8 1.2 30.9 63.2 5.9 0.5 0.1 6 19 Lesotho 1.3 1.8 2.0 1.5 0.9 41.7 53.1 5.2 0.8 0.1 23 33 Liberia 1.9 3.3 4.4 2.6 2.2 44.3 53.0 2.7 0.8 0.1 20 43 Libya 3.0 5.4 6.9 2.6 1.8 33.0 63.4 3.6 0.5 0.1 4 27 Lithuania 3.4 3.5 3.3 0.1 ­0.4 18.2 67.8 13.9 0.3 0.2 12 9 Macedonia, FYR 1.9 2.0 2.2 0.3 0.5 21.9 67.7 10.4 0.3 0.2 9 14 Madagascar 8.9 16.4 22.5 2.8 2.4 44.4 52.6 3.0 0.8 0.1 12 39 Malawi 6.2 10.7 13.6 2.5 1.8 44.7 51.9 3.5 0.9 0.1 25 45 Malaysia 13.8 24.3 29.6 2.6 1.5 33.3 62.4 4.3 0.5 0.1 5 22 Mali 6.6 11.4 15.6 2.5 2.4 47.2 50.0 2.9 0.9 0.1 22 48 Mauritania 1.6 2.8 3.6 2.5 2.0 43.1 53.7 3.1 0.8 0.1 15 35 Mauritius 1.0 1.2 1.4 1.0 0.9 25.2 68.5 6.3 0.4 0.1 7 17 Mexico 67.6 100.8 120.6 1.8 1.4 32.9 62.0 5.1 0.5 0.1 4 20 Moldova 4.0 4.3 4.1 0.3 ­0.2 21.1 67.9 11.1 0.3 0.2 13 11 Mongolia 1.7 2.4 2.9 1.8 1.3 32.5 63.5 4.0 0.5 0.1 6 23 Morocco 19.4 29.6 35.4 1.9 1.4 33.5 62.2 4.3 0.5 0.1 6 21 Mozambique 12.1 18.4 22.7 1.9 1.6 42.5 53.8 3.7 0.8 0.1 21 40 Myanmar 33.7 48.8 55.7 1.7 1.0 32.3 63.1 4.5 0.5 0.1 12 23 Namibia 1.0 2.0 2.3 3.0 1.1 41.8 54.4 3.8 0.8 0.1 21 35 Nepal 14.6 24.1 31.1 2.3 2.0 40.4 55.8 3.8 0.7 0.1 10 32 Netherlands 14.2 16.1 16.7 0.6 0.3 18.4 67.8 13.8 0.3 0.2 9 12 New Zealand 3.1 3.9 4.4 1.1 0.8 22.1 66.2 11.7 0.3 0.2 7 14 Nicaragua 2.9 5.3 7.0 2.7 2.0 41.5 55.4 3.1 0.7 0.1 5 29 Niger 5.6 11.4 16.3 3.3 2.7 48.9 48.8 2.3 1.0 0.0 20 49 Nigeria 71.1 132.8 169.4 2.8 1.9 43.7 53.7 2.6 0.8 0.0 17 39 Norway 4.1 4.5 4.7 0.5 0.3 19.8 65.2 15 0.3 0.2 10 12 Oman 1.1 2.5 3.4 3.8 2.2 42.3 55.1 2.7 0.8 0.0 3 26 Pakistan 82.7 144.9 192.8 2.5 2.2 40.6 56.0 3.3 0.7 0.1 8 33 Panama 2.0 2.9 3.5 1.9 1.2 30.4 63.9 5.7 0.5 0.1 5 20 Papua New Guinea 3.1 5.4 6.9 2.5 1.9 41.1 56.5 2.4 0.7 0.0 10 33 Paraguay 3.1 5.5 7.2 2.6 2.0 38.8 57.7 3.5 0.7 0.1 5 30 Peru 17.3 26.7 31.5 2.0 1.3 32.4 62.7 4.9 0.5 0.1 6 22 Philippines 48.0 79.9 98.2 2.3 1.6 36.5 59.6 3.9 0.6 0.1 6 26 Poland 35.6 38.6 38.4 0.4 0.0 18.2 69.4 12.4 0.3 0.2 9 9 Portugal 9.8 10.2 10.2 0.2 0.0 17.2 67.6 15.2 0.3 0.2 11 12 Puerto Rico 3.2 3.9 4.2 0.9 0.7 23.6 66.2 10.2 0.4 0.2 8 15 2004 World Development Indicators 39 2.1 Population dynamics Total Average annual Population age Dependency Crude Crude population population composition ratio death birth growth rate rate rate dependents as proportion of % working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1980 2002 2015 1980­2002 2002­15 2002 2002 2002 2002 2002 2002 2002 Romania 22.2 22.3 21.4 0.0 ­0.3 17.2 69.1 13.7 0.2 0.2 13 10 Russian Federation 139.0 144.1 134.5 0.2 ­0.5 16.9 70.2 12.9 0.2 0.2 15 10 Rwanda 5.2 8.2 10.0 2.1 1.6 46.6 50.3 3.1 0.9 0.1 22 44 Saudi Arabia 9.4 21.9 30.8 3.9 2.6 40.4 56.6 2.9 0.7 0.1 4 31 Senegal 5.5 10.0 12.8 2.7 1.9 44.0 53.3 2.7 0.8 0.1 13 35 Serbia and Montenegro 9.8 b 8.2 10.7 0.4 c 2.1 19.8 66.3 13.9 0.3 0.2 12 12 Sierra Leone 3.2 5.2 6.7 2.2 1.9 44.1 53.3 2.6 0.8 0.0 25 44 Singapore 2.4 4.2 4.8 2.5 1.1 21.1 71.4 7.5 0.3 0.1 5 11 Slovak Republic 5.0 5.4 5.4 0.3 0.0 18.8 69.8 11.4 0.3 0.2 10 11 Slovenia 1.9 2.0 1.9 0.1 ­0.2 15.2 70.4 14.4 0.2 0.2 10 9 Somalia 6.5 9.3 14.0 1.6 3.1 47.9 49.7 2.4 1.0 0.0 18 50 South Africa 27.6 45.3 47.0 2.3 0.3 32.1 63.4 4.5 0.5 0.1 20 25 Spain 37.4 40.9 41.5 0.4 0.1 15.0 68.0 17.0 0.2 0.2 9 10 Sri Lanka 14.6 19.0 21.9 1.2 1.1 25.6 67.8 6.5 0.4 0.1 6 18 Sudan 19.4 32.8 42.6 2.4 2.0 39.7 56.8 3.5 0.7 0.1 10 33 Swaziland 0.6 1.1 1.3 3.0 1.2 42.2 55.0 2.9 0.8 0.1 18 35 Sweden 8.3 8.9 9.0 0.3 0.1 17.7 64.8 17.5 0.3 0.3 11 11 Switzerland 6.3 7.3 7.5 0.6 0.2 16.7 67.8 15.5 0.2 0.2 9 10 Syrian Arab Republic 8.7 17.0 22.0 3.0 2.0 39.0 57.8 3.1 0.7 0.1 4 29 Tajikistan 4.0 6.3 7.2 2.1 1.0 37.6 57.9 4.6 0.6 0.1 7 23 Tanzania 18.6 35.2 43.9 2.9 1.7 45.0 52.6 2.4 0.9 0.0 18 38 Thailand 46.7 61.6 66.3 1.3 0.6 23.2 70.3 6.4 0.3 0.1 8 15 Togo 2.5 4.8 6.2 2.9 2.0 43.6 53.3 3.2 0.8 0.1 15 36 Trinidad and Tobago 1.1 1.3 1.4 0.8 0.8 24.3 69.3 6.4 0.4 0.1 7 16 Tunisia 6.4 9.8 11.5 1.9 1.3 28.2 65.8 6.0 0.4 0.1 6 18 Turkey 44.5 69.6 81.3 2.0 1.2 28.4 65.8 5.9 0.4 0.1 7 22 Turkmenistan 2.9 4.8 5.7 2.3 1.3 34.7 60.9 4.4 0.6 0.1 8 22 Uganda 12.8 24.6 33.6 3.0 2.4 49.0 49.1 1.9 1.0 0.0 18 44 Ukraine 50.0 48.7 44.7 ­0.1 ­0.7 16.5 68.8 14.7 0.2 0.2 15 9 United Arab Emirates 1.0 3.2 3.7 5.1 1.1 25.5 71.6 2.9 0.4 0.0 4 17 United Kingdom 56.3 59.2 59.6 0.2 0.0 18.4 65.6 16.1 0.3 0.2 10 11 United States 227.2 288.4 319.9 1.1 0.8 21.1 66.4 12.5 0.3 0.2 9 14 Uruguay 2.9 3.4 3.6 0.6 0.6 24.5 62.9 12.6 0.4 0.2 10 16 Uzbekistan 16.0 25.3 30.0 2.1 1.3 35.4 60.0 4.6 0.6 0.1 6 20 Venezuela, RB 15.1 25.1 30.3 2.3 1.4 33.0 62.5 4.5 0.5 0.1 5 23 Vietnam 53.7 80.4 92.4 1.8 1.1 31.4 63.3 5.3 0.5 0.1 6 19 West Bank and Gaza .. 3.2 4.9 .. 3.2 45.8 50.9 3.2 0.9 0.1 4 35 Yemen, Rep. 8.5 18.6 27.3 3.5 2.9 45.7 51.6 2.7 0.9 0.1 10 41 Zambia 5.7 10.2 11.9 2.6 1.2 44.9 52.9 2.2 0.8 0.0 23 39 Zimbabwe 7.1 13.0 14.1 2.7 0.6 44.0 52.8 3.1 0.8 0.1 21 29 World 4,430.1 s 6,198.5 s 7,090.7 s 1.5 w 1.0 w 29.2 w 63.7 w 7.1 w 0.5 w 0.1 w 9 w 21 w Low income 1,561.8 2,494.6 3,044.0 2.1 1.5 36.5 59.3 4.2 0.6 0.1 11 29 Middle income 2,038.1 2,737.8 3,039.0 1.3 0.8 26.4 66.5 7.1 0.4 0.1 8 17 Lower middle income 1,801.0 2,408.5 2,658.4 1.3 0.8 26.1 66.9 7.0 0.4 0.1 8 17 Upper middle income 237.0 329.3 380.6 1.5 1.1 28.9 63.7 7.4 0.5 0.1 6 19 Low & middle income 3,599.8 5,232.4 6,083.0 1.7 1.2 31.2 63.1 5.7 0.5 0.1 9 22 East Asia & Pacific 1,359.4 1,838.3 2,036.9 1.4 0.8 26.3 67.2 6.5 0.4 0.1 8 16 Europe & Central Asia 425.8 472.9 478.2 0.5 0.1 20.9 67.9 11.2 0.3 0.2 12 13 Latin America & Carib. 356.4 524.9 619.4 1.8 1.3 30.9 63.6 5.5 0.5 0.1 6 21 Middle East & N. Africa 173.7 305.8 382.7 2.6 1.7 35.3 60.7 4.0 0.6 0.1 6 24 South Asia 901.3 1,401.5 1,683.7 2.0 1.4 34.2 61.2 4.6 0.6 0.1 9 26 Sub-Saharan Africa 383.2 688.9 882.1 2.7 1.9 43.8 53.3 3.0 0.8 0.1 18 39 High income 830.2 966.2 1,007.7 0.7 0.3 18.3 67.3 14.4 0.3 0.2 9 12 Europe EMU 285.5 305.5 305.2 0.3 0.0 16.0 67.2 16.8 0.2 0.2 10 10 a. Estimate does not account for recent refugee flows. b. Includes population for Kosovo until 2001. c. Data are for 1980­2001. 40 2004 World Development Indicators PEOPLE 2.1 Population dynamics About the data Definitions Population estimates are usually based on national rates and declining mortality rates are now reflected · Total population of an economy includes all resi- population censuses, but the frequency and quality in the larger share of the working-age population. dents regardless of legal status or citizenship-- of these vary by country. Most countries conduct a Dependency ratios take into account the variations except for refugees not permanently settled in the complete enumeration no more than once a decade. in the proportions of children, elderly people, and country of asylum, who are generally considered part Pre- and post-census estimates are interpolations or working-age people in the population. Separate cal- of the population of their country of origin. The val- extrapolations based on demographic models. Errors culations of young-age and old-age dependency sug- ues shown are midyear estimates for 1980 and and undercounting occur even in high-income coun- gest the burden of dependency that the working-age 2002 and projections for 2015. · Average annual tries; in developing countries such errors may be population must bear in relation to children and the population growth rate is the exponential change for substantial because of limits in the transport, com- elderly. But dependency ratios show the age compo- the period indicated. See Statistical methods for munications, and other resources required to con- sition of a population, not economic dependency. more information. · Population age composition duct a full census. Some children and elderly people are part of the refers to the percentage of the total population that The quality and reliability of official demographic labor force, and many working-age people are not. is in specific age groups. · Dependency ratio is the data are also affected by the public trust in the gov- The vital rates shown in the table are based on ratio of dependents--people younger than 15 or ernment, the government's commitment to full and data derived from birth and death registration sys- older than 64--to the working-age population--those accurate enumeration, the confidentiality and protec- tems, censuses, and sample surveys conducted by ages 15­64. · Crude death rate and crude birth tion against misuse accorded to census data, and national statistical offices, United Nations agencies, rate are the number of deaths and the number of live the independence of census agencies from undue and other organizations. The estimates for 2002 for births occurring during the year, per 1,000 popula- political influence. Moreover, the international com- many countries are based on extrapolations of levels tion estimated at midyear. Subtracting the crude parability of population indicators is limited by differ- and trends measured in earlier years. death rate from the crude birth rate provides the rate ences in the concepts, definitions, data collection Vital registers are the preferred source of these of natural increase, which is equal to the population procedures, and estimation methods used by nation- data, but in many developing countries systems for growth rate in the absence of migration. al statistical agencies and other organizations that registering births and deaths do not exist or are collect population data. incomplete because of deficiencies in the coverage of Of the 152 economies listed in the table, 125 events or of geographic areas. Many developing coun- (about 82 percent) conducted a census between tries carry out special household surveys that esti- 1995 and 2003. The currentness of a census, along mate vital rates by asking respondents about births with the availability of complementary data from sur- and deaths in the recent past. Estimates derived in veys or registration systems, is one of many objec- this way are subject to sampling errors as well as tive ways to judge the quality of demographic data. In errors due to inaccurate recall by the respondents. some European countries registration systems offer The United Nations Statistics Division monitors the complete information on population in the absence completeness of vital registration systems. The of a census. See Primary data documentation for the share of countries with at least 90 percent complete most recent census or survey year and for the com- vital registration increased from 45 percent in 1988 Data sources pleteness of registration. to 55 percent in 2002. Still, some of the most pop- The World Bank's population estimates are pro- Current population estimates for developing coun- ulous developing countries--China, India, Indonesia, duced by its Human Development Network and tries that lack recent census-based data, and pre- and Brazil, Pakistan, Bangladesh, Nigeria--do not have Development Data Group in consultation with its post-census estimates for countries with census data, complete vital registration systems. Fewer than 30 operational staff and country offices. Important are provided by national statistical offices, the United percent of births and 40 percent of deaths worldwide inputs to the World Bank's demographic work Nations Population Division, and other agencies. The are thought to be registered and reported. come from the following sources: census reports standard estimation method requires fertility, mortali- International migration is the only other factor besides and other statistical publications from national ty, and net migration data, which are often collected birth and death rates that directly determines a coun- statistical offices; Demographic and Health from sample surveys, some of which may be small or try's population growth. From 1990 to 2000 the number Surveys conducted by national agencies, Macro limited in coverage. The population estimates are the of migrants in high-income countries increased by 23 mil- International, and the U.S. Centers for Disease product of demographic modeling and so are suscep- lion. About 175 million people currently live outside their Control and Prevention; United Nations Statistics tible to biases and errors because of shortcomings in home country, accounting for about 3 percent of the Division, Population and Vital Statistics Report the model as well as in the data. Population projec- world's population. Estimating international migration is (quarterly); United Nations Population Division, tions are made using the cohort component method. difficult. At any time many people are located outside World Population Prospects: The 2002 Revision; The growth rate of the total population conceals their home country as tourists, workers, or refugees or Eurostat, Demographic Statistics (various years); the fact that different age groups may grow at very for other reasons. Standards relating to the duration and Centro Latinoamericano de Demografía, Boletín different rates. In many developing countries the purpose of international moves that qualify as migration Demográfico (various years); and U.S. Bureau of population under 15 was earlier growing rapidly but vary, and accurate estimates require information on the Census, International Database. is now starting to shrink. Previously high fertility flows into and out of countries that is difficult to collect. 2004 World Development Indicators 41 2.2 Labor force structure Labor force Labor force participation rate Average annual % ages 15­64 Total growth rate Female Male Female millions % % of labor force 1980 2002 1980 2002 1980 2002 1980­2002 1980 2002 Afghanistan 89.3 87.6 a 49.8 50.3 a 6.8 11.7 a 2.4 34.8 35.8 a Albania 86.1 85.5 60.6 65.8 1.2 1.6 1.3 38.8 41.5 Algeria 80.4 79.8 19.1 33.8 4.8 11.0 3.7 21.4 29.0 Angola 91.7 90.1 77.8 74.8 3.5 6.1 2.5 47.0 46.2 Argentina 86.4 84.3 32.6 44.1 10.7 15.7 1.8 27.6 34.4 Armenia 77.4 78.2 68.1 71.1 1.4 1.6 0.4 47.9 48.6 Australia 86.6 82.8 52.0 67.1 6.7 10.0 1.8 36.8 44.0 Austria 84.9 78.6 54.4 56.6 3.4 3.8 0.5 40.5 40.4 Azerbaijan 77.8 78.0 67.4 61.3 2.7 3.7 1.4 47.5 44.7 Bangladesh 90.9 87.8 70.2 67.9 40.3 72.4 2.7 42.3 42.5 Belarus 83.4 80.9 74.3 73.4 5.1 5.3 0.2 49.9 48.9 Belgium 79.8 72.3 41.3 51.8 3.9 4.3 0.4 33.9 41.1 Benin 86.9 82.5 77.8 75.4 1.7 3.0 2.7 47.0 48.3 Bolivia 85.9 83.5 39.6 49.4 2.0 3.6 2.7 33.3 38.0 Bosnia and Herzegovina 77.8 78.0 37.0 49.1 1.6 1.9 0.8 32.8 38.2 Botswana 84.9 83.9 72.2 66.5 0.4 0.8 3.0 50.1 45.1 Brazil 89.4 87.2 35.7 47.0 47.7 81.7 2.5 28.4 35.5 Bulgaria 82.7 77.3 70.4 70.8 4.6 4.1 ­0.6 45.3 48.0 Burkina Faso 94.2 89.9 82.8 77.7 3.8 5.8 1.9 47.6 46.5 Burundi 93.9 93.9 86.9 85.4 2.3 3.9 2.4 50.2 48.6 Cambodia 85.9 86.2 85.2 84.8 3.7 6.6 2.7 55.4 51.5 Cameroon 89.8 86.0 49.6 51.7 3.7 6.5 2.6 36.8 38.2 Canada 86.0 82.8 57.3 72.0 12.2 16.8 1.5 39.5 46.0 Central African Republic .. .. .. .. 1.2 1.8 1.9 .. .. Chad 91.7 89.1 68.3 70.6 2.2 4.0 2.7 43.4 44.8 Chile 81.4 81.9 28.7 43.8 3.8 6.5 2.4 26.3 34.5 China 91.4 89.2 75.5 79.5 538.7 769.3 1.6 43.2 45.2 Hong Kong, China 86.1 85.0 50.5 56.3 2.5 3.6 1.7 34.3 37.2 Colombia 83.1 83.5 26.6 52.2 9.4 19.4 3.3 26.2 39.1 Congo, Dem. Rep. 87.6 84.7 65.8 62.5 12.4 21.4 2.5 44.5 43.3 Congo, Rep. 86.3 83.4 57.2 58.7 0.8 1.5 3.1 42.4 43.5 Costa Rica 88.8 84.3 24.3 40.9 0.8 1.6 3.2 20.8 31.6 Côte d'Ivoire 91.5 87.3 45.5 45.5 3.3 6.7 3.2 32.2 33.6 Croatia 80.5 75.2 53.2 59.7 2.2 2.1 ­0.1 40.2 44.4 Cuba 83.4 85.1 39.7 57.4 3.7 5.6 1.9 31.4 39.9 Czech Republic 84.8 82.6 75.1 74.5 5.3 5.7 0.3 47.1 47.2 Denmark 88.3 84.4 71.3 76.7 2.7 2.9 0.3 44.0 46.5 Dominican Republic 86.3 86.7 30.5 44.0 2.1 3.9 2.8 24.7 31.4 Ecuador 86.9 85.6 22.5 35.7 2.5 5.1 3.2 20.1 28.7 Egypt, Arab Rep. 83.5 82.3 29.3 38.3 14.3 25.9 2.7 26.5 31.0 El Salvador 89.6 87.2 32.2 50.7 1.6 2.8 2.7 26.5 37.3 Eritrea 88.4 86.6 78.1 76.6 1.2 2.2 2.6 47.4 47.4 Estonia 85.4 81.7 79.3 74.1 0.8 0.8 ­0.3 50.6 48.9 Ethiopia 86.9 85.6 60.2 58.6 16.9 28.9 2.4 42.3 41.0 Finland 79.3 75.6 68.3 71.7 2.4 2.6 0.3 46.5 48.2 France 81.6 75.2 55.2 62.3 23.8 27.0 0.6 40.1 45.3 Gabon 87.7 85.3 67.6 66.5 0.4 0.6 2.2 45.0 44.8 Gambia, The 93.0 90.0 71.2 70.7 0.3 0.7 3.4 44.8 45.2 Georgia 81.1 79.2 71.0 66.6 2.6 2.6 0.0 49.3 46.8 Germany 86.6 81.0 56.2 62.8 37.5 41.1 0.4 40.1 42.4 Ghana 83.0 82.4 82.8 81.0 5.2 9.7 2.8 51.0 50.4 Greece 83.5 78.3 31.8 48.7 3.8 4.6 0.9 27.9 38.2 Guatemala 91.7 88.3 27.6 39.9 2.3 4.5 3.0 22.4 30.1 Guinea 91.7 87.0 83.2 79.6 2.3 3.7 2.1 47.1 47.2 Guinea-Bissau 92.4 90.6 59.7 59.5 0.4 0.7 2.4 39.9 40.5 Haiti 85.5 82.4 64.2 58.1 2.5 3.6 1.6 44.6 42.8 42 2004 World Development Indicators PEOPLE 2.2 Labor force structure Labor force Labor force participation rate Average annual % ages 15­64 Total growth rate Female Male Female millions % % of labor force 1980 2002 1980 2002 1980 2002 1980­2002 1980 2002 Honduras 90.4 87.1 31.9 43.6 1.2 2.6 3.6 25.2 32.6 Hungary 84.8 78.2 62.0 61.1 5.1 4.9 ­0.2 43.3 44.8 India 88.6 86.8 47.8 45.0 299.5 470.2 2.0 33.7 32.5 Indonesia 85.8 84.7 45.6 59.1 58.6 104.2 2.6 35.2 41.2 Iran, Islamic Rep. 83.9 79.8 20.6 32.8 11.7 21.1 2.7 20.4 28.4 Iraq 80.1 76.3 16.3 20.4 3.5 6.8 3.0 17.3 20.4 Ireland 85.0 79.4 34.7 44.7 1.3 1.7 1.3 28.1 35.0 Israel 81.9 79.2 42.0 57.5 1.5 2.9 3.1 33.7 41.7 Italy 81.8 78.7 39.2 50.3 22.6 25.7 0.6 32.9 38.7 Jamaica 85.7 83.8 72.6 74.8 1.0 1.4 1.7 46.3 46.2 Japan 86.1 84.9 52.1 62.4 57.2 68.0 0.8 37.9 41.7 Jordan 78.7 79.3 14.6 29.4 0.5 1.6 5.0 14.7 25.6 Kazakhstan 82.3 79.9 70.5 68.9 7.0 7.4 0.2 47.6 47.1 Kenya 91.7 89.2 77.7 76.8 7.8 16.3 3.3 46.0 46.1 Korea, Dem. Rep. 82.5 84.5 65.7 66.8 7.5 11.8 2.1 44.8 43.3 Korea, Rep. 77.6 79.8 50.2 59.1 15.5 24.6 2.1 38.7 41.8 Kuwait 86.3 79.8 21.0 43.9 0.5 1.0 3.1 13.1 32.1 Kyrgyz Republic 79.9 77.7 68.8 68.1 1.5 2.2 1.6 47.5 47.2 Lao PDR .. .. .. .. 1.7 2.6 2.1 .. .. Latvia 84.8 82.4 77.9 74.6 1.4 1.3 ­0.5 50.8 50.5 Lebanon 77.7 81.1 21.4 33.3 0.8 1.6 2.9 22.6 30.1 Lesotho 87.8 85.0 50.0 49.9 0.5 0.7 1.4 37.9 37.0 Liberia 87.3 83.6 55.7 56.1 0.8 1.3 2.3 38.4 39.6 Libya 85.6 77.5 23.3 27.1 0.9 1.6 2.3 18.6 24.0 Lithuania 83.2 81.1 74.7 70.9 1.8 1.8 0.0 49.7 48.0 Macedonia, FYR 80.2 76.2 46.8 57.3 0.8 1.0 0.8 36.1 42.0 Madagascar 91.3 88.9 72.5 70.5 4.3 7.8 2.7 45.2 44.7 Malawi 89.6 86.4 81.4 78.4 3.1 5.2 2.3 50.6 48.4 Malaysia 84.6 81.2 42.8 51.3 5.3 10.3 3.0 33.7 38.3 Mali 92.3 89.7 75.9 73.6 3.4 5.6 2.2 46.7 46.1 Mauritania 91.4 87.5 71.5 65.0 0.8 1.3 2.3 45.0 43.5 Mauritius 85.2 83.7 28.5 42.0 0.3 0.5 1.9 25.7 33.0 Mexico 85.8 85.6 31.1 42.7 22.0 42.3 3.0 26.9 33.8 Moldova 82.8 79.5 74.6 69.9 2.1 2.2 0.1 50.3 48.4 Mongolia 90.4 86.2 75.7 77.3 0.8 1.2 2.2 45.7 47.1 Morocco 84.6 82.6 38.1 44.3 7.0 12.1 2.5 33.5 34.9 Mozambique 92.6 90.3 86.8 83.3 6.7 9.6 1.6 49.0 48.4 Myanmar 90.4 89.4 69.7 68.5 17.1 26.1 1.9 43.7 43.4 Namibia 87.7 82.9 55.2 56.9 0.4 0.8 2.8 40.1 41.0 Nepal 90.6 86.2 58.6 58.4 7.1 11.3 2.1 38.8 40.5 Netherlands 81.0 78.2 38.2 56.3 5.6 7.5 1.3 31.5 40.9 New Zealand 85.8 82.2 46.0 68.1 1.3 2.0 1.8 34.3 45.2 Nicaragua 88.6 86.2 34.8 51.2 1.0 2.2 3.6 27.6 36.6 Niger 95.2 92.6 73.6 71.1 2.8 5.4 3.0 44.6 44.3 Nigeria 89.1 86.2 50.0 49.7 29.5 52.9 2.7 36.2 36.7 Norway 83.5 81.0 59.8 73.9 1.9 2.4 0.9 40.5 46.5 Oman 88.3 77.9 7.4 22.1 0.3 0.7 3.3 6.2 18.9 Pakistan 88.2 86.4 27.7 38.7 29.3 55.3 2.9 22.7 29.5 Panama 82.4 82.8 37.3 47.5 0.7 1.3 2.8 29.9 35.7 Papua New Guinea 90.2 87.7 71.2 69.1 1.5 2.7 2.5 41.7 42.4 Paraguay 91.9 87.6 34.1 39.5 1.1 2.1 2.8 26.7 30.4 Peru 82.0 81.6 25.8 37.7 5.4 10.4 3.0 23.9 31.9 Philippines 84.5 83.1 46.0 51.8 18.7 34.2 2.7 35.0 38.0 Poland 84.2 77.8 67.7 66.2 18.5 19.9 0.3 45.3 46.5 Portugal 88.5 82.4 53.4 63.3 4.6 5.2 0.5 38.7 44.1 Puerto Rico 72.5 74.1 31.3 42.2 1.0 1.5 1.7 31.8 37.8 2004 World Development Indicators 43 2.2 Labor force structure Labor force Labor force participation rate Average annual % ages 15­64 Total growth rate Female Male Female millions % % of labor force 1980 2002 1980 2002 1980 2002 1980­2002 1980 2002 Romania 83.6 77.0 69.1 61.3 10.9 10.7 ­0.1 45.8 44.5 Russian Federation 84.3 79.7 74.7 72.2 76.0 77.6 0.1 49.4 49.2 Rwanda 95.1 94.4 87.4 85.4 2.6 4.4 2.4 49.1 48.7 Saudi Arabia 86.3 81.1 9.6 24.5 2.8 7.2 4.4 7.6 17.7 Senegal 88.9 86.8 63.2 63.5 2.5 4.4 2.5 42.2 42.6 Serbia and Montenegro 81.4 76.4 50.5 58.6 4.5 b 3.9 0.6 c 38.7 b 43.1 Sierra Leone 86.8 84.6 44.6 46.9 1.2 2.0 2.1 35.5 37.1 Singapore 84.7 82.6 47.4 54.7 1.1 2.0 2.8 34.6 39.2 Slovak Republic 83.5 82.4 69.4 74.3 2.5 3.0 0.8 45.3 47.7 Slovenia 81.9 75.4 67.0 65.1 1.0 1.0 0.2 45.8 46.6 Somalia 89.6 87.2 66.2 64.6 3.0 4.0 1.3 43.4 43.4 South Africa 85.1 82.0 46.5 50.2 10.3 18.1 2.5 35.1 37.9 Spain 84.5 80.0 32.5 48.5 14.0 18.1 1.2 28.3 37.5 Sri Lanka 83.7 81.8 32.3 47.0 5.4 8.4 2.0 26.9 36.9 Sudan 88.6 85.9 30.8 36.5 7.1 13.2 2.8 26.9 30.0 Swaziland 86.4 83.1 41.5 44.5 0.2 0.4 3.2 33.5 37.8 Sweden 85.4 83.9 69.3 81.2 4.2 4.8 0.6 43.8 48.1 Switzerland 89.9 90.2 51.9 65.4 3.1 3.9 1.1 36.7 40.8 Syrian Arab Republic 82.1 80.5 23.6 31.1 2.5 5.6 3.7 23.5 27.6 Tajikistan 79.6 77.3 68.3 63.6 1.5 2.5 2.3 46.9 45.2 Tanzania 89.9 88.3 86.0 82.7 9.5 18.1 2.9 49.8 49.0 Thailand 89.3 89.9 79.7 77.9 24.4 37.5 2.0 47.4 46.2 Togo 89.9 87.2 54.7 55.0 1.1 2.0 2.7 39.3 40.0 Trinidad and Tobago 85.6 81.1 39.7 49.7 0.4 0.6 1.5 31.4 34.9 Tunisia 84.8 83.2 34.5 40.6 2.2 4.0 2.7 28.9 32.1 Turkey 87.5 84.9 47.8 53.6 18.7 33.7 2.7 35.5 38.1 Turkmenistan 81.4 80.4 69.9 67.4 1.2 2.1 2.7 47.0 45.9 Uganda 93.4 91.0 83.3 81.2 6.6 12.1 2.7 47.9 47.6 Ukraine 82.7 78.4 73.7 69.7 26.4 24.9 ­0.3 50.2 48.8 United Arab Emirates 94.9 87.9 16.0 34.2 0.6 1.6 4.7 5.1 15.9 United Kingdom 89.2 83.3 57.0 67.1 26.9 29.6 0.4 38.9 44.3 United States 83.8 81.0 58.2 70.1 110.1 148.3 1.4 41.0 46.2 Uruguay 85.3 82.5 37.3 59.4 1.2 1.6 1.3 30.8 42.2 Uzbekistan 78.6 77.9 70.4 68.1 6.5 11.0 2.4 48.0 46.9 Venezuela, RB 83.9 83.0 32.3 47.3 5.2 10.5 3.2 26.7 35.4 Vietnam 89.9 83.8 74.9 77.5 25.6 41.8 2.2 48.1 48.7 West Bank and Gaza .. .. .. .. .. .. .. .. .. Yemen, Rep. 83.0 84.1 28.5 31.9 2.5 5.9 3.9 32.5 28.3 Zambia 89.5 87.2 69.2 66.8 2.4 4.4 2.8 45.4 44.6 Zimbabwe 87.4 86.3 67.4 67.0 3.2 6.1 2.9 44.4 44.5 World 87.5 w 85.3 w 57.3 w 60.8 w 2,036.6 s 3,028.6 s 1.8 w 39.1 w 40.7 w Low income 88.4 86.4 53.8 54.4 683.4 1,138.6 2.3 37.4 37.7 Middle income 88.2 85.9 61.9 65.1 978.8 1,419.4 1.7 40.5 42.2 Lower middle income 88.7 86.3 64.2 67.2 885.1 1,276.7 1.7 41.2 42.8 Upper middle income 84.8 82.5 44.3 49.0 93.7 142.7 1.9 34.2 37.0 Low & middle income 88.3 86.1 58.5 60.3 1,662.3 2,558.0 2.0 39.2 40.2 East Asia & Pacific 90.3 88.1 70.8 75.1 704.1 1,049.3 1.8 42.6 44.5 Europe & Central Asia 83.6 79.8 69.0 66.7 214.1 239.3 0.5 46.7 46.3 Latin America & Carib. 86.7 85.4 33.2 46.0 128.8 229.6 2.6 27.8 35.2 Middle East & N. Africa 83.2 80.8 24.8 33.8 53.9 105.0 3.0 23.8 28.6 South Asia 88.7 86.7 47.9 47.1 388.7 629.8 2.2 33.8 33.6 Sub-Saharan Africa 89.2 86.6 63.0 62.2 172.7 305.1 2.6 42.0 42.0 High income 84.4 81.1 52.6 63.5 374.3 470.6 1.0 38.4 43.4 Europe EMU 83.7 78.7 47.2 56.9 123.0 141.6 0.6 36.4 41.4 a. Estimate does not account for recent refugee flows. b. Includes labor force for Kosovo until 2001. c. Data are for 1980­2001. 44 2004 World Development Indicators PEOPLE 2.2 Labor force structure About the data Definitions The labor force is the supply of labor available for the But in many developing countries children under 15 · Labor force participation rate is the proportion of production of goods and services in an economy. It work full or part time. And in some high-income coun- the population ages 15­64 that is economically includes people who are currently employed and peo- tries many workers postpone retirement past age 65. active: all people who supply labor for the production ple who are unemployed but seeking work as well as As a result, labor force participation rates calculated of goods and services during a specified period. first-time job-seekers. Not everyone who works is in this way may systematically over- or under-esti- · Total labor force comprises people who meet the included, however. Unpaid workers, family workers, mate actual rates. High participation rates are found ILO definition of the economically active population. and students are among those usually omitted, and in Sub-Saharan Africa, where men and women can- It includes both the employed and the unemployed. in some countries members of the military are not not afford to forgo work, because of a lack of social While national practices vary in the treatment of counted. The size of the labor force tends to vary dur- protection. The largest gap between men and women such groups as the armed forces and seasonal or ing the year as seasonal workers enter and leave it. in labor force participation is observed in the Middle part-time workers, the labor force generally includes Data on the labor force are compiled by the East and North Africa, where low participation of the armed forces, the unemployed, and first-time job- International Labour Organization (ILO) from labor women in the work force also brings down the over- seekers, but excludes homemakers and other unpaid force surveys, censuses, establishment censuses all labor force participation rate. caregivers and workers in the informal sector. and surveys, and various types of administrative In general, estimates of women in the labor force · Average annual growth rate of the labor force is records such as employment exchange registers and are lower than those of men and are not comparable calculated using the exponential endpoint method unemployment insurance schemes. For some coun- internationally, reflecting the fact that for women, (see Statistical methods for more information). tries a combination of sources is used. While the demographic, social, legal, and cultural trends and · Females as a percentage of the labor force show resulting statistics may provide rough estimates of norms determine whether their activities are regard- the extent to which women are active in the labor the labor force, they are not comparable across ed as economic. In many countries large numbers of force. countries because of the noncomparability of the women work on farms or in other family enterprises original data and the different ways the original without pay, while others work in or near their homes, sources may be combined. mixing work and family activities during the day. For international comparisons the most compre- Countries differ in the criteria used to determine the hensive source is labor force surveys. Despite the extent to which such workers are to be counted as ILO's efforts to encourage the use of international part of the labor force. In most economies the gap standards, labor force data are not fully comparable between male and female labor force participation because of differences among countries, and some- rates has been narrowing since 1980. times within countries, in their scope and coverage. In some countries data on the labor force refer to people above a specific age, while in others there is no specific age provision. The reference period of the census or survey is another important source of dif- ferences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in oth- ers the data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregular intervals, the estimated labor force may be significantly smaller than the numbers actually work- ing (ILO, Yearbook of Labour Statistics 1997). The labor force estimates in the table were calcu- lated by World Bank staff by applying labor force par- ticipation rates from the ILO database to World Bank Data sources population estimates to create a series consistent The labor force participation rates are from the with these population estimates. This procedure ILO database Estimates and Projections of the sometimes results in estimates of labor force size Economically Active Population, 1950­2010. The that differ slightly from those in the ILO's Yearbook ILO publishes estimates of the economically of Labour Statistics. The labor force participation active population in its Yearbook of Labour rate of the population ages 15­64 provides an Statistics. indication of the relative size of the supply of labor. 2004 World Development Indicators 45 2.3 Employment by economic activity Agriculture a Industry a Services a Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b Afghanistan 66 .. 86 .. 9 .. 12 .. 26 .. 2 .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria 27 .. 69 .. 33 .. 6 .. 40 .. 25 .. Angola 67 .. 87 .. 13 .. 1 .. 20 .. 11 .. Argentina .. 1 .. 0 c .. 30 .. 12 .. 69 .. 87 Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 8 6 4 3 39 30 16 10 53 64 80 87 Austria .. 5 .. 6 .. 43 .. 14 .. 52 .. 80 Azerbaijan .. 37 .. 43 .. 14 .. 7 .. 49 .. 50 Bangladesh .. 53 .. 77 .. 11 .. 9 .. 30 .. 12 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 4 .. 2 .. 44 .. 18 .. 51 .. 79 .. Benin 66 .. 69 .. 10 .. 4 .. 24 .. 27 .. Bolivia .. 6 .. 3 .. 39 .. 14 .. 55 .. 82 Bosnia and Herzegovina 26 .. 38 .. 45 .. 24 .. 30 .. 39 .. Botswana .. 22 .. 17 .. 26 .. 14 .. 51 .. 67 Brazil 34 24 20 16 30 27 13 10 36 49 67 74 Bulgaria .. .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 92 .. 93 .. 3 .. 2 .. 5 .. 5 .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 71 .. 70 .. 9 .. 12 .. 20 .. 18 Cameroon 65 .. 87 .. 11 .. 2 .. 24 .. 11 .. Canada 7 4 3 2 37 33 16 11 56 64 81 87 Central African Republic 79 .. 90 .. 5 .. 1 .. 15 .. 9 .. Chad 82 .. 95 .. 6 .. 0 c .. 12 .. 4 .. Chile 22 18 3 5 27 29 16 13 51 53 81 83 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China 2 0 c 1 0 c 47 27 56 10 52 73 43 90 Colombia 2 33 1 7 39 19 26 17 59 48 74 76 Congo, Dem. Rep. 62 .. 84 .. 18 .. 4 .. 20 .. 12 .. Congo, Rep. 42 .. 81 .. 20 .. 2 .. 38 .. 17 .. Costa Rica 34 22 6 4 25 27 20 15 40 51 74 80 Côte d'Ivoire 60 .. 75 .. 10 .. 5 .. 30 .. 20 .. Croatia .. 16 .. 15 .. 37 .. 21 .. 47 .. 63 Cuba 30 .. 10 .. 32 .. 22 .. 39 .. 68 .. Czech Republic 13 6 11 3 57 50 39 28 30 44 50 68 Denmark .. 5 .. 2 .. 36 .. 14 .. 59 .. 85 Dominican Republic .. 21 .. 2 .. 26 .. 17 .. 53 .. 81 Ecuador .. 10 .. 4 .. 30 .. 16 .. 60 .. 79 Egypt, Arab Rep. 45 27 10 39 21 25 13 7 33 48 69 54 El Salvador 51 34 10 4 21 25 21 22 28 42 69 74 Eritrea 79 .. 88 .. 7 .. 2 .. 14 .. 11 .. Estonia .. 10 .. 4 .. 42 .. 23 .. 48 .. 73 Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 15 7 12 4 45 40 23 14 39 53 65 82 France 10 2 7 1 45 34 22 13 46 64 71 86 Gabon 59 .. 74 .. 18 .. 6 .. 24 .. 21 .. Gambia, The 78 .. 93 .. 10 .. 3 .. 13 .. 5 .. Georgia .. 53 .. 53 .. 12 .. 6 .. 35 .. 41 Germany .. 3 .. 2 .. 44 .. 18 .. 52 .. 80 Ghana .. .. .. .. .. .. .. .. .. .. .. .. Greece 26 15 42 18 34 30 18 12 40 56 40 70 Guatemala .. 50 .. 18 .. 18 .. 23 .. 27 .. 56 Guinea 86 .. 97 .. 2 .. 1 .. 12 .. 3 .. Guinea-Bissau 81 .. 98 .. 3 .. 0 .. 17 .. 3 .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 46 2004 World Development Indicators PEOPLE 2.3 Employment by economic activity Agriculture a Industry a Services a Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b Honduras .. .. .. .. .. .. .. .. .. .. .. .. Hungary .. 9 .. 4 .. 42 .. 26 .. 49 .. 71 India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 57 .. 54 .. 13 .. 13 .. 29 .. 33 .. Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq 21 .. 62 .. 24 .. 11 .. 55 .. 28 .. Ireland .. 11 .. 2 .. 39 .. 14 .. 50 .. 83 Israel 8 3 4 1 39 34 16 12 52 62 79 86 Italy 13 6 16 5 43 39 28 20 44 55 56 75 Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan 9 5 13 5 40 37 28 21 51 57 58 73 Jordan .. .. .. .. .. .. .. .. .. .. .. .. Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya 23 20 25 16 24 24 9 10 53 57 65 75 Korea, Dem. Rep. 39 .. 52 .. 37 .. 20 .. 24 .. 28 .. Korea, Rep. .. 9 .. 12 .. 34 .. 19 .. 57 .. 70 Kuwait 2 .. 0 .. 36 .. 3 .. 62 .. 97 .. Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. .. .. Lao PDR 77 .. 82 .. 7 .. 4 .. 16 .. 13 .. Latvia .. 18 .. 12 .. 35 .. 16 .. 47 .. 72 Lebanon 13 .. 20 .. 29 .. 21 .. 58 .. 59 .. Lesotho 26 .. 64 .. 52 .. 5 .. 22 .. 31 .. Liberia 69 .. 89 .. 9 .. 1 .. 22 .. 10 .. Libya 16 .. 63 .. 29 .. 3 .. 55 .. 34 .. Lithuania .. 20 .. 12 .. 34 .. 21 .. 45 .. 67 Macedonia, FYR .. 23 .. 25 .. 36 .. 30 .. 41 .. 46 Madagascar 73 .. 93 .. 9 .. 2 .. 19 .. 5 .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 34 21 44 14 26 34 20 29 40 45 36 57 Mali 86 .. 92 .. 2 .. 1 .. 12 .. 7 .. Mauritania 65 .. 79 .. 11 .. 2 .. 25 .. 19 .. Mauritius 29 .. 30 .. 19 .. 40 .. 47 .. 31 .. Mexico .. 24 .. 6 .. 28 .. 22 .. 48 .. 72 Moldova .. 52 .. 50 .. 18 .. 10 .. 31 .. 40 Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco .. .. .. .. .. .. .. .. .. .. .. .. Mozambique 72 .. 97 .. 14 .. 1 .. 14 .. 2 .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 52 33 42 29 22 17 10 7 27 49 47 63 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands .. 4 .. 2 .. 31 .. 9 .. 64 .. 86 New Zealand 13 12 7 6 38 32 19 12 48 56 73 82 Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 10 6 6 2 41 33 13 9 49 58 81 88 Oman 52 .. 24 .. 21 .. 33 .. 27 .. 43 .. Pakistan .. 44 .. 73 .. 20 .. 9 .. 36 .. 18 Panama .. 29 .. 6 .. 20 .. 10 .. 51 .. 85 Papua New Guinea 76 .. 92 .. 8 .. 2 .. 16 .. 6 .. Paraguay 2 39 0 c 20 35 21 13 10 63 40 86 69 Peru .. 11 .. 6 .. 24 .. 10 .. 65 .. 84 Philippines 60 45 37 25 16 18 15 12 25 37 48 63 Poland .. 19 .. 19 .. 40 .. 18 .. 40 .. 63 Portugal .. 12 .. 14 .. 44 .. 23 .. 44 .. 63 Puerto Rico 8 3 0 c 0 c 27 27 24 14 65 69 75 86 2004 World Development Indicators 47 2.3 Employment by economic activity Agriculture a Industry a Services a Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b 1980 2000­02 b Romania 22 40 39 45 52 30 34 22 26 30 27 33 Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 88 .. 98 .. 5 .. 1 .. 7 .. 1 .. Saudi Arabia 45 .. 25 .. 17 .. 5 .. 39 .. 70 .. Senegal 74 .. 90 .. 9 .. 2 .. 17 .. 8 .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 63 .. 82 .. 20 .. 4 .. 17 .. 14 .. Singapore 2 0 c 1 0 c 33 31 40 18 65 69 59 81 Slovak Republic .. 8 .. 4 .. 48 .. 26 .. 44 .. 71 Slovenia .. 10 .. 10 .. 46 .. 29 .. 43 .. 61 Somalia 69 .. 90 .. 12 .. 2 .. 19 .. 8 .. South Africa .. .. .. .. .. .. .. .. .. .. .. .. Spain 20 8 18 5 42 42 21 15 38 51 61 81 Sri Lanka 44 .. 51 .. 19 .. 18 .. 30 .. 28 .. Sudan 66 .. 88 .. 9 .. 4 .. 24 .. 8 .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 8 3 3 1 45 36 16 11 47 61 81 88 Switzerland 8 5 5 3 47 36 23 13 46 59 72 84 Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand 68 50 74 48 13 20 8 17 20 30 18 35 Togo 70 .. 67 .. 12 .. 7 .. 19 .. 26 .. Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 33 .. 53 .. 30 .. 32 .. 37 .. 16 .. Turkey 4 24 9 56 36 28 31 15 60 48 60 29 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine .. 22 .. 17 .. 39 .. 22 .. 33 .. 55 United Arab Emirates 5 9 0 c 0 c 40 36 7 14 55 55 93 86 United Kingdom 4 2 1 1 48 36 23 11 49 62 76 88 United States 5 3 2 1 39 32 19 12 56 65 80 87 Uruguay .. 6 .. 2 .. 32 .. 14 .. 62 .. 85 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 20 15 2 2 31 28 18 12 49 57 79 86 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 22 9 25 26 43 32 25 11 36 58 50 62 Yemen, Rep. 60 .. 98 .. 19 .. 1 .. 21 .. 1 .. Zambia 69 .. 85 .. 13 .. 3 .. 19 .. 13 .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 16 .. 8 .. 32 .. 19 .. 51 .. 73 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. .. .. .. .. .. .. .. .. .. .. .. Latin America & Carib. .. 21 .. 9 .. 27 .. 14 .. 52 .. 76 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 8 4 6 3 41 35 22 15 51 60 72 82 Europe EMU .. 5 .. 4 .. 40 .. 16 .. 55 .. 80 a. Data may not add up to 100 because of workers not classified by sector. b. Data are for the most recent year available. c. Less than 0.5. 48 2004 World Development Indicators PEOPLE 2.3 Employment by economic activity About the data The International Labour Organization (ILO) classifies are classified according to their last job. But in some growth is centered in service occupations, where economic activity on the basis of the International countries the unemployed and people seeking their women often dominate, as has been the recent experi- Standard Industrial Classification (ISIC) of All first job are not classifiable by economic activity. ence in many countries. Economic Activities. Because this classification is Because of these differences, the size and distribu- There are several explanations for the rising impor- based on where work is performed (industry) rather tion of employment by economic activity may not be tance of service jobs for women. Many service than on what type of work is performed (occupation), fully comparable across countries (ILO, Yearbook of jobs--such as nursing and social and clerical work-- all of an enterprise's employees are classified under Labour Statistics 1996, p. 64). are considered "feminine" because of a perceived the same industry, regardless of their trade or occu- The ILO's Yearbook of Labour Statistics and its data- similarity to women's traditional roles. Women often pation. The categories should add up to 100 percent. base Key Indicators of the Labour Market report data do not receive the training needed to take advantage Where they do not, the differences arise because of by major divisions of the ISIC revision 2 or ISIC revision of changing employment opportunities. And the workers who cannot be classified by economic activity. 3. In this table the reported divisions or categories are greater availability of part-time work in service indus- Data on employment are drawn from labor force sur- aggregated into three broad groups: agriculture, indus- tries may lure more women, although it is not clear veys, household surveys, establishment censuses try, and services. Classification into such broad groups whether this is a cause or an effect. and surveys, administrative records of social insur- may obscure fundamental shifts within countries' ance schemes, and official national estimates. The industrial patterns. Most economies report economic Definitions concept of employment generally refers to people activity according to the ISIC revision 2, although a above a certain age who worked, or who held a job, group of economies moved to ISIC revision 3. The use · Agriculture corresponds to division 1 (ISIC revision during a reference period. Employment data include of one classification or another should not have a sig- 2) or tabulation categories A and B (ISIC revision 3) both full-time and part-time workers. There are, how- nificant impact on the information for the three broad and includes hunting, forestry, and fishing. · Industry ever, many differences in how countries define and sectors presented in this table. corresponds to divisions 2­5 (ISIC revision 2) or tab- measure employment status, particularly for students, The distribution of economic activity by gender reveals ulation categories C­F (ISIC revision 3) and includes part-time workers, members of the armed forces, and some interesting patterns. Industry accounts for a larg- mining and quarrying (including oil production), man- household or contributing family workers. Where the er share of male employment than female employment ufacturing, construction, and public utilities (electrici- armed forces are included, they are allocated to the worldwide, whereas a higher proportion of women work ty, gas, and water). · Services correspond to divi- service sector, causing that sector to be somewhat in the services sector. Employment in agriculture is also sions 6­9 (ISIC revision 2) or tabulation categories overstated relative to the service sector in economies male-dominated, although not as much as industry. G­P (ISIC revision 3) and include wholesale and retail where they are excluded. Where data are obtained Segregating one sex in a narrow range of occupations trade and restaurants and hotels; transport, storage, from establishment surveys, they cover only employ- significantly reduces economic efficiency by reducing and communications; financing, insurance, real ees; thus self-employed and contributing family work- labor market flexibility and thus the economy's ability to estate, and business services; and community, ers are excluded. In such cases the employment share adapt to change. This segregation is particularly harm- social, and personal services. of the agricultural sector is severely underreported. ful for women, who have a much narrower range of Countries also take very different approaches to labor market choices and lower levels of pay than men. the treatment of unemployed people. In most coun- But it is also detrimental to men when job losses are tries unemployed people with previous job experience concentrated in industries dominated by men and job 2.3a Women tend to suffer disproportionately from underemployment Underemployment as share of total employment (%) 15 Male Female 12 9 6 3 0 Paraguay Namibia Pakistan Thailand Time-related underemployment includes people who work less than the normal duration of work, as defined by national Data sources authorities, but who desire and seek to work additional hours. More women tend to be underemployed than men, as The employment data are from the ILO database discrimination and women's household responsibilities may make it more difficult for them to have stable and high-paid work. Key Indicators of the Labour Market, third edition. Source: International Labour Organization, Key Indicators of the Labour Market, third edition. 2004 World Development Indicators 49 2.4 Unemployment Unemployment Long-term Unemployment by level unemployment of educational attainment Male Female Total % of total unemployment % of male % of female % of total % of total unemployment Primary Secondary Tertiary labor force labor force labor force Male Female Total 1999­ 1999­ 1999­ 1980 2000­02 a 1980 2000­02 a 1980 2000­02 a 2000­02 a 2000­02 a 2000­02 a 2001 a 2001 a 2001 a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 18.8 .. 28.4 5.6 22.7 .. .. .. .. .. .. Algeria .. 33.9 .. 29.7 .. 29.8 .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. 14.1 .. 16.4 2.3 17.8 .. .. .. .. .. .. Armenia .. .. .. .. .. .. 72.2 70.8 71.6 .. .. .. Australia 5.0 6.2 7.4 5.8 5.9 6.0 25.9 17.1 22.1 54.3 31.5 14.0 Austria 1.6 3.5 2.3 3.8 1.9 3.6 25.8 24.2 25.1 36.0 57.6 6.4 Azerbaijan .. 1.1 .. 1.5 .. 1.3 .. .. .. 4.5 35.4 60.1 Bangladesh .. 3.2 .. 3.3 .. 3.3 .. .. .. 54.3 22.7 8.4 Belarus .. 1.9 .. 2.6 .. 2.3 .. .. .. 7.9 15.3 76.9 Belgium 5.5 6.2 15.0 7.8 9.1 6.9 45.8 53.3 49.4 50.0 34.9 15.1 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia .. 4.5 .. 6.2 .. 5.2 .. .. .. 60.2 32.5 4.4 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 14.7 .. 17.2 .. 15.8 .. .. .. .. .. .. Brazil 2.8 7.5 2.8 11.9 2.8 9.4 .. .. .. 26.1 20.2 2.5 Bulgaria .. 20.2 .. 18.4 .. 19.4 .. .. .. 36.7 53.0 10.3 Burkina Faso .. .. .. .. .. .. .. .. .. 46.8 19.3 5.6 Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 1.5 .. 2.2 .. 1.8 .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 7.0 8.1 8.2 7.1 7.5 7.7 9.9 8.4 9.3 30.7 30.3 39.0 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 10.6 7.5 10.0 8.5 10.4 7.8 .. .. .. 22.7 54.9 21.6 China .. .. .. .. 4.9 3.1 .. .. .. .. .. .. Hong Kong, China 3.9 8.4 3.4 6.0 3.8 7.3 .. .. .. .. .. .. Colombia 7.5 11.6 11.5 19.1 9.1 17.9 .. .. .. 22.8 57.2 17.2 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 5.3 5.6 7.8 7.9 5.9 6.4 8.9 13.3 10.9 71.6 15.2 10.0 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia 3.4 13.4 8.2 18.5 5.3 15.2 .. .. 56.4 19.1 71.3 9.1 Cuba .. .. .. .. .. 3.3 .. .. .. .. .. .. Czech Republic .. 5.9 .. 9.0 .. 7.3 50.2 51.0 50.6 27.3 69.1 3.6 Denmark 6.5 4.2 7.6 4.3 7.0 4.3 17.1 22.1 19.5 35.1 44.9 20.0 Dominican Republic .. 9.4 .. 26.0 .. 15.6 2.2 1.3 1.6 .. .. .. Ecuador .. 7.1 .. 16.2 .. 11.0 .. .. .. 26.8 50.8 20.2 Egypt, Arab Rep. 3.9 5.1 19.2 22.7 5.2 9.0 .. .. .. .. .. .. El Salvador .. 8.0 .. 3.5 12.9 6.2 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 12.9 .. 12.2 .. 12.6 .. .. .. 19.3 62.7 18.1 Ethiopia .. .. .. .. .. .. .. .. .. 26.9 61.3 8.1 Finland 4.6 9.0 4.7 9.1 4.7 9.0 30.0 22.6 26.2 38.2 45.8 16.0 France 4.1 7.9 9.1 10.1 6.1 8.9 30.2 33.1 31.7 .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 11.6 .. 10.7 .. 11.0 .. .. .. 5.5 33.1 61.4 Germany .. 8.7 .. 8.3 .. 8.6 44.9 48.7 46.6 26.8 60.4 12.8 Ghana .. .. .. .. .. .. .. .. .. .. .. .. Greece 3.3 6.2 5.7 14.6 2.4 9.6 47.1 55.7 52.4 35.1 49.4 14.5 Guatemala .. 2.5 .. 4.3 1.7 3.1 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 50 2004 World Development Indicators PEOPLE 2.4 Unemployment Unemployment Long-term Unemployment by level unemployment of educational attainment Male Female Total % of total unemployment % of male % of female % of total % of total unemployment Primary Secondary Tertiary labor force labor force labor force Male Female Total 1999­ 1999­ 1999­ 1980 2000­02 a 1980 2000­02 a 1980 2000­02 a 2000­02 a 2000­02 a 2000­02 a 2001 a 2001 a 2001 a Honduras 8.6 3.4 6.0 4.7 7.3 3.8 .. .. .. .. .. .. Hungary .. 6.1 .. 5.4 .. 5.8 47.1 41.7 44.8 35.4 60.5 4.1 India .. .. .. .. .. .. .. .. .. 29.0 40.3 30.7 Indonesia .. .. .. .. .. 6.1 .. .. .. 46.0 36.6 6.7 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 11.4 4.6 8.2 3.7 10.5 4.2 35.9 18.2 29.4 60.8 20.8 16.1 Israel 4.1 10.1 6.0 10.6 4.8 10.3 .. .. .. 20.7 44.2 34.1 Italy 4.8 6.9 13.2 12.2 7.6 9.0 58.0 61.6 59.9 49.1 41.9 7.2 Jamaica 16.3 .. 39.6 .. 27.3 .. 24.4 36.2 31.7 .. .. .. Japan 2.0 5.6 2.0 5.1 2.0 5.4 34.8 21.6 29.7 21.5 53.4 24.8 Jordan .. 11.8 .. 20.7 .. 13.2 .. .. .. .. .. .. Kazakhstan .. .. .. .. .. .. .. .. .. 7.2 52.5 40.3 Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 6.2 3.5 3.5 2.5 5.2 3.1 3.1 1.2 2.5 26.1 51.0 22.9 Kuwait .. 0.8 .. 0.6 .. 0.8 .. .. .. .. 11.9 2.7 Kyrgyz Republic .. .. .. .. .. 8.6 .. .. .. 33.4 55.7 10.9 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 14.1 .. 11.5 .. 12.8 .. .. .. 24.6 67.0 8.2 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 19.7 .. 14.2 .. 13.8 .. .. 57.8 15.4 55.8 28.8 Macedonia, FYR 15.6 31.7 32.8 32.3 22.0 31.9 .. .. .. 34.0 52.1 7.8 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia .. .. .. .. .. 3.9 .. .. .. .. .. .. Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. 5.6 .. 12.6 .. 8.0 .. .. .. 35.5 63.9 .. Mexico .. 2.4 .. 2.4 .. 2.4 1.0 0.3 0.7 51.5 23.9 22.2 Moldova .. 8.7 .. 5.9 .. 7.3 .. .. .. .. .. .. Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco .. .. .. .. .. .. .. .. .. .. .. .. Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia .. 28.3 .. 39.0 .. 33.8 .. .. .. .. .. .. Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 4.3 2.8 5.2 3.6 4.6 3.1 21.5 20.7 21.1 49.5 35.9 13.2 New Zealand .. 5.0 .. 5.3 .. 5.2 14.9 10.0 12.6 0.5 44.5 19.2 Nicaragua .. 12.8 .. 9.4 .. 11.2 .. .. .. 56.3 23.4 14.7 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 1.2 4.1 2.1 3.7 1.6 3.9 8.1 3.7 6.2 25.0 50.0 22.6 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 3.0 6.1 7.5 17.3 3.6 7.8 .. .. .. .. .. .. Panama 6.3 10.5 13.3 18.2 8.4 13.2 24.0 35.7 29.3 47.0 35.5 11.3 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 3.8 .. 4.8 .. 4.1 .. .. .. .. .. .. .. Peru .. 7.5 .. 10.0 .. 8.7 .. .. .. 15.8 54.9 28.3 Philippines 3.2 9.4 7.5 10.3 4.8 9.8 .. .. .. .. .. .. Poland .. 19.1 .. 20.9 .. 19.9 45.1 52.0 48.4 19.1 76.8 4.2 Portugal 3.3 4.2 12.1 6.1 6.7 5.1 31.9 31.4 31.6 73.3 13.6 8.1 Puerto Rico 19.5 13.0 12.3 9.1 17.1 11.4 .. .. .. .. .. .. 2004 World Development Indicators 51 2.4 Unemployment Unemployment Long-term Unemployment by level unemployment of educational attainment Male Female Total % of total unemployment % of male % of female % of total % of total unemployment Primary Secondary Tertiary labor force labor force labor force Male Female Total 1999­ 1999­ 1999­ 1980 2000­02 a 1980 2000­02 a 1980 2000­02 a 2000­02 a 2000­02 a 2000­02 a 2001 a 2001 a 2001 a Romania .. 7.1 .. 5.9 .. 6.6 .. .. .. 20.6 72.7 5.5 Russian Federation .. 9.3 .. 8.5 .. 8.9 .. .. .. 16.8 41.6 41.6 Rwanda .. .. .. .. .. .. .. .. .. 60.7 24.1 5.9 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro .. 22.6 .. 22.1 .. 22.3 .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.9 3.5 3.4 3.4 3.0 3.4 .. .. .. 25.5 26.9 32.0 Slovak Republic .. 18.6 .. 18.7 .. 18.6 .. .. .. 19.8 77.1 3.0 Slovenia .. 5.6 .. 6.3 .. 5.9 58.6 61.4 59.9 33.3 63.2 5.3 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 26.1 .. 33.3 .. 29.5 .. .. .. .. .. .. Spain 10.4 8.0 13.1 16.4 11.1 11.4 31.6 41.8 37.5 57.1 19.7 22.2 Sri Lanka .. 6.8 .. 11.2 .. 8.2 .. .. .. 41.0 .. 56.1 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 1.9 5.6 2.6 4.7 2.2 5.2 23.0 18.1 20.9 28.6 56.6 13.1 Switzerland 0.2 2.8 0.3 3.1 0.2 2.9 19.0 23.9 21.3 43.0 43.0 14.0 Syrian Arab Republic 3.8 8.0 3.8 23.9 3.9 11.2 .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand 1.0 1.8 0.7 1.7 0.8 1.8 .. .. .. 70.6 7.2 19.2 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 8.0 .. 14.0 .. 10.0 .. 20.3 34.7 27.6 38.2 60.7 0.8 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 9.0 10.9 23.0 9.9 10.9 10.6 26.4 34.5 28.5 60.1 29.0 8.4 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine .. 11.2 .. 11.0 .. 11.1 .. .. .. 8.6 27.3 64.1 United Arab Emirates .. 2.2 .. 2.6 .. 2.3 .. .. .. .. .. .. United Kingdom 8.3 5.6 4.8 4.4 6.8 5.1 26.4 17.0 22.8 33.7 44.4 12.7 United States 6.9 5.9 7.4 5.6 7.1 5.8 8.9 8.1 8.5 20.3 35.3 44.4 Uruguay .. 11.5 .. 19.7 .. 17.2 .. .. .. 50.7 21.2 27.8 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB .. 11.6 .. 14.6 5.9 12.8 .. .. .. 57.9 24.0 14.4 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 27.3 .. 14.1 .. 25.5 .. .. .. .. .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 32.7 .. 59.0 .. 42.2 .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. 16.4 81.8 0.8 World .. w .. w .. w .. w .. w .. w .. w .. w .. w 30.0 w 40.2 w 25.2 w Low income .. .. .. .. .. .. .. .. .. 30.0 41.4 27.9 Middle income .. .. .. .. 4.8 4.9 .. .. .. .. .. .. Lower middle income .. .. .. .. 4.9 4.3 .. .. .. .. .. .. Upper middle income .. .. .. .. .. 9.0 .. .. .. 34.8 52.5 11.3 Low & middle income .. .. .. .. .. .. .. .. .. 28.8 40.0 25.8 East Asia & Pacific .. .. .. .. 4.7 3.7 .. .. .. .. .. .. Europe & Central Asia .. 11.3 .. 11.1 .. 11.1 .. .. .. 21.3 45.8 32.6 Latin America & Carib. .. .. .. .. .. 9.2 .. .. .. 31.3 28.3 9.6 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. 29.3 40.3 31.0 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 5.5 5.4 7.0 6.7 6.0 6.2 24.7 22.8 24.1 31.1 41.8 25.9 Europe EMU 5.5 7.9 10.8 11.6 7.1 9.8 40.7 44.6 42.8 42.1 43.7 13.3 a. Data are for the most recent year available. 52 2004 World Development Indicators PEOPLE 2.4 Unemployment About the data Definitions Unemployment and total employment in an economy closely with social insurance schemes and registration · Unemployment refers to the share of the labor are the broadest indicators of economic activity as with such offices is a prerequisite for receipt of unem- force without work but available for and seeking reflected by the labor market. The International ployment benefits, the two sets of unemployment esti- employment. Definitions of labor force and unem- Labour Organization (ILO) defines the unemployed as mates tend to be comparable. Where registration is ployment differ by country (see About the data). members of the economically active population who voluntary and where employment offices function only ·Long-term unemployment refers to the number of are without work but available for and seeking work, in more populous areas, employment office statistics people with continuous periods of unemployment including people who have lost their jobs and those do not give a reliable indication of unemployment. extending for a year or longer, expressed as a per- who have voluntarily left work. Some unemployment Most commonly excluded from both these sources are centage of the total unemployed. · Unemployment is unavoidable in all economies. At any time some discouraged workers who have given up their job by level of educational attainment shows the unem- workers are temporarily unemployed--between jobs search because they believe that no employment ployed by level of educational attainment, as a per- as employers look for the right workers and workers opportunities exist or do not register as unemployed centage of the total unemployed. The levels of edu- search for better jobs. Such unemployment, often after their benefits have been exhausted. Thus meas- cational attainment accord with the International called frictional unemployment, results from the nor- ured unemployment may be higher in countries that Standard Classification of Education 1997 of the mal operation of labor markets. offer more or longer unemployment benefits. United Nations Educational, Cultural, and Scientific Changes in unemployment over time may reflect Women tend to be excluded from the unemploy- Organization (UNESCO). changes in the demand for and supply of labor, but ment count for various reasons. Women suffer more they may also reflect changes in reporting practices. from discrimination and from structural, social, and Ironically, low unemployment rates can often disguise cultural barriers that impede them from actively substantial poverty in a country, while high unem- seeking work. Also, women are often responsible for ployment rates can occur in countries with a high level the care of children and the elderly or for other of economic development and low incidence of pover- household affairs. They may not be available for ty. In countries without unemployment or welfare ben- work during the short reference period, as they need efits, people eke out a living in the informal sector. In to make arrangement before starting work. countries with well-developed safety nets, workers Furthermore, women are considered to be employed can afford to wait for suitable or desirable jobs. But when they are working part-time or in temporary jobs high and sustained unemployment indicates serious in the informal sector, despite the instability of inefficiencies in the allocation of resources. these jobs and that they may be actively looking for The ILO definition of unemployment notwithstand- more secure employment. ing, reference periods, the criteria for those consid- Long-term unemployment is measured in terms of ered to be seeking work, and the treatment of people duration, that is, the length of time that an unem- temporarily laid off and those seeking work for the ployed person has been without work and looking for first time vary across countries. In many developing a job. The underlying assumption is that shorter peri- countries it is especially difficult to measure employ- ods of joblessness are of less concern, especially ment and unemployment in agriculture. The timing of when the unemployed are covered by unemployment a survey, for example, can maximize the effects of benefits or similar forms of welfare support. The seasonal unemployment in agriculture. And informal length of time a person has been unemployed is diffi- sector employment is difficult to quantify where infor- cult to measure, because the ability to recall the mal activities are not registered and tracked. length of that time diminishes as the period of job- Data on unemployment are drawn from labor force lessness extends. Women's long-term unemployment sample surveys and general household sample sur- is likely to be lower in countries where women consti- veys, censuses, and other administrative records such tute a large share of the unpaid family workforce. as social insurance statistics, employment office sta- Women in such countries have more access than men tistics, and official estimates, which are usually based to nonmarket work and are more likely to drop out of on information drawn from one or more of the above the labor force and not be counted as unemployed. sources. Labor force surveys generally yield the most Unemployment by level of educational attainment comprehensive data because they include groups not provide insights into the relationship between the covered in other unemployment statistics, particularly educational attainment of workers and unemploy- people seeking work for the first time. These surveys ment. Besides the limitations to comparability raised generally use a definition of unemployment that follows for measuring unemployment, the different ways of the international recommendations more closely than classifying the level of education across countries that used by other sources and therefore generate sta- may also cause inconsistency. The level of education Data source tistics that are more comparable internationally. is supposed to be classified according to The unemployment data are from the ILO database In contrast, the quality and completeness of data International Standard Classification of Education Key Indicators of the Labour Market, third edition. from employment offices and social insurance pro- 1997 (ISCED97). For more information on ISCED97, grams vary widely. Where employment offices work see About the data for table 2.10. 2004 World Development Indicators 53 2.5 Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 2002 29.6 25.4 .. .. .. 2002 a <2 <0.5 11.8 2.0 Algeria 1995 30.3 14.7 22.6 1998 16.6 7.3 12.2 1995 a <2 <0.5 15.1 3.8 Angola .. .. .. .. .. .. .. .. .. .. Argentina 1995 .. 28.4 1998 29.9 2001 b 3.3 0.5 14.3 4.7 Armenia 1996 48.0 58.8 54.7 1998­99 44.8 60.4 53.7 1998 a 12.8 3.3 49.0 17.3 Australia .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. Azerbaijan 1995 .. .. 68.1 2001 .. .. 49.6 2001 a 3.7 <1 9.1 3.5 Bangladesh 1995­96 55.2 29.4 51.0 2000 53.0 36.6 49.8 2000 a 36.0 8.1 82.8 36.3 Belarus 1998 .. .. 33.0 2000 .. .. 41.9 2000 a <2 <0.5 <2 0.1 Belgium .. .. .. .. .. .. .. .. .. .. Benin 1995 .. .. 33.0 .. .. .. .. .. .. .. Bolivia 1997 77.3 63.2 1999 81.7 .. 62.7 1999 a 14.4 5.4 34.3 14.9 Bosnia and Herzegovina 2001­02 19.9 13.8 19.5 .. .. .. .. .. .. .. Botswana .. .. .. .. .. .. 1993 a 23.5 7.7 50.1 22.8 Brazil 1990 32.6 13.1 17.4 .. .. .. 2001 b 8.2 2.1 22.4 8.8 Bulgaria 1997 .. .. 36.0 2001 .. .. 12.8 2001 a 4.7 1.4 16.2 5.7 Burkina Faso 1994 51.0 10.4 44.5 1998 51.0 16.5 45.3 1998 a 44.9 14.4 81.0 40.6 Burundi 1990 36.0 43.0 .. .. .. 1998 a 58.4 24.9 89.2 51.3 Cambodia 1993­94 43.1 24.8 39.0 1997 40.1 21.1 36.1 1997 a 34.1 9.7 77.7 34.5 Cameroon 1996 59.6 41.4 53.3 2001 49.9 22.1 40.2 2001 a 17.1 4.1 50.6 19.3 Canada .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. 1993 a 66.6 38.1 84.0 58.4 Chad 1995­96 67.0 63.0 64.0 .. .. .. .. .. .. .. Chile 1996 .. .. 19.9 1998 .. .. 17.0 2000 b <2 <0.5 9.6 2.5 China 1996 7.9 <2 6.0 1998 4.6 <2 4.6 2001 a 16.6 3.9 46.7 18.4 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 1995 79.0 48.0 60.0 1999 79.0 55.0 64.0 1999 b 8.2 2.2 22.6 8.8 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. Costa Rica 1992 25.5 19.2 22.0 .. .. .. 2000 b 2.0 0.7 9.5 3.0 Côte d'Ivoire .. .. .. 1998 a 15.5 3.8 50.4 18.9 Croatia .. .. .. .. .. .. 2000 a <2 <0.5 <2 <0.5 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. 1996 b <2 <0.5 <2 <0.5 Denmark .. .. .. .. .. .. .. .. .. .. Djibouti 1996 86.5 .. 45.1 .. .. .. .. .. .. .. Dominican Republic 1992 49.0 19.3 33.9 1998 42.1 20.5 28.6 1998 b <2 <0.5 <2 <0.5 Ecuador 1994 47.0 25.0 35.0 .. .. .. 1998 b 17.7 7.1 40.8 17.7 Egypt, Arab Rep. 1995­96 23.3 22.5 22.9 1999­2000 .. .. 16.7 2000 a 3.1 <0.5 43.9 11.3 El Salvador 1992 55.7 43.1 48.3 .. .. .. 2000 b 31.1 14.1 58.0 29.7 Eritrea 1993­94 .. .. 53.0 .. .. .. .. .. .. .. Estonia 1995 14.7 6.8 8.9 .. .. .. 1998 a <2 <0.5 5.2 0.8 Ethiopia 1995­96 47.0 33.3 45.5 1999­2000 45.0 37.0 44.2 1999­2000 a 26.3 5.7 80.7 31.8 Finland .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The 1992 .. .. 64.0 1998 61.0 48.0 .. 1998 a 59.3 28.8 82.9 51.1 Georgia 1997 9.9 12.1 11.1 .. .. .. 2001 a 2.7 0.9 15.7 4.6 Germany .. .. .. .. .. .. .. .. .. .. Ghana 1992 50.0 1998 49.9 18.6 39.5 1999 a 44.8 17.3 78.5 40.8 Greece .. .. .. .. .. .. .. .. .. .. Guatemala 1989 71.9 33.7 57.9 2000 74.5 27.1 56.2 2000 b 16.0 4.6 37.4 16.0 Guinea 1994 .. .. 40.0 .. .. .. .. .. .. .. Guinea-Bissau 1991 .. .. 48.7 .. .. .. .. .. .. .. 54 2004 World Development Indicators PEOPLE 2.5 Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Guyana 1993 .. .. 43.2 1998 .. .. 35.0 1998 b <2 <0.5 6.1 1.7 Haiti 1987 .. .. 65.0 1995 66.0 .. .. .. .. .. .. Honduras 1992 46.0 56.0 50.0 1993 51.0 57.0 53.0 1998 b 23.8 11.6 44.4 23.1 Hungary 1993 .. .. 14.5 1997 .. .. 17.3 1998 b <2 <0.5 7.3 1.7 India 1993­94 37.3 32.4 36.0 1999­2000 30.2 24.7 28.6 1999­2000 a 34.7 8.2 79.9 35.3 Indonesia 1996 15.7 1999 27.1 2002 a 7.5 0.9 52.4 15.7 Iran, Islamic Rep. .. .. .. .. .. .. 1998 a <2 <0.5 7.3 1.5 Iraq .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. Jamaica 1995 37.0 .. 27.5 2000 25.1 .. 18.7 2000 a <2 <0.5 13.3 2.7 Japan .. .. .. .. .. .. .. .. .. .. Jordan 1991 .. .. 15.0 1997 .. .. 11.7 1997 a <2 <0.5 7.4 1.4 Kazakhstan 1996 39.0 30.0 34.6 .. .. .. 2001 a <2 <0.5 8.5 1.4 Kenya 1994 47.0 29.0 40.0 1997 53.0 49.0 52.0 1997 a 23.0 6.0 58.6 24.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. 1998 b <2 <0.5 <2 <0.5 Kuwait .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 1997 64.5 28.5 51.0 1999 69.7 49.0 64.1 2001 a <2 <0.5 27.2 5.9 Lao PDR 1993 48.7 33.1 45.0 1997­98 41.0 26.9 38.6 1997­98 a 26.3 6.3 73.2 29.6 Latvia .. .. .. .. .. .. 1998 a <2 <0.5 8.3 2.0 Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. 1995 a 36.4 19.0 56.1 33.1 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. 2000 a <2 <0.5 13.7 4.2 Macedonia, FYR .. .. .. .. .. .. 1998 a <2 <0.5 4.0 0.6 Madagascar 1997 76.0 63.2 73.3 1999 76.7 52.1 71.3 1999 a 49.1 18.3 83.3 44.0 Malawi 1990­91 .. .. 54.0 1997­98 66.5 54.9 65.3 1997­98 a 41.7 14.8 76.1 38.3 Malaysia 1989 .. .. 15.5 .. .. .. 1997 b <2 <0.5 9.3 2.0 Mali 1998 75.9 30.1 63.8 .. .. .. 1994 a 72.8 37.4 90.6 60.5 Mauritania 1996 65.5 30.1 50.0 2000 61.2 25.4 46.3 2000 a 25.9 7.6 63.1 26.8 Mauritius .. .. .. .. .. .. .. .. .. .. Mexico 1988 .. .. 10.1 .. .. .. 2000 b 9.9 3.7 26.3 10.9 Moldova 1997 26.7 .. 23.3 .. .. .. 2001 a 22.0 5.8 63.7 25.1 Mongolia 1995 33.1 38.5 36.3 .. .. .. 1995 a 13.9 3.1 50.0 17.5 Morocco 1990­91 18.0 7.6 13.1 1998­99 27.2 12.0 19.0 1999 a <2 <0.5 14.3 3.1 Mozambique 1996­97 71.3 62.0 69.4 .. .. .. 1996 a 37.9 12.0 78.4 36.8 Myanmar .. .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. 1993 b 34.9 14.0 55.8 30.4 Nepal 1995­96 44.0 23.0 42.0 .. .. .. 1995 a 37.7 9.7 82.5 37.5 Netherlands .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. Nicaragua 1993 76.1 31.9 50.3 1998 68.5 30.5 47.9 2001 a 45.1 16.7 79.9 41.2 Niger 1989­93 66.0 52.0 63.0 .. .. .. 1995 a 61.4 33.9 85.3 54.8 Nigeria 1985 49.5 31.7 43.0 1992­93 36.4 30.4 34.1 1997 a 70.2 34.9 90.8 59.0 Norway .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. Pakistan 1993 33.4 17.2 28.6 1998­99 35.9 24.2 32.6 1998 a 13.4 2.4 65.6 22.0 Panama 1997 64.9 15.3 37.3 .. .. .. 2000 b 7.2 2.3 17.6 7.4 Papua New Guinea 1996 41.3 16.1 37.5 .. .. .. .. .. .. .. Paraguay 1991 28.5 19.7 21.8 .. .. .. 1999 b 14.9 6.8 30.3 14.7 Peru 1994 67.0 46.1 53.5 1997 64.7 40.4 49.0 2000 b 18.1 9.1 37.7 18.5 Philippines 1994 53.1 28.0 40.6 1997 50.7 21.5 36.8 2000 a 14.6 2.7 46.4 17.2 Poland 1993 .. .. 23.8 .. .. .. 1999 b <2 <0.5 <2 <0.5 2004 World Development Indicators 55 2.5 Poverty National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % % % Portugal .. .. .. .. .. .. 1994 b <2 <0.5 <0.5 <0.5 Puerto Rico .. .. .. .. .. .. .. .. .. .. Romania 1994 27.9 20.4 21.5 .. .. .. 2000 a 2.1 0.6 20.5 5.2 Russian Federation 1994 .. .. 30.9 .. .. .. 2000 a 6.1 1.2 23.8 8.0 Rwanda 1993 .. .. 51.2 .. .. .. 1983­85 a 35.7 7.7 84.6 36.7 Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegal 1992 40.4 .. 33.4 .. .. .. 1995 a 26.3 7.0 67.8 28.2 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 1989 76.0 53.0 68.0 .. .. .. 1989 a 57.0 39.5 74.5 51.8 Singapore .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. 1996 b <2 <0.5 2.4 0.7 Slovenia .. .. .. .. .. .. 1998 a <2 <0.5 <2 <0.5 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. .. .. .. .. .. 1995 a 7.1 1.1 23.8 8.6 Spain .. .. .. .. .. .. .. .. .. .. Sri Lanka 1990­91 22.0 15.0 20.0 1995­96 27.0 15.0 25.0 1995­96 a 6.6 1.0 45.4 13.5 Sudan .. .. .. .. .. .. .. .. .. .. Swaziland 1995 .. .. 40.0 .. .. .. .. .. .. .. Sweden .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. 1998 a 10.3 2.6 50.8 16.3 Tanzania 1991 40.8 38.6 2000­01 38.7 35.7 1993 a 19.9 4.8 59.7 23.0 Thailand 1990 .. .. 18.0 1992 15.5 10.2 13.1 2000 a <2 <0.5 32.5 9.0 Togo 1987­89 .. .. 32.3 .. .. .. .. .. .. .. Trinidad and Tobago 1992 20.0 24.0 21.0 .. .. .. 1992 b 12.4 3.5 39.0 14.6 Tunisia 1990 13.1 3.5 7.4 1995 13.9 3.6 7.6 2000 a <2 <0.5 6.6 1.3 Turkey .. .. .. .. .. .. 2000 a <2 <0.5 10.3 2.5 Turkmenistan .. .. .. .. .. .. 1998 a 12.1 2.6 44.0 15.4 Uganda 1993 .. .. 55.0 1997 .. .. 44.0 .. .. .. .. Ukraine 1995 .. .. 31.7 .. .. .. 1999 b 2.9 0.6 45.7 16.3 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. 2000 b <2 <0.5 3.9 0.8 Uzbekistan 2000 30.5 22.5 27.5 .. .. .. 2000 a 21.8 5.4 77.5 28.9 Venezuela, RB 1989 .. .. 31.3 .. .. .. 1998 b 15.0 6.9 32.0 15.2 Vietnam 1993 57.2 25.9 50.9 .. .. .. 1998 a 17.7 3.3 63.7 22.9 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1998 45.0 30.8 41.8 .. .. .. 1998 a 15.7 4.5 45.2 15.0 Zambia 1996 82.8 46.0 69.2 1998 83.1 56.0 72.9 1998 a 63.7 32.7 87.4 55.4 Zimbabwe 1990­91 35.8 3.4 25.8 1995­96 48.0 7.9 34.9 1990­91 a 36.0 9.6 64.2 29.4 a. Based on expenditure. b. Based on income. 56 2004 World Development Indicators PEOPLE 2.5 Poverty About the data International comparisons of poverty data entail both living. As with international comparisons, when the difference. So income data are used to estimate conceptual and practical problems. Different coun- real value of the poverty line varies, it is not clear poverty directly, with no adjustment of the income tries have different definitions of poverty, and con- how meaningful such urban-rural comparisons are. mean. sistent comparisons between countries can be diffi- The problems of making poverty comparisons do In all cases the measures of poverty have been cult. Local poverty lines tend to have higher not end there. More issues arise in measuring calculated from primary data sources (tabulations or purchasing power in rich countries, where more gen- household living standards. The choice between household data) rather than existing estimates. erous standards are used than in poor countries. Is income and consumption as a welfare indicator is Estimation from tabulations requires an interpolation it reasonable to treat two people with the same stan- one issue. Income is generally more difficult to method; the method chosen was Lorenz curves with dard of living--in terms of their command over com- measure accurately, and consumption accords better flexible functional forms, which have proved reliable modities--differently because one happens to live in with the idea of the standard of living than does in past work. Empirical Lorenz curves were weighted a better-off country? Can we hold the real value of income, which can vary over time even if the stan- by household size, so they are based on percentiles the poverty line constant across countries, just as dard of living does not. But consumption data are not of population, not households. we do when making comparisons over time? always available, and when they are not there is lit- Poverty measures based on an international pover- tle choice but to use income. There are still other Definitions ty line attempt to do this. The commonly used $1 a problems. Household survey questionnaires can dif- day standard, measured in 1985 international prices fer widely, for example, in the number of distinct cat- · Survey year is the year in which the underlying data and adjusted to local currency using purchasing egories of consumer goods they identify. Survey qual- were collected. · Rural poverty rate is the percent- power parities (PPPs), was chosen for the World ity varies, and even similar surveys may not be age of the rural population living below the national Bank's World Development Report 1990: Poverty strictly comparable. rural poverty line. · Urban poverty rate is the per- because it is typical of the poverty lines in low-income Comparisons across countries at different levels of centage of the urban population living below the countries. PPP exchange rates, such as those from development also pose a potential problem, because national urban poverty line. · National poverty rate the Penn World Tables or the World Bank, are used of differences in the relative importance of con- is the percentage of the population living below the because they take into account the local prices of sumption of nonmarket goods. The local market national poverty line. National estimates are based goods and services not traded internationally. But value of all consumption in kind (including consump- on population-weighted subgroup estimates from PPP rates were designed not for making international tion from own production, particularly important in household surveys. · Population below $1 a day poverty comparisons but for comparing aggregates underdeveloped rural economies) should be included and population below $2 a day are the percentages from national accounts. Thus there is no certainty in the measure of total consumption expenditure. of the population living on less than $1.08 a day and that an international poverty line measures the same Similarly, the imputed profit from production of non- $2.15 a day at 1993 international prices. As a result degree of need or deprivation across countries. market goods should be included in income. This is of revisions in PPP exchange rates, poverty rates This year's edition of the World Development not always done, though such omissions were a far cannot be compared with poverty rates reported in Indicators (like those of the past four years) uses bigger problem in surveys before the 1980s. Most previous editions for individual countries. · Poverty 1993 consumption PPP estimates produced by the survey data now include valuations for consumption gap is the mean shortfall from the poverty line World Bank. The international poverty line, set at $1 or income from own production. Nonetheless, valua- (counting the nonpoor as having zero shortfall), a day in 1985 PPP terms, has been recalculated in tion methods vary. For example, some surveys use expressed as a percentage of the poverty line. This 1993 PPP terms at about $1.08 a day. Any revisions the price in the nearest market, while others use the measure reflects the depth of poverty as well as its in the PPP of a country to incorporate better price average farm gate selling price. incidence. indexes can produce dramatically different poverty Wherever possible, consumption has been used as lines in local currency. the welfare indicator for deciding who is poor. Where Data sources Problems also exist in comparing poverty meas- consumption data are unavailable, income data are The poverty measures are prepared by the World ures within countries. For example, the cost of living used. Beginning with last year's World Development Bank's Development Research Group. The national is typically higher in urban than in rural areas. So the Indicators, there has been a change in how income poverty lines are based on the World Bank's coun- urban monetary poverty line should be higher than surveys are used. Before that, average income was try poverty assessments. The international poverty the rural poverty line. But it is not always clear that adjusted to accord with consumption and income lines are based on nationally representative primary the difference between urban and rural poverty lines data from national accounts. This approach was test- household surveys conducted by national statistical found in practice properly reflects the difference in ed using data for more than 20 countries for which offices or by private agencies under the supervision the cost of living. In some countries the urban pover- the surveys provided both income and consumption of government or international agencies and ty line in common use has a higher real value than expenditure data. Income gave a higher mean than obtained from government statistical offices and does the rural poverty line. Sometimes the differ- consumption but also greater income inequality. World Bank country departments. The World Bank ence has been so large as to imply that the inci- These two effects roughly canceled each other out has prepared an annual review of its poverty work dence of poverty is greater in urban than in rural when poverty measures based on consumption were since 1993. Partnerships in Development: Progress areas, even though the reverse is found when adjust- compared with those based on income from the in the Fight against Poverty is forthcoming. ments are made only for differences in the cost of same survey; statistically, there was no significant 2004 World Development Indicators 57 2.6 Social indicators of poverty Survey year Prevalence of Under-five Child immunization Contraceptive Births attended child malnutrition mortality rate rate prevalence by skilled health staff a Weight for age Measles % of children % of children % of women under age 5 per 1,000 ages 12­23 months b ages 15­49 % of total Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile Armenia 2000 3 2 61 30 68 74 c 16 29 93 100 Bangladesh 2000 60 29 140 72 59 86 37 50 4 42 Benin 1996 37 19 208 110 49 80 1 9 34 98 Bolivia 1998 14 3 147 32 58 85 7 46 20 98 Brazil 1996 12 3 99 33 78 90 56 77 72 99 Burkina Faso 1998­99 38 26 239 155 33 69 2 16 18 75 Cambodia 2000 52 34 155 64 44 82 13 25 15 81 Cameroon 1998 33 9 199 87 37 78 1 17 28 89 Central African Republic 1994­95 37 20 193 98 31 80 1 9 14 82 Chad 1996­97 50 29 171 172 12 39 0 d 5 3 47 Colombia 2000 9 3 39 20 74 85 54 66 64 99 Comoros 1996 36 18 129 87 c 51 86 7 19 26 85 Côte d'Ivoire 1994 31 13 190 97 31 79 1 13 17 84 Egypt, Arab Rep. 2000 7 2 98 34 95 99 43 61 31 94 Eritrea 1995 51 25 152 104 37 92 0 d 19 5 74 Ethiopia 2000 49 37 159 147 18 52 3 23 1 25 Gabon 2000 19 8 93 55 34 71 6 18 67 97 Ghana 1998 33 12 139 52 61 87 8 18 18 86 Guatemala 1998 34 10 78 39 80 91 5 60 9 92 Guinea 1999 29 17 230 133 33 73 1 9 12 82 Haiti 2000 24 8 164 109 43 63 17 24 4 70 India 1999 61 26 141 46 28 81 29 55 16 84 Indonesia 1997 .. .. 109 29 59 85 46 57 21 89 Jordan 1997 9 3 42 25 90 93 28 47 91 99 Kazakhstan 1999 5 6 82 45 74 76 c 49 55 99 99 Kenya 1998 32 10 136 61 64 89 13 50 23 80 Kyrgyz Republic 1997 13 8 96 49 82 81 44 54 96 100 Madagascar 1997 45 32 195 101 32 79 2 24 30 89 Malawi 2000 33 13 231 149 80 90 20 40 43 83 Mali 2001 39 17 248 148 40 77 4 18 21 86 Mauritania 2000­01 37 18 98 78 42 86 0 d 17 15 93 Morocco 1992 17 2 112 39 62 95 18 48 5 78 Mozambique 1997 37 14 278 145 33 94 1 17 18 82 Namibia 1992 36 13 110 76 69 79 5 57 51 91 Nepal 2001 57 31 130 68 61 83 24 55 4 45 Nicaragua 2001 16 3 64 19 76 94 50 71 78 99 Niger 1998 52 37 282 184 23 66 1 18 4 63 Nigeria 1990 40 22 240 120 35 70 1 12 12 70 Pakistan 1990 54 26 125 74 28 75 1 23 5 55 Paraguay 1990 6 1 57 20 48 69 21 46 41 98 Peru 2000 15 1 93 18 81 92 37 58 21 99 Philippines 1998 .. .. 80 29 68 92 20 29 21 92 Rwanda 2000 27 14 246 154 84 89 2 15 17 60 Senegal 1997 .. .. 181 70 .. .. 1 24 20 86 South Africa 1998 .. .. 87 22 74 85 34 70 68 98 Tanzania 1999 32 22 160 135 63 89 6 32 29 83 Togo 1998 32 12 168 97 35 63 3 13 25 91 Turkey 1998 17 3 85 33 64 89 24 48 53 98 Uganda 2000­01 27 12 192 106 49 65 11 41 20 77 Uzbekistan 1996 25 13 70 50 96 93 46 52 92 100 Vietnam 1997 .. .. 63 23 64 88 47 56 49 99 Yemen, Rep. 1997 56 30 163 73 16 73 1 24 7 50 Zambia 2001 33 20 192 92 81 88 11 53 20 91 Zimbabwe 1999 18 6 100 62 80 86 41 67 57 94 a. Based on births in the five years before the survey. b. Refers to children who were immunized before 12 months or, in some cases, at any time before the survey (between 12­23 months). c. The data contain large sampling errors because of the small number of cases. d. Less than 0.5. 58 2004 World Development Indicators PEOPLE 2.6 Social indicators of poverty About the data Definitions The data in the table describe the health status and but do have detailed information on households' own- · Survey year is the year in which the underlying use of health services by individuals in different socioe- ership of consumer goods and access to a variety of data were collected. · Prevalence of child malnutri- conomic groups within countries. The data are from goods and services. Like income or consumption, the tion is the percentage of children whose weight for Demographic and Health Surveys conducted by Macro asset index defines disparities in primarily economic age is more than two standard deviations below the International with the support of the U.S. Agency for terms. It therefore excludes other possibilities of dis- median reference standard for their age as estab- International Development. These large-scale house- parities among groups, such as those based on gen- lished by the World Health Organization, the U.S. hold sample surveys, conducted periodically in devel- der, education, ethnic background, or other facets of Centers for Disease Control and Prevention, and the oping countries, collect information on a large number social exclusion. To that extent the index provides U.S. National Center for Health Statistics. The fig- of health, nutrition, and population measures as well only a partial view of the multidimensional concepts ures in the table are based on children under age as on respondents' social, demographic, and econom- of poverty, inequality, and inequity. three, four, or five years of age, depending on the ic characteristics using a standard set of question- Creating one index that includes all asset indica- country. · Under-five mortality rate is the probabili- naires. The data presented here draw on responses to tors limits the types of analysis that can be per- ty that a newborn baby will die before reaching age individual and household questionnaires. formed. In particular, the use of a unified index does five, if subject to current age-specific mortality rates. The table defines socioeconomic status in terms of not permit a disaggregated analysis to examine The probability is expressed as a rate per 1,000. a household's assets, including ownership of con- which asset indicators have a more or less important Data in the table are based on births in the 10 years sumer items, features of the household's dwelling, association with health status or use of health serv- preceding the survey and may therefore differ from and other characteristics related to wealth. Each ices. In addition, some asset indicators may reflect the estimates in table 2.19. · Child immunization household asset on which information was collected household wealth better in some countries than in rate is the percentage of children ages 12­23 was assigned a weight generated through principal others--or reflect different degrees of wealth in dif- months at the time of the survey who received a component analysis. The resulting scores were stan- ferent countries. Taking such information into dose of measles vaccine by 12 months, or at any dardized in relation to a standard normal distribution account and creating country-specific asset indexes time before the interview date. These data may dif- with a mean of zero and a standard deviation of one. with country-specific choices of asset indicators fer from those in table 2.15. · Contraceptive preva- The standardized scores were then used to create might produce a more effective and accurate index lence is the percentage of women who are practic- break points defining wealth quintiles, expressed as for each country. The asset index used in the table ing, or whose sexual partners are practicing, any quintiles of individuals in the population rather than does not have this flexibility. modern method of contraception. It is usually meas- quintiles of individuals at risk with respect to any one The analysis has been carried out for 54 countries, ured for married women ages 15­49. · Births health indicator. with the results issued in country reports. The table attended by skilled health staff are the percentage The choice of the asset index for defining socioe- shows the estimates for the poorest and richest of deliveries attended by personnel trained to give conomic status was based on pragmatic rather than quintiles only; the full set of estimates for more than the necessary supervision, care, and advice to conceptual considerations: Demographic and Health 70 indicators is available in the country reports (see women during pregnancy, labor, and the postpartum Surveys do not provide income or consumption data Data sources). period; to conduct deliveries on their own; and to care for newborns. Skilled health staff include 2.6a doctors, nurses, or trained midwives, but exclude Education lowers birth rates dramatically for rich women, but not for poor ones trained or untrained traditional birth attendants. Data in the tables are based on births in the five Lowest wealth quintile Highest wealth quintile years preceding the survey and may therefore differ Total fertility rate, 1995­2000 Total fertility rate, 1995­2000 from the estimates in table 2.16. 10 10 8 8 6 6 4 4 2 2 Data sources 0 0 0 20 40 60 80 100 0 20 40 60 80 100 The data are from an analysis of Demographic Women completing grade 5 (%) Women completing grade 5 (%) and Health Surveys by the World Bank and Macro It is well known that women's education strongly affects the number of children they bear. But the effect varies with International. Country reports are available at the wealth of the household. Education greatly reduces fertility rates among wealthy women, but the effect is very weak http://www.worldbank.org/poverty/health/data/ among poor women. index.htm. Source: Demographic and Health Survey data. 2004 World Development Indicators 59 2.7 Distribution of income or consumption Survey Gini Index Percentage share of income or consumption year Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20% 10% Afghanistan .. .. .. .. .. .. .. .. Albania 2002 a,b 28.2 3.8 9.1 13.5 17.3 22.8 37.4 22.4 Algeria 1995 a,b 35.3 2.8 7.0 11.6 16.1 22.7 42.6 26.8 Angola .. .. .. .. .. .. .. .. Argentina c 2001 d,e 52.2 1.0 3.1 7.2 12.3 21.0 56.4 38.9 Armenia 1998 a,b 37.9 2.6 6.7 11.3 15.4 21.6 45.1 29.7 Australia 1994 d,e 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 1997 d,e 30.0 3.1 8.1 13.2 17.3 22.9 38.5 23.5 Azerbaijan 2001 a,b 36.5 3.1 7.4 11.5 15.3 21.2 44.5 29.5 Bangladesh 2000 a,b 31.8 3.9 9.0 12.5 15.9 21.2 41.3 26.7 Belarus 2000 a,b 30.4 3.5 8.4 13.0 17.0 22.5 39.1 24.1 Belgium 1996 d,e 25.0 2.9 8.3 14.1 17.7 22.7 37.3 22.6 Benin .. .. .. .. .. .. .. .. Bolivia 1999 a,b 44.7 1.3 4.0 9.2 14.8 22.9 49.1 32.0 Bosnia and Herzegovina 2001 a,b 26.2 3.9 9.5 14.2 17.9 22.6 35.8 21.4 Botswana 1993 a,b 63.0 0.7 2.2 4.9 8.2 14.4 70.3 56.6 Brazil 1998 d,e 59.1 0.5 2.0 5.7 10.0 18.0 64.4 46.7 Bulgaria 2001 d,e 31.9 2.4 6.7 13.1 17.9 23.4 38.9 23.7 Burkina Faso 1998 a,b 48.2 1.8 4.5 7.4 10.6 16.7 60.7 46.3 Burundi 1998 a,b 33.3 1.7 5.1 10.3 15.1 21.5 48.0 32.8 Cambodia 1997 a,b 40.4 2.9 6.9 10.7 14.7 20.1 47.6 33.8 Cameroon 2001 a,b 44.6 2.3 5.6 9.3 13.7 20.4 50.9 35.4 Canada 1998 d,e 33.1 2.5 7.0 12.7 17.0 22.9 40.4 25.0 Central African Republic 1993 a,b 61.3 0.7 2.0 4.9 9.6 18.5 65.0 47.7 Chad .. .. .. .. .. .. .. .. Chile 2000 d,e 57.1 1.2 3.3 6.6 10.5 17.4 62.2 47.0 China 2001 a,b 44.7 1.8 4.7 9.0 14.2 22.1 50.0 33.1 Hong Kong, China 1996 d,e 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 1999 d,e 57.6 0.8 2.7 6.6 10.8 18.0 61.9 46.5 Congo, Dem. Rep. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. Costa Rica 2000 d,e 46.5 1.4 4.2 8.9 13.7 21.7 51.5 34.8 Côte d'Ivoire 1998 a,b 45.2 2.2 5.5 9.6 13.6 20.1 51.1 35.9 Croatia 2001 a,b 29.0 3.4 8.3 12.8 16.8 22.6 39.6 24.5 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996 d,e 25.4 4.3 10.3 14.5 17.7 21.7 35.9 22.4 Denmark 1997 d,e 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Dominican Republic 1998 d,e 47.4 2.1 5.1 8.6 13.0 20.0 53.3 37.9 Ecuador 1998 a,b 43.7 0.9 3.3 7.5 11.7 19.4 58.0 41.6 Egypt, Arab Rep. 1999 a,b 34.4 3.7 8.6 12.1 15.4 20.4 43.6 29.5 El Salvador 2000 d,e 53.2 0.9 2.9 7.4 12.4 20.2 57.1 40.6 Eritrea .. .. .. .. .. .. .. .. Estonia 2000 d,e 37.2 1.9 6.1 12.1 15.9 22.0 44.0 28.5 Ethiopia 2000 a,b 30.0 3.9 9.1 13.2 16.8 21.5 39.4 25.5 Finland 2000 d,e 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 France 1995 d,e 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon .. .. .. .. .. .. .. .. Gambia, The 1998 a,b 38.0 1.5 4.0 7.6 12.3 20.8 55.2 38.0 Georgia 2001 a,b 36.9 2.3 6.4 11.4 16.1 22.6 43.6 27.9 Germany 2000 d,e 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1 Ghana 1999 a,b 30.0 2.1 5.6 10.1 14.9 22.8 46.6 30.0 Greece 1998 d,e 35.4 2.9 7.1 11.4 15.8 22.0 43.6 28.5 Guatemala 2000 d,e 48.3 0.9 2.6 5.9 9.8 17.6 64.1 48.3 Guinea 1994 a,b 40.3 2.6 6.4 10.4 14.8 21.2 47.2 32.0 Guinea-Bissau 1993 a,b 47.0 2.1 5.2 8.8 13.1 19.4 53.4 39.3 Guyana 1999 a,b 43.2 1.3 4.5 9.9 14.5 21.4 49.7 33.8 Haiti .. .. .. .. .. .. .. .. 60 2004 World Development Indicators PEOPLE 2.7 Distribution of income or consumption Survey Gini Index Percentage share of income or consumption year Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20% 10% Honduras 1999 d,e 55.0 0.9 2.7 6.7 11.8 19.9 58.9 42.2 Hungary 1999 a,b 24.4 2.6 7.7 13.4 18.0 23.4 37.5 22.8 India 1999­2000 a,b 32.5 3.9 8.9 12.3 16.0 21.2 41.6 27.4 Indonesia 2002 a,b 34.3 3.6 8.4 11.9 15.4 21.0 43.3 28.5 Iran, Islamic Rep. 1998 a,b 43.0 2.0 5.1 9.4 14.1 21.5 49.9 33.7 Iraq .. .. .. .. .. .. .. .. Ireland 1996 d,e 35.9 2.8 7.1 11.8 15.8 22.0 43.3 27.6 Israel 1997 d,e 35.5 2.4 6.9 11.4 16.3 22.9 44.3 28.2 Italy 2000 d,e 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8 Jamaica 2000 a,b 37.9 2.7 6.7 10.7 15.0 21.7 46.0 30.3 Japan 1993 d,e 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 1997 a,b 36.4 3.3 7.6 11.4 15.5 21.1 44.4 29.8 Kazakhstan 2001 a,b 31.3 3.4 8.2 12.5 16.8 22.9 39.6 24.2 Kenya 1997 a,b 44.5 2.3 5.6 9.3 13.6 20.2 51.2 36.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998 d,e 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2001 a,b 29.0 3.9 9.1 13.2 16.9 22.5 38.3 23.3 Lao PDR 1997 a,b 37.0 3.2 7.6 11.4 15.3 20.8 45.0 30.6 Latvia 1998 d,e 32.4 2.9 7.6 12.9 17.1 22.1 40.3 25.9 Lebanon .. .. .. .. .. .. .. .. Lesotho 1995 a,b 63.2 0.5 1.5 4.3 8.9 18.8 66.5 48.3 Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 2000 a,b 31.9 3.2 7.9 12.7 16.9 22.6 40.0 24.9 Luxembourg 2000 d,e 30.8 3.5 8.4 12.9 17.1 22.7 38.9 23.8 Macedonia, FYR 1998 a,b 28.2 3.3 8.4 14.0 17.7 23.1 36.7 22.1 Madagascar 2001 a,b 47.5 1.9 4.9 8.5 12.7 20.4 53.5 36.6 Malawi 1997 a,b 50.3 1.9 4.9 8.5 12.3 18.3 56.1 42.2 Malaysia 1997 d,e 49.2 1.7 4.4 8.1 12.9 20.3 54.3 38.4 Mali 1994 a,b 50.5 1.8 4.6 8.0 11.9 19.3 56.2 40.4 Mauritania 2000 a,b 39.0 2.5 6.2 10.6 15.2 22.3 45.7 29.5 Mauritius .. .. .. .. .. .. .. .. Mexico 2000 d,e 54.6 1.0 3.1 7.2 11.7 19.0 59.1 43.1 Moldova 2001 a,b 36.2 2.8 7.1 11.5 15.8 22.0 43.7 28.4 Mongolia 1998 a,b 44.0 2.1 5.6 10.0 13.8 19.4 51.2 37.0 Morocco 1998­99 a,b 39.5 2.6 6.5 10.6 14.8 21.3 46.6 30.9 Mozambique 1996­97 a,b 39.6 2.5 6.5 10.8 15.1 21.1 46.5 31.7 Myanmar .. .. .. .. .. .. .. .. Namibia 1993 d,e 70.7 0.5 1.4 3.0 5.4 11.5 78.7 64.5 Nepal 1995­96 a,b 36.7 3.2 7.6 11.5 15.1 21.0 44.8 29.8 Netherlands 1994 d,e 32.6 2.8 7.3 12.7 17.2 22.8 40.1 25.1 New Zealand 1997 d,e 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8 Nicaragua 2001 d,e 55.1 1.2 3.6 7.2 11.3 18.3 59.7 45.0 Niger 1995 a,b 50.5 0.8 2.6 7.1 13.9 23.1 53.3 35.4 Nigeria 1996­97 a,b 50.6 1.6 4.4 8.2 12.5 19.3 55.7 40.8 Norway 2000 d,e 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 1998­99 a,b 33.0 3.7 8.8 12.5 15.9 20.6 42.3 28.3 Panama 2000 d,e 56.4 0.7 2.4 6.5 11.2 19.6 60.3 43.3 Papua New Guinea 1996 a,b 50.9 1.7 4.5 7.9 11.9 19.2 56.5 40.5 Paraguay 1999 d,e 56.8 0.6 2.2 6.5 11.5 19.5 60.2 43.6 Peru 2000 d,e 49.8 0.7 2.9 8.3 14.1 21.5 53.2 37.2 Philippines 2000 a,b 46.1 2.2 5.4 8.8 13.1 20.5 52.3 36.3 Poland 1999 a,b 31.6 2.9 7.3 11.8 16.2 22.2 42.5 27.4 Portugal 1997 d,e 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. 2004 World Development Indicators 61 2.7 Distribution of income or consumption Survey Gini Index Percentage share of income or consumption year Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20% 10% Romania 2000 a,b 30.3 3.3 8.2 13.1 17.4 22.9 38.4 23.6 Russian Federation 2000 a,b 45.6 1.8 4.9 9.5 14.1 20.3 51.3 36.0 Rwanda 1983­85 a,b 28.9 4.2 9.7 13.2 16.5 21.6 39.1 24.2 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 1995 a,b 41.3 2.6 6.4 10.3 14.5 20.6 48.2 33.5 Serbia and Montenegro .. .. .. .. .. .. .. .. Sierra Leone 1989 a,b 62.9 0.5 1.1 2.0 9.8 23.7 63.4 43.6 Singapore 1998 d,e 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8 Slovak Republic 1996 d,e 25.8 3.1 8.8 14.9 18.7 22.8 34.8 20.9 Slovenia 1998­99 d,e 28.4 3.6 9.1 14.2 18.1 22.9 35.7 21.4 Somalia .. .. .. .. .. .. .. .. South Africa 1995 a,b 59.3 0.7 2.0 4.3 8.3 18.9 66.5 46.9 Spain 1990 d,e 32.5 2.8 7.5 12.6 17.0 22.6 40.3 25.2 Sri Lanka 1995 a,b 34.4 3.5 8.0 11.8 15.8 21.5 42.8 28.0 St. Lucia 1995 d,e 42.6 2.0 5.2 9.9 14.8 21.8 48.3 32.5 Sudan .. .. .. .. .. .. .. .. Swaziland 1994 d,e 60.9 1.0 2.7 5.8 10.0 17.1 64.4 50.2 Sweden 2000 d,e 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2 Switzerland 1992 d,e 33.1 2.6 6.9 12.7 17.3 22.9 40.3 25.2 Syrian Arab Republic .. .. .. .. .. .. .. .. Tajikistan 1998 a,b 34.7 3.2 8.0 12.9 17.0 22.1 40.0 25.2 Tanzania 1993 a,b 38.2 2.8 6.8 11.0 15.1 21.6 45.5 30.1 Thailand 2000 a,b 43.2 2.5 6.1 9.5 13.5 20.9 50.0 33.8 Togo .. .. .. .. .. .. .. .. Trinidad and Tobago 1992 d,e 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 Tunisia 2000 a,b 39.8 2.3 6.0 10.3 14.8 21.7 47.3 31.5 Turkey 2000 a,b 40.0 2.3 6.1 10.6 14.9 21.8 46.7 30.7 Turkmenistan 1998 a,b 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31.7 Uganda 1999 a,b 43.0 2.3 5.9 10.0 14.0 20.3 49.7 34.9 Ukraine 1999 a,b 29.0 3.7 8.8 13.3 17.4 22.7 37.8 23.2 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 1999 d,e 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5 United States 2000 d,e 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9 Uruguay c 2000 d,e 44.6 1.8 4.8 9.3 14.2 21.6 50.1 33.5 Uzbekistan 2000 a,b 26.8 3.6 9.2 14.1 17.9 22.6 36.3 22.0 Venezuela, RB 1998 d,e 49.1 0.6 3.0 8.4 13.7 21.6 53.4 36.3 Vietnam 1998 a,b 36.1 3.6 8.0 11.4 15.2 20.9 44.5 29.9 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 1998 a,b 33.4 3.0 7.4 12.2 16.7 22.5 41.2 25.9 Zambia 1998 a,b 52.6 1.1 3.3 7.6 12.5 20.0 56.6 41.0 Zimbabwe 1995 a,b 56.8 1.8 4.6 8.1 12.2 19.3 55.7 40.3 a. Data refer to consumption shares by percentiles of population. b. Ranked by per capita consumption. c. Urban data. d. Data refer to income shares by percentiles of population. e. Ranked by per capita income. 62 2004 World Development Indicators PEOPLE 2.7 Distribution of income or consumption About the data Definitions Inequality in the distribution of income is reflected in Wherever possible, consumption has been used · Survey year is the year in which the underlying the percentage shares of income or consumption rather than income. Income distribution and Gini data were collected. · Gini index measures the accruing to segments of the population ranked by indexes for high-income countries are calculated extent to which the distribution of income (or, in income or consumption levels. The segments ranked directly from the Luxembourg Income Study data- some cases, consumption expenditure) among indi- lowest by personal income receive the smallest base, using an estimation method consistent with viduals or households within an economy deviates shares of total income. The Gini index provides a con- that applied for developing countries. from a perfectly equal distribution. A Lorenz curve venient summary measure of the degree of inequality. plots the cumulative percentages of total income Data on personal or household income or con- received against the cumulative number of recipi- sumption come from nationally representative ents, starting with the poorest individual or house- household surveys. The data in the table refer to dif- hold. The Gini index measures the area between the ferent years between 1989 and 2002. Footnotes to Lorenz curve and a hypothetical line of absolute the survey year indicate whether the rankings are equality, expressed as a percentage of the maximum based on per capita income or consumption. Each area under the line. Thus a Gini index of 0 represents distribution is based on percentiles of population-- perfect equality, while an index of 100 implies per- rather than of households--with households ranked fect inequality. · Percentage share of income or by income or expenditure per person. consumption is the share that accrues to subgroups Where the original data from the household survey of population indicated by deciles or quintiles. were available, they have been used to directly cal- Percentage shares by quintile may not sum to 100 culate the income (or consumption) shares by quin- because of rounding. tile. Otherwise shares have been estimated from the best available grouped data. The distribution data have been adjusted for house- hold size, providing a more consistent measure of per capita income or consumption. No adjustment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middle- income economies, see Ravallion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achiev- ing strict comparability is still impossible (see About the data for table 2.5). Two sources of noncomparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or con- sumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used usually differ among sur- veys. Consumption is usually a much better welfare Data sources indicator, particularly in developing countries. The data on distribution are compiled by the World Second, households differ in size (number of mem- Bank's Development Research Group using pri- bers) and in the extent of income sharing among mary household survey data obtained from gov- members. And individuals differ in age and consump- ernment statistical agencies and World Bank tion needs. Differences among countries in these country departments. The data for high-income respects may bias comparisons of distribution. economies are from the Luxembourg Income World Bank staff have made an effort to ensure Study database. that the data are as comparable as possible. 2004 World Development Indicators 63 2.8 Assessing vulnerability Urban informal Youth Children Female-headed Pension contributors Private sector employment unemployment in the households health labor force expenditure Male Female % of urban % of male % of female employment labor force labor force % of % of Male Female ages 15­24 ages 15­24 % ages 10­14 % of % of working-age total 1995­2001 a 1995­2001 a 1995­2002 a 1995­2002 a 1980 2002 Year total Year labor force population 2001 Afghanistan .. .. .. .. 28 24 .. .. .. 47.4 Albania .. .. .. .. 4 0 .. 1995 32.0 31.0 35.4 Algeria .. .. .. .. 7 0 .. 1997 31.0 23.0 25.0 Angola .. .. .. .. 30 26 .. .. .. 36.9 Argentina .. .. 31 33 8 2 .. 1995 53.0 39.0 46.6 Armenia .. .. .. .. 0 0 2000 28 2002 64.4 48.3 58.8 Australia .. .. 13 12 0 0 .. .. .. 32.1 Austria .. .. 5 6 0 0 .. 1993 95.8 76.6 30.7 Azerbaijan .. .. .. .. 0 0 .. 1996 52.0 46.0 24.9 Bangladesh .. .. 11 10 35 27 1999­2000 8 1993 3.5 2.6 55.8 Belarus .. .. .. .. 0 0 .. 1992 97.0 94.0 13.3 Belgium .. .. 16 15 0 0 .. 1995 86.2 65.9 28.3 Benin 50 41 .. .. 30 26 2001 20 1996 4.8 .. 53.1 Bolivia .. .. 7 10 19 10 1998 19 1999 14.8 13.3 33.7 Bosnia and Herzegovina .. .. .. .. 1 0 .. .. .. 63.2 Botswana .. .. 38 47 26 14 .. .. .. 33.8 Brazil 27 27 15 22 19 14 1996 20 1996 36.0 31.0 58.4 Bulgaria .. .. 42 35 0 0 .. 1994 64.0 63.0 17.9 Burkina Faso .. .. .. .. 71 40 1998­99 6 1993 3.1 3.0 .. Burundi .. .. .. .. 50 48 .. 1993 3.3 3.0 41.0 Cambodia .. .. .. .. 27 23 2000 25 .. .. 85.1 Cameroon .. .. .. .. 34 22 1998 22 1993 13.7 11.5 62.9 Canada .. .. 15 12 0 0 .. 1992 91.9 80.2 29.2 Central African Republic .. .. .. .. .. .. 1994­95 21 .. .. 48.8 Chad .. .. .. .. 42 36 1996­97 21 1990 1.1 1.0 24.0 Chile .. .. 17 22 0 0 .. 2001 54.8 34.9 56.0 China .. .. .. .. 30 6 .. 1994 17.6 17.4 62.8 Hong Kong, China .. .. 14 9 6 0 .. .. .. .. Colombia .. .. 32 41 12 6 2000 27 1999 35.0 29.3 34.3 Congo, Dem. Rep. .. .. .. .. 33 28 .. .. .. 55.6 Congo, Rep. .. .. .. .. 27 25 .. 1992 5.8 5.6 36.2 Costa Rica .. .. 12 16 10 4 .. 1998 50.6 38.5 31.5 Côte d'Ivoire .. .. .. .. 28 18 1998­99 14 1997 9.3 9.1 84.0 Croatia .. .. 35 40 0 0 .. 2001 67.0 57.0 18.2 Cuba .. .. .. .. 0 0 .. .. .. 13.8 Czech Republic .. .. 15 17 0 0 .. 1995 85.0 67.2 8.6 Denmark .. .. 9 5 0 0 .. 1993 89.6 88.0 17.6 Dominican Republic .. .. 16 34 25 12 1999 32 2001 26.8 17.7 63.9 Ecuador .. .. 11 20 9 4 .. 2002 23.2 14.9 49.7 Egypt, Arab Rep. .. .. 14 37 18 8 2000 11 1994 50.0 34.2 51.1 El Salvador .. .. 14 10 17 13 .. 1996 26.2 25.0 53.3 Eritrea .. .. .. .. 44 38 1995 30 .. .. 34.9 Estonia .. .. 19 26 0 0 .. 1995 76.0 67.0 22.2 Ethiopia 39 65 .. .. 46 41 2000 23 .. .. 59.5 Finland .. .. 21 20 0 0 .. 1993 90.3 83.6 24.4 France .. .. 18 23 0 0 .. 1993 88.4 74.6 24.0 Gabon .. .. .. .. 29 12 2000 25 1995 15.0 14.0 52.1 Gambia, The .. .. .. .. 44 33 .. .. .. 50.6 Georgia 21 7 20 20 0 0 .. 2000 41.7 40.2 62.2 Germany .. .. 11 8 0 0 .. 1995 94.2 82.3 25.1 Ghana .. .. .. .. 16 11 .. 1993 7.2 9.0 40.4 Greece .. .. 19 34 5 0 .. 1996 88.0 73.0 44.0 Guatemala .. .. .. .. 19 13 1998­99 19 1999 22.8 19.3 51.7 Guinea .. .. .. .. 41 30 1999 12 1993 1.5 1.8 45.9 Guinea-Bissau .. .. .. .. 43 36 .. .. .. 46.2 Haiti .. .. .. .. 33 22 2000 42 .. .. 46.6 64 2004 World Development Indicators PEOPLE 2.8 Assessing vulnerability Urban informal Youth Children Female-headed Pension contributors Private sector employment unemployment in the households health labor force expenditure Male Female % of urban % of male % of female employment labor force labor force % of % of Male Female ages 15­24 ages 15­24 % ages 10­14 % of % of working-age total 1995­2001 a 1995­2001 a 1995­2002 a 1995­2002 a 1980 2002 Year total Year labor force population 2001 Honduras .. .. 7 8 14 7 .. 1999 20.6 17.7 46.9 Hungary .. .. 13 12 0 0 .. 1996 77.0 65.0 25.0 India 54 41 .. .. 21 11 1998­99 10 1992 10.6 7.9 82.1 Indonesia .. .. 12 15 13 7 1997 12 1995 8.0 7.0 74.9 Iran, Islamic Rep. .. .. .. .. 14 2 .. 2000 30.0 15.9 58.1 Iraq .. .. .. .. 11 2 .. .. .. 68.2 Ireland .. .. 9 7 1 0 .. 1992 79.3 64.7 24.0 Israel .. .. 19 18 0 0 .. 1992 82.0 63.0 30.8 Italy .. .. 23 31 2 0 .. 1997 87.0 68.0 24.7 Jamaica .. .. 24 46 0 0 .. 1999 44.4 45.8 57.9 Japan .. .. 11 9 0 0 .. 1994 97.5 92.3 22.1 Jordan .. .. .. .. 4 0 1997 9 1995 40.0 25.0 53.0 Kazakhstan .. .. .. .. 0 0 1999 33 2001 38.0 28.3 39.6 Kenya .. .. .. .. 45 38 1998 31 1995 18.0 24.0 78.6 Korea, Dem. Rep. .. .. .. .. 3 0 .. .. .. 26.6 Korea, Rep. .. .. 10 7 0 0 .. 1996 58.0 43.0 55.6 Kuwait .. .. .. .. 0 0 .. .. .. 19.0 Kyrgyz Republic 33 25 .. .. 0 0 1997 26 1997 44.0 42.0 51.3 Lao PDR .. .. .. .. 31 25 .. .. .. 44.5 Latvia .. .. 20 21 0 0 .. 1995 60.5 52.3 47.5 Lebanon .. .. .. .. 5 0 .. .. .. .. Lesotho .. .. 38 59 28 20 .. .. .. 21.1 Liberia .. .. .. .. 26 14 .. .. .. 24.1 Libya .. .. .. .. 9 0 .. .. .. 44.0 Lithuania 50 27 31 26 0 0 .. 2002 77.0 60.0 29.5 Macedonia, FYR .. .. .. .. 1 0 .. 1995 49.0 47.0 15.1 Madagascar .. .. .. .. 40 33 1997 21 1993 5.4 4.8 34.1 Malawi .. .. .. .. 45 30 2000 26 .. .. 65.0 Malaysia .. .. .. .. 8 2 .. 1993 48.7 37.8 46.3 Mali .. .. .. .. 61 50 2001 11 1990 2.5 2.0 61.4 Mauritania .. .. .. .. 30 21 2000­01 29 1995 5.0 4.0 27.6 Mauritius .. .. .. .. 5 1 .. 1995 60.0 57.0 40.5 Mexico 18 22 5 6 9 4 .. 1997 30.0 31.0 55.7 Moldova .. .. .. .. 3 0 .. .. .. 44.2 Mongolia .. .. .. .. 4 1 .. 2002 61.4 49.1 27.7 Morocco .. .. 16 15 21 0 1992 16 2000 17.3 11.3 60.7 Mozambique .. .. .. .. 39 32 1997 26 1995 2.0 2.1 32.6 Myanmar .. .. .. .. 28 22 .. .. .. 82.2 Namibia .. .. 33 41 34 16 1992 30 .. .. 32.2 Nepal 60 76 .. .. 56 41 2001 16 .. .. 70.3 Netherlands .. .. 6 6 0 0 .. 1993 91.7 75.4 36.7 New Zealand .. .. 12 11 0 0 .. .. .. 23.2 Nicaragua .. .. 20 20 19 11 1997­98 30 1999 14.3 13.3 51.5 Niger .. .. .. .. 48 43 1998 13 1992 1.3 1.5 60.9 Nigeria .. .. .. .. 29 23 1999 16 1993 1.3 1.3 76.8 Norway .. .. 12 11 0 0 .. 1993 94.0 85.8 14.5 Oman .. .. .. .. 6 0 .. .. .. 19.3 Pakistan 64 61 11 29 23 14 1991 7 1993 3.5 2.1 75.6 Panama .. .. 25 37 6 2 .. 1998 51.6 40.7 31.0 Papua New Guinea .. .. .. .. 28 16 .. .. .. 11.0 Paraguay .. .. 12 17 15 5 1990 16 2001 18.0 12.0 61.7 Peru .. .. 13 14 4 2 2000 19 2001 31.0 19.0 45.0 Philippines 16 19 17 23 14 4 1998 14 1996 28.3 13.6 54.8 Poland .. .. 44 44 0 0 .. 1996 68.0 64.0 28.1 Portugal .. .. 10 14 8 1 .. 1996 84.3 80.0 31.0 Puerto Rico .. .. 23 16 0 0 .. .. .. .. 2004 World Development Indicators 65 2.8 Assessing vulnerability Urban informal Youth Children Female-headed Pension contributors Private sector employment unemployment in the households health labor force expenditure Male Female % of urban % of male % of female employment labor force labor force % of % of Male Female ages 15­24 ages 15­24 % ages 10­14 % of % of working-age total 1995­2001 a 1995­2001 a 1995­2002 a 1995­2002 a 1980 2002 Year total Year labor force population 2001 Romania .. .. 18 17 0 0 .. 1994 55.0 48.0 20.8 Russian Federation 10 9 24 26 0 0 .. .. .. 31.8 Rwanda .. .. .. .. 43 41 2000 36 1993 9.3 13.3 44.5 Saudi Arabia .. .. .. .. 5 0 .. .. .. 25.4 Senegal .. .. .. .. 43 26 1997 18 1998 4.3 4.7 41.2 Serbia and Montenegro .. .. .. .. 0 0 .. .. .. 20.8 Sierra Leone .. .. .. .. 19 13 .. .. .. 39.0 Singapore .. .. 4 6 2 0 .. 1995 73.0 56.0 66.5 Slovak Republic .. .. 39 36 0 0 .. 1996 73.0 72.0 10.7 Slovenia .. .. 15 18 0 0 .. 1995 86.0 68.7 25.1 Somalia .. .. .. .. 38 31 .. .. .. 55.4 South Africa 16 28 58 53 1 0 1998 41 .. .. 58.6 Spain .. .. 18 27 0 0 .. 1994 85.3 61.4 28.6 Sri Lanka .. .. 20 31 4 1 .. 1992 28.8 20.8 51.1 Sudan .. .. .. .. 33 27 .. 1995 12.1 12.0 81.3 Swaziland .. .. 42 48 17 12 .. .. .. 31.5 Sweden .. .. 14 12 0 0 .. 1994 91.1 88.9 14.8 Switzerland .. .. 7 4 0 0 .. 1994 98.1 96.8 42.9 Syrian Arab Republic .. .. .. .. 14 2 .. .. .. 56.1 Tajikistan .. .. .. .. 0 0 .. .. .. 71.1 Tanzania 60 85 .. .. 43 36 1999 23 1996 2.0 2.0 53.3 Thailand .. .. 7 6 25 11 .. 1999 18.0 17.0 42.9 Togo .. .. .. .. 36 26 1998 24 1997 15.9 15.0 51.4 Trinidad and Tobago .. .. 22 31 1 0 .. .. .. 56.7 Tunisia .. .. .. .. 6 0 .. 2000 40.0 23.0 24.3 Turkey 10 6 21 18 21 7 1998 10 1997 37.1 27.4 36.8 Turkmenistan .. .. .. .. 0 0 2000 26 .. .. 26.7 Uganda .. .. .. .. 49 43 2000­01 27 1994 8.2 .. 42.5 Ukraine 5 5 23 25 0 0 .. 1995 69.8 66.1 32.2 United Arab Emirates .. .. 6 6 0 0 .. .. .. 24.2 United Kingdom .. .. 13 9 0 0 .. 1994 89.7 84.5 17.8 United States .. .. 13 11 0 0 .. 1993 94.0 91.9 55.6 Uruguay .. .. 29 42 4 1 .. 1995 82.0 78.0 53.7 Uzbekistan .. .. .. .. 0 0 1996 22 .. .. 25.5 Venezuela, RB .. .. 20 28 4 0 .. 1999 23.6 18.2 37.9 Vietnam .. .. .. .. 22 4 1997 24 1998 8.4 10.0 71.5 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. .. .. 26 18 1997 9 .. .. 65.9 Zambia .. .. .. .. 19 15 2001­02 22 1994 10.2 7.9 46.9 Zimbabwe .. .. 17 11 37 26 1999 33 1995 12.0 10.0 54.7 World .. w .. w 20 w 11 w 40.8 w Low income .. .. 25 18 73.7 Middle income .. .. 21 5 48.9 Lower middle income .. .. 23 5 52.8 Upper middle income 21 27 6 2 42.3 Low & middle income .. .. 23 12 53.0 East Asia & Pacific .. .. 27 6 61.2 Europe & Central Asia .. .. 3 1 27.6 Latin America & Carib. 17 24 13 8 52.0 Middle East & N. Africa .. .. 14 4 40.7 South Asia .. .. 23 14 78.4 Sub-Saharan Africa .. .. 35 28 58.7 High income 12 13 0 0 37.9 Europe EMU 14 17 1 0 26.5 a. Data are for the most recent year available. 66 2004 World Development Indicators PEOPLE 2.8 Assessing vulnerability About the data Definitions As traditionally defined and measured, poverty is a Reliable estimates of child labor are difficult to · Urban informal sector employment is broadly char- static concept, and vulnerability a dynamic one. obtain. In many countries child labor is officially pre- acterized as employment in urban areas in units that Vulnerability reflects a household's resilience in the sumed not to exist and so is not included in surveys produce goods or services on a small scale with the face of shocks and the likelihood that a shock will or in official data. Underreporting also occurs primary objective of generating employment and lead to a decline in well-being. Thus it depends pri- because data exclude children engaged in agricul- income for those concerned. These units typically marily on the household's asset endowment and tural or household activities with their families. operate at a low level of organization, with little or no insurance mechanisms. Because poor people have Available statistics suggest that more boys than division between labor and capital as factors of pro- fewer assets and less diversified sources of income girls work. But the number of girls working is often duction. Labor relations are based on casual employ- than the better-off, fluctuations in income affect underestimated because surveys exclude girls work- ment, kinship, or social relationships rather than con- them more. ing as unregistered domestic help or doing full-time tractual arrangements. · Youth unemployment refers Poor households face many risks, and vulnerability household work to enable their parents to work out- to the share of the labor force ages 15­24 without is thus multidimensional. The indicators in the table side the home. work but available for and seeking employment. focus on individual risks--informal sector employ- The data on female-headed household are from Definitions of labor force and unemployment may dif- ment, youth unemployment, child labor, female- recent Demographic and Health Surveys. The defini- fer by country (see About the data). · Children in the headed household, income insecurity in old age, pri- tion and concept of the female-headed household dif- labor force refer to the share of children ages 10­14 vate health expenditure--and the extent to which fer greatly across economies, making cross-country active in the labor force. · Female-headed house- publicly provided services may be capable of miti- comparison difficult. In some cases it is assumed holds refer to the percentage of households with a gating some of these risks. Poor people face labor that a woman cannot be the head of any household female head. · Pension contributors refer to the market risks, often having to take up precarious, low- in which an adult male is present, because of sex- share of the labor force or working-age population quality jobs in the informal sector and to increase biased stereotype. Users need to be cautious when (here defined as ages 15­64) covered by a pension their household's labor market participation through interpreting the data. scheme. · Private health expenditure includes direct their children. Income security is a prime concern for The data on pension contributors come from (out-of-pocket) spending by households, private insur- the elderly. And affordable access to health care is a national sources, the International Labour ance, spending by nonprofit institutions serving primary concern for all poor people, for whom illness Organization, and International Monetary Fund coun- households (other than social insurance), and direct and injury have both direct and opportunity costs. try reports. Coverage by pension schemes may be service payments by private corporations. For informal sector employment, the data are from broad or even universal where eligibility is deter- labor force and special informal sector surveys, vari- mined by citizenship, residency, or income status. In Data sources ous household surveys, surveys of household indus- contribution-related schemes, however, eligibility is The data on urban informal sector employment and tries or economic activities, surveys of small and usually restricted to individuals who have made con- youth unemployment are from the International micro enterprises, and official estimates. The inter- tributions for a minimum number of years. Labour Organization (ILO) database Key Indicators national comparability of the data is affected by dif- Definitional issues--relating to the labor force, for of the Labour Market, third edition. The child labor ferences among countries in definitions and coverage example--may arise in comparing coverage by force participation rates are from the ILO database and in the treatment of domestic workers and those contribution-related schemes over time and across Estimates and Projections of the Economically who have a secondary job in the informal sector. The countries (for country-specific information, see Active Population, 1950­2010. The data on data in the table are based on national definitions of Palacios and Pallares-Miralles 2000). Coverage of female-headed household are from Demographic urban areas established by countries. For details on the share of the labor force covered by a pension and Health Surveys by Macro International. The these definitions, see the notes in Data sources. scheme may be overstated in countries that do not data on pension contributors are drawn from Youth unemployment is an important policy issue attempt to count informal sector workers as part of Robert Palacios and Montserrat Pallares-Miralles's for many economies. Experiencing unemployment the labor force. "International Patterns of Pension Provision" may permanently impair a young person's productive The expenditure on health in a country can be (2000), and updates. For further updates, notes, potential and future employment opportunities. In divided into two main categories by source of fund- and sources, go to "Knowledge and information" this table unemployment among youth ages 15­24 is ing: public and private. Public health expenditure on the World Bank's Web site on pensions presented, but the lower age limit for young people consists of spending by central and local govern- (http://www.worldbank.org/pensions). The data on could be determined by the minimum age for leaving ments, including social health insurance funds. private health expenditure for developing countries school, so age groups could differ across countries. Private health expenditure includes private insur- are largely from the World Health Organization's Also since this age group is likely to include school ance, direct out-of-pocket payments by households, World Health Report 2003 and updates, from leavers, the level of youth unemployment varies sig- spending by nonprofit institutions serving house- household surveys, and from World Bank poverty nificantly over the year as a result of different school holds, and direct payments by private corporations. assessments and sector studies. The data on pri- opening and closing dates. The youth unemployment In countries where the share of out-of-pocket vate health expenditure for member countries of rate shares similar limitations on comparability to spending is large, poor households may be particu- the Organisation for Economic Co-operation and the general unemployment rate. For further informa- larly vulnerable to the impoverishing effects of Development (OECD) are from the OECD. tion, see About the data for table 2.4. health care needs. 2004 World Development Indicators 67 2.9 Enhancing security Public expenditure Public expenditure Public on pensions on health expenditure on education Average Per student pension % of % of % of GDP % of % of per GDP GDP per capita Year GDP Year capita income 2001 2001/02 a 2001/02 a Afghanistan .. .. .. 2.7 .. .. Albania 1995 5.1 1995 36.4 2.4 .. .. Algeria 1997 2.1 1991 75.0 3.1 .. .. Angola .. .. .. 2.8 2.8 .. Argentina 1994 6.2 .. .. 5.1 4.6 14.5 Armenia 2002 2.5 1996 18.7 3.2 3.2 14.3 Australia 1997 5.9 1989 37.3 6.2 4.6 16.5 Austria 1995 14.9 1993 69.3 5.5 5.8 .. Azerbaijan 1996 2.5 1996 51.4 0.6 3.5 12.4 Bangladesh 1992 0.0 .. .. 1.5 2.3 11.2 Belarus 1997 7.7 1995 31.2 4.8 6.0 .. Belgium 1997 12.9 .. .. 6.4 5.9 .. Benin 1993 0.4 1993 189.7 2.1 3.3 14.2 Bolivia 2000 4.5 .. .. 3.5 5.5 15.0 Bosnia and Herzegovina .. .. .. 2.8 .. .. Botswana .. .. .. 4.4 8.6 7.1 Brazil 1997 9.8 .. .. 3.2 4.0 12.7 Bulgaria 1996 7.3 1995 39.3 3.9 3.2 15.6 Burkina Faso 1992 0.3 1992 207.3 2.0 .. .. Burundi 1991 0.2 1991 57.4 2.1 3.6 25.6 Cambodia .. .. .. 1.7 2.0 7.6 Cameroon 1993 0.4 .. .. 1.2 3.2 12.2 Canada 1997 5.4 1994 54.3 6.8 5.2 9.1 Central African Republic 1990 0.3 .. .. 2.3 1.9 .. Chad 1997 0.1 .. .. 2.0 2.5 d 14.2 Chile 2001 2.9 1993 56.1 3.1 3.9 15.1 China 1996 2.7 .. .. 2.0 2.2 11.5 Hong Kong, China .. .. .. .. .. .. Colombia 1994 1.1 1989 72.2 3.6 4.4 19.5 Congo, Dem. Rep. .. .. .. 1.5 .. .. Congo, Rep. 1992 0.9 .. .. 1.4 0.1 .. Costa Rica 1997 4.2 1993 76.1 4.9 4.7 19.1 Côte d'Ivoire 1997 0.3 .. .. 1.3 4.6 22.0 Croatia 2001 13.2 .. .. 7.3 4.2 5.8 Cuba 1992 12.6 .. .. 6.2 8.5 42.6 Czech Republic 1999 9.8 1996 37.0 6.7 4.4 20.6 Denmark 1997 8.8 1994 46.7 7.0 8.3 37.7 Dominican Republic 2000 0.8 2000 42.0 2.2 2.4 .. Ecuador 2002 1.4 2002 55.3 2.3 .. .. Egypt, Arab Rep. 1994 2.5 1994 45.0 1.9 .. .. El Salvador 1997 1.3 .. .. 3.7 2.5 8.6 Eritrea 2001 0.3 .. .. 3.7 2.7 .. Estonia 2002 6.7 1995 56.7 4.3 7.4 27.5 Ethiopia 1993 0.9 .. .. 1.4 4.8 .. Finland 1997 12.1 1994 57.4 5.3 5.9 .. France 1997 13.4 .. .. 7.3 5.8 .. Gabon .. .. .. 1.7 3.9 8.7 Gambia, The .. .. .. 3.2 2.7 .. Georgia 2000 2.7 1996 12.6 1.4 2.5 .. Germany 1997 12.1 1995 62.8 8.1 4.5 .. Ghana 1996 1.1 .. .. 2.8 4.1 .. Greece 1993 11.9 1990 85.6 5.2 3.8 .. Guatemala 1995 0.7 1995 27.6 2.3 1.7 .. Guinea .. .. .. 1.9 1.9 .. Guinea-Bissau .. .. .. 3.2 2.1 .. Haiti .. .. .. 2.7 .. .. 68 2004 World Development Indicators PEOPLE 2.9 Enhancing security Public expenditure Public expenditure Public on pensions on health expenditure on education Average Per student pension % of % of % of GDP % of % of per GDP GDP per capita Year GDP Year capita income 2001 2001/02 a 2001/02 a Honduras 1994 0.6 .. .. 3.2 4.0 .. Hungary 1996 9.7 1996 33.6 5.1 4.9 20.5 India .. .. .. 0.9 4.1 20.8 Indonesia .. .. .. 0.6 1.3 6.0 Iran, Islamic Rep. 1994 1.5 .. .. 2.7 5.0 15.7 Iraq .. .. .. 1.0 .. .. Ireland 1997 4.6 1993 77.9 4.9 4.3 .. Israel 1996 5.9 1992 48.1 6.0 7.3 23.0 Italy 1997 17.6 .. .. 6.3 4.6 .. Jamaica 1996 .. 1989 25.9 2.9 6.3 23.1 Japan 1997 6.9 1989 33.9 6.2 3.6 20.4 Jordan 1995 4.2 1995 144.0 4.5 4.6 15.5 Kazakhstan 2001 3.8 2001 23.0 1.9 4.4 .. Kenya 1993 0.5 .. .. 1.7 6.3 4.7 Korea, Dem. Rep. .. .. .. 1.9 .. .. Korea, Rep. 1997 1.3 .. .. 2.6 3.6 15.0 Kuwait 1990 3.5 .. .. 3.5 6.1 .. Kyrgyz Republic 1997 6.4 2001 45.0 1.9 3.1 12.8 Lao PDR .. .. .. 1.7 3.2 11.0 Latvia 1995 10.2 1994 47.6 3.4 5.9 23.7 Lebanon .. .. .. 2.2 2.9 .. Lesotho .. .. .. 4.3 10.0 .. Liberia .. .. .. 3.3 .. .. Libya .. .. .. 1.6 2.7 .. Lithuania 2002 7.1 1995 21.3 4.2 .. .. Macedonia, FYR 1998 8.7 1996 91.6 5.8 3.7 d 19.7 d Madagascar 1990 0.2 .. .. 1.3 2.5 .. Malawi .. .. .. 2.7 4.1 .. Malaysia 1999 6.5 .. .. 2.0 7.9 23.2 Mali 1991 0.4 .. .. 2.2 2.8 26.7 Mauritania 1992 0.2 .. .. 2.6 3.6 22.5 Mauritius 1999 4.4 .. .. 2.0 3.3 13.0 Mexico 2000 0.3 b .. .. 2.7 4.4 15.0 Moldova 1996 7.5 .. .. 2.8 4.0 .. Mongolia 2002 5.8 .. .. 4.6 6.2 .. Morocco 1994 1.8 1994 118.0 2.0 5.0 .. Mozambique 1996 0.0 .. .. 4.0 2.4 .. Myanmar .. .. .. 0.4 1.3 7.8 Namibia .. .. .. 4.7 8.1 28.2 Nepal .. .. .. 1.5 3.4 13.8 Netherlands 1997 11.1 1989 48.5 5.7 4.8 .. New Zealand 1997 6.5 .. .. 6.4 6.6 21.6 Nicaragua 1996 2.5 .. .. 3.8 5.0 .. Niger 1992 0.1 .. .. 1.4 2.3 26.3 Nigeria 1991 0.1 1991 40.5 0.8 .. .. Norway 1997 8.2 1994 49.9 6.8 6.8 25.9 Oman .. .. .. 2.4 3.9 17.5 Pakistan 1993 0.9 .. .. 1.0 1.8 .. Panama 1996 4.3 .. .. 4.8 4.3 19.1 Papua New Guinea .. .. .. 3.9 2.3 13.3 Paraguay 2000 0.8 c .. .. 3.0 4.7 15.8 Peru 2000 2.6 .. .. 2.6 3.3 .. Philippines 1993 1.0 .. .. 1.5 3.2 11.4 Poland 1997 15.5 1995 61.2 4.6 5.0 18.8 Portugal 1997 10.0 1989 44.6 6.3 5.8 .. Puerto Rico .. .. .. .. .. .. 2004 World Development Indicators 69 2.9 Enhancing security Public expenditure Public expenditure Public on pensions on health expenditure on education Average Per student pension % of % of % of GDP % of % of per GDP GDP per capita Year GDP Year capita income 2001 2001/02 a 2001/02 a Romania 1996 5.1 1994 34.1 5.2 3.5 .. Russian Federation 1996 5.7 1995 18.3 3.7 3.1 .. Rwanda .. .. .. 3.1 2.8 12.8 Saudi Arabia .. .. .. 3.4 8.3 .. Senegal 1998 1.5 1997 85.0 c 2.8 6.5 d 21.0 Serbia and Montenegro .. .. .. 6.5 .. .. Sierra Leone .. .. .. 2.6 1.0 .. Singapore 1996 1.4 .. .. 1.3 .. .. Slovak Republic 1994 9.1 1994 44.5 5.1 4.1 16.8 Slovenia 1996 13.6 1996 49.3 6.3 .. .. Somalia .. .. .. 1.2 .. .. South Africa .. .. .. 3.6 5.7 17.7 Spain 1997 10.9 1995 54.1 5.4 4.5 .. Sri Lanka 1996 2.4 .. .. 1.8 1.3 6.1 Sudan .. .. .. 0.6 .. .. Swaziland .. .. .. 2.3 5.5 19.2 Sweden 1997 11.1 1994 78.0 7.4 7.7 30.7 Switzerland 1997 13.4 1993 44.4 6.4 5.5 28.9 Syrian Arab Republic 1991 0.5 .. .. 2.4 4.1 .. Tajikistan 1996 3.0 .. .. 1.0 2.4 .. Tanzania .. .. .. 2.0 2.2 .. Thailand .. .. .. 2.1 5.0 17.7 Togo 1997 0.6 1993 178.8 1.5 4.8 16.0 Trinidad and Tobago 1996 0.6 .. .. 1.7 4.0 18.4 Tunisia 2000 4.2 1991 89.5 4.9 6.8 23.9 Turkey 1997 4.5 1993 56.0 4.4 3.7 16.4 Turkmenistan 1996 2.3 .. .. 3.0 .. .. Uganda 1997 0.8 .. .. 3.4 2.5 .. Ukraine 1996 8.6 1995 30.9 2.9 4.2 17.0 United Arab Emirates .. .. .. 2.6 1.9 10.0 United Kingdom 1997 10.3 .. .. 6.3 4.4 15.8 United States 1997 7.5 1989 33.0 6.2 4.9 20.8 Uruguay 1996 15.0 1996 64.1 5.1 2.5 9.9 Uzbekistan 1995 5.3 1995 45.8 2.7 .. .. Venezuela, RB 2001 2.7 .. .. 3.7 .. .. Vietnam 1998 1.6 .. .. 1.5 2.8 .. West Bank and Gaza .. .. .. .. .. .. Yemen, Rep. 1994 0.1 .. .. 1.5 10.0 .. Zambia 1993 0.1 .. .. 3.0 2.3 .. Zimbabwe .. .. .. 2.8 10.4 18.0 World 5.6 w 4.1 m .. m Low income 1.1 3.1 .. Middle income 3.1 4.5 .. Lower middle income 2.7 4.0 .. Upper middle income 3.7 4.4 15.0 Low & middle income 2.7 3.8 .. East Asia & Pacific 1.9 3.2 10.2 Europe & Central Asia 4.3 4.3 .. Latin America & Carib. 3.4 4.5 15.0 Middle East & N. Africa 2.8 4.3 .. South Asia 1.0 2.3 11.2 Sub-Saharan Africa 2.5 3.4 .. High income 6.3 5.2 .. Europe EMU 6.8 5.2 .. a. Data are preliminary. b. Refers only to the scheme for civil servants. c. Refers to system covering private sector workers. d. Data are for 2002/03. 70 2004 World Development Indicators PEOPLE 2.9 Enhancing security About the data Definitions Enhancing security for poor people means reducing can be interpreted as reflecting a country's effort in · Public expenditure on pensions includes all gov- their vulnerability to such risks as ill health, providing education. It often bears a weak relationship to the ernment expenditures on cash transfers to the eld- them the means to manage risk themselves, and output of the education system as reflected in edu- erly, the disabled, and survivors and the administra- strengthening market or public institutions for man- cational attainment. The pattern in this relationship tive costs of these programs. · Average pension is aging risk. The tools include microfinance programs, suggests wide variations across countries in the effi- estimated by dividing total pension expenditure by old age assistance and pensions, and public provi- ciency with which the government's resources are the number of pensioners. · Public expenditure on sion of basic health care and education. translated into education outcomes. Data for educa- health consists of recurrent and capital spending Public interventions and institutions can provide tion expenditure are reported for school years. from government (central and local) budgets, exter- services directly to poor people, although whether nal borrowings and grants (including donations from these work well for the poor is debated. State action international agencies and nongovernmental organi- is often ineffective, in part because governments zations), and social (or compulsory) health insurance can influence only a few of the many sources of well- funds. · Public expenditure on education consists being and in part because of difficulties in delivering of public spending on public education plus subsi- goods and services. The effectiveness of public pro- dies to private education at the primary, secondary, vision is further constrained by the fiscal resources and tertiary levels. at governments' disposal and the fact that state institutions may not be responsive to the needs of poor people. The data on public pension spending are from national sources and cover all government expendi- tures, including the administrative costs of pension programs. They cover noncontributory pensions or social assistance targeted to the elderly and dis- abled and spending by social insurance schemes for which contributions had previously been made. The pattern of spending in a country is correlated with its demographic structure--spending increases as the population ages. The lack of consistent national health accounting systems in most developing countries makes cross- country comparisons of health spending difficult. Compiling estimates of public health expenditures is complicated in countries where state or provincial and local governments are involved in financing and delivering health care because the data on public Data sources spending often are not aggregated. The data in the The data on pension spending are drawn from table are the product of an effort to collect all avail- Robert Palacios and Montserrat Pallares- able information on health expenditures from nation- Miralles's "International Patterns of Pension al and local government budgets, national accounts, Provision" (2000) and updates. For further household surveys, insurance publications, interna- updates, notes, and sources, go to "Knowledge tional donors, and existing tabulations. and information" on the World Bank's Web site on The data on education spending in the table refer pensions (http://www.worldbank.org/pensions). solely to public spending--government spending on The estimates of health expenditure come from public education plus subsidies for private education. the World Health Organization's World Health The data generally exclude foreign aid for education. Report 2003 and updates, from the Organisation They may also exclude spending by religious schools, for Economic Co-operation and Development for which play a significant role in many developing coun- its member countries, and from countries' nation- tries. Data for some countries and for some years al health accounts, supplemented by World Bank refer to spending by the ministry of education only country and sector studies. The data on educa- (excluding education expenditures by other ministries tion expenditure are from the UNESCO Institute and departments and local authorities). The share of for Statistics. gross domestic product (GDP) devoted to education 2004 World Development Indicators 71 2.10 Education inputs Public expenditure per student a Public expenditure Trained teachers Primary on education in primary pupil- education teacher ratio % of total % of GDP per capita government % of pupils per Primary Secondary Tertiary expenditure total teacher 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 2001/02 b 2001/02 b 2001/02 b Afghanistan .. .. .. .. .. .. .. .. 43 Albania .. .. .. .. .. .. .. .. 22 Algeria .. .. .. .. .. .. .. 97.1 28 Angola .. .. .. .. .. .. .. .. .. Argentina .. 12.4 .. 15.8 .. 17.8 13.7 67.0 20 Armenia .. .. .. 14.8 .. 38.9 .. .. 19 Australia 15.9 16.0 34.6 14.3 50.7 23.5 13.8 .. .. Austria 18.0 .. 23.6 .. 35.1 .. 15.1 .. 13 Azerbaijan .. .. .. 20.1 .. 14.0 23.1 100.0 16 Bangladesh .. 8.3 15.2 13.4 26.0 42.5 15.8 65.6 55 Belarus 25.7 .. 6.9 .. 17.8 .. .. 97.9 17 Belgium .. .. 27.1 .. 29.0 .. 11.6 .. 12 Benin .. 10.1 .. 18.5 .. .. .. 65.0 53 Bolivia .. 12.0 .. 10.2 .. 45.0 18.4 74.1 25 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. Botswana 7.4 6.0 44.2 5.5 161.5 88.6 .. 89.5 27 Brazil .. 10.7 .. 10.0 .. 48.5 10.4 91.9 23 Bulgaria 22.1 .. .. .. 30.9 .. .. .. 17 Burkina Faso .. .. .. .. .. .. .. 80.4 47 Burundi .. 11.6 117.8 61.7 .. 691.5 21.8 .. 49 Cambodia .. 7.4 .. 6.2 .. 42.0 10.1 96.0 56 Cameroon .. .. .. .. 302.0 .. 12.5 .. 61 Canada .. .. .. .. 27.5 47.2 .. .. 17 Central African Republic .. .. 17.7 .. 347.1 .. .. .. .. Chad 7.0 9.5 .. 28.4 .. 422.7 .. .. 71 Chile 8.4 14.3 7.7 14.7 27.1 19.2 17.5 94.9 32 China 5.4 .. 12.5 .. 102.4 .. .. 96.8 20 Hong Kong, China 8.0 .. .. .. 51.3 .. .. .. .. Colombia .. 16.4 10.4 18.5 33.0 38.5 18.0 .. 26 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. Congo, Rep. .. 0.4 .. .. .. 8.8 12.6 64.6 56 Costa Rica 7.8 14.6 15.8 20.2 45.8 45.8 21.1 89.5 24 Côte d'Ivoire .. 14.9 .. 49.4 328.5 .. 21.5 99.1 44 Croatia .. .. .. .. .. 36.4 .. 100.0 18 Cuba .. 32.7 .. 43.3 .. 96.5 16.8 100.0 14 Czech Republic .. 13.0 .. 22.3 45.9 32.8 9.7 .. 18 Denmark 21.9 23.4 31.2 37.7 40.4 69.0 15.3 .. 10 Dominican Republic 2.5 6.6 .. 5.0 .. .. 13.2 .. 33 Ecuador .. .. 9.5 .. 23.9 .. 8.0 68.6 24 Egypt, Arab Rep. .. .. .. .. 50.4 .. .. 99.9 22 El Salvador .. .. .. .. .. 9.3 19.4 .. 26 Eritrea .. .. .. .. .. 17.4 .. 72.6 44 Estonia .. 23.6 38.7 29.7 55.9 31.8 .. .. 14 Ethiopia 31.1 .. 46.1 .. 506.6 .. 13.8 69.3 57 Finland 21.8 .. 25.8 .. 40.3 .. 12.2 .. 16 France 11.9 .. 20.7 .. 22.9 .. 11.5 .. 19 Gabon .. 4.7 .. 18.9 .. .. .. 95.3 63 Gambia, The 13.1 .. 28.2 .. .. .. 14.2 73.1 38 Georgia .. .. .. .. .. .. 13.1 87.6 14 Germany .. .. .. .. .. .. 9.9 .. 15 Ghana .. .. .. .. .. .. .. 64.9 32 Greece 8.1 .. 12.4 .. 16.0 .. 7.0 .. 13 Guatemala 2.7 7.7 4.4 4.8 34.7 .. 11.4 100.0 30 Guinea 10.8 9.2 34.9 .. 572.0 .. 25.6 .. 47 Guinea-Bissau .. .. .. .. .. .. 4.8 35.1 44 Haiti 5.7 .. .. .. .. .. .. .. .. 72 2004 World Development Indicators PEOPLE 2.10 Education inputs Public expenditure per student a Public expenditure Trained teachers Primary on education in primary pupil- education teacher ratio % of total % of GDP per capita government % of pupils per Primary Secondary Tertiary expenditure total teacher 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 2001/02 b 2001/02 b 2001/02 b Honduras .. .. 16.5 .. 76.6 .. .. .. 34 Hungary 20.3 19.2 25.4 18.8 81.3 31.4 14.1 .. 11 India .. 13.7 13.6 23.0 92.0 85.8 12.7 .. 40 Indonesia .. 3.7 .. 7.3 .. 21.0 9.6 93.5 21 Iran, Islamic Rep. 6.2 11.6 14.1 13.6 79.7 81.5 21.7 97.9 24 Iraq .. .. .. .. .. .. .. .. 21 Ireland 11.1 .. 18.5 .. 36.1 .. 10.7 .. 22 Israel 12.7 21.0 27.6 22.4 32.7 29.9 .. .. 12 Italy 15.1 .. 21.4 .. .. .. 9.5 .. 11 Jamaica 10.8 15.7 14.0 24.5 132.3 70.5 12.3 79.5 34 Japan 18.6 21.4 18.4 21.0 .. 17.2 10.5 .. 20 Jordan .. 16.0 .. 19.0 78.9 .. 20.6 .. 20 Kazakhstan .. .. .. .. .. .. .. .. 19 Kenya 13.0 0.9 .. 2.2 .. 256.7 22.5 .. 32 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. 12.0 18.4 9.9 16.8 5.8 7.4 17.4 .. 32 Kuwait 35.4 .. 13.6 .. 353.8 .. .. .. 14 Kyrgyz Republic .. .. 28.4 .. 53.5 .. 18.6 49.3 24 Lao PDR 5.0 9.1 25.3 10.2 52.2 94.5 10.6 76.1 30 Latvia .. 23.1 19.4 24.7 18.6 22.0 .. .. 15 Lebanon .. 8.3 .. .. .. 9.8 11.1 14.9 17 Lesotho 16.7 21.4 61.8 52.9 609.1 .. 18.4 74.8 47 Liberia .. .. .. .. .. .. .. .. 38 Libya .. .. .. .. .. 24.9 .. .. .. Lithuania .. .. 26.9 .. 54.1 .. .. .. 16 Macedonia, FYR .. 16.6 c 30.9 17.1 c 62.5 20.8 c .. .. 18 c Madagascar .. 10.7 .. .. 167.9 191.6 .. .. 54 c Malawi 6.5 .. 44.0 .. 851.2 .. .. 51.2 .. Malaysia 12.4 17.0 16.9 27.5 116.6 83.5 25.2 .. 20 Mali .. 14.4 .. .. .. .. .. .. 56 Mauritania 16.9 14.0 85.2 44.0 396.3 .. .. .. 39 Mauritius 10.1 9.0 17.1 13.9 177.1 48.7 13.3 100.0 25 Mexico 3.5 11.8 8.3 13.8 23.6 45.2 22.6 .. 27 Moldova .. .. .. .. .. .. 15.0 .. 20 Mongolia .. .. .. .. 119.4 .. .. 92.9 32 Morocco .. 17.9 47.1 47.5 73.1 .. .. .. 28 Mozambique 11.0 .. 27.4 .. .. .. .. 59.9 66 Myanmar .. 5.8 .. 7.0 .. 28.5 18.1 85.4 33 Namibia .. .. 50.1 .. 259.5 .. 21.0 36.0 32 Nepal .. 12.5 8.1 11.8 90.8 82.3 13.9 51.8 40 Netherlands 12.1 .. 21.7 .. 54.1 .. 10.4 .. 10 New Zealand 17.1 19.6 15.0 21.9 67.8 25.1 .. .. 15 Nicaragua 10.0 .. 9.7 .. .. .. 13.0 72.9 37 Niger .. 16.8 105.4 56.7 .. 304.5 .. 72.7 41 Nigeria .. .. .. .. .. .. .. .. 40 Norway .. 26.8 17.2 17.1 27.7 40.9 16.2 .. .. Oman .. 12.6 17.2 20.8 56.3 50.2 .. 99.8 23 Pakistan .. .. 14.7 .. 155.1 .. 7.8 .. 44 Panama .. 10.5 12.9 13.8 43.5 41.2 7.3 75.7 24 Papua New Guinea .. 12.4 .. 19.2 .. .. .. 100.0 36 Paraguay 3.1 12.9 6.7 15.4 38.0 47.1 9.7 .. .. Peru .. .. .. .. .. .. 21.1 78.2 29 Philippines .. 11.8 .. 9.4 .. 13.9 .. .. 35 Poland .. 28.8 .. 11.8 .. 16.1 12.2 .. 11 Portugal 15.4 .. 18.0 .. 32.5 .. 12.8 .. 13 Puerto Rico .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 73 2.10 Education inputs Public expenditure per student a Public expenditure Trained teachers Primary on education in primary pupil- education teacher ratio % of total % of GDP per capita government % of pupils per Primary Secondary Tertiary expenditure total teacher 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 2001/02 b 2001/02 b 2001/02 b Romania 23.2 .. 5.0 .. 32.3 .. .. .. 20 Russian Federation .. .. .. .. .. 9.6 10.6 .. 17 Rwanda .. 6.9 .. 22.0 .. 575.0 .. 81.2 51 Saudi Arabia .. .. .. .. 133.2 .. .. 93.3 12 Senegal 17.3 13.8 .. .. .. .. .. 100.0 49 Serbia and Montenegro .. .. .. .. .. .. .. 100.0 20 Sierra Leone .. .. .. .. .. .. .. 60.7 31 Singapore .. .. 13.6 .. 43.4 .. .. .. .. Slovak Republic 22.8 11.4 7.9 16.8 63.7 27.9 13.8 .. 19 Slovenia 17.5 .. 15.4 .. 38.7 .. .. .. 13 Somalia .. .. .. .. .. .. .. .. .. South Africa .. 14.3 .. 18.3 90.9 56.8 18.1 67.6 37 Spain 11.8 .. 13.6 .. 18.0 .. 11.3 .. 14 Sri Lanka .. 10.0 .. .. 78.6 .. .. .. .. Sudan .. .. .. .. .. .. .. .. .. Swaziland 7.0 10.4 .. 29.7 305.1 253.2 .. 90.4 32 Sweden 46.5 24.3 18.8 27.8 38.6 52.0 13.6 .. 11 Switzerland 34.9 22.8 13.5 27.8 43.7 53.2 15.2 .. 14 Syrian Arab Republic .. 12.8 15.0 23.1 46.6 .. .. .. 24 Tajikistan .. .. .. .. .. .. .. 81.6 22 Tanzania .. .. .. .. .. .. .. .. 47 Thailand 13.3 15.9 15.9 13.0 .. 31.1 28.3 .. 19 Togo 8.3 11.0 36.4 26.0 572.8 297.7 23.2 80.5 35 Trinidad and Tobago 4.5 14.2 15.5 20.1 67.4 68.5 13.4 78.1 19 Tunisia .. 15.8 27.6 25.7 115.5 68.0 17.4 94.1 22 Turkey .. 11.6 8.4 13.8 .. 48.5 .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. 59 Ukraine 20.9 .. 9.5 16.9 19.5 35.3 15.0 99.7 19 United Arab Emirates .. .. .. .. .. .. .. .. 15 United Kingdom 15.1 13.6 26.5 14.5 40.9 25.7 11.4 .. 18 United States 20.2 18.0 22.1 22.5 20.2 23.0 15.5 .. 15 Uruguay .. 7.2 8.6 8.3 24.0 24.6 10.0 .. 21 Uzbekistan .. .. .. .. .. .. .. .. .. Venezuela, RB 2.4 .. 7.8 .. 36.3 .. .. .. .. Vietnam .. .. .. .. .. .. .. 87.0 26 West Bank and Gaza .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. .. .. .. .. 32.8 .. .. Zambia 5.6 .. .. .. .. .. .. 100.0 45 Zimbabwe 20.1 16.2 34.0 24.2 195.9 .. .. 95.3 38 World .. m .. m .. m .. m .. m .. m .. m 86.2 m 28 m Low income .. .. .. .. .. .. .. 76.1 40 Middle income .. .. .. .. .. .. .. 90.4 22 Lower middle income .. .. .. .. .. .. .. 93.0 22 Upper middle income .. 12.4 16.9 .. 61.8 30.6 13.7 77.7 21 Low & middle income .. .. .. .. .. .. .. 84.9 30 East Asia & Pacific .. 5.7 .. 10.4 .. .. .. 93.5 22 Europe & Central Asia .. .. .. .. 49.9 .. .. .. 17 Latin America & Carib. .. 13.1 9.9 .. .. 44.9 13.2 76.7 26 Middle East & N. Africa .. .. .. .. 76.0 .. .. 96.8 24 South Asia .. 8.7 14.7 10.4 90.8 60.4 13.0 66.9 42 Sub-Saharan Africa .. .. .. .. .. .. .. 80.4 45 High income 40.2 26.2 31.0 .. 47.1 66.5 11.5 .. 17 Europe EMU .. 14 a. Break in series between 1997 and 1998 due to change from International Standard Classification of Education 1976 (ISCED76) to ISCED97. For information on ISCED, see About the data. b. Data are preliminary. c. Data are for 2002/03. 74 2004 World Development Indicators PEOPLE 2.10 Education inputs About the data Data on education are compiled by the UNESCO practices vary with respect to whether parents or statistics. In 1998 UNESCO introduced ISCED97 and Institute for Statistics from official responses to sur- schools pay for books, uniforms, and other supplies. adjusted its data collection program and country veys and from reports provided by education author- For greater detail, see the country- and indicator- reporting of education statistics to this new classifi- ities in each country. Such data are used for moni- specific notes in the source. cation. The adjustments were made to ease the toring, policymaking, and resource allocation. For a The share of public expenditure devoted to educa- international compilation and comparison of educa- variety of reasons, however, education statistics gen- tion allows an assessment of the priority a govern- tion statistics and to take into account new types of erally fail to provide a complete and accurate picture ment assigns to education relative to other public learning opportunities and activities for both children of a country's education system. Statistics often lag investments. It also reflects a government's commit- and adults. Thus the time-series data for the years by two to three years, though an effort is being made ment to investing in human capital development. through 1997 are not consistent with those for 1998 to shorten the delay. Moreover, coverage and data The share of trained teachers in primary schools and later. Any time-series analysis should therefore collection methods vary across countries and over measures the quality of the teaching staff. It does not be undertaken with extreme caution. time within countries, so the results of comparisons take account of competencies acquired by teachers should be interpreted with caution. through their professional experience or self- Definitions The data on education spending in the table refer instruction, or of such factors as work experience, solely to public spending--government spending on teaching methods and materials, or classroom condi- · Public expenditure per student is public current public education plus subsidies for private education. tions, all of which may affect the quality of teaching. spending on education divided by the number of stu- The data generally exclude foreign aid for education. Since the training teachers receive varies greatly, care dents by level, as a percentage of gross domestic They may also exclude spending by religious schools, should be taken in comparing across countries. product (GDP) per capita. · Public expenditure on which play a significant role in many developing coun- The comparability of pupil-teacher ratios across education is current and capital public expenditure tries. Data for some countries and for some years countries is affected by the definition of teachers on education expressed as a percentage of total gov- refer to spending by the ministry of education only and by differences in class size by grade and in the ernment expenditure. · Trained teachers in primary (excluding education expenditures by other ministries number of hours taught. Moreover, the underlying education are the percentage of primary school and departments and local authorities). enrollment levels are subject to a variety of reporting teachers who have received the minimum organized Many developing countries have sought to supple- errors (for further discussion of enrollment data, see teacher training (preservice or in service) required ment public funds for education. Some countries About the data for table 2.11). While the pupil- for teaching. · Primary pupil-teacher ratio is the have adopted tuition fees to recover part of the cost teacher ratio is often used to compare the quality of number of pupils enrolled in primary school divided of providing education services or to encourage devel- schooling across countries, it is often weakly related by the number of primary school teachers (regard- opment of private schools. Charging fees raises diffi- to the value added of schooling systems (Behrman less of their teaching assignment). cult questions relating to equity, efficiency, access, and Rosenzweig 1994). and taxation, however, and some governments have Data for education are reported for school years. used scholarships, vouchers, and other methods of For two decades the International Standard public finance to counter criticism. Data for a few Classification of Education, 1976 (ISCED76), was countries include private spending, although national used to assemble, compile, and present education 2.10a Education suffers in primary schools with high teacher absence rates Teacher absence rate, 2000­03 (%) 30 25 20 15 10 5 0 Uganda India Indonesia Zambia Bangladesh Ecuador Papua Peru Data sources New Guinea The data are from the UNESCO Institute for The primary school teacher absence rate is the percentage of full-time teachers who were absent from a random sample Statistics, which compiles international data on of primary schools during a surprise visit, regardless of the reasons for their absence. Many teachers were absent for valid education in cooperation with national commis- reasons, but even authorized absences reduce the quantity and quality of primary education. sions and national statistical services. Source: Chaudhury and others 2004; NRI and World Bank 2003; Habyarimana and others 2003. 2004 World Development Indicators 75 2.11 Participation in education Gross enrollment Net enrollment ratio a ratio a % of relevant age group % of relevant age group Preprimary Primary Secondary Tertiary Primary Secondary 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b Afghanistan .. 27 23 9 12 2 .. .. .. .. .. Albania 44 100 107 78 78 7 15 .. 97 .. 74 Algeria 4 100 108 61 72 11 .. 93 95 54 62 Angola .. 92 .. 12 19 1 1 .. .. .. .. Argentina 61 106 120 71 100 39 57 .. 100 .. 81 Armenia 30 104 96 93 87 20 26 .. 85 .. 85 Australia 104 108 102 82 154 35 65 99 96 79 88 Austria 82 102 103 104 99 35 57 90 91 91 88 Azerbaijan 23 114 93 90 80 24 23 .. 80 .. 76 Bangladesh 19 72 98 19 47 4 6 64 87 18 44 Belarus 99 95 110 93 84 48 62 .. 94 .. 78 Belgium 112 101 105 103 154 40 58 97 100 88 .. Benin 6 58 104 12 26 3 4 49 71 .. 20 Bolivia 47 95 114 37 84 21 39 91 94 29 67 Bosnia and Herzegovina .. 70 .. 65 .. 15 .. .. .. .. .. Botswana .. 113 103 43 73 3 5 93 81 34 55 Brazil 67 106 148 38 108 11 18 86 97 15 72 Bulgaria 70 98 99 75 94 31 40 86 90 63 87 Burkina Faso 1 33 48 c 7 10 1 .. 27 35 7 8 Burundi 1 73 71 6 11 1 2 52 53 5 8 Cambodia 7 121 123 32 22 1 3 .. 86 .. 21 Cameroon 14 101 107 28 33 3 5 .. .. .. .. Canada 65 103 100 101 106 95 59 97 100 89 98 Central African Republic .. 65 66 12 .. 2 2 53 .. .. .. Chad .. 54 73 8 12 1 1 .. 58 .. 8 Chile 77 100 103 73 85 21 37 88 89 55 75 China 27 125 114 49 68 3 13 97 93 .. .. Hong Kong, China .. 102 .. 80 .. 19 .. .. .. .. .. Colombia 37 102 110 50 65 13 24 69 87 34 54 Congo, Dem. Rep. 1 70 .. 22 .. 2 .. 54 .. 15 .. Congo, Rep. 4 133 86 53 32 5 4 .. .. .. .. Costa Rica 115 101 108 42 67 27 21 86 91 36 51 Côte d'Ivoire 3 67 80 22 23 3 .. 47 63 .. .. Croatia 38 85 96 76 88 24 36 79 88 63 86 Cuba 111 98 100 89 89 21 27 92 96 69 83 Czech Republic 92 96 104 91 95 16 30 .. 90 .. 88 Denmark 90 98 102 109 128 36 59 98 99 87 89 Dominican Republic 35 97 126 40 67 20 .. .. 97 .. 41 Ecuador 73 116 117 55 59 20 .. .. 99 .. 50 Egypt, Arab Rep. 12 94 97 76 85 16 .. .. 90 .. 78 El Salvador 46 81 112 26 56 16 17 75 89 .. 46 Eritrea 5 46 61 15 28 .. 2 24 43 .. 21 Estonia 102 111 103 102 110 26 59 94 98 82 92 Ethiopia 2 c 33 62 c 14 17 c 1 2 .. 46 .. 15 Finland 54 99 102 116 126 49 85 99 100 93 95 France 114 108 105 99 108 40 54 100 100 86 92 Gabon 13 .. 134 .. 51 .. .. .. 78 .. .. Gambia, The 20 64 79 19 34 .. .. 51 73 18 28 Georgia 41 97 92 95 79 37 36 .. 91 .. .. Germany 101 101 103 98 99 34 .. 84 86 89 88 Ghana 41 75 81 36 38 1 3 .. 60 .. 30 Greece 70 98 97 93 96 36 61 94 95 83 85 Guatemala 55 78 103 23 33 8 .. .. 85 .. 28 Guinea .. 37 77 10 .. 1 .. .. 61 .. .. Guinea-Bissau 3 56 70 9 18 1 0 d .. 45 .. .. Haiti .. 74 .. 21 .. 1 .. 22 .. .. .. 76 2004 World Development Indicators PEOPLE 2.11 Participation in education Gross enrollment Net enrollment ratio a ratio a % of relevant age group % of relevant age group Preprimary Primary Secondary Tertiary Primary Secondary 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b Honduras 21 109 106 33 .. 9 14 89 87 21 .. Hungary 79 95 102 79 98 14 40 91 90 75 87 India 26 97 99 44 48 6 11 .. 83 .. .. Indonesia 20 115 111 44 58 9 15 97 92 38 47 Iran, Islamic Rep. 23 112 92 55 81 10 19 97 87 .. .. Iraq 5 111 99 47 38 13 14 79 91 37 33 Ireland 3 103 119 101 .. 29 47 91 90 80 .. Israel 112 95 114 85 93 34 53 .. 100 .. 88 Italy 96 103 101 83 96 32 50 .. 100 .. 88 Jamaica 87 101 101 65 84 7 17 96 95 64 75 Japan 84 100 101 97 102 30 48 100 100 97 100 Jordan 31 71 99 45 86 16 31 66 91 33 80 Kazakhstan 13 87 99 98 89 40 39 .. 90 .. 84 Kenya 44 95 96 24 32 2 4 .. 70 .. 24 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 79 105 100 90 94 39 82 100 99 86 91 Kuwait 73 60 94 43 85 12 .. 45 85 45 77 Kyrgyz Republic 14 111 102 100 85 14 44 .. 82 .. .. Lao PDR 8 105 115 25 41 1 4 61 83 15 31 Latvia 57 94 99 93 93 25 64 83 91 .. 89 Lebanon 74 120 103 73 77 29 45 .. 90 .. .. Lesotho 21 112 124 25 34 1 2 73 84 15 22 Liberia 56 29 105 14 .. 3 .. .. 70 .. .. Libya 8 105 114 86 105 15 58 96 .. .. .. Lithuania 53 91 104 92 98 34 59 .. 97 .. 92 Macedonia, FYR 29 99 99 56 85 17 24 94 93 .. 82 Madagascar 3 103 104 18 .. 3 2 .. 69 .. .. Malawi .. 68 .. 8 .. 1 .. 50 .. .. .. Malaysia 89 94 95 56 70 7 26 .. 95 .. 69 Mali 2 26 57 7 .. 1 2 21 .. 5 .. Mauritania .. 49 86 14 22 3 3 .. 67 .. 15 Mauritius 87 109 106 53 80 4 11 95 93 .. 62 Mexico 75 114 110 53 73 15 20 100 99 45 58 Moldova 39 93 85 80 72 36 29 .. 78 .. 68 Mongolia 32 97 99 82 76 14 35 .. 87 .. 71 Morocco 60 67 107 35 41 11 10 58 88 .. 31 Mozambique .. 67 99 8 13 0 d 1 47 60 7 11 Myanmar 2 106 90 23 39 4 11 .. 82 .. 35 Namibia 23 129 106 44 61 3 7 89 78 31 38 Nepal 13 108 122 33 44 5 5 .. 70 .. .. Netherlands 96 102 108 120 124 40 55 95 99 84 90 New Zealand 87 106 99 89 113 40 72 100 98 85 92 Nicaragua 26 94 105 40 57 8 .. 72 82 .. 37 Niger 1 29 40 7 6 1 1 25 34 6 5 Nigeria .. 91 96 25 .. 4 .. .. .. .. .. Norway 79 100 101 103 115 42 70 100 100 88 95 Oman 5 86 83 46 79 4 7 70 75 49 68 Pakistan 55 61 73 23 .. 3 .. .. 67 .. .. Panama 51 106 110 63 69 21 34 91 99 51 62 Papua New Guinea 39 72 77 12 23 3 .. .. 77 .. 23 Paraguay 30 105 112 31 64 8 18 93 92 26 50 Peru 60 118 121 67 .. 30 .. .. 100 .. .. Philippines 33 111 112 73 82 28 30 97 93 57 56 Poland 49 98 100 81 101 22 55 97 98 76 91 Portugal 70 123 121 67 114 23 50 100 .. 70 85 Puerto Rico .. 121 .. 61 .. 45 .. .. .. .. .. 2004 World Development Indicators 77 2.11 Participation in education Gross enrollment Net enrollment ratio a ratio a % of relevant age group % of relevant age group Preprimary Primary Secondary Tertiary Primary Secondary 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b 1990/91 2001/02 b Romania 73 91 99 92 82 10 27 77 93 .. 80 Russian Federation 92 109 114 93 92 52 68 .. .. .. .. Rwanda 3 70 117 8 14 1 2 66 96 7 .. Saudi Arabia 5 73 67 44 69 12 22 59 59 31 53 Senegal 3 c 59 75 16 19 3 .. 48 58 .. .. Serbia and Montenegro 44 72 99 63 89 18 36 69 75 62 .. Sierra Leone 4 50 76 17 .. 1 2 .. .. .. .. Singapore .. 104 .. 68 .. 19 .. .. .. .. .. Slovak Republic 81 98 103 87 87 19 30 .. 89 .. 75 Slovenia 75 108 100 91 106 24 61 .. 93 .. 96 Somalia .. 11 .. 6 .. 3 .. .. .. .. .. South Africa 35 122 105 74 86 13 15 99 90 51 62 Spain 102 109 107 104 114 37 57 100 100 .. 93 Sri Lanka .. 106 110 74 81 5 .. .. .. .. .. Sudan 20 53 59 24 32 3 .. .. 46 .. .. Swaziland .. 111 100 44 45 4 5 88 77 33 32 Sweden 74 100 110 90 149 32 70 100 100 85 96 Switzerland 95 90 107 99 100 26 42 84 99 80 88 Syrian Arab Republic 10 108 112 52 45 18 .. 95 98 46 39 Tajikistan 10 91 107 102 82 22 15 .. 98 .. 79 Tanzania .. 70 70 5 6 0 d 1 51 54 .. 5 Thailand 86 99 98 30 83 17 37 .. 86 .. .. Togo 3 109 124 24 36 3 4 75 92 18 27 Trinidad and Tobago 63 97 105 80 70 7 7 91 94 65 65 Tunisia 20 113 112 45 79 9 23 94 97 43 68 Turkey 7 99 94 47 76 13 25 89 88 41 .. Turkmenistan .. 91 .. 107 .. 22 .. .. .. .. .. Uganda 4 71 136 13 .. 1 3 .. .. .. .. Ukraine 72 89 90 93 97 47 57 .. 82 .. 91 United Arab Emirates 71 104 92 67 79 9 .. 94 81 59 72 United Kingdom 82 104 101 85 158 30 59 97 100 79 95 United States 58 102 100 93 94 75 71 96 94 86 87 Uruguay 63 109 108 81 101 30 38 91 90 .. 72 Uzbekistan 21 81 103 99 99 30 9 .. .. .. .. Venezuela, RB 52 96 106 35 69 29 18 88 92 19 57 Vietnam 43 103 103 32 70 2 10 .. 94 .. 65 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 0 d 58 81 58 46 4 .. .. 67 .. 35 Zambia .. 99 79 24 .. 2 2 .. 66 .. .. Zimbabwe 39 116 99 50 43 5 4 .. 83 .. 40 World 40 w 102 w 103 w 55 w 70 w 16 w 24 w .. w 88 w .. w .. w Low income 24 88 94 35 46 5 10 .. 80 .. .. Middle income 40 113 111 56 75 13 22 95 92 .. .. Lower middle income 36 115 112 55 75 12 20 95 91 .. .. Upper middle income 63 102 104 64 81 20 33 92 93 50 69 Low & middle income 33 102 103 47 63 10 17 .. 86 .. .. East Asia & Pacific 29 121 111 47 66 5 14 97 92 .. .. Europe & Central Asia 58 98 103 85 89 34 48 .. .. .. .. Latin America & Carib. 60 106 129 49 89 17 23 89 94 29 65 Middle East & N. Africa 21 96 96 57 70 12 .. .. 83 .. 54 South Asia 28 90 95 39 48 5 10 .. 82 .. .. Sub-Saharan Africa .. 74 87 23 .. 3 .. .. .. .. .. High income 90 103 102 94 106 47 61 98 97 87 91 Europe EMU 98 105 104 97 106 35 54 93 99 87 90 a. Break in series between 1997 and 1998 due to change from ISCED76 to ISCED97. For information on ISCED, see About the data for table 2.10. b. Data are preliminary. c. Data are for 2002/03. d. Less than 0.5. 78 2004 World Development Indicators PEOPLE 2.11 Participation in education About the data School enrollment data are reported to the UNESCO some education systems ages for children repeating which, other things equal, leads to underreporting of Institute for Statistics by national education authori- a grade may be deliberately or inadvertently underre- repeaters and overestimation of dropouts. ties. Enrollment ratios help to monitor two important ported. As an international indicator, the gross pri- Thus gross enrollment ratios indicate the capacity issues for universal primary education: whether the mary enrollment ratio has been used to indicate of each level of the education system, but a high Millennium Development Goal that implies achieving a broad levels of participation as well as school capac- ratio does not necessarily mean a successful educa- net primary enrollment ratio of 100 percent is on track, ity. It has an inherent weakness: the length of pri- tion system. The net enrollment ratio excludes over- and whether an education system has sufficient capac- mary education differs significantly across countries. age students in an attempt to capture more accu- ity to meet the needs of universal primary education, A short duration tends to increase the ratio, and a rately the system's coverage and internal efficiency. as indicated in part by its gross enrollment ratios. The long duration to decrease it (in part because there It does not solve the problem completely, however, gross enrollment ratio shows the share of children in are more dropouts among older children). because some children fall outside the official the population who are enrolled in school regardless of Other problems affecting cross-country compar- school age because of late or early entry rather than their age. Net enrollment ratios show the share of chil- isons of enrollment data stem from errors in esti- because of grade repetition. The difference between dren of primary school age who are enrolled in school mates of school-age populations. Age-gender struc- gross and net enrollment ratios shows the incidence and thus also the share who are not. tures from censuses or vital registration systems, the of overage and underage enrollments. Enrollment ratios, while a useful measure of partici- primary sources of data on school-age populations, pation in education, also have significant limitations. are commonly subject to underenumeration (espe- Definitions They are based on data collected during annual school cially of young children) aimed at circumventing laws surveys, which are typically conducted at the begin- or regulations; errors are also introduced when par- · Gross enrollment ratio is the ratio of total enroll- ning of the school year. They do not reflect actual rates ents round up children's ages. While census data are ment, regardless of age, to the population of the age of attendance or dropouts during the school year. And often adjusted for age bias, adjustments are rarely group that officially corresponds to the level of edu- school administrators may report exaggerated enroll- made for inadequate vital registration systems. cation shown. · Net enrollment ratio is the ratio of ments, especially if there is a financial incentive to do Compounding these problems, pre- and post-census children of official school age (as defined by the so. Often the number of teachers paid by the govern- estimates of school-age children are interpolations or national education system) who are enrolled in ment is related to the number of pupils enrolled. projections based on models that may miss impor- school to the population of the corresponding official Overage or underage enrollments frequently tant demographic events (see the discussion of school age. Based on the International Standard occur, particularly when parents prefer, for cultural or demographic data in About the data for table 2.1). Classification of Education 1997 (ISCED97). economic reasons, to have children start school at In using enrollment data, it is also important to con- · Preprimary education refers to the initial stage of other than the official age. Children's age at enroll- sider repetition rates. These rates are quite high in organized instruction, designed primarily to introduce ment may be inaccurately estimated or misstated, some developing countries, leading to a substantial very young children to a school-type environment. especially in communities where registration of number of overage children enrolled in each grade and · Primary education provides children with basic births is not strictly enforced. Parents who want to raising the gross enrollment ratio. A common error reading, writing, and mathematics skills along with enroll their underage children in primary school may that may also distort enrollment ratios is the lack of an elementary understanding of such subjects as do so by overstating the age of the children. And in distinction between new entrants and repeaters, history, geography, natural science, social science, art, and music. · Secondary education completes 2.11a the provision of basic education that began at the pri- Girls from rural areas and poor households have the lowest attendance rates in Guinea mary level and aims at laying the foundations for life- Net attendance ratio, 1999 long learning and human development by offering Male Female more subject- or skill-oriented instruction using more 100 100 specialized teachers. · Tertiary education, whether 80 80 or not leading to an advanced research qualification, normally requires, as a minimum condition of admis- 60 60 sion, the successful completion of education at the secondary level. 40 40 20 20 0 0 Urban Rural Richest quintile Poorest quintile Data source Household surveys can provide data on attendance at school that cannot usually be derived from administrative data. In Guinea more children attend school in urban areas than in rural areas, and more than four times as many rich children The data are from the UNESCO Institute for attend school as poor children. Regardless of location and wealth, more boys than girls attend school. Statistics. Source: Global Education Report 2003, UNESCO Institute for Statistics 2003. 2004 World Development Indicators 79 2.12 Education efficiency Apparent intake rate Share of cohort Primary Repeaters in in grade 1 reaching grade 5 completion rate primary school % of relevant % of relevant age group age group % of grade 1 students Total Male Female % of enrollment Male Female Male Female 2000/01­ 2000/01 ­ 2000/01 ­ Total Male Female 2001/02 a 2001/02 a 1990/91 2000/01 a 1990/91 2000/01 a 2002/03 a,b2002/03 a, b 2002/03 a,b2001/02 a 2001/02 a 2001/02 a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 103 101 .. .. .. .. 100 101 99 4.1 4.6 3.4 Algeria 102 100 95 95 93 97 96 96 95 11.7 14.2 9.0 Angola .. .. .. .. .. .. .. .. .. 29.0 29.0 29.0 Argentina 112 112 .. 91 .. 95 100 98 102 6.2 7.3 5.0 Armenia 97 95 .. .. .. .. 74 74 74 0.1 0.1 0.1 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria 108 105 .. .. .. .. .. .. .. .. .. .. Azerbaijan 91 88 .. .. .. .. 100 101 99 0.3 0.3 0.3 Bangladesh 106 108 .. 63 .. 68 77 76 78 6.3 6.7 6.0 Belarus .. .. .. .. .. .. 131 .. .. 0.3 0.6 0.6 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 127 96 55 89 56 78 45 58 32 20.1 20.1 20.1 Bolivia 119 121 .. 79 .. 77 89 91 87 2.7 2.9 2.5 Bosnia and Herzegovina .. .. .. .. .. .. 77 .. .. .. .. .. Botswana 114 110 94 87 98 92 91 87 95 3.2 4.0 2.5 Brazil 130 119 .. .. .. .. 82 .. .. 21.5 25.0 25.0 Bulgaria 98 98 91 .. 90 .. 94 95 92 2.4 2.8 2.0 Burkina Faso 53 39 71 68 68 71 29 34 24 14.0 c 17.5 17.7 Burundi 92 73 65 68 58 59 27 30 24 26.3 25.6 27.2 Cambodia 174 161 .. 71 .. 70 71 75 66 9.6 10.2 8.9 Cameroon 115 99 .. 63 .. 60 57 58 56 25.2 25.9 24.4 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 76 53 25 .. 22 .. .. .. .. .. .. .. Chad 94 70 58 58 43 48 22 31 13 25.5 25.3 25.9 Chile 97 96 .. 101 .. 101 96 95 97 2.0 2.4 1.6 China .. .. .. .. .. .. 102 .. .. 0.6 .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 130 125 71 59 50 63 90 87 92 6.6 7.3 5.9 Congo, Dem. Rep. .. .. 58 .. 50 .. .. .. .. .. .. .. Congo, Rep. 67 61 58 .. 67 .. 58 59 56 24.8 25.1 24.4 Costa Rica 101 101 81 93 84 95 90 89 92 8.2 9.5 6.9 Côte d'Ivoire 82 62 75 73 70 65 48 57 38 23.3 23.1 23.6 Croatia 97 98 .. .. .. .. 90 90 89 0.4 0.5 0.3 Cuba 95 96 .. 95 .. 96 100 101 99 1.2 1.7 0.6 Czech Republic 102 101 .. 98 .. 99 .. .. .. 1.1 1.3 0.9 Denmark 100 100 94 .. 94 .. .. .. .. .. .. .. Dominican Republic 148 137 .. 71 .. 79 95 91 100 5.9 7.1 4.6 Ecuador 139 138 40 77 41 79 99 99 99 2.0 2.3 1.8 Egypt, Arab Rep. 95 92 .. 99 .. 99 91 92 89 5.2 6.4 3.9 El Salvador 135 128 56 67 60 73 86 86 86 6.5 7.3 5.6 Eritrea 70 59 85 89 80 74 33 38 29 17.5 17.1 17.9 Estonia 98 94 92 100 94 99 103 108 98 2.3 3.2 1.3 Ethiopia 96 74 61 63 54 59 18 .. .. 9.9 c 8.6 c 11.7 c Finland 98 98 100 99 100 101 .. .. .. 0.5 0.7 0.3 France .. .. .. 98 .. 97 .. .. .. 4.2 4.2 4.2 Gabon 97 97 .. 102 .. 102 92 92 92 34.4 35.1 33.7 Gambia, The 88 88 85 75 89 63 69 77 60 10.6 10.7 10.5 Georgia 93 92 .. .. .. .. 92 92 91 0.3 0.5 0.2 Germany 100 99 .. .. .. .. .. .. .. 1.8 2.0 1.6 Ghana 86 84 81 67 79 65 59 61 57 5.2 5.3 5.0 Greece .. .. 99 .. 100 .. .. .. .. .. .. .. Guatemala 126 123 .. 57 .. 54 59 63 55 14.2 14.8 13.5 Guinea 77 67 64 90 48 77 .. .. .. 20.8 19.7 22.4 Guinea-Bissau 106 79 .. 41 .. 34 .. .. .. 24.0 23.6 24.5 Haiti .. .. .. .. .. .. .. .. .. .. .. .. 80 2004 World Development Indicators PEOPLE 2.12 Education efficiency Apparent intake rate Share of cohort Primary Repeaters in in grade 1 reaching grade 5 completion rate primary school % of relevant % of relevant age group age group % of grade 1 students Total Male Female % of enrollment Male Female Male Female 2000/01­ 2000/01 ­ 2000/01 ­ Total Male Female 2001/02 a 2001/02 a 1990/91 2000/01 a 1990/91 2000/01 a 2002/03 a,b2002/03 a, b 2002/03 a,b2001/02 a 2001/02 a 2001/02 a Honduras 138 138 .. .. .. .. 70 69 70 .. .. .. Hungary 99 97 .. .. .. .. .. .. .. 2.5 3.0 2.0 India 136 114 .. 59 .. 59 77 85 69 3.7 3.7 3.7 Indonesia 119 113 .. 87 .. 92 107 106 108 5.3 5.5 5.1 Iran, Islamic Rep. 86 86 91 94 89 94 123 125 120 4.3 5.2 3.3 Iraq 118 104 .. .. .. .. .. .. .. 12.3 14.1 10.0 Ireland 101 100 100 98 100 99 .. .. .. 1.6 1.7 1.4 Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy 96 95 100 95 100 98 .. .. .. 0.3 0.4 0.2 Jamaica 99 99 .. 88 .. 93 90 88 92 3.5 4.3 2.6 Japan .. .. 100 .. 100 .. .. .. .. .. .. .. Jordan 103 103 100 98 100 97 98 97 99 0.5 0.5 0.5 Kazakhstan 107 106 .. .. .. .. 99 99 99 0.2 0.2 0.1 Kenya 105 101 .. .. .. .. 56 54 58 .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 102 100 99 100 100 100 .. .. .. .. .. .. Kuwait 96 95 .. .. .. .. .. .. .. 2.8 2.9 2.7 Kyrgyz Republic 111 108 .. .. .. .. 94 96 92 0.2 0.2 0.1 Lao PDR 133 117 56 62 50 63 73 78 69 20.0 21.2 18.5 Latvia 94 93 .. .. .. .. 90 .. .. 2.0 2.7 1.2 Lebanon 98 96 .. 92 .. 96 68 65 71 8.7 10.1 7.2 Lesotho 158 139 58 60 83 74 65 55 75 19.7 22.1 17.3 Liberia 204 174 .. 44 .. 21 .. .. .. 2.7 2.4 3.0 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 102 100 .. .. .. .. 106 106 106 0.7 0.9 0.5 Macedonia, FYR 98 98 .. .. .. .. 95 96 95 0.1 0.1 0.1 Madagascar 119 116 22 33 21 34 41 40 41 29.0 c 31.5 29.4 Malawi .. .. 71 .. 57 .. 55 62 48 .. .. .. Malaysia 93 93 98 96 98 96 .. .. .. .. .. .. Mali 65 54 73 88 70 79 39 48 31 19.3 19.0 19.7 Mauritania 114 110 75 54 75 56 46 48 43 14.1 13.8 14.4 Mauritius 90 93 98 99 98 99 108 108 107 4.3 4.9 3.7 Mexico 110 110 81 88 82 89 96 96 97 5.5 6.5 4.4 Moldova 95 92 .. .. .. .. 80 80 80 .. .. .. Mongolia 100 103 .. .. .. .. 107 106 109 0.6 0.7 0.6 Morocco 119 115 75 84 76 83 68 72 64 12.6 14.1 10.8 Mozambique 126 112 37 56 28 47 22 27 17 22.9 22.5 23.4 Myanmar 116 117 .. 59 .. 61 71 71 71 0.7 0.7 0.7 Namibia 96 98 61 94 65 94 95 91 100 13.0 14.7 11.3 Nepal 128 117 52 57 52 69 73 78 67 21.6 21.8 21.4 Netherlands 99 98 .. .. .. .. .. .. .. .. .. .. New Zealand 99 98 90 .. 91 .. .. .. .. .. .. .. Nicaragua 142 134 51 51 57 58 75 71 79 6.7 7.7 5.7 Niger 67 48 61 73 65 68 21 25 17 8.6 8.5 8.7 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. .. 100 .. 100 .. .. .. .. .. .. .. Oman 74 74 95 96 96 96 72 75 69 4.3 5.2 3.3 Pakistan 107 80 .. .. .. .. .. .. .. .. .. .. Panama 120 117 .. 88 .. 89 86 85 87 5.6 6.6 4.6 Papua New Guinea 102 90 60 61 58 58 59 63 55 .. .. .. Paraguay 114 112 69 76 72 78 89 88 90 8.0 9.2 6.7 Peru 115 116 .. 88 .. 87 98 98 99 10.7 10.9 10.4 Philippines 137 127 .. 76 .. 83 90 87 94 2.3 2.9 1.6 Poland 98 97 .. 99 .. 99 95 94 95 0.6 1.0 0.2 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 81 2.12 Education efficiency Apparent intake rate Share of cohort Primary Repeaters in in grade 1 reaching grade 5 completion rate primary school % of relevant % of relevant age group age group % of grade 1 students Total Male Female % of enrollment Male Female Male Female 2000/01­ 2000/01 ­ 2000/01 ­ Total Male Female 2001/02 a 2001/02 a 1990/91 2000/01 a 1990/91 2000/01 a 2002/03 a, b 2002/03 a, b 2002/03 a,b2001/02 a 2001/02 a 2001/02 a Romania 102 102 .. .. .. .. 94 95 94 3.2 3.8 2.5 Russian Federation .. .. .. .. .. .. 99 .. .. 0.9 .. .. Rwanda 132 133 61 39 59 41 25 25 24 36.1 36.0 36.2 Saudi Arabia 68 67 82 94 84 94 66 66 66 5.2 6.3 3.9 Senegal 87 86 .. 70 .. 65 49 53 44 13.6 13.7 13.6 Serbia and Montenegro 98 99 .. .. .. .. .. .. .. 1.0 1.0 1.0 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 100 100 .. .. .. .. .. .. .. 2.4 2.6 2.1 Slovenia 106 106 .. .. .. .. 96 99 93 0.8 0.9 0.6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 117 116 72 .. 79 .. 90 89 91 8.8 10.2 7.3 Spain .. .. 100 .. 100 .. .. .. .. .. .. .. Sri Lanka .. .. 94 .. 95 .. 108 113 103 0.8 .. .. Sudan 58 48 90 .. 95 .. .. .. .. 11.3 10.9 11.8 Swaziland 100 96 74 69 78 79 74 77 72 16.7 18.9 14.3 Sweden .. .. 100 .. 100 .. .. .. .. .. .. .. Switzerland 92 96 76 101 75 101 .. .. .. 1.7 1.8 1.5 Syrian Arab Republic 124 121 94 93 94 92 89 93 85 6.8 7.7 5.7 Tajikistan 117 112 .. .. .. .. 101 104 98 0.4 0.3 0.4 Tanzania 107 100 77 79 81 83 58 57 59 2.5 2.5 2.5 Thailand 99 92 .. 92 .. 96 91 92 90 3.9 4.0 3.7 Togo 117 104 55 88 44 80 84 100 67 22.5 21.9 23.2 Trinidad and Tobago 100 96 96 98 96 101 108 107 110 8.0 8.4 7.5 Tunisia 98 99 92 95 78 96 98 99 98 9.8 11.5 8.0 Turkey .. .. 98 .. 97 .. 95 105 85 .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. 67 73 62 .. .. .. Ukraine 119 118 .. .. .. .. 98 98 97 0.2 0.2 0.2 United Arab Emirates 100 98 80 97 80 98 .. .. .. 2.8 3.2 2.4 United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 104 104 93 87 96 90 95 93 97 9.0 10.5 7.4 Uzbekistan 104 104 .. .. .. .. 98 98 98 .. .. .. Venezuela, RB 107 104 83 82 90 88 58 51 65 7.7 9.3 5.9 Vietnam 103 97 .. 90 .. 88 104 106 101 2.4 2.8 1.9 West Bank and Gaza .. .. .. .. .. .. 66 61 72 .. .. .. Yemen, Rep. 104 79 .. 80 .. 98 68 90 45 9.0 11.1 5.5 Zambia 86 87 .. 79 .. 75 59 64 54 6.2 6.5 5.9 Zimbabwe 121 118 96 .. 89 .. .. .. .. .. .. .. World 116 w 104 w .. w .. w .. w .. w .. w .. w .. w 5.6 w .. w .. w Low income 121 105 .. 66 .. 68 74 78 68 6.7 6.8 6.7 Middle income 98 98 .. .. .. .. 98 98 97 4.8 .. .. Lower middle income 98 97 .. .. .. .. 97 98 96 4.7 .. .. Upper middle income 101 101 .. 90 .. 92 89 88 90 5.2 6.2 4.2 Low & middle income 117 105 .. .. .. .. 86 88 82 5.7 .. .. East Asia & Pacific 96 97 .. .. .. .. 100 101 98 2.0 .. .. Europe & Central Asia 93 92 .. .. .. .. 97 99 95 .. .. .. Latin America & Carib. 125 118 .. .. .. .. 87 83 92 13.0 12.5 11.3 Middle East & N. Africa 96 95 .. 93 .. 95 91 94 87 7.8 9.3 6.1 South Asia 130 110 .. 59 .. 61 78 84 71 4.6 4.6 4.6 Sub-Saharan Africa 92 82 .. .. .. .. 48 d 51 d 44 d .. .. .. High income .. .. .. .. .. .. .. .. .. .. .. .. Europe EMU 99 98 .. .. .. .. .. .. .. 2.2 2.3 2.1 a. Data are preliminary. b. Data are for the most recent year available. c. Data are for 2002/03. d. Represent only 60% of the population. 82 2004 World Development Indicators PEOPLE 2.12 Education efficiency About the data Definitions Indicators of students' progress through school are requires setting curriculum standards and measuring · Apparent intake rate in grade 1 is the number of estimated by the UNESCO Institute for Statistics and students' learning progress against those standards new entrants in the first grade of primary education the World Bank. These indicators measure an edu- through standardized assessments or tests. regardless of age, expressed as a percentage of the cation system's success in extending coverage to all The World Bank and the UNESCO Institute for population of the official primary school entrance students, maintaining the flow of students from one Statistics are working jointly on development of the age. · Share of cohort reaching grade 5 is the per- grade to the next, and, ultimately, imparting a par- primary completion rate indicator. The primary com- centage of children enrolled in the first grade of pri- ticular level of education. pletion rate is increasingly used as a core indicator mary school who eventually reach grade 5. The esti- Apparent intake rate indicates the general level of of an education system's performance. It reflects mate is based on the reconstructed cohort method access to primary education. It also indicates the both the coverage of the education system and the (see About the data). · Primary completion rate is capacity of the education system to provide access to educational attainment of students. It is vital as a the percentage of students successfully completing primary education. Low apparent intake rates in grade key measure of educational outcome at the primary the last year of primary school. It is calculated by tak- 1 reflect the fact that many children do not enter pri- level and of progress on the Millennium Development ing the total number of students in the last grade of mary school even though school attendance, at least Goals and the Education for All initiative. However, primary school, minus the number of repeaters in through the primary level, is mandatory in all coun- because curricula and standards for school comple- that grade, divided by the total number of children tries. Because the apparent intake rate includes all tion vary across countries, a high rate of primary of official graduation age. · Repeaters in primary new entrants regardless of age, it can be more than completion does not necessarily mean high levels of school refer to the total number of pupils who are 100 percent. Once enrolled, students drop out for a student learning. enrolled in the same grade as in a previous year, variety of reasons, including low quality of schooling, The primary completion rate reflects the primary expressed as a percentage of the total enrollment. It discouragement over poor performance, and the direct cycle as nationally defined, ranging from three or four is calculated by taking the total number of students and indirect costs of schooling. Students' progress to years of primary education (in a very small number of in the last grade of primary school, minus the num- higher grades may also be limited by the availability of countries) to five or six years (in most countries) and ber of repeaters in that grade, divided by the total teachers, classrooms, and educational materials. seven or eight years (in a small number of countries). number of children of official graduation age. The cohort survival rate is estimated as the pro- The data shown in the table are for the proxy pri- portion of an entering cohort of grade 1 students mary completion rate, calculated by subtracting the that eventually reaches grade 5. It measures the number of students who repeat the final primary holding power and internal efficiency of an education grade from the number of students in that grade and system. Cohort survival rates approaching 100 per- dividing the result by the number of children of offi- cent indicate a high level of retention and a low level cial graduation age in the population. Data limita- of dropout. tions preclude adjusting this number for students Cohort survival rates are typically estimated from who drop out during the final year of primary school. data on enrollment and repetition by grade for two Thus proxy rates should be taken as an upper-bound consecutive years, in a procedure called the recon- estimate of the actual primary completion rate. structed cohort method. This method makes three The numerator may include overage children who simplifying assumptions: dropouts never return to have repeated one or more grades of primary school school; promotion, repetition, and dropout rates but are now graduating successfully as well as chil- remain constant over the entire period in which the dren who entered school early. The denominator is cohort is enrolled in school; and the same rates the number of children of official graduation age, apply to all pupils enrolled in a given grade, regard- which could cause the primary completion rate to less of whether they previously repeated a grade exceed 100 percent. There are other data limitations (Fredricksen 1993). Given these assumptions, cross- that contribute to completion rates exceeding 100 country comparisons should be made with caution, percent, such as the use of estimates for the popu- because other flows--caused by new entrants, reen- lation, the conduct of the school and population sur- trants, grade skipping, migration, or school transfers veys at different times of year, and other discrepan- Data sources during the school year--are not considered. cies in the numbers used in the calculation. The data on the apparent intake rate, the cohort The UNESCO Institute for Statistics measures Repeaters not only increase the cost of education reaching grade 5, and repeaters are from the cohort survival to grade 5 because research sug- for the family and for the school system, but also use UNESCO Institute for Statistics. The data on the gests that five to six years of schooling is a critical up limited school resources. Countries have different primary completion rate are compiled by staff in threshold for the achievement of sustainable basic policies on repetition and promotion of students; in the Development Data Group of the World Bank, literacy and numeracy skills. But the cohort survival some cases the number of repeaters is controlled in collaboration with the Education Anchor of the rate only indirectly reflects the quality of schooling, because of limited capacity of the school system. Human Development Network of the World Bank and a high rate does not guarantee these learning Care should be taken in cross-country comparisons and the UNESCO Institute for Statistics. outcomes. Measuring actual learning outcomes of this indicator. 2004 World Development Indicators 83 2.13 Education outcomes Adult literacy rate Youth literacy rate Expected years of schooling % ages 15 and older % ages 15­24 Male Female Male Female Male Female 1990 2002 a 1990 2002 a 1990 2002 a 1990 2002 a 1990/91 2000/01 1990/91 2000/01 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 87 99 b 67 98 b 97 99 b 92 99 b .. 11 .. 11 Algeria 64 78 41 60 86 94 68 86 11 .. 9 .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 96 97 96 97 98 98 98 99 .. 14 .. 15 Armenia 99 100 b 96 99 b 100 100 b 99 100 b .. 8 .. 9 Australia .. .. .. .. .. .. .. .. 13 17 13 17 Austria .. .. .. .. .. .. .. .. 15 15 14 15 Azerbaijan .. .. .. .. .. .. .. .. .. 11 .. 10 Bangladesh 44 50 24 31 51 58 33 41 6 8 4 8 Belarus 100 100 99 100 100 100 100 100 .. 12 .. 13 Belgium .. .. .. .. .. .. .. .. 14 16 14 16 Benin 38 55 15 26 57 73 25 38 .. 9 .. 5 Bolivia 87 93 b 70 81 b 96 99 b 89 96 b .. .. .. .. Bosnia and Herzegovina .. 98 .. 91 .. 100 .. 100 .. .. .. .. Botswana 66 76 70 82 79 85 87 93 10 12 11 12 Brazil 83 86 b 81 87 b 91 93 b 93 96 b .. 13 .. 14 Bulgaria 98 99 96 98 100 100 99 100 12 13 12 13 Burkina Faso 25 19 b 8 8 b 36 26 b 14 14 b 3 .. 2 .. Burundi 48 58 27 44 58 67 45 65 6 .. 4 .. Cambodia 78 81 49 59 81 85 66 76 .. 8 .. 7 Cameroon 69 77 c 48 60 c 86 .. 76 .. .. .. .. .. Canada .. .. .. .. .. .. .. .. 17 14 17 15 Central African Republic 47 65 c 21 33 c 66 70 c 39 47 c .. .. .. .. Chad 37 55 19 38 58 76 38 64 .. 7 .. 4 Chile 94 96 b 94 96 b 98 99 b 98 99 b .. 14 .. 13 China 87 95 b 69 87 b 97 99 b 93 99 b .. .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 89 92 88 92 94 97 96 98 .. 11 .. 11 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 77 89 58 77 95 98 90 97 .. .. .. .. Costa Rica 94 96 94 96 97 98 98 99 .. 10 .. 10 Côte d'Ivoire 51 .. 26 .. 65 70 c 40 52 c .. .. .. .. Croatia 99 99 b 95 97 b 100 100 b 100 100 b .. 12 .. 12 Cuba 95 97 95 97 99 100 99 100 12 12 13 12 Czech Republic .. .. .. .. .. .. .. .. .. 14 .. 14 Denmark .. .. .. .. .. .. .. .. 14 15 14 16 Dominican Republic 80 84 79 84 87 91 88 92 .. .. .. .. Ecuador 90 92 b 85 90 b 96 96 b 95 96 b .. .. .. .. Egypt, Arab Rep. 60 67 b 34 44 b 71 79 b 51 67 b .. 10 .. 10 El Salvador 76 82 69 77 85 90 83 88 .. 11 .. 11 Eritrea .. .. .. .. .. .. .. .. .. 6 .. 4 Estonia 100 100 b 100 100 b 100 100 b 100 100 b 12 14 12 15 Ethiopia 37 49 20 34 52 63 34 52 .. 6 .. 4 Finland .. .. .. .. .. .. .. .. 15 16 16 17 France .. .. .. .. .. .. .. .. 14 15 15 16 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. 6 .. 6 Germany .. .. .. .. .. .. .. .. 15 15 14 15 Ghana 70 82 47 66 88 94 75 90 .. 8 .. 7 Greece 98 99 92 96 99 100 100 100 13 15 13 15 Guatemala 69 77 53 62 80 86 66 74 .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 43 54 37 50 56 66 54 67 .. .. .. .. 84 2004 World Development Indicators PEOPLE 2.13 Education outcomes Adult literacy rate Youth literacy rate Expected years of schooling % ages 15 and older % ages 15­24 Male Female Male Female Male Female 1990 2002 a 1990 2002 a 1990 2002 a 1990 2002 a 1990/91 2000/01 1990/91 2000/01 Honduras 69 80 b 67 80 b 78 87 b 81 91 b .. .. .. .. Hungary 99 99 99 99 100 100 100 100 11 13 11 14 India 62 .. 36 .. 73 .. 54 .. .. .. .. .. Indonesia 87 92 73 83 97 99 93 98 10 .. 9 .. Iran, Islamic Rep. 72 84 c 54 70 c 92 .. 81 .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. 10 .. 8 Ireland .. .. .. .. .. .. .. .. 12 14 13 15 Israel 95 97 88 93 99 100 98 99 .. 14 .. 15 Italy 98 99 97 98 100 100 100 100 .. 15 .. 15 Jamaica 78 84 86 91 87 91 95 98 11 11 11 11 Japan .. .. .. .. .. .. .. .. .. 14 .. 14 Jordan 90 96 72 86 98 99 95 100 9 12 9 13 Kazakhstan 99 100 98 99 100 100 100 100 .. 12 .. 12 Kenya 81 90 61 79 93 96 87 95 .. 8 .. 8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. 14 16 13 14 Kuwait 79 85 73 81 88 92 87 94 7 8 7 9 Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. .. .. Lao PDR 70 77 43 55 79 86 61 73 9 9 6 7 Latvia 100 100 b 100 100 b 100 100 b 100 100 b .. 12 .. 14 Lebanon .. .. .. .. .. .. .. .. .. 13 .. 13 Lesotho 65 74 c 89 90 c 77 .. 97 .. 9 10 11 10 Liberia 55 72 23 39 75 86 39 55 .. 11 .. 8 Libya 83 92 51 71 99 100 83 94 .. .. .. .. Lithuania 100 100 b 99 100 b 100 100 b 100 100 b .. 14 .. 15 Macedonia, FYR .. .. .. .. .. .. .. .. .. 12 .. 12 Madagascar .. .. .. .. .. .. .. .. .. 6 .. 6 Malawi 69 76 36 49 76 82 51 63 .. .. .. .. Malaysia 87 92 b 74 85 b 95 97 b 94 97 b .. 12 .. 12 Mali 28 27 b 10 12 b 38 32 b 17 17 b 3 .. 1 .. Mauritania 46 51 24 31 56 57 36 42 .. 7 .. 6 Mauritius 85 88 b 75 81 b 91 94 b 91 95 b .. 12 .. 12 Mexico 91 93 b 84 89 b 96 97 b 94 96 b .. 12 .. 11 Moldova 99 100 96 99 100 100 100 100 .. 9 .. 10 Mongolia 98 98 b 97 98 b 99 97 b 99 98 b .. 9 .. 11 Morocco 53 63 25 38 68 77 42 61 .. 9 .. 7 Mozambique 49 62 18 31 66 77 32 49 4 7 3 5 Myanmar 87 89 74 81 90 92 86 91 .. 7 .. 7 Namibia 77 84 72 83 86 91 89 94 .. 12 .. 12 Nepal 47 62 14 26 67 78 27 46 .. .. .. .. Netherlands .. .. .. .. .. .. .. .. 15 16 15 16 New Zealand .. .. .. .. .. .. .. .. 14 16 15 17 Nicaragua 63 77 c 63 77 c 68 84 c 69 89 c .. .. .. .. Niger 18 25 5 9 25 34 9 15 .. 3 .. 2 Nigeria 59 74 38 59 81 91 66 87 .. .. .. .. Norway .. .. .. .. .. .. .. .. 14 16 14 18 Oman 67 82 38 65 95 100 75 97 10 9 9 9 Pakistan 49 53 b 20 29 b 63 65 b 31 42 b .. .. .. .. Panama 90 93 88 92 96 97 95 97 .. 12 .. 13 Papua New Guinea .. .. .. .. .. .. .. .. .. 6 .. 6 Paraguay 92 93 c 88 90 c 96 96 c 95 96 c 9 10 8 10 Peru 92 91 c 79 80 c 97 98 c 92 96 c .. 13 .. 11 Philippines 92 93 b 91 93 b 97 94 b 97 96 b .. 11 .. 12 Poland .. .. .. .. .. .. .. .. 12 14 12 15 Portugal 91 95 84 91 99 100 100 100 13 15 14 16 Puerto Rico 92 94 91 94 95 97 97 98 .. .. .. .. 2004 World Development Indicators 85 2.13 Education outcomes Adult literacy rate Youth literacy rate Expected years of schooling % ages 15 and older % ages 15­24 Male Female Male Female Male Female 1990 2002 a 1990 2002 a 1990 2002 a 1990 2002 a 1990/91 2000/01 1990/91 2000/01 Romania 99 98 b 96 96 b 99 98 b 99 98 b 11 12 11 12 Russian Federation 100 100 99 99 100 100 100 100 .. .. .. .. Rwanda 63 75 44 63 78 86 67 84 .. .. .. .. Saudi Arabia 76 84 50 69 91 95 79 92 9 .. 7 9 Senegal 38 49 19 30 50 61 30 44 .. .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. 10 .. 11 Sierra Leone .. .. .. .. .. .. .. .. .. 7 .. 5 Singapore 94 97 b 83 89 b 99 99 b 99 100 b .. .. .. .. Slovak Republic .. 100 b .. 100 b .. 100 b .. 100 b .. 13 .. 13 Slovenia 100 100 100 100 100 100 100 100 .. 14 .. 15 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 82 87 80 85 89 92 88 92 13 13 13 13 Spain 98 99 95 97 100 100 100 100 .. 15 .. 16 Sri Lanka 93 95 85 90 96 97 94 97 .. .. .. .. Sudan 60 71 32 49 76 84 54 74 .. .. .. .. Swaziland 74 82 70 80 85 90 85 92 11 13 10 12 Sweden .. .. .. .. .. .. .. .. 13 15 13 17 Switzerland .. .. .. .. .. .. .. .. 14 16 13 15 Syrian Arab Republic 82 91 48 74 92 97 67 93 11 .. 9 .. Tajikistan 99 100 b 97 99 b 100 100 b 100 100 b .. 11 .. 9 Tanzania 76 85 51 69 89 94 77 89 .. 5 .. 5 Thailand 95 95 b 89 91 b 99 98 b 98 98 b .. 11 .. 11 Togo 60 74 29 45 79 88 48 67 11 12 6 8 Trinidad and Tobago 98 99 96 98 100 100 100 100 11 11 11 12 Tunisia 72 83 47 63 93 98 75 91 11 14 10 14 Turkey 89 93 b 66 75 b 97 98 b 88 93 b .. .. .. .. Turkmenistan .. 99 b .. 98 b .. 100 b .. 100 b .. .. .. .. Uganda 69 79 43 59 80 86 60 74 .. .. .. .. Ukraine 100 100 99 100 100 100 100 100 .. 11 .. 12 United Arab Emirates 71 76 71 81 82 88 89 95 10 .. 11 .. United Kingdom .. .. .. .. .. .. .. .. 14 16 14 17 United States .. .. .. .. .. .. .. .. 15 15 16 16 Uruguay 96 97 97 98 98 99 99 99 .. 13 .. 14 Uzbekistan 99 100 98 99 100 100 100 100 .. .. .. .. Venezuela, RB 90 94 88 93 95 98 97 99 .. 10 .. 11 Vietnam 94 94 b 87 87 b 94 .. 94 .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 55 69 13 29 74 84 25 51 .. 11 .. 5 Zambia 79 86 59 74 86 91 76 87 .. 7 .. 7 Zimbabwe 87 94 75 86 97 99 91 96 .. 10 .. 9 World 79 w 84 w 63 w 71 w 87 w 89 w 78 w 83 w .. w .. w .. w .. w Low income 64 72 42 53 75 82 59 70 .. .. .. .. Middle income 88 92 75 83 95 97 91 94 .. .. .. .. Lower middle income 87 92 74 82 95 96 91 94 .. .. .. .. Upper middle income 92 95 88 92 97 98 95 98 .. .. .. .. Low & middle income 78 83 62 70 86 89 77 82 .. .. .. .. East Asia & Pacific 88 93 71 82 97 98 93 97 .. .. .. .. Europe & Central Asia 98 99 95 96 99 100 98 99 .. .. .. .. Latin America & Carib. 87 90 83 89 93 95 93 96 .. .. .. .. Middle East & N. Africa 66 76 40 55 81 87 61 75 .. .. .. .. South Asia 59 67 34 44 70 77 50 61 .. .. .. .. Sub-Saharan Africa 60 71 40 56 75 83 60 74 .. .. .. .. High income .. .. .. .. .. .. .. .. 15 .. 15 .. Europe EMU .. .. .. .. .. .. .. .. 15 .. 15 .. a. Data are preliminary. b. National estimates based on census data. c. National estimates based on survey data. 86 2004 World Development Indicators PEOPLE 2.13 Education outcomes About the data Many governments collect and publish statistics that national estimates are received from countries and Because the calculation of this indicator assumes indicate how their education systems are working are based on national censuses or household sur- that the probability of a child's being enrolled in and developing--statistics on enrollment and on veys during 1995­2004. The UNESCO Institute for school at any future age is equal to the current such efficiency indicators as repetition rates, pupil- Statistics estimates were assessed in July 2002. enrollment ratio for that age, it does not account for teacher ratios, and cohort progression through The estimation methodology can be reviewed at changes and trends in future enrollment ratios. The school. But until recently, despite an obvious interest www.uis.unesco.org. expected number of years and the expected number in what education achieves, few systems in high- Literacy statistics for most countries cover the pop- of grades completed are not necessarily consistent, income or developing countries had systematically ulation ages 15 and older, by five-year age groups, but because the first includes years spent in repetition. collected information on outcomes of education. some include younger ages or are confined to age Comparability across countries and over time may be Basic student outcomes include achievements in ranges that tend to inflate literacy rates. As an alter- affected by differences in the length of the school reading and mathematics judged against established native, the UNESCO Institute for Statistics has pro- year or changes in policies on automatic promotions standards. In many countries national learning posed the narrower age range of 15­24, which better and grade repetition. assessments are enabling ministries of education to captures the ability of participants in the formal edu- monitor progress in these outcomes. Internationally, cation system. The youth illiteracy rate reported in the Definitions the United Nations Educational, Scientific, and table measures the accumulated outcomes of primary Cultural Organization (UNESCO) Institute for Statistics education over the previous 10 years or so by indicat- · Adult literacy rate is the percentage of people ages has established literacy as an outcome indicator ing the proportion of people who have passed through 15 and older who can, with understanding, both read based on an internationally agreed definition. the primary education system without acquiring basic and write a short, simple statement about their every- The literacy rate is defined as the percentage of literacy and numeracy skills (or never entered the sys- day life. · Youth literacy rate is the literacy rate people who can, with understanding, both read and tem). Reasons for this may include difficulties in among people ages 15­24. · Expected years of write a short, simple statement about their everyday attending school or dropping out before reaching schooling are the average number of years of formal life. In practice, literacy is difficult to measure. To esti- grade 5 (see About the data for table 2.12) and there- schooling that children are expected to receive, includ- mate literacy using such a definition requires census by failing to achieve basic learning competencies. ing university education and years spent in repetition. or survey measurements under controlled conditions. Expected years of schooling is an estimate of the They reflect the underlying age-specific enrollment Many countries estimate the number of literate peo- total years of schooling that a typical child at the age ratios for primary, secondary, and tertiary education. ple from self-reported data. Some use educational of school entry will receive, including years spent on attainment data as a proxy but apply different lengths repetition, given the current patterns of enrollment of school attendance or level of completion. Because across cycles of education. It may also be interpreted definition and methodologies of data collection differ as an indicator of the total education resources, across countries, data need to be used with caution. measured in school years, that a child will acquire over The reported literacy data are national estimates his or her "lifetime" in school--or as an indicator of an or UNESCO Institute for Statistics estimates. The education system's overall level of development. 2.13a There is a strong positive relationship between primary school enrollment ratios and literacy among youth Literacy rate, 2002 (%) 100 80 60 40 20 Data sources 0 The data on literacy are national estimates col- 0 50 100 150 lected by the UNESCO Institute for Statistics and Gross primary enrollment ratio, 1990 estimates and projections by the UNESCO Children learn basic reading and writing along with other subjects in primary school. The primary school enrollment ratio Institute for Statistics, assessed in July 2002. and the literacy rate among young people (15­24) have a strong positive relationship, suggesting the push to achieve The data on expected years of schooling are from universal primary education will increase the number of literate young people. the UNESCO Institute for Statistics. Source: UNESCO Institute for Statistics. 2004 World Development Indicators 87 2.14 Health expenditure, services, and use Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita per 1,000 per 1,000 % of Total Public Public people people population days % of GDP % of GDP % of total $ 1995­ 1995­ 1995­ 1995­ 1995­ 2001 2001 2001 2001 1980 2002 a 1980 2002 a 2002 a 2002 a 2002 a Afghanistan 5.2 2.7 52.6 8 .. 0.1 .. .. .. .. .. Albania 3.7 2.4 64.6 48 1.4 1.4 4.3 3.3 .. .. .. Algeria 4.1 3.1 75.0 73 .. 1.0 .. 2.1 .. .. .. Angola 4.4 2.8 63.1 31 .. 0.1 .. .. .. .. .. Argentina 9.5 5.1 53.4 679 .. 2.7 .. 3.3 .. .. .. Armenia 7.8 3.2 41.2 28 3.2 2.9 8.4 4.3 8 15 2 Australia 9.2 6.2 67.9 1,741 .. 2.5 12.3 7.9 16 16 6 Austria 8.0 5.5 69.3 1,866 1.6 3.2 11.2 8.6 30 9 7 Azerbaijan 0.9 0.6 75.1 8 3.4 3.6 9.7 8.5 6 .. 1 Bangladesh 3.5 1.5 44.2 12 0.1 0.2 0.2 .. .. .. .. Belarus 5.6 4.8 86.7 68 3.0 4.5 12.5 12.6 26 18 11 Belgium 8.9 6.4 71.7 1,983 2.3 3.9 9.4 7.3 20 12 7 Benin 4.4 2.1 46.9 16 0.1 0.1 1.5 .. .. .. .. Bolivia 5.3 3.5 66.3 49 .. 1.3 .. 1.7 .. .. .. Bosnia and Herzegovina 7.5 2.8 36.8 85 1.0 1.4 4.8 3.2 .. 15 .. Botswana 6.6 4.4 66.2 190 0.1 .. 2.4 .. .. .. .. Brazil 7.6 3.2 41.6 222 .. 1.3 .. 3.1 0 b .. 2 Bulgaria 4.8 3.9 82.1 81 2.5 3.4 8.9 7.2 .. 12 .. Burkina Faso .. 2.0 .. .. 0.0 c 0.0 c .. 1.4 2 3 0 b Burundi 3.6 2.1 59.0 4 .. .. .. .. .. .. .. Cambodia 11.8 1.7 14.9 30 .. 0.3 .. .. .. .. .. Cameroon 3.3 1.2 37.1 20 .. 0.1 .. .. .. .. .. Canada 9.5 6.8 70.8 2,163 1.8 2.1 6.8 3.9 10 9 6 Central African Republic 4.5 2.3 51.2 12 0.0 c 0.0 c 1.6 .. .. .. .. Chad 2.6 2.0 76.0 5 .. .. .. .. .. .. .. Chile 7.0 3.1 44.0 296 .. 1.1 3.4 2.7 .. .. .. China 5.5 2.0 37.2 49 1.2 1.4 2.2 2.5 4 12 .. Hong Kong, China .. .. .. .. 0.8 1.3 4.0 .. .. .. .. Colombia 5.5 3.6 65.7 105 .. 1.2 1.6 1.5 .. .. .. Congo, Dem. Rep. 3.5 1.5 44.4 5 .. 0.1 .. .. .. .. .. Congo, Rep. 2.1 1.4 63.8 18 .. 0.3 .. .. .. .. .. Costa Rica 7.2 4.9 68.5 293 .. 0.9 3.3 1.7 9 6 1 Côte d'Ivoire 6.2 1.0 16.0 41 .. 0.1 .. .. .. .. .. Croatia 9.0 7.3 81.8 394 1.7 2.4 7.2 6.0 .. .. .. Cuba 7.2 6.2 86.2 185 .. 5.3 .. 5.1 .. .. .. Czech Republic 7.4 6.7 91.4 407 2.3 3.4 11.3 8.8 21 11 13 Denmark 8.4 7.0 82.4 2,545 2.2 3.4 8.1 4.5 20 6 6 Dominican Republic 6.1 2.2 36.1 153 .. 2.2 .. 1.5 .. .. .. Ecuador 4.5 2.3 50.3 76 .. 1.7 1.9 1.6 .. .. .. Egypt, Arab Rep. 3.9 1.9 48.9 46 1.1 1.6 2.0 2.1 3 6 4 El Salvador 8.0 3.7 46.7 174 0.3 1.1 .. 1.6 .. .. .. Eritrea 5.7 3.7 65.1 10 .. 0.0 c .. .. .. .. .. Estonia 5.5 4.3 77.8 226 2.9 3.1 12.2 6.7 18 9 5 Ethiopia 3.6 1.4 40.5 3 0.0 c .. 0.3 .. .. .. .. Finland 7.0 5.3 75.6 1,631 1.7 3.1 15.6 7.5 27 11 4 France 9.6 7.3 76.0 2,109 2.0 3.3 11.1 8.2 23 13 7 Gabon 3.6 1.7 47.9 127 .. .. .. .. .. .. .. Gambia, The 6.4 3.2 49.4 19 .. 0.0 c .. .. .. .. .. Georgia 3.6 1.4 37.8 22 4.1 3.9 10.2 4.3 5 11 1 Germany 10.8 8.1 74.9 2,412 2.3 3.3 11.5 9.1 24 12 7 Ghana 4.7 2.8 59.6 12 .. 0.1 .. .. .. .. .. Greece 9.4 5.2 56.0 1,001 2.4 4.4 6.2 4.9 15 8 3 Guatemala 4.8 2.3 48.3 86 .. 0.9 .. 1.0 .. .. .. Guinea 3.5 1.9 54.1 13 .. 0.1 .. .. .. .. .. Guinea-Bissau 5.9 3.2 53.8 8 0.1 0.2 1.9 .. .. .. .. Haiti 5.0 2.7 53.4 22 .. 0.2 0.7 0.7 .. .. .. 88 2004 World Development Indicators PEOPLE 2.14 Health expenditure, services, and use Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita per 1,000 per 1,000 % of Total Public Public people people population days % of GDP % of GDP % of total $ 1995­ 1995­ 1995­ 1995­ 1995­ 2001 2001 2001 2001 1980 2002 a 1980 2002 a 2002 a 2002 a 2002 a Honduras 6.1 3.2 53.1 59 .. 0.8 1.3 1.1 .. .. .. Hungary 6.8 5.1 75.0 345 2.3 2.9 9.1 8.2 24 9 12 India 5.1 0.9 17.9 24 0.4 .. 0.8 .. .. .. .. Indonesia 2.4 0.6 25.1 16 .. .. .. .. .. .. .. Iran, Islamic Rep. 6.6 2.7 41.9 363 .. 0.9 1.5 1.6 .. .. .. Iraq 3.2 1.0 31.8 225 0.6 0.6 1.9 1.5 .. .. .. Ireland 6.5 4.9 76.0 1,711 .. 2.4 13.0 9.7 15 8 .. Israel 8.7 6.0 69.2 1,641 3.1 3.7 6.8 6.2 .. .. .. Italy 8.4 6.3 75.3 1,584 2.6 4.3 9.6 4.9 18 8 6 Jamaica 6.8 2.9 42.1 191 .. 1.4 .. 2.1 .. .. .. Japan 8.0 6.2 77.9 2,627 1.3 1.9 13.7 16.5 10 40 14 Jordan 9.5 4.5 47.0 163 0.8 1.7 1.3 1.8 11 4 .. Kazakhstan 3.1 1.9 60.4 44 3.0 3.6 13.1 7.0 15 16 0 b Kenya 7.8 1.7 21.4 29 .. 0.1 .. .. .. .. .. Korea, Dem. Rep. 2.5 1.9 73.4 22 .. 3.0 .. .. .. .. .. Korea, Rep. 6.0 2.6 44.4 532 .. 1.4 1.7 6.1 6 13 9 Kuwait 4.3 3.5 81.0 630 1.7 1.9 4.1 2.8 .. .. .. Kyrgyz Republic 4.0 1.9 48.7 12 2.6 2.6 12.0 5.5 21 13 1 Lao PDR 3.1 1.7 55.5 10 .. 0.2 .. .. .. .. .. Latvia 6.4 3.4 52.5 210 3.6 2.9 13.9 8.2 21 14 4 Lebanon 12.4 2.2 18.0 .. .. 2.1 .. 2.7 17 4 .. Lesotho 5.5 4.3 78.9 23 .. 0.1 .. .. .. .. .. Liberia 4.3 3.3 75.9 1 .. 0.0 c .. .. .. .. .. Libya 2.9 1.6 56.0 143 1.3 1.3 .. 4.3 .. .. .. Lithuania 6.0 4.2 70.5 206 .. 4.0 12.1 9.2 24 11 7 Macedonia, FYR 6.8 5.8 84.9 115 1.3 2.2 5.2 4.8 9 12 3 Madagascar 2.0 1.3 65.9 6 .. 0.1 .. 0.4 1 5 1 Malawi 7.8 2.7 35.0 13 .. .. .. 1.3 .. .. 2 Malaysia 3.8 2.0 53.7 143 0.3 0.7 .. 2.0 .. .. .. Mali 4.3 1.7 38.6 11 0.0 c 0.1 .. 0.2 1 7 0 b Mauritania 3.6 2.6 72.4 12 .. 0.1 .. .. .. .. .. Mauritius 3.4 2.0 59.5 128 0.5 0.9 3.1 .. .. .. .. Mexico 6.1 2.7 44.3 370 .. 1.5 0.7 1.1 6 4 3 Moldova 5.1 2.8 55.8 18 2.8 2.7 12.1 5.9 19 18 8 Mongolia 6.4 4.6 72.3 25 .. 2.4 11.2 .. .. .. .. Morocco 5.1 2.0 39.3 59 .. 0.5 .. 1.0 3 7 .. Mozambique 5.9 4.0 67.4 11 0.0 c .. 1.1 .. .. .. .. Myanmar 2.1 0.4 17.8 197 .. 0.3 0.9 .. .. .. .. Namibia 7.0 4.7 67.8 110 .. 0.3 .. .. .. .. .. Nepal 5.2 1.5 29.7 12 0.0 c 0.0 c 0.2 0.2 .. .. .. Netherlands 8.9 5.7 63.3 2,138 1.9 3.3 12.3 10.8 10 33 6 New Zealand 8.3 6.4 76.8 1,073 1.6 2.2 10.2 6.2 13 8 4 Nicaragua 7.8 3.8 48.5 60 0.4 0.9 .. 1.5 .. .. .. Niger 3.7 1.4 39.1 6 .. 0.0 c .. 0.1 28 5 0 b Nigeria 3.4 0.8 23.2 15 0.1 .. 0.9 .. .. .. .. Norway 8.0 6.8 85.5 2,981 2.0 3.0 16.5 14.6 17 9 .. Oman 3.0 2.4 80.7 225 0.5 1.3 1.6 2.2 9 4 4 Pakistan 3.9 1.0 24.4 16 0.3 0.6 0.6 .. .. .. .. Panama 7.0 4.8 69.0 258 .. 1.7 .. 2.2 .. .. .. Papua New Guinea 4.4 3.9 89.0 24 0.1 0.1 5.5 .. .. .. .. Paraguay 8.0 3.0 38.3 97 .. 1.1 .. 1.3 .. .. .. Peru 4.7 2.6 55.0 97 0.7 0.9 .. 1.5 1 6 .. Philippines 3.3 1.5 45.2 30 0.1 1.2 1.7 .. .. .. .. Poland 6.1 4.6 71.9 289 1.8 2.2 5.6 4.9 16 8 6 Portugal 9.2 6.3 69.0 982 2.0 3.2 5.2 4.0 12 9 3 Puerto Rico .. .. .. .. .. 1.8 .. 3.3 .. .. .. 2004 World Development Indicators 89 2.14 Health expenditure, services, and use Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita per 1,000 per 1,000 % of Total Public Public people people population days % of GDP % of GDP % of total $ 1995­ 1995­ 1995­ 1995­ 1995­ 2001 2001 2001 2001 1980 2002 a 1980 2002 a 2002 a 2002 a 2002 a Romania 6.5 5.2 79.2 117 1.5 1.9 8.8 7.5 18 10 4 Russian Federation 5.4 3.7 68.2 115 .. 4.2 .. 10.8 22 17 8 Rwanda 5.5 3.1 55.5 11 0.0 c .. 1.5 .. .. .. .. Saudi Arabia 4.6 3.4 74.6 375 .. 1.7 .. 2.3 11 4 1 Senegal 4.8 2.8 58.8 22 .. 0.1 .. 0.4 .. 10 1 Serbia and Montenegro 8.2 6.5 79.2 103 .. 2.1 .. 5.3 .. 12 2 Sierra Leone 4.3 2.6 61.0 7 0.1 0.1 1.2 .. .. .. .. Singapore 3.9 1.3 33.5 816 0.9 1.6 4.0 .. .. .. .. Slovak Republic 5.7 5.1 89.3 216 .. 3.6 .. 7.8 19 10 .. Slovenia 8.4 6.3 74.9 821 1.8 2.2 7.0 5.2 .. .. .. Somalia 2.6 1.2 44.6 6 0.0 c 0.0 c .. .. .. .. .. South Africa 8.6 3.6 41.4 222 .. 0.6 .. .. .. .. .. Spain 7.5 5.4 71.4 1,088 .. 3.3 5.4 4.1 12 9 9 Sri Lanka 3.6 1.8 48.9 30 0.1 0.4 2.9 .. .. .. .. Sudan 3.5 0.6 18.7 14 0.1 0.1 0.9 .. .. .. .. Swaziland 3.3 2.3 68.5 41 .. 0.2 .. .. .. .. .. Sweden 8.7 7.4 85.2 2,150 2.2 3.0 15.1 3.6 18 6 3 Switzerland 11.1 6.4 57.1 3,779 2.4 3.5 .. 17.9 15 13 .. Syrian Arab Republic 5.4 2.4 43.9 65 0.4 1.3 1.1 1.4 .. .. .. Tajikistan 3.4 1.0 28.9 6 .. 2.1 .. 6.4 .. .. .. Tanzania 4.4 2.0 46.7 12 .. 0.0 c 1.4 .. .. .. .. Thailand 3.7 2.1 57.1 69 0.1 0.4 1.5 2.0 .. .. 1 Togo 2.8 1.5 48.6 8 0.1 0.1 .. .. .. .. .. Trinidad and Tobago 4.0 1.7 43.3 279 0.7 0.8 .. 5.1 .. .. .. Tunisia 6.4 4.9 75.7 134 0.3 0.7 2.1 1.7 .. .. .. Turkey 6.9 4.4 63.0 .. 0.6 1.3 2.2 2.6 8 6 3 Turkmenistan 4.1 3.0 73.3 57 2.8 3.0 10.5 7.1 .. .. .. Uganda 5.9 3.4 57.5 14 .. .. .. .. .. .. .. Ukraine 4.3 2.9 67.8 33 3.5 3.0 12.1 8.7 20 .. 10 United Arab Emirates 3.5 2.6 75.8 849 1.1 1.8 2.8 2.6 .. .. .. United Kingdom 7.6 6.3 82.2 1,835 1.3 2.0 8.1 4.1 15 10 5 United States 13.9 6.2 44.4 4,887 2.0 2.7 6.0 3.6 12 7 9 Uruguay 10.9 5.1 46.3 603 .. 3.7 .. 4.4 .. .. .. Uzbekistan 3.6 2.7 74.5 17 2.7 2.9 9.2 5.3 .. .. .. Venezuela, RB 6.0 3.7 62.1 307 0.8 2.4 0.3 1.5 .. .. .. Vietnam 5.1 1.5 28.5 21 0.2 0.5 3.5 1.7 8 7 .. West Bank and Gaza .. .. .. .. .. 0.5 .. 1.2 9 3 4 Yemen, Rep. 4.5 1.5 34.1 20 .. 0.2 .. 0.6 .. .. .. Zambia 5.7 3.0 53.1 19 0.1 0.1 .. .. .. .. .. Zimbabwe 6.2 2.8 45.3 45 0.2 0.1 3.0 .. .. .. .. World 9.8 w 5.6 w 59.2 w 500 w 1.1 w .. w 3.7 w .. w 9 w .. w .. w Low income 4.4 1.1 26.3 23 0.4 .. 1.2 .. .. .. .. Middle income 6.0 3.1 51.1 118 1.2 1.9 3.0 3.7 7 11 .. Lower middle income 5.8 2.7 47.2 85 1.2 2.0 2.9 3.7 6 12 .. Upper middle income 6.4 3.7 57.7 357 .. 1.8 3.8 3.4 11 6 5 Low & middle income 5.8 2.7 47.0 72 0.8 .. 2.2 .. .. .. .. East Asia & Pacific 4.9 1.9 38.8 48 1.0 1.4 2.2 2.5 4 12 .. Europe & Central Asia 5.8 4.3 72.4 123 .. 3.1 .. 8.9 18 13 6 Latin America & Carib. 7.0 3.4 48.0 255 .. 1.4 .. 2.2 2 .. 2 Middle East & N. Africa 4.9 2.8 59.3 166 .. .. .. .. .. .. .. South Asia 4.8 1.0 21.6 22 0.3 .. 0.7 .. .. .. .. Sub-Saharan Africa 6.0 2.5 41.3 29 .. .. .. .. .. .. .. High income 10.8 6.3 62.1 2,841 1.9 2.8 8.6 7.4 14 14 8 Europe EMU 9.3 6.8 73.5 1,856 2.2 3.5 9.9 8.0 20 12 7 a. Data are for the most recent year available. b. Less than 0.5. c. Less than 0.05. 90 2004 World Development Indicators PEOPLE 2.14 Health expenditure, services, and use About the data National health accounts track financial flows in the Indicators on health services (physicians and hospi- surgeries are counted as hospital admissions. And in health sector, including public and private expendi- tal beds per 1,000 people) and health care utilization many countries outpatient visits, especially emergency tures, by source of funding. In contrast with high- (inpatient admission rates, average length of stay, and visits, may result in double counting if a patient receives income countries, few developing countries have outpatient visits) come from a variety of sources (see treatment in more than one department. health accounts that are methodologically consistent Data sources). Data are lacking for many countries, with national accounting approaches. The difficulties and for others comparability is limited by differences Definitions in creating national health accounts go beyond data in definitions. In estimates of health personnel, for collection. To establish a national health accounting example, some countries incorrectly include retired · Total health expenditure is the sum of public and system, a country needs to define the boundaries of physicians (because deletions to physician rosters are private health expenditure. It covers the provision of the health care system and to define a taxonomy of made only periodically) or those working outside the health services (preventive and curative), family plan- health care delivery institutions. The accounting sys- health sector. There is no universally accepted defini- ning activities, nutrition activities, and emergency aid tem should be comprehensive and standardized, pro- tion of hospital beds. Moreover, figures on physicians designated for health but does not include provision viding not only accurate measures of financial flows and hospital beds are indicators of availability, not of of water and sanitation. · Public health expenditure but also information on the equity and efficiency of quality or use. They do not show how well trained the consists of recurrent and capital spending from health financing to inform health policy. physicians are or how well equipped the hospitals or government (central and local) budgets, external bor- The absence of consistent national health medical centers are. And physicians and hospital beds rowings and grants (including donations from inter- accounting systems in most developing countries tend to be concentrated in urban areas, so these indi- national agencies and nongovernmental organiza- makes cross-country comparisons of health spend- cators give only a partial view of health services avail- tions), and social (or compulsory) health insurance ing difficult. Records of private out-of-pocket spend- able to the entire population. funds. · Physicians are graduates of any faculty or ing are often lacking. And compiling estimates of The average length of stay in hospitals is an indicator school of medicine who are working in the country in public health expenditures is complicated in coun- of the efficiency of resource use. Longer stays may any medical field (practice, teaching, research). tries where state or provincial and local govern- reflect a waste of resources if patients are kept in hos- · Hospital beds include inpatient beds available in ments are involved in financing and delivering pitals beyond the time medically required, inflating public, private, general, and specialized hospitals health care, because the data on public spending demand for hospital beds and increasing hospital costs. and rehabilitation centers. In most cases beds for often are not aggregated. The data in the table are Aside from differences in cases and financing methods, both acute and chronic care are included. the product of an effort by the World Health cross-country variations in average length of stay may · Inpatient admission rate is the percentage of the Organization (WHO), the Organisation for Economic result from differences in the role of hospitals. Many population admitted to hospitals during a year. Co-operation and Development (OECD), and the developing countries do not have separate extended · Average length of stay is the average duration of World Bank to collect all available information on care facilities, so hospitals become the source of both inpatient hospital admissions. · Outpatient visits health expenditures from national and local govern- long-term and acute care. Other factors may also per capita are the number of visits to health care ment budgets, national accounts, household sur- explain the variations. Data for some countries may not facilities per capita, including repeat visits. veys, insurance publications, international donors, include all public and private hospitals. Admission rates and existing tabulations. may be overstated in some countries if outpatient 2.14a High health personnel absence rates lower the quality of health care Data sources Health personnel absence rate, 2000­03 (%) The estimates of health expenditure come mostly 40 from the WHO's World Health Report 2003 and 35 updates and from the OECD for its member coun- 30 tries, supplemented by World Bank poverty 25 assessments and country and sector studies. 20 Data are also drawn from World Bank public expen- 15 diture reviews, the International Monetary Fund's 10 Government Finance Statistics database, and 5 other studies. The data on private expenditure in 0 developing countries are drawn largely from house- India Indonesia Uganda Bangladesh Peru Papua hold surveys conducted by governments or by sta- New Guinea tistical or international organizations. The data on Health personnel absence rate is the percentage of full-time medical personnel who were absent from a random sample physicians, hospital beds, and utilization of of primary health centers during surprise visits. Some personnel were absent for valid reasons, but even authorized absences reduce the quantity and quality of primary health care. Absence rates tend to be higher in remote areas, affecting health services are from the WHO, OECD, and the quality of health care available in these areas. TransMONEE, supplemented by country data. Source: Chaudhury and others 2004; NRI and World Bank 2003; Habyarimana and others 2003. 2004 World Development Indicators 91 2.15 Disease prevention: coverage and quality Access to an Access to Tetanus Child immunization Children Tuberculosis DOTS improved improved vaccinations rate sleeping treatment detection water source sanitation under success rate facilities treated rate % of % of children bednets b % of % of % of % of pregnant ages 12­23 months a % of children registered estimated population population women Measles DPT under age 5 cases cases 1990 2000 1990 2000 2002 2002 2002 1999­2001 c 2001 2002 Afghanistan .. 13 .. 12 34 44 47 .. 84 19 Albania .. 97 .. 91 .. 96 98 .. 98 24 Algeria .. 89 .. 92 .. 81 86 .. 84 114 Angola .. 38 .. 44 62 74 47 2.3 66 91 Argentina 94 .. 82 .. .. 97 88 .. 64 51 Armenia .. .. .. .. .. 91 94 .. 90 28 Australia 100 100 100 100 .. 94 93 .. 66 25 Austria 100 100 100 100 .. 78 83 .. 64 41 Azerbaijan .. 78 .. 81 .. 97 97 1.4 66 43 Bangladesh 94 97 41 48 89 77 85 .. 84 32 Belarus .. 100 .. .. .. 99 99 .. .. .. Belgium .. .. .. .. .. 75 90 .. 64 64 Benin .. 63 20 23 66 78 79 7.4 79 98 Bolivia 71 83 52 70 .. 79 81 .. 82 75 Bosnia and Herzegovina .. .. .. .. .. 89 80 .. 98 47 Botswana 93 95 60 66 .. 90 97 .. 78 73 Brazil 83 87 71 76 .. 93 96 .. 67 10 Bulgaria .. 100 .. 100 .. 90 94 .. 87 43 Burkina Faso .. 42 .. 29 44 46 41 .. 65 18 Burundi 69 78 87 88 42 75 74 1.3 80 28 Cambodia .. 30 .. 17 36 52 54 .. 92 52 Cameroon 51 58 77 79 65 62 48 1.3 62 60 Canada 100 100 100 100 .. 96 97 .. 67 52 Central African Republic 48 70 24 25 63 35 40 1.5 61 49 Chad .. 27 18 29 39 55 40 0.6 .. 42 Chile 90 93 97 96 .. 95 94 .. 83 112 China 71 75 29 40 .. 65 79 .. 96 27 Hong Kong, China .. .. .. .. .. .. .. .. 78 51 Colombia 94 91 83 86 .. 89 85 0.7 85 9 Congo, Dem. Rep. .. 45 .. 21 44 45 43 0.7 77 52 Congo, Rep. .. 51 .. .. 41 37 41 .. 66 69 Costa Rica .. 95 .. 93 .. 94 94 .. 72 79 Côte d'Ivoire 80 81 46 52 80 56 54 1.1 73 25 Croatia .. .. .. .. .. 95 95 .. .. .. Cuba .. 91 .. 98 .. 98 99 .. 93 91 Czech Republic .. .. .. .. .. 97 98 .. 73 57 Denmark .. 100 .. .. .. 99 98 .. .. .. Dominican Republic 83 86 66 67 .. 92 72 .. 85 43 Ecuador 71 85 70 86 .. 80 89 .. 82 31 Egypt, Arab Rep. 94 97 87 98 70 97 97 .. 82 53 El Salvador 66 77 73 82 .. 93 81 .. 88 57 Eritrea .. 46 .. 13 50 84 83 .. 80 14 Estonia .. .. .. .. .. 95 97 .. 64 61 Ethiopia 25 24 8 12 24 52 56 .. 76 33 Finland 100 100 100 100 .. 96 98 .. .. .. France .. .. .. .. .. 85 98 .. .. .. Gabon .. 86 .. 53 50 55 38 .. 49 73 Gambia, The .. 62 .. 37 .. 90 90 14.7 71 73 Georgia .. 79 .. 100 .. 73 84 .. 67 50 Germany .. .. .. .. .. 89 97 .. 67 52 Ghana 53 73 61 72 73 81 80 .. 42 41 Greece .. .. .. .. .. 88 88 .. .. .. Guatemala 76 92 70 81 .. 92 84 1.2 85 45 Guinea 45 48 55 58 43 54 47 .. 74 54 Guinea-Bissau .. 56 44 56 41 47 50 7.4 51 43 Haiti 53 46 23 28 52 53 43 .. 75 41 92 2004 World Development Indicators PEOPLE 2.15 Disease prevention: coverage and quality Access to an Access to Tetanus Child immunization Children Tuberculosis DOTS improved improved vaccinations rate sleeping treatment detection water source sanitation under success rate facilities treated rate % of % of children bednets b % of % of % of % of pregnant ages 12­23 months a % of children registered estimated population population women Measles DPT under age 5 cases cases 1990 2000 1990 2000 2002 2002 2002 1999­2001 c 2001 2002 Honduras 83 88 61 75 .. 97 95 .. 86 114 Hungary 99 99 99 99 .. 99 99 .. 46 39 India 68 84 16 28 78 67 70 .. 85 31 Indonesia 71 78 47 55 81 76 75 0.1 86 30 Iran, Islamic Rep. .. 92 .. 83 .. 99 99 .. 84 60 Iraq .. 85 .. 79 70 90 81 .. 89 21 Ireland .. .. .. .. .. 73 84 .. .. .. Israel .. .. .. .. .. 95 97 .. 79 58 Italy .. .. .. .. .. 70 95 .. 40 63 Jamaica 93 92 99 99 .. 86 87 .. 78 68 Japan .. .. .. .. .. 98 95 .. 75 33 Jordan 97 96 98 99 .. 95 95 .. 86 72 Kazakhstan .. 91 .. 99 .. 95 95 .. 78 93 Kenya 45 57 80 87 60 78 84 2.9 80 49 Korea, Dem. Rep. .. 100 .. 99 .. .. .. .. 91 88 Korea, Rep. .. 92 .. 63 .. 97 97 .. .. .. Kuwait .. .. .. .. .. 99 98 .. .. .. Kyrgyz Republic .. 77 .. 100 .. 98 98 .. 81 45 Lao PDR .. 37 .. 30 35 55 55 .. 77 43 Latvia .. .. .. .. .. 98 97 .. 73 78 Lebanon .. 100 .. 99 .. 96 92 .. 91 68 Lesotho .. 78 .. 49 .. 70 79 .. 71 61 Liberia .. .. .. .. 41 57 51 .. .. .. Libya 71 72 97 97 .. 91 93 .. .. 106 Lithuania .. .. .. .. .. 98 95 .. 75 62 Macedonia, FYR .. .. .. .. .. 98 96 .. 88 37 Madagascar 44 47 36 42 35 61 62 0.2 69 62 Malawi 49 57 73 76 82 69 64 2.9 70 36 Malaysia .. .. .. .. .. 92 96 .. 79 78 Mali 55 65 70 69 32 33 57 .. 50 15 Mauritania 37 37 30 33 40 81 83 .. .. .. Mauritius 100 100 100 99 .. 84 88 .. 93 25 Mexico 80 88 70 74 .. 96 91 .. 83 73 Moldova .. 92 .. 99 .. 94 97 .. 66 19 Mongolia .. 60 .. 30 .. 98 98 .. 87 69 Morocco 75 80 58 68 .. 96 94 .. 87 83 Mozambique .. 57 .. 43 67 58 60 .. 77 45 Myanmar .. 72 .. 64 71 75 77 .. 81 73 Namibia 72 77 33 41 85 68 77 .. 68 76 Nepal 67 88 20 28 69 71 72 .. 88 64 Netherlands 100 100 100 100 .. 96 98 .. 76 54 New Zealand .. .. .. .. .. 85 90 .. 9 48 Nicaragua 70 77 76 85 .. 98 84 .. 83 85 Niger 53 59 15 20 36 48 23 1.0 .. 22 Nigeria 53 62 53 54 44 40 26 .. 79 12 Norway 100 100 .. .. .. 88 91 .. 87 26 Oman 37 39 84 92 .. 99 99 .. 90 106 Pakistan 83 90 36 62 56 57 63 .. 77 13 Panama .. 90 .. 92 .. 79 89 .. 65 88 Papua New Guinea 40 42 82 82 34 71 57 .. 67 15 Paraguay 63 78 93 94 .. 82 77 .. 86 8 Peru 74 80 60 71 .. 95 89 .. 90 84 Philippines 87 86 74 83 87 73 70 .. 88 58 Poland .. .. .. .. .. 98 99 .. 77 55 Portugal .. .. .. .. .. 87 96 .. 78 94 Puerto Rico .. .. .. .. .. .. .. .. 80 65 2004 World Development Indicators 93 2.15 Disease prevention: coverage and quality Access to an Access to Tetanus Child immunization Children Tuberculosis DOTS improved improved vaccinations rate sleeping treatment detection water source sanitation under success rate facilities treated rate % of % of children bednets b % of % of % of % of pregnant ages 12­23 months a % of children registered estimated population population women Measles DPT under age 5 cases cases 1990 2000 1990 2000 2002 2002 2002 1999­2001 c 2001 2002 Romania .. 58 .. 53 .. 98 99 .. 78 41 Russian Federation .. 99 .. .. .. 98 96 .. 67 6 Rwanda .. 41 .. 8 83 69 88 5.0 61 29 Saudi Arabia .. 95 .. 100 .. 97 95 .. 77 37 Senegal 72 78 57 70 75 54 60 1.7 53 54 Serbia and Montenegro .. 98 .. 100 .. 92 95 .. 88 22 Sierra Leone .. 57 .. 66 60 60 50 1.5 80 36 Singapore 100 100 100 100 .. 91 92 .. 88 39 Slovak Republic .. 100 .. 100 .. 99 99 .. 87 35 Slovenia 100 100 .. .. .. 94 92 .. 82 68 Somalia .. .. .. .. 60 45 40 0.3 86 28 South Africa 86 86 86 87 52 78 82 .. 65 96 Spain .. .. .. .. .. 97 96 .. .. .. Sri Lanka 68 77 85 94 .. 99 98 .. 80 79 Sudan 67 75 58 62 35 49 40 0.4 80 33 Swaziland .. .. .. .. .. 72 77 0.1 36 31 Sweden 100 100 100 100 .. 94 99 .. 62 59 Switzerland 100 100 100 100 .. 79 95 .. .. .. Syrian Arab Republic .. 80 .. 90 .. 98 99 .. 81 42 Tajikistan .. 60 .. 90 .. 84 84 1.9 .. 3 Tanzania 38 68 84 90 86 89 89 2.1 81 43 Thailand 80 84 79 96 .. 94 96 .. 75 73 Togo 51 54 37 34 38 58 64 2.0 55 6 Trinidad and Tobago 91 90 99 99 .. 88 89 .. .. .. Tunisia 75 80 76 84 .. 94 96 .. 90 92 Turkey 79 82 87 90 37 82 78 .. .. .. Turkmenistan .. .. .. .. .. 88 98 .. 75 36 Uganda 45 52 .. 79 50 77 72 0.2 56 47 Ukraine .. 98 .. 99 .. 99 99 .. .. .. United Arab Emirates .. .. .. .. .. 94 94 .. 62 25 United Kingdom 100 100 100 100 .. 83 91 .. .. .. United States 100 100 100 100 .. .. .. .. 70 87 Uruguay .. 98 .. 94 .. 92 93 .. 85 70 Uzbekistan .. 85 .. 89 .. 97 98 .. 76 24 Venezuela, RB .. 83 .. 68 .. 78 63 .. 80 65 Vietnam 55 77 29 47 89 96 75 15.8 93 82 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. 69 32 38 39 65 69 .. 80 49 Zambia 52 64 63 78 60 85 78 1.1 75 40 Zimbabwe 78 83 56 62 77 58 58 .. 71 46 World 74 w 81 w 45 w 55 w 72 w 75 w Low income 66 76 30 43 65 65 Middle income 76 82 47 60 80 85 Lower middle income 75 81 45 58 78 84 Upper middle income .. .. .. .. 94 90 Low & middle income 71 79 39 51 71 73 East Asia & Pacific 71 76 35 46 70 78 Europe & Central Asia .. 91 .. .. 93 92 Latin America & Carib. 82 86 72 77 91 88 Middle East & N. Africa .. 88 .. 85 92 92 South Asia 72 84 22 34 66 70 Sub-Saharan Africa 53 58 54 53 58 54 High income .. .. .. .. 90 95 Europe EMU .. .. .. .. 85 96 a. Refers to children who were immunized before 12 months or, in some cases, at any time before the survey (between 12­23 months). b. For malaria prevention only. c. Data are for the most recent year available. 94 2004 World Development Indicators PEOPLE 2.15 Disease prevention: coverage and quality About the data Definitions The indicators in the table are based on data provided and one booster shot during each subsequent preg- · Access to an improved water source refers to the to the World Health Organization (WHO) by member nancy, with five doses considered adequate for life- percentage of the population with reasonable access states as part of their efforts to monitor and evaluate time protection. Information on tetanus shots during to an adequate amount of water from an improved progress in implementing national health strategies. pregnancy is collected through surveys in which preg- source, such as a household connection, public Because reliable, observation-based statistical data nant respondents are asked to show antenatal cards standpipe, borehole, protected well or spring, or rain- for these indicators do not exist in some developing on which tetanus shots have been recorded. Because water collection. Unimproved sources include ven- countries, some of the data are estimated. not all women have antenatal cards, respondents are dors, tanker trucks, and unprotected wells and People's health is influenced by the environment in also asked about their receipt of these injections. Poor springs. Reasonable access is defined as the avail- which they live. Lack of clean water and basic sanita- recall may result in a downward bias in estimates of ability of at least 20 liters a person a day from a tion is the main reason diseases transmitted by feces the share of births protected. But in settings where source within 1 kilometer of the dwelling. · Access are so common in developing countries. The data on receiving injections is common, respondents may erro- to improved sanitation facilities refers to the per- access to an improved water source measure the neously report having received tetanus shots. centage of the population with at least adequate share of the population with ready access to water for Governments in developing countries usually access to excreta disposal facilities (private or domestic purposes. The data are based on surveys finance immunization against measles and diphthe- shared but not public) that can effectively prevent and estimates provided by governments to the Joint ria, pertussis (whooping cough), and tetanus (DPT) human, animal, and insect contact with excreta. Monitoring Programme of the WHO and United Nations as part of the basic public health package. In many Improved facilities range from simple but protected Children's Fund (UNICEF). The coverage rates for water developing countries, however, lack of precise infor- pit latrines to flush toilets with a sewerage connec- and sanitation are based on information from service mation on the size of the cohort of children under tion. To be effective, facilities must be correctly con- users on the facilities their households actually use one year of age makes immunization coverage diffi- structed and properly maintained. · Tetanus vacci- rather than on information from service providers, who cult to estimate. The data shown here are based on nations refer to the percentage of pregnant women may include nonfunctioning systems. Access to drink- an assessment of national immunization coverage who receive two tetanus toxoid injections during their ing water from an improved source does not ensure rates by the WHO and UNICEF. The assessment con- first pregnancy and one booster shot during each that the water is safe or adequate, as these charac- sidered both administrative data from service subsequent pregnancy, with five doses considered teristics are not tested at the time of the surveys. providers and household survey data on children's adequate for a lifetime. · Child immunization rate is Neonatal tetanus is an important cause of infant immunization histories. Based on the data available, the percentage of children ages 12­23 months who mortality in some developing countries. It can be pre- consideration of potential biases, and contributions received vaccinations before 12 months or at any vented through immunization of the mother during of local experts, the most likely true level of immu- time before the survey for four diseases--measles pregnancy. Recommended doses for full protection are nization coverage was determined for each year. and diphtheria, pertussis (whooping cough), and generally two tetanus shots during the first pregnancy Sleeping under treated bednets, if properly used tetanus (DPT). A child is considered adequately and maintained, is one of the most important malar- immunized against measles after receiving one dose 2.15a ia preventive strategies to limit human-mosquito con- of vaccine and against DPT after receiving three Children in rural households are tact. Studies have emphasized that mortality rates doses. · Children sleeping under treated bednets less likely to use bednets could be reduced by about 25­30 percent if every refer to the percentage of children under age five Children under five sleeping under bednets, 2000 (%) child under five in malaria-risk areas such as Africa who slept under an insecticide-impregnated bednet 40 slept under a treated bednet every night. to prevent malaria. · Tuberculosis treatment suc- Data on the success rate of tuberculosis treatment cess rate is the percentage of new, registered 30 are provided for countries that have implemented the smear-positive (infectious) cases that were cured or recommended control strategy: directly observed in which a full course of treatment was completed. 20 treatment, short course (DOTS). Countries that have · DOTS detection rate is the percentage of estimat- not adopted DOTS or have only recently done so are ed new infectious tuberculosis cases detected under 10 omitted because of lack of data or poor comparability the directly observed treatment, short course case or reliability of reported results. The treatment suc- detection and treatment strategy. 0 cess rate for tuberculosis provides a useful indicator Urban Rural of the quality of health services. A low rate or no suc- Data sources Untreated bednets Insecticide-treated bednets cess suggests that infectious patients may not be Data are drawn from a variety of sources, includ- receiving adequate treatment. An essential comple- ing WHO and UNICEF estimates of National Even though malaria is often more prevalent in rural areas, fewer children under age five sleep under a bednet ment to the tuberculosis treatment success rate is Immunization Coverage, the WHO's Global in rural areas than in urban ones. The ratio of urban-rural the DOTS detection rate, which indicates whether Tuberculosis Control Report 2003; UNICEF's difference is even greater for insecticide-treated bednets because they are more expensive than untreated bednets, there is adequate coverage by the recommended State of the World's Children 2004; and the WHO and retreatment of insecticide-treated nets is still case detection and treatment strategy. A country with and UNICEF's Global Water Supply and Sanitation uncommon, especially in rural areas. a high treatment success rate may still face big chal- Assessment 2000 Report. Source: WHO and UNICEF 2003. lenges if its DOTS detection rate remains low. 2004 World Development Indicators 95 2.16 Reproductive health Total fertility Adolescent Women at risk Contraceptive Births attended Maternal mortality rate fertility of unintended prevalence by skilled ratio rate pregnancy rate health staff births % of per 1,000 married % of per 100,000 live births births women women women National Modeled per woman ages 15­19 ages 15­49 ages 15­49 % of total estimates estimates 1980 2002 2002 1990­2002 a 1990­2002 a 1985 1995­2002 a 1985­2002 a 2000 Afghanistan 7.0 6.8 151 .. .. .. 12 .. 1,900 Albania 3.6 2.2 11 .. .. .. 99 20 55 Algeria 6.7 2.8 17 .. 51 .. 92 140 140 Angola 6.9 7.0 225 .. .. .. 45 .. 1,700 Argentina 3.3 2.4 60 .. .. .. 98 41 82 Armenia 2.3 1.1 35 12 61 .. 97 22 55 Australia 1.9 1.8 18 .. .. .. 100 .. 8 Austria 1.6 1.3 20 .. .. .. 100 b .. 4 Azerbaijan 3.2 2.1 44 .. 55 .. 84 25 94 Bangladesh 6.1 3.0 129 15 54 .. 12 380 380 Belarus 2.0 1.3 21 .. .. 100 100 14 35 Belgium 1.7 1.6 11 .. .. 100 100 b .. 10 Benin 7.0 5.3 103 27 19 .. 66 500 850 Bolivia 5.5 3.8 75 26 49 .. 69 390 420 Bosnia and Herzegovina 2.1 1.3 23 .. .. .. 100 10 31 Botswana 6.1 3.8 68 .. .. .. 94 330 100 Brazil 3.9 2.1 68 7 77 81 88 160 260 Bulgaria 2.0 1.3 49 .. .. .. .. 15 32 Burkina Faso 7.5 6.3 133 26 12 .. 31 480 1,000 Burundi 6.8 5.8 50 .. .. 19 25 .. 1,000 Cambodia 5.7 3.8 57 30 24 .. 32 440 450 Cameroon 6.4 4.6 127 20 19 .. 60 430 730 Canada 1.7 1.5 20 .. .. 100 98 .. 6 Central African Republic 5.8 4.6 124 16 15 .. 44 1,100 1,100 Chad 6.9 6.2 182 10 4 .. 16 830 1,100 Chile 2.8 2.2 43 .. .. .. 100 23 31 China 2.5 1.9 15 .. 83 .. 76 53 56 Hong Kong, China 2.0 1.0 6 .. .. .. .. .. .. Colombia 3.9 2.5 75 6 77 .. 86 78 130 Congo, Dem. Rep. 6.6 6.7 226 .. .. .. 61 950 990 Congo, Rep. 6.3 6.3 146 .. .. .. .. .. 510 Costa Rica 3.6 2.3 69 .. .. 97 98 29 43 Côte d'Ivoire 7.4 4.6 118 28 15 .. 63 600 690 Croatia 1.9 1.5 18 .. .. .. 100 2 8 Cuba 2.0 1.6 67 .. .. .. 100 30 33 Czech Republic 2.1 1.2 23 .. 69 .. 99 3 9 Denmark 1.5 1.7 8 .. .. 100 100 b 10 5 Dominican Republic 4.2 2.6 89 12 70 .. 98 230 b 150 Ecuador 5.0 2.8 64 .. 66 .. 69 160 130 Egypt, Arab Rep. 5.1 3.0 46 11 56 .. 61 84 84 El Salvador 4.9 2.9 87 .. 60 .. 90 120 150 Eritrea 7.5 4.8 101 28 8 .. 21 1,000 630 Estonia 2.0 1.3 26 .. .. .. .. 46 63 Ethiopia 6.6 5.6 135 36 8 .. 6 870 850 Finland 1.6 1.7 10 .. .. .. 100 b 6 6 France 1.9 1.9 10 .. 71 .. 99 b 10 17 Gabon 4.5 4.1 156 28 33 .. 86 520 420 Gambia, The 6.5 4.8 139 .. .. .. 55 .. 540 Georgia 2.3 1.1 27 .. 41 .. 96 67 32 Germany 1.4 1.4 14 .. .. 100 100 b 8 8 Ghana 6.5 4.1 81 23 22 .. 44 210 b 540 Greece 2.2 1.3 17 .. .. .. .. 1 9 Guatemala 6.3 4.3 100 23 38 35 41 190 240 Guinea 6.1 5.0 153 24 6 .. 35 530 740 Guinea-Bissau 7.1 6.6 215 .. .. .. 35 910 1,100 Haiti 5.9 4.2 72 40 28 .. 24 520 680 96 2004 World Development Indicators PEOPLE 2.16 Reproductive health Total fertility Adolescent Women at risk Contraceptive Births attended Maternal mortality rate fertility of unintended prevalence by skilled ratio rate pregnancy rate health staff births % of per 1,000 married % of per 100,000 live births births women women women National Modeled per woman ages 15­19 ages 15­49 ages 15­49 % of total estimates estimates 1980 2002 2002 1990­2002 a 1990­2002 a 1985 1995­2002 a 1985­2002 a 2000 Honduras 6.5 4.0 110 .. 62 41 56 110 110 Hungary 1.9 1.3 21 .. 73 .. .. 5 16 India 5.0 2.9 98 16 52 .. 43 540 540 Indonesia 4.3 2.3 52 9 57 36 64 380 230 Iran, Islamic Rep. 6.7 2.0 25 .. 73 .. 90 37 76 Iraq 6.4 4.1 35 .. .. .. 72 290 250 Ireland 3.2 1.9 15 .. 60 .. 100 6 5 Israel 3.2 2.7 19 .. .. 99 99 b 5 17 Italy 1.6 1.3 8 .. .. .. .. 7 5 Jamaica 3.7 2.3 84 .. 65 .. 95 97 87 Japan 1.8 1.3 3 .. .. .. 100 8 10 Jordan 6.8 3.5 30 14 56 .. 97 41 41 Kazakhstan 2.9 1.8 35 9 66 .. 99 50 210 Kenya 7.8 4.2 100 24 39 .. 44 590 1,000 Korea, Dem. Rep. 2.8 2.1 2 .. .. .. 97 110 67 Korea, Rep. 2.6 1.5 4 .. .. .. 100 20 20 Kuwait 5.3 2.5 30 .. .. 96 98 5 5 Kyrgyz Republic 4.1 2.4 29 12 60 .. 98 44 110 Lao PDR 6.7 4.8 91 .. 25 .. 19 530 650 Latvia 1.9 1.2 32 .. .. .. 100 25 42 Lebanon 4.0 2.2 23 .. 61 .. 89 100 b 150 Lesotho 5.5 4.3 77 .. 23 .. 60 .. 550 Liberia 6.8 5.8 196 .. .. .. 51 580 760 Libya 7.3 3.3 32 .. 45 .. 94 77 97 Lithuania 2.0 1.3 33 .. .. .. .. 13 13 Macedonia, FYR 2.5 1.8 31 .. .. .. 97 15 23 Madagascar 6.6 5.2 157 26 17 .. 46 490 550 Malawi 7.6 6.1 137 30 31 .. 56 1,100 1,800 Malaysia 4.2 2.8 23 .. .. .. 97 30 41 Mali 7.1 6.4 176 29 8 32 41 580 1,200 Mauritania 6.4 4.6 113 32 8 .. 57 750 1,000 Mauritius 2.7 2.0 39 .. 75 .. 99 21 24 Mexico 4.7 2.4 62 .. 65 .. 86 79 83 Moldova 2.4 1.4 44 .. 74 .. 99 44 36 Mongolia 5.3 2.4 45 .. 60 .. 97 160 110 Morocco 5.4 2.8 44 20 59 26 40 230 220 Mozambique 6.5 5.0 153 23 6 .. 44 1,100 1,000 Myanmar 4.9 2.8 29 .. .. .. 56 230 360 Namibia 5.9 4.8 103 22 29 .. 78 270 300 Nepal 6.1 4.2 112 28 39 .. 11 540 740 Netherlands 1.6 1.7 5 .. 75 .. 100 7 16 New Zealand 2.0 1.9 30 .. .. .. 100 15 7 Nicaragua 6.3 3.4 122 15 60 .. 67 120 230 Niger 8.0 7.1 205 17 8 .. 16 590 1,600 Nigeria 6.9 5.1 111 17 15 .. 42 .. 800 Norway 1.7 1.8 10 .. .. .. 100 b 6 16 Oman 9.9 4.0 54 .. 24 87 95 23 87 Pakistan 7.0 4.5 62 32 28 .. 20 530 500 Panama 3.7 2.4 75 .. .. .. 90 70 160 Papua New Guinea 5.8 4.3 68 .. 26 .. 53 370 b 300 Paraguay 5.2 3.8 75 15 57 .. 71 190 170 Peru 4.5 2.6 61 10 69 .. 59 190 410 Philippines 4.8 3.2 33 19 47 .. 58 170 200 Poland 2.3 1.3 15 .. .. 99 99 b 4 13 Portugal 2.2 1.5 23 .. .. .. 100 8 5 Puerto Rico 2.6 1.9 64 .. 78 .. .. .. 25 2004 World Development Indicators 97 2.16 Reproductive health Total fertility Adolescent Women at risk Contraceptive Births attended Maternal mortality rate fertility of unintended prevalence by skilled ratio rate pregnancy rate health staff births % of per 1,000 married % of per 100,000 live births births women women women National Modeled per woman ages 15­19 ages 15­49 ages 15­49 % of total estimates estimates 1980 2002 2002 1990­2002 a 1990­2002 a 1985 1995­2002 a 1985­2002 a 2000 Romania 2.4 1.3 41 .. 64 .. 98 34 49 Russian Federation 1.9 1.3 46 .. 34 .. 99 37 67 Rwanda 8.3 5.7 52 36 13 .. 31 1,100 1,400 Saudi Arabia 7.3 5.3 91 .. 21 .. 91 .. 23 Senegal 6.8 4.9 89 35 11 41 58 560 690 Serbia and Montenegro 2.3 1.7 32 .. .. .. 99 7 11 Sierra Leone 6.5 5.6 182 .. .. .. 42 1,800 2,000 Singapore 1.7 1.4 8 .. .. .. 100 6 30 Slovak Republic 2.3 1.3 25 .. .. .. .. 16 3 Slovenia 2.1 1.1 9 .. .. .. 100 b 17 17 Somalia 7.3 6.9 204 .. .. .. 34 .. 1,100 South Africa 4.6 2.8 43 15 62 .. 84 150 230 Spain 2.2 1.3 9 .. .. .. .. 0 4 Sri Lanka 3.5 2.1 20 .. .. .. 97 92 92 Sudan 6.1 4.4 56 .. 10 .. 86 b 550 590 Swaziland 6.2 4.2 103 .. .. .. 70 230 370 Sweden 1.7 1.6 9 .. .. 100 100 b 5 2 Switzerland 1.5 1.5 5 .. .. .. .. 5 7 Syrian Arab Republic 7.4 3.4 38 .. 45 .. 76 b 110 b 160 Tajikistan 5.6 2.9 24 .. .. .. 71 45 100 Tanzania 6.7 5.0 115 22 25 .. 36 530 1,500 Thailand 3.5 1.8 72 .. 72 .. 99 36 44 Togo 6.8 4.9 82 32 24 .. 49 480 570 Trinidad and Tobago 3.3 1.8 42 .. .. 98 96 70 160 Tunisia 5.2 2.1 10 .. 60 .. 90 69 120 Turkey 4.3 2.2 55 10 64 .. 81 130 b 70 Turkmenistan 4.9 2.7 16 10 62 .. 97 9 31 Uganda 7.2 6.0 182 35 23 .. 39 510 880 Ukraine 2.0 1.2 31 .. 72 .. 100 18 35 United Arab Emirates 5.4 3.0 64 .. .. .. 96 3 54 United Kingdom 1.9 1.7 27 .. .. .. 99 7 13 United States 1.8 2.1 47 .. 64 .. 99 8 17 Uruguay 2.7 2.2 64 .. .. .. 100 26 27 Uzbekistan 4.8 2.3 37 14 56 .. 96 34 24 Venezuela, RB 4.2 2.7 90 .. .. .. 94 60 96 Vietnam 5.0 1.9 28 7 79 .. 70 95 130 West Bank and Gaza .. 4.9 81 .. 42 .. .. .. .. Yemen, Rep. 7.9 6.0 97 39 21 .. 22 350 570 Zambia 7.0 5.1 129 27 26 .. 43 650 750 Zimbabwe 6.4 3.7 86 13 54 .. 73 700 1,100 World 3.7 w 2.6 w 63 w .. w .. w 60 w 403 w Low income 5.5 3.5 98 .. .. 41 657 Middle income 3.2 2.1 36 .. .. 80 106 Lower middle income 3.1 2.1 33 86 .. 78 112 Upper middle income 3.6 2.4 54 .. .. 92 67 Low & middle income 4.1 2.8 68 .. .. 56 440 East Asia & Pacific 3.1 2.1 25 83 .. 72 115 Europe & Central Asia 2.5 1.6 38 .. .. 93 58 Latin America & Carib. 4.1 2.5 70 .. .. 82 193 Middle East & N. Africa 6.2 3.1 41 53 .. 70 165 South Asia 5.3 3.2 98 50 .. 35 506 Sub-Saharan Africa 6.6 5.1 126 .. .. 44 917 High income 1.9 1.7 24 .. .. 99 13 Europe EMU 1.8 1.5 11 .. .. .. 10 a. Data are for most recent year available. b. Data refer to period other than specified, differ from the standard definition, or refer to only part of a country. 98 2004 World Development Indicators PEOPLE 2.16 Reproductive health About the data Reproductive health is a state of physical and mental because of menopause, infertility, or postpartum anovu- The maternal mortality ratios shown in the table as well-being in relation to the reproductive system and lation. Common reasons for not using contraception are national estimates are based on national surveys, its functions and processes. Means of achieving repro- lack of knowledge about contraceptive methods and vital registration, or surveillance or are derived from ductive health include education and services during concerns about their possible health side-effects. community and hospital records. Those shown as pregnancy and childbirth, provision of safe and effec- Contraceptive prevalence reflects all methods-- modeled estimates are based on an exercise carried tive contraception, and prevention and treatment of ineffective traditional methods as well as highly effec- out by the World Health Organization (WHO), United sexually transmitted diseases. The complications of tive modern methods. Contraceptive prevalence rates Nations Children's Fund (UNICEF), and United Nations pregnancy and childbirth are the leading cause of are obtained mainly from Demographic and Health Population Fund (UNFPA). In this exercise maternal death and disability among women of reproductive age Surveys and contraceptive prevalence surveys (see mortality was estimated with a regression model using in developing countries. Reproductive health services Primary data documentation for the most recent sur- information on fertility, birth attendants, and HIV will need to expand rapidly over the next two decades, vey year). Unmarried women are often excluded from prevalence. Neither set of ratios can be assumed to when the number of women and men of reproductive such surveys, which may bias the estimates. provide an accurate estimate of maternal mortality in age is projected to increase by more than 600 million. The share of births attended by skilled health staff is any of the countries in the table. Total and adolescent fertility rates are based on an indicator of a health system's ability to provide ade- data on registered live births from vital registration quate care for pregnant women. Good antenatal and Definitions systems or, in the absence of such systems, from cen- postnatal care improve maternal health and reduce suses or sample surveys. As long as the surveys are maternal and infant mortality. But data may not reflect · Total fertility rate is the number of children that fairly recent, the estimated rates are generally consid- such improvements because health information systems would be born to a woman if she were to live to the ered reliable measures of fertility in the recent past. are often weak, maternal deaths are underreported, and end of her childbearing years and bear children in Where no empirical information on age-specific fertili- rates of maternal mortality are difficult to measure. accordance with current age-specific fertility rates. ty rates is available, a model is used to estimate the Maternal mortality ratios are generally of unknown · Adolescent fertility rate is the number of births per share of births to adolescents. For countries without reliability, as are many other cause-specific mortality 1,000 women ages 15­19. · Women at risk of unin- vital registration systems, fertility rates are generally indicators. Household surveys such as the Demo- tended pregnancy are fertile, married women of repro- based on extrapolations from trends observed in cen- graphic and Health Surveys attempt to measure mater- ductive age who do not want to become pregnant and suses or surveys from earlier years. nal mortality by asking respondents about survivorship are not using contraception. · Contraceptive preva- An increasing number of couples in the developing of sisters. The main disadvantage of this method is that lence rate is the percentage of women who are prac- world want to limit or postpone childbearing but are not the estimates of maternal mortality that it produces per- ticing, or whose sexual partners are practicing, any using effective contraceptive methods. These couples tain to 12 years or so before the survey, making them form of contraception. It is usually measured for mar- face the risk of unintended pregnancy, shown in the unsuitable for monitoring recent changes or observing ried women ages 15­49 only. · Births attended by table as the percentage of married women of reproduc- the impact of interventions. In addition, measurement skilled health staff are the percentage of deliveries tive age who do not want to become pregnant but are of maternal mortality is subject to many types of errors. attended by personnel trained to give the necessary not using contraception (Bulatao 1998). Information on Even in high-income countries with vital registration sys- supervision, care, and advice to women during preg- this indicator is collected through surveys and excludes tems, misclassification of maternal deaths has been nancy, labor, and the postpartum period; to conduct women not exposed to the risk of unintended pregnancy found to lead to serious underestimation. deliveries on their own; and to care for newborns. · Maternal mortality ratio is the number of women 2.16a who die from pregnancy-related causes during preg- Does household wealth affect antenatal care? nancy and childbirth, per 100,000 live births. Pregnant women attending antenatal clinics, by wealth quintile (%) 100 80 60 Data sources 40 The data on reproductive health come from Demographic and Health Surveys, the WHO's 20 Coverage of Maternity Care (1997) and other WHO 0 sources, UNICEF, and national statistical offices. Poorest Second Third Fourth Richest Modeled estimates for maternal mortality ratios Across 22 countries in Sub-Saharan Africa rich women were about 1.5 times more likely to attend antenatal clinics are from Carla AbouZahr and Tessa Wardlaw's than were poor women. The lack of care can contribute to women's death during pregnancy or childbirth and can also "Maternal Mortality in 2000: Estimates Developed compromise the health and survival of their infants. by WHO, UNICEF, and UNFPA" (2003). Source: WHO and UNICEF 2003. 2004 World Development Indicators 99 2.17 Nutrition Prevalence Prevalence Prevalence Low- Exclusive Consumption Vitamin A of of child of birthweight breastfeeding of iodized supplemen- undernourishment malnutrition overweight babies salt tation % of children % of % of % of under age 5 children % of children % of children population Weight for age Height for age under % of births under 6 months households 6­59 months 1990­92 1999­2001 1996­2002 a 1996­2002 a Year age 5 1998­2002 a 1995­2002 a 1997­2002 a 2001 Afghanistan 58 70 b 49 48 1997 4.0 .. .. 2 84 Albania 5 c 4 14 32 2000 22.5 3 6 62 .. Algeria 5 6 6 18 2000 10.1 7 13 69 .. Angola 61 49 31 45 1996 0.5 12 11 35 75 Argentina <3 <3 5 12 1995­96 9.2 7 .. 90 e .. Armenia 55 c 51 3 13 2000­01 10.4 7 30 84 .. Australia .. .. 0 0 1995­96 5.2 7 .. .. .. Austria .. .. .. .. .. 7 .. .. .. Azerbaijan 37 c 21 17 20 2000 3.8 11 7 26 .. Bangladesh 35 32 48 45 1999­2000 0.4 30 46 70 90 Belarus <3 c 3 .. .. .. 5 .. 37 .. Belgium .. .. .. .. .. 8 e .. .. .. Benin 20 16 23 31 2001 1.8 16 38 72 95 Bolivia 26 22 8 27 1998 6.5 9 39 65 31 Bosnia and Herzegovina 13 c 8 4 10 2000 13.2 4 6 77 .. Botswana 18 24 13 23 2000 6.9 10 34 66 85 Brazil 12 9 6 11 1996 4.9 10 e 42 f 95 e .. Bulgaria 8 c 16 .. .. .. 10 .. .. .. Burkina Faso 22 17 34 37 1998­99 1.0 19 6 23 e 97 Burundi 49 70 45 57 1987 1.1 16 62 96 95 Cambodia 43 38 45 45 2000 2.0 11 12 14 57 Cameroon 33 27 22 29 1998 5.0 11 12 84 100 Canada .. .. .. .. .. 6 .. .. .. Central African Republic 50 44 .. .. 1995 0.8 14 17 86 90 Chad 58 34 28 29 2000 1.5 17 e 10 58 91 Chile 8 4 1 2 2002 8.0 5 73 f 100 .. China 17 d 11 d 10 14 2000 2.6 6 67 f 93 .. Hong Kong, China <3 <3 .. .. .. .. .. .. .. Colombia 17 13 7 14 2000 3.7 9 32 92 .. Congo, Dem. Rep. 31 75 31 38 2001 3.9 12 24 72 98 Congo, Rep. 37 30 .. .. 1987 0.7 .. 4 f .. 100 Costa Rica 7 6 5 6 1996 6.2 7 35 e, f 97 e .. Côte d'Ivoire 18 15 21 25 1998­99 2.5 17 10 31 97 Croatia 18 c 12 1 1 1995­96 5.9 6 23 90 .. Cuba 8 11 4 5 .. 6 41 73 .. Czech Republic <3 c <3 .. .. 1991 4.1 7 .. .. .. Denmark .. .. .. .. .. 5 .. .. .. Dominican Republic 27 25 5 6 1996 4.9 14 11 18 35 Ecuador 8 4 14 26 .. 16 29 f 99 50 Egypt, Arab Rep. 5 3 4 19 1995­96 8.6 12 57 28 .. El Salvador 12 14 12 23 1998 2.6 13 16 91 e .. Eritrea .. 61 40 38 1995­96 0.9 21 e 52 97 61 Estonia 10 c 4 .. .. .. 4 .. .. .. Ethiopia .. 42 47 52 2000 1.2 15 55 28 16 Finland .. .. .. .. .. 4 .. .. .. France .. .. .. .. .. 7 .. .. .. Gabon 11 7 12 21 2000­01 3.7 14 6 15 89 Gambia, The 22 27 17 19 .. 17 26 8 91 Georgia 45 c 26 3 12 1999 12.7 6 18 f 8 .. Germany .. .. .. .. .. 7 .. .. .. Ghana 35 12 25 26 1998­99 1.7 11 31 28 100 Greece .. .. .. .. .. 8 .. .. .. Guatemala 16 25 24 46 1998­99 4.4 13 39 49 .. Guinea 40 28 33 41 1999 2.7 12 11 12 93 Guinea-Bissau .. .. 25 30 .. 22 37 2 100 Haiti 65 49 17 23 2000 2.0 21 24 11 .. 100 2004 World Development Indicators PEOPLE 2.17 Nutrition Prevalence Prevalence Prevalence Low- Exclusive Consumption Vitamin A of of child of birthweight breastfeeding of iodized supplemen- undernourishment malnutrition overweight babies salt tation % of children % of % of % of under age 5 children % of children % of children population Weight for age Height for age under % of births under 6 months households 6­59 months 1990­92 1999­2001 1996­2002 a 1996­2002 a Year age 5 1998­2002 a 1995­2002 a 1997­2002 a 2001 Honduras 23 20 17 29 2001 2.2 14 35 80 62 Hungary <3 c <3 .. .. 1980­88 2.0 9 .. .. .. India 25 21 47 45 1998­99 2.2 30 37 f 50 25 Indonesia 9 6 25 .. 1995 4.0 10 e 42 65 61 Iran, Islamic Rep. 5 5 11 15 1998 4.3 7 e 44 94 .. Iraq 7 27 b 16 22 .. 15 12 40 .. Ireland .. .. .. .. .. 6 .. .. .. Israel .. .. .. .. .. 8 .. .. .. Italy .. .. .. .. 1975­77 4.4 6 .. .. .. Jamaica 14 9 4 4 1999 3.8 9 .. 100 .. Japan .. .. .. .. 1978­81 1.6 8 .. .. .. Jordan 4 6 5 8 1997 2.8 10 e 34 88 .. Kazakhstan <3 c 22 4 10 1999 3.0 8 36 20 .. Kenya 44 37 22 33 1993 3.5 11 5 91 90 Korea, Dem. Rep. 18 34 28 45 .. 7 97 f .. 99 Korea, Rep. <3 <3 .. .. .. 4 .. .. .. Kuwait 22 4 2 3 1996­97 5.7 7 12 f .. .. Kyrgyz Republic 28 c 7 6 25 1997 6.3 7 e 24 27 .. Lao PDR 29 22 40 41 .. 14 23 75 70 Latvia 3 c 6 .. .. .. 5 .. .. .. Lebanon 3 3 3 12 .. 6 27 f 87 .. Lesotho 27 25 18 45 .. 14 15 69 .. Liberia 33 42 27 40 1999­2000 2.3 .. 35 .. 100 Libya <3 <3 .. .. .. 7 e .. 90 e .. Lithuania 4 c <3 .. .. .. 4 .. .. .. Macedonia, FYR 15 c 10 6 7 1999 4.9 5 37 100 .. Madagascar 35 36 33 49 1997 2.0 14 41 52 73 Malawi 49 33 25 49 2000 4.3 16 44 49 63 Malaysia 3 <3 .. .. .. 10 29 f .. .. Mali 25 21 33 38 2001 1.5 23 38 74 74 Mauritania 14 10 32 35 .. 42 20 2 98 Mauritius 6 5 .. .. 1995 4.0 13 16 e, f 0 e .. Mexico 5 5 8 18 1998­99 5.3 9 38 e,f 90 .. Moldova 5 c 12 .. .. .. 5 .. 33 .. Mongolia 34 38 13 25 1999 4.8 8 51 45 93 Morocco 6 7 9 23 1992 6.8 11 e 66 f 41 .. Mozambique 69 53 26 36 1997 3.4 14 e 30 62 e 71 Myanmar 10 7 28 42 1997 7.7 15 11 48 97 Namibia 20 7 .. .. 1992 3.3 16 e 26 f 63 84 Nepal 18 17 48 51 2001 0.2 21 69 63 98 Netherlands .. .. .. .. 1980 1.6 .. .. .. .. New Zealand .. .. .. .. .. 6 .. 83 .. Nicaragua 30 29 10 20 1998 2.6 13 31 96 .. Niger 42 34 40 40 2000 0.8 17 1 15 89 Nigeria 13 8 31 34 1993 3.3 12 17 98 77 Norway .. .. .. .. .. 5 .. .. .. Oman .. .. 18 10 1998 1.0 8 .. 61 .. Pakistan 26 19 .. .. 1990­94 1.3 19 e 16 f 17 100 Panama 20 26 8 18 1997 4.2 10 e 25 95 .. Papua New Guinea 25 27 .. .. 1982­83 1.6 11 e 59 .. .. Paraguay 18 13 .. .. 1990 3.9 9 e 7 f 83 .. Peru 40 11 7 25 2000 7.6 11 e 71 93 6 Philippines 26 22 32 32 1998 1.0 20 37 24 84 Poland <3 c <3 .. .. .. 6 .. .. .. Portugal .. .. .. .. .. 8 .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 101 2.17 Nutrition Prevalence Prevalence Prevalence Low- Exclusive Consumption Vitamin A of of child of birthweight breastfeeding of iodized supplemen- undernourishment malnutrition overweight babies salt tation % of children % of % of % of under age 5 children % of children % of children population Weight for age Height for age under % of births under 6 months households 6­59 months 1990­92 1999­2001 1996­2002 a 1996­2002 a Year age 5 1998­2002 a 1995­2002 a 1997­2002 a 2001 Romania <3 c <3 3 10 2002 5.5 9 .. .. .. Russian Federation 4 c 4 6 11 .. 6 .. 30 e .. Rwanda 43 41 24 43 2000 4.0 9 84 90 94 Saudi Arabia 4 3 .. .. .. 11 e 31 f .. .. Senegal 23 24 23 25 2000 2.2 18 24 f 16 85 Serbia and Montenegro 5 c 9 2 5 1996 12.9 4 11 f 73 .. Sierra Leone 46 50 27 34 .. .. 4 23 91 Singapore .. .. .. .. 1970­77 0.5 8 .. .. .. Slovak Republic 4 c 5 .. .. .. 7 .. .. .. Slovenia 3 c <3 .. .. .. 6 .. .. .. Somalia 68 71 b 26 23 .. .. 9 .. 62 South Africa .. .. .. .. 1995 6.7 15 7 62 .. Spain .. .. .. .. .. 6 e .. .. .. Sri Lanka 29 25 33 .. 1987 0.1 22 54 f 88 .. Sudan 31 25 11 .. .. 31 16 1 92 Swaziland 10 12 10 30 .. 9 24 59 .. Sweden .. .. .. .. .. 4 .. .. .. Switzerland .. .. .. .. .. 6 .. .. .. Syrian Arab Republic 5 4 7 19 .. 6 81 f 40 .. Tajikistan 22 c 71 .. 31 .. 15 14 20 .. Tanzania 35 43 29 44 1999 1.7 13 32 67 93 Thailand 28 19 .. .. 1995 2.8 9 4 f 74 .. Togo 33 25 25 22 1998 1.5 15 18 67 77 Trinidad and Tobago 13 12 6 4 1987 3.0 23 2 1 .. Tunisia <3 <3 4 12 1996­97 4.5 7 46 97 .. Turkey <3 3 8 16 1998 2.2 16 7 64 .. Turkmenistan 15 c 7 12 22 .. 6 13 75 .. Uganda 23 19 23 39 1995 2.8 12 65 95 37 Ukraine <3 c 4 3 16 2000 20.1 5 22 5 .. United Arab Emirates 4 <3 7 .. .. 15 e 34 f .. .. United Kingdom .. .. .. .. .. 8 .. .. .. United States .. .. .. .. 1988­94 4.5 8 .. .. .. Uruguay 6 3 .. .. 1992­93 6.2 8 .. .. .. Uzbekistan 10 c 26 19 31 1996 14.4 7 16 19 .. Venezuela, RB 11 18 4 13 2000 3.2 7 7 f 90 .. Vietnam 27 19 34 37 2000 2.7 9 31 40 59 West Bank and Gaza .. .. 4 7 1996 2.3 .. .. .. .. Yemen, Rep. 35 33 46 52 1996 4.3 32 e 18 39 100 Zambia 45 50 28 47 2001­02 3.0 10 40 68 83 Zimbabwe 43 39 13 27 1999 7.0 11 33 93 .. World 21 w 17 w .. w .. w 15 w 66 w .. w Low income 26 24 42 .. 21 52 55 Middle income 15 10 .. 25 9 79 .. Lower middle income 16 11 9 17 9 78 .. Upper middle income .. .. .. .. 8 89 .. Low & middle income 21 17 .. .. 16 66 51 East Asia & Pacific 17 12 15 14 8 82 .. Europe & Central Asia .. 9 .. .. 9 36 .. Latin America & Carib. 14 11 9 19 10 88 .. Middle East & N. Africa 7 8 .. .. 12 58 .. South Asia 27 23 48 47 28 48 42 Sub-Saharan Africa 31 32 .. .. 14 62 76 High income .. .. .. .. 7 .. .. Europe EMU .. .. .. .. 7 .. .. a. Data are for the most recent year available. b. Data are for 1998­2000. c. Data are for 1993­95. d. Includes Taiwan, China. e. Data refer to period other than specified, differ from the standard definition, or refer to only part of a country. f. Refers to exclusive breastfeeding for less than four months. 102 2004 World Development Indicators PEOPLE 2.17 Nutrition About the data Definitions Data on undernourishment are produced by the Food from hospital records and household surveys. Many · Prevalence of undernourishment is the percent- and Agriculture Organization (FAO) based on the calo- births in developing countries take place at home, age of the population that is undernourished. ries available from local food production, trade, and and these births are seldom recorded. A hospital · Prevalence of child malnutrition is the percentage stocks; the number of calories needed by different birth may indicate higher income and therefore better of children under five whose weight for age or height age and gender groups; the proportion of the popula- nutrition, or it could indicate a higher-risk birth, pos- for age is more than two standard deviations below tion represented by each age group; and a coefficient sibly skewing the data on birthweights downward. The the median for the international reference population of distribution to take account of inequality in access data should therefore be treated with caution. ages 0­59 months. For children up to two years of to food (FAO 2000). From a policy and program stand- It is estimated that breastfeeding can save some age height is measured by recumbent length. For point, however, this measure has its limits. First, food 1.5 million children a year. Breast milk alone con- older children height is measured by stature while insecurity exists even where food availability is not a tains all the nutrients, antibodies, hormones, and standing. The reference population, adopted by the problem because of inadequate access of poor antioxidants an infant needs to thrive. It protects WHO in 1983, is based on children from the United households to food. Second, food insecurity is an babies from diarrhea and acute respiratory infec- States, who are assumed to be well nourished. individual or household phenomenon, and the aver- tions, stimulates their immune systems and · Prevalence of overweight is the percentage of age food available to each person, even corrected for response to vaccination, and according to some children under five whose weight for height is more possible effects of low income, is not a good predic- studies, confers cognitive benefits as well. The data than two standard deviations above the median for tor of food insecurity among the population. And third, on breastfeeding are derived from national surveys. the international reference population of the corre- nutrition security is determined not only by food secu- Iodine deficiency is the single most important sponding age, established by the U.S. National rity but also by the quality of care of mothers and chil- cause of preventable mental retardation, and it con- Center for Health Statistics and the WHO. · Low- dren and the quality of the household's health envi- tributes significantly to the risk of stillbirth and mis- birthweight babies are newborns weighing less than ronment (Smith and Haddad 2000). carriage. Iodized salt is the best source of iodine, 2,500 grams, with the measurement taken within Estimates of child malnutrition, based on weight and a global campaign to iodize edible salt is signifi- the first hours of life, before significant postnatal for age (underweight) and height for age (stunting), cantly reducing the risks (UNICEF, The State of the weight loss has occurred. · Exclusive breastfeeding are from national survey data. The proportion of chil- World's Children 1999). refers to the percentage of children less than 6 dren who are underweight is the most common indi- Vitamin A is essential for the functioning of the months old who are fed breast milk alone (no other cator of malnutrition. Being underweight, even mild- immune system. A child deficient in vitamin A faces liquids). · Consumption of iodized salt refers to the ly, increases the risk of death and inhibits cognitive a 23 percent greater risk of dying from a range of percentage of households that use edible salt forti- development in children. Moreover, it perpetuates childhood ailments such as measles, malaria, and fied with iodine. · Vitamin A supplementation refers the problem from one generation to the next, as mal- diarrhea. Improving the vitamin A status of pregnant to the percentage of children ages 6­59 months who nourished women are more likely to have low-birth- women helps reduce anemia, improves their resist- received at least one high-dose vitamin A capsule in weight babies. Height for age reflects linear growth ance to infection, and may reduce their risk of dying the previous six months. achieved pre- and postnatally, and a deficit indicates during pregnancy and childbirth. Giving vitamin A to long-term, cumulative effects of inadequacies of new mothers who are breastfeeding helps to protect health, diet, or care. It is often argued that stunting their children during the first months of life. is a proxy for multifaceted deprivation and is a better indicator of long term changes in malnutrition. Estimates of children who are overweight are also from national survey data. Overweight in children has become a growing concern in developing coun- tries. Researchers show an association between obesity in childhood and a high prevalence of dia- betes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blossner 2000). The survey data were analyzed in a Data sources standardized way by the World Health Organization Data are drawn from a variety of sources, includ- (WHO) to allow comparisons across countries. ing the FAO's State of Food Insecurity in the World Low birthweight, which is associated with maternal 2003; the United Nations Administrative malnutrition, raises the risk of infant mortality and Committee on Coordination, Subcommittee on stunts growth in infancy and childhood. There is also Nutrition's Update on the Nutrition Situation; the emerging evidence that low-birthweight babies are WHO's World Health Report 2003; and the United more prone to noncommunicable diseases such Nations Children's Fund's (UNICEF) State of the as diabetes and cardiovascular heart diseases. World's Children 2004. Estimates of low-birthweight infants are drawn mostly 2004 World Development Indicators 103 2.18 Health risk factors and future challenges Prevalence Incidence of Prevalence of HIV of smoking tuberculosis % of adults per 100,000 % of % ages 15­24 a Male Female people adults Male Female 2000 2000 2002 2001 2001 2001 Afghanistan .. .. 333 <0.01 .. .. Albania 60 18 27 <0.01 .. .. Algeria 44 7 52 0.10 .. .. Angola .. .. 335 5.50 2.23 5.74 Argentina 47 34 46 0.70 0.86 0.34 Armenia 64 1 77 0.20 0.22 0.06 Australia 21 18 6 0.10 0.12 0.01 Austria 30 19 15 0.20 0.22 0.12 Azerbaijan 30 1 82 <0.10 0.06 0.01 Bangladesh 54 24 221 <0.10 0.01 0.01 Belarus 55 5 83 0.30 0.58 0.19 Belgium 30 26 14 0.20 0.12 0.12 Benin .. .. 86 3.60 1.17 3.71 Bolivia 43 18 234 <0.10 0.11 0.05 Bosnia and Herzegovina .. .. 60 0.10 .. .. Botswana .. .. 657 38.80 16.08 37.49 Brazil 38 29 62 0.70 0.64 0.48 Bulgaria 49 24 48 <0.10 .. .. Burkina Faso .. .. 157 6.50 3.97 9.73 Burundi .. .. 359 8.30 4.95 11.05 Cambodia 66 8 549 2.70 0.96 2.48 Cameroon .. .. 188 11.80 5.44 12.67 Canada 27 23 6 0.30 0.28 0.17 Central African Republic .. .. 338 12.90 5.82 13.54 Chad 24 .. 222 3.60 2.38 4.28 Chile 26 18 18 0.30 0.35 0.13 China 67 4 113 0.10 0.16 0.09 Hong Kong, China .. .. 93 0.10 0.00 0.00 Colombia 24 21 45 0.40 0.85 0.19 Congo, Dem. Rep. .. 6 383 4.90 2.92 5.91 Congo, Rep. .. .. 395 7.20 3.28 7.80 Costa Rica 29 7 15 0.60 0.58 0.27 Côte d'Ivoire 42 2 412 9.70 2.91 8.31 Croatia 34 32 47 <0.10 0.00 0.00 Cuba 48 26 12 <0.10 0.09 0.05 Czech Republic 36 22 13 <0.10 0.00 0.00 Denmark 32 29 13 0.20 0.14 0.06 Dominican Republic 24 17 95 2.50 2.10 2.76 Ecuador 46 17 137 0.30 0.31 0.15 Egypt, Arab Rep. 35 2 29 <0.10 .. .. El Salvador 38 12 60 0.60 0.77 0.35 Eritrea .. .. 268 2.80 2.78 4.30 Estonia 44 20 55 1.00 2.48 0.62 Ethiopia .. .. 370 6.40 4.39 7.82 Finland 27 20 10 <0.10 0.04 0.03 France 39 30 14 0.30 0.26 0.17 Gabon .. .. 248 4.16 2.32 4.72 Gambia, The 34 2 230 1.60 0.52 1.35 Georgia 61 15 85 <0.10 0.08 0.02 Germany 39 31 10 0.10 0.10 0.05 Ghana 28 4 211 3.00 1.36 2.97 Greece 47 29 20 0.20 0.14 0.06 Guatemala 38 18 77 1.00 0.90 0.85 Guinea 60 44 215 1.54 0.57 1.43 Guinea-Bissau .. .. 196 2.80 1.06 2.98 Haiti 11 9 319 6.10 4.06 4.95 104 2004 World Development Indicators PEOPLE 2.18 Health risk factors and future challenges Prevalence Incidence of Prevalence of HIV of smoking tuberculosis % of adults per 100,000 % of % ages 15­24 a Male Female people adults Male Female 2000 2000 2002 2001 2001 2001 Honduras 36 11 86 1.60 1.20 1.50 Hungary 44 27 32 0.10 0.09 0.02 India 29 3 168 0.80 0.34 0.71 Indonesia 59 4 256 0.10 0.06 0.06 Iran, Islamic Rep. 27 3 29 <0.10 0.05 0.01 Iraq 40 5 167 <0.10 .. .. Ireland 32 31 13 0.10 0.06 0.05 Israel 33 24 10 0.10 0.06 0.06 Italy 32 17 8 0.40 0.28 0.26 Jamaica .. .. 8 1.20 0.82 0.86 Japan 53 13 33 <0.10 0.01 0.04 Jordan 48 10 5 <0.10 .. .. Kazakhstan 60 7 146 0.10 0.13 0.03 Kenya 67 32 540 6.70 b 6.01 15.56 Korea, Dem. Rep. .. .. 160 <0.01 .. .. Korea, Rep. 65 5 91 <0.10 0.03 0.01 Kuwait 30 2 26 0.12 .. .. Kyrgyz Republic 60 16 142 <0.10 0.00 0.00 Lao PDR 41 15 170 <0.10 0.05 0.03 Latvia 49 13 78 0.40 0.94 0.24 Lebanon 46 35 14 0.09 .. .. Lesotho 39 1 726 31.00 17.40 38.08 Liberia .. .. 247 2.80 .. .. Libya .. .. 21 0.20 .. .. Lithuania 51 16 66 0.10 0.16 0.05 Macedonia, FYR 40 32 41 <0.10 0.00 0.00 Madagascar .. .. 234 0.30 0.06 0.23 Malawi 20 9 431 15.00 6.35 14.89 Malaysia 49 4 95 0.40 0.70 0.12 Mali .. .. 334 1.70 c 1.37 2.08 Mauritania .. .. 188 0.52 0.38 0.59 Mauritius 45 3 64 0.10 0.04 0.04 Mexico 51 18 33 0.30 0.37 0.09 Moldova 46 18 154 0.20 0.46 0.14 Mongolia 68 26 209 <0.10 .. .. Morocco 35 2 114 0.10 .. .. Mozambique .. .. 436 13.00 6.13 14.67 Myanmar 44 22 154 1.99 1.04 1.72 Namibia 65 35 751 22.50 11.10 24.29 Nepal 48 29 190 0.50 0.26 0.28 Netherlands 37 29 8 0.20 0.20 0.09 New Zealand 25 25 11 0.10 0.05 0.01 Nicaragua .. .. 64 0.20 0.23 0.08 Niger .. .. 193 1.35 0.95 1.50 Nigeria 15 2 304 5.80 2.99 5.82 Norway 31 32 6 0.10 0.08 0.04 Oman 16 2 11 0.10 .. .. Pakistan 36 9 181 0.10 0.06 0.05 Panama 56 20 47 1.50 1.88 1.25 Papua New Guinea 46 28 254 0.70 0.33 0.39 Paraguay 24 6 70 0.11 0.13 0.04 Peru 42 16 202 0.40 0.41 0.18 Philippines 54 11 320 <0.10 0.01 0.01 Poland 44 25 32 0.10 0.09 0.05 Portugal 30 7 47 0.50 0.41 0.19 Puerto Rico .. .. 7 .. .. .. 2004 World Development Indicators 105 2.18 Health risk factors and future challenges Prevalence Incidence of Prevalence of HIV of smoking tuberculosis % of adults per 100,000 % of % ages 15­24 a Male Female people adults Male Female 2000 2000 2002 2001 2001 2001 Romania 62 25 148 <0.10 0.02 0.02 Russian Federation 63 10 126 0.90 1.87 0.67 Rwanda 7 4 389 8.90 4.91 11.20 Saudi Arabia 22 1 42 0.01 .. .. Senegal .. .. 242 0.50 0.19 0.54 Serbia and Montenegro 52 42 38 0.20 .. .. Sierra Leone .. .. 405 7.00 2.48 7.53 Singapore 27 3 43 0.20 0.14 0.16 Slovak Republic 55 30 24 <0.10 0.00 0.00 Slovenia 30 20 21 <0.10 0.00 0.00 Somalia .. .. 405 1.00 .. .. South Africa 42 11 558 15.60 d 10.66 25.64 Spain 42 25 30 0.50 0.51 0.24 Sri Lanka 26 2 54 <0.10 0.03 0.04 Sudan 24 1 217 2.60 1.08 3.13 Swaziland 25 2 1,067 33.40 15.23 39.49 Sweden 19 19 5 0.10 0.06 0.05 Switzerland 39 28 8 0.50 0.46 0.40 Syrian Arab Republic 51 10 44 0.01 .. .. Tajikistan .. .. 109 <0.10 0.00 0.00 Tanzania 50 12 363 7.80 3.55 8.06 Thailand 44 3 128 1.80 1.11 1.66 Togo .. .. 361 6.00 2.05 5.93 Trinidad and Tobago 42 8 13 2.50 2.41 3.23 Tunisia 62 8 23 0.04 .. .. Turkey 65 24 32 <0.10 .. .. Turkmenistan 27 1 94 <0.10 0.00 0.00 Uganda 52 17 377 5.00 1.99 4.63 Ukraine 51 19 95 1.00 1.96 0.88 United Arab Emirates 18 1 18 0.18 .. .. United Kingdom 27 26 12 0.10 0.10 0.05 United States 26 22 5 0.60 0.47 0.22 Uruguay 32 14 29 0.30 0.52 0.20 Uzbekistan 49 9 101 <0.10 0.01 0.00 Venezuela, RB 42 39 42 0.50 0.65 0.15 Vietnam 51 4 192 0.30 0.31 0.17 West Bank and Gaza .. .. 27 .. .. .. Yemen, Rep. 60 29 92 0.10 .. .. Zambia 35 10 668 15.60 e 8.06 20.98 Zimbabwe 34 1 683 33.70 12.38 33.01 World 46 w 11 w 142 w 1.27 w 0.83 w 1.57 w Low income 37 7 226 2.31 1.11 2.51 Middle income 56 10 108 0.69 0.68 0.91 Lower middle income 58 9 116 0.70 0.69 0.98 Upper middle income 44 21 43 0.57 0.56 0.44 Low & middle income 48 9 164 1.45 0.91 1.77 East Asia & Pacific 63 5 147 0.19 0.19 0.17 Europe & Central Asia 56 17 88 0.45 1.03 0.39 Latin America & Carib. 40 24 67 0.67 0.68 0.47 Middle East & N. Africa 37 6 57 0.10 .. .. South Asia 33 6 176 0.64 0.27 0.54 Sub-Saharan Africa .. .. 358 8.38 4.14 9.44 High income 36 21 18 0.33 0.26 0.14 Europe EMU 37 26 15 0.28 0.25 0.15 a. Average of high and low estimates. b. Demographic and Health Survey 2003. c. Demographic and Health Survey 2001. d. Demographic and Health Survey 2002. e. Demographic and Health Survey 2001/02. 106 2004 World Development Indicators PEOPLE 2.18 Health risk factors and future challenges About the data The limited availability of data on health status is a major onset of disease, the health impact of smoking in devel- occur in young adults, with young women especially vul- constraint in assessing the health situation in developing oping countries will increase rapidly in the next few nerable. The estimates of HIV prevalence are based on countries. Surveillance data are lacking for many major decades. Because the data present a one-time esti- extrapolations from data collected through surveys and public health concerns. Estimates of prevalence and inci- mate, with no information on the intensity or duration of surveillance of small, nonrepresentative groups. dence are available for some diseases but are often smoking, they should be interpreted with caution. Estimates from recent Demographic and Health unreliable and incomplete. National health authorities Tuberculosis is one of the main causes of death from Surveys (DHS) that have collected data on HIV/AIDS dif- differ widely in their capacity and willingness to collect or a single infectious agent among adults in developing fer from those of the Joint United Nations Programme on report information. To compensate for the paucity of countries. In high-income countries tuberculosis has HIV/AIDS (UNAIDS) and WHO, which are based on sur- data and ensure reasonable reliability and international reemerged largely as a result of cases among immi- veillance systems that focus on pregnant women who comparability, the World Health Organization (WHO) pre- grants. The estimates of tuberculosis incidence in the attend sentinel antenatal clinics. There are reasons to pares estimates in accordance with epidemiological table are based on a new approach in which reported be cautious about comparing the two sets of estimates. models and statistical standards. cases are adjusted using the ratio of case notifications DHS is a household survey that uses a representative Smoking is the most common form of tobacco use in to the estimated share of cases detected by panels of sample from the whole population, whereas surveillance many countries, and the prevalence of smoking is there- 80 epidemiologists convened by the WHO. data from antenatal clinics is limited to pregnant women. fore a good measure of the extent of the tobacco epi- Adult HIV prevalence rates reflect the rate of HIV infec- Representative household surveys also frequently pro- demic (Corrao and others 2000). While the prevalence tion in each country's population. Low national preva- vide better coverage of rural populations. However, the of smoking has been declining in some high-income lence rates can be very misleading, however. They often fact that some respondents refuse to participate or are countries, it has been increasing in many developing disguise serious epidemics that are initially concentrated absent from the household adds considerable uncer- countries. Tobacco use causes heart and other vascular in certain localities or among specific population groups tainty to survey-based HIV estimates, because the pos- diseases and cancers of the lung and other organs. and threaten to spill over into the wider population. In sible association of absence or refusal with higher HIV Given the long delay between starting to smoke and the many parts of the developing world most new infections prevalence is unknown. UNAIDS and WHO estimates are 2.18a generally based on surveillance systems that focus on pregnant women who attend sentinel antenatal clinics. HIV prevalence rates vary by method of data collection UNAIDS and WHO use a methodology to estimate HIV Prevalence rate (%) prevalence for the adult population (ages 15­49) that UNAIDS and WHO Demographic and Health assumes that prevalence among pregnant women is a Country surveillance data Survey data good approximation of prevalence among men and Zambia 21.5 15.6 women. However, this assumption might not apply to all South Africa 20.1 15.6 countries or over time. There are also other potential Kenya 15.0 6.7 Mali 1.7 1.7 biases associated with the use of antenatal clinic data, such as differences among women who attend antena- Recent household survey data from Demographic and Health Surveys show significantly lower HIV prevalence rates than those from UNAIDS and WHO, which are based on surveillance. This indicates that different data collection methodologies, tal clinics and those who do not. and their quality and coverage, record different prevalence rates. Source: UNAIDS and WHO 2002; Demographic and Health Survey data. Definitions 2.18b · Prevalence of smoking is the percentage of men In some countries men know more about preventing HIV than women do and women who smoke cigarettes. The age range Population with correct knowledge of HIV prevention (%) varies among countries but in most is 18 and older or 15 and older. · Incidence of tuberculosis is the esti- 40 Men Women mated number of new tuberculosis cases (pulmonary, smear positive, extrapulmonary). · Prevalence of HIV 30 is the percentage of people who are infected with HIV. 20 Data sources 10 The data are drawn from a variety of sources, including the WHO's World Health Report 2003, 0 Tobacco Atlas 2002, and Global Tuberculosis Uganda Malawi Tanzania Rwanda Haiti Dominican Republic Control Report 2003; the National Tobacco Information Online System (NATIONS) database More men correctly identified the ways of preventing HIV transmission and had fewer misconceptions about it than did women. Countries where populations are more informed about HIV transmission do not necessarily have a low HIV (http://apps.nccd.cdc.gov/nations/); and the Joint prevalence rate, because it takes time to change people's behavior. However, there is no doubt that knowledge is an United Nations Programme on HIV/AIDS (UNAIDS) important prerequisite for behavior change. and WHO's AIDS Epidemic Update 2002. Source: Demographic and Health Surveys, 1996­2000. 2004 World Development Indicators 107 2.19 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival at birth rate mortality rate rate to age 65 rate per 1,000 per 1,000 Male Female per 1,000 % of cohort years live births per 1,000 1997­ 1997­ Male Female Male Female 1980 2002 1980 2002 1980 2002 2002 a 2002 a 2000­02 a 2000­02 a 2002 2002 Afghanistan 40 43 183 165 280 257 .. .. 437 376 32 33 Albania 69 74 56 22 66 24 .. .. 209 95 77 85 Algeria 59 71 94 39 134 49 .. .. 155 119 73 79 Angola 41 47 158 154 265 260 .. .. 492 386 34 39 Argentina 70 74 33 16 38 19 .. .. 184 92 75 87 Armenia 73 75 22 30 80 35 5 3 223 106 70 83 Australia 74 79 11 6 13 6 .. .. 100 52 84 92 Austria 73 79 14 5 17 5 .. .. 122 58 83 91 Azerbaijan 68 65 91 76 117 96 .. .. 261 150 58 72 Bangladesh 49 62 129 48 205 73 28 38 262 252 59 61 Belarus 71 68 21 17 26 20 .. .. 381 133 54 81 Belgium 73 79 12 5 15 6 .. .. 126 65 82 91 Benin 48 53 126 93 213 151 72 79 384 328 43 50 Bolivia 52 64 112 56 170 71 26 29 264 219 59 67 Bosnia and Herzegovina 70 74 31 15 39 18 .. .. 200 93 75 86 Botswana 58 38 62 80 84 110 .. .. 703 669 13 18 Brazil 63 69 67 33 86 37 .. .. 259 136 62 79 Bulgaria 71 72 20 14 24 16 .. .. 239 103 69 83 Burkina Faso 44 43 140 107 247 207 131 128 559 507 28 32 Burundi 47 42 114 123 190 208 .. .. 648 603 26 29 Cambodia 39 54 110 96 190 138 34 30 386 334 42 48 Cameroon 50 48 105 95 173 166 69 75 488 440 35 41 Canada 75 79 11 5 13 7 .. .. 101 57 83 92 Central African Republic 46 42 121 115 189 180 .. .. 620 573 24 29 Chad 42 48 124 117 225 200 106 99 449 361 38 43 Chile 69 76 31 10 39 12 .. .. 151 67 79 89 China 67 71 49 30 64 38 .. .. 161 110 72 79 Hong Kong, China 74 80 .. .. .. .. .. .. 97 50 85 92 Colombia 66 72 40 19 56 23 4 3 238 115 71 83 Congo, Dem. Rep. 49 45 130 129 210 205 .. .. 571 493 31 35 Congo, Rep. 50 52 88 81 125 108 .. .. 475 406 35 44 Costa Rica 73 78 24 9 26 11 .. .. 131 78 82 90 Côte d'Ivoire 49 45 114 116 172 191 83 58 553 494 31 34 Croatia 70 74 20 7 23 8 .. .. 150 110 71 87 Cuba 74 77 22 7 22 9 .. .. 143 94 81 88 Czech Republic 70 75 17 4 19 5 .. .. 160 75 75 88 Denmark 74 77 9 4 10 4 .. .. 128 80 80 88 Dominican Republic 63 67 71 32 92 38 13 8 234 146 63 75 Ecuador 63 70 64 25 98 29 .. .. 199 120 72 77 Egypt, Arab Rep. 56 69 118 33 173 39 15 16 210 147 69 75 El Salvador 57 70 84 33 118 39 .. .. 250 148 68 81 Eritrea 44 51 141 59 210 80 55 50 493 441 37 42 Estonia 69 71 21 10 24 12 .. .. 316 114 60 85 Ethiopia 42 42 143 114 220 171 83 86 594 535 26 30 Finland 73 78 8 4 9 5 .. .. 144 61 80 91 France 74 79 10 4 13 6 .. .. 130 57 82 92 Gabon 48 53 75 63 105 85 32 33 380 330 45 51 Gambia, The 40 53 144 91 231 126 .. .. 373 320 40 47 Georgia 71 73 34 24 43 29 .. .. 250 133 71 87 Germany 73 78 13 4 16 5 .. .. 125 59 82 91 Ghana 53 55 96 60 157 97 53 51 379 326 47 51 Greece 74 78 20 5 23 5 .. .. 114 47 83 91 Guatemala 57 65 97 36 139 49 15 18 286 182 58 72 Guinea 40 46 175 106 300 165 101 98 432 366 32 33 Guinea-Bissau 39 45 173 130 290 211 .. .. 495 427 34 39 Haiti 51 52 132 79 195 123 52 54 524 373 38 47 108 2004 World Development Indicators PEOPLE 2.19 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival at birth rate mortality rate rate to age 65 rate per 1,000 per 1,000 Male Female per 1,000 % of cohort years live births per 1,000 1997­ 1997­ Male Female Male Female 1980 2002 1980 2002 1980 2002 2002 a 2002 a 2000­02 a 2000­02 a 2002 2002 Honduras 60 66 75 32 103 42 .. .. 221 157 59 72 Hungary 70 72 24 8 26 9 .. .. 295 123 67 85 India 54 63 113 65 173 90 25 37 250 191 62 66 Indonesia 55 67 79 32 125 43 19 20 227 175 64 72 Iran, Islamic Rep. 58 69 92 34 130 41 .. .. 170 139 71 75 Iraq 62 63 63 102 83 125 .. .. 258 208 63 67 Ireland 73 77 12 6 14 6 .. .. 108 62 80 89 Israel 73 79 16 6 19 6 .. .. 99 56 84 90 Italy 74 78 15 4 17 6 .. .. 110 53 81 91 Jamaica 71 76 28 17 34 20 .. .. 169 127 80 87 Japan 76 82 8 3 11 5 .. .. 98 44 86 94 Jordan 64 72 52 27 67 33 5 5 199 144 74 81 Kazakhstan 67 62 45 76 58 99 11 6 366 201 47 71 Kenya 55 46 73 78 115 122 36 38 578 529 28 33 Korea, Dem. Rep. 67 62 32 42 43 55 .. .. 238 192 55 62 Korea, Rep. 67 74 16 5 18 5 .. .. 186 71 72 86 Kuwait 71 77 29 9 35 10 .. .. 100 68 82 88 Kyrgyz Republic 65 65 90 52 115 61 10 11 335 299 56 75 Lao PDR 45 55 135 87 200 100 .. .. 355 299 45 50 Latvia 69 70 21 17 26 21 .. .. 328 122 60 84 Lebanon 65 71 38 28 44 32 .. .. 192 136 71 79 Lesotho 53 38 115 91 168 132 .. .. 667 630 15 20 Liberia 51 47 157 157 235 235 .. .. 448 385 33 37 Libya 60 72 55 16 70 19 .. .. 210 157 73 83 Lithuania 71 73 21 8 22 9 .. .. 286 106 66 87 Macedonia, FYR .. 73 53 22 69 26 .. .. 160 89 75 84 Madagascar 51 55 106 84 175 135 75 68 385 322 49 55 Malawi 44 38 157 113 265 182 101 102 701 653 20 23 Malaysia 67 73 31 8 42 8 .. .. 202 113 72 83 Mali 42 41 176 122 300 222 132 125 518 446 25 29 Mauritania 47 51 118 120 175 183 38 38 357 302 43 49 Mauritius 66 73 33 17 40 19 .. .. 228 109 70 85 Mexico 67 74 56 24 74 29 .. .. 180 101 75 85 Moldova 66 67 41 27 53 32 .. .. 325 165 58 75 Mongolia 58 65 97 58 140 71 .. .. 280 199 65 71 Morocco 58 68 99 39 144 43 .. .. 174 113 68 76 Mozambique 44 41 140 128 233 205 85 82 674 612 25 30 Myanmar 51 57 94 77 134 108 .. .. 343 245 46 58 Namibia 53 42 84 55 114 67 .. .. 695 661 22 25 Nepal 48 60 124 62 183 83 28 40 314 314 58 56 Netherlands 76 78 9 5 11 5 .. .. 95 64 83 90 New Zealand 73 78 13 6 16 6 .. .. 108 69 83 90 Nicaragua 59 69 85 32 120 41 12 11 225 161 67 77 Niger 40 46 191 155 320 264 184 202 473 308 30 37 Nigeria 46 45 108 100 216 201 66 69 443 393 33 36 Norway 76 79 9 4 11 4 .. .. 105 59 84 91 Oman 60 74 73 11 95 13 .. .. 187 135 78 84 Pakistan 55 64 105 76 156 101 .. .. 221 198 64 70 Panama 70 75 34 19 46 25 .. .. 145 93 78 86 Papua New Guinea 51 57 79 70 108 94 .. .. 359 329 49 53 Paraguay 67 71 46 26 61 30 .. .. 173 129 70 80 Peru 60 70 89 30 126 39 19 17 190 139 69 78 Philippines 61 70 55 28 81 37 21 19 249 142 70 77 Poland 70 74 21 8 24 9 .. .. 226 88 71 87 Portugal 71 76 25 5 31 6 .. .. 164 66 77 89 Puerto Rico 74 77 .. .. .. .. .. .. 148 55 76 91 2004 World Development Indicators 109 2.19 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival at birth rate mortality rate rate to age 65 rate per 1,000 per 1,000 Male Female per 1,000 % of cohort years live births per 1,000 1997­ 1997­ Male Female Male Female 1980 2002 1980 2002 1980 2002 2002 a 2002 a 2000­02 a 2000­02 a 2002 2002 Romania 69 70 29 19 36 21 .. .. 260 117 64 81 Russian Federation 67 66 28 18 35 21 .. .. 420 149 48 77 Rwanda 46 40 130 118 219 203 105 97 667 599 23 25 Saudi Arabia 61 73 65 23 85 28 .. .. 181 116 76 83 Senegal 45 52 128 79 218 138 76 74 355 303 38 47 Serbia and Montenegro 70 73 36 16 44 19 .. .. 180 100 73 83 Sierra Leone 35 37 192 165 336 284 .. .. 587 531 24 29 Singapore 71 78 11 3 13 4 .. .. 114 61 83 89 Slovak Republic 70 73 20 8 23 9 .. .. 204 82 70 86 Slovenia 70 76 16 4 18 5 .. .. 170 76 76 89 Somalia 43 47 133 133 225 225 .. .. 516 452 38 44 South Africa 57 46 64 52 91 65 18 13 621 583 27 33 Spain 76 78 13 5 16 6 .. .. 122 49 83 92 Sri Lanka 68 74 34 16 46 19 .. .. 244 124 76 84 Sudan 48 58 86 64 142 94 .. .. 341 291 53 58 Swaziland 52 44 99 106 143 149 .. .. 642 602 26 30 Sweden 76 80 7 3 9 3 .. .. 87 55 85 92 Switzerland 76 80 9 5 11 6 .. .. 99 58 85 93 Syrian Arab Republic 62 70 54 23 73 28 .. .. 170 132 69 79 Tajikistan 66 67 .. 90 .. 116 .. .. 293 204 62 75 Tanzania 50 43 106 104 175 165 61 58 569 520 27 31 Thailand 64 69 45 24 58 28 .. .. 245 150 67 77 Togo 49 50 106 87 176 140 73 65 460 406 37 42 Trinidad and Tobago 68 72 35 17 40 20 .. .. 209 133 74 82 Tunisia 62 73 72 21 100 26 .. .. 169 99 75 83 Turkey 61 70 103 35 133 41 10 13 218 120 69 79 Turkmenistan 64 65 86 70 109 86 19 17 280 156 57 72 Uganda 48 43 107 83 185 141 78 70 617 567 25 28 Ukraine 69 68 22 16 27 20 .. .. 365 135 56 80 United Arab Emirates 68 75 23 8 27 9 .. .. 143 93 80 85 United Kingdom 74 77 12 5 14 7 .. .. 108 65 81 89 United States 74 77 13 7 15 8 .. .. 135 80 81 91 Uruguay 70 75 37 14 42 15 .. .. 185 89 74 88 Uzbekistan 67 67 51 55 60 65 .. .. 282 176 63 77 Venezuela, RB 68 74 34 19 42 22 .. .. 178 99 75 85 Vietnam 60 70 44 20 66 26 10 13 203 139 68 78 West Bank and Gaza .. 73 .. .. .. .. .. .. 154 97 74 83 Yemen, Rep. 49 57 135 83 205 114 33 36 278 226 50 53 Zambia 50 37 90 102 155 182 89 74 725 687 16 21 Zimbabwe 55 39 69 76 108 123 35 31 650 612 18 20 World 63 w 67 w 79 w 55 w 119 w 81 w .. w .. w 234 w 166 w 69 w 78 w Low income 53 59 110 79 174 121 .. .. 310 259 64 69 Middle income 66 70 57 30 76 37 .. .. 211 128 63 80 Lower middle income 65 69 59 32 79 40 .. .. 212 131 61 78 Upper middle income 68 73 42 19 54 22 .. .. 197 103 68 82 Low & middle income 60 65 86 60 131 88 .. .. 255 186 64 73 East Asia & Pacific 64 69 56 32 79 42 .. .. 184 129 69 76 Europe & Central Asia 68 69 45 31 57 37 .. .. 317 137 59 80 Latin America & Carib. 65 71 61 28 82 34 .. .. 222 125 67 81 Middle East & N. Africa 58 69 94 44 134 54 .. .. 193 143 68 73 South Asia 54 63 115 68 176 95 25 37 252 202 62 65 Sub-Saharan Africa 48 46 116 103 197 174 .. .. 519 461 40 46 High income 74 78 12 5 15 7 .. .. 128 66 81 90 Europe EMU 74 78 13 4 16 6 .. .. 125 58 .. .. a. Data are for the most recent year available. 110 2004 World Development Indicators PEOPLE 2.19 Mortality About the data Mortality rates for different age groups--infants, (see Primary data documentation). Extrapolations high prevalence of smoking, a high-fat diet, exces- children, or adults--and overall indicators of based on outdated surveys may not be reliable for sive alcohol use, and stressful conditions related to mortality--life expectancy at birth or survival to a monitoring changes in health status or for compara- the economic transition. given age--are important indicators of health status tive analytical work. The percentage of a cohort surviving to age 65 in a country. Because data on the incidence and To produce harmonized estimates of infant and reflects both child and adult mortality rates. Like life prevalence of diseases (morbidity data) are fre- under-five mortality rates that make use of all avail- expectancy, it is a synthetic measure based on cur- quently unavailable, mortality rates are often used to able information in a transparent way, the United rent age-specific mortality rates and used in the con- identify vulnerable populations. And they are among Nations Children's Fund (UNICEF) and the World struction of life tables. It shows that even in coun- the indicators most frequently used to compare lev- Bank developed and adopted a methodology that fits tries where mortality is high, a certain share of the els of socioeconomic development across countries. a regression line to the relationship between mortal- current birth cohort will live well beyond the life The main sources of mortality data are vital regis- ity rates and their reference dates using weighted expectancy at birth, while in low-mortality countries tration systems and direct or indirect estimates based least squares. (For further discussion of methodolo- close to 90 percent will reach at least age 65. on sample surveys or censuses. A "complete" vital gy for childhood mortality estimates, see Hill and oth- registration system--one covering at least 90 percent ers 1999.) Some of the estimates shown in the table Definitions of vital events in the population--is the best source of this year are World Bank estimates. Estimates may age-specific mortality data. But such systems are fair- change after the harmonization process with UNICEF · Life expectancy at birth is the number of years a ly uncommon in developing countries. Thus estimates and the World Health Organization is completed. newborn infant would live if prevailing patterns of must be obtained from sample surveys or derived by Infant and child mortality rates are higher for boys mortality at the time of its birth were to stay the applying indirect estimation techniques to registration, than for girls in countries in which parental gender same throughout its life. · Infant mortality rate is census, or survey data. Survey data are subject to preferences are insignificant. Child mortality cap- the number of infants dying before reaching one year recall error, and surveys estimating infant deaths tures the effect of gender discrimination better than of age, per 1,000 live births in a given year. · Under- require large samples because households in which a does infant mortality, as malnutrition and medical five mortality rate is the probability that a newborn birth or an infant death has occurred during a given interventions are more important in this age group. baby will die before reaching age five, if subject to year cannot ordinarily be preselected for sampling. Where female child mortality is higher, as in some current age-specific mortality rates. The probability is Indirect estimates rely on estimated actuarial ("life") countries in South Asia, girls probably have unequal expressed as a rate per 1,000. · Child mortality tables that may be inappropriate for the population access to resources. rate is the probability of dying between the ages of concerned. Because life expectancy at birth is con- Adult mortality rates have increased in many coun- one and five, if subject to current age-specific mor- structed using infant mortality data and model life tries in Sub-Saharan Africa and Europe and Central tality rates. The probability is expressed as a rate per tables, similar reliability issues arise for this indicator. Asia. In Sub-Saharan Africa the increase stems from 1,000. · Adult mortality rate is the probability of Life expectancy at birth and age-specific mortality AIDS-related mortality and affects both men and dying between the ages of 15 and 60--that is, the rates are generally estimates based on vital regis- women. In Europe and Central Asia the causes are probability of a 15-year-old dying before reaching age tration or the most recent census or survey available more diverse and affect men more. They include a 60--if subject to current age-specific mortality rates between ages 15 and 60. · Survival to age 65 2.19a refers to the percentage of a cohort of newborn Under-five mortality rates are higher in poor households than in rich ones infants that would survive to age 65, if subject to cur- Under-five mortality per 1,000 people, by income quintile rent age-specific mortality rates. Uganda, 2000­01 Bangladesh, 2000 200 200 Male Female 150 150 100 100 50 50 Data sources The data are from the United Nations Statistics 0 0 Division's Population and Vital Statistics Report, Poorest Second Third Fourth Richest Poorest Second Third Fourth Richest publications and other releases from national sta- Higher under-five mortality rates for children from poor households than for those from wealthier households indicate the tistical offices, Demographic and Health Surveys deprivation among the poor. Under-five mortality is usually higher for boys than for girls, except in cases of parental from national sources and Macro International, discrimination against girls. and UNICEF's State of the World's Children 2004. Source: Demographic and Health Survey data. 2004 World Development Indicators 111 3 ENVIRONMENT E conomic development has led to dramatic improvements in the quality of life in developing countries, producing gains unparalleled in human history. But the picture is far from entirely positive. Gains have been unevenly distributed, and a large part of the world's population remains desperately poor. At the same time, natural resources--land, water, and forests--are being degraded at alarming rates in many countries, and environmental factors such as indoor and outdoor air pollution, waterborne diseases, and exposure to toxic chemicals threaten the health of millions of people. Addressing these concerns, successive international conferences, including the latest World Summit on Sustainable Development, have reaffirmed the commitment to eliminate poverty through environmentally sound and socially responsible economic development. If the vision of a world without poverty is to be realized, sustainable development is the key. A healthy environment is central to the international development agenda and an integral part of meeting the Millennium Development Goals (see section 1, World View). The Millennium Development Goals call for integrating principles of environmental sustainability into country policies and programs and reversing environmental losses. This requires measuring and monitoring the state of the environment and its changes as well as the links between the economy and the environment. Given such close links, there is a strong argument for developing indicators that integrate economic activity and environmental change. One approach that appears to hold much promise is environmental accounting. Aimed at deriving "greener" measures of national income, savings, and wealth, environmental accounting adds natural resources and pollutants to the assets and liabilities measured in the standard national accounts. But preparing full-fledged integrated environmental and economic accounts is costly, and not all countries are doing so. In the absence of such integrated accounts, physical indicators and descriptive statistics can provide useful information for monitoring the state of the environment. Many such indicators are presented here, but despite greater awareness of the importance of environmental issues and efforts to improve environmental data, information on many aspects of the environment remains sparse. The available data are often of uneven quality, 2004 World Development Indicators 113 relate to different periods, and are sometimes out of date. The lack people lack access to safe water, and more than 430 million live in of adequate data hampers efforts to measure the state of the envi- countries facing chronic and widespread water shortages--with water ronment and design sound policies. Another problem is that many stress (less than 1,700 cubic meters of freshwater available per per- environmental indicators are not meaningful at the national level. son a year) or water scarcity (less than 1,000 cubic meters; table 3.5). Climate change has impacts that go beyond national boundaries. Global per capita water supplies are declining, further growth in Other environmental factors such as air and water pollution may population and economic activity will add to the demand for water, have relevance only to the locality where they are measured. So and by 2050 the share of the world's population facing water global, regional, or city (tables 3.11 and 3.13) indicators are often stress could increase more than fivefold. These trends pose a sig- more meaningful than national aggregates. nificant challenge for meeting the Millennium Development Goal of halving the proportion of people without sustainable access to safe Fragile land and increasing demand for food drinking water by 2015. Almost three of every five people in developing countries--some 3 billion--live in rural areas (table 3.1). In many of these countries Energy use improves welfare, but has its consequences agriculture is still the main source of employment. But most of the The use of energy, especially electricity, is important in raising peo- land available to meet current and future food requirements is ple's standard of living. High-income countries use more than five already in production; any further expansion must necessarily times as much energy as developing countries on a per capita involve fragile and marginal lands. This is particularly so in devel- basis, and with only 15 percent of the world's population they use oping countries where population growth is high, poverty is endem- more than half its energy (table 3.7 and figure 3a). ic, and existing institutional capacities for land management are At the same time, energy use and electricity generation also have weak. Because land resources are finite, fragile, and nonrenew- environmental consequences. Generating energy produces emis- able, countries must meet their increased need for food and other sions of carbon dioxide, the main greenhouse gas contributing to agricultural products mainly by raising and sustaining crop and live- global warming. Anthropogenic (human caused) carbon dioxide stock yields and by using land more intensively. Low-income coun- emissions result primarily from fossil fuel combustion and tries are increasing the land under cereal production, but their use cement manufacturing, with high-income countries contributing half of agricultural machinery lags far behind that in other countries (table 3.8), Among countries in all income groups, per capita emis- (table 3.2). These countries, where current cereal yields are a third sions vary widely (figure 3b). The extent of environmental damage those in high-income countries, will have to expand their arable depends largely on how energy is generated. For example, burning land--a strategy that cannot be sustained for long (table 3.3). coal releases twice as much carbon dioxide as does burning an equivalent amount of natural gas (see About the data for table 3.8). Shrinking forests and threatened biodiversity A substantial number of the world's 1.2 billion extremely poor More urban--and more polluted people--those living on less than $1 a day--depend for their liveli- The world is become increasingly urban. Now urban areas are hoods on forests and forest products . But the forests are shrink- home to 48 percent of the world's population--two of five people ing, as is the diversity of the plants and animals they support. With in low- and middle-income countries and almost four of five in high- growth and development, forests are being converted to agricultur- income countries. Most of Latin America is as urbanized as al land and urban areas. At the beginning of the 20th century the Europe, with 76 percent of the population living in urban areas. earth had about 5 billion hectares of forested area. Now it has less Asia is urbanizing rapidly. Even such traditionally rural countries as than 4 billion hectares. The loss has been concentrated in develop- ing countries, driven by the growing demand for timber and agricul- 3a tural land, exacerbated by weak monitoring institutions. Low-income High-income countries use more than half the world's energy countries lost some 73 million hectares--about 8 percent of their forest--in the 1990s. By contrast, high-income countries reforested Global energy use, 2001 about 8 million hectares of forest in the same period (table 3.4). Closely linked to changes in land use is biodiversity, another important dimension of environmental sustainability. Many coun- Rest of United tries have designated a share of their land as protected areas the world States 21% 23% (table 3.4). But even where protected areas are increased and India environmental protections are effectively respected, losses of bio- 5% Japan logically diverse areas cannot be reversed. About 12 percent of the 5% Russian world's nearly 10,000 bird species are vulnerable or in immediate Federation Other China danger of extinction, as are 24 percent of the world's 4,800 mam- 6% high-income 11% countries mal species and an estimated 30 percent of all fish species. 29% A thirsty planet--and getting thirstier Water is crucial to economic growth and development--and to the sur- vival of both terrestrial and aquatic systems. But more than 1 billion Source: Table 3.7. 114 2004 World Development Indicators 3b policy instruments more than their effectiveness. Still, making a for- mal commitment is an essential first step toward taking action. Emissions of carbon dioxide vary widely, even among the five largest producers of emissions Beyond national environmental problems, governments are increasingly concerned about global environmental issues. To Carbon dioxide emissions (billions of metric tons) address these issues, countries have reached agreements and 6 1980 signed treaties on areas relating to the quality of life on earth (for 5 2000 example, figure 3c shows the decline in chlorofluorocarbons as a 4 result of such agreements). Many of these agreements resulted from the 1992 United Nations Conference on Environment and 3 Development in Rio de Janeiro, which produced Agenda 21--an 2 array of actions to address environmental challenges. But 10 years after Rio the World Summit on Sustainable Development recog- 1 nized that many of the proposed actions have yet to materialize. 0 United States China Russian Federation Japan India Adjusted net savings--moving toward a measure of sustainability Carbon dioxide emissions per capita (metric tons) The question of an economy's sustainability can be reduced to the 20 question of whether welfare is expected to decline along the future development path as a result of decisions made today. Because 15 flows of income and well-being are ultimately derived from the stocks of produced, natural, and human assets, a drop in the 10 aggregate value of these stocks must eventually lead to a decline in welfare. One measure of change in total assets is provided by 5 net adjusted savings--a measure of savings that accounts not only for a country's economic surplus but also for its depletion of natu- 0 ral resources, accumulation of pollutants and their damages, and United States China Russian Federation Japan India investments in human capital. The data limitations and the approx- Note: No data for 1980 are available for the Russian Federation. imations used in calculating net adjusted savings mean that these Source: Table 3.8 and World Bank staff estimates. estimates still must be used with caution (for more details on the assumptions made, see About the data for table 3.15). China, India, and Indonesia now have hundreds of millions of Many developing countries have low or negative adjusted net sav- people living in urban areas, with both the number of people and ings. Broadly speaking, the lowest adjusted savings rates are record- the share of the population in cities growing rapidly (table 3.10). ed for countries that depend heavily on resource rents, particularly Urbanization can yield important social benefits, improving access those endowed with minerals and fossil fuels. These rents account to public services such as education, health care, and cultural for a sizable share of GDP in many countries, suggesting that man- facilities (table 3.11). aging natural resources and resource revenues should receive even Urbanization can also lead to adverse environmental effects that more attention as these countries strive to ensure the sustainability require policy responses. Greater urbanization usually means of their economies and the welfare of future generations. greater pollution, which can overwhelm the natural capacities of air and water to absorb pollutants. The costs of controlling pollution 3c can be enormous. And pollution exposes people to severe health Emissions of some greenhouse and ozone-depleting gases have hazards. Several major urban air pollutants--lead, sulfur dioxide, begun to fall or slow since Rio suspended particulate matter--are known to harm human health Methane and gaseous chlorine emissions, parts per trillion (table 3.13). A big source of urban air pollution is motor vehicles, 3,000 whose numbers are strongly linked to rising income. The number of passenger cars in developing countries has increased from 16 cars Gaseous chlorine per 1,000 people in 1990 to 28 in 2001. At the same time, the number of passenger cars in high-income countries has increased 2,000 from 400 per 1,000 people to 440 (table 3.12). Methane Commitment to change--necessary, but not sufficient 1,000 The strength of environmental policies in any country reflects the pri- ority its government gives to problems of environmental degradation-- and that priority reflects the benefits expected from using scarce resources that have competing uses. But measuring governments' 0 1980 1985 1990 1995 2000 commitment to these goals is difficult. The indicators of government Source: World Research Institute 2002. commitment in table 3.14 are imperfect, measuring the existence of 2004 World Development Indicators 115 3.1 Rural environment and land use Rural population Rural Land area Land use population density average people annual % per sq. km thousand % of land area % of total growth of arable land sq. km Arable land Permanent cropland Other land 1980 2002 1980­2002 2001 2001 1980 2001 1980 2001 1980 2001 Afghanistan 84 77 2.2 268 652 12.1 12.1 0.2 0.2 87.7 87.6 Albania 66 56 0.0 309 27 21.4 21.1 4.3 4.4 74.4 74.5 Algeria 56 42 1.0 170 2,382 2.9 3.2 0.3 0.2 96.8 96.5 Angola 79 65 1.9 277 1,247 2.3 2.4 0.4 0.2 97.3 97.4 Argentina 17 12 ­0.4 13 2,737 10.6 12.3 0.4 0.5 89.0 87.2 Armenia 34 33 ­0.3 204 28 .. 17.6 .. 2.3 .. 80.1 Australia 14 9 ­1.0 3 7,682 5.7 6.5 0.0 0.0 94.2 93.4 Austria 33 32 0.2 187 83 18.6 16.9 1.2 0.9 80.2 82.2 Azerbaijan 47 48 1.4 230 87 .. 19.6 .. 2.7 .. 77.7 Bangladesh 85 74 1.5 1,228 130 68.3 62.1 2.0 3.1 29.6 34.8 Belarus 43 30 ­1.5 49 207 .. 29.5 .. 0.6 .. 69.9 Belgium 5 3 ­2.4 32 33 a 23.2 a 25.7 a 0.4 a 0.7 a 76.4 a 73.6 a Benin 73 56 1.7 182 111 13.6 18.1 0.8 2.4 85.7 79.5 Bolivia 55 37 0.4 110 1,084 1.8 2.7 0.1 0.2 98.1 97.1 Bosnia and Herzegovina 64 56 ­0.6 333 51 .. 13.6 .. 3.0 .. 83.4 Botswana 82 50 0.7 232 567 0.7 0.7 0.0 0.0 99.3 99.3 Brazil 33 18 ­1.2 54 8,457 5.3 7.0 0.9 0.9 93.7 92.1 Bulgaria 39 32 ­1.3 59 111 34.6 40.0 3.2 1.9 62.2 58.1 Burkina Faso 92 83 2.0 243 274 10.0 14.4 0.1 0.2 89.8 85.4 Burundi 96 90 2.2 699 26 36.2 35.0 12.5 14.0 51.3 50.9 Cambodia 88 82 2.5 274 177 11.3 21.0 0.4 0.6 88.3 78.4 Cameroon 69 50 1.2 130 465 12.7 12.8 2.2 2.6 85.1 84.6 Canada 24 21 0.4 14 9,221 4.9 5.0 0.0 0.0 95.0 95.0 Central African Republic 65 58 1.8 114 623 3.0 3.1 0.1 0.1 96.9 96.8 Chad 81 75 2.5 171 1,259 2.5 2.9 0.0 0.0 97.5 97.1 Chile 19 14 0.1 108 749 5.1 2.6 0.3 0.4 94.6 96.9 China b 80 62 0.1 561 9,327 10.4 15.4 0.4 1.2 89.3 83.4 Hong Kong, China 9 0 .. .. .. 7.0 .. 1.0 .. 92.0 .. Colombia 37 24 ­0.1 420 1,039 3.6 2.4 1.4 1.7 95.0 95.9 Congo, Dem. Rep. .. .. .. .. 2,267 2.9 3.0 0.4 0.5 96.6 96.5 Congo, Rep. 58 33 0.7 690 342 0.4 0.5 0.1 0.1 99.5 99.4 Costa Rica 53 40 1.2 697 51 5.5 4.4 4.4 5.9 90.1 89.7 Côte d'Ivoire 65 56 2.5 292 318 6.1 9.7 7.2 13.8 86.6 76.4 Croatia 50 41 ­1.0 128 56 .. 26.1 .. 2.3 .. 71.6 Cuba 32 24 ­0.6 76 110 23.9 33.1 6.4 7.6 69.7 59.3 Czech Republic 25 25 ­0.0 85 77 .. 39.8 .. 3.1 .. 57.1 Denmark 16 15 ­0.2 35 42 62.3 54.0 0.3 0.2 37.4 45.8 Dominican Republic 49 33 0.1 263 48 22.1 22.7 7.2 10.3 70.6 67.0 Ecuador 53 36 0.4 285 277 5.6 5.9 3.3 4.9 91.1 89.2 Egypt, Arab Rep. 56 57 2.3 1,306 995 2.3 2.9 0.2 0.5 97.5 96.6 El Salvador 56 38 ­0.3 370 21 26.9 31.9 11.7 12.1 61.4 56.1 Eritrea 86 80 2.4 679 101 .. 5.0 .. 0.0 .. 95.0 Estonia 30 31 ­0.3 62 42 .. 16.0 .. 0.4 .. 83.5 Ethiopia 90 84 2.3 517 1,000 .. 10.7 .. 0.8 .. 88.5 Finland 40 41 0.5 97 305 7.8 7.2 0.0 0.0 92.2 92.8 France 27 24 0.0 78 550 31.8 33.5 2.5 2.1 65.7 64.4 Gabon 50 17 ­2.0 71 258 1.1 1.3 0.6 0.7 98.2 98.1 Gambia, The 80 68 2.8 372 10 15.5 25.0 0.4 0.5 84.1 74.5 Georgia 48 43 ­0.4 286 69 .. 11.4 .. 3.9 .. 84.7 Germany 17 12 ­1.4 86 349 34.5 33.9 1.4 0.6 64.1 65.6 Ghana 69 63 2.4 343 228 8.4 16.3 7.5 9.7 84.2 74.1 Greece 42 39 0.1 154 129 22.5 21.1 7.9 8.8 69.6 70.1 Guatemala 63 60 2.3 516 108 11.7 12.5 4.4 5.0 83.9 82.4 Guinea 81 72 2.0 614 246 2.9 3.6 1.8 2.6 95.4 93.8 Guinea-Bissau 83 67 1.8 317 28 9.1 10.7 1.7 8.8 89.2 80.5 Haiti 76 63 1.1 664 28 28.3 28.3 11.6 11.6 60.1 60.1 116 2004 World Development Indicators ENVIRONMENT 3.1 Rural environment and land use Rural population Rural Land area Land use population density average people annual % per sq. km thousand % of land area % of total growth of arable land sq. km Arable land Permanent cropland Other land 1980 2002 1980­2002 2001 2001 1980 2001 1980 2001 1980 2001 Honduras 65 45 1.3 288 112 13.3 9.5 2.4 3.2 84.3 87.2 Hungary 43 35 ­1.2 78 92 54.4 50.1 3.3 2.1 42.2 47.8 India 77 72 1.6 460 2,973 54.8 54.4 1.8 2.7 43.4 42.9 Indonesia 78 57 0.2 591 1,812 9.9 11.3 4.4 7.2 85.6 81.5 Iran, Islamic Rep. 50 35 0.6 160 1,636 7.9 8.7 0.4 1.4 91.6 89.9 Iraq 34 32 2.5 134 437 12.0 13.1 0.4 0.8 87.6 86.1 Ireland 45 40 0.2 150 69 16.1 15.2 0.0 0.0 83.9 84.8 Israel 11 8 0.8 157 21 15.8 16.4 4.3 4.2 80.0 79.4 Italy 33 33 0.0 232 294 32.2 27.8 10.0 9.5 57.7 62.7 Jamaica 53 43 ­0.0 648 11 12.5 16.1 9.7 10.2 77.8 73.8 Japan 24 21 ­0.2 603 365 13.3 12.2 1.6 1.0 85.1 86.8 Jordan 40 21 1.0 449 89 3.4 2.7 0.4 1.8 96.2 95.5 Kazakhstan 46 44 ­0.2 31 2,700 .. 8.0 .. 0.1 .. 92.0 Kenya 84 65 1.7 439 569 6.7 8.1 0.8 1.0 92.5 90.9 Korea, Dem. Rep. 43 39 0.8 353 120 19.0 20.8 2.4 2.5 78.6 76.7 Korea, Rep. 43 17 ­3.2 491 99 20.9 17.2 1.4 2.0 77.8 80.9 Kuwait 9 4 ­1.6 684 18 0.1 0.7 0.0 0.1 99.9 99.2 Kyrgyz Republic 62 66 1.7 232 192 .. 7.3 .. 0.3 .. 92.4 Lao PDR 88 80 2.1 495 231 3.4 3.8 0.1 0.4 96.5 95.8 Latvia 32 40 0.6 51 62 .. 29.7 .. 0.5 .. 69.9 Lebanon 26 10 ­2.8 257 10 20.5 16.6 8.9 14.0 70.6 69.4 Lesotho 87 71 0.6 380 30 9.6 10.9 0.1 0.1 90.2 89.0 Liberia 65 54 1.7 461 96 3.9 3.9 2.1 2.3 94.0 93.8 Libya 31 12 ­1.7 36 1,760 1.0 1.0 0.2 0.2 98.8 98.8 Lithuania 39 31 ­0.9 37 65 .. 45.2 .. 0.9 .. 53.9 Macedonia, FYR 47 40 ­0.3 146 25 .. 22.3 .. 1.8 .. 75.9 Madagascar 81 69 2.1 378 582 4.4 5.1 0.9 1.0 94.8 93.9 Malawi 91 85 2.2 406 94 16.1 23.4 0.9 1.5 83.0 75.1 Malaysia 58 41 1.0 554 329 3.0 5.5 11.6 17.6 85.4 76.9 Mali 82 68 1.7 165 1,220 1.6 3.8 0.0 0.0 98.3 96.1 Mauritania 72 40 ­0.2 228 1,025 0.2 0.5 0.0 0.0 99.8 99.5 Mauritius 58 58 1.1 701 2 49.3 49.3 3.4 3.0 47.3 47.8 Mexico 34 25 0.5 102 1,909 12.1 13.0 0.8 1.3 87.1 85.7 Moldova 60 58 0.1 137 33 .. 55.3 .. 10.8 .. 33.9 Mongolia 48 43 1.3 87 1,567 0.8 0.8 0.0 0.0 99.2 99.2 Morocco 59 43 0.5 146 446 16.9 19.6 1.1 2.2 82.0 78.2 Mozambique 87 66 0.6 302 784 3.7 5.1 0.3 0.3 96.0 94.6 Myanmar 76 71 1.4 346 658 14.6 15.2 0.7 1.0 84.8 83.8 Namibia 77 68 2.5 164 823 0.8 1.0 0.0 0.0 99.2 99.0 Nepal 93 87 2.0 668 143 16.0 21.7 0.2 0.6 83.8 77.7 Netherlands 12 10 0.1 184 34 23.3 26.7 0.9 1.0 75.7 72.3 New Zealand 17 14 0.3 36 268 9.8 5.6 3.4 7.0 86.8 87.4 Nicaragua 50 43 2.1 117 121 8.8 15.9 1.4 1.9 89.7 82.1 Niger 87 78 2.8 195 1,267 2.8 3.5 0.0 0.0 97.2 96.4 Nigeria 73 54 1.5 251 911 30.6 31.3 2.8 3.0 66.6 65.7 Norway 29 25 ­0.3 128 307 2.7 2.9 .. .. .. .. Oman 68 23 ­1.2 1,533 310 0.1 0.1 0.1 0.1 99.8 99.7 Pakistan 72 66 2.2 438 771 25.9 27.9 0.4 0.9 73.7 71.3 Panama 50 43 1.2 230 74 5.8 7.4 1.6 2.0 92.5 90.7 Papua New Guinea 87 82 2.3 2,060 453 0.4 0.5 1.1 1.4 98.5 98.1 Paraguay 58 43 1.2 77 397 4.1 7.6 0.3 0.2 95.6 92.2 Peru 35 27 0.7 191 1,280 2.5 2.9 0.3 0.4 97.2 96.7 Philippines 63 40 0.3 564 298 17.5 18.9 14.8 16.8 67.7 64.3 Poland 42 37 ­0.2 104 304 48.0 45.9 1.1 1.1 50.9 53.0 Portugal 71 33 ­3.2 176 92 26.5 21.7 7.8 7.8 65.7 70.4 Puerto Rico 33 24 ­0.6 2,681 9 8.3 3.9 7.3 5.5 84.3 90.5 2004 World Development Indicators 117 3.1 Rural environment and land use Rural population Rural Land area Land use population density average people annual % per sq. km thousand % of land area % of total growth of arable land sq. km Arable land Permanent cropland Other land 1980 2002 1980­2002 2001 2001 1980 2001 1980 2001 1980 2001 Romania 51 45 ­0.6 107 230 42.7 40.8 2.9 2.3 54.4 56.9 Russian Federation 30 27 ­0.3 32 16,889 .. 7.3 .. 0.1 .. 92.6 Rwanda 95 94 2.0 743 25 30.8 40.5 10.3 12.2 58.9 47.3 Saudi Arabia 34 13 ­0.6 79 2,150 0.9 1.7 0.0 0.1 99.1 98.2 Senegal 64 51 1.6 203 193 12.2 12.8 0.0 0.2 87.8 87.0 Serbia and Montenegro 54 48 ­1.3 .. .. 28.0 .. 2.9 .. 69.1 .. Sierra Leone 76 62 1.3 644 72 6.3 7.0 0.7 0.9 93.0 92.1 Singapore 0 0 .. 0 1 3.3 1.6 9.8 0.0 86.9 98.4 Slovak Republic 48 42 ­0.3 158 48 .. 30.4 .. 2.8 .. 66.8 Slovenia 52 51 0.0 581 20 .. 8.6 .. 1.5 .. 89.9 Somalia 78 72 1.3 622 627 1.6 1.7 0.0 0.0 98.4 98.3 South Africa 52 42 1.3 129 1,221 10.2 12.1 0.7 0.8 89.1 87.1 Spain 27 22 ­0.6 69 499 31.1 26.1 9.9 9.9 59.0 64.1 Sri Lanka 78 77 1.1 1,607 65 13.2 13.9 15.9 15.7 70.9 70.4 Sudan 80 62 1.2 125 2,376 5.2 6.8 0.0 0.2 94.8 93.0 Swaziland 82 73 2.4 440 17 10.8 10.3 0.2 0.7 89.0 89.0 Sweden 17 17 0.3 55 412 7.2 6.5 0.0 0.0 92.8 93.4 Switzerland 43 33 ­0.6 571 40 9.9 10.4 0.5 0.6 89.6 89.0 Syrian Arab Republic 53 48 2.6 173 184 28.5 25.2 2.5 4.4 69.1 70.3 Tajikistan 66 72 2.5 485 141 .. 6.6 .. 0.9 .. 92.5 Tanzania 85 66 1.7 575 884 3.5 4.5 1.0 1.1 95.5 94.4 Thailand 83 80 1.1 326 511 32.3 29.4 3.5 6.5 64.2 64.2 Togo 77 66 2.2 123 54 35.9 46.1 1.6 2.2 62.6 51.6 Trinidad and Tobago 37 25 ­0.9 441 5 13.6 14.6 9.0 9.2 77.4 76.2 Tunisia 48 33 0.2 118 155 20.5 17.9 9.7 13.7 69.7 68.4 Turkey 56 33 ­0.3 97 770 32.9 30.9 4.1 3.3 63.0 65.8 Turkmenistan 53 55 2.5 148 470 .. 3.7 .. 0.1 .. 96.1 Uganda 91 85 2.7 401 197 20.7 25.9 8.1 10.7 71.2 63.5 Ukraine 38 32 ­1.0 48 579 .. 56.2 .. 1.6 .. 42.2 United Arab Emirates 29 12 1.3 773 84 0.2 0.6 0.1 2.2 99.7 97.2 United Kingdom 11 10 ­0.1 109 241 28.7 23.5 0.3 0.2 71.0 76.3 United States 26 22 0.3 37 9,159 20.6 19.1 0.2 0.2 79.2 80.6 Uruguay 15 8 ­2.3 20 175 8.0 7.4 0.3 0.2 91.7 92.3 Uzbekistan 59 63 2.4 352 414 .. 10.8 .. 0.8 .. 88.3 Venezuela, RB 21 13 0.1 122 882 3.4 2.9 0.8 0.9 95.8 96.1 Vietnam 81 75 1.5 923 325 18.2 20.0 1.9 6.0 79.8 74.1 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 81 75 3.2 923 528 2.6 2.8 0.2 0.2 97.2 97.0 Zambia 60 60 2.6 115 743 6.9 7.1 0.0 0.0 93.1 92.9 Zimbabwe 78 63 1.8 255 387 6.5 8.3 0.3 0.3 93.3 91.3 World 61 w 52 w 0.8 w 476 w 130,145 s 10.3 w 10.8 w 0.8 w 1.0 w 88.9 w 88.2 w Low income 78 69 1.6 510 32,424 11.7 12.5 1.0 1.5 87.3 86.0 Middle income 61 47 0.2 473 66,725 8.2 9.6 0.9 1.0 90.9 89.5 Lower middle income 65 51 0.2 492 54,034 8.6 9.9 1.0 0.9 90.3 89.2 Upper middle income 34 25 0.1 190 12,691 7.0 8.0 0.7 1.0 92.3 90.9 Low & middle income 68 58 0.9 494 99,149 9.6 10.5 1.0 1.1 89.4 88.3 East Asia & Pacific 79 62 0.3 568 15,885 10.1 13.3 1.5 2.7 88.5 84.0 Europe & Central Asia 41 36 ­0.1 124 23,722 37.1 11.2 3.1 0.4 59.8 88.4 Latin America & Carib. 35 24 0.0 203 20,053 6.4 7.4 0.9 1.0 92.8 91.6 Middle East & N. Africa 52 42 1.5 601 11,105 4.4 4.9 0.4 0.8 95.2 94.4 South Asia 78 72 1.7 553 4,781 42.5 42.5 1.5 2.2 56.1 55.3 Sub-Saharan Africa 79 67 1.8 350 23,603 5.5 6.7 0.7 0.9 93.8 92.4 High income 27 22 ­0.3 205 30,996 12.1 11.6 0.5 0.5 87.4 87.9 Europe EMU 27 22 ­0.5 139 2,436 27.3 25.7 4.8 4.6 67.9 69.7 a. Includes Luxembourg. b. Includes Taiwan, China. 118 2004 World Development Indicators ENVIRONMENT 3.1 Rural environment and land use About the data Definitions Indicators of rural development are sparse, as few in the category other, may be particularly unreliable · Rural population is calculated as the difference indicators are disaggregated between rural and because of differences in definitions and irregular between the total population and the urban popula- urban areas (for some that are, see tables 2.5, 3.5, surveys (see About the data for table 3.4). tion (see Definitions for tables 2.1 and 3.10). and 3.10). This table shows indicators of rural popu- · Rural population density is the rural population lation and land use. Rural population is approximat- divided by the arable land area. · Land area is a ed as the midyear nonurban population. country's total area, excluding area under inland The data in the table show that land use patterns water bodies, national claims to the continental are changing. They also indicate major differences in shelf, and exclusive economic zones. In most cases resource endowments and uses among countries. the definition of inland water bodies includes major True comparability of the data is limited, however, rivers and lakes. (See table 1.1 for the total surface by variations in definitions, statistical methods, and area of countries.) · Land use is broken into three the quality of data collection. Countries use different categories. · Arable land includes land defined by definitions of rural population and land use, for exam- the FAO as land under temporary crops (double- ple. The Food and Agriculture Organization (FAO), the cropped areas are counted once), temporary mead- primary compiler of these data, occasionally adjusts ows for mowing or for pasture, land under market or its definitions of land use categories and sometimes kitchen gardens, and land temporarily fallow. Land revises earlier data. (In 1985, for example, the FAO abandoned as a result of shifting cultivation is began to exclude from cropland the land used for excluded. · Permanent cropland is land cultivated shifting cultivation but currently lying fallow.) And fol- with crops that occupy the land for long periods and lowing FAO practice, this year's edition of World need not be replanted after each harvest, such as Development Indicators, like the previous five, breaks cocoa, coffee, and rubber. This category includes down the category cropland, used in the earliest edi- land under flowering shrubs, fruit trees, nut trees, tions, into arable land and permanent cropland. and vines, but excludes land under trees grown for Because the data reflect changes in data reporting wood or timber. · Other land includes forest and procedures as well as actual changes in land use, woodland as well as logged-over areas to be forest- apparent trends should be interpreted with caution. ed in the near future. Also included are uncultivated Satellite images show land use that differs from land, grassland not used for pasture, wetlands, that given by ground-based measures in both area wastelands, and built-up areas--residential, recre- under cultivation and type of land use. Moreover, land ational, and industrial lands and areas covered by use data in countries such as India are based on roads and other fabricated infrastructure. reporting systems that were designed for the collec- tion of tax revenue. Because taxes on land are no longer a major source of government revenue, the quality and coverage of land use data (except for crop- land) have declined. Data on forest area, aggregated 3.1a All regions are becoming less rural Data sources Rural population as a share of total population, by region (%) The data on urban population shares used to esti- 80 1980 2002 mate rural population come from the United 70 Nations Population Division's World Urbanization 60 Prospects: The 2001 Revision. The total popula- 50 tion figures are World Bank estimates. The data 40 on land area and land use are from the FAO's 30 electronic files, which may contain more recent 20 information than those published in its Production 10 Yearbook. The FAO gathers these data from 0 national agencies through annual questionnaires East Asia & Europe & Latin America Middle East & South Sub-Saharan World and by analyzing the results of national agricultur- Pacific Central Asia & Caribbean North Africa Asia Africa al censuses. Source: Table 3.1. 2004 World Development Indicators 119 3.2 Agricultural inputs Arable land Irrigated land Land under Fertilizer Agricultural machinery cereal consumption production tractors tractors hundreds of grams per 1,000 per 100 hectares % of thousands per hectare agricultural sq. km of per capita cropland of hectares of arable land workers arable land 1979­81 1999­2001 1979­81 1999­2001 1979­81 2000­02 1979­81 1999­2001 1979­81 1999­2001 1979­81 1999­2001 Afghanistan 0.50 0.30 31.1 29.6 3,037 2,302 62 12 0 0 1 1 Albania 0.22 0.19 53.0 48.6 367 183 1,556 277 15 11 173 140 Algeria 0.37 0.25 3.4 6.8 2,968 1,770 277 126 27 37 68 122 Angola 0.41 0.24 2.2 2.3 705 928 49 5 4 2 35 34 Argentina 1.03 0.91 5.2 4.5 11,154 10,714 39 253 132 205 63 89 Armenia .. 0.16 .. 51.3 .. 191 .. 122 .. 74 .. 369 Australia 2.97 2.58 3.5 4.7 15,986 17,097 269 478 751 705 75 64 Austria 0.20 0.17 0.2 0.3 1,062 821 2,615 1,591 945 1,737 2,084 2,371 Azerbaijan .. 0.21 .. 74.8 .. 735 .. 58 .. 31 .. 178 Bangladesh 0.10 0.06 17.1 49.6 10,823 11,712 459 1,662 0 0 5 7 Belarus .. 0.61 .. 2.1 .. 2,491 .. 1,288 .. 102 .. 118 Belgium a 0.08 0.08 1.7 4.7 426 336 5,323 3,549 917 1,299 1,416 1,266 Benin 0.43 0.31 0.3 0.5 525 921 11 211 0 0 1 1 Bolivia 0.36 0.35 6.6 4.2 559 754 22 25 4 4 21 20 Bosnia and Herzegovina .. 0.17 .. 0.4 .. 381 .. 581 .. 304 .. 433 Botswana 0.44 0.22 0.5 0.3 153 177 32 128 9 20 54 166 Brazil 0.37 0.34 3.0 4.4 20,612 17,799 777 1,103 31 61 118 139 Bulgaria 0.43 0.54 28.3 17.4 2,110 1,988 2,334 328 66 86 161 57 Burkina Faso 0.39 0.34 0.4 0.6 2,026 3,061 26 96 0 0 0 5 Burundi 0.23 0.13 4.2 5.9 203 205 11 41 0 0 1 2 Cambodia 0.29 0.31 5.8 7.1 1,264 2,014 45 0 0 0 6 5 Cameroon 0.67 0.39 0.2 0.5 1,021 747 56 83 0 0 1 1 Canada 1.86 1.48 1.3 1.6 19,561 17,106 416 550 827 1,870 144 160 Central African Republic 0.81 0.52 .. .. 194 180 5 3 0 0 0 0 Chad 0.70 0.45 0.4 0.6 907 1,900 6 49 0 0 1 0 Chile 0.34 0.13 31.1 82.7 820 626 338 2,421 43 55 90 273 China 0.10 0.11 45.1 36.3 94,647 83,012 1,494 2,562 2 2 76 70 Hong Kong, China 0.00 .. 37.5 .. 0 .. .. .. 0 .. 10 .. Colombia 0.13 0.06 7.7 20.2 1,361 1,147 812 2,397 8 6 77 80 Congo, Dem. Rep. 0.24 0.14 0.1 0.1 1,115 2,043 12 2 0 0 3 4 Congo, Rep. 0.08 0.05 0.6 0.5 19 10 27 286 2 1 49 40 Costa Rica 0.12 0.06 12.1 20.6 136 68 2,650 7,096 22 21 210 311 Côte d'Ivoire 0.24 0.19 1.0 1.0 1,008 1,381 261 217 1 1 16 12 Croatia .. 0.33 .. 0.2 .. 710 .. 1,445 .. 13 .. 16 Cuba 0.27 0.32 22.9 19.5 224 202 2,024 447 78 100 259 215 Czech Republic .. 0.30 .. 0.7 .. 1,615 .. 1,075 .. 192 .. 293 Denmark 0.52 0.43 14.5 19.5 1,818 1,550 2,453 1,516 973 1,132 708 547 Dominican Republic 0.19 0.13 11.7 17.2 149 168 572 869 3 3 20 17 Ecuador 0.20 0.13 24.8 29.0 419 867 471 1,177 6 11 40 90 Egypt, Arab Rep. 0.06 0.04 100.0 100.0 2,007 2,700 2,864 4,401 4 10 158 307 El Salvador 0.12 0.10 4.6 5.0 422 363 1,376 1,189 5 4 61 53 Eritrea .. 0.12 .. 4.2 .. 293 .. 212 .. 0 .. 10 Estonia .. 0.71 .. 0.4 .. 290 .. 393 .. 602 .. 575 Ethiopia .. 0.16 .. 1.7 .. 7,440 .. 150 .. 0 .. 3 Finland 0.49 0.42 2.5 2.9 1,190 1,170 2,024 1,384 721 1,355 893 889 France 0.32 0.31 7.2 13.4 9,804 9,106 3,260 2,367 737 1,411 836 687 Gabon 0.42 0.26 2.4 3.0 6 17 20 9 5 7 43 46 Gambia, The 0.26 0.18 0.6 0.8 54 137 136 40 0 0 3 2 Georgia .. 0.15 .. 44.2 .. 338 .. 521 .. 33 .. 221 Germany 0.15 0.14 3.7 4.0 7,692 7,001 4,249 2,367 624 1,018 1,340 873 Ghana 0.17 0.19 0.2 0.2 902 1,425 104 34 1 1 19 10 Greece 0.30 0.26 24.2 37.3 1,600 1,287 1,927 1,635 120 323 485 912 Guatemala 0.19 0.12 5.0 6.8 716 660 726 1,449 3 2 32 32 Guinea 0.16 0.12 7.9 6.3 708 742 16 36 0 0 2 6 Guinea-Bissau 0.32 0.22 5.6 3.1 142 154 24 60 0 0 1 1 Haiti 0.15 0.10 6.4 6.8 416 448 43 158 0 0 2 2 120 2004 World Development Indicators ENVIRONMENT 3.2 Agricultural inputs Arable land Irrigated land Land under Fertilizer Agricultural machinery cereal consumption production tractors tractors hundreds of grams per 1,000 per 100 hectares % of thousands per hectare agricultural sq. km of per capita cropland of hectares of arable land workers arable land 1979­81 1999­2001 1979­81 1999­2001 1979­81 2000­02 1979­81 1999­2001 1979­81 1999­2001 1979­81 1999­2001 Honduras 0.42 0.19 4.1 5.2 421 399 171 1,408 5 7 22 44 Hungary 0.47 0.46 3.6 4.6 2,878 2,936 2,906 835 59 209 111 228 India 0.24 0.16 22.8 32.2 104,350 97,956 345 1,074 2 6 24 94 Indonesia 0.12 0.10 16.2 14.4 11,825 15,004 645 1,243 0 1 5 35 Iran, Islamic Rep. 0.36 0.24 35.5 44.2 8,062 7,740 430 905 17 37 57 158 Iraq 0.40 0.24 32.1 60.8 2,159 2,526 172 668 23 91 44 107 Ireland 0.33 0.28 .. .. 425 287 5,373 5,871 607 1,031 1,289 1,586 Israel 0.08 0.05 49.3 46.0 129 84 2,384 2,696 304 350 809 728 Italy 0.17 0.14 19.3 24.2 5,082 4,187 2,295 2,078 370 1,219 1,117 1,973 Jamaica 0.06 0.07 10.1 8.8 4 2 1,231 1,095 9 12 208 177 Japan 0.04 0.04 56.0 54.7 2,724 2,017 4,131 3,162 209 745 2,723 4,601 Jordan 0.14 0.05 11.0 19.3 158 52 404 913 47 32 153 234 Kazakhstan .. 1.44 .. 10.8 .. 13,082 .. 19 .. 36 .. 24 Kenya 0.23 0.15 0.9 1.7 1,692 2,017 160 322 1 1 17 27 Korea, Dem. Rep. 0.13 0.11 44.0 52.1 1,625 1,278 3,346 1,061 12 20 196 280 Korea, Rep. 0.05 0.04 59.6 60.4 1,689 1,177 3,920 4,539 1 80 14 1,112 Kuwait 0.00 0.00 83.3 85.8 0 2 4,500 1,002 3 10 220 94 Kyrgyz Republic .. 0.28 .. 74.2 .. 618 .. 157 .. 46 .. 186 Lao PDR 0.24 0.17 13.3 18.2 751 765 35 107 0 1 7 12 Latvia .. 0.78 .. 1.1 .. 440 .. 305 .. 350 .. 302 Lebanon 0.07 0.04 28.3 32.0 34 53 1,663 3,105 28 177 141 453 Lesotho 0.23 0.19 0.3 0.3 203 236 150 247 6 6 47 61 Liberia 0.20 0.12 0.3 0.5 203 141 123 0 0 0 8 9 Libya 0.58 0.35 10.7 21.9 538 342 357 363 101 319 134 187 Lithuania .. 0.84 .. 0.2 .. 938 .. 533 .. 429 .. 347 Macedonia, FYR .. 0.28 .. 9.0 .. 207 .. 665 .. 449 .. 948 Madagascar 0.29 0.19 21.2 31.0 1,309 1,412 30 27 1 1 10 12 Malawi 0.25 0.21 1.1 1.3 1,155 1,602 203 195 0 0 8 7 Malaysia 0.07 0.08 6.7 4.8 729 705 4,273 6,695 4 25 77 239 Mali 0.31 0.43 4.5 3.0 1,346 2,769 61 95 0 1 5 6 Mauritania 0.13 0.18 22.8 9.8 125 185 57 30 1 1 13 8 Mauritius 0.10 0.08 15.0 19.5 0 0 2,547 3,667 4 6 33 37 Mexico 0.34 0.25 20.3 23.1 9,356 10,322 570 736 16 37 54 131 Moldova .. 0.42 .. 14.1 .. 989 .. 25 .. 84 .. 230 Mongolia 0.71 0.50 3.0 7.1 559 196 83 26 32 16 82 42 Morocco 0.39 0.31 15.0 13.5 4,414 5,181 268 415 7 10 34 49 Mozambique 0.24 0.22 2.1 2.6 1,077 1,894 107 40 1 1 20 15 Myanmar 0.28 0.21 10.4 18.3 5,133 6,880 111 180 1 1 9 11 Namibia 0.64 0.43 0.6 0.9 195 292 0 4 11 11 39 39 Nepal 0.16 0.13 22.5 36.2 2,251 3,308 98 260 0 0 10 15 Netherlands 0.06 0.06 58.5 59.9 225 223 8,620 4,755 560 603 2,238 1,644 New Zealand 0.84 0.39 5.2 8.6 193 141 1,879 5,317 619 448 352 501 Nicaragua 0.38 0.37 6.2 4.5 266 464 415 159 6 7 20 15 Niger 0.62 0.42 0.7 1.5 3,872 7,693 10 10 0 0 0 0 Nigeria 0.39 0.22 0.7 0.8 6,048 19,783 59 68 1 2 3 11 Norway 0.20 0.20 .. .. 311 326 3,146 2,196 824 1,263 1,603 1,511 Oman 0.02 0.02 74.5 78.2 2 2 475 1,690 1 1 43 40 Pakistan 0.24 0.15 72.7 81.6 10,693 12,300 525 1,362 5 13 50 150 Panama 0.22 0.19 5.0 5.1 166 123 692 584 27 20 122 93 Papua New Guinea 0.05 0.04 .. .. 2 3 452 530 1 1 82 56 Paraguay 0.52 0.55 3.4 2.2 307 638 44 227 14 23 45 57 Peru 0.19 0.14 32.3 28.4 732 1,217 381 715 5 4 37 36 Philippines 0.11 0.07 12.8 14.6 6,790 6,514 636 1,337 1 1 20 20 Poland 0.41 0.36 0.7 0.7 7,875 8,643 2,393 1,110 112 302 425 933 Portugal 0.25 0.20 20.1 24.0 1,099 543 1,113 1,146 72 260 351 848 Puerto Rico 0.02 0.01 27.2 47.6 1 1 .. .. .. .. .. .. 2004 World Development Indicators 121 3.2 Agricultural inputs Arable land Irrigated land Land under Fertilizer Agricultural machinery cereal consumption production tractors tractors hundreds of grams per 1,000 per 100 hectares % of thousands per hectare agricultural sq. km of per capita cropland of hectares of arable land workers arable land 1979­81 1999­2001 1979­81 1999­2001 1979­81 2000­02 1979­81 1999­2001 1979­81 1999­2001 1979­81 1999­2001 Romania 0.44 0.42 21.9 31.2 6,340 5,696 1,448 309 39 100 150 174 Russian Federation .. 0.85 .. 3.6 .. 41,919 .. 117 .. 96 .. 63 Rwanda 0.15 0.12 0.4 0.4 239 298 3 3 0 0 1 1 Saudi Arabia 0.20 0.17 28.9 42.8 388 615 228 1,036 2 16 10 27 Senegal 0.42 0.25 2.6 2.9 1,216 1,174 104 162 0 0 2 3 Serbia and Montenegro 0.73 .. 1.9 .. 4,310 .. 1,261 .. 140 .. 616 .. Sierra Leone 0.14 0.10 4.1 5.4 434 213 58 4 0 0 6 2 Singapore 0.00 0.00 .. .. .. .. 22,333 30,423 3 22 220 650 Slovak Republic .. 0.27 .. 11.2 .. .. .. 610 .. .. .. 164 Slovenia .. 0.09 .. 1.3 .. 102 .. 4,384 .. .. .. .. Somalia 0.15 0.12 13.3 18.7 638 671 9 5 1 1 17 17 South Africa 0.45 0.34 8.4 9.2 6,760 4,633 874 510 92 43 140 50 Spain 0.42 0.33 14.8 20.1 7,391 6,658 1,012 1,674 200 686 335 668 Sri Lanka 0.06 0.05 28.3 33.6 864 809 1,800 2,768 4 2 141 90 Sudan 0.64 0.52 14.4 11.7 4,447 7,468 51 32 2 2 8 7 Swaziland 0.30 0.17 34.0 36.8 70 56 1,050 343 29 34 173 219 Sweden 0.36 0.31 2.4 4.2 1,505 1,163 1,654 1,055 715 1,108 623 616 Switzerland 0.06 0.06 6.2 5.7 172 177 4,623 2,277 494 700 2,428 2,710 Syrian Arab Republic 0.60 0.29 9.6 22.5 2,642 3,028 250 731 29 68 54 212 Tajikistan .. 0.15 .. 68.3 .. 376 .. 114 .. 37 .. 325 Tanzania 0.16 0.12 3.1 3.3 2,834 2,980 110 56 1 1 35 19 Thailand 0.35 0.25 16.4 27.1 10,625 11,257 177 1,120 1 10 11 147 Togo 0.77 0.55 0.3 0.4 416 703 14 74 0 0 0 0 Trinidad and Tobago 0.06 0.06 2.9 3.3 4 3 1,064 1,163 50 54 337 360 Tunisia 0.51 0.30 4.8 7.7 1,416 782 212 389 30 37 79 123 Turkey 0.57 0.36 9.6 16.9 13,499 13,946 529 825 38 65 169 391 Turkmenistan .. 0.37 .. 100.1 .. 819 .. 603 .. 73 .. 289 Uganda 0.32 0.22 0.1 0.1 752 1,395 1 11 0 1 6 9 Ukraine .. 0.66 .. 7.2 .. 13,436 .. 136 .. 90 .. 101 United Arab Emirates 0.01 0.02 237.7 32.6 0 0 2,250 7,090 6 5 106 68 United Kingdom 0.12 0.10 2.0 1.8 3,930 3,203 3,191 3,251 726 931 744 860 United States 0.83 0.62 10.8 12.6 72,639 55,818 1,092 1,097 1,230 1,586 253 272 Uruguay 0.48 0.39 5.4 13.5 614 522 564 846 171 174 236 255 Uzbekistan .. 0.18 .. 88.6 .. 1,581 .. 1,637 .. 57 .. 380 Venezuela, RB 0.19 0.11 10.1 16.9 814 928 696 1,012 50 61 131 189 Vietnam 0.11 0.08 25.6 37.6 5,962 8,301 302 3,407 1 6 38 254 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 0.16 0.09 19.9 30.2 865 654 93 102 3 2 33 41 Zambia 0.89 0.53 0.4 0.9 595 641 145 65 2 2 9 11 Zimbabwe 0.35 0.25 3.1 3.5 1,633 1,685 610 520 7 7 66 75 World 0.25 w 0.23 w 17.5 w 19.6 w 588,621 s 666,427 s 860 w 988 w 19 w 20 w 173 w 189 w Low income 0.23 0.17 19.8 26.4 199,719 244,864 289 717 2 4 20 66 Middle income 0.18 0.24 22.6 19.4 232,191 289,922 941 1,020 8 12 110 127 Lower middle income 0.16 0.22 25.8 20.5 196,509 249,113 962 1,063 6 9 104 103 Upper middle income 0.36 0.32 11.5 13.4 35,682 40,809 871 804 50 117 133 253 Low & middle income 0.20 0.20 21.2 22.1 431,910 534,786 625 903 5 8 66 103 East Asia & Pacific 0.12 0.11 36.3 35.5 139,927 135,938 1,113 2,145 2 2 55 76 Europe & Central Asia .. 0.56 .. 10.9 .. 114,548 .. 335 .. 102 .. 171 Latin America & Carib. 0.36 0.29 10.8 12.5 49,845 48,623 536 815 25 40 87 119 Middle East & N. Africa 0.29 0.19 25.8 37.9 25,653 25,446 421 808 12 25 61 131 South Asia 0.23 0.15 28.7 39.9 132,128 128,481 360 1,081 2 5 25 92 Sub-Saharan Africa 0.32 0.24 4.0 4.2 46,978 81,750 158 128 3 1 23 15 High income 0.44 0.37 10.2 12.1 156,711 131,641 1,327 1,238 430 895 385 439 Europe EMU 0.23 0.21 14.1 19.4 35,996 31,617 2,703 2,170 427 911 879 984 a. Includes Luxembourg. 122 2004 World Development Indicators ENVIRONMENT 3.2 Agricultural inputs About the data Definitions Agricultural activities provide developing countries To smooth annual fluctuations in agricultural activ- · Arable land includes land defined by the FAO as with food and revenue, but they also can degrade ity, the indicators in the table have been averaged land under temporary crops (double-cropped areas natural resources. Poor farming practices can over three years. are counted once), temporary meadows for mowing cause soil erosion and loss of soil fertility. Efforts or for pasture, land under market or kitchen gardens, to increase productivity through the use of chemical and land temporarily fallow. Land abandoned as a fertilizers, pesticides, and intensive irrigation have result of shifting cultivation is excluded. · Irrigated environmental costs and health impacts. Excessive land refers to areas purposely provided with water, use of chemical fertilizers can alter the chemistry including land irrigated by controlled flooding. of soil. Pesticide poisoning is common in develop- · Cropland refers to arable land and permanent ing countries. And salinization of irrigated land cropland (see table 3.1). · Land under cereal pro- diminishes soil fertility. Thus inappropriate use of duction refers to harvested areas, although some inputs for agricultural production has far-reaching countries report only sown or cultivated area. effects. · Fertilizer consumption is the quantity of plant This table provides indicators of major inputs to nutrients used per unit of arable land. Fertilizer prod- agricultural production: land, fertilizer, and agricultur- ucts cover nitrogenous, potash, and phosphate fer- al machinery. There is no single correct mix of tilizers (including ground rock phosphate). Traditional inputs: appropriate levels and application rates vary nutrients--animal and plant manures--are not by country and over time, depending on the type of included. The time reference for fertilizer consump- crops, the climate and soils, and the production tion is the crop year (July through June). process used. · Agricultural machinery refers to wheel and crawler The data shown here and in table 3.3 are collect- tractors (excluding garden tractors) in use in agricul- ed by the Food and Agriculture Organization (FAO) ture at the end of the calendar year specified or dur- through annual questionnaires. The FAO tries to ing the first quarter of the following year. impose standard definitions and reporting methods, · Agricultural workers refer to all economically but exact consistency across countries and over time active people engaged principally in agriculture, is not possible. Data on agricultural employment, in forestry, hunting, or fishing. particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work per- formed by women and children. Fertilizer consumption measures the quantity of plant nutrients. Consumption is calculated as pro- duction plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. 3.2a The 10 countries with the most arable land per person in 1999­2001--and the 10 with the least Ares per capita Country Arable land Country Arable land Australia 258.1 Singapore 0.0 Canada 148.2 Kuwait 0.5 Kazakhstan 143.6 Puerto Rico 0.9 Argentina 90.6 Oman 1.6 Russian Federation 85.5 United Arab Emirates 1.8 Lithuania 83.7 Japan 3.5 Data sources Latvia 77.8 Korea, Rep. 3.6 The data are from electronic files that the FAO Estonia 71.0 Papua New Guinea 4.0 makes available to the World Bank and that may Ukraine 65.9 Lebanon 4.2 United States 62.5 Egypt, Arab Rep. 4.4 contain more recent information than those pub- Note: An are equals 100 square meters or 0.01 hectare. lished in the FAO's Production Yearbook. Source: Table 3.2. 2004 World Development Indicators 123 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production production production yield productivity index index index Agriculture value added kilograms per worker 1989­91 = 100 1989­91 = 100 1989­91 = 100 per hectare 1995 $ 1979­81 2000­02 1979­81 2000­02 1979­81 2000­02 1979­81 2000­02 1979­81 2000­02 Afghanistan .. .. .. .. .. .. 1,337 1,533 .. .. Albania .. .. .. .. .. .. 2,500 3,154 1,184 1,868 Algeria 77.4 128.0 68.8 136.2 54.6 128.9 656 1,343 1,357 1,919 Angola 101.9 195.7 89.9 172.5 83.8 137.2 526 606 .. 137 Argentina 83.6 165.2 91.7 142.5 100.9 108.8 2,184 3,374 7,148 10,317 Armenia .. 99.8 .. 79.3 .. 67.9 .. 2,049 .. 2,827 Australia 79.9 152.2 91.3 138.8 85.6 116.1 1,321 1,758 20,872 36,327 Austria 92.8 103.6 92.2 104.7 94.5 103.2 4,131 5,589 11,082 33,828 Azerbaijan .. 63.4 .. 83.7 .. 81.8 .. 2,583 .. 1,029 Bangladesh 80.2 135.6 79.3 138.3 81.3 142.1 1,938 3,312 232 318 Belarus .. 90.3 .. 62.1 .. 58.4 .. 2,369 .. 3,038 Belgium a 84.9 143.9 88.5 113.5 88.8 109.8 4,861 8,002 21,861 57,462 Benin 53.8 195.6 66.8 173.9 93.2 116.7 698 1,077 311 621 Bolivia 71.9 177.2 71.5 151.6 75.5 129.7 1,183 1,786 693 754 Bosnia and Herzegovina .. .. .. .. .. .. .. 3,186 .. 7,634 Botswana 86.4 89.8 87.3 89.8 87.6 89.7 203 156 657 575 Brazil 75.4 135.8 69.5 153.2 67.9 169.8 1,496 3,081 2,049 4,899 Bulgaria 107.7 66.9 105.5 68.2 96.3 62.9 3,853 2,961 2,754 8,282 Burkina Faso 59.3 166.8 62.7 157.9 59.9 147.8 575 968 133 185 Burundi 79.9 92.7 79.9 93.2 82.3 76.1 1,081 1,325 177 151 Cambodia 55.0 147.2 48.9 152.0 27.3 166.9 1,006 1,978 .. 422 Cameroon 87.3 141.6 80.6 138.3 61.3 121.8 849 1,696 826 1,213 Canada 77.6 106.7 79.7 123.5 88.3 142.2 2,173 2,521 16,002 43,064 Central African Republic 102.9 136.6 79.7 146.5 48.9 147.4 529 1,069 380 502 Chad 66.8 160.8 79.8 151.2 89.2 122.2 587 697 160 211 Chile 70.7 132.6 71.5 140.2 75.8 151.4 2,124 5,235 3,488 6,226 China 67.1 155.6 60.8 185.9 45.4 226.7 3,027 4,845 161 338 Hong Kong, China 133.6 .. 99.8 .. 194.3 .. 1,712 .. .. .. Colombia 84.1 106.4 75.5 120.3 72.6 122.4 2,452 3,411 3,034 3,619 Congo, Dem. Rep. 73.0 83.2 72.8 86.3 83.5 98.3 807 774 241 212 Congo, Rep. 86.4 127.9 83.8 130.3 81.6 135.5 838 779 385 469 Costa Rica 66.2 147.0 69.5 150.0 77.1 136.6 2,498 3,968 3,139 5,270 Côte d'Ivoire 73.7 133.8 70.6 136.5 73.9 139.3 867 1,213 945 1,046 Croatia .. 90.6 .. 68.5 .. 55.2 .. 4,748 .. 9,741 Cuba 84.1 66.3 90.1 70.9 96.0 71.6 2,458 2,519 .. .. Czech Republic .. 88.6 .. 78.0 .. 70.8 .. 4,297 .. 6,382 Denmark 65.2 89.9 83.3 106.0 95.0 118.6 4,040 5,912 19,350 63,131 Dominican Republic 96.5 89.6 85.2 107.8 68.8 138.2 3,024 4,525 2,129 3,458 Ecuador 78.2 143.2 77.4 153.8 73.0 170.1 1,633 2,122 3,839 3,310 Egypt, Arab Rep. 75.5 154.9 68.5 158.2 67.0 165.9 4,053 7,244 721 1,316 El Salvador 120.4 98.9 88.9 111.7 86.5 116.3 1,702 2,264 1,925 1,678 Eritrea .. 121.9 .. 116.3 .. 112.0 .. 351 .. 68 Estonia .. 76.8 .. 39.8 .. 33.8 .. 2,028 .. 3,650 Ethiopia .. 160.6 .. 152.6 .. 129.8 .. 1,293 .. 154 Finland 76.3 99.7 93.8 93.7 107.5 91.8 2,511 3,219 17,885 42,306 France 87.4 107.0 93.6 104.3 97.8 105.5 4,700 6,796 19,318 59,243 Gabon 76.2 121.4 79.0 116.7 86.6 118.9 1,718 1,652 1,814 2,102 Gambia, The 79.2 132.8 82.4 127.2 93.7 102.7 1,284 1,231 325 307 Georgia .. 43.7 .. 74.9 .. 93.8 .. 2,004 .. .. Germany 90.0 118.2 91.4 97.1 98.7 87.7 4,166 6,355 9,119 33,686 Ghana 67.0 190.0 68.5 181.2 78.7 127.3 807 1,191 671 571 Greece 86.8 110.6 91.2 101.3 99.9 94.0 3,090 3,555 8,600 13,860 Guatemala 85.8 131.8 68.0 136.2 76.9 130.3 1,578 1,758 2,143 2,115 Guinea 89.7 158.7 93.1 161.8 91.7 188.8 958 1,403 .. 286 Guinea-Bissau 64.9 147.2 68.3 142.2 78.0 127.2 711 972 237 324 Haiti 103.4 87.2 101.2 101.7 100.2 156.2 1,009 840 .. .. 124 2004 World Development Indicators ENVIRONMENT 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production production production yield productivity index index index Agriculture value added kilograms per worker 1989­91 = 100 1989­91 = 100 1989­91 = 100 per hectare 1995 $ 1979­81 2000­02 1979­81 2000­02 1979­81 2000­02 1979­81 2000­02 1979­81 2000­02 Honduras 90.4 114.1 88.3 121.1 81.0 153.8 1,170 1,382 696 1,037 Hungary 93.3 79.7 90.7 79.5 94.1 73.3 4,519 4,026 3,390 5,625 India 70.9 124.2 68.2 131.8 62.6 149.8 1,324 2,390 269 401 Indonesia 65.9 122.9 63.1 123.6 51.0 124.7 2,837 4,141 604 748 Iran, Islamic Rep. 57.5 151.5 61.2 154.8 68.0 158.3 1,108 2,163 2,165 3,737 Iraq 74.7 76.7 77.3 77.5 81.2 67.9 832 945 .. .. Ireland 93.6 111.3 83.5 106.7 83.5 107.7 4,733 7,053 .. .. Israel 99.8 97.4 85.0 115.3 78.4 127.6 1,840 2,853 .. .. Italy 106.1 101.9 101.4 102.3 93.0 105.1 3,548 4,815 11,090 27,064 Jamaica 101.4 127.5 93.6 125.9 85.5 126.2 1,667 1,002 1,123 1,487 Japan 108.3 87.1 94.1 91.6 85.1 93.2 5,252 5,879 17,378 33,077 Jordan 54.6 132.6 57.4 147.4 51.5 167.7 521 1,301 1,141 1,145 Kazakhstan .. 89.5 .. 73.5 .. 46.7 .. 1,149 .. 1,753 Kenya 70.2 123.1 65.6 122.2 60.5 118.0 1,364 1,516 265 213 Korea, Dem. Rep. .. .. .. .. .. .. 3,694 3,189 .. .. Korea, Rep. 87.8 114.3 77.5 132.3 52.4 159.9 4,986 6,118 3,765 14,251 Kuwait 37.1 198.1 81.0 229.0 94.5 211.2 3,124 2,206 .. .. Kyrgyz Republic .. 153.2 .. 132.5 .. 80.7 .. 2,742 .. 1,861 Lao PDR 73.5 177.5 70.3 186.4 56.0 188.8 1,402 3,140 .. 621 Latvia .. 78.7 .. 42.4 .. 31.1 .. 2,189 .. 2,773 Lebanon 49.9 100.4 60.6 108.9 95.0 157.0 1,307 2,575 .. 29,874 Lesotho 98.2 147.9 96.6 111.6 96.6 87.2 977 926 611 575 Liberia .. .. .. .. .. .. 1,251 983 .. .. Libya 76.3 129.4 78.7 134.1 68.4 134.9 430 631 .. .. Lithuania .. 76.5 .. 64.7 .. 52.6 .. 2,807 .. 3,431 Macedonia, FYR .. 94.9 .. 89.5 .. 89.9 .. 2,642 .. 4,243 Madagascar 83.1 108.5 83.8 115.8 87.7 114.2 1,664 2,007 158 155 Malawi 85.7 156.0 93.1 174.0 78.4 125.4 1,161 1,134 96 124 Malaysia 75.3 119.4 55.6 142.1 41.0 142.1 2,828 3,132 3,939 6,912 Mali 54.5 143.9 77.2 128.6 95.6 123.2 804 943 242 274 Mauritania 62.1 126.2 86.5 108.3 89.4 107.0 384 860 289 447 Mauritius 93.3 98.1 89.6 109.0 64.0 145.3 2,536 7,577 2,891 5,494 Mexico 86.5 123.6 85.3 135.7 86.2 150.1 2,164 2,870 1,482 1,813 Moldova .. 61.9 .. 51.1 .. 32.9 .. 2,345 .. 971 Mongolia 44.6 29.7 88.1 91.9 93.2 97.4 573 751 994 1,444 Morocco 54.8 91.8 55.8 103.6 59.8 124.6 811 1,129 1,146 1,513 Mozambique 109.9 141.1 100.7 127.5 85.8 103.9 603 848 .. 136 Myanmar 89.0 178.5 88.2 176.5 89.1 169.4 2,521 3,453 .. .. Namibia 80.1 126.9 107.6 96.8 116.0 93.3 377 400 1,064 1,545 Nepal 61.9 137.9 65.4 135.8 77.3 129.3 1,615 2,178 156 203 Netherlands 79.8 111.7 86.5 98.4 88.3 96.5 5,696 7,531 24,360 59,476 New Zealand 74.4 142.9 90.7 135.2 95.5 123.9 4,089 6,230 16,637 28,740 Nicaragua 124.1 141.3 117.8 154.3 139.7 148.1 1,475 1,761 1,549 1,618 Niger 89.2 147.5 97.5 140.1 110.0 128.9 440 417 229 197 Nigeria 51.7 156.0 57.2 155.8 83.3 145.3 1,265 1,105 417 729 Norway 94.8 77.6 93.9 91.0 96.2 97.4 3,634 3,760 17,138 37,073 Oman 60.1 160.3 62.1 163.1 61.5 144.5 982 2,319 .. .. Pakistan 65.6 122.8 66.3 152.7 59.5 171.9 1,608 2,266 416 716 Panama 96.9 83.3 85.5 105.8 71.3 138.6 1,524 2,753 2,122 2,967 Papua New Guinea 86.5 120.7 86.1 124.3 84.9 146.0 2,087 3,919 692 823 Paraguay 58.7 115.5 60.8 141.0 62.1 136.9 1,535 2,034 2,641 3,318 Peru 82.1 180.3 77.3 175.0 78.0 159.1 1,946 3,302 1,299 1,863 Philippines 88.3 123.1 86.1 137.1 73.8 177.8 1,611 2,692 1,381 1,458 Poland 84.6 84.0 87.9 86.0 98.0 83.3 2,345 3,072 .. 1,637 Portugal 85.0 91.7 72.2 102.2 71.8 122.3 1,102 2,702 3,796 7,567 Puerto Rico 131.3 67.9 99.8 84.0 90.3 89.4 7,970 1,731 .. .. 2004 World Development Indicators 125 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production production production yield productivity index index index Agriculture value added kilograms per worker 1989­91 = 100 1989­91 = 100 1989­91 = 100 per hectare 1995 $ 1979­81 2000­02 1979­81 2000­02 1979­81 2000­02 1979­81 2000­02 1979­81 2000­02 Romania 114.1 91.0 113.0 87.1 110.0 80.7 2,854 2,562 1,397 3,588 Russian Federation .. 86.1 .. 66.6 .. 52.6 .. 1,846 .. 3,822 Rwanda 84.9 115.4 85.8 117.3 80.3 112.3 1,134 1,011 271 254 Saudi Arabia 27.2 84.2 26.7 98.5 32.7 152.6 820 3,818 2,152 15,796 Senegal 77.2 111.0 74.1 122.4 65.7 147.0 690 755 345 354 Serbia and Montenegro 96.3 .. 94.3 .. 94.2 .. 3,601 .. .. .. Sierra Leone 80.3 75.4 84.5 84.0 84.1 126.6 1,249 1,234 674 359 Singapore 595.0 48.2 154.3 31.9 173.7 31.8 .. .. 16,664 42,920 Slovak Republic .. .. .. .. .. .. .. .. .. .. Slovenia .. 81.9 .. 100.9 .. 108.7 .. 5,452 .. 37,671 Somalia .. .. .. .. .. .. 474 547 .. .. South Africa 94.9 110.1 90.5 111.1 86.0 104.3 2,105 2,633 2,857 4,072 Spain 83.0 115.8 81.9 120.1 83.9 134.2 1,986 3,091 7,556 22,412 Sri Lanka 99.3 114.8 98.1 117.2 92.0 147.7 2,462 3,520 642 725 Sudan 127.1 165.9 105.2 167.5 89.3 161.1 645 600 .. .. Swaziland 72.5 85.2 81.1 99.9 99.9 126.4 1,345 1,512 1,752 1,936 Sweden 93.1 89.3 100.6 96.0 103.8 100.2 3,595 4,878 20,865 40,368 Switzerland 95.5 89.6 95.8 95.6 98.8 94.9 4,883 6,466 .. .. Syrian Arab Republic 100.7 177.2 93.6 163.6 72.1 136.1 1,156 2,114 2,206 2,636 Tajikistan .. 62.2 .. 60.5 .. 41.6 .. 1,561 .. 728 Tanzania 80.5 107.7 75.4 112.8 69.2 126.9 1,063 1,438 .. 187 Thailand 79.1 124.3 79.7 123.5 64.5 135.3 1,911 2,654 616 863 Togo 70.6 138.0 78.3 131.4 56.2 115.2 729 1,008 365 503 Trinidad and Tobago 121.5 87.9 111.1 127.8 96.9 157.3 3,167 2,807 3,536 3,034 Tunisia 68.1 98.4 66.3 115.0 60.3 164.4 828 2,218 1,743 3,115 Turkey 76.6 118.8 75.8 114.6 80.4 103.9 1,869 2,176 1,872 1,848 Turkmenistan .. 77.7 .. 131.6 .. 138.0 .. 2,621 .. 690 Uganda 67.5 138.9 69.7 136.7 81.9 130.6 1,555 1,651 .. 346 Ukraine .. 71.3 .. 52.4 .. 45.7 .. 2,399 .. 1,576 United Arab Emirates 38.9 659.7 42.7 549.9 42.2 200.6 2,224 414 .. .. United Kingdom 80.1 97.2 92.2 92.4 98.1 93.1 4,792 6,841 20,326 32,918 United States 98.6 118.3 94.5 122.5 89.0 123.6 4,151 5,830 20,672 53,907 Uruguay 86.8 135.3 87.1 124.8 85.9 110.4 1,644 3,243 6,563 8,177 Uzbekistan .. 89.0 .. 122.3 .. 114.8 .. 3,644 .. 1,449 Venezuela, RB 76.3 119.3 80.2 135.0 84.9 138.8 1,904 3,278 3,935 5,399 Vietnam 65.8 180.3 62.5 171.4 50.1 193.8 2,049 4,375 .. 256 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 82.3 133.6 74.8 142.6 68.9 160.4 1,038 966 .. 412 Zambia 64.6 96.2 73.0 107.2 86.2 130.2 1,676 1,481 186 194 Zimbabwe 77.8 113.9 83.3 108.6 89.7 121.5 1,359 872 310 355 World 79.1 w 131.5 w 78.8 w 133.1 w 79.6 w 136.4 w 1,605 w 2,233 w .. w .. w Low income 71.7 134.0 70.7 135.1 68.4 146.8 1,090 1,321 .. 415 Middle income 74.3 147.0 71.8 150.3 69.6 164.4 1,759 2,497 .. 820 Lower middle income 72.5 154.2 68.8 158.0 60.8 181.9 1,682 2,181 .. 713 Upper middle income 79.4 116.2 78.8 118.6 82.8 114.0 1,842 2,926 .. 3,937 Low & middle income 73.3 142.7 71.5 145.2 69.3 159.9 1,397 1,966 .. 626 East Asia & Pacific 68.5 166.1 63.4 170.6 47.9 214.6 2,034 3,147 .. .. Europe & Central Asia .. .. .. .. .. .. 2,854 2,640 .. 2,353 Latin America & Carib. 80.3 138.6 78.3 141.9 79.8 144.8 1,786 2,804 2,239 3,570 Middle East & N. Africa 66.0 136.4 64.8 137.1 64.1 145.3 925 1,726 .. 2,340 South Asia 71.9 131.6 69.6 133.3 64.0 154.3 1,510 2,222 285 412 Sub-Saharan Africa 75.4 132.3 78.3 133.5 84.1 124.4 895 1,064 419 360 High income 93.4 112.5 91.9 113.2 90.6 112.5 3,274 3,746 .. .. Europe EMU 90.7 105.3 91.4 105.2 93.9 101.9 4,035 5,517 .. 30,154 a. Includes Luxembourg. 126 2004 World Development Indicators ENVIRONMENT 3.3 Agricultural output and productivity About the data Definitions The agricultural production indexes in the table are nominal exchange rates unrelated to the purchasing · Crop production index shows agricultural produc- prepared by the Food and Agriculture Organization power of the domestic currency. tion for each period relative to the base period (FAO). The FAO obtains data from official and semi- Data on cereal yield may be affected by a variety of 1989­91. It includes all crops except fodder crops. official reports of crop yields, area under production, reporting and timing differences. The FAO allocates The regional and income group aggregates for the and livestock numbers. If data are not available, the production data to the calendar year in which the FAO's production indexes are calculated from the FAO makes estimates. The indexes are calculated bulk of the harvest took place. But most of a crop underlying values in international dollars, normalized using the Laspeyres formula: production quantities harvested near the end of a year will be used in the to the base period 1989­91. The data in this table of each commodity are weighted by average interna- following year. Cereal crops harvested for hay or har- are three-year averages. · Food production index tional commodity prices in the base period and vested green for food, feed, or silage, and those covers food crops that are considered edible and summed for each year. Because the FAO's indexes used for grazing, are generally excluded. But millet that contain nutrients. Coffee and tea are excluded are based on the concept of agriculture as a single and sorghum, which are grown as feed for livestock because, although edible, they have no nutritive enterprise, estimates of the amounts retained for and poultry in Europe and North America, are used value. · Livestock production index includes meat seed and feed are subtracted from the production as food in Africa, Asia, and countries of the former and milk from all sources, dairy products such as data to avoid double counting. The resulting aggre- Soviet Union. So some cereal crops are excluded cheese, and eggs, honey, raw silk, wool, and hides gate represents production available for any use from the data for some countries and included else- and skins. · Cereal yield, measured in kilograms per except as seed and feed. The FAO's indexes may dif- where, depending on their use. hectare of harvested land, includes wheat, rice, fer from other sources because of differences in cov- Agricultural productivity is measured by value maize, barley, oats, rye, millet, sorghum, buckwheat, erage, weights, concepts, time periods, calculation added per unit of input. (For further discussion of the and mixed grains. Production data on cereals refer to methods, and use of international prices. calculation of value added in national accounts, see crops harvested for dry grain only. Cereal crops har- To ease cross-country comparisons, the FAO uses About the data for tables 4.1 and 4.2.) Agricultural vested for hay or harvested green for food, feed, or international commodity prices to value production. value added includes that from forestry and fishing. silage, and those used for grazing, are excluded. These prices, expressed in international dollars (equiv- Thus interpretations of land productivity should be · Agricultural productivity refers to the ratio of agri- alent in purchasing power to the U.S. dollar), are made with caution. To smooth annual fluctuations in cultural value added, measured in constant 1995 derived using a Geary-Khamis formula applied to agri- agricultural activity, the indicators in the table have U.S. dollars, to the number of workers in agriculture. cultural outputs (see Inter-Secretariat Working Group been averaged over three years. on National Accounts 1993, sections 16.93­96). This method assigns a single price to each commodity so that, for example, one metric ton of wheat has the same price regardless of where it was produced. The use of international prices eliminates fluctuations in the value of output due to transitory movements of 3.3a The 15 countries with the highest cereal yield in 2001­03--and the 15 with the lowest Kilograms per hectare of arable land Country Cereal yield Country Cereal yield Belgium a 8,002 Botswana 156 Mauritius 7,577 Eritrea 351 Netherlands 7,531 Namibia 400 Egypt, Arab Rep. 7,244 United Arab Emirates 414 Ireland 7,053 Niger 417 United Kingdom 6,841 Somalia 547 Data sources France 6,796 Sudan 600 The agricultural production indexes are prepared Switzerland 6,466 Angola 606 by the FAO and published annually in its Germany 6,355 Libya 631 New Zealand 6,230 Chad 697 Production Yearbook. The FAO makes these data Korea, Rep. 6,118 Mongolia 751 and the data on cereal yield and agricultural Denmark 5,912 Senegal 755 employment available to the World Bank in elec- Japan 5,879 Congo, Dem. Rep. 774 tronic files that may contain more recent informa- United States 5,830 Congo, Rep. 779 tion than the published versions. For sources of Austria 5,589 Haiti 840 data on agricultural value added, see Data a. Includes Luxembourg. sources for table 4.2. Source: Table 3.3. 2004 World Development Indicators 127 3.4 Deforestation and biodiversity Forest area Average Mammals Birds Higher plants a Nationally annual protected deforestation areas % of % of thousand total Threatened Threatened Threatened thousand total sq. km land area sq. km % Species species Species species Species species sq. km land area 2000 2000 1990­2000 1990­2000 2002 2002 2002 2002 2002 2002 2003 b 2003 b Afghanistan 14 2.1 .. .. 119 13 181 11 4,000 1 2.0 0.3 Albania 10 36.2 78 0.8 68 3 193 3 3,031 0 1.0 3.8 Algeria 21 0.9 ­266 ­1.3 92 13 183 6 3,164 2 119.1 5.0 Angola 698 56.0 1,242 0.2 276 19 265 15 5,185 19 82.3 6.6 Argentina 346 12.7 2,851 0.8 320 34 362 39 9,372 42 180.6 6.6 Armenia 4 12.4 ­42 ­1.3 84 11 236 4 3,553 1 2.1 7.6 Australia 1,581 20.6 0 0.0 252 63 497 37 15,638 38 1,029.4 13.4 Austria 39 47.0 ­77 ­0.2 83 7 230 3 3,100 3 27.3 33.0 Azerbaijan 11 12.6 ­130 ­1.3 99 13 229 8 4,300 0 5.3 6.1 Bangladesh 13 10.2 ­165 ­1.3 125 23 166 23 5,000 12 1.0 0.8 Belarus 94 45.3 ­2,562 ­3.2 74 7 194 3 2,100 0 13.1 6.3 Belgium 7 c 22.2 c ­10 c ­0.2 c 58 11 191 2 1,550 0 0.9 2.6 Benin 27 24.0 699 2.3 188 8 112 2 2,500 11 12.6 11.4 Bolivia 531 48.9 1,611 0.3 316 24 504 28 17,367 70 145.3 13.4 Bosnia and Herzegovina 23 44.8 0 0.0 72 10 205 3 .. 1 0.3 0.5 Botswana 124 21.9 1,184 0.9 164 6 184 7 2,151 0 104.8 18.5 Brazil 5,325 63.0 22,264 0.4 394 81 686 114 56,215 .. 566.6 6.7 Bulgaria 37 33.4 ­204 ­0.6 81 14 248 10 3,572 0 5.0 4.5 Burkina Faso 71 25.9 152 0.2 147 7 138 2 1,100 2 31.5 11.5 Burundi 1 3.7 147 9.0 107 6 145 7 2,500 2 1.5 5.7 Cambodia 93 52.9 561 0.6 123 24 183 19 .. 29 32.7 18.5 Cameroon 239 51.3 2,218 0.9 409 40 165 15 8,260 155 20.9 4.5 Canada 2,446 26.5 0 0.0 193 14 310 8 3,270 1 1,023.5 11.1 Central African Republic 229 36.8 300 0.1 209 14 168 3 3,602 10 54.2 8.7 Chad 127 10.1 817 0.6 134 17 141 5 1,600 2 114.6 9.1 Chile 155 20.7 203 0.1 91 21 157 22 5,284 40 141.5 18.9 China 1,589 17.0 ­13,483 ­0.9 394 79 618 74 32,200 168 727.5 7.8 Hong Kong, China .. .. .. .. .. 1 .. 11 .. .. 0.5 .. Colombia 496 47.8 1,905 0.4 359 41 708 78 51,220 213 105.9 10.2 Congo, Dem. Rep. 1,352 59.6 5,324 0.4 200 15 130 3 6,000 33 113.4 5.0 Congo, Rep. 221 64.6 175 0.1 450 40 345 28 11,007 55 22.2 6.5 Costa Rica 20 38.5 158 0.8 205 14 279 13 12,119 109 11.7 23.0 Côte d'Ivoire 71 22.4 2,649 3.1 230 19 252 12 3,660 101 19.1 6.0 Croatia 18 31.9 ­20 ­0.1 76 9 224 4 4,288 0 4.2 7.5 Cuba 23 21.4 ­277 ­1.3 31 11 86 18 6,522 160 75.9 69.1 Czech Republic 26 34.1 ­5 ­0.0 81 8 205 2 1,900 4 12.4 16.1 Denmark 5 10.7 ­10 ­0.2 43 5 196 1 1,450 3 14.4 34.0 Dominican Republic 14 28.4 0 0.0 20 5 79 15 5,657 29 25.1 51.9 Ecuador 106 38.1 1,372 1.2 302 33 640 62 19,362 197 50.7 18.3 Egypt, Arab Rep. 1 0.1 ­20 ­3.4 98 13 123 7 2,076 2 96.6 9.7 El Salvador 1 5.8 72 4.6 135 2 141 0 2,911 23 0.1 0.4 Eritrea 16 15.7 54 0.3 112 12 138 7 .. 3 4.3 4.3 Estonia 21 48.7 ­125 ­0.6 65 4 204 3 1,630 0 5.0 11.8 Ethiopia 46 4.6 403 0.8 277 35 262 16 6,603 22 169.0 16.9 Finland 219 72.0 ­80 ­0.0 60 5 243 3 1,102 1 28.3 9.3 France 153 27.9 ­616 ­0.4 93 18 283 5 4,630 2 73.2 13.3 Gabon 218 84.7 101 0.0 190 15 156 5 6,651 71 1.8 0.7 Gambia, The 5 48.1 ­45 ­1.0 117 3 154 2 974 3 0.2 2.3 Georgia 30 43.0 0 0.0 107 13 208 3 4,350 .. 1.6 2.3 Germany 107 30.8 0 0.0 76 11 247 5 2,682 12 113.8 31.9 Ghana 63 27.8 1,200 1.7 222 14 206 8 3,725 115 12.7 5.6 Greece 36 27.9 ­300 ­0.9 95 13 255 7 4,992 2 4.6 3.6 Guatemala 29 26.3 537 1.7 250 6 221 6 8,681 77 21.7 20.0 Guinea 69 28.2 347 0.5 190 12 109 10 3,000 21 1.7 0.7 Guinea-Bissau 22 77.8 216 0.9 108 3 235 0 1,000 4 .. .. Haiti 1 3.2 70 5.7 20 4 62 14 5,242 27 0.1 0.4 128 2004 World Development Indicators ENVIRONMENT 3.4 Deforestation and biodiversity Forest area Average Mammals Birds Higher plants a Nationally annual protected deforestation areas % of % of thousand total Threatened Threatened Threatened thousand total sq. km land area sq. km % Species species Species species Species species sq. km land area 2000 2000 1990­2000 1990­2000 2002 2002 2002 2002 2002 2002 2003 b 2003 b Honduras 54 48.1 590 1.0 173 10 232 5 5,680 108 7.2 6.4 Hungary 18 19.9 ­72 ­0.4 83 9 208 8 2,214 1 6.5 7.0 India 641 21.6 ­381 ­0.1 390 88 458 72 18,664 244 154.6 5.2 Indonesia 1,050 58.0 13,124 1.2 515 147 929 114 29,375 384 373.2 20.6 Iran, Islamic Rep. 73 4.5 0 0.0 140 22 293 13 8,000 1 78.5 4.8 Iraq 8 1.8 0 0.0 81 11 140 11 .. 0 0.0 0.0 Ireland 7 9.6 ­170 ­3.0 25 5 143 1 950 1 1.2 1.7 Israel 1 6.4 ­50 ­4.9 116 14 162 12 2,317 0 3.3 15.8 Italy 100 34.0 ­295 ­0.3 90 14 250 5 5,599 3 23.2 7.9 Jamaica 3 30.0 54 1.5 24 5 75 12 3,308 206 .. .. Japan 241 66.1 ­34 ­0.0 188 37 210 34 5,565 11 24.8 6.8 Jordan 1 1.0 0 0.0 71 10 117 8 2,100 0 3.0 3.4 Kazakhstan 121 4.5 ­2,390 ­2.2 178 16 379 15 6,000 1 72.9 2.7 Kenya 171 30.0 931 0.5 359 51 344 24 6,506 98 45.5 8.0 Korea, Dem. Rep. 82 68.2 0 0.0 .. 13 150 19 2,898 3 3.1 2.6 Korea, Rep. 63 63.3 49 0.1 49 13 138 25 2,898 0 6.8 6.9 Kuwait 0 0.3 ­2 ­5.2 21 1 35 7 234 0 0.3 1.5 Kyrgyz Republic 10 5.2 ­228 ­2.6 83 7 168 4 4,500 1 28.9 12.5 Lao PDR 126 54.4 527 0.4 172 31 212 20 8,286 18 6.9 3.6 Latvia 29 47.1 ­127 ­0.4 83 4 216 3 1,153 0 8.3 13.4 Lebanon 0 3.5 1 0.3 57 5 116 7 3,000 0 0.1 0.5 Lesotho 0 0.5 0 0.0 33 3 123 7 1,591 0 0.1 0.2 Liberia 35 36.1 760 2.0 193 17 146 11 2,200 46 1.6 1.7 Libya 4 0.2 ­47 ­1.4 76 8 76 1 1,825 1 1.8 0.1 Lithuania 20 30.8 ­48 ­0.2 68 5 201 4 1,796 0 6.7 10.3 Macedonia, FYR 9 35.6 0 0.0 78 11 199 3 3,500 0 1.8 7.1 Madagascar 117 20.2 1,174 0.9 141 50 172 27 9,505 162 25.0 4.3 Malawi 26 27.6 707 2.4 195 8 219 11 3,765 14 10.5 11.2 Malaysia 193 58.7 2,377 1.2 300 50 254 37 15,500 681 18.7 5.7 Mali 132 10.8 993 0.7 137 13 191 4 1,741 6 45.1 3.7 Mauritania 3 0.3 98 2.7 61 10 172 2 1,100 0 17.4 1.7 Mauritius 0 7.9 1 0.6 .. 3 .. 9 .. .. 0.2 7.8 Mexico 552 28.9 6,306 1.1 491 70 440 39 26,071 .. 194.7 10.2 Moldova 3 9.9 ­7 ­0.2 68 6 175 5 1,752 0 0.5 1.4 Mongolia 106 6.8 600 0.5 133 14 274 16 2,823 0 180.1 11.5 Morocco 30 6.8 12 0.0 105 16 206 9 3,675 2 3.1 0.7 Mozambique 306 39.0 637 0.2 179 14 144 16 5,692 36 65.9 8.4 Myanmar 344 52.3 5,169 1.4 300 39 310 35 7,000 37 2.0 0.3 Namibia 80 9.8 734 0.9 250 15 201 11 3,174 5 112.0 13.6 Nepal 39 27.3 783 1.8 181 31 274 25 6,973 6 12.7 8.9 Netherlands 4 11.1 ­10 ­0.3 55 10 192 4 1,221 0 4.8 14.2 New Zealand 79 29.7 ­390 ­0.5 .. 8 .. 63 .. .. 79.3 29.6 Nicaragua 33 27.0 1,172 3.0 200 6 215 5 7,590 39 21.6 17.8 Niger 13 1.0 617 3.7 131 11 125 3 1,460 2 97.5 7.7 Nigeria 135 14.8 3,984 2.6 274 27 286 9 4,715 119 30.1 3.3 Norway 89 28.9 ­310 ­0.4 54 10 241 2 1,715 2 20.9 6.8 Oman 0 0.0 0 0.0 56 9 109 10 1,204 6 43.3 14.0 Pakistan 25 3.2 304 1.1 188 19 237 17 4,950 2 37.8 4.9 Panama 29 38.6 519 1.6 218 20 302 16 9,915 193 16.2 21.7 Papua New Guinea 306 67.6 1,129 0.4 214 58 414 32 11,544 142 10.4 2.3 Paraguay 234 58.8 1,230 0.5 305 10 233 26 7,851 10 13.9 3.5 Peru 652 50.9 2,688 0.4 460 49 695 76 17,144 269 78.1 6.1 Philippines 58 19.4 887 1.4 153 50 404 67 8,931 193 17.0 5.7 Poland 93 30.6 ­110 ­0.1 84 15 233 4 2,450 4 37.7 12.4 Portugal 37 40.1 ­570 ­1.7 63 17 235 7 5,050 15 6.0 6.6 Puerto Rico 2 25.8 5 0.2 .. 2 .. 8 .. .. 0.3 3.5 2004 World Development Indicators 129 3.4 Deforestation and biodiversity Forest area Average Mammals Birds Higher plants a Nationally annual protected deforestation areas % of % of thousand total Threatened Threatened Threatened thousand total sq. km land area sq. km % Species species Species species Species species sq. km land area 2000 2000 1990­2000 1990­2000 2002 2002 2002 2002 2002 2002 2003 b 2003 b Romania 64 28.0 ­147 ­0.2 84 17 257 8 3,400 1 10.8 4.7 Russian Federation 8,514 50.4 ­1,353 ­0.0 269 45 528 38 11,400 7 1,317.3 7.8 Rwanda 3 12.4 150 3.9 151 9 200 9 2,288 3 1.5 6.2 Saudi Arabia 15 0.7 0 0.0 77 8 125 15 2,028 3 823.3 38.3 Senegal 62 32.2 450 0.7 192 12 175 4 2,086 7 22.3 11.6 Serbia and Montenegro 29 .. 14 0.0 96 12 238 5 4,082 1 .. 3.3 Sierra Leone 11 14.7 361 2.9 147 12 172 10 2,090 43 1.5 2.1 Singapore 0 3.3 0 0.0 85 3 142 7 2,282 54 0.0 4.9 Slovak Republic 20 42.5 ­69 ­0.3 85 9 199 4 3,124 2 11.0 22.8 Slovenia 11 55.0 ­22 ­0.2 75 9 201 1 3,200 0 1.2 6.0 Somalia 75 12.0 769 1.0 171 19 179 10 3,028 17 5.0 0.8 South Africa 89 7.3 80 0.1 247 42 304 28 23,420 45 67.2 5.5 Spain 144 28.8 ­860 ­0.6 82 24 281 7 5,050 14 42.5 8.5 Sri Lanka 19 30.0 348 1.6 88 22 126 14 3,314 280 8.7 13.5 Sudan 616 25.9 9,589 1.4 267 23 280 6 3,137 17 123.6 5.2 Swaziland 5 30.3 ­58 ­1.2 .. 4 .. 5 .. .. 0.6 3.5 Sweden 271 65.9 ­6 ­0.0 60 7 259 2 1,750 3 37.5 9.1 Switzerland 12 30.3 ­43 ­0.4 75 5 199 2 3,030 2 11.9 30.0 Syrian Arab Republic 5 2.5 0 0.0 63 4 145 8 3,000 0 .. .. Tajikistan 4 2.8 ­20 ­0.5 84 9 210 7 5,000 2 5.9 4.2 Tanzania 388 43.9 913 0.2 316 42 229 33 10,008 236 263.3 29.8 Thailand 148 28.9 1,124 0.7 265 37 285 37 11,625 78 71.0 13.9 Togo 5 9.4 209 3.4 196 9 117 0 3,085 9 4.3 7.9 Trinidad and Tobago 3 50.5 22 0.8 100 1 131 1 2,259 1 0.3 6.0 Tunisia 5 3.3 ­11 ­0.2 78 11 165 5 2,196 0 0.5 0.3 Turkey 102 13.3 ­220 ­0.2 116 17 278 11 8,650 3 12.3 1.6 Turkmenistan 38 8.0 0 0.0 103 13 204 6 .. 0 19.7 4.2 Uganda 42 21.3 913 2.0 345 20 243 13 4,900 33 48.5 24.6 Ukraine 96 16.5 ­310 ­0.3 108 16 245 8 5,100 1 22.6 3.9 United Arab Emirates 3 3.8 ­78 ­2.8 25 3 34 8 .. 0 0.0 0.0 United Kingdom 26 10.7 ­200 ­0.8 50 12 229 2 1,623 13 50.3 20.9 United States 2,260 24.7 ­3,880 ­0.2 428 37 508 55 19,473 .. 2,372.2 25.9 Uruguay 13 7.4 ­501 ­5.0 81 6 115 11 2,278 1 0.5 0.3 Uzbekistan 20 4.8 ­46 ­0.2 97 9 203 9 4,800 1 8.3 2.0 Venezuela, RB 495 56.1 2,175 0.4 323 26 547 24 21,073 67 562.7 63.8 Vietnam 98 30.2 ­516 ­0.5 213 40 262 37 10,500 126 12.0 3.7 West Bank and Gaza .. .. .. .. .. 1 .. 1 .. .. .. .. Yemen, Rep. 4 0.9 92 1.8 66 5 93 12 1,650 52 .. .. Zambia 312 42.0 8,509 2.4 233 12 252 11 4,747 8 237.1 31.9 Zimbabwe 190 49.2 3,199 1.5 270 12 229 10 4,440 14 46.8 12.1 World 38,480 s 29.7 w 95,009 s 0.2 w 13,750.0 s 10.7 w Low income 9,031 27.1 73,087 0.8 2,665.5 8.4 Middle income 21,493 32.7 29,869 0.1 6,073.9 9.1 Lower middle income 19,065 31.8 14,730 ­0.1 3,891.0 7.2 Upper middle income 2,428 34.5 15,139 0.5 2,183.0 17.3 Low & middle income 30,525 30.9 102,956 0.3 8,739.5 8.9 East Asia & Pacific 4,238 27.2 11,613 0.2 1,454.8 9.2 Europe & Central Asia 9,464 39.7 ­8,143 ­0.1 1,610.2 6.8 Latin America & Carib. 9,438 47.1 45,873 0.5 2,237.8 11.2 Middle East & N. Africa 168 1.5 ­239 ­0.1 1,169.3 11.3 South Asia 782 16.3 889 0.1 228.6 4.8 Sub-Saharan Africa 6,436 27.3 52,963 0.8 2,038.8 8.7 High income 7,955 26.1 ­7,947 ­0.1 5,010.5 19.5 Europe EMU 846 37.0 ­2,978 ­0.3 324.9 13.5 a. Flowering plants only. b. Data may refer to earlier years. They are the most recent reported by the World Conservation Monitoring Center in 2003. c. Includes Luxembourg. 130 2004 World Development Indicators ENVIRONMENT 3.4 Deforestation and biodiversity About the data Definitions The estimates of forest area are from the Food and · National parks of national or international signifi- · Forest area is land under natural or planted stands Agriculture Organization's (FAO) State of the World's cance (not materially affected by human activity). of trees, whether productive or not. · Average annu- Forests 2003, which provides information on forest · Natural monuments and natural landscapes with al deforestation refers to the permanent conversion cover in 2000 and an estimate of forest cover in unique aspects. of natural forest area to other uses, including shifting 1990. The current survey is the latest global forest · Managed nature reserves and wildlife sanctuaries. cultivation, permanent agriculture, ranching, settle- assessment and the first to use a uniform global def- · Protected landscapes and seascapes (which ments, and infrastructure development. Deforested inition of forest. According to this assessment, the may include cultural landscapes). areas do not include areas logged but intended for global rate of net deforestation has slowed to 9.5 mil- Designating land as a protected area does not nec- regeneration or areas degraded by fuelwood gather- lion hectares a year, a rate 20 percent lower than that essarily mean that protection is in force. For small ing, acid precipitation, or forest fires. Negative num- previously reported. No breakdown of forest cover countries that may only have protected areas small- bers indicate an increase in forest area. · Mammals between natural forest and plantation is shown in the er than 1,000 hectares, this size limit in the defini- exclude whales and porpoises. · Birds refer to breed- table because of space limitations. (This breakdown tion will result in an underestimate of the extent and ing species and are listed for countries included with- is provided by the FAO only for developing countries.) number of protected areas. in their breeding ranges. · Higher plants refer to For this reason the deforestation data in the table Threatened species are defined according to the native vascular plant species. · Threatened species may underestimate the rate at which natural forest is IUCN's classification categories: endangered (in dan- are the number of species classified by the IUCN as disappearing in some countries. ger of extinction and unlikely to survive if causal fac- endangered, vulnerable, rare, indeterminate, out of Deforestation is a major cause of loss of biodiver- tors continue operating), vulnerable (likely to move danger, or insufficiently known. · Nationally protect- sity, and habitat conservation is vital for stemming into the endangered category in the near future if ed areas are totally or partially protected areas of at this loss. Conservation efforts traditionally have causal factors continue operating), rare (not endan- least 1,000 hectares that are designated as scientif- focused on protected areas, which have grown sub- gered or vulnerable but at risk), indeterminate ic reserves with limited public access, national parks, stantially in recent decades. Measures of species (known to be endangered, vulnerable, or rare but not natural monuments, nature reserves or wildlife sanc- richness are among the most straightforward ways to enough information is available to say which), out of tuaries, and protected landscapes and seascapes. indicate the importance of an area for biodiversity. danger (formerly included in one of the above cate- The data do not include sites protected under local or The number of small plants and animals is usually gories but now considered relatively secure because provincial law. Total land area is used to calculate the estimated by sampling plots. It is also important to appropriate conservation measures are in effect), percentage of total area protected (see table 3.1). know which aspects are under the most immediate and insufficiently known (suspected but not definite- threat. This, however, requires a large amount of data ly known to belong to one of the above categories). and time-consuming analysis. For this reason global Figures on species are not necessarily comparable analyses of the status of threatened species have across countries because taxonomic concepts and been carried out for few groups of organisms. Only for coverage vary. And while the number of birds and birds has the status of all species been assessed. An mammals is fairly well known, it is difficult to make estimated 45 percent of mammal species remain to an accurate count of plants. Although the data in the be assessed. For plants the World Conservation table should be interpreted with caution, especially Union's (IUCN) 1997 IUCN Red List of Threatened for numbers of threatened species (where knowledge Plants provides the first-ever comprehensive listing of is very incomplete), they do identify countries that threatened species on a global scale, the result of are major sources of global biodiversity and show more than 20 years' work by botanists from around national commitments to habitat protection. the world. Nearly 34,000 plant species, 12.5 percent The dataset on protected areas is tentative and is of the total, are threatened with extinction. being revised. Due to variations in consistency and The table shows information on protected areas, methodology of collection, the quality of the data are numbers of certain species, and numbers of those highly variable across countries. Some countries species under threat. The World Conservation update their information more frequently than others, Monitoring Centre (WCMC) compiles these data from some may have more accurate data on extent of cov- a variety of sources. Because of differences in defi- erage, and many underreport the number or extent of Data sources nitions and reporting practices, cross-country com- protected areas. The forestry data are from the FAO's State of the parability is limited. Compounding these problems, World's Forests 2003. The data on species are available data cover different periods. from the WCMC's electronic files and the IUCN's Nationally protected areas are areas of at least 2002 IUCN Red List of Threatened Animals and 1,000 hectares that fall into one of five management 1997 IUCN Red List of Threatened Plants. The categories defined by the WCMC: data on protected areas are from the United · Scientific reserves and strict nature reserves Nations Environment Programme and WCMC. with limited public access. 2004 World Development Indicators 131 3.5 Freshwater Renewable freshwater Annual freshwater withdrawals Access to improved resources water source Net flows Internal from other Total flows countries resources billion % of total billion billion per capita cu. m resources % for % for % for % of urban % of rural cu. m cu. m cu. m a 1980­ 1980­ agriculture industry domestic population population 2000 2000 2000 2000 b 2000 a,b 1987 1987 1987 1990 2000 1990 2000 Afghanistan 55 10.0 2,322 26.1 40.2 99 c 0 c 1 c .. 19 .. 11 Albania 27 15.7 13,524 1.4 3.3 71 0 29 .. 99 .. 95 Algeria 14 0.4 457 5.0 35.0 52 c 14 c 34 c .. 94 .. 82 Angola 184 .. 14,023 0.5 0.3 76 c 10 c 14 c .. 34 .. 40 Argentina 276 623.0 23,693 28.6 3.2 75 9 16 .. 97 .. 73 Armenia 9 1.5 3,455 2.9 27.4 66 4 30 .. 87 .. 45 Australia 492 0.0 25,022 14.6 3.0 33 2 65 100 100 100 100 Austria 55 29.0 10,437 2.4 2.9 9 58 33 100 100 100 100 Azerbaijan 8 21.0 3,561 16.5 56.7 70 25 5 .. 93 .. 58 Bangladesh 105 1,105.6 8,922 14.6 1.2 86 2 12 99 99 93 97 Belarus 37 20.8 5,844 2.7 4.7 35 43 22 .. 100 .. 100 Belgium 12 4.0 1,548 .. .. .. .. .. .. .. .. .. Benin 10 15.5 3,938 0.1 0.4 67 c 10 c 23 c .. 74 .. 55 Bolivia 304 7.2 35,271 1.2 0.4 87 3 10 91 95 47 64 Bosnia and Herzegovina 36 2.0 9,120 1.0 2.7 60 10 30 .. .. .. .. Botswana 3 11.8 8,586 0.1 0.7 48 c 20 c 32 c 100 100 88 90 Brazil 5,418 1,900.0 41,941 54.9 0.8 61 18 21 93 95 54 53 Bulgaria 21 0.2 2,662 13.9 65.6 22 75 3 .. 100 .. 100 Burkina Faso 13 2.0 1,226 0.4 2.8 81 c 0 c 19 c .. 66 .. 37 Burundi 4 .. 509 0.1 2.8 64 c 0 c 36 c 96 91 67 77 Cambodia 121 355.6 38,136 0.5 0.1 94 1 5 .. 54 .. 26 Cameroon 273 0.0 17,312 0.4 0.1 35 c 19 c 46 c 78 78 32 39 Canada 2,850 52.0 92,532 45.1 1.6 12 70 18 100 100 99 99 Central African Republic 141 .. 36,911 0.1 0.1 74 c 5 c 21 c 71 89 35 57 Chad 15 28.0 5,155 0.2 0.5 82 c 2 c 16 c .. 31 .. 26 Chile 884 0.0 56,707 20.3 2.3 84 11 5 98 99 49 58 China 2,812 17.2 2,210 525.5 18.6 78 18 5 99 94 60 66 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2,112 0.0 48,293 8.9 0.4 37 4 59 98 99 84 70 Congo, Dem. Rep. 900 313.0 23,517 0.4 0.0 23 c 16 c 61 c .. 89 .. 26 Congo, Rep. 222 610.0 227,509 0.0 0.0 11 c 27 c 62 c .. 71 .. 17 Costa Rica 112 .. 28,513 5.8 5.2 80 7 13 .. 99 .. 92 Côte d'Ivoire 77 .. 4,645 0.7 0.9 67 c 11 c 22 c 97 92 69 72 Croatia 38 33.7 15,991 0.8 1.1 0 50 50 .. .. .. .. Cuba 38 0.0 3,383 5.2 13.6 51 0 49 .. 95 .. 77 Czech Republic 13 1.0 1,391 2.7 19.0 2 57 41 .. .. .. .. Denmark 6 .. 1,116 1.2 20.0 43 27 30 .. 100 .. 100 Dominican Republic 21 .. 2,438 8.3 39.5 89 0 11 92 90 71 78 Ecuador 432 0.0 33,703 17.0 3.9 82 6 12 82 90 58 75 Egypt, Arab Rep. 2 66.7 1,032 66.0 96.4 82 c 11 c 7 c 97 99 92 96 El Salvador 18 .. 2,774 0.7 3.9 46 20 34 88 91 48 64 Eritrea 3 6.0 2,048 .. .. .. .. .. .. 63 .. 42 Estonia 13 0.1 9,426 0.2 1.6 5 39 56 .. .. .. .. Ethiopia 110 0.0 1,636 2.2 2.0 86 c 3 c 11 c 80 81 17 12 Finland 107 3.0 21,158 2.2 2.0 3 85 12 100 100 100 100 France 179 11.0 3,186 32.3 17.0 10 72 18 .. .. .. .. Gabon 164 0.0 124,715 0.1 0.1 6 c 22 c 72 c .. 95 .. 47 Gambia, The 3 5.0 5,760 0.0 0.0 91 c 2 c 7 c .. 80 .. 53 Georgia 58 8.4 12,845 3.5 5.3 59 20 21 .. 90 .. 61 Germany 107 71.0 2,158 46.3 26.0 20 69 11 .. .. .. .. Ghana 30 22.9 2,624 0.3 0.6 52 c 13 c 35 c 85 91 36 62 Greece 58 15.0 6,867 8.7 11.9 87 3 10 .. .. .. .. Guatemala 109 0.0 9,106 1.2 1.1 74 17 9 88 98 69 88 Guinea 226 0.0 29,184 0.7 0.3 87 c 3 c 10 c 72 72 36 36 Guinea-Bissau 16 11.0 18,659 0.0 0.0 36 c 4 c 60 c .. 79 .. 49 Haiti 13 .. 1,569 1.0 7.7 94 1 5 59 49 50 45 132 2004 World Development Indicators ENVIRONMENT 3.5 Freshwater Renewable freshwater Annual freshwater withdrawals Access to improved resources water source Net flows Internal from other Total flows countries resources billion % of total billion billion per capita cu. m resources % for % for % for % of urban % of rural cu. m cu. m cu. m a 1980­ 1980­ agriculture industry domestic population population 2000 2000 2000 2000 b 2000 a,b 1987 1987 1987 1990 2000 1990 2000 Honduras 96 0.0 14,109 1.5 1.6 91 5 4 89 95 78 81 Hungary 6 114.0 11,812 6.8 5.7 36 55 9 100 100 98 98 India 1,261 647.2 1,819 500.0 26.2 92 3 5 88 95 61 79 Indonesia 2,838 .. 13,405 74.3 2.6 93 1 6 92 90 62 69 Iran, Islamic Rep. 129 .. 1,961 70.0 54.5 92 2 6 .. 98 .. 83 Iraq 35 75.9 4,596 42.8 38.5 92 5 3 .. 96 .. 48 Ireland 49 3.0 13,265 0.8 1.5 10 74 16 .. .. .. .. Israel 1 0.9 259 1.6 94.1 54 c 7 c 39 c .. .. .. .. Italy 183 6.8 3,281 42.0 22.2 48 34 19 .. .. .. .. Jamaica 9 .. 3,592 0.9 9.6 77 7 15 98 98 87 85 Japan 430 0.0 3,382 91.4 21.3 64 17 19 .. .. .. .. Jordan 1 .. 135 1.0 .. 75 3 22 99 100 92 84 Kazakhstan 75 34.2 7,368 33.7 30.7 81 17 2 .. 98 .. 82 Kenya 20 10.0 963 2.0 6.6 76 c 4 c 20 c 91 88 31 42 Korea, Dem. Rep. 67 10.1 3,428 14.2 18.4 73 16 11 .. 100 .. 100 Korea, Rep. 65 4.9 1,465 23.7 34.0 63 11 26 .. 97 .. 71 Kuwait 0 0.0 .. 0.5 .. 60 2 37 .. .. .. .. Kyrgyz Republic 47 0.0 9,293 10.1 21.7 94 3 3 .. 98 .. 66 Lao PDR 190 143.1 60,307 1.0 0.3 82 10 8 .. 61 .. 29 Latvia 17 18.7 15,141 0.3 0.8 13 32 55 .. .. .. .. Lebanon 5 0.0 1,081 1.3 27.1 68 6 27 .. 100 .. 100 Lesotho 5 0.0 2,926 0.1 1.9 56 c 22 22 c .. 88 .. 74 Liberia 200 32.0 70,410 0.1 0.0 60 c 13 c 27 c .. .. .. .. Libya 1 .. 110 4.5 .. 84 c 3 c 13 c 72 72 68 68 Lithuania 16 9.3 7,178 0.3 1.2 3 16 81 .. .. .. .. Macedonia, FYR 5 1.0 3,140 1.9 29.7 74 15 12 .. .. .. .. Madagascar 337 0.0 20,503 16.3 4.8 99 c .. c 1 c 85 85 31 31 Malawi 16 1.1 1,601 0.9 5.2 86 c 3 c 10 c 90 95 43 44 Malaysia 580 .. 23,863 12.7 2.2 77 13 11 .. .. .. 94 Mali 60 40.0 8,792 1.4 1.4 97 c 1 c 2 c 65 74 52 61 Mauritania 0 11.0 4,093 1.6 14.0 92 2 6 34 34 40 40 Mauritius 2 0.0 1,815 .. .. 77 c 7 c 16 c 100 100 100 100 Mexico 409 49.0 4,543 77.8 17.0 78 5 17 90 95 52 69 Moldova 1 10.7 2,750 3.0 25.6 26 65 9 .. 97 .. 88 Mongolia 35 .. 14,210 0.4 1.1 53 27 20 .. 77 .. 30 Morocco 29 0.0 978 11.5 39.7 89 c 2 c 10 c 94 98 58 56 Mozambique 99 111.0 11,390 0.6 0.3 89 c 2 c 9 c .. 81 .. 41 Myanmar 881 165.0 21,432 4.0 0.4 90 3 7 .. 89 .. 66 Namibia 6 39.3 22,922 0.2 0.4 68 c 3 c 29 c 98 100 63 67 Nepal 198 12.0 8,713 29.0 13.8 99 0 1 93 94 64 87 Netherlands 11 80.0 5,637 7.8 8.6 34 61 5 100 100 100 100 New Zealand 327 0.0 83,016 2.0 0.6 44 10 46 100 100 .. .. Nicaragua 190 0.0 35,511 1.3 0.7 84 2 14 93 91 44 59 Niger 4 29.0 2,845 0.5 1.5 82 c 2 c 16 c 65 70 51 56 Nigeria 221 59.0 2,109 3.6 1.3 54 c 15 c 31 c 83 78 37 49 Norway 382 11.0 86,602 2.0 0.5 8 72 20 100 100 100 100 Oman 1 .. 394 1.2 .. 94 2 5 41 41 30 30 Pakistan 52 170.3 1,534 155.6 70.0 97 2 2 96 95 77 87 Panama 147 .. 50,136 1.6 1.1 70 2 28 .. 99 .. 79 Papua New Guinea 801 .. 148,940 0.1 0.0 49 22 29 88 88 32 32 Paraguay 94 .. 17,060 0.4 0.4 78 7 15 80 93 46 59 Peru 1,616 144.0 65,797 19.0 1.1 86 7 7 88 87 42 62 Philippines 479 0.0 5,992 55.4 11.6 88 4 8 93 91 82 79 Poland 54 8.0 1,595 12.3 20.0 11 76 13 .. .. .. .. Portugal 38 35.0 7,173 7.3 10.0 48 37 15 .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 133 3.5 Freshwater Renewable freshwater Annual freshwater withdrawals Access to improved resources water source Net flows Internal from other Total flows countries resources billion % of total billion billion per capita cu. m resources % for % for % for % of urban % of rural cu. m cu. m cu. m a 1980­ 1980­ agriculture industry domestic population population 2000 2000 2000 2000 b 2000 a,b 1987 1987 1987 1990 2000 1990 2000 Romania 42 170.0 9,520 26.0 12.2 59 33 8 .. 91 .. 16 Russian Federation 4,313 185.5 31,222 77.1 1.7 20 62 19 .. 100 .. 96 Rwanda 5 .. 637 0.8 15.4 94 c 2 c 5 c .. 60 .. 40 Saudi Arabia 2 .. 110 17.0 .. 90 1 9 .. 100 .. 64 Senegal 26 13.0 4,009 1.4 3.6 92 c 3 c 5 c 90 92 60 65 Serbia and Montenegro 44 144.0 23,039 13.0 6.9 8 c 86 c 6 c .. 99 .. 97 Sierra Leone 160 0.0 30,564 0.4 0.3 89 4 7 .. 75 .. 46 Singapore .. .. .. .. .. 4 51 45 100 100 100 .. Slovak Republic 13 70.0 15,356 1.8 2.2 .. .. .. .. 100 .. 100 Slovenia 19 0.0 9,521 1.3 7.0 1 c 80 c 20 c 100 100 100 100 Somalia 6 9.7 1,685 0.8 5.1 97 c 0 c 3 c .. .. .. .. South Africa 45 5.2 1,103 13.3 26.6 72 11 17 99 99 73 73 Spain 111 0.3 2,725 35.2 31.6 68 19 13 .. .. .. .. Sri Lanka 50 0.0 2,636 9.8 19.6 96 c 2 c 2 c 91 98 62 70 Sudan 30 119.0 4,544 17.8 11.9 94 c 1 c 4 c 86 86 60 69 Swaziland 3 1.9 4,136 .. .. 96 2 2 .. .. .. .. Sweden 171 12.2 20,529 2.9 1.6 9 55 36 100 100 100 100 Switzerland 40 13.0 7,325 1.2 2.2 4 73 23 100 100 100 100 Syrian Arab Republic 7 37.7 2,632 12.0 26.8 90 2 8 .. 94 .. 64 Tajikistan 66 13.3 12,706 11.9 14.9 92 c 4 c 3 c .. 93 .. 47 Tanzania 82 9.0 2,587 1.2 1.3 89 2 9 76 90 28 57 Thailand 210 199.9 6,653 33.1 8.1 91 c 4 c 5 c 87 95 78 81 Togo 12 0.5 2,521 0.1 0.8 25 13 62 82 85 38 38 Trinidad and Tobago 4 .. 2,914 0.3 7.9 6 c 26 c 68 c .. .. .. .. Tunisia 4 0.4 470 2.8 60.9 86 c 1 c 13 c 91 92 54 58 Turkey 227 7.6 3,369 35.5 15.1 73 12 16 83 81 72 86 Turkmenistan 1 59.5 12,706 23.8 39.1 98 1 1 .. .. .. .. Uganda 39 27.0 2,683 0.2 0.3 60 8 32 81 80 40 47 Ukraine 53 86.5 2,866 26.0 18.6 30 52 18 .. 100 .. 94 United Arab Emirates 0 .. 62 2.1 .. 67 9 24 .. .. .. .. United Kingdom 145 2.0 2,482 11.8 8.0 3 c 77 c 20 c 100 100 100 100 United States 2,800 18.0 9,772 467.3 16.6 42 45 13 100 100 100 100 Uruguay 59 74.0 39,572 0.7 0.5 91 3 6 .. 98 .. 93 Uzbekistan 16 98.1 4,527 58.1 50.8 94 2 4 .. 94 .. 79 Venezuela, RB 723 .. 28,796 4.1 0.6 46 10 44 .. 85 .. 70 Vietnam 367 524.7 11,081 54.3 6.1 87 10 4 86 95 48 72 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 4 .. 220 2.9 70.7 92 1 7 .. 74 .. 68 Zambia 80 35.8 11,324 1.7 1.5 77 c 7 c 16 c 88 88 28 48 Zimbabwe 14 .. 1,085 1.2 8.5 79 c 7 c 14 c 99 100 69 73 World 42,900 s 9,463.8 s 8,513 w 3,325 s 6 w 71 w 20 w 10 w 94 w 94 w 62 71 w Low income 11,185 4,815.6 6,416 1,041 7 92 4 5 88 90 59 70 Middle income 22,898 4,275.4 9,938 1,430 5 73 18 9 95 95 63 70 Lower middle income 19,341 3,260.3 9,401 1,229 5 73 19 9 96 95 63 70 Upper middle income 3,556 1,015.2 13,848 201 4 71 14 15 .. .. .. 77 Low & middle income 34,082 9,091.0 8,258 2,471 6 81 12 7 93 93 61 70 East Asia & Pacific 9,454 1,415.6 6,020 776 7 81 14 5 97 93 61 67 Europe & Central Asia 5,255 1,134.8 13,511 387 6 57 33 10 .. 96 .. 83 Latin America & Carib. 13,429 2,833.8 30,925 263 2 74 9 18 92 94 58 65 Middle East & N. Africa 234 183.1 1,377 238 57 88 5 7 .. 96 .. 78 South Asia 1,816 1,945.1 2,684 735 20 94 3 4 90 94 66 80 Sub-Saharan Africa 3,895 1,578.7 7,951 73 1 85 6 10 86 83 40 46 High income 8,818 372.8 .. 854 9 42 42 16 .. .. .. .. Europe EMU 910 258.8 3,826 185 16 38 47 15 .. .. .. .. a. River flows from other countries and river outflows are included when available. b. Data are for the most recent year available. c. Data refer to a year other than 1987 (see Primary data documentation). 134 2004 World Development Indicators ENVIRONMENT 3.5 Freshwater About the data Definitions The data on freshwater resources are based on esti- different times and with different levels of quality · Renewable freshwater resources refer to total mates of runoff into rivers and recharge of groundwater. and precision, these data must be used with caution, renewable resources, broken down between internal These estimates are based on different sources and particularly in case of water-short countries, notably flows (internal river flows and groundwater from rain- refer to different years, so cross-country comparisons in the Middle East. fall) in the country and net river flows from other should be made with caution. Because the data are col- The data on access to an improved water source countries. · Net flows from other countries refer to lected intermittently, they may hide significant variations measure the share of the population with reasonable river flows arising outside countries minus river out- in total renewable water resources from one year to the and ready access to an adequate amount of safe flows, when these data are available. · Freshwater next. The data also fail to distinguish between season- water for domestic purposes. An improved source resources per capita are calculated using the World al and geographic variations in water availability within can be any form of collection or piping used to make Bank's population estimates (see table 2.1). countries. Data for small countries and countries in arid water regularly available. While information on · Annual freshwater withdrawals refer to total water and semiarid zones are less reliable than those for larg- access to an improved water source is widely used, withdrawals, not counting evaporation losses from er countries and countries with greater rainfall. Finally, it is extremely subjective, and such terms as safe, storage basins. Withdrawals also include water from caution is also needed in comparing data on annual improved, adequate, and reasonable may have very desalination plants in countries where they are a sig- freshwater withdrawals, which are subject to variations different meanings in different countries despite offi- nificant source. Withdrawals can exceed 100 percent in collection and estimation methods. cial World Health Organization definitions (see of total renewable resources where extraction from The table shows both internal freshwater Definitions). Even in high-income countries treated nonrenewable aquifers or desalination plants is con- resources and river flows arising outside countries. water may not always be safe to drink. While access siderable or where there is significant water reuse. River outflows are also taken into account. However, to an improved water source is equated with con- Withdrawals for agriculture and industry are total because inflows and outflows may be estimated at nection to a public supply system, this does not take withdrawals for irrigation and livestock production into account variations in the quality and cost (broad- and for direct industrial use (including withdrawals 3.5a ly defined) of the service once connected. Thus for cooling thermoelectric plants). Withdrawals for The distribution of freshwater resources cross-country comparisons must be made cautious- domestic uses include drinking water, municipal use is uneven ly. Changes over time within countries may result or supply, and use for public services, commercial Internal freshwater flows, 2000 from changes in definitions or measurements. The establishments, and homes. · Access to an Middle East & definition in this table and in table 2.15 differs from improved water source refers to the percentage of South Asia North Africa 4% 1% that used for the city-level data shown in table 3.11, the population with reasonable access to an ade- Sub-Saharan which is more stringent. quate amount of water from an improved source, Africa 9% High such as a household connection, public standpipe, income 21% borehole, protected well or spring, or rainwater col- Europe & Central Asia lection. Unimproved sources include vendors, tanker 12% trucks, and unprotected wells and springs. East Asia Latin America Reasonable access is defined as the availability of at & Pacific & Caribbean 22% 31% least 20 liters a person a day from a source within 1 kilometer of the dwelling. Source: Table 3.5. 3.5b Latin America and the Caribbean has more than 20 times the freshwater resources per capita as the Middle East and North Africa Data sources Total freshwater resources per capita, 2000 (thousands of cubic meters) The data on freshwater resources and with- 35 drawals are compiled by the World Resources 30 Institute from various sources and published in 25 World Resources 2000­01 and World Resources 20 2002­03 (produced in collaboration with the 15 United Nations Environment Programme, United 10 Nations Development Programme, and World 5 Bank). These are supplemented by the Food and 0 Agriculture Organization's AQUASTAT data. The Latin America Europe & Sub-Saharan East Asia South Middle East & data on access to an improved water source & Caribbean Central Asia Africa & Pacific Asia North Africa come from the World Health Organization. Source: Table 3.5. 2004 World Development Indicators 135 3.6 Water pollution Emissions Industry shares of emissions of organic water pollutants of organic water pollutants % of total kilograms Stone, kilograms per day Primary Paper Food and ceramics, per day per worker metals and pulp Chemicals beverages and glass Textiles Wood Other 1980 2000 a 1980 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a Afghanistan 6,680 .. 0.17 .. .. .. .. .. .. .. .. .. Albania .. 6,512 .. 0.29 14.3 0.9 5.5 73.5 0.3 4.6 0.0 0.8 Algeria 60,290 45,645 0.19 0.24 23.4 2.0 3.3 59.5 0.7 7.6 0.8 ­0.0 Angola .. 1,472 .. 0.20 7.6 3.0 9.1 65.9 0.3 5.5 4.4 4.1 Argentina 244,711 177,882 0.18 0.21 6.5 12.5 8.0 59.4 0.1 7.4 1.5 4.5 Armenia .. 10,014 .. .. .. 3.9 .. 72.5 .. .. .. .. Australia 204,333 95,369 0.18 0.21 .. .. 6.5 81.7 0.1 2.8 3.1 .. Austria 108,416 80,789 0.16 0.13 14.9 18.2 11.1 32.8 0.4 5.1 5.3 12.1 Azerbaijan .. 45,025 .. 0.17 11.6 2.5 12.0 49.0 0.2 18.1 1.0 5.6 Bangladesh 66,713 273,082 0.16 0.14 1.8 6.8 6.6 23.2 0.1 64.1 0.5 .. Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 136,452 102,460 0.16 0.17 13.7 18.0 10.5 40.4 0.2 6.0 2.0 7.5 Benin 1,646 .. 0.28 .. .. .. 1.2 .. .. .. .. .. Bolivia 9,343 12,759 0.22 0.25 0.9 20.5 6.6 61.4 0.3 7.1 2.4 0.9 Bosnia and Herzegovina .. 8,903 .. 0.18 20.5 13.1 6.6 33.3 0.2 17.6 5.8 2.8 Botswana 1,307 4,635 0.24 0.20 1.7 15.8 0.8 56.4 0.2 17.2 1.4 1.8 Brazil 866,790 629,406 0.16 0.20 17.7 12.9 7.6 44.4 0.1 9.8 1.4 4.5 Bulgaria 152,125 107,945 0.13 0.17 11.7 7.9 8.2 48.1 0.1 17.0 2.0 6.6 Burkina Faso 2,385 2,598 0.29 0.22 3.5 1.1 5.8 73.8 0.1 4.1 10.1 1.9 Burundi 769 1,644 0.22 0.24 0.0 8.3 5.1 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 10,714 0.29 0.20 3.1 6.3 3.6 52.7 0.0 3.6 5.6 0.4 Canada 330,241 307,325 0.18 0.15 10.8 23.9 9.8 34.8 0.1 5.4 5.1 10.0 Central African Republic 861 670 0.26 0.17 0.0 .. 4.0 62.0 0.0 13.8 19.6 .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 44,371 72,850 0.21 0.24 6.9 11.3 8.9 62.7 0.1 5.0 2.6 2.5 China 3,377,105 6,204,237 0.14 0.14 20.6 10.8 15.3 28.4 0.5 14.8 0.8 8.9 Hong Kong, China 102,002 35,649 0.11 0.18 0.9 43.6 4.2 27.4 0.1 17.9 0.1 6.0 Colombia 96,055 93,879 0.19 0.21 3.1 16.2 10.3 53.2 0.2 14.2 1.0 2.4 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 1,039 .. 0.21 .. .. .. .. .. .. .. .. .. Costa Rica .. 32,914 .. 0.21 1.8 10.0 7.1 62.2 0.1 13.9 1.7 2.9 Côte d'Ivoire 15,414 12,401 0.23 0.24 .. 5.5 5.0 71.9 0.0 8.6 5.9 .. Croatia .. 48,447 .. 0.17 7.2 14.4 8.6 45.2 0.2 14.6 3.8 6.0 Cuba 120,703 .. 0.24 .. .. .. .. .. .. .. .. .. Czech Republic 316,429 258,413 0.13 0.13 23.2 9.5 7.9 31.5 0.4 12.2 2.0 12.8 Denmark 65,465 83,591 0.17 0.17 4.4 29.1 7.0 44.2 0.2 2.2 3.5 8.6 Dominican Republic 54,935 .. 0.38 .. .. .. 1.9 .. .. .. .. .. Ecuador 25,297 32,266 0.23 0.27 2.3 10.8 7.1 71.8 0.1 6.0 1.5 1.3 Egypt, Arab Rep. 169,146 203,633 0.19 0.20 11.8 7.9 8.3 49.8 0.3 18.9 0.4 2.9 El Salvador 9,390 22,760 0.24 0.18 2.1 10.2 7.1 43.5 0.1 34.1 0.5 1.4 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 16,754 21,533 0.22 0.23 1.9 10.8 .. 61.5 0.2 18.7 1.6 0.8 Finland 92,275 62,610 0.17 0.19 9.8 43.3 2.2 30.2 0.2 2.8 4.4 7.0 France 729,776 278,878 0.14 0.10 14.9 30.9 10.3 37.7 0.3 9.7 2.7 .. Gabon 2,661 1,886 0.15 0.26 0.0 6.0 5.0 79.7 0.1 1.2 6.9 1.2 Gambia, The 549 832 0.30 0.34 .. .. 1.7 .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany .. 792,194 .. 0.13 11.2 22.3 9.8 34.4 0.2 3.2 2.3 16.5 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 57,178 0.17 0.20 6.3 11.8 9.1 54.0 0.2 13.2 1.5 3.8 Guatemala 20,856 19,253 0.25 0.28 4.9 7.2 8.1 72.8 0.1 6.9 0.8 .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 4,734 .. 0.19 .. .. .. .. .. .. .. .. .. 136 2004 World Development Indicators ENVIRONMENT 3.6 Water pollution Emissions Industry shares of emissions of organic water pollutants of organic water pollutants % of total kilograms Stone, kilograms per day Primary Paper Food and ceramics, per day per worker metals and pulp Chemicals beverages and glass Textiles Wood Other 1980 2000 a 1980 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 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 Hungary 201,888 152,531 0.15 0.17 8.0 12.1 7.9 48.0 0.2 14.1 2.4 7.3 India 1,422,564 1,582,285 0.21 0.20 13.9 6.6 9.6 52.2 0.2 13.1 0.3 4.2 Indonesia 214,010 752,834 0.22 0.18 2.8 8.6 8.6 50.1 0.1 22.0 5.3 4.5 Iran, Islamic Rep. 72,334 101,900 0.15 0.17 20.6 8.0 8.0 39.7 0.5 17.3 0.7 5.4 Iraq 32,986 19,617 0.19 0.16 8.8 14.1 15.1 39.4 0.7 16.7 0.3 4.8 Ireland 43,544 49,144 0.19 0.15 1.3 14.2 11.4 56.4 0.2 3.1 1.6 11.8 Israel 39,113 54,149 0.15 0.16 3.7 19.7 9.4 43.9 0.2 12.1 1.8 9.3 Italy 442,712 495,411 0.13 0.13 9.5 16.9 10.8 30.3 0.3 16.0 3.7 12.5 Jamaica 11,123 17,507 0.25 0.29 6.9 7.2 3.8 70.8 0.1 9.8 1.3 0.0 Japan 1,456,016 1,332,302 0.14 0.15 7.4 21.8 8.9 41.7 0.2 5.4 1.7 12.7 Jordan 4,146 16,142 0.17 0.18 3.9 16.2 14.5 51.4 0.5 7.2 3.3 3.0 Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya 26,834 53,029 0.19 0.25 4.1 11.9 5.8 70.0 0.1 8.5 1.8 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 281,900 303,091 0.14 0.12 12.2 17.0 12.4 26.0 0.2 15.7 1.3 15.3 Kuwait 6,921 11,412 0.16 0.17 2.5 16.4 10.9 49.4 0.4 12.1 2.9 5.8 Kyrgyz Republic .. 20,700 .. 0.16 13.7 0.2 0.9 54.8 0.4 21.0 1.0 8.0 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 25,106 .. 0.19 2.8 8.7 0.8 64.5 0.1 11.5 9.7 .. Lebanon 14,586 14,899 0.20 0.19 0.9 15.6 3.3 60.7 0.5 10.2 4.6 3.4 Lesotho 993 3,123 0.24 0.16 1.2 4.0 0.7 39.7 0.1 51.3 0.6 2.3 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 3,532 .. 0.21 .. .. .. 11.0 .. .. .. 6.0 .. Lithuania .. 35,689 .. 0.18 1.2 11.2 5.0 55.6 0.2 17.6 0.4 .. Macedonia, FYR .. 23,490 .. 0.18 11.7 9.6 6.2 45.0 0.1 20.9 .. .. Madagascar 9,131 .. 0.23 .. .. .. 2.5 .. .. .. 1.7 .. Malawi 12,224 11,805 0.32 0.29 0.0 16.0 3.7 70.0 0.0 7.8 7.0 .. Malaysia 77,215 158,761 0.15 0.12 6.5 14.5 16.5 34.1 0.2 7.5 3.2 19.7 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 9,224 17,700 0.21 0.15 0.9 6.6 2.6 32.8 0.1 55.4 0.9 0.8 Mexico 130,993 296,093 0.22 0.20 7.8 12.5 10.4 55.6 0.2 7.5 1.3 3.7 Moldova .. 34,234 .. 0.29 0.2 4.0 1.4 81.7 0.2 10.8 4.9 .. Mongolia 9,254 7,939 0.19 0.18 1.8 4.3 2.9 64.2 0.3 24.6 0.9 .. Morocco 26,598 88,779 0.15 0.18 0.7 7.0 9.7 54.4 0.4 27.2 1.4 .. Mozambique .. 10,230 .. 0.31 1.1 7.1 4.1 81.2 0.1 5.8 2.9 .. Myanmar .. 3,356 .. 0.13 14.0 9.0 40.5 27.0 0.5 4.9 0.9 .. Namibia .. 7,350 .. 0.35 0.0 5.0 1.6 90.4 0.1 1.2 1.7 0.4 Nepal 18,692 26,550 0.25 0.14 1.5 8.1 3.9 43.3 1.2 39.3 1.2 1.6 Netherlands 165,416 124,182 0.18 0.18 7.3 26.7 11.3 43.0 0.2 2.3 2.1 7.4 New Zealand 59,012 46,099 0.21 0.22 3.2 21.7 5.2 57.3 0.1 4.6 .. .. Nicaragua 9,647 .. 0.28 .. .. .. 5.1 .. .. .. .. .. Niger 372 .. 0.19 .. .. .. .. .. .. .. 4.7 .. Nigeria 72,082 82,477 0.17 0.17 1.4 15.4 11.3 40.2 0.1 23.5 3.0 5.1 Norway 67,897 55,439 0.19 0.20 8.7 31.7 4.9 42.9 0.1 1.4 3.8 6.5 Oman .. 5,789 .. 0.16 6.1 13.1 6.9 50.4 0.8 14.1 0.3 5.9 Pakistan 75,125 100,821 0.17 0.18 11.6 7.0 8.1 39.9 0.2 30.3 0.5 2.1 Panama 8,121 11,462 0.26 0.31 1.6 13.7 3.8 74.6 0.2 4.2 .. .. Papua New Guinea 4,365 .. 0.22 .. .. .. 0.9 .. .. .. 0.3 .. Paraguay .. 3,250 .. 0.28 2.3 9.9 6.0 73.6 0.3 6.7 2.0 .. Peru 50,367 52,644 0.18 0.21 8.1 13.5 10.5 52.8 0.2 11.7 2.0 2.8 Philippines 182,052 201,952 0.19 0.18 5.2 9.8 7.3 54.5 0.2 16.4 2.6 4.3 Poland 580,869 388,153 0.14 0.16 13.8 6.2 6.8 48.8 0.4 13.6 5.4 5.1 Portugal 105,441 121,013 0.15 0.14 4.0 17.4 4.5 33.6 0.4 27.4 1.4 11.1 Puerto Rico 24,034 15,367 0.16 0.14 1.9 14.9 19.5 34.4 0.2 15.5 .. .. 2004 World Development Indicators 137 3.6 Water pollution Emissions Industry shares of emissions of organic water pollutants of organic water pollutants % of total kilograms Stone, kilograms per day Primary Paper Food and ceramics, per day per worker metals and pulp Chemicals beverages and glass Textiles Wood Other 1980 2000 a 1980 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a 2000 a Romania 343,145 333,168 0.12 0.14 17.1 6.7 8.3 34.3 0.3 18.5 4.8 9.4 Russian Federation .. 1,485,833 .. 0.16 17.7 7.4 9.3 46.8 0.3 6.9 2.1 9.5 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 18,181 24,436 0.12 0.14 4.4 15.9 5.8 45.1 1.0 3.8 2.0 6.8 Senegal 9,865 6,643 0.31 0.36 0.0 6.6 4.0 87.0 0.1 1.8 0.2 0.7 Serbia and Montenegro .. 101,535 .. 0.16 9.5 12.0 8.0 46.9 0.3 13.3 2.2 7.7 Sierra Leone 1,612 4,170 0.24 0.32 .. 9.6 5.5 82.3 0.1 2.0 2.2 .. Singapore 28,558 32,119 0.10 0.09 1.4 26.2 16.0 21.6 0.1 4.0 1.4 28.6 Slovak Republic .. 57,970 .. 0.15 17.2 12.7 7.9 37.5 0.3 11.9 2.7 9.9 Slovenia .. 38,601 .. 0.17 32.3 15.6 8.5 24.4 0.2 11.1 2.1 5.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 237,599 234,012 0.17 0.17 13.7 16.3 9.1 40.3 0.2 10.2 3.4 6.8 Spain 376,253 374,589 0.16 0.15 6.7 19.8 8.9 42.5 0.3 9.3 4.0 8.6 Sri Lanka 30,086 83,058 0.18 0.18 0.6 7.4 9.8 52.6 0.2 29.8 1.1 .. Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 2,826 2,009 0.26 0.23 .. 79.8 0.3 .. 0.2 16.5 2.0 .. Sweden 130,439 103,913 0.15 0.14 11.3 35.0 7.8 26.6 0.1 1.3 3.0 14.9 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 15,115 0.19 0.20 4.1 1.5 8.3 69.8 0.9 19.4 0.2 .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 21,084 35,155 0.21 0.25 1.5 9.4 4.9 69.3 0.1 14.0 1.5 1.4 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 .. .. .. 2.3 .. .. .. .. .. Trinidad and Tobago 7,835 11,787 0.18 0.28 4.4 10.9 18.3 72.6 0.1 2.9 1.3 .. Tunisia 20,294 46,052 0.16 0.16 5.8 8.0 6.5 41.1 0.4 33.5 1.5 3.3 Turkey 160,173 170,685 0.20 0.17 11.0 7.1 7.6 44.5 0.3 23.6 1.1 5.0 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine .. 499,886 .. 0.18 22.8 3.4 6.6 51.6 0.3 5.8 1.6 7.9 United Arab Emirates 4,524 .. 0.15 .. .. .. .. .. .. .. .. .. United Kingdom 964,510 569,736 0.15 0.15 7.2 30.4 10.0 32.1 0.2 5.6 2.5 12.0 United States 2,742,993 1,968,196 0.14 0.12 10.5 11.0 13.8 38.4 0.2 7.1 4.1 14.9 Uruguay 34,270 17,972 0.21 0.28 1.2 11.1 6.7 71.2 0.1 8.5 0.7 1.8 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 84,797 94,175 0.20 0.21 13.7 10.4 9.8 53.1 0.3 7.5 1.5 3.3 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. 7,823 .. 0.25 0.0 9.1 12.9 71.1 0.3 4.9 1.0 0.9 Zambia 13,605 11,433 0.23 0.22 3.4 10.8 6.9 63.6 0.2 9.3 2.9 2.4 Zimbabwe 32,681 26,810 0.20 0.19 5.2 10.2 4.6 54.2 0.3 16.3 2.8 3.1 Note: Industry shares may not sum to 100 percent because data may be from different years. a. Data refer to any year from 1993 to 2000. 138 2004 World Development Indicators ENVIRONMENT 3.6 Water pollution About the data Definitions Emissions of organic pollutants from industrial activ- water pollution are more widely understood and much · Emissions of organic water pollutants are meas- ities are a major cause of degradation of water qual- less expensive than those for air pollution. ured in terms of biochemical oxygen demand, which ity. Water quality and pollution levels are generally Hettige, Mani, and Wheeler (1998) used plant- and refers to the amount of oxygen that bacteria in water measured in terms of concentration or load--the sector-level information on emissions and employ- will consume in breaking down waste. This is a stan- rate of occurrence of a substance in an aqueous ment from 13 national environmental protection agen- dard water treatment test for the presence of organic solution. Polluting substances include organic mat- cies and sector-level information on output and pollutants. Emissions per worker are total emissions ter, metals, minerals, sediment, bacteria, and toxic employment from the United Nations Industrial divided by the number of industrial workers. chemicals. This table focuses on organic water pol- Development Organization (UNIDO). Their econometric · Industry shares of emissions of organic water pol- lution resulting from industrial activities. Because analysis found that the ratio of BOD to employment in lutants refer to emissions from manufacturing activi- water pollution tends to be sensitive to local condi- each industrial sector is about the same across ties as defined by two-digit divisions of the tions, the national-level data in the table may not countries. This finding allowed the authors to estimate International Standard Industrial Classification (ISIC) reflect the quality of water in specific locations. BOD loads across countries and over time. The esti- revision 2: primary metals (ISIC division 37), paper The data in the table come from an international mated BOD intensities per unit of employment were and pulp (34), chemicals (35), food and beverages study of industrial emissions that may be the first to multiplied by sectoral employment numbers from (31), stone, ceramics, and glass (36), textiles (32), include data from developing countries (Hettige, UNIDO's industry database for 1980­98. The esti- wood (33), and other (38 and 39). Mani, and Wheeler 1998). These data were updated mates of sectoral emissions were then totaled to get through 2000 by the World Bank's Development daily emissions of organic water pollutants in kilo- Research Group. Unlike estimates from earlier stud- grams per day for each country and year. The data in ies based on engineering or economic models, these the table were derived by updating these estimates estimates are based on actual measurements of through 2000. plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and the most reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste in terms of the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD. Data on water pollution are more readily available than other emissions data because most industrial pollution control programs start by regulating emis- sions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring 3.6a High- and middle-income countries account for most water pollution from organic waste Emissions of organic water pollutants, 1998 Data sources Low income, excluding India The data come from a 1998 study by Hemamala India 6% 7% Hettige, Muthukumara Mani, and David Wheeler, Middle High "Industrial Pollution in Economic Development: income, income excluding 36% Kuznets Revisited" (available at http://www. China 20% worldbank.org/nipr). These data were updated through 2000 by the World Bank's Development China 31% Research Group using the same methodology as the initial study. Sectoral employment numbers are from UNIDO's industry database. Source: World Bank staff estimates. 2004 World Development Indicators 139 3.7 Energy production and use Total energy Energy use Energy use production per capita Total Combustible thousands of thousands of renewables average average metric tons of metric tons of and waste annual kg of oil annual oil equivalent oil equivalent % of total % growth equivalent % growth 1990 2001 1990 2001 1990 2001 1990­2001 1990 2001 1990­2001 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 2,449 673 2,662 1,715 13.6 7.5 ­1.6 812 548 ­1.0 Algeria 104,559 144,330 23,926 29,438 0.1 0.3 1.6 956 955 ­0.3 Angola 28,652 43,559 6,280 8,454 68.8 68.7 2.7 672 663 ­0.1 Argentina 47,384 82,862 45,039 57,601 3.8 5.2 2.9 1,395 1,593 1.6 Armenia 263 602 4,298 2,297 0.0 0.0 ­2.3 1,231 744 ­0.9 Australia 157,712 250,436 87,536 115,627 4.5 4.5 2.7 5,130 5,956 1.5 Austria 8,080 9,717 25,042 30,721 9.8 10.4 1.6 3,241 3,825 1.3 Azerbaijan 18,150 19,581 16,675 11,582 0.0 0.0 ­4.0 2,259 1,428 ­5.0 Bangladesh 10,747 16,200 12,937 20,410 53.0 37.9 4.3 118 153 2.5 Belarus 4,103 3,533 39,703 24,415 1.5 4.3 ­3.9 3,886 2,449 ­3.6 Belgium 12,490 12,967 48,685 59,001 1.4 1.6 1.8 4,885 5,735 1.6 Benin 1,774 1,483 1,678 2,028 93.2 71.2 1.5 356 318 ­1.2 Bolivia 4,923 6,938 2,774 4,271 27.2 16.8 5.7 422 496 3.1 Bosnia and Herzegovina 3,642 3,277 4,474 4,359 3.6 4.1 5.1 1,086 1,074 4.7 Botswana .. .. .. .. .. .. .. .. .. .. Brazil 97,069 145,933 132,985 185,083 31.0 23.4 3.6 899 1,074 2.1 Bulgaria 9,613 10,297 28,820 19,476 0.6 2.8 ­2.6 3,306 2,428 ­2.0 Burkina Faso .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. Cameroon 12,090 12,485 5,031 6,445 75.9 79.0 2.4 431 417 ­0.2 Canada 273,680 379,207 209,090 248,184 3.9 4.2 1.8 7,524 7,985 0.8 Central African Republic .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile 7,641 8,673 13,630 23,801 19.6 17.7 6.1 1,041 1,545 4.6 China 902,689 1,138,617 870,441 1,139,369 23.0 19.0 2.8 767 896 1.8 Hong Kong, China 43 48 10,662 16,278 0.5 0.3 3.9 1,869 2,421 2.1 Colombia 48,445 73,920 25,014 29,245 23.3 17.9 1.5 715 680 ­0.4 Congo, Dem. Rep. 12,027 15,707 11,911 15,039 83.9 93.0 2.2 319 300 ­0.5 Congo, Rep. 9,005 13,668 1,056 931 69.4 64.9 ­2.8 423 262 ­5.8 Costa Rica 1,032 1,733 2,025 3,481 36.6 11.0 4.8 664 899 2.6 Côte d'Ivoire 3,395 6,177 4,420 6,497 71.9 66.6 4.1 375 402 1.1 Croatia 4,346 3,720 6,714 7,904 3.8 3.7 2.1 1,405 1,771 3.1 Cuba 6,271 6,656 16,524 13,651 33.7 24.4 ­0.5 1,555 1,216 ­1.0 Czech Republic 38,474 30,489 47,401 41,396 1.2 1.7 ­1.0 4,574 4,049 ­0.9 Denmark 9,835 27,171 17,609 19,783 6.6 9.2 0.7 3,426 3,692 0.3 Dominican Republic 1,031 1,485 4,139 7,810 24.2 18.4 6.5 586 921 4.7 Ecuador 16,400 22,872 6,054 8,727 13.6 8.4 3.4 590 692 1.4 Egypt, Arab Rep. 54,869 59,301 32,024 48,012 3.3 2.8 3.9 611 737 1.9 El Salvador 1,722 2,329 2,535 4,269 48.2 32.9 4.5 496 677 2.4 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia 4,118 2,989 6,271 4,697 2.9 11.5 ­2.2 4,091 3,444 ­1.0 Ethiopia 14,158 18,000 15,151 19,161 92.8 93.1 2.4 296 291 0.1 Finland 12,081 15,156 29,171 33,815 15.6 19.7 1.7 5,851 6,518 1.3 France 111,278 132,709 227,114 265,570 4.8 4.5 1.2 4,003 4,487 0.8 Gabon 14,630 14,788 1,287 1,702 57.7 55.7 2.4 1,350 1,322 ­0.4 Gambia, The .. .. .. .. .. .. .. .. .. .. Georgia 1,470 1,265 8,762 2,413 7.7 .. ­11.7 1,612 462 ­11.4 Germany 186,157 133,745 356,218 351,092 1.3 2.3 ­0.0 4,485 4,264 ­0.3 Ghana 4,392 5,995 5,337 8,180 73.1 66.3 4.1 349 410 1.6 Greece 9,200 9,965 22,181 28,704 4.0 3.5 2.4 2,183 2,710 2.1 Guatemala 3,390 5,230 4,477 7,313 67.9 53.3 4.8 512 626 2.1 Guinea .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 1,253 1,542 1,585 2,088 76.5 72.7 3.5 245 257 1.3 140 2004 World Development Indicators ENVIRONMENT 3.7 Energy production and use Total energy Energy use Energy use production per capita Total Combustible thousands of thousands of renewables average average metric tons of metric tons of and waste annual kg of oil annual oil equivalent oil equivalent % of total % growth equivalent % growth 1990 2001 1990 2001 1990 2001 1990­2001 1990 2001 1990­2001 Honduras 1,694 1,535 2,416 3,236 62.0 41.1 2.6 496 488 ­0.2 Hungary 14,239 10,824 28,467 25,340 1.3 1.6 ­0.7 2,746 2,487 ­0.4 India 333,978 438,099 363,153 531,453 48.4 38.5 3.6 427 515 1.8 Indonesia 161,518 234,314 92,815 152,304 43.9 31.5 4.4 521 729 2.9 Iran, Islamic Rep. 179,738 246,644 68,775 120,000 1.0 0.7 5.2 1,264 1,860 3.6 Iraq 106,715 123,296 20,841 28,476 0.1 0.1 4.2 1,153 1,202 1.6 Ireland 3,467 1,729 10,575 14,981 1.0 1.2 3.6 3,016 3,876 2.7 Israel 433 685 12,112 21,193 0.0 0.0 5.3 2,599 3,291 2.4 Italy 25,547 26,264 152,552 171,998 0.6 1.4 1.2 2,690 2,981 1.0 Jamaica 485 487 2,943 4,009 16.2 11.9 3.0 1,231 1,545 2.2 Japan 73,209 104,006 436,523 520,729 1.0 1.0 1.8 3,534 4,099 1.5 Jordan 162 280 3,499 5,116 0.1 0.1 3.7 1,104 1,017 ­0.1 Kazakhstan 89,007 83,752 79,661 40,324 0.1 0.2 ­7.7 4,823 2,705 ­6.6 Kenya 10,272 12,644 12,479 15,377 78.4 78.2 2.2 534 500 ­0.4 Korea, Dem. Rep. 28,725 19,251 32,874 20,440 2.9 4.9 ­5.1 1,647 914 ­6.1 Korea, Rep. 21,908 34,207 92,578 194,780 0.3 1.2 7.1 2,160 4,114 6.1 Kuwait 48,519 108,851 8,413 16,368 0.1 .. 9.9 3,959 7,195 6.3 Kyrgyz Republic 1,818 1,353 5,066 2,235 0.1 0.2 ­6.4 1,114 451 ­7.5 Lao PDR .. .. .. .. .. .. .. .. .. .. Latvia 794 1,717 5,979 4,297 8.1 .. ­3.4 2,272 1,822 ­2.2 Lebanon 143 161 2,309 5,434 4.5 2.3 8.0 635 1,239 6.1 Lesotho .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. Libya 73,173 74,363 11,541 15,992 1.1 0.9 2.1 2,680 2,994 0.1 Lithuania 4,189 4,144 11,077 8,023 1.5 8.2 ­2.5 2,994 2,304 ­1.9 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. Malaysia 48,727 77,623 22,455 51,608 9.5 4.6 7.2 1,234 2,168 4.6 Mali .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. Mexico 194,454 230,236 124,028 152,273 5.9 5.4 1.8 1,490 1,532 0.2 Moldova 58 62 6,884 3,140 0.5 1.9 ­8.3 1,582 735 ­8.1 Mongolia .. .. .. .. .. .. .. .. .. .. Morocco 773 583 6,725 11,006 4.7 4.0 4.4 280 377 2.6 Mozambique 6,846 7,560 7,203 7,687 94.4 88.3 0.0 509 425 ­2.2 Myanmar 10,651 15,275 10,683 12,159 84.4 77.4 1.7 264 252 0.1 Namibia 218 294 652 1,159 16.0 15.2 5.3 445 596 2.5 Nepal 5,501 7,338 5,806 8,416 93.4 85.2 3.4 320 357 0.9 Netherlands 60,316 60,437 66,491 77,214 1.1 1.6 1.1 4,447 4,814 0.5 New Zealand 12,256 14,932 14,016 18,294 4.9 6.4 2.8 4,065 4,714 1.6 Nicaragua 1,495 1,540 2,118 2,792 53.3 48.2 2.6 554 536 ­0.2 Niger .. .. .. .. .. .. .. .. .. .. Nigeria 150,453 207,024 70,905 95,444 79.8 77.5 2.5 737 735 ­0.3 Norway 120,304 226,570 21,492 26,607 4.8 5.6 1.9 5,066 5,896 1.3 Oman 38,312 64,534 4,562 9,984 .. .. 5.4 2,804 4,029 1.8 Pakistan 34,360 48,606 43,424 64,506 43.2 37.2 3.8 402 456 1.3 Panama 612 678 1,490 3,180 28.3 14.6 6.1 621 1,098 4.3 Papua New Guinea .. .. .. .. .. .. .. .. .. .. Paraguay 4,578 6,077 3,089 3,756 72.1 58.0 2.8 744 697 0.4 Peru 10,596 9,363 9,952 12,113 26.9 18.7 2.6 461 460 0.8 Philippines 15,901 20,006 28,292 42,151 34.8 23.1 4.2 463 538 1.9 Poland 99,228 79,861 99,847 90,570 2.2 4.8 ­0.7 2,619 2,344 ­0.8 Portugal 2,805 3,396 17,158 24,732 11.0 8.3 3.7 1,734 2,435 3.4 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 141 3.7 Energy production and use Total energy Energy use Energy use production per capita Total Combustible thousands of thousands of renewables average average metric tons of metric tons of and waste annual kg of oil annual oil equivalent oil equivalent % of total % growth equivalent % growth 1990 2001 1990 2001 1990 2001 1990­2001 1990 2001 1990­2001 Romania 40,834 28,222 62,403 36,841 1.0 6.4 ­3.8 2,689 1,644 ­3.5 Russian Federation 1,118,707 996,161 774,823 621,349 1.6 1.1 ­2.4 5,211 4,293 ­2.1 Rwanda .. .. .. .. .. .. .. .. .. .. Saudi Arabia 368,753 476,831 60,834 110,586 0.0 0.0 4.3 3,850 5,195 1.6 Senegal 1,362 1,765 2,238 3,179 60.6 55.5 3.4 305 325 0.8 Serbia and Montenegro 11,835 10,774 15,002 16,061 5.0 5.0 1.8 1,435 1,508 1.6 Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore .. 64 13,357 29,158 .. .. 5.8 4,384 7,058 2.8 Slovak Republic 5,273 6,550 21,426 18,717 0.8 1.9 ­0.9 4,056 3,480 ­1.1 Slovenia 2,765 3,161 5,008 6,838 5.3 6.6 3.3 2,508 3,459 3.3 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 114,534 145,287 91,229 107,738 11.4 11.7 1.9 2,592 2,404 ­0.3 Spain 34,648 33,022 91,209 127,381 4.5 3.2 3.1 2,349 3,127 2.7 Sri Lanka 4,191 4,462 5,516 7,923 71.0 52.9 3.8 339 423 2.5 Sudan 8,775 21,551 10,627 13,525 81.8 80.3 4.2 426 421 1.7 Swaziland .. .. .. .. .. .. .. .. .. .. Sweden 29,754 34,377 46,667 51,054 11.8 16.0 0.7 5,452 5,740 0.4 Switzerland 9,831 12,367 25,106 28,019 4.1 6.0 0.8 3,740 3,875 0.2 Syrian Arab Republic 22,570 34,377 11,928 13,955 0.0 0.0 2.6 984 841 ­0.3 Tajikistan 1,553 1,267 9,087 3,036 .. .. ­8.9 1,631 487 ­10.1 Tanzania 9,063 13,001 9,808 13,917 91.0 91.5 3.2 385 404 0.4 Thailand 25,908 40,059 43,215 75,542 33.9 17.1 5.2 777 1,235 4.4 Togo 778 1,056 1,001 1,422 77.7 74.3 4.5 290 305 1.6 Trinidad and Tobago 12,612 18,385 5,795 8,693 0.8 0.3 3.8 4,770 6,708 3.2 Tunisia 6,127 6,886 5,536 8,243 18.7 15.2 3.9 679 852 2.3 Turkey 25,857 26,154 53,005 72,458 13.6 8.7 3.8 944 1,057 2.0 Turkmenistan 48,822 50,443 11,307 15,309 .. .. 2.4 2,912 3,244 0.3 Uganda .. .. .. .. .. .. .. .. .. .. Ukraine 110,170 83,428 218,376 141,577 0.1 0.2 ­4.5 4,187 2,884 ­3.8 United Arab Emirates 108,472 144,566 17,611 32,624 0.2 0.1 5.3 9,550 10,860 1.2 United Kingdom 207,007 261,939 212,176 235,158 0.3 1.0 0.8 3,686 3,982 0.6 United States 1,650,408 1,711,814 1,927,572 2,281,414 3.2 3.1 1.7 7,728 7,996 0.4 Uruguay 1,149 1,211 2,251 2,703 24.3 15.6 2.4 725 809 1.7 Uzbekistan 40,461 55,630 44,994 50,650 .. .. 1.7 2,098 2,029 ­0.0 Venezuela, RB 148,854 216,020 43,918 54,856 1.2 1.0 2.2 2,252 2,227 0.1 Vietnam 24,988 50,346 24,690 39,356 76.5 58.3 4.5 373 495 2.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 9,792 22,687 2,626 3,560 2.9 2.2 2.2 221 197 ­1.1 Zambia 4,923 6,052 5,469 6,423 73.5 81.5 1.3 703 638 ­1.0 Zimbabwe 8,250 8,531 9,084 9,882 52.0 57.4 0.7 887 769 ­1.3 World 8,711,744 t 10,140,706 t 8,572,434 t 10,009,627 t 10.9 w 10.6 w 1.5 w 1,677 w 1,686 w 0.1 w Low income 980,692 1,330,614 923,451 1,233,424 52.0 46.2 2.7 480 518 0.7 Middle income 4,481,928 4,952,955 3,400,356 3,641,578 10.8 10.4 0.7 1,417 1,339 ­0.4 Lower middle income 3,356,996 3,544,563 2,825,424 2,929,430 12.0 11.9 0.4 1,337 1,226 ­0.7 Upper middle income 1,124,932 1,408,392 572,246 709,943 4.1 4.2 1.9 2,027 2,176 0.6 Low & middle income 5,462,620 6,283,569 4,311,187 4,850,856 18.9 18.9 1.1 1,012 966 ­0.4 East Asia & Pacific 1,219,107 1,595,491 1,138,460 1,550,628 26.4 21.0 3.1 715 854 1.9 Europe & Central Asia 1,860,581 1,516,768 1,716,074 1,273,037 1.9 2.3 ­3.0 3,681 2,684 ­3.1 Latin America & Carib. 613,090 845,705 455,450 595,827 18.2 14.5 2.8 1,051 1,151 1.2 Middle East & N. Africa 965,686 1,254,273 257,289 413,276 1.4 1.1 4.1 1,087 1,383 2.0 South Asia 388,777 514,705 437,361 642,291 48.9 39.2 3.6 391 469 1.7 Sub-Saharan Africa 415,379 556,627 321,164 405,176 56.7 57.6 2.3 693 661 ­0.3 High income 3,249,124 3,857,137 4,287,850 5,187,992 2.9 3.0 1.8 4,847 5,423 1.1 Europe EMU 466,100 439,167 1,049,967 1,189,043 3.1 3.5 1.1 3,580 3,904 0.8 142 2004 World Development Indicators ENVIRONMENT 3.7 Energy production and use About the data Definitions 3.7a In developing countries growth in energy use is close- · Total energy production refers to forms of primary ly related to growth in the modern sectors--industry, Energy use varies by country, even among energy--petroleum (crude oil, natural gas liquids, the five largest energy users motorized transport, and urban areas--but energy and oil from nonconventional sources), natural gas, use also reflects climatic, geographic, and economic Total energy use (millions of metric tons of oil equivalent) solid fuels (coal, lignite, and other derived fuels), and factors (such as the relative price of energy). Energy 2,500 combustible renewables and waste--and primary 1990 2001 use has been growing rapidly in low- and middle- electricity, all converted into oil equivalents (see 2,000 income countries, but high-income countries still use About the data). · Energy use refers to apparent more than five times as much on a per capita basis. 1,500 consumption, which is equal to indigenous produc- Energy data are compiled by the International Energy tion plus imports and stock changes, minus exports 1,000 Agency (IEA). IEA data for countries that are not mem- and fuels supplied to ships and aircraft engaged in bers of the Organisation for Economic Co-operation international transport (see About the data). 500 and Development (OECD) are based on national ener- · Combustible renewables and waste comprise gy data adjusted to conform to annual questionnaires 0 solid biomass, liquid biomass, biogas, industrial United China Russian India Japan completed by OECD member governments. States Federation waste, and municipal waste, measured as a per- Total energy use refers to the use of domestic pri- centage of total energy use. Energy use per capita (thousands of kg of oil equivalent) mary energy before transformation to other end-use 8 fuels (such as electricity and refined petroleum prod- 7 ucts). It includes energy from combustible renew- 6 ables and waste--solid biomass and animal 5 products, gas and liquid from biomass, and industri- 4 al and municipal waste. Biomass is defined as any 3 plant matter used directly as fuel or converted into 2 fuel, heat, or electricity. (The data series published in 1 World Development Indicators 1998 and earlier edi- 0 tions did not include energy from combustible renew- United China Russian India Japan States Federation ables and waste.) Data for combustible renewables Source: Table 3.7. and waste are often based on small surveys or other incomplete information. Thus the data give only a 3.7b broad impression of developments and are not strict- ly comparable between countries. The IEA reports People in high-income countries use more than five times as much energy as do people (see Data sources) include country notes that in low-income countries explain some of these differences. All forms of energy--primary energy and primary electricity--are Energy use per capita (thousands of kg of oil equivalent) converted into oil equivalents. To convert nuclear 6 1990 2001 electricity into oil equivalents, a notional thermal effi- 5 ciency of 33 percent is assumed; for hydroelectric 4 power 100 percent efficiency is assumed. 3 2 1 0 Low Lower Upper High income middle middle income income income Source: Table 3.7. Data sources The data on energy production and use come from IEA electronic files. The IEA's data are pub- lished in its annual publications, Energy Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries. 2004 World Development Indicators 143 3.8 Energy efficiency, dependency, and emissions GDP per unit Net energy Carbon dioxide emissions of energy use imports a 1995 PPP $ kg per per kg % of Total Per capita 1995 PPP $ oil equivalent energy use million metric tons metric tons of GDP 1990 2001 1990 2001 1990 2000 1990 2000 1990 2000 Afghanistan .. .. .. .. 2.6 0.9 0.1 0.0 .. .. Albania 3.5 6.4 8 61 7.3 2.9 2.2 0.9 0.8 0.3 Algeria 5.1 5.0 ­337 ­390 80.4 89.4 3.2 2.9 0.7 0.6 Angola 2.7 2.2 ­356 ­415 4.6 6.4 0.5 0.5 0.3 0.4 Argentina 5.8 6.8 ­5 ­44 109.7 138.2 3.4 3.9 0.4 0.3 Armenia 1.2 3.3 94 74 3.7 3.5 1.1 1.1 0.7 0.5 Australia 3.7 4.2 ­80 ­117 266.0 344.8 15.6 18.0 0.8 0.7 Austria 6.6 6.8 68 68 57.5 60.8 7.4 7.6 0.4 0.3 Azerbaijan 1.4 1.7 ­9 ­69 47.1 29.0 6.4 3.6 2.0 1.6 Bangladesh 9.1 9.7 17 21 15.4 29.3 0.1 0.2 0.1 0.2 Belarus 1.1 1.9 90 86 94.6 59.2 9.3 5.9 2.1 1.3 Belgium 4.1 4.3 74 78 100.5 102.2 10.1 10.0 0.5 0.4 Benin 2.1 2.9 ­6 27 0.6 1.6 0.1 0.3 0.2 0.3 Bolivia 4.5 4.3 ­77 ­62 5.5 11.1 0.8 1.3 0.4 0.6 Bosnia and Herzegovina .. 4.8 19 25 4.7 19.3 1.1 4.8 .. 1.0 Botswana .. .. .. .. 2.2 3.9 1.7 2.3 0.3 0.3 Brazil 6.6 6.2 27 21 202.6 307.5 1.4 1.8 0.2 0.3 Bulgaria 1.9 2.5 67 47 75.3 42.3 8.6 5.2 1.3 0.9 Burkina Faso .. .. .. .. 1.0 1.0 0.1 0.1 0.2 0.1 Burundi .. .. .. .. 0.2 0.2 0.0 0.0 0.0 0.1 Cambodia .. .. .. .. 0.5 0.5 0.0 0.0 0.0 0.0 Cameroon 4.4 4.2 ­140 ­94 1.5 6.5 0.1 0.4 0.1 0.3 Canada 2.9 3.2 ­31 ­53 428.8 435.9 15.4 14.2 0.7 0.5 Central African Republic .. .. .. .. 0.2 0.3 0.1 0.1 0.1 0.1 Chad .. .. .. .. 0.1 0.1 0.0 0.0 0.0 0.0 Chile 5.0 5.6 44 64 35.3 59.5 2.7 3.9 0.5 0.5 China 2.0 4.2 ­4 0 2,401.7 2,790.5 2.1 2.2 1.4 0.6 Hong Kong, China 9.7 9.9 100 100 26.2 33.1 4.6 5.0 0.3 0.2 Colombia 7.0 7.9 ­94 ­153 55.9 58.5 1.6 1.4 0.3 0.3 Congo, Dem. Rep. 4.2 1.9 ­1 ­4 4.1 2.7 0.1 0.1 0.1 0.1 Congo, Rep. 2.4 3.3 ­753 ­1,368 2.0 1.8 0.8 0.5 0.8 0.6 Costa Rica 8.5 8.3 49 50 2.9 5.4 1.0 1.4 0.2 0.2 Côte d'Ivoire 4.1 3.7 23 5 11.9 10.5 1.0 0.7 0.7 0.4 Croatia 4.3 4.7 35 53 16.8 19.6 3.5 4.4 0.6 0.5 Cuba .. .. 62 51 32.0 30.9 3.0 2.8 .. .. Czech Republic 2.7 3.2 19 26 137.9 118.8 13.4 11.6 1.2 0.9 Denmark 6.4 7.3 44 ­37 50.7 44.6 9.9 8.4 0.4 0.3 Dominican Republic 5.9 5.7 75 81 9.4 25.1 1.3 3.0 0.4 0.6 Ecuador 2.8 4.4 ­171 ­162 16.6 25.5 1.6 2.0 1.0 0.7 Egypt, Arab Rep. 4.3 4.5 ­71 ­24 75.4 142.2 1.4 2.2 0.6 0.7 El Salvador 6.6 6.2 32 45 2.6 6.7 0.5 1.1 0.2 0.3 Eritrea .. .. .. .. .. 0.6 .. 0.1 .. 0.2 Estonia 1.7 2.8 34 36 24.9 16.0 16.2 11.7 2.4 1.3 Ethiopia 1.8 2.2 7 6 3.0 5.6 0.1 0.1 0.1 0.1 Finland 3.4 3.6 59 55 52.9 53.4 10.6 10.3 0.5 0.4 France 5.0 5.3 51 50 357.5 362.4 6.3 6.2 0.3 0.3 Gabon 4.3 4.2 ­1,037 ­769 6.7 3.5 7.0 2.8 1.2 0.5 Gambia, The .. .. .. .. 0.2 0.3 0.2 0.2 0.1 0.1 Georgia 1.3 4.2 83 48 15.1 6.2 2.8 1.2 1.4 0.6 Germany 4.7 5.6 48 62 890.2 785.5 11.1 9.6 0.5 0.4 Ghana 4.1 4.3 18 27 3.5 5.9 0.2 0.3 0.2 0.2 Greece 5.7 5.8 59 65 72.2 89.6 7.1 8.5 0.6 0.6 Guatemala 6.1 5.7 24 28 5.1 9.9 0.6 0.9 0.2 0.2 Guinea .. .. .. .. 1.0 1.3 0.2 0.2 0.1 0.1 Guinea-Bissau .. .. .. .. 0.8 0.3 0.8 0.2 1.0 0.3 Haiti 8.3 5.8 21 26 1.0 1.4 0.2 0.2 0.1 0.1 144 2004 World Development Indicators ENVIRONMENT 3.8 Energy efficiency, dependency, and emissions GDP per unit Net energy Carbon dioxide emissions of energy use imports a 1995 PPP $ kg per per kg % of Total Per capita 1995 PPP $ oil equivalent energy use million metric tons metric tons of GDP 1990 2001 1990 2001 1990 2000 1990 2000 1990 2000 Honduras 4.3 4.6 30 53 2.6 4.8 0.5 0.7 0.2 0.3 Hungary 3.7 4.7 50 57 58.5 54.2 5.6 5.4 0.6 0.5 India 3.6 4.4 8 18 675.3 1,070.9 0.8 1.1 0.5 0.5 Indonesia 3.9 3.7 ­74 ­54 165.2 269.6 0.9 1.3 0.5 0.5 Iran, Islamic Rep. 3.3 3.0 ­161 ­106 212.4 310.3 3.9 4.9 0.9 0.9 Iraq .. .. ­412 ­333 49.3 76.3 2.7 3.3 .. .. Ireland 4.6 7.0 67 88 29.8 42.2 8.5 11.1 0.6 0.4 Israel 5.7 5.6 96 97 34.6 63.1 7.4 10.0 0.5 0.6 Italy 7.4 7.8 83 85 398.9 428.2 7.0 7.4 0.4 0.3 Jamaica 2.8 2.1 84 88 8.0 10.8 3.3 4.2 1.0 1.3 Japan 6.0 5.8 83 80 1,070.7 1,184.5 8.7 9.3 0.4 0.4 Jordan 3.2 3.7 95 95 10.2 15.6 3.2 3.2 0.9 0.8 Kazakhstan 0.9 1.7 ­12 ­108 252.7 121.3 15.3 8.1 3.5 2.0 Kenya 1.9 1.8 18 18 5.8 9.4 0.2 0.3 0.2 0.3 Korea, Dem. Rep. .. .. 13 6 244.6 188.9 12.3 8.5 .. .. Korea, Rep. 3.9 3.5 76 82 241.2 427.0 5.6 9.1 0.7 0.7 Kuwait 2.5 2.2 ­477 ­565 42.2 47.9 19.9 21.9 0.9 1.3 Kyrgyz Republic 1.6 3.2 64 39 11.0 4.6 2.4 0.9 1.4 0.7 Lao PDR .. .. .. .. 0.2 0.4 0.1 0.1 0.1 0.1 Latvia 2.5 4.1 87 60 12.7 6.0 4.8 2.5 0.8 0.4 Lebanon 3.7 3.2 94 97 9.1 15.2 2.5 3.5 1.1 0.9 Lesotho .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. 0.5 0.4 0.2 0.1 .. .. Libya .. .. ­534 ­365 37.8 57.1 8.8 10.9 .. .. Lithuania 2.6 3.7 62 48 21.4 11.9 5.8 3.4 0.8 0.4 Macedonia, FYR .. .. .. .. 10.6 11.2 5.5 5.5 0.9 0.9 Madagascar .. .. .. .. 0.9 2.3 0.1 0.1 0.1 0.2 Malawi .. .. .. .. 0.6 0.8 0.1 0.1 0.1 0.1 Malaysia 4.1 3.6 ­117 ­50 55.3 144.4 3.0 6.2 0.6 0.8 Mali .. .. .. .. 0.4 0.6 0.0 0.1 0.1 0.1 Mauritania .. .. .. .. 2.6 3.1 1.3 1.2 1.0 0.8 Mauritius .. .. .. .. 1.2 2.9 1.1 2.4 0.2 0.3 Mexico 4.7 5.3 ­57 ­51 305.4 424.0 3.7 4.3 0.5 0.5 Moldova 1.3 1.7 99 98 20.9 6.6 4.8 1.5 2.4 1.3 Mongolia .. .. .. .. 10.0 7.5 4.7 3.1 2.9 2.2 Morocco 11.0 9.0 89 95 23.5 36.5 1.0 1.3 0.3 0.4 Mozambique .. .. 5 2 1.0 1.2 0.1 0.1 .. .. Myanmar .. .. 0 ­26 4.1 9.1 0.1 0.2 .. .. Namibia 10.4 9.3 67 75 0.0 1.8 0.0 1.0 0.0 0.2 Nepal 3.0 3.5 5 13 0.6 3.4 0.0 0.1 0.0 0.1 Netherlands 4.5 5.2 9 22 150.0 138.9 10.0 8.7 0.5 0.3 New Zealand 3.9 4.0 13 18 23.6 32.1 6.8 8.3 0.4 0.5 Nicaragua 3.4 .. 29 45 2.6 3.7 0.7 0.7 0.4 0.4 Niger .. .. .. .. 1.1 1.2 0.1 0.1 0.2 0.2 Nigeria 1.1 1.1 ­112 ­117 88.7 36.1 0.9 0.3 1.2 0.4 Norway 4.8 5.5 ­460 ­752 31.7 49.9 7.5 11.1 0.3 0.3 Oman 3.8 3.0 ­740 ­546 11.5 19.8 7.1 8.2 0.7 0.7 Pakistan 3.7 3.8 21 25 67.9 104.8 0.6 0.8 0.4 0.4 Panama 6.6 5.1 59 79 3.1 6.3 1.3 2.2 0.3 0.4 Papua New Guinea .. .. .. .. 2.4 2.4 0.6 0.5 0.4 0.2 Paraguay 5.9 6.1 ­48 ­62 2.3 3.7 0.5 0.7 0.1 0.2 Peru 7.7 9.4 ­6 23 21.7 29.5 1.0 1.1 0.3 0.3 Philippines 7.4 6.8 44 53 44.3 77.5 0.7 1.0 0.2 0.3 Poland 2.5 3.9 1 12 347.6 301.3 9.1 7.8 1.4 0.9 Portugal 7.0 6.4 84 86 42.3 59.8 4.3 5.9 0.4 0.4 Puerto Rico .. .. .. .. 11.8 8.7 3.3 2.3 0.3 0.2 2004 World Development Indicators 145 3.8 Energy efficiency, dependency, and emissions GDP per unit Net energy Carbon dioxide emissions of energy use imports a 1995 PPP $ kg per per kg % of Total Per capita 1995 PPP $ oil equivalent energy use million metric tons metric tons of GDP 1990 2001 1990 2001 1990 2000 1990 2000 1990 2000 Romania 2.3 3.4 35 23 155.1 86.3 6.7 3.8 1.1 0.7 Russian Federation 1.5 1.6 ­44 ­60 1,984.0 1,435.1 13.3 9.9 1.7 1.5 Rwanda .. .. .. .. 0.5 0.6 0.1 0.1 0.1 0.1 Saudi Arabia 2.9 2.0 ­506 ­331 177.9 374.3 11.3 18.1 1.0 1.7 Senegal 4.1 4.3 39 44 2.9 4.2 0.4 0.4 0.3 0.3 Serbia and Montenegro .. .. 21 33 130.5 39.5 12.4 3.7 .. .. Sierra Leone .. .. .. .. 0.3 0.6 0.1 0.1 0.1 0.3 Singapore 3.1 2.9 .. 100 41.9 59.0 13.8 14.7 1.0 0.7 Slovak Republic 2.5 3.1 75 65 44.7 35.4 8.4 6.6 1.0 0.6 Slovenia 4.3 4.5 45 54 12.3 14.6 6.2 7.3 0.6 0.5 Somalia .. .. .. .. 0.0 .. 0.0 .. .. .. South Africa 3.4 3.5 ­26 ­35 291.1 327.3 8.3 7.4 0.9 0.9 Spain 6.2 6.0 62 74 211.8 282.9 5.5 7.0 0.4 0.4 Sri Lanka 6.4 7.3 24 44 3.9 10.2 0.2 0.6 0.1 0.2 Sudan 2.3 3.3 17 ­59 3.5 5.2 0.1 0.2 0.1 0.1 Swaziland .. .. .. .. 0.4 0.4 0.6 0.4 0.1 0.1 Sweden 3.7 4.0 36 33 48.5 46.9 5.7 5.3 0.3 0.2 Switzerland 7.1 7.0 61 56 42.7 39.1 6.4 5.4 0.2 0.2 Syrian Arab Republic 2.4 3.5 ­89 ­146 35.8 54.2 3.0 3.3 1.2 1.1 Tajikistan 0.9 1.7 83 58 20.6 4.0 3.7 0.6 2.6 0.9 Tanzania 1.2 1.2 8 7 2.3 4.3 0.1 0.1 0.2 0.3 Thailand 5.3 4.8 40 47 95.7 198.6 1.7 3.3 0.4 0.6 Togo 5.2 4.2 22 26 0.7 1.8 0.2 0.4 0.1 0.3 Trinidad and Tobago 1.4 1.3 ­118 ­111 16.9 26.4 13.9 20.5 2.1 2.4 Tunisia 6.3 7.0 ­11 16 13.3 18.4 1.6 1.9 0.4 0.3 Turkey 5.1 4.9 51 64 143.8 221.6 2.6 3.3 0.5 0.6 Turkmenistan 1.7 1.3 ­332 ­229 28.0 34.6 7.2 7.5 1.4 2.1 Uganda .. .. .. .. 0.8 1.5 0.0 0.1 0.1 0.1 Ukraine 1.6 1.4 50 41 600.0 342.8 11.5 6.9 1.7 1.9 United Arab Emirates .. .. ­516 ­343 60.9 58.9 33.0 21.0 .. .. United Kingdom 5.0 5.8 2 ­11 569.3 567.8 9.9 9.6 0.5 0.4 United States 3.4 4.0 14 25 4,815.9 5,601.5 19.3 19.8 0.7 0.6 Uruguay 8.9 9.7 49 55 3.9 5.4 1.3 1.6 0.2 0.2 Uzbekistan 0.7 0.7 10 ­10 113.3 118.6 5.3 4.8 3.7 3.5 Venezuela, RB 2.4 2.4 ­239 ­294 113.8 157.7 5.8 6.5 1.1 1.2 Vietnam 2.8 4.0 ­1 ­28 22.5 57.5 0.3 0.7 0.3 0.4 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 3.0 3.8 ­273 ­537 9.4 8.4 0.7 0.5 1.2 0.6 Zambia 1.2 1.2 10 6 2.4 1.8 0.3 0.2 0.4 0.3 Zimbabwe 2.8 2.8 9 14 16.6 14.8 1.6 1.2 0.6 0.5 World 3.5 w 4.2 w 0 w 0 w 21,297.5 t 22,994.5 t 4.1 w 3.8 w 0.7 w 0.6 w Low income 3.1 3.6 ­6 ­8 1,653.2 2,066.7 0.8 0.9 0.5 0.5 Middle income 2.7 3.7 ­32 ­36 9,169.8 9,129.1 3.8 3.4 1.0 0.7 Lower middle income 2.5 3.7 ­19 ­21 7,561.2 7,116.3 3.6 3.0 1.1 0.7 Upper middle income 3.7 4.0 ­97 ­98 1,608.5 2,012.0 5.7 6.2 0.8 0.7 Low & middle income 2.8 3.7 ­27 ­30 10,823.2 11,196.2 2.5 2.2 0.9 0.6 East Asia & Pacific .. .. ­7 ­3 3,051.3 3,752.3 1.9 2.1 1.0 0.6 Europe & Central Asia 1.9 2.2 ­8 ­19 4,818.2 3,162.6 10.3 6.7 1.4 1.2 Latin America & Carib. 5.4 5.7 ­35 ­42 962.7 1,357.4 2.2 2.7 0.4 0.4 Middle East & N. Africa 3.8 3.4 ­275 ­203 751.1 1,227.2 3.3 4.2 0.8 0.9 South Asia 3.8 4.6 11 20 765.9 1,220.3 0.7 0.9 0.5 0.4 Sub-Saharan Africa 2.5 2.5 ­29 ­37 471.8 478.8 0.9 0.7 0.6 0.5 High income 4.3 4.7 24 26 10,480.8 11,804.3 11.8 12.4 0.6 0.5 Europe EMU 5.3 5.8 56 63 2,463.9 2,414.6 8.4 8.0 0.4 0.4 a. A negative value indicates that a country is a net exporter. 146 2004 World Development Indicators ENVIRONMENT 3.8 Energy efficiency, dependency, and emissions About the data The ratio of GDP to energy use provides a measure facturing releases about half a metric ton of carbon because of the difficulty of apportioning these fuels of energy efficiency. To produce comparable and con- dioxide for each metric ton of cement produced. among the countries benefiting from that transport. sistent estimates of real GDP across countries rela- The Carbon Dioxide Information Analysis Center tive to physical inputs to GDP--that is, units of (CDIAC), sponsored by the U.S. Department of Definitions energy use--GDP is converted to 1995 constant Energy, calculates annual anthropogenic emissions of international dollars using purchasing power parity carbon dioxide. These calculations are based on data · GDP per unit of energy use is the PPP GDP per (PPP) rates. Differences in this ratio over time and on fossil fuel consumption (from the World Energy kilogram of oil equivalent of energy use. PPP GDP is across countries reflect in part structural changes in Data Set maintained by the United Nations Statistics gross domestic product converted to 1995 constant the economy, changes in the energy efficiency of par- Division) and data on world cement manufacturing international dollars using purchasing power parity ticular sectors, and differences in fuel mixes. (from the Cement Manufacturing Data Set maintained rates. An international dollar has the same purchas- Because commercial energy is widely traded, it is by the U.S. Bureau of Mines). Emissions of carbon ing power over GDP as a U.S. dollar has in the United necessary to distinguish between its production and dioxide are often calculated and reported in terms of States. · Net energy imports are estimated as ener- its use. Net energy imports show the extent to which their content of elemental carbon. For this table these gy use less production, both measured in oil equiva- an economy's use exceeds its domestic production. values were converted to the actual mass of carbon lents. A negative value indicates that the country is High-income countries are net energy importers; mid- dioxide by multiplying the carbon mass by 3.664 (the a net exporter. · Carbon dioxide emissions are dle-income countries have been their main suppliers. ratio of the mass of carbon to that of carbon dioxide). those stemming from the burning of fossil fuels and Carbon dioxide emissions, largely a by-product of Although the estimates of global carbon dioxide the manufacture of cement. They include carbon energy production and use (see table 3.7), account emissions are probably within 10 percent of actual dioxide produced during consumption of solid, liquid, for the largest share of greenhouse gases, which are emissions (as calculated from global average fuel and gas fuels and gas flaring. associated with global warming. Anthropogenic car- chemistry and use), country estimates may have larg- bon dioxide emissions result primarily from fossil er error bounds. Trends estimated from a consistent fuel combustion and cement manufacturing. In com- time series tend to be more accurate than individual bustion, different fossil fuels release different values. Each year the CDIAC recalculates the entire amounts of carbon dioxide for the same level of ener- time series from 1950 to the present, incorporating its gy use. Burning oil releases about 50 percent more most recent findings and the latest corrections to its carbon dioxide than burning natural gas, and burning database. Estimates do not include fuels supplied to coal releases about twice as much. Cement manu- ships and aircraft engaged in international transport 3.8a Per capita emissions of carbon dioxide vary, even among the five largest producers of emissions Carbon dioxide emissions (billions of metric tons) 6 1990 2000 5 4 3 2 1 0 United States China Russian Federation Japan India Per capita carbon dioxide emissions (metric tons) 20 15 Data sources 10 The underlying data on energy production and use are from electronic files of the International 5 Energy Agency. The data on carbon dioxide emis- 0 sions are from the CDIAC, Environmental United States China Russian Federation Japan India Sciences Division, Oak Ridge National Laboratory, in the U.S. state of Tennessee. Source: Table 3.8. 2004 World Development Indicators 147 3.9 Sources of electricity Electricity Access to Sources of electricity a production electricity % of % of total billion kwh population Hydropower Coal Oil Gas Nuclear power 1990 2001 2000 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan .. .. 2.0 .. .. .. .. .. .. .. .. .. .. Albania 3.2 3.7 .. 89.1 96.3 .. .. 10.9 3.7 .. .. .. .. Algeria 16.1 26.6 98.0 0.8 0.3 .. .. 5.4 2.9 93.7 96.8 .. .. Angola 0.8 1.6 12.0 86.2 63.2 .. .. 13.8 36.8 .. .. .. .. Argentina 51.0 90.2 94.6 35.6 41.1 1.3 1.7 9.7 2.0 39.0 46.7 14.3 7.8 Armenia 9.0 5.7 .. 33.8 16.8 .. .. 43.3 .. 22.9 48.6 .. 34.6 Australia 154.3 216.9 .. 9.2 7.6 77.1 78.3 2.7 1.3 10.6 12.1 .. .. Austria 49.3 62.4 .. 63.9 67.0 14.2 12.7 3.8 3.2 15.7 13.6 .. .. Azerbaijan 19.7 19.0 .. 8.9 6.9 .. .. 91.1 28.4 .. 64.8 .. .. Bangladesh 7.7 16.3 20.4 11.4 6.0 .. .. 4.3 9.4 84.3 84.6 .. .. Belarus 37.6 25.0 .. 0.0 0.1 .. .. 52.1 7.7 47.9 92.2 .. .. Belgium 70.3 78.6 .. 0.4 0.6 28.2 16.2 1.9 2.1 7.7 20.1 60.8 59.0 Benin 0.0 0.1 22.0 .. 2.3 .. .. 100.0 97.7 .. .. .. .. Bolivia 2.1 4.0 60.4 55.3 54.6 .. .. 5.3 17.4 37.6 26.1 .. .. Bosnia and Herzegovina 6.5 10.4 .. 52.2 48.8 47.8 50.7 .. 0.5 .. .. .. .. Botswana .. .. 22.0 .. .. .. .. .. .. .. .. .. .. Brazil 222.8 327.9 94.9 92.8 81.7 2.1 3.1 2.5 5.4 0.0 2.6 1.0 4.4 Bulgaria 42.1 43.5 .. 4.5 4.0 35.4 45.4 4.7 1.3 20.6 4.4 34.8 44.9 Burkina Faso .. .. 13.0 .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. 15.8 .. .. .. .. .. .. .. .. .. .. Cameroon 2.7 3.5 20.0 98.5 98.1 .. .. 1.5 1.9 .. .. .. .. Canada 481.9 587.9 .. 61.6 56.7 17.1 20.1 3.4 2.9 2.0 6.1 15.1 13.0 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. .. Chile 18.4 43.9 99.0 55.3 49.4 34.3 16.5 7.6 1.6 1.3 28.7 .. .. China 621.2 1471.7 98.6 20.4 18.9 71.2 76.2 7.9 3.2 0.5 0.4 .. 1.2 Hong Kong, China 28.9 32.4 .. .. .. 98.3 61.5 1.7 0.4 .. 38.1 .. .. Colombia 36.2 43.5 81.0 76.0 73.2 9.8 7.3 1.1 0.2 12.4 18.0 .. .. Congo, Dem. Rep. 5.6 5.7 6.7 99.6 99.7 .. .. 0.4 0.3 .. .. .. .. Congo, Rep. 0.5 0.3 20.9 99.4 99.7 .. .. 0.6 0.3 .. .. .. .. Costa Rica 3.5 6.9 95.7 97.5 81.5 .. .. 2.5 1.4 .. .. .. .. Côte d'Ivoire 2.0 4.9 50.0 72.4 36.7 .. .. 27.6 0.3 .. 63.0 .. .. Croatia 8.9 11.8 .. 48.8 52.7 .. 13.9 35.8 18.0 15.4 15.4 .. .. Cuba 15.0 15.3 97.0 0.6 0.5 .. .. 91.5 93.9 0.2 0.0 .. .. Czech Republic 62.6 74.2 .. 2.3 2.8 71.8 71.7 4.8 0.5 1.0 4.2 20.1 19.9 Denmark 26.0 37.7 .. 0.1 0.1 90.3 47.3 3.7 11.1 2.7 24.6 .. .. Dominican Republic 3.7 10.3 66.8 9.4 5.4 1.2 5.3 88.6 88.9 .. .. .. .. Ecuador 6.3 11.1 80.0 78.5 64.0 .. .. 21.5 36.0 .. .. .. .. Egypt, Arab Rep. 42.3 82.7 93.8 23.5 17.1 .. .. 36.9 14.7 39.6 68.2 .. .. El Salvador 2.2 3.9 70.8 73.5 29.8 .. .. 6.8 45.0 .. .. .. .. Eritrea .. .. 17.0 .. .. .. .. .. .. .. .. .. .. Estonia 11.8 8.5 .. 0.0 0.1 90.0 90.2 4.5 0.5 5.5 9.1 .. .. Ethiopia 1.2 1.8 4.7 88.4 98.7 .. .. 11.6 1.0 .. .. .. .. Finland 54.4 74.5 .. 20.0 17.7 33.0 23.5 3.1 0.9 8.6 15.5 35.3 30.6 France 416.8 546.0 .. 12.8 13.6 8.5 4.5 2.1 1.0 0.7 3.1 75.4 77.1 Gabon 1.0 1.4 31.0 72.1 63.1 .. .. 11.2 20.6 16.4 15.8 .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. .. Georgia 11.2 6.9 .. 58.3 79.9 .. .. 5.0 0.4 36.6 19.7 .. .. Germany 547.6 579.8 .. 3.2 3.5 58.8 51.9 1.9 1.1 7.4 9.9 27.8 29.5 Ghana 5.7 7.9 45.0 100.0 84.1 .. .. .. 15.9 .. .. .. .. Greece 34.8 53.1 .. 5.1 4.0 72.4 66.8 22.3 16.0 0.3 11.6 .. .. Guatemala 2.3 5.9 66.7 76.0 32.9 .. 8.5 9.0 44.1 .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. .. Haiti 0.6 0.5 34.0 76.5 51.7 .. .. 20.6 48.3 .. .. .. .. 148 2004 World Development Indicators ENVIRONMENT 3.9 Sources of electricity Electricity Access to Sources of electricity a production electricity % of % of total billion kwh population Hydropower Coal Oil Gas Nuclear power 1990 2001 2000 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras 2.3 4.0 54.5 98.3 59.5 .. .. 1.7 38.6 .. .. .. .. Hungary 28.4 36.4 .. 0.6 0.5 30.5 24.5 4.8 11.5 15.7 24.3 48.3 38.8 India 289.4 576.5 43.0 24.8 12.8 67.5 78.3 2.7 1.2 2.9 3.6 2.1 3.4 Indonesia 37.0 101.7 53.4 17.6 10.5 28.8 28.9 46.8 23.6 3.8 34.2 .. .. Iran, Islamic Rep. 59.1 130.1 97.9 10.3 3.9 .. .. 37.3 21.2 52.5 74.9 .. .. Iraq 24.0 34.9 95.0 10.8 1.8 .. .. 89.2 98.2 .. .. .. .. Ireland 14.2 24.6 .. 4.9 2.4 57.4 37.6 10.0 21.1 27.7 37.1 .. .. Israel 20.9 43.8 100.0 0.0 0.0 50.1 75.1 49.9 24.8 .. 0.0 .. .. Italy 213.2 271.9 .. 14.8 17.2 16.8 13.5 48.2 27.6 18.6 38.3 .. .. Jamaica 2.5 6.7 90.0 3.6 1.7 .. .. 92.4 96.7 .. .. .. .. Japan 850.8 1,033.2 .. 10.5 8.1 14.5 23.1 29.7 11.3 19.4 24.9 23.8 31.0 Jordan 3.6 7.5 95.0 0.3 0.6 .. .. 87.8 89.2 11.9 10.2 .. .. Kazakhstan 82.7 55.4 .. 8.3 14.6 72.3 69.9 8.8 4.9 10.6 10.6 .. .. Kenya 3.0 4.4 7.9 81.6 54.7 .. .. 7.6 34.4 .. .. .. .. Korea, Dem. Rep. 27.7 20.2 20.0 56.3 52.5 40.1 42.5 3.6 5.0 .. .. .. .. Korea, Rep. 105.4 281.5 .. 6.0 1.5 16.8 39.2 17.9 8.5 9.1 10.8 50.2 39.8 Kuwait 18.5 33.5 100.0 .. .. .. .. 17.1 76.6 82.9 23.4 .. .. Kyrgyz Republic 11.9 13.7 .. 77.4 90.9 9.1 4.5 .. .. 13.6 4.5 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. .. Latvia 3.8 4.3 .. 65.8 66.2 .. 1.0 7.9 2.2 26.3 30.5 .. .. Lebanon 1.5 8.2 95.0 33.3 4.1 .. .. 66.7 95.9 .. .. .. .. Lesotho .. .. 5.0 .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. .. Libya 16.8 21.5 99.8 .. .. .. .. 100.0 100.0 .. .. .. .. Lithuania 16.5 14.4 .. 1.9 2.3 .. .. 7.4 5.0 2.2 13.0 88.5 79.1 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. .. 8.0 .. .. .. .. .. .. .. .. .. .. Malawi .. .. 5.0 .. .. .. .. .. .. .. .. .. .. Malaysia 23.0 71.4 96.9 17.3 9.9 4.7 3.4 55.9 8.6 22.0 78.1 .. .. Mali .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. 100.0 .. .. .. .. .. .. .. .. .. .. Mexico 122.7 209.6 .. 19.1 13.6 6.3 11.1 57.3 44.2 10.6 24.0 2.4 4.2 Moldova 11.2 3.6 .. 2.3 2.0 34.4 3.3 26.4 0.9 36.9 93.7 .. .. Mongolia .. .. 90.0 .. .. .. .. .. .. .. .. .. .. Morocco 9.6 16.1 71.1 12.7 5.4 23.0 72.2 64.4 21.1 .. .. .. .. Mozambique 0.5 8.8 7.2 62.6 99.5 13.9 .. 23.6 0.5 0.2 0.0 .. .. Myanmar 2.5 5.7 5.0 48.1 32.1 1.6 .. 10.9 10.9 39.3 57.0 .. .. Namibia 1.4 1.4 34.0 95.2 96.7 1.5 0.4 3.3 2.9 .. .. .. .. Nepal 0.9 1.9 15.4 99.9 99.0 .. .. 0.1 1.0 .. .. .. .. Netherlands 71.9 93.7 .. 0.1 0.1 38.3 28.5 4.3 3.3 50.9 58.9 4.9 4.2 New Zealand 32.3 39.9 .. 72.3 53.8 1.5 3.7 0.0 .. 17.6 31.2 .. .. Nicaragua 1.4 2.5 48.0 28.8 8.0 .. .. 39.8 82.0 .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 12.6 18.1 40.0 34.9 38.2 0.2 .. 36.5 8.2 28.5 53.6 .. .. Norway 121.6 121.3 .. 99.6 99.3 0.2 0.2 0.0 0.0 .. 0.2 .. .. Oman 4.5 9.7 94.0 .. .. .. .. 18.4 17.7 81.6 82.3 .. .. Pakistan 37.7 72.4 52.9 44.9 26.2 0.1 0.4 20.6 36.0 33.6 34.3 0.8 3.2 Panama 2.7 5.1 76.1 83.2 48.8 .. .. 14.7 50.8 .. .. .. .. Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 27.2 45.4 74.7 99.9 99.9 .. .. 0.0 0.0 .. .. .. .. Peru 13.8 20.8 73.0 75.8 84.7 .. 0.9 21.5 9.7 1.7 3.8 .. .. Philippines 25.2 46.2 87.4 24.0 15.4 7.7 40.6 46.7 21.3 .. 0.1 .. .. Poland 134.4 143.7 .. 1.1 1.6 97.5 95.2 1.2 1.7 0.1 0.9 .. .. Portugal 28.4 46.2 .. 32.3 30.4 32.1 29.5 33.1 20.2 .. 15.6 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 149 3.9 Sources of electricity Electricity Access to Sources of electricity a production electricity % of % of total billion kwh population Hydropower Coal Oil Gas Nuclear power 1990 2001 2000 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania 64.3 53.9 .. 17.7 27.7 28.8 37.2 18.4 10.0 35.1 15.0 .. 10.1 Russian Federation 1008.5 889.3 .. 17.0 19.6 15.3 19.0 9.9 3.4 45.7 42.4 11.9 15.4 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 64.9 137.4 97.7 .. .. .. .. 61.5 63.5 38.5 36.5 .. .. Senegal 0.9 1.7 30.1 .. .. .. .. 98.0 100.0 2.0 0.1 .. .. Serbia and Montenegro 36.5 31.8 .. 31.1 36.5 65.4 60.9 1.9 1.0 1.6 1.6 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. .. Singapore 15.7 33.1 100.0 .. .. .. .. 100.0 52.7 .. 45.1 .. .. Slovak Republic 23.4 31.9 .. 8.0 15.5 32.2 19.5 3.4 2.2 4.9 8.5 51.4 53.7 Slovenia 12.1 14.5 .. 28.2 26.2 36.2 34.0 2.5 0.9 0.2 2.0 32.9 36.3 Somalia .. .. .. .. .. .. .. .. .. .. .. .. .. South Africa 165.4 211.5 66.1 0.6 1.0 94.3 94.0 .. .. .. .. 5.1 5.1 Spain 151.2 234.7 .. 16.8 17.5 40.1 30.6 5.7 10.5 1.0 10.0 35.9 27.1 Sri Lanka 3.2 6.6 62.0 99.8 47.0 .. .. 0.2 53.0 .. .. .. .. Sudan 1.5 2.6 30.0 63.2 48.3 .. .. 36.8 51.7 .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. .. Sweden 146.0 161.7 .. 49.7 49.0 1.2 2.1 0.8 1.7 0.3 0.2 46.7 44.6 Switzerland 54.6 70.5 .. 54.6 58.6 0.1 .. 0.5 0.1 0.6 1.2 43.3 38.0 Syrian Arab Republic 11.6 25.5 85.9 48.6 39.0 .. .. 32.4 19.9 18.9 41.1 .. .. Tajikistan 16.8 14.4 .. 94.7 97.7 .. .. .. .. 5.3 2.3 .. .. Tanzania 1.6 2.8 10.5 95.1 91.7 .. 3.2 4.9 5.1 .. .. .. .. Thailand 44.2 102.4 82.1 11.3 6.2 25.0 19.2 23.5 2.9 40.2 70.5 .. .. Togo 0.1 0.0 9.0 4.6 6.3 .. .. 95.4 93.8 .. .. .. .. Trinidad and Tobago 3.6 5.6 99.0 .. .. .. .. 0.1 0.1 99.0 99.4 .. .. Tunisia 5.8 11.2 94.6 0.8 0.5 .. .. 35.5 9.8 63.7 89.4 .. .. Turkey 57.5 122.7 .. 40.2 19.6 35.1 31.3 6.9 8.5 17.7 40.4 .. .. Turkmenistan 13.2 10.8 .. 0.0 0.0 .. .. .. .. 100.0 100.0 .. .. Uganda .. .. 3.7 .. .. .. .. .. .. .. .. .. .. Ukraine 252.5 172.8 .. 3.2 7.0 26.2 27.5 10.1 4.0 31.2 17.4 29.2 44.1 United Arab Emirates 17.1 40.2 96.0 .. .. .. .. 3.7 7.9 96.3 92.1 .. .. United Kingdom 317.8 383.5 .. 1.6 1.1 65.0 34.8 10.9 1.9 1.6 37.2 20.7 23.5 United States 3,181.5 3,863.8 .. 8.6 5.2 53.4 51.3 4.1 3.5 12.0 16.7 19.2 20.9 Uruguay 7.4 9.3 98.0 94.2 99.4 .. .. 5.1 0.2 .. .. .. .. Uzbekistan 50.9 47.9 .. 12.3 12.5 4.9 4.2 6.9 11.4 75.9 71.8 .. .. Venezuela, RB 59.3 90.0 94.0 62.3 67.2 .. .. 11.5 10.4 26.2 22.4 .. .. Vietnam 8.7 30.6 75.8 61.7 59.5 23.0 10.5 15.2 15.5 0.1 14.5 .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.7 3.1 50.0 .. .. .. .. 100.0 100.0 .. .. .. .. Zambia 8.0 8.2 12.0 99.2 99.4 0.5 0.2 0.3 0.4 .. .. .. .. Zimbabwe 9.4 7.9 39.7 40.5 37.8 59.5 61.8 .. 0.4 .. .. .. .. World 11,696.7 s 15,442.7 s .. w 18.1 w 16.6 w 38.0 w 38.8 w 11.3 w 7.4 w 13.9 w 18.3 w 17.2 w 17.2 w Low income 606.4 1,017.2 37.4 32.8 22.7 38.9 49.2 13.0 8.8 13.1 16.4 1.1 2.1 Middle income 3,756.1 5,133.7 94.0 21.2 22.5 34.7 38.8 15.2 10.1 20.4 20.9 7.6 6.9 Lower middle income 3,091.3 4,098.3 93.9 22.0 23.5 34.9 42.5 13.1 6.7 21.8 19.7 7.2 6.9 Upper middle income 664.8 1,035.4 .. 17.2 18.6 33.6 24.0 24.9 23.4 14.2 25.8 9.5 7.1 Low & middle income 4,362.5 6,150.9 65.1 22.8 22.5 35.3 40.5 14.9 9.8 19.4 20.2 6.7 6.1 East Asia & Pacific 789.5 1,849.8 87.3 21.6 18.3 60.8 65.1 13.2 5.2 3.6 9.5 .. 0.9 Europe & Central Asia 2,143.0 1,855.8 .. 12.9 16.9 31.6 31.0 12.7 4.3 29.5 31.4 12.3 16.0 Latin America & Carib. 607.0 962.2 86.6 63.7 56.5 3.8 4.8 19.0 17.7 9.5 15.5 2.1 3.1 Middle East & N. Africa 261.5 514.6 90.4 10.0 6.1 0.8 2.3 51.7 41.3 37.4 50.3 .. .. South Asia 338.9 673.7 40.8 27.6 14.7 57.7 67.1 4.7 5.6 8.1 8.8 1.9 3.2 Sub-Saharan Africa 222.5 294.8 24.7 18.4 19.7 72.6 69.1 3.4 2.9 1.7 4.4 3.8 3.6 High income 7,334.2 9,291.8 .. 15.4 12.7 39.7 37.6 9.2 5.9 10.6 17.1 23.4 24.5 Europe EMU 1,652.7 2,066.1 .. 11.0 12.4 34.4 27.0 9.5 6.9 8.7 15.3 35.5 35.3 a. Shares may not sum to 100 percent because some sources of generated electricity are not shown. 150 2004 World Development Indicators ENVIRONMENT 3.9 Sources of electricity About the data Use of energy in general, and access to electricity in some sources of generated electricity (such as wind, The IEA makes these estimates in consultation particular, are important in improving people's stan- solar, and geothermal) are not shown. with national statistical offices, oil companies, elec- dard of living. But electricity generation also can dam- The International Energy Agency (IEA) compiles tricity utilities, and national energy experts. The IEA age the environment. Whether such damage occurs data on energy inputs used to generate electricity. occasionally revises its time series to reflect political depends largely on how electricity is generated. For IEA data for countries that are not members of the changes. Since 1990, for example, it has construct- example, burning coal releases twice as much carbon Organisation for Economic Co-operation and ed energy statistics for countries of the former Soviet dioxide--a major contributor to global warming--as Development (OECD) are based on national energy Union. In addition, energy statistics for other coun- does burning an equivalent amount of natural gas data adjusted to conform to annual questionnaires tries have undergone continuous changes in cover- (see About the data for table 3.8). Nuclear energy completed by OECD member governments. In addi- age or methodology as more detailed energy does not generate carbon dioxide emissions, but it tion, estimates are sometimes made to complete accounts have become available in recent years. produces other dangerous waste products. The table major aggregates from which key data are missing, Breaks in series are therefore unavoidable. provides information on electricity production by and adjustments are made to compensate for differ- There is no single internationally accepted definition source. Shares may not sum to 100 percent because ences in definitions. for access to electricity. The definition used here cov- ers access at the household level--that is, the number 3.9a of people who have electricity in their home. It includes Sources of electricity generation have shifted differently in different income groups commercially sold electricity, both on-grid and off-grid. Sources of electricity generation, by income group (% of total production) For countries where access to electricity has been assessed through surveys by government agencies, Low-income countries 1990 2001 50 the definition also includes self-generated electricity. The data do not capture unauthorized connections. 40 30 Definitions 20 10 · Electricity production is measured at the termi- 0 Hydropower Coal Oil Gas Nuclear power nals of all alternator sets in a station. In addition to hydropower, coal, oil, gas, and nuclear power gener- Lower-middle-income countries ation, it covers generation by geothermal, solar, 50 wind, and tide and wave energy as well as that from 40 combustible renewables and waste. Production 30 includes the output of electricity plants designed to 20 produce electricity only, as well as that of combined 10 heat and power plants. · Access to electricity refers 0 to the number of people with access to electricity Hydropower Coal Oil Gas Nuclear power (both on-grid and off-grid) as a percentage of the Upper-middle-income countries total population (see table 2.1). · Sources of elec- 50 tricity refer to the inputs used to generate electrici- 40 ty: hydropower, coal, oil, gas, and nuclear power. · Hydropower refers to electricity produced by hydro- 30 electric power plants. · Oil refers to crude oil and 20 petroleum products. · Gas refers to natural gas but 10 not natural gas liquids. · Nuclear power refers to 0 Hydropower Coal Oil Gas Nuclear power electricity produced by nuclear power plants. High-income countries Data sources 50 The data on electricity production are from the 40 IEA's electronic files and its annual publications 30 Energy Statistics and Balances of Non-OECD 20 Countries, Energy Statistics of OECD Countries, 10 and Energy Balances of OECD Countries. Data on 0 access to electricity are from the IEA's World Hydropower Coal Oil Gas Nuclear power Energy Outlook 2002: Energy and Poverty. Source: Table 3.9. 2004 World Development Indicators 151 3.10 Urbanization Urban population Population in Population in Access to improved urban agglomerations largest city sanitation facilities of more than 1 million % of total % of total % of urban % of urban % of rural millions population population population population population 1980 2002 1980 2002 1980 2000 2015 1980 2001 1990 2000 1990 2000 Afghanistan 2.5 6.4 16 23 6 10 14 39 45 .. 25 .. 8 Albania 0.9 1.4 34 44 .. .. .. .. 22 .. 99 .. 85 Algeria 8.1 18.2 44 58 8 6 7 20 16 .. 99 .. 81 Angola 1.5 4.7 21 35 13 20 25 62 60 .. 70 .. 30 Argentina 23.3 33.6 83 88 42 41 40 43 37 .. 87 .. 47 Armenia 2.0 2.1 66 67 34 34 35 51 55 .. .. .. .. Australia 12.6 18.0 86 91 61 56 55 26 22 100 100 100 100 Austria 5.1 5.4 67 68 27 26 26 40 38 100 100 100 100 Azerbaijan 3.3 4.2 53 52 26 24 26 48 47 .. 90 .. 70 Bangladesh 12.7 35.5 15 26 6 13 18 26 38 81 71 31 41 Belarus 5.4 6.9 57 70 14 18 20 24 24 .. .. .. .. Belgium 9.4 10.1 95 97 12 11 11 13 11 .. .. .. .. Benin 0.9 2.9 27 44 .. .. .. .. 8 46 46 6 6 Bolivia 2.4 5.6 45 63 14 18 20 33 28 73 86 26 42 Bosnia and Herzegovina 1.5 1.8 36 44 .. .. .. .. 31 .. .. .. .. Botswana 0.2 0.9 18 50 .. .. .. .. 27 87 88 41 43 Brazil 81.2 143.5 67 82 32 34 34 16 13 82 84 38 43 Bulgaria 5.4 5.4 61 68 12 15 16 20 22 .. 100 .. 100 Burkina Faso 0.6 2.0 8 17 .. .. .. 45 45 .. 39 .. 27 Burundi 0.2 0.7 4 10 .. .. .. .. 54 65 68 89 90 Cambodia 0.8 2.2 12 18 .. .. .. 44 53 .. 56 .. 10 Cameroon 2.8 7.9 31 50 11 21 27 19 23 97 92 64 66 Canada 18.6 24.8 76 79 32 37 38 16 20 100 100 99 99 Central African Republic 0.8 1.6 35 42 .. .. .. .. 42 38 38 16 16 Chad 0.8 2.0 19 25 .. .. .. .. 38 70 81 4 13 Chile 9.1 13.5 81 86 33 36 37 41 42 98 96 92 97 China 192.8 481.8 20 38 13 14 17 6 3 57 69 18 27 Hong Kong, China 4.6 6.8 91 100 91 100 100 100 100 .. .. .. .. Colombia 17.8 33.2 63 76 26 32 35 21 21 96 96 55 56 Congo, Dem. Rep. .. .. .. .. 8 10 12 .. .. .. 54 .. 6 Congo, Rep. 0.8 2.4 42 67 27 41 44 263 158 .. .. .. .. Costa Rica 1.1 2.4 47 60 .. .. .. 56 43 .. 89 .. 97 Côte d'Ivoire 2.8 7.3 35 44 15 21 25 44 54 70 71 29 35 Croatia 2.3 2.6 50 59 .. .. .. 28 42 .. .. .. .. Cuba 6.6 8.5 68 76 20 20 20 29 27 .. 99 .. 95 Czech Republic 7.6 7.6 75 75 12 12 12 15 16 .. .. .. .. Denmark 4.3 4.6 84 85 27 26 26 32 29 .. .. .. .. Dominican Republic 2.9 5.7 51 67 34 61 67 50 47 70 70 60 60 Ecuador 3.7 8.2 47 64 23 32 37 29 27 88 92 49 74 Egypt, Arab Rep. 17.9 28.4 44 43 23 23 24 38 35 96 100 79 96 El Salvador 2.0 4.0 44 62 16 22 25 35 35 87 89 62 76 Eritrea 0.3 0.8 14 20 .. .. .. .. 63 .. 66 .. 1 Estonia 1.0 0.9 70 69 .. .. .. .. 42 .. 93 .. .. Ethiopia 4.0 10.9 10 16 3 4 6 30 27 24 33 6 7 Finland 2.9 3.1 60 59 13 23 25 24 31 100 100 100 100 France 39.5 45.0 73 76 21 21 20 23 22 .. .. .. .. Gabon 0.3 1.1 50 83 .. .. .. .. 55 .. 55 .. 43 Gambia, The 0.1 0.4 20 32 .. .. .. .. 100 .. 41 .. 35 Georgia 2.6 2.9 52 57 22 24 29 42 .. .. 100 .. 99 Germany 64.7 72.5 83 88 39 41 43 10 9 .. .. .. .. Ghana 3.4 7.4 31 37 9 10 14 30 27 56 74 64 70 Greece 5.6 6.4 58 61 31 30 29 54 49 .. .. .. .. Guatemala 2.6 4.8 37 40 11 28 32 29 72 82 83 62 79 Guinea 0.9 2.2 19 28 12 25 32 75 60 94 94 41 41 Guinea-Bissau 0.1 0.5 17 33 .. .. .. .. 74 87 95 33 44 Haiti 1.3 3.1 24 37 13 22 28 55 62 33 50 19 16 152 2004 World Development Indicators ENVIRONMENT 3.10 Urbanization Urban population Population in Population in Access to improved urban agglomerations largest city sanitation facilities of more than 1 million % of total % of total % of urban % of urban % of rural millions population population population population population 1980 2002 1980 2002 1980 2000 2015 1980 2001 1990 2000 1990 2000 Honduras 1.2 3.7 35 55 .. .. .. 33 28 88 93 41 55 Hungary 6.1 6.6 57 65 19 18 19 34 28 100 100 98 98 India 158.5 294.5 23 28 8 10 12 5 6 44 61 6 15 Indonesia 32.9 91.0 22 43 8 10 13 18 13 66 69 38 46 Iran, Islamic Rep. 19.4 42.9 50 65 21 23 24 26 17 .. 86 .. 79 Iraq 8.5 16.3 66 68 29 31 34 39 31 .. 93 .. 31 Ireland 1.9 2.3 55 60 .. .. .. 48 44 .. .. .. .. Israel 3.4 6.0 89 92 37 35 33 41 35 .. .. .. .. Italy 37.6 38.8 67 67 24 19 20 14 11 .. .. .. .. Jamaica 1.0 1.5 47 57 .. .. .. .. 46 99 99 99 99 Japan 89.0 100.5 76 79 34 38 39 25 26 .. .. .. .. Jordan 1.3 4.1 60 79 29 29 32 49 30 100 100 95 98 Kazakhstan 8.0 8.3 54 56 6 8 9 12 13 .. 100 .. 98 Kenya 2.7 11.0 16 35 5 8 10 32 22 91 96 77 82 Korea, Dem. Rep. 9.8 13.7 57 61 11 14 16 19 24 .. 99 .. 100 Korea, Rep. 21.7 39.5 57 83 40 47 45 38 26 .. 76 .. 4 Kuwait 1.2 2.2 91 96 60 60 55 67 46 .. .. .. .. Kyrgyz Republic 1.4 1.7 38 34 .. .. .. .. 43 .. 100 .. 100 Lao PDR 0.4 1.1 12 20 .. .. .. .. 62 .. 67 .. 19 Latvia 1.7 1.4 68 60 .. .. .. 49 53 .. .. .. .. Lebanon 2.2 4.0 74 90 40 47 48 55 53 .. 100 .. 87 Lesotho 0.2 0.5 13 29 .. .. .. .. 46 .. 72 .. 40 Liberia 0.7 1.5 35 46 .. .. .. .. 34 .. .. .. .. Libya 2.1 4.8 69 88 26 34 34 38 37 97 97 96 96 Lithuania 2.1 2.4 61 69 .. .. .. .. 24 .. .. .. .. Macedonia, FYR 1.0 1.2 53 60 .. .. .. .. 36 .. .. .. .. Madagascar 1.6 5.1 19 31 6 10 13 33 35 70 70 25 30 Malawi 0.6 1.7 9 15 .. .. .. .. 33 96 96 70 70 Malaysia 5.8 14.3 42 59 7 6 6 16 10 .. .. .. 98 Mali 1.2 3.6 18 32 .. .. .. 40 34 95 93 62 58 Mauritania 0.4 1.7 28 60 .. .. .. .. 39 44 44 19 19 Mauritius 0.4 0.5 42 42 .. .. .. .. 35 100 100 100 99 Mexico 44.8 75.4 66 75 28 28 25 29 25 87 88 26 34 Moldova 1.6 1.8 40 42 .. .. .. .. 37 .. 100 .. 98 Mongolia 0.9 1.4 52 57 .. .. .. 49 56 .. 46 .. 2 Morocco 8.0 16.8 41 57 15 18 20 26 21 88 86 31 44 Mozambique 1.6 6.3 13 34 6 17 21 35 19 .. 68 .. 26 Myanmar 8.1 14.0 24 29 7 9 11 27 33 .. 84 .. 57 Namibia 0.2 0.6 23 32 .. .. .. .. 38 84 96 14 17 Nepal 1.0 3.0 7 13 .. .. .. .. 26 69 73 15 22 Netherlands 12.5 14.5 88 90 14 14 14 8 8 100 100 100 100 New Zealand 2.6 3.4 83 86 .. .. .. 30 34 .. .. .. .. Nicaragua 1.5 3.0 50 57 .. .. .. 36 35 97 95 53 72 Niger 0.7 2.5 13 22 .. .. .. 37 35 71 79 4 5 Nigeria 19.1 60.7 27 46 8 12 15 13 15 69 66 44 45 Norway 2.9 3.4 71 75 .. .. .. 22 23 100 .. .. .. Oman 0.3 2.0 32 77 .. .. .. .. 28 98 98 61 61 Pakistan 23.2 48.9 28 34 15 21 25 22 22 77 95 17 43 Panama 1.0 1.7 50 57 .. .. .. 62 73 .. 99 .. 83 Papua New Guinea 0.4 1.0 13 18 .. .. .. .. 28 92 92 80 80 Paraguay 1.3 3.2 42 57 22 23 26 52 41 96 94 91 93 Peru 11.2 19.7 65 73 25 29 30 39 39 77 79 21 49 Philippines 18.0 48.1 37 60 14 16 17 33 22 85 93 63 69 Poland 20.6 24.2 58 63 18 18 19 16 14 .. .. .. .. Portugal 2.9 6.8 29 67 19 57 68 46 60 .. .. .. .. Puerto Rico 2.1 2.9 67 76 34 36 37 51 48 .. .. .. .. 2004 World Development Indicators 153 3.10 Urbanization Urban population Population in Population in Access to improved urban agglomerations largest city sanitation facilities of more than 1 million % of total % of total % of urban % of urban % of rural millions population population population population population 1980 2002 1980 2002 1980 2000 2015 1980 2001 1990 2000 1990 2000 Romania 10.9 12.4 49 55 9 9 10 18 16 .. 86 .. 10 Russian Federation 97.0 105.0 70 73 18 19 21 8 8 .. .. .. .. Rwanda 0.2 0.5 5 6 .. .. .. .. 76 .. 12 .. 8 Saudi Arabia 6.2 19.1 66 87 19 25 24 17 25 .. 100 .. 100 Senegal 2.0 4.8 36 49 17 22 27 48 46 86 94 38 48 Serbia and Montenegro 4.5 4.2 46 52 11 14 15 25 30 .. 100 .. 99 Sierra Leone 0.8 2.0 24 38 .. .. .. 47 43 .. 88 .. 53 Singapore 2.4 4.2 100 100 100 89 83 100 100 .. 100 .. .. Slovak Republic 2.6 3.1 52 58 .. .. .. .. 15 .. 100 .. 100 Slovenia 0.9 1.0 48 49 .. .. .. .. 26 100 .. .. .. Somalia 1.4 2.6 22 28 .. .. .. 27 48 .. .. .. .. South Africa 13.3 26.5 48 58 27 32 36 13 12 93 93 80 80 Spain 27.2 31.9 73 78 20 17 17 16 13 .. .. .. .. Sri Lanka 3.1 4.4 22 23 .. .. .. .. 16 94 97 82 93 Sudan 3.9 12.4 20 38 6 9 11 30 24 87 87 48 48 Swaziland 0.1 0.3 18 27 .. .. .. .. 28 .. .. .. .. Sweden 6.9 7.4 83 83 17 18 18 20 22 100 100 100 100 Switzerland 3.6 4.9 57 67 .. .. .. 20 19 100 100 100 100 Syrian Arab Republic 4.1 8.9 47 52 28 28 31 26 27 .. 98 .. 81 Tajikistan 1.4 1.7 34 28 .. .. .. .. 30 .. 97 .. 88 Tanzania 2.7 12.0 15 34 5 12 18 30 19 84 99 84 86 Thailand 8.0 12.5 17 20 10 12 15 59 61 95 96 75 96 Togo 0.6 1.6 23 34 .. .. .. .. 46 71 69 24 17 Trinidad and Tobago 0.7 1.0 63 75 .. .. .. .. 6 .. .. .. .. Tunisia 3.3 6.5 52 67 18 20 21 35 30 96 96 48 62 Turkey 19.5 46.4 44 67 19 27 30 23 21 97 97 70 70 Turkmenistan 1.3 2.2 47 45 .. .. .. .. 23 .. .. .. .. Uganda 1.1 3.7 9 15 .. .. .. 42 39 .. 93 .. 77 Ukraine 30.9 33.2 62 68 14 15 17 7 7 .. 100 .. 98 United Arab Emirates 0.7 2.8 71 88 .. .. .. 34 35 .. .. .. .. United Kingdom 50.0 53.1 89 90 25 23 23 15 15 100 100 100 100 United States 167.6 224.0 74 78 38 38 37 9 8 100 100 100 100 Uruguay 2.5 3.1 85 92 42 37 35 49 43 .. 95 .. 85 Uzbekistan 6.5 9.3 41 37 11 9 8 28 24 .. 97 .. 85 Venezuela, RB 12.0 21.9 79 87 28 29 30 21 15 .. 71 .. 48 Vietnam 10.3 20.1 19 25 14 13 14 33 24 52 82 23 38 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.6 4.7 19 25 .. .. .. 15 31 69 89 21 21 Zambia 2.3 4.1 40 40 9 16 22 23 41 86 99 48 64 Zimbabwe 1.6 4.8 22 37 9 14 19 39 40 70 71 50 57 World 1,741.3 s 2,953.1 s 39 w 48 w .. w .. w .. w 18 w 16 w 75 w 81 w 27 w 38 w Low income 348.3 763.1 22 31 .. .. .. 17 18 58 71 20 31 Middle income 785.9 1,438.9 39 53 .. .. .. 18 15 75 82 29 43 Lower middle income 629.7 1,190.5 35 49 16 18 21 16 13 72 81 28 42 Upper middle income 156.2 248.4 66 75 .. .. .. 29 26 .. .. .. 64 Low & middle income 1,134.2 2,202.0 32 42 .. .. .. 18 16 68 78 24 36 East Asia & Pacific 288.6 701.8 21 38 .. .. .. 13 9 61 72 24 36 Europe & Central Asia 249.2 301.0 59 64 16 18 20 15 15 .. .. .. .. Latin America & Carib. 231.8 401.1 65 76 29 32 32 27 24 85 86 41 52 Middle East & N. Africa 83.4 177.2 48 58 21 22 24 30 26 .. 94 .. 72 South Asia 201.1 392.9 22 28 8 12 14 9 11 52 66 11 21 Sub-Saharan Africa 80.2 227.8 21 33 .. .. .. 27 26 75 76 45 46 High income 607.1 751.1 73 78 .. .. .. 18 18 .. .. .. .. Europe EMU 209.5 237.3 73 78 26 27 27 17 16 .. .. .. .. 154 2004 World Development Indicators ENVIRONMENT 3.10 Urbanization About the data The population of a city or metropolitan area Estimates of the world's urban population would dreds of towns reclassified as cities in recent years. depends on the boundaries chosen. For example, in change significantly if China, India, and a few other Because the estimates in the table are based on 1990 Beijing, China, contained 2.3 million people in populous nations were to change their definition of national definitions of what constitutes a city or met- 87 square kilometers of "inner city" and 5.4 million urban centers. According to China's State Statistical ropolitan area, cross-country comparisons should be in 158 square kilometers of "core city." The popula- Bureau, by the end of 1996 urban residents account- made with caution. To estimate urban populations, tion of "inner city and inner suburban districts" was ed for about 43 percent of China's population, while the United Nations' ratios of urban to total population 6.3 million, and that of "inner city, inner and outer in 1994 only 20 percent of the population was con- were applied to the World Bank's estimates of total suburban districts, and inner and outer counties" sidered urban. In addition to the continuous migration population (see table 2.1). was 10.8 million. (For most countries the last defini- of people from rural to urban areas, one of the main The urban population with access to improved san- tion is used.) reasons for this shift was the rapid growth in the hun- itation facilities is defined as people with access to at least adequate excreta disposal facilities that can 3.10a effectively prevent human, animal, and insect con- More people now live in urban areas in low-income countries than in high-income countries . . . tact with excreta. The rural population with access is Urban population, by income group (millions) included to allow comparison of rural and urban 1,200 access. This definition and the definition of urban 1980 2002 areas vary, however, so comparisons between coun- 1,000 tries can be misleading. 800 600 Definitions 400 200 · Urban population is the midyear population of 0 areas defined as urban in each country and reported Low income Lower middle income Upper middle income High income to the United Nations (see About the data). . . . and the urban population is growing fastest in low- and lower-middle-income countries · Population in urban agglomerations of more than Urban population as share of total population, by income group (%) 1 million is the percentage of a country's population living in metropolitan areas that in 1990 had a popu- 80 70 lation of more than 1 million. · Population in largest 60 city is the percentage of a country's urban population 50 living in that country's largest metropolitan area. 40 · Access to improved sanitation facilities refers to 30 the percentage of the urban or rural population with 20 access to at least adequate excreta disposal facilities 10 (private or shared but not public) that can effectively 0 Low income Lower middle income Upper middle income High income prevent human, animal, and insect contact with exc- reta. Improved facilities range from simple but pro- Source: Table 3.10. tected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be cor- 3.10b rectly constructed and properly maintained. Latin America was as urban as the average high-income country in 2002 Urban population as share of total population, by region (%) High-income countries, 2002 80 70 1980 2002 60 Data sources 50 The data on urban population and the population 40 in urban agglomerations and in the largest city 30 come from the United Nations Population 20 Division's World Urbanization Prospects: The 10 2001 Revision. The total population figures are 0 World Bank estimates. The data on access to san- East Asia Europe & Latin America Middle East South Sub-Saharan itation in urban and rural areas are from the World & Pacific Central Asia & Caribbean & North Africa Asia Africa Health Organization. Source: Table 3.10. 2004 World Development Indicators 155 3.11 Urban environment City Urban Secure House Work Travel Households with Wastewater population tenure price to trips by time access to services treated annual public to work income trans- ratio portation Potable Sewerage % of water connection Electricity Telephone thousands population % minutes % % % % % 2000 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a Algeria Algiers 2,562 b 93.2 .. .. 75 .. .. .. .. 80 Argentina Buenos Aires 2,996 b 92.1 5.10 59 42 100 98 100 70 .. Córdoba 1,322 b 85.0 6.80 44 32 99 40 99 80 49 Rosario 1,248 b .. 5.7 .. 22 98 67 93 76 1 Armenia Yerevan 1,250 b 100.0 4.0 84 30 98 98 100 88 36 Bangladesh Chittagong 2,301 b .. 8.1 27 45 44 .. 95 .. .. Dhaka 10,000 b .. 16.7 9 45 60 22 90 7 .. Sylhet 242 b .. 6.0 10 50 29 0 93 40 .. Tangail 152 b 85.7 13.9 .. 30 12 0 90 12 .. Barbados Bridgetown .. 99.7 4.4 .. .. 98 5 99 78 7 Belize Belize City 55 b .. .. .. .. .. .. .. .. .. Bolivia Santa Cruz de la Sierra 1,065 c 87.0 29.3 .. 29 53 33 98 59 53 Bosnia and Herzegovina Sarajevo 522 c .. .. 100 12 95 90 100 .. .. Brazil Belém 1,638 c .. .. .. .. .. .. .. .. .. Icapui .. 91.7 4.5 .. 30 88 .. 90 33 .. Maranguape .. .. .. 30 20 73 .. .. .. .. Porto Alegre 3 b .. .. .. .. 99 87 100 .. .. Recife 3,088 b .. 12.5 46 35 89 41 100 29 33 Rio de Janeiro 10,192 b .. .. .. .. 88 80 10 .. .. Santo Andre 1,658 b 80.3 23.4 43 40 98 95 100 79 .. Bulgaria Bourgas .. .. 5.1 61 32 100 93 100 .. 93 Sofia 1,200 b 100.0 13.2 79 32 95 91 100 89 94 Troyan 24 b 100.0 3.7 44 22 99 82 100 45 .. Veliko Tarnovo .. 100.0 5.4 46 30 98 98 100 96 50 Burkina Faso Bobo-Dioulasso .. 100.0 .. .. .. 24 .. 29 6 .. Koudougou .. .. .. .. .. 30 .. 26 7 .. Ouagadougou 1,130 c 100.0 .. 2 .. 30 .. 47 11 19 Burundi Bujumbura 373 b 97.0 .. 48 25 26 62 57 19 21 Cambodia Phnom Penh 1,000 b .. 8.9 0 45 45 75 76 40 .. Cameroon Douala 1,148 b .. 13.4 .. 40 34 1 95 9 5 Yaoundé 968 b .. .. 42 45 34 1 95 9 24 Canada Hull 254 b 100.0 .. 16 .. 100 100 100 100 100 Central African Republic Bangui .. 94.0 .. 66 60 31 .. 18 11 0 Chad N'Djamena 998 c .. .. 35 .. 42 0 13 6 21 Chile Gran Concepción .. .. .. 57 35 100 91 95 69 6 Santiago de Chile 5,737 b .. .. 60 38 100 99 99 73 3 Tome .. .. .. .. .. 92 52 98 58 57 Valparaiso 851 b 91.8 .. 55 .. 98 92 97 63 100 Viña del mar 851 b 92.7 .. .. .. 97 97 98 65 93 Colombia Armenia .. 94.1 5.0 42 60 90 50 99 97 .. Marinilla 170 b 94.5 8.5 18 15 98 93 100 65 .. Medellin 2,901 b .. .. 38 35 100 99 100 87 .. Congo Brazzaville 989 b 87.9 .. 55 20 56 0 52 18 .. Côte d'Ivoire Abidjan 3,201 b .. 14.5 .. 45 26 15 41 5 45 Croatia Zagreb 2,497 b 96.5 7.8 56 31 98 97 100 94 .. Cuba Baracoa .. 96.2 .. .. .. 83 3 93 32 .. Camaguey .. 84.7 .. 2 60 72 47 97 .. .. Cienfuegos .. 96.3 4.0 .. 80 100 73 100 9 2 Ciudad Habana .. .. 8.5 58 83 100 85 100 14 .. Pinar Del Rio .. 96.4 .. .. 80 97 48 100 .. .. Santa Clara .. 98.8 .. 7 48 95 42 100 43 .. Czech Republic Brno .. .. .. 50 25 100 96 100 69 100 Prague 1,193 b 99.3 .. 55 22 99 100 100 100 .. Dem. Rep. of Congo Kinshasa 5,398 b 94.9 .. 72 57 72 0 66 1 .. Dominican Republic Santiago de los Caballeros 691 b .. .. .. 30 75 80 .. 71 80 Ecuador Ambato 286 b .. .. .. .. 90 81 91 87 .. 156 2004 World Development Indicators ENVIRONMENT 3.11 Urban environment City Urban Secure House Work Travel Households with Wastewater population tenure price to trips by time access to services treated annual public to work income trans- ratio portation Potable Sewerage % of water connection Electricity Telephone thousands population % minutes % % % % % 2000 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a Ecuador Cuenca .. 91.0 4.6 .. 25 97 92 97 48 82 Guayaquil 2,317 b 45.8 3.4 89 45 70 42 .. 44 9 Manta 126 b .. .. .. 30 70 52 98 40 .. Puyo 40 b .. 2.1 .. 15 80 30 90 60 .. Quito 1,531 b 93.8 2.4 .. 33 85 70 96 55 .. Tena .. .. 6.3 .. 5 80 60 .. .. .. El Salvador San Salvador 1,863 b 90.5 3.5 .. .. 82 80 98 70 .. Estonia Riik .. 99.5 .. .. .. 92 90 98 55 .. Tallin 397 c 98.8 6.4 .. 35 98 98 100 86 10 Gabon Libreville 523 c .. .. 80 30 55 0 95 45 44 Gambia Banjul 50 b 91.8 11.4 55 22 23 12 24 .. .. Georgia Tbilisi 1,310 c 100.0 9.4 .. .. .. 98 100 58 .. Ghana Accra 1,500 b .. 14.0 54 21 .. .. .. .. .. Kumasi 780 b 77.7 13.7 51 21 65 .. 95 51 .. Guatemala Quetzaltenango 333 b .. 4.3 .. 15 60 55 80 40 .. Guinea Conakry 1,824 c .. .. 26 45 30 32 54 6 .. Indonesia Jakarta 9,489 b 95.5 14.6 .. .. 50 65 99 .. 16 Semarang 1,076 b 80.2 .. .. .. 34 .. 85 .. .. Surabaya 2,373 b 97.6 3.4 18 35 41 56 89 71 .. Iraq Baghdad 4,797 c .. .. .. .. .. .. .. .. .. Italy Aversa .. .. .. .. .. .. .. .. .. 90 Jamaica Kingston 655 c .. .. .. .. 97 .. 88 .. 20 Montego Bay .. .. .. .. .. 78 .. 86 .. 15 Jordan Amman 1,621 b 97.3 6.1 21 25 98 81 99 62 54 Kenya Kisumu 134 b 97.3 8.5 43 24 38 31 49 .. 65 Mombasa .. .. .. 47 20 .. .. .. .. 50 Nairobi 2,310 c .. .. 71 57 89 .. .. .. 52 Korea, Rep Hanam 124 b .. 3.7 .. .. 81 68 100 100 81 Pusan 3,843 b 100.0 4.0 39 42 98 69 100 100 69 Seoul 10,389 b 98.6 5.7 71 60 100 99 100 .. 99 Kuwait Kuwait City 1,165 c .. 6.5 21 10 100 98 100 98 .. Kyrgyz Republic Bishkek 60 b 94.8 .. 95 35 30 23 100 20 15 Lao Vientiane 562 b 92.2 23.2 2 27 87 .. 100 87 20 Latvia Riga 775 c 97.4 15.6 .. .. 95 93 100 70 .. Lebanon Sin El Fil .. b .. 8.3 50 10 80 30 98 80 .. Liberia Monrovia 651 b 57.6 28.0 80 60 .. .. .. .. .. Libya Tripoli 1,773 b .. 0.8 18 20 97 90 99 6 40 Lithuania Vilnius 578 b 100.0 20.0 52 37 89 89 100 77 54 Madagascar Antananarivo 1,507 c .. .. .. .. .. .. .. .. .. Malawi Lilongwe 765 c .. .. 27 5 65 12 50 10 .. Malaysia Penang .. .. 7.2 55 40 99 .. 100 98 20 Mauritania Nouakchott 881 c 89.9 5.4 45 50 .. .. .. .. .. Mexico Ciudad Juárez 1,018 b .. .. 24 23 89 77 96 45 .. Moldova Chisinau .. .. .. 80 23 100 95 100 83 71 Mongolia Ulaanbaatar 627 b 51.6 7.8 80 30 60 60 100 90 96 Morocco Casablanca 3,292 b .. .. .. 30 83 93 91 .. .. Rabat 646 b .. .. 40 20 93 97 52 .. .. Myanmar Yangon 3,692 b .. 8.3 69 45 78 81 85 17 .. Nicaragua Leon .. 98.8 .. .. 15 78 .. 84 21 .. Niger Niamey 731 c 87.4 .. .. 30 33 0 51 4 .. Nigeria Ibadan 1,731 c 85.8 .. 46 45 26 12 41 .. .. Lagos 13,427 c 93.0 .. 48 60 .. .. 41 .. .. Oman Muscat 887 b .. .. .. 20 80 90 89 53 .. Panama Colón 132 b .. 14.2 .. 15 .. .. .. .. .. Paraguay Asunción 1,262 c 90.2 10.7 .. 25 46 8 86 17 .. Peru Cajamarca .. 90.0 3.9 .. 20 86 69 81 38 62 2004 World Development Indicators 157 3.11 Urban environment City Urban Secure House Work Travel Households with Wastewater population tenure price to trips by time access to services treated annual public to work income trans- ratio portation Potable Sewerage % of water connection Electricity Telephone thousands population % minutes % % % % % 2000 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a 1998 a Peru Huanuco 747 b .. 30.0 .. 20 57 28 80 32 .. Huaras 54 b .. 6.7 .. 15 .. .. 71 .. .. Iquitos 347 b 97.3 5.6 25 10 73 60 82 62 .. Lima 7,431 b 80.6 10.4 82 .. 75 71 99 .. 4 Tacna .. .. 4.0 .. 25 65 58 74 16 64 Tumbes .. .. .. .. 20 60 35 80 25 .. Philippines Cebu 2,189 b 95.0 13.3 .. 35 41 92 80 25 .. Poland Bydgoszcz .. 60.5 4.3 35 18 95 87 100 85 28 Gdansk 893 c .. 4.4 56 20 99 94 100 56 100 Katowice 3,487 c 27.8 1.7 29 36 99 94 100 75 67 Poznan .. 65.5 5.8 51 25 95 96 100 86 78 Qatar Doha 391 c .. .. .. .. .. .. .. .. .. Russian Federation Astrakhan .. 100.0 5.0 66 35 81 79 100 51 92 Belgorod .. 100.0 4.0 .. 25 90 89 100 51 96 Kostroma .. 100.0 6.9 68 20 88 84 100 46 96 Moscow 9,321 c 100.0 5.1 85 62 100 100 100 102 98 Nizhny Novgorod 1,458 c 100.0 6.9 79 35 98 98 100 64 98 Novomoscowsk .. 100.0 4.2 61 25 99 93 100 62 97 Omsk 1,216 c 99.7 3.9 86 43 87 87 100 41 89 Pushkin .. 100.0 9.6 60 15 99 99 100 89 100 Surgut .. 100.0 4.5 81 57 98 98 100 50 93 Veliky Novgorod .. 100.0 3.4 75 30 97 97 100 51 95 Rwanda Kigali 358 b .. 11.4 32 45 36 20 57 6 20 Samoa Apia 34 b .. 10.0 .. .. 60 0 98 96 .. Serbia and Montenegro Belgrad 1,182 b 96.5 13.5 72 40 95 86 100 86 20 Singapore Singapore 3,164 b 100.0 3.1 53 30 100 100 100 100 100 Slovenia Ljubljana 273 b 98.9 7.8 20 30 100 100 100 97 98 Spain Madrid 4,577 b .. .. 16 32 .. .. .. .. 100 Pamplona .. .. .. .. .. 100 .. 100 .. 79 Sweden Amal 13 b .. 2.9 .. .. 100 100 100 .. 100 Stockholm 736 b .. 6.0 48 28 100 100 100 .. 100 Umea 104 b .. 5.3 .. 16 100 100 100 .. 100 Switzerland Basel 170 b .. 12.3 .. .. 100 100 100 99 100 Syria Damascus 2,335 b .. 10.3 33 40 98 71 95 10 3 Thailand Bangkok 5,647 b 77.2 8.8 28 60 99 100 100 60 .. Chiang Mai 499 b 96.5 6.8 5 30 95 60 100 75 70 Togo Lomé 663 b 64.0 .. 40 30 .. 70 51 18 .. Trinidad and Tobago Port of Spain .. 78.6 .. 44 .. .. .. .. .. .. Tunisia Tunis 2,023 b .. 5.0 .. .. 75 47 95 27 83 Turkey Ankara 2,837 b 91.3 4.5 .. 32 97 98 100 .. 80 Uganda Entebbe 65 b 74.0 10.4 65 20 48 13 42 0 30 Jinja 92 b 82.0 15.4 49 12 65 43 55 5 30 Uruguay Montevideo 1,670 b 88.0 5.6 60 45 98 79 100 75 34 West Bank and Gaza Gaza 367 b 87.3 5.4 .. .. 85 38 99 38 .. Yemen Aden 1,200 b .. .. 78 20 .. .. 96 .. 30 Sana'a 1,200 b .. .. 78 20 30 9 96 .. 30 Zimbabwe Bulawayo 900 b 99.4 .. 75 15 100 100 98 .. 80 Chegutu .. 51.5 3.4 20 22 100 68 9 3 69 Gweru .. 94.0 .. .. 15 100 100 90 61 95 Harare 1,634 b 99.9 .. 32 45 100 100 88 42 .. Mutare 149 b .. .. 70 20 88 88 74 4 100 a. Data are preliminary. b. Data are for 1998 and are from United Nations Centre for Human Settlements. c. Data are for 2000 and are from the United Nations Population Division's World Urbanization Prospects: The 2001 Revision. 158 2004 World Development Indicators ENVIRONMENT 3.11 Urban environment About the data Definitions Despite the importance of cities and urban agglom- Programme. As a result, the database excludes a · Urban population refers to the population of the erations as home to almost half the world's people, large number of major cities. The table reflects this urban agglomeration, a contiguous inhabited territory data on many aspects of urban life are sparse. The bias as well as the criterion of data availability for the without regard to administrative boundaries. available data have been scattered among interna- indicators shown. · Secure tenure refers to the percentage of the pop- tional agencies with different mandates, and compil- The data should be used with care. Because dif- ulation protected from involuntary removal from land ing comparable data has been difficult. Even within ferent data collection methods and definitions may or residence--including subtenancy, residence in cities it is difficult to assemble an integrated data have been used, comparisons can be misleading. In social housing, and residences owned, purchased, or set. Urban areas are often spread across many juris- addition, the definitions used here for access to privately rented--except through due legal process. dictions with no single agency responsible for col- potable water and urban population are more strin- · House price to annual income ratio is the average lecting and reporting data for the entire area. Adding gent than those used for tables 3.5 and 3.10 (see house price divided by the average household to the difficulties of data collection are gaps and Definitions). income. · Work trips by public transportation are the overlaps in the data collection and reporting respon- percentage of trips to work made by bus or minibus, sibilities of different administrative units. Creating a tram, or train. Buses or minibuses are road vehicles comprehensive, comparable international data set is other than cars taking passengers on a farepaying further complicated by differences in the definition of basis. Other means of transport commonly used in an urban area and by uneven data quality. developing countries, such as taxi, ferry, rickshaw, or The United Nations Global Plan of Action calls for animal, are not included. · Travel time to work is the monitoring the changing role of the world's cities and average time in minutes, for all modes, for a one-way human settlements. The international agency with trip to work. Train and bus times include average walk- the mandate to assemble information on urban ing and waiting times, and car times include parking areas is the United Nations Centre for Human and walking to the workplace. · Households with Settlements (UNCHS, or Habitat). Its Urban access to services are the percentage of households Indicators Programme is intended to provide data for in formal settlements with access to potable water monitoring and evaluating the performance of urban and connections to sewerage, electricity, and tele- areas and for developing government policies and phone service. Households with access to potable strategies. These data are collected through ques- water are those with access to safe or potable drink- tionnaires completed by city officials in more than a ing water within 200 meters of the dwelling. hundred countries. · Potable water is water that is free from contami- The table shows selected indicators for more than nation and safe to drink without further treatment. 160 cities from the UNCHS data set. A few more · Wastewater treated is the percentage of all waste- indicators are included on the World Development water undergoing some form of treatment. Indicators CD-ROM. The selection of cities in the UNCHS database does not reflect population weights or the economic importance of cities and is therefore biased toward smaller cities. Moreover, it is based on demand for participation in the Urban Indicators 3.11a The use of public transportation for work trips varied widely across cities in 1998 Share of total Share of total Country City work trips (%) Country City work trips (%) Lao PDR Vientiane 2 Kyrgyz Republic Bishkek 95 Spain Madrid 16 Russian Federation Moscow 85 Canada Hull 16 Armenia Yerevan 84 Libya Tripoli 18 Peru Lima 82 Slovenia Ljubljana 20 Gabon Libreville 80 Kuwait Kuwait City 21 Liberia Monrovia 80 Data sources Jordan Amman 21 Mongolia Ulaanbaatar 80 The data are from the Global Urban Indicators Mexico Ciudad Juarez 24 Moldova Chisinau 80 database of the UNCHS and the United Nations Guinea Conakry 26 Bulgaria Sofia 79 Malawi Lilongwe 27 Yemen, Rep. Aden 78 Population Division's World Urbanization Prospects: The 2001 Revision. Source: Table 3.11. 2004 World Development Indicators 159 3.12 Traffic and congestion Motor vehicles Passenger Two-wheelers Road traffic Fuel prices cars per 1,000 per kilometer per 1,000 per 1,000 million vehicle $ per liter people of road people people kilometers Super Diesel 1990 1999­2001 1990 1999­2001 1990 1999­2001 1990 1999­2001 1990 1999­2001 2002 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. 0.34 0.27 Albania 11 66 3 11 2 43 3 1 .. 29 0.80 0.51 Algeria .. .. .. .. .. .. .. .. .. .. 0.22 0.10 Angola 19 .. .. .. 14 .. .. .. .. .. 0.19 0.13 Argentina 181 181 27 37 134 140 1 .. 43,119 27,458 0.63 0.46 Armenia 5 .. 2 .. 1 .. .. .. .. .. 0.42 0.29 Australia 530 .. 11 .. 450 .. 18 18 138,501 .. 0.50 0.48 Austria 421 536 30 22 387 495 71 77 .. .. 0.84 0.73 Azerbaijan 52 52 7 17 36 42 5 1 .. .. 0.37 0.16 Bangladesh 1 1 0 .. 0 0 1 1 .. .. 0.52 0.29 Belarus 61 112 13 .. 59 145 .. 52 10,026 4,964 0.50 0.36 Belgium 423 515 30 35 385 462 14 29 .. 156,633 1.04 0.80 Benin 3 .. 2 .. 2 .. 34 .. .. .. 0.54 0.41 Bolivia 41 53 6 8 25 .. 9 3 1,139 .. 0.69 0.42 Bosnia and Herzegovina 114 .. 24 .. 101 .. .. .. .. .. 0.74 0.74 Botswana 18 69 3 11 10 30 .. 1 .. .. 0.41 0.38 Brazil 88 .. 8 .. .. .. .. 28 .. .. 0.55 0.31 Bulgaria 163 273 39 60 146 234 55 64 .. 213 0.68 0.59 Burkina Faso 4 .. 3 .. 2 .. 9 .. .. .. 0.83 0.62 Burundi .. .. .. .. .. .. .. .. .. .. 0.58 0.54 Cambodia 1 6 0 49 0 .. 9 134 314 7,210 0.63 0.44 Cameroon 10 .. 3 .. 6 .. .. .. .. .. 0.68 0.57 Canada 605 580 20 20 468 458 12 11 .. 73,500 0.51 0.43 Central African Republic 1 1 0 0 1 0 0 .. 1,494 .. 1.00 0.87 Chad 2 .. 0 .. 1 .. 0 .. .. .. 0.79 0.77 Chile 81 133 13 25 52 87 2 2 .. .. 0.58 0.39 China 5 12 4 11 1 7 3 26 .. 840,960 0.42 0.37 Hong Kong, China 66 77 253 279 42 57 4 5 8,192 10,781 1.47 0.77 Colombia .. 51 .. 19 .. 43 8 12 50,945 41,587 0.44 0.24 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 0.70 0.69 Congo, Rep. 18 .. 3 .. 12 .. .. .. .. .. 0.69 0.48 Costa Rica 87 .. 7 13 55 .. 14 22 .. 551,139 0.64 0.44 Côte d'Ivoire 24 .. 6 .. 15 .. .. .. .. .. 0.85 0.60 Croatia .. 274 .. 44 .. 247 .. 14 .. 15,168 0.89 0.74 Cuba 37 32 16 6 18 16 19 16 .. .. 0.90 0.45 Czech Republic 246 364 46 67 228 335 113 73 .. 7,753 0.81 0.71 Denmark 368 420 27 31 320 359 9 13 36,304 45,165 1.09 0.94 Dominican Republic 75 .. 48 0 21 .. .. .. .. .. 0.49 0.27 Ecuador 35 48 8 14 31 43 2 2 10,306 17,528 0.55 0.27 Egypt, Arab Rep. 29 .. 33 .. 21 .. 6 .. .. .. 0.19 0.80 El Salvador 33 61 14 36 17 30 0 5 2,002 4,244 0.46 0.33 Eritrea 1 .. 1 .. 1 .. .. .. .. .. 0.36 0.25 Estonia 211 404 22 11 154 339 66 5 .. 6,539 0.58 0.56 Ethiopia 1 2 2 3 1 1 0 0 .. 26,450 0.52 0.32 Finland 441 461 29 31 386 403 12 35 39,750 46,010 1.12 0.80 France 494 575 32 38 405 477 55 40 422,000 519,400 1.05 0.80 Gabon 32 .. 4 .. 19 .. .. .. .. .. 0.69 0.53 Gambia, The 13 .. 5 .. 6 .. .. .. .. .. 0.46 0.40 Georgia 107 70 27 15 89 55 5 1 4,620 .. 0.48 0.41 Germany 405 .. 53 .. 386 516 18 56 446,000 589,500 1.03 0.82 Ghana .. .. .. .. .. .. .. .. .. 15,320 0.28 0.23 Greece 248 328 22 .. 171 254 120 220 .. 79,377 0.78 0.68 Guatemala .. 52 .. 119 .. 1 .. 12 .. 4,547 0.48 0.32 Guinea 4 .. 1 .. 2 .. .. .. .. .. 0.66 0.56 Guinea-Bissau 7 .. 2 .. 4 .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 0.54 0.30 160 2004 World Development Indicators ENVIRONMENT 3.12 Traffic and congestion Motor vehicles Passenger Two-wheelers Road traffic Fuel prices cars per 1,000 per kilometer per 1,000 per 1,000 million vehicle $ per liter people of road people people kilometers Super Diesel 1990 1999­2001 1990 1999­2001 1990 1999­2001 1990 1999­2001 1990 1999­2001 2002 2002 Honduras 22 60 10 28 .. 51 .. 14 3,288 .. 0.63 0.46 Hungary 212 271 21 16 188 237 16 14 22,898 23,670 0.94 0.85 India 4 10 2 .. 2 6 15 29 .. .. 0.66 0.41 Indonesia 16 25 10 .. 7 .. 34 59 .. .. 0.27 0.19 Iran, Islamic Rep. 34 .. 14 .. 25 .. 36 .. .. .. 0.07 0.02 Iraq 14 .. 6 .. 1 .. .. .. .. .. 0.02 0.01 Ireland 270 408 10 .. 227 349 6 8 24,205 .. 0.90 0.80 Israel 210 275 74 108 174 233 8 12 18,212 37,010 0.90 0.62 Italy 529 606 99 74 476 542 45 125 344,726 67,916 1.05 0.86 Jamaica .. .. .. .. .. .. .. .. .. .. 0.52 0.44 Japan 469 572 52 62 283 413 146 110 628,581 775,723 0.91 0.66 Jordan 60 .. 26 .. .. .. 0 .. 1,098 490,248 0.52 0.17 Kazakhstan 76 86 8 16 50 67 .. 8 18,248 .. 0.35 0.29 Kenya 12 11 5 4 10 8 1 1 5,170 .. 0.70 0.56 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 0.55 0.41 Korea, Rep. 79 255 60 120 48 171 32 59 30,464 67,266 1.09 0.51 Kuwait .. .. .. .. .. .. .. .. .. 4,450 0.20 0.18 Kyrgyz Republic 44 .. 10 .. 44 38 .. 4 5,220 1,933 0.39 0.25 Lao PDR 9 .. 3 .. 6 .. 18 .. .. .. 0.36 0.30 Latvia 135 281 6 11 106 235 76 9 3,932 .. 0.70 0.65 Lebanon 321 .. 183 .. 300 .. 13 .. .. .. 0.65 0.25 Lesotho 11 .. 4 .. 3 .. .. .. .. .. 0.50 0.47 Liberia 14 .. 4 .. 7 .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. 0.10 0.08 Lithuania 160 345 12 17 133 317 52 5 .. 872 0.69 0.59 Macedonia, FYR 132 170 30 27 121 .. 1 .. 3,102 .. 0.85 0.63 Madagascar 6 .. 2 .. 4 .. .. .. 41,500 .. 1.08 0.65 Malawi 4 .. 4 0 2 .. .. .. .. .. 0.66 0.62 Malaysia 124 .. 26 .. 101 .. 167 233 .. .. 0.35 0.19 Mali 3 .. 2 .. 2 .. .. .. .. .. 0.69 0.55 Mauritania 10 .. 3 .. 7 .. .. .. .. .. 0.63 0.39 Mauritius 59 106 35 64 44 78 54 101 .. 78 .. .. Mexico 119 159 41 44 82 107 3 .. 55,095 .. 0.62 0.47 Moldova 53 82 17 24 48 64 45 .. .. 557 0.45 0.31 Mongolia 21 31 1 2 6 18 22 10 340 2,093 0.38 0.37 Morocco 37 51 15 26 28 41 1 1 .. 16,834 0.87 0.55 Mozambique 4 .. 2 .. 3 .. .. .. 1,889 .. 0.46 0.43 Myanmar .. .. .. .. .. .. .. .. .. .. 0.36 0.28 Namibia 71 82 1 2 39 38 1 2 1,896 2,317 0.45 0.43 Nepal .. .. .. .. .. .. .. .. .. .. 0.66 0.34 Netherlands 405 428 58 58 368 384 44 25 90,150 109,955 1.12 0.81 New Zealand 524 696 19 .. 436 578 24 20 .. 35,200 0.55 0.33 Nicaragua 19 30 5 8 10 12 3 5 108 440 0.54 0.41 Niger 6 .. 4 .. 5 .. .. .. 178 .. 0.77 0.55 Nigeria 30 .. 21 .. 12 .. 5 .. .. .. 0.20 0.19 Norway 458 511 22 25 380 411 48 55 .. 32,589 1.23 1.18 Oman 130 .. 9 .. 83 .. 3 .. .. .. 0.31 0.26 Pakistan 6 9 4 5 4 5 8 15 .. 234,515 0.52 0.35 Panama 75 .. 18 .. 60 .. 2 3 .. .. 0.51 0.36 Papua New Guinea .. .. .. .. .. .. .. .. .. .. 0.53 0.34 Paraguay .. .. .. .. .. .. .. .. .. .. 0.56 0.34 Peru .. 43 .. 13 .. 27 .. .. .. .. 0.74 0.48 Philippines 10 32 4 12 7 10 6 16 6,189 9,548 0.35 0.27 Poland 168 307 18 32 138 259 36 21 59,608 138,100 0.83 0.68 Portugal 222 347 34 .. 162 321 5 77 28,623 47,943 0.97 0.71 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 161 3.12 Traffic and congestion Motor vehicles Passenger Two-wheelers Road traffic Fuel prices cars per 1,000 per kilometer per 1,000 per 1,000 million vehicle $ per liter people of road people people kilometers Super Diesel 1990 1999­2001 1990 1999­2001 1990 1999­2001 1990 1999­2001 1990 1999­2001 2002 2002 Romania 72 160 11 18 56 139 13 14 23,907 39,184 0.64 0.57 Russian Federation 87 176 14 48 65 132 .. 43 .. 59,522 0.35 0.25 Rwanda 2 .. 1 .. 1 .. .. .. .. .. 0.84 0.84 Saudi Arabia 165 .. 19 .. 98 .. 0 .. .. .. 0.24 0.10 Senegal 11 14 6 2 8 11 0 0 .. 4,013 0.75 0.53 Serbia and Montenegro 137 163 31 39 133 150 3 3 .. 1,428 0.74 0.66 Sierra Leone 10 0 4 .. 7 0 2 0 996 .. 0.51 0.50 Singapore 130 168 142 .. 89 122 40 32 .. .. 0.85 0.38 Slovak Republic 194 266 57 34 163 236 61 8 .. 543 0.74 0.70 Slovenia 306 465 42 46 289 426 8 6 5,620 9,449 0.76 0.67 Somalia 2 .. 1 .. 1 .. .. .. .. .. 0.35 0.29 South Africa 139 .. 26 .. 97 .. 8 4 .. .. 0.43 0.40 Spain 360 467 43 54 309 408 79 92 100,981 201,896 0.83 0.72 Sri Lanka 21 37 4 7 7 12 24 42 3,468 15,630 0.54 0.31 Sudan 9 .. 22 .. 8 .. .. .. .. .. 0.30 0.24 Swaziland 66 71 18 21 35 35 3 3 .. .. 0.47 0.44 Sweden 464 494 29 21 426 450 11 31 61,040 128,200 1.06 0.96 Switzerland 491 534 46 54 449 493 114 102 48,660 54,707 0.89 0.93 Syrian Arab Republic 26 29 10 .. 10 9 .. .. .. .. 0.53 0.18 Tajikistan 3 .. 1 .. 0 .. .. .. .. 1,730 0.36 0.24 Tanzania 5 .. 2 .. 1 .. .. .. .. .. 0.67 0.61 Thailand 46 .. 36 .. 14 .. 86 .. 45,769 .. 0.36 0.32 Togo 24 .. 11 .. 16 .. 8 .. .. .. 0.56 0.46 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. 0.40 0.21 Tunisia 48 79 19 .. 23 53 .. 1 .. 14,635 0.29 0.19 Turkey 50 85 8 14 34 63 10 15 27,041 52,631 1.02 0.78 Turkmenistan .. .. .. .. .. .. .. .. .. .. 0.02 0.01 Uganda 2 .. .. .. 1 .. 0 3 .. .. 0.83 0.70 Ukraine 63 .. 20 .. 63 104 .. 46 59,500 .. 0.47 0.34 United Arab Emirates 121 .. 52 .. 97 .. .. .. .. .. 0.29 0.30 United Kingdom 400 391 64 62 341 384 14 3 399,000 462,400 1.18 1.20 United States 758 779 30 34 573 481 17 15 2,527,441 2,653,043 0.40 0.39 Uruguay 138 .. 45 .. 122 .. 74 .. .. .. 0.46 0.20 Uzbekistan .. .. .. .. .. .. .. .. .. .. 0.38 0.26 Venezuela, RB .. .. .. .. .. .. .. .. .. .. 0.05 0.05 Vietnam .. .. .. .. .. .. 45 .. .. .. 0.34 0.27 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. 0.99 0.52 Yemen, Rep. 34 .. 8 .. 14 .. .. .. 8,681 .. 0.21 0.10 Zambia 14 .. 3 .. 8 .. .. .. .. .. 0.72 0.60 Zimbabwe .. .. .. .. .. .. .. .. .. .. 0.85 0.72 World 118 w .. w .. .. 91 w .. w .. .. .. .. 0.58 m 0.44 m Low income 7 11 .. .. 4 8 .. .. .. .. 0.54 0.41 Middle income 40 52 .. .. 26 40 .. .. .. .. 0.54 0.39 Lower middle income 25 34 .. .. 13 26 .. .. .. .. 0.52 0.36 Upper middle income 149 213 .. .. 114 166 .. .. .. .. 0.60 0.43 Low & middle income 26 37 .. .. 16 28 .. .. .. .. 0.54 0.40 East Asia & Pacific 9 17 .. .. 4 10 .. .. .. .. 0.36 0.31 Europe & Central Asia 97 199 .. .. 82 152 .. .. .. .. 0.64 0.56 Latin America & Carib. 100 108 .. .. .. 72 .. .. .. .. 0.54 0.36 Middle East & N. Africa 48 .. .. .. 31 .. .. .. .. .. 0.29 0.18 South Asia 4 10 .. .. 2 6 .. .. .. .. 0.54 0.34 Sub-Saharan Africa 21 .. .. .. 14 .. .. .. .. .. 0.64 0.51 High income 505 668 .. .. 396 436 .. .. .. .. 0.87 0.66 Europe EMU 429 553 .. .. 379 494 .. .. .. .. 1.00 0.80 162 2004 World Development Indicators ENVIRONMENT 3.12 Traffic and congestion About the data Definitions Traffic congestion in urban areas constrains eco- tions. Comparability also is limited when time-series · Motor vehicles include cars, buses, and freight nomic productivity, damages people's health, and data are reported. Moreover, the data do not capture vehicles but not two-wheelers. Population figures degrades the quality of their lives. The particulate air the quality or age of vehicles or the condition or width refer to the midyear population in the year for which pollution emitted by motor vehicles--the dust and of roads. Thus comparisons over time and between data are available. Roads refer to motorways, soot in exhaust--is proving to be far more damaging countries should be made with caution. highways, main or national roads, and secondary or to human health than was once believed. (For infor- The data on fuel prices are compiled by the regional roads. A motorway is a road specially mation on particulate matter and other air pollutants, German Agency for Technical Cooperation (GTZ) from designed and built for motor traffic that separates the see table 3.13.) its global network of regional offices and represen- traffic flowing in opposite directions. · Passenger In recent years ownership of passenger cars has tatives as well as other sources, including the cars refer to road motor vehicles, other than two- increased, and the expansion of economic activity Allgemeiner Deutscher Automobil Club (for Europe) wheelers, intended for the carriage of passengers has led to the transport by road of more goods and and a project of the Latin American Energy and designed to seat no more than nine people services over greater distances (see table 5.9). Organization (OLADE, for Latin America). Local prices (including the driver). · Two-wheelers refer to mope- These developments have increased demand for have been converted to U.S. dollars using the ds and motorcycles. · Road traffic is the number of roads and vehicles, adding to urban congestion, air exchange rate on the survey date as listed in the vehicles multiplied by the average distances they pollution, health hazards, traffic accidents, and international monetary table of the Financial Times. travel. · Fuel prices refer to the pump prices of the injuries. For countries with multiple exchange rates, the mar- most widely sold grade of gasoline and of diesel fuel. Congestion, the most visible cost of expanding ket, parallel, or black market rate was used rather Prices have been converted from the local currency to vehicle ownership, is reflected in the indicators in the than the official exchange rate. U.S. dollars (see About the data). table. Other relevant indicators--such as average vehicle speed in major cities or the cost of traffic congestion, which takes a heavy toll on economic productivity--are not included because data are incomplete or difficult to compare. The data in the table--except for those on fuel prices--are compiled by the International Road Federation (IRF) through questionnaires sent to national organizations. The IRF uses a hierarchy of sources to gather as much information as possible. The primary sources are national road associations. Where such an associa- tion lacks data or does not respond, other agencies are contacted, including road directorates, ministries of transport or public works, and central statistical offices. As a result, the compiled data are of uneven quality. The coverage of each indicator may differ across countries because of differences in defini- 3.12a The 10 countries with the most vehicles per 1,000 people in 2001--and the 10 with the fewest Vehicles per 1,000 people Country Motor vehicles Country Motor vehicles United States 779 Mongolia 31 New Zealand 696 Nicaragua 30 Italy 606 Syrian Arab Republic 29 Canada 580 Indonesia 25 France 575 India 10 Japan 572 Pakistan 9 Austria 536 Cambodia 6 Data sources Switzerland 534 Uganda 5 The data on vehicles and traffic are from the IRF's Belgium 515 Ethiopia 2 electronic files and its annual World Road Norway 511 Bangladesh 1 Statistics. The data on fuel prices are from the Note: Data are for the most recent year available between 1999 and 2001. GTZ's electronic files. Source: Table 3.12. 2004 World Development Indicators 163 3.13 Air pollution City City Particulate Sulfur Nitrogen About the data population matter dioxide dioxide In many towns and cities exposure to air pollution is the main environmental threat to human health. micrograms per micrograms per microgram per Long-term exposure to high levels of soot and small thousands cubic meter cubic meter cubic meter particles in the air contributes to a wide range of 2000 1999 1995­2001 a 1995­2001 a health effects, including respiratory diseases, lung Argentina Córdoba 1,370 52 .. 97 cancer, and heart disease. Particulate pollution, on Australia Melbourne 3,293 15 .. 30 its own or in combination with sulfur dioxide, leads Perth 1,245 15 5 19 Sydney 3,855 22 28 81 to an enormous burden of ill health. Austria Vienna 1,904 39 14 42 Emissions of sulfur dioxide and nitrogen oxides Belgium Brussels 983 31 20 48 lead to the deposition of acid rain and other acidic Brazil Rio de Janeiro 5,902 40 129 .. compounds over long distances. Acid deposition São Paulo 9,984 46 43 83 changes the chemical balance of soils and can lead Bulgaria Sofia 1,177 83 39 122 Canada Montreal 3,519 22 10 42 to the leaching of trace minerals and nutrients criti- Toronto 4,535 26 17 43 cal to trees and plants. Vancouver 1,880 15 14 37 Where coal is the primary fuel for power plants, Chile Santiago 4,522 73 29 81 steel mills, industrial boilers, and domestic heating, China Anshan 3,132 99 115 88 the result is usually high levels of urban air pollution-- Beijing 9,302 106 90 122 Changchun 3,766 88 21 64 especially particulates and sometimes sulfur Chengdu 4,401 103 77 74 dioxide--and, if the sulfur content of the coal is high, Chongquing 3,945 147 340 70 widespread acid deposition. Where coal is not an Dalian 4,389 60 61 100 important primary fuel or is used in plants with effec- Guangzhu 495 74 57 136 tive dust control, the worst emissions of air pollutants Guiyang 2,103 84 424 53 Harbin 4,545 91 23 30 stem from the combustion of petroleum products. Jinan 3,037 112 132 45 The data on sulfur dioxide and nitrogen dioxide con- Kunming 2,037 84 19 33 centrations are based on reports from urban monitor- Lanzhou 2,044 109 102 104 ing sites. Annual means (measured in micrograms per Liupanshui 2,330 70 102 .. cubic meter) are average concentrations observed at Nanchang 1,594 94 69 29 Pinxiang 1,754 80 75 .. these sites. Coverage is not comprehensive because Quingdao 2,316 .. 190 64 not all cities have monitoring systems. Shanghai 10,367 87 53 73 The data on particulate matter concentrations are Shenyang 5,881 120 99 73 estimates, for selected cities, of average annual Taiyuan 2,811 105 211 55 concentrations in residential areas away from air Tianjin 7,333 149 82 50 Urumqi 1,467 61 60 70 pollution "hotspots," such as industrial districts and Wuhan 4,842 94 40 43 transport corridors. The data have been extracted Zhengzhou 2,214 116 63 95 from a complete set of estimates developed by the Zibo 3,139 88 198 43 World Bank's Development Research Group and Colombia Bogota 5,442 33 .. .. Environment Department in a study of annual ambi- Croatia Zagreb 908 39 31 .. Cuba Havana 2,270 28 1 5 ent concentrations of particulate matter in world Czech Republic Prague 1,211 27 14 33 cities with populations exceeding 100,000 (Pandey Denmark Copenhagen 1,371 24 7 54 and others 2003). Ecuador Guayaquil 2,120 26 15 .. Pollutant concentrations are sensitive to local con- Quito 1,598 34 22 .. ditions, and even in the same city different monitor- Egypt, Arab Rep. Cairo 7,941 178 69 .. Finland Helsinki 1,095 22 4 35 ing sites may register different concentrations. Thus France Paris 9,851 15 14 57 these data should be considered only a general indi- Germany Berlin 3,555 25 18 26 cation of air quality in each city, and cross-country Frankfurt 668 22 11 45 comparisons should be made with caution. The cur- Munich 1,275 22 8 53 rent World Health Organization (WHO) air quality Ghana Accra 1,938 31 .. .. Greece Athens 3,229 50 34 64 guidelines for annual mean concentrations are 50 Hungary Budapest 1,958 26 39 51 micrograms per cubic meter for sulfur dioxide and 40 Iceland Reykjavik 164 21 5 42 micrograms for nitrogen dioxide. The WHO has set no India Ahmedabad 4,154 104 30 21 guidelines for particulate matter concentrations Bangalore 5,180 56 .. .. below which there are no appreciable health effects. 164 2004 World Development Indicators ENVIRONMENT 3.13 Air pollution City City Particulate Sulfur Nitrogen Definitions population matter dioxide dioxide · City population is the number of residents of the city or metropolitan area as defined by national micrograms per micrograms per microgram per authorities and reported to the United Nations. thousands cubic meter cubic meter cubic meter · Particulate matter refers to fine suspended partic- 2000 1999 1995­2001 a 1995­2001 a ulates less than 10 microns in diameter that are India Calcutta 13,822 153 49 34 capable of penetrating deep into the respiratory tract Chennai 6,002 .. 15 17 and causing significant health damage. The state of a Delhi 10,558 187 24 41 Hyderabad 5,448 51 12 17 country's technology and pollution controls is an Kanpur 2,546 136 15 14 important determinant of particulate matter concen- Lucknow 2,093 136 26 25 trations. · Sulfur dioxide is an air pollutant produced Mumbai 15,797 79 33 39 when fossil fuels containing sulfur are burned. It con- Nagpur 2,087 69 6 13 tributes to acid rain and can damage human health, Pune 3,128 58 Indonesia Jakarta 10,845 103 .. .. particularly that of the young and the elderly. Iran, Islamic Rep. Tehran 7,689 71 209 .. · Nitrogen dioxide is a poisonous, pungent gas Ireland Dublin 991 23 20 .. formed when nitric oxide combines with hydro- Italy Milan 1,381 36 31 248 carbons and sunlight, producing a photochemical Rome 2,713 35 .. .. reaction. These conditions occur in both natural and Torino 969 53 .. .. Japan Osaka 2,626 39 19 63 anthropogenic activities. Nitrogen dioxide is emitted Tokyo 12,483 43 18 68 by bacteria, motor vehicles, industrial activities, Yokohama 3,366 32 100 13 nitrogenous fertilizers, combustion of fuels and bio- Kenya Nairobi 2,383 49 .. .. mass, and aerobic decomposition of organic matter Korea, Rep Pusan 4,075 43 60 51 in soils and oceans. Seoul 11,548 45 44 60 Taegu 2,417 49 81 62 Malaysia Kuala Lumpur 1,530 24 24 .. Mexico Mexico City 18,017 69 74 130 Netherlands Amsterdam 1,131 37 10 58 New Zealand Auckland 989 15 3 20 Norway Oslo 805 23 8 43 Philippines Manila 10,432 60 33 .. Poland Lodz 873 45 21 43 Warsaw 1,716 49 16 32 Portugal Lisbon 3,318 30 8 52 Data sources Romania Bucharest 2,070 25 10 71 City population data are from the United Nations Russian Federation Moscow 8,811 27 109 .. Omsk 1,206 28 20 34 Population Division. The data on sulfur dioxide and Singapore Singapore 3,163 41 20 30 nitrogen dioxide concentrations are from the WHO's Slovak Republic Bratislava 456 22 21 27 Healthy Cities Air Management Information System South Africa Capetown 2,942 15 21 72 and the World Resources Institute, which relies on Durban 1,364 29 31 .. various national sources as well as, among others, Johannesburg 2,344 30 19 31 Spain Barcelona 1,645 43 11 43 the Organisation for Economic Co-operation and Madrid 3,068 37 24 66 Development's (OECD) OECD Environmental Data Sweden Stockholm 916 15 3 20 Compendium 1999, the U.S. Environmental Switzerland Zurich 980 24 11 39 Protection Agency's National Air Quality and Thailand Bangkok 7,296 82 11 23 Emissions Trends Report 1995, the Aerometric Turkey Ankara 3,702 53 55 46 Istanbul 9,286 62 120 .. Information Retrieval System (AIRS) Executive Ukraine Kiev 2,622 45 14 51 International database, and the United Nations United Kingdom Birmingham 2,344 17 9 45 Centre for Human Settlements' (UNCHS) Urban London 7,812 23 25 77 Indicators database. The data on particulate matter Manchester 2,325 19 26 49 concentrations are from a recent World Bank study United States Chicago 9,024 27 14 57 Los Angeles 16,195 38 9 74 by Kiran D. Pandey, Katharine Bolt, Uwe Deichman, New York 20,951 23 26 79 Kirk Hamilton, Bart Ostro, and David Wheeler, "The Venezuela, RB Caracas 3,488 18 33 57 Human Cost of Air Pollution: New Estimates for a. Data are for the most recent year available. Developing Countries" (2003). 2004 World Development Indicators 165 3.14 Government commitment 3.14a Environ- Biodiversity Participation in treaties a mental assessments, strategies strategies or The Kyoto Protocol on climate change or action action plans The Kyoto Protocol was adopted at the third con- plans ference of the parties to the United Nations Law Framework Convention on Climate Change, held in Climate Ozone CFC of the Biological Kyoto Kyoto, Japan, in December 1997 and was open for change b layer control Sea c diversity b Protocol signature from March 1998 onward. Afghanistan .. .. 2002 .. .. .. 2002 .. At the heart of the protocol are its legally bind- Albania 1993 .. 1995 1999 1999 2003 f 1994 f .. ing greenhouse gas emissions targets for industri- Algeria 2001 .. 1994 1992 1992 1996 1995 .. al and transition economies (known as "Annex I Angola .. .. 2000 2000 2000 1994 1998 .. Argentina 1992 .. 1994 1990 1990 1996 1995 2001 Parties"), which accounted for at least 55 percent Armenia .. .. 1994 1999 1999 2002 f 1993 d 2003 f of carbon dioxide emissions in 1990. The emis- Australia 1992 1994 1994 1987 1989 1995 1993 .. sions targets amount to an aggregate reduction of Austria .. .. 1994 1987 1989 1995 1994 2002 greenhouse gas emissions by all Annex I Parties Azerbaijan 1998 .. 1995 1996 1996 .. 2000 e 2000 f of at least 5 percent from 1990 levels during the Bangladesh 1991 1990 1994 1990 1990 2001 1994 2001 f Belarus .. .. 2000 1986 1988 .. 1993 .. commitment period, 2008­12. All Annex I Parties Belgium .. .. 1996 1988 1988 1998 1997 2002 have individual emissions targets, which were Benin 1993 .. 1994 1993 1993 1997 1994 2002 f decided in Kyoto after intensive negotiation and Bolivia 1994 1988 1995 1994 1994 1995 1995 1999 are listed in the protocol's Annex B. Bosnia and Herzegovina .. .. 2000 1992 1992 1994 g 2002 f .. The protocol's rules focus on: Botswana 1990 1991 1994 1991 1991 1994 1996 2003 f · Commitments, including legally binding emis- Brazil .. 1988 1994 1990 1990 1994 1994 2002 Bulgaria .. 1994 1995 1990 1990 1996 1996 2002 sions targets and general commitments. Burkina Faso 1993 .. 1994 1989 1989 .. 1993 .. · Implementation, including domestic steps and Burundi 1994 1989 1997 1997 1997 .. 1997 2001 f three novel implementing mechanisms. Cambodia 1999 .. 1996 2001 2001 .. 1995 f 2002 f · Minimization of impacts on developing coun- Cameroon .. 1989 1995 1989 1989 1994 1995 2002 f tries, including use of an Adaptation Fund. Canada 1990 1994 1994 1986 1988 2003 1993 2002 · Accounting, reporting, and review, including in- Central African Republic .. .. 1995 1993 1993 .. 1995 .. Chad 1990 .. 1994 1989 1994 .. 1994 .. depth review of national reporting. Chile .. 1993 1995 1990 1990 1997 1994 2002 · Compliance, including a Compliance Committee China 1994 1994 1994 1989 1991 1996 1993 2002 e to assess and deal with problem cases. Hong Kong, China .. .. .. .. .. .. .. .. In addition to emissions targets for Annex I Colombia 1998 1988 1995 1990 1993 .. 1995 2001 f Parties, the Kyoto Protocol also contains a set of gen- Congo, Dem. Rep. .. 1990 1995 1994 1994 1994 1995 .. Congo, Rep. .. 1990 1997 1994 1994 .. 1996 .. eral commitments that apply to all parties, such as: Costa Rica 1990 1992 1994 1991 1991 1994 1994 2002 · Improving the quality of emissions data. Côte d'Ivoire 1994 1991 1995 1993 1993 1994 1995 .. · Mounting national mitigation and adaptation Croatia 2001 2000 1996 1991 1991 1994 g 1997 .. programs. Cuba .. .. 1994 1992 1992 1994 1994 .. · Promoting environmentally friendly technology Czech Republic 1994 .. 1994 1993 1993 1996 1994 e 2001 e transfer. Denmark 1994 .. 1994 1988 1988 .. 1994 2002 Dominican Republic .. 1995 1999 1993 1993 .. 1996 2002 f · Cooperating in scientific research and interna- Ecuador 1993 1995 1994 1990 1990 .. 1993 2000 tional climate observation networks. Egypt, Arab Rep. 1992 1988 1995 1988 1988 1994 1994 .. · Supporting education, training, public aware- El Salvador 1994 1988 1996 1992 1992 .. 1994 1998 ness, and capacity building initiatives. Eritrea 1995 .. 1995 .. .. .. 1996 f .. The Protocol is subject to ratification, accept- Estonia 1998 .. 1994 1996 1996 .. 1994 2002 ance, approval, or accession by Parties to the Ethiopia 1994 1991 1994 1994 1994 .. 1994 .. Finland 1995 .. 1994 1986 1988 1996 1994 d 2002 Convention, which bind the parties to the protocol's France 1990 .. 1994 1987 1988 1996 1994 2002 e commitments, once the protocol comes into force. Gabon .. 1990 1998 1994 1994 1998 2000 .. The table contains the latest information on Gambia, The 1992 1989 1994 1990 1990 1998 1994 2001 f dates of signature and ratification from the Georgia 1998 .. 1994 1996 1996 1996 f 1994 f 1999 f Secretary-General of the United Nations, the depos- Germany .. .. 1994 1988 1988 1994 f 1994 2002 itory of the Kyoto Protocol. The dates are those of Ghana 1992 1988 1995 1989 1989 1994 1994 2003 f Greece .. .. 1994 1988 1988 1995 1994 2002 the receipt of the instrument of ratification, accept- Guatemala 1994 1988 1996 1987 1989 1997 1995 1999 ance, approval, or accession. As of November Guinea 1994 1988 1994 1992 1992 1994 1993 2000 f 2003, 84 parties had signed the Kyoto Protocol and Guinea-Bissau 1993 1991 1996 2002 2002 1994 1996 .. 120 parties had ratified or accepted it. Haiti 1999 .. 1996 2000 2000 1996 1996 .. 166 2004 World Development Indicators ENVIRONMENT 3.14 Government commitment 3.14b Environ- Biodiversity Participation in treaties a mental assessments, strategies strategies or Global atmospheric concentrations of chlorofluorocarbons have leveled off or action action plans plans Parts per trillion Law Climate Ozone CFC of the Biological Kyoto 600 change b layer control Sea c diversity b Protocol Chlorofluorocarbon-12 Honduras 1993 .. 1996 1993 1993 1994 1995 2000 500 Hungary 1995 .. 1994 1988 1989 2002 1994 2002 f India 1993 1994 1994 1991 1992 1995 1994 2002 f 400 Indonesia 1993 1993 1994 1992 1992 1994 1994 .. Iran, Islamic Rep. .. .. 1996 1990 1990 .. 1996 .. Iraq .. .. .. .. .. 1994 .. .. 300 Chlorofluorocarbon-11 Ireland .. .. 1994 1988 1988 1996 1996 2002 Israel .. .. 1996 1992 1992 .. 1995 .. 200 Italy .. .. 1994 1988 1988 1995 1994 2002 Jamaica 1994 .. 1995 1993 1993 1994 1995 1999 f Japan .. .. 1994 1988 1988 1996 1993 d 2002 d 100 Chlorofluorocarbon-113 Jordan 1991 .. 1994 1989 1989 1995 f 1994 2003 f Kazakhstan .. .. 1995 1998 1998 .. 1994 .. 0 Kenya 1994 1992 1994 1988 1988 1994 1994 .. 1980 1985 1990 1995 2001 Korea, Dem. Rep. .. .. 1995 1995 1995 .. 1995 e .. Korea, Rep. .. .. 1994 1992 1992 1996 1995 2002 Note: Chlorofluorocarbon-11, chlorofluorocarbon-12, and Kuwait .. .. 1995 1992 1992 1994 2002 .. chlorofluorocarbon-113 are potent depletors of strato- Kyrgyz Republic 1995 .. 2000 2000 2000 .. 1996 e 2003 f spheric ozone. Lao PDR 1995 .. 1995 1998 1998 1998 1996 e 2003 f Source: World Resources Institute and others 2002. Latvia .. .. 1995 1995 1995 .. 1996 2002 Lebanon .. .. 1995 1993 1993 1995 1995 .. Lesotho 1989 .. 1995 1994 1994 .. 1995 2000 f Liberia .. .. 2003 1996 1996 .. 2000 2002 f Libya .. .. 1999 1990 1990 .. 2001 .. Lithuania .. .. 1995 1995 1995 2003 f 1996 2003 Macedonia, FYR .. .. 1998 1994 1994 1994 g 1997 f .. Madagascar 1988 1991 1999 1996 1996 2001 1996 2003 f Malawi 1994 .. 1994 1991 1991 .. 1994 2001 f Malaysia 1991 1988 1994 1989 1989 1997 1994 2002 Mali .. 1989 1995 1994 1994 1994 1995 2002 Mauritania 1988 .. 1994 1994 1994 1996 1996 .. Mauritius 1990 .. 1994 1992 1992 1994 1993 2001 f Mexico .. 1988 1994 1987 1988 1994 1993 2000 Moldova 2002 .. 1995 1996 1996 .. 1996 2003 f Mongolia 1995 .. 1994 1996 1996 1997 1993 1999 f Morocco .. 1988 1996 1995 1995 .. 1995 2002 f Mozambique 1994 .. 1995 1994 1994 1997 1995 .. Myanmar .. 1989 1995 1993 1993 1996 1995 2003 f Namibia 1992 .. 1995 1993 1993 1994 1997 2003 f Nepal 1993 .. 1994 1994 1994 1998 1994 .. Netherlands 1994 .. 1994 1988 1988 1996 1994 d 2002 f New Zealand 1994 .. 1994 1987 1988 1996 1993 2002 Nicaragua 1994 .. 1996 1993 1993 2000 1996 1999 Niger .. 1991 1995 1992 1992 .. 1995 .. Nigeria 1990 1992 1994 1988 1988 1994 1994 .. Norway .. 1994 1994 1986 1988 1996 1993 2002 Oman .. .. 1995 1999 1999 1994 1995 .. Pakistan 1994 1991 1994 1992 1992 1997 1994 .. Panama 1990 .. 1995 1989 1989 1996 1995 1999 Papua New Guinea 1992 1993 1994 1992 1992 1997 1993 2002 Paraguay .. .. 1994 1992 1992 1994 1994 1999 Peru .. 1988 1994 1989 1993 .. 1993 2002 Philippines 1989 1989 1994 1991 1991 1994 1994 2003 Poland 1993 1991 1994 1990 1990 1998 1996 2002 Portugal 1995 .. 1994 1988 1988 1997 1994 2002 e Puerto Rico .. .. .. .. .. .. .. .. 2004 World Development Indicators 167 3.14 Government commitment 3.14c Environ- Biodiversity Participation in treaties a mental assessments, strategies strategies or Global focus on biodiversity and climate change or action action plans plans Allocation of funds for Global Environment Facility Law programs, February 1991­January 2004 Total allocation: $19,944 million Climate Ozone CFC of the Biological Kyoto change b layer control Sea c diversity b Protocol By region Romania 1995 .. 1994 1993 1993 1997 1994 2001 Russian Federation 1999 1994 1995 1986 1988 1997 1995 .. Global projects Regional projects 1% Rwanda 1991 .. 1998 2001 2001 .. 1996 .. 12% Saudi Arabia .. .. 1995 1993 1993 1996 2001 e .. Europe & Senegal 1984 1991 1995 1993 1993 1994 1995 2001 f Central Asia Asia 15% Serbia and Montenegro .. .. 2001 1992 1992 2001 g 2002 .. 38% Sierra Leone 1994 .. 1995 2001 2001 1995 1995 e .. Africa Singapore 1993 1995 1997 1989 1989 1994 1996 .. 19% Slovak Republic .. .. 1994 1993 1993 1996 1994 e 2002 Slovenia 1994 .. 1996 1992 1992 1994 g 1996 2002 Latin America & Caribbean Somalia .. .. .. 2001 2001 1994 .. .. 20% South Africa 1993 .. 1997 1990 1990 1997 2000 2002 f By focal area Spain .. .. 1994 1988 1988 1997 1994 2002 Sri Lanka 1994 1991 1994 1989 1989 1994 1994 2002 f Ozone depletion 2% Multiple areas Sudan .. .. 1994 1993 1993 1994 1996 .. Persistent organic 6% Swaziland .. .. 1997 1992 1992 .. 1995 .. pollutants International 1% Sweden .. .. 1994 1986 1988 1996 1994 2002 waters 11% Switzerland .. .. 1994 1987 1988 .. 1995 2003 Syrian Arab Republic 1999 .. 1996 1989 1989 .. 1996 .. Climate change Tajikistan .. .. 1998 1996 1998 .. 1997 e .. Biodiversity 54% Tanzania 1994 1988 1996 1993 1993 1994 1996 2002 f 26% Thailand .. .. 1995 1989 1989 .. .. 2002 Togo 1991 .. 1995 1991 1991 1994 1996 d .. Trinidad and Tobago .. .. 1994 1989 1989 1994 f 1996 1999 Tunisia 1994 1988 1994 1989 1989 1994 1993 2003 f Source: Global Environment Facility data. Turkey 1998 .. .. 1991 1991 .. 1997 .. Turkmenistan .. .. 1995 1993 1993 .. 1996 e 1999 Uganda 1994 1988 1994 1988 1988 1994 1993 2002 f Ukraine 1999 .. 1997 1986 1988 1999 1995 .. United Arab Emirates .. .. 1996 1989 1989 .. 2000 .. United Kingdom 1995 1994 1994 1987 1988 1997 f 1994 2002 United States 1995 1995 1994 1986 1988 .. .. .. Uruguay .. .. 1994 1989 1991 1994 1994 2001 Uzbekistan .. .. 1994 1993 1993 .. 1995 e 1999 Venezuela .. .. 1995 1988 1989 .. 1994 .. Vietnam .. 1993 1995 1994 1994 1994 1995 2002 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 1996 1992 1996 1996 1996 1994 1996 .. Zambia 1994 .. 1994 1990 1990 1994 1993 .. Zimbabwe 1987 .. 1994 1992 1992 1994 1995 .. a. Ratification of the treaty. b. The years shown refer to the year the treaty entered into force in that country. c. Convention became effective November 16, 1994. d. Acceptance. e. Approval. f. Accession. g. Succession. 168 2004 World Development Indicators ENVIRONMENT 3.14 Government commitment About the data Definitions National environmental strategies and participation and Development (the Earth Summit) in Rio de · Environmental strategies and action plans provide in international treaties on environmental issues pro- Janeiro, which produced Agenda 21--an array of a comprehensive, cross-sectoral analysis of conserva- vide some evidence of government commitment to actions to address environmental challenges: tion and resource management issues to help inte- sound environmental management. But the signing · The Framework Convention on Climate Change grate environmental concerns with the development of these treaties does not always imply ratification, aims to stabilize atmospheric concentrations of process. They include national conservation strate- nor does it guarantee that governments will comply greenhouse gases at levels that will prevent gies, national environmental action plans, national with treaty obligations. human activities from interfering dangerously environmental management strategies, and national In many countries efforts to halt environmental with the global climate. sustainable development strategies. The year shown degradation have failed, primarily because govern- · The Vienna Convention for the Protection of the for a country refers to the year in which a strategy or ments have neglected to make this issue a priority, a Ozone Layer aims to protect human health and action plan was adopted. · Biodiversity assessments, reflection of competing claims on scarce resources. the environment by promoting research on the strategies, and action plans include biodiversity pro- To address this problem, many countries are prepar- effects of changes in the ozone layer and on files (see About the data). · Participation in treaties ing national environmental strategies--some focus- alternative substances (such as substitutes for covers five international treaties (see About the data). ing narrowly on environmental issues, and others chlorofluorocarbons) and technologies, monitor- · Climate change refers to the Framework Convention integrating environmental, economic, and social con- ing the ozone layer, and taking measures to con- on Climate Change (signed in New York in 1992). cerns. Among such initiatives are conservation trol the activities that produce adverse effects. · Ozone layer refers to the Vienna Convention for the strategies and environmental action plans. Some · The Montreal Protocol for CFC Control requires Protection of the Ozone Layer (signed in 1985). · CFC countries have also prepared country environmental that countries help protect the earth from exces- control refers to the Montreal Protocol for profiles and biodiversity strategies and profiles. sive ultraviolet radiation by cutting chlorofluoro- Chlorofluorocarbon Control (formally, the Protocol on National conservation strategies--promoted by the carbon consumption by 20 percent over their Substances That Deplete the Ozone Layer, signed in World Conservation Union (IUCN)--provide a compre- 1986 level by 1994 and by 50 percent over their 1987). · Law of the Sea refers to the United Nations hensive, cross-sectoral analysis of conservation and 1986 level by 1999, with allowances for increas- Convention on the Law of the Sea (signed in Montego resource management issues to help integrate envi- es in consumption by developing countries. Bay, Jamaica, in 1982). · Biological diversity refers to ronmental concerns with the development process. · The United Nations Convention on the Law of the the Convention on Biological Diversity (signed at the Such strategies discuss current and future needs, Sea, which became effective in November 1994, Earth Summit in Rio de Janeiro in 1992). The year institutional capabilities, prevailing technical condi- establishes a comprehensive legal regime for shown for a country refers to the year in which a treaty tions, and the status of natural resources in a country. seas and oceans, establishes rules for environ- entered into force in that country. · Kyoto Protocol National environmental action plans, supported by mental standards and enforcement provisions, refers to the protocol on climate change adopted at the World Bank and other development agencies, and develops international rules and national leg- the third conference of the parties to the United describe a country's main environmental concerns, islation to prevent and control marine pollution. Nations Framework Convention on Climate Change, identify the principal causes of environmental prob- · The Convention on Biological Diversity promotes held in Kyoto, Japan, in December 1997 (for more lems, and formulate policies and actions to deal with conservation of biodiversity among nations details see box 3.14a). them (table 3.14a). These plans are a continuing through scientific and technological cooperation, process in which governments develop comprehen- access to financial and genetic resources, and sive environmental policies, recommend specific transfer of ecologically sound technologies. actions, and outline the investment strategies, legis- But 10 years after Rio the World Summit on lation, and institutional arrangements required to Sustainable Development recognized that many of implement them. the proposed actions have yet to materialize. To help Biodiversity profiles--prepared by the World developing countries comply with their obligations Conservation Monitoring Centre and the IUCN--pro- under these agreements, the Global Environment vide basic background on species diversity, protect- Facility (GEF) was created to focus on global improve- ed areas, major ecosystems and habitat types, and ment in biodiversity, climate change, international legislative and administrative support. In an effort to waters, and ozone layer depletion. The UNEP, United Data sources establish a scientific baseline for measuring Nations Development Programme (UNDP), and the The data are from the Secretariat of the United progress in biodiversity conservation, the United World Bank manage the GEF according to the policies Nations Framework Convention on Climate Nations Environment Programme (UNEP) coordinates of its governing body of country representatives. The Change, the Ozone Secretariat of the UNEP, the global biodiversity assessments. World Bank is responsible for the GEF Trust Fund and World Resources Institute, the UNEP, the U.S. To address global issues, many governments have is chair of the GEF. National Aeronautics and Space Administration's also signed international treaties and agreements Socioeconomic Data and Applications Center, and launched in the wake of the 1972 United Nations Center for International Earth Science Information Conference on Human Environment in Stockholm and Network. the 1992 United Nations Conference on Environment 2004 World Development Indicators 169 3.15 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted national of fixed national expenditure depletion depletion forest dioxide emissions net savings a capital savings depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 13.8 9.1 4.7 2.8 1.0 0.0 0.0 0.3 0.1 6.1 Algeria .. 11.1 .. 4.5 33.4 0.1 0.1 1.3 0.7 .. Angola 23.3 10.4 12.9 4.4 36.3 0.0 0.0 0.5 .. ­19.6 b Argentina 22.3 11.1 11.2 3.2 5.4 0.2 0.0 0.8 1.6 6.5 Armenia 13.9 8.5 5.3 1.8 0.0 0.1 0.0 1.1 2.0 4.0 Australia 19.7 16.2 3.5 5.2 1.2 1.4 0.0 0.6 0.1 5.4 Austria 21.4 14.4 7.0 5.0 0.1 0.0 0.0 0.2 0.2 11.5 Azerbaijan 21.5 15.0 6.5 3.0 38.7 0.0 0.0 5.2 1.0 ­35.3 Bangladesh 28.5 5.8 22.7 1.3 0.8 0.0 0.8 0.4 0.3 21.7 Belarus 18.6 9.3 9.3 5.4 2.2 0.0 0.0 3.4 0.0 9.2 Belgium 23.4 14.5 8.9 3.0 0.0 0.0 0.0 0.3 0.2 11.4 Benin 9.2 8.1 1.1 2.7 0.1 0.0 1.3 0.4 0.3 1.8 Bolivia 12.2 9.1 3.1 4.8 5.9 0.8 0.0 1.1 0.7 ­0.5 Bosnia and Herzegovina 7.5 8.5 ­1.0 .. 0.1 0.0 0.0 2.4 0.4 .. Botswana .. 11.8 .. 5.6 0.0 0.2 0.0 0.6 .. .. Brazil 19.7 10.8 8.9 4.7 2.9 1.1 0.0 0.5 0.2 9.0 Bulgaria 15.4 10.3 5.1 3.0 0.2 0.4 0.0 2.2 2.1 3.3 Burkina Faso 8.0 6.7 1.3 2.4 0.0 0.0 1.2 0.3 0.5 1.8 Burundi 11.0 6.4 4.6 3.9 0.0 0.1 10.4 0.2 0.1 ­2.3 Cambodia 18.5 7.3 11.2 1.8 0.0 0.0 0.9 0.1 0.1 11.9 Cameroon .. 8.9 .. 2.3 6.2 0.0 0.0 0.6 0.7 .. Canada 23.2 13.0 10.2 6.9 4.0 0.1 0.0 0.5 0.2 12.3 Central African Republic .. 7.8 .. 1.6 0.0 0.0 0.0 0.2 0.4 .. Chad .. 7.2 .. 1.4 0.0 0.0 0.0 0.1 .. .. Chile 24.5 10.0 14.4 3.4 0.2 4.7 0.0 0.7 1.0 11.2 China 43.7 9.0 34.7 2.0 2.7 0.2 0.0 2.2 1.0 30.7 Hong Kong, China 32.1 12.9 19.2 2.8 0.0 0.0 0.0 0.2 0.0 21.8 Colombia 13.7 10.5 3.2 3.1 6.5 0.2 0.0 0.5 0.1 ­1.0 Congo, Dem. Rep. .. 6.8 .. 0.9 1.8 0.0 0.0 0.3 0.0 .. Congo, Rep. 34.6 12.5 22.2 5.9 47.4 0.2 0.0 0.8 .. .. Costa Rica 15.1 5.9 9.2 5.2 0.0 0.0 0.3 0.2 0.3 13.5 Côte d'Ivoire 20.5 9.2 11.3 4.5 0.0 0.0 0.6 0.5 0.6 14.1 Croatia 21.2 11.5 9.7 .. 0.6 0.0 0.0 0.6 0.3 .. Cuba .. .. .. 6.1 .. .. .. .. .. .. Czech Republic 23.0 12.2 10.7 4.4 0.1 0.0 0.0 1.2 0.1 13.8 Denmark 23.4 15.4 8.0 7.7 0.3 0.0 0.0 0.2 0.1 15.0 Dominican Republic 20.4 5.3 15.1 1.7 0.0 0.3 0.0 0.9 0.2 15.4 Ecuador .. 10.6 .. 3.2 13.8 0.0 0.0 1.2 0.1 .. Egypt, Arab Rep. 15.1 9.5 5.6 4.4 4.6 0.1 0.2 1.0 1.4 2.7 El Salvador 14.2 10.4 3.8 2.2 0.0 0.0 0.6 0.3 0.2 4.9 Eritrea 21.7 5.2 16.6 1.4 0.0 0.0 0.0 0.5 0.5 16.9 Estonia 20.2 14.2 6.0 6.3 0.5 0.0 0.0 2.2 0.2 9.4 Ethiopia 15.4 6.1 9.3 4.0 0.0 0.1 12.8 0.6 0.3 ­0.5 Finland 27.0 16.1 11.0 7.0 0.0 0.0 0.0 0.3 0.1 17.5 France 21.1 12.4 8.7 5.6 0.0 0.0 0.0 0.2 0.0 14.0 Gabon 40.7 12.9 27.8 2.2 27.8 0.0 0.0 0.6 0.1 1.5 Gambia, The .. 8.4 .. 3.4 0.0 0.0 0.6 0.5 0.7 .. Georgia 15.0 15.7 ­0.7 4.3 0.5 0.0 0.0 1.0 2.5 ­0.5 Germany 20.4 14.8 5.5 4.4 0.1 0.0 0.0 0.3 0.1 9.5 Ghana 20.4 7.1 13.3 2.8 0.0 1.2 2.7 0.8 0.2 11.3 Greece 19.7 8.7 11.0 3.1 0.1 0.0 0.0 0.5 0.7 12.8 Guatemala 14.8 10.1 4.7 1.6 0.7 0.0 0.9 0.3 0.2 4.2 Guinea 17.1 8.1 9.0 2.0 0.0 1.7 1.9 0.3 0.6 6.5 Guinea-Bissau .. 7.7 .. .. 0.0 0.0 0.0 0.7 0.9 .. Haiti .. 1.9 .. 1.5 0.0 0.0 0.9 0.3 0.2 .. 170 2004 World Development Indicators ENVIRONMENT 3.15 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted national of fixed national expenditure depletion depletion forest dioxide emissions net savings a capital savings depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Honduras 23.3 5.6 17.7 3.5 0.0 0.2 0.0 0.5 0.2 20.4 Hungary 23.5 11.8 11.7 4.3 0.3 0.0 0.0 0.7 0.4 14.6 India 22.3 9.7 12.6 3.2 2.3 0.3 1.0 1.7 0.7 9.8 Indonesia 18.2 5.4 12.7 0.6 8.6 1.2 0.0 0.9 0.5 2.1 Iran, Islamic Rep. 38.9 9.7 29.2 3.5 29.7 0.2 0.0 2.1 0.7 0.1 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 28.0 12.5 15.5 5.7 0.0 0.1 0.0 0.4 0.1 20.6 Israel 13.4 13.2 0.2 6.9 0.0 0.1 0.0 0.4 0.0 6.7 Italy 19.8 13.6 6.1 4.7 0.1 0.0 0.0 0.2 0.2 10.3 Jamaica 20.7 11.6 9.1 5.8 0.0 1.2 0.0 0.9 0.3 12.4 Japan 27.2 15.8 11.4 3.6 0.0 0.0 0.0 0.2 0.4 14.4 Jordan 26.2 10.6 15.6 5.6 0.0 1.1 0.0 1.2 0.7 18.2 Kazakhstan 25.5 10.0 15.5 4.4 33.4 0.0 0.0 4.4 0.4 ­18.3 Kenya 13.7 7.9 5.7 6.1 0.0 0.0 0.1 0.4 0.2 11.0 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 27.3 12.0 15.3 3.7 0.0 0.0 0.0 0.7 0.8 17.5 Kuwait 19.4 6.7 12.7 5.0 42.2 0.0 0.0 0.8 2.0 ­27.3 Kyrgyz Republic 17.4 8.2 9.2 5.1 1.0 0.0 0.0 2.6 0.2 10.5 Lao PDR .. 8.0 .. 1.8 0.0 0.0 0.0 0.2 0.2 .. Latvia 19.6 10.8 8.9 6.1 0.0 0.0 0.0 0.7 0.3 13.9 Lebanon 2.1 10.3 ­8.2 2.5 0.0 0.0 0.0 0.6 0.6 ­6.8 Lesotho 22.0 6.5 15.4 6.4 0.0 0.0 2.5 0.0 0.4 18.9 Liberia .. 8.2 .. .. 0.0 0.2 3.3 1.1 0.0 .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania 17.4 10.1 7.2 5.2 0.3 0.0 0.0 0.9 0.7 10.5 Macedonia, FYR 12.9 9.8 3.1 4.9 0.0 0.0 0.0 2.0 0.3 5.8 Madagascar 8.5 7.9 0.6 1.9 0.0 0.0 0.0 0.3 0.2 2.0 Malawi 0.8 7.0 ­6.3 4.4 0.0 0.0 1.4 0.3 0.2 ­3.7 Malaysia 34.5 11.7 22.8 4.1 8.9 0.0 0.0 1.0 0.1 16.8 Mali 3.2 8.4 ­5.2 2.1 0.0 0.0 0.0 0.2 0.5 ­3.8 Mauritania .. 8.1 .. 3.7 0.0 20.5 0.9 2.4 .. .. Mauritius 27.7 10.8 16.9 3.3 0.0 0.0 0.0 0.4 .. .. Mexico 18.3 10.5 7.8 4.6 4.9 0.1 0.0 0.5 0.5 6.5 Moldova 14.4 7.3 7.2 10.3 0.0 0.0 0.0 3.3 0.5 13.6 Mongolia 26.7 12.1 14.6 5.7 0.0 2.3 0.0 4.8 0.5 12.7 Morocco 26.1 10.0 16.1 4.9 0.0 0.2 0.0 0.7 0.2 19.9 Mozambique 27.7 8.3 19.4 3.8 0.0 0.0 0.0 0.3 0.4 22.4 Myanmar 12.4 .. .. 0.9 .. .. .. .. .. .. Namibia 39.6 11.5 28.1 8.5 0.0 0.4 0.0 0.4 0.2 35.6 Nepal 22.1 2.4 19.8 3.2 0.0 0.0 4.2 0.4 0.1 18.2 Netherlands 22.2 15.0 7.2 4.9 0.1 0.0 0.0 0.3 0.4 11.3 New Zealand 19.4 10.8 8.6 6.9 0.9 0.1 0.0 0.4 0.0 14.2 Nicaragua 11.2 .. .. 3.7 0.0 0.1 0.9 0.6 0.0 .. Niger .. 7.1 .. 2.3 0.0 0.0 3.6 0.4 0.4 .. Nigeria 13.1 8.3 4.8 0.5 38.7 0.0 0.0 0.5 0.8 ­34.7 Norway 32.0 15.9 16.1 6.9 4.2 0.0 0.0 0.3 0.1 18.4 Oman .. 11.5 .. 4.1 40.3 0.0 0.0 0.6 .. .. Pakistan 25.6 7.7 17.9 2.3 3.6 0.0 1.0 1.2 1.0 13.4 Panama 24.2 7.6 16.6 4.8 0.0 0.0 0.0 0.6 0.3 20.4 Papua New Guinea .. 8.6 .. .. 10.0 4.2 0.0 0.5 0.0 .. Paraguay 14.2 9.0 5.2 3.9 0.0 0.0 0.0 0.5 0.4 8.3 Peru 17.2 10.4 6.7 2.6 0.9 1.4 0.0 0.3 0.6 6.0 Philippines 24.5 7.9 16.5 2.9 0.0 0.1 0.2 0.7 0.4 18.0 Poland 16.6 11.2 5.4 7.5 0.3 0.1 0.0 1.3 0.7 10.5 Portugal 19.3 15.3 4.0 5.3 0.0 0.0 0.0 0.3 0.4 8.6 Puerto Rico .. 7.4 .. .. 0.0 0.0 0.0 0.2 .. .. 2004 World Development Indicators 171 3.15 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted national of fixed national expenditure depletion depletion forest dioxide emissions net savings a capital savings depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Romania 19.9 9.8 10.0 3.6 2.3 0.0 0.0 1.4 0.2 9.7 Russian Federation 30.6 10.4 20.1 3.6 25.5 0.3 0.0 3.1 0.6 ­5.7 Rwanda 12.2 7.3 4.9 3.5 0.0 0.0 3.9 0.3 0.0 4.2 Saudi Arabia 28.9 10.0 18.9 7.2 42.2 0.0 0.0 0.8 1.0 ­17.9 Senegal 11.5 8.4 3.2 3.7 0.0 0.2 0.3 0.6 .. .. Serbia and Montenegro .. 9.6 .. .. 0.9 0.1 0.0 1.7 0.2 .. Sierra Leone .. 7.1 .. 0.9 0.0 0.0 5.2 0.4 0.4 .. Singapore 42.7 14.4 28.3 2.3 0.0 0.0 0.0 0.6 0.4 29.6 Slovak Republic 23.2 11.2 12.0 4.6 0.0 0.0 0.0 1.2 0.1 15.2 Slovenia 25.2 11.7 13.5 5.3 0.0 0.0 0.0 0.5 0.2 18.2 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 16.5 13.3 3.2 7.6 1.6 1.2 0.3 2.3 0.2 5.2 Spain 24.0 12.8 11.1 4.6 0.0 0.0 0.0 0.3 0.4 15.0 Sri Lanka 19.9 5.1 14.8 2.9 0.0 0.0 0.6 0.3 0.3 16.3 Sudan 13.1 8.3 4.8 0.9 0.0 0.1 0.0 0.2 0.6 4.8 Swaziland 7.2 8.9 ­1.7 5.1 0.0 0.0 0.0 0.2 0.1 3.1 Sweden 21.4 13.4 8.0 8.3 0.1 0.1 0.0 0.1 0.0 16.0 Switzerland 26.8 14.9 12.0 4.9 0.0 0.0 0.0 0.1 0.2 16.6 Syrian Arab Republic 24.3 10.4 13.9 2.6 27.5 0.1 0.0 1.7 0.8 ­13.6 Tajikistan 5.0 7.4 ­2.5 2.0 0.4 0.0 0.0 3.8 0.2 ­4.8 Tanzania 14.5 7.5 7.0 2.4 0.0 0.4 0.0 0.3 0.2 8.5 Thailand 30.4 14.9 15.5 3.6 0.8 0.0 0.3 1.1 0.4 16.5 Togo 8.0 7.8 0.2 4.2 0.0 0.6 3.9 1.0 0.3 ­1.4 Trinidad and Tobago 28.9 11.9 17.0 3.3 21.9 0.0 0.0 1.9 0.0 ­3.4 Tunisia 22.7 10.1 12.6 6.6 3.6 0.5 0.1 0.7 0.3 13.9 Turkey 16.7 7.0 9.8 2.2 0.3 0.0 0.0 0.7 1.2 9.8 Turkmenistan 36.3 9.7 26.6 .. 53.6 0.0 0.0 4.8 0.3 .. Uganda 15.8 7.6 8.2 1.9 0.0 0.0 5.6 0.2 0.0 4.3 Ukraine 27.1 19.0 8.1 6.4 7.6 0.0 0.0 6.3 1.0 ­0.5 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 14.4 11.3 3.1 5.3 0.6 0.0 0.0 0.2 0.1 7.5 United States 14.4 11.8 2.6 5.4 0.9 0.0 0.0 0.4 0.3 6.4 Uruguay 13.5 11.1 2.4 3.0 0.0 0.0 0.3 0.3 1.9 3.0 Uzbekistan 17.2 9.9 7.3 9.4 51.7 0.0 0.0 10.9 0.6 ­46.6 Venezuela, RB 26.6 7.4 19.2 4.3 27.0 0.3 0.0 1.0 0.0 ­4.8 Vietnam 33.6 8.1 25.4 2.8 6.7 0.0 0.7 1.0 0.4 19.3 West Bank and Gaza .. 7.3 .. .. 0.0 0.0 0.0 0.0 .. .. Yemen, Rep. 24.1 9.5 14.6 .. 36.0 0.0 0.0 1.1 0.5 .. Zambia .. 8.2 .. 2.0 0.0 1.1 0.0 0.4 .. .. Zimbabwe .. 9.0 .. 6.9 0.3 0.3 0.0 1.1 0.5 .. World 19.5 w 12.5 w 7.0 w 4.7 w 1.9 w 0.1 w 0.0 w 0.5 w 0.3 w 8.8 w Low income 21.5 8.4 13.1 2.6 5.9 0.4 0.8 1.3 0.6 6.7 Middle income 27.7 10.1 17.6 3.8 7.7 0.3 0.0 1.4 0.7 11.3 Lower middle income 30.8 9.9 20.9 3.2 6.6 0.3 0.1 1.7 0.7 14.6 Upper middle income 21.4 10.6 10.8 5.0 9.7 0.2 0.0 0.7 0.6 4.5 Low & middle income 26.6 9.8 16.8 3.6 7.4 0.3 0.2 1.4 0.6 10.5 East Asia & Pacific 38.8 9.2 29.6 2.2 3.4 0.3 0.1 1.8 0.8 25.5 Europe & Central Asia 22.7 10.5 12.2 4.8 9.7 0.1 .. 2.1 0.6 .. Latin America & Carib. 19.3 10.3 9.0 4.2 5.2 0.6 0.0 0.5 0.5 6.3 Middle East & N. Africa 23.4 10.0 13.4 5.2 26.3 0.1 0.0 1.3 0.9 ­10.0 South Asia 23.1 9.0 14.0 2.9 2.2 0.3 1.0 1.5 0.7 11.3 Sub-Saharan Africa 15.9 10.2 5.8 5.1 8.1 0.5 0.7 1.1 0.4 0.0 High income 17.4 13.1 4.3 5.0 0.7 0.0 .. 0.3 0.3 .. Europe EMU 21.1 13.8 7.4 4.8 0.0 0.0 .. 0.3 0.2 .. a. The cutoff date for these data is February 2004; later revisions are not captured in this table. b. Adjusted net savings do not include particulate emission damage. 172 2004 World Development Indicators ENVIRONMENT 3.15 Toward a broader measure of savings About the data Definitions Adjusted net savings measure the change in value of return on capital). Unit rents are then multiplied by · Gross national savings are calculated as the dif- a specified set of assets, excluding capital gains. If the physical quantity extracted or harvested in order ference between gross national income and public a country's net savings are positive and the account- to arrive at a depletion figure. This figure is one of a and private consumption, plus net current transfers. ing includes a sufficiently broad range of assets, range of depletion estimates that are possible, · Consumption of fixed capital represents the economic theory suggests that the present value of depending on the assumptions made about future replacement value of capital used up in the process social welfare is increasing. Conversely, persistently quantities, prices, and costs, and there is reason to of production. · Net national savings are equal to negative adjusted net savings indicate that an econ- believe that it is at the high end of the range. Some gross national savings less the value of consumption omy is on an unsustainable path. of the largest depletion estimates in the table should of fixed capital. · Education expenditure refers to Adjusted net savings are derived from standard therefore be viewed with caution. public current operating expenditures in education, national accounting measures of gross national sav- A positive net depletion figure for forest resources including wages and salaries and excluding capital ings by making four adjustments. First, estimates of implies that the harvest rate exceeds the rate of nat- investments in buildings and equipment. · Energy capital consumption of produced assets are deduct- ural growth; this is not the same as deforestation, depletion is equal to the product of unit resource ed to obtain net national savings. Second, current which represents a change in land use (see rents and the physical quantities of energy extracted. expenditures on education are added to net national Definitions for table 3.4). In principle, there should It covers coal, crude oil, and natural gas. · Mineral savings (in standard national accounting these be an addition to savings in countries where growth depletion is equal to the product of unit resource expenditures are treated as consumption). Third, exceeds harvest, but empirical estimates suggest rents and the physical quantities of minerals extract- estimates of the depletion of a variety of natural that most of this net growth is in forested areas that ed. It refers to tin, gold, lead, zinc, iron, copper, nick- resources are deducted to reflect the decline in cannot be exploited economically at present. el, silver, bauxite, and phosphate. · Net forest asset values associated with their extraction and Because the depletion estimates reflect only timber depletion is calculated as the product of unit harvest. And fourth, deductions are made for dam- values, they ignore all the external and nontimber resource rents and the excess of roundwood harvest age from carbon dioxide and particulate emissions. benefits associated with standing forests. over natural growth. · Carbon dioxide emissions The exercise treats education expenditures as an Pollution damage from emissions of carbon dioxide damage is estimated to be $20 per ton of carbon (the addition to savings effort. But because of the wide is calculated as the marginal social cost per unit mul- unit damage in 1995 U.S. dollars) times the number variability in the effectiveness of government educa- tiplied by the increase in the stock of carbon dioxide. of tons of carbon emitted. · Particulate emissions tion expenditures, these figures cannot be con- The unit damage figure represents the present value damage is calculated as the willingness to pay to strued as the value of investments in human of global damage to economic assets and to human avoid mortality attributable to particulate emissions. capital. The accounting for human capital is also welfare over the time the unit of pollution remains in · Adjusted net savings are equal to net national sav- incomplete because depreciation of human capital the atmosphere. ings plus education expenditure and minus energy is not estimated. Pollution damage from particulate emissions is depletion, mineral depletion, net forest depletion, and There are also gaps in the accounting of natural estimated by valuing the human health effects from carbon dioxide and particulate emissions damage. resource depletion and pollution costs. Key esti- exposure to particulate matter less than 10 microns mates missing on the resource side include the in diameter. The estimates are calculated as willing- Data sources value of fossil water extracted from aquifers, net ness to pay to avoid mortality attributable to partic- Gross national savings are derived from the World depletion of fish stocks, and depletion and degrada- ulate emissions (in particular, mortality relating to Bank's national accounts data files, described in tion of soils. Important pollutants affecting human cardiopulmonary disease in adults, lung cancer in the Economy section. Consumption of fixed capi- health and economic assets are excluded because adults, and acute respiratory infections in children). tal is from the United Nations Statistics Division's no internationally comparable data are widely avail- National Accounts Statistics: Main Aggregates able on damage from ground-level ozone or from sul- and Detailed Tables, 1997, extrapolated to 2002. fur oxides. The education expenditure data are from the Estimates of resource depletion are based on the United Nations Statistics Division's Statistical calculation of unit resource rents. An economic rent Yearbook 1997, extrapolated to 2002. The wide represents an excess return to a given factor of range of data sources and estimation methods production--in this case the returns from resource used to arrive at resource depletion estimates extraction or harvest are higher than the normal rate are described in a World Bank working paper, of return on capital. Natural resources give rise to "Estimating National Wealth" (Kunte and others rents because they are not produced; in contrast, for 1998). The unit damage figure for carbon dioxide produced goods and services competitive forces will emissions is from Fankhauser (1995). The esti- expand supply until economic profits are driven to mates of damage from particulate emissions are zero. For each type of resource and each country, from Pandey and others (2003). The conceptual unit resource rents are derived by taking the differ- underpinnings of the savings measure appear in ence between world prices and the average unit Hamilton and Clemens (1999). extraction or harvest costs (including a "normal" 2004 World Development Indicators 173 4 ECONOMY I n 2002 the world economy grew by 1.9 percent, a slight increase from 1.3 percent in 2001, but below the 2.7 percent annual average in the 1990s. The world's recorded output--and income--grew by more than $1.1 trillion. Lower-middle-income economies saw the fastest growth, followed by low-income economies. Upper-middle-income economies, affected by slowing investment and widespread uncertainty in financial markets, experienced negative growth. High-income economies, accounting for 81 percent of the world's gross domestic product (GDP), almost doubled their growth over 2001, from 0.9 percent to 1.6 percent (figure 4a). Over the past decade economic growth was fastest in East Asia and Pacific (averaging 7.3 percent a year) and South Asia (5.4 percent). Leading this growth were China and India, each accounting for more than 70 percent of its region's output. These two regions even did comparatively well in 2002, with East Asia registering 6.7 percent growth--demonstrating its continuing recovery from the financial crisis in 1998, when annual growth fell to 0.7 percent--and South Asia recording 4.3 percent growth, a slight decline over 2001. 4a Output declined in the transition Economic growth varies by region economies of Europe and Central Average annual growth (%) 8 Asia in the 1990s, but recovered 1980­90 in the early 2000s, averaging 3.5 1990­2000 6 2001 percent growth for 2001­02. 2002 Several countries of the former 4 Soviet Union, such as Armenia, Azerbaijan, Kazakhstan, Moldova, 2 Tajikistan, and Turkmenistan, have been registering growth rates of more than 7 percent, 0 buoyed by increased exports of natural gas and petroleum ­2 East Europe & South Middle East Sub- Latin High products. But in Russia growth Asia & Central Asia & North Saharan America & income Pacific Asia Africa Africa Caribbean declined from 5 percent in 2001 Note: No data are available for Europe and Central Asia for 1980­90. to 4.3 percent in 2002. Source: World Bank data files. 2004 World Development Indicators 175 In Latin America and the Caribbean and the Middle East and In developing economies services generated more than North Africa growth was faster in the 1990s than in the 1980s. half of GDP in 2002, compared with 71 percent in high- But in Latin America growth decelerated sharply in 2001 and income economies (table 4.2). But in East Asia and Pacific turned negative in 2002, with Argentina, Uruguay, and services produced only 38 percent of GDP in 2002, and from Venezuela experiencing large declines in growth and with 1990 to 2002 growth in manufacturing, at 9.8 percent a Mexico growing only 0.9 percent and Brazil only 1.5 percent. year, outpaced growth in services, at 6.4 percent. This trend However, the heavily indebted poor countries, many in Sub- reflects the rapid growth of manufacturing in China (11.9 per- Saharan Africa, registered 4.1 percent growth in 2002, follow- cent a year), which also had rapid expansion in services (8.8 ing 4.7 percent growth in 2001. As a result, Sub-Saharan Africa percent a year). did better in 2001 and 2002 than in the 1990s, when growth declined sharply. The contribution of trade With two decades of high growth, the East Asia and Pacific After expanding by 6.7 percent a year in 1990­2001, global region has nearly reached the GDP level of the Latin America trade (exports plus imports) grew by only 3.7 percent in 2002. and Caribbean region (figure 4b). By contrast GDP in the Europe High-income economies, which account for more than 75 per- and Central Asia region, almost equal to that of Latin America cent of global trade, grew by only 2.3 percent in 2002, recover- and the Caribbean in 1990, is now only about half its size after ing from a slowdown in 2001. But trade by low-income half a decade of negative growth. Steady growth has also economies grew by 5.6 percent. moved South Asia ahead of the Middle East and North Africa, Trade in ser vices has grown rapidly, but trade in but GDP per capita lags far behind in this populous region. merchandise--primary commodities and manufactured goods-- continues to dominate. In 2002 merchandise accounted for 81 Patterns of change percent of all exports of goods and commercial services, and Most developing economies are following familiar patterns of manufactured goods for 78 percent of merchandise exports growth, with agriculture giving way first to manufacturing and (tables 4.5 and 4.7). Exporters of primary nonfuel commodities later to services as the main source of income. But some, such saw their trade volumes increase, but a continuing decline in as Jordan and Panama, have moved directly from agriculture to their terms of trade left them with less income (table 4.4). The service-based economies. For most economies services have economies of Sub-Saharan Africa were hit particularly hard. been the fastest growing sector. In 1990­2002 the service The structure of trade in services is also changing. Transport sector grew by 3.6 percent a year in developing and transition services are being replaced in importance by travel services. In economies and by 3 percent in high-income economies. Among the 1990s high-income countries were the main exporters of developing regions South Asia had the fastest growth in serv- financial services. Now, many developing countries are emerg- ices in the 1990s, at 7 percent a year, and Europe and Central ing as exporters of these new services along with computer, Asia the slowest, at 0.8 percent (table 4.1). information, and business services. The share of low- and middle-income economies in these new service exports is 4b increasing slowly, rising by 1.1 percentage points between 1990 and 2002 (table 4.7). With two decades of rapid growth, East Asia and Pacific has caught up with Latin America and the Caribbean Increased globalization has enabled greater labor mobility, and worker remittances have been steadily growing in countries Gross domestic product (1995 $ billions) like India, resulting in favorable current account balances and 2,000 Latin America & Caribbean increased reserves. India has the ninth largest reserves, East Asia & ahead of many high-income countries. Japan has the largest Pacific reserves, followed by China. The increase in the price of gold 1,500 from $277 in 2001 to $343 in 2002 resulted in a considerable increase in the reserves of many countries (table 4.15). Europe & Central Asia Steady trends in consumption, investment, and saving 1,000 Most of the world's output goes to final consumption by house- holds (including individuals) and governments. The share of South Asia final consumption in world output has remained fairly constant Middle East & North Africa over time, averaging about 80 percent in 1990­2002 (table 500 4.9). Growth of per capita household consumption expenditure Sub-Saharan Africa provides an important indicator of the potential for reducing poverty. In 1990­2002 per capita consumption grew by 5.5 0 percent a year in East Asia and Pacific but rose by only 0.1 per- 1980 1985 1990 1995 2002 cent in Sub-Saharan Africa. It rose by 1.7 in Europe and Central Source: World Bank data files. Asia and by 2.7 percent in South Asia (table 4.10). 176 2004 World Development Indicators Output that is not consumed goes to exports (less imports) leading to greater fiscal stability, inflation rates and interest and gross capital formation (investment). Investment is rates have tended to decline (table 4.14). financed out of domestic and foreign savings. In 2002 the glob- al savings rate averaged 20 percent of total output. But global External debt increases averages disguise large differences between countries. In 2002 the external debt of low- and middle-income economies Savings rates are consistently lower in Sub-Saharan Africa. And increased by $74 billion, or about 3 percent of their total debt they tend to be volatile in countries dependent on commodity stock, reversing the decline in 2001. Middle-income economies exports. Gross domestic savings in the Middle East and North accounted for 75 percent of the increase. The increase was Africa rose from 23 percent of GDP in 1999 to 30 percent in $47 billion in Europe and Central Asia, $12 billion in South 2000 and 29 percent in 2002, buoyed by higher oil prices. The Asia, $11 billion in the Middle East and North Africa, and $8 highest savings rate was in East Asia and Pacific, where gross billion in Sub-Saharan Africa. By contrast, debt stocks fell by $2 domestic savings averaged above 35 percent during most of billion in East Asia, and $1 billion in Latin America and the the past decade and was 37 percent in 2002 (table 4.9). Caribbean (table 4.16). Debt management indicators are In 1990­2002 the rate of gross capital formation increased shown in table 4.17. by about 6.9 percent a year in East Asia and Pacific and 6.5 percent in South Asia, but declined by 6.6 percent in Data on the economy Europe and Central Asia. East Asia and Pacific continued to The indicators in this section measure changes in the size and have the highest investment rate in the world, at 32 percent of structure of the global economy and the varying effects of GDP in 2002. By contrast, investment averaged only 18 per- these changes on national economies. They include measures cent of GDP in Sub-Saharan Africa (tables 4.9 and 4.10). of macroeconomic performance (gross domestic product, con- sumption, investment, and international trade) and of stability Fiscal affairs (central government budgets, prices, the money supply, the Developing countries have had larger overall central govern- balance of payments, and external debt). Other important eco- ment deficits than high-income countries. But with the excep- nomic indicators appear throughout the book, especially in the tion of East Asia and Pacific and Latin America and the States and markets section (credit, investment, financial mar- Caribbean, deficits have been falling. The South Asia region kets, tax policies, exchange rates) and the Global links section has the largest deficit among the developing regions. Central (trade and tariffs, foreign investment, and aid flows). governments of developing economies had expenditures aver- Most of the indicators in this section remain the same as aging 21 percent of GDP in 1999 and revenues (mainly from last year, with a few exceptions. Tables 4.7 and 4.8 now break taxes on goods and services) averaging 17 percent of GDP, out insurance and financial services and computer, informa- leaving a fiscal deficit of about 3 percent of GDP after taking tion, and communications services. Balance of payments data grants into account (table 4.11). (table 4.15) are presented in calendar years for all countries Government expenditures go mostly to the purchase of except Bhutan and Myanmar, which are still in fiscal years. goods and services (including the wages and salaries of public Thus for countries whose data were previously reported in fis- employees) and to subsidies and current transfers to private cal years, such as Egypt, India, and Pakistan, this year's data and public enterprises and local governments. The rest go to will not be comparable with previous data. The switch from fis- interest payments and capital expenditures. In 2000 subsidies cal year to calendar year was made so that data will be con- and current transfers accounted for 59 percent of government sistent among countries and with the calendar year data in spending in high-income economies and 51 percent in Europe tables 4.5 and 4.6. and Central Asia, but only 14 percent in the Middle East and In table 4.17 the gross national income (GNI) and export val- North Africa (table 4.12). ues used as denominators for calculating the ratio of the pres- The sources of government revenue have been changing. ent value of debt are three-year averages instead of single year Taxes on income, profits, and capital gains generated 23 per- values. The switch, made to even out fluctuations in GNI and cent of current revenues in 1990, but their share fell to 18 per- exports, is consistent with the methodology followed in other cent in 2000, whereas taxes on goods and services rose from World Bank publications. Workers' remittances are not includ- 27 percent to 34 percent. High-income economies depended ed as part of exports. And because the level of public and pub- more on income taxes (26 percent) than did low- and middle- licly guaranteed debt is the primary concern of the Heavily income economies, which derived 35 percent of their revenue Indebted Poor Countries (HIPC) Debt Initiative and of the from taxes on goods and services and 9 percent from taxes on Millennium Development Goals, public and publicly guaranteed trade (table 4.13). debt service replaces total debt service as a ratio of GNI and Governments, because of their size, have a large effect on as a ratio of exports, and multilateral debt service as a ratio of economic performance. High taxes and subsidies can distort public and publicly guaranteed debt replaces public and publicly economic behavior, and large fiscal deficits make it harder to guaranteed debt service as a ratio of central government cur- manage the growth of the money supply and thus increase the rent revenue. The indicators dropped are still available on the likelihood of inflation. As governments have adopted policies World Development Indicators CD-ROM. 2004 World Development Indicators 177 4.a Recent economic performance Gross domestic Exports of goods Imports of goods GDP deflator Current account Total product and services and services balance reserves a months annual annual annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2002 2003 2002 2003 2002 2003 2002 2003 2002 2003 2003 2003 Algeria 4.1 6.8 4.7 6.8 17.8 4.6 1.0 7.3 .. 10.9 .. .. Argentina ­10.9 7.0 3.1 3.7 ­50.1 39.1 30.6 10.7 9.4 6.1 1,313 0.6 Armenia 12.9 12.0 29.0 40.2 14.2 33.8 2.3 4.0 ­6.3 ­7.1 550 4.3 Azerbaijan 10.6 9.3 16.6 8.1 49.8 39.2 0.7 5.9 ­12.6 ­26.9 732 1.8 Bangladesh 4.4 5.3 ­2.3 ­0.4 ­11.2 0.8 3.2 4.4 1.6 0.6 2,454 2.8 Bolivia 2.8 2.4 12.4 14.6 7.7 ­7.1 2.7 3.7 ­4.4 ­0.9 843 4.4 Bosnia and Herzegovina 3.9 3.5 5.3 11.5 ­1.9 10.6 2.1 1.1 ­38.2 ­17.7 1,418 4.1 Botswana 3.1 3.7 ­4.8 6.9 3.8 6.9 5.5 5.6 .. 9.0 5,853 29.0 Brazil 1.5 ­0.2 7.8 14.2 ­12.8 ­1.9 8.5 10.1 ­1.7 0.4 35,869 5.1 Bulgaria 4.8 4.7 6.2 14.3 4.7 21.2 3.9 2.5 ­4.4 ­7.0 5,774 5.4 Cameroon 4.4 4.2 1.6 3.8 3.4 ­2.6 0.7 1.1 .. ­3.1 114 0.4 Chile 2.1 4.0 5.6 5.7 0.4 7.1 2.6 3.0 ­0.9 ­1.4 18,707 9.1 China 8.0 8.2 29.4 22.7 27.5 31.0 ­0.3 1.1 2.8 1.1 410,049 10.7 Colombia 1.6 2.5 ­4.4 5.4 0.6 3.3 6.1 7.4 ­2.0 ­2.5 10,586 6.1 Costa Rica 3.0 5.0 5.1 8.5 7.0 4.5 9.1 9.3 ­5.6 ­5.9 1,300 1.7 Dominican Republic 4.1 ­1.3 13.0 8.0 13.9 ­7.0 6.4 28.0 ­4.0 4.5 665 0.9 Ecuador 3.4 2.5 0.9 1.8 17.2 1.7 11.8 8.8 ­5.0 ­3.3 1,072 1.4 Egypt, Arab Rep. 3.0 3.1 ­10.4 14.0 ­10.8 0.2 4.0 3.9 0.7 1.9 .. .. El Salvador 2.1 2.2 5.7 3.4 0.5 5.2 1.3 2.8 ­2.7 .. 1,607 .. Estonia 6.0 4.5 6.0 3.8 10.2 7.9 4.1 4.4 ­12.3 ­12.9 1,386 2.4 Ghana 4.5 4.7 ­1.7 2.7 ­4.4 7.7 22.8 27.6 ­0.5 ­0.5 811 2.4 Guatemala 2.2 2.4 ­3.2 5.2 4.2 2.9 8.0 5.5 ­5.1 ­4.3 2,667 4.3 Honduras 2.5 1.5 2.1 ­2.5 2.1 1.3 6.2 9.8 ­4.1 ­7.6 1,492 4.5 India 4.3 6.8 9.9 6.9 17.9 14.2 3.0 4.3 0.9 ­0.3 78,222 8.8 Indonesia 3.7 4.1 ­1.2 4.0 ­8.3 2.0 7.2 6.6 4.3 3.9 36,246 7.0 Iran, Islamic Rep. 6.7 6.2 2.5 2.5 16.1 13.1 21.5 23.4 .. ­1.5 23,706 7.6 Jamaica 1.1 3.0 ­5.3 .. 3.6 .. 8.0 10.0 ­14.2 ­11.6 1,037 2.2 Jordan 4.9 3.0 11.6 4.1 0.7 6.6 0.5 0.8 5.0 4.4 3,940 6.5 Kazakhstan 9.8 9.0 22.6 26.7 4.3 42.4 5.8 6.6 ­2.8 ­1.7 4,852 4.0 Kenya 1.0 1.3 ­18.5 5.7 ­16.7 8.8 8.7 7.2 .. ­1.4 1,564 4.1 Latvia 6.1 7.0 6.3 8.2 4.5 5.1 1.8 3.0 ­7.7 ­8.3 .. .. Lesotho 4.5 3.9 43.0 0.0 15.0 0.7 9.0 9.8 ­15.1 ­12.2 417 4.5 Lithuania 6.7 6.3 19.4 23.1 16.1 21.9 0.0 0.0 ­5.2 ­5.9 .. .. continues on page 180 178 2004 World Development Indicators ECONOMY 4.b Key macroeconomic indicators Nominal exchange rate Real effective Money and Gross Real interest Short- exchange rate quasi money domestic credit rate term debt a local currency units annual annual % of per $ % change 1995 = 100 % growth % growth % exports 2003 2002 2003 2002 2003 2002 2003 2002 2003 2002 2003 2002 Algeria 72.6 2.4 ­8.9 101.7 91.1 .. .. .. .. 7.4 8.2 .. Argentina 2.9 232.2 ­12.5 .. .. 19.7 26.8 97.1 ­4.0 16.2 7.6 46.6 Armenia 566.0 4.1 ­3.2 95.9 85.8 34.0 13.7 ­8.1 ­15.0 18.5 13.9 1.6 Azerbaijan 4,923.0 2.5 0.6 .. .. 14.6 30.8 84.2 27.1 16.5 16.7 2.9 Bangladesh 58.8 1.6 1.5 .. .. 13.3 14.7 12.2 6.7 12.4 5.7 5.3 Bolivia 7.8 9.8 4.5 115.4 97.7 ­6.9 6.4 4.9 3.4 17.5 9.7 21.5 Bosnia and Herzegovina 1.5 ­16.0 ­17.0 .. .. 9.4 8.0 27.7 21.3 10.4 7.1 3.2 Botswana 4.4 ­21.7 ­18.7 .. .. ­1.1 .. ­55.8 .. 9.9 5.9 0.5 Brazil 2.9 52.2 ­18.2 .. .. 23.0 2.3 21.5 7.3 50.1 44.9 31.2 Bulgaria 1.5 ­15.1 ­17.8 135.6 143.2 12.2 19.6 27.4 37.3 5.3 1.9 9.6 Cameroon 519.4 ­16.0 ­17.0 102.1 106.5 15.9 3.7 4.4 2.1 17.2 14.9 .. Chile 599.4 8.6 ­15.9 90.7 85.3 ­0.3 9.8 6.5 2.5 5.0 0.4 16.0 China 8.3 0.0 0.0 121.4 111.8 19.4 20.4 29.3 19.5 5.6 1.7 12.8 Colombia 2,780.8 24.5 ­2.9 90.4 77.2 13.6 10.2 14.2 11.5 9.7 5.9 22.1 Costa Rica 418.5 10.8 10.5 109.4 98.1 20.9 17.3 26.5 19.2 15.8 4.1 20.0 Dominican Republic 37.3 23.6 75.8 112.1 73.0 10.3 72.6 22.6 57.3 18.4 3.9 19.3 Ecuador 1.0 0.0 0.0 113.8 111.7 .. .. .. .. 2.9 4.9 30.3 Egypt, Arab Rep. 6.2 0.2 36.7 .. .. 12.6 .. 13.1 .. 9.4 ­0.8 17.3 El Salvador 8.8 0.0 0.0 .. .. ­3.1 2.0 ­1.4 10.5 .. .. 16.9 Estonia 12.4 ­15.6 ­16.9 .. .. 11.2 10.9 27.6 28.7 2.5 ­1.6 27.9 Ghana 8,753.9 15.3 5.8 81.0 81.6 48.9 .. 22.8 .. .. .. 22.6 Guatemala 8.0 ­2.4 3.0 .. .. 11.8 21.3 16.1 7.2 8.2 1.8 16.9 Honduras 17.7 6.3 4.9 .. .. 13.7 17.3 7.0 30.3 15.5 5.4 16.3 India 45.6 ­0.3 ­5.0 .. .. 16.8 11.9 16.0 9.8 8.2 1.7 5.2 Indonesia 8,465.0 ­14.0 ­5.3 .. .. 4.5 8.4 5.4 3.3 11.0 3.7 34.1 Iran, Islamic Rep. 8,272.1 354.2 4.0 198.1 187.3 27.5 23.0 29.4 42.5 .. .. 6.5 Jamaica 60.5 7.4 19.2 .. .. 12.0 12.6 30.0 68.3 9.7 4.3 16.9 Jordan 0.7 0.0 0.0 .. .. 8.6 16.2 6.2 4.4 9.7 4.7 8.0 Kazakhstan 144.2 2.9 ­6.7 .. .. 30.1 43.0 30.2 35.8 .. .. 9.9 Kenya 76.1 ­1.9 ­1.2 .. .. 11.7 12.2 9.2 9.3 9.0 .. 22.7 Latvia 0.5 ­6.9 ­8.9 .. .. 19.9 20.6 38.3 38.4 6.1 ­0.5 101.1 Lesotho 6.6 ­28.8 ­23.1 60.8 68.6 8.8 1.5 120.5 ­67.9 6.7 0.9 0.7 Lithuania 2.8 ­17.2 ­16.6 .. .. 16.9 18.3 22.3 44.1 6.9 0.8 27.5 continues on page 181 2004 World Development Indicators 179 4.a Recent economic performance Gross domestic Exports of goods Imports of goods GDP deflator Current account Total product and services and services balance reserves a months annual annual annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2002 2003 2002 2003 2002 2003 2002 2003 2002 2003 2003 2003 Macedonia, FYR 0.7 3.0 ­4.4 9.3 10.7 3.5 3.6 0.4 ­8.6 ­5.5 861 4.2 Malawi 1.8 5.9 ­3.8 ­0.6 17.6 ­16.6 17.5 6.9 ­10.6 ­12.5 .. .. Malaysia 4.1 4.6 3.6 5.3 6.2 10.6 3.6 3.0 7.6 8.3 .. .. Mauritius 4.4 4.5 9.4 2.6 5.2 4.1 5.1 5.3 5.7 3.3 1,151 5.1 Mexico 0.9 1.5 1.4 ­0.3 1.6 ­0.9 4.6 3.5 ­2.2 ­1.8 52,705 3.1 Moldova 7.2 6.0 14.6 11.8 13.9 13.3 8.1 11.9 ­6.4 ­8.2 258 1.9 Morocco 3.2 5.5 6.3 0.6 5.6 7.4 0.6 1.5 4.1 0.7 .. .. Nicaragua 1.0 2.3 ­3.3 ­5.1 ­0.5 ­7.1 5.3 6.1 ­22.2 ­17.6 447 2.5 Pakistan 2.8 5.8 10.3 18.8 4.5 20.2 3.1 4.0 6.6 5.9 9,630 7.0 Panama 0.8 2.5 ­4.2 2.3 5.3 8.1 1.2 1.2 ­1.3 ­3.8 1,269 2.9 Paraguay ­2.3 1.5 ­9.3 5.1 ­15.0 6.3 14.6 8.8 5.3 0.3 859 3.1 Peru 4.9 4.0 6.8 9.7 2.4 8.8 0.6 3.1 ­2.1 ­2.1 11,026 10.1 Philippines 4.4 4.2 3.6 3.5 4.7 6.2 4.9 3.3 5.4 2.6 16,115 3.9 Poland 1.0 3.5 3.1 13.6 ­5.3 8.3 1.7 1.0 ­2.6 ­0.1 31,747 6.4 Romania 4.3 4.8 16.9 8.2 12.1 6.4 24.2 16.0 ­3.3 ­4.9 7,794 4.3 Russian Federation 4.3 6.5 10.2 3.7 19.1 2.7 15.2 14.0 8.6 9.9 74,098 8.9 Senegal 1.1 6.3 1.7 1.5 1.9 ­0.1 2.7 0.8 ­9.5 ­6.6 555 2.9 Serbia and Montenegro 4.0 3.0 12.3 27.7 26.3 22.9 25.5 16.5 ­8.8 ­8.3 3,325 4.5 Slovak Republic 4.4 3.9 5.9 8.5 5.3 7.1 3.9 5.0 .. ­5.6 12,126 7.0 South Africa 3.0 3.0 ­1.4 ­0.9 3.1 ­0.7 8.5 4.1 0.3 0.5 7,495 2.4 Sri Lanka 4.0 5.5 5.6 5.5 11.2 7.9 8.3 5.1 ­1.6 ­2.2 2,200 3.2 Swaziland 1.6 2.2 1.6 ­6.0 1.6 ­4.0 13.5 9.0 ­3.8 ­6.3 272 1.8 Syrian Arab Republic 2.7 0.9 2.1 ­17.8 ­2.4 5.4 4.4 1.5 .. 0.1 4,450 6.8 Thailand 5.3 6.4 10.9 6.8 11.3 6.9 0.8 1.4 6.0 9.6 42,100 6.8 Trinidad and Tobago 2.7 4.0 ­9.6 10.2 2.5 6.9 0.8 2.7 .. 5.9 3,401 8.2 Tunisia 1.7 6.0 ­2.1 4.0 ­2.4 3.0 2.3 2.3 ­3.5 ­3.5 .. .. Turkey 7.8 4.8 4.8 4.2 20.0 11.6 43.8 25.2 ­0.8 ­3.2 36,832 5.8 Ukraine 4.8 7.5 9.1 5.0 3.7 10.4 3.2 5.1 7.7 6.5 6,874 3.4 Uruguay ­10.8 ­1.0 ­10.9 10.0 ­28.3 ­3.0 18.8 22.9 2.2 2.7 1,486 5.8 Uzbekistan 4.2 1.0 ­8.8 2.8 ­12.6 ­1.7 45.5 30.0 3.0 6.7 1,743 6.9 Venezuela, RB ­8.9 ­12.0 ­7.8 ­10.9 ­26.7 ­37.5 31.6 30.0 8.0 8.6 15,844 12.2 Zambia 3.3 4.2 11.4 11.1 3.5 4.1 19.9 20.1 .. ­14.8 245 1.5 Zimbabwe ­5.6 ­13.6 ­0.8 ­10.0 ­4.8 ­5.0 107.5 .. .. 0.6 .. .. Note: Data for 2003 are the latest preliminary estimates and may differ from those in earlier World Bank publications. a. International reserves including gold valued at London gold price. Source: World Bank staff estimates. 180 2004 World Development Indicators ECONOMY 4.b Key macroeconomic indicators Nominal exchange rate Real effective Money and Gross Real interest Short- exchange rate quasi money domestic credit rate term debt a local currency units annual annual % of per $ % change 1995 = 100 % growth % growth % exports 2003 2002 2003 2002 2003 2002 2003 2002 2003 2002 2003 2002 Macedonia, FYR 52.2 ­15.3 ­16.0 72.6 71.6 15.7 11.1 ­14.7 12.5 14.3 8.0 5.0 Malawi 108.4 29.5 34.6 115.0 85.4 20.7 30.8 75.8 44.9 28.1 35.8 27.1 Malaysia 3.8 0.0 0.0 91.3 81.7 3.1 8.2 7.5 9.1 2.7 0.0 7.6 Mauritius 26.1 ­3.9 ­10.6 .. .. 12.5 11.2 7.0 9.4 15.1 10.9 29.4 Mexico 11.2 12.8 9.0 .. .. 4.6 5.1 18.1 2.1 3.4 0.8 5.3 Moldova 13.2 5.6 ­4.4 100.2 97.5 38.6 30.4 25.2 23.6 14.3 0.2 6.2 Morocco 8.7 ­12.1 ­13.9 103.4 103.4 6.4 8.5 4.3 3.2 12.5 .. 10.9 Nicaragua 15.6 6.0 6.0 111.6 95.3 13.3 .. 4.7 .. 17.0 .. 42.8 Pakistan 57.2 ­3.8 ­2.3 90.0 83.9 16.8 18.4 1.6 11.0 .. .. 9.7 Panama 1.0 0.0 0.0 .. .. ­0.3 .. ­4.5 .. 9.7 7.8 4.4 Paraguay 6,115.0 51.7 ­13.9 75.6 68.2 3.1 7.5 13.8 ­25.9 21.0 25.8 15.6 Peru 3.5 2.0 ­1.5 .. .. 5.1 ­3.7 ­3.1 ­8.8 14.1 8.7 22.8 Philippines 55.6 3.3 4.7 85.6 74.4 10.4 5.1 5.5 7.6 4.1 1.3 12.2 Poland 3.7 ­3.7 ­2.6 133.4 115.9 ­2.8 4.0 2.6 7.9 10.7 .. 14.8 Romania 32,595.0 6.0 ­2.7 110.2 133.6 38.2 27.2 39.9 48.5 .. .. 2.9 Russian Federation 29.5 5.5 ­7.3 109.0 117.7 33.9 39.0 26.5 28.8 0.4 ­9.9 12.9 Senegal 519.4 ­16.0 ­17.0 .. .. 8.2 .. ­5.2 .. .. .. 16.8 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. 61.7 Slovak Republic 33.0 ­17.4 ­17.6 105.8 106.2 4.1 10.0 ­7.0 ­9.9 6.1 ­2.2 24.2 South Africa 6.6 ­28.8 ­23.1 62.6 84.8 14.5 7.7 7.8 29.3 6.6 5.1 19.8 Sri Lanka 96.7 3.8 0.0 .. .. 13.4 .. 8.1 .. 4.5 .. 6.8 Swaziland 6.6 ­28.8 ­23.1 .. . 13.1 7.8 ­206.4 164.7 4.0 ­0.9 5.6 Syrian Arab Republic 11.2 0.0 0.0 .. . 18.5 .. 0.1 .. 4.4 .. 66.7 Thailand 39.6 ­2.4 ­8.3 .. .. 1.4 6.6 7.8 2.0 6.1 2.6 13.9 Trinidad and Tobago 6.3 0.2 ­0.4 126.6 118.8 5.7 .. 11.0 .. 11.6 .. 18.5 Tunisia 1.2 ­9.1 ­9.4 96.2 91.9 4.4 7.6 4.6 5.9 .. .. 5.6 Turkey 1,396,638.0 13.3 ­15.0 .. .. 29.1 11.6 28.3 15.4 .. .. 25.7 Ukraine 5.3 0.6 0.0 112.4 96.5 42.3 47.4 28.9 39.3 21.4 3.4 2.4 Uruguay 29.3 84.2 7.7 87.9 66.3 28.2 ­1.6 71.6 4.0 91.4 .. 50.0 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. 11.0 Venezuela, RB 1,596.0 83.7 13.9 132.9 114.0 15.8 54.2 19.6 ­3.2 3.8 ­31.9 12.7 Zambia 4,770.7 13.2 10.1 115.8 114.3 31.1 16.8 12.1 ­6.2 21.1 5.0 9.5 Zimbabwe 826.4 0.0 1,401.7 .. .. 191.7 472.6 128.7 485.0 ­34.2 49.7 .. Note: Data for 2003 are preliminary and may not cover the entire year. a. More recent data on short-term debt are available on a Web site maintained by the Bank for International Settlements, the International Monetary Fund, the Organisation for Economic Co-operation and Development, and the World Bank: www.oecd.org/dac/debt. Source: International Monetary Fund, International Financial Statistics; World Bank, Debtor Reporting System. 2004 World Development Indicators 181 4.1 Growth of output Gross domestic Agriculture Industry Services product Total Manufacturing average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania a 1.5 5.4 1.9 3.7 2.1 2.5 .. 8.7 ­0.4 9.2 Algeria a 2.7 2.2 4.1 3.6 2.6 2.0 4.1 ­1.9 3.0 2.3 Angola 3.6 2.7 0.5 1.4 6.3 5.2 ­11.1 1.6 1.4 ­1.4 Argentina a ­0.7 2.7 0.7 2.9 ­1.3 1.8 ­0.8 0.9 0.0 3.1 Armenia a .. 0.4 .. 1.4 .. ­4.2 .. ­2.0 .. ­2.5 Australia a 3.4 3.8 3.2 3.8 3.1 2.8 1.9 2.2 3.8 4.3 Austria a 2.3 2.2 1.4 4.0 1.8 2.7 2.5 2.8 2.8 1.9 Azerbaijan a .. 1.2 .. 0.8 .. 1.8 .. ­14.1 .. 1.8 Bangladesh a 3.7 4.9 2.1 3.1 6.0 7.1 5.2 6.9 3.8 4.6 Belarus a .. ­0.1 .. ­3.5 .. ­0.7 .. 0.4 .. 0.5 Belgium a 2.1 2.1 2.2 2.6 2.4 2.0 .. 2.8 1.8 1.9 Benin 2.5 4.9 5.1 5.7 3.4 4.5 5.1 6.0 0.7 4.4 Bolivia a ­0.2 3.6 1.5 2.7 ­2.3 3.8 ­1.1 3.5 ­0.2 3.8 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 11.0 5.1 2.5 ­1.2 11.4 4.3 11.4 4.0 14.3 7.2 Brazil a 2.7 2.7 2.8 3.4 2.0 2.2 1.6 1.6 3.3 2.8 Bulgaria a 3.4 ­0.7 ­2.1 3.0 5.2 ­3.3 .. .. 4.7 ­3.3 Burkina Faso 3.6 4.0 3.1 3.7 3.8 2.8 2.0 1.6 3.8 4.6 Burundi a 4.4 ­1.8 3.1 ­0.7 4.5 ­2.6 5.7 ­8.0 5.6 ­1.0 Cambodia a .. 6.6 .. 3.2 .. 14.8 .. 17.8 .. 5.8 Cameroon a 3.4 2.4 2.2 5.6 5.9 0.7 5.0 2.7 2.1 0.6 Canada a 3.2 3.2 2.3 0.8 2.9 3.0 3.8 4.0 3.2 3.2 Central African Republic a 1.4 2.1 1.6 4.0 1.4 1.4 5.0 0.7 1.0 ­1.1 Chad a 6.1 2.5 2.3 3.8 8.1 4.6 .. .. 6.7 1.7 Chile a 4.2 5.9 5.9 2.1 3.5 5.4 3.4 3.8 2.9 4.5 China 10.3 9.7 5.9 3.9 11.1 12.6 10.8 11.9 13.5 8.8 Hong Kong, China a 6.8 3.8 .. .. .. .. .. .. .. .. Colombia a 3.7 2.3 2.9 ­1.5 5.0 1.4 3.5 ­1.5 3.1 3.7 Congo, Dem. Rep. 1.6 ­4.4 2.5 0.3 0.9 ­6.8 1.6 .. 1.3 ­10.5 Congo, Rep. 3.3 1.6 3.4 1.3 5.2 2.9 6.8 ­0.7 2.2 0.5 Costa Rica a 3.0 4.9 3.1 3.6 2.8 5.5 3.0 5.8 3.3 4.6 Côte d'Ivoire 0.7 2.8 0.3 3.4 4.4 4.2 3.0 3.4 ­0.1 2.0 Croatia a .. 1.3 .. ­1.3 .. ­1.0 .. ­1.5 .. 2.5 Cuba .. 3.9 .. 3.5 .. 5.0 .. 4.7 .. 3.2 Czech Republic a .. 1.3 .. 3.7 .. ­0.3 .. .. .. 2.1 Denmark a 2.0 2.5 2.6 2.8 2.0 2.4 1.3 2.2 1.9 2.5 Dominican Republic 3.1 6.0 ­1.0 3.9 3.0 6.7 2.3 4.6 4.2 6.0 Ecuador 2.1 1.9 4.5 ­0.4 1.3 2.0 0.1 1.1 1.8 2.5 Egypt, Arab Rep. a 5.4 4.5 2.7 3.2 3.3 4.6 .. 6.5 7.8 4.6 El Salvador 0.2 4.3 ­1.1 0.9 0.2 4.9 ­0.1 5.0 0.7 4.8 Eritrea a .. 4.3 .. ­1.4 .. 11.5 .. 8.1 .. 4.6 Estonia a 2.2 1.0 .. ­2.5 .. ­0.7 .. 7.1 .. 2.7 Ethiopia a 2.3 4.6 0.6 2.2 3.1 4.0 2.7 4.0 4.9 6.9 Finland a 3.3 2.9 ­0.4 1.5 3.2 4.4 .. 6.9 3.6 2.5 France a 2.4 1.9 1.3 1.9 1.4 1.5 1.3 2.4 3.0 2.1 Gabon 0.9 2.5 1.2 ­0.5 1.5 2.4 1.8 0.6 0.1 3.2 Gambia, The a 3.6 3.3 0.9 4.3 4.7 2.9 7.8 1.7 2.7 4.1 Georgia a 0.4 ­4.3 .. ­0.9 .. 7.8 .. .. .. 15.5 Germany a 2.3 1.6 1.6 1.6 1.4 ­0.1 .. 0.2 3.0 2.6 Ghana 3.0 4.3 1.0 3.5 3.3 3.2 3.9 ­1.4 5.7 5.4 Greece a 0.9 2.6 ­0.1 0.3 1.3 1.9 .. 1.9 0.9 3.0 Guatemala 0.8 4.0 1.2 2.7 ­0.2 3.9 0.0 2.6 0.9 4.5 Guinea a .. 4.3 .. 4.6 .. 4.8 .. 4.4 .. 3.3 Guinea-Bissau a 4.0 0.7 4.7 3.1 2.2 ­2.5 .. ­1.8 3.5 ­0.0 Haiti a ­0.2 ­1.0 ­0.1 ­4.4 ­1.7 ­2.6 ­1.7 ­8.1 0.9 0.8 182 2004 World Development Indicators ECONOMY 4.1 Growth of output Gross domestic Agriculture Industry Services product Total Manufacturing average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 Honduras a 2.7 3.1 2.7 2.1 3.3 3.5 3.7 4.1 2.5 3.9 Hungary a 1.3 2.2 1.7 ­0.8 0.2 3.9 .. 7.6 2.1 2.1 India a 5.7 5.8 3.1 2.7 6.9 6.0 7.4 6.6 6.9 7.9 Indonesia 6.1 3.6 3.6 1.9 7.3 4.5 12.8 5.9 6.5 3.4 Iran, Islamic Rep. a 1.7 3.8 4.5 4.2 3.3 ­2.0 4.5 5.5 ­1.0 8.1 Iraq ­6.8 .. .. .. .. .. .. .. .. .. Ireland a 3.2 7.8 .. .. .. .. .. .. .. .. Israel 3.5 4.6 .. .. .. .. .. .. .. .. Italy a 2.5 1.7 ­0.5 1.2 1.8 1.2 2.1 1.4 3.0 1.9 Jamaica 2.0 0.7 0.9 ­0.1 2.4 ­0.8 2.7 ­2.0 1.6 1.7 Japan a 4.1 1.3 1.3 ­2.9 4.2 ­0.0 .. 0.7 4.2 2.2 Jordan a 2.5 4.7 6.8 ­2.4 1.7 4.9 0.5 5.6 2.3 4.8 Kazakhstan a .. ­1.6 .. ­5.4 .. ­5.4 .. 5.3 .. ­1.4 Kenya a 4.2 1.9 3.3 1.2 3.9 1.5 4.9 1.8 4.9 2.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 8.9 5.6 3.0 1.8 11.4 6.2 12.1 7.6 8.4 5.6 Kuwait 1.3 2.9 14.7 .. 1.0 .. 2.3 .. 2.1 .. Kyrgyz Republic a .. ­2.2 .. 2.5 .. ­7.6 .. ­13.4 .. ­3.0 Lao PDR a 3.7 6.3 3.5 4.9 6.1 10.9 8.9 12.6 3.3 6.5 Latvia a 3.5 ­1.0 2.3 ­4.0 4.3 ­5.1 4.4 ­4.6 3.2 3.6 Lebanon a .. 4.9 .. 1.7 .. ­0.8 .. ­2.4 .. 3.0 Lesotho a 4.5 3.5 2.8 2.0 5.3 4.8 9.8 6.0 4.0 3.8 Liberia a ­7.0 7.4 .. 6.5 .. ­11.2 .. .. .. ­12.5 Libya a ­7.0 .. .. .. .. .. .. .. .. .. Lithuania a .. ­0.9 .. ­1.0 .. 4.2 .. 6.7 .. 5.5 Macedonia, FYR a .. ­0.1 .. ­0.2 .. ­2.1 .. ­4.0 .. 1.3 Madagascar a 1.1 2.1 2.5 1.9 0.9 2.1 2.1 2.2 0.3 2.4 Malawi a 2.5 3.1 2.0 6.8 2.9 0.7 3.6 ­1.0 3.3 2.1 Malaysia 5.3 6.2 3.4 0.3 6.8 7.5 9.3 8.8 4.9 6.4 Mali a 0.8 4.2 3.3 2.5 4.3 8.2 6.8 ­2.2 1.9 3.5 Mauritania a 1.8 4.4 1.7 3.7 4.9 2.4 ­2.1 ­1.0 0.4 5.9 Mauritius a 6.0 5.2 2.6 0.4 9.2 5.4 10.4 5.3 5.1 6.3 Mexico a 1.1 3.0 0.8 1.6 1.1 3.5 1.5 4.0 1.4 3.0 Moldova a 2.8 ­7.1 .. ­8.1 .. ­9.5 .. ­1.1 .. 0.4 Mongolia 5.4 1.5 1.4 3.2 6.6 0.1 .. .. 8.4 0.8 Morocco 4.2 2.6 6.7 0.1 3.0 3.3 4.1 2.8 4.2 2.9 Mozambique a ­0.1 6.9 6.6 5.1 ­4.5 14.1 .. 18.9 9.1 3.4 Myanmar 0.6 7.4 0.5 5.7 0.5 10.5 ­0.2 7.9 0.8 7.2 Namibia a 1.3 3.7 1.9 2.8 0.0 2.7 3.7 3.3 3.6 4.2 Nepal a 4.6 4.7 4.0 2.7 8.8 6.4 9.3 7.5 3.9 5.8 Netherlands a 2.4 2.9 3.6 1.7 1.6 1.7 .. 2.4 2.6 3.2 New Zealand a 1.9 3.2 4.1 3.0 1.0 2.2 .. 2.0 2.0 3.6 Nicaragua ­1.9 4.3 ­2.2 3.1 ­2.3 3.0 ­3.2 1.7 ­1.5 5.5 Niger ­0.1 2.6 1.7 3.2 ­1.7 2.2 ­2.7 2.8 ­0.7 2.3 Nigeria a 1.6 2.4 3.3 3.5 ­1.1 0.9 0.7 1.2 3.7 2.8 Norway a 3.0 3.6 0.1 1.8 4.0 3.2 0.2 2.3 2.8 3.8 Oman 8.4 4.3 7.9 .. 10.3 .. 20.6 .. 5.9 .. Pakistan a 6.3 3.6 4.0 3.8 7.7 3.9 8.1 4.0 6.8 4.3 Panama a 0.5 4.2 2.5 2.7 ­1.3 4.3 0.4 2.9 0.7 4.4 Papua New Guinea 1.9 3.1 1.8 3.2 1.9 3.7 0.1 3.8 2.0 2.6 Paraguay 2.5 1.8 3.6 2.1 0.3 3.0 4.0 0.8 3.1 1.0 Peru a ­0.1 4.1 3.0 5.3 0.1 4.4 ­0.2 3.3 ­0.4 3.7 Philippines 1.0 3.5 1.0 2.0 ­0.9 3.5 0.2 3.1 2.8 4.2 Poland a .. 4.3 .. 1.1 .. 5.8 .. 8.3 .. 4.4 Portugal a 3.2 2.8 1.5 ­0.2 3.4 2.9 .. 2.3 2.5 2.3 Puerto Rico 4.0 4.3 1.8 .. 3.6 .. 3.6 .. 4.6 .. 2004 World Development Indicators 183 4.1 Growth of output Gross domestic Agriculture Industry Services product Total Manufacturing average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 Romania a 1.3 ­0.2 1.9 ­1.4 ­1.0 ­0.3 .. .. .. 1.0 Russian Federation a .. ­2.7 .. ­1.9 .. ­4.5 .. .. .. ­0.6 Rwanda 2.2 1.7 0.5 4.3 2.5 ­1.0 2.6 ­3.7 3.6 0.5 Saudi Arabia ­1.3 2.1 12.5 1.7 ­3.8 1.8 6.2 5.4 0.6 2.4 Senegal 3.1 3.9 2.8 1.8 4.3 5.3 4.6 4.6 2.8 4.1 Serbia and Montenegro .. 0.1 .. .. .. .. .. .. .. .. Sierra Leone a 0.5 ­3.8 3.1 ­3.6 1.7 ­4.0 .. 5.0 ­0.9 ­2.9 Singapore 6.7 6.7 ­5.3 ­3.1 5.2 7.3 6.6 6.9 7.6 6.8 Slovak Republic a 2.0 2.3 1.6 2.5 2.0 ­3.4 .. 4.3 0.6 7.7 Slovenia a .. 4.1 .. ­0.1 .. 4.7 .. 4.7 .. 4.0 Somalia a 2.1 .. 3.3 .. 1.0 .. ­1.7 .. 0.9 .. South Africa a 1.0 2.2 2.9 1.2 0.7 1.3 1.1 1.6 2.4 2.8 Spain a 3.1 2.8 3.1 0.7 2.7 2.5 .. 3.9 3.3 3.0 Sri Lanka a 4.0 4.8 2.2 1.6 4.6 6.1 6.3 7.0 4.7 5.3 Sudan 2.3 5.5 1.8 9.0 1.6 6.1 4.8 2.0 4.5 3.1 Swaziland a 6.7 3.2 2.3 1.9 12.0 3.3 15.7 2.6 4.8 3.5 Sweden a 2.5 2.3 1.4 ­0.7 2.8 4.5 .. 8.6 2.4 1.8 Switzerland a 2.0 1.0 .. .. .. .. .. .. .. .. Syrian Arab Republic 1.5 4.7 ­0.6 4.5 6.6 8.7 .. 9.6 1.6 3.3 Tajikistan a 2.0 ­6.8 ­2.8 ­4.6 5.5 ­11.7 5.6 ­10.8 3.4 ­0.3 Tanzania b .. 3.5 .. 3.4 .. 4.1 .. 3.3 .. 3.3 Thailand 7.6 3.7 3.9 1.5 9.8 4.9 9.5 6.1 7.3 3.1 Togo 1.7 2.0 5.6 3.3 1.1 2.8 1.7 4.2 ­0.3 0.4 Trinidad and Tobago ­0.8 3.5 ­5.9 3.3 ­5.5 3.8 ­10.1 5.5 6.7 3.3 Tunisia 3.3 4.6 2.8 1.8 3.1 4.7 3.7 5.5 3.5 5.3 Turkey a 5.3 3.1 1.2 1.1 7.7 3.1 7.9 3.8 4.5 3.4 Turkmenistan a .. ­1.0 .. ­3.2 .. ­1.6 .. .. .. ­3.2 Uganda a 2.9 6.9 2.1 3.9 5.0 11.2 3.9 13.0 2.8 7.9 Ukraine a .. ­6.6 .. ­4.0 .. ­7.9 .. ­7.2 .. ­8.0 United Arab Emirates a ­2.1 4.2 9.6 .. ­4.2 .. 3.1 .. 3.6 .. United Kingdom a 3.2 2.6 2.4 ­1.1 3.3 1.2 .. .. 3.1 3.4 United States a 3.5 3.3 3.2 3.8 3.0 3.4 .. 3.9 3.3 3.7 Uruguay a 0.5 2.0 0.1 1.6 ­0.2 ­0.0 0.4 ­1.3 1.0 3.1 Uzbekistan .. 0.8 .. 1.3 .. ­2.1 .. .. .. 1.9 Venezuela, RB 1.1 1.1 3.1 1.3 1.7 1.8 4.4 1.3 0.5 0.5 Vietnam 4.6 7.6 2.8 4.2 4.4 11.4 1.9 11.2 7.1 7.1 West Bank and Gaza a .. ­0.8 .. ­4.2 .. ­6.7 .. ­0.5 .. 2.4 Yemen, Rep. .. 5.9 .. 5.6 .. 6.5 .. 3.0 .. 5.8 Zambia a 1.0 1.1 3.6 3.5 1.0 ­2.8 4.1 1.5 ­0.2 3.0 Zimbabwe a 3.6 1.1 3.1 2.9 3.2 ­1.1 2.8 ­2.0 3.0 2.0 World 3.3 w 2.7 w 2.6 w 1.8 w 3.1 w 2.1 w .. w 2.9 w 3.5 w 3.1 w Low income 4.7 4.3 3.0 2.7 5.6 4.7 7.9 5.7 5.4 5.4 Middle income 2.9 3.2 3.5 2.1 2.7 3.4 3.7 5.3 3.1 3.3 Lower middle income 4.0 3.2 3.7 2.2 4.0 3.6 4.4 5.7 4.4 3.3 Upper middle income 0.8 3.0 2.8 1.4 ­0.1 2.9 1.9 4.0 1.1 3.3 Low & middle income 3.2 3.4 3.4 2.3 3.1 3.6 4.2 5.3 3.4 3.6 East Asia & Pacific 7.5 7.3 4.6 3.1 8.5 9.7 9.5 9.8 8.6 6.4 Europe & Central Asia .. ­0.5 .. ­0.8 .. ­2.2 .. .. .. 0.8 Latin America & Carib. 1.7 2.9 2.3 2.3 1.4 2.6 1.4 2.0 1.9 3.0 Middle East & N. Africa 1.4 3.2 5.0 2.9 ­0.4 1.8 4.9 4.6 1.9 4.2 South Asia 5.5 5.4 3.1 2.9 6.9 5.9 7.3 6.3 6.4 7.0 Sub-Saharan Africa 1.6 2.6 2.3 2.8 1.3 1.9 1.7 1.9 2.4 2.8 High income 3.3 2.5 1.9 1.2 3.1 1.8 .. 2.3 3.5 3.0 Europe EMU 2.4 2.0 1.3 1.4 1.7 1.1 .. 1.5 2.9 2.4 a. Components are at basic prices. b. Data cover mainland Tanzania only. 184 2004 World Development Indicators ECONOMY 4.1 Growth of output About the data Definitions An economy's growth is measured by the change in estimating household outputs produced for home use, · Gross domestic product (GDP) at purchaser the volume of its output or in the real incomes of per- sales in informal markets, barter exchanges, and illic- prices is the sum of gross value added by all resi- sons resident in the economy. The 1993 United it or deliberately unreported activities. The consisten- dent producers in the economy plus any product Nations System of National Accounts (1993 SNA) cy and completeness of such estimates depend on taxes (less subsidies) not included in the valuation offers three plausible indicators from which to calcu- the skill and methods of the compiling statisticians of output. It is calculated without making deductions late growth: the volume of gross domestic product and the resources available to them. for depreciation of fabricated capital assets or for (GDP), real gross domestic income, and real gross depletion and degradation of natural resources. national income. The volume of GDP is the sum of Rebasing national accounts Value added is the net output of an industry after value added, measured at constant prices, by house- When countries rebase their national accounts, they adding up all outputs and subtracting intermediate holds, government, and the industries operating in the update the weights assigned to various components inputs. The industrial origin of value added is deter- economy. This year's edition of World Development to better reflect the current pattern of production or mined by the International Standard Industrial Indicators continues to follow the practice of past edi- uses of output. The new base year should represent Classification (ISIC) revision 3. · Agriculture corre- tions, measuring the growth of the economy by the normal operation of the economy--that is, it should sponds to ISIC divisions 1­5 and includes forestry change in GDP measured at constant prices. be a year without major shocks or distortions--but and fishing. · Industry covers mining, manufactur- Each industry's contribution to the growth in the the choice of base year is often constrained by lack ing (also reported as a separate subgroup), con- economy's output is measured by the growth in of data. Some developing countries have not struction, electricity, water, and gas (ISIC divisions value added by the industry. In principle, value rebased their national accounts for many years. 10­45). · Manufacturing corresponds to industries added in constant prices can be estimated by meas- Using an old base year can be misleading because belonging to ISIC divisions 15­37. · Services cor- uring the quantity of goods and services produced in implicit price and volume weights become progres- respond to ISIC divisions 50­99. This sector is a period, valuing them at an agreed set of base year sively less relevant and useful. derived as a residual (from GDP less agriculture and prices, and subtracting the cost of intermediate To obtain comparable series of constant price industry) and may not properly reflect the sum of inputs, also in constant prices. This double-deflation data, the World Bank rescales GDP and value added service output, including banking and financial serv- method, recommended by the 1993 SNA and its by industrial origin to a common reference year, cur- ices. For some countries it includes product taxes predecessors, requires detailed information on the rently 1995. This process gives rise to a discrepan- (minus subsidies) and may also include statistical structure of prices of inputs and outputs. cy between the rescaled GDP and the sum of the discrepancies. In many industries, however, value added is extrapo- rescaled components. Because allocating the dis- lated from the base year using single volume indexes crepancy would give rise to distortions in the growth of outputs or, more rarely, inputs. Particularly in the rates, the discrepancy is left unallocated. As a service industries, including most of government, value result, the weighted average of the growth rates of added in constant prices is often imputed from labor the components generally will not equal the GDP inputs, such as real wages or the number of employ- growth rate. ees. In the absence of well-defined measures of out- Growth rates of GDP and its components are calcu- put, measuring the growth of services remains difficult. lated using constant price data in the local currency. Data sources Moreover, technical progress can lead to improve- Regional and income group growth rates are calculat- The national accounts data for most developing ments in production processes and in the quality of ed after converting local currencies to constant price countries are collected from national statistical goods and services that, if not properly accounted U.S. dollars using an exchange rate in the common organizations and central banks by visiting and for, can distort measures of value added and thus of reference year. The growth rates in the table are aver- resident World Bank missions. The data for high- growth. When inputs are used to estimate output, as age annual compound growth rates. Methods of com- income economies come from data files of the is the case for nonmarket services, unmeasured puting growth rates and the alternative conversion Organisation for Economic Co-operation and technical progress leads to underestimates of the factor are described in Statistical methods. Development (for information on the OECD's volume of output. Similarly, unmeasured changes in national accounts series, see its monthly Main the quality of goods and services produced lead to Changes in the System of National Accounts Economic Indicators). The World Bank rescales underestimates of the value of output and value World Development Indicators adopted the terminol- constant price data to a common reference year. added. The result can be underestimates of growth ogy of the 1993 SNA in 2001. Although most coun- The complete national accounts time series is and productivity improvement, and overestimates of tries continue to compile their national accounts available on the World Development Indicators inflation. These issues are highly complex, and only according to the SNA version 3 (referred to as the 2004 CD-ROM. The United Nations Statistics a few high-income countries have attempted to intro- 1968 SNA), more and more are adopting the 1993 Division publishes detailed national accounts for duce any GDP adjustments for these factors. SNA. Some low-income countries still use concepts United Nations member countries in National Informal economic activities pose a particular meas- from the even older 1953 SNA guidelines, including Accounts Statistics: Main Aggregates and urement problem, especially in developing countries, valuations such as factor cost, in describing major Detailed Tables and publishes updates in the where much economic activity may go unrecorded. economic aggregates. Countries that use the 1993 Monthly Bulletin of Statistics. Obtaining a complete picture of the economy requires SNA are identified in Primary data documentation. 2004 World Development Indicators 185 4.2 Structure of output Gross domestic Agriculture Industry Services product Total Manufacturing $ millions % of GDP % of GDP % of GDP % of GDP 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. 52 .. 24 .. 18 .. 24 Albania a 2,102 4,835 36 25 48 19 .. 10 16 56 Algeria a 62,045 55,914 11 10 48 53 11 8 40 37 Angola 10,260 11,248 18 8 41 68 5 4 41 24 Argentina a 141,352 102,042 8 11 36 32 27 21 56 57 Armenia a 2,257 2,367 17 26 52 37 33 23 31 37 Australia a 310,588 409,420 4 4 29 26 14 12 67 71 Austria a 161,692 204,066 4 2 34 32 23 22 62 66 Azerbaijan a 4,991 6,090 30 16 33 52 19 20 37 32 Bangladesh a 30,129 47,563 30 23 21 26 13 16 48 51 Belarus a 17,370 14,304 24 11 47 37 39 31 29 52 Belgium a 197,174 245,395 2 1 33 27 .. 19 65 72 Benin 1,845 2,695 36 36 13 14 8 9 51 50 Bolivia a 4,868 7,801 17 15 39 33 18 15 44 52 Bosnia and Herzegovina .. 5,599 .. 18 .. 37 .. 23 .. 45 Botswana 3,791 5,273 5 2 57 48 5 4 39 50 Brazil a 461,952 452,387 8 6 39 21 25 13 53 73 Bulgaria a 20,726 15,486 17 13 49 28 .. 17 34 59 Burkina Faso 3,120 3,127 28 32 20 18 15 13 52 50 Burundi a 1,132 719 56 49 19 19 13 .. 25 31 Cambodia a 1,115 4,005 .. 36 .. 28 .. 20 .. 36 Cameroon a 11,152 9,060 25 43 29 20 15 11 46 38 Canada a 574,204 714,327 3 .. 32 .. 17 .. 65 .. Central African Republic a 1,488 1,046 48 57 20 22 11 9 33 21 Chad a 1,739 2,002 29 38 18 17 14 15 53 45 Chile a 30,323 64,153 9 9 41 34 20 16 50 57 China 354,644 1,266,052 27 15 42 51 33 35 31 34 Hong Kong, China a 75,433 161,531 0 0 25 13 17 5 74 87 Colombia a 40,274 80,925 17 14 38 30 21 16 45 56 Congo, Dem. Rep. 9,348 5,707 30 56 28 19 11 4 42 25 Congo, Rep. 2,799 3,017 13 6 41 63 8 5 46 30 Costa Rica a 5,713 16,837 18 8 29 29 22 22 53 62 Côte d'Ivoire 10,796 11,682 32 26 23 20 21 13 44 53 Croatia a 18,156 22,436 10 8 34 30 28 21 56 62 Cuba .. .. .. 7 .. 46 .. 37 .. 47 Czech Republic a 34,880 69,514 6 4 49 40 .. .. 45 57 Denmark a 133,361 172,928 4 3 27 27 18 17 69 71 Dominican Republic 7,074 21,651 13 12 31 33 18 16 55 55 Ecuador 10,356 24,311 13 9 38 28 19 11 49 63 Egypt, Arab Rep. a 43,130 89,854 19 17 29 33 18 19 52 50 El Salvador 4,807 14,284 17 9 27 30 22 23 56 61 Eritrea a 477 642 31 12 12 25 8 12 57 63 Estonia a 4,649 6,507 17 5 50 30 42 19 34 65 Ethiopia a 8,609 6,059 49 40 13 12 8 .. 38 48 Finland a 137,224 131,508 6 3 35 33 .. 26 59 64 France a 1,215,893 1,431,278 4 3 30 25 21 18 66 72 Gabon 5,952 4,971 7 8 43 46 6 5 50 46 Gambia, The a 317 357 29 26 13 14 7 5 58 60 Georgia a 7,738 3,396 32 21 33 23 24 .. 35 56 Germany a 1,671,312 1,984,095 2 1 39 30 28 23 59 69 Ghana 5,886 6,160 45 34 17 24 10 9 38 42 Greece a 84,075 132,824 11 7 28 22 .. 12 61 70 Guatemala 7,650 23,277 26 22 20 19 15 13 54 58 Guinea a 2,818 3,213 24 24 33 37 5 4 43 39 Guinea-Bissau a 244 203 61 62 19 13 8 10 21 25 Haiti a 2,864 3,435 .. .. .. .. .. .. .. .. 186 2004 World Development Indicators ECONOMY 4.2 Structure of output Gross domestic Agriculture Industry Services product Total Manufacturing $ millions % of GDP % of GDP % of GDP % of GDP 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras a 3,049 6,564 22 13 26 31 16 20 51 56 Hungary a 33,056 65,843 15 4 39 31 23 23 46 65 India a 316,937 510,177 31 23 28 27 17 16 41 51 Indonesia 114,426 172,911 19 17 39 44 21 25 41 38 Iran, Islamic Rep. a 120,404 108,243 24 12 29 39 12 14 48 49 Iraq 48,657 .. .. .. .. .. .. .. .. .. Ireland a 47,301 121,449 9 3 35 42 28 33 56 54 Israel 52,490 103,689 .. .. .. .. .. .. .. .. Italy a 1,102,437 1,184,273 4 3 34 29 25 21 63 69 Jamaica 4,592 7,871 7 6 40 31 19 14 52 63 Japan a 3,053,143 3,993,433 2 1 39 31 27 21 58 68 Jordan a 4,020 9,301 8 2 28 26 15 16 64 72 Kazakhstan a 26,931 24,637 27 9 45 39 9 16 29 53 Kenya a 8,551 12,330 29 16 19 19 12 13 52 65 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 252,622 476,690 9 4 43 41 29 29 48 55 Kuwait 18,428 35,369 1 .. 52 .. 12 .. 47 .. Kyrgyz Republic a 2,659 1,603 34 39 36 26 28 11 30 35 Lao PDR a 866 1,680 61 51 15 23 10 18 24 26 Latvia a 7,279 8,406 22 5 46 25 34 15 32 71 Lebanon a 2,838 17,294 .. 12 .. 21 .. 10 .. 67 Lesotho a 615 714 24 16 33 43 14 20 43 41 Liberia a 384 562 .. .. .. .. .. .. .. .. Libya a 28,905 19,131 .. .. .. .. .. .. .. .. Lithuania a 10,259 13,796 27 7 31 31 21 20 42 62 Macedonia, FYR a 4,472 3,791 9 12 46 30 36 19 46 57 Madagascar a 3,081 4,400 29 32 13 13 11 11 59 55 Malawi a 1,881 1,901 45 37 29 15 19 10 26 49 Malaysia 44,024 94,900 15 9 42 47 24 31 43 44 Mali a 2,421 3,364 46 34 16 30 9 3 39 36 Mauritania a 1,020 969 30 21 29 29 10 9 42 50 Mauritius a 2,383 4,533 13 7 33 31 25 23 54 62 Mexico a 262,710 637,203 8 4 28 27 21 19 64 69 Moldova a 3,549 1,624 43 24 33 25 .. 17 24 51 Mongolia .. 1,119 17 30 30 16 .. 5 52 54 Morocco 25,821 36,093 18 16 32 30 18 17 50 54 Mozambique a 2,463 3,599 37 23 18 34 10 13 44 43 Myanmar .. .. 57 57 11 10 8 7 32 33 Namibia a 2,350 2,904 12 11 38 31 14 11 50 58 Nepal a 3,628 5,549 52 41 16 22 6 8 32 38 Netherlands a 294,290 417,910 4 3 30 26 19 16 65 71 New Zealand a 43,618 58,581 7 .. 28 .. 19 .. 65 .. Nicaragua 1,009 4,003 31 18 21 25 17 14 48 57 Niger 2,481 2,171 35 40 16 17 7 7 49 43 Nigeria a 28,472 43,540 33 37 41 29 6 4 26 34 Norway a 116,107 190,477 4 2 36 38 13 .. 61 60 Oman 10,535 20,309 3 .. 58 .. 4 .. 39 .. Pakistan a 40,010 59,071 26 23 25 23 17 16 49 53 Panama a 5,313 12,296 9 6 15 14 9 6 76 80 Papua New Guinea 3,221 2,814 29 27 30 42 9 9 41 32 Paraguay 5,265 5,508 28 22 25 29 17 15 47 49 Peru a 26,294 56,517 9 8 27 28 18 16 64 64 Philippines 44,331 77,954 22 15 34 33 25 23 44 53 Poland a 58,976 189,021 8 3 50 30 .. 18 42 66 Portugal a 71,466 121,595 9 4 32 30 22 .. 60 66 Puerto Rico 30,604 67,897 1 1 42 43 40 40 57 56 2004 World Development Indicators 187 4.2 Structure of output Gross domestic Agriculture Industry Services product Total Manufacturing $ millions % of GDP % of GDP % of GDP % of GDP 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania a 38,299 45,749 24 13 50 38 34 17 26 49 Russian Federation a 516,814 346,520 17 6 48 34 .. .. 35 60 Rwanda 2,584 1,732 33 41 25 21 18 11 43 37 Saudi Arabia 116,778 188,479 6 5 49 51 9 10 45 44 Senegal 5,699 5,037 20 15 19 22 13 14 61 63 Serbia and Montenegro .. 15,681 .. 15 .. 32 .. .. .. 53 Sierra Leone a 650 783 32 53 13 32 5 5 55 16 Singapore 36,902 86,969 .. 0 .. 36 .. 28 .. 64 Slovak Republic a 15,485 23,682 7 4 59 29 .. 21 33 67 Slovenia a 12,673 21,960 6 3 46 36 35 27 49 61 Somalia a 917 .. 65 .. .. .. 5 .. .. .. South Africa a 112,014 104,242 5 4 40 32 24 19 55 64 Spain a 509,968 653,075 6 3 35 30 .. 18 59 66 Sri Lanka a 8,032 16,567 26 20 26 26 15 16 48 54 Sudan 13,167 13,516 .. 39 .. 18 .. 9 .. 43 Swaziland a 882 1,186 13 16 42 50 35 38 45 35 Sweden a 245,941 240,313 4 2 32 28 .. 23 64 70 Switzerland a 228,415 267,445 .. 1 .. 27 .. .. .. 72 Syrian Arab Republic 12,309 20,783 28 23 24 28 20 25 48 49 Tajikistan a 2,629 1,212 33 24 38 24 25 21 29 52 Tanzania b 4,259 9,382 46 44 18 16 9 8 36 39 Thailand 85,345 126,905 12 9 37 43 27 34 50 48 Togo 1,628 1,384 34 40 23 22 10 9 44 38 Trinidad and Tobago 5,068 9,628 3 2 46 42 9 7 51 56 Tunisia 12,291 21,024 16 10 30 29 17 19 54 60 Turkey a 150,642 183,665 18 13 30 27 20 17 52 60 Turkmenistan a 3,232 7,672 32 29 30 51 .. .. 38 20 Uganda a 4,304 5,803 57 32 11 22 6 10 32 46 Ukraine a 81,456 41,477 26 15 45 38 44 23 30 47 United Arab Emirates a 34,132 70,960 2 .. 64 .. 8 .. 35 .. United Kingdom a 989,564 1,566,283 2 1 35 26 23 17 63 73 United States a 5,750,800 10,383,100 2 2 28 23 19 15 70 75 Uruguay a 9,286 12,129 9 9 35 27 28 17 56 64 Uzbekistan 13,361 7,932 33 35 33 22 .. 9 34 44 Venezuela, RB 48,592 94,340 5 3 50 43 20 6 44 54 Vietnam 6,472 35,086 39 23 23 39 12 21 39 38 West Bank and Gaza a .. 3,396 .. 6 .. 13 .. 11 .. 80 Yemen, Rep. 4,828 9,984 24 15 27 40 9 5 49 44 Zambia a 3,288 3,697 21 22 51 26 36 12 28 52 Zimbabwe a 8,784 8,304 16 17 33 24 23 13 50 59 World 21,676,054 t 32,312,146 t 5 w 4 w 34 w 29 w 22 w 19 w 60 w 68 w Low income 765,007 1,123,865 29 24 30 30 17 17 41 46 Middle income 3,229,351 5,139,306 14 9 39 34 24 21 47 57 Lower middle income 2,326,049 3,426,319 16 10 39 34 26 22 44 56 Upper middle income 905,385 1,708,823 9 6 39 34 21 18 53 60 Low & middle income 3,991,257 6,259,154 16 11 38 33 23 20 46 55 East Asia & Pacific 674,196 1,833,073 24 15 40 47 29 32 37 38 Europe & Central Asia 1,099,616 1,132,845 17 9 44 32 .. .. 39 59 Latin America & Carib. 1,098,727 1,668,800 9 7 36 26 23 15 55 67 Middle East & N. Africa 424,126 670,722 15 11 38 41 13 13 47 48 South Asia 404,808 649,079 31 23 27 26 17 16 43 51 Sub-Saharan Africa 298,443 319,288 18 18 34 29 17 15 48 54 High income 17,683,764 26,052,812 3 2 33 27 22 19 64 71 Europe EMU 5,503,913 6,648,492 3 2 34 28 24 21 62 70 a. Components are at basic prices. b. Data cover mainland Tanzania only. 188 2004 World Development Indicators ECONOMY 4.2 Structure of output About the data An economy's gross domestic product (GDP) repre- agricultural inputs that cannot easily be allocated to Monetary Fund for the year shown. An alternative sents the sum of value added by all producers in that specific outputs are frequently "netted out" using conversion factor is applied if the official exchange economy. Value added is the value of the gross out- equally crude and ad hoc approximations. For further rate is judged to diverge by an exceptionally large put of producers less the value of intermediate discussion of the measurement of agricultural pro- margin from the rate effectively applied to transac- goods and services consumed in production, before duction, see About the data for table 3.3. tions in foreign currencies and traded products. taking account of the consumption of fixed capital in Ideally, industrial output should be measured the production process. Since 1968 the United through regular censuses and surveys of firms. But Definitions Nations System of National Accounts has called for in most developing countries such surveys are infre- estimates of value added to be valued at either basic quent, so earlier survey results must be extrapolated · Gross domestic product (GDP) at purchaser prices (excluding net taxes on products) or producer using an appropriate indicator. The choice of sam- prices is the sum of gross value added by all resi- prices (including net taxes on products paid by pro- pling unit, which may be the enterprise (where dent producers in the economy plus any product ducers but excluding sales or value added taxes). responses may be based on financial records) or the taxes (less subsidies) not included in the valuation Both valuations exclude transport charges that are establishment (where production units may be of output. It is calculated without making deductions invoiced separately by producers. Some countries, recorded separately), also affects the quality of the for depreciation of fabricated assets or for depletion however, report such data at purchaser prices--the data. Moreover, much industrial production is organ- and degradation of natural resources. Value added prices at which final sales are made (including trans- ized in unincorporated or owner-operated ventures is the net output of an industry after adding up all port charges)--which may affect estimates of the that are not captured by surveys aimed at the formal outputs and subtracting intermediate inputs. The distribution of output. Total GDP as shown in the sector. Even in large industries, where regular sur- industrial origin of value added is determined by the table and elsewhere in this book is measured at pur- veys are more likely, evasion of excise and other International Standard Industrial Classification chaser prices. Value added by industry is normally taxes and nondisclosure of income lower the esti- (ISIC) revision 3. · Agriculture corresponds to ISIC measured at basic prices. When value added is mates of value added. Such problems become more divisions 1­5 and includes forestry and fishing. measured at producer prices, this is noted in Primary acute as countries move from state control of indus- · Industry covers mining, manufacturing (also data documentation. try to private enterprise, because new firms enter reported as a separate subgroup), construction, While GDP estimates based on the production business and growing numbers of established firms electricity, water, and gas (ISIC divisions 10­45). approach are generally more reliable than estimates fail to report. In accordance with the System of · Manufacturing corresponds to industries belong- compiled from the income or expenditure side, dif- National Accounts, output should include all such ing to ISIC divisions 15­37. · Services correspond ferent countries use different definitions, methods, unreported activity as well as the value of illegal to ISIC divisions 50­99. This sector is derived as a and reporting standards. World Bank staff review the activities and other unrecorded, informal, or small- residual (from GDP less agriculture and industry) and quality of national accounts data and sometimes scale operations. Data on these activities need to be may not properly reflect the sum of service output, make adjustments to increase consistency with inter- collected using techniques other than conventional including banking and financial services. For some national guidelines. Nevertheless, significant dis- surveys of firms. countries it includes product taxes (minus subsidies) crepancies remain between international standards In industries dominated by large organizations and and may also include statistical discrepancies. and actual practice. Many statistical offices, espe- enterprises, such as public utilities, data on output, cially those in developing countries, face severe lim- employment, and wages are usually readily available itations in the resources, time, training, and budgets and reasonably reliable. But in the service industry Data sources required to produce reliable and comprehensive the many self-employed workers and one-person The national accounts data for most developing series of national accounts statistics. businesses are sometimes difficult to locate, and countries are collected from national statistical they have little incentive to respond to surveys, let organizations and central banks by visiting and Data problems in measuring output alone report their full earnings. Compounding these resident World Bank missions. The data for high- Among the difficulties faced by compilers of national problems are the many forms of economic activity income economies come from data files of the accounts is the extent of unreported economic activ- that go unrecorded, including the work that women Organisation for Economic Co-operation and ity in the informal or secondary economy. In devel- and children do for little or no pay. For further dis- Development (for information on the OECD's oping countries a large share of agricultural output is cussion of the problems of using national accounts national accounts series, see its monthly Main either not exchanged (because it is consumed within data, see Srinivasan (1994) and Heston (1994). Economic Indicators). The complete national the household) or not exchanged for money. accounts time series is available on the World Agricultural production often must be estimated Dollar conversion Development Indicators 2004 CD-ROM. The indirectly, using a combination of methods involving To produce national accounts aggregates that are United Nations Statistics Division publishes estimates of inputs, yields, and area under cultiva- measured in the same standard monetary units, the detailed national accounts for United Nations tion. This approach sometimes leads to crude value of output must be converted to a single com- member countries in National Accounts Statistics: approximations that can differ from the true values mon currency. The World Bank conventionally uses Main Aggregates and Detailed Tables and publish- over time and across crops for reasons other than the U.S. dollar and applies the average official es updates in the Monthly Bulletin of Statistics. climatic conditions or farming techniques. Similarly, exchange rate reported by the International 2004 World Development Indicators 189 4.3 Structure of manufacturing Manufacturing Food, Textiles Machinery Chemicals Other value added beverages, and clothing and transport manufacturing a and equipment tobacco $ millions % of total % of total % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 376 24 .. 33 .. .. .. .. .. 44 .. Algeria 6,452 3,897 13 33 17 8 .. .. .. .. 70 59 Angola 513 264 .. .. .. .. .. .. .. .. .. .. Argentina 37,868 46,877 20 30 10 7 13 15 12 12 46 36 Armenia 681 418 .. .. .. .. .. .. .. .. .. .. Australia 38,868 42,528 18 .. 6 .. 20 .. 7 .. 48 .. Austria 33,386 37,189 15 12 7 3 28 41 7 6 43 38 Azerbaijan 1,092 675 .. .. .. .. .. .. .. .. .. .. Bangladesh 3,839 6,933 24 22 38 33 7 16 17 10 15 19 Belarus 6,630 3,444 .. .. .. .. .. .. .. .. .. .. Belgium .. 39,986 17 19 7 6 .. .. 13 16 62 59 Benin 145 198 .. .. .. .. .. .. .. .. .. .. Bolivia 826 1,121 28 31 5 4 1 1 3 3 63 60 Bosnia and Herzegovina .. 480 12 .. 15 .. 18 .. 7 .. 49 .. Botswana 181 253 51 .. 12 .. .. .. .. .. 36 .. Brazil 89,966 80,280 14 .. 12 .. 27 .. .. .. 48 .. Bulgaria .. 1,985 22 20 9 10 19 5 5 .. 45 65 Burkina Faso 460 281 .. .. .. .. .. .. .. .. .. .. Burundi 134 60 83 .. 9 .. .. .. 2 .. 7 .. Cambodia 58 583 .. .. .. .. .. .. .. .. .. .. Cameroon 1,581 940 61 47 ­13 15 1 1 5 4 46 32 Canada 91,674 117,240 15 13 6 3 26 36 10 8 44 39 Central African Republic 154 81 57 .. 6 .. 2 .. 6 .. 28 .. Chad 239 152 .. .. .. .. .. .. .. .. .. .. Chile 5,613 10,663 25 32 7 4 5 5 10 14 52 45 China 116,573 375,455 15 14 15 11 24 30 13 12 34 33 Hong Kong, China 12,639 9,197 8 7 36 20 21 33 2 4 33 37 Colombia 8,034 12,207 31 33 15 9 9 5 14 17 31 35 Congo, Dem. Rep. 1,029 205 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 234 112 .. .. .. .. .. .. .. .. .. .. Costa Rica 1,107 3,677 47 46 8 6 7 6 9 12 30 29 Côte d'Ivoire 2,257 1,591 .. 42 .. 10 .. 3 .. 12 .. 33 Croatia 4,770 3,219 22 .. 15 .. 20 .. 8 .. 36 .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark 20,757 23,156 22 .. 4 .. 24 .. 12 .. 39 .. Dominican Republic 1,270 3,325 .. .. .. .. .. .. .. .. .. .. Ecuador 1,988 2,171 22 38 10 6 5 3 8 4 56 50 Egypt, Arab Rep. 7,296 17,969 19 18 15 12 9 12 14 16 43 42 El Salvador 1,044 3,031 36 29 14 28 4 3 24 16 23 24 Eritrea 35 67 .. .. .. .. .. .. .. .. .. .. Estonia 1,632 830 .. .. .. .. .. .. .. .. .. .. Ethiopia 624 .. 62 54 21 12 1 7 2 5 14 22 Finland .. 27,771 13 7 4 2 24 24 8 2 52 64 France 228,263 215,860 13 .. 6 .. 31 .. 9 .. 41 .. Gabon 332 205 45 .. 2 .. 1 .. 7 .. 45 .. Gambia, The 18 18 .. .. .. .. .. .. .. .. .. .. Georgia 1,773 .. .. .. .. .. .. .. .. .. .. .. Germany 456,400 385,839 .. .. .. .. .. .. .. .. .. .. Ghana 575 449 .. .. .. .. .. .. .. .. .. .. Greece .. 11,441 22 28 20 11 12 11 10 11 36 38 Guatemala 1,151 2,542 .. .. .. .. .. .. .. .. .. .. Guinea 126 121 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 19 21 .. .. .. .. .. .. .. .. .. .. Haiti .. .. 51 46 9 19 .. .. .. .. 40 34 190 2004 World Development Indicators ECONOMY 4.3 Structure of manufacturing Manufacturing Food, Textiles Machinery Chemicals Other value added beverages, and clothing and transport manufacturing a and equipment tobacco $ millions % of total % of total % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Honduras 443 1,025 45 42 10 22 3 2 5 5 36 29 Hungary 6,613 9,534 14 19 9 8 26 26 12 7 39 40 India 48,808 66,024 12 13 15 12 25 20 14 22 34 33 Indonesia 23,643 37,393 27 19 15 17 12 25 9 11 37 28 Iran, Islamic Rep. 14,503 15,456 12 .. 20 .. 20 .. 8 .. 40 .. Iraq .. .. 20 .. 16 .. 4 .. 11 .. 49 .. Ireland 11,982 28,130 27 16 4 1 29 31 16 36 24 16 Israel .. .. 14 12 9 9 32 32 9 5 37 42 Italy 247,930 203,247 8 10 13 12 34 26 7 8 37 44 Jamaica 853 1,018 41 48 5 7 .. .. .. .. 54 46 Japan 810,232 1,040,351 9 11 5 3 40 39 10 10 37 36 Jordan 520 1,125 28 28 7 6 4 5 15 17 47 45 Kazakhstan 1,941 3,139 .. .. .. .. .. .. .. .. .. .. Kenya 864 1,163 38 48 10 8 10 9 9 8 33 28 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 72,837 144,376 11 8 12 8 32 45 9 9 36 30 Kuwait 2,142 .. 4 7 3 4 2 4 3 2 88 83 Kyrgyz Republic 703 105 .. .. .. .. .. .. .. .. .. .. Lao PDR 85 292 .. .. .. .. .. .. .. .. .. .. Latvia 2,524 926 .. 39 .. 12 .. 15 .. 6 .. 29 Lebanon .. 1,560 .. .. .. .. .. .. .. .. .. .. Lesotho 71 131 .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 2,113 1,995 .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 1,411 621 20 32 26 18 14 15 9 11 31 24 Madagascar 314 430 .. .. .. .. .. .. .. .. .. .. Malawi 313 197 38 44 10 8 1 5 18 16 33 28 Malaysia 10,665 29,447 13 10 6 4 31 46 11 11 39 30 Mali 200 86 .. .. .. .. .. .. .. .. .. .. Mauritania 94 78 .. .. .. .. .. .. .. .. .. .. Mauritius 491 918 30 31 46 48 2 2 4 5 17 15 Mexico 49,992 107,195 22 25 5 4 24 28 18 15 32 28 Moldova .. 183 .. .. .. .. .. .. .. .. .. .. Mongolia .. 58 33 .. 37 .. 1 .. 1 .. 27 .. Morocco 4,753 5,858 22 36 17 16 8 8 12 13 41 27 Mozambique 230 442 .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 292 342 .. .. .. .. .. .. .. .. .. .. Nepal 209 486 37 35 31 34 1 3 5 6 25 23 Netherlands 51,978 55,742 21 23 3 2 25 25 16 14 35 35 New Zealand 7,574 8,479 28 31 8 .. 13 14 7 13 44 43 Nicaragua 170 553 .. .. .. .. .. .. .. .. .. .. Niger 163 122 37 20 29 9 .. .. .. .. 34 71 Nigeria 1,562 1,635 15 30 46 11 13 8 4 26 22 25 Norway 13,450 17,076 18 16 2 2 25 29 9 8 46 46 Oman 396 .. .. 12 .. 5 .. 4 .. 5 .. 74 Pakistan 6,184 8,637 24 23 27 26 9 13 15 16 25 22 Panama 502 713 51 51 8 5 2 .. 8 6 31 37 Papua New Guinea 289 286 .. .. .. .. .. .. .. .. .. .. Paraguay 883 1,033 55 .. 16 .. .. .. .. .. 29 .. Peru 3,926 7,707 23 26 11 10 8 6 9 10 49 49 Philippines 11,008 16,878 39 33 11 9 13 15 12 13 26 29 Poland .. 28,625 21 26 9 6 26 23 7 6 37 38 Portugal 13,631 18,926 15 .. 21 .. 13 .. 6 .. 45 .. Puerto Rico 12,126 23,375 16 8 5 3 18 15 44 58 17 15 2004 World Development Indicators 191 4.3 Structure of manufacturing Manufacturing Food, Textiles Machinery Chemicals Other value added beverages, and clothing and transport manufacturing a and equipment tobacco $ millions % of total % of total % of total % of total % of total 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 1990 2000 Romania 9,152 4,768 19 .. 18 .. 14 .. 4 .. 45 .. Russian Federation .. .. .. 16 .. 2 .. 19 .. 9 .. 54 Rwanda 473 205 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 10,049 18,235 .. .. .. .. .. .. .. .. .. .. Senegal 747 566 60 44 3 5 5 3 9 26 23 21 Serbia and Montenegro .. .. .. 28 .. 8 .. 13 .. 11 .. 40 Sierra Leone 31 28 .. .. .. .. .. .. .. .. .. .. Singapore .. 24,407 4 2 3 1 53 62 10 14 29 20 Slovak Republic .. 4,075 .. .. .. .. .. .. .. .. .. .. Slovenia 4,008 4,468 12 11 15 9 16 16 9 11 48 52 Somalia 41 .. .. .. .. .. .. .. .. .. .. .. South Africa 24,043 21,643 14 14 8 7 18 20 9 9 50 50 Spain .. 95,110 18 14 8 7 25 23 10 10 39 47 Sri Lanka 1,077 2,459 51 42 24 26 4 8 4 4 17 19 Sudan .. 1,059 .. .. .. .. .. .. .. .. .. .. Swaziland 250 348 69 .. 8 .. 1 .. 0 .. 22 .. Sweden .. 47,689 10 7 2 1 32 39 9 11 47 42 Switzerland .. .. 10 9 4 3 34 27 .. .. 53 60 Syrian Arab Republic 2,508 4,579 35 27 29 24 .. .. .. .. 36 49 Tajikistan 653 237 .. .. .. .. .. .. .. .. .. .. Tanzania b 361 624 51 45 3 0 6 5 11 7 28 43 Thailand 23,217 41,212 24 .. 30 .. 19 .. 2 .. 26 .. Togo 162 118 .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 438 599 30 .. 3 .. 3 .. 19 .. 44 .. Tunisia 2,075 3,537 19 18 20 33 5 9 4 9 52 31 Turkey 26,882 26,994 16 13 15 18 16 17 10 11 43 41 Turkmenistan .. 643 .. .. .. .. .. .. .. .. .. .. Uganda 230 527 .. .. .. .. .. .. .. .. .. .. Ukraine 32,977 5,099 .. .. .. .. .. .. .. .. .. .. United Arab Emirates 2,643 .. .. .. .. .. .. .. .. .. .. .. United Kingdom 206,727 232,507 13 .. 5 .. 32 .. 11 .. 38 .. United States 1,040,600 1,520,300 12 .. 5 .. 31 .. 12 .. 40 .. Uruguay 2,597 3,392 31 37 18 12 9 3 10 8 32 39 Uzbekistan .. 645 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 9,809 15,621 17 22 5 2 5 .. 9 .. 64 76 Vietnam 793 5,786 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 591 .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 449 493 .. .. .. .. .. .. .. .. .. .. Zambia 1,048 329 44 .. 11 .. 7 .. 9 .. 29 .. Zimbabwe 1,799 1,003 28 30 19 7 9 29 6 6 38 28 World 4,475,773 t 5,826,313 t Low income 112,968 149,818 Middle income 698,289 1,114,738 Lower middle income 490,903 806,107 Upper middle income 170,285 302,007 Low & middle income 755,166 1,263,141 East Asia & Pacific 188,907 514,058 Europe & Central Asia .. .. Latin America & Carib. 243,987 307,798 Middle East & N. Africa 47,699 81,370 South Asia 61,101 85,928 Sub-Saharan Africa 42,805 37,493 High income 3,708,270 4,573,059 Europe EMU 1,221,575 1,119,610 a. Includes unallocated data. b. Data cover mainland Tanzania only. 192 2004 World Development Indicators ECONOMY 4.3 Structure of manufacturing About the data The data on the distribution of manufacturing value United Nations International Standard Industrial as advertising, accounting, and many other service activ- added by industry are provided by the United Nations Classification (ISIC) revision 2. First published in 1948, ities. In some cases the processes may be carried out Industrial Development Organization (UNIDO). UNIDO the ISIC has its roots in the work of the League of by different technical units within the larger enterprise, obtains data on manufacturing value added from a vari- Nations Committee of Statistical Experts. The commit- but collecting data at such a detailed level is not practi- ety of national and international sources, including the tee's efforts, interrupted by the Second World War, were cal. Nor would it be useful to record production data at United Nations Statistics Division, the World Bank, the taken up by the United Nations Statistical Commission, the very highest level of a large, multiplant, multiproduct Organisation for Economic Co-operation and Develop- which at its first session appointed a committee on firm. The ISIC has therefore adopted as the definition of ment, and the International Monetary Fund. To improve industrial classification. The latest revision, ISIC revision an establishment "an enterprise or part of an enterprise comparability over time and across countries, UNIDO 3, was completed in 1989, and many countries have which independently engages in one, or predominantly supplements these data with information from industri- now switched to it. But revision 2 is still widely used for one, kind of economic activity at or from one al censuses, statistics supplied by national and inter- compiling cross-country data. Concordances matching location...for which data are available..." (United national organizations, unpublished data that it collects ISIC categories to national systems of classification and Nations 1990, p. 25). By design, this definition matches in the field, and estimates by the UNIDO Secretariat. to related systems such as the Standard International the reporting unit required for the production accounts of Nevertheless, coverage may be less than complete, par- Trade Classification (SITC) are readily available. the United Nations System of National Accounts. ticularly for the informal sector. To the extent that direct In establishing a classification system, compilers information on inputs and outputs is not available, esti- must define both the types of activities to be described Definitions mates may be used that may result in errors in industry and the organizational units whose activities are to be totals. Moreover, countries use different reference peri- reported. There are many possibilities, and the choices · Manufacturing value added is the sum of gross out- ods (calendar or fiscal year) and valuation methods made affect how the resulting statistics can be inter- put less the value of intermediate inputs used in pro- (basic, producer, or purchaser prices) to estimate value preted and how useful they are in analyzing economic duction for industries classified in ISIC major division added. (See also About the data for table 4.2.) behavior. The ISIC emphasizes commonalities in the 3. · Food, beverages, and tobacco correspond to The data on manufacturing value added in U.S. dollars production process and is explicitly not intended to ISIC division 31. · Textiles and clothing correspond are from the World Bank's national accounts files. These measure outputs (for which there is a newly developed to ISIC division 32. · Machinery and transport equip- figures may differ from those used by UNIDO to calculate Central Product Classification). Nevertheless, the ISIC ment comprise ISIC groups 382­84. · Chemicals cor- the shares of value added by industry, in part because views an activity as defined by "a process resulting in respond to ISIC groups 351 and 352. · Other of differences in exchange rates. Thus estimates of a homogeneous set of products" (United Nations 1990 manufacturing covers wood and related products (ISIC value added in a particular industry calculated by apply- [ISIC, series M, no. 4, rev. 3], p. 9). division 33), paper and related products (ISIC division ing the shares to total manufacturing value added will Firms typically use a multitude of processes to pro- 34), petroleum and related products (ISIC groups not match those from UNIDO sources. The classification duce a final product. For example, an automobile manu- 353­56), basic metals and mineral products (ISIC divi- of manufacturing industries in the table accords with the facturer engages in forging, welding, and painting as well sions 36 and 37), fabricated metal products and pro- fessional goods (ISIC groups 381 and 385), and other 4.3a industries (ISIC group 390). When data for textiles and Manufacturing continues to show strong growth in East Asia clothing, machinery and transport equipment, or Value added in manufacturing (1990 = 100) chemicals are shown in the table as not available, they 350 are included in "other manufacturing." East Asia & Pacific 300 Data sources The data on value added in manufacturing in U.S. 250 dollars are from the World Bank's national accounts files. The data used to calculate shares 200 South Asia Middle East & North Africa of value added by industry are provided to the World Bank in electronic files by UNIDO. The most 150 recent published source is UNIDO's International Latin America & Caribbean Yearbook of Industrial Statistics 2003. The ISIC 100 Sub-Saharan Africa system is described in the United Nations' International Standard Industrial Classification of 50 All Economic Activities, Third Revision (1990). The 1990 1992 1994 1996 1998 2000 2002 discussion of the ISIC draws on Jacob Ryten's Manufacturing continues to be the dominant sector in East Asia and Pacific. Growing by an average 10 percent a year in paper "Fifty Years of ISIC: Historical Origins and 1990­2002, value added in manufacturing has more than tripled. Future Perspectives" (1998). Source: World Bank data files. 2004 World Development Indicators 193 4.4 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 = 100 1980­90 1990­2001 1980­90 1990­2001 1980­90 1990­2001 1980­90 1990­2001 1990 2001 Afghanistan ­9.7 ­3.9 ­1.8 ­0.2 ­10.5 ­4.7 ­0.1 ­1.1 99 100 Albania a .. .. .. .. .. 13.3 .. 17.2 .. .. Algeria 3.3 2.3 ­8.0 2.0 ­4.4 3.0 ­2.7 1.2 128 174 Angola 10.0 5.6 ­1.8 7.3 16.5 6.6 3.7 8.0 118 109 Argentina 4.9 8.8 ­6.8 13.6 2.2 9.2 ­6.6 13.4 97 108 Armenia a .. .. .. .. .. ­6.8 .. 0.5 .. .. Australia a 6.3 7.1 6.0 8.7 6.6 4.7 6.4 5.4 117 105 Austria a 6.6 .. .. .. 10.2 5.3 8.7 3.8 .. .. Azerbaijan a .. .. .. .. .. ­6.4 .. 4.0 .. .. Bangladesh 8.4 28.2 3.0 23.3 8.0 14.2 3.5 9.6 100 92 Belarus a .. .. .. .. .. 13.9 .. 14.1 .. .. Belgium a, b 4.5 6.2 4.0 5.4 7.8 6.3 6.4 4.5 100 .. Benin 11.9 2.3 ­10.0 6.6 18.7 2.4 ­4.8 6.9 100 82 Bolivia 3.1 3.2 ­1.3 8.1 ­1.9 4.4 ­0.4 8.4 115 111 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 14.9 9.3 9.3 5.3 18.7 8.5 9.1 1.7 109 130 Brazil 6.2 5.4 0.8 14.7 5.2 5.6 ­1.8 10.9 60 91 Bulgaria a .. .. .. .. ­12.3 2.2 ­14.0 5.6 .. .. Burkina Faso ­0.3 11.4 3.8 1.6 7.8 10.4 4.4 1.5 91 83 Burundi 3.4 10.3 0.9 6.0 2.5 ­5.6 2.2 ­6.3 79 44 Cambodia .. .. .. .. .. .. .. .. .. .. Cameroon 8.4 2.3 4.8 5.5 2.4 ­0.3 0.1 2.1 90 95 Canada a 6.4 8.7 7.4 8.7 6.8 7.8 7.9 7.0 100 103 Central African Republic ­0.0 18.8 4.2 3.2 3.5 3.0 7.8 ­0.7 124 54 Chad 8.7 1.5 10.7 3.6 9.4 1.8 12.5 6.2 116 110 Chile 9.1 10.7 ­2.9 9.3 8.1 8.6 2.8 8.9 84 69 China .. 13.4 .. 8.1 .. 12.8 .. 10.3 .. 81 Hong Kong, China 15.3 7.7 13.8 8.2 16.7 7.3 14.7 7.7 101 102 Colombia 7.9 4.3 ­2.1 7.6 7.7 6.7 ­0.2 8.5 95 109 Congo, Dem. Rep. 14.8 ­10.0 37.8 ­6.1 7.7 ­6.7 26.7 ­0.5 108 125 Congo, Rep. 7.3 5.7 ­2.5 1.8 2.1 7.5 ­0.7 1.4 122 156 Costa Rica 3.7 12.6 5.2 13.8 4.7 14.8 4.4 12.8 72 95 Côte d'Ivoire 2.6 3.0 ­2.1 3.5 1.8 5.0 ­1.5 3.6 82 103 Croatia a .. .. .. .. .. 1.6 .. 7.7 .. .. Cuba ­1.1 ­1.0 ­0.5 3.1 ­0.9 ­1.5 1.5 2.3 96 92 Czech Republic a .. .. .. .. .. 9.9 .. 10.0 .. .. Denmark a 4.1 5.2 3.1 6.0 8.4 3.6 6.3 3.8 100 102 Dominican Republic ­0.9 3.3 0.8 12.8 ­2.1 3.9 3.3 12.9 97 104 Ecuador 7.1 5.6 ­1.9 6.5 ­0.5 6.0 ­1.3 7.9 141 114 Egypt, Arab Rep. 2.1 2.8 8.0 1.1 ­3.3 3.7 12.6 3.8 86 85 El Salvador ­4.6 3.0 4.5 7.5 ­4.7 8.9 2.4 10.6 69 80 Eritrea .. 26.0 .. 7.1 .. 24.4 .. 5.8 91 97 Estonia a .. .. .. .. .. 17.4 .. 18.9 .. .. Ethiopia ­0.4 6.8 3.6 3.1 ­1.0 9.2 3.9 7.7 90 79 Finland a 2.3 9.3 4.4 4.3 7.4 6.9 6.9 4.4 100 88 France a 3.6 6.4 3.7 5.6 7.5 3.8 6.5 3.2 97 96 Gabon 2.6 3.4 ­3.5 2.8 ­3.9 0.7 1.1 2.4 126 113 Gambia, The ­4.2 ­13.2 ­6.0 0.1 ­0.0 ­13.7 2.4 ­0.4 100 100 Georgia .. .. .. .. .. .. .. .. .. .. Germany a, c 4.5 5.9 4.9 4.3 9.2 3.7 7.1 3.2 102 95 Ghana ­17.2 10.0 ­20.1 9.7 ­2.6 10.6 ­0.4 9.0 94 101 Greece a 5.0 8.9 6.4 8.9 5.8 2.1 6.6 4.2 108 107 Guatemala ­1.1 8.2 0.1 10.2 ­2.3 8.9 0.5 11.0 98 83 Guinea .. 4.7 .. ­1.9 .. 0.8 .. ­2.3 135 100 Guinea-Bissau ­2.1 17.1 ­0.3 ­5.5 4.2 13.3 5.3 ­3.2 143 70 Haiti ­0.4 12.2 ­4.6 12.7 ­1.2 11.8 ­2.9 13.7 116 89 Data for Taiwan, China .. 5.3 .. 6.8 .. 6.6 .. 7.2 102 117 194 2004 World Development Indicators ECONOMY 4.4 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 = 100 1980­90 1990­2001 1980­90 1990­2001 1980­90 1990­2001 1980­90 1990­2001 1990 2001 Honduras 4.1 2.5 1.6 12.3 1.6 6.3 0.5 13.2 81 102 Hungary a 3.4 10.9 1.3 12.0 1.4 12.8 0.1 13.2 106 96 India 4.2 11.2 4.7 12.6 7.3 9.1 4.2 9.5 79 91 Indonesia 8.1 8.6 .. .. ­0.8 7.1 .. .. .. .. Iran, Islamic Rep. 17.1 ­0.9 ­2.4 ­7.5 7.2 1.2 0.2 ­6.5 170 225 Iraq 2.3 29.5 ­4.5 9.8 ­4.0 29.4 ­2.2 10.3 132 162 Ireland a 9.3 15.1 4.8 11.2 12.8 13.4 7.0 10.4 107 99 Israel a 6.9 9.6 5.8 8.2 8.3 10.5 5.9 7.3 97 106 Italy a 4.3 5.4 5.3 4.7 8.7 4.2 6.9 3.2 98 102 Jamaica 1.6 4.6 3.0 7.3 1.1 2.2 2.8 6.8 105 87 Japan a 5.1 2.3 6.6 5.1 8.9 3.3 5.1 4.3 91 88 Jordan 7.8 5.3 1.1 3.9 6.0 6.7 ­1.9 5.2 80 85 Kazakhstan a .. .. .. .. .. 12.4 .. 5.1 .. .. Kenya 1.7 4.1 2.5 6.6 ­1.1 5.6 1.7 5.6 68 88 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 12.3 15.4 11.7 9.3 15.0 9.1 12.0 6.5 96 69 Kuwait .. 20.0 .. .. .. 16.3 .. .. .. .. Kyrgyz Republic a .. .. .. .. .. 4.9 .. 4.7 .. .. Lao PDR a .. .. .. .. 11.0 13.2 6.6 10.5 .. .. Latvia a .. 7.2 .. .. .. 10.7 .. 17.4 .. .. Lebanon ­5.6 2.4 ­7.5 8.6 ­5.6 4.1 ­5.5 8.9 105 112 Lesotho 6.3 13.9 3.5 1.6 3.8 12.2 3.4 ­0.4 97 100 Liberia ­3.5 7.4 ­7.6 9.7 ­3.1 4.6 ­7.2 8.8 112 89 Libya 0.1 ­4.1 ­6.6 0.3 ­7.3 ­2.2 ­4.4 1.8 145 200 Lithuania a .. .. .. .. .. 9.5 .. 13.2 .. .. Macedonia, FYR a .. .. .. .. .. 1.8 .. 4.4 .. .. Madagascar ­2.2 4.7 ­4.4 6.2 ­1.0 9.7 ­2.4 7.4 102 148 Malawi 2.3 3.0 ­0.1 ­2.3 2.0 1.1 3.2 ­0.9 141 96 Malaysia 14.6 10.7 .. .. 8.6 14.0 .. .. .. .. Mali 4.4 11.5 3.0 5.3 6.1 7.5 2.7 3.8 122 95 Mauritania 4.1 2.1 ­2.9 4.3 8.2 ­2.4 ­1.8 0.2 96 96 Mauritius 11.5 2.4 11.8 3.1 14.3 3.1 12.8 3.1 104 113 Mexico 15.4 14.8 0.8 12.3 5.8 15.4 6.3 13.0 109 107 Moldova a .. .. .. .. .. ­0.2 .. 2.6 .. .. Mongolia 3.1 .. .. .. 5.0 ­1.7 5.0 0.8 .. .. Morocco 5.6 6.7 3.1 7.2 6.1 6.6 3.6 5.2 95 115 Mozambique ­9.5 18.8 ­2.7 3.0 ­9.6 12.9 0.1 1.6 115 79 Myanmar ­5.9 16.7 ­10.0 13.7 ­7.2 16.0 ­5.1 21.3 117 65 Namibia a .. 2.1 .. 4.8 .. 0.3 .. 3.6 115 98 Nepal a .. .. .. .. 8.1 10.8 6.9 8.2 .. .. Netherlands a 4.5 6.8 4.5 6.7 4.6 5.2 4.4 5.0 98 99 New Zealand a 3.5 4.4 4.3 5.5 6.2 3.5 5.4 4.8 103 106 Nicaragua ­4.8 10.1 ­3.5 8.8 ­5.8 9.5 ­3.1 11.0 119 71 Niger ­5.2 3.7 ­5.2 ­2.2 ­5.4 0.2 ­3.5 0.9 136 77 Nigeria ­4.4 2.6 ­21.4 2.8 ­8.4 3.7 ­15.6 3.5 162 157 Norway a 4.2 6.3 3.5 7.4 5.3 5.5 6.2 3.5 111 157 Oman 11.0 11.0 .. .. ­2.2 14.8 .. .. .. .. Pakistan 7.9 3.0 2.6 1.9 8.0 4.0 2.9 2.5 91 83 Panama ­0.6 6.1 ­6.6 6.4 ­0.6 9.1 ­3.6 7.3 69 100 Papua New Guinea 4.6 2.5 .. .. 4.8 5.9 .. .. .. .. Paraguay 12.6 1.2 10.1 2.3 11.5 2.7 4.2 3.6 87 84 Peru 2.7 9.6 ­2.0 9.3 ­1.5 8.3 1.3 9.2 93 78 Philippines 17.3 21.9 18.3 16.5 3.9 17.1 2.9 10.3 109 118 Poland a 4.8 9.8 1.5 17.2 1.4 9.9 ­3.2 16.7 86 95 Portugal a 11.9 .. .. .. 15.1 4.7 10.3 4.7 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 195 4.4 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 = 100 1980­90 1990­2001 1980­90 1990­2001 1980­90 1990­2001 1980­90 1990­2001 1990 2001 Romania a .. .. .. .. ­4.0 8.7 ­3.8 7.4 .. .. Russian Federation a .. .. .. .. .. 9.1 .. 2.5 .. .. Rwanda 3.3 ­4.2 2.3 1.2 ­0.3 ­1.4 3.3 ­0.8 36 72 Saudi Arabia ­6.3 1.6 .. .. ­13.3 3.5 .. .. .. .. Senegal 1.2 6.8 0.4 5.5 3.6 3.7 1.4 3.3 109 91 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone ­2.7 ­36.3 ­3.0 ­8.0 ­2.4 ­24.8 ­8.7 ­2.7 73 .. Singapore 12.2 10.8 8.5 7.1 9.9 8.6 8.1 6.6 111 92 Slovak Republic a .. .. .. .. .. 9.8 .. 10.7 .. .. Slovenia a .. .. .. .. .. 7.5 .. 8.3 .. .. Somalia ­1.5 ­0.5 ­11.1 2.3 ­1.1 ­2.4 ­9.2 1.7 99 82 South Africa a, d 1.6 5.0 ­0.9 7.7 0.7 2.3 ­1.4 5.0 99 100 Spain a 2.7 10.9 10.5 9.2 10.8 7.9 10.6 6.0 96 99 Sri Lanka 4.6 6.9 1.7 10.2 5.4 10.1 2.2 12.3 83 .. Sudan ­3.0 17.1 ­7.7 11.2 ­2.5 14.0 ­6.4 9.9 123 141 Swaziland 7.7 1.2 2.3 3.8 4.6 2.2 ­0.5 4.8 100 100 Sweden a 4.4 8.4 5.0 6.2 8.0 4.9 6.7 3.7 99 89 Switzerland a 3.7 .. .. .. 9.5 2.5 8.8 1.9 .. .. Syrian Arab Republic 19.4 1.2 ­1.0 8.2 2.4 0.8 ­8.5 10.2 131 .. Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania .. 6.5 .. ­0.8 .. 7.1 .. 0.7 110 95 Thailand 13.7 9.1 11.3 2.2 13.8 9.6 12.6 4.7 102 78 Togo ­1.2 9.3 0.6 5.7 1.1 7.1 2.0 5.0 134 107 Trinidad and Tobago ­10.9 3.6 ­20.4 10.2 ­9.4 7.3 ­12.3 11.3 117 172 Tunisia 4.9 5.8 1.6 4.8 3.4 5.9 2.7 5.1 103 99 Turkey 19.4 10.8 15.4 9.6 14.1 8.8 9.3 8.8 104 93 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda ­13.5 15.6 ­6.7 24.1 ­8.3 13.0 3.4 19.0 74 78 Ukraine a .. .. .. .. .. 7.0 .. 6.3 .. .. United Arab Emirates 8.9 2.1 ­1.3 9.2 ­0.8 4.0 0.7 11.2 174 213 United Kingdom a 4.5 6.2 6.7 6.8 5.9 4.7 8.5 4.9 101 104 United States a 3.6 6.2 7.2 8.8 5.7 6.6 8.2 9.1 98 99 Uruguay 4.4 5.3 1.2 8.8 4.5 4.0 ­1.3 8.3 100 87 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 3.4 5.1 ­4.0 5.3 ­4.4 5.7 ­3.3 5.6 142 132 Vietnam .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. a .. .. .. .. .. .. .. .. .. .. Zambia ­0.5 6.2 2.1 4.3 0.9 ­1.4 ­0.0 1.9 109 56 Zimbabwe 4.1 8.7 3.4 9.6 2.9 2.4 ­0.4 3.1 100 104 a. Data are from the International Monetary Fund's International Financial Statistics database. b. Includes Luxembourg. c. Data prior to 1990 refer to the Federal Republic of Germany before unification. d. Data refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 196 2004 World Development Indicators ECONOMY 4.4 Growth of merchandise trade About the data Definitions Data on international trade in goods are available which maintains the Commodity Trade (COMTRADE) · Export and import volumes are average annual from each country's balance of payments and cus- database. The United Nations Conference on Trade growth rates calculated for low- and middle-income toms records. While the balance of payments focus- and Development (UNCTAD) compiles a variety of economies from UNCTAD's quantum index series es on the financial transactions that accompany international trade statistics, including price and vol- and for high-income economies from export and trade, customs data record the direction of trade and ume indexes, based on the COMTRADE data. The import data deflated by the IMF's trade price defla- the physical quantities and value of goods entering IMF and the World Trade Organization also compile tors. · Export and import values are average annu- or leaving the customs area. Customs data may dif- data on trade prices and volumes. The growth rates al growth rates calculated from UNCTAD's value fer from data recorded in the balance of payments and terms of trade for low- and middle-income indexes or from current values of merchandise because of differences in valuation and the time of economies shown in the table were calculated from exports and imports. · Net barter terms of trade recording. The 1993 System of National Accounts index numbers compiled by UNCTAD. Volume meas- are calculated as the ratio of the export price index and the fifth edition of the International Monetary ures for high-income economies were derived by to the corresponding import price index measured Fund's (IMF) Balance of Payments Manual (1993) deflating the value of trade using deflators from the relative to the base year 1995. attempted to reconcile the definitions and reporting IMF's International Financial Statistics. In some standards for international trade statistics, but dif- cases price and volume indexes from different ferences in sources, timing, and national practices sources may vary significantly as a result of differ- limit comparability. Real growth rates derived from ences in estimation procedures. All indexes are trade volume indexes and terms of trade based on rescaled to a 1995 base year. Terms of trade were unit price indexes may therefore differ from those computed from the same indicators. derived from national accounts aggregates. The terms of trade measures the relative prices of Trade in goods, or merchandise trade, includes all a country's exports and imports. There are a number goods that add to or subtract from an economy's of ways to calculate terms of trade. The most com- material resources. Thus the total supply of goods in mon is the net barter (or commodity) terms of trade, an economy is made up of gross output plus imports constructed as the ratio of the export price index to less exports (currency in circulation, titles of owner- the import price index. When a country's net barter ship, and securities are excluded, but nonmonetary terms of trade increase, its exports are becoming gold is included). Trade data are collected on the more valuable or its imports cheaper. basis of a country's customs area, which in most cases is the same as its geographic area. Goods pro- vided as part of foreign aid are included, but goods destined for extraterritorial agencies (such as embassies) are not. Collecting and tabulating trade statistics are diffi- cult. Some developing countries lack the capacity to report timely data; this is a problem especially for countries that are landlocked and those whose terri- torial boundaries are porous. As a result, it is nec- essary to estimate their trade from the data reported by their partners. (For further discussion of the use of partner country reports, see About the data for table 6.2.) Countries that belong to common cus- toms unions may need to collect data through direct inquiry of companies. In some cases economic or political concerns may lead national authorities to suppress or misrepresent data on certain trade Data sources flows, such as oil, military equipment, or the exports The main source of trade data for developing of a dominant producer. In other cases reported countries is UNCTAD's annual Handbook of trade data may be distorted by deliberate under- or International Trade and Development Statistics. over-invoicing to effect capital transfers or avoid The IMF's International Financial Statistics taxes. And in some regions smuggling and black includes data on the export and import values market trading result in unreported trade flows. and deflators for high-income and selected devel- By international agreement customs data are oping economies. reported to the United Nations Statistics Division, 2004 World Development Indicators 197 4.5 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan 235 101 .. .. .. .. .. .. .. .. .. .. Albania 230 330 .. 4 .. 7 .. 1 .. 3 .. 86 Algeria 12,930 19,130 0 0 0 0 96 97 0 0 3 2 Angola 3,910 7,600 0 .. 0 .. 93 .. 6 .. 0 .. Argentina 12,353 25,352 56 46 4 2 8 17 2 4 29 31 Armenia .. 508 .. 16 .. 2 .. 4 .. 18 .. 61 Australia 39,752 65,034 22 22 10 5 21 22 20 16 24 29 Austria 41,265 78,694 3 6 4 2 1 2 3 3 88 82 Azerbaijan .. 2,168 .. 3 .. 1 .. 89 .. 0 .. 6 Bangladesh 1,671 6,093 14 7 7 1 1 0 .. 0 77 92 Belarus .. 8,100 .. 8 .. 4 .. 20 .. 1 .. 64 Belgium a 117,703 224,185 .. 9 .. 1 .. 4 .. 3 .. 79 Benin 288 365 15 23 56 71 15 0 0 0 13 6 Bolivia 926 1,310 19 34 8 3 25 27 44 20 5 17 Bosnia and Herzegovina 276 950 .. .. .. .. .. .. .. .. .. .. Botswana 1,784 2,510 .. 3 .. 0 .. 0 .. 5 .. 91 Brazil 31,414 60,362 28 28 3 4 2 4 14 8 52 54 Bulgaria 5,030 5,745 .. 10 .. 3 .. 9 .. 11 .. 61 Burkina Faso 152 166 .. 22 .. 56 .. 3 .. 0 .. 19 Burundi 75 30 .. 88 .. 1 .. .. .. 10 .. 1 Cambodia 86 1,500 .. .. .. .. .. .. .. .. .. .. Cameroon 2,002 1,700 20 21 14 20 50 47 7 4 9 7 Canada 127,629 252,394 9 7 9 5 10 13 9 4 59 63 Central African Republic 120 160 .. .. .. .. .. .. .. .. .. .. Chad 188 180 .. .. .. .. .. .. .. .. .. .. Chile 8,372 18,340 24 26 9 10 1 1 55 41 11 18 China 62,091 325,565 13 5 3 1 8 3 2 2 72 90 Hong Kong, China b 82,390 201,150 3 2 0 0 0 1 1 2 95 95 Colombia 6,766 12,001 33 19 4 6 37 36 0 1 25 38 Congo, Dem. Rep. 2,326 1,210 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 981 2,215 .. .. .. .. .. .. .. .. .. .. Costa Rica 1,448 5,258 58 31 5 3 1 1 1 1 27 63 Côte d'Ivoire 3,072 4,390 .. 59 .. 14 .. 11 .. 0 .. 21 Croatia 4,597 4,899 13 11 6 4 9 9 5 3 68 73 Cuba 5,100 1,500 .. 59 .. 0 .. 1 .. 29 .. 10 Czech Republic 12,170 38,403 .. 3 .. 2 .. 4 .. 1 .. 89 Denmark 36,870 57,045 27 19 3 3 3 6 1 1 60 66 Dominican Republic 2,170 5,183 21 41 0 .. 0 16 0 2 78 34 Ecuador 2,714 5,030 44 43 1 7 52 40 0 0 2 10 Egypt, Arab Rep. 3,477 4,381 10 9 10 0 29 34 9 5 42 35 El Salvador 582 2,992 57 33 1 1 2 5 3 3 38 58 Eritrea 15 14 .. .. .. .. .. .. .. .. .. .. Estonia .. 4,336 .. 12 .. 8 .. 5 .. 2 .. 72 Ethiopia 298 415 .. 69 .. 15 .. 0 .. 1 .. 14 Finland 26,571 44,836 2 2 10 6 1 3 4 3 83 85 France 216,588 331,780 16 11 2 1 2 2 3 2 77 81 Gabon 2,204 2,560 .. 1 .. 12 .. 83 .. 2 .. 2 Gambia, The 31 15 .. 81 .. 1 .. 0 .. 0 .. 17 Georgia .. 326 .. 26 .. 2 .. 9 .. 27 .. 35 Germany 421,100 613,093 5 4 1 1 1 1 3 2 89 86 Ghana 897 1,840 51 45 15 10 9 11 17 17 8 16 Greece 8,105 10,353 30 24 3 3 7 11 7 8 54 52 Guatemala 1,163 2,232 67 53 6 4 2 7 0 1 24 35 Guinea 671 750 .. 2 .. 0 .. 1 .. 68 .. 28 Guinea-Bissau 19 51 .. .. .. .. .. .. .. .. .. .. Haiti 160 280 14 .. 1 .. 0 .. 0 .. 85 .. Data for Taiwan, China 67,245 135,065 4 1 2 1 1 1 1 1 93 94 198 2004 World Development Indicators ECONOMY 4.5 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 831 1,270 82 64 4 5 1 0 4 6 9 26 Hungary 10,000 34,337 23 7 3 1 3 1 6 2 63 86 India 17,969 49,251 16 12 4 1 3 5 5 4 71 75 Indonesia 25,675 57,130 11 12 5 4 44 24 4 5 35 54 Iran, Islamic Rep. 19,305 24,440 .. 4 .. 0 .. 86 .. 1 .. 9 Iraq 12,380 13,520 .. .. .. .. .. .. .. .. .. .. Ireland 23,743 88,224 22 7 2 0 1 0 1 0 70 88 Israel 12,080 29,513 8 4 3 1 1 0 2 1 87 93 Italy 170,304 250,975 6 7 1 1 2 2 1 1 88 88 Jamaica 1,158 1,105 19 23 0 0 1 3 10 10 69 64 Japan 287,581 416,726 1 1 1 1 0 0 1 1 96 93 Jordan 1,064 2,743 11 15 0 0 0 0 38 17 51 68 Kazakhstan .. 9,709 .. 5 .. 1 .. 56 .. 18 .. 19 Kenya 1,031 2,094 49 32 6 11 13 31 3 2 29 24 Korea, Dem. Rep. 1,857 724 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 65,016 162,470 3 2 1 1 1 4 1 1 94 92 Kuwait 7,042 15,426 1 .. 0 .. 93 .. 0 .. 6 .. Kyrgyz Republic .. 486 .. 18 .. 23 .. 20 .. 6 .. 33 Lao PDR 79 298 .. .. .. .. .. .. .. .. .. .. Latvia .. 2,284 .. 10 .. 24 .. 1 .. 6 .. 59 Lebanon 494 1,046 .. 19 .. 6 .. 0 .. 6 .. 69 Lesotho 62 395 .. .. .. .. .. .. .. .. .. .. Liberia 868 220 .. .. .. .. .. .. .. .. .. .. Libya 13,225 10,970 0 .. 0 .. 94 .. 0 .. 5 .. Lithuania .. 5,560 .. 12 .. 4 .. 23 .. 2 .. 58 Macedonia, FYR 1,199 1,112 .. 16 .. 1 .. 4 .. 8 .. 70 Madagascar 319 785 73 .. 4 .. 1 .. 8 .. 14 .. Malawi 417 478 93 87 2 2 0 0 0 0 5 10 Malaysia 29,452 93,265 12 8 14 2 18 9 2 1 54 79 Mali 359 947 36 .. 62 .. .. .. 0 .. 2 .. Mauritania 469 315 .. .. .. .. .. .. .. .. .. .. Mauritius 1,194 1,755 32 26 1 0 1 0 0 0 66 73 Mexico 40,711 160,682 12 5 2 1 38 9 6 1 43 84 Moldova .. 667 .. 64 .. 3 .. 0 .. 2 .. 31 Mongolia 661 501 .. 6 .. 15 .. 1 .. 43 .. 36 Morocco 4,265 7,930 26 21 3 1 4 3 15 8 52 66 Mozambique 126 682 .. 23 .. 4 .. 10 .. 55 .. 8 Myanmar 325 3,015 51 .. 36 .. 0 .. 2 .. 10 .. Namibia 1,085 1,096 .. 36 .. 1 .. 1 .. 9 .. 52 Nepal 204 568 13 10 3 0 .. .. 0 0 83 67 Netherlands 131,775 244,304 20 19 4 4 10 1 3 2 59 74 New Zealand 9,394 14,363 47 49 18 13 4 2 6 4 23 28 Nicaragua 330 596 77 72 14 4 0 2 1 3 8 19 Niger 282 303 .. 38 .. 1 .. 0 .. 56 .. 3 Nigeria 13,596 15,107 1 0 1 0 97 100 0 0 1 0 Norway 34,047 60,971 7 7 2 1 48 61 10 6 33 22 Oman 5,508 11,172 1 6 0 0 92 77 1 1 5 15 Pakistan 5,615 9,913 9 11 10 1 1 2 0 0 79 85 Panama 340 846 75 79 1 1 0 6 1 1 21 12 Papua New Guinea 1,177 1,550 22 15 9 2 0 29 58 51 10 2 Paraguay 959 1,030 52 75 38 14 0 0 0 1 10 15 Peru 3,230 7,688 21 30 3 3 10 8 47 38 18 21 Philippines 8,117 36,265 19 5 2 0 2 1 8 1 38 50 Poland 14,320 41,010 13 8 3 1 11 5 9 4 59 82 Portugal 16,417 25,621 7 7 6 3 3 2 3 2 80 86 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 199 4.5 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 4,960 13,869 1 3 3 3 18 8 4 4 73 81 Russian Federation 40,000 106,858 .. 2 .. 4 .. 56 .. 8 .. 22 Rwanda 110 56 .. 56 .. 5 .. 0 .. 36 .. 3 Saudi Arabia 44,417 73,940 1 1 0 0 92 89 0 0 7 10 Senegal 761 1,055 53 16 3 3 12 23 9 6 23 51 Serbia and Montenegro 2,929 2,275 7 .. .. .. 2 .. 7 .. 79 .. Sierra Leone 138 49 .. .. .. .. .. .. .. .. .. .. Singapore b 52,730 125,177 5 2 3 0 18 8 2 1 72 85 Slovak Republic 6,355 14,367 .. 4 .. 2 .. 6 .. 3 .. 85 Slovenia 6,681 9,471 7 4 2 1 3 1 3 4 86 90 Somalia 150 145 .. .. .. .. .. .. .. .. .. .. South Africa c 23,549 29,723 7 11 3 3 6 12 9 11 36 63 Spain 55,642 119,131 15 15 2 1 5 3 2 2 75 78 Sri Lanka 1,912 4,699 34 21 6 2 1 0 2 2 54 74 Sudan 374 1,850 61 18 38 6 .. 72 0 0 1 3 Swaziland 556 820 .. 15 .. 12 .. 1 .. 0 .. 76 Sweden 57,540 81,137 2 3 7 5 3 3 3 3 83 81 Switzerland 63,784 87,876 3 3 1 0 0 0 3 4 94 93 Syrian Arab Republic 4,212 5,540 14 13 4 5 45 72 1 1 36 7 Tajikistan .. 738 .. 4 .. 13 .. 14 .. 56 .. 13 Tanzania 331 875 .. 61 .. 13 .. 0 .. 9 .. 17 Thailand 23,068 68,853 29 15 5 3 1 3 1 1 63 74 Togo 268 429 23 23 21 11 0 1 45 17 9 43 Trinidad and Tobago 2,080 4,594 5 5 0 0 67 49 1 0 27 46 Tunisia 3,526 6,799 11 7 1 1 17 9 2 1 69 82 Turkey 12,959 34,561 22 10 3 1 2 2 4 2 68 84 Turkmenistan .. 2,950 .. 0 .. 10 .. 81 .. 0 .. 7 Uganda 152 442 .. 73 .. 11 .. 7 .. 2 .. 8 Ukraine .. 17,954 .. 13 .. 2 .. 9 .. 8 .. 67 United Arab Emirates 23,544 47,275 8 1 1 .. 5 92 39 4 46 4 United Kingdom 185,172 279,647 7 5 1 0 8 8 3 2 79 79 United States 393,592 693,860 11 8 4 2 3 2 3 2 74 81 Uruguay 1,693 1,861 40 49 21 13 0 1 0 0 39 37 Uzbekistan .. 3,184 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17,497 26,890 2 2 0 0 80 82 7 4 10 13 Vietnam 2,404 16,530 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 692 3,240 75 .. 10 .. 8 .. 7 .. 1 .. Zambia 1,309 970 .. 10 .. 3 .. 2 .. 72 .. 14 Zimbabwe 1,726 1,760 44 26 7 12 1 1 16 22 31 38 World 3,452,501 t 6,454,929 t 10 w 7 w 3 w 2 w 8 w 7 w 4 w 2 w 74 w 78 w Low income 101,140 211,197 16 16 6 4 23 16 6 5 49 58 Middle income 550,042 1,447,025 17 8 4 2 23 24 6 3 48 60 Lower middle income 317,249 859,842 20 8 4 2 10 21 5 5 59 60 Upper middle income 232,440 587,183 14 9 5 2 39 27 6 2 35 60 Low & middle income 651,141 1,658,222 17 9 5 2 23 23 6 4 48 60 East Asia & Pacific 155,939 606,270 15 7 6 2 14 8 3 2 59 79 Europe & Central Asia d 136,412 357,686 .. 6 .. 3 .. 25 .. 5 .. 57 Latin America & Carib. 143,154 347,667 26 22 4 3 24 17 12 8 34 48 Middle East & N. Africa 125,520 184,863 4 4 1 1 79 75 2 1 15 19 South Asia 27,728 70,831 16 13 5 1 2 4 4 3 71 77 Sub-Saharan Africa 68,415 90,905 .. 17 .. 6 .. 29 .. 8 .. 35 High income 2,800,647 4,796,707 8 7 3 2 5 4 3 2 79 82 Europe EMU 1,229,213 2,031,196 10 9 2 1 3 2 2 2 81 83 Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Includes re-exports. c. Data on total merchandise exports for 1990 refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland); those for 2002 refer to South Africa only. Data on export commodity shares refer to the South African Customs Union. d. Data for 2002 include the intratrade of the Baltic states and the Commonwealth of Independent States. 200 2004 World Development Indicators ECONOMY 4.5 Structure of merchandise exports About the data Data on merchandise trade come from customs reporting practices, data on exports may not be fully revision 1. Most countries now report using later reports of goods movement into or out of an econo- comparable across economies. revisions of the SITC or the Harmonized System. my or from reports of the financial transactions relat- The data on total exports of goods (merchandise) in Concordance tables are used to convert data report- ed to merchandise trade recorded in the balance of this table come from the World Trade Organization ed in one system of nomenclature to another. The payments. Because of differences in timing and defi- (WTO). The WTO uses two main sources, national sta- conversion process may introduce some errors of nitions, estimates of trade flows from customs tistical offices and the IMF's International Financial classification, but conversions from later to early sys- reports are likely to differ from those based on the Statistics. It supplements these with the COMTRADE tems are generally reliable. Shares may not sum to balance of payments. Moreover, several international database and publications or databases of regional 100 percent because of unclassified trade. agencies process trade data, each making estimates organizations, specialized agencies, and economic to correct for unreported or misreported data, and groups (such as the Commonwealth of Independent Definitions this leads to other differences in the available data. States, the Economic Commission for Latin America The most detailed source of data on international and the Caribbean, Eurostat, the Food and Agri- · Merchandise exports are the f.o.b. value of goods trade in goods is the Commodity Trade (COMTRADE) culture Organization, the Organisation for Economic provided to the rest of the world, valued in U.S. dol- database maintained by the United Nations Statistics Co-operation and Development, and the Organization lars. · Food corresponds to the commodities in SITC Division. In addition, the International Monetary Fund of Petroleum Exporting Countries). It also consults pri- sections 0 (food and live animals), 1 (beverages and (IMF) collects customs-based data on exports and vate sources, such as country reports of the tobacco), and 4 (animal and vegetable oils and fats) imports of goods. The value of exports is recorded as Economist Intelligence Unit and press clippings. In and SITC division 22 (oil seeds, oil nuts, and oil ker- the cost of the goods delivered to the frontier of the recent years country Web sites and direct contacts nels). · Agricultural raw materials correspond to SITC exporting country for shipment--the free on board through email have helped to improve the collection of section 2 (crude materials except fuels) excluding divi- (f.o.b.) value. Many countries report trade data in up-to-date statistics for many countries, reducing the sions 22, 27 (crude fertilizers and minerals excluding U.S. dollars. When countries report in local currency, proportion of estimated figures. The WTO database coal, petroleum, and precious stones), and 28 (metal- the United Nations Statistics Division applies the now covers most of the major traders in Africa, Asia, liferous ores and scrap). · Fuels correspond to SITC average official exchange rate for the period shown. and Latin America, which together with the high-income section 3 (mineral fuels). · Ores and metals corre- Countries may report trade according to the gener- countries account for nearly 90 percent of total world spond to the commodities in SITC divisions 27, 28, al or special system of trade (see Primary data docu- trade. There has also been a remarkable improvement and 68 (nonferrous metals). · Manufactures corre- mentation). Under the general system exports in the availability of recent, reliable, and standardized spond to the commodities in SITC sections 5 (chemi- comprise outward-moving goods that are (a) goods figures for countries in Europe and Central Asia. cals), 6 (basic manufactures), 7 (machinery and wholly or partly produced in the country; (b) foreign The shares of exports by major commodity group transport equipment), and 8 (miscellaneous manufac- goods, neither transformed nor declared for domestic were estimated by World Bank staff from the COM- tured goods), excluding division 68. consumption in the country, that move outward TRADE database. The values of total exports reported from customs storage; and (c) goods previously here have not been fully reconciled with the estimates included as imports for domestic consumption but of exports of goods and services from the national subsequently exported without transformation. Under accounts (shown in table 4.9) or those from the bal- the special system exports comprise categories a ance of payments (table 4.15). and c. In some compilations categories b and c are The classification of commodity groups is based on classified as re-exports. Because of differences in the Standard International Trade Classification (SITC) 4.5a Data sources Some developing country regions are increasing their share of merchandise exports The WTO publishes data on world trade in its Merchandise exports Annual Report. The IMF publishes estimates of 1990 2002 total exports of goods in its International East Asia & Pacific 5% East Asia & Pacific 9% Financial Statistics and Direction of Trade Europe & Central Asia 4% Latin America & Caribbean 4% Europe & Central Asia 6% Statistics, as does the United Nations Statistics Middle East & North Africa 4% Sub-Saharan Africa 2% Latin America & Caribbean 5% Division in its Monthly Bulletin of Statistics. And South Asia 1% Middle East & North Africa 3% the United Nations Conference on Trade and Sub-Saharan Africa 1% South Asia 1% Development (UNCTAD) publishes data on the High income High income structure of exports and imports in its Handbook 80% 75% of International Trade and Development Statistics. Tariff line records of exports and The share of developing economies in world merchandise exports increased by 5 percentage points between 1990 and imports are compiled in the United Nations 2002. East Asia and Pacific was the biggest gainer, capturing an additional 4 percentage points. Statistics Division's COMTRADE database. Source: International Monetary Fund data files. 2004 World Development Indicators 201 4.6 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan 936 950 .. .. .. .. .. .. .. .. .. .. Albania 380 1,516 .. 20 .. 1 .. 9 .. 2 .. 68 Algeria 9,780 10,791 24 28 5 3 1 1 2 1 68 67 Angola 1,578 3,795 .. .. .. .. .. .. .. .. .. .. Argentina 4,076 8,988 4 5 4 2 8 5 6 3 78 84 Armenia .. 991 .. 21 .. 1 .. 18 .. 3 .. 57 Australia 41,985 72,689 5 5 2 1 6 8 1 1 84 84 Austria 49,146 77,984 5 7 3 3 6 6 4 3 81 81 Azerbaijan .. 1,665 .. 14 .. 1 .. 18 .. 2 .. 65 Bangladesh 3,618 7,914 19 16 5 7 16 5 3 2 56 69 Belarus .. 8,980 .. 11 .. 2 .. 26 .. 4 .. 51 Belgium a 119,702 210,548 .. 9 .. 2 .. 9 .. 3 .. 77 Benin 265 653 38 20 4 5 1 17 1 1 56 56 Bolivia 687 1,770 12 13 2 1 1 5 1 1 85 80 Bosnia and Herzegovina 360 3,425 .. .. .. .. .. .. .. .. .. .. Botswana 1,946 1,950 .. 14 .. 1 .. 7 .. 2 .. 72 Brazil 22,524 49,720 9 7 3 2 27 15 5 3 56 73 Bulgaria 5,100 7,897 8 5 3 1 36 5 4 6 49 65 Burkina Faso 536 577 .. 15 .. 1 .. 25 .. 1 .. 58 Burundi 231 129 .. 13 .. 3 .. 12 .. 2 .. 70 Cambodia 164 1,989 .. .. .. .. .. .. .. .. .. .. Cameroon 1,400 1,796 19 18 0 1 2 13 1 1 78 66 Canada 123,244 227,463 6 6 2 1 6 5 3 2 81 84 Central African Republic 154 110 .. .. .. .. .. .. .. .. .. .. Chad 285 780 .. .. .. .. .. .. .. .. .. .. Chile 7,742 17,093 4 8 2 1 16 16 1 1 75 73 China 53,345 295,203 9 3 6 4 2 7 3 5 80 80 Hong Kong, China 84,725 207,168 8 4 2 1 2 2 2 2 85 91 Colombia 5,590 12,738 7 12 4 2 6 2 3 2 77 81 Congo, Dem. Rep. 1,739 980 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 621 850 .. .. .. .. .. .. .. .. .. .. Costa Rica 1,990 7,175 8 8 2 1 10 7 2 1 66 83 Côte d'Ivoire 2,097 3,075 .. 23 .. 1 .. 21 .. 2 .. 54 Croatia 4,500 10,714 12 9 4 2 10 12 4 2 64 75 Cuba 4,600 4,161 .. 18 .. 1 .. 20 .. 1 .. 60 Czech Republic 12,880 40,756 .. 4 .. 2 .. 14 .. 3 .. 77 Denmark 33,333 49,381 12 12 3 3 7 4 2 2 73 77 Dominican Republic 3,006 8,882 .. 12 .. 2 .. 23 .. 1 .. 62 Ecuador 1,861 6,431 9 9 3 1 2 4 2 1 84 84 Egypt, Arab Rep. 12,412 12,552 32 28 7 4 3 4 2 3 56 51 El Salvador 1,263 5,190 14 18 3 2 15 13 4 1 63 65 Eritrea 278 375 .. .. .. .. .. .. .. .. .. .. Estonia .. 5,863 .. 12 .. 3 .. 7 .. 2 .. 76 Ethiopia 1,081 1,594 .. 11 .. 1 .. 12 .. 1 .. 74 Finland 27,001 33,577 5 6 2 3 12 12 4 5 76 73 France 234,436 329,322 10 9 3 2 10 9 4 3 74 78 Gabon 918 1,080 .. 18 .. 1 .. 4 .. 1 .. 75 Gambia, The 188 225 .. 35 .. 1 .. 12 .. 1 .. 51 Georgia .. 725 .. 19 .. 1 .. 23 .. 1 .. 57 Germany 355,686 493,712 10 7 3 2 8 8 4 3 72 71 Ghana 1,205 2,790 11 20 1 2 17 9 0 2 70 68 Greece 19,777 31,273 15 12 3 1 8 15 3 3 70 68 Guatemala 1,649 6,078 10 13 2 1 17 13 2 1 69 71 Guinea 723 620 .. 23 .. 1 .. 19 .. 0 .. 56 Guinea-Bissau 86 82 .. .. .. .. .. .. .. .. .. .. Haiti 332 1,130 .. .. .. .. .. .. .. .. .. .. Data for Taiwan, China 54,782 112,602 7 4 5 2 11 11 6 5 69 76 202 2004 World Development Indicators ECONOMY 4.6 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 935 2,940 10 16 1 1 16 13 1 1 71 69 Hungary 10,340 37,612 8 3 4 1 14 7 4 2 70 84 India 23,580 56,595 3 6 4 3 27 33 8 5 51 52 Indonesia 21,837 31,288 5 11 5 6 9 21 4 3 77 59 Iran, Islamic Rep. 20,322 22,190 .. 11 .. 2 .. 3 .. 2 .. 82 Iraq 7,660 12,000 .. .. .. .. .. .. .. .. .. .. Ireland 20,669 51,906 11 7 2 1 6 3 2 1 76 81 Israel 16,793 35,517 8 6 2 1 9 9 3 2 77 82 Italy 181,968 242,957 12 9 6 3 11 9 5 4 64 70 Jamaica 1,928 3,500 15 15 1 1 20 18 1 1 61 63 Japan 235,368 337,194 15 13 7 3 25 19 9 5 44 58 Jordan 2,600 4,962 26 17 2 2 18 15 1 2 51 62 Kazakhstan .. 6,491 .. 8 .. 1 .. 13 .. 3 .. 75 Kenya 2,223 3,277 9 12 3 2 20 17 2 1 66 67 Korea, Dem. Rep. 2,930 1,718 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 69,844 152,126 6 6 8 3 16 21 7 6 63 64 Kuwait 3,972 8,960 17 .. 1 .. 1 .. 2 .. 79 .. Kyrgyz Republic .. 589 .. 13 .. 2 .. 26 .. 4 .. 55 Lao PDR 185 431 .. .. .. .. .. .. .. .. .. .. Latvia .. 4,053 .. 13 .. 2 .. 9 .. 2 .. 74 Lebanon 2,529 6,447 .. 18 .. 2 .. 18 .. 2 .. 60 Lesotho 672 779 .. .. .. .. .. .. .. .. .. .. Liberia 570 675 .. .. .. .. .. .. .. .. .. .. Libya 5,336 5,700 23 .. 2 .. 0 .. 1 .. 74 .. Lithuania .. 7,739 .. 9 .. 3 .. 20 .. 1 .. 64 Macedonia, FYR 1,206 1,921 .. 14 .. 2 .. 14 .. 2 .. 44 Madagascar 651 1,150 11 .. 1 .. 17 .. 1 .. 69 .. Malawi 575 674 9 12 1 2 11 17 1 1 78 69 Malaysia 29,258 79,869 7 5 1 1 5 5 4 3 82 83 Mali 602 928 26 .. 1 .. 19 .. 1 .. 53 .. Mauritania 388 440 .. .. .. .. .. .. .. .. .. .. Mauritius 1,618 2,168 12 19 3 2 8 10 1 1 76 67 Mexico 43,548 173,087 15 6 4 1 4 3 3 2 75 87 Moldova .. 1,052 .. 13 .. 3 .. 22 .. 1 .. 61 Mongolia 924 659 .. 18 .. 1 .. 22 .. 1 .. 59 Morocco 6,922 11,644 10 14 6 3 17 16 6 2 61 65 Mozambique 878 1,340 .. 14 .. 1 .. 16 .. 0 .. 47 Myanmar 270 2,324 13 .. 1 .. 5 .. 0 .. 81 .. Namibia 1,163 1,450 .. 13 .. 1 .. 10 .. 2 .. 74 Nepal 672 1,419 15 13 7 4 9 16 2 3 67 49 Netherlands 126,098 219,788 13 12 2 2 10 9 3 3 71 74 New Zealand 9,501 15,077 7 9 1 1 8 9 3 2 81 79 Nicaragua 638 1,795 19 15 1 1 19 13 1 0 59 68 Niger 388 430 .. 44 .. 1 .. 13 .. 2 .. 40 Nigeria 5,627 7,547 6 20 1 1 0 1 2 2 67 76 Norway 27,231 34,812 6 7 2 2 4 4 6 5 82 81 Oman 2,681 6,005 19 21 1 1 4 2 1 3 69 69 Pakistan 7,411 11,233 17 12 4 5 21 27 4 3 54 53 Panama 1,539 2,982 12 13 1 1 16 17 1 1 70 68 Papua New Guinea 1,193 1,100 18 18 0 1 7 22 1 1 73 58 Paraguay 1,352 1,770 8 12 0 1 14 17 1 1 77 69 Peru 2,634 7,523 24 13 2 2 12 14 1 1 61 70 Philippines 13,042 35,229 10 8 2 1 15 9 3 2 53 56 Poland 11,570 55,113 8 6 3 2 22 9 4 3 63 80 Portugal 25,263 38,451 12 13 4 2 11 10 2 2 71 73 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 203 4.6 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw metals materials $ millions % of total % of total % of total % of total % of total 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 7,600 17,857 12 6 4 1 38 11 6 3 39 78 Russian Federation 33,100 60,520 .. 23 .. 1 .. 2 .. 2 .. 70 Rwanda 288 203 .. 16 .. 4 .. 16 .. 2 .. 62 Saudi Arabia 24,069 32,310 15 16 1 1 0 0 3 3 81 79 Senegal 1,219 1,560 29 26 2 2 16 15 2 2 51 55 Serbia and Montenegro 4,634 6,320 12 .. 5 .. 17 .. 3 .. 63 .. Sierra Leone 149 264 .. .. .. .. .. .. .. .. .. .. Singapore 60,774 116,441 6 4 2 0 16 13 2 2 73 80 Slovak Republic 6,670 16,492 .. 5 .. 2 .. 13 .. 3 .. 76 Slovenia 6,142 10,937 9 6 4 3 11 7 4 4 67 79 Somalia 95 195 .. .. .. .. .. .. .. .. .. .. South Africa b 18,399 29,267 8 5 2 1 1 13 1 2 75 70 Spain 87,715 154,701 11 10 3 2 12 11 4 3 71 74 Sri Lanka 2,688 6,104 19 14 2 1 13 14 1 2 65 68 Sudan 618 1,790 13 19 1 1 20 5 0 1 66 74 Swaziland 663 925 .. 20 .. 2 .. 2 .. 1 .. 72 Sweden 54,264 66,219 6 8 2 2 9 9 3 3 79 75 Switzerland 69,681 83,672 6 6 2 1 5 4 3 5 84 84 Syrian Arab Republic 2,400 5,220 31 16 2 4 3 3 1 3 62 64 Tajikistan .. 715 .. 10 .. 1 .. 37 .. 0 .. 51 Tanzania 1,027 1,687 .. 15 .. 2 .. 13 .. 1 .. 69 Thailand 33,045 64,721 5 5 5 3 9 12 4 3 75 76 Togo 581 650 22 22 1 1 8 15 1 2 67 60 Trinidad and Tobago 1,262 4,040 19 9 1 1 11 23 6 1 62 65 Tunisia 5,513 9,527 11 10 4 3 9 9 4 2 72 75 Turkey 22,302 49,663 8 4 4 4 21 14 5 5 61 68 Turkmenistan .. 2,453 .. 12 .. 0 .. 1 .. 1 .. 80 Uganda 288 1,710 .. 14 .. 3 .. 16 .. 1 .. 66 Ukraine .. 16,993 .. 6 .. 1 .. 39 .. 3 .. 48 United Arab Emirates 11,199 32,180 14 11 1 1 3 1 4 2 77 86 United Kingdom 222,977 345,321 10 8 3 2 6 4 4 2 75 79 United States 516,987 1,202,430 6 5 2 1 13 10 3 2 73 78 Uruguay 1,343 1,964 7 14 4 4 18 15 2 1 69 65 Uzbekistan .. 3,160 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 7,335 11,834 11 13 4 1 3 2 4 2 77 82 Vietnam 2,752 19,000 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,571 2,590 27 .. 1 .. 40 .. 1 .. 31 .. Zambia 1,220 1,270 .. 14 .. 2 .. 7 .. 2 .. 75 Zimbabwe 1,847 1,440 4 11 3 2 16 8 2 2 73 76 World 3,532,918 t 6,590,272 t 9 w 8 w 3 w 2 w 11 w 10 w 4 w 3 w 71 w 75 w Low income 106,125 197,606 8 11 4 4 17 24 5 3 64 57 Middle income 502,597 1,364,003 10 9 4 2 10 9 3 3 71 75 Lower middle income 323,769 821,000 10 9 4 3 11 10 3 3 69 72 Upper middle income 179,974 543,003 10 7 2 1 8 7 3 2 76 81 Low & middle income 609,669 1,561,609 10 9 4 2 11 11 4 3 70 73 East Asia & Pacific 160,493 535,235 7 6 4 3 6 9 3 4 77 76 Europe & Central Asia c 150,809 371,275 .. 10 .. 2 .. 12 .. 3 .. 72 Latin America & Carib. 119,568 343,449 11 9 3 2 13 9 3 2 69 78 Middle East & N. Africa 104,010 142,093 19 16 3 2 4 5 3 3 70 72 South Asia 39,124 84,787 9 8 4 3 23 30 6 4 54 54 Sub-Saharan Africa 57,515 84,770 .. 10 .. 2 .. 16 .. 1 .. 66 High income 2,913,452 5,028,663 9 8 3 2 11 10 4 3 71 75 Europe EMU 1,247,461 1,884,219 11 9 3 2 9 9 4 3 72 74 Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Data on total merchandise imports for 1990 refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland); those for 2002 refer to South Africa only. Data on import commodity shares refer to the South African Customs Union. c. Data for 2002 include the intratrade of the Baltic states and the Commonwealth of Independent States. 204 2004 World Development Indicators ECONOMY 4.6 Structure of merchandise imports About the data Definitions Data on imports of goods are derived from the same The data on total imports of goods (merchandise) · Merchandise imports are the c.i.f. value of goods sources as data on exports. In principle, world in this table come from the World Trade Organization purchased from the rest of the world valued in U.S. exports and imports should be identical. Similarly, (WTO). For further discussion of the WTO's sources dollars. · Food corresponds to the commodities in exports from an economy should equal the sum of and methodology, see About the data for table 4.5. SITC sections 0 (food and live animals), 1 (bever- imports by the rest of the world from that economy. The shares of imports by major commodity group ages and tobacco), and 4 (animal and vegetable oils But differences in timing and definitions result in dis- were estimated by World Bank staff from the United and fats) and SITC division 22 (oil seeds, oil nuts, crepancies in reported values at all levels. For fur- Nations Statistics Division's Commodity Trade (COM- and oil kernels). · Agricultural raw materials corre- ther discussion of indicators of merchandise trade, TRADE) database. The values of total imports report- spond to SITC section 2 (crude materials except see About the data for tables 4.4 and 4.5. ed here have not been fully reconciled with the fuels) excluding divisions 22, 27 (crude fertilizers The value of imports is generally recorded as the estimates of imports of goods and services from the and minerals excluding coal, petroleum, and pre- cost of the goods when purchased by the importer national accounts (shown in table 4.9) or those from cious stones), and 28 (metalliferous ores and plus the cost of transport and insurance to the fron- the balance of payments (table 4.15). scrap). · Fuels correspond to SITC section 3 (miner- tier of the importing country--the cost, insurance, The classification of commodity groups is based on al fuels). · Ores and metals correspond to the com- and freight (c.i.f.) value, corresponding to the landed the Standard International Trade Classification (SITC) modities in SITC divisions 27, 28, and 68 cost at the point of entry of foreign goods into the revision 1. Most countries now report using later (nonferrous metals). · Manufactures correspond to country. A few countries, including Australia, Canada, revisions of the SITC or the Harmonized System. the commodities in SITC sections 5 (chemicals), 6 and the United States, collect import data on a free Concordance tables are used to convert data report- (basic manufactures), 7 (machinery and transport on board (f.o.b.) basis and adjust them for freight ed in one system of nomenclature to another. The equipment), and 8 (miscellaneous manufactured and insurance costs. Many countries collect and conversion process may introduce some errors of goods), excluding division 68. report trade data in U.S. dollars. When countries classification, but conversions from later to early sys- report in local currency, the United Nations Statistics tems are generally reliable. Shares may not sum to Division applies the average official exchange rate 100 percent because of unclassified trade. for the period shown. Countries may report trade according to the gener- al or special system of trade (see Primary data doc- umentation). Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses and free trade zones. Goods transported through a coun- try en route to another are excluded. 4.6a Top 10 developing country exporters in 2002 Merchandise exports ($ billions) Data sources 350 The WTO publishes data on world trade in its 1990 300 Annual Report. The International Monetary Fund 2002 (IMF) publishes estimates of total imports of 250 goods in its International Financial Statistics and 200 Direction of Trade Statistics, as does the United 150 Nations Statistics Division in its Monthly Bulletin 100 of Statistics. And the United Nations Conference 50 on Trade and Development (UNCTAD) publishes 0 data on the structure of exports and imports in its China Mexico Russian Malaysia Saudi Thailand Brazil Indonesia India Poland Handbook of International Trade and Development Federation Arabia Statistics. Tariff line records of exports and China led the developing economies in merchandise exports in 2002, followed by Mexico. The top 10 economies accounted for 63 percent of exports of developing economies and 16 percent of world exports. imports are compiled in the United Nations Note: No data are available for the Russian Federation for 1990. Statistics Division's COMTRADE database. Source: World Trade Organization data files. 2004 World Development Indicators 205 4.7 Structure of service exports Commercial Transport Travel Insurance and Computer, service exports financial services information, communications, and other commercial services % of total % of total % of total % of total $ millions services services services services 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 32 552 20.0 3.4 11.1 88.2 2.2 2.0 66.7 6.4 Algeria 479 .. 41.7 .. 13.4 .. 5.9 .. 39.0 .. Angola 65 203 48.8 6.6 20.6 .. 4.6 13.7 26.1 79.7 Argentina 2,264 2,878 51.1 24.3 39.9 53.3 0.0 0.2 9.0 22.1 Armenia .. 176 .. 36.5 .. 35.9 .. 3.8 .. 23.8 Australia 9,833 17,443 35.5 23.9 43.2 49.2 4.2 5.1 17.2 21.8 Austria 22,755 34,647 6.4 16.7 59.0 32.1 2.9 6.4 31.7 44.8 Azerbaijan .. 321 .. 66.1 .. 15.9 .. .. .. 18.0 Bangladesh 296 305 12.9 30.4 6.4 18.6 0.1 4.7 80.6 46.3 Belarus .. 1,276 .. 55.7 .. 15.1 .. 0.5 .. 28.7 Belgium a 26,646 48,970 27.5 20.8 14.0 15.5 18.2 27.8 40.3 35.9 Benin 109 133 33.4 14.7 50.2 63.4 6.9 2.3 9.5 19.6 Bolivia 133 220 35.8 27.3 43.6 37.1 10.0 16.8 10.6 18.8 Bosnia and Herzegovina .. 300 .. 10.0 .. 37.3 .. 13.8 .. 38.9 Botswana 183 .. 20.4 .. 64.1 .. 8.2 .. 7.3 .. Brazil 3,706 8,844 36.4 18.0 37.3 22.6 3.1 6.7 23.2 52.7 Bulgaria 837 2,553 27.5 30.2 38.2 52.3 3.1 1.1 31.2 16.5 Burkina Faso 34 32 37.1 14.6 34.1 61.6 .. 0.4 28.9 23.4 Burundi 7 4 38.7 23.1 51.4 30.6 1.6 28.7 8.3 17.6 Cambodia 50 593 .. 15.0 .. 76.5 .. .. .. 8.4 Cameroon 369 .. 42.6 .. 14.4 .. 9.4 .. 33.6 .. Canada 18,350 36,272 23.0 19.0 34.7 29.4 .. 8.4 42.3 43.1 Central African Republic 17 .. 50.9 .. 16.0 .. 18.8 .. 14.3 .. Chad 23 .. 18.4 .. 34.1 .. 0.2 .. 47.3 .. Chile 1,786 3,878 40.0 55.7 29.8 18.9 4.9 2.4 25.3 23.0 China 5,748 39,381 47.1 14.5 30.2 51.8 3.9 0.7 18.7 33.1 Hong Kong, China .. 43,333 .. 30.5 .. 15.1 .. 8.3 .. 46.1 Colombia 1,548 1,789 31.3 30.1 26.2 53.8 17.1 2.0 25.5 14.1 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 65 158 53.9 23.6 12.9 16.1 .. 2.5 33.1 57.8 Costa Rica 583 1,854 16.3 13.2 48.9 62.6 1.5 1.2 34.8 23.0 Côte d'Ivoire 425 506 62.4 19.0 12.1 10.0 8.3 13.3 17.2 57.7 Croatia .. 5,549 .. 10.6 .. 68.7 .. 1.9 .. 18.8 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. 7,024 .. 24.7 .. 42.2 .. 2.8 .. 30.4 Denmark 12,731 27,182 32.5 54.5 26.2 21.6 2.3 .. 39.0 23.9 Dominican Republic 1,086 2,966 5.6 2.4 66.8 92.2 0.2 .. 27.3 5.4 Ecuador 508 917 47.6 36.9 37.0 48.8 9.3 0.2 6.1 14.1 Egypt, Arab Rep. 4,812 9,127 50.1 30.6 22.9 41.2 1.0 1.2 26.1 26.9 El Salvador 301 749 26.2 41.6 25.2 32.8 7.5 4.2 41.1 21.3 Eritrea 73 54 85.7 18.2 1.0 64.2 .. 1.0 13.3 16.6 Estonia 200 1,979 74.7 54.4 13.7 28.0 0.1 1.0 11.5 16.6 Ethiopia 261 450 80.6 55.6 2.1 16.0 0.7 1.6 16.6 26.8 Finland 4,562 6,400 38.4 24.2 25.8 24.7 0.1 0.2 35.6 51.0 France 74,948 85,912 21.7 21.9 27.0 38.1 14.8 2.5 36.4 37.5 Gabon 214 .. 33.4 .. 1.4 .. 5.7 .. 59.4 .. Gambia, The 53 .. 8.8 .. 87.9 .. 0.1 .. 3.3 .. Georgia .. 354 .. 52.0 .. 35.5 .. 4.9 .. 7.6 Germany 51,545 99,622 28.6 25.8 27.8 19.3 1.0 11.8 42.6 43.2 Ghana 79 539 49.2 21.3 5.6 66.5 2.7 1.1 42.6 11.1 Greece 6,514 20,125 4.9 40.0 39.7 49.6 0.1 1.1 55.2 9.3 Guatemala 313 1,048 7.4 8.8 37.6 58.6 1.9 5.3 53.0 27.3 Guinea 91 43 14.2 20.4 32.6 .. 0.1 1.7 53.1 77.9 Guinea-Bissau 4 .. 5.4 .. .. .. .. .. 94.6 .. Haiti 43 .. 19.8 .. 78.9 .. 1.3 .. 0.0 .. 206 2004 World Development Indicators ECONOMY 4.7 Structure of service exports Commercial Transport Travel Insurance and Computer, service exports financial services information, communications, and other commercial services % of total % of total % of total % of total $ millions services services services services 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 121 463 35.1 11.7 24.0 62.9 12.9 3.9 28.0 21.4 Hungary 2,677 7,726 1.6 8.9 36.8 42.4 0.2 2.1 61.4 46.7 India 4,610 24,553 20.8 10.3 33.8 12.3 2.7 1.5 42.7 75.9 Indonesia 2,488 6,517 2.8 16.2 86.5 81.1 .. 0.0 10.7 2.7 Iran, Islamic Rep. 343 1,357 10.5 49.4 8.2 36.9 6.4 10.7 74.9 2.9 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 3,286 28,134 31.1 5.7 44.4 11.0 .. 22.3 24.5 61.0 Israel 4,546 10,825 30.8 19.6 30.7 19.4 .. 0.1 38.8 60.8 Italy 48,579 59,374 21.0 15.4 33.9 45.3 5.5 3.3 39.6 36.1 Jamaica 976 1,888 18.0 19.5 77.0 64.0 1.4 2.0 3.6 14.4 Japan 41,384 64,909 .. 37.0 .. 5.4 .. 4.2 .. 53.4 Jordan 1,430 1,473 26.0 19.6 35.7 53.4 .. .. 38.3 27.1 Kazakhstan .. 1,432 .. 47.6 .. 43.4 .. 0.8 .. 8.2 Kenya 774 791 32.0 54.1 60.2 39.0 0.7 0.5 7.1 6.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 9,155 27,080 34.7 48.3 34.5 19.5 0.1 3.3 30.7 28.8 Kuwait 1,054 1,372 87.5 82.3 12.5 8.6 .. 7.9 ­0.0 1.2 Kyrgyz Republic .. 118 .. 31.7 .. 30.2 .. 3.3 .. 34.8 Lao PDR 11 127 74.8 18.0 24.3 82.0 0.9 .. .. .. Latvia 290 1,235 94.9 62.5 2.5 13.1 0.0 5.5 2.6 18.9 Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho 34 31 14.1 1.3 51.2 64.0 .. ­0.0 34.7 34.7 Liberia .. .. .. .. .. .. .. .. .. .. Libya 83 .. 83.8 .. 7.7 .. .. .. 8.5 .. Lithuania .. 1,451 .. 45.1 .. 34.8 .. 0.7 .. 19.4 Macedonia, FYR .. 220 .. 36.3 .. 17.7 .. 2.2 .. 43.8 Madagascar 129 158 32.1 26.8 31.3 22.9 0.3 0.7 36.3 49.6 Malawi 37 49 46.1 32.7 42.6 67.3 0.1 .. 11.2 0.0 Malaysia 3,769 14,753 31.8 19.3 44.7 48.2 0.1 1.4 23.5 31.0 Mali 71 140 31.0 17.0 54.3 62.9 4.9 2.9 9.8 17.3 Mauritania 14 .. 35.3 .. 64.7 .. .. .. ­0.0 .. Mauritius 478 1,132 32.9 24.1 51.1 54.0 0.1 1.8 15.8 20.0 Mexico 7,222 12,474 12.4 9.2 76.5 71.0 4.6 9.7 6.5 10.1 Moldova .. 201 .. 54.4 .. 23.2 .. 1.9 .. 20.6 Mongolia 48 179 41.8 21.8 10.4 72.7 4.6 0.8 43.2 4.7 Morocco 1,871 4,098 9.6 19.0 68.4 64.6 0.8 0.7 21.2 15.7 Mozambique 103 249 61.3 22.4 .. 25.6 .. .. 38.7 52.1 Myanmar 94 405 10.3 19.8 20.9 31.0 0.5 .. 68.3 49.2 Namibia 106 230 .. .. 81.0 95.2 5.9 0.6 13.1 4.2 Nepal 166 303 3.6 15.6 65.6 47.5 .. .. 30.8 36.9 Netherlands 28,478 54,573 45.4 32.4 14.6 14.1 0.8 2.0 39.2 51.4 New Zealand 2,415 5,041 43.4 23.8 42.7 57.6 ­0.3 0.9 14.2 17.7 Nicaragua 34 270 19.2 9.5 35.5 42.0 .. 0.9 45.3 47.7 Niger 22 .. 5.2 .. 59.5 .. 13.5 .. 21.8 .. Nigeria 965 .. 3.9 .. 2.5 .. 0.3 .. 93.3 .. Norway 12,452 19,116 68.7 56.8 12.6 11.4 0.4 4.7 18.3 27.1 Oman 68 349 15.3 45.5 84.7 41.0 .. 4.5 ­0.0 9.0 Pakistan 1,218 1,536 59.3 54.1 12.0 6.3 1.4 2.3 27.3 37.3 Panama 907 2,254 64.9 55.7 18.9 23.4 3.8 12.6 12.4 8.2 Papua New Guinea 198 285 11.2 7.5 12.0 1.8 0.5 1.8 76.3 88.9 Paraguay 404 506 18.3 13.8 21.1 11.6 .. 5.3 60.5 69.3 Peru 714 1,430 43.4 19.9 30.4 56.1 11.2 6.8 15.0 17.2 Philippines 2,897 3,029 8.5 20.8 16.1 57.4 0.5 2.2 74.9 19.5 Poland 3,200 10,030 57.3 32.6 11.2 43.0 4.0 3.5 27.6 20.9 Portugal 5,054 9,720 15.6 18.7 70.4 61.4 0.7 2.3 13.3 17.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 207 4.7 Structure of service exports Commercial Transport Travel Insurance and Computer, service exports financial services information, communications, and other commercial services % of total % of total % of total % of total $ millions services services services services 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 610 2,326 50.5 41.4 17.4 14.4 5.6 4.4 26.6 39.7 Russian Federation .. 13,453 .. 40.8 .. 31.1 .. 1.7 .. 26.3 Rwanda 31 48 56.1 25.7 32.8 65.3 1.0 .. 10.0 9.0 Saudi Arabia 3,031 5,184 .. .. .. .. .. .. .. .. Senegal 356 .. 19.1 .. 42.7 .. 0.5 .. 37.6 .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 45 .. 9.7 .. 76.2 .. 0.1 .. 14.1 .. Singapore 12,719 29,599 17.5 38.6 36.6 14.8 0.7 2.5 45.3 44.0 Slovak Republic .. 2,218 .. 44.9 .. 19.5 .. 2.2 .. 33.4 Slovenia 1,219 2,286 22.6 26.2 55.0 47.4 1.2 2.0 21.2 24.4 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 3,290 4,391 21.6 23.3 55.8 62.1 10.8 5.4 11.9 9.1 Spain 27,649 62,109 17.2 15.1 67.2 54.4 4.3 4.2 11.3 26.4 Sri Lanka 425 1,247 39.7 41.2 30.2 29.1 4.2 3.6 25.9 26.0 Sudan 134 45 14.1 36.7 15.7 50.9 0.5 0.9 69.7 11.5 Swaziland 102 113 24.5 9.4 29.2 23.3 .. .. 46.3 67.3 Sweden 13,453 23,508 35.8 22.0 21.7 19.1 9.1 4.3 33.5 54.5 Switzerland 18,233 27,856 16.3 11.6 40.6 28.2 23.3 34.7 19.7 25.5 Syrian Arab Republic 740 1,481 29.7 16.6 43.3 73.1 .. .. 27.0 10.3 Tajikistan .. 60 .. 75.7 .. 2.7 .. 1.9 .. 19.6 Tanzania 131 609 19.9 10.1 36.4 71.8 0.5 3.9 43.1 14.3 Thailand 6,292 15,232 21.1 21.4 68.7 51.9 0.2 0.6 10.0 26.1 Togo 114 53 26.9 23.1 50.7 20.4 13.7 2.8 8.6 53.6 Trinidad and Tobago 322 563 50.7 36.8 29.4 35.7 .. 14.0 19.9 13.6 Tunisia 1,575 2,603 23.0 23.5 64.8 58.5 1.5 2.4 10.7 15.6 Turkey 7,882 14,738 11.7 19.0 40.9 57.5 1.7 1.7 47.4 21.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 230 .. 16.2 .. 76.0 .. 1.6 .. 6.2 Ukraine .. 4,583 .. 73.8 .. 17.2 .. 0.5 .. 8.4 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 53,830 123,130 25.2 14.5 29.0 17.2 16.4 23.7 29.4 44.6 United States 132,880 272,630 28.1 17.0 37.9 31.3 3.5 6.9 30.5 44.9 Uruguay 460 752 36.9 34.6 51.8 46.7 1.0 10.0 10.3 8.7 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 1,121 960 40.9 36.0 44.2 45.7 0.2 0.1 14.7 18.1 Vietnam .. 2,948 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 82 129 27.2 15.7 48.8 29.3 .. .. 24.0 55.0 Zambia 94 114 68.9 37.2 13.5 58.3 4.1 4.5 13.4 0.1 Zimbabwe 253 .. 44.3 .. 25.3 .. 1.2 .. 29.2 .. World 750,300 s 1,511,226 s 25.0 w 22.4 w 32.9 w 30.5 w 6.6 w 8.3 w 36.3 w 39.2 w Low income 14,230 40,966 24.6 15.7 38.3 28.2 2.5 1.5 35.1 54.6 Middle income 78,877 225,630 26.9 23.3 42.0 46.9 3.2 2.5 28.5 27.3 Lower middle income 49,966 144,059 27.0 24.0 42.1 47.6 3.6 1.9 28.1 26.6 Upper middle income 28,911 81,571 26.7 22.2 41.8 45.6 2.6 3.7 29.2 28.7 Low & middle income 93,107 266,596 26.6 22.2 41.4 44.2 3.1 2.4 29.5 31.3 East Asia & Pacific 22,049 82,632 26.1 17.1 48.5 54.0 1.3 0.8 24.2 28.1 Europe & Central Asia 15,237 77,656 21.9 31.4 32.8 41.8 1.7 2.1 44.0 24.7 Latin America & Carib. 25,004 46,516 28.7 21.4 50.5 50.2 4.5 6.2 16.5 22.6 Middle East & N. Africa 14,513 22,615 26.7 19.9 30.5 38.7 .. 1.2 41.8 40.5 South Asia 6,816 27,994 27.9 14.2 30.1 13.8 2.4 1.7 39.7 70.3 Sub-Saharan Africa 9,487 10,833 28.1 26.3 39.7 48.9 5.8 4.2 26.8 19.5 High income 657,193 1,244,630 24.8 22.5 31.7 27.4 7.1 9.5 37.2 40.9 Europe EMU 300,015 480,820 23.9 21.0 33.2 32.1 7.2 8.6 35.8 38.3 a. Includes Luxembourg. 208 2004 World Development Indicators ECONOMY 4.7 Structure of service exports About the data Definitions Balance of payments statistics, the main source of captured by conventional balance of payments · Commercial service exports are total service information on international trade in services, have statistics is establishment trade--sales in the host exports minus exports of government services not many weaknesses. Some large economies--such as country by foreign affiliates. By contrast, cross- included elsewhere. International transactions in the former Soviet Union--did not report data on border intrafirm transactions in merchandise may be services are defined by the IMF's Balance of trade in services until recently. Disaggregation of reported as exports or imports in the balance of Payments Manual (1993) as the economic output of important components may be limited, and it varies payments. intangible commodities that may be produced, trans- significantly across countries. There are inconsisten- The data on exports of services in this table and ferred, and consumed at the same time. Definitions cies in the methods used to report items. And the on imports of services in table 4.8, unlike those in may vary among reporting economies. · Transport recording of major flows as net items is common (for editions before 2000, include only commercial serv- covers all transport services (sea, air, land, internal example, insurance transactions are often recorded ices and exclude the category "government services waterway, space, and pipeline) performed by resi- as premiums less claims). These factors contribute not included elsewhere." The data are compiled by dents of one economy for those of another and to a downward bias in the value of the service trade the IMF based on returns from national sources. involving the carriage of passengers, movement of reported in the balance of payments. Data on total trade in goods and services from the goods (freight), rental of carriers with crew, and relat- Efforts are being made to improve the coverage, IMF's Balance of Payments database are shown in ed support and auxiliary services. Excluded are quality, and consistency of these data. Eurostat and table 4.15. freight insurance, which is included in insurance the Organisation for Economic Co-operation and services; goods procured in ports by nonresident Development, for example, are working together to carriers and repairs of transport equipment, which improve the collection of statistics on trade in servic- are included in goods; repairs of harbors, railway es in member countries. In addition, the International facilities, and airfield facilities, which are included in Monetary Fund (IMF) has implemented the new clas- construction services; and rental of carriers without sification of trade in services introduced in the fifth crew, which is included in other services. · Travel edition of its Balance of Payments Manual (1993). covers goods and services acquired from an econo- Still, difficulties in capturing all the dimensions of my by travelers in that economy for their own use dur- international trade in services mean that the record ing visits of less than one year for business or is likely to remain incomplete. Cross-border intrafirm personal purposes. Travel services include the service transactions, which are usually not captured goods and services consumed by travelers, such as in the balance of payments, have increased in meals, lodging, and transport (within the economy recent years. One example of such transactions is visited), including car rental. · Insurance and finan- transnational corporations' use of mainframe com- cial services cover freight insurance on goods puters around the clock for data processing, exploit- exported and other direct insurance such as life ing time zone differences between their home insurance, financial intermediation services such as country and the host countries of their affiliates. commissions, foreign exchange transactions, and Another important dimension of service trade not brokerage services; and auxiliary services such as financial market operational and regulatory services. 4.7a · Computer, information, communications, and Top 10 developing country exporters of commercial services in 2002 other commercial services include such activities as Commercial services exports ($ billions) international telecommunications and postal and 40 courier services; computer data; news-related serv- 1990 ice transactions between residents and nonresi- 2002 30 dents; construction services; royalties and license fees; miscellaneous business, professional, and 20 technical services; and personal, cultural, and recre- ational services. 10 0 China India Malaysia Thailand Turkey Russian Mexico Poland Brazil Egypt, Data sources Federation Arab Rep. The data on exports of commercial services are Major exporters of merchandise trade also tend to be major exporters of commercial services. The exceptions are the fuel exporters--Saudi Arabia and Indonesia. These top 10 developing country exporters accounted for 61 percent of from the IMF. The IMF publishes balance of pay- commercial services exports of developing economies and 11 percent of world commercial services exports in 2002. ments data in its International Financial Statistics Note: No data are available for the Russian Federation for 1990. and Balance of Payments Statistics Yearbook. Source: International Monetary Fund data files. 2004 World Development Indicators 209 4.8 Structure of service imports Commercial Transport Travel Insurance and Computer, service imports financial services information, communications, and other commercial services % of total % of total % of total % of total $ millions services services services services 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 29 561 26.3 22.8 .. 65.1 2.9 9.0 70.8 3.1 Algeria 1,155 .. 58.1 .. 12.9 .. 9.8 .. 19.2 .. Angola 1,288 3,176 38.3 12.4 3.0 2.1 2.6 2.9 56.1 82.6 Argentina 2,876 4,360 32.6 21.8 40.7 53.4 1.7 2.0 26.7 22.9 Armenia .. 217 .. 60.0 .. 24.9 .. 5.2 .. 9.9 Australia 13,388 17,740 33.9 33.8 31.5 34.4 4.8 4.5 29.8 27.4 Austria 14,104 34,416 8.4 10.4 54.9 27.5 4.6 6.5 32.1 55.6 Azerbaijan .. 1,283 .. 13.5 .. 8.2 .. 1.0 .. 77.2 Bangladesh 554 1,391 71.1 71.2 14.1 14.5 6.6 7.4 8.3 6.8 Belarus .. 892 .. 15.3 .. 62.6 .. 1.7 .. 20.5 Belgium a 25,924 42,856 23.3 19.5 21.1 24.7 14.7 19.1 40.8 36.7 Benin 113 186 46.9 67.9 12.8 9.3 5.7 10.2 34.6 12.6 Bolivia 291 500 61.7 58.4 20.6 14.8 10.0 18.7 7.6 8.1 Bosnia and Herzegovina .. 228 .. 59.4 .. 21.3 .. 12.5 .. 6.7 Botswana 371 .. 57.5 .. 15.0 .. 5.5 .. 22.0 .. Brazil 6,733 13,631 44.4 26.6 22.4 17.6 2.7 9.2 30.5 46.6 Bulgaria 600 1,986 40.5 44.1 31.5 31.0 4.5 4.0 23.5 20.9 Burkina Faso 196 135 64.7 65.1 16.6 16.1 5.1 14.7 13.6 4.2 Burundi 59 33 62.6 55.2 29.0 42.1 6.3 1.4 2.2 1.3 Cambodia 64 372 24.5 57.4 .. 10.3 .. 4.7 75.5 27.7 Cameroon 1,018 .. 45.3 .. 27.5 .. 7.2 .. 20.1 .. Canada 27,479 41,932 21.1 21.6 39.8 28.2 .. 11.3 39.2 38.9 Central African Republic 166 .. 49.7 .. 30.6 .. 8.9 .. 10.7 .. Chad 223 .. 45.1 .. 31.2 .. 4.4 .. 19.2 .. Chile 1,982 4,771 47.4 45.0 21.5 16.5 3.3 8.0 27.9 30.5 China 4,113 46,080 78.9 29.5 11.4 33.4 2.3 7.2 7.4 29.8 Hong Kong, China .. 24,800 .. 26.6 .. 50.1 .. 4.8 .. 18.5 Colombia 1,683 3,249 34.9 37.0 27.0 33.0 13.7 13.1 24.4 17.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 748 917 18.4 13.1 15.2 7.7 1.6 3.9 64.9 75.4 Costa Rica 540 1,188 41.2 38.6 28.8 29.0 6.0 7.4 24.0 24.9 Côte d'Ivoire 1,518 1,341 32.1 39.6 11.1 21.6 4.7 11.1 52.0 27.7 Croatia .. 2,399 .. 18.7 .. 32.6 .. 5.5 .. 43.2 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. 6,372 .. 14.0 .. 25.1 .. 12.4 .. 48.5 Denmark 10,106 25,116 38.3 47.4 36.5 27.6 1.6 .. 23.6 25.0 Dominican Republic 435 1,241 40.0 60.3 33.1 23.8 4.1 9.1 22.8 6.8 Ecuador 755 1,505 41.6 43.2 23.2 24.2 8.1 5.1 27.2 27.5 Egypt, Arab Rep. 3,327 6,013 44.0 29.6 3.9 21.1 4.6 7.1 47.5 42.2 El Salvador 296 960 45.9 43.4 20.5 19.9 12.0 13.0 21.5 23.7 Eritrea .. 24 .. 27.6 .. 49.5 .. 1.9 .. 21.0 Estonia 123 1,404 76.3 54.3 15.4 16.4 0.3 1.1 8.0 28.2 Ethiopia 348 558 76.5 57.2 3.3 8.1 3.4 5.4 16.8 29.3 Finland 7,432 8,130 26.1 30.4 37.2 24.2 1.8 1.2 34.8 44.2 France 59,560 68,171 29.4 26.2 20.7 28.9 19.2 4.5 30.7 40.5 Gabon 984 .. 23.2 .. 13.9 .. 5.3 .. 57.6 .. Gambia, The 35 .. 65.1 .. 23.1 .. 9.0 .. 2.8 .. Georgia .. 316 .. 33.0 .. 47.1 .. 5.8 .. 14.1 Germany 84,336 149,107 20.3 20.4 46.3 35.8 1.0 3.4 32.4 40.4 Ghana 226 546 55.1 48.8 5.9 22.0 11.2 5.4 27.8 23.9 Greece 2,756 10,306 34.0 46.0 39.5 32.0 5.4 4.2 21.0 17.8 Guatemala 363 996 41.0 51.5 27.4 26.8 3.4 11.3 28.2 10.4 Guinea 243 156 57.5 30.1 12.2 19.7 5.5 5.8 24.8 44.4 Guinea-Bissau 17 .. 54.5 .. 19.8 .. 5.6 .. 20.0 .. Haiti 71 .. 47.9 .. 52.1 .. .. .. .. .. 210 2004 World Development Indicators ECONOMY 4.8 Structure of service imports Commercial Transport Travel Insurance and Computer, service imports financial services information, communications, and other commercial services % of total % of total % of total % of total $ millions services services services services 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 213 601 45.4 51.3 17.6 21.7 15.0 .. 22.0 26.9 Hungary 2,264 7,093 8.8 14.6 25.9 24.3 1.0 4.4 64.3 56.8 India 5,943 18,464 57.5 13.7 6.6 18.7 5.8 1.7 30.1 65.9 Indonesia 5,898 16,779 47.4 30.7 14.2 19.6 4.0 1.1 34.5 48.6 Iran, Islamic Rep. 3,703 1,577 47.3 72.4 9.2 13.0 10.8 13.5 32.8 1.1 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 5,145 40,393 24.3 4.4 22.6 9.3 1.9 11.3 51.2 75.0 Israel 4,825 11,269 39.6 37.3 29.7 22.6 4.4 3.4 26.2 36.8 Italy 46,602 61,485 23.7 22.2 22.1 27.5 10.4 3.9 43.8 46.4 Jamaica 667 1,603 47.9 38.3 17.0 16.1 6.7 8.5 28.4 37.1 Japan 84,281 106,612 .. 29.6 .. 25.0 .. 4.6 .. 40.9 Jordan 1,118 1,480 52.0 48.3 30.1 28.2 5.2 6.8 12.7 16.7 Kazakhstan .. 3,635 .. 19.1 .. 20.8 .. 2.5 .. 57.5 Kenya 598 764 66.2 48.8 6.4 18.7 8.9 10.0 18.5 22.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 10,050 35,145 39.8 30.4 27.5 25.8 0.3 1.7 32.4 42.1 Kuwait 2,805 4,880 31.9 35.8 65.5 61.9 1.2 1.5 1.4 0.8 Kyrgyz Republic .. 142 .. 38.0 .. 6.9 .. 15.1 .. 40.0 Lao PDR 25 5 73.0 99.0 .. 1.0 6.3 .. 20.6 .. Latvia 120 698 82.3 33.3 10.9 32.9 4.8 8.7 2.1 25.1 Lebanon .. .. .. .. .. .. .. .. .. .. Lesotho 48 45 67.9 68.0 24.7 30.7 5.6 1.1 1.7 0.1 Liberia .. .. .. .. .. .. .. .. .. .. Libya 926 .. 41.9 .. 45.7 .. 4.1 .. 8.3 .. Lithuania .. 878 .. 33.8 .. 37.1 .. 4.3 .. 24.7 Macedonia, FYR .. 270 .. 37.2 .. 16.5 .. 3.4 .. 42.9 Madagascar 172 317 43.5 45.4 23.4 28.6 3.5 1.5 29.5 24.5 Malawi 268 222 81.8 50.1 5.9 35.2 8.7 0.0 3.7 14.7 Malaysia 5,394 16,248 46.9 36.3 26.9 16.1 .. 3.3 26.2 44.3 Mali 352 414 57.4 64.0 15.8 8.6 1.9 3.5 24.9 23.9 Mauritania 126 .. 76.9 .. 18.3 .. 3.1 .. 1.7 .. Mauritius 407 779 51.6 36.6 23.0 26.2 5.5 4.8 19.9 32.4 Mexico 10,063 17,031 25.0 11.7 54.9 35.6 6.2 39.5 14.0 13.3 Moldova .. 231 .. 33.0 .. 37.3 .. 2.3 .. 27.3 Mongolia 155 260 56.2 38.3 0.8 45.8 6.3 2.7 36.8 13.2 Morocco 940 1,903 58.3 45.0 19.9 23.4 6.0 2.4 15.9 29.2 Mozambique 206 607 57.7 26.0 .. 18.8 4.3 3.2 38.1 52.0 Myanmar 73 364 35.4 82.1 22.6 7.6 2.5 .. 39.5 10.3 Namibia 341 226 46.9 37.1 17.9 24.6 6.8 5.8 28.5 32.5 Nepal 159 205 40.8 34.9 28.5 38.8 3.2 .. 27.5 26.3 Netherlands 28,995 56,478 37.7 22.8 25.4 23.0 1.0 4.1 35.9 50.1 New Zealand 3,251 4,682 40.6 35.2 29.5 31.8 2.5 4.0 27.5 29.0 Nicaragua 73 315 70.7 55.1 20.1 22.1 7.9 4.0 1.4 18.8 Niger 209 .. 68.3 .. 10.4 .. 4.3 .. 17.1 .. Nigeria 1,901 .. 33.6 .. 30.3 .. 3.1 .. 32.9 .. Norway 12,247 16,459 44.6 34.2 30.0 30.8 1.7 4.4 23.6 30.6 Oman 719 1,678 36.6 37.1 6.5 21.9 4.1 7.1 52.8 34.0 Pakistan 1,863 2,093 67.0 66.4 23.1 12.2 1.3 5.7 8.6 15.8 Panama 666 1,204 66.6 51.1 14.8 14.8 10.2 20.6 8.4 13.5 Papua New Guinea 393 662 35.6 26.1 12.8 5.8 4.0 7.3 47.6 60.8 Paraguay 361 294 61.6 59.4 19.8 22.0 11.4 17.4 7.3 1.1 Peru 1,070 2,371 43.5 39.7 27.6 26.0 10.9 10.3 18.0 23.9 Philippines 1,721 4,311 56.9 51.9 6.4 20.2 3.4 7.9 33.2 20.1 Poland 2,847 9,089 52.4 20.0 14.9 35.2 1.0 6.1 31.8 38.6 Portugal 3,772 6,578 48.4 33.1 23.0 34.6 5.1 5.0 23.5 27.3 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 211 4.8 Structure of service imports Commercial Transport Travel Insurance and Computer, service imports financial services information, communications, and other commercial services % of total % of total % of total % of total $ millions services services services services 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 787 2,304 65.5 36.2 13.1 17.2 7.3 7.0 14.1 39.5 Russian Federation .. 23,577 .. 12.1 .. 50.9 .. 2.9 .. 34.0 Rwanda 94 124 69.0 71.9 23.7 19.2 0.0 .. 7.3 8.9 Saudi Arabia 12,694 7,159 18.1 33.5 .. .. 2.2 3.7 79.7 62.7 Senegal 368 .. 60.1 .. 12.4 .. 8.8 .. 18.7 .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 67 .. 29.5 .. 32.7 .. 4.8 .. 33.0 .. Singapore 8,575 27,155 41.0 42.6 21.0 19.2 9.1 4.3 29.0 33.9 Slovak Republic .. 1,779 .. 24.4 .. 16.6 .. 4.9 .. 54.1 Slovenia 1,034 1,719 42.5 21.3 27.3 35.7 2.5 2.3 27.8 40.7 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 3,593 5,221 40.2 45.6 31.5 34.6 11.6 9.7 16.7 10.1 Spain 15,197 37,620 30.8 24.6 28.0 17.7 6.3 6.9 34.9 50.8 Sri Lanka 620 966 64.2 34.2 11.9 27.3 6.8 2.8 17.1 35.7 Sudan 202 784 31.9 87.5 25.4 11.7 4.9 0.1 37.8 0.8 Swaziland 171 134 6.1 15.4 20.6 24.6 .. 8.7 73.4 51.2 Sweden 16,959 23,732 23.2 14.8 37.1 30.4 7.9 3.6 31.7 51.1 Switzerland 11,093 16,980 33.7 25.6 53.0 38.9 1.4 4.8 12.0 30.6 Syrian Arab Republic 702 1,468 54.5 47.5 35.5 45.6 4.4 .. 5.7 6.9 Tajikistan .. 103 .. 79.0 .. 1.7 .. 6.3 .. 13.0 Tanzania 288 647 58.0 27.3 7.9 52.2 6.2 4.8 27.9 15.7 Thailand 6,160 16,573 58.0 43.0 23.3 19.9 5.5 5.9 13.2 31.2 Togo 217 129 56.9 71.8 18.4 3.7 9.1 15.3 15.5 9.2 Trinidad and Tobago 460 339 51.7 34.4 26.6 44.5 9.9 2.4 11.9 18.7 Tunisia 682 1,353 51.4 48.3 26.2 19.2 7.3 7.5 15.0 25.0 Turkey 2,794 6,283 32.2 30.7 18.6 29.9 9.6 15.8 49.2 23.6 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 195 530 58.3 .. .. .. 6.5 .. 35.2 .. Ukraine .. 3,143 .. 15.5 .. 20.9 .. 8.1 .. 55.5 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 44,713 101,408 33.2 24.2 41.0 41.4 2.4 6.3 23.4 28.1 United States 97,950 205,580 36.3 28.5 38.9 29.6 4.5 9.2 20.4 32.7 Uruguay 363 619 48.2 41.7 30.7 28.7 1.5 7.0 19.6 22.6 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 2,390 3,767 33.5 41.1 42.8 26.0 4.3 7.6 19.4 25.2 Vietnam .. 3,698 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 639 883 27.6 44.2 9.9 8.8 5.4 7.6 57.1 39.4 Zambia 370 328 76.8 67.7 14.6 13.3 5.3 1.7 3.3 17.3 Zimbabwe 460 .. 51.8 .. 14.4 .. 3.4 .. 30.4 .. World 779,679 s 1,475,405 s 28.3 w 26.1 w 28.6 w 28.7 w 6.0 w 6.8 w 38.2 w 38.6 w Low income 28,313 52,561 50.4 28.9 13.9 18.7 4.6 2.4 31.1 49.9 Middle income 92,233 240,279 40.6 29.1 22.5 28.8 5.2 9.0 32.4 33.1 Lower middle income 45,983 154,902 50.4 30.9 19.1 30.6 6.4 7.1 24.5 31.5 Upper middle income 46,250 85,376 30.8 25.9 25.8 25.6 3.8 12.4 40.3 36.0 Low & middle income 120,546 292,840 42.9 29.1 20.5 27.1 5.1 7.9 32.1 35.9 East Asia & Pacific 24,308 104,321 56.0 34.1 18.2 25.5 4.1 5.4 22.6 35.1 Europe & Central Asia 9,321 73,106 36.0 19.5 19.5 35.3 2.1 6.0 43.0 39.2 Latin America & Carib. 32,757 60,676 37.2 29.3 35.9 27.6 6.0 17.7 21.5 25.7 Middle East & N. Africa 26,605 18,791 33.3 36.2 7.9 13.1 4.7 5.4 54.1 45.3 South Asia 9,176 23,023 60.4 23.0 11.3 18.3 4.9 2.5 23.4 56.2 Sub-Saharan Africa 18,379 17,855 44.1 38.0 19.2 21.0 6.3 6.0 30.6 35.0 High income 659,133 1,182,565 25.7 25.4 30.1 29.1 6.1 6.5 39.3 39.3 Europe EMU 293,822 486,299 25.4 20.5 31.5 27.2 8.0 6.4 35.2 45.9 a. Includes Luxembourg. 212 2004 World Development Indicators ECONOMY 4.8 Structure of service imports About the data Definitions Trade in services differs from trade in goods · Commercial service imports are total service because services are produced and consumed at the imports minus imports of government services not same time. Thus services to a traveler may be con- included elsewhere. International transactions in sumed in the producing country (for example, use of services are defined by the IMF's Balance of a hotel room) but are classified as imports of the Payments Manual (1993) as the economic output of traveler's country. In other cases services may be intangible commodities that may be produced, trans- supplied from a remote location; for example, insur- ferred, and consumed at the same time. Definitions ance services may be supplied from one location may vary among reporting economies. · Transport and consumed in another. For further discussion of covers all transport services (sea, air, land, internal the problems of measuring trade in services, see waterway, space, and pipeline) performed by resi- About the data for table 4.7. dents of one economy for those of another and The data on exports of services in table 4.7 and on involving the carriage of passengers, movement of imports of services in this table, unlike those in edi- goods (freight), rental of carriers with crew, and relat- tions before 2000, include only commercial services ed support and auxiliary services. Excluded are and exclude the category "government services not freight insurance, which is included in insurance included elsewhere." The data are compiled by the services; goods procured in ports by nonresident International Monetary Fund (IMF) based on returns carriers and repairs of transport equipment, which from national sources. are included in goods; repairs of harbors, railway facilities, and airfield facilities, which are included in construction services; and rental of carriers without crew, which is included in other services. · Travel covers goods and services acquired from an econo- my by travelers in that economy for their own use dur- ing visits of less than one year for business or personal purposes. Travel services include the goods and services consumed by travelers, such as meals, lodging, and transport (within the economy visited), including car rental. · Insurance and finan- cial services cover freight insurance on goods imported and other direct insurance such as life insurance, financial intermediation services such as commissions, foreign exchange transactions, and brokerage services; and auxiliary services such as financial market operational and regulatory services. · Computer, information, communications, and other commercial services include such activities as 4.8a international telecommunications, and postal and Developing economies are consuming less transport services courier services; computer data; news-related serv- Commercial service imports (% of total) ice transactions between residents and nonresi- dents; construction services; royalties and license 1990 2002 fees; miscellaneous business, professional, and technical services; and personal, cultural, and recre- ational services. Other Other Transport 32% Transport 36% 29% 43% Insurance and financial Insurance Travel Travel services and financial 27% 20% Data sources 5% services The data on imports of commercial services are 8% from the IMF. The IMF publishes balance of pay- Between 1990 and 2002 travel, insurance and finance, and other services displaced transport as the most important ments data in its International Financial Statistics categories of service imports for developing economies. and Balance of Payments Statistics Yearbook. Source: International Monetary Fund data files. 2004 World Development Indicators 213 4.9 Structure of demand Household General Gross Exports Imports Gross domestic final government capital of goods of goods savings consumption final formation and services and services expenditure consumption expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. 108 .. 9 .. 16 .. 57 .. 89 .. ­16 Albania 61 93 19 8 29 23 15 19 23 43 21 ­1 Algeria 57 44 16 15 29 31 23 36 25 26 27 40 Angola 36 61 34 .. a 12 32 39 77 21 70 30 39 Argentina 77 61 3 12 14 12 10 28 5 13 20 27 Armenia 46 87 18 10 47 21 35 30 46 47 36 3 Australia 59 60 19 18 22 24 17 20 17 22 22 22 Austria 55 58 19 19 25 22 40 52 38 51 26 23 Azerbaijan 51 60 18 15 27 33 44 44 39 51 31 25 Bangladesh 86 77 4 5 17 23 6 14 14 19 10 18 Belarus 47 61 24 21 27 22 46 70 44 74 29 18 Belgium 55 55 20 21 22 19 71 82 69 78 24 23 Benin 87 81 11 13 14 18 14 14 26 26 2 6 Bolivia 77 75 12 15 13 15 23 22 24 27 11 10 Bosnia and Herzegovina .. 113 .. .. a .. 20 .. 26 .. 59 .. ­13 Botswana 33 28 24 33 37 25 55 51 50 37 43 38 Brazil 59 58 19 19 20 20 8 16 7 14 21 22 Bulgaria 60 69 18 18 26 20 33 53 37 60 22 13 Burkina Faso 82 82 13 13 18 18 11 9 24 22 5 5 Burundi 95 92 11 13 15 8 8 7 28 19 ­5 ­4 Cambodia 91 80 7 6 8 22 6 59 13 67 2 14 Cameroon 67 71 13 12 18 19 20 27 17 28 21 18 Canada 56 56 23 19 21 20 26 44 26 39 21 25 Central African Republic 86 78 15 12 12 15 15 12 28 17 ­1 10 Chad 88 86 10 8 16 59 13 12 28 65 2 6 Chile 62 61 10 12 25 23 35 36 31 32 28 27 China 50 43 12 13 35 40 18 29 14 26 38 43 Hong Kong, China 57 58 7 10 28 23 133 151 124 142 36 32 Colombia 66 66 9 21 19 15 21 20 15 21 24 14 Congo, Dem. Rep. 79 92 12 4 9 7 30 18 29 21 9 4 Congo, Rep. 62 32 14 18 16 23 54 81 46 54 24 50 Costa Rica 61 68 18 15 27 22 35 42 41 47 21 17 Côte d'Ivoire 72 60 17 11 7 10 32 48 27 30 11 28 Croatia 74 60 24 22 10 27 78 46 86 55 2 18 Cuba .. 70 .. 23 .. 10 .. 16 .. 18 .. 7 Czech Republic 49 53 23 21 25 28 45 65 43 67 28 26 Denmark 49 48 26 26 20 20 36 45 31 39 25 26 Dominican Republic 80 76 5 10 25 23 34 26 44 35 15 15 Ecuador 67 69 11 10 21 28 33 24 32 31 22 20 Egypt, Arab Rep. 73 79 11 10 29 17 20 16 33 23 16 10 El Salvador 89 90 10 8 14 16 19 27 31 41 1 2 Eritrea 104 92 22 38 8 26 11 29 45 85 ­26 ­30 Estonia 62 58 16 20 30 31 60 84 54 94 22 22 Ethiopia 74 78 18 19 12 21 8 16 12 34 7 2 Finland 50 51 22 22 30 20 23 38 24 30 29 28 France 55 55 22 24 23 19 21 27 22 25 22 21 Gabon 50 52 13 .. a 22 28 46 59 31 39 37 48 Gambia, The 76 83 14 13 22 21 60 54 72 72 11 4 Georgia 65 81 10 10 31 21 40 27 46 39 25 9 Germany 57 59 20 19 24 18 25 35 25 32 24 22 Ghana 85 83 9 10 14 20 17 43 26 55 5 7 Greece 72 67 15 16 23 23 18 21 28 27 13 17 Guatemala 84 85 7 8 14 19 21 16 25 28 10 7 Guinea 73 82 9 7 18 17 31 24 31 30 18 11 Guinea-Bissau 87 105 10 13 30 15 10 45 37 77 3 ­17 Haiti 81 103 8 .. a 13 21 18 13 20 36 11 ­3 214 2004 World Development Indicators ECONOMY 4.9 Structure of demand Household General Gross Exports Imports Gross domestic final government capital of goods of goods savings consumption final formation and services and services expenditure consumption expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 66 74 14 14 23 28 36 37 40 53 20 12 Hungary 61 67 11 11 25 24 31 64 29 67 28 22 India 66 65 12 13 24 23 7 15 9 16 23 22 Indonesia 59 71 9 8 31 14 25 35 24 29 32 21 Iran, Islamic Rep. 62 50 11 13 29 35 22 31 24 29 27 37 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 58 47 16 15 21 24 57 98 52 83 26 38 Israel 56 59 30 31 25 18 35 37 45 46 14 9 Italy 58 60 20 19 22 20 20 27 20 26 22 21 Jamaica 65 67 13 20 26 34 48 39 52 60 22 13 Japan 53 56 13 17 33 26 10 10 9 10 34 26 Jordan 74 75 25 23 32 23 62 46 93 67 1 3 Kazakhstan 52 60 18 12 32 27 74 47 75 46 30 28 Kenya 67 71 19 19 20 14 26 27 31 30 14 10 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 53 62 10 11 38 26 29 40 30 39 37 27 Kuwait 57 56 39 26 18 9 45 48 58 40 4 18 Kyrgyz Republic 71 67 25 18 24 19 29 39 50 43 4 15 Lao PDR .. .. 9 .. .. 22 11 .. 25 .. .. .. Latvia 53 63 9 21 40 27 48 45 49 56 39 17 Lebanon 140 95 25 14 18 18 18 14 100 41 ­64 ­9 Lesotho 121 82 23 33 49 40 16 51 109 107 ­44 ­15 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 48 58 24 17 19 14 40 48 31 36 27 26 Lithuania 57 62 19 21 33 22 52 54 61 60 24 17 Macedonia, FYR 72 77 19 22 19 20 26 38 36 57 9 0 Madagascar 86 84 8 8 17 14 17 16 28 23 6 8 Malawi 72 88 15 18 23 12 24 25 33 43 13 ­6 Malaysia 52 44 14 14 32 24 75 114 72 97 34 42 Mali 80 77 14 11 23 20 17 32 34 41 6 12 Mauritania 69 79 26 19 20 31 46 39 61 68 5 2 Mauritius 64 66 13 9 31 22 64 61 71 57 23 26 Mexico 70 70 8 12 23 20 19 27 20 29 22 18 Moldova 77 86 .. a 17 25 23 49 54 51 79 23 ­3 Mongolia 58 64 32 19 38 31 24 67 53 81 9 16 Morocco 65 62 15 20 25 23 26 32 32 37 19 18 Mozambique 101 59 12 11 16 45 8 24 36 38 ­12 30 Myanmar 89 88 .. a .. a 13 12 3 .. 5 .. 11 12 Namibia 51 48 31 28 34 24 52 48 67 49 18 23 Nepal 84 78 9 10 18 25 11 16 22 29 7 12 Netherlands 50 50 23 24 24 20 54 62 51 56 27 26 New Zealand 61 60 19 19 20 20 27 33 27 32 20 22 Nicaragua 59 78 43 16 19 32 25 23 46 49 ­2 6 Niger 84 84 15 12 8 13 15 16 22 25 1 4 Nigeria 56 55 15 27 15 23 43 38 29 44 29 17 Norway 49 67 21 .. a 23 19 40 41 34 27 30 33 Oman 27 43 38 23 13 13 53 57 31 35 35 34 Pakistan 74 74 15 11 19 15 16 19 23 19 11 14 Panama 60 63 18 13 17 25 38 28 34 29 21 24 Papua New Guinea 59 .. 25 .. 24 .. 41 .. 49 .. 16 .. Paraguay 77 84 6 8 23 20 33 31 39 43 17 8 Peru 74 72 8 10 16 18 16 16 14 17 18 18 Philippines 72 69 10 12 24 19 28 49 33 49 18 19 Poland 48 65 19 19 26 19 29 28 22 31 33 16 Portugal 63 61 16 21 28 28 33 31 39 41 21 18 Puerto Rico 65 .. 14 .. 17 .. 77 81 101 100 21 .. 2004 World Development Indicators 215 4.9 Structure of demand Household General Gross Exports Imports Gross domestic final government capital of goods of goods savings consumption final formation and services and services expenditure consumption expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 66 76 13 7 30 23 17 35 26 41 21 17 Russian Federation 49 51 21 17 30 21 18 35 18 24 30 32 Rwanda 84 87 10 12 15 19 6 8 14 25 6 1 Saudi Arabia 47 37 29 26 15 20 41 41 32 23 24 37 Senegal 76 76 15 14 14 20 25 31 30 41 9 10 Serbia and Montenegro .. 89 .. 18 .. 16 .. 21 .. 44 .. ­7 Sierra Leone 83 93 8 21 10 9 22 18 24 40 9 ­14 Singapore 47 42 10 13 36 21 .. .. .. .. 43 45 Slovak Republic 54 55 22 21 33 31 27 73 36 80 24 24 Slovenia 55 55 19 21 17 23 84 58 74 56 26 25 Somalia 112 .. .. a .. 16 .. 10 .. 38 .. ­12 .. South Africa 57 62 20 19 17 16 24 34 19 31 23 19 Spain 60 58 17 18 27 26 16 28 20 30 23 24 Sri Lanka 76 77 10 9 23 21 29 36 38 43 14 14 Sudan .. 79 .. .. a .. 20 .. 15 .. 13 .. 21 Swaziland 62 74 18 17 19 18 75 91 74 100 20 9 Sweden 47 49 29 28 24 17 29 43 28 37 25 23 Switzerland 57 76 14 .. a 28 17 36 44 36 38 29 24 Syrian Arab Republic 69 59 14 11 17 22 28 37 28 28 17 30 Tajikistan 74 82 9 9 25 23 28 58 35 72 17 10 Tanzania b 81 77 18 13 26 17 13 17 37 24 1 10 Thailand 57 58 9 11 41 24 34 65 42 57 34 31 Togo 71 86 14 10 27 22 33 33 45 50 15 5 Trinidad and Tobago 59 69 12 10 13 16 45 47 29 43 29 20 Tunisia 58 63 16 16 32 25 44 45 51 49 25 21 Turkey 69 71 11 13 24 16 13 30 18 30 20 16 Turkmenistan 49 49 23 15 40 37 .. 47 .. 47 28 36 Uganda 92 78 8 16 13 22 7 12 19 27 1 6 Ukraine 57 56 17 20 27 19 28 56 29 52 26 24 United Arab Emirates 39 .. 16 .. 20 .. 65 .. 40 .. 45 .. United Kingdom 63 66 20 20 20 16 24 26 27 28 18 14 United States 67 70 17 16 18 18 10 10 11 14 16 14 Uruguay 70 73 12 13 12 12 24 22 18 20 18 14 Uzbekistan 61 58 25 19 32 20 29 38 48 34 13 24 Venezuela, RB 62 65 8 6 10 17 39 29 20 17 29 29 Vietnam 84 66 12 6 13 32 36 56 45 60 3 28 West Bank and Gaza .. 79 .. 52 .. 4 .. 12 .. 47 .. ­31 Yemen, Rep. 74 70 17 14 15 17 14 38 20 39 9 16 Zambia 64 84 19 12 17 17 36 29 37 42 17 4 Zimbabwe 63 72 19 17 17 8 23 24 23 22 17 11 World 59 w 63 w 17 w 17 w 24 w 20 w 19 w 24 w 19 w 23 w 24 w 20 w Low income 67 69 12 12 23 20 17 25 19 25 21 19 Middle income 59 58 15 16 25 23 20 32 19 28 26 27 Lower middle income 58 57 15 16 27 25 17 29 17 26 27 28 Upper middle income 63 61 12 15 21 19 28 39 25 33 24 25 Low & middle income 61 59 14 15 25 23 20 31 19 28 25 26 East Asia & Pacific 54 51 11 12 34 32 25 41 24 37 34 37 Europe & Central Asia 55 61 18 16 28 21 23 40 24 38 26 23 Latin America & Carib. 65 63 13 16 19 19 14 21 12 19 21 22 Middle East & N. Africa 59 53 20 18 23 23 31 34 33 29 21 29 South Asia 69 68 12 12 23 22 9 17 12 18 20 20 Sub-Saharan Africa 63 66 18 18 17 18 27 33 26 34 19 17 High income 59 64 17 18 24 19 19 22 19 22 24 19 Europe EMU 56 57 20 20 24 20 27 36 27 33 23 22 a. Data on general government final consumption expenditure are not available separately; they are included in household final consumption expenditure. b. Data cover mainland Tanzania only. 216 2004 World Development Indicators ECONOMY 4.9 Structure of demand About the data Definitions Gross domestic product (GDP) from the expenditure guidelines are capital outlays on defense establish- · Household final consumption expenditure is the mar- side is made up of household final consumption ments that may be used by the general public, such as ket value of all goods and services, including durable expenditure, general government final consumption schools, airfields, and hospitals, and intangibles such products (such as cars, washing machines, and home computers), purchased by households. It excludes pur- expenditure, gross capital formation (private and as computer software and mineral exploration outlays. chases of dwellings but includes imputed rent for owner- public investment in fixed assets, changes in inven- Data on capital formation may be estimated from occupied dwellings. It also includes payments and fees tories, and net acquisitions of valuables), and net direct surveys of enterprises and administrative to governments to obtain permits and licenses. World exports (exports minus imports) of goods and serv- records or based on the commodity flow method using Development Indicators includes in household con- ices. Such expenditures are recorded in purchaser data from production, trade, and construction activi- sumption expenditure the expenditures of nonprofit prices and include net taxes on products. ties. The quality of data on fixed capital formation by institutions serving households, even when reported Because policymakers have tended to focus on fos- government depends on the quality of government separately by the country. In practice, household con- tering the growth of output, and because data on pro- accounting systems (which tend to be weak in devel- sumption expenditure may include any statistical dis- duction are easier to collect than data on spending, oping countries). Measures of fixed capital formation crepancy in the use of resources relative to the supply many countries generate their primary estimate of GDP by households and corporations--particularly capital of resources. · General government final consumption expenditure includes all government current expendi- using the production approach. Moreover, many coun- outlays by small, unincorporated enterprises--are tures for purchases of goods and services (including tries do not estimate all the separate components of usually unreliable. compensation of employees). It also includes most national expenditures but instead derive some of the Estimates of changes in inventories are rarely com- expenditures on national defense and security but main aggregates indirectly using GDP (based on the plete but usually include the most important activi- excludes government military expenditures that poten- production approach) as the control total. ties or commodities. In some countries these tially have wider public use and are part of government Household final consumption expenditure (private estimates are derived as a composite residual along capital formation. · Gross capital formation consists of consumption in the 1968 System of National with household final consumption expenditure. outlays on additions to the fixed assets of the economy, Accounts, or SNA) is often estimated as a residual, According to national accounts conventions, adjust- net changes in the level of inventories, and net acquisi- by subtracting from GDP all other known expendi- ments should be made for appreciation of the value tions of valuables. Fixed assets include land improve- tures. The resulting aggregate may incorporate fairly of inventory holdings due to price changes, but this ments (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the con- large discrepancies. When household consumption is not always done. In highly inflationary economies struction of roads, railways, and the like, including is calculated separately, many of the estimates are this element can be substantial. schools, offices, hospitals, private residential dwellings, based on household surveys, which tend to be one- Data on exports and imports are compiled from cus- and commercial and industrial buildings. Inventories are year studies with limited coverage. Thus the esti- toms reports and balance of payments data. Although stocks of goods held by firms to meet temporary or mates quickly become outdated and must be the data from the payments side provide reasonably unexpected fluctuations in production or sales, and supplemented by estimates using price- and quantity- reliable records of cross-border transactions, they may "work in progress." · Exports and imports of goods based statistical procedures. Complicating the not adhere strictly to the appropriate definitions of val- and services represent the value of all goods and other issue, in many developing countries the distinction uation and timing used in the balance of payments or market services provided to, or received from, the rest between cash outlays for personal business and correspond to the change-of-ownership criterion. This of the world. They include the value of merchandise, those for household use may be blurred. World issue has assumed greater significance with the freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, con- Development Indicators includes in household con- increasing globalization of international business. struction, financial, information, business, personal, sumption the expenditures of nonprofit institutions Neither customs nor balance of payments data usu- and government services. They exclude labor and prop- serving households. ally capture the illegal transactions that occur in many erty income (factor services in the 1968 SNA) as well General government final consumption expenditure countries. Goods carried by travelers across borders as transfer payments. · Gross domestic savings are (general government consumption in the 1968 SNA) in legal but unreported shuttle trade may further dis- calculated as GDP less total consumption. includes expenditures on goods and services for indi- tort trade statistics. Data sources vidual consumption as well as those on services for Domestic savings, a concept used by the World The national accounts indicators for most devel- collective consumption. Defense expenditures, Bank, represent the difference between GDP and oping countries are collected from national statis- including those on capital outlays (with certain total consumption. Domestic savings also satisfy the tical organizations and central banks by visiting exceptions), are treated as current spending. fundamental identity: exports minus imports equal and resident World Bank missions. The data for Gross capital formation (gross domestic investment domestic savings minus capital formation. Domestic high-income economies come from Organisation in the 1968 SNA) consists of outlays on additions to savings differ from savings as defined in the nation- for Economic Co-operation and Development data the economy's fixed assets plus net changes in the al accounts; the SNA concept of savings represents files (see the OECD's National Accounts of OECD level of inventories. It is generally obtained from the difference between disposable income and con- Countries, Detailed Tables 1970­2001, volumes reports by industry of acquisition and distinguishes sumption. For further discussion of the problems in 1 and 2). The United Nations Statistics Division only the broad categories of capital formation. The compiling national accounts, see Srinivasan (1994), publishes detailed national accounts for United 1993 SNA recognizes a third category of capital Heston (1994), and Ruggles (1994). For a classic Nations member countries in National Accounts formation: net acquisitions of valuables. Included analysis of the reliability of foreign trade and nation- Statistics: Main Aggregates and Detailed Tables and updates in the Monthly Bulletin of Statistics. in gross capital formation under the 1993 SNA al income statistics, see Morgenstern (1963). 2004 World Development Indicators 217 4.10 Growth of consumption and investment Household final General Gross consumption expenditure government final capital consumption formation expenditure Per capita average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1990 2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 1,271 4,496 .. 4.2 .. 4.8 .. 1.6 ­0.3 20.5 Algeria 35,265 24,745 1.5 0.9 ­1.4 ­0.9 0.7 3.4 ­1.8 0.3 Angola 3,674 .. ­3.6 .. .. .. 8.4 .. ­5.6 .. Argentina 109,038 62,158 .. 0.5 .. ­0.7 .. 1.3 ­5.2 2.5 Armenia 1,097 2,121 .. 1.1 .. 2.5 .. ­1.0 .. ­6.2 Australia 182,448 247,950 2.9 3.7 1.4 2.5 3.5 3.0 3.7 6.4 Austria 89,789 117,605 2.4 2.3 2.2 2.0 1.4 1.6 2.4 2.0 Azerbaijan 3,186 3,587 .. 11.3 .. 10.3 .. 7.0 .. 8.0 Bangladesh 24,988 36,548 3.0 2.8 0.4 1.0 2.9 4.9 6.9 9.0 Belarus 8,223 8,781 .. 0.9 .. 1.2 .. ­1.0 .. ­6.3 Belgium 109,154 135,445 2.0 1.9 1.9 1.6 1.1 1.6 2.9 1.9 Benin 1,602 2,183 1.9 3.4 ­1.2 0.7 0.5 5.8 ­5.3 12.8 Bolivia 3,741 5,835 1.2 3.4 ­0.9 0.9 ­3.8 3.4 0.8 5.1 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 1,260 1,537 6.3 4.1 2.7 1.5 14.9 7.6 7.6 2.6 Brazil a 273,952 263,710 1.2 4.7 ­0.7 3.2 7.3 0.2 3.3 0.6 Bulgaria 12,401 10,742 3.1 ­1.0 3.2 ­0.3 5.1 ­5.8 .. .. Burkina Faso 2,284 2,556 2.6 3.9 0.1 1.4 6.2 ­0.5 8.6 7.9 Burundi 1,070 655 3.4 ­1.7 0.5 ­3.7 3.2 ­1.6 6.9 1.2 Cambodia a 1,016 3,287 .. 4.7 .. 2.2 .. 7.7 .. 12.2 Cameroon 7,423 6,394 3.5 3.5 0.6 0.9 6.8 2.8 ­2.6 2.3 Canada 322,564 391,155 3.2 2.7 2.0 1.7 2.4 0.5 5.0 4.6 Central African Republic a 1,274 815 1.5 .. .. .. ­1.7 .. 10.0 .. Chad a 1,538 1,719 2.9 2.0 0.2 ­1.1 17.0 ­0.8 22.0 18.0 Chile 18,759 39,211 2.0 6.2 0.3 4.7 0.4 3.6 6.4 6.2 China 174,249 586,381 8.8 8.7 7.2 7.6 9.8 9.0 10.8 10.7 Hong Kong, China 42,723 93,401 6.6 3.5 5.2 1.8 5.3 3.3 3.9 4.3 Colombia 26,357 53,046 2.6 1.8 0.5 ­0.1 4.2 8.9 1.4 0.8 Congo, Dem. Rep. a 7,398 5,269 3.4 ­2.9 0.4 ­5.4 0.0 ­15.9 ­5.1 0.3 Congo, Rep. a 1,746 955 2.3 1.7 ­0.9 ­1.5 4.3 ­2.6 ­11.6 2.3 Costa Rica a 3,502 11,521 3.6 4.4 0.6 2.2 1.1 2.0 4.6 5.5 Côte d'Ivoire 7,766 7,048 1.5 3.1 ­2.1 0.2 ­0.1 0.8 ­10.4 4.5 Croatia 13,527 13,483 .. 3.0 .. 3.5 .. 0.1 .. 5.7 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 17,195 36,165 .. 2.7 .. 2.8 .. ­1.0 .. 4.8 Denmark 65,430 82,827 1.4 1.8 1.4 1.4 0.9 2.2 4.7 5.2 Dominican Republic a 5,633 16,408 3.9 5.6 1.7 3.8 ­3.2 13.6 4.5 6.2 Ecuador a 6,988 16,837 1.1 2.2 ­1.5 0.3 ­0.7 ­1.0 ­1.3 1.3 Egypt, Arab Rep. 30,933 71,236 4.6 4.3 2.0 2.3 3.1 2.5 0.0 5.5 El Salvador 4,273 12,847 0.8 4.8 ­0.2 2.8 0.1 2.4 2.2 6.0 Eritrea 496 592 .. ­0.3 .. ­2.9 .. 14.2 .. 10.7 Estonia 2,539 3,727 .. 1.2 .. 2.5 .. 4.4 .. 1.8 Ethiopia 6,382 4,756 0.7 5.6 ­2.4 3.2 4.0 9.5 4.9 5.8 Finland 68,341 66,204 3.9 2.1 3.4 1.8 3.2 1.1 3.3 1.8 France 672,960 784,209 2.2 1.6 1.7 1.2 2.6 1.9 3.3 2.0 Gabon a 2,961 3,040 1.5 2.1 ­1.6 ­0.6 ­0.6 4.0 ­5.7 3.2 Gambia, The 240 296 ­2.4 5.3 ­5.9 1.8 1.7 0.5 0.0 2.6 Georgia 5,231 2,799 .. 4.6 .. 5.0 .. 4.3 .. ­8.3 Germany 950,047 1,168,773 2.3 1.6 2.2 1.3 1.5 1.5 1.8 0.4 Ghana 5,016 5,093 2.8 1.3 ­0.6 ­1.1 2.4 4.7 3.3 1.1 Greece 60,164 89,446 2.0 2.4 1.5 2.0 1.1 1.6 ­0.7 4.5 Guatemala a 6,398 19,794 1.1 4.1 ­1.4 1.4 2.6 5.3 ­1.8 6.3 Guinea 2,068 2,625 .. 3.6 .. 1.1 .. 4.3 .. 2.3 Guinea-Bissau 212 213 0.8 1.5 ­1.9 ­1.4 7.2 2.1 12.9 ­12.3 Haiti 2,332 3,334 0.9 .. .. .. ­4.4 .. ­0.6 8.7 218 2004 World Development Indicators ECONOMY 4.10 Growth of consumption and investment Household final General Gross consumption expenditure government final capital consumption formation expenditure Per capita average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1990 2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 Honduras a 2,026 4,858 2.7 3.1 ­0.5 0.3 3.3 3.7 2.9 5.2 Hungary 20,290 42,860 1.3 0.9 1.7 1.1 1.9 1.5 ­0.9 7.6 India 215,762 328,706 4.2 4.9 2.0 3.1 7.3 6.4 6.2 6.9 Indonesia 65,010 122,193 5.3 5.8 3.4 4.3 4.6 0.9 7.7 ­2.1 Iran, Islamic Rep. 74,476 54,403 2.8 3.3 ­0.6 1.7 ­5.0 4.3 ­2.5 4.6 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 27,957 47,973 2.2 5.7 1.9 4.8 ­0.3 4.6 ­0.4 9.8 Israel 32,112 61,552 .. 4.2 .. 1.7 .. 3.0 .. ­1.5 Italy 634,194 713,186 2.9 1.7 2.8 1.5 2.9 0.4 2.1 1.8 Jamaica 2,980 5,859 .. .. .. .. .. .. .. .. Japan 1,618,040 2,282,911 3.6 1.5 3.0 1.2 3.6 3.0 5.5 ­0.1 Jordan 2,978 7,622 1.9 5.2 ­1.9 1.4 1.9 4.1 ­1.9 0.3 Kazakhstan a 12,856 14,392 .. ­5.5 .. ­4.6 .. ­4.5 .. ­11.8 Kenya 5,320 8,819 4.7 2.2 1.2 ­0.3 2.6 7.5 0.4 2.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 132,113 286,818 7.9 4.9 6.7 4.0 5.2 2.6 12.0 1.3 Kuwait 10,459 19,720 ­1.4 .. .. .. 2.2 .. ­4.5 .. Kyrgyz Republic 1,896 1,083 .. ­4.7 .. ­5.6 .. ­6.5 .. ­2.2 Lao PDR .. .. .. .. .. .. .. .. .. .. Latvia 3,365 5,274 2.3 ­1.6 1.8 ­0.4 5.0 1.8 3.4 ­6.5 Lebanon 3,961 16,921 .. 2.4 .. 0.7 .. 5.7 .. 4.1 Lesotho 746 585 1.3 ­0.4 ­0.8 ­1.5 3.6 6.7 5.0 1.0 Liberia .. .. .. .. .. .. .. .. .. .. Libya 13,999 10,970 .. .. .. .. .. .. .. .. Lithuania a 5,826 8,577 .. 4.9 .. 5.6 .. 1.6 .. 9.3 Macedonia, FYR 3,021 2,924 .. 2.1 .. 1.4 .. 1.5 .. 1.5 Madagascar 2,663 3,703 ­0.7 2.3 ­3.4 ­0.6 0.5 0.6 4.9 4.0 Malawi 1,345 1,665 1.5 4.9 ­1.7 2.9 6.3 ­1.5 ­2.8 ­13.7 Malaysia 22,806 41,971 3.3 4.9 0.4 2.4 2.7 5.6 3.1 3.5 Mali 1,943 2,230 0.6 3.2 ­1.9 0.7 7.9 5.5 3.6 3.5 Mauritania 705 762 1.4 3.9 ­0.9 1.1 ­3.8 2.0 6.9 9.1 Mauritius 1,519 2,983 6.2 4.8 5.3 3.6 3.3 4.8 10.3 4.0 Mexico 182,791 445,791 1.1 2.8 ­1.0 1.1 2.4 1.6 ­3.3 4.6 Moldova a 1,780 1,403 .. 8.6 .. 8.9 .. ­9.2 .. ­12.0 Mongolia a .. 744 .. .. .. .. .. .. .. .. Morocco 16,833 23,952 4.3 2.7 2.0 0.9 2.1 3.6 1.2 3.9 Mozambique a 2,481 2,124 ­1.6 1.5 ­3.1 ­0.7 ­1.1 6.2 3.8 14.0 Myanmar .. .. 0.6 3.9 .. .. .. .. ­4.1 15.3 Namibia 1,204 1,377 1.3 5.1 ­1.9 2.2 3.7 3.0 ­3.2 6.5 Nepal 3,060 4,336 .. .. .. .. .. .. .. .. Netherlands 145,871 209,068 1.7 2.8 1.1 2.2 2.2 2.1 3.3 2.8 New Zealand 26,632 34,955 2.1 3.1 1.2 2.0 1.6 2.6 3.0 5.1 Nicaragua a 592 3,123 ­3.6 6.1 ­6.2 3.2 3.4 ­2.6 ­4.8 12.6 Niger 2,079 1,814 0.0 1.8 ­3.1 ­1.7 4.4 0.8 ­7.1 4.0 Nigeria 15,816 24,135 ­2.6 0.2 ­5.5 ­2.7 ­3.5 ­1.8 ­8.5 5.4 Norway 57,047 73,067 2.2 3.5 1.9 2.9 2.4 2.7 0.9 4.5 Oman 2,810 8,752 .. .. .. .. .. .. 25.5 .. Pakistan 29,512 43,936 4.3 4.4 1.6 1.8 10.3 0.8 5.8 1.4 Panama a 3,022 5,673 2.1 4.1 ­0.0 2.4 1.2 2.6 ­8.9 8.0 Papua New Guinea 1,902 .. 0.4 5.2 ­2.1 2.6 ­0.1 2.2 ­0.9 1.3 Paraguay 4,063 4,649 2.4 3.2 ­0.5 0.8 1.5 4.0 ­0.8 ­1.6 Peru a 19,376 40,717 0.7 3.6 ­1.5 1.7 ­0.9 4.6 ­3.8 4.9 Philippines 31,566 53,307 2.6 3.7 0.2 1.4 0.6 3.3 ­2.1 3.7 Poland a 28,281 123,535 .. 4.8 .. 4.7 .. 3.1 .. 8.5 Portugal 44,679 67,078 2.6 2.8 2.4 2.5 5.0 2.9 3.0 5.4 Puerto Rico 19,827 .. 3.5 .. .. .. 5.1 .. 6.9 .. 2004 World Development Indicators 219 4.10 Growth of consumption and investment Household final General Gross consumption expenditure government final capital consumption formation expenditure Per capita average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1990 2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 Romania a 25,232 34,785 .. 1.9 .. 2.2 .. 0.7 .. ­2.7 Russian Federation 252,561 177,362 .. 0.3 .. 0.5 .. ­1.5 .. ­13.6 Rwanda a 2,162 1,503 1.2 2.2 ­1.8 0.8 5.2 0.7 4.3 2.7 Saudi Arabia 54,508 69,666 .. .. .. .. .. .. .. .. Senegal 4,353 3,820 2.1 2.9 ­0.8 0.2 3.3 4.1 5.2 8.0 Serbia and Montenegro .. 13,915 .. .. .. .. .. .. .. .. Sierra Leone 546 728 ­2.7 ­5.2 ­4.7 ­7.3 ­4.7 3.2 44.9 2.7 Singapore 17,019 37,360 5.8 5.5 3.9 2.6 6.6 9.3 3.1 4.5 Slovak Republic 8,350 13,133 3.8 1.8 3.5 1.6 4.8 1.4 0.0 5.8 Slovenia 6,917 11,697 .. 3.5 .. 3.5 .. 3.9 .. 9.3 Somalia .. .. 1.3 .. .. .. 7.0 .. ­2.6 .. South Africa 64,251 64,741 2.4 2.7 ­0.2 0.5 3.5 0.7 ­5.3 3.1 Spain 306,953 378,319 2.6 2.5 2.3 2.0 4.9 3.0 5.9 3.4 Sri Lanka a 6,143 12,736 4.0 4.7 2.9 3.4 7.3 10.9 0.6 5.3 Sudan .. 8,339 0.0 .. .. .. ­0.5 .. ­1.8 10.8 Swaziland a 547 883 5.3 3.5 2.1 0.4 1.4 3.4 ­0.4 1.5 Sweden 116,475 116,993 2.2 1.6 1.9 1.3 1.6 0.5 4.7 2.0 Switzerland 130,900 149,886 1.6 1.1 1.1 0.5 3.1 1.0 3.9 1.0 Syrian Arab Republic 8,458 12,289 3.6 2.0 0.2 ­0.8 ­3.6 0.3 ­5.3 2.1 Tajikistan 1,940 932 .. 0.9 .. ­0.3 4.1 ­11.2 ­4.3 ­10.1 Tanzania b 3,526 7,365 .. 3.5 .. 0.7 .. 1.2 .. ­0.3 Thailand 48,270 71,743 5.9 3.3 4.1 2.5 4.2 4.5 9.5 ­4.1 Togo 1,158 1,184 4.7 3.6 1.3 0.8 ­1.2 ­2.1 2.7 1.3 Trinidad and Tobago 2,975 6,424 ­1.3 2.0 ­2.5 1.5 ­1.7 1.3 ­6.3 8.4 Tunisia 7,152 13,152 2.9 4.5 0.3 2.9 3.8 4.2 ­1.8 3.7 Turkey 103,324 130,631 .. 3.1 .. 1.2 .. 4.4 .. 1.4 Turkmenistan 1,616 2,918 .. .. .. .. .. .. .. 4.3 Uganda 4,002 4,528 2.6 6.0 ­0.6 3.0 2.0 7.1 8.0 8.1 Ukraine 46,497 23,251 .. ­4.7 .. ­4.1 .. ­3.1 .. ­13.9 United Arab Emirates 12,726 .. 4.6 .. .. .. ­3.9 .. ­8.7 .. United Kingdom 619,782 1,034,301 4.0 3.3 3.8 3.1 0.8 1.4 6.4 3.7 United States 3,831,500 7,303,700 3.8 3.5 2.9 2.3 3.3 1.3 4.0 6.2 Uruguay a 6,525 8,836 0.7 3.2 0.1 2.5 1.8 1.5 ­6.6 2.3 Uzbekistan 8,204 4,569 .. .. .. .. .. .. .. 0.1 Venezuela, RB 30,170 60,977 1.3 0.4 ­1.2 ­1.7 2.0 0.4 ­5.3 2.3 Vietnam 5,485 22,780 .. 5.0 .. 3.6 .. 3.4 .. 17.2 West Bank and Gaza .. 2,756 .. ­1.0 .. ­5.1 .. 13.6 .. ­22.7 Yemen, Rep. 3,561 6,882 .. 3.6 .. 0.3 .. 2.2 .. 7.5 Zambia 2,078 3,110 1.8 ­2.3 ­1.3 ­4.5 ­3.4 ­6.5 ­4.3 6.4 Zimbabwe 5,543 6,020 3.7 0.4 ­0.0 ­1.5 4.7 ­2.9 3.6 ­6.2 World 12,863,243 t 20,040,617 t 3.3 w 2.8 w 1.5 w 1.4 w 3.0 w 1.9 w 3.9 w 2.5 w Low income 513,634 757,325 3.4 4.4 1.0 2.4 5.2 3.8 4.7 4.2 Middle income 1,879,875 2,964,278 2.4 3.5 0.8 2.3 .. 2.1 1.6 1.3 Lower middle income 1,323,786 1,913,292 3.3 3.8 1.6 2.7 5.5 2.2 3.4 0.3 Upper middle income 558,438 1,046,796 .. 2.6 .. 1.3 .. 2.0 ­2.5 4.5 Low & middle income 2,389,174 3,715,003 2.9 3.7 0.9 2.1 5.3 2.3 2.1 1.7 East Asia & Pacific 361,814 932,055 6.4 6.7 4.7 5.5 6.3 6.8 8.4 6.9 Europe & Central Asia 604,731 689,168 .. 1.2 .. 1.1 .. ­0.3 .. ­6.6 Latin America & Carib. 722,234 1,098,409 1.3 3.4 ­0.7 1.7 5.6 1.1 ­0.3 2.6 Middle East & N. Africa 238,847 361,722 .. .. .. .. .. .. .. .. South Asia 283,645 432,695 4.1 4.6 1.8 2.7 7.6 5.8 6.0 6.5 Sub-Saharan Africa 186,935 203,372 1.5 2.6 ­1.3 0.1 2.7 1.4 ­3.8 3.4 High income 10,474,011 16,329,652 3.3 2.6 2.7 1.9 2.8 1.8 4.2 2.7 Europe EMU 3,115,871 3,795,770 2.4 1.9 2.1 1.5 2.3 1.7 2.6 1.8 a. Household final consumption expenditure includes statistical discrepancy. b. Data cover mainland Tanzania only. 220 2004 World Development Indicators ECONOMY 4.10 Growth of consumption and investment About the data Definitions Measures of growth in consumption and capital for- methods.) Growth rates of household final consump- · Household final consumption expenditure is the mation are subject to two kinds of inaccuracy. The first tion expenditure, household final consumption market value of all goods and services, including stems from the difficulty of measuring expenditures at expenditure per capita, general government final con- durable products (such as cars, washing machines, current price levels, as described in About the data for sumption expenditure, and gross capital formation and home computers), purchased by households. It table 4.9. The second arises in deflating current price are estimated using constant price data. excludes purchases of dwellings but includes imputed data to measure volume growth, where results depend (Consumption and capital formation as shares of rent for owner-occupied dwellings. It also includes pay- on the relevance and reliability of the price indexes GDP are shown in table 4.9.) ments and fees to governments to obtain permits and and weights used. Measuring price changes is more To obtain government consumption in constant licenses. World Development Indicators includes in difficult for investment goods than for consumption prices, countries may deflate current values by apply- household consumption expenditure the expenditures goods because of the one-time nature of many invest- ing a wage (price) index or extrapolate from the of nonprofit institutions serving households, even ments and because the rate of technological progress change in government employment. Neither tech- when reported separately by the country. In practice, in capital goods makes capturing change in quality dif- nique captures improvements in productivity or household consumption expenditure may include any ficult. (An example is computers--prices have fallen changes in the quality of government services. statistical discrepancy in the use of resources relative as quality has improved.) Several countries estimate Deflators for household consumption are usually cal- to the supply of resources. · General government capital formation from the supply side, identifying cap- culated on the basis of the consumer price index. final consumption expenditure includes all govern- ital goods entering an economy directly from detailed Many countries estimate household consumption as ment current expenditures for purchases of goods and production and international trade statistics. This a residual that includes statistical discrepancies services (including compensation of employees). It means that the price indexes used in deflating pro- associated with the estimation of other expenditure also includes most expenditures on national defense duction and international trade, reflecting delivered or items, including changes in inventories; thus these and security but excludes government military expen- offered prices, will determine the deflator for capital estimates lack detailed breakdowns of household ditures that potentially have wider public use and are formation expenditures on the demand side. consumption expenditures. part of government capital formation. · Gross capi- The data in the table on household final consump- tal formation consists of outlays on additions to the tion expenditure (private consumption in the 1968 fixed assets of the economy, net changes in the level System of National Accounts), in current U.S. dollars, of inventories, and net acquisitions of valuables. are converted from national currencies using official Fixed assets include land improvements (fences, exchange rates or an alternative conversion factor as ditches, drains, and so on); plant, machinery, and noted in Primary data documentation. (For a discus- equipment purchases; and the construction of sion of alternative conversion factors, see Statistical roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and 4.10a commercial and industrial buildings. Inventories are Per capita consumption has risen in Asia, fallen in Africa stocks of goods held by firms to meet temporary or Per capita household consumption (1995 $) unexpected fluctuations in production or sales, and "work in progress." 600 East Asia & Pacific 500 Sub-Saharan Africa Data sources 400 The national accounts indicators for most develop- South Asia ing countries are collected from national statistical 300 organizations and central banks by visiting and res- ident World Bank missions. Data for high-income economies come from data files of the Organisation 200 for Economic Co-operation and Development (see the OECD's National Accounts of OECD Countries, 100 Detailed Tables, 1970­2001, volumes 1 and 2). 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 The United Nations Statistics Division publishes Starting from slightly lower per capita household consumption in 1980 than South Asia, East Asia and Pacific has raised detailed national accounts for United Nations mem- consumption dramatically and lowered poverty. In Sub-Saharan Africa, by contrast, which had per capita household ber countries in National Accounts Statistics: Main consumption of well more than twice that in East and South Asia in 1980, per capita household consumption has fallen below that of East Asia and Pacific. Aggregates and Detailed Tables and updates in the Monthly Bulletin of Statistics. Source: World Bank data files. 2004 World Development Indicators 221 4.11 Central government finances Current Total Overall Financing Domestic Debt and revenue a expenditure budget balance from abroad financing interest (including payments grants) Total Interest debt % of % of current % of GDP % of GDP % of GDP % of GDP % of GDP GDP revenue 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 2001 2001 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. 35.5 .. 31.2 .. 4.0 .. ­2.6 .. ­1.4 50.4 9.8 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 10.4 13.8 10.6 17.1 ­0.4 ­3.3 0.2 4.2 0.2 ­0.9 .. 27.5 Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 24.9 23.9 23.2 23.5 2.0 1.4 0.2 ­0.5 ­2.2 ­0.9 15.4 5.3 Austria 34.0 37.2 37.6 40.3 ­4.4 .. 0.5 .. 3.9 .. 62.3 8.9 Azerbaijan .. 17.6 .. 22.6 .. ­2.5 .. .. .. .. .. 2.5 Bangladesh .. 9.3 .. 12.7 .. ­2.8 .. 0.1 .. 2.7 40.1 15.7 Belarus 30.9 28.5 37.3 29.6 ­4.8 ­1.4 2.7 ­0.1 2.4 1.5 11.4 2.5 Belgium 42.7 .. 47.9 .. ­5.5 .. ­0.3 .. 5.8 .. .. .. Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 13.7 17.1 16.4 26.6 ­1.7 ­6.7 0.7 2.0 1.0 4.7 69.3 12.2 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 50.8 .. 33.6 .. 11.2 .. 0.0 .. ­11.3 .. .. .. Brazil 22.8 .. 34.9 .. ­5.8 .. .. .. .. .. .. .. Bulgaria 47.1 33.0 55.1 34.4 ­8.3 1.9 ­0.8 ­0.2 9.1 ­1.7 .. 11.2 Burkina Faso 9.7 .. 13.3 .. ­1.2 .. .. .. .. .. .. .. Burundi 18.2 17.9 28.7 26.1 ­3.3 ­4.7 4.9 3.3 ­1.6 1.5 183.9 13.2 Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 15.4 15.7 21.2 15.5 ­5.9 0.1 5.2 0.2 1.2 ­0.3 102.3 19.2 Canada 21.5 21.1 26.1 19.8 ­4.8 1.3 0.2 0.6 4.6 ­1.9 58.5 12.4 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad 6.7 .. 21.8 .. ­4.7 .. 5.0 .. ­0.3 .. .. .. Chile 20.6 22.8 20.4 23.1 0.8 ­0.3 .. 0.7 .. ­0.4 15.6 2.1 China 6.3 7.2 10.1 10.9 ­1.9 ­2.9 0.8 ­0.1 1.1 3.0 12.7 .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 12.6 12.4 11.6 18.8 3.9 ­7.0 .. 2.1 .. 4.9 29.3 26.8 Congo, Dem. Rep. 10.1 0.0 18.8 0.1 ­6.5 ­0.0 0.0 0.0 6.5 0.0 .. .. Congo, Rep. 22.5 31.2 35.6 25.7 ­14.1 5.8 .. 2.0 .. ­3.1 160.6 19.3 Costa Rica 23.0 22.2 25.6 23.6 ­3.1 ­1.2 0.3 1.4 2.8 ­0.2 38.4 17.9 Côte d'Ivoire 22.0 17.0 24.5 16.5 ­2.9 0.9 4.0 0.2 0.4 ­1.1 102.5 19.5 Croatia 33.0 40.2 37.6 45.3 ­4.6 ­2.5 0.0 1.4 4.7 1.1 .. 5.0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 33.5 .. 38.2 .. ­1.9 .. ­0.2 .. 2.1 16.7 2.7 Denmark 37.8 36.6 39.0 35.4 ­0.7 1.6 .. .. .. .. .. 10.8 Dominican Republic 12.0 16.9 11.7 16.0 0.6 1.0 ­0.0 ­1.0 ­0.6 ­0.0 20.7 4.5 Ecuador 18.7 .. 15.0 .. 3.8 .. .. .. .. .. .. .. Egypt, Arab Rep. 23.0 .. 27.8 .. ­5.7 .. ­0.7 .. 6.4 .. .. .. El Salvador .. 2.0 .. 2.5 .. ­0.3 .. 0.4 .. ­0.1 3.6 8.0 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 26.2 29.9 23.7 29.9 0.4 2.5 0.0 ­0.3 ­0.4 ­2.2 2.6 0.6 Ethiopia 17.3 19.0 27.1 26.6 ­9.8 ­5.0 2.8 2.8 7.0 2.2 101.4 10.8 Finland 30.5 .. 30.2 .. 0.2 .. 0.7 .. ­0.8 .. .. .. France 39.7 .. 41.8 .. ­2.1 .. 1.1 .. 1.0 .. .. .. Gabon 20.6 .. 20.2 .. 3.2 .. 2.7 .. ­5.8 .. .. .. Gambia, The 19.4 .. 23.6 .. ­0.8 .. .. .. .. .. .. .. Georgia .. 10.4 .. 10.9 .. 0.1 .. ­0.2 .. 0.0 67.0 16.6 Germany 25.9 .. 26.5 .. ­1.5 .. 0.5 0.6 1.0 ­0.1 20.0 .. Ghana 12.5 .. 13.2 .. 0.2 .. 1.3 .. ­1.5 .. .. .. Greece 27.8 .. 52.2 .. ­22.9 .. 1.6 .. 21.3 .. .. .. Guatemala .. .. .. .. .. .. .. .. .. .. .. .. Guinea 16.0 11.7 22.9 21.0 ­3.3 ­2.4 4.1 2.3 ­0.8 0.2 .. 37.1 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. 7.9 .. 10.5 .. ­2.3 .. ­0.2 .. 2.5 .. 6.1 222 2004 World Development Indicators ECONOMY 4.11 Central government finances Current Total Overall Financing Domestic Debt and revenue a expenditure budget balance from abroad financing interest (including payments grants) Total Interest debt % of % of current % of GDP % of GDP % of GDP % of GDP % of GDP GDP revenue 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 2001 2001 Honduras .. .. .. .. .. .. .. .. .. .. .. .. Hungary 52.9 37.1 52.1 41.5 0.8 ­3.8 ­0.5 3.3 ­0.3 0.5 53.1 12.9 India 12.6 13.0 16.3 17.3 ­7.6 ­4.7 0.6 0.1 7.1 4.6 57.7 37.1 Indonesia 18.8 21.2 18.4 24.8 0.4 ­1.2 0.7 0.5 ­1.1 0.7 45.2 21.6 Iran, Islamic Rep. 18.1 21.0 19.9 21.9 ­1.8 ­0.6 ­0.0 0.1 1.8 0.5 .. 0.7 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 33.6 .. 37.7 .. ­2.4 .. .. .. .. .. .. .. Israel 39.4 41.2 50.7 47.3 ­5.3 ­3.6 0.8 ­0.1 4.6 3.7 99.1 12.9 Italy 38.2 41.3 47.4 41.9 ­10.2 ­1.6 .. .. .. .. .. 15.5 Jamaica 26.6 33.9 24.3 38.8 3.6 ­2.7 .. .. .. .. 142.5 43.5 Japan 14.0 .. 15.3 .. ­1.5 .. .. .. .. .. .. .. Jordan 26.1 25.1 35.8 32.4 ­3.5 ­2.5 3.0 0.2 0.5 2.3 91.9 13.3 Kazakhstan .. 11.4 .. 14.6 .. ­0.4 .. 0.3 .. 0.1 17.7 10.0 Kenya 22.4 .. 27.5 .. ­3.8 .. 1.3 .. 4.5 .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 17.5 .. 16.2 .. ­0.7 .. ­0.2 .. 0.9 .. .. .. Kuwait 58.7 34.5 55.3 44.2 .. ­9.7 .. .. .. .. .. 4.0 Kyrgyz Republic .. 16.1 .. 17.7 .. 0.4 .. .. .. .. 99.3 8.8 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 25.8 .. 29.1 .. ­1.4 .. 2.2 .. ­0.8 14.8 3.9 Lebanon .. 19.5 .. 35.7 .. ­16.2 .. 8.1 .. 8.1 135.2 74.4 Lesotho 39.4 .. 51.7 .. ­1.1 .. 8.0 .. ­6.9 .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 31.9 24.7 28.9 26.6 1.4 ­0.4 .. 1.0 .. ­0.6 23.2 6.3 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar 11.6 11.7 16.0 17.1 ­1.1 ­2.4 2.1 1.7 ­1.2 0.5 .. 12.1 Malawi 19.8 .. 25.4 .. ­1.6 .. .. .. .. .. .. .. Malaysia 26.4 .. 29.3 .. ­2.0 .. ­0.7 .. 2.8 .. .. .. Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 24.3 20.3 24.3 24.5 ­0.4 0.9 ­0.5 ­2.9 0.9 1.9 32.5 13.6 Mexico 15.3 14.8 17.9 15.9 ­2.5 ­1.3 0.3 ­0.9 2.3 2.1 23.2 14.0 Moldova .. 21.3 .. 22.8 .. 1.1 .. ­2.7 .. 1.6 60.9 19.7 Mongolia 19.6 30.7 23.1 30.7 ­6.4 ­4.0 7.5 6.3 ­1.1 ­2.3 83.5 4.6 Morocco 26.4 29.6 28.8 32.5 ­2.2 ­2.5 3.9 ­1.5 ­1.6 4.0 72.8 16.5 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 10.5 5.3 16.0 8.7 ­5.1 ­3.4 0.0 ­0.0 5.1 3.4 .. .. Namibia 31.3 32.4 33.3 35.9 ­1.2 ­3.5 .. .. .. .. .. 7.0 Nepal 8.4 11.2 17.2 18.0 ­6.8 ­4.5 5.4 1.8 1.4 2.7 63.8 10.2 Netherlands 45.3 .. 49.8 .. ­4.3 .. ­0.3 .. 4.6 .. .. .. New Zealand 42.1 29.7 43.4 29.1 4.0 0.3 .. .. .. .. 30.4 6.9 Nicaragua 33.5 18.0 72.0 27.3 ­35.6 ­6.3 12.7 3.3 22.9 0.5 .. 13.4 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 42.2 40.1 41.1 35.4 0.5 ­3.8 ­0.6 3.7 0.0 0.1 19.9 3.8 Oman 38.9 27.0 39.5 29.9 ­0.8 ­4.2 ­3.9 3.1 4.7 1.1 19.9 4.6 Pakistan 19.1 15.6 22.4 21.6 ­5.4 ­4.7 2.3 2.2 3.1 2.5 90.0 58.4 Panama 25.6 22.5 23.7 23.5 3.0 0.3 ­3.4 1.4 0.4 ­1.7 .. 20.7 Papua New Guinea 25.2 23.0 34.7 31.4 ­3.5 ­2.8 0.4 1.7 3.0 1.0 63.9 19.0 Paraguay 12.3 17.2 9.4 18.5 2.9 ­0.8 ­0.9 .. ­2.1 .. .. 7.5 Peru 12.5 15.8 20.6 18.5 ­8.1 ­1.8 5.4 1.1 2.7 0.8 44.3 13.6 Philippines 16.2 15.3 19.6 19.2 ­3.5 ­4.0 0.4 0.6 3.1 3.4 64.9 31.2 Poland .. 29.6 .. 35.1 .. ­4.3 .. ­1.5 .. 5.7 38.8 9.5 Portugal 31.3 .. 37.6 .. ­4.4 .. ­1.3 .. 5.7 .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 223 4.11 Central government finances Current Total Overall Financing Domestic Debt and revenue a expenditure budget balance from abroad financing interest (including payments grants) Total Interest debt % of % of current % of GDP % of GDP % of GDP % of GDP % of GDP GDP revenue 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 2001 2001 Romania 34.4 26.7 33.8 30.4 0.9 ­3.0 0.0 0.8 ­0.9 2.2 .. 11.5 Russian Federation .. 26.8 .. 24.4 .. 3.4 .. ­2.6 .. ­0.9 48.8 9.5 Rwanda 10.8 .. 18.9 .. ­5.3 .. 2.5 .. 2.8 .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal .. 17.8 .. 21.8 .. ­2.0 .. 1.6 .. 0.4 72.8 5.0 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 5.6 7.1 8.3 20.9 ­2.5 ­8.5 0.5 1.1 2.0 7.4 247.4 81.8 Singapore 26.7 24.9 21.3 22.2 10.8 5.2 ­0.1 0.0 ­10.6 ­5.2 99.4 1.3 Slovak Republic .. 33.3 .. 39.1 .. ­3.2 .. 0.8 .. 2.4 42.2 9.3 Slovenia 39.8 37.4 38.6 38.9 0.3 ­1.0 0.1 0.4 ­0.4 0.6 26.4 4.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 26.3 27.7 30.1 28.8 ­4.1 ­1.0 ­0.0 3.4 4.1 ­2.4 46.8 17.5 Spain 29.3 .. 32.6 .. ­3.1 .. 0.7 .. 2.4 .. .. .. Sri Lanka 21.0 16.4 28.4 26.1 ­7.8 ­9.8 3.6 1.0 4.2 8.8 103.1 40.8 Sudan .. 8.0 .. 8.5 .. ­0.9 .. 0.1 .. 0.8 8.7 9.4 Swaziland 32.7 28.1 25.5 30.1 0.0 ­0.9 ­0.2 ­0.6 0.2 1.5 28.7 2.0 Sweden 41.3 38.0 38.1 37.9 0.9 0.1 ­0.3 ­5.3 ­0.7 5.2 .. 11.4 Switzerland 20.8 25.3 23.3 26.6 ­0.9 2.9 0.0 0.0 0.9 ­2.9 26.7 3.6 Syrian Arab Republic 21.9 23.9 21.8 23.2 0.3 0.7 .. 2.1 .. ­2.8 .. .. Tajikistan .. 11.5 .. 11.6 .. 0.1 .. 0.2 .. ­0.2 81.4 4.8 Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand 18.5 17.5 14.1 19.7 4.6 ­2.8 ­1.5 0.4 ­3.1 2.4 29.8 7.1 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 30.7 28.6 34.6 32.0 ­5.4 ­2.6 1.8 0.7 3.6 1.8 62.6 11.4 Turkey 13.7 29.1 17.4 49.5 ­3.0 ­19.6 ­0.0 ­1.9 3.0 21.5 99.9 85.1 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 10.9 .. 21.4 .. ­2.2 .. 3.3 .. ­1.2 39.6 10.7 Ukraine .. 26.6 .. 28.9 .. ­0.9 .. 0.2 .. 0.7 36.5 7.2 United Arab Emirates 1.6 3.4 11.5 9.9 0.4 0.0 0.0 0.0 ­0.4 ­0.0 .. .. United Kingdom 36.0 36.0 37.5 35.9 0.6 0.0 0.2 ­0.4 ­0.8 0.3 .. 7.7 United States 18.9 20.8 22.7 19.5 ­3.8 1.3 0.2 ­0.5 3.6 ­0.8 32.6 10.8 Uruguay 23.8 25.1 23.3 31.2 0.3 ­4.6 1.4 3.0 ­1.7 1.6 .. 9.7 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 23.7 21.2 20.7 25.1 0.0 ­4.3 1.0 0.3 ­1.0 4.0 .. 13.4 Vietnam .. 20.1 .. 24.3 .. ­2.9 .. 1.0 .. 1.9 .. 4.3 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 18.9 23.9 27.8 26.7 ­8.8 ­3.5 3.2 1.3 5.6 2.2 .. 9.8 Zambia .. .. .. .. .. .. .. .. .. .. .. .. Zimbabwe 24.1 .. 27.3 .. ­5.3 .. 0.9 .. 4.4 .. .. .. World 22.3 w .. w 25.3 w .. w ­2.8 w .. w 0.2 m 1.1 m 0.9 m 0.8 m .. m 11.3 m Low income 14.4 16.0 17.1 20.1 ­4.8 ­3.3 .. .. .. .. .. .. Middle income 16.8 17.7 21.4 21.3 ­2.7 ­3.2 .. 0.1 0.5 1.0 34.7 9.8 Lower middle income 16.9 .. 23.0 .. ­3.2 .. .. 0.4 1.1 1.2 53.9 11.4 Upper middle income 16.3 20.0 17.1 22.3 ­1.1 ­1.6 .. 1.0 0.2 1.1 23.4 9.3 Low & middle income 16.4 17.1 20.7 20.6 ­3.0 ­3.3 0.3 1.3 0.7 0.9 .. 11.8 East Asia & Pacific 11.7 10.8 13.8 15.0 ­0.9 ­3.7 0.4 1.4 2.8 1.4 52.3 13.9 Europe & Central Asia .. 28.3 .. 33.0 .. ­3.4 .. .. .. 0.6 40.5 9.3 Latin America & Carib. 18.6 .. 25.3 .. ­3.5 .. 0.2 .. 0.1 0.5 .. 13.4 Middle East & N. Africa .. .. .. .. .. .. 0.9 1.3 2.7 1.7 .. 12.2 South Asia 12.9 13.5 16.4 18.3 ­7.3 ­4.9 2.3 1.4 3.1 3.7 63.8 38.9 Sub-Saharan Africa 21.9 23.6 25.3 25.9 ­3.5 ­1.6 0.9 .. .. .. .. .. High income 23.5 .. 26.2 .. ­2.8 .. 0.2 .. 1.0 .. .. 7.5 Europe EMU 33.6 .. 37.2 .. ­4.0 .. 0.5 .. 3.1 .. .. .. a. Excluding grants. 224 2004 World Development Indicators ECONOMY 4.11 Central government finances About the data Definitions Tables 4.11­4.13 present an overview of the size government activities is usually incomplete. A key · Current revenue includes all revenue from taxes and role of central governments relative to national issue is the failure to include the quasi-fiscal opera- and current nontax revenues (other than grants), such economies. The International Monetary Fund's (IMF) tions of the central bank. Central bank losses arising as fines, fees, recoveries, and income from property Manual on Government Finance Statistics describes from monetary operations and subsidized financing or sales. · Total expenditure includes nonrepayable the government as the sector of the economy can result in sizable quasi-fiscal deficits. Such current and capital expenditures. It does not include responsible for "implementation of public policy deficits may also result from the operations of other government lending or repayments to the government through the provision of primarily nonmarket servic- financial intermediaries, such as public development or government acquisition of equity for public policy es and the transfer of income, supported mainly by finance institutions. Also missing from the data are purposes. · Overall budget balance is current and compulsory levies on other sectors" (1986, p. 3). governments' contingent liabilities for unfunded pen- capital revenue and official grants received, less total The definition of government generally excludes non- sion and national insurance plans. expenditure and lending minus repayments. financial public enterprises and public financial insti- Data on government revenues and expenditures are · Financing from abroad (obtained from nonresi- tutions (such as the central bank). collected by the IMF through questionnaires dents) and domestic financing (obtained from resi- A second edition of the Manual on Government distributed to member governments and by the dents) refer to the means by which a government Finance Statistics, harmonized with the 1993 Organisation for Economic Co-operation and Develop- provides financial resources to cover a budget deficit System of National Accounts, was released in 2001. ment. Despite the IMF's efforts to systematize and or allocates financial resources arising from a budget The new manual recommends an accrual accounting standardize the collection of public finance data, surplus. The data include all government liabilities-- method instead of the earlier cash-based method. statistics on public finance are often incomplete, other than those for currency issues or demand, time, However, most countries still follow the previous untimely, and not comparable across countries. or savings deposits with government--or claims on manual. Government finance statistics are reported in local others held by government, and changes in govern- Units of government meeting this definition exist at currency. The indicators here are shown as percent- ment holdings of cash and deposits. They exclude many levels, from local administrative units to the ages of GDP. Many countries report government government guarantees of the debt of others. · Debt highest level of national government. Inadequate sta- finance data according to fiscal years; see Primary is the entire stock of direct government fixed-term tistical coverage precludes the presentation of sub- data documentation for the timing of these years. For contractual obligations to others outstanding on a national data, however, making cross-country further discussion of government finance statistics, particular date. It includes domestic debt (such as comparisons potentially misleading. see About the data for tables 4.12 and 4.13. debt held by monetary authorities, deposit money Central government can refer to one of two banks, nonfinancial public enterprises, and house- accounting concepts: consolidated or budgetary. For holds) and foreign debt (such as debt to international most countries central government finance data have development institutions and foreign governments). It been consolidated into one account, but for others is the gross amount of government liabilities not only budgetary central government accounts are reduced by the amount of government claims against available. Countries reporting budgetary data are others. Because debt is a stock rather than a flow, it noted in Primary data documentation. Because budg- is measured as of a given date, usually the last day etary accounts do not necessarily include all central of the fiscal year. · Interest payments include inter- government units, the picture they provide of central est payments on government debt--including long- term bonds, long-term loans, and other debt 4.11a instruments--to both domestic and foreign residents. Some developing economies spend a large part of their current revenue on interest payments Central government interest payments as share of current revenue (%) 100 1995 80 2001 Data sources 60 The data on central government finances are from 40 the IMF's Government Finance Statistics Yearbook, 2003 and IMF data files. Each coun- 20 try's accounts are reported using the system of 0 common definitions and classifications in the Turkey nes IMF's Manual on Government Finance Statistics Leone Lanka India ra Lebanon Pakistan Jamaica Guinea gentina Sri Ar Colombia Philippi Indonesia (1986). See these sources for complete and Sier authoritative explanations of concepts, defini- Note: 2001 data refer to the most recent year for which data are available in 1998­2001. No data are available for Guinea for 1995. tions, and data sources. Source: International Monetary Fund, Government Finance Statistics data files. 2004 World Development Indicators 225 4.12 Central government expenditures Goods and Wages Interest Subsidies and Capital services and salaries a payments other current expenditure transfers % of total % of total % of total % of total % of total expenditure expenditure expenditure expenditure expenditure 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. Algeria .. 28 .. 20 .. 11 .. 34 .. 27 Angola .. .. .. .. .. .. .. .. .. .. Argentina 30 18 23 14 8 22 57 54 5 5 Armenia .. .. .. .. .. .. .. .. .. .. Australia 27 .. 2 .. 8 5 56 .. 9 .. Austria 25 25 10 10 9 8 57 61 9 5 Azerbaijan .. 31 .. 11 .. 2 .. 50 .. 17 Bangladesh .. 27 .. 18 .. 11 .. 25 .. 23 Belarus 37 24 2 11 2 2 46 61 16 13 Belgium 19 .. 14 .. 21 .. 56 .. 5 .. Benin .. .. .. .. .. .. .. .. .. .. Bolivia 63 41 36 24 6 8 16 37 15 14 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 51 .. 23 .. 2 .. 25 .. 21 .. Brazil 16 .. 9 .. 78 .. 39 .. 2 .. Bulgaria 35 28 3 8 10 11 52 51 3 11 Burkina Faso 60 .. 51 .. 6 .. 11 .. 23 .. Burundi 34 50 22 30 5 9 10 11 51 23 Cambodia .. .. .. .. .. .. .. .. .. .. Cameroon 51 52 39 32 5 19 13 15 26 14 Canada 21 19 9 9 20 13 57 66 2 2 Central African Republic .. .. .. .. .. .. .. .. .. .. Chad 41 .. 28 .. 2 .. 3 .. 56 .. Chile 28 27 18 19 10 2 51 56 11 15 China .. .. .. .. .. .. .. .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 26 19 18 14 10 18 42 41 22 22 Congo, Dem. Rep. 73 47 23 17 7 0 4 35 16 18 Congo, Rep. 56 32 49 20 22 23 20 8 2 37 Costa Rica 57 48 43 37 12 17 20 27 11 8 Côte d'Ivoire .. 56 .. 35 .. 20 .. 12 .. 11 Croatia 54 44 22 24 0 4 42 46 3 6 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. 14 .. 7 .. 2 .. 75 .. 9 Denmark 20 22 12 13 15 11 61 64 3 3 Dominican Republic 39 53 29 41 4 5 13 16 44 22 Ecuador 42 .. 38 .. 23 .. 16 .. 18 .. Egypt, Arab Rep. 42 .. 23 .. 14 .. 26 .. 17 .. El Salvador .. 62 .. 37 .. 6 .. 15 .. 22 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia 25 40 8 9 0 1 73 54 8 6 Ethiopia 77 52 40 18 5 8 9 31 16 19 Finland 20 .. 10 .. 3 .. 70 .. 7 .. France 26 .. 17 .. 5 .. 63 .. 6 .. Gabon 63 .. 37 .. 0 .. 6 .. 32 .. Gambia, The 41 .. 21 .. 16 .. 9 .. 34 .. Georgia .. 36 .. 11 .. 16 .. 48 .. 1 Germany 32 .. 8 .. 5 .. 58 .. 5 .. Ghana 50 .. 32 .. 11 .. 20 .. 19 .. Greece 31 .. 21 .. 20 .. 41 .. 8 .. Guatemala .. .. .. .. .. .. .. .. .. .. Guinea 37 29 18 19 7 21 4 8 53 36 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. 65 .. 42 .. 5 .. 8 .. 22 226 2004 World Development Indicators ECONOMY 4.12 Central government expenditures Goods and Wages Interest Subsidies and Capital services and salaries a payments other current expenditure transfers % of total % of total % of total % of total % of total expenditure expenditure expenditure expenditure expenditure 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras .. .. .. .. .. .. .. .. .. .. Hungary 27 18 6 9 6 12 64 57 4 13 India 24 22 11 9 22 28 43 41 11 9 Indonesia 23 18 16 8 13 19 21 39 43 24 Iran, Islamic Rep. 53 68 40 52 0 1 22 10 25 21 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 19 .. 14 .. 21 .. 54 .. 7 .. Israel 38 34 14 15 18 11 37 50 6 6 Italy 17 20 13 16 21 15 54 59 8 6 Jamaica 47 51 21 32 29 38 1 0 23 11 Japan 14 .. .. .. 19 .. 54 .. 13 .. Jordan 55 64 44 45 18 10 11 8 16 18 Kazakhstan .. 38 .. 8 .. 8 .. 42 .. 12 Kenya 51 .. 31 .. 19 .. 10 .. 20 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 35 .. 13 .. 4 .. 46 .. 15 .. Kuwait 62 58 31 35 0 3 20 26 18 13 Kyrgyz Republic .. 66 .. 29 .. 8 .. 15 .. 11 Lao PDR .. .. .. .. .. .. .. .. .. .. Latvia .. 25 .. 12 .. 3 .. 65 .. 6 Lebanon .. 30 .. 23 .. 41 .. 12 .. 17 Lesotho 40 .. 22 .. 11 .. 5 .. 45 .. Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania 12 46 6 16 .. 6 67 39 20 9 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar 37 36 25 23 9 8 9 6 43 38 Malawi 54 .. 23 .. 14 .. 8 .. 24 .. Malaysia 41 .. 26 .. 20 .. 16 .. 24 .. Mali .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. Mauritius 47 44 37 32 15 11 22 28 17 17 Mexico 25 24 18 17 45 13 17 52 14 10 Moldova .. 25 .. 12 .. 18 .. 54 .. 3 Mongolia 30 35 7 10 1 5 56 46 13 14 Morocco 48 46 35 36 16 15 8 16 28 22 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. 29 39 Namibia 73 63 46 44 1 6 10 17 15 14 Nepal .. .. .. .. .. 6 .. .. .. .. Netherlands 15 .. 9 .. 9 .. 70 .. 6 .. New Zealand 19 53 12 .. 15 7 64 37 2 3 Nicaragua 43 33 23 16 0 9 14 24 4 34 Niger .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. Norway 19 21 8 8 6 4 69 70 5 5 Oman 76 77 22 27 6 4 7 6 11 13 Pakistan 44 23 .. 4 25 42 20 27 12 7 Panama 64 47 49 34 8 20 26 24 2 9 Papua New Guinea 61 56 34 29 11 14 18 24 11 6 Paraguay 54 52 36 46 10 7 19 25 17 16 Peru 30 41 17 19 37 12 25 36 8 12 Philippines 44 49 29 28 34 25 7 18 16 8 Poland .. 16 .. 8 .. 8 .. 72 .. 4 Portugal 38 .. 27 .. 18 .. 33 .. 12 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 227 4.12 Central government expenditures Goods and Wages Interest Subsidies and Capital services and salaries a payments other current expenditure transfers % of total % of total % of total % of total % of total expenditure expenditure expenditure expenditure expenditure 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania 26 33 12 12 0 10 57 45 17 12 Russian Federation .. 37 .. 11 .. 10 .. 44 .. 9 Rwanda 53 .. 29 .. 5 .. 16 .. 33 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegal .. 42 .. 24 .. 4 .. 24 .. 29 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. Sierra Leone 77 60 35 46 18 28 1 6 8 11 Singapore 51 50 27 24 14 1 12 22 24 26 Slovak Republic .. 25 .. 14 .. 8 .. 55 .. 12 Slovenia 40 41 20 23 1 4 52 49 7 7 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 53 28 23 13 14 17 23 50 10 5 Spain 19 .. 13 .. 9 .. 63 .. 9 .. Sri Lanka 33 38 17 21 23 26 23 18 21 18 Sudan .. 74 .. 34 .. 9 .. 7 .. 10 Swaziland 62 57 42 32 3 2 11 22 24 19 Sweden 15 18 6 6 11 11 72 69 2 2 Switzerland 31 28 5 4 3 3 61 64 5 5 Syrian Arab Republic .. .. .. .. .. .. .. .. 27 36 Tajikistan .. 44 .. 13 .. 5 .. 35 .. 17 Tanzania .. .. .. .. .. .. .. .. .. .. Thailand 60 55 35 30 13 6 9 17 18 22 Togo .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. Tunisia 34 41 28 34 10 10 35 25 22 23 Turkey 52 24 38 18 18 50 16 20 13 7 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 30 .. 9 .. 5 .. 17 .. 47 Ukraine .. 30 .. 13 .. 7 .. 57 .. 6 United Arab Emirates 88 78 33 35 0 0 10 18 1 4 United Kingdom 30 29 13 6 9 8 52 59 10 4 United States 28 21 10 8 15 11 49 63 8 5 Uruguay 35 26 20 15 8 8 50 62 7 4 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 31 26 23 19 16 11 37 42 16 21 Vietnam .. .. .. .. .. 4 .. .. .. 34 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 64 54 55 39 8 9 6 18 33 17 Zambia .. .. .. .. .. .. .. .. .. .. Zimbabwe 56 .. 37 .. 16 .. 18 .. 10 .. World 39 m 37 m 23 m 18 m 10 m 9 m 23 m 31 m 13 m 13 m Low income .. .. .. .. .. .. .. .. .. .. Middle income 42 37 25 18 11 8 23 42 16 12 Lower middle income 43 41 29 24 13 10 21 23 17 15 Upper middle income 38 26 23 15 9 8 26 54 11 9 Low & middle income .. 39 .. 21 .. 9 .. 26 .. 16 East Asia & Pacific 42 .. 27 .. 10 11 16 .. 21 24 Europe & Central Asia .. 30 .. 12 .. 8 .. 51 .. 9 Latin America & Carib. 35 41 23 19 10 9 25 36 11 14 Middle East & N. Africa 53 50 35 35 10 11 11 14 23 19 South Asia 33 23 .. 9 23 27 23 27 12 9 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. High income 25 29 13 .. 11 7 56 59 7 5 Europe EMU 20 .. 13 .. 9 .. 57 .. 7 .. Note: Components include expenditures financed by grants in kind and other cash adjustments to total expenditure. a. Part of goods and services. 228 2004 World Development Indicators ECONOMY 4.12 Central government expenditures About the data Definitions Government expenditures include all nonrepayable by grants in kind and other cash adjustments (which · Goods and services include all government pay- payments, whether current or capital, requited or may be positive or negative) are not shown separate- ments in exchange for goods and services, whether unrequited. Total central government expenditure as ly. For further discussion of government finance sta- in the form of wages and salaries to employees or presented in the International Monetary Fund's (IMF) tistics, see About the data for tables 4.11 and 4.13. other purchases of goods and services. · Wages Government Finance Statistics Yearbook is a more and salaries consist of all payments in cash, but not limited measure of general government consumption in kind (such as food and housing), to employees in than that shown in the national accounts (see table return for services rendered, before deduction of 4.10) because it excludes consumption expenditures withholding taxes and employee contributions to by state and local governments. At the same time, social security and pension funds. · Interest pay- the IMF's concept of central government expenditure ments are payments made to domestic sectors and is broader than the national accounts definition to nonresidents for the use of borrowed money. because it includes government gross capital forma- (Repayment of principal is shown as a financing item, tion and transfer payments. and commission charges are shown as purchases of Expenditures can be measured either by function services.) Interest payments do not include pay- (health, defense, education) or by economic type ments by government as guarantor or surety of inter- (interest payments, wages and salaries, purchases est on the defaulted debts of others, which are of goods and services). Functional data are often classified as government lending. · Subsidies and incomplete, and coverage varies by country other current transfers include all unrequited, non- because functional responsibilities stretch across repayable transfers on current account to private and levels of government for which no data are avail- public enterprises and the cost to the public of able. Defense expenditures, usually the central gov- covering the cash operating deficits on sales to the ernment's responsibility, are shown in table 5.8. public by departmental enterprises. · Capital expen- For more information on education expenditures, diture is spending to acquire fixed capital assets, see table 2.10; for more on health expenditures, land, intangible assets, government stocks, and see table 2.14. nonmilitary, nonfinancial assets. Also included are The classification of expenditures by economic capital grants. type can also be problematic. For example, the dis- tinction between current and capital expenditure may be arbitrary, and subsidies to state-owned enterpris- es or banks may be disguised as capital financing. Subsidies may also be hidden in special contractual pricing for goods and services. Expenditure shares may not sum to 100 percent because adjustments to total expenditures financed 4.12a Interest payments are a large part of government expenditure for some developing economies Central government interest payments as share of total expenditure (%) 50 1995 40 2001 30 Data sources The data on central government expenditures are 20 from the IMF's Government Finance Statistics Yearbook, 2003 and IMF data files. Each coun- 10 try's accounts are reported using the system of 0 common definitions and classifications in the e Turkey India Rep. IMF's Manual on Government Finance Statistics Leone Lanka Pakistan Lebanon Jamaica gentina Guinea d'Ivoir ra Sri Ar Philippines (1986). See these sources for complete and Sier Congo, Côte authoritative explanations of concepts, defini- Note: Data for 2001 refer to the most recent year for which data are available in 1999­2001. No data are available for Guinea for 1995. tions, and data sources. Source: International Monetary Fund, Government Finance Statistics data files. 2004 World Development Indicators 229 4.13 Central government revenues Taxes on Social Taxes on Taxes on Other Nontax income, profits, security goods and international taxes revenue and capital taxes services trade gains % of total % of total % of total % of total % of total % of total current revenue current revenue current revenue current revenue current revenue current revenue 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. 70 .. 0 .. 8 .. 11 .. 1 .. 10 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2 18 44 23 20 36 14 4 10 9 10 9 Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 65 .. 0 .. 21 .. 4 .. 2 .. 8 8 Austria 19 25 37 40 25 25 1 0 9 4 9 6 Azerbaijan .. 22 .. 22 .. 40 .. 9 .. 2 .. 5 Bangladesh .. 11 .. 0 .. 40 .. 23 .. 1 .. 25 Belarus 12 10 32 38 40 36 5 6 9 3 2 7 Belgium 35 .. 35 .. 24 .. 0 .. 3 .. 3 .. Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 5 7 9 11 31 48 7 5 11 9 38 19 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 39 .. 0 .. 2 .. 13 .. 0 .. 46 .. Brazil 20 .. 31 .. 24 .. 2 .. 6 .. 16 .. Bulgaria 30 13 23 24 18 37 2 2 1 1 27 24 Burkina Faso 23 .. 0 .. 30 .. 33 .. 7 .. 8 .. Burundi 21 21 6 7 37 44 24 20 1 1 10 6 Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 18 21 6 0 21 26 14 28 4 4 28 20 Canada 51 52 16 21 17 17 3 1 0 0 13 9 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad 19 .. 0 .. 39 .. 24 .. 10 .. 8 .. Chile 12 20 8 7 43 46 12 5 3 4 21 18 China 31 6 0 0 18 75 14 10 0 4 37 6 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 29 34 0 0 30 39 20 7 1 5 19 14 Congo, Dem. Rep. 27 15 1 0 18 22 46 32 1 23 7 8 Congo, Rep. 26 5 0 0 16 20 21 8 2 1 35 66 Costa Rica 10 14 29 32 27 40 23 5 1 0 14 10 Côte d'Ivoire 16 20 7 9 27 21 29 42 11 5 9 4 Croatia 17 8 52 33 24 46 3 6 0 1 3 5 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 20 .. 44 .. 29 .. 1 .. 1 .. 4 Denmark 37 35 4 4 41 45 0 0 3 4 15 12 Dominican Republic 21 18 4 4 23 25 40 43 1 2 10 8 Ecuador 62 .. 0 .. 22 .. 13 .. 1 .. 2 .. Egypt, Arab Rep. 19 .. 15 .. 14 .. 14 .. 11 .. 27 .. El Salvador .. 15 .. 14 .. 36 .. 6 .. 11 .. 18 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 27 13 28 36 41 42 1 0 1 0 2 9 Ethiopia 29 22 0 0 25 17 15 26 2 3 30 32 Finland 31 .. 9 .. 47 .. 1 .. 3 .. 9 .. France 17 .. 44 .. 28 .. 0 .. 3 .. 7 .. Gabon 24 .. 1 .. 23 .. 18 .. 2 .. 32 .. Gambia, The 13 .. 0 .. 37 .. 43 .. 1 .. 6 .. Georgia .. 4 .. 20 .. 62 .. 6 .. 0 .. 9 Germany 16 .. 53 .. 24 .. 0 .. 0 .. 6 .. Ghana 23 .. 0 .. 30 .. 39 .. 0 .. 8 .. Greece 22 .. 29 .. 43 .. 0 .. 8 .. 8 .. Guatemala .. .. .. .. .. .. .. .. .. .. .. .. Guinea 9 10 0 1 15 5 47 77 0 4 28 4 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 230 2004 World Development Indicators ECONOMY 4.13 Central government revenues Taxes on Social Taxes on Taxes on Other Nontax income, profits, security goods and international taxes revenue and capital taxes services trade gains % of total % of total % of total % of total % of total % of total current revenue current revenue current revenue current revenue current revenue current revenue 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Honduras .. .. .. .. .. .. .. .. .. .. .. .. Hungary 18 21 29 31 31 34 6 2 0 2 16 10 India 15 29 0 0 36 29 29 18 0 0 20 24 Indonesia 62 31 0 2 24 25 6 3 3 3 5 36 Iran, Islamic Rep. 10 17 8 9 4 6 13 7 4 1 60 60 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 37 .. 15 .. 38 .. 0 .. 3 .. 7 .. Israel 36 40 9 15 33 29 2 1 4 3 14 12 Italy 37 36 29 30 29 24 0 0 2 3 3 7 Jamaica 36 30 0 0 30 33 12 7 9 7 13 23 Japan 69 .. 0 .. 17 .. 1 .. 7 .. 5 .. Jordan 16 12 0 0 21 36 27 17 7 10 29 24 Kazakhstan .. 24 .. 0 .. 53 .. 7 .. 0 .. 16 Kenya 30 .. 0 .. 43 .. 16 .. 1 .. 10 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 34 .. 5 .. 35 .. 12 .. 5 .. 9 .. Kuwait 1 1 0 6 0 0 2 3 0 0 97 90 Kyrgyz Republic .. 17 .. 0 .. 58 .. 3 .. 0 .. 23 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 14 .. 35 .. 42 .. 1 .. 0 .. 7 Lebanon .. 11 .. 0 .. 20 .. 28 .. 13 .. 28 Lesotho 11 .. 0 .. 21 .. 57 .. 0 .. 11 .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 20 11 28 31 40 48 1 1 3 0 8 9 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar 13 15 0 0 19 28 48 52 2 2 18 3 Malawi 37 .. 0 .. 33 .. 16 .. 1 .. 13 .. Malaysia 31 .. 1 .. 20 .. 18 .. 3 .. 28 .. Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 14 12 4 5 21 37 46 25 6 6 9 15 Mexico 31 34 13 10 56 62 6 4 2 1 11 10 Moldova .. 3 .. 32 .. 49 .. 6 .. 0 .. 10 Mongolia 24 8 14 17 31 41 17 8 0 1 15 25 Morocco 24 24 4 5 38 36 18 16 4 3 13 16 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 18 20 0 0 28 33 14 4 0 0 41 44 Namibia 34 32 0 0 25 21 27 37 1 1 13 8 Nepal 11 18 0 0 36 36 31 27 5 3 17 16 Netherlands 31 .. 35 .. 22 .. 0 .. 3 .. 9 .. New Zealand 53 64 0 0 27 27 2 2 3 1 15 6 Nicaragua 17 13 9 19 35 52 19 8 8 .. 13 8 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 16 20 24 23 34 36 1 0 1 1 24 20 Oman 23 21 0 0 1 1 2 3 1 2 73 73 Pakistan 9 23 0 0 30 38 31 12 0 6 30 21 Panama 17 18 20 19 17 .. 12 .. 3 4 31 37 Papua New Guinea 37 54 0 0 14 11 25 27 3 4 20 5 Paraguay 9 9 0 0 21 37 20 10 24 2 25 41 Peru 5 22 7 8 50 52 17 9 19 5 7 14 Philippines 28 40 0 0 31 26 25 17 3 4 13 13 Poland .. 17 .. 31 .. 37 .. 2 .. 1 .. 12 Portugal 23 .. 25 .. 34 .. 2 .. 4 .. 12 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 231 4.13 Central government revenues Taxes on Social Taxes on Taxes on Other Nontax income, profits, security goods and international taxes revenue and capital taxes services trade gains % of total % of total % of total % of total % of total % of total current revenue current revenue current revenue current revenue current revenue current revenue 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Romania 19 10 23 41 33 30 1 3 15 1 10 14 Russian Federation .. 9 .. 26 .. 35 .. 14 .. 0 .. 16 Rwanda 18 .. 7 .. 34 .. 26 .. 4 .. 12 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal .. 22 .. 0 .. 33 .. 37 .. 4 .. 4 Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 31 26 0 0 23 22 40 49 0 0 5 4 Singapore 26 33 0 0 16 19 2 2 14 9 43 38 Slovak Republic .. 17 .. 38 .. 31 .. 1 .. 1 .. 11 Slovenia 12 14 47 35 27 37 8 2 0 5 5 6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 51 54 2 2 34 33 4 3 2 3 8 5 Spain 32 .. 38 .. 22 .. 2 .. 0 .. 5 .. Sri Lanka 11 15 0 0 46 59 29 11 5 4 10 12 Sudan .. 15 .. 0 .. 35 .. 29 .. 1 .. 20 Swaziland 30 25 0 0 11 14 47 52 2 4 10 5 Sweden 18 14 31 33 29 27 1 0 9 15 13 11 Switzerland 15 16 51 47 23 25 1 1 3 4 7 7 Syrian Arab Republic 31 38 0 0 31 19 7 10 7 6 24 27 Tajikistan .. 3 .. 18 .. 53 .. 16 .. 1 .. 9 Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand 24 28 0 3 41 40 22 10 4 0 8 18 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 13 20 13 17 19 38 28 11 5 4 22 9 Turkey 43 35 0 0 32 40 6 1 3 7 15 17 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 20 .. 0 .. 29 .. 50 .. 1 .. 1 Ukraine .. 12 .. 36 .. 29 .. 4 .. 0 .. 18 United Arab Emirates 0 0 2 1 36 51 0 0 0 0 62 48 United Kingdom 39 40 17 17 28 31 0 0 7 7 9 5 United States 52 55 35 33 3 3 2 1 1 1 8 6 Uruguay 7 15 27 23 36 39 10 3 12 8 5 7 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 64 20 4 3 3 25 7 7 0 3 22 42 Vietnam .. 27 .. 0 .. 34 .. 18 .. 5 .. 16 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 26 18 0 0 10 9 17 10 5 2 43 61 Zambia .. .. .. .. .. .. .. .. .. .. .. .. Zimbabwe 45 .. 0 .. 26 .. 17 .. 1 .. 10 .. World 23 m 18 m 4 m 5 m 27 m 34 m 13 m 7 m 3 m 2 m 13 m 12 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income 21 15 4 14 25 37 14 5 3 2 16 14 Lower middle income 23 20 1 3 28 36 15 9 4 3 16 14 Upper middle income 17 17 10 31 21 37 12 3 2 1 18 10 Low & middle income 19 17 0 2 28 35 17 9 3 2 13 12 East Asia & Pacific 31 25 0 0 24 32 18 9 3 2 20 11 Europe & Central Asia .. 13 .. 31 .. 40 .. 3 .. 1 .. 10 Latin America & Carib. 17 15 9 11 27 39 13 6 3 3 14 18 Middle East & N. Africa 21 19 2 0 17 19 15 14 5 3 28 28 South Asia 11 21 0 0 36 37 30 15 3 3 18 18 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 32 26 17 19 28 27 1 1 3 3 9 9 Europe EMU 31 .. 35 .. 28 .. 0 .. 3 .. 7 .. Note: Components may not sum to 100 percent as a result of adjustments to tax revenue. 232 2004 World Development Indicators ECONOMY 4.13 Central government revenues About the data Definitions The International Monetary Fund (IMF) classifies and duties levied on goods and services). This dis- · Taxes on income, profits, and capital gains are government transactions as receipts or payments tinction may be a useful simplification, but it has no levied on the actual or presumptive net income of and according to whether they are repayable or non- particular analytical significance except with respect individuals, on the profits of enterprises, and on cap- repayable. If nonrepayable, they are classified as to the capacity to fix tax rates. ital gains, whether realized or not, on land, securities, capital (meant to be used in production for more Social security taxes do not reflect compulsory pay- or other assets. Intragovernmental payments are than a year) or current and as requited (involving ments made by employers to provident funds or other eliminated in consolidation. · Social security taxes payment in return for a benefit or service) or unre- agencies with a like purpose. Similarly, expenditures include employer and employee social security contri- quited. Revenues include all nonrepayable receipts from such funds are not reflected in government butions and those of self-employed and unemployed (other than grants), the most important of which are expenditure (see table 4.12). The revenue shares people. · Taxes on goods and services include gen- taxes. Grants are unrequited, nonrepayable, non- shown in this table may not sum to 100 percent eral sales and turnover or value added taxes, selec- compulsory receipts from other governments or because adjustments to tax revenues are not shown. tive excises on goods, selective taxes on services, from international organizations. Transactions are For further discussion of taxes and tax policies, taxes on the use of goods or property, and profits of generally recorded on a cash rather than an accrual see About the data for table 5.6. For further discus- fiscal monopolies. · Taxes on international trade basis. Measuring the accumulation of arrears on sion of government revenues and expenditures, see include import duties, export duties, profits of export revenues or payments on an accrual basis would About the data for tables 4.11 and 4.12. or import monopolies, exchange profits, and typically result in a higher deficit. Transactions with- exchange taxes. · Other taxes include employer pay- in a level of government are not included, but trans- roll or labor taxes, taxes on property, and taxes not actions between levels are included. In some cases allocable to other categories. They may include nega- the government budget may include transfers used tive values that are adjustments (for example, for to finance the deficits of autonomous, extrabud- taxes collected on behalf of state and local govern- getary agencies. ments and not allocable to individual tax categories). The IMF's Manual on Government Finance · Nontax revenue includes requited, nonrepayable Statistics (1986) describes taxes as compulsory, receipts for public purposes--such as fines, adminis- unrequited payments made to governments by indi- trative fees, or entrepreneurial income from govern- viduals, businesses, or institutions. Taxes tradition- ment ownership of property--and voluntary, ally have been classified as either direct (those unrequited, nonrepayable receipts other than from levied directly on the income or profits of individuals government sources. It does not include proceeds of and corporations) or indirect (sales and excise taxes grants and borrowing, funds arising from the repay- ment of previous lending by governments, incurrence 4.13a of liabilities, and proceeds from the sale of capital Poor countries rely more on indirect taxes assets. Indirect taxes as share of current revenue, 2000­01 (%) 100 80 60 Data sources The data on central government revenues are 40 from the IMF's Government Finance Statistics Yearbook, 2003 and IMF data files. Each coun- try's accounts are reported using the system of 20 common definitions and classifications in the IMF's Manual on Government Finance Statistics (1986). The IMF receives additional information 0 0 1,000 10,000 50,000 from the Organisation for Economic Co-operation GNI per capita ($) and Development on the tax revenues of some of Low-income economies Middle-income economies High-income economies its members. See the IMF sources for complete Low-income countries tend to rely on indirect taxes on international trade and on goods and services, while high-income and authoritative explanations of concepts, defi- countries prefer to tax income, property, and social security contributions. But in all groups there are exceptions. nitions, and data sources. Source: International Monetary Fund, Government Finance Statistics data files. 2004 World Development Indicators 233 4.14 Monetary indicators and prices Money and Claims on Claims on GDP Consumer Food quasi money private sector governments implicit price index price index and other deflator public entities M2 annual growth annual growth average annual average annual average annual annual % growth as % of M2 as % of M2 % growth % growth % growth 1990 2002 1990 2002 1990 2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 5.9 .. 2.9 .. 2.4 ­0.4 28.6 .. 21.6 .. 31.2 Algeria 11.4 24.8 12.2 2.6 3.2 ­5.6 8.3 15.7 9.1 14.0 9.7 14.7 Angola .. 158.6 .. 37.8 .. 28.8 5.9 584.3 .. 562.9 .. 223.1 Argentina 1,113.3 19.7 1,444.7 ­9.5 1,573.2 143.2 391.1 4.5 390.6 7.2 486.5 6.5 Armenia .. 34.0 .. 1.0 .. ­6.0 .. 142.2 .. 44.7 .. 81.5 Australia 12.8 13.2 15.3 10.8 ­2.2 1.0 7.2 1.8 7.9 2.3 7.4 2.9 Austria a .. .. .. .. .. .. 3.3 1.8 3.2 2.1 2.7 1.5 Azerbaijan .. 14.6 .. 9.7 .. 24.6 .. 78.6 .. 109.1 .. 109.8 Bangladesh 10.4 13.3 9.2 12.0 ­0.2 0.8 9.8 3.8 .. 5.1 10.8 4.8 Belarus .. 53.5 .. 35.7 .. 28.8 .. 283.6 .. 258.0 2.4 141.1 Belgium a .. .. .. .. .. .. 4.1 1.9 4.2 1.9 4.0 1.2 Benin 28.6 ­7.0 ­1.3 5.5 12.4 0.1 1.7 7.5 .. 7.2 ­3.5 10.5 Bolivia 52.8 ­6.9 40.8 ­1.6 18.0 8.1 326.9 7.5 322.5 7.5 321.8 6.8 Bosnia and Herzegovina .. 9.4 .. 18.6 .. ­0.6 .. 3.4 .. .. .. .. Botswana ­14.0 ­1.1 12.6 12.9 ­51.9 117.2 13.6 9.0 10.0 9.8 10.1 9.6 Brazil 1,289.2 23.0 1,566.4 11.8 3,093.6 29.9 284.0 139.8 285.6 134.1 314.0 ­15.7 Bulgaria 53.8 12.2 1.9 14.7 83.1 ­1.7 1.8 83.8 6.3 94.0 1.8 89.9 Burkina Faso ­0.5 0.6 3.6 11.7 ­1.5 ­12.1 3.3 4.8 3.4 4.9 0.7 5.0 Burundi 9.6 29.5 15.4 29.0 ­6.9 7.9 4.4 12.8 7.1 15.3 6.1 .. Cambodia .. 31.1 .. 5.6 .. ­2.2 .. 3.7 .. 4.7 .. 4.8 Cameroon ­1.7 15.9 0.9 6.1 ­3.0 ­2.4 5.6 4.5 8.7 5.5 .. 4.1 Canada 7.8 5.6 9.2 5.9 0.6 1.6 4.6 1.5 5.3 1.8 4.6 1.7 Central African Republic ­3.7 ­4.3 ­1.6 6.2 2.3 2.6 7.9 4.1 3.2 4.6 2.0 5.5 Chad ­2.4 26.6 1.3 8.9 ­17.3 ­3.7 1.4 7.1 0.6 7.7 ­5.3 ­0.7 Chile 24.2 ­0.3 21.7 14.8 16.3 0.2 20.7 7.1 20.6 7.7 20.7 6.8 China 28.9 19.4 26.5 13.4 1.5 1.4 5.7 5.5 .. 6.7 8.8 11.3 Hong Kong, China 8.5 0.5 7.9 ­2.6 ­1.0 3.3 7.8 2.5 .. 4.1 6.3 3.5 Colombia 33.0 13.6 107.1 8.2 23.9 8.1 24.7 19.0 22.7 18.0 24.6 16.4 Congo, Dem. Rep. 195.4 40.0 18.0 3.8 429.7 ­36.2 62.9 731.4 57.1 691.7 .. .. Congo, Rep. 18.5 13.1 5.1 ­15.7 ­12.6 3.0 0.5 8.5 0.9 7.9 4.3 8.1 Costa Rica 27.5 20.9 7.3 16.6 8.2 6.8 23.6 15.6 23.0 14.6 16.0 4.5 Côte d'Ivoire ­2.6 30.0 ­3.9 ­0.3 ­3.0 1.3 2.8 7.8 5.4 6.3 .. .. Croatia .. 9.6 .. 19.9 .. 3.1 .. 61.3 304.1 61.3 124.6 58.7 Cuba .. .. .. .. .. .. .. 1.1 .. .. .. .. Czech Republic .. 6.9 .. ­12.3 .. 11.6 .. 9.9 .. 6.7 .. 0.8 Denmark 6.5 4.2 3.0 13.9 ­3.1 1.6 5.8 2.0 5.6 2.1 4.8 2.1 Dominican Republic 42.5 10.3 19.1 15.9 1.1 3.6 21.6 8.9 22.4 8.3 25.4 7.8 Ecuador 48.9 21.4 17.2 26.0 ­27.4 ­20.3 ­5.4 3.8 35.8 38.6 40.7 37.8 Egypt, Arab Rep. 28.7 12.6 6.3 3.4 25.3 11.5 13.7 7.4 17.4 7.5 22.0 6.5 El Salvador ­17.5 ­3.1 ­24.2 3.0 10.2 ­4.5 16.3 6.3 19.6 7.2 21.5 7.9 Eritrea .. 20.3 .. 3.8 .. 17.8 .. 9.6 .. .. .. .. Estonia 76.5 11.2 27.6 12.7 ­6.8 1.2 2.3 40.3 .. 16.7 .. ­20.1 Ethiopia 18.5 13.3 0.3 ­0.6 21.7 1.3 3.5 5.6 4.0 4.0 3.8 ­3.6 Finland a .. .. .. .. .. .. 6.7 2.0 6.2 1.6 5.8 ­0.3 France a .. .. .. .. .. .. 5.8 1.5 5.8 1.6 5.7 1.5 Gabon 3.3 5.7 0.7 7.2 ­20.6 ­9.6 1.8 5.3 5.1 4.6 4.9 4.3 Gambia, The 8.4 35.3 7.8 13.9 ­35.4 0.2 17.9 4.6 20.0 4.0 20.3 3.7 Georgia .. 17.9 .. 14.4 .. 2.7 1.9 225.2 .. 17.7 .. 15.9 Germany a .. .. .. .. .. .. 2.7 1.7 2.2 b 2.1 .. 0.5 Ghana 13.3 48.9 4.9 13.6 9.9 10.6 42.1 26.4 39.1 27.4 33.1 25.0 Greece a .. .. .. .. .. .. 19.3 8.0 18.7 7.7 18.0 6.7 Guatemala 25.8 11.8 15.0 4.7 0.5 3.8 14.6 9.6 14.0 9.4 22.1 9.2 Guinea ­17.4 19.7 13.1 2.7 2.9 36.0 .. 5.0 .. .. .. 9.2 Guinea-Bissau 574.6 22.8 90.5 ­0.3 460.7 4.5 57.4 25.6 .. 27.3 .. .. Haiti 2.5 22.8 ­0.6 11.8 0.4 11.7 7.3 20.1 5.2 19.8 4.1 .. 234 2004 World Development Indicators ECONOMY 4.14 Monetary indicators and prices Money and Claims on Claims on GDP Consumer Food quasi money private sector governments implicit price index price index and other deflator public entities M2 annual growth annual growth average annual average annual average annual annual % growth as % of M2 as % of M2 % growth % growth % growth 1990 2002 1990 2002 1990 2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 Honduras 21.4 13.7 13.0 4.9 ­10.5 ­0.5 5.7 17.1 6.3 17.2 5.2 16.7 Hungary 29.2 14.9 23.0 13.8 69.7 9.3 8.9 17.4 9.6 18.1 9.5 17.4 India 15.1 16.8 5.9 10.7 10.5 4.9 8.2 7.2 8.6 8.3 8.8 7.9 Indonesia 44.6 4.5 66.9 6.3 ­6.7 ­0.8 8.5 15.6 8.3 14.1 8.7 16.7 Iran, Islamic Rep. 18.0 27.5 14.7 19.2 5.8 7.8 14.4 25.0 18.2 23.6 16.2 24.3 Iraq .. .. .. .. .. .. 10.3 .. .. .. .. .. Ireland a .. .. .. .. .. .. 6.6 3.8 6.8 2.6 6.0 2.9 Israel 19.4 6.9 18.5 8.6 4.9 ­3.2 101.1 8.9 101.7 8.3 102.4 7.4 Italy a .. .. .. .. .. .. 10.0 3.5 9.1 3.4 8.2 2.8 Jamaica 21.5 12.0 12.5 8.5 ­16.0 5.6 19.9 19.7 15.1 19.7 16.1 18.6 Japan 8.2 3.4 9.7 ­4.4 1.5 1.7 1.8 ­0.3 1.7 0.5 1.5 0.3 Jordan 8.3 8.6 4.7 1.7 1.0 3.1 4.3 2.7 5.7 3.1 4.7 3.4 Kazakhstan .. 30.1 .. 33.0 .. ­14.6 .. 141.0 .. 45.6 .. 106.1 Kenya 20.1 11.7 8.0 1.6 21.5 5.0 9.1 12.8 11.2 13.3 10.0 12.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 17.2 11.0 36.1 22.4 ­1.2 ­1.1 6.5 4.2 4.9 4.7 5.0 4.9 Kuwait ­100.0 4.8 ­89.7 9.0 ­23.0 1.5 ­2.8 2.6 2.9 1.9 1.6 1.5 Kyrgyz Republic .. 33.9 .. 3.9 .. 12.6 .. 82.5 .. 18.7 .. 45.9 Lao PDR 7.8 37.6 3.6 ­0.0 7.0 ­5.6 37.6 28.8 .. 30.0 .. .. Latvia .. 19.9 .. 25.7 .. 5.7 ­0.0 36.1 .. 21.7 .. 18.2 Lebanon 55.1 7.3 27.6 1.5 18.5 ­1.0 .. 13.4 .. .. .. 19.8 Lesotho 8.4 8.8 6.8 6.8 ­14.9 15.2 12.1 9.6 13.6 8.9 13.5 9.8 Liberia ­100.0 2.0 ­39.8 7.7 ­271.0 535.8 2.9 53.8 .. .. .. .. Libya 19.0 1.1 2.0 ­0.3 15.0 10.2 1.2 .. 7.5 6.3 .. .. Lithuania .. 16.9 .. 13.3 .. ­1.2 .. 53.1 .. 22.7 2.7 39.5 Macedonia, FYR .. 15.7 .. 3.2 .. ­14.3 .. 56.4 .. 6.5 .. .. Madagascar 4.5 8.0 23.8 0.3 ­14.8 9.8 17.1 17.0 16.6 16.8 15.7 19.1 Malawi 11.1 20.7 15.5 3.7 ­12.9 42.2 15.1 32.2 16.9 32.6 16.4 33.2 Malaysia 10.6 3.1 20.8 5.8 ­1.2 3.2 1.7 3.5 2.6 3.3 2.2 4.6 Mali ­4.9 27.9 0.1 14.1 ­13.4 ­2.8 4.5 6.7 .. 4.6 2.7 4.8 Mauritania 11.5 8.9 20.2 35.2 1.5 ­95.2 8.4 5.6 7.1 5.7 .. 6.3 Mauritius 21.2 12.5 10.8 5.8 0.8 1.2 9.4 6.0 6.9 6.6 7.8 7.5 Mexico 81.9 4.6 48.5 5.8 13.6 9.6 71.5 17.3 73.8 17.7 73.1 17.3 Moldova 358.0 38.6 53.3 21.9 469.1 6.9 .. 89.4 .. 18.5 .. 110.5 Mongolia 31.6 42.0 40.2 28.0 38.5 ­7.2 ­1.6 45.4 .. 39.0 .. .. Morocco 21.5 6.4 44.2 2.3 ­4.9 1.1 7.1 2.5 7.0 3.3 6.7 3.1 Mozambique 37.2 21.6 22.0 ­0.5 ­5.1 7.0 38.3 26.8 .. 26.6 .. .. Myanmar 37.7 34.6 12.8 16.7 24.2 18.8 12.2 24.6 11.5 25.4 11.9 27.5 Namibia 30.3 6.9 15.4 20.4 ­4.2 ­5.4 13.7 10.3 12.6 9.5 13.9 8.8 Nepal 18.5 3.8 5.7 10.7 7.3 8.1 11.1 7.2 10.2 7.2 10.5 8.4 Netherlands a .. .. .. .. .. .. 1.5 2.3 2.0 2.5 1.3 1.3 New Zealand 12.5 7.7 4.2 9.8 ­1.7 1.0 10.5 1.7 11.0 1.9 9.8 1.7 Nicaragua 7,677.8 13.3 4,932.9 9.8 12,679.2 2.0 422.3 30.7 535.7 24.6 69.2 22.6 Niger ­4.1 ­0.5 ­5.1 7.2 1.4 3.5 1.9 5.5 0.7 5.4 ­1.5 6.3 Nigeria 32.7 21.6 7.8 8.5 27.1 28.8 16.7 25.0 18.9 27.8 22.5 25.9 Norway 5.6 7.6 5.0 8.8 ­0.6 4.4 5.4 3.2 7.4 2.2 7.8 1.8 Oman 10.0 5.2 9.6 0.4 ­10.9 ­3.6 ­3.6 2.0 .. ­0.1 0.9 0.1 Pakistan 11.6 16.8 5.9 2.5 7.7 ­1.0 6.7 9.1 6.3 8.6 6.6 8.8 Panama 36.6 ­0.3 0.8 ­8.7 ­25.7 3.1 1.9 3.2 1.4 1.1 1.5 0.7 Papua New Guinea 4.3 4.0 ­0.9 ­3.1 8.8 18.1 5.3 7.4 5.6 10.0 4.6 9.6 Paraguay 54.4 3.1 32.0 0.2 ­9.2 9.8 24.4 11.3 21.9 12.0 24.9 10.5 Peru 6,384.9 5.1 2,123.7 ­0.4 2,129.5 ­2.2 220.2 20.4 246.1 20.9 221.8 18.7 Philippines 22.4 10.4 15.6 0.5 3.4 4.7 14.9 8.0 13.4 7.7 14.1 6.7 Poland 160.1 ­2.8 158.7 2.7 ­20.6 ­0.7 .. 19.8 50.9 21.0 52.4 17.9 Portugal a .. .. .. .. .. .. 17.9 4.9 17.1 4.1 16.7 3.4 Puerto Rico .. .. .. .. .. .. 3.5 3.1 .. .. 2.7 9.8 2004 World Development Indicators 235 4.14 Monetary indicators and prices Money and Claims on Claims on GDP Consumer Food quasi money private sector governments implicit price index price index and other deflator public entities M2 annual growth annual growth average annual average annual average annual annual % growth as % of M2 as % of M2 % growth % growth % growth 1990 2002 1990 2002 1990 2002 1980­90 1990­2002 1980­90 1990­2002 1980­90 1990­2002 Romania 26.4 38.2 .. 13.3 0.0 1.1 1.5 84.3 .. 85.5 4.3 69.1 Russian Federation .. 33.9 .. 20.9 .. 7.2 .. 121.1 .. 75.2 .. 122.1 Rwanda 5.6 12.6 ­10.0 6.3 26.8 ­8.8 4.0 11.7 3.9 13.4 6.4 12.7 Saudi Arabia 4.6 15.2 ­4.5 5.7 4.2 0.2 ­3.8 1.7 ­0.8 0.7 ­0.2 0.7 Senegal ­4.8 8.2 ­8.4 3.5 ­5.3 ­8.3 6.5 4.0 6.2 4.6 5.3 5.1 Serbia and Montenegro .. .. .. .. .. .. .. 57.1 .. .. .. 4.1 Sierra Leone 74.0 29.6 4.9 7.5 228.7 ­1.8 60.3 26.7 72.4 24.5 .. .. Singapore 20.0 ­0.3 13.7 ­8.8 ­4.9 ­4.0 2.0 0.7 1.6 1.5 1.0 1.5 Slovak Republic .. 4.1 .. 8.2 .. ­14.1 1.8 9.7 .. 8.3 1.6 14.7 Slovenia 123.0 12.3 96.1 9.1 ­10.4 ­3.7 .. 10.2 .. 10.8 129.5 21.3 Somalia .. .. .. .. .. .. 49.7 .. .. .. .. .. South Africa 11.4 14.5 13.7 6.7 1.8 4.1 15.5 9.1 14.8 8.2 15.2 9.4 Spain a .. .. .. .. .. .. 9.3 3.8 9.0 3.6 9.3 3.1 Sri Lanka 19.9 13.4 16.2 10.3 4.4 ­1.2 11.0 9.1 10.9 9.8 11.0 10.3 Sudan 48.8 30.3 12.6 17.9 29.4 6.4 38.4 51.9 37.6 66.8 .. .. Swaziland 0.6 13.1 20.5 16.7 ­13.1 42.1 10.7 12.1 14.6 9.2 13.3 11.9 Sweden 0.8 1.9 13.4 12.4 ­12.1 2.1 7.3 1.9 7.0 1.8 8.2 ­0.0 Switzerland 0.8 5.7 11.7 0.4 1.0 0.2 3.4 1.1 2.9 1.4 3.1 0.8 Syrian Arab Republic 26.1 18.5 3.4 0.7 11.4 ­0.7 15.3 6.9 23.2 4.9 25.0 3.8 Tajikistan .. 40.5 .. 26.1 .. 17.8 2.5 175.2 .. .. .. 477.3 Tanzania 41.9 25.1 22.6 10.2 80.6 ­4.0 .. 18.6 31.0 17.8 32.0 20.1 Thailand 26.7 1.4 30.0 11.9 ­4.0 ­0.1 3.9 3.6 3.5 4.3 2.7 4.9 Togo 9.5 ­2.2 1.8 ­4.0 6.9 ­6.4 4.8 6.3 2.5 7.2 1.1 1.7 Trinidad and Tobago 6.2 5.7 2.7 2.9 ­1.9 2.5 2.4 5.9 10.7 5.4 14.6 12.7 Tunisia 7.6 4.4 5.9 5.4 1.8 ­0.0 7.4 4.1 7.4 4.0 8.3 4.2 Turkey 53.2 29.1 42.9 3.3 0.4 29.3 45.3 71.8 44.9 75.5 18.3 31.8 Turkmenistan .. 83.3 .. 10.8 .. 59.0 .. 266.6 .. .. .. .. Uganda 60.2 25.0 0.0 5.5 ­0.9 28.1 113.8 9.5 102.5 8.5 .. 8.5 Ukraine .. 42.3 .. 29.2 .. 1.5 .. 183.4 .. 102.6 2.0 99.5 United Arab Emirates ­8.2 11.0 1.3 9.6 ­4.8 ­1.6 0.8 2.8 .. .. .. .. United Kingdom 10.5 5.1 13.1 10.1 1.0 1.1 5.8 2.8 5.8 2.7 4.5 1.7 United States 4.9 4.3 1.1 5.7 0.6 2.1 3.8 2.0 4.2 2.6 3.9 2.4 Uruguay 118.5 28.2 56.2 27.1 25.8 41.5 62.7 25.5 61.1 27.5 62.0 25.1 Uzbekistan .. .. .. .. .. .. .. 184.2 .. .. .. .. Venezuela, RB 64.9 15.8 17.6 0.6 45.3 14.5 19.3 40.8 20.9 43.2 35.1 39.5 Vietnam .. 13.3 .. 16.7 .. 2.7 222.2 12.5 .. 2.9 .. .. West Bank and Gaza .. .. .. .. .. .. .. 8.8 .. .. .. .. Yemen, Rep. 11.3 17.5 1.4 2.4 10.2 ­12.4 .. 19.8 .. 32.6 .. .. Zambia 47.9 31.1 22.8 2.3 195.2 27.0 42.2 44.7 48.5 42.8 48.7 Zimbabwe 15.1 191.7 13.5 106.2 5.0 45.4 11.6 32.3 13.8 36.1 15.1 40.1 Note: The inconsistencies in the growth rates of the GDP deflator and consumer and food price indexes are mainly due to uneven coverage of the time period. a. As members of the European Monetary Union, these countries share a single currency, the euro. b. Data prior to 1990 refer to the Federal Republic of Germany before unification. 236 2004 World Development Indicators ECONOMY 4.14 Monetary indicators and prices About the data Definitions Money and the financial accounts that record the financial derivatives and the net liabilities of the · Money and quasi money comprise the sum of cur- supply of money lie at the heart of a country's finan- banking system can also be difficult. The quality of rency outside banks, demand deposits other than those cial system. There are several commonly used defi- commercial bank reporting also may be adversely of the central government, and the time, savings, and nitions of the money supply. The narrowest, M1, affected by delays in reports from bank branches, foreign currency deposits of resident sectors other than encompasses currency held by the public and especially in countries where branch accounts are the central government. This definition of the money demand deposits with banks. M2 includes M1 plus not computerized. Thus the data in the balance supply, often called M2, corresponds to lines 34 and 35 time and savings deposits with banks that require a sheets of commercial banks may be based on pre- in the International Monetary Fund's (IMF) International notice for withdrawal. M3 includes M2 as well as var- liminary estimates subject to constant revision. This Financial Statistics (IFS). The change in money supply is ious money market instruments, such as certificates problem is likely to be even more serious for non- measured as the difference in end-of-year totals relative of deposit issued by banks, bank deposits denomi- bank financial intermediaries. to M2 in the preceding year. · Claims on private sector nated in foreign currency, and deposits with financial Controlling inflation is one of the primary goals of (IFS line 32d) include gross credit from the financial sys- institutions other than banks. However defined, monetary policy and is intimately linked to the growth tem to individuals, enterprises, nonfinancial public enti- money is a liability of the banking system, distin- in money supply. Inflation is measured by the rate of ties not included under net domestic credit, and guished from other bank liabilities by the special role increase in a price index, but actual price change can financial institutions not included elsewhere. · Claims it plays as a medium of exchange, a unit of account, also be negative. Which index is used depends on on governments and other public entities (IFS line and a store of value. which set of prices in the economy is being examined. 32an + 32b + 32bx + 32c) usually comprise direct cred- The banking system's assets include its net for- The GDP deflator reflects changes in prices for total it for specific purposes, such as financing the govern- eign assets and net domestic credit. Net domestic gross domestic product. The most general measure ment budget deficit; loans to state enterprises; credit includes credit extended to the private sector of the overall price level, it takes into account advances against future credit authorizations; and and general government and credit extended to the changes in government consumption, capital forma- purchases of treasury bills and bonds, net of deposits nonfinancial public sector in the form of investments tion (including inventory appreciation), international by the public sector. Public sector deposits with the in short- and long-term government securities and trade, and the main component, household final con- banking system also include sinking funds for the loans to state enterprises; liabilities to the public sumption expenditure. The GDP deflator is usually service of debt and temporary deposits of government and private sectors in the form of deposits with the derived implicitly as the ratio of current to constant revenues. · GDP implicit deflator measures the aver- banking system are netted out. Net domestic credit price GDP, resulting in a Paasche index. It is defective age annual rate of price change in the economy as a also includes credit to banking and nonbank financial as a general measure of inflation for use in policy whole for the periods shown. · Consumer price index institutions. because of the long lags in deriving estimates and reflects changes in the cost to the average consumer of Domestic credit is the main vehicle through which because it is often only an annual measure. acquiring a basket of goods and services that may be changes in the money supply are regulated, with cen- Consumer price indexes are more current and pro- fixed or may change at specified intervals, such as year- tral bank lending to the government often playing the duced more frequently. They are also constructed ly. The Laspeyres formula is generally used. · Food most important role. The central bank can regulate explicitly, based on surveys of the cost of a defined price index is a subindex of the consumer price index. lending to the private sector in several ways--for basket of consumer goods and services. example, by adjusting the cost of the refinancing facil- Nevertheless, consumer price indexes should be Data sources ities it provides to banks, by changing market interest interpreted with caution. The definition of a house- The monetary, financial, and consumer price index rates through open market operations, or by control- hold, the basket of goods chosen, and the geograph- data are published by the IMF in its monthly ling the availability of credit through changes in the ic (urban or rural) and income group coverage of International Financial Statistics and annual reserve requirements imposed on banks and ceilings consumer price surveys can all vary widely across International Financial Statistics Yearbook. The on the credit provided by banks to the private sector. countries. In addition, the weights are derived from IMF collects data on the financial systems of its Monetary accounts are derived from the balance household expenditure surveys, which, for budgetary member countries. The World Bank receives data sheets of financial institutions--the central bank, reasons, tend to be conducted infrequently in devel- from the IMF in electronic files that may contain commercial banks, and nonbank financial intermedi- oping countries, leading to poor comparability over more recent revisions than the published sources. aries. Although these balance sheets are usually reli- time. Although useful for measuring consumer price The GDP deflator data are from the World Bank's able, they are subject to errors of classification, inflation within a country, consumer price indexes are national accounts files. The food price index data valuation, and timing and to differences in account- of less value in making comparisons across coun- are from the United Nations Statistics Division's ing practices. For example, whether interest income tries. Food price indexes, like consumer price index- Statistical Yearbook and Monthly Bulletin of is recorded on an accrual or a cash basis can make es, should be interpreted with caution because of the Statistics. The discussion of monetary indicators a substantial difference, as can the treatment of high variability across countries in the items covered. draws from an IMF publication by Marcello Caiola, nonperforming assets. Valuation errors typically The least-squares method is used to calculate the A Manual for Country Economists (1995). Also see arise with respect to foreign exchange transactions, growth rates of the GDP implicit deflator, consumer the IMF's Monetary and Financial Statistics particularly in countries with flexible exchange rates price index, and food price index. Manual (2000) for guidelines for the presentation or in those that have undergone a currency devalua- of monetary and financial statistics. tion during the reporting period. The valuation of 2004 World Development Indicators 237 4.15 Balance of payments current account Goods and services Net income Net Current Total current account reserves a transfers balance $ millions Exports Imports $ millions $ millions $ millions $ millions 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. 638 .. Albania 354 915 485 2,076 ­2 128 15 625 ­118 ­408 .. 866 Algeria 13,462 .. 10,106 .. ­2,268 .. 333 .. 1,420 .. 2,703 25,151 Angola 3,992 8,573 3,385 7,796 ­765 ­1,531 ­77 91 ­236 ­1,431 .. 376 Argentina 14,800 28,654 6,846 13,011 ­4,400 ­6,465 998 413 4,552 9,592 6,222 10,492 Armenia .. 698 .. 1,107 .. 88 .. 173 .. ­148 1 440 Australia 49,843 82,975 53,056 88,635 ­13,176 ­11,541 439 ­64 ­15,950 ­17,264 19,319 21,567 Austria 63,694 108,865 61,580 104,594 ­942 ­2,082 ­6 ­1,615 1,166 575 17,228 13,182 Azerbaijan .. 2,667 .. 3,121 .. ­385 .. 70 .. ­768 0 722 Bangladesh 2,064 6,972 3,960 9,192 ­116 ­281 1,613 3,242 ­398 742 660 1,722 Belarus .. 9,264 .. 9,787 .. ­29 .. 174 .. ­378 .. 619 Belgium b 138,605 213,811 135,098 203,106 2,316 2,907 ­2,197 ­4,220 3,627 9,392 23,789 c 14,698 c Benin 364 555 454 790 ­25 ­17 97 126 ­18 ­126 69 616 Bolivia 977 1,534 1,086 2,049 ­249 ­201 159 369 ­199 ­347 511 893 Bosnia and Herzegovina .. 1,417 .. 4,751 .. 256 .. 939 .. ­2,139 .. 1,321 Botswana 2,005 2,651 1,987 2,229 ­106 ­279 69 ­47,313 ­19 ­47,169 3,331 5,474 Brazil 35,170 69,967 28,184 61,863 ­11,608 ­18,191 799 2,390 ­3,823 ­7,696 9,200 37,835 Bulgaria 6,950 8,286 8,027 9,287 ­758 ­228 125 549 ­1,710 ­679 670 4,846 Burkina Faso 349 273 758 687 0 ­26 332 116 ­77 ­324 305 313 Burundi 89 39 318 147 ­15 ­12 174 118 ­69 ­3 112 59 Cambodia 314 2,350 507 2,693 ­21 ­168 120 447 ­93 ­64 .. 913 Cameroon 2,508 .. 2,475 .. ­558 .. ­26 .. ­551 .. 37 640 Canada 149,538 301,274 149,118 269,721 ­19,388 ­17,514 ­796 871 ­19,764 14,909 23,530 37,189 Central African Republic 220 .. 410 .. ­22 .. 123 .. ­89 .. 123 127 Chad 271 .. 488 .. ­21 .. 192 .. ­46 .. 132 223 Chile 10,221 22,300 9,166 20,744 ­1,737 ­2,536 198 426 ­485 ­553 6,784 15,344 China 57,374 365,395 46,706 328,012 1,055 ­14,946 274 12,984 11,997 35,422 34,476 297,739 Hong Kong, China .. 243,633 .. 230,153 .. 2,806 .. ­1,896 .. 14,390 24,656 111,919 Colombia 8,679 14,160 6,858 15,392 ­2,305 ­2,812 1,026 2,406 542 ­1,639 4,869 10,844 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 261 .. Congo, Rep. 1,488 2,454 1,282 1,618 ­460 ­860 3 ­10 ­251 ­34 10 35 Costa Rica 1,963 7,141 2,346 7,724 ­233 ­532 192 169 ­424 ­946 525 1,497 Côte d'Ivoire 3,503 5,747 3,445 3,869 ­1,091 ­629 ­181 ­482 ­1,214 767 21 1,863 Croatia .. 10,545 .. 12,709 .. ­518 .. 1,076 .. ­1,606 167 5,885 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 45,562 .. 47,159 .. ­3,800 .. 912 .. ­4,485 .. 23,707 Denmark 48,902 82,768 41,415 72,394 ­5,708 ­2,771 ­408 ­2,612 1,372 4,991 11,226 27,719 Dominican Republic 1,832 8,238 2,233 10,166 ­249 ­1,135 371 2,188 ­280 ­875 69 475 Ecuador 3,262 6,173 2,519 7,742 ­1,210 ­1,306 107 1,654 ­360 ­1,222 1,009 1,004 Egypt, Arab Rep. 9,895 16,438 14,091 19,508 ­1,022 ­267 7,545 3,960 2,327 622 3,620 14,076 El Salvador 973 3,799 1,624 5,898 ­132 ­287 631 2,003 ­152 ­384 595 1,784 Eritrea .. 187 .. 552 .. ­6 450 286 188 ­85 .. 30 Estonia 664 5,504 711 6,119 ­13 ­331 97 144 36 ­802 198 1,003 Ethiopia 597 1,066 1,271 2,038 ­69 ­23 449 845 ­294 ­150 55 966 Finland 31,180 51,347 33,456 39,952 ­3,735 ­542 ­952 ­648 ­6,962 10,205 10,415 9,825 France 285,389 392,362 283,238 365,576 ­3,896 12,823 ­8,199 ­13,865 ­9,944 25,744 68,291 61,697 Gabon 2,730 3,399 1,812 2,022 ­617 ­718 ­134 ­75 168 584 279 144 Gambia, The 168 .. 192 .. ­11 .. 59 .. 23 .. 55 107 Georgia .. 975 .. 1,398 .. 19 .. 174 .. ­230 .. 198 Germany 474,654 721,017 428,619 643,327 20,593 ­5,997 ­21,954 ­25,108 44,674 46,586 104,547 89,143 Ghana 983 2,570 1,506 3,325 ­111 ­176 411 900 ­223 ­31 309 636 Greece 13,018 30,091 19,564 41,997 ­1,709 ­1,957 4,718 3,458 ­3,537 ­10,405 4,721 9,432 Guatemala 1,568 3,769 1,812 6,622 ­196 ­298 227 1,958 ­213 ­1,193 362 2,373 Guinea 829 976 953 999 ­149 ­69 70 46 ­203 ­46 80 171 Guinea-Bissau 26 .. 88 .. ­22 .. 39 .. ­45 .. 18 103 Haiti 318 .. 515 .. ­18 .. 193 .. ­22 .. 10 82 Data for Taiwan, China 74,172 151,058 67,015 130,241 4,362 7,353 ­596 ­2,492 10,923 25,678 77,653 166,304 238 2004 World Development Indicators ECONOMY 4.15 Balance of payments current account Goods and services Net income Net Current Total current account reserves a transfers balance $ millions Exports Imports $ millions $ millions $ millions $ millions 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 1,032 2,451 1,127 3,420 ­237 ­159 280 862 ­51 ­266 47 1,531 Hungary 12,035 42,599 11,017 44,104 ­1,427 ­1,586 787 447 379 ­2,644 1,185 10,383 India 22,911 77,602 29,527 83,850 ­3,257 ­3,886 2,837 14,790 ­7,036 4,656 5,637 71,608 Indonesia 29,295 65,826 27,511 52,706 ­5,190 ­7,048 418 1,751 ­2,988 7,823 8,657 32,032 Iran, Islamic Rep. 19,741 35,554 22,292 31,228 378 ­217 2,500 457 327 12,645 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 26,786 114,169 24,576 91,385 ­4,955 ­24,514 2,384 805 ­361 ­925 5,362 5,475 Israel 17,312 38,505 20,228 42,682 ­1,981 ­3,599 5,060 6,549 163 ­1,226 6,598 24,083 Italy 219,971 313,931 218,573 300,688 ­14,712 ­14,550 ­3,164 ­5,434 ­16,479 ­6,741 88,595 55,622 Jamaica 2,217 3,229 2,390 4,828 ­430 ­606 291 1,086 ­312 ­1,119 168 1,645 Japan 323,692 461,293 297,306 409,691 22,492 65,769 ­4,800 ­4,923 44,078 112,447 87,828 469,618 Jordan 2,511 4,283 3,569 6,186 ­214 111 1,045 2,260 ­227 468 1,139 4,116 Kazakhstan .. 11,615 .. 11,394 .. ­1,031 .. 113 .. ­696 .. 3,136 Kenya 2,228 3,295 2,705 3,670 ­418 ­122 368 580 ­527 84 236 1,068 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 73,295 190,696 76,360 183,977 ­87 451 1,150 ­1,078 ­2,003 6,092 14,916 121,498 Kuwait 8,268 17,015 7,169 14,037 7,738 3,360 ­4,951 ­2,145 3,886 4,192 2,929 10,078 Kyrgyz Republic .. 636 .. 697 .. ­60 .. 86 .. ­35 .. 317 Lao PDR 102 477 212 560 ­1 ­34 56 .. ­55 ­82 8 216 Latvia 1,090 3,828 997 4,728 2 ­7 96 260 191 ­647 .. 1,327 Lebanon 511 2,399 2,836 7,065 622 817 1,818 1,000 115 ­2,848 4,210 10,405 Lesotho 100 390 754 792 433 161 286 121 65 ­119 72 406 Liberia .. 163 .. 173 .. ­85 .. 43 .. ­52 0 3 Libya 11,468 .. 8,960 .. 174 .. ­481 .. 2,201 .. 7,225 15,892 Lithuania .. 7,492 .. 8,258 .. ­183 .. 229 .. ­721 107 2,420 Macedonia, FYR .. 1,364 .. 2,156 .. ­31 .. 498 .. ­325 .. 790 Madagascar 471 710 809 1,001 ­161 ­75 234 96 ­265 ­270 92 363 Malawi 443 472 549 795 ­80 ­38 99 161 ­86 ­201 142 170 Malaysia 32,665 108,261 31,765 91,696 ­1,872 ­6,595 102 ­2,780 ­870 7,190 10,659 34,623 Mali 420 1,157 830 1,211 ­37 ­241 225 136 ­221 ­310 198 594 Mauritania 471 .. 520 .. ­46 .. 86 .. ­10 .. 59 400 Mauritius 1,722 2,965 1,916 2,805 ­23 10 97 89 ­119 259 761 1,249 Mexico 48,805 173,503 51,915 186,339 ­8,316 ­11,436 3,975 10,268 ­7,451 ­14,004 10,217 50,671 Moldova .. 871 .. 1,283 .. 165 .. 155 .. ­92 0 269 Mongolia 493 708 1,096 946 ­44 ­5 7 138 ­640 ­105 23 398 Morocco 6,239 12,199 7,783 13,314 ­988 ­738 2,336 3,330 ­196 1,477 2,338 10,375 Mozambique 229 1,153 996 1,782 ­97 ­113 448 217 ­415 ­657 233 841 Myanmar d 319 2,741 603 2,968 ­192 ­367 39 286 ­436 ­309 410 549 Namibia 1,220 1,309 1,584 1,485 37 28 354 278 28 130 50 323 Nepal 422 884 834 1,446 14 ­4 109 390 ­289 ­165 354 1,070 Netherlands 159,304 262,898 147,652 244,133 ­620 ­2,441 ­2,943 ­6,208 8,089 10,116 34,401 18,948 New Zealand 11,683 19,625 11,699 18,770 ­1,576 ­3,181 138 57 ­1,453 ­2,269 4,129 3,739 Nicaragua 392 909 682 1,970 ­217 ­203 202 377 ­305 ­888 166 453 Niger 533 .. 728 .. ­54 .. 14 .. ­236 .. 226 134 Nigeria 14,550 17,151 6,909 15,526 ­2,738 ­2,090 85 1,466 4,988 1,001 4,129 7,567 Norway 47,078 79,358 38,910 52,290 ­2,700 465 ­1,476 ­2,385 3,992 25,148 15,788 21,088 Oman 5,577 11,423 3,342 6,988 ­254 ­588 ­874 .. 1,106 2,315 1,784 3,174 Pakistan 6,835 12,261 10,205 12,645 ­1,084 ­2,190 2,794 6,445 ­1,661 3,871 1,046 8,796 Panama 4,438 7,574 4,193 7,724 ­255 ­217 219 213 209 ­154 344 1,183 Papua New Guinea 1,381 2,098 1,509 1,594 ­103 ­230 156 13 ­76 286 427 343 Paraguay 2,514 2,859 2,169 2,715 2 34 43 116 390 294 675 641 Peru 4,120 9,192 4,087 9,932 ­1,733 ­1,509 281 1,043 ­1,419 ­1,206 1,891 9,721 Philippines 11,430 37,439 13,967 38,295 ­872 4,550 714 503 ­2,695 4,197 2,036 16,136 Poland 19,037 56,777 15,095 63,177 ­3,386 ­1,887 2,511 3,280 3,067 ­5,007 4,674 29,784 Portugal 21,554 36,864 27,146 45,857 ­96 ­3,135 5,507 3,315 ­181 ­8,813 20,579 17,701 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 239 4.15 Balance of payments current account Goods and services Net income Net Current Total current account reserves a transfers balance $ millions Exports Imports $ millions $ millions $ millions $ millions 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 6,380 16,223 9,901 18,825 161 ­459 106 1,536 ­3,254 ­1,525 1,374 8,372 Russian Federation .. 121,214 .. 85,188 .. ­6,117 .. ­5 .. 29,905 .. 48,326 Rwanda 143 132 354 435 ­16 ­19 143 195 ­85 ­126 44 244 Saudi Arabia 47,445 76,862 43,939 49,287 7,979 96 ­15,637 ­15,975 ­4,152 11,696 13,437 22,186 Senegal 1,453 1,549 1,840 2,066 ­129 ­168 153 207 ­363 ­478 22 637 Serbia and Montenegro .. 3,241 .. 6,857 .. ­111 .. 2,343 .. ­1,384 .. .. Sierra Leone 210 .. 215 .. ­71 .. 7 .. ­69 .. 5 85 Singapore 67,489 158,075 64,953 137,122 1,006 ­1,145 ­421 ­1,104 3,122 18,704 27,748 82,021 Slovak Republic .. 17,174 .. 18,843 .. ­459 .. 120 .. ­694 .. 9,196 Slovenia 7,900 12,764 6,930 12,452 ­38 ­71 46 134 978 375 112 7,063 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 27,742 35,571 21,016 32,034 ­4,271 ­2,691 ­321 ­556 2,134 290 2,583 7,817 Spain 83,595 188,552 100,870 196,780 ­3,533 ­9,890 2,799 2,176 ­18,009 ­15,942 57,238 40,303 Sri Lanka 2,293 5,967 2,965 7,103 ­167 ­251 541 1,123 ­298 ­264 447 1,652 Sudan 499 1,996 877 2,971 ­136 ­617 141 666 ­372 ­926 11 441 Swaziland 658 1,072 768 1,177 59 48 102 10 51 ­46 216 276 Sweden 70,560 105,298 70,490 89,903 ­4,473 ­1,907 ­1,936 ­2,864 ­6,339 10,624 20,324 19,171 Switzerland 96,927 129,854 96,388 111,148 8,746 11,485 ­2,329 ­4,179 6,955 26,011 61,284 61,276 Syrian Arab Republic 5,030 8,228 2,955 6,341 ­401 ­925 88 485 1,762 1,062 .. .. Tajikistan 185 708 238 868 0 ­58 .. 184 ­53 ­34 .. 90 Tanzania 538 1,568 1,474 2,224 ­185 ­16 562 420 ­559 ­251 193 1,529 Thailand 29,229 82,114 35,870 73,741 ­853 ­1,340 213 618 ­7,281 7,650 14,258 38,903 Togo 663 457 847 683 ­32 ­33 132 77 ­84 ­169 358 205 Trinidad and Tobago 2,289 4,521 1,427 4,183 ­397 ­510 ­6 33 459 416 513 2,049 Tunisia 5,203 9,538 6,039 10,431 ­455 ­984 828 1,131 ­463 ­746 867 2,365 Turkey 21,042 54,617 25,652 55,046 ­2,508 ­4,549 4,493 3,496 ­2,625 ­1,482 7,626 28,348 Turkmenistan 1,238 3,138 857 2,703 0 ­111 66 68 447 ­74 .. .. Uganda 178 720 686 1,643 ­48 ­136 293 707 ­263 ­353 44 934 Ukraine .. 23,351 .. 21,494 .. ­604 .. 1,921 .. 3,174 469 4,414 United Arab Emirates .. .. .. .. .. .. .. .. .. .. 4,891 15,355 United Kingdom 239,226 404,794 264,090 436,634 ­5,154 31,255 ­8,794 ­13,828 ­38,811 ­14,414 43,146 42,819 United States 535,260 974,107 616,120 1,392,145 28,560 ­3,968 ­26,660 ­58,852 ­78,960 ­480,859 173,094 157,763 Uruguay 2,158 2,708 1,659 2,525 ­321 10 8 69 186 262 1,446 772 Uzbekistan .. 2,985 .. 2,721 .. ­145 .. 120 .. 239 .. .. Venezuela, RB 18,806 27,716 9,451 17,474 ­774 ­2,654 ­302 ­165 8,279 7,423 12,733 12,107 Vietnam .. 19,654 .. 21,458 .. ­721 .. 1,921 .. ­604 .. 4,121 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,490 3,787 2,170 3,867 ­372 ­766 1,790 1,384 739 538 441 4,428 Zambia 1,360 1,080 1,897 1,585 ­437 ­108 380 32 ­594 ­553 201 535 Zimbabwe 2,012 .. 2,001 .. ­263 .. 112 .. ­140 .. 295 132 World 4,301,369 t 7,985,963 t 4,330,919 t 7,986,659 t Low income 118,587 273,925 135,542 279,942 Middle income 632,588 1,702,940 593,980 1,591,324 Lower middle income 356,798 1,017,575 356,720 952,348 Upper middle income 272,381 683,839 236,047 636,247 Low & middle income 752,042 1,976,803 730,892 1,871,332 East Asia & Pacific 166,647 691,152 165,402 620,489 Europe & Central Asia .. 452,206 .. 443,440 Latin America & Carib. 168,326 403,563 145,500 399,939 Middle East & N. Africa 127,663 210,917 134,989 178,855 South Asia 34,818 104,364 47,813 115,016 Sub-Saharan Africa 79,306 111,723 72,835 110,384 High income 3,542,141 6,007,813 3,583,425 6,113,233 Europe EMU 1,517,749 2,447,680 1,480,370 2,284,894 a. International reserves Including gold valued at London gold price. b. Includes Luxembourg. c. Excludes Luxembourg. d. Data are in fiscal years. 240 2004 World Development Indicators ECONOMY 4.15 Balance of payments current account About the data Definitions The balance of payments records an economy's trans- residence and ownership, and the exchange rate · Exports and imports of goods and services com- actions with the rest of the world. Balance of payments used to value transactions--contribute to net errors prise all transactions between residents of an econo- accounts are divided into two groups: the current and omissions. In addition, smuggling and other ille- my and the rest of the world involving a change in account, which records transactions in goods, servic- gal or quasi-legal transactions may be unrecorded or ownership of general merchandise, goods sent for es, income, and current transfers, and the capital and misrecorded. For further discussion of issues relat- processing and repairs, nonmonetary gold, and serv- financial account, which records capital transfers, ing to the recording of data on trade in goods and ices. · Net income refers to receipts and payments acquisition or disposal of nonproduced, nonfinancial services, see About the data for tables 4.4­4.8. of employee compensation for nonresident workers, assets, and transactions in financial assets and liabili- The concepts and definitions underlying the data in and investment income (receipts and payments on ties. The table presents data from the current account the table are based on the fifth edition of the direct investment, portfolio investment, and other with the addition of gross international reserves. International Monetary Fund's (IMF) Balance of investments and receipts on reserve assets). Income The balance of payments is a double-entry accounting Payments Manual (1993). The fifth edition redefined derived from the use of intangible assets is recorded system that shows all flows of goods and services into as capital transfers some transactions previously under business services. · Net current transfers are and out of an economy; all transfers that are the coun- included in the current account, such as debt for- recorded in the balance of payments whenever an terpart of real resources or financial claims provided to giveness, migrants' capital transfers, and foreign aid economy provides or receives goods, services, or by the rest of the world without a quid pro quo, such to acquire capital goods. Thus the current account income, or financial items without a quid pro quo. All as donations and grants; and all changes in residents' balance now reflects more accurately net current transfers not considered to be capital are current. claims on and liabilities to nonresidents that arise from transfer receipts in addition to transactions in goods, · Current account balance is the sum of net exports economic transactions. All transactions are recorded services (previously nonfactor services), and income of goods and services, net income, and net current twice--once as a credit and once as a debit. In princi- (previously factor income). Many countries maintain transfers. · Total reserves comprise holdings of mon- ple the net balance should be zero, but in practice the their data collection systems according to the fourth etary gold, special drawing rights, reserves of IMF accounts often do not balance. In these cases a bal- edition. Where necessary, the IMF converts data members held by the IMF, and holdings of foreign ancing item, net errors and omissions, is included. reported in such systems to conform to the fifth edi- exchange under the control of monetary authorities. Discrepancies may arise in the balance of pay- tion (see Primary data documentation). Values are in The gold component of these reserves is valued at ments because there is no single source for balance U.S. dollars converted at market exchange rates. year-end (31 December) London prices ($385.00 an of payments data and therefore no way to ensure The data in this table come from the IMF's Balance ounce in 1990, and $342.75 an ounce in 2002). that the data are fully consistent. Sources include of Payments and International Financial Statistics customs data, monetary accounts of the banking databases, supplemented by estimates by World system, external debt records, information provided Bank staff for countries for which the IMF does not by enterprises, surveys to estimate service transac- collect balance of payments statistics. In addition, tions, and foreign exchange records. Differences in World Bank staff make estimates of missing data for collection methods--such as in timing, definitions of up to three years prior to the current year. 4.15a Worker remittances are an important source of income for many developing economies Workers' remittances, 2002 % of % of merchandise merchandise Country $ billions trade Country $ billions trade Data sources Mexico 10 6 Jordan 2 70 More information about the design and compila- India 8 17 Brazil 2 3 tion of the balance of payments can be found in Spain 4 3 China 2 1 the IMF's Balance of Payments Manual, fifth edi- Pakistan 4 36 Guatemala 2 71 tion (1993), Balance of Payments Textbook Portugal 3 13 Ecuador 1 28 (1996a), and Balance of Payments Compilation Egypt, Arab Rep. 3 66 Yemen, Rep. 1 40 Morocco 3 36 Sri Lanka 1 27 Guide (1995). The balance of payments data are Bangladesh 3 47 Indonesia 1 2 published in the IMF's Balance of Payments Colombia 2 20 Greece 1 11 Statistics Yearbook and International Financial Serbia and Montenegro 2 92 Jamaica 1 102 Statistics. The World Bank exchanges data with Dominican Republic 2 37 Poland 1 3 the IMF through electronic files that in most cases Turkey 2 6 Tunisia 1 16 are more timely and cover a longer period than El Salvador 2 65 World total 76 the published sources. The IMF's International Remittances accounted for $76 billion in 2002, and 25 countries received more than $1 billion in remittances. Financial Statistics and Balance of Payments databases are available on CD-ROM. Source: International Monetary Fund, Balance of Payments data files. 2004 World Development Indicators 241 4.16 External debt Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt external debt $ millions IBRD loans and $ millions $ millions Total IDA credits $ millions $ millions 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 1,312 .. 1,200 .. 1,187 .. 476 .. 13 .. 81 Algeria 28,149 22,800 26,688 21,362 26,688 21,255 1,208 1,203 0 107 670 1,330 Angola 8,594 10,134 7,605 8,883 7,605 8,883 0 265 0 0 0 0 Argentina 62,233 132,314 48,676 103,140 46,876 74,661 2,609 8,513 1,800 28,479 3,083 14,340 Armenia .. 1,149 .. 941 .. 920 .. 538 .. 21 .. 195 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan .. 1,398 .. 1,037 .. 964 .. 314 .. 73 .. 279 Bangladesh 12,439 17,037 11,657 16,445 11,657 16,445 4,159 7,076 0 0 626 71 Belarus .. 908 .. 711 .. 710 .. 89 .. 1 .. 56 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 1,292 1,843 1,218 1,690 1,218 1,690 326 654 0 0 18 73 Bolivia 4,275 4,867 3,864 4,302 3,687 3,378 587 1,320 177 923 257 195 Bosnia and Herzegovina .. 2,515 .. 2,303 .. 2,282 .. 1,115 .. 21 .. 139 Botswana 553 480 547 464 547 464 169 16 0 0 0 0 Brazil 119,964 227,932 94,427 183,710 87,756 96,565 8,427 8,585 6,671 87,145 1,821 20,827 Bulgaria .. 10,462 .. 8,585 .. 7,474 .. 958 .. 1,111 .. 1,049 Burkina Faso 834 1,580 750 1,399 750 1,399 282 745 0 0 0 127 Burundi 907 1,204 851 1,095 851 1,095 398 648 0 0 43 13 Cambodia 1,845 2,907 1,683 2,594 1,683 2,594 0 306 0 0 27 96 Cameroon 6,657 8,502 5,577 7,417 5,347 7,240 871 988 230 177 121 307 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 698 1,066 624 980 624 980 265 399 0 0 37 33 Chad 524 1,281 464 1,148 464 1,148 186 632 0 0 31 107 Chile 19,226 41,945 14,687 38,188 10,425 6,792 1,874 562 4,263 31,396 1,156 0 China 55,301 168,255 45,515 120,370 45,515 88,531 5,881 20,677 0 31,839 469 0 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 17,222 33,853 15,784 30,052 14,671 21,177 3,874 2,355 1,113 8,876 0 0 Congo, Dem. Rep. 10,259 8,726 8,994 7,391 8,994 7,391 1,161 1,504 0 0 521 571 Congo, Rep. 4,947 5,152 4,200 3,974 4,200 3,974 239 207 0 0 11 33 Costa Rica 3,756 4,834 3,367 3,335 3,063 3,139 412 93 304 196 11 0 Côte d'Ivoire 17,251 11,816 13,223 10,369 10,665 9,110 1,920 2,068 2,558 1,259 431 491 Croatia .. 15,347 .. 14,984 .. 7,679 .. 588 .. 7,305 .. 0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 26,419 .. 15,661 .. 6,903 .. 185 .. 8,757 .. 0 Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 4,372 6,256 3,518 4,206 3,419 4,035 258 363 99 171 72 27 Ecuador 12,107 16,452 10,029 13,828 9,865 11,243 848 847 164 2,586 265 308 Egypt, Arab Rep. 33,017 30,750 28,438 27,282 27,438 26,624 2,401 1,859 1,000 658 125 0 El Salvador 2,149 5,828 1,938 4,837 1,913 4,712 164 385 26 126 0 0 Eritrea .. 528 .. 496 .. 496 .. 219 .. 0 .. 0 Estonia .. 4,741 .. 3,151 .. 482 .. 39 .. 2,669 .. 0 Ethiopia 8,630 6,523 8,479 6,313 8,479 6,313 851 2,756 0 0 6 143 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 3,983 3,533 3,150 3,231 3,150 3,231 69 50 0 0 140 67 Gambia, The 369 573 308 504 308 504 102 195 0 0 45 32 Georgia .. 1,838 .. 1,495 .. 1,444 .. 491 .. 51 .. 310 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 3,837 7,338 2,772 6,382 2,740 6,129 1,423 3,476 33 253 745 363 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 3,080 4,676 2,605 3,744 2,478 3,641 293 400 127 102 67 0 Guinea 2,476 3,401 2,253 2,972 2,253 2,972 420 1,096 0 0 52 139 Guinea-Bissau 692 699 630 662 630 662 146 237 0 0 5 23 Haiti 910 1,248 772 1,063 772 1,063 324 501 0 0 38 31 242 2004 World Development Indicators ECONOMY 4.16 External debt Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt external debt $ millions IBRD loans and $ millions $ millions Total IDA credits $ millions $ millions 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 3,718 5,395 3,487 4,675 3,420 4,212 635 1,119 66 463 32 197 Hungary 21,202 34,958 17,931 29,289 17,931 13,551 1,512 517 0 15,738 330 0 India 83,628 104,429 72,462 99,860 70,974 88,271 20,996 26,093 1,488 11,589 2,623 0 Indonesia 69,872 132,208 58,242 100,037 47,982 70,011 10,385 11,523 10,261 30,026 494 8,862 Iran, Islamic Rep. 9,020 9,154 1,797 6,797 1,797 6,578 86 400 0 219 0 0 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 4,748 5,477 4,045 4,678 4,011 4,593 672 495 34 86 357 24 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 8,333 8,094 7,202 7,076 7,202 7,076 593 1,072 0 0 94 483 Kazakhstan .. 17,538 .. 16,355 .. 3,209 .. 1,178 .. 13,146 .. 0 Kenya 7,058 6,031 5,641 5,188 4,761 5,139 2,056 2,460 880 49 482 88 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 1,797 .. 1,593 .. 1,394 .. 454 .. 199 .. 185 Lao PDR 1,768 2,664 1,758 2,620 1,758 2,620 131 474 0 0 8 43 Latvia .. 6,690 .. 2,512 .. 1,124 .. 263 .. 1,388 .. 16 Lebanon 1,779 17,077 358 14,530 358 13,829 34 313 0 701 0 0 Lesotho 396 637 378 611 378 611 112 255 0 0 15 22 Liberia 1,849 2,324 1,116 1,065 1,116 1,065 248 240 0 0 322 304 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 6,199 .. 3,955 .. 2,486 .. 279 .. 1,469 .. 121 Macedonia, FYR .. 1,619 .. 1,476 .. 1,262 .. 432 .. 214 .. 67 Madagascar 3,704 4,518 3,335 4,137 3,335 4,137 797 1,652 0 0 144 150 Malawi 1,558 2,912 1,385 2,688 1,382 2,688 854 1,773 3 0 115 95 Malaysia 15,328 48,557 13,422 40,188 11,592 26,200 1,102 719 1,830 13,988 0 0 Mali 2,468 2,803 2,337 2,487 2,337 2,487 498 1,134 0 0 69 166 Mauritania 2,113 2,309 1,806 1,984 1,806 1,984 264 547 0 0 70 113 Mauritius 984 1,803 910 911 762 832 195 107 148 79 22 0 Mexico 104,442 141,264 81,809 131,364 75,974 76,327 11,030 10,797 5,835 55,038 6,551 0 Moldova .. 1,349 .. 1,126 .. 846 .. 331 .. 280 .. 152 Mongolia .. 1,037 .. 950 .. 950 .. 181 .. 0 .. 43 Morocco 25,017 18,601 23,860 16,913 23,660 15,001 3,138 2,573 200 1,912 750 0 Mozambique 4,650 4,609 4,231 4,039 4,211 2,526 268 985 19 1,513 74 200 Myanmar 4,695 6,556 4,466 5,391 4,466 5,391 716 729 0 0 0 0 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 1,640 2,953 1,572 2,913 1,572 2,913 668 1,210 0 0 44 4 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 10,745 6,485 8,313 5,756 8,313 5,576 299 811 0 181 0 174 Niger 1,726 1,797 1,487 1,658 1,226 1,604 461 867 261 54 85 106 Nigeria 33,439 30,476 31,935 28,206 31,545 28,057 3,321 1,951 391 149 0 0 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman 2,736 4,639 2,400 3,451 2,400 1,979 52 0 0 1,471 0 0 Pakistan 20,663 33,672 16,643 30,100 16,506 28,102 3,922 8,143 138 1,998 836 2,032 Panama 6,506 8,298 3,855 7,877 3,855 6,408 462 287 0 1,469 272 50 Papua New Guinea 2,594 2,485 2,461 2,305 1,523 1,488 349 358 938 818 61 116 Paraguay 2,105 2,967 1,732 2,481 1,713 2,064 320 241 19 417 0 0 Peru 20,064 28,167 13,959 25,596 13,629 20,477 1,188 2,609 330 5,118 755 237 Philippines 30,580 61,121 25,241 53,877 24,040 39,575 4,044 3,533 1,201 14,303 912 1,686 Poland 49,364 69,521 39,261 60,637 39,261 29,374 55 2,385 0 31,263 509 0 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 243 4.16 External debt Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt external debt $ millions IBRD loans and $ millions $ millions Total IDA credits $ millions $ millions 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 1,140 14,683 230 13,780 223 8,112 0 2,173 7 5,668 0 428 Russian Federation .. 147,541 .. 124,738 .. 96,223 .. 6,599 .. 28,514 .. 6,481 Rwanda 712 1,435 664 1,305 664 1,305 340 826 0 0 0 85 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 3,736 3,918 3,000 3,372 2,940 3,339 835 1,578 60 33 314 253 Serbia and Montenegro a .. 12,688 .. 8,793 .. 8,514 .. 2,419 .. 280 .. 567 Sierra Leone 1,196 1,448 940 1,262 940 1,262 92 479 0 0 108 169 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. 13,013 .. 8,776 .. 4,295 .. 204 .. 4,481 .. 0 Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia 2,370 2,688 1,926 1,860 1,926 1,860 419 405 0 0 159 152 South Africa .. 25,041 .. 17,640 .. 9,427 0 13 .. 8,213 0 0 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 5,863 9,611 5,049 8,805 4,947 8,455 946 1,738 102 351 410 310 Sudan 14,762 16,389 9,651 9,539 9,155 9,043 1,048 1,192 496 496 956 573 Swaziland 243 342 238 274 238 274 44 13 0 0 0 0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 17,259 21,504 15,108 15,849 15,108 15,849 523 38 0 0 0 0 Tajikistan .. 1,153 .. 999 .. 912 .. 195 .. 87 .. 94 Tanzania 6,459 7,244 5,799 6,201 5,787 6,182 1,493 2,874 12 20 140 400 Thailand 28,095 59,212 19,771 46,902 12,460 22,628 2,530 2,428 7,311 24,274 1 391 Togo 1,281 1,581 1,081 1,338 1,081 1,338 398 632 0 0 87 52 Trinidad and Tobago 2,512 2,672 2,055 1,807 1,782 1,697 41 87 273 110 329 0 Tunisia 7,690 12,625 6,880 12,027 6,662 10,641 1,406 1,498 218 1,386 176 0 Turkey 49,424 131,556 39,924 94,278 38,870 61,823 6,429 5,456 1,054 32,455 0 22,086 Turkmenistan .. .. .. .. .. .. .. 31 .. .. .. 0 Uganda 2,583 4,100 2,160 3,690 2,160 3,690 969 2,576 0 0 282 257 Ukraine .. 13,555 .. 11,100 .. 8,348 .. 2,233 .. 2,752 .. 1,876 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 4,415 10,736 3,114 7,343 3,045 6,851 359 703 69 493 101 1,793 Uzbekistan .. 4,568 .. 4,175 .. 3,901 .. 275 .. 274 .. 62 Venezuela, RB 33,171 32,563 28,159 28,843 24,509 23,264 974 670 3,650 5,578 3,012 0 Vietnam 23,270 13,349 21,378 12,181 21,378 12,181 59 1,715 0 0 112 381 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6,352 5,290 5,160 4,563 5,160 4,563 602 1,384 0 0 0 386 Zambia 6,916 5,969 4,554 4,846 4,552 4,737 813 2,155 2 108 949 1,015 Zimbabwe 3,247 4,066 2,649 3,269 2,464 3,123 449 871 185 146 7 280 World .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s Low income 411,419 523,464 351,318 448,932 333,366 399,076 67,061 104,388 17,953 49,857 11,317 20,258 Middle income b 940,479 1,817,163 749,931 1,469,476 707,793 983,880 70,222 107,517 42,138 485,596 23,334 75,550 Lower middle income 583,682 1,149,118 477,625 925,294 453,753 651,767 49,234 80,019 23,872 273,527 7,811 59,160 Upper middle income b 356,797 668,045 272,306 544,182 254,040 332,112 20,988 27,498 18,266 212,069 15,523 16,390 Low & middle income b 1,351,898 2,340,627 1,101,250 1,918,408 1,041,159 1,382,955 137,283 211,905 60,091 535,453 34,651 95,809 East Asia & Pacific 234,092 499,133 194,633 388,064 172,998 272,783 25,306 42,764 21,635 115,281 2,085 11,618 Europe & Central Asia 217,224 545,842 176,378 434,625 171,457 276,350 10,429 30,214 4,921 158,275 1,305 34,245 Latin America & Carib. 444,227 727,944 352,476 613,916 327,447 384,961 35,841 42,072 25,029 228,956 18,297 38,302 Middle East & N. Africa 155,134 189,010 120,603 148,851 119,101 142,396 10,074 10,417 1,502 6,455 1,815 2,219 South Asia 124,395 168,349 107,527 158,723 105,799 144,785 30,717 44,349 1,727 13,938 4,537 2,416 Sub-Saharan Africa 176,826 210,350 149,632 174,229 144,355 161,681 24,916 42,089 5,276 12,548 6,612 7,009 High income Europe EMU a. Data for 1990 refer to the former Socialist Federal Republic of Yugoslavia. Data for 2002 are estimates and reflect borrowings by the former Socialist Federal Republic of of Yugoslavia that are not yet allocated to the successor republics. b. Includes data for Gibraltar not included in other tables. 244 2004 World Development Indicators ECONOMY 4.16 External debt About the data Definitions Data on the external debt of developing countries are the IMF's International Financial Statistics (line ae). · Total external debt is debt owed to nonresidents gathered by the World Bank through its Debtor Flow figures are converted at annual average repayable in foreign currency, goods, or services. It Reporting System. World Bank staff calculate the exchange rates (line rf). Projected debt service is is the sum of public, publicly guaranteed, and private indebtedness of these countries using loan-by-loan converted using end-of-period exchange rates. Debt nonguaranteed long-term debt, use of IMF credit, and reports submitted by them on long-term public and repayable in multiple currencies, goods, or services short-term debt. Short-term debt includes all debt publicly guaranteed borrowing, along with informa- and debt with a provision for maintenance of the having an original maturity of one year or less and tion on short-term debt collected by the countries or value of the currency of repayment are shown at interest in arrears on long-term debt. · Long-term collected from creditors through the reporting sys- book value. debt is debt that has an original or extended maturi- tems of the Bank for International Settlements and Because flow data are converted at annual aver- ty of more than one year. It has three components: the Organisation for Economic Co-operation and age exchange rates and stock data at end-of-period public, publicly guaranteed, and private nonguaran- Development. These data are supplemented by infor- exchange rates, year-to-year changes in debt out- teed debt. · Public and publicly guaranteed debt mation on loans and credits from major multilateral standing and disbursed are sometimes not equal to comprises the long-term external obligations of pub- banks, loan statements from official lending agen- net flows (disbursements less principal repayments); lic debtors, including the national government and cies in major creditor countries, and estimates by similarly, changes in debt outstanding, including political subdivisions (or an agency of either) and World Bank and International Monetary Fund (IMF) undisbursed debt, differ from commitments less autonomous public bodies, and the external obliga- staff. In addition, the table includes data on private repayments. Discrepancies are particularly signifi- tions of private debtors that are guaranteed for nonguaranteed debt for 80 countries either reported cant when exchange rates have moved sharply during repayment by a public entity. · IBRD loans and IDA to the World Bank or estimated by its staff. the year. Cancellations and reschedulings of other credits are extended by the World Bank. The The coverage, quality, and timeliness of debt data liabilities into long-term public debt also contribute to International Bank for Reconstruction and vary across countries. Coverage varies for both debt the differences. Development (IBRD) lends at market rates. The instruments and borrowers. With the widening spec- Variations in reporting rescheduled debt also International Development Association (IDA) pro- trum of debt instruments and investors and the affect cross-country comparability. For example, vides credits at concessional rates. · Private expansion of private nonguaranteed borrowing, com- rescheduling under the auspices of the Paris Club of nonguaranteed external debt consists of the long- prehensive coverage of long-term external debt official creditors may be subject to lags between the term external obligations of private debtors that are becomes more complex. Reporting countries differ in completion of the general rescheduling agreement not guaranteed for repayment by a public entity. their capacity to monitor debt, especially private and the completion of the specific, bilateral agree- · Use of IMF credit denotes repurchase obligations nonguaranteed debt. Even data on public and pub- ments that define the terms of the rescheduled debt. to the IMF for all uses of IMF resources (excluding licly guaranteed debt are affected by coverage and Other areas of inconsistency include country treat- those resulting from drawings on the reserve accuracy in reporting--again because of monitoring ment of arrears and of nonresident national deposits tranche). These obligations, shown for the end of the capacity and sometimes because of unwillingness to denominated in foreign currency. year specified, comprise purchases outstanding provide information. A key part often underreported under the credit tranches (including enlarged access is military debt. resources) and all special facilities (the buffer stock, Because debt data are normally reported in the compensatory financing, extended fund, and oil facil- currency of repayment, they have to be converted ities), trust fund loans, and operations under the into U.S. dollars to produce summary tables. Stock structural adjustment and enhanced structural figures (amount of debt outstanding) are converted adjustment facilities. using end-of-period exchange rates, as published in 4.16a Since 2000, GDP has been larger than external debt for the heavily indebted poor countries $ billions Data sources Total external debt GDP 250 The main sources of external debt information are reports to the World Bank through its Debtor 200 Reporting System from member countries that have received IBRD loans or IDA credits. 150 Additional information has been drawn from the 100 files of the World Bank and the IMF. Summary tables of the external debt of developing countries 50 are published annually in the World Bank's Global 0 Development Finance and on its Global 1995 1996 1997 1998 1999 2000 2001 2002 Development Finance CD-ROM. Source: World Bank data files. 2004 World Development Indicators 245 4.17 External debt management Indebtedness Present value Public and publicly Multilateral Short-term classification a of debt guaranteed debt service debt service debt % of exports of goods, % of exports of % of public and % of services, % of goods, services, publicly % of GNI and income GNI and income guaranteed total debt 2002 2002 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania L 20 91 .. 0.7 .. 3.4 .. 37.5 .. 2.4 Algeria L 42 .. 14.3 7.0 63.3 .. 5.0 25.2 2.8 0.5 Angola S 118 125 3.4 8.7 7.1 9.8 2.2 1.0 11.5 12.4 Argentina S 66 393 3.6 4.3 28.9 12.8 16.2 80.1 16.8 11.2 Armenia L 34 111 .. 2.1 .. 6.3 .. 54.6 .. 1.2 Australia .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. Azerbaijan L 21 46 .. 1.6 .. 3.4 .. 18.0 .. 5.9 Bangladesh L 22 155 1.6 1.3 23.3 8.9 22.8 48.7 1.3 3.1 Belarus L 7 10 .. 1.1 .. 1.7 .. 31.6 .. 15.6 Belgium .. .. .. .. .. .. .. .. .. .. .. Benin M 36 b 155 b 1.8 1.9 8.6 8.5 95.7 60.0 4.3 4.4 Bolivia L 23 b 111 b 5.9 2.8 27.6 13.1 67.6 89.7 3.6 7.6 Bosnia and Herzegovina L 34 98 .. 2.0 .. 6.6 .. 57.2 .. 2.9 Botswana L 8 13 2.9 1.2 4.3 2.0 61.3 69.5 1.0 3.3 Brazil S 48 342 1.3 5.0 15.7 29.4 43.5 15.5 19.8 10.3 Bulgaria M 79 136 .. 3.8 .. 6.8 .. 26.6 .. 7.9 Burkina Faso M 16 b 171 b 0.9 1.4 7.7 14.8 73.0 85.5 10.1 3.4 Burundi S 110 1,553 3.6 2.6 40.7 47.1 51.1 87.6 1.5 8.0 Cambodia M 68 114 2.6 0.2 .. 0.3 .. 60.3 7.3 7.4 Cameroon M 58 b .. b 3.0 3.2 12.6 .. 43.5 46.3 14.4 9.2 Canada .. .. .. .. .. .. .. .. .. .. .. Central African Republic S 78 .. 1.1 0.0 7.5 .. 50.0 100.0 5.4 4.9 Chad S 37 b .. b 0.4 1.2 2.4 .. 72.3 77.0 5.7 2.0 Chile M 63 173 5.6 2.4 15.1 6.3 35.7 31.3 17.6 9.0 China L 14 50 1.6 1.0 9.7 3.5 7.6 32.8 16.8 28.5 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia M 46 229 8.2 6.4 34.5 33.4 32.2 26.0 8.4 11.2 Congo, Dem. Rep. S 171 .. 1.6 7.5 .. .. 49.6 100.0 7.2 8.8 Congo, Rep. S 228 200 20.4 0.5 31.6 0.5 12.7 100.0 14.9 22.2 Costa Rica L 33 69 7.9 3.7 20.7 8.2 36.1 53.2 10.0 31.0 Côte d'Ivoire S 91 188 5.7 4.5 14.7 8.6 77.5 73.6 20.8 8.1 Croatia M 76 150 .. 7 .. 14 .. 6 .. 2.4 Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic L 46 62 .. 2.2 .. 3.0 .. 10.7 .. 40.7 Denmark .. .. .. .. .. .. .. .. .. .. .. Dominican Republic L .. 68 2.1 2.8 7.2 6.6 50.3 24.1 17.9 32.3 Ecuador S 95 300 9.6 5.7 26.6 20.9 34.8 40.5 15.0 14.1 Egypt, Arab Rep. L 28 150 5.9 2.0 23.2 10.6 18.7 25.5 13.5 11.3 El Salvador L 46 162 3.7 2.7 17.7 9.5 60.2 54.5 9.8 17.0 Eritrea M 40 200 .. 1.2 .. 4.5 .. 58.6 .. 5.9 Estonia S 86 89 .. 1.6 .. 1.7 .. 54.1 .. 33.5 Ethiopia S 66 b,c 386 b, c 2.3 1.6 33.1 8.9 14.5 79.0 1.7 1.0 Finland .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. Gabon S 87 107 1.9 9.2 3.8 11.0 32.6 11.3 17.4 6.7 Gambia, The M 77 b .. b 10.4 5.3 17.9 .. 25.4 51.9 4.3 6.5 Georgia M 42 144 .. 2.0 .. 6.2 .. 24.4 .. 1.8 Germany .. .. .. .. .. .. .. .. .. .. .. Ghana M 73 b 157 b 3.3 2.8 19.3 6.6 30.7 47.9 8.3 8.1 Greece .. .. .. .. .. .. .. .. .. .. .. Guatemala L 21 110 2.2 1.5 10.4 9.0 36.8 61.3 13.3 19.9 Guinea M 47 b 166 b 5.6 3.9 17.7 12.4 22.1 57.0 6.9 8.5 Guinea-Bissau S 235 b .. 2.4 6.1 21.8 .. 70.2 46.5 8.2 2.0 Haiti L 23 .. 0.5 0.4 4.4 .. 69.2 41.4 11.1 12.3 246 2004 World Development Indicators ECONOMY 4.17 External debt management Indebtedness Present value Public and publicly Multilateral Short-term classification a of debt guaranteed debt service debt service debt % of exports of goods, % of exports of % of public and % of services, % of goods, services, publicly % of GNI and income GNI and income guaranteed total debt 2002 2002 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras M 49 121 10.8 2.6 29.1 6.5 90.7 76.1 5.4 9.7 Hungary M 62 82 11.9 3.4 30.4 4.9 8.0 9.2 13.9 16.2 India L 17 115 .. .. .. .. .. .. 10.2 4.4 Indonesia S 89 191 6.8 4.0 24.9 9.8 22.5 42.1 15.9 17.6 Iran, Islamic Rep. L 7 25 0.2 1.2 1.3 3.6 30.4 8.1 80.1 25.7 Iraq .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. Jamaica S 82 163 11.6 10.9 20.7 22.9 38.6 22.1 7.3 14.2 Japan .. .. .. .. .. .. .. .. .. .. .. Jordan S 84 165 14.5 5.2 21.4 10.1 26.8 47.7 12.4 6.6 Kazakhstan M 80 151 .. 3.7 .. 7.4 .. 18.2 .. 6.7 Kenya M 40 147 6.3 2.8 22.7 10.4 44.7 31.8 13.2 12.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic S 93 221 .. 2.9 .. 7.0 .. 39.9 .. 1.0 Lao PDR S 87 .. 1.0 2.2 8.0 .. 53.6 62.8 0.1 0.0 Latvia S 85 176 .. 1.2 .. 2.4 .. 70.9 .. 62.2 Lebanon S 102 557 1.1 9.5 3.2 51.8 27.8 5.4 79.9 14.9 Lesotho L 45 78 2 7 4 11 45 68 1 0.6 Liberia S 559 1,686 .. .. .. .. 100.0 .. 22.2 41.1 Libya .. .. .. .. .. 3.8 .. .. .. .. .. Lithuania M 50 95 .. 4.2 .. 7.4 .. 18.7 .. 34.2 Macedonia, FYR L 37 87 .. 3.9 .. 10.5 .. 38.5 .. 4.7 Madagascar L 33 b 129 b 5.2 1.5 31.9 9.1 23.7 61.0 6.1 5.1 Malawi M 51 b 183 b 5.5 1.5 22.4 5.7 38.2 100.0 3.7 4.5 Malaysia M 57 44 8.7 6.4 10.6 5.1 9.9 4.4 12.4 17.2 Mali M 47 b 134 b 1.8 2.4 9.7 5.8 54.3 64.0 2.5 5.4 Mauritania M 66 b .. b 10.9 5.8 24.8 .. 73.8 69.8 11.2 9.2 Mauritius L 39 60 3.4 4.2 4.5 6.3 51.6 21.8 5.3 49.5 Mexico L 26 86 3.1 3.0 15.1 10.7 26.0 15.0 15.4 7.0 Moldova M 79 126 .. 6.1 .. 9.4 .. 40.1 .. 5.3 Mongolia M 69 107 .. 3.9 .. 6.1 .. 13.4 .. 4.3 Morocco M 51 147 5.9 8.5 23.1 23.9 39.8 37.4 1.6 9.1 Mozambique L 27 b 88 b 2.2 1.1 17.2 2.9 30.6 61.9 7.4 8.0 Myanmar S .. .. .. .. 17.7 .. 43.6 0.7 4.9 17.8 Namibia .. .. .. .. .. 0.1 .. .. .. .. .. Nepal M 31 147 1.5 1.7 12.1 9.8 36.8 72.7 1.5 1.2 Netherlands .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. Nicaragua S 77 301 .. 2.6 2.4 10.7 21.1 26.0 22.6 8.5 Niger M 26 b .. b 0.7 0.7 3.1 .. 71.3 89.9 8.9 1.8 Nigeria S 80 152 12.8 3.6 22.3 8.2 15.5 31.3 4.5 7.4 Norway .. .. .. .. .. .. .. .. .. .. .. Oman L 23 .. 6.9 4.4 12.0 .. 5.1 7.6 12.3 25.6 Pakistan M 45 238 3.5 3.5 19.8 16.8 40.3 56.4 15.4 4.6 Panama S 84 107 2.8 11.5 2.5 16.4 90.7 9.5 36.6 4.5 Papua New Guinea S 82 .. 8.7 5.2 18.2 .. 23.0 53.6 2.8 2.6 Paraguay L 42 96 5.6 3.4 11.5 6.2 35.9 68.1 17.7 16.4 Peru S 56 319 0.7 5.5 4.1 31.7 28.8 24.1 26.7 8.3 Philippines M 77 135 6.6 6.7 22.2 12.3 28.7 16.1 14.5 9.1 Poland L 38 124 1.5 2.0 4.3 6.3 9.2 8.4 19.4 12.8 Portugal .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 247 4.17 External debt management Indebtedness Present value Public and publicly Multilateral Short-term classification a of debt guaranteed debt service debt service debt % of exports of goods, % of exports of % of public and % of services, % of goods, services, publicly % of GNI and income GNI and income guaranteed total debt 2002 2002 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania L 37 106 .. 4.0 .. 11.0 .. 27.1 79.8 3.2 Russian Federation M 50 122 .. 2.6 .. 7.1 .. 8.9 .. 11.1 Rwanda S 40 b 453 b 0.6 1.1 10.2 13.2 60.7 61.8 6.6 3.2 Saudi Arabia .. .. .. .. .. 1.2 .. .. .. .. .. Senegal M 53 b 165 b 3.8 3.6 13.8 11.4 39.8 42.9 11.3 7.5 Serbia and Montenegro S 102 421 .. 0.7 .. 3.1 .. 86.0 .. 26.2 Sierra Leone S 103 b .. b 2.8 2.8 7.8 .. 26.1 43.5 12.4 1.1 Singapore .. .. .. .. .. .. .. .. .. .. .. Slovak Republic M 62 82 .. 3.8 .. 5.0 .. 10.4 .. 32.6 Slovenia .. .. .. .. .. .. .. .. .. .. .. Somalia S .. .. 0.8 .. .. .. 100.0 .. 12.0 25.1 South Africa L 22 66 .. 1.6 .. 4.2 .. 0.4 .. 29.6 Spain .. .. .. .. .. .. .. .. .. .. .. Sri Lanka M 48 122 3.6 3.2 11.9 8.7 13.8 18.7 6.9 5.2 Sudan S 136 851 0.1 0.0 4.5 0.0 100.0 100.0 28.1 38.3 Swaziland L 25 26 4.8 1.5 5.6 1.6 73.0 75.7 1.9 19.9 Sweden .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic S 114 270 9.3 0.8 20.3 1.9 3.5 55.9 12.5 26.3 Tajikistan S 88 124 .. 1.5 .. 2.5 .. 62.5 .. 5.2 Tanzania L 19 b, d 117 b, d 3.4 1.4 25.1 7.8 52.7 39.6 8.0 8.9 Thailand M 49 69 3.9 6.1 10.4 8.9 22.1 33.7 29.6 20.1 Togo S 92 251 3.8 0.1 8.6 0.2 40.8 100.0 8.8 12.1 Trinidad and Tobago L 35 61 7.3 2.4 14.6 4.7 4.7 44.5 5.1 32.4 Tunisia M 65 135 10.3 6.8 23.0 14.1 26.0 49.4 8.2 4.7 Turkey S 77 246 4.3 5.6 29.6 17.7 23.3 10.0 19.2 11.5 Turkmenistan M .. .. .. .. .. .. .. .. .. .. Uganda L 22 b 175 b 2.0 1.0 47.1 7.6 37.4 83.1 5.4 3.7 Ukraine L 35 59 .. 2.8 .. 4.9 .. 29.8 .. 4.3 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. Uruguay S 65 279 7.9 8.9 29.4 33.7 16.2 26.2 27.2 14.9 Uzbekistan M 57 136 .. 7.8 .. 20.2 .. 11.6 .. 7.2 Venezuela, RB .. 33 112 8.8 6.6 19.4 20.5 1.6 10.2 6.0 11.4 Vietnam L 35 61 2.4 3.1 .. 5.5 3.4 2.6 7.7 5.9 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. L 40 90 2.3 1.5 7.1 3.5 51.0 60.0 18.8 6.5 Zambia S 127 406 5.7 6.4 12.7 19.9 41.6 26.9 20.4 1.8 Zimbabwe M .. .. 4.3 .. 18.2 .. 24.0 16.9 18.2 12.7 World .. w .. w .. w .. w .. w .. .. w .. w Low income 3.5 2.8 21.4 10.9 25.4 44.3 11.9 10.4 Middle income 3.4 3.2 14.6 8.9 19.2 20.8 17.8 15.0 Lower middle income 3.1 3.0 17.3 9.1 21.2 22.3 16.8 14.3 Upper middle income 3.9 3.7 11.8 8.6 15.8 18.1 19.3 16.1 Low & middle income 3.4 3.1 15.9 9.2 20.6 24.7 16.0 13.9 East Asia & Pacific 3.6 2.3 13.7 5.5 17.5 27.4 16.0 19.9 Europe & Central Asia .. 3.2 18.3 7.6 17.1 12.7 18.2 14.1 Latin America & Carib. 3.0 4.2 17.7 16.1 26.6 22.4 16.5 10.4 Middle East & N. Africa 4.2 .. 13.3 .. 10.8 26.5 21.1 20.1 South Asia 2.1 2.4 23.1 14.5 25.0 49.2 9.9 4.3 Sub-Saharan Africa .. 2.6 .. 6.5 30.0 32.7 11.6 13.8 High income Europe EMU a. S = severely indebted, M = moderately indebted, L = less indebted. b. Data are from debt sustainability analyses undertaken as part of the Debt Initiative for Heavily Indebted Poor Countries (HIPCs). Present value estimates for these countries are for public and publicly guaranteed debt only. c. As of December 31, 2002, Ethiopia had yet to reach the completion point under the HIPC Debt Initiative. d. Data refer to mainland Tanzania only. 248 2004 World Development Indicators ECONOMY 4.17 External debt management About the data Definitions The indicators in the table measure the relative bur- Monetary Fund (IMF). When the discount rate is · Indebtedness classification refers to assessment den on developing countries of servicing external debt. greater than the interest rate of the loan, the present on a three-point scale: severely indebted (S), moder- The present value of external debt provides a measure value is less than the nominal sum of future debt ately indebted (M), and less indebted (L). · Present of future debt service obligations that can be com- service obligations. value of debt is the sum of short-term external debt pared with the current value of such indicators as The ratios in the table are used to assess the sus- plus the discounted sum of total debt service pay- gross national income (GNI) and exports of goods and tainability of a country's debt service obligations, but ments due on public, publicly guaranteed, and private services. The table shows the present value of total there are no absolute rules that determine what val- nonguaranteed long-term external debt over the life of debt service both as a percentage of GNI in 2002 and ues are too high. Empirical analysis of the experi- existing loans. · Public and publicly guaranteed debt as a percentage of exports in 2002. The ratios com- ence of developing countries and their debt service service is the sum of principal repayments and inter- pare total debt service obligations with the size of the performance has shown that debt service difficulties est actually paid in foreign currency, goods, or servic- economy and its ability to obtain foreign exchange become increasingly likely when the ratio of the es on long-term obligations of public debtors and through exports. The ratios shown here may differ present value of debt to exports reaches 200 per- long-term private obligations guaranteed by a public from those published elsewhere because estimates of cent. Still, what constitutes a sustainable debt bur- entity. · Multilateral debt service is the repayment of exports and GNI have been revised to incorporate data den varies from one country to another. Countries principal and interest to the World Bank, regional available as of February 1, 2004. Exports refer to with fast-growing economies and exports are likely to development banks, and other multilateral and inter- exports of goods, services, and income. Workers' be able to sustain higher debt levels. governmental agencies. · Short-term debt includes remittances are not included here, though they are The World Bank classifies countries by their level of all debt having an original maturity of one year or less included with income receipts in other World Bank pub- indebtedness for the purpose of developing debt and interest in arrears on long-term debt. lications such as Global Development Finance. management strategies. The most severely indebted The present value of external debt is calculated by countries may be eligible for debt relief under special discounting the debt service (interest plus amortiza- programs, such as the HIPC Debt Initiative. Indebted tion) due on long-term external debt over the life of countries may also apply to the Paris and London existing loans. Short-term debt is included at its face Clubs for renegotiation of obligations to public and value. The data on debt are in U.S. dollars converted private creditors. In 2002, countries with a present at official exchange rates (see About the data for value of debt service greater than 220 percent of table 4.16). The discount rate applied to long-term exports or 80 percent of GNI were classified as debt is determined by the currency of repayment severely indebted, countries that were not severely of the loan and is based on reference rates for indebted but whose present value of debt service commercial interest established by the Organisation exceeded 132 percent of exports or 48 percent of for Economic Co-operation and Development. Loans GNI were classified as moderately indebted, and from the International Bank for Reconstruction and countries that did not fall into either group were clas- Development (IBRD) and credits from the sified as less indebted. International Development Association (IDA) are dis- counted using a special drawing rights (SDR) refer- ence rate, as are obligations to the International 4.17a When the present value of a country's external debt exceeds 220 percent of exports or Data sources 80 percent of GNI the World Bank classifies it as severely indebted The main sources of external debt information are Ratio of present value debt to GNI, 2002 (%) reports to the World Bank through its Debtor 600 Reporting System from member countries that ts Liberia have received IBRD loans or IDA credits. 500 expor of Additional information has been drawn from the 400 files of the World Bank and the IMF. The data on 220% 300 GNI and exports of goods and services are from Congo, Rep. the World Bank's national accounts files and the 200 Sudan Burundi IMF's Balance of Payments database. Summary 100 tables of the external debt of developing countries 80% of GNI 0 are published annually in the World Bank's Global 0 300 600 900 1,200 1,500 1,800 Development Finance and on its Global Ratio of present value debt to exports, 2002 (%) Development Finance CD-ROM. Source: World Bank data files. 2004 World Development Indicators 249 5 STATESAND MARKETS S uccessful development requires that states complement markets, not substitute for them. States should focus on providing a good business environment--in which contracts are enforced, markets function, basic infrastructure is provided, and people (especially poor people) are empowered to participate. Government institutions can support the development of markets in many ways--by providing information, fostering competition, enforcing contracts, and helping to make credit available to entrepreneurs. By leveling the playing field, governments create opportunities for poor people to participate in markets and improve their standards of living and give them hope for a better future for their children. Good governance matters for long-term growth, but good policies and effective government spending also have immediate effects on people. Many governments are working with service providers and beneficiaries to improve public service delivery. For example, in Bangalore, India, a civil society group introduced report cards in 1994 rating user experiences with public services. The reports of poor quality and corruption were widely publicized, leading to improvements in service delivery and public governance. This section covers a broad range of indicators showing how effective and accountable government, together with energetic private initiative, produces employment opportunities and services that empower poor people. Its 12 tables cover three cross-cutting development themes: private sector development, public sector policies, and infrastructure, information, and telecommunications. Creating the conditions for private sector development Investment is the foundation of growth, and most investment comes from the private sector. But governments play an important role in providing a predictable environment in which people, ideas, and money work together productively and efficiently. This allows private firms operating in competitive markets to be the engines of growth and job creation, providing opportunities to escape poverty. Governments around the world are expanding opportunities for improved investment and business climates. State-owned enterprises are being privatized, trade barriers are being reduced, and improvements in regulations that enhance business activity are contributing to greater business opportunities and growth. 2004 World Development Indicators 251 Investment in infrastructure--whether in power, transport, housing, enforcement, insolvency procedures and cost, and labor regulations telecommunications, or water and sanitation--enables businesses (several indicators for these areas are included in table 5.3). A new to grow. And when private firms participate in infrastructure, bring- business environment indicator from the Doing Business database ing with them capital and know-how, they can improve access to is the employment laws index, constructed by examining the basic infrastructure services, a key to reducing poverty. detailed provisions of labor laws (table 5.3). In developing countries private firms participate mainly in telecommunications and energy, and in many countries invest- Public sector policies and institutions can improve ment has been robust. In Chile in 1990­95 investment in service delivery--and private sector business activities telecommunications projects with private participation totaled Improving people's standard of living by ensuring access to about $150 million, but in 1996­2002 it increased tenfold, to essential services such as health, education, safety, water, san- almost $1,600 million. India also saw a dramatic increase in pri- itation, and electricity is widely viewed as government's respon- vate participation in energy investment, which soared from sibility. An efficient and accountable public sector has institutions $2,888 million in 1990­95 to $9,680 million in 1996­2002. that are responsive to citizens, provide information, deliver serv- Substantial increases in investment with private participation ices efficiently and equitably, and help to enforce people's rights. have also occurred in water and sanitation. In China these invest- Making services work better, especially for poor people who often ments rose from $68 million in 1990­95 to $3,886 million in do not get their fair share of public spending on services, is a 1996­2002 (table 5.1). challenge that can be met by governments, citizens, and private The case for creating a good investment climate (sound macro- service providers working together. economic framework, and legal and regulatory framework, good Measuring the quality of public sector governance is difficult. For governance to overcome bureaucratic inefficiencies, and access to example, for public goods, including public service delivery, it is dif- key financial and infrastructure services) is simple: an economy ficult to exclude anyone from benefiting from them, so individuals needs a predictable environment in which people, ideas, and adopt a "free rider" position, resulting in fewer resources being allo- money can work together productively and efficiently. In the context cated to public goods. Another example is measurement of some of a sound macroeconomic framework, a good investment climate dimensions of governance, such as corruption. Corruption is almost strengthens governance and overcomes bureaucratic inefficien- impossible to measure directly because of its illegal and clandes- cies, improves access to key financial and infrastructure services, tine nature. And although no international benchmarks of good gov- and provides a sound legal and regulatory framework for enterpris- ernance have been established, and World Development Indicators es that promotes competition. Countries should focus on improv- does not report on national governance measures, research shows ing the investment climate for domestic entrepreneurs, but a better a strong positive correlation between the quality of institutions and investment climate will also attract foreign investors. And countries economic growth. A related finding is that as countries become rich- that receive more foreign investment--an important conduit er, institutions and governance do not necessarily improve. But for new technologies, management experience, and access to there is a strong positive causal effect going from better governance markets--enjoy faster growth and greater poverty reduction. to higher per capita incomes (Kaufmann and Kraay 2002). External perceptions of the investment climate are reflected in Despite the difficulty of measuring the quality of institutions risk ratings, and changes in sovereign risk ratings may affect coun- and governance, several international and regional initiatives are try risk and stock returns. One example is the Euromoney credit- under way to identify trends and the links to development: worthiness ratings, which rank the risk of investing in an economy · Country Policy and Institutional Assessments by the World from 0 (high risk) to 100 (low risk). Although many factors determine Bank include ratings covering economic management, struc- the level of foreign investment, countries with high risk, such as the tural policies, policies for social inclusion and equity, and pub- Democratic Republic of Congo, at 18, and Haiti, at 24, have very low lic sector management and institutions. Public sector foreign direct investment--0.6 percent of gross domestic product management and institutions include measures of property (GDP) for the Democratic Republic of Congo and 0.2 percent for rights and rule-based governance, quality of budgetary and Haiti. Countries with low perceived risk, such as the Czech Republic, financial management, efficiency of revenue mobilization, qual- at 66, and Slovenia, at 76, have much higher levels of foreign direct ity of public administration, and transparency, accountability, investment--13.4 percent for the Czech Republic and about 8.5 and corruption of the public sector. These assessments are percent for Slovenia (see table 5.1 for data on foreign direct invest- calculated for World Bank member countries that are eligible ment and table 5.2 for credit and risk ratings). Countries with low for lending by the International Development Association (IDA) perceived risk also have large stock markets relative to their GDP. (see www.worldbank.org/ida). The African Development Bank Market capitalization is about 74 percent of GDP in Chile, 93 per- conducts similar assessments. cent in Australia, and 131 percent in Malaysia (table 5.4). · Worldwide Governance Indicators from the World Bank Institute The World Bank's Doing Business database identifies regulations measure broad dimensions of governance such as voice and that enhance or constrain business investment, productivity, and accountability, political instability and violence, government effec- growth, providing indicators of the cost of doing business (see tiveness, regulatory burden, rule of law, and control of corruption. http://rru.worldbank.org/DoingBusiness/default.aspx). The busi- The database covers 199 countries and territories and draws on ness environment in a country is determined by many factors, 25 sources. Aggregating data from many sources reduces the including regulation of new entry, access to credit markets, contract measurement error from any single source. The database 252 2004 World Development Indicators 5a services, and exports and imports. (Nontax revenue is also impor- tant in some economies; see table 4.13.) A comparison of tax lev- Higher income economies often have less regulated labor markets than lower income economies els across countries provides an overview of the fiscal obligations and incentives facing the private sector. Central government tax Employment laws index, range 0 (less rigid) to 100 (very rigid) revenues range from 2­3 percent of GDP in Myanmar to more than 80 35 percent in Croatia, Israel, and Slovenia (table 5.6). 70 The level and progressivity of taxes on personal and corporate income influence incentives to work and invest. Marginal tax 60 rates on individual income range from 0 percent to 50 percent or more. Most marginal tax rates on corporate income are in the 50 20­30 percent range (table 5.6). 40 Tapping the benefits of infrastructure, information, 30 and telecommunications 20 Infrastructure has become an increasingly important part of the World Bank Group's development agenda and is central to the 10 Bank's efforts to help achieve the Millennium Development Goals (tables 1.2­1.4 and World view). There is widespread recognition of 0 United Kingdom Jamaica Sri Lanka India Peru Portugal the key role that infrastructure plays in helping to achieve these goals. Better quality infrastructure--and better access to it-- Factors such as the legal tradition (common law, French legal origin) and other political and efficiency considerations determine every country's labor regulations. contribute to the success of manufacturing and agricultural busi- Source: Doing Business database. nesses by strengthening employment prospects, productivity, and growth. Roads, rails, power, communication, and water and sanita- includes point estimates and margins of error, to help interpret tion systems deliver services that promote better health and edu- the estimates (see www.worldbank.org/wbi/governance/ cation. Better housing increases people's earning capacity and govdata2002/). assets. And good transportation and schooling advance gender · Business Environment and Enterprise Performance Surveys equality and the empowerment of women (table 1.5). New informa- are a joint European Bank for Reconstruction and Development tion and communications technologies offer vast opportunities for and World Bank survey covering 22 countries. Survey ques- economic growth, improved health, better service delivery, learning tions cover issues related to bureaucratic red tape and cor- through distance education, and social and cultural advances. ruption (see http://info.worldbank.org/governance/beeps/). Efficient transport is critical to the development of competitive · African Peer Review Mechanism (APRM), launched by the New economies (table 5.9). But measuring progress in transport is Partnership for Africa's Development, addresses four dimen- difficult. Data for most transport sectors are often not strictly sions of governance: democracy and political governance, eco- comparable across countries that do not consistently follow com- nomic governance and management, corporate governance, and mon definitions and specifications. Moreover, the data do not socioeconomic development. Sixteen countries have formally indicate the quality and level of service, which depend on such joined the APRM (see http://www.touchtech.biz/nepad/). factors as maintenance budgets, the availability of trained per- · Code of Good Practices on Fiscal Transparency was adopted sonnel, geographic and climatic conditions, and incentives and by the International Monetary Fund (IMF) in 1998 and updated competition to provide the best service at the lowest cost. in 2001. Countries volunteer to prepare a Fiscal Report on Recognizing the need for better data on infrastructure for analy- Standards and Codes. Key requirements of transparency cov- sis and project planning, World Bank staff are developing a new ered in the reports include roles and responsibilities in gov- database on infrastructure. World Development Indicators will ernment; full information disclosure to the public on fiscal report these data as they become available. activities; open procedures for budget preparation, execution, New information and communications technologies are helping and reporting; and fiscal information prepared according to people everywhere improve their quality of life by creating, using, internationally accepted standards of data quality and integrity and sharing information and knowledge (tables 5.10 and 5.11). (see http://www.imf.org/external/np/rosc/rosc.asp). Successful e-government applications such as Citizen Service Government functions and policies affect many areas of social Centers in Brazil; income tax on line in Brazil, Jordan, Mexico, and economic life: health and education, natural resources and and Singapore; and new business registration in China, Jamaica, environmental protection, fiscal and monetary stability, and flows and Jordan have resulted in more convenience, less corruption, of trade. Data related to these topics are presented in the lower costs, and greater transparency. The Internet has spread respective sections. This section provides data on key public sec- to every corner of the world, starting with only 8 countries online tor activities: tax policies, exchange rates, and defense expendi- in 1988 to 209 countries by 2003. But many countries still have tures (tables 5.6­5.8). a long way to go. In some countries, such as Bangladesh, Chad, Taxes are the principle source of revenue for most governments. Ethiopia, Myanmar, and Tajikistan, only 1­2 people per 1,000 They are levied mainly on income, profits, capital gains, goods and have access to the Internet (table 5.11). 2004 World Development Indicators 253 5.1 Private sector investment Domestic Foreign direct Investment in infrastructure projects credit to investment with private participation a private sector $ millions % of GDP % of GDP Telecommunications Energy Transport Water and sanitation 1990 2002 1990 2002 1990­95 1996­2002 1990­95 1996­2002 1990­95 1996­2002 1990­95 1996­2002 Afghanistan .. .. .. .. .. 70.0 .. .. .. .. .. .. Albania .. 6.8 0.0 2.8 .. 283.2 .. 8.0 .. .. .. .. Algeria 44.4 6.8 0.0 1.9 .. 501.5 2,300.0 .. .. .. .. .. Angola .. 4.7 ­3.3 11.7 .. 75.3 .. .. .. .. .. .. Argentina 15.6 15.3 1.3 0.8 11,907.0 13,452.2 12,035.1 13,470.3 5,991.7 8,385.5 5,166.0 3,071.5 Armenia 40.4 6.9 .. 4.7 .. 468.4 .. 12.0 .. 50.0 .. .. Australia 64.2 89.8 2.6 4.1 .. .. .. .. .. .. .. .. Austria 91.6 106.4 0.4 0.4 .. .. .. .. .. .. .. .. Azerbaijan 10.8 5.6 .. 22.9 14.0 144.6 .. 375.2 .. .. .. .. Bangladesh 16.7 28.9 0.0 0.1 146.0 594.4 .. 1,040.2 .. 25.0 .. .. Belarus .. 9.1 .. 1.7 10.0 180.3 .. 500.0 .. .. .. .. Belgium 37.0 76.3 4.1 .. .. .. .. .. .. .. .. .. Benin 20.3 11.8 3.4 1.5 .. 90.4 .. .. .. .. .. .. Bolivia 24.0 51.4 0.6 8.7 38.0 808.9 252.4 2,718.2 .. 185.3 .. 682.0 Bosnia and Herzegovina .. 36.3 .. 5.2 .. .. .. .. .. .. .. .. Botswana 9.4 18.4 2.5 0.7 .. 80.0 .. .. .. .. .. .. Brazil 38.9 35.5 0.2 3.7 .. 70,824.6 613.6 48,631.8 1,349.4 19,577.8 155.3 3,019.0 Bulgaria 7.2 18.4 0.5 3.9 64.0 547.3 .. .. .. .. .. 152.0 Burkina Faso 16.8 13.5 0.0 0.3 .. 36.6 .. 5.6 .. .. .. .. Burundi 13.7 26.1 0.1 0.0 0.5 15.6 .. .. .. .. .. .. Cambodia .. 6.8 0.0 1.3 31.6 155.7 .. 123.2 120.0 72.2 .. .. Cameroon 26.7 10.2 ­1.0 1.0 .. 266.1 .. 91.9 30.8 95.0 .. .. Canada 75.9 82.2 1.3 2.9 .. .. .. .. .. .. .. .. Central African Republic 7.2 5.7 0.0 0.4 1.1 .. .. .. .. .. 0.7 .. Chad 7.3 4.1 0.5 45.0 .. 13.0 .. .. .. .. .. .. Chile 47.2 68.1 2.2 2.7 148.9 1,574.8 2,260.0 6,457.3 539.9 6,709.6 67.5 3,886.1 China 87.7 136.5 1.0 3.9 .. 13,024.7 6,113.5 14,301.6 6,219.8 15,849.8 104.0 1,992.4 Hong Kong, China 163.7 150.1 .. 7.9 .. .. .. .. .. .. .. .. Colombia 30.8 25.1 1.2 2.5 1,551.2 1,551.0 1,813.2 5,762.2 1,008.8 1,597.4 .. 330.0 Congo, Dem. Rep. 1.8 0.7 ­0.2 0.6 .. 369.7 .. .. .. .. .. .. Congo, Rep. 15.7 2.9 0.0 11.0 4.6 111.9 .. 325.0 .. .. .. .. Costa Rica 15.8 30.1 2.8 3.9 .. .. 76.3 243.1 .. 161.0 .. .. Côte d'Ivoire 36.5 14.8 0.4 2.0 .. 827.4 147.2 223.0 .. 178.0 .. .. Croatia .. 51.6 .. 4.4 .. 1,425.5 .. 375.6 .. 672.2 .. 298.7 Cuba .. .. .. .. 371.0 60.0 .. 165.0 .. .. .. 600.0 Czech Republic .. 33.4 .. 13.4 876.0 7,960.9 356.0 4,718.9 263.7 126.7 36.5 314.6 Denmark 52.2 146.4 0.8 3.7 .. .. .. .. .. .. .. .. Dominican Republic 27.5 40.2 1.9 4.4 10.0 433.2 372.5 1,936.3 .. 833.9 .. .. Ecuador 13.6 27.9 1.2 5.2 51.2 728.8 .. 310.0 12.5 886.8 .. 550.0 Egypt, Arab Rep. 30.6 60.6 1.7 0.7 .. 2,895.4 .. 1,378.0 .. 1,057.2 6.0 .. El Salvador 20.1 40.3 0.0 1.5 .. 910.7 106.0 879.2 .. .. .. .. Eritrea .. 32.4 .. 3.3 .. 40.0 .. .. .. .. .. .. Estonia 20.2 29.2 2.1 4.4 211.7 629.0 .. 26.5 .. 299.4 .. 81.0 Ethiopia 19.5 26.7 0.1 1.2 .. .. .. .. .. .. .. .. Finland 86.5 60.0 0.6 6.2 .. .. .. .. .. .. .. .. France 96.1 87.2 1.1 3.6 .. .. .. .. .. .. .. .. Gabon 13.0 12.0 1.2 2.5 .. 35.0 .. 624.8 .. 46.7 .. .. Gambia, The 11.0 17.3 0.0 12.0 .. 6.6 .. .. .. .. .. .. Georgia .. 8.1 0.0 4.9 21.6 43.8 .. 36.0 .. .. .. .. Germany 90.6 118.9 0.2 1.9 .. .. .. .. .. .. .. .. Ghana 4.9 12.0 0.3 0.8 25.0 436.1 .. 132.8 .. 10.0 .. .. Greece 36.3 67.1 1.2 0.0 .. .. .. .. .. .. .. .. Guatemala 14.2 19.1 0.6 0.5 20.0 1,673.3 134.8 1,298.4 .. 33.8 .. .. Guinea 3.5 3.8 0.6 0.0 45.0 75.3 36.4 .. .. .. .. .. Guinea-Bissau 22.0 3.0 0.8 0.5 .. .. 23.2 .. .. .. .. .. Haiti 12.6 18.0 0.0 0.2 .. 19.5 4.7 .. .. .. .. .. 254 2004 World Development Indicators 5.1 STATES AND Private sector investment MARKET S Domestic Foreign direct Investment in infrastructure projects credit to investment with private participation a private sector $ millions % of GDP % of GDP Telecommunications Energy Transport Water and sanitation 1990 2002 1990 2002 1990­95 1996­2002 1990­95 1996­2002 1990­95 1996­2002 1990­95 1996­2002 Honduras 31.1 40.7 1.4 2.2 .. 71.1 95.3 86.8 .. 130.5 .. 220.0 Hungary 46.6 35.3 0.9 1.3 3,510.9 5,298.9 2,156.7 1,906.1 1,004.0 135.0 10.9 167.6 India 25.2 32.6 0.1 0.6 720.5 14,950.0 2,888.5 9,680.5 126.9 1,969.1 .. 216.0 Indonesia 46.9 22.3 1.0 ­0.9 3,549.0 9,215.5 3,202.5 7,534.7 1,204.9 2,314.6 3.8 919.5 Iran, Islamic Rep. 32.5 34.3 ­0.3 0.0 5.0 28.0 .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 47.6 110.3 1.3 20.3 .. .. .. .. .. .. .. .. Israel 57.6 97.8 0.3 1.6 .. .. .. .. .. .. .. .. Italy 56.5 82.3 0.6 1.2 .. .. .. .. .. .. .. .. Jamaica 36.1 15.7 3.0 6.1 .. 494.0 289.0 201.0 30.0 390.0 .. .. Japan 195.1 175.3 0.1 0.2 .. .. .. .. .. .. .. .. Jordan 72.3 73.5 0.9 0.6 43.0 967.9 .. .. .. 182.0 .. 209.0 Kazakhstan .. 18.6 0.4 10.5 30.0 1,849.5 .. 2,125.0 .. .. .. 40.0 Kenya 32.8 23.4 0.7 0.4 .. 107.0 .. 171.5 .. 53.4 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 65.5 115.6 0.3 0.4 2,650.0 17,600.0 .. 2,690.0 2,280.0 5,950.0 .. .. Kuwait 52.1 73.8 0.0 0.0 .. .. .. .. .. .. .. .. Kyrgyz Republic .. 4.2 0.0 0.3 .. 94.0 .. .. .. .. .. .. Lao PDR 1.0 8.4 0.7 1.5 .. 185.5 .. 535.5 .. 100.0 .. .. Latvia .. 29.0 0.6 4.5 230.0 894.9 .. 177.1 .. 75.0 .. .. Lebanon 79.4 90.8 0.2 1.5 100.0 550.9 .. .. .. 200.0 .. .. Lesotho 15.8 14.3 2.8 11.3 .. 33.5 .. .. .. .. .. .. Liberia 30.9 3.2 0.0 ­11.6 .. .. .. .. .. .. .. .. Libya 31.0 18.0 .. .. .. .. .. .. .. .. .. .. Lithuania .. 14.2 0.0 5.2 74.0 1,345.0 .. 20.0 .. .. .. .. Macedonia, FYR .. 17.7 .. 2.0 .. 607.3 .. .. .. .. .. .. Madagascar 16.9 9.3 0.7 0.2 5.0 10.1 .. .. .. 20.3 .. .. Malawi 10.9 4.1 1.2 0.3 8.0 25.5 .. .. .. 6.0 .. .. Malaysia 108.5 146.1 5.3 3.4 2,630.0 3,241.6 6,909.5 2,131.6 4,657.6 7,919.0 3,986.7 1,105.5 Mali 12.8 17.6 0.2 3.0 .. 42.7 0.1 747.0 .. .. .. .. Mauritania 43.5 31.7 0.7 1.2 .. 99.6 .. .. .. .. .. .. Mauritius 35.6 61.3 1.7 0.6 .. 365.6 .. 109.3 .. 42.6 .. .. Mexico 17.5 12.6 1.0 2.3 18,031.0 17,426.2 1.0 5,759.1 7,910.3 5,432.5 312.1 331.5 Moldova 5.9 17.6 0.0 6.8 .. 84.6 .. 85.3 .. .. .. .. Mongolia 19.0 18.8 .. 7.0 13.1 20.4 .. .. .. .. .. .. Morocco 34.0 54.4 0.6 1.2 .. 3,643.0 2,300.0 4,819.9 .. .. .. 1,000.0 Mozambique 17.6 2.1 0.4 11.3 .. 44.0 .. 1,200.0 .. 959.7 .. 0.6 Myanmar 4.7 12.1 .. .. 4.0 .. 394.0 .. .. 50.0 .. .. Namibia 22.6 48.4 .. .. 18.0 4.0 .. 5.0 .. 450.0 .. .. Nepal 12.8 30.7 0.0 0.2 .. 45.6 131.4 137.2 .. .. .. .. Netherlands 80.0 147.9 3.6 6.8 .. .. .. .. .. .. .. .. New Zealand 76.0 118.1 4.0 1.4 .. .. .. .. .. .. .. .. Nicaragua 112.6 30.8 0.0 4.3 9.9 162.2 .. 347.4 .. 104.0 .. .. Niger 12.3 5.0 1.6 0.4 .. 52.7 .. .. .. .. .. 4.9 Nigeria 9.4 17.8 2.1 2.9 .. 982.7 .. 225.0 .. 22.8 .. .. Norway 81.7 86.3 0.9 0.5 .. .. .. .. .. .. .. .. Oman 22.9 38.6 1.4 0.2 .. .. 204.5 998.3 .. 546.1 .. .. Pakistan 27.7 27.9 0.6 1.4 602.0 343.0 3,417.3 2,519.7 299.6 118.7 .. .. Panama 46.7 97.6 2.6 0.5 .. 1,429.2 .. 1,064.9 409.9 806.0 .. 25.0 Papua New Guinea 28.6 13.7 4.8 1.8 .. .. .. 65.0 .. .. .. 175.0 Paraguay 15.8 24.2 1.5 ­0.4 48.1 204.4 .. .. .. 58.0 .. .. Peru 11.8 23.1 0.2 4.2 2,568.7 5,412.0 1,207.8 3,095.7 6.6 315.8 .. 56.0 Philippines 22.3 36.4 1.2 1.4 1,279.0 6,700.0 6,831.3 7,013.1 300.0 2,007.5 .. 5,867.7 Poland 21.1 28.8 0.2 2.2 479.0 11,070.3 145.0 2,154.8 3.1 705.9 .. 22.1 Portugal 49.1 147.9 3.7 3.5 .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 255 5.1 Private sector investment Domestic Foreign direct Investment in infrastructure projects credit to investment with private participation a private sector $ millions % of GDP % of GDP Telecommunications Energy Transport Water and sanitation 1990 2002 1990 2002 1990­95 1996­2002 1990­95 1996­2002 1990­95 1996­2002 1990­95 1996­2002 Romania .. 8.3 0.0 2.5 5.0 2,735.0 .. 100.0 .. 23.4 .. 1,040.0 Russian Federation .. 17.6 0.0 0.9 918.0 6,467.2 1,100.0 2,295.3 .. 515.4 .. 108.0 Rwanda 6.9 10.3 0.3 0.2 .. 15.6 .. .. .. .. .. .. Saudi Arabia 54.7 58.2 .. .. .. .. .. .. .. .. .. .. Senegal 26.5 19.6 1.0 1.9 .. 406.8 .. 124.0 .. .. .. 6.3 Serbia and Montenegro .. .. .. 3.0 .. 1,929.5 .. .. .. .. .. .. Sierra Leone 3.2 3.5 5.0 0.6 .. 23.5 .. .. .. .. .. .. Singapore 96.8 115.5 15.1 7.0 .. .. .. .. .. .. .. .. Slovak Republic .. 40.6 .. 16.9 118.6 1,754.1 .. 3,184.6 .. .. .. .. Slovenia 34.9 39.2 0.9 8.5 .. .. .. .. .. .. .. .. Somalia .. .. 0.6 .. .. 2.0 .. .. .. .. .. .. South Africa 81.0 131.7 .. 0.7 1,072.8 10,654.8 3.0 1,244.3 .. 1,874.1 .. 212.5 Spain 80.2 111.1 2.7 3.3 .. .. .. .. .. .. .. .. Sri Lanka 19.6 28.5 0.5 1.5 43.6 849.6 21.7 286.6 .. 240.0 .. .. Sudan 4.8 5.0 0.0 4.7 .. 6.0 .. .. .. .. .. .. Swaziland 20.7 14.3 3.4 3.8 .. 33.6 .. .. .. .. .. .. Sweden 124.4 43.6 0.8 4.9 .. .. .. .. .. .. .. .. Switzerland 167.9 159.0 2.6 1.3 .. .. .. .. .. .. .. .. Syrian Arab Republic 7.5 8.0 0.6 1.1 .. 130.0 .. .. .. .. .. .. Tajikistan .. 18.8 0.5 0.7 .. 1.0 .. .. .. .. .. .. Tanzania 13.9 6.3 0.0 2.6 30.1 321.0 6.0 490.0 .. 23.0 .. .. Thailand 83.4 102.5 2.9 0.7 4,814.0 5,116.2 2,059.6 6,981.0 2,395.9 546.4 153.0 347.5 Togo 22.6 13.3 1.1 5.4 .. 5.0 .. .. .. .. .. .. Trinidad and Tobago 44.7 40.7 2.2 7.6 47.0 146.7 .. 207.0 .. .. .. 120.0 Tunisia 66.2 68.6 0.6 3.8 .. 277.0 627.0 265.0 .. .. .. .. Turkey 16.7 14.9 0.5 0.6 190.3 7,875.4 2,478.0 5,167.2 .. 724.8 .. 942.0 Turkmenistan .. 2.3 .. 1.3 .. .. .. .. .. .. .. .. Uganda 4.0 6.7 0.0 2.6 8.8 204.1 .. .. .. .. .. .. Ukraine 2.6 18.0 0.3 1.7 100.6 1,299.9 .. 160.0 .. .. .. .. United Arab Emirates 37.4 55.9 .. .. .. .. .. .. .. .. .. .. United Kingdom 115.8 142.6 3.4 1.8 .. .. .. .. .. .. .. .. United States 93.5 140.6 0.8 0.4 .. .. .. .. .. .. .. .. Uruguay 32.4 66.4 0.0 1.5 19.0 57.7 86.0 330.0 96.0 621.2 10.0 351.0 Uzbekistan .. .. 0.1 0.8 2.5 367.4 .. .. .. .. .. .. Venezuela, RB 25.4 9.8 0.9 0.7 4,603.3 6,446.7 .. 133.0 100.0 268.0 .. 44.0 Vietnam 2.5 43.1 2.8 4.0 128.0 18.0 .. 2,215.5 10.0 115.0 .. 212.8 West Bank and Gaza .. .. .. .. 65.0 410.6 .. 150.0 .. .. .. 9.5 Yemen, Rep. 6.1 6.2 ­2.7 1.1 25.0 340.0 .. .. .. 190.0 .. .. Zambia 8.9 6.2 6.2 5.3 .. 56.9 .. 289.4 .. .. .. .. Zimbabwe 23.0 37.0 ­0.1 0.3 .. 46.0 .. 603.0 18.0 70.0 .. .. World 97.5 w 118.1 w 1.0 w 2.0 w .. s .. s .. s .. s .. s .. s .. s .. s Low income 26.5 26.5 0.4 1.2 5,395.3 31,713.9 10,251.3 29,334.5 1,810.2 6,518.8 4.5 1,535.1 Middle income 42.9 62.2 0.9 2.8 56,414.0 228,094.2 53,083.3 156,285.5 32,299.2 80,769.9 10,008.0 27,245.8 Lower middle income 50.3 76.7 0.6 2.7 13,427.6 152,884.0 28,718.7 112,141.9 11,323.0 47,579.9 418.3 17,377.6 Upper middle income 27.3 34.5 1.4 2.9 42,986.4 75,210.2 24,364.6 44,143.6 20,976.2 33,190.0 9,589.7 9,868.2 Low & middle income 39.3 55.9 0.8 2.5 61,809.3 259,808.1 63,334.6 185,620.0 34,109.4 87,288.7 10,012.5 28,780.9 East Asia & Pacific 74.0 116.5 1.6 3.1 12,481.7 37,827.2 25,510.4 40,901.2 14,908.2 28,974.5 4,247.5 10,620.4 Europe & Central Asia .. 21.9 0.4 2.9 6,856.2 55,357.0 6,235.7 23,427.6 1,270.8 3,327.8 47.4 3,166.0 Latin America & Carib. 28.4 24.4 0.7 2.7 39,482.4 123,980.3 19,482.2 93,198.0 17,455.1 46,534.7 5,710.9 13,335.7 Middle East & N. Africa 39.5 50.2 0.6 0.9 238.0 9,744.3 5,431.5 7,611.2 .. 2,225.3 6.0 1,218.5 South Asia 24.6 31.8 0.1 0.7 1,512.1 16,852.6 6,458.9 13,664.2 426.5 2,352.8 .. 216.0 Sub-Saharan Africa 42.4 53.5 .. 2.5 1,238.9 16,046.7 215.9 6,817.8 48.8 3,873.6 0.7 224.3 High income 107.7 133.1 1.0 1.9 .. .. .. .. .. .. .. .. Europe EMU 79.8 102.8 1.1 5.0 .. .. .. .. .. .. .. .. a. Data refer to total for the period shown. 256 2004 World Development Indicators 5.1 STATES AND Private sector investment MARKET S About the data Definitions Private sector development and investment--that is, and extending their delivery to poor people. The · Domestic credit to private sector refers to finan- tapping private sector initiative and investment for privatization trend in infrastructure that began in the cial resources provided to the private sector--such socially useful purposes--are critical for poverty 1970s and 1980s took off in the 1990s. Developing as through loans, purchases of nonequity securities, reduction. In parallel with public sector efforts, pri- countries have been at the head of this wave, pio- and trade credits and other accounts receivable-- vate investment, especially in competitive markets, neering better approaches to providing infrastructure that establish a claim for repayment. For some coun- has tremendous potential to contribute to growth. services and reaping the benefits of greater compe- tries these claims include credit to public Private markets serve as the engine of productivity tition and customer focus. In 1990­2002 more than enterprises. · Foreign direct investment is net growth, creating productive jobs and higher incomes. 130 developing countries introduced private partici- inflows of investment to acquire a lasting manage- And with government playing a complementary role of pation in at least one infrastructure sector, awarding ment interest (10 percent or more of voting stock) in regulation, funding, and provision of services, private almost 2,500 projects attracting investment commit- an enterprise operating in an economy other than initiative and investment can help provide the basic ments of $750 billion. that of the investor. It is the sum of equity capital, services and conditions that empower the poor--by The data on investment in infrastructure projects with reinvestment of earnings, other long-term capital, improving health, education, and infrastructure. private participation refer to all investment (public and and short-term capital as shown in the balance of Credit is an important link in the money transmis- private) in projects in which a private company payments. · Investment in infrastructure projects sion process; it finances production, consumption, assumes operating risk during the operating period or with private participation covers infrastructure proj- and capital formation, which in turn affect the level assumes development and operating risk during the ects in telecommunications, energy (electricity and of economic activity. The data on domestic credit to contract period. Foreign state-owned companies are natural gas transmission and distribution), transport, the private sector are taken from the banking survey considered private entities for the purposes of this and water and sanitation that have reached financial of the International Monetary Fund's (IMF) measure. The data are from the World Bank's closure and directly or indirectly serve the public. International Financial Statistics or, when data are Private Participation in Infrastructure (PPI) Project Incinerators, movable assets, stand-alone solid unavailable, from its monetary survey. The monetary Database, which tracks almost 2,500 projects, newly waste projects, and small projects such as windmills survey includes monetary authorities (the central owned or managed by private companies, that are excluded. The types of projects included are bank), deposit money banks, and other banking insti- reached financial closure in low- and middle-income operation and management contracts, operation and tutions, such as finance companies, development economies in 1990­2002. For more information, see management contracts with major capital expendi- banks, and savings and loan institutions. In some http://www.worldbank.org/privatesector/ppi/ppi_ ture, greenfield projects (in which a private entity or cases credit to the private sector may include credit database.htm. a public-private joint venture builds and operates a to state-owned or partially state-owned enterprises. new facility), and divestiture. The statistics on foreign direct investment are based on balance of payments data reported by the IMF, supplemented by data on net foreign direct investment reported by the Organisation for Economic Co-operation and Development and official national sources. (For a detailed discussion of data on foreign direct investment, see About the data for table 6.7). Private participation in infrastructure has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, 5.1a Foreign direct investment has expanded rapidly in many developing countries, contributing to increased productivity Data sources Net inflows of foreign direct investment (% of GDP) The data on domestic credit are from the IMF's International Financial Statistics. The data on foreign 20 Slovak Republic direct investment are based on estimates compiled 15 by the IMF in its Balance of Payments Statistics Czech Republic Yearbook, supplemented by World Bank staff 10 Moldova estimates. The data on investment in infrastructure 5 projects with private participation are from the Togo World Bank's Private Participation in Infrastructure 0 (PPI) Project Database (http://www.worldbank.org/ 1995 1996 1997 1998 1999 2000 2001 2002 privatesector/ppi/ppi_database.htm). Source: World Bank data files. 2004 World Development Indicators 257 5.2 Investment climate Credit markets Composite Institutional Euromoney Moody's Standard & Poor's Creditor Public Private ICRG risk Investor country sovereign sovereign long-term rights registry bureau rating a credit credit- long-term debt rating a index coverage coverage rating a worthiness debt rating a range borrowers borrowers rating a 0 (weak) to per 1,000 per 1,000 Foreign Domestic Foreign Domestic 4 (strong) adults adults currency currency currency currency January January January December September September January January January January 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 Afghanistan .. .. .. .. 7.6 7.8 .. .. .. .. Albania 3 0 0 66.8 17.0 34.5 .. .. .. .. Algeria 1 0 0 66.3 41.6 41.3 .. .. .. .. Angola 3 19 0 53.3 17.0 26.9 .. .. .. .. Argentina 1 202 645 64.0 18.4 25.8 Caa1 B3 SD SD Armenia 2 0 0 62.3 17.9 33.8 .. .. .. .. Australia 3 0 897 81.8 84.3 91.7 Aaa Aaa AAA AAA Austria 3 10 366 86.0 90.3 92.4 Aaa Aaa AAA AAA Azerbaijan 3 0 0 69.0 30.4 43.3 .. .. .. .. Bangladesh 2 2 0 63.0 28.6 38.3 .. .. .. .. Belarus 2 .. 0 65.3 17.5 32.0 .. .. .. .. Belgium 2 82 50 85.3 87.2 90.9 Aa1 Aa1 AA+ AA+ Benin 1 2 0 .. 20.2 30.9 .. .. B+ B+ Bolivia 2 88 213 65.8 27.5 37.5 B3 B3 B­ B­ Bosnia and Herzegovina 3 0 80 .. 26.0 35.6 .. .. .. .. Botswana 3 0 615 79.8 62.2 60.3 A2 A1 A A+ Brazil 1 60 602 65.5 37.1 47.6 B2 B2 B+ BB Bulgaria 3 6 0 71.8 47.0 50.7 Ba2 Ba2 BB+ BBB Burkina Faso 1 2 0 57.8 17.8 31.0 .. .. .. .. Burundi 1 1 0 .. 10.5 25.0 .. .. .. .. Cambodia 2 0 0 .. 18.7 35.0 .. .. .. .. Cameroon 1 1 0 62.0 19.9 31.3 .. .. B B Canada 1 0 976 85.8 90.3 92.1 Aaa Aaa AAA AAA Central African Republic 2 1 0 .. 12.8 26.2 .. .. .. .. Chad 1 0 0 .. 14.4 27.7 .. .. .. .. Chile 2 284 1,000 77.0 65.2 66.3 Baa1 A1 A AA China 2 4 0 77.3 59.9 61.5 A2 .. BBB .. Hong Kong, China 4 0 242 82.0 67.8 80.6 A1 Aa3 A+ AA­ Colombia 0 0 269 63.5 37.2 47.2 Ba2 Baa2 BB BBB Congo, Dem. Rep. 2 0 0 47.0 7.3 18.4 .. .. .. .. Congo, Rep. 0 0 0 48.8 12.6 26.7 .. .. .. .. Costa Rica 1 10 78 72.0 44.4 54.8 Ba1 Ba1 BB BB+ Côte d'Ivoire 1 2 0 55.8 15.7 26.6 .. .. .. .. Croatia 3 0 0 72.0 50.9 57.1 Baa3 Baa1 BBB­ BBB+ Cuba .. .. .. 60.3 12.3 12.0 Caa1 .. .. .. Czech Republic 3 12 163 77.8 65.6 66.1 A1 A1 A­ A+ Denmark 3 0 71 85.5 91.0 95.3 Aaa Aaa AAA AAA Dominican Republic 2 .. 617 60.3 36.6 43.4 B2 B2 CCC CCC Ecuador 1 121 0 62.5 24.2 36.2 Caa2 Caa1 CCC+ CCC+ Egypt, Arab Rep. 1 .. 0 66.0 41.1 49.2 Ba1 Baa1 BB+ BBB­ El Salvador 3 197 192 69.8 46.4 49.3 Baa3 Baa2 BB+ BB+ Eritrea .. .. .. .. 12.0 26.0 .. .. .. .. Estonia .. .. .. 75.0 61.5 64.5 A1 A1 A­ A­ Ethiopia 3 0 0 59.3 16.1 29.4 .. .. .. .. Finland 1 0 842 86.8 90.6 93.8 Aaa Aaa AAA AAA France 0 15 0 79.0 91.7 91.1 Aaa Aaa AAA AAA Gabon .. .. .. 65.3 22.7 34.1 .. .. .. .. Gambia, The .. .. .. 67.0 17.8 32.2 .. .. .. .. Georgia .. 0 0 .. 18.4 26.5 .. .. .. .. Germany 3 6 813 81.8 86.8 90.3 Aaa Aaa AAA AAA Ghana 1 0 1 62.8 25.8 35.0 .. .. B+ B+ Greece 1 0 100 76.0 73.1 80.7 A1 A1 A+ A+ Guatemala 1 0 65 67.0 32.3 44.6 Ba2 Ba1 BB­ BB Guinea 1 0 0 62.0 16.5 27.2 .. .. .. .. Guinea-Bissau .. .. .. 46.5 10.6 23.0 .. .. .. .. Haiti 2 2 0 52.0 15.8 24.4 .. .. .. .. 258 2004 World Development Indicators 5.2 STATES AND Investment climate MARKET S Credit markets Composite Institutional Euromoney Moody's Standard & Poor's Creditor Public Private ICRG risk Investor country sovereign sovereign long-term rights registry bureau rating a credit credit- long-term debt rating a index coverage coverage rating a worthiness debt rating a range borrowers borrowers rating a 0 (weak) to per 1,000 per 1,000 Foreign Domestic Foreign Domestic 4 (strong) adults adults currency currency currency currency January January January December September September January January January January 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 Honduras 2 74 0 62.3 25.3 39.2 B2 B2 .. .. Hungary 2 0 17 76.5 65.4 68.8 A1 A1 A­ A India 3 0 0 69.0 48.0 54.9 Ba1 Ba2 BB BB+ Indonesia 2 4 0 60.8 30.3 40.0 B2 B2 B B+ Iran, Islamic Rep. 2 .. 0 70.5 36.6 45.1 .. .. .. .. Iraq .. .. .. 42.0 8.4 4.3 .. .. .. .. Ireland 1 0 917 87.3 87.5 92.3 Aaa Aaa AAA AAA Israel 3 0 64 72.5 53.4 68.0 A2 A2 A­ A+ Italy 1 63 482 80.0 83.1 86.9 Aa2 Aa2 AA AA Jamaica 2 0 0 69.5 27.8 43.3 B1 Ba2 B B+ Japan 2 0 907 86.5 77.2 90.0 Aa1 A2 AA­ AA­ Jordan 1 30 0 71.0 38.5 44.1 Ba2 Baa3 BB BBB Kazakhstan .. 0 0 72.3 41.4 50.3 Baa3 Baa1 BB+ BBB Kenya 4 0 526 65.8 24.6 36.1 .. .. .. .. Korea, Dem. Rep. .. .. .. 53.5 7.5 3.3 .. .. .. .. Korea, Rep. 3 0 672 80.8 68.5 67.7 A3 A3 A­ A+ Kuwait 2 0 207 86.3 79.2 73.9 A2 A2 A+ A+ Kyrgyz Republic .. 0 0 .. 16.7 28.1 .. .. .. .. Lao PDR 0 0 0 .. 19.8 33.0 .. .. .. .. Latvia .. 0 0 78.3 51.5 62.1 A2 A2 BBB+ A­ Lebanon 4 0 0 55.5 25.2 38.1 B2 B3 B­ B­ Lesotho 2 0 0 .. 29.5 33.7 .. .. .. .. Liberia .. .. .. 36.0 6.6 11.6 .. .. .. .. Libya .. .. .. 74.0 34.2 21.9 .. .. .. .. Lithuania .. 9 0 76.5 55.6 62.0 A3 A3 BBB+ A­ Macedonia, FYR 3 3 0 .. 25.3 36.1 .. .. .. .. Madagascar 2 3 0 60.0 15.8 28.0 .. .. .. .. Malawi 2 0 0 54.0 18.8 31.3 .. .. .. .. Malaysia 2 154 676 75.3 61.7 62.1 Baa1 A3 A­ A+ Mali 1 1 0 58.5 18.4 30.4 .. .. .. .. Mauritania 3 0 0 .. 18.6 26.7 .. .. .. .. Mauritius .. .. .. .. 53.9 54.9 Baa2 A2 .. .. Mexico 0 0 562 71.5 54.8 61.1 Baa2 Baa1 BBB­ A­ Moldova .. 0 0 64.5 18.7 31.5 Caa1 Caa1 .. .. Mongolia 1 23 0 63.8 22.9 37.3 .. .. B B Morocco 1 .. 0 75.0 49.4 53.8 Ba1 Ba1 BB BBB Mozambique 2 1 0 61.3 20.6 32.5 .. .. .. .. Myanmar .. .. .. 59.5 13.5 20.4 .. .. .. .. Namibia .. 0 .. 76.5 39.8 24.5 .. .. .. .. Nepal 2 0 0 .. 23.8 37.2 .. .. .. .. Netherlands 3 0 645 85.0 92.2 93.5 Aaa Aaa AAA AAA New Zealand 4 0 1,000 81.8 81.1 87.1 Aaa Aaa AA+ AAA Nicaragua 4 83 0 54.3 18.0 24.2 Caa1 B3 .. .. Niger 1 1 0 57.5 14.7 30.5 .. .. .. .. Nigeria 4 0 0 57.0 20.2 33.5 .. .. .. .. Norway 2 0 1,000 90.5 92.9 97.8 Aaa Aaa AAA AAA Oman 0 0 0 81.0 56.5 61.3 Baa2 Baa2 BBB BBB+ Pakistan 1 1 0 63.5 26.2 32.0 B2 B2 B BB­ Panama 4 0 428 71.5 45.0 49.8 Ba1 .. BB BB Papua New Guinea 2 0 0 59.0 28.9 37.3 B1 B1 B B+ Paraguay 2 0 .. 62.5 22.4 34.7 Caa1 Caa1 SD CCC Peru 0 133 267 68.3 38.3 45.5 Ba3 Baa3 BB­ BB+ Philippines 1 0 33 69.3 43.8 51.3 Ba1 Baa3 BB BBB Poland 2 0 665 75.0 61.1 64.0 A2 A2 BBB+ A­ Portugal 1 610 30 78.5 80.4 84.3 Aa2 Aa2 AA AA Puerto Rico 1 0 643 .. .. .. .. .. .. .. 2004 World Development Indicators 259 5.2 Investment climate Credit markets Composite Institutional Euromoney Moody's Standard & Poor's Creditor Public Private ICRG risk Investor country sovereign sovereign long-term rights registry bureau rating a credit credit- long-term debt rating a index coverage coverage rating a worthiness debt rating a range borrowers borrowers rating a 0 (weak) to per 1,000 per 1,000 Foreign Domestic Foreign Domestic 4 (strong) adults adults currency currency currency currency January January January December September September January January January January 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 Romania 0 1 0 70.5 41.3 49.8 Ba3 Ba3 BB BB+ Russian Federation 2 0 0 75.0 45.1 49.0 Baa3 Baa3 BB BB+ Rwanda 1 1 0 .. 8.2 24.2 .. .. .. .. Saudi Arabia 2 0 0 76.5 52.4 65.7 Baa2 Baa1 A+ A+ Senegal 1 3 0 64.8 29.2 39.6 .. .. B+ B+ Serbia and Montenegro 2 0 0 55.3 16.1 31.5 .. .. .. .. Sierra Leone 2 0 0 51.3 8.5 22.2 .. .. Singapore 3 0 640 87.5 84.2 89.1 Aaa Aaa AAA AAA Slovak Republic .. 3 0 74.3 57.8 59.1 A3 A3 BBB A­ Slovenia 3 16 0 79.5 69.2 76.1 Aa3 Aa3 A+ AA Somalia .. .. .. 45.5 6.5 13.2 .. .. .. .. South Africa 3 0 684 68.8 54.6 60.4 Baa2 A2 BBB A Spain 2 344 55 80.0 85.7 87.2 Aaa Aaa AA+ AA+ Sri Lanka 2 12 0 63.5 34.1 44.3 .. .. .. .. Sudan .. .. .. 54.3 10.5 26.4 .. .. .. .. Swaziland .. .. .. .. 30.7 33.1 .. .. .. .. Sweden 1 0 592 86.5 89.3 93.8 Aaa Aaa AA+ AAA Switzerland 1 0 213 91.0 94.0 97.5 Aaa Aaa AAA AAA Syrian Arab Republic .. 0 0 70.3 22.7 33.5 .. .. .. .. Tajikistan .. .. .. .. 14.3 29.9 .. .. .. .. Tanzania 2 0 0 57.8 21.8 37.0 .. .. .. .. Thailand 3 0 127 76.5 56.9 59.5 Baa1 Baa1 BBB A Togo 2 .. 0 58.3 17.3 28.1 .. .. .. .. Trinidad and Tobago .. .. .. 76.5 54.2 61.0 Baa3 Baa1 BBB A­ Tunisia 0 6 0 72.8 52.6 57.7 Baa2 Baa2 BBB A Turkey 2 10 266 62.8 32.4 45.2 B1 B3 B+ B+ Turkmenistan .. .. .. .. 20.8 32.2 B2 B2 .. .. Uganda .. 0 0 62.3 20.1 37.8 .. .. .. .. Ukraine 2 0 0 68.8 32.5 39.0 B1 B1 B B United Arab Emirates 2 15 0 84.5 64.7 72.3 A2 .. .. .. United Kingdom 4 0 813 83.8 92.3 93.9 Aaa Aaa AAA AAA United States 1 0 1,000 75.8 92.8 96.6 Aaa Aaa AAA AAA Uruguay .. 65 630 64.5 27.3 39.8 B3 B3 B­ B­ Uzbekistan 2 0 0 .. 20.5 33.9 .. .. .. .. Venezuela, RB 2 141 0 58.3 27.1 34.6 Caa1 Caa1 B­ B­ Vietnam 0 3 0 69.8 37.7 47.8 B1 .. BB­ BB West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 0 12 0 67.0 24.3 33.0 .. .. .. .. Zambia 1 0 0 53.0 15.3 26.3 .. .. .. .. Zimbabwe 4 0 0 34.3 11.0 22.6 .. .. .. .. World 2 u 24 u 182 u 68.9 m 30.4 m 39.6 m Low income 2 4 11 58.8 17.9 30.1 Middle income 2 32 168 70.4 39.8 47.2 Lower middle income 2 24 100 68.3 36.6 44.1 Upper middle income 2 46 288 75.0 54.0 60.6 Low & middle income 2 18 93 65.1 25.3 35.6 East Asia & Pacific 2 19 84 66.6 29.6 38.7 Europe & Central Asia 2 2 50 72.0 32.5 44.3 Latin America & Carib. 2 77 293 65.0 30.0 43.3 Middle East & N. Africa 1 6 0 70.5 38.5 44.1 South Asia 2 3 0 63.5 27.4 37.8 Sub-Saharan Africa 2 1 57 58.0 17.5 28.7 High income 2 40 491 83.4 86.3 90.6 Europe EMU 2 103 391 81.8 87.2 90.9 a. This copyrighted material is reprinted with permission from the following data providers: PRS Group, Inc., 6320 Fly Road, Suite 102, East Syracuse, NY 13057; Institutional Investor Inc., 225 Park Ave. South, New York, NY 10003; Euromoney Publications PLC, Nestor House, Playhouse Yard, London EC4V 5EX, UK; Moody's Investors Service, 99 Church Street, New York, NY 10007; and Standard & Poor's Rating Services, The McGraw-Hill Companies, Inc., 1221 Avenue of the Americas, New York, NY 10020. Prior written consent from the original data providers cited must be obtained for third-party use of these data. 260 2004 World Development Indicators 5.2 STATES AND Investment climate MARKET S About the data Definitions This year the table includes newly developed meas- be highly subjective, reflecting external perceptions · Creditor rights index measures four powers of ures of the credit market: a creditor rights index, pub- that do not always capture the actual situation in a secured lenders in liquidation and reorganization: lic credit registry coverage, and private credit bureau country. But these subjective perceptions are the there are restrictions on entering reorganization, coverage. The data are from the World Bank's Doing reality that policymakers face. Countries not rated by there is no automatic stay (or asset freeze), secured Business database. credit risk rating agencies typically do not attract reg- creditors are paid first, and management does not As investment portfolios become increasingly glob- istered flows of private capital. The risk ratings pre- stay in reorganization. · Public registry coverage and al, investors as well as governments seeking to attract sented here are included for their analytical private bureau coverage measure the number of bor- investment must have a good understanding of the usefulness and are not endorsed by the World Bank. rowers with records contained in either the public investment climate. This table includes data on credit The PRS Group's International Country Risk Guide credit registry or private credit bureau, expressed as market risks and indicators of creditworthiness ratings (ICRG) collects information on 22 components of a percentage of the adult population. A score of 0 from several major international rating services. risk, groups it into three major categories (political, indicates that a public registry or private bureau does Lack of access to credit is one of the biggest bar- financial, and economic), and converts it into a sin- not operate in the country. The maximum score is riers entrepreneurs face in starting and operating a gle numerical risk assessment ranging from 0 to 1,000. · Composite International Country Risk business. And this in turn affects growth in the econ- 100. Ratings below 50 indicate very high risk, and Guide (ICRG) risk rating is an overall index, ranging omy and opportunities for improved livelihoods. those above 80 very low risk. Ratings are updated from 0 to 100 (highest risk to lowest), based on 22 Information on credit histories made available in monthly. components of risk. · Institutional Investor credit credit registries is one way for creditors to assess Institutional Investor country credit ratings are rating ranks, from 0 to 100 (highest risk to lowest), risk and allocate credit more efficiently. Information based on information provided by leading interna- the chances of a country's default. · Euromoney on creditor rights and how well collateral systems tional banks. Responses are weighted using a for- country creditworthiness rating ranks, from 0 to 100 facilitate access to credit offers an additional insti- mula that gives more importance to responses from (highest risk to lowest), the risk of investing in an tutional solution to expanding credit. The creditor banks with greater worldwide exposure and more economy. · Moody's sovereign foreign or domestic rights index is an indicator of the powers of secured sophisticated country analysis systems. Countries currency long-term debt rating assesses the risk of lenders in liquidation and reorganization. This com- are rated on a scale of 0 to 100 (highest risk to low- lending to governments. An entity's capacity to meet posite index captures information on issues related est), and ratings are updated every six months. its senior financial obligations is rated from AAA to reorganization of insolvent companies, the ability Euromoney country creditworthiness ratings are (offering exceptional financial security) to C (usually in of secured creditors to seize collateral if there is a based on nine weighted categories (covering debt, default, with potential recovery values low). Modifiers reorganization, whether secured creditors are paid economic performance, political risk, and access to 1­3 are applied to ratings from AA to B, with 1 indi- first from proceeds from liquidating a bankrupt firm, financial and capital markets) that assess country cating a high ranking in the rating category. and whether management remains in power during a risk. The ratings, also on a scale of 0 to 100 (highest · Standard & Poor's sovereign foreign or domestic reorganization. The index ranges from 0 for weak risk to lowest), are based on polls of economists and currency long-term debt rating ranges from AAA creditor rights to 4 for strong creditor rights. A public political analysts supplemented by quantitative data (extremely strong capacity to meet financial commit- credit registry is a database owned by public author- such as debt ratios and access to capital markets. ments) to CCC (currently highly vulnerable). Ratings ities (usually the central bank or banking superviso- Moody's sovereign long-term debt ratings are opin- from AA to CCC may be modified by a plus or minus ry) that collect information on the standing of ions of the capacity of entities to honor senior unse- sign to show relative standing in the category. An borrowers in the financial system and make it avail- cured financial obligations and contracts obligor rated SD (selective default) has failed to pay able to financial institutions. A private credit bureau denominated in foreign currency (foreign currency one or more financial obligations when due. is a private firm or nonprofit organization that main- issuer ratings) or in domestic currency (domestic cur- tains a database on the standing of borrowers in the rency issuer ratings). financial system. Its primary role is to facilitate Standard & Poor's ratings of sovereign long-term exchange of information among banks and financial foreign and domestic currency debt are based on institutions. Coverage of public credit registries and current information furnished by obligors or obtained Data sources private credit bureaus provides an indication of how by Standard & Poor's from other sources it considers The data on credit markets are from the World many borrowers, as a percentage of the adult popu- reliable. A Standard & Poor's issuer credit rating Bank's Doing Business project (http://rru. lation, have information on their payment histories (one form of which is a sovereign credit rating) is a worldbank.org/DoingBusiness/). The country risk available in credit registries. A score of 0 indicates current opinion of an obligor's capacity and willing- and creditworthiness ratings are from the PRS that a public registry or private bureau does not oper- ness to pay its financial obligations as they come Group's monthly International Country Risk Guide ate in the country. The maximum score is 1,000. due (its creditworthiness). This opinion does not (http://www.ICRGonline.com); the monthly Institu- Most risk ratings are numerical or alphabetical apply to any specific financial obligation, as it does tional Investor; the monthly Euromoney; Moody's indexes, with a higher number or a letter closer to the not take into account the nature and provisions of Investors Service's Sovereign, Subnational and beginning of the alphabet meaning lower risk (a good obligations, their standing in bankruptcy or liquida- Sovereign-Guaranteed Issuers; and Standard & prospect). (For more on the rating processes of the tion, statutory preferences, or the legality and Poor's Sovereign List in Credit Week. rating agencies, see Data sources.) Risk ratings may enforceability of obligations. 2004 World Development Indicators 261 5.3 Business environment Entry regulations Contract enforcement Insolvency Labor regulations Cost to Cost to Time to % of GNI per capita Time enforce a Time to resolve Employment Number of start Cost to Minimum Procedures to enforce contract resolve insolvency laws index start-up a business register capital to enforce a contract % of insolvency % of insol- range procedures days a business requirement a contract days GNI per capita years vency estate 0 (less rigid) to January January January January January January January January January 100 (very rigid) 2003 2003 2003 2003 2003 2003 2003 2003 2003 January 2003 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 11 47 65 52 37 220 73 2.0 38 41 Algeria 18 29 32 73 20 387 13 3.5 4 46 Angola 14 146 838 174 46 865 16 .. .. 78 Argentina 15 68 8 0 32 300 9 2.8 18 66 Armenia 10 25 9 11 22 65 15 1.9 4 57 Australia 2 2 2 0 11 320 8 1.0 18 36 Austria 9 29 7 141 20 434 1 1.3 18 30 Azerbaijan 14 106 17 0 25 115 3 2.7 8 63 Bangladesh 7 30 76 0 15 270 270 .. .. 50 Belarus 19 118 27 111 19 135 44 2.2 4 77 Belgium 7 56 11 75 22 365 9 0.9 4 48 Benin 9 63 189 378 44 248 31 3.2 18 52 Bolivia 18 67 167 0 44 464 5 2.0 18 66 Bosnia and Herzegovina 12 59 52 379 31 630 21 1.9 8 49 Botswana 10 97 36 0 22 56 0 2.2 18 35 Brazil 15 152 12 0 16 380 2 10.0 8 78 Bulgaria 10 30 8 134 26 410 6 3.8 18 53 Burkina Faso 15 136 325 652 24 376 173 4.0 8 53 Burundi 11 17 253 0 62 367 28 .. .. 62 Cambodia 11 94 554 1,826 18 210 269 .. .. 54 Cameroon 12 37 191 244 46 548 63 2.0 18 44 Canada 2 3 1 0 17 425 28 0.8 4 34 Central African Republic .. .. .. .. .. .. 0 .. .. 62 Chad 19 73 395 652 50 604 58 10.0 38 66 Chile 10 28 12 0 21 200 15 5.8 18 50 China 11 46 14 3,856 20 180 32 2.6 18 47 Hong Kong, China 5 11 2 0 17 180 7 1.0 18 27 Colombia 19 60 27 0 37 527 6 3.0 1 59 Congo, Dem. Rep. 13 215 872 321 55 414 92 .. .. 60 Congo, Rep. 8 67 271 205 44 500 51 3.0 18 60 Costa Rica 11 80 21 0 21 370 23 2.5 18 63 Côte d'Ivoire 10 77 143 235 18 150 83 2.2 18 53 Croatia 13 50 18 51 20 330 7 3.1 18 65 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 10 88 12 110 16 270 19 9.2 38 36 Denmark 4 4 0 52 14 83 4 4.2 8 25 Dominican Republic 12 78 48 23 19 495 441 3.5 4 49 Ecuador 14 90 63 28 33 332 11 3.5 18 55 Egypt, Arab Rep. 13 43 61 789 19 202 31 4.3 18 59 El Salvador 12 115 130 550 42 240 7 .. .. 69 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia 8 44 422 1,756 24 895 35 2.2 8 51 Finland 4 33 3 32 19 240 16 0.9 1 55 France 10 53 3 32 21 210 4 2.4 18 50 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. Georgia 9 30 26 140 17 180 63 3.2 1 55 Germany 9 45 6 104 22 154 6 1.2 8 51 Ghana 10 84 112 1 21 90 24 1.6 18 35 Greece 16 45 70 145 15 315 8 2.2 8 67 Guatemala 13 39 67 37 19 1,460 20 4.0 18 65 Guinea 13 71 229 397 41 150 40 .. .. 60 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 12 203 199 210 41 76 18 .. .. 60 262 2004 World Development Indicators 5.3 STATES AND Business environment MARKET S Entry regulations Contract enforcement Insolvency Labor regulations Cost to Cost to Time to % of GNI per capita Time enforce a Time to resolve Employment Number of start Cost to Minimum Procedures to enforce contract resolve insolvency laws index start-up a business register capital to enforce a contract % of insolvency % of insol- range procedures days a business requirement a contract days GNI per capita years vency estate 0 (less rigid) to January January January January January January January January January 100 (very rigid) 2003 2003 2003 2003 2003 2003 2003 2003 2003 January 2003 Honduras 14 80 73 165 32 225 7 .. .. 56 Hungary 5 65 64 220 17 365 5 2.0 38 54 India 10 88 50 430 11 365 95 11.3 8 51 Indonesia 11 168 15 303 0 225 269 6.0 18 57 Iran, Islamic Rep. 9 48 7 7 23 150 6 1.8 8 52 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 3 12 10 0 16 183 7 0.4 8 49 Israel 5 34 5 0 19 315 34 4.0 38 38 Italy 9 23 24 50 16 645 4 1.3 18 59 Jamaica 7 31 16 0 14 202 42 1.1 18 34 Japan 11 31 11 71 16 60 6 0.6 4 37 Jordan 14 98 50 2,404 32 147 0 4.3 8 60 Kazakhstan 10 25 10 35 41 120 8 3.3 18 55 Kenya 11 61 54 0 25 255 50 4.6 18 34 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 12 33 18 403 23 75 5 1.5 4 51 Kuwait 12 33 2 911 17 195 4 4.2 1 41 Kyrgyz Republic 9 26 13 75 44 365 255 4.0 4 64 Lao PDR 9 198 20 151 .. .. 0 .. .. 54 Latvia 7 11 15 93 19 189 8 1.2 4 62 Lebanon 6 46 130 83 27 721 54 4.0 18 46 Lesotho 9 92 68 20 .. .. 0 .. .. 45 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania 9 26 6 74 17 74 13 1.2 18 64 Macedonia, FYR 13 48 13 138 27 509 43 3.6 38 50 Madagascar 15 67 63 31 29 166 120 2.2 18 61 Malawi 11 45 125 0 16 108 521 2.8 8 52 Malaysia 8 31 27 0 22 270 19 2.2 18 25 Mali 13 61 232 598 27 150 7 3.5 18 54 Mauritania 11 73 110 897 .. .. 0 8.0 8 59 Mauritius .. .. .. .. .. .. .. .. .. .. Mexico 7 51 19 88 47 325 10 2.0 18 77 Moldova 11 42 26 86 36 210 14 2.8 8 67 Mongolia 8 31 12 2,047 26 224 2 4.0 8 50 Morocco 11 36 19 763 17 192 9 1.9 18 51 Mozambique 15 153 100 30 18 540 9 .. .. 74 Myanmar .. .. .. .. .. .. .. .. .. .. Namibia 10 85 19 0 .. .. 0 .. .. 43 Nepal 8 25 191 0 24 350 44 5.0 8 45 Netherlands 7 11 14 71 21 39 1 2.6 1 54 New Zealand 3 3 0 0 19 50 12 2.0 4 32 Nicaragua 12 71 338 0 17 125 18 2.3 8 61 Niger 11 27 447 844 29 365 57 5.0 18 59 Nigeria 10 44 92 29 23 730 7 1.6 18 43 Norway 4 24 4 33 12 87 10 0.9 1 41 Oman 9 34 5 721 17 250 5 7.0 4 54 Pakistan 10 22 47 0 30 365 46 2.8 4 58 Panama 7 19 26 0 44 197 20 6.5 38 79 Papua New Guinea 7 69 26 0 22 270 41 .. .. 26 Paraguay 18 73 156 0 46 188 34 3.9 8 73 Peru 9 100 25 0 35 441 30 2.1 8 73 Philippines 11 59 24 10 28 164 104 5.7 38 60 Poland 12 31 20 21 18 1,000 11 1.5 18 55 Portugal 11 95 13 43 22 420 5 2.6 8 79 Puerto Rico 6 6 3 0 55 365 21 3.8 8 41 2004 World Development Indicators 263 5.3 Business environment Entry regulations Contract enforcement Insolvency Labor regulations Cost to Cost to Time to % of GNI per capita Time enforce a Time to resolve Employment Number of start Cost to Minimum Procedures to enforce contract resolve insolvency laws index start-up a business register capital to enforce a contract % of insolvency % of insol- range procedures days a business requirement a contract days GNI per capita years vency estate 0 (less rigid) to January January January January January January January January January 100 (very rigid) 2003 2003 2003 2003 2003 2003 2003 2003 2003 January 2003 Romania 6 27 12 3 28 225 13 3.2 8 54 Russian Federation 12 29 9 30 16 160 20 1.5 4 61 Rwanda 9 43 232 457 0 .. 87 .. .. 60 Saudi Arabia 14 95 131 1,611 19 195 0 3.0 18 36 Senegal 9 58 124 296 30 335 49 3.0 8 54 Serbia and Montenegro 10 44 13 357 40 1,028 20 7.3 38 56 Sierra Leone 9 26 1,298 0 48 114 8 2.5 38 67 Singapore 7 8 1 0 23 50 14 0.7 1 20 Slovak Republic 10 98 10 112 26 420 13 4.8 18 61 Slovenia 10 61 16 89 22 1,003 4 3.7 18 59 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 9 38 9 0 26 207 17 2.0 18 36 Spain 11 115 19 20 20 147 11 1.5 8 70 Sri Lanka 8 58 18 0 17 440 8 2.3 18 42 Sudan .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. Sweden 3 16 1 41 19 190 8 2.0 8 42 Switzerland 6 20 9 34 14 224 4 4.6 4 36 Syrian Arab Republic 10 42 17 5,627 36 596 31 4.1 8 45 Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania 13 35 199 0 14 127 4 3.0 8 61 Thailand 9 42 7 0 19 210 30 2.6 38 61 Togo 14 63 281 531 43 503 21 .. .. 57 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. Tunisia 10 46 16 352 14 7 4 2.5 8 57 Turkey 13 38 37 13 18 105 5 1.8 8 55 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 17 36 135 0 16 99 10 2.0 38 42 Ukraine 14 40 27 451 20 224 11 3.0 18 73 United Arab Emirates 10 29 25 404 27 559 11 5.0 38 45 United Kingdom 6 18 1 0 12 101 1 1.0 8 28 United States 5 4 1 0 17 365 0 3.0 4 22 Uruguay 10 27 47 699 38 360 14 4.0 8 39 Uzbekistan 9 33 16 64 34 258 2 3.3 4 55 Venezuela, RB 14 119 19 0 41 360 47 4.0 38 75 Vietnam 11 63 30 0 28 120 9 2.0 18 56 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 13 96 264 1,717 27 240 1 2.4 4 43 Zambia 6 40 24 138 16 188 16 3.7 8 46 Zimbabwe 10 122 285 0 13 197 40 2.3 18 27 World 10 u 57 u 93 u 297 u 25 u 307 u 36 u 3.2 u 14 u 53 u Low income 11 74 213 339 28 304 65 3.8 13 54 Middle income 11 57 36 369 26 332 27 3.4 17 56 Lower middle income 12 58 38 455 27 333 32 3.3 15 56 Upper middle income 10 56 33 204 25 329 15 3.6 20 55 Low & middle income 11 65 118 355 27 319 44 3.6 15 55 East Asia & Pacific 10 80 73 819 20 208 77 3.8 23 49 Europe & Central Asia 11 47 22 115 26 317 29 3.2 15 58 Latin America & Carib. 12 78 74 90 32 363 39 3.7 16 62 Middle East & N. Africa 12 56 67 1,286 23 281 14 3.5 11 50 South Asia 9 45 76 86 19 358 93 5.3 10 49 Sub-Saharan Africa 11 72 255 278 30 334 52 3.5 18 53 High income 7 30 9 99 19 267 8 2.1 10 44 Europe EMU 9 47 16 65 19 287 6 1.6 9 56 264 2004 World Development Indicators 5.3 STATES AND Business environment MARKET S About the data Definitions The table presents key indicators on the environment distressed companies is the insolvency system. Two · Start-up procedures are those required to start a for doing business. The indicators, covering entry indicators measure the time it takes to resolve insol- business. Procedures are interactions of a company regulations, contract enforcement, insolvency, and vency and the associated costs. With effective insol- with external parties (government agencies, lawyers, labor regulations, identify regulations that enhance vency systems, one may expect greater access and auditors, notaries, and the like), including interactions or constrain business investment, productivity, and better allocation of credit. required to obtain necessary permits and licenses growth. The data are from the World Bank's Doing All economies have labor regulations intended to and to complete all inscriptions, verifications, and Business database. protect the interests of workers and to guarantee a notifications to start operations. Data are for busi- A vibrant private sector is central to promoting minimum standard of living. These laws and institu- nesses with specific characteristics of ownership, growth and expanding opportunities for poor people. tions encompass four bodies of law: employment size, and type of production. · Time to start a busi- But encouraging firms to invest, improve productivity, laws, industrial relations laws, occupational health ness is the time, measured in calendar days, needed and create jobs requires a legal and regulatory envi- and safety laws, and social security laws. The to complete the required procedures for legally oper- ronment that fosters access to credit, protection of employment laws index is a simple average of the ating a business. If a procedure can be speeded up property rights, and efficient judicial, taxation, and flexibility of hiring index, the conditions of employ- at additional cost, the fastest procedure, independ- customs systems. The indicators in the table point to ment index, and the flexibility of firing index; each ent of cost, is chosen. Time spent gathering informa- the administrative and regulatory reforms and insti- index has values between 0 and 100, with higher val- tion about the registration process is excluded. tutions needed to create a favorable environment for ues indicating more rigid regulation. Flexibility of hir- · Cost to register a business is normalized by pre- doing business. ing covers the availability of part-time, fixed-term, senting it as a percentage of gross national income When entrepreneurs start a business, the first and family members' contracts. Conditions of (GNI) per capita. · Minimum capital requirement is obstacles they face are the administrative and legal employment cover working time requirements, includ- the amount that an entrepreneur needs to deposit in procedures required to register the new firm. ing mandatory minimum daily rest, maximum number a bank to obtain a company registration number. The Countries differ widely in how they regulate the entry of hours in a normal work week, premium for over- amount is typically specified in the commercial code of new businesses. In some countries the process is time work, and restrictions on weekly holidays; or company law and is often returned to the entre- straightforward and affordable. But in others the pro- mandatory payment for nonworking days, which preneur only when the company is dissolved. cedures are so burdensome that entrepreneurs may includes days of annual leave with pay and paid time · Procedures to enforce a contract are independent opt to run their business informally. off for holidays; and minimum wage legislation. actions, each defined as a procedure (mandated by The data on entry regulations are derived from a Flexibility of firing covers workers' legal protections law or court regulation) that demands interaction survey of the procedures that a typical domestic against dismissal, including the grounds for dis- between the parties or between them and the judge limited-liability company must complete before legal- missal; procedures for dismissal (individual and col- or court officer. · Time to enforce a contract is the ly starting operation. The data cover the number and lective); notice period; and severance payment. number of calendar days from the time the plaintiff duration of start-up procedures, the cost to register To ensure cross-country comparability, several files the lawsuit in court until the time of final deter- a business, and the minimum capital requirement. standard characteristics of a company are defined in mination and, in appropriate cases, payment. · Cost Contract enforcement is critical to enable busi- all surveys, such as size, ownership, location, legal to enforce a contract includes filing fees, court nesses to engage with new borrowers or customers. status, and type of activities undertaken. The data costs, and estimated attorney fees. · Time to Without good contract enforcement, trade and credit were collected through a study of laws and regula- resolve insolvency is the number of years from the will be restricted to a small community of people who tions in each country, surveys of regulators or private time of filing for insolvency in court until the time of have developed relationships through repeated deal- sector professionals on each topic, and cooperative resolution of distressed assets. · Cost to resolve ings or through the security of assets. The institution arrangements with private consulting firms and busi- insolvency includes filing fees, court costs, attorney that enforces contracts between debtors and credi- ness and law associations. fees, and payments to other professionals (account- tors, and suppliers and customers, is the court. ants, assessors), out of the insolvency estate. The The efficiency of contract enforcement is reflected costs are averages of the estimates of survey respon- in three indicators: the number of judicial procedures dents, who chose among six options: 0­2 percent, to resolve a dispute, the time it takes to enforce a 3­5 percent, 6­10 percent, 11­25 percent, 26­50 commercial contract, and the associated costs. The percent, and more than 50 percent. · Employment data are derived from structured surveys answered laws index is a composite index of three aspects of by attorneys at private law firms. The questionnaires labor regulations: flexibility of hiring, conditions of cover the step-by-step evolution of a commercial employment, and flexibility of firing. The index ranges case before local courts in the country's largest city. from 0 (less rigid) to 100 (more rigid). The continuing existence of unviable competitors is consistently rated by firms as one of the greatest Data source potential barriers to operation and growth. The insti- All data are from the World Bank's Doing Business tution that deals with the exit of unviable companies project (http://rru.worldbank.org/DoingBusiness/). and the rehabilitation of viable but financially 2004 World Development Indicators 265 5.4 Stock markets Market capitalization Market liquidity Turnover ratio Listed domestic S&P/IFC companies Investable index value of shares value traded traded as % of % change in $ millions % of GDP as % of GDP market capitalization number price index 1990 2003 1990 2002 1990 2002 1990 2003 1990 2003 2002 2003 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 3,270 38,927 2.3 100.9 0.6 1.3 33.6 1.7 179 107 ­51.4 131.4 Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 109,000 380,969 35.1 93.1 12.9 72.0 31.6 77.2 1,089 1,355 .. .. Austria 11,500 31,664 7.1 15.5 11.5 2.9 110.3 21.3 97 91 .. .. Azerbaijan .. .. .. .. .. .. .. .. .. .. .. .. Bangladesh 321 1,622 1.1 2.5 0.0 1.4 1.5 3.5 134 247 ­4.2 a 15.4 a Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 65,400 127,556 33.2 52.0 3.3 13.8 .. 247.9 182 143 .. .. Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia .. 1,560 .. 19.4 .. 0.0 .. 0.1 .. 29 .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 261 2,131 6.6 32.6 0.2 1.0 6.1 1.1 9 19 31.1 a 25.6 a Brazil 16,400 234,560 3.6 27.4 1.2 10.7 23.6 3.4 581 367 ­33.0 105.4 Bulgaria .. 1,755 .. 4.7 .. 1.1 .. 2.0 .. 356 62.5 a 189.2 a Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 242,000 575,316 42.1 80.5 12.4 56.8 26.7 68.2 1,144 3,756 .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 13,600 86,291 44.9 74.2 2.6 4.9 6.3 0.9 215 240 ­14.8 79.5 China 2,030 681,204 0.5 36.6 0.2 26.3 158.9 11.5 14 1,296 ­14.5 77.7 Hong Kong, China 83,400 463,108 110.6 286.7 45.9 130.4 43.1 43.5 284 968 .. .. Colombia 1,420 14,258 3.5 11.9 0.2 0.3 5.6 0.6 80 114 9.7 a 27.3 a Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 475 .. 5.5 .. .. .. 5.8 .. 82 .. .. .. Côte d'Ivoire 549 1,650 5.1 11.4 0.2 0.1 3.4 0.6 23 38 17.4 a 27.4 a Croatia .. 6,126 .. 17.7 .. 0.7 .. 0.7 2 66 44.2 a 12.8 a Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 17,663 .. 22.9 .. 8.8 .. 6.0 .. 63 38.9 54.4 Denmark 39,100 76,788 29.3 44.4 8.3 29.8 28.0 60.3 258 201 .. .. Dominican Republic .. .. .. .. .. .. .. .. .. .. .. .. Ecuador 69 2,153 0.6 7.2 .. 0.1 .. 0.2 65 30 23.4 a 14.6 a Egypt, Arab Rep. 1,760 27,073 4.1 29.0 0.3 2.8 .. 1.6 573 967 ­5.8 79.3 El Salvador .. 1,520 .. 11.0 .. 0.2 .. 1.5 .. 32 .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 3,790 .. 37.3 .. 3.7 .. 1.6 .. 14 66.3 a 41.5 a Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 22,700 138,833 16.5 105.6 2.9 134.2 .. 106.8 73 147 .. .. France 314,000 966,962 25.8 67.6 9.6 65.3 .. 88.0 578 772 .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany 355,000 685,970 21.2 34.6 30.0 62.1 139.3 140.5 413 715 .. .. Ghana 76 1,426 1.2 12.0 .. 0.2 .. 0.2 13 25 27.6 a 65.4 a Greece 15,200 68,741 18.1 51.8 4.7 18.7 36.3 26.0 145 341 ­31.2 .. Guatemala .. 232 .. 1.1 .. 0.0 .. 3.1 .. 10 .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 266 2004 World Development Indicators 5.4 STATES AND Stock markets MARKET S Market capitalization Market liquidity Turnover ratio Listed domestic S&P/IFC companies Investable index value of shares value traded traded as % of % change in $ millions % of GDP as % of GDP market capitalization number price index 1990 2003 1990 2002 1990 2002 1990 2003 1990 2003 2002 2003 Honduras 40 .. 1.3 .. .. .. .. .. 26 .. .. .. Hungary 505 16,729 1.5 19.9 0.3 9.0 6.3 4.6 21 49 34.6 28.6 India 38,600 279,093 12.2 25.7 6.9 38.6 65.9 14.1 2,435 5,644 6.8 76.5 Indonesia 8,080 54,659 7.1 17.3 3.5 7.5 75.8 3.8 125 333 33.3 69.7 Iran, Islamic Rep. 34,300 9,700 .. 8.5 .. 1.0 30.4 11.3 97 316 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. 59,938 .. 49.4 .. 27.1 .. 50.5 .. 62 .. .. Israel 3,320 75,719 6.3 43.8 10.5 53.3 95.8 5.9 216 576 ­26.6 59.5 Italy 149,000 477,075 13.5 40.3 3.9 45.6 26.8 109.1 220 295 .. .. Jamaica 911 8,500 19.8 74.2 0.7 1.8 3.4 0.3 44 39 40.0 a ­3.4 a Japan 2,920,000 2,126,075 95.6 53.2 52.5 39.4 43.8 71.0 2,071 3,058 ­10.1 b 37.8 b Jordan 2,000 10,963 49.7 76.2 10.1 14.4 20.0 3.6 105 161 ­2.1 a 65.4 a Kazakhstan .. 1,200 .. 5.4 .. 1.4 .. 26.5 .. 31 .. .. Kenya 453 4,178 5.3 11.5 0.1 0.3 2.2 0.7 54 51 42.2 a 186.2 a Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 111,000 329,616 43.9 52.2 30.1 166.2 61.3 17.8 669 1,563 5.8 33.3 Kuwait .. .. .. 56.1 .. 11.4 .. .. .. .. .. .. Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 1,141 .. 8.5 .. 1.5 .. 5.0 .. 56 ­14.1 a 62.6 a Lebanon .. 1,497 .. 8.1 .. 0.7 .. 0.6 .. 13 5.7 0.9 a Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 3,510 .. 10.6 .. 1.3 .. 0.8 .. 48 25.7 a 117.9 a Macedonia, FYR .. 46 .. 1.3 .. 0.1 .. 4.3 .. 2 .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. 156 .. 9.2 .. 1.3 .. 13.8 .. 8 .. .. Malaysia 48,600 168,376 110.4 130.7 24.7 29.1 24.6 3.3 282 897 ­2.6 25.5 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. 1,090 .. 113.3 .. .. .. .. .. 40 .. .. Mauritius 268 1,955 11.2 29.3 0.3 1.3 1.9 0.3 13 40 22.9 a 43.7 a Mexico 32,700 122,532 12.4 16.2 4.6 4.4 44.0 1.5 199 159 ­16.4 30.4 Moldova .. 350 .. 23.7 .. 14.2 .. 60.1 .. 22 .. .. Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 966 13,152 3.7 23.8 0.2 1.6 .. 0.9 71 53 ­8.1 44.0 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 21 308 0.7 5.9 .. 0.0 .. 0.0 3 13 22.5 37.1 a Nepal .. .. .. 14.6 .. 0.6 .. .. .. .. .. .. Netherlands 120,000 401,465 40.8 96.1 13.7 110.6 29.0 123.7 260 180 .. .. New Zealand 8,840 21,745 20.3 37.1 4.4 12.8 17.3 38.3 171 149 .. .. Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 1,370 9,494 4.8 13.2 0.0 1.1 0.9 0.9 131 200 ­0.3 a 57.5 a Norway 26,100 67,300 22.5 35.3 12.1 25.7 54.4 67.8 112 179 .. .. Oman 1,060 5,014 9.4 19.7 0.9 2.6 12.3 2.1 55 96 31.8 47.0 a Pakistan 2,850 16,579 7.1 17.3 0.6 44.1 8.7 40.1 487 701 112.0 a 50.4 a Panama 226 2,600 3.4 21.6 0.0 0.4 0.9 1.7 13 29 .. .. Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay .. .. .. .. .. .. .. .. .. .. .. .. Peru 812 16,055 3.1 23.7 0.4 2.0 19.3 0.5 294 197 33.5 88.1 Philippines 5,930 23,565 13.4 50.0 2.7 4.0 13.6 0.9 153 234 ­19.7 41.4 Poland 144 37,165 0.2 15.2 0.0 3.1 89.7 2.2 9 203 2.2 29.5 Portugal 9,200 42,846 12.9 35.2 2.4 16.7 16.9 52.4 181 63 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 267 5.4 Stock markets Market capitalization Market liquidity Turnover ratio Listed domestic S&P/IFC companies Investable index value of shares value traded traded as % of % change in $ millions % of GDP as % of GDP market capitalization number price index 1990 2003 1990 2002 1990 2002 1990 2003 1990 2003 2002 2003 Romania .. 5,584 .. 10.0 .. 0.9 .. 0.5 .. 4,484 96.7 a 42.5 a Russian Federation 244 230,786 0.0 35.8 .. 10.4 .. 3.0 13 214 34.8 68.5 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 48,200 157,302 36.7 39.7 1.7 18.9 .. 10.3 59 70 3.8 a 49.5 a Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 34,300 101,900 92.9 117.2 55.0 64.5 .. 39.3 150 434 .. .. Slovak Republic .. 2,779 .. 8.0 .. 3.3 .. 1.9 .. 306 23.6 a 57.2 a Slovenia .. 5,209 .. 21.0 .. 0.5 .. 1.4 24 32 78.3 a 42.1 a Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 138,000 267,745 123.2 177.5 7.3 75.6 .. 3.6 732 426 44.9 37.6 Spain 111,000 461,559 21.8 70.7 8.0 155.3 .. 211.1 427 2,986 .. .. Sri Lanka 917 2,711 11.4 10.1 0.5 1.9 5.8 1.2 175 244 28.4 a 35.6 a Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 17 127 1.9 10.0 .. 0.6 .. 6.7 1 5 .. .. Sweden 97,900 177,065 39.8 73.7 7.1 90.9 14.9 96.2 258 278 .. .. Switzerland 160,000 553,758 70.0 207.1 29.6 245.6 .. 100.5 182 258 .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania .. 398 .. 4.3 .. 0.1 .. 1.9 .. 4 .. .. Thailand 23,900 120,887 28.0 36.3 26.8 37.5 92.6 18.2 214 405 18.3 147.2 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 696 10,605 13.7 67.6 1.1 1.8 10.0 0.6 30 35 33.2 a 46.7 a Tunisia 533 2,464 4.3 10.1 0.2 1.1 3.3 0.9 13 46 ­2.5 a 14.9 a Turkey 19,100 68,379 12.7 18.5 3.9 38.5 42.5 28.5 110 284 ­33.5 113.2 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 36 .. 0.6 .. .. .. .. .. 2 .. .. Ukraine .. 4,303 .. 7.5 .. 0.3 .. 0.5 .. 149 26.7 a 40.3 a United Arab Emirates .. 7,881 .. 11.4 .. 0.0 .. 3.4 .. 12 .. .. United Kingdom 849,000 1,864,134 85.8 119.0 28.2 173.7 33.4 135.4 1,701 1,701 ­16.5 c 26.3 c United States 3,060,000 11,052,403 53.2 106.4 30.5 244.4 53.4 202.5 6,599 5,685 ­23.4 d 26.4 d Uruguay .. 153 .. 0.8 .. 0.0 .. 0.5 36 15 .. .. Uzbekistan .. .. .. 0.6 .. 0.2 .. .. .. .. .. .. Venezuela, RB 8,360 3,820 17.2 4.2 4.6 0.1 43.0 0.6 76 54 ­35.1 a 14.3 a Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 723 .. 17.9 .. 1.9 .. 10.3 .. 24 .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia .. 217 .. 6.0 .. 1.3 .. 22.5 .. 9 .. .. Zimbabwe 2,400 4,975 27.3 187.9 0.6 29.9 2.9 1.2 57 81 97.9 a ­74.8 a World 9,403,525 s 23,359,484 s 48.0 w 74.6 w 28.5 w 122.8 w 57.1 w 123.0 w 25,424 s 47,576 s Low income 54,623 197,220 9.8 22.6 4.7 27.5 53.8 139.6 3,446 7,322 Middle income 320,160 1,639,528 20.0 35.3 5.2 16.0 .. 44.1 4,231 13,307 Lower middle income 212,666 1,099,924 15.5 36.6 9.0 20.8 .. 56.3 3,146 10,725 Upper middle income 107,494 539,604 29.6 33.0 6.1 7.1 50.3 23.2 1,085 2,582 Low & middle income 374,783 1,836,748 18.8 33.3 5.2 17.8 .. 57.8 7,677 20,629 East Asia & Pacific 86,510 702,100 16.4 40.4 6.6 24.4 118.1 72.7 774 3,132 Europe & Central Asia 19,100 234,597 2.2 22.7 .. 12.3 .. 53.6 110 6,781 Latin America & Carib. 78,169 418,720 7.7 27.4 2.1 5.4 29.8 21.7 1,734 1,381 Middle East & N. Africa 5,259 124,210 27.4 26.1 2.2 6.0 .. 19.6 817 1,585 South Asia 42,688 144,070 10.8 22.7 5.6 35.4 54.0 180.3 3,231 6,839 Sub-Saharan Africa 143,057 213,051 52.2 47.3 .. 32.4 .. 23.7 1,011 911 High income 9,028,742 21,522,735 51.6 83.4 31.4 145.2 59.4 137.9 17,747 26,947 Europe EMU 1,183,500 3,485,194 21.7 52.4 14.2 67.4 .. 106.1 2,630 5,843 Note: Aggregates for market capitalization are unavailable for 2003; those shown refer to 2002. a. Data refer to the S&P/IFC Global index. b. Data refer to the Nikkei 225 index. c. Data refer to the FT 100 index. d. Data refer to the S&P 500 index. 268 2004 World Development Indicators 5.4 STATES AND Stock markets MARKET S About the data Definitions The development of an economy's financial markets is Market liquidity, the ability to easily buy and sell secu- · Market capitalization (also known as market closely related to its overall development. Well function- rities, is measured by dividing the total value traded by value) is the share price times the number of shares ing financial systems provide good and easily accessi- GDP. This indicator complements the market capitaliza- outstanding. · Market liquidity is the total value ble information. That lowers transaction costs, which in tion ratio by showing whether market size is matched by traded divided by GDP. Value traded is the total value turn improves resource allocation and boosts economic trading. The turnover ratio--the value of shares traded of shares traded during the period. · Turnover ratio growth. Both banking systems and stock markets as a percentage of market capitalization--is also a is the total value of shares traded during the period enhance growth, the main factor in poverty reduction. At measure of liquidity as well as of transaction costs. divided by the average market capitalization for the low levels of economic development commercial banks (High turnover indicates low transaction costs.) The period. Average market capitalization is calculated as tend to dominate the financial system, while at higher turnover ratio complements the ratio of value traded to the average of the end-of-period values for the cur- levels domestic stock markets tend to become more GDP, because the turnover ratio is related to the size of rent period and the previous period. · Listed domes- active and efficient relative to domestic banks. the market and the value traded ratio to the size of the tic companies are the domestically incorporated Open economies with sound macroeconomic poli- economy. A small, liquid market will have a high companies listed on the country's stock exchanges cies, good legal systems, and shareholder protection turnover ratio but a low value traded ratio. Liquidity is an at the end of the year. This indicator does not include attract capital and therefore have larger financial mar- important attribute of stock markets because, in theo- investment companies, mutual funds, or other col- kets. Recent research on stock market development ry, liquid markets improve the allocation of capital and lective investment vehicles. · S&P/IFC Investable shows that new communications technology and enhance prospects for long-term economic growth. A index price change is the U.S. dollar price change in increased financial integration have resulted in more more comprehensive measure of liquidity would include the stock markets covered by the S&P/IFCI country cross-border capital flows, a stronger presence of trading costs and the time and uncertainty in finding a index, supplemented by the S&P/IFCG country index. financial firms around the world, and the migration of counterpart in settling trades. stock exchange activities to international exchanges. Standard & Poor's maintains a series of indexes Many firms in emerging markets now cross-list on inter- for investors interested in investing in stock markets national exchanges, which provides them with lower in developing countries. At the core of the Standard cost capital and more liquidity-traded shares. However, & Poor's family of emerging market indexes, the this also means that exchanges in emerging markets S&P/IFCG index is intended to represent the most may not have enough financial activity to sustain them, active stocks in the markets it covers and to be the putting pressure on them to rethink their operations. broadest possible indicator of market movements. The stock market indicators in the table include The S&P/IFCI index, which applies the same calcula- measures of size (market capitalization, number of tion methodology as the S&P/IFCG index, is listed domestic companies) and liquidity (value traded designed to measure the returns foreign portfolio as a percentage of gross domestic product, value of investors might receive from investing in emerging shares traded as a percentage of market capitaliza- market stocks that are legally and practically open to tion). The comparability of such indicators between foreign portfolio investment. countries may be limited by conceptual and statistical Standard & Poor's Emerging Markets Data Base, weaknesses, such as inaccurate reporting and differ- the source for all the data in the table, provides reg- ences in accounting standards. The percentage ular updates on 54 emerging stock markets encom- change in stock market prices in U.S. dollars, from the passing more than 2,200 stocks. The S&P/IFCG Standard & Poor's Investable (S&P/IFCI) and Global index includes 34 markets and more than 1,900 (S&P/IFCG) country indexes, is an important measure stocks, and the S&P/IFCI index covers 30 markets of overall performance. Regulatory and institutional and close to 1,200 stocks. These indexes are wide- factors that can affect investor confidence, such as ly used benchmarks for international portfolio man- entry and exit restrictions, the existence of a securi- agement. See Standard & Poor's (2001b) for further Data sources ties and exchange commission, and the quality of laws information on the indexes. The data on stock markets are from Standard & to protect investors, may influence the functioning of Because markets included in Standard & Poor's Poor's Emerging Stock Markets Factbook 2003, stock markets but are not included in the table. emerging markets category vary widely in level of which draws on the Emerging Markets Data Base, Stock market size can be measured in a number of development, it is best to look at the entire category supplemented by other data from Standard & ways, and each may produce a different ranking of to identify the most significant market trends. And it Poor's. The firm collects data through an annual countries. Market capitalization shows the overall is useful to remember that stock market trends may survey of the world's stock exchanges, supple- size of the stock market in U.S. dollars and as a per- be distorted by currency conversions, especially when mented by information provided by its network of centage of GDP. The number of listed domestic com- a currency has registered a significant devaluation. correspondents and by Reuters. The GDP data panies is another measure of market size. Market About the data is based on Demirgüç-Kunt and are from the World Bank's national accounts data size is positively correlated with the ability to mobi- Levine (1996a), Beck and Levine (2001), and files. lize capital and diversify risk. Claessens, Klingebiel, and Schmukler (2002). 2004 World Development Indicators 269 5.5 Financial depth and efficiency Domestic credit Liquid Quasi-liquid Ratio of bank Interest rate Risk premium provided by liabilities liabilities liquid reserves to spread on lending banking sector bank assets Lending minus Prime lending deposit rate rate minus percentage treasury bill rate % of GDP % of GDP % of GDP % points percentage points 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 43.6 .. 61.5 .. 38.9 .. 10.5 2.1 6.8 .. 5.8 Algeria 74.5 29.1 73.5 49.0 24.8 19.7 1.3 12.5 .. 3.3 .. 6.7 Angola .. 5.5 .. 22.2 .. 15.3 .. 14.5 .. 48.6 .. .. Argentina 32.4 62.4 11.5 27.9 7.1 18.9 7.4 9.5 .. 12.4 .. .. Armenia 58.7 7.3 79.9 15.6 42.9 7.1 13.6 11.4 .. 11.5 .. 6.4 Australia 71.4 93.9 55.0 71.0 43.2 47.6 1.5 1.2 4.5 5.0 4.0 3.3 Austria 121.4 124.3 .. .. .. .. 2.1 .. .. .. .. .. Azerbaijan 65.9 8.7 38.6 13.3 13.4 6.7 4.5 10.2 .. 8.7 .. 3.3 Bangladesh 23.9 40.2 23.4 39.1 16.8 29.8 12.8 8.6 4.0 7.8 .. .. Belarus .. 17.5 .. 15.4 .. 10.1 .. 7.7 .. 10.0 .. .. Belgium 73.1 115.4 .. .. .. .. 0.2 .. 6.9 5.1 3.4 4.5 Benin 22.4 5.8 26.7 26.6 5.9 7.1 29.3 20.9 9.0 .. .. .. Bolivia 30.7 62.3 24.5 49.1 18.0 40.7 18.8 5.8 18.0 11.1 .. 8.2 Bosnia and Herzegovina .. 35.8 .. 46.3 .. 19.1 .. 10.5 .. 8.2 .. .. Botswana ­46.0 ­29.6 21.9 28.5 13.6 20.9 11.0 3.8 1.8 5.7 .. .. Brazil 89.8 64.8 26.4 33.1 18.5 25.0 7.6 23.6 .. 43.7 .. 43.4 Bulgaria 118.5 23.7 71.9 41.9 53.6 24.8 10.2 8.9 8.9 6.6 8.6 6.5 Burkina Faso 12.1 12.7 18.8 18.2 6.6 7.0 12.7 6.8 9.0 .. .. .. Burundi 23.2 35.1 18.2 22.3 6.5 7.3 2.8 3.9 .. .. .. .. Cambodia .. 6.0 .. 18.4 .. 13.2 .. 71.3 .. 13.7 .. .. Cameroon 31.2 16.3 22.6 20.2 10.1 8.0 3.4 28.0 11.0 13.0 .. .. Canada 82.3 92.6 74.3 78.4 59.8 54.4 1.6 0.6 4.2 3.4 1.3 1.6 Central African Republic 12.9 13.2 15.3 14.4 1.8 1.4 2.8 2.5 11.0 13.0 .. .. Chad 11.5 10.9 14.6 13.5 0.6 0.8 3.3 25.2 11.0 13.0 .. .. Chile 73.0 77.6 40.8 40.0 32.8 30.0 3.6 3.0 8.5 4.0 .. .. China 90.0 166.4 79.2 178.3 41.4 108.9 15.7 12.1 0.7 3.3 .. .. Hong Kong, China 154.9 144.5 179.4 238.9 164.7 219.8 0.1 0.2 3.3 4.7 2.7 3.7 Colombia 35.9 36.5 29.8 31.8 19.3 21.2 27.4 6.7 8.8 7.4 .. .. Congo, Dem. Rep. 25.3 0.2 12.9 4.8 2.1 1.8 49.0 6.2 .. .. .. .. Congo, Rep. 29.1 11.4 22.0 13.9 6.1 1.0 2.0 17.0 11.0 13.0 .. .. Costa Rica 29.9 36.9 42.7 39.8 30.0 25.9 68.5 12.3 11.4 15.0 .. .. Côte d'Ivoire 44.5 20.7 28.8 29.4 10.9 7.9 2.1 6.2 9.0 .. .. .. Croatia .. 63.8 .. 65.7 .. 48.2 .. 14.3 499.3 11.0 .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. 45.8 .. 75.5 .. 39.2 .. 3.8 .. 4.0 .. 3.5 Denmark 63.0 156.6 59.0 51.0 29.4 19.4 1.1 1.2 6.2 4.7 .. .. Dominican Republic 31.5 44.8 28.6 39.5 13.3 28.5 31.2 18.8 15.2 9.5 .. .. Ecuador 15.5 28.0 21.1 24.8 11.6 15.6 22.6 3.3 ­6.0 9.6 .. .. Egypt, Arab Rep. 106.8 109.9 87.9 94.1 60.7 74.5 17.1 17.1 7.0 4.5 .. 8.3 El Salvador 32.0 49.4 30.6 42.7 19.6 35.1 27.3 9.5 3.2 4.6 .. .. Eritrea .. 148.9 .. 152.5 .. 86.0 .. 24.1 .. .. .. .. Estonia 66.7 49.6 136.0 42.0 95.2 16.8 43.1 9.6 .. 4.0 .. .. Ethiopia 66.8 58.0 42.2 52.9 12.6 26.5 24.0 13.6 3.6 4.6 3.0 7.4 Finland 82.8 64.7 54.3 .. .. .. 4.1 .. 4.1 3.3 .. .. France 104.4 105.0 .. .. .. .. 1.0 .. 6.1 3.6 0.4 2.7 Gabon 20.0 18.8 17.8 17.3 6.6 7.3 2.0 8.9 11.0 13.0 .. .. Gambia, The 3.4 26.3 20.7 45.1 8.8 20.4 8.8 13.7 15.2 11.3 .. .. Georgia .. 19.6 .. 11.7 .. 5.6 .. 14.4 .. 22.0 .. ­11.6 Germany 104.4 144.7 69.6 .. .. .. 3.2 .. 4.5 7.0 3.5 6.7 Ghana 17.5 31.9 14.1 30.7 3.4 14.2 20.2 11.2 .. .. .. .. Greece 99.3 109.5 .. .. .. .. 13.9 17.2 8.1 4.7 3.6 3.9 Guatemala 17.4 15.7 21.2 30.6 11.8 18.1 31.8 22.0 5.1 9.9 .. .. Guinea 6.0 12.5 9.2 13.0 1.1 2.3 6.2 27.5 0.2 11.9 .. 4.7 Guinea-Bissau 77.5 16.1 68.9 60.9 4.4 0.9 10.8 17.2 13.1 .. .. .. Haiti 34.3 37.3 32.6 42.8 16.6 28.8 74.9 40.0 .. 17.4 .. 18.1 270 2004 World Development Indicators 5.5 STATES AND Financial depth and efficiency MARKET S Domestic credit Liquid Quasi-liquid Ratio of bank Interest rate Risk premium provided by liabilities liabilities liquid reserves to spread on lending banking sector bank assets Lending minus Prime lending deposit rate rate minus percentage treasury bill rate % of GDP % of GDP % of GDP % points percentage points 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 40.9 34.2 33.6 56.8 18.8 43.6 6.7 23.0 8.3 8.9 .. .. Hungary 105.5 53.0 43.8 47.2 19.0 27.8 11.2 5.2 4.1 2.8 ­1.4 1.3 India 51.5 58.5 43.1 63.2 28.1 45.7 14.8 5.6 .. .. .. .. Indonesia 45.5 59.5 40.4 54.9 29.1 43.2 4.2 11.1 3.3 3.4 .. .. Iran, Islamic Rep. 70.8 47.6 57.6 44.5 31.1 25.6 66.0 26.8 .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 55.2 110.6 44.5 .. .. .. 4.8 .. 5.0 3.7 0.4 .. Israel 106.2 93.6 70.2 104.6 63.6 96.7 11.9 8.9 12.0 3.9 11.4 2.5 Italy 89.4 99.6 70.5 .. .. .. 12.0 .. 7.3 4.3 1.7 2.5 Jamaica 32.2 27.6 47.2 49.3 35.0 33.7 37.4 22.3 6.6 9.9 4.3 3.0 Japan 259.6 312.5 182.3 201.5 155.3 132.0 1.6 3.7 3.4 1.8 .. .. Jordan 117.9 90.4 131.2 120.2 77.8 85.7 20.5 27.1 2.2 5.8 .. .. Kazakhstan .. 13.0 .. 19.2 .. 9.1 .. 4.4 .. .. .. .. Kenya 52.9 43.2 43.3 42.6 29.3 27.2 9.9 8.2 5.1 13.0 4.0 9.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 65.7 116.9 54.6 103.7 45.7 93.1 6.3 2.6 0.0 1.8 .. .. Kuwait 243.0 105.8 192.2 89.8 153.9 70.6 1.2 1.1 0.0 3.3 0.0 .. Kyrgyz Republic .. 11.4 .. 14.7 .. 4.4 .. 11.3 .. 18.9 .. 14.7 Lao PDR 5.1 12.6 7.2 19.6 3.1 16.4 3.4 26.5 2.5 23.3 .. 7.9 Latvia .. 39.6 .. 36.5 .. 16.3 .. 5.9 .. 4.7 .. 4.5 Lebanon 132.6 196.1 193.7 217.9 170.9 208.1 3.9 18.8 23.1 5.5 21.1 5.7 Lesotho 32.8 10.7 39.2 28.8 22.6 9.7 23.0 6.2 7.4 11.9 4.1 5.8 Liberia 319.5 168.7 101.9 8.4 20.8 1.5 67.3 56.3 0.0 14.0 .. .. Libya 104.1 50.3 68.1 41.3 13.7 9.0 26.4 24.0 1.5 4.0 1.5 1.5 Lithuania .. 18.3 .. 29.3 .. 12.8 .. 10.9 .. 5.1 .. 3.1 Macedonia, FYR .. 15.9 .. 28.6 .. 17.3 .. 7.5 .. 8.8 .. .. Madagascar 26.2 18.4 17.8 24.3 5.3 5.5 8.5 23.3 5.3 13.3 .. 15.0 Malawi 19.7 14.3 20.2 16.2 10.8 7.4 32.9 23.0 8.9 22.5 8.1 8.8 Malaysia 75.7 154.2 118.0 128.5 97.8 103.0 5.9 12.5 1.3 3.2 1.1 3.7 Mali 13.7 16.4 20.5 26.6 5.5 5.8 50.8 18.0 9.0 .. .. .. Mauritania 54.7 ­8.3 28.5 16.0 7.0 5.0 6.1 4.0 5.0 .. .. .. Mauritius 48.4 77.1 67.9 87.2 52.7 73.9 8.8 5.1 5.4 11.1 .. .. Mexico 36.6 26.6 22.8 24.5 16.4 14.5 4.3 11.1 .. 4.4 .. 1.1 Moldova 62.8 29.7 70.3 30.5 35.4 14.4 8.3 16.5 .. 9.3 .. 17.6 Mongolia 73.4 17.1 56.2 37.8 14.7 22.7 2.0 15.8 .. 15.2 .. .. Morocco 60.1 84.5 61.0 89.4 18.4 21.0 11.3 8.1 0.5 8.6 .. .. Mozambique 15.6 13.2 26.5 32.7 5.2 19.0 61.5 14.2 .. 8.7 .. ­2.0 Myanmar 32.8 35.1 27.9 33.5 7.8 13.1 286.7 16.8 2.1 5.5 .. .. Namibia 20.3 49.0 24.3 40.2 14.2 18.3 4.4 2.9 10.6 6.0 6.3 2.8 Nepal 28.9 43.2 32.2 51.5 18.5 34.9 12.7 22.2 2.5 2.9 6.5 2.7 Netherlands 103.6 160.4 .. .. .. .. 0.3 .. 8.4 1.2 .. .. New Zealand 80.6 118.2 77.0 89.2 64.0 74.4 0.8 0.5 4.4 4.5 2.2 4.3 Nicaragua 206.6 93.0 56.9 40.3 23.1 34.4 20.2 30.9 12.5 15.8 .. .. Niger 16.2 8.5 19.8 9.0 8.3 2.7 42.9 19.0 9.0 .. .. .. Nigeria 23.7 25.3 23.6 30.5 10.3 12.4 11.9 17.9 5.5 8.1 6.9 5.7 Norway 89.0 54.0 59.5 55.7 26.8 8.8 0.5 4.7 4.6 2.1 .. .. Oman 16.6 40.3 28.9 35.4 19.3 25.5 6.9 4.2 1.4 5.7 .. .. Pakistan 50.9 43.5 39.8 54.8 10.0 23.9 8.9 9.0 .. .. .. .. Panama 52.7 90.7 41.1 76.4 33.0 65.6 .. .. 3.6 5.6 .. .. Papua New Guinea 35.7 26.3 35.2 30.2 24.0 15.3 3.2 9.6 6.9 8.1 4.1 3.0 Paraguay 14.9 29.3 22.3 36.7 13.7 28.0 31.0 24.1 8.1 15.8 .. .. Peru 20.2 23.9 24.8 32.4 11.8 21.3 22.0 25.4 2,335.0 10.5 .. .. Philippines 26.9 60.5 37.0 63.0 28.4 51.1 20.9 8.5 4.6 4.5 0.4 3.6 Poland 19.5 36.2 34.0 42.7 17.2 28.1 20.6 5.6 462.5 5.9 ­5.0 3.4 Portugal 69.4 149.9 .. .. .. .. 29.0 .. 7.8 .. 8.3 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 271 5.5 Financial depth and efficiency Domestic credit Liquid Quasi-liquid Ratio of bank Interest rate Risk premium provided by liabilities liabilities liquid reserves to spread on lending banking sector bank assets Lending minus Prime lending deposit rate rate minus percentage treasury bill rate % of GDP % of GDP % of GDP % points percentage points 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 79.7 13.2 60.4 24.7 32.7 19.2 1.2 61.7 .. .. .. .. Russian Federation .. 26.6 .. 26.2 .. 12.4 .. 13.9 .. 10.8 .. 3.0 Rwanda 17.1 11.1 14.9 17.3 7.0 8.8 4.3 9.9 6.3 .. .. .. Saudi Arabia 52.7 70.1 42.9 54.0 19.6 25.3 5.6 10.0 .. .. .. .. Senegal 33.8 22.6 22.9 27.6 9.7 11.6 14.1 15.9 9.0 .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 36.3 48.4 18.1 22.9 3.6 7.9 64.1 9.0 12.0 13.9 5.0 7.0 Singapore 75.2 84.8 122.7 115.8 99.9 92.8 3.7 2.5 2.7 4.5 3.7 4.6 Slovak Republic .. 52.8 .. 65.3 .. 42.5 .. 5.2 .. 3.6 .. .. Slovenia 36.8 46.0 34.2 55.6 25.8 42.7 2.7 4.0 142.0 4.9 .. 4.4 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 97.8 150.9 44.6 50.1 27.2 18.3 3.3 2.7 2.1 5.0 3.2 4.6 Spain 107.0 129.6 .. .. .. .. 8.7 .. 5.4 1.8 1.8 1.0 Sri Lanka 38.0 43.6 34.9 39.3 22.6 30.5 9.9 8.1 ­6.4 4.0 ­1.1 0.7 Sudan 20.4 11.7 20.1 15.8 2.9 5.9 79.5 19.9 .. .. .. .. Swaziland 7.5 5.6 28.3 20.9 19.8 14.3 21.5 7.1 5.8 7.2 3.4 6.7 Sweden 135.9 75.2 50.7 .. .. .. 1.9 0.4 6.8 3.7 3.0 1.9 Switzerland 179.0 174.4 145.2 157.9 118.6 112.0 1.1 0.9 ­0.9 3.5 ­0.9 3.0 Syrian Arab Republic 56.6 26.7 54.7 79.2 10.5 31.7 46.0 9.1 5.0 5.0 .. .. Tajikistan .. 21.3 .. 8.4 .. 3.2 .. 11.0 .. 5.0 .. .. Tanzania 34.6 10.0 19.9 23.0 6.3 12.4 5.3 13.0 0.0 13.1 .. 12.9 Thailand 91.1 116.0 74.9 114.5 66.0 102.1 3.1 3.4 2.2 4.9 .. .. Togo 21.3 17.0 36.1 24.3 19.1 8.9 59.0 16.9 9.0 .. .. .. Trinidad and Tobago 58.5 41.5 54.6 51.8 42.7 38.7 13.5 13.4 6.9 7.7 5.4 7.7 Tunisia 62.5 74.4 51.5 60.0 26.7 37.0 1.6 3.4 .. .. .. .. Turkey 19.5 59.3 24.1 49.8 16.4 44.5 16.4 9.0 .. .. .. .. Turkmenistan .. 30.7 .. 20.4 .. 8.9 .. 6.7 .. .. .. .. Uganda 17.8 15.4 7.6 20.2 1.4 9.4 15.2 9.8 7.4 13.5 ­2.3 13.2 Ukraine 83.2 28.1 50.1 29.1 9.0 10.9 49.0 9.0 .. 17.4 .. .. United Arab Emirates 34.7 47.6 46.3 66.6 37.7 48.6 4.4 9.3 .. .. .. .. United Kingdom 121.2 145.3 .. .. .. .. 0.5 0.3 2.2 .. 0.7 0.1 United States 110.8 159.4 65.5 70.0 49.4 54.2 2.4 1.1 .. .. 2.5 3.1 Uruguay 46.7 93.0 58.1 72.5 51.5 67.0 31.2 12.4 76.6 37.4 .. .. Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 37.4 15.0 38.8 17.8 29.4 7.8 21.9 23.2 7.7 7.6 .. .. Vietnam 4.7 44.8 22.7 53.0 9.3 29.6 25.3 6.3 .. 2.6 .. 3.1 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 60.6 ­0.5 55.1 37.4 10.4 19.9 121.2 16.5 .. 4.7 .. 6.2 Zambia 67.8 46.7 21.8 22.3 10.6 14.0 33.7 22.8 9.4 21.9 9.2 10.7 Zimbabwe 41.7 58.7 41.8 61.3 30.3 24.8 12.2 18.8 2.9 18.1 3.3 8.0 World 121.2 w 150.7 w 83.3 w 97.5 w .. w 68.6 w 10.3 m 10.3 m 5.4 m 7.0 m .. m .. m Low income 44.7 48.6 36.9 52.1 22.0 35.6 12.8 15.1 7.4 13.0 .. .. Middle income 65.3 82.9 42.3 79.0 24.5 50.3 14.6 9.5 5.0 6.7 .. .. Lower middle income 75.0 100.7 48.8 97.4 28.7 61.9 17.9 9.3 5.6 8.4 .. .. Upper middle income 45.5 49.1 29.1 43.8 16.1 28.2 9.9 9.6 6.2 5.6 .. .. Low & middle income 60.9 76.9 41.2 74.2 24.0 47.7 13.2 11.3 6.6 8.7 .. .. East Asia & Pacific 76.4 143.8 63.1 150.6 37.2 97.0 5.1 12.1 2.2 4.9 .. .. Europe & Central Asia .. 36.9 .. 39.3 .. 24.9 .. 9.9 .. 8.2 .. .. Latin America & Carib. 59.1 42.7 25.2 29.8 17.6 20.2 22.3 18.8 8.2 9.9 .. .. Middle East & N. Africa 70.4 72.9 59.0 67.3 26.9 40.2 14.2 16.5 2.2 5.0 .. .. South Asia 48.8 55.3 41.0 59.8 25.2 42.0 12.7 8.6 2.5 7.3 .. .. Sub-Saharan Africa 56.9 65.5 32.1 36.2 16.8 15.4 11.9 13.7 8.2 13.0 .. .. High income 132.1 168.5 92.9 105.6 .. 78.7 2.0 1.2 4.6 3.8 2.4 3.5 Europe EMU 99.5 123.0 .. .. .. .. 4.1 .. 6.5 3.7 2.6 3.5 272 2004 World Development Indicators 5.5 STATES AND Financial depth and efficiency MARKET S About the data Definitions The organization and performance of financial activities the earnings on assets--or the interest rate spread. · Domestic credit provided by banking sector in a country affect economic growth through their impact A narrowing of the interest rate spread reduces trans- includes all credit to various sectors on a gross basis, on how businesses raise and manage funds. These action costs, which lowers the overall cost of invest- with the exception of credit to the central government, funds come from savings: savers accumulate claims on ment and is therefore crucial to economic growth. which is net. The banking sector includes monetary financial institutions, which pass the funds to their final Interest rates reflect the responsiveness of financial authorities, deposit money banks, and other banking users. But even if a country has savings, growth may not institutions to competition and price incentives. The institutions for which data are available (including materialize--because the financial system may fail to interest rate spread, also known as the intermedia- institutions that do not accept transferable deposits direct the savings to where they can be invested most tion margin, is a summary measure of a banking sys- but do incur such liabilities as time and savings efficiently. Enabling it to do so requires established pay- tem's efficiency (although if governments set interest deposits). Examples of other banking institutions ments systems, the availability of price information, a rates, the spreads become less reliable measures of include savings and mortgage loan institutions and way to manage uncertainty and control risk, and mecha- efficiency). The risk premium on lending can be building and loan associations. · Liquid liabilities are nisms to deal with problems of asymmetric information approximated by the spread between the lending rate also known as broad money, or M3. They include bank between parties to a financial transaction. to the private sector (line 60p in the International deposits of generally less than one year plus curren- As an economy develops, the indirect lending by Monetary Fund's International Financial Statistics, or cy. Liquid liabilities are the sum of currency and savers to investors becomes more efficient and grad- IFS) and the "risk free" treasury bill interest rate (IFS deposits in the central bank (M0); plus transferable ually increases financial assets relative to gross line 60c). A small spread indicates that the market deposits and electronic currency (M1); plus time and domestic product (GDP). More specialized savings and considers its best corporate customers to be low risk. savings deposits, foreign currency transferable financial institutions emerge and more financing instru- Interest rates are expressed as annual averages. deposits, certificates of deposit, and securities repur- ments become available, spreading risks and reducing In some countries financial markets are distorted by chase agreements (M2); plus travelers' checks, for- costs to liability holders. Securities markets mature, restrictions on foreign investment, selective credit con- eign currency time deposits, commercial paper, and allowing savers to invest their resources directly in trols, and controls on deposit and lending rates. Interest shares of mutual funds or market funds held by resi- financial assets issued by firms. Financial systems rates may reflect the diversion of resources to finance dents. The ratio of liquid liabilities to GDP indicates vary widely across countries: banks, nonbank financial the public sector deficit through statutory reserve the relative size of these readily available forms of institutions, and stock markets are larger, more active, requirements and direct borrowing from the banking sys- money--money that the owners can use to buy goods and more efficient in richer countries. tem. And where state-owned banks dominate the finan- and services without incurring any cost. · Quasi-liquid The ratio of domestic credit provided by the banking cial sector, noncommercial considerations may unduly liabilities are the M3 money supply less M1. · Ratio sector to GDP is used to measure the growth of the influence credit allocation. The indicators in the table pro- of bank liquid reserves to bank assets is the ratio of banking system because it reflects the extent to which vide quantitative assessments of each country's finan- domestic currency holdings and deposits with the savings are financial. In a few countries governments cial sector, but qualitative assessments of policies, laws, monetary authorities to claims on other governments, may hold international reserves as deposits in the and regulations are needed to analyze overall financial nonfinancial public enterprises, the private sector, banking system rather than in the central bank. Since conditions. Recent international financial crises highlight and other banking institutions. · Interest rate spread the claims on the central government are a net item the risks of weak financial intermediation, poor corporate is the interest rate charged by banks on loans to (claims on the central government minus central gov- governance, and deficient government policies. prime customers minus the interest rate paid by com- ernment deposits), this net figure may be negative, The accuracy of financial data depends on the qual- mercial or similar banks for demand, time, or savings resulting in a negative figure for domestic credit pro- ity of accounting systems, which are weak in some deposits. · Risk premium on lending is the interest vided by the banking sector. developing countries. Some indicators in the table are rate charged by banks on loans to prime private sec- Liquid liabilities are a general indicator of the size of highly correlated, particularly the ratios of domestic tor customers minus the "risk free" treasury bill inter- financial intermediaries relative to the size of the econ- credit, liquid liabilities, and quasi-liquid liabilities to est rate at which short-term government securities omy, or an overall measure of financial sector devel- GDP, because changes in liquid and quasi-liquid liabil- are issued or traded in the market. In some countries opment. Quasi-liquid liabilities are long-term deposits ities flow directly from changes in domestic credit. this spread may be negative, indicating that the mar- and assets--such as bonds, commercial paper, and Moreover, the precise definition of the financial aggre- ket considers its best corporate clients to be lower certificates of deposit--that can be converted into cur- gates presented varies by country. risk than the government. rency or demand deposits, but at a cost. The ratio of The indicators reported here do not capture the activ- bank liquid reserves to bank assets captures the bank- ities of the informal sector, which remains an important ing system's liquidity. In countries whose banking sys- source of finance in developing economies. Personal tem is liquid, adverse macroeconomic conditions credit or credit extended through community-based Data sources should be less likely to lead to banking and financial pooling of assets may be the only source of credit for The data on credit, liabilities, bank reserves, and crises. Data on domestic credit and liquid and quasi- small farmers, small businesses, and home-based pro- interest rates are collected from central banks and liquid liabilities are cited on an end-of-year basis. ducers. And in financially repressed economies the finance ministries and reported in the print and No less important than the size and structure of the rationing of formal credit forces many borrowers and electronic editions of the International Monetary financial sector is its efficiency, as indicated by the lenders to turn to the informal market, which is very Fund's International Financial Statistics. margin between the cost of mobilizing liabilities and expensive, or to self-financing and family savings. 2004 World Development Indicators 273 5.6 Tax policies Tax Taxes on Domestic taxes Export Import Highest marginal revenue income, on goods duties duties tax rate a profits, and and services capital gains % of value Individual Corporate % of % of added in industry % of % of on income GDP total taxes and services tax revenue tax revenue % over $ % 2002 1990 2002 1990 2002 1990 2002 1990 2002 2003 2003 2003 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria 32.0 .. 77.9 .. 3.4 .. 0.0 .. 12.1 .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 12.5 2.7 19.9 2.2 5.5 9.3 0.2 2.6 4.5 35 36,697 35 Armenia 14.6 .. 18.3 .. 5.7 .. 0.0 .. 4.9 20 .. 20 Australia .. 70.9 .. 5.9 .. 0.1 .. 4.4 .. 47 35,149 30 Austria .. 20.8 .. 10.0 .. 0.0 .. 1.6 .. 50 48,698 34 Azerbaijan .. .. .. .. .. .. .. .. .. 35 12,257 25 Bangladesh .. .. .. 0.0 .. .. .. .. .. .. .. .. Belarus 26.6 12.1 10.3 17.1 13.5 3.6 .. 0.4 .. .. .. .. Belgium .. 36.1 .. 11.5 .. 0.0 .. 0.0 .. 50 28,596 39 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 13.8 7.9 8.7 5.6 11.2 0.0 0.0 11.1 6.4 13 .. 25 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 71.7 .. 1.0 .. 0.0 .. 24.7 .. 25 18,560 15 Brazil .. 24.5 .. 7.1 .. 0.0 .. 2.5 .. 28 7,251 15 Bulgaria 25.2 40.6 17.0 9.9 16.2 0.0 0.0 2.5 2.6 29 3,982 24 Burkina Faso .. 24.7 .. 4.0 .. 1.1 .. 33.1 .. .. .. .. Burundi .. 23.4 .. 0.0 .. 3.1 .. 23.2 .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. 20 38,462 20 Cameroon .. 25.1 .. 4.3 .. 1.7 .. 18.9 .. .. .. .. Canada 19.3 59.1 57.3 4.0 .. 0.0 0.0 3.2 1.4 29 65,206 38 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad 7.2 20.3 .. 3.9 .. .. .. .. .. .. .. .. Chile 18.7 15.8 24.7 10.4 13.0 .. .. .. .. 40 6,127 17 China .. 49.8 .. 1.5 .. 0.0 .. 22.1 .. 45 12,048 .. Hong Kong, China .. .. .. .. .. .. .. .. .. 17 13,462 18 Colombia .. 36.4 .. 4.8 .. 2.0 .. 22.5 .. 35 29,426 39 Congo, Dem. Rep. 3.9 28.5 16.7 2.6 1.5 4.1 1.0 45.1 33.7 50 6,056 40 Congo, Rep. 10.5 40.2 16.0 0.0 6.5 0.0 0.0 32.3 23.2 .. .. .. Costa Rica 20.0 11.5 15.1 8.7 10.8 8.0 0.2 18.2 4.2 30 16,860 30 Côte d'Ivoire 16.3 18.1 21.0 8.9 4.8 3.7 15.3 28.4 27.6 10 3,837 35 Croatia 38.2 17.4 8.7 9.6 24.9 0.0 0.0 3.6 6.8 45 35,171 .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 32.1 .. 21.1 .. 11.3 .. 0.0 .. 1.4 32 10,988 31 Denmark 32.3 43.5 40.2 18.9 19.8 0.0 0.0 0.1 0.0 59 .. 30 Dominican Republic 15.6 23.8 19.6 3.1 4.8 0.1 0.0 41.4 44.1 25 16,637 25 Ecuador .. 62.9 .. 4.7 .. 0.3 .. 12.1 .. 25 54,400 25 Egypt, Arab Rep. .. 26.4 .. 4.1 .. 0.0 .. 18.9 .. 32 10,823 40 El Salvador 10.0 .. 18.6 0.0 0.8 .. 0.0 .. 7.3 .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 27.2 27.5 14.7 14.8 15.0 0.0 0.0 0.8 0.2 26 803 35 Ethiopia 15.3 40.9 39.4 9.1 5.4 2.8 0.4 18.0 41.4 35 .. 30 Finland .. 34.5 .. 17.5 .. 0.0 .. 1.0 .. 36 52,843 29 France .. 18.7 .. 13.1 .. 0.0 .. 0.0 .. .. .. 33 Gabon .. 35.9 .. 5.0 .. 2.8 .. 23.4 .. 50 .. .. Gambia, The .. 13.7 .. 12.2 .. 0.2 .. 45.6 .. .. .. .. Georgia 10.4 .. 3.8 .. 9.4 .. 0.0 .. 7.7 .. .. .. Germany .. 17.5 .. 6.9 .. 0.0 .. 0.0 .. 49 52,659 27 Ghana .. 25.1 .. 6.8 .. 12.4 .. 28.7 .. 30 5,647 33 Greece .. 23.3 .. 14.5 .. 0.0 .. 0.1 .. 40 22,402 35 Guatemala .. .. .. .. .. .. .. .. .. 31 38,028 31 Guinea .. 12.6 .. 3.2 .. 51.7 .. 11.2 .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 274 2004 World Development Indicators 5.6 STATES AND Tax policies MARKET S Tax Taxes on Domestic taxes Export Import Highest marginal revenue income, on goods duties duties tax rate a profits, and and services capital gains % of value Individual Corporate % of % of added in industry % of % of on income GDP total taxes and services tax revenue tax revenue % over $ % 2002 1990 2002 1990 2002 1990 2002 1990 2002 2003 2003 2003 Honduras .. .. .. .. .. .. .. .. .. .. .. .. Hungary 33.6 21.2 23.7 22.6 15.2 1.3 0.0 5.6 2.5 40 5,999 18 India 9.9 18.6 37.4 7.4 5.5 0.1 0.1 35.8 24.1 30 3,139 37 Indonesia 13.6 65.4 48.0 5.5 6.5 0.1 0.3 6.6 4.6 35 22,371 30 Iran, Islamic Rep. 8.5 24.7 41.7 1.0 1.6 0.0 0.0 18.6 14.4 35 125,345 25 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. 39.7 .. 15.5 .. 0.0 .. 0.0 .. 42 26,805 16 Israel 36.2 42.4 45.2 .. .. 0.0 0.0 1.4 0.7 50 50,886 36 Italy .. 37.7 .. 12.7 .. 0.0 .. 0.0 .. 45 67,011 34 Jamaica 26.1 41.5 39.0 0.0 11.8 0.0 0.0 14.0 9.3 25 2,363 33 Japan .. 73.0 .. 2.4 .. 0.0 .. 1.4 .. 37 148,478 30 Jordan 19.0 22.9 16.4 6.8 10.6 0.0 0.0 34.7 20.4 .. .. .. Kazakhstan 9.6 .. 28.9 .. 7.1 .. 0.3 .. 5.7 30 39,185 30 Kenya .. 32.9 .. 15.9 .. 0.0 .. 17.8 .. 30 5,720 30 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. 37.5 .. 6.7 .. 0.0 .. 13.0 .. 36 66,644 27 Kuwait .. 19.5 .. 0.0 .. 0.0 .. 76.8 .. 0 .. .. Kyrgyz Republic 12.4 .. 21.9 .. 16.0 .. .. .. .. .. .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 24.0 .. 15.4 .. 12.9 .. 0.0 .. 1.3 25 .. 19 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. 12.7 .. 13.0 .. 0.2 .. 63.6 .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 22.5 22.2 11.9 16.4 14.3 .. 0.0 .. 1.3 33 .. 15 Macedonia, FYR 33.0 .. 12.8 .. 18.3 .. .. .. 7.9 18 .. 15 Madagascar 11.3 15.7 15.7 3.4 5.2 8.5 0.0 50.1 53.5 .. .. .. Malawi .. 42.5 .. 13.9 .. 0.0 .. 18.7 .. .. .. .. Malaysia .. 42.5 .. 6.3 .. 9.7 .. 15.1 .. 28 65,789 28 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 17.3 15.2 14.0 7.0 9.2 4.6 0.0 45.7 29.3 25 862 25 Mexico 13.2 34.2 38.1 10.2 10.5 0.1 0.0 6.9 4.5 35 61,689 34 Moldova 20.5 .. 2.6 .. 18.2 .. 0.0 .. 5.6 .. .. .. Mongolia 23.0 28.2 10.5 9.3 18.2 0.0 0.4 19.6 9.8 .. .. .. Morocco .. 27.3 .. 12.1 .. 0.3 .. 20.3 .. 44 5,243 35 Mozambique .. .. .. .. .. .. .. .. .. 32 42,583 32 Myanmar 3.0 29.8 34.5 6.8 4.0 0.0 0.0 23.3 7.2 .. .. .. Namibia 29.7 39.4 35.3 9.9 8.6 3.6 .. 26.9 .. 36 17,241 35 Nepal 9.6 13.0 20.8 6.6 7.1 0.4 2.4 37.0 31.3 .. .. .. Netherlands .. 33.6 .. 11.5 .. 0.0 .. 0.0 .. 52 47,352 35 New Zealand 27.9 62.2 68.3 13.2 .. 0.0 0.0 2.5 1.8 39 31,561 33 Nicaragua 16.5 20.0 14.7 16.9 11.3 0.0 0.0 21.3 8.4 .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 21.7 .. 16.8 .. 0.1 .. 0.6 .. .. .. 28 Oman 7.4 87.6 77.1 0.3 .. 0.0 0.0 7.8 10.3 0 .. 12 Pakistan 12.9 12.8 31.1 8.6 8.4 0.0 0.0 44.4 10.8 35 11,111 45 Panama 14.1 24.4 29.4 4.8 .. 1.3 0.0 15.8 .. 30 200,000 30 Papua New Guinea .. 47.0 .. 5.0 .. 2.1 .. 29.3 .. 47 24,842 25 Paraguay 10.1 12.4 16.1 3.6 8.1 0.0 0.0 18.8 17.5 0 .. 30 Peru 13.6 5.8 25.1 8.2 9.7 7.6 0.0 9.9 10.5 30 45,863 27 Philippines 13.3 32.5 45.6 6.4 4.7 0.0 0.0 28.4 19.6 32 9,320 32 Poland 26.2 .. 18.8 0.0 13.1 .. 0.0 .. 2.1 40 18,278 27 Portugal .. 25.7 .. 13.0 .. 0.0 .. 2.6 .. 40 50,045 30 Puerto Rico .. .. .. .. .. .. .. .. .. 33 50,000 20 2004 World Development Indicators 275 5.6 Tax policies Tax Taxes on Domestic taxes Export Import Highest marginal revenue income, on goods duties duties tax rate a profits, and and services capital gains % of value Individual Corporate % of % of added in industry % of % of on income GDP total taxes and services tax revenue tax revenue % over $ % 2002 1990 2002 1990 2002 1990 2002 1990 2002 2003 2003 2003 Romania 22.8 21.0 12.0 16.0 10.6 0.0 0.0 0.6 3.4 40 3,743 25 Russian Federation 22.5 .. 10.8 .. 11.3 .. 11.2 .. 5.1 13 .. 24 Rwanda .. 20.0 .. 5.5 .. 7.4 .. 20.7 .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. 0 .. 0 Senegal 17.9 .. 22.8 0.0 7.4 .. .. .. .. .. .. .. Serbia and Montenegro .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. 33.0 .. 2.1 .. 0.4 .. 41.3 .. .. .. .. Singapore 15.4 44.6 52.7 .. 5.1 0.0 0.0 3.5 2.6 22 184,438 22 Slovak Republic 29.6 .. 19.7 .. 10.9 .. 0.0 .. 1.3 38 14,087 25 Slovenia 35.0 12.3 15.5 12.7 16.6 .. 0.0 .. 1.8 50 .. 25 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 26.3 55.0 57.0 10.3 10.3 0.0 0.0 3.9 2.9 40 30,380 30 Spain .. 34.0 .. 7.5 .. 0.0 .. 1.7 .. 29 44,794 35 Sri Lanka 14.5 12.0 16.9 14.7 13.6 4.2 0.0 27.4 12.7 30 3,708 30 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 26.7 33.2 26.4 5.2 6.6 2.0 0.0 50.5 54.7 33 4,215 30 Sweden .. 20.6 .. 14.5 .. 0.0 .. 0.6 .. 25 50,767 28 Switzerland 23.5 17.0 17.7 .. 6.7 0.0 0.0 6.9 1.1 .. .. 9 Syrian Arab Republic .. 40.2 .. 9.6 .. 1.3 .. 8.2 .. .. .. .. Tajikistan 10.5 .. 3.0 .. 9.4 .. 0.0 .. 17.1 .. .. .. Tanzania .. .. .. .. .. .. .. .. .. 30 7,074 30 Thailand 14.4 26.2 34.3 8.8 7.8 0.2 0.3 23.7 12.3 37 92,379 30 Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. .. .. 0.0 .. .. .. .. .. 30 7,937 30 Tunisia 26.0 16.0 22.3 7.1 12.5 0.4 0.1 35.1 12.5 .. .. .. Turkey 24.2 51.2 42.2 5.9 15.4 0.0 0.0 7.3 1.1 40 73,417 30 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 10.8 .. 20.1 0.0 5.3 .. 0.0 .. 50.3 30 2,860 30 Ukraine 21.7 .. 14.3 .. 10.5 .. 0.0 .. 4.4 40 3,826 30 United Arab Emirates .. 0.0 .. 0.6 .. .. .. .. .. 0 .. 0 United Kingdom .. 43.2 .. 11.3 .. 0.0 .. 0.0 .. 40 48,413 30 United States 17.7 56.1 55.5 0.7 0.7 0.0 0.0 1.7 1.0 39 311,950 35 Uruguay 23.3 7.1 16.5 9.4 10.4 0.6 0.1 8.1 3.0 0 .. 35 Uzbekistan .. .. .. .. .. .. .. .. .. 32 561 20 Venezuela, RB 12.2 82.2 34.0 0.8 5.9 0.0 0.0 7.1 12.1 34 72,000 34 Vietnam 16.4 .. 32.0 .. 9.0 .. 0.0 .. 22.8 .. .. 32 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. 44.9 .. 2.5 .. 0.0 .. 29.2 .. .. .. .. Zambia .. .. .. .. .. .. .. .. .. 30 368 35 Zimbabwe .. 49.7 .. 8.4 .. 0.0 .. 18.8 .. 45 26,249 30 a. These data are from PricewaterhouseCoopers's Individual Taxes: Worldwide Summaries 2003­2004 and Corporate Taxes: Worldwide Summaries 2003­2004, copyright 2003 by PricewaterhouseCoopers by permission of John Wiley and Sons, Inc. 276 2004 World Development Indicators 5.6 STATES AND Tax policies MARKET S About the data Definitions Taxes are the main source of revenue for many govern- because indirect taxes on goods originating from these · Tax revenue comprises compulsory transfers to the ments. The sources of the tax revenue received by gov- sectors are usually negligible. What is missing here is a central government for public purposes. Compulsory ernments and the relative contributions of these sources measure of the uniformity of these taxes across indus- transfers such as fines, penalties, and most social are determined by policy choices about where and how tries and along the value added chain of production. security contributions are excluded. Refunds and cor- to impose taxes and by changes in the structure of the Without such data, no clear inferences can be drawn rections of erroneously collected tax revenue are treat- economy. Tax policy may reflect concerns about distribu- about how neutral a tax system is between subsectors. ed as negative revenue. · Taxes on income, profits, and tional effects, economic efficiency (including corrections "Surplus" revenues raised by some governments by capital gains are levied on wages, salaries, tips, fees, for externalities), and the practical problems of adminis- charging higher prices for goods produced under monop- commissions, and other compensation for labor servic- tering a tax system. There is no ideal level of taxation. oly by state-owned enterprises are not counted as tax rev- es; interest, dividends, rent, and royalties; profits of But taxes influence incentives and thus the behavior of enues. Similarly, losses from charging below-market businesses, estates, and trusts; and capital gains and economic actors and the economy's competitiveness. prices for products are rarely identified as subsidies. losses. Social security contributions based on gross Taxes are compulsory transfers to governments from Export and import duties are shown separately pay, payroll, or number of employees are not included, individuals, businesses, or institutions. They include because the burden they impose on the economy (and but taxable portions of social security, pension, and service fees that are clearly out of proportion to the thus growth) is likely to be large. Export duties, typically other retirement account distributions are included. costs of providing the services but exclude fines, penal- levied on primary (particularly agricultural) products, · Domestic taxes on goods and services are all taxes ties, and compulsory social security contributions. often take the place of direct taxes on income and prof- and duties levied by central governments on the pro- Taxes are considered unrequited because governments its, but they reduce the incentive to export and encour- duction, extraction, sale, transfer, leasing, or delivery of provide nothing specifically in return for them, although age a shift to other products. High import duties penalize goods and rendering of services, or on the use of goods taxes typically are used to provide goods or services to consumers, create protective barriers--which promote or permission to use goods or perform activities. These individuals or communities on a collective basis. higher priced output and inefficient production--and include value added taxes, general sales taxes, single- The level of taxation is typically measured by tax rev- implicitly tax exports. By contrast, lower trade taxes stage and multistage taxes (where stage refers to stage enue as a share of gross domestic product (GDP). enhance openness--to foreign competition, knowledge, of production or distribution), excise taxes, motor vehicle Comparing levels of taxation across countries provides technologies, and resources--energizing development in taxes, and taxes on the extraction, processing, or pro- a quick overview of the fiscal obligations and incentives many ways. Seeing this pattern, some of the fastest duction of minerals or other products. · Export duties facing the private sector. In this table tax data in local growing economies have lowered import tariffs in recent are all levies collected on goods at the point of export. currencies are normalized by scaling values in the years. The simple mean import tariff in India, for exam- Rebates on exported goods that are repayments of pre- same units to ease cross-country comparisons. The ple, declined from almost 80 percent in 1990 to about viously paid general consumption taxes, excise taxes, or table shows only central government data, which may 30 percent in 2001. In some countries, such as mem- import duties are deducted from the gross amounts significantly understate the total tax burden, particular- bers of the European Union, most customs duties are receivable from these taxes, not from amounts receiv- ly in countries where provincial and municipal govern- collected by a supranational authority; these revenues able from export duties. · Import duties are all levies ments are large or have considerable tax authority. are not reported in the individual countries' accounts. collected on goods at the point of entry into the country. Low ratios of tax revenue to GDP may reflect weak The tax revenues collected by governments are the They include levies imposed for revenue or protection administration and large-scale tax avoidance or evasion. outcomes of systems that are often complex, containing purposes and determined on a specific or ad valorem Low ratios may also reflect the presence of a sizable par- many exceptions, exemptions, penalties, and other basis as long as they are restricted to imported prod- allel economy with unrecorded and undisclosed inducements that affect the incidence of taxes and thus ucts. · Highest marginal tax rate is the highest rate incomes. Tax revenue ratios tend to rise with income, influence the decisions of workers, managers, and entre- shown on the national level schedule of tax rates with higher income countries relying on taxes to finance preneurs. A potentially important influence on both applied to the annual taxable income of individuals and a much broader range of social services and social secu- domestic and international investors is a tax system's corporations. Also presented are the income levels for rity than lower income countries are able to provide. progressivity, as reflected in the highest marginal tax rate individuals above which the highest marginal tax rates As economies develop, their capacity to tax residents levied at the national level on individual and corporate levied at the national level apply. directly typically expands and indirect taxes become less income. Figures for individual marginal tax rates general- important as a source of revenue. Thus the share of ly refer to employment income. In some countries the Data sources taxes on income, profits, and capital gains is one meas- highest marginal tax rate is also the basic or flat rate, and The definitions used here are from the International ure of an economy's (and tax system's) level of develop- other surtaxes, deductions, and the like may apply. And Monetary Fund's (IMF) Manual on Government ment. In the early stages of development governments in many countries several different corporate tax rates Finance Statistics (2002). The data on tax rev- tend to rely on indirect taxes because the administrative may be levied, depending on the type of business (min- enues are from print and electronic editions of the costs of collecting them are relatively low. The two main ing, banking, insurance, agriculture, manufacturing), own- IMF's Government Finance Statistics Yearbook. The indirect taxes are international trade taxes (including cus- ership (domestic or foreign), volume of sales, or whether data on individual and corporate tax rates are toms revenues) and domestic taxes on goods and serv- surtaxes or exemptions are included. The corporate tax from PricewaterhouseCoopers's Individual Taxes: ices. The table shows these domestic taxes as a rates in the table are mainly general rates applied to Worldwide Summaries 2003­2004 and Corporate percentage of value added in industry and services. domestic companies. For more detailed information, see Taxes: Worldwide Summaries 2003­2004. Agriculture and mining are excluded from the denominator the country's laws, regulations, and tax treaties. 2004 World Development Indicators 277 5.7 Relative prices and exchange rates Exchange rate Official Purchasing Ratio of PPP Real Interest rate arrangements a exchange power parity (PPP) conversion effective rate conversion factor to exchange factor official rate exchange local local currency rate currency units to index % Classification Structure units to $ international $ 1995 = 100 Deposit Lending Real 2002 2002 2002 1990 2002 2002 2002 2002 2002 2002 Afghanistan MF U 3,000.00 .. .. .. .. .. .. .. Albania IF U 140.15 2.0 44.5 0.3 .. 8.5 15.3 8.7 Algeria MF U 79.68 5.0 24.7 0.3 101.7 5.3 8.5 7.4 Angola MF U 43.53 0.0 17.5 0.4 .. 48.7 97.3 ­2.9 Argentina MF U 3.06 0.3 0.8 0.2 .. 39.2 51.7 16.2 Armenia IF U 573.35 .. 141.9 0.2 95.9 9.6 21.1 18.5 Australia IF U 1.84 1.4 1.4 0.7 96.1 3.0 8.0 5.5 Austria Euro U 1.06 0.9 0.9 0.9 92.1 .. .. .. Azerbaijan MF U 4,860.82 1.1 1,127.8 0.2 .. 8.7 17.4 16.5 Bangladesh P U 57.89 9.6 11.9 0.2 .. 8.2 16.0 12.4 Belarus P U 1,790.92 .. 465.9 0.3 .. 26.9 36.9 ­3.6 Belgium Euro U 1.06 0.9 0.9 0.9 90.2 2.6 7.7 5.8 Benin EA/Euro U 696.99 160.7 267.2 0.4 .. 3.5 .. .. Bolivia P U 7.17 1.3 2.6 0.4 115.4 9.6 20.6 17.5 Bosnia and Herzegovina CB/Euro U 2.08 .. .. .. .. 4.5 12.7 10.4 Botswana P/Euro D 6.33 1.2 2.4 0.4 .. 10.3 16.0 9.9 Brazil IF U 2.92 0.0 1.0 0.3 .. 19.1 62.9 50.1 Bulgaria CB/Euro U 2.08 0.0 0.6 0.3 135.6 2.8 9.3 5.3 Burkina Faso EA/Euro U 696.99 136.3 168.0 0.2 .. 3.5 .. .. Burundi MF U 930.75 49.6 149.3 0.2 79.1 .. 19.5 5.8 Cambodia MF D 3,912.08 .. 610.5 0.2 .. 2.5 16.2 12.8 Cameroon EA/Euro U 696.99 171.8 210.9 0.3 102.1 5.0 18.0 17.2 Canada IF U 1.57 1.3 1.2 0.8 97.7 0.8 4.2 3.2 Central African Republic EA/Euro U 696.99 136.0 162.6 0.2 95.4 5.0 18.0 16.3 Chad EA/Euro U 696.99 106.1 163.6 0.2 .. 5.0 18.0 13.8 Chile IF U 688.94 149.5 288.7 0.4 90.7 3.8 7.8 5.0 China P U 8.28 1.2 1.8 0.2 121.4 2.0 5.3 5.6 Hong Kong, China CB U 7.80 6.4 6.9 0.9 .. 0.3 5.0 8.2 Colombia IF U 2,504.24 120.6 727.5 0.3 90.4 8.9 16.3 9.7 Congo, Dem. Rep. IF U 346.48 0.0 58.7 0.2 109.2 .. 66.8 35.3 Congo, Rep. EA/Euro U 696.99 387.9 587.7 0.8 .. 5.0 18.0 18.8 Costa Rica P U 359.82 32.8 173.8 0.5 109.4 11.5 26.4 15.8 Côte d'Ivoire EA/Euro U 696.99 168.0 324.3 0.5 103.7 3.5 .. .. Croatia MF U 7.87 .. 3.9 0.5 103.7 1.9 12.8 9.6 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic MF U 32.74 8.1 14.1 0.4 133.6 2.2 6.2 3.5 Denmark P U 7.89 8.1 8.2 1.0 96.7 2.4 7.1 6.2 Dominican Republic MF D 18.61 2.6 7.0 0.4 112.1 16.5 26.1 18.4 Ecuador EA/Other U 1.00 0.4 0.5 0.5 113.8 5.5 15.1 2.9 Egypt, Arab Rep. MF U 4.50 0.8 1.5 0.3 .. 9.3 13.8 9.4 El Salvador EA/Other U 8.75 2.4 4.0 0.5 .. 9.3 14.0 10.5 Eritrea P U 13.96 1.2 2.3 0.2 .. .. .. .. Estonia CB/Euro U 16.61 0.1 6.5 0.4 .. 2.7 6.7 2.5 Ethiopia MF U 8.57 0.7 1.0 0.1 79.4 4.1 8.7 16.9 Finland Euro U 1.06 1.0 1.0 1.0 90.2 1.5 4.8 3.5 France Euro U 1.06 1.0 0.9 0.9 89.8 3.0 6.6 4.7 Gabon EA/Euro U 696.99 341.2 399.5 0.6 91.4 5.0 18.0 11.4 Gambia, The MF U 19.92 1.8 3.0 0.2 68.4 12.7 24.0 3.6 Georgia IF U 2.20 .. 0.6 0.3 .. 9.8 31.8 23.9 Germany Euro U 1.06 1.0 0.9 0.9 86.6 2.7 9.7 8.0 Ghana MF U 7,932.70 94.9 1,134.2 0.1 81.0 16.2 .. .. Greece Euro U 1.06 0.4 0.7 0.7 100.0 2.8 7.4 3.6 Guatemala MF U 7.82 1.4 3.7 0.5 .. 6.9 16.9 8.2 Guinea P U 1,975.84 225.0 390.8 0.2 .. 7.4 19.4 7.4 Guinea-Bissau EA/Euro U 696.99 11.0 137.8 0.2 .. 3.5 .. .. Haiti MF U 29.25 1.2 7.2 0.2 .. 8.2 25.7 15.3 278 2004 World Development Indicators 5.7 STATES AND Relative prices and exchange rates MARKET S Exchange rate Official Purchasing Ratio of PPP Real Interest rate arrangements a exchange power parity (PPP) conversion effective rate conversion factor to exchange factor official rate exchange local local currency rate currency units to index % Classification Structure units to $ international $ 1995 = 100 Deposit Lending Real 2002 2002 2002 1990 2002 2002 2002 2002 2002 2002 Honduras P U 16.43 1.3 6.1 0.4 .. 13.7 22.7 15.5 Hungary P U 257.89 22.3 124.8 0.5 130.8 7.4 10.2 ­0.5 India MF U 48.61 4.9 8.8 0.2 .. .. 11.9 8.7 Indonesia MF U 9,311.19 642.6 2,357.7 0.3 .. 15.5 18.9 11.0 Iran, Islamic Rep. MF D 6,906.96 180.4 1,963.4 0.3 198.1 .. .. .. Iraq MF U 0.31 .. .. .. .. .. .. .. Ireland Euro U 1.06 0.8 0.9 0.9 99.0 0.1 3.8 ­2.7 Israel P U 4.74 1.8 3.8 0.8 102.5 6.0 9.9 5.2 Italy Euro U 1.06 0.7 0.8 0.8 110.0 1.4 5.8 2.9 Jamaica MF U 48.42 4.4 36.6 0.8 .. 8.6 18.5 9.7 Japan IF U 125.39 190.2 146.2 1.2 78.9 0.0 1.9 3.6 Jordan P U 0.71 0.3 0.3 0.4 .. 4.4 10.2 9.7 Kazakhstan MF U 153.28 .. 43.2 0.3 .. .. .. .. Kenya MF U 78.75 9.1 30.4 0.4 .. 5.5 18.5 9.0 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. IF U 1,251.09 563.7 738.7 0.6 .. 4.9 6.8 5.0 Kuwait P U 0.30 0.3 0.3 0.9 .. 3.2 6.5 3.0 Kyrgyz Republic MF U 46.94 .. 9.3 0.2 .. 5.9 24.8 22.0 Lao PDR MF D 10,056.33 174.9 1,884.2 0.2 .. 6.0 29.3 18.4 Latvia p U 0.62 .. 0.2 0.4 .. 3.2 8.0 6.1 Lebanon P U 1,507.50 307.0 1,346.0 0.9 .. 11.0 16.6 13.8 Lesotho P U 10.54 1.0 1.7 0.2 60.8 5.2 17.1 7.4 Liberia IF U 61.75 .. .. .. .. 6.2 20.2 ­7.1 Libya P U 1.27 .. .. .. .. 3.0 7.0 .. Lithuania CB/Euro U 3.68 .. 1.4 0.4 .. 1.7 6.8 6.9 Macedonia, FYR P U 64.35 .. 18.5 0.3 72.6 9.6 18.4 14.3 Madagascar IF U 6,831.96 516.0 2,456.7 0.4 .. 12.0 25.3 8.6 Malawi IF U 76.69 1.4 23.3 0.3 115.0 28.1 50.5 28.1 Malaysia P U 3.80 1.5 1.6 0.4 91.3 3.2 6.4 2.7 Mali EA/Euro U 696.99 141.4 222.8 0.3 .. 3.5 .. .. Mauritania MF U 271.74 36.5 42.7 0.2 .. .. .. .. Mauritius MF U 29.96 6.5 10.5 0.3 .. 9.9 21.0 15.1 Mexico IF U 9.66 1.5 6.8 0.7 .. 3.8 8.2 3.4 Moldova MF U 13.57 .. 3.5 0.3 100.2 14.2 23.5 14.3 Mongolia MF U 1,110.31 2.9 296.8 0.3 .. 13.2 28.4 21.1 Morocco P U 11.02 3.2 3.5 0.3 103.4 4.5 13.1 12.5 Mozambique IF U 23,677.96 321.5 4,406.7 0.2 .. 18.0 26.7 13.9 Myanmar MF D 6.57 .. .. .. .. 9.5 15.0 ­6.2 Namibia P U 10.54 0.9 2.5 0.2 .. 7.8 13.8 4.0 Nepal P U 77.88 6.8 12.7 0.2 .. 4.8 7.7 4.3 Netherlands Euro U 1.06 0.9 0.9 0.9 95.8 2.8 4.0 0.7 New Zealand IF U 2.16 1.6 1.5 0.7 88.9 5.3 9.8 8.0 Nicaragua P U 14.25 0.0 4.3 0.3 111.6 7.3 23.2 17.0 Niger EA/Euro U 696.99 122.3 165.4 0.2 .. 3.5 .. .. Nigeria MF M 120.58 3.8 46.2 0.4 90.3 16.7 24.8 11.8 Norway IF U 7.98 8.0 9.2 1.1 107.9 6.5 8.5 10.0 Oman P U 0.38 0.3 0.2 0.6 .. 2.9 8.5 6.6 Pakistan MF U 59.72 5.8 12.9 0.2 90.0 .. .. .. Panama EA/Other U 1.00 0.6 0.7 0.7 .. 5.0 10.6 9.3 Papua New Guinea IF U 3.90 0.5 0.9 0.2 81.2 5.8 13.9 1.5 Paraguay MF U 5,716.26 408.1 1,240.2 0.2 75.6 22.9 38.7 21.0 Peru IF U 3.52 0.1 1.5 0.4 .. 4.2 14.7 14.1 Philippines IF U 51.60 5.6 12.1 0.2 85.6 4.6 9.1 4.1 Poland IF U 4.08 0.2 1.9 0.5 133.4 6.2 12.1 10.3 Portugal Euro U 1.06 0.5 0.7 0.7 100.6 .. .. .. Puerto Rico .. .. .. 0.7 0.7 .. .. .. .. .. 2004 World Development Indicators 279 5.7 Relative prices and exchange rates Exchange rate Official Purchasing Ratio of PPP Real Interest rate arrangements a exchange power parity (PPP) conversion effective rate conversion factor to exchange factor official rate exchange local local currency rate currency units to index % Classification Structure units to $ international $ 1995 = 100 Deposit Lending Real 2002 2002 2002 1990 2002 2002 2002 2002 2002 2002 Romania P U 33,055.43 6.9 10,344.3 0.3 110.2 .. .. .. Russian Federation MF U 31.35 .. 9.2 0.3 109.0 5.0 15.7 0.4 Rwanda MF U 476.33 31.4 79.5 0.2 .. 8.0 .. .. Saudi Arabia P U 3.74 2.9 2.5 0.7 107.2 2.2 .. .. Senegal EA/Euro U 696.99 185.8 222.2 0.3 .. 3.5 .. .. Serbia and Montenegro MF U .. .. .. .. .. .. .. .. Sierra Leone IF D 2,099.03 30.0 599.7 0.3 94.1 8.2 22.2 17.6 Singapore MF U 1.79 1.9 1.6 0.9 93.7 0.9 5.4 5.2 Slovak Republic MF U 45.33 5.9 15.5 0.3 105.8 6.6 10.2 6.1 Slovenia P U 240.25 .. 144.9 0.6 .. 8.2 13.2 4.7 Somalia IF D .. .. .. .. .. .. .. .. South Africa IF U 10.54 1.0 2.4 0.2 62.6 10.8 15.8 6.6 Spain Euro U 1.06 0.6 0.8 0.7 98.3 2.5 4.3 ­0.1 Sri Lanka MF U 95.66 10.3 23.4 0.2 .. 9.2 13.2 4.5 Sudan P U 263.31 0.7 59.8 0.2 .. .. .. .. Swaziland P U 10.54 0.9 2.5 0.2 .. 8.0 15.3 1.5 Sweden IF U 9.74 9.9 10.1 1.0 89.8 2.2 5.8 4.5 Switzerland IF U 1.56 2.0 1.9 1.2 93.1 0.4 3.9 3.5 Syrian Arab Republic P M 11.23 10.3 16.9 1.5 .. 4.0 9.0 4.4 Tajikistan MF U 2.76 .. 0.5 0.2 .. 9.2 14.2 ­6.4 Tanzania IF U 966.58 76.0 444.9 0.5 .. 3.3 16.4 11.8 Thailand MF U 42.96 10.8 12.6 0.3 .. 2.0 6.9 6.1 Togo EA/Euro U 696.99 94.5 137.1 0.2 105.4 3.5 .. .. Trinidad and Tobago MF U 6.25 3.1 4.9 0.8 126.6 4.8 12.5 11.6 Tunisia P U 1.42 0.4 0.5 0.3 96.2 .. .. .. Turkey IF U 1,507,226.38 1,643.1 621,572.8 0.4 .. 50.5 .. .. Turkmenistan P D 5,200.00 .. 1,544.1 0.3 .. .. .. .. Uganda IF U 1,797.55 111.4 298.4 0.2 76.7 5.6 19.1 23.1 Ukraine P U 5.33 .. 0.9 0.2 112.4 7.9 25.3 21.4 United Arab Emirates P U 3.67 3.4 .. .. .. .. 8.1 .. United Kingdom IF U 0.67 0.6 0.7 1.0 130.4 .. 4.0 0.8 United States IF U 1.00 1.0 1.0 1.0 133.6 .. 4.7 3.5 Uruguay IF U 21.26 0.6 10.0 0.5 87.9 14.3 126.1 90.3 Uzbekistan MF M 236.61 .. 177.4 0.4 .. .. .. .. Venezuela, RB P U 1,160.95 24.7 810.6 0.7 132.9 29.0 36.6 3.8 Vietnam MF U 15,279.50 644.4 2,892.2 0.2 .. 6.4 9.1 4.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. IF U 175.63 20.4 108.3 0.6 .. 13.0 17.7 11.9 Zambia MF U 4,398.60 18.7 1,893.0 0.4 115.8 23.3 45.2 21.1 Zimbabwe P U 55.04 1.0 16.4 0.3 .. 18.4 36.5 ­34.2 a. Exchange rate arrangements are given for the end of the year in 2002. Exchange rate classifications include independent floating (IF), managed floating (MF), pegged (P), currency board (CB), and several exchange arrangements (EA): Euro that the currency is pegged to the euro, and other that the currency of another country is used as legal tender. Exchange rate structures include dual exchange rates (D), multiple exchange rates (M), and unitary rate (U). 280 2004 World Development Indicators 5.7 STATES AND Relative prices and exchange rates MARKET S About the data Definitions In a market-based economy the choices households, For most high-income countries the weights are · Exchange rate arrangements describe the producers, and governments make about the alloca- based on trade in manufactured goods with other arrangements furnished to the IMF by each member tion of resources are influenced by relative prices, high-income countries in 1989­91, and an index of country under article IV, section 2(a) of the IMF's including the real exchange rate, real wages, real relative, normalized unit labor costs is used as the Articles of Agreement. · Classification indicates how interest rates, and a host of other prices in the econ- deflator. (Normalization smooths a time series by the exchange rate is determined in the main market omy. Relative prices also reflect, to a large extent, the removing short-term fluctuations while retaining when there is more than one market: floating (man- choices of these agents. Thus relative prices convey changes of a large amplitude over the longer eco- aged or independent), pegged (conventional, within vital information about the interaction of economic nomic cycle.) For other countries the weights before horizontal bands, crawling peg, or crawling band), agents in an economy and with the rest of the world. 1990 take into account trade in manufactured and currency board (implicit legislative commitment to The exchange rate is the price of one currency in primary products in 1980­82, the weights from exchange domestic currency for a specified foreign terms of another. Official exchange rates and January 1990 onward take into account trade in currency at a fixed exchange rate), and exchange exchange rate arrangements are established by gov- 1988­90, and an index of relative changes in con- arrangement (currency is pegged to the French franc, ernments. (Other exchange rates fully recognized by sumer prices is used as the deflator. An increase in or another country's currency is used as legal governments include market rates, which are deter- the real effective exchange rate represents an appre- tender). · Structure shows whether countries have a mined largely by legal market forces, and for coun- ciation of the local currency. Because of conceptual unitary exchange rate or dual or multiple rates. tries maintaining multiple exchange arrangements, and data limitations, changes in real effective · Official exchange rate is the exchange rate deter- principal rates, secondary rates, and tertiary rates.) exchange rates should be interpreted with caution. mined by national authorities or the rate determined Also see Statistical methods for information on Many interest rates coexist in an economy, reflect- in the legally sanctioned exchange market. It is cal- alternative conversion factors used in the Atlas ing competitive conditions, the terms governing loans culated as an annual average based on monthly aver- method of calculating gross national income (GNI) and deposits, and differences in the position and sta- ages (local currency units relative to the U.S. dollar). per capita in U.S. dollars. tus of creditors and debtors. In some economies · Purchasing power parity (PPP) conversion factor The official or market exchange rate is often used interest rates are set by regulation or administrative is the number of units of a country's currency to compare prices in different currencies. Since fiat. In economies with imperfect markets, or where required to buy the same amount of goods and serv- exchange rates reflect at best the relative prices of reported nominal rates are not indicative of effective ices in the domestic market as a U.S. dollar would tradable goods, the volume of goods and services rates, it may be difficult to obtain data on interest buy in the United States. · Ratio of PPP conversion that a U.S. dollar buys in the United States may not rates that reflect actual market transactions. Deposit factor to official exchange rate is the result correspond to what a U.S. dollar converted to anoth- and lending rates are collected by the International obtained by dividing the PPP conversion factor by the er country's currency at the official exchange rate Monetary Fund (IMF) as representative interest rates official exchange rate. · Real effective exchange would buy in that country. Since identical volumes of offered by banks to resident customers. The terms rate is the nominal effective exchange rate (a meas- goods and services in different countries correspond and conditions attached to these rates differ by coun- ure of the value of a currency against a weighted to different values (and vice versa) when official try, however, limiting their comparability. Real interest average of several foreign currencies) divided by a exchange rates are used, an alternative method of rates are calculated by adjusting nominal rates by an price deflator or index of costs. · Deposit interest comparing prices across countries has been devel- estimate of the inflation rate in the economy. A nega- rate is the rate paid by commercial or similar banks oped. In this method national currency estimates of tive real interest rate indicates a loss in the purchas- for demand, time, or savings deposits. · Lending GNI are converted to a common unit of account by ing power of the principal. The real interest rates in interest rate is the rate charged by banks on loans using conversion factors that reflect equivalent pur- the table are calculated as ( i ­ P ) / ( 1 + P ), where to prime customers. · Real interest rate is the lend- chasing power. Purchasing power parity (PPP) con- i is the nominal interest rate and P is the inflation rate ing interest rate adjusted for inflation as measured version factors are based on price and expenditure (as measured by the GDP deflator). by the GDP deflator. surveys conducted by the International Comparison Program and represent the conversion factors applied to equalize price levels across countries. See About the data for table 1.1 for further discussion of Data sources the PPP conversion factor. The information on exchange rate arrangements The ratio of the PPP conversion factor to the offi- is from the IMF's Exchange Arrangements and cial exchange rate (also referred to as the national Exchange Restrictions Annual Report, 2003. The price level) makes it possible to compare the cost of official and real effective exchange rates and the bundle of goods that make up gross domestic deposit and lending rates are from the IMF's product (GDP) across countries. These national price International Financial Statistics. PPP conversion levels vary systematically, rising with GNI per capita. factors are from the World Bank. The real interest Real effective exchange rates are derived by deflat- rates are calculated using World Bank data on the ing a trade-weighted average of the nominal GDP deflator. exchange rates that apply between trading partners. 2004 World Development Indicators 281 5.8 Defense expenditures and arms transfers Military expenditures Armed forces Arms transfers personnel $ millions % of % of central Total % of 1990 prices GDP government expenditure thousands labor force Exports Imports 1992 2002 1992 2002 1992 1999 1992 1999 1992 2002 1992 2002 Afghanistan .. .. .. .. 45 .. 0.6 .. .. .. .. 31 Albania 4.6 1.2 .. 3.7 65 18 4.1 1.2 .. .. .. 0 Algeria 2.2 3.7 9.5 0.0 126 120 1.6 1.2 .. .. 16 464 Angola 12.0 3.7 .. .. 128 100 2.8 1.8 20 1 106 5 Argentina 1.4 1.2 12.0 8.1 65 73 0.5 0.5 15 3 16 210 Armenia 2.2 2.7 .. .. 20 50 1.2 3.2 .. .. 8 2 Australia 2.3 1.7 8.9 7.5 68 55 0.8 0.6 4 30 250 614 Austria 1.0 0.8 2.4 2.0 44 49 1.2 1.3 13 124 2 79 Azerbaijan 3.3 2.1 12.4 10.2 43 75 1.4 2.1 .. .. 64 3 Bangladesh 1.1 1.1 .. 11.2 107 110 0.2 0.2 .. .. 63 21 Belarus 1.5 1.4 4.1 4.5 102 65 1.9 1.2 8 333 .. 41 Belgium 1.8 1.3 3.7 3.2 79 42 1.9 1.0 20 14 64 29 Benin .. .. .. .. 7 8 0.3 0.3 .. .. .. .. Bolivia 2.1 1.7 10.6 6.1 32 33 1.2 1.0 .. .. 24 1 Bosnia and Herzegovina .. 9.5 .. .. 60 30 3.2 1.7 .. .. 0 25 Botswana 4.3 4.0 11.7 .. 7 8 1.2 1.1 .. .. 3 12 Brazil 1.1 1.6 3.7 5.2 296 300 0.4 0.4 61 18 66 154 Bulgaria 2.7 2.7 6.6 7.9 99 70 2.3 1.7 18 20 44 6 Burkina Faso 2.3 1.7 14.0 .. 9 9 0.2 0.2 .. .. .. .. Burundi 3.6 7.6 10.7 27.1 13 40 0.4 1.1 .. .. .. 1 Cambodia 4.7 2.7 .. .. 135 60 2.7 1.0 0 .. 2 22 Cameroon 1.5 1.4 8.4 10.4 12 15 0.2 0.2 .. .. 3 1 Canada 1.9 1.1 6.9 6.2 82 60 0.5 0.4 210 318 344 359 Central African Republic 1.6 .. .. .. 4 3 0.3 0.2 .. .. 1 .. Chad 2.7 1.4 .. .. 38 30 1.3 0.8 .. .. 8 15 Chile 3.4 2.9 16.2 12.4 92 88 1.8 1.4 1 1 182 56 China 2.7 2.5 32.5 19.2 3,160 2,400 0.5 0.3 642 818 1,163 2,307 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2.4 3.7 15.8 18.8 139 155 0.9 0.9 .. .. 32 119 Congo, Dem. Rep. .. .. .. .. 45 55 0.3 0.3 .. .. 2 14 Congo, Rep. .. .. .. .. 10 10 0.9 0.7 .. .. .. 0 Costa Rica .. .. .. .. 8 10 0.6 0.7 .. .. 3 .. Côte d'Ivoire 1.4 0.9 4.0 3.7 15 15 0.3 0.2 .. .. 1 7 Croatia 7.6 2.5 19.1 5.9 103 60 4.6 2.9 .. 2 24 2 Cuba .. .. .. .. 175 50 3.5 0.9 .. .. .. .. Czech Republic 2.3 2.1 6.2 5.4 107 54 1.9 0.9 265 85 .. 53 Denmark 1.9 1.6 4.8 4.3 28 27 1.0 0.9 190 9 42 7 Dominican Republic .. .. .. .. 22 30 0.7 0.8 .. .. .. 13 Ecuador 2.7 2.1 16.9 .. 57 58 1.5 1.2 .. .. 14 1 Egypt, Arab Rep. 3.6 2.7 10.5 10.2 424 430 2.2 1.8 10 25 995 638 El Salvador 2.0 0.8 .. 31.2 49 15 2.4 0.6 .. .. 3 3 Eritrea 21.4 27.5 .. .. 55 215 3.2 10.8 .. .. 14 180 Estonia 0.5 1.9 2.2 5.6 3 7 0.4 0.9 .. .. 1 1 Ethiopia 2.7 5.2 19.3 43.0 120 300 0.5 1.1 .. .. .. 20 Finland 1.9 1.2 4.6 4.4 33 35 1.3 1.3 3 12 441 24 France 3.4 2.5 7.6 6.4 522 421 2.1 1.6 845 1,617 387 22 Gabon .. 0.3 .. .. 7 7 1.4 1.2 .. .. .. .. Gambia, The 1.0 0.9 .. .. 1 1 0.2 0.2 .. .. .. .. Georgia .. 0.6 .. 4.9 25 14 0.9 0.5 .. 108 4 80 Germany 2.1 1.5 6.3 4.7 442 331 1.1 0.8 1,134 745 969 16 Ghana 0.6 0.6 3.6 .. 7 7 0.1 0.1 .. .. 10 9 Greece 4.5 4.3 15.5 15.6 208 204 4.8 4.5 15 11 1,994 567 Guatemala 1.3 0.6 .. .. 44 30 1.4 0.7 .. .. 10 1 Guinea 1.9 1.7 9.0 8.5 15 12 0.5 0.3 .. .. .. 5 Guinea-Bissau 0.3 3.1 .. .. 11 7 2.1 1.1 .. .. 1 .. Haiti .. .. .. .. 8 0 0.3 0.0 .. .. .. .. 282 2004 World Development Indicators 5.8 STATES AND Defense expenditures and arms transfers MARKET S Military expenditures Armed forces Arms transfers personnel $ millions % of % of central Total % of 1990 prices GDP government expenditure thousands labor force Exports Imports 1992 2002 1992 2002 1992 1999 1992 1999 1992 2002 1992 2002 Honduras .. .. .. .. 17 8 0.9 0.3 .. .. .. .. Hungary 2.4 1.8 4.3 4.4 78 51 1.6 1.1 21 24 1,021 14 India 2.3 2.6 .. .. 1,270 1,300 0.3 0.3 0 0 871 1,668 Indonesia 1.7 1.1 9.4 4.6 283 296 0.3 0.3 20 70 47 51 Iran, Islamic Rep. 1.9 4.8 11.2 17.2 528 460 3.2 2.4 1 0 386 298 Iraq .. .. .. .. 407 420 8.2 6.7 .. .. .. .. Ireland 1.2 0.7 3.0 2.8 13 14 1.0 0.9 .. 0 48 20 Israel 10.5 8.6 21.6 16.6 181 173 8.8 6.6 68 178 1,330 226 Italy 2.0 1.9 3.9 4.8 471 391 1.9 1.5 368 490 42 308 Jamaica .. .. .. .. 3 3 0.2 0.2 .. .. .. 5 Japan 0.9 1.0 4.5 .. 242 240 0.4 0.4 13 3 1,523 154 Jordan 8.2 8.4 27.8 26.5 100 102 9.8 7.3 73 5 1 149 Kazakhstan 1.0 0.9 .. 6.8 15 33 0.2 0.4 .. 9 .. 69 Kenya 1.9 1.6 7.9 5.8 24 24 0.2 0.2 .. .. 3 61 Korea, Dem. Rep. .. .. .. .. 1,200 1,000 11.3 8.6 225 32 45 3 Korea, Rep. 3.4 2.7 20.6 16.6 750 665 3.6 2.8 21 22 497 229 Kuwait 31.8 11.2 31.5 18.8 12 21 2.1 2.5 .. 82 897 27 Kyrgyz Republic 0.7 1.7 3.2 9.7 12 12 0.6 0.6 .. .. .. .. Lao PDR .. 2.1 .. .. 37 50 1.7 2.0 .. .. .. 34 Latvia 0.8 1.8 3.4 3.9 5 5 0.3 0.4 8 .. 0 3 Lebanon 8.0 4.7 25.7 14.0 37 58 3.1 3.9 .. 45 38 4 Lesotho 2.6 3.1 5.7 6.4 2 2 0.3 0.3 .. .. .. 6 Liberia 10.6 .. .. .. 2 .. 0.2 .. .. .. .. 8 Libya .. .. .. .. 85 85 6.6 5.8 8 11 .. 145 Lithuania 0.7 2.0 3.5 6.8 10 12 0.5 0.7 .. 3 74 7 Macedonia, FYR .. 2.8 .. .. 10 16 1.1 1.7 .. .. 27 133 Madagascar 1.2 1.2 6.6 7.1 21 20 0.4 0.3 .. .. .. .. Malawi 1.4 0.8 .. .. 10 5 0.2 0.1 .. 1 1 .. Malaysia 3.0 2.1 10.5 10.6 128 95 1.7 1.0 .. 8 16 213 Mali 2.4 2.0 .. .. 12 10 0.3 0.2 .. .. .. 7 Mauritania 3.5 1.9 .. .. 16 11 1.6 0.9 .. .. 27 9 Mauritius 0.4 0.2 1.5 0.8 1 2 0.2 0.4 .. .. 6 1 Mexico 0.5 0.5 3.3 3.2 175 255 0.5 0.6 .. .. 12 19 Moldova 0.5 0.3 .. 1.2 9 11 0.4 0.5 12 5 6 .. Mongolia 2.5 2.3 11.6 7.5 21 20 2.1 1.7 .. .. .. .. Morocco 4.3 4.1 14.4 12.4 195 195 2.1 1.7 .. .. 30 169 Mozambique 5.1 2.5 .. .. 50 8 0.6 0.1 .. .. .. 0 Myanmar 3.4 2.3 30.1 26.6 286 345 1.3 1.4 .. .. 52 208 Namibia 4.3 2.9 10.6 9.1 8 3 1.3 0.4 .. .. 14 11 Nepal 0.9 1.4 6.4 8.6 35 35 0.4 0.3 .. .. .. 8 Netherlands 2.4 1.6 4.7 4.0 90 54 1.3 0.7 285 260 143 236 New Zealand 1.6 1.1 4.3 4.0 11 10 0.6 0.5 4 13 61 17 Nicaragua 2.4 1.4 7.6 2.6 15 12 1.0 0.6 87 .. .. .. Niger 1.2 1.1 .. .. 5 6 0.1 0.1 .. .. 11 3 Nigeria 0.5 1.1 .. .. 76 77 0.2 0.2 .. .. 56 2 Norway 3.0 1.8 7.0 5.9 36 33 1.7 1.4 5 203 317 82 Oman 16.2 13.0 40.9 40.7 35 38 6.7 6.1 1 .. 20 48 Pakistan 6.1 4.5 27.7 21.6 580 590 1.4 1.2 1 8 .. .. Panama 1.2 1.2 4.8 4.2 11 13 1.1 1.1 .. .. 2 12 Papua New Guinea 1.3 0.8 4.2 3.3 4 4 0.2 0.2 .. .. 10 12 Paraguay 1.6 0.9 11.8 5.0 16 17 1.0 0.9 .. .. 1 6 Peru .. 1.3 .. 9.2 112 115 1.4 1.2 .. 5 132 4 Philippines 1.3 1.0 6.5 5.1 107 107 0.4 0.3 .. .. 59 17 Poland 2.3 1.8 5.5 5.3 270 187 1.4 0.9 49 43 20 258 Portugal 2.7 2.3 6.2 5.4 80 71 1.6 1.4 1 .. 6 103 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 283 5.8 Defense expenditures and arms transfers Military expenditures Armed forces Arms transfers personnel $ millions % of % of central Total % of 1990 prices GDP government expenditure thousands labor force Exports Imports 1992 2002 1992 2002 1992 1999 1992 1999 1992 2002 1992 2002 Romania 4.3 2.3 10.7 8.1 172 170 1.6 1.6 12 3 160 186 Russian Federation 5.5 4.0 21.1 15.4 1,900 900 2.5 1.2 2,384 5,941 86 170 Rwanda 4.4 3.6 21.6 .. 30 40 0.8 1.0 .. .. 2 14 Saudi Arabia 11.7 11.3 .. .. 172 190 3.1 2.9 13 .. 1,198 478 Senegal 1.8 1.5 .. 6.8 18 13 0.5 0.3 .. .. 1 .. Serbia and Montenegro .. 4.9 .. .. 137 105 2.8 2.1 24 7 0 0 Sierra Leone 2.5 2.2 17.7 .. 8 3 0.5 0.2 .. .. 1 13 Singapore 4.8 5.2 24.0 22.8 56 60 3.4 3.0 8 2 100 227 Slovak Republic 2.1 1.9 .. 4.9 33 36 1.2 1.2 157 40 181 27 Slovenia 2.2 1.5 5.8 3.5 15 10 1.5 1.0 .. .. 30 0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2.9 1.6 8.8 5.4 75 68 0.5 0.4 83 34 140 17 Spain 1.6 1.2 4.4 4.2 198 155 1.2 0.9 88 65 187 132 Sri Lanka 3.0 3.9 11.3 14.7 110 110 1.6 1.4 .. .. 21 9 Sudan 2.5 3.0 .. 27.4 82 105 0.8 0.9 .. .. 5 134 Swaziland 1.9 1.5 .. 5.2 3 3 1.1 0.8 .. .. .. 1 Sweden 2.6 1.9 5.6 5.4 70 52 1.5 1.1 182 120 47 45 Switzerland 1.8 1.1 7.0 4.2 31 39 0.8 1.0 283 11 170 36 Syrian Arab Republic 9.0 6.1 39.0 24.2 408 310 11.0 6.2 38 0 317 162 Tajikistan 0.4 1.2 .. 10.1 3 7 0.1 0.3 .. .. 24 .. Tanzania 1.9 1.3 .. .. 46 35 0.3 0.2 .. .. 20 .. Thailand 2.3 1.4 15.3 7.1 283 300 0.9 0.8 .. .. 395 150 Togo 2.9 .. .. .. 8 11 0.5 0.6 .. .. 3 7 Trinidad and Tobago .. .. .. .. 2 2 0.4 0.4 .. .. .. 1 Tunisia 1.9 1.6 5.8 5.2 35 35 1.1 0.9 .. .. 32 7 Turkey 3.7 5.0 18.8 10.0 704 789 2.7 2.5 .. 29 1,347 721 Turkmenistan 1.8 3.8 .. .. 28 15 1.8 0.8 .. .. .. .. Uganda 1.6 2.4 .. 10.1 70 50 0.7 0.5 .. .. .. 6 Ukraine 0.5 2.8 .. 9.8 430 340 1.6 1.3 232 270 .. .. United Arab Emirates 4.5 2.5 37.4 30.1 55 65 5.2 4.9 .. 28 204 452 United Kingdom 3.8 2.4 8.7 7.0 293 218 1.0 0.7 693 719 1,166 575 United States 4.8 3.4 21.1 16.0 1,920 1,490 1.5 1.0 12,108 3,941 198 346 Uruguay 2.1 1.3 8.0 4.2 25 24 1.8 1.6 .. 1 37 2 Uzbekistan 1.5 1.1 .. .. 40 60 0.5 0.6 .. 170 .. 5 Venezuela, RB 1.6 1.2 8.2 6.1 75 75 1.0 0.8 .. .. 48 50 Vietnam 3.4 .. 10.6 .. 857 485 2.4 1.2 .. .. .. 69 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 9.1 4.5 30.7 18.8 64 69 1.5 1.3 .. .. .. 496 Zambia 3.0 0.6 .. .. 16 17 0.5 0.4 .. .. .. 27 Zimbabwe 3.7 3.2 11.3 9.4 48 40 1.0 0.7 .. .. 57 8 World 3.0 w 2.4 w 11.3 w 11.0 w 24,533 t 21,198 t 0.9 w 0.7 w Low income 2.4 2.7 14.5 13.0 6,040 5,869 0.7 0.6 Middle income 3.1 2.6 13.4 11.9 12,071 9,931 1.0 0.7 Lower middle income 3.1 2.7 15.1 14.7 10,676 8,495 1.0 0.7 Upper middle income 3.0 2.6 8.5 6.1 1,395 1,436 1.3 1.1 Low & middle income 3.0 2.6 13.6 12.3 18,111 15,800 0.9 0.7 East Asia & Pacific 2.4 2.3 23.7 16.4 6,506 5,166 0.7 0.5 Europe & Central Asia 4.5 3.2 15.8 9.6 4,303 3,192 2.1 1.3 Latin America & Carib. 1.2 1.2 5.3 6.9 1,443 1,371 0.8 0.6 Middle East & N. Africa 7.9 6.9 .. .. 2,624 2,520 3.3 2.6 South Asia 2.7 2.7 16.8 14.7 2,152 2,153 0.4 0.4 Sub-Saharan Africa 2.5 1.8 8.4 .. 1,083 1,398 0.5 0.5 High income 3.0 2.4 11.1 11.0 6,422 5,398 1.4 1.1 Europe EMU 2.3 1.8 5.7 4.9 2,181 1,768 1.6 1.3 Note: Data for some countries are based on partial or uncertain data or rough estimates; see SIPRI (2003) and U.S. Department of State (2002). 284 2004 World Development Indicators 5.8 STATES AND Defense expenditures and arms transfers MARKET S About the data Although national defense is an important function of the national data provided. Because of the differences in match. These weapons are assigned a value in an index government and security from external threats con- definitions and the difficulty in verifying the accuracy and that reflects the military resource value of the weapons in tributes to economic development, high levels of completeness of data, the data on military spending are relation to the "core weapons." These matches are based defense spending burden the economy and may impede not strictly comparable across countries. on such characteristics as size, performance, and type of growth. Comparisons of defense spending between The data on armed forces are from the U.S. Department electronics, and adjustments are made for second-hand countries should take into account the many factors that of State's Bureau of Verification and Compliance, which weapons. More information on SIPRI's estimation meth- influence perceptions of vulnerability and risk, including attributes its data to unspecified U.S. government ods and sources of arms transfers is available at historical and cultural traditions, the length of borders sources. These data refer to military personnel on active http://projects.sipri.se/armstrade/atmethods.html. that need defending, the quality of relations with neigh- duty, including paramilitary forces. These data exclude Definitions bors, and the role of the armed forces in the body politic. civilians in the defense establishment and so are not con- Data on military expenditures as a share of gross sistent with the data on military spending on personnel. · Military expenditures data from SIPRI are derived from domestic product (GDP) are a rough indicator of the Moreover, because they exclude personnel not on active the NATO definition, which includes all current and capital portion of national resources used for military activities duty, they underestimate the share of the labor force work- expenditures on the armed forces, including peacekeep- and of the burden on the national economy. As an ing for the defense establishment. Because governments ing forces; defense ministries and other government agen- "input" measure, military spending is not directly relat- rarely report the size of their armed forces, such data typ- cies engaged in defense projects; paramilitary forces, if ed to the "output" of military activities, capabilities, or ically come from intelligence sources. these are judged to be trained and equipped for military military security. Data on defense spending from gov- The data on arms transfers are from SIPRI's Arms operations; and military space activities. Such expendi- ernments are often incomplete and unreliable. Even in Transfers Project, which reports on international flows tures include military and civil personnel, including retire- countries where the parliament vigilantly reviews gov- of conventional weapons. Data are collected from open ment pensions of military personnel and social services ernment budgets and spending, defense spending and sources, and since publicly available information is for personnel; operation and maintenance; procurement; arms transfers often do not receive close scrutiny. For inadequate for tracking all weapons and other military military research and development; and military aid (in the a detailed critique of the quality of such data, see Ball equipment, SIPRI covers only what it terms major con- military expenditures of the donor country). Excluded are (1984) and Happe and Wakeman-Linn (1994). ventional weapons. civil defense and current expenditures for previous mili- This and the previous edition of World Development SIPRI's data on arms transfers cover sales of tary activities, such as for veterans' benefits, demobiliza- Indicators use data on military expenditures and arms weapons, manufacturing licenses, and aid and gifts; tion, conversion, and destruction of weapons. This transfers from the Stockholm International Peace therefore the term arms transfers rather than arms definition cannot be applied for all countries, however, Research Institute (SIPRI). The data on military expen- trade is used. The transferred weapons must be trans- since that would require much more detailed information ditures as a percentage of GDP are from SIPRI, and mil- ferred voluntarily by the supplier, must have a military than is available about what is included in military budg- itary expenditures as a percentage of central purpose, and must be destined for the armed forces, ets and off-budget military expenditure items. (For exam- government expenditure are calculated from SIPRI data paramilitary forces, or intelligence agencies of another ple, military budgets might or might not cover civil on military expenditures and IMF data on central gov- country. SIPRI data also cover weapons supplied to or defense, reserves and auxiliary forces, police and para- ernment expenditures. from rebel forces in an armed conflict as well as arms military forces, dual-purpose forces such as military and SIPRI's primary source of military expenditure data is deliveries for which neither the supplier nor the recipi- civilian police, military grants in kind, pensions for military official data provided by national governments. These data ent can be identified with an acceptable degree of cer- personnel, and social security contributions paid by one are derived from national budget documents, defense tainty; these data are available in SIPRI's database. part of government to another.) · Armed forces personnel white papers, and other public documents from official SIPRI's estimates of arms transfers, presented in are active duty military personnel, including paramilitary government agencies, including governments' responses 1990 constant price US dollars, are designed as a trend- forces if these forces resemble regular units in their to questionnaires sent by SIPRI, the United Nations, or the measuring device in which similar weapons have similar organization, equipment, training, or mission. · Arms Organization for Security and Co-operation in Europe. values, reflecting both the value and quality of weapons transfers cover the supply of military weapons through Secondary sources include international statistics, such transferred. The trends presented in the tables are sales, aid, gifts, and those made through manufacturing as those of the North Atlantic Treaty Organization (NATO) based on actual deliveries only. SIPRI cautions that licenses. Data cover major conventional weapons such as and the International Monetary Fund's (IMF) Government these estimated values do not reflect financial value aircraft, armored vehicles, artillery, radar systems, mis- Finance Statistics Yearbook. Other secondary sources (payments for weapons transferred) for three reasons: siles, and ships designed for military use. Excluded are include country reports of the Economist Intelligence Unit, reliable data on the value of the transfer are not avail- transfers of other military equipment such as small arms country reports by IMF staff, and specialist journals and able; even when the value of a transfer is known, it usu- and light weapons, trucks, small artillery, ammunition, newspapers. Data on military expenditures presented in ally includes more than the actual conventional weapons support equipment, technology transfers, and other serv- the table may therefore differ from national source data. such as spares, support systems, and training; and ices. See About the data for more detail. Lack of sufficiently detailed data makes it difficult to even when the value of the transfer is known, details of apply a common definition of military expenditure global- the financial arrangements such as credit and loan con- Data sources ly, so SIPRI has adopted a definition (derived from the ditions and discounts are usually not known. The data on military expenditures and arms trans- NATO definition) as a guideline (see Definitions). This def- Given these measurement issues, SIPRI's method of fers are from SIPRI's Yearbook 2003: Armaments, inition cannot be applied for all countries, however, since estimating the transfer of military resources includes an Disarmament and International Security. The data that would require much more detailed information than evaluation of the technical parameters of the weapons. on armed forces personnel are from the Bureau of is available about what is included in military budgets and Weapons for which a price is not known are compared Verification and Compliance's World Military off-budget military expenditure items. In the many cases with the same weapons for which actual acquisition Expenditures and Arms Transfers 2000 (U.S. where SIPRI cannot make independent estimates, it uses prices are available ("core weapons") or for the closet Department of State 2002). 2004 World Development Indicators 285 5.9 Transport infrastructure Roads Railways Ports Air Goods Traffic Employee Ratio of Total road hauled Rail lines density productivity passenger Container network Paved roads million Total Electric traffic units traffic units tariffs to traffic Aircraft Passengers Air freight km % ton-km km km per km per employee freight tariffs TEU departures carried millions 1995­ 1995­ 1995­ 1996­ 1996­ 1996­ 1996­ 1996­ thousands thousands thousands ton-km 2001 a 2001 a 2001 a 2001 a 2001 a 2001 a 2001 a 2001 a 2001 2002 2002 2002 Afghanistan 21,000 13.3 .. .. .. .. .. .. .. 3 150 8 Albania 18,000 39.0 1,830 440 .. 334 39 .. .. 4 138 .. Algeria 104,000 68.9 .. 3,793 283 419 230 .. 338.2 48 3,027 18 Angola 51,429 10.4 .. .. .. .. .. .. .. 4 190 51 Argentina 215,471 29.4 .. 28,291 179 318 1,209 1.28 500.2 94 5,257 76 Armenia 15,918 96.3 39 842 784 465 80 0.30 .. 3 408 5 Australia 811,603 38.7 .. .. .. .. .. .. 4,272.0 356 32,483 1,497 Austria 200,000 100.0 16,100 5,780 3,493 4,261 482 1.14 .. 136 7,070 396 Azerbaijan 25,013 92.3 4,836 .. .. .. .. .. .. 8 575 76 Bangladesh 207,486 9.5 .. 2,768 .. 1,704 126 0.24 486.3 7 1,544 172 Belarus 75,302 89.0 8,982 5,512 874 7,857 630 .. .. 6 205 1 Belgium 149,028 78.3 17,487 3,471 2,705 4,445 373 1.07 5,757.6 134 2,342 655 Benin 6,787 20.0 .. .. .. .. .. .. .. 1 46 7 Bolivia 53,790 6.5 .. 3,163 .. 336 1,381 0.31 .. 21 1,509 15 Bosnia and Herzegovina 21,846 52.3 .. .. .. .. .. .. .. 4 66 1 Botswana 10,217 55.0 .. .. .. .. .. .. .. 7 175 0 Brazil 1,724,929 5.5 .. 25,652 1,220 1,805 3,970 .. 2,923.1 628 35,890 1,540 Bulgaria 37,286 94.0 168 4,290 2,708 1,846 216 0.89 .. 2 63 2 Burkina Faso 12,506 16.0 .. .. .. .. .. .. .. 1 53 7 Burundi 14,480 7.1 .. .. .. .. .. .. .. .. .. .. Cambodia 12,323 16.2 412 601 .. 228 69 0.39 .. 5 125 4 Cameroon 34,300 12.5 .. 1,006 .. 1,333 496 0.34 .. 5 243 43 Canada 901,903 35.3 84,752 39,400 .. 7,479 7,600 6.63 3,299.7 264 23,323 1,578 Central African Republic 23,810 2.7 60 .. .. .. .. .. .. 1 46 7 Chad 33,400 0.8 .. .. .. .. .. .. .. 1 46 7 Chile 79,605 20.2 .. 4,814 850 370 2,162 .. 1,147.2 78 4,987 1,098 China 1,698,012 91.0 633,040 58,656 14,864 30,262 1,155 1.19 55,717.5 b 932 83,672 5,014 Hong Kong, China 1,831 100.0 .. .. .. .. .. .. .. 91 15,636 5,715 Colombia 112,988 14.4 31 3,154 .. .. 1,795 .. 603.1 178 9,395 540 Congo, Dem. Rep. 157,000 .. .. 3,641 858 169 40 .. .. 5 47 7 Congo, Rep. 12,800 9.7 .. 900 .. 188 55 .. .. 5 128 7 Costa Rica 35,881 22.0 3,070 424 109 .. .. .. 563.8 26 620 13 Côte d'Ivoire 50,400 9.7 .. 639 .. 986 540 0.67 579.1 1 46 7 Croatia 28,275 84.6 6,783 2,726 983 1,280 163 0.80 .. 19 1,127 3 Cuba 60,858 49.0 .. 4,667 132 468 81 .. .. 10 589 40 Czech Republic 127,728 100.0 40,260 9,365 2,843 2,615 284 .. .. 47 2,801 27 Denmark 71,622 100.0 11,696 2,047 625 3,648 770 .. 457.3 98 6,322 185 Dominican Republic 12,600 49.4 .. .. .. .. .. .. 430.6 .. .. .. Ecuador 43,197 18.9 4,405 .. .. .. .. .. 462.5 15 1,292 6 Egypt, Arab Rep. 64,000 78.1 31,500 5,024 59 14,308 753 0.20 1,223.1 42 4,478 248 El Salvador 10,029 19.8 .. 1,202 503 .. 367 .. .. 19 1,804 12 Eritrea 4,010 21.8 .. .. .. .. .. .. .. .. .. .. Estonia 52,038 19.7 4,677 968 132 7,999 1,358 2.36 .. 7 254 1 Ethiopia 31,663 12.0 .. 781 .. .. .. .. .. 28 1,103 84 Finland 77,900 64.5 26,500 5,854 2,372 2,308 1,056 2.47 1,091.8 109 6,414 213 France 894,000 100.0 245,400 32,515 14,104 3,854 715 1.54 3,278.0 733 49,096 4,997 Gabon 8,464 9.9 .. 814 .. 2,087 894 .. .. 8 366 49 Gambia, The 2,700 35.4 .. .. .. .. .. .. .. .. .. .. Georgia 20,229 93.5 520 1,562 1,544 2,794 276 0.37 .. 2 112 2 Germany 230,735 99.1 226,982 36,652 19,079 4,128 681 2.77 9,122.3 782 61,043 7,196 Ghana 46,179 18.4 .. 953 .. 1,778 376 .. .. 4 256 19 Greece 117,000 91.8 13,909 2,299 .. 830 182 .. 1,660.5 113 7,579 81 Guatemala 14,118 34.5 .. .. .. .. .. .. 360.2 .. .. .. Guinea 30,500 16.5 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 4,400 10.3 .. .. .. .. .. .. .. .. .. .. Haiti 4,160 24.3 .. .. .. .. .. .. .. .. .. .. 286 2004 World Development Indicators 5.9 STATES AND Transport infrastructure MARKET S Roads Railways Ports Air Goods Traffic Employee Ratio of Total road hauled Rail lines density productivity passenger Container network Paved roads million Total Electric traffic units traffic units tariffs to traffic Aircraft Passengers Air freight km % ton-km km km per km per employee freight tariffs TEU departures carried millions 1995­ 1995­ 1995­ 1996­ 1996­ 1996­ 1996­ 1996­ thousands thousands thousands ton-km 2001 a 2001 a 2001 a 2001 a 2001 a 2001 a 2001 a 2001 a 2001 2002 2002 2002 Honduras 13,603 20.4 .. .. .. .. .. .. 406.4 .. .. .. Hungary 167,839 43.7 11,398 7,729 2,628 2,242 319 .. .. 33 2,134 27 India 3,319,644 45.7 958 62,759 14,261 11,725 467 0.31 3,243.0 242 18,225 550 Indonesia 342,700 46.3 .. 5,324 131 3,974 610 0.95 4,539.9 152 12,114 406 Iran, Islamic Rep. 167,157 56.3 .. 6,688 148 3,185 758 .. .. 90 10,085 82 Iraq 45,550 84.3 .. .. .. .. .. .. .. .. .. .. Ireland 92,500 94.1 5,900 1,915 37 982 171 .. 775.3 177 19,729 116 Israel 16,521 100.0 .. 925 .. 2,112 1,628 .. 1,461.0 39 3,731 1,058 Italy 479,688 100.0 219,800 16,499 10,937 4,102 618 1.42 7,918.3 351 28,245 1,394 Jamaica 18,700 70.1 .. .. .. .. .. .. 1,065.0 23 2,016 57 Japan 1,166,340 76.6 313,118 20,165 12,080 13,048 1,528 .. 13,501.4 648 109,247 8,102 Jordan 7,245 100.0 .. 293 .. 2,123 518 .. .. 16 1,300 197 Kazakhstan 82,638 93.9 5,497 13,545 3,725 9,981 1,069 .. .. 13 593 15 Kenya 63,942 12.1 .. 2,634 .. 699 184 .. .. 26 1,600 118 Korea, Dem. Rep. 31,200 6.4 .. .. .. .. .. .. .. 1 84 2 Korea, Rep. 86,990 74.5 74,504 3,123 668 12,456 1,323 1.43 11,542.7 243 34,512 7,913 Kuwait 4,450 80.6 .. .. .. .. .. .. .. 19 2,299 254 Kyrgyz Republic 18,500 91.1 1,220 .. .. .. .. .. .. 4 177 6 Lao PDR 21,716 44.5 .. .. .. .. .. .. .. 7 220 2 Latvia 69,732 38.6 5,359 2,331 258 5,834 917 .. .. 9 265 1 Lebanon 7,300 84.9 .. .. .. .. .. .. 298.9 11 874 81 Lesotho 5,940 18.3 .. .. .. .. .. .. .. .. .. .. Liberia 10,600 6.2 .. .. .. .. .. .. .. .. .. .. Libya 83,200 57.2 .. .. .. .. .. .. .. 6 559 0 Lithuania 76,573 91.3 8,274 1,905 122 4,171 611 .. .. 10 304 2 Macedonia, FYR 8,684 62.0 2,693 699 233 972 162 0.39 .. 2 166 0 Madagascar 49,827 11.6 .. .. .. .. .. .. .. 19 549 29 Malawi 28,400 18.5 .. 710 .. 159 176 0.25 .. 5 105 1 Malaysia 65,877 75.8 .. 1,622 152 1,368 370 0.87 7,541.7 186 16,208 1,924 Mali 15,100 12.1 .. 734 .. 658 322 .. .. 1 46 7 Mauritania 7,660 11.3 .. .. .. .. .. .. .. 2 106 0 Mauritius 2,000 98.0 .. .. .. .. .. .. .. 14 1,025 189 Mexico 329,532 32.8 197,958 17,697 250 2,660 3,925 .. 1,561.9 271 19,282 311 Moldova 12,691 86.1 964 .. .. .. .. .. .. 4 129 0 Mongolia 49,250 3.5 129 1,810 .. 2,963 394 .. .. 6 270 8 Morocco 57,698 56.0 2,952 1,907 1,003 3,425 610 0.86 375.8 38 3,146 51 Mozambique 30,400 18.7 110 .. .. .. .. .. .. 8 282 7 Myanmar 28,200 12.2 .. .. .. .. .. .. .. 21 1,186 2 Namibia 62,237 12.9 .. 2,382 .. 474 .. .. .. 5 222 21 Nepal 13,223 30.8 .. .. .. .. .. .. .. 13 681 18 Netherlands 116,500 90.0 32,700 2,802 2,061 6,631 752 2.56 6,741.7 250 22,931 4,204 New Zealand 92,207 63.1 .. 3,913 519 938 1,120 1.46 1,413.6 265 12,240 688 Nicaragua 19,032 11.0 .. .. .. .. .. .. .. 1 61 1 Niger 10,100 7.9 .. .. .. .. .. .. .. 1 46 7 Nigeria 194,394 30.9 .. 3,557 .. 287 65 0.10 .. 11 512 9 Norway 91,443 77.0 12,796 .. .. .. .. .. .. 271 13,706 185 Oman 32,800 30.0 .. .. .. .. .. .. 1,415.5 23 2,104 130 Pakistan 257,683 59.0 111,323 7,791 293 2,838 232 0.28 965.6 42 4,141 347 Panama 11,643 34.6 .. .. .. .. .. .. 1,248.4 22 1,048 22 Papua New Guinea 19,600 3.5 .. .. .. .. .. .. .. 31 1,235 24 Paraguay 29,500 50.8 .. .. .. .. .. .. .. 9 269 .. Peru 72,900 12.8 .. 1,691 .. 406 363 .. 537.6 31 1,879 102 Philippines 201,994 21.0 .. 491 .. 505 112 0.09 3,270.8 43 5,660 267 Poland 364,697 68.3 74,403 22,560 11,826 3,537 415 0.79 287.4 70 2,846 67 Portugal 68,732 86.0 14,200 2,814 904 2,066 465 .. 970.1 114 6,894 198 Puerto Rico 24,023 94.0 .. .. .. .. .. .. 1,426.2 .. .. .. 2004 World Development Indicators 287 5.9 Transport infrastructure Roads Railways Ports Air Goods Traffic Employee Ratio of Total road hauled Rail lines density productivity passenger Container network Paved roads million Total Electric traffic units traffic units tariffs to traffic Aircraft Passengers Air freight km % ton-km km km per km per employee freight tariffs TEU departures carried millions 1995­ 1995­ 1995­ 1996­ 1996­ 1996­ 1996­ 1996­ thousands thousands thousands ton-km 2001 a 2001 a 2001 a 2001 a 2001 a 2001 a 2001 a 2001 a 2001 2002 2002 2002 Romania 198,603 49.5 14,288 11,364 3,929 2,467 267 1.24 .. 18 961 9 Russian Federation 537,289 67.4 139 86,075 40,962 15,854 1,054 0.97 795.7 345 20,892 1,039 Rwanda 12,000 8.3 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 152,044 29.9 .. 1,390 .. 799 555 .. 1,930.1 109 13,564 862 Senegal 14,576 29.3 .. 906 .. 562 339 .. .. 3 245 7 Serbia and Montenegro 44,993 62.3 630 4,058 1,103 522 94 .. .. 20 1,186 4 Sierra Leone 11,330 7.9 .. .. .. .. .. .. .. 0 14 6 Singapore 3,066 100.0 .. .. .. .. .. .. 16,986.0 72 17,257 6,772 Slovak Republic 42,956 87.3 20,233 3,662 1,536 3,851 302 1.11 .. 2 39 1 Slovenia 20,236 100.0 5,695 .. .. 2,746 .. .. .. 15 721 5 Somalia 22,100 11.8 .. .. .. .. .. .. .. .. .. .. South Africa 362,099 20.3 .. 22,657 10,430 5,018 2,933 .. 1,801.6 122 8,167 783 Spain 663,795 99.0 98,145 13,866 7,523 2,295 842 .. 6,669.2 500 40,585 807 Sri Lanka 11,547 95.0 30 1,447 .. 2,271 189 0.11 1,764.7 11 1,741 203 Sudan 11,900 36.3 .. 4,599 .. 298 98 .. .. 8 409 33 Swaziland 3,107 .. .. .. .. .. .. .. .. 2 90 0 Sweden 212,961 78.6 32,000 10,068 7,405 2,492 2,144 2.34 914.9 201 12,696 267 Switzerland 71,176 .. 23,500 .. .. .. .. .. .. 243 13,292 1,028 Syrian Arab Republic 44,575 21.1 .. 1,771 .. 996 160 .. .. 13 824 25 Tajikistan 27,767 82.7 .. .. .. .. .. .. .. 6 397 4 Tanzania 88,200 4.2 .. 2,722 .. 598 181 0.41 .. 5 138 2 Thailand 57,403 98.5 .. 4,044 .. 3,342 660 0.75 3,800.9 98 18,112 1,824 Togo 7,520 31.6 .. .. .. .. .. .. .. 1 46 7 Trinidad and Tobago 8,320 51.1 .. .. .. .. .. .. 385.2 23 1,269 36 Tunisia 18,997 65.4 .. 2,260 60 1,010 341 1.87 .. 19 1,789 19 Turkey 354,373 35.5 151,421 8,671 1,752 1,798 330 1.20 1,777.1 106 10,640 381 Turkmenistan 24,000 81.2 .. .. .. .. .. .. .. 25 1,464 14 Uganda 27,000 6.7 .. 261 .. 805 131 .. .. 0 41 21 Ukraine 169,630 96.7 16,811 22,302 9,170 9,535 598 .. .. 34 1,512 12 United Arab Emirates 1,088 100.0 .. .. .. .. .. .. 5,872.2 55 9,667 2,079 United Kingdom 371,913 100.0 150,700 17,067 5,225 3,500 2,678 .. 7,059.6 906 71,892 4,941 United States 6,304,193 58.8 1,534,430 160,000 484 13,800 13,476 9.28 29,676.9 7,878 c 593,246 c 29,070 c Uruguay 8,983 90.0 .. 3,003 .. 127 191 .. 293.0 8 525 12 Uzbekistan 81,600 87.3 .. .. 619 4,830 304 .. .. 23 1,451 69 Venezuela, RB 96,155 33.6 .. 336 .. 161 180 0.21 1,078.0 167 6,370 33 Vietnam 93,300 25.1 .. 3,142 .. 1,624 154 0.88 1,290.6 43 4,082 151 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 67,000 11.5 .. .. .. .. .. .. 388.4 16 869 37 Zambia 91,440 22.0 .. 1,273 .. 144 610 0.27 .. 5 47 1 Zimbabwe 18,338 47.4 .. 2,759 311 1,977 454 0.60 .. 5 251 26 World 44.0 m .. s .. s .. m .. m .. m 259,736 s 20,481 s 1,615,074 s 116,626 s Low income 16.0 .. .. .. .. .. .. 791 53,966 2,330 Middle income 52.3 .. .. .. 610 .. 94,397 4,365 321,221 17,661 Lower middle income 52.7 .. .. .. 610 .. 76,710 3,094 236,701 12,669 Upper middle income 51.1 .. .. .. 583 .. 17,687 1,272 84,520 4,993 Low & middle income 30.9 .. .. .. .. .. 104,113 5,156 375,186 19,992 East Asia & Pacific 25.1 .. .. 2,293 382 .. 74,871 1,626 144,068 9,726 Europe & Central Asia 89.0 .. .. .. 304 .. .. 825 50,903 1,767 Latin America & Carib. 26.9 .. .. .. .. .. 12,058 1,626 94,234 3,938 Middle East & N. Africa 63.8 .. .. .. 555 .. .. 429 42,619 1,750 South Asia 36.9 .. .. .. .. 0.24 5,973 319 26,431 1,290 Sub-Saharan Africa 12.9 .. .. .. .. .. .. 331 16,931 1,520 High income 92.9 .. .. 3,648 770 .. 155,622 15,325 1,239,888 96,634 Europe EMU 92.9 124,467 63,215 3,854 618 .. 43,985 3,440 252,823 24,415 a. Data are for the latest year available in the period shown. b. Includes Hong Kong, China. c. Data cover only the carriers designated by the U.S. Department of Transportation as major and national air carriers. 288 2004 World Development Indicators 5.9 STATES AND Transport infrastructure MARKET S About the data Definitions Transport infrastructure--highways, railways, ports million. (Note that kilometers of track may exceed · Total road network covers motorways, highways, and waterways, and airports and air traffic control kilometers of line because of double and triple track- main or national roads, secondary or regional roads, systems--and the services that flow from it are cru- ing, yard tracks, and the like.) Railways whose traffic and all other roads in a country. · Paved roads are cial to the activities of households, producers, and density averages less than 500,000 traffic units per roads surfaced with crushed stone (macadam) and governments. Because performance indicators vary kilometer need to operate at low costs and very high hydrocarbon binder or bituminized agents, with con- significantly by transport mode and focus (whether labor productivity to survive. Labor is the most crete, or with cobblestones. · Goods hauled by road are the volume of goods transported by road vehicles, physical infrastructure or the services flowing from expensive factor of production for a railway, and most measured in millions of metric tons times kilometers that infrastructure), highly specialized and carefully railways have found that improving labor productivity traveled. · Total rail lines refer to the track length of specified indicators are required. The table provides is the most important factor in establishing econom- the railway lines. · Electric rail lines refer to the length selected indicators of the size, extent, and produc- ic viability. Employee productivity is heavily influ- of line with electric traction. This line can include over- tivity of roads, railways, and air transport systems enced by the balance of passenger and freight head catenary at various direct current or alternating and of the volume of traffic in these modes as well service, with productivity far lower in passenger serv- current voltages and third-rail direct current systems. as in ports. ice. In developing countries a ratio of passenger tar- · Railway traffic density is total traffic units divided by Data for transport sectors are not always interna- iffs to freight tariffs greater than 1 indicates an total rail lines; total traffic units are the sum of tionally comparable. Unlike for demographic statis- absence of significant cross-subsidies and a poten- passenger-kilometers (passengers times kilometers tics, national income accounts, and international tial to provide higher quality service. This ratio, like traveled) and freight ton-kilometers (metric tons of trade data, the collection of infrastructure data has the other railway indicators, has no normative value freight times kilometers traveled) divided by kilometers not been "internationalized." But data on roads are and is intended for relative analysis only. of line. · Railway employee productivity is annual out- collected by the International Road Federation (IRF), Measures of port container traffic, much of it com- put (in traffic units) per employee. · Ratio of railway and data on air transport by the International Civil modities of medium to high value added, give some passenger tariffs to freight tariffs is the average pas- Aviation Organization (ICAO). indication of economic growth in a country. But when senger fare (total passenger revenue divided by total National road associations are the primary source traffic is merely transshipment, much of the eco- passenger-kilometers) divided by the average freight of IRF data. In countries where such an association nomic benefit goes to the terminal operator and rate (total freight revenue divided by total ton- is lacking or does not respond, other agencies are ancillary services for ships and containers rather kilometers). A ratio of very much less than 1 indicates contacted, such as road directorates, ministries of than to the country more broadly. In transshipment a likelihood of passengers being cross-subsidized by transport or public works, or central statistical centers empty containers may account for as much freight tariffs. · Port container traffic measures the offices. As a result, the compiled data are of uneven as 40 percent of traffic. flow of containers from land to sea transport modes quality. Even when data are available, they are often The air transport data represent the total (interna- and vice versa in twenty-foot-equivalent units (TEUs), a of limited value because of incompatible definitions tional and domestic) scheduled traffic carried by the standard-size container. Data refer to coastal shipping as well as international journeys. Transshipment traffic (for example, in some countries a path used mainly air carriers registered in a country. Countries submit is counted as two lifts at the intermediate port (once to by animals may be considered a road, while in others air transport data to ICAO on the basis of standard off-load and again as an outbound lift) and includes a road must be registered with a state agency instructions and definitions issued by ICAO. In many empty units. · Aircraft departures are domestic and responsible for its maintenance), inappropriate geo- cases, however, the data include estimates by ICAO international takeoffs of air carriers registered in the graphic units, lack of timeliness, and variations in for nonreporting carriers. Where possible, these esti- country. · Air passengers carried include both domes- the nature of the terrain. mates are based on previous submissions supple- tic and international passengers of air carriers regis- Moreover, the quality of transport service (reliabili- mented by information published by the air carriers, tered in the country. · Air freight is the sum of the ty, transit time, and condition of goods delivered) is such as flight schedules. metric tons of freight, express, and diplomatic bags rarely measured, though it may be as important as The data represent the air traffic carried on sched- carried on each flight stage (the operation of an aircraft quantity in assessing an economy's transport sys- uled services, but changes in air transport regula- from takeoff to its next landing), multiplied by the stage tem. A new initiative is under way in the World Bank tions in Europe have made it more difficult to classify distance, by air carriers registered in the country. to improve data availability and consistency. traffic as scheduled or nonscheduled. Thus recent Information covering access, affordability, efficiency, increases shown for some European countries may Data sources quality, and fiscal and institutional aspects of the be due to changes in the classification of air traffic The data on roads are from the IRF's World Road transport sector will help to measure progress and rather than actual growth. For countries with few air Statistics. The data on railways are from a data- improve performance. carriers or only one, the addition or discontinuation base maintained by the World Bank's Transport The railways indicators focus on efficiency and pro- of a home-based air carrier may cause significant and Urban Development Department, Transport ductivity. Traffic density is an indication of the inten- changes in air traffic. Division. The data on port container traffic are from sity of use of a railway's largest investment--its Containerisation International's Containerisation track. Traffic densities for branch lines tend to range International Yearbook. And the data on air trans- around 500,000 traffic units per kilometer (see port are from the ICAO's Civil Aviation Statistics of Definitions), while those for mainlines range from the World and ICAO staff estimates. more than 5 million traffic units per kilometer to 100 2004 World Development Indicators 289 5.10 Power and communications Electric power Telephone mainlines a Mobile International phones a telecommunications a Transmission and In largest Consumption distribution city Cost of Outgoing Cost of per losses per per Waiting Faults Revenue local call per traffic call to U.S. capita % 1,000 1,000 list per 100 per per line $ per 1,000 minutes per $ per kwh of output people people thousands mainlines employee $ 3 minutes people subscriber 3 minutes 2001 2001 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Afghanistan .. .. 1 8 .. .. .. .. .. 1 .. .. Albania 1,123 51 71 94 98.5 57.2 65 1,139 0.02 276 282 2.47 Algeria 638 16 61 124 727.0 6.0 105 192 0.02 13 111 .. Angola 100 15 6 21 240.3 .. 38 1,633 0.09 9 404 3.11 Argentina 2,107 14 219 .. 93.1 .. 337 931 0.03 178 53 .. Armenia 1,127 26 143 212 64.1 60.0 92 151 0.02 19 67 .. Australia 9,292 7 539 .. 0.0 8.0 136 1,276 0.12 640 215 0.68 Austria 7,031 5 489 .. 0.0 5.7 228 1,315 0.19 786 312 .. Azerbaijan 1,846 13 113 270 55.4 48.0 113 93 0.10 107 35 5.52 Bangladesh 94 18 5 30 199.1 .. 29 593 0.03 8 77 2.47 Belarus 2,676 14 299 397 341.5 26.8 112 72 0.01 47 81 2.25 Belgium 7,596 5 494 .. .. 5.9 197 1,343 0.14 786 353 .. Benin 66 70 9 .. 23.0 6.0 48 1,044 0.28 32 294 5.76 Bolivia 403 12 68 109 .. .. 174 742 0.09 105 69 .. Bosnia and Herzegovina 1,444 17 237 502 .. .. 130 247 0.03 196 106 3.01 Botswana .. .. 87 .. .. .. 83 1,238 0.02 241 425 .. Brazil 1,729 17 223 311 200.0 3.0 400 546 0.03 201 21 .. Bulgaria 3,066 14 368 .. 145.8 3.5 104 318 0.02 333 48 1.45 Burkina Faso .. .. 5 42 12.4 19.7 51 984 0.10 8 307 2.58 Burundi .. .. 3 .. 4.7 .. 27 737 0.02 7 127 3.71 Cambodia .. .. 3 19 .. .. 61 705 0.03 28 278 .. Cameroon 170 26 7 .. .. .. 50 .. 0.06 43 208 .. Canada 15,385 8 635 .. 0.0 .. 237 1,053 .. 377 254 .. Central African Republic .. .. 2 .. 1.2 .. 23 1,196 0.43 3 466 12.93 Chad .. .. 2 8 .. 60.8 16 .. 0.11 4 363 9.11 Chile 2,557 7 230 333 32.3 25.0 179 698 0.10 428 79 2.18 China 893 7 167 584 .. .. .. 238 0.03 161 7 .. Hong Kong, China 5,541 12 565 577 0.0 .. 216 1,700 0.00 942 1,039 2.62 Colombia 818 22 179 327 1,174.7 45.5 229 499 0.03 106 40 .. Congo, Dem. Rep. 47 4 0 .. .. .. .. .. .. 11 .. .. Congo, Rep. 75 65 7 .. .. .. .. .. .. 67 .. .. Costa Rica 1,557 7 251 .. 15.8 4.2 213 351 0.03 111 125 1.93 Côte d'Ivoire .. .. 20 68 24.2 81.0 91 1,186 0.22 62 204 6.38 Croatia 2,683 21 417 .. 0.0 12.0 171 679 0.09 535 198 .. Cuba 1,069 15 51 121 .. 9.6 34 1,370 0.09 2 65 7.35 Czech Republic 4,977 7 362 666 25.1 8.3 153 890 0.13 849 107 0.83 Denmark 6,160 5 689 .. 0.0 8.0 173 1,091 0.08 833 214 .. Dominican Republic 822 26 110 .. .. .. 55 .. 0.06 207 245 .. Ecuador 631 25 110 133 14.5 35.3 275 336 0.03 121 48 1.75 Egypt, Arab Rep. 1,046 12 110 .. 206.1 0.5 140 335 0.02 67 36 2.57 El Salvador 595 13 103 .. 38.2 14.5 168 903 0.07 138 243 1.23 Eritrea .. .. 9 43 38.5 53.3 56 458 0.03 0 125 3.55 Estonia 3,764 16 351 422 4.1 16.3 136 881 0.09 650 217 0.74 Ethiopia 22 10 5 60 145.9 .. 47 295 0.02 1 36 7.05 Finland 14,899 4 523 .. 0.0 .. 124 1,735 0.13 867 172 1.06 France 6,682 6 569 .. 0.0 .. 232 944 0.12 647 139 .. Gabon 814 18 25 .. .. .. 32 2,771 0.22 215 854 .. Gambia, The .. .. 28 97 10.6 .. 34 760 0.03 73 352 3.46 Georgia 718 12 131 233 138.8 17.2 39 208 0.03 102 108 0.68 Germany 6,093 4 651 696 0.0 .. 232 1,084 0.09 727 190 0.35 Ghana 341 15 13 83 154.8 67.4 57 460 0.03 21 213 1.13 Greece 4,205 9 491 731 7.6 12.1 302 864 0.07 845 158 0.67 Guatemala 358 23 71 .. .. .. 236 593 0.08 131 172 .. Guinea .. .. 3 .. 1.4 .. 33 1,119 0.08 12 734 4.61 Guinea-Bissau .. .. 9 .. 5.1 70.5 46 .. .. 0 271 .. Haiti 36 53 16 .. .. .. 18 .. .. 17 .. .. 290 2004 World Development Indicators 5.10 STATES AND Power and communications MARKET S Electric power Telephone mainlines a Mobile International phones a telecommunications a Transmission and In largest Consumption distribution city Cost of Outgoing Cost of per losses per per Waiting Faults Revenue local call per traffic call to U.S. capita % 1,000 1,000 list per 100 per per line $ per 1,000 minutes per $ per kwh of output people people thousands mainlines employee $ 3 minutes people subscriber 3 minutes 2001 2001 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Honduras 508 21 48 .. 342.2 3.6 62 1,210 0.06 49 144 2.85 Hungary 2,998 13 361 588 7.8 .. 176 1,015 0.13 676 66 0.79 India 365 27 40 136 1,648.8 126.0 92 198 0.02 12 16 3.20 Indonesia 404 13 37 261 .. 20.0 181 300 0.03 55 37 .. Iran, Islamic Rep. 1,570 16 187 381 1,480.5 .. 258 104 0.01 33 21 7.70 Iraq 1,475 .. 28 .. .. .. .. .. .. 1 .. .. Ireland 5,415 8 502 .. .. 7.6 133 1,643 0.14 763 706 .. Israel 5,841 3 467 .. .. .. 249 1,190 0.02 955 385 .. Italy 4,813 7 481 .. 0.0 .. 358 1,288 0.11 939 169 .. Jamaica 2,343 8 170 .. 168.6 39.7 192 1,050 0.07 535 310 .. Japan 7,237 4 558 554 0.0 .. 490 1,609 0.07 637 37 1.67 Jordan 1,252 12 127 183 1.4 10.7 108 1,128 0.04 229 294 1.96 Kazakhstan 2,850 17 130 .. 168.3 .. 65 289 0.00 64 63 .. Kenya 117 21 10 77 134.0 220.9 17 1,482 0.07 42 75 5.84 Korea, Dem. Rep. .. .. 21 .. .. .. .. .. .. 0 .. .. Korea, Rep. 5,288 6 489 632 0.0 1.5 437 935 0.03 679 45 1.74 Kuwait 10,251 3 204 46 0.0 .. 66 1,778 0.00 519 394 1.50 Kyrgyz Republic 1,351 34 77 168 37.7 .. 50 110 0.09 10 46 8.92 Lao PDR .. .. 11 65 5.9 .. 45 437 0.02 10 138 6.37 Latvia 1,943 23 301 500 14.3 22.7 180 338 0.11 394 65 2.02 Lebanon 1,824 18 199 .. .. .. .. .. 0.07 227 149 .. Lesotho .. .. 13 64 21.1 72.8 80 415 0.11 42 64 2.31 Liberia .. .. 2 .. .. .. .. .. .. 1 868 .. Libya 4,020 .. 118 .. .. .. 43 .. .. 13 68 .. Lithuania 1,851 10 270 427 3.9 17.0 217 472 0.14 475 36 2.31 Macedonia, FYR .. .. 271 .. .. .. 143 406 0.01 177 116 .. Madagascar .. .. 4 9 1.8 42.5 25 1,614 0.07 10 111 7.41 Malawi .. .. 7 41 17.4 .. 17 625 0.06 8 435 0.06 Malaysia 2,731 6 190 .. 65.9 40.0 222 948 0.03 377 144 2.37 Mali .. .. 5 24 .. 177.6 37 1,159 0.07 5 300 12.28 Mauritania .. .. 12 .. .. .. 26 1,330 0.13 92 394 .. Mauritius .. .. 270 376 13.5 56.8 181 499 0.04 289 113 2.50 Mexico 1,643 14 147 156 .. 1.9 139 1,134 0.16 255 134 3.04 Moldova 785 47 161 350 107.3 4.9 95 136 0.02 77 75 2.21 Mongolia .. .. 53 99 37.8 28.4 30 443 0.02 89 37 4.92 Morocco 461 7 38 .. 5.0 24.8 74 1,465 0.15 209 226 1.63 Mozambique 266 3 5 .. 12.7 70.0 39 1,533 0.08 14 274 .. Myanmar 88 20 7 32 93.5 169.0 43 .. 0.05 1 27 0.36 Namibia .. .. 65 157 2.6 42.2 81 700 0.03 80 499 4.28 Nepal 61 21 14 315 317.3 88.1 70 257 0.01 1 102 .. Netherlands 6,199 4 618 .. 0.0 .. 169 1,313 0.11 745 260 .. New Zealand 8,792 11 448 .. 0.0 30.7 325 1,127 0.00 622 547 .. Nicaragua 268 30 32 .. .. 4.6 82 591 0.08 38 108 3.20 Niger .. .. 2 24 .. 104.6 16 848 0.10 1 292 8.77 Nigeria 82 38 6 12 .. .. 58 715 .. 13 124 .. Norway 24,881 7 734 .. 0.0 .. 221 1,549 0.15 844 165 0.31 Oman 3,078 17 84 .. 2.1 .. 105 2,238 0.07 171 729 0.79 Pakistan 358 26 25 .. 214.0 .. 58 395 0.02 8 35 3.60 Panama 1,340 22 122 284 .. 30.8 78 1,018 0.12 189 120 4.36 Papua New Guinea .. .. 12 115 0.2 .. 36 1,221 0.08 3 402 4.32 Paraguay 833 3 47 91 .. 3.4 25 1,069 0.09 288 104 0.82 Peru 692 11 66 .. 33.0 .. 372 690 0.08 86 82 .. Philippines 489 12 42 265 .. .. 273 824 0.00 191 52 .. Poland 2,490 10 295 .. 501.6 17.2 159 646 0.08 363 73 1.79 Portugal 3,932 9 421 .. .. 10.2 240 1,485 0.11 825 124 0.93 Puerto Rico .. .. 346 .. .. .. 261 1,534 .. 316 .. .. 2004 World Development Indicators 291 5.10 Power and communications Electric power Telephone mainlines a Mobile International phones a telecommunications a Transmission and In largest Consumption distribution city Cost of Outgoing Cost of per losses per per Waiting Faults Revenue local call per traffic call to U.S. capita % 1,000 1,000 list per 100 per per line $ per 1,000 minutes per $ per kwh of output people people thousands mainlines employee $ 3 minutes people subscriber 3 minutes 2001 2001 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Romania 1,620 13 194 .. 542.1 23.0 114 410 0.11 236 50 1.82 Russian Federation 4,270 12 242 .. 5,809.6 .. 75 209 .. 120 34 .. Rwanda .. .. 3 .. .. .. 61 934 0.09 14 245 .. Saudi Arabia 5,117 8 144 214 73.6 26.2 155 1,893 0.04 217 578 2.40 Senegal 130 19 22 71 9.8 17.3 152 852 0.10 55 294 1.81 Serbia and Montenegro .. .. 233 424 143.0 .. 178 146 0.01 257 123 2.08 Sierra Leone .. .. 5 .. .. .. 19 .. 0.03 13 336 .. Singapore 7,178 4 463 463 0.0 2.4 221 1,738 0.02 796 1,020 0.68 Slovak Republic 4,360 4 268 665 7.0 27.0 106 604 0.12 544 134 0.79 Slovenia 5,535 5 506 .. 0.5 22.5 227 671 0.07 835 106 0.52 Somalia .. .. 10 .. .. .. .. .. .. 3 .. .. South Africa 3,793 8 107 .. 50.0 48.2 116 1,102 0.09 304 117 0.58 Spain 4,933 9 506 .. .. .. 273 1,447 0.07 824 210 .. Sri Lanka 285 18 47 299 257.7 99.6 72 379 0.03 49 58 2.33 Sudan 67 15 21 80 444.0 .. 150 364 0.03 6 80 3.92 Swaziland .. .. 34 131 15.6 160.0 67 826 0.04 61 657 2.42 Sweden 14,916 7 736 .. 0.0 .. 304 1,189 0.11 889 188 0.32 Switzerland 7,474 5 744 .. 0.0 .. 231 1,771 0.15 789 481 .. Syrian Arab Republic 973 .. 123 156 2,805.9 50.0 84 238 0.01 23 90 4.81 Tajikistan 2,151 15 37 133 6.1 126.0 48 32 0.01 2 42 6.96 Tanzania 58 25 5 20 8.0 24.0 46 1,471 0.12 19 73 5.28 Thailand 1,508 9 105 452 710.2 19.8 222 637 0.07 260 52 1.54 Togo .. .. 10 35 27.5 6.2 57 823 0.10 35 349 2.15 Trinidad and Tobago 3,829 8 250 .. .. .. 100 958 0.04 278 218 2.22 Tunisia 987 11 117 .. 108.7 29.0 143 450 0.02 52 164 .. Turkey 1,391 19 281 388 142.9 37.4 297 275 0.13 347 34 2.09 Turkmenistan 1,231 13 77 .. 36.8 86.4 52 145 .. 2 64 .. Uganda .. .. 2 .. .. .. 23 .. 0.21 16 125 3.51 Ukraine 2,217 20 216 .. 2,158.7 .. 86 146 .. 84 36 .. United Arab Emirates 10,787 9 314 348 0.4 0.3 115 1,994 0.00 696 1,732 1.73 United Kingdom 5,653 8 591 .. 0.0 11.0 148 2,087 0.18 841 258 .. United States 11,714 6 646 .. .. 12.4 170 1,579 0.00 488 217 .. Uruguay 1,918 16 280 335 0.0 .. 168 751 0.17 193 87 4.88 Uzbekistan 1,634 9 66 248 38.9 87.4 69 118 0.01 7 36 13.95 Venezuela, RB 2,605 25 113 .. .. 2.0 192 1,033 0.04 256 104 .. Vietnam 325 14 48 .. .. .. 49 356 0.02 23 17 .. West Bank and Gaza .. .. 87 .. 0.7 97.0 188 353 0.05 93 132 1.03 Yemen, Rep. 109 26 28 80 704.8 .. 100 266 0.02 21 81 4.10 Zambia 585 3 8 22 11.6 90.8 28 808 0.09 13 178 6.45 Zimbabwe 810 21 25 76 158.9 .. 63 817 0.04 30 309 4.36 World 2,159 w 9 w 176 w 296 w .. s 26.4 m 105 m 890 m 0.06 m 110 m 155 m 2.09 m Low income 317 23 28 130 4,517.4 62.5 49 437 0.07 13 108 3.63 Middle income 1,447 11 167 406 .. 28.3 133 700 0.05 149 124 2.08 Lower middle income 1,304 11 164 524 .. 35.3 110 637 0.04 99 110 2.09 Upper middle income 2,505 12 190 .. .. 17.2 173 885 0.09 241 166 2.20 Low & middle income 938 13 100 270 .. 40.0 80 545 0.06 62 117 2.40 East Asia & Pacific 816 8 131 502 .. .. 45 440 0.03 24 44 4.62 Europe & Central Asia 2,774 13 228 .. 10,859.2 22.8 113 318 0.06 196 65 2.08 Latin America & Carib. 1,493 16 168 .. .. 9.6 161 709 0.06 126 172 2.22 Middle East & N. Africa 1,409 12 107 .. 6,099.3 10.1 140 1,128 0.04 52 213 2.18 South Asia 331 27 34 127 2,623.8 88.1 55 387 0.02 8 68 2.33 Sub-Saharan Africa 456 11 15 .. .. 56.8 56 984 0.09 16 208 3.55 High income 8,421 6 585 .. .. 8.3 197 1,343 0.07 698 285 0.93 Europe EMU 5,904 6 555 .. 14.1 6.8 215 1,395 0.13 805 181 0.77 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report 2003. Please cite the ITU for third-party use of these data. 292 2004 World Development Indicators 5.10 STATES AND Power and communications MARKET S About the data Definitions The quality of an economy's infrastructure, including generated by primary sources of energy--coal, oil, · Electric power consumption measures the produc- power and communications, is an important element in gas, nuclear, hydro, geothermal, wind, tide and wave, tion of power plants and combined heat and power investment decisions for both domestic and foreign and combustible renewables--where data are avail- plants less transmission, distribution, and transfor- investors. Government effort alone will not suffice to able. Neither production nor consumption data cap- mation losses and own use by heat and power plants. meet the need for investments in modern infrastructure; ture the reliability of supplies, including breakdowns, · Electric power transmission and distribution loss- public-private partnerships, especially those involving load factors, and frequency of outages. es are losses in transmission between sources of local providers and financiers, will be critical in lowering Over the past decade new financing and technology, supply and points of distribution and in distribution to costs and delivering value for money. In telecommuni- along with privatization and liberalization, have consumers, including pilferage. · Telephone main- cations, competition in the marketplace, along with spurred dramatic growth in telecommunications in lines are telephone lines connecting a customer's sound regulation, is lowering costs and improving the many countries. The table presents some common equipment to the public switched telephone network. quality of and access to services around the globe. performance indicators for telecommunications, Data are presented for the entire country and for the An economy's production and consumption of elec- including measures of supply and demand, service largest city. · Waiting list shows the number of appli- tricity is a basic indicator of its size and level of quality, productivity, economic and financial perform- cations for a connection to a mainline that have been development. Although a few countries export electric ance, and tariffs. The quality of data varies among held up by a lack of technical capacity. · Telephone power, most production is for domestic consumption. reporting countries as a result of differences in regu- mainline faults is the number of reported faults per Expanding the supply of electricity to meet the grow- latory obligations for the provision of data. 100 telephone mainlines. · Telephone mainlines per ing demand of increasingly urbanized and industrial- Demand for telecommunications is often measured employee are calculated by dividing the number of ized economies without incurring unacceptable social, by the sum of telephone mainlines and registered appli- mainlines by the number of telecommunications staff economic, and environmental costs is one of the cants for new connections. (A mainline is normally iden- (with part-time staff converted to full-time equiva- great challenges facing developing countries. tified by a unique number that is the one billed.) In some lents) employed by enterprises providing public Data on electric power production and consumption countries the list of registered applicants does not telecommunications services. · Revenue per line is are collected from national energy agencies by the reflect real current pending demand, which is often hid- the revenue received by firms per mainline for provid- International Energy Agency (IEA) and adjusted by the den or suppressed, reflecting an extremely short supply ing telecommunications services. · Cost of local call IEA to meet international definitions (for data on elec- that has discouraged potential applicants from applying is the cost of a three-minute, peak rate, fixed line call tricity production, see table 3.9). Electricity consump- for telephone service. And in some countries the wait- within the same exchange area using the subscriber's tion is equivalent to production less power plants' ing list may overstate demand because applicants have equipment (that is, not from a public phone). own use and transmission, distribution, and transfor- placed their names on the list several times to improve · Mobile phones refer to portable telephone sub- mation losses. It includes consumption by auxiliary their chances. Telephone mainline faults refer to the scribers to an automatic public mobile telephone stations, losses in transformers that are considered number of reported faults per 100 main telephone service using cellular technology that provides access integral parts of those stations, and electricity pro- lines. It is calculated by the total number of reported to the public switched telephone network, per 1,000 duced by pumping installations. It covers electricity faults for the year divided by the number of telephone people. · International telecommunications outgo- mainlines and multiplied by 100. The definition of fault ing traffic is the telephone traffic, measured in min- 5.10a varies among countries: some operators define faults utes per subscriber, that originates in the country and Mobile phone subscribers are approaching as including malfunctioning customer equipment while has a destination outside the country. · Cost of call (or surpassing) 500 per 1,000 people in others include only technical faults. The number of to U.S. is the cost of a three-minute, peak rate, fixed some developing and transition economies mainlines no longer reflects a telephone system's full line call from the country to the United States. Mobile phone subscribers per 1,000 people capacity because mobile telephones--whose use has been expanding rapidly in most countries, rich and 600 Croatia poor--provide an alternative point of access. 500 In addition to waiting list and mainline faults, the table includes two other measures of efficiency in 400 Malaysia telecommunications: mainlines per employee and rev- enue per mainline. Caution should be used in inter- Data sources 300 preting the estimates of mainlines per employee The data on electricity consumption and losses are South Africa because firms often subcontract part of their work. from the IEA's Energy Statistics and Balances of 200 The cross-country comparability of revenue per main- Non-OECD Countries 2000­2001, the IEA's Energy Argentina line may also be limited because, for example, some Statistics of OECD Countries 2000­2001, and the 100 countries do not require telecommunications providers United Nations Statistics Division's Energy Statistics Georgia 0 to submit financial information; the data usually do not Yearbook. The telecommunications data are from 1995 1998 2002 include revenues from mobile phones or radio, paging, the International Telecommunication Union's World Source: World Bank data files, based on International and data services; and there are definitional and Telecommunication Development Report 2003. Telecommunication Union data. accounting differences between countries. 2004 World Development Indicators 293 5.11 The information age Daily Radios Television a Personal Internet Information and newspapers computers communications technology expenditures Cable Total monthly price a Sets subscribers In Users 20 hours Secure per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 education per 1,000 of use % of monthly servers Per capita people people people people people a number people a $ GNI per capita number % of GDP $ 2000 2001 2002 2002 2002 2002 2002 2003 2003 2003 2002 2002 Afghanistan 5 114 14 0.0 .. .. 0 .. .. 1 .. .. Albania 35 260 318 2.3 11.7 .. 4 29 24.8 1 .. .. Algeria 27 244 114 0.0 7.7 .. 16 18 12.4 4 .. .. Angola 11 78 52 0.9 1.9 .. 3 79 143.3 1 .. .. Argentina 37 681 326 162.9 82.0 98,635 112 13 3.9 274 3.9 95 Armenia 5 264 229 1.2 15.8 .. 16 45 68.0 2 .. .. Australia 293 1,996 731 76.3 565.1 672,471 482 18 1.1 5,749 6.4 1,298 Austria 296 763 637 132.0 369.3 196,210 409 33 1.7 1,156 5.3 1,322 Azerbaijan 27 22 332 0.6 .. .. 37 108 183.0 1 .. .. Bangladesh 53 49 59 27.0 3.4 .. 2 20 66.8 1 .. .. Belarus 152 199 362 77.2 .. .. 82 13 11.3 6 .. .. Belgium 160 793 541 374.7 241.4 285,395 328 29 1.5 576 5.5 1,324 Benin 5 445 12 .. 2.2 .. 7 46 146.5 1 .. .. Bolivia 55 667 121 9.7 22.8 .. 32 22 29.8 10 .. .. Bosnia and Herzegovina 152 243 116 19.4 .. .. 26 7 6.9 4 .. .. Botswana 27 150 44 .. 40.7 .. 30 27 10.9 .. .. .. Brazil 43 433 349 13.8 74.8 774,363 82 28 11.8 1,580 8.3 205 Bulgaria 116 543 453 93.5 51.9 22,078 81 12 8.3 24 6.9 146 Burkina Faso 1 433 79 0.0 1.6 .. 2 45 247.5 .. .. .. Burundi 2 220 31 0.0 0.7 .. 1 81 971.3 .. .. .. Cambodia 2 119 8 .. 2.0 .. 2 57 245.8 1 .. .. Cameroon 7 161 75 .. 5.7 .. 4 52 110.7 1 .. .. Canada 159 1,047 691 252.9 487.0 1,306,715 513 13 0.7 10,785 5.9 1,352 Central African Republic 2 80 6 .. 2.0 .. 1 175 807.9 .. .. .. Chad 0 233 2 .. 1.7 .. 2 69 375.6 .. .. .. Chile 98 759 523 57.4 119.3 131,024 238 22 6.1 233 5.7 246 China .. 339 350 75.0 27.6 3,555,157 46 10 13.0 182 5.8 58 Hong Kong, China 792 686 504 90.6 422.0 173,161 430 4 0.2 768 4.6 1,025 Colombia 46 549 303 13.6 49.3 167,461 46 19 12.2 105 6.7 114 Congo, Dem. Rep. 3 385 2 .. .. .. 1 74 986.7 .. .. .. Congo, Rep. 8 109 13 .. 3.9 .. 2 121 207.8 .. .. .. Costa Rica 91 816 231 .. 197.2 12,320 193 26 7.6 144 .. .. Côte d'Ivoire 16 185 61 0.0 9.3 .. 5 67 132.1 1 .. .. Croatia 114 339 293 8.1 173.8 .. 180 17 4.4 107 7.5 364 Cuba 118 185 251 .. 31.8 .. 11 58 32.2 1 .. .. Czech Republic 254 803 538 94.4 177.4 62,900 256 21 4.5 229 7.2 489 Denmark 283 1,400 859 201.4 576.8 276,813 513 18 0.7 998 5.8 1,852 Dominican Republic 27 181 .. .. .. 44,792 36 33 17.1 22 .. .. Ecuador 96 422 237 33.8 31.1 99,334 42 32 26.3 23 .. .. Egypt, Arab Rep. 31 339 229 0.0 16.6 48,816 28 5 4.5 17 3.3 38 El Salvador 28 481 233 49.7 25.2 .. 46 48 27.8 23 .. .. Eritrea .. 464 50 0.0 2.5 .. 2 27 200.9 .. .. .. Estonia 176 1,136 502 107.0 210.3 .. 328 14 3.9 89 .. .. Ethiopia 0 189 6 0.0 1.5 .. 1 27 329.1 2 .. .. Finland 445 1,624 670 199.7 441.7 210,163 509 23 1.2 932 5.8 1,464 France 201 950 632 57.5 347.1 1,682,650 314 14 0.8 2,860 5.2 1,246 Gabon 30 488 308 11.5 19.2 .. 19 122 46.9 3 .. .. Gambia, The 2 394 15 .. 13.8 .. 18 27 116.2 .. .. .. Georgia 5 568 357 12.4 31.6 .. 15 26 48.4 4 .. .. Germany 305 570 661 249.9 431.3 2,379,660 412 14 0.7 8,451 5.2 1,252 Ghana 14 695 53 0.3 3.8 .. 8 44 194.8 5 .. .. Greece 23 478 519 0.0 81.7 117,911 155 38 3.9 205 4.8 604 Guatemala 33 79 145 .. 14.4 8,310 33 31 21.4 36 .. .. Guinea .. 52 47 0.0 5.5 .. 5 63 185.2 .. .. .. Guinea-Bissau 5 178 36 .. .. .. 4 105 840.7 .. .. .. Haiti 3 18 6 4.8 .. .. 10 130 354.5 3 .. .. 294 2004 World Development Indicators 5.11 STATES AND The information age MARKET S Daily Radios Television a Personal Internet Information and newspapers computers communications technology expenditures Cable Total monthly price a Sets subscribers In Users 20 hours Secure per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 education per 1,000 of use % of monthly servers Per capita people people people people people a number people a $ GNI per capita number % of GDP $ 2000 2001 2002 2002 2002 2002 2002 2003 2003 2003 2002 2002 Honduras 55 411 119 21.6 13.6 .. 25 41 52.9 16 .. .. Hungary 465 690 475 170.1 108.4 52,452 158 10 2.3 139 6.4 420 India 60 120 83 38.9 7.2 347,801 16 9 21.9 281 2.8 13 Indonesia 23 159 153 0.3 11.9 58,593 38 22 37.6 60 1.5 11 Iran, Islamic Rep. 28 281 173 .. 75.0 .. 48 6 4.2 1 .. .. Iraq 19 222 83 .. 8.3 .. 1 .. .. .. .. .. Ireland 150 695 694 143.0 420.8 141,360 271 28 1.4 784 4.0 1,256 Israel 290 526 330 184.0 242.6 .. 301 30 2.1 562 6.9 1,173 Italy 104 878 494 1.4 230.7 1,109,182 352 17 1.0 1,430 4.4 898 Jamaica 62 795 374 .. 53.9 .. 229 44 18.5 12 .. .. Japan 578 956 785 183.1 382.2 2,292,417 449 21 0.8 11,878 5.3 1,671 Jordan 75 372 177 0.3 37.5 .. 58 26 18.0 9 .. .. Kazakhstan .. 411 338 6.6 .. .. 16 34 27.4 3 .. .. Kenya 10 221 26 0.5 6.4 .. 13 46 152.4 4 .. .. Korea, Dem. Rep. 208 154 162 0.0 .. .. 0 .. .. .. .. .. Korea, Rep. 393 1,034 363 132.0 555.8 857,233 552 10 1.2 688 6.5 645 Kuwait 374 570 418 .. 120.6 .. 106 25 2.0 38 .. .. Kyrgyz Republic 27 110 49 3.1 12.7 .. 30 15 62.1 1 .. .. Lao PDR 4 148 52 0.0 3.3 .. 3 32 123.4 .. .. .. Latvia 135 700 850 132.2 171.7 .. 133 58 20.0 53 .. .. Lebanon 107 182 357 29.9 80.5 .. 117 37 11.1 16 .. .. Lesotho 8 61 35 .. .. .. 10 43 110.7 .. .. .. Liberia 12 274 25 .. .. .. 0 .. .. .. .. .. Libya 15 273 137 .. 23.4 .. 23 19 3.8 .. .. .. Lithuania 29 524 487 75.1 109.7 .. 144 34 11.2 29 .. .. Macedonia, FYR 21 205 282 .. .. .. 48 19 13.3 .. .. .. Madagascar 5 216 25 .. 4.4 .. 3 67 336.7 1 .. .. Malawi 3 499 4 0.0 1.3 .. 3 62 465.0 .. .. .. Malaysia 158 420 210 0.0 146.8 241,392 320 8 2.9 174 7.3 304 Mali 1 180 33 .. 1.4 .. 2 58 289.8 1 .. .. Mauritania 0 148 99 .. 10.8 .. 4 39 113.1 1 .. .. Mauritius 119 379 299 .. 116.5 .. 99 15 4.7 17 .. .. Mexico 94 330 282 24.3 82.0 302,325 98 23 4.6 416 4.4 2,097 Moldova 13 758 296 13.3 17.5 .. 34 19 49.6 7 .. .. Mongolia 30 50 79 18.5 28.4 .. 21 18 48.6 3 .. .. Morocco 28 243 167 .. 23.6 .. 24 25 25.5 15 .. .. Mozambique 2 44 14 .. 4.5 .. 2 51 290.2 2 .. .. Myanmar 9 66 8 .. 5.1 .. 1 43 180.9 .. .. .. Namibia 19 134 269 16.0 70.9 .. 27 33 22.5 9 .. .. Nepal 12 39 8 .. 3.7 .. 3 13 70.3 2 .. .. Netherlands 306 980 648 401.4 466.6 652,319 506 24 1.2 58 5.8 1,505 New Zealand 362 992 557 7.1 413.8 196,364 484 13 1.1 1,276 7.4 1,096 Nicaragua 30 270 123 10.8 27.9 .. 17 51 138.6 8 .. .. Niger 0 122 10 .. 0.6 .. 1 97 683.6 .. .. .. Nigeria 24 200 103 0.5 7.1 .. 3 85 353.7 3 .. .. Norway 569 3,324 884 184.5 528.3 268,861 503 26 0.8 726 4.1 1,703 Oman 29 621 553 0.0 35.0 .. 66 24 3.8 1 .. .. Pakistan 40 105 150 0.2 4.2 .. 10 16 45.7 25 .. .. Panama 62 300 191 .. 38.3 15,253 41 36 10.7 85 .. .. Papua New Guinea 14 86 21 4.2 58.7 .. 14 20 45.3 .. .. .. Paraguay 43 188 218 21.3 34.6 .. 17 36 37.3 4 .. .. Peru 0 269 172 16.6 43.0 32,308 93 33 19.2 73 .. .. Philippines 82 161 182 37.0 27.7 125,055 44 17 20.1 97 4.2 40 Poland 102 523 422 91.4 105.6 109,598 230 16 4.1 389 5.2 256 Portugal 32 301 413 122.1 134.9 169,230 194 21 2.3 319 5.8 697 Puerto Rico 126 761 339 91.2 .. 302,941 156 .. .. 63 .. .. 2004 World Development Indicators 295 5.11 The information age Daily Radios Television a Personal Internet Information and newspapers computers communications technology expenditures Cable Total monthly price a Sets subscribers In Users 20 hours Secure per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 education per 1,000 of use % of monthly servers Per capita people people people people people a number people a $ GNI per capita number % of GDP $ 2000 2001 2002 2002 2002 2002 2002 2003 2003 2003 2002 2002 Romania 300 358 697 152.2 69.2 36,754 83 26 17.1 30 4.3 88 Russian Federation 105 418 538 43.6 88.7 229,630 41 10 5.6 233 3.7 88 Rwanda 0 85 .. .. .. .. 3 67 348.3 1 .. .. Saudi Arabia 326 326 265 0.3 130.2 .. 62 35 4.9 26 4.6 369 Senegal 5 128 78 0.1 19.8 .. 10 41 103.7 3 .. .. Serbia and Montenegro 107 297 282 .. 27.1 .. 60 13 11.3 6 .. .. Sierra Leone 4 259 13 .. .. .. 2 12 102.9 1 .. .. Singapore 298 672 303 84.5 622.0 136,000 504 11 0.6 732 6.5 1,268 Slovak Republic 131 965 409 127.3 180.4 27,729 160 21 6.3 48 5.8 251 Slovenia 169 405 366 160.3 300.6 28,842 376 25 3.1 96 4.9 556 Somalia 1 60 14 .. .. .. 9 .. .. .. .. .. South Africa 32 336 177 0.0 72.6 364,722 68 33 15.4 648 9.2 225 Spain 100 330 564 19.9 196.0 636,590 156 21 1.7 1,964 4.5 734 Sri Lanka 29 215 117 0.3 13.2 .. 11 15 21.5 23 .. .. Sudan 26 461 386 0.0 6.1 .. 3 161 550.8 .. .. .. Swaziland 26 161 34 .. 24.2 .. 19 21 21.0 2 .. .. Sweden 410 2,811 965 246.0 621.3 541,805 573 22 1.1 1,595 6.5 1,765 Switzerland 373 1,002 552 376.2 708.7 405,134 351 22 0.7 1,931 6.2 2,259 Syrian Arab Republic 20 276 182 0.0 19.4 .. 13 55 58.6 1 .. .. Tajikistan 20 141 357 0.1 .. .. 1 54 362.3 .. .. .. Tanzania 4 406 45 0.2 4.2 .. 2 117 501.4 .. .. .. Thailand 64 235 300 12.9 39.8 230,000 78 7 4.2 179 4.7 94 Togo 2 263 123 .. 30.8 .. 41 30 134.9 .. .. .. Trinidad and Tobago 123 534 345 .. 79.5 .. 106 13 2.5 13 .. .. Tunisia 19 158 207 .. 30.7 .. 52 17 10.4 13 .. .. Turkey 111 470 423 14.2 44.6 123,907 73 20 9.5 496 4.6 122 Turkmenistan 7 279 182 .. .. .. 2 20 20.2 .. .. .. Uganda 2 122 18 0.3 3.3 .. 4 97 464.4 2 .. .. Ukraine 175 889 456 38.6 19.0 .. 18 17 26.0 28 .. .. United Arab Emirates 156 330 252 .. 129.0 .. 337 13 0.8 83 .. .. United Kingdom 329 1,445 950 57.2 405.7 2,099,346 423 24 1.1 13,540 6.1 1,600 United States 213 2,117 938 255.0 658.9 19,787,772 551 15 0.5 138,514 6.5 2,358 Uruguay 293 603 530 125.9 110.1 .. 119 26 7.3 39 .. .. Uzbekistan 3 456 280 3.7 .. .. 11 20 53.8 1 .. .. Venezuela, RB 206 294 186 36.3 60.9 104,297 51 19 5.7 106 4.4 147 Vietnam 4 109 197 .. 9.8 29,516 18 20 55.4 3 2.4 10 West Bank and Gaza .. .. 148 0.0 36.2 .. 30 25 32.8 .. .. .. Yemen, Rep. 15 65 308 .. 7.4 .. 5 31 75.3 1 .. .. Zambia 12 179 51 1.2 7.5 .. 5 33 118.7 .. .. .. Zimbabwe 18 362 56 2.1 51.6 .. 43 23 58.3 7 .. .. World .. w 419 w 275 w 65.5 w 100.8 w 131 u 37 u 88.7 u 217,255 s Low income 40 139 91 23.7 7.5 10 57 246.4 435 Middle income .. 360 326 57.6 45.4 80 29 18.9 6,686 Lower middle income .. 346 326 58.9 37.7 46 29 24.9 3,965 Upper middle income 123 466 326 47.1 100.5 149 30 8.6 2,721 Low & middle income .. 257 190 40.2 28.4 50 41 114.8 7,121 East Asia & Pacific .. 287 317 70.1 26.3 44 31 66.1 720 Europe & Central Asia 102 447 407 47.6 73.4 87 26 39.5 1,930 Latin America & Carib. 70 410 289 33.9 67.4 92 33 31.8 3,309 Middle East & N. Africa 33 277 200 .. 38.2 37 31 29.9 103 South Asia 60 112 84 37.3 6.8 14 30 58.6 333 Sub-Saharan Africa 12 198 69 0.3 11.9 16 64 268.8 726 High income 284 1,266 735 191.0 466.9 364 23 1.6 210,134 Europe EMU 209 813 597 158.1 317.5 331 24 1.5 18,846 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report 2003. Please cite the ITU for third-party use of these data. 296 2004 World Development Indicators 5.11 STATES AND The information age MARKET S About the data Definitions The digital and information revolution has changed The data on Internet users are based on estimates · Daily newspapers refer to those published at least the way the world learns, communicates, does busi- derived from reported counts of Internet service sub- four times a week and calculated as average circula- ness, and treats illnesses. New information and scribers or calculated by multiplying the number of tion (or copies printed) per 1,000 people. · Radios communications technologies offer vast opportuni- Internet hosts by an estimated multiplier. Internet refer to radio receivers in use for broadcasts to the ties for progress in all walks of life in all countries-- hosts are computers connected directly to the world- general public. · Television sets refer to those in use. opportunities for economic growth, improved health, wide network, each allowing many computer users to · Cable television subscribers are households that better service delivery, learning through distance access the Internet. This method may undercount subscribe to a multichannel television service deliv- education, and social and cultural advances. This the number of people actually using the Internet, par- ered by a fixed line connection. Some countries also table presents indicators of the penetration of the ticularly in developing countries, where many com- report subscribers to pay-television using wireless information economy--newspapers, radios, televi- mercial subscribers rent out computers connected to technology or those cabled to community antenna sys- sions, personal computers, and Internet use--as the Internet or pre-paid cards are used to access the tems. · Personal computers are self-contained com- well as some of the economics of the information Internet. Although survey methods used to estimate puters designed to be used by a single individual. age--Internet access charges, the number of secure the number of Internet hosts have improved in recent · Personal computers in education are those servers, and spending on information and communi- years, some measurement problems remain (see installed in primary and secondary schools and uni- cations technology. Zook 2000). For detailed analysis of Internet trends versities. · Internet users are people with access to The data on the number of daily newspapers in cir- by country, it is best to use the original source data. the worldwide network. · Total monthly price refers to culation and radio receivers in use are from statistical The table shows the total monthly Internet price, the sum of ISP and telephone usage charges for 20 surveys by the United Nations Educational, Scientific, which refers to the sum of Internet service provider hours of use and as a percentage of monthly GNI per and Cultural Organization (UNESCO). In some coun- (ISP) charges and telephone usage charges. The capita. · Secure servers are servers using encryption tries definitions, classifications, and methods of enu- Internet price is also calculated as a percentage of technology in Internet transactions. · Information meration do not entirely conform to UNESCO monthly GNI per capita. Data are generally derived and communications technology expenditures cover standards. For example, newspaper circulation data from the prices listed by the largest ISP and incum- external spending on information technology ("tangi- should refer to the number of copies distributed, but bent telephone company. The number of secure ble" spending on information technology products in some cases the figures reported are the number of servers, from the Netcraft Secure Server Survey, purchased by businesses, households, govern- copies printed. In addition, many countries impose gives an indication of how many companies are con- ments, and education institutions from vendors or radio and television license fees to help pay for public ducting encrypted transactions over the Internet. organizations outside the purchasing entity), internal broadcasting, discouraging radio and television own- The data on information and communications tech- spending on information technology ("intangible" ers from declaring ownership. Because of these and nology expenditures cover the world's 55 largest buy- spending on internally customized software, capital other data collection problems, estimates of the num- ers of such technology among countries and regions. depreciation, and the like), and spending on telecom- ber of newspapers and radios vary widely in reliability These account for 98 percent of global spending. munications and other office equipment. and should be interpreted with caution. Because of different regulatory requirements for The data for other electronic communications and the provision of data, complete measurement of the information technology are from the International telecommunications sector is not possible. Telecommunication Union (ITU), the Internet Telecommunications data are compiled through Software Consortium, Netcraft, the World annual questionnaires sent to telecommunications Information Technology and Services Alliance, and authorities and operating companies by the ITU. The Data sources the International Data Corporation. The ITU collects data are supplemented by annual reports and sta- The data on newspapers and radios are compiled data on television sets and cable television sub- tistical yearbooks of telecommunications ministries, by the UNESCO Institute for Statistics. The data on scribers through annual questionnaires sent to regulators, operators, and industry associations. In television sets, cable television subscribers, per- national broadcasting authorities and industry asso- some cases estimates are derived from ITU docu- sonal computers, Internet users, and Internet ciations. Some countries require that television sets ments or other references. access charges are from the ITU and are reported be registered. To the extent that households do not in the ITU's World Telecommunication Development register their televisions or do not register all of Report 2003 and the World Telecommunications them, the data on licensed sets may understate the Indicators Database (2003). The data on personal true number. computers in education and on information and The estimates of personal computers are derived communications technology expenditures are from from an annual ITU questionnaire, supplemented by Digital Planet 2002: The Global Information other sources. In many countries mainframe com- Economy by the World Information Technology and puters are used extensively. Since thousands of Services Alliance (WITSA), and the International users can be connected to a single mainframe com- Data Corporation. The data on secure servers are puter, the number of personal computers under- from Netcraft (http://www.netcraft.com/). states the total use of computers. 2004 World Development Indicators 297 5.12 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in and for exports license fees applications applications R&D technical R&D filed a filed b journal articles % of per million per million $ manufactured Receipts Payments Non- Non- people people % of GDP millions exports $ millions $ millions Residents residents Residents residents 1990­2001 c 1990­2001 c 1999 1996­2002 c 2002 2002 2002 2002 2001 2001 2001 2001 Afghanistan .. .. 0 .. .. .. .. .. .. .. .. .. Albania .. .. 17 .. 2 1 .. .. 0 129,865 0 2,070 Algeria .. .. 162 .. 21 4 .. .. 52 72,257 1,418 3,284 Angola .. .. 3 .. .. .. 4 0 .. .. .. .. Argentina 684 149 2,361 0.42 583 7 17 225 0 6,634 .. .. Armenia 1,534 223 142 0.2 3 2 .. .. 155 75,502 510 2,696 Australia 3,439 792 12,525 1.53 2,945 16 304 1,012 10,244 84,929 25,159 13,893 Austria 2,313 979 3,580 1.93 8,433 15 111 1,053 3,358 229,823 7,544 11,818 Azerbaijan 2,799 160 66 0.37 10 8 .. 2 0 75,462 0 2,055 Bangladesh 51 32 148 .. 10 0 0 3 .. .. .. .. Belarus 1,893 273 564 .. 212 4 1 3 945 75,750 1,885 4,846 Belgium 2,953 1,157 4,896 1.98 15,736 11 887 1,246 1,953 154,676 21,382 d 12,510 d Benin 174 53 20 .. 0 0 0 1 .. .. .. .. Bolivia 123 6 33 0.34 15 7 2 6 .. .. .. .. Bosnia and Herzegovina .. .. 9 .. .. .. .. .. 52 76,362 152 4,298 Botswana .. .. 41 .. 6 0 .. .. 2 56 .. .. Brazil 323 129 5,144 1.05 6,007 19 100 1,229 6,706 87,301 85,098 16,415 Bulgaria 1,167 472 801 0.55 85 3 4 23 291 77,331 3,508 5,894 Burkina Faso 16 15 23 0.19 2 7 .. 0 .. .. .. .. Burundi 21 32 3 .. 0 2 0 0 .. .. .. .. Cambodia .. .. 5 .. .. .. .. 6 .. .. 231 1,268 Cameroon 3 4 61 .. 1 1 .. .. .. .. .. .. Canada 2,978 1,035 19,685 1.94 22,662 14 1,689 3,651 5,737 92,752 17,314 21,778 Central African Republic 47 27 4 .. .. .. .. .. .. .. .. .. Chad .. .. 2 .. .. .. .. .. .. .. .. .. Chile 419 307 879 0.54 107 3 6 345 241 2,879 .. .. China 584 202 11,675 1.09 68,182 23 133 3,114 30,324 118,970 229,775 30,149 Hong Kong, China 1,998 100 1,817 0.44 2,688 17 196 491 74 8,840 5,458 15,487 Colombia 101 48 207 0.17 319 7 4 87 63 44,882 7,265 7,096 Congo, Dem. Rep. .. .. 6 .. .. .. .. .. .. .. .. .. Congo, Rep. 33 37 13 .. .. .. .. .. .. .. .. .. Costa Rica 530 .. 69 0.20 1,146 37 2 51 0 74,360 .. .. Côte d'Ivoire .. .. 40 .. 27 3 0 10 .. .. .. .. Croatia 1,187 347 545 0.98 432 12 85 77 456 76,035 992 6,111 Cuba 489 2,393 192 0.65 48 29 .. .. 4 75,687 0 2,090 Czech Republic 1,466 712 2,005 1.31 4,494 14 45 119 605 78,648 8,100 10,949 Denmark 3,476 2,594 4,131 2.15 8,089 22 .. .. 3,770 229,151 3,646 8,351 Dominican Republic .. .. 6 .. .. 1 .. 24 .. .. .. .. Ecuador 83 72 20 0.09 34 7 .. 44 0 28,909 4,832 5,011 Egypt, Arab Rep. 493 366 1,198 0.19 13 1 38 171 464 923 0 3,216 El Salvador 47 303 0 0.01 44 6 2 20 .. .. .. .. Eritrea .. .. 2 .. .. .. 0 0 .. .. .. .. Estonia 1,947 387 261 0.66 375 12 5 14 25 77,142 910 5,617 Ethiopia .. .. 95 .. 0 .. 0 0 3 4 .. .. Finland 7,110 .. 4,025 3.42 9,139 24 559 604 3,405 227,036 2,879 7,365 France 2,718 2,878 27,374 2.20 52,582 21 3,241 1,956 21,790 153,332 60,513 14,324 Gabon .. .. 20 .. 4 7 .. .. .. .. .. .. Gambia, The .. .. 17 .. 0 3 .. .. 1 150,081 .. .. Georgia 2,421 97 112 0.33 41 38 6 11 257 76,207 218 3,114 Germany 3,153 1,345 37,308 2.50 86,861 17 3,765 5,064 80,222 212,176 63,645 14,235 Ghana .. .. 73 .. 3 3 .. 0 2 150,194 .. .. Greece 1,400 554 2,241 0.68 524 10 13 288 78 155,268 5,879 6,240 Guatemala 103 111 14 .. 55 7 0 0 5 260 3,048 5,040 Guinea .. .. 2 .. 0 0 0 1 .. .. .. .. Guinea-Bissau .. .. 6 .. .. .. .. .. 0 .. .. .. Haiti .. .. 1 .. .. .. .. .. 1 5 .. .. 298 2004 World Development Indicators 5.12 STATES AND Science and technology MARKET S Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in and for exports license fees applications applications R&D technical R&D filed a filed b journal articles % of per million per million $ manufactured Receipts Payments Non- Non- people people % of GDP millions exports $ millions $ millions Residents residents Residents residents 1990­2001 c 1990­2001 c 1999 1996­2002 c 2002 2002 2002 2002 2001 2001 2001 2001 Honduras 73 256 11 .. 5 2 0 11 7 155 .. .. Hungary 1,440 510 1,958 0.95 7,364 25 350 399 1,019 78,181 4,755 10,673 India 157 115 9,217 .. 1,788 5 12 350 234 78,288 .. .. Indonesia .. .. 142 .. 5,070 16 .. .. 0 77,407 .. .. Iran, Islamic Rep. 590 174 624 .. 64 3 0 0 691 302 9,858 1,224 Iraq .. .. 21 .. .. .. .. .. .. .. .. .. Ireland 2,190 588 1,237 1.16 31,624 41 249 10,347 1,334 155,155 918 3,038 Israel 1,563 516 5,025 4.96 5,414 20 389 450 2,378 82,027 2,468 6,468 Italy 1,128 808 17,149 1.07 19,872 9 539 1,273 3,819 153,039 0 11,005 Jamaica .. .. 44 .. 1 0 6 32 3 66 599 2,394 Japan 5,321 667 47,826 3.09 94,730 24 10,422 11,021 388,390 108,231 104,655 19,133 Jordan 1,948 717 204 6.33 48 3 .. .. .. .. .. .. Kazakhstan 716 293 104 0.29 157 10 0 19 1,610 75,560 1,796 3,300 Kenya .. .. 252 .. 35 10 5 62 2 150,443 0 1,442 Korea, Dem. Rep. .. .. 1 .. .. .. .. .. 0 74,672 0 2,587 Korea, Rep. 2,880 564 6,675 2.96 46,438 32 826 2,979 74,001 116,021 86,408 20,729 Kuwait 212 53 260 0.20 .. .. 0 0 .. .. .. .. Kyrgyz Republic 581 49 10 0.19 6 6 3 3 84 75,489 59 2,382 Lao PDR .. .. 2 .. .. .. .. .. .. .. 14 563 Latvia 1,078 298 153 0.40 51 4 3 6 124 130,315 1,062 6,133 Lebanon .. .. 100 .. 16 3 .. .. 0 104 .. .. Lesotho .. .. 1 .. .. .. 11 0 1 150,361 0 1,009 Liberia .. .. 1 .. .. .. .. .. 0 76,005 0 1,018 Libya 361 493 19 .. .. .. .. .. .. .. .. .. Lithuania 2,303 492 214 0.63 130 5 0 11 70 130,287 1,323 5,994 Macedonia, FYR 387 29 36 .. 9 1 3 10 66 129,995 440 3,962 Madagascar 15 46 .. 0.13 .. .. 0 13 0 76,048 236 336 Malawi .. .. 36 .. 1 3 0 0 2 150,687 146 515 Malaysia 160 45 416 0.40 40,912 58 12 628 .. .. .. .. Mali .. .. 11 .. .. .. 0 1 .. .. .. .. Mauritania .. .. 2 .. .. .. .. .. .. .. .. .. Mauritius 360 157 16 0.28 29 2 0 2 .. .. .. .. Mexico 225 183 2,291 0.43 28,939 21 48 720 594 81,876 43,788 21,147 Moldova 329 1,641 92 0.62 8 4 1 1 437 75,549 .. .. Mongolia 531 116 8 .. 1 0 0 .. 106 76,133 206 3,189 Morocco .. .. 386 .. 439 11 11 41 0 74,468 0 3,499 Mozambique .. .. 14 .. 2 3 0 0 1 146,278 0 1,162 Myanmar .. .. 10 .. .. .. 0 0 .. .. .. .. Namibia .. .. 13 .. 6 1 4 2 .. .. .. .. Nepal .. .. 39 .. 0 0 .. .. .. .. .. .. Netherlands 2,572 .. 10,441 1.95 33,667 28 1,962 2,612 8,107 150,825 .. .. New Zealand 2,197 776 2,375 1.03 388 10 89 347 920 82,362 8,382 12,232 Nicaragua 73 33 8 0.15 6 5 .. .. 9 136 .. .. Niger .. .. 21 .. 0 8 .. .. .. .. .. .. Nigeria .. .. 397 .. 0 0 .. .. .. .. .. .. Norway 4,377 1,836 2,598 1.64 2,863 22 171 325 1,780 82,593 3,316 11,767 Oman 4 0 73 .. 36 2 .. .. 0 2,174 .. .. Pakistan 69 12 277 .. 59 1 2 18 58 1,168 4,852 2,392 Panama 95 213 37 0.44 1 1 0 41 7 153 .. .. Papua New Guinea .. .. 36 .. 11 19 .. .. .. .. .. .. Paraguay 166 231 4 0.00 7 3 184 1 .. .. .. .. Peru 229 1 56 0.11 24 2 2 56 48 944 6,940 6,983 Philippines 156 22 164 .. 11,488 65 1 230 0 13,598 .. .. Poland 1,473 507 4,523 0.67 915 3 34 557 2,218 78,856 12,434 13,358 Portugal 1,754 506 1,508 0.78 1,628 7 32 294 189 230,719 7,191 9,682 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 299 5.12 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in and for exports license fees applications applications R&D technical R&D filed a filed b journal articles % of per million per million $ manufactured Receipts Payments Non- Non- people people % of GDP millions exports $ millions $ millions Residents residents Residents residents 1990­2001 c 1990­2001 c 1999 1996­2002 c 2002 2002 2002 2002 2001 2001 2001 2001 Romania 879 584 785 0.40 390 3 3 85 1,148 130,602 5,374 7,208 Russian Federation 3,494 551 15,654 1.16 2,897 13 147 338 25,046 82,632 39,801 13,295 Rwanda .. 6 4 .. 0 1 0 0 0 4 .. .. Saudi Arabia .. .. 528 .. 30 0 0 0 46 683 .. .. Senegal 2 3 66 0.01 15 4 .. .. .. .. .. .. Serbia and Montenegro 2,389 515 546 .. .. .. .. .. 470 77,043 971 6,022 Sierra Leone .. .. 3 .. .. .. .. .. 1 150,465 0 1,038 Singapore 4,052 335 1,653 2.11 63,792 60 .. .. 0 79,026 0 3,079 Slovak Republic 1,774 790 871 0.62 386 3 16 58 260 77,131 2,158 8,958 Slovenia 2,258 877 599 1.63 488 5 7 78 344 130,599 1,009 7,481 Somalia .. .. 0 .. .. .. .. .. .. .. .. .. South Africa 992 303 2,018 .. 740 5 43 94 175 76,571 .. .. Spain 1,948 1,019 12,289 0.96 6,777 7 370 1,810 3,814 230,729 73,937 15,263 Sri Lanka 191 46 84 0.18 19 1 .. .. 0 76,095 .. .. Sudan .. .. 43 .. 4 7 0 0 5 150,388 0 1,063 Swaziland .. .. 6 .. 3 1 0 46 1 75,091 0 1,054 Sweden 5,186 3,164 8,326 4.61 10,760 16 1,505 888 7,133 224,350 6,603 8,552 Switzerland 3,592 1,399 6,993 2.64 17,077 21 .. .. 7,323 226,329 7,665 17,053 Syrian Arab Republic 29 24 55 0.18 2 1 .. .. 0 0 0 0 Tajikistan 660 .. 20 .. 37 42 0 1 0 75,462 0 1,965 Tanzania .. .. 92 .. 1 2 0 0 2 148,738 0 16 Thailand 74 74 470 0.10 15,234 31 7 1,104 1,117 4,548 .. .. Togo 102 65 11 .. 1 1 0 1 .. .. .. .. Trinidad and Tobago 456 882 37 0.14 75 3 .. .. 1 76,045 .. .. Tunisia 336 32 237 0.45 177 4 16 6 0 195 .. .. Turkey 306 26 2,761 0.64 568 2 0 107 425 228,914 19,885 8,544 Turkmenistan .. .. 0 .. 8 5 .. .. 0 75,440 0 1,803 Uganda 24 14 59 0.75 4 12 0 .. 2 150,406 0 14 Ukraine 2,118 594 2,194 0.95 572 5 4 110 7,234 77,196 6,854 7,320 United Arab Emirates .. .. 118 .. 17 2 .. .. 0 75,414 .. .. United Kingdom 2,666 1,014 39,711 1.90 71,481 31 7,701 5,993 34,500 230,206 50,601 20,490 United States 4,099 .. 163,526 2.80 162,345 32 44,142 19,258 190,907 184,750 181,713 34,861 Uruguay 276 52 144 0.24 19 3 0 10 44 572 .. .. Uzbekistan 1,754 312 236 .. .. .. .. .. 803 76,432 690 2,723 Venezuela, RB 193 32 448 0.44 94 3 0 58 56 2,292 .. .. Vietnam 274 .. 98 .. .. .. .. .. 0 76,542 0 2,422 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. 10 .. .. .. .. .. .. .. .. .. Zambia .. .. 26 .. 2 2 .. 0 8 3,178 213 617 Zimbabwe .. .. 85 .. 21 3 .. .. 2 150,320 1 17 World .. w .. w 528,627 s 2.46 w 1,149,146 s 21 w 79,611 s 82,187 s 939,267 s 10,814,596 s 1,263,071 s 630,592 s Low income .. .. 12,040 .. .. 9 36 420 2,008 2,642,403 6,866 33,611 Middle income 818 .. 64,710 0.66 182,644 19 1,361 10,299 81,357 3,317,058 505,531 247,653 Lower middle income 810 .. 46,694 0.89 97,450 17 753 7,034 75,937 2,057,922 430,009 158,713 Upper middle income 662 .. 18,016 0.53 84,405 21 608 3,265 5,420 1,259,136 75,522 88,940 Low & middle income .. .. 76,750 0.57 .. 17 1,397 10,718 83,365 5,959,461 512,397 281,264 East Asia & Pacific 584 202 13,055 1.09 .. 32 153 5,082 30,430 437,322 230,226 40,178 Europe & Central Asia 2,069 .. 34,679 0.96 16,726 10 695 1,898 43,800 2,493,388 113,877 151,290 Latin America & Carib. .. .. 12,018 0.52 38,457 16 407 2,980 7,383 766,888 151,570 66,176 Middle East & N. Africa .. .. 3,617 .. 880 2 65 218 1,253 151,002 11,276 11,223 South Asia 158 113 9,769 .. .. 4 18 371 292 155,551 4,852 3,096 Sub-Saharan Africa .. .. 3,612 .. .. 4 59 169 207 1,955,310 596 9,301 High income 3,284 .. 451,877 2.64 853,545 23 78,214 71,469 855,902 4,855,135 750,674 349,328 Europe EMU 2,302 996 122,077 2.13 278,406 17 10,963 25,404 128,297 2,283,274 243,888 105,480 Note: The original information on patent and trademark applications was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no respon- sibility with respect to the transformation of these data. a. Other patent applications filed in 2001 include those filed under the auspices of the African Regional Industrial Property Organization (ARIPO) (5 by residents, 75,101 by nonresidents), European Patent Office (67,330 by residents, 90,960 by nonresidents), and the Eurasian Patent Organization (491 by residents, 75,355 by nonresidents). b. Other trademark applications filed in 2001 include those filed under the auspices of the Office for Harmonization in the Internal Market (30,543 by residents, 18,342 by nonresidents) and ARIPO (6 by residents, 18 by nonresidents). c. Data are for the latest year available. d. Includes Luxembourg and the Netherlands. 300 2004 World Development Indicators 5.12 STATES AND Science and technology MARKET S About the data Definitions The best opportunities to improve living standards, importance. They may also reflect some bias toward · Researchers in R&D are people engaged in including new ways of reducing poverty, will come from English-language journals. professional R&D activity who have received tertiary level science and technology. Science, advancing rapidly in The method used for determining a country's high training to work in any field of science. · Technicians in virtually all fields--particularly in biotechnology--is technology exports was developed by the Organisation R&D are people engaged in professional R&D activity who have received vocational or technical training in any playing a growing economic role: countries able to for Economic Co-operation and Development in collabo- branch of knowledge or technology. Most such jobs access, generate, and apply relevant scientific knowl- ration with Eurostat. Termed the "product approach" to require three years beyond the first stage of secondary edge will have a competitive edge over those that can- distinguish it from a "sectoral approach," the method is education. · Scientific and technical journal articles not. And there is greater appreciation of the need for based on the calculation of R&D intensity (R&D expen- refer to scientific and engineering articles published in high-quality scientific input into public policy issues diture divided by total sales) for groups of products from the following fields: physics, biology, chemistry, mathe- such as regional and global environmental concerns. six countries (Germany, Italy, Japan, the Netherlands, matics, clinical medicine, biomedical research, engineer- Technological innovation, often fueled by government- Sweden, and the United States). Because industrial ing and technology, and earth and space sciences. led research and development (R&D), has been the sectors characterized by a few high-technology products · Expenditures for R&D are current and capital expen- driving force for industrial growth around the world. may also produce many low-technology products, the ditures on creative, systematic activity that increases the Science and technology cover a range of issues too product approach is more appropriate for analyzing stock of knowledge. Included are fundamental and complex and too broad to be quantified by any single set international trade than is the sectoral approach. To applied research and experimental development work of indicators, but those in the table shed light on coun- construct a list of high-technology manufactured prod- leading to new devices, products, or processes. · High- tries' "technological base"--the availability of skilled ucts (services are excluded), the R&D intensity was cal- technology exports are products with high R&D intensi- human resources, the number of scientific and techni- culated for products classified at the three-digit level of ty, such as in aerospace, computers, pharmaceuticals, cal articles published, the competitive edge countries the Standard International Trade Classification revision scientific instruments, and electrical machinery. · Royalty and license fees are payments and receipts enjoy in high-technology exports, sales and purchases 3. The final list was determined at the four- and five-digit between residents and nonresidents for the authorized of technology through royalties and licenses, and the levels. At these levels, since no R&D data were avail- use of intangible, nonproduced, nonfinancial assets and number of patent and trademark applications filed. able, final selection was based on patent data and proprietary rights (such as patents, copyrights, trade- The United Nations Educational, Scientific, and expert opinion. This method takes only R&D intensity marks, franchises, and industrial processes) and for the Cultural Organization (UNESCO) collects data on scien- into account. Other characteristics of high technology use, through licensing agreements, of produced originals tific researchers and technical workers and R&D expen- are also important, such as know-how, scientific and of prototypes (such as films and manuscripts). · Patent ditures from member states, mainly through technical personnel, and technology embodied in applications filed are applications filed with a national questionnaires and special surveys as well as from offi- patents; considering these characteristics would result patent office for exclusive rights to an invention--a prod- cial reports and publications, supplemented by infor- in a different list. (See Hatzichronoglou 1997 for further uct or process that provides a new way of doing some- mation from other national and international sources. details.) Moreover, the R&D for high-technology exports thing or offers a new technical solution to a problem. A UNESCO reports either the stock of researchers and may not have occurred in the reporting country. patent provides protection for the invention to the owner technicians or the number of economically active people Most countries have adopted systems that protect of the patent for a limited period, generally 20 years. qualified as such. UNESCO supplements these data patentable inventions. Under most patent legislation · Trademark applications filed are applications for reg- with estimates of qualified researchers by counting an idea, to be protected by law (patentable), must be istration of a trademark with a national or regional trade- people who have completed education at International new in the sense that it has not already been pub- mark office. Trademarks are distinctive signs that identify goods or services as those produced or provided Standard Classification of Education (ISCED) levels 6 lished or publicly used; it must be nonobvious (involve by a specific person or enterprise. A trademark provides and 7; qualified technicians are estimated using the an inventive step) in the sense that it would not have protection to the owner of the mark by ensuring the exclu- number of people who have completed education at occurred to any specialist in the industrial field had sive right to use it to identify goods or services or to ISCED level 5. The data are normally calculated in terms such a specialist been asked to find a solution to the authorize another to use it in return for payment. of full-time-equivalent staff. The information does not problem; and it must be capable of industrial applica- reflect the quality of training and education, which varies tion in the sense that it can be industrially manufac- Data sources widely. Similarly, R&D expenditures are no guarantee of tured or used. Information on patent applications filed The data on technical personnel and R&D expendi- progress; governments need to pay close attention to is shown separately for residents and nonresidents of tures are from UNESCO's Statistical Yearbook. The data on scientific and technical journal articles are the practices that make them effective. the country. from the National Science Foundation's Science and The counts of scientific and technical journal arti- A trademark provides protection to its owner by Engineering Indicators 2002. The information on high- cles include those published in a stable set of about ensuring the exclusive right to use it to identify goods technology exports is from the United Nations 5,000 of the world's most influential scientific and or services or to authorize another to use it in return Statistics Division's Commodity Trade (COMTRADE) technical journals, tracked since 1985 by the for payment. The period of protection varies, but a database. The data on royalty and license fees are Institute of Scientific Information's Science Citation trademark can be renewed indefinitely by paying addi- from the International Monetary Fund's Balance of Index (SCI) and Social Science Citation Index (SSCI). tional fees. The trademark system helps consumers Payments Statistics Yearbook, and the data on (See Definitions for the fields covered.) The SCI and identify and purchase a product or service whose patents and trademarks are from the World Intellectual SSCI databases cover the core set of scientific jour- nature and quality, indicated by its unique trademark, Property Organization's Industrial Property Statistics. nals but may exclude some of regional or local meet their needs. 2004 World Development Indicators 301 6 GLOBALLINKS I n the past 20 years the global economy has become increasingly integrated. International financial flows have grown. More people are on the move. And countries are exchanging more goods and services. In 2002 trade in goods and services as a share of world output reached 54 percent, up from 31 percent in 1980 (figure 6a). Several rounds of tariff reductions and expanding trade in services have spurred growth in trade among high- income economies. Developing economies' trade has recovered from a slowdown in the 1980s. Since 1992 the share of trade in their output, measured in constant dollars, has been growing as fast or faster than that of the high-income economies. Still, there are many obstacles to global integration. National policies that protect home industries from competition or subsidize their output distort patterns of trade and prevent developing countries from reaching their full potential. The movement of people, an important mode of trade in services, remains particularly restricted. Risk and uncertainty also inhibit the flow of finance, while development assistance may be directed more by political considerations than by development opportunities. Table 6.1 highlights additional trends in global integration. Movement of goods 6a High-income countries continue to More than half of world output is globally traded dominate the global scene. They Trade of goods and services as % of GDP (constant US$) account for more than three- 75 quarters of the world's gross Developing countries domestic product (GDP) and for World three-quarters of world trade. They 50 High income also remain the most important markets for low- and middle- income economies. In 2002, 17 25 percent of world trade moved from high-income countries to low- and middle-income economies. Trade 00 between developing economies is 1980 1985 1990 1995 2002 still relatively small, but it is Source: World Bank staff estimates. 2004 World Development Indicators 303 growing in importance. In 2002 the movement of goods 6b between low- and middle-income economies accounted for Aid after Monterrey 6 percent of world trade, but in the period 1992­2002 the nom- inal value of trade between developing economies grew faster Official development assistance (ODA) declined from 0.34 percent of donor than that between high-income countries and between high- countries' gross national income (GNI) in 1990 to 0.22 percent in 2001 income and developing economies (table 6.2). (table 6.9). At the United Nations Conference on Financing for Development The types of goods traded by developing economies have in Monterrey, Mexico, in March 2002, donor countries agreed to scale up been shifting. Exports of manufactures have grown at nearly their commitment on aid to developing economies to help them achieve the twice the rate of agricultural exports and account for more than Millennium Development Goals. Between 2001 and 2002, ODA flows half of exports from developing economies. In 2002, 68 per- began to increase, reaching 0.23 percent of donors' GNI in 2002. In com- cent of imports to high-income Organisation for Economic Co- ing years aid flows will continue to rise and by 2006, if countries keep their operation and Development (OECD) countries from commitments, aid is expected to reach 0.29 percent of donor GNI. middle-income countries were manufactured goods, up from 46 percent in 1992. Low-income economies also saw significant Will aid flows be enough to reach the Monterrey goals? increases, with shares rising from 38 percent in 1992 to 54 Net ODA 2002 ODA as % of GNI percent in 2002. Both middle- and low-income economies expe- Country ($ millions) 2002 2006 rienced declines in the value of exports of agricultural raw Austria 520 0.26 0.33 materials (table 6.3), during a period when many commodity Belgium 1,072 0.43 0.46 prices were falling (table 6.4). Denmark 1,643 0.96 0.83 Yet trade barriers continue to be a significant problem. The Finland 462 0.35 0.42 World Trade Organization's Fifth Ministerial Meeting in Cancun France a 5,486 0.38 0.47 in September 2003, which was supposed to move the Doha Germany 5,324 0.27 0.33 Round development agenda forward, produced disappointing Greece 276 0.21 0.33 results. Some 70 percent of the world's poor live in rural areas Ireland a 398 0.40 0.63 and earn incomes from agriculture, while two-thirds of the Italy 2,332 0.20 0.33 world's agricultural trade originates in OECD countries. This Luxembourg 147 0.77 1.00 occurs in part because rich countries subsidize their producers. Netherlands 3,338 0.81 0.80 Subsidies in OECD countries amount to $330 billion--with Portugal 323 0.27 0.33 some $250 billion going directly to producers. Spain 1,712 0.26 0.33 In addition, agricultural exports from developing countries to Sweden 1,991 0.83 0.87 high-income economies are four to seven times greater than United Kingdom 4,924 0.31 0.40 manufacturing exports. Reduced protection in agriculture would EU members, total 29,949 0.35 0.42 account for two-thirds of the gains from full global liberalization Australia b 989 0.26 0.26 of all merchandise trade. Although tariffs on manufactured Canada 2,006 0.28 0.34 goods are lower on average in high-income countries than in Japan 9,283 0.23 0.26 developing economies, rich countries place substantially lower New Zealand 122 0.22 0.26 tariffs on products from other industrial countries than on Norway 1,696 0.89 1.00 those from developing economies. But both high-income and Switzerland a 939 0.32 0.36 developing economies distort trade through tariffs. Latin United States c 13,290 0.13 0.17 American exporters of manufactures face tariffs in other mar- DAC members, total 58,274 0.23 0.29 kets in the region that are seven times higher than in high- income countries. Tariffs are six times higher in Sub-Saharan Africa than in high-income countries and twice as high in South Estimates are based on commitments made by donor countries at the United Nations International Conference on Financing for Development in March 2002. Asia. Protection also comes through nontariff barriers. Table 6.6 includes new estimates of the ad valorem equivalents of a. ODA/GNI ratio for 2006 interpolated between 2002 and year target scheduled to be nontariff barriers. attained. b. Estimated ODA/GNI of 0.26 percent in 2003/04. Since aid volumes are deter- mined in annual budgets, the same ratio is assumed in forward years. c. For 2006, assumes additional $5 billion from the Millennium Challenge Account, $2 billion from the Financial flows and aid Emergency Plan for AIDS Relief, phased spending from Iraq and Afghanistan reconstruction The downturn in foreign direct investment (FDI) that began in supplements, and 2 percent annual inflation to deflate from 2006 to 2002 prices. 2000 continued through 2002. World FDI grew from $202 bil- lion in 1990 to a peak of $1.5 trillion in 2000 and then dropped Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. off to $631 billion in 2002. Middle-income economies, which 304 2004 World Development Indicators receive the largest share of FDI flows to developing countries, 6c were hit hardest. FDI fell from $164 billion in 2001 to $134 bil- Immigrant labor plays an important role in some high-income lion. Flows to low-income economies increased slightly from economies $11 billion to $13 billion. The largest drops occurred in Latin Foreign labor as share of total labor force (%) America and the Caribbean, Middle East and North Africa, and 15 United States Sub-Saharan Africa. China's growth led to an increase in FDI flows in East Asia and Pacific, as did India's strength in South Asia (table 6.7). 12 Aid--which consists of official development assistance (ODA) and official aid to transition and certain high-income countries--continues to be a major source of financing for 9 developing economies. Net aid flows reached $70 billion in 2002, up from $54 billion in 1997. More than a quarter of net France aid flows went to Sub-Saharan Africa, which was equivalent to 6 32 percent of the region's gross capital formation, compared United Kingdom with an average of 4.4 percent for all developing economies Italy (table 6.10). 3 The poorest countries are not the only recipients of aid. In Japan 2002, excluding unallocated aid, middle-income countries 0 received almost half of total net aid. In dollar terms the largest 1990 1995 2001 aid recipients in 2002 were Pakistan ($2.1 billion), Recent inflows have pushed up the share of the foreign labor force in the United States and Mozambique ($2.1 billion), Serbia and Montenegro ($1.9 bil- Italy. In Japan foreign workers make up less than a quarter of a percent of the labor force. lion), West Bank and Gaza ($1.6 billion), and China ($1.5 bil- Source: Table 6.13. lion). The largest recipients of aid per capita were several small island states, as well as West Bank and Gaza ($500), Serbia and Montenegro ($237), Bosnia and Herzegovina ($143), Macedonia, FYR ($136), and Mauritania ($128). Only countries, seen in table 6.13, has become an important feature Mauritania is classified by the World Bank as a low-income of the global labor market. In 2001 some 1 million migrants economy. entered the United States and 350,000 entered Japan. Foreign and foreign-born persons now make up about 38 percent of the Movement of people population in Luxembourg and 9 percent each in Austria and The movement of people across borders can be important Germany. to both high-income and developing economies. Rich countries International tourism also plays a key role in movements of benefit from access to a larger labor force. And poor people. Although most travelers still come from high-income countries gain from higher salaries and remittances. In 2001 countries, there has been rapid growth in travelers from the remittances--current transfers by migrants who are employed developing world. While global tourism receipts have grown at or intend to remain employed for more than a year in a country an average annual rate of 5 percent since 1990, in the devel- in which they are considered residents--totaled $70 billion, oping world these have grown more than 9 percent. Global roughly equivalent to net aid flows. The total is even higher tourism receipts reached $473 billion in 2002, up from $265 when net income flows are included. In addition, workers often billion in 1990. During the same period receipts in the devel- bring back skills to their country of origin. oping world grew from $48 billion to $138 billion (table 6.14). Not all migrant flows are well recorded. Records are especially Tourism is a significant export earner and an important factor weak for illegal immigration, movements within countries, and in the balance of payments of most nations. And it has become flows between developing countries. But migration to OECD an important source of employment. 2004 World Development Indicators 305 6.1 Integration with the global economy Trade in goods Ratio of Growth Gross private Gross commercial in real capital flows foreign direct service exports trade less investment to merchandise growth in exports real GDP % of % of percentage % of % of GDP goods GDP % points GDP GDP 1990 2002 1990 2002 1990 2002 1990­2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 29.0 38.2 34.5 69.4 13.7 167.3 5.5 18.0 6.3 0.0 2.8 Algeria 36.6 53.5 55.0 82.2 3.7 .. ­0.5 2.6 .. 0.0 .. Angola 53.5 101.3 91.0 133.4 1.7 2.7 .. 10.1 30.7 3.3 22.7 Argentina 11.6 33.7 27.0 74.4 18.3 11.4 5.1 8.2 39.4 1.3 9.0 Armenia .. 63.3 .. 94.9 .. 34.7 ­10.4 .. 12.3 .. 4.7 Australia 26.3 33.5 68.7 96.5 24.7 25.8 3.4 9.3 20.0 3.7 6.3 Austria 55.9 76.8 140.5 209.0 55.1 44.0 4.1 9.8 41.9 1.5 3.8 Azerbaijan .. 62.9 .. .. .. 14.8 18.3 .. 54.3 .. 49.0 Bangladesh 17.6 29.4 .. .. 17.7 5.0 5.3 0.9 2.6 0.0 0.1 Belarus .. 119.4 .. 232.0 .. 15.7 ­3.4 .. 7.1 .. 3.2 Belgium 120.4 177.2 321.7 542.2 22.6 21.8 2.3 5.1 49.3 5.1 10.7 Benin 30.0 37.8 60.8 65.1 38.0 36.5 ­2.0 10.7 11.4 3.7 3.9 Bolivia 33.1 39.5 57.0 77.6 14.3 16.8 1.3 3.1 17.2 0.7 8.7 Bosnia and Herzegovina .. 78.1 .. .. .. 31.6 ­4.0 .. 29.5 .. 5.2 Botswana 98.4 84.6 .. .. 10.3 .. ­1.1 9.0 .. 4.4 .. Brazil 11.7 24.3 .. .. 11.8 14.7 4.8 1.9 13.2 0.4 4.4 Bulgaria 48.9 88.1 70.8 185.9 16.6 44.4 5.6 39.2 16.5 0.0 4.1 Burkina Faso 22.0 23.8 43.3 46.4 22.1 19.5 ­2.7 1.0 4.3 0.0 0.4 Burundi 27.0 22.1 35.1 .. 8.7 12.5 8.1 3.7 3.2 0.1 0.0 Cambodia 22.4 94.9 33.6 .. 57.8 39.5 8.5 3.2 5.5 1.7 1.6 Cameroon 30.5 38.6 .. .. 18.4 .. 2.4 15.5 .. 1.1 .. Canada 43.7 67.1 115.1 .. 14.4 14.4 4.2 8.1 13.4 2.7 7.3 Central African Republic 18.4 25.1 26.4 37.7 14.5 .. .. 2.2 .. 0.5 .. Chad 27.2 48.0 54.9 84.7 12.5 .. 4.6 5.6 .. 0.0 .. Chile 53.1 55.2 100.5 111.2 21.3 21.1 3.2 15.0 23.6 2.2 5.5 China 32.5 49.0 47.4 73.8 9.3 12.1 4.5 2.5 8.0 1.2 4.7 Hong Kong, China 221.5 252.8 772.3 2,020.6 .. 22.5 3.5 .. 92.4 .. 29.6 Colombia 30.7 30.6 .. .. 22.9 14.9 3.3 3.1 10.8 1.3 3.6 Congo, Dem. Rep. 43.5 38.4 74.5 50.6 .. .. 7.1 .. .. .. .. Congo, Rep. 57.2 101.7 107.0 141.9 6.7 7.1 1.6 6.6 37.4 0.0 19.4 Costa Rica 60.2 73.8 .. .. 40.3 35.3 3.8 7.0 10.5 2.9 4.8 Côte d'Ivoire 47.9 63.9 86.0 137.1 13.8 11.5 0.4 3.5 9.8 0.4 2.3 Croatia 88.8 69.6 164.8 140.3 .. 113.3 4.1 .. 31.4 .. 6.7 Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic 83.6 113.9 .. 232.4 .. 18.3 9.4 .. 28.6 .. 13.8 Denmark 52.6 61.9 144.1 171.2 34.5 47.7 3.0 15.1 12.1 2.0 6.7 Dominican Republic 73.2 65.0 163.2 146.0 50.1 57.2 ­0.3 5.0 6.9 1.9 4.6 Ecuador 44.2 47.1 .. .. 18.7 18.2 2.0 11.0 21.1 1.2 5.2 Egypt, Arab Rep. 36.8 18.8 72.9 35.6 138.4 208.3 ­2.1 6.8 6.6 1.7 0.8 El Salvador 38.4 57.3 87.6 146.9 51.7 25.0 6.9 2.0 15.3 0.8 1.6 Eritrea 37.6 60.4 65.0 104.4 484.7 386.6 1.0 32.5 9.8 .. 4.4 Estonia .. 156.7 .. 361.6 .. 45.6 10.0 3.7 30.1 2.0 8.1 Ethiopia 16.0 33.2 25.5 62.0 87.4 108.5 2.2 1.6 3.1 0.0 .. Finland 39.0 59.6 86.3 141.0 17.2 14.3 5.0 17.3 38.8 3.6 13.4 France 37.1 46.2 101.6 148.5 34.6 25.9 4.0 20.6 20.2 3.9 8.0 Gabon 52.5 73.2 97.7 .. 9.7 .. ­1.8 18.0 .. 8.4 .. Gambia, The 69.1 67.3 134.4 116.3 170.6 .. ­1.2 0.9 .. 0.0 .. Georgia .. 30.9 .. 66.7 .. 108.5 13.9 .. 9.6 .. 5.0 Germany 46.5 55.8 108.8 161.3 12.2 16.2 4.0 9.8 21.7 1.8 5.4 Ghana 35.7 75.2 58.0 129.3 8.8 29.3 4.4 2.7 4.4 0.3 0.8 Greece 33.2 31.3 83.5 89.1 80.4 194.4 3.8 3.9 22.6 1.2 1.0 Guatemala 36.8 35.7 .. .. 26.9 47.0 3.2 2.9 24.6 0.6 9.8 Guinea 49.5 42.7 85.5 63.6 13.6 5.8 ­1.0 3.9 2.1 0.6 0.0 Guinea-Bissau 43.0 61.6 53.3 86.1 19.4 .. 3.8 23.0 .. 0.0 .. Haiti 17.2 41.0 .. .. 26.7 .. 5.4 1.1 .. 0.3 .. 306 2004 World Development Indicators 6.1 GLOBAL LINK Integration with the global economy S Trade in goods Ratio of Growth Gross private Gross commercial in real capital flows foreign direct service exports trade less investment to merchandise growth in exports real GDP % of % of percentage % of % of GDP goods GDP % points GDP GDP 1990 2002 1990 2002 1990 2002 1990­2002 1990 2002 1990 2002 Honduras 57.9 64.1 106.4 126.7 14.5 36.4 ­0.4 7.2 5.6 1.4 2.2 Hungary 61.5 109.3 102.4 .. 26.8 22.5 8.8 4.6 19.3 0.0 3.9 India .. .. .. .. 25.7 49.9 .. .. .. .. .. Indonesia 41.5 51.1 68.1 82.6 9.7 11.3 0.8 4.1 5.4 1.0 2.1 Iran, Islamic Rep. 32.9 43.1 61.8 86.0 1.8 5.6 ­7.9 2.6 2.4 0.0 0.0 Iraq 41.2 .. .. .. .. .. .. .. .. .. .. Ireland 93.9 114.1 186.7 255.2 13.8 31.9 7.1 22.2 278.2 2.2 47.1 Israel 55.0 62.7 .. .. 37.6 36.7 1.4 6.5 10.8 0.7 3.0 Italy 32.0 41.7 83.3 121.1 28.5 23.7 3.5 10.6 13.7 1.3 2.7 Jamaica 67.2 58.5 162.2 174.2 84.2 170.9 ­1.1 8.4 27.1 3.0 7.1 Japan 17.1 18.9 44.1 64.2 14.4 15.6 2.6 5.4 15.3 1.7 1.4 Jordan 91.1 82.8 205.2 221.3 134.4 53.7 ­2.6 6.3 7.8 1.7 0.9 Kazakhstan .. 65.8 .. 128.9 .. 14.8 ­3.0 .. 34.2 .. 12.3 Kenya 38.1 43.6 68.5 100.6 75.0 37.8 1.4 3.6 5.4 0.7 0.0 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 53.4 66.0 102.7 152.0 14.1 16.7 6.9 5.6 7.4 0.7 1.0 Kuwait 59.8 68.9 112.9 .. 15.0 8.9 .. 19.3 18.9 1.3 0.5 Kyrgyz Republic .. 67.1 .. 99.3 .. 24.3 ­2.1 .. 11.6 .. 1.4 Lao PDR 30.5 43.4 40.2 .. 13.5 42.5 .. 3.7 1.4 0.7 1.4 Latvia .. 75.4 .. 202.6 .. 54.1 7.3 1.7 29.5 0.5 5.0 Lebanon 106.5 43.3 .. .. .. .. ­2.5 .. .. .. .. Lesotho 119.3 149.8 .. .. 54.9 7.7 0.6 9.6 10.5 2.8 10.3 Liberia 374.1 159.3 .. .. .. .. .. .. .. .. .. Libya 64.2 87.1 .. .. 0.6 .. .. 7.3 .. 0.9 .. Lithuania .. 96.4 .. 212.0 .. 26.1 8.8 .. 13.3 .. 5.3 Macedonia, FYR 103.8 80.0 168.9 150.4 .. 19.8 5.4 .. 14.6 .. 2.0 Madagascar 31.5 44.0 53.7 91.2 40.5 20.1 1.5 1.8 1.2 0.7 0.2 Malawi 52.7 60.6 70.6 108.9 8.8 10.3 ­2.1 3.2 3.2 0.0 0.3 Malaysia 133.4 182.4 232.3 347.4 12.8 15.8 3.3 10.3 19.9 5.3 5.8 Mali 39.7 60.7 63.4 87.4 19.7 14.8 2.7 2.0 22.9 0.2 12.2 Mauritania 84.1 76.8 134.0 133.9 3.0 .. ­1.2 48.8 .. 0.7 .. Mauritius 118.0 86.6 219.8 193.5 40.0 64.5 0.1 8.0 26.9 1.7 0.6 Mexico 32.1 52.4 78.9 148.7 17.7 7.8 9.2 9.2 6.3 1.0 2.4 Moldova .. 105.9 .. 191.0 .. 29.9 11.7 .. 17.8 .. 6.8 Mongolia .. 101.5 .. 227.6 7.3 35.7 .. .. 13.4 .. 6.8 Morocco 43.3 54.2 86.5 116.8 43.9 51.7 2.7 5.5 3.3 0.6 1.4 Mozambique 40.8 56.2 68.9 93.3 81.7 36.5 0.6 0.4 10.0 0.4 7.4 Myanmar .. .. .. .. 29.0 13.4 .. .. .. .. .. Namibia 95.6 87.7 190.3 182.7 9.7 21.0 ­0.6 16.5 26.8 5.0 4.8 Nepal 24.1 35.8 .. .. 81.5 53.3 .. 3.5 3.2 0.0 0.0 Netherlands 87.6 111.1 230.9 332.6 21.6 22.3 3.6 29.8 69.1 8.3 15.0 New Zealand 43.3 49.0 121.0 .. 25.7 35.1 2.3 17.8 9.2 11.5 4.0 Nicaragua .. 59.7 .. 138.9 10.4 45.2 .. .. 9.8 .. 4.3 Niger 27.0 33.8 49.9 57.2 7.8 .. ­2.6 2.8 .. 1.6 .. Nigeria 67.5 52.0 90.8 95.0 7.1 .. 2.1 5.9 .. 2.1 .. Norway 52.8 50.3 126.6 112.9 36.6 31.4 1.5 11.9 38.3 2.1 5.2 Oman 77.7 84.6 127.4 .. 1.2 3.1 .. 3.8 5.0 1.4 0.2 Pakistan 32.6 35.8 .. .. 21.7 15.5 ­1.5 4.2 5.3 0.6 1.4 Panama 35.4 31.1 .. .. 266.7 266.4 ­2.0 106.6 69.4 2.6 7.4 Papua New Guinea 73.6 94.2 123.9 147.6 16.8 18.4 0.7 5.7 15.8 4.8 2.2 Paraguay 43.9 50.8 82.8 106.6 42.1 49.1 ­3.8 5.4 19.1 1.5 8.3 Peru 22.3 26.9 .. .. 22.1 18.6 3.6 3.2 10.8 0.2 4.2 Philippines 47.7 91.7 84.7 194.1 35.7 8.4 3.1 4.4 41.2 1.2 1.5 Poland 43.9 50.9 75.2 121.0 22.3 24.5 9.9 11.0 10.2 0.2 3.7 Portugal 58.3 52.7 140.8 151.0 30.8 37.9 3.7 11.4 37.6 3.9 7.1 Puerto Rico .. .. .. .. .. .. ­0.4 .. .. .. .. 2004 World Development Indicators 307 6.1 Integration with the global economy Trade in goods Ratio of Growth Gross private Gross commercial in real capital flows foreign direct service exports trade less investment to merchandise growth in exports real GDP % of % of percentage % of % of GDP goods GDP % points GDP GDP 1990 2002 1990 2002 1990 2002 1990­2002 1990 2002 1990 2002 Romania 32.8 69.3 45.2 117.7 12.3 16.8 8.5 2.9 8.5 0.0 2.5 Russian Federation 16.5 48.3 35.0 105.1 .. 12.6 2.6 .. 12.2 .. 1.9 Rwanda 15.4 14.9 26.9 23.6 28.0 84.9 0.6 2.8 0.9 0.3 0.2 Saudi Arabia 58.6 56.4 107.5 99.8 6.8 7.0 .. 8.8 13.9 1.6 0.5 Senegal 34.7 51.9 90.0 141.8 46.8 .. 0.1 4.8 .. 1.3 .. Serbia and Montenegro .. 54.8 .. .. .. .. .. .. .. .. .. Sierra Leone 44.2 39.7 .. .. 32.8 .. ­13.4 11.0 .. 5.0 .. Singapore 307.6 277.8 .. 921.3 24.1 23.6 .. 54.2 47.8 20.6 11.7 Slovak Republic 110.8 130.2 192.1 302.5 .. 15.4 8.4 .. 29.6 .. 11.8 Slovenia 102.4 92.9 196.5 206.2 18.3 24.1 3.1 3.4 21.2 0.9 10.2 Somalia 26.7 .. 33.2 .. .. .. .. .. .. .. .. South Africa 37.4 a 56.6 a 73.6 a 136.6 a 14.0 14.8 3.2 2.2 10.1 0.2 1.4 Spain 28.1 41.9 70.6 117.3 49.7 52.1 6.5 11.4 26.9 3.4 6.2 Sri Lanka 57.3 65.2 .. .. 22.2 26.5 2.6 13.1 3.6 0.5 1.5 Sudan 4.1 26.5 .. .. 35.9 2.5 5.8 0.2 7.5 0.0 4.6 Swaziland 138.2 144.6 217.1 195.5 18.3 13.8 ­0.6 10.7 19.6 5.0 8.5 Sweden 45.5 61.3 112.0 167.8 23.4 29.0 4.6 33.1 29.3 6.8 14.5 Switzerland 58.4 64.1 .. 246.7 28.6 31.7 3.1 15.9 59.9 5.8 9.2 Syrian Arab Republic 53.7 51.8 102.4 90.0 17.6 26.7 3.6 18.0 16.8 0.0 1.5 Tajikistan .. 119.9 .. 152.7 .. 8.2 .. .. 10.6 .. 3.0 Tanzania 31.9 27.3 47.8 43.0 39.5 69.6 0.5 0.2 3.4 0.0 2.6 Thailand 65.7 105.6 132.2 205.0 27.3 22.1 2.9 13.5 13.6 3.0 0.8 Togo 52.1 78.0 92.6 126.4 42.6 12.4 ­0.5 9.6 14.4 1.1 6.2 Trinidad and Tobago 65.9 89.7 149.7 214.8 15.5 12.3 2.5 11.4 20.5 3.1 11.6 Tunisia 73.5 77.7 161.6 196.2 44.7 38.3 0.3 9.5 10.6 0.6 3.8 Turkey 23.4 45.9 44.5 105.5 60.8 42.6 6.8 4.3 7.7 0.5 0.7 Turkmenistan .. 70.4 .. .. .. .. 3.8 .. .. .. .. Uganda 10.2 36.7 .. .. 0.0 52.1 6.8 1.1 4.5 0.0 2.6 Ukraine .. 84.3 .. 145.7 .. 25.5 3.8 .. 11.8 .. 1.7 United Arab Emirates 101.8 .. 159.6 .. .. .. .. .. .. .. .. United Kingdom 41.2 39.9 102.6 123.9 29.1 44.0 3.9 35.3 60.3 7.4 23.8 United States 15.8 18.3 44.8 66.8 33.8 39.3 4.5 5.7 9.2 2.8 2.4 Uruguay 32.7 31.5 85.0 104.6 27.2 40.4 2.9 12.7 81.7 0.0 1.7 Uzbekistan .. 80.0 .. 130.5 .. .. ­1.2 .. .. .. .. Venezuela, RB 51.1 41.0 90.8 86.3 6.4 3.5 3.1 49.9 15.4 1.7 3.1 Vietnam 79.7 101.3 129.7 .. .. 17.8 .. .. 5.8 .. 4.0 West Bank and Gaza .. .. .. .. .. .. ­3.2 .. .. .. .. Yemen, Rep. 46.9 58.4 90.0 97.1 11.8 4.0 3.1 16.2 3.6 2.7 1.1 Zambia 76.9 60.6 102.3 113.2 7.2 11.8 2.0 64.7 9.3 6.2 3.8 Zimbabwe 40.7 38.5 74.5 97.5 14.7 .. 4.8 1.7 .. 0.1 .. World 32.5 w 40.3 w 80.2 w 116.0 w 21.5 23.1 w 10.1 w 20.8 w 2.7 w 6.0 w Low income 26.9 37.3 .. .. 14.6 19.4 3.0 4.4 0.5 1.7 Middle income 35.2 54.9 74.6 116.8 16.5 15.6 6.8 12.4 1.0 3.7 Lower middle income 30.6 49.2 63.2 98.0 18.7 16.8 4.1 11.0 0.8 3.6 Upper middle income 45.0 66.2 86.4 146.8 13.7 13.9 12.2 15.1 1.5 3.9 Low & middle income 33.4 51.8 74.5 115.0 16.2 16.1 6.0 11.1 0.9 3.3 East Asia & Pacific 47.0 63.4 78.5 104.6 14.1 13.6 5.0 10.2 1.7 4.1 Europe & Central Asia 28.8 64.3 53.3 132.1 29.6 21.7 .. 13.9 .. 3.7 Latin America & Carib. 23.1 41.2 66.4 132.0 17.5 13.4 7.9 13.7 0.9 4.0 Middle East & N. Africa 46.6 50.5 84.0 90.9 11.6 12.2 6.0 10.3 0.8 0.9 South Asia 16.5 24.2 .. .. 24.6 39.5 1.4 3.2 0.1 0.7 Sub-Saharan Africa 40.8 55.3 77.1 119.7 13.9 10.1 4.9 9.6 1.0 2.2 High income 32.3 37.6 80.9 117.2 23.2 25.5 10.9 22.9 3.0 6.6 Europe EMU 44.9 56.3 112.6 141.9 24.4 23.7 14.1 49.3 2.9 14.8 a. Data refer to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 308 2004 World Development Indicators 6.1 GLOBAL LINK Integration with the global economy S About the data Definitions The growing integration of societies and economies Trade and capital flows are converted to U.S. dol- · Trade in goods as a share of GDP is the sum of has helped reduce poverty in many countries. lars at the International Monetary Fund's average merchandise exports and imports divided by the Between 1990 and 2000 the number of people living official exchange rate for the year shown. An alter- value of GDP, all in current U.S. dollars. · Trade in on less than $1 a day declined by about 137 million. native conversion factor is applied if the official goods as a share of goods GDP is the sum of mer- Although global integration is a powerful force in exchange rate diverges by an exceptionally large mar- chandise exports and imports divided by the value of reducing poverty, more needs to be done--2 billion gin from the rate effectively applied to transactions GDP after subtracting value added in services, all in people are in danger of becoming marginal to the in foreign currencies and traded products. current U.S. dollars. · Ratio of commercial service world economy. All countries have a stake in helping exports to merchandise exports is total service developing countries integrate with the global econo- exports minus exports of government services not my and gain better access to rich country markets. included elsewhere over the f.o.b. value of goods One indication of increasing global economic inte- provided to the rest of the world, all in current U.S. gration is the growing importance of trade in the dollars. · Growth in real trade less growth in real world economy. Another is the increased size and GDP is the difference between annual growth in importance of private capital flows to developing trade of goods and services and annual growth in countries that have liberalized their financial mar- GDP. Growth rates are calculated using constant kets. This table presents standardized measures of price series taken from national accounts and are the size of trade and capital flows relative to gross expressed as a percentage. · Gross private capital domestic product (GDP). The numerators are based flows are the sum of the absolute values of direct, on gross flows that capture the two-way flow of goods portfolio, and other investment inflows and outflows and capital. In conventional balance of payments recorded in the balance of payments financial accounting exports are recorded as a credit and account, excluding changes in the assets and liabili- imports as a debit. And in the financial account ties of monetary authorities and general government. inward investment is a credit and outward invest- The indicator is calculated as a ratio to GDP in U.S. ment a debit. Thus net flows, the sum of credits and dollars. · Gross foreign direct investment is the debits, represent a balance in which many transac- sum of the absolute values of inflows and outflows tions are canceled out. Gross flows are a better of foreign direct investment recorded in the balance measure of integration because they show the total of payments financial account. It includes equity cap- value of financial transactions during a given period. ital, reinvestment of earnings, other long-term capi- Trade in goods (exports and imports) is shown rel- tal, and short-term capital. This indicator differs from ative to both total GDP and goods GDP (GDP less the standard measure of foreign direct investment, services such as storage, transport, communica- which captures only inward investment (see table tions, retail trade, business services, public adminis- 6.7). The indicator is calculated as a ratio to GDP in tration, restaurants and hotels, and social, U.S. dollars. community, and personal services). As a result of the growing share of services in GDP, trade as a share of total GDP appears to be declining for some Data sources economies. Comparing merchandise trade with GDP The data on merchandise trade are from the after deducting value added in services thus provides World Trade Organization. The data on GDP come a better measure of its relative size than does com- from the World Bank's national accounts files, paring it with total GDP, although this neglects the converted from national currencies to U.S. dollars growing service component of most goods output. using the official exchange rate, supplemented by Trade in services (such as transport, travel, an alternative conversion factor if the official finance, insurance, royalties, construction, communi- exchange rate is judged to diverge by an excep- cations, and cultural services) is an increasingly tionally large margin from the rate effectively important element of global integration. The differ- applied to transactions in foreign currencies and ence between the growth of real trade in goods and traded products. The data on real trade and GDP services and the growth of GDP helps to identify growth come from the World Bank's national economies that have integrated with the global econ- accounts files. Gross private capital flows and for- omy by liberalizing trade, lowering barriers to foreign eign direct investment were calculated using the investment, and harnessing their abundant labor to International Monetary Fund's Balance of gain a competitive advantage in labor-intensive man- Payments database. ufactures and services. 2004 World Development Indicators 309 6.2 Direction and growth of merchandise trade Direction of trade, 2002 High-income importers % of world trade Other All European United Other All high high Union Japan States industrial industrial income income Source of exports High-income economies 29.9 2.9 11.5 5.9 50.2 7.2 57.4 Industrial economies 28.4 1.8 9.3 5.5 45.0 5.1 50.1 European Union 23.3 0.6 3.6 2.1 29.6 1.7 31.3 Japan 1.0 1.9 0.3 3.2 1.6 4.8 United States 2.3 0.8 2.9 6.0 1.3 7.3 Other industrial economies 1.8 0.4 3.9 0.2 6.3 0.4 6.7 Other high-income economies 1.6 1.1 2.2 0.4 5.2 2.0 7.3 Low- and middle-income economies 6.4 1.9 6.3 0.8 15.5 3.6 19.1 East Asia & Pacific 1.4 1.4 1.9 0.4 5.1 2.5 7.6 Europe & Central Asia 2.7 0.0 0.2 0.2 3.2 0.2 3.4 Latin America & Caribbean 0.6 0.1 3.2 0.1 4.1 0.2 4.3 Middle East & N. Africa 0.9 0.3 0.3 0.1 1.6 0.4 2.0 South Asia 0.3 0.0 0.3 0.0 0.6 0.2 0.8 Sub-Saharan Africa 0.5 0.1 0.3 0.0 0.9 0.1 1.0 World 36.3 4.8 17.8 6.8 65.7 10.8 76.5 Low- and middle-income importers % of world trade Europe Latin Middle All low- East Asia & Central America East & South Sub-Saharan & middle- & Pacific Asia & Caribbean N. Africa Asia Africa income World Source of exports High-income economies 6.3 3.4 1.7 1.6 0.7 0.8 17.2 74.6 Industrial economies 3.2 3.3 1.5 1.4 0.5 0.7 12.9 63.0 European Union 0.9 3.0 0.5 1.0 0.3 0.5 6.8 38.1 Japan 1.3 0.1 0.2 0.1 0.1 0.1 1.8 6.5 United States 0.8 0.2 0.8 0.2 0.1 0.1 3.6 10.9 Other industrial economies 0.3 0.1 0.1 0.1 0.1 0.0 0.7 7.5 Other high-income economies 3.1 0.1 0.1 0.1 0.2 0.1 4.3 11.6 Low- and middle-income economies 1.7 1.6 1.0 0.6 0.4 0.4 6.3 25.4 East Asia & Pacific 0.9 0.2 0.1 0.2 0.2 0.1 1.9 9.5 Europe & Central Asia 0.2 1.1 0.0 0.2 0.0 0.0 1.6 5.1 Latin America & Caribbean 0.1 0.0 0.7 0.1 0.0 0.0 1.3 5.6 Middle East & N. Africa 0.2 0.1 0.0 0.1 0.1 0.0 0.7 2.7 South Asia 0.1 0.0 0.0 0.1 0.1 0.0 0.3 1.1 Sub-Saharan Africa 0.1 0.0 0.0 0.0 0.0 0.2 0.4 1.4 World 8.0 4.9 2.7 2.2 1.1 1.3 23.5 100.0 310 2004 World Development Indicators 6.2 GLOBAL LINK Direction and growth of merchandise trade S Nominal growth of trade, 1992­2002 High-income importers annual % growth Other All European United Other All high high Union Japan States industrial industrial income income Source of exports High-income economies 3.3 2.4 6.1 4.2 3.9 4.8 4.0 Industrial economies 3.2 1.6 6.5 4.3 3.9 3.9 3.9 European Union 3.5 3.3 8.3 3.3 4.0 5.6 4.0 Japan ­0.9 2.2 ­0.1 0.9 2.5 1.4 United States 2.9 0.7 5.7 3.8 3.8 3.8 Other industrial economies 2.9 0.8 7.7 4.2 5.4 3.5 5.3 Other high-income economies 4.5 3.8 4.6 3.7 4.3 7.5 5.1 Low- and middle-income economies 7.0 6.9 11.7 9.3 8.7 8.0 8.6 East Asia & Pacific 12.1 9.3 14.4 13.0 12.0 8.0 10.5 Europe & Central Asia 11.2 0.2 12.7 11.7 11.0 10.9 11.0 Latin America & Caribbean 2.4 ­0.5 12.3 10.0 9.4 9.7 9.4 Middle East & N. Africa 2.7 3.0 3.7 4.0 3.0 4.4 3.3 South Asia 6.1 0.0 10.9 7.3 7.4 9.5 7.8 Sub-Saharan Africa 6.6 17.6 5.6 4.9 6.7 16.8 7.3 World 3.9 3.9 7.7 4.7 4.9 5.7 5.0 Low- and middle-income importers annual % growth Europe Latin Middle All low- East Asia & Central America East & South Sub-Saharan & middle- & Pacific Asia & Caribbean N. Africa Asia Africa income World Source of exports High-income economies 8.7 7.5 2.9 0.9 5.1 1.3 6.5 4.5 Industrial economies 7.3 7.8 3.0 0.7 4.0 1.0 5.8 4.2 European Union 7.2 8.6 3.1 1.5 4.6 1.5 6.5 4.4 Japan 7.1 4.0 ­0.1 ­3.2 ­0.4 ­3.5 4.0 2.1 United States 8.2 1.8 3.9 ­2.0 5.4 1.1 6.0 4.5 Other industrial economies 6.0 2.4 1.5 4.3 4.5 3.1 4.2 5.2 Other high-income economies 10.5 ­1.0 2.8 1.7 7.5 1.2 8.7 6.3 Low- and middle-income economies 14.9 3.2 7.1 6.3 10.2 9.0 9.2 8.7 East Asia & Pacific 16.5 10.7 16.7 10.0 14.7 15.8 14.3 11.2 Europe & Central Asia 6.3 8.7 7.0 5.7 8.3 10.9 7.8 9.9 Latin America & Caribbean 12.9 6.1 7.0 4.8 14.8 5.3 8.1 9.1 Middle East & N. Africa 16.1 2.9 ­2.1 6.0 4.6 8.7 6.7 4.1 South Asia 15.5 5.2 19.0 4.6 9.8 11.5 9.7 8.3 Sub-Saharan Africa 27.6 17.5 10.0 5.7 8.7 11.4 13.5 8.8 World 9.7 6.0 4.2 2.1 6.7 3.3 7.1 5.4 2004 World Development Indicators 311 6.2 Direction and growth of merchandise trade About the data Definitions The table provides estimates of the flow of trade in published period average exchange rates (series rf · Merchandise trade includes all trade in goods; trade goods between groups of economies. The data are or rh, monthly averages of the market or official in services is excluded. · High-income economies are from the International Monetary Fund's (IMF) rates) for the reporting country or, if those are not those classified as such by the World Bank (see inside Direction of Trade database. All high-income coun- available, monthly average rates in New York. front cover). · Industrial economies are those classi- tries and 22 of the 156 developing countries report Because imports are reported at cost, insurance, fied as such in the IMF's Direction of Trade Statistics trade on a timely basis, covering about 80 percent of and freight (c.i.f.) valuations, and exports at free on Yearbook. They include the countries of the European trade for recent years. Trade by less timely reporters board (f.o.b.) valuations, the IMF adjusts country Union, Japan, the United States, and the other indus- and by countries that do not report is estimated reports of import values by dividing them by 1.10 to trial economies listed below. · European Union com- using reports of partner countries. Because the estimate equivalent export values. This approxima- prises Austria, Belgium, Denmark, Finland, France, largest exporting and importing countries are reliable tion is more or less accurate, depending on the set Germany, Greece, Ireland, Italy, Luxembourg, the reporters, a large portion of the missing trade flows of partners and the items traded. Other factors Netherlands, Portugal, Spain, Sweden, and the United can be estimated from partner reports. Partner affecting the accuracy of trade data include lags in Kingdom. · Other industrial economies include country data may introduce discrepancies due to reporting, recording differences across countries, Australia, Canada, Iceland, New Zealand, Norway, and smuggling, confidentiality, different exchange rates, and whether the country reports trade according to Switzerland. · Other high-income economies include overreporting of transit trade, inclusion or exclusion the general or special system of trade. (For further Antigua and Barbuda, Aruba, The Bahamas, Bahrain, of freight rates, and different points of valuation and discussion of the measurement of exports and Barbados, Bermuda, Brunei, Cyprus, Faeroe Islands, times of recording. imports, see About the data for tables 4.5 and 4.6.) French Polynesia, Greenland, Guam, Hong Kong In addition, estimates of trade within the European The regional trade flows shown in the table were (China), Israel, the Republic of Korea, Kuwait, Macao Union (EU) have been significantly affected by calculated from current price values. The growth rates (China), Malta, Netherlands Antilles, New Caledonia, changes in reporting methods following the creation presented are in nominal terms; that is, they include Qatar, Singapore, Slovenia, Taiwan (China), and the of a customs union. The new system for collecting the effects of changes in both volumes and prices. United Arab Emirates. · Low- and middle-income data on trade between EU members--Intrastat, intro- regional groupings are based on World Bank classifi- duced in 1993--has less exhaustive coverage than cations and may differ from those used by other the previous customs-based system and has resulted organizations. in some problems of asymmetry (estimated imports are about 5 percent less than exports). Despite these issues, only a small portion of world trade is esti- mated to be omitted from the IMF's Direction of Trade Statistics Yearbook and Direction of Trade database. Most countries report their trade data in national currencies, which are converted using the IMF's 6.2a Rich markets for developing country exports Developing economy exports as % of world trade, 2002 10 High income 8 Developing countries 6 4 2 0 Data sources East Asia Europe & Latin America Middle East & South Sub-Saharan Intercountry trade flows are published in the & Pacific Central Asia & Caribbean North Africa Asia Africa IMF's Direction of Trade Statistics Yearbook and High-income countries continue to be the principal trading partners of developing countries. Yet trade between and within Direction of Trade Statistics Quarterly; the data in developing countries continues to grow. At 9.5 percent, East Asia and Pacific is the developing region with the largest the table were calculated using the IMF's exports as a share of world trade. Sub-Saharan Africa's share, although small, has been growing. Direction of Trade database. Source: International Monetary Fund, Direction of Trade database. 312 2004 World Development Indicators 6.3 OECD trade with low- and GLOBAL LINK middle-income economies S Exports to low-income economies High-income European Japan United States OECD countries Union 1992 2002 1992 2002 a 1992 2002 1992 2002 $ billions Food 6.1 8.2 3.4 4.2 0.1 0.1 1.7 2.2 Cereals 2.7 2.2 1.0 0.9 0.0 0.0 1.2 1.0 Agricultural raw materials 1.9 2.9 0.4 0.7 0.2 0.2 0.6 0.9 Ores and nonferrous metals 1.4 2.1 0.6 0.8 0.1 0.2 0.3 0.2 Fuels 1.9 2.1 0.8 0.8 0.1 0.1 0.2 0.2 Crude petroleum 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 1.3 1.4 0.8 0.7 0.1 0.1 0.2 0.2 Manufactured goods 57.4 69.6 30.0 34.4 12.9 13.1 6.3 8.7 Chemical products 8.1 10.3 4.5 5.6 1.0 1.1 1.3 1.5 Mach. and transport equip. 33.6 38.2 16.4 17.5 9.0 8.5 4.2 5.6 Other 15.7 21.1 9.1 11.3 2.9 3.4 0.9 1.7 Miscellaneous goods 1.5 3.3 0.6 1.2 0.1 0.3 0.3 0.7 Total 70.3 88.2 35.8 42.1 13.4 14.1 9.3 12.9 % of total exports Food 8.7 9.3 9.5 10.0 0.6 0.4 18.0 17.0 Cereals 3.8 2.5 2.7 2.2 0.1 0.2 13.3 7.5 Agricultural raw materials 2.8 3.2 1.2 1.7 1.6 1.7 6.7 7.3 Ores and nonferrous metals 2.0 2.4 1.6 2.0 0.7 1.8 2.8 1.3 Fuels 2.7 2.4 2.4 1.8 0.6 0.9 1.7 1.3 Crude petroleum 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 1.8 1.6 2.3 1.8 0.6 0.7 1.7 1.3 Manufactured goods 81.7 78.9 83.6 81.7 95.9 93.0 67.9 67.8 Chemical products 11.6 11.7 12.5 13.2 7.2 8.1 13.5 11.3 Mach. and transport equip. 47.8 43.3 45.7 41.6 67.0 60.4 45.3 43.4 Other 22.3 23.9 25.4 26.9 21.7 24.4 9.1 13.1 Miscellaneous goods 2.2 3.8 1.7 2.8 0.6 2.2 2.8 5.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Imports from low-income economies $ billions Food 11.5 17.5 7.1 9.5 2.1 2.8 1.6 3.9 Cereals 0.1 0.4 0.1 0.2 0.0 0.0 0.0 0.1 Agricultural raw materials 4.4 5.0 2.5 2.8 0.8 0.6 0.7 0.8 Ores and nonferrous metals 5.1 5.6 2.0 2.4 2.1 2.3 0.4 0.2 Fuels 29.4 37.0 8.4 9.0 8.8 8.6 9.2 11.8 Crude petroleum 22.2 26.8 7.9 7.7 3.6 3.0 8.8 10.8 Petroleum products 2.3 3.0 0.3 0.5 0.9 0.7 0.5 1.0 Manufactured goods 31.6 76.0 16.0 32.8 3.9 7.6 8.8 28.9 Chemical products 1.3 4.1 0.7 1.6 0.1 0.6 0.2 1.2 Mach. and transport equip. 1.9 9.3 1.0 3.9 0.1 1.9 0.4 2.6 Other 28.5 62.5 14.3 27.4 3.6 5.1 8.2 25.1 Miscellaneous goods 0.4 1.0 0.2 0.4 0.1 0.1 0.1 0.3 Total 82.4 142.0 36.2 56.8 17.8 22.1 20.9 45.9 % of total imports Food 13.9 12.3 19.6 16.7 11.9 12.6 7.6 8.5 Cereals 0.2 0.3 0.3 0.3 0.0 0.1 0.1 0.2 Agricultural raw materials 5.4 3.5 6.9 4.9 4.6 2.8 3.5 1.8 Ores and nonferrous metals 6.2 3.9 5.6 4.2 11.9 10.4 2.0 0.5 Fuels 35.7 26.1 23.2 15.9 49.5 39.2 44.2 25.7 Crude petroleum 26.9 18.9 21.9 13.6 20.4 13.7 41.9 23.4 Petroleum products 2.7 2.1 0.9 0.9 5.3 3.2 2.2 2.2 Manufactured goods 38.4 53.5 44.1 57.7 21.7 34.4 42.3 62.8 Chemical products 1.5 2.9 1.8 2.8 0.8 2.6 1.0 2.5 Mach. and transport equip. 2.3 6.6 2.9 6.8 0.6 8.8 1.9 5.6 Other 34.6 44.0 39.4 48.2 20.3 23.0 39.3 54.6 Miscellaneous goods 0.5 0.7 0.6 0.7 0.4 0.7 0.4 0.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 2004 World Development Indicators 313 6.3 OECD trade with low- and middle-income economies Exports to middle-income economies High-income European Japan United States OECD countries Union 1992 2002 1992 2002 a 1992 2002 1992 2002 $ billions Food 35.8 44.7 16.7 19.9 0.3 0.3 13.0 16.7 Cereals 13.8 10.6 4.7 3.4 0.1 0.0 5.8 5.5 Agricultural raw materials 7.4 14.1 2.3 4.9 0.5 1.0 3.0 5.1 Ores and nonferrous metals 6.4 15.0 2.2 5.8 0.6 2.0 2.0 3.5 Fuels 8.1 14.5 2.8 4.7 0.5 0.5 3.3 5.1 Crude petroleum 0.9 1.2 0.5 0.4 0.0 0.0 0.0 0.0 Petroleum products 5.4 9.8 2.2 3.7 0.4 0.5 2.2 3.9 Manufactured goods 311.1 619.2 139.4 307.9 60.7 92.5 85.3 153.8 Chemical products 38.5 86.5 20.5 45.4 3.4 8.1 10.6 20.0 Mach. and transport equip. 185.0 361.1 77.3 169.9 41.7 62.6 53.9 93.8 Other 87.5 171.6 41.6 92.6 15.6 21.8 20.8 39.9 Miscellaneous goods 10.3 22.6 3.2 6.6 0.6 3.3 5.2 9.1 Total 379.0 730.0 166.6 349.8 63.2 99.6 111.8 193.2 % of total exports Food 9.5 6.1 10.0 5.7 0.5 0.3 11.6 8.6 Cereals 3.6 1.5 2.8 1.0 0.1 0.0 5.2 2.8 Agricultural raw materials 2.0 1.9 1.4 1.4 0.8 1.0 2.7 2.6 Ores and nonferrous metals 1.7 2.1 1.3 1.7 1.0 2.0 1.8 1.8 Fuels 2.1 2.0 1.7 1.4 0.7 0.5 3.0 2.6 Crude petroleum 0.2 0.2 0.3 0.1 0.0 0.0 0.0 0.0 Petroleum products 1.4 1.3 1.3 1.0 0.6 0.5 2.0 2.0 Manufactured goods 82.1 84.8 83.7 88.0 96.1 92.9 76.3 79.6 Chemical products 10.2 11.9 12.3 13.0 5.3 8.1 9.5 10.4 Mach. and transport equip. 48.8 49.5 46.4 48.6 66.0 62.9 48.2 48.5 Other 23.1 23.5 25.0 26.5 24.7 21.9 18.6 20.7 Miscellaneous goods 2.7 3.1 1.9 1.9 0.9 3.3 4.7 4.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Imports from middle-income economies $ billions Food 59.5 81.8 30.0 36.5 10.0 14.5 14.2 22.4 Cereals 2.1 4.4 0.4 1.9 0.6 0.5 0.2 0.6 Agricultural raw materials 15.2 17.9 7.3 9.6 4.2 2.9 1.9 3.8 Ores and nonferrous metals 24.8 39.6 12.4 17.3 6.0 7.0 4.2 8.9 Fuels 122.7 182.3 61.1 76.4 20.5 24.1 30.6 61.2 Crude petroleum 87.9 134.7 41.6 52.7 14.5 17.4 23.4 49.7 Petroleum products 19.6 24.9 9.2 11.6 2.4 1.2 6.8 9.8 Manufactured goods 196.1 730.2 75.6 254.1 19.0 75.6 85.1 335.6 Chemical products 13.9 34.7 7.2 15.0 1.8 3.5 3.3 11.3 Mach. and transport equip. 59.6 333.8 17.5 107.5 3.7 32.1 33.6 164.8 Other 122.6 361.6 50.9 131.6 13.5 39.9 48.2 159.5 Miscellaneous goods 7.4 15.7 3.3 2.2 0.6 1.6 3.2 11.5 Total 425.7 1,067.4 189.7 396.1 60.4 125.7 139.2 443.3 % of total imports Food 14.0 7.7 15.8 9.2 16.6 11.5 10.2 5.1 Cereals 0.5 0.4 0.2 0.5 1.1 0.4 0.2 0.1 Agricultural raw materials 3.6 1.7 3.9 2.4 7.0 2.3 1.3 0.9 Ores and nonferrous metals 5.8 3.7 6.5 4.4 10.0 5.6 3.0 2.0 Fuels 28.8 17.1 32.2 19.3 33.9 19.2 22.0 13.8 Crude petroleum 20.6 12.6 21.9 13.3 24.0 13.8 16.8 11.2 Petroleum products 4.6 2.3 4.8 2.9 4.0 1.0 4.9 2.2 Manufactured goods 46.1 68.4 39.9 64.1 31.4 60.2 61.1 75.7 Chemical products 3.3 3.3 3.8 3.8 3.0 2.8 2.3 2.5 Mach. and transport equip. 14.0 31.3 9.2 27.1 6.1 25.6 24.2 37.2 Other 28.8 33.9 26.8 33.2 22.4 31.8 34.6 36.0 Miscellaneous goods 1.7 1.5 1.7 0.6 1.0 1.3 2.3 2.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 a. Data for Belgium, Greece, and Luxembourg are for 2001. 314 2004 World Development Indicators 6.3 OECD trade with low- and GLOBAL LINK middle-income economies S About the data Definitions Developing countries are becoming increasingly goods from developing countries is imposed by other The product groups in the table are defined in accor- important in the global trading system. Since the developing countries). The growing trade between dance with the Standard International Trade early 1990s trade between high-income members of developing countries strengthens the case for reduc- Classification (SITC) revision 1: food (0, 1, 22, and the Organisation for Economic Co-operation and ing these barriers. Despite the growth in trade 4) and cereals (04); agricultural raw materials (2 Development (OECD) and low- and middle-income between developing countries, high-income OECD excluding 22, 27, and 28); ores and nonferrous met- economies has grown faster than trade between countries remain the developing world's most impor- als (27, 28, and 68); fuels (3), crude petroleum OECD members. The increased trade benefits con- tant partners. (331), and petroleum products (332); manufactured sumers and producers. But as the World Trade The aggregate flows in the table were compiled goods (5­8 excluding 68), chemical products (5), Organization's (WTO) Ministerial Conference in from intercountry flows recorded in the United machinery and transport equipment (7), and other Doha, Qatar, in October 2001 showed, achieving a Nations Statistics Division's Commodity Trade (COM- manufactured goods (6 and 8 excluding 68); and more prodevelopment outcome from trade remains TRADE) database. Partner country reports by high- miscellaneous goods (9). · Exports are all mer- a major challenge. Meeting this challenge will income OECD countries were used for both exports chandise exports by high-income OECD countries to require strengthening international consultation. and imports. Exports are recorded free on board low-income and middle-income economies as record- Negotiations after the Doha meetings have been (f.o.b.); imports include insurance and freight ed in the United Nations Statistics Division's COM- launched on services, agriculture, manufactures, charges (c.i.f.). Because of differences in sources of TRADE database. · Imports are all merchandise WTO rules, the environment, dispute settlement, data, timing, and treatment of missing data, the data imports by high-income OECD countries from low- intellectual property rights protection, and disci- in this table may not be fully comparable with those income and middle-income economies as recorded plines on regional integration. These negotiations used to calculate the direction of trade statistics in in the United Nations Statistics Division's COM- are scheduled to be concluded by 2005. table 6.2 or the aggregate flows in tables 4.4­4.6. TRADE database. · High-income OECD countries in Trade flows between high-income OECD members For further discussion of merchandise trade statis- 2002 were Australia, Austria, Belgium, Canada, and low- and middle-income economies reflect the tics, see About the data for tables 4.4­4.6 and 6.2. Denmark, Finland, France, Germany, Greece, changing mix of exports to and imports from devel- Iceland, Ireland, Italy, Japan, the Republic of Korea, oping economies. While food imports from middle- Luxembourg, the Netherlands, New Zealand, Norway, income countries have continued to fall as a share of Portugal, Spain, Sweden, Switzerland, the United OECD imports, food imports from low-income coun- Kingdom, and the United States. · European Union tries to high-income countries have increased as a comprises Austria, Belgium, Denmark, Finland, share of U.S. and Japanese imports. The share of France, Germany, Greece, Ireland, Italy, Luxembourg, manufactures in total goods imports to high-income the Netherlands, Portugal, Spain, Sweden, and the countries has grown dramatically for both low- and United Kingdom. middle-income countries. Moreover, trade between developing countries has grown substantially over the past decade. This growth has resulted from many factors, including developing countries' increasing share of world output and the liberalization of their trade. Yet trade barriers remain high (more than 70 percent of the tariff burden faced by manufactured 6.3a Manufactured goods from developing countries dominated imports by OECD countries in 2002 Imports by high-income OECD countries (% of total imports) 80 Data sources Low income Middle income 70 COMTRADE data are available in electronic form 60 from the United Nations Statistics Division. 50 Although not as comprehensive as the underlying 40 COMTRADE records, detailed statistics on inter- 30 national trade are published annually in the United 20 Nations Conference on Trade and Development's 10 (UNCTAD) Handbook of International Trade and 0 Development Statistics and the United Nations Manufactured Fuels Food Ores and Agricultural Statistics Division's International Trade Statistics goods nonferrous metals raw materials Yearbook. Source: United Nations Statistics Division, COMTRADE database. 2004 World Development Indicators 315 6.4 Primary commodity prices 1970 1980 1990 1995 1997 1998 1999 2000 2001 2002 2003 World Bank commodity price index (1990 = 100) Non-energy commodities 156 159 100 104 114 99 89 89 84 89 91 Agriculture 163 175 100 112 124 108 93 90 85 93 95 Beverages 203 230 100 129 165 141 108 91 76 91 87 Food 166 177 100 100 112 105 88 87 91 97 96 Raw materials 130 133 100 116 110 88 89 94 82 89 98 Fertilizers 108 164 100 88 116 123 115 109 105 108 106 Metals and minerals 144 120 100 87 87 76 74 85 80 78 82 Petroleum 19 204 100 64 81 57 80 127 113 117 126 Steel products a 111 100 100 91 86 75 69 79 71 73 79 MUV G-5 index 28 79 100 117 104 100 99 97 94 93 100 Commodity prices (1990 prices) Agricultural raw materials Cotton (cents/kg) 225 260 182 182 169 145 118 134 112 109 140 Logs, Cameroon ($/cu. m) a 153 319 343 290 275 287 271 283 282 .. .. Logs, Malaysian ($/cu. m) 154 248 177 218 230 163 188 195 169 175 187 Rubber (cents/kg) 145 181 86 135 98 72 63 71 64 83 106 Sawnwood, Malaysian ($/cu. m) 625 503 533 632 641 486 605 614 510 565 551 Tobacco ($/mt) 3,836 2,888 3,392 2,258 3,411 3,349 3,064 3,063 3,185 2,947 2,643 Beverages (cents/kg) Cocoa 240 330 127 122 156 168 114 93 113 191 175 Coffee, robustas 330 411 118 237 168 183 150 94 64 71 81 Coffee, Arabica 409 440 197 285 403 299 231 198 146 146 142 Tea, avg., 3 auctions 298 211 206 127 199 205 185 193 169 162 152 Energy Coal, Australian ($/mt) .. 49.67 39.67 33.63 33.90 29.34 26.08 27.01 34.26 29.05 27.83 Coal, U.S. ($/mt) .. 54.69 41.67 33.46 35.15 34.51 33.41 34.02 47.56 42.97 .. Natural gas, Europe ($/mmbtu) .. 4.31 2.55 2.33 2.65 2.43 2.15 3.97 4.30 3.28 3.91 Natural gas, U.S. ($/mmbtu) 0.59 1.97 1.70 1.47 2.40 2.09 2.28 4.44 4.19 3.60 5.49 Petroleum ($/bbl) 4.31 46.78 22.88 14.68 18.52 13.12 18.20 29.05 25.82 26.76 28.90 About the data Primary commodities--raw or partially processed the prices paid by importers are used. Annual price steel products, which are not included in the non- materials that will be transformed into finished series are generally simple averages based on high- energy commodity price index. goods--are often the most significant exports of er frequency data. The constant price series in the The MUV index is a composite index of prices for developing countries, and revenues obtained from table is deflated using the manufactures unit value manufactured exports from the five major (G-5) them have an important effect on living standards. (MUV) index for the G-5 countries (see below). industrial countries (France, Germany, Japan, the Price data for primary commodities are collected The commodity price indexes are calculated as United Kingdom, and the United States) to low- and from a variety of sources, including trade journals, Laspeyres index numbers, in which the fixed weights middle-income economies, valued in U.S. dollars. international study groups, government market sur- are the 1987­89 export values for low- and middle- The index covers products in groups 5­8 of the veys, newspaper and wire service reports, and com- income economies, rebased to 1990. Each index Standard International Trade Classification (SITC) modity exchange spot and near-term forward prices. represents a fixed basket of primary commodity revision 1. To construct the MUV G-5 index, unit This table is based on frequently updated price exports. The non-energy commodity price index con- value indexes for each country are combined using reports. When possible, the prices received by tains 37 price series for 31 non-energy commodities. weights determined by each country's export share. exporters are used; if export prices are unavailable, Separate indexes are compiled for petroleum and for 316 2004 World Development Indicators 6.4 GLOBAL LINK Primary commodity prices S 1970 1980 1990 1995 1997 1998 1999 2000 2001 2002 2003 Commodity prices (continued) (1990 prices) Fertilizers ($/mt) Phosphate rock 39 59 40 30 40 43 44 45 44 43 38 TSP 152 229 132 128 166 174 156 142 135 143 149 Food Fats and oils ($/mt) Coconut oil 1,417 855 336 572 634 660 742 463 337 452 467 Groundnut oil 1,350 1,090 964 847 976 913 793 734 721 738 1,242 Palm oil 927 740 290 536 527 674 439 319 303 419 443 Soybeans 417 376 247 221 285 244 203 218 208 228 264 Soybean meal 367 332 200 168 266 171 153 194 192 188 211 Soybean oil 1,021 758 447 534 545 628 430 348 375 488 554 Grains ($/mt) Grain sorghum 185 164 104 102 106 98 85 91 101 109 107 Maize 208 159 109 105 113 102 91 91 95 107 105 Rice 450 521 271 274 293 305 250 208 183 206 198 Wheat 196 219 136 151 154 127 113 117 134 159 146 Other food Bananas ($/mt) 590 481 541 380 499 491 376 436 618 568 375 Beef (cents/kg) 465 350 256 163 179 173 186 199 226 226 198 Oranges ($/mt) 599 496 531 454 443 444 434 374 631 606 682 Sugar, EU domestic (cents/kg) 40 62 58 59 61 60 60 57 56 59 60 Sugar, U.S. domestic (cents/kg) 59 84 51 43 47 49 47 44 50 50 47 Sugar, world (cents/kg) 29 80 28 25 24 20 14 19 20 16 16 Metals and minerals Aluminum ($/mt) 1,982 1,847 1,639 1,542 1,545 1,363 1,371 1,594 1,531 1,449 1,431 Copper ($/mt) 5,038 2,769 2,661 2,508 2,199 1,660 1,584 1,866 1,673 1,674 1,779 Iron ore (cents/dmtu) 35 36 32 24 29 31 28 30 32 31 31 Lead (cents/kg) 108 115 81 54 60 53 51 47 50 49 51 Nickel ($/mt) 10,148 8,271 8,864 7,028 6,691 4,647 6,055 8,888 6,303 7,271 9,627 Tin (cents/kg) 1,310 2,128 609 531 545 556 544 559 475 436 489 Zinc (cents/kg) 105 97 151 88 127 103 108 116 94 84 83 a. Series not included in the non-energy index. Definitions · Non-energy commodity price index covers the 31 iron ore, lead, nickel, tin, and zinc. · Petroleum and sources, see "Commodity Price Data" (also non-energy primary commodities that make up the price index refers to the average spot price of Brent, known as the "Pink Sheet") at the Global Prospects agriculture, fertilizer, and metals and minerals Dubai, and West Texas Intermediate crude oils, Web site (http://www.worldbank.org/prospects). indexes. · Agriculture includes beverages, food, equally weighted. · Steel products price index is and agricultural raw material. · Beverages include the composite price index for eight steel products cocoa, coffee, and tea. · Food includes rice, wheat, based on quotations free on board (f.o.b.) Japan maize, sorghum, soybeans, soybean oil, soybean excluding shipments to China and the United Data sources meal, palm oil, coconut oil, groundnut oil, bananas, States, weighted by product shares of apparent Commodity price data and the G-5 MUV index beef, oranges, and sugar. · Agricultural raw mate- combined consumption (volume of deliveries) for are compiled by the World Bank's Development rials include cotton, timber (logs and sawnwood), Germany, Japan, and the United States. · MUV G-5 Prospects Group. Monthly updates of com- natural rubber, and tobacco. · Fertilizers include index is the manufactures unit value index for G-5 modity prices are available on the Web at phosphate rock and triple superphosphate (TSP). country exports to low- and middle-income http://www.worldbank.org/prospects. · Metals and minerals include aluminum, copper, economies. · Commodity prices--for definitions 2004 World Development Indicators 317 6.5 Regional trade blocs Merchandise exports within bloc $ millions 1970 1980 1990 1995 1997 1998 1999 2000 2001 2002 High-income and low- and middle-income economies APEC a 58,633 357,697 901,560 1,688,708 1,869,192 1,734,386 1,896,213 2,262,159 2,070,710 2,166,764 CEFTA 1,157 7,766 4,235 12,118 13,169 14,234 13,226 15,123 17,054 19,180 European Union 76,451 456,857 981,260 1,259,699 1,159,112 1,223,801 1,396,574 1,407,525 1,396,252 1,473,375 NAFTA 22,078 102,218 226,273 394,472 496,423 521,649 581,161 676,440 639,138 626,985 Latin America and the Caribbean ACS 758 4,892 5,398 11,049 12,032 12,505 11,252 15,773 14,984 16,937 Andean Group 97 1,161 1,312 4,812 5,524 5,408 3,929 4,785 5,461 5,026 CACM 287 1,174 667 1,594 1,993 2,010 2,175 2,418 2,394 2,598 CARICOM 52 576 448 867 968 1,020 1,136 1,050 1,202 1,221 Central American Group of Four 176 692 399 1,026 1,302 1,230 1,369 1,582 1,546 1,678 Group of Three 59 706 1,046 3,460 3,944 3,921 2,912 3,544 4,178 3,647 LAIA 1,263 10,981 12,331 35,299 44,700 42,959 34,785 42,593 40,755 35,755 MERCOSUR 451 3,424 4,127 14,199 20,680 20,352 15,313 17,884 15,244 10,341 OECS .. 8 29 39 34 36 37 38 40 43 Africa CEMAC 22 75 139 120 161 153 127 102 120 131 CEPGL 3 2 7 8 6 8 9 10 11 12 COMESA 392 609 910 1,244 1,391 1,342 1,357 1,556 1,639 1,801 Cross-Border Initiative 209 447 613 1,002 1,144 1,156 964 1,066 947 1,019 ECCAS 162 89 163 163 211 198 179 196 217 236 ECOWAS 86 692 1,557 1,936 2,244 2,350 2,364 2,873 2,794 3,009 Indian Ocean Commission 23 39 73 127 75 95 91 105 135 136 MRU 1 7 0 1 7 2 4 5 4 5 SADC 483 617 1,630 3,373 4,471 3,865 4,224 4,452 4,132 4,268 UDEAC 22 75 139 120 160 152 126 101 119 130 UEMOA 52 460 621 560 707 752 805 741 776 875 Middle East and Asia Arab Common Market 102 661 911 1,368 1,146 978 951 1,312 1,728 1,857 ASEAN 1,456 13,350 28,648 81,911 88,773 72,352 80,415 101,848 90,105 95,473 Bangkok Agreement 132 1,464 4,476 12,066 13,684 12,851 14,463 16,844 16,733 18,299 EAEG 9,197 98,532 281,067 634,606 669,833 549,010 612,415 772,420 698,550 779,364 ECO 63 15,891 1,243 4,746 4,929 4,031 3,903 4,485 4,457 4,915 GCC 156 4,632 6,906 6,832 8,124 7,358 7,306 7,218 6,959 6,922 SAARC 99 613 863 2,024 2,174 2,466 2,180 2,614 2,828 2,999 UMA 60 109 958 1,109 924 881 919 1,104 1,136 1,178 Note: Regional bloc memberships are as follows: Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand, the United States, and Vietnam; Central European Free Trade Area (CEFTA), Bulgaria, the Czech Republic, Hungary, Poland, Romania, the Slovak Republic, and Slovenia; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom; North American Free Trade Area (NAFTA), Canada, Mexico, and the United States; Association of Caribbean States (ACS), Antigua and Barbuda, the Bahamas, Barbados, Belize, Colombia, Costa Rica, Cuba, Dominica, the Dominican Republic, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, Trinidad and Tobago, and República Bolivariana de Venezuela; Andean Group, Bolivia, Colombia, Ecuador, Peru, and República Bolivariana de Venezuela; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas (part of the Caribbean Community but not of the Common Market), Barbados, Belize, Dominica, Grenada, Guyana, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Group of Four, El Salvador, Guatemala, Honduras, and Nicaragua; Group of Three, Colombia, Mexico, and República Bolivariana de Venezuela; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and República Bolivariana de Venezuela; Southern Cone Common Market (MERCOSUR), Argentina, Brazil, Paraguay, and Uruguay; Organization of Eastern Caribbean States (OECS), Antigua and Barbuda, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Economic and Monetary Community of Central Africa (CEMAC), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Principe; Economic Community of the Countries of the Great Lakes (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; Common Market for Eastern and Southern Africa (COMESA), Angola, Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Tanzania, Zambia, 318 2004 World Development Indicators 6.5 GLOBAL LINK Regional trade blocs S Merchandise exports within bloc % of total bloc exports 1970 1980 1990 1995 1997 1998 1999 2000 2001 2002 High-income and low- and middle-income economies APEC a 57.8 57.9 68.3 71.8 71.6 69.7 71.8 73.1 72.6 73.3 CEFTA 12.9 14.8 9.9 14.6 13.4 13.0 12.1 12.2 12.4 12.2 European Union 59.5 60.8 65.9 62.4 55.4 56.8 62.9 61.6 60.8 60.6 NAFTA 36.0 33.6 41.4 46.2 49.1 51.7 54.6 55.7 55.5 56.7 Latin America and the Caribbean ACS 9.6 8.7 8.4 8.5 7.0 7.2 5.6 6.4 6.5 7.1 Andean Group 1.8 3.8 4.1 12.0 10.8 12.8 8.8 7.9 10.3 9.5 CACM 26.1 24.4 15.3 21.8 18.7 15.8 13.6 14.8 15.5 11.1 CARICOM 4.2 5.3 8.1 12.1 14.4 17.3 16.9 14.7 14.0 12.5 Central American Group of Four 20.1 18.1 13.7 22.2 20.2 17.1 14.6 15.1 14.8 12.8 Group of Three 1.1 1.8 2.0 3.2 2.7 2.6 1.7 1.7 2.1 1.8 LAIA 9.9 13.7 10.8 17.1 17.0 16.7 12.7 12.8 12.8 11.1 MERCOSUR 9.4 11.6 8.9 20.3 24.8 25.0 20.6 20.8 17.2 11.6 OECS .. 9.1 8.1 12.6 10.7 12.0 13.1 10.0 5.3 3.8 Africa CEMAC 4.8 1.6 2.3 2.1 2.0 2.3 1.7 1.0 1.3 1.5 CEPGL 0.4 0.1 0.5 0.5 0.4 0.6 0.8 0.8 0.8 0.7 COMESA 8.7 6.0 6.3 7.0 7.1 7.7 7.4 5.7 7.0 6.4 Cross-Border Initiative 9.3 8.8 10.3 11.9 12.7 13.9 12.1 10.6 10.0 10.2 ECCAS 9.6 1.4 1.4 1.5 1.5 1.8 1.3 1.1 1.3 1.3 ECOWAS 2.9 10.1 7.9 9.0 8.6 10.7 10.4 9.5 9.6 10.6 Indian Ocean Commission 8.4 3.9 4.1 6.0 3.9 4.7 4.8 4.2 5.5 5.3 MRU 0.2 0.8 0.0 0.1 0.5 0.1 0.4 0.4 0.3 0.2 SADC 8.0 2.0 4.8 8.7 10.4 10.4 11.9 11.9 10.2 9.3 COMESA 4.9 1.6 2.3 2.1 2.0 2.3 1.7 1.0 1.3 1.5 UDEAC 4.9 1.6 2.3 2.1 2.0 2.3 1.7 1.0 1.3 1.5 UEMOA 6.5 9.6 13.0 10.3 11.8 11.0 13.1 13.1 14.3 12.3 Middle East and Asia Arab Common Market 2.2 2.4 2.7 6.7 4.1 4.8 3.3 3.0 4.5 4.8 ASEAN 22.9 18.7 19.8 25.4 24.9 21.9 22.4 23.9 23.3 23.7 Bangkok Agreement 2.7 3.7 3.7 5.0 5.1 5.0 5.1 5.1 5.5 5.6 EAEG 28.9 35.6 39.7 47.9 47.8 42.0 43.8 46.6 46.6 48.2 ECO 1.5 73.2 3.2 7.9 7.5 6.8 5.8 5.6 5.5 5.9 GCC 2.9 3.0 8.0 6.8 6.5 8.0 6.7 4.5 4.5 4.6 SAARC 3.2 4.8 3.2 4.4 4.2 4.8 4.0 4.1 4.3 4.2 UMA 1.4 0.3 2.9 3.8 2.7 3.3 2.5 2.3 2.6 2.7 and Zimbabwe; Cross-Border Initiative, Burundi, Comoros, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe; Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of the Congo, the Republic of Congo, Equatorial Guinea, Gabon, Rwanda, and São Tomé and Principe; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d'Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Reunion, and Seychelles; Mano River Union (MRU), Guinea, Liberia, and Sierra Leone; Southern African Development Community (SADC; formerly Southern African Development Coordination Conference), Angola, Botswana, the Democratic Republic of the Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Central African Customs and Economic Union (UDEAC; formerly Union Douanière et Economique de l'Afrique Centrale), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo; Arab Common Market, the Arab Republic of Egypt, Iraq, Jordan, Libya, Mauritania, the Syrian Arab Republic, and the Republic of Yemen; Association of South-East Asian Nations (ASEAN), Brunei, Cambodia, Indonesia, the Lao People's Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Bangkok Agreement, Bangladesh, India, the Republic of Korea, the Lao People's Democratic Republic, the Philippines, Sri Lanka, and Thailand; East Asian Economic Caucus (EAEC), Brunei, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, the Philippines, Singapore, Taiwan (China), and Thailand; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and Uzbekistan; Gulf Cooperation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; South Asian Association for Regional Cooperation (SAARC), Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; and Arab Maghreb Union (UMA), Algeria, Libya, Mauritania, Morocco, and Tunisia. a. No preferential trade agreement. 2004 World Development Indicators 319 6.5 Regional trade blocs Total merchandise exports by bloc % of world exports 1970 1980 1990 1995 1997 1998 1999 2000 2001 2002 High-income and low- and middle-income economies APEC a 36.0 33.7 39.0 46.3 47.3 46.1 46.6 48.6 46.5 46.0 CEFTA 3.2 2.9 1.3 1.6 1.8 2.0 1.9 2.0 2.2 2.4 European Union 45.6 41.0 44.0 39.7 37.9 39.9 39.2 35.9 37.4 37.9 NAFTA 21.7 16.6 16.2 16.8 18.3 18.7 18.8 19.1 18.8 17.2 Latin America and the Caribbean ACS 2.8 3.1 1.9 2.6 3.1 3.2 3.5 3.8 3.7 3.7 Andean Group 1.9 1.7 0.9 0.8 0.9 0.8 0.8 1.0 0.9 0.8 CACM 0.4 0.3 0.1 0.1 0.2 0.2 0.3 0.3 0.3 0.4 CARICOM 0.4 0.6 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.2 Central American Group of Four 0.3 0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 Group of Three 1.8 2.1 1.5 2.1 2.7 2.8 3.0 3.3 3.2 3.1 LAIA 4.5 4.4 3.4 4.1 4.8 4.8 4.8 5.2 5.2 5.0 MERCOSUR 1.7 1.6 1.4 1.4 1.5 1.5 1.3 1.3 1.4 1.4 OECS .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Africa CEMAC 0.2 0.3 0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.1 CEPGL 0.3 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 COMESA 1.6 0.6 0.4 0.4 0.4 0.3 0.3 0.4 0.4 0.4 Cross-Border Initiative 0.8 0.3 0.2 0.2 0.2 0.2 0.1 0.2 0.2 0.2 ECCAS 0.6 0.3 0.3 0.2 0.3 0.2 0.2 0.3 0.3 0.3 ECOWAS 1.1 0.4 0.6 0.4 0.5 0.4 0.4 0.5 0.5 0.4 Indian Ocean Commission 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MRU 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 SADC 2.2 1.6 1.0 0.8 0.8 0.7 0.6 0.6 0.7 0.7 UDEAC 0.2 0.3 0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.1 UEMOA 0.3 0.3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Middle East and Asia Arab Common Market 1.6 1.5 1.0 0.4 0.5 0.4 0.5 0.7 0.6 0.6 ASEAN 2.0 3.7 4.1 6.1 6.6 5.8 5.6 6.9 6.0 6.3 Bangkok Agreement 1.6 2.1 3.5 4.6 5.0 4.6 4.5 5.4 4.7 5.1 EAEC 11.3 15.1 20.9 26.1 25.4 24.2 24.7 26.1 24.4 25.2 ECO 1.5 1.2 1.1 1.2 1.2 1.1 1.2 1.3 1.3 1.3 GCC 1.9 8.5 2.5 2.0 2.3 1.7 1.9 2.5 2.5 2.3 SAARC 1.1 0.7 0.8 0.9 0.9 0.9 1.0 1.0 1.1 1.1 UMA 1.5 2.3 1.0 0.6 0.6 0.5 0.6 0.8 0.7 0.7 320 2004 World Development Indicators 6.5 GLOBAL LINK Regional trade blocs S About the data Definitions Trade blocs are groups of countries that have estab- certain groups may be understated. Data on trade · Merchandise exports within bloc are the sum of lished special preferential arrangements governing between developing and high-income countries are merchandise exports by members of a trade bloc to trade between members. Although in some cases the generally complete. other members of the bloc. They are shown both in preferences--such as lower tariff duties or exemp- Membership in the trade blocs shown is based on U.S. dollars and as a percentage of total merchan- tions from quantitative restrictions--may be no the most recent information available, from the dise exports by the bloc. · Total merchandise greater than those available to other trading partners, World Bank Policy Research Report Trade Blocs exports by bloc as a share of world exports are the the general purpose of such arrangements is to (2000a) and from consultation with the World Bank's ratio of the bloc's total merchandise exports (within encourage exports by bloc members to one another-- international trade unit. Although bloc exports have the bloc and to the rest of the world) to total mer- sometimes called intratrade. been calculated back to 1970 on the basis of current chandise exports by all economies in the world. Most countries are members of a regional trade membership, most of the blocs came into existence bloc, and more than a third of the world's trade takes in later years and their membership may have place within such arrangements. While trade blocs changed over time. For this reason, and because sys- vary widely in structure, they all have the same main tems of preferences also change over time, intra- objective: to reduce trade barriers among member trade in earlier years may not have been affected by countries. But effective integration requires more than the same preferences as in recent years. In addition, reducing tariffs and quotas. Economic gains from com- some countries belong to more than one trade bloc, petition and scale may not be achieved unless other so shares of world exports exceed 100 percent. barriers that divide markets and impede the free flow Exports of blocs include all commodity trade, which of goods, services, and investments are lifted. For may include items not specified in trade bloc agree- example, many regional trade blocs retain contingent ments. Differences from previously published esti- protections or restrictions on intrabloc trade. These mates may be due to changes in bloc membership or include antidumping, countervailing duties, and "emer- to revisions in the underlying data. gency protection" to address balance of payments problems or to protect an industry from surges in imports. Other barriers include differing product stan- dards, discrimination in public procurement, and cum- bersome and costly border formalities. Membership in a regional trade bloc may reduce the frictional costs of trade, increase the credibility of reform initiatives, and strengthen security among partners. But making it work effectively is a challenge for any government. All sectors of an economy may be affected, and some sectors may expand while others contract, so it is important to weigh the potential costs and benefits that membership may bring. The table shows the value of merchandise intra- trade for important regional trade blocs (service exports are excluded) as well as the size of intratrade relative to each bloc's total exports of Data sources goods and the share of the bloc's total exports in Data on merchandise trade flows are published in world exports. Although the Asia Pacific Economic the IMF's Direction of Trade Statistics Yearbook Cooperation (APEC) has no preferential arrange- and Direction of Trade Statistics Quarterly; the ments, it is included in the table because of the vol- data in the table were calculated using the IMF's ume of trade between its members. Direction of Trade database. The United Nations The data on country exports are drawn from the Conference on Trade and Development (UNCTAD) International Monetary Fund's (IMF) Direction of publishes data on intratrade in its Handbook of Trade database and should be broadly consistent International Trade and Development Statistics. with those from other sources, such as the United The information on trade bloc membership is from Nations Statistics Division's Commodity Trade (COM- the World Bank Policy Research Report Trade TRADE) database. However, trade flows between Blocs (2000a) and the World Bank's international many developing countries, particularly in Africa, are trade unit. not well recorded. Thus the value of intratrade for 2004 World Development Indicators 321 6.6 Tariff barriers All products Primary products Manufactured products % Share of Share of Ad valorem % % Simple Simple Weighted lines with lines with equivalent Simple Weighted Simple Weighted Binding mean mean mean international specific of nontariff mean mean mean mean Year coverage bound rate tariff tariff peaks rates barriers a tariff tariff tariff tariff Albania 1997 .. .. 17.7 16.0 59.8 0.0 .. 15.7 12.8 17.2 15.2 2001 100.0 7.0 11.8 12.4 40.0 0.0 0.6 12.1 10.6 11.6 11.6 Algeria 1993 .. .. 20.9 16.1 43.7 0.0 .. 22.5 8.9 21.7 18.7 2002 .. .. 18.8 15.3 43.5 0.0 1.1 19.7 12.8 18.3 13.1 Argentina 1992 .. .. 14.9 13.4 36.8 0.0 .. 8.1 5.8 14.8 13.6 2002 100.0 31.9 14.6 11.9 50.3 0.0 4.7 10.9 8.0 14.8 12.2 Australia 1991 b .. .. 14.4 10.3 36.3 0.7 .. 3.0 1.7 14.1 10.5 2002 b 97.0 9.9 5.9 3.9 8.4 0.7 0.6 1.5 0.8 5.6 4.3 Bangladesh 1989 .. .. 110.5 131.0 98.9 1.0 .. 79.8 53.6 108.6 109.6 2002 16.1 163.8 19.3 23.0 45.9 0.0 1.7 22.4 20.1 19.3 21.1 Belarus 1996 .. .. 11.8 7.8 30.2 0.0 .. 9.4 6.5 12.6 10.5 2002 .. .. 11.1 8.0 19.4 1.1 0.0 11.1 7.0 11.6 10.3 Belize 1996 22.0 21.1 48.8 42.2 .. 23.0 10.5 20.4 18.8 2001 98.0 58.2 12.6 13.3 41.7 0.0 .. 19.7 9.9 11.6 11.2 Benin 2001 .. .. 14.5 15.5 57.9 0.0 .. 15.5 12.9 14.1 12.4 2002 39.4 28.3 14.5 15.5 57.6 0.0 .. 15.5 12.9 14.0 12.4 Bhutan 1996 .. .. 16.1 14.4 52.2 2.7 .. 21.2 9.7 17.0 16.8 2002 .. .. 16.3 14.3 45.4 0.0 .. 24.4 14.3 16.4 15.0 Bolivia 1993 .. .. 9.7 9.2 0.0 0.0 .. 10.0 10.0 9.7 9.3 2001 100.0 40.0 9.4 8.8 0.0 0.0 0.8 9.8 9.1 9.3 8.8 Brazil 1989 .. .. 43.5 35.6 92.9 0.2 .. 31.5 18.6 44.0 37.1 2002 99.9 31.4 14.9 9.9 43.1 0.0 2.4 10.9 4.8 15.1 12.0 Burkina Faso 1993 .. .. 25.9 23.5 75.3 0.0 .. 27.5 23.1 25.5 20.3 2002 39.2 41.9 13.1 11.2 48.1 0.0 .. 15.0 15.2 12.8 9.2 Cameroon 1994 .. .. 19.2 15.3 52.7 0.0 .. 23.9 14.9 18.8 13.6 2002 13.6 79.9 18.7 15.8 51.7 0.0 0.1 21.7 18.1 18.0 13.9 Canada 1989 b .. .. 10.8 6.4 18.4 2.4 .. 4.3 2.6 10.5 6.6 2002 b 100.0 5.1 5.1 1.1 13.0 3.3 1.5 1.9 0.5 4.7 1.0 Central African Republic 1995 .. .. 17.1 14.3 50.0 0.7 .. 19.1 13.7 16.8 13.1 2002 62.5 36.2 18.3 20.0 51.3 0.0 .. 24.3 25.5 17.7 13.4 Chad 1995 b .. .. 15.9 17.0 44.2 0.0 .. 19.0 15.9 15.6 13.4 2002 13.7 79.9 16.8 14.2 43.8 0.0 .. 21.4 24.0 16.5 13.3 Chile 1992 .. .. 11.0 11.0 0.0 0.0 .. 11.0 11.0 11.0 10.9 2002 100.0 25.1 7.0 7.0 0.0 0.0 1.0 7.0 7.0 7.0 6.9 China 1992 .. .. 41.6 35.3 79.5 0.0 .. 36.3 14.0 40.6 35.6 2001 .. .. 15.1 12.8 41.7 0.7 1.5 16.0 19.2 15.0 12.8 Colombia 1991 .. .. 5.6 4.3 0.3 0.0 .. 7.0 7.5 5.8 6.1 2002 100.0 42.9 12.8 10.1 23.0 0.0 3.4 12.9 13.2 12.7 10.8 Congo, Rep. 1994 .. .. 20.8 16.6 61.4 0.0 .. 24.4 20.5 20.3 14.6 2002 16.3 27.5 19.6 18.0 55.2 0.0 .. 23.7 21.3 18.8 16.1 Costa Rica 1995 b .. .. 10.4 8.7 29.8 0.0 .. 12.9 10.5 9.9 8.0 2001 .. .. 6.6 5.8 0.1 0.0 0.2 10.4 7.9 6.1 3.7 Côte d'Ivoire 1993 .. .. 24.3 23.1 73.0 0.0 .. 26.5 21.6 24.1 22.5 2002 33.1 11.1 12.8 12.0 45.7 0.0 2.0 14.8 10.7 12.6 10.3 Cuba 1993 .. .. 14.1 12.3 30.5 0.0 .. 12.1 7.2 14.0 12.9 2002 30.9 21.2 12.3 9.4 12.5 0.1 .. 11.5 5.8 12.0 11.0 Czech Republic 1996 .. .. 6.4 5.4 2.4 0.0 .. 5.9 4.1 6.5 6.2 2002 .. .. 5.0 4.1 3.4 0.0 1.1 5.5 3.9 5.0 4.3 Dominican Republic 1997 .. .. 15.7 17.4 33.1 0.0 .. 18.0 10.4 15.2 17.7 2001 100.0 34.9 9.7 10.1 29.1 0.1 .. 13.1 8.0 9.4 9.8 Ecuador 1993 .. .. 9.4 6.4 19.8 0.0 .. 9.1 6.4 9.3 8.3 2002 99.8 21.7 12.5 10.5 22.8 0.0 .. 12.3 10.8 12.3 10.7 Egypt, Arab Rep. 1995 .. .. 23.3 17.1 53.5 0.6 .. 25.9 7.6 24.0 22.2 2002 98.9 37.2 18.4 13.4 44.6 10.0 0.1 18.2 6.6 19.0 16.4 El Salvador 1995 .. .. 10.4 8.8 28.0 0.0 .. 12.8 10.2 9.8 8.7 2001 100.0 36.6 7.5 6.1 12.1 0.0 6.9 10.6 8.0 6.9 6.0 Equatorial Guinea 1998 .. .. 19.4 13.3 56.7 0.2 .. 24.6 23.6 18.5 13.6 2002 .. .. 18.6 14.0 51.0 0.1 .. 24.1 23.0 17.9 13.1 Ethiopia c 1995 .. .. 35.2 16.9 75.8 0.2 .. 36.9 18.4 32.3 18.0 2001 .. .. 21.3 16.5 57.8 0.3 0.0 22.0 6.3 20.3 15.2 European Union 1988 b .. .. 2.6 3.0 1.7 12.4 .. 5.8 2.7 2.6 4.3 2002 b 100.0 4.1 3.1 2.4 1.3 4.7 1.5 3.4 1.5 2.9 2.9 Gabon 1995 .. .. 20.5 16.1 60.3 0.0 .. 24.4 20.2 19.9 15.2 2002 100.0 21.4 20.2 15.8 58.6 0.0 0.2 24.1 20.2 19.6 13.5 Ghana 1993 .. .. 15.3 10.5 45.1 0.0 .. 19.4 14.1 13.8 9.2 2000 14.3 92.4 15.2 9.5 47.3 0.0 0.1 21.4 28.2 14.0 8.9 Guatemala 1995 .. .. 10.0 8.4 25.8 0.0 .. 12.6 10.2 9.4 8.0 2001 34.6 36.6 7.8 5.8 14.3 0.0 0.0 9.8 7.9 7.2 5.8 Guinea-Bissau 2001 .. .. 14.1 16.1 55.1 0.0 .. 17.0 18.6 13.4 12.4 2002 97.7 48.7 13.6 13.9 51.8 0.0 .. 16.3 15.2 12.6 10.4 Guyana 1996 .. .. 22.6 20.6 47.8 43.6 .. 28.0 15.5 21.0 19.0 2001 100.0 56.7 12.2 10.6 40.2 0.4 .. 20.1 14.5 11.3 9.8 322 2004 World Development Indicators 6.6 GLOBAL LINK Tariff barriers S All products Primary products Manufactured products % Share of Share of Ad valorem % % Simple Simple Weighted lines with lines with equivalent Simple Weighted Simple Weighted Binding mean mean mean international specific of nontariff mean mean mean mean Year coverage bound rate tariff tariff peaks rates barriers a tariff tariff tariff tariff Honduras 1995 .. .. 9.8 10.3 25.6 0.0 .. 13.0 12.9 9.2 7.5 2001 100.0 32.5 7.5 7.3 11.9 0.7 0.0 10.7 11.4 7.1 6.2 Hungary 1991 b .. .. 12.0 9.6 15.0 0.0 .. 13.5 5.5 12.1 11.7 2002 96.3 9.7 8.3 7.5 4.8 0.0 1.0 18.0 7.2 7.7 8.0 India 1990 b .. .. 76.6 49.8 98.4 0.5 .. 69.8 25.4 79.9 70.8 2001 b 73.8 49.8 31.0 21.0 94.9 0.2 3.2 32.8 22.7 30.8 28.4 Indonesia 1989 b .. .. 18.7 12.0 48.5 0.1 .. 18.1 5.9 19.2 15.1 2001 b 96.6 37.5 6.0 3.9 1.9 0.0 0.5 6.0 2.4 6.2 5.2 Jamaica 1996 .. .. 21.7 21.8 46.0 45.7 .. 23.7 14.2 20.7 20.9 2001 100.0 49.8 9.1 7.8 38.9 0.2 .. 15.3 9.5 8.5 10.1 Japan 1988 b .. .. 4.0 3.4 8.6 8.4 .. 8.3 4.4 3.5 2.7 2002 b 99.6 2.9 2.9 2.2 6.9 1.4 1.6 5.2 2.5 2.4 1.7 Jordan 2000 .. .. 24.0 20.7 63.7 0.4 .. 28.0 17.0 23.3 19.8 2002 100.0 16.3 16.2 11.3 43.9 0.1 10.2 21.8 11.7 15.9 13.1 Kenya 1994 b .. .. 32.4 25.5 91.0 0.0 .. 32.4 17.0 31.9 23.3 2001 14.6 95.6 20.0 14.4 41.3 0.1 0.3 20.9 15.3 19.6 12.5 Korea, Rep. 1988 .. .. 18.7 14.7 76.8 10.6 .. 19.3 8.2 18.6 17.0 2002 94.5 16.1 7.9 5.7 3.1 0.7 0.0 12.0 6.1 7.4 4.7 Kyrgyz Republic 1995 .. .. 0.0 0.0 0.0 9.6 .. 0.0 0.0 0.0 0.0 2002 99.9 7.4 8.4 7.8 9.9 0.0 .. 8.2 6.3 8.2 7.1 Lao PDR 2000 .. .. 8.7 14.5 8.3 0.6 .. 15.6 14.7 8.6 12.6 2001 b .. .. 8.6 12.2 7.9 0.0 .. 15.9 17.3 8.8 11.9 Latvia 1996 .. .. 3.6 3.0 0.5 0.0 .. 6.5 1.5 3.3 2.6 2001 100.0 12.7 3.4 2.5 0.7 0.0 0.4 8.2 5.4 2.7 1.5 Lebanon 1999 .. .. 15.3 13.1 31.2 0.1 .. 13.1 11.2 14.4 12.7 2002 .. .. 6.4 8.0 9.8 0.0 3.7 13.7 10.2 5.9 6.6 Libya 1996 .. .. 21.8 17.0 57.9 0.2 .. 24.9 9.6 22.5 25.6 2002 .. .. 18.8 15.9 45.7 0.7 .. 18.1 15.7 19.9 29.0 Lithuania 1995 b .. .. 3.0 2.1 4.3 0.0 .. 6.2 3.7 2.6 1.8 2002 b 100.0 9.3 0.7 0.5 1.3 0.0 0.4 3.5 1.5 0.5 0.3 Madagascar 1995 .. .. 7.9 4.8 8.0 0.0 .. 6.3 2.9 7.6 6.3 2001 29.7 27.4 5.5 2.9 6.5 0.0 0.6 6.1 1.4 5.4 4.3 Malawi 1994 .. .. 32.7 29.9 89.3 0.0 .. 29.1 12.9 31.9 26.6 2001 b 26.1 82.7 13.8 12.5 43.0 0.0 .. 12.8 11.1 12.8 11.7 Malaysia 1988 b .. .. 14.4 11.5 50.7 5.5 .. 10.8 4.6 14.9 10.8 2001 b 83.7 14.5 7.5 4.6 19.5 0.4 1.7 4.4 2.4 8.1 4.7 Mali 1995 .. .. 16.5 9.5 43.1 0.0 .. 19.5 13.4 16.0 8.5 2002 40.1 28.8 12.9 11.4 46.7 0.0 .. 15.1 12.1 12.6 9.9 Mauritius 1995 b .. .. 35.7 22.5 63.7 0.0 .. 26.0 25.7 37.2 22.9 2002 14.9 114.8 25.1 15.8 40.2 0.1 0.0 20.1 9.0 25.8 14.4 Mexico 1991 .. .. 14.7 12.7 20.9 0.0 .. 13.4 8.3 14.6 13.0 2002 b 99.9 34.9 16.2 4.9 43.7 0.4 1.8 14.5 7.0 15.8 4.7 Moldova 1996 .. .. 6.4 3.3 19.7 0.0 .. 11.3 1.5 4.7 2.3 2001 .. .. 5.3 3.9 0.0 0.2 .. 8.9 2.6 4.5 2.9 Morocco 1993 .. .. 64.6 47.0 97.9 0.0 .. 55.0 30.2 65.0 55.2 2002 100.0 41.2 27.7 28.2 76.9 0.0 0.5 35.7 27.7 28.0 26.2 Mozambique 1994 .. .. 5.0 5.0 0.0 0.0 .. 5.0 5.0 5.0 5.0 2002 b 100.0 99.6 12.3 9.4 36.4 0.0 .. 14.8 11.0 11.6 8.7 Nepal 1993 .. .. 21.8 18.1 60.8 0.1 .. 11.8 9.3 22.9 21.0 2002 .. .. 13.1 14.3 14.8 0.1 .. 16.0 8.3 13.8 17.8 New Zealand 1992 .. .. 10.5 8.5 37.5 1.5 .. 5.5 4.0 10.7 9.4 2002 b 99.9 10.3 4.3 2.8 9.8 7.1 2.2 1.7 0.5 4.2 3.6 Nicaragua 1995 b .. .. 7.9 4.0 20.5 0.0 .. 7.7 7.1 7.4 4.6 2002 b 100.0 41.7 4.4 2.3 0.0 0.0 .. 6.2 3.6 4.1 2.5 Niger 2001 .. .. 14.6 14.1 57.5 0.0 .. 15.1 12.9 14.2 12.7 2002 96.8 44.3 14.5 14.1 57.2 0.0 .. 15.1 12.9 14.2 12.7 Nigeria 1988 .. .. 25.5 20.0 63.8 0.1 .. 33.3 32.4 25.3 21.4 2002 19.0 118.8 26.6 15.8 56.3 0.7 0.4 40.1 20.6 24.9 15.5 Norway 1988 b .. .. 2.1 1.0 5.8 6.4 .. 0.6 0.2 2.1 0.8 2002 b 100.0 3.0 0.8 0.7 1.9 7.0 0.3 2.4 2.1 0.6 0.2 Oman 1992 .. .. 5.2 5.1 0.8 0.0 .. 7.2 14.2 5.1 5.4 2002 100.0 13.8 7.7 6.7 0.2 2.3 0.9 9.5 31.6 7.6 6.5 Pakistan 1995 .. .. 50.1 45.5 93.9 3.5 .. 43.4 24.0 51.1 50.8 2002 38.0 60.5 16.9 15.2 54.9 0.0 .. 17.9 11.2 17.5 19.1 Panama 1997 .. .. 12.1 9.7 35.3 0.2 .. 17.8 9.6 11.8 11.0 2001 100.0 23.5 7.9 5.7 0.1 0.2 .. 11.4 5.9 7.7 7.4 Papua New Guinea 1997 .. .. 19.1 13.4 32.2 0.4 .. 33.2 21.8 18.5 13.7 2002 100.0 31.7 6.3 2.7 21.9 0.3 .. 17.5 6.7 5.8 3.2 Paraguay 1991 .. .. 16.1 13.9 44.0 0.0 .. 14.1 3.6 15.7 14.5 2001 100.0 33.5 13.9 12.5 33.2 0.0 1.7 12.8 8.2 13.6 11.9 Peru 1993 .. .. 18.2 16.8 25.6 0.0 .. 18.3 15.8 18.0 16.6 2000 100.0 30.1 13.4 12.6 14.6 0.0 1.7 15.6 13.9 13.0 12.3 2004 World Development Indicators 323 6.6 Tariff barriers All products Primary products Manufactured products % Share of Share of Ad valorem % % Simple Simple Weighted lines with lines with equivalent Simple Weighted Simple Weighted Binding mean mean mean international specific of nontariff mean mean mean mean Year coverage bound rate tariff tariff peaks rates barriers a tariff tariff tariff tariff Philippines 1988 .. .. 27.7 21.1 74.7 0.1 .. 29.9 18.5 27.9 23.4 2002 66.8 25.6 4.8 2.8 0.3 0.0 0.4 6.7 5.4 5.0 2.6 Poland 1991 .. .. 11.8 9.5 24.0 0.0 .. 11.9 8.2 12.2 11.2 2003 b .. .. 4.0 2.0 7.1 4.7 1.2 12.4 4.0 3.1 1.4 Romania 1991 .. .. 19.2 10.7 54.8 0.0 .. 20.0 8.1 18.9 17.9 2001 b 100.0 40.4 11.3 7.3 26.2 0.0 2.5 18.0 11.2 10.6 7.2 Russian Federation 1993 .. .. 8.9 7.2 2.3 0.0 .. 3.1 3.9 9.5 7.4 2002 .. .. 9.9 8.4 10.0 16.8 .. 9.7 8.3 10.5 8.9 Rwanda 1993 .. .. 39.6 29.6 65.9 1.1 .. 60.7 24.9 37.4 25.5 2001 b 100.0 89.3 10.0 6.6 12.1 0.0 1.4 13.2 6.8 9.5 5.9 Saudi Arabia 1994 .. .. 12.3 11.1 9.6 0.1 .. 12.0 9.1 12.4 11.5 2000 .. .. 12.3 11.4 8.6 3.5 0.9 11.7 7.9 12.2 11.4 Senegal 2001 .. .. 14.0 8.6 53.6 0.0 .. 14.5 8.3 13.8 10.4 2002 100.0 30.0 13.9 8.4 52.9 0.0 0.0 14.4 8.2 13.7 9.9 Singapore 1989 .. .. 0.4 0.2 0.0 0.3 .. 0.2 2.5 0.4 0.6 2002 69.2 6.9 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 Slovenia 1999 .. .. 11.9 10.9 23.7 2.5 .. 9.5 7.5 11.7 12.1 2001 100.0 24.3 11.6 10.2 26.3 0.0 0.6 9.8 7.5 11.2 10.5 South Africa d 1988 .. .. 11.4 7.7 31.9 19.9 .. 4.8 3.6 11.8 12.3 2001 b 98.0 17.8 9.8 3.6 38.0 1.6 0.5 7.5 2.0 9.5 5.8 Sri Lanka 1990 .. .. 27.1 31.5 53.6 0.8 .. 32.4 32.3 26.6 24.2 2001 b 22.6 42.7 8.4 4.2 18.1 0.4 0.0 13.9 11.3 8.7 5.0 Switzerland e 1990 .. .. .. .. .. 51.7 .. .. .. .. .. 2001 99.8 1.7 1.9 0.8 .. 39.2 1.2 15.0 9.5 1.1 0.2 Taiwan, China 1989 .. .. 11.1 9.7 13.7 0.6 .. 14.5 8.6 10.8 10.5 2002 .. .. 6.9 3.3 7.5 2.1 .. 9.7 4.1 6.4 3.0 Tanzania 1993 .. .. 16.7 19.0 45.6 0.0 .. 22.7 19.9 15.3 15.0 2000 13.3 120.0 19.1 15.4 74.7 0.0 0.0 19.9 13.2 18.4 13.0 Thailand 1989 .. .. 38.7 31.7 76.5 18.7 .. 30.0 24.3 39.0 34.9 2001 b 74.7 25.7 14.7 8.7 49.4 0.4 0.3 16.2 4.7 14.6 9.7 Togo 2001 .. .. 14.5 11.5 58.7 0.0 .. 14.7 10.5 14.2 11.2 2002 13.7 80.0 14.5 11.5 58.4 0.0 .. 14.7 10.5 14.1 11.2 Trinidad and Tobago 1991 .. .. 18.5 11.2 40.6 0.0 .. 24.8 10.9 17.8 14.1 2002 100.0 55.7 9.6 2.9 38.1 0.0 0.2 15.5 5.8 9.2 4.7 Tunisia 1990 .. .. 28.6 29.9 98.1 0.0 .. 25.1 17.4 28.3 28.5 2002 57.4 57.7 30.2 27.4 86.4 0.0 0.8 44.7 26.7 28.7 25.5 Turkey 1993 b .. .. 7.5 5.7 6.1 0.0 .. 6.3 7.9 7.4 5.3 1999 49.5 28.4 7.1 4.5 6.4 0.4 0.2 16.6 5.5 6.2 5.3 Turkmenistan 1998 .. .. 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 2002 .. .. 5.5 1.7 12.2 3.3 .. 16.0 13.2 3.7 1.1 Uganda 1994 b .. .. 17.5 15.0 57.3 0.0 .. 19.4 17.4 16.8 12.3 2002 b 15.7 73.3 8.0 6.8 0.0 0.0 0.1 10.0 8.8 7.7 6.1 Ukraine 1995 .. .. 8.0 4.3 11.5 0.0 .. 8.9 2.7 7.3 4.3 2002 .. .. 7.9 4.4 10.5 6.9 0.0 7.1 1.5 7.9 6.4 United States 1989 b .. .. 5.9 5.2 9.6 13.2 .. 2.5 2.0 5.5 4.1 2002 b 100.0 3.6 4.1 2.6 7.0 7.5 1.6 2.7 1.1 3.8 2.0 Uruguay 1992 .. .. 6.7 6.7 0.0 0.0 .. 7.9 5.8 7.0 5.8 2001 b 100.0 31.7 13.3 6.5 38.9 0.0 1.9 8.9 2.8 13.4 8.1 Venezuela, RB 1992 .. .. 17.4 12.9 48.2 0.4 .. 16.3 14.7 17.1 16.5 2000 .. .. 13.5 11.3 25.1 0.0 1.4 13.5 13.6 13.4 13.3 Vietnam 1994 .. .. 14.1 13.0 36.7 0.8 .. 20.9 46.7 13.9 13.1 2001 .. .. 15.0 17.4 37.9 0.0 .. 19.6 20.7 14.7 16.3 Zambia 1993 .. .. 26.2 18.1 94.1 0.0 .. 30.0 12.4 25.2 20.0 2002 17.1 106.4 13.9 8.4 36.5 2.3 0.2 17.3 12.6 13.3 8.3 Zimbabwe 1996 b .. .. 41.2 37.3 95.9 0.4 .. 34.2 40.4 41.3 38.8 2001 21.4 94.3 20.4 12.0 45.7 1.4 .. 20.7 7.0 20.6 14.2 a. Ad valorem equivalents of nontariff barriers are calculated for the year 2000 only. b. Rates are either partially or fully recorded applied rates. All other simple and weighted tariff rates are most favored nation rates. c. Excludes Eritrea. d. Data refer to South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). e. Data for Switzerland include all specific rates converted to their ad valorem equivalents. 324 2004 World Development Indicators 6.6 GLOBAL LINK Tariff barriers S About the data Poor people in developing countries work primarily in eign prices. They include administrative price fixing, vol- Two other measures of tariff coverage are shown: agriculture and labor-intensive manufactures, sectors untary export price restraints, variable charges, the share of tariff lines with international peaks (those that confront the greatest trade barriers. Removing bar- antidumping measures, and countervailing measures. for which ad valorem tariff rates exceed 15 percent) riers to merchandise trade could increase growth by Countries typically maintain a hierarchy of trade pref- and the share of tariff lines with specific duties (those about 0.5 percent a year in these countries--even more erences applicable to specific trading partners. The tar- not covered by ad valorem rates). Some countries--for if trade in services (retailing, business, financial, and iff rates used in calculating the indicators in the table example, Switzerland--apply only specific duties. telecommunications services) were also liberalized. are most favored nation rates unless they are specified The indicators were calculated from data supplied by In general, tariffs in high-income countries on imports as applied rates. Effectively applied rates are those in the United Nations Conference on Trade and Develop- from developing countries, though low, are four times effect for partners in preferential trade agreements ment (UNCTAD) and the World Trade Organization (WTO). those collected from other high-income countries. But such as the North American Free Trade Agreement. The Data are classified using the Harmonized System of protection is also an issue for developing countries, difference between most favored nation and applied trade at the six- or eight-digit level. Tariff line data were which maintain high tariffs on agricultural commodities, rates can be substantial. For example, the weighted matched to Standard International Trade Classification labor-intensive manufactures, and other products and average of Slovenia's 2001 most favored nation rates (SITC) revision 2 codes to define commodity groups and services. In some developing regions new trade policies is 10.2 percent, while the effectively applied rate in import weights. Import weights were calculated using could make the difference between achieving important 2000 averaged less than 2 percent. As more countries the United Nations Statistics Division's Commodity Millennium Development Goals--reducing poverty, low- report their free trade agreements, suspensions of tar- Trade (COMTRADE) database. Data are shown only for ering maternal and child mortality rates, improving edu- iffs, or other special preferences, World Development the first and last year for which complete data are avail- cational attainment--and falling far short. Indicators will include their effectively applied rates. able. To conserve space, data for the European Union Countries use a combination of tariff and nontariff Three measures of average tariffs are shown: the are shown instead of data for individual members. measures to regulate imports. The most common form simple and the weighted mean rates and simple Definitions of tariff is an ad valorem duty, based on the value of bound rates. The most favored nation or applied rates the import, but tariffs may also be levied on a specific, are calculated using all traded items, while bound · Primary products are commodities classified in SITC or per unit, basis or may combine ad valorem and spe- rates are based on all products in a country's tariff revision 2 sections 0­4 plus division 68 (nonferrous cific rates. Tariffs may be used to raise fiscal revenues schedule. Weighted mean tariffs are weighted by the metals). · Manufactured products are commodities or to protect domestic industries from foreign competi- value of the country's trade with each trading partner. classified in SITC revision 2 sections 5­8 excluding tion--or both. Nontariff barriers, which limit the quanti- Simple averages are often a better indicator of tariff division 68. · Binding coverage is the percentage of ty of imports of a particular good, include quotas, protection than weighted averages, which are biased product lines with an agreed bound rate. · Simple prohibitions, licensing schemes, export restraint downward because higher tariffs discourage trade and mean bound rate is the unweighted average of all the arrangements, and health and quarantine measures. reduce the weights applied to these tariffs. Bound lines in the tariff schedule in which bound rates have Nontariff barriers are generally considered less rates have resulted from trade negotiations that are been set. · Simple mean tariff is the unweighted desirable than tariffs because changes in an exporting incorporated into a country's schedule of concessions average of effectively applied rates or most favored country's efficiency and costs no longer result in and are thus enforceable. If a contracting party raises nation rates for all products subject to tariffs calculat- changes in market share in the importing country. a tariff to a higher level than its bound rate, benefici- ed for all traded goods. · Weighted mean tariff is the Further, the quotas or licenses that regulate trade aries of the earlier binding have a right to receive com- average of effectively applied rates or most favored become very valuable, and resources are often wast- pensation, usually as reduced tariffs on other nation rates weighted by the product import shares ed in attempts to acquire these assets. A high per- products they export to the country. If the beneficiar- corresponding to each partner country. · Share of centage of products subject to nontariff barriers ies are not compensated, they may retaliate by raising lines with international peaks is the share of lines in suggests a protectionist trade regime, but the fre- their own tariffs against an equivalent value of the orig- the tariff schedule with tariff rates that exceed 15 per- quency of nontariff barriers does not measure how inal country's exports. Specific duties (not expressed cent. · Share of lines with specific rates is the share much they restrict trade. Moreover, a wide range of as a proportion of declared value) are not included in of lines in the tariff schedule that are set on a per unit domestic policies and regulations (such as health reg- the table, except for Switzerland. Work is under way to basis or that combine ad valorem and per unit rates. ulations) may act as nontariff barriers. complete the estimations for ad valorem equivalents · Ad valorem equivalent of nontariff barriers are the Estimates of ad valorem equivalents of nontariff bar- of specific duties for all countries. simple average of core nontariff barriers transformed riers are given at the six-digit level of the Harmonized Some countries set fairly uniform tariff rates across into a price effect using import demand elasticities; System, which provides the simple averages of core all imports. Others are more selective, setting high tar- they are calculated for traded products only. nontariff barriers, including quantity control measures iffs to protect favored domestic industries. The stan- such as nonautomatic licensing, quotas, prohibitions, dard deviation of tariffs is a measure of the dispersion and export restraint arrangements but excluding tariff- of tariff rates around their mean value. Highly dis- Data sources quotas and enterprise-specific restrictions; financial persed rates increase the costs of protection substan- All indicators in the table were calculated by World measures, which include advance payment require- tially. But these nominal tariff rates tell only part of the Bank staff using the World Integrated Trade ments, multiple exchange rates, and restrictive official story. The effective rate of protection--the degree to Solution (WITS) system. Tariff data were provided foreign exchange allocation and exclude regulations on which the value added in an industry is protected--may by UNCTAD and the WTO. Data on global imports terms of payment, transfer delays, and queuing; and exceed the nominal rate if the tariff system systemati- come from the United Nations Statistics price control measures, which affect the cost of imports cally differentiates among imports of raw materials, Division's COMTRADE database. based on differences between domestic prices and for- intermediate products, and finished goods. 2004 World Development Indicators 325 6.7 Global private financial flows Net private Foreign direct Portfolio investment flows Bank and capital flows investment trade-related lending $ millions $ millions $ millions Bonds Equity $ millions 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania .. 136 .. 135 .. 0 .. 0 .. 1 Algeria ­424 1,023 0 1,065 ­16 0 0 0 ­409 ­42 Angola 235 1,420 ­335 1,312 0 0 0 0 570 108 Argentina ­216 681 1,836 785 ­857 86 0 ­99 ­1,195 ­91 Armenia .. 108 .. 111 .. 0 .. 1 .. ­4 Australia .. .. 8,111 16,622 .. .. .. .. .. .. Austria .. .. 653 886 .. .. .. .. .. .. Azerbaijan .. 1,313 .. 1,392 .. 0 .. 0 .. ­79 Bangladesh 59 132 3 47 0 0 0 0 55 85 Belarus .. 227 .. 247 .. 0 .. 0 .. ­21 Belgium .. .. 8,047 .. .. .. .. .. .. .. Benin 62 41 62 41 0 0 0 0 0 0 Bolivia 3 601 27 677 0 0 0 0 ­24 ­76 Bosnia and Herzegovina .. 299 0 293 .. 0 .. 0 .. 6 Botswana 77 35 96 37 0 0 0 0 ­19 ­2 Brazil 666 9,861 989 16,566 129 1,519 103 1,981 ­555 ­10,205 Bulgaria .. 808 .. 600 .. ­79 .. ­23 .. 310 Burkina Faso 0 8 1 8 0 0 0 0 ­1 0 Burundi ­5 ­2 1 0 0 0 0 0 ­6 ­2 Cambodia 0 54 0 54 0 0 0 0 0 0 Cameroon ­124 38 ­113 86 0 0 0 0 ­12 ­49 Canada .. .. 7,581 20,501 .. .. .. .. .. .. Central African Republic 0 4 1 4 0 0 0 0 ­1 0 Chad 9 900 9 901 0 0 0 0 ­1 ­1 Chile 2,216 2,781 661 1,713 ­7 1,614 367 ­317 1,194 ­230 China 8,107 47,107 3,487 49,308 ­48 ­1,289 0 2,249 4,668 ­3,161 Hong Kong, China .. .. .. 12,794 .. .. .. .. .. .. Colombia 345 947 500 2,023 ­4 68 0 17 ­151 ­1,161 Congo, Dem. Rep. ­27 32 ­15 32 0 0 0 0 ­12 0 Congo, Rep. ­100 331 0 331 0 0 0 0 ­100 0 Costa Rica 22 602 163 662 ­42 ­44 0 0 ­99 ­16 Côte d'Ivoire 57 117 48 230 ­1 0 0 1 10 ­114 Croatia .. 3,604 .. 980 .. ­27 .. 78 .. 2,573 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic .. 10,382 .. 9,323 .. 180 .. ­265 .. 1,143 Denmark .. .. 1,132 6,410 .. .. .. .. .. .. Dominican Republic 129 1,351 133 961 0 ­20 0 0 ­3 410 Ecuador 184 2,103 126 1,275 0 0 0 1 58 826 Egypt, Arab Rep. 668 437 734 647 ­1 0 0 ­212 ­65 3 El Salvador 7 1,419 2 208 0 1,252 0 0 6 ­40 Eritrea .. 21 .. 21 .. 0 .. 0 .. 0 Estonia .. 1,586 .. 285 .. 219 .. 0 .. 1,083 Ethiopia ­45 71 12 75 0 0 0 0 ­57 ­4 Finland .. .. 812 8,156 .. .. .. .. .. .. France .. .. 13,183 52,020 .. .. .. .. .. .. Gabon 103 139 74 123 0 0 0 0 29 16 Gambia, The ­8 42 0 43 0 0 0 0 ­8 0 Georgia .. 149 .. 165 .. 0 .. 0 .. ­17 Germany .. .. 3,005 37,296 .. .. .. .. .. .. Ghana ­5 27 15 50 0 0 0 0 ­20 ­23 Greece .. .. 1,005 53 .. .. .. .. .. .. Guatemala 44 61 48 110 ­11 ­31 0 0 7 ­19 Guinea ­1 0 18 0 0 0 0 0 ­19 0 Guinea-Bissau 2 1 2 1 0 0 0 0 0 0 Haiti 0 6 0 6 0 0 0 0 0 0 326 2004 World Development Indicators 6.7 GLOBAL LINK Global private financial flows S Net private Foreign direct Portfolio investment flows Bank and capital flows investment trade-related lending $ millions $ millions $ millions Bonds Equity $ millions 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 75 100 44 143 0 0 0 0 32 ­43 Hungary ­147 221 311 854 921 ­742 0 ­137 ­1,379 247 India 1,842 4,944 237 3,030 147 ­272 0 967 1,458 1,219 Indonesia 2,923 ­6,966 1,093 ­1,513 26 ­406 0 877 1,804 ­5,924 Iran, Islamic Rep. ­392 816 ­362 37 0 0 0 0 ­30 779 Iraq .. .. .. .. .. .. .. .. .. .. Ireland .. .. 627 24,697 .. .. .. .. .. .. Israel .. .. 151 1,649 .. .. .. .. .. .. Italy .. .. 6,411 14,699 .. .. .. .. .. .. Jamaica 92 540 138 481 0 70 0 0 ­46 ­11 Japan .. .. 1,777 9,087 .. .. .. .. .. .. Jordan 252 ­31 38 56 0 ­11 0 ­52 214 ­24 Kazakhstan .. 4,431 .. 2,583 .. ­50 .. 39 .. 1,859 Kenya 122 39 57 50 0 0 0 0 65 ­12 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. 788 1,972 .. .. .. .. .. .. Kuwait .. .. 0 7 .. .. .. .. .. .. Kyrgyz Republic .. ­54 .. 5 .. 0 .. 0 .. ­59 Lao PDR 6 25 6 25 0 0 0 0 0 0 Latvia .. 496 .. 382 .. 0 .. 22 .. 91 Lebanon 13 4,803 7 257 0 4,626 0 4 6 ­84 Lesotho 17 73 17 81 0 0 0 0 0 ­8 Liberia 0 ­65 0 ­65 0 0 0 0 0 0 Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. 760 .. 712 .. ­200 .. 6 .. 242 Macedonia, FYR .. 113 .. 77 .. 0 .. 0 .. 35 Madagascar 7 8 22 8 0 0 0 0 ­15 0 Malawi 26 6 23 6 0 0 1 0 2 0 Malaysia 476 4,807 2,332 3,203 ­1,239 1,962 0 ­250 ­617 ­110 Mali 5 102 6 102 0 0 0 0 ­1 0 Mauritania 5 16 7 12 0 0 0 0 ­1 4 Mauritius 86 ­43 41 28 0 0 0 0 45 ­71 Mexico 9,600 10,261 2,549 14,622 661 ­3,899 1,995 ­104 4,396 ­359 Moldova .. 77 .. 111 .. ­43 .. 2 .. 8 Mongolia .. 78 .. 78 .. 0 .. 0 .. 0 Morocco 483 15 165 428 0 ­31 0 ­14 318 ­369 Mozambique 35 381 9 406 0 0 0 0 26 ­25 Myanmar 155 69 163 129 0 0 0 0 ­8 ­60 Namibia .. .. .. .. .. .. .. .. .. .. Nepal ­14 9 0 10 0 0 0 0 ­14 0 Netherlands .. .. 10,676 28,534 .. .. .. .. .. .. New Zealand .. .. 1,735 823 .. .. .. .. .. .. Nicaragua 20 206 0 174 0 0 0 0 20 32 Niger 51 0 41 8 0 0 0 0 10 ­8 Nigeria 467 639 588 1,281 0 ­452 0 0 ­121 ­190 Norway .. .. 1,003 1,008 .. .. .. .. .. .. Oman ­257 ­1,131 142 40 0 ­225 0 ­13 ­400 ­933 Pakistan 182 379 245 823 0 ­178 0 79 ­63 ­345 Panama 129 180 136 57 ­2 13 ­1 0 ­4 110 Papua New Guinea 204 ­46 155 50 0 0 0 0 49 ­96 Paraguay 68 34 77 ­22 0 0 0 0 ­9 56 Peru 59 3,131 41 2,391 0 720 0 ­9 18 30 Philippines 639 3,549 530 1,111 395 1,540 0 410 ­286 488 Poland 71 5,075 89 4,131 0 1,307 0 ­830 ­18 468 Portugal .. .. 2,610 4,235 .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. 2004 World Development Indicators 327 6.7 Global private financial flows Net private Foreign direct Portfolio investment flows Bank and capital flows investment trade-related lending $ millions $ millions $ millions Bonds Equity $ millions 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 4 3,173 0 1,144 0 ­28 0 21 4 2,037 Russian Federation .. 8,011 .. 3,009 .. 2,745 .. 2,626 .. ­370 Rwanda 6 3 8 3 0 0 0 0 ­2 0 Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegal 43 94 57 93 0 0 1 0 ­15 1 Serbia and Montenegro .. 507 .. 475 .. 0 .. 0 .. 32 Sierra Leone 36 5 32 5 0 0 0 0 4 0 Singapore .. .. 5,575 6,097 .. .. .. .. .. .. Slovak Republic .. 5,460 .. 4,012 .. ­189 .. 0 .. 1,637 Slovenia .. .. .. 1,865 .. .. .. .. .. .. Somalia 6 0 6 0 0 0 0 0 0 0 South Africa .. 783 .. 739 .. 3,187 .. ­388 .. ­2,754 Spain .. .. 13,984 21,284 .. .. .. .. .. .. Sri Lanka 54 206 43 242 0 0 0 0 10 ­36 Sudan 0 633 0 633 0 0 0 0 0 0 Swaziland 26 45 30 45 0 0 ­2 0 ­2 0 Sweden .. .. 1,982 11,828 .. .. .. .. .. .. Switzerland .. .. 5,987 3,599 .. .. .. .. .. .. Syrian Arab Republic 63 224 72 225 0 0 0 0 ­9 ­1 Tajikistan .. ­10 .. 9 .. 0 .. 2 .. ­20 Tanzania 5 214 0 240 0 0 0 0 5 ­26 Thailand 4,371 ­1,992 2,444 900 ­87 ­1,010 440 207 1,574 ­2,089 Togo 23 75 18 75 0 0 4 0 0 0 Trinidad and Tobago ­68 736 109 736 ­52 0 0 0 ­126 0 Tunisia ­116 1,625 76 795 ­60 650 5 6 ­137 174 Turkey 1,836 7,582 684 1,037 597 956 89 ­16 466 5,605 Turkmenistan .. .. .. 100 .. .. .. 0 .. .. Uganda 16 149 0 150 0 0 0 0 16 ­1 Ukraine .. ­576 .. 693 .. 101 .. ­1,958 .. 588 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. 33,504 28,180 .. .. .. .. .. .. United States .. .. 48,490 39,633 .. .. .. .. .. .. Uruguay ­192 107 0 177 ­16 77 0 ­39 ­176 ­108 Uzbekistan .. ­11 .. 65 .. 0 .. 0 .. ­76 Venezuela, RB ­126 ­1,639 451 690 345 ­1,066 0 75 ­922 ­1,337 Vietnam 180 759 180 1,400 0 0 0 0 0 ­641 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 30 114 ­131 114 0 0 0 0 161 0 Zambia 194 186 203 197 0 0 0 0 ­9 ­12 Zimbabwe 85 ­3 ­12 26 ­30 0 0 0 127 ­29 World .. s .. s 202,476 s 630,827 s .. s .. s .. s .. s .. s .. s Low income 6,820 7,151 2,764 12,941 142 ­1,351 6 1,927 3,908 ­6,365 Middle income 36,872 146,679 21,269 134,145 933 14,090 2,997 3,018 11,673 ­4,574 Lower middle income 21,964 98,852 10,180 91,104 1,270 10,259 636 4,887 9,878 ­7,397 Upper middle income 14,908 47,828 11,089 43,041 ­336 3,832 2,361 ­1,869 1,795 2,824 Low & middle income 43,692 153,831 24,032 147,086 1,076 12,739 3,004 4,945 15,581 ­10,939 East Asia & Pacific 17,179 47,524 10,512 54,834 ­952 798 439 3,493 7,180 ­11,601 Europe & Central Asia 7,490 53,739 1,227 32,931 1,893 4,149 89 ­433 4,281 17,092 Latin America & Carib. 13,199 34,544 8,181 44,682 145 498 2,464 1,507 2,408 ­12,143 Middle East & N. Africa 2,266 5,359 2,604 2,653 ­126 5,010 5 ­281 ­217 ­2,023 South Asia 2,129 5,697 536 4,164 147 ­450 1 1,046 1,446 938 Sub-Saharan Africa 1,429 6,968 972 7,822 ­31 2,735 6 ­387 482 ­3,202 High income .. .. 178,443 483,741 .. .. .. .. .. .. Europe EMU .. .. 60,540 320,893 .. .. .. .. .. .. 328 2004 World Development Indicators 6.7 GLOBAL LINK Global private financial flows S About the data Definitions The data on foreign direct investment are based on transactions reported by market sources. · Net private capital flows consist of private debt balance of payments data reported by the Transactions of public and publicly guaranteed bonds and nondebt flows. Private debt flows include com- International Monetary Fund (IMF), supplemented by are reported through the Debtor Reporting System by mercial bank lending, bonds, and other private cred- data on net foreign direct investment reported by the World Bank member economies that have received its, as well as foreign direct investment and portfolio Organisation for Economic Co-operation and either loans from the International Bank for equity investment. · Foreign direct investment is net Development (OECD) and official national sources. Reconstruction and Development or credits from the inflows of investment to acquire a lasting manage- The internationally accepted definition of foreign International Development Association. Information on ment interest (10 percent or more of voting stock) in direct investment is provided in the fifth edition of the private nonguaranteed bonds is collected from market an enterprise operating in an economy other than that IMF's Balance of Payments Manual (1993). Under sources, because official national sources reporting of the investor. It is the sum of equity capital, rein- this definition foreign direct investment has three to the Debtor Reporting System are not asked to vestment of earnings, other long-term capital, and components: equity investment, reinvested earnings, report the breakdown between private nonguaranteed short-term capital, as shown in the balance of pay- and short- and long-term intercompany loans between bonds and private nonguaranteed loans. Information ments. · Portfolio investment flows are net and parent firms and foreign affiliates. But many coun- on transactions by nonresidents in local equity mar- include non-debt-creating portfolio equity flows (the tries fail to report reinvested earnings, and the defi- kets is gathered from national authorities, investment sum of country funds, depository receipts, and direct nition of long-term loans differs among countries. positions of mutual funds, and market sources. purchases of shares by foreign investors) and portfo- Foreign direct investment, as distinguished from The volume of portfolio investment reported by the lio debt flows (bond issues purchased by foreign other kinds of international investment, is made to World Bank generally differs from that reported by investors). · Bank and trade-related lending covers establish a lasting interest in or effective manage- other sources because of differences in the sources, commercial bank lending and other private credits. ment control over an enterprise in another country. As in the classification of economies, and in the method a guideline, the IMF suggests that investments used to adjust and disaggregate reported informa- should account for at least 10 percent of voting stock tion. Differences in reporting arise particularly for to be counted as foreign direct investment. In prac- foreign investments in local equity markets because tice, many countries set a higher threshold. clarity, adequate disaggregation, and comprehensive The OECD has also published a definition, in con- and periodic reporting are lacking in many developing sultation with the IMF, Eurostat, and the United economies. By contrast, capital flows through inter- Nations. Because of the multiplicity of sources and national debt and equity instruments are well record- differences in definitions and reporting methods, ed, and for these the differences in reporting lie there may be more than one estimate of foreign primarily in the classification of economies, the direct investment for a country and data may not be exchange rates used, whether particular install- comparable across countries. ments of the transactions are included, and the Foreign direct investment data do not give a com- treatment of certain offshore issuances. plete picture of international investment in an econ- omy. Balance of payments data on foreign direct investment do not include capital raised locally, which has become an important source of financing for investment projects in some developing coun- tries. In addition, foreign direct investment data cap- ture only cross-border investment flows involving equity participation and thus omit nonequity cross- border transactions such as intrafirm flows of goods and services. For a detailed discussion of the data issues, see the World Bank's World Debt Tables 1993­94 (volume 1, chapter 3). Portfolio flow data are compiled from several market Data sources and official sources, including Euromoney databases The data are compiled from a variety of public and and publications; Micropal; Lipper Analytical Services; private sources, including the World Bank's published reports of private investment houses, cen- Debtor Reporting System, the IMF's International tral banks, national securities and exchange commis- Financial Statistics and Balance of Payments sions, and national stock exchanges; and the World databases, and other sources mentioned in About Bank's Debtor Reporting System. the data. These data are also published in the Gross statistics on international bond and equity World Bank's Global Development Finance 2004. issues are produced by aggregating individual 2004 World Development Indicators 329 6.8 Net financial flows from Development Assistance Committee members Net flows to part I countries Official development assistance Other Private flows Net grants Total official by NGOs net flows flows Contributions Foreign Bilateral Multilateral Private Bilateral Bilateral to multilateral direct portfolio portfolio export Total grants loans institutions Total investment investment investment credits 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 $ millions Australia 989 774 .. 215 31 ­433 ­103 ­331 .. .. 248 834 Austria 520 367 ­2 156 ­36 1,325 1,029 .. .. 296 57 1,866 Belgium 1,072 736 ­25 360 106 86 555 .. .. ­469 74 1,337 Canada 2,006 1,527 ­24 503 ­424 188 829 ­604 .. ­37 276 2,046 Denmark 1,643 1,019 19 605 ­3 ­63 ­63 .. .. .. .. 1,577 Finland 462 248 4 211 3 ­676 ­5 ­720 .. 48 10 ­200 France 5,486 3,874 ­259 1,871 635 ­1,392 2,915 ­2,859 .. ­1,448 .. 4,729 Germany 5,324 3,904 ­576 1,997 3,710 ­1,124 1,760 ­2,496 ­676 287 823 8,733 Greece 276 107 .. 169 .. 40 40 .. .. .. 6 322 Ireland 398 267 .. 131 .. 986 .. 986 .. .. 86 1,469 Italy 2,332 1,083 ­77 1,326 ­370 ­563 639 ­3,250 .. 2,048 .. 1,399 Japan 9,283 4,373 2,320 2,591 ­4,208 ­573 6,362 ­3,077 ­2,804 ­1,054 157 4,659 Luxembourg 147 116 .. 31 .. .. .. .. .. .. 2 148 Netherlands 3,338 2,585 ­136 889 229 ­5,310 281 ­7,395 946 859 257 ­1,487 New Zealand 122 92 .. 30 2 17 17 .. .. .. 23 164 Norway 1,696 1,143 2 551 .. 131 23 .. .. 109 452 2,279 Portugal 323 183 3 137 ­1 ­150 ­360 .. .. 210 .. 171 Spain 1,712 769 229 714 54 6,404 6,540 .. .. ­136 .. 8,171 Sweden 1,991 1,242 8 741 2 199 296 .. .. ­97 19 2,211 Switzerland 939 750 15 174 3 1,089 1,222 .. .. ­133 202 2,234 United Kingdom 4,924 3,384 121 1,419 ­4 13,547 13,940 840 .. ­1,233 353 18,820 United States 13,290 11,251 ­681 2,720 227 5,173 12,928 ­7,930 ­590 765 5,720 24,410 Total 58,274 39,793 941 17,540 ­45 18,899 48,844 ­26,835 ­3,124 14 8,765 85,893 Net flows to part II countries Official aid Other Private flows Net grants Total official by NGOs net flows flows Contributions Foreign Bilateral Private Bilateral Bilateral to multilateral direct portfolio export Total grants loans institutions Total investment investment credits 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 $ millions Australia 7 4 .. 4 13 1,747 572 1,174 .. 248 2,015 Austria 196 142 0 55 .. 3,215 3,215 .. .. 8 3,420 Belgium 97 6 6 85 ­24 ­2,527 ­2,497 0 ­30 10 ­2,443 Canada 104 104 .. .. ­106 5,603 5,534 76 ­7 .. 5,602 Denmark 167 90 5 72 19 431 431 .. .. .. 617 Finland 67 33 ­1 35 ­1 1,043 390 519 134 0 1,109 France 1,464 1,083 ­20 401 21 4,352 1,925 2,626 ­199 .. 5,837 Germany 780 347 ­81 514 ­505 10,980 7,734 4,692 ­1,446 78 11,333 Greece 16 16 .. .. .. 216 216 .. .. 1 234 Ireland 26 1 .. 25 .. .. .. .. .. .. 26 Italy .. .. .. .. 25 ­199 197 ­469 73 .. ­173 Japan 99 123 ­66 43 ­896 6,150 6,182 ­349 318 .. 5,353 Luxembourg 10 3 .. 7 .. .. .. .. .. .. 10 Netherlands 211 138 ­6 79 .. ­1,061 2,775 ­4,066 230 .. ­850 New Zealand 1 0 .. 0 .. .. .. .. .. .. 1 Norway 45 43 .. 2 0 1,084 1,082 .. 1 .. 1,129 Portugal 33 1 .. 32 ­2 71 57 .. 14 .. 102 Spain 11 11 .. .. .. 206 206 .. .. .. 218 Sweden 107 100 0 7 ­2 ­1,261 ­1,288 0 27 .. ­1,155 Switzerland 66 57 1 9 2 1,302 1,320 0 ­17 9 1,379 United Kingdom 494 92 ­4 407 .. 8,121 5,350 2,880 ­110 6 8,621 United States 2,313 2,418 ­173 69 ­52 4,182 21,372 ­17,120 ­70 3,146 9,589 Total 6,317 4,813 ­342 1,846 ­1,508 43,655 54,774 ­10,036 ­1,083 3,508 51,972 Note: Data may not sum to totals because of gaps in reporting. 330 2004 World Development Indicators 6.8 Net financial flows from Development GLOBAL LINK Assistance Committee members S About the data Definitions The high-income members of the Development Assist- and flows reported by the United Nations, all United · Official development assistance comprises grants and ance Committee (DAC) of the Organisation for Economic Nations agencies revised their data to include only reg- loans (net of repayments of principal) that meet the DAC Co-operation and Development (OECD) are the main ular budgetary expenditures since 1990 (except for the definition of ODA and are made to countries and territo- source of official external finance for developing coun- World Food Programme and the United Nations High ries in part I of the DAC list of aid recipients. · Official aid tries. This table shows the flow of official and private Commissioner for Refugees, which revised their data comprises grants and loans (net of repayments) that financial resources from DAC members to official and pri- from 1996 onward). meet the criteria for ODA and are made to countries and vate recipients in developing and transition economies. DAC maintains a list of countries and territories that territories in part II of the DAC list of aid recipients. DAC exists to help its members coordinate their devel- are aid recipients. Part I of the list comprises devel- · Bilateral grants are transfers of money or in kind for opment assistance and to encourage the expansion oping countries and territories considered by DAC which no repayment is required. · Bilateral loans are and improve the effectiveness of the aggregate members to be eligible for ODA. Part II comprises loans extended by governments or official agencies that resources flowing to recipient economies. In this capac- economies in transition: more advanced countries of have a grant element of at least 25 percent (calculated at ity DAC monitors the flow of all financial resources, but Central and Eastern Europe, the countries of the for- a rate of discount of 10 percent). · Contributions to its main concern is official development assistance mer Soviet Union, and certain advanced developing multilateral institutions are concessional funding (ODA). DAC has three criteria for ODA: It is undertaken countries and territories. Flows to these recipients received by multilateral institutions from DAC members in by the official sector. It promotes the economic devel- that meet the criteria for ODA are termed official aid. the form of grants or capital subscriptions. · Other offi- opment and welfare of developing countries as a main The data in the table were compiled from replies by cial flows are transactions by the official sector whose objective. And it is provided on concessional terms, with DAC member countries to questionnaires issued by the main objective is other than development or whose grant a grant element of at least 25 percent on loans (calcu- DAC Secretariat. Net flows of ODA, official aid, and other element is less than 25 percent. · Private flows consist lated at a rate of discount of 10 percent). official resources are defined as gross disbursements of of flows at market terms financed from private sector This definition excludes nonconcessional flows from grants and loans minus repayments of principal on earli- resources in donor countries. They include changes in official creditors, which are classified as "other official er loans. Because the data are based on donor country holdings of private long-term assets by residents of the flows," and military aid, which is not recorded in DAC reports, they do not provide a complete picture of the reporting country. · Foreign direct investment is invest- statistics. The definition includes food aid, capital proj- resources received by developing and transition ment by residents of DAC member countries to acquire a ects, emergency relief, technical cooperation, and post- economies, for two reasons. First, flows from DAC mem- lasting management interest (at least 10 percent of vot- conflict peacekeeping efforts. Also included are bers are only part of the aggregate resource flows to ing stock) in an enterprise operating in the recipient coun- contributions to multilateral institutions, such as the these economies. Second, the data that record contri- try. The data reflect changes in the net worth of United Nations and its specialized agencies, and butions to multilateral institutions measure the flow of subsidiaries in recipient countries whose parent company concessional funding to the multilateral develop- resources made available to those institutions by DAC is in the DAC source country. · Bilateral portfolio invest- ment banks. In 1999, to avoid double counting members, not the flow of resources from those institu- ment covers bank lending and the purchase of bonds, extrabudgetary expenditures reported by DAC countries tions to developing and transition economies. shares, and real estate by residents of DAC member countries in recipient countries. · Multilateral portfolio 6.8a investment records the transactions of private banks and Who were the largest donors in 2002? nonbanks in DAC member countries in the securities Official development assistance issued by multilateral institutions. · Private export credits are loans extended to recipient countries by the private sector in DAC member countries to promote trade; they may be supported by an official guarantee. · Net Others Spain 3% United grants by NGOs are private grants by nongovernmental 15% States organizations, net of subsidies from the official sector. Sweden 3% 23% · Total net flows comprise ODA or official aid flows, other Canada 3% official flows, private flows, and net grants by NGOs. Italy 4% Japan Data sources 16% Netherlands 6% The data on financial flows are compiled by DAC United and published in its annual statistical report, Kingdom France 9% Geographical Distribution of Financial Flows to Aid Germany 9% 9% Recipients, and its annual Development Coopera- tion Report. Data are available electronically on the OECD's International Development Statistics Disbursements from three countries made up almost half of total net ODA flows in 2002. The top five contributed two-thirds of the total amount. CD-ROM and to registered users at http://www. oecd.org/dataoecd/50/17/5037721.htm. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. 2004 World Development Indicators 331 6.9 Aid flows from Development Assistance Committee members Net flows to part I countries Net official Untied aid a development assistance average annual % Per capita of change in volume b donor country b % of general % of bilateral $ millions % of GNI 1996­97 to $ government disbursement ODA commitments 1997 2002 1997 2002 2001­02 1997 2002 1997 2002 1997 2002 Australia 1,061 989 0.27 0.26 2.4 43 47 0.70 0.68 63.1 56.7 Austria 495 520 0.24 0.26 5.2 51 61 0.44 0.49 60.6 69.0 Belgium 764 1,072 0.31 0.43 7.1 63 97 0.61 0.87 49.9 .. Canada 2,045 2,006 0.34 0.28 ­0.6 65 64 0.72 0.67 33.4 61.4 Denmark 1,637 1,643 0.97 0.96 2.8 266 286 1.67 1.71 71.6 82.1 Finland 379 462 0.32 0.35 5.1 63 83 0.55 0.70 76.8 82.5 France 6,307 5,486 0.45 0.38 ­2.6 89 86 0.82 0.72 65.1 91.5 Germany 5,857 5,324 0.28 0.27 ­0.5 58 60 0.56 0.55 73.6 86.6 Greece 173 276 0.14 0.21 9.6 14 23 0.30 0.44 .. 13.9 Ireland 187 398 0.31 0.40 14.3 47 93 0.63 0.97 .. 100.0 Italy 1,266 2,332 0.11 0.20 4.6 19 37 0.21 0.41 45.6 .. Japan 9,358 9,283 0.21 0.23 3.0 70 76 0.61 0.60 99.6 82.8 Luxembourg 95 147 0.55 0.77 13.5 198 316 1.25 1.57 95.1 .. Netherlands 2,947 3,338 0.81 0.81 3.6 170 190 1.62 1.68 90.0 88.6 New Zealand 154 122 0.26 0.22 3.5 28 28 0.56 0.54 .. 76.0 Norway 1,306 1,696 0.84 0.89 2.8 291 333 1.76 1.87 91.1 99.1 Portugal 250 323 0.25 0.27 6.7 23 28 0.53 0.57 99.0 33.0 Spain 1,234 1,712 0.24 0.26 9.5 28 38 0.53 0.66 0.0 59.9 Sweden 1,731 1,991 0.79 0.83 5.2 151 207 1.11 1.42 74.5 78.5 Switzerland 911 939 0.34 0.32 2.3 114 118 .. .. 94.9 95.1 United Kingdom 3,433 4,924 0.26 0.31 6.5 56 78 0.63 0.77 71.7 100.0 United States 6,878 13,290 0.09 0.13 6.8 28 46 0.24 0.36 .. .. Total or average 48,465 58,274 0.22 0.23 3.5 53 65 0.54 0.59 83.2 84.8 Net flows to part II countries Net official aid average annual % Per capita of change in volume b donor country b $ millions % of GNI 1996­97 to $ 1997 2002 1997 2002 2001­02 1997 2002 Australia 0 7 0.00 0.00 10.7 0 0 Austria 181 196 0.09 0.10 4.8 19 23 Belgium 59 97 0.02 0.04 12.0 5 9 Canada 157 104 0.03 0.01 ­4.3 5 3 Denmark 133 167 0.08 0.10 10.3 22 29 Finland 71 67 0.06 0.05 3.3 12 12 France 574 1,464 0.04 0.10 22.2 8 23 Germany 660 780 0.03 0.04 ­1.0 7 9 Greece 9 16 0.01 0.01 20.6 1 1 Ireland 1 26 0.00 0.03 94.4 0 6 Italy 241 .. 0.02 .. .. 4 .. Japan 84 99 0.00 0.00 ­4.5 1 1 Luxembourg 2 10 0.01 0.05 38.1 5 22 Netherlands 7 211 0.00 0.05 88.8 0 12 New Zealand 0 1 0.00 0.00 86.8 0 0 Norway 55 45 0.04 0.02 ­6.6 12 9 Portugal 18 33 0.02 0.03 13.5 2 3 Spain 3 11 0.00 0.00 ­20.9 0 0 Sweden 148 107 0.07 0.04 ­1.7 13 11 Switzerland 75 66 0.03 0.02 ­2.5 9 8 United Kingdom 337 494 0.03 0.03 5.4 6 8 United States 2,516 2,313 0.03 0.02 ­3.4 10 8 Total or average 5,331 6,317 0.02 0.03 3.3 6 7 a. Excluding administrative costs and technical cooperation. b. At 2001 exchange rates and prices. 332 2004 World Development Indicators 6.9 Aid flows from Development GLOBAL LINK Assistance Committee members S About the data Effective aid supports institutional development and research, stipends and tuition costs for aid-financed mismanaging aid receipts, but they may also be moti- policy reforms, which are at the heart of successful students in donor countries, or payment of experts vated by a desire to benefit suppliers in the donor development. To be effective, especially in reducing hired by donor countries. Second, donors record their country. The same volume of aid may have different global poverty, aid requires partnerships among recip- concessional funding (usually grants) to multilateral purchasing power depending on the relative costs of ient countries, aid agencies, and donor countries. It agencies when they make payments, while the agen- suppliers in countries to which the aid is tied and the also requires improvements in economic policies and cies make funds available to recipients with a time lag degree to which each recipient's aid basket is untied. institutions. Where traditional methods of nurturing and in many cases in the form of soft loans where such reforms have failed, aid agencies need to find donors' grants have been used to reduce the interest Definitions alternative approaches and new opportunities. burden over the life of the loan. As part of its work, the Development Assistance Aid as a share of gross national income (GNI), aid · Net official development assistance and net offi- Committee (DAC) of the Organisation for Economic per capita, and ODA as a share of the general gov- cial aid record the actual international transfer by the Co-operation and Development (OECD) assesses the ernment disbursements of the donor are calculated donor of financial resources or of goods or services aid performance of member countries relative to the by the OECD. The denominators used in calculating valued at the cost to the donor, less any repayments size of their economies. As measured here, aid com- these ratios may differ from corresponding values of loan principal during the same period. Data are prises bilateral disbursements of concessional elsewhere in this book because of differences in tim- shown at current prices and dollar exchange rates. financing to recipient countries plus the provision by ing or definitions. · Aid as a percentage of GNI shows the donor's con- donor governments of concessional financing to mul- DAC members have progressively introduced the new tributions of ODA or official aid as a share of its tilateral institutions. Volume amounts, at constant United Nations System of National Accounts (adopted gross national income. · Average annual percentage prices and exchange rates, are used to measure the in 1993), which replaced gross national product (GNP) change in volume and aid per capita of donor coun- change in real resources provided over time. Aid with GNI. Because GNI includes items not included in try are calculated using 2001 exchange rates and flows to part I recipients--official development GNP, ratios of ODA to GNI are slightly smaller than the prices. · Aid as a percentage of general government assistance (ODA)--are tabulated separately from previously reported ratios of ODA to GNP. disbursements shows the donor's contributions of those to part II recipients--official aid (see About the The proportion of untied aid is reported here ODA as a share of public spending. · Untied aid is data for table 6.8 for more information on the dis- because tying arrangements may prevent recipients the share of ODA that is not subject to restrictions by tinction between the two types of aid flows). from obtaining the best value for their money and so donors on procurement sources. Measures of aid flows from the perspective of donors reduce the value of the aid received. Tying arrange- differ from aid receipts from the perspective of recipi- ments require recipients to purchase goods and serv- ents for two main reasons. First, aid flows include ices from the donor country or from a specified group expenditure items about which recipients may have no of countries. They may be justified on the grounds precise information, such as development-oriented that they prevent a recipient from misappropriating or 6.9a Official development assistance from selected non-DAC donors, 1998­2002 Net disbursements ($ millions) Donor 1998 1999 2000 2001 2002 OECD members (non-DAC) Czech Republic 16 15 16 26 45 Iceland 7 8 9 10 13 Korea, Rep. 183 317 212 265 279 Poland 19 20 29 36 14 Slovak Republic .. 7 6 8 7 Turkey 69 120 82 64 73 Arab countries Kuwait 278 147 165 73 20 Data sources Saudi Arabia 288 185 295 490 2,478 United Arab Emirates 63 92 150 127 156 The data on financial flows are compiled by DAC Other donors and published in its annual statistical report, Israel 87 114 164 a 76 a 114 a Geographical Distribution of Financial Flows to Aid Other b 27 0 1 2 3 Recipients, and its annual Development Coopera- Total 1,037 1,026 1,128 1,178 3,201 tion Report. Data are available electronically on the Note: China also provides aid but does not disclose the amount. OECD's International Development Statistics a. These figures include $66.8 million in 2000, $50.1 million in 2001, and $87.8 million in 2002 for first-year sustenance expenses for people arriving from developing countries (many of which are experiencing civil war or severe unrest) or who CD-ROM and to registered users at http://www. have left their country for humanitarian or political reasons. b. Includes Estonia, Latvia, Lithuania, and Taiwan, China. oecd.org/dataoecd/50/17/5037721.htm. Source: Organisation for Economic Co-operation and Development. 2004 World Development Indicators 333 6.10 Aid dependency Net official Aid per capita Aid dependency ratios development assistance or official aid Aid as Aid as Aid as % of % of % of central Aid as gross capital imports of government $ millions $ % of GNI formation goods and services expenditure 1997 2002 1997 2002 1997 2002 1997 2002 1997 2002 1997 2002 Afghanistan 230 1,285 10 46 .. .. .. .. .. .. .. .. Albania 166 317 53 101 7.5 6.4 75.8 28.8 20.2 15.1 25.3 .. Algeria 250 361 9 12 0.5 0.7 2.2 2.1 .. .. 1.7 1.1 Angola 355 421 31 32 5.5 4.3 18.2 11.6 5.7 4.5 .. .. Argentina 105 0 3 0 0.0 0.0 0.2 0.0 0.2 0.0 0.2 0.3 Armenia 166 293 52 96 9.6 12.0 53.2 59.2 16.8 25.4 .. .. Australia Austria Azerbaijan 184 349 23 43 4.7 6.1 13.6 17.5 8.6 9.9 24.2 .. Bangladesh 1,011 913 8 7 2.3 1.8 11.5 8.3 12.6 9.6 .. .. Belarus 55 39 5 4 0.4 0.3 1.5 1.3 0.6 0.4 1.2 1.1 Belgium Benin 221 220 38 34 10.4 8.3 55.7 45.9 27.8 26.3 .. .. Bolivia 700 681 89 77 9.1 9.0 45.0 59.2 29.7 28.9 40.0 34.2 Bosnia and Herzegovina 862 587 236 143 26.1 10.0 59.2 53.4 .. 12.1 .. .. Botswana 122 38 77 22 2.4 0.8 8.3 2.9 3.9 1.3 .. .. Brazil 288 376 2 2 0.0 0.1 0.2 0.4 0.3 0.5 0.1 .. Bulgaria 220 381 26 48 2.2 2.5 21.5 12.5 3.5 3.9 6.5 7.4 Burkina Faso 368 473 35 40 14.2 15.2 56.5 82.8 .. 65.1 .. .. Burundi 56 172 9 24 6.0 24.2 72.9 303.8 35.5 107.7 24.5 .. Cambodia 335 487 30 39 10.1 12.7 66.0 54.7 25.6 16.7 .. .. Cameroon 499 632 35 40 5.9 7.3 33.9 37.6 .. .. .. .. Canada Central African Republic 91 60 26 16 9.2 5.8 92.6 38.6 .. .. .. .. Chad 228 233 32 28 14.5 11.8 102.0 19.8 .. .. .. .. Chile 129 ­23 9 ­1 0.2 ­0.0 0.6 ­0.2 0.5 ­0.1 0.8 0.4 China 2,054 1,476 2 1 0.2 0.1 0.6 0.3 1.1 0.4 2.8 .. Hong Kong, China 8 4 1 1 0.0 0.0 0.0 0.0 .. 0.0 .. .. Colombia 196 441 5 10 0.2 0.6 0.9 3.6 0.9 2.3 1.1 .. Congo, Dem. Rep. 158 807 3 16 5.5 14.7 102.8 199.5 .. .. 26.2 .. Congo, Rep. 270 420 86 115 16.2 19.1 52.0 59.7 14.0 16.9 30.8 10.5 Costa Rica ­8 5 ­2 1 ­0.1 0.0 ­0.3 0.1 ­0.1 0.1 ­0.3 0.1 Côte d'Ivoire 446 1,069 30 65 4.1 9.6 26.4 87.4 9.0 23.0 17.4 9.6 Croatia 40 166 9 37 0.2 0.8 0.7 2.7 0.3 1.2 0.5 1.3 Cuba 65 61 6 5 0.3 .. 3.7 .. .. .. .. .. Czech Republic 117 393 11 38 0.2 0.6 0.7 2.0 0.3 0.7 0.6 1.4 Denmark Dominican Republic 71 157 9 18 0.5 0.8 2.3 3.1 0.8 1.4 2.8 .. Ecuador 155 216 13 17 0.7 1.0 3.0 3.2 2.1 2.4 .. .. Egypt, Arab Rep. 1,985 1,286 33 19 2.6 1.4 14.4 8.5 9.0 6.3 8.6 .. El Salvador 279 233 47 36 2.5 1.7 16.5 10.0 6.3 3.7 .. 67.8 Eritrea 123 230 33 54 14.3 30.8 57.5 135.6 .. 40.7 .. .. Estonia 66 69 47 51 1.5 1.1 4.6 3.4 1.5 1.0 4.5 4.1 Ethiopia 579 1,307 10 19 8.4 21.7 53.3 105.2 39.6 63.0 38.7 .. Finland France Gabon 39 72 33 55 0.8 1.7 2.4 5.1 1.4 2.5 .. .. Gambia, The 39 61 33 44 9.7 17.3 55.1 79.0 13.2 .. .. .. Georgia 242 313 45 60 6.5 9.2 58.0 43.6 16.4 20.5 39.8 74.6 Germany Ghana 494 653 27 32 7.3 10.8 28.9 53.8 17.7 18.6 .. .. Greece Guatemala 264 249 25 21 1.5 1.1 10.9 5.7 5.9 3.5 .. .. Guinea 381 250 55 32 10.4 7.9 42.7 46.4 39.9 23.2 .. .. Guinea-Bissau 124 59 99 41 48.9 30.5 212.4 198.7 120.8 .. .. .. Haiti 325 156 43 19 9.9 4.5 40.3 22.1 35.9 .. 93.4 .. 334 2004 World Development Indicators 6.10 GLOBAL LINK Aid dependency S Net official Aid per capita Aid dependency ratios development assistance or official aid Aid as Aid as Aid as % of % of % of central Aid as gross capital imports of government $ millions $ % of GNI formation goods and services expenditure 1997 2002 1997 2002 1997 2002 1997 2002 1997 2002 1997 2002 Honduras 297 435 50 64 6.6 6.8 19.5 23.8 10.6 11.9 .. .. Hungary 180 471 18 46 0.4 0.7 1.5 3.0 0.7 1.0 0.9 1.9 India 1,648 1,463 2 1 0.4 0.3 1.8 1.3 2.6 1.6 2.6 2.1 Indonesia 810 1,308 4 6 0.4 0.8 1.2 5.3 1.1 2.1 2.1 4.3 Iran, Islamic Rep. 200 116 3 2 0.2 0.1 0.9 0.3 1.1 0.4 0.5 .. Iraq 220 116 10 5 .. .. .. .. .. .. .. .. Ireland Israel 1,196 754 205 115 1.2 0.7 4.8 4.0 2.8 1.5 2.5 0.3 Italy Jamaica 72 24 28 9 1.1 0.3 3.3 0.9 1.6 0.4 2.7 1.8 Japan Jordan 462 534 104 103 6.6 5.8 24.8 25.0 8.2 8.1 19.5 15.1 Kazakhstan 140 188 9 13 0.6 0.8 4.1 2.8 1.6 1.5 3.2 4.6 Kenya 448 393 16 13 4.3 3.2 27.1 23.5 11.1 10.3 17.2 .. Korea, Dem. Rep. 88 267 4 12 .. .. .. .. .. .. .. .. Korea, Rep. ­160 ­82 ­3 ­2 ­0.0 ­0.0 ­0.1 ­0.1 ­0.1 ­0.0 ­0.2 .. Kuwait 0 5 0 2 0.0 0.0 0.0 0.1 0.0 0.0 0.0 .. Kyrgyz Republic 240 186 51 37 14.1 12.0 62.5 62.7 27.0 24.4 60.6 70.0 Lao PDR 329 278 67 50 19.3 17.3 69.4 .. 44.4 .. .. .. Latvia 81 86 33 37 1.4 1.0 6.3 3.8 2.3 1.7 4.6 4.8 Lebanon 251 456 61 103 1.6 2.5 6.4 14.7 3.1 5.9 4.0 .. Lesotho 92 76 54 43 6.8 8.7 17.1 26.7 7.6 9.5 18.1 .. Liberia 76 52 26 16 28.8 11.0 .. .. .. .. .. .. Libya 7 10 1 2 .. .. 0.2 0.4 0.1 .. .. .. Lithuania 104 147 29 42 1.1 1.1 4.2 4.7 1.6 1.7 3.9 4.1 Macedonia, FYR 98 277 49 136 2.7 7.4 12.5 37.0 5.1 12.4 .. .. Madagascar 834 373 59 23 24.1 8.6 183.5 59.4 69.8 33.8 147.4 .. Malawi 344 377 36 35 13.8 20.2 111.3 160.0 35.9 44.9 .. .. Malaysia ­240 86 ­11 4 ­0.3 0.1 ­0.6 0.4 ­0.2 0.1 ­1.2 .. Mali 429 472 43 42 17.7 15.1 84.1 69.1 44.4 31.8 .. .. Mauritania 238 355 98 128 22.8 45.4 123.5 116.5 42.8 .. .. .. Mauritius 43 24 38 20 1.0 0.5 3.6 2.4 1.6 0.8 4.5 2.1 Mexico 105 136 1 1 0.0 0.0 0.1 0.1 0.1 0.1 0.2 .. Moldova 65 142 15 33 3.3 8.0 14.2 38.4 4.3 10.2 8.1 33.4 Mongolia 251 208 108 85 28.1 18.6 96.8 60.8 43.6 21.6 112.4 65.7 Morocco 464 636 17 21 1.4 1.8 6.7 7.8 3.9 4.4 4.5 .. Mozambique 948 2,058 57 112 29.5 60.4 135.6 127.9 80.3 103.4 .. .. Myanmar 50 121 1 2 .. .. .. .. 1.9 .. 0.3 .. Namibia 166 135 96 68 4.1 4.2 22.6 19.3 7.1 8.3 12.6 .. Nepal 402 365 19 15 8.2 6.6 32.2 26.8 20.7 24.2 49.5 37.6 Netherlands New Zealand Nicaragua 411 517 88 97 24.1 13.6 66.0 40.3 21.8 23.7 60.4 84.9 Niger 333 298 34 26 18.3 13.8 166.1 107.7 .. .. .. .. Nigeria 200 314 2 2 0.6 0.8 3.2 3.1 1.1 1.8 .. .. Norway Oman 65 41 29 16 0.4 0.2 2.3 1.6 0.9 .. 1.4 0.0 Pakistan 596 2,144 5 15 1.0 3.6 5.3 24.7 3.8 14.3 4.5 15.2 Panama 46 35 17 12 0.5 0.3 1.7 1.1 0.4 0.4 2.0 .. Papua New Guinea 346 203 73 38 7.4 7.5 33.5 .. 12.6 .. 24.0 .. Paraguay 108 57 22 10 1.1 1.0 4.8 3.8 2.1 2.0 6.8 4.8 Peru 395 491 16 18 0.7 0.9 2.8 4.7 2.9 4.2 3.9 4.6 Philippines 689 560 10 7 0.8 0.7 3.4 3.7 1.3 1.3 4.3 4.2 Poland 861 1,160 22 30 0.6 0.6 2.4 3.2 1.8 1.7 1.5 1.5 Portugal Puerto Rico 2004 World Development Indicators 335 6.10 Aid dependency Net official Aid per capita Aid dependency ratios development assistance or official aid Aid as Aid as Aid as % of % of % of central Aid as gross capital imports of government $ millions $ % of GNI formation goods and services expenditure 1997 2002 1997 2002 1997 2002 1997 2002 1997 2002 1997 2002 Romania 219 701 10 31 0.6 1.5 3.0 6.6 1.7 3.6 2.0 5.3 Russian Federation 793 1,301 5 9 0.2 0.4 0.9 1.8 0.8 1.3 .. 1.5 Rwanda 230 356 32 44 12.5 20.8 89.8 109.2 45.8 77.1 .. .. Saudi Arabia 11 27 1 1 0.0 0.0 0.0 0.1 0.0 0.1 .. .. Senegal 423 449 48 46 9.8 9.2 54.2 45.3 24.8 19.9 50.5 41.0 Serbia and Montenegro 97 1,931 9 237 .. 12.4 4.8 76.6 1.9 27.5 .. .. Sierra Leone 119 353 25 68 14.3 47.0 278.9 514.7 .. .. 81.3 .. Singapore 3 7 1 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Slovak Republic 71 189 13 35 0.3 0.8 1.0 2.6 0.5 1.0 0.8 2.1 Slovenia 99 171 50 87 0.5 0.8 2.3 3.3 0.9 1.3 1.4 1.7 Somalia 81 194 10 21 .. .. .. .. .. .. .. .. South Africa 496 657 12 14 0.3 0.6 2.0 4.0 1.3 1.8 1.1 1.3 Spain Sri Lanka 331 344 19 18 2.2 2.1 9.0 9.9 4.7 4.6 8.5 7.6 Sudan 139 351 5 11 1.3 2.7 6.6 13.3 8.7 9.7 .. .. Swaziland 28 25 29 23 1.8 2.0 9.6 11.6 2.1 1.9 .. .. Sweden Switzerland Syrian Arab Republic 197 81 13 5 1.4 0.4 6.4 1.8 3.2 1.1 1.2 .. Tajikistan 86 168 14 27 8.0 14.6 39.5 61.2 9.8 18.2 .. .. Tanzania 945 1,233 30 35 12.5 13.2 82.5 78.7 44.4 53.3 .. .. Thailand 626 296 11 5 0.4 0.2 1.2 1.0 0.8 0.4 2.1 1.2 Togo 125 51 31 11 8.5 3.8 51.3 17.0 16.4 6.9 .. .. Trinidad and Tobago 33 ­7 26 ­6 0.6 ­0.1 1.6 ­0.5 0.9 ­0.1 .. .. Tunisia 194 475 21 49 1.1 2.4 3.9 9.0 2.0 4.1 3.2 .. Turkey 7 636 0 9 0.0 0.4 0.0 2.1 0.0 1.0 0.0 0.2 Turkmenistan 12 41 3 8 0.4 .. 1.2 1.7 0.7 .. .. .. Uganda 813 638 38 26 13.0 11.2 77.2 50.7 46.0 35.4 .. 65.5 Ukraine 268 484 5 10 0.5 1.2 2.5 6.1 1.2 2.2 .. 4.7 United Arab Emirates 2 4 1 1 0.0 .. 0.0 .. .. .. 0.0 .. United Kingdom United States Uruguay 34 13 11 4 0.2 0.1 1.0 0.9 0.7 0.5 0.5 0.3 Uzbekistan 140 189 6 7 1.3 2.4 5.7 11.8 3.0 6.5 .. .. Venezuela, RB 9 57 0 2 0.0 0.1 0.0 0.4 0.0 0.3 0.0 0.1 Vietnam 998 1,277 13 16 3.8 3.6 13.1 11.3 7.0 5.7 16.5 16.2 West Bank and Gaza 603 1,616 230 500 13.1 42.9 35.9 1,349.0 .. .. .. .. Yemen, Rep. 356 584 22 31 5.6 6.3 20.8 35.1 9.5 12.2 16.1 .. Zambia 610 641 66 63 16.5 18.1 107.0 99.4 37.3 36.6 .. .. Zimbabwe 336 201 28 15 4.2 .. 22.0 29.2 .. .. 11.1 .. World 54,482 s 69,814 s 9 w 11 w 0.2 w 0.2 w 0.8 w 1.0 w 0.7 w 0.7 w .. .. Low income 21,534 29,622 9 12 2.1 2.7 8.9 13.1 7.9 9.5 .. .. Middle income 18,914 25,382 7 9 0.4 0.5 1.4 1.9 1.3 1.4 .. .. Lower middle income 15,853 19,979 7 8 0.5 0.6 1.7 2.1 1.8 1.9 .. .. Upper middle income 2,578 4,018 8 12 0.2 0.2 0.7 1.2 0.4 0.6 .. .. Low & middle income 52,324 67,945 11 13 0.9 1.1 3.4 4.4 3.0 3.3 .. .. East Asia & Pacific 6,939 7,340 4 4 0.5 0.4 1.3 1.2 1.4 1.1 .. .. Europe & Central Asia 7,121 12,819 15 27 0.7 1.1 2.7 5.3 1.8 2.7 .. .. Latin America & Carib. 5,399 5,108 11 10 0.3 0.3 1.2 1.6 1.2 1.1 .. .. Middle East & N. Africa 5,440 6,527 20 21 0.9 1.0 4.5 4.3 3.2 3.4 .. .. South Asia 4,313 6,615 3 5 0.8 1.0 3.6 4.9 4.5 5.3 .. .. Sub-Saharan Africa 14,976 19,406 24 28 4.5 6.3 24.5 32.2 12.4 15.3 .. .. High income Europe EMU Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. 336 2004 World Development Indicators 6.10 GLOBAL LINK Aid dependency S About the data Definitions Ratios of aid to gross national income (GNI), gross Expenditures on technical cooperation do not always · Net official development assistance consists of capital formation, imports, and government spend- directly benefit the economy to the extent that they disbursements of loans made on concessional terms ing provide a measure of the recipient country's defray costs incurred outside the country on the (net of repayments of principal) and grants by official dependency on aid. But care must be taken in draw- salaries and benefits of technical experts and the agencies of the members of DAC, by multilateral ing policy conclusions. For foreign policy reasons overhead costs of firms supplying technical services. institutions, and by non-DAC countries to promote some countries have traditionally received large In 1999, to avoid double counting extrabudgetary economic development and welfare in countries and amounts of aid. Thus aid dependency ratios may expenditures reported by DAC countries and flows territories in part I of the DAC list of aid recipients. It reveal as much about a donor's interest as they do reported by the United Nations, all United Nations includes loans with a grant element of at least 25 about a recipient's needs. Ratios in Sub-Saharan agencies revised their data since 1990 to include percent (calculated at a rate of discount of 10 per- Africa are generally much higher than those in other only regular budgetary expenditures (except for the cent). · Net official aid refers to aid flows (net of regions, and they increased in the 1980s. These World Food Programme and the United Nations High repayments) from official donors to countries and high ratios are due only in part to aid flows. Many Commissioner for Refugees, which revised their data territories in part II of the DAC list of aid recipients: African countries saw severe erosion in their terms from 1996 onward). These revisions have affected more advanced countries of Central and Eastern of trade in the 1980s, which, along with weak poli- net ODA and official aid and, as a result, aid per capi- Europe, the countries of the former Soviet Union, cies, contributed to falling incomes, imports, and ta and aid dependency ratios. and certain advanced developing countries and terri- investment. Thus the increase in aid dependency Because the table relies on information from tories. Official aid is provided under terms and con- ratios reflects events affecting both the numerator donors, it is not consistent with information recorded ditions similar to those for ODA. · Aid per capita and the denominator. by recipients in the balance of payments, which often includes both ODA and official aid. · Aid dependen- As defined here, aid includes official development excludes all or some technical assistance-- cy ratios are calculated using values in U.S. dollars assistance (ODA) and official aid (see About the data particularly payments to expatriates made directly by converted at official exchange rates. For definitions for table 6.8). The data cover loans and grants from the donor. Similarly, grant commodity aid may not of GNI, gross capital formation, imports of goods and Development Assistance Committee (DAC) member always be recorded in trade data or in the balance of services, and central government expenditure, see countries, multilateral organizations, and non-DAC payments. Moreover, DAC statistics exclude purely Definitions for tables 1.1, 4.9, and 4.12. donors. They do not reflect aid given by recipient military aid. countries to other developing countries. As a result, The nominal values used here may overstate the some countries that are net donors (such as Saudi real value of aid to the recipient. Changes in inter- Arabia) are shown in the table as aid recipients (see national prices and in exchange rates can reduce the table 6.9a). purchasing power of aid. The practice of tying aid, The data in the table do not distinguish among dif- still prevalent though declining in importance, also ferent types of aid (program, project, or food aid; tends to reduce its purchasing power (see About the emergency assistance; postconflict peacekeeping data for table 6.9). assistance; or technical cooperation), each of which The values for population, GNI, gross capital for- may have very different effects on the economy. mation, imports of goods and services, and central government expenditure used in computing the 6.10a ratios are taken from World Bank and International Where did aid go in 2002? Monetary Fund (IMF) databases. The aggregates Net aid also refer to World Bank definitions. Therefore the Latin America ratios shown may differ somewhat from those com- & Caribbean puted and published by the Organisation for Data sources 9% Middle East Economic Co-operation and Development (OECD). Aid The data on financial flows are compiled by DAC & North not allocated by country or region--including admin- and published in its annual statistical report, Africa Sub-Saharan 11% Africa istrative costs, research on development issues, and Geographical Distribution of Financial Flows to South 34% Asia aid to nongovernmental organizations--is included in Aid Recipients, and in its annual Development 11% the world total. Thus regional and income group Cooperation Report. Data are available in electron- East Asia Europe & & Pacific totals do not sum to the world total. ic format on the OECD's International Development 13% Central Asia Statistics CD-ROM and to registered users at 22% http://www.oecd.org/dataoecd/50/17/5037721. East Asia and Pacific has received a smaller share of htm. The data on population, GNI, gross capital for- total net aid flows, declining from 16 to 13 percent, while mation, imports of goods and services, and cen- flows to Europe and Central Asia increased from 16 to 22 percent. tral government expenditure are from World Bank Source: Organisation for Economic Co-operation and and IMF databases. Development, Development Assistance Committee. 2004 World Development Indicators 337 6.11 Distribution of net aid by Development Assistance Committee members Total Ten major DAC donors Other DAC donors United United States Japan France Germany Kingdom Netherlands Canada Sweden Norway Denmark 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 $ millions Afghanistan 985.9 367.6 31.7 11.9 92.6 130.8 88.3 35.8 27.5 60.9 7.8 131.2 Albania 177.2 61.8 4.0 3.0 24.7 4.9 11.6 1.3 4.0 5.8 3.3 52.9 Algeria 122.8 5.2 ­2.2 89.6 ­3.9 .. 0.4 0.4 0.7 3.2 0.0 29.6 Angola 286.4 105.6 27.2 9.9 16.5 10.2 27.7 2.6 14.1 22.2 1.0 49.4 Argentina 51.9 1.6 12.9 11.7 13.1 .. 0.3 2.1 0.2 0.1 0.0 10.0 Armenia 171.4 114.3 11.4 3.8 19.9 1.7 7.2 0.7 1.1 3.3 0.2 7.8 Australia Austria Azerbaijan 232.2 61.5 141.8 2.9 9.8 0.5 4.1 0.8 0.4 3.5 .. 6.9 Bangladesh 520.8 72.1 122.7 7.3 30.0 101.8 44.3 30.9 15.0 16.6 37.3 42.7 Belarus 26.0 8.4 0.2 2.8 6.8 0.1 1.0 0.1 2.9 0.2 1.0 2.6 Belgium Benin 140.1 23.4 4.5 40.5 24.0 0.1 2.4 2.4 0.1 0.1 23.6 19.0 Bolivia 482.2 127.7 37.5 33.9 71.9 14.2 62.6 14.6 16.4 3.3 30.6 69.4 Bosnia and Herzegovina 292.3 75.8 14.7 2.4 19.4 7.3 37.3 6.9 27.0 23.8 0.4 77.2 Botswana 36.7 22.4 ­0.1 0.6 4.5 2.2 1.9 0.2 0.6 3.2 0.8 0.6 Brazil 197.6 ­37.1 117.6 20.5 31.9 16.6 14.7 6.0 2.0 2.9 0.4 22.1 Bulgaria 189.2 47.5 36.7 14.9 49.2 7.0 7.9 1.3 0.2 0.5 3.3 20.7 Burkina Faso 229.9 16.2 10.0 53.9 19.4 0.3 37.3 8.6 7.5 0.4 23.0 53.3 Burundi 84.7 21.2 0.1 7.1 2.7 1.2 9.6 1.8 3.6 10.2 .. 27.2 Cambodia 272.8 44.4 98.6 24.6 18.4 13.2 9.3 4.9 14.5 3.1 6.6 35.0 Cameroon 436.2 13.1 7.5 119.0 67.0 43.5 7.5 80.3 0.0 5.7 17.0 75.5 Canada Central African Republic 39.6 0.8 12.9 16.5 7.1 0.4 0.4 0.1 0.2 .. .. 1.2 Chad 67.0 7.0 0.1 34.8 13.0 .. 0.8 0.5 0.0 .. .. 10.8 Chile ­13.8 ­18.4 ­39.6 11.8 18.7 0.3 3.3 1.6 0.9 0.5 0.0 7.1 China 1,212.8 17.0 828.7 77.2 149.9 36.1 17.9 30.0 6.4 12.2 6.3 31.1 Hong Kong, China 4.0 .. 2.2 1.6 0.0 .. 0.1 .. .. .. .. 0.1 Colombia 426.1 306.3 4.3 13.0 21.4 3.2 15.2 6.1 6.9 7.7 0.2 41.9 Congo, Dem. Rep. 351.0 80.0 0.9 0.8 21.1 14.9 135.0 9.8 7.7 12.5 .. 68.4 Congo, Rep. 41.4 5.9 0.2 23.7 2.6 0.3 0.2 0.4 2.2 0.4 .. 5.6 Costa Rica 4.5 ­23.7 ­2.8 4.8 3.1 ­0.1 6.2 3.7 1.1 0.5 0.0 11.7 Côte d'Ivoire 831.1 53.1 5.2 531.3 31.1 11.7 24.3 78.7 0.2 0.5 0.7 94.2 Croatia 82.1 49.5 0.5 2.5 2.2 2.1 1.6 1.1 5.5 13.2 0.1 3.7 Cuba 49.6 4.6 3.7 2.8 4.3 0.6 1.7 5.4 1.9 1.2 .. 23.5 Czech Republic 48.5 2.5 1.6 8.3 16.3 1.3 1.2 0.3 1.2 0.3 2.4 13.1 Denmark Dominican Republic 138.2 15.7 42.7 5.9 8.0 25.9 1.4 1.0 0.2 0.4 0.4 36.6 Ecuador 205.1 65.0 28.3 6.8 16.4 0.6 10.5 9.2 0.6 2.3 4.8 60.6 Egypt, Arab Rep. 1,124.2 845.8 12.9 100.1 61.9 12.2 17.1 10.3 2.2 0.6 16.1 44.9 El Salvador 217.9 62.0 32.9 3.0 15.2 11.1 8.4 3.3 5.3 1.7 1.3 73.8 Eritrea 120.7 44.9 4.3 4.3 3.7 1.2 12.6 1.1 4.2 13.5 10.2 20.8 Estonia 16.9 ­7.7 0.6 1.3 2.4 0.2 0.6 0.3 3.4 0.7 12.2 3.1 Ethiopia 489.2 156.4 50.5 10.2 40.6 43.7 34.8 6.9 21.3 28.5 2.7 93.7 Finland France Gabon 49.5 2.3 3.8 41.0 0.5 0.2 0.3 0.9 .. .. .. 0.5 Gambia, The 17.5 2.8 8.2 0.4 1.8 1.7 0.3 0.6 0.5 0.7 0.2 0.4 Georgia 209.6 133.3 18.6 1.9 21.0 3.9 8.9 0.7 2.0 4.4 0.3 14.4 Germany Ghana 406.2 68.9 23.6 10.2 34.0 123.7 59.6 12.4 1.4 0.7 51.5 20.4 Greece Guatemala 199.6 64.7 29.4 1.4 19.0 0.6 20.6 10.2 11.3 11.5 1.9 29.0 Guinea 125.6 47.7 18.6 22.6 15.4 2.7 4.0 4.3 0.5 0.6 .. 9.4 Guinea-Bissau 25.8 3.8 0.1 4.0 1.4 .. 3.6 0.3 1.8 0.0 0.3 10.5 Haiti 125.4 69.9 9.3 17.2 4.3 0.2 4.2 10.2 0.4 1.7 0.1 8.1 338 2004 World Development Indicators 6.11 Distribution of net aid by Development GLOBAL LINK Assistance Committee members S Total Ten major DAC donors Other DAC donors United United States Japan France Germany Kingdom Netherlands Canada Sweden Norway Denmark 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Honduras 297.9 97.3 94.9 3.9 13.4 1.9 8.9 7.1 11.0 0.9 12.7 46.0 Hungary 40.3 1.9 6.9 7.5 11.5 2.4 1.2 0.4 0.2 0.3 0.3 7.8 India 785.3 ­3.8 493.6 ­135.9 ­26.1 343.7 59.4 16.0 8.2 8.5 8.5 13.2 Indonesia 1,162.0 225.8 538.3 44.8 78.4 31.7 127.3 11.6 1.6 6.1 1.9 94.7 Iran, Islamic Rep. 81.5 0.2 17.5 7.9 31.8 2.8 3.8 .. 0.0 5.3 .. 12.1 Iraq 85.1 0.0 0.1 2.0 18.4 13.7 15.8 0.3 4.5 17.9 .. 12.3 Ireland Israel 749.3 786.8 0.6 6.2 ­50.4 .. 2.0 .. 1.5 .. .. 2.6 Italy Jamaica ­3.6 ­11.0 ­6.6 ­0.9 ­0.8 7.4 1.6 7.1 0.1 0.6 .. ­1.2 Japan Jordan 370.9 286.8 ­0.2 3.2 51.1 5.0 0.3 3.7 4.2 2.3 0.1 14.5 Kazakhstan 143.9 74.0 30.1 2.3 13.1 1.1 2.0 0.7 0.5 1.8 0.4 18.0 Kenya 288.1 102.4 17.4 17.6 27.1 54.4 12.7 7.3 14.4 3.0 9.7 22.2 Korea, Dem. Rep. 187.8 131.2 .. 0.5 33.2 3.0 0.6 0.2 4.3 3.6 .. 11.4 Korea, Rep. ­79.8 ­44.6 ­47.2 11.5 ­0.2 .. 0.1 .. .. .. .. 0.7 Kuwait 3.0 .. 0.1 1.4 1.5 .. 0.0 .. .. .. .. .. Kyrgyz Republic 95.2 51.7 8.1 0.4 11.0 4.5 1.7 0.7 0.8 1.3 0.6 14.4 Lao PDR 177.8 8.5 90.1 14.9 12.0 1.0 2.0 1.5 15.4 5.7 5.0 21.9 Latvia 26.2 0.8 0.4 1.4 3.8 0.1 0.4 0.5 5.7 0.8 9.8 2.5 Lebanon 102.4 36.2 10.1 33.2 7.2 0.2 0.4 1.3 1.1 5.3 .. 7.4 Lesotho 29.7 6.0 3.9 ­0.9 4.7 1.7 0.7 0.2 0.3 0.4 0.8 11.9 Liberia 27.0 15.1 0.0 1.7 ­2.1 2.9 2.9 0.3 1.1 1.9 0.1 3.1 Libya 4.4 .. 0.2 1.6 1.7 .. 0.1 .. .. .. .. 0.8 Lithuania 36.0 ­1.5 1.5 2.0 6.9 0.1 0.7 0.8 13.3 1.0 8.9 2.3 Macedonia, FYR 179.8 50.5 3.8 2.0 16.8 7.6 17.6 2.0 6.2 11.7 1.0 60.6 Madagascar 125.9 41.7 7.6 46.3 8.6 0.7 0.4 1.2 0.1 5.7 0.0 13.5 Malawi 224.9 61.2 18.8 5.1 24.0 50.2 16.9 8.5 7.7 15.6 7.8 9.3 Malaysia 85.4 1.1 54.2 ­2.7 4.5 ­0.1 0.9 0.7 .. 0.3 26.0 0.6 Mali 256.8 49.2 17.0 63.6 28.0 6.8 38.2 13.6 9.1 7.1 0.1 24.1 Mauritania 146.6 5.5 13.0 20.0 25.6 19.4 27.6 2.2 0.3 0.5 .. 32.5 Mauritius 3.5 0.2 0.7 ­0.2 1.4 0.2 0.1 0.2 .. 0.3 .. 0.6 Mexico 92.6 84.0 ­6.6 ­0.2 15.0 2.6 3.3 3.9 0.4 0.4 ­0.2 ­9.8 Moldova 86.3 56.9 5.9 2.3 2.4 3.3 3.5 0.3 4.6 1.1 1.5 4.6 Mongolia 141.3 20.4 79.0 1.0 23.2 0.6 2.6 0.9 2.4 2.6 0.9 7.7 Morocco 218.7 ­13.3 40.8 145.8 16.9 .. 1.2 4.4 0.8 0.2 ­0.9 22.8 Mozambique 1,661.0 159.7 69.7 431.6 156.9 48.0 52.0 9.0 45.3 38.7 51.9 598.2 Myanmar 79.1 4.8 49.4 1.5 1.7 6.5 4.2 1.3 0.9 3.9 0.9 4.1 Namibia 84.8 17.0 3.2 3.0 18.3 3.0 4.8 0.7 9.4 3.4 1.9 20.2 Nepal 279.4 32.6 97.5 ­1.9 34.5 36.9 7.3 4.2 3.6 13.1 25.4 26.3 Netherlands New Zealand Nicaragua 287.2 66.7 31.4 0.9 34.5 0.4 26.0 7.7 38.7 9.1 25.0 47.0 Niger 114.5 16.3 13.3 34.4 14.9 0.6 1.8 5.3 0.1 2.4 6.8 18.7 Nigeria 215.0 76.1 19.1 8.8 37.7 41.7 2.8 18.1 1.5 3.1 0.0 6.2 Norway Oman ­0.4 ­4.9 3.7 0.6 0.1 .. 0.0 .. .. .. .. 0.1 Pakistan 702.5 209.0 301.1 2.5 76.2 66.9 12.2 7.8 1.6 10.3 ­1.2 16.2 Panama 23.3 6.0 5.3 0.8 1.7 0.2 0.5 0.8 .. .. 2.0 6.0 Papua New Guinea 197.1 0.2 4.4 0.6 3.2 .. 1.3 0.0 0.1 0.2 .. 187.2 Paraguay 50.8 11.2 26.8 0.2 3.5 ­0.2 1.4 0.9 1.3 0.6 .. 5.1 Peru 463.0 143.6 119.6 4.9 24.3 84.4 12.9 7.9 3.9 1.4 2.1 57.9 Philippines 509.1 78.6 318.0 ­2.4 14.5 1.3 25.9 15.6 1.6 1.0 2.2 52.9 Poland 388.6 3.1 ­3.8 159.6 37.3 3.2 1.1 70.8 4.1 1.1 14.3 97.9 Portugal Puerto Rico 2004 World Development Indicators 339 6.11 Distribution of net aid by Development Assistance Committee members Total Ten major DAC donors Other DAC donors United United States Japan France Germany Kingdom Netherlands Canada Sweden Norway Denmark 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Romania 176.6 54.7 29.6 23.6 29.7 9.7 6.8 2.2 0.6 1.3 6.0 12.5 Russian Federation 1,109.3 858.8 5.3 23.8 54.1 41.5 8.2 12.8 31.7 22.7 13.2 37.2 Rwanda 199.1 46.4 0.4 6.6 10.8 52.6 19.6 5.6 15.6 6.1 0.5 35.0 Saudi Arabia 13.4 .. 9.0 3.7 0.6 .. 0.0 0.0 .. 0.0 .. 0.1 Senegal 242.8 37.1 37.8 104.5 13.2 0.6 10.4 9.8 0.3 1.4 0.9 26.9 Serbia and Montenegro 1,921.3 495.4 0.3 103.7 531.4 459.7 61.9 .. 24.5 22.1 8.0 214.2 Sierra Leone 225.3 70.1 0.1 3.6 15.9 54.3 20.6 3.3 1.7 10.6 0.4 44.6 Singapore 7.1 .. 2.0 3.2 1.5 0.1 0.0 0.2 .. .. .. 0.1 Slovak Republic 39.2 4.9 3.6 4.1 6.7 4.3 1.5 0.7 0.2 0.3 3.9 9.0 Slovenia 2.4 0.3 0.3 1.4 ­2.9 0.2 0.0 0.0 0.0 0.1 .. 3.0 Somalia 102.4 35.4 .. 0.4 2.8 3.1 13.1 0.2 5.5 25.4 1.4 15.2 South Africa 375.3 89.4 4.7 25.4 42.4 47.0 45.6 9.5 22.0 17.5 18.8 53.2 Spain Sri Lanka 188.5 ­11.0 118.9 ­2.5 7.8 7.7 18.6 3.5 15.0 21.5 0.2 8.8 Sudan 232.3 119.6 1.2 2.4 14.5 13.5 22.7 4.9 9.6 23.3 0.6 19.9 Swaziland 6.6 ­0.2 4.5 0.0 ­2.1 ­1.4 1.1 0.2 0.2 0.2 ­0.2 4.3 Sweden Switzerland Syrian Arab Republic 25.0 .. 15.8 13.5 ­12.8 0.1 2.3 0.2 0.2 0.8 .. 4.9 Tajikistan 128.8 75.9 27.0 0.2 10.2 3.3 0.6 1.5 2.0 1.4 0.1 6.8 Tanzania 902.8 85.4 58.2 16.0 23.2 103.2 138.3 8.3 61.4 46.7 69.9 292.3 Thailand 280.4 36.4 222.4 ­7.1 ­4.2 0.3 2.4 2.5 3.6 1.6 8.5 14.1 Togo 39.2 6.7 0.3 18.7 8.1 0.5 0.5 1.0 0.1 0.2 0.1 3.1 Trinidad and Tobago 5.7 0.6 2.7 0.8 0.1 0.2 0.0 1.3 .. .. .. 0.1 Tunisia 144.6 ­20.8 63.3 96.6 ­5.2 .. ­3.2 0.8 0.1 0.1 .. 12.9 Turkey 99.0 144.5 ­15.9 9.1 ­71.0 ­0.7 0.3 1.1 1.7 4.2 0.0 25.7 Turkmenistan 26.0 12.1 11.4 0.4 0.8 0.2 0.0 0.4 .. 0.2 .. 0.4 Uganda 466.1 109.4 8.1 5.5 33.9 84.0 43.5 6.4 23.4 32.6 43.1 76.3 Ukraine 358.2 255.5 1.6 6.8 44.6 12.5 2.8 14.0 5.0 0.2 5.1 10.1 United Arab Emirates 3.7 0.4 0.1 2.6 0.5 0.1 .. .. .. .. .. 0.0 United Kingdom United States Uruguay 6.8 ­1.7 4.1 2.4 2.0 0.0 0.1 0.7 0.1 .. .. ­0.9 Uzbekistan 152.9 74.3 40.2 1.6 21.6 1.4 0.7 0.5 0.1 2.6 .. 10.0 Venezuela, RB 42.0 10.9 3.7 5.0 3.0 0.1 0.3 1.4 0.1 0.1 .. 17.5 Vietnam 746.0 14.7 374.7 77.8 41.7 26.5 30.1 20.0 24.4 7.9 48.4 79.9 West Bank and Gaza 410.2 138.1 12.8 15.6 37.9 23.8 13.9 8.9 28.0 50.9 5.5 74.8 Yemen, Rep. 119.4 24.1 6.0 4.2 28.4 7.8 40.8 0.5 0.4 0.4 .. 7.0 Zambia 359.5 48.3 68.4 10.1 44.2 28.1 35.5 12.2 19.4 29.1 32.2 32.1 Zimbabwe 177.8 47.0 23.6 3.2 10.3 28.7 22.3 6.5 8.3 7.2 5.6 15.0 World 45,249.8 s 12,814.5 s 6,748.3 s 4,677.7 s 3,593.5 s 3,593.3 s 2,624.9 s 1,607.2 s 1,350.3 s 1,188.3 s 1,133.5 s 5,918.3 s Low income 17,698.7 3,559.1 3,141.6 1,788.8 1,368.2 1,648.9 1,325.7 496.4 463.6 533.4 541.3 2,831.6 Middle income 15,415.8 5,022.6 2,555.7 1,444.8 1,609.6 991.4 553.0 447.7 315.0 363.8 268.1 1,844.3 Lower middle income 13,328.4 4,744.1 2,421.5 952.0 1,390.9 885.7 502.7 218.5 264.9 272.3 156.1 1,519.7 Upper middle income 1,467.5 179.2 118.3 433.6 164.2 96.4 26.2 95.6 37.9 28.4 80.6 207.2 Low & middle income 43,667.3 12,042.3 6,780.0 3,904.1 3,637.0 3,592.8 2,528.2 1,606.5 1,348.8 1,187.7 1,133.5 5,906.4 East Asia & Pacific 5,742.1 778.1 2,755.2 317.8 378.7 138.3 226.5 96.4 79.8 56.0 107.0 808.3 Europe & Central Asia 7,112.1 3,004.4 401.6 422.8 902.6 593.2 196.1 233.6 183.9 184.6 143.1 846.1 Latin America & Carib. 3,891.7 1,207.3 583.2 174.6 355.2 292.4 218.8 143.1 126.0 60.1 89.5 641.5 Middle East & N. Africa 2,914.9 1,310.6 195.9 561.7 243.4 65.7 93.8 31.8 44.2 87.4 20.9 259.6 South Asia 3,518.0 667.2 1,190.0 ­118.6 216.3 688.2 233.8 99.0 71.3 133.4 88.3 249.1 Sub-Saharan Africa 11,675.0 2,369.4 579.5 2,129.8 935.0 1,022.0 939.5 374.1 404.5 447.8 392.5 2,080.8 High income Europe EMU Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. 340 2004 World Development Indicators 6.11 Distribution of net aid by Development GLOBAL LINK Assistance Committee members S About the data The data in the table show net bilateral aid to low- research on development issues, and aid to non- access to information on such aid expenditures as and middle-income economies from members of the governmental organizations--is included in the world development-oriented research, stipends and tuition Development Assistance Committee (DAC) of the total; thus regional and income group totals do not costs for aid-financed students in donor countries, or Organisation for Economic Co-operation and sum to the world total. payment of experts hired by donor countries. Development (OECD). The DAC compilation of the In 1999 all United Nations agencies revised their Moreover, a full accounting would include donor data includes aid to some countries and territories data since 1990 to include only regular budgetary country contributions to multilateral institutions, the not shown in the table and small quantities of aid to expenditures (except for the World Food Programme flow of resources from multilateral institutions to unspecified economies that are recorded only at the and the United Nations High Commissioner for recipient countries, and flows from countries that are regional or global level. Aid to countries and territo- Refugees, which revised their data from 1996 not members of DAC. ries not shown in the table has been assigned to onward). They did so to avoid double counting extra- The expenditures that countries report as official regional totals based on the World Bank's regional budgetary expenditures reported by DAC countries development assistance (ODA) have changed. For classification system. Aid to unspecified economies and flows reported by the United Nations. example, some DAC members have reported as ODA has been included in regional totals and, when pos- The data in the table are based on donor country the aid provided to refugees during the first 12 sible, in income group totals. Aid not allocated by reports of bilateral programs, which may differ from months of their stay within the donor's borders. country or region--including administrative costs, reports by recipient countries. Recipients may lack Some of the aid recipients shown in the table are also aid donors. See table 6.9a for a summary of 6.11a ODA from non-DAC countries. Top aid recipients from top DAC donors reflect historical alliances and geopolitical events Definitions Total bilateral aid, 2002 United States Japan · Net aid comprises net bilateral official develop- Russian Federation 7% ment assistance to part I recipients and net bilater- China 12% Egypt, Arab Rep. 7% al official aid to part II recipients (see About the data Indonesia 8% Israel 6% for table 6.8). · Other DAC donors are Australia, Serbia and Montenegro 4% Austria, Belgium, Finland, Greece, Ireland, Italy, India 7% Afghanistan 3% Luxembourg, New Zealand, Portugal, Spain, and Others Others Vietnam 6% Switzerland. 73% 62% Phillippines 5% France Germany Côte d'Ivoire 11% Serbia and Montenegro 15% Mozambique 9% Mozambique 4% Poland 3% China 4% Morocco 3% Afghanistan 3% Others Cameroon 3% Others Indonesia 2% 71% 72% United Kingdom Netherlands Tanzania 5% Serbia and Montenegro 13% Congo, Dem. Rep. 5% Indonesia 5% Data sources India 10% Afghanistan 3% Data on financial flows are compiled by DAC Bolivia 2% and published in its annual statistical report, Afghanistan 4% Others Others Ghana 3% Geographical Distribution of Financial Flows to Aid 67% 80% Tanzania 3% Recipients, and its annual Development Coopera- tion Report. Data are available electronically on the OECD's International Development Statistics This figure shows the distribution of aid from the top six donors to their top five recipients in 2002. Serbia and Montenegro CD-ROM and to registered users at http://www. and Afghanistan drew a large share of aid from donors in 2002. oecd.org/dataoecd/50/17/5037721.htm. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. 2004 World Development Indicators 341 6.12 Net financial flows from multilateral institutions International financial institutions United Nations Total $ millions Regional development World Bank IMF banks Conces- Non- Conces- Non- $ millions IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Afghanistan .. .. .. .. .. .. .. 9.0 9.0 9.2 2.0 15.4 44.6 Albania 78.9 0.0 ­2.9 ­5.7 0.0 3.5 18.7 1.4 0.4 0.7 0.5 3.1 98.5 Algeria 0.0 ­129.5 0.0 ­297.5 0.0 ­33.3 ­155.5 0.8 1.3 0.9 4.6 6.2 ­602.0 Angola 17.9 0.0 0.0 0.0 ­0.3 ­2.6 ­0.1 1.7 2.4 5.3 30.3 15.8 70.4 Argentina 0.0 ­928.3 0.0 ­743.0 0.0 ­502.3 0.0 0.3 0.3 0.6 .. 2.8 ­2,169.5 Armenia 66.5 ­0.4 15.0 ­7.3 0.0 ­6.1 ­9.4 0.8 0.3 0.6 1.3 3.5 64.8 Australia Austria Azerbaijan 56.9 0.0 7.8 ­46.4 0.0 0.6 11.2 2.2 0.8 0.9 3.3 3.7 40.9 Bangladesh 195.0 ­5.5 ­22.9 ­65.1 84.6 6.2 56.6 14.4 10.3 11.3 25.4 13.3 323.7 Belarus 0.0 ­9.8 0.0 ­30.3 0.0 ­15.3 ­5.7 0.2 0.2 .. .. 1.5 ­59.1 Belgium Benin 20.1 0.0 ­4.6 0.0 1.1 ­0.3 21.2 2.8 3.6 1.6 1.4 3.9 50.8 Bolivia 96.4 0.0 ­17.1 0.0 89.2 ­54.4 76.8 1.0 3.2 1.2 2.8 3.2 202.3 Bosnia and Herzegovina 96.8 ­23.1 0.0 18.2 0.0 ­7.5 0.5 1.1 0.1 0.5 .. 20.1 106.7 Botswana ­0.5 ­3.5 0.0 0.0 ­1.5 ­12.0 ­12.4 0.5 1.3 1.3 .. 3.1 ­23.7 Brazil 0.0 337.6 0.0 11,246.8 0.0 853.1 ­6.4 0.4 0.9 1.2 .. 131.0 12,564.6 Bulgaria 0.0 2.1 0.0 ­144.0 0.0 ­13.7 31.4 0.7 0.2 .. .. 2.0 ­121.4 Burkina Faso 65.3 0.0 6.4 0.0 37.9 ­1.8 1.8 5.0 1.9 4.0 2.2 4.3 126.9 Burundi 25.0 0.0 ­2.5 12.5 0.0 0.0 ­0.4 5.4 1.5 2.5 5.4 11.5 60.9 Cambodia 47.2 0.0 10.8 ­1.4 69.9 0.0 7.0 3.1 3.6 3.5 3.2 5.4 152.4 Cameroon 41.8 ­21.0 41.2 0.0 20.6 ­40.2 ­11.2 1.7 2.3 2.8 1.6 3.4 42.8 Canada Central African Republic 0.7 0.0 0.0 0.0 0.0 ­0.2 0.0 3.3 1.0 2.0 2.7 4.6 14.1 Chad 70.3 0.3 12.7 0.0 11.2 0.0 5.7 3.7 2.7 2.4 1.7 3.8 114.4 Chile ­0.7 ­172.0 0.0 0.0 ­1.3 ­76.2 ­0.3 ­4.6 0.2 0.6 .. 1.6 ­252.7 China 94.7 ­576.9 0.0 0.0 0.0 2.2 ­13.7 9.7 4.6 11.4 12.1 12.3 ­443.7 Hong Kong, China .. .. .. .. .. .. .. 0.0 .. .. .. 0.0 0.0 Colombia ­0.7 248.8 0.0 0.0 ­13.2 ­424.1 33.5 0.4 0.9 0.8 0.7 7.5 ­145.5 Congo, Dem. Rep. 275.2 ­81.5 358.8 ­203.4 ­32.0 0.0 0.0 6.2 1.7 18.8 10.3 42.8 397.0 Congo, Rep. ­0.3 ­6.5 ­3.6 ­4.7 0.0 0.0 0.0 1.4 0.7 1.7 0.3 8.8 ­2.2 Costa Rica ­0.2 ­11.1 0.0 0.0 ­10.9 ­22.5 ­44.7 0.2 0.4 0.6 .. 2.2 ­86.2 Côte d'Ivoire 161.2 ­89.9 ­10.6 0.0 34.3 ­109.4 ­1.9 2.5 2.0 3.1 1.6 8.6 1.5 Croatia 0.0 104.8 0.0 ­125.9 0.0 11.5 40.6 0.1 .. .. .. 9.9 41.0 Cuba .. .. .. .. .. .. .. 0.6 1.0 0.3 1.2 2.2 5.3 Czech Republic 0.0 ­41.0 0.0 0.0 0.0 0.0 39.7 0.1 .. .. .. 1.5 0.3 Denmark Dominican Republic ­0.7 32.6 0.0 ­25.7 ­16.1 80.2 ­1.7 0.3 1.1 0.6 0.4 1.6 72.5 Ecuador ­1.1 ­60.0 0.0 97.9 ­21.8 ­25.4 8.1 0.2 1.5 0.8 1.5 4.0 5.6 Egypt, Arab Rep. 20.5 ­46.6 0.0 0.0 0.6 ­65.0 14.4 1.5 1.1 2.7 3.2 7.3 ­60.4 El Salvador ­0.8 36.0 0.0 0.0 ­17.6 107.8 64.8 0.4 1.1 0.7 0.1 1.1 193.5 Eritrea 46.2 0.0 0.0 0.0 11.9 0.0 7.6 2.7 2.0 1.1 2.1 18.0 91.6 Estonia 0.0 ­35.2 0.0 ­13.8 0.0 ­4.3 0.7 .. 0.0 .. .. 0.2 ­52.3 Ethiopia 459.5 0.0 33.0 0.0 73.3 ­19.7 27.3 13.3 3.8 14.0 23.5 32.2 660.4 Finland France Gabon 0.0 ­5.9 0.0 ­13.1 0.0 ­12.5 8.9 .. 0.3 0.6 0.0 3.2 ­18.5 Gambia, The 14.3 0.0 3.7 0.0 4.7 ­0.7 4.8 2.6 0.5 0.7 0.9 2.3 33.8 Georgia 61.3 0.0 11.2 ­12.0 0.0 4.3 0.5 1.4 0.3 0.7 0.9 5.3 73.8 Germany Ghana 88.7 ­1.2 63.4 0.0 36.1 ­17.7 6.5 3.1 3.4 3.3 1.0 4.6 191.3 Greece Guatemala 0.0 69.5 0.0 0.0 ­6.4 163.3 ­12.5 0.8 13.5 0.8 3.2 1.8 233.9 Guinea 28.4 0.0 6.6 0.0 0.7 ­12.4 ­13.7 0.8 0.5 2.1 3.1 26.2 42.3 Guinea-Bissau 3.8 0.0 ­1.2 ­0.3 ­0.8 ­0.3 ­1.0 2.3 0.7 1.1 1.7 1.9 7.9 Haiti ­0.1 0.0 ­1.9 ­7.3 1.3 0.0 ­0.3 2.7 3.3 2.8 3.6 1.4 5.4 342 2004 World Development Indicators 6.12 Net financial flows from GLOBAL LINK multilateral institutions S International financial institutions United Nations Total $ millions Regional development World Bank IMF banks Conces- Non- Conces- Non- $ millions IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Honduras 45.6 ­9.7 ­4.2 ­30.8 39.4 ­19.2 7.8 1.1 2.0 1.1 2.1 1.6 36.8 Hungary 0.0 ­62.9 0.0 0.0 0.0 ­18.9 201.3 0.3 .. .. .. 1.8 121.6 India 429.8 ­2,383.4 0.0 0.0 0.0 ­1,341.7 ­24.5 21.2 13.2 30.4 9.4 35.5 ­3,210.1 Indonesia 59.8 ­706.0 0.0 ­950.2 8.0 384.1 ­37.6 4.1 6.2 5.1 0.4 20.3 ­1,205.8 Iran, Islamic Rep. 0.0 ­56.3 0.0 0.0 0.0 0.0 0.0 0.9 2.4 1.8 0.8 23.8 ­26.6 Iraq .. .. .. .. .. .. .. 0.7 0.4 1.7 1.6 13.8 18.2 Ireland Israel .. .. .. .. .. .. .. .. .. .. .. 0.3 0.3 Italy Jamaica 0.0 39.9 0.0 ­18.7 ­4.6 92.2 ­0.3 0.2 0.3 0.6 .. 1.6 111.1 Japan Jordan ­2.6 110.7 0.0 13.6 0.0 0.0 ­3.3 0.5 0.8 0.7 1.7 83.3 205.3 Kazakhstan 0.0 35.9 0.0 0.0 0.0 4.4 19.6 0.7 0.6 0.9 .. 2.1 64.2 Kenya 23.6 ­11.8 ­18.2 0.0 ­1.0 ­8.1 ­8.2 4.6 4.9 4.7 10.5 30.2 31.2 Korea, Dem. Rep. .. .. .. .. .. .. .. 0.7 1.1 2.1 0.1 4.1 7.9 Korea, Rep. .. .. .. .. .. .. .. 0.0 .. .. .. 1.5 1.5 Kuwait .. .. .. .. .. .. .. .. .. .. .. 0.3 0.3 Kyrgyz Republic 33.4 0.0 ­1.4 ­7.0 27.2 ­7.4 6.4 1.5 0.6 0.9 .. 1.7 56.0 Lao PDR 27.2 0.0 2.6 0.0 43.7 0.0 1.8 1.1 1.8 1.5 2.7 2.0 84.5 Latvia 0.0 ­2.8 0.0 ­9.9 0.0 ­12.7 18.3 0.1 0.1 .. .. 0.5 ­6.3 Lebanon 0.0 35.7 0.0 0.0 0.0 0.0 ­23.0 0.5 0.6 0.6 .. 53.5 67.8 Lesotho 18.5 ­21.3 5.3 0.0 3.7 ­1.5 ­1.9 0.9 0.5 1.3 3.5 0.9 9.7 Liberia 0.0 0.0 0.0 ­0.3 0.0 0.0 0.0 0.8 0.5 1.5 5.0 11.6 19.1 Libya .. .. .. .. .. .. .. .. .. .. .. 4.7 4.7 Lithuania 0.0 ­9.7 0.0 ­40.2 0.0 ­7.8 ­25.8 0.2 0.1 .. .. 0.4 ­82.8 Macedonia, FYR 18.2 9.6 ­1.2 ­7.6 0.0 ­23.9 33.4 0.4 .. 0.6 0.0 5.5 35.0 Madagascar 157.3 0.0 13.1 0.0 3.5 ­5.8 5.7 5.5 1.7 5.1 4.2 4.7 195.0 Malawi 45.8 ­1.6 ­7.3 22.5 17.4 ­2.3 0.0 2.8 2.9 4.9 7.0 4.4 96.5 Malaysia 0.0 ­70.0 0.0 0.0 0.0 ­30.3 ­2.4 0.4 0.2 0.4 .. 1.0 ­100.8 Mali 87.5 0.0 ­9.6 0.0 5.2 0.0 5.5 3.6 2.2 5.3 4.7 3.2 107.5 Mauritania 40.7 0.0 10.0 0.0 11.0 2.1 19.5 0.6 2.0 1.4 5.0 3.4 95.8 Mauritius ­0.6 19.6 0.0 0.0 ­0.1 48.3 2.1 0.2 0.2 0.5 .. 1.0 71.1 Mexico 0.0 ­86.4 0.0 0.0 0.0 598.0 0.0 0.8 4.7 1.2 .. 6.3 524.6 Moldova 21.9 ­4.4 12.0 ­17.8 0.0 ­3.1 ­9.6 0.8 0.2 0.6 .. 1.3 1.9 Mongolia 13.3 0.0 ­7.7 0.0 26.0 0.0 1.4 1.2 2.0 0.9 .. 3.4 40.6 Morocco ­1.4 ­222.3 0.0 0.0 2.7 ­284.4 193.6 1.0 0.9 1.5 1.3 3.0 ­304.1 Mozambique 146.9 0.0 5.9 0.0 68.1 ­1.1 15.0 4.0 5.9 6.5 5.7 6.7 263.6 Myanmar 0.0 0.0 0.0 0.0 0.0 0.0 ­0.9 6.5 1.5 7.4 0.8 11.0 26.3 Namibia .. .. .. .. .. .. .. ­0.2 1.2 1.2 0.9 7.3 10.4 Nepal 14.5 0.0 ­4.5 0.0 1.9 0.0 2.9 6.5 3.3 3.3 7.5 14.2 49.6 Netherlands New Zealand Nicaragua 71.7 0.0 4.7 0.0 100.8 ­1.1 20.3 2.2 2.0 0.7 2.1 1.4 204.8 Niger 68.3 0.0 19.6 0.0 17.0 0.0 22.7 3.4 2.9 6.1 4.0 4.3 148.3 Nigeria 7.6 ­176.1 0.0 0.0 27.1 ­80.6 ­1.8 12.7 6.4 18.3 .. 24.1 ­162.3 Norway Oman 0.0 ­1.4 0.0 0.0 0.0 0.0 ­9.9 .. 0.0 0.4 .. 1.5 ­9.4 Pakistan 851.3 ­208.5 297.0 ­222.0 153.4 106.3 ­173.7 6.4 4.2 11.0 4.4 34.5 864.2 Panama 0.0 3.2 0.0 ­8.1 ­9.5 50.3 4.0 0.2 0.5 0.6 .. 1.8 43.1 Papua New Guinea ­3.4 ­14.2 0.0 0.0 ­2.4 ­2.5 ­0.7 1.6 0.8 1.1 .. 1.9 ­17.8 Paraguay ­1.4 ­1.9 0.0 0.0 ­10.4 13.8 ­13.9 0.2 0.6 0.6 .. 0.9 ­11.6 Peru 0.0 ­16.8 0.0 ­173.3 ­5.0 168.8 307.0 0.7 6.4 1.0 2.1 9.6 300.4 Philippines ­5.2 ­143.8 0.0 ­405.3 ­1.2 ­18.6 ­0.7 2.3 3.3 2.8 .. 3.7 ­562.7 Poland 0.0 ­33.3 0.0 0.0 0.0 0.0 0.0 0.3 0.1 .. .. 1.5 ­31.5 Portugal Puerto Rico 2004 World Development Indicators 343 6.12 Net financial flows from multilateral institutions International financial institutions United Nations Total $ millions Regional development World Bank IMF banks Conces- Non- Conces- Non- $ millions IDA IBRD sional concessional sional concessional Others UNDP UNFPA UNICEF WFP Others $ millions 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 Romania 0.0 214.3 0.0 9.0 0.0 9.9 ­16.1 0.5 0.4 0.7 .. 2.0 220.7 Russian Federation 0.0 ­296.3 0.0 ­1,486.0 0.0 ­15.4 0.1 0.3 0.6 .. 1.0 17.6 ­1,778.0 Rwanda 73.2 0.0 0.7 ­2.5 6.5 ­0.1 ­1.7 2.1 1.7 3.1 4.8 10.1 98.1 Saudi Arabia .. .. .. .. .. .. .. .. 0.0 0.6 .. 12.8 13.4 Senegal 108.4 0.0 ­10.8 0.0 18.2 ­14.6 32.9 3.4 2.2 1.9 3.7 3.9 149.1 Serbia and Montenegro 159.4 0.0 0.0 0.0 0.0 0.0 84.2 .. 2.0 0.5 ­0.4 1.6 247.4 Sierra Leone 43.0 0.0 35.5 0.0 14.2 0.0 ­0.5 2.8 1.1 3.0 6.5 24.8 130.3 Singapore .. .. .. .. .. .. .. .. .. .. .. 0.3 0.3 Slovak Republic 0.0 ­21.9 0.0 0.0 0.0 ­1.2 2.1 0.2 .. .. .. 1.8 ­19.0 Slovenia .. .. .. .. .. .. .. 0.1 .. .. .. 0.8 0.9 Somalia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 0.6 4.5 1.4 13.1 23.7 South Africa 0.0 5.1 0.0 0.0 0.0 ­0.8 0.0 1.8 1.5 1.6 .. 6.7 15.9 Spain Sri Lanka 58.9 ­4.4 ­50.8 125.2 70.0 37.2 4.1 1.7 1.2 0.7 4.2 10.2 258.2 Sudan ­0.3 0.0 0.0 ­22.0 0.0 0.0 0.0 2.0 2.1 4.4 11.3 26.8 24.1 Swaziland ­0.3 0.1 0.0 0.0 2.7 ­2.6 2.4 0.2 0.6 1.5 0.1 2.2 6.8 Sweden Switzerland Syrian Arab Republic ­1.5 ­6.2 0.0 0.0 0.0 0.0 ­49.6 1.1 4.0 0.8 0.9 33.6 ­16.9 Tajikistan 9.6 0.0 ­14.2 ­9.7 14.1 0.0 ­7.5 2.4 0.7 1.2 4.9 1.8 3.3 Tanzania 142.8 ­2.4 47.0 0.0 14.2 ­0.8 9.4 5.6 7.5 6.9 7.7 33.3 271.1 Thailand ­3.4 ­695.8 0.0 ­1,360.0 ­2.6 ­1,274.1 ­74.9 0.3 0.3 0.9 .. 9.3 ­3,400.1 Togo 6.8 0.0 ­9.4 0.0 0.0 0.0 2.4 1.6 1.1 1.6 .. 1.7 5.7 Trinidad and Tobago 0.0 ­3.1 0.0 0.0 ­0.1 ­19.7 6.3 0.1 0.0 .. .. 2.0 ­14.5 Tunisia ­2.1 ­36.9 0.0 0.0 0.0 85.2 69.8 0.4 0.4 0.7 .. 1.6 119.0 Turkey ­5.9 594.3 0.0 6,490.9 0.0 0.0 ­103.3 0.9 0.9 0.8 .. 6.3 6,984.8 Turkmenistan 0.0 0.7 .. .. .. .. .. 0.5 0.6 0.9 .. 0.8 3.5 Uganda 95.8 0.0 ­17.0 0.0 14.7 ­0.7 ­4.1 4.0 5.4 5.0 14.9 20.2 138.0 Ukraine 0.0 ­76.4 0.0 ­182.3 0.0 ­26.0 ­61.8 1.4 0.6 .. .. 4.4 ­340.0 United Arab Emirates .. .. .. .. .. .. .. ­0.1 .. .. .. 0.5 0.5 United Kingdom United States Uruguay 0.0 158.3 0.0 1,559.8 ­1.7 488.2 ­3.8 0.3 0.1 0.6 .. 1.3 2,203.0 Uzbekistan 0.0 21.5 0.0 ­21.5 6.8 5.2 0.0 1.2 0.6 1.9 .. 1.4 16.9 Venezuela, RB 0.0 ­169.1 0.0 0.0 0.0 45.7 271.0 0.3 0.7 0.7 .. 3.7 152.9 Vietnam 258.9 0.0 ­9.0 ­5.2 175.1 20.0 6.6 4.1 2.9 4.0 .. 6.6 464.1 West Bank and Gaza .. .. .. .. .. .. .. 3.5 1.3 1.6 5.4 238.1 249.9 Yemen, Rep. 63.9 0.0 0.0 ­17.6 0.0 0.0 ­1.3 5.1 2.8 3.0 3.5 7.0 66.4 Zambia 140.5 ­6.4 109.8 0.0 22.8 ­11.0 ­15.9 2.8 2.4 3.7 11.0 20.0 279.8 Zimbabwe 0.0 0.0 ­1.1 ­1.9 0.0 ­0.7 1.6 2.3 1.0 1.8 4.2 6.2 13.4 World .. s .. s .. s .. s .. s .. s .. s 277.9 s 312.5 s 571.4 s 351.6 s 2,156.0 s .. s Low income 4,753.6 ­3,720.2 959.7 ­1,583.3 1,166.6 ­1,159.7 ­6.0 232.1 160.2 264.0 281.5 651.4 2,000.0 Middle income 771.0 ­2,013.3 ­53.9 14,691.3 118.3 ­128.9 924.5 46.5 80.8 63.3 58.3 920.5 15,478.4 Lower middle income 758.1 ­682.5 ­53.9 14,083.9 143.4 ­664.8 428.7 44.6 67.1 53.1 58.3 712.2 14,948.2 Upper middle income 12.8 ­1,330.9 0.0 607.4 ­25.2 535.9 495.8 1.8 9.8 9.8 0.0 123.1 440.3 Low & middle income 5,524.6 ­5,733.5 905.8 13,108.0 1,284.9 ­1,288.6 918.6 278.9 312.5 571.4 351.6 2,149.1 18,383.4 East Asia & Pacific 489.4 ­2,210.6 ­3.3 ­2,722.1 320.6 ­919.0 ­119.8 38.6 32.6 44.3 19.2 92.7 ­4,937.4 Europe & Central Asia 597.0 366.0 26.2 4,609.8 48.1 ­95.9 231.0 19.8 10.3 16.3 11.4 147.1 5,987.0 Latin America & Carib. 230.1 ­530.2 ­20.4 11,899.2 134.3 1,526.4 767.1 11.2 47.7 21.8 19.9 231.0 14,338.1 Middle East & N. Africa 94.1 ­352.8 5.9 ­303.8 7.4 ­297.5 37.5 16.6 17.3 18.6 25.6 543.8 ­187.2 South Asia 1,557.4 ­2,601.9 218.9 ­161.9 327.6 ­1,192.0 ­121.1 60.7 42.3 67.3 55.3 126.4 ­1,620.8 Sub-Saharan Africa 2,556.6 ­404.1 678.4 ­213.3 447.0 ­310.5 123.8 131.8 98.2 168.2 213.8 616.1 4,106.0 High income Europe EMU Note: The aggregates for the regional development banks, United Nations, and total net financial flows include amounts for economies not specified elsewhere. 344 2004 World Development Indicators 6.12 Net financial flows from GLOBAL LINK multilateral institutions S About the data Definitions The table shows concessional and nonconcessional Eligibility is based principally on a country's per capi- · Net financial flows in this table are disbursements financial flows from the major multilateral ta income and eligibility under IDA, the World Bank's of public or publicly guaranteed loans and credits, less institutions--the World Bank, the International concessional window. repayments of principal. · IDA is the International Monetary Fund (IMF), regional development banks, Regional development banks also maintain conces- Development Association, the concessional loan win- United Nations agencies, and regional groups such sional windows for funds. Loans from the major region- dow of the World Bank. · IBRD is the International as the Commission of the European Communities. al development banks--the African Development Bank for Reconstruction and Development, the found- Much of the data comes from the World Bank's Bank, Asian Development Bank, and Inter-American ing and largest member of the World Bank Group. Debtor Reporting System. Development Bank--are recorded in the table accord- · IMF is the International Monetary Fund. Its noncon- The multilateral development banks fund their non- ing to each institution's classification. cessional lending consists of the credit it provides to concessional lending operations primarily by selling In 1999 all United Nations agencies revised their its members, mainly to meet their balance of pay- low-interest, highly rated bonds (the World Bank, for data since 1990 to include only regular budgetary ments needs. It provides concessional assistance example, has an AAA rating) backed by prudent lend- expenditures (except for the World Food Programme through the Poverty Reduction and Growth Facility and ing and financial policies and the strong financial sup- and the United Nations High Commissioner for the IMF Trust Fund. · Regional development banks port of their members. These funds are then on-lent Refugees, which revised their data from 1996 include the African Development Bank, in Abidjan, at slightly higher interest rates and with relatively long onward). They did so to avoid double counting extra- Côte d'Ivoire, which lends to all of Africa, including maturities (15­20 years) to developing countries. budgetary expenditures reported by DAC countries North Africa; the Asian Development Bank, in Manila, Lending terms vary with market conditions and the and flows reported by the United Nations. Philippines, which serves countries in South and policies of the banks. Central Asia and East Asia and Pacific; the European Concessional flows from bilateral donors are Bank for Reconstruction and Development, in London, defined by the Development Assistance Committee United Kingdom, which serves countries in Europe and (DAC) of the Organisation for Economic Co-operation Central Asia; the European Development Fund, in and Development (OECD) as financial flows contain- Brussels, Belgium, which serves countries in Africa, ing a grant element of at least 25 percent. The grant the Caribbean, and the Pacific; and the Inter-American element of loans is evaluated assuming a nominal Development Bank, in Washington, D.C., which is the market interest rate of 10 percent. The grant ele- principal development bank of the Americas. ment is nil for a loan carrying a 10 percent interest Concessional financial flows cover disbursements rate, and it is 100 percent for a grant, which requires made through concessional lending facilities. no repayment. Concessional flows from multilateral Nonconcessional financial flows cover all other dis- development agencies are credits provided through bursements. · Others is a residual category in the their concessional lending facilities. The cost of World Bank's Debtor Reporting System. It includes these loans is reduced through subsidies provided such institutions as the Caribbean Development Bank by donors or drawn from other resources available to and the European Investment Bank. · United Nations the agencies. Grants provided by multilateral agen- includes the United Nations Development Programme cies are not included in the net flows. (UNDP), United Nations Population Fund (UNFPA), All concessional lending by the World Bank is car- United Nations Children's Fund (UNICEF), World Food ried out by the International Development Programme (WFP), and other United Nations agencies, Association (IDA). Eligibility for IDA resources is such as the United Nations High Commissioner for based on gross national income (GNI) per capita; Refugees, United Nations Relief and Works Agency for countries must also meet performance standards Palestine Refugees in the Near East, and United assessed by World Bank staff. Since July 1, 2003, Nations Regular Programme for Technical Assistance. the GNI per capita cutoff has been set at $735, measured in 2002 using the World Bank Atlas Data sources method (see Users guide). In exceptional circum- The data on net financial flows from international stances IDA extends eligibility temporarily to coun- financial institutions come from the World Bank's tries that are above the cutoff and are undertaking Debtor Reporting System. These data are published major adjustment efforts but are not creditworthy for in the World Bank's Global Development Finance lending by the International Bank for Reconstruction 2004 and electronically as GDF Online. The data on and Development (IBRD). An exception has also aid from United Nations agencies come from the been made for small island economies. Lending by DAC annual Development Cooperation Report. Data the International Finance Corporation is not included are available in electronic format on the OECD's in this table. International Development Statistics CD-ROM and The IMF makes concessional funds available to registered users at http://www.oecd.org/ through its Poverty Reduction and Growth Facility, dataoecd/50/17/5037721.htm. which replaced the Enhanced Structural Adjustment Facility in 1999, and through the IMF Trust Fund. 2004 World Development Indicators 345 6.13 Foreign labor and population in selected OECD countries Foreign population a Foreign labor force b Inflows of foreign population % of total % of total Total Asylum seekers thousands population labor force thousands c thousands 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Austria 456 764 5.9 9.4 7.4 11.0 .. 75 23 30 Belgium 905 847 9.1 8.2 7.1 8.9 50 66 13 25 Denmark 161 267 3.1 5.0 2.4 3.5 15 25 13 10 Finland 26 99 0.5 1.9 .. 1.7 6 11 3 2 France 3,597 3,263 6.3 5.6 6.2 6.2 102 d 141 d 55 47 Germany 5,343 7,319 8.4 8.9 .. 9.1 e 842 685 193 88 Ireland 80 151 2.3 3.9 2.6 4.6 .. 28 d 0 10 Italy 781 1,363 1.4 2.4 1.4 3.8 .. 233 d 5 13 Japan 1,075 1,778 0.9 1.4 0.1 0.2 224 351 .. 0 Luxembourg 113 167 29.4 37.5 45.2 e 61.7 e 9 11 0 1 Netherlands 692 690 4.6 4.3 3.1 .. 81 95 21 33 Norway 143 186 3.4 4.1 2.3 4.9 16 25 4 15 Portugal 108 224 1.1 2.2 1.0 2.0 .. 14 d 0 0 Spain 279 1,109 0.7 2.7 0.6 3.4 .. .. 9 9 Sweden 484 476 5.6 5.3 5.4 5.1 53 44 29 24 Switzerland 1,100 1,419 16.3 19.7 18.9 18.1 101 100 36 21 United Kingdom 1,723 2,587 3.2 4.4 3.3 4.4 175 373 38 92 Foreign-born population a Foreign-born Inflows of foreign population labor force b % of total % of total Total Asylum seekers thousands population labor force thousands c,d thousands 1990 2001 1990 2001 1990 2001 1990 2001 1990 2001 Australia 3,965 4,482 22.9 23.1 25.7 24.2 121 88.9 4 13 Canada 4,343 5,448 16.1 18.2 18.5 19.9 214 250 37 42 United States 19,767 f 31,811 g 7.9 f 11.1 g 9.4 13.9 1,536 1,064 74 63 a. Data are from population registers or from registers of foreigners, except for Australia (1991­2001); Canada (1991­2001); France (1990­99); and the United States (censuses); Italy, Portugal, and Spain (residence permits); and Ireland and the United Kingdom (labor force surveys) and refer to the population on December 31 of the year indicated. b. Data include the unem- ployed, except in Italy, Luxembourg, the Netherlands, Norway, and the United Kingdom. Cross-border and seasonal workers are excluded unless otherwise noted. c. Inflow data are based on population registers and are not fully comparable because the criteria governing who gets registered differ from country to country. Counts for the Netherlands, Norway, and (especially) Germany include substantial numbers of asylum seekers. d. Data are based on residence permits or other sources. e. Includes cross-border workers. f. From the U.S. Census Bureau, 1990 Census of Population. g. From the U.S. Census Bureau, Current Population Report (March 2000). 346 2004 World Development Indicators 6.13 Foreign labor and population in GLOBAL LINK selected OECD countries S About the data Definitions The data in the table are based on national definitions OECD countries are not the only ones that receive · Foreign (or foreign-born) population is the number and data collection practices and are not fully compa- substantial migration flows. Migrant workers make of foreign or foreign-born residents in a country. rable across countries. Japan and the European mem- up a significant share of the labor force in Gulf · Foreign (or foreign-born) labor force as a percent- bers of the Organisation for Economic Co-operation countries and in southern Africa, and people are dis- age of total labor force is the share of foreign or and Development (OECD) have traditionally defined for- placed by wars and natural disasters throughout the foreign-born workers in a country's workforce. eigners by nationality of descent. Australia, Canada, world. Systematic recording of migration flows is dif- · Inflows of foreign population are the gross arrivals and the United States use place of birth, which is ficult, however, especially in poor countries and of immigrants in the country shown. The total does not closer to the concept used in the United Nations' def- those affected by civil disorder. include asylum seekers, except as noted. · Asylum inition of the immigrant stock. Few countries, however, seekers are immigrants who apply for permission to apply just one criterion in all circumstances. For this remain in a country for humanitarian reasons. and other reasons, data based on the concept of for- eign nationality and data based on the concept of for- eign born cannot be completely reconciled. See the notes to the table for other breaks in comparability between countries and over time. Data on the size of the foreign labor force are also problematic. Countries use different permit systems to gather information on immigrants. Some countries issue a single permit for residence and work, while others issue separate residence and work permits. Differences in immigration laws across countries, particularly with respect to immigrants' access to employment, greatly affect the recording and meas- urement of migration and reduce the international comparability of raw data. The data exclude tempo- rary visitors and tourists (see table 6.14). 6.13a Migration to OECD countries is growing Foreign population (% of total population) Luxembourg Australia Switzerland Canada 1990 United States 2001 Austria Germany Belgium France Sweden Denmark United Kingdom Netherlands Norway Ireland Spain Data sources Italy International migration data are collected by the Portugal OECD through information provided by national Finland correspondents to the Continuous Reporting Japan System on Migration (SOPEMI) network, which 0 5 10 15 20 25 30 35 40 provides an annual overview of trends and poli- The proportion of foreigners has increased in most OECD countries over the past 11 years. Only Belgium, France, Sweden, and the Netherlands have shown small declines. cies. The data appear in the OECD's Trends in Note: Australia, Canada, and the United States refer to foreign born. International Migration 2003. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. 2004 World Development Indicators 347 6.14 Travel and tourism International tourism International tourism receipts International tourism expenditures thousands Inbound tourists Outbound tourists $ millions % of exports $ millions % of imports 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Afghanistan .. .. .. .. .. .. .. .. 1 .. .. .. Albania 30 34 .. .. 4 487 1.1 53.2 4 366 0.8 17.6 Algeria 1,137 988 3,828 1,257 64 133 0.5 .. 149 193 1.5 .. Angola 67 91 .. .. 13 22 0.3 0.3 38 66 1.1 1.0 Argentina 1,930 2,820 2,398 3,008 1,131 2,547 7.6 8.2 1,505 3,800 22.0 13.8 Armenia 15 123 .. 110 .. 63 .. 9.0 .. 54 .. 4.9 Australia 2,215 4,841 2,170 3,461 4,088 8,087 8.2 9.7 4,535 6,116 8.5 6.9 Austria 19,011 18,611 2,572 3,907 13,417 11,237 21.1 10.3 7,748 9,391 12.6 9.0 Azerbaijan 77 834 .. 1,130 42 51 .. 1.9 .. 106 .. 3.4 Bangladesh 115 207 388 1,075 11 57 0.5 0.8 78 202 2.0 2.2 Belarus .. 61 .. 1,386 .. 193 .. 2.1 .. 559 .. 5.7 Belgium 5,147 6,724 6,453 6,773 3,721 6,892 2.7 3.2 5,477 10,435 4.1 4.8 Benin 110 72 418 .. 55 60 15.1 10.8 15 7 3.3 0.9 Bolivia 254 308 242 240 91 156 9.3 10.3 130 118 12.0 6.0 Bosnia and Herzegovina .. 160 .. .. .. 112 .. 7.9 .. 49 .. 1.0 Botswana 543 1,037 192 .. 117 309 5.8 11.7 56 .. 2.8 .. Brazil 1,091 3,783 1,188 1,861 1,444 3,120 4.1 4.5 1,559 2,380 5.5 3.8 Bulgaria 1,586 3,433 2,395 3,188 320 1,344 4.6 16.2 189 616 2.4 6.6 Burkina Faso 74 149 .. .. 11 34 3.2 13.1 32 .. 4.2 .. Burundi 109 36 24 35 4 1 4.5 2.6 17 14 5.3 9.5 Cambodia 17 787 .. .. 50 379 15.9 16.1 .. 38 .. 1.4 Cameroon 89 221 .. .. 53 39 2.1 .. 279 .. 11.3 .. Canada 15,209 20,057 20,415 17,705 6,339 9,700 4.2 3.2 10,931 9,929 7.3 3.7 Central African Republic 6 .. .. .. 3 .. 1.4 .. 51 .. 12.4 .. Chad 9 32 24 39 8 .. 3.0 .. 70 .. 14.4 .. Chile 943 1,412 768 1,938 540 845 5.3 3.8 426 793 4.6 3.8 China 10,484 36,803 2,134 16,600 2,218 20,385 3.9 5.6 470 15,398 1.0 4.7 Hong Kong, China 6,581 16,566 2,043 4,709 5,032 10,117 .. 4.2 .. 12,417 .. 5.4 Colombia 813 541 781 1,241 406 962 4.7 6.8 454 1,072 6.6 7.0 Congo, Dem. Rep. 55 103 .. .. 7 .. .. .. 16 .. .. .. Congo, Rep. 33 19 .. .. 8 25 0.5 1.0 113 70 8.8 4.3 Costa Rica 435 1,113 191 .. 275 1,078 14.0 15.1 148 367 6.3 4.8 Côte d'Ivoire 196 .. 2 .. 51 50 1.5 0.9 169 290 4.9 7.5 Croatia 7,049 6,944 .. .. 1,704 3,811 .. 36.1 729 781 .. 6.1 Cuba 327 1,656 12 .. 243 1,633 .. .. .. .. .. .. Czech Republic 7,278 4,579 13,380 .. 419 2,941 .. 6.5 455 1,575 .. 3.3 Denmark 1,838 2,010 2,530 .. 3,322 5,785 6.8 7.0 3,676 6,856 8.9 9.5 Dominican Republic 1,305 2,811 137 .. 900 2,736 49.1 33.2 144 295 6.4 2.9 Ecuador 362 654 181 598 188 447 5.8 7.2 175 364 6.9 4.7 Egypt, Arab Rep. 2,411 4,906 2,012 3,074 1,100 3,764 11.1 22.9 129 1,278 0.9 6.6 El Salvador 194 951 525 1,001 18 342 1.8 9.0 61 229 3.8 3.9 Eritrea 169 101 .. .. .. 73 .. 39.1 .. .. .. .. Estonia 372 1,360 80 1,658 27 555 4.1 10.1 19 231 2.7 3.8 Ethiopia 79 148 89 .. 25 75 4.2 7.7 11 45 0.9 2.2 Finland 1,572 2,875 1,169 5,824 1,167 1,573 3.7 3.1 2,791 1,966 8.3 4.9 France 52,497 77,012 19,430 17,404 20,184 32,329 7.1 8.2 12,423 19,460 4.4 5.3 Gabon 109 212 161 .. 3 7 0.1 0.2 137 170 7.6 8.7 Gambia, The 100 75 .. .. 26 .. 15.5 .. 8 .. 4.2 .. Georgia .. 298 .. 317 .. 472 .. 48.4 .. 174 .. 12.4 Germany 17,045 17,969 .. 73,300 14,288 19,158 3.0 2.7 33,771 53,196 7.9 8.3 Ghana 146 483 .. .. 81 358 8.2 13.9 13 120 0.9 3.6 Greece 8,873 14,180 1,651 .. 2,587 9,741 19.9 32.4 1,090 2,450 5.6 5.8 Guatemala 509 884 289 629 185 612 11.8 16.2 100 267 5.5 4.0 Guinea .. 43 .. .. 30 31 3.6 3.2 30 21 3.1 2.1 Guinea-Bissau .. 8 .. .. .. .. .. .. .. .. .. .. Haiti 144 142 .. .. 46 54 14.5 .. 37 .. 7.2 .. 348 2004 World Development Indicators 6.14 GLOBAL LINK Travel and tourism S International tourism International tourism receipts International tourism expenditures thousands Inbound tourists Outbound tourists $ millions % of exports $ millions % of imports 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Honduras 290 550 196 285 29 342 2.8 14.0 38 185 3.4 5.4 Hungary 3,693 3,013 13,596 12,966 824 3,273 6.8 7.7 477 1,722 4.3 3.9 India 1,707 2,384 2,281 4,205 1,513 2,923 6.6 3.8 393 3,449 1.3 4.1 Indonesia 2,178 5,033 688 .. 2,105 4,306 7.2 6.5 836 3,368 3.0 6.4 Iran, Islamic Rep. 154 1,402 788 2,400 61 1,122 0.3 3.9 340 238 1.5 1.1 Iraq 748 127 239 .. 55 .. .. .. .. .. .. .. Ireland 3,666 6,476 1,798 4,634 1,459 3,089 5.4 2.7 1,163 3,741 4.7 4.1 Israel 1,063 862 883 3,273 1,396 1,197 8.1 3.1 1,442 2,547 7.1 6.0 Italy 26,679 39,799 .. 25,126 16,458 26,915 7.5 8.6 10,304 16,935 4.7 5.6 Jamaica 989 1,266 .. .. 740 1,209 33.4 37.4 114 258 4.8 5.3 Japan 3,236 5,239 10,997 16,523 3,578 3,499 1.1 0.8 24,928 26,681 8.4 6.5 Jordan 572 1,622 1,143 1,726 512 786 20.4 18.4 336 416 9.4 6.7 Kazakhstan .. 2,832 .. 2,274 .. 621 .. 5.3 .. 756 .. 6.6 Kenya 814 838 210 .. 443 297 19.9 9.0 38 143 1.4 3.6 Korea, Dem. Rep. 115 .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2,959 5,347 1,561 7,123 3,559 5,277 4.9 2.8 3,166 7,642 4.1 4.2 Kuwait 15 73 .. .. 132 119 1.6 0.7 1,837 3,021 25.6 21.5 Kyrgyz Republic .. .. .. .. 2 36 .. 5.7 .. 10 .. 1.4 Lao PDR 14 215 .. .. 3 113 2.9 21.8 1 8 0.5 1.4 Latvia .. 848 .. 2,306 7 161 0.6 4.2 13 230 1.3 4.9 Lebanon 210 956 .. .. .. 956 .. 39.8 .. .. .. .. Lesotho 171 .. 254 .. 17 20 17.0 5.1 12 14 1.6 1.8 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 96 174 425 .. 6 .. 0.1 .. 424 .. 4.7 .. Lithuania 780 1,271 .. 3,584 .. 383 .. 6.3 .. 218 .. 3.3 Macedonia, FYR 562 99 .. .. 45 23 .. 1.6 .. .. .. .. Madagascar 53 170 34 .. 40 115 8.5 9.0 40 115 4.9 7.8 Malawi 130 285 .. .. 16 125 3.6 26.5 16 78 2.9 9.8 Malaysia 7,446 13,292 14,920 36,248 1,667 6,785 5.1 6.3 1,450 2,618 4.6 2.9 Mali 44 96 .. .. 47 71 11.2 11.0 62 41 7.5 4.4 Mauritania .. 30 .. .. 9 .. 1.9 .. 23 .. 4.4 .. Mauritius 292 682 89 162 244 612 14.2 20.6 94 204 4.9 7.3 Mexico 17,176 19,667 7,357 11,948 5,467 8,858 11.2 5.1 5,519 6,060 10.6 3.3 Moldova 226 18 129 52 .. 47 .. 5.4 .. 86 .. 6.7 Mongolia 147 198 .. .. 5 130 1.0 18.4 1 119 0.1 12.6 Morocco 4,024 4,193 1,202 1,533 1,259 2,152 20.2 17.6 184 444 2.4 3.3 Mozambique .. .. .. .. .. 144 .. 12.5 .. 296 .. 16.6 Myanmar 21 217 .. .. 9 45 2.8 1.6 16 27 2.7 0.9 Namibia 213 670 .. .. 85 404 7.0 29.6 63 .. 4.0 .. Nepal 255 275 82 200 64 107 15.2 12.1 45 80 5.4 4.7 Netherlands 5,795 9,595 9,000 16,760 4,155 7,706 2.6 2.9 7,376 12,919 5.0 5.3 New Zealand 976 2,045 717 1,293 1,030 2,918 8.8 14.9 958 1,480 8.2 7.9 Nicaragua 106 472 173 532 12 116 3.1 12.8 15 69 2.2 3.5 Niger 21 52 18 .. 17 .. 3.2 .. 44 28 6.0 .. Nigeria 190 831 56 .. 25 156 0.2 0.8 576 700 8.3 4.9 Norway 1,955 3,107 508 .. 1,570 2,738 3.3 3.5 3,679 5,814 9.5 11.1 Oman 149 602 .. .. 69 116 1.2 1.0 47 367 1.4 5.3 Pakistan 424 498 .. .. 156 105 2.3 0.9 440 179 4.3 1.4 Panama 214 534 151 200 179 679 4.0 9.0 99 178 2.4 2.3 Papua New Guinea 41 54 66 92 41 101 3.0 4.8 50 .. 3.3 .. Paraguay 280 250 264 141 128 62 5.1 2.2 103 65 4.7 2.4 Peru 317 862 329 859 217 801 5.3 8.7 295 616 7.2 6.2 Philippines 1,025 1,933 1,137 1,968 1,306 1,741 11.4 4.7 111 871 0.8 2.3 Poland 3,400 13,980 22,131 45,043 358 4,500 1.9 7.9 423 3,200 2.8 5.1 Portugal 8,020 11,666 192 .. 3,555 5,919 16.5 16.1 867 2,274 3.2 5.0 Puerto Rico 2,560 3,087 996 1,227 1,366 2,486 .. .. 630 928 .. .. 2004 World Development Indicators 349 6.14 Travel and tourism International tourism International tourism receipts International tourism expenditures thousands Inbound tourists Outbound tourists $ millions % of exports $ millions % of imports 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 Romania 3,009 3,204 11,247 5,757 106 612 1.7 3.8 103 396 1.0 2.1 Russian Federation 3,009 7,943 4,150 20,343 752 4,188 .. 3.5 .. 12,005 .. 14.1 Rwanda .. 113 .. .. 10 31 7.0 23.4 23 24 6.5 5.5 Saudi Arabia 2,209 7,511 .. 7,896 1,884 3,420 4.0 4.7 .. 7,356 .. 14.9 Senegal 246 427 .. .. 167 140 11.5 10.9 105 .. 5.7 .. Serbia and Montenegro 1,186 448 .. .. 419 77 .. 2.4 .. .. .. .. Sierra Leone 98 28 .. 27 19 12 9.1 .. 4 6 1.9 .. Singapore 4,842 6,996 1,237 4,399 4,937 4,932 7.3 3.1 1,893 5,213 2.9 3.8 Slovak Republic 822 1,399 188 437 70 724 .. 4.2 181 442 .. 2.3 Slovenia 650 1,302 .. 2,055 721 1,083 8.5 8.5 282 614 4.1 4.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 1,029 6,550 616 2,794 992 2,728 3.6 7.7 1,117 1,804 5.3 5.6 Spain 34,085 51,748 21,878 3,748 18,593 33,609 22.2 17.8 4,254 6,638 4.2 3.4 Sri Lanka 298 337 297 533 132 253 5.8 4.2 74 253 2.5 3.6 Sudan 33 52 219 .. 21 56 4.2 3.3 51 91 5.8 3.1 Swaziland 263 256 .. .. 30 26 4.6 2.4 35 33 4.6 2.8 Sweden 1,900 7,458 8,691 12,300 2,906 4,233 4.1 4.0 6,286 6,816 8.9 7.6 Switzerland 13,200 10,000 9,627 11,427 7,411 7,628 7.6 5.9 5,873 6,427 6.1 5.8 Syrian Arab Republic 562 1,658 1,041 4,362 320 1,366 6.4 16.6 249 610 8.4 10.2 Tajikistan .. 4 .. 3 .. 2 .. 0.3 .. 2 .. 0.2 Tanzania 153 550 301 .. 65 730 12.1 46.5 23 337 1.6 15.2 Thailand 5,299 10,873 883 2,044 4,326 7,902 14.8 9.6 854 3,303 2.4 4.5 Togo 103 57 .. .. 58 11 8.7 2.6 40 5 4.7 0.8 Trinidad and Tobago 195 379 254 .. 95 224 4.2 5.0 122 151 8.6 3.8 Tunisia 3,204 5,064 1,727 1,669 948 1,422 18.2 14.9 179 260 3.0 2.5 Turkey 4,799 12,782 2,917 5,130 3,225 9,010 15.3 16.5 520 1,881 2.0 3.4 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 69 254 .. 152 10 185 5.6 25.7 8 .. 1.2 .. Ukraine .. 6,326 .. 9,270 .. 2,992 .. 12.8 .. 2,087 .. 9.7 United Arab Emirates 633 5,445 .. .. 169 1,328 .. .. .. .. .. .. United Kingdom 18,013 24,180 31,150 59,377 13,762 17,591 5.8 4.3 17,560 40,409 6.6 9.3 United States 39,362 41,892 44,623 56,359 43,007 66,547 8.0 6.8 37,349 58,044 6.1 4.2 Uruguay 1,267 1,258 .. 530 262 318 12.1 11.7 111 178 6.7 7.0 Uzbekistan .. 332 .. 264 .. 68 .. 2.3 .. .. .. .. Venezuela, RB 525 432 309 881 496 468 2.6 1.7 1,023 1,418 10.8 8.1 Vietnam 250 1,599 .. .. 85 .. .. .. .. .. .. .. West Bank and Gaza .. 7 .. .. .. 0 .. .. .. .. .. .. Yemen, Rep. 52 76 .. .. 20 38 1.3 1.0 64 78 2.9 2.0 Zambia 141 565 .. .. 41 117 3.0 11.1 54 44 2.8 3.3 Zimbabwe 605 2,068 352 .. 60 76 3.0 .. 66 .. 3.3 .. World 448,870 t 692,292 t 380,321 t 692,300 t 264,889 t 472,506 t 6.1 w 5.9 w 268,743 t 449,218 t 6.3 w 5.9 w Low income 10,477 23,235 .. .. 5,892 12,570 5.0 5.5 4,551 10,718 3.8 4.8 Middle income 115,149 215,705 130,244 273,535 42,455 126,123 6.8 7.4 29,525 79,801 5.0 5.6 Lower middle income 55,932 130,729 49,323 98,526 25,307 77,080 7.5 7.7 .. 51,214 3.1 5.8 Upper middle income 58,506 84,172 91,270 .. 17,171 48,887 5.9 6.9 15,183 28,357 7.9 5.2 Low & middle income 127,339 242,033 157,717 .. 48,376 138,458 6.5 7.2 34,183 92,314 4.8 5.5 East Asia & Pacific 28,191 73,291 21,595 61,131 12,577 43,448 7.3 6.3 3,947 26,658 2.4 4.4 Europe & Central Asia 42,782 75,225 124,053 183,289 9,756 36,977 7.4 8.2 .. 28,911 2.6 7.2 Latin America & Carib. 30,253 43,682 16,209 27,174 13,500 28,249 7.9 6.4 12,349 19,019 9.1 4.1 Middle East & N. Africa 15,665 27,947 16,504 21,501 .. .. 4.9 8.9 3,126 .. .. 11.0 South Asia 3,054 4,254 3,503 6,964 1,968 3,774 5.7 3.6 1,048 4,265 2.1 3.6 Sub-Saharan Africa 7,217 19,836 .. .. 3,106 7,557 3.9 6.7 3,641 5,489 5.6 5.4 High income 314,680 442,385 223,939 349,895 215,805 336,311 6.1 5.5 232,521 357,414 6.5 6.0 Europe EMU 183,210 257,531 .. .. 100,855 160,354 6.6 6.8 88,022 141,301 6.0 6.3 350 2004 World Development Indicators 6.14 GLOBAL LINK Travel and tourism S About the data Definitions Tourism is defined as the activities of people traveling The data in the table are from the World Tourism · International inbound tourists (overnight visitors) to and staying in places outside their usual environ- Organization. The data on international inbound and are the number of tourists who travel to a country ment for no more than one consecutive year for outbound tourists refer to the number of arrivals and other than that in which they have their usual resi- leisure, business, and other purposes not related to departures of visitors within the reference period, dence, but outside their usual environment, for a peri- an activity remunerated from within the place visited. not to the number of people traveling. Thus a person od not exceeding 12 months and whose main The social and economic phenomenon of tourism has who makes several trips to a country during a given purpose in visiting is other than an activity remuner- grown substantially over the past quarter of a century. period is counted each time as a new arrival. ated from within the country visited. · International In the past, descriptions of tourism focused on the International visitors include tourists (overnight visi- outbound tourists are the number of departures that characteristics of visitors, such as the purpose of tors), same-day visitors, cruise passengers, and people make from their country of usual residence to their visit and the conditions in which they traveled crew members. any other country for any purpose other than a remu- and stayed. Now, there is a growing awareness of the Regional and income group aggregates are based nerated activity in the country visited. · International direct, indirect, and induced effects of tourism on on the World Bank's classification of countries and tourism receipts are expenditures by international employment, value added, personal income, govern- differ from those shown in the World Tourism inbound visitors, including payments to national carri- ment income, and the like. Organization's publications. Countries not shown in ers for international transport. These receipts include Statistical information on tourism is based mainly the table but for which data are available are includ- any other prepayment made for goods or services on data on arrivals and overnight stays along with bal- ed in the regional and income group totals. World received in the destination country. They also may ance of payments information. But these do not com- totals are no longer calculated by the World Tourism include receipts from same-day visitors, except in pletely capture the economic phenomenon of Organization. The aggregates in the table are calcu- cases where these are important enough to justify a tourism. Thus governments, businesses, and citizens lated using the World Bank's weighted aggregation separate classification. Their share in exports is cal- may not receive the information needed for effective methodology (see Statistical methods) and differ culated as a ratio to exports of goods and services public policies and efficient business operations. from aggregates provided by the World Tourism (for definition of exports of goods and services see Although the World Tourism Organization reports that Organization. Definitions for table 4.9). · International tourism progress has been made in harmonizing definitions expenditures are expenditures of international out- and measurement units, differences in national prac- bound visitors in other countries, including payments tices still prevent full international comparability. By to foreign carriers for international transport. These 2005 the World Tourism Organization will improve expenditures may include those by residents travel- coverage of tourism expenditure data by adding the ing abroad as same-day visitors, except in cases balance of payments category "international passen- where these are so important as to justify a separate ger transportation" to "travel." classification. Their share in imports is calculated as Credible data are needed on the scale and signifi- a ratio to imports of goods and services (for defini- cance of tourism. Information on the role tourism tion of imports of goods and services see Definitions plays in national economies throughout the world is for table 4.9). particularly deficient. 6.14a Tourism is highest in high-income countries $ billions, 2002 400 300 200 Data sources The visitor and expenditure data are available in the World Tourism Organization's Yearbook of 100 Tourism Statistics and Compendium of Tourism 0 Statistics, 2002. The data in the table were Low income Middle income High income updated from electronic files provided by the Receipts Expenditures World Tourism Organization. The data on exports and imports are from the International Monetary Tourism receipts are almost three times larger in high- income economies than in middle-income economies. Fund's International Financial Statistics and World Expenditures are more than five times the size. Bank staff estimates. Source: World Tourism Organization. 2004 World Development Indicators 351 PRIMARY DATA DOCUMENTATION The World Bank is not a primary data collection agency for most issues other than living standards surveys and debt. As a major user of socioeconomic data, however, the World Bank places particular emphasis on data documentation to inform users of data in economic analysis and policymaking. The tables in this section provide information on the sources, treatment, and currentness of the principal demographic, economic, and environmental indicators in World Development Indicators. Differences in the methods and conventions used by the primary data collectors--usually national statistical agencies, central banks, and customs services--may give rise to significant discrepancies over time both among and within countries. Delays in reporting data and the use of old surveys as the base for current estimates may severely compromise the quality of national data. Although data quality is improving in some countries, many developing coun- tries lack the resources to train and maintain the skilled staff and obtain the equipment needed to measure and report demographic, economic, and environ- mental trends in an accurate and timely way. The World Bank recognizes the need for reliable data to measure living standards, track and evaluate economic trends, and plan and monitor development projects. Thus, working with bilateral and other multilateral agencies, it continues to fund and participate in technical assistance projects to improve statistical organization and basic data methods, collection, and dissemination. The World Bank is working at several levels to meet the challenge of improv- ing the quality of the data that it collates and disseminates. With a view to strengthening national capacity the Bank conducts technical assistance, training, and surveys at the country level in the following areas: · Poverty assessments in most borrower member countries. · Living standards measurement and other household and farm surveys with national statistical agency partners. · National accounts and inflation. · Price and expenditure surveys for the International Comparison Program. · Projects to improve statistics in the countries of the former Soviet Union. · External debt management. · Environmental and economic accounting. 2004 World Development Indicators 353 PRIMARY DATA DOCUMENTATION National currency Fiscal National accounts Balance of payments Government IMF year and trade finance data end dissemi- nation Balance of stan- Alternative PPP Payments dard Reporting Base SNA price conversion survey Manual External System Accounting period year valuation factor a year in use debt of trade concept Afghanistan Afghan afghani Mar. 20 FY 1975 VAB Albania Albanian lek Dec. 31 CY 1990 b VAB 1996 BPM5 Actual G C G Algeria Algerian dinar Dec. 31 CY 1980 VAB BPM5 Actual S B Angola Angolan kwanza Dec. 31 CY 1997 VAP 1991­96 BPM4 Estimate S G Argentina Argentine peso Dec. 31 CY 1993 VAB 1971­84 1996 BPM5 Preliminary S C S* Armenia Armenian dram Dec. 31 CY 1996 b,c VAB 1990­95 2000 BPM5 Actual S S* Australia Australian dollar Jun. 30 FY 1995 b,c VAB 2000 BPM5 G C S* Austria Euro Dec. 31 CY 1995 b VAB 2000 BPM5 S C S* Azerbaijan Azeri manat Dec. 31 CY 2000 b,c VAB 1992­95 2000 BPM5 Actual G C G Bangladesh Bangladesh taka Jun. 30 FY 1996 b VAB 1960­2002 1996 BPM5 Actual G G Belarus Belarussian rubel Dec. 31 CY 1990 b,c VAB 1990­95 2000 BPM5 Actual G C Belgium Euro Dec. 31 CY 1995 b VAB 2000 BPM5 S C S* Benin CFA franc Dec. 31 CY 1985 VAP 1992 1996 BPM5 Actual S G Bolivia Boliviano Dec. 31 CY 1990 b VAB 1960­85 1996 BPM5 Actual S C G Bosnia and Herzegovina Convertible mark Dec. 31 CY 1996 c VAB BPM5 Actual Botswana Botswana pula Dec. 31 CY 1994 VAP 1996 BPM5 Actual G B G Brazil Brazilian real Dec. 31 CY 1995 VAB 1996 BPM5 Preliminary S C S* Bulgaria Bulgarian lev Dec. 31 CY 2002 b,c VAB 1978­89, 1991­92 2000 BPM5 Actual G C S* Burkina Faso CFA franc Dec. 31 CY 1990 VAP 1992­93 BPM4 Actual G C G Burundi Burundi franc Dec. 31 CY 1980 VAB BPM5 Preliminary S C Cambodia Cambodian riel Dec. 31 CY 2000 VAB BPM5 Preliminary G G Cameroon CFA franc Dec. 31 CY 1990 VAB 1965­2001 1996 BPM5 Preliminary S C G Canada Canadian dollar Mar. 31 CY 1995 b VAB 2000 BPM5 G C S* Central African Republic CFA franc Dec. 31 CY 1987 VAB BPM4 Estimate S Chad CFA franc Dec. 31 CY 1995 VAB BPM5 Preliminary S C G Chile Chilean peso Dec. 31 CY 1986 VAB 1996 BPM5 Actual S C S* China Chinese yuan Dec. 31 CY 1990 VAP 1978­93 1986 BPM5 Estimate S B G Hong Kong, China Hong Kong dollar Dec. 31 CY 2000 VAB 1996 BPM5 G S* Colombia Colombian peso Dec. 31 CY 1994 VAB 1992­94 BPM5 Actual S B S* Congo, Dem. Rep. Congo franc Dec. 31 CY 1987 VAP 1999­2001 BPM5 Preliminary S C Congo, Rep. CFA franc Dec. 31 CY 1978 VAP 1999­2001 1996 BPM5 Estimate S C G Costa Rica Costa Rican colon Dec. 31 CY 1991 b VAB BPM5 Actual S C S* Côte d'Ivoire CFA franc Dec. 31 CY 1996 VAP 1996 BPM5 Estimate S C G Croatia Croatian kuna Dec. 31 CY 1997 b VAB 2000 BPM5 Actual G C S* Cuba Cuban peso Dec. 31 CY 1984 VAP G Czech Republic Czech koruna Dec. 31 CY 1995 b VAB 2000 BPM5 Preliminary G C S* Denmark Danish krone Dec. 31 CY 1995 b VAB 2000 BPM5 G C S* Dominican Republic Dominican peso Dec. 31 CY 1990 VAP BPM5 Actual G C Ecuador U.S. dollar Dec. 31 CY 2000 VAP 1996 BPM5 Estimate S B S* Egypt, Arab Rep. Egyptian pound Jun. 30 FY 1992 VAB 1965­91 1996 BPM5 Actual S C El Salvador Salvadoran colone Dec. 31 CY 1990 VAP 1982­90 BPM5 Actual S B S* Eritrea Eritrean nakfa Dec. 31 CY 1992 VAB BPM4 Actual Estonia Estonian kroon Dec. 31 CY 2000 b VAB 1991­95 2000 BPM5 Actual G C S* Ethiopia Ethiopian birr Jul. 7 FY 1981 VAB 1965­2002 BPM5 Preliminary G B G Finland Euro Dec. 31 CY 1995 b VAB 2000 BPM5 G C S* France Euro Dec. 31 CY 1995 b,c VAB 2000 BPM5 S C S* Gabon CFA franc Dec. 31 CY 1991 VAP 1993 1996 BPM5 Preliminary S B G Gambia, The Gambian dalasi Jun. 30 CY 1987 VAB BPM5 Actual G B G Georgia Georgian lari Dec. 31 CY 1996 b VAB 1990­95 2000 BPM5 Actual G C Germany Euro Dec. 31 CY 1995 b VAB 2000 BPM5 S C S* Ghana Ghanaian cedi Dec. 31 CY 1975 VAP 1973­87 BPM5 Actual G B Greece Euro Dec. 31 CY 1995 b,c VAB 2000 BPM5 Estimate S C S* Guatemala Guatemalan quetzal Dec. 31 CY 1958 VAP BPM5 Actual S B Guinea Guinean franc Dec. 31 CY 1994 VAB 1996 BPM5 Estimate S C G Guinea-Bissau CFA franc Dec. 31 CY 1986 VAB 1970­86 BPM5 Estimate G G Haiti Haitian gourde Sep. 30 FY 1976 VAB 1991 BPM5 Preliminary G 354 2004 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of Vital Latest Latest Latest Latest population education, or health most recent registration agricultural industrial trade water census household survey income and complete census data data withdrawal (including expenditure data registration- data based censuses) Afghanistan MICS, 2000 1987 Albania 1989 MICS, 2000 LSMS, 2002 Yes 1995 1990 2002 1995 Algeria 1998 MICS, 2000 HLSS, 1995 1973 1996 2000 1995 Angola 1970 MICS, 2000 Priority survey, 1995 1964­65 1991 1987 Argentina 2001 EPH, 2002 Yes 1988 1996 2002 1995 Armenia 2001 DHS, 2000 LSMS, 1996 Yes 2002 1994 Australia 2001 Yes 1990 1992 2002 1985 Austria 2001 Yes 1990 2000 2002 1991 Azerbaijan 1999 MICS, 2000 HBS, 2001 Yes 2002 1995 Bangladesh 2001 Special, 2001 1976 1997 2001 1990 Belarus 1999 IES, 2000 Yes 1994 2002 1990 Belgium 2001 Yes 1990 1997 2001 .. Benin 2002 DHS, 2001 1992­93 1981 2001 1994 Bolivia 2001 DHS, 2003 EH, 2002 1997 2002 1987 Bosnia and Herzegovina 1991 MICS, 2000 LSMS, 2001 Yes 1991 1995 Botswana 2001 MICS, 2000 HIES, 1993­94 1993 1995 2001 1992 Brazil 2000 DHS, 1996 PNAD, 2001 1996 1995 2002 1992 Bulgaria 2001 LSMS, 2001 Yes 1996 2001 1988 Burkina Faso 1996 DHS, 2003 Priority survey, 1998 1993 2001 1992 Burundi 1990 MICS, 2000 Priority survey, 1998 1991 2002 1987 Cambodia 1998 DHS, 2000 SES, 1997 1987 Cameroon 1987 DHS, 2004 Priority survey, 2001 1972­73 1999 2002 1987 Canada 2001 Yes 1991 2000 2002 1991 Central African Republic 1988 MICS, 2000 EPI, 1993 1993 1996 1987 Chad 1993 MICS, 2000 1987 Chile 2002 CASEN, 2000 Yes 1997 2000 2002 1987 China 2000 Population, 1995 HHS (Rural/Urban), 1998 1996 2000 2002 1993 Hong Kong, China 2001 Yes 2000 Colombia 1993 DHS, 2000 ECV, 2003 1988 1999 2002 1996 Congo, Dem. Rep. 1984 MICS, 2000 1990 1990 Congo, Rep. 1996 1986 1988 1995 1987 Costa Rica 2000 CDC, 1993 EHPM, 2000 Yes 1973 2000 2002 1997 Côte d'Ivoire 1998 MICS, 2000 LSMS, 1998 1974­75 1997 2002 1987 Croatia 2001 HBS, 2001 Yes 1992 2002 1996 Cuba 2002 MICS, 2000 Yes 1989 2001 1995 Czech Republic 2001 CDC, 1993 Microcensus 1997 Yes .. 2002 1991 Denmark 2001 Yes 1989 1991 2002 1990 Dominican Republic 2002 DHS, 2002 ENFT, 1998 1971 1984 2001 1994 Ecuador 2001 CDC, 1999 LSMS, 1998 1997 1999 2002 1997 Egypt, Arab Rep. 1996 SPA, 2002 HECS, 1999 Yes 1989­90 1996 2002 1996 El Salvador 1992 CDC, 1994 EHPM, 2000 Yes 1970­71 1998 2002 1992 Eritrea 1984 DHS, 2002 Estonia 2000 Yes 1994 2002 1995 Ethiopia 1994 DHS, 2000 ICES, 2000 1988­89 1996 2002 1987 Finland 2000 Yes 1990 1999 2002 1991 France 1999 Yes 1988 1995 2002 1999 Gabon 1993 DHS, 2000 1974­75 1995 2000 1987 Gambia, The 2003 MICS, 2000 HHS, 1998 1993 2000 1982 Georgia, Rep. 2002 MICS, 2000 Yes 2001 1990 Germany 1995 Yes 1993 2002 1991 Ghana 2000 SPA, 2002; DHS, 2003 LSMS, 1998/99 1984 1995 2001 1997 Greece 2001 Yes 1993 2000 2001 1980 Guatemala 2002 DHS, 1998­99 LSMS, 2000; ENCOVI, 2000 Yes 1979 1988 2002 1992 Guinea 1996 DHS, 1999 LSMS, 1994 1996 2001 1987 Guinea-Bissau 1991 MICS, 2000 IES, 1993 1988 1995 1991 Haiti 2003 DHS, 2000 1971 1996 1997 1991 2004 World Development Indicators 355 PRIMARY DATA DOCUMENTATION National currency Fiscal National accounts Balance of payments Government IMF year and trade finance data end dissemi- nation Balance of stan- Alternative PPP Payments dard Reporting Base SNA price conversion survey Manual External System Accounting period year valuation factor a year in use debt of trade concept Honduras Honduran lempira Dec. 31 CY 1978 VAB 1988­89 BPM5 Actual S Hungary Hungarian forint Dec. 31 CY 2000 b VAB 2000 BPM5 Actual S C S* India Indian rupee Mar. 31 FY 1993 VAB 1960­2002 BPM5 Actual G C S* Indonesia Indonesian rupiah Mar. 31 CY 1993 VAP 1996 BPM5 Preliminary S C S* Iran, Islamic Rep. Iranian rial Mar. 20 FY 1982 VAB 1980­90 1996 BPM5 Actual G C Iraq Iraqi dinar Dec. 31 CY 1969 VAB S Ireland Euro Dec. 31 CY 1995 b VAB 2000 BPM5 G C S* Israel Israeli new shekel Dec. 31 CY 2000 b VAP 2000 BPM5 S C S* Italy Euro Dec. 31 CY 1995 b VAB 2000 BPM5 S C S* Jamaica Jamaica dollar Dec. 31 CY 1996 VAP 1996 BPM5 Actual G C G Japan Japanese yen Mar. 31 CY 1995 VAB 2000 BPM5 G C S* Jordan Jordan dinar Dec. 31 CY 1994 VAB 1996 BPM5 Actual G B G Kazakhstan Kazakh tenge Dec. 31 CY 1995 b,c VAB 1987­95 2000 BPM5 Actual G C S* Kenya Kenya shilling Jun. 30 CY 1982 VAB 1996 BPM5 Actual G B G Korea, Dem. Rep. Democratic Republic of Korea won Dec. 31 CY .. .. BPM5 Korea, Rep. Korean won Dec. 31 CY 1995 b VAP 2000 BPM5 Actual S C S* Kuwait Kuwaiti dinar Jun. 30 CY 1984 VAP BPM5 S C G Kyrgyz Republic Kyrgyz som Dec. 31 CY 1995 b,c VAB 1990­95 2000 BPM5 Actual G B G Lao PDR Lao kip Dec. 31 CY 1990 VAB 1993 BPM5 Preliminary G Latvia Latvian lat Dec. 31 CY 2000 b VAB 1991­95 1996 BPM5 Actual S C S* Lebanon Lebanese pound Dec. 31 CY 1994 VAB BPM4 Actual G C G Lesotho Lesotho loti Mar. 31 CY 1995 VAB BPM5 Actual G C G Libya Libyan dinar Dec. 31 CY 1975 VAB 1986 BPM5 G Liberia Liberian dollar Dec. 31 CY 1992 VAB Estimate Lithuania Lithuanian litas Dec. 31 CY 2000 b VAB 1990­95 2000 BPM5 Actual G C S* Macedonia, FYR Macedonian denar Dec. 31 CY 1997 b VAB 2000 BPM5 Actual G G Madagascar Malagasy franc Dec. 31 CY 1984 VAB 1996 BPM5 Preliminary S C Malawi Malawi kwacha Mar. 31 CY 1994 VAB 1996 BPM5 Estimate G B G Malaysia Malaysian ringgit Dec. 31 CY 1987 VAP 1993 BPM5 Estimate G C S* Mali CFA franc Dec. 31 CY 1987 VAB 1996 BPM4 Actual G G Mauritania Mauritanian ouguiya Dec. 31 CY 1985 VAB BPM4 Actual G Mauritius Mauritian rupee Jun. 30 FY 1998 VAB 1996 BPM5 Actual G C G Mexico Mexican new peso Dec. 31 CY 1993 b VAB 2000 BPM5 Actual G C S* Moldova Moldovan leu Dec. 31 CY 1996 VAB 1987­95 2000 BPM5 Actual G C Mongolia Mongolian tugrik Dec. 31 CY 1998 VAP 2000 BPM5 Actual S C G Morocco Moroccan dirham Dec. 31 CY 1980 VAP 1996 BPM5 Preliminary S C Mozambique Mozambican metical Dec. 31 CY 1995 VAB 1992­95 BPM5 Estimate S G Myanmar Myanmar kyat Mar. 31 FY 1985 VAP BPM5 Estimate G C Namibia Namibia dollar Mar. 31 CY 1995 VAB BPM5 Estimate B G Nepal Nepalese rupee Jul. 14 FY 1985 VAB 1966­2002 1996 BPM5 Actual S C G Netherlands Euro Dec. 31 CY 1995 b,c VAB 2000 BPM5 S C S* New Zealand New Zealand dollar Mar. 31 FY 1995 VAB 2000 BPM5 G B Nicaragua Nicaraguan gold cordoba Dec. 31 CY 1998 VAP 1965­93 BPM5 Actual S C Niger CFA franc Dec. 31 CY 1987 VAP 1993 BPM5 Preliminary S G Nigeria Nigerian naira Dec. 31 CY 1987 VAB 1971­98 1996 BPM5 Estimate G G Norway Norwegian krone Dec. 31 CY 1995 b,c VAB 2000 BPM5 G C S* Oman Rial Omani Dec. 31 CY 1978 VAP 1996 BPM5 Actual G B G Pakistan Pakistan rupee Jun. 30 FY 1981 VAB 1972­2002 1996 BPM5 Preliminary G C G Panama Panamanian balboa Dec. 31 CY 1982 c VAP 1996 BPM5 Actual S C G Papua New Guinea Papua New Guinea kina Dec. 31 CY 1983 VAP 1989 BPM5 Actual G B Paraguay Paraguayan guarani Dec. 31 CY 1982 VAP 1982­88 BPM5 Actual S C G Peru Peruvian new sol Dec. 31 CY 1994 VAB 1985­91 1996 BPM5 Actual S C S* Philippines Philippine peso Dec. 31 CY 1985 VAP 1996 BPM5 Actual G B S* Poland Polish zloty Dec. 31 CY 2002 b,c VAB 2000 BPM5 Actual S C S* Portugal Euro Dec. 31 CY 1995 b VAB 2000 BPM5 S C S* Puerto Rico U.S. dollar Jun. 30 FY 1954 VAP G 356 2004 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of Vital Latest Latest Latest Latest population education, or health most recent registration agricultural industrial trade water census household survey income and complete census data data withdrawal (including expenditure data registration- data based censuses) Honduras 2001 CDC, 1994 EPHPM, 1999 1993 1996 2002 1992 Hungary 2001 FBS, 1998 Yes 1994 2000 2002 1991 India 2001 Benchmark, 1998­2002 LSMS, 1997­98 d 1986 2000 2002 1990 Indonesia 2000 DHS, 2002; Special, 2002 SUSENAS, 2002 1993 2000 2002 1990 Iran, Islamic Republic 1996 Demographic, 1995 SECH, 1998 1988 1993 2002 1993 Iraq 1997 MICS, 2000 1981 1992 1990 Ireland 2002 Yes 1991 1999 2002 1980 Israel 1995 Yes 1983 1996 2002 1997 Italy 2001 Yes 1990 2000 2002 1998 Jamaica 2001 CDC, 1997; MICS, 2000 LSMS, 2000 Yes 1979 1996 2002 1993 Japan 2000 Yes 1990 2000 2002 1992 Jordan 1994 DHS, 2002 HIES, 1997 1997 1997 2002 1993 Kazakhstan 1999 DHS, 1999 HBS, 2001 Yes 2001 1993 Kenya 1999 DHS, 2003 WMS II, 1997 1981 2000 2002 1990 Korea, Dem. Rep. 1993 MICS, 2000 1987 Korea, Rep. 2000 1991 2000 2002 1994 Kuwait 1995 FHS, 1996 Yes 1970 1999 1999 1994 Kyrgyz Rep. 1999 DHS, 1997 HBS, 2001 Yes 2002 1994 Lao PDR 1995 MICS, 2000 ECS I, 1997 1999 1987 Latvia, Rep. 2000 HBS, 1998 Yes 1994 1996 2002 1994 Lebanon 1970 MICS, 2000 1999 2001 1996 Lesotho 2001 MICS, 2000 1989­90 1985 1987 Libya 1995 MICS, 2000 1987 1998 1999 Liberia 1987 Lithuania 2001 LSMS, 2000 Yes 1994 2002 1995 Macedonia, FYR 2002 Yes 1994 1996 2001 1996 Madagascar 1993 MICS, 2000; DHS, 2003 Priority survey, 2001 1984 1988 1999 1984 Malawi 1998 EdData, 2002 HHS, 1997 1992­93 1998 2001 1994 Malaysia 2000 HIBAS, 1997 Yes 1999 2002 1995 Mali 1998 DHS, 2001 EMCES, 1994 1978 1997 1987 Mauritania 2000 Special, 2003 LSMS, 2000 1985 1996 1985 Mauritius 2000 CDC, 1991 Yes 1997 2002 .. Mexico 2000 Population, 1995 ENIGH, 2002 Yes 1991 2000 2002 1998 Moldova 1989 MICS, 2000 HBS, 2000 Yes 2002 1992 Mongolia 2000 MICS, 2000 LSMS/Integrated Survey, 1998 1995 2002 1993 Morocco 1994 DHS, 2003 LSMS, 1998/99 1997 2000 2002 1998 Mozambique 1997 Interim, 2003 NHS, 1996/97 2001 1992 Myanmar 1983 MICS, 2000 1993 1992 1987 Namibia 2001 DHS, 2000 NHIES, 1993 1995 1994 2001 1991 Nepal 2001 DHS, 2001 LSMS, 1996 1992 1996 2000 1994 Netherlands 2002 Yes 1989 2000 2002 1991 New Zealand 2001 Yes 1990 1996 2002 1991 Nicaragua 1995 DHS, 2001 LSMS, 2001 1963 2002 1998 Niger 2001 MICS, 2000 LSMS, 1995 1980 1998 2001 1988 Nigeria 1991 DHS, 2003 NCS, 1996 1960 1996 2000 1987 Norway 2001 Yes 1989 1999 2002 1985 Oman 2003 FHS, 1995 1979 2000 2002 1991 Pakistan 1998 RHS, 2000­01 PIHS, 1998/99 1990 1996 2002 1991 Panama 2000 LSMS, 1997 EH, 2000 1990 1999 2002 1990 Papua New Guinea 2000 DHS, 1996 HGS, 1997 2000 1987 Paraguay 2002 CDC, 1998 EPH, 2003 1991 1991 2002 1987 Peru 1993 DHS, 2000 ENAHO, 2003 1994 1996 2002 1992 Philippines 2000 DHS, 2003 FIES, 2000 1991 1997 2002 1995 Poland 2002 HBS, 1998 Yes 1990 2000 2002 1991 Portugal 2001 Yes 1989 1995 2002 1990 Puerto Rico 2000 Yes 1987 2000 2004 World Development Indicators 357 PRIMARY DATA DOCUMENTATION National currency Fiscal National accounts Balance of payments Government IMF year and trade finance data end dissemi- nation Balance of stan- Alternative PPP Payments dard Reporting Base SNA price conversion survey Manual External System Accounting period year valuation factor a year in use debt of trade concept Romania Romanian leu Dec. 31 CY 1998 c VAB 1987­89, 1992 2000 BPM5 Actual S C G Russian Federation Russian ruble Dec. 31 CY 2000 b, c VAB 1987­95 2000 BPM5 Estimate G C Rwanda Rwanda franc Dec. 31 CY 1995 VAP BPM5 Estimate G C G Saudi Arabia Saudi Arabian riyal Dec. 31 CY 1999 VAP BPM4 Estimate G Senegal CFA franc Dec. 31 CY 1987 VAP 1996 BPM5 Preliminary S B G Serbia and Montenegro Yugoslav new dinar Dec. 31 CY 1994 VAP Preliminary Sierra Leone Sierra Leonean leone Jun. 30 CY 1990 VAB 1971­79, 1987 1996 BPM5 Actual G B G Singapore Singapore dollar Mar. 31 CY 1995 VAB 1996 BPM5 G C S* Slovak Republic Slovak koruna Dec. 31 CY 1995 b VAP 2000 BPM5 Actual G C S* Slovenia Slovenian tolar Dec. 31 CY 2000 b VAB 2000 BPM5 Actual S C S* Somalia Somali shilling Dec. 31 CY 1985 VAB 1977­90 Estimate South Africa South African rand Mar. 31 CY 1995 VAB BPM5 Preliminary S C S* Spain Euro Dec. 31 CY 1995 b VAB 2000 BPM5 S C S* Sri Lanka Sri Lankan rupee Dec. 31 CY 1996 VAB 1996 BPM5 Actual G B G Sudan Sudanese dinar Dec. 31 CY 1982 VAB 1970­95 BPM5 Estimate G B G Swaziland Lilangeni Jun. 30 FY 1985 VAB Actual B G Sweden Swedish krona Jun. 30 CY 1995 c VAB 2000 BPM5 G C S* Switzerland Swiss franc Dec. 31 CY 1995 VAB 2000 BPM5 Estimate S C S* Syrian Arab Republic Syrian pound Dec. 31 CY 1995 VAP 1970­2002 1996 BPM5 Estimate S C Tajikistan Tajik somoni Dec. 31 CY 1985 b VAB 1990­95 2000 BPM5 Actual G C Tanzania Tanzania shilling Dec. 31 CY 1992 VAB 1996 BPM5 Estimate S G Thailand Thai baht Sep. 30 CY 1988 VAP 1996 BPM5 Preliminary G C S* Togo CFA franc Dec. 31 CY 1978 VAP BPM5 Preliminary S G Trinidad and Tobago Trinidad and Tobago dollar Dec. 31 CY 1985 VAP 1996 BPM5 Preliminary S C Tunisia Tunisian dinar Dec. 31 CY 1990 VAP 1996 BPM5 Actual G C S* Turkey Turkish lira Dec. 31 CY 1987 VAB 2000 BPM5 Actual S C S* Turkmenistan Turkmen manat Dec. 31 CY 1987 b VAB 1987­95 2000 BPM5 G Uganda Uganda shilling Jun. 30 FY 1998 VAB 1980­99 BPM5 Actual G B G Ukraine Ukrainian hryvnia Dec. 31 CY 1990 b, c VAB 1990­95 2000 BPM5 Actual G C S* United Arab Emirates U.A.E. dirham Dec. 31 CY 1985 VAB BPM4 G B United Kingdom Pound sterling Dec. 31 CY 1995 b VAB 2000 BPM5 G C S* United States U.S. dollar Sep. 30 CY 1995 c VAB 2000 BPM5 G C S* Uruguay Uruguayan peso Dec. 31 CY 1983 VAP 1996 BPM5 Actual S C S* Uzbekistan Uzbek sum Dec. 31 CY 1997 c VAB 1990­95 2000 BPM5 Actual G Venezuela, R.B. Venezuelan bolivar Dec. 31 CY 1984 VAB 1996 BPM5 Actual G C G Vietnam Vietnamese dong Dec. 31 CY 1994 VAP 1991 1996 BPM4 Preliminary G B G West Bank and Gaza Israeli new shekel Dec. 31 CY 1998 VAB Yemen, Rep. Yemen rial Dec. 31 CY 1990 VAP 1991­96 1996 BPM5 Actual G B G Zambia Zambian kwacha Dec. 31 CY 1994 VAB 1990­92 1996 BPM5 Preliminary G B G Zimbabwe Zimbabwe dollar Jun. 30 CY 1990 VAB 1991, 1998 1996 BPM5 Preliminary G C G Note: For explanation of the abbreviations used in the table see the notes. a. World Bank estimates including adjustments for fiscal year reporting. b. Country uses the 1993 System of National Accounts methodology. c. Original chained constant price data are rescaled. d. For Uttar Pradesh and Bihar. 358 2004 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of Vital Latest Latest Latest Latest population education, or health most recent registration agricultural industrial trade water census household survey income and complete census data data withdrawal (including expenditure data registration- data based censuses) Romania 2002 CDC, 1999 LSMS, 2000 Yes 1993 1995 1994 Russian Federation 2002 LSMS, 1992 LMS, Round 9, 2000 Yes 1994­95 2000 2002 1994 Rwanda 2002 SPA, 2001 LSMS, 1998 1984 1986 2002 1993 Saudi Arabia 1992 Demographic, 1999 1983 2002 1992 Senegal 2002 DHS, 2004 ESASM, 1994 1960 1997 2002 1987 Serbia and Montenegro 2002 MICS, 2000 Yes Sierra Leone 1985 MICS, 2000 SHEHEA, 1989­90 1985 1993 1987 Singapore 2000 General household, 1995 Yes 2000 2002 1975 Slovak Republic 2001 Microcensus, 1996 Yes 1994 2002 1991 Slovenia 2002 Yes 1991 1998 2002 1996 Somalia 1987 MICS, 2000 1987 South Africa 2001 DHS, 1998 IES, 1994/95 2000 2002 1990 Spain 2001 Yes 1989 2000 2002 1997 Sri Lanka 2001 DHS, 1993 SES, 1995/96 Yes 1982 1999 2002 1990 Sudan 1993 MICS, 2000 2002 1995 Swaziland 1997 MICS, 2000 SHIES, 1994 1995 2002 .. Sweden 1990 Yes 1981 2000 2002 1991 Switzerland 2000 Yes 1990 1997 2002 1991 Syrian Arab Republic 1994 MICS, 2000 1981 1998 2002 1995 Tajikistan 2000 MICS, 2000 LSMS, 1999 Yes 1994 2000 1994 Tanzania 2002 AIS, 2003 LSMS, 1993 1995 1999 2001 1994 Thailand 2000 DHS, 1987 SES, 2002 1993 1994 2001 1990 Togo 1981 MICS, 2000 1996 2002 1987 Trinidad and Tobago 2000 MICS, 2000 LSMS, 1992 Yes 1982 1995 2002 1997 Tunisia 1994 MICS, 2000 1961 2000 2002 1996 Turkey 2000 DHS, 1998 LSMS, 2000 1991 2000 2002 1997 Turkmenistan 1995 DHS, 2000 LSMS, 1998 Yes 2000 1994 Uganda 2002 AIS, 2004 NIHS II, 1999 1991 2002 1970 Ukraine 2001 MICS, 2000 HIES, 1999 Yes 2002 1992 United Arab Emirates 1995 1998 1981 2001 1995 United Kingdom 2001 Yes 1993 1995 2002 1991 United States 2000 Current population, 1997 Yes 1997 1995 2002 1990 Uruguay 1996 ECH, 2000 Yes 1990 1997 2002 1965 Uzbekistan 1989 Special, 2002 FBS, 2000 Yes 1994 Venezuela, R.B. 2001 MICS, 2000 EHM, 2000 Yes 1997­98 1999 2002 1970 Vietnam 1999 MICS, 2000; DHS 2002 LSMS, 1997/98 1994 1990 West Bank and Gaza 1997 Demographic, 1995 1971 Yemen, Rep. 1994 DHS, 1997 HBS, 1998 1982­85 1998 1990 Zambia 2000 EdData, 2002 LCMS II, 1998 1990 1994 2002 1994 Zimbabwe 2002 DHS, 1999 ICES, 1995 1960 1996 2002 1987 2004 World Development Indicators 359 Primary data documentation notes · Fiscal year end is the date of the end of the fiscal Monetary Fund's (IMF) Balance of Payments Manual guide member countries in disseminating compre- year for the central government. Fiscal years for (1977), and BPM5 to the fifth edition (1993). Since hensive, timely, accessible, and reliable economic, other levels of government and the reporting years 1995 the IMF has adjusted all balance of payments financial, and socio-demographic statistics. IMF for statistical surveys may differ, but if a country is data to BPM5 conventions, but some countries con- member countries voluntarily elect to participate in designated as a fiscal year reporter in the following tinue to report using the older system. · External either the SDDS or the GDDS. Both the GDDS and the column, the date shown is the end of its national debt shows debt reporting status for 2000 data. SDDS are expected to enhance the availability of accounts reporting period. · Reporting period for Actual indicates that data are as reported, prelimi- timely and comprehensive data and therefore con- national accounts and balance of payments data is nary that data are preliminary and include an element tribute to the pursuit of sound macroeconomic poli- designated as either calendar year (CY) or fiscal year of staff estimation, and estimate that data are staff cies; the SDDS is also expected to improve the (FY). Most economies report their national accounts estimates. · System of trade refers to the general functioning of financial markets. · Latest population and balance of payments data using calendar years, trade system (G) or the special trade system (S). For census shows the most recent year in which a census but some use fiscal years, which straddle two calen- imports under the general trade system both goods was conducted and for which at least preliminary dar years. In World Development Indicators fiscal entering directly for domestic consumption and goods results have been released. · Latest demographic, year data are assigned to the calendar year that con- entered into customs storage are recorded as education, or health household survey gives infor- tains the larger share of the fiscal year. If a country's imports at the time of their first arrival; under the spe- mation on the household surveys used in compiling fiscal year ends before June 30, the data are shown cial trade system goods are recorded as imports demographic, education, and health data presented in the first year of the fiscal period; if the fiscal year when declared for domestic consumption whether at in section 2. CDC is Centers for Disease Control and ends on or after June 30, the data are shown in the time of entry or on withdrawal from customs storage. Prevention, DHS is Demographic and Health Survey, second year of the period. Balance of payments data Exports under the general system comprise outward- FHS is Family Health Survey, LSMS is Living are shown by calendar year and so are not compara- moving goods: (a) national goods wholly or partly pro- Standards Measurement Study, MICS is Multiple ble to national accounts data for countries that duced in the country; (b) foreign goods, neither Indicator Cluster Survey, RHS is Reproductive report their national accounts on a fiscal year basis. transformed nor declared for domestic consumption Health Survey, and SPA is Service Provision · Base year is the year used as the base period for in the country, that move outward from customs stor- Assessment. · Source of most recent income and constant price calculations in the country's national age; and (c) nationalized goods that have been expenditure data shows household surveys that col- accounts. Price indexes derived from national declared from domestic consumption and move out- lect income and expenditure data. HBS is Household accounts aggregates, such as the GDP deflator, ward without having been transformed. Under the Budget Survey; IECS is Income, Expenditure, and express the price level relative to prices in the base special system of trade, exports comprise categories Consumption Survey; IES is Income and Expenditure year. Constant price data reported in World (a) and (c). In some compilations categories (b) and Survey; LSMS is Living Standards Measurement Development Indicators are rescaled to a common (c) are classified as re-exports. Direct transit trade, Study; and SES is Socioeconomic Survey. · Vital 1995 reference year. See About the data for table consisting of goods entering or leaving for transport registration complete identifies countries judged to 4.1 for further discussion. · SNA price valuation purposes only, is excluded from both import and have complete registries of vital (birth and death) sta- shows whether value added in the national accounts export statistics. See About the data for tables 4.5 tistics by the United Nations Department of Economic is reported at basic prices (VAB) or at producer and 4.6 for further discussion. · Government finance and Social Information and Policy Analysis, Statistical prices (VAP). Producer prices include the value of accounting concept describes the accounting basis Division, and reported in Population and Vital taxes paid by producers and thus tend to overstate for reporting central government financial data. For Statistics Reports. Countries with complete vital sta- value added in production. See About the data for most countries government finance data have been tistics registries may have more accurate and more tables 4.1 and 4.2 for further discussion of national consolidated (C) into one set of accounts capturing all timely demographic indicators. · Latest agricultural accounts valuation. · Alternative conversion factor the central government's fiscal activities. Budgetary census shows the most recent year in which an agri- identifies the countries and years for which a World central government accounts (B) exclude central gov- cultural census was conducted and reported to the Bank­estimated conversion factor has been used in ernment units. See About the data for tables 4.11, Food and Agriculture Organization. · Latest industri- place of the official exchange rate (line rf in the 4.12, and 4.13 for further details. · IMF data dis- al data refer to the most recent year for which manu- IMF's International Financial Statistics). Estimates semination standard shows the countries that sub- facturing value added data at the three-digit level of also include adjustments to correspond to the fiscal scribe to the IMF's Special Data Dissemination the International Standard Industrial Classification years in which national accounts data have been Standard (SDDS) or the General Data Dissemination (revision 2 or revision 3) are available in the United reported. See Statistical methods for further discus- System (GDDS). S refers to countries that subscribe Nations Industrial Development Organization data- sion of the use of alternative conversion factors. to the SDDS; S* indicates subscribers that have base. · Latest trade data shows the most recent · PPP survey year refers to the latest available sur- posted data on the Dissemination Standards Bulletin year for which structure of merchandise trade data vey year for the International Comparison Program's Board Web site [dsbb.imf.org]; while G refers to coun- are available in the United Nations Statistical estimates of purchasing power parities (PPPs). tries that subscribe to the GDDS. The SDDS was Division's Commodity Trade (COMTRADE) database. · Balance of Payments Manual in use refers to the established by the IMF for member countries that · Latest water withdrawal data refer to the most classification system used for compiling and report- have or that might seek access to international capi- recent year for which data have been compiled from a ing data on balance of payments items in table 4.15. tal markets, to guide them in providing their econom- variety of sources. See About the data for table 3.5 BPM4 refers to the fourth edition of the International ic and financial data to the public. The GDDS helps for more information. 360 2004 World Development Indicators ACRONYMS AND ABBREVIATIONS Technical terms Organizations AIDS acquired immunodeficiency syndrome ADB Asian Development Bank BOD biochemical oxygen demand AfDB African Development Bank CFC chlorofluorocarbon APEC Asia Pacific Economic Cooperation c.i.f. cost, insurance, and freight CDC Centers for Disease Control and Prevention COMTRADE United Nations Statistics Division's Commodity Trade database CDIAC Carbon Dioxide Information Analysis Center CO carbon dioxide CEC Commission of the European Communities 2 cu. m cubic meter DAC Development Assistance Committee of the OECD DHS Demographic and Health Survey EBRD European Bank for Reconstruction and Development DMTU dry metric ton unit EDF European Development Fund DOTS directly observed treatment, short-course (strategy) EFTA European Free Trade Area DPT diphtheria, pertussis, and tetanus EIB European Investment Bank DRS World Bank's Debtor Reporting System EMU European Monetary Union ESAF Enhanced Structural Adjustment Facility EU European Union f.o.b. free on board Eurostat Statistical Office of the European Communities GDP gross domestic product FAO Food and Agriculture Organization GEMS Global Environment Monitoring System G-5 France, Germany, Japan, United Kingdom, and United States GIS geographic information system G-7 G-5 plus Canada and Italy GNI gross national income (formerly referred to as gross national product) G-8 G-7 plus Russian Federation ha hectare GEF Global Environment Facility HIPC heavily indebted poor country IBRD International Bank for Reconstruction and Development HIV human immunodeficiency virus ICAO International Civil Aviation Organization ICD International Classification of Diseases ICP International Comparison Programme ICSE International Classification of Status in Employment ICSID International Centre for Settlement of Investment Disputes ICT information and communications technology IDA International Development Association IP Internet Protocol IDB Inter-American Development Bank ISCED International Standard Classification of Education IDC International Data Corporation ISIC International Standard Industrial Classification IEA International Energy Agency ISP Internet service provider IFC International Finance Corporation kg kilogram ILO International Labour Organization km kilometer IMF International Monetary Fund kwh kilowatt-hour IRF International Road Federation LIBOR London interbank offered rate ITU International Telecommunication Union LSMS Living Standards Measurement Study IUCN World Conservation Union M0 currency and coins (monetary base) MIGA Multilateral Investment Guarantee Agency M1 narrow money (currency and demand deposits) NAFTA North American Free Trade Agreement M2 money plus quasi money NATO North Atlantic Treaty Organization M3 broad money or liquid liabilities NSF National Science Foundation MICS Multiple Indicator Cluster Survey OECD Organisation for Economic Co-operation and Development mmbtu millions of British thermal units PAHO Pan American Health Organization mt metric ton PARIS21 Partnership in Statistics for Development in the 21st Century MUV manufactures unit value S&P Standard & Poor's NEAP national environmental action plan UIP Urban Indicators Programme NGO nongovernmental organization UIS UNESCO Institute for Statistics NO nitrogen dioxide UN United Nations 2 ODA official development assistance UNAIDS Joint United Nations Programme on HIV/AIDS PC personal computer UNCED United Nations Conference on Environment and Development PM10 particulate matter smaller than 10 microns UNCHS United Nations Centre for Human Settlements (Habitat) PPI private participation in infrastructure UNCTAD United Nations Conference on Trade and Development PPP purchasing power parity UNDP United Nations Development Programme PRGF Poverty Reduction and Growth Facility UNECE United Nations Economic Commission for Europe R&D research and development UNEP United Nations Environment Programme SDR special drawing right UNESCO United Nations Educational, Scientific, and Cultural Organization SITC Standard International Trade Classification UNFPA United Nations Population Fund SNA System of National Accounts UNHCR United Nations High Commissioner for Refugees SOPEMI Continuous Reporting System on Migration UNICEF United Nations Children's Fund SO sulfur dioxide UNIDO United Nations Industrial Development Organization 2 sq. km square kilometer UNRISD United Nations Research Institute for Social Development STD sexually transmitted disease UNSD United Nations Statistics Division TB tuberculosis USAID U.S. Agency for International Development TEU twenty-foot equivalent unit WCMC World Conservation Monitoring Centre TFP total factor productivity WFP World Food Programme ton-km metric ton-kilometer WHO World Health Organization TSP total suspended particulates WIPO World Intellectual Property Organization TU traffic unit WITSA World Information Technology and Services Alliance WTO World Trade Organization WWF World Wildlife Fund 2004 World Development Indicators 361 STATISTICAL METHODS This section describes some of the statistical procedures used in preparing the · Aggregates of ratios are denoted by a w when calculated as weighted averages of World Development Indicators. It covers the methods employed for calculating the ratios (using the value of the denominator or, in some cases, another indica- regional and income group aggregates and for calculating growth rates, and it tor as a weight) and denoted by a u when calculated as unweighted averages. The describes the World Bank's Atlas method for deriving the conversion factor used aggregate ratios are based on available data, including data for economies not to estimate gross national income (GNI) and GNI per capita in U.S. dollars. Other shown in the main tables. Missing values are assumed to have the same average statistical procedures and calculations are described in the About the data sec- value as the available data. No aggregate is calculated if missing data account for tions following each table. more than a third of the value of weights in the benchmark year. In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing Aggregation rules values for missing data according to the above rules for computing totals. Aggregates based on the World Bank's regional and income classifications of · Aggregate growth rates are denoted by a w when calculated as a weighted aver- economies appear at the end of most tables. The countries included in these age of growth rates. In a few cases growth rates may be computed from time classifications are shown on the flaps on the front and back covers of the book. series of group totals. Growth rates are not calculated if more than half the Most tables also include aggregates for the member countries of the European observations in a period are missing. For further discussion of methods of Monetary Union (EMU). Members of the EMU on 1 January 2004 were Austria, computing growth rates see below. Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the · Aggregates denoted by an m are medians of the values shown in the table. No Netherlands, Portugal, and Spain. Other classifications, such as the European value is shown if more than half the observations for countries with a popula- Union and regional trade blocs, are documented in About the data for the tables tion of more than 1 million are missing. in which they appear. Exceptions to the rules occur throughout the book. Depending on the judgment Because of missing data, aggregates for groups of economies should be treat- of World Bank analysts, the aggregates may be based on as little as 50 percent of ed as approximations of unknown totals or average values. Regional and income the available data. In other cases, where missing or excluded values are judged to group aggregates are based on the largest available set of data, including values be small or irrelevant, aggregates are based only on the data shown in the tables. for the 152 economies shown in the main tables, other economies shown in table 1.6, and Taiwan, China. The aggregation rules are intended to yield estimates for Growth rates a consistent set of economies from one period to the next and for all indicators. Growth rates are calculated as annual averages and represented as percentages. Small differences between sums of subgroup aggregates and overall totals and Except where noted, growth rates of values are computed from constant price averages may occur because of the approximations used. In addition, compilation series. Three principal methods are used to calculate growth rates: least squares, errors and data reporting practices may cause discrepancies in theoretically iden- exponential endpoint, and geometric endpoint. Rates of change from one period tical aggregates such as world exports and world imports. to the next are calculated as proportional changes from the earlier period. Five methods of aggregation are used in the World Development Indicators: · For group and world totals denoted in the tables by a t, missing data are Least-squares growth rate. Least-squares growth rates are used wherever imputed based on the relationship of the sum of available data to the total there is a sufficiently long time series to permit a reliable calculation. No growth in the year of the previous estimate. The imputation process works forward rate is calculated if more than half the observations in a period are missing. The and backward from 1995. Missing values in 1995 are imputed using one of least-squares growth rate, r, is estimated by fitting a linear regression trend line several proxy variables for which complete data are available in that year. The to the logarithmic annual values of the variable in the relevant period. The regres- imputed value is calculated so that it (or its proxy) bears the same relation- sion equation takes the form ship to the total of available data. Imputed values are usually not calculated ln X = a + bt, t if missing data account for more than a third of the total in the benchmark year. The variables used as proxies are GNI in U.S. dollars, total population, which is equivalent to the logarithmic transformation of the compound growth exports and imports of goods and services in U.S. dollars, and value added equation, in agriculture, industry, manufacturing, and services in U.S. dollars. X = X (1 + r )t. t o · Aggregates marked by an s are sums of available data. Missing values are not imputed. Sums are not computed if more than a third of the observations In this equation X is the variable, t is time, and a = ln X and b = ln (1 + r) are o in the series or a proxy for the series are missing in a given year. parameters to be estimated. If b* is the least-squares estimate of b, then the 362 2004 World Development Indicators average annual growth rate, r, is obtained as [exp(b*) ­ 1] and is multiplied by The inflation rate for Japan, the United Kingdom, the United States, and the 100 for expression as a percentage. The calculated growth rate is an average Euro Zone, representing international inflation, is measured by the change in the rate that is representative of the available observations over the entire period. It SDR deflator. (Special drawing rights, or SDRs, are the International Monetary does not necessarily match the actual growth rate between any two periods. Fund's unit of account.) The SDR deflator is calculated as a weighted average of these countries' GDP deflators in SDR terms, the weights being the amount of Exponential growth rate. The growth rate between two points in time for cer- each country's currency in one SDR unit. Weights vary over time because both tain demographic indicators, notably labor force and population, is calculated the composition of the SDR and the relative exchange rates for each currency from the equation change. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conversion r = ln(p /p )/n, factor is then applied to a country's GNI. The resulting GNI in U.S. dollars is divid- n 1 ed by the midyear population to derive GNI per capita. where p and p are the last and first observations in the period, n is the num- When official exchange rates are deemed to be unreliable or unrepresentative n 1 ber of years in the period, and ln is the natural logarithm operator. This growth of the effective exchange rate during a period, an alternative estimate of the rate is based on a model of continuous, exponential growth between two points exchange rate is used in the Atlas formula (see below). in time. It does not take into account the intermediate values of the series. Nor The following formulas describe the calculation of the Atlas conversion factor does it correspond to the annual rate of change measured at a one-year interval, for year t: which is given by (p ­ p )/p . n n ­1 n ­1 Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at and the calculation of GNI per capita in U.S. dollars for year t : intervals, in which case the compound growth model is appropriate. The average growth rate over n periods is calculated as Y$ = (Y /N )/e ,* t t t t r = exp[ln(p /p )/n] ­ 1. where e* is the Atlas conversion factor (national currency to the U.S. dollar) for n 1 t year t, e is the average annual exchange rate (national currency to the U.S. t Like the exponential growth rate, it does not take into account intermediate val- dollar) for year t, p is the GDP deflator for year t, pS is the SDR deflator in U.S. $ t t ues of the series. dollar terms for year t, Y$ is the Atlas GNI per capita in U.S. dollars in year t, Y t t is current GNI (local currency) for year t, and N is the midyear population for t World Bank Atlas method year t. In calculating GNI and GNI per capita in U.S. dollars for certain operational pur- poses, the World Bank uses the Atlas conversion factor. The purpose of the Atlas Alternative conversion factors conversion factor is to reduce the impact of exchange rate fluctuations in the The World Bank systematically assesses the appropriateness of official cross-country comparison of national incomes. exchange rates as conversion factors. An alternative conversion factor is used The Atlas conversion factor for any year is the average of a country's when the official exchange rate is judged to diverge by an exceptionally large mar- exchange rate (or alternative conversion factor) for that year and its exchange gin from the rate effectively applied to domestic transactions of foreign curren- rates for the two preceding years, adjusted for the difference between the rate of cies and traded products. This applies to only a small number of countries, as inflation in the country and that in Japan, the United Kingdom, the United States, shown in Primary data documentation. Alternative conversion factors are used in and the Euro Zone. A country's inflation rate is measured by the change in its the Atlas methodology and elsewhere in the World Development Indicators as GDP deflator. single-year conversion factors. 2004 World Development Indicators 363 CREDITS This book draws on a wide range of World Bank reports and numerous external White of the World Resources Institute, Ricardo Quercioli of the International sources, listed in the bibliography following this section. Many people inside and Energy Agency, Orio Tampieri of the Food and Agriculture Organization, Laura outside the World Bank helped in writing and producing World Development Battlebury of the World Conservation Monitoring Centre, Gerhard Metchies of GTZ, Indicators. The team would like to particularly acknowledge the help and encour- and Christine Auclair, Moses Ayiemba, Bildad Kagai, Guenter Karl, Pauline Maingi, agement of François Bourgignon, Senior Vice President and Chief Economist. The and Markanley Rai of the Urban Indicators Programme, United Nations Centre for team is also grateful to those who provided valuable comments on the entire book. Human Settlements. Mehdi Akhlaghi managed the databases for this section, and The poverty estimates were prepared by Shaohua Chen of the World Bank's Poverty Mayhar Eshragh-Tabary contributed with research and data analysis. The World Monitoring Group with help from Prem Sangraula and Johan Mistiaen. This note Bank's Environment Department and Rural Development Department devoted identifies those who made specific contributions. Numerous others, too many to substantial staff resources to the book, for which the team is very grateful. M. H. acknowledge here, helped in many ways for which the team is extremely grateful. Saeed Ordoubadi wrote the introduction to the section with valuable comments from Sarwar Lateef, Eric Swanson, and Bruce Ross-Larson, who also edited the 1. World view text. Other contributions were made by Susmita Dasgupta, Craig Meisner, Kiran Section 1 was prepared by Eric Swanson and K. M. Vijayalakshmi. Eric Swanson Pandey, and David Wheeler (air and water pollution) and Giovanni Ruta and Kirk wrote the introduction with input from Sulekha Patel and Saeed Ordoubadi. Amy Hamilton (adjusted savings). Valuable comments were also provided by Azamat Heyman, Masako Hiraga, and Vivienne Wang assisted in developing and prepar- Abdymomunov, Julian Bandiaks, Zeljko Bogetic, Gohar Gyulumyan, Kirk Hamilton, ing tables and figures. Valuable suggestions were received from members of the Aurelien Kruse, Mark Lundell, Evgenij Najdov, Luc Razafimandimby, Giovanni Ruta, World Bank's Human Development Network. Yonas Biru and William Prince pro- Marcin Sasin, Monica Singh, and Jean van Houtte. vided substantial assistance with the data, preparing the estimates of gross national income in purchasing power parity terms. The team is grateful to Guy 4. Economy Karsenty and Jurgen Richtering of the World Trade Organization for providing the Section 4 was prepared by K. M. Vijayalakshmi in close collaboration with the market access indicators and to Wayne Mitchell of the IMF for providing the HIPC Macroeconomic Data Team of the World Bank's Development Data Group, led by indicators in table 1.4. Vinoda Basnayake assisted in preparing the table. Soong Sup Lee. Eric Swanson and K. M. Vijayalakshmi wrote the introduction with valuable suggestions from Sarwar Lateef. Contributions to the section were pro- 2. People vided by Azita Amjadi (trade) and Ibrahim Levent (external debt). The national Section 2 was prepared by Masako Hiraga in partnership with the World Bank's accounts data for low- and middle-income economies were gathered from the Human Development Network and the Development Research Group in the World Bank's regional staff through the annual Unified Survey. Maja Bresslauer, Development Economics Vice Presidency. Vivienne Wang provided invaluable assis- Kay Chung, Victor Gabor, and Soong Sup Lee worked on updating, estimating, tance in data and table preparation. Sulekha Patel wrote the introduction, with input and validating the databases for national accounts. The national accounts data from Sarwar Lateef. Contributions to the section were provided by Eduard Bos and for OECD countries were processed by Mehdi Akhlaghi. The team is grateful to Emi Suzuki (demography, health, and nutrition); Shaohua Chen and Martin Ravallion Guy Karsenty and Andreas Maurer of the World Trade Organization and Sanja (poverty and income distribution); Montserrat Pallares-Miralles (vulnerability and Blazevic, Arunas Butkevicius, and Aurelie von Wartensleben of the United security); Barbara Bruns, Saida Mamodova, Raymond Muhula, and Lianqin Wang Nations Conference on Trade and Development, for providing data on trade in (education); Lucia Fort and Maria Estela Rivero-Fuentes (gender) and Eldaw Abdalla goods; to Tetsuo Yamada for help in obtaining the United Nations Industrial Suliman (social indicators of poverty). The team is also grateful to Rosario Garcia, Development Organization database; and to C. Patel for helpful comments. Jens Johansen, and Olivier Labe at the UNESCO Institute for Statistics for their spe- cial effort to provide us with the education data. Comments and suggestions at var- 5. States and markets ious stages of production also came from Eric Swanson. Section 5 was prepared by David Cieslikowski and Mahyar Eshragh-Tabary in part- nership with the World Bank's Private Sector and Infrastructure Network, its Poverty 3. Environment Reduction and Economic Management Network, the World Bank Institute, the Section 3 was prepared by M. H. Saeed Ordoubadi and Mayhar Eshragh-Tabary in International Finance Corporation, and external partners. David Cieslikowski wrote partnership with the World Bank's Environmentally and Socially Sustainable the introduction to the section. Other contributors include Ada Karina Izaguirre and Development Network and in collaboration with the World Bank's Development Kathy Khuu (privatization and infrastructure projects); Andrew Newby of Euromoney Research Group and Transportation, Water, and Urban Development Department. (credit ratings); Simeon Djankov and Caralee McLeish (business environment); Alka Important contributions were made by Christian Layke, Daniel Prager, and Robin Banerjee and Isilay Cabuk (Standard & Poor's emerging stock market indexes); 364 2004 World Development Indicators Yonas Biru (purchasing power parity conversion factors); Esperanza Magpantay and overall design direction, editing, and layout, led by Meta de Coquereaumont and Michael Minges of the International Telecommunication Union (communications and Bruce Ross-Larson. The editing and production team consisted of Joseph information); Peter Roberts (transport); Jane Degerlund of Containerisation Costello, Elizabeth McCrocklin, Christopher Trott, and Elaine Wilson. International (ports); Maria Helena Capelli Miguel of the United Nations Educational, Communications Development's London partner, Grundy & Northedge, provided Scientific and Cultural Organization Institute for Statistics (culture, research and art direction and design. Staff from External Affairs oversaw publication and dis- development, scientists and engineers); Anders Halvorsen of the World Information semination of the book. Technology and Services Alliance; Stephen Minton of International Data Corporation (information and communications technology); Dan Gallik of the U.S. Department of Client services State (military personnel); Bjorn Hagelin and Petter Stålenheim of the Stockholm The Development Data Group's Client Services Team (Azita Amjadi, Richard Fix, International Peace Research Institute (military expenditures and arms transfers); Naomi Halewood, Gonca Okur, and William Prince) contributed to the design and and Lise McLeod of the World Intellectual Property Organization (patents data). planning of World Development Indicators and The World Bank Atlas and helped coordinate work with the Office of the Publisher. 6. Global links Section 6 was prepared by Amy Heyman in collaboration with Eric Swanson and Administrative assistance and office technology support external partners. Substantial input came from Azita Amjadi, Jerzy Rozanski (tar- Estela Zamora provided administrative assistance and assisted in updating the iffs), and Ibrahim Levent (financial data). Other contributors include Bernard databases. Jean-Pierre Djomalieu, Gytis Kanchas, Nacer Megherbi, and Shahin Hoekman, Francis Ng, and Marcelo Olarreaga (trade); Betty Dow (commodity Outadi provided information technology support prices); Aki Kuwahara of the United Nations Conference on Trade and Development; Cecile Thoreau and Pauline Fron of the Organisation for Economic Publishing and dissemination Co-operation and Development (OECD) (migration); Yasmin Ahmad, Brian The Office of the Publisher, under the direction of Dirk Koehler, provided valuable Hammond, and Aimee Nichols of the OECD (aid flows); and Antonio Massieu and assistance throughout the production process. Randi Park coordinated printing Azucena Pernia of the World Tourism Organization (tourism data). Mehdi Akhlaghi and supervised marketing and distribution. Chris Neal of the Development provided valuable technical assistance. Economics Vice President's office and Carl Hanlor of External Affairs managed the communications strategy, and the Regional Operations Group headed by Paul Other parts Mitchell helped coordinate the overseas release. Preparation of the maps on the inside covers was coordinated by Jeff Lecksell and Greg Prakas of the World Bank's Map Design Unit. The Users guide was pre- World Development Indicators CD-ROM pared by David Cieslikowski. Statistical methods was written by Eric Swanson. Programming and testing were carried out by Reza Farivari and his team: Azita Primary data documentation was coordinated by K. M. Vijayalakshmi, who served Amjadi, Ying Chi, Ramgopal Erabelly, Nacer Megherbi, Shahin Outadi, and as database administrator. Gladys Gicker and Estela Zamora assisted in updat- William Prince. Masako Hiraga produced the social indicators tables. William ing the Primary data documentation table. Mehdi Akhlaghi was responsible for Prince coordinated user interface design and overall production and provided database updates and aggregation. Acronyms and abbreviations was prepared by quality assurance. Amy Heyman. The index was collated by Richard Fix and Gonca Okur. WDI Online Database management Design, programming, and testing were carried out by Reza Farivari and his team: Database management was coordinated by Mehdi Akhlaghi with the cross-team par- Mehdi Akhlagi, Azita Amjadi, Ying Chi, Shahin Outadi, and Nacer Megherbi. ticipation of Development Data Group staff to create an integrated World Develop- William Prince coordinated production and provided quality assurance. Cybèle ment Indicators database. This database was used to generate the tables for World Bourgougnon of the Office of the Publisher was responsible for implementation Development Indicators and related products such as WDI Online, The World Bank of WDI Online and management of the subscription service. Atlas, The Little Data Book, and the World Development Indicators CD-ROM. Client feedback Design, production, and editing The team is grateful to the many people who have taken the time to provide com- Richard Fix coordinated all stages of production with Communications ment on its publications. Their feedback and suggestions have helped improve Development Incorporated. Communications Development Incorporated provided this year's edition. 2004 World Development Indicators 365 BIBLIOGRAPHY AbouZahr, Carla. 2000. "Maternal Mortality." OECD Observer (223): 29­30. Chen, Shaohua, and Martin Ravallion. 2000. "How Did the World's Poorest Fare AbouZahr, Carla, Tessa Wardlaw. 2003. "Maternal Mortality in 2000: Estimates in the 1990s?" Policy Research Working Paper 2409. World Bank, developed by WHO, UNICEF, and UNFPA." Development Research Group, Washington, D.C. Ahmad, Sultan. 1992. "Regression Estimates of Per Capita GDP Based on Collier, Paul, and David Dollar. 1999. "Aid Allocation and Poverty Reduction." Purchasing Power Parities." Policy Research Working Paper 956. World Bank, Policy Research Working Paper 2041. World Bank, Development Research International Economics Department, Washington, D.C. Group, Washington, D.C. ------. 1994. "Improving Inter-Spatial and Inter-Temporal Comparability of ------. 2001. "Can the World Cut Poverty in Half? How Policy Reform and Effective National Accounts." Journal of Development Economics 44: 53­75. Aid Can Meet the International Development Goals." Policy Research Working Allen, Richard, Salvatore Schiavo-Campo, and Thomas Columkill Garrity. 2004. Paper 2403. World Bank, Development Research Group, Washington, D.C. Assessing and Reforming Public Financial Management: A New Approach. Collins, Wanda W., Emile A. Frison, and Suzanne L. Sharrock. 1997. "Global Washington, D.C.: World Bank. Programs: A New Vision in Agricultural Research." Issues in Agriculture (World Ball, Nicole. 1984. "Measuring Third World Security Expenditure: A Research Bank, Consultative Group on International Agricultural Research, Washington, Note." World Development 12(2): 157­64. D.C.) 12: 1­28. Barro, Robert J. 1991. "Economic Growth in a Cross-Section of Countries." Commission of the European Communities, International Monetary Fund, Quarterly Journal of Economics 106(2): 407­44. Organisation for Economic Co-operation and Development, United Nations, Beck, Thorsten, and Ross Levine. 2001. "Stock Markets, Banks, and Growth: and World Bank. 2002. System of Environmental and Economic Accounts: Correlation or Causality?" Policy Research Working Paper 2670. World Bank, SEEA 2000. New York. Development Research Group, Washington, D.C. Containerisation International. 2004. Containerisation International Yearbook Behrman, Jere R., and Mark R. Rosenzweig. 1994. "Caveat Emptor: Cross- 2004. London. Country Data on Education and the Labor Force." Journal of Development Corrao, Marlo Ann, G. Emmanuel Guindon, Namita Sharma, and Donna Economics 44: 147­71. Fakhrabadi Shokoohi, eds. 2000. Tobacco Control Country Profiles. Atlanta: Bhalla, Surjit. 2002. Imagine There Is No Country: Poverty, Inequality, and American Cancer Society. Growth in the Era of Globalization. Washington, D.C.: Institute for Deaton, Angus. 2002. "Counting the World's Poor: Problems and Possible International Economics. Solutions." World Bank Research Observer 16(2): 125­47. Bloom, David E., and Jeffrey G. Williamson. 1998. "Demographic Transitions Demirgüç-Kunt, Asli, and Enrica Detragiache. 1997. "The Determinants of and Economic Miracles in Emerging Asia." World Bank Economic Review Banking Crises: Evidence from Developed and Developing Countries." Working 12(3): 419­55. paper. World Bank and International Monetary Fund, Washington, D.C. Brown, Lester R., and others. 1999. Vital Signs 1999: The Environmental Trends Demirgüç-Kunt, Asli, and Ross Levine. 1996a. "Stock Market Development and That Are Shaping Our Future. New York and London: W. W. Norton for Financial Intermediaries: Stylized Facts." World Bank Economic Review 10(2): Worldwatch Institute. 291­321. Brown, Lester R., Christopher Flavin, Hilary F. French, and others. 1998. State ------. 1996b. "Stock Markets, Corporate Finance, and Economic Growth: An of the World 1998. Washington, D.C.: Worldwatch Institute. Overview." World Bank Economic Review 10(2): 223­39. Brown, Lester R., Michael Renner, Christopher Flavin, and others. 1998. Vital ------. 1999. "Bank-Based and Market-Based Financial Systems: Cross-Country Signs 1998. Washington, D.C.: Worldwatch Institute. Comparisons." Policy Research Working Paper 2143. World Bank, Bruns, Barbara, Alain Mingat, and Ramahatra Rakotomalala. 2003. A Chance Development Research Group, Washington, D.C. for Every Child. Washington, D.C.: World Bank. de Onis, Mercedes, and Monika Blossner. 2000. "The WHO Global Database on Bulatao, Rodolfo. 1998. The Value of Family Planning Programs in Developing Child Growth and Malnutrition: Methodology and Applications." International Countries. Santa Monica, Calif.: Rand. Journal of Epidemiology 32: 518­26. Caiola, Marcello. 1995. A Manual for Country Economists. Training Series 1, Development Committee. September 22, 2003. SecM2003-0370: "Supporting vol. 1. Washington, D.C.: International Monetary Fund. Sound Policies with Adequate and Appropriate Financing: Implementing the Chaudhury, Nazmul, Jeffrey S. Hammer, Michael Kremer, Karthik Muralidharan, Monterrey Consensus at the Country Level." and F. Halsey Rogers. 2004. "Teacher and Health Care Provider DKT International. 1998. 1997 Contraceptive Social Marketing Statistics. Absenteeism: A Multi-Country Study." World Bank, Washington, D.C. Washington, D.C. 366 2004 World Development Indicators Doyle, John J., and Gabrielle J. Persley, eds. 1996. Enabling the Safe Use of Gardner, Robert. 1998. "Education." Demographic and Health Surveys, Biotechnology: Principles and Practice. Environmentally Sustainable Develop- Comparative Study 29. Macro International, Calverton, Md. ment Studies and Monographs Series, no. 10. Washington, D.C.: World Bank. Gardner-Outlaw, Tom, and Robert Engelman. 1997. "Sustaining Water, Easing Drucker, Peter F. 1994. "The Age of Social Transformation." Atlantic Monthly Scarcity: A Second Update." Population Action International, Washington, D.C. 274 (November). Goldfinger, Charles. 1994. L'utile et le futile: L'économie de l'immatériel. Paris: Easterly, William. 2000. "Growth Implosions, Debt Explosions, and My Aunt Editions Odile Jacob. Marilyn: Do Growth Slowdowns Cause Public Debt Crises?" Policy Research GTZ (German Agency for Technical Cooperation). 2002. Fuel Prices and Working Paper 2531. World Bank, Development Research Group, Taxation. Eschborn, Germany. Washington, D.C. Gupta, Sanjeev, Hamid Davoodi, and Erwin Tiongson. 2000. "Corruption and the Euromoney. 2003. September. London. Provision of Health Care and Education Services." IMF Working Paper Eurostat (Statistical Office of the European Communities). Various years. 00/116. International Monetary Fund, Washington, D.C. Demographic Statistics. Luxembourg. Gupta, Sanjeev, Brian Hammond, and Eric Swanson. 2000. "Setting the Goals." ------. Various years. Statistical Yearbook. Luxembourg. OECD Observer (223): 15­17. Evenson, Robert E., and Carl E. Pray. 1994. "Measuring Food Production (with Habyarimana, James, Jishnu Das, Stefan Dercon, and Pramila Krishnan. 2003. Reference to South Asia)." Journal of Development Economics 44: 173­97. "Sense and Absence: Absenteeism and Learning in Zambian Schools." World Faiz, Asif, Christopher S. Weaver, and Michael P. Walsh. 1996. Air Pollution Bank, Washington, D.C. from Motor Vehicles: Standards and Technologies for Controlling Emissions. Hamilton, Kirk, and Michael Clemens. 1999. "Genuine Savings Rates in Washington, D.C.: World Bank. Developing Countries." World Bank Economic Review 13(2): 333­56. Fallon, Peter, and Zafiris Tzannatos. 1998. Child Labor: Issues and Directions Happe, Nancy, and John Wakeman-Linn. 1994. "Military Expenditures and Arms for the World Bank. Washington, D.C.: World Bank. Trade: Alternative Data Sources." IMF Working Paper 94/69. International Fankhauser, Samuel. 1995. Valuing Climate Change: The Economics of the Monetary Fund, Policy Development and Review Department, Washington, D.C. Greenhouse. London: Earthscan. Harrison, Ann. 1995. "Factor Markets and Trade Policy Reform." World Bank, FAO (Food and Agriculture Organization). 1986. "Inter-Country Comparisons of Washington, D.C. Agricultural Production Aggregates." Economic and Social Development Paper Hatzichronoglou, Thomas. 1997. "Revision of the High-Technology Sector and 61. Rome. Product Classification." STI Working Paper 1997/2. OECD Directorate for ------. 1996. Food Aid in Figures 1994. Vol. 12. Rome. Science, Technology, and Industry, Paris. ------. 2003. State of the World's Forests 2003. Rome. Heck, W. W. 1989. "Assessment of Crop Losses from Air Pollutants in the U.S." ------. Various years. Fertilizer Yearbook. FAO Statistics Series. Rome. In J. J. McKenzie and M. T. El Ashry, eds., Air Pollution's Toll on Forests and ------. Various years. Production Yearbook. FAO Statistics Series. Rome. Crops. New Haven, Conn.: Yale University Press. ------. Various years. The State of Food Insecurity in the World. Rome. Heggie, Ian G. 1995. Management and Financing of Roads: An Agenda for ------. Various years. Trade Yearbook. FAO Statistics Series. Rome. Reform. World Bank Technical Paper 275. Washington, D.C. Frankel, Jeffrey. 1993. "Quantifying International Capital Mobility in the 1990s." Heston, Alan. 1994. "A Brief Review of Some Problems in Using National In Jeffrey Frankel, ed., On Exchange Rates. Cambridge, Mass.: MIT Press. Accounts Data in Level of Output Comparisons and Growth Studies." Journal Frankhauser, Pierre. 1994. "Fractales, tissus urbains et reseaux de transport." of Development Economics 44: 29­52. Revue d'economie politique 104: 435­55. Hettige, Hemamala, Muthukumara Mani, and David Wheeler. 1998. "Industrial Fredricksen, Birger. 1993. Statistics of Education in Developing Countries: An Pollution in Economic Development: Kuznets Revisited." Policy Research Introduction to Their Collection and Analysis. Paris: UNESCO. Working Paper 1876. World Bank, Development Research Group, Gallup, John L., and Jeffrey D. Sachs. 1998. "The Economic Burden of Malaria." Washington, D.C. Harvard Institute for International Development, Cambridge, Mass. Hill, Kenneth, Carla AbouZahr, and Tessa Wardlaw. 2001. "Estimates of Gannon, Colin, and Zmarak Shalizi. 1995. "The Use of Sectoral and Project Maternal Mortality for 1995." Bulletin of the World Health Organization 79(3): Performance Indicators in Bank-Financed Transport Operations." TWU 182­93. Discussion Paper 21. World Bank, Transportation, Water, and Urban Hill, Kenneth, Rohini Pande, Mary Mahe, and Gareth Jones. 1999. Trends in Development Department, Washington, D.C. Child Mortality in the Developing World: 1960 to 1996. New York: UNICEF. 2004 World Development Indicators 367 BIBLIOGRAPHY IEA (International Energy Agency). 2002. World Energy Outlook: Energy and Inter-Secretariat Working Group on National Accounts (Commission of the Poverty. Paris. European Communities, International Monetary Fund, Organisation for ------. Various years. Energy Balances of OECD Countries. Paris. Economic Co-operation and Development, United Nations, and World ------. Various years. Energy Statistics and Balances of Non-OECD Countries. Bank). 1993. System of National Accounts. Brussels, Luxembourg, New Paris. York, and Washington, D.C. ------. Various years. Energy Statistics of OECD Countries. Paris. IPCC (Intergovernmental Panel on Climate Change). 2001. Climate Change IFPRI (International Food Policy Research Institute). 1999. Soil Degradation: A 2001. Cambridge: Cambridge University Press. Threat to Developing-Country Food Security by 2020. Washington, D.C. ITU (International Telecommunication Union). 2003a. World Telecommunication ILO (International Labour Organization). 1990. ILO Manual on Concepts and Development Report 2003. Geneva. Methods. Geneva: International Labour Office. ------. 2003b. World Telecommunications Indicators Database. Geneva. ------. Various years. Key Indicators of the Labour Market 2001­2002. Geneva: IUCN (World Conservation Union). 1998. 1997 IUCN Red List of Threatened International Labour Office. Plants. Gland, Switzerland. ------. Various years. Sources and Methods: Labour Statistics (formerly ------. 2000. 2000 IUCN Red List of Threatened Animals. Gland, Switzerland. Statistical Sources and Methods). Geneva: International Labour Office. Journal of Development Economics. 1994. Special issue on Database for ------. Various years. Yearbook of Labour Statistics. Geneva: International Development Analysis. Edited by T. N. Srinivasan. Vol. 44, no. 1. Labour Office. Kaminsky, Graciela L., Saul Lizondo, and Carmen M. Reinhart. 1997. "Leading IMF (International Monetary Fund). 1977. Balance of Payments Manual. 4th ed. Indicators of Currency Crises." Policy Research Working Paper 1852. World Washington, D.C. Bank, Latin America and the Caribbean Region, Office of the Chief ------. 1993. Balance of Payments Manual. 5th ed. Washington, D.C. Economist, Washington, D.C. ------. 1995. Balance of Payments Compilation Guide. Washington, D.C. Kaufmann, Daniel, and Art Kraay. 2002. "Growth Without Governance." Policy ------. 1996a. Balance of Payments Textbook. Washington, D.C. Research Working Paper 2928. World Bank, Washington, D.C. ------. 2000. Monetary and Financial Statistics Manual. Washington, D.C. Kaufmann, Daniel, Art Kraay, and Massimo Mastruzzi. 2003. "Governance ------. 2001. A Manual on Government Finance Statistics. Washington, D.C. Matters III: Governance Indicators for 1996­2002." Policy Research Working ------. 2002. Exchange Arrangements and Exchange Restrictions Annual Paper 3106. World Bank, Washington, D.C. Report, 2002. Washington, D.C. Klasen, Stephen. 1999. "Does Gender Inequality Reduce Growth and Develop- ------. Various issues. Direction of Trade Statistics. Quarterly. Washington, D.C. ment? Evidence from Cross-Country Regressions." World Bank, 2001, back- ------. Various issues. International Financial Statistics. Monthly. Washington, D.C. ground paper for Engendering Development--Through Gender Equality in ------. Various years. Balance of Payments Statistics Yearbook. Parts 1 and 2. Rights, Resources, and Voice. Washington, D.C. Knetter, Michael. 1994. Why Are Retail Prices in Japan So High? Evidence from ------. Various years. Direction of Trade Statistics Yearbook. Washington, D.C. German Export Prices. NBER Working Paper 4894. Cambridge, Mass.: ------. Various years. Government Finance Statistics Yearbook. Washington, D.C. National Bureau of Economic Research. ------. Various years. International Financial Statistics Yearbook. Washington, D.C. Kunte, Arundhati, Kirk Hamilton, John Dixon, and Michael Clemens. 1998. IMF (International Monetary Fund), OECD (Organisation for Economic Co-opera- "Estimating National Wealth: Methodology and Results." Environmental Eco- tion and Development), United Nations, and World Bank. 2000. A Better World nomics Series, no. 57. World Bank, Environment Department, Washington, for All: Progress towards the International Development Goals. Washington, D.C. D.C. Institutional Investor. 2003. September. New York. Lanjouw, Jean O., and Peter Lanjouw. 2001. "The Rural Non-Farm Sector: Issues International Civil Aviation Organization. 2002. Civil Aviation Statistics of the and Evidence from Developing Countries." Agricultural Economics 26: 1­23. World, 1999­2000. Montreal. Lanjouw, Peter, and Gershon Feder. 2001. Rural Nonfarm Activities and Rural International Road Federation. 2002. World Road Statistics 2002. Geneva. Development: From Experience toward Strategy. Rural Strategy Discussion International Working Group of External Debt Compilers (Bank for International Paper 4. Washington, D.C.: World Bank. Settlements, International Monetary Fund, Organisation for Economic Co- Lewis, Karen K. 1995. "Puzzles in International Financial Markets." In Gene operation and Development, and World Bank). 1987. External Debt Grossman and Kenneth Rogoff, eds., Handbook of International Economics. Definitions. Washington, D.C. Vol. 3. Amsterdam: North Holland. 368 2004 World Development Indicators Lewis, Stephen R., Jr. 1989. "Primary Exporting Countries." In Hollis Chenery ------. Various years. National Accounts. Vol. 1, Main Aggregates. Paris. and T. N. Srinivasan, eds., Handbook of Development Economics. Vol. 2. ------. Various years. National Accounts. Vol. 2, Detailed Tables. Paris. Amsterdam: North Holland. ------. Various years. Trends in International Migration: Continuous Reporting Lovei, Magdolna. 1997. "Toward Effective Pollution Management." Environment System on Migration. Paris. Matters (fall): 52­53. Organisation for Economic Co-operation and Development (OECD), Development Lucas, R. E. 1988. "On the Mechanics of Economic Development." Journal of Assistance Committee. Various years. Development Cooperation. Paris. Monetary Economics 22: 3­22. ------. Various years. Geographical Distribution of Financial Flows to Aid Mani, Muthukumara, and David Wheeler. 1997. "In Search of Pollution Havens? Recipients: Disbursements, Commitments, Country Indicators. Paris. Dirty Industry in the World Economy, 1960­95." World Bank, Policy Research O'Meara, Molly. 1999. "Reinventing Cities for People and the Planet." Department, Washington, D.C. Worldwatch Paper 147. Worldwatch Institute, Washington, D.C. McCarthy, F. Desmond, and Holger Wolf. 2001. "Comparative Life Expectancy in Palacios, Robert, and Montserrat Pallares-Miralles. 2000. "International Africa." Policy Research Working Paper 2668. World Bank, Development Patterns of Pension Provision." Social Protection Discussion Paper 0009. Research Group, Washington, D.C. World Bank, Human Development Network, Washington, D.C. Midgley, Peter. 1994. Urban Transport in Asia: An Operational Agenda for the Pandey, Kiran Dev, Katharine Bolt, Uwe Deichmann, Kirk Hamilton, Bart Ostro, 1990s. World Bank Technical Paper 224. Washington, D.C. and David Wheeler. 2003. "The Human Cost of Air Pollution: New Estimates Moody's Investors Service. 2003. Sovereign, Sub-national and Sovereign- for Developing Countries." World Bank, Development Research Group and Guaranteed Issuers. January. New York. Environment Department, Washington, D.C. Morgenstern, Oskar. 1963. On the Accuracy of Economic Observations. Pearce, David, and Giles Atkinson. 1993. "Capital Theory and the Measurement Princeton, N.J.: Princeton University Press. of Sustainable Development: An Indicator of Weak Sustainability." Ecological Morisset, Jacques. 2000. "Foreign Direct Investment in Africa: Policies Also Economics 8: 103­08. Matter." Policy Research Working Paper 2481. World Bank, Washington, D.C. Pilling, David. 1999. "In Sickness and in Wealth." Financial Times, 22 October. Murray, Christopher J. L., and Alan D. Lopez. 1996. The Global Burden of Plucknett, Donald L. 1991. "Saving Lives through Agricultural Research." Issues Disease. Cambridge, Mass.: Harvard University Press. in Agriculture (World Bank, Consultative Group on International Agricultural National Science Foundation. 2003. Science and Engineering Indicators 2003. Research, Washington, D.C.) 16. Arlington, Va. Population Reference Bureau. 2002. 2002 Women of Our World. Washington, D.C. Netcraft. 2003. Netcraft Secure Server Survey. [http://www.netcraft.com/]. PricewaterhouseCoopers. 2003a. Corporate Taxes 2003­2004: Worldwide NRI and World Bank. 2003. "Public Expenditure and Service Delivery in Papua Summaries. New York. New Guinea: Draft." World Bank: Washington, D.C. ------. 2003b. Individual Taxes 2003­2004: Worldwide Summaries. New York. Obstfeldt, Maurice. 1995. "International Capital Mobility in the 1990s." In P. B. PRS Group, Inc. 2002. International Country Risk Guide. CD-ROM. December. Kenen, ed., Understanding Interdependence: The Macroeconomics of the East Syracuse, N.Y. Open Economy. Princeton, N.J.: Princeton University Press. Rama, Martin, and Raquel Artecona. 2002. "A Database of Labor Market Obstfeldt, Maurice, and Kenneth Rogoff. 1996. Foundations of International Indicators across Countries." World Bank, Development Research Group, Macroeconomics. Cambridge, Mass.: MIT Press. Washington, D.C. OECD (Organisation for Economic Co-operation and Development). 1985. Ravallion, Martin. 1996. "Poverty and Growth: Lessons from 40 Years of Data Measuring Health Care 1960­1983: Expenditure, Costs, Performance. Paris. on India's Poor." DECNote 20. World Bank, Development Economics Vice ------. 1996. Trade, Employment, and Labour Standards: A Study of Core Presidency, Washington, D.C. Workers' Rights and International Trade. Paris. ------. 2001. "Growth, Inequality and Poverty: Looking Beyond Averages." Policy ------. 1997. Employment Outlook. Paris. Research Working Paper 2558. World Bank, Development Research Group, ------. 1999. OECD Environmental Data: Compendium 1999. Paris. Washington, D.C. ------. 2003. Agricultural Policies in OECD Countries: Monitoring and Evaluation ------. 2002. "Counting the World's Poor: A Comment." World Bank Research 2003. Paris Observer 16(2): 149­56. ------. Various issues. Main Economic Indicators. Monthly. Paris. Ravallion, Martin, and Shaohua Chen. 1996. "What Can New Survey Data Tell ------. Various years. International Development Statistics. CD-ROM. Paris. Us about the Recent Changes in Living Standards in Developing and 2004 World Development Indicators 369 BIBLIOGRAPHY Transitional Economies?" World Bank, Policy Research Department, ------. 2000. Fourth Report on the World Nutrition Situation. Geneva. Washington, D.C. UNAIDS (Joint United Nations Programme on HIV/AIDS) and WHO (World ------. 1997. "Can High-Inequality Developing Countries Escape Absolute Health Organization). 2002. AIDS Epidemic Update. December. Poverty?" Economic Letters 56: 51­57. UNCTAD (United Nations Conference on Trade and Development). 2003. The Ravallion, Martin, Gaurav Datt, and Dominique van de Walle. 1991. Least Developed Countries Report. Geneva. "Quantifying Absolute Poverty in the Developing World." Review of Income ------. Various years. Handbook of International Trade and Development and Wealth 37: 345­61. Statistics. Geneva. Reddy, Sanjay, and Thomas Pogge. 2002. "How Not to Count The Poor." Working UNEP (United Nations Environment Programme). 1991. Urban Air Pollution. paper. Barnard College and Columbia University, New York. Nairobi. Rodrik, Dani. 1996. "Labor Standards in International Trade: Do They Matter and UNESCO (United Nations Educational, Scientific, and Cultural Organization) What Do We Do About Them?" Overseas Development Council, Washington, D.C. Institute for Statistics. 2003. Global Education Digest 2003. Paris. Romer, P. M. 1986. "Increasing Returns and Long-Run Growth." Journal of UNESCO (United Nations Educational, Scientific, and Cultural Organization) Political Economy 94: 1002­37. and OECD (Organisation for Economic Co-operation and Development). Ruggles, Robert. 1994. "Issues Relating to the UN System of National Accounts 2003. Financing Education: Investments and Returns--Analysis of the World and Developing Countries." Journal of Development Economics 44(1): 87­102. Education Indicators. 2002 ed. Paris. Ryten, Jacob. 1998. "Fifty Years of ISIC: Historical Origins and Future Perspec- UNICEF (United Nations Children's Fund). 2001. Progress Since the World tives." ECA/STAT.AC. 63/22. United Nations Statistics Division, New York. Summit for Children: A Statistical Review. New York. Sala-i-Martin, Xavier. 2002. The Disturbing `Rise' in Global Income Inequality. ------. Various years. The State of the World's Children. New York: Oxford NBER Working Paper 8904. Cambridge, Mass.: National Bureau of Economic University Press. Research. UNIDO (United Nations Industrial Development Organization). Various years. Sen, Amartya. 1988. "The Concept of Development." In Hollis Chenery and T. N. International Yearbook of Industrial Statistics. Vienna. Srinivasan, eds., Handbook of Development Economics. Vol. 1. Amsterdam: United Nations. 1947. Measurement of National Income and the Construction of North Holland. Social Accounts. New York. Shiklovanov, Igor. 1993. "World Fresh Water Resources." In Peter H. Gleick, ed., Water ------. 1968. A System of National Accounts: Studies and Methods. Series F, in Crisis: A Guide to Fresh Water Resources. New York: Oxford University Press. no. 2, rev. 3. New York. SIPRI (Stockholm International Peace Research Institute). 2003. SIPRI ------. 1990. International Standard Industrial Classification of All Economic Yearbook 2003: Armaments, Disarmament, and International Security. Activities, Third Revision. Statistical Papers Series M, no. 4, rev. 3. New York. Oxford: Oxford University Press. ------. 1992. Handbook of the International Comparison Programme. Studies in Srinivasan, T. N. 1994. "Database for Development Analysis: An Overview." Methods Series F, no. 62. New York. Journal of Development Economics 44(1): 3­28. ------. 1993. SNA Handbook on Integrated Environmental and Economic Standard & Poor's. 2000. The S&P Emerging Market Indices Methodology, Accounting. Statistical Office of the United Nations. Series F, no. 61. New York. Definitions, and Practices. New York. ------. 1999. Integrated Environmental and Economic Accounting: An ------. 2003. Emerging Stock Markets Factbook 2003. New York. Operational Manual. Studies in Methods Series F, no. 78. New York. ------. 2004. Credit Week. January. New York. ------. 2000a. We the Peoples: The Role of the United Nations in the 21st Taylor, Alan M. 1996a. International Capital Mobility in History: Purchasing Century. New York. Power Parity in the Long Run. NBER Working Paper 5742. Cambridge, Mass.: ------. 2000b. The World's Women 2000: Trends and Statistics. Department of National Bureau of Economic Research. Economic and Social Affairs. New York. ------. 1996b. International Capital Mobility in History: The Saving-Investment United Nations Economic and Social Commission for Western Asia. 1997. Relationship. NBER Working Paper 5743. Cambridge, Mass.: National Bureau Purchasing Power Parities: Volume and Price Level Comparisons for the of Economic Research. Middle East, 1993. E/ESCWA/STAT/1997/2. Amman. UNACC/SCN (United Nations Administrative Committee on Co-ordination, United Nations Population Division. 2000. World Population Prospects: The Subcommittee on Nutrition). Various years. Update on the Nutrition 2000 Revision. New York. Situation. Geneva. ------. 2002. World Urbanization Prospects: The 2001 Revision. New York. 370 2004 World Development Indicators ------. 2002. International Migration Report 2003. New York. ------. 2003. The Africa Malaria Report 2003. Geneva. ------. Various years. Levels and Trends of Contraceptive Use. New York. WITSA (World Information Technology and Services Alliance). 2002. Digital United Nations Statistics Division. 1985. National Accounts Statistics: Planet 2002: The Global Information Economy. Based on research by Compendium of Income Distribution Statistics. New York. International Data Corporation. Vienna, Va. ------. Various issues. Monthly Bulletin of Statistics. New York. Wolf, Holger C. 1997. Patterns of Intra- and Inter-State Trade. NBER Working ------. Various years. Energy Statistics Yearbook. New York. Paper 5939. Cambridge, Mass.: National Bureau of Economic Research. ------. Various years. International Trade Statistics Yearbook. New York. World Bank. 1990. World Development Report 1990: Poverty. New York: Oxford ------. Various years. National Accounts Statistics: Main Aggregates and University Press. Detailed Tables. Parts 1 and 2. New York. ------. 1992. World Development Report 1992: Development and the ------. Various years. National Income Accounts. New York. Environment. New York: Oxford University Press. ------. Various years. Population and Vital Statistics Report. New York. ------. 1995. "China's GDP in U.S. Dollars, Based on Purchasing Power Parity." ------. Various years. Statistical Yearbook. New York. Policy Research Working Paper 1415. Washington, D.C. U.S. Census Bureau. 1990. 1990 Census of Population Listing. Washington, D.C. ------. 1996a. Environment Matters (summer). Environment Department, ------. 2000. Current Population Report. March. Washington, D.C. Washington, D.C. U.S. Department of Health and Human Services. 1997. Social Security Systems ------. 1996b. Livable Cities for the 21st Century. A Directions in Development throughout the World. Washington, D.C. book. Washington, D.C. U.S. Department of State, Bureau of Verification and Compliance. 2002. World ------. 1996c. National Environmental Strategies: Learning from Experience. Military Expenditures and Arms Transfers 2000. Washington, D.C. Environment Department, Washington, D.C. U.S. Environmental Protection Agency. 1995. National Air Quality and Emissions ------. 1997a. Can the Environment Wait? Priorities for East Asia. Washington, Trends Report 1995. Washington, D.C. D.C. Wagstaff, Adam, and Harold Alderman. 2001. "Life and Death on a Dollar a Day: ------. 1997b. Expanding the Measure of Wealth: Indicators of Environmentally Does It Matter Where You Live?" World Bank, Washington, D.C. Sustainable Development. Environmentally Sustainable Development Studies Walsh, Michael P. 1994. "Motor Vehicle Pollution Control: An Increasingly Critical and Monographs Series, no. 17. Washington, D.C. Issue for Developing Countries." World Bank, Washington, D.C. ------. 1997d. Rural Development: From Vision to Action. Environmentally Watson, Robert, John A. Dixon, Steven P. Hamburg, Anthony C. Janetos, and Sustainable Development Studies and Monographs Series, no. 12. Richard H. Moss. 1998. Protecting Our Planet, Securing Our Future: Linkages Washington, D.C. among Global Environmental Issues and Human Needs. A joint publication of ------. 1997e. Sector Strategy: Health, Nutrition, and Population. Human the United Nations Environment Programme, U.S. National Aeronautics and Development Network, Washington, D.C. Space Administration, and World Bank, Nairobi and Washington, D.C. ------. 1999a. Fuel for Thought: Environmental Strategy for the Energy Sector. WCMC (World Conservation Monitoring Centre). 1992. Global Biodiversity: Environment Department, Energy, Mining, and Telecommunications Status of the Earth's Living Resources. London: Chapman and Hall. Department, and International Finance Corporation, Washington, D.C. ------. 1994. Biodiversity Data Sourcebook. Cambridge: World Conservation ------. 1999b. Greening Industry: New Roles for Communities, Markets, and Press. Governments. A World Bank Policy Research Report. New York: Oxford WHO (World Health Organization). 1983. International Classification of University Press. Diseases. 10th rev. Geneva. ------. 1999c. Health, Nutrition, and Population Indicators: A Statistical ------. 1997. Coverage of Maternity Care. Geneva. Handbook. Human Development Network, Washington, D.C. ------. 2002b. The Tobacco Atlas. Geneva. ------. 1999d. Toward a Virtuous Circle: A Nutrition Review of the Middle East ------. Various years. Global Tuberculosis Control Report. Geneva. and North Africa. Middle East and North Africa Working Paper Series, no. 17. ------. Various years. World Health Report. Geneva. Washington, D.C. ------. Various years. World Health Statistics Annual. Geneva. ------. 2000a. Trade Blocs. A World Bank Policy Research Report. New York: WHO (World Health Organization) and UNICEF (United Nations Children's Oxford University Press. Fund). 2000. Global Water Supply and Sanitation Assessment 2000 Report. ------. 2000b. World Development Report 2000/2001: Attacking Poverty. New Geneva. York: Oxford University Press. 2004 World Development Indicators 371 BIBLIOGRAPHY ------. 2001. "Malaria at a Glance." Health, Nutrition, and Population ------. 2003b. "The Millennium Development Goals for Health: Rising to the Department, Washington, D.C. [www.worldbank.org/hnp/malaria]. Challenges." World Bank, Washington, D.C. ------. 2002a. "Achieving Education for All by 2015: Simulation Results for 47 ------. 2004. Doing Business in 2004: Understanding Regulation. Washington, Low-Income Countries." Africa Region and Human Development Network, D.C.: Copublication of the World Bank and Oxford University Press. Education Department, Washington, D.C. ------. Various issues. Global Commodity Markets. Quarterly. Washington, D.C. ------. 2002b. A Case for Aid: Building a Consensus for Development ------. Various years. Global Development Finance (formerly World Debt Tables). Assistance. Washington, D.C. Washington, D.C. (Also available on CD-ROM.) ------. 2002c. Financial Impact of the HIPC Initiative: First 24 Country Cases. ------. Various years. Global Economic Prospects and the Developing Countries. [http://www.worldbank.org/hipc]. Washington, D.C. ------. 2002e. Globalization, Growth, and Poverty: Building an Inclusive World ------. Various years. World Development Indicators. Washington, D.C. Economy. A World Bank Policy Research Report. New York: Oxford University World Energy Council. 1995. Global Energy Perspectives to 2050 and Beyond. Press. London. ------. 2002f. Private Sector Development Strategy: Directions for the World World Intellectual Property Organization. 2003. Industrial Property Statistics. Bank Group. Private Sector Development Network, Washington, D.C. Publication A. Geneva. ------. 2002g. Social Protection in Latin America and the Caribbean. Fact Sheet. World Resources Institute, UNEP (United Nations Environment Programme), Latin America and the Caribbean Region and Human Development Network, UNDP (United Nations Development Programme), and World Bank. Various Washington, D.C. years. World Resources: A Guide to the Global Environment. New York: Oxford ------. 2002h. World Development Report 2002: Building Institutions for University Press. Markets. New York: Oxford University Press. World Tourism Organization. 2002a. Compendium of Tourism Statistics 2002. ------. 2002i. World Development Report 2003: Transforming Growth-- Madrid. Neighbor, Nature, Future. Draft. Washington, D.C. ------. 2002b. Yearbook of Tourism Statistics. Vols. 1 and 2. Madrid. ------. 2002j. The Environment and the Millennium Development Goals. WTO (World Trade Organization). Various years. Annual Report. Geneva. Washington, D.C. Zook, Matthew. 2000. "Internet Metrics: Using Host and Domain Counts to Map ------. 2002k. Poverty Reduction and the World Bank: Operationalizing World the Internet." International Journal on Knowledge Infrastructure Development Report 2000/2001. Washington, D.C. Development, Management and Regulation (University of California at ------. 2003a. World Bank Atlas. Washington, D.C. Berkeley) 24(6/7). 372 2004 World Development Indicators INDEX OF INDICATORS References are to table numbers. A total 6.10 net concessional flows Agriculture from international financial institutions 6.12 cereal from United Nations agencies 6.12 area under production 3.2 exports as share of total exports 6.3 net official development assistance and official aid by DAC members exports, total 6.3 as share of general government disbursements 6.9 imports as share of total imports 6.3 as share of GNI of donor country 1.4, 6.9 imports, total 6.3 average annual change in volume 6.9 yield 3.3 by type 6.8 fertilizer from major donors, by recipient 6.11 commodity prices 6.4 for basic social services as share of total ODA commitments 1.4 consumption, per hectare of arable land 3.2 for debt relief as share of total ODA commitments 1.4 food per capita of donor country 6.9 commodity prices 6.4 total 6.8, 6.9, 6.11 exports as share of total exports 6.3 untied aid 6.9 exports, total 6.3 imports as share of total imports 6.3 AIDS--see HIV, prevalence of imports, total 6.3 freshwater withdrawals for, as share of total 3.5 Air pollution--see Pollution labor force as share of total, male and female 2.3 land Air transport arable, as share of land area 3.1 aircraft departures 5.9 arable, per capita 3.2 air freight 5.9 area under cereal production 3.2 passengers carried 5.9 irrigated, as share of cropland 3.2 permanent cropland as share of land area 3.1 Asylum seekers--see Migration machinery B tractors per 100 square kilometers of arable land 3.2 tractors per 1,000 agricultural workers 3.2 production indexes Balance of payments crop 3.3 current account balance 4.15 food 3.3 exports and imports of goods and services 4.15 livestock 3.3 gross international reserves 4.15 value added net current transfers 4.15 annual growth of 4.1 net income 4.15 as share of GDP 4.2 See also Exports; Imports; Investment; Private capital flows; Trade per worker 3.3 Bank and trade-related lending 6.7 Aid by recipient Biological diversity aid dependency ratios 6.10 assessment, date prepared, by country 3.14 per capita 6.10 species 3.4 2004 World Development Indicators 373 threatened species 3.4 Consumption treaty 3.14 distribution of--see Income, distribution fixed capital 3.15 Birds government, general species 3.4 annual growth of 4.10 threatened species 3.4 as share of GDP 4.9 private Birth rate, crude 2.1 annual growth of 1.4, 4.10 as share of GDP 4.9 Births attended by skilled health staff 1.2, 2.6, 2.16 per capita, annual growth of 1.2, 4.10 relative price level 4.12 Birthweight, low 2.17 total 4.10 See also Purchasing power parity (PPP) C Contraceptive prevalence rate 2.17 Carbon dioxide damage 3.15 Contract enforcement emissions costs of 5.3 per capita 1.3, 3.8 number of procedures 5.3 per 1995 U.S. dollar of GDP 3.8 time required for 5.3 total 1.6, 3.8 Country risk Cities composite ICRG risk ratings 5.2 air pollution 3.13 Euromoney country creditworthiness ratings 5.2 environment 3.11 Institutional Investor credit ratings 5.2 population Moody's sovereign long-term debt ratings 5.2 in largest city 3.10 Standard & Poor's sovereign long-term debt ratings 5.2 in selected cities 3.13 in urban agglomerations of more than one million 3.10 Credit, domestic telephone mainlines in largest city 5.10 from banking sector 5.5 urban population 3.10, 3.11 to private sector 5.1 See also Urban environment Credit, markets Commodity prices and price indexes 6.4 creditor rights index 5.2 private bureau coverage 5.2 Communications--see Internet, users; Newspapers; Radios; public registry coverage 5.2 Telecommunications, international; Television Current account balance 4.15 Computers See also Balance of payments in education 5.11 D per 1,000 people 5.11 DAC (Development Assistance Committee)--see Aid 374 2004 World Development Indicators Death rate, crude 2.1 female to male enrollment in primary and secondary schools 2.11 See also Mortality rate gross, by level 2.11 net, by level 2.11 Debt, external net intake rate, grade 1 2.12 debt service, total 4.17 primary completion rate 2.12 long term 4.16 public spending on present value of 4.17 as share of GDP 2.9 private nonguaranteed 4.16 as share of total government expenditure 2.10 public and publicly guaranteed per student, as share of GDP per capita 2.9 debt service 4.17 per student, by level 2.10 IBRD loans and IDA credits 4.16 pupil-teacher ratio, primary level 2.10 IMF credit, use of 4.16 repeaters in primary school 2.12 total 4.16 teachers, primary, trained 2.10 ratings 5.2 unemployment by level of educational attainment 2.4 short term 4.17 total 4.16 Electricity consumption 5.10 Defense distribution losses 5.10 armed forces personnel production as share of labor force 5.8 sources of 3.9 total 5.8 total 3.9 arms transfers exports 5.8 Employment imports 5.8 employment laws index 5.3 military expenditure in agriculture, male and female 2.3 as share of central government expenditure 5.8 in industry, male and female 2.3 as share of GDP 5.8 in informal sector, urban male and female 2.8 Deforestation 3.4 total 2.8 in services, male and female 2.3 Density--see Population, density Endangered species--see Biological diversity, threatened species Development assistance--see Aid Energy Distribution of income or consumption--see Income, distribution commercial, use of annual growth of 3.7 E efficiency of 3.8 GDP per unit of 3.8 Education per capita 3.7 attainment total 3.7 share of cohort reaching grade 5, male and female 2.12 depletion as share of GDP 3.15 expected years of schooling 2.13 emissions--see Pollution enrollment ratio imports, net 3.7 2004 World Development Indicators 375 INDEX OF INDICATORS production, commercial 3.7 transport 4.7 See also Electricity travel 4.7, 6.14 See also Trade Entry regulations for business F cost to register a business as share of GNI per capita 5.3 minimum capital requirement as share of GNI per capita 5.3 start-up procedures, number of 5.3 Fertility rate time to start up a business 5.3 adolescent 2.16 total 2.7, 2.16 Environmental profile, date prepared 3.14 Financial debt and efficiency--see Liquidity; Monetary indicators Environmental strategy, year adopted 3.14 Financial flows, net Euromoney country creditworthiness ratings 5.2 from DAC members 6.8 from multilateral institutions Exchange rates international financial institutions 6.12 arrangements 5.7 total 6.12 official, local currency units to U.S. dollar 5.7 United Nations 6.12 ratio of PPP conversion factor to official exchange rate 5.7 official development assistance and official aid real effective 5.7 grants from NGOs 6.8 See also Purchasing power parity (PPP) other official flows 6.8 private 6.8 Exports total 6.8 arms 5.8 See also Aid duties on 5.6 goods and services Foreign direct investment, net--see Investment; Private capital flows, net as share of GDP 4.9 total 4.15 Forest high-technology area share of manufactured exports 5.12 as share of total land area 3.4 total 5.12 total 3.4 merchandise deforestation, average annual 3.4 by high-income OECD countries, by product 6.3 depletion of 3.15 by regional trade blocs 6.5 direction of trade 6.2 Freshwater high technology 5.12 annual withdrawals of structure of 4.5 as share of total resources 3.5 total 4.5 for agriculture 3.5 value, annual growth of 4.4, 6.2 for domestic use 3.5 volume, annual growth of 4.4 for industry 3.5 services flows structure of 4.7 internal 3.5 total 4.7 from other countries 3.5 376 2004 World Development Indicators resources per capita 3.5 revenues, current volume of 3.5 nontax 4.13 See also Water, access to improved source of tax, by source 4.13, 5.6 Fuel prices 3.12 Gross capital formation annual growth of 4.10 G as share of GDP 4.9 Gender differences Gross domestic product (GDP) in education annual growth of 1.1, 1.6, 4.1 enrollment, primary and secondary 1.2 implicit deflator--see Prices expected years of schooling 2.13 per capita, annual growth of 1.1, 1.6 in employment 2.3 total 4.2 in HIV prevalence, youth 2.18 in labor force participation 1.5, 2.2 Gross domestic savings as share of GDP 4.9 in literacy adult 2.13 Gross foreign direct investment--see Investment youth 1.5, 2.13 in life expectancy 1.5 Gross national income (GNI) in mortality per capita adult 2.19 in 2001 PPP dollars 1.1, 1.6 child 2.19 in 2001 U.S. dollars 1.1, 1.6 in smoking 2.18 rank 1.1 in survival to 65 2.19 rank unpaid family workers 1.5 in 2001 PPP dollars 1.1 women in parliaments 1.5 in 2001 U.S. dollars 1.1 total Gini index 2.7 in 2001 PPP dollars 1.1, 1.6 in 2001 U.S. dollars 1.1, 1.6 Government, central debt Gross national savings as share of GNI 3.15 as share of GDP 4.11 H interest as share of current revenue 4.11 interest as share of total expenditure 4.12 expenditures Health care as share of GDP 4.11 average length of hospital stay 2.14 by economic type 4.12 hospital beds per 1,000 people 2.14 military 5.8 immunization 2.15 financing inpatient admission rate 2.14 domestic 4.11 outpatient visits per capita 2.14 from abroad 4.11 pregnant women receiving prenatal care 1.5 overall deficit 4.11 physicians per 1,000 people 2.14 revenues as share of GDP 4.11 2004 World Development Indicators 377 INDEX OF INDICATORS I reproductive births attended by skilled health staff 1.2, 2.6, 2.16 contraceptive prevalence rate 2.16 Immunization fertility rate child 2.15 adolescent 2.16 DPT, share of children under 12 months 2.15 total 2.16 measles, share of children under 12 months 2.15 low-birthweight babies 2.17 maternal mortality ratio 1.2, 2.16 Imports women at risk of unwanted pregnancy 2.16 arms 5.7 tetanus vaccinations 2.15 duties on 5.5 tuberculosis energy, as share of commercial energy use 3.7 DOTS detection rate 2.15 goods and services treatment success rate 2.15 as share of GDP 4.9 total 4.15 Health expenditure merchandise as share of GDP 2.14 by high-income OECD countries, by product 6.3 per capita 2.14 structure of 4.6 private 2.14 total 4.6 public 2.9, 2.14 value, annual growth of 4.4, 6.2 total 2.14 volume, annual growth of 4.4, 6.2 services Health risks structure of 4.8 anemia, prevalence of 2.17 total 4.8 HIV, prevalence of 1.3, 2.18 transport 4.8 iodized salt consumption 2.17 travel 4.8, 6.14 malnutrition, child 1.2, 2.6, 2.17 See also Trade overweight children, prevalence of 2.17 smoking 2.18 Income tuberculosis, incidence of 1.3, 2.18 distribution undernourishment, prevalence of 2.17 Gini index 2.7 percentage shares of 1.2, 2.7 Heavily indebted poor countries (HIPCs) survey year 2.7 completion point 1.4 urban house price to income ratio, selected cities 3.11 decision point 1.4 nominal debt service relief 1.4 Indebtedness classification 4.17 HIV, prevalence of 1.3, 2.18 Industry, value added annual growth of 4.1 Hospital beds--see Health care as share of GDP 4.2 Housing, selected cities Inflation--see Prices population with secure tenure 3.11 price to income ratio 3.11 378 2004 World Development Indicators Information and communications technology expenditures Investment as share of GDP 5.11 foreign direct, gross, as share of GDP 6.1 per capita 5.11 foreign direct, net as share of GDP 5.1 Insolvency total 6.7 costs to resolve 5.3 government capital expenditure 4.12 time to resolve 5.3 infrastructure, private participation in energy 5.1 Institutional Investor credit ratings 5.2 telecommunications 5.1 transport 5.1 Integration, global economic, indicators of 6.1 water and sanitation 5.1 portfolio Interest payments--see Government, central, debt bonds 6.7 equity 6.7 Interest rates See also Gross capital formation deposit 5.7 lending 5.7 Iodized salt, consumption of 2.17 real 5.7 L risk premium on lending 5.5 spreads 5.5 Labor force International Bank for Reconstruction and Development (IBRD) annual growth of 2.2 IBRD loans and IDA credits 4.16 armed forces 5.8 net financial flows from 6.12 children ages 10­14 in 2.8 female 2.2 International Country Risk Guide (ICRG) composite risk ratings 5.3 foreign, in OECD countries 6.13 in agriculture, as share of total, male and female 2.3 International Development Association (IDA) in industry, as share of total, male and female 2.3 IBRD loans and IDA credits 4.16 in services, as share of total, male and female 2.3 net concessional flows from 6.12 maternity leave benefits 1.5 participation International Monetary Fund (IMF) gender differences in 1.5 net financial flows from 6.12 of population ages 15­64 2.2 use of IMF credit 4.16 total 2.2 women in parliaments 1.5 Internet See also Employment; Migration; Unemployment costs per 20 hours of use 5.11 Labor regulations, employment laws index 5.3 as share of monthly GNI per capita 5.11 secure servers 5.11 Land area users 5.11 arable--see Agriculture, land of selected cities 3.11 See also Protected areas; Surface area 2004 World Development Indicators 379 INDEX OF INDICATORS Land use Merchandise area under cereal production 3.2 exports by type 3.1 agricultural raw materials 4.5 irrigated land 3.2 food 4.5 fuels 4.5 Life expectancy at birth manufactures 4.5 gender differences in 1.5 ores and metals 4.5 total 1.6, 2.19 total 4.5 imports Liquidity agricultural raw materials 4.6 bank liquid reserves to bank assets 5.5 food 4.6 liquid liabilities 5.5 fuels 4.6 quasi-liquid liabilities 5.5 manufactures 4.6 See also Monetary indicators ores and metals 4.6 total 4.6 Literacy rate trade adult, male and female 2.13 direction of 6.2 gender differences in 1.5 growth of 4.4, 6.2 total, for other economies 1.6 youth, male and female 2.13 Migration foreign labor force in OECD countries as share of total labor force 6.13 M foreign population in OECD countries 6.13 inflows of foreign population Malnutrition, in children under five 1.2, 2.6, 2.17 asylum seekers 6.13 total 6.13 Mammals species 3.4 Millennium Development Goals, indicators for threatened species 3.4 aid as share of GNI of donor country 1.4, 6.9 Manufacturing as share of total ODA commitments 1.4 structure of 4.3 access to improved water source 1.3, 2.15, 3.5 value added access to improved sanitation facilities 1.3, 2.15, 3.10 annual growth of 4.1 births attended by skilled health staff 1.2, 2.6, 2.16 as share of GDP 4.2 carbon dioxide emissions per capita 1.3, 3.8 total 4.3 child malnutrition 1.2, 2.6, 2.17 consumption, national share of poorest quintile 1.2, 2.7 Market access to high-income countries female to male enrollments, primary and secondary 1.2 goods admitted free of tariffs 1.4 heavily indebted poor countries (HIPCs) support to agriculture 1.4 completion point 1.4 tariffs on exports from low- and middle-income countries decision point 1.4 agricultural products 1.4 nominal debt service relief 1.4 textiles and clothing 1.4 HIV, prevalence of, among 15- to 24-year-olds female 1.3, 2.18 380 2004 World Development Indicators male 1.3, 2.18 malnutrition, child 1.2, 2.6, 2.17 maternal mortality ratio 1.2, 2.16 overweight children, prevalence of 2.17 net primary enrollment ratio 2.11 undernourishment, prevalence of 2.17 telephone lines 1.3, 5.10 vitamin A supplementation 2.17 tuberculosis, incidence of 1.3, 2.18 O under-five mortality rate 1.2, 2.19 unemployment among 15- to 24-year-olds 1.3, 2.4 Official aid--see Aid Minerals, depletion of 3.15 Official development assistance--see Aid Monetary indicators claims on governments and other public entities 4.14 Official flows, other 6.8 claims on private sector 4.14 P Money and quasi money (M2), annual growth of 4.14 Passenger cars per 1,000 people 3.12 Moody's sovereign long-term debt ratings 5.2 Patent applications filed 5.12 Mortality rate adult, male and female 2.19 Pension child, male and female 2.19 average, as share of per capita income 2.9 children under five 1.2, 2.19 contributors 2.9 infant 2.6, 2.19 public expenditure on, as share of GDP 2.9 maternal 1.2, 2.16 Physicians--see Health care Motor vehicles passenger cars 3.12 Plants, higher per kilometer of road 3.12 species 3.4 per 1,000 people 3.12 threatened species 3.4 two-wheelers 3.12 See also Roads; Traffic Pollution carbon dioxide damage as share of GDP 3.15 N carbon dioxide emissions per capita 3.8 Nationally protected areas--see Protected areas per PPP dollar of GDP 3.8 total 3.8 Net adjusted savings 3.15 nitrogen dioxide, selected cities 3.13 Newspapers, daily 5.11 organic water pollutants, emissions of by industry 3.6 Nutrition per day 3.6 anemia, prevalence of 2.17 per worker 3.6 breastfeeding 2.17 sulfur dioxide, selected cities 3.13 iodized salt consumption 2.17 suspended particulate matter, selected cities 3.13 2004 World Development Indicators 381 INDEX OF INDICATORS Population body mass index, women with low 2.6 age dependency ratio 2.1 fertility rate 2.6, 2.16 annual growth of 2.1 malnutrition, child 1.2, 2.6, 2.17 by age group mortality rate, infant 2.6, 2.19 0­14 2.1 survey year 2.6 15­64 2.1 65 and above 2.1 Power--see Electricity, production density rural 3.1 Pregnancy, risk of unwanted 2.16 total 1.1, 1.6 female, as share of total 1.5 Prenatal care 1.5 foreign, in OECD countries 6.13 rural Prices annual growth of 3.1 commodity prices and price indexes 6.4 as share of total 3.1 consumer, annual growth of 4.14 total 1.1, 1.6, 2.1 food, annual growth of 4.14 urban fuel 3.12 as share of total 3.10 GDP implicit deflator, annual growth of 4.14 in largest city 3.10 terms of trade 4.4 in selected cities 3.11, 3.13 in urban agglomerations 3.10 Private capital flows total 3.10 gross, as share of GDP 6.1 See also Migration net bank and trade-related lending 6.7 Portfolio investment flows from DAC members 6.8 bonds 6.7 foreign direct investment 6.7 equity 6.7 portfolio investment 6.7 See also Investment Ports, container traffic in 5.9 Productivity Poverty in agriculture, value added per worker 3.3 international poverty line population below $1 a day 2.5 Protected areas population below $2 a day 2.5 as share of total land area 3.4 poverty gap at $1 a day 2.5 size of 3.4 poverty gap at $2 a day 2.5 survey year 2.5 Purchasing power parity (PPP) national poverty line conversion factor 5.7 population below 2.5 gross national income 1.1, 1.6 rural 2.5 survey year 2.5 urban 2.5 social indicators of 382 2004 World Development Indicators R S Radios 5.11 S&P/IFC Investable Index 5.4 Railways Sanitation lines households with sewerage connections, selected cities 3.11 electric 5.9 population with access to total 5.9 rural 3.10 productivity of, per employee 5.9 total 1.3, 2.16 tariffs, ratio of passenger to freight 5.9 urban 3.10 traffic density 5.9 Savings Regional development banks, net financial flows from 6.12 gross domestic 4.9 gross national 3.15 Relative prices (PPP)--see Purchasing power parity (PPP) net adjusted 3.15 Research and development domestic 3.15 expenditures for 5.12 researchers 5.12 Schooling--see Education technicians 5.12 Science and engineering Reserves, gross international--see Balance of payments researchers in R&D 5.12 scientific and technical journal articles 5.12 Risk ratings--see Country risk See also Research and development Roads Services goods hauled by 5.9 exports paved, as share of total 5.9 structure of 4.7 total network 5.9 total 4.7 traffic 3.12 imports structure of 4.8 Royalty and license fees total 4.8 payments 5.12 value added receipts 5.12 annual growth of 4.1 as share of GDP 4.2 Rural environment Sewerage connections, selected cities 3.11 access to improved water source 3.5 access to sanitation 3.10 Smoking, prevalence of, male and female 2.19 population annual growth of 3.1 Standard & Poor's sovereign long-term debt ratings 5.2 as share of total 3.1 density 3.1 Stock markets listed domestic companies 5.4 2004 World Development Indicators 383 INDEX OF INDICATORS market capitalization tax revenue as share of GDP 5.6 as share of GDP 5.4 total 5.4 Technology--see Computers; Exports, merchandise, high technology; Internet, market liquidity 5.4 users; Research and development; Science and engineering; S&P/IFC Investable Index 5.4 Telecommunications, international turnover ratio 5.4 Telecommunications, international Sulfur dioxide emissions--see Pollution cost of call to United States 5.10 outgoing traffic 5.10 Surface area 1.1, 1.6 See also Land area Telephones cost of local call 5.10 Suspended particulate matter--see Pollution fixed line and mobile phone subscribers 1.3 mainlines T faults 5.10 per 1,000 people Tariffs in largest city 5.10 all products national 5.10 mean tariff 6.6 revenue per line 5.10 standard deviation 6.6 waiting list 5.10 manufactured goods mobile 5.10 mean tariff 6.6 standard deviation 6.6 Television primary products cable subscribers per 1,000 people 5.11 mean tariff 6.6 sets per 1,000 people 5.11 standard deviation 6.6 See also Taxes and tax policies, duties Terms of trade, net barter 4.4 Taxes and tax policies Tetanus vaccinations, pregnant women 2.16 duties on exports 5.6 Threatened species--see Biological diversity on imports 5.6 See also Tariffs Tourism, international goods and service taxes, domestic 4.13, 5.6 expenditures 6.14 highest marginal tax rate inbound tourists, by country 6.14 corporate 5.6 outbound tourists, by country 6.14 individual 5.6 receipts 6.14 income, profit, and capital gains taxes as share of total revenue 4.13 Trade as share of total taxes 5.6 arms transfers 5.8 international trade taxes 4.13 changes in, as share of GDP 6.1 other taxes 4.13 exports plus imports as share of GDP 6.1 social security taxes 4.13 384 2004 World Development Indicators U merchandise as share of goods GDP 6.1 direction of, by region 6.2 UNDP, net concessional flows from 6.12 export value 4.4, 6.2 export volume 4.4 Unemployment import value 4.4, 6.2 incidence of long term import volume 4.4 male and female 2.4 nominal growth of, by region 6.2 total 2.4 OECD trade by commodity 6.3 rate real growth in, less growth in real GDP 6.1 by level of educational attainment 2.4 services for 15- to 24-year-olds 1.3 transport 4.7, 4.8 travel 4.7, 4.8 UNFPA, net concessional flows from 6.12 See also Balance of payments; Exports; Imports UNICEF, net concessional flows from 6.12 Trade blocs, regional exports within bloc 6.5 United Nations agencies, net concessional flows from 6.12 total exports, by bloc 6.5 Urban environment Trademark applications filed 5.12 access to sanitation 3.10 population Trade policies--see Tariffs as share of total 3.10 in largest city 3.10 Traffic in urban agglomerations of more than one million 3.10 accidents, people injured or killed by 3.2 total 3.10 road traffic 3.2 selected cities See also Roads area 3.11 households with Transport--see Air transport; Railways; Roads; Traffic; Urban environment access to potable water 3.11 regular waste collection 3.11 Treaties, participation in sewerage connections 3.11 biological diversity 3.14 house price to income ratio 3.11 CFC control 3.14 population 3.11 climate change 3.14 travel time to work 3.11 Law of the Sea 3.14 work trips by public transportation 3.11 ozone layer 3.14 See also Pollution; Population; Water, access to improved source of; Sanitation Tuberculosis V incidence of 1.3, 2.18 treatment success rate 2.15 Value added as share of GDP in agriculture 4.2 2004 World Development Indicators 385 INDEX OF INDICATORS in industry 4.2 Waste collection, households with access to 3.11 in manufacturing 4.2 in services 4.2 Water, access to improved source of growth of population with, as share of total 1.3, 2.15 in agriculture 4.1 rural 3.5 in industry 4.1 urban 3.5 in manufacturing 4.1 urban households with 3.11 in services 4.1 per worker, in agriculture 3.3 WFP, net concessional flows from 6.12 total, in manufacturing 4.3 World Bank, net financial flows from 6.12 W See also International Bank for Reconstruction and Development; International Development Association Wage, as share of total government expenditure 4.12 386 2004 World Development Indicators The world by region Low- and middle-income economies High-income economies Classified according to East Asia and Pacific Middle East and Nor th Africa OECD No data World Bank analytical grouping Europe and Central Asia South Asia Other Latin America and the Caribbean Sub-Saharan Africa MAP The world by region East Asia and Pacific Brazil Burkina Faso Luxembourg * American Samoa Chile Burundi Netherlands * Cambodia Colombia Cameroon New Zealand REGION China Costa Rica Cape Verde Norway Fiji Cuba Central African Republic Portugal * Indonesia Dominica Chad Spain * Kiribati Dominican Republic Comoros Sweden Korea, Dem. Rep. Ecuador Congo, Dem. Rep. Switzerland Lao PDR El Salvador Congo, Rep. United Kingdom Malaysia Grenada Côte d'Ivoire United States Marshall Islands Guatemala Equatorial Guinea Micronesia, Fed. Sts. Guyana Eritrea Other high income Mongolia Haiti Ethiopia Andorra Myanmar Honduras Gabon Antigua and Barbuda Northern Mariana Islands Jamaica Gambia, The Aruba Palau Mexico Ghana Bahamas, The Papua New Guinea Nicaragua Guinea Bahrain Philippines Panama Guinea-Bissau Barbados Samoa Paraguay Kenya Bermuda Solomon Islands Peru Lesotho Brunei Thailand St. Kitts and Nevis Liberia Cayman Islands Timor-Leste St. Lucia Madagascar Channel Islands Tonga St. Vincent and the Malawi Cyprus Vanuatu Grenadines Mali Faeroe Islands Vietnam Suriname Mauritania French Polynesia Trinidad and Tobago Mauritius Greenland Europe and Central Uruguay Mayotte Guam Asia Venezuela, RB Mozambique Hong Kong, China Albania Namibia Isle of Man Armenia Middle East and Niger Israel Azerbaijan North Africa Nigeria Kuwait Belarus Algeria Rwanda Liechtenstein Bosnia and Herzegovina Djibouti São Tomé and Principe Macao, China Bulgaria Egypt, Arab Rep. Senegal Malta Croatia Iran, Islamic Rep. Seychelles Monaco Czech Republic Iraq Sierra Leone Netherlands Antilles Estonia Jordan Somalia New Caledonia Georgia Lebanon South Africa Puerto Rico Hungary Libya Sudan Qatar Kazakhstan Morocco Swaziland San Marino Kyrgyz Republic Oman Tanzania Singapore Latvia Saudi Arabia Togo Slovenia Lithuania Syrian Arab Republic Uganda United Arab Emirates Macedonia, FYR Tunisia Zambia Virgin Islands (U.S.) Moldova West Bank and Gaza Zimbabwe Poland Yemen, Rep. Romania High income OECD Russian Federation South Asia Australia Serbia and Montenegro Afghanistan Austria * Slovak Republic Bangladesh Belgium * Tajikistan Bhutan Canada Turkey India Denmark Turkmenistan Maldives Finland * Ukraine Nepal France * Uzbekistan Pakistan Germany * Sri Lanka Greece * Latin America and the Iceland Caribbean Sub-Saharan Africa Ireland * Argentina Angola Italy * * Member of the Belize Benin Japan European Monetary Bolivia Botswana Korea, Rep. Union