PIECING TOGETHER POVERTY THE PUZZLE PIECING TOGETHER POVERTY THE PUZZLE © 2018 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved 1 2 3 4 21 20 19 18 This work is a product of the staff of The World Bank with external contributions. The findings, interpre- tations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other informa- tion shown on any map in this work do not imply any judgment on the part of The World Bank concern- ing the legal status of any territory or the endorsement or acceptance of such boundaries. 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Examples of components can include, but are not limited to, tables, figures, or images. All queries on rights and licenses should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; e-mail: pubrights@worldbank.org. ISBN (paper): 978-1-4648-1330-6 ISBN (electronic): 978-1-4648-1360-3 DOI: 10.1596/978-1-4648-1330-6 Cover design: Patricia Hord.Graphik Design Library of Congress Cataloging-in-Publication Data has been requested. Contents Foreword xi Acknowledgments xiii About the Team xv Abbreviations xix Overview 1 Staying focused on the poorest 4 Monitoring progress in a growing world 7 Piecing together the poverty puzzle 12 Introduction 15 1 Ending Extreme Poverty: Progress, but Uneven and Slowing 19 Monitoring extreme poverty: A quarter century of progress 19 Nowcasts and forecasts to 2030 22 Uneven progress: A regional profile of poverty reduction 24 Drilling down: The countries with the most poor 29 Socioeconomic and demographic profile of global poverty 37 Conclusions 39 Annex 1A Historical global and regional poverty estimates 41 Annex 1B Validation check of the 2030 poverty projections 46 Notes 47 2 Shared Prosperity: Mixed Progress 49 Beyond extreme poverty: A focus on the bottom 40 49 Continued progress in most economies though some are falling short 50 The poorest countries have limited information about shared prosperity 54 Growth at the bottom and the top is not always even 55 Who are the bottom 40? 58 Monitoring the twin goals 59 v Annex 2A Shared prosperity definitions 62 Annex 2B Shared prosperity estimates by economy 63 Notes 66 3 Higher Standards for a Growing World 67 Introduction 67 Higher poverty lines for everyone: US$3.20 and US$5.50 a day 68 Higher lines tailored to country circumstances: Societal poverty 72 Why not simply use national poverty lines? 79 Conclusion 81 Annex 3A Historical global and regional poverty estimates 83 Notes 85 4 Beyond Monetary Poverty 87 Why look beyond monetary poverty? 88 Considerations for constructing multidimensional poverty measures 90 A first global picture 96 A deeper look 103 Conclusion 108 Annex 4A Comparison of indicators used in multidimensional poverty measures 110 Annex 4B Multidimensional poverty measures: A formalization 111 Annex 4C Statistical tables 113 Notes 120 Spotlight 4.1 National multidimensional poverty indexes 122 5 Inside the Household: Poor Children, Women, and Men 125 Introduction 125 Beyond headship: Gender and age profiles of the global poor 128 Differences in resources and poverty within households 132 An individual perspective on multidimensional poverty 140 Conclusion 144 Annex 5A Technical note: Estimating intrahousehold resource shares 147 Notes 148 Appendix A Data Details 151 Main databases for the report 151 Classification of economies 152 Chapter 1 data and methodology 154 Chapter 2 data and methodology 159 Chapter 3 data and methodology 161 Chapter 4 data and methodology 162 Chapter 5 data and methodology 165 Note 166 References 167 vi POVERTY AND SHARED PROSPERITY 2018 Boxes 1.1 Alignment of the SDGs and the Twin 4.2 Incorporating Aspects of Quality Goals of the World Bank Group 20 into Multidimensional Poverty 1.2 Chapter 1: Data Overview 21 Measures 95 1.3 India: Issues with the 2015 Poverty 4.3 Chapter 4: Data Overview 97 Estimate and 2030 Forecasts 32 5.1 Differences in Needs and 2.1 The Global Database of Shared Equivalence Scales 127 Prosperity 50 5.2 Chapter 5: Data Overview 128 2.2 Country Stories 53 5.3 Dietary Diversity as an Indicator of 2.3 The Shared Prosperity Premium and Individual-Level Food Security 135 Other Concepts of Inequality 56 5.4 Child Poverty 141 4.1 Early Applications of Multidimensional 5.5 Gender and Socioemotional Dimensions Poverty Measurement 90 of Poverty: Participatory Studies 145 Figures O.1 Global Extreme Poverty Rate and 1.10 Household Size and Dependency Headcount, 1990–2015 2 Ratio in Sub-Saharan Africa 34 O.2 Share of Individuals in 1.11 Under-Five Mortality, Fertility, and Multidimensional Poverty, circa 2013 5 Population Growth in Sub-Saharan O.3 Percent of Females and Males Living Africa 35 in Households in Extreme Poverty, 1.12 Concentration of Extreme Poverty by Age Group, circa 2013 6 in Fragile and Conflict-Affected O.4 National and Societal Poverty Lines Situations 36 in a Growing World 9 1.13 Fragile Situations Perform Poorly in O.5 Societal Poverty, Global Estimates, Multiple Constituent Components 1990–2015 9 of Fragility 37 O.6 Contribution to Multidimensional 1A.1 Global Total Consumption Gap of Poverty, by Dimension, Selected the Extreme Poor, 1990–2015 41 Countries 11 1B.1 Projections to 2015 of Global Extreme O.7 Gender Gaps, Individual Poverty 46 Multidimensional Poverty, Selected 2.1 Shared Prosperity, 91 Economies, Countries 12 circa 2010–15 51 1.1 Global Extreme Poverty Rate and 2.2 Shared Prosperity Estimates, Headcount, 1990–2015 21 91 Economies, by Region, Group, 1.2 Projections to 2030 of Global Extreme and Income 55 Poverty 23 2.3 Correlation between Shared 1.3 Number of Extreme Poor by Region, Prosperity and the Shared Prosperity 1990–2030 25 Premium, 91 Economies 57 1.4 Regional GDP per Capita Growth 2.4 Growth across Deciles of the Income and Average Growth for the Extreme Distribution, Selected Countries 58 Poor, 1990–2017 25 2.5 Extreme Poverty and the Bottom 40, 1.5 Extreme Poverty Rate by Region and Selected Countries, circa 2015 59 Country, 2015 28 2.6 Mean Income, by Distribution 1.6 Extreme Poverty, Regional and World Decile, Selected Countries, 2015 59 Trends, 1990–2015 29 2.7 Share of Consumption or Income, 1.7 Rate and Number of the Extreme Poor, by Decile, Selected Countries, by Income Group, 2015 29 circa 2015 60 1.8 Global Distribution of the Extreme 2.8 Shared Prosperity and Changes Poor by Region and Country, 2015 30 in Extreme Poverty, 91 Economies, 1.9 Projections to 2030 for the Five circa 2010–15 61 Countries with the Most Extreme Poor 2.9 Shared Prosperity among the Poorest in 2015 31 Economies, circa 2010–15 61 CONTENTS vii 2B.1 The Shared Prosperity Premium, 4.8 Contribution to Multidimensional 91 Economies, by Region or Income Poverty (M), by Dimension, Classification 66 Selected Countries 106 3.1 Consumption and Income 4.9 The Poor, by Sociodemographic Distributions, 1990 and 2015 71 Characteristics, Selected Countries 107 3.2 National Poverty Lines and 4C.1 Share of Individuals in Multidimensional Economic Development 73 Poverty, by Region, circa 2013 119 3.3 Societal Poverty Line 75 5.1 Percent of Females and Males Living 3.4 Societal Poverty, Global Estimates, in Households in Extreme Poverty, 1990–2015 77 by Age Group, circa 2013 130 3.5 Change in the Societal Poverty Line 5.2 Distribution of People Living in from Growth 78 Households in Extreme Poverty, by Sex 3.6 Societal Poverty and Shared Prosperity and Employment Status, circa 2013 131 in Costa Rica and Ecuador 79 5.3 Distribution of Households in 3.7 Comparing National and Societal Extreme Poverty, by Demographic Poverty Lines and Rates, Vietnam, Typology, circa 2013 131 1993–2015 81 5.4 Distribution of Households in 4.1 Share of Individuals in Extreme Poverty, by Economic Multidimensional Poverty, Typology, circa 2013 132 119 Economies, circa 2013 98 5.5 The Gender Gap in Food Consumption 4.2 Share of Individuals Deprived in at over the Life Cycle, China 134 Least a Given Number of Indicators, 5.6 Caloric Shortfalls of Male Heads 119 Economies, circa 2013 100 and Other Household Members, 4.3 Contribution of Indicators to the Bangladesh 136 Adjusted Headcount Measure (M), 5.7 Estimated Consumption Allocation, 119 Economies, circa 2013 102 Men, Women, and Children, 4.4 Difference in the Share of the Poor Bangladesh and Malawi 139 in Rural Areas, Multidimensional 5.8 Individual Poverty Rates, Nuclear Headcount versus Monetary Households, Bangladesh and Malawi 140 Headcount, 119 Economies, 5.9 Gender Gaps, Education, and circa 2013 103 Nutrition Deprivation, Selected 4.5 Contribution to Monetary and Countries 142 Multidimensional Poverty, by 5.10 Gender Gaps, Individual Household Type, 119 Economies, Multidimensional Poverty, circa 2013 103 Selected Countries 143 4.6 Share of Individuals Deprived in at A.1 Use of Income/Consumption to Least a Given Number of Indicators, Measure Poverty 157 Selected Countries 105 A.2 Shared Prosperity Indicators Are 4.7 The Headcount Ratio, by Alternative Less Likely in Economies at Lower Poverty Measures, Selected Countries 106 GDP per Capita 160 Maps O.1 Shared Prosperity across the World, 2.1 Shared Prosperity across the World, 91 Economies, circa 2010–15 10 91 Economies, circa 2010–15 52 1.1 Extreme Poverty Rate by Country, 4.1 Provincial Poverty Rates, Ecuador 108 2015 27 Tables O.1 Poverty at Higher Poverty Lines, 1.2 Education and Access to Services US$3.20 and US$5.50 (2011 PPP) 8 among the Extreme Poor and Nonpoor 1.1 Age and Gender Profile of the Households 39 Extreme Poor, 2015 38 viii POVERTY AND SHARED PROSPERITY 2018 1A.1 Global and Regional Extreme Poverty, 4.6 The Multidimensionally Poor and 1990–2015 42 the Breadth of Deprivation, by Number 1A.2 Extreme Poverty, by Economy, of Deprivations, 119 Economies, 2015 43 circa 2013 101 2.1 Shared Prosperity and Shared 4.7 Regional Contributions to Prosperity Premium, 91 Economies, Multidimensional Poverty, 119 Economies, circa 2013 101 Summary Table, circa 2010–15 50 4.8 Percent of Individuals Deprived, B2.3.1 Number of Economies with Top by Indicator, Selected Countries 104 Incomes Estimated in the World Inequality Database and in the Poverty 4A.1 Dimensions and Indicators 110 and Shared Prosperity 2018 Report 56 4C.1 People Living in Monetary or 2B.1 Shared Prosperity Estimates, 91 Multidimensional Poverty, by Economies, circa 2010–15 63 Rural-Urban Areas, 119 Economies, circa 2013 113 2B.2 Changes in Shared Prosperity, 4C.2 People Living in Monetary or 67 Economies, circa 2008–13 to Multdimensional Poverty, by circa 2010–15 65 Household Type, 119 Economies, 2B.3 Changes in the Shared Prosperity circa 2013 113 Premium, 67 Economies, 4C.3 Individuals in Households Deprived circa 2008–13 to circa 2010–15 65 in Each Indicator, 119 Economies, 3.1 National Poverty Lines, circa 2011 69 circa 2013 114 3.2 Poverty at Higher Poverty Lines, 4C.4 Multidimensional Poverty across US$3.20 and US$5.50 (2011 PPP) 70 Alternative Measures, 119 Economies, 3.3 Average Societal Poverty Lines, circa 2013 117 by Region and Income Classification, 5.1 Households in Extreme Poverty, 1990–2015 76 Rates and Distribution by Headship, 3.4 Societal Poverty Rates, 1990–2015 77 circa 2013 129 5.2 Recent Data Sets on Individualized 3A.1 Historical Trends, Global Poverty Consumption 133 Estimates, 1990–2015 83 5.3 Individuals Misclassified by the 3A.2 Historical Trends, Regional Poverty Household Measure of Caloric Rates, 1990–2015 84 Availability 136 3A.3 Historical Trends, Regional Number 5.4 Indicators and Dimensions, the of Extreme Poor, 1990–2015 84 Individual and Household 4.1 Dimensions of Well-Being and Multidimensional Poverty Measure 142 Indicators of Deprivation 93 A.1 Overview of Principal Data Sources 4.2 Indicator Weights: Analysis of Three by Chapter 152 Dimensions 96 A.2 Shared Prosperity Availability across 4.3 Indicator Weights: Analysis of Five Rounds 161 Dimensions 96 A.3 Surveys Used in Chapter 1 and 4.4 People Living in Monetary or Chapter 4 in Cases Where Different Multidimensional Poverty, Survey Rounds Are Used 163 119 Economies, circa 2013 97 A.4 Household Surveys, Six-Country 4.5 Individuals in Households Deprived Sample 164 in Each Indicator, 119 Economies, A.5 Household Surveys for Case Studies circa 2013 99 and Sharing Rule Estimates 166 CONTENTS ix Foreword Five years ago, the World Bank Group set two overarching goals: to end extreme poverty by 2030, and to promote shared prosperity by boosting the incomes of the bottom 40 percent of the population in each country. As this year’s Poverty and Shared Prosperity report documents, the world continues to make progress toward eliminating poverty. In 2015, approximately one-tenth of the world’s population lived in extreme poverty—the lowest poverty rate in recorded history. This is an impressive achievement, considering that in 1990, more than a third of people on earth lived in extreme poverty. Since we last reported on global poverty two years ago, the number of poor has diminished by 68 million. But we cannot take success for granted. Poverty is on the rise in several countries in Sub-Saharan Africa, as well as in fragile and conflict-affected situations. In many countries, the bottom 40 percent of the population is getting left behind; in some countries, the living standard of the poorest 40 percent is actually declining. To reach our goal of bringing extreme poverty below 3 percent by 2030, the world’s poorest countries must grow at a rate that far surpasses their historical experience. There is no room for complacency. We must intensify the effort to promote economic growth in the lagging countries and ensure that the poorest 40 percent of the population benefits more from economic progress. Reducing extreme poverty to less than 3 percent by 2030 remains a considerable challenge, and it will continue to be our focus. At the same time, most of the world’s poor now live in middle-income countries, and our research indicates that those countries tend to have a more demanding view of poverty. Drawing on national poverty lines, we now also report poverty rates at two higher thresholds—$3.20 per day and $5.50 per day—which are typical of standards in lower- and upper-middle-income countries. These thresholds are a recognition that the concept of poverty itself is dependent on one’s social circumstances. What is a luxury in one society could be a necessity in another. Even if minimum physical needs are met, people cannot be said to lead flourishing lives if they are not able to conduct themselves with dignity in the society in which they live. The societal poverty rate presented in this report gauges people’s well-being by the standard of their surroundings. Poverty encompasses a shortfall in income and consumption, but also low educational achievement, poor health and nutritional outcomes, lack of access to basic services, and a hazardous living environment. If we hope to tackle poverty “in all its forms everywhere” as the Sustainable Development Goals call for, we must understand and measure poverty in all of its manifestations. This report presents results of the World Bank’s first exercise in multidimensional global poverty measurement to account for multiple and overlapping components of poverty. xi Traditionally, poverty is measured at the household level, but because there is inequality within households, there are undoubtedly people living in poverty within nonpoor households. Current data and methods do not permit us to account for inequality within households in most countries, so a chapter of the report examines select country studies where this accounting is possible, and it describes how it affects the profile of poverty, including by gender and age. The twin goals of ending extreme poverty and boosting shared prosperity will continue to guide our work. The new suite of poverty lines and measures broadens our conception of poverty. As this report shows, taking such an expansive view only reinforces how far we still need to go to rid the world of poverty in all of its dimensions. Jim Yong Kim President World Bank Group xii POVERTY AND SHARED PROSPERITY 2018 Acknowledgments This report was prepared by a team co-led by Dean Jolliffe and María Ana Lugo. The core team included Bénédicte Leroy de la Brière, Jed Friedman, Isis Gaddis, Roy Katayama, Daniel Gerszon Mahler, Mario Negre, David Newhouse, Minh Cong Nguyen, Espen Beer Prydz, Maika Schmidt, Dhiraj Sharma, and Judy Yang. The extended team included Sabina Alkire, Luis Alberto Andrés, Paola Buitrago Hernandez, Samuel Freije-Rodríguez, Xavier Godinot, Stephan Klasen, Rahul Lahoti, Christoph Lakner, Sylvie Lambert, Valérie Lechene, Libbet Loughnan, Carolina Mejía-Mantilla, Ana María Muñoz Boudet, Rakesh Gupta N. Ramasubbaiah, Raul Santaeulalia- Llopis, Kenneth Simler, Sharad Tandon, Robert Walker, Alexander Wolf, and Ruoxuan Wu, all of whom provided key inputs. Karem Nathalia Edwards de Izquierdo, Pamela Gaye Gunio, and Estella Malayika provided overall support to the report team. The work has been carried out under the general direction of Francisco H. G. Ferreira, Haishan Fu, Caren Grown, and Carolina Sánchez-Páramo. The team is also grateful for guidance and advice from Kaushik Basu, Shantayanan Devarajan, Akihiko Nishio, and Carlos Silva-Jáuregui. Elizabeth Howton, Mark Felsenthal, and Venkat Gopalakrishnan led the communication and messaging of the report, with inputs from Indira Chand, Paul Gallagher, Mary Donaldson Lewis, Mikael Reventar, Victoria Smith, and Divyanshi Wadhwa. Additional support was provided by the Media and Web & Social Media teams of External and Corporate Relations. Robert Zimmerman, Honora Mara, and Stuart Grudgings provided editing services. Patricia Katayama, from the World Bank’s Development Economics unit, was the acquisitions editor. The production of the report and overview were managed by World Bank Publications, Global Corporate Solutions unit, with Susan Graham as the production editor and Deborah Appel-Barker as the print coordinator, and with help from Bruno Bonansea (cartography), Aziz Gokdemir, and Susan Mandel. Patricia Hord designed the overview booklet and the report covers. This report would not have been possible without inputs from many different people, including data inputs from the PovcalNet and Data 4 Goals teams, in particular, Raul Andrés Castaneda Aguilar, João Pedro Wagner De Azevedo, Shaohua Chen, José Montes, Prem Sangraula, Nobuo Yoshida, and Qinghua Zhao. Others who helped support this report include Edouard Al-Dahdah, Aziz Atamanov, Ciro Avitabile, Sophie Charlotte Emi Ayling, M. Abul Kalam Azad, Leandro Ezequiel Chalela, Urmila Chatterjee, Mickey Chopra, Reno Dewina, Ritika D’Souza, Patrick Hoang-Vu Eozenou, María Gabriela Farfán Bertrán, Deon Filmer, Tony Henri Mathias Jany Fujs, Roberta Gatti, María Eugenia Genoni, Michele Gragnolati, Faya Hayati, Ruth Hill, Talip Kilic, Aart Kraay, Caterina Ruggeri Laderchi, Kihoon Lee, Vasco Molini, Rose Mungai, Rinku Murgai, Huyen Khanh Nguyen, Nga Thi Viet Nguyen, Gbemisola Oseni Siwatu, Sergio Olivieri, Utz Pape, Husnul Rizal, Aude-Sophie Rodella, Halsey Rogers, Shwetlena Sabarwal, xiii Sarosh Sattar, Prem Sangraula, William Hutchins Seitz, Umar Serajuddin, Hiroki Uematsu, Aibek Baibagysh Uulu, Malarvizhi Veerappan, Pallavi Vyas, Matthew Wai-Poi, and Alberto Zezza. The team also benefited from discussions with the following groups within the World Bank: Poverty and Equity Global Practice—Development Economics Working Group; Water Supply, Sanitation, and Hygiene; and the Human Capital Project. The team gratefully acknowledges advice from the peer reviewers: Andrea Brandolini, José Cuesta, Jesko Hentschel, and Salman Zaidi. The team also appreciates the many helpful comments received from Junaid Kamal Ahmad, Abdallah Al Dardari, Sabina Alkire, Kathleen Beegle, Ted Haoquan Chu, James Foster, Caroline Heider, Ejaz Syed Ghani, Alex Gibbs, Michele Gragnolati, Talip Kilic, Luis Felipe López-Calva, William F. Maloney, Mahmoud Mohieldin, Samia Msadek, Martin Rama, Nagaraja Rao Harshadeep, Julie Ruel Bergeron, Elizabeth N. Ruppert Bulmer, Sudhir Shetty, Hans Timmer, Philip Verwimp, and Dominique van de Walle. In addition, the team gratefully acknowledges help from the many people who have commented on various drafts of the chapters as well as from those who have provided assistance in the preparation of this report. And, finally, this report would not have been possible without the hard work and dedication of the thousands of enumerators and survey respondents around the world who have graciously shared the details of their lives and the many facets of poverty. The report is a joint project of the Development Data and Research Groups in the Development Economics Vice Presidency and the Poverty and Equity Global Practice in the Equitable Growth, Finance and Institutions Vice Presidency of the World Bank. Financing from the UK government helped support analytical work on the societal and extreme poverty measures. xiv POVERTY AND SHARED PROSPERITY 2018 About the Team Co-Leads of the Report Dean Jolliffe is a lead economist in the Development Data Group at the World Bank. He is a member of the Living Standards Measurement Study team and co-lead of the team that works on global poverty measurement (PovcalNet). Previously, he worked in the Research Group and the South Asia region of the World Bank. Prior to joining the World Bank, he was a research economist with the Economic Research Service of the U.S. Department of Agriculture, an assistant professor at Charles University Center for Economic Research and Graduate Education in Prague, an adjunct professor at the Johns Hopkins University School of Advanced International Studies, an adjunct professor at the Georgetown University Public Policy Institute, and a postdoctoral fellow at the International Food Policy Research Institute. Dean holds appointments as a research fellow with the Institute for the Study of Labor, as a co-opted council member of the International Association for Research in Income and Wealth, and as a fellow of the Global Labor Organization. He received his PhD in economics from Princeton University. María Ana Lugo is a senior economist in the Poverty and Equity Global Practice at the World Bank. Her current work focuses on issues of poverty and well-being measurement, multidimensional poverty, economic mobility, inequality of opportunities, and fiscal incidence analysis, with an emphasis on Latin American countries. She is currently a council member of the Society for the Study of Economic Inequality. Prior to joining the World Bank, she was a postdoctoral fellow in economics at the University of Oxford, researcher at the Oxford Poverty and Human Development Initiative, tutor at Brasenose College (Oxford), researcher at the Universidad de General Sarmiento in Buenos Aires, and analyst at the Ministry of Social Development in Argentina. She holds a PhD in economics from the University of Oxford, and a bachelor’s degree in economics from the Universidad de Buenos Aires, Argentina. Core Team Bénédicte Leroy de la Brière is a lead economist with the Social Protection and Jobs Global Practice at the World Bank. She previously served in the Gender Group, where she contributed to the development of the gender strategy and coordinated analysis on women’s welfare and work. Bénédicte’s interests include the design, implementation, and evaluation of social assistance programs and strategies and their relationship to early childhood development, women’s empowerment, and household economic resilience. She has worked for the Food xv and Agriculture Organization of the United Nations, the International Food Policy Research Institute, the U.K. Department for International Development, and the government of Brazil, mostly in Latin America and Africa. Bénédicte holds a PhD in agricultural and resource economics from the University of California at Berkeley. Mark Felsenthal is a communications officer in the Development Economics Vice Presidency of the World Bank, where he does outreach for the Bank’s macroeconomic forecasting and surveillance, including the flagship Global Economic Prospects report. At the Bank, he has done communications work on financial inclusion, on operations in East Asia and Pacific, and for the office of the managing director. Prior to joining the Bank, he covered the White House and the Federal Reserve for Reuters and was an information officer for UNICEF. He holds an MS from Columbia University and a BA from Middlebury College. Jed Friedman is a senior economist in the Development Research Group (Poverty and Inequality Team) at the World Bank. His research interests include the measurement of well- being and poverty as well as the evaluation of health and social policies. Jed’s current work involves investigating the effectiveness of health financing reforms in Kyrgyzstan, Zambia, and Zimbabwe; the nutritional and development gains from early childhood investment programs in India and the Philippines; and the incorporation of new approaches to survey-based well-being measurement in Malawi and Peru. Jed holds a BA in philosophy from Stanford University and a PhD in economics from the University of Michigan. Isis Gaddis is a senior economist with the World Bank’s Gender Global Theme Department. She previously served as a poverty economist for Tanzania, based in Dar es Salaam. Isis co- authored the 2016 World Bank Africa Region flagship report Poverty in a Rising Africa. Her main research interest is empirical microeconomics, with a focus on the measurement and analysis of poverty and inequality, gender, labor, and public service delivery. She holds a PhD in economics from the University of Göttingen, where she was a member of the development economics research group from 2006 to 2012. Venkat Gopalakrishnan is an online communications officer with the Poverty and Equity Global Practice of the World Bank. He providies communications support, including message development, dissemination strategies, social media outreach, and media engagement, for various products and reports across the World Bank. For the past nine years, he has worked with the World Bank on some of the major international development issues. Previously, he worked on The Hindu, one of India’s leading English-language daily newspapers, as a senior copy editor. He has an MBA in finance from St. Joseph’s University in Philadelphia and a master’s in mass communication and journalism from the University of Madras, India. Elizabeth Howton is the senior communications officer for the Poverty and Equity Global Practice. Previously, she worked with the infoDev program, which helps start-up entrepreneurs in developing countries grow their businesses. Before that, she was the World Bank Group’s Global Web editor. She joined the World Bank in 2012 as an online communications officer for the South Asia Region. Her experience prior to the World Bank includes 10 years as an editor at the San Jose Mercury News in California’s Silicon Valley. She was a Knight Science Journalism Fellow at the Massachusetts Institute of Technology and earned a bachelor’s degree from Stanford University and a master’s degree from George Washington University. xvi POVERTY AND SHARED PROSPERITY 2018 Roy Katayama is a senior economist in the Poverty and Equity Global Practice at the World Bank. His current work focuses on the design of data collection methods suitable for fragile situations, iterative beneficiary monitoring, enhanced digital census cartography, and statistical capacity building in the Central African Republic. During his time at the World Bank, he has led analytical work on poverty and inequality, poverty measurement, poverty maps, geospatial analysis of development, welfare impact of shocks, targeting of social safety nets, and systematic country diagnostics. He has extensive experience working in Sub-Saharan Africa and previously served as a Peace Corps volunteer in Gabon. He holds an MPA in international development (MPA/ID) from Harvard University. Daniel Gerszon Mahler is a Young Professional in the Poverty and Equity Global Practice. His research deals with the intersection between inequality, welfare measurement, and behavioral science. Prior to joining the World Bank, Daniel was a visiting fellow in Harvard University’s Department of Government and worked for the Danish Ministry of Foreign Affairs and the Danish Ministry for Economic Affairs. Daniel holds a PhD in economics from the University of Copenhagen. Mario Negre is a senior researcher at the German Development Institute and a regular consultant for the World Bank. From 2014 to 2016, he was a senior economist in the World Bank’s Development Research Group (Poverty and Inequality Team). He has worked at the European Parliament, first as an adviser to the chairman of the Development Committee and then for all external relations committees. His fields of specialization are pro-poor growth, inclusiveness, inequality, and poverty measurement, as well as development cooperation policy, particularly European. Mario holds a BSc in physics from the University of Barcelona, an MA in development policies from the University of Bremen, and a PhD in development economics from the Jawaharlal Nehru University, India. David Newhouse is a senior economist in the Poverty and Equity Global Practice. Since joining the practice in 2013, he has led or contributed to the World Bank’s analysis of poverty in India, Pakistan, and Sri Lanka; the nature of global and child poverty; and the use of satellite imagery for poverty measurement. He was formerly a labor economist in the Social Protection and Labor Practice, where he helped lead efforts to analyze the policy response to the 2008 financial crisis. David holds a PhD in economics from Cornell University and has published numerous journal articles and a book on labor, poverty, health, and education in developing countries. Minh Cong Nguyen is a senior data scientist in the Poverty and Equity Global Practice of the World Bank. His research interests include poverty, inequality, imputation methods, and data systems. He currently co-leads the Europe and Central Asia Team for Statistical Development and also co-leads the Data for Goals team. He has worked as a consultant with the Africa Region, the South Asia Region, the Human Development Network, and the Private Sector Development Network at the World Bank. Minh has a PhD in economics (applied microeconometrics) from American University. Espen Beer Prydz is an economist working on measurement of poverty and inequality with the World Bank’s Development Data Group. He has previously worked with the World Bank in Cambodia, Indonesia, and South Sudan on poverty, social protection, and economic policy. ABOUT THE TEAM xvii Prior to joining the World Bank, he worked with the Organisation for Economic Co-operation and Development’s Development Centre and The Abdul Latif Jameel Poverty Action Lab. Espen is a Norwegian national and holds an MPA in international development (MPA/ID) from Harvard University and a BSc from the London School of Economics. Maika Schmidt is a consultant in the Poverty and Equity Global Practice and Development Research Group (Poverty and Inequality Team). Her research interests are poverty, inequality, and early childhood development, with a focus on measurement issues, specifically multi- dimensional indicators. Maika has worked for the Deutsche Gesellschaft für Internationale Zusammenarbeit. She holds a BSc in economics from the University of Mannheim, a master’s from the Barcelona Graduate School of Economics, and a master of research from Pompeu Fabra University, Barcelona. She is currently pursuing her PhD in development economics from the University of Sussex. Dhiraj Sharma is an economist in the Poverty and Equity Global Practice. His work focuses on welfare measurement, poverty diagnostics, and policy analysis. He has led or contributed to the analysis of poverty in Ghana, Iraq, and Nepal, and he also led impact evaluations in Nepal. His current work focuses on welfare analysis and statistical capacity building in countries in the Middle East and North Africa region. His recent work in that region includes research on the impact of refugee influx on host communities and the factors that shape attitudes toward refugees. Dhiraj holds a PhD in applied economics from Ohio State University. Judy Yang is an economist in the Poverty and Equity Global Practice. Prior to this position, she worked for teams in the Middle East and North Africa Chief Economist’s Office, the Africa region, and the Enterprise Surveys group. Before joining the World Bank, she worked at the U.S. Department of Labor. Judy holds a PhD in economics from Georgetown University. Her research interests include migration, the business environment, household welfare, and access to finance. xviii POVERTY AND SHARED PROSPERITY 2018 Abbreviations CPI consumer price index FCS fragile and conflict-affected situations GDP gross domestic product GDSP Global Database of Shared Prosperity GMD Global Monitoring Database GNI gross national income HFCE household final consumption expenditure IDA International Development Association IPL international poverty line LMIC lower-middle-income country MDG Millennium Development Goal MMRP Modified Mixed Reference Period MPI Multidimensional Poverty Index MRP Mixed Reference Period NSS National Sample Survey (India) OECD Organisation for Economic Co-operation and Development OPHI Oxford Poverty and Human Development Initiative PPP purchasing power parity SDG Sustainable Development Goal SP shared prosperity SPL societal poverty line SPP shared prosperity premium UMIC upper-middle-income country UNDP United Nations Development Programme URP Uniform Reference Period WDI World Development Indicators WID World Inequality Database WIR World Inequality Report xix Overview The world has made remarkable and un- Back in 1990, 36 percent of the world’s precedented progress in reducing extreme people lived in extreme poverty, defined by poverty over the past quarter century. In the IPL as consumption (or income) less than 2015, more than a billion fewer people were US$1.90 a day in 2011 purchasing power par- living in extreme poverty than in 1990. The ity (PPP). By 2015, that share had plunged to progress has been driven by strong global 10 percent, down from 11.2 percent in 2013. growth and the rising wealth of many devel- The number of people living in extreme pov- oping countries, particularly in the world’s erty stood at 736 million in 2015, down from most populous regions of East Asia and Pa- nearly 2 billion in 1990 (figure O.1). cific and South Asia. This impressive progress Despite the more sluggish global growth has brought us closer to achieving the World of recent years, the total count of people in Bank’s target of reducing extreme poverty to extreme poverty declined by more than 68 less than 3 percent of the world’s population million people between 2013 and 2015—a by 2030. Half of all countries included in the number roughly equivalent to the population global poverty counts already have less than 3 of Thailand or the United Kingdom. Tens of percent of their populations living under the millions of people have escaped poverty every international poverty line (IPL), which de- year since 1990, reducing the global poverty fines extreme poverty for global monitoring. rate by an average of 1 percentage point per Despite this good news, the fight against year between 1990 and 2015. extreme poverty is far from over—and in Much of the progress in the past quarter some ways is getting harder. The number of century has been in East Asia and Pacific, poor worldwide remains unacceptably high, where China’s economic rise has helped lift and it is increasingly clear that the benefits of millions of people out of extreme poverty. economic growth have been shared unevenly The countries of this region went from an across regions and countries. Even as much average poverty rate of 62 percent in 1990 of the world leaves extreme poverty behind, to less than 3 percent in 2015. More recently, poverty is becoming more entrenched and South Asia has made impressive inroads harder to root out in certain areas, particu- against extreme poverty, helping to reduce larly in countries burdened by violent con- the global rate further. The number of poor flict and weak institutions. Poor households in South Asia dropped to 216 million people are overwhelmingly located in rural areas, in 2015, compared to half a billion in 1990. have a large number of children, and suffer These two regions have fared well on the from a lack of education. World Bank’s other core goal—to increase They are ill served in essential elements of shared prosperity to ensure that the rela- well-being such as health care and sanitation, tively poor in societies are participating in and often are exposed to natural hazards and and benefiting from economic success. This physical insecurity. goal is measured by monitoring the aver- 1 FIGURE O.1 Global Extreme Poverty Rate and Headcount, 1990–2015 50 1,895 2,000 1,878 45 1,703 1,800 1,729 40 1,610 1,600 35.9 35 33.9 1,352 1,400 30 29.4 28.6 1,223 1,200 Poverty rate (%) Millions 25 25.7 1,000 963 20.8 804 20 800 18.1 736 15 13.7 600 11.2 10 400 10.0 5 200 0 0 1990 1995 2000 2005 2010 2015 Number of people who live below US$1.90 a day (2011 PPP) (right axis) Share of people who live below US$1.90 a day (2011 PPP) Source: Most recent estimates, based on 2015 data using PovcalNet. Note: PPP = purchasing power parity. age consumption (or income) growth rate Whereas the average poverty rate for other of the poorest 40 percent of the population regions was below 13 percent as of 2015, it (the bottom 40) within each and every coun- stood at about 41 percent in Sub-Saharan Af- try. On that score, the progress in East Asia rica. Of the world’s 28 poorest countries, 27 and Pacific and South Asia is all the more are in Sub-Saharan Africa, all with poverty impressive because the economic growth in rates above 30 percent. those regions is being shared. On average, the In short, extreme poverty is increasingly consumption (or income) of the bottom 40 becoming a Sub-Saharan African problem. in these two regions grew by 4.7 percent and Sub-Saharan African countries have struggled 2.6 percent per year, respectively, according to partly because of their high reliance on ex- the latest estimates for 2010–15. tractive industries that have weaker ties to the But the huge progress against poverty consumption and income levels of the poor, in these regions contrasts sharply with the the prevalence of conflict, and their vulner- much slower pace of poverty reduction in ability to natural disasters such as droughts. Sub-Saharan Africa. Extreme poverty is be- Despite faster growth in some Sub-Saharan coming more concentrated there because of African economies, such as Burkina Faso and the region’s slower rates of growth, problems Rwanda, the region has also struggled to im- caused by conflict and weak institutions, and prove shared prosperity. The bottom 40 in the a lack of success in channeling growth into dozen Sub-Saharan African countries cov- poverty reduction. Sub-Saharan Africa now ered by the indicator saw their consumption accounts for most of the world’s poor, and— (or income) rise by an average of 1.8 percent unlike most of the rest of the world—the per year in 2010–15 (slightly below the global total number of poor there is increasing. The average of 1.9 percent per year). More worry- number of people living in extreme poverty ing, however, is that the consumption (or in- in the region has grown from an estimated come) level of the bottom 40 shrank in a third 278 million in 1990 to 413 million in 2015. of those 12 countries. 2 POVERTY AND SHARED PROSPERITY 2018 The stark contrast between Asia and Af- billions of people living above US$1.90, who rica explains why it is getting harder to re- are still very poor by the standards of their duce poverty globally. Although overall own societies. Now that extreme poverty progress against poverty has been steady, continues to be high in some regions while not all regions have shared in global growth heading down to single digits in most of the and some are being left behind. As extreme rest of the world, we need to build a more poverty becomes rarer, there is less scope for complete picture of what is meant by a world gains to shift to different regions and coun- free of poverty. Certainly, the world could not tries. With extreme poverty in East Asia and be said to be free of poverty if most countries Pacific down to 2.3 percent in 2015, for ex- achieve the 3 percent rate while large pock- ample, the region has little more to give in ets of extreme poverty linger. To have a better terms of reducing the global rate. A similar understanding of what it means to end pov- trend is well under way in South Asia. erty, we need more ways of measuring and The result is a slowdown in overall pov- conceptualizing the problem. We need more erty reduction that makes it unlikely the pieces of the puzzle to better understand World Bank’s 2030 target will be met. From what a world free of poverty means. 2013 to 2015, global poverty declined by 0.6 The World Bank’s focus remains on lifting percentage points per year, well below the people from extreme poverty, and the IPL 25-year average of a percentage point a year. will continue to be a crucial way of monitor- Our forecasts suggest that the rate of reduc- ing this progress. But we also need to recog- tion further slowed between 2015 and 2018 nize that societies have not stopped thinking to less than half a percentage point per year. or caring about poverty even if it has become Looking ahead to 2030, forecasts indicate much less apparent in its extreme forms. that the world would need to grow at an un- There is a need to expand our understand- usually strong pace in order to meet the 3 ing of poverty as a complex, multifaceted percent target. For example, the target would problem and identify pockets of people who be met if all countries grow at an average are impoverished but who have remained annual rate of 6 percent and the consump- unnoticed. tion (or income) of the bottom 40 grows 2 To do so, we introduce three new pieces percentage points faster than the average. of the poverty puzzle. The addition of these Alternatively, the landmark could be reached new ways to measure and conceptualize pov- if all countries grow at an average pace of 8 erty follows from the recommendations of percent. But, in either of these scenarios, ex- the Commission on Global Poverty, led by treme poverty would still be in double digits Professor Sir A. B. Atkinson, to consider com- in Sub-Saharan Africa by 2030. plementary indicators to the core indicator of In an alternate scenario where all coun- extreme poverty (in Monitoring Global Pov- tries grow in line with the average in their erty published by the World Bank in 2017). region over the last 10 years, our forecasts The new measures recognize that people can indicate that the global poverty rate would be defined as poor relative to their societies be above 5 percent in 2030. This “business even at consumption levels well above the as usual” scenario leads to a bifurcated world US$1.90 level. They also broaden our view where more than a quarter of the people in of poverty to include elements of basic well- Sub-Saharan Africa live in extreme poverty being such as access to sanitation and core whereas it is less than 2 percent in most of health services. Finally, they go beyond the the rest of the world. household level in a first attempt to measure These contrasting regional poverty trends poverty as it affects individuals. have two important implications. First, the These new measures will help both in primary focus of the international commu- those countries where extreme poverty is nity’s efforts to eliminate the worst forms currently at very low levels and in countries of deprivation must remain firmly in Sub- where it is pervasive. Even while maintaining Saharan Africa and those few other countries a focus on the poorest countries of the world, elsewhere with very high poverty rates. At the with this broader view we can better un- same time, we must not forget the plight of derstand the various dimensions of poverty OVERVIEW 3 globally. And that better understanding can status as the country with the most poor is provide guidance for policy and help identify ending—Nigeria either already is, or soon areas of greatest need. will be, the country with the most poor peo- The new measures can also help us moni- ple. The extreme poverty rate and the num- tor progress in reducing poverty in a growing ber of poor in South Asia have been steadily world. Even in those countries where extreme declining and are expected to continue that deprivation rates are very low, there con- trend. The result of this trend is a shift in pov- tinue to be significant concerns about pov- erty from South Asia to Sub-Saharan Africa. erty more broadly defined. Having enough By 2030, the share of the extreme poor liv- money is critical to living a life free of pov- ing in Sub-Saharan Africa could be as large as erty, but it is not all that matters. To truly end 87 percent on the basis of historical growth poverty, we need to better monitor people’s rates. Even if every other country in the world progress in achieving nonmonetary aspects had zero extreme poverty by 2030, the aver- of well-being, such as safe drinking water and age rate in Sub-Saharan Africa would have to access to education. decrease from the 2015 rate of 41 percent to When it comes to measuring extreme about 17 percent for the global average to be poverty, the US$1.90 yardstick is used to as- 3 percent. That would require an unprece- sess how well people are doing relative to the dented annual growth rate for the region. basic needs in the world’s poorest countries. Stronger economic growth and renewed But, for people living in countries with higher efforts to resolve violent conflicts will be cru- overall consumption (or income) levels, there cial to speed up the rate of poverty reduction is value in monitoring progress with higher in Sub-Saharan Africa and elsewhere. But poverty lines that reflect the greater needs business as usual will not be enough. More in a growing world. By using these new lines needs to be done to ensure that growth is in- and measures in coordination with the ex- clusive, with a stronger focus on raising the isting measure of extreme poverty—both in productive capacity of the poor. those countries with high rates of extreme If Sub-Saharan African and other fragile poverty and those that have nearly vanquished situations are to have a chance of reaching extreme poverty—we can better monitor the 3 percent goal, not only will their growth poverty in all countries, in multiple aspects rates have to be high but consumption (or of life, and for all individuals in every house- income) levels among the bottom 40 in their hold. This broader monitoring promises to societies will also have to rise at a higher rate. give us a more nuanced understanding of Yet, in two-thirds of the 13 extremely poor the nature of poverty in all its forms, so we countries (with poverty rates above 10 per- can develop better policy tools to tackle the cent) covered by the World Bank’s shared problem. prosperity indicator, average consumption (or income) levels of the bottom 40 are grow- Staying focused on the ing at a slower rate than the global average of 1.9 percent per year. That is a worrying trend poorest for the poorest economies and conflict-af- Ending extreme poverty will require a re- fected situations, precisely the countries least newed focus on Sub-Saharan Africa and likely to reach the 2030 target. states suffering from weak institutions and A second and crucial worry is that data conflict. Estimates for 2015 indicate that needed to assess shared prosperity are weak- India, with 176 million poor people, contin- est in the very countries that most need them ued to have the highest number of people in to improve. Only 1 in 4 low-income countries poverty and accounted for nearly a quarter of and 4 of the 35 recognized fragile and conflict- the global poor. The extreme poverty rate is affected situations have data that allow us to significantly lower in India relative to the av- monitor shared prosperity over time. Because erage rate in Sub-Saharan Africa, but because a lack of reliable data is associated with slow of its large population, India’s total number growth in consumption (or income) for the of poor is still large. In a sign of change, how- poorest, the situation could even be worse ever, forecasts for 2018 suggest that India’s than currently observed. 4 POVERTY AND SHARED PROSPERITY 2018 In the fragile situations that are covered by rity. Someone may earn more than US$1.90 a data, the recent trend is discouraging. After day but still feel poor if lacking access to such falling sharply between 2005 and 2011, the basic needs. Equally, someone earning less rate of extreme poverty in these countries than that could be in even direr need without rose to 35.9 percent in 2015 from a low of clean water to drink or a safe environment 34.4 percent in 2011. The share of the global for his or her family. poor in these countries has risen steadily This expanded, “multidimensional” view since 2010 to reach 23 percent in 2015. reveals a world in which poverty is a much In many low-income countries, the bot- broader, more entrenched problem, under- tom 40 live on less than US$1.90 a day and lining the importance of investing more in disproportionately live in rural areas, making human capital. At the global level, the share of them vulnerable to disruptions caused by the poor according to a multidimensional defini- climate. Uganda, for example, has suffered tion that includes consumption, education, significant setbacks in poverty reduction and and access to basic infrastructure is approx- shared prosperity largely due to droughts imately 50 percent higher than when relying and pests that affected harvests starting in solely on monetary poverty. In Sub-Saharan 2016. Uganda’s poverty rate rose from 35.9 Africa, more than in any other region, short- percent in 2012 to 41.6 percent in 2016. Real falls in one dimension go hand in hand with consumption for its bottom 40 shrank by 2.2 other deficiencies. Low levels of consumption percent a year. are often accompanied by challenges in non- As we seek to end poverty, we also need monetary dimensions. to recognize that being poor is not defined Figure O.2 presents the share of the popula- just by inadequate consumption or a lack of tion in Sub-Saharan Africa and South Asia that income. Other aspects of life are critical for are considered multidimensionally deprived well-being, including education, access to according to consumption (blue oval), edu- basic infrastructure, health care, and secu- cation for children and adults (orange oval), FIGURE O.2 Share of Individuals in Multidimensional Poverty, circa 2013 a. Sub-Saharan Africa b. South Asia Basic infrastructure 2.3 Basic infrastructure Education Monetary 0.4 0.7 3.4 12.4 2.8 17 10.9 7 28.2 1.3 Monetary 2.9 0.2 1.4 Education Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database). Note: The diagram shows the share of population that is multidimensionally poor, and the dimensions they are deprived in. The size of the ovals is scaled such that they represent the respective proportions in each of the regions. For example, the numbers in the blue oval for Sub-Saharan Africa add up to 44.9 percent, which is the monetary headcount ratio. Adding up all the numbers for Sub-Saharan Africa results in 64.3 percent, which is the proportion of people that are multidimensionally deprived. (Numbers may not add to totals because of rounding.) OVERVIEW 5 and access to basic infrastructure services in- and conflict with productive activities. This cluding drinking water, sanitation, and elec- tension is often most pronounced among tricity (yellow oval). Almost half of the multi- the poorest countries and the poorest groups dimensional poor in Sub-Saharan Africa (28.2 in society. For example, the average gender percent out of a total of 64.3 percent multi- gap in poverty rates for 20–34-year-olds in dimensionally poor) experience simultaneous Sub-Saharan Africa is 7 percentage points, deprivations in consumption, education, and compared to a global average of 2 percentage access to some basic infrastructure service. points (figure O.3) and virtually zero in Eu- This proportion contrasts with other regions, rope and Central Asia. including South Asia, in which only a quarter There is evidence from studies in sev- of the multidimensionally poor suffer depri- eral countries that resources are not shared vations in all three of these dimensions. The equally within poor households, especially implication is that in Sub-Saharan Africa, the when it comes to more prized consumption cumulative deprivations reinforce one another items. Evidence also shows complex dynam- and make it much harder to fight poverty. ics at work within households that go beyond To build a true picture of poverty as ex- gender and age divides. For example, a wom- perienced by individuals, we also need to go an’s poverty status may be related to her posi- beyond the traditional household-level mea- tion as mother versus wife of the household sures to consider how resources are shared head. among families. Women and children tend Another way to explore disparities within to have disproportionately less access to re- the household is to look at how food is shared sources and basic services, especially in the within families. In Bangladesh, for example, poorest countries. Women in poorer coun- household survey data reveal that household tries often withdraw from the labor force and heads—mostly men—have much smaller lose their earning potential when they reach calorie shortfalls than individuals who are reproductive age. The gender gap in poverty not household heads. Such differences are in- rates is largest during the reproductive years visible in standard measures of poverty. when care and domestic responsibilities, When we estimate individual poverty rates which are socially assigned to women, overlap on the basis of broader consumption patterns FIGURE O.3 Percent of Females and Males Living in Households in Extreme Poverty, by Age Group, circa 2013 25 20 Poverty rate (%) 15 10 5 0 0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 Age groups Males Females Source: Muñoz Boudet et al. 2018. Note: Data are from 89 countries. 6 POVERTY AND SHARED PROSPERITY 2018 including nonfood goods, women fare slightly the world relative to the measure of extreme better than men in Bangladesh. In Malawi, by poverty, which is forecast now to be in single contrast, women have a significantly higher digits. Nearly half the world (46 percent) lives poverty rate (73 percent) than men (49 per- on less than US$5.50 per day, a standard that cent). Children in both countries suffer from defines poverty in a typical upper-middle- significantly higher poverty rates. income country (table O.1). A quarter of the We need more comprehensive data to world lives on less than US$3.20 per day. deepen our understanding of how poverty These higher poverty lines also portray a affects individuals and to assess how social different regional story of poverty reduction programs can be better tailored to meet their from the US$1.90 line. The Middle East and needs. The initial findings of this approach North Africa is a case in point. In 1990, ex- suggest that current assistance programs risk treme poverty in the region was 6 percent, missing many poor people who are hidden in and in 2015, it was 5 percent. This discour- nonpoor households. aging picture of very little progress in reduc- ing extreme poverty looks different when Monitoring progress in a examined through the lens of the US$3.20 line. Over this same time period, the coun- growing world tries of the Middle East and North Africa As the world grows wealthier and extreme reduced the proportion of people living on poverty becomes rarer, legitimate questions less than US$3.20 from 27 percent to 16 per- arise over whether US$1.90 (2011 PPP) is too cent. Important progress in reducing poverty low to define whether someone is poor in all in this region is hidden when one examines countries of the world. Even as the number of only extreme poverty. The US$5.50 line, re- extreme poor declines, many people continue flecting basic needs in upper-middle-income to live in poverty when measured by stan- countries, presents two distressing findings: dards that are more appropriate for a wealth- (1) almost half the world lives on less than ier world. The success in reducing extreme US$5.50 per day, and (2) in the regions of the poverty allows us to broaden our focus to as- Middle East and North Africa, South Asia, sess whether such people are also benefitting and Sub-Saharan Africa, despite progress in from economic development. reducing their poverty rates, more people Two decades ago, 60 percent of the global were living on less than US$5.50 in 2015 than population lived in low-income countries. in 1990 due to their growing populations. By 2015, that had fallen to 9 percent, mean- As we seek a broader understanding of ing that the majority of people and most of poverty, it is important to recognize that the world’s poor now live in middle-income what constitutes a basic need can vary de- countries. To reflect this shift and the rise in pending on a country’s level of consumption what may constitute basic needs for many or income. In a poorer country, for example, people, the World Bank now reports on two participating in the job market may require higher-value poverty lines of US$3.20 and only clothing and food, whereas someone in US$5.50 per person per day, expressed in a richer society may also need access to the 2011 PPP. The value of these lines is derived internet, transportation, and a cell phone. from the typical poverty line in lower- and The cost of performing the same function upper-middle-income countries, respectively, may differ across countries depending on in the same way that the value of the IPL is their overall level of consumption or income. derived from the typical poverty line for To assess this type of poverty, the World some of the poorest countries in the world. Bank is introducing the societal poverty line These higher-valued poverty lines therefore (SPL) as a complement to its existing lines. reflect social assessments of what defines The SPL is a combination of the absolute IPL minimum basic needs in countries at these and a poverty line that is relative to the me- income levels. dian consumption (or income) level of each As may be expected, these two standards country. Specifically, it is equal in value to ei- for measuring poverty portray a less encour- ther the IPL or US$1.00 plus half of daily me- aging picture of the level of well-being in dian consumption in the country, whichever OVERVIEW 7 TABLE O.1 Poverty at Higher Poverty Lines, US$3.20 and US$5.50 (2011 PPP) Poverty rate by Percentage point region at US$3.20 1990 1999 2008 2013 2015 change, 1990–2015 East Asia and Pacific 85.3 67.1 37.4 17.5 12.5 –72.8 Europe and Central Asia 9.9a 21.1 7.5 5.7 5.4 –4.6 Latin America and the 28.3 27.0 15.7 11.4 10.8 –17.5 Caribbean Middle East and North 26.8 21.7 16.7 14.4 16.3 –10.5 Africa South Asia 81.7 76.0a 67.9 53.9 48.6a –33.1 Sub-Saharan Africa 74.9 78.3 72.2 67.8 66.3 –8.6 Rest of the world 0.8 0.8 0.7 0.8 0.9 0.1 World 55.1 50.6 38.2 28.8 26.3 –28.9 Poverty rate by Percentage point region at US$5.50 1990 1999 2008 2013 2015 change, 1990–2015 East Asia and Pacific 95.2 87.0 63.6 42.4 34.9 –60.3 Europe and Central Asia 25.3a 44.5 17.1 14.1 14.0 –11.3 Latin America and the 48.6 47.0 33.3 27.2 26.4 –22.2 Caribbean Middle East and North 58.8 54.5 46.6 42.3 42.5 –16.3 Africa South Asia 95.3 93.1a 89.8 84.2 81.4 a –14 Sub-Saharan Africa 88.5 90.5 88.1 85.4 84.5 –4.1 Rest of the world 1.7 1.3 1.2 1.5 1.5 –0.2 World 67.0 66.8 56.5 48.7 46.0 –21.0 Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), World Bank. Note: PPP = purchasing power parity. a. The estimate is based on regional population coverage of less than 40 percent. The criteria for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. is greater. This means that, for the poorest of corresponds on average with how all coun- countries, the value of the SPL will never be tries of the world define being poor. less than the IPL. But, after a certain point When poverty is defined this way, the as countries get richer, the value of the SPL number of people who are poor stood at 2.1 will increase as the consumption level of the billion as of 2015, almost three times more median individual in that country increases. than those living under the US$1.90 level This increasing value of the SPL corresponds (figure O.5). Strikingly, the number of peo- with the fact that the value of national pov- ple identified as poor by the SPL has largely erty lines typically increases as countries stayed the same over the last 25 years even as grow richer. In fact, the SPL is constructed the number in extreme poverty has plunged. in such a way that it directly corresponds to The global rate of societal poverty has fallen the average value of national poverty lines at steadily since 1990, but still at a much slower different levels of (median) consumption for rate than the decline of extreme poverty. In each country of the world. Figure O.4 illus- 1990, the rate of societal poverty (45 per- trates how the value of the societal poverty cent) was about one-fourth greater than the line (in dark blue) runs through the middle rate of extreme poverty (36 percent). For of the national poverty lines (in light blue) many low-income countries, societal and ex- at different levels of median consumption in treme poverty were the same. The economic each country. In this sense, societal poverty growth of the past quarter century means provides a global measure of poverty that significantly fewer countries in 2015 have 8 POVERTY AND SHARED PROSPERITY 2018 FIGURE O.4 National and Societal Poverty Lines in a Growing World 20 10 Poverty line (2011 US$ PPP, per day) 5 1.9 1 1 1.9 5 10 20 40 Median consumption (or income) (2011 US$ PPP, per day) National poverty line Societal poverty line Source: Based on data and analysis from Jolliffe and Prydz (2016, 2017). Note: Both axes use log scales. PPP = purchasing power parity. FIGURE O.5 Societal Poverty, Global Estimates, 1990–2015 a. Poverty rate b. Number of poor 50 2,500 40 2,000 Poverty rate (%) Millions 30 1,500 20 1,000 10 500 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Societal poverty Extreme poverty Source: Updated analysis from Jolliffe and Prydz (2017). Note: The international poverty line reflects the extreme poverty rate (in panel a) and the headcount (in panel b) as assessed by the US$1.90 per day threshold (2011 purchasing power parity). The societal poverty line provides the same information for societal poverty. an SPL that is the same as their IPL, and the Whereas societal poverty is based on a rate of societal poverty (28 percent) is almost poverty line that is in part relative to the me- three times the rate of extreme poverty (10 dian consumption levels across countries, the percent). shared prosperity measure monitored by the OVERVIEW 9 MAP O.1 Shared Prosperity across the World, 91 Economies, circa 2010–15 Consumption or income growth among the bottom 40 percent of the population Sources: GDSP (Global Database of Shared Prosperity) fall 2018 edition. Note: The map shows annualized growth rates in mean household per capita consumption or income among the poorest 40 percent of the population in each country. World Bank is similarly relative to how indi- setbacks on the measure even if several econ- viduals are doing in each and every country. omies in the region, whose bottom 40 suf- By assessing how the bottom 40 are doing in fered large declines linked to the 2008 finan- each economy, the World Bank’s measure of cial crisis, are now recovering. This is the case shared prosperity is relevant to countries of in Estonia, Latvia, and Lithuania, where cur- all income levels. Overall, the news on shared rent levels of shared prosperity are above 6 prosperity is positive, with almost 80 percent percent a year. The mixed progress on shared of the countries for which data are available prosperity highlights the need to renew our showing income growth for the bottom 40 focus on inclusive growth. (map O.1). But the progress was restrained Shared prosperity and societal poverty by modest global growth and, despite the capture different aspects of how the relatively overall improvement, some countries have less well-off are doing in each country. But experienced slowdowns and even reversals in the two measures are nonetheless linked, as shared prosperity. an example of two upper-middle-income Latin America and the Caribbean, for ex- countries—Costa Rica and Ecuador—shows. ample, saw less growth in shared prosperity Between 2011 and 2016, both countries’ from 2010 to 2015 than in previous years as economies grew at similar rates. But the its economies cooled amid a downturn in bottom 40 in Ecuador did better than their global commodity prices. Many countries counterparts in Costa Rica, growing their in Europe and Central Asia also experienced income by a percentage point more than the 10 POVERTY AND SHARED PROSPERITY 2018 mean in the country. Costa Rica’s bottom 40 greater than those living in monetary poverty. grew in line with their country’s mean. As a This means that the challenge in securing result, societal poverty fell faster in Ecuador higher living standards for the population of than in Costa Rica. South Asia is far more daunting when poverty Our view of poverty expands again when in all its forms is considered. Although South we define it not just as a shortage of money Asia is expected to meet the goal of reducing but also as a lack of basic elements of well- extreme poverty to below 3 percent by 2030, being. Many countries have made great many people will still be living in unsatisfac- strides in reducing monetary poverty but still tory conditions if the region does not make lag in crucial areas—such as basic infrastruc- progress on other components of well-being. ture, education, and security—that have a The multidimensional approach high- very real impact on people’s quality of life. In lights how the ways deprivations interact vary the Middle East and North Africa and Latin widely from country to country. In richer re- America and the Caribbean, despite the low gions such as Latin America and the Carib- prevalence of monetary poverty (less than 6 bean, the Middle East and North Africa, and percent), almost one in seven people lacks East Asia and Pacific, nonmonetary depriva- adequate sanitation. tions are much less associated with monetary South Asia is another case in point. De- ones than in other regions. In a sample of six spite having made progress in poverty re- countries, the multidimensional approach duction, the region’s shortfalls in education can be extended to include, in addition to remain high for both adults and children and education and access to basic infrastruc- are not strongly associated with monetary ture services, two other dimensions: health poverty. In addition, the number of people and nutrition, and security from crime and in the region living in households without natural disaster (figure O.6). The higher- access to an acceptable standard of drinking income countries of Ecuador, Iraq, and Mex- water, adequate sanitation, or electricity is far ico suffer from higher crime rates and greater FIGURE O.6 Contribution to Multidimensional Poverty, by Dimension, Selected Countries 100 Contribution to total poverty (%) 80 60 40 20 0 3 5 3 5 3 5 3 5 3 5 3 5 Dimensions Ecuador Indonesia Iraq Mexico Tanzania Uganda Monetary Education Services Health Security Sources: Calculations are based on Ecuador’s Encuesta de Condiciones de Vida 2013–14; Indonesian Family Life Survey, 2014; Iraq Household Socio-Economic Survey, 2012; Mexican Family Life Survey, 2009–12; Tanzania’s National Panel Survey, 2012–13; Uganda National Panel Survey 2013–14. See annex 4B for details. Note: The figure shows the contribution of each dimension to the multidimensional poverty measure based on the dimensional breakdown method of Alkire et al. 2015. OVERVIEW 11 FIGURE O.7 Gender Gaps, Individual Multidimensional Poverty, Selected Countries 80 Share who are multidimensionally poor (%) 70 60 50 40 30 20 10 0 Ecuador Indonesia Iraq Mexico Tanzania Men Women Source: Klasen and Lahoti forthcoming. insecurity than the lower-income countries This more nuanced picture highlights new included in the analysis. In Indonesia, multi- pockets of poverty and can help in formu- dimensional poverty is largely driven by poor lating policies to address them. For example, outcomes in children’s health and nutrition. policies to expand infrastructure and social Including additional dimensions of depri- services should take into account the differ- vation in our measures of poverty can pro- ent needs of women, children, and men. In vide valuable insight into how policies can be some regions, improvements in access to ed- directed to have the most effect on poverty. ucation can particularly help women, who The profile of the poor can change as we take continue to be held back by gender inequali- a multidimensional view of poverty. For ex- ties in schooling. ample, a five-dimension picture of Indonesia shows that the country may need a stronger focus on combatting health care depriva- Piecing together the poverty tions, whereas efforts in Ecuador may be bet- puzzle ter directed toward education and security, This report provides a more complete picture particularly in urban areas. of poverty that reinforces much of the posi- The multidimensional approach, when tive story revealed by the tremendous prog- combined with data at the individual level, ress in reducing extreme poverty over the can also provide new insights into who is last quarter century. But it also uncovers pre- poor. Applying this approach to five of the viously hidden details about the nature and six countries reveals that poverty is greater extent of poverty throughout the world. Par- among women than men, especially in Iraq ticularly distressing findings are that extreme (figure O.7). Women are revealed as multi- poverty is becoming entrenched in a handful dimensionally poorer than men in all five of countries and that the pace of poverty re- countries, and the gender gap may be even duction will soon decelerate significantly. wider for specific vulnerable groups. Widows, Reaching the target of reducing extreme for example, are found to be significantly poverty to less than 3 percent by 2030 will poorer than widowers in all countries except require a redoubling of efforts and greater Ecuador. focus on those countries where poverty is 12 POVERTY AND SHARED PROSPERITY 2018 the worst. The work of the World Bank will get by 2030 will require more than busi- continue to focus on monetary poverty with ness as usual: the region will need strong respect to the IPL; however, truly bringing an and sustained economic growth, signifi- end to global poverty requires thinking more cant improvements in the living standards broadly and recognizing the greater complex- of the bottom 40 throughout Sub-Saharan ity inherent in the concept of poverty around Africa at a scale not seen in recent history, the world. and substantial investments in people. Going forward, the World Bank will con- 2. The new measures can enhance policy tinue its focus on reporting progress toward dialogue. Welfare monitoring and policy the twin goals of ending extreme poverty and dialogue at the country level will continue boosting shared prosperity. But, to assure that to be based on national poverty mea- poverty is also tracked in a relevant manner sures. Grounded in tools that countries in countries with very low levels of extreme already use to monitor progress, the lines poverty, our regular poverty updates will also and measures introduced here open new include progress at the two higher poverty possibilities for countries to benchmark lines of US$3.20 and US$5.50 and on the their performance against relevant com- new societal poverty line. Likewise, the next parators using a richer set of instruments. global poverty update in 2020 will report on This is particularly the case in middle-in- advances in multidimensional poverty for the come countries, where extreme poverty is countries where data are available. Between less prevalent, but where the higher pov- global updates, these new measures will be- erty lines and the new multidimensional come part of our biannual country reports poverty measure reveal there is still much on poverty and shared prosperity—Poverty work to be done. and Equity Briefs. The use of these new measures for global 3. Data investments are critical. World poverty monitoring and the findings of the Bank investments in data have helped report have three important and distinct im- provide a more comprehensive pic- plications for the work and priorities of the ture of poverty, but there is a need for World Bank: continued and deeper investment in data. More and better welfare data are 1. Transformational change is needed in needed to compare poverty across time, Sub-Saharan Africa and conflict-affected for multiple dimensions, for all indi- areas. The battle against extreme poverty viduals, and particularly among low- will be won or lost in Sub-Saharan Africa income and conflict-affected countries. and fragile and conflict-affected settings. Very few of these countries have shared Global extreme poverty is increasingly prosperity estimates, and few countries becoming a Sub-Saharan phenomenon, have data for estimating all dimensions of and the share of the poor in fragile and poverty. Ensuring that no one is left be- conflict-affected situations is growing. hind in the fight against extreme poverty Of all regions, Sub-Saharan Africa has requires that we expand investments in one of the worst performances in shared country systems and capacity to measure prosperity and the poor there suffer from and monitor welfare in a timely, compa- multiple deprivations more than in any rable manner using both traditional and other region. Reaching the 3 percent tar- newer types of data and methods. OVERVIEW 13 Introduction The last 25 years have seen tremendous prog- erty), 27 are in Sub-Saharan Africa, all with ress toward the goal of ending extreme pov- rates above 30 percent. erty. The share of the global population living Second, the pace of poverty reduction has in extreme poverty as measured by the inter- slowed in recent years. Over the 25 years from national poverty line (IPL, currently valued 1990 to 2015, the global extreme poverty at US$1.90 in 2011 purchasing power par- rate fell by slightly more than 25 percentage ity dollars) fell from 35.9 percent in 1990 to points, or an average decline of 1 percentage 11.2 percent in 2013. As noted in this Poverty point a year; however, over the two years be- and Shared Prosperity report, an additional tween 2013 and 2015, it declined by only 1.2 68 million people were lifted out of extreme points, or 0.6 percentage points a year. One poverty between 2013 and 2015—the last of the main reasons for the slowdown is the year for which we have globally comparable growing concentration of extreme poverty in data—to bring the global rate to a historical Sub-Saharan Africa, where the combination low of 10 percent. of slower than average economic growth, However, a more careful look at these often concentrated in capital-intensive sec- numbers, particularly in recent years, reveals tors, higher than average population growth, two concerning and interrelated trends. First, low levels of human capital and access to progress toward the elimination of extreme basic infrastructure, and increased levels of poverty has been uneven. Whereas in 1990 80 fragility and conflict, has resulted in limited percent of the extreme poor lived in East Asia progress in poverty reduction and, conse- and Pacific or South Asia, in 2015 more than quently, the region’s growing number of peo- half of the global poor resided in Sub-Saharan ple living in extreme poverty. Africa. The changing regional concentration If economic growth over the next 15 years of extreme poverty reflects the highly uneven is similar to historical growth patterns, re- rate of poverty reduction across regions and gional disparities will only become larger over countries of the world. Four of the six devel- time: forecasts for 2030 put the share of the oping regions had extreme poverty rates below global extreme poor residing in Sub-Saharan 10 percent in 2015, compared to a rate of over Africa at about 87 percent and extreme pov- 40 percent for Sub-Saharan Africa. Similarly, erty rates in the double digits for many coun- of the 164 countries for which the World Bank tries in the region. Even in a forecast where monitors extreme poverty, more than half— countries grow at a rate of 8 percent per 84 countries—had already reached levels year, significantly above historical averages below 3 percent as of 2015. In contrast, three- in the region and the world, the prevalence fourths of countries in Sub-Saharan Africa of extreme poverty in Sub-Saharan Africa had extreme poverty rates above 18 percent in would still be in double digits (13.4 percent), 2015; of the world’s 28 poorest countries (that whereas the average for the rest of the world is, those with the highest rates of extreme pov- would be close to zero (0.4 percent). 15 Reaching the goal of reducing global ex- extreme poverty at the global level is inatten- treme poverty to less than 3 percent by 2030 tive to how progress is distributed across the will require that the countries of Sub-Saharan world. The shared prosperity indicator was Africa realize historically unprecedented and built to ensure the monitoring of progress sustained economic growth rates. But it will in all countries. Ending poverty and sharing also require that this growth be highly inclu- in prosperity cannot happen in a satisfactory sive, not just globally but in every country, be- way if the need for equitable and sustainable cause a world where extreme poverty is elim- economic development is ignored in certain inated everywhere except in one region does regions or countries. not portray a picture of a world free of poverty. To complete this picture of what poverty Similarly, as the world gets richer and means, we need more information. Just as progress is made in the battle against ex- one can recognize the picture in a puzzle only treme poverty, we must not forget that many when enough of the pieces are in place, so around the world, and particularly in middle- too must there be more pieces of the puzzle income countries, still live in deprivation, to better bring the state of poverty into full unable to meet their basic needs, even if their view. A more comprehensive picture helps us income levels are higher than the IPL. In the understand what meeting the goal requires. early 1990s, when extreme poverty was perva- The rest of the report introduces three new sive in most regions of the world, focusing the pieces to the poverty puzzle, broadening the world’s attention on one core indicator served way poverty is defined and measured. To do as a galvanizing force for coordinated action. this, the report goes beyond extreme mone- It was not necessarily a weakness that progress tary poverty to start the process of monitor- in this indicator could be attained through ing poverty in all its forms. The new lines and significant improvements in some regions or measures introduced in this report allow one countries. With the high global prevalence of to better monitor poverty in all countries, in extreme poverty, a rapid reduction of extreme multiple aspects of life, and for all individuals poverty was critical. And in this dimension in every household. They also reflect the first there has been tremendous success. Now that steps taken by the World Bank in respond- the extreme poverty rate is in single digits (as ing to recommendations from the Atkinson indicated by the 2018 nowcast) and is becom- Commission on Global Poverty (World Bank ing increasingly concentrated, finishing the 2017b), and present an evolving view of pov- job will require constructing a more detailed erty and shared prosperity. and comprehensive picture of what is meant Chapter 3 expands on the notion intro- by a world free of poverty. duced with the shared prosperity indicator, This report builds on the desire to con- that it is important to monitor progress in struct a more complete picture of what it all countries. The chapter presents two new means to live in a world free of poverty and sets of monetary poverty lines intended to in which all prosper. A key point of the report complement the IPL of US$1.90 a day. First, is that we must broaden our view of poverty. it presents higher poverty lines, at US$3.20 After an update on global extreme poverty and US$5.50 per day, reflecting typical na- in chapter 1, the remaining chapters of this tional poverty thresholds in middle-income report can be viewed as expanding our un- countries. In addition, the chapter introduces derstanding of poverty. Chapter 2 provides a concept of societal poverty that reflects dif- an update on shared prosperity as measured ferences in the overall level of well-being in by growth in consumption or income of the each country. The societal poverty line is bottom 40 percent of the population in each constructed to reflect social and economic country for the period around 2010–15. One assessments of basic needs in each and every important reason the concept of monitor- country. It integrates both the idea of mon- ing shared prosperity was introduced was itoring absolute extreme poverty and the to expand our view of how to think about more relative notion of ensuring that the less poverty reduction and growth. Monitoring well-off in each society benefit as that soci- 16 POVERTY AND SHARED PROSPERITY 2018 ety grows. In this way it reflects both abso- equality within households, there undoubt- lute poverty and the relative notion of shared edly are people living in poverty within prosperity. nonpoor households, as well as nonpoor Chapter 4 previews a new multidimen- individuals living in poor households. Chap- sional poverty measure, which goes beyond ter 5 sheds light on this issue, with a focus consumption or income poverty by adding on differences by sex and between children nonmonetary dimensions into the measure. and adults. Current data and methods do Access to education, health, electricity, water, not permit accounting for inequality within sanitation, and physical and environmental households in most countries, so the chapter security are critical for well-being. Because examines select country studies where this many of these goods cannot be purchased in is possible and describes how this affects the the market, they are typically not included in global profile of poverty. the measure of extreme poverty. This work Pieced together, the chapters of this re- builds on the tradition pioneered by the port provide a more comprehensive picture United Nations Development Programme of poverty that reinforces much of the posi- and the Oxford Poverty and Human Develop- tive story revealed by the tremendous prog- ment Initiative with the Global Multidimen- ress in reducing extreme poverty over the last sional Poverty Index, and complements it by quarter century. But they also uncover some placing the monetary measure of well-being previously hidden details about the nature alongside nonmonetary dimensions. For 119 and extent of poverty throughout the world. countries, consumption poverty is combined Monetary poverty with respect to the IPL will with education and access to basic utilities for continue to be the focus of the World Bank’s circa 2013. In addition, the chapter explores, work. Alarming findings from the forecasts for only six countries, the addition of dimen- reported in the first chapter are that extreme sions on health and nutrition and on security poverty appears to be entrenched in a hand- from crime and natural disaster. Extending ful of countries and that the pace of poverty and complementing the monetary measure reduction will soon decelerate significantly. with deprivation in other dimensions gives a The goal of ending extreme poverty as mea- more comprehensive picture and helps better sured by the IPL itself will require a redou- understand the interaction among the various bling of efforts and a greater focus on those dimensions of poverty. countries where poverty is the worst. But, to Finally, in most countries of the world, truly bring an end to poverty, we now also poverty is measured at the household level, need to think more broadly and recognize the implicitly assuming that everyone in a poor greater complexity inherent in the concept of household is poor. But, because there is in- poverty around the world. INTRODUCTION 17 Ending Extreme Poverty: 1 Progress, but Uneven and Slowing Chapter 1 presents the latest data on global and regional extreme poverty rates using the inter- national poverty line of US$1.90 in 2011 purchasing power parity dollars. The chapter discusses the trends, the geographical concentration, and the profile of extreme poverty. It also reflects on data coverage and methodological issues and their consequences on global estimates. Extreme poverty declined to 10 percent of the world’s population in 2015, meaning 1 person in every 10 in the world was living in extreme poverty. This rate dropped from nearly 36 percent in 1990, resulting in a world with more than a billion fewer people living in extreme poverty. Although this progress is remarkable, 10 percent equates to 736 million people still living in extreme poverty in 2015, and there is evidence that the pace of poverty reduction is starting to decelerate. There remain significant challenges to reaching the goal of a world free of poverty. Meeting the global target of reducing extreme poverty to less than 3 percent will require substantially greater efforts. Monitoring extreme poverty: billion fewer people lived in extreme poverty in 2015 than in 1990. Not only are there now A quarter century of progress fewer poor people but, on average, the poor The World Bank is committed to eradicating are also now less poor. In 1990, the average poverty. The twin goals of ending extreme shortfall between what the poor consumed poverty and promoting shared prosperity in a and the IPL was 35 percent (of the IPL). This sustainable manner accord well with the post- shortfall shrank to an average of 31 percent in 2015 development agenda and the Sustain- 2015. The total consumption shortfall of the able Development Goals (SDGs) to ensure poor (the sum of all consumption shortfalls that all people can fulfill their potential in of the poor) in 2015 had shrunk to about one- dignity and equality and in a healthy environ- third of its size from 1990. (For more details ment (box 1.1). Monitoring global poverty is on the consumption shortfall of the poor, and critical for tracking progress and identifying the depth and severity of poverty, see annex areas that require additional policy actions. 1A.) Despite this impressive progress in terms In 2015, an estimated 736 million people of the declining poverty rate, the number of were living below the international poverty poor, and the consumption shortfall of the line (IPL), currently set at US$1.90 in 2011 poor, the number of people living in extreme purchasing power parity (PPP) dollars. This poverty globally remains unacceptably high. count of people living in extreme poverty is The World Bank has set a specific target to down from 1.9 billion people in 1990. Despite help guide the work in eradicating poverty: the world population increasing by more than reduce the global share of people living in 2 billion people over this period, more than a extreme poverty to less than 3 percent. Over 19 BOX 1.1 Alignment of the SDGs and the Twin Goals of the World Bank Group On April 20, 2013, the Board of Executive building on the Millenium Development Directors of the World Bank adopted Goals (MDGs). Ending poverty in all its two ambitious goals: ending extreme forms and dimensions is the first of the 17 poverty globally and promoting shared SDGs. The General Assembly Resolution prosperity in every country in a sustainable recognizes that eradicating poverty is way. Progress toward the first of these the greatest global challenge and an goals is measured by monitoring the share indispensable requirement for sustainable of the global population living below the development. international poverty line. The World Bank The SDGs and the World Bank’s twin set a target of reducing extreme poverty to goals are aligned. The goals of ending less than 3 percent by 2030 and to ensure extreme poverty within a generation continued focus and steady progress and promoting shared prosperity in a toward the goal, the institution set an sustainable manner accord with the 2030 interim target of 9 percent by 2020. Agenda for Sustainable Development to The second goal is not defined ensure that all human beings can fulfill globally, but rather tracks progress at the their potential in dignity and equality and country level. Progress on the shared in a healthy environment. In contrast to prosperity goal is measured by the growth the SDGs, the World Bank’s twin goals do in the average consumption or income not set distinct country-specific targets expenditure of the poorest 40 percent or targets for the multiple dimensions of the population (the bottom 40) in a of poverty, equity, and sustainability. country. This goal is not associated with a However, the World Bank recognizes target in 2030, but it reflects the aim that that poverty is multidimensional, and every country should promote the welfare sustainability is critical. The pursuit of of its least privileged citizens for a more these goals will require the concerted inclusive and equitable society. effort of all stakeholders. Over the years, On September 25, 2015, the United the World Bank has collaborated with the Nations General Assembly adopted the 17 United Nations in nearly every region and Sustainable Development Goals (SDGs) sector, and its engagement has deepened and 169 targets as part of the 2030 since the adoption of the MDGs, and now Agenda for Sustainable Development, with the SDGs. the last decades, remarkable progress has this trend of steady poverty reduction, the been made in reducing extreme poverty world is clearly on track to reach the interim (figure 1.1; see box 1.2 for details on the data poverty target of 9 percent by 2020 set by the used). The world attained the first Millen- World Bank to monitor progress toward the nium Development Goal—to cut the 1990 2030 goal.1 Forecasts for 2018 indicate that poverty rate in half by 2015—six years ahead this target has already been surpassed. of schedule. With continued reductions, the Reducing extreme poverty to 3 percent by global poverty rate—the share of the world’s 2030 from 10 percent in 2015 will require an population living below the IPL—dropped additional 7-percentage-point reduction in from about 36 percent in 1990 to 10 per- the poverty rate in 15 years. If, over the last 25 cent in 2015, that is, more than a 70 percent years, poverty has steadily declined at 1 per- reduction. centage point a year, it would seem reasonable Over the 25 years from 1990 to 2015, the to assume that the world is well on track to re- global rate of extreme poverty fell by slightly ducing poverty by at least 7 percentage points more than 25 percentage points, or an average over the next 15 years. The rate of poverty re- decline of 1 percentage point a year. (Gauged duction could be cut in half to a 1-percentage- according to today’s population, 1 percent point decline every two years, and the world equates to about 76 million people.) Given would still reach the 3 percent target. 20 POVERTY AND SHARED PROSPERITY 2018 FIGURE 1.1 Global Extreme Poverty Rate and Headcount, 1990–2015 50 1,895 2,000 1,878 45 1,703 1,800 1,729 40 1,610 1,600 35.9 35 33.9 1,352 1,400 30 29.4 28.6 1,223 1,200 Poverty rate (%) Millions 25 25.7 1,000 963 20.8 804 20 800 18.1 736 15 13.7 600 11.2 10 400 10.0 5 200 0 0 1990 1995 2000 2005 2010 2015 Number of people who live below $1.90 a day (2011 PPP) (right axis) Share of people who live below $1.90 a day (2011 PPP) Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: PPP = purchasing power parity. BOX 1.2 Chapter 1: Data Overview Data source than consumption has been increasing The data for this chapter come from over time. The differences between PovcalNet, which is an online analysis income and consumption measures tool for global poverty monitoring hosted matter for comparing trends and patterns by the World Bank (http://iresearch. in poverty. To assure that the income worldbank.org/PovcalNet). PovcalNet and consumption levels from different was developed with the purpose of countries are comparable, they need to be public replication of the World Bank’s expressed in the same unit. To this end, poverty measures for the IPL. PovcalNet consumer price indexes and purchasing contains poverty estimates from more power parities are applied. Because than 1,600 household surveys spanning the frequency and timing of household 164 countries.a Most of the surveys in surveys vary across countries, comparable PovcalNet are harmonized through the country-level estimates require projecting Global Monitoring Database, the World the survey data to the reference year Bank’s repository of household surveys. for which global poverty is expressed, here 2015. When the timing of surveys Derivation of country-level estimates does not align with the reference year, The national poverty rates from household PovcalNet “lines up” the survey estimates surveys are based on measures of to the reference year. household consumption or income. In the current 2015 estimates, about 40 Derivation of regional/global estimates percent of the countries covered use To arrive at a regional and global income, but the use of income rather estimate of poverty, population-weighted (continued) ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 21 BOX 1.2 Chapter 1: Data Overview (continued) average poverty rates are calculated estimate of the number of poor is the for each region.b Some countries have product of the population-weighted mean no household survey data to monitor of the regional poverty rates and the total poverty. No direct value is imputed for world population. these countries; rather it is assumed that the average for the region based on the Further information countries with data available is the same For further information regarding the data as the regional average for all countries. sources, geographical regions, data issues, The number of poor in each region is the and assumptions underlying the global, product of the region’s poverty rate and regional, and country-level estimates, see the total regional population. The global appendix A at the end of the report. a. The term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics. b. Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models (Source: World Development Indicators). Despite this optimistic portrait of the which is three years out of date. Why in 2018 path toward the target, there are reasons for is poverty reported for 2015? The global esti- concern. One reason is the existence of some mates are based on household surveys from evidence that the rate of poverty reduction 164 countries, and these surveys are carried has recently slowed. Between 2011 and 2013, out independently, typically by national sta- extreme poverty declined by 2.5 percentage tistical offices or national planning ministries. points, but, over the two years between 2013 The surveys are complex and lengthy, requir- and 2015, it declined by only 1.2 points. Al- ing significant amounts of labor and time though this change in the rate of poverty to be implemented effectively; and, in most reduction over these two years should be in- countries, they are not carried out every year. terpreted with caution because of data chal- Countries implement household surveys that lenges, it is a first potential signal of change. measure poverty status once every three to To assess whether this recent change in the five years (Serajuddin et al. 2015). It also takes path of poverty reduction is an aberration or time to gather, process, and analyze these a warning sign of what the future holds, fore- data. There is thus frequently a lag between casts of how extreme poverty may evolve up the completion of the survey fieldwork and to 2030 can be very informative. Such fore- the publication of the data for the global pov- casts should be viewed with caution though, erty counts (Independent Evaluation Group because the factors that affect global poverty 2015). For these reasons, 2015 is the most re- reduction are complex, and because the fu- cent year for which there are sufficient data to ture is uncertain. For example, economic estimate a global poverty rate.2 (For details on growth is a key factor in reducing poverty, how data are shifted forward and backward in but it can be volatile and difficult to predict. time to produce the 2015 estimate, see appen- Nonetheless, without forecasts, it is not pos- dix A at the end of the report.) sible to clarify whether the current trajectory However, if assumptions are made about is adequate to reach the target. the relationship between economic growth as observed in national accounts (such as Nowcasts and forecasts to the real growth in gross domestic product [GDP]) and in surveys, as well as on popula- 2030 tion projections, it is possible to nowcast the The current estimate of the global extreme global poverty rate in 2018 and also generate poverty rate—10 percent—refers to 2015, scenarios about global poverty in 2030.3 To 22 POVERTY AND SHARED PROSPERITY 2018 nowcast poverty in 2018, it is assumed that provides another piece of evidence that there each household’s welfare grows at a fraction seems to be a significant slowdown in the of the growth in GDP per capita. Only a frac- rate of global poverty reduction. From 2013 tion of the growth in GDP per capita is passed to 2015, poverty declined by 0.6 percentage through to the welfare vector because there points per year; this is slower than the 25-year is a historical divergence between growth average decline of a percentage point per year. in consumption or income observed in sur- Between 2015 and 2018 the nowcast suggests veys and the growth observed in national ac- that the rate of poverty reduction has further counts. The fraction that is passed through to slowed to less than half a point per year. the welfare vector is based on examining past Projecting global poverty to 2030 is more data on the average relationship between sur- challenging, but it is possible to consider how vey means and national accounts data (Prydz, global poverty may evolve under different Jolliffe, and Serajuddin, forthcoming).4 With scenarios. Four scenarios are considered as this approach, it is assumed that the scaled described below. The first scenario assumes growth accrued equally (in proportionate that every country grows at its average growth terms) to everyone in a country regardless of rate from 2005–15. This growth rate is then individual income level. If inequality changed used to “grow” the household survey mean from 2015 to 2018, this assumption will not over time, in a way that does not change the hold, and poverty will be higher or lower de- level of inequality. This approach makes it pos- pending on the change in inequality (World sible to move the entire distribution of con- Bank 2016b; Lakner, Negre, and Prydz 2014). sumption or income forward in time, starting Under these assumptions, the 2018 nowcast with the 2018 nowcast and moving up to 2030. for the global extreme poverty rate is 8.6 per- The second scenario is like the first, ex- cent (figure 1.2). This means that the 2020 in- cept for one difference: the growth rate for terim target has likely already been achieved. each country is not its historical average, but One implication of this estimate is that it rather the historical average for its region. FIGURE 1.2 Projections to 2030 of Global Extreme Poverty 14 12 10 8.6 Poverty rate (%) 8 Growth assumptions 6 Historical country growth Historical regional growth 4 2 x historical regional growth 3% target 6% annual growth + 2 pp premium 2 0 2012 2015 2018 2021 2024 2027 2030 Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/; World Development Indicators; World Economic Outlook; Global Economic Prospects; Economist Intelligence Unit. Note: The 2018 nowcast uses realized and projected growth in GDP per capita and household final consumption expenditure per capita from 2015 to 2018 to grow the 2015 welfare vector. “Historical country (regional) growth” assumes that the annual growth rates countries (regions) experienced from 2005 to 2015 continue from 2018 to 2030. “6% annual growth + 2 pp premium” assumes that all countries grow by 6 percent annually from 2018 to 2030, and that the bottom 40 percent on average grow with an additional 2 percentage points (pp). All assumed growth rates are real, per capita growth. ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 23 For each region, the average annualized real 10-year historic average growth rate (based growth rate between 2005 and 2015 is esti- on growth from 2000 to 2010) was almost 4 mated and then used as the growth rate for percent, but this was sustained for only a few each country in the region. The third sce- years and has since declined slightly. nario is identical to the second but uses twice How can it be that poverty has declined the historical regional growth averages. These by 25 percentage points over the last 25 years, three scenarios all assume that inequality in yet the only forecasts that suggest poverty the country remains unchanged until 2030. will be reduced by 7 percentage points over The final scenario explores what happens the next 15 years are based on unprecedented if growth is pro-poor; if the bottom 40 per- growth patterns and rates? cent on average grows faster than the coun- try as a whole. This scenario, not anchored to Uneven progress: A regional any empirical data, assumes that each coun- try grows by 6 percent annually toward 2030, profile of poverty reduction but that the bottom 40 percent, on average, There are several parts to the answer of this grows by 8 percent annually (while the top question and many of them hinge on the gen- 60 percent grows at 4.7 percent, resulting in eral idea that progress has been uneven, which the average of 6 percent). Because the bottom is linked to the theme of this report. A slightly 40 percent grows at a rate that is 2 percentage more specific answer to the question posed points faster than the average, this is referred above is that not all regions have shared in the to as a shared prosperity premium of 2 per- benefits of the global reduction in poverty. centage points. In all these scenarios, growth To better understand why the simulations rates in either GDP per capita or household forecast a challenging path for reaching the final consumption expenditure (HFCE) per target, it is useful to examine the changing capita are rescaled to account for the differ- regional profile of poverty that has been ence between survey means and national ac- brought about by the differing rates of pov- counts as discussed above.5 erty reduction. Between 1990 and 2015, the The scenarios based on growth rates that regional profile of poverty has changed sig- correspond with historical performance of nificantly. In 2015, more than half of the the countries, or of the average performance global poor resided in Sub-Saharan Africa of the region do not come close to reaching and more than 85 percent of the poor re- the target (figure 1.2). Both scenarios suggest sided in either Sub-Saharan Africa or South global poverty rates in the range of 6 per- Asia (figure 1.3). The remaining 14 percent cent in 2030. The third scenario, where it is of the global poor, or about 106 million poor assumed that all countries grow by twice the people, lived in the other four regions or in average regional growth rate over the past ten high-income economies.7 years, also falls short of the 3 percent target. This is a dramatic shift from 1990, when This scenario predicts a global extreme pov- over half of the poor were living in East Asia erty rate of 3.7 percent in 2030. and Pacific. The two regions with the most This is an alarming finding. poor people in 1990 were East Asia and Pa- The only scenario where the 3 percent tar- cific and South Asia, which were home to 80 get is met is when a real annual growth rate of percent of the poor. With China’s rapid re- 6 percent and a shared prosperity premium duction of poverty, the concentration of the of 2 percentage points are assumed.6 The global poor shifted from East Asia and Pa- most important element of this scenario is cific in the 1990s to South Asia in 2002, and that Sub-Saharan Africa is assumed to grow then to Sub-Saharan Africa in 2010. In South steadily at this rate for 12 straight years up Asia, both the poverty rate and number of through 2030. In considering this scenario, it poor have been steadily declining, but, given is useful to note that between 2000 and 2015 the sheer size of the populations, the con- Sub-Saharan Africa has never had a 10-year tribution to global poverty continues to be average growth rate near 6 percent—let alone high. This contrasts with Sub-Saharan Africa, 8 percent for the bottom 40. The highest av- where the total count of poor people in this erage growth rate was around 2010, when its region has been increasing, essentially lead- 24 POVERTY AND SHARED PROSPERITY 2018 FIGURE 1.3 Number of Extreme Poor by Region, 1990–2030 2,000 1,800 1,600 World 1,400 1,200 Millions of poor 1,000 800 600 400 200 0 1990 1995 2000 2005 2010 2015 2020 2025 2030 Sub-Saharan Africa Latin America and the Caribbean Rest of the world South Asia Middle East and North Africa East Asia and Pacific Europe and Central Asia Source: PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet/. World Bank, Washington, DC, World Development Indicators; World Economic Outlook; Global Economic Prospects; Economist Intelligence Unit. ing to the shifting concentration of poverty FIGURE 1.4 Regional GDP per Capita Growth and Average Growth for from South Asia to Sub-Saharan Africa. the Extreme Poor, 1990–2017 This pattern is likely to continue in the coming decade. Simulations show that, as the 10 number of extreme poor continues to decline in South Asia, the forecasts based on histor- ical regional performance indicate that there will be no matching decline in poverty in Sub- Annual growth (%) 5 Saharan Africa (figure 1.3). In 2030, the share of the global poor residing in Sub-Saharan Africa is forecasted to be about 87 percent, if economic growth over the next 12 years is sim- ilar to historical growth patterns. (For more 0 details on the simulations, see annex 1B.) One important reason for the changing regional concentration of extreme poverty, and the projected increase in the share of the –5 global poor residing in Sub-Saharan Africa, 1990 1995 2000 2005 2010 2015 is the regional differences in per capita GDP growth. Focusing on the three regions that East Asia and Pacific (population-weighted growth) have accounted for the bulk of the poor, the Sub-Saharan Africa (population-weighted growth) average annual growth rate since 1990 has South Asia (population-weighted growth) consistently been highest in the East Asia and Global average growth for the extreme poor Pacific region (between 5 and 10 percent), fol- Source: PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet/. World Bank, lowed by South Asia, and then Sub-Saharan Washington, DC, World Development Indicators. Africa. South Asia has maintained an average Note: The orange line reflects the average growth rate as experienced by the population of people in extreme poverty. It is a weighted average of country growth rates where the weights are the number growth rate between 5 and 6 percent over the of extreme poor in each country. All curves fit a local polynomial through the annual growth rates to last decade (figure 1.4). The average growth smooth out year-to-year fluctuations. ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 25 rate in Sub-Saharan Africa has rarely exceeded ing the global poverty count will occur only 5 percent and has decreased in recent years. if progress is primarily made in those coun- Growth is an important driver of poverty tries where poverty is greatest. This is not reduction, and, throughout the 1990s and to say that countries with extreme poverty early 2000s, the vast majority of the poor lived rates below 3 percent cannot make further in countries with relatively high growth rates. progress. Where there is poverty, there is still Over the last few years, as the concentration much work to be done. But the core indicator of poverty has shifted to Sub-Saharan Africa, the World Bank will track up through 2030 is the majority of the poor now live in countries to reduce the global rate of extreme poverty with lower-than-average growth rates (figure to less than 3 percent. 1.4). The orange line in figure 1.4 reflects this If the goal is a world free of poverty, why change because it is a weighted average of is progress monitored toward 3 percent and country growth rates where the weights are not zero percent? The 3 percent target comes the number of extreme poor in each coun- from both empirical and conceptual consid- try. As the concentration of poor moved from erations. Empirically, poverty in some coun- high-growth to low-growth countries, this tries remains deep, entrenched, and wide- shift led to a significant deceleration in the spread; and, when the target was initially set, 3 rate at which poverty has been declining. percent was considered an ambitious but fea- Not only has the growth rate in the coun- sible target (Jolliffe et al. 2015). Conceptually, tries with the most poor declined in recent however, there is also an important reason for years but the conversion of growth to poverty setting the target at some level greater than reduction—the growth elasticity of poverty— zero percent. The purpose of a target is to has also historically been lower in Sub-Saha- assist in efforts to attain goals. For targets to ran Africa. Hence, a given growth rate buys help, they need to be credibly measured and less poverty reduction in Sub-Saharan Africa monitored. The key conceptual concern then than in most other regions of the world. is that, in general, sample surveys from large The changing regional concentration of populations cannot measure rare outcomes extreme poverty reflects the highly uneven well. As countries make progress toward elim- rate of poverty reduction across countries inating extreme poverty, the accuracy with of the world. Of the 164 countries for which which samples can measure the increasingly the World Bank monitors poverty, more than lower rates deteriorates. In particular, sample half—84 countries—have already reached surveys cannot reliably measure the complete rates below 3 percent as of 2015. The median eradication of a phenomenon in a popula- poverty rate of the 164 countries in 2015 is 2.7 tion. In part for this reason, progress is moni- percent; this median in 2018 is estimated to be tored toward 3 percent, which can be credibly 1.9 percent. This success in having more than measured and is also an ambitious goal. half the countries of the world with poverty Map 1.1 shows the countries that have rates below 3 percent is also part of the reason extreme poverty rates in 2015 of less than why the world is now starting to experience 3 percent and highlights the countries that a slowdown in the rate of poverty reduction. have reached the interim 9 percent target set There are now fewer countries than before for 2020. In addition to the 84 countries with with large populations of poor people. Pre- poverty rates less than 3 percent, there are 23 viously, progress in poverty reduction could countries with poverty rates less than 9 per- shift over time from one country or region to cent. Two-thirds of the countries have rates another, but now there is less scope for this. less than 9 percent. Of the remaining one- The slowdown that is observed at the global third, though, the story is different. In about level does not mean that poverty reduction is half of these countries, the poverty rate is declining in every country; however, it does greater than 30 percent; and, in 11 countries, mean that the number of countries where the poverty rate is greater than 50 percent. there have been significant declines in the The impressive progress in terms of reducing number of poor people is shrinking. global poverty to 10 percent masks signifi- As extreme poverty becomes increasingly cant variation in success at the country level concentrated, significant progress in reduc- in reducing extreme poverty. 26 POVERTY AND SHARED PROSPERITY 2018 MAP 1.1 Extreme Poverty Rate by Country, 2015 Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Map 1.1 also marks countries with ex- 41 percent live below the IPL (figure 1.5). treme poverty rates between 9 and 18 percent It hasn’t always been like this. In 1990, the in 2015. This subsample has been created average poverty rate in countries from the using the simplistic assumption that these East Asia and Pacific region was higher; but, countries, if they succeed in reducing poverty whereas the rates in these countries quickly by 1 percentage point a year, will have pov- declined over the years, the decline in the erty rates less than 3 percent by 2030. There poverty rate in Sub-Saharan Africa was much are 121 countries with rates at or below 18 slower (figure 1.6). Although the percentage percent in 2015, and only 43 countries have of poor in Sub-Saharan Africa has slowly de- extreme poverty rates that are higher than clined, this decline has not been fast enough this. A closer examination of these countries to counter a growing population—the total provides more evidence as to why the 2030 population of poor people there has steadily forecasts indicate that attaining the 3 percent increased from 1990 to 2015 (table 1A.1 in target will be a hard battle. annex 1A).8 Economic growth and pro-poor The map reveals that most of the 43 coun- policies in Sub-Saharan Africa over the last tries with poverty rates above 18 percent 25 years have had anemic effects on reducing are in Sub-Saharan Africa. Three-fourths poverty. For simulations that use historical of Sub-Saharan African countries had pov- average growth rates as estimates for future erty rates above 18 percent in 2015, and, growth, the predicted future path of pov- of the world’s 28 poorest countries (that is, erty reduction in Sub-Saharan Africa is in- those with the highest rates of poverty), 27 adequate to bring global extreme poverty to are in Sub-Saharan Africa, all with poverty below 3 percent. rates above 30 percent. In 11 countries, all in Although extreme poverty is compara- Sub-Saharan Africa, more than half the pop- tively much lower in the Middle East and ulation live in extreme poverty (figure 1.5). North Africa, the rate increased to 5.0 percent In all regions except for Sub-Saharan Af- in 2015, up from 2.6 percent in 2013, while rica, the regional average is well below 18 per- the number of poor almost doubled from cent, whereas in Sub-Saharan Africa about 9.5 million in 2013 to 18.6 million in 2015. ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 27 FIGURE 1.5 Extreme Poverty Rate by Region and Country, 2015 77 Sub-Saharan 70 Africa 65 (41%) 72 78 62 73 55 75 52 58 50 31 East Asia and Pacific 15 (2%) South Asiaa (12%) Rest of the world (1%) 2 14 Europe and Central Asia (1%) Middle East and North Africa (5%) Latin America and the Caribbean 28 41 (4%) Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: Population-weighted regional average shown in parentheses. Each spike represents a country and all countries within a region are the same color. Within each region, spikes are numbered with the poverty rate if they have the highest rate within the region or if their poverty rate is greater than 50 percent. a. This estimate is based on a regional population coverage less than 40 percent. The criterion for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. These recent estimates should be interpreted ravel, the risks of falling back into economic with caution because of underlying data deprivation must be managed efficiently and challenges, but they are, nonetheless, a stark collectively (World Bank 2013). If not, the reminder that past gains cannot be taken for risks can turn into economic, environmen- granted. To ensure that progress does not un- tal and political crises, as in the Middle East 28 POVERTY AND SHARED PROSPERITY 2018 and North Africa, where fragility and conflict FIGURE 1.6 Extreme Poverty, Regional and World Trends, 1990–2015 in the region are impacting livelihoods and manifesting in the recent spike in poverty. East Asia and Pacific 60 Sub-Saharan Africa Drilling down: The countries 50 with the most poor South Asia Over time, many of the countries with Poverty rate (%) 40 high poverty numbers, including Bangla- World desh, India, Indonesia, Kenya, and Nigeria, 30 have grown their economies out of low- income-country status and are now middle- income countries. With this growth, most 20 of the extreme poor have also moved from Latin America and the Caribbean being in low-income to being in middle- 10 income countries, and nearly two-thirds of Middle East and North Africa the world’s poor people now reside in Europe and Central Asia 0 middle-income countries (figure 1.7). How- 1990 1995 2000 2005 2010 2015 ever, as more countries shift from low- to middle-income status, so does the popula- Source: PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet/. World Bank, Washington, DC, tion share. As of 2015, 5.5 billion people lived Note: The regional estimates for Europe and Central Asia in 1990 and South Asia in 1999 and 2015 in middle-income countries as opposed to are based on regional population coverage of less than 40 percent. The criterion for estimating survey about 640 million in low-income countries, population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. Because of the low coverage, these numbers are censored in explaining why most of the extreme poor— PovcalNet. over 400 million—now reside in lower- middle-income countries. As countries de- velop and per capita GDP increases, poverty FIGURE 1.7 Rate and Headcount of Extreme Poor, by Income Group, 2015 rates tend to fall as economic opportunities 45 450 are expanded. This general trend can be seen in figure 1.7, with the poverty rate declin- 40 400 ing from 42 percent for low-income coun- 35 350 tries to 14 percent for lower-middle-income countries, and close to 2 percent for upper- 30 300 Poverty rate (%) Millions of poor middle-income countries. This situation is 25 250 promising for continued poverty reduction if 20 200 more poor people can benefit from economic growth. Conversely, nearly every low-income 15 150 country is in Sub-Saharan Africa (and a few 100 10 countries in other regions, namely Afghan- istan, Haiti, the Democratic People’s Re- 5 50 public of Korea, and Nepal according to the 0 0 fiscal year 2018 classification), highlighting Low income Lower-middle income Upper-middle income the need to stimulate and sustain economic Population-weighted poverty rate growth in low-income countries. Number of poor (right axis) Drilling down a bit further into the coun- Source: PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet/. World Bank, tries that have the largest population of poor Washington, DC, people, figure 1.8 represents all countries by the share of the global poor in 2015. Half of the people living in extreme poverty in 2015 and Nigeria) are the five topping the list of can be found in just five countries. The most countries with the greatest number of ex- populous countries in South Asia (Bangla- treme poor. India, with over 170 million desh and India) and Sub-Saharan Africa poor people in 2015, has the highest num- (Democratic Republic of Congo, Ethiopia, ber of poor people and accounts for nearly a ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 29 FIGURE 1.8 Global Distribution of the Extreme Poor by Region and Country, 2015 eria Nig In do ne . s ia ep .R Dem g o, Ea d P n Co an s t ac Ban As ific gla ia des h pia Et hio Tanzania Sub-Sa hara Afric a n Madagas h As ia ca r Sout Keny a Mo ia zamb I nd iq ue Source: PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet/. World Bank, Washington, DC. Note: The inner circle is divided proportionally to each region’s share of the total population living in extreme poverty. The outer circle is similarly proportionate, but at the country level. The 10 countries with the most extreme poor in the world are listed. quarter of global poverty. In the South Asia the verge of switching). But the uncertainty region, four out of five extreme poor reside in about when they have switched or will switch India. Despite a poverty rate of 13.4 percent, also reflects a series of difficult measurement India’s large population of 1.3 billion results issues related to global poverty counts. Dis- in a high number of extreme poor. To achieve cussing some of these issues is useful because the global poverty goal, progress in poverty it can help convey a sense of the level of reduction needs to continue in India. (im)precision of the poverty counts, and it India’s placement as the country with the allows for transparency in the strengths and most poor people in the world is likely to weaknesses of the data and methods. change in the near future. In fact, projections In the case of Nigeria, there is one key con- indicate that Nigeria may already have over- cern with current poverty estimates. Both the taken India. The uncertainty about whether 2015 estimate and the 2018 nowcast for Nige- India or Nigeria is currently the country with ria are based on household survey data col- the most poor people is in part simply be- lected in 2009. To estimate extreme poverty cause the countries are near a crossing point in 2015 for Nigeria, the survey mean from the (having either recently switched or being on 2009 data was increased at a rate equal to the 30 POVERTY AND SHARED PROSPERITY 2018 country’s GDP per capita growth rate (which and how one asks has a significant effect on is estimated annually) and it is assumed that how people respond (Backiny-Yetna, Steele, the level of inequality was unchanged over and Djima 2017; Beegle et al. 2012; Gibson, those six years. Similarly, for 2018, the mean Huang, and Rozelle 2003; Jolliffe 2001). Over is shifted forward on the basis of nine years of the years, changes have been introduced in growth estimates and assuming unchanged the recall period in the NSS Consumer Ex- inequality. Although growth measured in penditure Survey, the official instrument for surveys used for poverty estimation is cor- estimating poverty in India. The extreme related with growth as measured by national poverty rate for India as reported here is cur- accounts data such as GDP, there can be size- rently based on an old questionnaire design. able differences and these differences can With the next NSS data that will be made have substantial impact on estimated poverty publicly available, it will no longer be possi- rates. Similarly, if the assumption that the ble to estimate consumption using the same distribution (or inequality) has not changed questions and the extreme poverty measure since 2009 is wrong, this too can lead to sub- will be estimated using a new questionnaire stantial error in the estimated poverty rate design. The 2018 nowcast estimates for India (Jolliffe et al. 2015). indicate that switching from the old to the There are two important measurement is- new questionnaire results in a significantly sues that also temper confidence in the India higher level of total consumption that re- poverty estimates. The first is similar to the classifies more than 50 million people from issue for Nigeria. The last round of poverty poor to not poor. Whenever the next round data available was collected in 2011–12. For of NSS data is released (using the new ques- India, however, an additional round of the tionnaire), backcasted estimates of poverty in National Sample Survey (NSS), collected in 2015 will most likely show significantly fewer 2014–15, has the same socioeconomic and people living in extreme poverty (figure 1.9). demographic information as the 2011–12 For more details on these measurement is- round, and both provide data on household sues for India, see box 1.3. expenditures on services and durables. The 2014–15 NSS also contains three additional FIGURE 1.9 Projections to 2030 for the Five Countries with the Most schedules with consumption data that were Extreme Poor in 2015 designed to test the questionnaire design, but these data are not in the public domain 250 and were not available for analysis. Given the importance of India to the total poverty 200 count, and the availability of the same so- cioeconomic, demographic, geographic, and Millions of poor limited consumption data at two points in 150 time, a model of consumption was estimated on the basis of the common variables at these 100 two points in time. The change in the char- acteristics of the population of India is lev- eraged to estimate how much consumption 50 increased over time (in a manner that avoids assuming that inequality did not change). For the cases of both India and Nigeria, the lack 0 2012 2015 2018 2021 2024 2027 2030 of recent data available for analysis results in poverty estimates that are almost certainly India URP India MMRP Nigeria Congo, Dem. Rep. Ethiopia Bangladesh much less precise than many other estimates in this report. Source: PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet/. World Bank, The other measurement issue is that there Washington, DC, World Development Indicators; World Economic Outlook; Global Economic Prospects. Note: India URP (Uniform Reference Period) relies on poverty estimates and projections based on a are many different ways to ask survey re- uniform recall period; India MMRP (Modified Mixed Reference Period) relies on poverty estimates and spondents about their consumption habits, projections based on the modified mixed recall period. ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 31 BOX 1.3 India: Issues with the 2015 Poverty Estimate and 2030 Forecasts The 2015 estimate, 2018 nowcast, households in the 2011–12 survey areas) to calibrate the growth rate in and 2030 forecasts for India merit would be given a growth rate of survey mean consumption between special mention given both the 21 percent, and poverty in 2015 2011–12 and 2014–15. The fraction importance of India to the global would be estimated using this of growth from national accounts poverty count and the particularly adjusted welfare vector. Given that is passed through to growth challenging measurement issues. India’s importance for the global in the survey mean implied by this One source of the problem arises poverty rate, and the availability of procedure is 55.9 percent for urban from the fact that only a subset a newer survey (albeit without a India and 73.3 percent for rural of the 2014–15 survey data was full consumption aggregate), it was India. Earlier projections had used a released by the government. felt that this extrapolation method pass-through of 57 percent (for both There are two key issues, the first needed to be cross-validated. urban and rural areas), which was of which is linked to how survey For this reason, the 2015 based on the observed historical data from 2011–12 and 2014–15 poverty estimate for India is based relationship between the survey are used to estimate poverty in on survey-to-survey imputation and national accounts growth India for 2015. The second issue method to estimate the growth rates (Jolliffe et al. 2015, chapter 1, is linked to a change in how India rate in HFCE. The method uses the footnote 14; Ravallion 2003). measures consumption, which 2014–15 National Sample Survey The new method used for India is the foundation of the poverty (NSS) that collected consumption marks the first time the World estimate. information on only a small subset Bank is using inputs from survey- of items but included questions to-survey imputation methods. 2015 poverty estimates for India: on several correlates of household Thus, there can be a variation in the Imputing consumption consumption like household size, poverty estimate obtained from the The usual methodology for lining age composition of the household, new method and the conventional up countries to the reference year caste status, and labor market HFCE-based method. The 2015 (for this report, 2015) is based indicators. In the first step, a model extreme poverty rate for India with on two assumptions: the survey of the relationship between per the imputation-based growth rate is mean grows at the same rate as capita household consumption 2.5 percentage points higher than HFCE or GDP per capita, and there and household characteristics is with the HFCE growth rate (13.4 is no change in the distribution of developed using the NSS data percent versus 10.9 percent). consumption. These assumptions from 2004–5, 2009–10, and In the coming years, when may be reasonable when adjusting 2011–12. These surveys have the countries do not have surveys with over a short period of time, but full consumption questions as well full consumption modules, but have they become problematic as the as the variables used in the model. other smaller surveys with partial distance between the survey In the second step, the estimated coverage, similar methods may be year and the lineup year increases relationship is imposed on the applied to minimize reliance on the (Jolliffe et al. 2015). 2014–15 data to predict household two assumptions implicit in the The latest survey with official consumption and poverty status. HFCE approach. Household surveys poverty estimates for India was See Newhouse and Vyas (2018) with full consumption modules conducted in 2011–12, so a 2015 for more details on the modeling are undoubtedly the preferred lineup would imply adjusting exercise. approach, and only in exceptional the survey forward four years. PovcalNet uses the poverty rates cases will the imputation approach With an HFCE growth rate of 21 at US$1.90 estimated by Newhouse be relied upon. percent in India from 2011–12 to and Vyas (2018) (10.0 percent for The new imputation approach 2015, the welfare aggregate for all urban and 16.8 percent for rural implies that the poverty estimate (continued) With the cautions in mind that consump- Nigeria is now the country with the most tion in 2015 for both India and Nigeria is poor people in the world (figure 1.9). When based on projections, not direct enumera- examining a scenario where the consumption tions of consumption from recent household measure for India is based on the new ques- surveys, the nowcast for 2018 suggests that tionnaire rather than the old one, the esti- 32 POVERTY AND SHARED PROSPERITY 2018 BOX 1.3 India: Issues with the 2015 Poverty Estimate and 2030 Forecasts (continued) for India in 2013 needs to also method under which questions on The choice of method be updated. It has been revised household expenditure data for all can significantly affect total from 16.5 percent to 17.8 percent. items were asked for the previous household consumption and The new estimate is based on an 30-day period. After a series of poverty estimates. The official average of the estimate from the experiments in the “thin” survey 2004–05 poverty rate for India 2011–12 survey and the 2014–15 rounds from 1994–95 to 1998, the with the URP-based consumption survey, where, prior to averaging, Mixed Reference Period (MRP) data was 27.5 percent. The the estimates have been lined up method was introduced in the corresponding figure for the MRP- to 2013 using the HFCE-based 1999–2000 survey round in which based consumption data was 21.8 approach described above. This expenditure on food, pan, and percent (Government of India lineup is based on a shorter time tobacco was collected using 7-day 2007). These changes did not, period where the two assumptions and 30-day recall periods, and the however, affect the estimates are less problematic. expenditure data for five nonfood of extreme poverty because the items—clothing, footwear, durable World Bank continued to use Changes in how consumption data goods, education expenses, and the URP-based aggregate for are collected: Questionnaire design institutional medical expenses— international poverty monitoring Recall period affects reported were collected using a 365-day to maintain comparability with consumption through two main recall period (Deaton and Kozel historical estimates. The poverty channels: memory decay and 2005). estimates and forecasts for India telescoping. A longer recall With the 2011–12 round of presented here, based on MMRP period is better at encompassing the NSS, the Modified Mixed (figure 1.9), similarly indicate a expenditure on infrequently Reference Period (MMRP) was significant decline in the number of purchased items, but it can introduced where the recall period poor people. An important caveat, lead to underreporting if was set at 7 days for perishable however, is that the difference respondents forget about the items, 365 days for the five low- in the count of extreme poor as past purchases. Despite lower frequency items, and 30 days for measured by URP and MMRP average consumption, measured the remaining items (Government dissipates with economic growth. poverty might be lower under of India, Planning Commission In the most recent “thick” round the longer recall period because 2014). For the sake of comparability of the NSS Consumer Expenditure it captures the purchases of low- over time, the World Bank global Survey, India has phased out frequency items of households in poverty count has been based on the URP as well as the MRP the lower parts of the distribution. consumption measures derived questions, which means extreme Short recall periods can mitigate from the URP instrument. With poverty can no longer be tracked underreporting but can lead to the next NSS Consumption and using the URP-aggregate. The telescoping, where respondents Expenditure Survey, India is no next update of global poverty will mistakenly report the consumption longer enumerating consumption likely show a sizeable drop in the that took place outside of the with the URP. This means that the extreme poverty, both because of reference period. global poverty count produced by economic growth and because of Until 1993–94, the consumption the World Bank will soon no longer India’s switch to the MMRP-based data in India were collected using be based on the URP for India and consumption aggregate. the Uniform Reference Period (URP) a switch to the MMRP will occur. mates indicate that Nigeria overtook India in Drilling down: Africa and fragile 2015 as the country with the most poor peo- and conflict-affected situations ple in the world. These projections are based on old surveys and strong assumptions, but, In 2002, Sub-Saharan Africa was home to less if the historically observed patterns in India than a quarter of the world’s extreme poor, and Nigeria continue, Nigeria either already whereas, in 2015, more extreme poor lived is or soon will be the country with the most in the region (413 million) than everywhere people living in extreme poverty. else in the world combined. If this trend con- ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 33 FIGURE 1.10 Household Size and Dependency Ratio in Sub-Saharan Africa a. Household size b. Dependency ratio 8 160 7 140 6 120 5 100 4 80 3 60 2 40 1 20 0 0 Nonpoor households Poor households Nonpoor households Poor households Circa 2004 Circa 2011 Source: World Bank Africa Poverty database. Note: The median years for the base period and the terminal period are 2004 and 2011, respectively. Dependency ratio is the ratio of dependents (people younger than 15 or older than 64) to the working-age population (ages 15–64). tinues as the forecasts suggest, extreme pov- the region, the fast rate of population growth erty will soon become a predominantly Afri- has led to the increase in the total popula- can phenomenon. An important first step in tion of poor people in Sub-Saharan Africa. tackling poverty in the region is to better un- These demographic features of the region derstand the factors associated with poverty will continue to pose a challenge for poverty in Sub-Saharan Africa. reduction, a point that was anticipated by the One such factor is the demographic struc- first World Development Report on poverty ture of the household. In many parts of (World Bank 1990). the world, the poor generally live in larger A second contributing factor for the slow households and have more economically decline in extreme poverty in Sub-Saharan dependent members per working-age adult Africa is that growth in this region has been (Castaneda et al. 2016). In many regions of less effective in reaching the poor than growth the world, the ratio of dependent household in other regions. One indicator of this is the members to working-age adults is declining. region’s low growth elasticity of poverty. For However, this is not the case in Sub-Saharan every percentage increase in GDP per capita, Africa. Household surveys from the region poverty in a typical non-African developing show no appreciable decrease in average country falls by 2 percent, whereas in a typ- household size or in the dependency ratio ical African country it falls by only 0.7 per- over the 2000s (figure 1.10). cent (Christiaensen, Chuhan-Pole, and Sanoh The good news of a declining under-5 2013). There is a caveat to the elasticity com- mortality rate in Sub-Saharan Africa, and parison—the level of poverty is much higher elsewhere in the world (figure 1.11, panel a), in Sub-Saharan Africa so a smaller percentage has combined with a relatively small drop in change in a higher level can still be a signif- the total fertility rate to keep Sub-Saharan icant reduction in poverty—but the general Africa’s population growing at a higher rate point is that growth in Sub-Saharan Africa than that of every other region in the world has been less effective in reducing poverty (figure 1.11, panels b and c) (Canning, Raja, than elsewhere. Some of the leading explana- and Yazbeck 2015; Groth and May 2017). Al- tions for this ineffectiveness of growth in re- though poverty rates have declined slightly in ducing poverty include the overall high levels 34 POVERTY AND SHARED PROSPERITY 2018 FIGURE 1.11 Under-5 Mortality, Fertility, and Population Growth in Sub-Saharan Africa a. Under-five mortality rate b. Total fertility rate 200 7 180 6 160 Deaths (U5) per 1,000 births 140 5 Births per woman 120 4 100 3 80 60 2 40 1 20 0 0 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 c. Population growth rate 4 3 Sub-Saharan Africa Annual growth (%) East Asia and Pacific Latin America and the Caribbean 2 South Asia Europe and Central Asia 1 Middle East and North Africa World 0 1990 1995 2000 2005 2010 2015 Source: World Development Indicators (http://databank.worldbank.org/data/source/world-development-indicators). of inequality in several countries and growth After falling sharply between 2005 and 2011, that is predominantly in capital-intensive sec- the poverty rate has since gone up: in 2015, tors like natural resource extraction. the poverty rate in 35 economies in FCS was As the global poverty rate declines, there 35.9 percent, up from a low of 34.4 percent in is concern that extreme poverty will become 2011. The share of the global poor living in a phenomenon increasingly associated with FCS has risen steadily since 2010, culminat- institutional fragility and conflict. It is also ing in 23 percent of all poor people in 2015 the case that most people (54 percent) liv- (figure 1.12, panel b).12 ing in fragile and conflict-affected situations This rise has not come about because pop- (FCS) in 2015 are in Sub-Saharan Africa.9 ulous countries have joined the ranks of frag- To see if there is evidence that poverty is al- ile situations, except for a small drop between ready beginning to pool in FCS, trends in the 2005 and 2008, the share of the world popu- poverty rate and the share of the global poor lation living in fragile situations has stayed living in fragile situations are analyzed.10 level through much of the period (figure 1.12, Figure 1.12, panel a, shows the poverty rate panel c). Were more countries to become in economies in FCS from 2005 to 2015.11 fragile, the goal of rooting out global poverty ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 35 FIGURE 1.12 Concentration of Extreme Poverty in Fragile and Conflict-Affected Situations a. Poverty rate b. Share of the global poor 100 50 80 40 Share of poor (%) Poverty rate (%) 60 81.1 78.5 76.8 30 86.0 84.8 52.4 20 41.6 40 34.4 34.8 35.9 10 20 18.9 21.5 23.2 14.0 15.2 0 0 2005 2008 2011 2013 2015 2005 2008 2011 2013 2015 c. Share of the total population 100 80 Share of population (%) 60 92.4 93.9 93.9 93.0 93.4 40 20 7.6 6.1 6.1 7.0 6.6 0 2005 2008 2011 2013 2015 Fragile and conflict-affected situations Not fragile and conflict-affected Source: PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet/. World Bank, Washington, DC. Harmonized List of Fragile Situations (http://www.worldbank.org/en/topic/fragilityconflictviolence/brief/harmonized-list-of-fragile-situations) Note: See appendix A for more details on the list of countries in fragile and conflict-affected situations. would only get more challenging. Panels b and (World Bank 2017a ).13 For illustration, figure c together also reveal the “poverty burden” 1.13 plots the performance of countries on a borne by the economies in FCS: they have 6.6 few fundamental indicators of economic and percent of the global population but 23 per- institutional quality against poverty rates. In cent of the poor, which is 3.5 times higher than general, there is a negative correlation be- would be expected if poverty were equally tween poverty rates and the strength of in- prevalent everywhere. Despite this significant stitutions; countries with high poverty rates pooling of poverty, these estimates almost cer- have lower financial penetration (panel a; tainly undercount the extent of poverty in FCS correlation = −0.59), poorer business climate for several reasons, including technical mea- (panel b; correlation = −0.62), weaker rule of surement reasons such as missing data on ref- law (panel c; correlation = −0.46), and higher ugees and displaced persons (see appendix A). perceived corruption (panel d; correlation = Fragility comprises many elements, and −0.43). Notably, fragile situations (marked in countries that are in fragile situations are red) are often among the poorest performers characterized by policy failures and institu- in these areas, falling in the bottom quintile tional weaknesses in multiple dimensions of the distribution. They must make signifi- 36 POVERTY AND SHARED PROSPERITY 2018 FIGURE 1.13 Fragile Situations Perform Poorly in Multiple Constituent Components of Fragility a. Financial Inclusion index, 2014 b. Doing Business score, 2015 80 80 Poverty rate (%), 2015 Poverty rate (%), 2015 60 60 40 40 20 20 0 0 0 20 40 60 80 100 30 40 50 60 70 80 90 c. Rule of Law score, 2015 d. Control of Corruption score, 2015 80 80 Poverty rate (%), 2015 Poverty rate (%), 2015 60 60 40 40 20 20 0 0 −2 −1 0 1 2 −2 −1 0 1 2 3 Fragile and conflict-affected situations Not fragile and conflict-affected 20th percentile Source: PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet; World Bank, Washington, DC. The Global Findex Database (https://globalfindex.worldbank .org/); Doing Business (http://www.doingbusiness.org/); and Worldwide Governance Indicators (http://info.worldbank.org/governance/wgi/index.aspx#home). Note: Financial Inclusion Index is the proportion of individuals with a bank account in 2014. Doing Business indicator is the “Distance to Frontier” score for 2014. The 2015 Rule of Law Indicator and the Control of Corruption Indicator are drawn from the Worldwide Governance Indicators (WGI) project. These indicators are used as guideposts to set the World Bank’s CPIA ratings (http://pubdocs.worldbank.org/en/600961531149299007/CPIA-Criteria-2017.pdf). cant progress on several constituent compo- opment programs in proper locations, and nents of fragility simultaneously to relieve the target the beneficiary population accurately, constraints to economic growth and poverty it is critically important to know where the reduction. poor live, what conditions they live in, and how they earn a living. This description of the Socioeconomic and poor is frequently done within each country, informing country dialogue on how best to demographic profile of improve the well-being of the less well off in global poverty society. But researchers and policy makers To devise an appropriate poverty reduction can also learn a great deal by examining a strategy, it is not enough to merely know how global profile of the poor. This examination many people are poor. In order to choose the can aid the international development com- right poverty reduction policies, place devel- munity to better target poverty alleviation ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 37 programs as well as areas of well-being re- A stronger correlation is observed between quiring emphasis. poverty and educational achievement. Of the The profile of the poor is based on har- adults with no education, more than a fifth monized household surveys from 91 coun- are in poverty. There is a premium to having tries in the Global Monitoring Database had even some schooling: the poverty rate (GMD).14 It is an update of a previous pro- more than halves for adults with incomplete file that was based on the harmonized data primary education, whereas poverty is all for 89 countries for 2013.15 The sample used but absent among adults with some tertiary for this profiling covers about 76 percent of education. Given that intergenerational mo- the world’s population and 86 percent of the bility in education is low in low- and middle- extreme poor in 2015. The data demands for income countries, there is a danger that this the global poverty profile are more stringent pattern will carry over to the next generation than that for the global poverty update. It re- as well (Narayan et al. 2018). Increasing labor quires harmonization of additional variables productivity in agriculture and improving like age, gender, education, and sector of human capital to facilitate labor migration work from diverse household surveys, which into high-productivity sectors and locations is why the poverty profile is available for only are key to poverty reduction. a subset of countries and for an earlier date. The fertility rate is usually higher among Globally, extreme poverty continues to the poor. As a result, poor households are be disproportionately and overwhelmingly usually large and have many children. There rural. The poverty rate in rural areas (17.2 are on average 7.7 members and 3.5 children percent) is more than three times as high as under the age of 14 in the world’s extremely that in urban areas (5.3 percent); with ap- poor households. Just under a fifth of chil- proximately 54 percent of the world’s popu- dren under the age of 14 live in poverty, and, lation, rural areas account for 79 percent of despite representing only about a quarter of the total poor. Rural poverty is strongly as- the population, they make up more than two- sociated with the sector of employment; the fifths of the absolute poor (table 1.1). There extreme poverty rate is higher among agri- is suggestion of increasing concentration of cultural workers, and they constitute almost poverty among children, with children under two-thirds of the extreme poor. But nonfarm the age of 14 constituting a marginally larger employment does not guarantee an escape share of the poor in 2015 (45.7 percent) than from poverty; a significant share of poor in 2013 (44.2 percent).16 Children who grow adults in both urban and rural areas is em- up in poverty acquire less human capital be- ployed in nonagricultural sectors. cause of inadequate or low-quality schooling and undernutrition. This makes childhood poverty especially pernicious because it per- TABLE 1.1 Age and Gender Profile of the Extreme Poor, 2015 petuates intergenerational poverty. Share of the Share of the The current state of data limits the abil- Poverty rate (%) poor (%) population (%) ity to understand the prevalence of poverty Age group by gender and age. Household surveys collect 0–14 19.3 45.7 27.4 information on total household consump- 15–24 11.7 16.9 16.6 tion. They typically do not differentiate how 25–34 9.4 13.0 15.9 35–44 8.7 10.1 13.4 resources are allocated within a household. 45–54 6.4 6.4 11.6 For analytical purposes, it is assumed that all 55–64 5.9 4.2 8.2 household members have equal needs and 65 and up 5.9 3.6 6.9 that total consumption is distributed equally Total 11.5 100.0 100.0 within a household. The equal distribution assumption distorts the picture of poverty Gender Male 11.7 50.3 49.6 if there is inequality within households. For Female 11.4 49.7 50.4 example, the profile shows that males and females are equally likely to be in poverty. Source: Estimates based on the harmonized household surveys in 91 countries, circa 2015, GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, Poverty Chapter 5 takes up this issue in detail and and Equity Global Practice, World Bank, Washington, DC. proposes methodological changes in house- 38 POVERTY AND SHARED PROSPERITY 2018 hold surveys to capture the intrahousehold TABLE 1.2 Education and Access to Services among the Extreme distribution of consumption. In the mean- Poor and Nonpoor Households time, differences in poverty by gender and Share of households (%) age will be informed by assuming someone Poor Nonpoor is poor if he or she lives in a poor household. No adult member has completed primary education 53.1 12.2 The poor lack not just income. Poverty At least one school-age child (up to grade 8) is out of school 22.8 3.4 also materializes as low educational attain- Household does not have access to limited-standard source of 37.0 8.6 ment, poor health and nutrition outcomes, drinking water exposure to physical insecurity and natu- Household does not have access to limited-standard sanitation 66.8 16.3 ral hazards, and substandard living condi- facilities tions. Globally, a large share of extreme poor Household does not have access to electricity 67.8 7.1 households has no adult member with pri- Source: Estimates based on the harmonized household surveys in 119 countries, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, mary schooling, and in many households at Poverty and Equity Global Practice, World Bank, Washington, DC. least one child of school age (up to grade 8) is out of school (table 1.2). The poor are also poorly served in essential services like accept- able standards of drinking water, adequate cline by a percentage point a year the global sanitation facilities, and electricity (table 1.2). poverty rate declined by half a percentage Low levels of human capital and poor point a year, the world would still meet the access to basic services undermine labor 3 percent target. Despite this scope for the productivity of the poor, often their most pace to significantly slacken, all forecasts for important source of income, trapping them 2030 considered in this chapter that are based in income poverty. Increasingly, however, on countries or regions growing in line with poverty is understood as encompassing more their recent historic performance indicate than just income. Sufficient education, good that the world will fall well short of the target. health, a safe living environment, and pro- Part of the explanation for the deceleration vision of basic services are desired for their in poverty reduction is that not all regions intrinsic value, beyond their instrumental have shared in the global economic growth value in raising income. Chapter 4 takes a of the last quarter century, nor have all re- panoramic view of poverty as the inability gions succeeded in ensuring that the poor to reach a sufficiency threshold in monetary have fully shared in the benefits of growth. terms as well as in a wide range of nonmon- Sub-Saharan Africa has had inadequate levels etary dimensions that directly affect an indi- of growth and inadequate poverty reduction vidual’s well-being. from growth, and this has resulted in the in- crease of the total number of people in this region living in extreme poverty. In 1990, 278 Conclusions million people in Sub-Saharan Africa lived in Between 1990 and 2015, the world made extreme poverty; by 2015, this increased to an steady progress toward the target of reduc- estimated 413 million people. Forecasts based ing the number of people living in extreme on historic average growth rates predict that poverty to less than 3 percent globally by the number of people living in extreme pov- 2030. The extreme poverty rate dropped on erty in the region will remain above 400 mil- average 1 percentage point every year, falling lion in 2030. from 35.9 percent in 1990 to 10.0 percent in A related reason why poverty reduction 2015. As a result of this decline, there were is slowing is that previously progress rested well over a billion fewer people living in pov- heavily on the success of the countries of erty despite a global population that had in- East Asia and Pacific and South Asia in re- creased by more than 2 billion people during ducing the total number of people living in this period. With the estimated extreme pov- extreme poverty. The countries of East Asia erty rate at 10 percent in 2015, the target of and Pacific have experienced remarkable 3 percent by 2030 could be attained even if reductions in extreme poverty. In 1990, the rate of poverty reduction was cut in half. there were 987 million people living in ex- That is to say, if instead of continuing to de- treme poverty in this region, and this num- ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 39 ber dropped to 47 million people by 2015. erage poverty rates in all regions of the world On average, each year ended with about 38 except for Africa are below 2 percent; how- million fewer people living in extreme pov- ever, the forecasted average extreme poverty erty in the East Asia and Pacific region. But rate for Sub-Saharan Africa is above 25 per- now, with the prevalence of extreme poverty cent. Even in a forecast based on an assumed below 3 percent, and the number of poor in real growth rate of 8 percent, the 3 percent this region contributing only about 6 percent global target is met but extreme poverty in to the total population of poor, there are few Sub-Saharan Africa is in double digits (13.4 remaining gains to be made in this region in percent). terms of having a significant effect in reduc- This sort of outcome, where extreme pov- ing the global poverty rate. erty is eliminated throughout the world ex- Although there are still many extreme cept in one region where it is in double dig- poor in South Asia, a similar story will most its certainly does not portray a picture of a likely soon occur there, and this is good news. world free of poverty. This, then, is one of the In 1990, more than a half billion people in key messages of this report: it is time to go South Asia lived in extreme poverty; by 2015, beyond the focus on bringing down the aver- this dropped to 216 million people. A rela- age global poverty rate to 3 percent to reach tively large portion of the extreme poor still the goals of eradicating extreme poverty and live in South Asia, but the forecasts indicate ensure that all share in the benefits of eco- (combined with the anticipated change in nomic development. how consumption is measured in India) that A key point of this report is that the view the total number of poor there is rapidly de- of poverty needs to be broadened. Now that clining. The success in reducing extreme pov- the extreme poverty rate is less than 3 percent erty in many regions of the world means that in half the countries of the world and is be- the majority of the remaining gains in pov- coming increasingly concentrated, finishing erty reduction must come from the countries the job will require constructing a more de- of Sub-Saharan Africa. tailed and complete picture of what is meant The unevenness of the progress in global by a world free of poverty. To do this, the next poverty reduction brings into focus the rel- chapters in this report go beyond extreme ative strengths and weaknesses in how prog- income poverty to start the process of moni- ress toward the goal of a world free of poverty toring poverty in all its forms. New measures is monitored. In various forecasts assuming introduced in this report allow one to better that countries continue to grow in line with monitor poverty in all countries, in multiple their recent performance (or with the aver- aspects of life, and for all individuals in every age historic growth rate of their region), av- household. 40 POVERTY AND SHARED PROSPERITY 2018 Annex 1A Historical global and regional poverty estimates This annex contains tables of historical pov- FIGURE 1A.1 Global Total Consumption Gap erty rates at the global, regional, and coun- of the Extreme Poor, 1990–2015 try levels. Poverty rates do not speak to the 1,400 1,276 distribution of consumption (or income) among the poor, meaning that the poor may 1,200 fare worse in certain countries than in others. Million of US$ (2011 PPP) 1,000 For this reason, the poverty rates are comple- mented with other measures of poverty: the 800 poverty gap, the poverty gap divided by the 600 poverty rate, and the squared poverty gap (Foster, Greer, and Thorbecke 1984). 400 433 The poverty gap measures the average dis- 200 tance to the poverty line, where people above the poverty line are given a distance of zero. 0 This measure reflects both the share of poor 1990 1995 2000 2005 2010 2015 and the average daily consumption of the Source: PovcalNet (online analysis tool), World Bank, Washington, poor, but expressed as the average shortfall DC, http://iresearch.worldbank.org/PovcalNet/. among the entire population. If two countries Note: PPP = purchasing power parity. have the same poverty rate, but the poor in the first country have a daily consumption Although both the poverty gap and the pov- of US$1.50, whereas in the other they have a erty gap divided by the poverty rate are sen- daily consumption of US$1.80, then the pov- sitive to the average level of consumption (or erty gap will indicate a higher depth of poverty income) among the poor, they do not account in the first country. When the poverty gap is for inequality among the poor. The squared divided by the poverty rate, the resulting num- poverty gap—which is the average squared dis- ber shows the average distance to the poverty tance to the poverty line, where people above line, or average consumption shortfall among the poverty line have a distance of zero—is the poor. If the average consumption shortfall sensitive to inequality among the poor. Sup- of the poor is 0.25, then poor individuals on pose that two countries have the same poverty average consume 25 percent less than the value rate, and the poor in both countries on average of the IPL, or US$1.43 per day ( (1-0.25)*IPL ). consume US$1.50 daily. Suppose further that, Since 1990, both of these complementary in one of the countries, all the poor consume measures of poverty have improved. The total US$1.50, whereas the other country has many consumption gap of the poor (the sum of all people consuming much less. The squared consumption shortfalls of the poor) shrank poverty gap measures this latter country, with from US$1,276 million (2011 PPP) in 1990 greater inequality among the poor, as having to US$433 million (2011 PPP) in 2015 (figure more severe form of poverty. 1A.1). This improvement reflects both that An issue to keep in mind with these com- the share of people living in extreme poverty plementary poverty measures is that they are has decreased and that the average income of more sensitive to whether poverty is mea- the poor has increased over this time interval. sured with consumption or income. Whereas ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 41 poverty estimates based on income can be using income is faring poorly in these com- zero—and even negative—in a given period plementary measures, and it makes it difficult because of negative income shocks, they to compare the depth of poverty across coun- rarely get close to zero when consumption is tries using consumption and income (see ap- used. This makes it more likely that a country pendix A for more discussion on this). TABLE 1A.1 Global and Regional Extreme Poverty, 1990–2015 a. Global extreme poverty, 1990–2015 Squared Year Poverty rate (%) Poverty gap (%) poverty gap Poor (millions) Population (millions) 1990 35.9 12.7 6.1 1,894.8 5,284.9 1993 33.9 11.9 5.8 1,877.5 5,542.9 1996 29.4 9.8 4.7 1,703.2 5,792.6 1999 28.6 9.5 4.5 1,728.6 6,038.1 2002 25.6 8.3 3.9 1,609.9 6,276.8 2005 20.7 6.3 2.9 1,352.2 6,517.0 2008 18.1 5.4 2.4 1,223.2 6,763.7 2011 13.7 4.1 1.9 963.0 7,012.8 2013 11.2 3.4 1.6 804.2 7,182.9 2015 10.0 3.1 1.5 735.9 7,355.2 b. Extreme poverty rates, by region, 1990–2015 Percent Region 1990 1993 1996 1999 2002 2005 2008 2011 2013 2015 East Asia and Pacific 61.6 54.0 41.1 38.8 29.9 19.1 15.1 8.6 3.6 2.3 Europe and Central Asia 2.9 a 5.0 7.2 7.8 5.9 4.9 2.8 2.1 1.6 1.5 Latin America and the Caribbean 14.2 13.2 13.8 13.6 11.8 9.9 6.9 5.6 4.6 4.1 Middle East and North Africa 6.2 6.7 5.8 3.8 3.2 3.0 2.7 2.7 2.6 5.0 South Asia 47.3 44.9 40.3 39.3 a 38.6 33.7 29.5 19.8 16.2 12.4 a Sub-Saharan Africa 54.3 58.9 58.2 57.7 56.4 50.7 47.8 45.1 42.5 41.1 Sum of regions 43.1 40.6 35.1 34.0 30.4 24.5 21.3 16.1 13.1 11.6 Rest of the world 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.7 World 35.9 33.9 29.4 28.6 25.6 20.7 18.1 13.7 11.2 10.0 c. Number of extreme poor, by region, 1990–2015 Millions Region 1990 1993 1996 1999 2002 2005 2008 2011 2013 2015 East Asia and Pacific 987.1 902.0 712.9 695.9 552.5 361.6 292.8 169.6 73.1 47.2 Europe and Central Asia 13.3 a 23.4 33.8 36.7 27.6 22.9 13.3 9.8 7.7 7.1 Latin America and the Caribbean 62.6 61.3 67.7 69.7 63.2 54.9 39.9 33.8 28.0 25.9 Middle East and North Africa 14.2 16.6 15.3 10.6 9.4 9.4 8.8 9.2 9.5 18.6 South Asia 535.9 542.1 518.0 534.4 a 554.3 510.4 467.0 328.0 274.5 216.4 a Sub-Saharan Africa 277.5 327.3 350.7 376.1 398.0 387.7 396.4 406.4 405.1 413.3 Sum of regions 1,890.5 1,872.7 1,698.3 1,723.5 1,605.0 1,346.9 1,218.1 956.9 797.8 728.5 Rest of the world 4.3 4.9 4.9 5.0 4.9 5.3 5.1 6.2 6.4 7.3 World 1,894.8 1,877.5 1,703.2 1,728.6 1,609.9 1,352.2 1,223.2 963.0 804.2 735.9 Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), World Bank. Note: Sum of regions was previously referred to as developing world. a. This estimate is based on a regional population coverage of less than 40 percent. The criterion for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. 42 POVERTY AND SHARED PROSPERITY 2018 TABLE 1A.2 Extreme Poverty, by Economy, 2015 Number of poor Poverty rate Poverty gap Poverty gap/rate Economy Survey year(s) (millions) (%) (%) (%) Albania 2012 0.0 0.9 0.2 20.0 Algeria 2011.17 0.1 0.4 0.1 37.1 Angola 2008.5 7.8 27.9 8.7 31.2 Argentina 2014 and 2016 0.3 0.6 0.3 45.3 Armenia 2015 0.1 1.9 0.4 18.8 Australia 2010 0.1 0.5 0.3 62.0 Austria 2015 0.1 0.8 0.5 72.0 Azerbaijan 2005 0.0 0.0 0.0 Bangladesh 2010 and 2016 24.4 15.2 2.8 18.1 Belarus 2015 0.0 0.0 0.0 Belgium 2015 0.0 0.0 0.0 Belize 1999 0.0 13.9 6.0 43.1 Benin 2015 5.2 49.6 22.4 45.1 Bhutan 2012 and 2017 0.0 1.7 0.3 16.3 Bolivia 2015 0.7 6.4 2.8 44.3 Bosnia and Herzegovina 2015 0.0 0.2 0.1 30.0 Botswana 2009.25 0.3 12.8 3.7 29.3 Brazil 2015 6.9 3.4 1.2 34.5 Bulgaria 2014 0.1 1.2 0.5 36.3 Burkina Faso 2014 7.8 42.8 10.8 25.2 Burundi 2013.5 7.6 74.7 32.9 44.0 Cabo Verde 2007.33 0.0 7.2 1.7 23.0 Cameroon 2014 5.2 22.8 7.1 31.3 Canada 2013 0.2 0.5 0.2 32.0 Central African Republic 2008 3.5 77.7 44.0 56.6 Chad 2011 4.8 34.1 13.2 38.7 Chile 2015 0.2 1.3 0.8 58.5 China 2015 10.0 0.7 0.2 21.9 Colombia 2015 2.2 4.5 1.7 38.2 Comoros 2013.5 0.1 18.2 6.5 35.7 Congo, Dem. Rep. 2012.4 55.1 72.3 34.6 47.9 Congo, Rep. 2011 1.7 34.9 13.5 38.7 Costa Rica 2015 0.1 1.5 0.6 38.8 Côte d’Ivoire 2015 6.5 28.2 9.1 32.4 Croatia 2015 0.0 0.8 0.4 46.7 Cyprus 2015 0.0 0.0 0.0 Czech Republic 2015 0.0 0.0 0.0 Denmark 2015 0.0 0.2 0.1 57.1 Djibouti 2013 0.2 18.6 6.3 33.9 Dominican Republic 2015 0.2 1.9 0.5 25.5 Ecuador 2015 0.6 3.4 1.2 35.8 Egypt, Arab Rep. 2015 1.3 1.4 0.2 11.9 El Salvador 2015 0.1 1.9 0.4 20.7 Estonia 2015 0.0 0.5 0.4 78.7 Eswatini 2009.25 0.5 39.0 14.8 37.9 Ethiopia 2010.5 and 2015.5 27.0 27.0 7.7 28.6 Fiji 2013.24 0.0 1.0 0.2 16.7 Finland 2015 0.0 0.0 0.0 France 2015 0.0 0.0 0.0 Gabon 2005 and 2017 0.1 4.1 1.0 24.1 Gambia, The 2010.08 and 2015.31 0.2 11.1 2.5 22.9 Georgia 2015 0.1 4.0 1.0 24.7 Germany 2015 0.0 0.0 0.0 Ghana 2012.8 3.0 10.9 3.1 28.7 Greece 2015 0.2 1.5 0.8 52.7 Guatemala 2014 1.3 7.9 2.3 29.3 Guinea 2012 4.0 33.0 9.4 28.4 Guinea-Bissau 2010 1.2 65.3 29.4 44.9 (continued) ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 43 TABLE 1A.2 Extreme Poverty, by Economy, 2015 (continued) Number of poor Poverty rate Poverty gap Poverty gap/rate Economy Survey year(s) (millions) (%) (%) (%) Guyana 1998 0.1 6.5 1.9 28.9 Haiti 2012 2.5 23.7 7.6 32.1 Honduras 2015 1.4 16.2 5.6 34.9 Hungary 2015 0.0 0.5 0.3 61.2 Iceland 2014 0.0 0.0 0.0 India* 2011.5 175.7 13.4 2.4 17.7 Indonesia 2015 18.5 7.2 1.2 16.6 Iran, Islamic Rep. 2014 0.3 0.4 0.1 16.2 Iraq 2012 0.8 2.2 0.3 14.8 Ireland 2015 0.0 0.2 0.2 95.7 Israel 2012 0.0 0.5 0.3 54.2 Italy 2015 1.2 2.0 1.4 70.5 Jamaica 2004 0.1 1.8 0.4 22.8 Japan 2008 0.3 0.2 0.2 68.2 Jordan 2010.24 0.0 0.2 0.0 16.7 Kazakhstan 2015 0.0 0.0 0.0 Kenya 2005.38 and 2015.67 17.6 37.3 11.9 31.9 Kiribati 2006 0.0 11.8 3.0 25.4 Korea, Rep. 2012 0.1 0.3 0.1 44.0 Kosovo 2015 0.0 0.4 0.1 20.0 Kyrgyz Republic 2015 0.2 2.5 0.5 18.5 Lao PDR 2012.25 0.9 14.0 2.9 20.7 Latvia 2015 0.0 0.7 0.4 47.3 Lebanon 2011.77 0.0 0.0 0.0 Lesotho 2010 1.2 54.8 28.1 51.3 Liberia 2014 1.8 40.2 12.3 30.7 Lithuania 2015 0.0 0.8 0.5 72.0 Luxembourg 2015 0.0 0.2 0.2 95.0 Macedonia, FYR 2014 0.1 5.0 2.4 47.2 Madagascar 2012 18.8 77.5 38.8 50.1 Malawi 2010.23 12.2 69.6 31.7 45.6 Malaysia 2013 and 2015.33 0.0 0.0 0.0 Maldives 2009.5 0.0 4.1 0.8 20.3 Mali 2009.89 8.3 47.8 14.5 30.4 Malta 2015 0.0 0.0 0.0 Mauritania 2014 0.3 6.2 1.5 23.9 Mauritius 2012 0.0 0.4 0.1 17.5 Mexico 2014 and 2016 4.2 3.3 0.8 24.4 Micronesia, Fed. Sts. 2013 0.0 15.4 5.5 35.9 Moldova 2015 0.0 0.0 0.0 Mongolia 2014 and 2016 0.0 0.2 0.0 10.0 Montenegro 2014 0.0 0.0 0.0 Morocco 2013.5 0.3 0.9 0.2 17.4 Mozambique 2014.44 17.4 62.2 27.3 43.8 Myanmar 2015 3.3 6.4 1.5 23.1 Namibia 2009.54 and 2015.27 0.3 13.4 4.5 33.8 Nepal 2010.17 2.0 7.0 1.4 19.8 Netherlands 2015 0.0 0.0 0.0 Nicaragua 2014 0.2 2.9 0.6 22.3 Niger 2014 8.9 44.5 13.5 30.2 Nigeria 2009.83 86.5 47.8 18.6 38.9 Norway 2015 0.0 0.2 0.0 16.7 Pakistan 2013.5 and 2015.5 9.9 5.2 0.7 13.2 Panama 2015 0.1 2.0 0.5 26.8 Papua New Guinea 2009.67 2.3 28.4 10.3 36.3 Paraguay 2015 0.1 1.9 0.4 21.7 Peru 2015 1.1 3.6 1.0 27.3 (continued) 44 POVERTY AND SHARED PROSPERITY 2018 TABLE 1A.2 Extreme Poverty, by Economy, 2015 (continued) Number of poor Poverty rate Poverty gap Poverty gap/rate Economy Survey year(s) (millions) (%) (%) (%) Philippines 2015 8.5 8.3 1.6 18.9 Poland 2015 0.0 0.0 0.0 Portugal 2015 0.1 0.5 0.3 50.0 Romania 2015 1.1 5.7 1.9 33.4 Russian Federation 2015 0.0 0.0 0.0 Rwanda 2013.75 6.0 51.5 17.6 34.2 Samoa 2008 0.0 1.1 0.1 10.5 São Tomé and Príncipe 2010 0.1 26.0 6.2 24.0 Senegal 2011.29 5.3 35.7 11.4 31.9 Serbia 2015 0.0 0.1 0.0 30.0 Seychelles 2013 0.0 1.0 0.4 40.6 Sierra Leone 2011 3.5 48.4 14.8 30.5 Slovak Republic 2015 0.0 0.7 0.3 35.1 Slovenia 2015 0.0 0.0 0.0 Solomon Islands 2013 0.1 24.7 6.7 26.9 South Africa 2014.83 10.4 18.9 6.2 32.8 South Sudan 2009 8.7 73.3 40.0 54.6 Spain 2015 0.5 1.0 0.6 64.6 Sri Lanka 2012.5 and 2016 0.2 0.8 0.1 11.7 St. Lucia 1995 0.1 28.3 9.8 34.6 Sudan 2009 3.0 7.7 2.0 25.8 Suriname 1999 0.1 18.8 14.5 77.0 Sweden 2015 0.0 0.5 0.3 50.0 Switzerland 2015 0.0 0.0 0.0 Syrian Arab Republic 2004 4.0 21.2 4.8 22.4 Tajikistan 2015 0.4 4.8 1.1 21.8 Tanzania 2011.77 21.9 40.7 11.7 28.9 Thailand 2015 0.0 0.0 0.0 33.3 Timor-Leste 2014 0.4 31.2 6.9 22.0 Togo 2015 3.6 49.2 19.9 40.5 Tonga 2009 0.0 1.0 0.2 21.4 Trinidad and Tobago 1992 0.0 0.6 0.2 35.7 Tunisia 2010.41 0.1 0.9 0.2 18.3 Turkey 2015 0.2 0.3 0.1 21.4 Turkmenistan 1998 0.2 2.8 0.4 15.5 Tuvalu 2010 0.0 2.4 0.2 6.8 Uganda 2012.45 and 2016.5 15.8 39.2 12.3 31.2 Ukraine 2015 0.1 0.1 0.0 8.3 United Kingdom 2015 0.1 0.2 0.1 39.1 United States 2013 and 2016 3.7 1.2 1.0 82.8 Uruguay 2015 0.0 0.1 0.0 23.1 Uzbekistan 2003 4.4 14.0 3.8 26.8 Vanuatu 2010 0.0 12.8 3.2 24.7 Venezuela, RB 2006 2.8 8.9 6.6 74.7 Vietnam 2014 and 2016 2.1 2.3 0.4 19.1 West Bank and Gaza 2011 and 2016.75 0.0 0.6 0.1 15.3 Yemen, Rep. 2014 11.0 40.9 12.0 29.3 Zambia 2015 9.3 57.5 29.5 51.3 Zimbabwe 2011 2.5 16.0 3.5 21.5 Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), World Bank. Note: The year column refers to the year of the survey that is used to calculate the 2015 estimate as listed in PovcalNet. Note that for economies that use EU-SILC surveys, the survey year is backdated by one year to align with the reference period for the income data in the survey (for example, the 2016 survey is listed as 2015). If one year is listed, and this year is different from 2015, the poverty estimate from the year of the survey has been extrapolated to 2015. If two years are listed, the 2015 estimates are based on interpolations between these two surveys. For more information on how these interpolations and extrapolations are carried out, see appendix A. The decimal year notation is used when data are collected over two calendar years. The number before the decimal point refers to the first year of data collection, while the numbers after the decimal point show the proportion of data collected in the second year. For example, the Algerian survey (2011.17) was conducted in 2011 and 2012, with 17 percent of the data collected in 2012. Pov. rate is the poverty rate, or the percentage of the population living on less than the IPL (international poverty line). Pov. gap is the average consumption shortfall of the population where the nonpoor have no shortfall (as described above). Pov. gap / pov. rate is the average consumption shortfall of the poor (as described above). * indicates that the 2015 estimate for India is based on an imputation described in box 1.3. ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 45 Annex 1B Validation check of the 2030 poverty projections The poverty projections to 2030 are based FIGURE 1B.1 Projections to 2015 of Global on several critical assumptions regarding Extreme Poverty countries’ future growth rates and the nature of this growth. The global poverty patterns 35 in 2030 may look very different if these as- sumptions are not met. The soundness of 30 the 2030 forecasts can be assessed indirectly Poverty rate (%) by pretending that the poverty levels and 25 growth rates from 2002 to 2015 are unknown and applying the forecast methodology to 20 2002–15. For example, one can use the coun- try-specific and regional growth rates from 15 1992 to 2002 to predict poverty rates from 2002 to 2015. With this approach, the 2015 10 forecasts can be benchmarked against the re- alized poverty levels in 2015. This would help 5 1990 1995 2000 2005 2010 2015 uncover the sensitivity of the assumptions behind the projections and hence give an in- Projection using average regional growth rates from 1992 to 2002 dication of the uncertainty surrounding the Projection using average country growth rates 2030 projections. from 1992 to 2002 Using this approach, the global rate of Projection using 6 percent annual growth rate extreme poverty is predicted to be 13.4 per- Actual trend cent in 2015—well above the actual rate of Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), 10.0 percent (figure 1B.1). This is largely World Bank. Note: The figure assumes 2002 is the latest year of data and because the regional growth rates in Sub- applies the forecasting methods used toward 2030 to obtain pov- Saharan Africa are severely underestimated erty “forecasts” for 2002–15. This can be benchmarked against using historical growth data.17 The regional realized poverty levels, and hence allows for an assessment of the soundness of the 2030 projections. growth rate in GDP per capita in Sub- Saharan Africa from 1992 to 2002 was 0.7 percent, whereas the actual growth in GDP the actual global poverty rate observed in per capita from 2002 to 2015 turned out to 2015, the 2002–15 projections would need to be several percentage points higher. Hence, use annual growth rates in the range of 6 per- the historical growth rates were not a good cent per year. indication of the future growth rates, and the Although this example speaks to the in- projections overestimate the amount of pov- herent uncertainty of making long-term pov- erty in Sub-Saharan Africa in 2015. In other erty projections, even with an annual growth regions, such as East Asia and Pacific and rate across the globe of 6 percent until 2030, South Asia, the projections are very close to the projections still do not predict that the 3 the actual poverty levels in 2015. To match percent target will be met. 46 POVERTY AND SHARED PROSPERITY 2018 Notes 1. The interim target of a poverty rate of 9 per- 0.544 for the Middle East and North Africa, cent was set by the World Bank Group presi- 0.912 for South Asia, 0.748 for Sub-Saharan dent at the 2013 Annual Meetings: http://www Africa, and 0.892 for the rest of the world. .worldbank.org/en/news/speech/2013/10/11 5. GDP rates are used for Sub-Saharan Africa /world-bank-group-president-jim-yong-kim- and for countries without HFCE growth rates. speech-annual-meetings-plenary. The same pass-through rates are applied as in 2. Survey coverage is assessed by considering the nowcast. The average regional growth rate surveys within a two-year window on either is weighted using each country’s population in side of 2015, that is, surveys conducted be- 2015 as the weight. tween 2013 and 2017. By this criterion, two- 6. Projections based on a global growth rate of thirds of the world is covered by a survey for 8 percent and no shared prosperity premium the 2015 poverty update. The coverage would are nearly identical to the 6 percent growth be lower for more recent years. and 2 percentage point premium scenario, 3. The core poverty numbers reported in this and thus also get the global rate below 3 per- chapter are for 2015. These numbers are re- cent by 2030. In general, a mean growth rate ferred to as estimates. References to a nowcast of x percent combined with a shared pros- indicate that the poverty rate is a forecasted perity premium of y percent is nearly identi- estimate up to the current point in time, cal to a growth rate of x + y percent and no which for this report is 2018. Because this re- shared prosperity premium. Projections using lies largely on realized growth rates and pop- a 7 percent global growth rate and no shared ulation figures, it should, in principle, be more prosperity premium, or a 5 percent growth reliable than a forecast. References to forecasts rate and a 2-percentage-point premium, get are used when the prediction is more remote very close to the 3 percent target. in the future, later than the nowcast, typically 7. East Asia and Pacific (6.4 percent), Latin 2030. Forecasts are based on assumed growth America and the Caribbean (3.5 percent), the rates and predictions of population figures and Middle East and North Africa (2.5 percent), are estimated with significantly less precision. Europe and Central Asia (1.0 percent), and 4. The growth rates used are from the World the rest of the world (1.0 percent). Development Indicators. Pass-through rates 8. Some evidence suggests that, if price differ- are essentially estimated by comparing av- ences within countries are accounted for, the erage differences between national accounts reduction in Sub-Saharan Africa has been and household surveys. The mean national greater than the numbers suggested here consumption or income from each country’s (Beegle et al. 2016). For more information on household survey is compared with either the impact of price differences within coun- GDP or household final consumption expen- tries on poverty, see appendix A. diture (HFCE) from national accounts. HFCE 9. Of the 35 economies in FCS in 2015, 16 (45.7 is the preferred measure in most countries, percent) were in Sub-Saharan Africa. In terms except in Sub-Saharan Africa where GDP is of population, of the 481.1 million people liv- used for estimating pass-through factors and ing in FCS, 259.8 million (54.0 percent) were growth rates. If GDP and HFCE data are un- in Sub-Saharan Africa. More details on how available, growth forecasts from the Global countries are determined to be in FCS are Economic Prospects are used. If these are also given in appendix A. unavailable, growth forecasts from the World 10. The analysis uses a “rolling” roster of fragile Economic Outlook are used. For Syria, no es- situations, that is, the set of fragile situations timates are available in these sources. Instead, can change from one year of the analysis to data from the Economist’s Intelligence Unit are the next. relied upon. The fraction of GDP/HFCE per 11. This analysis goes back only to 2005 because capita that is assumed to pass through to the the World Bank classification of fragile situa- welfare vector is as follows: 0.785 for East Asia tions began that year. and Pacific, 0.773 for Europe and Central Asia, 12. The aggregate FCS poverty rate is the 0.829 for Latin America and the Caribbean, population-weighted mean of the poverty ENDING EXTREME POVERTY: PROGRESS, BUT UNEVEN AND SLOWING 47 rates of all economies in FCS. The number of 15. The 2013 profile and the methodological de- poor is a product of the FCS poverty rate and tails are reported in Castaneda et al. (2016). the total population living in FCS. This leads 16. The 2013 estimates are based on a different set to a slightly higher estimate of the total num- of 89 countries (Castaneda et al. 2016). When ber of poor (744 million versus PovcalNet es- the 2013 profiling is repeated using the same timate of 736 million), but the discrepancy is 91 countries from 2015, children constitute inconsequential to the current discussion. 44.9 percent of the poor. 13. The World Bank’s definition of fragility is based 17. Another reason for the discrepancy is that the on the Country Policy and Institutional Assess- projections used here assume that only a frac- ment (CPIA), which assesses the conducive- tion of the growth rates observed in national ness of a country’s policies and institutions to accounts translates into growth in the con- poverty reduction, sustainable growth, and the sumption aggregate observed in surveys. The effective use of development assistance. The actual poverty numbers, in contrast, assume CPIA comprises 16 indicators clustered in four that all growth observed in national accounts dimensions: economic management, structural translates into growth in the consumption ag- policies, policies for social inclusion and equity, gregate. This implies that the projections are and public sector management and institutions. more pessimistic than the actual estimates for 14. Please refer to appendix A for more informa- countries where the 2015 estimate is based on tion on the GMD. extrapolation. 48 POVERTY AND SHARED PROSPERITY 2018 Shared Prosperity: 2 Mixed Progress This chapter reports on the progress achieved in promoting shared prosperity, defined as the growth in the average consumption or income of the poorest 40 percent of the population (the bottom 40). Introduced as one of two twin goals by the World Bank in 2013 along with ending extreme poverty, fostering shared prosperity embodies notions of economic growth and equity. Shared prosperity is examined by country rather than globally. The latest available data, on 91 economies, paint a mixed albeit moderately positive picture. The bottom 40 were doing well in most economies for which data are available in about 2010–15. Overall, the incomes of the bottom 40 grew in 70 of the 91 economies monitored, and, in more than half the bottom 40 obtained a larger share of the total income. Good performance in shared prosperity is primarily but not exclusively found in South Asia, East Asia and Pacific, Latin America and the Caribbean, and the Baltic countries in Northern Europe. However, slow economic progress is hindering shared prosperity in some regions, particularly in Europe and Central Asia, and other high-income countries, which experienced negative or low levels of shared prosperity. More worrying, among the countries with high rates of poverty (most of which are located in Sub-Saharan Africa), income growth at the bottom has on average been lower than in the rest of the world. In addition, the picture of shared prosperity among the poorest economies as well as those in fragile and conflict-affected situations is only partial because data on the shared prosperity indicator remain limited. Beyond extreme poverty: ity is shared within each country. Thus, even in higher-income economies where extreme A focus on the bottom 40 poverty rates are low, the shared prosperity Promoting shared prosperity involves ensur- goal is still highly relevant. ing that the relatively poor in every country To estimate shared prosperity, two com- are able to participate in and benefit from parable surveys are needed. In this report, economic success. Progress toward this goal the selected surveys were for circa 2010 and is monitored through an indicator that mea- circa 2015 (box 2.1). The survey data are used sures the annualized growth rates in average to calculate changes in consumption or in- consumption or income among the poorest come. This presents a greater data challenge 40 percent of the population in each country than the calculation of a global poverty rate (the bottom 40).1 Irrespective of the prev- (chapter 1). Therefore, the set of countries alence of extreme poverty, this measure is included in the sample is smaller. The shared meaningful as a gauge of how well prosper- prosperity measure is reported for 91 econ- 49 ulation coverage is lower than in the earlier report, when it represented 75 percent of the BOX 2.1 The Global Database of global population. Shared Prosperity Shared prosperity estimates are Continued progress in most calculated using household surveys and are presented in the Global economies though some are Database of Shared Prosperity (GDSP). falling short The present report is grounded on the In this sample of 91 economies, the bottom sixth edition of the GDSP (the fall 2018 40 are mostly doing well. The incomes of release), which features data on 91 economies circa 2010–15. For details, the poorest 40 percent were growing in 70 of please refer to appendix A. the 91 economies circa 2010–15. The simple average of the annualized consumption or income growth rate among the bottom 40 was 1.9 percent (table 2.1). The performance in shared prosperity omies in which the combined population across the world ranges from an annualized is 4.6 billion, representing 62 percent of the 8.4 percent decline in income among the bot- world’s population in 2015. Compared to the tom 40 in Greece to an annualized growth of previous report with data for circa 2008–13, 9.1 percent in China (see figure 2.1 and map the number of economies included in the 2.1).2 There are clear regularities in perfor- present report is higher (91 rather than 83 mance across regions and income groups, economies). However, given that a few large though with some exceptions. Three groups countries, such as India, are excluded in this of economies can be identified on the basis of round because of lack of data, the global pop- their performance in shared prosperity. TABLE 2.1 Shared Prosperity and Shared Prosperity Premium, 91 Economies, Summary Table, circa 2010–15 SP indicator available Economies, number Average SP Population, Number of % of total Growth in SP Premium Average SP Premium Region millions economies population mean > 0 SP > 0 >0 (%) (p.p) East Asia and Pacific 2,036.6 8 94.6 7 8 7 4.73 1.33 Europe and Central Asia 487.0 26 89.9 18 20 13 2.22 0.15 Latin America and the Caribbean 626.5 16 87.8 15 16 14 3.19 0.98 Middle East and North Africa 371.6 3 47.8 1 2 2 0.98 1.33 South Asia 1,744.2 4 21.3 4 4 0 2.62 –0.56 Sub-Saharan Africa 1,005.6 12 32.4 9 8 5 1.84 –0.55 Rest of the world 1,083.6 22 71.7 14 12 10 -0.27 –0.33 Fragile and conflict-affected 485.1 4 7.6 2 3 3 2.03 0.80 IDA and Blend 1,539.3 20 42.7 16 17 10 2.16 –0.11 Low income 641.9 7 35.1 6 5 3 2.06 –0.67 Lower-middle income 2,970.0 24 36.1 19 21 13 2.56 0.30 Upper-middle income 2,560.4 28 93.7 21 24 20 2.61 0.77 High income 1,182.9 32 73.6 22 20 15 0.85 –0.20 Total 7,355.2 91 62.1 68 70 51 1.94 0.20 Sources: GDSP (Global Database of Shared Prosperity) fall 2018 edition, http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity; World Bank, Washington, DC, PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: IDA = International Development Association; Blend = IDA-eligible countries but also creditworthy for some borrowing from the International Bank for Reconstruction and Development; SP = shared prosperity; the indicator measures growth in the average household per capita consumption or income of the bottom 40. Shared prosperity premium = the difference in growth in the average consumption or income of the bottom 40 and the mean, in percentage points (p.p.). Population coverage refers to 2015. The list of econ- omies in fragility and conflict-affected situations is based on data for 2015. The shared prosperity indicator is close to zero (between −0.15 and 0.15 percent) in three countries: Iceland, Niger, and Romania. 50 POVERTY AND SHARED PROSPERITY 2018 FIGURE 2.1 Shared Prosperity, 91 Economies, circa 2010–15 China Latvia Malaysia Lithuania Vietnam Georgia Thailand East Asia Macedonia, FYR Indonesia and Pacific Estonia Philippines Kazakhstan Mongolia Belarus Fiji Kosovo Moldova Chile Turkey Nicaragua Poland Paraguay Tajikistan Dominican Republic Armenia El Salvador Russian Federation Panama Latin America and the Caribbean Czech Republic Europe and Brazil Hungary Central Asia Colombia Kyrgyz Republic Uruguay Croatia Peru Bulgaria Ecuador Romania Costa Rica Bosnia and Herzegovina Bolivia Slovak Republic Honduras Slovenia Mexico Ukraine Argentina Serbia Montenegro Sri Lanka Pakistan South Asia Egypt, Arab Rep. Bhutan Middle East Iran, Islamic Rep. Bangladesh and North Africa West Bank and Gaza Malta Burkina Faso Norway Namibia Sweden Rwanda Ireland Mauritania United States Togo Sub-Saharan Africa Switzerland Ethiopia Netherlands Mozambique France Côte d'Ivoire Belgium Rest of the world Niger Denmark Zambia Finland South Africa United Kingdom Uganda Iceland Germany –5 0 5 Canada Annualized growth in mean Austria incomes or consumption (%) Portugal Italy Luxembourg Spain Cyprus Bottom 40 (shared prosperity) Greece Total population –5 0 5 Annualized growth in mean incomes or consumption (%) Source: GDSP fall 2018 edition, World Bank, Washington, DC, http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity. Note: The figure shows annualized growth in mean household per capita consumption or income (see annex 2B). SHARED PROSPERITY: MIXED PROGRESS 51 MAP 2.1 Shared Prosperity across the World, 91 Economies, circa 2010–15 Source: GDSP (Global Database of Shared Prosperity) fall 2018 edition, World Bank, Washington, DC. Note: The map shows annualized growth in mean household per capita consumption or income (see appendix A). The first group consists of, by and large, a treme poverty, and the region now consists large part of the developing world in which of mainly middle-income countries (World the incomes of those in the bottom 40 are Bank 2018a). The success in South Asia, as growing, in some cases strongly. This is pri- mentioned in the previous chapter, was more marily, though not exclusively, the case of recent than in East Asia and Pacific but is still economies in East Asia and Pacific, South persistent. Asia, and Latin America and the Caribbean. In many Latin American and Caribbean On average, the incomes of the bottom 40 in countries, the progress in lifting incomes these regions grew by 4.7 percent, 2.6 per- of those at the bottom has been widespread cent, and 3.2 percent per year, respectively since the early 2000s and is still strong despite (table 2.1). In some cases, such as in various the more recent slowdown. After a decade of countries in East Asia and Pacific, current strong economic growth and shared prosper- high levels of shared prosperity represent ity, largely driven by favorable commodity a continuation of over a quarter century of prices and expanded social protection sys- strong and broadly shared economic growth tems (Ferreira et al. 2013), regional growth driven by labor-intensive development com- has slowed since 2012 as international condi- bined with investment in human capital, tions deteriorated. The economic slowdown which particularly benefitted the lower part translated into slower poverty reduction and of the distribution (Birdsall et al. 1993; Com- more sluggish income growth among the mission on Growth and Development 2008) middle class, particularly in South American (see box 2.2). This success means that more countries (Calvo-González et al. 2017; World than a billion people have risen out of ex- Bank 2018b). The income of the bottom 40 52 POVERTY AND SHARED PROSPERITY 2018 BOX 2.2 Country Stories With contributions from Kenneth Simler, Samuel Freije-Rodriguez, Rakesh Gupta N. Ramasubbaiah, and Carolina Mejia-Mantilla. Rising East Asia: the dysfunctional wage-setting and skill premiums in several high- China and Malaysia practices for low-paid workers income European and non-European As described in chapter 1, the (Del Carpio and Pabon 2014). economies (Katz and Autor 1999; success of economies in East The increase of minimum wages Goldin and Katz 2007; Katz and Asia and Pacific in drastically has also been linked to strong Margo 2014; Ganong and Shoag reducing poverty in the last few reductions in inequality in other 2017; Ridao-Cano and Bodewig decades is unparalleled. Solid countries such as Brazil (World 2018; Bussolo et al. 2018). educational foundations and strong Bank 2016a). In contrast, household export-oriented growth from income growth was lower in Droughts and pests manufacturing have been some 2013–15, about 6 percent per year, affecting Uganda of the fundamental growth drivers and almost distribution-neutral. Between 2012 and 2016, Uganda in the region. The high rates of experienced a setback in terms Stagnated incomes at the income growth among the bottom of reducing poverty and boosting bottom in high-income 40 continue to be observed in the shared prosperity, trends that had countries last five years. been observed throughout the The fast growth of consumption Inequality in the developed world decade leading up to 2012. The per capita among the bottom 40 has recently been the focus of poverty headcount ratio (under the in China is supported by faster intensified public debate. Rich international poverty line) increased growth in rural than in urban evidence using different and new from 35.9 to 41.6 percent, and household disposable income. For estimation methods and sources consumption for the bottom 40 the period 2013–15, the higher of data on welfare distributions for shrank by 2.15 percent per year, income dynamism in rural areas Western Europe and the United more than the 0.96 percent per is driven by household operations States emerging from the last year decline for the average (family business or farm incomes), decade suggest that the top 1 consumption. Behind the reversal which accrue 2.8 percentage points percent are getting a larger share of fortune were the drought and (out of 10.1) of disposable income of national income since the 1980s pests that affected the agricultural growth in rural households, but only and that the incomes of those at the sector for the better part of 2016 0.8 percentage points (out of 8.6) bottom 50 percent have remained and the beginning of 2017. Given in urban households. This indicates stagnant or even declined (Alvaredo that households engaged in that traditional economic activities et al. 2018). In the United States, agriculture remain highly vulnerable continue to have a significant for example, estimates suggest that to weather and price shocks, these influence in the growth of the the average pre-tax income for this problems affected the livelihood rural economy. Higher disposable latter group has stagnated at about $16,000 (in constant 2014 dollars) of rural households in particular. income entailed a higher increase in since 1980 (Piketty et al. 2018). Estimates using panel data show consumption expenditure in almost The question of lack of income that the lack of rainfall and low all consumption items for rural growth for the median worker (a prices contribute substantially residents. In Malaysia, the rapid income comprehensive description can to lower income for Ugandan growth among the bottom 40 be found in Shambaugh and Nunn agricultural households. A 10 (see figure 2.4) from 2011 to 2018) is complex but has been percent decline in water sufficiency 2015 is fundamentally driven addressed by several studies in the (rainfall) decreases crop income by extraordinary performance recent literature. Some explanatory by 9.9 percent, while a 10 percent between 2011 and 2013—when factors focus on the emergence decline in the price of maize or wages rose sharply and overall of superstar firms that led to beans lowers crop income by 4.5 or income of the bottom 40 grew increasing monopolistic rents and 9.2 percent, respectively (Hill and at an annual rate of 12 percent. a declining labor share, which did Mejia-Mantilla 2017). The effects The timing of the increase in labor not benefit lower-skilled workers are higher for poorer households as earnings coincides with minimum during this period (Autor et al. 2017; they are more vulnerable to adverse wage legislation passed in 2012, Barth et al. 2016). Others stress shocks. For these households, a which introduced minimum the fact that technological change, 10 percent decline in rainfall and wages for the first time, relevant combined with the educational a 10 percent decline in maize and to all workers except domestic landscape, has dampened median bean prices result in a 13.4 percent employees. In part, the minimum wage income growth (and increased and 13.0 percent decline in crop wage was put in place to address polarization of the wage distribution) income, respectively. SHARED PROSPERITY: MIXED PROGRESS 53 grew 1.4 percentage points more slowly per 2017; Ridao-Cano and Bodewig 2018; Bus- year in circa 2010–15 than in circa 2008–13 solo et al. 2018). (See also box 2.2). (reported in the previous edition of this re- Finally, there is also cause for concern port) with average annualized rates of 3.2 among some of the poorest economies and percent compared to 4.6 percent in the pre- those in fragile and conflict-affected situa- vious period (annex 2B, table 2B.2.). Still, tions. On average, the incomes of the bottom shared prosperity continued to be high in 40 in Sub-Saharan Africa grew at 1.8 percent many countries in the region. In Chile, in- per year, a pace slightly lower than in the total comes of the bottom 40 grew at a rate of 6.0 sample. But this number is the average among percent per year in 2010–15, driven by soar- economies where incomes of the bottom 40 ing hourly wages and a strong public transfer declined or grew below 1 percentage point system protecting the most vulnerable. (over a third of African economies) and other Within this first group of good performers economies in which income growth was in shared prosperity, the Baltic states—Es- strong, such as Burkina Faso and Rwanda. tonia, Latvia, and Lithuania—were able to The negative performance in countries with recover vigorously after the 2008 and 2013 high poverty rates like Uganda and Zambia is crises. Between 2010 and 2015, incomes of likely related to the poor performance of the the bottom 40 in these countries grew at agriculture sector, in part due to adverse cli- rates above 6 percent per year. These coun- mate shocks and pests (see box 2.2). Among tries were among those that experienced the four conflict-affected economies with avail- largest gross domestic product declines and able data, two had low or negative income fiscal deficits during the years of the crisis growth for the bottom 40. Although Côte (OECD 2012), and implemented large fis- d’Ivoire’s shared prosperity of 0.7 is still low, cal consolidations programs (Sutherland, it represents a recovery from a decade of po- Hoeller, and Merola 2012). Starting in 2011, litical and economic crisis. In the Middle East they experienced some of the strongest eco- and North Africa, the poor performance in nomic growth recovery relative to other Eu- West Bank and Gaza reflects to a large extent ropean countries (De Agostini et al. 2015; the economic despair in Gaza, despite prog- World Bank 2018c). ress in West Bank, which was largely restricted A second group includes relatively rich to urban areas. A second important source economies, with low prevalence of extreme of concern among these poor or conflict- poverty (in the single digit), in which in- affected economies is that their coverage of comes of the bottom 40 are growing slowly, the shared prosperity indicator is low, an stagnating, or even losing ground. This is issue highlighted in the next section. the case of several Eastern and Western Eu- ropean countries, such as Greece and Spain, The poorest countries have as well as of other high-income economies, limited information about such as the United States. On average, the in- comes of the bottom 40 in the so-called rest shared prosperity of the world contracted 0.3 percent per year Of the 164 countries with an available in- in circa 2010–15. In some countries such as ternational poverty rate, only a quarter Greece, Portugal, and Spain, the negative of low-income economies and 4 of the 35 performance reflects, to a greater extent, the recognized as being in fragile and conflict- slow recovery from the European debt crisis affected situations also have a shared prosper- (IMF 2017; World Bank 2018c). In richer ity indicator.3 As a consequence, the coverage economies such as the United Kingdom and of shared prosperity in Sub-Saharan Africa the United States, more structural processes is limited: only 12 of the 45 economies for that led to the stagnation of incomes at the which poverty estimates are available in the bottom since the 1980s, or more recently region are included (figure 2.2). In contrast, in continental European countries such as 84 percent of the high-income economies are Germany and Poland, which are sometimes represented in the shared prosperity analy- linked to polarization of wages and regu- sis. Of the 57 countries with extreme poverty lations (Alvaredo et al. 2018; Piketty et al. rates above 10 percent, only 13 have a shared 54 POVERTY AND SHARED PROSPERITY 2018 prosperity indicator. Two countries that con- FIGURE 2.2 Shared Prosperity Estimates, 91 Economies, by Region, centrate a high proportion of the world’s Group, and Income poor, India and Nigeria, are excluded because they lack comparable data across time. Popu- East Asia and Pacific lation coverage is also limited among econo- Europe and Central Asia mies grouped by other World Bank country Latin America and the Caribbean categories, such as small island nations for Middle East and North Africa which there is no shared prosperity indicator South Asia available. Sub-Saharan Africa Because this round excludes many poorer Rest of the world countries as well as those in fragile and conflict-affected situations, the picture on Fragile and conflict affected shared prosperity for these economies is only IDA and Blend partial. The computation of the shared pros- perity measure relies on frequent and com- Low income parable data collection (appendix A). This is Lower-middle income often associated with a country’s level of de- Upper-middle income velopment because data collection depends High income on the capacity of a national statistics office. Stronger commitments to narrowing the data 0 10 20 30 40 50 60 70 gap are needed if the shared prosperity goal is Number of economies to be monitored globally in a timely fashion Positive shared prosperity Negative shared prosperity (Independent Evaluation Group 2017).4 No shared prosperity measure Sources: GDSP (Global Database of Shared Prosperity) fall 2018 edition, World Bank, Washington, DC, Growth at the bottom and http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity; PovcalNet the top is not always even (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: The count is based on the 164 economies on which direct estimates of the poverty rate are avail- The incomes or consumption of the bot- able through PovcalNet. IDA = International Development Association; Blend = IDA-eligible countries but also creditworthy for some borrowing from the International Bank for Reconstruction and Development; tom 40 depend directly on both the average No shared prosperity measure = economies with poverty rates reported in PovcalNet, but insufficient growth within the economy and the share of data to compute a shared prosperity indicator. national income that accrues to the bottom 40 (Rosenblatt and McGavock 2013; World Bank 2016b) (annex 2A). Improvements at prosperity. The number of economies exhib- the bottom may thus derive from the fact iting a positive premium is less (51) than the that society in general is doing better—that number showing a positive shared prosperity is, the tide lifts all boats. Improvements may indicator (70) (table 2.1.). The implication also arise from progressive shifts in the dis- is that, in almost half the economies moni- tribution of economic gains (Lakner, Negre, tored, the consumption or income share of and Prydz 2014, 2015). The shared prosperity the bottom 40 is growing more slowly than premium represents an effort to capture such the average, suggesting that the distribution progressive shifts. It is defined as the differ- in these countries is worsening because the ence between the annual income growth rate bottom 40 are getting a smaller share of total among the bottom 40 and the annual growth income. Globally, the average shared pros- rate of the mean in the economy. A positive perity premium is small. The simple average premium indicates that the incomes or con- across all economies in the sample is 0.2 per- sumption of the bottom 40 are increasing at centage points. an above average rate and that the bottom 40 The regions with higher average premi- are obtaining a larger share of overall con- ums are East Asia and Pacific, the Middle East sumption or income (see box 2.3 for a com- and North Africa, and Latin America and the parison with other concepts of inequality Caribbean. In these regions, the incomes of based on income shares). the bottom 40 grew by 1.3, 1.3, and 1.0 per- Achieving progress is more elusive in the centage points above the mean, respectively. shared prosperity premium than in shared These regions also include a larger share of SHARED PROSPERITY: MIXED PROGRESS 55 BOX 2.3 The Shared Prosperity Premium and Other Concepts of Inequality The shared prosperity premium distribution to determine whether countries for which the analysis calculated on the basis of the the rich are becoming richer. is performed differs from WIR. 2010–15 sample shows that, in 51 Although the WIR uses data on • The absence of the top income of the 91 economies, the bottom top earners from administrative tax earners in household surveys. 40 are obtaining a larger share of records only for 10 countries,b this Often, household surveys tend total income in their countries. This type of data is currently available to suffer from nonresponse or suggests that, in a little more than underreporting at the top of the for 58 countries in the World half of the economies, inequality distribution. Therefore, to obtain Inequality Database for at least one has been declining. However, the reliable data on the top earners, year. In the dataset, high-income perceptions of the public and the studies focusing on the rich, such and upper-middle-income countries World Inequality Report 2018 (WIR) as the WIR, tend to be based are more represented than low- and do not seem to agree that within- on tax records, complementing lower-middle-income countries. Of country inequality is narrowing in a household surveys. Yet, for a the 58 with some information on majority of countries.a According to large part of the developing top incomes, 32 are also included the global picture displayed in the world, tax records are not readily in the present chapter. The large WIR, inequality has been widening available, and thus the present majority of the economies in both over the past few decades, and the chapter is not able to account datasets (almost 80 percent) are richest people in each country are for underreporting at the top. upper-middle- and high-income increasing their share of national The implication is that the economies, in which it was shown incomes at an alarming pace. analysis from the chapter differs that the progress in terms of the This mismatch in interpretations from the WIR both because shared prosperity premium was of inequality trends stems partly consumption or income at the more limited than in the rest of the from differences in the definition top is not properly accounted for world. Table B2.3.1 compares both of inequality, as well as from and because the subset of samples. differences in the supporting data. TABLE B2.3.1 Number of Economies with Top Incomes • Inequality at the top versus Estimated in the World Inequality Database and in the inequality at the bottom. The Poverty and Shared Prosperity Report shared prosperity premium focuses on the bottom of the Both WID Income group and PSPR Only WID Only PSPR national income distribution as a High income 18 13 14 gauge of inequality. It reflects an Upper-middle income 9 3 19 assessment of whether the poor Lower-middle income 4 6 20 are catching up or falling farther Low income 1 4 6 behind. Meanwhile, the WIR Note: PSPR = Poverty and Shared Prosperity (this report); WID = World Inequality focuses on the top of the income Database. a. Several perception-based surveys in East Asia and Pacific indicate that respondents feel income disparities are too large (World Bank 2018a). For World Inequality Report 2018, see Alvaredo et al. (2018). b. The WIR uses fiscal and national accounts data to scale up the income distributions to match national income estimates for a large number of countries. But the distributional information used comes from only 10 countries (Brazil, China, Côte d’Ivoire, France, Germany, India, Lebanon, Russian Federation, United Kingdom, and United States). These are used to predict income dynamics in their neighboring countries to obtain regional and global income inequality estimates. economies with positive shared prosperity In the four South Asian economies included premiums, with all but one or two in each in the sample, incomes among the bottom region for which the incomes of the bottom 40 are growing, but at a slower pace than 40 grew at a faster rate than the rest of the the mean. In addition, half the countries in economy (figure 2B.1). Europe and Central Asia and more than half In contrast, higher concentrations of in Sub-Saharan Africa have negative shared shared prosperity premiums close to zero or prosperity premiums. These two regions negative are found in the other four regions. are unique in that they house the lowest 56 POVERTY AND SHARED PROSPERITY 2018 and most negative shared prosperity pre- FIGURE 2.3 Correlation between Shared Prosperity and the Shared miums (Armenia, Mozambique, and Zam- Prosperity Premium, 91 Economies bia), as well as some of the highest premi- 5 ums (Burkina Faso and the Former Yugoslav Republic of Macedonia). This dichotomous 4 trend in inequality in Sub-Saharan African Shared prosperity premium (percentage points) has already been highlighted by Beegle et al. 3 (2016), who find increasing and decreasing inequality without a clear pattern across 2 economies (that is, no clear association with 1 resource status, income levels, or initial levels of inequality). 0 Relative to the previous report, the aver- age shared prosperity premium across all –1 countries was slightly lower in 2010–15 than –2 in 2008–13 (table 2B.3). Because of the lim- ited sample coverage in some of the regions, –3 comparisons focus on the three subgroups of countries for which data coverage is more sta- –4 ble and extensive across the two periods (see appendix A on comparability across rounds): –5 –10 –8 –6 –4 –2 0 2 4 6 8 10 Europe and Central Asia, Latin America and the Caribbean, and the rest of the world. The Shared prosperity (%) decline in the premium was more pronounced Sources: GDSP (Global Database of Shared Prosperity), fall 2018, World Bank, Washington, DC, http:// in Latin America and the Caribbean, suggest- www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity; PovcalNet (online ing not only that the economic slowdown in analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. this region dampened the performance in consumption or income growth among the bottom 40, but also that overall consumption comes of the bottom 40 grew at a more rapid or income growth was not as equalizing as it rate relative to the average. had been in the past. This is the case, for ex- If the shared prosperity indicator is neg- ample, among several South American coun- ative, the shared prosperity premium is al- tries, such as Peru and Uruguay, in which the most always negative as well (see figure 2.3). rates of income growth among the bottom Of the 21 economies with negative shared 40 were about 3 percentage points above prosperity indicators, 19 also present nega- the respective mean in 2008–13, whereas the tive premiums.5 This occurs in Europe and corresponding gap in 2010–15 was closer to Central Asia, Sub-Saharan Africa, and the 1 percentage point. rest of the industrialized countries (rest of There is a positive correlation between the world). Greece, Spain, and Zambia are shared prosperity and the shared prosperity examples shown in figure 2.4, panel b. This premium (figure 2.3). Of the 91 economies, means not only that incomes among the bot- 49 achieved both a positive shared prosper- tom 40 are shrinking rather than growing, ity indicator (absolute growth among the but also that the decline is more profound bottom 40) and a positive shared prosperity among the bottom 40 than across the rest of premium (relative growth among the bottom the distribution. This result is consistent with 40). This is the case of most countries in Latin the evidence showing that the poor are more America and the Caribbean and in East Asia highly exposed to downturns and shocks and and Pacific, but also in 12 of the economies of that policies that safeguard them against such Europe and Central Asia. As examples, figure risks—safety nets and insurance—can help 2.4, panel a, shows three cases, Latvia, Peru, guarantee that prosperity is shared. Poorer and the Malaysia, in which incomes grew households are also much more likely to re- across the entire distribution, whereas the in- duce consumption in response to shocks SHARED PROSPERITY: MIXED PROGRESS 57 FIGURE 2.4 Growth across Deciles of the Income Distribution, Selected Countries a. Positive shared prosperity: Positive premium b. Negative shared prosperity: Negative premium (3 of 49 countries) (3 of 19 countries) 12 6 4 10 2 Annualized growth rate (%) Annualized growth rate (%) 8 0 –2 6 –4 4 –6 –8 2 –10 0 –12 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Deciles Deciles Latvia Malaysia Peru Greece Spain Zambia Sources: GDSP (Global Database of Shared Prosperity), World Bank, Washington, DC, http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity; PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: The bars illustrate the growth in the mean, by decile. The bottom 40 are in the left bars, in orange and red. because they are also less likely to maintain more highly developed countries with almost savings (World Bank 2013). no extreme poverty, children are more likely to live in relatively more deprived households. In addition, people in the bottom 40 dif- Who are the bottom 40? fer significantly across countries. In terms People in the bottom 40 differ from those liv- of consumption or income, in most low- ing in the top 60, in terms not only of their income economies, such as Togo and Zam- income but also of their characteristics. A bia, everyone in the bottom 40 lives on less closer look at who makes up the bottom 40 in than US$1.90 a day (figure 2.5). In contrast, a country may offer insights into the groups in more well-developed countries, only a that are relatively more deprived. It can also small share of the bottom 40 are living in guide national policy makers in identifying extreme poverty. problem areas. Differences in income levels among the Compared with the top 60, people in the bottom 40 across countries reflect not only bottom 40 live disproportionally in rural the wealth of these economies as a whole areas and attain less education than the rest of but also how the bottom 40 fare relative to society. In addition, children are more likely the rest of the population. Although the bot- to be among the bottom 40 than among the tom 40 in Croatia are consistently doing bet- top 60. In Côte d’Ivoire, for example, children ter than the bottom 40 in Brazil, the rich in under 15 years of age constitute about half Brazil are much richer than the top earners the bottom 40, whereas they make up only in Croatia (figure 2.6). This reflects the fact a third of the top 60. Similarly, in the Philip- that Brazil is much more unequal than Cro- pines, children under 15 represent more than atia. The average daily income of the richest 40 percent of the bottom 40 but less than 25 decile in Brazilian society is more than 30 percent of the top 60. This pattern is repeated times higher than the average daily income across all countries and regions in the current of the poorest decile, whereas the equiva- sample. Chapter 1 concludes that children are lent ratio in Croatia is 8. Findings are similar more likely than adults to live in extreme pov- among high-income economies with negligi- erty. The present chapter finds that, even in ble poverty rates: for example, the bottom 40 58 POVERTY AND SHARED PROSPERITY 2018 FIGURE 2.5 Extreme Poverty and the Bottom FIGURE 2.6 Mean Income, by Distribution Decile, Selected Countries, 40, Selected Countries, circa 2015 2015 Zambia 120 Daily consumption or income per capita Togo Niger 100 Burkina Faso Côte d’Ivoire 80 Honduras Bangladesh Bolivia 60 Philippines Romania 40 Serbia Mauritania Tajikistan 20 Colombia Georgia 0 Mexico 1 2 3 4 5 6 7 8 9 10 Peru Deciles of the income distribution Brazil Greece Belgium Brazil Croatia Dominican Republic United Kingdom Zambia International poverty line Bulgaria Kyrgyz Republic Source: PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet/. World Bank, Costa Rica Washington, DC. Spain Note: The shaded area indicates the bottom 40. The lines represent the average daily consumption or Lithuania income per capita by decile, expressed in 2011 purchasing power parity (PPP) U.S. dollars. Slovak Republic Hungary Sri Lanka the bottom 40 receive less than 25 percent Croatia of the overall income (figure 2.7). In several United Kingdom Eastern European countries, such as Ukraine, Belgium the share is almost 25 percent. At the other Iran, Islamic Rep. Turkey extreme is Zambia, where the bottom 40 re- France ceive less than 10 percent of the pie. Similar, Uruguay though less extreme, is the situation in several Ukraine Latin American countries in which inequality 0 20 40 60 tends to be high. Share of people living on less than $1.90 per day (%) Source: GDSP (Global Database of Shared Prosperity), fall 2018, Monitoring the twin goals World Bank, Washington, DC, http://www.worldbank.org/en The joint monitoring of poverty and shared /topic/poverty/brief/global-database-of-shared-prosperity and PovcalNet (online analysis tool), World Bank, Washington, DC, prosperity shines a spotlight on the extreme http://iresearch.worldbank.org/PovcalNet/. poor and the less well-off in each country. In this way, the most vulnerable can be iden- in Belgium have higher average incomes than tified no matter the corner of the world in the United Kingdom, even though the richest which they live and, at the same time, their 10 percent are richer in the United Kingdom progress highlighted. This section addresses than in Belgium. this progress on the twin goals across the 91 The relative position of the bottom 40— economies for which the shared prosperity how deprived they are compared with the indicator can be calculated among the 164 rest of the population—also varies largely economies on which the international pov- across countries. The shared prosperity pre- erty rate is available. mium captures whether the bottom 40 are There is a strong correlation between the receiving a larger or smaller share of the twin goals, and most economies are per- overall pie. How large is this piece of the pie forming well in both poverty reduction and accruing to the bottom 40 across countries? boosting shared prosperity (figure 2.8, top In all economies on which data are available, left quadrant). In most of the 91 economies SHARED PROSPERITY: MIXED PROGRESS 59 FIGURE 2.7 Share of Consumption or Income, by Decile, Selected Countries, circa 2015 Zambia Honduras Brazil Bolivia Mexico Peru Nicaragua Togo Philippines Turkey Côte d'Ivoire Uruguay Romania Iran, Islamic Rep. Sri Lanka Bulgaria Tajikistan Niger United Kingdom Mauritania Armenia Burkina Faso Mongolia Bangladesh Hungary Belgium Norway Ukraine 0 10 20 30 40 50 60 70 80 90 100 Share of total household per capita consumption or income Decile 1 (poorest) Decile 10 (richest) Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. monitored, if the shared prosperity indicator can be challenging, and economic growth in is positive, then the poverty rate is falling. Re- these economies does not necessarily align gionally, circa 2010–15, all countries in East with large welfare improvements among the Asia and Pacific and in Latin America and poorest in society (Bussolo and López-Calva the Caribbean enjoyed a reduction in poverty 2014). and positive shared prosperity. In terms of The risk of failing to reach the goal of making progress on the twin goals, much can reducing poverty below 3 percent by 2030 be learned from these two regions. is greatest among the economies with ex- In contrast, some economies have per- treme poverty rates above the global aver- formed poorly in achieving progress on the age of about 10 percent (figure 2.9). All but twin goals. In these economies, poverty rates one of these economies are in Sub-Saharan rose, and the shared prosperity measure was Africa, with the exception being in Central negative in circa 2010–15 (see figure 2.8, bot- America. Although only a fourth of the ex- tom right quadrant). Of the 13 economies tremely poor economies are included in the in this situation, only two also exhibited ini- shared prosperity sample (13 out of 57), an tially high rates of extreme poverty (South examination of their shared prosperity mea- Africa and Uganda). The rest are European sure in 2010–15 is not encouraging for many countries with extremely low international of them.6 Except for a few countries, such as poverty rates, and the changes in poverty are Burkina Faso, Namibia, and Rwanda, if these thus also slight. Achieving equitable growth economies are to have a chance of reaching 60 POVERTY AND SHARED PROSPERITY 2018 the 3 percent goal by 2030, growth rates will FIGURE 2.8 Shared Prosperity and Changes in Extreme Poverty, have to be high and incomes among the bot- 91 Economies, circa 2010–15 tom 40 will have to rise at an even higher rate. 10 Instead, in two-thirds of these countries, av- Shared prosperity is positive (the bottom 40 is growing) erage incomes among the bottom 40 are in- 8 creasing at an annual rate below the global Annualized growth rates of the bottom 40 (%) average of 1.9 percent, and, in most of these, 6 consumption growth is slower for the bottom 40 than for the mean in the country. 4 To conclude, although most countries 2 have made progress in shared prosperity, the results are mixed. This is in part due to the 0 Shared prosperity is negative fact that in several richer economies incomes (the bottom 40 is shrinking) of the bottom 40 are growing slowly or not at –2 all. But there is also cause for concern at the –4 very bottom—largely in Sub-Saharan Afri- can and in economies in fragile and conflict- –6 affected situations. This concern takes two forms: First, data –8 scarcity among the poorest and most fragile –10 situations continues to be an issue, so cover- –3 –2 –1 0 1 2 age of the shared prosperity measure in these Change in poverty rate (p.p.) countries is limited. This means that where Poverty is falling Poverty is rising we need the most light we have the least. Sec- East Asia and Pacific South Asia ond, where there are data (the 13 countries), Europe and Central Asia Sub-Saharan Africa progress looks decidedly more mixed than Latin America and the Caribbean Rest of the world among the middle-income success stories. As Middle East and North Africa mentioned in chapter 1, reaching the global Sources: GDSP (Global Database of Shared Prosperity), fall 2018, World Bank, Washington, DC, target of reducing extreme poverty to less http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity; PovcalNet than 3 percent by 2030 will require greater (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: Changes in poverty are measured as the annual percentage point change in the international attention to inclusive growth in the world’s poverty rate based on the US$1.90-a-day poverty line. Changes in poverty are measured over the same poorest countries. period as shared prosperity. FIGURE 2.9 Shared Prosperity among the Poorest Economies, circa 2010–15 Shared prosperity 2015 Poverty Economy Type period rate (%) Mozambique c 2008–14 62.2 Mozambique Zambia c 2010–15 57.5 Zambia Rwanda c 2010–13 51.5 Rwanda Togo c 2011–15 49.2 Togo Niger c 2011–14 44.5 Niger Burkina Faso c 2009–14 42.8 Burkina Faso Uganda c 2012–16 39.2 Uganda Côte d'Ivoire c 2008–15 28.2 Côte d’Ivoire Ethiopia c 2010–15 27.0 Ethiopia South Africa c 2010–14 18.9 South Africa Honduras i 2011–16 16.2 Honduras Bangladesh c 2010–16 15.2 Bangladesh Namibia c 2009–15 13.4 Namibia Note: The column “Type” denotes whether the data reported are based on –4 –2 0 2 4 6 8 consumption (c) or income (i) data. The 2015 poverty rates have been lined-up to Annualized growth in consumption or income (%) 2015 using interpolation or extrapolation methods. See appendix A for details. Bottom 40 (shared prosperity) Total population Sources: GDSP (Global Database of Shared Prosperity), fall 2018, http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity; World Bank, Wash- ington, DC, PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. SHARED PROSPERITY: MIXED PROGRESS 61 Annex 2A Shared prosperity definitions The definition of shared of overall income that accrues to the bottom prosperity 40, or both. This can be analytically expressed as follows: The shared prosperity measure represents the annualized growth rate of the mean house- g40 = gmean + gshareB40 , (2A.1) hold per capita consumption or income of where g40 is the income growth among the the poorest 40 percent of the population (the bottom 40; gmean is the growth in the mean; bottom 40), where the bottom 40 are deter- and gshareB40 is the growth in the income share mined by their rank in household per capita of the bottom 40. consumption or income. Unlike global and Although not an inequality indicator, the regional poverty estimates that are popula- second term may be considered as the distri- tion weighted, global and regional means of butional term that accounts for changes in shared prosperity are simple averages. This the proportion of total income growth that is because the shared prosperity indicator is accrues to the bottom 40. This is precisely the purely national in focus. shared prosperity premium (SPP), which is obtained by rearranging equation (2A.1) as follows: The definition of shared prosperity premium gshareB40 = g40 – gmean ≡ SPP (2A.2) The World Bank’s second twin goal, boosting This change in the share, or premium, does shared prosperity, is sometimes character- not directly measure the inequality in a soci- ized as a growth indicator and sometimes as ety. But it is a (limited) measure of distribu- an indicator of inequality. In fact, it is a bit tional changes. If the incomes of the bottom of both. Growth in the average consumption 40 grow at a rate that is above (or below) av- (or income) of the bottom 40 can stem from erage, then inequality—at least between the the rising mean consumption (or income) of bottom 40 and the rest of the distribution— the overall population, changes in the share will tend to narrow (or widen). 62 POVERTY AND SHARED PROSPERITY 2018 Annex 2B Shared prosperity estimates by economy TABLE 2B.1 Shared Prosperity Estimates, 91 Economies, circa 2010–15 Annualized growth in Mean consumption or income per capitaa mean consumption or income per capitaa,b Initial year Most recent year Total Total Total Bottom 40 population Bottom 40 Population Bottom 40 Population Economy Periodc Typed (%) (%) ($ a day, PPP) ($ a day, PPP) ($ a day, PPP) ($ a day, PPP) Chinaf 2013–15 C 9.11 7.37 3.91 9.46 4.65 10.90 Fiji 2008–13 c 1.17 –0.51 3.33 7.65 3.52 7.47 Indonesia 2015–17 c 4.77 4.79 2.51 5.68 2.75 6.24 Mongolia 2010–16 c 1.86 1.42 4.01 8.05 4.48 8.77 Malaysia 2011–15 i 8.30 5.95 7.89 21.76 11.14 27.95 Philippines 2009–15 i 2.43 1.38 2.38 6.75 2.74 7.33 Thailand 2010–15 c 5.03 3.04 5.67 13.29 7.24 15.43 Vietnam 2010–16 c 5.17 3.75 3.29 7.61 4.46 9.49 Armenia 2011–16 c 2.25 4.58 3.16 5.66 3.53 7.08 Bulgariag 2009–14 i 0.43 2.11 8.15 16.86 8.32 18.72 Bosnia and Herzegovina 2011–15 c –0.45 –0.79 9.51 19.26 9.34 18.65 Belarus 2011–16 c 4.06 3.46 9.40 16.34 11.47 19.37 Czech Republicg 2010–15 i 1.42 1.03 15.98 26.79 17.15 28.20 Estoniag 2010–15 i 6.15 6.62 10.71 21.07 14.44 29.04 Georgia 2011–16 c 6.44 4.32 2.46 5.97 3.36 7.38 Croatiag 2010–15 i 0.47 –0.12 9.28 18.82 9.49 18.71 Hungaryg 2010–15 i 1.19 1.73 10.55 19.57 11.19 21.33 Kazakhstan 2010–15 c 4.09 3.47 5.50 9.58 6.72 11.36 Kyrgyz Republic 2011–16 c 0.59 –0.03 3.07 5.30 3.16 5.29 Kosovo 2012–15 c 3.50 1.57 4.66 8.39 5.17 8.79 Lithuaniag 2010–15 i 6.65 8.10 7.91 16.79 10.91 24.79 Latviag 2010–15 i 7.52 6.47 7.74 16.93 11.11 23.16 Moldova 2011–16 c 2.79 0.39 4.92 9.19 5.65 9.37 Macedonia, FYR 2009–14 I 6.20 1.90 3.36 9.46 4.55 10.42 Montenegro 2009–14 c –2.73 –2.27 8.64 16.27 7.52 14.51 Polandg 2010–15 i 2.52 2.07 11.00 22.29 12.46 24.70 Romaniag 2010–15 i 0.06 1.14 4.25 9.71 4.26 10.27 Russian Federation 2010–15 c 1.62 0.48 9.29 21.84 10.07 22.36 Serbiag 2012–15 i –1.70 –0.88 4.69 12.04 4.45 11.72 Slovak Republicg 2010–15 i –0.62 –0.61 13.17 22.95 12.77 22.25 Sloveniag 2010–15 i –0.78 –0.56 21.12 34.70 20.31 33.74 Tajikistan 2009–15 c 2.30 3.58 2.69 5.13 3.08 6.34 Turkey 2011–16 c 2.53 3.47 6.45 15.73 7.30 18.66 Ukraine 2011–16 c –0.85 –0.69 7.34 11.90 7.03 11.50 Argentinae 2011–16 i 0.15 0.00 8.44 23.25 8.51 23.26 Bolivia 2011–16 i 1.67 1.06 4.07 12.56 4.42 13.24 Brazil 2011–15 i 3.80 2.19 4.77 17.66 5.54 19.25 Chile 2009–15 i 5.97 5.49 5.21 15.69 7.37 21.63 Colombia 2011–16 i 3.49 1.48 3.57 13.27 4.24 14.28 Costa Rica 2011–16 i 2.00 1.95 6.69 21.42 7.39 23.59 Dominican Republic 2011–16 i 4.46 3.53 4.22 12.54 5.24 14.92 Ecuador 2011–16 i 2.95 1.92 4.10 12.26 4.74 13.49 Honduras 2011–16 i 1.17 –1.95 2.15 9.13 2.28 8.28 Mexico 2010–14 i 0.51 0.74 3.88 11.41 3.96 11.75 Nicaragua 2009–14 i 5.64 6.52 2.94 7.90 3.87 10.83 (continued) SHARED PROSPERITY: MIXED PROGRESS 63 TABLE 2B.1 Shared Prosperity Estimates, 91 Economies, circa 2010–15 (continued) Annualized growth in Mean consumption or income per capitaa mean consumption or income per capitaa,b Initial year Most recent year Total Total Total Bottom 40 population Bottom 40 Population Bottom 40 Population Economy Periodc Typed (%) (%) ($ a day, PPP) ($ a day, PPP) ($ a day, PPP) ($ a day, PPP) Panama 2011–16 i 4.00 3.89 5.74 20.40 6.98 24.70 Peru 2011–16 i 3.08 2.18 4.11 12.04 4.79 13.41 Paraguay 2011–16 i 4.90 1.65 4.21 15.02 5.35 16.30 El Salvador 2011–16 i 4.08 2.93 3.46 8.86 4.22 10.23 Uruguay 2011–16 i 3.18 1.76 9.10 23.94 10.64 26.13 Egypt, Arab Rep. 2010–12 c 2.58 0.78 2.84 5.17 2.99 5.25 Iran, Islamic Rep. 2009–14 c 1.25 –1.27 6.60 17.42 7.02 16.34 West Bank and Gaza 2011–16 c –0.89 –0.55 5.30 10.84 5.03 10.50 Bangladesh 2010–16 c 1.35 1.54 1.88 3.52 2.03 3.86 Bhutan 2012–17 c 1.63 1.67 3.54 8.08 3.83 8.78 Sri Lanka 2012–16 c 4.80 5.28 3.37 7.51 3.98 8.99 Pakistan 2010–15 c 2.72 4.25 2.28 4.01 2.60 4.94 Burkina Faso 2009–14 c 5.84 2.93 1.04 2.39 1.38 2.76 Côte d’Ivoire 2008–15 c 0.74 –0.22 1.46 3.91 1.53 3.84 Ethiopia 2010–15 c 1.67 4.91 1.48 2.88 1.61 3.66 Mozambique 2008–14 c 1.52 5.36 0.72 1.96 0.78 2.65 Mauritania 2008–14 c 3.17 1.44 2.37 5.27 2.86 5.74 Namibia 2009–15 c 5.73 6.64 1.75 7.79 2.41 11.27 Niger 2011–14 c –0.06 3.26 1.27 2.35 1.27 2.59 Rwanda 2010–13 c 4.82 2.78 0.90 2.43 1.03 2.63 Togo 2011–15 c 2.76 0.82 0.89 2.63 0.99 2.71 Uganda 2012–16 c –2.15 –0.96 1.39 3.32 1.28 3.19 South Africa 2010–14 c –1.34 –1.23 2.12 11.80 1.99 11.11 Zambia 2010–15 c –0.59 2.93 0.68 2.59 0.66 2.99 Austriag 2010–15 i –0.47 –0.28 29.76 56.03 29.07 55.26 Belgiumg 2010–15 i 0.57 0.48 26.73 47.73 27.50 48.89 Canada 2010–13 I –0.24 0.83 27.36 55.97 27.16 57.37 Switzerlandg 2010–15 i 0.98 0.84 31.99 63.63 33.59 66.35 Cyprusg 2010–15 i –4.34 –3.04 27.05 50.63 21.66 43.38 Greeceg 2010–15 i –8.35 –6.98 14.56 31.08 9.41 21.65 Germany 2010–15 I –0.18 0.59 28.13 52.31 27.88 53.88 Denmarkg 2010–15 i 0.57 0.45 28.97 50.77 29.80 51.93 Spaing 2010–15 i –2.16 –1.53 17.74 39.51 15.90 36.58 Finlandg 2010–15 i 0.53 0.17 28.13 48.95 28.89 49.36 Franceg 2010–15 i 0.74 0.21 26.41 52.68 27.40 53.23 United Kingdomg 2010–15 i 0.26 0.11 22.00 46.34 22.29 46.60 Irelandg 2010–15 i 1.69 1.14 22.19 43.74 24.13 46.29 Icelandg 2009–14 i –0.13 –0.47 29.23 51.35 29.04 50.15 Italyg 2010–15 i –2.13 –1.08 19.88 42.44 17.85 40.19 Luxembourgg 2010–15 i –2.14 –0.44 36.83 70.80 33.04 69.24 Maltag 2010–15 i 3.57 3.48 19.49 35.76 23.22 42.43 Netherlandsg 2010–15 i 0.95 0.66 27.90 50.25 29.25 51.92 Norwayg 2010–15 i 2.11 2.95 36.54 61.31 40.57 70.92 Portugalg 2010–15 i –0.87 –0.74 13.11 27.85 12.55 26.84 Swedeng 2010–15 i 1.80 2.40 26.97 47.84 29.49 53.85 United States 2010–16 I 1.31 1.67 24.38 62.43 26.36 68.93 Source: GDSP (Global Database of Shared Prosperity), fall 2018, World Bank, Washington, DC, PovcalNet (online analysis tool), http://iresearch.worldbank.org/PovcalNet. World Bank, Washington, DC. Note: PPP = purchasing power parity. a. Based on real mean per capita consumption or income measured at 2011 Purchasing Power Parity (PPP) using PovcalNet (http://iresearch.worldbank.org/PovcalNet). b. The annualized growth rate is computed as (Mean in year 2/Mean in year 1)^(1/(Reference year 2 – Reference year 1)) – 1. c. Refers to the year in which the underlying household survey data were collected and, in cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. See appendix A for criteria in selecting shared prosperity periods. d. Denotes whether the data reported are based on consumption (c) or income (i) data. Capital letters indicate that grouped data were used. e. Covers urban areas only. f. See Chen et al. (2018) for details on how the shared prosperity estimate for China is calculated. g. Source from World Bank (forthcoming). “Living and Leaving. Housing, Mobility and Welfare in the European Union,” World Bank Regional Report. 64 POVERTY AND SHARED PROSPERITY 2018 TABLE 2B.2 Changes in Shared Prosperity, 67 Economies, circa 2008–13 to circa 2010–15 Economies, number Average SP Higher SP in Lower SP in Average change Region Total circa 2010–15 circa 2010–15 Circa 2008–13 Circa 2010–15 in SP East Asia and Pacific 6 5 1 5.82 4.73 –1.09 Europe and Central Asia 22 12 10 1.51 2.41 0.90 Latin America and the Caribbean 14 4 10 4.56 3.21 –1.35 Middle East and North Africa 1 0 1 3.07 1.25 –1.82 South Asia 3 1 2 3.86 3.05 –0.81 Sub-Saharan Africa 1 0 1 4.09 –2.15 –6.24 Rest of the world 20 13 7 –1.10 –0.46 0.64 Total 67 35 32 1.92 1.87 –0.05 Source: GDSP (Global Database of Shared Prosperity), World Bank, Washington, DC, http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity. Note: SP = shared prosperity; the indicator measures growth in the average consumption or income of the bottom 40. The 2008–13 release refers to the version included in Poverty and Shared Prosperity 2016 (World Bank 2016b). The 2010–15 release refers to the version used in the present report. Regional and global averages of shared prosper- ity refer to simple averages across country means. TABLE 2B.3 Changes in the Shared Prosperity Premium, 67 Economies, circa 2008–13 to circa 2010–15 Economies, number Average SPP Higher SPP in Lower SPP in Average change Region Total circa 2010–15 circa 2010–15 Circa 2008–13 Circa 2010–15 in SPP East Asia and Pacific 6 4 2 0.91 1.10 0.19 Europe and Central Asiaa 22 11 10 0.30 0.21 –0.09 Latin America and the Caribbean 14 4 10 1.51 1.20 –0.31 Middle East and North Africa 1 0 1 4.27 2.52 –1.75 South Asia 3 0 3 0.27 –0.69 –0.96 Sub-Saharan Africa 1 0 1 2.24 –1.19 –3.43 Rest of the world 20 7 13 –0.09 –0.32 –0.23 Total 67 26 40 0.58 0.31 –0.27 Source: GDSP (Global Database of Shared Prosperity), World Bank, Washington, DC, http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity. Note: SPP = shared prosperity premium, which refers to the difference in the consumption or income growth of the bottom 40 and the mean of the country. The 2008–13 release refers to the version included in Poverty and Shared Prosperity 2016 (World Bank 2016b). The 2010–15 release refers to the version covered in the present report. Regional and global averages of shared prosperity refer to simple averages across country means. a. The SPP for FYR Macedonia is the same for both circa 2010–15 and circa 2008–13. SHARED PROSPERITY: MIXED PROGRESS 65 FIGURE 2B.1 The Shared Prosperity Premium, 91 Economies, by or consumption for measuring poverty and Region or Income Classification changes over time, see the section on chapter 1 in appendix A. See also boxes 1.1 and 4.4 in East Asia and Pacific World Bank (2016b). Europe and Central Asia 2. Estimates for China are based on PovcalNet Latin America and the Caribbean (see appendix A for further details). Middle East and North Africa 3. The economies in fragile and conflict-affected South Asia situations included are Côte d’Ivoire, Kosovo, Sub-Saharan Africa Togo, and West Bank and Gaza. Rest of the world 4. As of August 8, 2018, the World Bank consid- ered that 83 economies exhibited moderate or extreme data deprivation. Data deprivation Fragile and conflict-affected occurs if a country conducts fewer than two household surveys in a 10-year period (Sera- Low income juddin et al. 2015). Recognizing that the poor- Lower-middle income est countries are more data challenged, the Upper-middle income World Bank pledged in 2015 to help the poorest High income countries improve the frequency of data collec- tion to one household survey every three years. 0 10 20 30 40 50 60 5. A positive premium occurs in association with Number of economies a negative shared prosperity indicator in only Positive premium Negative premium No shared prosperity measure two cases, namely, Bosnia and Herzegovina and Sources: GDSP (Global Database of Shared Prosperity), fall 2018, World Bank, Washington, DC, http:// Iceland. In these countries, the entire growth www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity; PovcalNet (online distribution is negative, shared prosperity is analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. also negative though close to zero, and incomes Note: The count is based on the 164 economies on which PovcalNet includes direct estimates of pov- erty. Premium refers to the shared prosperity premium. “Positive premium” indicates that the income of among the top 60 are declining even more rap- the bottom 40 grew at a faster rate than the average. “Negative premium” indicates that the incomes idly than the incomes of the bottom 40. of the bottom 40 grew at a slower rate than the average in the country. “No shared prosperity measure” 6. The sample of economies in which shared indicates that a poverty rate is reported in PovcalNet for the economy, but that the data are inadequate for computing shared prosperity. prosperity can be measured in circa 2010–15 (13 of the 57 countries with poverty rates above 10 percent) is small, but similar conclusions would be reached if older time spells for shared Notes prosperity are considered—thus increasing the 1. Survey income and consumption are used coverage among economies with poverty rates herein as equivalent aggregates. The assump- above 10 percent. Taking this expanded sample, tion that they can be used interchangeably is a in the five countries with the highest level of requirement of the global poverty and shared poverty at the US$1.90 a day poverty line, none prosperity exercise given that country data of which is included in the present round on are often available on only one or the other. shared prosperity, four have a negative shared For more on the implications of using income prosperity and all have a negative premium. 66 POVERTY AND SHARED PROSPERITY 2018 Higher Standards 3 for a Growing World This chapter presents two new sets of monetary poverty lines intended to complement the international poverty line (IPL) of US$1.90 a day. First, two higher poverty lines, at US$3.20 and US$5.50 per day, are presented, reflecting typical national poverty thresholds in middle- income countries. Second, the chapter introduces a global societal poverty line (SPL) reflecting how monetary definitions of poverty at the national level vary with the overall income in a society. The SPL counts individuals as poor if they are living either on less than the IPL or on less than US$1.00 a day plus half the median value of consumption or income of their nation. The two sets of complementary poverty lines enrich our understanding of global monetary poverty. They reveal that global poverty rates are higher and being reduced more slowly than is indicated by assessments using the IPL. Although only 10 percent of the world population was living on less than US$1.90 per person per day in 2015, a quarter of the world was living on less than US$3.20 per person per day, and close to half the world was living on less than US$5.50 per person per day. The societal poverty rate declined by about a third between 1990 and 2015, dropping from approximately 45 percent to 28 percent. The chapter shows that the elimination of monetary poverty, more broadly defined, is still a distant goal. Introduction In 2013, the World Bank set a target of re- in each country of how much someone needs ducing extreme poverty as assessed by the to meet basic needs and live a life free of pov- international poverty line (IPL) to less than erty. These national poverty lines came from 3 percent of the global population by 2030. some of the poorest countries in the world, A frequent and important question posed and the US$1.90 value was an average of na- when monitoring progress toward the goal tional poverty lines from 15 of these very poor of ending poverty is whether the IPL, cur- countries (Ferreira et al. 2016). The inference rently valued at US$1.90 in 2011 purchasing is that, if US$1.90 defines the cost of basic power parity (PPP) U.S. dollars, is too severe needs in some of the poorest countries of the a threshold for defining whether someone world, then it can be viewed as an absolute is poor or not. Or, is US$1.90 per day really minimum threshold for defining poverty in all enough to live a life free of extreme poverty? countries. This approach for setting the IPL is One element of the answer involves exam- therefore guided by decisions made in some of ining the reason this amount was initially se- the poorest countries of the world and, in this lected. The value of the IPL was derived from way, respectful of national values and choices. a set of national poverty lines—lines that re- In addition to reflecting national values flected social and economic assessments made and choices, the IPL also has the desirable 67 attribute that it is fixed in real terms over time of the global population. By 2015, however, and across countries. The value of the line only 9 percent of the global population was will be regularly adjusted to reflect changing living in low-income countries (Fantom and prices over time so that it maintains a con- Serajuddin 2016). Because most of the ex- stant value through 2030 in each country of treme poor are now living in middle-income the world. Fixing the real value of the IPL in countries, and most of the total population this way ensures that the 3 percent by 2030 is in middle- and high-income countries, the target will not be shifted to make it easier or use of average assessments of basic needs in more difficult to reach. low-income countries is gradually becoming Additionally, the value of the IPL is con- less relevant in many countries of the world. verted into local currencies using the 2011 To address this concern in part, the World PPP index to lock in corresponding amounts Bank has introduced a new set of poverty of each local currency that can purchase lines that are higher in value and more rele- approximately the same amount of basic vant to current economic conditions. Look- goods within each country. Uniformity in ing beyond the IPL helps us better under- purchasing power across countries is desir- stand what poverty means in different parts able because it guarantees that the yardstick of the world. This chapter discusses two ways of material well-being used in each country in which the World Bank will now also report is comparable with the yardsticks used in on poverty, by assessing complementary pov- all other countries. The comparable value erty lines that will help guide efforts to de- of the line makes certain that, if individuals liver on the broader objective of establishing are identified as poor in one country because a world free of poverty. they are not able to acquire a basic bundle of goods, they would also be identified as poor in other countries if unable to purchase a Higher poverty lines for similarly valued bundle of goods. everyone: US$3.20 and “Measurable, time-bound goals are crucial US$5.50 a day to focusing our work,” explains World Bank President Jim Yong Kim (2016). The decision Although maintaining the value of the IPL to fix the purchasing power of the IPL over fixed in real terms is essential to monitoring time (up through 2030), and over all coun- progress toward achieving the 2030 poverty tries of the world, ensures that the goal line target, recognizing that how countries and the for this time-bound target is not changed. global community define poverty and basic All of these attributes of the IPL have been needs can change is also imperative. “The ne- persuasive in helping the global community cessities of life are not fixed” argues Townsend reach agreement around the poverty goal. (1979, 915). “They are continuously being The success of the IPL in fostering coordina- adapted and augmented as changes take place tion in the international community on the in society and its products.” issue of poverty is evident in the incorpora- To address the concern that the value of tion of the IPL in first the Millennium Devel- the IPL could be viewed as too extreme for opment Goals (MDGs) and now the Sustain- much of the world or that the necessities able Development Goals (SDGs).1 of life are greater now than previously, the Although the World Bank will continue to World Bank also uses poverty lines that are focus on the 3 percent target as assessed by higher in value. The values of these lines have the IPL, there are, nonetheless, reasonable been identified in a manner similar to the concerns with the current valuation of the IPL, that is, they reflect social and economic IPL. One source of concern is simply that, assessments made by governments; however, when those national poverty lines were con- the assessments are more recent, and they are structed for the 15 poor countries, 60 percent also produced in countries that are, on av- of the global population was living in low- erage, richer than those upon which the IPL income countries. The average value of these is based. national poverty lines was meaningful for the These complementary lines reflect typical vast majority of the poor and a large portion poverty assessments in lower-middle-income 68 POVERTY AND SHARED PROSPERITY 2018 countries (LMICs) and upper-middle-income TABLE 3.1 National Poverty Lines, circa 2011 countries (UMICs) in recent years.2 Specifi- Economy, income classification Median Mean cally, the lines are the median values of LMIC Low income 1.90 2.20 and UMIC national poverty lines in about Lower-middle income 3.20 3.90 2011 (Jolliffe and Prydz 2016). The value Upper-middle income 5.50 5.60 of the poverty line based on assessments High income 21.70 21.20 of needs in LMICs is US$3.20 per person Source: Jolliffe and Prydz 2016. per day expressed in 2011 PPP U.S. dollars, Note: Values are rounded to nearest 0.10. Economies are classified on the basis of official World Bank whereas the value of the line based on typi- income classifications, which rely on measures of per capita gross national income. Estimates are based cal basic needs in UMICs is US$5.50 (table on national poverty lines in 126 economies. The selected poverty line for each economy is the one that is closest in time to 2011. 3.1). Although these lines may sometimes be referred to as LMIC and UMIC lines, this does not mean that, for example, the LMIC Table 3.2 shows the change since 1990 in line can be applied only in the case of LMICs. the proportion of people living on less than The two poverty lines simply offer higher val- US$3.20 or less than US$5.50 a day. The find- ues that reflect assessments of basic needs in ings illustrated in the table suggest that the these two groups of countries. (The values success in reducing extreme poverty has not are based on a large database of harmonized been completely matched by reductions in the national poverty lines in about 2011; see ap- relative size of the population living on less pendix A for details.) than these higher-valued lines. Like the MDG As with the IPL, the intention is that the of halving extreme poverty as measured by the value of these LMIC and UMIC lines will IPL, the proportion of people living on less remain fixed in real terms, thereby allowing than US$3.20 a day was also halved between poverty reduction to be monitored also at 1990 and 2015. However, in contrast to the higher global poverty lines.3 Because they are MDG, which was met about six years ahead complementary lines based on more recent of schedule, the proportion of people living social assessments of basic needs, the lines on less than US$3.20 was only halved by 2014, will maintain greater relevance as poverty re- five years after the MDG target was reached. duction is monitored over the next 15 years. Measured according to the US$5.50 line, the The decision to use social assessments from success in improving the well-being of people middle-income countries also reflects the living in poverty must be additionally tem- overall growth in the global economy. Using pered. In 1990, approximately two-thirds of LMIC and UMIC median national poverty the population of the world was living on less lines as the basis for the complementary lines than US$5.50 a day. By 2015, this proportion means that these new lines better reflect the had fallen, but it had not been halved. Slightly situations in countries that are home to most less than half (46 percent) of the world was of the global population and most of the still living on less than US$5.50 a day in 2015. global poor. Figure 3.1, panel a, illustrates why the rate Chapter 1 in this report shows the tre- at which extreme poverty is being reduced is mendous progress the world has made in re- not matched by reductions in the share of the ducing extreme poverty as measured by the world population living on less than US$3.20 IPL. As one remarkable example, target 1.A or US$5.50. In 1990, there was a concentra- of MDG 1, to cut the poverty rate of 1990 in tion of people who were consuming just less half by 2015, was reached approximately six than the US$1.90 threshold, as revealed by the years ahead of schedule. This is true whether distribution peaking to the left of this value.4 we examine the global poverty rate or the Although one-third of the world’s population global poverty rate less several high-income consumed less than US$1.90, most of those countries. This extraordinary success allows people consumed at rates between US$1.00 us to broaden our focus to ensure that those and US$1.90. Economic development shifted people who may not be poor as measured by the distribution to the right, moving the the IPL, but who struggle nonetheless to sat- hump over the US$1.90 threshold, leading to a isfy their basic needs, also benefit from eco- rapid reduction in the number of people con- nomic development. suming less than US$1.90. In contrast, panel HIGHER STANDARDS FOR A GROWING WORLD 69 TABLE 3.2 Poverty at Higher Poverty Lines, US$3.20 and US$5.50 (2011 PPP) a. Poverty rate by region at US$3.20 (%) Percentage point Region(s) 1990 1999 2008 2013 2015 change, 1990–2015 East Asia and Pacific 85.3 67.1 37.4 17.5 12.5 −72.8 Europe and Central Asia 9.9a 21.1 7.5 5.7 5.4 −4.6 Latin America and the Caribbean 28.3 27 15.7 11.4 10.8 −17.5 Middle East and North Africa 26.8 21.7 16.7 14.4 16.3 −10.5 South Asia 81.7 76a 67.9 53.9 48.6a −33.1 Sub-Saharan Africa 74.9 78.3 72.2 67.8 66.3 −8.6 Sum of regions 66.4 60.1 45 33.7 30.7 −35.7 Rest of the world 0.8 0.8 0.7 0.8 0.9 0.1 World 55.1 50.6 38.2 28.8 26.3 −28.9 b. Poverty rate by region at US$5.50 (%) Percentage point Region(s) 1990 1999 2008 2013 2015 change, 1990–2015 East Asia and Pacific 95.2 87 63.6 42.4 34.9 −60.3 Europe and Central Asia 25.3a 44.5 17.1 14.1 14 −11.3 Latin America and the Caribbean 48.6 47 33.3 27.2 26.4 −22.2 Middle East and North Africa 58.8 54.5 46.6 42.3 42.5 −16.3 South Asia 95.3 93.1a 89.8 84.2 81.4a −14 Sub-Saharan Africa 88.5 90.5 88.1 85.4 84.5 −4.1 Sum of regions 80.5 79.3 66.5 57 53.7 −26.7 Rest of the world 1.7 1.3 1.2 1.5 1.5 −0.2 World 67 66.8 56.5 48.7 46 −21 Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: The criteria for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. PPP = purchasing power parity. a. This estimate is based on less than 40 percent of regional population coverage. a shows that a significantly smaller share of with significantly fewer people now living people was living on more than US$1.90 but below the $1.90 threshold, future growth will less than US$3.20. So the economic growth not lift as many people past this threshold that led to a rapid reduction in extreme pov- as previously experienced. Thus, the reduc- erty could not carry as many people above tion in extreme poverty will be tempered, al- the US$3.20 threshold. This narrative is sim- though the potential for progress in reducing ilar in the case of the US$5.50 line: economic the share of the world’s population living on growth carried significantly fewer people past less than US$5.50 a day will be significant. the US$5.50 threshold. This reinforces the conclusion in chapter 1 The global distribution of consumption that the slowdown in the rate of decline of for 2015 offers useful insights into what one extreme poverty will likely continue. may expect in the near future (as illustrated In addition to providing insight on the po- by the histogram in figure 3.1, panel b). In tential for global poverty reduction in the near- 2015, the peak in the consumption distribu- term future, these higher lines also present tion had shifted to the right and is now be- clear regional differences in the profile of the tween US$3.20 and US$5.50. Only about 10 people living in extreme poverty or nearly so. percent of the global population is still living The countries in East Asia and Pacific not only on less than US$1.90 a day. An implication of had the largest reductions in extreme poverty, this is that growth in the near future will shift but they also experienced the largest reductions the distribution further to the right, leading in the proportion of people living on less than to a rapid reduction in the share of people US$3.20 and US$5.50 (figure 3.1, panels c and living on less than US$5.50 a day. However, d). Between 1990 and 2015, the proportion of 70 POVERTY AND SHARED PROSPERITY 2018 FIGURE 3.1 Consumption and Income Distributions, 1990 and 2015 a. World, 1990 b. World, 2015 1,500 1,500 Population (millions) Population (millions) 1,000 1,000 500 500 0 0 1− 1 −1 .5 60 60 1.9 .9 1.9 .9 .5 20 0 40 0 80 80 80 80 20 0 40 0 10 0 10 0 3.2 3.2 5.5 .5 60 5.5 .5 60 3.2 .2 − −2 −4 −2 −4 −1 −1 <0 1 1 <0 0.5 0.5 −5 −3 −5 >1 >1 − − −1 −1 1− − Consumption/income per day (2011 US$ PPP) Consumption/income per day (2011 US$ PPP) c. East Asia and Pacific, 1990 d. East Asia and Pacific, 2015 800 800 Population (millions) 600 Population (millions) 600 400 400 200 200 0 0 −1 60 60 .5 1.9 .9 1.9 .9 −1 20 0 40 0 80 80 20 0 40 0 80 80 10 0 10 0 3.2 .2 5.5 .5 60 3.2 .2 5.5 .5 60 .5 −2 −4 −2 −4 −1 −1 <0 1 1 0.5 −3 −5 −3 −5 <0 >1 >1 0.5 − − −1 −1 1− 1− Consumption/income per day (2011 US$ PPP) Consumption/income per day (2011 US$ PPP) e. South Asia, 1990 f. South Asia, 2015a 600 600 Population (millions) Population (millions) 400 400 200 200 0 0 −1 60 60 1.9 .9 1.9 .9 20 0 40 0 80 80 20 0 40 0 80 80 1− 1 .5 10 0 10 0 3.2 3.2 5.5 .5 60 3.2 .2 5.5 .5 60 .5 −2 −4 − −2 −4 −1 −1 1 1 <0 0.5 −5 −3 −5 >1 >1 <0 − − 0.5 −1 −1 1− − Consumption/income per day (2011 US$ PPP) Consumption/income per day (2011 US$ PPP) g. Sub-Saharan Africa, 1990 h. Sub-Saharan Africa, 2015 250 250 Population (millions) Population (millions) 200 200 150 150 100 100 50 50 0 0 −1 .5 60 60 1.9 .9 1.9 .9 −1 80 80 20 0 40 0 20 0 40 0 80 80 10 0 10 0 60 3.2 .2 5.5 .5 3.2 .2 5.5 .5 60 .5 −2 −4 −2 −4 −1 −1 <0 0.5 1 1 −3 −5 −3 −5 >1 >1 <0 0.5 − − −1 −1 1− 1− Consumption/income per day (2011 US$ PPP) Consumption/income per day (2011 US$ PPP) Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: Bins were purposely selected to highlight US$1.90, US$3.20, and US$5.50 poverty lines. The size of the selected bins produces a histogram that approximates the shape of the estimated density function of the log of income/consumption. a. This estimate is based on less than 40 percent of regional population coverage. HIGHER STANDARDS FOR A GROWING WORLD 71 people living on less than each of these three Higher lines tailored to thresholds declined by nearly 60 percentage country circumstances: points. This can be seen in panels c and d in the large rightward shift of the distribution Societal poverty between 1990 and 2015. This massive prog- The second set of complementary poverty ress over every threshold was experienced only lines the World Bank is now reporting are tai- in East Asia and Pacific. In the other regions, lored to the specific levels of economic devel- progress in reducing poverty at the various opment of each country and are designed to thresholds has been much more modest. measure societal poverty. The introduction Figure 3.1, panel e, reveals that in South Asia of this measure is in direct response to rec- the peak of the consumption distribution was ommendations of the Commission on Global slightly below US$1.90 in 1990. By 2015, most Poverty, led by Professor Sir A. B. Atkinson, people now lived on more than US$1.90 but to “introduce a societal head count measure less than US$3.20 (figure 3.1, panel f). There of global consumption poverty that takes was a large decline—35 percentage points—in account, above an appropriate level, of the the share of people living on less than US$1.90. standard of living in the country in question, There was also a decline (60 percent) in the thus combining fixed and relative elements of number of people living below US$1.90 (table poverty” (World Bank 2017, xxi). 1A.1). The story for South Asia changes, how- A key attribute of the IPL is that it is con- ever, when we examine the US$3.20 poverty verted into local currencies using the 2011 threshold. The percentage of the total pop- PPP U.S. dollars to ensure that the value of ulation living below this threshold declined the line reflects approximately the same pur- substantially over this time, but because of chasing power in all countries (see earlier a growing population, the number of people discussion). If an individual who is able to living on less than US$3.20 declined by only 8 buy US$2.00 worth of goods in one country percent over this 25-year period. In contrast to each day is not considered poor, then an in- East Asia where the peak of the distribution es- dividual who is able to consume at that same sentially shifted past the US$5.50 threshold, in level in another country will also not be poor. South Asia the peak of the distribution of con- Everyone is assessed by the same standard re- sumption essentially shifted from just below gardless of where they live. This guiding prin- US$1.90 to just below US$3.20. ciple of the monitoring of extreme poverty In the case of Sub-Saharan Africa (fig- ensures that the material well-being of people ure 3.1, panels g and h), the distribution has can be assessed and compared meaningfully shifted rightward only very slightly. Although across the world. chapter 1 reported that extreme poverty Although ensuring equality in the yard- declined by 13 percentage points in Sub- stick of poverty is desirable, there are some Saharan Africa between 1990 and 2015, panel trade-offs in making this choice. One trade- d reveals that the peak of the consumption off in particular helped guide the World Bank distribution was essentially around US$1.90 toward the development of a new comple- in both 1990 and 2015. The decline in the mentary poverty line, the societal poverty prevalence of extreme poverty coincided with line (SPL). Fixing the value of the line in nearly a 50 percent increase in the number of constant PPP terms across all countries en- people living in extreme poverty during this sures that the bundle of goods that can be time period. Overall, the population of Sub- purchased is the same. As economies grow, Saharan Africa nearly doubled in this time however, this bundle is becoming a less use- period, with one of the largest increases in ful indicator of basic needs in many places. population being for those living on less than For example, in 2015, the extreme poverty US$3.20 and more than US$1.90. Economic rate was less than 3 percent in more than half growth slightly outpaced population growth the 164 countries in which the World Bank resulting in a distribution of consumption monitors extreme poverty; and the majority that shifted only slightly to the right but grew of the world no longer lives in low-income significantly larger, reflecting the near dou- economies. For many countries, the social bling of the population. 72 POVERTY AND SHARED PROSPERITY 2018 relevance of the IPL has lessened over time on a relative notion of the poverty line re- as their economies have grown. This is largely volves around the fact that participation in due to the observance that needs change as society with dignity may require more goods the world becomes richer (Townsend 1979). in a richer country than in a poorer coun- A very closely related point is that, as try. Social participation might thus be more countries grow richer, uniformity in the con- closely related to the concept of meeting sumption bundle may not result in the same basic needs in the poorest of countries, but level of well-being everywhere. Carrying out in richer countries the ability to participate in basic functions of life might require more society might be costlier. goods in some countries than in others, and This conceptual point, that the very defini- fixing the consumption bundle could result tion of basic needs in terms of goods and ser- in unequal assessment of people across the vices may vary across countries, appears to be world in terms of their ability to function empirically supported. Figure 3.2 shows that in society in a socially acceptable manner. there is significant variation across countries Another way to express this is that ensuring in how basic needs are defined, as expressed equality across countries in terms of carrying in national poverty lines. The analysis in the out the same basic functions of life in each figure is based on 699 estimated national society may result in a poverty line that takes poverty lines—all of which are expressed in different monetary values (Sen 1983). For ex- comparable purchasing power terms. It re- ample, participating in the labor market may veals a strong positive correlation between the be viewed as a minimal social functioning; median level of consumption in each country the cost of this functioning, however, may and the assessment of basic needs. Analysis require only clothing and food in a poor soci- on a different set of national poverty lines has ety, whereas in a richer society it may require similarly shown that the values of absolute access to the internet, transportation, and a national poverty lines range across countries cell phone, in addition to clothing and food. from US$0.63 a day to more than US$9.00 a Another example that more directly builds day (in 2005 PPP U.S. dollars) and that higher FIGURE 3.2 National Poverty Lines and Economic Development a. Level scale b. Log scale 20 20 Poverty line (2011 US$ PPP) Poverty line (2011 US$ PPP) 10 15 5 10 2 5 1 0 0 0 10 20 30 0 1 2 5 10 20 40 Median consumption/income per day Median consumption/income per day (2011 US$ PPP) (2011 US$ PPP) Full sample 2011 sample Predicted 10th and 90th percentiles Source: Jolliffe and Prydz 2016. Note: Both panels plot 699 harmonized national poverty lines. Dark dots indicate the 104 poverty lines that are closest to 2011 (one unique line for each country), excluding lines prior to 2000. Both panels plot the same data. Panel a plots the lines on actual values. Panel b plots these same values, but the axis values of the plots are log transformations. Lines in panel b are predicted (conditional bivariate) 10th and 90th percentile lines. All axis values are expressed in 2011 purchasing power parity (PPP) U.S. dollars. HIGHER STANDARDS FOR A GROWING WORLD 73 poverty lines correspond to relatively more of median consumption (or income) per day well-off economies (Ravallion 2010). in that country. If US$1.00 plus half the me- This finding is not merely a cross- dian consumption is less than the IPL, then sectional association. If the definition of pov- the SPL is equal to the IPL. In many countries, erty changes as countries grow richer on aver- this value is greater than US$1.90, and this age, national poverty lines should be changing greater value then becomes the SPL. More for- in real terms over time. This is indeed what mally, the SPL adopted by the World Bank is is observed. A few specific examples follow. In calculated in 2011 PPP U.S. dollars as follows: 2011, the government of India raised the real SPL = max (US$1.90, US$1.00 value of the urban poverty line by more that + 0.5 ‫ ן‬median consumption).7 (3.1) 40 percent, increasing it from Rs 33 to Rs 47 per person per day. The change in rural pov- For example, in a country in which the erty lines was significantly less, about 19 per- median consumption per person is US$1.60 cent, increasing from Rs 27 to Rs 32. At about per day, the IPL is greater than US$1.00 this time, China increased the real value of the plus half of US$1.60, so the value of the SPL rural poverty line by more than 75 percent is US$1.90.8 Alternatively, in a country in (Addison and Niño-Zarazúa 2012). Many which the median consumption is US$3.00 governments have increased the real value per day, the SPL is US$2.50 (US$1.00 + of national poverty lines in recognition that 0.5 ‫ ן‬US$3.00). In defining societal poverty their economies have grown so significantly in this way, Jolliffe and Prydz (2017), build that the concept of basic needs has changed on the important contributions of Atkinson fundamentally. After 15 years of keeping the and Bourguignon (2001), Chen and Raval- real value of the national poverty line con- lion (2013), Foster (1998), and Ravallion and stant, the government of Nepal raised the real Chen (2011). value of its poverty line in 2011 (CBS 2012). By this definition, societal poverty rep- Similarly, the government of Jordan increased resents a combination of extreme poverty, the real value of the poverty line by about 10 which is fixed in value for everyone, and a percent in 2011 (Jolliffe and Serajuddin 2018; relative dimension of well-being that differs World Bank 2009).5 Absolute national poverty in every country depending on the median lines are behaving like relative poverty lines in level of consumption in that country. Figure that they are becoming higher as countries get 3.3 illustrates how the SPL changes as the richer. “It can be agreed that a sustained in- median consumption in a country increases. crease in average living standards is likely to In countries with low median consumption lead eventually to more generous perceptions (less than US$1.80 per person per day), a rise of what ‘poverty’ means in a given society,” in median consumption does not change the notes Ravallion (1998, 29). SPL. Indeed, the SPL has the same value as the IPL in all countries with median con- sumption at less than US$1.80. However, as Characteristics of the societal countries with median consumption at more poverty line than US$1.80 become richer, and the median To reflect this viewpoint, the World Bank consumption increases, the value of the SPL will now initiate reporting on societal pov- also rises. The climbing cost of social partic- erty, which is based on a poverty line that ipation as the economy grows is reflected in is adjusted for the median level of well- the positive slope of the line. being in each country.6 First, according to The slope of one-half, the rate at which the definition of societal poverty used by the the SPL is rising as countries become richer, World Bank, individuals living in extreme comes from the empirical association ob- poverty as measured by the IPL are also suf- served between national poverty lines and fering from societal poverty. Second, the new different measures of overall consumption measure considers that individuals are suffer- in society. It indicates that, on average, the ing from societal poverty if they are living on national poverty lines are increasing at a less than US$1.00 a day plus half of the value rate equal to half the median consumption 74 POVERTY AND SHARED PROSPERITY 2018 FIGURE 3.3 Societal Poverty Line for country poverty rates.10 Similarly, Eu- ropean countries typically set national pov- erty thresholds at 50 percent or 60 percent of median disposable household income Societal poverty line (Vecchi 2015). The gradient of 50 percent coincides with SDG indicator 10.2.1 on in- Slope = 0.5 equality, namely, the proportion of peo- ple living below 50 percent of the median income, by sex, age, and disability status.11 Similarly, the intercept of US$1.00 per person per day in 2011 PPP U.S. dollars cor- $1.90 responds in value with some relevant empir- ical findings. Ravallion (2016) estimates an $1.00 empirical lower bound on consumption in part to address the issue of how to monitor the concept of leaving no one behind. His 0 analysis indicates that the value of this con- Median national consumption (or income) per day sumption floor is US$0.67 in 2005 PPP U.S. Source: Jolliffe and Prydz 2017. dollars, which is US$1.00 after conversion to Note: The lower bound is equal to the international poverty line, 2011 PPP.12 There are also analyses that aim which is currently valued at US$1.90 in 2011 purchasing power to estimate minimum biological needs—a parity U.S. dollars. The slope is equal to 0.5. The intercept is US$1.00. The kink point in the figure is at a median national con- concept that differs significantly from socially sumption or income of US$1.80. acceptable ways of meeting basic needs. The value of these minimum needs tends to be about US$1.00 (Lindgren 2015).13 in the countries. The slope of one-half and The SPL is estimated by first extracting the the intercept of US$1.00 are the values that median level of daily per capita consumption most closely fit the data provided by the na- (or income) for each national distribution tional poverty lines and overall consumption from PovcalNet, then following the formula in each country. This observed relationship in equation (3.1) to derive a set of country- between national poverty lines and national specific values of the SPL.14 If this value is well-being determines the formula for mea- greater than US$1.90, the SPL is passed to suring societal poverty.9 In an important PovcalNet, which reports the poverty rate as- sense, the SPL and the IPL share the same sociated with this line. This rate is the societal empirical underpinning. Both are anchored poverty rate. (If the SPL < US$1.90, then so- in the distribution of national poverty lines, cietal poverty is simply the same as extreme which represent countries’ own judgements poverty estimated in chapter 1.) of what poverty means for them. Whereas the By design, the SPL rises with growth. The IPL focuses narrowly—and deliberately—on population-weighted average SPL across all the choices of some of the poorest countries, countries increased from US$5.30 in 1990 to the SPL is built on information from across about US$6.90 in 2015, reflecting the steady, the whole range of levels of development. global growth in real median consumption In addition to fitting the data well, the during that time. The SPL growth rate has slope coefficient of half the median is widely been much stronger in higher-income coun- used by many countries and organizations as tries. Among today’s UMICs, the mean SPL a measure of relative poverty and inclusion. nearly doubled over the same time period, ris- In the academic literature on poverty, this ing from US$3.00 in 1990 to US$5.80 in 2015. slope has been a subject of discussion for a In contrast, the average SPL only slightly in- long time, and, in policy, the Organisation creased in value in low-income countries over for Economic Co-operation and Develop- this period—in large part because of changes ment uses 50 percent of median household in country composition of these income income as the headline poverty indicator categories. HIGHER STANDARDS FOR A GROWING WORLD 75 TABLE 3.3 Average Societal Poverty Lines, by Region and Income Classification, 1990–2015 Percentage point a. Region(s) 1990 1999 2008 2013 2015 change, 1990–2015 East Asia and Pacific 2.0 2.2 3.2 4.3 4.8 2.8 Europe and Central Asia 5.9a 4.4 7.1 7.8 7.6 1.8 Latin America and the Caribbean 3.9 4.1 5.2 6.1 6.1 2.2 Middle East and North Africa 3.6 3.8 4.5 4.7 4.6 1.0 South Asia 2.0 2.1a 2.2 2.5 2.6a 0.6 Sub-Saharan Africa 2.1 2.1 2.2 2.3 2.3 0.2 Sum of regions 2.7 2.7 3.4 4.0 4.1 1.4 Rest of the world 17.8 19.8 22.1 22.0 22.8 5.0 World 5.3 5.4 6.2 6.7 6.9 1.6 Percentage point b. Income group 1990 1999 2008 2013 2015 change, 1990–2015 Low income 2.1 2.1 2.1 2.2 2.2 0.1 Lower-middle income 2.2 2.2 2.5 2.8 2.9 0.7 Upper-middle income 3.0 3.0 4.4 5.4 5.8 2.8 High income 16.4 18.2 20.4 20.5 21.2 4.8 Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: The table presents (population-weighted) average of the value of country societal poverty lines, evaluated at US$1.00 + 50 per- cent ‫ ן‬median consumption (or income) with a lower bound of US$1.90 (2011 PPP). Current (2018) World Bank income classifications have been used. The criteria for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. PPP = purchasing power parity. a. This estimate is based on less than 40 percent of regional population coverage. Table 3.3 reveals significant differences in rate of societal poverty, as measured by the the pattern of the regional growth of the SPL. SPL. It also displays the count and rate of abso- For example, the mean SPL in South Asia, lute extreme poverty as measured by the IPL of East Asia and Pacific, and Sub-Saharan Africa US$1.90 a day. The first striking aspect of the in 1990 was just slightly higher than the IPL of figure is that, although the total count of peo- US$1.90. Because of strong economic growth ple living in extreme poverty has declined rap- in East Asia and Pacific, the mean line more idly, the number of people who are identified than doubled, to US$4.80 per day in 2015. In as societally poor has largely stayed the same contrast, in Sub-Saharan Africa, which has ex- over the 25 years, between 1990 and 2015. perienced much weaker overall growth, there In contrast, the share of the global popu- has been little change in the value of the SPL, lation that is societally poor has fallen steadily increasing only by $0.20 since 1990. since 1990, but at a much slower pace than the decline in extreme poverty (figure 3.4, panel a). This divergence in the rate of decline am- Profile of societal poverty plifies the distinction between the two mea- Global counts of extreme poverty are based sures. Table 3.4 shows that, in 1990, the societal on data from PovcalNet (described in appen- poverty rate, at 44.5 percent, was estimated dix A), and so too are the estimates of societal at about 9 percentage points higher than the poverty presented in this chapter.15 Using the extreme poverty rate (35.9 percent, as seen in country-specific SPL and following the same figure 3.4, panel a). By 2015, the gap between aggregation and lining-up methods as in the societal and extreme poverty, in terms of the case of the extreme poverty estimates reported percentage point difference (18.4), had more in chapter 1, the estimated societal poverty than doubled. In a growing global economy, headcount was approximately 2.1 billion peo- this divergence is an expected outcome, and ple in 2015.16 This is almost three times more the magnitude of the change in the difference than the global count of people living on less in the rates over the decades highlights the than US$1.90 a day, which was estimated at distinction in the informational content in approximately 736 million in 2015. Figure 3.4 these measures. In the 1980s and early 1990s, displays the change in both the count and the the societal poverty rate and the extreme pov- 76 POVERTY AND SHARED PROSPERITY 2018 FIGURE 3.4 Societal and Extreme Poverty, Global Estimates, 1990–2015 a. Poverty rate b. Number of poor 50 2,500 40 2,000 Poverty rate (%) Millions 30 1,500 20 1,000 10 500 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 Societal poverty Extreme poverty Note: Panel a shows the rate of extreme poverty based on the international poverty line (US$1.90, 2011 PPP) and societal poverty based on the societal poverty line. Panel b shows the corresponding number of people who are poor by both lines. PPP = purchasing power parity. erty rate were largely similar concepts because Similar to the case of regional profiles of most of the world population was living in absolute poverty, Sub-Saharan Africa stands countries with low median national consump- out because of the substantially higher rates of tion, whereby the IPL and the SPL were either societal poverty. Although the societal poverty identical or close in value. They largely por- rate has declined 9 percentage points over the trayed the same picture of poverty. But now, as last 25 years in Sub-Saharan Africa, the overall countries have grown richer, and median con- rate is still almost half the population, 49 per- sumption is above US$1.80 in many countries cent, in 2015. In contrast, societal poverty had of the world, the SPL is capturing significantly dropped 38 percentage points in the East Asia more information about the distributional as- and Pacific region, reducing by more than half pects of growth. the rate of 63.4 percent in 1990, to 25.1 per- TABLE 3.4 Societal Poverty Rates, 1990–2015 Percent Percentage point a. Region(s) 1990 1999 2008 2013 2015 change, 1990–2015 East Asia and Pacific 63.4 46.6 34.7 27.2 25.1 −38.3 Europe and Central Asia 22.2a 27.0 19.4 17.7 17.3 −4.9 Latin America and the Caribbean 33.9 34.0 29.4 27.5 26.9 −7.0 Middle East and North Africa 28.6 26.6 23.7 21.5 22.9 −5.7 South Asia 51.0 46.9a 42.0 35.4 32.9a −18.0 Sub-Saharan Africa 57.9 61.2 53.3 49.9 49.0 −9.0 Sum of regions 50.6 44.3 37.0 31.9 30.6 −20.0 Rest of the world 15.5 15.2 15.4 16.0 16.0 0.5 World 44.5 39.7 33.7 29.6 28.4 −16.1 Percentage point b. Income group 1990 1999 2008 2013 2015 change, 1990–2015 Low income 63.6 65.0 55.6 51.4 51.2 −12.3 Lower-middle income 50.5 46.7 40.3 34.9 32.9 −17.6 Upper-middle income 50.8 39.7 30.4 24.7 23.5 −27.3 High income 15.8 15.8 15.9 16.4 16.3 0.5 Source: PovcalNet (online analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. Note: World Bank income classifications are current as of 2018. Change is measured in percentage points (pp). “Sum of regions” was previously referred to as “developing world” for which PovcalNet monitors poverty. a. The criteria for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. HIGHER STANDARDS FOR A GROWING WORLD 77 cent in 2015. All developing regions have seen of the SPL increases in percentage terms at an overall decline in societal poverty rates a rate that is slower than the percentage in- since 1990, especially during the 2000s. In crease in economic growth. This means that, contrast societal poverty has been stubbornly if median consumption doubles, the SPL in- static, at about 16.0 percent in aggregate, in creases, but by an amount less than double. the mainly high-income countries in the “rest Because the percent increase in the SPL of the world” category, though remaining will always be less than the percent increase lower than in all the developing regions. in median consumption, distribution- A similar pattern emerges in the lower neutral growth will reduce societal poverty. half of table 3.4, which presents societal pov- By construction, the percentage increase in erty rates by country income classifications. the SPL in response to a percentage increase Countries are shown in their income classifi- in median consumption differs among rich cation as of 2018. So a country identified as a countries relative to poor countries. For the UMIC in 2018 was not necessarily a UMIC in poorest countries, among which median con- 1990. It might have grown economically into sumption is less than US$1.80 a day, growth that classification, and this happened often. in median consumption does not change Partly for this reason, the largest declines in the value of the SPL. If a country’s median societal poverty occurred among UMICs. consumption grows sufficiently and crosses The countries classified as UMICs in 2018 the US$1.80 kink point, then the SPL will had realized some of the highest economic increase slightly (see figure 3.1). Figure 3.5 growth rates over the preceding 25 years. shows that for a typical country that has The analysis of societal poverty by in- reached high-income status, that is, median come classification confounds two issues. consumption around US$40 a day, the SPL Economic growth is an important engine of rises at a percentage rate that is nearly equal poverty reduction, but growth alone is a less to the percentage increase in median con- effective vehicle for reducing societal poverty sumption. For the richest of countries, dou- if a country is already in the higher-income bling median consumption nearly doubles category. This is because societal poverty is the value of the SPL. In contrast, increasing a hybrid concept that mixes elements of ab- the median consumption for countries whose solute and relative poverty (Foster 1998). An median consumption is less than US$1.80 implication of this hybrid concept (more has no effect on the SPL if the SPL has less specifically, the lower bound at the IPL and value than the IPL. the positive intercept at one) is that the value An alternative way to interpret this is that, among low-income countries, improvements FIGURE 3.5 Change in the Societal Poverty Line from Growth in societal poverty are highly correlated with improvements in extreme poverty; in fact, 1.0 they are identical in the poorest countries. Among high-income countries, the shared 0.8 prosperity premium is highly correlated with reductions in societal poverty. Positive 0.6 shared prosperity, combined with a shared Elasticity prosperity premium, indicates that a country 0.4 is growing and that the poorest in the coun- try are benefitting more from this growth. 0.2 In high-income countries, this is precisely what is needed to reduce societal poverty. 0 In this way, societal poverty combines infor- 1 2 5 10 20 40 mation about reductions in extreme poverty Median income/consumption (2011 US$ PPP, log scale) (discussed in chapter 1) and the notions of Societal poverty line, elasticity Fixed share of societal poverty line shared prosperity and the shared prosperity premium (discussed in chapter 2). Note: Vertical lines indicate the average national median consumption or income in 2013 for World Bank income classification groupings (from left to right): low-income (US$2.1/day), lower-middle-income Figure 3.6 illustrates this by displaying (US$3.7), upper-middle-income (US$9.3), and high-income (US$40) countries. PPP = purchasing power parity. the case of two UMICs, Costa Rica and Ec- 78 POVERTY AND SHARED PROSPERITY 2018 FIGURE 3.6 Societal Poverty and Shared shared prosperity premium and the reduc- Prosperity in Costa Rica and Ecuador tion in societal poverty, but at a lower level (about 0.4). Improvement in societal pov- 28 erty in low-income countries is driven much more by reductions in extreme poverty. Because societal poverty is a hybrid of abso- 27 lute and relative poverty concepts, it provides a natural bridge between the dual goals of re- Poverty rate (%) ducing extreme poverty and increasing shared prosperity. Among the poorest countries, the 26 value of the SPL is primarily determined by the IPL, and policies that promote reductions in extreme poverty will be the same as policies 25 that reduce societal poverty. As countries be- come wealthier, the SPL is increasingly deter- mined by the relative component of the pov- 24 erty line, which means that policies that focus 2011 2012 2013 2014 2015 2016 on raising the shared prosperity premium— the difference between the growth rate of the Costa Rica Ecuador bottom 40 and the average growth rate in a Note: The figure shows the decline in societal poverty for Ecuador country—will be more effective in reducing and Costa Rica over a time period where both countries had sim- societal poverty than policies that simply pro- ilar levels of economic growth. Societal poverty declined by more in Ecuador because the poor shared to a much larger extent in the mote growth in overall national income. economic growth. Why not simply use national uador. Between 2011 and 2016, both coun- tries exhibited comparable overall economic poverty lines? growth. The average annual growth in survey The social and economic assessments made consumption was 1.95 percent in Costa Rica by governments in setting national poverty and 1.92 percent in Ecuador. However, the lines underpin essentially all global poverty level of shared prosperity during this period lines, including the IPL, the higher lines of was greater in Ecuador than in Costa Rica. US$3.20 and US$5.50 (based on the me- In Costa Rica, growth among the bottom dian national poverty lines in LMICs and 40 percent of the income distribution (the UMICs), and now the SPL.17 Despite the im- bottom 40) was essentially the same as the portance of using assessments of basic needs growth in mean consumption. In contrast, undertaken by countries, this report reflects the bottom 40 grew a full percentage point a purposeful decision not to allow these as- more than the mean in Ecuador, resulting in sessments alone to completely determine the a shared prosperity premium. Although the value of the SPL. An assumption underlying level of growth was the same, the decline in the SPL is that the cost of social participation societal poverty was greater in Ecuador over rises with the level of economic development the period because of the difference in shared (as evidenced by the positive income gradient prosperity. An examination across all UMICs of national poverty lines), but does not vary and high-income countries for which data across countries at the same income.18 are available on shared prosperity reveals a This differs greatly from a proposal that strong correlation (equal to 0.6) between each and every national poverty line should the shared prosperity premium and the re- be used as a global SPL (Gentilini and duction in societal poverty. Improvement in Sumner 2012). Such a definition of societal societal poverty in UMICs and high-income poverty would certainly show respect for the countries requires economic growth in which judgment of the government of each coun- the poor disproportionately share. An exam- try, but it would suffer from the problem that ination of LMICs and low-income countries countries with the same level of median con- likewise indicates a correlation between the sumption could have different assessments HIGHER STANDARDS FOR A GROWING WORLD 79 of basic needs. The premise of global soci- In addition, the use of national poverty etal poverty is that it captures the idea that lines to count societal poverty is also prob- participation in society becomes costlier as lematic over time. As societies prosper, the countries become richer and that it is also real value of the threshold used to determine meant to serve as a tool for global poverty who is considered poor tends to increase. In monitoring. This latter element, that the SPL poorer countries, this is typically a stepwise is a global poverty line, means that it should process. A poverty line is held static in real allow comparisons across countries or over terms for several years or even several decades, time. The use of national poverty lines as the and then it is revised and held static again for SPL is problematic on both these counts. a long time. The length of time between the National poverty lines do not rise strictly revisions depends on the country and the rate in parallel with economic development, nor of growth experienced. The World Bank’s SPL are they fixed in value as is the IPL. Figure aims to capture how national poverty lines 3.2 shows that there are many cases in which evolve as countries grow and thus provide a a country may exhibit higher median con- consistently defined measure of poverty that sumption than some other country but have mirrors how societies typically measure pov- a lower national poverty line. There are also erty. The global SPL is derived from a global many cases in which countries at the same relationship between overall economic devel- level of economic development rely on vastly opment and observed national poverty lines different assessments of basic needs. If one across societies, and this averaging over all were to construct a global SPL based on the countries helps improve comparability. An sum of national poverty lines, then two peo- example from Vietnam follows. ple who consume at the same level and living In 1993, the General Statistics Office of in countries at the same level of economic Vietnam set a national poverty line that development might be treated differently in would reflect basic needs at the time. The line the global aggregation of societal poverty. An was equivalent to approximately US$2.05 a awkward implication of the use of national day at 2011 PPP U.S. dollars, which was kept poverty lines directly, without any averaging, roughly constant in real purchasing power is that the global aggregation based on this until 2010.19 Between 1992 and 2008, living rule would embody a counterintuitive social standards improved twofold, and poverty judgement that someone who is poor in one measured at the 1993 line fell from 58.0 per- country may not be identified as poor if his or cent to 14.5 percent. When a new survey was her well-being were assessed in a richer coun- conducted in 2010, a fresh welfare measure try with a lower national poverty line. and poverty methodology were developed to Figure 3.2, panel b, also includes predicted capture living standards and poverty more lines at the 90th and 10th percentiles from the effectively and reflect current basic needs. bivariate (quantile) regression of the poverty The new poverty line was set at a value equiv- line on median consumption. These predicted alent to approximately US$3.50 a day at 2011 lines have similar slopes, and the ratio of these PPP, with a corresponding estimated poverty lines in levels is approximately 2 over the en- rate of 21 percent. tire range. This suggests that, at any given level Figure 3.7 shows how the value of the na- of national well-being, the range in values of tional poverty line, SPLs, and correspond- national poverty lines is large. The most gen- ing headcount ratio have evolved in Viet- erous line is consistently about twice as large nam. The SPL in 1993 was US$1.92 a day, as the least generous line. This result is prob- only slightly below the national threshold of lematic for the proposal to construct a global US$2.05. When the economy grew rapidly count of the poor that treats the poverty line in the early 2000s, the value of the SPL rose. of each country as the relevant threshold. Al- In 2010, when the new poverty line was set, lowing for such significant differences in the the SPL was US$3.80, a little above the na- definition of basic needs across countries that tional poverty line; for the latest survey, it was are essentially at the same level of well-being US$4.90. Whereas the national poverty line is inconsistent with the idea that needs may is fixed in intervals, and goes up in discrete rise as economic development expands. steps, the SPL has risen more smoothly, fol- 80 POVERTY AND SHARED PROSPERITY 2018 FIGURE 3.7 Comparing National and Societal Poverty Lines and Rates, Vietnam, 1993–2015 a. Comparison of poverty lines b. Poverty rate 5 70 4 60 Poverty line (2011 US$ PPP) 50 3 Percent 40 2 30 20 1 10 0 0 93 95 97 99 01 03 05 07 09 11 13 15 93 95 97 99 01 03 05 07 09 11 13 15 19 19 19 19 20 20 20 20 20 20 20 20 19 19 19 19 20 20 20 20 20 20 20 20 Vietnam 1993 Vietnam 2010 Societal poverty Note: PPP = purchasing power parity. lowing the average trend of the national pov- US$3.20 per person per day, and slightly less erty line. In 2009, prior to the large increase than half of the world’s population is living on in the national poverty line, the SPL defini- less than US$5.50. The introduction of these tion of basic needs was much closer to the yet lines is motivated primarily by noting that to be determined national poverty line defi- the world has grown richer, and now most nition of basic needs in 2010 than to the defi- of the extreme poor no longer live in low- nition in 1993. Because the SPL was smoothly income countries but rather are in middle- updated as the country prospered, the 2009 income countries. The relevance of an IPL SPL was likely a better reflection of the social based on national poverty lines from low- assessment of basic needs at that point than income countries has gradually diminished the existing definition based on the 1993 na- with time. The motivation for these new tional poverty line value. higher lines could just as easily be made by recognizing that it is difficult to precisely identify thresholds and legitimate to have Conclusion differing views on what defines basic needs This chapter discusses two new sets of pov- (Atkinson 1987). The higher lines can help erty lines that the World Bank will use to re- address this concern. port on global poverty, and that are intended There are a couple of key takeaways from to complement the monitoring of poverty as these higher poverty lines. First, the rate of measured with respect to the IPL. One set has the reduction in extreme poverty in recent complementary poverty lines that are fixed decades has not been matched by a similarly at values greater than the IPL. These lines paced reduction in the share of people living reflect typical assessments of basic needs, on less than US$3.20 or US$5.50. More than as measured in national poverty lines, for a 80 percent of the population of South Asia set of LMICs and UMICs and are valued at and Sub-Saharan Africa still live on less than US$3.20 and US$5.50 (2011 PPP). The basic US$5.50 a day. Second, a large share of the descriptive statistics of the fixed poverty lines world’s population is living on slightly less are quite striking. As chapter 1 describes, 10 than US$5.50. A reasonable expectation is percent of the population is living on less that, if it continues, global economic growth than US$1.90. This chapter highlights that will produce a rapid reduction in the count one-fourth of the world is living on less than of people below this threshold. HIGHER STANDARDS FOR A GROWING WORLD 81 The other new poverty line that the World the highest rate of all regions in 1990 (63.4 Bank is now reporting is the SPL, which is a percent) to one of the lower rates (25.1 per- mixture of the fixed-in-value IPL and a line cent) in 2015. This impressive performance that rises in value with median consumption in reducing societal poverty was driven in in a country. According to this line, individ- large part by the extraordinary success in uals are considered poor if they are living eradicating extreme poverty. either on less than the IPL or on a dollar a The focus of monitoring poverty reduc- day, plus 50 percent of median consumption tion will continue to be on the progress in in their country of residence. The decision bringing extreme poverty below 3 percent, to anchor the SPL in a median measure of but it is clear that this measure of poverty well-being fits the data well (as assessed by is becoming less helpful in the majority of regressions of national poverty lines on con- countries, which already exhibit rates near sumption) and corresponds to existing defi- zero. Even though extreme poverty rates may nitions of relative poverty in many countries. be well below 3 percent in many countries, The proposed SPL is also relevant to SDG this does not mean that poverty is no lon- target 10.2 aimed at the social, economic, and ger a problem in these societies. The higher political inclusion of all. The indicator asso- poverty lines, set in accord with typical na- ciated with this target is the share of people tional poverty lines from countries classified living on less than 50 percent of the median as lower-middle and upper-middle income, income. Although the focus of this SDG is on provide useful guides for monitoring prog- reducing inequality and improving inclusion, ress on the basis of lines that are fixed in real it overlaps with the idea of monitoring soci- terms over time. For middle-income coun- etal poverty. As countries grow, societal pov- tries, these are useful markers for measuring erty provides information on the extent to progress that aligns with the definition of which the poor share in the growth. basic needs in middle-income countries from The rate of decline in societal poverty 2011. For lower-income countries, they could has been slower than the rate of decline in be viewed as markers for more aspirational extreme poverty. This is to be expected: the targets in poverty reduction. value of the SPL rises as the economy grows. Similarly, the measure of societal poverty Societal poverty has declined by about a third provides a global tool to measure poverty in across the world, dropping from approxi- accord with how countries assess changing mately 44.5 percent to 28.4 percent between standards of basic needs; however, in contrast 1990 and 2015. The reduction in extreme to the US$3.20 and US$5.50 lines, the real poverty was about twice this rate, declining value of these lines changes over time as the by about 72 percent, dropping from 35.9 country grows richer. Although the SPL can percent to approximately 10.0 percent. In change in real terms over time, it is constant the 1990s, when extreme poverty was more in value across countries that are at the same widespread, the difference between societal level of median consumption or income. Be- poverty and extreme poverty was relatively cause the SPL is constructed to reflect, on modest. In 2015, the societal poverty rate was average, national poverty lines at different almost three times larger than the extreme levels of median consumption or income, it poverty rate. The continued decline in ex- provides a useful measure of global poverty treme poverty will likely lead to greater diver- that aligns well with national assessments of gence in the informational content of these poverty. Keeping the IPL fixed is highly desir- two measures. able because it allows the progress toward an Another useful takeaway from the exam- unmoving target to be monitored, but, as the ination of societal poverty is the differential world advances toward the eradication of ex- performance across regions. Most regions treme poverty, the US$1.90 poverty line will experienced a fairly modest reduction in the become increasingly less relevant in many prevalence of societal poverty. The exceptions countries. In contrast, because the SPL yard- were the economies of East Asia and Pacific. stick is explicitly a function of the well-being Societal poverty was cut by more than half of each country, it is, by construction, rele- there between 1990 and 2015, declining from vant for all countries over time. 82 POVERTY AND SHARED PROSPERITY 2018 Annex 3A Historical global and regional poverty estimates TABLE 3A.1 Historical Trends, Global Poverty Estimates, 1990–2015 a. US$3.20 (2011 PPP) Poverty Squared Population Year Poverty rate (%) Poverty gap (%) poverty gap Poor (millions) (millions) 1990 55.1 26.6 15.5 2,914.0 5,284.9 1993 54.4 25.6 14.7 3,013.4 5,542.9 1996 51.7 22.8 12.7 2,993.8 5,792.6 1999 50.6 22.3 12.4 3,056.1 6,038.1 2002 47.2 20.2 11.0 2,962.7 6,276.8 2005 42.2 16.9 8.8 2,753.3 6,517.0 2008 38.2 14.9 7.7 2,586.9 6,763.7 2011 32.8 12.1 6.0 2,298.8 7,012.8 2013 28.8 10.2 5.0 2,071.7 7,182.9 2015 26.3 9.2 4.6 1,932.7 7,355.2 b. US$5.50 (2011 PPP) Poverty Squared Population Year Poverty rate (%) Poverty gap (%) poverty gap Poor (millions) (millions) 1990 67.0 41.5 28.8 3,540.5 5,284.9 1993 67.9 40.9 28.0 3,761.2 5,542.9 1996 67.3 38.7 25.6 3,900.0 5,792.6 1999 66.8 38.1 25.1 4,035.2 6,038.1 2002 64.0 35.6 23.0 4,018.2 6,276.8 2005 60.4 31.9 19.9 3,939.4 6,517.0 2008 56.5 29.0 17.8 3,823.7 6,763.7 2011 52.2 25.3 15.0 3,662.3 7,012.8 2013 48.7 22.6 13.1 3,498.3 7,182.9 2015 46.0 20.9 12.0 3,386.5 7,355.2 Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), World Bank. Note: PPP = purchasing power parity. HIGHER STANDARDS FOR A GROWING WORLD 83 TABLE 3A.2 Historical Trends, Regional Poverty Rates, 1990–2015 Percent a. US$3.20 (2011 PPP) Poverty rates Region 1990 1993 1996 1999 2002 2005 2008 2011 2013 2015 East Asia and Pacific 85.3 79.7 70.6 67.1 57.2 45.4 37.4 26.5 17.5 12.5 Europe and Central Asia 9.9a 15.1 19.2 21.1 14.9 11.8 7.5 6.6 5.7 5.4 Latin America and the Caribbean 28.3 27.1 27.7 27.0 24.9 21.4 15.7 13.1 11.4 10.8 Middle East and North Africa 26.8 28.9 28.0 21.7 19.6 18.8 16.7 14.9 14.4 16.3 South Asia 81.7 80.4 77.3 76.0a 75.5 71.5 67.9 58.9 53.9 48.6a Sub-Saharan Africa 74.9 78.2 78.0 78.3 78.2 74.8 72.2 70.1 67.8 66.3 Sum of regions 66.4 65.1 61.6 60.1 55.9 49.9 45.0 38.5 33.7 30.7 Rest of the world 0.8 0.8 0.7 0.8 0.7 0.7 0.7 0.8 0.8 0.9 World 55.1 54.4 51.7 50.6 47.2 42.2 38.2 32.8 28.8 26.3 b. US$5.50 (2011 PPP) Poverty rates Region 1990 1993 1996 1999 2002 2005 2008 2011 2013 2015 East Asia and Pacific 95.2 93.2 89.3 87.0 79.9 71.7 63.6 52.3 42.4 34.9 Europe and Central Asia 25.3a 35.9 41.2 44.5 34.5 26.5 17.1 15.4 14.1 14.0 Latin America and the Caribbean 48.6 48.0 48.2 47.0 45.1 40.9 33.3 29.6 27.2 26.4 Middle East and North Africa 58.8 59.4 59.6 54.5 51.4 49.5 46.6 43.0 42.3 42.5 South Asia 95.3 95.0 93.9 93.1a 92.8 91.0 89.8 86.4 84.2 81.4a Sub-Saharan Africa 88.5 90.4 90.2 90.5 90.9 89.9 88.1 86.9 85.4 84.5 Sum of regions 80.5 81.2 80.2 79.3 75.7 71.3 66.5 61.2 57.0 53.7 Rest of the world 1.7 1.6 1.4 1.3 1.3 1.2 1.2 1.4 1.5 1.5 World 67.0 67.9 67.3 66.8 64.0 60.4 56.5 52.2 48.7 46.0 Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), World Bank. Note: The criteria for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. “Sum of regions” was previously referred to as “developing world.” PPP = purchasing power parity. a. This estimate is based on less than 40 percent of regional population coverage. TABLE 3A.3 Historical Trends, Regional Number of Extreme Poor, 1990–2015 Millions a. Number of poor at US$3.20 (2011 PPP) Region 1990 1993 1996 1999 2002 2005 2008 2011 2013 2015 East Asia and Pacific 1,366.5 1,332.1 1,224.7 1,205.4 1,057.1 859.5 723.8 524.0 352.2 254.2 Europe and Central Asia 46.1a 70.8 90.4 99.4 70.2 55.4 35.6 31.6 27.7 26.2 Latin America and the Caribbean 124.5 125.9 135.7 138.4 133.0 118.8 90.8 78.3 70.0 67.5 Middle East and North Africa 61.5 71.2 73.4 60.4 57.4 58.2 54.6 51.2 51.5 60.6 South Asia 925.3 971.5 992.5 1,034.4a 1,085.5 1,081.5 1,075.8 973.5 916.0 847.2a Sub-Saharan Africa 383.2 434.7 470.0 510.5 552.3 572.5 599.1 631.8 645.4 667.0 Sum of regions 2,907.1 3,006.2 2,986.7 3,048.6 2,955.5 2,745.9 2,579.6 2,290.3 2,062.8 1,922.9 Rest of the world 6.8 7.2 7.1 7.5 7.2 7.4 7.3 8.5 8.9 9.8 World 2,914.0 3,013.4 2,993.8 3,056.1 2,962.7 2,753.3 2,586.9 2,298.8 2,071.7 1,932.7 b. Number of poor at US$5.50 (2011 PPP) Region 1990 1993 1996 1999 2002 2005 2008 2011 2013 2015 East Asia and Pacific 1,525.3 1,557.7 1,550.2 1,562.2 1,476.0 1,357.5 1,231.0 1,035.2 851.7 710.4 Europe and Central Asia 117.3a 168.5 194.0 209.7 161.8 124.4 81.0 73.7 67.8 68.2 Latin America and the Caribbean 214.4 223.1 235.8 240.8 241.1 227.6 192.5 177.2 166.9 165.4 Middle East and North Africa 135.1 146.4 156.3 151.6 150.9 152.9 151.9 148.3 151.7 157.9 South Asia 1,080.1 1,148.5 1,206.7 1,267.6a 1,334.1 1,377.0 1,423.1 1,429.6 1,431.0 1,419.0a Sub-Saharan Africa 452.8 502.6 543.5 590.3 641.5 687.4 731.7 783.4 813.1 849.5 Sum of regions 3,525.0 3,746.8 3,886.5 4,022.2 4,005.4 3,926.9 3,811.2 3,647.4 3,482.2 3,370.3 Rest of the world 15.5 14.4 13.5 13.0 12.9 12.6 12.5 15.0 16.1 16.1 World 3,540.5 3,761.2 3,900.0 4,035.2 4,018.2 3,939.4 3,823.7 3,662.3 3,498.3 3,386.5 Source: PovcalNet (http://iresearch.worldbank.org/PovcalNet/), World Bank. Note: The criteria for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted within two years of the reference year. “Sum of regions” was previously referred to as “developing world.” PPP = purchasing power parity. a. This estimate is based on less than 40 percent of regional population coverage. 84 POVERTY AND SHARED PROSPERITY 2018 Notes 1. Target 1.A of the MDGs is to halve, between “a ‘societal’ head count measure of global con- 1990 and 2015, the proportion of people sumption poverty.” whose income is less than one dollar a day. The 7. In the relatively small number of countries indicator for monitoring progress in achiev- in which extreme poverty is assessed using ing the target was fixed at the proportion of income rather than consumption, the SPL is people living on less than the World Bank IPL similarly defined in terms of income instead of US$1.25 a day (in 2005 PPP values). Sim- of consumption. ilarly, target 1.1 of the SDGs, to be achieved 8. If median consumption is US$1.60, then by 2030, is to eradicate extreme poverty for all US$1.00 + half of US$1.60 is US$1.80. This people everywhere, measured as people living value is less than the IPL of US$1.90; so, in on less than $1.90 a day, the IPL. See Millen- this case, the SPL is set at the lower bound, nium Development Goals Indicators (data- US$1.90. base), Development Indicators Unit, Statistics 9. For a detailed discussion of the fit of the SPL Division, United Nations, New York, http:// with national poverty lines and how this fit mdgs.un.org/unsd/mdg/Host.aspx?Content= compares with other candidate specifications, Indicators%2fOfficialList.htm; “Sustainable see Jolliffe and Prydz (2017). Development Goals: 17 Goals to Transform 10. See Fuchs (1967); “Poverty Rate” (indicator), Our World,” United Nations, New York, http:// Organisation for Economic Co-operation and www.un.org/sustainabledevelopment/. Development, Paris (accessed January 26, 2. The World Bank classification of countries 2017), https://doi.org/10.1787/0fe1315d-en. according to regions and income groups 11. For details on each of the 17 SDGs, including is followed here. For details on income metadata and indicators, see “Compilation of classification, see Fantom and Serajuddin Metadata for the Proposed Global Indicators (2016). For the World Bank regions, see for the Review of the 2030 Agenda for Sus- “Select a Region,” in “Where We Work,” tainable Development,” Inter-agency Expert http://www.worldbank.org/en/country. Group on SDG Indicators, Statistics Division, 3. There may be different interpretations of what Department of Economic and Social Affairs, “fixed in real terms” means. Here it means that United Nations, New York. http://unstats.un the lines are converted to domestic currency .org/sdgs/iaeg-sdgs/metadata-compilation/. in 2011 prices, using the 2011 PPP conversion The decision that the cost of social partici- factors, and are thereafter adjusted over time pation is increasing in median consumption by the main domestic consumer price index rather than, say, average consumption is dis- used in each country. cussed in detail in Jolliffe and Prydz (2017) 4. The bin sizes of the consumption distributions and is consistent with arguments made by have been selected to correspond to key thresh- Aaberge and Atkinson (2013), Birdsall and olds at US$1.90, US$3.20, and US$5.50. The Meyer (2015), and Stiglitz, Sen, and Fitoussi statement then about most people consuming (2010) that the median is a better represen- just less than US$1.90 is affected by the selected tation of the material well-being of a country bin sizes. But an estimated density function of relative to the mean and is also a simple way of the log of consumption closely corresponds to capturing distributional aspects of well-being. the shape of the histogram displayed. 12. See Ferreira et al. (2016) for a discussion on 5. For more examples of countries that have inflating 2005 PPP values into 2011 PPP changed the value of their national pov- values. They assert that, on average, US$1.90 erty lines, see the online appendix of Jolliffe in 2011 PPP U.S. dollars maintains the same and Prydz (2016), at https://static-content purchasing power as US$1.25 in 2005 PPP for .springer.com/esm/art%3A10.1007%2Fs10888 the set of 15 poor countries that determine the -016-9327-5/MediaObjects/10888_2016_9327 IPL. They also demonstrate that this inflation _MOESM1_ESM.pdf. rate of about 52 percent maintains an average 6. The motivation for referring to the line as the purchasing power for essentially all countries SPL is drawn from the World Bank (2017, in the PovcalNet database for which they esti- xxi), which recommends the introduction of mate poverty (and have measures of PPP in HIGHER STANDARDS FOR A GROWING WORLD 85 both years). Inflating US$0.67 by 52 percent data used in the rest of this report. See Pov- results in US$1.01. Furthermore, direct rees- calNet (online analysis tool), World Bank, timation of Ravallion’s (2016) consumption http://iresearch.worldbank.org/PovcalNet/. floor using 2011 PPP gives a value of US$1.00 16. Household survey data do not exist for every at 2011 PPP. country in every year, but all global poverty es- 13. Similarly, Allen (2017, table 11) estimates the timates are for a specific year. To overcome the lowest cost of a diet consisting of 2,100 calo- data gaps, survey data are projected forward ries per day with 50 grams of protein and 34 and, sometimes, backcast to produce country grams of protein across several countries. The poverty rates for each year. For an overview of lowest value he estimates is US$0.98 in 2011 the methods, see Ferreira et al. (2016); Jolliffe PPP terms for Zimbabwe. et al. (2015). 14. See PovcalNet (online analysis tool), World 17. The idea that national poverty lines represent Bank, Washington, DC. http://iresearch.world social assessments of minimum needs has bank.org/PovcalNet/. The estimates cited here been a motivating argument behind the use were produced from the version of PovcalNet of the IPL for many years. Ravallion, Datt, updated on October 1, 2016. China, India, and and van de Walle (1991) and the World Bank Indonesia have separate rural and urban dis- (1990) interpret national poverty lines in tributions in PovcalNet, and no national me- some of the poorest countries as representa- dian is readily available. For these countries, tive of absolute minimum needs and use them the national median is derived by combining in calculating the dollar-a-day IPL. the rural and urban population-weighted 18. The claim is not being made that this report distributions available in PovcalNet and esti- empirically disentangles whether the rising mating the median of the joint national dis- value of national poverty lines reflects the tribution. The resulting national median is growing cost of social participation (as is as- used in defining the SPL for these countries. sumed here) or simply reflects a definition of For high-income countries, the alignment basic needs that is more generous, resulting in of the surveys closest to the reference years greater utility. For a discussion of this iden- is replicated using National Accounts data, tification challenge, see Ravallion and Chen the method in the PovcalNet reference-year (2017). aggregation. 19. The 1993 value was estimated from the na- 15. The profile of societal poverty presented tional headcount ratio and an internation- here is based on estimates from PovcalNet as ally harmonized welfare vector, following the of September 2018, the same version of the method of Jolliffe and Prydz (2016). 86 POVERTY AND SHARED PROSPERITY 2018 Beyond Monetary Poverty 4 This chapter reports on the results of the World Bank’s first exercise of multidimensional global poverty measurement. Information on consumption or income is the traditional basis for the World Bank’s poverty estimates, including the estimates reported in chapters 1–3. However, in many settings, important aspects of well-being, such as access to quality health care or a secure community, are not captured by standard monetary measures. To address this concern, an established tradition of multidimensional poverty measurement measures these nonmone- tary dimensions directly and aggregates them into an index. The United Nations Development Programme’s Multidimensional Poverty Index (Global MPI), produced in conjunction with the Oxford Poverty and Human Development Initiative, is a foremost example of such a multi- dimensional poverty measure. The analysis in this chapter complements the Global MPI by placing the monetary measure of well-being alongside nonmonetary dimensions. By doing so, this chapter explores the share of the deprived population that is missed by a sole reliance on monetary poverty as well as the extent to which monetary and nonmonetary deprivations are jointly presented across different contexts. The first exercise provides a global picture using comparable data across 119 economies for circa 2013 (representing 45 percent of the world’s population) combining consumption or income with measures of education and access to basic infrastructure services. Accounting for these aspects of well-being alters the perception of global poverty. The share of poor increases by 50 percent—from 12 percent living below the international poverty line to 18 percent deprived in at least one of the three dimensions of well-being. Across this sample, only a small minority of the poor is deprived in only one dimension: more than a third of the poor suffer simultaneous deprivations in all three dimensions. More than in any other region of the world, in Sub-Saharan Africa shortfalls in one dimension occur alongside deprivations in other dimensions. In South Asia, the relatively high incidence of deprivations in education and sanitation imply that poverty rates could be more than twice as high when these nonmonetary dimensions are added. A second complementary exercise for a smaller set of countries (six) explores the inclusion of two additional nonmonetary dimensions. When measures of health and household security (the risk of experiencing crime or a natural disaster) are included alongside the previous three dimensions, the profile of the poor changes. In most countries, the share of the poor living in female-headed households is greater than when the nonmonetary dimensions are excluded and, in some countries, the poor also have a significantly higher presence in urban areas. 87 Why look beyond monetary various goods taking their relative prices into account, these relative prices serve as natural poverty? weights with which to aggregate those quan- Consider the following hypothetical exam- tities consumed.1 That is why they form the ple. Two families have the same income, say basis for the first three chapters in this report. US$3.00 per person per day. However, only It is why poverty has typically been defined one family has access to adequate water, sani- in terms of whether a household’s income tation, and electricity, whereas the other lives reaches or surpasses a monetary threshold, the in an area lacking the necessary infrastruc- poverty line, which represents the minimum ture for basic services, such as a power grid or amount needed to purchase a sufficient quan- water mains. Members of this second family tity of essential goods and services. will still consume water and use energy for Yet the point of the example is that lighting and cooking, but they may have to monetary-based measures do not encompass spend hours per week fetching water from all aspects of human well-being. One reason a well, or pay higher prices to obtain lower- for this is that not all goods and services that quality water from a truck. For sanitation, matter to people are obtained exclusively they may use a private or communal latrine, through markets. Consequently, the prices without the convenience or hygiene benefits necessary to cost these goods and services ei- of a sewerage connection. And with no ac- ther do not exist or do not accurately reflect cess to an electricity grid, the second family’s their true consumption value (World Bank choice set for lighting and power options is 2017b). Common examples of nonmarket severely reduced. Both households will spend goods without prices are public goods such some of their US$3.00 per person per day to as a clean environment and a secure commu- meet their energy and water needs. Yet, be- nity. Examples of goods with prices that often cause their choice sets (including the prices do not reflect true consumption value include they face) are so different, the differences in those that require large public investments their living standards arising from the access to make them available—the provision of a that the first family enjoys are not captured power grid is often necessary before a house- by a monetary measure of poverty alone. hold can access electricity. Other core services The first family clearly enjoys a higher stan- at least partially provided through systems dard of living than the second, but a welfare supported by direct government spending judgment that considers only their incomes include health care and education. General will pronounce them equally well-off. This is government health expenditure accounts for an example of when public action—or lack more than half of total global health expen- thereof—can directly affect the well-being diture. Likewise, governments on average of households by expanding—or not—their spend the equivalent of nearly 5 percent of choice sets in ways that incomes and prices fail the gross domestic product (GDP) of their to fully internalize. It is possible that, under economies on education. The presence of a broader assessment of poverty, the second such goods renders the traditional monetary family might be considered poor or deprived, welfare measure incomplete with respect to a even though its daily income is above the in- variety of core aspects of well-being. ternational poverty line of US$1.90 per day. This chapter presents a broader picture of To be clear: Income (or consumption ex- well-being than that found in chapters 1–3, penditures valued at prevailing market prices) by considering a notion of poverty that rec- is hugely important for human well-being. ognizes the centrality of the monetary mea- Indeed, income and consumption are the sure, but looks to complement it by explicitly workhorse metrics of individual welfare in treating access to key nonmarket goods as economic analysis. They summarize a house- separate dimensions of well-being. Specif- hold’s capacity to purchase multiple goods and ically, the chapter previews a multidimen- services that are crucial for well-being, such sional poverty measure derived from stan- as food, clothing, and shelter. And they do so dardized data for 119 economies that provide with one remarkable property: because con- a global picture for circa 2013. The multidi- sumers choose the quantities they consume of mensional measure is anchored on consump- 88 POVERTY AND SHARED PROSPERITY 2018 tion or income as one dimension of welfare, and national level (box 4.1). The capability and includes several direct measures of access framework inspired the development of the to education and utilities (such as electricity, first global efforts to measure poverty multi- water, and sanitation) as additional dimen- dimensionally. These were carried out by the sions. Although this multidimensional mea- United Nations Development Programme sure has wide country coverage, it still lacks (UNDP), through the Human Poverty Index information on other important dimensions in the late 1990s (UNDP 1997) and, more re- of well-being including health care and nu- cently, through the Global Multidimensional trition, as well as security from crime and Poverty Index (Global MPI), introduced natural disasters. Consequently, in a more in the 2010 Human Development Report exploratory manner, the chapter extends the (UNDP 2010), developed with the Oxford analysis by adding these dimensions for a Poverty and Human Development Initiative smaller subset of countries for which infor- (OPHI), and reported annually for over 100 mation for all these dimensions can be cap- countries. At the country level, an increasing tured within the same household survey. number of governments are choosing to ex- The two exercises—one with broad coun- pand or complement their poverty measures try coverage, but fewer dimensions than one with multidimensional indicators (see spot- would ideally like, and the other with a rela- light 4.1 at the end of this chapter). The ef- tively extensive set of dimensions, but available forts of the UNDP, OPHI, and most govern- only as a pilot for a few countries—represent ments build on influential research by Sabina the World Bank’s first steps toward including Alkire and James Foster (see, for example, multidimensional poverty indicators in the Alkire and Foster 2011). set of complementary indicators of global The efforts here are also indebted to these poverty, as suggested by the Commission on previous efforts by other researchers, gov- Global Poverty (World Bank 2017b). Going ernments, and international institutions. In forward, the World Bank will monitor prog- addition, they follow on the World Develop- ress on multidimensional poverty at the global ment Report (WDR) 2000/01 Attacking Pov- level using the three-dimensional measures erty (World Bank 2001), which recognized the presented in this chapter, while continuing its many dimensions of poverty and considered efforts to incorporate the dimensions missing deprivations in education and health alongside from the global analysis for future rounds. income in its analysis of the evolution of pov- This approach adopts a living standards erty. The present report goes beyond the WDR perspective, in that each dimension is valued 2000/01 by taking advantage of richer house- instrumentally, that is, each dimension rep- hold-level data that combine monetary and resents the ability to command goods and nonmonetary indicators to present deprivation services that households value for other ends in each domain as well as measures that aggre- (in other words, consuming or owning these gate these different deprivations. This proposal commodities allows for the satisfaction of follows from the recommendations of the different needs and wants). But it is also con- Commission on Global Poverty, led by Profes- sistent with the capability framework, which sor Sir A. B. Atkinson, to consider complemen- calls for expanding the evaluative space for tary indicators to monetary poverty “where a assessing welfare (Sen 1987). The capability dashboard approach is proposed as part of the approach advocates for a broader perspective Complementary Indicators, . . . together with a to capture the “plurality of different features measure of the extent of overlapping depriva- of our lives and concerns” (Sen 2009, 233). In tions” (World Bank 2017b, 100). this approach people have varying abilities to The present exercise is also related to the convert resources into the opportunity to be Sustainable Development Goals (SDGs) es- and do what they most value—that is, into tablished by the United Nations in 2015, what Sen terms “capabilities.” which include a call for governments to re- Of course, measuring poverty multi- port on their progress in improving the na- dimensionally is not a new endeavor. In- tional multidimensional poverty indicator deed, multidimensional poverty measures (Indicator 1.2.2 of SDG 1, end poverty in have become widespread both at the global all its forms everywhere).2 The focus of this BEYOND MONETARY POVERTY 89 BOX 4.1 Early Applications of Multidimensional Poverty Measurement The approach followed in this children, housing conditions, access the Human Poverty Index, chapter builds on previous to basic services, and the economic which appeared in the Human applications of the multidimensional capacity of household members. Development Reports from 1997 poverty concept. There is a long The basic needs indicators are to 2009 measuring country- history of assessing the deprivation generally calculated using census level aggregate deprivations in of individuals by combining multiple data. health, education, and standard of components of well-being. Inspired The Mexican government living. The Global MPI combines by empirical studies in the 1970s has taken a lead in adopting a 10 indicators grouped in three and early 1980s, various European multidimensional approach in the dimensions, namely, education, countries have been measuring official poverty measure. Following health, and standard of living, and the share of the population that a comprehensive consultative identifies each person as poor or is deprived in a select number of process initiated in 2006, and nonpoor according to how many socially perceived necessities as a grounded on a human rights deprivations they face (Alkire and core indicator of social exclusion.a perspective, the government, Santos 2010; Alkire et al. 2015). In many of these cases, such as since 2010, has measured poverty This work has been adapted and in Ireland, the United Kingdom, as the share of the population adopted by many developing and, later, the European Union, the that is deprived simultaneously in countries (see spotlight 4.1). assessment of multiple deprivations monetary terms and in at least one The 2018 edition of the Global combines income poverty with of six social indicators reflecting MPI includes 105 countries, the counting of these material core social rights. These indicators with a population coverage of 75 deprivations.b Since the 1980s, cover gaps in education, access to percent of the global population many countries in Latin America health services, access to social (OPHI 2018). A comparison of the have complemented monetary security, access to basic residential indicators included in the Global poverty measures developed services, housing conditions, and MPI, as well as the Mexican through household surveys with access to food (CONEVAL 2010). poverty measure and (selected an indicator of unsatisfied basic Since 2010, OPHI and the indicators) for Europe 2020 and the needs that counts the number of UNDP have been computing multidimensional poverty measures deprivations in several indicators, the Global MPI for over 100 presented in the chapter, is found in including school enrollments among countries. The Global MPI replaced annex 4A. a. The Level-of-Living Survey in Sweden and Townsend (1979) and Mack and Lansley (1985) in the United Kingdom are considered pioneers in Europe in this approach. Excellent reviews on early applications include Aaberge and Brandolini (2015) and Alkire et al. (2015). For the Swedish survey, see LNU (The Swedish Level-of-Living Survey) (database), Swedish Institute for Social Research, Stockholm University, Stockholm, https://www.sofi.su.se/english/2.17851/research/three-research-units/lnu-level-of-living. b. In Ireland, “consistent poverty” is measured as the population share that is both income poor and deprived in two or more essential items. In the United Kingdom, a similar approach has been used since 2010 to measure child poverty. In the European Union, the Europe 2020 poverty and social exclusion headline indicator combines income poverty (the at-risk-of-poverty rate), household quasi-joblessness, and severe material deprivation (lacking at least four of nine items that are considered fundamental to enjoying an adequate standard of living). See Atkinson et al. (2002); Marlier et al. (2007). chapter, on steps to develop a useful global harmonization, several key insights emerge multidimensional poverty measure, should from the analysis. not be taken as a preference for such a global measure over possibly richer country-level Considerations measures when assessing national progress. for constructing The requirement of a global multidimen- multidimensional poverty sional poverty measure for standardized household indicators across many countries measures necessarily limits indicator choice to the rel- This is the initial step by the World Bank to atively few that are consistently measured. expand the space of assessment beyond the Nonetheless, despite this constraint of data monetary to explicitly include access to non- 90 POVERTY AND SHARED PROSPERITY 2018 market goods and services that are essen- former is not available) captures people’s tial for well-being. In addition to a measure access to certain crucial goods and ser- based on economic resources, it incorporates vices, including food, clothing, and shelter. a core set of indicators for nonmonetary di- The consumption measure uses market mensions and presents results on the extent prices to aggregate across the various con- to which these deprivations arise and overlap. sumption goods.3 Market prices reflect the Furthermore, it presents summary measures ability of people to purchase goods and that combine the information into a single services, while allowing for variation in index, the multidimensional poverty head- individual preferences. Other aspects of count ratio. well-being on which prices are not avail- Broadening the poverty measure to in- able or are arguably not a good representa- corporate additional directly measured com- tion of value should therefore complement ponents involves two steps. First, one must monetary poverty. Public goods as well as select the dimensions, the indicators, and private goods that are heavily subsidized the respective sufficiency thresholds for each are cases in which prices either do not exist indicator. For example, in the case of the ed- or, if they do exist, do not closely represent ucational dimension, one possible indicator the household’s valuation of the good. could be school enrollment for the school- age children in the household, and the suf- • Relevance. The indicators included should ficiency threshold is that all children are in be relevant in that they are widely ac- school (and therefore every household mem- knowledged to represent essential aspects ber is considered deprived if at least one child of well-being. Indicator thresholds should is not enrolled). To consider the existence of reflect minimum basic needs, comparable multiple deprivations occurring in the case with the US$1.90 per person per day pov- of a same individual, all indicators need to be erty lines. The SDGs and other similar ini- observed or inferred for the same individual, tiatives provide useful guidance. typically from the same data source. Second, • Data availability. Indicators should ideally the information on each dimension is then be derived from the same data source (typ- aggregated into one index. Summary indexes ically a household survey). One of the key can be applied to generate rankings across features of the multidimensional approach population groups and countries, while ac- is that it can be used to assess the extent knowledging the multiplicity of deprivations. to which deprivation in one dimension is This section briefly discusses the proposed related to deprivation in other dimensions choices in each of these two stages. for the same individual. However, because of the requirement about data sources, the Selected dimensions and choice of the dimensions and indicators to indicators be included will ultimately be shaped by the availability of meaningful data. The selection of the dimensions and indica- tors relevant to the measurement of standards • Parsimony. The multidimensional mea- of living is never simple. Possessing a clear sure should be parsimonious. It should conceptual framework to advise this process involve only a small number of judiciously is therefore fundamental. The approach to selected dimensions to lend prominence to the selection of the nonmonetary indicators multidimensionality, while ensuring suffi- is guided by the idea that poverty, at least in cient population coverage. part, represents an inability to reach a min- imum standard of material well-being com- Because of data limitations, there exists a prising both market and nonmarket goods. trade-off between the number of dimensions The choice of dimensions is informed by (measured by harmonized indicators) that the following core principles: can be included in the multidimensional pov- erty measure and the number of countries • Centrality of private consumption. Pri- that can be included in the analysis. For ex- vate consumption (or income, when the ample, comprehensive assessments of health BEYOND MONETARY POVERTY 91 services and health outcomes are rarely avail- Most often, this is a measure of school able in the same household survey that also enrollment (among children and youth contains the lengthy questionnaires typically of school age) or educational attainment necessary to measure consumption poverty. (among adults). The education dimension For this reason, the chapter conducts two here similarly has these two components. complementary exercises. To get a global These indicators are available for many picture, the next section presents an analy- countries and are standardized in recent sis including a large number of economies surveys across 119 economies. (119, covering 45 percent of the world’s 3. Access to basic infrastructure. The third population) and includes three dimensions, dimension encompasses access to key ser- including consumption, represented by six vices that often require large-scale public indicators. The second exercise uses data for a investments to make them widely avail- much smaller set of countries (six) to explore able. Access to electricity and a certain the impact of adding two additional dimen- standard of drinking water and sanitation sions. The analysis that follows should be un- are critical for economic activity and sur- derstood as an initial exploration to generate vival (related to SDGs 6 and 7). Although a consistent, conceptually robust, and prac- many individuals pay for the provision of tical proposal for expanding current poverty these services (through utility bills or oth- measurement methods to include other non- erwise), the choice set available to users monetary dimensions of well-being. (and their prices) depends to a large ex- The five well-being dimensions consid- tent on the initial investments that gov- ered in this chapter are the following: ernments have made on electricity grids and water and sewer networks. This pub- 1. Monetary well-being. The first dimen- lic action often determines the price and sion is the monetary measure of well-be- quality of the service provided.4 For the ing that the World Bank uses as its prin- 119-economy sample, indicators can be cipal poverty measure: the consumption standardized across multipurpose house- or income per person per day, valued at hold surveys to reflect wider definitions 2011 purchasing power parity (PPP) U.S. of “at least limited” drinking water and dollars, that is available to the individuals “at least limited” sanitation used in the in the household (SDG target 1.1). This is SDG monitoring, whereas, for the smaller the well-being measure and threshold fea- six-country sample, the chosen indicator tured in chapter 1 of this report. The di- applies a more stringent definition also mension encompasses the range of goods used under the SDG framework of access and services that can be purchased at mar- to “at least basic” water and sanitation.5 ket prices. The sufficiency threshold is the international poverty line, currently set at 4. Health and nutrition. Health is widely US$1.90 per person per day. Individuals considered a core dimension of well- living in households in which per capita being. It is the focus of SDG 3: ensure income falls below this cutoff are consid- healthy lives and promote well-being for ered deprived in the monetary dimension all at all ages. As in other cases, health care of well-being. is typically not supplied entirely through the market or valued entirely at market 2. Education. Although education may be prices. The empirical challenge of in- available through private or public institu- cluding this dimension for a large set of tions, provision among a large share of the countries limits the feasibility of investi- population is fully or partially subsidized gating health and nutrition meaningfully in most countries. The price that families in the 119-economy sample. However, must pay therefore does not adequately for a smaller selection of countries, one represent the value of the service. Indexes may analyze indicators of access to for- of multidimensional poverty typically mal health care services as well as direct include at least one indicator of access individual assessments of nutrition. Four to formal education (related to SDG 4). indicators are included in the health and 92 POVERTY AND SHARED PROSPERITY 2018 nutrition dimension: facility-based birth incidence of crime at the household level delivery, vaccination among children, as well as the threat of crime, often defined the incidence of child stunting, and un- by the incidence of crime in the commu- dernourishment among adult women. nity. The six-country study includes this Whereas nutrition is intimately linked to indicator. In addition, this dimension in- food consumption—and thus can be ar- corporates a measure of the prevalence gued to be already indirectly included in of natural disasters that severely affected monetary poverty—stunting and malnu- households’ well-being beyond short- trition are also reflective of exposure to term losses in consumption. Although illness and lack of nutritional knowledge information on the incidence of natural as well as possible unequal access of re- disasters is sometimes captured in shock sources within households. modules in household surveys—such as in the six-country study analyzed in this 5. Household security. A final dimension chapter—other environmental qualities considers the risks to which households essential for a good life, such as air free of are exposed and for which insurance or pollution, are most often not included and mitigation programs, where they exist, thus cannot be incorporated at this stage.6 are often partially or fully supplied by the government. One of the basic functions of Table 4.1 illustrates the individual indica- government is to ensure that the daily lives tors. Appendix A contains technical details of the population are free of the fear of on indicator definitions. exposure to violence and crime. Although One limitation of the approach followed few living standard–type surveys ade- in this chapter is that it relies on indicators quately cover the relevant issues, some do that are readily available in standard house- contain questions designed to measure the hold surveys. For many of the dimensions TABLE 4.1 Dimensions of Well-Being and Indicators of Deprivation Dimensions Three dimensions (119 economies) Five dimensions (6 countries) Monetary Daily consumption or income is less than US$1.90 Daily consumption or income is less than US$1.90 per person poverty per person Education At least one school-age child up to the age of grade 8 At least one school-age child up to the age of grade 8 is not enrolled in school is not enrolled in school No adult in the household (age of grade 9 or above) No adult in the household (age of grade 9 or above) has completed primary has completed primary education education Access to basic The household lacks access to limited-standard The household lacks access to a basic-standard drinking water (“limited-standard” infrastructure drinking water with an added criterion of the source being within a round trip time of 30 minutes) The household lacks access to limited-standard The household lacks access to basic-standard sanitation (“limited-standard” with sanitation an added criterion of the facility for the exclusive use of the household) The household has no access to electricity The household has no access to electricity Health and Any woman age 15–49 with a live birth in the last 36 months did not deliver at a nutrition health facilitya Any child age 12–59 months did not receive DPT3 vaccinationa Any child age 0–59 months is stunted (HAZ < −2) Any woman age 15–49 is undernourished (BMI < 18.5) Security The household has been subject to crime in the previous 12 months or lives in a community in which crime is prevalent The household has been affected by a natural disaster (including flooding, drought, earthquake) in the previous 12 months Note: BMI < 18.5 = body mass index below 18.5 (underweight); DPT3 = diphtheria-pertussis-tetanus vaccine; HAZ < −2 = the height-for-age Z-score is below −2, that is, more than two standard deviations below the reference population mean. “Limited-standard” drinking water is drinking water that comes from an improved source (for example, piped, borehole, protected dug well, rainwater, or delivered water). “Limited-standard” sanitation means using improved sanitation facilities (for example, flush/pour flush to piped sewer system, septic tank, or a composting latrine). a. If the indicator is not applicable, for example if the household includes no women who gave birth in the previous 36 months, the household is classified as deprived if the relevant deprivation rates in the subregion of residence are sufficiently high. Specifically, the deprivation threshold is set such that the share of individuals in nonapplicable house- holds that are classified as deprived equals the national share of deprived individuals in applicable households who actually experienced a recent birth or have a child under age 6. BEYOND MONETARY POVERTY 93 considered, relevant information on the im- combine household information on well- portant aspect of service quality is sometimes being across dimensions into a single num- available in specialized surveys, but not in ber. Such indicators facilitate comparisons standard household surveys that also record across countries and time, especially if the other data on well-being. Essential infor- extent of deprivation within countries varies mation on quality thus cannot be used for across dimensions under consideration. various indicators here (box 4.2). If this in- Any aggregation of indicators into a single formation becomes available through multi- index invariably involves a decision on how purpose household surveys in the future or if each of the indicators is to be weighted. There a method can be developed to apply relevant are various approaches to the selection of administrative data at a sufficiently granular weights, including those stipulated by policy level, then subsequent measures of multi- makers and those that are based on a poll of dimensional well-being may reflect quality the preferences among the target population more accurately. (Decancq and Lugo 2013). Although there are One dimension often featured in multidi- advantages and disadvantages to each of the mensional well-being indexes, but not con- methods, the approach chosen here follows sidered here, is employment in a stable, dig- standard practice in the field. Dimensions are nified job. Employment may matter beyond weighted equally, and within each dimension the monetary benefits individuals derive each indicator is also equally weighted. The from it because jobs can give people a sense result is that each indicator has a different of self-esteem and help them stay connected weight depending on the number of elements with society. An unstable employment con- within its dimension. Weights must also ad- tract could be detrimental to well-being be- just as the number of considered dimensions cause of the financial and other risks associ- changes, as illustrated in tables 4.2 and 4.3, ated with such jobs. Employment is not part where the number of dimensions rises from of the multidimensional poverty measure three to five.7 presented here for two reasons. First, many of The main summary measure presented in the frequently used indicators of employment the chapter is the multidimensional poverty in high-income countries, such as unemploy- headcount ratio, denoted by H. This index ment and wage employment, are not as rel- describes the share of people who are consid- evant in low-income countries, which have ered multidimensionally deprived and par- very different labor market structures (Lugo allels the headcount measure used for global 2007). Second, whatever relevant indicators poverty monitoring (the poverty rate). Indi- of employment exist, these indicators are not viduals are considered multidimensionally available or not sufficiently harmonized in deprived if they fall short of the threshold the different surveys considered here. in at least one dimension or in a combina- tion of indicators equivalent in weight to a full dimension. In other words, in the three- Aggregating multiple indicators dimension exercise, households will be con- into a single index sidered poor if they are deprived in indica- Each of the five dimensions discussed above tors whose weight adds up to 1/3 or more. is considered fundamental to well-being, Analogously, in the five-dimension exercise, even if other, equally important aspects of the weights on all deprivations must add up living standards are missing. They are im- to 1/5 or more for a household to be clas- portant not only separately but also in the sified as poor. For example, in the three- way they are often present or absent together. dimension case, every person who lives in a The chapter therefore examines the share of household without access to water and sani- people deprived according to each separate tation and with a child who does not attend indicator, along with measures that capture school is considered multidimensionally de- the degree to which these deprivations arise prived, whereas members of another house- together by counting the number of depriva- hold may be deprived because the household tions that individuals experience. In addition, income does not meet basic needs. The index the chapter presents summary indicators that is thus a simple expression of an approach 94 POVERTY AND SHARED PROSPERITY 2018 BOX 4.2 Incorporating Aspects of Quality into Multidimensional Poverty Measures The measure of multidimensional that could be included in standard Organization–United Nations poverty considered in this chapter household surveys, a possible Children’s Fund Joint Monitoring does not contain sufficient solution may involve national or Programme for Water Supply, information to thoroughly assess subnational indicators of learning Sanitation and Hygiene (JMP) household well-being in all major outcomes. Recently, the World developed an operational model dimensions, especially as it Bank has harmonized data gathered for monitoring SDG 6, on safely relates to the quality of services through international educational managed drinking water, sanitation, utilized. Although such information testing programs—such as the and hygiene.b Safely managed sometimes becomes available Latin American Laboratory for drinking water sources are basic through specialized surveys, these Assessment of the Quality of drinking water sources located in specialized surveys often do not Education, the Program for the the household, available as needed, include all relevant dimensions Analysis of Education Systems and compliant with standards of poverty. Therefore, the data of Confemen, the Program for on fecal and chemical content. requirement is too large for International Student Assessment, Similarly, safely managed sanitation multidimensional poverty indicators the Southern and Eastern Africa services are basic sanitation to be accurately and consistently Consortium for Monitoring facilities that are not shared and estimated across countries. In Educational Quality, and the Trends through which excreta are safely practice, this means that the in International Mathematics disposed in situ or transported and indicators of multidimensional and Science Study—to allow treated off-site. poverty considered here are for comparable indicators of Measures of quality could restricted to reporting on the learning to be computed across improve the indicator on electricity. access of households to services, countries.a These data are core In many countries, households but not the quality of these to the newly designed Human may have access to electricity, services. Going forward, additional Capital Index (HCI) that the World but, because of frequent power efforts are needed to collect richer Bank is presenting as part of the outages, the service is unreliable. data that include both access and Human Capital Project (World This ought to be incorporated so quality of services. Bank 2019). The HCI is a measure the indicator captures the benefits Ensuring inclusive, equitable of human capital, designed as an derived from the electricity rather education of high quality is indicator of each country’s future than only a binary measure of one of the core SDGs. Access labor productivity, going beyond access. Likewise, the quality of to education is considered a years of schooling. Specifically, maternal care could be incorporated fundamental right, but it needs the HCI combines, for each into the indicator on the births at to lead to “relevant and effective country, information on the level of health facilities. Many pregnant learning outcomes” (SDG target education adjusted for quality and women may deliver at facilities, 4.1). An ideal indicator of education indicators of health status (stunting but the conditions of the facilities in a multidimensional poverty and mortality) (Kraay 2018). and the expertise of the people index ought to be the attainment The core drinking water and assisting the delivery can vary by individuals of a basic level of sanitation indicators of SDG 6.1 greatly. Accurate data on the quality learning capability (World Bank and 6.2 focus on the concept of of the facilities and the skills of 2018d). Although indicators that safely managed, which contains the staff assisting in the deliveries account for learning outcomes a quality dimension that is not would improve the accuracy of the are rare and might prove difficult captured in the indicators described health service indicator. to calculate through questions in this chapter. The World Health a. See LLECE (Latin American Laboratory for Assessment of the Quality of Education), Regional Bureau for Education in Latin America and the Caribbean, United Nations Educational, Scientific and Cultural Organization, Santiago, Chile; http://www.unesco.org/new/en/santiago/education/education-assessment-llece/;PASEC (Program for the Analysis of Education Systems of Confemen) (database), PASEC and Conference of the Ministers of Education of French-Speaking Countries, Dakar, Senegal, http://www.pasec.confemen.org/donnees/; PISA (Programme for International Student Assessment) (database), Organisation for Economic Co-operation and Development, Paris, http://www.oecd.org/pisa/pisaproducts/; SACMEQ (Southern and Eastern Africa Consortium for Monitoring Educational Quality) (database), SACMEQ, Gaborone, Botswana, http://www.sacmeq.org/ReadingMathScores; TIMSS (Trends in International Mathematics and Science Study) (database), International Association for the Evaluation of Educational Achievement, Amsterdam, http://www.iea.nl/timss. b. See JMP (WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene) (database), United Nations Children’s Fund, New York; World Health Organization, Geneva, https://washdata.org/data. BEYOND MONETARY POVERTY 95 TABLE 4.2 Indicator Weights: Analysis of TABLE 4.3 Indicator Weights: Analysis of Three Dimensions Five Dimensions Three dimensions Weights Five dimensions Weights Income per capita 1/3 Income per capita 1/5 Child school enrollment 1/6 Child school enrollment 1/10 Adult school attainment 1/6 Adult school attainment 1/10 Limited-standard drinking water 1/9 Basic-standard drinking water 1/15 Limited-standard sanitation 1/9 Basic-standard sanitation 1/15 Electricity 1/9 Electricity 1/15 Coverage of key health services 1/10 Malnourishment (child and adult) 1/10 whereby the number of deprivations that Incidence of crime 1/10 people suffer are counted (Atkinson 2003). Incidence of natural disaster 1/10 The chapter also presents two alterna- tive multidimensional poverty indexes (see annex 4B for a formalization of the mea- in table 4.1, namely educational attainment sures). The first one, the adjusted head- among adults and access to limited-standard count measure M, combines the incidence sanitation. Considering these two indicators of poverty H with the average breadth of alongside monetary poverty and using a sam- deprivation suffered by each poor person, ple of 119 economies for circa 2013 (on data, as proposed by Alkire and Foster (2011). see box 4.3.), the exercise finds 12 percent of In addition, the chapter uses a measure the people to be monetarily poor, but, among that penalizes for the compounding ef- them, only one individual in five is deprived fect of multiple deprivations experienced only in the monetary dimension.8 The rest of by the same household (Chakravarty and the 12 percent are deprived at least in either D’Ambrosio 2006; Datt, forthcoming). As educational attainment or access to limited- a result, if a household is deprived in any standard drinking water, with 5 percent of two indicators, its deprivation will be con- individuals experiencing deprivations in all sidered greater than the sum of the depri- three dimensions. At the same time, many vations of two other households each only individuals are not monetarily poor but are deprived on a single indicator. The measure deprived in other aspects of well-being. is referred to as the distribution-sensitive This observation raises several questions: multidimensional measure, denoted by D. How does our view of global poverty change By incorporating information of the extent if poverty is defined as insufficiency not only of deprivation suffered by individuals, both in monetary resources but also in a range of these measures bring valuable elements to nonmonetary attributes that directly affect the analysis. Although the three measures people’s well-being? Who are the new poor? (H, M, and D) are presented in the chapter, In how many ways are they deprived? How do precedence is given to the multidimensional different regions fare if a wide-angle view of poverty headcount ratio H because it is the poverty is considered? Insights into the dif- closest analogue to the monetary poverty ferential prevalence, nature, and distribution headcount ratio, used to monitor the first of of multidimensional poverty in contrast to the World Bank’s twin goals (see chapter 1 of monetary poverty can be important for the this report). formulation of effective poverty reduction policies. Highlighting the additional depriva- tions experienced by the extreme poor sen- A first global picture sitizes policy makers to the importance of Expanding a poverty measure to include improving those aspects of human welfare nonmonetary aspects brings into focus not captured by the monetary measure alone. deprivations that may otherwise remain hid- This is even more important as more people den. For example, consider a slight extension leave extreme poverty behind because a siz- of the monetary poverty measure: the addi- able share of the non-income-poor popula- tion of only two of the indicators described tion experiences other deprivations. 96 POVERTY AND SHARED PROSPERITY 2018 Table 4.4 describes the share of people who are poor because of either monetary depriva- BOX 4.3 Chapter 4: Data Overview tion or multidimensional poverty as defined by the three dimensions and six indicators il- This chapter relies on information from the harmonized lustrated in table 4.1. The indicators cover the household surveys in the Global Monitoring Database (GMD) for dimensions of monetary poverty, education circa 2013. Surveys have been included in the multidimensional (two indicators), and access to basic infrastruc- poverty analysis if they satisfy the following criteria: ture (three indicators). Approximately one • They include a monetary welfare measure (income or individual in eight (11.8 percent) in the 119- expenditure) and indicators on education and basic economy sample in circa 2013 lives in a house- infrastructure access that may be used to construct a hold experiencing monetary poverty, whereas multidimensional poverty measure. almost one person in five (18.3 percent) lives • The surveys were conducted within three years of 2013, that in a multidimensionally deprived household.9 is, from 2010 to 2016. The multidimensional measure yields a more The extreme poverty rate (headcount ratio) reported in this expansive view of poverty by counting as poor chapter cannot be compared to the information presented in any individual with a cumulative deprivation chapter 1 for practical and methodological reasons. For more above the critical threshold of 1/3. details, see appendix A. The monetary poverty measure presented in chapter 1 outlines a bipolar world, with Africa on one end (a high poverty rate) and all the other regions, South Asia included, A different image of the world emerges on the other end (a relatively low poverty through the multidimensional lens. The rate). The separation of Sub-Saharan Africa poverty rate in Sub-Saharan Africa contin- from the other regions is seen more clearly ues to be worryingly high, with almost two when looking at the poverty trends over the in three individuals (64.3 percent) living in last 25 years. East Asia and Pacific, South multidimensional poverty in circa 2013. This Asia, and Sub-Saharan Africa all started is an increase of 40 percent from an already with a relatively high poverty rate in 1990; high monetary poverty rate of 44.9 percent. however, while poverty declined rapidly in South Asia, however, changes even more dra- the first two regions, the decline was much matically. In South Asia, more than twice as slower in Sub-Saharan Africa. Consequently, many people are multidimensionally poor as Sub-Saharan Africa today comprises most monetarily poor (table 4.4). of the world’s poor. If the trend contin- This raises important questions about ues, by 2030 the extreme poor will almost the success of poverty reduction in South exclusively be in this region. Asia. The challenge in securing higher living TABLE 4.4 People Living in Monetary or Multidimensional Poverty, 119 Economies, circa 2013 Monetary Multidimensional Headcount Share of the poor Headcount Share of the poor Number of Population Region ratio (%) ratio (H) (%) economies coverage (%) East Asia and Pacific 5.3 8.1 7.5 7.3 13 28.9 Europe and Central Asia 0.3 0.4 1.1 0.8 17 90.0 Latin America and the Caribbean 3.9 5.7 6.1 5.8 17 91.5 Middle East and North Africa 3.2 2.2 5.9 2.6 9 72.1 South Asia 11.9 12.3 26.6 17.7 5 23.0 Sub-Saharan Africa 44.9 70.9 64.3 65.4 29 60.7 Rest of the world 0.5 0.5 0.5 0.3 29 39.6 Total 11.8 100.0 18.3 100.0 119 45.0 Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measure- ment and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. Note: The reported multidimensional headcount ratio is estimated on the basis of three dimensions—monetary, education, and basic infrastructure access, as defined in table 4.1—and an overall poverty cutoff of one-third of the weighted deprivations. The data are derived from household surveys conducted in about 2013 (+/−3 years). Because of the unavailability or incomparability of data, analysis does not include all countries. The last column shows the percentage of regional or global populations covered by the surveys. Percentages may not sum to 100 because of rounding. BEYOND MONETARY POVERTY 97 standards for the population of South Asia is FIGURE 4.1 Share of Individuals in more daunting when poverty in all its forms Multidimensional Poverty, 119 Economies, is considered. Although South Asia is ex- circa 2013 pected to meet the goal of reducing extreme Basic infrastructure poverty below 3 percent by 2030, many peo- ple will still be living in unsatisfactory con- 0.7 ditions if no progress is made in the other components of well-being. 3.3 It is apparent from table 4.4 that the multi- dimensional poverty headcount is always higher than the monetary poverty headcount. 5.2 This regularity arises because of the relative importance assigned to each component and the stipulated overall poverty threshold 6.6 that determines if a household is considered multidimensionally poor. If a household is deprived in at least one dimension, then the members are considered multidimension- 0.6 0.6 1.3 ally poor. Because the monetary dimension Monetary Education is measured using only one indicator, any- one who is income poor is automatically also Source: Estimates based on the harmonized household surveys in poor under the broader poverty concept. The 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity difference between the headcounts therefore Building, Poverty and Equity Global Practice, World Bank, Wash- hinges on those individuals among whom ington, DC. the privation is a result of a shortfall in the Note: The diagram shows the share of population that is multi- dimensionally poor, and the dimensions they are deprived in. For nonmonetary dimensions of life despite their example, the numbers in the blue oval add up to 11.8 percent, ability to command sufficient financial re- which is the monetary headcount. Adding up all numbers in the sources to cross the monetary poverty thresh- figure results in 18.3 percent, which is the proportion of people that are multidimensionally deprived. old. These households would be deemed nonpoor under the narrower poverty con- cept on the basis of insufficiency in monetary cause of the relatively low correlation in depri- resources, leaving policy makers with an un- vations across dimensions. In these countries, duly optimistic assessment of poverty from a a household that is deprived in education at- multidimensional perspective. tainment has a high probability of being de- The underlying structure of the depriva- prived in school enrollment as well, making tion experienced by the multidimensionally its members multidimensionally poor. But the poor is depicted in figure 4.1. There is a large correlation between the monetary dimension degree of overlap between dimensions. Only and the education indicators is weak, which a small minority of the multidimensionally means the same households are not deprived poor are deprived in only one dimension, in the monetary dimension. This adds new whereas more than a third are simultaneously households to the count of the poor. deprived in all three dimensions. The over- Because the difference in poverty incidence lap is highest in Sub-Saharan Africa (annex according to the two measures is the result 4C, figure 4C.1). A larger overlap between of cumulative nonmonetary deprivations, it dimensions indicates a larger extent of in- is natural to inquire about the components terdependence, which implies that policy in- most responsible for the difference. Table terventions targeted exclusively toward one 4.5 presents the poverty headcount ratio at dimension may not reduce multidimensional US$1.90 a day as well as the deprivation rate poverty and therefore a multipronged ap- associated with each of the five nonmonetary proach might be required. indicators. Despite having made progress in Going from monetary to multidimen- poverty reduction, the countries included in sional poverty, the poverty rate more than the sample for South Asia still are highly de- doubles in the five South Asian countries be- prived in the education dimension. An issue 98 POVERTY AND SHARED PROSPERITY 2018 TABLE 4.5 Individuals in Households Deprived in Each Indicator, 119 Economies, circa 2013 Educational Educational Drinking Monetary attainment enrollment Electricity Sanitation water Region (%) (%) (%) (%) (%) (%) East Asia and Pacific 5.3 7.5 3.2 4.5 14.0 11.3 Europe and Central Asia 0.3 0.9 5.6 0.5 6.8 2.6 Latin America and the Caribbean 3.9 12.2 2.7 3.3 15.6 6.4 Middle East and North Africa 3.2 11.1 7.9 3.8 14.6 4.2 South Asia 11.9 31.6 22.6 23.8 39.5 7.0 Sub-Saharan Africa 44.9 46.2 20.8 64.8 61.9 33.9 Rest of the world 0.5 1.2 0.0 0.0 0.6 0.0 Total 11.8 17.0 9.0 15.9 23.8 10.9 Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measure- ment and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. Note: The definition of the indicators and the deprivation thresholds are as follows: Monetary poverty: a household is deprived if income or expenditure, in 2011 purchasing power parity U.S. dollars, is less than US$1.90 per person per day. Educational attainment: a household is deprived if no adult (grade 9 equivalent age or above) has completed primary education. Educational enrollment: a household is deprived if at least one child (grade 8 equivalent age or below) is not enrolled in school. Electricity: a household is deprived if it does not have access to electricity. Sanitation: a household is deprived if it does not have access to even a limited standard of sanitation. Drinking water: a house- hold is deprived if it does not have access to even a limited standard of drinking water. The data reported refer to the share of people living in households deprived according to each indicator. of apparent global concern is poor sanitation: rate, but Pakistan’s level of deprivation in approximately a quarter of the population in education attainment and enrollment is far the 119-economy sample lives in households higher than that of Vietnam (Table 4C.4). lacking access to even a limited standard of These countries typify the development ex- sanitation. The populations in regions with perience of the two regions. Expansion in low monetary poverty like East Asia and Pa- access to education preceded or was contem- cific, Latin America and the Caribbean, and poraneous with the growth in income in East the Middle East and North Africa suffer a san- Asia, whereas despite rising incomes human itation deprivation rate several times as high development has lagged in South Asia (World as that in the monetary dimension. Globally, Bank 2018d). Iraq experiences the highest almost one individual in six is not connected deprivation in the education dimension, and to electricity. Yet this is overwhelmingly a it is one of the few countries where school South Asian and Sub-Saharan African phe- enrollment outcome is worse than education nomenon: approximately one South Asian in attainment. Over the last 15 years, access to four and two Sub-Saharan Africans in three schooling in Iraq has been disrupted because lack electricity at home. of conflict, which is a reminder that progress An examination of deprivation rates, one cannot be taken for granted, especially in indicator at a time, generally confirms that fragile and conflict-affected situations. the regional ranking for any one indicator is The examination of indicator deprivation consistent with the others. Regions more de- rates does not reveal information about the prived in one indicator are highly likely to be simultaneity of deprivations. To consider this more deprived in other indicators. However, aspect, other tools are needed. One of the there are anomalies. For example, the Europe simplest approaches involves counting the and Central Asia region shows the lowest in- number of indicators in which people are de- cidence of monetary poverty; however, the prived contemporaneously. Figure 4.2 shows share of people deprived in school enroll- the shares of individuals deprived according ment in the region is higher than in both the to the maximum of six indicators. Approxi- East Asia and Pacific and the Latin America mately 60 percent of people in the 119 econ- and Caribbean regions. omies are not deprived in any of the six indi- Important insights on the pattern of de- cators. More than 80 percent of Sub-Saharan velopment can be gleaned from country Africans exhibit at least one deprivation, but outcomes as well. For example, Pakistan and a smaller share of South Asians (65.6 per- Vietnam both have a low absolute poverty cent) experience at least one deprivation; as BEYOND MONETARY POVERTY 99 FIGURE 4.2 Share of Individuals Deprived in at Least a Given Number The adjusted headcount measure M defined of Indicators, 119 Economies, circa 2013 in the previous section is sensitive to both the incidence and breadth of multidimen- 100 sional poverty. If a poor household becomes 90 deprived in additional elements, the changes are registered by the measure—something 80 that will not be captured by the headcount H. Share of population (%) 70 The adjusted headcount measure, however, does not take into account the deprivations 60 of households deemed to be multidimension- 50 ally nonpoor. This can ignore a substantial portion of deprivation. Of the total popula- 40 tion in the sample, 15.5 percent is deprived 30 in only one indicator and another 8.2 per- cent deprived in two indicators (table 4.6). A 20 subset of these households is not identified as multidimensionally poor because their 10 total weighted deprivation does not cross the 0 poverty threshold of one-third. In fact, most 1 2 3 4 5 6 individuals experiencing one deprivation Number of indicators deprived in and two-thirds of individuals experiencing East Asia and Pacific Rest of the world two deprivations are not multidimensionally Europe and Central Asia South Asia poor. They face an average of 0.13 and 0.25 Latin America and the Caribbean Sub-Saharan Africa weighted deprivations, respectively, which is Middle East and North Africa Total missed by the intensity-sensitive measure. Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD The picture of poverty can shift yet again (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, under the distribution-sensitive measure D, Poverty and Equity Global Practice, World Bank, Washington, DC. the third measure, because it differs from the adjusted headcount measure in two crucial ways. Unlike the adjusted headcount mea- the number of deprivations rises, a large gap sure, the distribution-sensitive measure is opens between South Asia and Sub-Saharan not associated with a prespecified poverty Africa. Whereas 20.5 percent of South Asia’s threshold so it counts deprivations experi- population is deprived in three or more in- enced by all households. Second, it penalizes dicators, 55.1 percent of Africans are so de- compounding deprivations such that poverty prived. On the shares experiencing four or is higher when one household experiences more deprivations, South Asia catches up to two deprivations than when two households the world at large. Thus, in addition to the experience one deprivation each. relatively larger share of Sub-Saharan Afri- The regional estimates for multidimen- cans who are deprived in each dimension, sional headcount, adjusted headcount, and Sub-Saharan Africans suffer from a greater distribution-sensitive measures are presented average number of deprivations than people in table 4.7. Because the scales of the two elsewhere. measures do not lend themselves to easy comparison, the focus is on the regional con- tribution to global poverty under each ap- Incorporating breadth of poverty proach. Moving from multidimensional pov- into the measurement erty headcount (H) to the intensity-sensitive Summarizing the information on the num- measure (M), the concentration of poverty ber of deprivations into a single index proves shifts further to Africa. This shift is driven by useful in making comparisons across popula- the breadth of deprivation in Sub-Saharan tions and across time. Aggregate multidimen- Africa, which is twice as high as in South Asia sional poverty measures provide an easy way and several times higher than in other re- to rank countries and monitor their progress. gions of the world (table 4.7). 100 POVERTY AND SHARED PROSPERITY 2018 TABLE 4.6 The Multidimensionally Poor and the Breadth of Deprivation, by Number of Deprivations, 119 Economies, circa 2013 Multidimensional poverty status Breadth of deprivation Number of Share of the population deprivations (%) Nonpoor (%) Poor (%) Nonpoor Poor 0 62.0 62.0 0.0 0.00 n.a. 1 15.5 14.1 1.4 0.13 0.33 2 8.2 5.7 2.5 0.25 0.43 3 6.0 0.0 6.0 n.a. 0.48 4 4.8 0.0 4.8 n.a. 0.65 5 2.8 0.0 2.8 n.a. 0.83 6 0.7 0.0 0.7 n.a. 1.00 Total 100.0 81.7 18.3 0.04 0.58 Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measure- ment and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. Note: A household is multidimensionally poor if it is deprived in more than a third of weighted deprivations. Breadth of deprivation refers to the average number of deprivations relative to the total number of indicators. It varies from 0 to 1, where 1 represents a person deprived in all six indicators. The shares may not sum to 100 because of rounding. n.a. = not applicable. TABLE 4.7 Regional Contributions to Multidimensional Poverty, 119 Economies, circa 2013 Multidimensional Adjusted headcount Distribution-sensitive headcount (H) measure (M) measure (D) Breadth of Share of the Region deprivation population (%) H Contribution (%) M Contribution (%) D Contribution (%) East Asia and Pacific 0.07 17.8 7.5 7.3 0.03 5.8 0.02 5.5 Europe and Central Asia 0.02 13.3 1.1 0.8 0.00 0.5 0.01 0.9 Latin America and the Caribbean 0.07 17.4 6.1 5.8 0.03 4.7 0.02 5.1 Middle East and North Africa 0.06 8.1 5.9 2.6 0.03 2.1 0.02 2.2 South Asia 0.21 12.1 26.6 17.7 0.14 15.9 0.09 15.2 Sub-Saharan Africa 0.44 18.6 64.3 65.4 0.40 70.8 0.29 70.9 Rest of the world 0.00 12.7 0.5 0.3 0.00 0.2 0.00 0.2 Total 0.14 100.0 18.3 100.0 0.11 100.0 0.07 100.0 Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measure- ment and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. Note: Breadth of deprivation refers to the average number of deprivations relative to the total number of indicators. It varies from 0 to 1, where 1 represents a person deprived in all six indicators. The distribution of global poverty is An appealing feature of the adjusted head- subject to two countervailing effects when count measure M is that the overall measure going from the intensity-sensitive measure can be easily decomposed into the relative (M) to the distribution-sensitive measure contribution of each indicator. Such de- (D). Counting all deprivations pushes the compositions matter for understanding the distribution of poverty to regions that have drivers of multidimensional poverty, and the few multidimensionally poor but many who sectors that ought to be given priority in the suffer from at least one deprivation. At the design of poverty-alleviating policies. If the same time, assigning more importance to poverty rate is high because of income in- compounding deprivations pulls it toward sufficiency, a focus on economic growth or regions with high breadth of deprivation. income support is appropriate; but, if edu- The first effect more than offsets the second cation or access to utilities plays a dominant in Europe and Central Asia, Latin America role in multidimensional poverty, invest- and the Caribbean, and the Middle East and ments in the corresponding sectors may yield North Africa, resulting in a slightly higher the highest returns to poverty reduction. contribution of these regions to global pov- In high-income countries, multidimen- erty under D than under M (table 4.7). sional poverty, though extremely low, almost BEYOND MONETARY POVERTY 101 FIGURE 4.3 Contribution of Indicators to the Adjusted Headcount Measure (M), 119 Economies, circa 2013 Total Sub-Saharan Africa South Asia Rest of the world Middle East and North Africa Latin America and the Caribbean Europe and Central Asia East Asia and Pacific 0 20 40 60 80 100 Percent Monetary Educational attainment Educational enrollment Electricity Limited-standard sanitation Limited-standard water Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. exclusively arises because of insufficient in- poverty is predominantly a rural phenome- come given the near-universal access to edu- non: 45.8 percent of the total sample popula- cation and infrastructure services (figure 4.3). tion is rural, but 81.3 percent of the monetary For the multidimensionally poor in Europe poor are living in rural areas (annex 4C, table and Central Asia, access to electricity is a much 4C.1). If poverty is considered more broadly more important driver of poverty than else- with the multidimensional lens, the distribu- where. The comparison across Sub-Saharan tion of poverty tilts even more toward rural Africa and South Asia reveals how the underly- areas. Thus, 83.5 percent of the multidimen- ing structure of deprivations differs across the sionally poor are rural dwellers, implying that, two regions. In South Asia, the education di- relative to urban households, rural house- mension has a disproportionate contribution holds suffer cumulatively more deprivations to poverty (46 percent), whereas the contribu- in access to education and essential utilities. tion of monetary poverty is relatively low (24.6 The most pronounced shifts of poverty to- percent). In Sub-Saharan Africa, the services ward rural areas are observed in East Asia and (39.7) and the monetary (36.1) dimensions Pacific and in Latin America and the Carib- contribute the most to multidimensional pov- bean (figure 4.4). In these regions, the shift in erty, and the education dimension contributes the composition is largely driven by depriva- the least (24.2 percent). This may suggest a dif- tions in limited-standard sanitation and adult ferent policy focus in the two regions. The pri- educational attainment. In contrast, poverty ority in these South Asian countries should be becomes more urban in the Middle East and wider access to education whereas expansion North Africa and South Asia, suggesting that of basic infrastructure services will have the urban residents in these regions, although not strongest impact in Sub-Saharan Africa. monetarily poor, experience deprivations in some of these additional aspects of life. With respect to household composition, Who are the monetarily and households with children are overrepresented multidimensionally poor? among both the monetary poor and the mul- As the definition of poverty broadens to in- tidimensionally poor, regardless of the gender clude additional aspects of deprivation, the or number of adults in the household (figure composition of the poor changes. Monetary 4.5; also annex 4C, table 4C.2).10 The shift 102 POVERTY AND SHARED PROSPERITY 2018 from an exclusively monetary approach to a FIGURE 4.4 Difference in the Share of the Poor in Rural Areas, multidimensional account of poverty does Multidimensional Headcount vs. Monetary Headcount, 119 not substantially change the demographic Economies, circa 2013 composition of the poor, though house- 10 holds with only one adult woman (with or without children) represent a slightly larger 8 share in the latter case (8.8 percent compared Percentage point difference with 8.1 percent). All indicators included in 6 this chapter are measured at the household level and thus do not distinguish differences 4 within households. The estimates also assume 2 that resources are distributed equally within a household, that all household members 0 have similar needs, and that there are no scale economies in larger households. Assessing –2 individual well-being requires measuring intrahousehold resource allocation and the –4 East Asia Europe Latin Middle Rest South Sub- Total needs of each household member. Chapter 5 and and America East and of the Asia Saharan investigates methods that estimate individual Pacific Central and the North world Africa Asia Caribbean Africa well-being from underlying household data.11 Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, A deeper look Poverty and Equity Global Practice, World Bank, Washington, DC. Note: The lines indicate the difference in percentage points of the rural share of the poor when com- Extending monetary poverty by including paring multidimensional and monetary poverty. A positive value indicates that the rural share of the poor measures of access to education and basic in- is greater with the multidimensional measure. frastructure services changes the understand- ing of poverty. However, even this extension FIGURE 4.5 Contribution to Monetary and Multidimensional Poverty, to three dimensions fails to capture other by Household Type, 119 Economies, circa 2013 key dimensions of well-being. This section augments multidimensional poverty by also 100 including measures of access to health care 90 services and lack of security. The analysis is carried out on six countries for which in- 80 formation on households from a single data 70 source is available. This exercise is exploratory in nature and the numbers presented might 60 Percent diverge from recent official sources (and even 50 from the analysis performed in the previous 40 section) because in all but one country the analysis is based on different household sur- 30 veys than the one used for calculating mone- 20 tary poverty. Instead, it uses surveys that are comprehensive enough to include the addi- 10 tional dimensions. The purpose of the exer- 0 cise is to illustrate the gains and insights that Population Monetary poor Multidimensional poor could emerge if this information was avail- Only children Two adults, with child able for a larger set of countries. Only seniors One adult male, with no children Accounting for the two extra dimensions Multiple adults, with no children One adult male, with children of well-being further enhances the under- Multiple adults, with children One adult female, with no children standing of poverty. The proportion of peo- Two adults, without child One adult female, with children ple identified as poor under the expanded Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD definition is higher than with the three- (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, dimensional measure, suggesting that the Poverty and Equity Global Practice, World Bank, Washington, DC. BEYOND MONETARY POVERTY 103 share of individuals who are unnoticed by tively balanced view of how countries might monetary poverty measures could be even fare after the multidimensional poverty mea- higher than reported in the previous section. sure is extended.12 Including health and security can also shift Summary analysis of the data reveals that the common understanding of who the poor deprivation rates vary greatly by country (table are and where they are located. Specifically, 4.8). Monetary poverty ranges from 2 percent acknowledging deprivations along these two in Ecuador to 44 percent in Tanzania.13 Only dimensions reveals that a larger share of the 1 percent of the population does not have ac- poor live in female-headed households and, cess to electricity in Ecuador, Indonesia, and in several cases, shifts poverty back toward Iraq, whereas the same measure is as high as urban areas. 87 percent in Uganda. The countries also ex- hibit different deprivation rates in the newly added dimensions. More than 43 percent of The six-country sample individuals in Tanzania live in households The extended measure of poverty is com- where at least one child is stunted, whereas puted for six countries—Ecuador, Indonesia, the same deprivation rate for Mexico is 15 Iraq, Mexico, Tanzania, and Uganda—and percent. The country ranking on the crime covers the years 2009–14 (see appendix A for indicator is nearly the reverse of the rankings details on the surveys used). These countries on the other indicators. The upper-middle- have primarily been chosen on the basis of income countries in the sample—Ecuador, data availability. In each of these countries, Iraq, and Mexico—suffer from high crime a household survey has been conducted re- rates and substantial insecurity in comparison cently that collected information relevant to with the low-income countries, Tanzania and the five dimensions of poverty in a compa- Uganda. The share of individuals affected by rable manner. The six countries include low- a natural disaster also differs markedly across income, lower-middle-income, and upper- the six countries. Uganda stands out as the middle-income countries, as well as all World least well performing country; there, nearly Bank regions except Europe and Central Asia a third of the population was affected by a and South Asia. They therefore offer a rela- drought in the year leading up to the survey. TABLE 4.8 Share of Individuals Deprived, by Indicator, Selected Countries Percent Dimension Indicator Ecuador Indonesia Iraq Mexico Tanzania Uganda Monetary poverty Daily consumption < $1.9 2.0 3.5 2.5 9.2 43.6 35.8 Education Any school-aged child is not enrolled in school 2.2 3.6 26.0 10.4 32.2 15.4 No adult has completed primary education 4.8 5.3 12.6 5.3 13.9 26.1 Access to basic No access to basic-standard drinking water 11.3 19.0 13.4 3.7 54.6 54.0 infrastructure No access to basic-standard sanitation 14.1 26.6 13.5 19.4 74.5 77.0 No access to electricity 1.2 0.8 0.7 4.3 79.7 87.2 Health No facility delivery 6.8 16.6 11.7 4.6 36.7 30.8 No DPT3 vaccination 3.6 33.6 — 11.9 — 8.4 Any child is stunted 25.7 41.8 40.5 15.0 43.4 40.7 Any female is malnourished 3.5 10.5 6.0 5.3 13.6 — Security Experienced or in threat of crime 33.0 6.9 21.1 16.4 1.8 5.1 Affected by natural disaster 2.9 0.9 3.0 0.1 5.6 32.3 Source: Calculations based on Ecuador’s Encuesta de Condiciones de Vida 2013–14; Indonesian Family Life Survey, 2014; Iraq Household Socio-Economic Survey, 2012; Mexican Family Life Survey, 2009–12; Tanzania’s National Panel Survey, 2012–13; Uganda National Panel Survey 2013–14. See appendix A for details. Note: Monetary poverty rates might differ from recent official estimates because, in all cases except for Iraq, this exploratory analysis is based on different household surveys than the ones used to calculate official monetary poverty, as reported in chapter 1 and earlier in this chapter. When an indicator is not available for the particular country, weights are shifted to the other indicators in the dimension. A household has access to a basic-standard drinking water if its drinking water comes from an improved source (for example, piped, borehole, protected dug well, rainwater, or delivered water) within a round trip time of 30 minutes. A household has access to basic-standard sanitation if it is using improved sanitation facilities (for example, flush/pour flush to piped sewer system, septic tank, or a composting latrine) and the facility is for the exclusive use of the household. — = not available; DPT3 = diphtheria-pertussis-tetanus vaccine. 104 POVERTY AND SHARED PROSPERITY 2018 With the addition of health and security FIGURE 4.6 Share of Individuals Deprived in at Least a Given Number indicators, the share of individuals deprived of Indicators, Selected Countries in at least one indicator is troublingly high 100 (figure 4.6). In Tanzania and Uganda, as many as 95 percent of the population is de- prived in at least one indicator. Even in the 80 Share of the population (%) top-performing countries, Ecuador and Mexico, more than half the population is deprived in at least 1 of the 12 indicators. If a household is considered worthy of atten- 60 tion when it is deprived in any of the rele- vant indicators, then monetary poverty and even multidimensional poverty measures in 40 three dimensions fail to capture many house- holds. The number of deprivations people experience declines rapidly as the deprived 20 indicator count increases, and virtually no one is deprived in all 12 indicators (or 11 or 10) in any country. Yet the decline occurs 0 more quickly in some countries than in oth- 1 2 3 4 5 6 7 8 9 10 11 12 ers. In Tanzania and Uganda, about half of Number of indicators deprived in the population is deprived in five indicators, Ecuador Indonesia Iraq highlighting the compounded disadvantages Mexico Tanzania Uganda many households suffer in these countries. Source: Calculations based on Ecuador’s Encuesta de Condiciones de Vida 2013–14; Indonesian Family Life Survey, 2014; Iraq Household Socio-Economic Survey, 2012; Mexican Family Life Survey, 2009–12; Tanzania’s National Panel Survey, 2012–13; Uganda National Panel Survey 2013–14. See appendix A for Comparing alternative measures details. of poverty Because of the frequency of cumulative more stringent definitions in the services di- deprivations, headcount ratios rise several- mension, or the correlational structure link- fold in some countries if one shifts from ing the various dimensions. The last reason monetary poverty to the multidimensional may be less apparent, but it is conceptually poverty measure in five dimensions (figure important: if households deprived in any 4.7). In Iraq, 2.5 percent of the population of the added dimensions were already de- are counted among the monetary poor; 10.4 prived according to the three-dimension percent are poor if three dimensions are measures, implying that the correlation be- considered (with a cutoff of one-third); and tween the deprivations are high, then adding 28.4 percent are poor if five dimensions are new dimensions need not raise the poverty considered (with a cut-off of one-fifth). Pov- headcount rates. Conversely, if the new di- erty rates climb by an average 41 percent if mensions are uncorrelated or, especially, neg- the five-dimension measure is used instead of atively correlated with deprivation according the three-dimension measure. Clearly, as the to the three-dimension measure, then the ad- poverty measure becomes more comprehen- dition of the new dimensions may lead to an sive and deprivation in a single dimension upward surge in poverty rates. Similar to the (or indicators whose weights add up to that three-dimension multidimensional measure of a single dimension) continues to define above, decompositions of the adjusted head- poverty, the count of individuals living in count ratios (M) can be used to unpack how poverty rises. much the different dimensions contribute to The headcount ratios mask the dimen- poverty in each of the countries studied. sions and indicators driving the rise in pov- The addition of the health and security erty rates, and those dimensions and indica- dimensions to the three-dimension measure tors vary across countries. The increase may shifts the drivers of poverty in several coun- be caused by any of the added dimensions, tries (figure 4.8). Measured in three dimen- BEYOND MONETARY POVERTY 105 FIGURE 4.7 The Headcount Ratio, by Alternative Poverty Measures, Selected Countries 80 76.0 71.9 70 63.3 60 54.8 Share of population (%) 50 43.6 40 35.8 30 28.4 20 16.1 16.2 13.3 10.3 10.4 9.2 10 5.9 7.3 2.0 3.5 2.5 0 Ecuador Indonesia Iraq Mexico Tanzania Uganda Monetary poverty Multidimensional poverty (3 dimensions) Multidimensional poverty (5 dimensions) Source: Calculations based on Ecuador’s Encuesta de Condiciones de Vida 2013–14; Indonesian Family Life Survey, 2014; Iraq Household Socio-Economic Survey, 2012; Mexican Family Life Survey, 2009–12; Tanzania’s National Panel Survey, 2012–13; Uganda National Panel Survey 2013–14. See appendix A for details. Note: The figure shows the share of the population that is considered poor under three different definitions of poverty. Monetary poverty = individuals living on less than US$1.90 a day. Multidimensional poverty (three dimensions) = individuals deprived in at least 33 percent of the (weighted) indicators according to the multidimensional headcount measure; the dimensions considered are monetary poverty, edu- cation and access to basic infrastructure. Multidimensional poverty (five dimensions) = individuals deprived in at least 20 percent of the (weighted) indicators according to the multidimensional headcount measure and considering all five dimensions. Each dimension in the three-dimension measure is weighted 0.33. Each dimension in the five-dimension measure is weighted 0.20. In the multidimension mea- sures, each indicator is weighted equally within dimensions. Monetary poverty rates might differ from recent official estimates because, in all cases except for Iraq, this exploratory analysis is based on different household surveys than the ones used to calculate official mon- etary poverty, as reported in chapter 1 and earlier in this chapter. FIGURE 4.8 Contribution to Multidimensional Poverty (M), by Dimension, Selected Countries 100 Contribution to total poverty (%) 80 60 40 20 0 3 5 3 5 3 5 3 5 3 5 3 5 Dimensions Ecuador Indonesia Iraq Mexico Tanzania Uganda Monetary Education Basic infrastructure Health Security Source: Calculations based on Ecuador’s Encuesta de Condiciones de Vida 2013–14; Indonesian Family Life Survey, 2014; Iraq Household Socio-Economic Survey, 2012; Mexican Family Life Survey, 2009–12; Tanzania’s National Panel Survey, 2012–13; Uganda National Panel Survey 2013–14. See appendix A for details. Note: The figure shows the contribution of each dimension to the adjusted-headcount ratio M based on the dimensional breakdown method of Alkire et al. (2015). 106 POVERTY AND SHARED PROSPERITY 2018 sions, deprivations in the education dimen- of the households that suffer from crime do sion are behind two-thirds of the headcount not experience other deprivations, and hence ratio in Iraq. If the five-dimension measure do not meet the criteria for classification is used, the role of educational deprivations among the poor. Consequently, security con- decreases noticeably, and the two extra di- tributes only modestly to multidimensional mensions are behind roughly half the poverty poverty in Mexico. In Tanzania and Uganda, headcount. Particularly, health deprivations health care deprivations are positively cor- emerge as an area with large contributions to related with monetary poverty, education poverty in Iraq. In contrast, in Tanzania and deprivations, and deprivation in services. Uganda, the two new dimensions account Yet, because many households already meet for only 20 percent of poverty; and, in both the cutoff for classification among the poor the three-dimension measure and the five- without adding the health care dimension, dimension measure, monetary poverty and the dimension does not contribute much to lack of access to basic infrastructure services the ranks of the poor. are the major contributors to poverty. These effects are partially driven by the Poverty profiling with five extent to which the deprivations tend to ap- dimensions of well-being pear together, and the number of depriva- tions experienced by households. In Ecuador The correlational structure between the di- and Mexico, monetary poverty and threat of mensions of well-being and their association crime are negatively correlated, implying that with population characteristics may change the two indicators capture different types the composition of the poor and the corre- of households; households that suffer from sponding policy actions needed to reduce monetary poverty are less likely to suffer from poverty. In Ecuador and Iraq, where the deprivations associated with crime relative to contribution to poverty from the security households that do not suffer from monetary dimension is relatively large, many of the poverty. When deprivations linked to crime individuals suffering from threats of crime are included in the measure of multidimen- reside in urban centers. As a result, the share sional poverty, many new households may be of the poor who reside in urban areas in Iraq added to the ranks of the poor, which is the rises from 31 percent to 44 percent if the case in Ecuador. In the case of Mexico, many focus shifts from monetary poverty to five- FIGURE 4.9 The Poor, by Sociodemographic Characteristics, Selected Countries a. Poor living in urban areas b. Poor living in female-headed households 60 14 50 12 Share of poor (%) Share of poor (%) 10 40 8 30 6 20 4 10 2 0 0 or ia q ico ia a or ia q o ia a Ira d Ira ic nd es an es an d ad an ex ex ua a on on nz nz u Ug Ug M M Ec Ec Ta Ta Ind Ind Monetary poor Monetary poor Multidimensionally poor (5 dimensions) Multidimensionally poor (5 dimensions) Source: Calculations based on Ecuador’s Encuesta de Condiciones de Vida 2013–14; Indonesian Family Life Survey, 2014; Iraq Household Socio-Economic Survey, 2012; Mexican Family Life Survey, 2009–12; Tanzania’s National Panel Survey, 2012–13; Uganda National Panel Survey 2013–14. See appendix A for details. BEYOND MONETARY POVERTY 107 MAP 4.1 Provincial Poverty Rates, Ecuador a. Monetary measure b. Three-dimension measure c. Five-dimension measure PA C I FI C PACIFIC PAC I F I C OCEAN OCE AN OC E AN QUITO QUITO QUITO Poverty rate (%) > 35 35 30 25 20 15 10 5 0 IBRD 43980 | OCTOBER 2018 Source: Calculations based on Ecuador’s Encuesta de Condiciones de Vida 2013–14. See appendix A for details. dimension poverty, and similarly from 18 within a country. In Ecuador, for example, percent to 37 percent in Ecuador (figure 4.9). the thinly populated province of Pastaza is In contrast, in Mexico, Tanzania and Uganda, one of several eastern provinces with high where the security addition had a relatively poverty rates according to the monetary pov- small contribution to total poverty, urban erty measure, but it has an exceptionally high poverty rates change only marginally in re- poverty rate according to the extended mul- sponse to the addition of more dimensions. tidimensional poverty measure (map 4.1). In Indonesia, where health deprivations Similar changes occur in other countries, make up the greatest contribution to poverty, suggesting that the geographical concentra- the share of poor in urban areas decreases, tion of poverty shifts if more dimensions are suggesting that lack of health care primarily considered. This may have important impli- is germane to rural areas. cations for policies aiming to eliminate the Adding more dimensions also highlights pockets of poverty and for the allocation of differences in the types of households con- resources across regions within a country. sidered poor. If the five-dimension measure is used instead of the monetary poverty mea- sure, the share of the poor living in female- Conclusion headed households, defined as households in Monetary poverty is the World Bank’s work- which the only adult is a woman, increases in horse measure to assess progress in poverty all six countries in the sample except Tanza- reduction across the world. This chapter ex- nia. In Indonesia, the shift causes the poverty amines the effects of extending the measure of rate among individuals in female-headed poverty by adding nonmonetary dimensions households to rise from less than the average in an attempt to broaden the measurement of rate to more than the average rate, hence tar- well-being. The analysis should be viewed as geting female-headed households becomes a starting point for a deeper investigation of an important means to combat poverty. the measurement of poverty that recognizes As the composition of poverty changes, that many dimensions of well-being are not so does the spatial concentration of poverty all readily available through markets. 108 POVERTY AND SHARED PROSPERITY 2018 In addition to income and consumption, human suffering. Although this appreciation up to four other dimensions of poverty are is not new or original, elevating additional included in the analysis, represented by a aspects of well-being to the same level as total of 12 indicators of well-being. Although consumption or income poverty can high- there are many other valuable indicators that light the relevance of those aspects in com- could have been included in the portfolio of parison to an exclusive focus on monetary nonmonetary indicators, the selected indica- poverty. tors satisfy explicit principles, including the Going forward, the World Bank will mon- centrality of private consumption, data avail- itor progress on multidimensional poverty ability and parsimony. using the three-dimension poverty head- The consideration of access to education count presented in this chapter. However, the and basic infrastructure alongside income, in empirical challenges of a multidimensional a sample of 119 economies for circa 2013 re- poverty measure, especially at the global veals that about a third of those that are mul- level, are great. The analysis described in this tidimensionally deprived are not captured by chapter relies heavily on available data for the monetary poverty. The most prevalent depri- various components of well-being. The data vation is access to adequate sanitation, which on 119 economies had to have been stan- is associated with higher deprivation rates dardized so indicators on education and util- than income. In the exploratory analysis for ities could be examined alongside consump- six countries in which indicators of health, tion. However, household consumption or nutrition, and security are added to the anal- income surveys often lack adequate informa- ysis of poverty, new aspects of deprivation tion on many key aspects of well-being, such are uncovered. In some cases, the incidence as health, nutrition, and security. Thus, the of crime or the threat of crime is weakly or extended analysis on additional dimensions even negatively associated with monetary of poverty was restricted to six countries. poverty. This implies that the characteristics These exercises are also suboptimal because of the poor shift as the definition of poverty information on the quality of the related ser- is broadened to include security. For several vices is missing. Richer datasets harmonized countries, a larger share of the multidimen- with respect to the measurement of essential sional poor live in urban areas and in fe- service access and quality are needed. This male-headed households. appeal does not necessarily mean that already A growing toolbox for the assessment of lengthy household survey questionnaires well-being enhances the understanding of should be lengthened further. Where possi- poverty. In some regions, deprivations in one ble, alternative information sources, such as dimension are accompanied by deprivations administrative data or vital statistics, can be in other dimensions, whereas this is not the combined with survey data at relatively little case for other regions. This has important additional cost in order to broaden the un- implications for policies aimed at reducing derstanding of well-being. BEYOND MONETARY POVERTY 109 Annex 4A Comparison of indicators used in multidimensional poverty measures TABLE 4A.1 Dimensions and Indicators World Bank EU Social Indicators (3 and 5 dimensions) UNDP–OPHI MPI Mexico (selected) Monetary (and Consumption or income Income below national Income below 60% of median living standard) below $1.90 well-being threshold national equivalized income Housing Housing Assets Assets Basic Electricity Electricity Electricity infrastructure Drinking water Drinking water Drinking water Sanitation Sanitation Sanitation Cooking fuel Cooking fuel Education Adult school attainment Adult school attainment Complete level of education Early school-leavers (ages 18–24) (years of schooling) Child school enrollment Child school attendance School attendance Health and Coverage of vaccination Child mortality Infant mortality nutrition Coverage of birth attendance Coverage of health service Nutrition (children and adults) Nutrition (children and adults) Access to food Life expectancy Self-reported unmet need for health care Security Incidence of crime Incidence of natural disasters Employment Access to social security Jobless households Employment of older workers Sources: OPHI 2018; and World Bank 2017b. Note: Indicators in blue reflect those that are included in the World Bank’s multidimensional poverty measure for five dimensions. EU = European Union; MPI = Multidimensional Poverty Index; OPHI = Oxford Poverty and Human Development Initiative; UNDP = United Nations Development Programme. 110 POVERTY AND SHARED PROSPERITY 2018 Annex 4B Multidimensional poverty measures: A formalization The adjusted headcount measure M was developed by Alkire and Foster (2011), as a special case of the Alkire–Foster family of multidimensional poverty measures. One of the main char- acteristics of the measure is that it uses a dual cutoff. The first cutoff is the specific sufficiency threshold for each dimension. The second cutoff is often identified by the parameter k and rep- resents the number of (weighted) deprivations needed before an individual may be considered multidimensionally deprived. The deprivations among individuals who are poor in at least k dimensions are aggregated for an entire society as follows: L G N  4IJ ) \6 - 4] S  [ f[ 2J ^ Q _ (IJ g ( \+I V -] (4B.1) . 5J I=: J=: where yij is the achievement of person i on dimension j ; z j is the sufficiency threshold for di- mension j ; Iij is a dimension-specific indicator function that takes the value of 1 if yij < zj and 0 otherwise; α is a parameter of the measure’s sensitivity to the depth of poverty; and I(ci ≥ k) is a poverty indicator function that equals 1 if the number of (weighted) dimensions in which the individual is deprived is at least equal to the parameter k. The measure M(α, k ; y) is de- composable across population groups, which can facilitate a regional analysis and is useful for targeting. It also satisfies several desirable properties, including dimensional breakdown, which is useful to understand the contribution of each dimension to overall poverty. The most common application of the measure involves setting α equal to zero. This special case is known as the adjusted headcount ratio (M), and is defined as the share of multidimensionally poor households multiplied by the average number of deprivations expe- rienced by the multidimensionally poor. This case is used more frequently because, in many applications, some indicators are categorical, and thus higher values of α are not appropriate. This measure can be seen as more appealing than the multidimensional headcount H because it incorporates information on the breadth of poverty. The special case included in the pres- ent chapter is as follows: For α = 0, k = 1 3 , then L G   ) S  [ d[ 2J (IJ e ( X+I V Z  S ' R % (4B.2) . I=: J=: where H is the multidimensional headcount rate, that is, the share of individuals who are multidimensionally deprived, and A is the average number of deprivations among those in- dividuals who are multidimensionally deprived. This chapter first reports H as a summary measure across countries and regions and then H ϫ A. BEYOND MONETARY POVERTY 111 Datt (forthcoming) proposes an alternative family of multidimensional poverty measures, known as the distribution-sensitive multidimensional poverty measures. The measure pro- posed does not make use of a dual cutoff, recognizing the essentiality of every deprivation. Every deprivation is counted toward the measurement of poverty even if a person is deprived in a single indicator with low weight. In addition, the measure penalizes for any compounding effect of deprivations characterized by parameter β . The larger the value of β , the higher the weight it places on the cumulative deprivations. O L G N  4IJ for 6 V )\6 7 4] S  [ f[ 2J ^ Q _ (IJ g ;  ,/06  V 77U  U (4B.3) . 5J I=: J=: Although M(α, β, y) is sensitive to the intensity of deprivation suffered by individuals, it does not satisfy the dimensional breakdown (unlike the previous measure). If some of the indicators are binary, as in the case of this chapter, α is set at 0, and M(α, β, y) coincides with the measure of social exclusion presented by Chakravarty and D’Ambrosio (2006). The measure is also a member of the M-gamma class of indicators pre- sented in Alkire and Foster (2016). The measure used in the chapter is defined as follows: ; L G  & S )\  4] S  [ f[ 2J (J W4IJ T 5J Yg  (4B.4) . I=: J=: 112 POVERTY AND SHARED PROSPERITY 2018 Annex 4C Statistical tables TABLE 4C.1 People Living in Monetary or Multidimensional Poverty, by Rural-Urban Areas, 119 Economies, circa 2013 Monetary Multidimensional headcount ratio (%) headcount ratio (%) Rural share of total Rural share Rural share Economies Population Region population (%) Rural Urban of the poor Rural Urban of the poor (number) coverage (%) East Asia and Pacific 55.7 6.5 3.9 67.8 10.2 4.2 75.5 13 28.9 Europe and Central Asia 33.5 0.5 0.2 52.7 1.8 0.8 52.2 17 90.0 Latin America and the Caribbean 21.0 11.2 1.9 61.0 19.9 2.5 68.2 17 91.5 Middle East and North Africa 43.6 6.4 0.9 84.8 11.5 1.9 82.2 9 72.1 South Asia 70.6 15.2 3.9 90.3 33.3 10.5 88.4 5 23.0 Sub-Saharan Africa 67.0 55.9 22.6 83.4 81.8 28.8 85.2 29 60.7 Rest of the world 24.6 0.6 0.4 30.7 0.6 0.4 30.7 29 39.6 Total 45.8 21.0 4.1 81.3 33.6 5.6 83.5 119 45.0 Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measure- ment and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. Note: Location of residence is missing for 1 percent of the total sample. TABLE 4C.2 People Living in Monetary or Multdimensional Poverty, by Household Type, 119 Economies, circa 2013 Monetary poverty Multidimensional poverty Population Profile share (%) Headcount ratio (%) Share of the poor (%) Headcount ratio (%) Share of the poor (%) One adult female, with child 4.4 20.3 7.63 32.7 7.92 One adult female, without child 2.8 1.8 0.42 5.5 0.84 One adult male, with child 0.8 17.1 1.09 30.3 1.25 One adult male, without child 2.4 1.7 0.35 5.5 0.74 Two adults, with child 37.3 14.9 47.48 23.5 48.29 Two adults, without child 7.7 1.6 1.04 4.4 1.86 Multiple adults, with child 31.1 15.1 39.92 21.4 36.59 Multiple adults, without child 9.7 1.8 1.53 3.0 1.6 Only seniors 3.9 1.4 0.45 3.9 0.83 Only children 0.0 24.9 0.08 38.7 0.08 Total 100.0 11.7 100.0 18.2 100.0 Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measure- ment and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. Note: The monetary and multidimensional poverty rates in this table differ slightly from those reported in tables 4.5, 4.6, and 4.7 because household type cannot be constructed for 0.4 percent of the sample because of missing information. BEYOND MONETARY POVERTY 113 Economy-level estimates supply and sanitation). Despite best efforts to harmonize country-specific questionnaires to The estimates in tables 4C.3 and 4C.4 are the standard definition, there could be some derived from household surveys included in discrepancies with measures reported else- the GMD, for circa 2013. The surveys collect where. Therefore, the estimates must be taken information on total household income or as the best possible estimate under the strin- expenditure for monetary poverty estima- gent data requirement of joint observation of tion, as well as information on a host of other monetary as well as nonmonetary dimensions topics, including education enrollment, adult of well-being. Finally, both education indica- education attainment, and access to basic in- tors are household-level indicators (that is, the frastructure services, which permits the con- number of individuals living in a household in struction of the multidimensional poverty which one child is not attending school). This measure. However, there is large heteroge- means that the table of each country’s educa- neity in how the questions are worded, how tional deprivations presented in the chapter detailed the response choices are, and how cannot be directly compared to official esti- closely they match the standard definitions mates from the United Nations Educational, of access (for example, as defined by the Joint Scientific and Cultural Organization, which Monitoring Programme for drinking water are based on individual-level indicators. TABLE 4C.3 Individuals in Households Deprived in Each Indicator, 119 Economies, circa 2013 Deprivation rate (share of population) Education Education Drinking Monetary attainment enrollment Electricity Sanitation water Country Year (%) (%) (%) (%) (%) (%) Albania 2012 1.06 2.27 6.73 0.50 3.04 0.34 Argentina 2014 0.74 1.31 0.98 0.00 0.72 0.05 Armenia 2013 2.24 0.09 1.89 0.50 10.64 0.06 Austria 2013 0.34 0.00 0.00 0.00 0.96 0.00 Bangladesh 2010 19.63 29.14 16.83 43.63 48.32 3.75 Belarus 2013 0.00 0.79 0.00 — 10.60 0.00 Belgium 2013 0.14 1.41 0.00 0.00 2.10 0.00 Benin 2015 49.55 61.61 25.45 69.02 70.67 26.87 Bhutan 2012 2.17 49.96 6.53 12.90 33.76 1.73 Bolivia 2014 5.80 18.33 4.39 9.60 32.58 7.88 Bosnia and Herzegovina 2015 0.20 1.70 30.77 0.04 1.55 8.79 Brazil 2014 2.76 19.50 0.94 0.33 24.67 4.13 Bulgaria 2013 1.77 0.94 0.00 0.00 20.64 0.00 Burundi 2013 71.73 66.34 18.89 93.07 94.31 18.87 Cameroon 2014 23.83 24.39 15.94 1.20 38.93 23.20 Chad 2011 38.43 67.86 5.87 95.51 92.68 56.05 Chile 2013 0.92 4.64 0.39 0.33 0.47 0.91 Colombia 2014 5.03 6.71 3.03 2.39 9.70 5.04 Congo, Dem. Rep. 2012 77.08 28.73 26.94 85.50 70.08 47.90 Congo, Rep. 2011 36.96 13.39 2.25 40.90 47.29 20.23 Costa Rica 2014 1.45 4.43 1.01 0.57 1.92 0.39 Croatia 2013 0.75 0.26 0.00 0.00 1.93 0.00 Cyprus 2013 0.05 1.81 0.00 0.00 1.03 0.00 Czech Republic 2013 0.05 0.01 0.00 0.00 0.70 0.00 Côte d’Ivoire 2015 28.21 53.15 25.57 37.42 59.47 23.28 Denmark 2013 0.31 1.40 0.00 0.00 0.48 0.00 Djibouti 2012 18.32 32.98 4.05 45.41 37.33 9.25 Dominican Republic 2013 2.37 17.03 1.64 1.37 4.28 27.28 Ecuador 2014 2.63 4.74 1.97 0.96 5.57 4.28 Egypt, Arab Rep. 2012 2.29 14.15 7.16 0.28 11.43 1.08 El Salvador 2014 2.97 28.66 5.54 4.49 2.63 1.30 Estonia 2013 0.86 0.07 0.00 0.00 7.21 0.00 (continued) 114 POVERTY AND SHARED PROSPERITY 2018 TABLE 4C.3 Individuals in Households Deprived in Each Indicator, 119 Economies, circa 2013 (continued) Deprivation rate (share of population) Education Education Drinking Monetary attainment enrollment Electricity Sanitation water Country Year (%) (%) (%) (%) (%) (%) Ethiopia 2010 33.56 72.39 37.98 82.53 95.56 50.55 Fiji 2013 1.37 0.79 1.43 10.00 7.15 8.27 Finland 2013 0.09 0.76 0.00 0.00 0.61 0.00 France 2013 0.06 1.04 0.00 0.00 0.47 0.00 Gambia, The 2010 25.08 20.12 10.59 67.10 16.44 11.37 Georgia 2013 6.88 0.19 0.97 0.00 6.04 0.18 Germany 2011 0.04 0.00 0.00 0.00 0.92 0.00 Ghana 2012 12.05 17.08 8.56 33.55 77.09 13.65 Greece 2013 0.96 2.63 0.00 0.00 0.48 0.00 Guatemala 2014 8.65 24.85 18.35 16.52 46.72 8.45 Guinea 2012 35.27 53.73 7.70 0.00 65.66 31.25 Guinea-Bissau 2010 67.08 44.12 5.81 97.09 65.77 36.35 Haiti 2012 23.49 23.18 9.00 64.31 68.80 33.50 Honduras 2013 17.32 14.05 16.56 13.92 19.55 9.85 Hungary 2013 0.11 0.01 0.00 0.00 5.33 0.00 Iceland 2013 0.05 0.06 0.00 0.00 0.00 0.00 Indonesia 2016 6.49 4.99 1.74 2.38 16.51 10.68 Iran, Islamic Rep. 2013 0.11 4.49 1.38 0.12 — 2.44 Iraq 2012 2.46 13.55 22.69 0.66 0.95 10.01 Ireland 2013 0.65 0.76 0.00 0.00 0.12 — Italy 2013 1.37 1.29 0.00 0.00 0.76 0.00 Jordan 2010 0.12 1.83 2.99 0.00 0.00 0.24 Kazakhstan 2013 0.02 0.00 0.00 0.00 0.01 0.38 Kosovo 2013 0.29 0.73 59.40 0.24 — 2.67 Kyrgyz Republic 2013 3.26 0.21 0.00 5.29 0.50 10.67 Lao PDR 2012 22.75 13.45 14.45 11.13 32.10 44.34 Latvia 2013 1.14 0.77 0.00 0.00 14.68 0.00 Lebanon 2011 0.00 9.24 2.25 0.94 — 0.86 Lesotho 2010 59.65 21.25 10.52 83.63 — — Liberia 2014 38.61 40.56 2.83 95.67 53.39 19.13 Lithuania 2013 0.71 0.55 0.00 0.00 12.48 — Luxembourg 2013 0.09 0.64 0.00 0.00 0.08 0.00 Madagascar 2012 77.63 82.46 34.63 27.98 89.48 58.84 Malawi 2010 71.38 47.72 3.12 5.14 26.43 19.41 Malta 2013 0.03 0.52 0.00 0.00 0.09 0.00 Mauritania 2014 5.97 54.26 8.31 62.54 49.30 23.54 Mexico 2012 3.93 6.08 2.76 0.81 4.43 7.41 Micronesia, Fed. Sts. 2013 15.96 8.75 27.99 23.63 19.06 4.97 Moldova 2013 0.08 0.23 0.62 0.09 0.00 28.86 Mongolia 2016 0.50 5.96 3.16 0.17 9.56 12.82 Montenegro 2013 1.04 13.00 37.73 0.99 12.35 4.75 Mozambique 2014 62.90 54.91 33.31 72.76 71.29 40.77 Myanmar 2015 6.36 17.75 13.70 16.20 20.12 29.43 Nepal 2010 14.99 28.56 9.51 31.47 47.32 16.78 Netherlands 2013 0.09 0.58 0.00 0.00 0.01 0.00 Nicaragua 2014 3.24 14.11 8.06 19.98 42.74 12.49 Niger 2014 44.54 70.58 11.71 87.03 83.74 48.54 Norway 2013 0.13 0.78 0.00 0.00 0.00 0.00 Pakistan 2013 6.07 37.09 31.65 8.13 35.11 7.90 Paraguay 2014 2.41 7.76 3.14 0.92 10.95 6.67 Peru 2014 3.72 5.83 1.09 6.80 8.98 14.37 Philippines 2015 6.58 4.52 4.40 9.13 6.78 10.61 Poland 2015 0.00 1.16 2.63 0.00 2.92 0.57 Portugal 2013 0.86 3.57 0.00 0.00 0.95 0.00 (continued) BEYOND MONETARY POVERTY 115 TABLE 4C.3 Individuals in Households Deprived in Each Indicator, 119 Economies, circa 2013 (continued) Deprivation rate (share of population) Education Education Drinking Monetary attainment enrollment Electricity Sanitation water Country Year (%) (%) (%) (%) (%) (%) Romania 2013 0.00 0.29 4.78 1.63 33.43 29.76 Russian Federation 2013 0.01 0.02 1.12 1.01 1.28 0.00 Rwanda 2013 59.49 37.54 4.34 80.55 15.03 25.58 São Tomé and Príncipe 2010 32.28 26.74 17.52 — 60.87 7.03 Senegal 2011 37.98 41.16 6.41 47.05 28.70 18.21 Serbia 2013 0.29 4.28 0.00 0.07 5.03 0.29 Seychelles 2013 1.06 94.93 0.00 — — 8.70 Sierra Leone 2011 52.21 42.49 0.99 0.00 51.03 30.35 Slovak Republic 2013 0.28 0.01 0.00 0.00 1.29 0.00 Slovenia 2013 0.02 0.00 0.00 0.00 0.56 0.00 Solomon Islands 2013 25.14 11.40 13.54 53.83 58.52 25.46 South Africa 2014 18.85 2.26 1.54 4.09 4.17 6.38 Spain 2013 1.16 4.02 0.00 0.00 0.13 0.00 Sri Lanka 2016 0.73 3.78 4.01 2.47 1.15 11.02 Sweden 2013 0.64 0.89 0.00 0.00 0.00 0.00 Switzerland 2013 0.04 0.00 0.00 0.00 0.16 0.00 Tajikistan 2015 4.81 0.31 22.49 2.03 3.51 26.31 Tanzania 2011 49.09 60.61 26.47 84.28 40.79 31.77 Thailand 2013 0.04 15.07 0.67 0.15 0.26 2.68 Timor-Leste 2014 30.31 21.20 0.31 27.20 48.60 22.10 Togo 2015 49.15 26.57 2.32 — 51.82 40.63 Tunisia 2010 1.99 22.55 3.05 0.53 33.32 5.48 Turkey 2013 0.33 3.21 4.22 0.00 2.86 0.68 Tuvalu 2010 3.26 4.54 6.09 9.20 11.54 0.03 Uganda 2012 35.86 47.91 18.48 91.12 72.06 25.97 Ukraine 2013 0.00 0.50 28.94 0.00 27.10 0.00 United Kingdom 2013 0.16 0.48 0.00 0.00 0.40 0.00 Uruguay 2014 0.11 3.04 1.25 0.25 1.52 0.15 Vanuatu 2010 13.15 18.48 14.63 55.93 45.63 19.13 Vietnam 2014 2.64 5.85 1.29 0.89 19.84 7.09 West Bank and Gaza 2011 0.20 3.23 5.49 0.32 1.38 1.95 Yemen, Rep. 2014 18.82 15.95 15.74 33.89 42.53 14.02 Zambia 2015 57.50 24.37 30.37 69.21 59.80 30.67 Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. Note: The definition of the indicators and the deprivation thresholds are as follows. Monetary poverty: a household is deprived if income or expenditure, in 2011 purchasing power parity U.S. dollars, is less than US$1.90 per person per day. Educational attainment: a house- hold is deprived if no adult (grade 9 equivalent age or above) has completed primary education. Educational enrollment: a household is deprived if at least one child (grade 8 equivalent age or below) is not enrolled in school. Electricity: a household is deprived if it does not have access to electricity. Sanitation: a household is deprived if it does not have access to even a limited standard of sanitation. Drinking water: a household is deprived if it does not have access to even a limited standard of drinking water. The data reported refer to the share of people living in households deprived according to each indicator. — = not available. 116 POVERTY AND SHARED PROSPERITY 2018 TABLE 4C.4 Multidimensional Poverty across Alternative Measures, 119 Economies, circa 2013 Number of deprivations (share of population) Multidimensional Adjusted Distribution- headcount (H) headcount sensitive 0 1 2 3 4 5 6 Country (%) measure (M) measure (D) (%) (%) (%) (%) (%) (%) (%) Albania 1.21 0.005 0.005 87.7 11.0 1.0 0.1 0.1 0.0 0.0 Argentina 0.77 0.003 0.002 96.4 3.4 0.2 0.0 0.0 0.0 0.0 Armenia 2.24 0.008 0.005 85.4 13.9 0.7 0.0 0.0 0.0 0.0 Austria 0.34 0.001 0.000 98.7 1.3 0.0 0.0 0.0 0.0 0.0 Bangladesh 32.22 0.178 0.121 27.9 23.9 21.3 15.6 8.3 2.7 0.2 Belarus 8.56 0.029 0.011 89.2 10.4 0.4 0.0 0.0 0.0 0.0 Belgium 0.14 0.000 0.001 96.5 3.4 0.1 0.0 0.0 0.0 0.0 Benin 71.74 0.462 0.332 10.4 11.2 13.1 20.7 23.9 16.2 4.5 Bhutan 11.09 0.050 0.046 35.0 33.8 22.4 7.0 1.7 0.1 0.0 Bolivia 11.88 0.060 0.044 57.8 20.6 11.6 6.3 3.0 0.7 0.1 Bosnia and Herzegovina 1.05 0.005 0.013 61.5 34.5 3.6 0.3 0.1 0.0 0.0 Brazil 4.59 0.021 0.021 62.6 26.0 8.6 2.2 0.5 0.0 0.0 Bulgaria 1.77 0.008 0.006 78.7 19.3 1.8 0.1 0.0 0.0 0.0 Burundi 86.54 0.589 0.429 3.0 2.9 8.6 23.6 40.7 18.0 3.3 Cameroon 36.60 0.210 0.142 42.5 21.2 15.3 11.5 7.0 2.5 0.0 Chad 85.47 0.496 0.323 1.5 3.6 9.8 28.4 36.9 18.2 1.5 Chile 1.01 0.004 0.003 93.0 6.5 0.4 0.1 0.0 0.0 0.0 Colombia 6.54 0.028 0.018 78.6 14.4 4.4 1.7 0.7 0.2 0.0 Congo, Dem. Rep. 83.16 0.560 0.404 7.7 7.1 10.6 21.1 28.6 19.4 5.6 Congo, Rep. 42.64 0.228 0.141 27.0 28.5 23.6 15.4 5.0 0.5 0.0 Costa Rica 1.70 0.007 0.005 91.9 7.0 0.8 0.2 0.1 0.0 0.0 Croatia 0.75 0.003 0.001 97.1 2.8 0.1 0.0 0.0 0.0 0.0 Cyprus 0.05 0.000 0.001 97.4 2.3 0.3 0.0 0.0 0.0 0.0 Czech Republic 0.05 0.000 0.000 99.2 0.7 0.0 0.0 0.0 0.0 0.0 Côte d’Ivoire 49.89 0.294 0.206 16.9 19.0 20.7 18.9 14.5 8.1 1.9 Denmark 0.31 0.001 0.001 97.9 1.9 0.1 0.0 0.0 0.0 0.0 Djibouti 27.86 0.162 0.115 35.9 23.5 16.2 11.6 7.4 5.4 0.1 Dominican Republic 5.24 0.023 0.021 62.3 25.2 9.5 2.4 0.6 0.1 0.0 Ecuador 3.34 0.014 0.009 85.0 11.2 2.7 0.8 0.2 0.0 0.0 Egypt, Arab Rep. 4.12 0.017 0.015 70.6 23.5 5.1 0.7 0.1 0.0 0.0 El Salvador 6.50 0.028 0.022 66.2 24.8 6.8 1.9 0.3 0.1 0.0 Estonia 0.86 0.003 0.002 92.0 7.9 0.1 0.0 0.0 0.0 0.0 Ethiopia 82.18 0.523 0.372 2.6 7.3 11.0 18.4 28.1 24.0 8.6 Fiji 2.38 0.009 0.008 78.2 15.9 4.8 1.0 0.1 0.0 0.0 Finland 0.09 0.000 0.000 98.6 1.4 0.0 0.0 0.0 0.0 0.0 France 0.06 0.000 0.000 98.4 1.5 0.0 0.0 0.0 0.0 0.0 Gambia, The 30.83 0.161 0.104 21.5 31.7 26.8 15.3 3.9 0.8 0.0 Georgia 6.89 0.024 0.009 86.6 12.6 0.8 0.0 0.0 0.0 0.0 Germany 0.04 0.000 0.000 99.0 1.0 0.0 0.0 0.0 0.0 0.0 Ghana 26.02 0.137 0.095 19.6 36.9 19.7 13.6 7.2 2.6 0.5 Greece 0.96 0.003 0.002 96.0 3.8 0.1 0.0 0.0 0.0 0.0 Guatemala 21.56 0.110 0.075 39.4 24.3 18.3 11.1 5.2 1.6 0.2 Guinea 46.20 0.257 0.168 19.0 18.3 26.3 24.0 11.4 0.9 0.0 Guinea-Bissau 79.70 0.495 0.334 1.0 11.0 18.4 25.5 29.7 13.1 1.2 Haiti 43.90 0.248 0.169 15.0 22.1 21.3 19.0 14.1 6.9 1.6 Honduras 22.48 0.118 0.075 53.9 22.4 10.8 6.8 4.1 1.6 0.3 Hungary 0.11 0.000 0.001 94.6 5.4 0.0 0.0 0.0 0.0 0.0 Iceland 0.05 0.000 0.000 99.9 0.1 0.0 0.0 0.0 0.0 0.0 Indonesia 8.03 0.034 0.021 70.6 19.7 6.9 2.0 0.6 0.2 0.0 Iran, Islamic Rep. 0.70 0.002 0.003 92.1 7.2 0.6 0.0 0.0 0.0 0.0 Iraq 7.26 0.031 0.024 63.6 25.8 8.0 1.9 0.6 0.1 0.0 Ireland 0.65 0.002 0.001 98.5 1.5 0.0 0.0 0.0 0.0 0.0 Italy 1.37 0.005 0.002 96.6 3.3 0.0 0.0 0.0 0.0 0.0 Jordan 0.33 0.001 0.002 95.1 4.6 0.3 0.0 0.0 0.0 0.0 Kazakhstan 0.02 0.000 0.000 99.6 0.4 0.0 0.0 0.0 0.0 0.0 Kosovo 2.31 0.008 0.020 39.2 58.6 2.0 0.2 0.1 0.0 0.0 (continued) BEYOND MONETARY POVERTY 117 TABLE 4C.4 Multidimensional Poverty across Alternative Measures, 119 Economies, circa 2013 (continued) Number of deprivations (share of population) Multidimensional Adjusted Distribution- headcount (H) headcount sensitive 0 1 2 3 4 5 6 Country (%) measure (M) measure (D) (%) (%) (%) (%) (%) (%) (%) Kyrgyz Republic 3.26 0.012 0.007 81.8 16.6 1.6 0.0 0.0 0.0 0.0 Lao PDR 28.77 0.151 0.099 24.0 41.4 17.3 10.4 4.4 1.9 0.6 Latvia 1.14 0.005 0.004 84.3 14.9 0.8 0.0 0.0 0.0 0.0 Lebanon 0.76 0.003 0.004 87.5 11.8 0.8 0.0 0.0 0.0 0.0 Lesotho 90.88 0.529 0.342 8.3 28.7 44.9 16.2 2.0 0.0 0.0 Liberia 53.24 0.312 0.211 2.6 24.2 25.3 22.9 18.8 6.2 0.1 Lithuania 1.06 0.004 0.005 86.8 12.6 0.6 0.0 0.0 0.0 0.0 Luxembourg 0.09 0.000 0.000 99.2 0.8 0.0 0.0 0.0 0.0 0.0 Madagascar 85.35 0.669 0.560 2.5 7.1 13.3 19.7 31.5 23.5 2.4 Malawi 75.07 0.385 0.216 16.4 27.6 32.3 19.2 4.4 0.2 0.0 Malta 0.03 0.000 0.000 99.4 0.6 0.0 0.0 0.0 0.0 0.0 Mauritania 43.25 0.206 0.119 21.0 18.6 18.8 22.1 16.0 3.3 0.1 Mexico 4.59 0.019 0.012 80.6 14.5 3.9 0.7 0.2 0.0 0.0 Micronesia, Fed. Sts. 20.84 0.103 0.067 42.4 30.4 16.0 7.3 3.4 0.5 0.0 Moldova 0.08 0.000 0.004 70.6 28.8 0.5 0.0 0.0 0.0 0.0 Mongolia 1.27 0.005 0.008 75.5 17.6 6.2 0.7 0.0 0.0 0.0 Montenegro 9.48 0.041 0.032 51.8 34.5 8.2 3.4 1.4 0.6 0.0 Mozambique 76.61 0.543 0.419 12.6 8.6 8.9 14.2 22.3 22.4 10.9 Myanmar 15.32 0.078 0.057 44.1 28.2 14.5 8.2 3.3 1.4 0.2 Nepal 28.17 0.150 0.102 32.1 24.8 19.3 13.9 6.9 2.4 0.7 Netherlands 0.09 0.000 0.000 99.3 0.7 0.0 0.0 0.0 0.0 0.0 Nicaragua 15.00 0.071 0.048 45.9 28.9 11.7 7.8 3.5 1.9 0.2 Niger 79.16 0.500 0.348 6.2 6.3 9.1 18.6 34.6 23.5 1.7 Norway 0.13 0.000 0.000 99.1 0.9 0.0 0.0 0.0 0.0 0.0 Pakistan 24.38 0.119 0.079 35.2 28.8 19.1 10.6 4.5 1.6 0.1 Paraguay 3.92 0.018 0.014 77.3 15.9 4.9 1.6 0.3 0.1 0.0 Peru 6.15 0.027 0.018 74.3 15.2 6.8 2.7 0.8 0.1 0.0 Philippines 8.70 0.040 0.025 74.2 15.7 5.8 2.7 1.2 0.3 0.0 Poland 0.08 0.000 0.002 93.5 5.9 0.6 0.0 0.0 0.0 0.0 Portugal 0.86 0.003 0.002 94.8 5.0 0.2 0.0 0.0 0.0 0.0 Romania 3.49 0.013 0.019 62.8 8.3 25.4 3.2 0.3 0.0 0.0 Russian Federation 0.99 0.003 0.002 96.6 3.4 0.0 0.0 0.0 0.0 0.0 Rwanda 63.13 0.362 0.226 12.9 15.7 27.5 27.0 13.6 3.1 0.2 São Tomé and Príncipe 51.11 0.247 0.139 20.2 33.6 30.7 12.5 2.7 0.2 0.0 Senegal 46.69 0.268 0.174 31.1 17.2 16.7 16.5 13.3 4.7 0.4 Serbia 0.38 0.002 0.003 91.0 8.2 0.7 0.1 0.0 0.0 0.0 Seychelles 9.69 0.048 0.048 4.6 86.2 9.2 0.1 0.0 0.0 0.0 Sierra Leone 56.91 0.298 0.173 21.5 22.1 24.6 21.8 10.0 0.1 0.0 Slovak Republic 0.28 0.001 0.000 98.5 1.5 0.0 0.0 0.0 0.0 0.0 Slovenia 0.02 0.000 0.000 99.4 0.6 0.0 0.0 0.0 0.0 0.0 Solomon Islands 37.62 0.193 0.124 14.2 24.4 33.0 18.3 7.6 2.3 0.1 South Africa 19.21 0.073 0.031 70.6 22.9 5.4 1.0 0.0 0.0 0.0 Spain 1.16 0.004 0.003 94.8 5.1 0.1 0.0 0.0 0.0 0.0 Sri Lanka 1.12 0.005 0.006 80.1 17.1 2.3 0.4 0.1 0.0 0.0 Sweden 0.64 0.002 0.001 98.6 1.3 0.1 0.0 0.0 0.0 0.0 Switzerland 0.04 0.000 0.000 99.8 0.2 0.0 0.0 0.0 0.0 0.0 Tajikistan 5.43 0.024 0.024 54.2 34.4 9.7 1.5 0.3 0.1 0.0 Tanzania 67.19 0.434 0.315 8.2 12.0 18.2 23.9 19.8 13.4 4.4 Thailand 0.28 0.001 0.005 82.2 16.9 0.8 0.1 0.0 0.0 0.0 Timor-Leste 39.49 0.192 0.111 28.6 25.3 22.6 15.8 6.5 1.1 0.0 Togo 60.66 0.343 0.216 26.0 19.8 22.4 22.0 9.8 0.2 0.0 Tunisia 4.16 0.020 0.024 52.6 31.0 13.5 2.6 0.3 0.0 0.0 Turkey 0.62 0.002 0.004 90.1 8.6 1.0 0.2 0.0 0.0 0.0 Tuvalu 3.88 0.015 0.012 70.7 24.4 4.3 0.5 0.0 0.0 0.0 Uganda 65.03 0.384 0.261 5.8 9.9 21.3 27.9 22.6 10.8 1.8 Ukraine 0.07 0.000 0.015 51.5 40.6 8.0 0.0 0.0 0.0 0.0 (continued) 118 POVERTY AND SHARED PROSPERITY 2018 TABLE 4C.4 Multidimensional Poverty across Alternative Measures, 119 Economies, circa 2013 (continued) Number of deprivations (share of population) Multidimensional Adjusted Distribution- headcount (H) headcount sensitive 0 1 2 3 4 5 6 Country (%) measure (M) measure (D) (%) (%) (%) (%) (%) (%) (%) United Kingdom 0.16 0.001 0.000 99.0 1.0 0.0 0.0 0.0 0.0 0.0 Uruguay 0.19 0.001 0.002 94.1 5.6 0.3 0.1 0.0 0.0 0.0 Vanuatu 33.12 0.154 0.095 22.4 25.3 26.5 17.4 6.7 1.6 0.3 Vietnam 3.78 0.019 0.016 74.8 16.3 6.3 1.9 0.6 0.1 0.0 West Bank and Gaza 0.49 0.002 0.004 88.4 10.6 0.9 0.1 0.0 0.0 0.0 Yemen, Rep. 29.98 0.154 0.098 34.0 24.5 18.2 14.6 7.1 1.4 0.1 Zambia 63.69 0.432 0.318 19.9 11.4 10.6 17.1 21.5 14.8 4.7 Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measure- ment and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. FIGURE 4C.1 Share of Individuals in Multidimensional Poverty, by Region, circa 2013 a. Sub-Saharan Africa Basic infrastructure Monetary 2.3 2.9 12.4 17 28.2 0.2 1.4 Education b. South Asia c. East Asia and Pacific d. Latin America and the Caribbean Basic infrastructure Basic infrastructure Education 0.5 Monetary 1.1 0.4 0.7 3.4 2 1.7 1.5 1.1 0.4 Basic 1 0.5 0.2 infrastructure 2.8 0.3 0.1 Education 1.2 Education 10.9 Monetary 2 Monetary 7 e. Middle East and North Africa f. Europe and Central Asia Basic infrastructure Basic infrastructure 1.3 0.2 0.6 1.4 1 Monetary 0.1 1.1 0.2 0.04 0.2 0.6 0.5 1.1 0.02 Monetary Education Education Source: Estimates based on the harmonized household surveys in 119 economies, circa 2013, GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. Note: The diagrams show the fraction of the regional population that is multidimensionally poor, and the dimensions the poor are deprived in. BEYOND MONETARY POVERTY 119 Notes 1. Economists describe this result formally by explains how the core SDG drinking water saying that utility-maximizing consumers and sanitation indicators focus on a concept will, in choosing their consumption bundles, of “safely managed,” but there are relatively end up equating their marginal rates of sub- few datasets available with all necessary criteria stitution (a ratio of marginal utilities) to the (and data sources beyond household surveys relative prices. Hence, so long as markets func- are needed for some aspects of safely man- tion well, relative prices are natural weights aged sanitation services). SDG monitoring also with which to aggregate goods and services. uses the less-stringent concepts of “limited” Where markets work less well, the case for and “basic” access adopted in this report, for adding separate dimensions of well-being is which data availability is higher, and because stronger (Ravallion 2011; Ferreira and Lugo of the strong relevance of the concepts glob- 2013). In addition, even when there are prices ally. SDG “limited-standard” drinking water is for some of these dimensions, such as school drinking water that comes from an improved fees for private schooling, these might not be source (for example, piped, borehole, pro- reflective of the “value of a poorly maintained tected dug well, rainwater, delivered water). village school without a regular teacher. The SDG “basic-standard” drinking water has an implications of debt owed to a landlord may added criterion of being within a roundtrip not be captured by ruling interest rates. The time of 30 minutes. SDG “limited-standard” value of health services depends on the cir- sanitation means using improved sanitation cumstances of the individual and household” facilities (for example, flush/pour flush to (World Bank 2017b, 155). piped sewer system, septic tank, a compost- 2. See Sustainable Development Knowledge Plat- ing latrine). SDG “basic-standard” sanitation form (database), Department of Economic has an added criterion of being for the exclu- and Social Affairs, United Nations, New York, sive use of the household (these concepts are https://sustainabledevelopment.un.org/. reflected in table 4.1). Thus “safely managed” 3. So long as markets work reasonably well, is a subset of “basic-standard,” which is a sub- prices—the weights for the quantity of goods set of “limited-standard,” with each additional and services consumed—bear a very close criterion meaning fewer datasets currently relationship to the marginal contribution of available for analysis. Graphs at the World those goods to well-being. In technical terms, Bank Atlas http://datatopics.worldbank.org the ratio of two prices equals their marginal /sdgatlas/SDG-06-clean-water-and-sanitation rate of substitution between the two goods. .html make this clearer, and at the website of When externalities or other imperfections the WHO/UNICEF JMP, custodian agency for distort the market price, then a shadow price monitoring these indicators globally: https:// can be used in principle to value a good. How- washdata.org/monitoring. ever, the information required to estimate an 6. The quality of the environment in which the accurate shadow price is high, and frequently individual resides matters greatly for well- shadow prices cannot be estimated with much being. Although environmental degradation accuracy. Typically, when there is no adequate can be partially offset through market pur- comparator, or the distortion is too great, one chases, such as flood insurance, these sorts moves to add the good or service in question of goods and services are not widely available as a separate dimension. and, in any case, often only partly alleviate the 4. Hentschel and Lanjouw (2000) distinguish physical, mental, and health costs imposed three reasons for the price of public utilities when environmental disasters strike. to vary across consumers: rationed markets, 7. Not all indicators are applicable to every public subsidies, and increasing marginal household. For example, not every household tariff rates. The authors present a method to has a child below the school age for grade 8 impute the value of consumption of basic (necessary for the school enrollment indica- utilities irrespective of the source of water, tor). In these cases, the weight for the missing cooking fuel, or electricity to be incorporated indicator is shifted to other indicators within into the consumption aggregate. At present, the dimension so that each dimensional data are not available at a large scale across weight is unchanged. The same process occurs countries and thus cannot be implemented. if the information on an indicator for a house- 5. “At least limited” and “at least basic” drinking hold is missing, even if the indicator is appli- water and sanitation reflect the key concepts cable. Because of this reweighting process, few of SDG monitoring in this exercise. Box 4.2 households are ignored because of missing 120 POVERTY AND SHARED PROSPERITY 2018 data. Indeed, only households on which infor- survey from 2014, is lower than the lined-up mation is missing on all the indicators that estimate in 2015 of 41 percent. constitute a dimension are not considered in 9. These figures may not be representative of the the analysis. entire region because of incomplete popula- 8. The share of monetary poor differs from tion coverage. The coverage in East Asia and the numbers presented in chapter 1. This is Pacific and South Asia is particularly low (see primarily because the estimates presented last column of table 4.4.): China and India in chapter 4 are from around 2013, and not are not a part of this exercise because of data “lined up” to 2015, as is the case in chapter 1 availability. (see appendix A for details on why the num- 10. See the demographic composition typology bers presented in this chapter differ slightly, proposed in Muñoz Boudet et al. (2018) for and for how survey estimates are lined up to the methodology followed. a reference year). The 2015 lined-up head- 11. Some studies show that households with chil- count ratio for the 119 economies covered dren will likely appear more disadvantaged here is 11.2 rather than 11.8 percent. This under a child-specific multidimensional pov- difference mostly reflects that countries had erty measure (Hjelm et al. 2016). positive growth rates from 2013 to 2015, and 12. The Indonesian survey is not nationally rep- hence reduced poverty during that time. For resentative. The sample is representative of example, the headcount ratio presented here about 83 percent of the population and cov- for the Lao People’s Democratic Republic, ers 13 of the 27 provinces in the country. The which is based on a survey from 2012, is near provinces excluded tend to be less developed. 23 percent, whereas the 2015 lined-up esti- 13. The surveys are not necessarily the same sur- mate for Lao PDR is 14 percent. Conversely, veys used for official national poverty esti- the recent crisis unfolding in the Republic of mates. The monetary poverty rate cited here Yemen means that the headcount ratio of 19 may therefore vary somewhat from official percent presented in this chapter based on a estimates. BEYOND MONETARY POVERTY 121 SPOTLIGHT 4.1 National Multidimensional Poverty Indexes Prepared by Sabina Alkire, Oxford Poverty and Human Development Initiative National Multidimensional Poverty Indexes (MPIs) are increasingly being adopted as official permanent poverty statistics, usually standing alongside and complementing national mone- tary poverty statistics (Alkire and Foster 2011; Alkire et al. 2015; UNECE 2017; OPHI 2018). Updated usually every one to two years, national MPIs are used to shape and energize effective policy actions. They are reported against SDG Indicator 1.2.2. The Atkinson Commission report Monitoring Global Poverty placed great emphasis on na- tional poverty estimates, both monetary and multidimensional, given their central relevance to national policy and public debate. In the case of poverty measurement, the report advocated jointly reporting the global and national poverty measures in national poverty reports. The report also scrutinized the dimensions and indicators covered by national MPIs, and observed that their data requirements are modest: most require 38–70 questions compared to 450 or more survey questions for monetary poverty measures (World Bank 2017b, 172). But how do national MPIs differ from the global MPI that the United Nations Develop- ment Programme and Oxford Poverty and Human Development Initiative have published since 2010 (UNDP 2010; Alkire and Santos 2014) or from the ones presented in this chapter? The difference is similar to the difference between the US$1.90 per day measure of extreme poverty globally and national monetary poverty measures. That is, whereas the global poverty measures are computed in a standardized format for every country, national poverty mea- sures are tailored to the contours of poverty and the policy priorities of each context. Also, na- tional measures are computed by national statistical offices, using national survey data. Thus, national MPIs may have different dimensions and indicators; their deprivation cutoffs may reflect the aspirations, context, or national plan of the country; and the weights and poverty cutoff are set so as to identify poverty according to national definitions. Nearly all national MPIs cover health, education, and living standards. The Atkinson report recommended six nonmonetary dimensions for the global MPI including employment, and many national MPIs already include a dimension on work. National MPIs cannot be compared across countries precisely because the definitions differ. However, the great advantage of national MPIs is that they can be—and indeed are being— used to guide policy in powerfully practical ways. In particular, the following are the main uses to date of national MPIs by the increasing community of countries that use these indexes. • Complement monetary poverty. The national MPI makes visible a set of nonmonetary deprivations. The value added is seen by exploring mismatches. For example, Chile’s MPI made visible situations of high poverty in Atacama, a low-monetary poverty region in the country. Bhutan (2017) found that the district of Gasa, which had lowest monetary poverty, had the highest MPI because of missing services and infrastructure. 122 POVERTY AND SHARED PROSPERITY 2018 • Ease communication. The headcount ratio of monetary poverty, which is widely used, is always compared to the headcount ratio of multidimensional poverty. And the national MPI often accords with participatory work showing how people are poor. El Salvador’s MPI was informed by a study on poverty “as Viewed by Its Protagonists” linking poor people’s voices to each indicator of the MPI (UNDP 2014). • Monitor trends. In every country, the national MPI tracks the trend of multidimensional poverty over time, nationally and by rural-urban regions, subnational regions, and social groups, providing a rigorous overview of progress. • Allocate resources. The national MPI is regularly used to shape both sectoral and regional budget allocation across regions and across sectors. For example, Bhutan’s district allocation formula uses the MPI. • Leave no one behind. The national MPI is disaggregated by population subgroups to see who is the poorest. Changes over time are reported across subgroups, to establish whether the poorest regions are catching up—or whether their progress is slower than less poor re- gions, so that the poorest regions are gradually being left behind. For example, Pakistan’s poorest district, Musakhel, reduced poverty the fastest over the period 2005–15. • Target households. The national MPI structure is used to identify which poor people are to be recipients of certain benefits. This is usually done using a different data source: a census, partial census, or eligibility applications. For example, Costa Rica targets households ac- cording to their deprivation scores on the national MPI. • Coordinate policies. The national MPI is used as a management tool to coordinate poli- cies across sectors and across levels of government, and to design and monitor integrated, multisectoral policies that bridge silos. Although practices vary, this measure-to-manage ap- proach is mainly used when data are updated every one to two years. For example, Colombia has a Ministerial Round Table chaired by the President, which meets regularly to accelerate progress in reducing its MPI—which is updated annually. • Be transparent. Many countries post the computational files required to replicate their of- ficial national MPI online. For example, Mexico’s CONEVAL both launches its MPI and posts online tables two weeks after cleaned data are received. In many cases, methodological notes, data tables, microdata, and presentations are also online, so citizens can easily learn and interact. Thus, national MPIs provide a headline and high-resolution information panel on subna- tional conditions across population groups and across the joint distribution of deprivations in different dimensions of poverty. Although most cannot be compared cross-nationally,a they do complement official national monetary poverty statistics by providing policy-relevant evi- dence on poverty in other forms and dimensions. Details of national MPIs and of their policy applications are available on the website of the Multidimensional Poverty Peer Network, a South–South Network that convenes countries using or designing or exploring national MPIs (see www.mppn.org). a. Nepal adopted the global MPI, with slight adaptation, as its national MPI in 2018, partly in order to avail such comparisons. BEYOND MONETARY POVERTY 123 Inside the Household: 5 Poor Children, Women, and Men The aim of this chapter is to enter the household to try and answer an apparently simple question: how many children, women, and men are poor? The common approach assigns all individuals within a household to the same poverty status as the household. However, this masks potential differences in poverty among household members. Ignoring these decreases the effectiveness of common approaches to targeting poverty reduction interventions and the take-up of these interventions because they do not address the needs and constraints of the poorest individuals. The chapter begins with an analysis of global poverty data, including comparisons between male- and female-headed households, and introducing alternative household classifications related to the number of adults and income earners. Despite maintaining the concept of poverty based on the household, the analysis provides insights into sex and age differences among the poor. Next, evidence is presented on intrahousehold differences in resource allo- cation, first, by relying on a few datasets that provide information on consumption among individuals and, second, by applying models of intrahousehold resource allocation. A broader exploration of adult poverty follows according to the multidimensional approach introduced in chapter 4 but including individual-level information on educational attainment and body mass index. The accumulated evidence of numerous studies and data sources suggests that women and children are often disproportionately affected by poverty albeit with considerable varia- tion across countries and across types of households. Sex differences in poverty are largest during the reproductive years, when care and domestic responsibilities, which are socially assigned to women, overlap and conflict with productive activities. This tension is often most pronounced among the poorest countries and the poorest groups in society. Introduction How many women are poor? How many households in which they live. This masks poor children are there? These seem straight- differences in poverty among the individuals forward questions, but there are no straight- within the same household. forward answers. Most poverty measures, In the absence of poverty data on individ- including most of those presented earlier in uals, perceptions about differences in pov- this report, refer to households. Individuals erty by sex and age are rarely supported by are typically classified as poor or nonpoor evidence. Consider, for example, the widely in accordance with the poverty status of the cited claim that 70 percent of the world’s 125 poor are women. There is a solid consensus in retrofitting household-level data to the that the empirical data do not substantiate individual. Advancing our understanding of this claim and that the statistic is false (Chant the poverty of individuals requires a renewed 2008; Green 2010; Greenberg 2014; Quisum- emphasis on data collection and investments bing, Haddad, and Peña 2001; Sánchez- in survey data collection methodologies fo- Páramo and Muñoz Boudet 2018). A com- cused on the individual. mon lens on the gender dimension of pov- More reliable poverty estimates on indi- erty is the difference between female- and viduals would facilitate a better understand- male-headed households. The concept of ing of the characteristics of poverty and its household head is, however, often ill-defined intergenerational transmission, the interven- and may even be misleading, for example, if tions appropriate for different types of indi- vulnerable widows and more affluent single viduals, and the more effective targeting of women are lumped under a single category social protection and broader development of female-headed households and then used programs. Such programs often rely on ap- as a proxy for women in general (Bradshaw, proaches targeted to households but may fail Chant, and Linneker 2017; Grown 2010, to reach potentially poor beneficiaries if many 2014; Milazzo and van de Walle 2017). of these live in households not identified as Drawing on new work conducted for this poor (Brown, Ravallion, and van de Walle, report, and a review of the existing literature, forthcoming). this chapter revisits what we know about the Measuring the monetary poverty of in- poverty of individuals, with a focus on differ- dividuals requires two main pieces of infor- ences by sex and between children and adults. mation. The first is information on how total Child poverty, though related to the poverty household resources are allocated among of women, is a distinct issue. This chapter household members. This is an intuitive idea, considers both because they are the two di- but one vexed with theoretical and practical mensions prioritized for the disaggregation of challenges. Data on the food consumption of the global poverty figure (World Bank 2017b, individuals are difficult to collect whenever 114). The accumulated evidence from many household members consume meals together. studies and data sources suggests that women Other consumption items, such as housing or and children are often disproportionately consumer durables, are shared among house- affected by poverty, but with considerable hold members and often cannot be allocated variation across countries. Sex differences in to specific individuals even in principle. Be- poverty are largest during the reproductive cause of these and other challenges, living years when, because of social norms, women standards surveys, the main data source for face strong trade-offs between reproductive measurements of monetary poverty, typi- care and domestic responsibilities on the cally collect most data on the consumption of one hand and productive activities on the households as a single entity. Poverty analysis other hand. The tension is often most pro- thus remains fixed on the household. The sec- nounced in the poorest countries and among ond key ingredient is information on the ways the poorest groups in society. In addition, basic needs differ across household members, women’s intrahousehold bargaining power for example, by sex and age, and across house- and poverty appear to be related to their po- holds of different sizes and compositions to sition within the household, for example, as assess whether differences in resources trans- the first or more junior wife of the principal late into differences in well-being and poverty. male, his mother, and so on. This underscores Even though not the primary focus of this that gender, age, and status within the house- chapter, the need to measure the poverty of hold are interrelated dimensions, which can individuals highlights the need to revisit the be difficult to disentangle. broader issue of equivalence scales (box 5.1). A secondary objective of the chapter is to This chapter highlights various methods test the boundaries on methods for identify- that can be used to measure poverty among ing the poor, whether they live in poor house- individuals and explore the effects of gen- holds or not, and to highlight the challenges der and age differences on poverty data. The 126 POVERTY AND SHARED PROSPERITY 2018 BOX 5.1 Differences in Needs and Equivalence Scales Global poverty estimates use data national poverty assessments in associated with children (Folbre, on consumption or income per both developing and high-income Murray-Close, and Suh 2018). In capita to measure poverty. Similarly, countries, including member addition, adjusting consumption the international poverty line, which countries of the Organisation or income by an equivalence scale is anchored on the average cost of for Economic Co-operation and requires recalibrating the poverty meeting basic needs in the poorest Development (OECD), routinely use line (Ravallion 2015). Central to societies, is expressed in per capita equivalence scales. The failure to this recalibration is the choice terms. This per capita approach account for equivalence scales will of a household with “reference assumes that needs do not vary overestimate poverty in regions demographics,” which may also vary across the members of households where households are large and from country to country. The use of and that there are no economies contain lots of children, such as Sub- a per capita scale in global poverty of scale in larger households. Both Saharan Africa, compared to regions monitoring therefore imposes assumptions are subject to criticism. where households are small and comparability across countries and is Caloric needs vary by sex, age, contain few children, such as Europe also transparent and easy to explain physical activity (often related to and Central Asia and to some extent no matter how problematic it may be occupation), and so on and are thus East Asia and Pacific and Latin in the details (Ferreira et al. 2016). not the same across all household America and the Caribbean. The question of how to adjust members. For example, a person The main problem with adopting for differences in needs arises engaged in heavy agricultural work an equivalence scale approach in even more prominently once the typically requires more calories global poverty monitoring is that focus of the analysis moves inside than an office worker. Likewise, there is no consensus on what the household. A comparison of shared household public goods may the best scale for this purpose inequality in consumption between represent an advantage for larger would be across a wide range of adults and children or between men households even at the same level countries. For example, nutrition- and women remains incomplete of per capita consumption. One based equivalence scales, which if we do not also consider way to adjust for such differences account for differences in needs differences in needs between in household size and composition by sex and age and are used in these groups. (See also the section is to use equivalence scales, the many low-income countries, may on “Differences in resources and discussion of which goes back be less appropriate in higher- poverty within households” in which to the seminal work by Engel income countries where food all the country studies have adopted (1895) and Rothbarth (1943) (see constitutes a smaller relative share some variant of an equivalence Coulter, Cowell, and Jenkins 1992; in total consumption. Similarly, scale.) Measuring the poverty of Deaton 1997). Equivalence scales the economies of scale in shared individuals would require not only approximate the consumption needs goods may be offset by the estimating intrahousehold resource of a household of a given size and greater need for health care and allocation but also adjusting for demographic composition relative education expenditures (Abdu the differences in needs among to a reference household (usually and Delamonica 2017) and the individuals living in the same a household consisting of a single failure to value nonmarket (time household and between households adult, or a single adult male). Many and resources) expenditures of different sizes. starting point, in the next section, titled “Be- household. This assumption is inadequate yond headship: Gender and age profiles of for a clear understanding of the differences the global poor,” is the monetary poverty within households and biases country pov- estimates introduced in chapter 1, which erty rates and the demographic profile of represent the current state of play in global poverty if there are systematic differences by poverty monitoring. In comparing per capita sex and age in the household. Despite these household consumption against the interna- limitations, even the current data can provide tional poverty line, which is also expressed meaningful though incomplete insights into in per capita terms, this approach assumes sex and age differences in poverty if the anal- that resources are shared equally and that ysis probes more deeply than comparisons needs are the same across all members of a of female- and male-headed households to INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 127 explore differences by household composi- Beyond headship: Gender and tion and over the life cycle. age profiles of the global poor The subsequent section of the chapter, titled “Differences in resources and poverty This section analyzes data from the Global within households” presents evidence on in- Monitoring Database (GMD), which is a col- trahousehold differences in resource alloca- lection of globally harmonized household tion, thus relaxing the assumption of equal survey data the World Bank uses to monitor sharing among household members. A few global poverty and shared prosperity (box specialized datasets provide information, 5.2).1 The global poverty figures rely on a for at least some aspects of consumption, concept of poverty based on the household on how much is allocated to whom within (though expressed in per capita terms) and the household. Invoking assumptions about classify individuals as poor or nonpoor ac- household behavior and equivalence scales, cording to the poverty status of the house- a growing academic literature provides es- holds in which they live. Although this ap- timates on resource allocation across indi- proach cannot reveal differences in poverty vidual household members on the basis of within households, innovative ways to analyze (largely) household-level data. the data can reveal meaningful, though in- In the penultimate section, the chapter complete, information on sex and age differ- describes a broader examination of well- ences, which are explored in this section. being and poverty among adult household This section shows that, although the pro- members based on the multidimensional ap- portion of women and men living in poor proach introduced in chapter 4. Straightfor- households is similar on aggregate, the pro- ward documentation on gender differences portions vary by women’s and men’s marital in nonmonetary dimensions of well-being status, the presence of children and depen- may be derived from data collected on indi- dents in their households, whether or when viduals, rather than households. An example they join the labor market, and their respon- is education, for which indicators of educa- sibilities within the family. Children and other tional attainment have been used for many dependents are an important factor of vul- years to compare achievements and depri- nerability, particularly among women during vations between women and men. Likewise, their reproductive years. Care responsibili- anthropometric data, such as weight, height, ties, especially borne by women, are greatest and the body mass index (BMI), which are during those years in the life cycle that tend commonly used to measure malnutrition, also to be the best for income generation. Re- refer to individuals, not households. These lying on the economic activity of more adults, data are used to provide perspective on multi- both women and men, helps shield the house- dimensional poverty among individuals. hold against poverty, though doing so requires BOX 5.2 Chapter 5: Data Overview This section relies on information from quality concerns in the economic the harmonized sample of 104 household participation variables, 18 countries were surveys for 89 countries in the 2013 Global dropped for the analysis of employment Monitoring Database (GMD).a Additional and economic typology of households. labor data from the International Income Because of low coverage in the Middle Distribution dataset were merged for 17 East and North Africa (4.1 percent), the countries in Sub-Saharan Africa (Muñoz results from this region are not presented. Boudet et al. 2018). Because of remaining a. GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capac- ity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. 128 POVERTY AND SHARED PROSPERITY 2018 quality and affordable care services for chil- groups, and might privilege one sex over the dren, the sick, and the elderly. Formal school- other. Globally, self-reported female-headed ing is also a strong protective factor against households account for 23 percent of all poverty, especially for women. Interventions households, but only 16 percent of poor aimed at reducing poverty need to consider households. Although this shows that the both household structure and individual char- poverty rate is lower among these households acteristics to increase their chances of success. than among male-headed households, we can The rates of women and men living in learn little else (table 5.1). poor households are similar in the 89-country dataset used here (12.8 percent and 12.3 per- Poverty by age cent, respectively2). These poverty rates vary across regions, but gender differences are only Nearly one child in five3 lives in a poor house- statistically significant in South Asia. World- hold. Children are twice as likely as adults wide, this translates to 104 women in poor to live in poor households. This primarily households for every 100 men. In South Asia, reflects the fact that the poor tend to live in the corresponding comparison is 109 women large households with more children. Chil- for every 100 men. These differences become dren are the poorest across all regions, but starker at specific ages. the patterns vary by region. For example, in Sub-Saharan Africa, 49.3 percent of girls and 49.5 percent of boys live in poor households Beyond headship and boys represent a slightly larger share Many global and country-level analyses of (51 percent) of poor children than girls do. poverty compare female- and male-headed Differences with other age groups are even households to highlight sex differences in starker: boys and girls under 15 years of age poverty. However, the concept of the female- are 10 percentage points more likely to live head is often difficult to interpret. First, it in a poor household than their young adult typically combines women who have never (ages 15–24) counterparts, and girls are 17.2 married, women who are widowed or di- percentage points more likely than females vorced, and some women who are married. A above 60. In contrast, in South Asia, girls related concern is that the headship concept are poorer than boys (22.2 and 20.1 percent, risks conflating gender gaps with differences respectively) and slightly more numerous caused by demographic composition. For than boys among the poor (50.5 percent), example, many female-headed households but differences in poverty rates between chil- contain children but not adult males, whereas dren and older adults—although sizable— most male-headed households contain adult are smaller than in other regions. women and children. Second, self-reported The rates of women and men who are household headship reflects social norms and living in poor households decline sharply as views about who is understood as the head of children reach adulthood, and they tend to the household, for example, the main bread- stabilize after women and men reach 50 years winner, the main decision maker, the oldest of age. Starting in their early 20s and up to age man, and so on. These norms may vary across 34, women are 2 percentage points more likely countries, within countries, or across income than men to live in poor households, which TABLE 5.1 Households in Extreme Poverty, Rates and Distribution by Headship, circa 2013 Percent Share of poor Poverty rate households Share of total households Female-headed households 5.8 16.4 23.5 Male-headed households 9.0 83.6 76.5 All households 8.2 100.0 100.0 Source: Muñoz Boudet et al. 2018. Note: Data are from 89 countries. INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 129 FIGURE 5.1 Percent of Females and Males Living in Households in Extreme Poverty, by Age Group, circa 2013 25 20 Poverty rate (%) 15 10 5 0 0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 Age groups Males Females Source: Muñoz Boudet et al. 2018. Note: Data are from 89 countries. is a significant, sizable difference (figure 5.1). capacity (proxied by employment status) of In this age group, an average of 120 women individuals. This allows for a closer look at are living in poor households for every 100 how these characteristics build on the age men. This gender gap coincides with the peak and sex differences. productive and reproductive ages of men and Formal schooling is inversely correlated women, and can be related to factors such as with poverty among both women and men. household formation4 and income genera- Of the poor population ages 15 or above, 41 tion for both men and women, and the im- percent have no education. Women represent plications of such processes on their welfare. 62.3 percent of the poor population ages 15 It is well documented that female labor force or above with no schooling, but only 36.9 participation declines during women’s repro- percent of the poor with tertiary schooling. ductive years, particularly if they have young The share of women living in poor house- children (Aguero and Marks 2008; Cruces and holds diminishes strongly with schooling. Galiani 2007; Goldin and Katz 2002). Among The association between employment and the 20–34 age group, the gender gap in pov- poverty varies by sex and type of employ- erty rates ranges from 0.12 percentage points ment. In the prime productive years, between in Europe and Central Asia to 7.1 percent- 25 and 54 years of age, women represent 86 age points in Sub-Saharan Africa. In this age percent of those out of the labor force and group, the gaps are wider in the poorest coun- 60 percent of those who are unpaid work- tries, especially the 17 countries with overall ers. In poor households, while most men poverty rates above 35 percent, that is, Haiti are paid workers or self-employed, over and 16 Sub-Saharan African countries. half of women are not in the labor force. Globally, 40 percent of poor men are self- employed, compared with only 19 percent of Schooling, the labor market, women (figure 5.2). In Sub-Saharan Africa and gender differences and South Asia, self-employment is closely Household surveys collect information on associated with poverty for men, but slightly educational attainment and income-earning less so for women. 130 POVERTY AND SHARED PROSPERITY 2018 Household structure and FIGURE 5.2 Distribution of People Living in Households in Extreme gender differences Poverty, by Sex and Employment Status, circa 2013 The analysis demonstrates that household 100 10.3 composition, particularly the presence of de- 80 12.4 pendents and the type of earners, influences 50.1 gender differences in poverty over the life 60 Percent cycle. Building on the framework introduced 40.2 in Grown and Valodia (2010), this subsection 40 17.8 illustrates two ways to classify households: a demographic typology and an economic one. 20 34.9 19.0 The demographic typology is based on the 11.7 adult composition of the household, start- 0 ing with the age and sex of the adults (18–64 Men Women years) who live in the household and distin- Paid worker Self-employed Unpaid worker guishing separate categories for the elderly or Unemployed Out of labor force seniors (ages 65 years or above) and children Source: Muñoz Boudet et al. 2018. (under age 18). The economic typology is Note: Data are from 71 countries. Ages are 25–54. based on the presence and sex of all earners in the household and of the dependents who depend on the income of the earners. Earn- married or cohabiting—with children account ers are defined as any individuals ages 15 or for the largest share of poor households (figure above who are engaged in any economic ac- 5.3). They are overrepresented among the poor, tivity for pay or profit.5 Dependents here in- representing 31 percent of all households but clude nonearners ages 18–64 (unpaid family accounting for 42 percent of poor households. workers, and those that are unemployed or Adult-couple households with children and not in the labor force) and traditional depen- other adults, that is, extended family house- dents (children and seniors). holds, which represent 17 percent of all house- Within the lens of the household demo- holds, account for the second-largest share graphic typology, adult-couple households— among poor households (28 percent), and consisting of two adults of opposite sex who are they are also overrepresented among the poor. FIGURE 5.3 Distribution of Households in Extreme Poverty, by Demographic Typology, circa 2013 Other adults One adult, female combinations with with children, children 7.2% (4.6%) 6.1% (3.2%) Other adults One adult only, Senior(s) combinations only, no no children (other than children, 2.8% (11.2%) a couple) 2.1% (6.2%) without children, Multiple adults, Adult 3.9% only female couple, no Adult couple with children and (13.6%) with children children Adult couple with children, 41.5% (30.6%) other adults, 28.2% (17.1%) 2.4%, (1.1%) 1.9%, (8.2%) Source: Muñoz Boudet et al. 2018. Note: The percentages in the cells refer to the share of the type among poor households; the numbers in parentheses refer to the share of the typology among all households. The figure shows typologies that represent at least 2 percent of either poor or all households. Data are from 89 countries. INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 131 FIGURE 5.4 Distribution of Households in Extreme Poverty, by Economic Typology, circa 2013 Female earner with children Head couple earner with children and nonearner only, 8.2% (10.2%) 4.9% (2.4%) Female Children and nonearner, 14.2% (4.9%) Multiple earners earner with Head couple earner with nonearner, children only, with children and 2.7% (4.9%) 2.4% (1.3%) nonearner, 4.7% (4.9%) Male earner Senior nonearner, with 2.3% (6.7%) non- Multiple earners earner Multiple earners with children and with children only, Adults, all nonearners, 1.9% Male earner with children and nonearner, 36.2% (21.0%) nonearner, 10.9% (7.1%) 3.2% (2.6%) 2.1% (4.4%) (6.0%) Source: Muñoz Boudet et al. 2018. Note: The percentages in the cells refer to the share of the type among poor households; the numbers in parentheses refer to the share of the typology among all households. The figure shows typologies that represent at least 2 percent of either poor or all households. Data are from 71 countries. Meanwhile, adult-couple households without in households with children and nonearners children are less likely to be poor (8 percent of (42 percent in households where there is only all households; 2 percent of the poor). Other a male earner and 15 percent in households types of households gain relevance depend- with multiple earners). ing on the region. Adult woman households with children are disproportionately rep- Differences in resources and resented among the poor in Latin America and the Caribbean and in Sub-Saharan Af- poverty within households rica. Three poor women in four live in adult- The previous section summarizes what the couple households with children only or with data used to monitor global poverty reveal other adults, and this proportion increases to about gender and age differences in the pro- four poor women in five for the 20–34 years file of poverty, while maintaining the (gen- age group. erally implicit) assumption that resources The analysis of poverty using the eco- under the per capita measure are shared nomic typology confirms that households equally. A more comprehensive measurement with traditional dependents (children below of gender and age differences in the profile of 15 or seniors) fare less well than households poverty requires a relaxation in the assump- without dependents (figure 5.4). In most tion of equal sharing to consider intrahouse- cases, the presence of a nonearner, age 18–64, hold differences in resource allocation. also raises the poverty rate. Households with Measuring intrahousehold inequalities in no earners, combined with the presence of resource allocation and poverty in household children, are the household type most at risk surveys is not an easy task. Accurate data on of poverty (14 percent of the poor while they food consumption across individuals in a account for less than 5 percent of house- household are difficult to collect whenever holds), followed by households with a single household members cook together and share woman earner and dependents (5 percent of meals. Some household surveys collect such the poor and 2 percent of the population) data using a 24-hour recall or direct obser- and households with a male earner only, a vation (weighting, measuring by resident nonearner and children (36 percent of the enumerators), but these methods are time- poor while they account for 21 percent of the consuming and intrusive. Other consump- population). Poor women are concentrated tion items, such as housing, are de facto 132 POVERTY AND SHARED PROSPERITY 2018 public goods within the household that are suggest that total inequality in calorie ade- shared among household members and can- quacy among individuals is significantly un- not be allocated to specific individuals even derestimated, by 30 to 40 percent, if inequal- in principle (Case and Deaton 2002; Klasen ity within households is ignored. 2007). The following section reports findings More recent data collection efforts in Af- from four recent country surveys that collect rica and Asia have allowed a fresh look at data on consumption among individuals. intrahousehold differences in poverty across These case studies are then complemented various contexts and social settings (De Vreyer by model-based estimates of poverty in two and Lambert 2017 on Senegal; D’Souza and countries. Modeling allows the resource Tandon 2018 on Bangladesh; Mercier and shares of men, women, and children to be es- Verwimp 2017 on Burundi; Santaeulàlia- timated over the entire consumption basket Llopis and Zheng 2017 on China).6 Though even though individual consumption data these studies individualize only a few separate are only available on a few items, thus provid- components of consumption (table 5.2), they ing a more complete picture of intrahouse- reveal interesting differences in resource allo- hold resource sharing. cation among women, men, and children. The evidence in this section shows that in- trahousehold differences in consumption and Individual-level data on poverty are widespread. In most cases, women consumption and children are allocated a smaller share of Starting in the 1980s, a few specialized studies the households’ resources than men.7 Intra- have collected data on consumption among household inequalities in resource allocation individuals, often with a focus on food appear to be more pronounced for nonfood (Behrman and Deolalikar 1990; Haddad, items than for core food items, hinting at a Hoddinott, and Alderman 1997; Haddad and degree of solidarity within families. Similar Kanbur 1990; Pitt, Rosenzweig, and Hassan to the previous section, we find evidence of 1990). An early example of this literature is complex dynamics within households, linked the work of Haddad and Kanbur (1990) who to life cycle and status that extend beyond investigate intrahousehold inequality in food simple gender or age divides. For example, in- consumption in the Philippines through the trahousehold bargaining power and poverty lens of calorie adequacy, that is, calorie intake among women are related to their relation- relative to standardized calorie requirements ship with the principal male, such as first ver- by age, sex, and pregnancy status. These data sus second wife, or mother versus wife. TABLE 5.2 Recent Datasets on Individualized Consumption Country Survey Year(s) Representativeness Items individualized and data collection method Bangladesh Bangladesh 2011–12, 2015 National (rural) Food (24-hour recall by the woman in charge of cooking) Integrated Household Survey 1, 2 Burundi Panel Priority 2012 The 2012 wave is a Food and clothing (respondents were asked to specify the share of Survey follow-up of a 1998 household expenditures going to the main adult man, woman, sons, nationally representative daughters, and other household members) survey China China Health 1989, 1991, 1993, Select provinces Food, alcohol, and cigarettes (24-hour recall over three days, plus and Nutrition 1997, 2000, 2004, household food inventory) Survey 2006, 2009, 2011, 2015 Senegal Poverty and 2006–07, National Most consumption is captured at the cell level (for example, clothing, Family Structure 2010–12 mobile phones, transport, food outside the home); food consumed at home Survey is individualized based on accounts about which meals are shared and estimates of the preparation costs Note: The italicized years are used in the case studies. INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 133 China Typical household-level data miss a sub- In China, intrahousehold and gender dy- stantial portion of inequality among indi- namics over the past 20 years have evolved viduals. A comparison of an individual-level against the backdrop of rapid economic and measure of extended food consumption to a demographic change. The China Health and household-level measure, where the latter is Nutrition Survey data allow the computation normalized for differences in household de- of an individual measure of extended food mographic composition using equivalence consumption, which includes all food items scales highlights this clearly. In the rural as well as alcohol and tobacco (Santaeulàlia- (urban) subsamples, household consumption Llopis and Zheng 2017).8 In 1991, extended per adult equivalent misses about 41 percent food consumption was twice as high among (38 percent) of individual inequality. This is men as among women, and, by 2009, this ratio again driven primarily by individual inequality had risen to 2.3. This gender gap is, however, in the consumption of alcohol, tobacco, coffee, largely accounted for by four items—tea, cof- and tea. Core food consumption inequality fee, alcohol, and tobacco—that are consumed among small children ages 0–5 is about twice disproportionately by men and may reflect as high as the inequality among adults. different degrees of control over resources or social norms about acceptable behavior Burundi for men and women. Excluding these items Burundi is one of the poorest countries in gives a narrower measure of core food con- Africa, with a legacy of conflict and violence. sumption, according to which consumption Mercier and Verwimp (2017) use a household is about 12 percent greater among men than survey conducted in 2012 that asked mostly fe- among women, a ratio that has remained male respondents to specify how categories of close to constant and could reflect differences consumption goods were allocated within the in caloric need between men and women. household to examine intrahousehold con- Analysis over the life cycle shows that the sumption inequality.9 The data show a gender gender gap in extended food consumption gap in clothing and food expenditures (the starts to emerge at about age 15 and peaks latter less pronounced) that benefits women. between the ages of 45 and 55, after which it Among children, the consumption shares of declines sharply (figure 5.5). In contrast, the food and clothing appear to be balanced be- gender gap in core food consumption peaks tween boys and girls. The large share of miss- much earlier, at around age 17–18, and stays ing values in item groups other than food and at a similar level until age 50. clothing prevents additional analysis. FIGURE 5.5 The Gender Gap in Food Consumption over the Life Cycle, China a. Extended food consumption b. Core food consumption 6,000 3.5 2,000 3.5 Annual consumption (US$) Annual consumption (US$) 3.0 1,800 3.0 5,000 2.5 2.5 Gender gap Gender gap 4,000 1,600 2.0 2.0 3,000 1,400 1.5 1.5 1,200 2,000 1.0 1.0 1,000 1,000 0.5 0.5 0 5 10 15 20 25 30 35 40 45 50 55 60 65 0 5 10 15 20 25 30 35 40 45 50 55 60 65 Age (years) Age (years) Men Women Gender gap (right axis) No gender gap Source: Based on Santaeulàlia-Llopis and Zheng 2017 and their supplementary material. Note: The gender gap is the ratio of male-to-female consumption, based on a regression with age dummies and time fixed effects (pooling data from 1989, 1991, 1993, 1997, 2000, 2004, 2006, and 2009). 134 POVERTY AND SHARED PROSPERITY 2018 Assuming equal sharing among siblings ual consumption depends more on a person’s of the same sex, irrespective of age, one may position within the household than on age. use the reported resource shares for food and In Burundi, unlike in the other countries clothing to compute a partially individual- discussed in this section, women appear to ized measure of consumption. Taking into be less poor than men. This highlights the account differences in caloric needs by sex context specificity of intrahousehold distri- and age through the use of equivalence scales bution rules. However, another potential ex- yields poverty rates of 65 percent among planation for the higher consumption shares men, 56 percent among women, and 77 per- among women may be that women overes- cent among children. Because of the dispro- timate their consumption relative to that of portionate incidence of child poverty, chil- their husbands, for example because of inter- dren make up 68 percent of the hidden poor, nalized social norms or because they are not that is, poor individuals living in nonpoor aware of some components of consumption households, significantly more than their among their husbands, such as food con- share in the sample population (56 percent). sumed away from home. Relying on one Mirroring the results from Senegal below, the (female) respondent who reports about other age effect becomes insignificant if the analy- members’ consumption (see also box 5.3 for sis controls for the household member’s sta- alternative measures of food security) may tus within the family, suggesting that individ- generate some measurement error. BOX 5.3 Dietary Diversity as an Indicator of Individual-Level Food Security The four case studies show in question. Some measures Although the dietary diversity of intrahousehold inequalities in additionally account for how often mothers and their young children the consumption of calories and a given food (or items from a given tends to be strongly correlated, nutrients, a pattern also found food group) is consumed. Common children often consume fewer to varying degrees in Ethiopia metrics for dietary diversity are food groups than their mothers (Coates et al. 2017), India Household and Individual Dietary (Amugsi, Mittelmark, and Oduro (Fledderjohann et al. 2014), Nepal Diversity Scores (Maxwell, Vaitla, 2015; Nguyen et al. 2013). In (Harris-Fry et al. 2018), and South and Coates 2014), which count the Bangladesh, even more food East Asia (Bühler, Hartje, and number of food groups consumed secure households have poor infant Grote 2018). A double burden over a 24-hour recall period by diets (Owais et al. 2016). Among of malnutrition—simultaneous the whole household (reflects children in Nepal, older children presence of undernourished the household economic ability have better dietary diversity, and overweight individuals—is to access a variety of foods) or but their diets are more likely to occurring in many households individual members (reflects dietary deteriorate when the household and countries, for example, in quality and nutrient adequacy experiences a negative shock. middle-income countries, stunted [Arimond et al. 2010; Moursi et al. Younger children have less diverse children living with obese mothers 2008; Savy et al. 2005; Torheim but more stable diets (Finaret et al. (Aitsi-Selmi 2015). et al. 2004]). 2018). In India, children’s diets vary An alternative to the collection Individual-level dietary diversity by age and sex, with girls’ diets of individual food consumption indicators are strongly correlated less diverse than boys’—especially could be dietary diversity. It is with the three common measures in adolescence (Aurino 2017). routinely collected for vulnerable of child undernutrition: stunting, In sum, individual-level dietary individuals (infants and their wasting, and underweight diversity metrics are a promising mothers) in household health (Arimond and Ruel 2004; approach to assess individual food surveys, but less frequently Chandrasekhar et al. 2017; Headey security (Bühler, Hartje, and Grote collected for individuals in and Ecker 2013; Mallard et al. 2016; 2018; Headey and Ecker 2013; household consumption surveys. Rah et al. 2010; Ruel 2003). Across Leroy et al. 2015). Adding these Dietary diversity indicators countries, even a very simple questions to existing household capture the number of food dietary diversity measure is better consumption surveys could items or groups consumed, at predicting malnutrition than provide an alternative source of often weighted according to the calorie deprivation (Headey and information about differences nutritional importance of the food Ecker 2013). within households. INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 135 FIGURE 5.6 Caloric Shortfalls of Male Heads and Other Household ries and micronutrients are classified as ad- Members, Bangladesh equately nourished, and those who do not are classified as undernourished. Similarly, 0.0006 a household is adequately nourished if the total household caloric availability exceeds the sum of the individual dietary require- ments. The analysis reveals that male heads 0.0004 have much smaller caloric and micronutri- Density ent shortfalls than other household members (figure 5.6). 0.0002 These differences lead to the misclassifica- tion of individuals relative to their household status, that is, undernourished individuals in adequately nourished households or ade- 0 quately nourished individuals in undernour- −6,000 −4,000 −2,000 0 2,000 4,000 ished households. Overall, the proportion of Caloric shortfall misclassification varies between 18 percent and 30 percent according to the type of mem- Male heads Other household members ber (first row of table 5.3) but in adequately Source: D’Souza and Tandon 2018. nourished households, 55 percent of boys and 47 percent of girls are undernourished Bangladesh (whereas only 22 percent of heads and 9 per- A significant portion of the population in cent of spouses are undernourished, third row Bangladesh is undernourished in terms of of table 5.3). calories and specific micronutrients. Studies have also repeatedly demonstrated inequi- Senegal table intrahousehold resource distribution. The household structure in Senegal, as in D’Souza and Tandon (2018) use the Bangla- other West African countries, is complex be- desh Integrated Household Survey to explore cause of polygamy and the frequent presence intrahousehold differences in undernourish- of foster children. This offers opportunities ment.10 The analysis draws on data of 3,060 to explore intrahousehold inequality within rural households with male heads who are extended families. The 2006/07 Poverty and married and whose spouses are present, but Family Structure Survey, described in De without pregnant or lactating women. In- Vreyer et al. (2008), can be used to construct dividual shortfalls from minimum dietary a relatively individualized measure of con- energy requirements are computed. Individ- sumption and poverty status. To reflect intra- uals who meet these requirements in calo- household structure and resource allocation TABLE 5.3 Individuals Misclassified by the Household Measure of Caloric Availability Measure Male heads Spouses Boys Girls Other adults All households Share 0.24 0.18 0.30 0.28 0.22 Number 3,060 3,060 2,462 2,342 1,722 Adequately nourished households Share 0.22 0.09 0.55 0.47 0.15 Number 1,901 1,901 1,257 1,207 1,207 Undernourished households Share 0.26 0.32 0.05 0.09 0.39 Number 1,159 1,159 1,205 1,135 515 Source: D’Souza and Tandon 2018. Note: Shares = population-weighted means of undernourished individuals in adequately nourished households and adequately nourished individuals in undernourished households. Number = observations. 136 POVERTY AND SHARED PROSPERITY 2018 more accurately, each household is divided ated with the household-reported male head. into cells whereby the household-reported The same is true for sisters versus brothers. head forms a cell with unaccompanied de- Cells headed by women in a leviratic union— pendent members; each wife of the head and that is widows who “remarried” their former her children and any other dependents then husband’s brother or other male relative— form separate cells, as do other adults with have a higher probability of being poor, at dependents, for example, married brothers. an odd ratio of 1.4 relative to women in their This cell structure is characteristic of house- first marriage, but the difference is not sta- holds in Senegal. tistically significant (De Vreyer and Lambert The cell consumption data show that in- 2017 and their supplementary material). trahousehold inequality accounts for almost 14 percent of total consumption inequality Taken as a whole, these studies give an idea in Senegal, driven largely by intrahousehold of the potential misclassification of individ- inequality in nonfood consumption. About uals with respect to households’ poverty clas- 13 percent of the poor live in nonpoor house- sification: many poor individuals do not live holds and are hence invisible in standard in poor households. In addition, they point measures of poverty (De Vreyer and Lambert out complex relationships between sex, age, 2017). There are also important gender dif- and status within the household, especially in ferences. Per capita consumption is 33 per- nonnuclear households, making it difficult to cent greater among cells headed by a man disentangle those effects. Furthermore, there than among those headed by a woman, and are potentially complex interactions between this difference is statistically significant. This the way the data were collected (for example, pro-male-headed cell gap in consumption single or multiple respondents in the house- narrows if the analysis controls for education hold, direct enumerator observation), the because literacy and numeracy outcomes variable analyzed (caloric intake, food con- are worse among women than among men. sumption, total consumption), and the level of The remaining gender difference appears to disaggregation (individual-level analysis, cells/ be mainly attributable to the greater depen- subgroups of household members, or broad dency ratio in female-headed cells because categories such as children/women/men). children are ascribed to their mother’s cell (and not their father’s) if the mother is pres- Estimating individual ent in the household (De Vreyer and Lambert consumption from 2017 and their supplementary material). household-level data The social roles ascribed to women imply that their position in the household and their Collecting data on individual-level consump- marital status are much more strongly asso- tion is costly and not always feasible in the ciated with their material well-being than is context of large-scale household surveys. the case for men. The mothers and daughters Even specialized datasets, such as the ones of the household-reported male head, and, presented earlier in this section, tend to in- to a lesser extent, his junior wives tend to be dividualize only some components of the found in the least favored positions in the overall consumption basket and thus provide household, whereas no equivalent consump- a partial picture of sharing within house- tion penalty exists among fathers and sons. holds. Moreover, basing our understanding Widowed women, whether remarried or not, of intrahousehold differences in well-being are also particularly vulnerable. These gender and poverty on differences in the consump- differences in per capita consumption extend tion of specific consumption items is prob- to poverty. A cell headed by a daughter of the lematic if preferences over those items differ household-reported male head is 2.5 times between household members. For example, more likely to be poor than the cell associated even if men disproportionately consume with the household head, whereas there is no alcohol and tobacco, women might spend significant difference in poverty status be- more on other items so that any subset of tween cells headed by sons and those associ- items cannot provide the full picture (Tian, INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 137 Yu, and Klasen 2018). An alternative ap- imposes strong assumptions on the ways in proach is to model intrahousehold resource which households and individuals behave, allocation on the basis of the observed behav- and those assumptions are open to criti- ior of the household and a structural model cism (Basu 2006; Cuesta 2006; Doss 1996; that describes the preferences of household Sen 1990; Udry 1996; World Bank 2017b). members and how they make decisions (for For example, this literature is largely based example, the collective household model pi- on the standard assumption of utility max- oneered by Chiappori 1988, 1992). Armed imization and does not consider alternative with this structural model, and exploiting the explanations of human behavior. Likewise, fact that many household surveys collect con- the collective model assumes that all house- sumption data of one or two items in a way hold decisions are efficient—in other words, that can be “assigned” to individuals, demand whatever decision the household takes, no al- functions can be estimated that allow for ternative decision would have been preferred teasing out how resources are shared inside by all its members. This rules out inefficient the household even if data on consumption bargaining outcomes, whereby households of most items are collected at the household may get trapped in situations where at least level (see annex 5A for further details). This one household member could be made bet- approach has two main advantages. First, it ter off without making the others worse off allows an estimation of the resource shares (see Basu 2006; World Bank 2017b). Because of women, men, and children over the entire of these assumptions, and additional econo- consumption basket and therefore provides metric challenges in estimating the sharing a more complete picture of the allocation rules empirically, model-based estimations of of resources within households. Second, be- individual resource shares warrant additional cause the data requirements are modest, this validation and sensitivity analysis before they approach could open the door to estimating can be used in routine poverty monitoring. individual-level poverty in many countries, As a first step in this direction, we use the beyond the select few case studies discussed model proposed by Dunbar, Lewbel, and in the previous section. A small but growing Pendakur (2013) to estimate consistent in- literature uses model-based estimates of in- trahousehold differences in resource allo- trahousehold resource allocation to explore cation and poverty in nuclear households differences in poverty between women and in two countries (Bangladesh and Malawi). men or between adults and children in devel- The model has the advantage that it is con- oping countries.11 siderably less complex than previous ap- Estimating individual poverty in this proaches, which enhances transparency and way requires that at least some parts of the makes estimating individual resource shares household consumption basket can be as- across countries more feasible using the same signed to individuals. In other words, one method (see annex 5A). Figure 5.7 shows esti- observes who within the household con- mates of resource shares in Bangladesh (pool- sumes what—either because the underly- ing data for 2011/12 and 2015), with either ing household survey disaggregates items food or clothing as the assignable good, and in in such a way (for example, men’s clothing, Malawi in 2004/05 and 2010/11, with clothing women’s clothing, and children’s clothing), as the assignable good.12 The horizontal axis or because the survey asks respondents to gives the percentage of household resources, assign an item to specific household mem- both the point estimate and the confidence bers. These data requirements are modest. In interval, that are allocated to an individual of fact, most studies rely on a single assignable type j living in a household of type s, holding good, typically clothing, that is disaggregated the other household characteristics fixed at among men, women, and children in many their mean. On the vertical axis are the types standard household surveys. However, the of individuals and household sizes. The share underlying structural model estimates the of household resources that goes to children resource shares of men, women, and chil- has been divided by the number of children. dren over the entire consumption basket. The The results on Bangladesh in figure 5.7, flip side of this is that the structural model panel a, which use food as the assignable 138 POVERTY AND SHARED PROSPERITY 2018 FIGURE 5.7 Estimated Consumption Allocation, Men, Women, and Children, Bangladesh and Malawi a. Bangladesh, 2012–15 (food) b. Bangladesh, 2012–15 (clothing) Man in 1-child hh Man in 1-child hh Man in 2-child hh Man in 2-child hh Man in 3-child hh Man in 3-child hh Man in 4-child hh Man in 4-child hh Woman in 1-child hh Woman in 1-child hh Woman in 2-child hh Woman in 2-child hh Woman in 3-child hh Woman in 3-child hh Woman in 4-child hh Woman in 4-child hh Child in 1-child hh Child in 1-child hh Child in 2-child hh Child in 2-child hh Child in 3-child hh Child in 3-child hh Child in 4-child hh Child in 4-child hh 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 Resource share (%) Resource share (%) c. Malawi, 2004–5 (clothing) d. Malawi, 2010–11 (clothing) Man in 1-child hh Man in 1-child hh Man in 2-child hh Man in 2-child hh Man in 3-child hh Man in 3-child hh Man in 4-child hh Man in 4-child hh Woman in 1-child hh Woman in 1-child hh Woman in 2-child hh Woman in 2-child hh Woman in 3-child hh Woman in 3-child hh Woman in 4-child hh Woman in 4-child hh Child in 1-child hh Child in 1-child hh Child in 2-child hh Child in 2-child hh Child in 3-child hh Child in 3-child hh Child in 4-child hh Child in 4-child hh 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 Resource share (%) Resource share (%) Source: Gaddis et al., forthcoming. Note: The horizontal axis gives the percentage of household (hh) resources, both the point estimate and the confidence interval, that are allocated to an individual of type j living in a household of type s, holding the other household characteristics fixed at their mean. On the vertical axis are the types of individuals and household sizes. The share of household resources that goes to children has been divided by the number of children. hh = household. good, show that, in households with one or with two children, 29 percent in households two children, men receive about 37 percent of with three children, and 26 percent in house- the resources. The share of resources going to holds with four children. Among the children, men is smaller in households with three chil- an only child receives, on average, about 21 dren (31 percent) and in households with four percent of the resources. In households with children (27 percent). In households with one multiple children, each child receives between child, women’s resource shares are larger than 12 percent and 14 percent of the resources. those of men (42 percent), but their resource The broad patterns in resource allocation shares decline more steeply as the number of for Bangladesh are similar if one uses cloth- children increases, to 35 percent in households ing as the assignable good (figure 5.7, panel INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 139 b), which lends credibility to the estimation ferences in results underscore the need to fur- method.13 However, the precision is much ther explore the robustness of model-based es- greater with food, presumably because of timates of intrahousehold resource allocation. food’s larger share in household consump- In Malawi in 2004/05 (figure 5.7, panel tion (33 percent versus 3 percent). Moreover, c), one finds that the share of household re- in households with more than one child, sources going to men does not vary with the the resource shares of women are somewhat number of children. It is greater than the smaller, and the resource shares of children share of resources going to women, though are larger if the estimation is based on food. the confidence intervals overlap. The share of These estimates suggest inequalities in resources going to women also does not vary the way resources are shared among house- significantly with the number of children. hold members, particularly between adults The share of resources going to each child is and children. However, unlike the nutrition- not significantly different in households with centered Bangladesh case study presented ear- one, two, or three children, but it is smaller lier, the estimates in this section do not sug- when there is a fourth child. Focusing on the gest that women fare worse than men. One confidence intervals together with the point explanation for this divergence could be that estimates, the results on Malawi in 2010/11 D’ Souza and Tandon (2018) use a measure are qualitatively similar (figure 5.7, panel d) of needs; another is that we are looking at a apart from the fact that the resource share of different sample—nuclear households here, men is greater in households with one child compared with all couple-households, exclud- than in households with more children.14 ing pregnant and lactating women, in D’Souza One may use the resource shares to esti- and Tandon (2018). Yet another explanation is mate poverty rates among men, women, and that, per definition, the approach used in this children, depending on the size of the rele- section uses information on the assignable vant household. This requires additional as- good to estimate individual-level resource al- sumptions about household economies of location over the entire consumption basket, scale and the relative needs of children. The beyond just food and nutrition. Still, these dif- estimates here follow Dunbar, Lewbel, and Pendakur (2013) in relying on an equivalence scale used by the OECD. Figure 5.8 summa- FIGURE 5.8 Individual Rates of Poverty, Nuclear Households, rizes the information on Bangladesh (using Bangladesh and Malawi the more precise estimates based on food 100 as the assignable good) and on Malawi (using the latest available survey). In both countries, 90 88 the estimated poverty rates are significantly 80 higher among children than among adults. 73 The model estimates that women are poorer 70 68 than men in Malawi, but not in Bangladesh. Poverty rate (%) 60 However, these results only apply to nuclear 49 households. These make up the largest share 50 46 45 of poor households globally but are often less 40 poor than extended multigenerational house- 30 holds (see the previous section). 20 An individual perspective on 10 multidimensional poverty 0 The chapter now builds on the multidi- Bangladesh, 2012–15 Malawi, 2010–11 mensional approach described in chapter 4, Men Women Children which captured deprivations in education, health and nutrition, access to services, and Source: Gaddis et al., forthcoming. Note: Based on estimated resource shares in figure 5.7, panel a, Bangladesh, using food as the assign- security, in addition to monetary poverty. able good; panel d, Malawi, using the 2010–11 data. Bringing the multidimensional approach to 140 POVERTY AND SHARED PROSPERITY 2018 individuals takes advantage of the fact that, in Data on five countries—Ecuador, Indo- most household surveys, in contrast to con- nesia, Iraq, Mexico, and Tanzania—are used sumption expenditures, nonmonetary indi- to exemplify how one might apply the multi- cators in a few key dimensions of well-being, dimensional poverty measure to the indi- such as education and nutrition, are often vidual.15 The focus is on adults (18+ years) collected on an individual basis. For example, because some of the indicators are not di- educational attainment is often lower among rectly valid for infants and young children, adult women than among adult men because such as educational attainment or the BMI, of past gender gaps in school enrollments, and because a multidimensional measure of and these differences within the household child poverty should consider child-specific can be captured by a measure of multidimen- vulnerabilities (box 5.4). sional poverty among individual household The analysis uses the same five dimensions members. of multidimensional poverty as the country The multidimensional poverty measure in- case studies in chapter 4.16 The datasets have troduced in chapter 4 combines monetary and been selected on the basis of the availabil- nonmonetary dimensions of well-being, but ity of information on individuals, but the it relies on households as the unit of analysis. surveys provide information only about in- By way of illustration, consider the dimension dividual deprivations in the education and of education. The measure retroactively col- health-nutrition dimensions. The individual lapses the information about the educational multidimensional poverty measure considers attainment of individual household members adults deprived in the education dimension if into an indicator for the household, whereby they have not completed primary schooling, the household is deprived if no adult member and they are considered deprived in the nu- has completed primary education. Like the trition indicator of the health and nutrition monetary poverty estimates in chapter 1, the dimension if they are undernourished (table household multidimensional poverty mea- 5.4). The other dimensions—monetary pov- sure in chapter 4 cannot provide insights into erty, access to services, and security—and the differences within households. health indicator of the health and nutrition BOX 5.4 Child Poverty Children growing up in extreme as adults, and the transmission and face immediate threats poverty require special attention. of poverty down the generations, such as gender-based violence, They experience poverty differently including through early marriage. recruitment as child soldiers, and than adults, and their needs and Beyond this sad and avoidable discrimination in the provision of vulnerabilities change rapidly in impact on human life and potential, basic services. Irregular migration, ways that are foreign to adults neglecting children fails to build the displacement, and trafficking create (Abdu and Delamonica 2017). human capital the world needs for multiple dangers for children; girls, Key dimensions of poverty sustained economic prosperity. especially, are disadvantaged among children include health, The numbers are stark: because of gender inequalities. information, nutrition, education, Children are more than twice Children living in poverty water, sanitation, and housing. as likely as adults to be living in often experience stress, anger, Poverty causes poor children to poor households (the results are frustration, sadness, and miss out on a good start in life. robust to the use of 32 different hopelessness because of the The consequences of inadequate equivalence scales, and the repeated instances of discrimination nutrition, deficient early stimulation youngest children are the least well and social exclusion they encounter, and learning, and exposure to off [Newhouse, Suárez-Becerra, which may lead them to drop out of stress and shame last a lifetime. and Evans 2017]). More than school, lose friends, and become They lead to stunted development, half (58 percent) of the children exposed to risks that more well off low capacity in the skills needed for in fragile and conflict-affected children and adults never have to work, restrained future productivity situations live in poor households face (Save the Children 2016). INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 141 TABLE 5.4 Indicators and Dimensions, the Individual and Household Multidimensional Poverty Measure Deprived if Weight Dimension Individuals Households (%) Monetary poverty Daily consumption per capita < US$1.90 20 No adult has completed primary school Education Adult has not completed primary school 20 Any school-aged child is not attending school Health and nutrition Any woman (ages 15–49) experiencing a live birth in the previous 36 months did not deliver at a facility 20a Any child (ages 12–59 months) did not receive a DPT3 vaccination Any woman (ages 15–49) is undernourished (BMI < 18.5) Adult undernourished (BMI < 18.5) Any child (ages 0–59 months) is stunted Access to services No access to an improved source of water within a round trip distance of 30 minutes 20 No access to improved sanitation facilities for use exclusively by the household No access to electricity Security Household has been negatively affected by crime in the previous 12 months or lives in an area where more than 20% of 20 households have been negatively affected by crime Note: Dimensions on which data on individuals are available are shaded gray. BMI < 18.5 = body mass index below 18.5 (underweight); DPT3 = diphtheria-pertussis-tetanus vaccine. a. Health and nutrition each has a weight of 10 percent. dimension may be analyzed meaningfully in those dimensions that can be measured only among households with the existing among individuals. Nonetheless, even a par- data. Thus, the multidimensional poverty tially individualized multidimensional pov- measure is de facto only partially individ- erty measure reveals that multidimensional ualized; only 30 percent of deprivations are poverty is greater among women than among measured among individuals. This is a clear men in the countries under examination, limitation because one must fall back on the driven by women’s disadvantaged position in assumption of equal sharing among house- educational attainment. hold members in the other indicators and di- Figure 5.9 shows the share of men and mensions (70 percent), and this dilutes what- women who are deprived in the two indica- ever intrahousehold inequality one may find tors on which data on individuals are avail- FIGURE 5.9 Gender Gaps, Education and Nutrition Deprivation, Selected Countries a. Education b. Nutrition 50 50 45 45 Share deprived in education (%) Share deprived in nutrition (%) 40 40 35 35 30 30 25 25 20 20 15 15 10 10 5 5 0 0 Ecuador Indonesia Iraq Mexico Tanzania Ecuador Indonesia Iraq Mexico Tanzania Men, household measure Women, household measure Men, individual measure Women, individual measure Source: Klasen and Lahoti, forthcoming. 142 POVERTY AND SHARED PROSPERITY 2018 able: education and nutrition. For each coun- vation and poverty using a household-level try and indicator, deprivation rates among approach (Klasen and Lahoti 2016). men and women are compared through two The share of men and women who are approaches: one relying on the household, multidimensionally poor, measured across whereby all household members are assigned individuals, is shown in figure 5.10. Multidi- the same deprivation status, and the other mensional poverty is more prevalent among relying on the individual, measuring individ- women than among men in all countries, ual deprivations directly.17 with the largest gender gap in Iraq (54 percent In education (figure 5.9, panel a), the versus 38 percent). Klasen and Lahoti (forth- household approach reveals some gender coming) show that a significant gender gap differences in education deprivation that in multidimensional poverty is also found in tend to disadvantage women, showing that India. women are more likely than men to live in a These gender gaps may even be wider household where no adult has completed pri- among the most vulnerable groups. For exam- mary school. These gender differences, which ple, in all countries but Ecuador, widows are are muted under the household approach, significantly more likely to be multidimen- are amplified if the data on individuals are sionally poor than widowers, and the gender used. In the five countries under examina- gap ranges from 8 percentage points in Iraq to tion, women are much more likely to be de- 19 percentage points in Mexico (Klasen and prived in education than men if deprivations Lahoti, forthcoming). This highlights widow- are measured across individuals, especially in hood as an important vulnerability factor Iraq (a gap of 19 percentage points). In ad- among women, which is not revealed in the dition to these gender gaps, an individual, household multidimensional poverty mea- whether a man or a woman, is more likely sure (Djuikom and van de Walle 2018). to be considered deprived in education if The gender gaps illustrated in figure 5.10 the measure of deprivation is applied across are probably still an underestimation of the individuals. This reflects the fact that the true extent of gender inequality in multidi- household education indicator is defined in mensional poverty. Because of data limita- an expansive way, that is, all household mem- tions, even the individual multidimensional bers are considered nondeprived if any adult poverty measure individualizes only some in the household has completed primary school, irrespective of who in the household FIGURE 5.10 Gender Gaps, Individual Multidimensional Poverty, benefited from education and whether there Selected Countries is any systematic gender bias. (Klasen and La- hoti 2016 show that defining deprivation in 80 this way will lead to an underestimation of Share who are multidimensionally poor (%) deprivation and poverty rates using a house- 70 hold-level approach because typically many 60 deprived individuals live in households where one member has the required education.) 50 In terms of nutrition (figure 5.9, panel b), gender gaps are small, even if measured 40 with reference to individuals, and they do not show a consistent pattern.18 Unlike the 30 case of education, a person is less likely to be 20 considered deprived in nutrition under the individual approach than under the house- 10 hold approach. This is because the household nutrition indicator is defined restrictively, 0 that is, all household members are considered Ecuador Indonesia Iraq Mexico Tanzania deprived if any adult in the household is un- Men Women dernourished, which will overestimate depri- Source: Klasen and Lahoti, forthcoming. INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 143 of the dimensions in which one may expect on simultaneous activities (watching a child to find variations within households and while selling at the market) also hide the pro- systematic gender differences. As discussed found effect these differences have for labor in the previous section, intrahousehold in- force participation decisions, types of jobs, equalities in consumption may disadvantage and hours spent working for pay or profit. women and children. But, because none of Participatory poverty research often shows the datasets used here allows estimates of re- that, although insufficient financial means source allocation across individuals, the in- are central to the experience of destitution dividual multidimensional poverty measure among poor people, they are interlocked with must fall back to reliance on (unsatisfactory) other dimensions, such as voicelessness, so- assumptions about equal sharing associated cial exclusion, shame, exposure to violence, with the monetary poverty dimension. Sim- lack of access to basic infrastructure and ser- ilarly, other studies have shown a gender di- vices, lack of education, poor physical and mension in access to services. For example, mental health, and illness. Box 5.5 summa- the individual deprivation measure, a new rizes findings from recent and ongoing par- gender-sensitive multidimensional measure ticipatory analysis of poverty (Narayan et al. of poverty, illustrates how men and women 2000a; Walker and Godinot 2018).19 are affected differently by lack of access to services because of social norms assigning Conclusion domestic work to women (Hunt 2017; IDM 2017). A more refined individual multidi- This chapter starts with a question: How mensional poverty measure would also cap- many women and children are poor? De- ture women’s and men’s exposure to all forms spite the conceptual challenges in answering of violence under the security dimension. this question and the data limitations, accu- Some forms of violence, particularly gender- mulating evidence using different methods based violence and especially intimate part- and data sources confirms the existence of a ner violence, are more frequently experi- pattern of consumption inequality between enced by women than by men (Stöckl et al. children and adults and between women and 2013; UBOS and ICF International 2017). In men in the household. The results suggest contrast, men are more susceptible to recruit- that women are disproportionately affected ment in gangs and armed groups. An individ- by poverty. Likewise, the global poverty data ual measure of exposure to violence could re- and country studies show that children are veal such differences within households and poorer than adults, which is partly driven by lean toward greater intrahousehold variation demographic patterns of fertility and house- in the multidimensional poverty measure. hold formation. However, the picture of how Another direction for expanding the in- much poorer children are in relation to adults dividual multidimensional poverty measure is sensitive to assumptions about the relative along gender lines would be to broaden the needs of children, which requires further set of dimensions, to include time use and investigation beyond the scope of this chap- socioemotional dimensions of poverty. As ter. In several countries, households seem to discussed earlier, patterns of time use are very share basic food items somewhat equitably, different between men and women, especially but inequality among gender lines is stronger in the presence of children. Many studies for more prized consumption items. (World Bank 2011; Bardasi and Wodon 2010; These general patterns mask contextual Blackden and Wodon 2006; Rubiano Matu- variation related to the position of individu- levich and Viollaz 2018) show the persistent als in the life cycle (marital status and parent- gap between time spent in market and non- hood), their status within the household (the market activities, with women consistently sons, first wife, or mother of a man who is the spending twice as much time as men in the household head hold higher relative status latter (household chores, child and elderly than his daughters or more junior wives), and care) and often having less leisure time. their human capital and position in the labor Data limitations on the actual distribution of market (schooling and employment status). time between care and household chores and Because of gendered social norms that view 144 POVERTY AND SHARED PROSPERITY 2018 BOX 5.5 Gender and Socioemotional Dimensions of Poverty: Participatory Studies The World Bank (2017b) recognizes Provisional findings indicate role. Whereas women may face that in-depth consultation with that, while lack of financial sexual exploitation and gender- people experiencing poverty is resources and the inability to based violence, especially as essential to an understanding of meet basic needs are central, domestic workers, men face the true nature of the multifaceted both women and men frequently exploitation and discrimination phenomenon of poverty. The associate these needs with their as casual laborers. Children find Voices of the Poor reports (Narayan direct consequences in terms themselves socially excluded at et al. 2000a, 2000b) highlight of physical and mental health. school, singled out if they are the importance of nonmonetary Shame, fear, depression, worry, unable to afford the totem items dimensions, access to services, and anger emerge as integral of their peers. They are often and gender norms. Under the components of the experience of embarrassed to invite friends home strain of vast social, economic, poverty. Poverty is also relational. to their substandard housing. and political transformation, poor As a group, people living in In rural areas, people living in household members reflect on the poverty experience oppression, poverty may lack basic social and contradiction between purported exploitation, humiliation, and the infrastructure service provision gender roles—homemaker for denial of rights, including the locally, whereas, in cities, point of women and breadwinner for denial of rights to health care and use charges deny them access. men—and the reality of women education. As individuals, they Gender roles imply that lack of performing income-earning tasks, experience social isolation, stigma, proximate clean water affects more which increases their time poverty. and discrimination. Beyond their the time and lives of women (and Under stress, men are more likely intrinsic importance, these factors children) who are responsible for to abuse alcohol, and domestic also contribute to a lack of social fetching it, cooking, and cleaning. violence spreads. All these factors and political voice and to relative Stigma is more contagious in rural affect children negatively. powerlessness, all often resulting settings, afflicting all members of Following the same approach, in social exclusion. extended families, than in urban people living in extreme poverty Both women and men areas, where social life is more in Bangladesh, Bolivia, France, emphasize these dimensions, individualized. Although poverty is Tanzania, the United Kingdom, but they experience them pain, people experiencing it often and the United States are leading differently. Gender roles mean demonstrate resourcefulness; research with the International that women feel stress and stigma they acquire knowledge and skills Movement ATD Fourth World and in the context of care and family that could be useful to others, and Oxford University to understand responsibilities under tightly they feel they have a positive and the dimensions of poverty that constrained domestic budgets. valuable contribution to offer to matter most in their lives (Walker Men can feel emasculated if they society. and Godinot 2018). cannot fulfill their breadwinning unpaid work as a female prerogative, women Gender gaps are also pervasive in other face a strong trade-off between reproductive key components of welfare. Although gender and productive functions, and mothers who gaps in school enrollments have narrowed do not work for pay are especially likely to significantly over the past decades (and in live in poor households. Adult couples with some countries reversed), adult women dependent children or other nonearners around the world continue to be disadvan- ages 18–64 in the household are overly rep- taged in educational attainment because resented among the poor. These gender gaps of past (and sometimes present) gender in- in poverty are stronger in Sub-Saharan Af- equalities in access to schooling. Participatory rica and South Asia; within countries, these research also highlights gender differences in inequities seem stronger among the poorest, the socioemotional dimensions of poverty. which has strong implications for reaching Advancing our understanding of poverty the twin goals, reducing poverty and sharing among individuals requires a renewed em- prosperity. phasis on individual-level data collection. INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 145 This chapter has touched upon various data ising but need to be put to the test in addi- gaps limiting our understanding of individual tional validation studies and extended to poverty. Three broad directions for data col- more complex household structures (beyond lection and methodological survey research nuclear households). Specialized data collec- emerge from this discussion. First, although tions and participatory research could help to full individual-level consumption data col- test some of the key assumptions underlying lection remains infeasible for most living these methods and explore the sensitivity of standards surveys, there may be some scope results to alternative assumptions. Further to collect partially individualized consump- investigations of how relative needs and pref- tion data. This could take the form either of erences differ inside the household would fielding an individual-level module to a sub- allow for a better understanding of whether set of households or of identifying a subset an unequal resource allocation translates into of consumption items (beyond clothing) that differences in well-being and poverty. can signal inequalities within households The findings of this chapter have import- and that can be collected for individuals (or, ant implications for policies and interven- at the minimum, for men, women, girls, and tions to alleviate poverty and enhance shared boys) in a reliable and cost-effective way. prosperity. Given the importance of maternal Advancing this type of data collection would health and education for the formation of facilitate the application of the collective children’s human capital in many contexts, model to estimate intrahousehold resource better understanding intrahousehold poverty shares. Second, expanding individual-level could help design more effective interventions data collection on nonmonetary dimensions, to weaken its intergenerational transmission. such as time use, violence, access to services Understanding differences in poverty levels and assets, and some of the socioemotional between different household members is im- dimensions highlighted by participatory re- portant for the effective targeting of poverty search, would allow for the advance of multi- reduction programs. At present, commonly dimensional measures of individual poverty used household targeting of social assistance and analysis of the intersectionality of depri- programs may miss a significant share of the vations. Third, additional methodological poor: those people hidden in overall nonpoor research is needed to shed light on the differ- households. Understanding how gender and ence, in terms of accuracy and cost, between age affect the demand for basic services is key self- and proxy-reporting for data referring to to making sure that interventions to expand individuals. The marginal cost of individual- basic infrastructure and social services ad- level data collection is strongly influenced dress the differentiated needs and constraints by whether survey enumerators need to in- of the poorest. Factoring in the potential terview multiple household members (thus impacts of interventions on time use would allowing for repeat visits to the household), benefit women disproportionately. Finally, which has major implications for survey op- better understanding of the socioemotional erations. Existing research highlights the im- dimensions of poverty would help increase portance of respondent selection for data on the take-up of programs and strengthen their assets and labor (on assets: Kilic and Moylan design and implementation by lifting rele- 2016; Doss, Kieran, and Kilic 2017; on labor: vant social and psychological barriers and de- Bardasi et al. 2011; Dammert and Galdo creasing stigma. As more poverty alleviation 2013), but similar investigations would be programs focus on productive inclusion, the useful for other dimensions of living stan- success of active and enabling policies that dards and welfare, including consumption. stress agency and entrepreneurial initiative In terms of research, recent advances in also depends on fostering the mindset that the application of the collective bargaining help poor people and society recognize their model to household survey data are prom- potential. 146 POVERTY AND SHARED PROSPERITY 2018 Annex 5A Technical note: Estimating intrahousehold resource shares The basic approach by singles and by couples, with assumptions on economies of scale for the public goods. Most studies estimating intrahousehold re- An alternative route, which is followed in source shares are based on the collective this chapter, is to use information on the household model (Chiappori 1988, 1992). consumption of assignable goods, that is, The collective model recognizes that house- goods that are consumed only by one type hold members have their individual prefer- of individual in the household. For assign- ences and assumes Pareto efficiency, that is, able goods, the household’s consumption whatever decision the household takes, no al- is also the consumption of the individual, ternative decision would have been preferred so that the household’s budget share for an by all its members. In this model, it is as if assignable good (observed) is equal to the each household member (that is, woman, product of an individual’s resource share by man, or child) is allocated a fraction of the the budget share the individual would choose household’s total resources (that individual’s subject to his or her own shadow budget resource share), which the individual then constraint (both unobserved). The estimates allocates according to his or her own prefer- presented in this section, which are based on ences. Each household member determines the approach proposed by Dunbar, Lewbel, his or her demand for each consumption and Pendakur (2013), make some further item by maximizing his or her utility func- assumptions of similarity of certain aspects tion, subject to the individual’s resource of preferences.20 The resource shares are constraint (that is, determined by resource identified from the observation of the bud- share) and a vector of shadow prices. These get shares of assignable goods (see below for shadow prices are equivalent to market prices details). for private goods, but lower than market prices for goods that are shared by multiple The model underlying household members. (Bourguignon and Chi- appori 1992; Browning et al 1994; Chiappori individual resource shares and Meghir 2015.) There are two routes to Households are supposed to be composed recover individual resource shares from ob- of one adult man, one adult woman, and s served household expenditures. One is to children. Each household member is of type assume that preferences of adults in couples j, where j = m, f, c for the adult man, adult are no different from preferences of singles. woman, and children, respectively. Following Consumption by adults in couples is then de- Dunbar, Lewbel, and Pendakur (2013), the duced from the observation of consumption demand system can be written as follows: *KM S  8KM \5]\6KM \5] P  79 \8KM \5]3]] *HM S  8HM \5]\6HM \5] P  79 \8HM \5]3]] (5A.1) 8FM \5] *FM S  8FM \5] b6FM \5] P  79  ` 3ac 1 INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 147 where Wj,s is the household budget share of in this section points to differences not fully member j’s assignable good in a household accounted by those. with s children; hj,s(z) is the resource share 8. The China Health and Nutrition Survey is a of household member of type j in a house- panel dataset that has tracked food consump- hold with s children; x is the household’s total tion among individuals in about 6,800 house- nondurable expenditure; and z is a set of so- holds in nine provinces since the early 1990s. ciodemographic characteristics of the house- The survey records the quantity (in grams) hold. The last equation in the demand system of a variety of food items, including alcohol (5.1) gives the household budget share of the and tobacco, that each household member children’s assignable good. The children are consumed at and between meals, at home jointly treated as one member of the house- and away from home, during three days at a hold; this requires the simplifying assump- level of detail suitable for nutritional analysis. tion that resources are shared equally among Local prices are used to compute a monetary the children. measure of consumption. The term in parentheses in each equation 9. The Burundi survey included a module on of the demand system (5.1)—aj,s(z) + b0 individual consumption, which asked a sin- ln(hj,s(z)x)—is referred to as j ’s latent bud- gle respondent, a woman household mem- get share (for j = m, f, and the corresponding ber considered responsible for the household term for children). The latent budget share is budget, to specify the share of household linear in the log of individual resources. consumption dedicated to five groups of in- dividuals: the main adult man, the main adult woman, the sons, the daughters, and all other Notes household members. In about two-thirds of 1. This section draws on Muñoz Boudet et al. households, the woman respondents reported (2018). that they were the wives of the household 2. These rates are higher than the rates in chap- heads whereas, in the remaining third, they ter 1 because they are based on a subset of reported that they headed the households. countries and household surveys (see box 10. The Bangladesh Integrated Household Survey 5.2). Corresponding rates for the 2015 GMD was conducted between December 2011 and data are 11.4 and 11.7 percent for women and March 2012. It covered 5,000 households and men, respectively. was representative of rural Bangladesh. The 3. In 2015, 19.3 percent of those ages 0–14 lived survey recorded individual food consump- in poor households. tion, in grams, for over 300 food items for 4. Average age at marriage by country was 25 every household member during the previous years for women (minimum 17.2 and max- 24 hours, as reported by the woman in charge imum 33.8 years) and 28.4 years for men of cooking and serving. (minimum 21.7 and maximum 36.5 years) 11. See Bargain, Donni, and Kwenda (2014) on (World Marriage Data 2015 using the latest Côte d’Ivoire; Bargain, Kwenda, and Ntuli data for 2013). (2017) on South Africa; Bargain, Lacroix, 5. Farmers are considered earners, even if they and Tiberti (2018) on Bangladesh; Belete produce mostly for subsistence purposes, un- (2018) on Ethiopia; Brown, Calvi, and Pen- less they are classified in the survey as unpaid glase (2018) on Bangladesh; Cuesta (2006) on family workers. Chile; Dunbar, Lewbel, and Pendakur (2013) 6. To the best of our knowledge, these are the on Malawi. few relatively recent datasets that collect 12. The results are based on pooling the Bangla- consumption data with the level of detail desh Integrated Household Survey 2011–12 necessary for intrahousehold analysis and a and 2015 and on using the Malawi Integrated significant geographical coverage. Other ex- Household Survey 2004–05 and 2010–11. isting datasets are either limited in geographic 13. See Bargain, Lacroix, and Tiberti (2018) for a scope, are outdated, or can only assign a small similar validation study. proportion of consumption to individuals. 14. The resource shares are estimated less pre- 7. Although these smaller shares may reflect dif- cisely in Malawi than in Bangladesh, even in ferences in needs or preferences, the evidence comparisons with resource shares estimated 148 POVERTY AND SHARED PROSPERITY 2018 on the basis of expenditures on clothing. This indicator (any woman [ages 15–49] in the may arise because of differences in sample size household is undernourished) and the indi- (4,149 households in Bangladesh against 3,045 vidual indicator (the adult is undernourished). in Malawi in 2004/05). The additional estima- 18. In addition, most surveys are characterized by tion of resource shares in Tanzania based on numerous missing values for nutrition among pooling the 2012–13 and 2014–15 datasets individuals, which reduces the reliability of did not yield interpretable results. The sample this indicator. This is because household sur- size was considerably smaller, with only 1,552 vey protocols typically allow for only a limited observations, which may explain why the esti- number of revisits to each household. House- mation results were inconclusive. hold members who are not at home during 15. Details on the datasets used are presented in the first visit and subsequent revisits are not chapter 4. This section does not include a dis- measured. cussion of Uganda, because anthropometric 19. In this ongoing work to gain insight on the information is not available on adults in that dimensions of poverty in six countries, each country. national team of 10–15 people is responsible 16. Following chapter 4, the individual multi- for the local design, execution, and analysis dimensional poverty measure gives equal of the research. Each team includes people weight to each dimension (0.2), and all in- who are poor, but also academics and prac- dicators within a dimension are weighted titioners who provide services or advocate for equally. The only exception is the health and the poor. Outreach is undertaken among peo- nutrition dimension; the two subdimensions ple of working age, the elderly, and children, (health, nutrition) are weighted equally. For all of whom participate in detailed moderated the Alkire-Foster (2011) measure, α = 0 is discussion, first, within peer groups of people used, and a household classified multidimen- with similar experiences and, then, in mixed sionally poor if it is deprived in at least 0.2 of groups that explore relationships across di- the weighted indicators (k = 0.2). The results mensions and seek consensual conclusions. are qualitatively similar for different parame- 20. The first is that Engel curves for the assign- ters of the Alkire-Foster (2011) measure and able good have the same shape across house- for the Datt (forthcoming) measure. hold members. The second is that preferences 17. In education, the approach compares the share are similar across household types, where of adults deprived according to the household household types are differentiated by the indicator (no adult has completed primary number of children living in the household. school) and the individual indicator (the adult These assumptions can be used in isolation has not completed primary school). In nu- or jointly (as done here) to identify the share trition, the approach compares the share of of resources accruing to each member of the adults deprived according to the household household. INSIDE THE HOUSEHOLD: POOR CHILDREN, WOMEN, AND MEN 149 Appendix A Data Details The poverty and shared prosperity measures and regional estimates and to facilitate com- and supporting analysis in this report are parisons across countries, PovcalNet aligns based on household surveys from around the the surveys to specific reference years (for world. Because the variables available in the additional details, see the chapter 1 section household surveys differ across countries and of this appendix). This report is based on the years, the country coverage varies from chap- September 2018 vintage of PovcalNet. The ter to chapter according to the data require- PovcalNet poverty measures are used for the ments for the analysis. As the data require- analysis of global poverty at the IPL in chap- ments become more demanding, the subset ter 1 and for the analysis of poverty at higher of countries that can meet them decreases. poverty lines in chapter 3 (table A.1). Thus, the same country coverage is not possi- ble across all five chapters of this report. Global Database of This data appendix first provides an over- view of the main data sources for this report Shared Prosperity along with country classification definitions The Global Database of Shared Prosperity applicable throughout the report. In the sub- (GDSP) includes the most recent figures on sequent sections, chapter-specific data and annualized consumption or income growth methodological issues, such as survey se- of the bottom 40 percent of the popula- lection criteria, definitions, additional data tion (the bottom 40) and related indicators sources, and key measurement issues are de- over similar time periods and intervals. All scribed separately for each of the five chapters. numbers were vetted by an internal Techni- cal Working Group. This report is based on Main databases for the the sixth edition of the GDSP (the fall 2018 release), which features data on 91 econo- report mies circa 2010–15 (http://www.worldbank .org/en/topic/poverty/brief/global-database PovcalNet -of-shared-prosperity). The harmonized sur- PovcalNet is an online tool for global pov- veys for the GDSP are all sourced from the erty monitoring hosted by the World Bank Global Monitoring Database (see below). The (http://iresearch.worldbank.org/PovcalNet). GDSP is the main data source for the shared PovcalNet was developed with the purpose prosperity analysis presented in chapter 2 of of public replication of the World Bank’s this report (see table A.1). poverty measures at the international pov- erty line (IPL). PovcalNet contains poverty Global Monitoring Database estimates from more than 1,600 household surveys spanning 164 economies and over 40 The Global Monitoring Database (GMD) is years, from 1977 to 2017. To produce global the World Bank’s repository of multitopic 151 TABLE A.1 Overview of Principal Data Sources by Chapter Global Monitoring Global Database of Database PovcalNet Shared Prosperity Chapter 1: Ending Extreme Poverty Fall 2018 release, Fall 2018 release, data from circa 2015 data from 1977–2017 Chapter 2: Shared Prosperity Fall 2018 release, Fall 2018 release, data from circa 2010–15 data from circa 2010–15 Chapter 3: Higher Standards for a Fall 2018 release, Growing World data from 1977–2017 Chapter 4: Beyond Monetary Poverty Fall 2017 release, data from circa 2013 Chapter 5: Inside the Household Fall 2016 release, data from circa 2013 income and expenditure household surveys By income used to monitor global poverty and shared prosperity.1 As of June 2018, the GMD con- The World Bank updates annually the income tains more than 1,100 household surveys classification of economies. The income clas- conducted in 156 economies. For a few econ- sification used in this report is based on the omies, the welfare aggregate of the GMD World Bank’s 2018 fiscal year classifications. spans up to 46 years, from 1971 to 2017, According to fiscal 2018 definitions, low- whereas for most other economies, coverage income economies are defined as those with a is significantly less. The household survey gross national income (GNI) per capita, cal- data are typically collected by national statis- culated using the World Bank Atlas method, tical offices in each country, and then com- of US$1,005 or less in 2016; lower-middle- piled, processed, and vetted for inclusion in income economies are those with a GNI per the GMD by the World Bank’s internal Tech- capita between US$1,006 and 3,955; upper- nical Working Group. Selected variables have middle-income economies are those with been harmonized to the extent possible such a GNI per capita between US$3,956 and that levels and trends in poverty and other 12,235; and high-income economies are key sociodemographic attributes can be rea- those with a GNI per capita of US$12,236 or sonably compared across and within coun- more. The list of economies by income and tries over time. The GMD’s harmonized mi- lending classification is available at https:// crodata are used in PovcalNet and the GDSP. datahelpdesk.worldbank.org/knowledgebase In this report, the GMD is used for the /articles/906519-world-bank-country-and global poverty profile in chapter 1, the multi- -lending-groups. dimensional poverty measures in chapter 4, and the individual poverty measures in chap- By geographical region ter 5. Whereas chapter 1 uses the latest version of the GMD, analyses in chapters 4 and 5 are In this report, the six geographical regions based on previous versions (see table A.1). comprise (1) low- and middle-income econ- omies, and (2) economies eligible to receive loans from the World Bank (such as Chile) Classification of economies and recently graduated economies (such The economy classifications by income level, as Estonia). The aggregate for the six geo- geographical region, and fragile and conflict- graphical regions is reported as the “sum of affected situation are described in this sec- regions,” which in previous publications was tion. The term country, used interchange- often referred to as the “developing world.” ably with economy, does not imply political The economies excluded from the six geo- independence but refers to any territory for graphical regions (as defined above), mostly which authorities report separate social or high-income economies, are grouped in a economic statistics. category called “rest of the world” in this 152 POVERTY AND SHARED PROSPERITY 2018 report. This group was often referred to as Madagascar; Malawi; Mali; Mauritania; Mau- “other high-income” or “industrialized econ- ritius; Mozambique; Namibia; Niger; Nige- omies” in previous publications. ria; Rwanda; São Tomé and Príncipe; Sene- The economies in each of the six regions gal; Seychelles; Sierra Leone; Somalia; South and the “rest of the world” category are listed Africa; South Sudan; Sudan; Tanzania; Togo; below. Uganda; Zambia; Zimbabwe. East Asia and Pacific: American Samoa; Rest of the world: Andorra; Antigua and Cambodia; China; Fiji; Indonesia; Kiribati; Barbuda; Aruba; Australia; Austria; The Ba- Democratic People’s Republic of Korea; Lao hamas; Bahrain; Belgium; Bermuda; British People’s Democratic Republic; Malaysia; Mar- Virgin Islands; Brunei Darussalam; Canada; shall Islands; Federated States of Micronesia; Cayman Islands; Channel Islands; Curaçao; Mongolia; Myanmar; Northern Mariana Is- Cyprus; Denmark; Faroe Islands; Finland; lands; Palau; Papua New Guinea; Philippines; France; French Polynesia; Germany; Gibral- Samoa; Solomon Islands; Thailand; Timor- tar; Greece; Greenland; Guam; Hong Kong Leste; Tonga; Tuvalu; Vanuatu; Vietnam. SAR, China; Iceland; Ireland; Isle of Man; Is- Europe and Central Asia: Albania; Ar- rael; Italy; Japan; Republic of Korea; Kuwait; menia; Azerbaijan; Belarus; Bosnia and Her- Liechtenstein; Luxembourg; Macao SAR, zegovina; Bulgaria; Croatia; Czech Repub- China; Malta; Monaco; Nauru; Netherlands; lic; Estonia; Georgia; Hungary; Kazakhstan; New Caledonia; New Zealand; Norway; Por- Kosovo; Kyrgyz Republic; Latvia; Lithuania; tugal; Puerto Rico; Qatar; San Marino; Saudi former Yugoslav Republic of Macedonia; Arabia; Singapore; Sint Maarten (Dutch Moldova; Montenegro; Poland; Romania; part); Spain; St. Martin (French part); Swe- Russian Federation; Serbia; Slovak Republic; den; Switzerland; Taiwan, China; Turks and Slovenia; Tajikistan; Turkey; Turkmenistan; Caicos Islands; United Arab Emirates; United Ukraine; Uzbekistan. Kingdom; United States; U.S. Virgin Islands. Latin America and the Caribbean: Argentina; Barbados; Belize; Bolivia; Brazil; By fragile and conflict-affected Chile; Colombia; Costa Rica; Cuba; Domi- situation nica; Dominican Republic; Ecuador; El Sal- vador; Grenada; Guatemala; Guyana; Haiti; Economies with fragile situations are primar- Honduras; Jamaica; Mexico; Nicaragua; ily International Development Association– Panama; Paraguay; Peru; St. Kitts and Nevis; eligible countries and nonmember or in- St. Lucia; St. Vincent and the Grenadines; active countries and territories with a 3.2 Suriname; Trinidad and Tobago; Uruguay; or lower harmonized average of the World República Bolivariana de Venezuela. Bank’s Country Policy and Institutional As- Middle East and North Africa: Algeria; sessment (CPIA) rating and the correspond- Djibouti; Arab Republic of Egypt; Islamic Re- ing rating by a regional development bank, public of Iran; Iraq; Jordan; Lebanon; Libya; or with a United Nations or regional peace- Morocco; Oman; Syrian Arab Republic; Tuni- building and political mission (for example sia; West Bank and Gaza; Republic of Yemen. by the African Union, European Union, or South Asia: Afghanistan; Bangladesh; Organization of American States) or peace- Bhutan; India; Maldives; Nepal; Pakistan; Sri keeping mission (for example, by the Afri- Lanka. can Union, European Union, North Atlan- Sub-Saharan Africa: Angola; Benin; tic Treaty Organization, or Organization of Botswana; Burkina Faso; Burundi; Cabo American States) during the last three years. Verde; Cameroon; Central African Repub- The group excludes World Bank countries lic; Chad; Comoros; Democratic Republic (for which the CPIA scores are not publicly of Congo; Republic of Congo; Côte d’Ivoire; disclosed) unless they have a peacekeeping or Equatorial Guinea; Eritrea; Eswatini; Ethi- political/peacebuilding mission. This defini- opia; Gabon; The Gambia; Ghana; Guinea; tion is pursuant to an agreement between the Guinea-Bissau; Kenya; Lesotho; Liberia; World Bank and other multilateral develop- APPENDIX A: DATA DETAILS 153 ment banks at the start of the International putation estimates for India are not counted Development Association 15 round in 2007. toward the 40 percent, which means the South The World Bank releases annually the Asia coverage for 2015 is below the threshold. Harmonized List of Fragile Situations. The The recent availability of additional survey first such list was compiled in fiscal 2006 data has filled gaps in the regional poverty and has gone through a series of changes trend for the Middle East and North Africa. in terms of classification from the Low- In the 2016 edition of the Poverty and Shared Income Countries Under Stress List (2006– Prosperity Report, the estimates for the Mid- 09), to the Fragile States List (2010), to the dle East and North Africa region were not re- current Harmonized List of Fragile Situa- ported for 1999, 2002, and after 2008 because tions (2011–15). The concept and the list of low population coverage of the data. In have evolved as the World Bank’s under- the current edition, regional estimates for the standing of the development challenges in Middle East and North Africa are reported countries affected by violence and instabil- for all years. ity has matured. The lists of economies by year are available at http://www.worldbank India .org/en/topic/fragilityconflictviolence/brief /harmonized-list-of-fragile-situations. Although the most recent round of National Sample Survey (NSS) data that the Govern- ment of India uses for poverty estimation Chapter 1 data and was collected in 2011–12, a subsequent round methodology of the NSS was collected in 2014–15. This The World Bank now reports global and re- more recent survey collects socioeconomic gional poverty estimates every two years, co- and demographic information similar to the inciding with the publication of the Poverty 2011–12 NSS and earlier NSS rounds. But and Shared Prosperity report. Up until 2008, the 2014–15 NSS cannot be used for direct the frequency of the global estimates was poverty estimation because the consumption every three years. Because new surveys be- data on only a small subset of items have been come available and existing survey and aux- released. Given the importance of India to the iliary data are sometimes updated, the global global poverty count, and the unique situa- and regional estimates are revised regularly. tion of having common socioeconomic and The 2018 edition of global poverty esti- demographic data in the 2014–15 NSS (and mates is based on the most recent data avail- found in earlier NSSs), a model of consump- able. This section explains notable changes tion has been estimated on the basis of the since the 2016 edition of global poverty esti- common socioeconomic, demographic, and mates, discusses some key measurement is- geographic characteristics of the population sues, and describes the auxiliary data, includ- (Newhouse and Vyas 2018). This allows for ing purchasing power parity (PPP) conversion an estimate of poverty at the IPL for India in factors, consumer price indexes (CPIs), popu- 2014–15, which is then lined up to 2015 and lation data, and national accounts data. used as the poverty estimate for India in chap- ter 1 (for details on the lineup method, see the section “Estimating global and regional pov- Household survey data for erty: The ‘lineup,’” below). For further details poverty monitoring on the consumption model for India, see box Poverty rates for a region are marked with a 1.3 in chapter 1. note if the available household surveys cover less than 40 percent of the population in the Auxiliary data: PPP, CPI, region. The criterion for estimating survey population, and national accounts population coverage is whether at least one survey used in the reference year estimate was PPP conversion factors. The poverty esti- conducted within two years of the reference mates for all countries are based on con- year. For the purpose of this chapter, the im- sumption PPPs from the 2011 round of data 154 POVERTY AND SHARED PROSPERITY 2018 collection coordinated by the International The CPI, population, and national ac- Comparison Program. The PPP conversion counts data used for the latest global esti- factors include benchmark countries where mates are available on the PovcalNet site actual price surveys were conducted, and (http://iresearch.worldbank.org/PovcalNet regression-based PPP estimates where such /Data.aspx). For additional details on recent surveys were not conducted or not appro- changes and data updates, see the What’s priate for poverty measurement. Since the New notes of the Global Poverty Monitoring 2016 edition of the Poverty and Shared Pros- Technical Notes (http://iresearch.worldbank perity Report, the 2011 PPP conversion fac- .org/PovcalNet/whatIsNew.aspx). tors for Egypt, Iraq, Jordan, Lao PDR, and the Republic of Yemen have been revised Estimating global and regional (Atamanov, Jolliffe, and Prydz 2018). poverty: The “lineup” CPI. The primary source of CPI data used for global poverty measurement is the In- Because the household surveys necessary to ternational Monetary Fund’s International measure poverty are conducted in different Finance Statistics (IFS) monthly series. Pre- years and at varying frequencies across coun- viously, the World Development Indica- tries, producing global and regional poverty tors (WDI) annual series were used. When estimates entails bringing each of the country- monthly IFS series are not available or not level poverty estimates to a common reference appropriate for poverty monitoring, other or “lineup” year. For countries with surveys sources are used. China and India use rural available in the reference year, the direct es- and urban CPIs provided by the national sta- timates of poverty from the surveys are used. tistical offices, six countries use national se- For other countries, the poverty estimates ries provided by the national statistical offices are imputed for the reference year using the (the Islamic Republic of Iran, Iraq, Kenya, country’s recent household survey data and Maldives, Nicaragua, and República Boli- real growth rates from national accounts data. variana de Venezuela), and five countries use The procedures for doing this depend on the CPIs implied from the surveys (Bangladesh, survey years available for the country. Ghana, Lao PDR, Malawi, and Tajikistan). A When a survey is available only prior to more detailed description of CPIs used for the reference year, the consumption (or in- global poverty monitoring is available in Lak- come) vector from the latest survey is extrap- ner et al. (2018). olated forward to the reference year using real Population. The primary source of pop- growth rates of per capita GDP (or HFCE) ulation data is the December 2017 version of obtained from national accounts. Each ob- the WDI. For additional details see Chen et al. servation in the welfare distribution is multi- (2018). plied by the growth rate in per capita GDP National accounts. The primary source (or HFCE) between the reference year and of per capita gross domestic product (GDP) the time of the survey. Poverty measures and household final consumption expendi- can then be estimated for the reference year. ture (HFCE) data is the December 2017 ver- This procedure assumes distribution-neutral sion of the WDI. Per capita GDP is used for growth—that is, no change in inequality— countries in Sub-Saharan Africa and in coun- and that the growth in national accounts is tries for which HFCE is not available. Every- fully transmitted to growth in household where else, per capita HFCE is used. A more consumption or income. If the only available detailed description of the national accounts surveys are after the reference year, a similar data used for global poverty monitoring will approach is applied to extrapolate backward. be available on the PovcalNet website. For When surveys are available both before nowcasts, growth projections for recent years and after the reference year, information are taken from the World Bank’s Global Eco- from both surveys is used to interpolate pov- nomic Prospects, and from the International erty. In these cases, the welfare vectors (that Monetary Fund’s World Economic Outlook, is, per capita consumption or income) from when the former is unavailable. the two surveys are both lined up to the ref- APPENDIX A: DATA DETAILS 155 erence year using growth rates of per capita consistent with a “truly global” approach to GDP (or HFCE). After this, the poverty rate poverty measurement (World Bank 2017b, is calculated for each of the two lined-up 47). The Commission therefore advised the surveys and then averaged, with each point inclusion of all economies in the global pov- weighted by the relative distance of the sur- erty measures. For further discussion, see vey year to the reference year. The surveys Ferreira, Lakner, and Sanchez (2017). are lined up to the reference year using two different interpolation methods. The default Key poverty measurement method is applied when the growth in the issues survey mean between the two surveys is of the same sign as the real growth in per capita There are many technical details on how global GDP (or HFCE) from the first survey to the poverty is measured. Ferreira et al. (2016) pro- reference year, and from the reference year to vide a good overview of many of these issues, the second survey. With this default method, particularly concerning the valuation of the the growth in welfare from the time of the most recent IPL at US$1.90 in 2011 PPPs. For survey to the reference year is proportional a more in-depth discussion of select measure- to the relative growth in per capita GDP (or ment and data issues, see also Jolliffe et al. HFCE) over the same period. The first step (2015). Two key measurement concerns are entails imputing the survey mean at the refer- discussed below. These two areas are currently ence year using the following formula: being examined, and potential methodss for improvement are being considered.             , (A.1)      Consumption- and income-based measures of well-being where tr indicates the reference year, t1 indi- National poverty rates are based on measures cates the time of the first survey, t2 indicates of consumption or income. Countries typ- the time of the second survey (such that t2 > ically choose the measure that can be more tr > t1), and m indicates the survey mean at accurately measured while balancing con- the specified time. Upon computing mt r, each cerns about respondent burden. On the one element of the welfare vector from the first hand, consumption measures of poverty re- survey is grown or shrunk by the rate  , quire a wide range of questions and are thus  more time consuming. Income measures, on while each element of the welfare vector from the other hand, are difficult to obtain when the second survey is grown or shrunk by the a large fraction of the population works in  the informal sector or is self-employed, and rate  . The alternative method involves income data are not collected for tax pur- extrapolating the consumption vector to the poses. This is frequently the case in poorer reference year for each of the two surveys countries, which therefore often opt for using the real growth rates of per capita GDP using consumption (figure A.1). None of the (or HFCE). The mechanics of the extrapola- low-income countries uses income, but this tion and interpolation are described in more share increases to 10 percent, 40 percent, and detail in box 6.4 in Jolliffe et al. (2015). 97 percent for lower-middle-, upper-middle-, and high-income countries, respectively. As living standards have improved, so has the A truly global approach to share of countries using income-based mea- poverty measurement sures of poverty, and it will likely continue to All economies are now included in the global do so (figure A.1). poverty estimates. Previously, the practice Both approaches to measuring poverty was to assume that economies in the “rest of have advantages and disadvantages. The con- the world” category have zero extreme pov- sumption approach is arguably more con- erty. As pointed out in the Commission on nected to economic welfare. Whereas income Global Poverty report, this assumption is in- is valuable because it allows individuals to 156 POVERTY AND SHARED PROSPERITY 2018 FIGURE A.1 Use of Income/Consumption to Measure Poverty a. By income group, 2015 97.4 b. Over time 100 40 Percentage of economies using income Percentage of economies using income 80 60 35 40.4 40 30 20 9.6 0 0 25 Low Lower- Upper- High income middle middle income 1999 2002 2005 2008 2011 2014 income income Source: PovcalNet, World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/. purchase goods, consumption is valuable for rather than consumption. For a given poverty its own sake. Income measures of poverty also rate, poor households also tend to be further suffer from the disadvantage that incomes below the poverty line when income is used. might be very low—even negative—in a given This is explained by the earlier point about period. Negative incomes are often not an ac- very low incomes: whereas it is plausible that curate depiction of the well-being of a house- households have a zero income in a given time hold, so currently negative values are being period, subsistence requires a minimum level discarded. This is particularly relevant for of consumption, which is strictly above zero. self-employed individuals who tend to experi- The differences also matter for nowcasting ence large income shocks at greater frequen- and making poverty projections for the fu- cies. At a theoretical level, consumption will ture. Typically, such projections are made by likely be smoothed to safeguard against such assuming a fixed growth rate of household shocks, preventing consumption-based mea- consumption/income over time. If some sures of poverty from being as vulnerable to households have zero income or a negative large shocks as income-based measures. A income, then, regardless of how large growth household that has managed to save sufficient rates are assumed to be, those households will resources may not suffer greatly from a nega- never be projected to move out of poverty. tive income shock. Consumption-based mea- sures of poverty, conversely, are often more Accounting for spatial price time demanding, require detailed price data, differences across and within and often post fieldwork adjustments, such as countries rent imputations, which can matter greatly Welfare is measured by aggregating a house- for the final poverty estimates. Income mea- hold’s total value of consumption or total sures need not rely on more than a handful of income over a defined time period and then questions and can, at times, be verified from dividing by household size. When converted at other sources. market exchange rates, US$100 can buy differ- The differences between income and con- ent quantities and qualities of goods and ser- sumption measures matter for comparing vices in say Nigeria than in the United States. trends and patterns in poverty. Given that in- When comparing poverty rates across coun- comes can be very low and negative, poverty tries, local currencies are converted to PPP rates are typically higher when income is used dollars to account for differences in the pur- APPENDIX A: DATA DETAILS 157 chasing power across countries, ensuring that well-being relative to other countries at the a dollar can purchase approximately the same same nominal level of average consumption. bundle of goods and services across countries. Much work is yet to be done to assure that Important differences in price levels also similar practices are applied in various coun- appear within countries. Suppose a house- tries. Ferreira et al. (2016) contains more in- hold pays $1.00 for a kilo of rice in an urban formation on the methods applied in differ- center, whereas a rural household in the same ent countries. country pays only $0.50 for a similar quality and amount of rice. Assume more generally Data for global and regional that prices for all goods are twice as high in poverty profiles urban areas. If both households consumed the same quantity of goods, and if one were The global poverty profile for 2015 in chapter to assess poverty on the basis of the self- 1 is an update of the global profile of the poor reported value of goods and services con- first reported in Castaneda et al. (2016) for sumed without accounting for these price 2013. The methodological details of poverty differences, one would conclude that the rural profiling are presented in the original paper. household in this scenario is poorer than the The current exercise uses the 2018 vintage of urban household. From a welfare perspective, the GMD, covering 91 economies and more however, both households are consuming than 5.6 billion people, and lines up the the same items and are at approximately the survey-based poverty estimates to 2015. The same level of well-being. To properly com- exercise also uses recent population projec- pare the welfare levels of the two households, tions from the United Nations Department one would need to account for the differences of Economic and Social Affairs to adjust (that in price levels that the two households face. is, post-stratify) the sampling weights to the This example highlights the importance “lineup” year. of spatial price adjustments within countries. For the Sub-Saharan Africa regional pov- If certain households are deemed poorer erty profile, the analysis of demographic solely because they face different price lev- characteristics presented in this section els, then policy responses to poverty within builds on the harmonized 24-country data countries may be misinformed. Because price from the book Poverty in a Rising Africa. The differences can vary greatly within a country, book examines the trends in poverty and in- accounting for regional price differences can equality in Sub-Saharan Africa using com- have vast implications for subnational pro- parable surveys (Beegle et al. 2016). Of the files of poverty, allocation of resources, and 148 surveys conducted in 48 Sub-Saharan the design of poverty reduction strategies. As African countries between 1990 and 2012, national poverty is falling in many countries two or more surveys were comparable in only around the world, it is becoming increasingly 27 of 48 countries, and the data were avail- important to correctly identify the remaining able for 24 of the 27 countries. The current areas where poverty reduction lags. Without analysis adds Burundi (2006 and 2013) and spatial price adjustments, a national poverty Seychelles (2006 and 2013); uses more re- line could overestimate poverty in areas with cent data for Cameroon (2014), Côte d’Ivoire low prices, typically rural areas, and underes- (2015), Madagascar (2012), Rwanda (2013), timate poverty in areas with high prices, typ- and Togo (2015); and drops Mauritius, re- ically urban areas. sulting in a 25-country sample with a slightly Current measurement practices comprise different compostion. For the set of countries a wide range of methods to account for dif- and surveys included in the present analysis, ferences in the cost of living across regions, or the median year for the base period is 2004 across rural and urban areas. Some countries and the median year for the terminal period peg prices to the price level of the capital re- is 2011. The countries represent 73 percent gion, or a large city. With this approach, the of the total population of Sub-Saharan Af- mean of the spatially adjusted welfare aggre- rica in 2015, and the average poverty rates gate is larger than the mean without adjust- for the two periods are 59.7 and 47.7 per- ments, essentially inflating the overall level of cent, respectively. These figures are different 158 POVERTY AND SHARED PROSPERITY 2018 from but close to the poverty rates for Sub- Because of the low prevalence of refugees Saharan Africa around the same time—56.9 in general and their concentration in dense percent in 2002 and 44.9 percent in 2011 from geographical pockets, it might be difficult to PovcalNet. The discrepancy arises because draw a nationally representative sample using PovcalNet includes a wider range of surveys. conventional sampling methods. Refugees and internally displaced persons are highly Missing data on forcibly mobile, especially when the crisis is unfold- ing, which complicates the survey effort. displaced persons Even when the displaced households can be Worldwide, it is estimated that there are located, the nonresponse rate might be high nearly 70 million people in 2017 who have because of their wariness of divulging per- been forcibly displaced because of persecu- sonal information. The problem with non- tion, conflict, and generalized violence. Over response can become more severe when the the last 10 years, the number of forcibly dis- survey needs to interview vulnerable popula- placed persons has increased by more than 50 tions like women (for example, for birth his- percent (UNHCR 2018). As the number of tory) and children (for example, for anthro- forcibly displaced persons—refugees, asylum pometric measures). seekers, and internally displaced persons— In sum, socioeconomic surveys on dis- continues to increase, it becomes essential to placed persons are marked with incomplete measure their welfare for an accurate moni- coverage, unrepresentative samples, and pos- toring of global poverty. However, there are sibly larger-than-usual sampling and non- many challenges in monitoring the welfare of sampling errors, which results in an under- the displaced persons. Many countries do not estimate of the level of global poverty and an count refugees as part of the usual resident undercount of the number of poor. To im- population in the population census, and prove the ability to get a complete picture of the census enumeration often excludes refu- the poverty situation in the world, and to un- gee camps and temporary reception centers derstand how policy can affect the well-being where refugees are housed. The exclusion of of displaced persons, a first step is to ensure refugees from the population census implies that they are included in population censuses they are not a part of the sampling frame and the national sample surveys of the coun- used in household surveys. Similarly, typical try of their residence. sample designs for household surveys used for poverty measurement explicitly exclude Chapter 2 data and people living in institutions or camps and without an address. methodology Administrative registration databases Welfare aggregate maintained by government agencies or inter- national organizations like the United Nations The mean of the bottom 40 within each High Commissioner for Refugees are not well country refers to the average household per integrated into the data systems of national capita consumption or income among this statistical offices throughout the world, nor segment of the population. The choice of do these data correspond well with definitions consumption or income depends on the data in household surveys. For example, the unit available for each economy, and in most cases of record in administrative databases is typi- is consistent with the welfare aggregate used cally a case (for example, border crossing that to measure poverty (see annex 2B, table 2B.1). can occur multiple times for an individual) or For China, shared prosperity is estimated application, which does not match the defini- by PovcalNet using grouped data. Because tion of a household, the unit of analysis for grouped data are provided separately for sample surveys. This difference makes admin- urban and rural populations, the bottom 40 istrative databases challenging to use as sam- percent of the national population must be pling frames of the population of displaced estimated. The bottom 40 are identified using persons (Expert Group on Refugee and Inter- the national poverty gap and choosing a pov- nally Displaced Persons Statistics 2018). erty line that corresponds to the threshold APPENDIX A: DATA DETAILS 159 consumption level of the national bottom 40 prosperity estimate because of stricter data percent. PovcalNet uses a parametric Lorenz requirements. Economies are included in the curve fitted to grouped data, with an adjust- fall 2018 edition of the GDSP if the following ment for differences in price levels between requirements are met: urban and rural areas, and urban–rural pop- • Two relevant household surveys have been ulation shares from the WDI. Because shared conducted and have yielded comparable prosperity is estimated using grouped data data. for China, it is approximate and may differ from using official microdata (see Chen et al. • Among comparable surveys, one must be 2018 for details). conducted within two years of 2010, and In countries in Europe and Central Asia the other within two years of 2015. For using household per capita income as the example, the Solomon Islands cannot be welfare aggregate, households with nega- included because, although two rounds tive incomes are included when estimating of a comparable household survey have shared prosperity. been conducted (in 2005 and 2013), 2005 is more than two years from 2010. China is an exception to this rule because a survey Surveys used to calculate shared break between 2012 and 2013 means that prosperity surveys conducted around 2010 and 2015 Among the 164 economies with a poverty are not comparable. The shared prosperity estimate, significantly fewer have a shared period used for China is 2013–15. • The period between the selected initial and end years should range between three and FIGURE A.2 Shared Prosperity Indicators Are Less Likely in seven years. For example, a shared pros- Economies at Lower GDP per Capita perity period of 2012–13 meets the second 80 selection criterion but would not be al- lowed because it does not meet this third requirement. • In cases where multiple surveys can fulfill 60 these criteria, the most recent survey years Extreme poverty rate (%) are typically chosen. 40 Factors affecting the inclusion of economies in the GDSP The computation of the shared prosperity 20 measure relies on frequent data collection, which may depend on the capacity of a na- tional statistics office—often related to the level of development of a country. Among the 0 107 economies with a poverty rate below 10 percent in 2015 measured by the IPL, 78 also 0 0 0 00 00 0 00 00 00 00 00 10 20 50 00 1,0 5, 0 ,0 ,0 ,0 ,0 0,0 have a shared prosperity estimate for 2010–15 2, 10 20 50 0 20 10 GDP per capita (logarithmic scale) (figure A.2). Meanwhile, among 57 economies Economies with a poverty and shared prosperity indicator with a poverty rate at more than 10 percent, Economies with a poverty indicator but no shared prosperity indicator only 13 have a shared prosperity indicator. Population coverage is also limited among Sources: GDSP (Global Database of Shared Prosperity), fall 2018, World Bank, Washington, DC, http:// www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity; PovcalNet (online economies grouped by other World Bank analysis tool), World Bank, Washington, DC, http://iresearch.worldbank.org/PovcalNet/; WDI (World country categories, such as vulnerable, poor, Development Indicators) (database), World Bank, Washington, DC, http://data.worldbank.org/products or small nations. For example, a shared pros- /wdi. Note: Based on data on 164 economies in PovcalNet associated with direct estimates of poverty. perity measure is not available on any of the Poverty rates are based on the PovcalNet 2015 lineup. 15 small island nations. 160 POVERTY AND SHARED PROSPERITY 2018 Comparison of shared prosperity omies with data updates were mainly in Eu- across rounds of GDSP rope and Central Asia, Latin America and the Caribbean, and other high-income countries Comparing the performance in shared pros- (the rest of the world). Therefore, only in perity across rounds has limitations. The cur- these regions can trends in shared prosperity rent release of the GDSP includes 91 econo- be reliably examined. At the other extreme, mies. Since the circa 2008–13 GDSP used in new household survey data in the Middle Poverty and Shared Prosperity 2016 (World East and North Africa and in Sub-Saharan Bank 2016b), 19 countries have been added, Africa are scarcer, and shared prosperity esti- and 10 countries removed because they no mates were updated in only one economy per longer fulfill the data requirements (table region following the publication of Poverty A.2). Of the 72 economies occurring in both and Shared Prosperity 2016. rounds, the shared prosperity measure has not been updated in five—Mexico, Mon- tenegro, Nicaragua, Rwanda, and Togo— Chapter 3 data and because no new surveys have become avail- methodology able or, in the case of Mexico, because of a break in the survey series. A comparison of Poverty rates at higher poverty shared prosperity indicators can be carried lines out in 67 economies across rounds. The country sample changed across the The poverty estimates at the higher poverty releases of the GDSP for two main reasons: lines presented in chapter 3 are extracted from PovcalNet. See the discussion in the chapter 1 1. Data requirements were met in one round section of this appendix for details on house- but not in the next because appropriate hold surveys, auxiliary data, and measure- data within the established time frame ment issues. For India, the poverty estimates were not available or because of a lack of are extrapolated using 2011–12 survey data data comparability. Between circa 2008– and the pass-through rate described in box 1.3 13 and circa 2010–15, 10 countries were in chapter 1. Poverty rates at the societal pov- removed for these reasons. erty line are also estimated from PovcalNet. 2. Some countries that did not previously meet data requirements do so now. Be- tween circa 2008–13 and circa 2010–15, 19 Database of harmonized countries were added for this reason. This national poverty lines occurs when countries collect new house- A database of harmonized national poverty hold surveys, following a long gap. lines is used to derive the societal poverty Despite these challenges, the fall 2018 line presented in chapter 3. Jolliffe and Prydz GDSP contains updated values of shared (2016) construct a set of national poverty prosperity for three-quarters of the sample lines by combining national poverty rates (67 economies) used in Poverty and Shared from national sources, reported in the World Prosperity 2016 (World Bank 2016b). Econ- Bank’s databases, with corresponding con- sumption and income distributions from PovcalNet used for international poverty TABLE A.2 Shared Prosperity Availability estimates. Because the consumption and in- across Rounds come distributions used are all expressed in Number of per capita PPP terms, the estimated national GDSP round economies poverty lines are all expressed in comparable Circa 2008–13 82 per capita PPP dollars. The national poverty Removed 10 lines are harmonized in terms of the unit Added 19 of measure in the sense that they are all ex- Circa 2010–15 91 pressed in per capita terms. Circa 2008–13 and circa 2010–15 72 Following this approach, rather than col- With updated shared prosperity measure 67 lecting publicly reported poverty lines, al- APPENDIX A: DATA DETAILS 161 lows for a substantial increase of the set of indicators and maintaining cross-country countries for which we have national poverty comparability. thresholds. This approach also results in a se- Most of the surveys used in the analysis ries of historic and current poverty lines that were conducted during 2012–14 (88 coun- allows one to subset on a specific year cor- tries). No household income and expenditure responding to the most recent International survey data were available for the populous Comparison Program reference year (for ex- African countries of Nigeria and Sudan in ample, 2011). the 2010–16 period, which explains the low Subsetting on national poverty lines regional population coverage in Sub-Saharan closest to 2011 both provides recent socio- Africa (see table 4.4). The population cover- economic assessments of basic needs and age for the rest of the world category is small reduces the reliance on CPI data for lining because of limited coverage in the GMD. Be- up the poverty lines to a common year. The cause of the selection criteria above, the set of larger database contains 864 harmonized countries differs from that in chapter 1. national poverty lines. The analysis of the circa-2011 national poverty lines for the lower-middle-income and upper-middle- Differences from chapter 1 income country lines is based on a subsample poverty estimates of 126 lines; and the estimation of the soci- The extreme poverty rates (headcount ratios) etal poverty line, discussed in this chapter, reported in this chapter cannot be compared is based on a subsample of 699 harmonized to the information presented in chapter 1 for national poverty lines. For more details on three practical and methodological reasons. the construction of the database of harmo- First, if a survey was available for a country nized national poverty lines, see Jolliffe and in both 2013 and 2015, the 2013 data are used Prydz (2016); and for discussion of the data in this chapter to minimize the overall dis- underlying the estimation of the societal pov- persion in survey years. Second, to examine erty line, see Jolliffe and Prydz (2017). For a the simultaneous incidence of deprivations, discussion of the precision of these harmo- only unit-record data are used in this chapter, nized lines, see the online appendix to their which limits the number of countries consid- paper at https://static-content.springer.com ered. In contrast, grouped data also enter into /esm/art%3A10.1007%2Fs10888-016-9327-5 the estimation of the global poverty rate re- /MediaObjects/10888_2016_9327_MOESM1_ ported in chapter 1 if unit-record data are un- ESM.pdf. available. China is a notable example where only grouped data are available. This explains Chapter 4 data and the low population coverage of the East Asia methodology and Pacific region in this chapter. Third, PovcalNet relies on recent surveys to impute Chapter 4 uses data from the harmonized the headcount ratio for the lineup year, 2015, household surveys from the 2017 edition of assuming distribution-neutral growth. These the GMD. Surveys have been included in the adjustments are not made in this chapter be- multidimensional poverty analysis if they cause the lineup process cannot be applied to satisfy the following criteria: the other indicators of well-being. A full list • They include a monetary welfare measure of the countries for which different surveys (consumption or income) and indicators are used in chapter 1 (for the 2015 estimates) on education and service access that may and chapter 4 is included in table A.3. be used to construct a multidimensional poverty measure. Six-country sample • The surveys were conducted within three The extended multidimensional analyses cov- years of 2013, that is, from 2010 to 2016. ering five dimensions of poverty are based on The circa 2013 restriction strikes a balance the household surveys for the six countries in between maximizing country coverage of table A.4. Except for Iraq, the surveys are not 162 POVERTY AND SHARED PROSPERITY 2018 TABLE A.3 Surveys Used in Chapter 1 and Chapter 4 in Cases Where Different Survey Rounds Are Used Survey(s) used in chapter 1 Economy Survey used in chapter 4 for extreme poverty Argentina EPHC 2014 EPHC 2014 and EPHC 2016 Armenia ILCS 2013 ILCS 2015 Austria EU-SILC 2014 EU-SILC 2016 Bangladesh HIES 2010 HIES 2010 and HIES 2016 Belarus HHS 2013 HHS 2015 Belgium EU-SILC 2014 EU-SILC 2016 Bhutan BLSS 2012 BLSS 2012 and BLSS 2017 Bolivia EH 2014 EH 2015 Brazil PNAD 2014 PNAD 2015 Chile CASEN 2013 CASEN 2015 Colombia GEIH 2014 GEIH 2015 Costa Rica ENAHO 2014 ENAHO 2015 Croatia EU-SILC 2014 EU-SILC 2016 Cyprus EU-SILC 2014 EU-SILC 2016 Czech Republic EU-SILC 2014 EU-SILC 2016 Denmark EU-SILC 2014 EU-SILC 2016 Dominican Republic ENFT 2013 ENFT 2015 Ecuador ENEMDU 2014 ENEMDU 2015 Egypt, Arab Rep. HIECS 2012 HIECS 2015 El Salvador EHPM 2014 EHPM 2015 Estonia EU-SILC 2014 EU-SILC 2016 Ethiopia HICES 2010 HICES 2010 & HICES 2015 Finland EU-SILC 2014 EU-SILC 2016 France EU-SILC 2014 EU-SILC 2016 Gambia, The IHS 2010 IHS 2010 and IHS 2015 Georgia HIS 2013 HIS 2015 Germany EU-SILC 2012 EU-SILC 2016 Greece EU-SILC 2014 EU-SILC 2016 Honduras EPHPM 2013 EPHPM 2015 Hungary EU-SILC 2014 EU-SILC 2016 Indonesia SUSENAS 2016 SUSENAS 2015 Iran, Islamic Rep. HEIS 2013 HEIS 2014 Ireland EU-SILC 2014 EU-SILC 2016 Italy EU-SILC 2014 EU-SILC 2016 Kazakhstan HBS 2013 HBS 2015 Kosovo HBS 2013 HBS 2015 Kyrgyz Republic KIHS 2013 KIHS 2015 Latvia EU-SILC 2014 EU-SILC 2016 Lithuania EU-SILC 2014 EU-SILC 2016 Luxembourg EU-SILC 2014 EU-SILC 2016 Malta EU-SILC 2014 EU-SILC 2016 Mexico ENIGH 2012 ENIGH 2014 and ENIGH 2016 Moldova HBS 2013 HBS 2015 Mongolia HSES 2016 HSES 2014 and HSES 2016 Montenegro HBS 2013 HBS 2014 Netherlands EU-SILC 2014 EU-SILC 2016 Norway EU-SILC 2014 EU-SILC 2016 Pakistan PSLM 2013 PSLM 2013 and PSLM 2015 Paraguay EPH 2014 EPH 2015 Peru ENAHO 2014 ENAHO 2015 Portugal EU-SILC 2014 EU-SILC 2016 Romania HBS 2013 EU-SILC 2016 Russian Federation HBS 2013 HBS 2015 Serbia HBS 2013 HBS 2015 Slovak Republic EU-SILC 2014 EU-SILC 2016 Slovenia EU-SILC 2014 EU-SILC 2016 (continued) APPENDIX A: DATA DETAILS 163 TABLE A.3 Surveys Used in Chapter 1 and Chapter 4 in Cases Where Different Survey Rounds Are Used (continued) Survey(s) used in chapter 1 Economy Survey used in chapter 4 for extreme poverty Spain EU-SILC 2014 EU-SILC 2016 Sri Lanka HIES 2016 HIES 2012 and HIES 2016 Sweden EU-SILC 2014 EU-SILC 2016 Switzerland EU-SILC 2014 EU-SILC 2016 Thailand SES 2013 SES 2015 Turkey HICES 2013 HICES 2015 Uganda UNHS 2012 UNHS 2012 and UNHS 2016 Ukraine HLCS 2013 HLCS 2015 United Kingdom EU-SILC 2014 EU-SILC 2016 Uruguay ECH 2014 ECH 2015 Vietnam VHLSS 2014 VHLSS 2014 and VHLSS 2016 West Bank and Gaza PECS 2011 PECS 2011 and PECS 2016 Source: GMD (Global Monitoring Database), Global Solution Group on Welfare Measurement and Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. Note: Only economies where different survey rounds are used for chapter 4 and the 2015 poverty estimates of chapter 1 are listed. For economies where EU-SILC is used, the income data is from the year prior to the survey. For example, the EU-SILC 2016 survey uses data from 2015. Romania is the only economy where both the survey year and the type of survey differ from chapter 1 to chapter 4. the same surveys used for official national applicable, and the deprivation in the edu- poverty estimates. Therefore, the monetary cation dimension is measured solely using poverty headcount ratios cited in this section the adult school attainment indicator. may vary from official estimates. • Adult school attainment. Individuals are considered deprived if no adult (at or above the age one is normally at when Definitions of indicators attending the ninth grade) in the house- Monetary poverty hold has completed primary education. • Income per capita. A person is considered Access to basic infrastructure deprived if the household consumption or income per person per day falls below the • Electricity. A person is considered de- IPL, currently set at US$1.90 in 2011 PPPs. prived if the household has no access to electrification from any source, that is, grid Education electricity or self-generation. • Child school enrollment. Individuals • Limited-standard drinking water. A per- are considered deprived if they live in a son is considered deprived if the household household in which at least one school- has no access to even a limited standard of aged child up to the age of grade 8 is not drinking water. For a selection of coun- enrolled in school. If a household has no tries, a variation closer to the Sustainable child up to this age, this indicator is not Development Goals’ safely managed drink- ing water concept is available: a house- TABLE A.4 Household Surveys, Six-Country Sample hold is considered deprived if it has no Country Year Survey access to basic drinking water (a limited- standard source that is within a round-trip Ecuador 2013–14 Encuesta de Condiciones de Vida Indonesia 2014 Indonesian Family Life Survey time of 30 minutes). For more informa- Iraq 2012 Iraq Household Socio-Economic Survey tion, see https://washdata.org/monitoring. Mexico 2009–12 Mexican Family Life Survey • Limited-standard sanitation. A person is Tanzania 2012–13 National Panel Survey considered deprived if the household has no Uganda 2013–14 Uganda National Panel Survey access to even a limited standard of sanita- 164 POVERTY AND SHARED PROSPERITY 2018 tion facilities, that is, a sanitation facility that cable households who actually experienced hygienically separates excreta from human a recent birth or have a child younger than 6 contact. For a selection of countries, exclu- years. sivity of the facility is also taken into con- sideration. In those countries, a household Security is considered deprived if it lacks a limited- • Incidence of crime. A person is considered standard facility that is used only by mem- deprived if anyone in the household has bers of the same household. The addition experienced crime in the previous year or of this criterion to “limited” is called “basic- lives in a neighborhood where at least 20 standard” sanitation. For more information, percent of households contain at least one see https://washdata.org/monitoring. individual who experienced crime in the previous year. Health and nutrition • Incidence of natural disaster. Individuals • Birth delivery. A person is considered de- are considered deprived if their household prived if any woman in the household be- has experienced a severe shock (a loss of tween the ages of 15 to 49 has given birth income, property, or livestock) because of (live) in the previous 36 months, and the drought, flooding, earthquake, or other delivery did not occur in a formal facility. natural disaster in the previous 12 months. • Vaccination. A person is considered de- prived if the household has any child be- tween the ages of 12 to 59 months who has Chapter 5 data and not received a third diphtheria-pertussis- methodology tetanus vaccination. This section uses the harmonized household • Child stunting. A person is considered surveys from the 2016 release, circa 2013 deprived if the household has any child data, edition of the GMD. Even though GMD between the ages of 0 to 59 months who data for circa 2013 was used for chapters 4 is stunted (the height-for-age Z-score is and 5, the set of countries covered differs below −2, that is, more than two standard because different variables are required for deviations below the reference population the analysis. The combined sample of the median). data used in chapter 5 contains records rep- resenting 5.2 billion individuals in 89 coun- • Undernourishment. A person is consid- tries, with estimates of poverty figures lined ered deprived if any woman between the up—that is, extrapolated—to 2013 and then ages of 15 to 49 in the household is under- updated to 2016. The data include welfare nourished (her body mass index is below aggregates based on a money metric, either 18.5 [underweight]). household per capita consumption or in- The measure of access to formal health come, depending on the concept used in care is not applicable to all households be- each country (see chapter 1 discussion above cause a significant share of households have for details). Nearly 83 percent of the sample not experienced a birth in the previous three originates in middle-income countries. East years or do not have a child younger than 5 Asia and Pacific and South Asia account for years. For such households, access to health nearly two-thirds of the sample. The GMD services is approximated by the share of indi- sample has a high regional coverage of de- viduals in applicable households in the same veloping countries in East Asia and Pacific, community who are observed to be deprived. South Asia, Latin America and the Carib- The deprivation threshold for the rate of bean, and Europe and Central Asia (above health service access is set such that the share 87 percent) and partial coverage of Sub- of individuals in nonapplicable households Saharan Africa (74 percent). Additional that are classified as deprived equals the na- labor data from the International Income tional share of deprived individuals in appli- Distribution dataset were merged for 17 APPENDIX A: DATA DETAILS 165 TABLE A.5 Household Surveys for Case Studies and Sharing Rule Estimates Country Survey Year(s) Case studies Bangladesh Bangladesh Integrated Household Survey 2011–12 China China Health and Nutrition Survey 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009 Senegal Poverty and Family Structure Survey 2006–07 Burundi Panel Priority Survey 2012 Sharing rule estimates Bangladesh Bangladesh Integrated Household Survey 2011–12, 2015 Malawi Malawi Integrated Household Survey 2004–05, 2010–11 Tanzania National Panel Survey 2012–13, 2014–15 countries in Sub-Saharan Africa (Muñoz An individual perspective on Boudet et al. 2018). Because of remaining multidimensional poverty quality concerns in the economic participa- tion variables, 18 countries were dropped for This section uses the same household surveys the economic typology of households. Be- that were used in the six-country sample in cause of low coverage in the Middle East and chapter 4 (see table A.4), except Uganda is North Africa (4.1 percent), the results from excluded because the survey did not collect this region are not presented. anthropometric information for adults. Differences in resources and Note poverty within households 1. GMD (Global Monitoring Database), Global This section draws on the household surveys Solutions Group on Welfare Measurement and in table A.5. Capacity Building, Poverty and Equity Global Practice, World Bank, Washington, DC. 166 POVERTY AND SHARED PROSPERITY 2018 References Aaberge, Rolf, and Andrea Brandolini. 2015. 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Forthcoming. “Living and Leaving: Hous- World Bank. ing, Mobility, and Welfare in the European ———. 2009. “Jordan—Poverty Update.” Report Union.” World Bank Regional Report, World 47951, World Bank, Washington, DC. Bank, Washington, DC. 176 POVERTY AND SHARED PROSPERITY 2018 ECO-AUDIT Environmental Benefits Statement The World Bank Group is committed to reducing its environmental footprint. In support of this commitment, we leverage electronic publishing options and print- on-demand technology, which is located in regional hubs worldwide. Together, these initiatives enable print runs to be lowered and shipping distances decreased, resulting in reduced paper consumption, chemical use, greenhouse gas emissions, and waste. We follow the recommended standards for paper use set by the Green Press Initiative. The majority of our books are printed on Forest Stewardship Council (FSC)–certified paper, with nearly all containing 50–100 percent recycled con- tent. 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