Public Disclosure Authorized Global Poverty Monitoring Technical Note 22 April 2022 Update to the Public Disclosure Authorized Multidimensional Poverty Measure What’s New Carolina Diaz-Bonilla and Carlos Sabatino Public Disclosure Authorized May 2022 Keywords: Multidimensional Poverty Measure, April 2022. Public Disclosure Authorized Development Data Group Development Research Group Poverty and Equity Global Practice Group Global Poverty Monitoring Technical Note 22 Abstract The April 2022 update presents the 3rd edition of the World Bank’s Multidimensional Poverty Measure (MPM), based on updates to the Global Monitoring Database (GMD). The MPM is an index that captures the percentage of households in a country deprived along three dimensions of well-being – monetary poverty, education, and basic infrastructure services – to provide a more complete picture of poverty. The latest MPM data provides country estimates for 123 economies in the GMD circa 2018, revising estimates published in March 2021. Some changes reflect the availability of more recent survey data. Other changes are due to the addition of new economies to the dataset, the release of new population data, and new monetary poverty estimates. The accompanying online dashboard containing the data and results presented in this document has also been updated. The dashboard allows users to visualize MPM data and modify the weights used when aggregating the different indicators in the MPM headcount ratio. All authors are with the World Bank. Corresponding author: Carolina Diaz-Bonilla (cdiazbonilla@worldbank.org). This work could not be completed without the contributions from the Data for Goals (D4G), regional and country teams. Regional teams: Minh Cong Nguyen, David Newhouse, Hernan Winkler, Ifeanyi Nzegwu Edochie, Ikuko Uochi, Jose Montes, Laura Liliana Moreno Herrera, Reno Dewina, Rose Mungai, Sergio Olivieri. D4G team: Nobuo Yoshida, Silvia Malgioglio, Haoyu Wu. This note has been cleared by Benu Bidani. The Global Poverty Monitoring Technical Note Series publishes short papers that document methodological aspects of the World Bank’s global poverty estimates. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Global Poverty Monitoring Technical Notes are available at http://iresearch.worldbank.org/PovcalNet/. Contents 1. Introduction ................................................................................................................................. 1 1.1. What is the Multidimensional Poverty Measure? ........................................................ 1 1.2. Methodology, Usage, and Data .................................................................................... 2 2. Revision to the MPM circa 2018: What’s New .......................................................................... 4 2.1. Data source................................................................................................................... 4 2.2. Key results ................................................................................................................... 5 3. Online dashboard and setting up your own weighting for the MPM .......................................... 9 4. References ................................................................................................................................. 11 Appendix ....................................................................................................................................... 12 1. Introduction 1.1. What is the Multidimensional Poverty Measure? The World Bank’s Multidimensional Poverty Measure (MPM) seeks to assess a broader understanding of poverty that takes into account multiple dimensions of well-being beyond just monetary poverty. However, given the goal of estimating the MPM in a standard way for as many countries as possible, data limitations result in a trade-off between the number of dimensions that can be included and the number of countries that have the required harmonized indicators. The World Banks’ MPM, therefore includes education and access to basic infrastructure, which are generally available in household surveys across the world, as additional dimensions beyond the monetary poverty dimension. The World Bank’s measure takes inspiration and guidance from other prominent multidimensional measures, particularly the Multidimensional Poverty Index (MPI) developed by UNDP and Oxford University. The MPM and MPI differ in one important aspect: the MPM includes the monetary poverty dimension, measured as having household income or consumption per capita that is less than $1.90 per day (the International Poverty Line, 2011 PPP). While monetary poverty is strongly correlated with deprivations in other domains, this correlation is far from perfect. The Poverty and Shared Prosperity 2020 report (World Bank, 2020) shows that over a third of those experiencing multidimensional poverty are not captured by the monetary headcount ratio, in line with prior editions of the report (World Bank, 2018). A country’s MPM is at least as high as or higher than monetary poverty, reflecting the additional role of nonmonetary dimensions to poverty and their importance to general well-being. By incorporating the different dimensions, the MPM can present the extent to which these deprivations arise and overlap. Households that are income poor as well as deprived in non-monetary dimensions face worse levels of well-being than households that are only income poor but have good access to services and education. In addition, a focus on non-monetary deprivations for the income poor helps to highlight to policymakers the importance of improving those other aspects of human welfare that the monetary measure may not capture well. It is also useful to measure deprivations in basic 1 services faced by non-income-poor households, as these households face different constraints to well-being. This becomes more important as more people leave extreme poverty while continuing to experience other nonmonetary deprivations. A poverty measure that includes nonmonetary aspects thus highlights deprivations that may otherwise remain hidden. Securing higher living standards for a population becomes more challenging when poverty in all its forms is considered, but it can provide policymakers a roadmap for and a means of monitoring improvements in welfare. The MPM does not contain all the information needed to fully assess well-being in all major dimensions (due to the type of data limitations mentioned above) and it also cannot fully account for the quality of services for those dimensions that are included. Data on quality of services is too demanding on the underlying household surveys and difficult to collect accurately and consistently across countries. Therefore, the indicator of multidimensional poverty considered here is restricted to reporting on dimensions of access and not on the quality of these services. Additional efforts would be needed to collect richer data that includes information on quality across all countries. 1.2. Methodology, Usage, and Data To broaden the poverty measure to include multiple nonmonetary dimensions, one must first select the dimensions, the indicators, and the sufficiency thresholds for each indicator. The MPM maps six indicators into three dimensions of well-being (monetary standard of living, education, and basic infrastructure services). The six indicators are consumption or income, educational attainment, educational enrollment, drinking water, sanitation, and electricity. These indicators are defined as 1-0 variables, and thus incorporate sufficiency thresholds by definition, where “1” means the individual or household is deprived in that indicator (see Table 3 for the detailed indicator definition). Summarizing the information on the number of deprivations into a single index proves useful in making comparisons across populations and, in some cases, across time. However, any aggregation of indicators into a single index invariably involves a decision on how each of the indicators is to be weighted. In the official MPM, dimensions are weighted equally, and within 2 each dimension each indicator is also equally weighted (see Table 3 below). Individuals are considered multidimensionally deprived if they fall short of the threshold in at least one dimension or a combination of indicators equivalent in weight to a full dimension. In other words, households will be considered poor if they are deprived in indicators whose weight adds up to 1/3 or more. Because the monetary dimension is measured using only one indicator, anyone who is income poor is automatically also poor under the broader multidimensional poverty concept. The latest estimates for each indicator in each country are derived from standardized and recent surveys in the World Bank’s Global Monitoring Database, April 2022.1 The latest regional and global estimates are calculated for circa 2018, using household survey data collected within a three-year window from 2015 to 2021 for 123 economies. These harmonized surveys collect information on total household consumption or income for monetary poverty estimation as well as information on a host of other topics, including education enrollment, adult education attainment, and access to basic infrastructure services, which permits the construction of the MPM. However, there is considerable heterogeneity in how the questions are worded, how detailed the response choices are, and how closely they match the standard definitions of access (for example, as defined by the Joint Monitoring Programme for Water Supply and Sanitation). Despite best efforts to harmonize country-specific questionnaires to the standard definition, discrepancies with measures reported elsewhere may arise. The global MPM estimate is reported if it fulfills the same coverage rules applied to the World Bank’s global monetary poverty measures. These coverage rules stipulate that the data needs to be available for at least 50 percent of the global population and at least 50 percent of the population living in low-income and lower-middle-income countries. The regional MPM aggregates are reported if data covers at least 50 percent of the regional population. 1 The Global Monitoring Database (GMD) is a set of harmonized household surveys maintained by the Data for Goals (D4G) team of the Poverty and Equity Global Practice at the World Bank. The GMD is an ex-post harmonization effort based on available multitopic household surveys, including household budget surveys and the Living Standards Measurement Study household surveys. The data are stored on secure servers accessible only to subscribed or approved users. The GMD accounts for most of the welfare aggregates included in PovcalNet in recent years and in the World Bank’s new Poverty and Inequality Platform (PIP) that is replacing PovcalNet (pip.worldbank.org). The Luxembourg Income Study (LIS) data are the other main source of information included in PovcalNet and PIP. 3 The countries included in the circa 2018 MPM reported here are different from those included in the previous report (circa year 2017), preventing meaningful comparisons of regional and global estimates. Although the countries between circa years can also vary for the World Bank’s monetary poverty measures, the practice of lining up survey-year estimates to a common reference year (that ensures that the same number of countries is available in all years) has many fewer assumptions than would be required to undertake this same exercise for the multidimensional poverty measure.2 Therefore, the global and regional monetary poverty measures that the World Bank traditionally reports in PovcalNet (and now in the Poverty and Inequality Platform, PIP) can be meaningfully compared over time. Not only do countries vary in the case of the MPM, but the survey data can overlap for some countries. The MPM estimates published in World Bank (2020) were reported for a circa 2017 reference year that thus includes surveys in the period between 2014 and 2020, which overlap with the 2015 to 2021 period used for the current 2018 reference year. Therefore, for some countries, the same survey-year estimate would be used in both reference years. These limitations hinder the possibility of comparing the regional and global MPM estimates between the two editions. 2. Revision to the MPM circa 2018: What’s New 2.1. Data source The estimates of multidimensional poverty are derived from household surveys included in the World Bank’s Global Monitoring Database. The April 2022 update of the country data can be found in the What’s New of the new Poverty and Inequality Platform (PIP) that replaced PovcalNet (see pip.worldbank.org/publication). The updates include changes in some welfare aggregates for improved harmonization; updates to CPI, national accounts, and population input data; and the addition of a large number of new country-years. The new harmonized surveys include 10 new 2018 surveys in West Africa, new imputed poverty estimates for Nigeria, recent 2020 household survey data (for 10 countries in Latin America and the Caribbean [LAC], 5 countries in Europe and Central Asia [ECA], and 2 countries in East Asia and the Pacific [EAP]), and one recent 2021 2 See PovcalNet (now PIP) and the first part of Chapter 1 in World Bank, 2020. The line-up method uses growth in national accounts to extrapolate and interpolate from the survey years, as described in Prydz et al. (2019) and annex 1.A of World Bank (2020). 4 household survey for Indonesia. The global and regional MPMs are now derived using household surveys collected for circa 2018 (a three-year window ranging from 2015 to 2021) for 123 countries. Rather than only show the 123 countries used to estimate the global and regional MPM (those countries that fall within the circa 2018 window), the full list of 150 countries with available MPM data have been made available for users. This includes the most recent data as well as historical data for previous editions. See section 3 below for information on the updated Multidimensional Poverty Website where users can visualize and download the data. 2.2. Key results The MPM builds on monetary extreme poverty, the focal point of the World Bank’s monitoring of global poverty, along with access to education and basic infrastructure. The MPM is at least as high as or higher than the monetary poverty headcount in a country, to reflect the additional role of nonmonetary dimensions in increasing multidimensional poverty. Figure 1 illustrates this point by plotting the correlation between monetary poverty and multidimensional poverty; the distance from the 45-degree line highlights in which economies the difference between the two figures is greatest. This difference might be as large as 46 percentage points as in Chad (which has a monetary poverty rate of 33.2 percent and an MPM of 79.4 percent) or relatively low at 2.7 percentage points as in Zimbabwe (which has a monetary poverty rate of 39.5 percent and an MPM of 42.2 percent). From the sample of 123 economies in the 2022 MPM, Table 1 reports the aggregate regional and global estimates, weighting each economy by its population in 2018 (the circa year for this edition of the MPM). 5 Figure 1. Correlation between Monetary and Multidimensional Poverty Headcount (circa 2018) Source: Global Monitoring Database, April 2022. Note: The figure shows the relationship between the monetary poverty headcount (horizontal axis) and the multidimensional poverty headcount (vertical axis) for 123 economies. The full list of economies can be found in the annex table. The dashed line is the 45-degree line. 6 Table 1. Monetary and Multidimensional Poverty Headcount, by Region and the World, circa 2018 Monetary Multidimensional poverty, Number of Population Region poverty, headcount ratio headcount ratio economies coverage (%) a (%) (%) East Asia & Pacific 2.5 4.4 14 30 Europe & Central Asia 0.3 2.2 25 89 Latin America & Caribbean 4.0 4.7 14 87 Middle East & North Africa 2.3 2.9 5 51 South Asia 7.8 17.3 5 22 Sub-Saharan Africa 37.2 55.2 35 73 Rest of the World 0.7 1.3 25 78 All regions 9.6 15.0 123 51 b Source: Global Monitoring Database, April 2022. Note: The monetary headcount is based on the international poverty line $1.90. Regional and total estimates are population-weighted averages of survey-year estimates for 123 economies and are not comparable to the monetary poverty measures presented in PovcalNet (which is being replaced by PIP). The multidimensional poverty measure headcount indicates the share of the population in each region defined as multidimensionally poor. Number of economies is the number of economies in each region for which information is available in the window between 2015 and 2021, for a circa 2018 reporting year. The coverage rule applied to the estimates is identical to that used for the World Bank’s global monetary poverty measures (e.g., see annex 1A of World Bank, 2020). Regions without sufficient population coverage are shown in light grey. a. Data coverage differs across regions. The data cover as much as 81 percent of the population in Sub-Saharan Africa and as little as 22 percent of the population in South Asia. The coverage for South Asia is low because no household survey is available for India between 2009 and 2020. Because of the absence of data on China and India, the regional coverage of South Asia and East Asia and Pacific is insufficient. b. The table conforms to both coverage criteria used for the global poverty estimate. The global population coverage is 50.84 percent and in low-income and lower-middle-income countries is 50.67 percent (also see annex 1A of World Bank, 2020). As with monetary poverty, Sub-Saharan Africa experiences the highest levels of deprivation in multidimensional poverty, with more than half of the population multidimensionally poor. Although 23 percent of the population lives in households in which at least one school-age child is not enrolled in school (Table 2), this is the dimension under which the lowest share of individuals is deprived in the region, suggesting some progress for future generations. Table 2 shows important differences when comparing monetary poverty to deprivations in each of the indicators. About a third of those who are multidimensionally deprived are not captured by monetary poverty. The gap is particularly striking between sanitation and monetary poverty in Europe and Central Asia, Latin America and the Caribbean, and the Middle East and North Africa; but it is also large when looking at educational attainment. For example, Latin America and the Caribbean and the Middle East and North Africa show a 2-percentage point difference in their 7 poverty monetary headcount, but larger differences in educational enrollment and sanitation. On the one hand, the share of the population living in households with at least one school-age child not enrolled in school is roughly a third higher in the Middle East and North Africa than in Latin America and the Caribbean (likely related to the negative effects of conflict in the region). On the other hand, the share of population lacking appropriate sanitation is close to 16 percent in Latin America and the Caribbean, more than twice that of the Middle East and North Africa and of Europe and Central Asia. Table 2. Share of population Deprived in Each Indicator, 123 Economies, circa 2018 Educational Educational Drinking Monetary Electricity Sanitation Region attainment enrollment water (%) (%) (%) (%) (%) (%) East Asia & Pacific 2.5 10.2 1.5 6.3 11.1 7.1 Europe & Central Asia 0.3 1.2 1.6 1.7 6.6 4.4 Latin America & 4.0 8.8 1.8 0.8 15.8 2.7 Caribbean Middle East & North 2.3 8.5 2.8 0.5 3.0 1.3 Africa South Asia 7.8 20.5 19.1 14.8 35.5 5.3 Sub-Saharan Africa 37.2 35.7 23.0 48.0 65.0 28.8 Rest of the World 0.7 1.0 0.3 0.0 0.2 0.2 All regions 9.6 12.7 7.4 12.5 21.7 8.5 Source: Global Monitoring Database, April 2022. Note: This table shows the share of population living in households deprived in each indicator of the multidimensional poverty measure. The monetary poverty headcount is based on the international poverty line. Regional and total estimates are population weighted averages of survey-year estimates for 123 economies. Regions without sufficient population coverage are shown in light grey. See Table 1 for a discussion of the coverage rule. The underlying structure of deprivations experienced by the multidimensionally poor is depicted in Figure 2 below. In most countries, there is a large degree of overlap between dimensions. Only a small minority of the multidimensionally poor are deprived in only one dimension, whereas more than a third are simultaneously deprived in all three dimensions. The overlap is highest in Sub- Saharan Africa. A larger overlap between dimensions indicates a larger extent of interdependence, which implies that policy interventions targeted exclusively toward one dimension may not reduce multidimensional poverty and therefore a multipronged approach might be required. 8 Figure 2. Share of individuals in multidimensional poverty, circa 2018 Latin America and the Caribbean Sub-Saharan Africa Middle East and North Africa All Regions Source: Global Monitoring Database, April 2022. Note: The figure shows the overlap in different dimensions of the multidimensional poverty measure at the household level. It shows the share of households (in percent) deprived in all indicators and in each combination of the monetary, education, and basic infrastructure dimensions. Only Latin America and the Caribbean, the Middle East and North Africa, and Sub-Saharan Africa are shown because these regions have sufficient population coverage (over 50 percent). 3. Online dashboard and setting up your own weighting for the MPM A full list of 150 countries with available MPM data will be available to visualize and download on the Multidimensional Poverty Website, along with historical data for previous MPM editions. Users will also be able to visualize and explore the data through an interactive dashboard (Figure 3). The dashboard also allows users to test the sensitivity of the MPM to different assumptions and priorities by changing the weights of dimensions and deprivation thresholds. 9 Figure 3. Multidimensional Poverty Measure (MPM) Dashboard Many countries now track multidimensional poverty at the national and subnational level as a complement to monetary poverty. Exploring how to calculate and properly weigh the different components within the measure may help create a tool that is more tailored to country needs. In the MPM, the three dimensions are weighted equally, and within each dimension each indicator is also equally weighted (see Table 3 for an overview). Individuals are considered multidimensionally deprived if they fall short of the threshold in at least one dimension or in a combination of indicators equivalent in weight to a full dimension. In other words, households will be considered poor if they are deprived in indicators whose weight adds up to 1/3 or more. With this update, users will continue to be able to modify the weights being used through the updated multidimensional poverty dashboard. Different views on what constitutes well-being and deprivation can thus continue to be accommodated; users can reflect their own perspectives on deprivation and relative importance of each dimension or indicators within dimensions. 10 Table 3. Multidimensional Poverty Measure Indicators and Weights Dimension Parameter Weight Monetary Daily consumption or income is less than US$ 1.90 per person. 1/3 At least one school-age child up to the age of grade 8 is not enrolled 1/6 in school. Education No adult in the household (age of grade 9 or above) has completed 1/6 primary education. The household lacks access to limited-standard drinking water. 1/9 Access to basic The household lacks access to limited-standard sanitation. 1/9 infrastructure The household has no access to electricity. 1/9 Source: World Bank, 2018. 4. References Prydz, Espen Beer, Dean M. Jolliffe, Christoph Lakner, Daniel Gerszon Mahler, and Prem Sangraula. “National Accounts Data used in Global Poverty Measurement.” Global Poverty Monitoring Technical Note 8. Washington, DC: World Bank. 2019. https://ideas.repec.org/p/wbk/wbgpmt/8.html. World Bank. 2018. Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle. https://www.worldbank.org/en/publication/poverty-and-shared-prosperity-2018 World Bank. 2020. Poverty and Shared Prosperity 2020: Reversals of Fortune. https://www.worldbank.org/en/publication/poverty-and-shared-prosperity 11 5. Annex Deprivation rate (share of population) Multi- dimensional Survey Survey Welfare Educational Educational Drinking Economy Monetary Electricity Sanitation poverty year name type attainment enrollment water (%) (%) (%) headcount ratio (%) (%) (%) (%) Albania 2018 HBS c 0.1 0.2 - 0.1 6.6 9.6 0.4 Angola 2018 IDREA c 49.9 29.8 27.4 52.6 53.6 32.1 58.0 Argentina 2020 EPHC-S2 i 1.6 1.4 0.7 0.0 0.8 0.1 1.6 Armenia 2020 ILCS c 0.4 0.1 1.9 0.0 0.8 1.6 0.4 Australia 2018 SIH-LIS I 0.5 1.7 - 0.0 0.0 - 2.2 Austria 2020 EU-SILC i 0.7 0.0 - 0.0 0.8 0.7 0.7 Bangladesh 2016 HIES c 14.3 22.0 8.4 23.6 54.5 2.8 21.2 Belarus 2019 HHS c 0.0 0.0 - - 4.6 3.3 3.2 Belgium 2020 EU-SILC i 0.2 0.6 - 0.0 0.7 0.4 0.8 Benin 2018 EHCVM c 19.2 50.2 31.5 54.3 80.0 22.1 53.1 Bhutan 2017 BLSS c 1.5 40.8 4.1 1.9 14.3 0.4 3.9 Bolivia 2020 EH i 4.4 14.1 2.1 4.4 17.9 6.6 7.8 Botswana 2015 BMTHS c 14.1 8.2 4.2 35.5 52.0 3.7 20.0 PNADC- Brazil 2019 i 4.9 15.0 0.4 0.2 34.3 1.8 5.6 E1 Bulgaria 2020 EU-SILC i 0.9 0.6 - 0.0 13.2 7.4 1.4 Burkina Faso 2018 EHCVM c 33.7 56.4 50.9 47.2 69.6 19.7 61.4 Cabo Verde 2015 IDRF c 3.4 11.7 2.7 9.9 30.2 11.1 6.5 Chad 2018 EHCVM c 33.2 69.0 34.9 90.0 87.0 34.8 79.4 Chile 2020 CASEN i 0.7 3.4 3.3 - 1.4 0.8 1.0 Colombia 2020 GEIH i 6.6 5.4 2.9 1.5 8.8 3.0 7.1 Costa Rica 2020 ENAHO i 2.1 3.9 0.5 0.1 1.4 0.1 2.1 Côte d'Ivoire 2018 EHCVM c 9.2 48.6 30.4 18.1 64.4 20.7 36.4 12 Croatia 2020 EU-SILC i 0.3 0.3 - 0.0 1.4 0.9 0.6 Cyprus 2020 EU-SILC i 0.2 1.1 - 0.0 0.4 0.5 1.2 Czech Republic 2020 EU-SILC i 0.0 0.0 - 0.0 0.3 0.2 0.0 Denmark 2020 EU-SILC i 0.5 0.5 - 0.0 0.5 2.0 0.9 Djibouti 2017 EDAM c 17.0 30.1 18.0 34.2 45.4 7.1 27.9 Dominican ECNFT- 2020 i 0.8 13.1 8.8 0.6 5.7 5.2 2.7 Republic Q03 Ecuador 2020 ENEMDU i 6.5 3.8 2.3 2.8 4.8 4.6 6.9 Egypt, Arab Rep. 2017 HIECS c 3.8 10.6 4.2 0.5 3.2 0.8 4.7 El Salvador 2019 EHPM i 1.3 24.8 3.9 2.1 9.4 3.1 4.3 Estonia 2020 EU-SILC i 0.6 0.1 - 0.0 3.9 5.2 0.7 Ethiopia 2015 HICES c 30.8 66.7 31.2 64.1 95.9 42.7 73.5 Fiji 2019 HIES c 1.8 0.6 1.9 4.5 5.1 12.0 2.1 Finland 2020 EU-SILC i 0.0 0.9 - 0.0 0.3 0.3 1.0 France 2019 EU-SILC i 0.1 1.6 - 0.0 0.5 0.5 1.7 Gabon 2017 EGEP c 3.4 11.3 7.9 8.6 68.2 11.5 9.1 Gambia, The 2015 IHS c 10.3 29.9 6.1 8.0 58.2 8.2 15.5 Georgia 2020 HIS c 4.2 0.1 1.2 0.0 9.5 5.7 4.3 GSOEP- Germany 2018 I 0.1 2.7 2.5 0.0 0.0 - 0.2 LIS Ghana 2016 GLSS-VII c 12.7 15.1 9.0 19.5 79.9 40.8 23.2 Greece 2020 EU-SILC i 1.1 2.0 - 0.0 0.3 0.1 3.0 Guinea 2018 EHCVM c 23.2 61.3 25.0 56.4 71.1 21.0 54.3 Guinea-Bissau 2018 EHCVM c 24.7 41.0 30.1 42.1 63.0 21.6 47.7 Honduras 2019 EPHPM i 14.7 10.1 10.0 6.7 5.8 5.7 16.6 Hungary 2020 EU-SILC i 0.3 0.0 - 0.0 1.6 1.6 0.4 Iceland 2018 EU-SILC i 0.2 0.0 - 0.0 0.0 0.2 0.2 SUSENA Indonesia 2021 c 2.2 3.8 1.2 0.8 11.6 6.5 2.8 S 13 Iran, Islamic Rep. 2019 HEIS c 0.6 4.4 0.8 0.0 1.9 1.6 0.7 Irfieland 2019 EU-SILC i 0.0 0.7 - 0.0 0.2 0.2 0.7 Israel 2018 HES-LIS I 0.3 0.7 - 0.0 0.0 - 0.9 Italy 2019 EU-SILC i 1.6 1.3 - 0.0 0.8 0.7 2.9 Kazakhstan 2018 HBS c 0.0 0.0 - 0.0 0.5 0.7 0.0 Kenya 2015 IHBS c 37.1 22.5 6.1 56.9 69.0 32.2 50.1 HIES- Korea, Rep. 2016 I 0.1 0.0 - 0.0 0.0 - 0.1 FHES-LIS Kiribati 2019 HIES c 1.3 0.6 6.0 - 83.8 17.1 20.7 Kosovo 2017 HBS c 0.4 0.5 23.6 0.2 1.4 0.7 0.8 Kyrgyz Republic 2020 KIHS c 1.1 0.0 0.1 0.0 0.1 4.6 1.1 Lao PDR 2018 LECS c 10.0 12.8 5.7 1.7 22.5 7.8 12.8 Latvia 2020 EU-SILC i 0.5 0.1 - 0.0 7.9 8.9 0.6 Lesotho 2017 CMSHBS c 27.2 18.1 4.8 58.7 55.1 13.7 36.6 Liberia 2016 HIES c 44.4 30.5 54.1 79.7 61.8 25.7 64.0 Lithuania 2020 EU-SILC i 0.6 0.4 - 0.0 7.6 7.2 1.0 Luxembourg 2020 EU-SILC i 0.4 0.5 - 0.0 0.1 0.1 0.9 Macedonia, FYR 2019 SILC-C i 3.4 0.4 - 0.0 5.1 - 3.7 Malawi 2019 IHS-IV c 73.5 54.3 3.7 88.8 75.1 11.4 80.3 Malaysia 2016 HIS i 0.0 0.7 0.6 0.6 13.2 1.6 0.2 Maldives 2019 HIES c 0.0 0.0 1.9 1.9 4.8 0.0 0.0 Mali 2018 EHCVM c 16.3 66.6 28.2 23.9 51.9 23.8 44.1 Malta 2020 EU-SILC i 0.3 0.1 - 0.0 0.1 0.0 0.4 Marshall Islands 2019 HIES c 0.8 1.0 3.4 1.1 29.0 1.7 1.0 Mauritius 2017 HBS c 0.2 7.2 0.2 0.2 - - 0.4 ENIGHN Mexico 2020 i 3.1 3.8 2.5 0.2 1.3 3.9 3.4 S Moldova 2019 HBS c 0.0 2.5 0.5 0.0 25.5 16.9 0.8 Mongolia 2018 HSES c 0.5 2.7 3.2 0.2 10.4 13.0 1.7 Myanmar 2017 MLCS c 1.4 28.0 6.8 50.9 9.7 20.6 15.0 14 Namibia 2015 NHIES c 13.8 11.3 6.1 53.8 68.3 9.2 26.3 Netherlands 2020 EU-SILC i 0.2 1.6 - 0.0 0.0 0.1 1.8 Niger 2018 EHCVM c 41.4 79.7 28.0 78.7 85.2 37.5 78.5 Nigeria 2018 LSS c 39.1 17.6 20.3 39.4 44.9 27.5 47.3 Norway 2020 EU-SILC i 0.3 1.7 - 0.0 0.0 0.5 2.0 Pakistan 2018 HIES c 3.6 21.1 28.8 9.3 24.8 6.5 16.0 Paraguay 2020 EPH i 0.8 4.9 2.0 0.3 6.8 1.6 1.3 Peru 2020 ENAHO i 4.4 4.8 1.2 3.7 11.5 5.5 5.6 Philippines 2015 FIES i 6.1 4.0 0.0 9.1 16.4 9.7 7.9 Poland 2019 HBS c 0.0 0.0 0.4 0.0 1.0 0.1 0.0 Portugal 2020 EU-SILC i 0.1 1.5 - 0.0 0.6 0.6 1.7 Romania 2018 HBS c 0.0 0.2 1.8 0.1 18.0 1.0 0.1 Russian 2020 HBS c 0.0 0.9 0.7 5.1 7.7 8.6 5.0 Federation Rwanda 2016 EICV-V c 56.5 36.9 4.3 64.0 28.1 24.5 61.1 Samoa 2018 HIES c 8.1 0.1 10.5 0.4 0.6 1.3 8.1 Sao Tome and 2017 IOF c 25.6 19.5 4.3 31.2 62.0 8.2 33.8 Principe Senegal 2018 EHCVM c 7.6 42.0 31.9 26.6 37.4 15.2 31.5 Serbia 2019 HBS c 0.0 1.7 0.7 0.1 1.5 0.1 0.2 Seychelles 2018 HBS i 0.5 0.4 - 0.0 0.2 5.5 0.8 Sierra Leone 2018 SLIHS c 43.0 28.7 18.7 68.7 87.2 33.8 61.7 Slovak Republic 2020 EU-SILC i 0.1 0.0 - 0.0 1.0 0.7 0.1 Slovenia 2020 EU-SILC i 0.0 0.0 - 0.0 0.1 0.1 0.0 Somalia 2017 SHFS-W2 c 68.6 59.2 56.3 50.6 39.4 11.8 82.6 South Sudan 2016 HFS-W3 c 76.5 39.3 62.2 - 88.1 13.9 87.5 Spain 2020 EU-SILC i 0.9 2.7 - 0.0 0.4 0.2 3.6 Sri Lanka 2016 HIES c 0.9 3.8 4.0 2.5 0.8 12.5 1.4 Swaziland 2016 HIES c 29.1 10.7 0.3 35.7 46.5 27.9 35.1 Sweden 2020 EU-SILC i 0.5 2.0 - 0.0 0.0 0.1 2.4 15 Switzerland 2019 EU-SILC i 0.2 0.0 - 0.0 0.1 0.1 0.2 HSITAFI Tajikistan 2015 c 4.1 0.3 26.8 2.0 3.5 39.4 5.1 EN Tanzania 2018 HBS c 49.4 13.2 19.5 44.3 71.5 29.2 57.8 Thailand 2020 SES c 0.0 13.4 0.5 0.1 0.2 0.5 0.2 Togo 2018 EHCVM c 24.1 32.7 14.0 47.4 83.7 25.3 44.6 Tonga 2015 HIES c 1.0 1.9 0.8 8.3 0.4 0.1 1.0 NSHBCS Tunisia 2015 c 0.2 20.2 2.1 0.2 6.5 2.1 1.6 L Turkey 2019 HICES c 0.4 3.3 3.0 0.0 5.3 0.1 0.6 Tuvalu 2010 HIES c 3.3 4.5 6.1 9.2 11.5 2.4 3.9 FIDES- Taiwan, China 2016 I 0.1 0.9 1.2 0.0 0.0 - 0.1 LIS Uganda 2019 UNHS c 41.0 31.4 11.8 41.3 71.1 23.7 51.3 Ukraine 2020 HLCS c 0.0 1.6 - 0.0 12.4 0.0 1.7 United Kingdom 2016 EU-SILC i 0.7 0.5 - 0.0 0.4 0.6 1.2 CPS- United States 2019 ASEC- I 0.9 0.2 - 0.0 0.0 - 1.1 LIS Uruguay 2019 ECH i 0.1 2.0 0.7 0.1 1.0 0.5 0.1 Vanuatu 2019 NSDP c 8.6 25.7 13.4 1.4 43.0 11.8 14.4 Vietnam 2018 VHLSS c 1.8 11.8 1.7 0.4 11.1 4.7 3.0 West Bank and 2016 PECS c 0.8 1.2 5.8 0.0 0.1 3.2 0.9 Gaza LCMS- Zambia 2015 c 58.7 24.4 30.4 69.2 60.0 34.4 64.9 VII Zimbabwe 2019 PICES c 39.5 0.9 6.0 38.0 38.3 19.3 42.2 Source: Global Monitoring Database, April 2022. Note: Estimates are based on harmonized household surveys in 123 economies, latest data after 2009, that are part of the Global Monitoring Database, Data for Goals, Poverty and Equity Global Practice, World Bank, Washington, DC. The definitions 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 US dollars, is less than US$1.90 per person per day. The estimates in this table for Australia, Canada, Germany, Israel, Japan, Korea Rep., and the United States are based on the microdata available from the Luxembourg Income Study. Educational attainment: a household is deprived if no adult (grade 9 equivalent age or older) has completed primary education. Educational enrollment: a household is deprived if at least one school-age child up to the (equivalent) age of grade 8 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 limited- standard sanitation. Drinking water: a household is deprived if it does not have access to limited-standard drinking water. The data reported refer to the share of people living in households deprived according to each indicator. – = not available. 16