Global Poverty Monitoring Technical Note 17 March 2021 Update to the Multidimensional Poverty Measure: What’s New Minh Cong Nguyen, Haoyu Wu, Christoph Lakner, and Marta Schoch March 2021 Keywords: Multidimensional Poverty Measure, March 2021. Development Data Group Development Research Group Poverty and Equity Global Practice Group GLOBAL POVERTY MONITORING TECHNICAL NOTE 17 Abstract The March 2021 update to the Multidimensional Poverty Measure (MPM) involves changes to the data underlying the multidimensional poverty estimates based on the Global Monitoring Database (GMD). This update reports new estimates for circa 2017, revising the estimates that were first published in October 2020. Some changes reflect the availability of more recent survey data for the economies already part of the GMD. Other changes are due to the addition of 9 new economies to the dataset, the release of new population data and new monetary poverty estimates. Notably, this update accompanies the launch of an online dashboard containing the data and results presented in this document. This includes an online tool that allows users to modify the weights used when aggregating the different indicators in the MPM headcount ratio. All authors are with the World Bank. Corresponding author: Minh C. Nguyen (mnguyen3@worldbank.org). This work could not be completed without the contributions from the Data4Goals, regional and country teams. Regional teams: Aziz Atamanov, Carolina Diaz-Bonilla, David Newhouse, Hernan Winkler, Ifeanyi Nzegwu Edochie, Ikuko Uochi, Jose Montes, Laura Liliana Moreno Herrera, Reno Dewina, Rose Mungai. 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/. GLOBAL POVERTY MONITORING TECHNICAL NOTE 17 Contents 1. Introduction ............................................................................................................................. 1 1.1. What is the Multidimensional Poverty Measure? ........................................................... 1 1.2. Methodology and Usage ................................................................................................. 1 2. Revision to the MPM circa 2017: what’s new ........................................................................ 2 2.1. Data source...................................................................................................................... 2 2.2. Key results ...................................................................................................................... 3 3. Setting up your own weighting for MPM ............................................................................... 8 4. MPM circa 2017 – corrections to previously published estimates ......................................... 8 5. References ............................................................................................................................. 11 Appendix ....................................................................................................................................... 12 1. Introduction 1.1. What is the Multidimensional Poverty Measure? The Multidimensional Poverty Measure (MPM) seeks to understand poverty beyond just a monetary dimension by including access to education and basic infrastructure along with the monetary headcount ratio at the $1.90 poverty line. 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 but differs from them in one important aspect: it includes Monetary poverty (measured as having a daily consumption less than $1.90 in 2011 PPP) as one of the dimensions. While monetary poverty is strongly correlated with deprivations in other domains, this correlation is far from perfect. The Poverty and Shared Prosperity 2020 (World Bank, 2020) report shows that over a third of those experiencing multidimensional poverty are not captured by the monetary headcount ratio, in line with the findings of the previous edition of the report (World Bank, 2018). A country’s MPM is at least as high as or higher than the monetary poverty, reflecting the additional role of nonmonetary dimensions in increasing multidimensional poverty and their importance to general well-being. 1.2. Methodology and Usage To construct the MPM, six indicators (consumption or income, educational attainment, educational enrollment, drinking water, sanitation, and electricity), are mapped into three dimensions of well- being (monetary standard of living, education, and basic infrastructure services). The latest estimates for each indicator are derived from standardized and recent surveys in the World Bank’s Global Monitoring Database, March 2021.1 The latest estimates for all the regions are available for circa 2017, using household survey data collected within a three-years window from 2014 to 2020 from 124 economies. 1 The Global Monitoring Database (GMD) is a set of harmonized household surveys maintained by the Data for Goals 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. 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. The Luxembourg Income Study (LIS) data are the other main source of information included in PovcalNet. 1 The countries included in the circa 2017 MPM reported here are not the same as those included in the previous report (circa year 2013), preventing meaningful comparisons of regional and global estimates. The same is true for the monetary poverty measures that the World Bank has traditionally been reporting (e.g. see PovcalNet and the first part of Chapter 1 in World Bank, 2020). However, in the case of the monetary poverty measures, lining up survey-year estimates to a common reference year ensures that the same numbers of countries are available in all years, although it requires additional assumptions.2 Moreover, the estimates published in World Bank (2018) were reported for a circa 2013 reference year, including surveys in the period between 2010 and 2016, which overlaps with the 2014 to 2020 period used for the 2017 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. Summarizing the information on the number of deprivations into a single index proves useful in making comparisons across populations and 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 MPM, dimensions are weighted equally, and within 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 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. 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. 2. Revision to the MPM circa 2017: what’s new 2.1. Data source The estimates of multidimensional poverty are largely derived from household surveys included in the World Bank’s GMD for circa 2017 (ranging from 2014 to 2020). These surveys account for 2 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). 2 most of the welfare aggregates included in PovcalNet in recent years. 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 could arise. 2.2. Key results The MPM builds on monetary extreme poverty, which remains the focal point of the World Bank’s monitoring of global poverty and is included as one of the MPM dimensions, 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 measures is greatest. This difference might be as large as 43 percentage points (Ethiopia, which has a monetary poverty rate of 30.8% and an MPM of 73.5%) or relatively low as in Rwanda (4.5 percentage points; monetary poverty rate of 56.5% and an MPM of 61.0%). From the sample of 124 economies, Table 1 reports the aggregate regional and global estimates, weighting each economy by its population in 2017 (the circa year for the MPM). As with monetary poverty, Sub-Saharan Africa experiences the highest levels of deprivations in multidimensional poverty, with more than half of the population multidimensionally poor. Although 18 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. 3 Figure 1 Correlation between Monetary and Multidimensional Poverty Headcount, circa 2017 Source: Global Monitoring Database, March 2021. Note: The figure shows the relationship between the monetary poverty headcount (horizontal axis) and the multidimensional poverty headcount (vertical axis) for 124 economies. The full list of economies can be found in the annex table. The dashed line is the 45-degree line. 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 4 Caribbean and the Middle East and North Africa show a difference of less than 1 percentage point in their 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 more than three times 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 Middle East and North Africa). On the other hand, the share of population lacking appropriate sanitation is close to 18 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 1 Monetary and Multidimensional Poverty Headcount, by Region and the World, circa 2017 Region Monetary Multidimensional Number of Population poverty, poverty, headcount economies coverage headcount ratio (%) (%)a ratio (%) East Asia and Pacific 3.1 5.4 10 30 Europe and Central Asia 0.3 1.7 26 90 Latin America and the Caribbean 3.5 4.9 16 90 Middle East and North Africa 4.2 6.9 6 58 South Asia 8.3 20.1 5 22 Sub-Saharan Africa 37.9 54.2 36 81 Rest of the World 0.7 1.3 25 79 All regions 10.4 16.1 124 53b Source: Global Monitoring Database, March 2021. Note: The monetary headcount is based on the international poverty line. Regional and total estimates are population-weighted averages of survey-year estimates for 124 economies and are not comparable to the monetary poverty measures presented in PovcalNet. 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 2014 and 2020, for a circa 2017 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 90 percent of the population in Latin America and the Caribbean 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 2014 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 53 percent and in low-income and lower-middle-income countries it is 53 percent (also see annex 1A of World Bank, 2020). 5 Table 2 Share of population Deprived in Each Indicator, 124 Economies, circa 2017 Region Monetary Educational Educational Electricity Sanitation Drinking (%) attainment enrollment (%) (%) water (%) (%) (%) East Asia and Pacific 3.1 9.3 1.7 6.7 15.9 8.2 Europe and Central Asia 0.3 0.9 2.1 2.3 8.1 4.5 Latin America and the 3.5 9.8 2.1 1.5 18.0 3.1 Caribbean Middle East and North Africa 4.2 9.5 8.1 4.7 7.9 2.9 South Asia 8.3 27.7 19.0 14.8 38.8 5.8 Sub-Saharan Africa 37.9 32.7 20.5 44.3 63.2 29.6 Rest of the World 0.7 0.7 0.0 0.4 0.2 All regions 10.4 13.4 10.0 12.6 27.5 9.4 Source: Global Monitoring Database, March 2021. 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 124 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 the deprivation experienced by the multidimensionally poor is depicted in Figure 2 for the regions with sufficient population coverage. 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 (Figure 2C). 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. 6 Figure 2 Share of individuals in multidimensional poverty, 124 countries, circa 2017 Source: Global Monitoring Database, March 2021. 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. 7 3. Setting up your own weighting for MPM 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. Because the monetary dimension is measured using only one indicator, anyone who is income poor is automatically also poor under the multidimensional poverty measure. With this update, we are presenting an online tool that allows users to modify the weights being used. Different views on what constitutes well-being and deprivation can this be accommodated; users can reflect their own perspectives on deprivation and relative importance of each dimension or indicators within dimensions. 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 Education At least one school-age child up to the age of grade 8 is not enrolled in school. 1/6 No adult in the household (age of grade 9 or above) has completed primary 1/6 education. Access to basic 1/9 infrastructure The household lacks access to limited-standard drinking water. The household lacks access to limited-standard sanitation. 1/9 The household has no access to electricity. 1/9 Source: World Bank, 2018. 4. MPM circa 2017 – corrections to previously published estimates This update presents the first revision to the MPM estimates for a circa 2017 reporting year. These estimates were first published in the Poverty and Shared Prosperity report 2020 (World Bank, 2020). The differences between these two versions can be explained by the release of more recent survey data for countries already in the MPM database and by the addition of 9 new countries. Minor differences are also to be expected because of the release of new population data between the publication of the Poverty and Shared Prosperity report and the data used for this update. 8 Additional differences are due to an error in the calculation of the estimates for the world (“All regions”) that were originally published in World Bank (2020).3 In particular, the MPM for the world (“All regions”) should have been calculated as the population-weighted average of all countries with MPM data, and was instead wrongly calculated taking the weighted average of the regional numbers using full population totals (which include countries without an MPM estimate). The difference are the missing countries: the MPM should be computed only over the countries with data (not assuming the regional average for the missing countries). The error only affects the estimate for the world, while the regional numbers are unaffected. Tables 4 and 5 and Figure 3 show the original and corrected estimates, using the September 2020 vintage of the MPM database throughout. The other MPM results in World Bank (2020) are unaffected. The differences with the estimates shown in Table 1 and 2 and Figure 2 above are explained by new data becoming available in the March 2021 vintage of the database. Table 4 Correction to Table 1.1 in World Bank (2020): Monetary and Multidimensional Poverty Headcount, by Region and the World, circa 2017 Monetary poverty, Multidimensional poverty, Region headcount ratio (%) headcount ratio (%) Original Corrected Original Corrected East Asia and Pacific 4.1 4.1 6.2 6.2 Europe and Central Asia 0.3 0.3 1.6 1.6 Latin America and the Caribbean 3.4 3.4 6.8 6.8 Middle East and North Africa 4.2 4.2 6.8 6.8 South Asia 8.1 8.1 15.0 15.0 Sub-Saharan Africa 38.5 38.5 53.8 53.8 Rest of the World 0.8 0.8 1.4 1.4 All regions 9.1 10.4 14.0 15.5 Source: Global Monitoring Database, September 2020. Note: This table corrects Table 1.1 in World Bank (2020). The other columns in the table are unaffected. 114 economies are included. The global population coverage is 50 percent and in low-income and lower-middle-income countries it is 51 percent. 3 The estimates published in the report have now been corrected as described in this section. The current version of the report (with the corrected values) can be found here: https://www.worldbank.org/en/publication/poverty-and- shared-prosperity. 9 Table 5 Correction to Table 1.2 in World Bank (2020): Share of Population Deprived in Each Indicator, circa 2017 Educational Educational Electricity Sanitation Drinking attainment enrollment (%) (%) water (%) Region (%) (%) Org. Corr. Org. Corr. Org. Corr. Org. Corr. Org. Corr. East Asia and Pacific 7.0 7.0 3.0 3.0 4.2 4.2 13.6 13.6 10.2 10.2 Europe and Central 0.8 0.8 2.6 2.6 1.6 1.6 8.8 8.8 3.3 3.3 Asia Latin America and the 9.3 9.3 2.6 2.6 1.7 1.7 18.8 18.8 3.1 3.1 Caribbean Middle East and North 9.4 9.4 8.1 8.1 4.7 4.7 7.8 7.8 2.9 2.9 Africa South Asia 31.4 31.4 6.4 6.4 15.2 15.2 37.3 37.3 5.8 5.8 Sub-Saharan Africa 32.3 32.3 19.5 19.5 46.2 46.2 59.9 59.9 29.3 29.3 Rest of the World 0.8 0.8 0.0 0.0 0.0 0.2 0.2 0.2 0.2 All regions 15.4 13.2 5.9 7.0 11.7 12.4 23.6 22.7 9.0 9.4 Source: Global Monitoring Database, September 2020. Note: This table corrects Table 1.2 in World Bank (2020). The change in the monetary dimension is shown in Table 4. Figure 3 Correction to Figure 1.13d in World Bank (2020): Deprivation in Multiple Dimensions, circa 2017 a) Original b) Corrected 10 5. 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. World Bank. 2020. Poverty and Shared Prosperity 2020: Reversals of Fortune. 11 Appendix Table A.1 Individuals in households deprived in each indicator, 124 economies, circa 2017 Deprivation rate (share of population) Multi- dimensional Survey Welfare Educational Educational Economy Survey name Monetary Electricity Sanitation Drinking poverty year type attainment enrollment (%) (%) (%) water (%) headcount (%) (%) ratio (%) Albania 2017 HBS c 1.3 0.3 – 0.1 7 9.3 1.5 Angola 2018 IDREA c 49.9 29.8 27.4 52.6 53.6 32.1 58 Argentina 2019 EPHC-S2 i 1.5 1.5 0.6 0 0.4 0.3 1.5 Armenia 2019 ILCS c 1.1 0 2 0 2.4 1.1 1.1 Australia 2014 SIH-LIS i 0.7 0.3 – 0 – 0 1 Austria 2018 EU-SILC i 0.6 0 – – 0.5 0.2 0.6 Bangladesh 2016 HIES c 14.3 22 8.4 23.6 54.5 2.8 21.2 Belarus 2019 HHS c 0 0 – – 4.6 3.3 3.2 Belgium 2018 EU-SILC i 0.2 0.5 – – 0.7 0.3 0.7 Benin 2015 EMICOV c 49.6 61.6 25.5 69 70.7 26.9 71.8 Bhutan 2017 BLSS c 1.5 40.8 4.1 1.9 14.3 0.4 3.9 Bolivia 2019 EH i 3.2 13.2 2.2 4.9 16.3 7.4 6.6 Botswana 2015 BMTHS c 14.1 8.2 4.2 35.5 52 3.7 20 Brazil 2019 PNADC-E1 i 4.6 16 0.4 0.2 34.2 1.7 5.3 Bulgaria 2018 EU-SILC i 0.9 0.8 – 0 13.7 7.9 1.5 Burkina Faso 2014 EMC c 43.8 64.7 58 85.2 63.3 20.6 74.8 Cabo Verde 2015 IDRF c 3.4 11.7 2.7 9.9 30.2 11.1 6.5 Cameroon 2014 ECAM-IV c 26 24.4 15.9 1.2 38.9 23.2 37.7 Canada 2017 CIS-LIS i 0.3 0.1 – 0 – 0 0.4 Chile 2017 CASEN i 0.3 4 0.4 0.3 0.6 0.1 0.4 Colombia 2019 GEIH i 4.9 5.1 2.8 1.3 8.2 2.4 5.5 Comoros 2014 EESIC c 19.1 15.3 7.3 28.5 67.2 6.4 26.7 Costa Rica 2019 ENAHO i 1 4.5 0.5 0.3 1.5 0.4 1.1 12 Côte d'Ivoire 2015 ENV c 29.8 53.2 25.6 37.4 59.5 23.3 50.8 Croatia 2018 EU-SILC i 0.5 0.3 – 0 1.1 0.9 0.7 Cyprus 2018 EU-SILC i 0 1.3 – 0 0.5 0.5 1.3 Czech Republic 2018 EU-SILC i 0 0 – 0 0.4 0.2 0 Denmark 2018 EU-SILC i 0.6 0.3 – 0 0.3 1.8 0.9 Djibouti 2017 EDAM c 17 30.1 18 34.2 45.4 7.1 27.9 Dominican Republic 2019 ECNFT-Q03 i 0.6 12.4 6.3 0.7 6.5 5.9 2.2 Ecuador 2019 ENEMDU i 3.6 3.9 2.9 1.4 3.6 4.3 4.2 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 2018 EU-SILC i 0.5 0 – 0 4.7 5.8 0.5 Ethiopia 2015 HICES c 30.8 66.7 31.2 64.1 95.9 42.7 73.5 Finland 2018 EU-SILC i 0.1 1 – 0 0.3 0.3 1.1 France 2018 EU-SILC i 0.1 1.6 – 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 58.2 8.2 15.5 Georgia 2019 HIS c 3.8 0 0.7 0 1.6 7 3.8 Germany 2016 GSOEP-LIS i 0.1 0.3 – 0 – 0 0.4 Ghana 2016 GLSS-VII c 12.7 15.1 9 19.5 79.9 40.8 23.2 Greece 2018 EU-SILC i 0.7 1.8 – 0 0.3 0.4 2.4 Guatemala 2014 ENCOVI i 8.8 24.8 18.3 16.5 46.7 8.4 21.6 Honduras 2019 EPHPM i 14.7 10.1 10 6.7 5.8 5.7 16.6 Hungary 2018 EU-SILC i 0.4 0 – 0 3 2.8 0.4 Iceland 2017 EU-SILC i 0.2 0 – 0 0 0.2 0.2 Indonesia 2018 SUSENAS c 3.6 5 1.6 1.5 22.9 8.9 5 Iran, Islamic Rep. 2018 HEIS c 0.5 4.5 1 0 2.2 1.8 0.7 Ireland 2017 EU-SILC i 0.2 0.4 – 0 0.3 0.1 0.6 Israel 2016 HES-LIS i 0 0.3 – 0 – 0 0.3 Italy 2017 EU-SILC i 1.6 1.3 – 0 0.6 0.5 2.9 Kazakhstan 2018 HBS c 0 0 – 0 0.5 0.7 0 Kenya 2015 IHBS c 37.1 22.5 6.1 56.9 69 32.2 50.1 13 Korea, Rep. 2016 HIES-FHES-LIS i 0 0.2 – 0 – 0 0.2 Kosovo 2017 HBS c 0.4 0.5 23.6 0.2 1.4 0.7 0.8 Kyrgyz Republic 2019 KIHS c 0.6 0 – 48.2 0.2 7.8 0.6 Lao PDR 2018 LECS c 10 12.8 5.7 1.7 23.7 7.8 12.8 Latvia 2018 EU-SILC i 0.6 0.2 – 0 8.8 10.2 0.8 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 Lithuania 2018 EU-SILC i 1 0.4 – 0 9.9 9.5 1.4 Luxembourg 2018 EU-SILC i 0.6 0.4 – 0 0.1 0.3 0.9 Macedonia, FYR 2018 SILC-C i 3.4 0.4 – 0 5.1 0 3.7 Malawi 2016 IHS-IV c 69.2 56.2 3.1 6.7 48.1 12.9 75.2 Malaysia 2015 HIS i 0 0.7 0.6 0.6 13.2 1.6 0.2 Maldives 2016 HIES c 0 2.2 1.5 0 0.4 0.9 0 Malta 2018 EU-SILC i 0.2 0.1 – 0 0.1 0.1 0.3 Mauritania 2014 EPCV c 6 54.3 8.3 54.1 49.3 38.6 45.5 Mauritius 2017 HBS c 0.2 7.2 0.2 0.2 – – 0.4 Mexico 2018 ENIGHNS i 1.7 4.3 2.5 0.5 6.2 4.2 3.3 Moldova 2018 HBS c 0 0.1 0.4 0 26.7 17.2 0.3 Mongolia 2018 HSES c 0.5 2.7 3.2 0.2 10.4 13 1.7 Montenegro 2014 HBS c 0 0.1 – 1.4 2.5 1.2 1.2 Mozambique 2014 IOF c 63.7 54.9 33.3 14.6 71.3 41.1 73.2 Myanmar 2017 MLCS c 1.4 28 6.8 50.9 9.7 20.6 15 Namibia 2015 NHIES c 13.8 11.3 6.1 53.8 68.3 9.2 26.3 Netherlands 2018 EU-SILC i 0.4 1.2 – 0 0 0.1 1.6 Nicaragua 2014 EMNV i 3.4 14.1 8.1 20 42.7 12.5 15.2 Niger 2014 ECVMA c 45.4 70.6 11.7 87 83.7 48.5 79.3 Nigeria 2018 LSS c 39.1 17.6 20.3 39.4 44.9 27.6 47.3 Norway 2018 EU-SILC i 0.5 2.1 – 0 0 0.4 2.6 Pakistan 2018 PSLM c 4.4 34.6 28.8 9.3 30.9 7.4 21.2 Paraguay 2019 EPH i 0.9 6.3 1.9 0.3 9 2.1 1.7 Peru 2019 ENAHO i 2.2 5.4 0.8 4.1 12.1 6.2 3.9 14 Philippines 2015 FIES i 6.1 7 0 9.1 16.4 9.7 7.9 Poland 2019 HBS c 0 0 0.4 0 1 0.1 0 Portugal 2018 EU-SILC i 0.3 2.3 – 0 0.7 0.8 2.6 Romania 2016 HBS c 0 0.3 2.9 0.9 21.6 1.6 0.8 Russian Federation 2015 HBS c 0 0.2 0.3 4.5 10.7 8.3 3.3 Rwanda 2016 EICV-V c 56.5 36.9 4.3 64 28.1 24.5 61 Sao Tome and Principe 2017 IOF c 35.2 20.2 4.2 27.4 62.1 8.8 41.8 Serbia 2018 HBS c 0 3.2 0.7 0.1 2.1 0.2 0.1 Seychelles 2018 HBS i 0.5 0.4 – 0 0.2 5.5 0.8 Sierra Leone 2018 SLIHS c 43 28.7 18.7 68.7 87.2 33.8 61.7 Slovak Republic 2018 EU-SILC i 0.2 0 – 0 1.8 1.4 0.2 Slovenia 2018 EU-SILC i 0 0 – 0 0.1 0.1 0 Somalia 2017 SHFS-W2 c 68.6 59.2 56.3 50.6 39.4 11.8 82.6 South Africa 2014 LCS c 18.7 2.3 2.3 4.1 35.2 10.4 20 South Sudan 2016 HFS-W3 c 76.4 39.3 62.2 – 88.1 13.9 87.5 Spain 2018 EU-SILC i 1 3 – 0 0.4 0.3 4 Sri Lanka 2016 HIES c 0.9 3.8 4 2.5 0.8 12.5 1.4 Sudan 2014 NBHS c 12.2 40.2 22.7 48.5 92.9 44.9 51.5 Swaziland 2016 HIES c 29.1 10.7 0.3 35.7 46.5 27.9 35.1 Sweden 2018 EU-SILC i 0.9 1.1 – 0 0 0.1 1.9 Switzerland 2018 EU-SILC i 0.2 0 – 0 0.1 0.1 0.2 Tajikistan 2015 HSITAFIEN c 4.1 0.3 26.8 2 3.5 39.4 5.1 Tanzania 2018 HBS c 49.4 13.2 19.5 44.3 71.5 29.2 57.8 Thailand 2019 SES c 0.1 15 0.5 0.1 0.2 0.9 0.2 Timor Leste 2014 TLSLS c 22 21.1 16.4 27.4 39.6 22.1 34.1 Togo 2015 QUIBB c 51.3 26.7 2.3 0 51.8 40.6 62.1 Tonga 2015 HIES c 1 1.9 0.8 8.3 0.4 0.1 1 Tunisia 2015 NSHBCSL c 0.2 20.2 2.1 0.2 6.5 2.1 1.6 Turkey 2019 HICES c 0.4 3.3 3 0 5.3 0.1 0.6 Uganda 2016 UNHS c 41.3 34.8 14 61.2 77.6 22.9 57 Ukraine 2019 HLCS c 0 1.4 – 0 15.3 0 1.4 15 United Kingdom 2015 EU-SILC i 0.7 0.5 – 0 0.4 0.6 1.2 United States 2018 CPS-ASEC-LIS i 1 0.2 – 0 – 0 1.2 Uruguay 2019 ECH i 0.1 2 0.7 0.1 1 0.5 0.1 Vietnam 2018 VHLSS c 1.8 11.8 1.7 0.4 11.1 4.7 3 West Bank and Gaza 2016 PECS c 0.8 1.2 5.8 0 0.1 3.2 0.9 Yemen, Rep. 2014 HBS c 18.3 16 44.5 33.9 41.2 14 34.6 Zambia 2015 LCMS-VII c 58.7 24.4 30.4 69.2 60 34.4 64.9 Zimbabwe 2019 PICES c 39.5 0.9 0.3 38 38.3 19.3 41.7 Source: Global Monitoring Database, March 2021. Note: Estimates are based on harmonized household surveys in 124 economies, circa 2017, 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, 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. c=consumption, i=income. 16