Global Poverty Monitoring Technical Note 26 October 2022 Update to the Multidimensional Poverty Measure What’s New Carolina Diaz-Bonilla Carlos Sabatino Haoyu Wu Minh Cong Nguyen October 2022 Keywords: Multidimensional Poverty Measure, October 2022. Development Data Group Development Research Group Poverty and Equity Global Practice Group GLOBAL POVERTY MONITORING TECHNICAL NOTE 26 Abstract The October 2022 update presents the 4th 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 data provides country estimates for 123 economies in the GMD circa 2018, revising estimates published in April 2022. This new edition recalculates the MPM using the international poverty line at $2.15 in 2017 PPP. The accompanying 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: Diana Sanchez Castro, Elizabeth Foster, Ifeanyi Nzegwu Edochie, Ikuko Uochi, Jose Montes, Minh Cong Nguyen, Laura Moreno Herrera, Nobuo Yoshida, Reno Dewina, Rose Mungai, Sergio Olivieri. Additional D4G: Daniel Gerszon Mahler. 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 ............................................................................................................................. 2 1.1. What is the Multidimensional Poverty Measure? ........................................................... 2 1.2. Methodology, Usage, and Data ....................................................................................... 3 2. Revisions in the 4th edition of the MPM: What’s New .......................................................... 5 2.1. Data source...................................................................................................................... 5 2.2. Key results ...................................................................................................................... 6 3. Online dashboard and modifying weights in the MPM ........................................................ 12 4. References ............................................................................................................................. 14 5. Annex .................................................................................................................................... 16 1 1. Introduction 1.1. What is the Multidimensional Poverty Measure? The World Bank’s Multidimensional Poverty Measure (MPM) presents a broader understanding of poverty beyond just the monetary dimension by incorporating access to education and basic infrastructure as additional dimension of well-being. It aims to thus highlight additional deprivations experienced by poor households beyond the monetary headcount ratio at the $2.15 international poverty line. In order to estimate 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. Both education and access to basic infrastructure are generally available in household surveys across the world. 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 $2.15 per day, the new International Poverty Line at 2017 PPPs published by the World Bank in 2022 (Jolliffe et al., 2022). While monetary poverty is strongly correlated with deprivations in other domains, this correlation is far from perfect. The Poverty and Shared Prosperity 2022 report (World Bank, 2022) shows that almost 4 out of 10 multidimensionally poor individuals (39 percent) are not captured by monetary poverty, as they are deprived in nonmonetary dimensions alone. 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. A focus on non-monetary deprivations for the income-poor highlights to policymakers the importance of improving other aspects of human welfare that may not be well-captured by the monetary measure alone. For example, 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. It is also useful to measure deprivations in basic services faced by non-income-poor households, including households that leave extreme 2 poverty but continue to experience nonmonetary deprivations, as these households face different constraints to well-being. 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. In addition to the type of data limitations mentioned earlier, which reduce the information available to fully assess well-being in all major dimensions, the MPM 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 The MPM is composed of six indicators that are mapped into three dimensions of well-being (monetary standard of living, education, and basic infrastructure services). The six indicators that were standardized for countries across the world are consumption or income, educational attainment, educational enrollment, drinking water, sanitation, and electricity. These indicators are defined as 0-1 variables, where “1” means the individual or household is deprived in that indicator. Thus, in addition to selecting the dimensions and the indicators, one must also select the deprivation thresholds for each indicator. As an example, the threshold selected for the educational enrollment indicator is that at least one school-age child up to the age of grade 8 is not enrolled in school. Table 3 presents the detailed indicator definitions. The MPM summarizes the information on the number of deprivations into a single index, therefore requiring a decision on how each of the indicators is to be weighted. Aggregating indicators into a single index allows for comparisons across populations and across time. In the World Bank’s MPM, dimensions are weighted equally, and within each dimension each indicator is also equally weighted (see Table 3 below). As explained in previous editions of the World Bank’s MPM, individuals are considered multidimensionally deprived if they fall short of the threshold in at least 3 one of the three dimensions, or alternatively if they fall short in a combination of indicators that together are equal in weight to a full dimension. In other words, if a household faces deprivations in indicators whose weight adds up to 1/3 or more that household will be considered poor. Because the monetary dimension is measured using only one indicator, and there are three equally weighted dimensions, anyone who is income poor is automatically also poor under the broader multidimensional poverty concept. The standardized and recent surveys in the World Bank’s Global Monitoring Database (GMD) for October 2022 provide the latest estimates for each indicator in each country in this edition of the MPM.1 The latest regional and global estimates are calculated for circa 2018, using household survey data collected within a six-year window from 2015 to 2021 for 123 economies. The GMD’s harmonized multitopic income and expenditure surveys collect information on total household income or consumption in order to measure monetary poverty. These surveys also collect 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. Nevertheless, the underlying surveys are country-specific and collected by national statistical offices, resulting understandably in considerable heterogeneity in the wording of questions, the response choices to questions, or the definitions of access applied. Therefore, despite best efforts to harmonize country-specific questionnaires to standard definitions, such as for example those used by the Joint Monitoring Programme for Water Supply and Sanitation, the measures reported here may differ from those reported elsewhere. 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 1 The Global Monitoring Database (GMD) is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity. The household survey data are typically collected by national statistical offices in each country, and then compiled, processed, and harmonized. The process is coordinated by the Data for Goals (D4G) team and supported by the six regional statistics teams in the Poverty and Equity Global Practice. The Global Poverty & Inequality Data Team (GPID) in the Development Economics Data Group (DECDG) also contributes historical data from before 1990 and recent survey data from Luxemburg Income Studies (LIS). Selected variables have been harmonized to the extent possible such that levels and trends in poverty and other key sociodemographic attributes can be reasonably compared across and within countries over time. The GMD’s harmonized microdata are currently used in the Poverty and Inequality Platform (PIP), the World Bank’s Multidimensional Poverty Measure (WB MPM), the Global Database of Shared Prosperity (GDSP), and Poverty and Shared Prosperity Reports. 4 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. The countries included in the circa 2018 MPM reported here are different from those included in the circa 2017 MPM report (Nguyen, 2021), requiring caution regarding 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 its Poverty and Inequality Platform (PIP) (previously known as PovcalNet) 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 includes surveys in the period between 2014 and 2020, which overlaps 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 imply that any comparison of the regional and global MPM estimates between the two editions should be done with caution and be dependent on the share of new data. 2. Revisions in the 4th edition of the MPM: What’s New 2.1. Data source As mentioned earlier, the estimates of multidimensional poverty are derived from household surveys included in the World Bank’s Global Monitoring Database. The October 2022 update of the country data can be found in the What’s New of the Poverty and Inequality Platform (PIP) (see Castaneda et al., 2022b). Compared to the 3rd edition of the MPM released in April 2022 (see Diaz- 2 See PIP methodological handbook 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). 5 Bonilla and Sabatino, 2022),3 this edition updates the definition of monetary poverty, which is now defined as households with income or consumption per capita that is less than $2.15 per day, the new International Poverty Line at 2017 PPP used by the World Bank since September 2022 (replacing $1.90 at 2011 PPP). Although 123 countries are used to estimate the global and regional MPM (those countries that fall within the circa 2018 window), the full list of 149 countries with available MPM data are 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 dashboard where users can visualize and download the data. 2.2. Key results The MPM is constructed from three dimensions: monetary extreme poverty based on the international poverty line (the focal point of the World Bank’s monitoring of global poverty), access to education, and access to basic infrastructure. The definition of multidimensional poverty applied ensures that the MPM is at least as high as or higher than the monetary poverty headcount in a country, and the nonmonetary dimensions provide an additional role 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 estimates 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). Table 1 below reports the aggregate regional and global estimates from the sample of 123 economies in the 4th edition MPM, weighting each economy by its population in 2018. 3 The 3rd edition presented 10 new 2018 surveys in West Africa, new imputed poverty estimates for Nigeria, and 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). 6 Figure 1. Correlation between Monetary and Multidimensional Poverty Headcount (circa 2018) Source: Global Monitoring Database, October 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. The insert zooms in on the countries in the bottom left corner of the chart. 7 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 3.8 6.0 14 30 Europe & Central Asia 0.3 2.1 25 89 Latin America & Caribbean 3.8 4.6 14 87 Middle East & North Africa 1.7 2.4 5 51 South Asia 8.2 17.4 5 22 Sub-Saharan Africa 32.4 52.6 35 73 Rest of the World 0.7 1.4 25 78 All regions 9.0 14.8 123 51 b Source: Global Monitoring Database, October 2022. Note: The monetary headcount is based on the international poverty line $2.15. 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 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 89 percent of the population in Europe & Central Asia and as little as 22 percent of the population in South Asia. The coverage for South Asia is low because no multidimensional poverty data is available for India between 2014 and 2021. Due to 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 for low-income and lower-middle-income countries is 51 percent, the same as the coverage for the overall global population (also see annex 1A of World Bank, 2020). Table 2 shows important differences when comparing monetary poverty to deprivations in each of the indicators. Close to 40 percent of households that are multidimensionally deprived are not captured by monetary poverty. Sub-Saharan Africa experiences the highest levels of deprivation in multidimensional poverty, with over half of all households 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. Across each indicator, Sub-Saharan Africa and South Asia, respectively, have the highest and second-highest percentage of population who experience a deprivation in that indicator, except in the case of drinking water, for which East Asia and Pacific has the second-worst performance (although this regional 8 comparison may be complicated by the relatively low population coverage of the East Asia and Pacific and South Asia regions). In terms of deprivations in individual indicators, the most prevalent is sanitation, with 22.8 percent of the covered population living with less than adequate sanitation. After sanitation, the most prevalent deprivations occur with adult educational attainment (12.9 percent) and access to electricity (12.7 percent). 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 monetary poverty headcount, but a large difference in 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 17 percent in Latin America and the Caribbean, more than twice that of the Middle East and North Africa, and Europe and Central Asia. Nearly half of all households in Sub-Saharan Africa have no access to electricity. Figure 2 depicts via Venn diagrams the underlying structure of deprivations experienced by the multidimensionally poor. In most countries, there is a large degree of overlap between dimensions. As has been the case in previous editions, only a small minority of the multidimensionally poor are deprived in only one dimension, more than a third are simultaneously deprived in all three dimensions, and Sub-Saharan Africa shows the highest overlap of deprivations. These results indicate a large interdependence of deprivation, especially in Sub-Saharan Africa, suggesting that multipronged policy interventions might be required rather than targeting one dimension at a time. 9 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 3.8 8.7 1.7 6.6 15.9 8.2 Europe & Central Asia 0.3 0.9 2.2 1.7 7.1 4.5 Latin America & 3.8 9.4 1.6 1.0 16.6 2.9 Caribbean Middle East & North 1.7 8.6 2.8 0.5 3.1 1.4 Africa South Asia 8.2 20.5 19.1 14.8 35.5 5.3 Sub-Saharan Africa 32.4 35.7 23.0 48.7 65.1 28.9 Rest of the World 0.7 1.0 2.2 0.0 0.2 0.5 All regions 9.0 12.9 9.7 12.7 22.8 10.1 Source: Global Monitoring Database, October 2022. Note: This table shows the share of population living in households deprived in an indicator of the multidimensional poverty measure. The monetary poverty headcount is based on the international poverty line of 2.15$ at 2017PPP. Regional and total estimates are population weighted averages of survey-year estimates for 123 economies. Population data from 2018. Number of economies is the number of economies in each region for which information is available in the window between 2014 and 2021, for a circa 2018 reporting year. The coverage rule applied to the estimates is identical to that used in the rest of the chapter; details can be found in annex 1A of World Bank (2020). Regions without sufficient population coverage are shown in light grey. See Table 1 for a discussion of the coverage rule. 10 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, October 2022. Note: The figure shows the share of population that is multidimensionally poor and the dimensions in which they are deprived. For example, in All regions, the numbers in the yellow oval add up to 8.9 percent, which is the monetary headcount. Adding up all numbers in the figure results in 14.7 percent, which is the proportion of people who are multidimensionally deprived. Estimates are based on harmonized household surveys in 123 economies, circa 2018. 11 3. Online dashboard and modifying weights in the MPM A full list of 149 countries with available MPM data is available to visualize and download on the Multidimensional Poverty Dashboard, 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). Furthermore, the dashboard allows users to test the sensitivity of the MPM to different assumptions and priorities by changing the weights of dimensions and deprivation thresholds. 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. 12 With this update, users will continue to be able to modify the weights being used through the updated 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. Table 3. Multidimensional Poverty Measure Indicators and Weights Dimension Parameter Weight Monetary Daily consumption or income is less than US$ 2.15 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, 2022. 13 4. References Castaneda Aguilar, R. Andres; Dewina, Reno; Diaz-Bonilla, Carolina; Edochie, Ifeanyi N.; Fujs, Tony H. M. J.; Jolliffe, Dean; Lain, Jonathan; Lakner, Christoph; Ibarra, Gabriel Lara; Mahler, Daniel G.; Meyer, Moritz; Montes, Jose; Moreno Herrera, Laura L.; Mungai, Rose; Newhouse, David; Nguyen, Minh C.; Sanchez Castro, Diana; Schoch, Marta; Sousa, Liliana D.; Tetteh-Baah, Samuel K.; Uochi, Ikuko; Viveros Mendoza, Martha C.; Wu, Haoya; Yonzan, Nishant; Yoshida, Nobuo. 2022a. April 2022 Update to the Poverty and Inequality Platform (PIP): What's New. Global Poverty Monitoring Technical Note; 20. Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/37479 Castaneda Aguilar, R. Andres; Diaz-Bonilla, Carolina; Fujs, Tony H. M. J.; Jolliff, Dean; Lakner, Christoph; Mahler, Daniel G.; Nguyen, Minh C.; Schoch, Marta; Tetteh-Baah, Samuel K.; Viveros Mendoza, Martha C.; Wu, Haoyu; Yonzan, Nishant. 2022b. September 2022 Update to the Poverty and Inequality Platform (PIP): What’s New. Global Poverty Monitoring Technical Note;24. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/38023 Diaz-Bonilla, Carolina; Sabatino, Carlos. 2022. April 2022 Update to the Multidimensional Poverty Measure: What’s New. Global Poverty Monitoring Technical Note; No. 22. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/37491 Jolliffe, Dean Mitchell; Mahler, Daniel Gerszon; Lakner, Christoph; Atamanov, Aziz; Tetteh Baah, Samuel Kofi. 2022. Assessing the Impact of the 2017 PPPs on the International Poverty Line and Global Poverty (English). Policy Research working paper, no. WPS 9941. Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/353811645450974574/Assessing-the-Impact- of-the-2017-PPPs-on-the-International-Poverty-Line-and-Global-Poverty Nguyen, Minh Cong; Wu, Haoyu; Lakner, Christoph; Schoch, Marta. 2021. March 2021 Update to the Multidimensional Poverty Measure: What’s New. Global Poverty Monitoring Technical Note; No. 17. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/35390 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://documents.worldbank.org/en/publication/documents- reports/documentdetail/664751553100573765/national-accounts-data-used-in-global- poverty-measurement Yang, Judy; Nguyen, Minh Cong. 2021. March 2021 Update to the Global Database of Shared Prosperity: What’s New. Global Poverty Monitoring Technical Note; No. 16. World Bank, Washington, DC https://openknowledge.worldbank.org/handle/10986/35389 14 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://openknowledge.worldbank.org/handle/10986/34496 World Bank. 2022. Poverty and Shared Prosperity 2022: Correcting Course. https://www.worldbank.org/en/publication/poverty-and-shared-prosperity 15 5. Annex Table 4. Individuals in households deprived in each indicator, 149 economies, latest year available 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 0.0 0.2 - 0.1 6.6 9.6 0.3 Angola 2018 IDREA c 31.1 29.8 27.4 52.6 53.6 32.1 47.2 Argentina 2020 EPHC-S2 i 1.1 1.4 0.7 0.0 0.8 0.1 1.1 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 13.5 22.0 8.4 23.6 54.5 2.8 20.5 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 19.9 50.2 31.5 54.3 80.0 22.1 53.3 Bhutan 2017 BLSS c 0.9 40.8 4.1 1.9 14.3 0.4 3.3 Bolivia 2020 EH i 3.1 14.1 2.1 4.4 17.9 6.6 6.6 Botswana 2015 BMTHS c 15.0 8.2 4.2 35.5 52.0 3.7 20.8 PNADC- Brazil 2019 E1 i 5.4 15.0 0.4 0.2 34.3 1.8 6.1 Bulgaria 2020 EU-SILC i 0.9 0.6 - 0.0 13.2 7.4 1.4 Burkina Faso 2018 EHCVM c 30.5 56.4 50.9 47.2 69.6 19.7 60.4 Burundi 2013 ECVMB c 65.1 66.3 18.9 91.8 94.3 20.6 85.2 Cabo Verde 2015 IDRF c 4.6 11.7 2.7 9.9 30.2 11.1 7.6 Cameroon 2014 ECAM-IV c 25.7 24.4 15.9 1.2 38.9 23.2 37.5 Chad 2018 EHCVM c 30.9 69.0 34.9 90.0 87.0 34.8 79.3 Chile 2020 CASEN i 0.7 3.4 3.3 - 1.4 0.8 1.0 Colombia 2019 GEIH i 5.3 5.1 2.8 1.3 8.2 2.4 5.9 Comoros 2013 EESIC c 18.6 15.3 7.3 28.5 67.2 6.4 26.3 16 Congo, Democratic Republic of 2012 E123 c 69.7 22.5 8.0 83.0 80.0 47.9 78.3 Congo, Republic of 2011 ECOM c 35.4 13.4 2.3 29.9 47.3 23.4 41.6 Costa Rica 2020 ENAHO i 2.2 3.9 0.5 0.1 1.4 0.1 2.3 Côte d'Ivoire 2018 EHCVM c 11.4 48.6 30.4 18.1 64.4 20.7 37.3 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 19.1 30.1 18.0 34.2 45.4 7.1 29.3 Dominican ECNFT- Republic 2020 Q03 i 1.1 13.1 8.8 0.6 5.7 5.2 2.9 ENEMD Ecuador 2020 U i 6.5 3.8 2.3 2.8 4.8 4.6 6.9 Egypt, Arab Rep. 2017 HIECS c 2.5 10.6 4.2 0.5 3.2 0.8 3.5 El Salvador 2019 EHPM i 1.4 24.8 3.9 2.1 9.4 3.1 4.4 Estonia 2020 EU-SILC i 0.7 0.1 - 0.0 3.9 5.2 0.8 Ethiopia 2015 HICES c 27.0 66.7 31.2 64.1 95.9 42.7 72.7 Fiji 2019 HIES c 1.3 0.6 1.9 4.5 5.1 12.0 1.6 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 2.5 11.3 7.9 8.6 68.2 11.5 8.4 Gambia, The 2015 IHS c 13.4 29.9 6.1 8.0 58.2 8.2 18.3 Georgia 2020 HIS c 5.8 0.1 1.2 0.0 9.5 5.7 5.8 GSOEP- Germany 2018 LIS I 0.1 2.7 2.5 0.0 0.0 - 0.2 Ghana 2016 GLSS-VII c 25.3 15.1 9.0 19.5 79.9 40.8 32.9 Greece 2020 EU-SILC i 1.0 2.0 - 0.0 0.3 0.1 3.0 Guatemala 2014 ENCOVI i 9.5 24.8 18.3 16.5 46.7 8.4 22.2 Guinea 2018 EHCVM c 13.8 61.3 25.0 56.4 71.1 21.0 51.7 Guinea-Bissau 2018 EHCVM c 21.7 41.0 30.1 42.1 63.0 21.6 46.1 17 Haiti 2012 ECVMAS c 29.2 23.2 9.0 64.3 68.8 33.5 46.8 Honduras 2019 EPHPM i 12.6 10.1 10.0 6.7 5.8 5.7 14.8 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 Indonesia 2021 SUSENAS c 3.6 3.8 1.2 0.8 11.6 6.5 4.1 Iran, Islamic Rep. 2019 HEIS c 1.1 4.4 0.8 0.0 1.9 1.6 1.2 Iraq 2012 IHSES c 0.1 13.6 22.7 0.1 0.9 10.0 5.5 Ireland 2019 EU-SILC i 0.0 0.7 - 0.0 0.2 0.2 0.7 Israel 2018 HES-LIS I 0.4 0.7 - 0.0 0.0 - 1.1 Italy 2019 EU-SILC i 1.6 1.3 - 0.0 0.8 0.7 2.9 Japan 2013 JHPS-LIS I 0.7 8.8 0.5 0.0 0.0 - 0.8 Jordan 2010 HEIS c 0.0 1.8 3.0 0.0 0.0 0.2 0.3 Kazakhstan 2018 HBS c 0.0 0.0 - 0.0 0.5 0.7 0.0 Kenya 2015 IHBS c 29.4 22.5 6.1 56.9 69.0 32.2 45.4 Kiribati 2019 HIES c 1.7 0.6 6.0 - 83.8 17.1 21.0 HIES- Korea, Rep. 2016 FHES-LIS I 0.1 0.0 - 0.0 0.0 - 0.1 Kosovo 2017 HBS c 0.4 0.5 23.6 0.2 1.4 0.7 0.8 Kyrgyz Republic 2020 KIHS c 1.3 0.0 0.1 0.0 0.1 4.6 1.3 Lao PDR 2018 LECS c 7.1 12.8 5.7 1.7 22.5 7.8 10.3 Latvia 2020 EU-SILC i 0.5 0.1 - 0.0 7.9 8.9 0.6 Lebanon 2011 HBS c 0.0 9.2 2.3 0.9 30.7 0.9 0.7 Lesotho 2017 CMSHBS c 32.4 18.1 4.8 58.7 55.1 13.7 40.7 Liberia 2016 HIES c 27.6 30.5 54.1 79.7 61.8 25.7 56.6 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 Madagascar 2012 ENSOMD c 80.7 49.0 34.7 13.0 76.9 59.9 82.9 Malawi 2016 IHS-V c 70.1 54.3 3.7 88.8 75.1 11.4 78.3 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 18 Mali 2018 EHCVM c 14.8 66.6 28.2 23.9 51.9 23.8 43.7 Malta 2020 EU-SILC i 0.3 0.1 - 0.0 0.1 0.0 0.4 Marshall Islands 2019 HIES c 0.9 1.0 3.4 1.1 29.0 1.7 1.1 Mauritania 2014 EPCV c 6.5 54.3 8.3 54.1 49.3 38.6 45.7 Mauritius 2017 HBS c 0.1 7.2 0.2 0.2 - - 0.4 ENIGHN Mexico 2020 S i 3.1 3.8 2.5 0.2 1.3 3.9 3.4 Micronesia, Federated States of 2013 HIES c 16.0 8.7 28.0 23.6 42.8 5.2 22.7 Moldova 2019 HBS c 0.0 2.5 0.5 0.0 25.5 16.9 0.8 Mongolia 2018 HSES c 0.7 2.7 3.2 0.2 10.4 13.0 2.0 Montenegro 2014 HBS c 0.0 0.1 - 1.4 2.5 1.2 1.2 Morocco 2013 ENCDM c 1.4 12.7 6.8 2.4 12.9 8.7 5.8 Mozambique 2014 IOF c 64.6 54.9 33.3 14.6 71.3 41.1 73.7 Myanmar 2017 MLCS c 2.0 28.0 6.8 50.9 9.7 20.6 15.4 Namibia 2015 NHIES c 15.6 11.3 6.1 53.8 68.3 9.2 27.5 Nauru 2012 HIES c 1.4 15.2 4.2 0.8 22.5 3.8 1.8 Nepal 2010 LSS-III c 8.2 28.6 9.5 31.5 66.7 16.8 26.5 Netherlands 2020 EU-SILC i 0.2 1.6 - 0.0 0.0 0.1 1.8 Nicaragua 2014 EMNV i 3.9 14.1 8.1 20.0 42.7 12.5 15.6 Niger 2018 EHCVM c 50.6 79.7 28.0 78.7 85.2 37.5 80.0 Nigeria 2018 LSS c 30.9 17.6 20.3 39.4 44.9 27.5 41.8 Norway 2020 EU-SILC i 0.3 1.7 - 0.0 0.0 0.5 2.0 Pakistan 2018 HIES c 4.9 21.1 28.8 9.3 24.8 6.5 16.7 Papua New Guinea 2009 HIES c 39.7 22.2 9.0 82.6 79.8 69.2 74.7 Paraguay 2020 EPH i 0.8 4.9 2.0 0.3 6.8 1.6 1.3 Peru 2020 ENAHO i 5.8 4.8 1.2 3.7 11.5 5.5 7.0 Philippines 2015 FIES i 6.5 4.0 0.0 9.1 16.4 9.7 8.2 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 19 Romania 2018 HBS c 0.0 0.2 1.8 0.1 18.0 1.0 0.1 Russian Federation 2020 HBS c 0.0 0.9 0.7 5.1 7.7 8.6 5.0 Rwanda 2016 EICV-V c 52.0 36.9 4.3 64.0 28.1 24.5 57.4 Sao Tome and Principe 2017 IOF c 15.6 19.5 4.3 31.2 62.0 8.2 24.9 Senegal 2018 EHCVM c 9.3 42.0 31.9 26.6 37.4 15.2 32.3 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 0.9 Sierra Leone 2018 SLIHS c 26.0 28.7 18.7 68.7 87.2 33.8 54.0 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 Solomon Islands 2012 HIES c 26.6 11.4 13.5 53.8 58.5 25.5 38.7 Somalia 2017 SHFS-W2 c 70.7 59.2 56.3 50.6 39.4 11.8 83.8 South Africa 2014 LCS c 20.5 2.3 2.3 4.1 35.2 10.4 21.7 South Sudan 2016 HFS-W3 c 67.3 39.3 62.2 - 88.1 13.9 84.9 Spain 2020 EU-SILC i 0.9 2.7 - 0.0 0.4 0.2 3.6 Sri Lanka 2016 HIES c 1.3 3.8 4.0 2.5 0.8 12.5 1.7 Sudan 2014 NBHS c 15.3 40.2 22.7 48.5 92.9 44.9 52.5 Eswatini 2016 HIES c 36.1 10.7 0.3 35.7 46.5 27.9 40.8 Sweden 2020 EU-SILC i 0.6 2.0 - 0.0 0.0 0.1 2.5 Switzerland 2019 EU-SILC i 0.2 0.0 - 0.0 0.1 0.1 0.2 FIDES- Taiwan, China 2016 LIS I 0.1 0.9 1.2 0.0 0.0 - 0.1 HSITAFI Tajikistan 2015 EN c 6.1 0.3 26.8 2.0 3.5 39.4 7.0 Tanzania 2018 HBS c 44.9 13.2 19.5 44.3 71.5 29.2 54.6 Thailand 2020 SES c 0.0 13.4 0.5 0.1 0.2 0.5 0.2 Timor Leste 2014 TLSLS c 8.0 21.1 16.4 27.4 39.6 22.1 23.5 Togo 2018 EHCVM c 28.1 32.7 14.0 47.4 83.7 25.3 46.4 Tonga 2015 HIES c 1.1 1.9 0.8 8.3 0.4 0.1 1.1 Tunisia 2015 NSHBCSL c 0.1 20.2 2.1 0.2 6.5 2.1 1.5 20 Türkiye 2019 HICES c 0.4 3.3 3.0 0.0 5.3 0.1 0.6 Tuvalu 2010 HIES c 3.6 4.5 6.1 9.2 11.5 2.4 4.3 Uganda 2019 UNHS c 42.2 31.4 11.8 41.3 71.1 23.7 52.3 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-LIS I 1.0 0.2 - 0.0 0.0 - 1.1 Uruguay 2019 ECH i 0.1 2.0 0.7 0.1 1.0 0.5 0.1 Vanuatu 2019 NSDP c 10.0 25.7 13.4 1.4 43.0 11.8 15.4 Vietnam 2018 VHLSS c 1.2 11.8 1.7 0.4 11.1 4.7 2.5 West Bank and Gaza 2016 PECS c 0.5 1.2 5.8 0.0 0.1 3.2 0.6 Yemen, Rep. 2014 HBS c 19.8 16.0 44.5 33.9 41.2 14.0 35.4 Zambia 2015 LCMS-VII c 61.4 24.4 30.4 69.2 60.0 34.4 66.5 Zimbabwe 2019 PICES c 39.8 0.9 6.0 38.0 38.3 19.3 42.4 Source: Global Monitoring Database, October 2022. Note: Estimates are based on harmonized household surveys in 149 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 2017 purchasing power parity US dollars, is less than US$2.15 per person per day. The estimates in this table for Australia, Canada, Germany, Israel, Japan, Korea Rep., Taiwan, China, 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. 21