Global Poverty Monitoring Technical Note 39 September 2024 Update to the Poverty and Inequality Platform (PIP) What’s New Danielle V. Aron, R. Andres Castaneda Aguilar, Carolina Diaz-Bonilla, Tony H. M. J. Fujs, Diana C. García R., Ruth Hill, Lali Jularbal, Christoph Lakner, Gabriel Lara Ibarra, Daniel G. Mahler, Minh C. Nguyen, Samuel Nursamsu, Carlos Sabatino, Zurab Sajaia, William Seitz, Bambang Suharnoko Sjahrir, Samuel K. Tetteh-Baah, Martha C. Viveros Mendoza, Hernan Winkler, Haoyu Wu, and Nishant Yonzan September 2024 Keywords: What’s New; September 2024; Prosperity Gap; Inequality; Nowcasts; Growth Incidence Curve; Poverty Decomposition; Bottom censoring Development Data Group Development Research Group GLOBAL POVERTY MONITORING TECHNICAL NOTE 39 Poverty and Equity Global Practice Group Abstract The September 2024 update to the Poverty and Inequality Platform (PIP) introduces several changes to the data underlying the global poverty estimates. This document details these changes and the methodological reasons behind them. The database now includes 16 new country-years, bringing the total number of surveys to nearly 2,400. This update incorporates new methodologies for measuring global poverty and introduces new indicators of shared prosperity: the Prosperity Gap and the number of economies with high income inequality. It also incorporates two new analytical dashboards: growth incidence curves and poverty decompositions. Depending on the availability of recent survey data, global and regional poverty estimates are reported up to 2022. For the first time, PIP also includes country-level, regional, and global poverty nowcast estimates up to 2024. The September 2024 PIP update presents the poverty and inequality data underlying the forthcoming World Bank’s Poverty, Prosperity, and Planet Report 2024. All authors were with the World Bank at the time of writing. Corresponding authors: Christoph Lakner (clakner@worldbank.org) and Minh C. Nguyen (mnguyen3@worldbank.org). The authors are thankful for comments and guidance received from Deon Filmer, Haishan Fu, and Luis-Felipe Lopez-Calva. We would also like to thank the countless Poverty Economists that have provided data and documentation, and patiently answered our questions. Without them the database of household surveys that underpins the World Bank’s global poverty measures would not exist. The authors gratefully acknowledge financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme. This note has been cleared by Umar Serajuddin. 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 pip.worldbank.org/. Contents 1. Introduction ................................................................................................................................. 2 2. Nowcasting poverty .................................................................................................................... 5 3. New measures of shared prosperity ............................................................................................ 6 3.1. Prosperity Gap ..................................................................................................................... 6 3.2. Number of high-inequality countries ................................................................................... 7 4. Bottom coding of welfare distributions ...................................................................................... 7 5. Synthetic distributions from grouped data .................................................................................. 8 6. New Analytical Dashboards ....................................................................................................... 9 6.1 Growth Incidence Curve ....................................................................................................... 9 6.2 Decomposition of Poverty Changes.................................................................................... 10 7. Changes to welfare distributions............................................................................................... 11 7.1. Spatial deflation in Indonesia............................................................................................. 11 7.2. Luxembourg Income Study (LIS) ...................................................................................... 14 7.3. Russia ................................................................................................................................. 15 8. Economy-years removed .......................................................................................................... 17 8.1. Sierra Leone 1989 .............................................................................................................. 17 9. Economy-years added ............................................................................................................... 17 10. Comparability database ........................................................................................................... 17 11. References ............................................................................................................................... 18 12. Appendix ................................................................................................................................. 20 12.1. Complete list of new country-years ................................................................................. 20 12.2. CPI data sources ............................................................................................................... 21 1 1. Introduction The September 2024 global poverty update by the World Bank revises previously published poverty and inequality estimates up to 2022. These revisions up to 2022 apply to all regions except the Middle East and North Africa, and Sub-Saharan Africa, where there is currently a lack of sufficient recent data (Table 1). The most recent poverty estimates for these two regions are 2018 and 2019, respectively, with more recent estimates being projections based on limited recent survey data. With this update, for the first time, the Poverty and Inequality Platform (PIP) provides country-level, regional and global poverty nowcasts until the current year (2024). Section 2 briefly discusses the methodology used for nowcasting poverty. This release largely confirms the poverty trends in recent years, as previously published by Castaneda et al. (2024) and Yonzan et al. (2023). In 2020, the COVID-19 pandemic caused global extreme poverty to rise by 0.85 percentage points, reaching 9.7 percent. In the subsequent years, global poverty declined as part of an economic recovery, though unevenly across countries and regions. By now, global extreme poverty has returned to pre-pandemic levels. However, low- and lower-middle-income countries have been less resilient, facing additional global shocks of inflationary pressures following Russia’s invasion of Ukraine in 2022, which slowed down their pace of economic recovery. The Middle East and North Africa region has experienced by far the largest regression in extreme poverty over the past few years, even before the hit of COVID-19. This is largely explained by the concentration of the extreme poor in fragile- and conflict-affected Syria and Yemen. The lack of recent data has also constrained the estimation of reliable estimates for the Middle East and North Africa compared to other regions. In 2022, extreme poverty was estimated to be lower than 2019 pre-pandemic levels for regions with more recent data, such as Latin America and the Caribbean and South Asia (see Table 1). South Asia, in particular, continued to experience the largest reduction in poverty, with significant improvements observed by 2024. Latin America and the Caribbean saw a reduction in extreme poverty in 2020 as a result of fiscal stimulus used by governments to mitigate the economic impacts of the pandemic (World Bank, 2022). In East Asia and Pacific, Europe and Central Asia, and the Other High Income countries, extreme poverty is low around one percent or less. 2 Table 1 Percentage of population living in poverty by region, 2019 – 2024 $2.15 (2017 PPP) $6.85 (2017 PPP) Region 2019 2020 2021 2022 2023 2024 2019 2020 2021 2022 2023 2024 East Asia & Pacific 1.0 1.1 1.1 1.0 0.9 0.8 32.4 32.9 27.8 27.4 26.1 24.7 Europe & Central Asia 0.5 0.5 0.5 0.5 0.5 0.5 10.8 10.3 8.6 8.2 7.7 7.4 Latin America & Caribbean 4.2 3.8 4.5 3.5 3.4 3.3 27.2 27.6 28.4 25.2 24.7 24.3 Middle East & North Africa 4.6 5.3 5.9 6.1 6.4 6.7 45.4 48.0 47.0 45.5 45.2 44.9 Other High Income Countries 0.6 0.4 0.3 0.6 0.6 0.6 1.3 1.2 1.0 1.3 1.3 1.2 South Asia 10.6 13.0 11.4 9.7 8.7 7.6 80.4 81.9 80.9 78.8 77.3 75.6 Sub-Saharan Africa 36.7 37.9 37.6 37.0 36.9 36.5 87.3 88.0 87.8 87.7 87.6 87.3 Eastern and Southern Africa 43.1 44.5 44.2 43.6 43.7 43.2 88.2 88.9 88.7 88.5 88.5 88.3 Western and Central Africa 27.3 28.3 27.9 27.3 27.0 26.5 85.8 86.7 86.5 86.4 86.2 85.9 World 8.8 9.7 9.5 9.0 8.8 8.5 46.3 47.2 45.7 44.9 44.3 43.6 Source: PIP Note: All regional and global poverty estimates for 2023 and 2024 are nowcasts. They are greyed out, as well as region-years where there is insufficient data coverage. There is sufficient regional data coverage if at least 50% of the population have survey data within a three-year window either side of the reference year. There is global data coverage if, in addition, at least 50% of the population in low- and lower-middle-income countries have survey data. Data coverage is computed with a break in 2020, such that data collected during the COVID-19 pandemic do not count for coverage in pre-pandemic years and data collected prior to the pandemic do not count for coverage in the pandemic years and later. See Castaneda et al. (2024) for more details. Table 1 shows poverty estimates at the $2.15 (2017 PPP) and $6.85 (2017 PPP) poverty lines. Poverty estimates are available in PIP for any poverty line, including the $3.65 (2017 PPP) line. The 2011 PPP-based estimates are also available in PIP. At $6.85, a poverty line more typical of upper-middle-income countries, in 2020, global poverty increased by half a percentage point to 47.2 percent. However, since 2021, the trend has reverted to the pre-pandemic decline. This finding aligns with the faster recovery observed in more prosperous regions, considering that Sub-Saharan Africa accounts for a smaller share of the global poor at this higher line compared to the extreme poverty line. Table 2 documents the revisions to regional and global poverty estimates (at poverty lines of $2.15 and $6.85) between the March and September 2024 data vintages for the latest year with sufficient data coverage. The revisions are very small. The global poverty headcount ratio at the International Poverty Line ($2.15 per person per day, 2017 PPP) has remained rounded to 9 percent, with a marginal upward revision in the total number of extreme poor individuals from 712 to 713 million. The 1M increase seen in 2022 is primarily due to increased poverty levels in Other High Income countries (from 0.3 percent to 0.6 percent at the $2.15 poverty line since last vintage), explained by newly available data. At the $6.85 poverty line, the global poverty rate decreased by 0.6 points, equivalent to 42 million fewer poor people. These downward revisions in the number of poor 3 individuals are driven by poverty reductions in East Asia Pacific, Europe and Central Asia and South Asia regions. Table 2 Poverty estimates reported for the latest year with sufficient data coverage, changes between March and September 2024 PIP vintage by region and poverty line $2.15 (2017 PPP) $6.85 (2017 PPP) Data coverage Headcount ratio Number of poor Headcount ratio Number of poor Region Year (%) (%) (mil) (%) (mil) Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep 2024 2024 2024 2024 2024 2024 2024 2024 2024 2024 East Asia & Pacific 2022 94.4 94.4 1.0 1.0 22.4 20.3 29.2 27.4 621.5 584.2 Europe & Central Asia 2022 93.1 93.1 0.5 0.5 2.2 2.4 8.6 8.2 42.4 40.3 Latin America & Caribbean 2022 85.8 85.8 3.5 3.5 22.6 22.6 25.2 25.2 165.0 165.0 Middle East & North Africa 2018 51.3 51.3 4.7 4.7 19.1 18.9 45.1 45.1 181.8 181.7 Other High Income Countries 2022 63.2 75.4 0.3 0.6 3.5 7.1 1.0 1.3 10.9 14.3 South Asia 2022 82.8 84.4 9.7 9.7 186.9 186.2 79.2 78.8 1519.5 1513.3 Sub-Saharan Africa 2019 54.1 54.1 36.7 36.7 411.2 411.2 87.3 87.3 978.6 978.6 Eastern and Southern Africa 2018 57.9 57.9 42.8 42.8 277.9 277.9 88.0 88.0 571.9 571.9 Western and Central Africa 2019 90.0 90.0 27.3 27.3 123.9 123.9 85.8 85.8 390.0 390.0 World 2022 74.4 76.5 9.0 9.0 711.9 712.8 45.5 44.9 3616.2 3573.9 Source: PIP Note: Data coverage represents the share of population having survey data within a three-year window either side of the reference year. In 2022, the population share in low- and lower-middle-income countries covered by a recent survey increases from 63.9% in the March 2024 PIP update to 64.5% in the September 2024 PIP update. Data coverage is computed with a break in 2020, such that data collected during the COVID-19 pandemic do not count for coverage in pre-pandemic years and data collected prior to the pandemic do not count for coverage in the pandemic years and later. See Castaneda et al. (2024) for more details about the coverage rules. Table 1 shows poverty estimates at the $2.15 (2017 PPP) and $6.85 (2017 PPP) poverty lines. Poverty estimates are available in PIP for any poverty line, including the $3.65 (2017 PPP) line. The 2011 PPP-based estimates are also available in PIP. For each region, the latest year with population coverage is shown in the table; when this is not 2022, it is shaded grey. The above changes observed in regional and global poverty estimates are explained by changes to the survey database in the Poverty and Inequality Platform (PIP). Table 3 provides an overview of the survey data used in this update. Revisions have been made to 69 welfare distributions from the previous update to improve the quality of the data (see Section 7) and 16 country-years have been added (see Section 9), bringing the total number of distributions to 2,384.1 PIP now has survey data for 170 countries, including Qatar, the newest economy added to the database. 1 A distribution is defined as a unique combination of country, year, and data type (income or consumption). There are country-years with both income and consumption data. 4 Table 3 Overview of survey data by PIP vintage Description March 2024 September 2024 Difference Distributions 2367 2384 17 Country-years 2283 2298 15 Countries 169 170 1 Country-years with income and consumption 84 86 2 Surveys revised 69 Surveys removed 1 Note: A distribution is defined as a unique combination of country, year, and data type. There are country-years with both income and consumption data. 2. Nowcasting poverty For the first time, this edition of the Poverty and Inequality Platform (PIP) includes nowcasted estimates of poverty until the current year. Two types of nowcasts are presented: estimates using a common global model and those using country-specific local models. A dedicated visualization at www.pip.worldbank.org/nowcasts provides access to all the various nowcasts. The results from the global model can also be accessed via the API, and the Stata and R wrappers. The method underpinning the global model is identical to how PIP extrapolates welfare vectors forward to a common reference year. In short, this method assumes that welfare grows proportionally with national accounts aggregates, and that inequality remains unchanged. More details are available here. GDP estimates and nowcasts are sourced from the World Bank's Macro and Poverty Outlook or Global Economic Prospects, complemented with nowcasts from IMF's World Economic Outlook when necessary. The global model produces results for countries, regions, and the world. The estimates using country-specific methodologies are taken from the World Bank’s Macro & Poverty Outlook (https://www.worldbank.org/en/publication/macro-poverty-outlook). These estimates are made by World Bank staff who are experts on estimating poverty and inequality in a particular country. They use a range of methodologies that differ across countries and over time. Further information about these methods is available as country-level metadata on the PIP nowcast website. The results for the local model are available at the country-level and for select regions. 5 Users are advised to use the estimates based on the local model when they are interested in a single country. The main use of the global model is to generate comparable results for all countries that can be aggregated to produce estimates for regions and the world. 3. New measures of shared prosperity 3.1. Prosperity Gap With this September 2024 update, the World Bank’s new measure of shared prosperity, the Prosperity Gap, has been added to the suite of poverty and inequality measures in the Poverty and Inequality Platform (PIP) (World Bank, 2024a). The prosperity gap is the average factor by which incomes need to be multiplied to bring everyone in the world to the prosperity standard of $25 per person per day (expressed in 2017 PPP dollars). It has a pro-poor weighting scheme, so that individuals who are further behind from the prosperity standard contribute proportionally more to the prosperity gap than individuals closer to the standard (Kraay ⓡ al. 2023; World Bank, forthcoming). The prosperity gap replaces the World Bank’s previous measure of shared prosperity, namely growth in the mean of the bottom 40 percent of the population , which is still reported in the World Development Indicators.2 The new measure of shared prosperity accounts for inequality in the distribution while the previous measure does not. The new measure addresses several other limitations of the old measure, such as the lack of sub-group decomposability and its stringent data demands (Kraay ⓡ al. 2023). For further details on the new measure and how it relates to the growth in the bottom 40, see the forthcoming Poverty, Prosperity, and Planet report (World Bank, forthcoming). Like the poverty headcount measure, survey, lined-up, and aggregate estimates of the prosperity gap are provided in the Poverty and Inequality Platform (PIP). The prosperity gap is “lined up” for all years beginning from 1981 and aggregated across regions and for the world. The lining up and aggregating of the Prosperity Gap uses the same methods as for the poverty headcount measure (World Bank, 2024b). 2 It can also easily be estimated from the decile shares and mean reported in PIP. 6 3.2. Number of high-inequality countries PIP now includes the count of countries with a Gini index of greater than 40 based on the most recent household survey for a country. Haddad et al. (2024) document the rationale behind the choice of the Gini index and the threshold. The count is available in the aggregate PIP output, along with regional and global poverty and inequality estimates. 4. Bottom coding of welfare distributions Data at the bottom of consumption and income distributions are prone to measurement errors (Ravallion, 2016). Zero (or very low) consumption is not plausible, given that a minimum consumption is required for human survival. As many as 13 consumption surveys in PIP had observations with zero consumption. For income surveys, very low, zero, and even negative incomes are more plausible as individuals can finance consumption by drawing down savings. Following PovcalNet, all poverty and inequality indicators in PIP were previously estimated using consumption or income distributions truncated at zero (i.e., observations with a negative value were dropped). In addition, ad-hoc adjustments were made for those indicators that are defined for strictly positive observations: for example, in the case of the mean log deviation, zero values were replaced with a small positive value, while zero values were dropped in the case of the Watts index. With the September 2024 PIP update, all poverty and inequality indicators, as well as the new Prosperity Gap measure, are calculated using income and consumption distributions that (a) do not include negative incomes (i.e., they are dropped as before), and (b) all other observations below $0.25 per person per day are replaced with $0.25 per person per day. 3 This threshold applies to welfare vectors expressed in the 2017 PPP dollars; the corresponding threshold for welfare vectors expressed in the 2011 PPP dollars is $0.22. For details on the need to bottom code, the thresholds used, methods explored, and the effect on indicators, see Yonzan ⓡ al. (forthcoming). 3 For now, only survey distributions have been bottom-censored, which are then used in the line-up. Lined-up distributions extrapolated and interpolated from (censored) survey distributions will be re-censored in subsequent updates. This does not affect the poverty headcount ratio, and the impact on the poverty gap and squared poverty gap are expected to be small since these are not very bottom-sensitive. Distributional measures such as the Gini index and the mean log deviation are not lined-up and are thus unaffected. 7 The bottom coding does not affect the headcount ratio (all individuals are identified as poor either way) but are relevant for distribution-sensitive measures, such as the Gini index, mean log deviation, poverty gap, Watts index, and the Prosperity Gap. Small positive values can have an extreme influence on distribution sensitive indices (Cowell and Flachaire, 2007; Cowell and Victoria-Feser, 2006). The introduction of the bottom coding leads to small revisions in all of PIP’s estimates. Section 7 below lists the country-years that saw additional revisions. 5. Synthetic distributions from grouped data Most surveys in PIP are unit-record data, but in some cases, only grouped data, derived from surveys, are available. Grouped data are aggregated data representing usually 5, 10, 20 or 100 quantiles of the welfare distribution. Poverty and distributional indicators for grouped data are calculated in PIP by fitting a parametric Lorenz Curve to the data, which can be done using the corresponding quantiles as well as the overall welfare mean. Two approaches are considered for each fit: the General Quadratic (GQ) Lorenz and the Beta Lorenz functions. Before the September 2024 PIP update, the poverty and distributional indicators were computed according to analytical derivations found in the literature for each indicator (Datt, 1998; Kakwani, 1980; Krause, 2013; Rohde, 2008; Villaseñor and Arnold, 1989). From this update forward, after selecting the best fit (either GQ or Beta Lorenz) for each country-year that uses grouped data, the parametric Lorenz curve and mean is used to generate synthetic data. The synthetic data are then treated like any unit-record data for computing indicators. For further details, see the methodological handbook and code. This change was made because of the introduction of the bottom censoring which can be easily applied to the synthetic vector. More generally, the use of a synthetic vector for the grouped data allows for more flexibility and consistency across PIP as it can be used to calculate new indicators not derived analytically in the literature (like the Prosperity Gap), and it can undergo the same pre- processing applied to unit-record data (like the bottom censoring). 8 6. New Analytical Dashboards This PIP edition introduces two new dashboards: Growth Incidence Curves and Poverty Decompositions, both of which are powerful tools for understanding distributional dynamics. While the growth incidence curve allows users to examine how economic growth is distributed across various population segments, the poverty decomposition tool breaks down the changes in poverty rates over time. The integration of both tools is particularly valuable for determining whether growth is inclusive or concentrated within specific groups. The tools use the same income and consumption vectors that are used in PIP’s main statistics.4 6.1 Growth Incidence Curve A growth incidence curve (GIC) shows how consumption or income growth is distributed across different percentiles of the distribution to understand if economic growth has been ‘pro-poor,’ i.e., if the gains from economic growth are relatively larger among those that are poor initially. It is created by plotting the annualized growth in per capita mean income (y-axis) for each percentile of the population (x-axis). Calculations involve obtaining the annualized growth between mean income in the first year and over mean income in the final year in a defined period, for each percentile group of the population. For more detailed explanations for the methodology behind the GIC and associated indicators, see Ravallion and Chen (2003). We follow Lakner and Milanovic (2016) in using the mean income of a percentile group. The growth indicators used to generate the GIC are calculated using PIP’s percentile data. The GIC features the anonymous growth of percentiles of the income distribution, showing how different segments (percentiles) of the population fare in terms of growth, rather than tracking the income changes of specific individuals over time. The results in the tails of the distribution (bottom 5 and top 95 percentiles) must be interpreted carefully due to small sample sizes, and measurement errors from difficulties in capturing the true income for the very rich or very poor. 4 For simplicity, the following description will largely refer to income. 9 6.2 Decomposition of Poverty Changes The aim of poverty decompositions is to understand how much of the changes in poverty are due to either (a) economic growth or (b) changes in the distribution. The total change in poverty between times 1 and 2 can then be expressed as the sum of the changes from both forces. The growth component measures the effect of changes in the average income on poverty, assuming the income distribution is the same as in the initial period. This is calculated by applying the poverty measure to the average income at 2 but using the income distribution from 1 . Simply put, the growth component quantifies the change in poverty that would occur if only average income changed, with the income distribution held constant. On the other hand, the distribution component measures the effect of changes in income distribution on poverty, assuming the average income remains the same as in 1 . This is calculated by applying the poverty measure to the income distribution at 2 while using the average income from 1 . That is to say, the redistribution component quantifies the change in poverty that would occur if only the income distribution changed, with average income held constant. See Datt and Ravallion (1992) for more details on the general methodology. The specific growth and redistribution components used are the Shapley values computed in a similar manner to the drdecomp command in Stata, following the Shapley and non-parametric methodology suggested by Shorrocks (2013) and Kolenikov and Shorrocks (2003). The original Datt and Ravallion (1992) approach can give different results if the initial or the final period is used as the starting point, and there may be a residual component. The Shapley approach addresses these issues by averaging the forward and backward decomposition. Calculations are made using 1,000-bin data from each PIP survey, which is similar to PIP’s 100- bin database. This 1,000-bin database is exclusively used for decompositions and is not currently available for public download. Since decomposition estimates are based on 1,000-bin data, there might be a two decimal place difference in the results compared to results computed with the full microdata (used in main PIP estimates). 10 7. Changes to welfare distributions 7.1. Spatial deflation in Indonesia Formerly, Indonesia’s consumption aggregates were not spatially deflated. To partially address that issue, urban and rural PPP conversion factors were used, resulting in PIP reporting estimates at the national, urban, and rural level separately which accounted for differential price levels to some extent. From 2024, adjustments to household welfare that account for spatial differences in the cost of living are applied, which eliminates the need for reporting separate PIP estimates for rural and urban areas. As a result of these adjustments, the past practice of applying separate urban and rural PPP conversion factors is no longer needed; now a single PPP conversion factor together with a spatially deflated aggregate is used. Different approaches were used in three periods: 1984- 1999, 2000-2001, and 2002 onwards. 2002-2023: An annual district-level (kabupaten/kota-level) spatial deflator was introduced, based on a Paasche-type index. The index included food, fuel, energy, and rent components derived from the consumption modules of the Survei Sosial Ekonomi Nasional (SUSENAS)—the primary official household survey used for poverty measurement in Indonesia. Household-level food, fuel, and energy unit values included in the index were directly observed, while the rent component of the index was calculated using a hedonic estimation technique to estimate the value of a standardized housing unit in each domain. An exception is made in 2013 and 2014, where the welfare aggregate is deflated using a province urban and rural-level spatial deflator in accordance to the data representativeness. Decerf et al. (forthcoming) has further details. 2000-2001: The necessary price data to calculate the spatial deflator was unavailable prior to 2002. For 2000 and 2001, the lowest subnational identifier is the country’s main seven regions (Sumatra, Java and Bali, Kalimantan, Sulawesi, Nusa Tenggara, Maluku, and Papua). For these two years, the consumption aggregate was adjusted using the simple average of the newly available deflator at the regional level between 2003-2005. The time period 2003-2005 was selected to average out idiosyncrasies from only using a single year of data with using price differences close to 2000 and 2001. 2002 could not be used because it has a different regional coverage. 11 1984-1999: Prior to 2000, the lowest subnational information is an urban/rural identifier. Given this data environment, from 1984 through 1999, estimates were revised using the simple average of the new 2002-2004 urban and rural spatial deflators. For all periods, additional adjustments were made to ensure consistency with the application of the official CPI temporal deflator. As the components of the national CPI of Indonesia are weighted by the consumption patterns in urban areas only, an adjustment was made for the purposes of poverty and welfare measurement to use the urban cost of living average as the reference price for the spatial deflator. Once the series has been temporally deflated, the welfare vectors are adjusted by the ratio between national and urban prices before the national PPP is applied. In addition, the national aggregate for Indonesia no longer uses urban and rural population weights from WDI. From this vintage, the national aggregate is constructed using urban and rural population weights from the household survey from 1993 onwards. For 1984, 1987, and 1990 urban and rural population weights for WDI are still used as survey population weights are unavailable for these years. Table 4 provides a comparison of national poverty and inequality rates since the previous release. 12 Table 4 Changes to poverty and inequality estimates, Indonesia 1984-2023 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Mar Sep Mar Sep Mar Sep Mar Sep Country Year 2024 2024 2024 2024 2024 2024 2024 2024 Indonesia 1984 74.17 74.30 92.43 92.96 98.72 98.84 33.46 32.30 Indonesia 1987 74.32 74.57 93.33 93.33 98.76 98.84 31.40 30.41 Indonesia 1990 62.75 61.77 88.05 88.52 97.62 97.81 32.27 31.07 Indonesia 1993 62.10 61.19 87.54 88.03 97.38 97.64 33.16 31.76 Indonesia 1996 51.28 49.35 81.19 81.26 95.40 95.74 35.63 34.21 Indonesia 1998 69.12 68.31 90.69 91.10 98.45 98.62 32.18 30.85 Indonesia 1999 45.97 43.99 81.76 81.96 96.60 96.92 32.08 30.79 Indonesia 2000 43.60 43.84 81.63 80.99 96.86 96.54 29.46 30.31 Indonesia 2001 39.87 40.34 79.34 78.32 96.03 95.62 30.02 31.17 Indonesia 2002 26.79 23.45 67.71 68.28 91.85 93.27 32.83 30.16 Indonesia 2003 26.45 22.82 65.48 66.48 91.03 93.19 33.03 29.32 Indonesia 2004 27.01 24.24 65.75 66.65 91.13 92.69 33.85 30.42 Indonesia 2005 24.64 20.13 63.46 63.03 90.53 92.43 34.06 29.90 Indonesia 2006 30.59 26.16 68.09 67.52 91.42 93.26 35.30 31.52 Indonesia 2007 25.25 21.55 60.82 60.87 87.95 89.75 36.66 33.23 Indonesia 2008 24.75 19.19 59.73 58.00 88.10 89.25 36.06 32.63 Indonesia 2009 20.96 18.85 57.51 58.44 87.21 89.13 35.99 33.43 Indonesia 2010 18.25 16.46 50.67 51.95 82.49 85.05 37.21 34.59 Indonesia 2011 15.69 14.10 47.62 48.69 79.32 81.67 40.46 37.93 Indonesia 2012 13.74 10.58 46.06 44.75 78.50 80.03 40.46 37.15 Indonesia 2013 11.18 10.81 43.57 44.94 76.15 78.13 40.79 38.93 Indonesia 2014 9.26 9.68 40.46 42.82 74.93 77.46 40.18 38.78 Indonesia 2015 8.28 8.45 35.85 38.40 72.95 76.90 40.40 38.21 Indonesia 2016 7.52 6.67 33.45 34.15 68.25 71.47 39.30 36.89 Indonesia 2017 6.62 4.30 29.68 26.33 65.26 65.20 38.79 36.37 Indonesia 2018 5.42 4.41 26.41 25.45 62.84 64.65 38.41 36.34 Indonesia 2019 4.38 3.36 24.67 23.44 61.92 63.68 37.61 35.36 Indonesia 2020 3.83 2.83 23.46 21.77 60.68 62.48 37.61 35.34 Indonesia 2021 3.55 2.86 22.39 21.32 60.64 62.67 37.92 35.50 Indonesia 2022 2.47 2.18 20.22 19.10 60.41 62.63 37.92 35.51 Indonesia 2023 1.88 1.82 18.07 17.52 58.81 61.77 38.31 36.06 Note: The revisions shown in the table for the Gini index also include the impact of the bottom censoring that is introduced as part of the September update. The bottom censoring does not affect the poverty estimates. 13 7.2. Luxembourg Income Study (LIS) As in previous editions, welfare data for the following nine economies is drawn from the Luxembourg Income Study (LIS) published by the LIS Data Center: Australia, Canada, Germany, Israel, Japan, South Korea, United States, United Kingdom and Taiwan, China.5 Additionally, PIP includes some historical LIS data (typically before the early 2000s, prior to the existence of EU- SILC) for some European countries that currently use the EU-SILC.6 The break in comparability (between LIS and EU-SILC) is indicated through PIP’s main outputs.7 In all cases we use disposable income per capita in the form of 400 bins (see Chen et al., 2018 for more details). For this release, LIS data was downloaded on 18 March 2024. The following 8 country-years have been added to PIP, as they became available in LIS during the past year: • CHE (Switzerland): 2004 • DEU (Germany): 2020 • KOR (South Korea): 2017, 2018, 2019, 2020, 2021 • USA (United States): 2022 Finally, the following 30 country-years have been revised, as explained in more detail on the LIS website: • AUT (Austria): 1995 • CAN (Canada): 1997 • DEU (Germany): 1992, 1993, 1995-2000, 2002-2005, 2007, 2008, 2010, 2011, 2013-2019 • FRA (France): 1984, 2000 • GBR (United Kingdom): 1995 • KOR (South Korea): 2016 • USA (United States); 2021 5 The term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics. 6 These additional pre-EUSILC surveys were introduced in the March 2020 update (see Atamanov et al. 2020a). 7 The comparability between surveys is indicated through the variables comparable_spell and survey_comparability available in the main outputs on the PIP’s website, Stata command and API. For more on comparability see PIP’s Methodological Manual. 14 7.3. Russia As far as possible, poverty and inequality series for Russia since 2014 are now based on survey data excluding Crimea. The current practice in the World Development Indicators (WDI) is to include Crimea with Ukraine and not Russia. Population data are reported this way for Russia and Ukraine.8 With this update, Russia’s surveys since 2014 in the Poverty and Inequality Platform no longer include observations collected in Crimea to be consistent with the WDI’s practice. The recent Ukraine surveys (the most recent survey is from 2020) do not include Crimea since the annexation by Russia. Therefore, Ukraine’s poverty rate is estimated excluding Crimea. When estimating the number of poor and creating regional and global aggregates, Ukraine is weighted by the WDI population which includes Crimea. Implicitly, this means that Crimea is assigned the poverty rate of Ukraine (excluding Crimea) in PIP.9 Russia’s consumption surveys for 2015, 2019 and 2020, as well as the income surveys for 2014- 2018 have been revised accordingly. For the consumption surveys in 2016, 2017 and 2018, the subnational identifier is currently not available in the Global Monitoring Database (the main source for PIP survey data), so Crimea cannot be excluded at this point. The impacts are very small. With this update, three new income surveys for Russia (2019-2021) are added to PIP which also exclude Crimea. Table 5 Changes to poverty and inequality estimates, Russia consumption surveys Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Mar Sep Mar Sep Mar Sep Mar Sep Country Year 2024 2024 2024 2024 2024 2024 2024 2024 Russia 2015 0.04 0.03 0.41 0.39 5.54 5.36 37.74 37.71 Russia 2019 0.03 0.03 0.27 0.27 4.16 4.16 37.69 37.74 Russia 2020 0.01 0.01 0.29 0.27 4.08 4.04 36.03 36.07 Note: The revisions shown in the table for the Gini index also include the impact of the bottom censoring that is introduced as part of the September update. The bottom censoring does not affect the poverty estimates. 8 However, as an exception to the rule, national accounts data for Russia are provided including Crimea. To create GDP per capita in WDI, the population number is revised to include Crimea with Russia. 9 It is akin to assigning the state of Borno the poverty rate of Nigeria (excluding Borno state) in the 2018/2019 Nigeria survey because the state of Borno could not be reached for data collection (Castaneda et al., 2020). 15 Table 6 Changes to poverty and inequality estimates, Russia income surveys Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Mar Sep Mar Sep Mar Sep Mar Sep Country Year 2024 2024 2024 2024 2024 2024 2024 2024 Russia 2014 0.31 0.30 0.84 0.81 3.06 2.90 37.09 36.89 Russia 2015 0.29 0.29 0.81 0.80 3.48 3.45 36.56 36.52 Russia 2016 0.25 0.25 0.79 0.78 3.55 3.51 36.75 36.72 Russia 2017 0.27 0.26 0.78 0.77 3.28 3.27 35.46 35.45 Russia 2018 0.15 0.15 0.41 0.41 2.55 2.57 35.26 35.28 Note: The revisions shown in the table for the Gini index also include the impact of the bottom censoring that is introduced as part of the September update. The bottom censoring does not affect the poverty estimates. 16 8. Economy-years removed 8.1. Sierra Leone 1989 The 1989 Sierra Leone survey data have been removed from the Poverty and Inequality Platform. These are grouped data with more than 30% of the distribution living below $0.25 per person per day. Distributional statistics are missing because the algorithm fails to fit a valid Lorenz curve for computing distributional measures. Therefore, it is not possible to derive a valid synthetic distribution from the Lorenz function (see Section 5). Hence this dataset is now excluded from the PIP database. 9. Economy-years added Table A1 in the Appendix gives the complete list of new economy-years added to the PIP database. A total of 16 new economy-years were added. 10. Comparability database Since September 2019, we provide metadata on comparability of poverty estimates within countries over time. The assessment of comparability is country-dependent and relies on the accumulation of knowledge from past and current Bank staff in the countries, as well as close dialogue with national data producers with knowledge of survey design and methodology (see Atamanov et al. [2019] for more information on reasons that break comparability). More information about the comparability database and how to use it is available at https://worldbank.github.io/PIP-Methodology/welfareaggregate.html#comparability. The PIP website also indicates comparability in its main output. 17 11. References Atamanov, A., Castaneda Aguilar, R.A., Diaz-Bonilla, C., Jolliffe, D., Lakner, C., Mahler, D.G., Montes, J., Moreno Herrera, L.L., Newhouse, D., Nguyen, M.C., Prydz, E.B., Sangraula, P., Tandon, S.A., Yang, J., 2019. September 2019 PovcalNet Update, Global Poverty Monitoring Technical Note 10. Washington, D.C. https://doi.org/10.1596/32478 Castaneda, R.A.A., Diaz-Bonilla, C., Fujs, T., Lakner, C., Minh, N., Tetteh Baah, S.K., Viveros, M., 2024. March 2024 global poverty update from the World Bank: first estimates of global poverty until 2022 from survey data. URL https://blogs.worldbank.org/en/opendata/march-2024-global-poverty-update-from-the- world-bank--first-esti (accessed 5.26.22). Castaneda, R.A.A., Fujs, T., Jolliffe, D., Lakner, C., Gerszon Mahler, D., Nguyen, M.C., Schoch, M., Vargas Mogollon, D.L., Viveros Mendoza, M.C., Baah, S.K.T., 2020. September 2020 PovcalNet Update: What’s New (Global Poverty Monitoring Technical Note). World Bank. Cowell, F.A., Flachaire, E., 2007. Income distribution and inequality measurement: The problem of extreme values. Journal of Econometrics 141, 1044–1072. https://doi.org/10.1016/j.jeconom.2007.01.001 Cowell, F.A., Victoria-Feser, M.-P., 2006. Distributional Dominance with Trimmed Data. Journal of Business & Economic Statistics 24, 291–300. Datt, G., 1998. Computational tools for poverty measurement and analysis. IFPRI FCND Discussion Paper 50. Datt, G., Ravallion, M., 1992. Growth and redistribution components of changes in poverty measures: A decomposition with applications to Brazil and India in the 1980s. Journal of Development Economics 38, 275–295. https://doi.org/10.1016/0304-3878(92)90001-P Decerf, B., Nursamsu, S., Seitz, W., forthcoming. Assessing Temporal and Spatial Deflators for Indonesia’s Monetary Measures of Welfare. Haddad, C.N., Mahler, D.G., Diaz-Bonilla, C., Hill, R., Lakner, C., Lara Ibarra, G., 2024. The World Bank’s New Inequality Indicator: The Number of Countries with High Inequality. World Bank Policy Research Working Paper Series. Kakwani, N., 1980. On a class of poverty measures. Econometrica: Journal of the Econometric Society 437–446. Kolenikov, S., Shorrocks, A.F., 2003. A Decomposition Analysis of Regional Poverty in Russia, UNU-WIDER Discussion Paper 2003/74. United Nations University. Kraay, A.C., Lakner, C., Ozler, B., Decerf, B.M.A., Jolliffe, D.M., Sterck, O.C.B., Yonzan, N., 2023. A New Distribution Sensitive Index for Measuring Welfare, Poverty, and Inequality (No. 10470), Policy Research Working Paper Series. The World Bank. Krause, M., 2013. Corrigendum to “Elliptical Lorenz Curves”[J. Econom. 40 (1989) 327–338]. Journal of Econometrics 1, 44. Lakner, C., Mahler, D.G., Nguyen, M.C., Azevedo, J.P., Chen, S., Jolliffe, D., 2018. Consumer price indices used in global poverty measurement. Global Poverty Monitoring Technical Note 8. Lakner, C., Milanovic, B., 2016. Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession. The World Bank Economic Review 30, 203–232. https://doi.org/10.1093/wber/lhv039 18 Ravallion, M., 2016. Are the world’s poorest being left behind? | Journal of Economic Growth. Journal of Economic Growth 21, 139–164. https://doi.org/10.1007/s10887-016-9126-7 Ravallion, M., Chen, S., 2003. Measuring pro-poor growth. Economics Letters 78, 93–99. https://doi.org/10.1016/S0165-1765(02)00205-7 Rohde, N., 2008. Lorenz Curves and Generalised Entropy Inequality Measures, in: Chotikapanich, D. (Ed.), Modeling Income Distributions and Lorenz Curves. Springer New York, New York, NY, pp. 271–283. https://doi.org/10.1007/978-0-387-72796-7_15 Shorrocks, A.F., 2013. Decomposition procedures for distributional analysis: a unified framework based on the Shapley value. J Econ Inequal 11, 99–126. https://doi.org/10.1007/s10888-011-9214-z Villaseñor, J., Arnold, B.C., 1989. Elliptical lorenz curves. Journal of econometrics 40, 327–338. World Bank, 2024a. New World Bank Group Scorecard FY24-FY30 : Driving Action, Measuring Results. World Bank, 2024b. Poverty and Inequality Platform Methodology Handbook. World Bank, 2022. Poverty and Shared Prosperity 2022: Correcting Course, Poverty and Shared Prosperity. World Bank, Washington, DC. World Bank, forthcoming. Poverty, Prosperity, and Planet Report 2024: Pathways out of the Polycrisis. World Bank, Washington, D.C. Yonzan, N., Mahler, D.G., Lakner, C., 2023. Poverty is back to pre-COVID levels globally, but not for low-income countries. Poverty is back to pre-COVID levels globally, but not for low-income countries. URL https://blogs.worldbank.org/opendata/poverty-back-pre- covid-levels-globally-not-low-income-countries (accessed 10.31.23). Yonzan, N., Nguyen, M.C., Lakner, C., Kraay, A., Jolliffe, D.M., Wu, H., Ibarra, G.L., forthcoming. Bottom-coding for the measurement of global poverty and inequality. 19 12. Appendix 12.1. Complete list of new country-years Table A1. Economies-years added in September 2024 PIP update Economy Year Survey Name China 2021 CNIHS Costa Rica 2023 ENAHO Ecuador 2023 ENEMDU Georgia 2022 HIS Germany 2020 GSOEP-LIS Korea, Rep. 2017 SHFLC-LIS Korea, Rep. 2018 SHFLC-LIS Korea, Rep. 2019 SHFLC-LIS Korea, Rep. 2020 SHFLC-LIS Korea, Rep. 2021 SHFLC-LIS Kyrgyz Republic 2022 KIHS Nepal 2022 LSS-IV Qatar 2017 HIES Russian Federation 2021 VNDN Switzerland 2004 IES-LIS United States 2022 CPS-ASEC-LIS 20 12.2. CPI data sources Table A2 lists the source of CPI used for each economy-year reported in PIP. The columns in the table are defined as follows: • Code: The 3-letter economy code used by the World Bank: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world- bankcountryand-lending-groups • Economy: Name of economy • Year(s): Welfare reporting year, i.e., the year for which the welfare has been reported. If the survey collects income for the previous year, it is the year prior to the survey. • CPI period: Common time period to which the welfare aggregates in the survey have been deflated. The letter Y denotes that the CPI period is identical to the year column. When the welfare aggregate has been deflated to a particular month within the welfare reporting year, the month is indicated by a number between 1 and 12, preceded by an M, and similarly with a Q for quarters. The letter W indicates that a weighted CPI is used, as described in equation 1 in (Lakner et al., 2018). • CPI source: Source of the deflator used. The source is given by the abbreviation, the frequency of the CPI, and the vintage; e.g. IFS-M-202311 denotes the monthly IFS database version November 2023. For economy-specific deflators, the description is given in the text or further details are available upon request. 21 Table A2. Source of temporal deflators used in September 2024 PIP update Code Economy Survey Year(s) CPI period Source HBS 2000 W IFS-M-202311 AGO Angola IBEP-MICS 2008 W IFS-M-202311 IDREA 2018 W IFS-M-202311 EWS 1996 Y IFS-M-202311 LSMS 2002-2012 Y IFS-M-202311 ALB Albania HBS 2014-2020 Y IFS-M-202311 SILC-C 2017-2019 (prev. year)Y IFS-M-202311 United Arab HIES 2014 W IFS-M-202311 ARE Emirates 2019 Y IFS-M-202311 EPH 1980-1987 Y NSO 1991-2002 M9 NSO ARG Argentina - urban EPHC-S2 2003-2022 M7-M12 NSO 2007-2014 M7-M12 Private estimates ARM Armenia ILCS ALL Y IFS-M-202311 IHS-LIS 1981 Y IFS-A-202311 IDS-LIS 1985 Y IFS-A-202311 AUS Australia SIHCA-LIS 1989 Y IFS-A-202311 SIH-LIS 1995-2018 Y IFS-A-202311 SIH-HES-LIS 2004-2016 Y IFS-A-202311 ECHP-LIS 1994-2000 Y IFS-M-202311 AUT Austria EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 SLC 1995 Y IFS-M-202311 AZE Azerbaijan HBS 2001-2005 Y IFS-M-202311 EDCM 1992 Y IFS-M-202311 EP 1998 W IFS-M-202311 BDI Burundi QUIBB 2006 Y IFS-M-202311 ECVMB 2013 W IFS-M-202311 EICVMB 2020 W IFS-M-202311 SEP-LIS 1985-1997 Y IFS-M-202311 PSBH-ECHP- BEL Belgium LIS 1995-2000 Y IFS-M-202311 EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 QUIBB 2003 Y IFS-M-202311 EMICOV 2011 W IFS-M-202311 BEN Benin 2015 Y IFS-M-202311 EHCVM 2018 M10 IFS-M-202311 2021 M11 IFS-M-202311 EP-I 1994 W IFS-M-202311 EP-II 1998 Y IFS-M-202311 BFA Burkina Faso ECVM 2003-2009 Y IFS-M-202311 EMC 2014 Y IFS-M-202311 EHCVM 2018 M9 IFS-M-202311 22 2021 M10 IFS-M-202311 HHES 1983-1985 W WEO-A-202310 1988-1991 W IFS-A-202311 BGD Bangladesh 1995 W Survey HIES 2000-2022 Y Survey HBS 1989 Y IFS-A-202311 1992-1994 Y IFS-M-202311 BGR Bulgaria HIS 1995-2001 Y IFS-M-202311 MTHS 2003-2007 Y IFS-M-202311 EU-SILC 2007-2022 (prev. year)Y IFS-M-202311 Bosnia and LSMS 2001-2004 Y WEO-A-202310 BIH Herzegovina HBS 2007-2011 Y IFS-M-202311 FBS 1993-1995 Y IFS-M-202311 BLR Belarus HHS 1998-2020 Y IFS-M-202311 LFS 1993-1999 Y IFS-A-202311 BLZ Belize HBS 1995 Y IFS-A-202311 SLC 1996 Y IFS-A-202311 Bolivia - urban EPF 1990 W IFS-M-202311 EIH 1992 M11 IFS-M-202311 Bolivia ENE 1997 M11 IFS-M-202311 ECH 1999 M10 IFS-M-202311 BOL 2000 M11 IFS-M-202311 EH 2001-2005 M11 IFS-M-202311 ECH 2004 M10 IFS-M-202311 EH 2006-2016 M10 IFS-M-202311 2017-2021 M11 IFS-M-202311 PNAD 1981-2011 M9 IFS-M-202311 BRA Brazil PNADC-E1 2012-2022 Y IFS-M-202311 PNADC-E5 2020-2021 Y IFS-M-202311 BLSS 2003-2017 Y Previous WDI/IFS BTN Bhutan 2022 M1-M8 Previous WDI/IFS HIES 1985-2002 W IFS-M-202311 BWA Botswana CWIS 2009 W IFS-M-202311 BMTHS 2015 W IFS-M-202311 EPCM 1992 W IFS-M-202311 Central African CAF ECASEB 2008 Y IFS-M-202311 Republic EHCVM 2021 M5 IFS-M-202311 SCF-LIS 1971-1995 Y IFS-M-202311 CAN Canada SLID-LIS 1996-2011 Y IFS-M-202311 CIS-LIS 2012-2019 Y IFS-M-202311 SIWS-LIS 1982 Y IFS-M-202311 NPS-LIS 1992 Y IFS-M-202311 CHE Switzerland IES-LIS 2000-2004 Y IFS-M-202311 EU-SILC 2007-2021 (prev. year)Y IFS-M-202311 23 CASEN 1987 Y IFS-M-202311 CHL Chile 1990-2022 M11 IFS-M-202311 CRHS-CUHS 1981-2011 Y NSO CHN China CNIHS 2012-2021 Y NSO EPAM 1985-1988 W IFS-M-202311 EP 1992 W IFS-M-202311 CIV Côte d'Ivoire ENV 1995-2015 Y IFS-M-202311 EHCVM 2018 M10 IFS-M-202311 2021 M11 IFS-M-202311 ECAM-I 1996 Y IFS-M-202311 ECAM-II 2001 Y IFS-M-202311 CMR Cameroon ECAM-III 2007 Y IFS-M-202311 ECAM-IV 2014 Y IFS-M-202311 ECAM-V 2021 M10 IFS-M-202311 E123 2004-2012 W IFS-M-202311 COD Congo, Dem. Rep. EGI-ODD 2020 Y WEO-A-202310 ECOM 2005 Y IFS-M-202311 COG Congo, Rep. 2011 W IFS-M-202311 Colombia - urban ENH 1980-1988 Y IFS-M-202311 1989-1991 M11 IFS-M-202311 COL Colombia 1992-2000 M11 IFS-M-202311 ECH 2001-2005 M11 IFS-M-202311 GEIH 2008-2022 M11 IFS-M-202311 EIM 2004 Y IFS-M-202311 COM Comoros EESIC 2013 Y IFS-M-202311 IDRF 2001 W IFS-M-202311 CPV Cabo Verde QUIBB 2007 W IFS-M-202311 IDRF 2015 Y IFS-M-202311 ENH 1981-1986 Y IFS-M-202311 EHPM 1989 Y IFS-M-202311 CRI Costa Rica 1990-2009 M7 IFS-M-202311 ENAHO 2010-2023 M7 IFS-M-202311 CYP Cyprus EU-SILC ALL (prev. year)Y IFS-M-202311 MC-LIS 1992-2002 Y IFS-M-202311 CZE Czech Republic CM 1993 Y IFS-M-202311 EU-SILC 2005-2022 (prev. year)Y IFS-M-202311 DEU Germany LIS ALL Y IFS-M-202311 EDAM 2002-2013 Y IFS-M-202311 DJI Djibouti 2017 M5 IFS-M-202311 LM-LIS 1987-2000 Y IFS-M-202311 DNK Denmark EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 ENGSLF 1986-1989 Y IFS-M-202311 Dominican DOM ICS 1992 M6 IFS-M-202311 Republic ENFT 1996 M2 IFS-M-202311 24 1997 M4 IFS-M-202311 2000-2016 M9 IFS-M-202311 ECNFT-Q03 2017-2022 Y IFS-M-202311 EDCM 1988 Y IFS-M-202311 DZA Algeria ENMNV 1995 Y IFS-M-202311 ENCNVM 2011 W IFS-M-202311 Ecuador - urban EPED 1987 Y IFS-M-202311 Ecuador ECV 1994 M6-M10 IFS-M-202311 Ecuador - urban EPED 1995 M11 IFS-M-202311 ECU 1998 M6 IFS-M-202311 (prev. Ecuador ECV 1999 year)M10-M9 IFS-M-202311 EPED 2000 M11 IFS-M-202311 ENEMDU 2003-2023 M11 IFS-M-202311 HIECS 1990-2012 W IFS-M-202311 EGY Egypt, Arab Rep. 2015 Y IFS-M-202311 2017-2019 W IFS-M-202311 HBS-LIS 1980-1990 Y IFS-M-202311 ESP Spain ECHP-LIS 1993-2000 Y IFS-M-202311 EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 HIES 1993-1998 Y IFS-M-202311 EST Estonia HBS 2000-2004 Y IFS-M-202311 EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 HICES 1981 W IFS-M-202311 Ethiopia - rural 1995-2010 W IFS-M-202311 ETH Ethiopia 2015 M12 IFS-M-202311 HCES 2021 M12 IFS-M-202311 IDS-LIS 1987-2000 Y IFS-M-202311 FIN Finland EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 FJI Fiji HIES ALL W IFS-M-202311 TIS-LIS 1970-1990 Y IFS-M-202311 FRA France TSIS-LIS 1996-2002 Y IFS-M-202311 EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 Micronesia, Fed. Sts. - urban CPH 2000 Y IFS-A-202311 FSM Micronesia, Fed. Sts. HIES 2005-2013 Y IFS-A-202311 GAB Gabon EGEP ALL Y IFS-M-202311 FES-LIS 1968-1993 Y IFS-M-202311 GBR United Kingdom FRS-LIS 1994-2021 Y IFS-M-202311 GEO Georgia HIS ALL Y IFS-M-202311 GLSS-I 1987 W IFS-M-202311 GLSS-II 1988 W IFS-M-202311 GHA Ghana GLSS-III 1991 W IFS-M-202311 GLSS-IV 1998 W IFS-M-202311 25 GLSS-V 2005 W Survey GLSS-VI 2012 W Survey GLSS-VII 2016 W Survey ESIP 1991 Y WEO-A-202310 EIBC 1994 W WEO-A-202310 GIN Guinea EIBEP 2002 W WEO-A-202310 ELEP 2007-2012 Y IFS-M-202311 EHCVM 2018 W IFS-M-202311 HPS 1998 Y IFS-M-202311 GMB Gambia, The HIS 2003 W IFS-M-202311 HIS 2010-2020 W IFS-M-202311 ILJF 1991 Y IFS-M-202311 ICOF 1993 Y IFS-M-202311 ILAP-I 2002 Y IFS-M-202311 GNB Guinea-Bissau ILAP-II 2010 Y IFS-M-202311 EHCVM 2018 W IFS-M-202311 2021 M11 IFS-M-202311 ECHP-LIS 1995-2000 Y IFS-M-202311 GRC Greece EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 GRD Grenada SLCHB 2018 M4 IFS-M-202311 ENSD 1986 W IFS-M-202311 1989 Y IFS-M-202311 GTM Guatemala ENIGF 1998 M8 IFS-M-202311 ENCOVI 2000 M6-M11 IFS-M-202311 2006-2014 M7 IFS-M-202311 GLSMS 1992 W WEO-A-202310 GUY Guyana 1998 Y IFS-M-202311 Honduras - urban ECSFT 1986 Y IFS-M-202311 Honduras EPHPM 1989 Y IFS-M-202311 HND 1990-1993 M5 IFS-M-202311 1994 M9 IFS-M-202311 1995-2019 M5 IFS-M-202311 HBS 1988-2010 Y IFS-M-202311 HRV Croatia EU-SILC 2010-2022 (prev. year)Y IFS-M-202311 ECVH 2001 M5 IFS-M-202311 HTI Haiti ECVMAS 2012 M10 IFS-M-202311 HBS 1987-2007 Y IFS-M-202311 HHP-LIS 1991-1994 Y IFS-M-202311 HUN Hungary THMS-LIS 1999 Y IFS-M-202311 EU-SILC 2005-2022 (prev. year)Y IFS-M-202311 SUSENAS 1984-1999 Y IFS-M-202311 IDN Indonesia 2000-2007 M2 IFS-M-202311 2008-2023 M3 IFS-M-202311 M7-(next IND India NSS 1977 year)M6 NSO 26 1983 Y NSO M7-(next NSS-SCH1 1987-2011 year)M6 NSO M4-(next CPHS 2015-2021 year)M3 NSO SIDPUSS-LIS 1987 Y IFS-M-202311 LIS-ECHP-LIS 1994-2000 Y IFS-M-202311 IRL Ireland SILC-LIS 2002 Y IFS-M-202311 EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 SECH 1986 Y IFS-A-202311 1990-1998 Y IFS-M-202311 IRN Iran, Islamic Rep. HEIS 2005-2009 W IFS-M-202311 M4-(next 2011-2022 year)M3 IFS-M-202311 IHSES 2006 W COSIT IRQ Iraq 2012 Y COSIT ISL Iceland EU-SILC ALL (prev. year)Y IFS-M-202311 ISR Israel HES-LIS ALL Y IFS-M-202311 SHIW-LIS 1977-2002 Y IFS-M-202311 ITA Italy EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 SLC 1988 M9 IFS-M-202311 M11-(next 1990-1993 year)M3 IFS-M-202311 JAM Jamaica 1996 M5-M8 IFS-M-202311 1999 M6-M8 IFS-M-202311 2002-2004 M6 IFS-M-202311 JSLC 2018-2021 Y IFS-M-202311 HEIS 1986 W IFS-M-202311 JOR Jordan 1992-1997 Y IFS-M-202311 2002-2010 W IFS-M-202311 JPN Japan JHPS-LIS ALL Y IFS-M-202311 HBS 1993-2021 Y IFS-M-202311 KAZ Kazakhstan LSMS 1996 Y IFS-M-202311 WMS-I 1992 Y NSO WMS-II 1994 Y NSO WMS-III 1997 Y NSO KEN Kenya IHBS 2005-2015 W NSO KCHS 2020 M6 NSO 2021 M7 KPMS 1998 Y IFS-M-202311 KGZ Kyrgyz Republic HBS 2000-2003 Y IFS-M-202311 KIHS 2004-2022 Y IFS-M-202311 HIES 2006 Y IFS-M-202311 KIR Kiribati 2019 W IFS-M-202311 HIES-FHES- KOR Korea, Rep. LIS 2006-2014 Y IFS-M-202311 27 SHFLC-LIS 2016-2021 Y IFS-M-202311 LECS 1992 W IFS-A-202311 LAO Lao PDR 1997 W IFS-M-202311 2002-2018 W Survey LBN Lebanon HBS 2011 (next year)M5 IFS-M-202311 CWIQ 2007 Y IFS-M-202311 LBR Liberia HIES 2014-2016 Y IFS-M-202311 LSMS 1995 Y IFS-M-202311 LCA St. Lucia SLCHBS 2015 M11 IFS-M-202311 LFSS 1985 Y IFS-M-202311 HIES 1990 W IFS-M-202311 SES 1995 W IFS-M-202311 LKA Sri Lanka HIES 2002 Y IFS-M-202311 2006-2012 W IFS-M-202311 2016-2019 Y IFS-M-202311 HBS 1986 W WEO-A-202310 NHECS 1994 W WEO-A-202310 LSO Lesotho HBS 2002 W IFS-M-202311 CMSHBS 2017 M8 IFS-M-202311 HBS 1993-2008 Y IFS-M-202311 LTU Lithuania EU-SILC 2005-2022 (prev. year)Y IFS-M-202311 PSELL-LIS 1985-1993 Y IFS-M-202311 PSELL-ECHP- LUX Luxembourg LIS 1994-2001 Y IFS-M-202311 SEP-SILC-LIS 2002 Y IFS-M-202311 EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 HBS 1993-2009 Y IFS-M-202311 LVA Latvia EU-SILC 2005-2022 (prev. year)Y IFS-M-202311 ECDM 1984 W IFS-M-202311 MAR Morocco ENNVM 1990-2006 W IFS-M-202311 ENCDM 2000-2013 W IFS-M-202311 MDA Moldova HBS ALL Y IFS-M-202311 EB 1980 Y IFS-M-202311 EPM 1993 W IFS-M-202311 MDG Madagascar 1997-2010 Y IFS-M-202311 ENSOMD 2012 W IFS-M-202311 HIES 2002-2009 W IFS-M-202311 MDV Maldives 2016 Y IFS-M-202311 2019 M11 IFS-M-202311 ENIGH 1984-2014 M8 IFS-M-202311 MEX Mexico ENIGHNS 2016-2022 M8 IFS-M-202311 MHL Marshall Islands HIES 2019 W WEO-A-202310 MKD North Macedonia HBS 1998-2008 Y IFS-M-202311 SILC-C 2010-2020 (prev. year)Y IFS-M-202311 MLI Mali EMCES 1994 Y IFS-A-202311 28 EMEP 2001 W IFS-M-202311 ELIM 2006-2009 W IFS-M-202311 EHCVM 2018-2021 M10 IFS-M-202311 MLT Malta EU-SILC ALL (prev. year)Y IFS-M-202311 MPLCS 2015 M1 IFS-M-202311 MMR Myanmar MLCS 2017 Q1 IFS-M-202311 HBS 2005-2014 Y IFS-M-202311 MNE Montenegro SILC-C 2013-2022 (prev. year)Y IFS-M-202311 LSMS 1995-1998 Y IFS-M-202311 HIES-LSMS 2002 W IFS-M-202311 MNG Mongolia HSES 2007 W IFS-M-202311 2010-2022 Y IFS-M-202311 NHS 1996 W WEO-A-202310 MOZ Mozambique IAF 2002 W WEO-A-202310 IOF 2008-2019 W IFS-M-202311 EPCV 1987 Y IFS-M-202311 EP 1993 Y IFS-M-202311 MRT Mauritania EPCV 1995-2008 W IFS-M-202311 2014 Y IFS-M-202311 2019 M11 IFS-M-202311 HBS 2006 W IFS-M-202311 MUS Mauritius 2012-2017 Y IFS-M-202311 IHS-I 1997 W IFS-M-202311 IHS-II 2004 W Survey MWI Malawi IHS-III 2010 W Survey IHS-IV 2016 M4 Survey IHS-V 2019 M4 Survey HIS 1984-1997 Y IFS-M-202311 (prev. year)M7- (prev. 2004 year)M12 IFS-M-202311 (prev. MYS Malaysia year)M7- (prev. 2007 year)M10 IFS-M-202311 2009 W IFS-M-202311 2012-2016 Y IFS-M-202311 HIESBA 2019 W IFS-M-202311 HIS 2022 W IFS-M-202311 NHIES 1993 W WEO-A-202310 NAM Namibia 2003-2015 W IFS-M-202311 ENBCM 1992-2007 W IFS-M-202311 NER Niger EPCES 1994 W IFS-M-202311 ENCVM 2005 Y IFS-M-202311 29 ECVMA 2011-2014 Y IFS-M-202311 EHCVM 2018 M10 IFS-M-202311 2021 M11 IFS-M-202311 NCS 1985 W IFS-M-202311 1992-1996 Y IFS-M-202311 LSS 2003 W IFS-M-202311 GHSP-W1 2010 M3-M4 IFS-M-202311 NGA Nigeria GHSP-W2 2012 M3-M4 IFS-M-202311 GHSP-W3 2015 M3-M4 IFS-M-202311 (next year)M3-(next LSS 2018 year)M4 IFS-M-202311 EMNV 1993 M2 NSO 1998 M6 NSO NIC Nicaragua 2001 M6 IFS-M-202311 2005-2009 M8 IFS-M-202311 2014 M8-M10 IFS-M-202311 AVO-LIS 1983-1990 Y IFS-M-202311 NLD Netherlands SEP-LIS 1993-1999 Y IFS-M-202311 EU-SILC 2005-2022 (prev. year)Y IFS-M-202311 IDS-LIS 1979-2000 Y IFS-M-202311 NOR Norway EU-SILC 2004-2020 (prev. year)Y IFS-M-202311 MHBS 1984 W IFS-M-202311 LSS-I 1995 W IFS-M-202311 NPL Nepal LSS-II 2003 W IFS-M-202311 LSS-III 2010 W IFS-M-202311 M6-(next LSS-IV 2022 year)M5 IFS-M-202311 NRU Nauru HIES 2012 W IFS-M-202311 HIES 1987 Y IFS-M-202311 1990-1998 W IFS-M-202311 PAK Pakistan IHS 1996 W IFS-M-202311 PIHS 2001 M6 IFS-M-202311 HIES 2004-2018 (next year)M1 IFS-M-202311 EMO 1979-1989 Y IFS-M-202311 PAN Panama 1991 M7 IFS-M-202311 EH 1995-2023 M7 IFS-M-202311 ENNIV 1985 W IFS-M-202311 1994 Y IFS-M-202311 PER Peru ENAHO 1997-2002 Q4 IFS-M-202311 2003 M5-M12 IFS-M-202311 2004-2022 Y IFS-M-202311 PHL Philippines FIES ALL Y IFS-M-202311 HIES 1996 Y IFS-A-202311 PNG Papua New Guinea 2009 W IFS-A-202311 30 HBS 1985-1987 Y IFS-A-202311 HBS-LIS 1986 Y IFS-A-202311 POL Poland HBS 1989-2019 Y IFS-M-202311 HBS-LIS 1992-1999 Y IFS-M-202311 EU-SILC 2005-2022 (prev. year)Y IFS-M-202311 PRT Portugal EU-SILC ALL (prev. year)Y IFS-M-202311 EH 1990 M7 IFS-M-202311 1995 M8-M11 IFS-M-202311 EIH 1997 (next year)M2 IFS-M-202311 EPH 1999 M9 IFS-M-202311 EIH 2001 M3 IFS-M-202311 EPH 2002 M11 IFS-M-202311 PRY Paraguay 2003 M9 IFS-M-202311 2004 M10 IFS-M-202311 2005 M11 IFS-M-202311 2006 M12 IFS-M-202311 2007-2008 M10 IFS-M-202311 2009 M11 IFS-M-202311 2010-2022 M10 IFS-M-202311 West Bank and PECS 2004-2011 Y IFS-M-202311 PSE Gaza 2016 W IFS-M-202311 QAT Qatar HIES 2017 W IFS-M-202311 HBS 1989 Y Milanovic (1998) MC 1992 Y IFS-M-202311 HIS 1994-1999 Y IFS-M-202311 ROU Romania IHS-LIS 1995-1997 Y IFS-M-202311 IHS 1998-2000 Y IFS-M-202311 HBS 2001-2021 Y IFS-M-202311 EU-SILC 2007-2022 (prev. year)Y IFS-M-202311 HBS 1993-2020 Y IFS-M-202311 RUS Russian Federation VNDN 2015-2021 (prev. year)Y IFS-M-202311 2022 (prev. year)Y WEO-A-202310 Rwanda - rural ENBCM 1984 W IFS-M-202311 Rwanda EICV-I 2000 W IFS-M-202311 EICV-II 2005 W IFS-M-202311 RWA EICV-III 2010 (next year)M1 IFS-M-202311 EICV-IV 2013 (next year)M1 IFS-M-202311 EICV-V 2016 (next year)M1 IFS-M-202311 NBHS 2009 Y IFS-M-202311 SDN Sudan 2014 M11 IFS-M-202311 EP 1991 W IFS-M-202311 ESAM 1994 W IFS-M-202311 SEN Senegal ESAM-II 2001 W IFS-M-202311 ESPS-I 2005 W IFS-M-202311 ESPS-II 2011 W IFS-M-202311 31 EHCVM 2018 M9 IFS-M-202311 2021 M11 IFS-M-202311 SLB Solomon Islands HIES ALL W IFS-M-202311 SLIHS 2003 W WEO-A-202310 SLE Sierra Leone 2011-2018 Y IFS-M-202311 EHPM 1989 Y IFS-M-202311 M10-(next 1991 year)M4 IFS-M-202311 SLV El Salvador 1995-1999 Y IFS-M-202311 2000-2007 M12 IFS-M-202311 2008-2022 M11 IFS-M-202311 LSMS 2002 Y IFS-M-202311 SRB Serbia HBS 2003-2019 Y IFS-M-202311 EU-SILC 2013-2022 (prev. year)Y IFS-M-202311 NBHS 2009 Y IFS-M-202311 SSD South Sudan (prev. HFS-W3 2016 year)M7 IFS-M-202311 São Tomé and IOF 2000 W IFS-M-202311 STP Principe 2010-2017 Y IFS-M-202311 Suriname - urban EHS 1999 Y IFS-M-202311 SUR Suriname SSLC 2022 Y IFS-M-202311 MC-LIS 1992-1996 Y IFS-M-202311 SVK Slovak Republic HBS 2004-2009 Y IFS-M-202311 EU-SILC 2005-2022 (prev. year)Y IFS-M-202311 IES 1987-1993 Y IFS-M-202311 HBS-LIS 1997-1999 Y IFS-M-202311 SVN Slovenia HBS 1998-2003 Y IFS-M-202311 EU-SILC 2005-2022 (prev. year)Y IFS-M-202311 HIS-LIS 1975-2002 Y IFS-M-202311 SWE Sweden EU-SILC 2004-2022 (prev. year)Y IFS-M-202311 SWZ Eswatini HIES ALL W IFS-M-202311 HES 1999 W IFS-M-202311 HBS 2006 W IFS-M-202311 SYC Seychelles 2013 Y IFS-M-202311 2018 W IFS-M-202311 HIES 1996-2007 W IFS-M-202111 Syrian Arab SYR 2009 Y IFS-M-202111 Republic HNAP 2022 Y IFS/IMF/Economist/EIU ECOSIT-II 2003 Y IFS-M-202311 ECOSIT-III 2011 Y IFS-M-202311 TCD Chad EHCVM 2018 W IFS-M-202311 2022 M2 IFS-M-202311 QUIBB 2006-2015 Y IFS-M-202311 TGO Togo EHCVM 2018-2021 M10 IFS-M-202311 THA Thailand SES ALL Y IFS-M-202311 32 TLSS 1999 Y WEO-A-202310 2003-2007 Y Survey TJK Tajikistan HBS 2004 Y Survey TLSS 2009 Y IFS-M-202311 HSITAFIEN 2015 Y IFS-M-202311 TKM Turkmenistan LSMS 1998 Y WEO-A-202310 TLSS 2001 Y WEO-A-202310 TLS Timor-Leste TLSLS 2007-2014 Y IFS-M-202311 HIES 2000 W IFS-M-202311 TON Tonga 2009-2021 Y IFS-M-202311 Trinidad and SLC 1988 Y IFS-M-202311 TTO Tobago PHC 1992 Y IFS-M-202311 HBCS 1985 Y IFS-A-202311 1990 Y IFS-M-202311 TUN Tunisia LSS 1995-2000 Y IFS-M-202311 NSHBCSL 2005-2021 W IFS-M-202311 HICES 1987-2019 Y IFS-M-202311 TUR Türkiye SILC-C 2018-2022 (prev. year)Y IFS-M-202311 TUV Tuvalu HIES 2010 Y IFS-A-202311 TWN Taiwan, China FIDES-LIS ALL Y WEO-A-202310 HBS 1991 W IFS-A-202311 2000 W IFS-M-202311 TZA Tanzania 2007 Y IFS-M-202311 2011-2018 W IFS-M-202311 HBS 1989 Y WEO-A-202310 NIHS 1992 W WEO-A-202310 UGA Uganda 1996-1999 W IFS-M-202311 UNHS 2002-2019 W IFS-M-202311 HS 1992-1993 Y IFS-M-202311 UKR Ukraine HIES 1995-1996 Y IFS-M-202311 HLCS 1999-2020 Y IFS-M-202311 Uruguay - urban ENH 1981-1989 Y IFS-M-202311 (prev. ECH 1992-2005 year)M12 IFS-M-202311 URY (prev. Uruguay 2006-2022 year)M12 IFS-M-202311 (prev. ECH-S2 2021 year)M12 IFS-M-202311 CPS-LIS 1963-2001 Y IFS-M-202311 USA United States CPS-ASEC-LIS 2002-2022 Y IFS-M-202311 HBS 1998-2003 Y WEO-A-202310 UZB Uzbekistan 2022 Y IFS-M-202311 EHM 1981-1989 Y NSO VEN Venezuela, RB 1992-2006 M12 NSO VNM Viet Nam VLSS 1992 W WEO-A-202310 33 1997 W IFS-M-202311 VHLSS 2002-2022 M1 IFS-M-202311 HIES 2010 Y IFS-A-202311 VUT Vanuatu NSDP 2019 W IFS-A-202311 HIES 2002-2008 Y IFS-M-202311 WSM Samoa 2013 W IFS-M-202311 XKX Kosovo HBS ALL Y IFS-M-202311 HBS 1998 Y IFS-M-202311 YEM Yemen, Rep. 2005 W IFS-M-202311 2014 Y IFS-M-202311 KIDS 1993 Y IFS-M-202311 HIES 2000 W IFS-M-202311 ZAF South Africa IES 2005-2010 (next year)M6 IFS-M-202311 LCS 2008 W IFS-M-202311 2014 (next year)M6 IFS-M-202311 HBS 1991-1993 Y IFS-M-202311 LCMS-I 1996 Y IFS-M-202311 LCMS-II 1998 Y IFS-M-202311 LCMS-III 2002 W IFS-M-202311 ZMB Zambia LCMS-IV 2004 W IFS-M-202311 LCMS-V 2006 W IFS-M-202311 LCMS-VI 2010 Y IFS-M-202311 LCMS-VII 2015 Y IFS-M-202311 ICES 2011 Y IFS-M-202311 ZWE Zimbabwe PICES 2017-2019 Y Survey 34