Global Poverty Monitoring Technical Note 44 June 2025 Update to the Poverty and Inequality Platform (PIP) What’s New Federica Alfani, Danielle V. Aron, Aziz Atamanov, R. Andres Castaneda Aguilar, Carolina Diaz-Bonilla, Nancy P. Devpura, Reno Dewina, Arden Finn, Tony Fujs, Maria Fernanda Gonzalez, Nandini Krishnan, Nishtha Kocchar, Naresh Kumar, Christoph Lakner, Gabriel Lara Ibarra, Diego Lestani, Julia Liniado, Jonas Lønborg, Daniel G. Mahler, Carolina Mejía-Mantilla, Veronica Montalva, Laura L. Moreno, Minh C. Nguyen, Eliana Rubiano, Zurab Sajaia, Diana M. Sanchez, Ganesh K. Seshan, Samuel K. Tetteh-Baah, Martha C. Viveros Mendoza, Haoyu Wu, Nishant Yonzan, and Ayago Wambile. June 2025 Keywords: What’s New; June 2025; Poverty, Inequality, 2021 PPPs. Development Data Group Development Research Group Poverty and Equity Global Department GLOBAL POVERTY MONITORING TECHNICAL NOTE 44 Abstract The June 2025 update to the Poverty and Inequality Platform (PIP) introduces several important changes to the data underlying the global poverty estimates. The most important change is the adoption of the 2021 Purchasing Power Parities (PPPs). In addition, new data for India has been incorporated and the existing series adjusted for comparability. This document details the changes to underlying data and the methodological reasons behind them. Depending on the availability of recent survey data, global and regional poverty estimates are reported up to 2023, together with nowcasts up to 2025. The PIP database now includes 74 new country-years, bringing the total number of surveys to over 2,400, for 172 economies. 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. Adoption of 2021 Purchasing Power Parities (PPPs) ................................................................. 5 3. Adjustments to India data ........................................................................................................... 8 3.1 New data for 2022/23 and adjustments to 2011/12 data ....................................................... 9 3.1.1 Change in the recall period ............................................................................................ 9 3.1.2 Construction of the welfare aggregate ......................................................................... 10 3.1.3 Revision of price deflators ........................................................................................... 11 3.1.4 Caveats: Comparability between 2011/12 and 2022/23, and inequality estimates ...... 12 3.1.5 Final estimates ............................................................................................................. 12 3.2 Adjustments to data prior to 2011 ....................................................................................... 13 3.2.1 Converting between MMRP and URP ......................................................................... 14 3.2.2 Spatial deflation with national poverty lines ............................................................... 15 4. Changes to welfare distributions............................................................................................... 18 4.1. Botswana 2002, 2009, 2015 ............................................................................................... 18 4.3. EU-SILC ............................................................................................................................ 19 4.4. Guatemala 2000, 2006, 2014 ............................................................................................. 19 4.5. Honduras 2014- 2019 ......................................................................................................... 20 4.6. Jamaica 2018, 2021 ............................................................................................................ 20 4.7. Luxembourg Income Study (LIS) ...................................................................................... 21 4.8. Paraguay 2022 .................................................................................................................... 22 4.9. Thailand 2016 .................................................................................................................... 22 4.10. Tunisia 2010..................................................................................................................... 23 4.11. Uzbekistan 2022............................................................................................................... 24 5. Country-years removed ............................................................................................................. 24 6. Economy-years added ............................................................................................................... 24 6.1 Belize 2018 and Barbados 2016 ......................................................................................... 24 6.2 Guatemala 2023 .................................................................................................................. 25 6.3 Honduras 2023 .................................................................................................................... 25 6.4 Lebanon 2022...................................................................................................................... 26 6.5 West Bank and Gaza 2023 .................................................................................................. 26 7. Changes to CPI data .................................................................................................................. 27 8. Changes to national accounts and population data ................................................................... 27 9. Comparability database............................................................................................................. 27 10. Flagging data points with non-national survey coverage ....................................................... 28 References ..................................................................................................................................... 29 A. Appendix .................................................................................................................................. 31 A.1. Complete list of new country-years .................................................................................. 31 A.2. CPI data sources ................................................................................................................ 34 1 1. Introduction The June 2025 global poverty update by the World Bank revises previously published poverty and inequality estimates and now includes nowcasted estimates up to 2025. Regional aggregates are published until 2023 for all regions, except for Sub-Saharan Africa where data coverage is limited after 2019. The post-2019 estimates available for Sub-Saharan Africa and the West & Central Africa subregion are projections based on the most recently available survey data ( Table 1). In summary, this update brings an additional 74 country-year datapoints to the PIP database, whilst improving existing data for another 90 country-years. The PIP database now contains over 2,400 survey-year observations from 172 countries. This update also includes the adoption of the 2021 Purchasing Power Parities (PPPs), which were published in May 2024 by the International Comparison Program. The adoption of the 2021 PPPs also implies a revision to the global poverty lines: The three global poverty lines have been revised from US$2.15 to US$3.00, from US$3.65 to US$4.20, and from US$6.85 to US$8.30. In keeping with the existing methodology, the global poverty lines are based on countries’ own national poverty lines. The PPP revision reflects the most recent data on these national poverty lines which imply an upward revision by more than suggested by pure price changes, especially for the international poverty line (defining extreme poverty), as well as the typical poverty line of upper- middle-income countries (see Foster ⓡ al., (2025) for more details). Due to India’s population size, the methodological revision to its historical series as well as the inclusion of the new 2022 survey data, significantly affects the estimates for South Asia and the world. The overall revisions to the previously published global poverty estimates reflect the combined effect from these different forces. Sections 2 and 3 below expand separately on the effects that these revisions have on the global and regional estimates presented in this first section. Table 1 documents revisions to the regional and global poverty estimates between the September 2024 data vintage (in 2017 PPPs) and the June 2025 data vintage (in 2021 PPPs). The global poverty headcount in 2022 is revised up from 9.0 to 10.5 percent, resulting in a change in the population living below the international poverty line from 713 to 838 million. Most of this upward 2 revision stems from Sub-Saharan Africa, home to two thirds of the global extreme poor. For this region, the poverty rate is revised up from 37.0 to 45.5 percent of the population, equivalent to an increase in the extreme poor population from 448 to 559 million people. Upward revisions are evident in all regions except for South Asia, where the poverty rate drops from 9.7 to 7.3 percent in 2022 (45 million fewer extreme poor individuals) driven mainly by updated data from India. Table 1 Poverty estimates for reference year 2022, changes between September 2024 and June 2025 vintage by region and poverty lines $2.15 / $3.00 $4.20 (2021PPP) $8.30 (2021PPP) Survey Poverty rate Number of Poverty rate Number of Poverty rate Number of Region Coverage (%) poor (mil) (%) poor (mil) (%) poor (mil) (%) Jun Sep Jun Sep Jun Sep Jun Sep Jun Sep Jun Sep Jun 2025 2024 2025 2024 2025 2024 2025 2024 2025 2024 2025 2024 2025 East Asia & Pacific 94.4 1.0 2.5 20.3 54.0 5.4 6.7 115.2 143.0 27.4 31.8 584.2 679.2 Europe & Central Asia 93.4 0.5 1.1 2.4 5.3 1.7 2.4 8.5 12.0 8.2 12.0 40.3 59.3 Latin America & Caribbean 90.0 3.5 5.2 22.6 33.6 8.9 9.4 58.2 61.1 25.2 28.6 165.0 185.2 Middle East & North Africa 66.7 6.1 8.5 26.1 37.1 16.2 15.0 69.4 65.4 45.5 49.8 195.1 217.8 Other High Income Countries 90.8 0.6 0.7 7.1 7.8 0.8 0.9 9.3 9.9 1.3 1.6 14.3 18.2 South Asia 84.1 9.7 7.3 186.2 141.5 38.8 27.7 744.5 535.7 78.8 82.1 1513.3 1585.5 Sub-Saharan Africa 48.8 37.0 45.5 448.0 558.8 64.2 63.1 777.3 775.8 87.7 88.4 1061.7 1086.6 Eastern and Southern Africa 54.0 43.6 53.4 314.0 390.9 68.4 69.5 492.8 508.3 88.5 89.9 638.1 658.0 Western and Central Africa 41.1 27.3 33.8 134.0 167.9 58.0 53.8 284.6 267.5 86.4 86.2 423.7 428.6 World 82.5 9.0 10.5 712.8 838.0 22.4 20.1 1782.6 1603.0 44.9 48.0 3573.9 3831.8 Note: Regions with insufficient data coverage are highlighted in gray. There is sufficient regional data coverage if at least 50% of the population has survey data within a three-year window on 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. Global survey coverage for low- and lower-middle-income countries in 2022 is 81.1%. In contrast, at the $4.20 poverty line, typical of lower-middle-income countries, estimated global poverty in 2022 is revised downward from 22.4 percent of the population to 20.1 percent. This decrease is mainly driven by a significant downward revision of poverty in South Asia from 38.8 to 27.7 percent (equivalent to 209 million fewer poor individuals), driven by the new data for India. All other regions see only small revisions in poverty rates across vintages at this poverty line. At the $6.85 poverty line, upward revisions are evident across all regions, with global poverty rising by 3 percentage points from 45 to 48 percent of the global population. 3 Turning to nowcasted estimates (Table 2), South Asia is the region that is estimated to experience the most significant improvements in extreme poverty through to 2025. Globally, extreme poverty is projected to decrease from 10.5 percent in 2022 to 9.9 percent in 2025. The lack of population coverage for Sub-Saharan Africa, where most of the extreme poor live, causes substantial uncertainty over these nowcasts. This is explained by the unavailability of recent survey data in populous countries in the region, most notably Nigeria, where the last survey is from 2018. Table 2 Percentage of population living in poverty by region, 2020 – 2025 $3.00 (2021 PPP) $8.30 (2021PPP) Region 2020 2021 2022 2023 2024 2025 2020 2021 2022 2023 2024 2025 East Asia & Pacific 2.9 2.8 2.5 2.3 2.0 1.9 37.7 32.0 31.8 30.3 28.9 27.7 Europe & Central Asia 1.1 1.1 1.1 1.0 0.9 0.9 13.9 12.0 12.0 10.1 9.6 9.1 Latin America & Caribbean 5.8 6.5 5.2 4.7 4.6 4.6 31.1 31.7 28.6 27.3 26.9 26.5 Middle East & North Africa 8.6 8.6 8.5 8.7 9.0 9.4 53.0 51.4 49.8 49.0 49.0 48.7 Other High Income Countries 0.5 0.5 0.7 0.7 0.7 0.7 1.5 1.3 1.6 1.5 1.5 1.5 South Asia 10.3 8.8 7.3 6.3 5.4 4.8 85.2 83.8 82.1 80.1 78.1 76.1 Sub-Saharan Africa 46.2 46.1 45.5 45.2 45.1 44.4 88.5 88.5 88.4 88.3 88.3 88.0 Eastern and Southern Africa 53.8 54.0 53.4 53.2 53.5 52.8 89.8 90.0 89.9 90.1 90.2 90.0 Western and Central Africa 35.2 34.5 33.8 33.3 32.8 32.0 86.5 86.3 86.2 85.8 85.6 85.1 World 11.2 11.0 10.5 10.2 10.0 9.9 50.4 48.6 48.0 47.0 46.3 45.5 Source: Poverty & Inequality Platform (PIP) Note: All regional and global poverty estimates for 2024 and 2025 are nowcasts. These predicted levels are highlighted in grey, which also includes region-years where there is insufficient data coverage. There is sufficient regional data coverage if at least 50% of the population has survey data within a three-year window on 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 2 shows poverty estimates at the $3.00 (2021 PPP) and $8.30 (2021 PPP) poverty lines. Poverty estimates are available in PIP for any poverty line, including the $4.20 (2021 PPP) line, as well as estimates based on the previously used 2017 PPPs. At $8.30 – the poverty line typical of upper-middle-income countries – the share of people living below this threshold globally has steadily decreased since 2020 from 50.4 to 45.5 percent in 2025. The greatest improvements are observed in more prosperous regions, particularly East Asia & Pacific. It is important to consider that Sub-Saharan Africa accounts for a smaller share of the global poor at this higher line compared to the extreme poverty line. The above changes observed in regional and global poverty estimates are explained by the PPP revision, but also by changes to the surveys included in the Poverty and Inequality Platform (PIP). 4 Table 3 provides an overview of the survey data used in this update. Revisions have been made to 90 welfare distributions from the previous update to improve the quality of the data (see Section 4). 81 new country-years have been added (see Section 6) and 7 removed (see Section 5), bringing the total number of distributions to 2,460.1 PIP now has survey data for 172 countries, including Barbados and Equatorial Guinea, the newest economies added to the database. Table 3 Overview of survey data by PIP vintage September June Description Difference 2024 2025 Distributions 2,384 2,460 76 Country-years with income and consumption 86 88 2 Country-years 2,298 2,372 74 Countries 170 172 2 Surveys revised 69 90 Surveys removed 1 7 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. This update also incorporates the latest versions of other input data such as consumer price indices (CPI), population, and national accounts data from our standard sources, including the World Development Indicators (WDI), World Economic Outlook (WEO), and Maddison Project Database (MPD). See Sections 7 and 8 for more details on the changes to the auxiliary data. 2. Adoption of 2021 Purchasing Power Parities (PPPs) The June 2025 update of PIP adopts the 2021 PPPs released by the International Comparison Program (ICP) in May 2024 (World Bank, 2024a). The 2021 PPPs provide updated information on the price levels of goods and services across countries, while keeping the methodology unchanged from the 2017 PPPs (World Bank, 2024a). In adopting these new PPPs, the World Bank is therefore consistent with its response to the Atkinson Commission recommendation, which had called for keeping the 2011 PPPs. The World Bank’s response was that it will adopt future 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. 5 ICP rounds if new PPPs are driven by new price information and not changes in the ICP methodology. Furthermore, in keeping with the approach applied when updating to the 2017 PPPs, a legacy series using the older 2017 PPPs will still be available in PIP (Castaneda et al., 2022). This section provides a brief overview of the implications of adopting the 2021 PPPs, while a more detailed analysis can be found in Foster ⓡ al., (2025). With the adoption of the 2021 PPPs, the global poverty lines are revised from $2.15 to $3.00 for the international poverty line, from $3.65 to $4.20 for the line typical of lower-middle-income countries and from $6.85 to $8.30 for the line typical of upper-middle-income countries. These increases are the result of both the adoption of the 2021 PPPs and updated national poverty lines available. In relative terms, the upward revision to the international poverty line is the most significant (an increase of around 40%), followed by the line typical of upper-middle income countries (an increase of around 20%). The change in the IPL (and to a lesser extent the UMIC line) is explained by the upward revision in the underlying national poverty lines. In other words, between the 2017 and 2021 rounds of the PPPs, the value of the typical national poverty line in low-income countries increased significantly. This in turn is related to improvements in how these countries collect data on consumption, especially in West Africa. The new and improved surveys collect significantly more (measured) consumption, and national poverty lines are then also adjusted to this new methodology. Put differently, the new international poverty line reflects better information on the cost of basic needs in the poorest countries around the world. Foster ⓡ al., (2025) provide more details, including decompositions into the various factors. Figures Figure 1 and Figure 2 below show the global and regional poverty trends with both 2017 and 2021 PPPs at all three poverty lines, as well as the September 2024 and June 2025 data vintages. The main takeaway of this comparison is that the poverty trends at the global and regional level are consistent irrespective of the PPPs applied. Nevertheless, the switch from the 2017 PPPs to 2021 PPPs can have important implications for global, regional, and country-level poverty levels. 6 As has been the case also for the adoption of earlier updates to PPPs, a thorough review has been undertaken to assess the 2021 PPPs before their adoption into World Bank poverty metrics (Foster ⓡ al., 2025). The review results in a decision to deviate from the PPPs published by the ICP for measuring global poverty for 4 countries. These countries include Egypt, Guinea, São Tomé and Príncipe, and Sudan. In general, fewer exceptions are necessary in this round, compared to earlier rounds: For example, eight exceptions were made in the 2017 cycle of the ICP, and six in the 2011 cycle. Figure 1 Global Poverty Trends, 1990-2030 Source: Foster ⓡ al. (2025) Note: This figure shows global poverty trends using two vintages of PIP data, namely the old September 2024 vintage and the current June 2025 vintage. Poverty is estimated from both PIP data vintages using the $2.15, $3.65, and $6.85 poverty lines, expressed in 2017 PPP terms. Poverty is also estimated from current vintage of PIP data using the $3.00, $4.20, and $8.30 poverty lines, expressed in 2021 PPP terms. With the September 2024 PIP data, poverty estimates for 2023 and 2024 are nowcasts, which are indicated in the chart with light grey color. With the June 2025 PIP data, poverty estimates for 2024 and 2025 are nowcasts, which are indicated in the chart with a darker grey color.The figure also includes projections to 2030 (also indicated in dark grey color) that rely on national accounts growth, as the nowcasts. 7 Figure 2 Regional poverty trends, 1990 – 2025 Source: Foster ⓡ al. (2025) Note: See Fig. 1. The figure includes nowcasts. The region Rest of the World is dropped for presentational purposes; poverty is almost non-existent in this group of high-income countries at all three lines. 3. Adjustments to India data The World Bank is updating the international poverty estimates for India based on the 2022-23 Household Consumption Expenditure Survey (CES), released by the National Sample Survey (NSS) Office after an 11-year gap. The update also involves shifting from 2017 to 2021 Purchasing Power Parity (PPP). Methodological adjustments have been introduced, with key changes being: (a) a shift from uniform reference period (URP) to modified mixed reference period (MMRP); (b) transitioning from an expenditure aggregate to a welfare aggregate better aligned with international best practices on the treatment of lumpy expenses, and the consumption of subsidized items; and (c) revising the spatial and temporal price deflators, bringing them more in line with other countries. 8 The existing PIP estimates are revised as follows: (1) Similar adjustments have been made to the 2022/23 and 2011/12 surveys, yet comparisons across surveys are subject to caveats. The new series should be seen as the best attempt to improve the welfare measurement and maximize comparability between 2011/12 and 2022/23. (2) The estimates from 2015/16 to 2021/22, which were based on the Consumer Pyramids Household Survey (CPHS) in the absence of official survey data, have been removed since they are not comparable to the new estimates for 2011/12 and 2022/23. (3) Adjustments have been made to the data from 1977 to 2009 to improve comparability with the new series. All of this is detailed in what follows. A methodological document provides additional technical details on the construction of the welfare aggregate for 2011/12 and 2022/23, and the measurement of poverty and inequality in India (World Bank, 2025). 3.1 New data for 2022/23 and adjustments to 2011/12 data 3.1.1 Change in the recall period From URP to MMRP. The 2022/23 CES has fully transitioned to the modified mixed reference period (MMRP). Before this update, India estimates published in PIP had used the uniform reference period (URP). The MMRP is considered an improvement and alignment with international best practices, allowing respondents to recall and report purchases over more relevant recall periods (7 or 30-day recall for frequently purchased items, 365 days for infrequent purchases). The 2011/12 estimates are also replaced by MMRP for consistency—the 2011/12 CES collected two parallel samples, for URP and MMRP. By itself, this transition shifts the poverty trend significantly. In 2011/12, the MMRP lowers poverty by systematically increasing reported consumption in certain categories. 9 3.1.2 Construction of the welfare aggregate The revised estimates attempt to align India better with poverty measurement guidelines and best practices, and replace the household expenditure aggregate with a welfare aggregate, including the following changes: Welfare contribution of subsidized items. Purchases via the Public Distribution System (PDS) were re-valued to better reflect their contribution to household welfare, rather than their purchase value at subsidized price or zero-cost. Prices of PDS food rations and kerosene are imputed by finding a market-equivalent unit value (UV)—calculated as the weighted median UV across market purchases of similar items in the most local cluster with sufficient data. It is assumed that market-purchases are suitable substitutes for PDS items. The method is applied consistently in 2011/12 and 2022/23, although an expanding PDS results in more imputed items in 2022/23. A similar method is used to value the welfare contribution of school uniforms and footwear provided for free to households with students in public institutions—the median UV from local market purchases is used. Durable purchases are excluded since data limitations prevent accounting for their use value. The CES questionnaire lacks the necessary data to calculate the use-value or flow of services derived from durables. Instead, it only contains information on durable purchases over the last 365 days, which are lumpy and infrequent, and do not capture the services derived from any stock of durables that was purchased before this period. Including the purchase value of new durables alone would bias and distort the distribution of welfare. Hospitalization and other non-food items are excluded. Hospitalization expenses are excluded as they are not welfare-enhancing. Other non-food items (e.g., jewelry and wristwatches) are excluded because they primarily serve as stores of value rather than consumption (in addition, there is no information on the stock of these items). Furthermore, all these expenses are lumpy and infrequent and do not reflect typical consumption. House rental values for renters are excluded due to difficulties in consistently capturing the value of housing services for renters and homeowners. Previously, actual rent payments for renters alone were included in the consumption aggregate, which distorts welfare if homeowners’ housing services are not similarly accounted for. India’s thin rental market, particularly in rural areas [3% 10 of households report actual rents], along with the inconsistent collection of dwelling characteristics in the CES, hinder the estimation of an unbiased and accurate hedonic rents model. Despite efforts, robust estimates of housing services could not be obtained, and actual rent payments are excluded to avoid bias and re-ranking of households due to missing data. 3.1.3 Revision of price deflators Before this update, separate urban-rural PPPs were used and there was no temporal deflation during the survey period.2 With this update, more detailed spatial deflators are introduced, and the intra-temporal deflators account for price variation during the survey period. The objective of price deflation is to ensure comparability of welfare measures across different time periods and regions. The following sequential adjustments are made: (1) Intra-temporal adjustment: This deflator accounts for price variations over the 12-month survey period (July 2011 to June 2012 and August 2022 to July 2023, respectively). The monthly household expenditure on each item is adjusted for within-survey-year price variations, using monthly Consumer Price Index (CPI), disaggregated at the state, sector (urban/rural), and subgroup level. This urban-rural CPI series is sourced from the Ministry of Statistics and Programme Implementation (MoSPI) (2024). (2) Spatial adjustment: Fisher Price Indices are constructed following the Tendulkar Committee (2009) methodology to adjust for price variation across states and urban-rural areas. The indices are computed separately for each state’s rural and urban areas across 15 commodity groups, for which the survey provides information on quantities purchased (food, clothing, and footwear). The Fisher indices are then used to deflate expenditures at the commodity group level for each household (all-India representing the spatial reference). As a result, each household has a unique adjustment factor that reflects variations in both consumption patterns and regional price levels. After these adjustments, the national CPI—retrieved from the International Financial Statistics IFS)— and PPP are used to convert the welfare aggregate to 2021 PPP-adjusted US dollars. PIP no longer uses separate urban and rural PPPs and CPIs for India. In addition, PIP no longer uses 2 National poverty calculations in India built-in spatial deflation by constructing state-sector specific poverty lines. However, the single value of the IPL calls for a spatial deflation of the welfare aggregate. 11 rural and urban population shares from the Ministry of Health of India but only national population data from the World Development Indicators. 3.1.4 Caveats: Comparability between 2011/12 and 2022/23, and inequality estimates Overall, changes introduced in the 2022/23 CES are a positive step towards alignment with international best practices. Nonetheless, they pose challenges for comparability. The introduction of Computed Assisted Personal Interviewing (CAPI) in 2022/23 improves data quality but challenges comparability. The increased number of consumption items surveyed (from 347 to 405) can capture shifting consumption patterns but potentially inflates reported consumption. Use of three distinct questionnaires (food, consumables, durables) across multiple visits—enhances accuracy but may increase reported consumption by reducing interview fatigue. An additional rural stratum was created in the 2022/23 CES to include villages closer to district headquarters and larger towns. Similarly, the second-stage stratum (SSS) selection criteria changed between surveys. Several factors raise concerns that the inequality estimates based on the CES might be underestimated. These include: (a) growing concerns regarding the survey’s inability to capture households at the top of the distribution, and (b) the fact that adjustments (e.g., exclusion of housing services, and the flow of services or use-value from durables) are more likely to lead to an underestimation of consumption among better-off households. Both effects contribute to an overall underestimation of inequality. Therefore, caution is warranted in interpreting inequality estimates. 3.1.5 Final estimates Tables 4 to 6 present the September 2024 and June 2025 vintages of India estimates included in PIP, respectively. As noted above, the years 2015-2021 are no longer available in the June 2025 vintage. 12 Table 4 Poverty and inequality estimates, India 2011-2021 (September 2024 vintage) $2.15 a day $3.65 a day $6.85 a day Recall Microdata (2017 PPP) (2017 PPP) (2017 PPP) Gini Year Method period source Poverty Population Poverty Population Poverty Population Index rate (%) (million) rate (%) (million) rate (%) (million) 2011 URP Survey CES 2011-12 22.86 289.41 62.97 797.19 90.26 1,142.78 35.40 estimate 2015 URP Imputation CPHS 18.75 248.80 60.97 808.98 88.92 1,179.86 34.69 2016 URP Imputation CPHS 18.12 243.31 59.88 803.93 88.73 1,191.28 34.75 2017 URP Imputation CPHS 13.38 181.68 54.44 739.18 85.40 1,159.59 35.90 2018 URP Imputation CPHS 11.10 152.30 46.94 644.30 82.66 1,134.49 34.55 2019 URP Imputation CPHS 13.20 183.07 43.97 609.61 80.72 1,119.10 33.81 2020 URP Imputation CPHS 15.45 216.24 48.25 675.05 82.97 1,160.85 33.78 2021 URP Imputation CPHS 12.92 182.13 44.05 621.02 81.76 1,152.83 32.78 Table 5 Poverty and inequality estimates, India 2011 and 2022 (new vintage, 2017 PPP) $2.15 a day $3.65 a day $6.85 a day Recall Microdata (2017 PPP) (2017 PPP) (2017 PPP) Gini Year Method period source Poverty Population Poverty Population Poverty Population Index rate (%) (million) rate (%) (million) rate (%) (million) Survey CES 2011 MMRP 16.22 205.99 61.75 784.19 92.43 1,173.81 28.78 estimate 2011-12 Survey CES 2022 MMRP 2.35 33.67 28.12 402.89 81.93 1,173.86 25.51 estimate 2022-23 Note: While the headline estimates in PIP now use 2021 PPPs, the legacy 2017 PPP series which is also available is used for comparisons with the previous vintage. Table 6 Poverty and inequality estimates, India 2011 and 2022 (2021 PPP) $3.00 a day $4.20 a day $8.30 a day Recall Microdata (2021 PPP) (2021 PPP) (2021 PPP) Gini Year Method period source Poverty Population Poverty Population Poverty Population Index rate (%) (million) rate (%) (million) rate (%) (million) Survey CES 2011 MMRP 27.12 344.41 57.68 732.51 92.48 1,174.45 28.78 estimate 2011-12 Survey CES 2022 MMRP 5.25 75.22 23.89 342.29 82.06 1,175.72 25.51 estimate 2022-23 3.2 Adjustments to data prior to 2011 The adjustments detailed above do not involve data before 2011. Thus, estimates for the pre-2011 period rely on consumption aggregates that do not account for spatial price variation (except for 13 the urban-rural PPPs) or adjustments for free and subsidized food items, and rely on the URP. To enhance comparability with the more recent consumption aggregates, two significant adjustments have been implemented to the historic data. Firstly, a specific adjustment factor, defined at the sector-consumption level, has been applied to the nominal URP vectors to better align them with the nominal MMRP vectors. Secondly, national poverty lines have been used to deflate the pre- 2011 consumption vectors spatially. It is important to recognize that due to the absence of MMRP data and spatial deflators for the pre-2011 period, these estimates are subject to a larger uncertainty than the more recent estimates. However, they are done in a best effort to improve comparability with the new survey data, similar to efforts for Nigeria (Castaneda Aguilar et al. 2022). PIP includes six years for India prior to 2011. The three later years (1993, 2004 and 2009) are included in PIP as microdata. The first three surveys (1977, 1983 and 1988) are included as grouped data. For the years with microdata, the spatial deflation is done at the state-sector level, while for the years with grouped data, it can occur only at the sector-level. In future PIP updates, it might be possible to replace the grouped data with microdata, which would allow the spatial deflation to be done at a finer spatial level. 3.2.1 Converting between MMRP and URP In the 2011/12 CES survey, consumption aggregates relying on the URP and the MMRP were collected (as well as the MRP, mixed recall period). This information was collected on a different set of households but can nonetheless be used to understand how the two measures of consumption are related. We take the nominal URP consumption distribution and the nominal MMRP consumption distribution (including the accounting of free and subsidized food as explained in the prior section) and collapse them to the percentile-sector level. This allows us to express the consumption of the MMRP aggregate relative to the URP aggregate as a function of the URP consumption level (Figure 3). As an example, Figure 3 suggests that urban households with a per capita URP consumption of around 20,000 rupees in 2021 prices are predicted to have around 20% higher MMRP consumption than URP consumption. We create a smooth fitted line through the percentiles and apply these smoothed factors to URP aggregates from 1977, 1983, 1988, 1993, 2004, and 2009. If households in the prior surveys have 14 URP consumption aggregates smaller or larger than the factors shown in Figure 3, we apply the factor closest to their consumption level. Figure 3 Relationship between 2011/12 URP and MMRP aggregate Note: Circles indicate relationships at the percentile levels as observed in the data. Lines are fitted through the percentile data and indicate the conversion factors applied to the older rounds of the NSS. A critical assumption underlying this adjustment is that the relationship between URP and MMRP is time-invariant at the consumption-sector level. Another critical assumption is that the share of spending on subsidized items is time-invariant at the consumption-sector level. The policies behind free items that are included in the 2022/23 welfare aggregate did not exist in 2011 and before, and are therefore not part of the adjustment factors applied. 3.2.2 Spatial deflation with national poverty lines The MMRP aggregates from 2011/12 and 2021/23 apply a spatial deflation based on implicit price information in the survey. Such spatial deflation has not been calculated in prior rounds. To increase the comparability between the older and newer data, we use state-sector level national poverty lines as spatial deflators. Differences in these national poverty lines reflect varying costs of obtaining a bundle representing the same welfare level across geographic areas. The lines are 15 stated in Table 7. After applying the lines, we rescale the spatially-deflated mean such that it stays equivalent to the nominal mean. For 1977, 1983, and 1988, where state identifiers are not available in the PIP version of the consumption data, sector (urban/rural) national poverty lines are used.3 Table 7 National poverty lines used for spatial deflation (rupees per person per month)   Rural Urban   1977 1983 1988 1993 2004 2009 1977 1983 1988 1993 2004 2009 All 57 90 115       70 116 162       Andhra Pradesh       244 433 694       282 563 926 Assam       266 478 692       307 600 871 Bihar       234 433 656       283 526 775 Chhattisgarh       342 399 617       342 514 807 Delhi       315 541 748       320 642 1040 Goa       316 609 931       306 671 1025 Gujarat       279 502 726       321 659 951 Haryana       294 529 792       312 626 975 Himachal Pradesh       273 520 708       316 606 888 Jharkhand         405 616         531 831 Karnataka       267 418 629       295 588 908 Kerala       287 537 775       289 585 831 Madhya Pradesh       232 408 632       276 532 772 Maharashtra       269 485 744       329 632 961 Manipur       322 578 871       366 641 955 Meghalaya       284 503 687       393 746 990 Mizoram       317 639 850       356 700 939 Nagaland       382 687 1017       410 783 1148 Odisha       224 408 567       279 497 736 Puducherry       220 385 641       264 506 778 Punjab       287 544 830       342 643 961 Rajasthan       272 478 755       301 568 846 Sikkim       267 532 729       362 742 1035 Tamil Nadu       253 442 639       288 560 801 Tripura       276 450 663       317 556 783 Uttar Pradesh       245 435 664       282 532 800 Uttarakhand         486 720         602 899 West Bengal       236 445 643       295 573 831 Source: Government of India Planning Commission (2014) and World Bank’s South Asia Regional Micro Database (SARMD). Lakdawala methodology for 1977, 1983,1988; and Tendulkar methodology for 1993, 2004, 2009. 3 For these years, PIP uses grouped data. For the years with microdata, the spatial deflation is done at the state-sector level, while for the years with grouped data, it can occur only at the sector-level. In future PIP updates, it might be possible to replace the grouped data with microdata, which would allow the spatial deflation to be done at a finer spatial level. 16 The impact on Indian poverty rates of these adjustments is shown in Table 8. The table combines the impact of the two adjustments made above as well as the switch from separate urban and rural PPPs and CPIs to a national PPP and CPI. The adjustments lead to downwards revisions in India’s historical extreme poverty estimates by 9 and 19 percentage points, and reductions in the Gini index of about 5 points. Table 8 Changes to historical poverty and inequality estimates, India 1977-2009 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Sep Jun Sep Jun Sep Jun Sep Jun Country Year 2024 2025 2024 2025 2024 2025 2024 2025 India 1977/78 63.54 46.88 89.12 85.08 97.77 97.40 33.21 29.69 India 1983 56.68 38.05 86.63 80.84 97.38 97.16 32.01 27.20 India 1987/88 50.93 38.56 84.16 81.48 96.66 97.00 32.46 27.53 India 1993/94 47.96 33.60 82.58 79.23 96.66 97.28 31.56 26.11 India 2004/05 40.58 32.55 77.46 77.52 94.75 96.31 34.01 27.68 India 2009/10 33.46 22.50 72.50 69.80 93.31 94.93 34.89 27.83 Source: Poverty & Inequality Platform (PIP). Note: While the headline estimates in PIP now use 2021 PPPs, the legacy 2017 PPP series which is also available is used for comparisons with the previous vintage. Figure 4 breaks down what is driving these changes over the years with microdata by estimating the poverty rates if only the spatial deflation (and the switch from urban-rural to national CPIs and PPPs) is applied and not the adjustment from URP to MMRP. The changes mostly come from the URP-MMRP adjustment. About 80% of the reduction in inequality comes from this adjustment. The spatial deflation increases the poverty rate at the $2.15 line, which is more than compensated by the decrease caused by the URP-MMRP adjustment. The non-parallel trends between September 2024 and June 2025 poverty rate vintages stem from the spatial adjustment. 17 Figure 4 Changes to historical poverty and inequality estimates, India 1993-2009 $2.15 poverty rates Gini index 60 40 50 35 40 30 30 25 20 10 20 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010 Sep 2024 June 2025 June 2025 (only spatial) Sep 2024 June 2025 June 2025 (only spatial) Source: Poverty & Inequality Platform (PIP) and authors’ calculations. Note: A comparison between September 2024 and June 2025 (only spatial) captures the impact of the change in the spatial deflation (which includes the adoption of state-sector poverty lines, and a national CPI and PPP). The difference between the two series for the June vintage shows the impact of the URP to MMRP conversion. 4. Changes to welfare distributions 4.1. Botswana 2002, 2009, 2015 Botswana’s nominal consumption aggregates for the last three available income and expenditure surveys (2002/03, 2009/10, and 2015/16) have been replaced with spatially deflated consumption aggregates to be consistent with poverty estimates undertaken by Statistics Botswana. The official estimates of poverty used by Statistics Botswana already account for spatial differences in the cost of living by using Poverty Datum Lines (PDLs). The PDLs are household- and region-specific poverty lines which embed an adjustment for differences in the purchasing power across Botswana. A simple estimate of the spatial cost of living index (SPI) is the ratio of each PDL to the average national PDL. Background information can be found in Appendix 8: “Technical report on Botswana’s household surveys and the creation of a comparable spatially-deflated consumption aggregate” of the Botswana Poverty Assessment (World Bank, 2024b). 18 Table 9 Changes to poverty and inequality estimates, Botswana 2002, 2009 and 2015 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Sep Jun Sep Jun Sep Jun Sep Jun Country Year 2024 2025 2024 2025 2024 2025 2024 2025 Botswana 2002 29.14 27.12 49.56 48.74 68.60 68.85 64.73 61.54 Botswana 2009 17.69 15.06 36.67 33.27 60.37 59.53 60.46 56.93 Botswana 2015 15.43 14.22 38.00 35.32 63.51 60.50 53.33 54.91 Note: While the headline estimates in PIP now use 2021 PPPs, the legacy 2017 PPP series which is also available is used for comparisons with the previous vintage. 4.3. EU-SILC All historical EU-SILC data have been updated to data released in December 2021. The updates for each country-year are documented on the Eurostat website [CIRCABC → Eurostat → EUSILC → Library → data_dissemination → udb_user_database]. Further information on EU-SILC data can be found at: https://ec.europa.eu/eurostat/documents/203647/771732/Datasetsavailability-table.pdf and https://ec.europa.eu/eurostat/documents/203647/203704/EU+SILC+DOI+2021rel2.pdf 4.4. Guatemala 2000, 2006, 2014 The Socioeconomic Database for Latin America and the Caribbean (SEDLAC)’s harmonization of the income aggregate has changed for 2000, 2006 and 2014. Changes stem from considering the overtime payment for workers, which was previously omitted from the labor income variable. The omission had impacted the calculation of total household income in earlier versions of the harmonized data. Table 10 Changes to poverty and inequality estimates, Guatemala 2000, 2006 and 2014 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Sep Jun Sep Jun Sep Jun Sep Jun Country Year 2024 2025 2024 2025 2024 2025 2024 2025 Guatemala 2000 54.17 54.17 Guatemala 2006 11.65 11.65 25.24 25.17 48.62 48.21 54.57 54.54 Guatemala 2014 55.40 55.36 48.28 48.33 Note: While the headline estimates in PIP now use 2021 PPPs, the legacy 2017 PPP series which is also available is used for comparisons with the previous vintage. For empty cells, there were no changes in poverty or inequality (at four decimals of precision). 19 4.5. Honduras 2014- 2019 The minimum working age in Honduras has been updated from 10 years or older, to 5 years or older. This change affects labor market variables such as employed, unemployed, and economically active population, and consequently impacts the indicator that flags coherent income observations (SEDLAC variable cohh=14). Only coherent observations are included in the sample. Table 11 Changes to poverty and inequality estimates, Honduras 2014-2019 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Sep Jun Sep Jun Sep Jun Sep Jun Country Year 2024 2025 2024 2025 2024 2025 2024 2025 Honduras 2014 Honduras 2015 Honduras 2016 14.61 14.58 28.31 28.29 50.51 50.48 49.81 49.80 Honduras 2017 13.86 13.85 27.51 27.52 51.18 51.17 49.44 49.44 Honduras 2018 14.23 14.19 27.25 27.23 51.31 51.33 48.94 48.93 Honduras 2019 12.74 12.74 26.43 26.45 49.54 49.56 48.16 48.17 Note: While the headline estimates in PIP now use 2021 PPPs, the legacy 2017 PPP series which is also available is used for comparisons with the previous vintage. For empty cells, there were no changes in poverty or inequality (at four decimals of precision). 4.6. Jamaica 2018, 2021 In Jamaica as in other Caribbean countries, the value of housing services for renters is calculated as the actual rent paid for the dwelling and for non-renters is estimated using the self-assessment method. The slight changes result from a correction in the treatment of housing costs. In this revision, outliers and missing values in both actual and self-reported implicit rents were imputed using hedonic regression, which had been erroneously omitted previously. Table 12 Changes to poverty and inequality estimates, Jamaica 2018 and 2021 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Sep Jun Sep Jun Sep Jun Sep Jun Country Year 2024 2025 2024 2025 2024 2025 2024 2025 Jamaica 2018 0.06 0.06 0.56 0.56 9.08 9.04 35.64 35.33 Jamaica 2021 0.31 0.31 2.40 2.40 13.91 13.66 40.21 39.87 Note: While the headline estimates in PIP now use 2021 PPPs, the legacy 2017 PPP series which is also available is used for comparisons with the previous vintage. 4 In the SEDLAC harmonization, some observations are identified as incoherent. For example, this applies to individual observations that are identified as employed but record no income in the main occupation 20 4.7. 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 12 December 2024. The following 12 country-years have been added to PIP, as they became available in LIS: • CAN (Canada): 2020 • JPN (Japan): 2009, 2011, 2012, 2014-2020 • USA (United States): 2023 Finally, underlying data for the following 38 country-years have been updated by LIS, as explained in more detail on their website: • AUT (Austria): 1995 • CAN (Canada): 1997 • ESP (Spain): 1985 • FRA (France): 1984, 2000 • GBR (United Kingdom): 1995 • JPN (Japan): 2008, 2010, 2013 • KOR (South Korea): 2016 • LUX (Luxembourg): 1990, 1995, 1997, 1998, 2002 • USA (United States): 1963-1966, 1970, 2004-2020, 2022 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 (Atamanov et al., 2020). 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. 21 4.8. Paraguay 2022 Since 2017, Paraguay’s Institute of Statistics (INE) has been conducting the Encuesta Permanente de Hogares Continua (EPHC) alongside the traditional Encuesta Permanente de Hogares (EPH). In 2023, INE fully transitioned to the EPHC and discontinued the EPH. This transition came with two significant methodological changes: • The EPHC uses new expansion factors based on the 2022 Population and Housing Census, while the EPH series used factors based on the 2015 population projections. • The EPHC poverty estimates are now calculated using annual data, whereas up until 2022 they used only data from the last quarter of each year. INE released both the 2022 and 2023 EPHC datasets in March 2024, with these updated methodologies applied. Apart from the addition of 2023 data for Paraguay into PIP with this update, given these methodological changes and to align with Paraguay’s official poverty monitoring approach, the 2022 EPH data is being replaced with the 2022 EPHC. New poverty and inequality estimates are described in Table 13. It is important to know that, due to the different expansion factors and data collection periods, the estimates from 2021 and earlier (based on EPH) will not be directly comparable to the new series beginning in 2022 (based on EPHC). Table 13 Changes to poverty and inequality estimates, Paraguay 2022 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Sep Jun Sep Jun Sep Jun Sep Jun Country Year 2024 2025 2024 2025 2024 2025 2024 2025 Paraguay 2022 1.33 1.68 5.61 6.21 19.90 20.60 45.11 44.84 Note: While the headline estimates in PIP now use 2021 PPPs, the legacy 2017 PPP series which is also available is used for comparisons with the previous vintage. 4.9. Thailand 2016 Household size has been corrected for this survey year by excluding domestic workers from the household. This follows the definition of household size applied by the NSO for poverty measurement. The change in household size affects the population weight and per capita 22 consumption. Therefore, the poverty rate for a $6.85 line increases from 15.73 to 15.75, and the Gini index decreases from 36.89 to 36.69. Table 14 Changes to poverty and inequality estimates, Thailand 2016 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Sep Jun Sep Jun Sep Jun Sep Jun Country Year 2024 2025 2024 2025 2024 2025 2024 2025 Thailand 2016 0.04 0.036 0.98 0.98 15.73 15.75 36.89 36.69 Note: While the headline estimates in PIP now use 2021 PPPs, the legacy 2017 PPP series which is also available is used for comparisons with the previous vintage. 4.10. Tunisia 2010 Tunisia’s National Institute of Statistics revised the 2010 welfare aggregate to ensure comparability of poverty estimates with the subsequent series for 2015 and 2021. The primary revision involved changes in the approach used to impute some housing expenditures that were provided free-of-charge for specific household categories. The previous approach used to impute the regional medians, while the current approach imputes household-specific numbers based on an empirical model. As a result, the new comparable welfare aggregate is also adopted for global monitoring of poverty and inequality. These revisions to the welfare aggregate lead to lower poverty rates, and to a significant change in inequality. The latter effect occurs considering that household-specific imputation is likely to increase variation in imputed values, compared to regional imputation of the median values. Table 15 Changes to poverty and inequality estimates, Tunisia 2010 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $2.15 $3.65 $6.85 Sep Jun Sep Jun Sep Jun Sep Jun Country Year 2024 2025 2024 2025 2024 2025 2024 2025 Tunisia 2010 1.50 1.37 7.43 7.00 31.05 28.99 35.81 38.47 Note: While the headline estimates in PIP now use 2021 PPPs, the legacy 2017 PPP series which is also available is used for comparisons with the previous vintage. 23 4.11. Uzbekistan 2022 With the addition of Uzbekistan’s Household Budget Survey (HBS) income data for years 2021 and 2023, the classification for the existing 2022 welfare aggregate was corrected from consumption to income. These corrections did not lead to any changes in the value of poverty or inequality indicators. 5. Country-years removed • India: As explained in Section 3, after data adjustments, a total of seven years were removed from the India series from 2015 through 2021. For the entire series, the separate urban and rural estimates were removed. • Madagascar: The data for Madagascar are undergoing a revision, so they are not displayed on the website since the adoption of the 2021 PPPs. They are included in the aggregates. • Ukraine: For Ukraine, the latest survey predates Russia’s invasion of Ukraine, so there is considerable uncertainty over recent poverty trends in the country. As a result, the series is not shown on the PIP website. Estimates using a global methodology are still included in the long- run regional and global aggregates. Poverty trends at the national poverty line for the more recent period using a microsimulation model, are available here. 6. Economy-years added A total of 81 new economy-years were added in this update. Table A1 in the Appendix gives the complete list of new economy-years added to the PIP database. Additional details are provided for specific cases: 6.1 Belize 2018 and Barbados 2016 The Poverty and Equity GP team carried out a project to harmonize consumption data across Caribbean countries. By standardizing surveys, they created consistent consumption aggregates to accurately measure household well-being aligning with World Bank guidelines. In this recent 24 update, the team expanded their efforts by incorporating consumption-based welfare aggregates for two additional countries: Belize (2018) and Barbados (2016). 6.2 Guatemala 2023 In August 2024, Guatemala's Instituto Nacional de Estadística (INE) released official poverty estimates for the first time in nine years, based on the 2023 ENCOVI. For the national official estimates, Guatemala uses a consumption-based methodology to measure monetary poverty that has followed the World Bank guidelines (Deaton and Zaidi, 2002; Mancini and Vecchi, 2022). In 2023, several aspects of the methodology used to estimate official poverty in Guatemala were updated. First, new poverty lines were estimated using updated caloric requirements. Second, to improve the accuracy of the estimates, the methodology shifted from a single poverty line for the entire country to two poverty lines: one for urban areas and the other one for rural areas. Third, four updates according to new guidelines from Mancini and Vecchi (2022) were incorporated: (i) inclusion of in-kind donations received from family, friends, and non-governmental institutions; (ii) use of a new formula to estimate the use value of durable goods based on a “cost approach” with a geometric model to estimate the annual depreciation rate; (iii) inclusion of all items listed in the household food consumption section; and (iv) the exclusion of homemade products made from food inputs. To make the series comparable across time, the new methodology was used in previous years to estimate the consumption aggregate and measured against the new poverty line. 6.3 Honduras 2023 In January 2020, the Government of Honduras, in close collaboration with the World Bank, updated its official poverty measurement methodology for the years 2014-2019. The new official income aggregate included improvements in data cleaning, annualized labor income for salaried workers receiving 13th and 14th salaries, and imputed rent (for more details, see Arayavechkit et al. (2021)). The World Bank applied the new methodology to the years 2011-2013 to extend the comparable series in Honduras, ensuring consistency and reliability across different years. In this 25 update, these improvements were also incorporated into the new 2023 data, enhancing the comprehensive analysis of poverty trends over a longer timeframe. 6.4 Lebanon 2022 The Lebanon Household Survey (LHS), co-funded by the World Bank, WFP, and UNHCR, was conducted between December 2022 and May 2023. It covered Lebanese, Syrians and other nationals (except for Palestinians in camps and gatherings). Data collected includes information on demographics, education, health, employment, expenditures, assets, income, and coping strategies. While the survey aimed to be nationally representative, several areas were inaccessible, restricting the final sample to around 4,200 households in five out of eight governorates. The lack of observational data for 2022 in the excluded governorates of Baalbek El-Hermel, El-Nabatieh, and South Lebanon precludes a robust assessment of the state of poverty in those areas and, by extension, for the country. The decision to use this partial data was made on the basis that the latest poverty number for Lebanon -coming from the 2011/12 Household Budget Survey- is extremely outdated and precedes the COVID-19 pandemic and socio-economic crisis the country underwent afterward. 6.5 West Bank and Gaza 2023 The Palestinian Expenditure and Consumption Survey (PECS) is a multi-purpose survey focusing on household budgets and living standards, used to calculate official poverty estimates for Palestinian territories. The 2023 PECS was conducted from January to December 2023, with three quarters of data collected in Gaza and four quarters in the West Bank and East Jerusalem. The final sample included 4,373 households comprising 21,797 individuals. Due to the outbreak of war in October 2023, it was not possible to collect Q4 data in Gaza, though data collection continued in the West Bank and East Jerusalem. Poverty estimates for 2023 use three-quarters weights for Gaza and annual weights for the West Bank, ensuring the use of all available data, and leveraging off the fact that the survey was designed so that each quarter is representative. The official methodology employs a Laspeyres index using prices for 139 food and nonfood products from urban areas to reflect price variation in the official aggregate. This index is constructed quarterly for three areas: West Bank, East Jerusalem, and 26 Gaza. As the survey was interrupted in the last quarter in Gaza, the price index uses the third quarter's national price averages as the reference. It should be noted that the impact of the war in Gaza is not captured, though impacts of the first three months of the war on the West Bank are. 7. Changes to CPI data The baseline source of CPI data has been updated to the IMF’s International Financial Statistics (IFS) as of 5 November 2024. Lakner et al. (2018) provide an overview of the various CPI series that are used in PIP. Table A2 in the Appendix to this note gives the up-to-date source of the deflator for all countries included in PIP as of the current update. 8. Changes to national accounts and population data We have incorporated new national accounts and population data from the latest vintages of our standard sources. The primary source of national accounts data is the January 2025 vintage of the WDI. As before, when WDI data are missing, data from the IMF’s WEO, October 2024 version are used. Supplementary data from the MPD, 2023 version are further used to fill in for missing observations. For a more complete series, national accounts data are chained on backward or forward using growth rates in WEO data, or MPD data, when WDI data are missing. The population data has also been revised to the January 2025 vintage of the WDI. 9. 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://datanalytics.worldbank.org/PIP-Methodology/welfareaggregate.html#comparability. The PIP website also indicates comparability in its main output. 27 10. Flagging data points with non-national survey coverage Starting with this June 2025 update, PIP will begin documenting cases when surveys that are used for estimation of a country’s poverty rate are based on surveys will less than national coverage. Such situations can emerge, for instance, because national statistical offices are not able to collect information in certain areas of the country due to security reasons. Table 16 below shows the countries that will be flagged as having partial survey coverage. As better documentation of other data collection efforts becomes available, PIP will continue updating the information available. Table 16 Countries that will be flagged as having partial survey coverage Country Year Regions not adequately captured in the survey Myanmar 2017 Northern parts of Rakhine State Honduras 2011 and later Departments ‘Gracias a Dios’ and ‘Islas de Bahia’ South Sudan 2016 Three states: Jonglei, Unity, and Upper Nile Nigeria 2018/19 Borno Excluding the Autonomous Republic of Crimea, the city of Ukraine 2014 and later Sevastopol, and parts of Donetsk and Luhansk regions Lebanon 2022 Baalbek El-Hermel, El-Nabatieh and South Lebanon governorates Source: Authors’ compilation using World Bank’s Household Survey Scorecard and Poverty Monitoring Database. 28 References Arayavechkit, T., Atamanov, A., Barreto Herrera, K., Belhaj Hassine Belghith, N., Castaneda Aguilar, R.A., Fujs, T., Dewina, R., Diaz-Bonilla, C., Edochie, I., Jolliffe, D., Lakner, C., Mahler, D., Montes, J., Moreno Herrera, L., Mungai, R., Newhouse, D., Nguyen, M., Sanchez Castro, D., Schoch, M., Sharma, D., Simler, K., Swinkel, R., Takamatsu, S., Uochi, I., Viveros Mendoza, M., Yonzan, N., Yoshida, N., Wu, H., 2021. March 2021 PovcalNet Update: What’s New (Global Poverty Monitoring Technical Note Series No. 15). The World Bank. 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 Atamanov, A., Castaneda Aguilar, R.A., Fujs, T.H., Dewina, R., Diaz-Bonilla, C., Mahler, D.G., Jolliffe, D., Lakner, C., Matytsin, M., Montes, J., 2020. March 2020 PovcalNet Update: What’s New (Global Poverty Monitoring Technical Note No. 11). Castaneda, R.A.A., Diaz-Bonilla, C., Fujs, T., Jolliffe, D., Lakner, C., Mahler, D., Nguyen, M., Schoch, M., Tetteh-Baah, S., Viveros Mendoza, M., Wu, H., Yonzan, N., 2022. September 2022 Update to the Poverty and Inequality Platform (PIP): What’s New (Global Poverty Monitoring Technical Note Series No. 24). The World Bank. 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). Deaton, A., Zaidi, S., 2002. Guidelines for Constructing Consumption Aggregates for Welfare Analysis, Living Standards Measurement Study Working Paper. World Bank, Washington, DC. Foster, E.M. ⓡ Jolliffe, D. ⓡ Lara Ibarra, G. ⓡ Lakner, C. ⓡ Tetteh-Baah, S.K. , 2025. Global Poverty Revisited Using 2021 PPPs and New Data on Consumption (World Bank Policy Research Working Paper Series No. 11137). World Bank, Washington, DC. Government of India Planning Commission, 2014. Report of the Expert Group to Review the Methodology for Measurement of Poverty. 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. Mancini, G., Vecchi, G., 2022. On the Construction of a Consumption Aggregate for Inequality and Poverty Analysis. World Bank. Ministry of Statistics and Programme Implementation (MoSPI), 2024. Factsheet of Household Consumption Expenditure Survey (HCES) 2022-23. India National Statistical Office (NSO), Delhi. World Bank, 2024a. Purchasing Power Parities and the Size of World Economies: Results from the International Comparison Program 2021. World Bank, Washington, D.C. World Bank, 2024b. Botswana Poverty Assessment: Renewing Pathways for Poverty and Inequality Reduction. World Bank, Washington, D.C. 29 World Bank, 2025. India: Trends in Poverty, 2011-12 to 2022-23. Methodology Note. World Bank, Washington, D.C. https://documents.worldbank.org/en/publication/documents- reports/documentdetail/099060325033540333 30 A. Appendix A.1. Complete list of new country-years Table A1. Economies-years added in June 2025 PIP update Economy Year Survey Name Argentina 2023 EPHC-S2 Armenia 2023 ILCS Austria 2022 EU-SILC Belgium 2022 EU-SILC Bulgaria 2022 EU-SILC Belize 2018 HBS Bolivia 2022 EH Bolivia 2023 EH Brazil 2023 PNADC-E1 Barbados 2016 BSLC Canada 2020 CIS-LIS Switzerland 2021 EU-SILC Colombia 2023 GEIH Costa Rica 2024 ENAHO Cyprus 2022 EU-SILC Czechia 2022 EU-SILC Denmark 2022 EU-SILC Dominican Republic 2023 ECNFT-Q03 Egypt, Arab Rep. 2021 HIECS Spain 2022 EU-SILC Estonia 2022 EU-SILC Ethiopia 2021 HCES Finland 2022 EU-SILC France 2022 EU-SILC Georgia 2023 HIS Equatorial Guinea 2022 ENH2 Greece 2022 EU-SILC Guatemala 2023 ENCOVI Honduras 2023 EPHPM Croatia 2022 EU-SILC Hungary 2022 EU-SILC Indonesia 2024 SUSENAS India 2022 HCES Ireland 2022 EU-SILC Iran 2023 HEIS Iraq 2023 IHSES 31 Iceland 2018 EU-SILC Italy 2022 EU-SILC Japan 2009 JHPS-KHPS-LIS Japan 2011 JHPS-KHPS-LIS Japan 2012 JHPS-KHPS-LIS Japan 2014 JHPS-KHPS-LIS Japan 2015 JHPS-KHPS-LIS Japan 2016 JHPS-KHPS-LIS Japan 2017 JHPS-KHPS-LIS Japan 2018 JHPS-KHPS-LIS Japan 2019 JHPS-KHPS-LIS Japan 2020 JHPS-KHPS-LIS Kosovo 2018 SILC-C Kosovo 2019 SILC-C Kosovo 2020 SILC-C Kosovo 2021 SILC-C Lebanon 2022 LHS Lithuania 2022 EU-SILC Luxembourg 2022 EU-SILC Latvia 2022 EU-SILC Moldova 2022 HBS Malta 2021 EU-SILC Malta 2022 EU-SILC Norway 2020 EU-SILC Norway 2021 EU-SILC Norway 2022 EU-SILC Peru 2023 ENAHO Philippines 2023 FIES Poland 2022 EU-SILC Portugal 2022 EU-SILC Paraguay 2023 EPHC West Bank and Gaza 2023 PECS Romania 2022 EU-SILC El Salvador 2023 EHPM Serbia 2022 EU-SILC Slovak Republic 2022 EU-SILC Slovenia 2022 EU-SILC Sweden 2022 EU-SILC Thailand 2022 SES Thailand 2023 SES Türkiye 2022 SILC-C Uruguay 2023 ECH United States 2023 CPS-ASEC-LIS 32 Uzbekistan 2021 HBS Uzbekistan 2023 HBS 33 A.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-202411 denotes the monthly IFS database version November 2024. For economy-specific deflators, the description is given in the text or further details are available upon request. 34 Table A2. Source of temporal deflators used in June 2025 PIP update Code Economy Survey Year(s) CPI period Source HBS 2000 W IFS-M-202411 AGO Angola IBEP-MICS 2008 W IFS-M-202411 IDREA 2018 W IFS-M-202411 EWS 1996 Y IFS-M-202411 LSMS 2002-2012 Y IFS-M-202411 ALB Albania HBS 2014-2020 Y IFS-M-202411 SILC-C 2017-2019 (prev. year)Y IFS-M-202411 United Arab HIES 2014 W IFS-M-202411 ARE Emirates 2019 Y IFS-M-202411 EPH 1980-1987 Y NSO 1991-2002 M9 NSO ARG Argentina - urban EPHC-S2 2003-2023 M7-M12 NSO 2007-2014 M7-M12 Private estimates ARM Armenia ILCS ALL Y IFS-M-202411 IHS-LIS 1981 Y IFS-A-202411 IDS-LIS 1985 Y IFS-A-202411 AUS Australia SIHCA-LIS 1989 Y IFS-A-202411 SIH-LIS 1995-2018 Y IFS-A-202411 SIH-HES-LIS 2004-2016 Y IFS-A-202411 ECHP-LIS 1994-2000 Y IFS-M-202411 AUT Austria EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 SLC 1995 Y IFS-M-202411 AZE Azerbaijan HBS 2001-2005 Y IFS-M-202411 EDCM 1992 Y IFS-M-202411 EP 1998 W IFS-M-202411 BDI Burundi QUIBB 2006 Y IFS-M-202411 ECVMB 2013 W IFS-M-202411 EICVMB 2020 W IFS-M-202411 BEL Belgium SEP-LIS 1985-1997 Y IFS-M-202411 PSBH-ECHP-LIS 1995-2000 Y IFS-M-202411 EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 QUIBB 2003 Y IFS-M-202411 EMICOV 2011 W IFS-M-202411 BEN Benin 2015 Y IFS-M-202411 EHCVM 2018 M10 IFS-M-202411 2021 M11 IFS-M-202411 EP-I 1994 W IFS-M-202411 EP-II 1998 Y IFS-M-202411 BFA Burkina Faso ECVM 2003-2009 Y IFS-M-202411 EMC 2014 Y IFS-M-202411 EHCVM 2018 M9 IFS-M-202411 35 2021 M10 IFS-M-202411 HHES 1983-1985 W WEO-A-202410 1988-1991 W IFS-A-202411 BGD Bangladesh 1995 W Survey HIES 2000-2022 Y Survey HBS 1989 Y IFS-A-202411 1992-1994 Y IFS-M-202411 BGR Bulgaria IHS 1995-2001 Y IFS-M-202411 MTHS 2003-2007 Y IFS-M-202411 EU-SILC 2007-2023 (prev. year)Y IFS-M-202411 Bosnia and LSMS 2001-2004 Y WEO-A-202410 BIH Herzegovina HBS 2007-2011 Y IFS-M-202411 FBS 1993-1995 Y IFS-M-202411 BLR Belarus HHS 1998-2020 Y IFS-M-202411 LFS 1993-1999 Y IFS-A-202411 HBS 1995 Y IFS-A-202411 BLZ Belize SLC 1996 Y IFS-A-202411 HBS 2018 W IFS-A-202411 Bolivia - urban EPF 1990 W IFS-M-202411 EIH 1992 M11 IFS-M-202411 Bolivia ENE 1997 M11 IFS-M-202411 ECH 1999 M10 IFS-M-202411 BOL 2000 M11 IFS-M-202411 EH 2001-2005 M11 IFS-M-202411 ECH 2004 M10 IFS-M-202411 EH 2006-2016 M10 IFS-M-202411 2017-2023 M11 IFS-M-202411 PNAD 1981-2011 M9 IFS-M-202411 BRA Brazil PNADC-E1 2012-2023 Y IFS-M-202411 PNADC-E5 2020-2021 Y IFS-M-202411 BRB Barbados BSLC 2016 M2 IFS-M-202411 BLSS 2003-2017 Y Previous WDI/IFS BTN Bhutan 2022 M1-M8 Previous WDI/IFS HIES 1985-2002 W IFS-M-202411 BWA Botswana CWIS 2009 W IFS-M-202411 BMTHS 2015 W IFS-M-202411 EPCM 1992 W IFS-M-202411 Central African CAF ECASEB 2008 Y IFS-M-202411 Republic EHCVM 2021 M5 IFS-M-202411 SCF-LIS 1971-1995 Y IFS-M-202411 CAN Canada SLID-LIS 1996-2011 Y IFS-M-202411 CIS-LIS 2012-2020 Y IFS-M-202411 SIWS-LIS 1982 Y IFS-M-202411 CHE Switzerland NPS-LIS 1992 Y IFS-M-202411 36 IES-LIS 2000-2004 Y IFS-M-202411 EU-SILC 2007-2022 (prev. year)Y IFS-M-202411 CASEN 1987 Y IFS-M-202411 CHL Chile 1990-2022 M11 IFS-M-202411 CRHS-CUHS 1981-2011 Y NSO CHN China CNIHS 2012-2021 Y NSO EPAM 1985-1988 W IFS-M-202411 EP 1992 W IFS-M-202411 CIV Côte d'Ivoire ENV 1995-2015 Y IFS-M-202411 EHCVM 2018 M10 IFS-M-202411 2021 M11 IFS-M-202411 ECAM-I 1996 Y IFS-M-202411 ECAM-II 2001 Y IFS-M-202411 CMR Cameroon ECAM-III 2007 Y IFS-M-202411 ECAM-IV 2014 Y IFS-M-202411 ECAM-V 2021 M10 IFS-M-202411 Congo, Dem. E123 2004-2012 W IFS-M-202411 COD Rep. EGI-ODD 2020 Y WEO-A-202410 ECOM 2005 Y IFS-M-202411 COG Congo, Rep. 2011 W IFS-M-202411 Colombia - urban ENH 1980-1988 Y IFS-M-202411 1989-1991 M11 IFS-M-202411 COL Colombia 1992-2000 M11 IFS-M-202411 ECH 2001-2005 M11 IFS-M-202411 GEIH 2008-2023 M11 IFS-M-202411 EIM 2004 Y IFS-M-202411 COM Comoros EESIC 2013 Y IFS-M-202411 IDRF 2001 W IFS-M-202411 CPV Cabo Verde QUIBB 2007 W IFS-M-202411 IDRF 2015 Y IFS-M-202411 ENH 1981-1986 Y IFS-M-202411 EHPM 1989 Y IFS-M-202411 CRI Costa Rica 1990-2009 M7 IFS-M-202411 ENAHO 2010-2024 M7 IFS-M-202411 CYP Cyprus EU-SILC ALL (prev. year)Y IFS-M-202411 MC-LIS 1992-2002 Y IFS-M-202411 CZE Czech Republic CM 1993 Y IFS-M-202411 EU-SILC 2005-2023 (prev. year)Y IFS-M-202411 DEU Germany LIS ALL Y IFS-M-202411 EDAM 2002-2013 Y IFS-M-202411 DJI Djibouti 2017 M5 IFS-M-202411 LM-LIS 1987-2000 Y IFS-M-202411 DNK Denmark EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 DOM ENGSLF 1986-1989 Y IFS-M-202411 37 ICS 1992 M6 IFS-M-202411 ENFT 1996 M2 IFS-M-202411 Dominican 1997 M4 IFS-M-202411 Republic 2000-2016 M9 IFS-M-202411 ECNFT-Q03 2017-2023 Y IFS-M-202411 EDCM 1988 Y IFS-M-202411 DZA Algeria ENMNV 1995 Y IFS-M-202411 ENCNVM 2011 W IFS-M-202411 Ecuador - urban EPED 1987 Y IFS-M-202411 Ecuador ECV 1994 M6-M10 IFS-M-202411 Ecuador - urban EPED 1995 M11 IFS-M-202411 ECU 1998 M6 IFS-M-202411 (prev. Ecuador ECV 1999 year)M10-M9 IFS-M-202411 EPED 2000 M11 IFS-M-202411 ENEMDU 2003-2023 M11 IFS-M-202411 HIECS 1990-2012 W IFS-M-202411 EGY Egypt, Arab Rep. 2015 Y IFS-M-202411 2017-2021 W IFS-M-202411 HBS-LIS 1980-1990 Y IFS-M-202411 HBCS-LIS 1985 Y IFS-M-202411 ESP Spain ECHP-LIS 1993-2000 Y IFS-M-202411 EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 HIES 1993-1998 Y IFS-M-202411 EST Estonia HBS 2000-2004 Y IFS-M-202411 EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 Ethiopia - rural HICES 1981 W IFS-M-202411 Ethiopia 1995-2010 W IFS-M-202411 ETH 2015 M12 IFS-M-202411 HCES 2021 M12 IFS-M-202411 IDS-LIS 1987-2000 Y IFS-M-202411 FIN Finland EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 FJI Fiji HIES ALL W IFS-M-202411 TIS-LIS 1970-1990 Y IFS-M-202411 FRA France TSIS-LIS 1996-2002 Y IFS-M-202411 EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 Micronesia, Fed. Sts. - urban CPH 2000 Y IFS-A-202411 FSM Micronesia, Fed. Sts. HIES 2005-2013 Y IFS-A-202411 GAB Gabon EGEP ALL Y IFS-M-202411 FES-LIS 1968-1993 Y IFS-M-202411 GBR United Kingdom FRS-LIS 1994-2021 Y IFS-M-202411 GEO Georgia HIS ALL Y IFS-M-202411 38 GLSS-I 1987 W IFS-M-202411 GLSS-II 1988 W IFS-M-202411 GLSS-III 1991 W IFS-M-202411 GHA Ghana GLSS-IV 1998 W IFS-M-202411 GLSS-V 2005 W Survey GLSS-VI 2012 W Survey GLSS-VII 2016 W Survey ESIP 1991 Y WEO-A-202410 EIBC 1994 W WEO-A-202410 GIN Guinea EIBEP 2002 W WEO-A-202410 ELEP 2007-2012 Y IFS-M-202411 EHCVM 2018 W IFS-M-202411 HPS 1998 Y IFS-M-202411 GMB Gambia, The HIS 2003 W IFS-M-202411 IHS 2010-2020 W IFS-M-202411 ILJF 1991 Y IFS-M-202411 ICOF 1993 Y IFS-M-202411 ILAP-I 2002 Y IFS-M-202411 GNB Guinea-Bissau ILAP-II 2010 Y IFS-M-202411 EHCVM 2018 W IFS-M-202411 2021 M11 IFS-M-202411 M8-(next GNQ Equatorial Guinea ENH2 2022 year)M11 IFS-M-202411 ECHP-LIS 1995-2000 Y IFS-M-202411 GRC Greece EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 GRD Grenada SLCHB 2018 M4 IFS-M-202411 ENSD 1986 W IFS-M-202411 1989 Y IFS-M-202411 GTM Guatemala ENIGF 1998 M8 IFS-M-202411 ENCOVI 2000 M6-M11 IFS-M-202411 2006-2023 M7 IFS-M-202411 GLSMS 1992 W WEO-A-202410 GUY Guyana 1998 Y IFS-M-202411 Honduras - urban ECSFT 1986 Y IFS-M-202411 Honduras EPHPM 1989 Y IFS-M-202411 HND 1990-1993 M5 IFS-M-202411 1994 M9 IFS-M-202411 1995-2023 M5 IFS-M-202411 HBS 1988-2010 Y IFS-M-202411 HRV Croatia EU-SILC 2010-2023 (prev. year)Y IFS-M-202411 ECVH 2001 M5 IFS-M-202411 HTI Haiti ECVMAS 2012 M10 IFS-M-202411 HBS 1987-2007 Y IFS-M-202411 HUN Hungary HHP-LIS 1991-1994 Y IFS-M-202411 39 THMS-LIS 1999 Y IFS-M-202411 EU-SILC 2005-2023 (prev. year)Y IFS-M-202411 SUSENAS 1984-1999 Y IFS-M-202411 IDN Indonesia 2000-2007 M2 IFS-M-202411 2008-2024 M3 IFS-M-202411 M7-(next NSS 1977 year)M6 NSO 1983 Y NSO M7-(next IND India NSS-SCH1 1987-2009 year)M6 NSO M7-(next NSS-SCH2 2011 year)M6 NSO M8-(next HCES 2022 year)M7 NSO SIDPUSS-LIS 1987 Y IFS-M-202411 LIS-ECHP-LIS 1994-2000 Y IFS-M-202411 IRL Ireland SILC-LIS 2002 Y IFS-M-202411 EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 SECH 1986 Y IFS-A-202411 1990-1998 Y IFS-M-202411 IRN Iran, Islamic Rep. HEIS 2005-2009 W IFS-M-202411 M4-(next 2011-2023 year)M3 IFS-M-202411 IHSES 2006 W COSIT IRQ Iraq 2012 Y COSIT 2023 Y COSIT/IFS ISL Iceland EU-SILC ALL (prev. year)Y IFS-M-202411 ISR Israel HES-LIS ALL Y IFS-M-202411 SHIW-LIS 1977-2002 Y IFS-M-202411 ITA Italy EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 SLC 1988 M9 IFS-M-202411 M11-(next 1990-1993 year)M3 IFS-M-202411 JAM Jamaica 1996 M5-M8 IFS-M-202411 1999 M6-M8 IFS-M-202411 2002-2004 M6 IFS-M-202411 JSLC 2018-2021 Y IFS-M-202411 HEIS 1986 W IFS-M-202411 JOR Jordan 1992-1997 Y IFS-M-202411 2002-2010 W IFS-M-202411 JPN Japan JHPS-KHPS-LIS ALL Y IFS-M-202411 HBS 1993-2021 Y IFS-M-202411 KAZ Kazakhstan LSMS 1996 Y IFS-M-202411 WMS-I 1992 Y NSO KEN Kenya WMS-II 1994 Y NSO 40 WMS-III 1997 Y NSO IHBS 2005-2015 W NSO KCHS 2020 M6 NSO 2021 M7 NSO/IFS KPMS 1998 Y IFS-M-202411 KGZ Kyrgyz Republic HBS 2000-2003 Y IFS-M-202411 KIHS 2004-2022 Y IFS-M-202411 HIES 2006 Y IFS-M-202411 KIR Kiribati 2019 W IFS-M-202411 HIES-FHES-LIS 2006-2014 Y IFS-M-202411 KOR Korea, Rep. SHFLC-LIS 2016-2021 Y IFS-M-202411 LECS 1992 W IFS-A-202411 LAO Lao PDR 1997 W IFS-M-202411 2002-2018 W Survey HBS 2011 (next year)M5 IFS-M-202411 LBN Lebanon LHS 2022 (next year)M1 IFS-M-202411 CWIQ 2007 Y IFS-M-202411 LBR Liberia HIES 2014-2016 Y IFS-M-202411 LSMS 1995 Y IFS-M-202411 LCA St. Lucia SLCHBS 2015 M11 IFS-M-202411 LFSS 1985 Y IFS-M-202411 HIES 1990 W IFS-M-202411 SES 1995 W IFS-M-202411 LKA Sri Lanka HIES 2002 Y IFS-M-202411 2006-2012 W IFS-M-202411 2016-2019 Y IFS-M-202411 HBS 1986 W WEO-A-202410 NHECS 1994 W WEO-A-202410 LSO Lesotho HBS 2002 W IFS-M-202411 CMSHBS 2017 M8 IFS-M-202411 HBS 1993-2008 Y IFS-M-202411 LTU Lithuania EU-SILC 2005-2023 (prev. year)Y IFS-M-202411 PSELL-LIS 1985-1993 Y IFS-M-202411 PSELL-ECHP- LUX Luxembourg LIS 1994-2001 Y IFS-M-202411 SEP-SILC-LIS 2002 Y IFS-M-202411 EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 HBS 1993-2009 Y IFS-M-202411 LVA Latvia EU-SILC 2005-2023 (prev. year)Y IFS-M-202411 ECDM 1984 W IFS-M-202411 MAR Morocco ENNVM 1990-2006 W IFS-M-202411 ENCDM 2000-2013 W IFS-M-202411 MDA Moldova HBS ALL Y IFS-M-202411 MDG Madagascar EB 1980 Y IFS-M-202411 41 EPM 1993 W IFS-M-202411 1997-2010 Y IFS-M-202411 ENSOMD 2012 W IFS-M-202411 HIES 2002-2009 W IFS-M-202411 MDV Maldives 2016 Y IFS-M-202411 2019 M11 IFS-M-202411 ENIGH 1984-2014 M8 IFS-M-202411 MEX Mexico ENIGHNS 2016-2022 M8 IFS-M-202411 MHL Marshall Islands HIES 2019 W WEO-A-202410 HBS 1998-2008 Y IFS-M-202411 MKD North Macedonia SILC-C 2010-2020 (prev. year)Y IFS-M-202411 EMCES 1994 Y IFS-A-202411 EMEP 2001 W IFS-M-202411 MLI Mali ELIM 2006-2009 W IFS-M-202411 EHCVM 2018-2021 M10 IFS-M-202411 MLT Malta EU-SILC ALL (prev. year)Y IFS-M-202411 MPLCS 2015 M1 IFS-M-202411 MMR Myanmar MLCS 2017 Q1 IFS-M-202411 HBS 2005-2014 Y IFS-M-202411 MNE Montenegro SILC-C 2013-2022 (prev. year)Y IFS-M-202411 LSMS 1995-1998 Y IFS-M-202411 HIES-LSMS 2002 W IFS-M-202411 MNG Mongolia HSES 2007 W IFS-M-202411 2010-2022 Y IFS-M-202411 NHS 1996 W WEO-A-202410 MOZ Mozambique IAF 2002 W WEO-A-202410 IOF 2008-2019 W IFS-M-202411 EPCV 1987 Y IFS-M-202411 EP 1993 Y IFS-M-202411 MRT Mauritania EPCV 1995-2008 W IFS-M-202411 2014 Y IFS-M-202411 2019 M11 IFS-M-202411 HBS 2006 W IFS-M-202411 MUS Mauritius 2012-2017 Y IFS-M-202411 IHS-I 1997 W IFS-M-202411 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-202411 (prev. MYS Malaysia year)M7- (prev. 2004 year)M12 IFS-M-202411 42 (prev. year)M7- (prev. 2007 year)M10 IFS-M-202411 2009 W IFS-M-202411 2012-2016 Y IFS-M-202411 HIESBA 2019 W IFS-M-202411 HIS 2022 W IFS-M-202411 NHIES 1993 W WEO-A-202410 NAM Namibia 2003-2015 W IFS-M-202411 ENBCM 1992-2007 W IFS-M-202411 EPCES 1994 W IFS-M-202411 ENCVM 2005 Y IFS-M-202411 NER Niger ECVMA 2011-2014 Y IFS-M-202411 EHCVM 2018 M10 IFS-M-202411 2021 M11 IFS-M-202411 NCS 1985 W IFS-M-202411 1992-1996 Y IFS-M-202411 LSS 2003 W IFS-M-202411 GHSP-W1 2010 M3-M4 IFS-M-202411 NGA Nigeria GHSP-W2 2012 M3-M4 IFS-M-202411 GHSP-W3 2015 M3-M4 IFS-M-202411 (next year)M3-(next LSS 2018 year)M4 IFS-M-202411 EMNV 1993 M2 NSO 1998 M6 NSO NIC Nicaragua 2001 M6 IFS-M-202411 2005-2009 M8 IFS-M-202411 2014 M8-M10 IFS-M-202411 AVO-LIS 1983-1990 Y IFS-M-202411 NLD Netherlands SEP-LIS 1993-1999 Y IFS-M-202411 EU-SILC 2005-2022 (prev. year)Y IFS-M-202411 IDS-LIS 1979-2000 Y IFS-M-202411 NOR Norway EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 MHBS 1984 W IFS-M-202411 LSS-I 1995 W IFS-M-202411 NPL Nepal LSS-II 2003 W IFS-M-202411 LSS-III 2010 W IFS-M-202411 M6-(next LSS-IV 2022 year)M5 IFS-M-202411 NRU Nauru HIES 2012 W IFS-M-202411 HIES 1987 Y IFS-M-202411 PAK Pakistan 1990-1998 W IFS-M-202411 IHS 1996 W IFS-M-202411 43 PIHS 2001 M6 IFS-M-202411 HIES 2004-2018 (next year)M1 IFS-M-202411 EMO 1979-1989 Y IFS-M-202411 PAN Panama 1991 M7 IFS-M-202411 EH 1995-2023 M7 IFS-M-202411 Peru ENNIV 1985 W IFS-M-202411 1994 Y IFS-M-202411 PER ENAHO 1997-2002 Q4 IFS-M-202411 2003 M5-M12 IFS-M-202411 2004-2023 Y IFS-M-202411 PHL Philippines FIES ALL Y IFS-M-202411 Papua New HIES 1996 Y IFS-A-202411 PNG Guinea 2009 W IFS-A-202411 HBS 1985-1987 Y IFS-A-202411 HBS-LIS 1986 Y IFS-A-202411 POL Poland HBS 1989-2019 Y IFS-M-202411 HBS-LIS 1992-1999 Y IFS-M-202411 EU-SILC 2005-2023 (prev. year)Y IFS-M-202411 PRT Portugal EU-SILC ALL (prev. year)Y IFS-M-202411 EH 1990 M7 IFS-M-202411 1995 M8-M11 IFS-M-202411 EIH 1997 (next year)M2 IFS-M-202411 EPH 1999 M9 IFS-M-202411 EIH 2001 M3 IFS-M-202411 EPH 2002 M11 IFS-M-202411 2003 M9 IFS-M-202411 PRY Paraguay 2004 M10 IFS-M-202411 2005 M11 IFS-M-202411 2006 M12 IFS-M-202411 2007-2008 M10 IFS-M-202411 2009 M11 IFS-M-202411 2010-2021 M10 IFS-M-202411 EPHC 2022-2023 M6 IFS-M-202411 PECS 2004-2011 Y IFS-M-202411 West Bank and PSE 2016 W IFS-M-202411 Gaza 2023 Q2 IFS-M-202411 QAT Qatar HIES 2017 W IFS-M-202411 HBS 1989 Y Milanovic (1998) MC 1992 Y IFS-M-202411 HIS 1994-1999 Y IFS-M-202411 ROU Romania IHS-LIS 1995-1997 Y IFS-M-202411 IHS 1998-2000 Y IFS-M-202411 HBS 2001-2021 Y IFS-M-202411 EU-SILC 2007-2023 (prev. year)Y IFS-M-202411 44 HBS 1993-2020 Y IFS-M-202411 Russian RUS VNDN 2015-2021 (prev. year)Y IFS-M-202411 Federation 2022 (prev. year)Y WEO-A-202410 Rwanda - rural ENBCM 1984 W IFS-M-202411 RWA Rwanda EICV-I 2000 W IFS-M-202411 EICV-II 2005 W IFS-M-202411 EICV-III 2010 (next year)M1 IFS-M-202411 EICV-IV 2013 (next year)M1 IFS-M-202411 EICV-V 2016 (next year)M1 IFS-M-202411 NBHS 2009 Y IFS-M-202411 SDN Sudan 2014 M11 IFS-M-202411 EP 1991 W IFS-M-202411 ESAM 1994 W IFS-M-202411 ESAM-II 2001 W IFS-M-202411 SEN Senegal ESPS-I 2005 W IFS-M-202411 ESPS-II 2011 W IFS-M-202411 EHCVM 2018 M9 IFS-M-202411 2021 M11 IFS-M-202411 SLB Solomon Islands HIES ALL W IFS-M-202411 SLIHS 2003 W WEO-A-202410 SLE Sierra Leone 2011-2018 Y IFS-M-202411 EHPM 1989 Y IFS-M-202411 M10-(next 1991 year)M4 IFS-M-202411 SLV El Salvador 1995-1999 Y IFS-M-202411 2000-2007 M12 IFS-M-202411 2008-2023 M11 IFS-M-202411 LSMS 2002 Y IFS-M-202411 SRB Serbia HBS 2003-2019 Y IFS-M-202411 EU-SILC 2013-2023 (prev. year)Y IFS-M-202411 NBHS 2009 Y IFS-M-202411 SSD South Sudan (prev. HFS-W3 2016 year)M7 IFS-M-202411 São Tomé and IOF 2000 W IFS-M-202411 STP Principe 2010-2017 Y IFS-M-202411 Suriname - urban EHS 1999 Y IFS-M-202411 SUR Suriname SSLC 2022 Y IFS-M-202411 MC-LIS 1992-1996 Y IFS-M-202411 SVK Slovak Republic HBS 2004-2009 Y IFS-M-202411 EU-SILC 2005-2023 (prev. year)Y IFS-M-202411 IES 1987-1993 Y IFS-M-202411 HBS-LIS 1997-1999 Y IFS-M-202411 SVN Slovenia HBS 1998-2003 Y IFS-M-202411 EU-SILC 2005-2023 (prev. year)Y IFS-M-202411 45 HIS-LIS 1975-2002 Y IFS-M-202411 SWE Sweden EU-SILC 2004-2023 (prev. year)Y IFS-M-202411 SWZ Eswatini HIES ALL W IFS-M-202411 HES 1999 W IFS-M-202411 HBS 2006 W IFS-M-202411 SYC Seychelles 2013 Y IFS-M-202411 2018 W IFS-M-202411 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-202411 ECOSIT-III 2011 Y IFS-M-202411 TCD Chad EHCVM 2018 W IFS-M-202411 2022 M2 IFS-M-202411 QUIBB 2006-2015 Y IFS-M-202411 TGO Togo EHCVM 2018-2021 M10 IFS-M-202411 THA Thailand SES ALL Y IFS-M-202411 TLSS 1999 Y WEO-A-202410 2003-2007 Y Survey TJK Tajikistan HBS 2004 Y Survey TLSS 2009 Y IFS-M-202411 HSITAFIEN 2015 Y IFS-M-202411 TKM Turkmenistan LSMS 1998 Y WEO-A-202410 TLSS 2001 Y WEO-A-202410 TLS Timor-Leste TLSLS 2007-2014 Y IFS-M-202411 HIES 2000 W IFS-M-202411 TON Tonga 2009-2021 Y IFS-M-202411 Trinidad and SLC 1988 Y IFS-M-202411 TTO Tobago PHC 1992 Y IFS-M-202411 HBCS 1985 Y IFS-A-202411 1990 Y IFS-M-202411 TUN Tunisia LSS 1995-2000 Y IFS-M-202411 NSHBCSL 2005-2015 W IFS-M-202411 M3-(next 2021 year)M3 IFS-M-202411 HICES 1987-2019 Y IFS-M-202411 TUR Türkiye SILC-C 2018-2023 (prev. year)Y IFS-M-202411 TUV Tuvalu HIES 2010 Y IFS-A-202411 TWN Taiwan, China FIDES-LIS ALL Y WEO-A-202410 HBS 1991 W IFS-A-202411 2000 W IFS-M-202411 TZA Tanzania 2007 Y IFS-M-202411 2011-2018 W IFS-M-202411 UGA Uganda HBS 1989 Y WEO-A-202410 46 NIHS 1992 W WEO-A-202410 1996-1999 W IFS-M-202411 UNHS 2002-2019 W IFS-M-202411 HS 1992-1993 Y IFS-M-202411 UKR Ukraine HIES 1995-1996 Y IFS-M-202411 HLCS 1999-2020 Y IFS-M-202411 Uruguay - urban ENH 1981-1989 Y IFS-M-202411 (prev. ECH 1992-2005 year)M12 IFS-M-202411 URY (prev. Uruguay 2006-2023 year)M12 IFS-M-202411 (prev. ECH-S2 2021 year)M12 IFS-M-202411 CPS-LIS 1963-2001 Y IFS-M-202411 USA United States CPS-ASEC-LIS 2002-2023 Y IFS-M-202411 HBS 1998-2023 Y WEO-A-202410 UZB Uzbekistan 2021-2022 Y IFS-M-202411 EHM 1981-1989 Y NSO VEN Venezuela, RB 1992-2006 M12 NSO VLSS 1992 W WEO-A-202410 VNM Viet Nam 1997 W IFS-M-202411 VHLSS 2002-2022 M1 IFS-M-202411 HIES 2010 Y IFS-A-202411 VUT Vanuatu NSDP 2019 W IFS-A-202411 HIES 2002-2008 Y IFS-M-202411 WSM Samoa 2013 W IFS-M-202411 HBS 2003-2017 Y IFS-M-202411 XKX Kosovo SILC-C 2018-2022 (prev. year)Y IFS-M-202411 HBS 1998 Y IFS-M-202411 YEM Yemen, Rep. 2005 W IFS-M-202411 2014 Y IFS-M-202411 KIDS 1993 Y IFS-M-202411 HIES 2000 W IFS-M-202411 ZAF South Africa IES 2005-2010 (next year)M6 IFS-M-202411 LCS 2008 W IFS-M-202411 2014 (next year)M6 IFS-M-202411 HBS 1991-1993 Y IFS-M-202411 LCMS-I 1996 Y IFS-M-202411 LCMS-II 1998 Y IFS-M-202411 LCMS-III 2002 W IFS-M-202411 ZMB Zambia LCMS-IV 2004 W IFS-M-202411 LCMS-V 2006 W IFS-M-202411 LCMS-VI 2010 Y IFS-M-202411 LCMS-VII 2015 Y IFS-M-202411 47 LCMS-VIII 2022 Y IFS-M-202411 Implicit from national ICES 2011 Y ZWE Zimbabwe account PICES 2017-2019 Y Survey 48