Global Poverty Monitoring Technical Note 47 September 2025 Update to the Poverty and Inequality Platform (PIP) What’s New Agustin Arakaki, Danielle Aron, R. Andres Castaneda Aguilar, Tony Fujs, Ivan Gachet, Christoph Lakner, Gabriel Lara Ibarra, Jonas Lønborg, Daniel G. Mahler, Kelly Y. Montoya Munoz, Francis Mulangu, Mario Negre Rossignoli, Olive Umuhire Nsababera, Minh C. Nguyen, Sergio Olivieri, Ana Maria Oviedo, Juan Carlos Parra, Zander Prinsloo, Diana M. Sanchez, Tomoyuki Sho, Samuel K. Tetteh-Baah, Leopoldo Tornarolli, Martha C. Viveros Mendoza, Haoyu Wu, Nishant Yonzan and Nobuo Yoshida. September 2025 Keywords: What’s New; September 2025; Missing countries; Regional definition Development Data Group Development Research Group Poverty and Equity Global Department GLOBAL POVERTY MONITORING TECHNICAL NOTE 47 Abstract The September 2025 update to the Poverty and Inequality Platform (PIP) introduces changes to the data underlying the global poverty estimates. This document details the changes to underlying data and the reasons behind them. It also explains the change in methodology used for countries without data, as well as a minor change in how surveys are interpolated. Finally, the regional classification used in PIP has been aligned with the World Bank classification as of July 2025, although users continue to be able to construct their own regional aggregates from the underlying country-level data. 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 55 new country-years, bringing the total number of distributions to over 2,500 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 partial 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. New Method for Predicting Welfare Distributions for Countries Without Any Survey Data .... 7 3. Changes in Line-up distribution methodology ......................................................................... 10 4. Changes to welfare distributions............................................................................................... 12 4.1. Bangladesh 2022 ................................................................................................................ 12 4.2. Ecuador 2003, 2005 and 2006 ........................................................................................... 13 4.3. EU-SILC ............................................................................................................................ 14 4.4. Luxembourg Income Study (LIS) ...................................................................................... 14 4.5. Mozambique 2019 ............................................................................................................. 15 4.6. Paraguay 2022 and 2023 .................................................................................................... 16 4.7. Uruguay 2000..................................................................................................................... 16 5. Country-years removed ............................................................................................................. 17 5.1. Hungary 2018-2022 ......................................................................................................... 17 6. Economy-years added ............................................................................................................... 17 6.1. Madagascar ........................................................................................................................ 18 6.2. Rwanda .............................................................................................................................. 18 7. Changes to CPI data .................................................................................................................. 18 7.1. Madagascar 2012-2021 ...................................................................................................... 19 8. Changes to national accounts and population data ................................................................... 20 9. Comparability database............................................................................................................. 20 10. Flagging data points with non-national survey coverage ....................................................... 21 References ..................................................................................................................................... 22 A. Appendix .................................................................................................................................. 24 A.1. Extrapolations ................................................................................................................... 24 A.2. Interpolations .................................................................................................................... 25 A.2.1 “Same direction” interpolation.................................................................................... 26 A.2.2 “Diverging directions” interpolation ........................................................................... 27 B. Appendix .................................................................................................................................. 28 B.1. Complete list of new country-years .................................................................................. 28 B.2. CPI data sources ................................................................................................................ 30 1 1. Introduction The September 2025 global poverty update by the World Bank revises previously published poverty and inequality estimates including an update of the nowcasted estimates up to 2025. Regional aggregates are published until 2024 for all regions, now including Sub-Saharan Africa where the inclusion of 2022 survey data for Nigeria results in improved coverage for the region. Moreover, methodological enhancements are included in this release to the way welfare distributions for countries without data are predicted, as well as minor changes to the line-up distribution methodology (sections 2 and 3). With regards to the underlying survey data, this update brings an additional 55 country-year datapoints to the PIP database, whilst improving existing data for another 11 country-years. The PIP database now contains over 2,500 distributions from 172 countries. This update also marks a change to the regional groupings used in PIP. Prior to this update, PIP used its own regional definition that was grounded in the World Bank’s regional classification, but for which some high-income economies were excluded from the geographical regions. These economies were included as a separate group referred to as “Other High Income”. With this update, PIP applies the World Bank regional classification, where the high-income economies are included in their respective geographic regions. This means that the “Other High Income” grouping is no longer used, and that a new region, “North America”, now exists. Users should also be aware that the World Bank recently moved Afghanistan and Pakistan from the South Asia region to the Middle East & North Africa region. With these joint regional changes, the only region that remains unchanged is Sub-Saharan Africa. The new regional classification can be found in Section 5.6 of the PIP Methodological Handbook. Several additional materials are available to better understand the implications of the changes in the regional classification. PIP’s “Further Indicators & Data” page includes a new dashboard (“Aggregates”) that compares the old and new regional classifications, and up-to-date aggregates for the old regions are also available there. The PIP Stata package provides access to the new regions by default but allows the option to use the old regions (see codes in Table 1). Finally, the 2 PIP Stata package will also include an example of how users can construct alternative aggregates (regional and other groups) from the available country-year information. Table 1 Comparison of New and Old regional classification and codes New regional classification Old regional classification Region Name Code Region Name Code East Asia & Pacific EAS East Asia & Pacific EAP Europe & Central Asia ECS Europe & Central Asia ECA Latin America & Caribbean LCN Latin America & Caribbean LAC Middle East, North Africa, Afghanistan & Pakistan MEA Middle East & North Africa MNA North America NAC Other High Income Countries OHI South Asia SAS South Asia SAR1 Sub-Saharan Africa SSF Sub-Saharan Africa SSA Eastern and Southern Africa AFE Eastern and Southern Africa AFE Western and Central Africa AFW Western and Central Africa AFW World WLD World WLD Note: 1 - In previous vintages of PIP, the region code for South Asia was SAS. To allow for distinction between the former and current regional classification of South Asia, the code for the former grouping is changed to SAR in the September version of PIP. The new codes also align with the codes used in the World Development Indicators. Table 2 reports the poverty rates by region based on the current update (and new regional classification), including nowcasted estimates. Globally, extreme poverty is estimated at 10.3 percent in 2024 and projected to decrease to 10.1 percent in 2025. Most of this decrease is driven by an expected continuing decrease in the extreme poverty rate in South Asia. With the inclusion of data from Nigeria, regional coverage is now reached for Western and Central Africa. On the other hand, updating the line-up year from 2023 to 2024 results in a loss of coverage for Eastern and Southern Africa, where survey coverage drops from 52.7 to 37.8 percent. Nevertheless, this update results in coverage for all of Sub-Saharan Africa with estimates for the region now based on data covering 56.7 percent of the Sub-Saharan population around 2024 (Table 2). However, regional data coverage for the period since COVID is lost for the Middle East and North Africa region following the inclusion of Afghanistan and Pakistan, with the latest survey data for the latter being from 2018 and no survey data currently available for Afghanistan in PIP. 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 50.5 to 46.3 percent in 2024 3 and is expected to decline further to 45.5 percent by 2025. The greatest improvements are observed in more prosperous regions, particularly East Asia & Pacific. Table 2 Percentage of population living in poverty by region, 2020 – 2025 Survey $3.00 (2021 PPP) $8.30 (2021PPP) Region coverage (%) in 2024 2020 2021 2022 2023 2024 2025 2020 2021 2022 2023 2024 2025 East Asia & Pacific 87.7 2.7 2.6 2.4 2.2 2.0 1.9 34.4 29.4 29.8 27.7 26.4 25.3 Europe & Central Asia 82.1 0.8 0.8 0.7 0.6 0.5 0.5 8.1 7.0 6.7 5.5 5.1 4.9 Latin America & Caribbean 89.6 5.7 6.3 5.1 4.6 4.5 4.4 30.4 31.0 28.0 26.9 25.9 25.5 Middle East, North Africa, 38.5 12.1 12.0 11.6 11.6 11.8 11.8 61.6 60.7 59.6 59.0 58.9 58.6 Afghanistan and Pakistan North America 100.0 0.3 0.2 1.1 1.0 1.0 1.0 1.2 0.9 1.9 1.8 1.7 1.7 South Asia 98.7 9.4 7.9 6.1 4.7 3.8 3.1 84.8 83.4 81.4 78.8 76.5 74.1 Sub-Saharan Africa 56.7 46.4 46.6 46.4 46.5 46.0 45.2 88.7 89.0 89.0 89.2 89.0 88.7 Eastern and Southern Africa 37.8 53.4 53.5 53.1 53.3 52.9 52.1 89.9 90.1 90.0 90.3 90.2 90.0 Western and Central Africa 84.5 36.2 36.4 36.5 36.6 35.7 35.0 87.1 87.4 87.7 87.7 87.3 86.8 World 80.2 11.4 11.2 10.8 10.5 10.3 10.1 50.5 48.8 48.3 47.1 46.3 45.5 Source: Poverty & Inequality Platform (PIP) Note: The new regional classification is used. All regional and global poverty estimates for 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. While 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. Global survey coverage for low- and lower-middle-income countries in 2024 is 81.3%. Table 3 compares the regional and global poverty estimates for 2023 from the September 2025 data vintage with the most recent release of June 2025 (in 2021 PPPs). For comparison purposes between data vintages, this is done using the latest available line-up year across the two vintages and using the former regional classification, with the numbers thus differing from the regional classification used in Table 2. Globally, the extreme poverty rate in 2023 is revised upwards from 10.2 percent in the June vintage to 10.5 percent in the September vintage. This is due primarily to an upward revision of extreme poverty in Western and Central Africa, which increases the extreme 4 poverty rate for Sub-Saharan Africa from 45.2 to 46.5 percent.1 The updated methodology for predicting welfare in countries without survey data also results in an upward revision of extreme poverty in the former South Asia region, primarily due to a higher estimated poverty level in Afghanistan.2 The number of people living in extreme poverty globally in 2023 is revised upwards from 821.7 million people in the June vintage to 848.5 million people in the September vintage. Table 3 Poverty estimates reported for the latest year with sufficient data coverage (2023), changes between June and September 2025 vintages by region and poverty lines Survey $3.00 $4.20 $8.30 Coverage Headcount Number of Headcount Number of Headcount Number of Region (%) ratio (%) poor (mil) ratio (%) poor (mil) ratio (%) poor (mil) Sep Jun Sep Jun Sep Jun Sep Jun Sep Jun Sep Jun Sep 2025 2025 2025 2025 2025 2025 2025 2025 2025 2025 2025 2025 2025 East Asia & Pacific 94.4 2.3 2.4 48.5 51.2 6.1 6.4 131.4 137.3 30.3 30.7 648.3 657.0 Europe & Central Asia 93.5 1.0 0.8 4.8 4.2 2.1 1.9 10.6 9.5 10.1 9.5 49.9 46.9 Latin America & Caribbean 90.1 4.7 4.6 30.7 30.2 8.8 8.7 57.6 56.9 27.3 27.0 178.3 176.7 Middle East & North Africa 66.7 8.7 8.3 38.8 37.1 15.1 14.5 67.0 64.4 49.0 48.4 218.2 215.3 Other High Income 93.0 0.7 0.6 7.7 6.8 0.9 0.7 10.0 8.4 1.5 1.4 17.4 16.2 South Asia 84.0 6.3 6.8 122.1 133.0 24.7 25.0 482.8 488.8 80.1 80.2 1,563.7 1,566.0 Sub-Saharan Africa 66.4 45.2 46.5 569.3 586.0 62.9 64.7 792.8 814.8 88.3 89.2 1,113.1 1,124.1 Eastern and Southern 53.7 53.2 53.3 399.5 399.7 69.5 69.8 521.6 523.8 90.1 90.3 675.9 677.3 Africa Western and Central 85.0 33.3 36.6 169.8 186.3 53.2 57.1 271.2 290.9 85.8 87.7 437.2 446.8 Africa World Total 85.4 10.2 10.5 821.7 848.5 19.3 19.6 1,552.3 1,580.1 47.0 47.1 3,788.9 3,802.1 Source: Poverty & Inequality Platform (PIP) 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 2023 is 84.1%. 1 The change in regional classification does not affect Sub-Saharan Africa. 2 Since this table uses the old regional classification, Afghanistan is included in South Asia. With this update, it will no longer be included in South Asia, but instead with the Middle East, North Africa, Afghanistan and Pakistan region (see Table 2). 5 Similar revisions are observed at the higher poverty lines of $4.20 and $8.30 respectively. The poverty rate at $4.20 is revised from 19.3 to 19.6 percent – i.e. from 1,552.3 million to 1,580.1 million people. For the $8.30 poverty line the rate is revised from 47.0 to 47.1 percent – i.e. from 3,788.9 million to 3,802.1 million people. By comparing the most recent figures across Tables 2 and 3, it is furthermore possible to see the implications of the change in the regional classification. For instance, the regional change of Afghanistan and Pakistan results in a decline in the estimated poverty rate for South Asia and an increase in the Middle East region. In 2023, the poverty rate in the new Middle East, North Africa, Afghanistan and Pakistan region is 11.6 percent, compared with 8.3 percent in the former Middle East and North Africa region. For South Asia, the extreme poverty rate falls from 6.8 percent to 4.7 percent in 2023 with the new classification. The above changes observed in regional and global poverty estimates are explained by the newly available data and changes to the surveys included in the Poverty and Inequality Platform (PIP) including the revised methodology and the update of the regional classification. Table 4 provides an overview of the survey data used in this update. Revisions have been made to 11 welfare distributions from the previous update to improve the quality of the data (see Section 4). 55 new country-years have been added (see Section 6) and 5 removed (see Section 5), bringing the total number of distributions to 2,510.3 Table 4 Overview of survey data by PIP vintage June September Description Difference 2025 2025 Distributions 2,460 2,510 50 Country-years with income and consumption 88 88 0 Country-years 2,372 2,422 50 Countries 172 172 0 Surveys revised 90 11 Surveys removed 7 5 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. 3 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. 6 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. New Method for Predicting Welfare Distributions for Countries Without Any Survey Data This PIP update changes how poverty is predicted for countries that do not have a poverty estimate in PIP. These countries are missing primarily because of a lack of survey data, but in some cases they are missing due to the absence of national accounts data that is used in extrapolating or interpolating surveys. These predictions are made for the exclusive purpose of estimating the regional number of poor, the global number of poor, and computing the global poverty rate. They are not intended to be used as a country-specific poverty estimate. Prior to this update, these countries were assigned the regional poverty rate – that is, the average (population weighted) poverty rate of the countries in the same region with available poverty rates. From this update, the method developed in Mahler et al. (2025) has been adopted. The approach uses a handful of variables that are available in all (or nearly all) economies to predict full welfare distributions. Four models are used depending on whether GDP per capita data is available and depending on whether welfare should be predicted in 2021 PPPs or 2017 PPPs. The preferred model is referred to as Tier 1 and is used when GDP per capita is available, while the Tier 2 model without GDP per capita is used elsewhere. Welfare, , for country and year is predicted as follows: ln(, ) = , + ln ( ) 1 − , Here and are vectors of variables predictive of welfare and and are vectors of corresponding coefficients, both shown in Table 5. represents quantiles—equally spaced values 7 between 0 and 1. In particular, we calculate welfare at 10,000 equally spaced quantile points. Thus, the model is used to approximate the full distribution of welfare . Table 5 Coefficients estimated to predict full distributions for missing countries Outcome variable: Log welfare, ln() 2021 PPPs 2017 PPPs Tier 1 Tier 2 Tier 1 Tier 2 GDP GDP GDP GDP available unavailable available unavailable Intercept -2.2640 1.6046 -2.5147 1.4189 Log GDP per capita (2021 or 2017 PPP) 0.4316 0.4915 Log under-5 mortality (per 1,000 live births) -0.1532 -0.2566 -0.1645 -0.2323 Life expectancy (years) 0.0142 0.0197 0.0090 0.0190 Rural population share (0-100) -0.0027 -0.0077 -0.0013 -0.0078 Income group Low income Base Base Lower-middle income 0.2219 0.2090 Upper-middle income 0.4486 0.5520 High income 1.1389 1.2483 Welfare type (income =1, consumption = 0) -4.1402 -4.4971 Welfare type * log GDP per capita 0.4328 0.4708 Intercept 0.3465 0.3453 0.3719 0.3181 I[Europe & Central Asia] -0.0264 -0.0229 -0.0574 -0.0037 I[Latin America & Caribbean] 0.1482 0.1526 0.1232 0.1752 I[Sub-Saharan Africa] 0.0609 0.0665 0.0246 0.0683 Source: Mahler et al. (2025) The output from Table 5 can be used to directly predict poverty rates if one isolates , which can then be interpreted as a poverty rate, and is interpreted as the corresponding poverty line, as per the following equation 8 1 −1 exp(, ) , , = [1 + ( ) ] Suppose we want to predict the extreme poverty rate in Libya (a missing country in PIP) in 2024. GDP per capita in 2021 PPPs is available, so we can use the Tier 1 model. GDP per capita in 2021 PPPs is $12,276, under-5 mortality is 14.8 per 1,000 live births, life expectancy at birth is 71.1 years, and the rural population share is 18.1. To predict a consumption-based poverty rate, we first evaluate the following equation , = −2.2640 + 0.4316 ∗ log(12276) − 0.1532 ∗ log(14.8) + 0.0142 ∗ 71.1 − 0.0027 ∗ 14.8 = 2.34762 Using upper-middle income poverty line of $8.300, the predicted poverty rate becomes4 1 −1 exp(2.34762) 0.3465 ,2024 = [1 + ( ) ] = 33.9% 8.30 With the September 2025 PIP update, this method is now applied to 47 economies without any data in PIP. These economies make up less than 3% of the global population, and hence this revision does not have a sizeable impact on global poverty counts. A quantification of the impact with 2017 PPPs is given in Annex B.4 of Mahler et al. (2025). They found the extreme poverty rate to increase by 0.14 percentage points because of implementing this method and the poverty rate at the upper-middle-income poverty line to increase by 0.21 percentage points. The small increases are driven by the fact that the countries without any data tend to be poorer than their region (the regional poverty rates were assigned to these countries under the old method). For example, PIP does not have any lined-up poverty estimates for Afghanistan, Somalia, Venezuela, Cambodia, and North Korea, all of which are poorer than the region to which they belong. Due to the large possible errors from any poverty imputation model, the poverty rates recovered using the method above are not reported on the PIP website. They are produced to be included in 4 PIP uses more decimals than what is shown in Table 4 and these calculations. When using more decimals, the predicted poverty rate becomes 33.74%. 9 the regional and global aggregates. Users can see the poverty rates generated from this method through the PIP Stata command by using the fillgaps option. 3. Changes in Line-up distribution methodology In PIP, countries that have surveys at some points in time, but not in the exact year for which a global or regional aggregate is reported (the “reference year”), are interpolated and extrapolated using growth in national accounts adjusted for a passthrough rate. Before this update, PIP interpolated or extrapolated mean income or consumption, which was then used to “lineup” poverty for the reference year (see Appendix A). This update implements the following change to the backend: Instead of only extrapolating/interpolating the mean, the entire distribution is extrapolated/interpolated, which adds more flexibility. For example, the bottom-coding threshold can now be applied in each reference year. Also, in the future any welfare measure (such as inequality measures) could be estimated in the reference year, not just the poverty rate and mean, which are currently reported. For extrapolations, the methodology is the same as before where the survey from the last (first) available year is extrapolated forward (backward) using the growth in national accounts adjusted by a passthrough rate. For details, see Appendix A.1. For interpolations, PIP now constructs a single distribution for the reference year by (i) extrapolating forward or backward the two surveys on either side of the reference year to the reference year using growth in national accounts. The scaling factor remains the same as the prior methodology and makes use of the “same direction” or “diverging directions” interpolation approaches (see Appendix A.2), (ii) stacking the interpolated surveys, (iii) reweighting the survey by the surveys’ relative proximity to the target year, and (iv) rescaling the weight of the stacked distribution to match the population totals in the target year. The choice of whether to use the consumption or income-based aggregates remains the same and can be found in section 5.4 of the PIP Methodology Handbook. Previously, indicators were computed separately from each survey, lined-up to the reference year, and then averaged using distance-based weights. For details on the previous approach see Appendix A. 10 This new approach will make the bottom coding of the lined-up distributions consistent with what is done for the surveys. The September 2024 What’s New discussed the process and motivation for (a) dropping negative incomes/consumption and (b) replacing all values below the relevant bottom-threshold ($0.28/day in 2021 $PPP, $0.25/day in 2017 $PPP, or $0.22/day in 2011 $PPP) with the bottom-threshold value (Aron et al., 2024). Before this update, the survey distributions were bottom censored, which were then used in the line-up. However, the lined-up distributions extrapolated and interpolated from the (censored) survey distributions were not re-censored.5 With this update, the lined-up distributions are censored in every reference year. After attaining the lined-up distributions, they are converted to 20,000 points for each distribution (i.e. per reporting level and reference year). This is done for computational reasons, since some of the survey distribution files are very large. It is important to note that these collapsed distributions are used only for the line-up, while the survey-year estimates are based on the full microdata files. This may result in minor differences, typically at the 4th decimal, between poverty estimates reported in the survey-years, and those that are lined-up and used for the aggregates.6 The bottom code, and hence the switch in lineup methodology, does not affect measures that are not distributionally sensitive like the poverty headcount ratio (unless one chooses a poverty line close to the bottom-censoring threshold). For the poverty gap (FGT1) and squared poverty gap (FGT2), the magnitude and direction of changes will depend on the mass of the distribution near the floor and the growth rate. Any revisions are expected to be small because these measures are not very bottom-sensitive (Kraay et al., 2023). Among the measures currently reported for lineup years in PIP, the Prosperity Gap would be most affected by the bottom code due to the measures bottom sensitivity. For the September 2024 and the June 2025 PIP updates, an ad hoc solution was applied where the Prosperity Gap for lineup 5 For example, consider a country that has no surveys in an earlier year, so a more recent survey is extrapolated backwards. In the survey year, the distribution would have been bottom-censored, so there are no observations below $0.28 (in 2021 PPPs). However, assuming that the country has had positive growth over the earlier years, extrapolating this survey backwards might introduce some observations below the bottom-censoring threshold. 6 For some cases, such as Germany in 1995, the difference can be slightly larger (at the first decimal of the poverty rate) due to the fact that a) the underlying distribution (extracted from the Luxembourg Income Study) only has 400 observations, b) the poverty line falls in the very tails of the distribution (less than 0.3 percent). Regardless, the absolute effect of this imprecision is very small even in these cases. 11 years was calculated using the 1000 bin distribution. The 1000 bin distribution proxies a welfare distribution for each country and can be bottom coded. This update removes this temporary solution that was in place in the last two data releases. The changes to the line-up calculation can be summarized as follows: • Any welfare measures reported for survey-year distributions are unaffected. If a country has a survey in the reference year, the reference-year distribution is also unchanged. • For countries that do not have a survey in the reference year, the bottom-censoring in those references years can change. This will not affect the lined-up poverty estimates, since the poverty rate is not distribution-sensitive. It may have small effects on the lined-up poverty gap, squared poverty gap or mean. • The reference-year estimates for the prosperity gap change because they are now based on the censored lined-up microdata rather than the 1000 bin distribution. This does not affect the estimates for the prosperity gap in the survey-years. • In general, the methods for lining up (e.g., choosing between income and consumption surveys, the pass-through rates, or the choice in national accounts growth rates) all remain unchanged. 4. Changes to welfare distributions 4.1. Bangladesh 2022 For Bangladesh 2022, the welfare aggregate is now spatially deflated to account for cost-of-living differences across regions in Bangladesh using a mean-preserving approach. This adjustment has three steps:7 (i) poverty lines are constructed at the level of 16 divisions, relying on the spatial price variation from unit values in the household surveys;8 (ii) a spatial adjustment factor is 7 For further details about this methodology, see World Bank (2013). 88 The 16 divisions are regions split by urban, rural, as well as some metropolitan areas. For the food component of each poverty line, 11 food items or categories (as recommended by Ravallion and Sen (1996)) are priced by the median unit value in each division from the reference population (2nd to 6th decile) in the household survey. Using these 16 food poverty lines, the non-food component is defined using the upper bound in Ravallion’s cost of basic needs 12 calculated by dividing the poverty line for each observation by the average poverty line across all observations; and (iii) the consumption aggregate is re-estimated by dividing it by this spatial adjustment factor, ensuring that its mean remains unchanged. The spatial deflation is only applied in the 2022 survey round, so the consumption aggregate is not comparable with the country’s previous estimates. The 2022 round also included several additional improvements to the data quality, such as creating a rigorous process in selecting quality enumerators; conducting residential training; introducing Computer Assisted Personal Interviewing (CAPI), the Classification of Individual Consumption by Purpose (COICOP), the use of weighing scales, and the strict collection of diary data with continuous monitoring and intense supervision in the field. These steps also substantially increased the number of food and non-food items collected. Table 6 Changes to poverty and inequality estimates, Bangladesh 2022 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $3.00 $4.20 $8.30 Jun Sep Jun Sep Jun Sep Jun Sep Country Year 2025 2025 2025 2025 2025 2025 2025 2025 Bangladesh 2022 8.01 5.91 24.43 20.46 72.16 71.50 33.37 30.93 4.2. Ecuador 2003, 2005 and 2006 Previously, labor income was excluded for individuals whose employment status was missing if they had not worked during the last week. However, this approach overlooked those who may have worked during the rest of the month but not in the reference week. In the current version, this issue has been corrected, ensuring that labor income is properly accounted even if employment status was missing for the last week. Changes to the poverty and inequality estimates are only detectable at the second decimal. approach (Ravallion, 2016). The price variation is based on unit values observed in the household survey. It is important to note that the 2022 survey had improved data collection through the use of weighing scales, as well as the use of market prices to validate responses. 13 Table 7 Changes to poverty and inequality estimates, Ecuador 2003, 2005 and 2006 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $3.00 $4.20 $8.30 Jun Sep Jun Sep Jun Sep Jun Sep Country Year 2025 2025 2025 2025 2025 2025 2025 2025 Ecuador 2003 18.87 18.86 29.86 29.84 58.74 58.71 53.52 53.51 Ecuador 2005 51.87 51.86 53.05 53.06 Ecuador 2006 52.25 52.25 Note: For empty cells, there were no changes in poverty or inequality (at two decimals precision). 4.3. EU-SILC All historical EU-SILC data have been updated to data released in July 2025. The updates for each country-year are documented on the Eurostat website [Eurostat → Data → Microdata → EU statistics on income and living conditions]. Further information on EU-SILC data can be found at: https://ec.europa.eu/eurostat/documents/203647/771732/Datasets-availability-table.pdf and https://ec.europa.eu/eurostat/documents/203647/22127502/EUSILC_DOI_2025.pdf/f5e5ef59- 19e6-c7bf-7df1-10f7f653a0ab?t=1756397495096. 4.4. 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.9 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.10 The break in comparability (between LIS and EU-SILC) is indicated through PIP’s main outputs.11 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 21 July 2025. 9 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. 10 These additional pre-EUSILC surveys were introduced in the March 2020 update (Atamanov et al., 2020). 11 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 The following 3 country-years have been added to PIP, as they became available in LIS: • AUS (Australia): 2020 • CAN (Canada): 2021 • GRC (Greece): 2002 Finally, the underlying data for the following 3 country-years have been updated by LIS, as explained in more detail on their website: • GRC (Greece): 2000 • POL (Poland): 1995, 1999 4.5. Mozambique 2019 As part of the latest revision of IOF2019, a methodological update was made to the depreciation rate used in the treatment of durables. The nominal interest rate was adjusted from zero to 10.25 percent, aligning the approach with standard practice. This change results in modest shifts in the welfare aggregate: mean per capita consumption increases from $2.30 to $2.37 (2021 PPP), the Gini coefficient moves from 50.26 to 50.74, and the poverty rate at the international poverty line declines by approximately 0.6 percentage points. These refinements improve consistency in the estimation framework while leaving the overall poverty and distributional profile broadly unchanged. To compute food consumption from the diary, it is essential to consider both sources of information: food purchased and food produced by the household. By summing up these two sources, we find that there are no households with missing observations in the diary and only 160 households, or roughly 1 percent, declaring zero food consumption from both these sources. Applying Lasso imputations for the 160 households that reported zero food consumption from both food purchases and own production results in no change in the poverty rate, and inequality decreases slightly by 0.06 Gini points. Consistent with previous work done for the IOF 2019/20 (0.7% of households reporting zero total food consumption) and IOF 2014/15 (3.3%), we retained 15 these 1 percent of households with zero food consumption as reported in the diary in the raw data. Note that total consumption is bottom censored in PIP at $0.28 (2021 PPPs). Table 8 Changes to poverty and inequality estimates, Mozambique 2019 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $3.00 $4.20 $8.30 Jun Sep Jun Sep Jun Sep Jun Sep Country Year 2025 2025 2025 2025 2025 2025 2025 2025 Mozambique 2019 82.24 81.63 89.43 89.01 96.83 96.54 50.26 50.74 4.6. Paraguay 2022 and 2023 The Paraguayan National Statistics Institute (INE) has released new data for the 2022 and 2023 Encuesta Permanente de Hogares Continua (EPHC). In this update, the monetary value of school lunches provided under the “Hambre Cero” Feeding Program has been incorporated. This addition recognizes that the benefit represents a significant in-kind income for households. The update specifically applies to individuals in the sample who attend a formal educational institution and reported receiving this food benefit during the month prior to the survey date. This change is being adopted with this update and will be used going forward. Further information can be found here. The effect on poverty and inequality is reported in the table below. Table 9 Changes to poverty and inequality estimates, Paraguay 2022 and 2023 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $3.00 $4.20 $8.30 Jun Sep Jun Sep Jun Sep Jun Sep Country Year 2025 2025 2025 2025 2025 2025 2025 2025 Paraguay 2022 3.48 3.23 7.58 7.19 26.34 25.83 44.84 44.59 Paraguay 2023 2.56 2.39 6.08 5.79 23.23 22.80 44.38 44.18 4.7. Uruguay 2000 There was a correction to the variable that captures the labor income of people who have a secondary occupation. Some of these observations were mistakenly treated as missing values in labor income instead of positive income, which has now been corrected. Specifically, the incomes were recorded as missing because secondary job tenure had been erroneously classified as missing 16 (since there was no direct survey question asking about secondary employment), but other information available in the survey questionnaire could be used to identify whether the respondent had a secondary job. This correction affects the indicator which flags “coherent” income observations (SEDLAC variable cohh=112) and, therefore, the overall household income. Table 10 Changes to poverty and inequality estimates, Uruguay 2000 Poverty rate (%) Poverty rate (%) Poverty rate (%) Gini Index $3.00 $4.20 $8.30 Jun Sep Jun Sep Jun Sep Jun Sep Country Year 2025 2025 2025 2025 2025 2025 2025 2025 Uruguay 2000 0.88 0.83 2.09 1.97 12.34 11.72 42.91 43.37 5. Country-years removed 5.1. Hungary 2018-2022 Recently, Eurostat removed Hungarian microdata from the EU Statistics on Income and Living Conditions (EU-SILC) database from 2019 through 2024. Consequently, the data are also removed from PIP, where the affected data falls within the reference years 2018 through 2022.13 For more information, see this Information Note from Eurostat: https://ec.europa.eu/eurostat/databrowser-backend/api/public/explanatory- notes/get/Info_note_ILC_HU_20250605.pdf 6. Economy-years added A total of 55 new economy-years were added in this update. Table B1 in the Appendix B gives the complete list of new economy-years added to the PIP database. Additional details are provided for specific cases: 12 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. Only coherent observations are included in the sample. 13 The year variable in PIP captures the year for which welfare is collected. For example, the EU-SILC survey for 2019 collects income for the previous year. 17 6.1. Madagascar During the previous (June 2025) data release, data for Madagascar underwent a revision and was temporarily removed from the PIP website. This process is now complete (also see Section 7.1) and the data has been reinstated on the website. 6.2. Rwanda In May 2025, the National Institute of Statistics of Rwanda (NISR) released official poverty estimates for the first time in seven years, based on the 2023/2024 Enquête Intégrale sur les Conditions de Vie des ménages (EICV7). Beginning with EICV7, Rwanda’s consumption aggregate and poverty measurement incorporated several methodological improvements to align with evolving international best practices and World Bank guidelines (Deaton and Zaidi, 2002; Mancini and Vecchi, 2022). In 2025, new national official poverty lines were estimated using updated caloric requirements and a revised adult-equivalence scale; the consumption aggregate shifted from food acquisition to food consumption; spatial and temporal deflation moved from regional-level to household-level Paasche indices; and four updates were made to the consumption aggregate: (i) inclusion of food eaten away from home and school meals; (ii) a minor revision to the depreciation-rate formula for estimating the use value of durable goods; (iii) application of a hedonic model to treat outliers in reported rents; and (iv) inclusion of own-production of firewood. EICV7 also introduced fewer household visits with a shorter recall period for food consumption and separated questions on food consumption versus acquisition, alongside expanded detail on food eaten away from home and household-level price deflation. These changes mean that consumption and poverty estimates from EICV7 are not comparable with earlier rounds, resulting in a break in the international poverty rate series. A detailed description of the methodology can be found in the National Institute of Statistics of Rwanda 2025). 7. Changes to CPI data The baseline source of CPI data has been updated to the IMF’s International Financial Statistics (IFS) as of 11 June 2025. Lakner et al. (2018) provide an overview of the various CPI series that 18 are used in PIP. Table B2 in the Appendix B to this note gives the up-to-date source of the deflator for all countries included in PIP as of the current update. 7.1. Madagascar 2012-2021 An assessment of Madagascar’s poverty trends using survey-to-survey imputation (SWIFT) demonstrates that standard CPI-based methods understate inflation, leading to misleading inferences of poverty reduction. Details are available in Yoshida et al. (2025). Yoshida et al. (2025). Key findings show that: I. Survey Comparability The 2021/22 Enquête Périodique auprès des Ménages (EPM) contains a broader consumption module than the one in the 2012 Enquête Nationale sur le Suivi des Objectifs du Millénaire pour le Développement (ENSOMD), which could overstate welfare growth. However, daily per-capita calorie intake and food budget shares changed little between 2010 and 2021, indicating that survey- design effects are limited and the two rounds remain broadly comparable for trend analysis. II. CPI Underestimation of Inflation Between 2012 and 2021, official CPI inflation was 1.76, whereas survey-based food price indices imply 2.66–2.99, and cost-per-calorie measures suggest 3.61. Methodological weaknesses in the CPI—including outdated 2012 weights, coverage restricted to seven urban centers, obsolete outlet samples, and reliance on phone-based collection after 2020—contribute to a systematic understatement of the true cost of living. III. SWIFT-Based Correction Survey of Wellbeing via Instant and Frequent Tracking (SWIFT) is a survey-to-survey imputation method that trains prediction models on the most recent full-consumption survey (EPM 2021/22) and imputes household expenditures in earlier surveys at constant (2021) prices. For Madagascar, expenditures from ENSOMD 2012/13 are imputed in 2021 terms, and comparison with the original nominal 2012 expenditures yields an implicit inflation factor. The ratio of the medians implies an inflation rate of 3.27, substantially above the CPI-based figure. The corresponding international 19 poverty headcount rates (2021 PPP, $3/day) are 67.3 percent in 2012 and 69.2 percent in 2021, pointing to stagnation rather than decline (as suggested by the series using the official CPI). IV. Extended Poverty Trends, 2001–2021 Pre-2012 surveys are comparable, and CPI estimates are considered reliable, while post-2012 inflation is replaced with SWIFT-based estimates. The resulting poverty series shows a decline between 2001 and 2005, a sharp rise between 2005 and 2010, and stagnation from 2010 to 2021. This trajectory contrasts sharply with the standard CPI-based series, which suggests steady improvement. V. Cross-Checks Since 2012 Other indicators reinforce the SWIFT-based poverty profile. GDP per capita, asset ownership, agricultural productivity, employment structure, and human capital accumulation all stagnated or deteriorated over the period. These trends corroborate the finding that poverty in Madagascar has remained persistently high, consistent with multidimensional and non-monetary measures of welfare. 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 July 2025 vintage of the WDI. As before, when WDI data are missing, data from the IMF’s WEO, April 2025 version are used. Supplementary data from the Maddison Project Database (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 July 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 20 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. 10. Flagging data points with non-national survey coverage Starting with the June 2025 update, PIP began 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 11 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. The table does not include datapoints that in PIP are already flagged as having only rural or urban coverage, such as Argentina, where the household survey only covers urban areas. Table 11 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 2018/19 Borno Nigeria 2022/23 Taraba and Zamfara states. 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. 21 References Alfani, F., Aaron, D. V., Atamanov, A., Castaneda Aguilar R. A., Diaz-Bonilla, C., Devpura, N. P., Dewina, R., Finn, A., Fujs, T., Gonzalez, M. F., Krishnan, N., Kocchar, N., Kumar, N., Lakner, C., Lara Ibarra, G., Lestani, D., Liniado, J., Lønborg, J. H., Mahler, D. G., Mejía-Mantilla, C., Montalva, V., Moreno, L. L., Nguyen, M. C., Rubiano, E., Sajaia, Z., Sanchez, D. M., Seshan, G. K., Tetteh-Baah, S. K., Viveros Mendoza, M. C., Wu, H., Yonzan, N., Wambile, A., 2025. June 2025 Update to the Poverty and Inequality Platform (PIP). The World Bank. Aron, D., Aguilar, R.A.C., Diaz-Bonilla, C., Fujs, T.H.M.J., Rojas, D.G., Hill, R., Jularbal, L., Lakner, C., Ibarra, G.L., Mahler, D.G., Nguyen, M.C., Nursamsu, S., Sabatino, C., Sajaia, Z., Seitz, W., Sjahrir, B.S., Tetteh-Baah, S.K., Mendoza, M.C.V., Winkler, H., Wu, H., Yonzan, N., 2024. September 2024 Update to the Poverty and Inequality Platform (PIP): What’s New (No. 39), Global Poverty Monitoring Technical Note Series. 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., 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). Chen, S., Jolliffe, D.M., Lakner, C., Lee, K., Mahler, D.G., Mungai, R., Nguyen, M.C., Prydz, E.B., Sangraula, P., Sharma, D., Yang, J., Zhao, Q., 2018. September 2018 PovcalNet Update: What’s New. Global Poverty Monitoring Technical Note Series, Global Poverty Monitoring Technical Note Series. Chen, S., Ravallion, M., 2004. How Have the World’s Poorest Fared since the Early 1980s? The World Bank Research Observer 19, 141–169. https://doi.org/10.1093/wbro/lkh020 Deaton, A., Zaidi, S., 2002. Guidelines for Constructing Consumption Aggregates for Welfare Analysis, Living Standards Measurement Study Working Paper. World Bank, Washington, DC. Jolliffe, D., Prydz, E.B., 2015. Global poverty goals and prices: how purchasing power parity matters (Policy Working Paper Series 7256). World Bank, Washington, DC. 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. 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. 22 Mahler, D.G., Castañeda Aguilar, R.A., Newhouse, D., 2022. Nowcasting Global Poverty. The World Bank Economic Review 36, 835–856. https://doi.org/10.1093/wber/lhac017 Mahler, D.G., Schoch, M., Lakner, C., Nguyen, M., Montes, J., 2025. Predicting Income Distributions from Almost Nothing, Policy Research Working Paper Series, no. 11034. World Bank, Washington, D.C. Mancini, G., Vecchi, G., 2022. On the Construction of a Consumption Aggregate for Inequality and Poverty Analysis. World Bank. National Institute of Statistics of Rwanda (NISR), 2025. Seventh Integrated Household Living Conditions Survey (EICV7) Report. National Institute of Statistics of Rwanda (NISR). Prydz, E.B., Jolliffe, D., Serajuddin, U., 2022. Disparities in Assessments of Living Standards Using National Accounts and Household Surveys. Review of Income and Wealth 68, S385–S420. https://doi.org/10.1111/roiw.12577 Ravallion, M., 2016. The Economics of Poverty: History, Measurement, and Policy. Oxford University Press, Oxford, New York. Ravallion, M., 2003. Measuring Aggregate Welfare in Developing Countries: How Well Do National Accounts and Surveys Agree? The Review of Economics and Statistics 85, 645– 652. Ravallion, M., Sen, B., 1996. When Method Matters: Monitoring Poverty in Bangladesh. Economic Development and Cultural Change 44, 761–792. World Bank, 2018. Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle. World Bank, Washington, DC. World Bank, 2013. Bangladesh - Poverty assessment: Assessing a decade of progress in reducing poverty, 2000-2010 (No. 31), Bangladesh Development Series. World Bank, Washington, D.C. Yoshida, N., Mulangu, F., Oviedo, A.M., Wu, H., Sho, T., Aron, D., Nsababera, O.U., 2025. Estimating a Trend in the National Poverty Rate 2012–2021 in Madagascar., Global Poverty Monitoring Technical Note 48. 23 A. Appendix A.1. Extrapolations For countries that do not have welfare aggregates at a specific reference year, but which do have earlier welfare aggregates available, their most recent aggregate is extrapolated forward using growth rates from national accounts, either real GDP per capita or real Household Final Consumption Expenditure (HFCE) per capita. Only a fraction of growth is passed through to the survey welfare aggregate based on evidence suggesting a misalignment between growth in survey means and national accounts (Prydz et al., 2022; Ravallion, 2003). PIP uses a passthrough rate of 0.7 for consumption aggregates and 1 (meaning full passthrough) for income aggregates. The selection of a 0.7 passthrough rate, and the decision to only apply this to countries that use a consumption aggregate is based on empirical evidence from Mahler et al. (2022). The extrapolation is implemented by first finding the growth in national accounts, ,+1 , between the survey year , and the following year + 1, and scaling the survey welfare distribution , by (a fraction of) this factor. This continues year-by-year until the reference year of interest, , is reached. Concretely, this means that the extrapolated welfare distribution, , is given by: −1 =   ∏(1 + ℎℎ ⋅ ,+1 ) = where ,+1 is growth in real GDP per capita or real HFCE per capita as explained in the national accounts data section, and ℎℎ equals 0.7 or 1. Poverty for the reference year is then estimated using this extrapolated distribution. A similar approach is used to extrapolate backwards, when the earliest survey estimate available is more recent than the desired reference year. The extrapolation method assumes distribution-neutral growth, i.e. that everyone’s welfare grows at the same rate. This implies that inequality is assumed to stay constant. The example below illustrates the consumption distribution from the 2016 Ghanaian survey expressed in 2021 PPP-adjusted dollars, and the 2023 Ghanaian distribution when the survey distribution is extrapolated to 2023 using the method described above. The 2016 daily consumption mean is $4.91 and the growth rate in real GDP per capita between 2016 and 2023 is 24 14%. The positive growth rate pushes the extrapolated distribution to the right, representing increased consumption levels, causing the share of extreme poor – those living below the $3.00 poverty line – to decrease from 40.5% in 2016 to 34.5% in 2023. When household surveys span two calendar years (e.g., a survey starts in July and runs until June of the following year), the national accounts data used for extrapolating the survey forward is the weighted average of the two years. The weights of the national accounts data are determined by the share of the fieldwork that took place in each calendar year. A.2. Interpolations In cases where the reference year falls between two surveys, poverty is interpolated for the reference year using the surveys on each side of the reference year. One of two approaches is used depending on the correspondence in growth between national accounts and survey data. 25 Denoting by 1 the survey predating the reference year and 2 the survey after the reference year, “same direction” interpolation is used when growth in the survey mean, 1,2 , between the two surveys is of the same sign as (1) the growth in national accounts from the first survey to the reference year 1 , and (2) from the reference year to the second survey ,2 . This means that same direction interpolation is used when (1,2 ) = (1 , ) = (,2 ). “Diverging directions” interpolation is used when the equation above does not hold. The main difference between the two is that the “same direction” interpolation estimates how much of the growth in national accounts had accrued by the reference year, and uses this to back out a predicted survey mean at the reference year. When the survey mean and national accounts grow in opposite directions this is not meaningful, and an alternative approach is used. Interpolations are never done between consumption and income aggregates. Whenever both are available, specific rules are used to determine which aggregate to use. A.2.1 “Same direction” interpolation “Same direction” interpolation works in three steps. First, the survey mean at the reference year, , is estimated using the following interpolation formula: ∏−1 =1 (1 + ℎℎ  ⋅ , +1 ) − 1 = (2 − 1 ) × 2 −1 + 1 ∏=1 (1 + ℎℎ  ⋅ , +1 )−1 Second, the welfare distributions at the two surveys are scaled to reflect this mean: 1 = ⋅  1 and  2 = ⋅     1 2 2 After this alignment, there will be two distributions with the same mean for the reference year but with different distributional shapes. At the third step, poverty is estimated from each of these distributions, and the final poverty estimate at the reference year is the weighted average poverty rate from both distributions where 26 each poverty estimate is weighted by the inverse of the relative distance between the survey year and the reference year: 0,1 (2 − ) + 0,2 ( − 1 ) 0, = 2 − 1 For example, if a reference year falls two years after the first survey and one year before the second survey, the poverty estimate from the first survey is given a weight of 1/3 and the estimate from the second survey a weight of 2/3. A.2.2 “Diverging directions” interpolation If the growth rates in surveys and national accounts diverge, an approach similar to the extrapolation is applied to the two closest surveys; poverty is extrapolated forward by (a fraction of) the national account growth rate using the early survey and backwards using the later survey. Poverty for the reference year is estimated using both distributions and the estimates are averaged using the formula above. The mechanics of the extrapolation and interpolation (without accounting for passthrough rates) are described in Chen and Ravallion (2004), box 6.4 in Jolliffe and Prydz (2015), Ravallion (2003), and in Appendix A of World Bank (2018). 27 B. Appendix B.1. Complete list of new country-years Table B1. Economies-years added in September 2025 PIP update Economy Year Survey Name Argentina 2024 EPHC-S2 Australia 2020 SIH-LIS Austria 2023 EU-SILC Belgium 2023 EU-SILC Bulgaria 2023 EU-SILC Canada 2021 CIS-LIS China 2022 CNIHS Croatia 2023 EU-SILC Cyprus 2023 EU-SILC Czechia 2023 EU-SILC Denmark 2023 EU-SILC Dominican Republic 2024 ECNFT-Q03 Ecuador 2024 ENEMDU Estonia 2023 EU-SILC Finland 2023 EU-SILC France 2023 EU-SILC Georgia 2024 HIS Greece 2002 SILC-LIS Greece 2023 EU-SILC Honduras 2024 EPHPM Iceland 2019 EU-SILC Ireland 2023 EU-SILC Italy 2023 EU-SILC Kenya 2022 KCHS Kosovo 2022 SILC-C Kyrgyz Republic 2023 KIHS Latvia 2023 EU-SILC Lithuania 2023 EU-SILC Luxembourg 2023 EU-SILC Madagascar 2021 EPM Moldova 2020 HBS Moldova 2023 HBS Mozambique 2022 IOF Nigeria 2022 LSS Norway 2023 EU-SILC Panama 2024 EH 28 Paraguay 2024 EPHC Peru 2024 ENAHO Poland 2023 EU-SILC Portugal 2023 EU-SILC Romania 2023 EU-SILC Russian Federation 2022 VNDN Russian Federation 2023 VNDN Rwanda 2023 EICV7 Slovak Republic 2023 EU-SILC Slovenia 2023 EU-SILC Spain 2023 EU-SILC Sweden 2023 EU-SILC Switzerland 2022 EU-SILC Tajikistan 2021 HBS Tajikistan 2022 HBS Tajikistan 2023 HBS Tajikistan 2024 HBS Uruguay 2024 ECH Uzbekistan 2024 HBS 29 B.2. CPI data sources Table B2 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. 30 Table B2. Source of temporal deflators used in September 2025 PIP update Code Economy Survey Year(s) CPI period Source HBS 2000 W IFS-M-202506 AGO Angola IBEP-MICS 2008 W IFS-M-202506 IDREA 2018 W IFS-M-202506 EWS 1996 Y IFS-M-202506 LSMS 2002-2012 Y IFS-M-202506 ALB Albania HBS 2014-2020 Y IFS-M-202506 SILC-C 2017-2019 (prev. year)Y IFS-M-202506 United Arab HIES 2014 W IFS-M-202506 ARE Emirates 2019 Y IFS-M-202506 EPH 1980-1987 Y NSO 1991-2002 M9 NSO ARG Argentina - urban EPHC-S2 2003-2024 M7-M12 NSO 2007-2014 M7-M12 Private estimates ARM Armenia ILCS ALL Y IFS-M-202506 IHS-LIS 1981 Y IFS-A-202506 IDS-LIS 1985 Y IFS-A-202506 AUS Australia SIHCA-LIS 1989 Y IFS-A-202506 SIH-LIS 1995-2020 Y IFS-A-202506 SIH-HES-LIS 2004-2016 Y IFS-A-202506 ECHP-LIS 1994-2000 Y IFS-M-202506 AUT Austria EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 SLC 1995 Y IFS-M-202506 AZE Azerbaijan HBS 2001-2005 Y IFS-M-202506 EDCM 1992 Y IFS-M-202506 EP 1998 W IFS-M-202506 BDI Burundi QUIBB 2006 Y IFS-M-202506 ECVMB 2013 W IFS-M-202506 EICVMB 2020 W IFS-M-202506 BEL Belgium SEP-LIS 1985-1997 Y IFS-M-202506 PSBH-ECHP-LIS 1995-2000 Y IFS-M-202506 EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 QUIBB 2003 Y IFS-M-202506 EMICOV 2011 W IFS-M-202506 BEN Benin 2015 Y IFS-M-202506 EHCVM 2018 M10 IFS-M-202506 2021 M11 IFS-M-202506 EP-I 1994 W IFS-M-202506 EP-II 1998 Y IFS-M-202506 BFA Burkina Faso ECVM 2003-2009 Y IFS-M-202506 EMC 2014 Y IFS-M-202506 31 EHCVM 2018 M9 IFS-M-202506 2021 M10 IFS-M-202506 HHES 1983-1985 W WEO-A-202504 1988-1991 W IFS-A-202506 BGD Bangladesh 1995 W Survey HIES 2000-2022 Y Survey HBS 1989 Y IFS-A-202506 1992-1994 Y IFS-M-202506 BGR Bulgaria IHS 1995-2001 Y IFS-M-202506 MTHS 2003-2007 Y IFS-M-202506 EU-SILC 2007-2024 (prev. year)Y IFS-M-202506 Bosnia and LSMS 2001-2004 Y WEO-A-202504 BIH Herzegovina HBS 2007-2011 Y IFS-M-202506 FBS 1993-1995 Y IFS-M-202506 BLR Belarus HHS 1998-2020 Y IFS-M-202506 LFS 1993-1999 Y IFS-A-202506 HBS 1995 Y IFS-A-202506 BLZ Belize SLC 1996 Y IFS-A-202506 HBS 2018 W IFS-A-202506 Bolivia - urban EPF 1990 W IFS-M-202506 EIH 1992 M11 IFS-M-202506 Bolivia ENE 1997 M11 IFS-M-202506 ECH 1999 M10 IFS-M-202506 BOL 2000 M11 IFS-M-202506 EH 2001-2005 M11 IFS-M-202506 ECH 2004 M10 IFS-M-202506 EH 2006-2016 M10 IFS-M-202506 2017-2023 M11 IFS-M-202506 PNAD 1981-2011 M9 IFS-M-202506 BRA Brazil PNADC-E1 2012-2023 Y IFS-M-202506 PNADC-E5 2020-2021 Y IFS-M-202506 BRB Barbados BSLC 2016 M2 IFS-M-202506 BLSS 2003-2017 Y Previous WDI/IFS BTN Bhutan 2022 M1-M8 Previous WDI/IFS HIES 1985-2002 W IFS-M-202506 BWA Botswana CWIS 2009 W IFS-M-202506 BMTHS 2015 W IFS-M-202506 EPCM 1992 W IFS-M-202506 Central African CAF ECASEB 2008 Y IFS-M-202506 Republic EHCVM 2021 M5 IFS-M-202506 SCF-LIS 1971-1995 Y IFS-M-202506 CAN Canada SLID-LIS 1996-2011 Y IFS-M-202506 CIS-LIS 2012-2021 Y IFS-M-202506 CHE Switzerland SIWS-LIS 1982 Y IFS-M-202506 32 NPS-LIS 1992 Y IFS-M-202506 IES-LIS 2000-2004 Y IFS-M-202506 EU-SILC 2007-2023 (prev. year)Y IFS-M-202506 CASEN 1987 Y IFS-M-202506 CHL Chile 1990-2022 M11 IFS-M-202506 CRHS-CUHS 1981-2011 Y NSO CHN China CNIHS 2012-2022 Y NSO EPAM 1985-1988 W IFS-M-202506 EP 1992 W IFS-M-202506 CIV Côte d'Ivoire ENV 1995-2015 Y IFS-M-202506 EHCVM 2018 M10 IFS-M-202506 2021 M11 IFS-M-202506 ECAM-I 1996 Y IFS-M-202506 ECAM-II 2001 Y IFS-M-202506 CMR Cameroon ECAM-III 2007 Y IFS-M-202506 ECAM-IV 2014 Y IFS-M-202506 ECAM-V 2021 M10 IFS-M-202506 Congo, Dem. E123 2004-2012 W IFS-M-202506 COD Rep. EGI-ODD 2020 Y WEO-A-202504 ECOM 2005 Y IFS-M-202506 COG Congo, Rep. 2011 W IFS-M-202506 Colombia - urban ENH 1980-1988 Y IFS-M-202506 1989-1991 M11 IFS-M-202506 COL Colombia 1992-2000 M11 IFS-M-202506 ECH 2001-2005 M11 IFS-M-202506 GEIH 2008-2023 M11 IFS-M-202506 EIM 2004 Y IFS-M-202506 COM Comoros EESIC 2013 Y IFS-M-202506 IDRF 2001 W IFS-M-202506 CPV Cabo Verde QUIBB 2007 W IFS-M-202506 IDRF 2015 Y IFS-M-202506 ENH 1981-1986 Y IFS-M-202506 EHPM 1989 Y IFS-M-202506 CRI Costa Rica 1990-2009 M7 IFS-M-202506 ENAHO 2010-2024 M7 IFS-M-202506 CYP Cyprus EU-SILC ALL (prev. year)Y IFS-M-202506 MC-LIS 1992-2002 Y IFS-M-202506 CZE Czech Republic CM 1993 Y IFS-M-202506 EU-SILC 2005-2024 (prev. year)Y IFS-M-202506 DEU Germany LIS ALL Y IFS-M-202506 EDAM 2002-2013 Y IFS-M-202506 DJI Djibouti 2017 M5 IFS-M-202506 LM-LIS 1987-2000 Y IFS-M-202506 DNK Denmark EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 33 ENGSLF 1986-1989 Y IFS-M-202506 ICS 1992 M6 IFS-M-202506 Dominican ENFT 1996 M2 IFS-M-202506 DOM Republic 1997 M4 IFS-M-202506 2000-2016 M9 IFS-M-202506 ECNFT-Q03 2017-2024 Y IFS-M-202506 EDCM 1988 Y IFS-M-202506 DZA Algeria ENMNV 1995 Y IFS-M-202506 ENCNVM 2011 W IFS-M-202506 Ecuador - urban EPED 1987 Y IFS-M-202506 Ecuador ECV 1994 M6-M10 IFS-M-202506 Ecuador - urban EPED 1995 M11 IFS-M-202506 ECU 1998 M6 IFS-M-202506 (prev. Ecuador ECV 1999 year)M10-M9 IFS-M-202506 EPED 2000 M11 IFS-M-202506 ENEMDU 2003-2024 M11 IFS-M-202506 HIECS 1990-2012 W IFS-M-202506 EGY Egypt, Arab Rep. 2015 Y IFS-M-202506 2017-2021 W IFS-M-202506 HBS-LIS 1980-1990 Y IFS-M-202506 HBCS-LIS 1985 Y IFS-M-202506 ESP Spain ECHP-LIS 1993-2000 Y IFS-M-202506 EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 HIES 1993-1998 Y IFS-M-202506 EST Estonia HBS 2000-2004 Y IFS-M-202506 EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 Ethiopia - rural HICES 1981 W IFS-M-202506 Ethiopia 1995-2010 W IFS-M-202506 ETH 2015 M12 IFS-M-202506 HCES 2021 M12 IFS-M-202506 IDS-LIS 1987-2000 Y IFS-M-202506 FIN Finland EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 FJI Fiji HIES ALL W IFS-M-202506 TIS-LIS 1970-1990 Y IFS-M-202506 FRA France TSIS-LIS 1996-2002 Y IFS-M-202506 EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 Micronesia, Fed. Sts. - urban CPH 2000 Y IFS-A-202506 FSM Micronesia, Fed. Sts. HIES 2005-2013 Y IFS-A-202506 GAB Gabon EGEP ALL Y IFS-M-202506 FES-LIS 1968-1993 Y IFS-M-202506 GBR United Kingdom FRS-LIS 1994-2021 Y IFS-M-202506 34 GEO Georgia HIS ALL Y IFS-M-202506 GLSS-I 1987 W IFS-M-202506 GLSS-II 1988 W IFS-M-202506 GLSS-III 1991 W IFS-M-202506 GHA Ghana GLSS-IV 1998 W IFS-M-202506 GLSS-V 2005 W Survey GLSS-VI 2012 W Survey GLSS-VII 2016 W Survey ESIP 1991 Y WEO-A-202504 EIBC 1994 W WEO-A-202504 GIN Guinea EIBEP 2002 W WEO-A-202504 ELEP 2007-2012 Y IFS-M-202506 EHCVM 2018 W IFS-M-202506 HPS 1998 Y IFS-M-202506 GMB Gambia, The HIS 2003 W IFS-M-202506 IHS 2010-2020 W IFS-M-202506 ILJF 1991 Y IFS-M-202506 ICOF 1993 Y IFS-M-202506 ILAP-I 2002 Y IFS-M-202506 GNB Guinea-Bissau ILAP-II 2010 Y IFS-M-202506 EHCVM 2018 W IFS-M-202506 2021 M11 IFS-M-202506 M8-(next GNQ Equatorial Guinea ENH2 2022 year)M11 IFS-M-202506 ECHP-LIS 1995-2000 Y IFS-M-202506 GRC Greece SILC-LIS 2002 Y IFS-M-202506 EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 GRD Grenada SLCHB 2018 M4 IFS-M-202506 ENSD 1986 W IFS-M-202506 1989 Y IFS-M-202506 GTM Guatemala ENIGF 1998 M8 IFS-M-202506 ENCOVI 2000 M6-M11 IFS-M-202506 2006-2023 M7 IFS-M-202506 GLSMS 1992 W WEO-A-202504 GUY Guyana 1998 Y IFS-M-202506 Honduras - urban ECSFT 1986 Y IFS-M-202506 Honduras EPHPM 1989 Y IFS-M-202506 HND 1990-1993 M5 IFS-M-202506 1994 M9 IFS-M-202506 1995-2024 M5 IFS-M-202506 HBS 1988-2010 Y IFS-M-202506 HRV Croatia EU-SILC 2010-2024 (prev. year)Y IFS-M-202506 ECVH 2001 M5 IFS-M-202506 HTI Haiti ECVMAS 2012 M10 IFS-M-202506 35 HBS 1987-2007 Y IFS-M-202506 HHP-LIS 1991-1994 Y IFS-M-202506 HUN Hungary THMS-LIS 1999 Y IFS-M-202506 EU-SILC 2005-2018 (prev. year)Y IFS-M-202506 SUSENAS 1984-1999 Y IFS-M-202506 IDN Indonesia 2000-2007 M2 IFS-M-202506 2008-2024 M3 IFS-M-202506 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-202506 LIS-ECHP-LIS 1994-2000 Y IFS-M-202506 IRL Ireland SILC-LIS 2002 Y IFS-M-202506 EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 SECH 1986 Y IFS-A-202506 1990-1998 Y IFS-M-202506 IRN Iran, Islamic Rep. HEIS 2005-2009 W IFS-M-202506 M4-(next 2011-2023 year)M3 IFS-M-202506 IHSES 2006 W COSIT IRQ Iraq 2012 Y COSIT 2023 Y COSIT/IFS ISL Iceland EU-SILC ALL (prev. year)Y IFS-M-202506 ISR Israel HES-LIS ALL Y IFS-M-202506 SHIW-LIS 1977-2002 Y IFS-M-202506 ITA Italy EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 SLC 1988 M9 IFS-M-202506 M11-(next 1990-1993 year)M3 IFS-M-202506 JAM Jamaica 1996 M5-M8 IFS-M-202506 1999 M6-M8 IFS-M-202506 2002-2004 M6 IFS-M-202506 JSLC 2018-2021 Y IFS-M-202506 HEIS 1986 W IFS-M-202506 JOR Jordan 1992-1997 Y IFS-M-202506 2002-2010 W IFS-M-202506 JPN Japan JHPS-KHPS-LIS ALL Y IFS-M-202506 HBS 1993-2021 Y IFS-M-202506 KAZ Kazakhstan LSMS 1996 Y IFS-M-202506 36 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 NSO/IFS 2022 M3-M12 NSO/IFS KPMS 1998 Y IFS-M-202506 KGZ Kyrgyz Republic HBS 2000-2003 Y IFS-M-202506 KIHS 2004-2023 Y IFS-M-202506 HIES 2006 Y IFS-M-202506 KIR Kiribati 2019 W IFS-M-202506 HIES-FHES-LIS 2006-2014 Y IFS-M-202506 KOR Korea, Rep. SHFLC-LIS 2016-2021 Y IFS-M-202506 LECS 1992 W IFS-A-202506 LAO Lao PDR 1997 W IFS-M-202506 2002-2018 W Survey HBS 2011 (next year)M5 IFS-M-202506 LBN Lebanon LHS 2022 (next year)M1 IFS-M-202506 CWIQ 2007 Y IFS-M-202506 LBR Liberia HIES 2014-2016 Y IFS-M-202506 LSMS 1995 Y IFS-M-202506 LCA St. Lucia SLCHBS 2015 M11 IFS-M-202506 LFSS 1985 Y IFS-M-202506 HIES 1990 W IFS-M-202506 SES 1995 W IFS-M-202506 LKA Sri Lanka HIES 2002 Y IFS-M-202506 2006-2012 W IFS-M-202506 2016-2019 Y IFS-M-202506 HBS 1986 W WEO-A-202504 NHECS 1994 W WEO-A-202504 LSO Lesotho HBS 2002 W IFS-M-202506 CMSHBS 2017 M8 IFS-M-202506 HBS 1993-2008 Y IFS-M-202506 LTU Lithuania EU-SILC 2005-2024 (prev. year)Y IFS-M-202506 PSELL-LIS 1985-1993 Y IFS-M-202506 PSELL-ECHP- LUX Luxembourg LIS 1994-2001 Y IFS-M-202506 SEP-SILC-LIS 2002 Y IFS-M-202506 EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 HBS 1993-2009 Y IFS-M-202506 LVA Latvia EU-SILC 2005-2024 (prev. year)Y IFS-M-202506 ECDM 1984 W IFS-M-202506 MAR Morocco ENNVM 1990-2006 W IFS-M-202506 37 ENCDM 2000-2013 W IFS-M-202506 MDA Moldova HBS ALL Y IFS-M-202506 EB 1980 Y IFS-M-202506 EPM 1993 W IFS-M-202506 MDG Madagascar 1997-2010 Y IFS-M-202506 ENSOMD 2012 W IFS-M-202506 EPM 2021 W IFS-M-202506 HIES 2002-2009 W IFS-M-202506 MDV Maldives 2016 Y IFS-M-202506 2019 M11 IFS-M-202506 ENIGH 1984-2014 M8 IFS-M-202506 MEX Mexico ENIGHNS 2016-2022 M8 IFS-M-202506 MHL Marshall Islands HIES 2019 W WEO-A-202504 HBS 1998-2008 Y IFS-M-202506 MKD North Macedonia SILC-C 2010-2020 (prev. year)Y IFS-M-202506 EMCES 1994 Y IFS-A-202506 EMEP 2001 W IFS-M-202506 MLI Mali ELIM 2006-2009 W IFS-M-202506 EHCVM 2018-2021 M10 IFS-M-202506 MLT Malta EU-SILC ALL (prev. year)Y IFS-M-202506 MPLCS 2015 M1 IFS-M-202506 MMR Myanmar MLCS 2017 Q1 IFS-M-202506 HBS 2005-2014 Y IFS-M-202506 MNE Montenegro SILC-C 2013-2022 (prev. year)Y IFS-M-202506 LSMS 1995-1998 Y IFS-M-202506 HIES-LSMS 2002 W IFS-M-202506 MNG Mongolia HSES 2007 W IFS-M-202506 2010-2022 Y IFS-M-202506 NHS 1996 W WEO-A-202504 IAF 2002 W WEO-A-202504 MOZ Mozambique IOF 2008-2019 W IFS-M-202506 2022 M1 IFS-M-202506 EPCV 1987 Y IFS-M-202506 EP 1993 Y IFS-M-202506 MRT Mauritania EPCV 1995-2008 W IFS-M-202506 2014 Y IFS-M-202506 2019 M11 IFS-M-202506 HBS 2006 W IFS-M-202506 MUS Mauritius 2012-2017 Y IFS-M-202506 IHS-I 1997 W IFS-M-202506 IHS-II 2004 W Survey MWI Malawi IHS-III 2010 W Survey IHS-IV 2016 M4 Survey IHS-V 2019 M4 Survey 38 HIS 1984-1997 Y IFS-M-202506 (prev. year)M7-(prev. 2004 year)M12 IFS-M-202506 (prev. MYS Malaysia year)M7-(prev. 2007 year)M10 IFS-M-202506 2009 W IFS-M-202506 2012-2016 Y IFS-M-202506 HIESBA 2019 W IFS-M-202506 HIS 2022 W IFS-M-202506 NHIES 1993 W WEO-A-202504 NAM Namibia 2003-2015 W IFS-M-202506 ENBCM 1992-2007 W IFS-M-202506 EPCES 1994 W IFS-M-202506 ENCVM 2005 Y IFS-M-202506 NER Niger ECVMA 2011-2014 Y IFS-M-202506 EHCVM 2018 M10 IFS-M-202506 2021 M11 IFS-M-202506 NCS 1985 W IFS-M-202506 1992-1996 Y IFS-M-202506 LSS 2003 W IFS-M-202506 GHSP-W1 2010 M3-M4 IFS-M-202506 NGA Nigeria GHSP-W2 2012 M3-M4 IFS-M-202506 GHSP-W3 2015 M3-M4 IFS-M-202506 (next year)M3- LSS 2018 (next year)M4 IFS-M-202506 2022 (next year)M6 IFS-M-202506 EMNV 1993 M2 NSO 1998 M6 NSO NIC Nicaragua 2001 M6 IFS-M-202506 2005-2009 M8 IFS-M-202506 2014 M8-M10 IFS-M-202506 AVO-LIS 1983-1990 Y IFS-M-202506 NLD Netherlands SEP-LIS 1993-1999 Y IFS-M-202506 EU-SILC 2005-2022 (prev. year)Y IFS-M-202506 IDS-LIS 1979-2000 Y IFS-M-202506 NOR Norway EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 MHBS 1984 W IFS-M-202506 LSS-I 1995 W IFS-M-202506 NPL Nepal LSS-II 2003 W IFS-M-202506 LSS-III 2010 W IFS-M-202506 M6-(next LSS-IV 2022 year)M5 IFS-M-202506 NRU Nauru HIES 2012 W IFS-M-202506 39 HIES 1987 Y IFS-M-202506 1990-1998 W IFS-M-202506 PAK Pakistan IHS 1996 W IFS-M-202506 PIHS 2001 M6 IFS-M-202506 HIES 2004-2018 (next year)M1 IFS-M-202506 EMO 1979-1989 Y IFS-M-202506 PAN Panama 1991 M7 IFS-M-202506 EH 1995-2024 M7 IFS-M-202506 Peru ENNIV 1985 W IFS-M-202506 1994 Y IFS-M-202506 PER ENAHO 1997-2002 Q4 IFS-M-202506 2003 M5-M12 IFS-M-202506 2004-2024 Y IFS-M-202506 PHL Philippines FIES ALL Y IFS-M-202506 Papua New HIES 1996 Y IFS-A-202506 PNG Guinea 2009 W IFS-A-202506 HBS 1985-1987 Y IFS-A-202506 HBS-LIS 1986 Y IFS-A-202506 POL Poland HBS 1989-2019 Y IFS-M-202506 HBS-LIS 1992-1999 Y IFS-M-202506 EU-SILC 2005-2024 (prev. year)Y IFS-M-202506 PRT Portugal EU-SILC ALL (prev. year)Y IFS-M-202506 EH 1990 M7 IFS-M-202506 1995 M8-M11 IFS-M-202506 EIH 1997 (next year)M2 IFS-M-202506 EPH 1999 M9 IFS-M-202506 EIH 2001 M3 IFS-M-202506 EPH 2002 M11 IFS-M-202506 2003 M9 IFS-M-202506 PRY Paraguay 2004 M10 IFS-M-202506 2005 M11 IFS-M-202506 2006 M12 IFS-M-202506 2007-2008 M10 IFS-M-202506 2009 M11 IFS-M-202506 2010-2021 M10 IFS-M-202506 EPHC 2022-2024 M6 IFS-M-202506 PECS 2004-2011 Y IFS-M-202506 West Bank and PSE 2016 W IFS-M-202506 Gaza 2023 Q2 IFS-M-202506 QAT Qatar HIES 2017 W IFS-M-202506 HBS 1989 Y Milanovic (1998) MC 1992 Y IFS-M-202506 ROU Romania HIS 1994-1999 Y IFS-M-202506 IHS-LIS 1995-1997 Y IFS-M-202506 40 IHS 1998-2000 Y IFS-M-202506 HBS 2001-2021 Y IFS-M-202506 EU-SILC 2007-2024 (prev. year)Y IFS-M-202506 HBS 1993-2020 Y IFS-M-202506 Russian RUS VNDN 2015-2021 (prev. year)Y IFS-M-202506 Federation 2022-2024 (prev. year)Y WEO-A-202504 Rwanda - rural ENBCM 1984 W IFS-M-202506 RWA Rwanda EICV-I 2000 W IFS-M-202506 EICV-II 2005 W IFS-M-202506 EICV-III 2010 (next year)M1 IFS-M-202506 EICV-IV 2013 (next year)M1 IFS-M-202506 EICV-V 2016 (next year)M1 IFS-M-202506 EICV7 2023 (next year)M1 IFS-M-202506 NBHS 2009 Y IFS-M-202506 SDN Sudan 2014 M11 IFS-M-202506 EP 1991 W IFS-M-202506 ESAM 1994 W IFS-M-202506 ESAM-II 2001 W IFS-M-202506 SEN Senegal ESPS-I 2005 W IFS-M-202506 ESPS-II 2011 W IFS-M-202506 EHCVM 2018 M9 IFS-M-202506 2021 M11 IFS-M-202506 SLB Solomon Islands HIES ALL W IFS-M-202506 SLIHS 2003 W WEO-A-202504 SLE Sierra Leone 2011-2018 Y IFS-M-202506 EHPM 1989 Y IFS-M-202506 M10-(next 1991 year)M4 IFS-M-202506 SLV El Salvador 1995-1999 Y IFS-M-202506 2000-2007 M12 IFS-M-202506 2008-2023 M11 IFS-M-202506 LSMS 2002 Y IFS-M-202506 SRB Serbia HBS 2003-2019 Y IFS-M-202506 EU-SILC 2013-2023 (prev. year)Y IFS-M-202506 NBHS 2009 Y IFS-M-202506 SSD South Sudan HFS-W3 2016 (prev. year)M7 IFS-M-202506 São Tomé and IOF 2000 W IFS-M-202506 STP Principe 2010-2017 Y IFS-M-202506 Suriname - urban EHS 1999 Y IFS-M-202506 SUR Suriname SSLC 2022 Y IFS-M-202506 MC-LIS 1992-1996 Y IFS-M-202506 SVK Slovak Republic HBS 2004-2009 Y IFS-M-202506 EU-SILC 2005-2024 (prev. year)Y IFS-M-202506 SVN Slovenia IES 1987-1993 Y IFS-M-202506 41 HBS-LIS 1997-1999 Y IFS-M-202506 HBS 1998-2003 Y IFS-M-202506 EU-SILC 2005-2024 (prev. year)Y IFS-M-202506 HIS-LIS 1975-2002 Y IFS-M-202506 SWE Sweden EU-SILC 2004-2024 (prev. year)Y IFS-M-202506 SWZ Eswatini HIES ALL W IFS-M-202506 HES 1999 W IFS-M-202506 HBS 2006 W IFS-M-202506 SYC Seychelles 2013 Y IFS-M-202506 2018 W IFS-M-202506 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-202506 ECOSIT-III 2011 Y IFS-M-202506 TCD Chad EHCVM 2018 W IFS-M-202506 2022 M2 IFS-M-202506 QUIBB 2006-2015 Y IFS-M-202506 TGO Togo EHCVM 2018-2021 M10 IFS-M-202506 THA Thailand SES ALL Y IFS-M-202506 TLSS 1999 Y WEO-A-202504 2003-2007 Y Survey HBS 2004 Y Survey TJK Tajikistan TLSS 2009 Y IFS-M-202506 HSITAFIEN 2015 Y IFS-M-202506 HBS 2021-2024 Y IFS-M-202506 TKM Turkmenistan LSMS 1998 Y WEO-A-202504 TLSS 2001 Y WEO-A-202504 TLS Timor-Leste TLSLS 2007-2014 Y IFS-M-202506 HIES 2000 W IFS-M-202506 TON Tonga 2009-2021 Y IFS-M-202506 Trinidad and SLC 1988 Y IFS-M-202506 TTO Tobago PHC 1992 Y IFS-M-202506 HBCS 1985 Y IFS-A-202506 1990 Y IFS-M-202506 TUN Tunisia LSS 1995-2000 Y IFS-M-202506 NSHBCSL 2005-2015 W IFS-M-202506 M3-(next 2021 year)M3 IFS-M-202506 HICES 1987-2019 Y IFS-M-202506 TUR Türkiye SILC-C 2018-2023 (prev. year)Y IFS-M-202506 TUV Tuvalu HIES 2010 Y IFS-A-202506 TWN Taiwan, China FIDES-LIS ALL Y WEO-A-202504 TZA Tanzania HBS 1991 W IFS-A-202506 42 2000 W IFS-M-202506 2007 Y IFS-M-202506 2011-2018 W IFS-M-202506 HBS 1989 Y WEO-A-202504 NIHS 1992 W WEO-A-202504 UGA Uganda 1996-1999 W IFS-M-202506 UNHS 2002-2019 W IFS-M-202506 HS 1992-1993 Y IFS-M-202506 UKR Ukraine HIES 1995-1996 Y IFS-M-202506 HLCS 1999-2020 Y IFS-M-202506 Uruguay - 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