Global Poverty Monitoring Technical Note 32 September 2023 Update to the Poverty and Inequality Platform (PIP) What’s New Danielle Victoria Aron, R. Andres Castaneda Aguilar, Carolina Diaz-Bonilla, Maria Gabriela Farfan Betran, Elizabeth Mary Foster, Tony H. M. J. Fujs, Dean Jolliffe, Nandini Krishnan, Christoph Lakner, Gabriel Lara Ibarra, Daniel G. Mahler, Laura Moreno Herrera, Minh C. Nguyen, Diana M. Sanchez Castro, Samuel K. Tetteh-Baah, Martha C. Viveros Mendoza, Haoyu Wu, and Nishant Yonzan September 2023 Keywords: What’s New; September 2023; COVID-19. Development Data Group Development Research Group Poverty and Equity Global Practice Group 1 GLOBAL POVERTY MONITORING TECHNICAL NOTE 32 Abstract The September 2023 update to the Poverty and Inequality Platform (PIP) involves several changes to the data underlying the global poverty estimates. In particular, some welfare aggregates have been revised, and the CPI, national accounts, and population input data have been updated. This document explains these changes in detail and the reasoning behind them. Moreover, 63 new country-years have been added, bringing the total number of surveys to more than 2,200. Global poverty estimates are reported up to 2019 and earlier years have been revised. Regional poverty estimates in 2020 and 2021 are reported only for regions with sufficient survey data coverage during the COVID- 19 pandemic. All authors are with the World Bank. 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. Changes to welfare aggregates.................................................................................................... 5 2.1. Costa Rica 2021 ................................................................................................................... 5 2.2. India 2019/20 ....................................................................................................................... 5 2.3. Luxembourg Income Study (LIS) ........................................................................................ 5 2.4. West African countries ........................................................................................................ 6 2.5. Zambia ................................................................................................................................. 7 3. India ............................................................................................................................................ 8 4. Economy-years added ............................................................................................................... 12 5. Changes to CPI data .................................................................................................................. 13 6. Changes to National Accounts and Population data ................................................................. 13 7. Comparability database............................................................................................................. 14 8. References ................................................................................................................................. 15 9. Appendix ................................................................................................................................... 16 9.1. CPI data sources ................................................................................................................. 16 1 1. Introduction The September 2023 global poverty update from the World Bank revises the previously published global and regional estimates from 1981 to 2021. New survey data have been added to the Poverty and Inequality Platform (PIP) covering the period of the COVID-19 pandemic, making it possible to report regional poverty estimates for 2020 for all regions except Sub-Saharan Africa, and the Middle East and North Africa. Regional estimates for South Asia are now available until 2021. The 2021 poverty estimates for Latin America and the Caribbean have been revised slightly (e.g., from 4.7% to 4.6% at the $2.15 poverty line). Thus, this September 2023 PIP update provides more data that shed light on poverty in most of the world’s regions during the pandemic years (see more details below and in Table 2). However, the lack of sufficient data coverage in low- and lower- middle-income countries, particularly in Sub-Saharan Africa, still limits the global poverty series to 2019. Table 1 documents revisions to the regional and global poverty estimates between the March 2023 data vintage and the September 2023 data vintage for the 2019 reference year at the three global poverty lines. Poverty estimates remain virtually unchanged, except for South Asia and Sub- Saharan Africa where there are some upward revisions. For example, the rate of extreme poverty, as measured by the international poverty line of $2.15, increases by 1.9 percentage points to 10.5% for South Asia and by 0.5 percentage points to 35.4% for Sub-Saharan Africa. Globally, extreme poverty in 2019 is estimated to increase from 8.5% to 9%, representing 41 million more people living in extreme poverty in that year. India accounts for almost 70% of this global change in extreme poverty. At the $3.65 poverty line, India accounts for 40% of the slight upward revision of the global poverty rate from 23.6% to 24.1%. At the $6.85 poverty line, virtually no change is observed in global poverty estimates. As discussed in more detail in Section 3, the 2019/2020 India survey estimate has been revised to create a comparable trend with the estimates for 2020/21 and 2021/22 that are added with this update. Overall, limited or no changes are observed in regional poverty estimates in 2019 due to the updating of the auxiliary data in this update, including consumer price indices (CPIs), population, GDP, and household final consumption expenditure (HFCE). A total of 63 new survey data were 2 added to the PIP database, bringing the total number of surveys to 2,259. In a large part, these new surveys are historical data for rich countries, such as Canada, Luxembourg, and the United States. Only two surveys were added for the year 2019 and 13 more surveys were added for the pandemic years. Table 1 Poverty estimates for reference year 2019, changes between March 2023 and September 2023 vintage by region and poverty lines $2.15 (2017 PPP) $3.65 (2017 PPP) $6.85 (2017 PPP) Survey Headcount Number of Headcount Number of poor Headcount Number of poor Coverage Region ratio (%) poor (mil) ratio (%) (mil) ratio (%) (mil) 2019 (%) Sep Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 East Asia & Pacific 97.4 1.2 1.2 24.6 24.6 7.6 7.6 161.0 161.0 32.1 32.1 676.4 676.4 Europe & Central Asia 87.4 2.3 2.2 11.2 11.1 6.1 6.1 30.1 30.1 15.0 15.0 74.2 74.3 Latin America & Caribbean 86.7 4.3 4.3 27.7 27.7 10.6 10.6 67.7 67.7 28.0 28.0 179.4 179.5 Middle East & North Africa 48.3 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Other High Income 82.3 0.6 0.6 6.6 6.6 0.8 0.8 8.7 8.7 1.3 1.3 14.7 14.7 South Asia 96.4 8.6 10.5 160.9 196.3 42.3 43.7 788.0 814.3 82.3 81.8 1532.2 1523.1 Sub-Saharan Africa 62.0 34.9 35.4 391.3 397.4 62.3 62.9 698.2 705.6 86.4 86.9 969.2 974.5 Eastern & Southern Africa 41.8 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Western & Central Africa 91.7 27.3 27.3 123.8 123.9 57.2 57.4 259.7 260.6 85.1 85.5 386.6 388.2 World 85.8 8.5 9.0 659.2 700.6 23.6 24.1 1831.0 1864.9 46.9 46.9 3634.4 3633.1 Note: Poverty estimates in 2019 are not reported for Eastern and Southern Africa, and Middle East and North Africa due to a limited survey data coverage of less than 50% of the regional population; however, the available data are incorporated into the poverty estimates for Sub-Saharan Africa and the world, respectively. Survey coverage for low- and lower-middle-income countries for 2019 is 81.1%. The 2011 PPP-based estimates are also available in PIP. Regional poverty estimates are reported only for regions with sufficient survey data coverage during the COVID-19 pandemic. The new surveys collected in the pandemic years and added to the PIP database have increased data coverage to 62% of the world’s population in 2020 and 34% in 2021 (see Table 2). Given the greater data coverage in this update, 2020 poverty estimates are reported for five regions (East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, South Asia, and the group of other high-income countries) and 2021 poverty estimates are reported for two regions (Latin America and the Caribbean, and South Asia). COVID-19 did not lead to a significant rise in (extreme) poverty in East Asia and the Pacific, 3 Europe and Central Asia, Latin America and the Caribbean, and high-income countries in 2020, which all have relatively low (extreme) poverty rates. However, in South Asia extreme poverty increased by 2.5 percentage points, followed by a recovery in 2021. In contrast, after a slight decline in extreme poverty for Latin America and the Caribbean in 2020, extreme poverty increased by 0.7 percentage points in 2021 (see Lara Ibarra and Vale (2023) for more details on Brazil in 2020 and 2021, which is an important contributor to these regional changes). See World Bank (2022) for a more detailed discussion of the effect of COVID-19 on global poverty and in particular the role of fiscal policy in mitigating any adverse effects. For the remaining regions and the world, there is limited data coverage for the respective pandemic years to report poverty estimates (see Table 2). As a rule, a region is considered to have adequate data coverage if at least 50% of its population have survey data covering them in the reference year. For the world, an additional coverage rule requires that at least 50% of the population in low- and lower-middle-income countries should have survey data coverage in the reference year. These 1-year coverage rules applied in the pandemic years are stricter than the conventional 3-year coverage rules applied in normal years. However, these new coverage rules are necessary to ensure that poverty is estimated from survey data collected during the COVID-19 pandemic, and not pre- pandemic survey data extrapolated forward (Castaneda et al. 2023). It is a conservative approach that is adopted due to the exceptional volatility in economic conditions over this period. Table 2 Poverty estimates reported for the pandemic years 2020 2021 $2.15 (2017 PPP) $3.65 (2017 PPP) $2.15 (2017 PPP) $3.65 (2017 PPP) Region Head Head Head Head Coverage Millions Millions of Coverage Millions Millions count count count count (%) of poor poor (%) of poor of poor (%) (%) (%) (%) East Asia & Pacific 87 1.2 26.3 7.2 152.8 22 Europe & Central Asia 56 2.3 11.2 6.2 30.9 2 Latin America & Caribbean 84 3.9 25.5 10.2 65.6 63 4.6 29.8 10.8 70.5 Middle East & North Africa 0 0 Other High Income 59 0.4 4.0 0.6 6.5 30 South Asia 74 13.1 245.7 47.3 891.2 74 10.9 207.5 44.3 842.7 Sub-Saharan Africa 11 5 World 62 34 LIC/LMIC 48 45 Note: Coverage presents the share of population with data coverage in 2020 or 2021. Regions with missing poverty data do not have at least 50% of their population with data coverage. LIC/LMIC represents low- and lower-middle-income countries. 4 2. Changes to welfare aggregates 2.1. Costa Rica 2021 The temporal deflator used within the survey was updated for this year. In the 2021 data, this deflation had erroneously used a wrong deflator. This has been corrected and the effect on poverty estimates (at the three absolute lines used by the World Bank) are visible at first and second decimals precision. Table 3 Changes to poverty and inequality estimates, Costa Rica 2021 Poverty rate $2.15 Poverty rate $3.65 Poverty rate $6.85 Gini Index Mar Sep Mar Sep Mar Mar Sep Country Year 2023 2023 2023 2023 2023 Sep 2023 2023 2023 Costa Rica 2021 1.227 1.242 3.692 3.713 14.276 14.483 48.691 48.679 2.2. India 2019/20 New survey estimates have been included for 2020/21 and 2021/22. The 2019/2020 estimate has been revised to create a comparable trend. See Section 3 for a more detailed description. Table 4 Changes to poverty and inequality estimates, India 2019 Poverty rate $2.15 Poverty rate $3.65 Poverty rate $6.85 Gini Index Mar Sep Mar Sep Mar Sep Mar Sep Country Year 2023 2023 2023 2023 2023 2023 2023 2023 India 2019 10.0 12.7 44.8 45.9 83.8 82.4 35.7 35.0 2.3. Luxembourg Income Study (LIS) As in the March 2023 PIP update, welfare data for the following nine economies continues to be 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.1 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. 1 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. 5 The break in comparability (between LIS and EU-SILC) is indicated in the comparability database.2 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 17 July 2023. The following 50 country-years have been added to PIP, as they became available in LIS during the past year: • CAN (Canada): 1973, 1977, 1979, 1982, 1984-1986, 1988-1990, 1992, 1993, 1995, 2019 • LUX (Luxembourg): 1986-1990, 1992, 1993, 1995, 1996, 1998, 1999, 2001, 2002 • ESP (Spain): 1993, 1994, 1996-1999 • SWE (Sweden): 2002 • USA (United States): 1963-1973, 1975-1978, 2021. Finally, the following 21 country-years have been revised, as explained in more detail on the LIS website: • CAN (Canada): 1975, 1981, 1987, 1991, 1994 • ESP (Spain): 1995, 2000 • LUX (Luxembourg) 1985, 1991, 1994, 1997, 2000 • USA (United States): 1974, 1979, 1980-1986. 2.4. West African countries Existing survey data were revised for seven countries that participated in the 2018/2019 West African Economic and Monetary Union (WAEMU) survey harmonization program: Benin, Burkina Faso, Cote d’Ivoire, Mali, Niger, Senegal, and Togo. The main source of the revisions is the adjustment of temporal deflators, affecting all countries. The original temporal deflators (while internally consistent) did not allow for conversion into the prices of the 2017 ICP reference year under the current framework. To correct this, a new series of temporal deflators was constructed 2 These additional pre-EUSILC surveys were introduced in the March 2020 update (Atamanov et al. 2020). The comparability database is released together with the global poverty data see Atamanov et al. (2019) and PIP’s Methodological Handbook. Comparability is also indicated in the main output on the PIP website, the PIP Stata command and the PIP API. 6 that adjusts to the price level of the first month of fieldwork and can then be converted into the prices of the 2017 ICP reference year to account for inflation. In addition, minor revisions and corrections were made to the survey data for Benin, Burkina Faso, Mali, Senegal, and Togo. Table 5 Changes to poverty and inequality estimates, WAEMU countries 2018 Poverty rate $2.15 Poverty rate $3.65 Poverty rate $6.85 Gini index Mar Sept Mar Sept Mar Sept Mar Sept Country Year 2023 2023 2023 2023 2023 2023 2023 2023 Benin 2018 19.9 20.1 53.2 53.2 83.4 83.6 37.8 37.9 Burkina Faso 2018 30.5 31.2 59.8 63.1 81.1 87.2 47.3 43.0 Cote d'Ivoire 2018 11.4 11.5 39.6 39.7 75.4 75.6 37.2 37.2 Mali 2018 14.8 15.2 47.5 48.2 80.5 81.0 36.1 36.0 Niger 2018 50.6 50.9 81.1 81.2 95.0 95.0 37.3 37.3 Senegal 2018 9.3 9.2 37.4 37.6 74.4 74.4 38.1 38.3 Togo 2018 28.1 28.4 56.8 56.9 84.0 84.0 42.4 42.5 2.5. Zambia Updates have been made to Zambia’s consumption aggregate and associated poverty rates for 2010 and 2015 to create comparability across rounds and ensure consistency with changes to the poverty methodology introduced by the Central Statistical office of Zambia (2016). Price adjustments were made to add within-survey temporal and spatial deflators using province-level CPI. The consumption components were updated to include services from durable goods, and exclude loan payments and other big expenses. Electricity and water consumption were also imputed for households that reported no expenditure. Monthly estimates are now calculated using 4.3 weeks in a month rather than 4. Lastly, the survey weights for 2010 were adjusted to use the actual 2010 population census rather than the 2010 population estimates from the 2010 Living Conditions Monitoring Survey (LCMS), which were based on projections from the 2000 population census. Urban and rural populations were also matched by province using post-stratification. After restoring comparability across rounds but before adopting the change from nominal to real consumption, the poverty trend from 2010 to 2015 shows a much smaller decline in poverty. At the international poverty line, the 2010 poverty rate falls from 68.5 to 64.8 percent, resulting in a 3.4 percentage point decline between 2010 and 2015 compared with the 7-percentage-point decline 7 previously reported. The re-estimated Gini index shows lower inequality in 2010, changing from 55.6 to 53.5. As a result, between 2010 and 2015 inequality has increased by 2 points more than previously reported. The adoption of real consumption aggregates instead of nominal aggregates changes the poverty and inequality levels for both 2010 and 2015, but it has minor implications on the trends. Poverty rates are about 0.5 percentage points lower, and the Gini coefficient is between 1.2 and 1.5 points lower. Table 6 Changes to poverty and inequality estimates, Zambia 2010, 2015 Poverty rate $2.15 Poverty rate $3.65 Poverty rate $6.85 Gini index Mar Sept Mar Sept Mar Sept Mar Sept Country Year 2023 2023 2023 2023 2023 2023 2023 2023 Zambia 2010 68.5 64.4 82.9 80.8 93.0 92.5 55.6 52.0 Zambia 2015 61.4 60.8 77.5 78.0 90.7 91.0 57.1 55.9 3. India This update includes new estimates for 2020/21 and 2021/22 and revised estimates for 2019/20. In September 2022, PIP included estimates for five years – 2015/16, 2016/17, 2017/18, 2018/19, and 2019/20 – using imputed consumption based on the methodology Roy and Van der Weide (2022) proposed. The authors used the Consumer Pyramids Household Survey (CPHS) conducted by the Center for Monitoring Indian Economy (CMIE), a private data company. Official data for poverty estimation has been unavailable for over a decade. The 2011/12 National Sample Survey (NSS) is the most recently available official data source for poverty measurement. The 2017/18 NSS round was collected but kept unreleased to the public due to data quality concerns by the government of India (see Box 1.2 in World Bank, 2020). The CPHS data cannot be directly used to measure poverty for two reasons. First, the national representativeness of the survey has been questioned due to its sample design and geographic coverage. Sociodemographic indicators obtained from the CPHS significantly differ from those in other nationally representative surveys. Second, the construction of the CPHS consumption 8 aggregate is not directly comparable to the NSS consumption aggregate (Dreze and Somanchi 2021). Roy and Van der Weide (2022) proposed a methodology to address these drawbacks. CPHS quarterly waves from 2015 to 2020 were used to create datasets from April to March to approximate the Indian fiscal year, with one randomly-selected interview per household.3 The survey weights were adjusted to transform the data into a nationally representative dataset (Roy and Van der Weide 2022). The reweighting process sought to: (i) obtain a vector of weights representative of the fiscal year, whereas the original CMIE weights were estimated for each wave separately, and (ii) improve the national representativeness of the survey data. The authors used a maximum entropy or minimum cross-entropy criterion to calibrate the household weights of the April to March datasets.4 The methodology calibrates survey data to various targets, matching the means. The authors sequentially adjusted the weights to match target indicators drawn from two nationally representative surveys, the National Family Health Survey (NFHS IV, 2015-16) and the concurrent Periodic Labor Force Survey (PLFS). Finally, consumption was imputed using two approaches.. In approach 1, a vector of NSS-type consumption was imputed using a Survey to Survey method. In approach 2, the CPHS measure was transformed into an NSS-type consumption. Fifty draws were generated in approach 2. In the attempt to estimate poverty rates for 2020/21 and 2021/22 following the proposed methodology, the circumstances below emerged: • Phone interviews were conducted in 2020 and 2021 due to lockdowns related to the COVID-19 pandemic, adding additional biases to the CPHS data, typically collected in person. • A new National Family Health Survey (NFHS-V, 2019-21) round became available. The authors adjusted the population weights in the first step using state-sector5 indicators of 3 Because of the quarterly structure of the CPHS data, the same household may have been present up to three times within the same fiscal year. 4 The “maxentropy” Stata ado file was used (Wittenberg, M 2010). 5 Sector refers to the urban-rural division in the context of India. 9 assets, demographics, and education observed in the NFHS-IV (2015-16), the latest available data at the time of analysis. The new NFHS-V data presented the opportunity to update the targets to a more contemporary reference. • Consumption under approach 2 could not be estimated for the urban sector for 2020/21. This may be related to the change in survey modality. COVID-induced mobility restrictions during this time forced the survey data collection agency to switch to phone-based surveys. Consumption under approach 2 can be estimated for later years when the survey firm switched to face-to-face interviews. • New population projections by state and sector became available from the Ministry of Health. This source is deemed more appropriate as the series is more consistent with national estimates by the UN World Population Prospects than the population expansion derived from the PLFS. Based on this, the new and revised estimates deviate from the previous estimates in the following aspects: • The 2020/21 and 2021/22 estimates are based on Approach 1. Two hundred vectors were imputed following the Approach 1 methodology to minimize the chance that one random draw of the error term could drive the results. • The 2020/21 and 2021/22 CMIE weights were adjusted using the newly available NFHS- V. • 2019/20 was updated in the PIP series such that the estimates are comparable with 2020/21 and 2021/22. • An alternative algorithm was implemented to adjust the target variables considered in the max-entropy procedure when the algorithm failed to converge. Weights for all observations were adjusted by combining information from both NFHS and PLFS. • The weights in each urban/rural area and state were expanded to match population projections by the Ministry of Health. Then, the urban/rural population shares were adjusted to the population shares in the World Development Indicators, as is done for all years in India. 10 • Only estimates using the total sample (including households interviewed by phone) were introduced to PIP. These decisions have implications for the comparability of the series. There are breaks in comparability from 2016/17 to 2017/18 due to the change in target survey(s) used for reweighting and from 2018/19 to 2019/20 due to the change from approach 2 to 1. Table 7 Old series (September 2022) vs. revised series (September 2023) Revised/New series (September 2023) Old series (March 2023) Poverty rate Poverty rate Gini index Gini index Year $2.15 $3.65 $6.85 $2.15 $3.65 $6.85 2011/12 22.5% 62.3% 89.9% 35.7 Approach 2, reweight to NFHSIV targets. 2015/16 18.7% 60.9% 88.9% 34.7 2016/17 18.1% 59.8% 88.7% 34.8 Approach 2, reweight to PLFS or NFHSIV targets. 2017/18 13.4% 54.3% 85.3% 35.9 2018/19 11.1% 46.8% 82.6% 34.6 2019/20 10.0% 44.8% 83.8% 35.7 Approach 1, reweight to PLFS and NFHSV targets; all observations reweighted. 2019/20 12.7% 45.9% 82.4% 35.0 2020/21 14.7% 49.7% 84.0% 34.8 2021/22 11.9% 46.5% 83.0% 34.2 11 4. Economy-years added Table 8 below has the list of 63 new economy-years added to the PIP database. Table 8 Economy-years added in the September 2023 PIP update Economy Year Survey Name Bangladesh 2022 HIES Bhutan 2022 BLSS Canada 1973 SCF-LIS Canada 1977 SCF-LIS Canada 1979 SCF-LIS Canada 1982 SCF-LIS Canada 1984 SCF-LIS Canada 1985 SCF-LIS Canada 1986 SCF-LIS Canada 1988 SCF-LIS Canada 1989 SCF-LIS Canada 1990 SCF-LIS Canada 1992 SCF-LIS Canada 1993 SCF-LIS Canada 1995 SCF-LIS Canada 2019 CIS-LIS Central African Republic 2021 EHCVM China 2020 CNIHS Costa Rica 2022 ENAHO Ecuador 2022 ENEMDU El Salvador 2022 EHPM India 2020 CPHS India 2021 CPHS Kenya 2020 KCHS Kenya 2021 KCHS Luxembourg 1986 PSELL-LIS Luxembourg 1987 PSELL-LIS Luxembourg 1988 PSELL-LIS Luxembourg 1989 PSELL-LIS Luxembourg 1990 PSELL-LIS Luxembourg 1992 PSELL-LIS Luxembourg 1993 PSELL-LIS Luxembourg 1995 PSELL-ECHP-LIS Luxembourg 1996 PSELL-ECHP-LIS Luxembourg 1998 PSELL-ECHP-LIS Luxembourg 1999 PSELL-ECHP-LIS Luxembourg 2001 PSELL-ECHP-LIS Luxembourg 2002 SEP-SILC-LIS Mozambique 2019 IOF 12 Paraguay 2022 EPH Spain 1993 ECHP-LIS Spain 1994 ECHP-LIS Spain 1996 ECHP-LIS Spain 1997 ECHP-LIS Spain 1998 ECHP-LIS Spain 1999 ECHP-LIS Sweden 2002 HIS-LIS United States 1963 CPS-LIS United States 1964 CPS-LIS United States 1965 CPS-LIS United States 1966 CPS-LIS United States 1967 CPS-LIS United States 1968 CPS-LIS United States 1969 CPS-LIS United States 1970 CPS-LIS United States 1971 CPS-LIS United States 1972 CPS-LIS United States 1973 CPS-LIS United States 1975 CPS-LIS United States 1976 CPS-LIS United States 1977 CPS-LIS United States 1978 CPS-LIS United States 2021 CPS-ASEC-LIS 5. Changes to CPI data The baseline source of CPI data has been updated to the IMF’s International Financial Statistics (IFS) as of 1 November 2022. Lakner et al. (2018) provide an overview of the various CPI series that are used in PIP. Table A1 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. 6. 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 June 2023 vintage of the World Development Indicators (WDI). As done in the previous update, when WDI data are missing, data from the IMF’s World Economic Outlook (WEO), April 2023 version are used. Supplementary data from the Maddison Project Database (MPD), 2020 version are further used to 13 fill 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 have also been revised to the June 2023 vintage of the WDI. Compared to the December 2022 vintage of WDI used for the previous PIP update, there have been slight revisions to population data. Sri Lanka has the largest revision of more than half a million people added to the 2012 population number. 7. Comparability database Since September 2019, we provide metadata on comparability of poverty estimates within countries over time. The assessment of comparability is country-dependent and relies on the accumulation of knowledge from past and current Bank staff in the countries, as well as close dialogue with national data producers with knowledge of survey design and methodology (see Atamanov et al. (2019) for more information on reasons that break comparability). More information about the comparability database and how to use it is available at https://worldbank.github.io/PIP-Methodology/welfareaggregate.html#comparability. The PIP website also indicates comparability in its main output. 14 8. References Atamanov, Aziz, R. Andres Castaneda Aguilar, Carolina Diaz-Bonilla, Dean Jolliffe, Christoph Lakner, Daniel Gerszon Mahler, Jose Montes, et al. 2019. “September 2019 PovcalNet Update.” Global Poverty Monitoring Technical Note 10. Washington, D.C. https://doi.org/10.1596/32478. Atamanov, Aziz, R. Andres Castaneda Aguilar, Tony H.M.J. Fujs, Reno Dewina, Carolina Diaz- Bonilla, Daniel Gerszon Mahler, Dean Jolliffe, et al. 2020. “March 2020 PovcalNet Update.” Global Poverty Monitoring Technical Note 11. Washington, DC. https://doi.org/10.1596/33496. Castaneda, R Andres Aguilar, Carolina Diaz-Bonilla, Tony H M J Fujs, Dean Jolliffe, Aphichoke Kotikula, Christoph Lakner, Gabriel Lara Ibarra, et al. 2023. “March 2023 Update to the Poverty and Inequality Platform (PIP): What’s New.” Global Poverty Monitoring Technical Note 27. Washington, DC. https://documents1.worldbank.org/curated/en/099923403272329672/pdf/IDU089370bcb04 8b9044fd0ab49037249b87aef6.pdf. Central Statistical office of Zambia. 2016. “The Methodology for Consumption-Poverty Estimation and Poverty Trends in Zambia in 2010-2015.” Dreze, Jean, and Anmol Somanchi. 2021. “View: New Barometer of India’s Economy Fails to Reflect Deprivations of Poor Households.” The Economic Times, June 2021. https://economictimes.indiatimes.com/opinion/et-commentary/view-the-new-barometerof- indias-economy-fails-to-reflect-the-deprivations-of- poorhouseholds/articleshow/83696115.cms. Lakner, Christoph, Daniel Gerszon Mahler, Minh C Nguyen, Joao Pedro Azevedo, Shaohua Chen, Dean Jolliffe, and Prem Sangraula. 2018. “Consumer Price Indices Used in Global Poverty Measurement.” Global Poverty Monitoring Technical Note 4. Washington, DC. Lara Ibarra, Gabriel, and Ricardo Campante Vale. 2023. “Brazil 2021 Data Update: Methodological Adjustments to the World Bank’s Poverty and Inequality Estimates.” Global Poverty Monitoring Technical Note 28. Washington, D.C. Roy, Sutirtha Sinha, Roy van der Weide. 2022. “Poverty in India Has Declined over the Last Decade But Not As Much As Previously Thought.” Policy Research Working Paper, no. 9994, World Bank. Wittenberg, Martin. 2010. "An introduction to maximum entropy and minimum cross-entropy estimation using Stata." The Stata Journal 10, no. 3: 315-330. World Bank. 2020. Poverty and Shared Prosperity 2020: Reversals of Fortune. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-1602-4. World Bank. 2022. Poverty and Shared Prosperity 2022: Correcting Course. Washington, DC: World Bank. https://elibrary.worldbank.org/doi/epdf/10.1596/978-1-4648-1893-6. 15 9. Appendix 9.1. CPI data sources Table A1 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-202211 denotes the monthly IFS database version November 2022. For economy-specific deflators, the description is given in the text or further details are available upon request. 16 Table A1. Source of temporal deflators used in the September 2023 PIP update Code Economy Survey Year(s) CPI period Source HBS 2000 W IFS-M-202211 AGO Angola IBEP-MICS 2008 W IFS-M-202211 IDREA 2018 W IFS-M-202211 EWS 1996 Y IFS-M-202211 LSMS 2002-2012 Y IFS-M-202211 ALB Albania HBS 2014-2020 Y IFS-M-202211 SILC-C 2017-2019 (prev. year)Y IFS-M-202211 HIES 2014 W IFS-M-202211 ARE United Arab Emirates 2019 Y IFS-M-202211 EPH 1980-1987 Y NSO 1991-2002 M9 NSO ARG Argentina - urban EPHC-S2 2003-2021 M7-M12 NSO 2007-2014 M7-M12 Private estimates ARM Armenia ILCS ALL Y IFS-M-202211 IHS-LIS 1981 Y IFS-A-202211 IDS-LIS 1985 Y IFS-A-202211 AUS Australia SIHCA-LIS 1989 Y IFS-A-202211 SIH-LIS 1995-2018 Y IFS-A-202211 SIH-HES-LIS 2004-2016 Y IFS-A-202211 ECHP-LIS 1994-2000 Y IFS-M-202211 AUT Austria MC-LIS 1995 Y IFS-M-202211 EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 SLC 1995 Y IFS-M-202211 AZE Azerbaijan HBS 2001-2005 Y IFS-M-202211 EDCM 1992 Y IFS-M-202211 EP 1998 W IFS-M-202211 BDI Burundi QUIBB 2006 Y IFS-M-202211 ECVMB 2013 W IFS-M-202211 SEP-LIS 1985-1997 Y IFS-M-202211 BEL Belgium PSBH-ECHP-LIS 1995-2000 Y IFS-M-202211 EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 QUIBB 2003 Y IFS-M-202211 EMICOV 2011 W IFS-M-202211 BEN Benin 2015 Y IFS-M-202211 EHCVM 2018 M10 IFS-M-202211 EP-I 1994 W IFS-M-202211 BFA Burkina Faso EP-II 1998 Y IFS-M-202211 ECVM 2003-2009 Y IFS-M-202211 17 EMC 2014 Y IFS-M-202211 EHCVM 2018 M9 IFS-M-202211 HHES 1983-1985 W WEO-A-202210 1988-1991 W IFS-A-202211 BGD Bangladesh 1995 W Survey HIES 2000-2022 Y Survey HBS 1989 Y IFS-A-202211 1992-1994 Y IFS-M-202211 BGR Bulgaria IHS 1995-2001 Y IFS-M-202211 MTHS 2003-2007 Y IFS-M-202211 EU-SILC 2007-2021 (prev. year)Y IFS-M-202211 LSMS 2001-2004 Y WEO-A-202210 BIH Bosnia and Herzegovina HBS 2007-2011 Y IFS-M-202211 FBS 1993-1995 Y IFS-M-202211 BLR Belarus HHS 1998-2020 Y IFS-M-202211 LFS 1993-1999 Y IFS-A-202211 BLZ Belize HBS 1995 Y IFS-A-202211 SLC 1996 Y IFS-A-202211 Bolivia - urban EPF 1990 W IFS-M-202211 EIH 1992 M11 IFS-M-202211 Bolivia ENE 1997 M11 IFS-M-202211 ECH 1999 M10 IFS-M-202211 BOL 2000 M11 IFS-M-202211 EH 2001-2005 M11 IFS-M-202211 ECH 2004 M10 IFS-M-202211 EH 2006-2016 M10 IFS-M-202211 2017-2021 M11 IFS-M-202211 PNAD 1981-2011 M9 IFS-M-202211 BRA Brazil PNADC-E1 2012-2019 Y IFS-M-202211 PNADC-E5 2020-2021 Y IFS-M-202211 Previous BLSS 2003-2017 Y WDI/IFS BTN Bhutan Previous 2022 M1-M8 WDI/IFS HIES 1985-2002 W IFS-M-202211 BWA Botswana CWIS 2009 W IFS-M-202211 BMTHS 2015 W IFS-M-202211 EPCM 1992 W IFS-M-202211 CAF Central African Republic ECASEB 2008 Y IFS-M-202211 EHCVM 2021 M5 IFS-M-202211 SCF-LIS 1971-1995 Y IFS-M-202211 CAN Canada SLID-LIS 1996-2011 Y IFS-M-202211 CIS-LIS 2012-2019 Y IFS-M-202211 18 SIWS-LIS 1982 Y IFS-M-202211 NPS-LIS 1992 Y IFS-M-202211 CHE Switzerland IES-LIS 2000-2002 Y IFS-M-202211 EU-SILC 2007-2019 (prev. year)Y IFS-M-202211 CASEN 1987 Y IFS-M-202211 CHL Chile 1990-2020 M11 IFS-M-202211 CRHS-CUHS 1981-2011 Y NSO CHN China CNIHS 2012-2020 Y NSO EPAM 1985-1988 W IFS-M-202211 EP 1992 W IFS-M-202211 CIV Côte d'Ivoire ENV 1995-2015 Y IFS-M-202211 EHCVM 2018 M10 IFS-M-202211 ECAM-I 1996 Y IFS-M-202211 ECAM-II 2001 Y IFS-M-202211 CMR Cameroon ECAM-III 2007 Y IFS-M-202211 ECAM-IV 2014 Y IFS-M-202211 COD Congo, Dem. Rep. E123 ALL W IFS-M-202211 ECOM 2005 Y IFS-M-202211 COG Congo, Rep. 2011 W IFS-M-202211 Colombia - urban ENH 1980-1988 Y IFS-M-202211 1989-1991 M11 IFS-M-202211 COL Colombia 1992-2000 M11 IFS-M-202211 ECH 2001-2005 M11 IFS-M-202211 GEIH 2008-2021 M11 IFS-M-202211 EIM 2004 Y IFS-M-202211 COM Comoros EESIC 2013 Y IFS-M-202211 IDRF 2001 W IFS-M-202211 CPV Cabo Verde QUIBB 2007 W IFS-M-202211 IDRF 2015 Y IFS-M-202211 ENH 1981-1986 Y IFS-M-202211 EHPM 1989 Y IFS-M-202211 CRI Costa Rica 1990-2009 M7 IFS-M-202211 ENAHO 2010-2022 M7 IFS-M-202211 CYP Cyprus EU-SILC ALL (prev. year)Y IFS-M-202211 MC-LIS 1992-2002 Y IFS-M-202211 CZE Czech Republic CM 1993 Y IFS-M-202211 EU-SILC 2005-2021 (prev. year)Y IFS-M-202211 DEU Germany LIS ALL Y IFS-M-202211 EDAM 2002-2013 Y IFS-M-202211 DJI Djibouti 2017 M5 IFS-M-202211 LM-LIS 1987-2000 Y IFS-M-202211 DNK Denmark EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 19 ENGSLF 1986-1989 Y IFS-M-202211 ICS 1992 M6 IFS-M-202211 ENFT 1996 M2 IFS-M-202211 DOM Dominican Republic 1997 M4 IFS-M-202211 2000-2016 M9 IFS-M-202211 ECNFT-Q03 2017-2021 Y IFS-M-202211 EDCM 1988 Y IFS-M-202211 DZA Algeria ENMNV 1995 Y IFS-M-202211 ENCNVM 2011 W IFS-M-202211 Ecuador - urban EPED 1987 Y IFS-M-202211 Ecuador ECV 1994 M6-M10 IFS-M-202211 Ecuador - urban EPED 1995 M11 IFS-M-202211 ECU 1998 M6 IFS-M-202211 (prev. Ecuador ECV 1999 year)M10-M9 IFS-M-202211 EPED 2000 M11 IFS-M-202211 ENEMDU 2003-2022 M11 IFS-M-202211 HIECS 1990-2012 W IFS-M-202211 EGY Egypt, Arab Rep. 2015 Y IFS-M-202211 2017-2019 W IFS-M-202211 HBS-LIS 1980-1990 Y IFS-M-202211 ESP Spain ECHP-LIS 1993-2000 Y IFS-M-202211 EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 HIES 1993-1998 Y IFS-M-202211 EST Estonia HBS 2000-2004 Y IFS-M-202211 EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 Ethiopia - rural HICES 1981 W IFS-M-202211 ETH Ethiopia 1995-2010 W IFS-M-202211 2015 M12 IFS-M-202211 IDS-LIS 1987-2000 Y IFS-M-202211 FIN Finland EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 FJI Fiji HIES ALL W IFS-M-202211 TIS-LIS 1970-1990 Y IFS-M-202211 FRA France TSIS-LIS 1996-2002 Y IFS-M-202211 EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 Micronesia, Fed. Sts. - urban CPH 2000 Y IFS-A-202211 FSM Micronesia, Fed. Sts. HIES 2005-2013 Y IFS-A-202211 GAB Gabon EGEP ALL Y IFS-M-202211 FES-LIS 1968-1993 Y IFS-M-202211 GBR United Kingdom FRS-LIS 1994-2020 Y IFS-M-202211 GEO Georgia HIS ALL Y IFS-M-202211 GHA Ghana GLSS-I 1987 W IFS-M-202211 20 GLSS-II 1988 W IFS-M-202211 GLSS-III 1991 W IFS-M-202211 GLSS-IV 1998 W IFS-M-202211 GLSS-V 2005 W Survey GLSS-VI 2012 W Survey GLSS-VII 2016 W Survey ESIP 1991 Y WEO-A-202210 EIBC 1994 W WEO-A-202210 GIN Guinea EIBEP 2002 W WEO-A-202210 ELEP 2007-2012 Y IFS-M-202211 EHCVM 2018 W IFS-M-202211 HPS 1998 Y IFS-M-202211 GMB Gambia, The HIS 2003 W IFS-M-202211 IHS 2010-2020 W IFS-M-202211 ILJF 1991 Y IFS-M-202211 ICOF 1993 Y IFS-M-202211 GNB Guinea-Bissau ILAP-I 2002 Y IFS-M-202211 ILAP-II 2010 Y IFS-M-202211 EHCVM 2018 W IFS-M-202211 ECHP-LIS 1995-2000 Y IFS-M-202211 GRC Greece EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 ENSD 1986 W IFS-M-202211 1989 Y IFS-M-202211 GTM Guatemala ENIGF 1998 M8 IFS-M-202211 ENCOVI 2000 M6-M11 IFS-M-202211 2006-2014 M7 IFS-M-202211 GLSMS 1992 W WEO-A-202210 GUY Guyana 1998 Y IFS-M-202211 Honduras - urban ECSFT 1986 Y IFS-M-202211 Honduras EPHPM 1989 Y IFS-M-202211 HND 1990-1993 M5 IFS-M-202211 1994 M9 IFS-M-202211 1995-2019 M5 IFS-M-202211 HBS 1988-2010 Y IFS-M-202211 HRV Croatia EU-SILC 2010-2021 (prev. year)Y IFS-M-202211 ECVH 2001 M5 IFS-M-202211 HTI Haiti ECVMAS 2012 M10 IFS-M-202211 HBS 1987-2007 Y IFS-M-202211 HHP-LIS 1991-1994 Y IFS-M-202211 HUN Hungary THMS-LIS 1999 Y IFS-M-202211 EU-SILC 2005-2021 (prev. year)Y IFS-M-202211 IDN Indonesia SUSENAS 1984-1999 Y IFS-M-202211 21 2000-2007 M2 IFS-M-202211 2008-2022 M3 IFS-M-202211 M7-(next NSS 1977 year)M6 NSO 1983 Y NSO IND India M7-(next NSS-SCH1 1987-2011 year)M6 NSO M4-(next CPHS 2015-2021 year)M3 NSO SIDPUSS-LIS 1987 Y IFS-M-202211 LIS-ECHP-LIS 1994-2000 Y IFS-M-202211 IRL Ireland SILC-LIS 2002 Y IFS-M-202211 EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 SECH 1986 Y IFS-A-202211 1990-1998 Y IFS-M-202211 IRN Iran, Islamic Rep. HEIS 2005-2009 W IFS-M-202211 M4-(next 2013-2019 year)M3 IFS-M-202211 IHSES 2006 W COSIT IRQ Iraq 2012 Y COSIT ISL Iceland EU-SILC ALL (prev. year)Y IFS-M-202211 ISR Israel HES-LIS ALL Y IFS-M-202211 SHIW-LIS 1986-2000 Y IFS-M-202211 ITA Italy EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 1988 M9 IFS-M-202211 M11-(next 1990-1993 year)M3 IFS-M-202211 JAM Jamaica SLC 1996 M5-M8 IFS-M-202211 1999 M6-M8 IFS-M-202211 2002-2004 M6 IFS-M-202211 1986 W IFS-M-202211 JOR Jordan HEIS 1992-1997 Y IFS-M-202211 2002-2010 W IFS-M-202211 JPN Japan JHPS-LIS ALL Y IFS-M-202211 HBS 1993-2018 Y IFS-M-202211 KAZ Kazakhstan LSMS 1996 Y IFS-M-202211 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 KPMS 1998 Y IFS-M-202211 KGZ Kyrgyz Republic HBS 2000-2003 Y IFS-M-202211 22 KIHS 2004-2020 Y IFS-M-202211 HIES 2006 Y IFS-M-202211 KIR Kiribati 2019 W IFS-M-202211 KOR Korea, Rep. HIES-FHES-LIS ALL Y IFS-M-202211 LECS 1992 W IFS-A-202211 LAO Lao PDR 1997 W IFS-M-202211 2002-2018 W Survey (next LBN Lebanon HBS 2011 year)M5 IFS-M-202211 CWIQ 2007 Y IFS-M-202211 LBR Liberia HIES 2014-2016 Y IFS-M-202211 LSMS 1995 Y IFS-M-202211 LCA St. Lucia SLC-HBS 2016 M1 IFS-M-202211 LFSS 1985 Y IFS-M-202211 HIES 1990 W IFS-M-202211 SES 1995 W IFS-M-202211 LKA Sri Lanka HIES 2002 Y IFS-M-202211 2006-2012 W IFS-M-202211 2016-2019 Y IFS-M-202211 HBS 1986 W WEO-A-202210 NHECS 1994 W WEO-A-202210 LSO Lesotho HBS 2002 W IFS-M-202211 CMSHBS 2017 M8 IFS-M-202211 HBS 1993-2008 Y IFS-M-202211 LTU Lithuania EU-SILC 2005-2021 (prev. year)Y IFS-M-202211 PSELL-LIS 1985-1993 Y IFS-M-202211 PSELL-ECHP- LUX Luxembourg LIS 1994-2001 Y IFS-M-202211 SEP-SILC-LIS 2002 Y IFS-M-202211 EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 HBS 1993-2009 Y IFS-M-202211 LVA Latvia EU-SILC 2005-2021 (prev. year)Y IFS-M-202211 ECDM 1984 W IFS-M-202211 MAR Morocco ENNVM 1990-2006 W IFS-M-202211 ENCDM 2000-2013 W IFS-M-202211 MDA Moldova HBS ALL Y IFS-M-202211 EB 1980 Y IFS-M-202211 EPM 1993 W IFS-M-202211 MDG Madagascar 1997-2010 Y IFS-M-202211 ENSOMD 2012 W IFS-M-202211 HIES 2002-2009 W IFS-M-202211 MDV Maldives 2016 Y IFS-M-202211 2019 M11 IFS-M-202211 23 ENIGH 1984-2014 M8 IFS-M-202211 MEX Mexico ENIGHNS 2016-2020 M8 IFS-M-202211 MHL Marshall Islands HIES 2019 W WEO-A-202210 HBS 1998-2008 Y IFS-M-202211 MKD North Macedonia SILC-C 2010-2020 (prev. year)Y IFS-M-202211 EMCES 1994 Y IFS-A-202211 EMEP 2001 W IFS-M-202211 MLI Mali ELIM 2006-2009 W IFS-M-202211 EHCVM 2018 M10 IFS-M-202211 MLT Malta EU-SILC ALL (prev. year)Y IFS-M-202211 MPLCS 2015 M1 IFS-M-202211 MMR Myanmar MLCS 2017 Q1 IFS-M-202211 HBS 2005-2014 Y IFS-M-202211 MNE Montenegro SILC-C 2013-2019 (prev. year)Y IFS-M-202211 LSMS 1995-1998 Y IFS-M-202211 HIES-LSMS 2002 W IFS-M-202211 MNG Mongolia HSES 2007 W IFS-M-202211 2010-2018 Y IFS-M-202211 NHS 1996 W WEO-A-202210 MOZ Mozambique IAF 2002 W WEO-A-202210 IOF 2008-2019 W IFS-M-202211 EPCV 1987 Y IFS-M-202211 EP 1993 Y IFS-M-202211 MRT Mauritania EPCV 1995-2008 W IFS-M-202211 2014 Y IFS-M-202211 HBS 2006 W IFS-M-202211 MUS Mauritius 2012-2017 Y IFS-M-202211 IHS-I 1997 W IFS-M-202211 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-202211 (prev. year)M7- (prev. 2004 year)M12 IFS-M-202211 (prev. MYS Malaysia year)M7- (prev. 2007 year)M10 IFS-M-202211 2009 W IFS-M-202211 2012-2016 Y IFS-M-202211 HIESBA 2019 W IFS-M-202211 24 NHIES 1993 W WEO-A-202210 NAM Namibia 2003-2015 W IFS-M-202211 ENBCM 1992-2007 W IFS-M-202211 EPCES 1994 W IFS-M-202211 NER Niger ENCVM 2005 Y IFS-M-202211 ECVMA 2011-2014 Y IFS-M-202211 EHCVM 2018 M10 IFS-M-202211 NCS 1985 W IFS-M-202211 1992-1996 Y IFS-M-202211 LSS 2003 W IFS-M-202211 GHSP-W1 2010 M3-M4 IFS-M-202211 NGA Nigeria GHSP-W2 2012 M3-M4 IFS-M-202211 GHSP-W3 2015 M3-M4 IFS-M-202211 (next year)M3- (next LSS 2018 year)M4 IFS-M-202211 EMNV 1993 M2 NSO 1998 M6 NSO NIC Nicaragua 2001 M6 IFS-M-202211 2005-2009 M8 IFS-M-202211 2014 M8-M10 IFS-M-202211 AVO-LIS 1983-1990 Y IFS-M-202211 NLD Netherlands SEP-LIS 1993-1999 Y IFS-M-202211 EU-SILC 2005-2021 (prev. year)Y IFS-M-202211 IDS-LIS 1979-2000 Y IFS-M-202211 NOR Norway EU-SILC 2004-2020 (prev. year)Y IFS-M-202211 MHBS 1984 W IFS-M-202211 LSS-I 1995 W IFS-M-202211 NPL Nepal LSS-II 2003 W IFS-M-202211 LSS-III 2010 W IFS-M-202211 NRU Nauru HIES 2012 W WEO-A-202210 HIES 1987 Y IFS-M-202211 1990-1998 W IFS-M-202211 PAK Pakistan IHS 1996 W IFS-M-202211 PIHS 2001 M6 IFS-M-202211 (next HIES 2004-2018 year)M1 IFS-M-202211 EMO 1979-1989 Y IFS-M-202211 PAN Panama 1991 M7 IFS-M-202211 EH 1995-2021 M7 IFS-M-202211 Peru ENNIV 1985 W IFS-M-202211 PER 1994 Y IFS-M-202211 25 ENAHO 1997-2002 Q4 IFS-M-202211 2003 M5-M12 IFS-M-202211 2004-2021 Y IFS-M-202211 PHL Philippines FIES ALL Y IFS-M-202211 HIES 1996 Y IFS-A-202211 PNG Papua New Guinea 2009 W IFS-A-202211 HBS 1985-1987 Y IFS-A-202211 HBS-LIS 1986 Y IFS-A-202211 POL Poland HBS 1989-2019 Y IFS-M-202211 HBS-LIS 1992-1999 Y IFS-M-202211 EU-SILC 2005-2020 (prev. year)Y IFS-M-202211 PRT Portugal EU-SILC ALL (prev. year)Y IFS-M-202211 EH 1990 M7 IFS-M-202211 1995 M8-M11 IFS-M-202211 (next EIH 1997 year)M2 IFS-M-202211 EPH 1999 M9 IFS-M-202211 EIH 2001 M3 IFS-M-202211 EPH 2002 M11 IFS-M-202211 PRY Paraguay 2003 M9 IFS-M-202211 2004 M10 IFS-M-202211 2005 M11 IFS-M-202211 2006 M12 IFS-M-202211 2007-2008 M10 IFS-M-202211 2009 M11 IFS-M-202211 2010-2022 M10 IFS-M-202211 PECS 2004-2011 Y IFS-M-202211 PSE West Bank and Gaza 2016 W IFS-M-202211 HBS 1989 Y Milanovic (1998) MC 1992 Y IFS-M-202211 HIS 1994-1999 Y IFS-M-202211 ROU Romania IHS-LIS 1995-1997 Y IFS-M-202211 IHS 1998-2000 Y IFS-M-202211 HBS 2001-2018 Y IFS-M-202211 EU-SILC 2007-2021 (prev. year)Y IFS-M-202211 HBS 1993-2020 Y IFS-M-202211 RUS Russian Federation VNDN 2015-2019 (prev. year)Y IFS-M-202211 Rwanda - rural ENBCM 1984 W IFS-M-202211 Rwanda EICV-I 2000 W IFS-M-202211 RWA EICV-II 2005 W IFS-M-202211 (next EICV-III 2010 year)M1 IFS-M-202211 26 (next EICV-IV 2013 year)M1 IFS-M-202211 (next EICV-V 2016 year)M1 IFS-M-202211 NBHS 2009 Y IFS-M-202211 SDN Sudan 2014 M11 IFS-M-202211 EP 1991 W IFS-M-202211 ESAM 1994 W IFS-M-202211 ESAM-II 2001 W IFS-M-202211 SEN Senegal ESPS-I 2005 W IFS-M-202211 ESPS-II 2011 W IFS-M-202211 EHCVM 2018 M9 IFS-M-202211 SLB Solomon Islands HIES ALL W IFS-M-202211 HEEAS 1989 W WEO-A-202210 SLE Sierra Leone SLIHS 2003 W WEO-A-202210 2011-2018 Y IFS-M-202211 EHPM 1989 Y IFS-M-202211 M10-(next 1991 year)M4 IFS-M-202211 SLV El Salvador 1995-1999 Y IFS-M-202211 2000-2007 M12 IFS-M-202211 2008-2022 M11 IFS-M-202211 LSMS 2002 Y IFS-M-202211 SRB Serbia HBS 2003-2019 Y IFS-M-202211 EU-SILC 2013-2021 (prev. year)Y IFS-M-202211 NBHS 2009 Y IFS-M-202211 SSD South Sudan (prev. HFS-W3 2016 year)M7 IFS-M-202211 IOF 2000 W IFS-M-202211 STP São Tomé and Principe 2010-2017 Y IFS-M-202211 SUR Suriname - urban EHS 1999 Y IFS-M-202211 MC-LIS 1992-1996 Y IFS-M-202211 SVK Slovak Republic HBS 2004-2009 Y IFS-M-202211 EU-SILC 2005-2020 (prev. year)Y IFS-M-202211 IES 1987-1993 Y IFS-M-202211 HBS-LIS 1997-1999 Y IFS-M-202211 SVN Slovenia HBS 1998-2003 Y IFS-M-202211 EU-SILC 2005-2021 (prev. year)Y IFS-M-202211 HIS-LIS 1975-2002 Y IFS-M-202211 SWE Sweden EU-SILC 2004-2021 (prev. year)Y IFS-M-202211 SWZ Eswatini HIES ALL W IFS-M-202211 HES 1999 W IFS-M-202211 SYC Seychelles HBS 2006 W IFS-M-202211 27 2013 Y IFS-M-202211 2018 W IFS-M-202211 SYR Syrian Arab Republic HIES ALL W IFS-M-202111 ECOSIT-II 2003 Y IFS-M-202211 TCD Chad ECOSIT-III 2011 Y IFS-M-202211 EHCVM 2018 W IFS-M-202211 QUIBB 2006-2015 Y IFS-M-202211 TGO Togo EHCVM 2018 M10 IFS-M-202211 THA Thailand SES ALL Y IFS-M-202211 TLSS 1999 Y WEO-A-202210 2003-2007 Y Survey TJK Tajikistan HBS 2004 Y Survey TLSS 2009 Y IFS-M-202211 HSITAFIEN 2015 Y IFS-M-202211 TKM Turkmenistan LSMS 1998 Y WEO-A-202210 TLSS 2001 Y WEO-A-202210 TLS Timor-Leste TLSLS 2007-2014 Y IFS-M-202211 HIES 2000 W IFS-M-202211 TON Tonga 2009-2015 Y IFS-M-202211 SLC 1988 Y IFS-M-202211 TTO Trinidad and Tobago PHC 1992 Y IFS-M-202211 HBCS 1985 Y IFS-A-202211 1990 Y IFS-M-202211 TUN Tunisia LSS 1995-2000 Y IFS-M-202211 NSHBCSL 2005-2015 W IFS-M-202211 TUR Turkey HICES ALL Y IFS-M-202211 TUV Tuvalu HIES 2010 Y IFS-A-202211 TWN Taiwan, China FIDES-LIS ALL Y WEO-A-202210 HBS 1991 W IFS-A-202211 2000 W IFS-M-202211 TZA Tanzania 2007 Y IFS-M-202211 2011-2018 W IFS-M-202211 HBS 1989 Y WEO-A-202210 NIHS 1992 W WEO-A-202210 UGA Uganda 1996-1999 W IFS-M-202211 UNHS 2002-2019 W IFS-M-202211 HS 1992-1993 Y IFS-M-202211 UKR Ukraine HIES 1995-1996 Y IFS-M-202211 HLCS 1999-2020 Y IFS-M-202211 Uruguay - urban ENH 1981-1989 Y IFS-M-202211 URY (prev. ECH 1992-2005 year)M12 IFS-M-202211 28 (prev. Uruguay 2006-2020 year)M12 IFS-M-202211 (prev. ECH-S2 2021 year)M12 IFS-M-202211 CPS-LIS 1963-2001 Y IFS-M-202211 USA United States CPS-ASEC-LIS 2002-2021 Y IFS-M-202211 UZB Uzbekistan HBS ALL Y WEO-A-202210 EHM 1981-1989 Y NSO VEN Venezuela, RB 1992-2006 M12 NSO VLSS 1992 W WEO-A-202210 VNM Vietnam 1997 W IFS-M-202211 VHLSS 2002-2020 M1 IFS-M-202211 HIES 2010 Y IFS-A-202211 VUT Vanuatu NSDP 2019 W IFS-A-202211 HIES 2002-2008 Y IFS-M-202211 WSM Samoa 2013 W IFS-M-202211 XKX Kosovo HBS ALL Y IFS-M-202211 HBS 1998 Y IFS-M-202211 YEM Yemen, Rep. 2005 W IFS-M-202211 2014 Y IFS-M-202211 KIDS 1993 Y IFS-M-202211 HIES 2000 W IFS-M-202211 (next ZAF South Africa IES 2005-2010 year)M6 IFS-M-202211 LCS 2008 W IFS-M-202211 (next 2014 year)M6 IFS-M-202211 HBS 1991-1993 Y IFS-M-202211 LCMS-I 1996 Y IFS-M-202211 LCMS-II 1998 Y IFS-M-202211 LCMS-III 2002 W IFS-M-202211 ZMB Zambia LCMS-IV 2004 W IFS-M-202211 LCMS-V 2006 W IFS-M-202211 LCMS-VI 2010 Y IFS-M-202211 LCMS-VII 2015 Y IFS-M-202211 ZWE Zimbabwe ICES 2011 Y IFS-M-202211 PICES 2017-2019 Y Survey 29