Global Poverty Monitoring Technical Note 14 September 2020 PovcalNet Update What’s New R. Andres Castaneda Aguilar, Tony Fujs, Dean Jolliffe, Christoph Lakner, Daniel Gerszon Mahler, Minh C. Nguyen, Marta Schoch, David L. Vargas Mogollon, Martha C. Viveros Mendoza, Samuel Kofi Tetteh Baah, Nishant Yonzan, and Nobuo Yoshida September 2020 Keywords: What’s New; September 2020. Development Data Group Development Research Group Poverty and Equity Global Practice Group GLOBAL POVERTY MONITORING TECHNICAL NOTE 14 Abstract The September 2020 update to PovcalNet mainly involves the adoption of the revised 2011 PPPs for the estimation of global poverty. In addition, the coverage rules for reporting regional and global poverty aggregates have been reviewed, resulting in small adjustments. Historical regional and global aggregates are now reported with an annual frequency instead of intervals with varying lengths. Only two surveys have been added and some welfare aggregates have been revised compared with the March 2020 update. National accounts and population input data have been updated. This document explains these changes and the rationale behind them in detail. The data and associated estimates are used for the analysis of global poverty in the forthcoming Poverty and Shared Prosperity Report 2020. 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 Samuel Freije-Rodriguez, Haishan Fu, Jonathan Lain, David Newhouse, Berk Özler Carolina Sánchez-Páramo, Sarosh Sattar, Umar Serajuddin and Tara Vishwanath. We would also like to thank the countless Poverty Economists that have provided data and documentation; without them the database of household surveys that underpins the World Bank’s global poverty measures would not exist. This note has been cleared by Berk Özler and 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 http://iresearch.worldbank.org/PovcalNet/. Contents 1. Introduction ............................................................................................................................. 2 2. Annual regional and global poverty estimates, review of population coverage rule .............. 4 3. Revised 2011 PPPs ................................................................................................................. 8 3.1. Country-specific PPP adjustments .................................................................................. 9 4. Surveys added ....................................................................................................................... 10 4.1. Nigeria 2018/2019 ........................................................................................................ 10 4.2. Canada 2017.................................................................................................................. 11 5. India line-up .......................................................................................................................... 12 6. Revisions to welfare aggregates............................................................................................ 12 6.1. Changes to the US data in LIS ...................................................................................... 12 6.2. National inequality estimates for India, Indonesia and China ...................................... 13 7. Changes to national accounts data ........................................................................................ 13 8. Changes to population data ................................................................................................... 14 9. References ............................................................................................................................. 15 A. Appendix 1 – Imputation of revised 2011 PPPs ................................................................... 16 A1.1. Egypt, Iraq, Jordan, Laos, Myanmar, Yemen ................................................................. 16 A1.2. China, India, Indonesia ................................................................................................... 17 B. Appendix 2 – CPI data sources ............................................................................................. 18 C. Appendix 3 – Gini coefficients ............................................................................................. 32 D. Appendix 4 – National accounts data sources ...................................................................... 33 1 1. Introduction The September 2020 global poverty update from the World Bank presents new global poverty estimates for 2017 and revises the previously published historical global and regional estimates. This note describes and explains the changes made in this update. The revisions occur as a result of the adoption of revised 2011 PPPs, the addition of new survey data, the update of national accounts, as well as other (small) changes to the existing data. Table 1 indicates the global and regional poverty estimates for 2017, which are presented in more detail in the forthcoming 2020 Poverty and Shared Prosperity report (World Bank, 2020). In 2017, an estimated 689 million people were living below the international poverty line (IPL), set at $1.90 PPP U.S. dollars. The global poverty rate, the share of the world’s population living below the IPL, stood at 9.2%.1 Sub-Saharan Africa accounted for more than 60% of the world’s population below the IPL and had the highest regional poverty rate, at 41.0%. Around a quarter of the world population (24.1%) lived on less than $3.20 and 43.6% on less than $5.50, poverty lines that are typical of lower-middle and upper-middle income countries, respectively. Table 1. Poverty estimates for reference year 2017, different poverty lines $1.90 $3.20 $5.50 Survey Head- Head- Head- Region coverage Number Number Number count count count (%) of poor of poor of poor ratio ratio ratio (mil) (mil) (mil) (%) (%) (%) East Asia and Pacific 97.1 1.4 29 8.7 179 28.2 583 Europe and Central Asia 89.5 1.3 6 4.7 23 12.6 62 Latin America and the Caribbean 90.2 3.9 24 9.5 60 23.1 146 Middle East and North Africa 58.2 6.3 24 18.5 71 43.4 165 Rest of the World 77.7 0.6 7 0.8 9 1.3 14 South Asia 21.8 n/a n/a n/a n/a n/a n/a Sub-Saharan Africa 79.0 41.0 431 67.3 707 86.1 905 World 70.7 9.2 689 24.1 1811 43.6 3271 Source: PovcalNet Note: Survey coverage is assessed within a three-year window either side of 2017, i.e. including surveys that were conducted between 2014 and 2020 (see Section 2 below). The estimates for South Asia are not displayed since the region has a survey coverage less than 50% of the region’s total population. At the global level, the surveys available within a three-year window either side of 2017 represent 52% of the population living in low-income and lower- middle income countries. 1 Even though the estimates for South Asia are not shown in Table 1, they are included in the total for the World. 2 Table 2 reports the differences in regional poverty estimates in 2018 between the March 2020 and September 2020 PovcalNet vintages. In general, the differences are small. Venezuela explains the reduction in poverty in Latin America and the Caribbean (0.6 percentage points at the IPL).2 The inclusion of new survey data for Nigeria explains the reduction in Sub-Saharan Africa (14 million fewer poor people). For most of the regions, poverty rates decrease with the September 2020 update at all poverty lines.3 The exception is the Middle East and North Africa, where the poverty estimates increase at the higher global poverty lines (the same is true for Sub-Saharan Africa at $5.50). Table 2. Regional poverty rates in 2018: March 2020 vs. September 2020 PovcalNet update $1.90: $1.90: $3.20: $3.20: $5.50: $5.50: Headcount Number of Headcount Number of Headcount Number of Region ratio (%) poor (mil) ratio (%) poor (mil) ratio (%) poor (mil) Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep East Asia and 1.3 1.2 28 25 7.6 7.2 159 149 25.6 25.0 532 520 Pacific Europe and 1.2 1.1 6 6 4.5 4.3 22 21 12.1 11.9 60 59 Central Asia Latin America 4.4 3.8 28 24 10.4 9.3 66 59 24.2 22.6 154 144 & Caribbean Middle East and 7.2 7.2 28 28 19.8 20.3 77 79 44.8 45.0 174 174 North Africa Rest of the 0.7 0.6 7 7 0.8 0.8 9 9 1.3 1.3 14 14 World South Asia n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Sub-Saharan 41.6* 40.2 447* 433 66.8* 66.6 718* 718 85.6* 86.0 920* 927 Africa Source: PovcalNet Note: The estimates for South Asia are not displayed due to insufficient population coverage. The March 2020 update did not report the regional estimates for Sub-Saharan Africa for the same reason; those unreported estimates are shown here to assess the impact of the data revisions. 2 The updated national accounts data excludes recent data for Venezuela, so it cannot be lined-up to 2018. Without a lined-up poverty estimate, like any missing country, Venezuela is assigned the regional poverty rate, which is lower than the lined-up estimate from the March 2020 update. 3 Table 2 reports the net effect of several changes to the underlying data. For example, the revisions to the 2011 PPPs by themselves increased the poverty rate in Sub-Saharan Africa, but this was offset by the new Nigeria survey. 3 2. Annual regional and global poverty estimates, review of population coverage rule In this update, for the first time, PovcalNet is reporting lined-up global and regional poverty numbers for every year. Previously, poverty estimates were reported at varying intervals and for the following years: every three years from 1981 to 2008, annually from 2010 to 2013, followed by 2015 and 2018. Figure 1 compares the lined-up global poverty estimates for the March 2020 update with the lined-up estimates for the September 2020 update. Figure 2 shows the comparison by region. Figure 1. Global line-up estimates of extreme poverty Source: PovcalNet Notes: Extreme poverty is measured as the share of population living on less than $1.90 per day. Interactive graph depicting global line-up estimates of extreme poverty, available here. The introduction of annual line-up years makes it easier to compare changes over time, as it standardizes the distance between line-up years. With the added granularity it is also possible to see that COVID-19 is likely to cause the first increase in global poverty since 1998, when the Asian Financial Crisis hit (Mahler et al., 2020). This was not apparent from the line-up years available previously, which included 1996 and 1999, but not the years in between. 4 Figure 2. Regional line-up estimates of extreme poverty Source: PovcalNet Notes: Extreme poverty is measured as the share of population living on less than $1.90 per day. Interactive graphs depicting line-up estimates of extreme poverty at the regional level, available here. The annual line-up series is noisier, though, especially before 1990. This is largely explained by the line-up estimates at the country level switching from income to consumption.4 This treatment of consumption and income data is not new, but the effect is now more visible with more granular line-up estimates. In fact, there has been no change in the way poverty estimates are calculated; the underlying country-level estimates have always been estimated for every year, but simply had 4 China, for example, has income-based poverty estimates at and before 1987 and consumption-based estimates from 1990 onwards. The 1990 estimate has a higher poverty rate at $1.90 than the 1987 estimate. PovcalNet does not interpolate between income and consumption-based estimates but rather extrapolates from the nearest survey. In this case, this means that the 1988 lined-up estimate is based on forward-extrapolated 1987 income estimate while the 1989 lined-up estimate is based on the backwards extrapolated consumption estimate. This creates a sharp break in the lined-up estimates, which is visible in the regional East Asia and Pacific poverty rates. Though this break was also apparent before, the introduction of lined-up estimates for 1987 and 1988 makes it more salient. 5 not been aggregated. We are working on refining the line-up methods and possibly change how line-up estimates switch between the use of income and consumption estimates. Together with the introduction of the annual line-up years, the population coverage rules applied to report regional and global aggregates have also been revised slightly. These rules are used to determine whether a particular line-up year has sufficient population coverage to allow the reporting of regional and global poverty aggregates. It is important to highlight that these changes do not affect how regional and global poverty aggregates are estimated; they only affect whether an estimate is displayed. Three main changes have been made. First, the coverage rules now include data for survey years within 3 years either side of a line-up year. This change makes this rule somewhat more lenient, but represents a small change compared to the old rule. Under the old rule, a country was included if the survey used is less than three years to the line-up year.5 Under the new rule, a country is considered covered if the distance to the line- up year is less than or equal to three years. This change simplifies the coverage rule and does not require making decisions for surveys that overlap multiple years, shown in PovcalNet as ‘decimal’ years (see footnote 4). The second change increases the threshold of population coverage at the regional level from 40% to 50% of the population. For regions in which the surveys within 3 years either side of the line- up year account for less than half of the regional population, the regional poverty estimate is not reported. This is a stricter parameter compared to the previous version of the coverage rule and balances the previous requirement. The 40-percent and 50-percent thresholds are both somewhat arbitrary but requiring a coverage of half of the regional population seems more intuitive. The third change introduces an additional requirement for the global poverty aggregate to ensure sufficient population coverage of countries where most of the poor live. Specifically, it tries to 5 The old rule has often been communicated as including surveys within 2 years either side of a line-up year. In practice, however, surveys that span two years were included even if only a small share of the survey actually fell within the two-year window. Surveys that span two years are reported in PovcalNet with a decimal year, with the decimals indicating the share of the survey conducted in the second year. For example, the 2012.25 survey for Lao PDR means that 75% of the survey was conducted in 2012 and 25% in 2013. Similarly, for the 2017.92 survey in Tanzania, 8% of the survey was conducted in 2017 and 92% of the survey in 2018. Under the old rule, both these surveys are included in the 2015 reference year. In other words, the old rule included surveys for 2015 as long as the survey year was less than 2018 and greater than 2012. 6 avoid a situation whereby the global population threshold is met by having recent data in the high- income countries, East Asia, and Latin America, which together account for a small share of the global extreme poor today. Under this requirement, global poverty estimates are reported only if data is representative of at least 50 percent of the population in low-income and lower-middle income countries (LIC/LMIC countries), since over 90 percent of the poor live in these countries.6 This requirement, however, is only applied to the global poverty estimate, not to the regional estimates.7 The World Bank classification of countries according to income groups in the line-up year is used.8 By using the income classification in the line-up year, this rule also accounts for how the regional composition of global poverty has shifted over time.9 The adoption of the new coverage rules makes little or no change in the reporting of regional poverty numbers, especially in the latest line-up years. Poverty estimates for the Latin America and the Caribbean (LAC) and East Asia and Pacific (EAP) regions can be reported over the entire line-up years under both the old and the new rules. Poverty estimates can be reported for Sub- Saharan Africa between 1990 and 2018 under both old and new rules. Poverty numbers cannot be reported for South Asia for the periods 1997-2001 and 2015-2018 under both old and new rules. For the Middle East and North Africa, line-up poverty estimates cannot be reported until 1983 (1984) under the old (new) rules. And for Europe and Central Asia, line-up poverty estimates cannot be reported until 1990 (1989) under the old (new) rules. Using these new rules, estimates of the global extreme poverty rate stop in 2017, while for individual regions we have information up to 2018, except for South Asia where the regional estimate is only reported until 2014. For 2018, the population coverage of LIC/LMIC countries with recent data is less than the 50% threshold; without this new requirement, the population 6 In 2017, more than 93% of the extreme global poor lived in low and lower-middle income countries. An alternative requirement would have been to compute the population coverage of the countries with the most poor. However, this would have created a certain circularity, since the objective of this rule is to assess whether the population coverage is sufficient to estimate which countries these are. 7 It does not make sense to apply this rule at the regional level, since some regions have only few LIC/LMIC countries that account for a small share of the regional population. For example, in Latin America, currently, the only LIC/LMIC countries are Bolivia, El Salvador, Haiti, Honduras and Nicaragua. These countries account for around 7% of the regional population. 8 For details on income classification, see the World Bank's classification of countries by income (Fantom & Serajuddin, 2016). 9 For example, East Asia accounted for around two-fifths of global poverty in the late-1990s compared with around 4 percent in 2017. China, which has been an important contributor to this change, has since then left the group of LIC/LMIC countries. 7 coverage threshold for reporting on global poverty would have been reached. Reporting the most recent regional estimates for which the coverage rules are satisfied is an attempt to provide the most up-to-date poverty estimates and to recognize the efforts by many countries to collect timely household survey data to monitor global poverty. 3. Revised 2011 PPPs Purchasing power parities (PPPs) are price indices that measure how much it costs to purchase a basket of goods and services in one country relative to purchasing the same basket in a reference country. Put differently, they express how much of a country’s currency will exchange for one unit of the currency of a reference country, typically the US, in real terms. Market exchange rates do not take into account non-tradable services, which are often cheaper in developing countries, where factors of production (e.g., labor) are not as expensive as in rich countries (i.e., the Balassa- Samuelson effect). All the poverty estimates included in this chapter adjust for differences in relative price levels across countries using the revised 2011 PPPs released by the International Comparison Program (ICP) in May 2020. The original 2011 PPPs were revised, mainly in light of the rebasing of national accounts data in several countries. The underlying price data remain unchanged. Since the PPPs are multilateral price indices, revisions to national accounts weights in one or a few countries translate into changes in PPP estimates for all countries. The revised 2011 PPPs have relatively small effects on global poverty estimates, as analyzed in greater detail in Atamanov et al. (2020b). The global headcount ratio increases by 0.24 percentage points (equivalent to 17.7 million more poor people) in 2017. When compared with the adoption of the 2005 PPPs replacing the 1993 PPPs, which increased global poverty by 400 million people, this change in poverty is small (Chen & Ravallion, 2010). Historically, ICP rounds have not only reflected new price information but also changes in ICP methodologies (e.g., the change from 2005 to 2011 PPPs). With this concern in mind, the Atkinson Commission on Global Poverty has recommended against adopting future ICP rounds (World Bank, 2017). Thus, the 2017 PPPs, which were published together with the revised 2011 PPPs, are not currently used for global poverty measurement as more analysis will be needed to examine their comparability. However, it is necessary to adopt the revised 2011 PPPs, as they incorporate 8 new information from national accounts. This is similar to how PovcalNet periodically revises its other input data, such as CPI, GDP or population estimates, to reflect the most recent accurate information. PPPs are also used in the derivation of the global poverty lines. When updated with the revised 2011 PPPs, the IPL still rounds to $1.90 per person per day (the updated underlying estimate is $1.87) (Atamanov et al., 2020b). The higher lines—$3.20 and $5.50 per person per day—are derived as the median implicit national poverty lines corresponding to lower-middle income countries and upper-middle income countries (Jolliffe & Prydz, 2016). When updated with the revised 2011 PPPs, the $3.20 line also remains unchanged, but the $5.50 line increases by approximately $0.15 (Atamanov et al., 2020b). Over time the World Bank’s global poverty lines have been widely used in the development community, such that they could be considered as parameters in estimating global poverty and there is a cost to revising them frequently. While changes in PPPs could result in a different estimate, it is important to recognize that the poverty line is a parameter chosen—using a reasonable method—to monitor progress in different parts of the global distribution of income or consumption. To this end, the World Bank has decided to keep all global poverty lines unchanged, including the Societal Poverty Line (SPL). 3.1. Country-specific PPP adjustments For Egypt, Iraq, Jordan, Laos, Myanmar and Yemen, PovcalNet uses imputed PPPs considered to be more appropriate than the official PPPs (Atamanov et al., 2018). The imputed PPP estimates are out-of-sample predictions based on a variant of the seemingly unrelated regression (SURE) model the ICP uses for estimating PPPs for non-benchmark countries. Using the same imputation method, revised 2011 PPPs have been imputed for these countries (Atamanov et al., 2020b). See Appendix 1, Table A1.1 for the original 2011 PPPs imputed for these economies, as well as the revised 2011 PPPs imputed using both old and new input data. PovcalNet currently uses those estimates with new input data. For global poverty estimation, PovcalNet uses rural and urban PPPs for China, India and Indonesia, to take into account the ‘urban bias’ in the ICP price data collection (Chen & Ravallion, 2008, 2010; Ferreira et al., 2016). These location-specific PPPs are imputed using the official national PPP estimates, the ratio of urban to rural poverty lines, and the urban share in the ICP price data 9 collection (see the formula given in the online Appendix of Ferreira et al. (2016) and Lakner et al. (2015)). For China, India and Indonesia, the rural and urban PPPs have been updated using the official national estimates for revised 2011 PPPs (while the other parameters remain unchanged) (Atamanov et al., 2020b). See Appendix 1, Table A1.2 for the original and revised 2011 PPPs for both rural and urban China, India and Indonesia. Using the rural and urban revised 2011 PPPs for India, the poverty estimate for 2014/15 that has been estimated by Newhouse & Vyas, (2019) has been updated. This estimate is derived using a survey-to-survey imputation methodology. PovcalNet uses it to calibrate a pass-through rate to estimate poverty in India between 2011 and 2015 for the purposes of the global poverty aggregate (following Chen et al., 2018).10 The 2014/15 survey-to-survey poverty estimates for rural and urban India with the original and revised 2011 PPPs are shown in Table 3. Table 3: Survey-to-survey poverty estimates for India (2014/15) at $1.90 PPP Poverty rate (%) Original 2011 PPP – Rural 16.8 Original 2011 PPP – Urban 10.1 Revised 2011 PPP – Rural 18.1 Revised 2011 PPP – Urban 10.7 Source: Newhouse & Vyas (2019) and updates by the same authors. The Syrian Arab Republic has no revised 2011 PPP estimate, so the original 2011 PPP estimate is still used. More details on how the revised 2011 PPPs affect the measurement of global poverty can be found in Atamanov et al. (2020b). 4. Surveys added 4.1. Nigeria 2018/2019 This PovcalNet update includes newly published household survey data from the Nigeria Living Standards Survey (NLSS) 2018/19 (Nigerian National Bureau of Statistics, 2020). This survey was conducted over 12 months for a final total sample of approximately 22,000 households. The survey 10 The implied fraction of household final consumption expenditure (HFCE) per capita growth that is passed through to growth in the survey mean is 69.9% for rural India and 55.1% for urban India from 2011/12 to 2014/15. 10 is representative at the national, zonal (6 zones), state (36+1), and rural/urban levels. The household survey contains information on household consumption, including a module on consumption from home production. The survey provides new data for estimating poverty in Nigeria, one of the countries with the largest extreme poor population according to the IPL. Some issues regarding the survey deserve additional discussion. First, the NLSS is not representative of the Borno state, which accounts for 2.5% of the Nigerian population. This is because parts of the state became inaccessible over the course of the survey. Only 530 households were reached (i.e., 15 Local Government Areas (LGAs) out of original 27 LGAs). For both national and international poverty estimates, Borno state is excluded from the data. In the regional and global aggregates, PovcalNet weights Nigeria using the national population, so Borno state is implicitly assumed to have the Nigerian poverty rate (excluding Borno state).11 Second, the NLSS survey is not comparable to the previous data used in PovcalNet, the Harmonized Nigeria Living Standards Survey (HNLSS) 2009/2010 (see comparability database). There has been a change in the questionnaire design which affects how the household consumption aggregate has been constructed. Another difference between the two surveys is that the HNLSS 2009/2010 was not spatially deflated while the NLSS 2018/2019 is both spatially and temporally deflated using food unit values. The survey runs from September of 2018 to October 2019 and the reference price is the median at the national level (i.e., April 2019). With the spatial deflation the national poverty rate over the survey period at the international poverty line is 39.1% (without deflation it would be 42.5%).12 4.2. Canada 2017 One survey has been added for Canada (2017), which is incorporated as binned data (400 quantile groups) from the Luxembourg Income Study (LIS). For further details on the LIS data, see Section 6.1. 11 Alternatively, Borno state could have been treated as a missing country, in which case it would have been assigned the regional (i.e., Sub-Saharan African) poverty rate for the purposes of the global aggregation. Our approach thus assumes that the Nigerian national poverty rate is a better predictor for Borno state than the Sub-Saharan regional poverty rate. 12 Using the national poverty line, the Nigerian National Bureau of Statistics reports a poverty estimate of 40.1% (including a spatial price deflation). 11 5. India line-up Annual growth in household final consumption expenditure (HFCE) per capita from the World Development Indicators (WDI) is used to line-up poverty estimates for rural and urban India, based on the latest survey available for 2011/12, for the purposes of estimating global poverty. Only a fraction of the growth in HFCE per capita is passed through to survey consumption. From 2011 to 2015, the pass-through rates have been calibrated using the poverty rates estimated by Newhouse & Vyas (2019), as described earlier in Section 3 (also see Chen et al. (2018)). After 2015, the pass-through factor is based on estimates from historic data. The pass-through factor accounts for the difference in growth rates between HFCE per capita in national accounts and household consumption expenditure as recorded in surveys. Using all comparable consumption surveys available in PovcalNet, a pass-through rate of 67% is estimated.13 This estimate is applied to the HFCE per capita growth in WDI for India after 2015. This estimate is in line with the existing literature on this issue (Datt et al., 2003; Deaton & Kozel, 2005; Lakner et al., 2020; Sen, 2000), and also very close to the pass-through factors calibrated for the period 2011/12 to 2014/15. Further details, including alternative methods to estimate poverty in India in 2017, will be available in the forthcoming Poverty and Shared Prosperity report and the associated background paper (Edochie et al., 2020). See Section 7 for more details on the treatment of national accounts data for India. 6. Revisions to welfare aggregates 6.1. Changes to the US data in LIS We continue to use the LIS data for the following eight economies: Australia; Canada; Germany; Israel; Japan; South Korea; Taiwan, China; and United States. In addition, we continue to use the LIS data for countries that use EU-SILC in recent years (typically from the early 2000s). In June 2020, several changes were made to the US data in the LIS database that affect disposable household income (DHI), which is the welfare aggregate used by PovcalNet. The US series in PovcalNet has thus been updated (downloaded on 19 June 2020). Data for all other LIS countries have remained unchanged since the March 2020 PovcalNet update 13 Lakner et al. (2020) show that using a machine learning method finds significant differences in terms of pass- through rates between consumption and income surveys. 12 (downloaded on 6 February 2020). As before, we use disposable income per capita from the LIS data in the form of 400 bins (see Chen et al. (2018) for more details). 6.2. National inequality estimates for India, Indonesia and China We discovered and rectified an error in the estimation of the national Gini coefficients for India and Indonesia that was introduced in the March 2020 PovcalNet update.14 The urban/rural spatial price adjustment had not been applied correctly. This has now been fixed along with applying the revised 2011 PPPs. Appendix 3, Table A3.1 shows the observations for India and Indonesia for which there are differences between the March 2020 and September 2020 updates. Country-year observations with differences less than 0.01 percentage points are not shown in the table. Unlike the distributions for India and Indonesia, there was no such error in the distributions for China, which are based on grouped data. These estimates have also been updated, but since the urban-to-rural PPP ratio is the same in the revised and original 2011 PPPs (see Appendix 1, Table A1.2), all the country-year differences in the national Gini coefficient for China are less than 0.01pp and are therefore not shown in Table A3.1. 7. Changes to national accounts data The national accounts data used to adjust survey data to reference years have been updated. Methodological details and the choice of data sources are available in Prydz et al. (2019). The primary source of national accounts data in this update is the May 2020 version of the World Development Indicators (WDI). For the following special cases, supplementary data are obtained from the April 2020 version of the World Economic Outlook (WEO) where WDI data are missing: Angola (2019), Djibouti (2016-2018), Iran (2018), South Sudan (2016-2018), Syrian Arab Republic (2008-2010), and Taiwan, China (1981-2018). Apart from these cases, there are other special series and other sources of national accounts data in the March 2020 vintage of PovcalNet that have not changed, such as the Madison Project Database (Atamanov et al., 2020a). A full overview of national accounts data used in the update, including special series, is available in Appendix 4. 14 This does not affect the national estimates of poverty or the mean. The new estimates for the Gini are very close to the estimates in the September 2019 vintage. 13 For India, adjustments are made to the national accounts data, as in the previous PovcalNet update. A method that adjusts HFCE growth by incorporating findings of a survey-to-survey imputation for 2014/15 is used for the line-up years from 2011 to 2015 (see Sections 3 and 5). After 2015, growth rates in national accounts are adjusted with a pass-through rate of 67%, as described in Section 5. 8. Changes to population data We did a general update of population data as published in the July 2020 vintage of World Development Indicators (WDI). All 218 economies have been included in the update, including Eritrea which was missed in the March 2020 PovcalNet update. 14 9. References Atamanov, A., Aguilar, R. A. C., Fujs, T. H. M. J., Dewina, R., Diaz-bonilla, C., Mahler, D. G., Jolliffe, D., Lakner, C., Matytsin, M., Montes, J., Herrera, L. L. M., Mungai, R., Newhouse, D., Nguyen, M. C., Parada, F. J., Urquiza, G., Silwal, A. R., Castro, D. M. S., Schoch, M., … Wu, H. (2020 a). March 2020 PovcalNet update: What’s new. Global Poverty Monitoring Technical Note, 11(March). 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Washington, DC: World Bank. World Bank. (2020). Poverty and Shared Prosperity Report 2020: Reversing reversals of fortune . Washington, DC: World Bank. 15 A. Appendix 1 – Imputation of revised 2011 PPPs A1.1. Egypt, Iraq, Jordan, Laos, Myanmar, Yemen Table A1.1: Raw and imputed 2011 PPPs (1) (2) (3) (4) (5) (6) Country Original 2011 PPP Revised 2011 PPP Raw Imputed Raw Imputed Imputed (old input data) (new input data) Egypt 1.80 2.78 1.71 2.74 2.87 Iraq 573.42 1003.8 477.56 993.81 939.22 Jordan 0.32 0.45 0.33 0.44 0.44 Lao 2914.85 3325.2 3124.08 3273.48 3248.44 Myanmar 275.83 320.6 278.39 310.73 296.14 Yemen 82.09 111.3 76.77 109.18 109.53 Source: Atamanov et al. (2020b), Table A.1. Notes: The imputed PPP estimates are out-of-sample predictions based on a variant of the seemingly unrelated regression (SURE) model the ICP uses for estimating PPPs for non-benchmark countries (Atamanov et al., 2020b). Columns (2) and (4) report the raw, official PPPs for household final consumption expenditure, including non-profit institutions serving households (NPISHs), obtained from the ICP. Column (3) shows the original 2011 PPPs imputed by Atamanov et al. (2018). Column (5) shows imputed PPPs using revised 2011 PPPs but otherwise the same input data as in Column (3). Column (6) shows imputed 2011 PPPs using both revised 2011 PPPs and new input data. The World Bank currently uses the estimates in column (6) for global poverty measurement. 16 A1.2. China, India, Indonesia Table A1.2: Imputed rural and urban PPPs a. China Original 2011 PPP Revised 2011 PPP Ratio of urban to rural poverty line 1.29 1.29 ICP urban shares 0.76 0.76 Rural PPP 3.038 3.039 Urban PPP 3.904 3.905 National PPP 3.696 3.698 b. India Ratio of urban to rural poverty line 1.22 1.22 ICP urban shares 0.74 0.74 Rural PPP 12.908 13.173 Urban PPP 15.695 16.018 National PPP 14.975 15.283 c. Indonesia Ratio of urban to rural poverty line 1.18 1.18 ICP urban shares 0.61 0.61 Rural PPP 3678.414 3498.876 Urban PPP 4352.751 4140.299 National PPP 4091.939 3892.218 Source: Atamanov et al. (2020b), Table A.2. Note: National PPPs are from the ICP. Further details in the text. 17 B. Appendix 2 – CPI data sources Table A2.1 lists the source of CPI used for each country-year observation reported in PovcalNet. The columns in the table are defined as follows: • Code: The 3-letter country code used by the World Bank: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country- and-lending-groups • Economy name: 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. This is identical to the year variable used in PovcalNet. • 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-201911 denotes the monthly IFS database version November 2019. For country-specific deflators, the description is given in the text or further details are available upon request. 18 Table A2.1. Source of temporal deflator used in PovcalNet Code Economy name Survey Year(s) CPI period Source HBS 2000 W IFS-M-201911 AGO Angola IBEP-MICS 2008 W IFS-M-201911 IDREA 2018 W IFS-M-201911 EWS 1996 Y IFS-M-201911 ALB Albania LSMS 2002-2012 Y IFS-M-201911 ARE United Arab Emirates HIES 2014 W IFS-M-201911 1980-1987 Y CEDLAS May 25 18 EPH 1991-2002 M9 NSO ARG Argentina - urban 2003-2018 M7-M12 NSO EPHC-S2 2007-2014 M7-M12 Private estimates ARM Armenia ILCS All Y IFS-M-201911 HIS-LIS 1981 Y IFS-A-201911 IDS-LIS 1985 Y IFS-A-201911 AUS Australia SIH-LIS 1989-2014 Y IFS-A-201911 SIH-HES-LIS 2004-2010 Y IFS-A-201911 MC-LIS 1987-1995 Y IFS-M-201911 AUT Austria ECHP-LIS 1994-2000 Y IFS-M-201911 EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 ALZ 1995 Y IFS-M-201911 AZE Azerbaijan HBS 2001-2005 Y IFS-M-201911 HSMTSA 2008 Y IFS-M-201911 EDCM 1992 Y IFS-M-201911 EP 1998 W IFS-M-201911 BDI Burundi QUIBB 2006 Y IFS-M-201911 ECVMB 2013 W IFS-M-201911 SEP-LIS 1985-1997 Y IFS-M-201911 BEL Belgium PSBH-ECHP-LIS 1995-2000 Y IFS-M-201911 EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 QUIBB 2003 Y IFS-M-201911 BEN Benin 2011 W IFS-M-201911 EMICOV 2015 Y IFS-M-201911 EP-I 1994 W IFS-M-201911 EP-II 1998 Y IFS-M-201911 BFA Burkina Faso ECVM 2003-2009 Y IFS-M-201911 EMC 2014 Y IFS-M-201911 1983-1985 W WEO-A-201910 HHES 1988-1991 W IFS-A-201911 BGD Bangladesh 1995 W Survey HIES 2000-2016 Y Survey 19 1989 Y IFS-A-201911 HBS 1992-1994 Y IFS-M-201911 BGR Bulgaria IHS 1995-2001 Y IFS-M-201911 MTHS 2003-2007 Y IFS-M-201911 EU-SILC 2007-2018 (prev. year) Y IFS-M-201911 Bosnia and LSMS 2001-2004 Y WEO-A-201910 BIH Herzegovina HBS 2007-2015 Y IFS-M-201911 1988 Y Previous WDI/IFS FBS BLR Belarus 1993-1995 Y IFS-M-201911 HHS 1998-2018 Y IFS-M-201911 LFS 1993-1999 Y WEO-A-201910 BLZ Belize HBS 1995 Y WEO-A-201910 SLC 1996 Y WEO-A-201910 Bolivia EPF 1990 W IFS-M-201911 Bolivia - urban EIH 1992 M11 IFS-M-201911 ENE 1997 M11 IFS-M-201911 1999 M10 IFS-M-201911 ECH BOL 2000 M11 IFS-M-201911 Bolivia EH 2001-2005 M11 IFS-M-201911 ECH 2004 M10 IFS-M-201911 2006-2016 M10 IFS-M-201911 EH 2017-2018 M11 IFS-M-201911 PNAD 1981-2015 M9 IFS-M-201911 BRA Brazil PNADC-E1 2012-2018 Y IFS-M-201911 BTN Bhutan BLSS All Y Previous WDI/IFS HIES 1985-2002 W IFS-M-201911 BWA Botswana CWIS 2009 W IFS-M-201911 BMTHS 2015 W IFS-M-201911 Central African EPCM 1992 W IFS-M-201911 CAF Republic ECASEB 2003-2008 Y IFS-M-201911 SCF-LIS 1971-1997 Y IFS-M-201911 CAN Canada SLID-LIS 1998-2010 Y IFS-M-201911 CIS-LIS 2012-2017 Y IFS-M-201911 SIWS-LIS 1982 Y IFS-M-201911 NPS-LIS 1992 Y IFS-M-201911 CHE Switzerland IES-LIS 2000-2002 Y IFS-M-201911 EU-SILC 2007-2018 (prev. year) Y IFS-M-201911 1987 Y IFS-M-201911 CHL Chile CASEN 1990-2017 M11 IFS-M-201911 China - rural CRHS-CUHS 1981-2011 Y NSO CHN China - urban 1981-2011 Y NSO 20 China - rural CNIHS 2012-2016 Y NSO China - urban 2012-2016 Y NSO EPAM 1985-1988 W IFS-M-201911 CIV Côte d'Ivoire EP 1992 W IFS-M-201911 ENV 1995-2015 Y IFS-M-201911 ECAM-I 1996 Y IFS-M-201911 ECAM-II 2001 Y IFS-M-201911 CMR Cameroon ECAM-III 2007 Y IFS-M-201911 ECAM-IV 2014 Y IFS-M-201911 COD Congo, Dem. Rep. E123 All W IFS-M-201911 COG Congo, Rep. ECOM All Y IFS-M-201911 1980-1988 Y IFS-M-201911 Colombia - urban ENH 1989-1991 M11 IFS-M-201911 COL ENH 1992-2000 M11 IFS-M-201911 Colombia ECH 2001-2005 M11 IFS-M-201911 GEIH 2008-2018 M11 IFS-M-201911 EIM 2004 Y IFS-M-201911 COM Comoros EESIC 2013 Y IFS-M-201911 IDRF 2001 W IFS-M-201911 CPV Cabo Verde QUIBB 2007 W IFS-M-201911 IDRF 2015 Y IFS-M-201911 ENH 1981-1986 Y IFS-M-201911 1989 Y IFS-M-201911 CRI Costa Rica EHPM 1990-2009 M7 IFS-M-201911 ENAHO 2010-2018 M7 IFS-M-201911 CYP Cyprus EU-SILC All (prev. year) Y IFS-M-201911 CM 1988 Y Previous WDI/IFS MC-LIS 1992-2002 Y IFS-M-201911 CZE Czech Republic CM 1993 Y IFS-M-201911 EU-SILC 2005-2018 (prev. year) Y IFS-M-201911 1973-1983 Y IFS-M-201911 DEU Germany LIS 1981 Y IFS-M-201911 1984-2016 Y IFS-M-201911 2002-2013 Y IFS-M-201911 DJI Djibouti EDAM 2017 M5 IFS-M-201911 LM-LIS 1987-2000 Y IFS-M-201911 DNK Denmark EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 ENGSLF 1986-1989 Y IFS-M-201911 ICS 1992 M6 IFS-M-201911 DOM Dominican Republic 1996 M2 IFS-M-201911 ENFT 1997 M4 IFS-M-201911 21 2000-2016 M9 IFS-M-201911 ECNFT-Q03 2017-2018 Y IFS-M-201911 EDCM 1988 Y IFS-M-201911 DZA Algeria ENMNV 1995 Y IFS-M-201911 ENCNVM 2011 W IFS-M-201911 EPED 1987 Y IFS-M-201911 Ecuador ECV 1994 M6-M10 IFS-M-201911 Ecuador - urban EPED 1995 M11 IFS-M-201911 ECU EPED 1998 M6 IFS-M-201911 (prev. year) ECV 1999 IFS-M-201911 Ecuador M10-M9 EPED 2000 M11 IFS-M-201911 ENEMDU 2003-2018 M11 IFS-M-201911 1990-2012 W IFS-M-201911 EGY Egypt, Arab Rep. HIECS 2015 Y IFS-M-201911 2017 W IFS-M-201911 HBS-LIS 1980-1990 Y IFS-M-201911 ESP Spain ECHP-LIS 1995-2000 Y IFS-M-201911 EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 1988 Y Previous WDI/IFS HIES 1993-1998 Y IFS-M-201911 EST Estonia HBS 2000-2004 Y IFS-M-201911 EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 Ethiopia - rural HICES 1981 W IFS-M-201911 ETH 1995-2010 W IFS-M-201911 Ethiopia HICES 2015 M12 IFS-M-201911 IDS-LIS 1987-2000 Y IFS-M-201911 FIN Finland EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 FJI Fiji HIES All W IFS-M-201911 HBS-LIS 1978-2000 Y IFS-M-201911 FRA France EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 Micronesia, Fed. Sts. - CPH 2000 Y IFS-A-201911 FSM urban Micronesia, Fed. Sts. HIES 2005-2013 Y IFS-A-201911 GAB Gabon EGEP All Y IFS-M-201911 FES-LIS 1969-1995 Y IFS-M-201911 GBR United Kingdom FRS-LIS 1994-1999 Y IFS-M-201911 EU-SILC 2005-2017 (prev. year) Y IFS-M-201911 SGH 1996-1997 Y IFS-M-201811 GEO Georgia 1997-2004 Y IFS-M-201811 HIS 2005-2018 Y IFS-M-201911 GHA Ghana GLSS-I 1987 W IFS-M-201911 22 GLSS-II 1988 W IFS-M-201911 GLSS-III 1991 W IFS-M-201911 GLSS-IV 1998 W IFS-M-201911 GLSS-V 2005 W Survey GLSS-VI 2012 W Survey GLSS-VII 2016 W Survey ESIP 1991 Y WEO-A-201910 EIBC 1994 W WEO-A-201910 GIN Guinea EIBEP 2002 W WEO-A-201910 ELEP 2007-2012 Y IFS-M-201911 HPS 1998 Y IFS-M-201911 GMB Gambia, The HIS 2003 W IFS-M-201911 IHS 2010-2015 W IFS-M-201911 ILJF 1991 Y IFS-M-201911 ICOF 1993 Y IFS-M-201911 GNB Guinea-Bissau ILAP-I 2002 Y IFS-M-201911 ILAP-II 2010 Y IFS-M-201911 ECHP-LIS 1995-2000 Y IFS-M-201911 GRC Greece EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 1986 W IFS-M-201911 ENSD 1989 Y IFS-M-201911 GTM Guatemala ENIGF 1998 M8 IFS-M-201911 2000 M6-M11 IFS-M-201911 ENCOVI 2006-2014 M7 IFS-M-201911 GLSMS 1992 W WEO-A-201910 GUY Guyana 1998 Y IFS-M-201911 Honduras - urban ECSFT 1986 Y IFS-M-201911 1989 Y IFS-M-201911 HND 1990-1993 M5 IFS-M-201911 Honduras EPHPM 1994 M9 IFS-M-201911 1995-2018 M5 IFS-M-201911 HBS 1988-2010 Y IFS-M-201911 HRV Croatia EU-SILC 2010-2018 (prev. year) Y IFS-M-201911 ECVH 2001 M5 IFS-M-201911 HTI Haiti ECVMAS 2012 M10 IFS-M-201911 HBS 1987-2007 Y IFS-M-201911 HHP-LIS 1991-1994 Y IFS-M-201911 HUN Hungary THMS-LIS 1999 Y IFS-M-201911 EU-SILC 2005-2018 (prev. year) Y IFS-M-201911 1984-1999 Y IFS-M-201911 IDN Indonesia SUSENAS 2000-2007 M2 IFS-M-201911 23 2008-2018 M3 IFS-M-201911 India - rural NSS 1977-1983 Y NSO India - urban 1977-1983 Y NSO IND India - rural NSS-SCH1 1987-2011 W NSO India - urban 1987-2011 W NSO SIDPUSS-LIS 1987 Y IFS-M-201911 IRL Ireland LIS-ECHP-LIS 1994-2000 Y IFS-M-201911 EU-SILC 2004-2017 (prev. year) Y IFS-M-201911 SECH 1986-1998 Y CBI IRN Iran, Islamic Rep. HEIS 2005-2017 Y CBI M11-(next 2006 COSIT IRQ Iraq IHSES year) M12 2012 Y COSIT ISL Iceland EU-SILC All (prev. year) Y IFS-M-201911 ISR Israel HES-LIS All Y IFS-M-201911 SHIW-LIS 1986-2000 Y IFS-M-201911 ITA Italy EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 1988 M9 IFS-M-201911 M11-(next 1990-1993 IFS-M-201911 year) M3 JAM Jamaica SLC 1996 M5-M8 IFS-M-201911 1999 M6-M8 IFS-M-201911 2002-2004 M6 IFS-M-201911 1986 W IFS-M-201911 JOR Jordan HEIS 1992-1997 Y IFS-M-201911 2002-2010 W IFS-M-201911 JPN Japan JHPS-LIS ALL Y IFS-M-201911 1988 Y Previous WDI/IFS HBS KAZ Kazakhstan 1993-2017 Y IFS-M-201911 LSMS 1996 Y IFS-M-201911 WMS-I 1992 Y NSO WMS-II 1994 Y NSO KEN Kenya WMS-III 1997 Y NSO IHBS 2005-2015 W NSO PMS 1988 Y Previous WDI/IFS 1993 Y Previous WDI/IFS KGZ Kyrgyz Republic HBS 1998-2003 Y IFS-M-201911 KIHS 2004-2018 Y IFS-M-201911 KHM Cambodia CSES All Y IFS-M-201911 KIR Kiribati HIES 2006 Y IFS-M-201911 KOR Korea, Rep. HIES-FHES-LIS All Y IFS-M-201911 LAO Lao PDR LECS 1997 W IFS-M-201911 24 2002-2012 W Survey (next year) LBN Lebanon HBS 2011 IFS-M-201911 M5 CWIQ 2007 Y IFS-M-201911 LBR Liberia HIES 2014-2016 Y IFS-M-201911 LSMS 1995 Y IFS-M-201911 LCA St. Lucia SLC-HBS 2016 M1 IFS-M-201911 LFSS 1985 Y IFS-M-201911 HIES 1990 W IFS-M-201911 SES 1995 W IFS-M-201911 LKA Sri Lanka 2002 Y IFS-M-201911 HIES 2006-2012 W IFS-M-201911 2016 Y IFS-M-201911 HBS 1986 W WEO-A-201910 NHECS 1994 W WEO-A-201910 LSO Lesotho HBS 2002 W IFS-M-201911 2010 Y IFS-M-201911 CMSHBS 2017 M8 IFS-M-201911 1988 Y Previous WDI/IFS HBS LTU Lithuania 1993-2008 Y IFS-M-201911 EU-SILC 2005-2018 (prev. year) Y IFS-M-201911 PSELL-LIS 1985-1991 Y IFS-M-201911 LUX Luxembourg PSELL-ECHP-LIS 1994-2000 Y IFS-M-201911 EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 1988 Y Previous WDI/IFS HBS LVA Latvia 1993-2009 Y IFS-M-201911 EU-SILC 2005-2018 (prev. year) Y IFS-M-201911 ECDM 1984 W IFS-M-201911 ENCV 1990 W IFS-M-201911 MAR Morocco ENNVM 1998-2006 W IFS-M-201911 ENCDM 2000-2013 W IFS-M-201911 1988-1992 Y Previous WDI/IFS MDA Moldova HBS 1997-2018 Y IFS-M-201911 EB 1980 Y IFS-M-201911 1993 W IFS-M-201911 MDG Madagascar EPM 1997-2010 Y IFS-M-201911 ENSOMD 2012 Y IFS-M-201911 2002-2009 W IFS-M-201911 MDV Maldives HIES 2016 Y IFS-M-201911 ENIGH 1984-2014 M8 IFS-M-201911 MEX Mexico ENIGHNS 2016-2018 M8 IFS-M-201911 MKD North Macedonia HBS 1998-2008 Y IFS-M-201911 25 SILC-C 2010-2018 (prev. year) Y IFS-M-201911 EMCES 1994 Y IFS-A-201911 EMEP 2001 W IFS-M-201911 MLI Mali 2006 Y IFS-M-201911 ELIM 2009 W IFS-M-201911 MLT Malta EU-SILC ALL (prev. year) Y IFS-M-201911 MPLCS 2015 M1 IFS-M-201911 MMR Myanmar MLCS 2017 Q1 IFS-M-201911 HBS 2005-2014 Y IFS-M-201911 MNE Montenegro SILC-C 2013-2016 (prev. year) Y IFS-M-201911 LSMS 1995-1998 Y IFS-M-201911 LFS 2002 Y IFS-M-201911 MNG Mongolia LSS 2007 W IFS-M-201911 HSES 2010-2018 Y IFS-M-201911 NHS 1996 W WEO-A-201910 MOZ Mozambique IAF 2002 W WEO-A-201910 IOF 2008-2014 W WEO-A-201910 EPCV 1987 Y IFS-M-201911 EP 1993 Y IFS-M-201911 MRT Mauritania 1995 W IFS-M-201911 EPCV 2000-2014 Y IFS-M-201911 2006 W IFS-M-201911 MUS Mauritius HBS 2012-2017 Y IFS-M-201911 IHS-I 1997 W IFS-M-201911 IHS-II 2004 W Survey MWI Malawi IHS-III 2010 W Survey IHS-IV 2016 M04 Survey 1984-2007 Y IFS-M-201911 2009 W IFS-M-201911 MYS Malaysia HIS 2012-2014 Y IFS-M-201911 2016 W IFS-M-201911 1993 W WEO-A-201910 NAM Namibia NHIES 2003-2015 W IFS-M-201911 ENBCM 1992-2007 W IFS-M-201911 EPCES 1994 W IFS-M-201911 NER Niger ENCVM 2005 Y IFS-M-201911 ECVMA 2011-2014 Y IFS-M-201911 1985 W IFS-M-201911 NCS NGA Nigeria 1992-1996 Y IFS-M-201911 LSS 2003-2009 W IFS-M-201911 26 (next year) 2018 M3-(next IFS-M-201911 year) M4 1993 M2 NSO 1998 M6 NSO NIC Nicaragua EMNV 2001 M6 IFS-M-201911 2005-2009 M8 IFS-M-201911 2014 M8-M10 IFS-M-201911 AVO-LIS 1983-1990 Y IFS-M-201911 NLD Netherlands SEP-LIS 1993-1999 Y IFS-M-201911 EU-SILC 2005-2018 (prev. year) Y IFS-M-201911 IDS-LIS 1979-2000 Y IFS-M-201911 NOR Norway EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 MHBS 1984 W IFS-M-201911 LSS-I 1995 W IFS-M-201911 NPL Nepal LSS-II 2003 W IFS-M-201911 LSS-III 2010 W IFS-M-201911 1987 Y IFS-M-201911 HIES 1990-1998 W IFS-M-201911 PAK Pakistan IHS2 1996 W IFS-M-201911 PIHS 2001 W IFS-M-201911 PSLM 2004-2015 W IFS-M-201911 1979-1989 Y IFS-M-201911 EMO PAN Panama 1991 M7 IFS-M-201911 EH 1995-2018 M7 IFS-M-201911 1985 W IFS-M-201911 ENNIV 1994 Y IFS-M-201911 PER Peru 1997-2002 Q4 IFS-M-201911 ENAHO 2003 M5-M12 IFS-M-201911 2004-2018 Y IFS-M-201911 PHL Philippines FIES All Y IFS-M-201911 1996 Y IFS-A-201911 PNG Papua New Guinea HIES 2009 W IFS-A-201911 HBS 1985-1987 Y IFS-A-201911 HBS-LIS 1986 Y IFS-A-201911 POL Poland HBS 1989-2016 Y IFS-M-201911 HBS-LIS 1992-1999 Y IFS-M-201911 EU-SILC 2005-2018 (prev. year) Y IFS-M-201911 PRT Portugal EU-SILC All (prev. year) Y IFS-M-201911 1990 M7 IFS-M-201911 PRY Paraguay EH 1995 M8-M11 IFS-M-201911 27 (next year) EIH 1997 IFS-M-201911 M2 EPH 1999 M9 IFS-M-201911 EIH 2001 M3 IFS-M-201911 2002 M11 IFS-M-201911 2003 M9 IFS-M-201911 2004 M10 IFS-M-201911 2005 M11 IFS-M-201911 EPH 2006 M12 IFS-M-201911 2007-2008 M10 IFS-M-201911 2009 M11 IFS-M-201911 2010-2018 M10 IFS-M-201911 2004-2011 Y IFS-M-201911 PSE West Bank and Gaza PECS 2016 W IFS-M-201911 HBS 1989 Y Milanovic (1998) MC 1992 Y IFS-M-201911 HIS 1994 Y IFS-M-201911 ROU Romania IHS-LIS 1995-1997 Y IFS-M-201911 IHS 1998-2000 Y IFS-M-201911 HBS 1999-2016 Y IFS-M-201911 EU-SILC 2007-2018 (prev. year) Y IFS-M-201911 RLMS 1988 Y Previous WDI/IFS RUS Russian Federation HBS 1993-2018 Y IFS-M-201911 RLMS 2001 Y IFS-M-201911 ENBCM 1984 W IFS-M-201911 EICV-I 2000 W IFS-M-201911 EICV-II 2005 W IFS-M-201911 (next year) RWA Rwanda EICV-III 2010 IFS-M-201911 M1 (next year) EICV-IV 2013 IFS-M-201911 M1 (next year) EICV-V 2016 IFS-M-201911 M1 2009 Y IFS-M-201911 SDN Sudan NBHS 2014 M11 IFS-M-201911 EP 1991 W IFS-M-201911 ESAM 1994 W IFS-M-201911 SEN Senegal ESAM-II 2001 Y IFS-M-201911 ESPS-I 2005 W IFS-M-201911 ESPS-II 2011 W IFS-M-201911 SLB Solomon Islands HIES All Y IFS-M-201911 HEEAS 1989 W WEO-A-201910 SLE Sierra Leone SLIHS 2003 W WEO-A-201910 28 2011-2018 Y IFS-M-201911 1989 Y IFS-M-201911 M10-(next 1991 IFS-M-201911 year) M4 SLV El Salvador EHPM 1995-1999 Y IFS-M-201911 2000-2007 M12 IFS-M-201911 2008-2018 M11 IFS-M-201911 LSMS 2002 Y IFS-M-201911 SRB Serbia HBS 2003-2018 Y IFS-M-201911 EU-SILC 2013-2018 (prev. year) Y IFS-M-201911 SSD South Sudan NBHS 2009 Y IFS-M-201911 2000 W IFS-M-201911 STP São Tomé and Principe IOF 2010-2017 Y IFS-M-201911 SUR Suriname EHS 1999 Y IFS-M-201911 MC-LIS 1992-1996 Y IFS-M-201911 SVK Slovak Republic HBS 2004-2009 Y IFS-M-201911 EU-SILC 2005-2017 (prev. year) Y IFS-M-201911 IES 1987-1993 Y IFS-M-201911 HBS-LIS 1997-1999 Y IFS-M-201911 SVN Slovenia HBS 1998-2003 Y IFS-M-201911 EU-SILC 2005-2018 (prev. year) Y IFS-M-201911 LLS-RD-LIS 1967 Y IFS-M-201911 SWE Sweden HIS-LIS 1975-2000 Y IFS-M-201911 EU-SILC 2004-2018 (prev. year) Y IFS-M-201911 1994-2000 W IFS-M-201911 SWZ Eswatini HIES 2001 Y IFS-M-201911 2009-2016 W IFS-M-201911 HES 1999 W IFS-M-201911 SYC Seychelles 2006 W IFS-M-201911 HBS 2013 Y IFS-M-201911 SYR Syrian Arab Republic HBS 2004 Y IFS-M-201911 ECOSIT-II 2003 Y IFS-M-201911 TCD Chad ECOSIT-III 2011 Y IFS-M-201911 TGO Togo QUIBB All Y IFS-M-201911 THA Thailand SES All Y IFS-M-201911 1999 Y WEO-A-201910 TLSS 2003-2007 Y Survey TJK Tajikistan HBS 2004 Y Survey TLSS 2009 Y IFS-M-201911 HSITAFIEN 2015 Y IFS-M-201911 TKM Turkmenistan LSMS 1998 Y WEO-A-201910 TLS Timor-Leste TLSS 2001 Y WEO-A-201910 29 TLSLS 2007-2014 Y IFS-M-201911 TON Tonga HIES All Y IFS-M-201911 SLC 1988 Y IFS-M-201911 TTO Trinidad and Tobago PHC 1992 Y IFS-M-201911 1985 Y IFS-A-201911 HBCS 1990 Y IFS-M-201911 TUN Tunisia LSS 1995-2000 Y IFS-M-201911 NSHBCSL 2005-2015 W IFS-M-201911 TUR Turkey HICES All Y IFS-M-201911 TUV Tuvalu HIES 2010 Y WEO-A-201910 TWN Taiwan, China FIDES-LIS All Y WEO-A-201910 1991 W IFS-A-201911 2000 W IFS-M-201911 TZA Tanzania HBS 2007 Y IFS-M-201911 2011-2018 W IFS-M-201911 HBS 1989 Y WEO-A-201910 1992 W WEO-A-201910 UGA Uganda NIHS 1996-1999 W IFS-M-201911 UNHS 2002-2016 W IFS-M-201911 1988 Y Previous WDI/IFS HS 1992-1993 Y IFS-M-201911 UKR Ukraine HIES 1995-1996 Y IFS-M-201911 HBS 1999 Y IFS-M-201911 HLCS 2002-2018 Y IFS-M-201911 Uruguay ENH 1981-1989 Y IFS-M-201911 (prev. year) URY Uruguay - urban ECH 1992-2005 IFS-M-201911 M12 (prev. year) Uruguay ECH 2006-2018 IFS-M-201911 M12 CPS-LIS 1974-2000 Y IFS-M-201911 USA United States CPS-ASEC-LIS 2004-2018 Y IFS-M-201911 UZB Uzbekistan HBS All Y WEO-A-201910 1981-1989 Y NSO VEN Venezuela, RB EHM 1992-2006 M12 NSO 1992 W WEO-A-201910 VLSS VNM Vietnam 1998 W IFS-M-201911 VHLSS 2002-2018 M1 IFS-M-201911 VUT Vanuatu HIES 2010 Y IFS-A-201911 2002-2008 Y IFS-M-201911 WSM Samoa HIES 2013 W IFS-M-201911 XKX Kosovo HBS All Y IFS-M-201911 YEM Yemen, Rep. HBS 1998 Y IFS-M-201911 30 2005 W IFS-M-201911 2014 Y IFS-M-201911 KIDS 1993 Y IFS-M-201911 1996 Y IFS-M-201911 HIES 2000 W IFS-M-201911 ZAF South Africa (next year) IES 2005-2010 IFS-M-201911 M6 2008 W IFS-M-201911 LCS (next year) 2014 IFS-M-201911 M6 HBS 1991-1993 Y IFS-M-201911 LCMS-I 1996 Y IFS-M-201911 LCMS-II 1998 Y IFS-M-201911 LCMSIII 2002 W IFS-M-201911 ZMB Zambia LCMS-IV 2004 W IFS-M-201911 LCMS-V 2006 W IFS-M-201911 LCMS-VI 2010 Y IFS-M-201911 LCMS-VII 2015 Y IFS-M-201911 ICES 2011 Y IFS-M-201911 ZWE Zimbabwe PICES 2017 Y IFS-M-201911 31 C. Appendix 3 – Gini coefficients Table A3.1. Estimates of Gini coefficient Gini (%) Gini (%) Country Year Mar 2020 Sept 2020 Difference (pp) India 1993 32.71 31.70 -1.01 India 2004 36.83 34.41 -2.43 India 2009 37.51 35.38 -2.13 India 2011 37.83 35.71 -2.12 Indonesia 1993 31.96 32.02 0.05 Indonesia 1996 34.41 34.47 0.06 Indonesia 1998 31.07 31.12 0.05 Indonesia 1999 31.03 31.08 0.05 Indonesia 2000 30.25 28.58 -1.67 Indonesia 2001 30.89 29.03 -1.87 Indonesia 2002 33.77 31.72 -2.04 Indonesia 2003 33.96 31.90 -2.06 Indonesia 2004 34.78 32.74 -2.03 Indonesia 2005 34.92 33.02 -1.90 Indonesia 2006 36.15 34.29 -1.86 Indonesia 2007 37.47 35.70 -1.77 Indonesia 2008 36.83 35.15 -1.67 Indonesia 2009 36.74 35.11 -1.64 Indonesia 2010 37.88 36.41 -1.47 Indonesia 2011 41.08 39.73 -1.36 Indonesia 2012 41.12 39.68 -1.44 Indonesia 2013 41.47 39.97 -1.50 Indonesia 2014 40.82 39.42 -1.39 Indonesia 2015 40.98 39.73 -1.25 Indonesia 2016 39.90 38.60 -1.30 Indonesia 2017 39.37 38.11 -1.27 Indonesia 2018 38.97 37.77 -1.19 Source: PovcalNet Note: This table shows the Gini coefficients (%) in the March 2020 and September 2020 PovcalNet updates as well as the differences. Small differences less than 0.01pp are not shown in this table. 32 D. Appendix 4 – National accounts data sources Legend for Tables A4.1 and A4.2 Code – World Bank economy/country code Sources Sources M – Madison Project Dataset W – World Development Indicators, May 2020 S – Special Country Series Coverage N – National U – Urban R – Rural 33 Coverage Table A4.1. Gross Domestic Product (GDP) per capita 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Code AGO N S S S S S WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW ALB N WWWWWWWWWWWWWWWWWWW WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW ARE N WWWW WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW ARG N WWWWWWWWWWWWWWWWWWW WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW ARM N W WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW AUS N W W W W W W W W W W W W W W W W W W W WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW AUT N W W W W W W W W W W W W W W W W W W W WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW AZE N W WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW BDI N WWWWWWWWWWWWWWWWWWW WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW BEL N WWWWWWWWWWWWWWWWWWW WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW BEN N 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Household Final Consumption Expenditures (HFCE) per capita Coverage 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Code ALB N WWWWWWWWWWWWWWWWWWW ARE N WWWWWWWWWWWWWWWWWW ARG N WWWWWWWWWWWWWWWWWWWWWWWWWW ARM N WWWWWWWWWWWWWWWWWWWWWWWWW AUS N W WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW AUT N WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW AZE N WWWWWWWWWWWWWWWWWWWWW BDI N WWWWWWWWWWWWWWWWWWWWWW BEL N WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW BEN N W WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW BFA N WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW BGD N W WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW BGR N WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW BIH N WWWWWWWWWWWWW BLR N 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