The World Bank Economic Review, 39(3), 2025, 497–521 https://doi.org10.1093/wber/lhae035 Article Poverty and Prices: Assessing the Impact of the 2017 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 PPPs on the International Poverty Line and Global Poverty Dean Jolliffe r , Daniel Gerszon Mahler r , Christoph Lakner r , Aziz Atamanov r , and Samuel Kofi Tetteh-Baah Abstract Purchasing power parities (PPPs) are used to estimate the international poverty line (IPL) in a common currency and account for relative price differences across countries when measuring global poverty. This paper assesses the impact of the 2017 PPPs on the nominal value of the IPL and global poverty. Updating the $1.90 IPL in 2011 PPP dollars to 2017 PPP dollars results in an IPL of $2.15—a finding that is robust to various methods. Based on an updated IPL of $2.15, the global extreme poverty rate in 2017 falls from the previously estimated 9.3 to 9.1 percent, reducing the count of people who are poor by 15 million. This is a modest change compared with previous updates of PPP data. The paper also assesses the methodological stability between the 2011 and 2017 PPPs, scrutinizes large changes at the country level, and updates alternative, complementary poverty lines with the 2017 PPPs. JEL classification: I32 Keywords: 2017 PPPs, global poverty, the international poverty line; societal poverty; Sustainable Development Goals Dean Jolliffe (corresponding author) is a lead economist at the World Bank, Washington, DC, USA; his email address is djolliffe@worldbank.org. Daniel Gerszon Mahler is a senior economist at the World Bank; his email address is dmahler@worldbank.org. Christoph Lakner is a program manager at the World Bank; his email ad- dress is clakner@worldbank.org. Aziz Atamanov is a senior economist at the World Bank; his email address is aatamanov@worldbank.org. Samuel Kofi Tetteh-Baah is an economist at the World Bank; his email address is stettehbaah@worldbank.org. The author ordering was constructed through American Economic Association’s randomiza- tion tool (confirmation code: R-TVbwJ99ttd). The authors would like to thank the staff of the International Comparison Program Global Office at the World Bank, particularly Nada Hamadeh, Marko Rissanen, William Vigil Oliver, Maurice Ns- abimana, and Mizuki Yamanaka for providing the PPP data and metadata, patiently answering technical questions, and for their feedback on earlier drafts. The authors are also thankful for helpful comments and guidance from Laurence Chandy, Ste- fan Dercon, Francisco Ferreira, Deon Filmer, Haishan Fu, Luis Felipe Lopez-Calva, Ana Revenga, Carolina Sanchez-Paramo, and Umar Serajuddin and the anonymous referees of this journal. Many thanks are also due to World Bank poverty economists and members of the World Bank’s Global Poverty Working Group (GPWG) for their feedback, as well as to Judy Yang, who was involved in the earlier stages of this project. The authors would also like to thank Angus Deaton, Bob Allen, and members of the International Comparison Program’s Technical Advisory Group for comments. The authors gratefully acknowledge financial support from the Knowledge for Change program and from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme. The findings, interpretations, and conclusions expressed in this C 2024 International Bank for Reconstruction and Development / The World Bank. Published by Oxford University Press 498 Jolliffe et al. 1. Introduction The ambition to eliminate global extreme poverty by 2030 is at the forefront of development policy. It is the first of the Sustainable Development Goals (SDGs), as well as part of the World Bank’s core mission. To realize this ambition, it is first necessary to be able to count the number of poor people in the world and to know where they live. Some of the most challenging issues in estimating global poverty center Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 around updating the international poverty line (IPL)—a poverty line common to the world derived from the national poverty lines of some of the poorest countries in the world (Ravallion, Chen, and Sangraula 2009; Ferreira et al. 2016)—to account for changing relative prices across the world and to keep it at a comparable value across countries. The principal data that measure relative price differences across countries are the purchasing power parities (PPPs) produced by the International Comparison Program (ICP). As evidence that PPPs are at the center of the challenges faced in global poverty measurement, their use in estimating the IPL over time has been the subject of considerable debate (Deaton 2010; Deaton and Heston 2010; Ravallion 2014; Kakwani and Son 2016; Deaton and Aten 2017; Inklaar and Rao 2017). One important example of this is the update from the 1993 PPPs with the 2005 PPPs, when Chen and Ravallion (2010)reported that the world was much poorer than previously believed (under the 1993 PPPs). Deaton (2010) showed that the IPL as revised by Chen and Ravallion, along with the introduction of the updated PPPs, led to an increase in the estimation of poverty for the year 1993 of half a billion people. Deaton and Aten (2017) also later asserted that the 2005 ICP round overstated price levels in most countries in the developing world, particularly owing to a new methodological approach. Both the methodological change in the PPPs, and the assertion by Deaton (2010) that the method used for estimating the IPL when updating with the 2005 PPPs was inappropriate, led to concerns that an estimated number of 500 million more people living in extreme poverty in 2005 deeply confounds both real and methodological changes. With this background, this article analyzes the 2017 PPPs to make three contributions to the literature on global poverty measurement. First, it provides evidence that is consistent with the claim that the ICP methodology over the 2011 and 2017 rounds had stabilized, where methodological stability is under- stood to mean the decisions on how to handle country-level price data for the estimation of the global conversion factors.1 Despite this stability, this article finds that for certain countries the 2017 PPPs yield large differences to estimated poverty as compared to using the 2011 PPPs. In several cases, this is because of country-level improvements in the price data. Second, the article derives global poverty lines with the 2017 PPPs. It generates a database of over 1,400 national poverty lines—the largest to date—and follows Jolliffe and Prydz (2016) by calculating the IPL as the median of recent national poverty lines of low-income countries. This yields a line of $2.15 in 2017 PPP, compared to $1.90 in 2011 PPP. It is shown that this value is robust to varying the set of countries and set of poverty lines, and hence reliably reflects the typical standard by which the poorest countries of the world judge their citizens to be impoverished. In addressing concerns of the Atkinson Commission on Global Poverty (World Bank 2017) that adopting new PPP data might result in “shifting the goalpost,” the article shows that the $2.15 IPL in 2017 PPP results in global poverty rates that are essentially the same as those estimated at the $1.90 IPL in 2011 PPP for recent years. Beyond the IPL, the study also computes with the 2017 PPPs the higher poverty lines the World Bank uses to monitor poverty in countries with a low prevalence of extreme poverty—$3.20 and $5.50 in 2011 PPPs. These higher poverty lines reflect the median national poverty line of lower-middle-income countries paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated orga- nizations, or those of the Executive Directors of the World Bank or the governments they represent. A supplementary online appendix for this article can be found at The World Bank Economic Review website. 1 See Deaton and Schreyer (2022) for additional evidence also suggesting consistency in the methodological approach to estimating the PPPs over these rounds. The World Bank Economic Review 499 and upper-middle-income countries, respectively. With the 2017 PPPs, the articles compute these lines to be approximately $3.65 and $6.85 per person per day. It also updates the Bank’s societal poverty line (SPL) originally defined as $1 + 50 percent of median consumption (with a lower bound set at $1.90) in 2011 PPPs to $1.15 + 50 percent of median consumption (with the lower bound set at $2.15) in 2017 PPPs. Third, the article analyzes the impact on the global and regional poverty estimates from using these Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 global poverty lines and the 2017 PPPs. The 2017 PPPs slightly increase historical estimates of extreme poverty and slightly decrease extreme poverty since 2014, compared with the 2011 PPPs. Extreme poverty decreases marginally by 0.2 percentage points (pp) in 2017 with the 2017 PPPs. Compared to past adop- tions of new PPP rounds, this is a minor change. The global count of extreme poor reduces by 15 million, which is largely driven by 34 million fewer poor people in Sub-Saharan Africa, while poverty increases slightly in all other regions. However, Sub-Saharan Africa remains the region with the largest share of extreme poor in 2017; from 62 percent with the revised 2011 PPPs to 58 percent with the 2017 PPPs. Nigeria accounts for almost half of the reduction in extreme poverty in Sub-Saharan Africa. There is also a limited change in global poverty with the revised poverty line of $3.65 in 2017 PPP, as declines in Sub-Saharan Africa and the Middle East & North Africa are offset by increases in East Asia & the Pacific and Europe & Central Asia. The revision of the $5.50 line (2011 PPP) to $6.85 (2017 PPP) increases poverty markedly globally by 4.3pp. This increase is seen in all regions with about two-thirds of the added poor living in China, India, and Indonesia alone. With the updated parameters of the SPL, about 17.6 million more people would be living in societal poverty in the world in 2017. Societal poverty slightly increases in all regions, except Sub-Saharan Africa and the Middle East & North Africa. The rest of the paper is organized as follows. Section 2 describes the data that are used. Section 3 assesses the stability of the 2017 PPPs, relative to the 2005 and 2011 PPPs. Section 4 proposes updates to the IPL, the higher global poverty lines, and the societal poverty line in terms of the 2017 PPPs. Section 5 documents the changes observed in global, regional, and country-level poverty estimates with the 2017 PPPs. Section 6 discusses the drivers of the changes observed in the global poverty estimates as well as the global poverty lines. Section 7 concludes. 2. Data The data used in the analysis can be categorized into five different headings: (1) country distributions of household consumption or income, (2) national poverty lines and national poverty rates, (3) consumer price indices (CPIs), (4) PPPs and market exchange rates, and (5) gross domestic product (GDP) per capita. Distributions of consumption or incomes: For the analysis of poverty trends over several years, the paper uses household-level data from 1,971 distributions from PovcalNet, which is the World Bank’s database for global poverty estimates.2 When focusing on poverty in 2017, the article draws on data from 167 economies that represent over 97 percent of the world’s population. Some economies have more surveys than others, depending on how often surveys are conducted. There is an average of 10 surveys for all economies spanning the period between 1991 and 2017, but more advanced economies are more likely to have annual distributions. For economies without annual data, annual poverty estimates are produced by extrapolating forward or backward from surveys, or interpolating between surveys, using national accounts growth rates and assuming no distributional changes (Prydz et al. 2019). While this is a strong assumption to impose, it performs relatively well (Mahler, Castaneda Aguilar, and Newhouse 2022) and has the advantage that the same set of countries can be compared over time. For most countries, the distributions are based on household-level data from nationally representative household surveys. For a 2 PovcalNet was replaced by the Poverty and Inequality Platform in 2022. This paper is based on the June 2021 vintage of PovcalNet. 500 Jolliffe et al. few countries, including China, the distributions are constructed from grouped data (i.e., quantiles of the distribution such as ventiles or percentiles). Though efforts are made to make the welfare distributions comparable within and across countries, incomparabilities exist with respect to whether income or consumption is used to measure welfare (102 countries use consumption aggregates, while 65 countries use income aggregates for the 2017 poverty estimates), and because survey design (Beegle et al. 2012), fieldwork (Jolliffe and Serajuddin 2018), and Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 welfare aggregate construction (Lanjouw and Lanjouw 2001) often differ across space and time. In a limited number of cases where national statistical offices have not produced or released household survey data for political or capacity reasons, alternative surveys are relied upon to impute consumption. This is the case, for example, for Nigeria and India (Lain, Schoch, and Vishwanath 2022; Roy and van der Weide 2022). These imputations add uncertainty to the welfare aggregates used. More details on the sources of data, imputations, and incomparability issues are available in World Bank (2023). National poverty lines and poverty rates: The article estimates global poverty lines using national poverty lines and national poverty rates covering a wide range of countries. The analysis obtains a total of 1,438 country-year observations of national poverty rates from the World Bank’s Poverty and Equity Database and the Organization for Economic Development and Co-operation (OECD) (for more details, see Table S2.1 in the supplementary online appendix). In addition, the article also uses the national poverty lines of 15 poor countries used by Ferreira et al. (2016), which Ravallion, Chen, and Sangraula (2009) originally derived. Consumer price indices (CPIs): CPI data are required to adjust income distributions to obtain a measure of household welfare in real terms. CPI data are also required to inflate national poverty lines to the ICP reference year. The article uses the CPI data used in PovcalNet (Lakner et al. 2018). The main source of CPI data is the International Monetary Fund’s International Financial Statistics (IFS). Following the PovcalNet methods, the study uses measures of inflation imputed from household survey data for a few countries, including Bangladesh, Ghana, the Lao People’s Democratic Republic, Malawi, and Tajikistan (see Lakner at al. 2018). Purchasing power parities (PPPs) and market exchange rates: When estimating global poverty, both income distributions and national poverty lines are converted into a common, internationally comparable currency unit using conversion factors. PPPs are preferred to market exchange rates as conversion factors, since market exchange rates only equilibrate the relative prices of tradable goods across countries. PPPs incorporate the relative prices of nontradable services (e.g., getting a haircut) across countries, which tend to be less expensive in developing countries, where labor costs tend to be lower (a phenomenon known as the Balassa-Samuelson-Penn effect). PPPs are price indices that express the currency units of one country in terms of the currency units of a reference country, typically the United States. In other words, PPPs measure how much it costs to purchase a basket of goods and services in one country compared to how much it costs to purchase the same basket of goods and services in the United States. This article obtains estimates of PPPs and market exchange rates from the ICP. It uses PPP estimates from the 2005, 2011 (original and revised), and 2017 rounds.3 It uses the PPPs for final household consumption expenditure, which includes nonprofit institutions serving households (NPISHs).4 Citing urban bias in the ICP price data collection, previous studies have imputed subnational (rural/urban) PPPs for the three most populous 3 Together with the 2017 PPPs, the ICP also published revised 2011 PPPs. The revisions to the 2011 PPPs reflect changes made to national accounts expenditures that are used as weights to aggregate elementary PPPs. The revised 2011 PPPs had only a small effect on the global poverty rates, relative to the original 2011 PPPs (see Section S1 in the supplementary materials). 4 For the 2011 PPP estimates, the article follows Ferreira et al. (2016) and uses predicted PPP estimates for six countries— the Arab Republic of Egypt, Iraq, Jordan, Lao PDR, Myanmar, and the Republic of Yemen—based on a regression model similar to that used by the ICP for nonbenchmark countries. Ferreira et al. (2016) note that there were large differences in the price changes implied by the 2005 and 2011 PPPs and domestic CPI inflation for these six countries (see also, The World Bank Economic Review 501 countries in the developing world, namely China, India, and Indonesia, when estimating global poverty (Chen and Ravallion 2008, 2010; Ferreira et al. 2016). The article follows these studies and imputes rural and urban revised 2011 PPPs and 2017 PPPs for these three countries, using the share of price collection outlets in urban areas, the ratio of urban to rural poverty lines in the ICP reference year, and the national PPP as inputs (see table S2.4 in the supplementary online appendix for more details). Gross domestic product (GDP) per capita: The study uses data on GDP per capita, denominated in Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 2011 PPP, 2017 PPP, and current USD, for the analyses done in this paper. It obtained these data from the June 2021 vintage of the World Development Indicators (WDI). 3. Assessing the 2017 PPPs The ICP has released PPPs on an irregular basis, with updates occurring in 1970, 1973, 1975, 1980, 1985, 1993, 2005, 2011, and 2017. With several of these releases, the ICP methodology evolved, largely improving the coverage and quality of PPP estimates, but leaving a concern that part of the observed changes in PPPs may have been due to methodological changes (and not price changes). The aim of this section is to establish that between 2011 and 2017 the methodology had stabilized and that most of the observed change in the PPPs reflects price changes. Examining Stability between the 2011 and 2017 PPPs at the Global Level During the long periods between releases of PPPs, much can change in the world that affects relative prices across countries. Even though PPPs are extrapolated to each of the interim years by comparing inflation— typically as measured by the consumer price index (CPI) for applications related to monitoring poverty— in each country relative to a base country (typically the United States), the infrequency of the benchmark years has led to cases where estimated poverty rates have changed significantly when introducing new PPP data. As Jolliffe and Lakner (2023) note, these changes may reflect real changes due to shifts in relative prices not picked up in the ratio of CPIs, but they may also reflect issues linked to methodological changes and data challenges. Broadly speaking, there are four main components of the ICP methodology by which stability between the 2011 and 2017 rounds can be assessed (more details are provided in section S3 in the supplementary online appendix). First, the adoption of a common classification of expenditure items across countries, and relatedly, the basket of goods and services priced. The basket of goods is directly linked to the classification of expenditures used by the ICP, and the ICP relies on the final expenditures classification on GDP from the System of National Accounts (SNA). While the 2005 and original 2011 rounds were based on the 1993 SNA, the revised 2011 and 2017 rounds are based on the 2008 SNA.5 While there were some minor changes at the basic-heading level of the ICP,6 such as how questions about food stocks are framed, these changes have not influenced the basket of goods or produced changes in the broader classification scheme. An implication of this is that there has been relative stability over these latest two rounds in the classification of expenditures. Second, the computation of the PPPs. While there was a major change in the method used in linking prices within and between regions from the “ring” method in 2005 to the “Global Core List” method Atamanov et al. 2018). For 2017, the article uses the official 2017 PPPs as published by the ICP except for 14 countries (see section 3 below and table S2.3 for more details). 5 For details of the 2008 SNA classification of expenditures, see International Monetary Fund (2009). 6 Basic headings in the ICP nomenclature refer to detailed expenditure categories containing similar item varieties. For example, “Rice” basic heading contains several rice varieties. The changes at the basic-heading level are more structural than substantive (e.g., “opening value of inventories” and “closing value of inventories” in the 2011 round have been merged as “changes in inventories” in the 2017 round). 502 Jolliffe et al. in 2011, the computational steps followed to produce the final global PPPs have remained unchanged since the 2011 round (World Bank 2015, 2020a). The Country Product Dummy (CPD) method is used to estimate PPPs at the basic-heading level, while the Gini, Eltetö, Köves, and Szulc (GEKS) method is used to estimate aggregated PPPs, which are both transitive and base-country invariant. Third, the set of participating countries. The calculation of a country’s PPP partially depends on other countries’ PPPs—it’s a multilateral price index. The number of participating countries declined from 199 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 to 176 between the 2011 and 2017 rounds, which is mostly explained by several small Pacific Island economies dropping out. The loss of these countries though, largely did not affect the global results because the Pacific Island economies were linked to the other participating economies via three bridge- countries (i.e., selected countries that additionally collect prices on a range of items from another region)— Australia (Eurostat-OECD), Fiji (Asia and the Pacific), and New Zealand (Eurostat-OECD). Fourth, the spatial coverage of the ICP price collection. The spatial coverage may vary over time within countries. In the 2005 round, for instance, price survey outlets were predominantly in capital cities and urban areas (World Bank 2008). In India, 74 percent of prices were collected from urban areas in the 2011 round, while 64 percent of prices were collected from urban areas in the 2017 round. In OECD countries, prices have been largely collected from urban areas in both rounds. These differences do not necessarily imply overestimation or underestimation of national prices, as the ICP requests countries to submit nationally representative prices.7 Under the assumption that countries submit nationally repre- sentative prices, changes to this component should not affect the methodological stability of the 2011 and 2017 PPPs. The absence of sufficient documentation at the country level makes it difficult to assess whether this is indeed the case. In addition to examining the ICP methodology by these four components, the article uses the ratio of actual to extrapolated PPPs to assess stability empirically (Deaton and Heston 2010; Deaton and Aten 2017). PPPs are extrapolated from the past ICP rounds using a factor equal to domestic inflation relative to U.S. inflation, and then compared to the actual PPP estimate. While there are methodological and data reasons for why the extrapolated (based on CPI data) and actual PPP estimates may differ, over relatively short periods of time, the divergences should not be substantial. Substantial deviations suggest issues with either the quality of the PPPs or CPIs. Deaton and Aten (2017) use systematic deviations in the realized PPPs and extrapolated estimates to argue that the ICP overstated prices in the 2005 round, especially in developing countries, which was later reversed in the 2011 round. Figure 1 shows that the ratio of 2017 PPPs to 2017 PPPs extrapolated from the 2005 round is sys- tematically lower than 1, while the ratio of 2017 PPPs to 2017 PPPs extrapolated from the revised 2011 round is more uniformly distributed around a mean of almost 1. The revised 2011 PPPs can better pre- dict the 2017 PPPs. This evidence is consistent with a claim of stability in the PPP estimates between the 2011 and 2017 rounds (especially when compared with the 2005 PPPs), and it is also an initial indication that revising the global poverty estimates from 2011 PPP terms to estimates expressed in 2017 PPP terms should not significantly alter the level of global poverty. In sum, this article asserts that the observed stability between 2011 and 2017 of the four components of the ICP methodology suggests that observed changes in relative price levels between the 2011 and 2017 PPPs are due to actual price changes and not due to changes in methodology. Further, the relative improvement in the correspondence between actual 2017 PPPs and extrapolated PPPs when using the 2011 PPPs instead of the 2005 PPPs (fig. 1), similarly speaks to the interpretation that observed changes in relative price levels between 2011 and 2017 are more likely due to actual prices changes (and not methodological changes). 7 National statistical offices are instructed to leverage data from existing national temporal and spatial price indices to construct national average prices, although it is not possible to obtain the data or methodological details to assess whether the corrections result in unbiased estimates of national prices. The World Bank Economic Review 503 Figure 1. Ratio of Actual to Extrapolated PPPs. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from PovcalNet, June 2021 and other sources described below. Note: This figure shows the ratio of actual to extrapolated PPPs as a function of GDP per capita. The actual PPPs are household final consumption PPPs from the 2017 round. The extrapolations are based on official PPPs from the 2005 or revised 2011 rounds together with CPIs mainly from the IMF International Financial Statistics (IFS). Eurostat PPPs have been used for OECD countries. Eurostat-OECD countries have a ratio of close to 1, as they publish annual PPP numbers, and therefore see only minimal revisions at new rounds of the ICP. Panel (A) shows 2005 PPPs extrapolated to 2017. For the full sample of 143 observations, the ratio of actual to extrapolated PPPs has a mean of 0.822 with a standard deviation of 0.18. Panel (B) shows revised 2011 PPPs extrapolated to 2017. For the full sample of 187 observations, the ratio of actual to extrapolated PPPs has a mean of 0.975 with a standard deviation of 0.11. The difference in means between panels (A) and (B) is statistically significant. Examining Stability between the 2011 and 2017 PPPs at the Country Level Though the ICP methodology may have stabilized and the changes between 2011 and 2017 are smaller than earlier rounds, the changes in poverty at the country level could still be large. To some extent, this too can be seen in fig. 1. The countries where the 2011 PPPs extrapolated to 2017 (fig. 1, panel B) are equal or close to the actual 2017 PPP values (i.e., countries on the horizontal line identifying a ratio of 1) will experience no or little change in the estimated level of poverty for 2017. Those countries where the ratio is significantly different from 1 will likely have substantial changes in their estimated poverty rate for 2017 when switching from the extrapolated 2011 PPPs to the actual 2017 PPPs. While many countries have ratios that differ from 1, the correlation between the actual and extrapolated 2017 PPPs is 0.97. This is an increase from a correlation of 0.91 for the actual 2011 PPPs and the 2011 PPPs extrapolated from the 2005 PPPs. The improved correlation is consistent with the inference of improved methodological stability— more of the change in PPPs is explained by a change in CPIs between 2011 and 2017, as compared to 2005 to 2011. Divergence from a ratio of 1 can result from different factors causing changes in PPPs that are not getting picked up in CPIs, or changes in CPIs that are not affecting PPPs. For example, changes in PPPs can arise from: (1) geographical changes in ICP price collection (with an imperfect adjustment to national average prices); or (2) changes in the share of items in the ICP price survey for which prices were actually collected (for example, Angola priced 18 percent of items in the 2011 round, which almost doubled to 35 percent in the 2017 round). Furthermore, the 2017 ICP round has benefited from technological advances 504 Jolliffe et al. that have reportedly improved data quality assurance across all regions (World Bank 2020b). It is also important to note that the extrapolation of different PPPs to a common year requires the use of national CPIs, which may have measurement issues, such as following nonstandard consumption classifications, or using expenditure weights and baskets that are outdated or not nationally representative (Berry et al. 2019). More generally, methodological differences in how the two indices are constructed, along with differences in how the price data are collected, can readily lead to a ratio that differs from 1.8 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 A slightly different way of considering how to assess changes in estimated poverty rates resulting from updating PPPs is discussed in Ferreira et al. (2016), who summarize the changes in PPPs and CPIs using the delta ratio. The delta ratio is the ratio of the relative change in CPI between 2011 and 2017 to the relative change in PPP between 2011 and 2017. It equals the ratio of the PPP-denominated mean income levels, and is thus closely related to the change in poverty.9 The delta ratio is given by: CPI2017 PPP2017 YPPP2017 δ= / = CPI2011 PPP2011 YPPP2011 where CPI2017 is the CPI in 2017, CPI2011 is the CPI in 2011, PPP2017 is the 2017 PPP, and PPP2011 is the (revised) 2011 PPP. YPPP2017 is income per capita in 2017 PPPs and YPPP2011 is income per capita in (revised) 2011 PPPs. One way to think of delta is that it converts a 2011 PPP-denominated value into a 2017 PPP-denominated value. Since PPPs are generally expressed in US dollars (i.e., the numeraire), for the United States the delta ratio boils down to the relative change in CPI between 2011 and 2017 (i.e., δ = 1.0897 or an inflation rate of 8.97 percent). For Angola, with a delta ratio of 1.80, the same bundle of goods and services in Angola that would cost $1.00 in 2011 (converted by 2011 PPP into LCU) would cost $1.80 in 2017 (converted by 2017 PPP into LCU). This has immediate implications for global poverty estimation. The IPL is expressed in US dollars (converted into local currency with PPPs) and the appreciation of the Angolan Kwanza relative to the US dollar means that fewer Kwanza are needed to realize a consumption level equal to the IPL. Thus, the 2017 poverty rate for Angola will fall when shifting to the actual 2017 PPPs from the extrapolated 2017 PPPs (i.e., the extrapolated PPP based on the ratio of CPI in Angola relative to CPI in US). Large differences in the movements of PPPs and CPIs—outlier delta ratios—indicate large changes in poverty when moving to the 2017 PPPs. Ferreira et al. (2016) identified countries with delta ratios of two standard deviations from the mean as outliers. If the delta ratio for a country is an outlier, it is not known if this is caused by the 2011 PPP, 2017 PPP, and/or the CPI. A large change in the poverty rate between the revised 2011 and 2017 PPPs does not mean that the 2017 PPP-based poverty rate is wrong, but it is indicative of a divergence from expectation that merits examination. Even if the data collection and methodology stabilized between any two rounds, the delta ratio would still deviate from the U.S. inflation rate since CPIs and PPPs are constructed very differently (Locker and Faerber 1984; Dalgaard and Sørensen 2002; McCarthy 2013). Figure 2 shows the delta ratio between 2011 and 2017 PPPs and indicates the mean and cut-offs outside of which a country is considered as an outlier. The mean delta ratio is 1.13, which incidentally is also the relative change in the IPL, which is derived in a later part of the paper (i.e., $2.15 in 2017 PPP compared 8 It is useful to note that even though the extrapolated PPPs are constructed to update the PPP over time, there are many measurement reasons why the extrapolated PPPs (a bilateral adjustment) will diverge from the actual, multilateral PPP index. This is a fundamental point from price-index theory that a set of bilateral price indices will differ from a multilateral price index (e.g., the PPP index) even if the bundles of goods are the same (Hill 2004; Locker and Faerber 1984). 9 Note that the discussion of fig. 1 focused on extrapolated PPPs, which is the method used for creating a time series of PPPs based on inflating each country’s PPP by the ratio of its CPI to that of the United States. The delta ratio can also be expressed as the inverse of the actual to extrapolated PPPs multiplied by the U.S. inflation between the two rounds. The World Bank Economic Review 505 Figure 2. Real Changes in PPP-Adjusted Dollars between 2011 and 2017 PPPs (Delta Ratio). Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from PovcalNet, June 2021 and other sources described below. Note: The horizontal axis is on a log scale. In contrast to fig. 1, this graph includes only benchmark countries in both 2011 and 2017 ICP rounds. Panel (A) uses 2011 PPPs and CPIs currently used for global poverty monitoring, and panel (B) uses official 2011 PPPs (from the ICP) and CPIs (from IFS). Official 2017 PPPs are used in both cases. with $1.90 in 2011 PPP). This means that extreme poverty decreases in countries with a delta ratio higher than 1.13 and increases where delta is less than 1.13. Panels (A) and (B) use slightly different 2011 PPPs and CPIs, which results in somewhat different outliers to be identified. The article uses PPPs and CPIs for global poverty monitoring in panel (A), while panel (B) uses those published by the ICP and IFS.10 The delta ratio is slightly higher on average with greater dispersion in panel (A) than in panel (B). Taken together, there are 14 outlier countries: Angola, Belize, Bolivia, Central African Republic, Egypt, Ghana, Guinea, Iraq, Jordan, Liberia, Nigeria, São Tomé and Príncipe, Suriname, and Trinidad and Tobago. As was done with the 2011 PPPs (Ferreira et al. 2016), for these outlier countries the analysis has conducted a series of additional checks to decide whether to use the official PPPs or predicted PPPs for global poverty monitoring.11 Apart from the delta criterion, the article has also included a PPP residual criterion to identify outlier countries. The analysis defines the PPP residual as the log difference between published and predicted PPP (using the ICP’s model for nonbenchmark country PPP estimation) and selects as an outlier any country whose residual is more than two standard deviations from the mean. The residual criterion is added to address the concern that the delta criterion fails to unambiguously identify whether there are issues with PPPs or CPIs. The study selects Egypt, Suriname, Central African Republic, Djibouti, and Sudan based on the residual criterion (three of which were already selected based on the 10 The differences are explained by the revised 2011 PPPs, which are predicted for exception countries (Egypt, Iraq, Jordan, Lao PDR, and Myanmar) and survey-based CPIs used for some countries (such as Ghana). The Republic of Yemen, which also has a predicted 2011 PPP, drops from the analysis since it is not included in the 2017 ICP. 11 Deaton (2001) similarly argues that with a new ICP round, the poverty estimates should be subjected to detailed, local scrutiny. 506 Jolliffe et al. delta criterion). The article also includes as outliers the countries whose revised, predicted 2011 PPPs were estimated by Atamanov et al. (2018) and used by Ferreira et al. (2016) (Egypt, Iraq, Jordan, Lao PDR, and Myanmar). For all countries selected as outliers, the article considers a range of indicators that could provide some evidence on whether official or predicted 2017 PPPs are more appropriate for these countries.12 To examine whether the quality of CPIs rather than PPPs might be the reason why a country is an outlier, Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 the analysis assesses the CPI expenditure classification used, the reference year of the CPI weights, and the spatial coverage of the expenditure data used in deriving CPI weights (Berry et al. 2019). To examine the quality of the PPPs, the article looks at the changes in the share of items priced between the 2011 and 2017 rounds.13 It also compares 2017-PPP-based poverty rates with correlates of poverty that do not rely on PPPs, such as the multidimensional poverty index (MPI) from the Oxford Poverty and Human Development Initiative (OPHI) (Alkire, Kanagaratnam, and Suppa 2020) and the age-dependency ratio from the World Development Indicators (WDI). Last but not least, the article incorporates the assessments of the country poverty economists—World Bank staff specialized in the measurement of poverty in a country—who provide country-specific nuances. Section S4 in the supplementary online appendix has the details of all the steps that were followed to identify and deal with outlier countries. Given this analysis, this article deviates from the official PPPs for eight countries (Belize, Egypt, Guinea, Iraq, Nigeria, São Tomé and Príncipe, Sudan, and Trinidad and Tobago). For these countries, the article uses the geometric average of official and predicted 2017 PPPs. In the absence of definitive evidence on which PPP is appropriate, the study argues that using the averages of the official and predicted PPPs for these eight countries provides more robust PPPs.14 It uses extrapolated 2017 PPPs for a few other excep- tion countries that do not have official 2017 PPPs, including Kiribati, Nauru, the Syrian Arab Republic, Tuvalu, the República Bolivariana de Venezuela, and the Republic of Yemen. The study conducts sensitivity analysis to understand the extent to which the alternative PPPs influence the IPL and extreme poverty estimates. The IPL remains robust to alternative PPPs and global and regional trends are virtually unaffected by the alternative PPPs (see fig. S4.4 in the supplementary online appendix). Global extreme poverty would decline by 0.4pp (instead of 0.2pp) in 2017 if the official 2017 PPPs were used throughout (see table S4.7 in the supplementary online appendix). This change is largely driven by Sub-Saharan Africa (particularly Nigeria) and the Middle East & North Africa (particularly Egypt). 4. Derivation of Global Poverty Lines National poverty lines underpin the construction of the World Bank’s global poverty lines. A national poverty line typically expresses the minimum amount of expenditure expected to cover the basic needs of a person, usually including food, clothing, and other nonfood items. By relying on national poverty lines to guide the determination of global poverty lines, this article relies on countries’ own judgments of what it means to be poor. National statistical offices are responsible for deriving the national poverty lines in 12 The analysis predicts 2017 PPPs using the seemingly unrelated regressions (SUR) model the ICP uses to predict PPPs for nonbenchmark economies, while excluding from the regression those economies which are subjected to greater scrutiny. With the revised 2011 PPPs, a slightly different model was used, which particularly addressed concerns in the Middle East & North Africa region (e.g., by including a conflict dummy in the model) (Atamanov et al. 2018). Since this region does not stand out this time around, it was decided to use the ICP model without any adjustments. 13 Unfortunately, it is not possible to assess the changes in the geographical coverage of ICP price data collection between rounds, as (reliable) metadata is lacking from the 2011 round for all regions. 14 Using the geometric average is similar to the ICP’s approach for interpolating between ICP rounds, where forward and backward extrapolations are averaged (Inklaar and Rao 2020). Furthermore, invoking a Bayesian interpretation, Chen and Ravallion (2010) take the geometric average between mean consumption in surveys and national accounts. Recently, Ravallion suggested taking a weighted average of the revised 2011 and 2017 PPPs (Ravallion 2020). The World Bank Economic Review 507 most low- and middle-income countries.15 National poverty lines are expressed in domestic currencies, and PPPs are used to convert different currencies into a common, comparable unit. PPPs are therefore instrumental in setting global poverty lines. The IPL has historically been derived as a summary measure (e.g., mean or median) of the national poverty lines of some of the poorest countries in the world. For example, the IPL of $1.25 per person per day was the mean of PPP-adjusted national poverty lines of 15 of the poorest countries in the world, as Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 judged by countries’ household final consumption expenditure per capita around 2008 when the 2005 PPPs were released (Ravallion et al. 2009). When the 2011 PPPs were released in 2014, the same 15 national poverty lines were used, but were then converted to 2011 PPPs yielding an IPL of $1.88, rounded to $1.90 (Ferreira et al. 2015; Ferreira, Joliffe, and Prydz 2016). This paper maintains the principle that the IPL should be constructed as the typical poverty line of the poorest countries in the world but, following Jolliffe and Prydz (2016), makes four changes to the methodology, addressing four issues with earlier derivations of the IPL. First, the national poverty lines of the 15 poor countries were reported in different units, for instance, some are in adult equivalent terms and others in per capita terms, creating incomparability in the final PPP-adjusted national poverty lines (Jolliffe and Prydz 2016). Second, the 15 countries were selected based on data showing that, for countries with per capita household final consumption expenditure at the level of these countries, national poverty lines were uncorrelated to per capita household final consumption expenditure (Ravallion, Chen, and Sangraula 2009). But later evidence with more data found that national poverty lines are positively correlated to per capita household final consumption expenditure at all levels (Jolliffe and Prydz 2016). Hence, increasing the sample to more than 15 countries might induce greater statistical support. Third, using national poverty lines constructed primarily in the 1990s for setting global poverty lines for measuring poverty today would rely heavily on CPI series over a long period of time from countries with typically weak statistical capacity. This would make the IPL sensitive to revisions made to historical CPI data from the 15 poor countries. Finally, using the mean of the national poverty lines might make the final IPL vulnerable to outliers, compared to the median. In an attempt to address these four issues, this article follows the “harmonized poverty line” approach of Jolliffe and Prydz (2016) to derive the IPL in 2017 PPPs. Rather than converting national poverty lines using PPPs and CPIs, the harmonized poverty line approach matches national poverty rates with con- sumption/income distributions in PovcalNet, which are already expressed in the same units; per capita PPP terms.16 For each country, the percentile of the PovcalNet distribution that corresponds to the reported national poverty rate is found (in other words, we query the inverse cumulative distribution function). This yields national poverty lines expressed in per capita PPP terms for all countries. Jolliffe and Prydz (2016) selected surveys conducted as close as possible to the 2011 ICP reference year, such that only one survey per country is used for deriving the IPL. Here we will do the same with 2017 as the reference year, thus minimizing the reliance on CPI series to move national poverty lines to the 2017 ICP reference year. We again follow Jolliffe and Prydz (2016) and use all low-income countries rather than 15 countries, thus increasing statistical support, and take the median poverty line instead of the mean to make the IPL robust to outliers. The next section describes the results of using this methodology to update the IPL and shows that the IPL is robust to choosing a broader set of countries, a broader set of national poverty lines, and 15 Low-capacity national statistical offices often do so in collaboration with the World Bank. In such cases, the goal of the Bank is to assure that the national poverty lines reflect countries’ own normative judgments and are based on the latest advances in poverty estimation. 16 There are thus a conceptual and practical reason for this approach. First, national poverty lines may be in different units (e.g., adult-equivalents), and using the harmonized lines avoids this problem since these lines are all expressed in per capita terms. Second, the national poverty rates are readily available in WDI for most countries, while the national poverty lines are not. For the 2017 analysis in this paper, we are able to estimate harmonized national poverty lines for 157 countries. 508 Jolliffe et al. Table 1. Updating Global Poverty Lines with Harmonized National Poverty Lines Income classification (A) Original 2011 PPP (B) Revised 2011 PPP (C) 2017 PPP Median Mean N Median Mean N Median Mean N Low-income 1.91 2.23 33 1.85 2.22 33 2.15 2.42 28 Lower-middle 3.21 3.88 32 3.21 3.89 32 3.63 3.95 54 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Upper-middle 5.47 5.62 32 5.65 5.64 32 6.85 7.06 37 High-income 21.70 21.19 29 21.70 21.31 29 24.36 23.36 38 Observations 126 126 157 Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: In panels (A) and (B), the analysis uses the harmonized poverty lines Jolliffe and Prydz (2016) derived. Panel (A) replicates the results of Jolliffe and Prydz (2016). Panel (B) uses revised 2011 PPPs to update the analysis. Panel (C) is based on new survey data together with changes in PPPs to 2017 PPPs and changes in CPIs between 2011 and 2017 (see section S5 in the supplementary online appendix). Countries are categorized according to the World Bank’s income classification in the years the surveys were conducted. In panel (C), the line that is closest to 2017 is selected, one for each country. If harmonized national poverty lines are available for 2016 and 2018 but not 2017, 2018 is selected. using an equivalent poverty line—a line that retains the global poverty rate in a prespecified reference year (Kakwani and Son 2016).17 We also update the societal poverty line (Jolliffe and Prydz 2021) as well as two higher lines Jolliffe and Prydz (2016) derived (see the following subsection below for more details). Global Poverty Lines Based on Harmonized National Poverty Lines Jolliffe and Prydz (2016) estimate a median harmonized national poverty line of $1.91 in 2011 PPPs from 33 low-income countries. This finding provided evidence in support of the IPL of $1.90 per person per day. They also estimated $3.21 and $5.47 as the median harmonized national poverty lines for 32 lower- and 32 upper-middle-income countries, respectively. These lines, rounded to $3.20 and $5.50, have been adopted by the World Bank as additional global poverty lines. Using the same approach to update the global poverty lines with the 2017 PPPs would ensure consistency in the Bank’s methodology in monitoring global poverty. When the analysis of Jolliffe and Prydz (2016) is updated with the revised 2011 PPPs, the median lines become $1.85, $3.21, and $5.65 (see table 1). The World Bank decided to keep the global poverty lines unchanged with the revised 2011 PPPs, including the $5.50 line (World Bank 2020a), and we follow that approach here when using the revised 2011 PPPs. For the 2017 PPPs, we use new harmonized poverty lines to estimate global poverty lines. We select for each country one survey that was conducted in 2017 or the closest year. Eighty-four percent of selected surveys were conducted after 2011, and 43 percent were conducted in 2017. More than three-quarters of selected surveys were conducted within three years from 2017. Compared with Jolliffe and Prydz (2016), new countries have been included in the analysis of the harmonized national poverty lines (e.g., Algeria, Bangladesh, and Egypt), and some of the countries in the low-income category moved up to the lower-middle-income category (e.g., Ghana, Kenya, Pakistan, São Tomé and Príncipe, and Tajikistan). Countries are categorized into low, lower-middle, upper-middle, and high-income countries based on the World Bank’s income classification in the years the surveys were conducted. Based on this approach, the median poverty line for low-income countries, or the IPL, is estimated to be $2.15 (2017 PPP). The higher lines using data from lower-middle-income countries and upper-middle-income countries are estimated to be $3.65 and $6.85, respectively, in 2017 PPP. These lines are used with the 2017 PPPs in the rest of the paper. Figure 3 illustrates the global poverty lines graphically.18 17 There are alternative methods that could be used to derive global poverty lines than the ones that were used. Global poverty lines, for example, could be estimated using PPPs that equilibrate the least-cost diets across countries (Allen 2017; Bai and Masters 2020). 18 Section S6 in the supplementary online appendix lists the four global poverty lines with the 2011 PPPs and 2017 PPPs in 2020 local currency units for each country with available data. The World Bank Economic Review 509 Figure 3. Global Poverty Lines Using Harmonized National Poverty Lines. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: This chart is based on the data summarized in table 1, panel (C). The national poverty lines are the harmonized national poverty lines, expressed in 2017 PPP per person per day. GDP is also given in 2017 PPP per person per day. The global poverty lines are the median poverty lines for low-income countries (LIC), lower- middle-income countries (LMIC), upper-middle-income countries (UMIC), and high-income countries (HIC), rounded to the nearest five cents. Income classifications are determined using a slightly different measure—GNI per capita with the Atlas method (hence GDP per capita does not perfectly order countries according to their income classification). There are fewer observations of GNI per capita, which is why GDP per capita is used here. Uncertainty of the Estimated IPL The estimated value of these global poverty lines rests on many different data sources and a series of assumptions. Even in deriving the harmonized national poverty lines, there are at least five sources of uncertainty stemming from the different data sources, including the PPPs, the CPIs, the (value of the unharmonized) national poverty lines, the welfare distributions, and the GNIs, which are used to classify low-income countries. The magnitude of these uncertainties and their codependence is largely unknown, making it difficult to calculate confidence intervals of the IPL. In addition, how each of these data sources is used entails a series of assumptions, each of which can readily introduce error as well. The following discussion examines the potential magnitude of error from several different sources. As a first step, we provide an estimate of error associated with one consideration. The median value of the harmonized, national poverty lines from 28 LICs is the estimated IPL. One source of sampling error then is the selection of the 28 lines from the population of poverty lines from all LICs. Following a common approach for estimating sampling error for small samples, we use a (studentized) bootstrap estimator of the variance (based on 10,000 replications.) A caveat though is that while the sample is small, these 28 LICs cover 90 percent of LICs; so we therefore use a finite-population correction to shrink the bootstrap estimate of the standard error. The resulting 95 percent confidence interval around the estimated IPL is $2.04–$2.26, when accounting for this one source of error. An issue with the approach above is that it assumes that the group of low-income countries is the appropriate set of poor countries (as implicitly assumed in the bootstrapping exercise). Given the positive slope between national income and national poverty lines, one faces a trade-off between including more countries in the calculation—and hence increasing statistical support—and focusing on only the poorest 510 Jolliffe et al. Figure 4. Cumulative Mean and Cumulative Median of Harmonized National Poverty Lines. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from PovcalNet, June 2021. Note: The harmonized national poverty line is in 2017 PPP-adjusted dollars per person per day. The number of countries indicates the count of countries ranked by GDP per capita in 2017 PPP-adjusted dollars from the poorest to the richest. For example, 20 indicates the poorest 20 countries. This chart is based on the data summarized in table 1, panel (C) but the number of countries is capped at 100 to better illustrate the IPL. countries. To check the robustness of the $2.15 IPL derived above, to changing the set of countries used to estimate the line, we order countries by GDP per capita in 2017 PPP and plot the cumulative median of harmonized poverty lines in 2017 PPPs, sequentially increasing the pool of countries starting with the country with the lowest GDP per person in 2017 (see table S2.5 in the supplementary online appendix for details). This analysis aims to address a critique from Deaton (2010) who demonstrates the sensitivity of the estimated IPL from excluding one country, when based on a small sample of countries. Figure 4 shows that when using between 11 and 41 of the poorest countries, the median poverty line is $2.15. This suggests that the estimated IPL is not sensitive to enlarging or shrinking the set of countries used to find the median national poverty line. The assumption to use the set of LIC countries to estimate the IPL introduces very little uncertainty in the estimated value.19 As noted above, poverty estimates are based on consumption measures for some countries and income in others. This creates significant concerns over comparability across countries (and over time if a country switches from consumption to income, or vice versa), which merit particular attention in this section on uncertainty of the estimated poverty lines. The estimated poverty lines in MICs are sensitive to the decision to pool consumption and income national poverty lines. This is particularly true in lower-middle-income countries (LMICs) where about 10 percent of the lines are income based. If one were to estimate separate lines for income- and consumption-based measures, the median income-based poverty line in LMICs is 19 We temper this claim, though, by noting that if it were to use the 15 national poverty lines selected by Ravallion, Chen, and Sangraula (2009), convert them to 2017 PPPs values, and take the average (as done in Ferreira et al. 2016), the resulting value is $2.08 (see table S2.9 in section S2 of the supplementary online appendix for details). The reason this differs from fig. 4 is that these 15 countries were not selected based on sorting them by income level but simply by data availability at that time. The World Bank Economic Review 511 $6.66; and $3.49 for consumption-based lines. The difference is also large, though less pronounced, in upper-middle-income countries (UMICs), where the median consumption-based line is $6.25 and that for income is $7.28. While this highlights an area of substantial variation and leads towards the proposal to use differing lines depending on whether the underlying well-being measure is consumption or income, there are two tempering comments. The first is that this variation does not exist in the case of the IPL. Within the set of Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 LICs, there is no country that has an income-based poverty line, so $2.15 is unaffected by this concern. Second, in the case of LMICs, where the income-based line is almost twice as large as the consumption- based line, it is noteworthy that there are only 5 LMICs with income-based measures, while there are 49 LMICs with consumption-based measures. Any inference about the difference between income and consumption-based measures for LMICs would have little statistical support (due to the small sample of LMICs using income). In contrast, the $1.03 difference between income and consumption-based poverty lines in UMICs is based on relatively equal distribution of lines with greater statistical support.20 Despite these cautions, the general point about improving the coherence between the poverty line and the underlying welfare metric is a very important concern, and it is not just limited to the conceptual differences between income and consumption. It is also the case that even across consumption aggregates, there is variation in the conceptual framework (e.g., whether they include use-value of durable goods or not) and substantial variation in questionnaire design that produces noncomparabilities (e.g., length of recall period). This concern of noncomparabilities across the welfare vectors underpinning the estimated poverty lines (and, probably more importantly, informing the poverty and inequality estimates) is clearly an area in need of further analysis. Another assumption made in estimating the IPL that may introduce uncertainty is our assumption to base the estimate on the harmonized, national poverty line for each country from circa 2017. Many low-income countries have multiple national poverty lines available estimated at different points in time (and from different data sources) that frequently vary over time in real terms. To assess whether the results change if we were to use older national poverty lines of low-income countries rather than the ones centered around 2017, we use harmonized national poverty lines from circa 2011, as in Jolliffe and Prydz (2016), and still get an estimated IPL of $2.15 (see table S2.6 in the supplementary online appendix). When pooling together the over 1,300 harmonized national poverty lines that were newly derived, an IPL of $2.16 is obtained (table S2.6 in the supplementary online appendix). One might worry about using old harmonized national poverty lines given that the statistical capacity of most countries is increasing over time and given that using old poverty lines relies on many years of CPI data. If we restrict the sample to poverty lines at most 10 years old with respect to the PPP year, we arrive at an IPL of $2.14 whether we use the pooled sample or only the poverty line closest to 2017 for each country (see tables S2.6 and S2.7). All of these examples suggest that relatively little uncertainty is introduced into the estimated IPL from the selection of the circa 2017 sample of harmonized, national poverty lines. Another way to get at the uncertainty behind the estimated IPL is to use equivalent poverty lines, a concept introduced by Kakwani and Son (2016) at the country level, as an alternative method to derive the IPL. The idea behind this method is to estimate the IPL with the 2017 PPPs by choosing a line that retains the global poverty rate in a pre-specified reference year. See section S5 in the supplementary online appendix for the derivation of equivalent poverty lines. Table 2 presents the results of this analysis. The equivalent IPL that retains the global poverty rate rounds to $2.15 in 2017 PPPs (rounding to the nearest $0.05) when using either 2012, 2015, or 2017 as the reference year.21 Our preferred year for evaluating 20 Twenty UMICs have consumption-based measures of wellbeing, compared to 17 UMICs which are income based. 21 As reference years, the study chose 2010, which is the year that had the most recent global poverty estimate when the World Bank’s goal of ending extreme poverty was set; 2012, which is the year with the most recent global poverty estimate when the UN agreed on the SDGs; 2015, which is the year that had the most recent global poverty estimate 512 Jolliffe et al. Table 2. Equivalent International Poverty Line, 2017 PPP Region Year Headcount, % (Revised 2011 PPP) Equivalent IPL (2017 PPP) World 2010 16.02 2.119 World 2012 12.89 2.128 World 2015 10.14 2.161 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 World 2017 9.27 2.173 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table shows equivalent international poverty lines that keep global poverty headcounts constant in prespecified reference years. the equivalent IPL is 2017, which is both the latest ICP reference year and the year with the latest global poverty data. The equivalent IPL for 2017 is $2.173. Updating the Societal Poverty Line Just as the value of the IPL, and the higher lines reflecting typical assessments of basic needs in middle- income countries, are expressed in PPP dollar terms, so too is the World Bank’s societal poverty line (SPL). The SPL was introduced by World Bank (2018) in response to the Atkinson Commission on Global Poverty (World Bank 2017) recommendation to report a measure of poverty that combines fixed and rel- ative elements of poverty. The SPL, estimated for each country separately, was initially parameterized by Jolliffe and Prydz (2021) as: max(IPL, $1 + 50 percent of median consumption), with two parameters expressed in 2011 PPPs—the IPL and the intercept. With the 2017 update of the PPPs, these two param- eters similarly need to be updated. The first parameter, the IPL, is updated to $2.15 in 2017 PPPs, leaving only the intercept of $1 in need of an update. We examine two methodological approaches to consider how best to update this value. First, we follow an approach similar to how we updated the higher lines by replicating the methodology used to determine these values except now using the 2017 PPPs and more recent data. As with the IPL and the higher poverty lines, we consider different samples and approaches. Following Jolliffe and Prydz (2021), we regress harmonized national poverty lines on median values of consumption (or income, depending on the welfare aggregate used in the country). We have collected metadata on the type of poverty line from most countries and subsample on all countries that have ab- solute poverty lines. Table 3 shows the estimated intercept and slope coefficients when considering three such specifications. We first consider a sample (n = 119) where there is one observation for each country with an absolute poverty line. For each country, we select the observation that is closest in time to 2017 (and refer to this as the circa-2017 sample). We then consider the subsample of all (n = 815) country-year observations, allowing multiple observations per country capturing some within-country variation over time in their values. Two different weighting schemes are used; both cases ensure that all countries are weighted equally, but in one specification, we weight more heavily the data points that are closer in time to 2017 (see table notes for details). Across these three specifications, the intercept ranges from 1.15 to 1.17 (in 2017 PPPs). While that variation is not substantial, it’s noteworthy that these are very imprecisely estimated parameters with 95 percent confidence intervals that are almost $1 in width (e.g., $1.15 ± $0.49 for the circa-2017 sample). when Atkinson wrote his report; and 2017, which is the year with the latest global poverty numbers at the time of writing. table S2.8 reports a slight variant of the equivalent poverty lines. The estimated international poverty line in this case is derived as the weighted sum of the equivalent regional poverty lines that hold constant regional poverty rates in a particular reference year. The weights correspond to the regional shares of the global poor in the reference year. This approach suggests an IPL between $2.16 and $2.24 with the 2017 PPPs. The World Bank Economic Review 513 Table 3. Updating the Societal Poverty Line with New Survey Data and 2017 PPPs Description Intercept Slope N Delta ratio One line per country—circa-2017 sample 1.15 0.47 119 1.15 (0.249) (0.043) (0.014) Pool all lines—uniform weights 1.16 0.52 815 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 (0.213) (0.043) Pool all lines—higher weights for lines closer to 2017 1.17 0.49 815 (0.235) (0.044) Source: Authors’ calculations from PovcalNet, June 2021. Note: Using the same specification as in Jolliffe and Prydz (2021) and the data used in updating the absolute poverty lines in this paper (table 1, panel C), the parameters are updated with the 2017 PPPs. This table excludes all national poverty lines that are defined as relative lines (which is typical of high-income countries). For the circa-2017 sample, every country with an absolute poverty line is included but only for the year that is closest to the 2017 ICP benchmark year. In the larger samples (n = 815), the weighting schemes differ. In one scheme, every observation has a weight equal to the inverse of the total number of observations for each country, so that each country has a sum of weights equal to 1. In the other scheme, the analysis weights the observations to give greater importance to those close to 2017 and to give equal weight to each country (see Jolliffe r et al. 2022 for details). All parameter estimates are statistically significant at the 1 percent level. Standard errors of the estimates are in parentheses. For the circa-2017 sample, the standard error is $0.25, but this drops only a few cents when considering sample sizes that are nearly seven-fold larger.22 The magnitude of the confidence intervals is concerning and leads us to consider a complementary approach to updating the intercept. This complementary approach is used to assess the sensitivity of the estimated intercept. Part of the reason why the confidence intervals around the intercept are so large is because the intercept is an out-of-sample prediction. The intercept essentially represents a minimum cost of basic needs under which no person could survive, no matter how poor a particular country is. No country provides a direct estimate of this value; it can only be inferred by imposing modeling assumptions. This is conceptually very different from how we approached updating the IPL, which reflects a median value over a large sample of national poverty lines and is a well-supported, within-sample estimate. The alternative method considered is to update the intercept based on the delta ratio, which directly converts 2011 PPP values for each country into 2017 PPP values. For the subsample of countries that use absolute poverty lines (i.e., those countries used in this part of the analysis), table 3 reports an average value of the delta ratio of 1.15. If using this estimation approach to update the intercept, the revised intercept would be $1.15 in 2017 PPPs. Based on these findings, we assert that an appropriate updating of the SPL in 2017 PPPs is parame- terized as: max(IPL, $1.15 + 50 percent of median consumption). This inference is based on three main points coming from the two approaches. The first is that both methods result in estimated intercepts that fall within the narrow range of ($1.15, $1.17), with two of the four estimates being $1.15. Second, while the confidence intervals from the estimates based on replicating the methodology of Jolliffe-Prydz are large, the confidence interval based on the delta method is very tight ($1.15 ± $0.03) indicating signif- icantly greater precision from this estimation approach. And, finally, it should be noted that all of the estimated intercepts, when rounded to the nearest five cents (as is done for the other poverty lines), are equal to $1.15. 5. Global, Regional, and Country-Level Poverty Estimates This section compares global, regional, and country-level poverty estimates over time with the 2017 PPPs, relative to the revised 2011 PPPs—at the values of the global poverty lines associated with each (e.g., $2.15 at 2017 PPPs and $1.90 at 2011 PPPs). While updating global poverty estimates with the 2017 PPPs does 22 This relatively minor decline in standard errors is due to the significant intra-country correlation of the error term (and correcting for this with the sandwich variance estimator). 514 Jolliffe et al. Figure 5. Global and Regional Trends in Extreme Poverty. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from PovcalNet, June 2021. Note: This figure shows the trends in extreme poverty at the global and regional levels. The international poverty lines used are $1.90/day (revised 2011 PPP) and $2.15/day (2017 PPP). create some noteworthy changes, the overall message is one of stability between the 2011 and 2017 PPP- based estimates, particularly when evaluated at the IPL. That the ratio of the extrapolated and actual 2017 PPPs is centered on 1 (fig. 1), is one piece of evidence that one would not anticipate a significant change in the overall level of poverty. This is, of course, conditional on how the corresponding poverty line is estimated for the 2017 PPPs, but as will be shown below, over a set of various methods for updating the IPL, the global poverty headcount is fairly stable. Similarly, when examining the data from fig. 1, both high levels of correlation and rank correlation between actual and extrapolated 2017 PPPs are observed. This too is a piece of evidence that there may not be a large re-alignment in the geographic distribution of poverty when updating the IPL and revising the poverty profiles. Nonetheless, there are some noteworthy changes, which are discussed below. Fig. 5 shows regional and global trends in extreme poverty between 1991 and 2017 with the two sets of PPPs. Extreme poverty is measured as the share of the population living on less than $1.90 or $2.15 a day expressed in revised 2011 PPP or 2017 PPP, respectively. At the global level, the revised 2011 PPPs and 2017 PPPs induce relatively small changes to extreme poverty. Between 1991 to 2017, extreme poverty in the world falls from 36.05 percent to 9.27 percent with the revised 2011 PPPs, and from 37.46 percent to 9.07 percent with the 2017 PPPs. In 2017, extreme poverty in the world decreases marginally by 0.2pp with the 2017 PPPs. The 2017 PPPs slightly increase historical estimates of extreme poverty and slightly decrease extreme poverty since 2014. Regional trends in extreme poverty are similar for both revised 2011 and 2017 PPPs. There is progress against poverty across regions, except for the Middle East & North Africa where conflict and fragility reverse the progress made (World Bank 2020a). Extreme poverty more than doubles in Europe & Central Asia (but from a low level), and slightly increases in all other regions, except Sub-Saharan Africa. Extreme The World Bank Economic Review 515 Table 4. Changes in Extreme Poverty In 2017, Top 10 Countries Poverty rate, % Poverty rate, % Change in Change in Change in Country (2011 PPP) (2017 PPP) poverty, pp poverty, % millions of poor Nigeria 41.36 33.41 − 7.96 − 19.23 − 15.19 Uzbekistan 12.70 34.35 21.65 170.53 7.01 − 8.24 − 11.34 − 6.70 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Congo, Dem. Rep. 72.65 64.42 Indonesia 4.46 6.55 2.09 47.01 5.54 Angola 45.07 26.50 − 18.57 − 41.20 − 5.54 Kenya 35.11 27.50 − 7.61 − 21.67 − 3.82 Ethiopia 24.66 21.16 − 3.50 − 14.18 − 3.72 Ghana 12.34 24.68 12.33 99.91 3.59 Pakistan 4.02 5.44 1.42 35.35 2.95 China 0.35 0.55 0.19 55.05 2.69 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table compares extreme poverty between the revised 2011 PPPs and 2017 PPPs at the country level. The top 10 countries with the largest absolute changes in millions of people in extreme poverty are shown in this table. Countries are ranked in descending order of absolute changes in millions of poor. When a country does not have a survey in 2017, recent surveys are extrapolated or interpolated using growth rates in national accounts data, particularly GDP or household final consumption expenditure (HFCE). poverty reduces substantially in Sub-Saharan Africa (34 million fewer poor people), driving down the global count of the extreme poor by 15 million. Sub-Saharan Africa still has the largest share of millions of extreme poor, though it falls from 62 percent with the revised 2011 PPPs to 58 percent with the 2017 PPPs.23 To understand the regional trends further, table 4 shows the 10 countries with the largest absolute changes in millions of people in extreme poverty.24 The change observed in Europe & Central Asia is driven by Uzbekistan, where poverty increases by 22pp (equivalent to 7 million more poor people).25 About half of the change in extreme poverty in Sub-Saharan Africa is driven by Nigeria, where poverty falls by 8pp (equivalent to 15 million fewer poor people). Changes in poverty at the global level are also relatively small when the $3.65 line is used, as regional changes offset each other. Poverty increases in East Asia & the Pacific, Europe & Central Asia, and Latin America & the Caribbean, while poverty decreases in the Middle East & North Africa and Sub-Saharan Africa. In 2017, global poverty increases by 0.6pp, or 43 million more poor people, with the 2017 PPPs at the $3.65 line. The largest changes in millions of poor in 2017 are observed in East Asia & the Pacific (43 million more poor people), and Sub-Saharan Africa (24 million fewer poor people). The change in East Asia & the Pacific is mainly driven by China and Indonesia, while the change in Sub-Saharan Africa is mainly driven by Nigeria. In contrast, using the $6.85 line (2017 PPP) relative to a $5.50 line (2011 PPP) markedly increases estimates of poverty in all regions, including Sub-Saharan Africa. With the 2017 PPPs, about 321 million more people in the world would be considered poor in 2017 by the standards of 23 For these results in tabular form across all regions, see Jolliffe et al. (2022). For further discussion on estimated changes in poverty for Sub-Saharan Africa, see Tetteh-Baah and Lakner (2022). 24 The 2017 estimates for all countries are used. As explained above, when there is no survey in 2017, these numbers are estimated using growth rates from national accounts and assuming no distributional changes. 25 This is the first time the ICP provides a PPP estimate for Uzbekistan based on actual prices. The price data collection was based on an experimental approach, meaning that prices were collected for only actual individual consumption and individual consumption expenditure by households, but not for all the components included in GDP. The original and revised 2011 PPPs were predicted by the ICP like other nonbenchmark countries. The 2017 PPP estimate is deemed more credible, but potentially not comparable to estimates from previous rounds. This might explain why Uzbekistan has large changes in poverty. 516 Jolliffe et al. upper-middle-income countries. About 129 million of them live in China, and 64 percent of them live in China, India, and Indonesia alone.26 If the societal poverty line were strongly relative (without any fixed or absolute elements), as in the OECD where the poverty line is 60 percent of the median, revisions to PPP data would not change global and regional poverty trends. The global welfare distribution would change by the same factor as the societal poverty line. Unlike the OECD poverty line, the SPL has two terms expressed in PPPs (i.e., the Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 IPL and the intercept) whose revision drives the changes observed in societal poverty trends when moving to the 2017 PPPs. In fact, the changes in societal poverty trends with the 2017 PPPs are small and similar to the changes observed in extreme poverty with the 2017 PPPs. Societal poverty slightly increases globally and, in all regions, except Sub-Saharan Africa and the Middle East & North Africa. About 17.6 million more people would be living in societal poverty in the world in 2017 when moving from the revised 2011 to 2017 PPPs.27 6. Discussion While aggregate changes are small, the 2017 PPPs compared to the 2011 PPPs imply lower price levels in relatively poor countries and higher price levels in relatively rich countries. Poverty in Poorer versus Richer Countries In 2017, the share of the world’s population living in poverty in low-income countries systematically decreases while the share of the world’s poor living in upper-middle-income countries systematically in- creases at all three global poverty lines (see fig. S2.1 in the supplementary online appendix). The largest change occurs at the highest line, where the share of people in upper-middle-income countries considered as poor by the standards of these countries increases from 15 percent to 19 percent. Some of this change is driven by upward revisions in the national poverty lines of these countries (see further down in this section for more details). It is possible to make sense of the global changes in poverty and the shifting shares of poverty across income groups by investigating (1) the changes in poverty at the country level and (2) the aggregation of country-level poverty estimates. The global pattern that is observed could be explained by the former if the real changes in welfare when moving to the 2017 PPPs ) are systematically higher for low-income coun- tries than for middle-income countries (which would be reflected by an income gradient in the delta ratio). As the IPL increases by 13 percent from $1.90 to $2.15, extreme poverty increases in countries whose delta ratios fall short of 1.13 and decreases in countries whose delta ratios exceed 1.13. Averaging delta over each income group, one observes that it is greatest for LICs (well over 1.13) and then declines while moving into higher income classifications. These results suggest that low-income countries are slightly richer with the 2017 PPPs, hence slightly shifting the concentration of extreme poverty away from the poorest countries. The global pattern observed could also be driven by the fact that populous countries carry a larger weight when aggregating country-level poverty estimates. For low-income countries, the delta ratio is even higher when population weighted, and lower for upper-middle-income countries; sug- 26 For a full set of results at the $3.65 line, see tables S2.10 and S2.11 in the supplementary online appendix; for the $6.85 line, see tables S2.12 and S2.13 in the supplementary online appendix. For figures displaying the regional changes at these two lines, see Jolliffe r et al. 2022. 27 A few large countries drive this change, including China and Indonesia from East Asia & the Pacific, Nigeria and the Democratic Republic of Congo from Sub-Saharan Africa, India and Pakistan from South Asia, Iraq and the Arab Republic of Egypt from the Middle East & North Africa, and Uzbekistan from Europe and Central Asia. For a full set of results at the SPL, see tables S2.14 and S2.15 in the supplementary online appendix; for corresponding figure, see Jolliffe r et al. 2022. The World Bank Economic Review 517 gesting that the shift in poverty to relatively rich countries is partly driven by a few populous countries in the world.28 Extreme Poverty in Sub-Saharan Africa This subsection presents additional analysis to better understand the changes in Sub-Saharan Africa, which shows the largest changes in extreme poverty as a result of the 2017 PPPs. Since Sub-Saharan Africa ac- Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 counts for nearly two-thirds of the global extreme poor, it is not surprising that the region drives the global changes, but it is important to understand whether the changes are stemming primarily from sys- tematic issues with the new (or old) round of PPPs in the region. On the whole, the 2017 ICP round has benefited from improved data quality assurance across all regions (World Bank 2020b). The article investigates the quality of PPPs by comparing the residuals between published and predicted price level indices (PLIs) across regions.29 The article predicts PLIs from country-level characteristics (e.g., GDP per capita, export and import shares, and dependency ratio), following the official ICP model used to esti- mate PPPs for nonbenchmark economies (for the model specification and more details, see section S4 in the supplementary online appendix). Sub-Saharan Africa does not have systematically different residuals, suggesting that there is no evidence of systematic errors in the PPPs for Sub-Saharan Africa. The study also considers the quality of CPIs for Sub-Saharan African countries relative to other regions. It draws on metadata covering 196 countries to assess the quality and coverage of official CPI data used in the global poverty estimates (Berry et al. 2019). The article examines three indicators, including whether the CPI expenditure categories follows the accepted classification standard, the reference year used for the expenditure weights, and the spatial coverage of the expenditure weights. Most countries in the world (77 percent) follow the standard Classification of Individual Consumption According to Purpose (COICOP) classification, with the Sub-Saharan Africa region not being very different (71 percent). Similarly, the average reference year used for CPI weights in Sub-Saharan Africa is 2011.0, which is close to the world’s average of 2011.9. Finally, the share of CPI expenditure weights with national coverage in Sub-Saharan Africa (73 percent) is also similar to the world (79 percent). No evidence is found that the substantial reduction in extreme poverty in Sub-Saharan Africa is related to PPP or CPI quality issues. While there are obviously changes in rankings within the region, African countries are actually quite evenly split between increasing and decreasing poverty: Extreme poverty is lower with the 2017 PPPs in 24 countries and higher in 21 countries, with most countries being quite stable (see fig. S2.4 in the supplementary online appendix). Most of the overall change in the region is being driven by a few large countries, including Nigeria, the Democratic Republic of Congo, Angola, Kenya, and Ethiopia. Nigeria alone accounts for about half of the change in the millions of extremely poor people in the region. Drivers of Changes in the Global Poverty Lines This subsection attempts to explain the drivers of the revisions to the global poverty lines.30 It pays particular attention to the large change at the highest line from $5.50 to $6.85. The observed changes in the global poverty lines are not only due to the change in PPPs but also to three other factors: (1) the 28 For example, Ethiopia and the Democratic Republic of Congo are populous countries driving the decline in extreme poverty in low-income countries with the 2017 PPPs. At the LMIC line, the changes in poverty in the populous countries offset each other; for example, the poverty counts increase in Indonesia by 13 million and decrease in Nigeria by 14 million. At the highest line, poverty increases in China, Brazil, the Russian Federation, and Mexico with the 2017 PPPs, driving down the delta ratio for upper-middle-income countries markedly when population weighted. See discussion in section S2 of the supplementary online appendix, in particular, fig. S2.2. 29 The PLI is the ratio of the PPP conversion factor to the market exchange rate. See Jolliffe r et al. (2022, appendix S4) for more details. 30 The authors thank Francisco Ferreira and Benoît Decerf for encouraging them to delve deeper into this point. 518 Jolliffe et al. national poverty lines used, (2) income group classifications, and (3) the set of countries with national poverty lines and welfare aggregates available. This paper aims to gauge whether any of the other three factors are driving changes to the poverty lines. To isolate the various factors, the analysis updates the harmonized national poverty lines previously used by Jolliffe and Prydz (2016) with the 2017 PPPs while keeping everything else constant, and sequentially accounts for each of the three factors above.31 First the analysis revisits the result of maintaining the exact same sample of national poverty lines but updates Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 the PPPs from the 2011 round to the 2017 round. As argued in section 4, this generates an IPL of $2.15, meaning that the revision to the IPL can entirely be explained by the new PPPs, regardless of the other factors.32 For the poverty line typical of lower-middle-income countries, the analysis arrives at a poverty line of $3.68, quite close to the final one when accounting for all changes ($3.63), meaning that nearly all of the updates to that line can be explained by the changes to PPPs. For the line typical of upper-middle- income countries, revising only the PPPs brings the line to $6.32, quite far from the final line ($6.85) meaning that the PPP changes cannot explain all of the increase of that line. Next, the article accounts for the fact that the countries Jolliffe and Prydz (2016) used for their analysis might have more recent national poverty lines. Updating the set of national poverty lines has negligible impacts on the IPL and the lower-middle-income line but increases the upper-middle-income line notably, from $6.32 to $7.15. Part of this increase can be explained by some of these countries now being high- income countries, but when removing those countries, the line still sits at $6.95 (see table S2.16 in the supplementary online appendix for more details). The increase in the upper-middle-income line is then driven by increases in the real value of national poverty lines of upper-middle-income countries. Poverty in upper-middle-income countries is more likely to reflect the concept of relative poverty, which means that one expects the real value of national poverty lines to increase over time with economic growth (Smeeding 2016). The fact that national poverty lines and welfare aggregates are now available for more countries than Jolliffe and Prydz (2016) only has a minimal impact on the value of the global lines. Even for countries whose national poverty lines are based on concepts of absolute poverty, it is known that as countries get wealthier, they tend to increase over longer periods of time the real value of their national poverty line (Ravallion 1998; Jolliffe and Prydz 2016). Between the release of the 2011 PPPs and 2017 PPPs, on average countries got wealthier. This means that one would expect some of the national poverty lines close to 2017 to be higher in real terms than the national poverty lines close to 2011. How is this consistent with the finding that the real value of the IPL and lower-middle-income line is driven by PPPs and not by changes to the real value of national poverty lines? The answer lies in the use of income groups to define the global poverty lines. Suppose a poor country grew between the release of the 2011 PPPs and 2017 PPPs and as a consequence adopted a higher national poverty line. Suppose further that the country graduated from being a low-income country to a lower-middle income country as a result of this growth. Then it is not obvious that the increase in the real value of their national poverty line will increase the median national poverty line of low-income countries or lower-middle income countries. As long as the median income level within each income group has not changed between 2011 and 2017, then—even if countries’ national poverty lines increase as they grow—it does not need to be the case that the global poverty lines that are derived on expectation increase in real value. 31 This decomposition is path dependent, meaning that the order in which the analysis accounts for the various factors matters. The results remain qualitatively the same regardless of which order is used. See table S2.16 in the supplementary online appendix. 32 Though the analysis has changed the method by which it derives the IPL—it now uses the harmonized national poverty line approach to update the IPL, similar to what is used for the higher lines—the article asserts that this change in methodology is not a factor in changing the value of the IPL since this approach also resulted in a $1.90 poverty line with the 2011 PPPs (Jolliffe and Prydz 2016). When deriving the IPL, the article already showed that it was robust to using the exact same poverty lines as Jolliffe and Prydz (2016). The World Bank Economic Review 519 7. Conclusion This paper analyzes the impact of the 2017 PPPs on three important issues related to global poverty monitoring. First, it analyzes the stability of the 2017 PPPs relative to the 2011 PPPs, the previous round of PPPs. The adoption of previous ICP rounds has resulted in large revisions to global poverty estimates, especially the 2005 round that added half a billion more people to the estimated number of extremely Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 poor people in the developing world (Deaton 2010; Chen and Ravallion 2010). Against this background, the Atkinson Commission on global poverty argued that future ICP rounds should not be adopted until 2030, the target date for the SDGs (World Bank 2017; see also Deaton 2001 and Klasen et al. 2016 for a similar argument made earlier). This paper shows that the ICP methodology has remained relatively stable between the 2011 and 2017 rounds. While changes at the country level could still be important, no evidence is found of substantial broad-based changes, which characterized earlier rounds and motivated the Atkinson recommendation. Second, the paper estimates revised global poverty lines with the 2017 PPPs. The IPL that was derived to measure extreme poverty is $2.15 per person per day in 2017 PPPs. The analysis employs the harmonized national poverty line technique (Jolliffe and Prydz 2016) to arrive at this line and shows that it is consistent with an equivalent poverty lines approach (Kakwani and Son 2016). As such, the IPL this paper derives keeps the global poverty rate largely at the levels that were observed when the SDGs were set. Therefore, the 2017 PPPs do not significantly shift the goalposts. This article estimates the line that is more typical of lower-middle-income countries (i.e., $3.20 in 2011 PPP) to be $3.65 in 2017 PPP, and the line common in upper-middle-income countries (i.e., $5.50 in 2011 PPP) to be $6.85 in 2017 PPP. The Bank’s societal poverty line, which was originally defined as max($1.90, $1 + 50 percent of median consumption) with the 2011 PPPs, is also updated to max($2.15, $1.15 + 50 percent of median consumption) with the 2017 PPPs. Third, this paper analyzes the impact of these global poverty lines and the 2017 PPPs on the global poverty counts. While the changes at the upper-middle-income line are larger, the 2017 PPPs have small implications for extreme poverty and poverty at the lower-middle-income line. Between 1991 and 2017, extreme poverty in the world falls from 36.05 percent to 9.27 percent with the revised 2011 PPPs, and from 37.46 percent to 9.07 percent with the 2017 PPPs. This is equivalent to a decrease in the estimated number of poor people in the world by 15 million in 2017 (or 0.2pp). This is a very small change compared to prior PPP rounds, which is consistent with the methodological stability between the 2011 and 2017 ICP rounds. Data availability The data underlying this article will be shared on reasonable request to the corresponding author. References Alkire, S., U. Kanagaratnam, and N. Suppa. 2020. “The Global Multidimensional Poverty Index (MPI).” OPHI MPI Methodological Note 49. Oxford, UK: Oxford Poverty and Human Development Initiative, University of Oxford. Allen, R.C. 2017. “Absolute Poverty: When Necessity Displaces Desire.” American Economic Review 107(12): 3690– 721. Atamanov, A., D. Jolliffe, C. Lakner, and E.B. Prydz. 2018. “Purchasing Power Parities Used in Global Poverty Mea- surement.” Global Poverty Monitoring Technical Note 5. The World Bank. Washington, DC, USA. Bai, Y., and W.A. Masters. 2020. “Retail Food Prices at Purchasing Power Parity Exchange Rates: A First Look at Aggregate ICP 2017 Data.” World Bank Data Blog. https://blogs.worldbank.org/opendata/retail- food- prices- purc hasing- power- parity- exchange- rates- first- look- aggregate- icp- 2017. Accessed August 27, 2024. Beegle, K., J.D. Weerdt, J. Friedman, and J. Gibson. 2012. “Methods of Household Consumption Measurement through Surveys: Experimental Results from Tanzania.” Journal of Development Economics 1(98): 3–18. 520 Jolliffe et al. Berry, F., B. Graf, M. Stanger, and M. Ylä-Jarkko. 2019. “Price Statistics Compilation in 196 Economies: The Relevance for Policy Analysis.” IMF Working Paper 163. International Monetary Fund. Washington, DC, USA. Chen, S., and M. Ravallion. 2008. “China Is Poorer than We Thought, but No Less Successful in the Fight against Poverty.” World Bank Policy Research Working Paper 4621. The World Bank. Washington, DC. Chen, S., and M. Ravallion. 2010. “The Developing World Is Poorer than We Thought, but No Less Successful in the Fight against Poverty.” Quarterly Journal of Economics 125(4): 1577–625. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Dalgaard, E., and H.S. Sørensen. 2002. “Consistency between PPP Benchmarks and National Price and Volume In- dices." Paper presented at the 27th General Conference of the International Association for Research on Income and Wealth. Stockholm, Sweden. Deaton, A. 2001. “Counting the World’s Poor: Problems and Possible Solutions.” The World Bank Research Observer 16(2): 125–47. Deaton, A. 2010. “Price Indexes, Inequality, and the Measurement of World Poverty.” American Economic Review 100(1): 5–34. Deaton, A., and B. Aten. 2017. “Trying to Understand the PPPs in ICP 2011: Why Are the Results so Different.” American Economic Journal: Macroeconomics 9(1): 243–64. Deaton, A., and A. Heston. 2010. “Understanding PPPs and PPP-Based National Accounts.” American Economic Journal: Macroeconomics 2(4): 1–35. Deaton, A., and P. Schreyer. 2022. “GDP, Wellbeing, and Health: Thoughts on the 2017 Round of the International Comparison Program.” Review of Income and Wealth 68(1): 1–15. Ferreira, F.H.G., S. Chen, A. Dabalen, Y. Dikhanov, N. Hamadeh, D. Jolliffe, A. Narayan, et al. 2016. “A Global Count of the Extreme Poor in 2012: Data Issues, Methodology and Initial Results.” Journal of Economic Inequality 14(2): 141–72. Ferreira, F.H.G., D. Jolliffe, and E.B. Prydz. 2015. “The International Poverty Line Has Just Been Raised to $1.90 a Day, but Global Poverty Is Basically Unchanged. How Is That Even Possible?” World Bank Blog: Let’s Talk Development. https://blogs.worldbank.org/developmenttalk/international- poverty- line- has- just- been- raised- 190- day- global- poverty- basically- unchanged- how- even. Accessed August 27, 2024. Hill, R. 2004. “Constructing Price Indexes across Space and Time: The Case of the European Union.” American Economic Review 94(5): 1379–410. Inklaar, R., and D.S.P. Rao. 2017. “Cross-Country Income Levels over Time: Did the Developing World Suddenly Become Much Richer?” American Economic Journal: Macroeconomics 9(1): 265–90. Inklaar, R., and P. Rao. 2020. “ICP PPP Time Series Implementation.” Washington, DC. https://thedocs.worldbank. org/en/doc/f32e966db6ec404699b3381b32e2e589-0050022021/original/2-01-RA-Item-01-ICP-PPP-Time-Serie s- Implementation- Rao- and- Inklaar.pdf. Accessed August 27, 2024. International Monetary Fund. 2009.System of National Accounts. Washington, D.C.: International Monetary Fund. Jolliffe, D., and C. Lakner. 2023. “Measuring Global Poverty in a Changing World.” In Handbook of Labor, Human Resources and Population Economics, edited by K.F. Zimmermann, 1–25. Cham: Springer International Publishing,. Jolliffe, D. r , D.G. Mahler r , C. Lakner r , A. Atamanov r , and S.K. Tetteh-Baah. 2022. Assessing the Impact of the 2017 PPPs on the International Poverty Line and Global Poverty. Policy Research Working Paper Series no. 9941. The World Bank. Washington, DC. Jolliffe, D., and E.B. Prydz. 2016. “Estimating International Poverty Lines from Comparable National Thresholds.” Journal of Economic Inequality 14(2): 185–98. Jolliffe, D., and E.B. Prydz. 2021. “Societal Poverty: A Relative and Relevant Measure.” World Bank Economic Review 35(1): 180–206. Jolliffe, D., and U. Serajuddin. 2018. “Noncomparable Poverty Comparisons.” Journal of Development Studies 54(3): 523–36. Kakwani, N., and H.H. Son. 2016. “Global Poverty Estimates Based on 2011 Purchasing Power Parity: Where Should the New Poverty Line Be Drawn?” Journal of Economic Inequality 14(2): 173–84. Klasen, S., T. Krivobokova, F. Greb, R. Lahoti, S. Hidayat, and P. Manuel. 2016. “International Income Poverty Mea- surement: Which Way Now?” Journal of Economic Inequality 14: 199–225. Lain, J.W., M. Schoch, and T. Vishwanath. 2022. "Estimating a Poverty Trend for Nigeria between 2009 and 2019." Policy Research Working Paper 9974. World Bank. Washington, DC. USA. The World Bank Economic Review 521 Lakner, C., D.G. Mahler, M.C. Nguyen, J.P. Azevedo, S. Chen, and D. Jolliffe. 2018. “Consumer Price Indices Used in Global Poverty Measurement.” Global Poverty Monitoring Technical Note 8. World Bank. Washington, DC, USA. Lanjouw, J.O., and P. Lanjouw. 2001. “How to Compare Apples and Oranges: Poverty Measurement Based on Dif- ferent Definitions of Consumption.” Review of Income and Wealth 47(1): 25–42. Locker, H.K., and H.D. Faerber. 1984. “Space and Time Comparisons of Purchasing Power Parities and Real Values.” Review of Income and Wealth 30(1): 53–83. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Mahler, D.G., R.A. Castaneda Aguilar, and D. Newhouse. 2022. “Nowcasting Global Poverty.” World Bank Economic Review 36(4): 835–56. McCarthy, P. 2013. “Extrapolating PPPs and Comparing ICP Benchmark Results.” In Measuring the Real Size of the World Economy, edited by the International Comparison Program. 473–505. World Bank. Washington, DC, USA. Prydz, E.B., D. Jolliffe, C. Lakner, D.G. Mahler, and P. Sangraula. 2019. “National Accounts Data Used in Global Poverty Measurement.” Global Poverty Monitoring Technical Note 8. World Bank. Washington, DC. USA. Ravalllion, M. 1998. “Poverty Lines in Theory and Practice.” LSMS Working Paper 133. World Bank. Washington, DC, USA. Ravallion, M. 2014. “An Exploration of the International Comparison Program’s New Global Economic Landscape.” National Bureau of Economic Research Working Paper Series, Working Paper no. 20338. https://doi.org/10.3386/ w20338. Ravallion, M. 2020. “Book Review of Measuring Poverty around the World.” Journal of Economic Inequality 18(1): 131–36. Ravallion, M., S. Chen, and P. Sangraula. 2009. “Dollar a Day Revisited.” World Bank Economic Review 23(2): 163–84. Roy, S., and R. van der Weide. 2022."Poverty in India has Declined over the Last Decade but not as Much as Previously Thought." Policy Research Working Paper 9994. World Bank. Washington, DC, USA. Smeeding, T.M. 2016. “Poverty Measurement.” In The Oxford Handbook of the Social Science of Poverty, edited by David Brady, and Linda M. Burton. Oxford: Oxford University Press, chapter 2. Tetteh-Baah, S.K., and C. Lakner. 2022. “A New, Nuanced Narrative of Poverty in Sub-Saharan Africa with the 2017 PPPs.” Conference paper, International Association for Research on Income and Wealth Conference on Measuring Income, Wealth and Wellbeing in Africa. Arusha: Tanzania. November 11, 2022. World Bank. 2008. Global Purchasing Power Parities and Real Expenditures: 2005 International Comparison Pro- gram. Washington, DC: World Bank. World Bank. 2015. Purchasing Power Parities and the Real Size of World Economies: A Comprehensive Report of the 2011 International Comparison Program. Washington, DC: World Bank. World Bank. 2017. Monitoring Global Poverty: Report of the Commission on Global Poverty. Washington, DC: World Bank. World Bank. 2018. Poverty and Shared Prosperity Report 2018: Piecing Together the Poverty Puzzle. Washington, DC: World Bank. World Bank. 2020a. Poverty and Shared Prosperity Report 2020: Reversing Reversals of Fortune. Washington, DC: World Bank. World Bank. 2020b. Purchasing Power Parities and the Size of World Economies: Results from the 2017 International Comparison Program. Washington, DC: World Bank. World Bank. 2023. “Poverty and Inequality Platform Methodology Handbook.” Edition 2023-09. https://datanalyti cs.worldbank.org/PIP-Methodology/. Accessed September 30, 2023. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Supplementary Online Appendix Poverty and Prices: Assessing the Impact of the 2017 PPPs on the International Poverty Line and Global Poverty Dean Jolliffe r , Daniel Gerszon Mahler r , Christoph Lakner r , Aziz Atamanov r , and Samuel Kofi Tetteh-Baah S1: Revised 2011 PPPs In May 2020, the ICP released new PPP estimates for the 2017 reference year, and also revised PPP estimates originally published for the 2011 round. The 2011 PPPs were revised primarily to account for revisions to national accounts expenditures. The ICP expenditure classification for the original 2011 round was based on the 1993 System of National Accounts. For the 2017 round, the ICP adopted the new 2008 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 System of National Accounts, which was also used to revise the original 2011 PPPs. The underlying price data were largely unchanged in the revised 2011 round. For nonbenchmark economies—countries that do not collect prices to compute PPPs as part of the ICP exercise—new input data were used to impute PPPs. For details on the revisions, see World Bank (2020) and Tetteh-Baah et al. (2020). Using the harmonized poverty line approach (Jolliffe and Prydz 2016), the revised 2011 PPPs do not change the international Table S1.1. Updating the $1.90 IPL with Revised 2011 PPPs. Poverty line, original Poverty line, update Poverty line, update Poverty line, update Country 2011 PPP (1) PPP only (2) CPI only (3) both PPP and CPI (4) Ethiopia 2.03 1.98 2.02 1.97 Ghana 3.07 3.11 3.01 3.04 Gambia, The 1.82 1.81 1.82 1.81 Guinea-Bissau 2.16 2.08 2.16 2.08 Mali 2.15 2.13 2.15 2.13 Mozambique 1.26 1.24 1.33 1.30 Malawi 1.34 1.33 1.29 1.28 Niger 1.49 1.48 1.49 1.48 Nepal 1.47 1.47 1.38 1.38 Rwanda 1.50 1.47 1.51 1.49 Sierra Leone 2.73 2.64 2.10 2.04 Chad 1.28 1.29 1.36 1.36 Tajikistan 3.18 3.35 3.18 3.35 Tanzania 0.88 0.88 0.88 0.88 Uganda 1.77 1.77 1.71 1.72 Mean 1.88 1.87 1.83 1.82 Source: Authors’ calculations and compilation. Note: Column (1) reproduces the national poverty lines from Ferreira et al. (2016). When only PPPs are updated, the IPL still rounds to $1.87 (see column (2)). Since the derivation by Ferreira et al. (2016), some of the CPIs used to convert the national poverty lines to 2011 prices have been updated. When CPIs are updated, or when both CPIs and PPPs are updated, the IPL rounds to $1.80 (columns (3) and (4)). This is mainly driven by CPI revisions in Ghana, Malawi, Sierra Leone, and Tajikistan. See Appendix S5.1 for the derivations. Table S1.2. Global and Regional Changes in Poverty in 2017 at $1.90/Day Poverty rate, Poverty rate, Change in Change in Change in millions Region % (original) % (revised) poverty, pp poverty, % of poor World 9.04 9.27 0.24 2.65 17.98 East Asia & Pacific 1.61 1.41 − 0.20 − 12.27 − 4.08 Europe & Central Asia 1.36 1.30 − 0.06 − 4.74 − 0.32 Latin America & Caribbean 3.73 3.77 0.03 0.94 0.22 Middle East & North Africa 6.43 6.34 − 0.09 − 1.45 − 0.36 Rest of the World 0.68 0.68 0.00 0.00 0.00 South Asia 9.02 9.65 0.63 7.00 11.33 Sub-Saharan Africa 40.12 41.18 1.06 2.65 11.18 Source: Authors’ calculations. Note: This table compares extreme poverty between the original and revised 2011 PPPs at the global and regional levels. Extreme poverty is measured as the share of the population living below the international poverty line, which is $1.90/day. Table S1.3. Countries with the Largest Changes in Millions of Poor in 2017 at $1.90/Day Poverty rate, Poverty rate, Change in Change in Change in millions Country % (original) % (revised) poverty, pp poverty, % of poor India 9.71 10.55 0.85 8.71 11.32 Nigeria 38.65 41.36 2.71 7.02 5.18 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Indonesia 5.71 4.46 -1.25 -21.92 -3.31 Ethiopia 23.14 24.66 1.52 6.56 1.62 Angola 41.18 45.07 3.89 9.44 1.16 Congo, Dem. Rep. 72.00 72.65 0.65 0.91 0.53 Cameroon 21.73 23.66 1.93 8.87 0.47 Egypt, Arab Rep. 2.58 2.97 0.39 15.03 0.37 Côte d’Ivoire 24.02 25.49 1.47 6.14 0.36 Myanmar 2.01 1.36 − 0.65 − 32.21 − 0.35 Source: Authors’ calculations. Note: This table compares extreme poverty in 2017 estimated with the original and revised 2011 PPPs at the country level. The global poverty line used is $1.90/day. The top 10 countries with the largest absolute changes in millions of poor are shown here. Countries are ranked in descending order of absolute changes in millions of poor. Table S1.4. Global and Regional Changes in Poverty in 2017 at $3.20/Day Poverty rate, Poverty rate, Change in Change in Change in millions Region % (original) % (revised) poverty, pp poverty, % of poor World 23.96 24.25 0.29 1.20 21.52 East Asia & Pacific 8.92 8.43 − 0.49 − 5.53 − 10.19 Europe & Central Asia 4.87 4.63 − 0.23 − 4.78 − 1.14 Latin America & Caribbean 9.26 9.31 0.05 0.56 0.33 Middle East & North Africa 17.99 18.30 0.32 1.76 1.21 Rest of the World 0.89 0.89 0.00 0.00 0.00 South Asia 42.26 43.43 1.16 2.75 20.85 Sub-Saharan Africa 66.34 67.34 1.00 1.50 10.47 Source: Authors’ calculations. Note: This table compares poverty in 2017 estimated with the original and revised 2011 PPPs at the global and regional levels. The poverty line used is $3.20/day. Table S1.5. Countries with the Largest Change in Millions of Poor in 2017 at $3.20/Day Poverty rate, Poverty rate, Change in Change in Change in millions Country % (original) % (revised) poverty, pp poverty, % of poor India 43.65 45.17 1.52 3.48 20.34 Indonesia 27.25 24.64 − 2.62 − 9.60 − 6.93 Nigeria 69.34 71.74 2.41 3.47 4.59 Egypt, Arab Rep. 22.22 24.79 2.57 11.56 2.48 Ethiopia 59.11 61.33 2.22 3.76 2.36 Myanmar 19.35 14.95 − 4.40 − 22.72 − 2.35 Angola 63.88 68.03 4.15 6.50 1.24 Iraq 15.12 12.19 − 2.94 − 19.41 − 1.10 Cameroon 42.15 44.58 2.43 5.76 0.60 Bangladesh 47.85 47.49 − 0.36 − 0.75 − 0.58 Source: Authors’ calculations. Note: This table compares extreme poverty in 2017 estimated with the original and revised 2011 PPPs at the country level. The poverty line used is $3.20/day. The top 10 countries with the largest absolute changes in millions of poor are shown here. Countries are ranked in descending order of absolute changes in millions of poor. Table S1.6. Global and Regional Changes in Poverty in 2017 at $5.50/Day Poverty rate, Poverty rate, Change in Change in Change in millions Region % (original) % (revised) poverty, pp poverty, % of poor World 43.47 43.53 0.07 0.15 4.93 East Asia & Pacific 28.21 27.62 − 0.59 − 2.10 − 12.27 − 0.27 − 2.07 − 1.31 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Europe & Central Asia 12.89 12.63 Latin America & Caribbean 22.87 22.97 0.09 0.41 0.60 Middle East & North Africa 42.85 43.08 0.23 0.54 0.88 Rest of the World 1.29 1.29 0.00 0.27 0.04 South Asia 77.80 78.48 0.68 0.87 12.20 Sub-Saharan Africa 85.77 86.23 0.46 0.53 4.80 Source: Authors’ calculations. Note: This table compares poverty in 2017 estimated with the original and revised 2011 PPPs at the global and regional levels. The poverty line used is $5.50/day. Table S1.7. Countries with the Largest Change in Millions of Poor in 2017 at $5.50/Day Poverty rate, Poverty rate, Change in Change in Change in millions Country % (original) % (revised) poverty, pp poverty, % of poor India 78.64 79.52 0.87 1.11 11.69 Indonesia 58.66 55.79 − 2.87 − 4.90 − 7.60 Myanmar 60.81 54.30 − 6.52 − 10.71 − 3.48 Nigeria 90.61 91.80 1.19 1.31 2.27 Egypt, Arab Rep. 66.76 69.11 2.34 3.51 2.26 Iraq 53.11 47.59 − 5.52 − 10.40 − 2.07 Russian Federation 2.53 3.82 1.29 51.11 1.87 Iran, Islamic Rep. 11.30 13.24 1.94 17.16 1.56 Argentina 9.07 10.96 1.89 20.83 0.83 Angola 83.56 86.29 2.73 3.27 0.81 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table compares poverty in 2017 estimated with the original and revised 2011 PPPs at the country level. The poverty lines used is $5.50/day. The top 10 countries with the largest absolute changes in millions of poor are shown here. Countries are ranked in descending order of absolute changes in millions of poor. poverty line of $1.90 or the $3.20 line (the $5.50 line slightly increases by $0.15) (Atamanov et al. 2020). Table S1.1 updates the IPL with the revised 2011 PPPs using the approach by Ravallion, Chen, and Sangraula (2009). Table S1.1 shows how revisions to the historic CPIs affect the IPL; when the analysis takes the national poverty lines denominated in the original 2011 PPPs in Ferreira et al. (2016) as given and consider only the revisions to the 2011 PPPs, the IPL remains at $1.90. Compared to the original 2011 PPPs, the revised 2011 PPPs increase extreme poverty in the world in 2017 by 0.24pp, representing 18.0 million more poor people (see table S1.2). This result is mainly driven by the two poorest regions—Sub-Saharan Africa (11 million more poor people) and South Asia (11 million more poor people). These regional numbers are in turn largely driven by Nigeria (5 million poorer) and India (11 million poorer). Table S1.3 shows the top 10 countries with the largest absolute changes in extreme poverty in 2017. With a poverty line of $3.20 a day, global poverty increases by 0.29pp, or 21.5 million. Poverty falls in East Asia & the Pacific (10 million), which is offset by large increases in South Asia (21 million) and Sub-Saharan Africa (10 million). These changes are largely driven by India, Indonesia, and Nigeria. See tables S1.4 and S1.5 for the full set of results. With a poverty line of $5.50 a day, global poverty slightly increases by 0.07pp, or 5 million. Poverty again falls in East Asia & the Pacific (12 million), which is offset by a similar increase in poverty in South Asia (12 million). These changes are largely driven by Indonesia and India, respectively. See tables S1.6 and S1.7 for the full set of results. S2: Additional Tables and Figures Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Figure S2.1. Share of Global Poor by Income Group in 2017. Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: This chart shows the share of global poor in 2017 in lower-income countries (LIC), lower-middle-income countries (LMIC), upper-middle-income countries (UMIC), and high-income countries (HIC) based on the LIC, LMIC, and UMIC global poverty lines. Figure S2.2. Delta Ratio by Income Group. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: This chart shows the mean delta ratio for each income group based on the World Bank’s income classification for 2017 (see more details on the delta ratio in section 3 of the paper). The delta ratios are either weighted by countries’ population sizes in 2017 or weighted equally. The weights are population sizes of countries in 2017. The relative changes in global poverty lines between 2011 and 2017 PPPs are marked in the chart. Table S2.1. Country-Year Observations of National Poverty Rates Source All Matched into PovcalNet Poverty and Equity database 1,315 1,315 OECD 123 66 Total 1,438 1,381 Source: Authors’ compilation from the World Bank’s Poverty and Equity database and OECD. Note: 1. Most of the national poverty rates were obtained from the World Bank’s Poverty and Equity database. These data can be found in the following series from https://databank.worldbank.org/source/poverty- and- equity: Poverty headcount ratio at national poverty lines (% of population), including noncomparable values (SI.POV.NAHC.NC). The series was downloaded on July 11, 2021. 2. The remaining data on national poverty rates were obtained from the Organization for Economic Co-operation and Development (OECD). The researchers down- loaded the series PVT6A: Poverty rate after taxes and transfers, Poverty line 60% (i.e., share of population living on below 60% of median disposable income) on December 19, 2020 from the OECD website: https://data-explorer.oecd.org/. The OECD data were used only for countries with missing data in the Poverty and Equity database, or where the OECD time series is longer. Figure S2.3. Comparison of Residuals in Price Level Indices (PLIs) across Regions. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from PovcalNet, June 2021 and 2017 ICP Report. Note: This graph shows the mean log residuals and absolute mean log residuals in price level indices (PLIs) for the 2017 PPP for household consumption expenditure across regions. The residual is defined as the published PLI (log) minus predicted PLI (log). Error bars represent 95 percent confidence intervals. Figure S2.4. Changes in extreme poverty in Sub-Saharan Africa, 2017. Source: Authors’ calculations from PovcalNet, June 2021. Note: This chart shows estimates of extreme poverty in 2017 for countries in Sub-Saharan Africa. For countries without a survey in 2017, the estimates are based on extrapolations or interpolations from recent surveys. Extreme poverty is measured using the IPL of $1.9 (revised 2011 PPP) or $2.15 (2017 PPP). Marker size is proportional to absolute change in millions of poor. The dotted line is a 45-degree line. Table S2.2. Exception 2011 PPPs for Global Poverty Monitoring Country Original (official) Original (imputed) Revised (official) Revised (imputed) 1. Egypt. Arab Rep. 1.80 2.78 1.71 2.87 2. Iraq 573.42 1003.80 477.56 939.22 3. Jordan 0.32 0.45 0.33 0.44 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 4. Lao PDR 2914.85 3325.20 3124.08 3248.44 5. Myanmar 275.83 320.60 278.39 296.14 6. Yemen, Rep. 82.09 111.30 76.77 109.53 Source: Authors’ compilations from Atamanov et al. (2020), table A.1. Note: See main text, Atamanov et al. (2018) and Atamanov et al. (2020) for details. Whenever the (revised) 2011 PPPs are used in the main text, the PPPs in the last column are used. Table S2.3. Exception 2017 PPPs used in this paper Revised 2017 PPP 2017 PPP 2017 PPP Country 2011 PPP (1) (official) (2) (imputed) (3) (exception) (4) Note (5) 1. Belize 1.17 1.48 1.16 1.31 Average 2. Egypt, Arab Rep. 2.87 3.41 6.88 4.84 Average 3. Guinea 2599.89 3213.98 3696.76 3446.93 Average 4. Iraq 939.22 555.39 619.35 586.50 Average 5. Nigeria 83.58 112.10 136.44 123.67 Average 6. São Tomé and Príncipe 10.49 10.76 12.14 11.43 Average 7. Sudan 1.46 5.38 7.77 6.46 Average 8. Trinidad and Tobago 4.52 4.21 5.16 4.66 Average 9. Kiribati 1.07 0.98 Extrapolated 10. Nauru 1.21 1.38 Extrapolated 11. Syrian Arab Republic 22.26 151.78 Extrapolated 12. Tuvalu 1.17 1.25 Extrapolated 13. Venezuela, RB 2.94 315.91 Extrapolated 14. Yemen, Rep. 109.53 255.68 Extrapolated Source: Authors’ calculations and compilation from 2011 ICP Report and 2017 ICP Report. Note: The PPPs in columns (1) and (4) are used in this paper. The geometric averages of official and imputed 2017 PPPs (column 4) are used for the exception countries. For countries without official 2017 PPPs, extrapolated PPPs are used: PPPs are extrapolated from the revised 2011 PPP currently used for global poverty monitoring, together with domestic and U.S. inflation between 2011 and 2017. See section 3of the main paper for details. Table S2.4. Rural/Urban PPPs Used in Global Poverty Monitoring Country, variable Original 2011 PPP Revised 2011 PPP 2017 PPP A) China Ratio of urban to rural poverty line (ω ) 1.29 1.29 1.24 ICP urban share of outlets (λ) 0.76 0.76 0.79 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Rural PPP 3.038 3.039 3.495 Urban PPP 3.904 3.905 4.318 National PPP 3.696 3.698 4.147 B) India Ratio of urban to rural poverty line (ω ) 1.22 1.22 1.22 ICP urban share of outlets (λ ) 0.74 0.74 0.64 Rural PPP 12.908 13.173 17.092 Urban PPP 15.695 16.018 20.787 National PPP 14.975 15.283 19.469 C) Indonesia Ratio of urban to rural poverty line (ω ) 1.18 1.18 1.07 ICP urban share of outlets (λ ) 0.61 0.61 0.98 Rural PPP 3678.414 3498.876 4746.852 Urban PPP 4352.751 4140.299 5098.196 National PPP 4091.939 3892.218 5089.686 Source: Adapted from Atamanov et al. (2020), table A.2. Note: See main text for details. The urban and rural PPPs are computed as follows: Rural PPP = National PPP ω ×λ + ( 1 − λ ) Urban PPP = ω∗Rural PPP. See online appendix of Ferreira et al. (2016) for details. Table S2.5. Cumulative Mean and Median of Poverty Lines of the Poorest Countries No. Country Year Harmonized national poverty lines, 2017 PPP Country-specific Cumulative mean Cumulative median 1 Burundi 2013 2.14 2.14 2.14 2 Congo, Dem. Rep. 2012 1.90 2.02 2.02 3 Malawi 2016 1.68 1.91 1.90 4 Central African Republic 2008 2.16 1.97 2.02 5 Niger 2014 1.87 1.95 1.90 6 Mozambique 2014 1.49 1.88 1.89 7 Togo 2015 2.17 1.92 1.90 8 Liberia 2016 3.13 2.07 2.02 9 Madagascar 2012 1.64 2.02 1.90 10 Sierra Leone 2018 3.24 2.14 2.02 11 Chad 2011 2.66 2.19 2.14 12 Guinea-Bissau 2010 2.28 2.20 2.15 13 Ethiopia 2015 2.04 2.19 2.14 14 Burkina Faso 2014 2.16 2.18 2.15 15 Rwanda 2016 1.73 2.15 2.14 16 Guinea 2012 3.40 2.23 2.15 17 Mali 2009 1.95 2.22 2.14 18 Gambia, The 2015 3.87 2.31 2.15 19 Uganda 2016 1.49 2.26 2.14 20 Kiribati 2006 2.73 2.29 2.15 21 Nepal 2010 2.93 2.32 2.16 22 Tanzania 2017 1.62 2.29 2.15 23 Solomon Islands 2012 1.61 2.26 2.14 Table S2.5. Continued No. Country Year Harmonized national poverty lines, 2017 PPP Country-specific Cumulative mean Cumulative median 24 Senegal 2011 2.36 2.26 2.15 25 Lesotho 2017 3.22 2.30 2.16 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 26 Comoros 2004 4.18 2.37 2.16 27 Tajikistan 2015 4.01 2.43 2.16 28 Haiti 2012 3.67 2.48 2.17 29 Benin 2015 1.77 2.45 2.16 30 Zimbabwe 2017 2.01 2.44 2.16 31 Uzbekistan 2003 0.91 2.39 2.16 32 Papua New Guinea 2009 2.16 2.38 2.16 33 Vanuatu 2010 2.04 2.37 2.16 34 Timor-Leste 2014 3.59 2.41 2.16 35 Tuvalu 2010 4.16 2.46 2.16 36 Sudan 2009 3.58 2.49 2.16 37 Cameroon 2014 2.91 2.50 2.16 38 Zambia 2015 1.78 2.48 2.16 39 Micronesia, Federated States of 2013 3.66 2.51 2.16 40 Kenya 2015 2.41 2.51 2.17 Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: Countries are shown in ascending order of GDP per capita (2017 PPP), starting with the poorest country. The full sample consists of 157 countries, excluding South Sudan, the Syrian Arab Republic, the Republic of Yemen (whose GDP per capita data are missing). Only the first 40 countries are listed here due to space constraints. Table S2.6. Updating Global Poverty Lines Using Harmonized Poverty Lines: Different Options Income (A) Harmonized lines (Jolliffe and (C) Own harmonized lines (pooled, classification Prydz 2016) (B) Own harmonized lines (pooled) since 2007) Median Mean N Median Mean N Median Mean N Low-income 2.15 2.53 33 2.16 2.43 177 2.14 2.35 59 Lower-middle 3.68 4.49 32 3.80 4.20 394 3.59 4.04 214 Upper-middle 6.32 6.18 32 6.76 7.06 343 6.76 7.04 257 High-income 23.65 23.34 29 24.15 23.19 460 23.97 23.08 359 Observations 126 1,374 889 Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: Panel (A) uses the same harmonized poverty lines originally derived by Jolliffe and Prydz (2016). Panel (B) and (C) pool together the harmonized poverty lines that have been derived for this paper. The total sample of harmonized poverty lines is 1,381, but 7 lines from the 1980s drop out because they could not be matched into the World Bank’s historical income classification, which begins from 1988. Panels (B) and (C) provide weighted medians and means for country-year observations based on the income classification in the year in which the surveys were conducted. The analysis weights the observations to give greater importance to those close to 2017 and to give equal weight to each country: In the first step, the analysis assigns a survey conducted in 2017 a weight of 1, a survey conducted in 2016 or 2018 a weight of 1/2, a survey conducted in 2015 or 2019 a weight of 1/3, and so on. Secondly, the weights are rescaled such that each country has a total weight of 1. For example, if a country has three harmonized poverty lines for 2016, 2017, and 2018, the corresponding weights will be 0.25, 0.5, and 0.25. The weights are based on the relevant data set (N = 1,374) or subsample in question (N = 889). The sample in panel (A) has one harmonized national poverty for each country, so each country has a weight of 1 (there are 126 countries in panel (A), 157 in panel (B), and 150 in panel (C)). The oldest lines (n = 2) in panel (A) are from 2001 (i.e., 10 years before the 2011 ICP reference year). Panel (C) includes only lines since 2007 (i.e., 10 years before the 2017 ICP). When one line is selected for each country from the most recent lines (since 2007), the proposed international poverty line would be $2.14 (see table S2.7). Table S2.7. Updating Global Poverty Lines Using Harmonized Poverty Lines: Full Sample vs. Subsample Income classification (A) Full sample (B) Subsample (since 2007) Median Mean N Median Mean N Low-income 2.15 2.42 28 2.14 2.31 25 Lower-middle 3.63 3.95 54 3.59 3.93 51 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Upper-middle 6.85 7.05 37 6.95 7.08 36 High-income 24.36 23.36 38 24.36 23.36 38 Observations 157 150 Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: Panel (A) replicates Panel (C) from table 1 in the main text. The line that is closest to 2017 is selected, one for each country. If harmonized national poverty lines are available for 2016 and 2018 but not 2017, 2018 is selected. Panel (B) excludes 7 lines derived from surveys conducted more than 10 years before the 2017 ICP reference year: AZE2001, COM2004, JAM2004, KIR2006, SYR2003, UZB2003, VEN2006. Table S2.8. Equivalent International Poverty Line, 2017 PPP: Different Options Headcount, % Share of Weighted (Revised 2011 PPP) Equivalent IPL global poor equivalent Region Year (1) (2) (2017 PPP) (3) (4) IPL (5) World 2010 16.02 2.119 1.00 2.16 East Asia & Pacific 2010 10.79 1.973 0.19 0.38 Europe & Central Asia 2010 2.37 1.801 0.01 0.02 Latin America & Caribbean 2010 6.03 2.058 0.03 0.07 Middle East & North Africa 2010 2.04 2.215 0.01 0.01 Rest of the world 2010 0.50 2.076 0.00 0.01 South Asia 2010 25.95 2.146 0.38 0.82 Sub-Saharan Africa 2010 47.47 2.274 0.37 0.85 World 2012 12.89 2.128 1.00 2.19 East Asia & Pacific 2012 6.88 1.979 0.15 0.30 Europe & Central Asia 2012 1.88 1.801 0.01 0.03 Latin America & Caribbean 2012 4.63 2.072 0.03 0.06 Middle East & North Africa 2012 2.23 2.185 0.01 0.02 Rest of the world 2012 0.57 2.114 0.01 0.01 South Asia 2012 19.15 2.147 0.35 0.76 Sub-Saharan Africa 2012 43.90 2.284 0.44 1.01 World 2015 10.14 2.161 1.00 2.22 East Asia & Pacific 2015 2.06 2.012 0.06 0.11 Europe & Central Asia 2015 1.51 1.801 0.01 0.02 Latin America & Caribbean 2015 3.69 2.067 0.03 0.06 Middle East & North Africa 2015 4.28 2.143 0.02 0.05 Rest of the world 2015 0.73 2.132 0.01 0.02 South Asia 2015 13.18 2.148 0.31 0.67 Sub-Saharan Africa 2015 41.95 2.294 0.56 1.29 World 2017 9.27 2.173 1.00 2.23 East Asia & Pacific 2017 1.41 2.023 0.04 0.08 Europe & Central Asia 2017 1.30 1.801 0.01 0.02 Latin America & Caribbean 2017 3.77 2.055 0.03 0.07 Middle East & North Africa 2017 6.34 2.147 0.03 0.07 Rest of the world 2017 0.68 2.143 0.01 0.02 South Asia 2017 9.65 2.147 0.25 0.53 Sub-Saharan Africa 2017 41.18 2.297 0.62 1.43 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table shows equivalent poverty lines in column (3) that keep the global and regional poverty headcount ratios in column (2) constant in a pre-specified reference year (year given in column (1)). The results for the world in column (3) are presented in the main text in table 2. Column (5) provides an alternative approach, where the equivalent international poverty line for the world is calculated as the weighted average of the regional equivalent poverty lines (in column (3)), with the weights being the regions’ respective shares of total millions of poor in column (4). The weights are based on the revised 2011 PPPs (with a $1.90 poverty line). Table S2.9. Updating the $1.90 IPL with 2017 PPPs Update PPP & CPI Update PPP & CPI Take $1.90 as Poverty line original (update CPI in II (do not update given, update PPP Country 2011 PPP (1) $1.90) (2) CPI in $1.90) (3) & CPI (4) Ethiopia 2.03 2.37 2.38 2.23 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Ghana 3.07 2.28 2.33 1.44 Gambia, The 1.82 1.88 1.88 1.97 Guinea-Bissau 2.16 2.45 2.45 2.15 Mali 2.15 2.49 2.49 2.20 Mozambique 1.26 1.44 1.37 2.06 Malawi 1.34 1.55 1.61 2.28 Niger 1.49 1.47 1.47 1.88 Nepal 1.47 1.83 1.95 2.52 Rwanda 1.5 1.81 1.79 2.27 Sierra Leone 2.73 2.88 3.73 2.60 Chad 1.28 1.60 1.51 2.24 Tajikistan 3.18 3.27 3.27 1.95 Tanzania 0.88 1.06 1.06 2.29 Uganda 1.77 1.90 1.96 2.11 Mean 1.88 2.02 2.08 2.15 Source: Authors’ calculations from PovcalNet, June 2021 and compilations from Ferreira et al. (2016). Note: Column (1) reproduces national poverty lines from Ferreira et al. (2016). Column (2) updates both PPPs and CPIs to the 2017 ICP reference year, while incorporating the revisions to the CPI data used in deriving the $1.90 line. Column (3) updates both PPPs and CPIs to the 2017 ICP reference year, without incorporating the revisions to CPI data used in deriving the $1.90 line. Column (4) takes the $1.90 line as given for all countries and updates the $1.90 line with both PPPs and updated CPIs to the 2017 reference year. See section S5.2 of the supplementary online appendix for the derivations. If the analysis had updated the IPL with U.S. inflation between 2011 and 2017 (i.e., approximately 9%), the new IPL would be $2.07. Table S2.10. Global and Regional Changes in Poverty in 2017 at Lower-Middle-Income Poverty Line Poverty rate, % Poverty rate, % Change in Change in Change in Region (2011 PPP) (2017 PPP) poverty, pp poverty, % millions of poor World 24.25 24.82 0.57 2.35 42.79 East Asia & Pacific 8.43 10.52 2.09 24.85 43.30 Europe & Central 4.63 6.70 2.06 44.51 10.13 Asia Latin America & 9.31 10.14 0.83 8.87 5.21 Caribbean Middle East & 18.30 16.32 -1.99 -10.85 -7.57 North Africa Rest of the world 0.89 0.91 0.02 2.31 0.23 South Asia 43.43 44.29 0.86 1.98 15.43 Sub-Saharan Africa 67.34 65.06 -2.28 -3.39 -23.94 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table compares poverty in 2017 estimated with the revised 2011 and 2017 PPPs at the global and regional levels. The poverty lines used are $3.20/day (2011 PPP) and $3.65/day (2017 PPP). Table S2.11. Countries with the Largest Changes in Millions of Poor in 2017 at Lower-Middle-Income Poverty Line Poverty rate, % Poverty rate, % Change in Change in Change in Country (2011 PPP) (2017 PPP) poverty, pp poverty, % millions of poor China 3.80 5.72 1.92 50.41 26.56 Nigeria 71.74 64.67 -7.08 -9.86 -13.50 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Indonesia 24.64 29.59 4.95 20.10 13.11 Pakistan 33.95 38.86 4.91 14.47 10.21 Uzbekistan 43.66 71.46 27.80 63.68 9.00 India 45.17 45.81 0.64 1.41 8.53 Angola 68.03 47.81 -20.23 -29.73 -6.03 Egypt, Arab Rep. 24.79 18.58 -6.21 -25.05 -5.99 Ghana 28.72 48.13 19.41 67.56 5.65 Ethiopia 61.33 56.50 -4.83 -7.88 -5.14 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table compares poverty in 2017 estimated with the revised 2011 and 2017 PPPs at the country level. The poverty lines used are $3.20/day (2011 PPP) and $3.65/day (2017 PPP). The top 10 countries with the largest absolute changes in millions of poor are shown here. Countries are ranked in descending order of absolute changes in millions of poor. Table S2.12. Global and Regional Changes in Poverty in 2017 at Upper-Middle-Income Poverty Line Poverty rate, % Poverty rate, % Change in Change in Change in Region (2011 PPP) (2017 PPP) poverty, pp poverty, % millions of poor World 43.53 47.81 4.28 9.83 321.22 East Asia & Pacific 27.62 36.21 8.60 31.13 177.79 Europe & Central 12.63 16.43 3.81 30.15 18.70 Asia Latin America & 22.97 27.59 4.63 20.15 29.16 Caribbean Middle East & 43.08 44.09 1.00 2.33 3.83 North Africa Rest of the World 1.29 1.49 0.20 15.56 2.20 South Asia 78.48 82.66 4.18 5.33 74.96 Sub-Saharan Africa 86.23 87.61 1.39 1.61 14.58 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table compares poverty in 2017 estimated with the revised 2011 and 2017 PPPs at the global and regional levels. The poverty lines used are $5.50/day (2011 PPP) and $6.85/day (2017 PPP). Table S2.13. Countries with the Largest Changes In Millions of Poor in 2017 at Upper-Middle-Income Poverty Line Poverty rate, % Poverty rate, % Change in Change in Change in Country (2011 PPP) (2017 PPP) poverty, pp poverty, % millions of poor China 19.88 29.18 9.30 46.77 128.91 India 79.52 83.37 3.85 4.85 51.59 Indonesia 55.79 65.17 9.38 16.81 24.82 Pakistan 74.52 82.05 7.53 10.10 15.65 Brazil 20.23 25.93 5.70 28.15 11.84 Iraq 47.59 21.04 − 26.55 − 55.79 − 9.97 Myanmar 54.3 68.2 13.91 25.61 7.42 Ghana 54.34 77.94 23.60 43.44 6.87 Philippines 60.02 66.25 6.23 10.38 6.55 Iran, Islamic Rep. 13.24 20.94 7.70 58.13 6.21 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table compares poverty in 2017 estimated with the revised 2011 and 2017 PPPs at the country level. The global poverty lines used are $5.50/day (2011 PPP) and $6.85/day (2017 PPP). The top 10 countries with the largest absolute changes in millions of poor are shown here. Countries are ranked in descending order of absolute changes in millions of poor. Table S2.14. Global and Regional Changes in Societal Poverty, 2017 Poverty rate, % Poverty rate, % Change in Change in Change in Region (2011 PPP) (2017 PPP) poverty, pp poverty, % millions of poor World 27.31 27.55 0.23 0.86 17.59 East Asia & Pacific 22.58 23.55 0.97 4.29 20.04 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Europe & Central Asia 17.10 18.18 1.09 6.35 5.33 Latin America & Caribbean 26.36 26.67 0.31 1.18 1.96 Middle East & North Africa 26.01 25.47 − 0.54 − 2.09 − 2.07 Rest of the World 15.19 15.25 0.06 0.39 0.66 South Asia 31.32 31.84 0.52 1.65 9.29 Sub-Saharan Africa 48.28 46.60 − 1.68 − 3.48 − 17.62 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table compares societal poverty in 2017 estimated with the revised 2011 and 2017 PPPs at the global and regional levels. With the revised 2011 PPP, the societal poverty line (SPL) used is given as max($1.90, $1.00 + 0.5 ∗ median consumption/income). With the 2017 PPP, the societal poverty line (SPL) used is given as max($2.15, $1.15 + 0.5 ∗ median consumption/income). Table S2.15. Countries with the Largest Changes in Millions Living in Societal Poverty, 2017 Poverty rate, % Poverty rate, % Change in Change in Change in Country (2011 PPP) (2017 PPP) poverty, pp poverty, % millions of poor China 21.04 22.07 1.03 4.88 14.25 Nigeria 41.36 37.57 − 3.79 − 9.16 − 7.23 Congo, Dem. Rep. 72.65 64.42 − 8.24 − 11.34 − 6.7 India 31.43 31.87 0.45 1.42 5.98 Uzbekistan 32.18 44.93 12.75 39.62 4.13 Indonesia 29.36 30.86 1.49 5.09 3.95 Pakistan 30.88 32.69 1.81 5.87 3.77 Angola 51.81 43.58 − 8.24 − 15.9 − 2.46 Iraq 21.31 15.82 − 5.49 − 25.78 − 2.06 Egypt, Arab Rep. 30.53 28.54 − 1.99 − 6.52 − 1.92 Source: Authors’ calculations from PovcalNet, June 2021. Note: This table compares societal poverty in 2017 estimated with the revised 2011 and 2017 PPPs at the country level. With the revised 2011 PPP, the societal poverty line (SPL) used is given as max($1.90, $1.00 + 0.5 ∗ median consumption/income). With the 2017 PPP, the societal poverty line (SPL) used is given as max($2.15, $1.15 + 0.5 ∗ median consumption/income). The top 10 countries with the largest absolute changes in millions of poor are shown here. Countries are ranked in descending order of absolute changes in millions of poor. Table S2.16. Decomposition of Changes to Global Poverty Lines Jolliffe and Update with Update Prydz (original revised 2011 Update with Update national income Update country 2011 PPP) (1) PPP (2) 2017 PPP (3) poverty lines (4) Obs groups (5) Obs availability (6) Obs Low-income 1.91 1.85 2.15 2.17 33 2.16 25 2.15 28 Lower-middle 3.21 3.21 3.68 3.76 32 3.58 37 3.63 54 Upper-middle 5.47 5.65 6.32 7.15 32 6.95 30 6.85 37 High-income 21.70 21.70 23.65 27.39 29 24.31 34 24.36 38 Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: The table shows what is driving the changes to the global poverty lines, starting from the global lines replicating Jolliffe and Prydz (2016) in column (1), to the final global poverty lines derived in this paper in column (6). The intermediate columns all make one change to the derivation of the harmonized national poverty lines, allowing for a (path dependent) decomposition of what is driving the final estimates. Columns (2) and (3) update the harmonized poverty lines from Jolliffe and Prydz (2016) with the revised 2011 PPPs and 2017 PPPs, respectively. Column (4) updates with new harmonized poverty lines derived from new survey data for the exact same countries that Jolliffe and Prydz (2016) used. Column (5) updates the income classification for the countries that with new data have changed income groups. Column (6) adds the countries for which national poverty lines have been made available after Jolliffe and Prydz (2016) made their analysis and removes the countries for which the national poverty lines or welfare aggregates have been removed due to quality concerns. S3: More Details on the Stability of the ICP Methodology The System of National Accounts (SNA) is the most widely used framework for measuring economic activities across countries. The conceptual framework of the ICP methodology is centered around the SNA’s definition of GDP from the expenditure side; the sum of final expenditures on consumption, gross capital formation, and net exports (World Bank 2013, 2020). The most recent ICP rounds were based Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 on different versions of the SNA: the 2005 and original 2011 rounds were based on the 1993 SNA, whereas the revised 2011 and 2017 rounds were based on the 2008 SNA. Even though both versions share the same fundamental structure, the 2008 SNA introduced some changes to the 1993 SNA (OECD 2013; World Bank 2020). These changes have implications for GDP, either by re-allocating components and/or changing the level of GDP. For example, research and development (R&D) has been capitalized for the first time, which has two implications: (1) it reclassifies R&D from government final consumption expenditure to government gross fixed capital formation, and (2) it adds to the level of GDP via the cost of depreciation that has to be imputed and included (OECD 2013).33 A few changes have been introduced into the ICP expenditure classification in the 2017 round at different levels of aggregation, mainly in light of the SNA changes. The ICP classifies expenditure and price data into main aggregates, categories, groups, classes, and basic headings, in descending order.34 For example, gross capital formation is a new main aggregate of GDP that now combines two main aggregates in the (original) 2011 round (i.e., gross fixed capital formation and changes in inventories and acquisitions less disposal of valuables). Even though there are still 155 basic headings in the 2017 round as in the 2011 round, some basic headings have been merged (e.g., opening value of inventories and closing value of inventories in the 2011 round have been merged as changes in inventories in the 2017 round), and new ones have been added (e.g., actual and imputed rentals for housing was a basic heading in the 2011 round but is now separated as actual rentals for housing and imputed rentals for housing under the housing rentals category). A key point to note is that aggregation is done from the level of basic headings up to the desired aggregate (e.g., GDP, household final consumption, or other levels) so that all these changes at the level of basic headings do not have a significant impact on the aggregates. Apart from changes in national accounts structures caused by SNA revisions, national accounts expenditure data of countries get routinely revised or rebased to incorporate new information. This primarily explains the revisions to the original 2011 PPPs. The overall methodology for selecting the basket of goods and services the ICP collects prices on has remained quite stable. The ICP relies on sampling theory to determine the number of products to be priced in each basic heading, the number of survey outlets, and the number of times surveys will be conducted in the reference year. For example, only 10–15 items may be priced for the rice basic heading while 70– 100 items may be priced for the more heterogeneous garments basic heading to obtain a similar level of precision in the estimates for the basic-headings PPPs (World Bank 2013). The exact items that are priced, as well as where or when surveys are exactly conducted, are subject to expert advice ex ante and validation ex post. The list of items also requires at least partial updates for each comparison (e.g., IT equipment, cars, home entertainment, etc.). In the 2005 round, there was a separate price and expenditure survey with a “ring” list of 1,000 items that were priced in 18 selected “ring” countries across all regions, to link prices 33 As an example, OECD (2013) shows that the capitalization of R&D increases Australia’s GDP by 1.25–1.5%. Other SNA changes relate to the classification and/or valuation of weapon systems and ammunitions, computer software and databases, financial intermediation services indirectly measured (FISIM), output of central banks, and output for own use, among others. A more detailed description of the SNA changes can be found in OECD (2013). 34 A basic heading consists of a well-defined group of goods or services at the lowest level of the ICP expenditure classifi- cation. Rice and bread are examples of basic headings under the class of bread and cereals, which forms a part of the group called food. Food is within the Food and nonalcoholic beverages category, under the main aggregate of individual consumption by households. PPPs are first estimated at the level of basic headings and are aggregated along higher levels of aggregation up to the level of GDP, which sums up the main aggregates of expenditure. within and across regions. The ring list of items has been found to have a bias for traded goods, which are often unrepresentative, scarce, and expensive in developing countries (Deaton and Aten 2017). As a result, the 2005 round overstated price levels in most developing countries. The lessons learned from the 2005 round brought about significant improvements in ICP product specification. In subsequent rounds, the list of items in the price surveys conducted in all participating economies has been carefully chosen to ensure that the items are both representative within countries and comparable across countries, as much Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 as possible (World Bank 2015, 2013, 2020). A global core list (GCL) of items is priced as part of the price and expenditure surveys, for linking prices within and across regions. The GCL items are marked as “important” or “less important,” and prices are weighted accordingly, so that price levels in developing countries are not overstated. Each country decides its own sets of important and less important items, depending on their national consumption patterns. Also, importance indicators apply only within basic headings, not across. While the general approach of obtaining the price and expenditure data remains similar, especially between the 2011 and 2017 rounds, data quality assurance has been improved with the latest available technology, particularly in the 2017 round (World Bank 2020). As described in more detail in World Bank (2013; 2015; 2020), the computational steps the ICP fol- lows to produce the final global PPPs have also remained largely unchanged. Since the major change involving the linking of regional prices—namely, the 2005 “ring” method vs. 2011 or 2017 “Global Core List” (GCL) method—the common elements of PPP estimation have not changed. At the global level, the Country Product Dummy (CPD) method is used to estimate PPPs. This method estimates a regression model that produces, in one step, PPPs that are both transitive and base-country invariant. The resulting PPPs are multilateral (i.e., the basic-heading PPPs for one economy depend on those of all other participat- ing economies). PPPs are calculated for countries within regions, first at the basic heading level using the Country Product Dummy (CPD) method and then at the aggregate level using the Gini, Eltetö and Köves, and Szulc (GEKS) method, using prices in the GCL and a regional list, and expressed in the currency of a reference country in the region. Finally, the PPPs are linked across regions solely using items in the GCL to produce the global PPPs, expressed in U.S. dollars, in a way that ensures regional fixity (i.e., the ratio of real expenditures between any pair of participating economies within a region remains the same after linking prices across regions). The ICP data coverage varies across rounds, which potentially impacts the global PPPs used for mea- suring global poverty. Given that the PPPs are multilateral, PPPs would, in principle, be affected if the number of economies changes between any two ICP rounds. In practice, however, the total number of countries participating is less of an issue due to the regional fixity principle. In particular, the differences in country coverage between the 2011 and 2017 rounds mainly come from small Pacific Islands (e.g., Cook Islands and Kiribati) that participated in the 2011 round. However, since these economies were linked via three bridge-countries that participated from other regions—Australia (Eurostat-OECD), Fiji (Asia and the Pacific), and New Zealand (Eurostat-OECD)—the inclusion of these islands did not im- pact the rest of the countries in any manner. Overall, comparability of different ICP rounds may still be an issue in cases where countries move regions (e.g., Colombia and Costa Rica have been moved from Latin America and the Caribbean to OECD in the 2017 round) or where countries join or drop from the comparison altogether. Within countries, the price data coverage also varies between rounds, although with the currently available metadata it is not possible to assess this everywhere for past ICP rounds. In the 2005 round, for instance, price survey outlets were predominantly in capital cities and urban areas (World Bank 2008). In the 2017 round, this is still the case in some countries, including many high-income countries. Survey frames do not necessarily imply overestimation or underestimation of national prices, as the ICP requests countries to submit nationally representative annual prices. However, the ICP acknowledges that national average prices are often hard to achieve in large economies with settlements and populations that are substantially rural (World Bank 2015), and ultimately the ICP does not know whether prices have been adjusted appropriately. S4: Deciding when to Impute 2017 PPPs for Exception Countries While the article argues that the ICP methodology has largely stabilized, the change from 2011 to 2017 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 PPPs can still lead to erratic changes in poverty estimates at the country level. Section S4 of the supple- mentary online appendix describes the analysis the study has done to identify the countries where it was decided to deviate from the official 2017 PPPs. Step 1: Select all countries where it is worthwhile to take a deeper look Countries that fulfill any of the following conditions are selected for more detailed analysis: a) Is there a large change in the real value of welfare when moving to the 2017 PPPs? As described in the main text and following Ferreira et al. (2016), the study selects all countries with delta ratios more than two standard deviations from the mean using the CPIs and revised 2011 PPPs used for global poverty monitoring. From fig. 2A(in the main text), the analysis selects Angola, Belize, the Arab Republic of Egypt, Ghana, Guinea, Iraq, Jordan, Liberia, São Tomé and Príncipe, and Suriname. b) Is there a big difference in the movements of CPIs and PPPs? The analysis selects all countries with delta ratios more than two standard deviations from the mean using IFS CPIs and official 2011 PPPs. This is very similar to the previous condition, except for the slightly different CPIs and PPPs (see the main text for details). From fig. 2A(in the main text), the analysis selects Angola, Belize, Bolivia, Central African Republic, Guinea, Liberia, Nigeria, São Tomé and Príncipe, Suriname, and Trinidad and Tobago. c) Are the 2017 PPPs very different from what would be expected based on the country’s characteristics? The analysis selects all countries whose log difference in the PPP residual is more than two standard deviations from the mean. The PPP residual is defined as the difference in the logs of the published PPP and predicted PPP. PPPs are predicted using the seemingly unrelated regressions (SUR) model the ICP uses to predict PPPs for nonbenchmark countries. The ICP model for predicting 2017 PPPs for nonbenchmark countries is given as: PLIi − PLIUSA = b ∗ (Xi − XUSA ) + ei where PLIi is the price level index of country i, calculated as the ratio of the PPP conversion factor to the market exchange rate, and X is a vector of explanatory variables: GDP per capita in U.S. dollars (based on market exchange rates), imports as a share of GDP, exports as a share of GDP, age dependency ratio, and dummies for Sub-Saharan Africa, the EU, island economies, and land-locked developing economies. Interaction terms between GDP per capita and the country-group-dummy variables are also included.35 From fig. S4.1, the analysis selects Barbados, Central African Republic, Egypt, Djibouti, Sudan, and Suriname. Barbados has no surveys in PovcalNet, so it is dropped from the set of countries. d) Are revised 2011 PPPs currently imputed for global poverty monitoring? 35 Note that this model is not targeted towards predicting the price levels of the poor, for which other variables may be more relevant. This is a general issue with PPPs and CPIs used for global poverty monitoring. The authors thank Stefan Dercon for raising this issue. The study nonetheless uses the model by the ICP to stay consistent with the PPPs used for benchmark countries, which likewise are not tailored towards the prices faced by the poor. Figure S4.1. Outlier Countries Based on the Residual Criterion. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from World Development Indicators (WDI), June 2021 and 2017 ICP Report. Note: PPP residual is defined as the difference in the logs of the published PPP and predicted PPP (based on the ICP model used for nonbenchmark countries). The study also includes countries whose revised 2011 PPPs are currently imputed (Ferreira et al. 2016; Atamanov et al. 2018; Atamanov et al. 2020). This adds Egypt, Iraq, Jordan, the Lao People’s Democratic Republic, Myanmar, and the Republic of Yemen. The Republic of Yemen is not included in the rest of the investigation because it has no official 2017 PPP. In summary, step 1 of the analysis selects the following countries for a more detailed analysis: Angola, Belize, Bolivia, Central African Republic, Djibouti, Egypt, Ghana, Guinea, Iraq, Jordan, Lao PDR, Liberia, Myanmar, Nigeria, São Tomé and Príncipe, Sudan, Suriname, Trinidad and Tobago. Step 2: Remove Countries where Imputing PPPs Would Lead to Even Larger Changes For the countries identified in step 1, the analysis predicts PPPs using the ICP model (while excluding all the countries that have been identified in step 1). If the predicted PPP makes the delta ratio more extreme, the country is removed from the list of countries that are worth a deeper look. The reason for this is that the study’s proposed solution to dealing with countries selected as outliers will be to partially rely on imputed PPPs. If imputed PPPs make the country an even stronger outlier, the proposed solution will not improve the situation. From table S4.1, using imputed PPPs would not improve the situation for Angola, Central African Republic, Ghana, Jordan, and Liberia, so they are excluded from the subsequent analysis. Table S4.1. Delta ratios with Official and Imputed 2017 PPP Country Delta ratio Delta ratio increases/decreases further? Official PPP Imputed PPP 1. Angola 1.80 2.14 Yes 2. Belize 0.83 1.05 No Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 3. Bolivia 1.43 1.33 No 4. Central African Republic 1.24 1.93 Yes 5. Egypt, Arab Rep. 1.77 0.88 No 6. Ghana 0.75 0.73 Yes 7. Guinea 1.46 1.27 No 8. Iraq 1.90 1.70 No 9. Jordan 1.53 1.57 Yes 10. Liberia 1.50 1.81 Yes 11. Nigeria 1.44 1.18 No 12. São Tomé and Príncipe 1.46 1.30 No 13. Suriname 1.45 0.96 No 14. Trinidad and Tobago 1.43 1.17 No Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: The countries whose delta ratios become more extreme with the imputed PPP are in bold. When the delta ratio increases further, poverty decreases further. When the delta ratio decreases further, poverty increases further. Also see fig. S4.3, panel (A). Step 3: Criteria to Suggest That the 2017 PPPs May Be Problematic The study uses four criteria to evaluate whether the 2017 PPP for a country may be problematic. Given that no information can cast definite doubt on the 2017 PPPs—if so, the ICP team would have likely dealt with the issue already—each of these criteria provides a signal of evidence on whether the 2017 PPPs are fit for global poverty monitoring. a) Are the CPIs fit for purpose? As described in the main text, an extreme delta ratio could arise from issues with either PPP or the CPI. The study evaluates the quality of the CPI by whether it classifies expenditures using COICOP, and whether the weights have national coverage and are no older than 10 years from 2017. If none of these issues applies, there is no evidence that there is a problem with the CPIs, which may suggest that the problem could be with the PPPs, creating an argument for imputing PPPs (see table S4.2). b) Is there a large decline in the share of items priced? The share of items priced is defined as the ratio of the sum of global core list and regional items priced to the sum of all global and regional items. Figure S4.2plots the share of items priced in the 2017 round against the 2011 round. For countries in the dark color, the log difference in the share of items priced in 2011 and 2017 is more than two standard deviations from the mean (evaluated over all countries, not just the ones plotted). Among the countries identified above, only Angola shows up as an outlier by this criterion. In Angola, the share of items priced almost doubles between the two rounds, which is an improvement. None of the countries under consideration has a large decline in the share of items priced. c) Does the imputed PPP make the poverty rate more consistent with related indicators that do not rely on PPPs, such as the multidimensional poverty index (MPI) and the age dependency ratio (ADR)? The analysis regresses the monetary poverty rate (in 2017 PPPs with a $2.15 IPL) on the related indica- tor (MPI or ADR) with regional dummies and interactions. The countries identified in step 1 are excluded from the regression. Using the estimated parameters, the analysis then predicts the monetary poverty rate Table S4.2. CPI Quality Indicators Country CPI classification system CPI expenditure weights coverage CPI weights reference year Flag 1. Belize COICOP National 2009 Yes 2. Bolivia Other National 2016 No 3. Djibouti Other Capital City 2013 No Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 4. Egypt, Arab Rep. COICOP National 2010 Yes 5. Guinea Other Capital City 2002 No 6. Iraq COICOP National 2012 Yes 7. Lao PDR Other National 2010 No 8. Myanmar Other National 2012 No 9. Nigeria Other National 2004 No 10. São Tomé and Príncipe COICOP National 2014 Yes 11. Sudan Other National 2007 No 12. Suriname COICOP National 2014 Yes 13. Trinidad and Tobago COICOP National 2009 Yes 14. Angola COICOP National 2009 Yes 15. Central African Republic Other National 2005 No 16. Ghana Survey National 2005.67 No 17. Jordan Other National 2010 No 18. Liberia COICOP National 2016 Yes Source: Authors’ compilations from Berry et al. (2019). Note: Countries with a “Yes” have no obvious problems with their CPIs. Countries dropped in step 2 above are still added (in italics) for completeness. Figure S4.2. Share of Items Priced: 2011 vs. 2017 Rounds. Source: Authors’ calculations from 2017 ICP Report. Note: Countries with a marked change in the share of items priced are shown in dark color, including Angola (AGO), Bermuda (BMU), Bonaire (BON), Equatorial Guinea (GNQ), Jamaica (JAM), and Panama (PAN). Outlier countries under consideration are shown in light color, including those that should be dropped based on step 2. All other countries that participated in the 2011 and 2017 rounds are suppressed. The dotted line is a 45-degree line. Table S4.3. Multidimensional Poverty Index and Monetary Poverty Log difference between Log difference between predicted poverty from predicted poverty from MPI and poverty with MPI and poverty with Country Year official 2017 PPP imputed 2017 PPP Flag 1. Belize 2015 1.79 1.45 Yes Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 2. Bolivia 2008 0.12 0.06 Yes 3. Djibouti 2006 1.37 0.56 Yes 4. Egypt, Arab Rep. 2014 2.21 2.43 No 5. Guinea 2018 1.36 0.96 Yes 6. Iraq 2018 3.41 2.17 Yes 7. Lao PDR 2017 0.57 0.77 No 8. Myanmar 2015 1.37 1.54 No 9. Nigeria 2018 0.26 0.08 Yes 10. São Tomé and 2014 0.21 0.43 No Príncipe 11. Sudan 2014 1.42 0.37 Yes 12. Suriname 2018 1.84 2.05 No 13. Trinidad and 2011 1.82 1.46 Yes Tobago 14. Angola 2015 0.55 0.86 No 15. Central African 2010 0.07 0.37 No Republic 16. Ghana 2014 0.18 0.22 No 17. Jordan 2017 0.41 0.13 Yes 18. Liberia 2013 0.46 0.95 No Source: Authors’ calculations from PovcalNet, June 2021, World Development Indicators (WDI), June 2021, and Alkire et al. (2020). Note: The analysis uses the multidimensional poverty index (MPI) from the Oxford Poverty and Human Development Initiative (OPHI) for the latest available year, which is indicated in the table (Alkire et al. 2020). These estimates are matched with the lined-up, annual estimates of poverty in PovcalNet. For countries with a “Yes,” the poverty rate with imputed PPP is more in tune with what would be expected from the MPI than with the official PPP. Countries that are dropped in step 2 above are still added (in italics) for completeness. for these countries. Finally, the study computes the difference between the predicted poverty rate and (a) the poverty rate with official 2017 PPP and (b) the poverty rate with imputed 2017 PPP. The analysis creates a flag for a country if the former difference is greater than the latter difference (see table S4.3 and S4.4). Such a case would imply that the poverty rate with imputed 2017 PPPs is closer to the study’s MPI- or ADR-based priors than the poverty rate with official 2017 PPPs. d) Does the poverty economist find the official 2017 PPP less appropriate? Following Ferreira et al. (2016), the authors consulted with country poverty economists (PE)—World Bank staff specialized in the measurement of poverty in a country—to assess whether the imputed PPP seems more appropriate for the countries identified in step 1. The study’s partial reliance on poverty economists reflects the authors’ view that the prior three steps might have missed relevant country-specific information and nuances. There is, however, a trade-off between using country-specific information and preserving comparability across countries. For that reason, the authors consider the judgment of poverty economists as one input alongside the other indicators. Poverty economists could not influence the set of countries that were considered outliers nor the PPPs that were used for countries not considered outliers. Their responses are provided in table S4.5. Step 4: Decide on When to Use Imputed PPPs For the 18 countries identified, the analysis considers two options: either the official PPP, or the average between the imputed PPP (using the ICP model) and the official PPP. The study prefers the average of Table S4.4. Age Dependency Ratio and Monetary Poverty Log difference between Log difference between predicted poverty from predicted poverty from ADR and poverty with ADR and poverty with Country Year official 2017 PPP imputed 2017 PPP Flag 1. Belize 1999 0.40 0.09 Yes Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 2. Bolivia 2019 0.76 0.59 Yes 3. Djibouti 2017 3.22 2.38 Yes 4. Egypt, Arab Rep. 2017 1.07 2.28 No 5. Guinea 2012 1.01 0.69 Yes 6. Iraq 2012 3.90 2.98 Yes 7. Lao PDR 2018 0.90 0.66 Yes 8. Myanmar 2017 0.66 0.38 Yes 9. Nigeria 2018 0.47 0.12 Yes 10. São Tomé and 2017 0.43 0.18 Yes Príncipe 11. Sudan 2014 1.23 0.19 Yes 12. Suriname 1999 1.25 1.53 No 13. Trinidad and 1992 1.20 0.55 Yes Tobago 14. Angola 2018 0.72 0.94 No 15. Central African 2008 0.36 0.06 Yes Republic 16. Ghana 2016 0.47 0.51 No 17. Jordan 2010 3.75 3.75 No 18. Liberia 2016 0.09 0.52 No Source: Authors’ calculations from PovcalNet, June 2021 and World Development Indicators (WDI), June 2021. Note: The analysis uses estimates of poverty for the latest survey year available in PovcalNet, which is indicated in the table. These estimates are matched with annual estimates of the age dependency ratio (ADR) in WDI. For countries with a “Yes,” the poverty rate with imputed PPP is more in tune with what would be expected from ADR than with the official PPP. The study uses “Age dependency ratio, young (% of working-age population),” the ratio of younger dependents (i.e., people younger than 15) to the working-age population (i.e., those aged 15–64) from WDI for the analysis reported here. The flags are the same when the study uses “Age dependency ratio (% of working-age population),” the ratio of all dependents (i.e., people younger than 15 or older than 64) to the working-age population (i.e., those aged 15–64). Countries that should be dropped based on step 2 above are still added (in italics) for completeness. official and imputed, instead of the imputed PPP by itself, because the authors think both PPPs represent signals that are worthy accounting for. The choice between these options is guided by the analysis in step 3 (see table S4.5). The more flags a country has, the more likely the researchers are to decide not to use the official PPP. In the end, it was decided to use the average of official and imputed PPPs for Belize, Egypt, Guinea, Iraq, Nigeria, São Tomé and Príncipe, Sudan, and Trinidad and Tobago, while for the remaining outlier countries the study uses the official 2017 PPPs. This is equivalent to all countries with at least three flags and Egypt. According to the official PPP, Egypt has the lowest price level index in the world (World Bank 2020), and Egypt thus represents an extreme outlier. Egypt is also the only country that is identified as possibly having a problematic 2017 PPP estimate based on the delta criterion, PPP residual criterion, and countries with exceptional 2011 PPPs (see table S4.5, panel (B)). See fig. 4.3 and table S4.6 for the poverty estimates for all 18 countries under review for the year 2017 with different PPPs. Figure S4.3. Poverty Estimates in 2017 with Different PPPs. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from PovcalNet, June 2021 and 2017 ICP Report. Note: This chart shows extreme poverty estimates in 2017 using revised 2011 and 2017 PPPs. Panel (A) shows the poverty estimates for countries dropped in step 2 because using an imputed PPP would make the delta ratio even more extreme. Panel (B) shows outlier countries where there is little evidence against using the official PPP to estimate poverty. Panel (C) shows outlier countries where the study proposes to use the average of official and imputed PPPs. This figure uses the international poverty lines of $1.90 (2011 PPP) and $2.15 (2017 PPP) for estimating extreme poverty. See table S4.6 for the poverty estimates and associated delta ratios. Table S4.5. Summary of Responses Flags (sum of Country Panel (A) Panel (B) panel A Yes’s) PPP Problem Items Delta ratio residual with CPIs priced MPI ADR PE outlier outlier 2011 PPP Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 1. Belize Yes No Yes Yes Yes Yes No No 4 2. Bolivia No No Yes Yes No Yes No No 2 3. Djibouti No No Yes Yes No No Yes No 2 4. Egypt, Arab Rep. Yes No No No Yes Yes Yes Yes 2 5. Guinea No No Yes Yes Yes Yes No No 3 6. Iraq Yes No Yes Yes Yes Yes No Yes 4 7. Lao PDR No No No Yes Yes Yes No Yes 2 8. Myanmar No No No Yes No Yes No Yes 1 9. Nigeria No No Yes Yes Yes Yes No No 3 10. São Tomé and Yes No No Yes Yes Yes No No 3 Príncipe 11. Sudan No No Yes Yes Yes No Yes No 3 12. Suriname Yes No No No N/A Yes Yes No 1 13. Trinidad and Yes No Yes Yes Yes Yes No No 4 Tobago 14. Angola Yes No No No No Yes No No 1 15. Central African No No No Yes No Yes Yes No 1 Republic 16. Ghana No No No No No Yes No No 0 17. Jordan No No Yes No No Yes No Yes 1 18. Liberia Yes No No No No Yes No No 1 Source: Authors’ calculations and compilations. Note: Panel (A) shows the factors identified in step 3 providing suggestive evidence as to whether there might be issues with the official 2017. Panel (B) includes the questions raised in step 1 with which the analysis selected the 18 problem countries. There is no poverty economist (PE) for Suriname. Figure S4.4. Sensitivity of Extreme Poverty Trends to Alternative PPPs. Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Source: Authors’ calculations from PovcalNet, June 2021 and 2017 ICP Report. Note: This figure shows the trends in extreme poverty at the global and regional levels using the revised 2011 PPPs and 2017 PPPs. The solid red line uses 2017 PPPs officially published by the ICP, except for 14 countries where alternative PPPs are used (see table S2.3), while the dashed red line uses only 2017 PPPs officially published by the ICP. Table S4.6. Delta Ratios and Poverty Changes in 2017 Country 2011 PPP poverty 2017 PPP (official) 2017 PPP (imputed) 2017 PPP (average) Poverty Change Delta Poverty Change Delta Poverty Change Delta (A) Use official 2017 PPP: Imputed PPP leads to even larger changes Jordan 0.10 0.04 −0.06 1.53 0.03 −0.07 1.57 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Ghana 12.34 24.68 12.33 0.75 25.72 13.38 0.73 Liberia 44.55 27.64 −16.91 1.50 17.97 −26.58 1.81 Angola 45.07 26.50 −18.57 1.80 20.41 −24.66 2.14 Central African Republic 72.37 69.03 −3.35 1.24 49.44 −22.93 1.93 (B) Use official 2017 PPP: Official PPP seems appropriate Myanmar 1.36 1.99 0.63 1.05 1.51 0.14 1.10 Bolivia 6.55 4.93 −1.61 1.43 5.68 −0.87 1.33 Lao PDR 10.72 7.61 −3.11 1.24 6.26 −4.46 1.31 Djibouti 17.01 19.08 2.07 1.06 8.24 −8.77 1.63 Suriname 17.53 15.53 −2.01 1.45 19.30 1.77 0.96 (C) Use average 2017 PPP Trinidad and Tobago 0.28 0.17 −0.10 1.43 0.26 −0.02 1.17 0.21 −0.07 1.29 Iraq 1.24 0.02 −1.22 1.90 0.09 −1.15 1.70 0.05 −1.19 1.80 Egypt, Arab Rep. 2.97 0.31 −2.65 1.77 9.78 6.81 0.88 1.93 −1.04 1.24 Sudan 10.22 7.39 −2.83 1.26 21.78 11.56 0.87 12.72 2.50 1.05 Belize 13.66 20.05 6.39 0.83 14.39 0.73 1.05 17.50 3.83 0.93 Guinea 24.33 12.67 −11.66 1.46 18.84 −5.49 1.27 15.90 −8.43 1.36 São Tomé and Príncipe 35.64 22.94 −12.70 1.46 29.20 −6.44 1.30 26.12 −9.52 1.38 Nigeria 41.36 28.09 −13.27 1.44 38.86 −2.50 1.18 33.41 −7.96 1.31 Source: Authors’ calculations from PovcalNet, June 2021 and 2017 ICP Report. Note: Poverty is in percentages (%), and poverty change is in percentage points (pp). Poverty estimates with the average of official and imputed PPPs are not applicable for panels (A) and (B). Countries are sorted in ascending order of poverty rate, as measured by the revised 2011 PPP. Columns entitled "delta" refer to the delta ratio. Table S4.7. Sensitivity of Changes in Extreme Poverty in 2017 to Alternative PPPs Region A. 2017 PPP B. 2017 PPP (ICP) Change in poverty, pp Change in millions of poor Change in poverty, pp Change in millions of poor World −0.20 −15.36 −0.41 −30.96 East Asia & Pacific 0.41 8.45 0.41 8.45 Europe & Central Asia 1.48 7.26 1.48 7.26 Latin America & Caribbean 0.31 1.93 0.31 1.94 Middle East & North Africa 0.02 0.09 −0.72 −2.76 Rest of the World 0.00 0.01 0.00 0.01 South Asia 0.06 1.01 0.06 1.01 Sub-Saharan Africa −3.25 −34.10 −4.46 −46.8 6 Source: Authors’ calculations from PovcalNet, June 2021 and 2017 ICP. Note: This table shows the changes to extreme poverty estimates and millions of poor when moving from the revised 2011 PPPs to the 2017 PPPs. Extreme poverty is estimated using $1.90 (2011 PPP) and $2.15 (2017 PPP). Panel (A) uses 2017 PPPs officially published by the ICP, except for 14 countries where alternative PPPs are used (see table S2.3), while panel (B) uses only 2017 PPPs officially published by the ICP. S5: Methodological Details S5.1: Update $1.90 with Revised 2011 PPPs Equation S1 expresses how the national poverty lines of the 15 poor countries denominated in the original 2011 PPPs were originally derived by Ferreira et al. (2016) from the national poverty lines estimated in Ravallion et al. (2009): CPI2011i PPP2005i PL2011i = PL2005i ∗ ∗ (S1) CPI2005i PPP2011i where: r PL2011 is the national poverty line in the original 2011 PPP Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 r PL2005 is the national poverty line Ravallion et al. (2009) estimated in 2005 PPP r CPI2011 is the consumer price index (CPI) for 2011 as used by Ferreira et al. (2016) r CPI2005 is the consumer price index (CPI) for 2005 as used by Ferreira et al. (2016) r PPP2011 is the original 2011 PPP r PPP2005 is the 2005 PPP The international poverty line (IPL), denominated in the original 2011 PPP, is given as: 15 1 IPL2011 = ∗ PL2011i (S2) 15 i =1 The analysis uses the following equation to update the IPL in (S2) with the revised 2011 PPPs only. CPI2011i PPP2005i PL2011i = PL2005i ∗ ∗ (S3) CPI2005i PPPr2011i where: r PPPr2011 is the revised 2011 PPP The analysis uses the following equation to update the IPL in (S2) with CPI revisions only. CPIr2011i PPP2005i PL2011i = PL2005i ∗ ∗ (S4) CPIr2005i PPP2011i where: r CPIr2011 is the updated consumer price index (CPI) for 2011 r CPIr2005 is the updated consumer price index (CPI) for 2005 The analysis uses the following equation to update the IPL in (S2) with both PPP and CPI revisions. CPIr2011i PPP2005i PL2011i = PL2005i ∗ ∗ (S5) CPIr2005i PPPr2011i S5.2: Update $1.90 with 2017 PPPs Using equation (S5) above, the following expression updates the 15 national poverty lines selected by Ravallion et al. (2009) to 2017, while updating the CPIs used by Ferreira et al. (2016) CPIr2011i CPI2017i PPP2005i PPPr2011i PL2017i = PL2005i ∗ ∗ ∗ ∗ (S6) CPIr2005i CPIr2011i PPPr2011i PPP2017i Here PL2017 is the national poverty line in 2017 PPP. Similarly, the following equation updates the poverty lines to 2017 while using the CPIs that Ferreira et al. (2016) had originally used, CPI2011i CPI2017i PPP2005i PPPr2011i PL2017i = PL2005i ∗ ∗ ∗ ∗ (S7) CPI2005i CPIr2011i PPPr2011i PPP2017i Lastly, when taking $1.90 as given for all 15 countries, and updating both PPP and CPI to 2017 the analysis uses the following expression: PPPr2011i CPI2017i PL2017i = 1.9 ∗ ∗ (S8) PPP2017i CPIr2011i In all these three cases, the international poverty line (IPL), denominated in 2017 PPP, is given as: 15 1 IPL2017 = ∗ PL2017i (S9) 15 i =1 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 S5.3: Derive Harmonized National Poverty Lines The harmonized national poverty line is derived from the following equation. z F (z ) = ∫ f (y ) dy = P (S10) 0 where: r F is a cumulative distribution function of consumption or income, denominated in original or revised 2011 PPP per person per day (as in PovcalNet) r f (y ) is a probability distribution function of consumption or income, denominated in original or revised 2011 PPP per person per day (as in PovcalNet) r P is the national poverty headcount ratio reported in WDI r z is the harmonized national poverty line that yields a poverty headcount ratio of P S5.4: Update Harmonized Poverty Lines with revised 2011 PPPs Let z2011 be z in equation (S10) if the income distribution is denominated in the original 2011 PPP, and let zr2011 be z in equation (S10) above if the income distribution is denominated in the revised 2011 PPP. Then PPP2011 zr2011 = z2011 ∗ (S11) PPPr2011 S5.5: Update Harmonized Poverty Lines with 2017 PPPs The harmonized poverty line, denominated in 2017 PPP, is derived as: PPPr2011 CPI2017 z2017 = zr2011 ∗ ∗ (S12) PPP2017 CPI2011 S5.6: Derive equivalent poverty lines Let the global poverty rate in a reference year (e.g., 2010) be given as: 1.9 F (1.9 ) = ∫ f (y (PPP2011 ) ) dy = P∗ (S13) 0 with an international poverty line of $1.90 per person per day in revised 2011 PPP and a global welfare (probability distribution) function, f (y(. ) ) expressing daily income per person in revised 2011 PPP. P∗ is the global poverty rate. The equivalent poverty line in 2017 PPP, z ˜ , is derived by solving the following equation for a given reference year. ˜ z ˜ ) = ∫ f (y (PPP2017 ) ) dy = P∗ F (z (S14) 0 with a global welfare (probability distribution) function, f (y(. ) ) expressing daily income per person in 2017 PPP. Convert PovcalNet Distributions from Revised 2011 PPPs to Original 2011 PPPs The income distribution, denominated in original 2011 PPP, is derived as: PPPr2011 y2011 = y_r2011 ∗ (S15) PPP2011 where: Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 r y_r2011 is the income distribution, denominated in revised 2011 PPP S5.8: Convert PovcalNet Distributions from Revised 2011 PPPs to 2017 PPPs The income distribution, denominated in 2017 PPP, is derived as: PPPr2011 CPI2017 y2017 = y_r2011 ∗ ∗ (S16) PPP2017 CPIr2011 S5.9: Explain Changes in Poverty with Adopting the 2017 PPPs The change in poverty is a function of the relative change in income per capita (given a poverty line and the distribution). Re-arranging (S16), y2017 CPI2017 PPP2017 = / = δ (S17) y_r2011 CPI2011 PPPr2011 Thus, the rate of change in income per capita depends on the rates of change in CPI and PPP. If, for example, the PPP increases faster than the CPI between the 2011 and 2017 ICP reference years, income per capita decreases and poverty increases at a given poverty line. S6: Global Poverty Lines in Local Currency Units The following table expresses the four global poverty lines typical of low-income countries (LIC), lower- middle-income countries (LMIC), upper-middle-income countries (UMIC), and high-income countries (HIC) in local currency units in 2020 prices. Global poverty lines determined from both revised 2011 and 2017 PPP rounds are provided in the table. For example, the 2011 PPP exchange rate for Albania is 54.65 Albanian lek per U.S. dollar, and the inflation in Albania from 2011 to 2020 was 16.97%. This means that the $1.90 line in 2011 USD equates 1.90$∗54.65 lek$ ∗1.1697 = 121.46 lek in 2020 prices, which is the first entry in the table below. Table S6.1. Global Poverty Lines in Local Currency Units in 2020 Prices Economy PPP LIC LMIC UMIC HIC Albania 2011 121.46 204.56 351.59 1387.19 Albania 2017 113.84 193.26 362.70 1289.82 Algeria 2011 88.81 149.58 257.09 1014.33 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Algeria 2017 89.44 151.84 284.96 1013.37 Angola 2011 627.76 1057.28 1817.20 7169.70 Angola 2017 394.99 670.56 1258.45 4475.30 Argentina 2011 85.10 143.33 246.35 971.96 Argentina 2017 68.04 115.51 216.78 770.91 Armenia 2011 382.20 643.70 1106.36 4365.09 Armenia 2017 378.64 642.81 1206.37 4290.11 Australia 2011 3.39 5.71 9.81 38.72 Australia 2017 3.43 5.83 10.94 38.90 Austria 2011 1.87 3.16 5.43 21.41 Austria 2017 1.88 3.18 5.98 21.25 Azerbaijan 2011 0.92 1.55 2.66 10.49 Azerbaijan 2017 1.13 1.92 3.60 12.79 Bangladesh 2011 76.02 128.04 220.06 868.25 Bangladesh 2017 74.74 126.89 238.13 846.85 Belarus 2011 1.23 2.08 3.57 14.08 Belarus 2017 1.55 2.63 4.94 17.55 Belgium 2011 1.90 3.20 5.50 21.70 Belgium 2017 1.90 3.22 6.04 21.47 Belize 2011 2.33 3.93 6.75 26.64 Belize 2017 2.83 4.81 9.02 32.07 Benin 2011 475.67 801.12 1376.93 5432.61 Benin 2017 485.82 824.77 1547.85 5504.48 Bhutan 2011 53.19 89.58 153.96 607.45 Bhutan 2017 48.86 82.95 155.68 553.63 Bolivia 2011 7.48 12.60 21.66 85.47 Bolivia 2017 5.90 10.02 18.81 66.89 Bosnia and Herzegovina 2011 1.63 2.74 4.71 18.56 Bosnia and Herzegovina 2017 1.70 2.88 5.40 19.22 Botswana 2011 11.39 19.19 32.98 130.11 Botswana 2017 11.64 19.76 37.09 131.90 Brazil 2011 5.08 8.56 14.71 58.05 Brazil 2017 5.55 9.43 17.69 62.92 Bulgaria 2011 1.68 2.84 4.87 19.23 Bulgaria 2017 1.70 2.89 5.43 19.31 Burkina Faso 2011 455.35 766.91 1318.13 5200.63 Burkina Faso 2017 431.67 732.84 1375.32 4890.93 Burundi 2011 1681.06 2831.25 4866.22 19199.44 Burundi 2017 1457.43 2474.25 4643.45 16513.07 Cabo Verde 2011 97.16 163.64 281.26 1109.69 Cabo Verde 2017 105.43 178.99 335.92 1194.60 Cameroon 2011 544.41 916.90 1575.91 6217.70 Cameroon 2017 540.32 917.28 1721.48 6121.92 Canada 2011 2.78 4.69 8.06 31.80 Canada 2017 2.90 4.93 9.26 32.91 Central African Republic 2011 774.54 1304.49 2242.10 8846.10 Central African Republic 2017 704.13 1195.38 2243.38 7977.92 Chad 2011 572.98 965.02 1658.63 6544.07 Chad 2017 551.18 935.72 1756.08 6244.98 Chile 2011 966.73 1628.17 2798.42 11041.05 Chile 2017 1114.85 1892.64 3551.95 12631.46 China 2011 8.53 14.36 24.68 97.38 Table S6.1. Continued Economy PPP LIC LMIC UMIC HIC China 2017 9.59 16.28 30.56 108.66 Colombia 2011 3215.11 5414.92 9306.89 36719.89 Colombia 2017 3343.95 5676.94 10653.98 37887.74 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Comoros 2011 493.11 830.50 1427.42 5631.81 Comoros 2017 488.03 828.52 1554.89 5529.52 Congo, Dem. Rep. 2011 2440.70 4110.65 7065.17 27875.32 Congo, Dem. Rep. 2017 2043.47 3469.15 6510.59 23152.99 Congo, Rep. 2011 720.26 1213.07 2084.96 8226.10 Congo, Rep. 2017 647.20 1098.73 2062.00 7332.88 Costa Rica 2011 821.50 1383.58 2378.03 9382.40 Costa Rica 2017 842.26 1429.89 2683.49 9543.05 Côte d’Ivoire 2011 504.03 848.89 1459.02 5756.51 Côte d’Ivoire 2017 540.13 916.97 1720.89 6119.85 Croatia 2011 8.63 14.54 24.99 98.59 Croatia 2017 8.40 14.26 26.77 95.19 Cyprus 2011 1.42 2.39 4.12 16.24 Cyprus 2017 1.45 2.46 4.62 16.45 Czech Republic 2011 32.34 54.47 93.63 369.41 Czech Republic 2017 31.81 54.00 101.35 360.41 Denmark 2011 17.08 28.77 49.45 195.11 Denmark 2017 17.31 29.38 55.14 196.08 Djibouti 2011 222.70 375.08 644.66 2543.48 Djibouti 2017 237.14 402.59 755.55 2686.89 Dominican Republic 2011 51.15 86.15 148.07 584.22 Dominican Republic 2017 57.38 97.42 182.82 650.16 Ecuador 2011 1.23 2.08 3.57 14.08 Ecuador 2017 1.23 2.09 3.92 13.95 Egypt, Arab Rep. 2011 15.03 25.31 43.50 171.62 Egypt, Arab Rep. 2017 13.66 23.19 43.52 154.77 El Salvador 2011 1.08 1.81 3.11 12.29 El Salvador 2017 1.12 1.89 3.56 12.64 Estonia 2011 1.28 2.15 3.70 14.58 Estonia 2017 1.35 2.29 4.30 15.30 Eswatini 2011 12.76 21.49 36.94 145.73 Eswatini 2017 14.69 24.94 46.81 166.47 Ethiopia 2011 30.81 51.88 89.17 351.84 Ethiopia 2017 28.99 49.22 92.36 328.47 Fiji 2011 2.32 3.91 6.71 26.49 Fiji 2017 2.19 3.71 6.97 24.78 Finland 2011 2.01 3.39 5.83 23.00 Finland 2017 2.06 3.49 6.55 23.28 France 2011 1.81 3.05 5.24 20.67 France 2017 1.86 3.15 5.92 21.06 Gabon 2011 836.53 1408.90 2421.54 9554.08 Gabon 2017 745.16 1265.05 2374.13 8442.89 Gambia, The 2011 36.20 60.97 104.79 413.43 Gambia, The 2017 39.28 66.68 125.14 445.02 Georgia 2011 2.06 3.46 5.95 23.47 Georgia 2017 2.25 3.82 7.17 25.50 Germany 2011 1.75 2.94 5.05 19.94 Germany 2017 1.76 2.98 5.59 19.89 Ghana 2011 2.95 4.97 8.55 33.72 Ghana 2017 4.45 7.56 14.19 50.47 Greece 2011 1.42 2.39 4.12 16.24 Table S6.1. Continued Economy PPP LIC LMIC UMIC HIC Greece 2017 1.38 2.35 4.41 15.68 Guatemala 2011 10.27 17.30 29.73 117.31 Guatemala 2017 10.51 17.85 33.50 119.12 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Guinea 2011 11838.66 19938.80 34269.82 135210.00 Guinea 2017 9854.55 16729.82 31397.07 111654.39 Guinea-Bissau 2011 534.37 899.99 1546.86 6103.08 Guinea-Bissau 2017 509.72 865.34 1624.00 5775.28 Guyana 2011 255.94 431.06 740.88 2923.11 Guyana 2017 258.90 439.52 824.86 2933.38 Haiti 2011 97.28 163.85 281.61 1111.08 Haiti 2017 109.64 186.13 349.32 1242.25 Honduras 2011 28.41 47.86 82.25 324.53 Honduras 2017 26.26 44.58 83.66 297.51 Hungary 2011 315.21 530.88 912.45 3600.02 Hungary 2017 347.97 590.75 1108.66 3942.64 Iceland 2011 341.30 574.82 987.98 3898.03 Iceland 2017 364.06 618.05 1159.91 4124.87 India 2011 49.17 82.81 142.33 561.56 India 2017 48.12 81.69 153.31 545.20 Indonesia 2011 10815.32 18215.28 31307.52 123522.39 Indonesia 2017 11858.56 20131.97 37781.92 134360.22 Iran, Islamic Rep. 2011 60201.79 101392.49 174268.34 687567.82 Iran, Islamic Rep. 2017 67542.39 114664.99 215193.20 765271.01 Iraq 2011 2019.23 3400.80 5845.13 23061.70 Iraq 2017 1270.34 2156.62 4047.36 14393.24 Ireland 2011 1.88 3.17 5.46 21.53 Ireland 2017 2.11 3.58 6.72 23.91 Israel 2011 8.40 14.15 24.31 95.93 Israel 2017 9.14 15.52 29.12 103.57 Italy 2011 1.66 2.80 4.82 19.00 Italy 2017 1.68 2.85 5.34 18.99 Jamaica 2011 189.92 319.86 549.76 2169.06 Jamaica 2017 171.65 291.41 546.90 1944.88 Japan 2011 231.41 389.75 669.88 2642.98 Japan 2017 246.49 418.46 785.34 2792.81 Jordan 2011 1.01 1.71 2.93 11.58 Jordan 2017 0.75 1.27 2.38 8.47 Kazakhstan 2011 279.37 470.52 808.71 3190.71 Kazakhstan 2017 320.79 544.59 1022.04 3634.57 Kenya 2011 118.85 200.17 344.05 1357.43 Kenya 2017 103.95 176.47 331.19 1177.77 Kiribati 2011 2.04 3.44 5.91 23.30 Kiribati 2017 2.12 3.60 6.75 24.01 Korea, Rep. 2011 1910.14 3217.08 5529.35 21815.79 Korea, Rep. 2017 2145.06 3641.61 6834.25 24304.00 Kosovo 2011 0.77 1.29 2.22 8.77 Kosovo 2017 0.80 1.35 2.54 9.03 Kyrgyz Republic 2011 43.94 74.01 127.20 501.85 Kyrgyz Republic 2017 45.48 77.21 144.90 515.29 Lao PDR 2011 8193.58 13799.72 23718.27 93579.36 Lao PDR 2017 7466.18 12675.14 23787.58 84593.50 Latvia 2011 1.21 2.04 3.50 13.80 Latvia 2017 1.26 2.14 4.01 14.25 Lebanon 2011 3613.81 6086.42 10461.04 41273.54 Lebanon 2017 3308.51 5616.78 10541.07 37486.20 Table S6.1. Continued Economy PPP LIC LMIC UMIC HIC Lesotho 2011 11.62 19.57 33.63 132.70 Lesotho 2017 13.04 22.14 41.55 147.76 Liberia 2011 250.73 422.27 725.78 2863.55 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Liberia 2017 189.34 321.44 603.25 2145.28 Lithuania 2011 1.12 1.88 3.23 12.75 Lithuania 2017 1.12 1.90 3.56 12.65 Luxembourg 2011 2.09 3.52 6.05 23.85 Luxembourg 2017 2.16 3.67 6.89 24.50 Madagascar 2011 2333.90 3930.79 6756.04 26655.65 Madagascar 2017 2474.20 4200.39 7882.93 28033.31 Malawi 2011 743.51 1252.22 2152.25 8491.62 Malawi 2017 694.72 1179.40 2213.39 7871.28 Malaysia 2011 3.51 5.91 10.15 40.06 Malaysia 2017 3.73 6.33 11.88 42.24 Maldives 2011 20.26 34.12 58.65 231.40 Maldives 2017 20.79 35.29 66.22 235.51 Mali 2011 452.14 761.50 1308.83 5163.95 Mali 2017 437.23 742.27 1393.02 4953.88 Malta 2011 1.33 2.25 3.86 15.23 Malta 2017 1.40 2.37 4.45 15.83 Mauritania 2011 280.46 472.35 811.85 3203.12 Mauritania 2017 288.80 490.29 920.14 3272.20 Mauritius 2011 44.11 74.28 127.68 503.74 Mauritius 2017 41.16 69.88 131.15 466.39 Mexico 2011 23.90 40.24 69.17 272.91 Mexico 2017 23.83 40.46 75.93 270.03 Micronesia 2011 2.10 3.54 6.09 24.03 Micronesia 2017 2.18 3.70 6.95 24.72 Moldova 2011 16.46 27.72 47.65 188.01 Moldova 2017 15.38 26.11 49.01 174.29 Mongolia 2011 2065.69 3479.06 5979.63 23592.35 Mongolia 2017 2232.10 3789.38 7111.57 25290.20 Montenegro 2011 0.94 1.59 2.73 10.77 Montenegro 2017 0.92 1.57 2.95 10.48 Morocco 2011 8.69 14.63 25.15 99.23 Morocco 2017 9.49 16.11 30.23 107.49 Mozambique 2011 50.86 85.67 147.24 580.92 Mozambique 2017 52.08 88.41 165.92 590.03 Myanmar 2011 952.66 1604.49 2757.71 10880.43 Myanmar 2017 1030.63 1749.67 3283.62 11677.24 Namibia 2011 15.32 25.80 44.35 174.98 Namibia 2017 16.40 27.84 52.24 185.79 Nauru 2011 3.01 5.07 8.72 34.40 Nauru 2017 3.13 5.31 9.96 35.44 Nepal 2011 88.78 149.53 257.01 1014.01 Nepal 2017 75.71 128.53 241.21 857.81 Netherlands 2011 1.91 3.21 5.52 21.77 Netherlands 2017 1.93 3.28 6.15 21.88 Nicaragua 2011 27.44 46.21 79.43 313.38 Nicaragua 2017 28.42 48.25 90.55 322.00 Niger 2011 478.41 805.74 1384.86 5463.90 Niger 2017 544.56 924.49 1735.00 6170.01 Nigeria 2011 434.05 731.04 1256.47 4957.36 Nigeria 2017 376.01 638.34 1197.97 4260.23 North Macedonia 2011 49.54 83.44 143.41 565.80 North Macedonia 2017 49.31 83.71 157.10 558.68 Table S6.1. Continued Economy PPP LIC LMIC UMIC HIC Norway 2011 22.43 37.78 64.93 256.19 Norway 2017 24.56 41.70 78.26 278.29 Pakistan 2011 86.60 145.86 250.69 989.08 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Pakistan 2017 91.16 154.75 290.43 1032.82 Panama 2011 1.19 2.01 3.45 13.63 Panama 2017 1.08 1.83 3.44 12.23 Papua New Guinea 2011 6.36 10.71 18.41 72.63 Papua New Guinea 2017 6.60 11.21 21.04 74.82 Paraguay 2011 5759.64 9700.45 16672.65 65781.19 Paraguay 2017 5937.75 10080.37 18917.96 67276.12 Peru 2011 3.84 6.46 11.10 43.81 Peru 2017 4.29 7.28 13.67 48.61 Philippines 2011 45.31 76.31 131.16 517.48 Philippines 2017 46.14 78.33 147.00 522.77 Poland 2011 4.07 6.86 11.79 46.53 Poland 2017 4.26 7.23 13.58 48.28 Portugal 2011 1.40 2.36 4.05 15.99 Portugal 2017 1.43 2.43 4.56 16.23 Romania 2011 4.13 6.96 11.96 47.18 Romania 2017 4.33 7.35 13.78 49.02 Russian Federation 2011 61.08 102.87 176.80 697.57 Russian Federation 2017 60.24 102.27 191.94 682.58 Rwanda 2011 768.69 1294.63 2225.15 8779.23 Rwanda 2017 714.66 1213.26 2276.94 8097.26 Samoa 2011 3.97 6.69 11.49 45.34 Samoa 2017 4.12 7.00 13.13 46.71 São Tomé and Príncipe 2011 38.16 64.27 110.47 435.84 São Tomé and Príncipe 2017 31.35 53.22 99.89 355.22 Senegal 2011 513.16 864.27 1485.47 5860.84 Senegal 2017 537.73 912.88 1713.22 6092.57 Serbia 2011 109.10 183.74 315.81 1246.01 Serbia 2017 108.84 184.77 346.77 1233.17 Seychelles 2011 20.06 33.79 58.07 229.11 Seychelles 2017 20.74 35.20 66.07 234.95 Sierra Leone 2011 8628.24 14531.77 24976.48 98543.55 Sierra Leone 2017 6915.81 11740.79 22034.09 78357.73 Slovak Republic 2011 1.19 2.01 3.46 13.64 Slovak Republic 2017 1.34 2.27 4.26 15.14 Slovenia 2011 1.40 2.36 4.06 16.03 Slovenia 2017 1.42 2.41 4.52 16.08 Solomon Islands 2011 17.29 29.12 50.06 197.50 Solomon Islands 2017 17.96 30.49 57.21 203.46 South Africa 2011 14.93 25.15 43.23 170.55 South Africa 2017 15.82 26.85 50.39 179.20 South Sudan 2011 429.05 722.62 1241.99 4900.23 South Sudan 2017 334.51 567.89 1065.78 3790.12 Spain 2011 1.61 2.70 4.65 18.33 Spain 2017 1.54 2.62 4.91 17.47 Sri Lanka 2011 126.36 212.82 365.78 1443.19 Sri Lanka 2017 133.94 227.39 426.74 1517.57 St. Lucia 2011 4.19 7.06 12.13 47.87 St. Lucia 2017 4.50 7.64 14.34 51.00 Sudan 2011 79.29 133.55 229.54 905.63 Sudan 2017 85.78 145.62 273.29 971.87 Suriname 2011 12.19 20.53 35.29 139.25 Table S6.1. Continued Economy PPP LIC LMIC UMIC HIC Suriname 2017 9.50 16.13 30.27 107.64 Sweden 2011 18.49 31.14 53.52 211.15 Sweden 2017 20.68 35.11 65.89 234.31 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Switzerland 2011 2.88 4.86 8.35 32.94 Switzerland 2017 2.94 4.99 9.36 33.28 Syrian Arab Republic 2011 759.41 1279 2198.28 8673.21 Syrian Arab Republic 2017 788.58 1338.75 2512.45 8934.79 Taiwan, China 2011 32.98 55.55 95.48 376.71 Taiwan, China 2017 36.35 61.72 115.82 411.89 Tajikistan 2011 5.85 9.85 16.93 66.82 Tajikistan 2017 6.78 11.51 21.60 76.83 Tanzania 2011 1921.85 3236.81 5563.26 21949.60 Tanzania 2017 1794.46 3046.41 5717.24 20331.69 Thailand 2011 26.23 44.17 75.92 299.53 Thailand 2017 28.83 48.94 91.85 326.65 Timor-Leste 2011 1.23 2.08 3.57 14.10 Timor-Leste 2017 0.94 1.60 3.01 10.70 Togo 2011 500.81 843.47 1449.71 5719.77 Togo 2017 548.62 931.38 1747.93 6215.99 Tonga 2011 3.93 6.62 11.37 44.88 Tonga 2017 4.08 6.93 13.00 46.23 Trinidad and Tobago 2011 11.77 19.82 34.07 134.42 Trinidad and Tobago 2017 10.29 17.46 32.78 116.56 Tunisia 2011 2.10 3.54 6.09 24.03 Tunisia 2017 1.93 3.28 6.15 21.87 Turkey 2011 5.33 8.98 15.43 60.87 Turkey 2017 5.33 9.04 16.97 60.35 Turkmenistan 2011 5.11 8.60 14.78 58.31 Turkmenistan 2017 4.58 7.78 14.59 51.89 Tuvalu 2011 2.78 4.68 8.05 31.77 Tuvalu 2017 2.89 4.90 9.20 32.72 Uganda 2011 2812.38 4736.65 8141.11 32120.40 Uganda 2017 2876.69 4883.68 9165.27 32593.57 Ukraine 2011 16.20 27.29 46.90 185.06 Ukraine 2017 18.52 31.45 59.01 209.87 United Arab Emirates 2011 5.06 8.53 14.65 57.82 United Arab Emirates 2017 6.03 10.24 19.22 68.35 United Kingdom 2011 1.72 2.89 4.97 19.60 United Kingdom 2017 1.76 2.99 5.61 19.96 United States 2011 2.19 3.68 6.33 24.97 United States 2017 2.27 3.85 7.23 25.72 Uruguay 2011 65.02 109.51 188.21 742.59 Uruguay 2017 68.09 115.59 216.93 771.43 Uzbekistan 2011 3337.31 5620.74 9660.64 38115.62 Uzbekistan 2017 4968.84 8435.47 15830.96 56298.13 Vanuatu 2011 270.05 454.81 781.71 3084.20 Vanuatu 2017 280.42 476.06 893.43 3177.22 Venezuela, RB 2011 2103457343.59 3542664999.72 6088955468.27 24023697029.37 Venezuela, RB 2017 2184265599.02 3708171830.90 6959171792.23 24748237205.67 Vietnam 2011 20343.08 34262.03 58887.87 232339.41 Vietnam 2017 18441.94 31308.41 58756.87 208951.45 West Bank and Gaza 2011 5.03 8.47 14.56 57.43 Table S6.1. Continued Economy PPP LIC LMIC UMIC HIC West Bank and Gaza 2017 4.47 7.59 14.24 50.66 Yemen, Rep. 2011 931.33 1568.56 2695.96 10636.77 Yemen, Rep. 2017 967.11 1641.84 3081.26 10957.58 Downloaded from https://academic.oup.com/wber/article/39/3/497/7759190 by World Bank Publications user on 04 August 2025 Zambia 2011 11.46 19.29 33.16 130.83 Zambia 2017 12.33 20.93 39.29 139.72 Zimbabwe 2011 12.95 21.82 37.50 147.94 Zimbabwe 2017 13.02 22.11 41.49 147.54 Source: Authors’ calculations from PovcalNet, June 2021, 2011 ICP Report, and 2017 ICP. Note: Poverty increases (decreases) when the global poverty line in local currency units increases (decreases) with the 2017 PPP, relative to the revised 2011 PPP. These results are mechanically similar to the results from the analysis of the delta ratio. For example, a relatively high delta ratio of 1.80 for Angola indicates a reduction in extreme poverty (see table S4.6), as the local currency units of the IPL in 2017 PPP reduce relative to the revised 2011 PPP. References for the Supplementary Online Appendix Alkire, S., U. Kanagaratnam, and N. Suppa. 2020. “The Global Multidimensional Poverty Index (MPI).” OPHI MPI Methodological Note 49. Oxford Poverty and Human Development Initiative. University of Oxford. Oxford, UK. Atamanov, A., D. Jolliffe, C. Lakner, and E.B. Prydz. 2018. “Purchasing Power Parities Used in Global Poverty Mea- surement.” Global Poverty Monitoring Technical Note 5. World Bank. Washington, DC, USA. Atamanov, A., C. Lakner, D.G. Mahler, S.K. Tetteh-Baah, and J. Yang. 2020. “The Effect of New PPP Estimates on Global Poverty: A First Look.” Global Poverty Monitoring Technical Note 12. World Bank. Washington, DC, USA. Berry, F., B. Graf, M. Stanger, and M. Ylä-Jarkko. 2019. “Price Statistics Compilation in 196 Economies: The Relevance for Policy Analysis.” Working Paper 163. International Monetary Fund. Washington, DC, USA Deaton, A., and B. Aten. 2017. “Trying to Understand the PPPs in ICP 2011: Why Are the Results so Different.” American Economic Journal: Macroeconomics 9(1): 243–64. Ferreira, F.H.G., S. Chen, A. Dabalen, Y. Dikhanov, N. Hamadeh, D. Jolliffe, A. Narayan et al. 2016. “A Global Count of the Extreme Poor in 2012: Data Issues, Methodology and Initial Results.” Journal of Economic Inequality 14(2): 141–72. Jolliffe, D., and E.B. Prydz. 2016. “Estimating International Poverty Lines from Comparable National Thresholds.” Journal of Economic Inequality 14(2): 185–98. OECD. 2013. “The 2008 SNA – Changes from the 1993 SNA.” In National Accounts at a Glance 2013, 90–92. Paris: OECD Publishing. Ravallion, M., S. Chen, and P. Sangraula. 2009. “Dollar a Day Revisited.” World Bank Economic Review 23(2): 163–84. Tetteh-Baah, S.K., A. Atamanov, C. Lakner, D.G. Mahler, M. Rissanen, W. Vigil-Oliver, M. Yamanaka, and J. Yang. 2020. “Why Have the 2011 PPPs Been Revised and What Does It Mean for Estimates of Poverty?” World Bank Data Blog. 2020. World Bank. 2008. Global Purchasing Power Parities and Real Expenditures: 2005 International Comparison Pro- gram. Washington, DC: World Bank. World Bank. 2013. Measuring the Real Size of the World Economy: The Framework, Methodology, and Results of the International Comparison Program (ICP). Washington, DC: World Bank. World Bank. 2015. Purchasing Power Parities and the Real Size of World Economies: A Comprehensive Report of the 2011 International Comparison Program. Washington, DC: World Bank. World Bank. 2020. Purchasing Power Parities and the Size of World Economies: Results from the 2017 International Comparison Program. Washington, DC: World Bank. C 2024 International Bank for Reconstruction and Development / The World Bank. Published by Oxford University Press