Global Poverty Monitoring Technical Note 7 March 2019 PovcalNet Update What’s New Aziz Atamanov, R. Andres Castaneda Aguilar, Paul A. Corral Rodas, Reno Dewina, Carolina Diaz-Bonilla, Dean M. Jolliffe, Christoph Lakner, Kihoon Lee, Jose Montes, Laura Liliana Moreno Herrera, Rose Mungai, David Newhouse, Minh C. Nguyen, Espen Beer Prydz, Prem Sangraula and Judy Yang March 21, 2019 Keywords: What’s New; March 2019; CPI, National Accounts, Inequality. Development Data Group Development Research Group Poverty and Equity Global Practice Group GLOBAL POVERTY MONITORING TECHNICAL NOTE 7 Abstract The March 2019 update to PovcalNet involves several changes to the data underlying the global poverty estimates. Some welfare aggregates have been changed for improved harmonization, and the national accounts and population input data have been updated. This document explains these changes in detail and the reasoning behind them. Emphasis is given to the update of the CPIs series released by the IMF on November 2018 and the changes to the national inequality measures in China, India, and Indonesia. In addition to the changes listed here, 50 new country-years have been added, bringing the total number of surveys to 1657. All authors are with the World Bank. Corresponding authors: Espen Beer Prydz (eprydz@worldbank.org) and Minh C. Nguyen (mnguyen3@worldbank.org). The authors are thankful for comments and guidance received from Benu Bidani, Francisco Ferreira, Haishan Fu, Rinku Murgai, and Carolina Sánchez-Páramo. This note has been cleared by Haishan Fu. The Global Poverty Monitoring Technical Note Series publishes short papers that document methodological aspects of the World Bank’s global poverty estimates. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Global Poverty Monitoring Technical Notes are available at http://iresearch.worldbank.org/PovcalNet/. Contents 1. Introduction ............................................................................................................................. 2 2. Changes to the welfare aggregates .......................................................................................... 4 2.1. Azerbaijan (2002-2005) .................................................................................................. 4 2.2. Belarus (2015, 2016) ....................................................................................................... 4 2.3. Brazil (2001-2015) .......................................................................................................... 5 2.4. Colombia (2001-2005, 2008-2015) ................................................................................ 5 2.5. Dominican Republic (2000-2016) .................................................................................. 6 2.6. El Salvador (2008) .......................................................................................................... 7 2.7. Georgia (2002-2016) ....................................................................................................... 7 2.8. Guatemala (2000, 2014).................................................................................................. 8 2.9. Honduras (2001) ............................................................................................................. 8 2.10. Kazakhstan (2001-2015) ................................................................................................. 8 2.11. Kosovo (2015) ................................................................................................................ 8 2.12. Latvia (1997) ................................................................................................................... 9 2.13. Macedonia, FYR (2010-2016) ........................................................................................ 9 2.14. Malawi (1997, 2004, 2010) ............................................................................................. 9 2.15. Panama (2000-2015). ...................................................................................................... 9 2.16. Peru (2000-2016) .......................................................................................................... 10 2.17. Romania (2006, 2007, 2016) ........................................................................................ 11 2.18. Rwanda (2010, 2013) .................................................................................................... 11 2.19. SEDLAC (household members) ................................................................................... 11 2.20. Tonga (2009) ................................................................................................................. 11 3. Changes to CPI data .............................................................................................................. 12 3.1. Revisions of China CPI Series ...................................................................................... 12 4. Changes to National Accounts Data ..................................................................................... 13 5. National inequality measures for China, India and Indonesia .............................................. 13 5.1. Methodology for micro data ......................................................................................... 15 5.2. Methodology for grouped data...................................................................................... 15 5.3. Overview of the estimates ............................................................................................. 16 6. Country-years added/removed .............................................................................................. 16 References ..................................................................................................................................... 19 Appendix 1 – CPI Data sources .................................................................................................... 20 Table A1. 1. Source of temporal deflator used in PovcalNet ................................................... 20 Appendix 2 – National Accounts Data Sources ............................................................................ 32 1 1. Introduction The March 2019 global poverty update from the World Bank presents an update of the poverty estimates for 2015 and revises the previously published global and regional estimates from 1981 to 2013. The update includes 51 new surveys that have been received and processed, the adoption of an updated series of consumer price index (CPI) values released by the IMF in November 2018, and several changes to the existing data. This document outlines the changes made to the underlying data by country and explains the reasons why the changes have been made. In general, most of the changes reflect improvements in the welfare aggregate based on new harmonization efforts and updates of supporting data, such as national accounts data and CPI, to most recent vintages. Some of the changes also involve corrections of minor errors in the construction of the welfare aggregate. Table 1 illustrates the impact of the data updates on global poverty for the reference year 2015, which were first published in September 2018, with a minor revision published in February 2019. With the present update, the estimate of the global $1.90 headcount ratio decreases slightly from 9.98% to 9.94%, whereas the number of poor decreased from 734 million to 731 million people. Compared with recent updates this is a relatively small change (e.g. in September 2018, the estimate for the 2013 reference year changed by 21 million, see Chen et al., 2018). The reduction of the estimated poor population by 3 million people (or 0.04 percentage points) at the global level can be largely explained by changes in Middle East and North Africa (decline by 3.0 million). A change to the national accounts series for Yemen explains the decline in reference year estimates for the Middle East and North Africa. At the country level there are many other smaller changes, due to revisions of welfare aggregates and CPI series. 2 Table 1. Poverty headcount and number of poor differences between September 2018* and March 2019 $1.90 $3.20 $5.50 number of headcount number of poor headcount number of poor headcount poor (mil) ratio (%) (mil) ratio (%) (mil) ratio (%) Region Sep Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep Mar 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 East Asia and 47.2 47.0 2.3 2.3 254.2 253.8 12.5 12.4 710.4 710.3 34.9 34.8 Pacific Europe and Central 7.1 7.1 1.5 1.5 26.2 26.2 5.4 5.4 68.2 68.1 14.0 14.0 Asia Latin America and 24.3 24.3 3.9 3.9 66.3 66.3 10.6 10.6 164.8 164.6 26.3 26.3 the Caribbean Middle East and 18.6 15.7 5.0 4.2 60.6 58.1 16.3 15.6 157.9 156.6 42.5 42.1 North Africa Other high Income 7.3 7.4 0.7 0.7 9.8 10.0 0.9 0.9 16.1 16.2 1.5 1.5 South Asia n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Sub-Saharan Africa 413.3 413.0 41.1 41.0 667.0 667.4 66.3 66.3 849.5 850.1 84.5 84.5 World less Other 726.9 723.7 11.6 11.5 1921.7 1919.3 30.6 30.6 3369.7 3368.6 53.7 53.7 High Income World Total 734.3 731.0 10.0 9.9 1931.5 1929.3 26.3 26.2 3385.8 3384.8 46.0 46.0 *Note: the September 2018 numbers include minor revisions for LAC made to the estimates in February 2019 and are therefore not exactly the same as the numbers presented in Chen et al, 2018. See “What’s New” on PovcalNet website for details. The new surveys added have helped improve availability of country estimates for many countries, and slightly improved the overall measure of population coverage of household surveys, compared with the update of September 2018 (Table 2). 3 Table 2. Population coverage of household surveys by region Coverage Region Sep Mar 2018 2019 East Asia and Pacific 97.6 97.6 Europe and Central Asia 89.9 89.9 Latin America and the Caribbean 89.8 89.9 Middle East and North Africa 64.6 67.7 Other high Income 71.7 74.7 South Asia 21.4 21.4 Sub-Saharan Africa 52.7 54.7 World Total 66.7 67.6 Note: The criterion for estimating survey population coverage is whether at least one survey used in the reference year estimate was conducted less than three years of the reference year, which means the South Asia coverage for 2015 is below the threshold. 2. Changes to the welfare aggregates 2.1. Azerbaijan (2002-2005) The welfare aggregate has been revised to a new version (including consumption items that were missed and correcting some minor mistakes). These changes result in substantial changes in inequality (Table 3). Table 3 Gini index in Azerbaijan: Comparison of September 2018 and March 2019 versions September March Year Difference 2018 2019 2002 17.36 25.28 7.91 2003 18.81 26.83 8.03 2004 16.23 26.62 10.39 2005 16.64 26.55 9.92 2.2. Belarus (2015, 2016) For the 2015 and 2016 surveys, purchase of real estate was removed from the welfare aggregate. The item “Expenditures for construction and purchase of real estate” was mistakenly included as rent. The effects on the poverty headcount at $5.5 are small, while the impact on inequality is somewhat larger (Table 4). 4 Table 4. Poverty and Inequality in Belarus: Comparison of September 2018 and March 2019 versions Poverty headcount at $5.5 (%) Gini index Year Sep-2018 Mar-2019 Sep-2018 Mar-2019 2015 0.72 0.87 26.73 25.63 2016 0.67 0.71 26.99 25.31 2.3. Brazil (2001-2015) The labor income aggregate has been revised to include some earnings from the non-primary occupation that were previously excluded. This has a very small impact on poverty. In addition, the variables for education and availability of water have been revised, which results in a (small) change in the hedonic model for imputing rents, which in turn affects poverty. The overall changes to the poverty headcount and the Gini index are in the second decimal or smaller (Table 5). Table 5. Poverty and Inequality in Brazil: Comparison of September 2018 and March 2019 versions Poverty headcount at $1.9 (%) Gini index Year Sep-2018 Mar-2019 Sep-2018 Mar-2019 2001 11.60 11.59 58.41 58.41 2002 10.31 10.31 58.11 58.11 2003 11.09 11.09 57.56 57.56 2004 9.73 9.73 56.48 56.48 2005 8.64 8.64 56.32 56.32 2006 7.20 7.20 55.65 55.65 2007 6.81 6.81 54.93 54.93 2008 5.59 5.59 54.05 54.04 2009 5.41 5.41 53.67 53.69 2011 4.73 4.72 52.94 52.95 2012 3.77 3.77 52.60 52.69 2013 3.83 3.82 52.77 52.77 2014 2.76 2.76 51.46 51.47 2015 3.36 3.37 51.33 51.32 2.4. Colombia (2001-2005, 2008-2015) The urban/rural price deflation was changed from the method applied to all SEDLAC (Socio- Economic Database for Latin America and the Caribbean) surveys (rural incomes inflated by 15%, see Ferreira et al., 2016) to the spatial price deflator used by the national statistics office (NSO) at 5 the department level (Spanish departamentos).1 The revision was first mentioned in Atamanov et al., 2018, but was first implemented with this update. This results in a small change in the poverty rates. In addition, the new income aggregate includes a few new items. From 2001 to 2005 changes in poverty and inequality are negligible, but from 2008 to 2015 changes in the poverty headcount are around 0.2 percentage points (Table 6). Table 6. Poverty and Inequality in Colombia: Comparison of September 2018 and March 2019 versions Poverty headcount at $1.9 (%) Gini index Year Sep-2018 Mar-2019 Sep-2018 Mar-2019 2008 10.40 10.35 55.39 55.54 2009 9.02 8.87 54.47 54.39 2010 7.85 7.72 54.78 54.73 2011 6.36 6.26 53.62 53.52 2012 6.35 6.20 52.85 52.75 2013 5.74 5.68 52.85 52.82 2014 5.03 5.03 52.80 52.73 2015 4.53 4.54 51.14 51.10 2.5. Dominican Republic (2000-2016) The NSO adjusted the sampling weights in the household survey to match the official population projections of the NSO. The changes in poverty and inequality are small (Table 7). Table 7. Poverty and Inequality in the Dominican Republic: Comparison of September 2018 and March 2019 versions Poverty headcount at $1.9 (%) Gini index Year Sep-2018 Mar-2019 Sep-2018 Mar-2019 2000 5.52 5.46 51.98 51.52 2001 4.01 3.92 50.37 50.00 2002 5.99 5.62 49.96 49.68 2003 6.95 6.65 52.03 52.13 2004 8.80 8.47 51.99 52.06 2005 5.80 5.64 49.93 49.97 2006 4.54 4.34 51.82 51.96 2007 4.52 4.35 48.60 48.86 2008 3.85 3.68 47.82 48.11 1 Therefore, among the SEDLAC countries, only for Colombia and Peru a spatial price index (provided by the NSO) is used; for the other countries rural incomes are inflated by 15%. 6 2009 3.29 3.14 48.51 48.88 2010 2.64 2.49 46.95 47.33 2011 2.90 2.89 47.35 47.71 2012 2.68 2.60 45.61 46.13 2013 2.37 2.17 46.97 47.66 2014 2.27 2.08 44.06 44.28 2015 1.92 1.78 44.69 45.18 2016 1.60 1.64 45.28 45.72 2.6. El Salvador (2008) There was a small change in the rent imputation. The effect on the poverty headcount or inequality measures is minimal; in the fifth decimal. 2.7. Georgia (2002-2016) As a result of the Population Census conducted in 2014, all survey weights were revised going back to 2002. Additionally, the welfare vector was modified to use the most up to date COICOP (Classification of Individual Consumption According to Purpose) item classifications from the NSO of Georgia (GEOSTAT). Changes in poverty and inequality are small (Table 8). Table 8. Poverty and Inequality in Georgia: Comparison of September 2018 and March 2019 versions Poverty headcount at $1.9 (%) Gini index Year Sep-2018 Mar-2019 Sep-2018 Mar-2019 2002 11.49 10.51 37.28 37.25 2003 11.60 10.75 36.73 36.66 2004 11.30 10.14 36.34 36.18 2005 12.06 10.86 37.31 37.38 2006 11.64 10.54 36.90 36.87 2007 13.26 11.83 38.26 38.11 2008 12.40 10.41 38.21 38.53 2009 10.40 10.25 38.06 38.24 2010 13.33 12.18 40.06 39.46 2011 11.65 11.28 39.55 39.62 2012 8.95 8.62 38.84 38.95 2013 6.88 6.61 38.35 38.59 2014 5.28 4.99 37.29 37.55 2015 3.97 3.76 36.43 36.50 2016 4.19 3.93 36.49 36.64 7 2.8. Guatemala (2000, 2014) Because of the general changes in the SEDLAC project explained in 2.19 below, poverty headcount and inequality in Guatemala changed slightly in 2000 and 2014 (Table 9). Table 9. Poverty and Inequality in Guatemala: Comparison of September 2018 and March 2019 versions Poverty headcount at $1.9 (%) Gini index Year Sep-18 Mar-19 Sep-18 Mar-19 2000 9.23 9.23 54.24 54.18 2014 8.65 8.66 48.31 48.28 2.9. Honduras (2001) The raw data had been merged incorrectly. The merge was corrected and a new harmonized version was created. The changes are summarized in the table below (Table 10). Table 10. Poverty and Inequality in Honduras 2001: Comparison of September 2018 and March 2019 versions Indicator Sep-2018 Mar-2019 Difference Poverty headcount at $1.9 (%) 20.56 21.29 0.73 Gini index 55.24 55.58 0.33 2.10. Kazakhstan (2001-2015) The rent component (actual rents) was removed for the whole series starting in 2001, as there are only about 3-5% households with actual rent reporting in the welfare aggregates. The welfare aggregate already excluded imputed rents, so this change ensures that renters and owner-occupiers are treated similarly. This change has only a small impact on poverty and inequality. For instance, the largest change in the poverty headcount is an increase of 0.27 percentage points from 8.90% to 9.17% at $5.5 a day in 2012. 2.11. Kosovo (2015) Some mismatches between COICOP items for 2015 and 2016 were fixed. The outlier adjustment for the welfare vector now uses individual weights rather than household weights. The resulting changes in poverty and inequality are very small. For instance, in 2015 the poverty headcount at 8 $5.5 a day changes from 21.50% to 21.37%, whereas the Gini index changes from 26.37 to 26.45 for the same year. 2.12. Latvia (1997) The wrong distribution was provided for Latvia 1997. The correct distribution replaces previous 1997 survey. 2.13. Macedonia, FYR (2010-2016) New microdata, previously unavailable, were added to the PovcalNet repository, replacing old group-data files. 2.14. Malawi (1997, 2004, 2010) A new datapoint for Malawi 2016 has been added. Similar to the method used between the 2004 and 2010 surveys, the inflation rate in Malawi does not use the IFS CPIs but uses prices observed in the surveys. With this approach, the inflation from 2010 to 2016 is estimated to be 271.1 percent (National Statistics Office of Malawi and the World Bank (2018), p. 11). Since Malawi now has a survey on each side of 2011, the entire CPI series is rebased according to formula 2 in Lakner et al. (2018). This has caused slight changes to the 1997, 2004 and 2010 poverty estimates, while leaving inequality unchanged (Table 11). Table 11. Poverty and Inequality in Malawi: Comparison of September 2018 and March 2019 versions Poverty headcount at $1.9 (%) Gini index Year Sep-18 Mar-19 Sep-18 Mar-19 1997 62.78 63.28 65.76 65.76 2004 72.83 73.41 39.87 39.87 2010 71.38 71.72 45.48 45.48 2.15. Panama (2000-2015). Because of the general changes in the SEDLAC project explained in 2.19 below, the poverty headcount and inequality in Panama changed slightly from 2004 to 2015. From 2000 to 2003 both series remain unchanged (Table 12). 9 Table 12. Poverty and Inequality in Panama: Comparison of September 2018 and March 2019 versions Poverty headcount at $1.9 (%) Gini index Year Sep-18 Mar-19 Sep-18 Mar-19 2000 12.37 12.37 56.84 56.84 2001 15.55 15.55 56.88 56.88 2002 11.22 11.22 56.24 56.24 2003 11.17 11.17 55.77 55.77 2004 10.24 10.12 54.88 54.78 2005 10.01 9.96 53.87 53.81 2006 10.53 10.51 54.68 54.65 2007 7.80 7.77 52.82 52.69 2008 6.97 5.21 53.43 52.69 2009 3.18 3.17 51.87 51.77 2010 4.48 4.49 51.71 51.61 2011 3.04 3.04 51.38 51.35 2012 4.15 4.12 51.83 51.74 2013 2.81 2.79 51.50 51.46 2014 3.53 3.51 50.58 50.47 2015 1.98 1.98 50.81 50.81 2.16. Peru (2000-2016) Because of the general changes in the SEDLAC project explained in 2.19 below, poverty headcount and inequality in Peru changed slightly from 2000 to 2016 (Table 13). Table 13. Poverty and Inequality in Peru: Comparison of September 2018 and March 2019 versions Poverty headcount at $1.9 (%) Gini index Year Sep-18 Mar-19 Sep-18 Mar-19 2000 16.39 16.27 49.36 49.08 2001 17.14 17.18 51.55 51.32 2002 15.10 15.01 53.77 53.59 2003 11.89 11.75 53.46 53.08 2004 13.58 13.46 50.26 49.88 2005 15.49 15.34 50.78 50.45 2006 13.54 13.34 50.55 50.33 2007 11.11 11.01 50.41 50.03 2008 8.94 8.96 47.82 47.47 2009 7.04 6.99 47.31 47.02 2010 5.50 5.46 45.73 45.54 2011 5.20 5.19 44.91 44.66 2012 4.72 4.73 44.66 44.45 2013 4.32 4.31 44.11 43.89 10 2014 3.72 3.68 43.36 43.15 2015 3.55 3.55 43.50 43.36 2016 3.49 3.47 43.78 43.65 2.17. Romania (2006, 2007, 2016) Observations with a missing value in the weight variable caused issues in merging the individual data to the household level data. The estimation code has now been modified to address this problem. For 2006 and 2007, this change has no effect on poverty and inequality. However, for 2016, the poverty headcount at $5.5 slightly declines from 18.49% to 18.37%, whereas the Gini coefficient increases from 28.32 to 28.34 for the same year. 2.18. Rwanda (2010, 2013) The welfare vectors originally provided by the NSO were in January 2011 and January 2014 prices, respectively. Prior to this update, the welfare aggregate had been adjusted to decimal year prices using an outdated inflation rate. With this update, the original welfare aggregates in January 2011 and January 2014 prices are used. As a result, the poverty headcount changes slightly from 62.63% to 62.29% in 2010 and from 56.0% to 56.84% in 2013 at $1.9 a day. The Gini index remains unchanged. 2.19. SEDLAC (household members) The PovcalNet data for Latin America and the Caribbean are taken from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC). SEDLAC has been developed by the Center for Distributional, Labor and Social Studies (CEDLAS) of the Universidad Nacional de La Plata in Argentina, in partnership with World Bank’s Poverty and Equity Group’s Latin America team. The SEDLAC harmonization has been revised such that only domestic workers and individuals who pay rent (tenants) are excluded as members of the household. This change directly affects the per capita welfare aggregate of some households, which in turn affects the poverty rate. The country/years affected by this change are Panama (2000-2015), Guatemala (2000, 2014), and Peru (2000-2016). 2.20. Tonga (2009) Gifts given were removed since it is considered double counting. This change also makes the welfare aggregate more consistent with the 2015 survey. The effects on the estimates are minimal. 11 3. Changes to CPI data The baseline source of CPI data has been updated to the IMF’s International Financial Statistics (IFS) as of December 2018. Lakner et al. (2018) provide an overview of the various CPI series that are used in PovcalNet. Table A.1 in the Appendix to this note gives the up-to-date source of the deflator for all countries included in PovcalNet as of the current update. 3.1. Revisions of China CPI Series Urban and rural CPIs for China have been revised based on the most recent data from the National Bureau of Statistics (NBS) and China’s poverty estimates have been updated accordingly. The changes in the rural poverty headcount ratio were larger than those in the urban headcount, and they are concentrated before 2010. The rural headcount is adjusted downward by 0.66 percentage point at most before 2008, with no changes after that point (at two decimal points). For urban China, the biggest (downward) change in headcount is 0.28 percentage point in 1981 and there are changes only in the second decimal after the 1980s and 1990s. Table 14 shows the impact of these revisions on the poverty headcount ratio, for urban, rural and national China. Table 14. Revisions of China CPIs: Comparison of poverty headcount ratio (in %, at $1.90) Rural Urban National Year Sep-18 Mar-19 Sep-18 Mar-19 Sep-18 Mar-19 1981 95.59 95.36 59.42 59.14 88.32 88.07 1984 85.22 84.56 42.59 42.36 75.75 75.19 1987 72.55 72.06 24.27 24.08 60.84 60.42 1990 78.95 78.53 32.16 31.99 66.58 66.22 1993 71.83 71.40 20.86 20.71 57.00 56.64 1996 55.26 54.77 13.85 13.74 42.05 41.67 1999 56.38 55.91 10.96 10.87 40.54 40.21 2002 48.80 48.36 4.95 4.90 31.95 31.66 2005 30.63 30.24 2.69 2.66 18.75 18.51 2008 26.25 26.58 1.33 1.34 14.65 14.83 2010 21.30 21.30 0.74 0.75 11.18 11.18 2011 15.44 15.44 0.54 0.54 7.90 7.91 12 2012 12.98 12.98 0.42 0.42 6.47 6.48 2013 3.38 3.38 0.51 0.51 1.85 1.86 2014 2.39 2.39 0.49 0.49 1.36 1.36 2015 1.25 1.25 0.32 0.31 0.73 0.73 Methodology From 1981 to 2005, both rural and urban CPIs adopted by PovcalNet are from the China Statistical Yearbook. Due to high food price inflation in the mid-2000s, alternative CPIs were used over this period in an attempt to capture price changes for the poor: From 2006 to 2010, the rural CPI is computed as the weighted average of food and non-food rural CPIs, using the expenditure shares of the poorest 5 percent of households within rural areas. The urban CPI between 2006 and 2011 is estimated with a similar methodology, using urban CPIs and expenditure shares of the poorest 5 percent of households within urban areas. From 2010 onwards, the rural CPI series is derived from changes in the rural poverty line. Starting in 2012, the urban series is directly taken from the official urban price indices by the NBS. For more information on the CPI source, see Lakner et al. (2018). 4. Changes to National Accounts Data The national accounts data used to adjust survey data to reference years have been updated. Methodological details and choice of data sources are available in a new technical note with this update (see Prydz et al, 2019). The primary series is national accounts data from WDI December 2018, supplemented with historical data from the Madison Project Database.2 Some special country series are used in a few cases. A full overview of national accounts data used in the update, including special series, is available in Appendix 2. 5. National inequality measures for China, India and Indonesia PovcalNet uses separate rural and urban distributions for China, India and Indonesia, the three most populous developing countries. By having separate distributions, PovcalNet is able to control for spatial price differences between rural and urban areas in a flexible manner (e.g. users can 2 We use Maddison Project Database, version 2018. See Bolt et al (2018) and Prydz et al (2019) for details. 13 easily apply alternative assumptions about rural-urban price differences). In the case of China, the urban and rural areas used to be surveyed separately until 2012 when the nationally integrated survey began. In every survey year, PovcalNet reports distributional estimates (e.g. mean, median, poverty headcount, Gini) separately for rural and urban areas. Furthermore, the mean and poverty measures are also reported nationally, by simply re-weighting the rural and urban estimates. National inequality estimates, however, were previously only reported in selected years. With this update, national inequality measures for China, India and Indonesia are provided in all survey years available in PovcalNet, and the existing estimates have been revised. Full details of the methodology and comparisons with estimates published in the literature are given in the Appendix to Ferreira et al. (forthcoming). It is important to note that these are estimates produced by the PovcalNet team. They may differ from the series produced by National Statistical Offices due to different methodological choices, such as the choice of spatial price deflator and whether to adjust the WDI population totals. In the case of China, for example, the NSO reports a Gini based on income, while our series uses consumption expenditure – inequality in consumption tends to be lower than in terms of income, and may also show a different trend (e.g. see Alvaredo and Gasparini, 2015, and World Bank, 2016). In addition to the distributional data, the estimation of the national inequality measures uses the following data: Welfare aggregates are deflated into 2011 constant local currency using the CPI. Both China and India use separate CPI series for rural and urban, while in Indonesia a national deflator is used (see Lakner et al (2018) for more information on the CPIs used). In addition, the welfare aggregate is adjusted for price level differences using 2011 purchasing power parity (PPP) exchange rates. To capture rural-urban price differences, different PPPs are used for the rural and urban areas in China, India and Indonesia (see the online Appendix of Ferreira et al. (2016) for the urban/rural adjustment factors being used). Finally, in the construction of the national distribution, urban and rural distributions are weighted using the urban and rural population from the World Development Indicators (WDI).3 3 In India, the survey fieldwork does not coincide with a calendar year. For example, the fieldwork for the 2011.5 survey is split half-half across 2011 and 2012. From WDI, we average the population values reported in 2011 and 2012. 14 5.1. Methodology for micro data Among these three countries, microdata are available in PovcalNet for India in 1993.5, 2004.5, 2009.5, and 2011.5, and for Indonesia in 1993, 1996 and annually from 1998 to 2017. The sampling weights are post-stratified by the ratio of the census population to the sum of sampling weights for rural and urban separately. The formula is as follows: , ̃ , = , ∗ (1) ∑ , ̃ where , is the rescaled weight, , is the original survey weight, , is total census population ̃ (from WDI) for subsample (either urban or rural). , is the rescaled weight which is used to compute the national estimates. The main reason for rescaling survey weights in this way is to preserve consistency with the national estimates of poverty and the mean that are derived as a weighted average of the rural and urban estimates (where the weights are taken from the WDI). The survey weights and WDI may imply different urban/rural weights for several reasons, such as sampling frames based on an older census. Furthermore, for the grouped data (see below), only the WDI weights can be used, so we use them throughout for consistency. 5.2. Methodology for grouped data Due to the absence of micro data, we rely on grouped data for China, and for India and Indonesia in earlier years to derive the national distribution. Based on the grouped data, PovcalNet fits two parametric Lorenz curves, the general quadratic (GQ) and beta Lorenz curves, and reports the corresponding parameter estimates in the detailed output page. 4 We extract these parameter estimates and create a distribution of 100,000 quantiles (separately for urban and rural areas) using the following formula: () = × ′ (, ) (2) 4 For example, the relevant URL for rural China in 2015: http://iresearch.worldbank.org/PovcalNet/Detail.aspx?Format=Detail&C0=CHN_1&PPP0=3.69611&PL0=1.90&Y 0=2015&NumOfCountries=1 15 where is mean consumption, ′(, ) is the slope of the Lorenz curve with a vector of parameters and () is th quantile of the consumption distribution (e.g. x(0.5) is the median). The expressions for the slopes of the Lorenz curve for the beta and GQ functional forms are given in Datt (1992). These simulated data, weighted by the urban and rural WDI population numbers, are then used to calculate the national inequality measures. 5.3. Overview of the estimates For the years for which national inequality measures were published in September 2018, Table 15 compares the Gini estimates across the two vintages. The differences for China are in the second decimal, while they are around half a Gini point for India and Indonesia. For India and Indonesia, the revision can be explained by a mistake in the way the survey weights had been rescaled (essentially the fraction in equation 1 above was reversed). Furthermore, for India, the welfare aggregate is now adjusted by the half-year urban and rural CPI (from 2011.5 to 2011, the base year for the PPP), such that the aggregate is identical to what is used for estimating poverty. For Indonesia, there was also an update to the WDI population data. Table 15. National Gini for China, India and Indonesia: Comparison of September 2018 and March 2019 versions Gini Country Year Type Sep-2018 Mar-2019 Difference China 2008 grouped 42.83 42.91 0.08 China 2012 grouped 42.16 42.21 0.05 India 2011.5 micro 35.15 35.71 0.56 Indonesia 2013 micro 39.47 39.94 0.47 6. Country-years added/removed 50 New country-years have been added to PovcalNet. These surveys are listed in Table 16. Table 16. New Country-years added Economy Years Survey Name Argentina 2017 EPHC-S2: Encuesta Permanente de Hogares Continua - Semester 2. Armenia 2017 ILCS: Integrated Living Conditions Survey Australia 2004,2014 SIH: Survey of Income and Housing [400 bins extracted from LIS] 16 Belarus 2017 HHS: Household Sample Survey Bolivia 2017 EH: Encuesta de Hogares Brazil 2016, 2017 PNADC: Pesquisa Nacional por Amostra de Domicilios Continua5 Botswana 2017 BMTHS: Botswana Multi-Topic Household Survey Chile 2017 CASEN: Encuesta de Caracterización Socioeconómica Nacional Colombia 2017 GEIH: Gran Encuesta Integrada de Hogares Costa Rica 2017 ENAHO: Encuesta Nacional de Hogares Germany 1995, 1998, 2002, GSOEP: German Social Economic Panel Study [400 2003, 2005, 2008, bins extracted from LIS] 2009 Djibouti 2017 EDAM: Enquete Djiboutienne Aupres de Menages Ecuador 2017 ENEMDU: Encuesta Nacional de Empleo, Desempleo y Subempleo El Salvador 2017 EHPM: Encuesta de Hogares de Propósitos Múltiples Georgia 2017 HIS: Household Integrated Survey Ghana 2016 GLSS-VII: Ghana Living Standards Survey Honduras 2017 EPHPM: Encuesta Permanente de Hogares de Propósitos Múltiples Iran, Islamic Rep. 2015-2016 HIES: Household Income and Expenditure Survey Israel 2014, 2016 Household Expenditure Survey [400 bins extracted from LIS] Kazakhstan 2016-2017 HBS: Household Budget Survey Kosovo 2017 HBS: Household Budget Survey Kyrgyz Republic 2017 KIHS: Kyrgys Integrated Household Survey Liberia 2016 HIES: Household Income and Expenditure Survey St. Lucia 2016 HBS: Household Budget Survey Moldova 2017 HBS: Household Budget Survey Macedonia 2015 SILC-C: Survey of Income and Living Conditions Malawi 2016 IHS-IV: Integrated Household Survey Panama 2017 EH: Encuesta de Hogares Paraguay 2017 EPH: Encuesta Permanente de Hogares Peru 2017 ENAHO: Encuesta Nacional de Hogares Rwanda 2016 EICV-V: Enquête Intégrale sur les Conditions de Vie des ménages Sudan 2014 NBHS: National Baseline Household Survey Thailand 2016, 2017 SES: Standardization of Socio-Economic Status Tonga 2015 HIES: Household Income and Expenditure Survey Tunisia 2015 NSHBCSL: National Survey of Household Budget, Consumption and Standard of Living Uruguay 2017 ECH: Encuesta Continua de Hogares Samoa 2002, 2013 HIES: Household Income and Expenditure Survey 5 The new PNADC survey is not comparable with the previous survey PNAD (available from 1981 to 2015). 17 Note: One country-year has been removed with this update. The 2015 survey for Bosnia and Herzegovina has been removed until further revisions of the methodology used to construct the consumption aggregate for international comparisons are complete. Analyses conducted by the regional statistical team found that the current welfare aggregate may be unsuitable for regional comparisons due to a very different method for construction of the consumption aggregate; particularly the use of imputed rents, compared with methods generally used in the region. 18 References Alvaredo, Facundo, and Leonardo Gasparini. 2015. “Recent Trends in Inequality and Poverty in Developing Countries.” In Handbook of Income Distribution, vol. 2A, edited by Anthony B. Atkinson and François Bourguignon, 697–805. Handbooks in Economics. Amsterdam: North-Holland. Atamanov, Aziz; Azevedo, Joao Pedro Wagner De; Castaneda Aguilar, Raul Andres; Chen, Shaohua; Corral Rodas, Paul Andres; Dewina, Reno; Diaz-Bonilla, Carolina; Jolliffe, Dean Mitchell; Lakner, Christoph; Lee, Kihoon; Mahler, Daniel Gerszon; Montes, Jose; Mungai, Rose; Nguyen, Minh Cong; Prydz, Espen Beer; Sangraula, Prem; Scott, Kinnon; Wambile, Ayago Esmubancha; Yang, Judy; Zhao, Qinghua. 2018. April 2018 Povcalnet Update : What’s New (English). Global Poverty Monitoring Technical Note; no. 1. Washington, D.C. : World Bank Group. Bolt, Jutta, Robert Inklaar, Herman de Jong and Jan Luiten van Zanden (2018), “Rebasing ‘Maddison’: new income comparisons and the shape of long-run economic development,” Maddison Project Working paper 10. Chen, Shaohua, Dean M. Jolliffe, Christoph Lakner, Kihoon Lee, Daniel Gerszon Mahler, Rose Mungai, Minh C. Nguyen, Espen Beer Prydz, Prem Sangraula, Dhiraj Sharma, Judy Yang and Qinghua Zhao. 2018. “September 2018 PovcalNet Update: What's new.” Global Poverty Monitoring Technical Note. No. 2. Washington, DC: World Bank. Datt, Gaurav. 1992. “Computational Tools for Poverty Measurement and Analysis.” Mimeo Ferreira, Francisco, Shaohua Chen, Andrew Dabalen, Yuri Dikhanov, Nada Hamadeh, Dean Jolliffe, Ambar Narayan, Espen B. Prydz, Ana Revenga, Prem Sangraula, Umar Serajuddin, and Nobuo Yoshida. 2016. “A Global Count of the Extreme Poor in 2012: Data Issues, Methodology and Initial Results.” The Journal of Economic Inequality, 14(2), 141-172. Ferreira, Francisco, Christoph Lakner and Ani Rudra Silwal. Forthcoming. “Inequality increasing everywhere? Conflicting evidence from an updated global database of household surveys.” World Bank. Lakner, Christoph, Daniel Gerszon Mahler, Minh C. Nguyen, Joao Pedro Azevedo, Shaohua Chen, Dean M. Jolliffe, Espen Beer Prydz and Prem Sangraula. 2018. “Consumer Price Indices used in Global Poverty Measurement.” Global Poverty Monitoring Technical Note 4. Washington, DC: World Bank. National Statistics Office of Malawi and the World Bank. 2018. “Methodology for Poverty Measurement in Malawi (2016/17).” http://documents.worldbank.org/curated/en/575101534874113572/pdf/129570- WP-P164692-Malawi-Poverty-Technical-Note-DISCLOSED-PUBLIC.pdf. Prydz, Espen Beer, Dean M. Jolliffe, Christoph Lakner, Daniel Gerszon Mahler, and Prem Sangraula. 2019. “National Accounts Data used in Global Poverty Measurement.” Global Poverty Monitoring Technical Note 8. Washington, DC: World Bank. World Bank. 2016. Poverty and Shared Prosperity 2016: Taking on Inequality. Washington, DC: World Bank 19 Appendix 1 – CPI Data sources Table A1. 1 lists the source of CPI used for each country-year reported in PovcalNet. The columns in the table are defined as follows: • Code: The 3-letter country code used by the World Bank: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country- and-lending-groups • Country name: Name of country • Year(s): Welfare reporting year, i.e. the year for which the welfare has been reported. If the survey collects income for the previous year, it is the year prior to the survey. This is identical to the year variable used in PovcalNet. • CPI period: Common time period to which the welfare aggregates in the survey have been deflated. The letter Y denotes that the CPI period is identical to the year column. When the welfare aggregate has been deflated to a particular month within the welfare reporting year, the month is indicated by a number between 1 and 12, preceded by an M, and similarly with a Q for quarters. The letter W indicates that a weighted CPI is used, as described in equation 1 in Lakner et al. (2018). • CPI source: Source of the deflator used. The source is given by the abbreviation, the frequency of the CPI, and the vintage; e.g. IFS-M-201712 denotes the monthly IFS database version December 2017. For country-specific deflators, the description is given in the text or further details are available upon request. Table A1. 1. Source of temporal deflator used in PovcalNet Code Country Name Survey Year(s) CPI period Source AGO Angola - urban HBS 2000 W IFS-M-201811 Angola IBEP-MICS 2008 W IFS-M-201811 ALB Albania EWS 1996 Y IFS-M-201811 LSMS 2002-2012 Y IFS-M-201811 ARG Argentina - urban EPH 1980-1987 Y CEDLAS May 25 18 1991-2002 M9 NSO EPHC 2003-2006 M7-M12 NSO 2007-2014 M7-M12 Private estimates EPHC-S2 2007-2014 M7-M12 Private estimates 2016 M7-M12 NSO 20 2017 M7-M12 NSO ARM Armenia ILCS ALL Y IFS-M-201811 AUS Australia IHS 1981-1985 Y IFS-A-201811 SIHCA 1989 Y IFS-A-201811 SIH 1995-2008 Y IFS-A-201811 HES 2010-2014 Y IFS-A-201811 AUT Austria EU-SILC ALL (prev. year)Y IFS-M-201811 AZE Azerbaijan ASLC 1995 Y IFS-M-201811 HBS 2001-2005 Y IFS-M-201811 HSMTSA 2008 Y IFS-M-201811 BDI Burundi EDCM 1992 Y IFS-M-201811 EPENCVP 1998 W IFS-M-201811 QUIBB 2006 Y IFS-M-201811 ECVMB 2013 W IFS-M-201811 BEL Belgium EU-SILC ALL (prev. year)Y IFS-M-201811 BEN Benin QUIBB 2003 Y IFS-M-201811 EMICOV 2011 W IFS-M-201811 2015 Y IFS-M-201811 BFA Burkina Faso EP-I 1994 W IFS-M-201811 EP-II 1998 Y IFS-M-201811 ECVM 2003-2009 Y IFS-M-201811 EMC 2014 Y IFS-M-201811 BGD Bangladesh HHES 1983-1985 W WEO-A-201810 1988-1991 W IFS-A-201811 1995 W Survey HIES 2000-2016 Y Survey BGR Bulgaria HBS 1989 Y IFS-A-201811 1992-1994 Y IFS-M-201811 IHS 1995-2001 Y IFS-M-201811 MTHS 2003 Y IFS-M-201811 EU-SILC 2007 (prev. year)Y IFS-M-201811 MTHS 2007 Y IFS-M-201811 EU-SILC 2008-2017 (prev. year)Y IFS-M-201811 BIH Bosnia and Herzegovina LSMS 2001-2004 Y WEO-A-201810 HBS 2007-2015 Y IFS-M-201811 BLR Belarus FBS 1993-1995 Y IFS-M-201811 HBS 1998-1999 Y IFS-M-201811 HHS 2000-2017 Y IFS-M-201811 BLZ Belize LFS 1993-1999 Y WEO-A-201810 HBS 1995 Y WEO-A-201810 SLC 1996 Y WEO-A-201810 21 BOL Bolivia - urban EPF 1990 W IFS-M-201806 Bolivia EIH 1992 M11 IFS-M-201806 ENE 1997 M11 IFS-M-201806 ECH 1999 M10 IFS-M-201806 2000 M11 IFS-M-201806 EH 2001-2002 M11 IFS-M-201806 ECH 2004 M10 IFS-M-201806 EH 2005 M11 IFS-M-201806 2006-2015 M10 IFS-M-201806 EPF 2016 M10 IFS-M-201806 EH 2017 M11 IFS-M-201806 BRA Brazil PNAD 1981-2015 M9 IFS-M-201811 PNADC 2016-2017 Y IFS-M-201811 BTN Bhutan BLSS ALL Y Previous WDI/IFS BWA Botswana HIES 1985-2002 W IFS-M-201811 CWIS 2009 W IFS-M-201811 BMTHS 2015 W IFS-M-201811 CAF Central African EPI 1992 W IFS-M-201811 Republic Central African ESCVM 2003 Y IFS-M-201811 Republic - rural Central African ECASEB 2008 Y IFS-M-201811 Republic CAN Canada SCF 1981-1997 Y IFS-M-201811 SLID 1998-2010 Y IFS-M-201811 CIS 2013 Y IFS-M-201811 CHE Switzerland EU-SILC ALL (prev. year)Y IFS-M-201811 CHL Chile CASEN 1987 Y IFS-M-201806 1990-2017 M11 IFS-M-201806 CHN China - rural CRHS 1981-2011 Y NSO China - urban 1981-2011 Y NSO China - rural CNIHS 2012-2015 Y NSO China - urban 2012-2015 Y NSO CIV Côte d'Ivoire EPAM 1985-1988 W IFS-M-201811 EP 1992 W IFS-M-201811 ENV 1995-2015 Y IFS-M-201811 CMR Cameroon ECAM-I 1996 Y IFS-M-201811 ECAM-II 2001 Y IFS-M-201811 ECAM-III 2007 Y IFS-M-201811 ECAM-IV 2014 Y IFS-M-201811 COD Congo, Dem. Rep. E123 ALL W IFS-M-201811 COG Congo, Rep. ECOM ALL Y IFS-M-201811 22 COL Colombia - urban ENH 1980 Y IFS-M-201811 Colombia 1988 Y IFS-M-201811 Colombia - urban 1989 M11 IFS-M-201811 Colombia 1991-2000 M11 IFS-M-201811 ECH 2001-2005 M11 IFS-M-201811 GEIH 2008-2017 M11 IFS-M-201811 COM Comoros EIM 2004 Y IFS-M-201811 EDMC 2013 Y IFS-M-201811 CPV Cabo Verde IDRF 2001 W IFS-M-201811 QUIBB 2007 W IFS-M-201811 CRI Costa Rica ENH 1981-1986 Y IFS-M-201811 EHPM 1989 Y IFS-M-201811 1990-2009 M7 IFS-M-201811 ENAHO 2010-2017 M7 IFS-M-201811 CYP Cyprus EU-SILC ALL (prev. year)Y IFS-M-201811 CZE Czech Republic CMC 1993-1996 Y IFS-M-201811 EU-SILC 2005-2017 (prev. year)Y IFS-M-201811 DEU Germany LIS 1991-2006 Y IFS-M-201811 2007-2012 (prev. year)Y IFS-M-201811 2013-2015 Y IFS-M-201811 DJI Djibouti EDAM 2002-2013 Y IFS-M-201811 2017 M5 IFS-M-201811 DNK Denmark EU-SILC ALL (prev. year)Y IFS-M-201811 DOM Dominican Republic ENGSF 1986-1989 Y IFS-M-201806 ENIGH 1992 M6 IFS-M-201806 ENFT 1996 M2 IFS-M-201806 1997 M4 IFS-M-201806 2000-2016 M9 IFS-M-201806 2017 Y IFS-M-201806 DZA Algeria EDCM 1988 Y IFS-M-201811 ENMNV 1995 Y IFS-M-201811 ENCNVM 2011 W IFS-M-201811 ECU Ecuador - urban EPED 1987 Y IFS-M-201806 Ecuador ECV 1994 M6-M10 IFS-M-201806 EPED 1995 M11 IFS-M-201806 1998 M6 IFS-M-201806 ECV 1999 (prev. year)M10- IFS-M-201806 M9 EPED 2000 M11 IFS-M-201806 ENEMDU 2003-2017 M11 IFS-M-201806 EGY Egypt, Arab Rep. HIES 1990-1999 W IFS-M-201811 HIECS 2004-2012 W IFS-M-201811 23 2015 Y IFS-M-201811 ESP Spain EU-SILC ALL (prev. year)Y IFS-M-201811 EST Estonia HIES 1993-1998 Y IFS-M-201811 HBS 2000-2003 Y IFS-M-201811 EU-SILC 2004 (prev. year)Y IFS-M-201811 HBS 2004 Y IFS-M-201811 EU-SILC 2005-2017 (prev. year)Y IFS-M-201811 ETH Ethiopia HICES ALL W IFS-M-201811 FIN Finland EU-SILC ALL (prev. year)Y IFS-M-201811 FJI Fiji HIES ALL W IFS-M-201811 FRA France EU-SILC ALL (prev. year)Y IFS-M-201811 FSM Micronesia, Fed. Sts. - CPH 2000 Y IFS-A-201811 urban Micronesia, Fed. Sts. HIES 2005-2013 Y IFS-A-201811 GAB Gabon EGEP 2005 Y IFS-M-201811 2017 Y WEO-A-201810 GBR United Kingdom EU-SILC ALL (prev. year)Y IFS-M-201811 GEO Georgia SGH 1996-1997 Y IFS-M-201811 HIS 1998-2017 Y IFS-M-201811 GHA Ghana GLSS-I 1987 W IFS-M-201811 GLSS-II 1988 W IFS-M-201811 GLSS-III 1991 W IFS-M-201811 GLSS-IV 1998 W IFS-M-201811 GLSS-V 2005 W Survey GLSS-VI 2012 W Survey GLSS-VII 2016 W Survey GIN Guinea ESIP 1991 Y WEO-A-201810 EIBC 1994 W WEO-A-201810 EIBEP 2002 W WEO-A-201810 ELEP 2007-2012 Y IFS-M-201811 GMB Gambia, The HPS 1998 Y IFS-M-201811 HIS 2003 W IFS-M-201811 IHS 2010-2015 W IFS-M-201811 GNB Guinea-Bissau ILJF 1991 Y IFS-M-201811 ICOF 1993 Y IFS-M-201811 ILAP-I 2002 Y IFS-M-201811 ILAP-II 2010 Y IFS-M-201811 GRC Greece EU-SILC ALL (prev. year)Y IFS-M-201811 GTM Guatemala ENSD 1986 W IFS-M-201811 1989 Y IFS-M-201811 ENIGF 1998 M8 IFS-M-201811 ENCOVI 2000 M6-M11 IFS-M-201811 24 2006-2014 M7 IFS-M-201811 GUY Guyana GLSMS 1992 W WEO-A-201810 1998 Y IFS-M-201811 HND Honduras - urban ECSFT 1986 Y IFS-M-201811 Honduras EPHPM 1989 Y IFS-M-201811 1990-1993 M5 IFS-M-201811 1994 M9 IFS-M-201811 1995-2017 M5 IFS-M-201811 HRV Croatia HBS 1988-2010 Y IFS-M-201811 EU-SILC 2010-2017 (prev. year)Y IFS-M-201811 HTI Haiti ECVH 2001 M5 IFS-M-201811 ECVMAS 2012 M10 IFS-M-201811 HUN Hungary HBS 1987-2007 Y IFS-M-201811 EU-SILC 2005-2017 (prev. year)Y IFS-M-201811 IDN Indonesia SUSENAS 1984-1999 Y IFS-M-201811 2000-2007 M2 IFS-M-201811 2008-2017 M3 IFS-M-201811 IND India - rural NSS 1983 Y NSO India - urban 1983 Y NSO India - rural NSS-SCH1 1987-2011 W NSO India - urban 1987-2011 W NSO IRL Ireland EU-SILC ALL (prev. year)Y IFS-M-201811 IRN Iran, Islamic Rep. SECH 1986-1998 Y CBI HEIS 2005-2016 Y CBI IRQ Iraq IHSES 2006 M11-(next COSIT year)M12 2012 Y COSIT ISL Iceland EU-SILC ALL (prev. year)Y IFS-M-201811 ISR Israel HES ALL Y IFS-M-201811 ITA Italy EU-SILC ALL (prev. year)Y IFS-M-201811 JAM Jamaica JSLC 1988 M9 IFS-M-201811 1990-1993 M11-(next IFS-M-201811 year)M3 1996 M5-M8 IFS-M-201811 1999 M6-M8 IFS-M-201811 2002-2004 M6 IFS-M-201811 JOR Jordan HEIS 1986 W IFS-M-201811 1992-1997 Y IFS-M-201811 2002-2010 W IFS-M-201811 JPN Japan JHPS 2008 Y IFS-M-201811 KAZ Kazakhstan HBS 1993-2017 Y IFS-M-201811 LSMS 1996 Y IFS-M-201811 25 KEN Kenya WMS-I 1992 Y NSO WMS-II 1994 Y NSO WMS-III 1997 Y NSO IHBS 2005-2015 W NSO KGZ Kyrgyz Republic PMS 1998 Y IFS-M-201811 HBS 2000-2003 Y IFS-M-201811 KIHS 2004-2017 Y IFS-M-201811 KHM Cambodia CSES ALL Y IFS-M-201811 KIR Kiribati HIES 2006 Y IFS-M-201811 KOR Korea, Rep. FHES ALL Y IFS-M-201811 LAO Lao PDR LECS 1997 W IFS-M-201811 2002-2012 W Survey LBN Lebanon HBS 2011 (next year)M5 IFS-M-201811 LBR Liberia CWIQ 2007 Y IFS-M-201811 HIES 2014-2016 Y IFS-M-201811 LCA St. Lucia LSMS 1995 Y IFS-M-201811 SLCHB 2016 M1 IFS-M-201811 LKA Sri Lanka LFSS 1985 Y IFS-M-201811 HIES 1990 W IFS-M-201811 SES 1995 W IFS-M-201811 HIES 2002 Y IFS-M-201811 2006-2012 W IFS-M-201811 2016 Y IFS-M-201811 LSO Lesotho HBS 1986 W WEO-A-201810 NHECS 1994 W WEO-A-201810 HBS 2002 W IFS-M-201811 CMSHBS 2010 Y IFS-M-201811 2017 W IFS-M-201811 LTU Lithuania HBS 1993-2004 Y IFS-M-201811 EU-SILC 2005-2008 (prev. year)Y IFS-M-201811 HBS 2008 Y IFS-M-201811 EU-SILC 2009-2017 (prev. year)Y IFS-M-201811 LUX Luxembourg EU-SILC ALL (prev. year)Y IFS-M-201811 LVA Latvia HBS 1993-2009 Y IFS-M-201811 EU-SILC 2005-2017 (prev. year)Y IFS-M-201811 MAR Morocco ECDM 1984 W IFS-M-201811 ENCV 1990 W IFS-M-201811 ENNVM 1998-2006 W IFS-M-201811 ENCDM 2013 W IFS-M-201811 MDA Moldova HBS 1997-2017 Y IFS-M-201811 MDG Madagascar EBMR 1980 Y IFS-M-201811 26 EPM 1993 W IFS-M-201811 1997-2010 Y IFS-M-201811 ENSOMD 2012 Y IFS-M-201811 MDV Maldives HIES ALL W IFS-M-201811 MEX Mexico ENIGH 1984-2014 M8 IFS-M-201811 NSENIGH 2016 M8 IFS-M-201811 MKD Macedonia, FYR HBS 1998-2008 Y IFS-M-201811 EU-SILC 2009 Y IFS-M-201811 SILC-C 2010-2016 (prev. year)Y IFS-M-201811 MLI Mali - rural EMCES 1994 Y IFS-A-201811 Mali EMEP 2001 W IFS-M-201811 ELIM 2006 Y IFS-M-201811 2009 W IFS-M-201811 MLT Malta EU-SILC ALL (prev. year)Y IFS-M-201811 MMR Myanmar MPLCS 2015 M1 IFS-M-201811 MNE Montenegro HBS ALL Y IFS-M-201811 MNG Mongolia LSMS 1995-1998 Y IFS-M-201811 LFS 2002 Y IFS-M-201811 LSS 2007 W IFS-M-201811 HSES 2010-2016 Y IFS-M-201811 MOZ Mozambique NHS 1996 W WEO-A-201810 IAF 2002 W WEO-A-201810 IOF 2008-2014 W WEO-A-201810 MRT Mauritania EPCV 1987-1993 Y IFS-M-201811 1995 W IFS-M-201811 2000-2014 Y IFS-M-201811 MUS Mauritius HBS 2006 W IFS-M-201811 2012-2017 Y IFS-M-201811 MWI Malawi IHS-I 1997 W IFS-M-201811 IHS-II 2004 W Survey IHS-III 2010 W Survey IHS-IV 2016 M04 Survey MYS Malaysia HIBAS 1984-2007 Y IFS-M-201811 HIS 1987 Y IFS-M-201811 NAM Namibia NHIES 1993 W WEO-A-201810 2003-2015 W IFS-M-201811 NER Niger ENBC 1992 W IFS-M-201811 EPCES 1994 W IFS-M-201811 ENCVM 2005 Y IFS-M-201811 ENBCM 2007 W IFS-M-201811 ECVMA 2011-2014 Y IFS-M-201811 27 NGA Nigeria NCS 1985 W IFS-M-201811 1992-1996 Y IFS-M-201811 LSS 2003-2009 W IFS-M-201811 NIC Nicaragua EMNV 1993 M2 NSO 1998 M6 NSO 2001 M6 IFS-M-201811 2005-2009 M8 IFS-M-201811 2014 M8-M10 IFS-M-201811 NLD Netherlands EU-SILC ALL (prev. year)Y IFS-M-201811 NOR Norway EU-SILC ALL (prev. year)Y IFS-M-201811 NPL Nepal MHBS 1984 W IFS-M-201806 LSS-I 1995 W IFS-M-201806 LSS-II 2003 W IFS-M-201806 LSS-III 2010 W IFS-M-201806 PAK Pakistan HIES 1987 Y IFS-M-201811 1990-1998 W IFS-M-201811 IHS 1996 W IFS-M-201811 PIHS 2001 W IFS-M-201811 PSLM 2004-2015 W IFS-M-201811 PAN Panama EMO 1989 Y IFS-M-201806 1991 M7 IFS-M-201806 EH 1995-2017 M7 IFS-M-201806 PER Peru ENNIV 1985 W IFS-M-201811 1994 Y IFS-M-201811 ENAHO 1997-2002 Q4 IFS-M-201811 2003 M5-M12 IFS-M-201811 2004-2017 Y IFS-M-201811 PHL Philippines FIES ALL Y IFS-M-201811 PNG Papua New Guinea HIES 1996 Y IFS-A-201811 2009 W IFS-A-201811 POL Poland HBS 1985-1987 Y IFS-A-201811 1989-2016 Y IFS-M-201811 EU-SILC 2005-2017 (prev. year)Y IFS-M-201811 PRT Portugal EU-SILC ALL (prev. year)Y IFS-M-201811 PRY Paraguay EH 1990 M7 IFS-M-201811 1995 M8-M11 IFS-M-201811 EIH 1997 (next year)M2 IFS-M-201811 EPH 1999 M9 IFS-M-201811 EIH 2001 M3 IFS-M-201811 EPH 2002 M11 IFS-M-201811 2003 M9 IFS-M-201811 28 2004 M10 IFS-M-201811 2005 M11 IFS-M-201811 2006 M12 IFS-M-201811 2007-2008 M10 IFS-M-201811 2009 M11 IFS-M-201811 2010-2017 M10 IFS-M-201811 PSE West Bank and Gaza PECS 2004-2011 Y IFS-M-201811 2016 W IFS-M-201811 ROU Romania HBS 1989 Y Milanovic (1998) IHS 1994-2000 Y IFS-M-201811 HBS 1999-2006 Y IFS-M-201811 EU-SILC 2007-2017 (prev. year)Y IFS-M-201811 RUS Russian Federation RLMS 1993-2001 Y IFS-M-201811 HBS 1996-2015 Y IFS-M-201811 RWA Rwanda - urban ENBCM 1984 W IFS-M-201811 Rwanda EICV-I 2000 W IFS-M-201811 EICV-II 2005 W IFS-M-201811 EICV-III 2010 (next year)M1 IFS-M-201811 EICV-IV 2013 (next year)M1 IFS-M-201811 EICV-V 2016 (next year)M1 IFS-M-201811 SDN Sudan NBHS 2009 Y IFS-M-201811 2014 M11 IFS-M-201811 SEN Senegal EP 1991 W IFS-M-201811 ESAM 1994 W IFS-M-201811 ESAM-II 2001 Y IFS-M-201811 ESPS-I 2005 W IFS-M-201811 ESPS-II 2011 W IFS-M-201811 SLB Solomon Islands HIES ALL Y IFS-M-201811 SLE Sierra Leone HEEAS 1989 W WEO-A-201810 SLIHS 2003 W WEO-A-201810 2011 Y IFS-M-201806 SLV El Salvador - urban EHPM 1989 Y IFS-M-201811 El Salvador 1991 M10-(next IFS-M-201811 year)M4 1995-1999 Y IFS-M-201811 2000-2007 M12 IFS-M-201811 2008-2016 M11 IFS-M-201811 2017 M12 IFS-M-201811 SRB Serbia LSMS 2002 Y IFS-M-201811 HBS 2003-2015 Y IFS-M-201811 EU-SILC 2013-2017 (prev. year)Y IFS-M-201811 SSD South Sudan NBHS 2009 Y IFS-M-201811 29 STP São Tomé and Principe IOF 2000 W IFS-M-201811 2010 Y IFS-M-201811 SUR Suriname - urban EHS 1999 Y IFS-M-201811 SVK Slovak Republic SMC 1996 Y IFS-M-201811 HBS 2004-2009 Y IFS-M-201811 EU-SILC 2005-2017 (prev. year)Y IFS-M-201811 SVN Slovenia IES 1993 Y IFS-M-201811 HBS 1998-2003 Y IFS-M-201811 EU-SILC 2005-2017 (prev. year)Y IFS-M-201811 SWE Sweden EU-SILC ALL (prev. year)Y IFS-M-201811 SWZ Swaziland SHIES 1994 W WEO-A-201810 HIES 2000 W WEO-A-201810 2001 Y WEO-A-201810 2009-2016 W WEO-A-201810 SYC Seychelles HBS 1999-2006 W IFS-M-201811 2013 Y IFS-M-201811 SYR Syrian Arab Republic HBS 2004 Y IFS-M-201811 TCD Chad ECOSIT-II 2003 Y IFS-M-201811 ECOSIT-III 2011 Y IFS-M-201811 TGO Togo QUIBB ALL Y IFS-M-201806 THA Thailand SES ALL Y IFS-M-201811 TJK Tajikistan TLSS 1999 Y WEO-A-201810 2003-2007 Y Survey HBS 2004 Y Survey TLSS 2009 Y IFS-M-201811 HSITAFIEN 2015 Y IFS-M-201811 TKM Turkmenistan LSMS 1998 Y WEO-A-201810 TLS Timor-Leste TLSS 2001 Y WEO-A-201810 TLSLS 2007-2014 Y IFS-M-201811 TON Tonga HIES ALL Y IFS-M-201811 TTO Trinidad and Tobago PHC 1988 Y IFS-M-201811 SLC 1992 Y IFS-M-201811 TUN Tunisia HBCS 1985 Y IFS-A-201811 1990 Y IFS-M-201811 LSS 1995-2000 Y IFS-M-201811 NSHBCSL 2005-2015 W IFS-M-201811 TUR Turkey HICES ALL Y IFS-M-201811 TUV Tuvalu HIES 2010 Y WEO-A-201810 TZA Tanzania HBS 1991 W IFS-A-201811 2000 W IFS-M-201811 2007 Y IFS-M-201811 30 2011 W IFS-M-201811 UGA Uganda HBS 1989 Y WEO-A-201810 NIHS 1992 W WEO-A-201810 1996-1999 W IFS-M-201811 UNHS 2002-2016 W IFS-M-201811 UKR Ukraine HS 1992-1993 Y IFS-M-201811 HIES 1995-1996 Y IFS-M-201811 HBS 1999 Y IFS-M-201811 HLCS 2002-2016 Y IFS-M-201811 URY Uruguay - urban ENH 1981-1989 Y IFS-M-201811 Uruguay ECH 1992-2017 (prev. year)M12 IFS-M-201811 USA United States CPS ALL Y IFS-M-201811 UZB Uzbekistan HBS ALL Y WEO-A-201810 VEN Venezuela, RB EHM 1981-1989 Y NSO 1992-2006 M12 NSO VNM Vietnam VLSS 1992 W WEO-A-201810 1998 W IFS-M-201811 VHLSS 2002-2016 M1 IFS-M-201811 VUT Vanuatu HIES 2010 Y IFS-A-201811 WSM Samoa HIES 2002-2008 Y IFS-M-201811 2013 W IFS-M-201811 XKX Kosovo HBS ALL Y IFS-M-201811 YEM Yemen, Rep. HBS 1998 Y IFS-M-201811 2005 W IFS-M-201811 2014 Y IFS-M-201811 ZAF South Africa KIDS 1993 Y IFS-M-201811 HIES 1996 Y IFS-M-201811 2000 W IFS-M-201811 IES 2005 (next year)M6 IFS-M-201811 LCS 2008 W IFS-M-201811 IES 2010 (next year)M6 IFS-M-201811 LCS 2014 (next year)M6 IFS-M-201811 ZMB Zambia HBS 1991-1993 Y IFS-M-201811 LCMS-I 1996 Y IFS-M-201811 LCMS-II 1998 Y IFS-M-201811 LCMS-III 2002 W IFS-M-201811 LCMS-IV 2004 W IFS-M-201811 LCMS-V 2006 W IFS-M-201811 LCMS-VI 2010 Y IFS-M-201811 LCMS 2015 Y IFS-M-201811 ZWE Zimbabwe ICES 2011 Y IFS-M-201811 31 Appendix 2 – National Accounts Data Sources This appendix provides details of national accounts data used in aligning estimates to reference years (see Prydz et al, 2019 for methodological details). The primary source of national accounts data in this update is the December 2018 version of the World Development Indicators. For historical data this is supplemented with the Madison Project Database (MDP), 2018 version, for years prior to 2000. In addition, the following special country series are used: • Djibouti GDP data from the WDI 2017 December edition is used up to 2015. From 2015 until 2017, annualized survey growth between the 2013 and 2017 surveys are used. Data is not available in the most recent WDI edition (December 2018). • Liberia and South Sudan GDP from the WDI 2017 December edition is used. • Syrian Arab Republic: WDI 2017 is used up to 2009 and linked with national accounts growth rates from Gobat and Kostial (2016), as described in Atamanov et al (2018) for recent years. • India 2011-2015: The reference year estimates for India from 2012 to 2015 are based on a method which adjusts HFCE growth by incorporating findings of a poverty imputation for 2014.5. Growth rates in national accounts are adjusted to match the results from the poverty imputation. The method is described in greater detail in Chen et al (2018) and Newhouse and Vyas (2018). A complete overview is available in Table A2. 1 (GDP per capita) and Table A2. 2 (HFCE per capita). Legend Tables A2.1 and A2.2 Code – World Bank economy/country Sources (See beginning of Appendix for details) code - M – Madison Project Dataset Cov – Coverage - W – World Development Indicators, N – National December 2018 U – Urban - S – Special Country Series R – Rural 32 Table A2. 1. Gross Domestic Product (GDP) per capita 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 GDP Cov Code AGO N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ALB N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ARG N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ARM N M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W AUS N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W AUT N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W AZE N M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W BDI N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BEL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BEN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BFA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BGD N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BGR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BIH N M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W BLR N M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W BLZ N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BOL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BRA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BTN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BWA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CAF N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CAN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CHE N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CHL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CHN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CHN R W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CHN U W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CIV N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CMR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W COD N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W COG N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W COL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W COM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CPV N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CRI N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CYP N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CZE N M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W DEU N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W DJI N M M M M M M M M M S S S S S S S S S S S S S S S S S S S S S S S S S S S S DNK N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W DOM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W DZA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W 33 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 GDP Cov Code ECU N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W EGY N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ESP N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W EST N M M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W ETH N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W FIN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W FJI N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W FRA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W FSM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GAB N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GBR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GEO N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GHA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GIN N M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GMB N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GNB N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GRC N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GTM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GUY N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W HND N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W HRV N M M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W HTI N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W HUN N M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W IDN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IDN R W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IDN U W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IND N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IND R W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IND U W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IRL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IRN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IRQ N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ISL N M M M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W ISR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ITA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W JAM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W JOR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W JPN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W KAZ N M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W KEN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W KGZ N M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W KIR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W KOR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LAO N M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W 34 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 GDP Cov Code LBN N M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LBR N S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S LCA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LKA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LSO N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LTU N M M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W LUX N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LVA N M M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W MAR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MDA N M M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W MDG N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MDV N W W W W W W W W W W W W W W W W W W W W W W W MEX N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MKD N M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W MLI N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MLT N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MMR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MNE N M M M M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W MNG N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MOZ N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MRT N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MUS N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MWI N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MYS N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NAM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NER N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NGA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NIC N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NLD N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NOR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NPL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PAK N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PAN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PER N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PHL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PNG N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W POL N M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W PRT N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PRY N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PSE N M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W ROU N M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W RUS N M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W RWA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SDN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W 35 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 GDP Cov Code SEN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SLB N W W W W W W W W W W W W W W W W W W W W W W W W W W W W SLE N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SLV N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SRB N M M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W SSD N S S S S S S S S STP N W W W W W W W W W W W W W W W W W SUR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SVK N M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W SVN N M M M M M M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W SWE N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SWZ N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SYC N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SYR N S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S TCD N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TGO N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W THA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TJK N M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TKM N M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TLS N W W W W W W W W W W W W W W W W W W TON N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TTO N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TUN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TUR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TUV N W W W W W W W W W W W W W W W W W W W W W W W W W W W W TZA N M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W UGA N M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W UKR N M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W URY N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W USA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W UZB N M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W VEN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W VNM N M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W VUT N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W WSM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W XKX N W W W W W W W W W W W W W W W W W W YEM N M M M M M M M M M W W W W W W W W W W W W W W W W W W W W W W W W W W W W ZAF N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ZMB N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ZWE N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W 36 Table A2. 2. Household Final Consumption Expenditures (HFCE) per capita 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Cov HFCE Code AGO N W W W W W W W W W W W W W W W W ALB N W W W W W W W W W W W W W W W W W W W W W W ARG N W W W W W W W W W W W W W W W W W W W W W W W W W ARM N W W W W W W W W W W W W W W W W W W W W W W W W AUS N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W AUT N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W AZE N W W W W W W W W W W W W W W W W W W W W W BDI N W W W W W W W W W W W W W W W W W W W W BEL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BEN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BFA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BGD N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BGR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BIH N W W W W W W W W W W W W BLR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W BLZ N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BOL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BRA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W BTN N W W W W W W W W W W W W W W W W W W BWA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CAF N W W W W W W W W W W CAN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CHE N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CHL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CHN N W W W W W W W W W W W W W W W W W W W W W W W CHN R W W W W W W W W W W W W W W W W W W W W W W W CHN U W W W W W W W W W W W W W W W W W W W W W W W CIV N W W W W W W W W W W CMR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W COD N W W W W W W W W W W W W W W W W W W W W W W W W W COG N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W COL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W COM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CPV N W W W W W W W W W W W CRI N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CYP N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W CZE N W W W W W W W W W W W W W W W W W W W W W W W W W W W W DEU N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W DJI N DNK N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W DOM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W DZA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W 37 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Cov HFCE Code ECU N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W EGY N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ESP N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W EST N W W W W W W W W W W W W W W W W W W W W W W W W W ETH N FIN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W FJI N FRA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W FSM N GAB N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GBR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GEO N W W W W W W W W GHA N W W W W W W W W W W W W GIN N W W W W W W W W W W W GMB N W GNB N W W W W W W W W W W W W W W W W W W GRC N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GTM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W GUY N W HND N W W W W W W W W W W W W W W W W W W HRV N W W W W W W W W W W W W W W W W W W W W W W W HTI N HUN N W W W W W W W W W W W W W W W W W W W W W W W W W W W IDN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IDN R W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IDN U W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IND N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IND R W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W S S S S W W IND U W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W S S S S IRL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IRN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W IRQ N W ISL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W ISR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ITA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W JAM N W W W W W W W W W W W JOR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W JPN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W KAZ N W W W W W W W W W W W W W W W W W W W W W W W W W W W W KEN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W KGZ N W W W W W W W W W W W W W W W W W W W W W W W W W KIR N KOR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LAO N W W W W W W W W W W W W W W W W W W 38 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Cov HFCE Code LBN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W LBR N S S S S S S S S S S S S S S S S S LCA N W LKA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LSO N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LTU N W W W W W W W W W W W W W W W W W W W W W W W LUX N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W LVA N W W W W W W W W W W W W W W W W W W W W W W W W MAR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MDA N W W W W W W W W W W W W W W W W W W W W W W MDG N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MDV N MEX N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MKD N W W W W W W W W W W W W W W W W W W W W W W W W W W W W MLI N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MLT N W W W W W W W W W W W W W W W W W W MMR N W MNE N W W W W W W W W W W W W W W W W W W MNG N W W W W W W W W MOZ N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MRT N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MUS N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W MWI N W W W W W W W W W W W W W W W W MYS N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NAM N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NER N W W W W W W W W W W W W NGA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NIC N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NLD N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NOR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W NPL N W W W W W W W W W W W W W W W W W PAK N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PAN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PER N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PHL N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PNG N POL N W W W W W W W W W W W W W W W W W W W W W W W PRT N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PRY N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W PSE N W W W W W W W W W W W W W W W W W W W W W W W W ROU N W W W W W W W W W W W W W W W W W W W W W W W W W W W W RUS N W W W W W W W W W W W W W W W W W W W W W W W W W W W W RWA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SDN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W 39 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Cov HFCE Code SEN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SLB N SLE N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SLV N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SRB N W W W W W W W W W W W W W W W W W W W W W W W SSD N S S S S S S S S STP N SUR N W SVK N W W W W W W W W W W W W W W W W W W W W W W W W W SVN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W SWE N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SWZ N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W SYC N W SYR N TCD N W W W W W W W W W W W TGO N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W THA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TJK N W W W W W W W W W W W W W W W W W W W W W TKM N W TLS N W W W W W W W W W W W W W W W W W TON N W TTO N TUN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TUR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W TUV N TZA N W W W W W W W W W W W W W W W W W W W W W W W W W W W UGA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W UKR N W W W W W W W W W W W W W W W W W W W W W W W W W W W W URY N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W USA N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W UZB N W VEN N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W VNM N W W W W W W W W W W W W W W W W W W W W W W W W VUT N W W W W W W W W W W W WSM N XKX N W W W W W W W W W W W W YEM N ZAF N W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W ZMB N W ZWE N W W W W W W W W W 40