Abstract
We introduce two separate datasets [The Global Consumption Dataset (GCD) and The Global Income Dataset (GID)] making possible an unprecedented portrait of consumption and income of persons over time, within and across countries, around the world. The current benchmark version of the dataset presents estimates of monthly real consumption and income for every percentile of the population (a “consumption/income profile”) for more than 160 countries and more than half a century (1960–2015). We describe the construction of the datasets and demonstrate possible uses by presenting some sample results concerning the distribution of consumption, poverty and inequality in the world.
Appendix: Country Splits and Unifications
I. Countries Undergoing Splits
There are countries in our database which experienced splits and for which we have data pertaining to the entities both before and after this event. Examples of the affected pre-existing entities include Czechoslovakia, Ethiopia, Indonesia, Malaysia, Pakistan, the USSR and Yugoslavia. Other countries which experienced splits over the period, such as Sudan, are ones for which we do not yet have sufficient data to incorporate them separately.
I.1 Former Socialist Economies
For Czechoslavakia, the USSR and Yugoslavia we apply the following procedure. In most of these cases, we have data on means and distributions for the countries emerging from the split from near to the year in which they were formed and some distributional data on the combined country for the earlier period. As some of the countries undergoing splits were also formerly socialist economies, problems of available of distributional and mean information are compounded by the often lesser availability of household data for such countries.
Our approach to handling these countries is to attempt to assign a pseudo-mean and pseudo-distribution to the constituent countries even prior to the split, while recognizing that the assigned values may in fact be more characteristic of the unified country, and thus recommending appropriate interpretative caution. Such an approach allows us to estimate trends for the individual constituent units over a longer period, as well as to construct and report aggregates (whether prior to or after the split). Each of these possible choices has its benefits and costs in terms of statistical meaningfulness vs. inter-temporal comparability.
For the affected countries mentioned above, we use distributional data from the combined country to create a pseudo-distribution of each of the countries undergoing splits for years when the countries were one, as we do not possess distributional data for the individual constituent countries. We recognize that this is an inadequate assumption, in part because the distribution for the unified country reflects differences in income between successor countries as well as within them.
Although we do possess some mean estimates for the combined countries from national accounts or independent academic and institutional estimates, we do not use these for the constituent countries both because this would mean using the same mean for all constituent countries in the earlier years and because we don not have reliable national-level inflation data for the pre-split period to convert these estimates into units of a common PPP base year after the split. We therefore instead estimate means for the countries undergoing splits by extrapolating backward using real per-capita-income growth rate data, which is available in many instances. Where growth rate information for the constituent countries is not available, we use the growth rate of the combined country as a proxy for the growth rate of each of the countries resulting from the split. In particular, for the three countries undergoing splits mentioned above, we employ data on growth rates for the constituent countries from 1980 onwards from the TED. Prior to 1980 we use the growth rates for the combined entities as provided by Angus Maddison.[41]
Table A1 has details on the data used for each of the combined countries undergoing splits.
Countries undergoing splits or reunions.
| Survey years | Growth data | Source of growth rate data | Inflation data | |
|---|---|---|---|---|
| Yugoslavia | 1968–1990 | 1961–2006 | Angus Maddison | |
| Bosnia and Herzegovina | 2001–2007 | 1991–2015 | TED and WDI | 1994– |
| Croatia | 1988–2008 | 1981–2015 | TED and WDI | 1985– |
| Montenegro | 2005–2011 | 1991–2013 | TED and WDI | 2000– |
| Serbia | 2002–2010 | 1991–2013 | TED and WDI | 1994– |
| Slovenia | 1987–2011 | 1981–2015 | TED and WDI | 1992– |
| Macedonia, FYR | 1994–2008 | 2015 | TED and WDI | 1990– |
| Kosovo | 2003–2006 | 2000– | ||
| USSR | 1980–1989 | 1961–1990 | Angus Maddison | |
| Armenia | 1996–2012 | 1981–2015 | TED and WDI | 1990– |
| Azerbaijan | 1995–2008 | 1981–2015 | TED and WDI | 1990– |
| Belarus | 1988–2011 | 1981–2015 | TED and WDI | 1990– |
| Estonia | 1988–2011 | 1981–2015 | TED and WDI | 1992– |
| Georgia | 1996–2012 | 1966–2015 | TED and WDI | 1965– |
| Kazakhstan | 1988–2010 | 1981–2015 | TED and WDI | 1990– |
| Kyrgyz Republic | 1988–2011 | 1981–2015 | TED and WDI | 1987– |
| Latvia | 1988–2011 | 1966–2015 | TED and WDI | 1965– |
| Lithuania | 1988–2011 | 1981–2015 | TED and WDI | 1990– |
| Moldova | 1988–2011 | 1981–2015 | TED and WDI | 1989– |
| Russia | 1988–2010 | 1961–2015 | TED and WDI | 1989– |
| Tajikistan | 1999–2009 | 1981–2015 | TED and WDI | 1985– |
| Turkmenistan | 1988–1998 | 1981–2015 | TED and WDI | 1987– |
| Ukraine | 1985–2010 | 1981–2015 | TED and WDI | 1987– |
| Uzbekistan | 1988–2003 | 1981–2015 | TED and WDI | 1987– |
| Czechoslovakia | 1964–1992 | 1961–2006 | Angus Maddison | 1975– |
| Czech Republic | 1988–2011 | 1971–2015 | TED and WDI | 1990– |
| Slovakia | 1988–2011 | 1986–2015 | TED and WDI | 1992– |
II. Other Countries Undergoing Splits
For the other countries undergoing splits, we have made specific assumptions, for instance about the coverage of surveys from before and after the split. These are mentioned in the online appendix on country assumptions (see gcip.info under “Documentation”).
III. Countries Undergoing Unification
Among the countries undergoing unification during the time interval covered by the database are Germany and Yemen. We do not have sufficient information for the constituent parts of Yemen prior to or posterior to its unification to form a picture of the country at this time.
For Germany, we use West Germany’s distribution and mean for all of Germany prior to unification. We are actively interested in finding and integrating East German data from prior to unification so as to improve upon this inadequate approach.
References
Aaberg, R. and I. Melby (1998) “The Sensitivity of Income Inequality to Choice of Equivalence Scales,” Review of Income and Wealth, 44(4):565–569.10.1111/j.1475-4991.1998.tb00299.xSearch in Google Scholar
Atkinson, A. and A. Brandolini (2001) “Promise and Pitfalls in the Use of ‘Secondary’ Data-Sets: Income Inequality in OECD Countries As a Case Study,” Journal of Economic Literature, 39(3):771–799.10.1257/jel.39.3.771Search in Google Scholar
Atkinson, A. B. and A. Brandolini (2009) “On Data: A Case Study of the Evolution of Income Inequality Across Time and Across Countries,” Cambridge Journal of Economics, 33(3):381–404.10.1093/cje/bel013Search in Google Scholar
Anand, S. and P. Morduch (1996) “Poverty and the Population Problem: Evidence from Bangladesh,” Harvard Institute for International Development, Development Discussion Paper No. 559. Available at: http://www.cid.harvard.edu/hiid/559.pdf.Search in Google Scholar
Anand, S. and P. Segal (2008) “What do we Know about Global Income Inequality?” Journal of Economic Literature, 46(1):57–94.10.1257/jel.46.1.57Search in Google Scholar
Banks, J. and P. Johnson (1994) “Equivalence Scale Relativities Revisited,” Economic Journal, 104:883–890.10.2307/2234982Search in Google Scholar
Blaylock, J. R. (1991) “The Impact of Equivalence Scales on the Analysis of Income and Food Spending Distributions,” Western Journal of Agricultural Economics, 16(1):11–20.Search in Google Scholar
Buhmann, B., L. Rainwater, G. Schmaus and T. Smeeding (1988) “Equivalence Scales, Well-Being, Inequality, and Poverty: Sensitivity Estimates Across Ten Countries Using The Luxembourg Income Study (Lis) Database,” Review of Income and Wealth, 34(2):115–142.10.1111/j.1475-4991.1988.tb00564.xSearch in Google Scholar
Chen, S. and M. Ravallion (2004) “How Have the World’s Poorest Fared Since the Early 1980s?” World Bank Research Observer, 19(2):141–169.10.1596/1813-9450-3341Search in Google Scholar
Chotikapanich, D. (1993) “A comparison of Alternative Functional Forms for the Lorenz Curve,” Economics Letters, 41(2):129–138.10.1016/0165-1765(93)90186-GSearch in Google Scholar
Coulter, F. A. E., F. A. Cowell and S. P. Jenkins. (1992a) “Differences in Needs and Assessment of Income Distributions,” Bulletin of Economic Research, 44:77–124.10.1111/j.1467-8586.1992.tb00538.xSearch in Google Scholar
Coulter, F. A. E., F. A. Cowell and S. P. Jenkins (1992b) “Equivalence Scale Relativities and the Extent of Inequality and Poverty,” Economic Journal, 102:1067–1082.10.2307/2234376Search in Google Scholar
Cowell, F. A. and M. Mercader-Prats (1999) Equivalence Scales and Inequality. LSE STICERD Working Paper No. 27. Available at http://eprints.lse.ac.uk/2190/1/Equivalence_of_Scales_and_Inequality.pdf.10.1007/978-94-011-4413-1_15Search in Google Scholar
Datt, G. (1998) Computational Tools for Poverty Measurement and Analysis. Washington, D.C.: International Food Policy Research Institute.Search in Google Scholar
Deaton, A. (2005) “Measuring Poverty in a Growing World (or Measuring Growth in a Poor World),” Review of Economics and Statistics, 87(2):395.10.3386/w9822Search in Google Scholar
Deininger, K. and L. Squire (1996) “A New Data Set Measuring Income Inequality,” The World Bank Economic Review, 10(3):565–591.10.1093/wber/10.3.565Search in Google Scholar
Dykstra, S., B. Dykstra and J. Sandefur (2014) We Just Ran 23 Million Queries of the World Bank’s Website. Center for Global Development, Working Paper No. 362.Search in Google Scholar
Edward, P. and A. Sumner (2013) The Geography of Inequality: Where and by How Much Has Income Distribution Changed since 1990? Center for Global Development, Working Paper No. 362.Search in Google Scholar
Ferreira, F. H. G., S. Chen, A. L. Dabalen, Y. M. Dikhanov, N. Hamadeh, D. M. Jolliffe, A. Narayan, E. B. Prydz, A. L. Revenga, P. Sangraula, U. Serajuddin and N. Yoshida (2015) A Global Count of the Extreme Poor in 2012: Data Issues, Methodology and Initial Results. Policy Research Working Paper No. WPS 7432. Washington, D.C.: World Bank Group. Available at: http://documents.worldbank.org/curated/en/2015/10/25114899/global-count-extreme-poor-2012-data-issues-methodology-initial-results.10.1596/1813-9450-7432Search in Google Scholar
Jain, S. (1975) Size Distribution of Income: A Compilation of Data.” Washington, DC: World Bank, pp. 101–104.Search in Google Scholar
Jayadev, A., Lahoti, R. and S. G. Reddy (2015a) Who Got What, Then and Now? A Fifty Years Overview from the Global Consumption and Income Project. Working Paper, May 4th.10.2139/ssrn.2602268Search in Google Scholar
Jayadev, A., R. Lahoti and S. G. Reddy (2015b) The Middle Muddle: Conceptualizing and Measuring the Global Middle Class. Working Paper.10.2139/ssrn.2694624Search in Google Scholar
Jenkins, S. P. (2015). “World Income Inequality Databases: An Assessment of WIID and SWIID,” The Journal of Economic Inequality, 13(4):629–671.10.1007/s10888-015-9305-3Search in Google Scholar
Kakwani, N. C. (1980) “Functional Forms for Estimating the Lorenz Curve: A Reply,” Econometrica, 48(4):1063–1064.10.2307/1912949Search in Google Scholar
Lakner, C. and B. Milanovic (2013) Global Income Distribution: from the Fall of the Berlin Wall to the Great Recession. World Bank Working Paper No. 6719, December.10.1596/1813-9450-6719Search in Google Scholar
Luxembourg Income Study Database (LIS), www.lisdatacenter.org (multiple countries; June 1–June 3, 2014). Luxembourg: LIS.Search in Google Scholar
Milanovic, B. (2002) “True World Income Distribution, 1988 And 1993: First Calculation Based On Household Surveys Alone,” The Economic Journal, 112(476):51–92.10.1596/1813-9450-2244Search in Google Scholar
Milanovic, B. (2005) Worlds Apart: Measuring International and Global Inequality. Princeton, N.J.: Princeton University Press.Search in Google Scholar
Minoiu, C. and S. G. Reddy (2009) “The Estimation of Poverty and Inequality through Parametric Estimation of Lorenz Curves: An Evaluation,” Journal of Income Distribution, 18(2):160–178.10.25071/1874-6322.23370Search in Google Scholar
Niño Zarazúa, M., L. Roope and F. Tarp (2014) “Global Interpersonal Inequality: Trends and Measurement”. WIDER Working Paper 2014/004. Helsinki: UNU-WIDER.10.35188/UNU-WIDER/2014/725-7Search in Google Scholar
Nurkse, R. (1953) The Problem of Capital Formation in Less-Developed Countries. Oxford University Press, Vol. 33, pp. 1–337.Search in Google Scholar
Pinkovskiy, M. and X. Sala-i-Martin (2009) Parametric Estimations of the World Distribution of Income. NBER Working Paper No. 15433.10.3386/w15433Search in Google Scholar
Quah, D. T. (1996) “Twin Peaks: Growth and Convergence in Models of Distribution Dynamics,” Economic Journal, 106(437):1045–1055.10.2307/2235377Search in Google Scholar
Rasche, R. H., J. Gaffney, A. Y. C. Koo and N. Obst (1980) “Functional Forms for Estimating the Lorenz Curve,” Econometrica, 48(4):1061–1062.10.2307/1912948Search in Google Scholar
Reddy, S. G. and R. Lahoti (2015) “$1.90 Per Day: What Does It Say?” (No. 189). Courant Research Centre: Poverty, Equity and Growth-Discussion Papers.10.2139/ssrn.2685096Search in Google Scholar
Sefil, S. (2015) “Sensitivity of Turkish Income Distributions to Choice of Equivalence Scale,” Topics in Middle Eastern and African Economies, 17(1).Search in Google Scholar
Smith, L. C., O. Dupriez and N. Troubat (2014) Assessment of the Reliability and Relevance of the Food Data Collected In National Household Consumption and Expenditure Surveys. International Household Survey Network Working Paper.Search in Google Scholar
Solt, F. (2009) “Standardizing the World Income Inequality Database,” Social Science Quarterly, 90(2):231–242.10.1111/j.1540-6237.2009.00614.xSearch in Google Scholar
Stiglitz, J. E., A. Sen and J.-F. Fitoussi (2010) Mismeasuring Our Lives: Why GDP Doesn’t Add Up. New York: New Press.Search in Google Scholar
Smeeding, T. and J. P. Latner (2015) “PovcalNet, WDI and ‘All the Ginis’: A Critical Review,” The Journal of Economic Inequality, 13(4):603–628.10.1007/s10888-015-9312-4Search in Google Scholar
Villaseñor, J. and B. C. Arnold (1989) “Elliptical Lorenz Curves,” Journal of Econometrics, 40(2):327–338.10.1016/0304-4076(89)90089-4Search in Google Scholar
Weisbrod, J., S. Vollmer, Sebastian and H. Holzmann (2007) “Perspectives on the World Income Distribution: Beyond Twin Peaks Towards Welfare Conclusions.”.Search in Google Scholar
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