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The Global Consumption and Income Project (GCIP): An Overview

  • Rahul Lahoti , Arjun Jayadev EMAIL logo and Sanjay Reddy

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.

Table A1:

Countries undergoing splits or reunions.

Survey yearsGrowth dataSource of growth rate dataInflation data
Yugoslavia1968–19901961–2006Angus Maddison
Bosnia and Herzegovina2001–20071991–2015TED and WDI1994–
Croatia1988–20081981–2015TED and WDI1985–
Montenegro2005–20111991–2013TED and WDI2000–
Serbia2002–20101991–2013TED and WDI1994–
Slovenia1987–20111981–2015TED and WDI1992–
Macedonia, FYR1994–20082015TED and WDI1990–
Kosovo2003–20062000–
USSR1980–19891961–1990Angus Maddison
Armenia1996–20121981–2015TED and WDI1990–
Azerbaijan1995–20081981–2015TED and WDI1990–
Belarus1988–20111981–2015TED and WDI1990–
Estonia1988–20111981–2015TED and WDI1992–
Georgia1996–20121966–2015TED and WDI1965–
Kazakhstan1988–20101981–2015TED and WDI1990–
Kyrgyz Republic1988–20111981–2015TED and WDI1987–
Latvia1988–20111966–2015TED and WDI1965–
Lithuania1988–20111981–2015TED and WDI1990–
Moldova1988–20111981–2015TED and WDI1989–
Russia1988–20101961–2015TED and WDI1989–
Tajikistan1999–20091981–2015TED and WDI1985–
Turkmenistan1988–19981981–2015TED and WDI1987–
Ukraine1985–20101981–2015TED and WDI1987–
Uzbekistan1988–20031981–2015TED and WDI1987–
Czechoslovakia1964–19921961–2006Angus Maddison1975–
Czech Republic1988–20111971–2015TED and WDI1990–
Slovakia1988–20111986–2015TED and WDI1992–

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.

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Published Online: 2016-7-6
Published in Print: 2016-6-1

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