Introducing the Global Inequality Project (GIP) national income dataset

To study global inequality between countries, we use a dataset of Gross Domestic Product (GDP) per person, derived from the World Inequality Database (WID). The dataset covers 173 countries, representing 99.87% of the global population, for all years from 1960 to 2023. GDP is represented using three different currency concepts for conversion to US dollars: constant MER, variable MER, and PPP (see our note here for more information about what these currency concepts mean and how they should be interpreted).

The full GIP national income dataset, including metadata, is available for download as an excel file here.

In this short note, we explain how the GIP national income dataset was processed from the WID, including extrapolations and adjustments that we made to fill in missing datapoints.

Constant MER and PPP

The default GDP/cap series provided by the World Inequality Database is expressed in Local Currency Units (LCU) at constant 2023 prices.¹ In the WID, this variable is coded as agdproi999. To convert these series to constant MER and PPP, we followed the WID’s ‘Distributional National Accounts’ (DINA) guidelines. For each country, we converted all datapoints to constant MER using the 2023 value of that country’s market exchange rate (code = xlcusxi999). To convert to PPP, we used the 2023 value of the relevant country’s PPP exchange rate (xlcuspi999).

One challenge to these calculations has to do with the 23 successor states of the former- USSR, Yugoslavia, and Czechoslovakia. For these states, GDP/cap is not available in the 1960s (although precise coverage varies by country). To overcome this problem, we used the growth rate of the relevant former state to extrapolate the GDP/cap of the successor states back to missing years.

For instance, data on Azerbaijan’s income is only available from 1970 onwards. As such, we extrapolated Azerbaijan’s income from 1969 back to 1960, using the per capita growth rate of the Soviet Union. This assumption – i.e., that all constituent republics of the USSR grew at the same rate – is obviously simplistic, so the figures for the successor states of the USSR should be interpreted with caution in the earliest years. The same applies to the successor states of Yugoslavia and Czechoslovakia.

For a full list of the country-years that were estimated through extrapolation, please download the metadata as an excel file at the top of this page.

Variable MER

Converting to variable MER requires a few additional steps. First, we adjusted the GDP, LCU data (agdproi999) to current prices using each countries’ GDP deflator (inyixxi999). Second, we converted these series to US dollars using the relevant market exchange rate in each year (xlcusxi999).¹ Finally, we deflated these estimates to 2023 prices using the World Bank’s SDR deflator – a special price index that attempts to measure inflation on the world market as a whole.

Adjustment for defunct states

Once again, the 23 successor states of the former- USSR, Yugoslavia, and Czechoslovakia pose a challenge to the calculations. There is no direct exchange rate data for these states prior to 1990, as these states did not exist at that time.² This makes it impossible to calculate GDP, variable MER for these 23 countries using the method described above.

Data is, however, available for the former states themselves. The WID includes data on the GDP of the former states in local currency units, along with GDP deflators to render these figures in current prices, and annual exchange rate data from 1970 to 1989. Using the same method applied to all other countries (described above) we were able to calculate GDP per capita, variable MER, for the USSR, Yugoslavia, and Czechoslovakia, from 1970 to 1989.³ We then assumed that the variable MER income of each successor state was equal to the variable MER income of the relevant former state, multiplied by a country-specific adjustment factor. In each year, the adjustment factor was assumed to equal the ratio of the successor state’s per capita income (in PPP terms) to the per capita income of the aggregate former-state (also in PPP terms).⁴ It should be noted that this approach was only used for the years 1970 to 1989. From 1990 onwards we followed the same approach that was applied to all other countries.

Adjustments for low data coverage

Another challenge to calculating variable MER data is that many countries are missing price deflators or exchange rate data for all or part of the 1960s. Without price deflators it is not possible to convert GDP from constant LCU to current LCU, and without exchange rate data it is not possible to convert from LCU to USD. 

To overcome this problem, we extrapolated the variable-MER GDP/cap series back to 1960 from the earliest available year of data, using the growth rate of real GDP per capita (adjusted for domestic inflation). One can think of this in terms of extrapolating back with the growth rate of GDP/cap in constant LCU, constant PPP, or constant MER. The growth rates are the same, regardless of the currency concept used, since they are all indexed to changes in domestic prices. In practice, we performed the calculations with the growth rate of GDP in constant 2023 LCU (WID’s agdproi999 variable).

The main limitation to this approach is that datapoints based on extrapolations won’t reflect changes in a given country’s market exchange rate. Our method implicitly assumes that the ‘exchange rate deviation index’ (i.e., the ratio of PPP to MER) was constant in extrapolated years, so that all changes in a country’s market income can be accounted for by the growth of incomes relative to domestic prices. As such, the extrapolated series do not allow us to capture changes in inequality that arise due to shifts in the international terms of trade or the bargaining power of different countries on the world market (for more information about this problem, see our discussion of the three currency concepts used in the GIP national income dataset).

Despite this issue, we believe that our method of extrapolation is preferable to the alternative of simply omitting countries from the dataset. The growth rate of GDP relative to domestic prices is the best available evidence on changes in income in the early years of data for some countries. While it does not capture all elements of a country’s position in the world market, it is close enough to permit analysis.

Using the growth rates of GDP relative to domestic prices, we were able to extrapolate back to 1960 for 85 countries with missing GDP, MER data. Where necessary, the growth rates of the former-USSR, Yugoslavia, and Czechoslovakia were applied to their successor states. This allows us to analyse long term trends in international inequality without major gaps in country coverage.

For a full list of the country-years that were estimated through extrapolation, please download the metadata as an excel file at the top of this page.

How to cite this dataset

Users of the GIP National Income Dataset should cite the World Inequality Database and the Global Inequality Project. The following references can be used:

World Inequality Database (2024). WID.world [Database]. Retrieved November 2024, from https://wid.world/

Sullivan, D., Zoomkawala, H., & Hickel, J. (2024). Global Inequality Project National Income Dataset [Dataset].

Notes

Suggested citation: Sullivan, D., Hickel, J., & Zoomkawala, H. (2025). “Introducing the Global Inequality Project national income database”, Global Inequality Project. Accessed at: https://globalinequality.org/introducing-the-global-inequality-project-national-income-dataset

1. Local currency values are expressed in the equivalent of each country’s current currency, even for historical periods when a different currency was used. This is perhaps best illustrated with an example. Germany’s GDP (LCU) and LCU exchange rate series are expressed in the equivalent of euros in all years, even though Germany used the Deutsche mark (DEM) prior to 1999. In years prior to the adoption of the euro, the value of the ‘German euro’ is equivalent to the value of the DEM divided by the 1999 exchange rate between the euro and the DEM (i.e., 1 EUR = 1.95583 DEM).

2.The World Inequality Database does provide historical estimates for the successor states’ exchange rates, but these are not based on direct data from the United Nations. Instead, they are derived by extrapolating the 1990 value of each countries’ exchange rate back to 1970. In most cases, this is done by applying the movements of the relevant former state’s exchange rate to its successors. However, for those countries that have recently adopted the Euro as their currency, the WID team applies the average exchange rate of countries that are now in the Eurozone.

These indirect estimates are not appropriate for our purposes. The WID’s method of extrapolation can lead to estimated exchange rates that do not align with the actual historical exchange rates. This problem is especially acute in cases where WID have applied the movements of the Eurozone countries’ exchange rates to post-socialist states that had nothing to do with the Eurozone at that time.

3. In the case of the USSR, it was necessary to make a small adjustment to the exchange rate data. The WID’s GDP, LCU series is measured in the equivalent of Russian roubles (RUB) – i.e., the currency used by the Russian federation since 1998. However, the WID’s exchange rate data is presented in terms of Soviet roubles (SUR) per US dollar. To account for the redenomination of the rouble in 1998 (where the value of 1 RUR was set at 1,000 SUR), we divided the USSR’s historical exchange rate by 1,000. This was necessary to ensure that the exchange rate data is compatible with the GDP data. It should be noted that we checked the results against the original United Nations’ data, to confirm their accuracy.

4. To calculate adjustment factors for the successor states of the USSR and Czechoslovakia, we used the GDP, PPP data from the WID. However, in the case of the Yugoslav successor states, this leads to unrealistically high adjustment factors. In the WID, the GDP, PPP of Yugoslavia’s successor states are 2 – 4x higher than the GDP, PPP of Yugoslavia itself, which is obviously inaccurate. To get around this issue, we calculated correction factors for the Yugoslav republics using GDP, PPP data from the 2023 version of the Maddison database. See, Bolt, J., & van Zanden, J.L. (2024). Maddison style estimates of the evolution of the world economy: A new 2023 update. Journal of Economic Surveys, 1-41. DOI: 10.1111/joes.12618.