Constant MER, variable MER, and PPP
What does it all mean?

To study global inequality between countries, we use a dataset of Gross Domestic Product (GDP) per person, derived from the World Inequality Database (WID). GDP is a measure of the total production of an economy, as valued in market prices, and also represents the total income of all people in the country.

For the purposes of international comparison, it is necessary to express all countries’ GDP in a common currency (at the Global Inequality Project, we use the US dollar), and to adjust for inflation over time by representing incomes in terms of the price of a common base year (we use the year 2023).

At the Global Inequality Project (GIP), we take three different approaches to currency conversions. These are called variable MER, constant MER, and PPP. In this short note, we describe what these three currency concepts mean and we explain how they should be interpreted. For readers interested in understanding how we calculated income data using these approaches, please consult the technical notes on processing the inputs from the WID.

Variable Market Exchange Rates

One way to standardize incomes across countries is to convert all countries’ GDP into US dollars using the market exchange rate (MER), sometimes called the foreign exchange (FX), in each year. One can then convert the resulting time series to 2023 prices using the World Bank’s “SDR deflator” – a price index that attempts to measure inflation on the world market as a whole.¹

This method – which we call “variable MER” because the exchange rate varies from year to year – tells us about changes in purchasing power over the world market, measured in 2023 prices. One can think of this as the amount of money a person actually has available, which they can use to appropriate human labour and social resources on the world market.

The variable-MER method suffers from a few problems, however. First, market exchange rate data is not available for some countries during the 1960s, and so we are forced to fill in the gaps with assumptions – for more information, see the technical note on how we processed the data.

Second, historical records of official exchange rates are not always very reliable. This is known to be a problem in the former-Soviet Bloc and other planned economies, where the government-fixed exchange rate was not always reflective of the rate effectively applied to actual foreign exchange transactions. The dataset we use at the GIP incorporates adjustments made by economists at the United Nations to account for limitations in official MER data. But it is difficult to confirm whether this fully addresses the problem. 

Finally, the World Bank’s SDR deflator only accounts for price changes in the US, UK, Japan, the Euro Area, and, to a much lesser extent, China. While this reflects the reality that these countries play a dominant role in international trade and therefore have a more significant effect on international prices than other countries do, it nevertheless means that the SDR deflator may be imperfect as a measure of international inflation.

Constant Market Exchange Rates

This brings us to the second method of currency conversion, which the World Bank refers to as the ‘constant dollar’ approach. Under this approach, each country’s exchange rate is held constant at the value of a specific reference year (in our case 2023), so that economic growth is not affected by changes in international prices or exchange rates.²

One way to think about this method is that it measures income in terms of the purchasing power that the US dollar had over a country’s domestic output in 2023. This is done by first deflating a country’s GDP in local currency units (LCU) to the price level in 2023, before then converting the entire series to US dollars using the 2023 exchange rate.

The main benefit of the ‘constant MER’ approach is that it relies on relatively recent exchange rate data, which is often regarded as more reliable than historical data on past exchange rates. But the obvious limitation of this approach is that it cannot tell us about 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.³ This issue is particularly problematic in the 1980s, as the available evidence indicates that most countries in the Global South saw a deterioration in their real exchange rate under structural adjustment programmes (SAPs).⁴  SAPs worked to cheapen labour and resources in the Global South, through currency devaluation, deflationary fiscal policy, and the removal of wage protections. This meant that holders of Southern currencies could purchase fewer goods on the world market, while holders of Northern currencies were able to purchase more goods. The constant MER method ignores these processes and, as a result, it does not accurately capture the worsening of world market inequality that occurred in the 1980s.

Purchasing Power Parity

Finally, a third option is to convert GDP to US dollars with what’s called the Purchasing Power Parity (PPP) exchange rate, which adjusts for international differences in the price of domestically-produced goods and services.

In the PPP approach, all countries’ GDP are expressed in terms of the US price level in the base year (again, we use the year 2023). Countries where prices are lower than the US see their incomes adjusted upwards, while countries with higher prices see their incomes adjusted down. Because low- and middle-income countries tend to have lower prices than high-income countries, the PPP approach points to lower overall levels of inequality than do market exchange rates.

It is crucial to understand that PPP- and MER-based figures fundamentally measure different types of inequality. While PPP exchange rates are useful for measuring inequalities in consumption of domestically-produced goods and services, they cannot be used to study inequality in purchasing power over internationally traded goods and relational forms of inequality on the world market.⁵

Giovanni Arrighi, a leading scholar in the study of global inequality, writes that:

“While PPP data allow for a more adequate description of trends in material consumption, FX-based data are a better measure of differences in the relative level of income/wealth among residents of different countries in the global economy. Wealth in a global economy is the command that people have over one another’s goods and services on the world market. PPP-adjusted data actually obscure what we seek to measure. For example, even though a book produced in India or China may be significantly less expensive than a book produced in the United States, purchasing any of these books takes a smaller percentage of the income of the average resident of the United States than it would take to make the same purchases for the average resident of India or China. One can think of this in terms of the difference it makes in the ability of differently located universities to maintain a world-class library.”⁶

Scholars of international relations note that MER-based data is also more useful than PPPs for assessing differences in the geopolitical and military strength of nations. According to Gilboy and Heginbotham:

“PPP methods adjust for purchasing power for locally produced and priced goods. PPP methods are not appropriate for measuring relative international wealth and power that is based on internationally traded goods and services. PPPs are not designed to measure relative market share or relative capabilities in international finance and trade. Nor are PPPs designed to measure advanced industrial, technological, or military power, because substantial elements of these are comprised of manufactured goods and components sourced from international markets at international market prices.”⁷

Summary 

The table below provides a summary of the three currency concepts used in the GIP, and the way that they should be interpreted.

At the Global Inequality Project, we generally use constant MER as the default when graphing data on global income inequality. This provides a useful middle ground between PPP figures (which do not capture relational forms of inequality on the world market) and variable MER figures (which are highly sensitive to price fluctuations and exchange rate movements).

However, because all approaches to measurement have their strengths and limitations, we make time series of all three currency concepts available, where possible, via the dropdown menu at the bottom of each graph.

References

Arrighi, G., Silver, B.J., & Brewer, B.D. (2003). ‘Industrial convergence, globalization, and the persistence of the North-South divide.’ Studies in Comparative International Development, 38(1), 3-31.

Gilboy, G.J., & Heginbotham, E. (2012). Chinese and Indian strategic behaviour: Growing power and alarm. Cambridge University Press.

Hickel, J., Sullivan, D., & Zoomkawala, H. (2021). ‘Plunder in the post-colonial era: Quantifying drain from the Global South through unequal exchange, 1960-2018.’ New Political Economy, 26(6), 1030-1047.

Köhler, G. (1998). ‘The structure of global money and world tables of unequal exchange.’ Journal of World-Systems Research, 4(2), 145-168.

Ricci, A. (2022). ‘Global locational inequality: Assessing unequal exchange effects.’ Environment and Planning A: Economy and Space, 54(7), 1323-1340.

Smith, J. (2016). Imperialism in the twenty-first century: Globalization, super-exploitation, and capitalism’s final crisis. Monthly Review Press.

World Inequality Lab (2024). Distributional national accounts guidelines: Methods and concepts used in the World Inequality Database. Available here.