Measuring the welfare of countries using GDP per capita an average figure tells us nothing about the distribution of income within that country. In 2018, for example, Equatorial Guinea generated a GDP per capita of US$20,181, by far the highest in Africa, but most of the income generated from oil and gas remains in the hands of a corrupt elite leaving the vast majority of the population in dire poverty earning a living from subsistence farming. In all other respects – life expectancy, infant mortality, educational attainment etc., the country scores poorly compared with many other of its African neighbours albeit with far lower levels of GDP per capita.
The distribution of income in an economy matters and can be measured on a country by country basis by a Gini Coefficient which varies from a value of 0 – a completely egalitarian society- to 100 – all of the income of a country is in the hands of one person or one household depending upon the unit of measurement.
The combination of GDP per capita with the Gini coefficient is a useful gauge of the extent to which an economy’s inhabitants find mass market goods and services affordable and provides valuable information to portfolio investors and to development agencies. Although it is generally the case that developed economies are more equal than emerging markets, especially those where corruption leads to rent capture, there is not an automatic relationship between GDP per capita and the degree of inequality. For example, South Africa had a GDP per capita of US$13,324 in 2020 not far above the level of Indonesia at US$11,867 in Purchasing Power Parity terms, but with a Gini coefficient of 65.4 compared to 37.3, the distribution of income in South Africa is far more unequally distributed.
This report analyses international inequality data from the World Bank, UN University and WID.world using published Gini coefficients for 155+ countries analysed by World Economics. In some cases data is not available for every year in a sequence and or is years out of date.
The Gini coefficient can vary between 0 and 1, but in economic data it does not approach the two extremes. In the datasets analysed the coefficient varies from the most equal the Slovak Republic with a value of 23.2 in 2020 to the most unequal Eswatini with a value of 66.6 in 2019.
The median value of the data set is 43.5, Georgia and most European countries bunch in the range 20 to 36, although some countries lie outside this range as does the United States with a Gini coefficient of 41.5 and Saudi Arabia with a value of 54.4.
Inequality of income is prevalent in sub-Saharan Africa with 32 countries lying in the final 2 quintiles ranging from 46.7 in Chad to 66.7 in Eswatini. It is also high among countries in Latin America and the Caribbean with Colombia registering a value of 54.1 and Brazil a value of 55.9.
See Full Gini Coefficient Data by Country
Time series data on the Gini coefficient can also be used to track trends in the distribution of income over time, by country, by region and globally. A Gini studies showed that global income inequality has been increasing over the long-term with the aggregate Gini coefficient rising steadily by 2002 then peaking before declining to 0.59 by 2019. Another later World Bank study for a shorter period demonstrates a continuous decline as a result of globalisation raising incomes in China and India.
A study by UNICEF in 2011 confirmed the findings of this paper that there are significant regional variations in income inequality across the world. Data from 2008, showed that Latin America and the Caribbean region had the highest net income Gini index in the world at 48.3, on unweighted average basis in 2008. The next highest regional averages were: sub-Saharan Africa (44.2), Asia (40.4), the Middle East and North Africa (39.2), Eastern Europe and Central Asia (35.4), and High-income Countries (30.9). South Africa recorded the highest income Gini index score of 67.8.
The Gini coefficient is not perfect. Countries with relatively low numbers such as Belgium (27.2) and Sweden (29.3) do indicate relatively equal distributions with average high GDP per capita. In contrast, others such as the former Soviet or communist controlled countries such as the Slovak Republic (26.4), Moldova (35.1), or the Belarus, with lower levels of GDP per capita suggest a instead a more equal distribution of relative poverty compared with other more developed European nations. In these nations, benefits that are difficult to value such as subsidised housing, medical care, education or other such services are difficult to value and are not measured adequately by a Gini coefficient.
The Gini coefficient shows higher levels of income equality in many emerging economies in Latin America, Africa and Asia, but in subsistence-driven and informal economies recording problems bias the coefficient upwards. The value and distribution of the incomes from informal or underground economy is difficult to quantify and different assumptions and quantifications of these incomes will produce different Gini coefficients.
Finally, in affluent countries with higher relative GDP per capita, the Gini coefficient measures income and not net worth. Most of a country’s wealth may be concentrated in the hands of a small number of people even if income distribution is relatively equal. Large holdings of corporate or sovereign debt, which pays low interest in the current environment, could give an individual a low income but a high net worth. Nevertheless, investors in a country are more interested in the propensity to consume of the average household rather than the savings habits of a rich minority, so the Gini coefficient is still useful.
The World Economics Inequality Index is based on the Gini Coefficient data from multiple respected sources including the World Bank, UN University, and WID.world (using Top 10% Income Inequality data). The coefficient for the latest available year is analysed on a country-by-country basis, and in cases where there is multiple data for the same year that varies in value, a simple average is calculated from the sources.
The resulting data is then scaled from 0 to 100 using the standard deviation of the dataset. A score of 0 indicates the most unequal country in the world, while a score of 100 denotes the most equal. Moreover, the data is further categorised into quintiles labelled as grades A-E. Grade A corresponds to "Very low inequality," whereas grade E implies “very high levels of inequality”.
See Inequality Rankings by Country