Measuring Inequality
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 the 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 63.0 compared to 38.2, the distribution of income in South Africa is far more unequally distributed.
Global Inequality Data
This paper analyses international inequality data from the World Bank using published Gini coefficients for 150 of the 154 countries analysed in the World Economics GDP Data Quality Index (DQI), although there are a number of gaps and some data is out of date. Some of these gaps have been filled using information from the World Inequality Database, but there a number of complete gaps which are understandable such as Bahrain, Oman, Kuwait, Libya and the Bahamas and others which are not such as Singapore and Hong Kong. On the other hand in many cases data is not available for every year in a sequence and or is years out of date. The vintage of the latest Gini coefficient data ranges from 1992 for Trinidad and Tobago to 2019 for Brazil with an average of 5 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 data set analysed the coefficient varies from the most equal Slovenia with a value of 24.6 to the most unequal South Africa with a value of 63.0.
The median value of the data set is 37.9, Georgia and most European countries bunch in the range 20 to 36, although Greece, 32.9, Spain, 34.7, the United Kingdom, 35.1 and Luxembourg, 35.4, lie outside this range as does the United States with a Gini coefficient of 41.4 and Saudi Arabia with a value of 45.9.
Inequality of income is prevalent in sub-Saharan Africa with seventeen countries lying in the final quartile ranging from 43.7 in Rwanda to 63.0 in South Africa. It is also high among countries in Latin America and the Caribbean with Colombia registering a value of 51.3 and Brazil a value of 53.4.
See Full Gini Coefficient Data by Country
Inequality Over Time
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 World Bank study 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.
Conclusion
The Gini coefficient is not perfect. Countries with relatively low numbers such as Belgium (27.2) and Sweden (30.0) 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(25.0), Moldova (25.7), or the Ukraine, 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 index 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.