Measuring Income Distribution: A Primer on the Gini Coefficient
The U.S. Bureau of Economic Analysis (BEA) recently released updated prototype statistics showing how personal income is distributed across households in each state and the District of Columbia. These statistics include Gini coefficients, a widely used measure of the concentration of income within a population, such as the residents of a country or U.S. state.
The Gini coefficient summarizes how much the distribution of income (or other measures, like consumption or wealth) among individuals or households deviates from a perfectly equal distribution. The value ranges from 0 to 1, where:
- 0 represents perfect equality—everyone has the same income.
- 1 represents perfect inequality—one person has all the income.
As illustrated in chart 1, the Gini coefficient is calculated from the Lorenz curve, a graphical representation of the distribution of income. The x-axis represents the cumulative percentage of the population (from poorest to richest), and the y-axis represents the cumulative percentage of income earned by these individuals. As an example of how the Lorenz curve is defined, the point highlighted in the chart shows a case in which the lowest 50 percent of earners received 25 percent of the income. The 45-degree “line of equality” represents perfect equality—each percent share of the population (along the horizontal axis) earns that same percent share of income (on the vertical axis).
The Lorenz curve always lies at or below this line, and the further the Lorenz curve bends away from the 45-degree line, the more concentrated is the distribution among higher income individuals. Finally, the Gini coefficient is computed as twice the area between the line of equality and the Lorenz curve.
Different Lorenz curves can be used to compare income concentration across countries, time periods, or population groups. Chart 2 compares two sample Lorenz curves and their Gini coefficients.
Both Lorenz curves in chart 2 end at (1,1), representing total population and total income. Lorenz curve 2, which has a Gini of 0.50, bows more sharply from the line of equality, indicating a less equal distribution—that is, a larger share of income is concentrated among richer individuals. Lorenz curve 1 has a shallower bow and a Gini of 0.20, indicating a more equal distribution.
Chart 3 shows the Gini coefficients of the 50 U.S. states and the District of Columbia, based on 2023 statistics, ranging from a low of 0.38 in West Virginia to a high of 0.52 in Wyoming. See BEA's distribution of personal income product page for an interactive version of this map that includes Gini coefficients and other income data from 2012–2023.
To better understand how countries compare in terms of income distributions, table 1 provides selected national Gini coefficients. The years vary for the Gini coefficients that are shown, reflecting the latest available statistics. The South Africa measure from 2014 is the highest Gini coefficient ever recorded for country-level income. Note these Gini coefficients are not directly comparable to those produced by BEA, as they adhere to a narrower definition of income than the personal income concept of BEA's distributional accounts statistics.
| Country | Year | Gini coefficient | Notes |
|---|---|---|---|
| Brazil | 2023 | 0.516 | Upper-middle-income country with persistent inequality |
| United States | 2023 | 0.418 | High-income country |
| Spain | 2022 | 0.336 | Moderate European income equality |
| Guatemala | 2023 | 0.452 | Developing economy in Latin America |
| Sweden | 2022 | 0.316 | Among the most equal countries in Europe |
| South Africa | 2014 | 0.63 | Highest recorded (national) inequality |
U.S. Bureau of Economic Analysis
- A lower Gini coefficient indicates a more equal income distribution; a higher Gini indicates a distribution where more income is concentrated among high earners.
- Developed countries usually have Gini values between 0.25 and 0.45, while emerging markets may have values greater than 0.50.
- While the Gini is simple and widely used, it has limitations. For example, it does not indicate where the income is concentrated: relative equality within the lower income group with high inequality for higher incomes can have a similar Gini coefficient to a very low concentration of income among the lowest earners and relatively equal upper income groups. As such, they are most useful to compare economies of similar levels of development.
In sum, the Gini coefficient is a powerful but imperfect tool. It provides a concise summary of distributional information, but it is best combined with other metrics and qualitative understanding for full insight.
- Our World in Data: Gini coefficient trends and visualizations.
- World Bank: Poverty and Inequality Platform (source of Gini data).
