Gini Coefficient

The Gini coefficient, also called the Gini index or Gini ratio, is a statistic that summarizes how unequally something, usually income or wealth, is distributed across a population. A value of 0 means perfect equality, where every person or household has exactly the same amount. A value of 1 (or 100, when written as an index) means maximum inequality, where a single person has everything. Most national income Ginis fall between about 0.25 and 0.65.

The measure was developed by Italian statistician Corrado Gini in 1912 and is published by national statistics offices, the World Bank, the OECD and others. It is the most common single indicator of economic inequality, though it is usually read alongside top-income shares and poverty rates.

Imagine lining everyone up from poorest to richest and then walking along the line, keeping a running total of what share of the population you have passed and what share of the income they hold. In a perfectly equal country, passing 30% of the people means passing 30% of the income. In an unequal one, you might pass half the people and only a sixth of the income.

The Gini coefficient turns that gap between "fair share" and "actual share" into one number. The more the actual shares lag behind the population shares, the higher the score.

The Gini is defined using the Lorenz curve, introduced by economist Max Lorenz in 1905. The horizontal axis shows the cumulative share of people, from poorest to richest; the vertical axis shows the cumulative share of income they receive. A 45-degree line represents perfect equality. The actual Lorenz curve sags below it. If $A$ is the area between the line of equality and the Lorenz curve, and $B$ is the area under the Lorenz curve, then:

Because $A + B = 0.5$ when both axes run from 0 to 1, this is the same as $G = 2A$.

For a list of incomes, the Gini can be calculated directly from the average gap between every pair of people:

• $x_i$ and $x_j$ are the incomes of persons $i$ and $j$.
• $n$ is the number of people.
• $\mu$ is the mean income.

Take four people earning 10, 20, 30 and 40 thousand dollars. The mean is 25. The absolute differences between each distinct pair are 10, 20, 30, 10, 20 and 10, which add to 100; counting each pair in both directions doubles this to 200. The Gini is 200 ÷ (2 × 4² × 25) = 200 ÷ 800 = 0.25. If one person earned 100 and the other three earned nothing, the mean would still be 25 but the double sum would be 600, giving 600 ÷ 800 = 0.75; with very large populations, that extreme case approaches 1.

Values depend heavily on whether income is measured before or after taxes and transfer payments, and per person or per household. In World Bank data, South Africa has among the highest income Ginis in the world, above 0.6, while several European countries, including Slovakia, Slovenia and the Nordic countries, are among the lowest, below 0.3. The United States has one of the highest Ginis among rich countries; the US Census Bureau found that in 2024 its Gini for household income was 8.7% lower when measured after taxes than on pre-tax money income. Wealth Ginis are much higher than income Ginis almost everywhere, often 0.7 or more, because many households have little or negative net worth while assets such as shares, housing and businesses are concentrated. Comparing pre-tax and post-tax Ginis shows how much a progressive tax system and welfare spending redistribute.

Governments track the Gini to monitor the effects of fiscal policy, minimum wages and labor market changes, and international bodies use it in development targets such as Sustainable Development Goal 10 on reducing inequality. Researchers use it in studies linking inequality to growth, health, crime and political outcomes, and to analyze trends such as the Great Compression and later rise in inequality. Outside economics, the same formula is used to measure concentration in market shares, biodiversity and the spread of credit scores.

• It hides the shape of the distribution. Two countries can have the same Gini with very different patterns, for example one with a large poor group and another with a tiny very rich group. Top-share measures and the Palma ratio give more detail at the extremes, and researchers often model the top tail with a Pareto distribution.
• It is most sensitive to the middle. Changes at the very top or bottom move the Gini less than changes around the middle of the distribution.
• Data problems. Surveys tend to miss the very rich and undercount income from the informal economy and from capital, such as capital gains. Many developing countries measure consumption rather than income, which gives lower Ginis.
• Age and life cycle. A society with many students and retirees can show a higher annual Gini even if lifetime incomes are similar, a point linked to the life-cycle hypothesis.
• Not a welfare measure. A country can lower its Gini while everyone gets poorer, or raise it while everyone gets richer; it says nothing about average living standards measured by GDP or income.

• World Bank: Gini index data
• US Census Bureau: Income in the United States: 2024