Gini coefficient: a measure of statistical inequality

Calculation and key properties

The Gini coefficient is linked to the Lorenz curve, which plots the cumulative share of the quantity held by the cumulative share of the population. The Gini is the ratio of the area between the Lorenz curve and the line of perfect equality to the total area under the equality line. It can also be computed from individual data by a double-sum of absolute differences or approximated from grouped data.

• Range: 0 (equality) to 1 (inequality) or 0–100.
• Scale invariance: multiplying all values by a constant does not change the coefficient.
• Population invariance: replicating the population leaves the value unchanged.
• Transfer principle: a small transfer from richer to poorer that reduces differences lowers the Gini.

History and development

The measure was introduced by the Italian statistician Corrado Gini in 1912. Since its introduction it has become a standard indicator in economics and social statistics. Analysts have developed alternative formulations, sampling corrections, and methods to compute the index from grouped surveys or tax records. For more on Gini's life and work see a brief biographical note.

Uses, examples and interpretation

Governments, international organizations and researchers use the Gini coefficient to compare inequality across countries, regions or over time. For example, rising Gini values often signal growing disparity, while declines may reflect redistributive policies or economic changes. Because it compresses distributional information into one number, it is convenient for comparison but must be interpreted with context: two populations can have the same Gini yet very different distribution shapes.

Limitations and notable facts

The Gini coefficient has recognized limitations. It is less sensitive to where inequality occurs (top versus middle), can be biased by underreported top incomes, and is not straightforwardly additive across subgroups without special weighting. Alternative measures (Theil index, variance-based statistics, percentile ratios) are sometimes used to highlight different aspects of inequality. Despite caveats, the Gini remains a widely used, easily communicated summary of distributional concentration.