Gini Coefficient Calculator

The Gini Coefficient (also called the Gini Index or Gini Ratio) is a statistical measure of inequality in a distribution, developed by Italian statistician Corrado Gini in 1912. It ranges from 0 (perfect equality — everyone has the same share) to 1 (maximum inequality — one entity has everything). It is the most widely used single-number summary of inequality in economics and social sciences.

The Gini Coefficient is calculated using the Lorenz Curve, which plots the cumulative share of income against the cumulative share of the population. The formula is G = A / (A + B), where A is the area between the line of perfect equality and the Lorenz Curve, and B is the area under the Lorenz Curve. Equivalently, G = 1 − 2B. For empirical data, it can also be computed as G = Σ Σ |xi − xj| / (2n²x̄).

A Lorenz Curve is a graphical representation of income or wealth distribution. The x-axis shows the cumulative percentage of the population (from poorest to richest), and the y-axis shows their cumulative share of total income or wealth. A perfectly equal distribution would be a 45-degree straight line (the line of perfect equality). The further the Lorenz Curve bows below that line, the greater the inequality.

There is no single 'good' value — it depends on context. Generally: 0.0–0.25 is considered low inequality, 0.25–0.40 is moderate inequality, 0.40–0.60 is high inequality, and above 0.60 is very high inequality. Scandinavian countries typically have Gini coefficients around 0.25–0.30, while the US is around 0.39–0.41, and highly unequal nations can exceed 0.60.

The US Gini Coefficient for household income is approximately 0.39–0.41 as of recent measurements, placing it among the higher-inequality developed nations. For comparison, most Western European countries have Gini coefficients in the 0.28–0.36 range. The US wealth (net worth) Gini is considerably higher, often estimated above 0.85.

Under standard definitions using income or wealth (non-negative values), the Gini Coefficient cannot be negative. However, if a dataset contains negative values (such as net worth including debt), it is theoretically possible to compute a Gini above 1 or, depending on the formula variant, negative values. For typical applications with non-negative data, G always falls between 0 and 1.

Yes. While it is most commonly applied to income and wealth distributions, the Gini Coefficient can measure inequality in any distribution — market share concentration, land ownership, educational attainment, health outcomes, or even ecological diversity. The calculation method is identical; only the interpretation of what 'inequality' means changes with context.

The Gini Coefficient condenses an entire distribution into a single number, which means two very different distributions can yield the same Gini value. It does not tell you where in the distribution inequality is concentrated (top, middle, or bottom). It is also sensitive to sample size with small datasets and does not account for factors like cost of living differences, household size, or non-monetary welfare.