Gini Coefficient

The Gini coefficient is a statistical measure used to quantify income or wealth distribution inequality. Ranging from 0 (perfect equality) to 1 (absolute inequality), it’s a widely used tool in economics, despite limitations like not accounting for income levels or inequality sources.

Gini Coefficient Definition

This is a measure of inequality in a distribution, a concept developed by the Italian statistician and sociologist Corrado Gini.

Value Range

The Gini coefficient ranges between 0 and 1. In this scale, 0 expresses perfect equality, where everyone has the same income or wealth. Conversely, a Gini coefficient of 1 signals absolute inequality, where one person has all the income or wealth and others have none.


The coefficient is calculated as the ratio of the area between the line of perfect equality (or Line of Equality) and the observed Lorenz Curve to the area underneath the Line of Equality. The Lorenz curve plots the cumulative income or wealth of the population (y-axis) against the cumulative percentage of the population (x-axis).

Use in Economics

The Gini coefficient is widely used in economics, particularly in studies of income or wealth distribution. This includes understanding the gap between rich and poor, both within and between nations.


A high Gini coefficient (closer to 1) indicates a high degree of inequality in the distribution. A low Gini coefficient (closer to 0) indicates a more equal distribution.


The Gini coefficient does not take into account the absolute level of income or wealth, it only reflects the distribution. Moreover, it’s not sensitive to where the inequality occurs – for instance, whether it’s among the poor, the middle class, or the rich. It’s also not specific about the nature or causes of inequality.


Different factors can affect the Gini coefficient, including social, economic, and political policies and conditions. Changes in these factors over time can lead to changes in the Gini coefficient.

Application beyond Economics

The Gini coefficient has also been used in a variety of fields outside of economics, including ecology (to measure biodiversity), health (to measure inequality in health outcomes), and technology (to measure dispersion in usage).