Gini Coefficient
The Gini coefficient (also called the Gini ratio or Gini index) is a statistical measure of the degree of inequality in the distribution of income or wealth within a population. It was developed by the Italian statistician Corrado Gini in 1912. A Gini coefficient of 0 expresses perfect equality, in which every individual receives the same income, while a value of 1 (or 100%) expresses maximal inequality, in which one individual receives all the income.[1]
Definition and calculation
The Gini coefficient is defined as the ratio of the area between the Lorenz curve and the line of perfect equality to the total area below the line of perfect equality. The value ranges from 0 to 1, and is often multiplied by 100 and reported as a percentage.[1]
- Gini coefficient = A ÷ (A + B)
where A is the area between the Lorenz curve and the line of perfect equality, and B is the area beneath the Lorenz curve.
Interpretation
A lower Gini coefficient indicates a more equal distribution, while a higher value indicates greater inequality. Broad bands of interpretation are sometimes used:
| Gini coefficient | Interpretation |
|---|---|
| Below 0.2 | Highly equal |
| 0.2 – 0.3 | Relatively equal |
| 0.3 – 0.4 | Moderately unequal |
| 0.4 – 0.5 | Highly unequal |
| Above 0.5 | Extremely unequal |
A value of 0.4 is sometimes cited as a warning threshold, above which social tensions and instability may increase, though this is a rule of thumb rather than a precise limit.[1]
Uses and limitations
The Gini coefficient is widely used by governments and international organisations to compare inequality across countries and to track changes in inequality over time. It is most commonly calculated for income, but can also be computed for wealth, consumption, or other distributions. Its limitations include:
- It is a single number and does not reveal the structure of inequality, so two very different distributions may share the same Gini coefficient.
- It is sensitive to differences in the definition of income and to the granularity of the underlying data.
- Comparisons across countries are affected by differences in how income is measured and by the treatment of taxes and transfers.
The Gini coefficient is often used alongside the Lorenz curve and other poverty measures for a more complete picture.[1]