Gini Coefficient Calculator - Income Inequality

The Gini coefficient summarizes dispersion in a non-negative distribution. A result of zero represents perfect equality, where every observation has the same value. Values closer to one indicate greater concentration, although the maximum for a finite sample is slightly below one. The Gini coefficient calculator sorts the entered values and applies the standard weighted-rank formula. Currency and unit selections provide context but do not affect the dimensionless coefficient. Multiplying every observation by the same positive number leaves the Gini unchanged. Input design matters more than the arithmetic. Each value should represent the same concept, period, unit, and population. Do not casually mix monthly and annual income, household and individual observations, or income and wealth. Decide whether values are before tax, after tax, market income, disposable income, or net wealth. Zero values are allowed and can materially increase measured inequality. Negative values are rejected because common Gini formulas and interpretations become problematic when debts or losses produce negative observations. The coefficient is not a complete picture of distribution. Two datasets can have the same Gini while concentrating differences in very different parts of the population. It does not identify who has high or low values, describe mobility over time, or reveal group disparities. Sample weighting also matters. Official household surveys usually apply population weights and equivalence scales, neither of which this simple unweighted list supports. Small samples can produce unstable results, and missing high-value observations can bias wealth or income estimates downward. Use the mean, observation count, quantiles, Lorenz curve, and source documentation alongside the Gini. Compare coefficients only when definitions and populations are consistent. Labels such as low, moderate, and high inequality are broad descriptive aids, not universal policy thresholds. For official analysis, use microdata methods that handle survey weights, taxes, transfers, household size, and uncertainty. This tool is useful for classroom examples, quick checks, and small unweighted datasets. It does not establish fairness, diagnose causes, or replace a complete statistical analysis. Protect personal data: enter anonymized or aggregated values rather than identifiable financial records.