Gini Coefficient Calculator

Written by Great Medico

Updated September 25, 2026 •2 min read

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A finance formula can look precise even when the assumptions behind it are doing most of the work. Gini Coefficient Calculator keeps those assumptions visible and turns the fields on this page into one focused result. On this page, it converts the two Lorenz-curve area inputs into a Gini coefficient.

What this calculator does

Gini Coefficient Calculator converts the two Lorenz-curve area inputs into a Gini coefficient. Its visible inputs are Area A, Area B. The article follows those fields and the calculation that is actually available on this page; it does not silently add live market feeds, tax tables, legal eligibility tests, or other variables that are not present in the tool.

How to use it

Enter Area A, Area B. Use the units and percentage scale shown beside each field, and keep values on the same time basis when the formula compares income, rates, prices, balances, or work hours.

How the calculation works

With the two area inputs shown here, the calculator uses Gini = Area A ÷ (Area A + Area B). This is the Lorenz-curve area form of the coefficient.

Example

Using the page’s demonstration values (Area A = 0.2; Area B = 0.3) and leaving the remaining defaults unchanged, the calculator returns 0.4 for gini coefficient. Replace the sample inputs with values from the same period and definition before interpreting your own result.

How to interpret the result

Read the result as a model of the economic relationship represented by the inputs, not as a forecast of what an economy, market, currency, or policy authority will do next. Economic data are definition-sensitive: nominal versus real values, time periods, population bases, and price indexes must be aligned before comparing results.

Limitations and notes

This area-based version assumes Area A and B correctly represent the space around a valid Lorenz curve; it does not derive those areas from a raw income dataset. Simplified macroeconomic formulas hold other influences constant. Revisions to source data, measurement definitions, expectations, policy responses, market frictions, and nonlinear behavior can make real-world outcomes differ from the clean relationship shown here.