What this Gini Coefficient Calculator does
The Gini Coefficient Calculator estimates income inequality for a population divided into two groups. It uses each group’s population share and income share to approximate the area beneath a Lorenz curve, then expresses the result as a coefficient from 0 to 1. A value closer to 0 indicates a more equal distribution in the data represented. A value closer to 1 indicates greater concentration of income.
This version is a small grouped-data worksheet. It accepts the population percentage and income percentage for each of two groups, with the groups arranged from lower to higher income. It does not inspect election results, fetch official statistics, infer missing shares, or calculate a full distribution from individual records. Its estimate depends on the two-group representation and the inputs you provide.
The calculator title is Gini Coefficient Calculator, and its description is “Estimate the Gini coefficient from grouped population shares and cumulative income shares. Enter each group's population percentage and its corresponding income percentage in ascending income order.” The result is labeled Estimated Gini Coefficient. This is useful for exploratory civic analysis when a dataset has been summarized into two groups, such as lower- and higher-income portions of a population.
How to use the Gini Coefficient Calculator
Enter four percentages. The first input is the population share in group 1, and the second is its income share. The next two inputs describe group 2 in the same order. Population shares should sum to 100%, and income shares should also sum to 100%. The form limits each population share to 1–99% so neither group has zero population.
- Sort the groups by income level, from the lower-income group to the higher-income group.
- Enter the population percentage represented by each group.
- Enter each group’s percentage of the total income being measured.
- Check that both pairs of shares add to 100% before interpreting the estimate.
For example, suppose the lower-income half of a population receives 20% of the measured income, while the upper-income half receives 80%. Enter 50 for group 1 population, 20 for group 1 income, 50 for group 2 population, and 80 for group 2 income. The formula converts percentages to proportions and applies a two-segment trapezoid approximation to the Lorenz curve. The area is 0.30, so the calculation is 1 − (2 × 0.30), producing a Gini estimate of 0.40. The output has no units because it is a ratio.
The defaults show this same worked example. Replace them with shares from a clearly defined dataset. If the shares do not total 100%, the result may still be computed, but it no longer has the intended interpretation as a normalized two-group distribution.
How the Gini Coefficient formula works
The calculator approximates the Lorenz curve, which plots the cumulative population share against the cumulative share of income owned or received. At the starting point, both cumulative shares are zero. After the first group, cumulative population is the population share of group 1 and cumulative income is its income share. At the final point, both cumulative totals should equal 1.
For each segment, the formula uses the trapezoid area: the change in population share multiplied by the average of the income shares at the segment’s endpoints. With two groups, it adds the area from zero to the first group and the area from the first group to the total population. The conventional grouped-data estimate is one minus twice this area. The calculator’s formula writes those two trapezoids directly from the four inputs, converting percentage values by dividing them by 100.
This approach assumes income is distributed evenly within each group for purposes of the approximation. Actual income distributions may vary substantially inside a group, so a two-group estimate can miss inequality that more detailed data would reveal. More groups usually allow a closer representation of the curve, provided the underlying data are reliable and consistently defined.
Use cases for the Gini Coefficient
A Gini estimate can help summarize a distribution in a compact way. In civic data work, a reader might compare income concentration across two places, time periods, or population definitions when each source has been reduced to comparable group shares. The number can help frame a question for further analysis, such as whether distributional patterns changed alongside a policy or demographic shift.
Before comparing two estimates, confirm that the figures use the same income definition, population coverage, time period, and treatment of taxes or transfers. Household income, individual earnings, and disposable income describe different things. A coefficient alone does not identify the cause of a pattern, show which households gained or lost, or indicate how people voted. It is a summary measure, not a complete account of economic conditions.
For classroom exercises, the two-group structure also makes the calculation easy to trace. Students can change one share at a time and see how a more concentrated distribution affects the area under the curve. That exercise is strongest when the figures are labeled as hypothetical or tied to a documented dataset.
Other factors to consider when calculating inequality
Data choices affect the meaning of the result. A dataset may cover individuals or households, include market income or disposable income, and describe a particular year or a longer period. Household size adjustments can also affect how resources are compared. The coefficient will not resolve those choices for you, so record them alongside the result.
Grouping also discards detail. Two groups with identical total shares may contain very different distributions within each group. The order matters as well. Group 1 should represent the lower-income portion and group 2 the higher-income portion for the formula’s Lorenz-curve interpretation. If groups overlap or are not ordered by income, the estimate may be misleading.
Use the result as a descriptive statistic and keep the source, definitions, and calculation method visible. For a formal report, use the source dataset’s established method or a suitably detailed distribution rather than relying on this two-group approximation. Avoid interpreting small differences as meaningful unless the data quality and uncertainty support that conclusion.
Frequently asked questions
What does a Gini coefficient of zero mean?
A value of zero represents perfect equality under the measure, meaning the cumulative income share tracks the cumulative population share throughout the distribution. It is an ideal reference point, and real-world results depend on the population and income definition used.
Can the coefficient be reported as a percentage?
The coefficient is conventionally shown between 0 and 1. Some sources multiply it by 100 and report a scale from 0 to 100. Keep the scale explicit when comparing published figures. This calculator displays the 0-to-1 form.
Why must the shares add up to 100?
The formula assumes the two groups together represent the full population and the full measured income. When either pair does not total 100%, the curve endpoint differs from the intended total, and the output is not a normalized estimate.
Does this calculator measure wealth inequality?
No. The inputs represent income shares. Wealth includes assets and liabilities and is a different distribution. A wealth inequality calculation needs suitable wealth data and a method designed for that measure.
Is a two-group estimate as precise as a full-data calculation?
No. It approximates the curve using only two segments and assumes an even distribution within each group for the trapezoid calculation. More detailed grouped or individual data can capture variation that this estimate cannot show.