Gini Coefficient - Income and Wealth Inequality Index

The Gini coefficient is a single number that summarizes how evenly a quantity is shared across a population, and it is most often used to describe income or wealth inequality. Enter a list of values and the Gini coefficient calculator returns a dimensionless index between 0 (everyone has the same amount) and 1 (one observation holds everything).

• • Income inequality research: Compute a quick Gini from a household or worker income sample to compare how unequal two populations are before publishing or reporting.
• • Wealth portfolio analysis: Paste net-worth or account-balance values for a group of clients and read off the index to discuss concentration risk in plain language.
• • Classroom and exam checks: Verify hand-calculated Gini answers by reproducing the mean absolute difference formula against any small data set.
• • Policy and grant writing: Pair the calculator with a Lorenz curve visualization to support a narrative on inequality reduction or the impact of a transfer program.

The coefficient is named after Italian statistician Corrado Gini, who introduced the index in 1912 to compress the Lorenz curve into one number that is easy to compare across time or across countries.

Because the index is unitless, you can apply it to any non-negative distribution: dollars, hours of screen time, kilograms of food distributed per household, or tokens earned in a community program.

To see how a single household or individual salary compares to the rest of the country, US Income Percentile Calculator reads the official Census distribution and prints the matching percentile.

The calculator parses every numeric token in your input, validates that the values are non-negative, and then applies the mean absolute difference formula that is standard in the World Bank Poverty and Inequality Platform and the OECD Income Distribution Database.

• x_i: An individual value from your list (income, wealth, or any non-negative quantity).
• n: The number of values recognized after parsing commas, spaces, tabs, semicolons, and new lines.
• mean(x): The arithmetic mean of all parsed values, used as the normalization constant that makes the index unitless.
• |x_i - x_j|: Absolute difference between every pair of observations, summed over all n-squared ordered pairs.

If you type two equal values the calculator returns 0, because every pairwise difference is zero. If every value in your list is zero the result is also 0 by convention. If one observation holds all of the mass and the rest are zero, the calculator returns (n-1)/n, which approaches 1 as n grows.

According to World Bank Data, the Gini coefficient measures the area between the Lorenz curve and the line of perfect equality, expressed as a ratio between 0 and 1

When you want to compare the Gini against an alternative inequality summary such as a log-normal fit, Geometric Mean Calculator computes the geometric mean that often anchors that comparison.

Four ideas come up every time the index is reported, and each one shapes how a user should read the number the calculator prints.

These four concepts together explain why the same Gini value can come from very different distributions, and why two distributions with the same mean can still have different inequality scores.

Because the Gini formula divides by the arithmetic mean, Weighted Average Calculator is a useful sanity check whenever you want to confirm that the average you are normalizing against is the right one.

Paste your data into the textarea, watch the index update in real time, and read the inequality band for an interpretation.

1. 1 Gather your values: Collect the income, wealth, or other non-negative numbers you want to summarize. A spreadsheet column copied straight from Excel or Google Sheets works perfectly.
2. 2 Paste into the values box: Drop the numbers into the textarea. Separators can be commas, spaces, tabs, semicolons, or new lines; the calculator handles all of them in any combination.
3. 3 Read the result: The result panel shows the Gini coefficient, the inequality band, the number of values parsed, and the arithmetic mean of your list.
4. 4 Check the inequality band: Compare the band to common OECD and World Bank thresholds: below 0.2 is low, 0.2 to 0.3 is moderate, 0.3 to 0.4 is high, 0.4 to 0.5 is very high, and above 0.5 is extreme.
5. 5 Adjust and re-check: Edit the list, remove a stray negative entry, or append a sensitivity scenario, and the calculator updates the Gini in real time without a separate recalculate click.
6. 6 Export or copy the result: Highlight the printed Gini and copy it into your report, paper, or slide. For a Lorenz curve picture, pair the number with the US Income Percentile Calculator to anchor it in a familiar reference distribution.

Ten salaries (35000, 38000, 42000, 45000, 48000, 52000, 60000, 72000, 95000, 150000) produce a Gini of about 0.21, which lands in the low-to-moderate band; the single $150,000 observation widens the distribution a little but does not push the index into the high-inequality band because the rest of the values are tightly grouped.

After you compute the Gini, Disposable Income Calculator helps you trace how much of each household's gross income actually remains after taxes and transfers.

The index is one of the most cited inequality metrics because it collapses a long distribution into a single comparable number, and this calculator makes the math transparent.

• • Compare populations at a glance: Replace pages of distribution tables with one dimensionless score that you can put in a chart, a sentence, or a footnote.
• • Stay close to the source formula: The page shows the exact mean absolute difference formula instead of a black box, so you can audit the math line by line.
• • Reuse any non-negative data: Paste income, wealth, hours, weights, or scores. The index is scale-independent, so the unit of the input does not change the answer.
• • Catch input errors early: Validation messages flag negative values, empty lists, and all-zero inputs before the formula produces a misleading number.
• • Combine with related tools: Pair the result with the US Income Percentile Calculator, the Disposable Income Calculator, or the Net Income Calculator to give the index real-world context.
• • Teach and learn the formula: Use the worked example as a classroom reference and let students reproduce it by hand to verify the result.

These benefits are most useful when the index is paired with a Lorenz curve, because the Gini alone can hide which part of the distribution drives the inequality.

When you want to convert those inequality numbers into per-person take-home pay before recomputing a different Gini, Net Income Calculator gives you the per-period net quickly.

Several characteristics of your data and your definition of income or wealth can move the index up or down even when the underlying population feels similar.

• • The index does not distinguish between a distribution that is unequal at the bottom and one that is unequal at the top; two very different Lorenz curves can share the same Gini value.
• • The index is undefined when any value is negative, so distributions that include losses or debts must be transformed (for example, by shifting all values by a constant) before the formula is meaningful.
• • Reporting a Gini without the Lorenz curve or without the mean can mislead readers, because a high index in a small sample may simply reflect a single outlier.

These caveats are the same ones the OECD Income Distribution Database and the U.S. Census Bureau document when they publish national Gini numbers, so cite one of those sources when the result drives a decision.

According to OECD Income Distribution Database, the Gini index ranges from 0 to 1, with higher values indicating greater income inequality, and OECD reports national values ranging from about 0.25 to roughly 0.50

According to U.S. Census Bureau, the Gini index for U.S. household income was 0.485 in 2023

Because the Gini is sensitive to which slice of income you include, Discretionary Income Calculator isolates the spendable portion so you can recompute a post-transfer Gini on a clean subset.