Gini Coefficient Calculator

What Is the Gini Coefficient Calculator?

The Gini Coefficient Calculator measures income inequality within any dataset — whether you're analysing a country, a company's payroll, a classroom's stipends, or a survey sample. Enter your income values (individually or as grouped brackets with headcounts) and the calculator returns the Gini coefficient, bias-corrected Gini, Palma ratio, quintile income shares, and a comparison against World Bank country data.

The Gini coefficient was developed by Italian statistician and demographer Corrado Gini and published in his 1912 paper Variabilità e mutabilità. It has since become the standard measure for income and wealth inequality in economics, sociology, and public policy. The World Bank tracks Gini coefficients for over 160 countries and uses the index to monitor progress on reducing economic inequality under the Sustainable Development Goals.

Our calculator goes beyond a simple Gini output. It also computes the bias-corrected Gini (G* = G × n/(n−1)), which matters when your sample is small; the Palma ratio, which captures elite concentration better than the Gini alone; and quintile income shares that approximate the full Lorenz curve without requiring a chart. If you are studying income mobility, the Female Delusion Calculator applies similar probabilistic breakdowns to demographic criteria matching.

How the Lorenz Curve Underlies Every Gini Score

To understand what the Gini coefficient means, you need to understand the Lorenz curve. Plot the population on the x-axis from poorest to richest (0% to 100%) and cumulative income share on the y-axis (0% to 100%). In a perfectly equal distribution, the bottom 10% earn 10% of income, the bottom 50% earn 50%, and so on — the Lorenz curve is a straight 45-degree diagonal, called the "line of perfect equality."

In reality, the Lorenz curve bows downward because the poor receive less than their population share. The Gini coefficient is the area between the diagonal and the actual Lorenz curve, divided by the total area under the diagonal (which equals 0.5). Mathematically, for a sorted list of n incomes x₁ ≤ x₂ ≤ ... ≤ xₙ with total T, the Gini equals (2 × Σ rank_i × x_i) / (n × T) − (n+1)/n. This formula avoids numerical integration and gives an exact result from discrete data.

The quintile share table in our output approximates the Lorenz curve at five evenly-spaced points. You can see how far each quintile's cumulative income share falls below the line of perfect equality. The wider the gap in Q1 and Q2 (the bottom 40%), the more the Lorenz curve bows, and the higher the Gini. The OECD regularly publishes Lorenz curves for member countries alongside their Gini values as part of the Society at a Glance report series.

What Gini Scores Mean Across Countries

The global range of Gini coefficients spans roughly 0.238 (Slovakia) to 0.630 (South Africa), with a global mean of approximately 0.37. Understanding where different values sit on this spectrum helps contextualise any result. Here are five broad tiers:

• Below 0.30 — Low inequality: Central and Northern Europe dominate this range. Slovakia, Slovenia, Czech Republic, Sweden, Norway, and Denmark all sit here. High social transfers, compressed wage structures, and strong union bargaining keep distributions tight. These countries typically show Q1 income shares above 9% and top-20% shares below 37%.
• 0.30–0.35 — Moderate-low: Germany, France, Canada, Australia, and Japan occupy this band. Market inequality is higher but post-tax redistribution brings the disposable income Gini into this moderate range. Q1 shares typically run 6–9%.
• 0.35–0.40 — Moderate: The United States (0.394), the United Kingdom (0.351), and Spain (approximately 0.345) fall here. A visible but manageable gap between middle and top earners. Mean incomes exceed medians by 20–35%.
• 0.40–0.50 — High: Mexico, Turkey, China, and parts of Southeast Asia. The top 20% typically captures over 50% of income. Q1 shares drop below 5%. The ratio of mean to median income often exceeds 1.5.
• Above 0.50 — Very high to extreme: Brazil (0.516), Colombia (0.539), Namibia (0.591), and South Africa (0.630). These are the highest sustained Gini values in World Bank records. Q1 income shares can fall to 2–3%, and the top 10% alone may capture 45–55% of total income.

All country values referenced here are post-tax/transfer disposable income Gini from the World Bank Development Indicators database, which provides the most internationally comparable series. Pre-tax market income Gini values are typically 15–25 percentage points higher, which is why US pre-tax estimates near 0.50 are common in academic literature even as the post-transfer Gini sits near 0.39.

The Palma Ratio vs the Gini Index: Which Should You Use?

The Gini coefficient is the most recognised inequality measure, but it has a mathematical weakness: it is most sensitive to differences in the middle of the distribution and relatively insensitive to changes at the very top or very bottom. A transfer from the 1st percentile to the 5th percentile barely moves the Gini, and neither does a transfer from the 95th percentile to the 99th percentile.

Cambridge economist Gabriel Palma identified a striking empirical regularity in 2011: across a wide range of countries and time periods, the middle 50% of earners (those in the 50th to 90th percentiles) tend to capture a relatively stable 50% of income — roughly the same share everywhere. Almost all the cross-country variation in inequality comes from the contest between the top 10% and the bottom 40%. The Palma ratio, defined as the income share of the top 10% divided by the income share of the bottom 40%, captures exactly this variation.

A Palma ratio of 1.0 means the top decile and the bottom four deciles earn the same aggregate income — a rough marker of relatively equitable distribution. Most developed economies sit between 1.0 and 1.5. Values above 2.0 indicate strong elite concentration. South Africa's Palma ratio exceeds 7.0. For tracking changes in elite capture over time — or for understanding within-organisation pay equity, as in the case study above — many researchers now prefer the Palma ratio to the Gini precisely because it focuses on where inequality actually differs between places.

Our calculator shows both: use the Gini for international comparison and historical tracking, and the Palma ratio when your question is specifically about whether top earners are pulling away from the bottom. For related income-distribution analysis, the Debt-to-Income Ratio Calculator and Income Tax Calculator can help contextualise individual financial positions within these distributions.

Accuracy and Limitations of This Calculator

The Gini formula used here — G = (2 × Σ rank × income) / (n × total) − (n+1)/n — is an exact, closed-form result for discrete data. No numerical integration or curve-fitting is involved. For grouped bracket data, we use the midpoint income as a representative value for everyone in the bracket. This is the standard approach for survey data (used by the US Census Bureau, Eurostat, and OECD) but introduces approximation error if the income distribution within a bracket is skewed. Using narrower brackets improves accuracy.

The bias-corrected Gini (G* = G × n/(n−1)) is important for small samples. For n = 5, the standard Gini can understate true inequality by 20%; for n = 30, the error is under 3%; for n = 100+, it is negligible. For sample sizes below 30, always report G* rather than G.

The US Census Bureau notes several additional limitations: the Gini treats income as individual rather than household (sharing economies reduce apparent inequality); it ignores non-monetary income (benefits, in-kind transfers); and it uses pre-defined income concepts that vary by country, making some cross-national comparisons misleading. For organisational or classroom datasets, also note that small populations make the Gini volatile — a single extreme value can shift the result substantially.

The Most Common Gini Coefficient Misinterpretation

The single most common error in interpreting Gini results is conflating inequality with poverty. A perfectly equal distribution of extremely low incomes has a Gini of 0.00. A country where everyone earns $200 per month has no measured inequality and complete absolute poverty simultaneously. The Gini is a relative measure — it tells you how income is spread across the population but says nothing about whether that income is enough to live on.

This is why international agencies always pair the Gini with absolute poverty headcount ratios and median income figures. A Gini of 0.35 in a country with a median income of $60,000 is a very different situation from a Gini of 0.35 in a country with a median income of $3,000. Similarly, reducing inequality by compressing downward (everyone becomes poorer at a similar rate) is not a policy success even if the Gini falls. When using this calculator, always note the mean and median incomes alongside the Gini to avoid drawing conclusions about living standards from the inequality metric alone. The UN Human Development Index explicitly combines income inequality with life expectancy and education to avoid exactly this misinterpretation.