What the Gini Coefficient Measures

The Gini coefficient measures how unevenly income is spread across a population, on a scale from 0 to 1. A score of 0 would mean every household receives exactly the same income. A score of 1 would mean one household receives everything and the rest receive nothing. The U.S. Census Bureau publishes the figure for the United States annually, and recent readings have sat in the range of about 0.47 to 0.49, among the higher values recorded for a wealthy country.

How the number gets built

Statisticians start by lining up every household from lowest income to highest, then plot the cumulative share of income against the cumulative share of households. That plot is the Lorenz curve. In a perfectly equal society the curve would be a straight diagonal line, because the bottom 20 percent of households would hold 20 percent of income, the bottom half would hold half, and so on.

Real distributions sag below that diagonal. The Gini coefficient measures the size of the sag: the area between the diagonal and the actual curve, divided by the total area under the diagonal. A deeper sag produces a bigger number. The arithmetic compresses an entire distribution into one decimal, which is both the reason the measure gets used and the reason it gets misread.

A worked example

Take five households earning $20,000, $40,000, $60,000, $80,000 and $100,000, for a total of $300,000. The poorest household holds about 6.7 percent of the income, the poorest two hold 20 percent, the poorest three hold 40 percent, and the poorest four hold 66.7 percent. Those points trace a modest sag, and the Gini for this group works out to about 0.27.

Now move $50,000 from the middle household to the top one. The spread becomes $20,000, $40,000, $10,000, $80,000 and $150,000. Total income has not changed at all, but the distribution has stretched, and the Gini rises to roughly 0.41. The lesson sits in that pair of numbers: the coefficient responds to the shape of the distribution and ignores its size. A country can grow richer every year while this number climbs.

What the coefficient hides

One decimal cannot tell you where inequality lives. Two countries can post identical Gini scores for opposite reasons, one with a very poor bottom decile and a normal top, the other with an ordinary bottom and an extreme top. Policy aimed at the wrong end would miss in both cases. Economists pair the Gini with decile or percentile shares for exactly this reason.

The measure also covers income rather than wealth. Income is what arrives this year. Wealth is what has accumulated, and the Federal Reserve’s Survey of Consumer Finances shows wealth concentrated far more sharply than income. A household with no savings and a household with substantial assets can report the same salary and sit in the same income decile while living different lives.

Timing matters too. Census reports income Gini both before and after taxes and transfers, and the two differ meaningfully, because tax credits, Social Security and public benefits redistribute income after the market has distributed it. A quoted Gini figure without that qualifier invites an argument where two people cite real numbers and talk past each other.

Why a single statistic drives so much argument

The coefficient is comparable across countries and across decades, which makes it the default citation in any inequality discussion. That convenience carries a cost. Because the number summarizes everything, people reach for it to settle questions it cannot answer, including whether a particular household can afford rent.

Affordability is a different measurement problem. The median U.S. home sale price ran roughly $400,000 to $420,000 in 2024 according to National Association of Realtors and Census data, against median household income of about $80,000 (U.S. Census Bureau, 2023). That ratio of about five to one, compared with roughly three to one in the 1980s, describes a specific barrier that no Gini reading captures. The federal minimum wage has held at $7.25 an hour since 2009, per the U.S. Department of Labor, and that fact would be invisible in a distribution summary.

Groups working on wage and cost issues tend to argue in those concrete terms instead. Fight For A Living Wage, a nonpartisan grassroots 501(c)(3), frames the problem as affordability across housing, health care, childcare, food, transport and education rather than as a single inequality statistic. Whether or not you share that framing, it points at a real limitation: distributional measures describe spread, and households experience prices.

How the United States compares

Cross-country comparison is where the measure does its best work, with one caveat: the agency doing the measuring has to define income the same way in both places. International bodies publish harmonized series for this reason, and on those series the United States posts a higher income Gini than most other high-income countries. Much of the gap traces to the post-tax, post-transfer stage rather than to market earnings, since countries with similar pre-tax distributions end up further apart once their tax and benefit systems run.

State-level figures inside the United States vary as well, and Census publishes Gini estimates by state through the American Community Survey. Dense states with large financial and technology sectors tend to score higher, partly because very high earners pull the top of the distribution upward and partly because those same states contain large low-wage service workforces. A state ranking tells you about the spread within its borders and nothing about the cost of living inside them.

Reading a Gini figure responsibly

Check four things before you repeat one. Find out whether the figure covers households or individuals, since the two give different answers. Find out whether it is pre-tax or post-tax. Note the year, because small annual moves get overstated in headlines. Confirm the source, and prefer the statistical agency that produced it over a secondary summary.

Then treat the number as an opening question. A rising Gini tells you the distribution stretched and says nothing about which end moved. Pull the income shares by quintile from Census tables and the wealth shares from the Federal Reserve, and the story usually resolves into something specific enough to discuss.

The coefficient earns its place as a comparison tool. It fails as a conclusion. Anyone who cites it as proof of a particular cause, or of a particular remedy, has asked one decimal to carry an argument it was never built to hold.