Income Inequality: Gini Coefficient, Lorenz Curve & Poverty Measures

Income inequality — how unevenly income is distributed across a population — is one of the most debated topics in economics. The Gini coefficient and Lorenz curve are the primary tools economists use to measure it. Whether you are analyzing economic development, evaluating how labor markets determine wages, or studying for an economics exam, understanding these measurement tools is essential.

This guide covers the Gini coefficient and Lorenz curve, how to interpret and calculate them, poverty measurement, the causes of income inequality, redistribution policies, and the philosophical trade-offs between equality and efficiency — all grounded in the same supply and demand framework that underpins modern economics.

What Is the Gini Coefficient?

The Gini coefficient is a single number between 0 and 1 that summarizes how unevenly income is distributed across a population. A Gini of 0 represents perfect equality (every household earns the same), while a Gini of 1 represents perfect inequality (one household earns everything). Economists typically measure inequality using quintile analysis — dividing households into five equal groups ranked by income and comparing each group’s share of total income.

In the United States, income inequality is substantial. According to the U.S. Census Bureau’s American Community Survey, the approximate shares of total household income by quintile are:

These figures reflect pre-tax household money income as defined by the Census Bureau — which includes wages, salaries, Social Security, and other cash benefits, but excludes taxes and noncash transfers like SNAP and Medicaid. After accounting for progressive taxation and noncash benefits, the distribution is less unequal — a distinction that matters when comparing inequality across countries with different tax and transfer systems.

The Lorenz Curve

The Lorenz curve is the graphical foundation behind the Gini coefficient. It plots the cumulative percentage of the population (ranked from poorest to richest) on the x-axis against the cumulative percentage of total income they earn on the y-axis.

If income were distributed perfectly equally, the Lorenz curve would lie exactly on the 45-degree line — the bottom 20% would earn 20% of income, the bottom 40% would earn 40%, and so on. In reality, the Lorenz curve bows below this line. Using the U.S. quintile data above, the Lorenz curve passes through approximately these points:

• (20, 3.0) — the poorest 20% earn only 3.0% of total income
• (40, 11.6) — the poorest 40% earn 11.6%
• (60, 25.9) — the poorest 60% earn 25.9%
• (80, 48.5) — the poorest 80% earn 48.5%
• (100, 100) — the entire population earns 100%

The farther the Lorenz curve bows away from the 45-degree line, the greater the inequality. Area A is the region between the equality line and the Lorenz curve. Area B is the region below the Lorenz curve. Together, A + B fills the entire triangle below the 45-degree line.

The Gini Coefficient Formula

The Gini coefficient is calculated directly from the Lorenz curve:

Since the total triangle under the 45-degree line (A + B) has an area of 0.5 in a unit square, the formula simplifies to a practical discrete form:

When G = 0, the Lorenz curve lies on the equality line (B = 0.5, so 1 − 2(0.5) = 0). When G = 1, the Lorenz curve hugs the axes (B = 0, so 1 − 2(0) = 1). In practice, no country reaches either extreme.

The following table compares Gini coefficients across countries. Note: the World Bank reports Gini on a 0–100 scale; the values below are converted to the 0–1 convention used throughout this article.

Source: World Bank, SI.POV.GINI indicator. Years reflect most recent available observation.

Interpreting Gini Coefficient Values

While Gini values fall on a continuous scale, the following ranges serve as approximate rules of thumb — they are not standardized thresholds and should be interpreted with context:

How to Calculate the Gini Coefficient

Here is a step-by-step calculation for a simplified five-household economy:

Poverty: Measurement and the Poverty Line

While the Gini coefficient measures how income is distributed, poverty measures whether individuals fall below an absolute threshold of economic well-being. These are distinct concepts that provide different information about an economy.

The official U.S. poverty measure defines poverty using money-income thresholds that vary by family size and composition, updated annually for inflation. (The measure historically originated from roughly three times the cost of an adequate food budget, but today it functions as a set of fixed thresholds.) Two important caveats apply: the official measure counts only pre-tax money income, excluding noncash benefits like SNAP and Medicaid; and it does not adjust for geographic cost-of-living differences, treating a dollar of income in Manhattan identically to a dollar in rural Mississippi.

The poverty rate is the percentage of the population with income below the poverty line. The poverty gap measures how far below the poverty line poor families fall on average — capturing the depth of poverty, not just its incidence. In many European countries, poverty is measured on a relative basis — typically income below 60% of the national median — which conflates poverty with inequality to some degree.

Poverty rates in the United States correlate with demographic characteristics. According to Census data, poverty rates are higher for children, for single-parent households, and for Black and Hispanic households compared to the overall average. As Mankiw notes in his discussion of economic mobility, roughly one in four American families falls below the poverty line at some point during a 10-year period, but fewer than 3% remain poor for 8 or more consecutive years — suggesting that for most families, poverty is transitory rather than permanent.

Causes of Income Inequality

Economists identify several factors that contribute to income inequality. Each represents an empirical observation supported by research, and their relative importance is debated:

• Education and human capital — Returns to education have increased substantially. Workers with college degrees earn significantly more on average than those without, and the gap has widened over recent decades.
• Ability, effort, and luck — Natural talents, work ethic, and chance events (health, economic conditions at career entry) all contribute to income variation across individuals.
• Technological change and the skill premium — Technology tends to increase demand for skilled workers while reducing demand for routine tasks, widening the gap between high-skill and low-skill wages. This is closely related to how the marginal product of labor determines wages in competitive factor markets.
• Globalization — International trade with lower-wage countries can reduce wages for domestic unskilled workers while benefiting skilled workers and capital owners.
• Wealth inheritance and intergenerational mobility — Some inequality persists across generations through inherited wealth, though economic mobility in the United States remains substantial.
• Winner-take-all markets — In industries like entertainment, technology, and professional sports, small differences in talent or timing can produce enormous differences in income.

Institutional factors — including labor market regulations, discrimination, and household structure (e.g., the rise of dual-income households and single-parent families) — also shape the income distribution.

Redistribution Policies

Governments use several policy tools to alter the income distribution. Each involves trade-offs between equity and economic efficiency:

• Progressive taxation — Higher tax rates on higher incomes reduce after-tax inequality. The trade-off: higher marginal rates can reduce incentives for work, saving, and investment.
• Earned Income Tax Credit (EITC) — A tax credit for low-income working families that phases in with earned income. It encourages labor force participation, unlike traditional welfare programs that may reduce work incentives. The trade-off: it provides limited help to those unable to work.
• Transfer programs — Social Security provides cash benefits that are counted in official money-income statistics. However, noncash programs — SNAP (food assistance), Medicaid (health coverage), and housing vouchers — reduce material deprivation but are not counted as money income in standard inequality and poverty statistics, meaning their equalizing effect is understated in official measures.
• Minimum wage laws — Set a floor on hourly wages. For a full analysis of the minimum wage debate — including employment effects and the monopsony model — see Minimum Wage Economics.

The effectiveness and desirability of these policies depends partly on empirical questions (how large are the efficiency costs?) and partly on philosophical judgments about the appropriate balance between equality and individual liberty.

The Equality-Efficiency Trade-off

Economist Arthur Okun described redistribution using the metaphor of a leaky bucket: transferring income from higher earners to lower earners is like carrying water in a bucket with holes. Some resources are inevitably lost in transit — through administrative costs, reduced work incentives, and economic distortion. The central question is how much leakage society should tolerate in pursuit of a more equal distribution.

Three major philosophical frameworks offer different answers to this question:

These are simplified representations of complex philosophical traditions, and most real-world policy reflects elements of multiple perspectives. Economic analysis can quantify the trade-offs — how much efficiency is lost per unit of equality gained — but it cannot resolve the underlying value judgment about how much equality society should pursue. That is a question of political philosophy, not economics.

Common Mistakes

1. Confusing income inequality with poverty. Inequality measures how unevenly income is distributed; poverty measures whether people fall below an absolute threshold. A country can reduce poverty while inequality increases — if economic growth lifts the bottom but benefits the top even more. Conversely, a severe recession can reduce inequality (by disproportionately harming high earners) while increasing poverty.

2. Ignoring in-kind transfers and taxes when measuring inequality. Official U.S. inequality and poverty statistics are based on pre-tax money income, which excludes SNAP, Medicaid, housing assistance, the EITC, and the effects of taxation. Including these substantially reduces measured inequality and poverty. Comparing raw figures across countries without noting whether the data is pre-tax or post-tax is misleading.

3. Comparing Gini coefficients across countries without standardizing definitions. Different countries measure income differently — pre-tax vs. post-tax, household vs. individual, income vs. consumption. The survey year also varies: as shown in the country comparison table above, the most recent World Bank Gini observations range from 2014 (South Africa) to 2023 (United States, Sweden, Brazil). A raw comparison without standardizing these factors can produce incorrect conclusions.

4. Assuming income inequality always increases. In the United States, inequality actually decreased substantially from the 1930s through the 1970s — a period economists call the “Great Compression.” It then increased from the late 1970s onward. The trend is neither monotonic nor universal: different countries have followed different trajectories depending on their institutions, technology adoption, and policy choices.

5. Treating the Gini coefficient as a measure of prosperity or living standards. The Gini measures only the distribution of income, not its level. A country where everyone earns $5,000 per year would have a Gini near zero, but no one would call it prosperous. Conversely, a country with rapid economic growth may see its Gini rise temporarily even as absolute living standards improve for everyone. The Gini is a distributional metric, not a welfare metric.

Limitations of the Gini Coefficient

1. Snapshot in time. The Gini measures annual income distribution, but lifetime income is more relevant to economic welfare. A society with high annual inequality but strong economic mobility may have lower lifetime inequality. As standard textbook illustrations note, roughly 25% of American families fall below the poverty line for at least one year in a 10-year span, but fewer than 3% are poor for 8 or more consecutive years.

2. Does not capture wealth inequality. Income and wealth are different. Wealth inequality (the distribution of net assets) is typically much higher than income inequality because wealth accumulates over time and across generations.

3. Ignores household size and composition. A household of one earning $50,000 and a household of five earning $50,000 have very different living standards, but most Gini calculations treat them equally.

4. Insensitive to where inequality occurs. The Gini treats a transfer from the middle class to the wealthy the same as a transfer from the poor to the middle class, even though the welfare implications are different.