Income inequality describes how unevenly income is spread across a population. In an intermediate micro context, this matters because factor markets determine wages and returns to capital, and those returns don't land equally on everyone. The tools in this section let you quantify how unequal a distribution is, compare distributions across countries or time periods, and evaluate whether policy interventions actually reduce inequality.

Defining and quantifying income inequality

Income inequality exists whenever income is distributed unevenly across individuals or households in a population. To measure it, economists divide the population into ordered segments and compare their income shares.

Population segments used in measurement:

Quintiles divide the population into five equal groups (each 20%), ranked from lowest to highest income
Deciles divide into ten equal groups (each 10%), giving finer resolution

Statistical measures of inequality:

Range: the gap between the highest and lowest incomes. Simple but heavily influenced by outliers.
Variance and standard deviation: capture how spread out incomes are around the mean. Higher values mean more dispersion. These are scale-dependent, though, so they're hard to use for comparisons across countries with different average income levels.

Ratio-based measures compare specific parts of the distribution:

90/10 ratio: income at the 90th percentile divided by income at the 10th percentile. A 90/10 ratio of 12 means someone near the top earns 12 times what someone near the bottom earns.
Palma ratio: income share of the top 10% divided by the income share of the bottom 40%. This ratio highlights the tails of the distribution, where most of the action in inequality tends to happen. The middle 50% of the distribution tends to capture a relatively stable share of income across countries, which is why the Palma ratio focuses on the extremes.

Time-series analysis tracks these measures over multiple years to identify trends. For example, the 90/10 ratio in the U.S. has risen substantially since the 1980s, indicating growing inequality at the extremes.

Advanced analytical techniques

Beyond snapshot measures, economists use several deeper approaches:

Income mobility studies track the same individuals over time to see whether people move up or down the income ladder. High inequality with high mobility tells a different story than high inequality with low mobility.
Decomposition analysis breaks total inequality into components by income source. You can ask: how much of overall inequality comes from differences in labor income versus capital income (dividends, interest, rents)?
Wealth inequality complements income inequality because wealth (accumulated assets minus debts) is typically distributed far more unequally than income. Measures include the net worth Gini coefficient and wealth-to-income ratios.
International comparisons require purchasing power parity (PPP) adjustments so that differences in price levels across countries don't distort the comparison.
Multidimensional indices go beyond money to incorporate factors like education and health, recognizing that well-being isn't captured by income alone.

Lorenz curve and Gini coefficient

Defining and quantifying income inequality, Lorenz curve - Wikipedia

Understanding the Lorenz curve

The Lorenz curve is the primary graphical tool for visualizing income inequality. It plots the cumulative percentage of the population (ranked from poorest to richest) on the horizontal axis against the cumulative percentage of total income they receive on the vertical axis.

How to read it:

Start at the origin (0%, 0%). The poorest 0% of the population earns 0% of total income.
Move along the curve to the right. Each point tells you what share of total income the bottom $x\%$ of the population earns.
The curve always ends at (100%, 100%) because the entire population earns all the income.

The line of perfect equality is the 45-degree diagonal from (0,0) to (100,100). Along this line, every percentage of the population earns exactly that same percentage of income (the bottom 20% earns 20%, the bottom 50% earns 50%, etc.).

The further the Lorenz curve bows away from the diagonal, the greater the inequality. If the bottom 40% of the population earns only 15% of total income, the curve will be well below the diagonal at that point.

You can compare two distributions by plotting their Lorenz curves on the same graph. If one curve lies entirely inside (closer to the diagonal than) another, that distribution is unambiguously more equal. This is called Lorenz dominance. When curves cross, the ranking is ambiguous, and you need a summary statistic like the Gini coefficient to make a comparison. Keep in mind, though, that different summary statistics can rank crossing Lorenz curves differently depending on which part of the distribution they weight most heavily.

The Lorenz curve isn't limited to income. You can use it for wealth, land ownership, or any variable where you want to visualize how evenly something is distributed.

Calculating and interpreting the Gini coefficient

The Gini coefficient converts the Lorenz curve into a single number. It's defined as:

$G = \frac{A}{A + B}$

where $A$ is the area between the line of perfect equality and the Lorenz curve, and $B$ is the area under the Lorenz curve. Since the total area of the triangle under the diagonal equals $\frac{1}{2}$ , we know $A + B = \frac{1}{2}$ , and the formula simplifies to:

$G = 2A = 1 - 2B$

Computing the Gini from data (step-by-step):

If you're given quintile data (or any grouped data), you can approximate the Gini using the trapezoidal method:

Rank the population from poorest to richest and calculate cumulative income shares for each group. For example, if the bottom quintile earns 5% and the second quintile earns 10%, the cumulative shares are 5% and 15%.
Plot these cumulative shares to form the Lorenz curve. Your points will be (0, 0), (0.20, 0.05), (0.40, 0.15), and so on up to (1.0, 1.0).
Calculate the area under the Lorenz curve ( $B$ ) using the trapezoid rule: sum the areas of the trapezoids formed between each pair of adjacent points.
Plug into $G = 1 - 2B$ .

Interpreting the value:

$G = 0$ : perfect equality (everyone earns the same)
$G = 1$ : perfect inequality (one person earns everything)
Most countries fall between 0.25 and 0.60. For reference, Sweden's Gini is around 0.28, the U.S. is around 0.39, and South Africa is around 0.63.

Limitations you should know:

Two very different distributions can produce the same Gini coefficient. A country with a squeezed middle class and a country with a small ultra-rich elite could have identical Gini values, even though the shape of inequality differs. This is exactly the crossing-Lorenz-curves problem.
The Gini is most sensitive to changes in the middle of the distribution and less sensitive to changes at the extremes. If you care specifically about poverty or top-end concentration, the Gini may understate what's happening.
It doesn't capture demographic information. A Gini of 0.40 doesn't tell you who is at the top or bottom.

Because of these limitations, economists often use complementary measures alongside the Gini:

Theil index: decomposable, meaning you can break it into "within-group" and "between-group" inequality (e.g., inequality within regions vs. between regions). This is especially useful for understanding sources of inequality.
Atkinson index: incorporates a parameter $\varepsilon$ reflecting society's aversion to inequality. Higher $\varepsilon$ values weight the bottom of the distribution more heavily, so you can tailor the measure to different normative priorities.

Factors contributing to income inequality

Defining and quantifying income inequality, Income Inequality: Measurement and Causes | OS Microeconomics 2e

Educational and skill-based factors

Education is one of the strongest predictors of income. On average, workers with a bachelor's degree earn significantly more over their lifetimes than workers with only a high school diploma. But the relationship between education and inequality is more nuanced than "more school = more money."

Skill-biased technological change (SBTC) is a central concept here. As technology advances, demand rises for workers who can complement that technology (programmers, analysts, engineers), pushing their wages up. At the same time, technology can substitute for routine tasks performed by lower-skilled workers, reducing demand and wages for those jobs. This widens the wage gap between high-skilled and low-skilled workers.

In factor market terms, SBTC shifts the marginal revenue product curve for skilled labor rightward while shifting it leftward (or holding it flat) for unskilled labor. The result is diverging equilibrium wages.

Other education-related factors:

Returns to education vary by field. An engineering degree and an arts degree both represent four years of college, but average earnings differ substantially.
Access to quality education correlates with socioeconomic background. Children from wealthier families tend to attend better-resourced schools, creating a feedback loop that perpetuates inequality across generations.
Educational policy shapes long-term inequality through public school funding, higher education affordability, and vocational training programs.

Structural and institutional factors

Beyond individual skills, the structure of the economy and its institutions play a major role:

Labor market discrimination creates wage gaps based on race, gender, ethnicity, or other characteristics. Even controlling for education and experience, persistent gaps remain in many labor markets.

Globalization shifts relative labor demand. In developed countries, trade with lower-wage nations can reduce demand for low-skilled domestic workers while benefiting high-skilled workers and capital owners. The Stolper-Samuelson theorem from trade theory formalizes this: trade tends to raise returns to a country's abundant factor and lower returns to its scarce factor.

Institutional factors directly shape wage structures:

Stronger labor unions tend to compress the wage distribution by raising wages at the bottom and middle
Minimum wage laws set a floor on earnings
Collective bargaining rights determine workers' ability to negotiate

Intergenerational transfers perpetuate inequality over time through inheritance, access to social and professional networks, and unequal educational opportunities. A family's wealth position in one generation strongly predicts the next generation's income.

Market concentration matters too. When firms have monopsony power in labor markets, they pay workers below their marginal revenue product, capturing the surplus as profit that flows to owners and top executives. The financialization of the economy, where financial sector profits and executive compensation packages grow relative to the rest of the economy, amplifies this effect.

Government policies and income inequality

Redistributive policies and their effects

Governments can reduce income inequality through two main channels: progressive taxation and transfer payments.

Progressive taxation means tax rates rise with income. Someone earning $500,000 faces a higher marginal tax rate than someone earning $50,000. This compresses the after-tax income distribution relative to the pre-tax distribution.

Transfer payments directly supplement incomes at the bottom:

Unemployment benefits
Social security / public pensions
Welfare and income assistance programs

Measuring policy effectiveness: Compare the Gini coefficient (or other inequality metrics) before and after taxes and transfers. The gap between the pre-tax Gini and the post-tax/transfer Gini tells you how much redistribution the government is actually achieving. Nordic countries, for example, have relatively high pre-tax inequality but achieve much lower post-tax inequality through aggressive redistribution.

Tax expenditures (deductions, credits, exemptions) have mixed effects. Deductions for mortgage interest or retirement savings tend to benefit higher-income taxpayers who itemize. Refundable tax credits like the Earned Income Tax Credit (EITC) in the U.S. are specifically designed to support lower-income households and have been shown to significantly reduce poverty rates among working families.

Long-term strategies and considerations

Redistribution addresses inequality after the fact. Long-term strategies aim to reduce inequality at its source by changing the distribution of human capital and opportunity:

Education access: universal early childhood education, affordable college, and skills retraining programs all aim to equalize earning potential before people enter the labor market
Labor market policies: minimum wage adjustments, worker protections, and anti-discrimination enforcement shape how factor markets distribute income
Addressing automation: as technology displaces certain jobs, policies like retraining programs or portable benefits help workers adapt

Trade-offs to keep in mind:

Redistribution can create disincentive effects. Very high marginal tax rates may discourage additional work or investment. This is the classic equity-efficiency trade-off: reducing inequality (equity) may come at the cost of reduced output or slower growth (efficiency). The magnitude of this trade-off is an empirical question, not a settled one.
Complex programs carry administrative costs that reduce their net impact.
International tax coordination is increasingly important as capital moves freely across borders, making it easier for high earners and corporations to avoid domestic taxation.

The core challenge in policy design is balancing equity with efficiency. The analytical tools in this unit give you a framework for evaluating those trade-offs rigorously rather than relying on intuition alone.