Welfare Economics
The Gini Coefficient: Measuring Inequality With One Number
The Gini Coefficient compresses the entire shape of a country's income (or wealth) distribution into a single number between 0 and 1, where 0 means everyone earns exactly the same and 1 means one person has everything. It does this geometrically: it measures how far the real distribution sags away from perfect equality, using the area between the diagonal 45° line and the Lorenz curve that plots cumulative income against cumulative population. Its appeal is brutal simplicity — you can say "the U.S. is 0.41, Denmark is 0.28" and instantly rank them — but that same one-number compression is also its deepest weakness.- Named afterCorrado Gini (Italian statistician)
- First described1912, in Variabilità e mutabilità
- Range0 (perfect equality) → 1 (perfect inequality)
- GeometryGini = A / (A + B), area vs Lorenz curve
- Most equal (income)Nordic states, Slovenia ≈ 0.25–0.28
- Most unequalSouth Africa ≈ 0.63 (post-apartheid legacy)
Interactive visualization
Press play, or step through manually. The visualization is yours to drive — try it before reading on.
Watch the 60-second explainer
A condensed visual walkthrough — narrated, captioned, under a minute.
The Lorenz curve: the picture the number summarizes
Start with the object the Gini actually measures. Line up everyone from poorest to richest. On the x-axis plot the cumulative share of the population (0% to 100%); on the y-axis plot the cumulative share of income they collectively hold. The result is the Lorenz curve (Max Lorenz, 1905).
If income were perfectly equal, the poorest 20% of people would earn 20% of income, the poorest 60% would earn 60%, and so on — a straight 45° diagonal called the line of perfect equality. In any real economy the poor hold less than their headcount share, so the curve sags below the diagonal: the bottom 60% of people might hold only 30% of income. The deeper the sag, the more unequal the society.
The Gini turns that sag into a number. Let A be the area between the diagonal and the Lorenz curve, and B the area under the Lorenz curve. Then:
Gini = A / (A + B)
Because the whole triangle below the diagonal has area A + B = ½, this is equivalently Gini = 2A = 1 − 2B. Perfect equality: the curve is the diagonal, A = 0, Gini = 0. Perfect inequality (one person has everything): the curve runs flat along the bottom then jumps to 100%, A fills the whole triangle, Gini → 1.
A worked example you can do by hand
Take a toy economy of 5 people with incomes 1, 2, 3, 4, 10 (total = 20). Sort them (already sorted) and build cumulative income shares:
- Poorest 20% (1 person): 1/20 = 5%
- Poorest 40%: 3/20 = 15%
- Poorest 60%: 6/20 = 30%
- Poorest 80%: 10/20 = 50%
- Everyone (100%): 20/20 = 100%
Those five points define the Lorenz curve. A fast discrete formula avoids measuring areas by hand. Sort incomes yᵢ ascending, i = 1…n:
Gini = ( 2 · ∑ i·yᵢ ) / ( n · ∑ yᵢ ) − (n + 1) / n
Here ∑ i·yᵢ = 1·1 + 2·2 + 3·3 + 4·4 + 5·10 = 1 + 4 + 9 + 16 + 50 = 80, ∑ yᵢ = 20, n = 5:
Gini = (2 · 80) / (5 · 20) − 6/5 = 160/100 − 1.2 = 1.6 − 1.2 = 0.40
So this five-person economy scores 0.40 — roughly the level of the United States. Now double the top earner to 20 (incomes 1, 2, 3, 4, 20, total 30): the same formula gives ∑ i·yᵢ = 1 + 4 + 9 + 16 + 100 = 130, and Gini = (2·130)/(5·30) − 1.2 = 260/150 − 1.2 = 1.733 − 1.2 ≈ 0.53. One rich person pulling away moved the whole number sharply — a preview of what the Gini does and doesn't see.
Why one number is both the point and the problem
The Gini's power is that it satisfies four properties economists want in an inequality index: it is (1) scale-independent — multiply everyone's income by 10 and it doesn't change, so it measures relative not absolute gaps; (2) population-independent — merging two identical countries leaves it unchanged; (3) anonymous — only the distribution of income matters, not who holds it; and (4) it obeys the Pigou–Dalton transfer principle — moving a dollar from a richer to a poorer person (without reordering them) always lowers it.
But collapsing a whole curve into one scalar means very different societies can share the same Gini. A country where the middle class is squeezed and a country where the poorest are destitute can both read 0.40. The Gini is mathematically most sensitive to changes near the middle of the distribution (where most people are) and relatively insensitive to the extreme tails. That is exactly backwards from what dominates political debate — the top 1% and outright poverty. Two Lorenz curves can even cross: one society more equal at the bottom, the other more equal at the top. When curves cross, the Gini still spits out a ranking, but that ranking is not robust — a different, equally reasonable index could flip it.
Real numbers, real countries, real history
Cross-country Gini figures (World Bank / OECD, disposable income after taxes and transfers) tell a clear story:
- Slovenia, Slovakia, Nordic states, Czechia: ≈ 0.24–0.28 — the most equal market economies on Earth, driven by heavy transfers.
- Germany, France, Canada: ≈ 0.29–0.33.
- United States: ≈ 0.39–0.41 — high for a rich country.
- Brazil, Colombia: ≈ 0.49–0.53.
- South Africa: ≈ 0.63 — the highest measured, a durable legacy of apartheid.
Two subtleties matter enormously. First, pre-tax vs post-tax: the U.S. market-income (pre-tax) Gini is ~0.51, but taxes and transfers pull the disposable-income Gini down to ~0.39 — the ~0.12 gap is the redistributive state made visible. Second, income vs wealth: wealth is always far more concentrated. The U.S. wealth Gini is roughly 0.85, because millions of households have near-zero or negative net worth while a sliver holds most assets. Historically, the U.S. income Gini fell through the mid-20th-century "Great Compression" to a low around 0.35 in the late 1960s, then rose steadily from the 1980s — a widely cited empirical anchor of the modern inequality debate.
Assumptions, pitfalls, and what the number quietly hides
Every Gini rests on choices that can move it more than the underlying reality:
- Unit of analysis: individuals vs households? Household Ginis are lower because incomes are pooled. Adjusting for household size ("equivalized" income) changes the figure again.
- What counts as income: including non-cash transfers, imputed rent, capital gains, or the government services people consume can shift a country's Gini by several points.
- The life-cycle illusion: a society of identical people who are simply at different ages — students poor, mid-career rich, retirees drawing down — shows a positive Gini even with zero lifetime inequality. A snapshot cannot see mobility. Lifetime Ginis are typically well below annual ones.
- Data truncation: household surveys systematically miss the very rich (non-response, capped top-coding), so official Ginis understate top-end inequality; tax-record studies (Piketty–Saez–Zucman) tend to read higher.
- Informal economies: in developing countries, unmeasured cash and subsistence output blur the picture.
None of this makes the Gini useless — but it means a comparison is only valid when the two Ginis were built the same way, from the same income definition and unit. Comparing a household post-tax Gini to an individual pre-tax one is a category error people make constantly.
The misconception: Gini is not a percentage of anything
The single most common error is treating the Gini as a share — reading 0.40 as "the top 40% own everything" or "40% of income is unequal." It is neither. It is a normalized area ratio with no direct real-world referent; there is no group whose income share equals the Gini. A cleaner intuition: the Gini equals half the average absolute income difference between two randomly chosen people, expressed as a fraction of mean income. Formally Gini = (mean absolute difference) / (2 × mean income). So a Gini of 0.40 means that if you pick two people at random, the expected gap between their incomes is about 80% of the mean income (2 × 0.40). That is a concrete, checkable statement — unlike the phantom "40%."
A second subtle point: a rising Gini does not automatically mean the poor got poorer. Because the measure is purely relative, everyone's real income can grow while the Gini rises — if the rich grew faster. Conversely a recession that hammers the top can lower the Gini while making almost everyone worse off. The Gini answers "how spread out?", never "how well off?" — which is why it should always be read alongside a level statistic like median income or a poverty rate.
| Measure | What it captures | Strength | Weakness |
|---|---|---|---|
| Gini coefficient | Overall dispersion; area under Lorenz curve | One intuitive number, 0–1, comparable across countries | Insensitive to WHERE inequality happens; hides distribution shape |
| Palma ratio | Income of top 10% ÷ bottom 40% | Directly tracks the extremes that dominate politics | Ignores the entire middle of the distribution |
| Theil index (T) | Entropy-based; ∑ shares × log(shares) | Decomposable into within-group + between-group | Not bounded to a clean 0–1; less intuitive |
| 90/10 or 80/20 ratio | Ratio of a high percentile to a low one | Trivial to explain and compute | Discards everyone between the two percentiles |
| Top 1% income share | Fraction of income going to the richest 1% | Captures the tail Gini smoothes over | Says nothing about the poor or the middle |
Frequently asked questions
What is a 'good' Gini coefficient?
There is no objective threshold, but as a rough guide: below ~0.30 is considered relatively equal (most of Western Europe, especially the Nordics at 0.25–0.28), 0.30–0.40 is moderate, and above ~0.45 signals high inequality often linked to social and political strain. The U.S. sits around 0.39–0.41 for post-tax income; South Africa's ~0.63 is the world's highest. What counts as 'good' is a value judgment, not a statistical fact.
Why can't the Gini coefficient be negative or above 1?
It is defined as an area ratio A/(A+B) where the Lorenz curve lies between the diagonal (equality) and the bottom-right corner (total inequality). A is bounded between 0 and the full triangle's area, so the ratio is bounded 0 to 1. A negative Gini would require someone to hold a negative cumulative income share along the sorted curve — impossible for non-negative incomes. (Wealth data with large negative net worth can technically push a raw Gini above 1, which is one reason wealth inequality is usually reported differently.)
What's the difference between the Gini coefficient and the Gini index?
They are the same measure on different scales. The Gini coefficient runs 0 to 1; the Gini index is simply the coefficient multiplied by 100, so it runs 0 to 100. A coefficient of 0.41 and an index of 41 are identical. The World Bank publishes the index (0–100) form; academic papers usually use the coefficient (0–1).
Why do income and wealth Ginis differ so much?
Wealth (net worth) is far more concentrated than annual income because it accumulates over a lifetime, compounds through returns, and can be inherited — while many households have zero or negative net worth (debt exceeding assets). The U.S. income Gini is around 0.39 but its wealth Gini is roughly 0.85. Always check which one a headline is quoting; they describe very different things.
If two countries have the same Gini, are they equally unequal?
Not necessarily. The Gini compresses a whole Lorenz curve into one number, and two very different curves can produce the same value — one country unequal because its bottom is destitute, another because its top pulls far away, both reading 0.40. When Lorenz curves cross, the Gini still gives a ranking, but that ranking isn't robust. This is why analysts pair the Gini with the Palma ratio or top-income shares to see the shape it hides.
How is the Gini related to the Lorenz curve exactly?
The Gini IS a summary of the Lorenz curve. Let A be the area between the 45° equality line and the Lorenz curve, and B the area under the curve. Since the triangle below the diagonal has area ½, Gini = A/(A+B) = 2A = 1 − 2B. When the curve hugs the diagonal (A→0) the Gini →0; when it sags to the corner (A fills the triangle) the Gini →1. Everything the Gini 'knows' is contained in that curve's shape.