Gini Coefficient
The Gini Coefficient is a statistical measure of income or wealth inequality within a population, ranging from 0 (perfect equality, where everyone earns the same) to 1 (perfect inequality, where one person earns everything), with higher values indicating greater concentration of income or wealth.
Italian statistician Corrado Gini developed the coefficient in 1912 as a tool to quantify the degree of inequality in a distribution. It is derived from the Lorenz Curve, a graphical representation of cumulative income share (vertical axis) against cumulative population share (horizontal axis). The Gini Coefficient equals the area between the Lorenz Curve and the line of perfect equality, divided by the total area below the line of perfect equality.
A Gini of 0 would mean every person has identical income — the Lorenz Curve would lie exactly on the 45-degree equality line. A Gini of 1 would mean one person earns everything — the Lorenz Curve would hug the bottom and right edges of the graph. Most real economies fall between 0.25 and 0.65, with Nordic countries at the low end and highly unequal developing economies at the high end.
The United States has one of the higher Gini coefficients among developed economies. Its pre-tax Gini (roughly 0.50) falls to around 0.39 after taxes and transfers, reflecting meaningful redistribution through progressive taxation and social programs, but the after-tax figure still exceeds most Western European counterparts. Wealth Gini coefficients (rather than income) are even higher in the US, often estimated above 0.85, reflecting extreme concentration at the top.
The Gini Coefficient has limitations worth understanding. Two countries with very different income distributions can produce identical Gini values if their Lorenz Curves happen to cross. It also says nothing about absolute living standards — a country with a low Gini but very low average incomes may have worse living conditions than a higher-Gini country with a substantially higher GDP per capita.
For investors and market analysts, inequality metrics matter in several ways. High and rising inequality tends to dampen consumer spending growth because upper-income households have lower marginal propensities to consume. It can also generate political pressure for redistribution, including higher corporate taxes, capital gains taxes, or wealth levies — all of which affect asset valuations. Research by economists including Thomas Piketty has elevated inequality to a central macro-financial concern over the past decade.