Gini Index

What Is the Gini Index?

The Gini index, or Gini coefficient, measures income distribution across a population. Developed by the Italian statistician Corrado Gini in 1912, it often serves as a gauge of economic inequality, measuring income distribution or, less commonly, wealth distribution among a population.

The coefficient ranges from 0 (or 0%) to 1 (or 100%), with 0 representing perfect equality and 1 representing perfect inequality. Values over 1 are theoretically possible due to negative income or wealth.

Understanding the Gini Index

A country in which every resident has the same income would have an income Gini coefficient of 0. Conversely, a country in which one resident earned all the income, while everyone else earned nothing, would have an income Gini coefficient of 1.

The same analysis can apply to wealth distribution (the "wealth Gini coefficient"), but because wealth is more difficult to measure than income, Gini coefficients usually refer to income and appear simply as the "Gini coefficient" or "Gini index," without specifying that they refer to income. Wealth Gini coefficients tend to be much higher than those for income.

The Gini coefficient is an important tool for analyzing income or wealth distribution within a country or region, but it should not be mistaken for an absolute measurement of income or wealth. A high-income country and a low-income one can have the same Gini coefficient, as long as incomes are distributed similarly within each: For instance, Turkey and the United States both have income Gini coefficients of around 0.39-0.40, according to the Organisation for Economic Co-operation and Development (OECD), despite Turkey's vastly lower gross domestic product (GDP) per person.

Graphical Representation of the Gini Index

The Gini index is often represented graphically through the Lorenz curve, which shows income (or wealth) distribution by plotting the population percentile by income on the horizontal axis and cumulative income on the vertical axis. The Gini coefficient is equal to the area below the line of perfect equality (0.5 by definition) minus the area below the Lorenz curve, divided by the area below the line of perfect equality. In other words, it is double the area between the Lorenz curve and the line of perfect equality.

In the graph below, the 47th percentile corresponds to 10.46% in Haiti and 17.42% in Bolivia, meaning that the bottom 47% of Haitians take in 10.46% of their nation's total income and the bottom 47% of Bolivians take in 17.42% of theirs. The straight line represents a hypothetically equal society: The bottom 47% take in 47% of national income. 

To estimate the income Gini coefficient for Haiti in 2012, we would find the area below its Lorenz curve: around 0.2. Subtracting that figure from 0.5 (the area under the line of equality), we get 0.3, which we then divide by 0.5. This yields an approximate Gini of 0.6 or 60%.

Another way of thinking about the Gini coefficient is as a measure of deviation from perfect equality. The further a Lorenz curve deviates from the perfectly equal straight line (which represents a Gini coefficient of 0), the higher the Gini coefficient and the less equal the society. In the example above, Haiti is more unequal than Bolivia.

The Gini Index Around the World

Global Gini

The Gini coefficient experienced sustained growth during the 19th and 20th centuries. In 1820, the global Gini coefficient stood at 0.50, while in 1980 and 1992, the figure was 0.657.

Source: World Bank.

COVID-19 is likely to have a further negative impact on income equality. According to the World Bank, the Gini coefficient has historically increased about 1.5 points in the five years following major epidemics, such as Ebola and Zika. Economists believe COVID-19 triggered an annual 1.2 to 1.9 percentage points increase in the Gini coefficient for 2020 and 2021.

Gini within countries

Below are the income Gini coefficients of every country for which the CIA World Factbook provides data:

Some of the world's poorest countries have some of the world's highest Gini coefficients, while many of the lowest Gini coefficients are found in wealthier European countries. However, the relationship between income inequality and GDP per capita is not one of perfect negative correlation, and the relationship has varied over time.

Michail Moatsos of Utrecht University and Joery Baten of Tuebingen University show that from 1820 to 1929, inequality rose slightly—then tapered off—as GDP per capita increased. From 1950 to 1970, inequality tended to fall off as GDP per capita rose above a certain threshold. From 1980 to 2000, inequality fell with higher GDP per capita then curved back up sharply.

Limitations of the Gini Index

Though useful for analyzing economic inequality, the Gini coefficient has some shortcomings.

The metric's accuracy is dependent on reliable GDP and income data. Shadow economies and informal economic activity are present in every country. Informal economic activity tends to represent a larger portion of true economic production in developing countries and at the lower end of the income distribution within countries. In both cases, this means that the Gini index of measured incomes will overstate true income inequality. Accurate wealth data is even more difficult to come by due to the popularity of tax havens.

Another flaw is that very different income distributions can result in identical Gini coefficients. Because the Gini attempts to distill a two-dimensional area (the gap between the Lorenz curve and the equality line) down to a single number, it obscures information about the "shape" of inequality. In everyday terms, this would be similar to describing the contents of a photo solely by its length along one edge, or the simple average brightness value of the pixels.

Though using the Lorenz curve as a supplement can provide more information in this respect, it also does not show demographic variations among subgroups within the distribution, such as the distribution of incomes across age, race, or social groups. In that vein, understanding demographics can be important for understanding what a given Gini coefficient represents. For example, a large retired population pushes the Gini higher.