Understanding the Gini Index: Global Income Inequality Insights

What Is the Gini Index?

Developed by Italian statistician Corrado Gini in 1912, the Gini index is a critical tool for measuring income inequality within a nation by analyzing the distribution of income among its population.

The index, ranging from 0 to 1, indicates perfect equality at 0 and perfect inequality at 1.

Notably, South Africa exhibits the highest level of income inequality with a Gini index of 63.0%, as reported by the World Bank, while Norway demonstrates a low level of inequality with a Gini index of 22.7%. The United States, with a Gini index of 39.8%, highlights significant income disparity for a developed nation. Understanding these figures is crucial for gauging economic inequality across different countries.

How the Gini Index Works

A country in which every resident has the same income would have an income Gini coefficient of 0. 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 are reported 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.

Even in affluent countries, the Gini index measures net income rather than net worth, so a nation’s wealth can be concentrated in the hands of a small number of people even if income distribution is relatively equal.

 The Gini coefficient helps analyze income or wealth distribution in a country or region, but it isn't an absolute measure of these.

High and low-income countries can have the same Gini coefficient if their income distribution is similar. For example, Turkey and the U.S. have similar Gini coefficients, according to the OECD, even though Turkey has a much lower GDP per person.

Visualizing the Gini Index: Understanding the Lorenz Curve

The Gini index is often represented graphically through the Lorenz curve, as depicted below, 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.

A Global Perspective on the Gini Index

Global Gini

The Gini coefficient showed a sustained growth in inequality worldwide 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.

A later estimate by the World Inequality Lab, from 2020, estimates the global Gini coefficient at 0.67.

Source: The World Bank

COVID-19 had a further negative impact on income equality. According to the World Bank, the Gini coefficient increased by about 1.5 points in the five years following major epidemics, such as Ebola and Zika. Economists believe COVID-19 triggered an increase of 0.5 points in the Gini coefficient from 2019 to 2020, the largest single-year increase in global inequality since World War II.

Country-by-Country Gini Coefficient Insights

Below are the income Gini coefficients of every country for which the U.S. Central Intelligence Agency (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 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.

Understanding the 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 that obscure the amounts of money held by the wealthiest.

Different income distributions can yield the same Gini coefficient. The Gini distills a complex range into a single number, which can hide details about 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.

While the Lorenz curve provides more detail, it doesn't show demographic variations like income distribution across age, race, or social groups.

Understanding demographics helps explain what a Gini coefficient represents. A large retired population, for example, can increase the Gini.

The Bottom Line

If the gap between rich and poor continues to increase, the evaluation of the income gap can become more important. And the Gini index can provide a good starting point when it comes to measuring income inequality.

Knowing the Gini index numbers is no panacea, but it can quantify and track the direction in which a society is moving, which may open the door for dialogue and potential solutions.

But keep in mind that there are limitations associated with using this measure. The coefficient is only as reliable as the data used to calculate it, and a single-number reading cannot tell the whole story.