What is the 'Gini Index'

The Gini index or Gini coefficient is a statistical measure of distribution developed by the Italian statistician Corrado Gini in 1912. It is often used 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.

BREAKING DOWN 'Gini Index'

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 be applied 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 "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: Turkey and the U.S. both had income Gini coefficients around 0.39 in 2014, according to the OECD, though Turkey's GDP per person was less than half the U.S.'s (in 2010 dollar terms). 

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%. The CIA gives the actual Gini for Haiti in 2012 as 60.8% (see below). This figure represents extremely high inequality; only Micronesia, the Central African Republic, South Africa, and Lesotho are more unequal, according to the CIA.

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

Christoph Lakner of the World Bank and Branko Milanovic of the City University of New York estimate that the global income Gini coefficient was 0.705 in 2008, down from 0.722 in 1988. Figures vary considerably, however. DELTA economists François Bourguignon and Christian Morrisson estimate that the figure was 0.657 in both 1980 and 1992. Bourguignon and Morrisson's work shows a sustained growth in inequality since 1820 when the global Gini coefficient was 0.500. Lakner and Milanovic's shows a decline in inequality around the turn of the 20th century, as does a 2015 book by Bourguignon:

Graph showing global inequality from 1820 to 2010

Source: World Bank.

Economic expansion in Latin America, Asia, and Eastern Europe has driven much of the recent decline in income inequality. While inequality between countries has fallen in recent decades, however, inequality within countries has risen.

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 (Central African Republic) have some of the world's highest Gini coefficients (61.3), while some of the wealthiest (Denmark) have some of the lowest (28.8). Yet 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 head increased. From 1950 to 1970, inequality tended to fall off as GDP per head rose above a certain threshold. From 1980 to 2000 inequality fell with higher GDP per head then curved back up sharply.

Three graphs showing the behaviour of GDP at three different moments in time.

Correlation between Gini coefficients and GDP per capita in three time periods. Source: Moatsos and Baten.

Shortcomings

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, and these transactions tend to represent a larger portion of true economic production in developing countries. 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. While using the Lorenz curve as a supplement can provide more information in that respect, it does not show demographic variations among subgroups within the distribution. 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.

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