Gini coefficient

In economics, the Gini coefficient (/ˈni/ JEE-nee), also known as the Gini index or Gini ratio, is a measure of statistical dispersion intended to represent the income inequality or the wealth inequality within a nation or a social group. The Gini coefficient was developed by the statistician and sociologist Corrado Gini.

World map of income inequality Gini coefficients by country (as %). Based on World Bank data ranging from 1992 to 2020.[1]
  •   Above 50
  •   Between 45 to 50
  •   Between 40 to 45
  •   Between 35 to 40
  •   Between 30 to 35
  •   Below 30
  •   No data
A map showing Gini coefficients for wealth within countries for 2019[2]
Global share of wealth by wealth group. Redrawn from data from Credit Suisse, 2021[3]

The Gini coefficient measures the inequality among values of a frequency distribution, such as the levels of income. A Gini coefficient of 0 reflects perfect equality, where all income or wealth values are the same, while a Gini coefficient of 1 (or 100%) reflects maximal inequality among values. For example, if everyone has the same income, the Gini coefficient will be 0. In contrast, if for a large number of people only one person has all the income or consumption and all others have none, the Gini coefficient will be 1.[4][5]

The Gini coefficient was proposed by Corrado Gini as a measure of inequality of income or wealth.[6] For OECD countries, in the late 20th century, considering the effect of taxes and transfer payments, the income Gini coefficient ranged between 0.24 and 0.49, with Slovenia being the lowest and Mexico the highest.[7] African countries had the highest pre-tax Gini coefficients in 2008–2009, with South Africa having the world's highest, variously estimated to be 0.63 to 0.7,[8][9] although this figure drops to 0.52 after social assistance is taken into account, and drops again to 0.47 after taxation.[10] The global income Gini coefficient in 2005 has been estimated to be between 0.61 and 0.68 by various sources.[11][12]

There are some issues in interpreting a Gini coefficient; the same value may result from many different distribution curves. The demographic structure should be taken into account. Countries with an aging population or with an increased birth rate experience an increasing pre-tax Gini coefficient even if real income distribution for working adults remains constant. Scholars have devised over a dozen variants of the Gini coefficient.[13][14][15]

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