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Gini coefficient

Measure of inequality of a distribution / From Wikipedia, the free encyclopedia

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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, the wealth inequality, or the consumption inequality[3] within a nation or a social group. It was developed by Italian statistician and sociologist Corrado Gini.

Map of income inequality Gini coefficients by country (%). Based on World Bank data ranging from 1992 to 2020.[1]
  •   Above 50
  •   Between 45 and 50
  •   Between 40 and 45
  •   Between 35 and 40
  •   Between 30 and 35
  •   Below 30
  •   No data
A different map showing wealth Gini coefficients within countries for 2019[2]
Share of income of the top 1% for selected developed countries, 1975 to 2015

The Gini coefficient measures the inequality among the values of a frequency distribution, such as 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, a situation where a single individual has all the income while all others have none.[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, estimated to be 0.63 to 0.7.[8][9] However, this figure drops to 0.52 after social assistance is taken into account, and drops again to 0.47 after taxation.[10] The country with the lowest Gini coefficient is Slovenia, with a Gini coefficient of 0.232.[11] The Gini coefficient of the global income in 2005 has been estimated to be between 0.61 and 0.68 by various sources.[12][13]

There are some issues in interpreting a Gini coefficient, as the same value may result from many different distribution curves. To mitigate this, the demographic structure should be taken into account. Countries with an aging population, or those with an increased birth rate, experience an increasing pre-tax Gini coefficient even if real income distribution for working adults remains constant. Many scholars have devised over a dozen variants of the Gini coefficient.[14][15][16]

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