U.S. income distribution of individual earnings for full-time, year-round workers from 1990 to 2016 (by Gini-coefficient)

U.S. income distribution of individual earnings 1990-2016, by Gini-coefficient This statistic shows the Gini coefficient, an index for measuring income distribution, for individual earnings for full-time, year-round workers in the U.S. from 1990 to 2016. A Gini coefficient of 0 expresses perfect equality, where all would have the same income,a Gini coefficient of 1 expresses maximal inequality among values. In 2016, the Gini coefficient for individual income was 0.42.
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Gini coefficient
'900.36
'910.36
'920.36
'930.39
'940.4
'950.39
'960.39
'970.39
'980.39
'990.4
'000.41
'010.41
'020.41
'030.4
'040.41
'050.41
'060.41
'070.39
'080.4
'090.4
'100.4
'110.41
'120.41
'130.42
'140.42
'150.41
`160.42
Gini coefficient
'900.36
'910.36
'920.36
'930.39
'940.4
'950.39
'960.39
'970.39
'980.39
'990.4
'000.41
'010.41
'020.41
'030.4
'040.41
'050.41
'060.41
'070.39
'080.4
'090.4
'100.4
'110.41
'120.41
'130.42
'140.42
'150.41
`160.42
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Description Source More information
This statistic shows the Gini coefficient, an index for measuring income distribution, for individual earnings for full-time, year-round workers in the U.S. from 1990 to 2016. A Gini coefficient of 0 expresses perfect equality, where all would have the same income,a Gini coefficient of 1 expresses maximal inequality among values. In 2016, the Gini coefficient for individual income was 0.42.
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Release date
August 2017
Region
United States
Survey time period
1990 to 2016
Supplementary notes
* The Gini coefficient measures the inequality among values of a frequency distribution (for example levels of income). A Gini coefficient of zero expresses perfect equality where all values are the same (for example, where everyone has an exactly equal income). A Gini coefficient of one (100 on the percentile scale) expresses maximal inequality among values (for example where only one person has all the income).
Data have been rounded to provide better understanding of the statistic.

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