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The inequality income metric should be independent of the aggregate level of income. This may be stated as: = where α is a positive real number. Population independence Similarly, the income inequality metric should not depend on whether an economy has a large or small population.
Income inequality and income mobility trends have been different for men and women workers between 1937 and the 2000s. When men and women are considered together, the Gini coefficient-based Shorrocks index trends imply long-term income inequality has been substantially reduced among all workers, in recent decades for the United States. [67]
The MLD of household income has been defined as [1] = = ¯ where N is the number of households, is the income of household i, and ¯ is the mean of .Naturally the same formula can be used for positive variables other than income and for units of observation other than households.
The most commonly used index from the family, FGT 2, puts higher weight on the poverty of the poorest individuals, making it a combined measure of poverty and income inequality and a popular choice within development economics. The indices were introduced in a 1984 paper by economists Erik Thorbecke, Joel Greer, and James Foster. [1] [2]
In this case, the number of people counted as poor could increase while their income rises. There are several different income inequality metrics; one example is the Gini coefficient. Although absolute poverty is more common in developing countries, poverty and inequality exist across the world.
The Hoover is the total amount (as a percentage of the national-income) by which people have less than their equal income-share. The Hoover Index can be calculated by the following subtraction: The percentage of the people getting less than their equal-share (i.e. less than the national mean income), minus their percentage of the national income.
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The Lorenz curve is invariant under positive scaling. If X is a random variable, for any positive number c the random variable c X has the same Lorenz curve as X. The Lorenz curve is flipped twice, once about F = 0.5 and once about L = 0.5, by negation. If X is a random variable with Lorenz curve L X (F), then −X has the Lorenz curve: