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Generalized entropy index

The generalized entropy index has been proposed as a measure of income inequality in a population.[1] It is derived from information theory as a measure of redundancy in data. In information theory a measure of redundancy can be interpreted as non-randomness or data compression; thus this interpretation also applies to this index. In additional interpretation of the index is as biodiversity as entropy has also been proposed as a measure of diversity.[2]

FormulaEdit

The formula for general entropy for real values of   is:

 

where N is the number of cases (e.g., households or families),   is the income for case i and   is a parameter which regulates the weight given to distances between incomes at different parts of the income distribution. For large   the index is especially sensitive to the existence of large incomes, whereas for small   the index is especially sensitive to the existence of small incomes.

An Atkinson index for any inequality aversion parameter can be derived from a generalized entropy index under the restriction that   - i.e an Atkinson index with high inequality aversion is derived from a GE index with small  . Moreover, it is the unique class of inequality measures that is a monotone transformation of the Atkinson index and which is additive decomposable. Many popular indices, including Gini index, do not satisfy additive decomposability.[1]

The formula for deriving an Atkinson index with inequality aversion parameter   under the restriction   is given by:

 

 

Note that the generalized entropy index has several income inequality metrics as special cases. For example, GE(0) is the mean log deviation, GE(1) is the Theil index, and GE(2) is half the squared coefficient of variation.

See alsoEdit

ReferencesEdit

  1. ^ a b Shorrocks, A. F. (1980). "The Class of Additively Decomposable Inequality Measures". Econometrica. 48 (3): 613–625. doi:10.2307/1913126. JSTOR 1913126.
  2. ^ Pielou, E.C. (December 1966). "The measurement of diversity in different types of biological collections". Journal of Theoretical Biology. 13: 131–144. doi:10.1016/0022-5193(66)90013-0.