Atkinson index

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The Atkinson index (also known as the Atkinson measure or Atkinson inequality measure) is a measure of income inequality developed by British economist Anthony Barnes Atkinson. The measure is useful in determining which end of the distribution contributed most to the observed inequality.[1]


The index can be turned into a normative measure by imposing a coefficient \varepsilon to weight incomes. Greater weight can be placed on changes in a given portion of the income distribution by choosing \varepsilon, the level of "inequality aversion", appropriately. The Atkinson index becomes more sensitive to changes at the lower end of the income distribution as \varepsilon approaches 1. Conversely, as the level of inequality aversion falls (that is, as \varepsilon approaches 0) the Atkinson becomes more sensitive to changes in the upper end of the income distribution.

The Atkinson index is defined as:

& \mbox{for}\ \varepsilon \in \left[0,1\right)\cup\left(1,+\infty\right) \\
& \mbox{for}\ \varepsilon=1,

where y_{i} is individual income (i = 1, 2, ..., N) and \mu is the mean income.

Atkinson index relies on the following axioms:

  1. The index is symmetric in its arguments: A_\varepsilon(y_1,\ldots,y_N)=A_\varepsilon(y_{\sigma(1)},\ldots,y_{\sigma(N)}) for any permutation \sigma.
  2. The index is non-negative, and is equal to zero only if all incomes are the same: A_\varepsilon(y_1,\ldots,y_N) = 0 iff y_i = \mu for all i.
  3. The index satisfies the principle of transfers: if a transfer \Delta>0 is made from an individual with income y_i to another one with income y_j such that y_i - \Delta > y_j + \Delta, then the inequality index cannot increase.
  4. The index satisfies population replication axiom: if a new population is formed by replicating the existing population an arbitrary number of times, the inequality remains the same: A_\varepsilon(\{y_1,\ldots,y_N\},\ldots,\{y_1,\ldots,y_N\})=A_\varepsilon(y_1,\ldots,y_N)
  5. The index satisfies mean independence, or income homogeneity, axiom: if all incomes are multiplied by a positive constant, the inequality remains the same: A_\varepsilon(y_1,\ldots,y_N) = A_\varepsilon( ky_1,\ldots,ky_N) for any k>0.
  6. The index is subgroup decomposable.[2] This means that overall inequality in the population can be computed as the sum of the corresponding Atkinson indices within each group, and the Atkinson index of the group mean incomes:

A_\varepsilon(y_{gi}: g=1,\ldots,G, i=1,\ldots,N_g) = \sum_{g=1}^G w_g A_\varepsilon( y_{g1}, \ldots, y_{g,N_g}) + A_\varepsilon(\mu_1, \ldots, \mu_G)

where g indexes groups, i, individuals within groups, \mu_g is the mean income in group g, and the weights w_g depend on \mu_g, \mu, N and N_g. The class of the subgroup-decomposable inequality indices is very restrictive. Many popular indices, including Gini index, do not satisfy this property.

See also[edit]


  1. ^ inter alia "Income, Poverty, and Health Insurance Coverage in the United States: 2010", U.S. Census Bureau, 2011, p.10
  2. ^ Shorrocks, AF (1980). The class of additively decomposable inequality indices. Econometrica, 48 (3), 613–625, doi:10.2307/1913126


External links[edit]


  • Free Online Calculator computes the Gini Coefficient, plots the Lorenz curve, and computes many other measures of concentration for any dataset
  • Free Calculator: Online and downloadable scripts (Python and Lua) for Atkinson, Gini, and Hoover inequalities
  • Users of the R data analysis software can install the "ineq" package which allows for computation of a variety of inequality indices including Gini, Atkinson, Theil.
  • A MATLAB Inequality Package, including code for computing Gini, Atkinson, Theil indexes and for plotting the Lorenz Curve. Many examples are available.
  • Stata inequality packages: ineqdeco to decompose inequality by groups; svygei and svyatk to compute design-consistent variances for the generalized entropy and Atkinson indices; glcurve to obtain generalized Lorenz curve. You can type ssc install ineqdeco etc. in Stata prompt to install these packages.