Josephson phase

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.

Definition

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:

$A_{\varepsilon }(y_{1},\ldots ,y_{N})={\begin{cases}1-{\frac {1}{\mu }}\left({\frac {1}{N}}\sum _{i=1}^{N}y_{i}^{1-\varepsilon }\right)^{1/(1-\varepsilon )}&{\mbox{for}}\ \varepsilon \in \left[0,1\right)\cup \left(1,+\infty \right)\\1-{\frac {1}{\mu }}\left(\prod _{i=1}^{N}y_{i}\right)^{1/N}&{\mbox{for}}\ \varepsilon =1,\end{cases}}$

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

The entropy measure developed by Atkinson can be computed from a "normalized Theil index". This, however, only applies to the Theil index ${I_{1}}$, which is derived from the "generalized entropy class" with ${\epsilon }=1$. The Atkinson index is computed using the function $1-e^{-T}$.

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. 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

• Income inequality metrics
• Generalized entropy index
• Gini index

References

• Atkinson, AB (1970) On the measurement of economic inequality. Journal of Economic Theory, 2 (3), pp. 244–263, 21 year-old Glazier James Grippo from Edam, enjoys hang gliding, industrial property developers in singapore developers in singapore and camping. Finds the entire world an motivating place we have spent 4 months at Alejandro de Humboldt National Park.. The original paper proposing this inequality index.
• Allison PD (1978) Measures of Inequality, American Sociological Review, 43, pp. 865–880. Presents a technical discussion of the Atkinson measure's properties.
• Biewen M, Jenkins SP (2003). Estimation of Generalized Entropy and Atkinson Inequality Indices from Complex Survey Data. IZA Discussion Paper #763. Provides statistical inference for Atkinson indices.
• Lambert, P. (2002). Distribution and redistribution of income. 3rd edition, Manchester Univ Press, ISBN 978-0-7190-5732-8.
• Sen A, Foster JE (1997) On Economic Inequality, Oxford University Press, ISBN 978-0-19-828193-1. (Python script for a selection of formulas in the book)
• World Income Inequality Database, from World Institute for Development Economics Research
• Income Inequality, 1947-1998, from United States Census Bureau.

External links

Software:

• 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.