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Decomposition of Gini and the generalized entropy inequality measures [PDF]

Economics Bulletin, 2003
In this article we provide an overview of the Gini decomposition and the generalized entropy inequality measures, a free access to their computation, an application on French wages, and a different way than Dagum to demonstrate that the Gini index is a more convenient measure than those issued from entropy: Theil, Hirschman-Herfindahl and Bourguignon.
Stéphane Mussard, Michel Terraza, Françoise Seyte   +2 more
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Inequality decomposition analysis, the Lorenz curve and the Gini coefficient

Social Choice and Welfare
We compare two alternative procedures for decomposing the Lorenz curve and the Gini coefficient into within-groups and between-groups contributions: the standard additive decomposition (Bhattacharya and Mahalanobis in J Am Stat Assoc 62(317):143–161, 1967) and another inspired by the path-independent decomposition (Foster and Shneyerov in J Econ Theory
Fleurbaey, Marc, Lambert, Peter, Moramarco, Domenico, Peragine, Vito   +3 more
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On Decomposition of the Gini Index of Equality [PDF]

, 2005
The purpose of this paper is to show that the Gini index of equality is: (i) subgroup decomposable throughout interpersonal comparisons; (ii) decomposable by income source; (iii) decomposable both by subgroup and income source; (iv) and decomposable in a multidimensional context permitting statistical inference on equality components.
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On Dagum’s Decomposition of the Gini Coefficient [PDF]

, 2008
To measure the contributions to inequality from population subgroups, the Gini coefficient is often decomposed into inequality within groups, inequality between groups and a residual term arising from the overlapping of income distributions from different groups.
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A Note on the Dagum Decomposition of the Gini Inequality Index

SSRN Electronic Journal, 2007
In this paper we reconsider the Dagum decomposition of the Gini index and we compare this decomposition with the decompositions proposed by Mookherjee and Shorrocks and by Lambert and Aronson. In so doing, a deeper insight into the meaning of the overlapping term is given and an alternative expression for this term is obtained.
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Consumer Expenditures and Inequality: An Analysis Based on Decomposition of the Gini Coefficient

The Review of Economics and Statistics, 1993
Inequality in U.S. consumption expenditures is examined using the Lerman and Yitzhaki covariance method for decomposing the Gini coefficient by factors. From the decomposition, nonparametric estimates of elasticities with respect to total expenditures are derived. Using data from the 1987 U.S.
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Decomposition of the Gini coefficient using Stata [PDF]

, 2009
The Gini coefficient is widely used to measure inequality in the distribution of income, consumption, and other welfare proxies. Decomposing this measure can help you understand the determinants of inequality. In this presentation, I will use income data from Mexico to illustrate a user-written command, descogini, that implements the Gini decomposition
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Approximation and decomposition of Gini, Pietra–Ricci and Theil inequality measures

Empirical Economics, 2011
Two related problems are dealt with in this article, concerning some popular inequality indices proposed by Gini, Pietra–Ricci and Theil: (1) the calculation of the index when only a frequency distribution is available, thus needing some kind of approximation; and (2) a reasonable decomposition of the index calculated for a mixture, with components ...
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Notes on the Gini index decomposition

2004
The measurement of inequality between performed by resorting to the Gini index decomposition allows to avoid the major difficulties related to the traditional income inequality measures decompositions and, at the same time, allows to correctly evaluate and compare the effects of different factors on total inequality. When a population is divided in two
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On the decomposition of the Gini's mean difference and concentration ratio

2005
In this paper we propose a decomposition for the Gini's mean difference of a real variate obtained as the sum of c variates observed on a finite population. The decomposition is mainly based on two different sortings of the values of each variate: one is the natural sorting of the values, the other one is obtained by ranking the values according to the
RADAELLI, PAOLO, ZENGA, MICHELE
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