Abstracts
Résumé
La lecture de la littérature indique que la mesure des inégalités de revenus s’est largement développée depuis les années 1970. L’étude des inégalités mesurées sur les revenus des individus est nécessaire mais non suffisante pour appréhender la complexité des déterminants des inégalités. En ce sens, les techniques de décompositions des mesures d’inégalité en sources de revenu sont intéressantes. Elles permettent de mettre en évidence de nouveaux indices statistiques dont la structure autorise l’analyse des sources de rémunération (salaires, primes, taxes, pensions, etc.), les corrélations de ces sources aux rangs des individus dans la société ou leurs parts dans le revenu moyen. Notre analyse s’effectue autour de la décomposition de la mesure de Gini, les débats qu’elle a pu susciter et les améliorations de la méthode en partant de la notion de pseudo-Gini à celle de Gini étendu. Les dernières méthodes en date sont aussi exposées afin de mettre en exergue les possibilités de généralisation en ce domaine en utilisant soit l’analyse économétrique soit la valeur de Shapley.
Abstract
The literature indicates that income inequality measures have been developed since the 70’s. In order to apprehend the underlying components of the inequality measures, it is necessary but not sufficient to study overall incomes. In this respect, decomposing inequality indices by income source is welcome. The methodology brings out new statistical indices, for which it is possible to analyze factor components (wages, fringe benefits, taxes, pensions, etc.), correlations between the income sources and the ranks of individuals within the society, and the contribution of each source in the mean income. The paper draws an analysis of the Gini decomposition by income source, the debates between researchers, and the development of the decomposition technique, starting from the concept of pseudo-Gini to that of extended Gini. The last approaches of the literature are exposed in order to point out the possibilities of generalization, using either econometric models or the Shapley value.
Appendices
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