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A Regression-Based Shapley Decomposition for Inequality Measures

Author

Listed:
  • Kévin Fourrey

    (CEET - Centre d'études de l'emploi et du travail - CNAM - Conservatoire National des Arts et Métiers [CNAM] - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche - Ministère du Travail, de l'Emploi et de la Santé, ERUDITE - Equipe de Recherche sur l’Utilisation des Données Individuelles en lien avec la Théorie Economique - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel, TEPP - Travail, Emploi et Politiques Publiques - UPEM - Université Paris-Est Marne-la-Vallée - CNRS - Centre National de la Recherche Scientifique)

Abstract

This article proposes an innovative tool to decompose the inequality of an outcome distribution between a set of attributes contributing to that inequality, based on econometric models. We use the recent developments of the Shapley decomposition by Chantreuil et al. (2019) that we apply more broadly to a case where the outcome distribution by sources is not predefined by a natural additive structure. We show that this development is highly relevant in applied studies and that it has a number of advantages compared to the existing regression-based decompositions of inequality measures. An example of application to wage inequalities in France is given, with a focus on the attribute of gender.

Suggested Citation

  • Kévin Fourrey, 2023. "A Regression-Based Shapley Decomposition for Inequality Measures," Post-Print hal-04083219, HAL.
    • Handle: RePEc:hal:journl:hal-04083219
    DOI: 10.2307/48718079
    as

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