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- handle: 10670/1.z7cvq8
- hal: hal-04083219
- doi: 10.2307/48718079
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Mots-clés
Decomposition of Inequality Measures Distributive Interactions Shapley Value Gender InequalitySujets proches
Social inequality Social equality Inequality EgalitarianismCiter ce document
Kévin Fourrey, « A Regression-Based Shapley Decomposition for Inequality Measures », HAL-SHS : sociologie, ID : 10.2307/48718079
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Résumé
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.