Intersectionality: Affirmative Action with Multidimensional Identities

Jean-Paul Carvalho (), Bary Pradelski () and Cole Williams
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Jean-Paul Carvalho: University of Oxford
Bary Pradelski: LIG - Laboratoire d'Informatique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, MFO - Maison Française d'Oxford - MEAE - Ministère de l'Europe et des Affaires étrangères - CNRS - Centre National de la Recherche Scientifique, POLARIS - Performance analysis and optimization of LARge Infrastructures and Systems - Centre Inria de l'Université Grenoble Alpes - Inria - Institut National de Recherche en Informatique et en Automatique - LIG - Laboratoire d'Informatique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes

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Abstract: Studying the design of affirmative action policies when identities are multidimensional, we provide a formal demonstration of the importance of intersectionality. Prevailing affirmative action policies are based only on one identity dimension (e.g., race, gender, socioeconomic class). We find that any such nonintersectional policy can almost never achieve a representative outcome. In fact, nonintersectional policies often increase the underrepresentation of underrepresented groups in a manner undetected by standard measures. Examples based on race and gender reveal significant hidden inequality arising from nonintersectional policies. We show how to construct intersectional policies that achieve proportional representation.

Date: 2024-09-13
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Published in Management Science, 2024, ⟨10.1287/mnsc.2023.03839⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04788142

DOI: 10.1287/mnsc.2023.03839

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