Intersectionality: Affirmative Action with Multidimensional Identities
研究了当身份是多维时平权政策的设计，证明非交叉性政策几乎无法实现代表性结果，甚至可能加剧未被标准措施察觉的隐性不平等，并展示了如何构建实现比例代表性的交叉性政策。
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. This paper was accepted by Dorothea Kübler, behavioral economics and decision analysis. Funding: This work was supported by the Austrian Science Fund [FG 6-G, I 3487] and the Agence Nationale de la Recherche [ANR-19-CE48-0018-01, ANR-19-P3IA-0003, and ANR-23-PEIA-0003].