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Gender-Trait and Ethnic Biases in Russian Regions: Ethnic Integration, Regional Economies, and Women in Local Politics
Published online by Cambridge University Press: 21 February 2020
Abstract
Under what conditions are voters likely to invoke gender-trait stereotypes and ethnic biases when they evaluate the performance of local political leaders? We test arguments from role congruity and social identity theories and employ intersectional analysis to explore voter reliance on gender-trait stereotypes and ethnic biases across four diverse Russian regions. We find that the structure of the regional economy matters for the reduction of gender-trait stereotypes, while spatial and labor market integration matters for the dissipation of ethnic favoritism in leader evaluation. Our findings have important implications for policy makers aiming to reduce political biases in diverse contexts. On the one hand, we encourage scholars to pay careful attention to stereotypes of underrepresented social groups and evaluate how they can translate into understanding the traits associated with good leadership. On the other hand, we demonstrate that the roots of social biases stem from complex structural phenomena, such as limited labor opportunities for women and minorities, and thus require transformative political and economic changes.
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- Research Article
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- Copyright © The Author(s) 2020. Published by Cambridge University Press on behalf of The Women and Politics Research Section of the American Political Science Association
Footnotes
This research is supported by a National Science Foundation grant (ARC #0756122). The authors would like to thank our research partners in the Russian regions for their help in conducting the surveys—Dekabrina Vinokurova, Yury Zhegusov, Erzhena Gylykova, Lyudmila Sandakova, Tatyana Titova, Vyacheslav Kozlov, and Marina Kalinina—as well as our large team of research assistants in each region. This article was one of the last manuscripts that Professor Matland worked on. Richard Matland passed away in August 2018.
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