Racial bias, colorism, and overcorrection
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917203648970752 |
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| author | Colombe, Kenneth Krumer, Alex Lavelle-Hill, Rosa Pawlowski, Tim |
| author_facet | Colombe, Kenneth Krumer, Alex Lavelle-Hill, Rosa Pawlowski, Tim |
| contents | This paper examines whether increased awareness can affect racial bias and colorism. We exploit a natural experiment arising from the widespread publicity of Price and Wolfers (2010), which served as an external shock, intensifying scrutiny of racial bias in men's basketball officiating. We investigate refereeing decisions in a similar setting, the Women's National Basketball Association (WNBA), which is known as a progressive institution with a longstanding commitment to diversity, equity, and inclusion (DEI) policy. We apply state-of-the-art artificial intelligence and machine learning techniques to systematically predict race and objectively measure skin tone. Our empirical strategy exploits the quasi-random assignment of referees to games, combined with high-dimensional fixed effects, to estimate the relationship between the racial and skin tone compositions of referees and players, as well as foul-calling behavior. Our results show no significant racial bias before the intense media coverage. However, afterward, we find evidence of overcorrection: a player earns fewer fouls when facing more referees from the opposite race and skin tone. Even though this overcorrection seems to wear off over time, we highlight the need to consider baseline levels of bias before applying any prescription with direct relevance to policymakers and organizations, given the recent discourse on DEI. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_10585 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Racial bias, colorism, and overcorrection Colombe, Kenneth Krumer, Alex Lavelle-Hill, Rosa Pawlowski, Tim General Economics Economics This paper examines whether increased awareness can affect racial bias and colorism. We exploit a natural experiment arising from the widespread publicity of Price and Wolfers (2010), which served as an external shock, intensifying scrutiny of racial bias in men's basketball officiating. We investigate refereeing decisions in a similar setting, the Women's National Basketball Association (WNBA), which is known as a progressive institution with a longstanding commitment to diversity, equity, and inclusion (DEI) policy. We apply state-of-the-art artificial intelligence and machine learning techniques to systematically predict race and objectively measure skin tone. Our empirical strategy exploits the quasi-random assignment of referees to games, combined with high-dimensional fixed effects, to estimate the relationship between the racial and skin tone compositions of referees and players, as well as foul-calling behavior. Our results show no significant racial bias before the intense media coverage. However, afterward, we find evidence of overcorrection: a player earns fewer fouls when facing more referees from the opposite race and skin tone. Even though this overcorrection seems to wear off over time, we highlight the need to consider baseline levels of bias before applying any prescription with direct relevance to policymakers and organizations, given the recent discourse on DEI. |
| title | Racial bias, colorism, and overcorrection |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2508.10585 |