Gendered Navigation of Advice and Suboptimal Behavior in Matching Algorithms: Evidence from the Residency Match

• 成果类型: Article
• 署名作者: Skowronek, Samuel E.; He, Joyce C.
• 署名单位: University of California System; University of California Los Angeles
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.2024.19652
• 发表日期: 2026
• 关键词: sex-differences; HELP-SEEKING; Knowledge transfer; school choice; S-FRAME; I-FRAME; women; men; WORKPLACE; AGENCY

摘要

Two-sided matching algorithms have been deployed at an increasing rate in labor markets all over the world in part because they can result in more equitable labor market matches. To achieve this desirable result, institutions using these algorithms often engage in a translation process to provide advice to market participants about how to optimally interact with the algorithm. We draw on theories of gendered agency to theorize that men may be more likely than women to engage in independent advice seeking-an agentic way of navigating one's understanding by seeking out additional advice about how the algorithm works to form their own understanding beyond the baseline advice provided by institutions-and that this tendency leads men to have more success with the algorithm because of their deeper understanding. We test these predictions in the context of the National Residency Matching Program (NRMP), which uses a two-sided matching algorithm to match graduating medical students to residencies in the United States. Using archival data of medical students' responses in an incentivized simulation of the NRMP and 66 interviews with medical students going through the match, we find evidence supporting these hypotheses. Men are more likely than women to seek additional advice beyond the baseline guidance, improving their understanding and success with the algorithm. These findings advance prior literature by demonstrating that group-based disparities may occur even when the algorithm itself is unbiased because individuals navigate understanding of these novel algorithms in ways shaped by their identities (i.e., gender).