Reactions, Revisions, and Rehiring: Changes in Employers' Gendered Preferences in Online Labor Markets

被引:0
作者
Daviss, Claire [1 ]
Leung, Ming D. [2 ]
机构
[1] Stanford Univ, Sociol Dept, Stanford, CA 94305 USA
[2] Univ Calif Irvine, Paul Merage Sch Business, Irvine, CA 92697 USA
关键词
diversity in organizations; organizational processes; organizational learning; occupations and professions; DOUBLE STANDARDS; STEREOTYPE CONTENT; TEAM PERFORMANCE; WOMEN; COMPETENCE; WORK; DISCRIMINATION; FAMILIARITY; PREJUDICE; EXPOSURE;
D O I
10.1287/orsc.2023.17383
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Employers in online labor marketplaces prefer women over men because of stereotypes that women are more trustworthy. These stereotypes are especially salient in this context because of the uncertainty in online transactions. Yet, employers' interactions with women and men workers might moderate the influence of trustworthiness stereotypes and, by extension, employers' hiring preferences for individual women and men as well as gender categories in general. By exploiting rare access to large-scale, longitudinal hiring data from an online labor marketplace, we examine how employers' interactions with workers shape their subsequent hiring preferences. Using linear probability models with job fixed effects, we find that employers prefer rehiring workers with whom they previously had positive interactions over unknown workers. Men workers benefit more than women workers from positive interactions, suggesting that these interactions decrease the influence of negative stereotypes about men's trustworthiness. We additionally find that employers prefer hiring workers of the same gender as workers with whom they previously had positive interactions, suggesting that employers' interactions with individual women and men shape their preferences for hiring women and men in general. These findings point to a nuanced theoretical relationship between employers' interactions with workers and their social category preferences in hiring under conditions of immense uncertainty.
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页数:22
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