JOURNAL ARTICLE

Explicit and Implicit Belief-Based Gender Discrimination: A Hiring Experiment.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 2. P. 1600 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Barron, Kai; Ditlmann, Ruth; Gehrig, Stefan; Schweighofer-Kodritsch, Sebastian 3 of 3

Abstract

This paper investigates how gender stereotypes translate into discriminatory hiring actions by distinguishing between explicit and implicit belief-based discrimination in a controlled experimental setting that rules out taste-based discrimination. Using a hiring experiment with 240 participants acting as employers choosing between male and female candidates with systematically varied qualifications, the study finds significant aggregate gender bias favoring men both when candidates are equally qualified (explicit discrimination) and when they differ in qualifications (where discrimination is less obvious). By analyzing within-subject decision patterns, the authors identify implicit discrimination: some employers who do not explicitly discriminate against women still favor male candidates in complex decisions where bias is less detectable, suggesting a tension between holding gender stereotypes and adhering to social norms against overt discrimination. The findings highlight the importance of the decision environment in shaping discriminatory behavior and have implications for designing policies that address both overt and subtle forms of gender bias in hiring.

Additional Information

  • Source:Management Science (INFORMS). 2025/02, Vol. 71, Issue 2, p1600
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2022.01229
  • Accession Number:182990746
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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