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      Gender composition predicts gender bias: A meta-reanalysis of hiring discrimination audit experiments

      1 , 2
      Science Advances
      American Association for the Advancement of Science (AAAS)

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          Abstract

          Since 1983, more than 70 employment audit experiments, carried out in more than 26 countries across five continents, have randomized the gender of fictitious applicants to measure the extent of hiring discrimination on the basis of gender. The results are mixed: Some studies find discrimination against men, and others find discrimination against women. We reconcile these heterogeneous findings through a “meta-reanalysis” of the average effects of being described as a woman (versus a man), conditional on occupation. We find a strongly positive gender gradient. In (relatively better paying) occupations dominated by men, the effect of being a woman is negative, while in the (relatively lower paying) occupations dominated by women, the effect is positive. In this way, heterogeneous employment discrimination on the basis of gender preserves status quo gender distributions and earnings gaps. These patterns hold among both minority and majority status applicants.

          Abstract

          Summarizing 57 hiring audits, we find pro-woman bias in women-dominated contexts and pro-man bias in men-dominated contexts.

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Birds of a Feather: Homophily in Social Networks

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              Role congruity theory of prejudice toward female leaders.

              A role congruity theory of prejudice toward female leaders proposes that perceived incongruity between the female gender role and leadership roles leads to 2 forms of prejudice: (a) perceiving women less favorably than men as potential occupants of leadership roles and (b) evaluating behavior that fulfills the prescriptions of a leader role less favorably when it is enacted by a woman. One consequence is that attitudes are less positive toward female than male leaders and potential leaders. Other consequences are that it is more difficult for women to become leaders and to achieve success in leadership roles. Evidence from varied research paradigms substantiates that these consequences occur, especially in situations that heighten perceptions of incongruity between the female gender role and leadership roles.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Science Advances
                Sci. Adv.
                American Association for the Advancement of Science (AAAS)
                2375-2548
                May 05 2023
                May 05 2023
                : 9
                : 18
                Affiliations
                [1 ]Centre for the Experimental-Philosophical Study of Discrimination (CEPDISC), Department of Political Science, Aarhus University, Aarhus, Denmark.
                [2 ]Department of Political Science, Yale University, New Haven, CT, USA.
                Article
                10.1126/sciadv.ade7979
                37146136
                16bb08bb-64f5-496b-a615-0e98cc395cd0
                © 2023
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