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Gendered Language in Job Advertisements Relates to Gender Sorting in Public Labor Markets: A Multi-Source Analysis

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  • Sievert, Martin

    (Leiden University)

  • Vogel, Dominik

    (University of Hamburg)

  • Döring, Matthias

Abstract

Increasing gender diversity constitutes a desirable goal for policymakers and recruiters in public organizations. However, contemporary research lacks focus on gender sorting, referring to structural self-selection among job seekers in the public labor market. Since job advertisements are the initial step when targeting candidates, we investigate how they contribute to gender sorting. We address research gaps concerning gendered recruitment practices and theorize two mechanisms of gender sorting: gendered language and the gender of the contact person in job advertisements. We test these theoretical arguments using a unique multi-source dataset consisting of real job advertisements, a survey among recruiters issuing the job advertisements, and organization-level data (n = 1,859). Results from hierarchical linear models indicate that feminine wording relates to a higher number and share of applications by women. A female contact person did not exhibit statistically significant effects. This study offers robust empirical evidence showcasing the relevance of gendered language. The research contributes to public management research by providing interdisciplinary theorizing about why, structurally, women may be less inclined to apply for some public sector jobs. We derive theoretical implications for policymakers and recruitment in public administrations and emphasize the relevance of gender sorting mechanisms in public sector recruiting.

Suggested Citation

  • Sievert, Martin & Vogel, Dominik & Döring, Matthias, 2024. "Gendered Language in Job Advertisements Relates to Gender Sorting in Public Labor Markets: A Multi-Source Analysis," SocArXiv u6z5e, Center for Open Science.
  • Handle: RePEc:osf:socarx:u6z5e
    DOI: 10.31219/osf.io/u6z5e
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    References listed on IDEAS

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    1. repec:wyi:journl:002164 is not listed on IDEAS
    2. Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
    3. Peter Kuhn & Kailing Shen, 2013. "Gender Discrimination in Job Ads: Evidence from China," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(1), pages 287-336.
    4. Harry J. Holzer & David Neumark, 2006. "Affirmative action: What do we know?," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 25(2), pages 463-490.
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