Gendered language in job ads and applicant behavior: Evidence from India

Sugat Chaturvedi, Kanika Mahajan and Zahra Siddique

Labour Economics, 2025, vol. 96, issue C

Abstract: We examine employers’ gender preferences using 157,888 job ads posted on an online job portal in India which received 6.45 million applications. About 8% of the job ads include an explicit gender preference. We apply text analysis methods on job titles and detailed job descriptions to construct measures indicating how predictive the job ad text is of employers’ explicit gender preferences. We find that advertised wages are lower in jobs where employers prefer women, even when this preference is implicitly retrieved through text analysis, and that these jobs attract a larger share of female applicants. We find that explicit gender requests by employers explain 7% of the gender wage gap in applied-for-jobs between comparable men and women after accounting for a wide range of controls. Implicit gender associations in the job ad text, together with explicit requests, explain 17% of this gap. We then systematically uncover gendered words or attributes employers associate with men and women. We find that hard skills-related female-gendered words have lower returns but attract a higher share of female applicants, while male-gendered words indicating decreased flexibility (e.g., frequent travel or unusual working hours) have higher returns but result in a smaller share of female applicants. Finally, we identify words in job ads associated with a higher female applicant share, which can be leveraged in future experimental research and assist organizations looking to attract a diverse applicant pool.

Keywords: Gender; Stereotypes; Discrimination; Job search; Machine learning (search for similar items in EconPapers)
JEL-codes: J16 J63 J71 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:labeco:v:96:y:2025:i:c:s0927537125000508

DOI: 10.1016/j.labeco.2025.102726

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