To Discriminate, or Not to Discriminate, in Online Job Ads

Tho Pham (), Daniel Schaefer () and Carl Singleton
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Tho Pham: University of York
Daniel Schaefer: Johannes Kepler University Linz

No 19024, IZA Discussion Papers from IZA Network @ LISER

Abstract: How do employers use and adjust recruitment signals and wage offers when they target applicants of a particular gender? Using online vacancy data from Vietnam and natural language processing methods, we construct an index measuring the direction and intensity of implicit gendered language in job requirements. We document widespread implicit gendered framing and substantial variation across repeated postings for the same firm and job, even where explicit gender requests remain unchanged. When explicit requests do change, we find that the two signals move together: adding a gender request strengthens wording oriented toward that gender, while removing it weakens such wording. Both signals are also associated with posted wages. Within positions, adding a male request is accompanied by 3% higher wages, and adding a female request by 4% lower wages. More male-coded wording is likewise associated with higher wages. A stylised directed-search model can rationalise these patterns when applicants' wage responsiveness differs by gender and inconsistent recruitment signals discourage applicants.

Keywords: gender discrimination; online vacancy data; wage posting; text analysis; directed search (search for similar items in EconPapers)
JEL-codes: J16 J63 J71 (search for similar items in EconPapers)
Date: 2026-10
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