Artificial Intelligence-Related Digital Skills and Employment Outcomes for Underrepresented Women

Nick Drydakis ()
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Nick Drydakis: Anglia Ruskin University

No 18964, IZA Discussion Papers from IZA Network @ LISER

Abstract: This study examines whether signalling AI-related digital skills improves employment outcomes for women from underrepresented groups in England, defined by race, age, sexual orientation, and autism-spectrum disclosure. The study finds that underrepresented women receive fewer interview invitations and are considered for lower-paid vacancies than majority-group women. In pooled analyses, signalling AI-related digital skills increases interview invitations for underrepresented applicants, but does not eliminate their disadvantage. These findings are consistent with AI Capital and productivity-signalling frameworks, as employers appear to value AI-related capabilities while the returns to such credentials remain constrained by persistent demographic inequalities. The study therefore demonstrates that positive returns to AI-related skills and labour-market disadvantage can coexist. Its broader implication is that digital upskilling can strengthen the recruitment prospects of underrepresented women, but cannot by itself deliver parity in employment outcomes. A dual policy response is therefore required, combining wider and more equitable access to AI education and training with stronger anti-discrimination enforcement.

Keywords: AI Capital; artificial intelligence; skills; discrimination; hiring; wages; sexual orientation; race; age; autism spectrum (search for similar items in EconPapers)
JEL-codes: C93 J14 J15 J16 J24 J31 J64 J71 M51 O33 (search for similar items in EconPapers)
Date: 2026-09
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