@TechReport{iza:izadps:dp18964,
            	author={Drydakis, Nick},
            	title={Artificial Intelligence-Related Digital Skills and Employment Outcomes for Underrepresented Women},
            	year={2026},
	month={Sep},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={18964},
            	url={https://www.iza.org/publications/dp18964},
	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},
            }