The Elusive Returns to AI Skills: Evidence from a Field Experiment - Teo Firpo; Lukas Niemann; Anastasia Danilov (Ludwig-Maximilians-Universität München und Humboldt-Universität zu Berlin, Collaborative Research Center Transregio 190 - Rationality and Competition, 2025)
As firms increasingly adopt Artificial Intelligence (AI) technologies, how they adjust hiring practices for skilled workers remains unclear. This paper investigates whether AI-related skills are rewarded in talent recruitment by conducting a large-scale correspondence study in the United Kingdom. We submit 1,185 résumés to vacancies across a range of occupations, randomly assigning the presence or absence of advanced AI-related qualifications. These AI qualifications are added to résumés as voluntary signals and not explicitly requested in the job postings. We find no statistically significant effect of listing AI qualifications in résumés on interview callback rates. However, a heterogeneity analysis reveals some positive and significant effects for positions in Engineering and Marketing. These results are robust to controlling for the total number of skills listed in job ads, the degree of match between résumés and job descriptions, and the level of expertise required. In an exploratory analysis, we find stronger employer responses to AI-related skills in industries with lower exposure to AI technologies. These findings suggest that the labor market valuation of AI-related qualifications is context-dependent and shaped by sectoral innovation dynamics.
Keywords: O33; J23; J24; I26; 330; return to skills; technological change; labor market; hiring; signaling; human capital; field experiment; AI-related skills