The gender gap in attitudes toward workplace technological change

We provide the first systematic analysis of how attitudes toward workplace automation and artificial intelligence (AI) vary by gender, using survey data from ten countries. Our analyses reveal a significant gender gap in the perceived fairness of automation and AI, similar in magnitude to that of job offshoring. Drawing on the literature on economic shocks, we examine four explanations based on gender differences in (a) economic self-interest, (b) technological knowledge, (c) sociotropic concerns and (d) social status perceptions. Including these variables in our models, however, narrows the observed gender gap by only 40%. To better understand the sources of attitudinal variation by gender, we rely on Kitagawa-Oaxaca-Blinder decomposition, which shows that distributional differences in group characteristics, specifically women's lower levels of technological knowledge and self-reported social status, account for approximately one-third of the gap, while the other two-thirds are explained by differences in how specific variables differentially influence attitudes.