Automation, Gender, and Inequality: Which Tradeoffs do Policymakers Face?

This paper examines the role of automation in shaping gender inequality among high-skilled and low-skilled workers in the United States. We develop an R&D-based growth model of automation in which we endogenize disparities between men and women and between high-skilled and low-skilled labor through education choices. Automation substitutes for routine, brawn-intensive tasks, while it complements high-skilled, brain-intensive ones. Our framework predicts that automation increases demand for high-skilled workers, raising female participation in knowledge production but also widening within-gender and between-skills inequality. Redistributive transfers to low-skilled workers, financed through robot taxation, reduce high-skilled employment, lower innovation, and slow down economic growth despite compressing within-gender and between-skills inequality. Education subsidies expand the share of skilled workers and foster innovation but come at the cost of greater within-gender and between-skills inequality. Subsidies targeted on women reduce between-gender inequality, but they can raise within-gender inequality and slow down economic growth. Finally, in the case of the presence of norms and institutions that are detrimental to gender equality, female empowerment can reduce inequality and raise economic growth at the same time.