Abstract:

Progress in industrial robotics and information technologies has meant that advanced economies have experienced a signiﬁcant drop in the fraction of the population employed in middle wage, “routine task-intensive”occupations. Applying machine learning techniques, we identify the types of individuals who would otherwise be employed in such occupations, if not for advances in automation technology, and track their labor market outcomes. Based on these ﬁndings, we develop a quantitative, heterogeneous agent, general equilibrium model of labor force participation, occupational choice, and capital investment to study the aggregate and distributional effects of advancing automation. We use this framework as a laboratory to evaluate various public policies aimed at addressing the disappearance of routine employment and its consequent impacts on inequality.