Training Time, Robots and Technological Unemployment

Fenicia Cossu, Alessio Moro and Michelle Petersen Rendall

No 19343, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We show that labor training requirements for high-skilled occupations increased in the U.S. from 2006 to 2019. These greater training requirements reduce the extent to which workers displaced from shrinking occupations can relocate to expanding (high-skilled) occupations, thus affecting both the equilibrium occupational structure and the unemployment level. We build a quantitative model in which labor is displaced by task-replacing technological change embodied in robots (“tasks shock†) and the extent of occupational switching depends on the destination occupations' training requirements. We find that: (i) task-displacing technological change increases steady-state unemployment, but it reduces unemployment along the transition; (ii) in contrast, a comparable shock to capital embodied technological change produces larger unemployment rates with respect to the tasks shock, both in the transition and the steady state; and (iii) greater training requirements in high-skilled occupations increase steady-state unemployment and affects the occupational structure along the transition, but their effect depends on the size of the technological shock.

JEL-codes: E24 J24 J63 O33 (search for similar items in EconPapers)
Date: 2024-08
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