Automation, Learning, and Career Dynamics

Hassan Afrouzi (), Andres Blanco (), Andrés Drenik () and Erik Hurst ()
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Hassan Afrouzi: Columbia University Department of Economics and NBER
Andres Blanco: Federal Reserve Bank of Atlanta and Emory University
Andrés Drenik: University of Texas at Austin Department of Economics and NBER
Erik Hurst: The University of Chicago Booth School of Business and NBER

No 2026-61, Working Papers from Becker Friedman Institute for Research In Economics

Abstract: We study how an automating technology affects career dynamics, human capital, and welfare in an economy where workers acquire skill through the tasks they perform. In a continuous-time general equilibrium model, learning-by-doing is determined jointly with the share of tasks automated, the frontier of tasks managers maintain, and the worker-to-manager career transition. Economies with high learning capacity admit pairs of stationary equilibria strictly ranked by the aggregate learning rate. Cheaper technology has opposite effects across the two: in the high-learning equilibrium, it raises welfare through the learning channel itself; in the low-learning equilibrium, it tips the economy into a human-capital trap. The planner's first-best combines a tax on automation profits with a subsidy on frontier-maintenance expenditures at a common rate.

JEL-codes: E23 E24 J24 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2026
New Economics Papers: this item is included in nep-mic
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