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Automation and Polarization

Author

Listed:
  • Daron Acemoglu
  • Jonas Loebbing
Abstract
We develop an assignment model of automation. Each of a continuum of tasks of variable complexity is assigned to either capital or one of a continuum of labor skills. We characterize conditions for interior automation, whereby tasks of intermediate complexity are assigned to capital. Interior automation arises when the most skilled workers have a comparative advantage in the most complex tasks relative to capital, and because the wages of the least skilled workers are sufficiently low relative to their productivity and the effective cost of capital in low-complexity tasks. Minimum wages and other sources of higher wages at the bottom make interior automation less likely. Starting with interior automation, a reduction in the cost of capital (or an increase in capital productivity) causes employment and wage polarization. Specifically, further automation pushes workers into tasks at the lower and upper ends of the task distribution. It also monotonically increases the skill premium above a skill threshold and reduces the skill premium below this threshold. Moreover, automation tends to reduce the real wage of workers with comparative advantage profiles close to that of capital. We show that large enough increases in capital productivity ultimately induce a transition to low-skill automation and qualitatively alter the effects of automation - thereafter inducing monotone increases in skill premia rather than wage polarization.

Suggested Citation

  • Daron Acemoglu & Jonas Loebbing, 2022. "Automation and Polarization," NBER Working Papers 30528, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30528
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    References listed on IDEAS

    as
    1. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    2. Daron Acemoglu, 1998. "Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1055-1089.
    3. Berg, Andrew & Buffie, Edward F. & Zanna, Luis-Felipe, 2018. "Should we fear the robot revolution? (The correct answer is yes)," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 117-148.
    4. Teulings, Coen N, 1995. "The Wage Distribution in a Model of the Assignment of Skills to Jobs," Journal of Political Economy, University of Chicago Press, vol. 103(2), pages 280-315, April.
    5. Heckman, James J & Sedlacek, Guilherme, 1985. "Heterogeneity, Aggregation, and Market Wage Functions: An Empirical Model of Self-selection in the Labor Market," Journal of Political Economy, University of Chicago Press, vol. 93(6), pages 1077-1125, December.
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    Cited by:

    1. Heyman, Fredrik & Olsson, Martin, 2022. "Long-Run Effects of Technological Change: The Impact of Automation and Robots on Intergenerational Mobility," Working Paper Series 1451, Research Institute of Industrial Economics, revised 29 Jun 2023.
    2. Enrique Ide & Eduard Talamas, 2023. "Artificial Intelligence in the Knowledge Economy," Papers 2312.05481, arXiv.org, revised Dec 2024.
    3. Rude, Johanna, 2024. "Demographic Change, Automation and the Role of Education," MPRA Paper 120876, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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