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Demographics and Automation

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Listed:
  • Daron Acemoglu
  • Pascual Restrepo

Abstract

We argue theoretically and document empirically that aging leads to greater (industrial) automation, and in particular, to more intensive use and development of robots. Using US data, we document that robots substitute for middle-aged workers (those between the ages of 36 and 55). We then show that demographic change—corresponding to an increasing ratio of older to middle-aged workers—is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across US commuting zones. We also provide evidence of more rapid development of automation technologies in countries undergoing greater demographic change. Our directed technological change model further predicts that the induced adoption of automation technology should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation. Both of these predictions receive support from country-industry variation in the adoption of robots. Our model also implies that the productivity implications of aging are ambiguous when technology responds to demographic change, but we should expect productivity to increase and labor share to decline relatively in industries that are most amenable to automation, and this is indeed the pattern we find in the data.

Suggested Citation

  • Daron Acemoglu & Pascual Restrepo, 2018. "Demographics and Automation," NBER Working Papers 24421, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24421
    Note: AG EFG LS
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Demographics and Automation
      by maximorossi in NEP-LTV blog on 2018-10-24 12:48:21

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    Cited by:

    1. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: an empirically grounded conceptualization," International Journal of Production Economics, Elsevier, vol. 223(C).
    2. Robert L. Clark & Beth M. Ritter, 2020. "How Are Employers Responding to an Aging Workforce?," NBER Working Papers 26633, National Bureau of Economic Research, Inc.
    3. Pietro Checcucci, 2019. "The Silver Innovation. Older workers characteristics and digitalisation of the economy," Working Papers 0040, ASTRIL - Associazione Studi e Ricerche Interdisciplinari sul Lavoro.
    4. Vincent Vandenberghe, 2020. "Differentiating Retirement Age to Compensate for Health Differences," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2020015, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    5. Kuhn, Michael & Prettner, Klaus, 2020. "Rising longevity, increasing the retirement age, and the consequences for knowledge-based long-run growth," Hohenheim Discussion Papers in Business, Economics and Social Sciences 02-2020, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    6. Krenz, Astrid & Prettner, Klaus & Strulik, Holger, 2018. "Robots, reshoring, and the lot of low-skilled workers," Center for European, Governance and Economic Development Research Discussion Papers 351, University of Goettingen, Department of Economics.
    7. Jung, Yeonha, 2020. "The long reach of cotton in the US South: Tenant farming, mechanization, and low-skill manufacturing," Journal of Development Economics, Elsevier, vol. 143(C).
    8. Henrik Schwabe & Fulvio Castellacci, 2020. "Automation, workers’ skills and job satisfaction," Working Papers on Innovation Studies 20200526, Centre for Technology, Innovation and Culture, University of Oslo.
    9. Gasteiger, Emanuel & Prettner, Klaus, 2020. "Automation, stagnation, and the implications of a robot tax," ECON WPS - Vienna University of Technology Working Papers in Economic Theory and Policy 02/2020, Vienna University of Technology, Institute for Mathematical Methods in Economics, Research Group Economics (ECON).
    10. Abeliansky, Ana & Strulik, Holger, 2020. "Health and aging before and after retirement," Center for European, Governance and Economic Development Research Discussion Papers 397, University of Goettingen, Department of Economics.
    11. Francisco Perez-Arce & Maria J. Prados & Tarra Kohli, 2018. "The Decline in the U.S. Labor Force Participation Rate," Working Papers wp385, University of Michigan, Michigan Retirement Research Center.
    12. Gunes Arkadas Asik & Mohamed Ali Marouani & Michelle Marshalian & Ulas Karakoc, 2018. "Productivity, Structural Change and Skills Dynamics in Tunisia and Turkey," Working Papers 1269, Economic Research Forum, revised 10 Dec 2018.
    13. Christine Lewis & Patrice Ollivaud, 2020. "Policies for Switzerland’s ageing society," OECD Economics Department Working Papers 1600, OECD Publishing.
    14. Dechezleprêtre, Antoine & Hémous, David & olsen, morten & Zanella, carlo, 2019. "Automating Labor: Evidence from Firm-level Patent Data," CEPR Discussion Papers 14249, C.E.P.R. Discussion Papers.

    More about this item

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries

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