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Automation: Theory, Evidence, and Outlook

Author

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  • Pascual Restrepo

    (Department of Economics, Boston University, Boston, Massachusetts, USA)

Abstract

This article reviews the literature on automation and its impact on labor markets, wages, factor shares, and productivity. I first introduce the task model and explain why this framework offers a compelling way to think about recent labor market trends and the effects of automation technologies. The task model clarifies that automation technologies operate by substituting capital for labor in a widening range of tasks. This substitution reduces costs, creating a positive productivity effect, but it also reduces employment opportunities for workers displaced from automated tasks, creating a negative displacement effect. I survey the empirical literature and conclude that there is wide qualitative support for the implications of task models and the displacement effects of automation. I conclude by discussing shortcomings of the existing literature and avenues for future research.

Suggested Citation

  • Pascual Restrepo, 2024. "Automation: Theory, Evidence, and Outlook," Annual Review of Economics, Annual Reviews, vol. 16(1), pages 1-25, August.
    • Handle: RePEc:anr:reveco:v:16:y:2024:p:1-25
    DOI: 10.1146/annurev-economics-090523-113355
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    Cited by:

    1. Ann Harrison, 2024. "Disentangling Various Explanations for the Declining Labor Share: Evidence from Millions of Firm Records," NBER Working Papers 32015, National Bureau of Economic Research, Inc.
    2. Giovanni Dosi & Marcelo C. Pereira & Andrea Roventini & Maria Enrica Virgillito, 2024. "The political economy of complex evolving systems: the case of declining unionization and rising inequalities," LEM Papers Series 2024/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Armanda Cetrulo & Dario Guarascio & Maria Enrica Virgillito, 2024. "Two neglected origins of inequality: hierarchical power and care work," LEM Papers Series 2024/04, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    4. Laetitia Hauret & Ursula Holtgrewe & Sandra M. Leitner & Ludivine Martin, 2024. "Offshoring, technological change and the quality of work in the EU: On the mediating role of trade unions," wiiw Working Papers 258, The Vienna Institute for International Economic Studies, wiiw.
    5. Abeliansky, Ana Lucia & Prettner, Klaus & Rodriguez-Crespo, Ernesto, 2024. "Climate change and automation: the emission effects of robot adoption," Department of Economics Working Paper Series 370, WU Vienna University of Economics and Business.
    6. Schneider, Florian, 2024. "Do robots boost productivity? A quantitative meta-study," MPRA Paper 123392, University Library of Munich, Germany.
    7. Caleb Peppiatt, 2024. "The Future of Work: Inequality, Artificial Intelligence, and What Can Be Done About It. A Literature Review," Papers 2408.13300, arXiv.org.
    8. Guillermo Cruces & Verónica Amarante & Estefanía Lotitto, 2024. "Generative Artificial Intelligence and Its Implications for Labor Markets in Developing Countries: A Review Essay," CEDLAS, Working Papers 0343, CEDLAS, Universidad Nacional de La Plata.

    More about this item

    Keywords

    technology; robots; tasks; productivity; automation; wages; inequality;
    All these keywords.

    JEL classification:

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

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