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Explaining the labor share: automation vs labor market institutions

Author

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  • Luís Guimarães

    (Queen's University Belfast and cef.up)

  • Pedro Mazeda Gil

    (cef.up, FEP, Universidade do Porto)

Abstract

We propose a simple model to assess the evolution of the US labor share and how automation affects employment. In our model, heterogeneous firms may choose a manual technology and hire a worker subject to matching frictions. Alternatively, they may choose an automated technology and produce using only machines (robots). Our model offers three main insights. First, automation-augmenting shocks reduce the labor share but increase employment and wages. Second, labor market institutions play an almost insignificant role in explaining the labor share. Third, the US labor share only (clearly) fell after 1987 because of a contemporaneous acceleration of automation's productivity.

Suggested Citation

  • Luís Guimarães & Pedro Mazeda Gil, 2019. "Explaining the labor share: automation vs labor market institutions," CEF.UP Working Papers 1901, Universidade do Porto, Faculdade de Economia do Porto.
    • Handle: RePEc:por:cetedp:1901
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    Cited by:

    1. Abeliansky, Ana Lucia & Prettner, Klaus, 2017. "Automation and demographic change," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168215, Verein für Socialpolitik / German Economic Association.
    2. Abeliansky, Ana Lucia & Prettner, Klaus, 2021. "Population growth and automation density: theory and cross-country evidence," Department of Economics Working Paper Series 315, WU Vienna University of Economics and Business.
    3. Abeliansky, Ana & Algur, Eda & Bloom, David E. & Prettner, Klaus, 2020. "The Future of Work: Challenges for Job Creation Due to Global Demographic Change and Automation," IZA Discussion Papers 12962, Institute of Labor Economics (IZA).
    4. Xiaomeng Zhang & Theodore Palivos & Xiangbo Liu, 2022. "Aging and automation in economies with search frictions," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(2), pages 621-642, April.
    5. Gasteiger, Emanuel & Prettner, Klaus, 2022. "Automation, Stagnation, And The Implications Of A Robot Tax," Macroeconomic Dynamics, Cambridge University Press, vol. 26(1), pages 218-249, January.
    6. Ana L. ABELIANSKY & Eda ALGUR & David E. BLOOM & Klaus PRETTNER, 2020. "The future of work: Meeting the global challenges of demographic change and automation," International Labour Review, International Labour Organization, vol. 159(3), pages 285-306, September.
    7. Guimarães, Luis & Gil, Pedro, 2019. "Looking ahead at the effects of automation in an economy with matching frictions," MPRA Paper 96238, University Library of Munich, Germany.
    8. Sequeira, Tiago Neves & Garrido, Susana & Santos, Marcelo, 2021. "Robots are not always bad for employment and wages," International Economics, Elsevier, vol. 167(C), pages 108-119.

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    More about this item

    Keywords

    Automation; Labor Share; Technology Choice; Employment; Matching Frictions.;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • 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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