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Explaining the labor share: automation vs labor market institutions

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  • Luis Guimaraes
  • Pedro Mazeda Gil

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

  • Luis Guimaraes & Pedro Mazeda Gil, 2019. "Explaining the labor share: automation vs labor market institutions," Economics Working Papers 19-01, Queen's Management School, Queen's University Belfast.
  • Handle: RePEc:qub:wpaper:1901
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    Cited by:

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    3. Jacobs, Arthur, 2023. "Capital-augmenting technical change in the context of untapped automation opportunities," Mathematical Social Sciences, Elsevier, vol. 123(C), pages 155-166.
    4. Hugo Hopenhayn & Julian Neira & Rish Singhania, 2022. "From Population Growth to Firm Demographics: Implications for Concentration, Entrepreneurship and the Labor Share," Econometrica, Econometric Society, vol. 90(4), pages 1879-1914, July.
    5. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.
    6. Kostøl, Fredrik B. & Svarstad, Elin, 2023. "Trade Unions and the Process of Technological Change," Labour Economics, Elsevier, vol. 84(C).
    7. 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.
    8. Guimarães, Luís & Mazeda Gil, Pedro, 2022. "Looking ahead at the effects of automation in an economy with matching frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    9. Yuan, Sai & Zhou, Ran & Li, Mengna & Lv, Chengchao, 2023. "Investigating the influence of digital technology application on employee compensation," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    10. 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).
    11. 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.
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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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