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Explaining the labor share: automation vs labor market institutions

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  • Guimarães, Luis
  • Gil, Pedro

Abstract

In this paper, we build a theoretical model to study the effects of automation and labor market institutions on the labor share. In our model, firms choose between two technologies: an automated technology and a manual technology. In this context, the labor share reflects both the average wage level (versus output) and the distribution of firms between the two technologies. Our model offers three main insights. First, automation-augmenting shocks reduce the labor share but increase employment and wages. Second, labor market institutions (relative to automation) play an almost insignificant role in explaining the labor share. Third, our model suggests that the US labor share only (clearly) falls after the late 1980’s because of a contemporaneous acceleration of automation’s productivity.

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  • Guimarães, Luis & Gil, Pedro, 2019. "Explaining the labor share: automation vs labor market institutions," MPRA Paper 92062, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:92062
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    Cited by:

    1. Abeliansky, Ana & Prettner, Klaus, 2017. "Automation and demographic change," University of Göttingen Working Papers in Economics 310, University of Goettingen, Department of Economics.
    2. Arthur Jacobs, 2023. "Capitalist-Worker Wealth Distribution in a Task-Based Model of Automation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 23/1064, Ghent University, Faculty of Economics and Business Administration.
    3. Ana Lucia Abeliansky & Klaus Prettner, 2021. "Population Growth and Automation Density: Theory and CrossCountry Evidence," VID Working Papers 2102, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna.
    4. Kostøl, Fredrik B. & Svarstad, Elin, 2023. "Trade Unions and the Process of Technological Change," Labour Economics, Elsevier, vol. 84(C).
    5. 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.
    6. 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).
    7. 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).
    8. 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.
    9. Abeliansky, Ana Lucia & Prettner, Klaus, 2023. "Automation and population growth: Theory and cross-country evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 208(C), pages 345-358.
    10. Cui, Guanghui & Zhang, Yi & Ma, Jingwen & Yao, Wenyun, 2023. "Does environmental regulation affect the labor income share of manufacturing enterprises? Evidence from China," Economic Modelling, Elsevier, vol. 123(C).
    11. 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.
    12. 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.
    13. Jacobs, Arthur, 2023. "Capital-augmenting technical change in the context of untapped automation opportunities," Mathematical Social Sciences, Elsevier, vol. 123(C), pages 155-166.
    14. 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.
    15. 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).
    16. 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.

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    More about this item

    Keywords

    Automation; Labor Share; Technology Choice; Employment; Labor-Market 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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