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Robots and Workers: Evidence from the Netherlands

Author

Listed:
  • Acemoglu, Daron

    (MIT)

  • Koster, Hans R.A.

    (Vrije Universiteit Amsterdam)

  • Ozgen, Ceren

    (University of Birmingham)

Abstract

We estimate the effects of robot adoption on firm-level and worker-level outcomes in the Netherlands using a large employer-employee panel dataset spanning 2009-2020. Our firm-level results confirm previous findings, with positive effects on value added and hours worked for robot-adopting firms and negative outcomes on competitors in the same industry. Our worker-level results show that directly-affected workers (e.g., bluecollar workers performing routine or replaceable tasks) face lower earnings and employment rates, while other workers indirectly gain from robot adoption. We also find that the negative effects from competitors' robot adoption load on directly-affected workers, while other workers benefit from this industry-level robot adoption. Overall, our results highlight the uneven effects of automation on the workforce.

Suggested Citation

  • Acemoglu, Daron & Koster, Hans R.A. & Ozgen, Ceren, 2023. "Robots and Workers: Evidence from the Netherlands," IZA Discussion Papers 15997, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp15997
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    References listed on IDEAS

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    1. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    2. Daron Acemoglu & Claire Lelarge & Pascual Restrepo, 2020. "Competing with Robots: Firm-Level Evidence from France," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 383-388, May.
    3. Matthias Kehrig & Nicolas Vincent, 2021. "The Micro-Level Anatomy of the Labor Share Decline," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(2), pages 1031-1087.
    4. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    5. Adrian Adermon & Magnus Gustavsson, 2015. "Job Polarization and Task-Biased Technological Change: Evidence from Sweden, 1975–2005," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(3), pages 878-917, July.
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    Citations

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    Cited by:

    1. Derick Almeida & Tiago Neves Sequeira, 2024. "Robots at work: New evidence with recent data," Manchester School, University of Manchester, vol. 92(6), pages 700-722, December.
    2. Deng, Liuchun & Müller, Steffen & Plümpe, Verena & Stegmaier, Jens, 2024. "Robots, occupations, and worker age: A production-unit analysis of employment," European Economic Review, Elsevier, vol. 170(C).
    3. Pilar Beneito & Maria Garcia-Vega & Oscar Vicente-Chirivella & Guillaume Wilemme, 2024. "Robots and firms’ labour search: The role of temporary work agencies," Discussion Papers 2024-02, University of Nottingham, GEP.
    4. Xin, Baogui & Ye, Xiaopu, 2024. "Robotics applications, inclusive employment and income disparity," Technology in Society, Elsevier, vol. 78(C).
    5. Winston LeCroy, Craig & Morrill Nisbet, Kristin & Schmidt, Michele C., 2024. "Randomized controlled trial of the parents as teacher home visiting program: Outcomes of the intervention on families at six month follow-up," Children and Youth Services Review, Elsevier, vol. 165(C).
    6. Chen, Kaiming & Chen, Xiaoqian & Wang, Zhan-ao & Zvarych, Roman, 2024. "Does artificial intelligence promote common prosperity within enterprises? —Evidence from Chinese-listed companies in the service industry," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    7. Brzozowski, Michał & Siwińska-Gorzelak, Joanna, 2024. "Did robots make wages less responsive to unemployment?," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    8. Mauro Caselli & Edwin Fourrier-Nicolai & Andrea Fracasso & Sergio Scicchitano, 2024. "Digital Technologies and Firms’ Employment and Training," CESifo Working Paper Series 11056, CESifo.
    9. Ryoo, Yuhosua & Jeon, Yongwoog Andy & Kim, WooJin, 2024. "The blame shift: Robot service failures hold service firms more accountable," Journal of Business Research, Elsevier, vol. 171(C).
    10. Teng, Lefa & Sun, Chuluo & Chen, Yifei & Lever, Michael W. & Foti, Lianne, 2024. "Partner or servant? The influence of robot role positioning on consumers’ brand evaluations," Journal of Business Research, Elsevier, vol. 180(C).

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    More about this item

    Keywords

    robots; workers; technology; productivity; the Netherlands;
    All these keywords.

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • 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

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