IDEAS home Printed from https://ideas.repec.org/p/war/wpaper/2025-01.html

Industrial robots and workers’ well-being in Europe

Author

Listed:
  • Honorata Bogusz

    (University of Warsaw, Faculty of Economic Sciences)

  • Daniela Bellani

    (Università Cattolica, Milano)

Abstract

In the 21st century, advancements in technologies such as industrial robots have raised concerns about their impact on employment and wages, prompting extensive research. However, their effects on workers’ subjective well-being remain underexplored. This study addresses this gap ¬by examining whether workers experience a decline in well-being due to a loss of agency or maintain it by leveraging human skills to adapt to automation. Using data from the International Federation of Robotics, Eurostat, and the European Social Survey (2002–2018), we link robot density at the country-industry-year level to workers’ life satisfaction, happiness, job influence, and health. Employing an instrumental variables approach, we find that robot adoption negatively affects medium-educated workers’ well-being, particularly its eudaimonic dimension, supporting the decreasing agency thesis. In contrast, low- and highly educated workers experience positive effects. These impacts are more pronounced among women and weaker in countries with robust compensatory social policies.

Suggested Citation

  • Honorata Bogusz & Daniela Bellani, 2025. "Industrial robots and workers’ well-being in Europe," Working Papers 2025-01, Faculty of Economic Sciences, University of Warsaw.
  • Handle: RePEc:war:wpaper:2025-01
    as

    Download full text from publisher

    File URL: https://www.wne.uw.edu.pl/download_file/5249/0
    File Function: First version, 2025
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    2. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    3. Burton G. Malkiel, 2005. "Reflections on the Efficient Market Hypothesis: 30 Years Later," The Financial Review, Eastern Finance Association, vol. 40(1), pages 1-9, February.
    4. Fischer, Thomas & Krauss, Christopher, 2017. "Deep learning with long short-term memory networks for financial market predictions," FAU Discussion Papers in Economics 11/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    5. Yuze Lu & Hailong Zhang & Qiwen Guo, 2023. "Stock and market index prediction using Informer network," Papers 2305.14382, arXiv.org.
    6. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    7. Kim, Jae H. & Shamsuddin, Abul & Lim, Kian-Ping, 2011. "Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 868-879.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Filip Stefaniuk & Robert Ślepaczuk, 2024. "The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Me," Working Papers 2024-27, Faculty of Economic Sciences, University of Warsaw.
    2. Filip Stefaniuk & Robert 'Slepaczuk, 2025. "Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data," Papers 2503.18096, arXiv.org.
    3. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    4. Bianchi, Robert J. & Drew, Michael E. & Fan, John Hua, 2016. "Commodities momentum: A behavioral perspective," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 133-150.
    5. Zhu (Drew) Zhang & Jie Yuan & Amulya Gupta, 2024. "Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility," INFORMS Journal on Computing, INFORMS, vol. 36(6), pages 1400-1416, December.
    6. Amélie Charles & Olivier Darné & Jae H. Kim, 2014. "Precious metals shine? A market efficiency perspective," Working Papers hal-01010516, HAL.
    7. Kin-Boon Tang & Shao-Jye Wong & Shih-Kuei Lin & Szu-Lang Liao, 2020. "Excess volatility and market efficiency in government bond markets: the ASEAN-5 context," Journal of Asset Management, Palgrave Macmillan, vol. 21(2), pages 154-165, March.
    8. Md Lutfur Rahman & Mahbub Khan & Samuel A. Vigne & Gazi Salah Uddin, 2021. "Equity return predictability, its determinants, and profitable trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 162-186, January.
    9. Rahman, Oriana & Semenov, Andrei, 2025. "Subjective probabilities under behavioral heuristics," International Review of Economics & Finance, Elsevier, vol. 98(C).
    10. Charfeddine, Lanouar & Khediri, Karim Ben & Aye, Goodness C. & Gupta, Rangan, 2018. "Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 632-647.
    11. Chopra, Ritika & Sharma, Gagan Deep & Pereira, Vijay, 2024. "Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction," Technovation, Elsevier, vol. 135(C).
    12. Won Joong Kim & Gunho Jung & Sun-Yong Choi, 2020. "Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning," Complexity, Hindawi, vol. 2020, pages 1-23, July.
    13. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2015. "Will precious metals shine? A market efficiency perspective," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 284-291.
    14. Shi, Huai-Long & Zhou, Wei-Xing, 2017. "Wax and wane of the cross-sectional momentum and contrarian effects: Evidence from the Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 397-407.
    15. Navaz Naghavi & Wee-Yeap Lau, 2014. "Exploring the nexus between financial openness and informational efficiency -- does the quality of institution matter?," Applied Economics, Taylor & Francis Journals, vol. 46(7), pages 674-685, March.
    16. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2019. "Structural instability and predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    17. Banerjee, Ameet Kumar & Sensoy, Ahmet & Goodell, John W. & Mahapatra, Biplab, 2024. "Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period," Finance Research Letters, Elsevier, vol. 59(C).
    18. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    19. Mirzaee Ghazani, Majid & Khalili Araghi, Mansour, 2014. "Evaluation of the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the Tehran stock exchange," Research in International Business and Finance, Elsevier, vol. 32(C), pages 50-59.
    20. Asif, Raheel & Frömmel, Michael, 2022. "Testing Long memory in exchange rates and its implications for the adaptive market hypothesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:war:wpaper:2025-01. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jacek Rapacz (email available below). General contact details of provider: https://edirc.repec.org/data/fesuwpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.