IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwwuw/wuwp352.html
   My bibliography  Save this paper

Infection Risk at Work, Automatability, and Employment

Author

Listed:
  • Ana L. Abeliansky

    (Department of Economics, Vienna University of Economics and Business)

  • Klaus Prettner

    (Department of Economics, Vienna University of Economics and Business)

  • Roman Stoellinger

    (Department of Economics, Vienna University of Economics and Business)

Abstract

We propose a model of production featuring the trade-off between employing workers versus employing robots and analyze the extent to which this trade-off is altered by the emergence of a highly transmissible infectious disease. Since workers are - in contrast to robots - susceptible to pathogens and also spread them at the workplace, the emergence of a new infectious disease should reduce demand for human labor. According to the model, the reduction in labor demand concerns automatable occupations and increases with the viral transmission risk. We test the model's predictions using Austrian employment data over the period 2015-2021, during which the COVID-19 pandemic increased the infection risk at the workplace substantially. We find a negative effect on occupation-level employment emanating from the higher viral transmission risk in the COVID years. As predicted by the model, a reduction in employment is detectable for automatable occupations but not for non-automatable occupations.

Suggested Citation

  • Ana L. Abeliansky & Klaus Prettner & Roman Stoellinger, 2023. "Infection Risk at Work, Automatability, and Employment," Department of Economics Working Papers wuwp352, Vienna University of Economics and Business, Department of Economics.
    • Handle: RePEc:wiw:wiwwuw:wuwp352
    Note: PDF Document
    as

    Download full text from publisher

    File URL: https://research.wu.ac.at/ws/portalfiles/portal/59341742/WP352.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Fischer, Kai & Reade, J. James & Schmal, W. Benedikt, 2022. "What cannot be cured must be endured: The long-lasting effect of a COVID-19 infection on workplace productivity," Labour Economics, Elsevier, vol. 79(C).
    2. Alex Chernoff & Casey Warman, 2023. "COVID-19 and implications for automation," Applied Economics, Taylor & Francis Journals, vol. 55(17), pages 1939-1957, April.
    3. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    4. Sara Flisi & Giulia Santangelo, 2022. "Occupations in the European Labour Market During the COVID-19 Pandemic," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(2), pages 120-126, March.
    5. Pichler, Stefan & Ziebarth, Nicolas R., 2017. "The pros and cons of sick pay schemes: Testing for contagious presenteeism and noncontagious absenteeism behavior," Journal of Public Economics, Elsevier, vol. 156(C), pages 14-33.
    6. Krenz, Astrid & Prettner, Klaus & Strulik, Holger, 2021. "Robots, reshoring, and the lot of low-skilled workers," European Economic Review, Elsevier, vol. 136(C).
    7. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    8. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    9. Anton Korinek & Joseph E. Stiglitz, 2021. "Artificial Intelligence, Globalization, and Strategies for Economic Development," NBER Working Papers 28453, National Bureau of Economic Research, Inc.
    10. David Hémous & Morten Olsen, 2022. "The Rise of the Machines: Automation, Horizontal Innovation, and Income Inequality," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 179-223, January.
    11. David E. Bloom & Michael Kuhn & Klaus Prettner, 2022. "Modern Infectious Diseases: Macroeconomic Impacts and Policy Responses," Journal of Economic Literature, American Economic Association, vol. 60(1), pages 85-131, March.
    12. de Vries, Gaaitzen J. & Gentile, Elisabetta & Miroudot, Sébastien & Wacker, Konstantin M., 2020. "The rise of robots and the fall of routine jobs," Labour Economics, Elsevier, vol. 66(C).
    13. Daron Acemoglu & Pascual Restrepo, 2022. "Demographics and Automation [Automation and Demographic Change]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(1), pages 1-44.
    14. Anthony Barr & Darlene Booth-Bell & Kristen Broady & Ryan Perry, 2023. "The Covid-19 Pandemic Spurred Growth in Automation: What Does this Mean for Minority Workers?," Working Paper Series WP 2023-06, Federal Reserve Bank of Chicago.
    15. Prettner, Klaus & Strulik, Holger, 2020. "Innovation, automation, and inequality: Policy challenges in the race against the machine," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 249-265.
    16. Porto, Edoardo Di & Naticchioni, Paolo & Scrutinio, Vincenzo, 2022. "Lockdown, essential sectors, and Covid-19: Lessons from Italy," Journal of Health Economics, Elsevier, vol. 81(C).
    17. Blanas, Sotiris & Oikonomou, Rigas, 2023. "COVID-induced economic uncertainty, tasks and occupational demand," Labour Economics, Elsevier, vol. 81(C).
    18. Arntz, Melanie & Gregory, Terry & Zierahn, Ulrich, 2017. "Revisiting the risk of automation," Economics Letters, Elsevier, vol. 159(C), pages 157-160.
    19. 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.
    20. Houštecká, Anna & Koh, Dongya & Santaeulàlia-Llopis, Raül, 2021. "Contagion at work: Occupations, industries and human contact," Journal of Public Economics, Elsevier, vol. 200(C).
    21. Jones, Charles I, 1995. "R&D-Based Models of Economic Growth," Journal of Political Economy, University of Chicago Press, vol. 103(4), pages 759-784, August.
    22. 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.
    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. 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.
    2. Prettner, Klaus, 2023. "Stagnant wages in the face of rising labor productivity: The potential role of industrial robots," Finance Research Letters, Elsevier, vol. 58(PD).
    3. 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.
    4. 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.
    5. David E. Bloom & Klaus Prettner & Jamel Saadaoui & Mario Veruete, 2023. "Artificial intelligence and the skill premium," Department of Economics Working Papers wuwp353, Vienna University of Economics and Business, Department of Economics.
    6. Franziska Brall & Ramona Schmid, 2023. "Automation, robots and wage inequality in Germany: A decomposition analysis," LABOUR, CEIS, vol. 37(1), pages 33-95, March.
    7. Jurkat, Anne & Klump, Rainer & Schneider, Florian, 2023. "Robots and Wages: A Meta-Analysis," EconStor Preprints 274156, ZBW - Leibniz Information Centre for Economics.
    8. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.
    9. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    10. Krenz, Astrid & Strulik, Holger, 2022. "Automation and the Fall and Rise of the Servant Economy," VfS Annual Conference 2022 (Basel): Big Data in Economics 264034, Verein für Socialpolitik / German Economic Association.
    11. Stähler, Nikolai, 2021. "The Impact of Aging and Automation on the Macroeconomy and Inequality," Journal of Macroeconomics, Elsevier, vol. 67(C).
    12. 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.
    13. Arthur Jacobs & Freddy Heylen, 2021. "Demographic change, secular stagnation and inequality: automation as a blessing?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1030, Ghent University, Faculty of Economics and Business Administration.
    14. Röser, Florian & Niemann, Stefan & Angelini, Daniele, 2023. "Fiscal Policy and Human Capital in the Race Against the Machine," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277672, Verein für Socialpolitik / German Economic Association.
    15. Basso, Henrique S. & Jimeno, Juan F., 2021. "From secular stagnation to robocalypse? Implications of demographic and technological changes," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 833-847.
    16. Gries, Thomas & Naudé, Wim, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 56, pages 1-12.
    17. Hémous, David & Dechezleprêtre, Antoine & Olsen, Morten & Zanella, carlo, 2019. "Automating Labor: Evidence from Firm-level Patent Data," CEPR Discussion Papers 14249, C.E.P.R. Discussion Papers.
    18. David Autor & Caroline Chin & Anna M. Salomons & Bryan Seegmiller, 2022. "New Frontiers: The Origins and Content of New Work, 1940–2018," NBER Working Papers 30389, National Bureau of Economic Research, Inc.
    19. repec:hal:spmain:info:hdl:2441/7n49nkmngd8448a5ts5gt5ade0 is not listed on IDEAS
    20. Krenz, Astrid & Prettner, Klaus & Strulik, Holger, 2021. "Robots, reshoring, and the lot of low-skilled workers," European Economic Review, Elsevier, vol. 136(C).
    21. Arntz, Melanie & Gregory, Terry & Zierahn, Ulrich, 2019. "Digitalization and the Future of Work: Macroeconomic Consequences," IZA Discussion Papers 12428, Institute of Labor Economics (IZA).

    More about this item

    Keywords

    Automation; robots; pandemics; viral transmission risk; occupational employment; shadow cost of human labor;
    All these keywords.

    JEL classification:

    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J32 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Nonwage Labor Costs and Benefits; Retirement Plans; Private Pensions
    • 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:wiw:wiwwuw:wuwp352. 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: Department of Economics (email available below). General contact details of provider: http://www.wu.ac.at/economics/en .

    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.