IDEAS home Printed from https://ideas.repec.org/p/iso/educat/0213.html
   My bibliography  Save this paper

How Scary is the Risk of Automation? Evidence from a Large Survey Experiment

Author

Listed:
  • Maria A. Cattaneo
  • Christian Gschwendt
  • Stefan C. Wolter

Abstract

Advances in technology have always reshaped labor markets, increasing demand for highly skilled workers and automating human labor in many areas, leading to job creation but also losses. However, emerging AI innovations like ChatGPT may reduce labor demand in occupations previously considered "safe" from automation. While initial studies suggest that individuals adjust their educational and career choices to mitigate automation risk, the subjective monetary value of reduced automation risk is unknown. This study quantifies this value by assessing individuals' preferences for occupations for a hypothetical child in a discrete-choice experiment with almost 6'000 participants. The results show that survey respondents' willingness to accept lower wages for an occupation with a lower exposure to automation of 10 percentage points is substantial and amounts to almost 20 percent of an annual gross wage. Although the preferences are quite homogeneous, there are still some significant differences in willingness to pay between groups, with men, younger people, those with higher levels of education, and those with a higher risk tolerance showing a lower willingness to pay.

Suggested Citation

  • Maria A. Cattaneo & Christian Gschwendt & Stefan C. Wolter, 2024. "How Scary is the Risk of Automation? Evidence from a Large Survey Experiment," Economics of Education Working Paper Series 0213, University of Zurich, Department of Business Administration (IBW).
    • Handle: RePEc:iso:educat:0213
    as

    Download full text from publisher

    File URL: http://repec.business.uzh.ch/RePEc/iso/leadinghouse/0213_lhwpaper.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nir Jaimovich & Henry E. Siu, 2020. "Job Polarization and Jobless Recoveries," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 129-147, March.
    2. Cortes, Guido Matias & Jaimovich, Nir & Nekarda, Christopher J. & Siu, Henry E., 2020. "The dynamics of disappearing routine jobs: A flows approach," Labour Economics, Elsevier, vol. 65(C).
    3. Lawrence F. Katz & Kevin M. Murphy, 1992. "Changes in Relative Wages, 1963–1987: Supply and Demand Factors," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(1), pages 35-78.
    4. Maarten Goos & Alan Manning & Anna Salomons, 2009. "Job Polarization in Europe," American Economic Review, American Economic Association, vol. 99(2), pages 58-63, May.
    5. Golin, M. & Rauh, C., 2022. "The Impact of Fear of Automation," Janeway Institute Working Papers 2229, Faculty of Economics, University of Cambridge.
    6. Arne Hole & Julie Kolstad, 2012. "Mixed logit estimation of willingness to pay distributions: a comparison of models in preference and WTP space using data from a health-related choice experiment," Empirical Economics, Springer, vol. 42(2), pages 445-469, April.
    7. Philipp Lergetporer & Katharina Wedel & Katharina Werner, 2023. "Automatability of Occupations, Workers’ Labor-Market Expectations, and Willingness to Train," CESifo Working Paper Series 10862, CESifo.
    8. Jonas Tungodden & Alexander Willén, 2023. "When Parents Decide: Gender Differences in Competitiveness," Journal of Political Economy, University of Chicago Press, vol. 131(3), pages 751-801.
    9. 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.
    10. Lergetporer, Philipp & Wedel, Katharina & Werner, Katharina, 2023. "Automatability of Occupations, Workers' Labor-Market Expectations, and Willingness to Train," IZA Discussion Papers 16687, Institute of Labor Economics (IZA).
    11. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74(2), pages 132-132.
    12. Heß, Pascal & Janssen, Simon & Leber, Ute, 2023. "The effect of automation technology on workers’ training participation," Economics of Education Review, Elsevier, vol. 96(C).
    13. Philipp Lergetporer & Katharina Wedel & Katharina Werner, 2023. "Automatability of occupations, workers’ labor-market expectations, and willingness to train," Munich Papers in Political Economy 32, Munich School of Politics and Public Policy and the School of Management at the Technical University of Munich.
    14. Christian Gschwendt, 2022. "Routine job dynamics in the Swiss labor market," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-21, December.
    15. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    16. Xiang Hui & Oren Reshef & Luofeng Zhou, 2023. "The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market," CESifo Working Paper Series 10601, CESifo.
    17. Philipp Lergetporer & Katharina Wedel & Katharina Werner, 2023. "Automatability of Occupations, Workers' Labor-market Expectations, and Willingness to Train," Rationality and Competition Discussion Paper Series 488, CRC TRR 190 Rationality and Competition.
    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. James Harrigan & Ariell Reshef & Farid Toubal, 2016. "The March of the Techies: Technology, Trade, and Job Polarization in France, 1994-2007," Working Papers 2016-15, CEPII research center.
    2. Daniel Goller & Christian Gschwendt & Stefan C. Wolter, 2023. ""This time it's different" Generative Artificial Intelligence and Occupational Choice," Economics of Education Working Paper Series 0209, University of Zurich, Department of Business Administration (IBW).
    3. 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.
    4. Antonio Martins-Neto & Nanditha Mathew & Pierre Mohnen & Tania Treibich, 2021. "Is There Job Polarization in Developing Economies? A Review and Outlook," CESifo Working Paper Series 9444, CESifo.
    5. Gaggl, Paul & Kaufmann, Sylvia, 2020. "The cyclical component of labor market polarization and jobless recoveries in the US," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 334-347.
    6. Cortes, Guido Matias & Jaimovich, Nir & Siu, Henry E., 2017. "Disappearing routine jobs: Who, how, and why?," Journal of Monetary Economics, Elsevier, vol. 91(C), pages 69-87.
    7. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    8. Christian Gschwendt, 2022. "Routine job dynamics in the Swiss labor market," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-21, December.
    9. Chuan, Amanda & Zhang, Weilong, 2023. "Non-college Occupations, Workplace Routinization, and the Gender Gap in College Enrollment," IZA Discussion Papers 16089, Institute of Labor Economics (IZA).
    10. Chuan, A. & Zhang, W., 2021. "Non-College Occupations, Workplace Routinization, and the Gender Gap in College Enrollment," Cambridge Working Papers in Economics 2177, Faculty of Economics, University of Cambridge.
    11. Nicola Cassandro & Marco Centra & Dario Guarascio & Piero Esposito, 2021. "What drives employment–unemployment transitions? Evidence from Italian task-based data," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(3), pages 1109-1147, October.
    12. Carlos Medina & Christian Posso, 2010. "Technical Change and Polarization of the Labor Market: Evidence for Brazil, Colombia and Mexico," Borradores de Economia 614, Banco de la Republica de Colombia.
    13. David J. Deming, 2017. "The Growing Importance of Social Skills in the Labor Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 1593-1640.
    14. Maya Eden & Paul Gaggl, 2018. "On the Welfare Implications of Automation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 29, pages 15-43, July.
    15. Davide Consoli & Francesco Vona & Francesco Rentocchini, 2016. "That was then, this is now: skills and routinization in the 2000s," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 25(5), pages 847-866.
    16. Ross, Matthew B., 2017. "Routine-biased technical change: Panel evidence of task orientation and wage effects," Labour Economics, Elsevier, vol. 48(C), pages 198-214.
    17. Anders Frederiksen & Odile Poulsen, 2016. "Income Inequality: The Consequences Of Skill-Upgrading When Firms Have Hierarchical Organizational Structures," Economic Inquiry, Western Economic Association International, vol. 54(2), pages 1224-1239, April.
    18. Gaetano Basso, 2020. "The Evolution of the Occupational Structure in Italy, 2007–2017," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(2), pages 673-704, November.
    19. Christopher L. Foote & Richard W. Ryan, 2015. "Labor-Market Polarization over the Business Cycle," NBER Macroeconomics Annual, University of Chicago Press, vol. 29(1), pages 371-413.
    20. Ronald Bachmann & Merve Cim & Colin Green, 2019. "Long‐Run Patterns of Labour Market Polarization: Evidence from German Micro Data," British Journal of Industrial Relations, London School of Economics, vol. 57(2), pages 350-376, June.

    More about this item

    Keywords

    Artificial intelligence; automation; willingness to pay; survey experiment;
    All these keywords.

    JEL classification:

    • 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

    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:iso:educat:0213. 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: Sara Brunner (email available below). General contact details of provider: https://edirc.repec.org/data/isuzhch.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.