IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2305.11900.html
   My bibliography  Save this paper

ChatGPT and the Labor Market: Unraveling the Effect of AI Discussions on Students' Earnings Expectations

Author

Listed:
  • Samir Huseynov

Abstract

This paper investigates the causal impact of negatively and positively toned ChatGPT Artificial Intelligence (AI) discussions on US students' anticipated labor market outcomes. Our findings reveal students reduce their confidence regarding their future earnings prospects after exposure to AI debates, and this effect is more pronounced after reading discussion excerpts with a negative tone. Unlike STEM majors, students in Non-STEM fields show asymmetric and pessimistic belief changes, suggesting that they might feel more vulnerable to emerging AI technologies. Pessimistic belief updates regarding future earnings are also prevalent among non-male students, indicating widespread AI concerns among vulnerable student subgroups. Educators, administrators, and policymakers may regularly engage with students to address their concerns and enhance educational curricula to better prepare them for a future that AI will inevitably shape.

Suggested Citation

  • Samir Huseynov, 2023. "ChatGPT and the Labor Market: Unraveling the Effect of AI Discussions on Students' Earnings Expectations," Papers 2305.11900, arXiv.org, revised Aug 2023.
    • Handle: RePEc:arx:papers:2305.11900
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2305.11900
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Christoph Drobner, 2022. "Motivated Beliefs and Anticipation of Uncertainty Resolution," American Economic Review: Insights, American Economic Association, vol. 4(1), pages 89-105, March.
    2. 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.
    3. Zarifhonarvar, Ali, 2023. "Economics of ChatGPT: A Labor Market View on the Occupational Impact of Artificial Intelligence," EconStor Preprints 268826, ZBW - Leibniz Information Centre for Economics.
    4. Kai Barron, 2021. "Belief updating: does the ‘good-news, bad-news’ asymmetry extend to purely financial domains?," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 31-58, March.
    5. Christoph Drobner & Sebastian J. Goerg, 2024. "Motivated Belief Updating and Rationalization of Information," Management Science, INFORMS, vol. 70(7), pages 4583-4592, July.
    6. Alexander Coutts, 2019. "Good news and bad news are still news: experimental evidence on belief updating," Experimental Economics, Springer;Economic Science Association, vol. 22(2), pages 369-395, June.
    7. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    8. Tyna Eloundou & Sam Manning & Pamela Mishkin & Daniel Rock, 2023. "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models," Papers 2303.10130, arXiv.org, revised Aug 2023.
    9. Andrea L. Eisfeldt & Gregor Schubert & Miao Ben Zhang, 2023. "Generative AI and Firm Values," NBER Working Papers 31222, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).

    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. Charlotte Cordes & Jana Friedrichsen & Simeon Schudy, 2023. "Motivated Procrastination," Rationality and Competition Discussion Paper Series 471, CRC TRR 190 Rationality and Competition.
    2. Castagnetti, Alessandro & Schmacker, Renke, 2022. "Protecting the ego: Motivated information selection and updating," European Economic Review, Elsevier, vol. 142(C).
    3. Barron, Kai & Becker, Anna & Huck, Steffen, 2025. "Motivated political reasoning: On the emergence of belief-value constellations," European Economic Review, Elsevier, vol. 172(C).
    4. Albanesi, Stefania & Dias da Silva, Antonio & Jimeno, Juan Francisco & Lamo, Ana & Wabitsch, Alena, 2023. "New Technologies and Jobs in Europe," CEPR Discussion Papers 18220, C.E.P.R. Discussion Papers.
    5. Ozge Demirci & Jonas Hannane & Xinrong Zhu, 2024. "Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms," CESifo Working Paper Series 11276, CESifo.
    6. Markus M. Möbius & Muriel Niederle & Paul Niehaus & Tanya S. Rosenblat, 2022. "Managing Self-Confidence: Theory and Experimental Evidence," Management Science, INFORMS, vol. 68(11), pages 7793-7817, November.
    7. Heluo, Yuxi & Fabel, Oliver, 2024. "Job computerization, occupational employment and wages: A comparative study of the United States, Germany, and Japan," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    8. Cavalan, Quentin & de Gardelle, Vincent & Vergnaud, Jean-Christophe, 2023. "No evidence of biased updating in beliefs about absolute performance: A replication and generalization of Grossman and Owens (2012)," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 530-548.
    9. Harry Moroz & Mariana Viollaz, 2024. "The Future of Work in Central America and the Dominican Republic," World Bank Publications - Reports 42043, The World Bank Group.
    10. Cavalan, Quentin & de Gardelle, Vincent & Vergnaud, Jean-Christophe, 2022. "I did most of the work! Three sources of bias in bargaining with joint production," Journal of Economic Psychology, Elsevier, vol. 93(C).
    11. Enrico Maria Fenoaltea & Dario Mazzilli & Aurelio Patelli & Angelica Sbardella & Andrea Tacchella & Andrea Zaccaria & Marco Trombetti & Luciano Pietronero, 2024. "Follow the money: a startup-based measure of AI exposure across occupations, industries and regions," Papers 2412.04924, arXiv.org, revised Dec 2024.
    12. Gordon H. Hanson, 2021. "Immigration and Regional Specialization in AI," NBER Working Papers 28671, National Bureau of Economic Research, Inc.
    13. Banerjee, Ritwik & Gupta, Nabanita Datta & Villeval, Marie Claire, 2020. "Feedback spillovers across tasks, self-confidence and competitiveness," Games and Economic Behavior, Elsevier, vol. 123(C), pages 127-170.
    14. Michael Thaler, 2024. "The Fake News Effect: Experimentally Identifying Motivated Reasoning Using Trust in News," American Economic Journal: Microeconomics, American Economic Association, vol. 16(2), pages 1-38, May.
    15. Dzung Bui & Lena Draeger & Bernd Hayo & Giang NghiemŸ, 2020. "Consumer Sentiment During the COVID-19 Pandemic: The Role of Others' Beliefs," MAGKS Papers on Economics 202049, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    16. Hagenbach, Jeanne & ,, 2022. "Motivated Skepticism," CEPR Discussion Papers 17478, C.E.P.R. Discussion Papers.
    17. Kai Barron, 2021. "Belief updating: does the ‘good-news, bad-news’ asymmetry extend to purely financial domains?," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 31-58, March.
    18. Michael Thaler, 2021. "The Supply of Motivated Beliefs," Papers 2111.06062, arXiv.org, revised Sep 2023.
    19. Aksoy, Cevat Giray & Özcan, Berkay & Philipp, Julia, 2021. "Robots and the gender pay gap in Europe," European Economic Review, Elsevier, vol. 134(C).
    20. Jeanne Hagenbach & Nicolas Jacquemet & Philipp Sternal, 2024. "The motivated memory of noise," Documents de travail du Centre d'Economie de la Sorbonne 24010, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

    More about this item

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

    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:arx:papers:2305.11900. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.