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Are CEOs expected utility maximizers?

Author

Listed:
  • List, John A.
  • Mason, Charles F.
Abstract
Are individuals expected utility maximizers? This question represents much more than academic curiosity. In a normative sense, at stake are the fundamental underpinnings of the bulk of the last half-century's models of choice under uncertainty. From a positive perspective, the ubiquitous use of benefit-cost analysis across government agencies renders the expected utility maximization paradigm literally the only game in town. In this study, we advance the literature by exploring CEO's preferences over small probability, high loss lotteries. Using undergraduate students as our experimental control group, we find that both our CEO and student subject pools exhibit frequent and large departures from expected utility theory. In addition, as the extreme payoffs become more likely CEOs exhibit greater aversion to risk. Our results suggest that use of the expected utility paradigm in decision making substantially underestimates society's willingness to pay to reduce risk in small probability, high loss events.

Suggested Citation

  • List, John A. & Mason, Charles F., 2011. "Are CEOs expected utility maximizers?," Journal of Econometrics, Elsevier, vol. 162(1), pages 114-123, May.
    • Handle: RePEc:eee:econom:v:162:y:2011:i:1:p:114-123
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    References listed on IDEAS

    as
    1. List, John A. & Mason, Charles F., 2011. "Are CEOs expected utility maximizers?," Journal of Econometrics, Elsevier, vol. 162(1), pages 114-123, May.
    2. Yaari, Menahem E, 1987. "The Dual Theory of Choice under Risk," Econometrica, Econometric Society, vol. 55(1), pages 95-115, January.
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    4. Michael S. Haigh & John A. List, 2005. "Do Professional Traders Exhibit Myopic Loss Aversion? An Experimental Analysis," Journal of Finance, American Finance Association, vol. 60(1), pages 523-534, February.
    5. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
    6. List John A., 2007. "Field Experiments: A Bridge between Lab and Naturally Occurring Data," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 6(2), pages 1-47, April.
    7. Ernst Fehr & John A. List, 2004. "The Hidden Costs and Returns of Incentives-Trust and Trustworthiness Among CEOs," Journal of the European Economic Association, MIT Press, vol. 2(5), pages 743-771, September.
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    12. Chris Starmer, 2000. "Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk," Journal of Economic Literature, American Economic Association, vol. 38(2), pages 332-382, June.
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    21. Charles Mason & Jason Shogren & Chad Settle & John List, 2005. "Investigating Risky Choices Over Losses Using Experimental Data," Journal of Risk and Uncertainty, Springer, vol. 31(2), pages 187-215, September.
    22. Steven D. Levitt & John A. List, 2007. "What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 153-174, Spring.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. John A. List, 2014. "Using Field Experiments to Change the Template of How We Teach Economics," The Journal of Economic Education, Taylor & Francis Journals, vol. 45(2), pages 81-89, June.
    2. Peter Gordon Roetzel & Burkhard Pedell & Daniel Groninger, 2020. "Information load in escalation situations: combustive agent or counteractive measure?," Journal of Business Economics, Springer, vol. 90(5), pages 757-786, June.
    3. Hallsworth, Michael & List, John A. & Metcalfe, Robert D. & Vlaev, Ivo, 2017. "The behavioralist as tax collector: Using natural field experiments to enhance tax compliance," Journal of Public Economics, Elsevier, vol. 148(C), pages 14-31.
    4. Pushkar Maitra & Ananta Neelim, 2021. "Behavioural characteristics, stability of preferences and entrepreneurial success," Chapters, in: Ananish Chaudhuri (ed.), A Research Agenda for Experimental Economics, chapter 5, pages 93-118, Edward Elgar Publishing.
    5. Luca Corazzini & Stefano Galavotti & Rupert Sausgruber & Paola Valbonesi, 2017. "Allotment in first-price auctions: an experimental investigation," Experimental Economics, Springer;Economic Science Association, vol. 20(1), pages 70-99, March.
    6. List, John A. & Mason, Charles F., 2011. "Are CEOs expected utility maximizers?," Journal of Econometrics, Elsevier, vol. 162(1), pages 114-123, May.
    7. Lefebvre, Mathieu & Vieider, Ferdinand M., 2014. "Risk taking of executives under different incentive contracts: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 97(C), pages 27-36.
    8. Dillon Bowen, 2022. "Simple models predict behavior at least as well as behavioral scientists," Papers 2208.01167, arXiv.org.
    9. Lata Gangadharan & Tarun Jain & Pushkar Maitra & Joe Vecci, 2022. "Lab-in-the-field experiments: perspectives from research on gender," The Japanese Economic Review, Springer, vol. 73(1), pages 31-59, January.
    10. Hakan J. Holm & Sonja Opper & Victor Nee, 2013. "Entrepreneurs Under Uncertainty: An Economic Experiment in China," Management Science, INFORMS, vol. 59(7), pages 1671-1687, July.
    11. Jeffrey Flory & Uri Gneezy & Kenneth Leonard & John List, 2012. "Sex, competitiveness, and investment in offspring: On the origin of preferences," Artefactual Field Experiments 00072, The Field Experiments Website.
    12. Matthew Gould & Matthew D. Rablen, 2019. "Are World Leaders Loss Averse?," CESifo Working Paper Series 7763, CESifo.
    13. Steffen Andersen & Amalia Girolamo & Glenn Harrison & Morten Lau, 2014. "Risk and time preferences of entrepreneurs: evidence from a Danish field experiment," Theory and Decision, Springer, vol. 77(3), pages 341-357, October.

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    More about this item

    Keywords

    Decision making under uncertainty High stakes Experiments;

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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