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How to reveal people's preferences: Comparing time consistency and predictive power of multiple price list risk elicitation methods

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  • Tamás Csermely

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

  • Alexander Rabas

    (Department of Economics, University of Vienna)

Abstract

The question of how to measure and classify people’s risk preferences is of substantial importance in the field of Economics. Inspired by the multitude of ways used to elicit risk preferences, we conduct a holistic investigation of the most prevalent method, the multiple price list (MPL) and its derivations. In accordance with previous literature, we find that revealed preferences differ under various and even the same versions of the MPL. Thus, an arbitrary selection of a particular risk assessment method can lead to biased results especially if researchers investigate its connection to other phenomena. In order to resolve this issue, we determine the most stable version of the MPL by using multiple measures of within-method consistency, and the version with the highest forecast accuracy by using behavior in two economically relevant games as benchmarks. A derivation of the well-known method by Holt and Laury (2002), where the highest payoff is varied instead of probabilities, emerges as the best MPL method in both dimensions.

Suggested Citation

  • Tamás Csermely & Alexander Rabas, 2014. "How to reveal people's preferences: Comparing time consistency and predictive power of multiple price list risk elicitation methods," Department of Economics Working Papers wuwp185, Vienna University of Economics and Business, Department of Economics.
    • Handle: RePEc:wiw:wiwwuw:wuwp185
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    References listed on IDEAS

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    1. Hans-Martin Gaudecker & Arthur Soest & Erik Wengström, 2012. "Experts in experiments," Journal of Risk and Uncertainty, Springer, vol. 45(2), pages 159-190, October.
    2. Peter P. Wakker, 2008. "Explaining the characteristics of the power (CRRA) utility family," Health Economics, John Wiley & Sons, Ltd., vol. 17(12), pages 1329-1344, December.
    3. Walker, James M. & Smith, Vernon L. & Cox, James C., 1987. "Bidding behavior in first price sealed bid auctions : Use of computerized Nash competitors," Economics Letters, Elsevier, vol. 23(3), pages 239-244.
    4. Wakker,Peter P., 2010. "Prospect Theory," Cambridge Books, Cambridge University Press, number 9780521765015, November.
    5. von Gaudecker, H.M. & van Soest, A.H.O. & Wengstrom, E., 2008. "Selection and Mode Effects in Risk Preference Elicitation Experiments," Discussion Paper 2008-11, Tilburg University, Center for Economic Research.
    6. Peter P. Wakker, 2008. "Explaining the characteristics of the power (CRRA) utility family," Health Economics, John Wiley & Sons, Ltd., vol. 17(12), pages 1329-1344.
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    Cited by:

    1. Paolo Crosetto & Antonio Filippin, 2016. "A theoretical and experimental appraisal of four risk elicitation methods," Experimental Economics, Springer;Economic Science Association, vol. 19(3), pages 613-641, September.

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    More about this item

    Keywords

    Risk; MPL; Experiment; Revealed Preferences;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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