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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

    () (Doctoral School of Operations Management and Logistics
    Vienna University of Economics and Business, Institute for Public Sector Economics
    Lauder Business School)

  • Alexander Rabas

    (Doctoral School of Operations Management and Logistics)

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 our experiment, we find that revealed preferences differ under various versions of MPLs as well as yield unstable results within a 30-minute time frame. We determine the most stable elicitation method with the highest forecast accuracy by using multiple measures of within-method consistency and by using behavior in two economically relevant games as benchmarks. A derivation of the well-known method by Holt and Laury (American Economic Review 92(5):1644–1655, 2002), where the highest payoff is varied instead of probabilities, emerges as the best MPL method in both dimensions. As we pinpoint each MPL characteristic’s effect on the revealed preference and its consistency, our results have implications for preference elicitation procedures in general.

Suggested Citation

  • Tamás Csermely & Alexander Rabas, 2016. "How to reveal people’s preferences: Comparing time consistency and predictive power of multiple price list risk elicitation methods," Journal of Risk and Uncertainty, Springer, vol. 53(2), pages 107-136, December.
  • Handle: RePEc:kap:jrisku:v:53:y:2016:i:2:d:10.1007_s11166-016-9247-6
    DOI: 10.1007/s11166-016-9247-6
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    More about this item

    Keywords

    Risk; Multiple price list; MPL; Revealed preferences; Risk preference elicitation methods;
    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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