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A parametric analysis of prospect theory’s functionals for the general population

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  • Adam Booij
  • Bernard Praag
  • Gijs Kuilen

Abstract

This paper presents the results of an experiment that completely measures the utility function and probability weighting function for different positive and negative monetary outcomes, using a representative sample of N = 1935 from the general public. The results confirm earlier findings in the lab, suggesting that utility is less pronounced than what is found in classical measurements where expected utility is assumed. Utility for losses is found to be convex, consistent with diminishing sensitivity, and the obtained loss aversion coefficient of 1.6 is moderate but in agreement with contemporary evidence. The estimated probability weighing functions have an inverse-S shape and they imply pessimism in both domains. These results show that probability weighting is also an important phenomenon in the general population. Women and lower educated individuals are found to be more risk averse, in agreement with common findings. Unlike previous studies that ascribed gender differences in risk attitudes solely to differences in the degree utility curvature, however, our results show that this finding is primarily driven by loss aversion and, for women, also by a more pessimistic psychological response towards the probability of obtaining the best possible outcome.
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  • Adam Booij & Bernard Praag & Gijs Kuilen, 2010. "A parametric analysis of prospect theory’s functionals for the general population," Theory and Decision, Springer, vol. 68(1), pages 115-148, February.
    • Handle: RePEc:kap:theord:v:68:y:2010:i:1:p:115-148
    DOI: 10.1007/s11238-009-9144-4
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    More about this item

    Keywords

    Prospect theory; Utility for gains and losses; Loss aversion; Subjective probability weighting;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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