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Measuring risk aversion and the wealth effect

In: Risk Aversion in Experiments

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  • Frank Heinemann

Abstract

Measuring risk aversion is sensitive to assumptions about the wealth in subjects’ utility functions. Data from the same subjects in low- and high-stake lottery decisions allow estimating the wealth in a pre-specified one-parameter utility function simultaneously with risk aversion. This paper first shows how wealth estimates can be identified assuming constant relative risk aversion (CRRA). Using the data from a recent experiment by Holt and Laury (2002a), it is shown that most subjects’ behavior is consistent with CRRA at some wealth level. However, for realistic wealth levels most subjects’ behavior implies a decreasing relative risk aversion. An alternative explanation is that subjects do not fully integrate their wealth with income from the experiment. Within-subject data do not allow discriminating between the two hypotheses. Using between-subject data, maximum-likelihood estimates of a hybrid utility function indicate that aggregate behavior can be described by expected utility from income rather than expected utility from final wealth and partial relative risk aversion is increasing in the scale of payoffs.

Suggested Citation

  • Frank Heinemann, 2008. "Measuring risk aversion and the wealth effect," Research in Experimental Economics, in: Risk Aversion in Experiments, pages 293-313, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:rexezz:s0193-2306(08)00005-7
    DOI: 10.1016/S0193-2306(08)00005-7
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    References listed on IDEAS

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    1. Matthew Rabin & Richard H. Thaler, 2013. "Anomalies: Risk aversion," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 27, pages 467-480, World Scientific Publishing Co. Pte. Ltd..
    2. Matthew Rabin, 2000. "Risk Aversion and Expected-Utility Theory: A Calibration Theorem," Econometrica, Econometric Society, vol. 68(5), pages 1281-1292, September.
    3. Uri Gneezy & Jan Potters, 1997. "An Experiment on Risk Taking and Evaluation Periods," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 631-645.
    4. Charles A. Holt & Susan K. Laury, 2002. "Risk Aversion and Incentive Effects," American Economic Review, American Economic Association, vol. 92(5), pages 1644-1655, December.
    5. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    6. Laura Schechter, 2007. "Risk aversion and expected-utility theory: A calibration exercise," Journal of Risk and Uncertainty, Springer, vol. 35(1), pages 67-76, August.
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    Cited by:

    1. Young-Il Kim & Jungmin Lee, 2012. "Estimating Risk Aversion Using Individual-Level Survey Data," Korean Economic Review, Korean Economic Association, vol. 28, pages 221-239.
    2. Johansson-Stenman, Olof, 2006. "A Note on the Risk Behavior and Death of Homo Economicus," Working Papers in Economics 221, University of Gothenburg, Department of Economics.
    3. Booij, Adam S. & van Praag, Bernard M.S., 2009. "A simultaneous approach to the estimation of risk aversion and the subjective time discount rate," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 374-388, May.
    4. Johansson-Stenman, Olof, 2010. "Risk aversion and expected utility of consumption over time," Games and Economic Behavior, Elsevier, vol. 68(1), pages 208-219, January.
    5. Richard Engelbrecht-Wiggans & Elena Katok, 2006. "E-sourcing in Procurement: Theory and Behavior in Reverse Auctions with Noncompetitive Contracts," Management Science, INFORMS, vol. 52(4), pages 581-596, April.

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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • 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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