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Behavioral Welfare Economics and Risk Preferences: A Bayesian Approach

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  • Xiaoxue Sherry Gao
  • Glenn W. Harrison
  • Rusty Tchernis

Abstract

We propose the use of Bayesian estimation of risk preferences of individuals for applications of behavioral welfare economics to evaluate observed choices that involve risk. Bayesian estimation provides more systematic control of the use of informative priors over inferences about risk preferences for each individual in a sample. We demonstrate that these methods make a difference to the rigorous normative evaluation of decisions in a case study of insurance purchases. We also show that hierarchical Bayesian methods can be used to infer welfare reliably and efficiently even with significantly reduced demands on the number of choices that each subject has to make. Finally, we illustrate the natural use of Bayesian methods in the adaptive evaluation of welfare.

Suggested Citation

  • Xiaoxue Sherry Gao & Glenn W. Harrison & Rusty Tchernis, 2020. "Behavioral Welfare Economics and Risk Preferences: A Bayesian Approach," NBER Working Papers 27685, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27685
    Note: CF EEE EH PE
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    References listed on IDEAS

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    6. Glenn W. Harrison & Jia Min Ng, 2016. "Evaluating The Expected Welfare Gain From Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(1), pages 91-120, January.
    7. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
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    11. Guido W. Imbens, 2021. "Statistical Significance, p-Values, and the Reporting of Uncertainty," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 157-174, Summer.
    12. Glenn W. Harrison & E. Elisabet Rutström, 2008. "Risk Aversion in the Laboratory," Research in Experimental Economics, in: Risk Aversion in Experiments, pages 41-196, Emerald Group Publishing Limited.
    13. Maximilian Kasy & Anja Sautmann, 2021. "Adaptive Treatment Assignment in Experiments for Policy Choice," Econometrica, Econometric Society, vol. 89(1), pages 113-132, January.
    14. A Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Osman Shami & Alexander Teytelboym, 2024. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," Journal of the European Economic Association, European Economic Association, vol. 22(2), pages 781-836.
    15. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    16. Glenn W. Harrison, 2011. "Experimental methods and the welfare evaluation of policy lotteries," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 38(3), pages 335-360, August.
    17. Olivier Toubia & Eric Johnson & Theodoros Evgeniou & Philippe Delquié, 2013. "Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters," Management Science, INFORMS, vol. 59(3), pages 613-640, June.
    18. Jonathan Chapman & Erik Snowberg & Stephanie Wang & Colin Camerer, 2018. "Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)," CESifo Working Paper Series 7262, CESifo.
    19. Glenn W. Harrison & Jia Min Ng, 2018. "Welfare effects of insurance contract non-performance," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(1), pages 39-76, May.
    20. Daniel Cavagnaro & Mark Pitt & Richard Gonzalez & Jay Myung, 2013. "Discriminating among probability weighting functions using adaptive design optimization," Journal of Risk and Uncertainty, Springer, vol. 47(3), pages 255-289, December.
    21. Steffen Andersen & Glenn W. Harrison & Morten I. Lau & E. Elisabet Rutström, 2008. "Eliciting Risk and Time Preferences," Econometrica, Econometric Society, vol. 76(3), pages 583-618, May.
    22. Steffen Andersen & Glenn W. Harrison & Morten I. Lau & E. Elisabet Rutström, 2018. "Multiattribute Utility Theory, Intertemporal Utility, And Correlation Aversion," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 537-555, May.
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    24. Glenn Harrison & E. Rutström, 2009. "Expected utility theory and prospect theory: one wedding and a decent funeral," Experimental Economics, Springer;Economic Science Association, vol. 12(2), pages 133-158, June.
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    Cited by:

    1. Glenn W. Harrison & Andre Hofmeyr & Harold Kincaid & Brian Monroe & Don Ross & Mark Schneider & J. Todd Swarthout, 2022. "Subjective beliefs and economic preferences during the COVID-19 pandemic," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 795-823, June.
    2. Harrison, Glenn W. & Kairies-Schwarz, Nadja & Han, Johann, 2024. "Deductibles and health care utilization: An experiment on the role of forward-looking behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 224(C), pages 717-748.
    3. Glenn W. Harrison, 2024. "Risk preferences and risk perceptions in insurance experiments: some methodological challenges," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 49(1), pages 127-161, March.
    4. Robert Finger & Viviana Garcia & Chloe McCallum & Jens Rommel, 2024. "A note on European farmers' preferences under cumulative prospect theory," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 465-472, February.
    5. Patrick Schmidt, 2025. "Eliciting Ambiguity with Mixing Bets," American Economic Journal: Microeconomics, American Economic Association, vol. 17(1), pages 354-388, February.
    6. Glenn Harrison & Karlijn Morsink & Mark Schneider, 2022. "Literacy and the quality of index insurance decisions," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 47(1), pages 66-97, March.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D6 - Microeconomics - - Welfare Economics
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G40 - Financial Economics - - Behavioral Finance - - - General

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