IDEAS home Printed from https://ideas.repec.org/a/kap/expeco/v26y2023i2d10.1007_s10683-022-09751-0.html
   My bibliography  Save this article

Behavioral welfare economics and risk preferences: a Bayesian approach

Author

Listed:
  • Xiaoxue Sherry Gao

    (University of Massachusetts Amherst)

  • Glenn W. Harrison

    (Robinson College of Business, Georgia State University
    University of Cape Town)

  • Rusty Tchernis

    (Georgia State University)

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, 2023. "Behavioral welfare economics and risk preferences: a Bayesian approach," Experimental Economics, Springer;Economic Science Association, vol. 26(2), pages 273-303, April.
    • Handle: RePEc:kap:expeco:v:26:y:2023:i:2:d:10.1007_s10683-022-09751-0
    DOI: 10.1007/s10683-022-09751-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10683-022-09751-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10683-022-09751-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. A. Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Shami & Alexander Teytelboym, 2020. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," CSAE Working Paper Series 2020-20, Centre for the Study of African Economies, University of Oxford.
    2. 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.
    3. 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.
    4. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    5. Yaari, Menahem E, 1987. "The Dual Theory of Choice under Risk," Econometrica, Econometric Society, vol. 55(1), pages 95-115, January.
    6. Regier, Dean A. & Ryan, Mandy & Phimister, Euan & Marra, Carlo A., 2009. "Bayesian and classical estimation of mixed logit: An application to genetic testing," Journal of Health Economics, Elsevier, vol. 28(3), pages 598-610, May.
    7. Jonathan Chapman & Erik Snowberg & Stephanie Wang & Colin Camerer, 2018. "Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)," NBER Working Papers 25072, National Bureau of Economic Research, Inc.
    8. 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.
    9. 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.
    10. 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.
    11. Daniel R. Cavagnaro & Richard Gonzalez & Jay I. Myung & Mark A. Pitt, 2013. "Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach," Management Science, INFORMS, vol. 59(2), pages 358-375, February.
    12. Harrison, Glenn W, 1990. "Risk Attitudes in First-Price Auction Experiments: A Bayesian Analysis," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 541-546, August.
    13. 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.
    14. Glenn W. Harrison & Jia Min Ng, 2018. "Welfare effects of insurance contract non-performance," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(1), pages 39-76, May.
    15. Quiggin, John, 1982. "A theory of anticipated utility," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 323-343, December.
    16. 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.
    17. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    18. 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.
    19. 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.
    20. Maximilian Kasy & Anja Sautmann, 2021. "Adaptive Treatment Assignment in Experiments for Policy Choice," Econometrica, Econometric Society, vol. 89(1), pages 113-132, January.
    21. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    22. 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.
    23. Glenn W. Harrison & Don Ross, 2018. "Varieties of paternalism and the heterogeneity of utility structures," Journal of Economic Methodology, Taylor & Francis Journals, vol. 25(1), pages 42-67, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Kpegli, Yao Thibaut & Corgnet, Brice & Zylbersztejn, Adam, 2023. "All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components," Journal of Mathematical Economics, Elsevier, vol. 104(C).
    3. Glenn W. Harrison & Jia Min Ng, 2019. "Behavioral insurance and economic theory: A literature review," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 22(2), pages 133-182, July.
    4. Fidanoski, Filip & Johnson, Timothy, 2023. "A z-Tree implementation of the Dynamic Experiments for Estimating Preferences [DEEP] method," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    5. Glenn W. Harrison, 2019. "The behavioral welfare economics of insurance," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 44(2), pages 137-175, September.
    6. Andersen, Steffen & Harrison, Glenn W. & Lau, Morten Igel & Rutström, Elisabet E., 2010. "Behavioral econometrics for psychologists," Journal of Economic Psychology, Elsevier, vol. 31(4), pages 553-576, August.
    7. Bocquého, Géraldine & Deschamps, Marc & Helstroffer, Jenny & Jacob, Julien & Joxhe, Majlinda, 2023. "Modelling refugee migration under cognitive biases: Experimental evidence and policy," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 103(C).
    8. Holden, Stein T. & Tilahun, Mesfin & Sommervoll, Dag Einar, 2022. "Is diminishing impatience in time-dated risky prospects explained by probability weighting?," CLTS Working Papers 3/22, Norwegian University of Life Sciences, Centre for Land Tenure Studies.
    9. 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.
    10. Glenn W. Harrison & Morten I. Lau & Hong Il Yoo, 2020. "Risk Attitudes, Sample Selection, and Attrition in a Longitudinal Field Experiment," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 552-568, July.
    11. Glenn W. Harrison & J. Todd Swarthout, 2019. "Eye-tracking and economic theories of choice under risk," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 26-37, August.
    12. Geng, Kexin & Wang, Yacan & Cherchi, Elisabetta & Guarda, Pablo, 2023. "Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    13. Glenn W. Harrison & Andre Hofmeyr & Don Ross & J. Todd Swarthout, 2018. "Risk Preferences, Time Preferences, and Smoking Behavior," Southern Economic Journal, John Wiley & Sons, vol. 85(2), pages 313-348, October.
    14. Andreas C. Drichoutis & Rodolfo M. Nayga, 2022. "On the stability of risk and time preferences amid the COVID-19 pandemic," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 759-794, June.
    15. Glenn W. Harrison & Jia Min Ng, 2018. "Welfare effects of insurance contract non-performance," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(1), pages 39-76, May.
    16. Wilcox, Nathaniel T., 2011. "'Stochastically more risk averse:' A contextual theory of stochastic discrete choice under risk," Journal of Econometrics, Elsevier, vol. 162(1), pages 89-104, May.
    17. Géraldine Bocquého & Julien Jacob & Marielle Brunette, 2023. "Prospect theory in multiple price list experiments: further insights on behaviour in the loss domain," Theory and Decision, Springer, vol. 94(4), pages 593-636, May.
    18. Filiz-Ozbay, Emel & Guryan, Jonathan & Hyndman, Kyle & Kearney, Melissa & Ozbay, Erkut Y., 2015. "Do lottery payments induce savings behavior? Evidence from the lab," Journal of Public Economics, Elsevier, vol. 126(C), pages 1-24.
    19. Gauriot, Romain & Heger, Stephanie A. & Slonim, Robert, 2020. "Altruism or diminishing marginal utility?," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 24-48.
    20. Holden, Stein T. & Tilahun, Mesfin, 2019. "How related are risk preferences and time preferences?," CLTS Working Papers 4/19, Norwegian University of Life Sciences, Centre for Land Tenure Studies, revised 16 Oct 2019.

    More about this item

    Keywords

    Behavioral welfare economics; Bayesian analysis; Risk preferences; Insurance;
    All these keywords.

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:expeco:v:26:y:2023:i:2:d:10.1007_s10683-022-09751-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.