IDEAS home Printed from https://ideas.repec.org/a/eee/jeborg/v162y2019icp106-119.html
   My bibliography  Save this article

Estimating parametric loss aversion with prospect theory: Recognising and dealing with size dependence

Author

Listed:
  • Balcombe, Kelvin
  • Bardsley, Nicholas
  • Dadzie, Sam
  • Fraser, Iain

Abstract

Parameteric identification of loss aversion requires either the imposition of rotational symmetry on the utility function or a point dependent normalization condition. In this paper, we propose a new approach in which point dependence is reduced by integration over normalization points. To illustrate our approach, we consider a sample of Ghanaian farmers’ risk preferences over the gain, loss and mixed domains. Using Bayesian econometric methods, we find support for Prospect Theory albeit with substantial behavioral variation across individuals plus mild overweighting of losses compared to gains. We also show that the majority of respondents are mildly loss averse especially as the size of the payoffs increase.

Suggested Citation

  • Balcombe, Kelvin & Bardsley, Nicholas & Dadzie, Sam & Fraser, Iain, 2019. "Estimating parametric loss aversion with prospect theory: Recognising and dealing with size dependence," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 106-119.
  • Handle: RePEc:eee:jeborg:v:162:y:2019:i:c:p:106-119
    DOI: 10.1016/j.jebo.2019.04.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167268119301209
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Glenn W. Harrison & J. Todd Swarthout, 2016. "Cumulative Prospect Theory in the Laboratory: A Reconsideration," Experimental Economics Center Working Paper Series 2016-04, Experimental Economics Center, Andrew Young School of Policy Studies, Georgia State University.
    2. 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.
    3. 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.
    4. Horst Zank, 2010. "On probabilities and loss aversion," Theory and Decision, Springer, vol. 68(3), pages 243-261, March.
    5. 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.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    7. Kelvin Balcombe & Iain Fraser, 2015. "Parametric preference functionals under risk in the gain domain: A Bayesian analysis," Journal of Risk and Uncertainty, Springer, vol. 50(2), pages 161-187, April.
    8. Mohammed Abdellaoui & Han Bleichrodt & Corina Paraschiv, 2007. "Loss Aversion Under Prospect Theory: A Parameter-Free Measurement," Management Science, INFORMS, vol. 53(10), pages 1659-1674, October.
    9. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    10. Chateauneuf, Alain & Wakker, Peter, 1999. "An Axiomatization of Cumulative Prospect Theory for Decision under Risk," Journal of Risk and Uncertainty, Springer, vol. 18(2), pages 137-145, August.
    11. Patrick S. Ward & Vartika Singh, 2015. "Using Field Experiments to Elicit Risk and Ambiguity Preferences: Behavioural Factors and the Adoption of New Agricultural Technologies in Rural India," Journal of Development Studies, Taylor & Francis Journals, vol. 51(6), pages 707-724, June.
    12. Peter P. Wakker, 2008. "Explaining the characteristics of the power (CRRA) utility family," Health Economics, John Wiley & Sons, Ltd., vol. 17(12), pages 1329-1344.
    13. Kobberling, Veronika & Wakker, Peter P., 2005. "An index of loss aversion," Journal of Economic Theory, Elsevier, vol. 122(1), pages 119-131, May.
    14. Henry Stott, 2006. "Cumulative prospect theory's functional menagerie," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 101-130, March.
    15. Tomomi Tanaka & Colin F. Camerer & Quang Nguyen, 2010. "Risk and Time Preferences: Linking Experimental and Household Survey Data from Vietnam," American Economic Review, American Economic Association, vol. 100(1), pages 557-571, March.
    16. Liu, Elaine M. & Huang, JiKun, 2013. "Risk preferences and pesticide use by cotton farmers in China," Journal of Development Economics, Elsevier, vol. 103(C), pages 202-215.
    17. Eyal Ert & Ido Erev, 2013. "On the descriptive value of loss aversion in decisions under risk: Six clarifications," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 8(3), pages 214-235, May.
    18. Wakker, Peter & Tversky, Amos, 1993. "An Axiomatization of Cumulative Prospect Theory," Journal of Risk and Uncertainty, Springer, vol. 7(2), pages 147-175, October.
    19. Schmidt, Ulrich & Traub, Stefan, 2002. "An Experimental Test of Loss Aversion," Journal of Risk and Uncertainty, Springer, vol. 25(3), pages 233-249, November.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Prospect theory; Loss aversion; Hierachical Bayes methods;

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services

    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:eee:jeborg:v:162:y:2019:i:c:p:106-119. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/jebo .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.