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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    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.
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    17. Horst Zank, 2010. "On probabilities and loss aversion," Theory and Decision, Springer, vol. 68(3), pages 243-261, March.
    18. 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.
    19. Kobberling, Veronika & Wakker, Peter P., 2005. "An index of loss aversion," Journal of Economic Theory, Elsevier, vol. 122(1), pages 119-131, May.
    20. repec:cup:judgdm:v:8:y:2013:i:3:p:214-235 is not listed on IDEAS
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    Cited by:

    1. Toritseju Begho & Kelvin Balcombe, 2023. "Attitudes to Risk and Uncertainty: New Insights From an Experiment Using Interval Prospects," SAGE Open, , vol. 13(3), pages 21582440231, July.
    2. Otto, Philipp E. & Schmidt, Lennard, 2021. "Reservation price uncertainty: Loss, virtue, or emotional heterogeneity?," Journal of Economic Psychology, Elsevier, vol. 87(C).
    3. Liu, Zhenya & Zhan, Yaosong, 2022. "Investor behavior and filter rule revisiting," Journal of Behavioral and Experimental Finance, Elsevier, vol. 33(C).
    4. Balcombe, Kelvin & Fraser, Iain, 2024. "A Note on an Alternative Approach to Experimental Design of Lottery Prospects," MPRA Paper 119743, University Library of Munich, Germany.
    5. Lei Chen & Ying-Ming Wang & Yan Huang, 2020. "Cross-efficiency aggregation method based on prospect consensus process," Annals of Operations Research, Springer, vol. 288(1), pages 115-135, May.
    6. Johannes G. Jaspersen & Marc A. Ragin & Justin R. Sydnor, 2019. "Predicting Insurance Demand from Risk Attitudes," NBER Working Papers 26508, National Bureau of Economic Research, Inc.
    7. Johannes G. Jaspersen & Marc A. Ragin & Justin R. Sydnor, 2022. "Predicting insurance demand from risk attitudes," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(1), pages 63-96, March.
    8. Nilanjan Dutta & Arshinder Kaur, 2023. "Enabling socially responsible operations: A decision-making model for a firm contracting with decision-biased smallholders," Annals of Operations Research, Springer, vol. 320(1), pages 509-533, January.

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    More about this item

    Keywords

    Prospect theory; Loss aversion; Hierachical Bayes methods;
    All these keywords.

    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

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