IDEAS home Printed from https://ideas.repec.org/p/hal/wpceem/hal-01947417.html
   My bibliography  Save this paper

On the role of probability weighting on WTP for crop insurance with and without yield skewness

Author

Listed:
  • Douadia Bougherara

    (CEE-M - Centre d'Economie de l'Environnement - Montpellier - FRE2010 - Montpellier SupAgro - Institut national d’études supérieures agronomiques de Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - INRA - Institut National de la Recherche Agronomique - CNRS - Centre National de la Recherche Scientifique - UM - Université de Montpellier)

  • Laurent Piet

    (SMART-LERECO - Structures et Marché Agricoles, Ressources et Territoires - INRA - Institut National de la Recherche Agronomique - AGROCAMPUS OUEST - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

Abstract

A growing number of studies in finance and economics seek to explain insurance choices using the assumptions advanced by behavioral economics. One recent example in agricultural economics is the use of cumulative prospect theory (CPT) to explain farmer choices regarding crop insurance coverage levels (Babcock, 2015). We build upon this framework by deriving willingness to pay (WTP) for insurance programs under alternative assumptions, thus extending the model to incorporate farmer decisions regarding whether or not to purchase insurance. Our contribution is twofold. First, we study the sensitivity of farmer WTP for crop insurance to the inclusion of CPT parameters. We find that loss aversion and probability distortion increase WTP for insurance while risk aversion decreases it. Probability distortion in losses plays a particularly important role. Second, we study the impact of yield distribution skewness on farmer WTP assuming CPT preferences. We find that WTP decreases when the distribution of yields moves from negatively- to positively-skewed and that the combined effect of probability weighting in losses and skewness has a large negative impact on farmer WTP for crop insurance.

Suggested Citation

  • Douadia Bougherara & Laurent Piet, 2018. "On the role of probability weighting on WTP for crop insurance with and without yield skewness," CEE-M Working Papers hal-01947417, CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro.
  • Handle: RePEc:hal:wpceem:hal-01947417
    Note: View the original document on HAL open archive server: https://hal.umontpellier.fr/hal-01947417
    as

    Download full text from publisher

    File URL: https://hal.umontpellier.fr/hal-01947417/document
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Levon Barseghyan & Francesca Molinari & Ted O'Donoghue & Joshua C. Teitelbaum, 2013. "The Nature of Risk Preferences: Evidence from Insurance Choices," American Economic Review, American Economic Association, vol. 103(6), pages 2499-2529, October.
    2. Douadia Bougherara & Xavier Gassmann & Laurent Piet & Arnaud Reynaud, 2017. "Structural estimation of farmers’ risk and ambiguity preferences: a field experiment," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 44(5), pages 782-808.
    3. Jerry R. Skees & J. Roy Black & Barry J. Barnett, 1997. "Designing and Rating an Area Yield Crop Insurance Contract," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 430-438.
    4. 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.
    5. Richard E. Just & Quinn Weninger, 1999. "Are Crop Yields Normally Distributed?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(2), pages 287-304.
    6. Bruce J. Sherrick & Fabio C. Zanini & Gary D. Schnitkey & Scott H. Irwin, 2004. "Crop Insurance Valuation under Alternative Yield Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 406-419.
    7. Quang Nguyen & Colin Camerer & Tomomi Tanaka, 2010. "Risk and Time Preferences Linking Experimental and Household Data from Vietnam," Post-Print halshs-00547090, HAL.
    8. Nguyen, Quang, 2010. "How nurture can shape preferences: an experimental study on risk preferences of Vietnamese fishers," Environment and Development Economics, Cambridge University Press, vol. 15(5), pages 609-631, October.
    9. Adelchi Azzalini, 2005. "The Skew‐normal Distribution and Related Multivariate Families," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(2), pages 159-188, June.
    10. Smith, Vincent H. & Chouinard, Hayley H. & Baquet, Alan E., 1994. "Almost Ideal Area Yield Crop Insurance Contracts," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 23(1), pages 1-9, April.
    11. David R. Just & Sivalai V. Khantachavana & Richard E. Just, 2010. "Empirical Challenges for Risk Preferences and Production," Annual Review of Resource Economics, Annual Reviews, vol. 2(1), pages 13-31, October.
    12. Jean-Marc Bourgeon & Robert G. Chambers, 2003. "Optimal Area-Yield Crop Insurance Reconsidered," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(3), pages 590-604.
    13. Douadia Bougherara & Xavier Gassmann & Laurent Piet & Arnaud Reynaud, 2017. "Corrigendum: Structural estimation of farmers’ risk and ambiguity preferences: a field experiment," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 44(5), pages 809-809.
    14. Géraldine Bocquého & Florence Jacquet & Arnaud Reynaud, 2014. "Expected utility or prospect theory maximisers? Assessing farmers' risk behaviour from field-experiment data," Post-Print hal-01000072, HAL.
    15. Bernardi, Mauro & Maruotti, Antonello & Petrella, Lea, 2012. "Skew mixture models for loss distributions: A Bayesian approach," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 617-623.
    16. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    17. Xiaodong Du & Hongli Feng & David A. Hennessy, 2017. "Rationality of Choices in Subsidized Crop Insurance Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(3), pages 732-756.
    18. 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.
    19. Keith H. Coble & Thomas O. Knight & Rulon D. Pope & Jeffery R. Williams, 1996. "Modeling Farm-Level Crop Insurance Demand with Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 439-447.
    20. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
    21. Géraldine Bocquého & Florence Jacquet & Arnaud Reynaud, 2014. "Expected utility or prospect theory maximisers? Assessing farmers' risk behaviour from field-experiment data," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 41(1), pages 135-172, February.
    22. Elaine M. Liu, 2013. "Time to Change What to Sow: Risk Preferences and Technology Adoption Decisions of Cotton Farmers in China," The Review of Economics and Statistics, MIT Press, vol. 95(4), pages 1386-1403, October.
    23. Octavio A. Ramirez & Sukant Misra & James Field, 2003. "Crop-Yield Distributions Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 108-120.
    24. Ardian Harri & Cumhur Erdem & Keith H. Coble & Thomas O. Knight, 2009. "Crop Yield Distributions: A Reconciliation of Previous Research and Statistical Tests for Normality," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 31(1), pages 163-182.
    25. Steven J. Humphrey & Arjan Verschoor, 2004. "Decision-making Under Risk among Small Farmers in East Uganda," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 13(1), pages 44-101, March.
    26. Laurence Ball & N. Gregory Mankiw, 1995. "Relative-Price Changes as Aggregate Supply Shocks," The Quarterly Journal of Economics, Oxford University Press, vol. 110(1), pages 161-193.
    27. Nicholas Barberis & Ming Huang, 2008. "Stocks as Lotteries: The Implications of Probability Weighting for Security Prices," American Economic Review, American Economic Association, vol. 98(5), pages 2066-2100, December.
    28. Koen Jochmans, 2017. "Two-Way Models for Gravity," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 478-485, July.
    29. Chen, Yi-Yi & Schmidt, Peter & Wang, Hung-Jen, 2014. "Consistent estimation of the fixed effects stochastic frontier model," Journal of Econometrics, Elsevier, vol. 181(2), pages 65-76.
    30. Bruce A. Babcock, 2015. "Using Cumulative Prospect Theory to Explain Anomalous Crop Insurance Coverage Choice," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(5), pages 1371-1384.
    31. Michael W. Robbins & T. Kirk White, 2011. "Farm Commodity Payments and Imputation in the Agricultural Resource Management Survey," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(2), pages 606-612.
    32. Eling, Martin, 2012. "Fitting insurance claims to skewed distributions: Are the skew-normal and skew-student good models?," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 239-248.
    33. Peat, Maurice & Svec, Jiri & Wang, Jue, 2014. "Reporting bias in incomplete information model," Economics Letters, Elsevier, vol. 123(1), pages 45-49.
    34. Jean-Paul Chavas & Robert G. Chambers & Rulon D. Pope, 2010. "Production Economics and Farm Management: a Century of Contributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(2), pages 356-375.
    35. Justin Sydnor, 2010. "(Over)insuring Modest Risks," American Economic Journal: Applied Economics, American Economic Association, vol. 2(4), pages 177-199, October.
    36. 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.
    37. Nicholas Barberis, 2013. "The Psychology of Tail Events: Progress and Challenges," American Economic Review, American Economic Association, vol. 103(3), pages 611-616, May.
    38. Nicholas C. Barberis, 2013. "Thirty Years of Prospect Theory in Economics: A Review and Assessment," Journal of Economic Perspectives, American Economic Association, vol. 27(1), pages 173-196, Winter.
    39. Mario J. Miranda, 1991. "Area-Yield Crop Insurance Reconsidered," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(2), pages 233-242.
    40. A. Azzalini & A. Capitanio, 1999. "Statistical applications of the multivariate skew normal distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 579-602.
    41. Koen Jochmans, 2017. "Two-Way Models for Gravity," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 478-485, July.
    42. Kim, Woo Chang & Fabozzi, Frank J. & Cheridito, Patrick & Fox, Charles, 2014. "Controlling portfolio skewness and kurtosis without directly optimizing third and fourth moments," Economics Letters, Elsevier, vol. 122(2), pages 154-158.
    43. Barry K. Goodwin & Vincent H. Smith, 2013. "What Harm Is Done By Subsidizing Crop Insurance?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(2), pages 489-497.
    44. Olivier Mahul, 1999. "Optimum Area Yield Crop Insurance," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(1), pages 75-82.
    45. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    46. Carl H. Nelson, 1990. "The Influence of Distributional Assumptions on the Calculation of Crop Insurance Premia," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 12(1), pages 71-78.
    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. Géraldine Bocquého & Julien Jacob & Marielle Brunette, 2020. "Prospect theory in experiments: behaviour in loss domain and framing effects," Working Papers hal-02987294, HAL.
    2. Doidge, Mary & Feng, Hongli & Hennessy, David A., 2018. "Farmers’ valuation of changes to crop insurance coverage level – a test of third generation prospect theory," 2018 Annual Meeting, August 5-7, Washington, D.C. 274478, Agricultural and Applied Economics Association.
    3. Fezzi, Carlo & Menapace, Luisa & Raffaelli, Roberta, 2021. "Estimating risk preferences integrating insurance choices with subjective beliefs," European Economic Review, Elsevier, vol. 135(C).
    4. Bontemps, Christophe & Bougherara, Douadia & Nauges, Céline, 2020. "Do Risk Preferences Really Matter? The Case of Pesticide Use in Agriculture," TSE Working Papers 20-1095, Toulouse School of Economics (TSE).
    5. Golo-Friedrich Bauermeister & Daniel Hermann & Oliver Musshoff, 2018. "Consistency of determined risk attitudes and probability weightings across different elicitation methods," Theory and Decision, Springer, vol. 84(4), pages 627-644, June.
    6. Bougherara, Douadia & Piet, Laurent, 2014. "The Impact of Farmers’ Risk Preferences on the Design of an Individual Yield Crop Insurance," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 183082, European Association of Agricultural Economists.
    7. Stephen G Dimmock & Roy Kouwenberg & Olivia S Mitchell & Kim Peijnenburg, 2021. "Household Portfolio Underdiversification and Probability Weighting: Evidence from the Field," Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4524-4563.
    8. Xiaodong Du & Hongli Feng & David A. Hennessy, 2017. "Rationality of Choices in Subsidized Crop Insurance Markets," American Journal of Agricultural Economics, John Wiley & Sons, vol. 99(3), pages 732-756, April.
    9. Shuoli Zhao & Chengyan Yue, 2020. "Risk preferences of commodity crop producers and specialty crop producers: An application of prospect theory," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 359-372, May.
    10. Lampe, Immanuel & Würtenberger, Daniel, 2020. "Loss aversion and the demand for index insurance," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 678-693.
    11. Thomas Sproul & Clayton P. Michaud, 2017. "Heterogeneity in loss aversion: evidence from field elicitations," Agricultural Finance Review, Emerald Group Publishing, vol. 77(1), pages 196-216, May.
    12. Douadia Bougherara & Lana Friesen & Céline Nauges, 2021. "Risk Taking with Left- and Right-Skewed Lotteries," Journal of Risk and Uncertainty, Springer, vol. 62(1), pages 89-112, February.
    13. Gary Charness & Thomas Garcia & Theo Offerman & Marie Claire Villeval, 2020. "Do measures of risk attitude in the laboratory predict behavior under risk in and outside of the laboratory?," Journal of Risk and Uncertainty, Springer, vol. 60(2), pages 99-123, April.
    14. Hongli Feng & Xiaodong Du & David A. Hennessy, 2020. "Depressed demand for crop insurance contracts, and a rationale based on third generation Prospect Theory," Agricultural Economics, International Association of Agricultural Economists, vol. 51(1), pages 59-73, January.
    15. Immanuel Lampe & Daniel Würtenberger, 2019. "Loss Aversion And The Demand For Index Insurance," Working Papers on Finance 1907, University of St. Gallen, School of Finance.
    16. Camille Tevenart & Marielle Brunette, 2021. "Role of Farmers’ Risk and Ambiguity Preferences on Fertilization Decisions: An Experiment," Sustainability, MDPI, vol. 13(17), pages 1-27, August.
    17. Thomas Epper & Helga Fehr-Duda, 2012. "The missing link: unifying risk taking and time discounting," ECON - Working Papers 096, Department of Economics - University of Zurich, revised Oct 2018.
    18. Heutel, Garth, 2019. "Prospect theory and energy efficiency," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 236-254.
    19. Galarza, Francisco, 2009. "Choices under Risk in Rural Peru," MPRA Paper 17708, University Library of Munich, Germany.
    20. Geraldine Bocquého & Marc Deschamps & Jenny Helstroffer & Julien Jacob & Majlinda Joxhe, 2018. "Risk and Refugee Migration," Working Papers of BETA 2018-16, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

    More about this item

    Keywords

    skewness; Crop Insurance; Cumulative Prospect Theory; premium subsidy;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hal:wpceem:hal-01947417. 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: . General contact details of provider: https://edirc.repec.org/data/lamplfr.html .

    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: Laurent Garnier (email available below). General contact details of provider: https://edirc.repec.org/data/lamplfr.html .

    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.