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Depressed demand for crop insurance contracts, and a rationale based on third generation Prospect Theory

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  • Hongli Feng
  • Xiaodong Du
  • David A. Hennessy

Abstract

When actuarially fair insurance for a major risk is available then standard economic theory posits that those subject to the risk should insure. In agriculture, it is common for producers to decline contract offers where presubsidy premiums just cover average losses and subsidies are substantial. This paper seeks to shed light on why demand is curtailed. In a mail survey of U.S. corn and soybean producers we solicited Willingness to Pay (WTP) for actuarially fair insurance at different coverage levels. We find demand to be so low that median WTP is no larger than fair premium when adjusted down by current subsidy rates, which pay for one half or more of most premium charges. WTP as a share of the fair rate is especially low when risk of loss is high. There is limited evidence that respondents appreciate the convex relationship between coverage level and expected indemnity payoff. Third generation Prospect Theory is shown to be consistent with observed findings. In particular, a strong distaste for paying premium can be rationalized by loss aversion. Furthermore, when high revenue outcomes are more likely than not, that is, negative skewness, then higher loss aversion, greater decision weight distortions and greater risk aversion will decrease WTP.

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  • 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.
  • Handle: RePEc:bla:agecon:v:51:y:2020:i:1:p:59-73
    DOI: 10.1111/agec.12541
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    2. Du, Xiaodong & Feng, Hongli & Hennessy, David A., 2021. "Implications of WTP-AFP Discrepancy, Premium Subsidy Reduction and Program Changes in U.S. Crop Insurance," 2021 Annual Meeting, August 1-3, Austin, Texas 313881, Agricultural and Applied Economics Association.
    3. Alexis H. Villacis & Jeffrey R. Alwang & Victor Barrera, 2021. "Linking risk preferences and risk perceptions of climate change: A prospect theory approach," Agricultural Economics, International Association of Agricultural Economists, vol. 52(5), pages 863-877, September.
    4. Paloch Suchato & Taro Mieno & Karina Schoengold & Timothy Foster, 2022. "The potential for moral hazard behavior in irrigation decisions under crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 53(2), pages 257-273, March.
    5. Ranjan Kumar Ghosh & Shweta Gupta & Vartika Singh & Patrick S. Ward, 2021. "Demand for Crop Insurance in Developing Countries: New Evidence from India," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(1), pages 293-320, February.
    6. Xuche Gong & David A. Hennessy & Hongli Feng, 2023. "Systemic risk, relative subsidy rates, and area yield insurance choice," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(3), pages 888-913, May.
    7. Stigler, Matthieu M. & Lobell, David, 2020. "Suitability of index insurance: new insights from satellite data," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304663, Agricultural and Applied Economics Association.
    8. Tobias Dalhaus & Barry J Barnett & Robert Finger, 2020. "Behavioral weather insurance: Applying cumulative prospect theory to agricultural insurance design under narrow framing," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-25, May.
    9. Sofia Kislingerová & Jindřich Špička, 2022. "Factors Influencing the Take-Up of Agricultural Insurance and the Entry into the Mutual Fund: A Case Study of the Czech Republic," JRFM, MDPI, vol. 15(8), pages 1-17, August.
    10. Matthieu Stigler & David Lobell, 2020. "On the benefits of index insurance in US agriculture: a large-scale analysis using satellite data," Papers 2011.12544, arXiv.org, revised Nov 2021.
    11. Luigi Biagini & Simone Severini, 2022. "Can Machine Learning discover the determining factors in participation in insurance schemes? A comparative analysis," Papers 2212.03092, arXiv.org, revised Dec 2022.

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