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Accounting for short samples and heterogeneous experience in rating crop insurance

Author

Listed:
  • Julia I. Borman
  • Barry K. Goodwin
  • Keith H. Coble
  • Thomas O. Knight
  • Rod Rejesus

Abstract

Purpose - The purpose of this paper is to be an academic inquiry into rating issues confronted by the US Federal Crop Insurance program stemming from changes in participation rates as well as the weighting of data to reflect longer‐run weather patterns. Design/methodology/approach - The authors investigate two specific approaches that differ from those adopted by the Risk Management Agency, building upon standard maximum likelihood and Bayesian estimation techniques that consider parametric densities for the loss‐cost ratio. Findings - Both approaches indicate that incorporating weights into the priors for Bayesian estimation can inform the distribution. Originality/value - In most cases, the authors' results indicate that including weighting into priors for Bayesian estimation implied lower premium rates than found using standard methods.

Suggested Citation

  • Julia I. Borman & Barry K. Goodwin & Keith H. Coble & Thomas O. Knight & Rod Rejesus, 2013. "Accounting for short samples and heterogeneous experience in rating crop insurance," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 73(1), pages 88-101, May.
    • Handle: RePEc:eme:afrpps:v:73:y:2013:i:1:p:88-101
    DOI: 10.1108/00021461311321339
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    Citations

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    Cited by:

    1. Rejesus, R. & Park, S. & Zheng, X. & Goodwin, G., 2018. "How does a Fraud Mitigation Program Influence Insurance Claims filing Behavior? Evidence from the "Spot Check List" Program in U.S. Crop Insurance," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277452, International Association of Agricultural Economists.
    2. Sungkwol Park & Xiaoyong Zheng & Roderick M. Rejesus & Barry K. Goodwin, 2022. "Somebody's watching me! Impacts of the spot check list program in U.S. crop insurance," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 921-946, May.
    3. 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.
    4. Rejesus, Roderick M. & Coble, Keith H. & Miller, Mary France & Boyles, Ryan & Goodwin, Barry K & Knight, Thomas O., 2015. "Accounting for Weather Probabilities in Crop Insurance Rating," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(2), pages 1-19, May.
    5. Zhiwei Shen & Martin Odening & Ostap Okhrin, 2016. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(2), pages 237-269.
    6. Porth, Lysa & Tan, Ken Seng & Zhu, Wenjun, 2016. "A Relational Model for Predicting Farm-Level Crop Yield Distributions in the Absence of Farm-Level Data," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236278, Agricultural and Applied Economics Association.

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