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Premium misrating in federal crop insurance programs: scale, geography, and fiscal impacts

Author

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  • Zhangliang Chen
  • Sandy Dall'Erba
  • Bruce J. Sherrick

Abstract

Purpose - Federal crop insurance programs are the primary risk management programs of the US farm programs. Currently, these programs have been criticized for being disproportionally in favor of the riskier areas. Despite previous researchers having widely speculated its existence, a formal study of the scale, spatial pattern and fiscal impacts of such misrating phenomenon is still missing in the literature. Design/methodology/approach - This paper first purposes an empirically testable definition of misrating, and then detects the scale of the misrating phenomenon by using over two million actuarial records collected by United States Department of Agriculture (USDA's) risk management agency since 1989. Furthermore, multiple spatial statistics methods have been adopted to study the spatial patterns of the misrating statuses. Finally, the paper builds a simple theoretical model to study the potential fiscal impacts of any policy attempts to mitigate the misrating issue. Findings - The result reveals that roughly 40% of the counties display some degree of misrating. Furthermore, the distribution of misrating displays a significant pattern of positive global spatial autocorrelation, which reflects the existence of regional clusters of premium rate mispricing. Last but not least, the paper concludes that whether an attempt toward fair rating decreases the total program outlay or not relies on the demand elasticity of crop insurance in both overrated and underrated regions. Originality/value - This paper offers the first attempt to quantify the scale, identify the spatial pattern and evaluate the fiscal impact of the premium misrating in federal crop insurance programs.

Suggested Citation

  • Zhangliang Chen & Sandy Dall'Erba & Bruce J. Sherrick, 2020. "Premium misrating in federal crop insurance programs: scale, geography, and fiscal impacts," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 80(5), pages 693-713, June.
  • Handle: RePEc:eme:afrpps:afr-12-2019-0131
    DOI: 10.1108/AFR-12-2019-0131
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    Cited by:

    1. Matthew Stuart & Cindy Yu & David A. Hennessy, 2023. "The Impact of Stocks on Correlations of Crop Yields and Prices and on Revenue Insurance Premiums using Semiparametric Quantile Regression," Papers 2308.11805, arXiv.org.

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