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Empirically Evaluating the Flexibility of the Johnson Family of Distributions: A Crop Insurance Application

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  • Lu, Yue
  • Ramirez, Octavio A.
  • Rejesus, Roderick M.
  • Knight, Thomas O.
  • Sherrick, Bruce J.

Abstract

This article examines the flexibility of the Johnson system of distributions by assessing its per-formance in terms of modeling crop yields for the purpose of setting actuarially fair crop in-surance premiums. Using data from corn farms in Illinois coupled with Monte Carlo simula-tion procedures, we found that average crop insurance premiums computed on the basis of the Johnson system provide reasonably accurate estimates even when the data are normal or come from a non-normal distribution other than the Johnson system (i.e., a beta). These results sug-gest that there is potential for using the Johnson system to rate previously uninsured crops that do not have historical insurance performance data upon which to base premium calculations.

Suggested Citation

  • Lu, Yue & Ramirez, Octavio A. & Rejesus, Roderick M. & Knight, Thomas O. & Sherrick, Bruce J., 2008. "Empirically Evaluating the Flexibility of the Johnson Family of Distributions: A Crop Insurance Application," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 37(1), pages 1-13, April.
    • Handle: RePEc:ags:arerjl:44740
    DOI: 10.22004/ag.econ.44740
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    References listed on IDEAS

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    1. 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.
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    9. Stokes, Jeffrey R., 2000. "A Derivative Security Approach To Setting Crop Revenue Coverage Insurance Premiums," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(1), pages 1-18, July.
    10. Bailey Norwood & Matthew C. Roberts & Jayson L. Lusk, 2004. "Ranking Crop Yield Models Using Out-of-Sample Likelihood Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1032-1043.
    11. Barry K. Goodwin & Alan P. Ker, 1998. "Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 139-153.
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    Cited by:

    1. Tack, Jesse, 2013. "A Nested Test for Common Yield Distributions with Applications to U.S. Corn," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(1), pages 1-14, April.
    2. Ramirez, Octavio A. & Carpio, Carlos E., 2015. "Are the Federal Crop Insurance Subsidies Equitably Distributed? Evidence from a Monte Carlo Simulation Analysis," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(3), pages 1-19, September.
    3. Ramirez, Octavio A. & Carpio, Carlos E. & Rejesus, Roderick M., 2011. "Can Crop Insurance Premiums Be Reliably Estimated?," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 40(1), pages 1-14, April.
    4. Ghahremanzadeh, Mohammad & Mohammadrezaei, Rassul & Dashti, Ghader & Ainollahi, Moharram, 2018. "Designing a whole-farm revenue insurance for agricultural crops in Zanjan province of Iran," Economia Agraria y Recursos Naturales, Spanish Association of Agricultural Economists, vol. 17(02), January.

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    Risk and Uncertainty;

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