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

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Author Info

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

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    Bibliographic Info

    Article provided by Northeastern Agricultural and Resource Economics Association in its journal Agricultural and Resource Economics Review.

    Volume (Year): 37 (2008)
    Issue (Month): 1 (April)
    Pages:

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    Handle: RePEc:ags:arerjl:44740

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    Web page: http://www.narea.org/
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    Related research

    Keywords: crop insurance; crop yield modeling; Johnson distribution; premium rate setting; Risk and Uncertainty;

    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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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.
    2. Babcock, Bruce A. & Hart, Chad E. & Hayes, Dermot J., 2004. "Actuarial Fairness of Crop Insurance Rates with Constant Rate Relativities," Staff General Research Papers 11283, Iowa State University, Department of Economics.
    3. 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.
    4. Keith H. Coble & Thomas O. Knight & Rulon D. Pope & Jeffery R. Williams, 1997. "An Expected-Indemnity Approach to the Measurement of Moral Hazard in Crop Insurance," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 216-226.
    5. 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.
    6. 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(01), July.
    7. Alan P. Ker & Keith Coble, 2003. "Modeling Conditional Yield Densities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 291-304.
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    Cited by:
    1. 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), April.
    2. 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), April.

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