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Premium estimation inaccuracy and the actuarial performance of the US crop insurance program

  • Octavio A. Ramirez
  • Carlos A. Carpio

Purpose – The purpose of this paper is to explore the impact of the levels of inaccuracy associated with three different premium estimation methods, one of which attempts to mimic the protocol currently used by the Risk Management Agency (RMA), on the actuarial performance of the US crop insurance program. Design/methodology/approach – The analyses are conducted using empirically-grounded simulation and other computational methods, under various plausible assumptions about the producer's risk aversion behavior and knowledge of his/her actuarially fair premium. Findings – Regardless of the assumed producer knowledge and behavior, it is concluded that the persistently high government subsidy levels required to keep the program solvent could be solely explained by the inaccuracy in the RMA's premium estimates. In other words, the observed need for large subsidies does not necessarily imply that the program is systematically favoring less efficient farmers or particular crops or production areas. Also, contrary to the commonly accepted “adverse selection” argument, it is shown that farmers having more information about their actuarially fair premiums than the insurer is not the reason why high subsidies are needed. Actuarial performance, however, could be improved by using the more elaborate methods exemplified in the paper, as well as larger sample sizes for premium estimation. Originality/value – The paper provides conclusions and recommendations that could substantially reduce the amount of public subsidies needed to keep the US crop insurance program solvent.

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Article provided by Emerald Group Publishing in its journal Agricultural Finance Review.

Volume (Year): 72 (2012)
Issue (Month): 1 (March)
Pages: 117-133

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Handle: RePEc:eme:afrpps:v:72:y:2012:i:1:p:117-133
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  1. Barry J. Barnett, 2000. "The U.S. Federal Crop Insurance Program," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 48(4), pages 539-551, December.
  2. Harwood, Joy L. & Heifner, Richard G. & Coble, Keith H. & Perry, Janet E. & Somwaru, Agapi, 1999. "Managing Risk in Farming: Concepts, Research, and Analysis," Agricultural Economics Reports 34081, United States Department of Agriculture, Economic Research Service.
  3. Alan P. Ker, 2001. "Private Insurance Company Involvement in the U.S. Crop Insurance Program," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 49(4), pages 557-566, December.
  4. Ramirez, Octavio A. & Carpio, Carlos E. & Rejesus, Roderick M., 2009. "Can Crop Insurance Premiums be Reliably Estimated?," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49465, Agricultural and Applied Economics Association.
  5. Roderick M. Rejesus & Barry K. Goodwin & Keith H. Coble & Thomas O. Knight, 2010. "Evaluation of the reference yield calculation method in crop insurance," Agricultural Finance Review, Emerald Group Publishing, vol. 70(3), pages 427-445, November.
  6. Joseph W. Glauber, 2004. "Crop Insurance Reconsidered," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(5), pages 1179-1195.
  7. Keith H. Coble & Thomas O. Knight & Rulon D. Pope & Jeffery R. Williams, 1996. "Modeling Farm-Level Crop Insurance Demand with Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 439-447.
  8. Ramirez, Octavio A. & McDonald, Tanya U. & Carpio, Carlos E., 2010. "A Flexible Parametric Family for the Modeling and Simulation of Yield Distributions," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 42(02), May.
  9. Octavio A. Ramirez & Sukant Misra & James Field, 2003. "Crop-Yield Distributions Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 108-120.
  10. Carriquiry, Miguel A. & Babcock, Bruce A. & Hart, Chad E., 2008. "Using a Farmer's Beta for Improved Estimation of Expected Yields," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 33(01), April.
  11. 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.
  12. Octavio A. Ram�rez, 1997. "Estimation and Use of a Multivariate Parametric Model for Simulating Heteroskedastic, Correlated, Nonnormal Random Variables: The Case of Corn Belt Corn, Soybean, and Wheat Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 191-205.
  13. Octavio A. Ram�rez & Tanya McDonald, 2006. "Ranking Crop Yield Models: A Comment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(4), pages 1105-1110.
  14. 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.
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