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Patterns of Collusion in the U.S. Crop Insurance Program: An Empirical Analysis

Published online by Cambridge University Press:  28 April 2015

Roderick M. Rejesus ,
Bertis B. Little ,
Ashley C. Lovell ,
Mike Cross  and
Michael Shucking
Roderick M. Rejesus
Affiliation:
Department of Agricultural and Applied Economics, Texas Tech University, Lubbock, TX
Bertis B. Little
Affiliation:
Center for Agribusiness Excellence, Tarleton State University, Stephenville, TX
Ashley C. Lovell
Affiliation:
Center for Agribusiness Excellence, Tarleton State University, Stephenville, TX
Mike Cross
Affiliation:
Planning Systems Inc., Stephenville, TX
Michael Shucking
Affiliation:
Planning Systems Inc., Stephenville, TX
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Abstract

This article analyzes anomalous patterns of agent, adjuster, and producer claim outcomes and determines the most likely pattern of collusion that is suggestive of fraud, waste, and abuse in the federal crop insurance program. Log–linear analysis of Poisson-distributed counts of anomalous entities is used to examine potential patterns of collusion. The most likely pattern of collusion present in the crop insurance program is where agents, adjusters, and producers nonrecursively interact with each other to coordinate their behavior. However, if a priori an intermediary is known to initiate and coordinate the collusion, a pattern where the producer acts as the intermediary is the most likely pattern of collusion evidenced in the data. These results have important implications for insurance program design and compliance.

Keywords

abusecollusioncrop insuranceempirical analysisfraudwasteG22Q12Q18Q19
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 36 , Issue 2 , August 2004 , pp. 449 - 465
Copyright
Copyright © Southern Agricultural Economics Association 2004

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References

Agresti, A.An Introduction to Categorical Data Analysis. New York: John Wiley & Sons, Inc., 1996.Google Scholar
Alger, I., and Ma, C.A.. “Moral Hazard, Insurance, and Some Collusion.” Journal of Economic Behavior and Organization 50(February 2003):225–47.CrossRefGoogle Scholar
Allingham, M.G., and Sandmo, A.. “Income Tax Evasion: A Theoretical Analysis.” Journal of Public Economics 1(Nov. 1972):323–38.CrossRefGoogle Scholar
Becker, G.S. “Crime and Punishment: An Economic Approach.” Journal of Political Economy 76(Mar-April 1968):169–217.CrossRefGoogle Scholar
Crop Insurance Industry. Combating Fraud, Waste, and Abuse in the Crop Insurance Program. A report by the Crop Insurance Industry, January 2001. Internet site: http://www.amag.com/library/pdf/fraudreport_jan01.pdf (Accessed: October 24, 2001).Google Scholar
Gilbert, G.N.Modelling Society: An Introduction to Loglinear Analysis for Social Researchers. London: George Allen and Unwin, 1981.Google Scholar
Harwood, J., Heifner, D., Coble, K., Perry, J., and Somwara, A.. Managing Risk in Farming: Concepts, Research, and Analysis. Washington, DC: U.S. Department of Agriculture, Economic Research Service, Agricultural Economic Report No. 774, 1999.Google Scholar
Ker, A.P. “Private Insurance Companies and the U.S. Crop Insurance Program.” Choices 14(Third Quarter 1999):39–40.Google Scholar
Ker, A.P. “Private Insurance Company Involvement in the U.S. Crop Insurance Program.” Canadian Journal of Agricultural Economics 49(December 2001):557–66.CrossRefGoogle Scholar
Kilman, S. “Abuses Plague Program To Insure Farmers' Crops.” Wall Street Journal, New York, May 5, 2003.Google Scholar
McCullagh, P., and Neider, J.A.. Generalized Linear Models, 2nd ed. New York: Chapman and Hall, 1983.CrossRefGoogle Scholar
Neider, J.A., and Wedderburn, R.W.M.. “Generalized Linear Models.” Journal of the Royal Statistical Society A (General). 135,3(1972):370–84.Google Scholar
Picard, P. “Economic Analysis of Insurance Fraud.” Handbook of Insurance. Dionne, G., ed. Boston: Kluwer Academic Publishers, 2000.Google Scholar
RMA. Standard Reinsurance Agreement, 1999. Internet site: http://www.rma.usda.govs/pubs/ra (Accessed August 2, 2003).Google Scholar
RMA. Risk Management Agency Program Compliance and Integrity Annual Report to Congress, 2002. Internet site: http://www.rma.usda.gov/pubs/2002/ComplianceReport.pdf (Accessed December 15, 2002).Google Scholar
Srinivasan, T.N. “Tax Evasion: A Model.” Journal of Public Economics 2(Nov. 1973):339–46.CrossRefGoogle Scholar