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How does a Fraud Mitigation Program Influence Insurance Claims filing Behavior? Evidence from the "Spot Check List" Program in U.S. Crop Insurance

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  • Rejesus, R.
  • Park, S.
  • Zheng, X.
  • Goodwin, G.

Abstract

The "Spot Check List" (SCL) approach is an important tool developed to help detect and deter fraud, waste, and abuse in the U.S. crop insurance program. This article carefully examines whether the SCL approach affects producers' claims filing behavior and provides insights with regards to the effectiveness of this program. Using proprietary county-level SCL data and panel data econometric procedures (which controls for both observable and unobservable confounding factors), we find evidence that counties with more than three producers included in the SCL tend to have better actuarial performance (i.e., less claims) after being informed about listing in the SCL. This indicates that the SCL procedure seem to be a promising tool for fraud mitigation in the Federal crop insurance program. Acknowledgement :

Suggested Citation

  • Rejesus, R. & Park, S. & Zheng, X. & Goodwin, G., 2018. "How does a Fraud Mitigation Program Influence Insurance Claims filing Behavior? Evidence from the "Spot Check List" Program in U.S. Crop Insurance," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277452, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae18:277452
    DOI: 10.22004/ag.econ.277452
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    1. Lacker, Jeffrey M & Weinberg, John A, 1989. "Optimal Contracts under Costly State Falsification," Journal of Political Economy, University of Chicago Press, vol. 97(6), pages 1345-1363, December.
    2. Wolfram Schlenker & Michael J. Roberts, 2006. "Nonlinear Effects of Weather on Corn Yields," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 28(3), pages 391-398.
    3. James Vercammen & G. Cornelis van Kooten, 1994. "Moral Hazard Cycles in Individual-Coverage Crop Insurance," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 76(2), pages 250-261.
    4. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    5. Francis Annan & Wolfram Schlenker, 2015. "Federal Crop Insurance and the Disincentive to Adapt to Extreme Heat," American Economic Review, American Economic Association, vol. 105(5), pages 262-266, May.
    6. Rejesus, Roderick M. & Little, Bertis B. & Lovell, Ashley C. & Cross, Mike H. & Shucking, Michael, 2004. "Patterns of Collusion in the U.S. Crop Insurance Program: An Empirical Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 36(2), pages 1-17, August.
    7. Joseph A. Atwood & James F. Robison-Cox & Saleem Shaik, 2006. "Estimating the Prevalence and Cost of Yield-Switching Fraud in the Federal Crop Insurance Program," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(2), pages 365-381.
    8. Townsend, Robert M., 1979. "Optimal contracts and competitive markets with costly state verification," Journal of Economic Theory, Elsevier, vol. 21(2), pages 265-293, October.
    9. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    10. Knight, Thomas O. & Coble, Keith H., 1999. "Actuarial Effects of Unit Structure in the U.S. Actual Production History Crop Insurance Program," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 31(3), pages 519-535, December.
    11. Richard Blundell & Stephen Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
    12. Julia I. Borman & Barry K. Goodwin & Keith H. Coble & Thomas O. Knight & Rod Rejesus, 2013. "Accounting for short samples and heterogeneous experience in rating crop insurance," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 73(1), pages 88-101, May.
    13. Thomas O. Knight & Keith H. Coble & Barry K. Goodwin & Roderick M. Rejesus & Sangtaek Seo, 2010. "Developing Variable Unit-Structure Premium Rate Differentials in Crop Insurance," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 141-151.
    14. Marra, Michele C. & Schurle, Bryan W., 1994. "Kansas Wheat Yield Risk Measures And Aggregation: A Meta- Analysis Approach," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 19(01), pages 1-9, July.
    15. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    16. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    17. R M Rejesus, 2003. "Ex post Moral Hazard in Crop Insurance: Costly State Verification or Falsification?," Economic Issues Journal Articles, Economic Issues, vol. 8(2), pages 29-46, September.
    18. Calum G. Turvey, 2012. "Whole Farm Income Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(2), pages 515-540, June.
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