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Estimating the Prevalence and Cost of Yield-Switching Fraud in the Federal Crop Insurance Program

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  • Joseph A. Atwood
  • James F. Robison-Cox
  • Saleem Shaik

Abstract

Producers who manipulate and switch their reported crop-yields between separately insured units can increase their insurance indemnities substantially. A statistical model that identifies potential yield switching is developed. The unrestricted statistical model is singular and is identified by imposing a mixture of system-estimable and system-nonestimable restrictions. Lower bound estimates of yield-switching fraud incidence and costs are obtained by applying the model to 207,067 multiple unit producers who purchased crop insurance in 1998. Copyright 2006, Oxford University Press.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:ajagec:v:88:y:2006:i:2:p:365-381
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    File URL: http://hdl.handle.net/10.1111/j.1467-8276.2006.00864.x
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    References listed on IDEAS

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    Cited by:

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    2. Sungkwol Park & Xiaoyong Zheng & Roderick M. Rejesus & Barry K. Goodwin, 2022. "Somebody's watching me! Impacts of the spot check list program in U.S. crop insurance," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 921-946, May.
    3. Paloch Suchato & Taro Mieno & Karina Schoengold & Timothy Foster, 2022. "The potential for moral hazard behavior in irrigation decisions under crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 53(2), pages 257-273, March.
    4. Joshua D. Woodard & Leslie J. Verteramo‐Chiu, 2017. "Efficiency Impacts of Utilizing Soil Data in the Pricing of the Federal Crop Insurance Program," American Journal of Agricultural Economics, John Wiley & Sons, vol. 99(3), pages 757-772, April.
    5. Sankalp Sharma & Cory G. Walters, 2020. "Influence of farm size and insured type on crop insurance returns," Agribusiness, John Wiley & Sons, Ltd., vol. 36(3), pages 440-452, June.
    6. Denisa BANULESCU-RADU & Meryem YANKOL-SCHALCK, 2021. "Fraud detection in the era of Machine Learning: a household insurance case," LEO Working Papers / DR LEO 2904, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    7. Sharma, S. & Walters, C., 2018. "Influence of Farm and Lease Type on Crop Insurance Returns," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277399, International Association of Agricultural Economists.
    8. Yuehua Zhang & Ying Cao & H. Holly Wang, 2018. "Cheating? The Case of Producers’ Under‐Reporting Behavior in Hog Insurance in China," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 66(3), pages 489-510, September.

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