Contract elements, growing conditions, and anomalous claims behaviour in U.SAuthor(s): Sungkwol Park; Barry K. Goodwin; Xiaoyong Zheng; Roderick M. Rejesus
Source: The Geneva Papers on Risk and Insurance - Issues and Practice
Publication Series: Scientific Journal (JRNL)
Station: Southern Research Station
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We investigate contract elements and growing conditions associated with anoma- lous claims behaviour in the U.S. Federal Crop Insurance Program. In this study the measure of “anomalous claims behaviour” is based on the number of producers (in a county) placed on the “Spot Check List” (SCL) a list generated from government compliance efforts that aim to detect and deter fraud, waste, and abuse in the U.S. Federal Crop Insurance Program. Using county level data and various econometric approaches that control for features of this data set (e.g., the count nature of the dependent variable, censoring, potential endogeneity, and spatial/temporal dependence), we find that the following crop insurance contract attributes influence the extent of anomalous claims behaviour in a county: (a) the ability to insure indi- vidual fields through “optional units”; (b) the coverage level choice; and (c) the total number of acres insured. In addition, our empirical analyses suggest that anomalous claims behaviour significantly increases when extreme weather events occur (e.g., droughts, floods) and when economic conditions are unfavourable (i.e., high input costs that lower profit levels). Results from this study have important implications for addressing potential underwriting vulnerabilities in crop insurance contracts and the frequency of more rigorous compliance inspections.
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CitationPark, Sungkwol; Goodwin, Barry K.; Zheng, Xiaoyong; Rejesus, Roderick M. 2019. Contract elements, growing conditions, and anomalous claims behaviour in U.S. crop insurance. The Geneva Papers on Risk and Insurance - Issues and Practice. 45(1): 157-183. https://doi.org/10.1057/s41288-019-00143-9.
KeywordsSpot Check List, Insurance fraud, Crop insurance, Simulated maximum likelihood estimation · Control function approach, Block bootstrap
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