Contract elements, growing conditions, and anomalous claims behaviour in U.S. crop insurance
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Contract elements, growing conditions, and anomalous claims behaviour in U.S. crop insurance Sungkwol Park1 · Barry K. Goodwin2 · Xiaoyong Zheng2 · Roderick M. Rejesus2 Received: 7 January 2019 / Accepted: 17 July 2019 © The Geneva Association 2019
Abstract We investigate contract elements and growing conditions associated with anomalous 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 individual 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. Keywords Spot Check List · Insurance fraud · Crop insurance · Simulated maximum likelihood estimation · Control function approach · Block bootstrap
Electronic supplementary material The online version of this article (https://doi.org/10.1057/s4128 8-019-00143-9) contains supplementary material, which is available to authorised users. * Roderick M. Rejesus [email protected] 1
Ministry of Economy and Finance, Seoul, South Korea
2
Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, NC 27695, USA
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Introduction The Risk Management Agency (RMA) of the U.S. Department of Agriculture (USDA) is in charge of administering the U.S. Crop Insurance Program. As part of its efforts to detect and deter fraud, waste, and abuse in the Crop Insurance Program, the RMA developed and implemented the so-called Spot Check List (SCL) programme in 2001 (USDA-RMA 2011). Under this SCL effort, the RMA and their partners utilise complex and proprietary algorithms to analyse a massive data warehouse that contains extensive crop insurance contract data as well as information from other related databases (e.g., weather data and other administrative data from other USDA agencies). The aim of the SCL process is to detect individual producers whose claims behaviour demonstrates atypical patterns that are indicative of potential fraud, waste, and abuse (or
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