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

Roderick Rejesus, S. Park, Xiaoyong Zheng () and G. Goodwin

No 277452, 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia from International Association of Agricultural Economists

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 :

Keywords: Risk; and; Uncertainty (search for similar items in EconPapers)
Date: 2018-07
New Economics Papers: this item is included in nep-ias
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/277452/files/673.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:iaae18:277452

DOI: 10.22004/ag.econ.277452

Access Statistics for this paper

More papers in 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia from International Association of Agricultural Economists Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().