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Big assumptions for small samples in crop insurance

Ashley Elaine Hungerford (Economic Research Services, Washington, District of Columbia, USA)
Barry Goodwin (Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, North Carolina, USA)

Agricultural Finance Review

ISSN: 0002-1466

Article publication date: 28 October 2014

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Abstract

Purpose

The purpose of this paper is to investigate the effects of crop insurance premiums being determined by small samples of yields that are spatially correlated. If spatial autocorrelation and small sample size are not properly accounted for in premium ratings, the premium rates may inaccurately reflect the risk of a loss.

Design/methodology/approach

The paper first examines the spatial autocorrelation among county-level yields of corn and soybeans in the Corn Belt by calculating Moran's I and the effective spatial degrees of freedom. After establishing the existence of spatial autocorrelation, copula models are used to estimate the joint distribution of corn yields and the joint distribution of soybean yields for a group of nine counties in Illinois. Bootstrap samples of the corn and soybean yields are generated to estimate copula models with the purpose of creating sampling distributions.

Findings

The estimated bootstrap confidence intervals demonstrate that the copula parameter estimates and the premium rates derived from the parameter estimates can vary greatly. There is also evidence of bias in the parameter estimates.

Originality/value

Although small samples will always be an issue in crop insurance ratings and assumptions must be made for the federal crop insurance program to operate at its current scale, this analysis sheds light on some of the issues caused by using small samples and will hopefully lead to the mitigation of these small sample issues.

Keywords

Acknowledgements

This paper was presented in Finance Section paper session at the 2013 Agricultural and Applied Economics Association (AAEA) annual meeting, August 4-6, in Washington, DC. The papers in these sessions are not subjected to the journal's standard refereeing process. Reviews conducted by N. Paulson and/or C.G. Turvey.

The views expressed are those of the authors and should not be attributed to the Economic Research Service or USDA. Goodwin was supported by the US Forest Service and the North Carolina Agricultural Research Service.

Citation

Elaine Hungerford, A. and Goodwin, B. (2014), "Big assumptions for small samples in crop insurance", Agricultural Finance Review, Vol. 74 No. 4, pp. 477-491. https://doi.org/10.1108/AFR-09-2014-0025

Publisher

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Emerald Group Publishing Limited

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