Spatial Pattern of Yield Distributions: Implications for Crop Insurance

Abstract

Despite the potential benefits of larger datasets for crop insurance ratings, pooling yields with similar distributions is not a common practice. The current USDA-RMA county insurance ratings do not consider information across state lines, a politically driven assumption that ignores a wealth of climate and agronomic evidence suggesting that growing regions are not constrained by state boundaries. We test the appropriateness of this assumption, and provide empirical grounds for benefits of pooling datasets.We find evidence in favor of pooling across state lines, with poolable counties sometimes being as far as 2,500 miles apart. An out-of-sample performance exercise suggests our proposed pooling framework out-performs a no-pooling alternative, and supports the hypothesis that economic losses should be expected as a result of not adopting our pooling framework. Our findings have strong empirical and policy implications for accurate modeling of yield distributions and vis-à-vis the rating of crop insurance products.

A novel approach for modelling pattern and spatial dependence structures between climate variables by combining mixture models with copula models

“…These two variables are the key climate variables and have strong impacts on agriculture in Scania, one of the most fertile parts of Northern Europe. Currently, due to the impacts of climate change on agriculture, many researchers focus on modelling agricultural production, meteorology‐induced disasters, crop insurance and prices (Okhrin et al ., Annan et al ., Goodwin and Hungerford, ). Laux et al .…”

Value of Incorporating ENSO Forecast in Crop Insurance Programs

“…Because crop yield distributions from geographically proximate areas tend to resemble one another due to common environmental and climatic conditions, pooling data from adjacent areas can often improve yield estimation. The benefit of pooling information from multiple units has been well established in the literature (Goodwin and Ker 1998;Goodwin and Mahul 2004;Annan et al 2013;Ker, Tolhurst, and Liu 2015;Zhang 2017). In this study, we adopt the flexible semiparametric density ratio approach developed by Zhang (2017).…”

“…We follow the standard procedure used by Harri et al (2011), Annan et al (2013), and Ker, Tolhurst, and Liu (2015) in our investigation of policy selection. Insurance companies use their own estimates to assess the profitability of insurance policies.…”

Assessing effects of federal crop insurance supply on acreage and yield of specialty crops

“…Much research focuses on federal crop insurance, but mostly in the context of field crops. Some of the research analyzes the demand for crop insurance (e.g., Coble, Knight, Pope, & Williams, 1996;Goodwin, 1993;Hazell, Bassoco, & Arcia, 1986;Mishra & Goodwin, 2003 or producer responses to crop insurance (e.g., Du, Hennessy, & Feng, 2013;Miao, Feng, Hennessy, & Du, 2016;Quiggin, Karagiannis, & Stanton, 1993), whereas others explore the feasibility and design of the FCIP (e.g., Annan, Tack, Harri, & Coble, 2013;Chalise, Coble, Barnett, & Miller, 2017;Chambers, 1989;Coble, Knight, Pope, & Williams, 1997;Gardner & Kramer, 1986;Goodwin, 2001;Goodwin & Ker, 1998;Ker & Goodwin, 2000;Ker, Tolhurst, & Liu, 2015;Nelson & Loehman, 1987;Quiggin et al, 1993;Skees & Reed, 1986;Woodard, Pavlista, Schnitkey, Burgener, & Ward, 2012;Woodard, Sherrick, & Schnitkey, 2011;Woodard & Verteramo-Chiu, 2017).…”