Incorporating historical weather information in crop insurance rating

Abstract

Crop insurance programs rely on conditional predictive distributions of loss random variables (e.g., yield, revenue, loss costs, etc.) to determine probabilities and magnitudes of loss. The loss variables may be related to stochastic variables that are not known at the time the policy is priced. Such is the case for weather; weather is stochastic, realizations are not known when the crop insurance policy is sold, and there is often additional historical information on weather relative to the loss variable itself. We provide a Bayesian methodology for incorporating historical weather information in crop insurance rating. We apply the method in empirical applications to county-level U.S. corn yields and loss cost ratios in the Midwest. The historical weather-conditioned distributions differ from those based on shorter samples. In the yield distribution setting, additional temporal weather information leads to economic gains relative to other rating approaches; the magnitude of these gains increases with the amount of historical weather information included in the analysis.

A semiparametric spatio‐temporal model of crop yield trend and its implication to insurance rating

“…We are inclined to maintain this deterministic feature when extending a temporal trend model to a spatio-temporal model. Although it is possible to incorporate some auxiliary variables such as soil and climate condition to the trend model (Liu & Ramsey, 2023;Miller et al, 2021;Perry et al, 2020;Tack et al, 2012Tack et al, , 2014Tsiboe & Tack, 2022), a simple deterministic spatio-temporal trend seems to be a reasonable choice for our purpose.…”

Heterogeneous impacts of the spot check‐list program in US crop insurance: A disaggregated analysis at the crop level

“…As a result, higher‐risk farmers are more likely to purchase crop insurance, resulting in larger than expected indemnity payments. Many studies in the literature have examined how the actuarial performance of FCIP can be improved through better rate‐making procedures to address adverse selection (e.g., Knight et al, 2010; Liu & Ramesey, 2022; Tack & Ubilava, 2015; Tsiboe & Tack, 2022; Yi et al, 2020).…”