Heterogeneity, climate change, and crop yield distributions: Solvency implications for publicly subsidized crop insurance programs

Abstract

Climate change continues to fuel concern about the future cost of publicly subsidized crop insurance programs in developed nations. These changes in climate are expected to alter the upper and lower tails of crop yield distributions differently. This may best be captured by modeling the climate–yield relationship heterogeneously across different parts of the yield distribution. To this end, we consider a mixture model with the parameters expressed as nonparametric functions (to capture any nonlinearities) of weather variables estimated by machine learning methods (neural net). By doing so, we are able to identify possibly heterogeneous effects of climate change on each component, the mixing probabilities, and thus all moments of the yield distribution. We find changing climate alters, quite significantly, the entire shape of the yield distribution. The overall probability of the lower tail tends to increase as temperatures rise, to the point where some yield distributions become positively skewed. Across a range of climate change scenarios, premium rates for fixed guarantees are expected to rise 20–66% relative to no climate change by 2040. However, if we allow the yield guarantees to also fall because of additional losses from climate change, premium rates (albeit not comparable given yield guarantees are different) increase notably less (6–14%), suggesting less solvency issues than first thought.