Accounting for geographic basis risk in heat index insurance: How spatial interpolation can reduce the cost of risk

Abstract

Extreme heat events cause periodic damage to crop yields and may pose a threat to the income of farmers. Weather index insurance provides payouts to farmers in the case of measurable weather extremes to keep production going. However, its viability depends crucially on the accuracy of local weather indices to predict yield damages from adverse weather conditions. So far, extreme heat indices are poorly represented in weather index insurance. In this study, we construct indices of extreme heat using observations at the nearest weather station and estimates for each county using three interpolation techniques: inverse-distance weighting, ordinary kriging, and regression kriging. Applying these indices to insurance against heat damage to corn in Illinois and Iowa, we show that heat index insurance reduces relative risk premiums by 27%-29% and that interpolated indices outperform the nearest-neighbor index by around 2%-3% in terms of relative risk reduction. Further, we find that the advantage of interpolation over a nearest-neighbor index in terms of relative risk reduction increases as the sample of weather stations is reduced. These findings suggest that heat index insurance can work even when weather data are spatially sparse, which delivers important implications for insurance practice and policy makers. Further, our public code repository provides a rich toolbox of methods to be used for other perils, crops, and regions. Our results are therefore not only replicable but also constitute a cornerstone for projects to come.