![]() ![]() ![]() These were chosen because they are among the most important corn producers in the United States ( USDA 2019) and have comparable climates. We simulate index insurance contracts to insure extreme heat for corn production in 131 counties in Illinois and Iowa. heat insurance policies and future research. Third, our supplementary code provides a rich toolbox of methods to not only replicate our results but to be additionally used to inform future U.S. Second, we focus on extreme events in both the yield and temperature distribution by using quantile regression and cooling degree-days. First, we show that heat index insurance based on interpolated temperature data reduces the financial exposure to heat events and can thus be used to insure farmers, especially when weather station data are scarce. We extend the literature in three dimensions. 2019) reduces the basis risk of heat index insurance. In this article, we draw from the geostatistics literature and show how interpolation ( Cressie 1988 Wu and Li 2013 Roznik et al. Therefore, it is key to use weather data that best capture extreme events at the production location to keep basis risk low and the weather index insurance market functioning ( Dalhaus and Finger 2016). ![]() In WII, the insurance payout is a function of weather rather than observed losses, which makes any discrepancy between payout and loss, that is, basis risk, the main adoption hurdle ( Clarke 2016 Woodard and Garcia 2008 Barnett et al. Therefore, weather index insurance (WII) may complement indemnity-based products, especially for systemic perils such as heat, because information asymmetries between insurer and the insured are minimized ( Belasco et al. There are also monitoring costs associated with on-farm loss adjustment. Traditional indemnity-based crop insurance comes at the cost of adverse selection and moral hazard ( Glauber 2013 Goodwin and Smith 2013), which puts an additional burden on insurers by incentivizing farmers to take on more risk in production ( Annan and Schlenker 2015). Therefore, efficient risk management is crucial in order to protect farmers’ incomes when extreme weather conditions occur. corn production, and climate change is expected to further exacerbate heat stress ( Schlenker and Roberts 2009). Our results are therefore not only replicable but also constitute a cornerstone for projects to come.Įxtreme heat events can cause substantial losses in U.S. Further, our public code repository provides a rich toolbox of methods to be used for other perils, crops, and regions. 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, 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. 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. 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. So far, extreme heat indices are poorly represented in weather index insurance. However, its viability depends crucially on the accuracy of local weather indices to predict yield damages from adverse weather conditions. Weather index insurance provides payouts to farmers in the case of measurable weather extremes to keep production going. So what we've done to move from (a, b) to (b, a) is reflect over the line y=x.Extreme heat events cause periodic damage to crop yields and may pose a threat to the income of farmers. Notice that y=x has a slope of 1, and our segment has a slope of -1. So the midpoint of the segment must lie on the line y=x. So the midpoint has y-coordinate (b+a)/2. So the midpoint has the x-coordinate (a+b)/2. Let's find the midpoint of our line segment. That's interesting if we have a point on a function and want to find the corresponding point on the inverse function, we slide along a line of slope -1. Let's look at how we get from (a, b) to (b, a). So if (a, b) is on our original function, then (b, a) is on the inverse. If we consider the inverse function, it will contain each of these points, but with the coordinates switched. We can think of a function as a collection of points in the plane. ![]()
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