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Accounting for Geographic Basis Risk in Heat Index Insurance: How Spatial Interpolation Can Reduce the Cost of Risk

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  • 1 Swedish University of Agricultural Sciences, Uppsala, Sweden
  • | 2 Business Economics Group, Wageningen University and Research, Wageningen, Netherlands
  • | 3 Agricultural Economics and Policy Group, ETH Zürich, Zurich, Switzerland
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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.

Significance Statement

Extreme heat is an important threat to crops and one that could be exacerbated by climate change. Heat index insurance that compensates farmers when temperatures reach destructive levels has been promoted to reduce moral hazard or as an option where traditional insurance is difficult to implement, such as in developing countries where transaction costs can be prohibitive. This study is to our knowledge the first paper to simultaneously design heat indices using interpolation, simulating the amount of risk farmers would avoid from purchasing index insurance, and show how the risk management effectiveness of the contracts depend on the access to weather stations. We show that heat index insurance significantly reduces risk to corn producers in two states in the U.S. Midwest and corroborate recent results that interpolated temperature estimates outperform observations at local weather stations.

Leppert’s current affiliation: Durham University Business School, Durham, United Kingdom.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Daniel Leppert, daniel.j.leppert@durham.ac.uk

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.

Significance Statement

Extreme heat is an important threat to crops and one that could be exacerbated by climate change. Heat index insurance that compensates farmers when temperatures reach destructive levels has been promoted to reduce moral hazard or as an option where traditional insurance is difficult to implement, such as in developing countries where transaction costs can be prohibitive. This study is to our knowledge the first paper to simultaneously design heat indices using interpolation, simulating the amount of risk farmers would avoid from purchasing index insurance, and show how the risk management effectiveness of the contracts depend on the access to weather stations. We show that heat index insurance significantly reduces risk to corn producers in two states in the U.S. Midwest and corroborate recent results that interpolated temperature estimates outperform observations at local weather stations.

Leppert’s current affiliation: Durham University Business School, Durham, United Kingdom.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Daniel Leppert, daniel.j.leppert@durham.ac.uk
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