Mitigation of Data Scarcity in Crop Insurance Pricing by Exploiting Spatial Information: A Propagation-Separation Approach

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In view of the rapid expansion of crop insurance worldwide, a sound and accurate pricing approach of insurance contracts is of utmost importance to maintain sustainable and viable risk management solutions for producers, insurers and governments. Unlike most forms of insurance where sufficient information exists to estimate reliable loss distributions and insurance premiums, agricultural insurance ratemaking is plagued by the fact that crop data are spatially correlated and scarce, usually 50 observations at most. The lack of historic data may lead to incorrect estimations of loss distributions and introduces a new model risk into the insurer's decision problem. A practical approach to deal with data scarcity in insurance industry is to utilize spatially nearby data to complement the limited historical observations, but the incorporation of such yield data is often on an ad hoc basis, lacking a formal, sound mathematical framework. Mixing data from dissimilar distributions would lead to adverse selection problems. Therefore, the objective of this research project is to develop a formal and rigorous framework to exploit the spatial information for mitigating data scarcity in crop insurance pricing. Particularly, we will adopt and extend the adaptive local smoothing model (also referred to the Propagation-Separation approach) that allows to identify local homogeneous areas and estimate parameters of the common distribution in a more flexible and efficient way. The project will further provide a comprehensive comparison of different statistical methods as well as empirical applications to draw a clearer picture of the conditions under which spatial information can be most helpful.