A Spatial Econometric Approach to Designing and Rating Scalable Index Insurance in the Presence of Missing Data

Joshua D Woodard (), Apurba Shee () and Andrew Mude ()
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Joshua D Woodard: Charles H. Dyson School of Applied Economics and Management, Cornell University, 351B Warren Hall, Ithaca, NY 14850 U.S.A.
Apurba Shee: Environment and Production Technology Division, International Food Policy Research Institute, Arusha, Tanzania.
Andrew Mude: International Livestock Research Institute, Nairobi, Kenya.

The Geneva Papers on Risk and Insurance - Issues and Practice, 2016, vol. 41, issue 2, 259-279

Abstract: Index-Based Livestock Insurance has emerged as a promising market-based solution for insuring livestock against drought-related mortality. The objective of this work is to develop an explicit spatial econometric framework to estimate insurable indexes that can be integrated within a general insurance pricing framework. We explore the problem of estimating spatial panel models when there are missing dependent variable observations and cross-sectional dependence, and implement an estimable procedure which employs an iterative method. We also develop an out-of-sample efficient cross-validation mixing method to optimise the degree of index aggregation in the context of spatial index models.

Date: 2016
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