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

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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.)

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.

Suggested Citation

  • Joshua D Woodard & Apurba Shee & Andrew Mude, 2016. "A Spatial Econometric Approach to Designing and Rating Scalable Index Insurance in the Presence of Missing Data," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 41(2), pages 259-279, April.
    • Handle: RePEc:pal:gpprii:v:41:y:2016:i:2:p:259-279
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    Cited by:

    1. Aditya Kusuma & Bethanna Jackson & Ilan Noy, 2018. "A viable and cost-effective weather index insurance for rice in Indonesia," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(2), pages 186-218, September.
    2. Joshua D. Woodard & Jing Yi, 2020. "Estimation of Insurance Deductible Demand Under Endogenous Premium Rates," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(2), pages 477-500, June.
    3. Li, Hong & Porth, Lysa & Tan, Ken Seng & Zhu, Wenjun, 2021. "Improved index insurance design and yield estimation using a dynamic factor forecasting approach," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 208-221.
    4. Xu, Hao & Gardoni, Paolo, 2020. "Conditional formulation for the calibration of multi-level random fields with incomplete data," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

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