This paper describes a novel effort at developing index-based insurance for locationaveraged livestock mortality as a means to fill an important void in the risk management instruments available to protect the main asset of pastoralists in the arid and semi-arid lands of Kenya, where insurance markets are effectively absent and uninsured risk exposure is a main cause of the existence of poverty traps. We describe the detailed methodology in designing such insurance contract with the underlying index uniquely constructed off explicit statistical predictions established using longitudinal observations of household-level herd mortality, fit to high quality, objectively verifiable remotely sensed vegetation data not manipulable by either party to the contract and available at low cost and in near-real time. The resulting index performs very well out of sample, both when tested against other complementing household-level herd mortality data from the same region and period and when compared qualitatively with community level drought experiences over the past 27 years. We describe contract pricing and potential risk exposures of the underwriter using a rich time series of satellite-based vegetation data available from 1982-present. And finally, implementation opportunities and challenges are discussed to spur the product's pilot potential.
This paper evaluates the welfare impacts of an index-based livestock insurance designed to compensate for satellite-based predicted livestock mortality in northern Kenya, where previous work has established the presence of poverty traps. We simulate household wealth dynamics based on rich panel and experimental data. The bifurcated livestock dynamics associated with the poverty trap gives rise to insurance valuation that is highly nonlinear in herd size. Estimated willingness to pay among vulnerable groups who most need insurance is, on average, lower than commercially viable rates. Targeted premium subsidization nonetheless appears to offer more cost-effective poverty reduction than needbased direct transfers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.