Creating safety nets through semi-parametric index-based insurance: A simulation for Northern Ghana
In West Africa, farm income is highly exposed to risks from crop failure in the drier, inland areas, and from fluctuations in (world market) prices in the wetter coastal areas. As individuals and even extended families are poorly equipped to deal with these, provision of social safety nets is required Our paper reviews the situation in Ghana and the way in which the new financial instrument of index-based insurance might contribute to better it, focusing on the estimation of a crop indemnification scheme for farmers in Northern Ghana. It recalls that in a poor rural area like Northern Ghana, provision of social safety almost coincides with food security management, and must, therefore, distinguish three basic subtasks: distributing income entitlements (possibly indemnification payments from insurance) to the poor, ensuring collection of taxes (possibly insurance premiums) to fund the arrangement, and assuring delivery of staple goods, such as food to the all households, including the poor. We point out that crop insurance, in any form can at best entitle the poor, and with adequate premiums, become adequately funded, albeit that current experience suggests that farmers tend to be reluctant and to find it difficult to fulfill their obligations. Our main remark is, however, that unless the actual availability of goods is assured, the indemnification from crop insurance will under droughts only cause prices to rise and channel away scarce food from the uninsured to the insured. In short, in poor areas such as Northern Ghana co-ordinated food security management is key, particularly under severe droughts, with crop insurance possibly playing a role in the spheres of entitlement and taxation. Turning to the modalities of crop insurance, we mention the advantages of the index-based approach, which as compared to the individualized contracts of commercial insurance greatly reduces transaction costs by basing the indemnification payments on objectively and easily measurable variables, such as rainfall data collected at weather stations, and world prices of main export goods. Our contribution is an improvement of the indemnification schedules. Rather than specifying a synthetic schedule or estimating is as a parametric form, we estimate it as an optimal indemnification that minimizes farmers' risk of having their income drop below the poverty line, while restricting the indemnification to be an unknown function of index variables on weather and prices. We adapt kernel learning technique to conduct this estimation, so as to ensure that the schedule is self-financing, up to a subsidy. Our application is for Northern Ghana where poverty is highest and farming conditions are most risky. We test the scheme's performance as a social safety net in terms of its capacity to reduce basis risk and alleviate poverty. Although our schedule definitely outperforms the parametric forms, basis risk and associated poverty remain considerable.
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