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Modelling Demand for Weather Index Insurance as Adaptation Instrument to Drought Risk Event in Central-West Nigeria

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dc.contributor.author Awolala, David Olufemi
dc.date.accessioned 2020-11-25T10:39:59Z
dc.date.available 2020-11-25T10:39:59Z
dc.date.issued 2016-04
dc.identifier.uri http://197.159.135.214/jspui/handle/123456789/124
dc.description A Thesis submitted to the West African Science Service Center on Climate Change and Adapted Land Use and Université Cheikh Anta Diop, Dakar in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Climate Change and Economics en_US
dc.description.abstract There has been increase in both the frequency of extreme weather events and the number of vulnerable human population. Converging results from climate model simulations projected that extreme climatic events will result in a long-term income losses and by 2020, half of the Nigeria‘s agro-ecological zones will be food insecure. Failures of traditional crop insurance programmes to provide financial protection and exclusion from formal financial services have further increased farmers exposure to more severe impact of extreme weather risk events and poverty trap. With ideal levels of adaptation, some residual impacts would still lead to economic losses. Parametric weather insurance is a global consensus to transfer farmers‘ risks and increase supports for agricultural lending in developing economies. This study focus on modelling of future demand for weather index insurance as an adaptation instrument to drought risk events in Central-West Nigeria. Results show that monthly rainfall uncertainties keep expanding significantly during the most critical maize growth period threatening crop performance. Radiation index of dryness of 1.394, at an evaporation rate of 949 mm/year and rainfall deficit of 366 mm/year signal that the region is rapidly shifting towards aridity. About 65% of respondents are willing to insure in weather index insurance. The marginal effects from heckman ordered probit selection model indicated that increase in farmers‘ experience, farm size cultivated, access to farmers‘ groups and linkages, income, extension services and awareness of agricultural insurance will significantly increase farmers‘ willingness to insure. Socioeconomic drivers of willingness to pay for weather insurance policy are access to early seasonal weather forecast, farm income livelihood and dependence on rain water collection will significantly increase farmers‘ decision to buy rainfall insurance product. Interval regression estimated mean WTP as N457.28 per mm of rainfall deficit (std. err. 27.951) per farmer with a significant log pseudolikelihood function of -36.56. The market demand for rainfall index insurance is elastic. Mode of insurance uptake reveals that 72.5% would buy after-harvest lumpsum weather insurance, 55.7% opted for group insurance and after initial take up, only 31.1% would continue if there is no payout for next 5 years. Rainfall index insurance is economically feasible with an annual economic profit of N323million and commercially viable with a sustainability index of 0.19 in expected economic benefit (EEB) at 10% interest rate. Economic loss is triggered at rainfall below 377mm threshold during crop development stage while loss is triggered at below 140 mm rainfall in the reproduction stage of maize growth by crossing these critical thresholds of rainfall requirements. The prototype weather index maize insurance contract designed for the most critical crop growth phases payout indemnity capable of securing income losses and build farmers‘ resilience. en_US
dc.description.sponsorship The Federal Ministry of Education and Research en_US
dc.language.iso en en_US
dc.publisher WASCAL en_US
dc.subject Financial adaptation en_US
dc.subject Risk transfer en_US
dc.subject Weather index insurance en_US
dc.subject Demand model en_US
dc.subject Nigeria en_US
dc.title Modelling Demand for Weather Index Insurance as Adaptation Instrument to Drought Risk Event in Central-West Nigeria en_US
dc.type Thesis en_US


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