Evidence on the welfare impacts of index-based insurance (IBI) is scant. We use two-round panel data on households who had access to adopt IBI in the Rift-valley zone of south-eastern Ethiopia. Difference-in-difference method with fixed-effect estimation technique is used to reduce potential program placement and individual self-selection biases arising from time-invariant unobserved heterogeneity. Results reveal that adoption of IBI indeed causally increased the level of consumption and investment in high-risk high-return agricultural inputs. Accounting for the intensity of adoption through a flexible model specification, results suggest that repeated adoption of IBI has cumulative lasting effect on these outcomes.Farm households in Africa must cope with bad conditions as to soil quality, weather and infrastructure. The variability of rainfall causes yields to vary strongly from one year to the next. With yields already low (due to poor soil condition) these variations can be life threatening. Meanwhile, inadequate infrastructure makes it difficult to help the households with access to financial services, insurance and inputs that could stabilize their access to resources, and enhance yields. Solving a single aspect, say bringing inputs to the farm, will not be sufficient as credit is also needed. But credit can only be provided if sufficient likelihood exists that loans will be repaid. Here, insurance can help. If insurance of the loan makes it attractive enough for the lender, a package can be composed of inputs, with credit and insurance, that solves all these problems with one bundle. Yet, the households will remain exposed to some risks as insuring against all is prohibitively expensive. What is the appropriate degree of insurance in such bundles? That is the core question addressed in this research. It aims at supplying inputs to farmers on credit, with insurance, in such a way that a good balance is found between the benefits and risks to the farmers and the profits and risks to the credit provider. We investigate the possibilities for such a balanced approach in Kenya and Ethiopia in collaboration with a large insurance provider and a farmers organisation. Together with them we collect information on the costs, benefits and risks involved in using the inputs, the alternatives open to them, and the costs and benefits involved in providing credit to finance the purchase of inputs, with and without an insurance against crop failure. With all this information, we go and talk to the stakeholders concerned to find out how they would respond if more or less insurance would be provided. Will credit suppliers lower their prices, if repayment of loan is more likely because the crop is insured? Will households decide to take higher yielding (but more risky) crops if part of the downside risk is insured? We establish this for the parties concerned in Kenya and Ethiopia, but also in other African countries. Having established how these stakeholders respond to changes in insurance, we can proceed to derive what the best degree of insurance might be. And this is then finally tested in a field experiment. With this knowledge we can help other suppliers of insurance and credit, and farm organisations to establish similar packages that are adapted to the local conditions for input supply, and financial services.

Data used in this study were collected from smallholders in the Rift-valley zone in south-eastern Ethiopian. A two-round survey with two-year intervals (2015–2017) was administered on 1143 randomly selected IBI-adopter and non-adopter households. Recruitment of households included in these two surveys was worked out as follows. First, we selected three districts, namely Bora, AJK and Arsi Negele, out of the five districts where OIC implemented IBI. Second, we identified a random sample of kebeles within the three districts , including those kebeles covered by IBI as well as those that OIC did not cover. Finally, sample households were randomly drawn from all these selected kebeles. In the first round of survey that we conducted during January-April, 2015, data were collected from a total of 1143 households, out of which 461 were adopters and 682 were non-adopters of IBIs, over the period 2013-14. The dataset covers information on household, village and IBI intervention, including household demographic characteristics, investment in agricultural inputs, consumption, use of financial services as well as village infrastructure and access to markets. The same questionnaire used in the baseline survey was also administered in the end-line survey. The questionnaires did take about 3 hours per interview . Respondent attrition was minimal. Only four households who were considered during the baseline were not covered during the end line survey. This study is thus based on a balanced panel of 1139 households, of which 596 were adopters and 543 were non-adopters, during the second survey observation. Over the two survey periods, adoption or treatment status was changed in subsequent years, with some households joining IBI adoption and others dropping out. In addition, uptake payout data were collected from OIC, and cross-checked with the responses of the households in the survey. An advantage of these data in studying the impact of IBI is that the baseline observation in 2015 coincides with the massive expansion of IBI in villages that were rank-filed during the initial two years of intervention, to be considered in the subsequent intervention periods. This enables us to identify the impact of IBI adoption using 2015 as baseline information for both adopters and non-borrowers. Moreover, there is little reason to believe that OIC’s expansion to other villages has been systematic and endogenous to village outcomes. In principle, if a kebele is considered for IBI implementation, all residents in that kebele were eligible to buy IBI. However, households may have self-selected into IBI adoption, and participation can be endogenous at the individual level, which we explicitly tackle in the empirical analysis. We measure the impact of IBI adoption on two welfare indicators: household consumption and investment in high-risk high-return inputs. Both set of variables are continuous in nature. Household consumption is an aggregate of selected food and non-food consumption. Food items consumed both from own sources and from purchases over a period of one week were included . Necessary adjustments are made to make measured items and units. To minimize measurement error from heterogeneity in age among household members, per capita consumption is used.