This paper analyzes the dynamic nature of rainfall insurance purchasing decisions. Customers of the Indan microfinance institution BASIX who receive an insurance payout are 9 to 22 percentage points more likely to purchase insurance the following year. This effect cannot be satisfactorily explained by trust, learning, or direct effects ofweather, leading to the conclusion that it is driven by behavioral effects. Overall, low repurchasing rates even after payouts suggest that the studied rainfall index insurance products are likely to continue struggling to achieve significant sales at market prices.

This paper analyzes the dynamic nature of rainfall insurance purchasing decisions, specifically looking at whether and why receiving an insurance payout induces a greater chance of purchasing insurance again the next year. This analysis uses customer data from the Indian micro-finance institution BASIX, and finds that receiving an insurance payout is associated with a 9 to 22 percentage points increased probability of purchasing insurance the following year. This affect appears to be driven by behavioral effects of receiving a payout, and cannot be explained by trust, learning, or direct effects of weather. Overall, low repurchasing rates even after payouts suggest that current rainfall index insurance products are likely to continue struggling to achieve significant sales at market prices.

This paper develops a novel methodology to estimate the degree of spatial basis risk for an arbitrary rainfall index insurance instrument. It relies on a widelyused stochastic rainfall generator, extendedto accommodate nontraditional dependence patterns—in particular spatial upper-tail dependence in rainfall—through a copula function. The methodology is applied to a recentlylaunched index product insuring against excess rainfall in Uruguay. The model is first calibrated using historical daily rainfall data from the national network of weather stations, complemented with a unique,high-resolution dataset from a dense network of 34 automatic weather stations around the study area. The degree of downside spatial basis risk is then estimated by Monte Carlo simulations and the results are linked to both a theoretical model of the demand for index insurance and to farmers’ perceptions about the product.

This thesis has two research chapters regarding the government provided Rainfall Index Insurance for Pasture, Rangeland, and Forage (RI-PRF). In the first chapter, we empirically examined whether charity hazard exists between Rainfall Index Insurance (RI-PRF) and government mandated Livestock Forage Program by estimating the demand for RI-PRF. Evidence was found that lagged LFP payments significantly increase the marginal effects of participating in RI-PRF in three of the five states and in the combined model. These results support the opposite of charity hazard where LFP payments improve the probability of purchasing RI-PRF. In our other models, the results provide evidence in support and against charity hazard in RI-PRF participation. As a result, we cannot definitively accept or reject our hypothesis that charity hazard exists in RI-PRF participation. In the second chapter, we examined the relation between RI-PRF insurance interval selection and financial outcomes from forage production for two locations in the Sandhills of Nebraska. We find that the risk reducing effectiveness of the monthly insurance interval depends upon expected precipitation. Our results indicate that insurance scenarios containing monthly intervals with high expected precipitation (during the growing season) reduced producer risk. Whereas insurance scenarios containing monthly intervals with low expected precipitation (non-growing season) did not result in reducing producer risk. In addition, insurance intervals with low expected precipitation offered the highest net returns to insurance participation at one location, had higher premiums and therefore, higher government cost through additional subsidy dollars per acre.

Weather index insurance has been attracting much attention from academics and policy makers. When deciding whether and how much insurance to obtain, farmers face a trade-off: while an increasing acre coverage reduces the weather risk, it increases the basis risk. This dissertation investigates the demand for rainfall index insurance in India. Chapter 1 presents the subsidy experiment that was conducted in India and describes the key variables collected. It offers directions for quantitative research using the analyzed dataset. In Chapter 2, I particularly focus on the relationships among basis risk, weather risk, and the risk aversion of potential insurance buyers. Based on the subsidy experiment, I develop a structural model, and estimate the risk aversion parameters. The estimated risk aversion is found to be consistent with the observed inelasticity of demand for many farmers. I also show that a heterogeneity of socio-economic characteristics affects the level of the estimated risk aversion. I find that age, education, and literacy are negatively correlated with the estimated risk aversion. Finally, I derive an aggregate demand and conduct a counterfactual analysis to quantify the effect of basis risk. I find that basis risk is important enough that only farmers within approximately 4km to a weather station can actually reduce the overall risk by purchasing this insurance.

Rainfall index insurance provides a payout based on measured local rainfall during key phases of the agricultural season, and in principle can help rural households diversify a key source of idiosyncratic risk. This paper describes basic features of rainfall insurance contracts offered in India since 2003, and documents stylized facts about market demand and the distribution of payouts. The authors summarize the results of previous research on this market, which provides evidence that price, liquidity constraints, and trust all present significant barriers to increased take-up. They also discuss potential future prospects for rainfall insurance and other index insurance products.