Demand and Design Considerations for Smallholder Farmers’ Weather Index Insurance Products

This cumulative dissertation discusses a range of topics related to the demand for and considerations around product design of weather index insurance instruments aimed at smallholder farmers’ risk management. The second chapter, “Demand for a Simple Weather Insurance Product in India: Theory and Evidence,” uses survey and administrative data from a project conducted in Madhya Pradesh, India during the 2010-2012 period. As part of this project, a new index insurance product was introduced to protect smallholder farmers of rainfed soybean from both deficit and excess rainfall during two consecutive Kharif (summer) seasons. In order to assess the drivers behind the demand for insurance the study induced exogenous variation along three dimensions: (1) Spatial basis risk / Distance to the weather station (by installing three new randomly-positioned weather stations), (2) Insurance premium (by offering random discounts), and (3) Product understanding (by randomly varying the intensity of training across villages). The chapter relies on a standard expected utility theory framework by Clarke (2016) to develop a series of hypotheses about the responsiveness of demand to price, spatial basis risk, and farmer’s risk aversion. Demand is found to behave as predicted: falling with price and basis risk and hump-shaped in risk aversion. Moreover, there is evidence of differential price sensitivity at different levels of basis risk, as predicted by the model. With respect to product understanding, the evidence suggests that increased incentives to learn or learning by using are more effective at increasing both understanding and demand. Furthermore, the chapter contributes to the scarce evidence on the dynamics of the demand for insurance by analyzing a two-year panel of insurance purchases. While the effect of premium subsidies persists over time, that of investments in new weather stations diminishes and the effect of increased training in the first season seems to disappear altogether during the second season. Importantly, while having previously purchased insurance does not encourage future uptake, receiving a payout does, potentially reflecting issues of trust in the product or the insurance company. The third chapter, “Estimating Spatial Basis Risk in Rainfall Index Insurance: Methodology and Application to Excess Rainfall Insurance in Uruguay,” tackles the important topic of basis risk in weather index insurance in more depth. In particular, the chapter sets out to estimate the actual extent of spatial or geographic basis risk and compare this to farmers‘ perceptions, as captured from survey data. A novel methodology is developed to estimate the degree of spatial basis risk for an arbitrary rainfall index insurance instrument. The methodology relies on a widely-used stochastic rainfall generator by Wilks (1998), extended to accommodate non-traditional dependence patterns through a copula function. In particular, the model intends to capture spatial upper tail dependence in rainfall, or the tendency for extreme rainfall (as that related to extensive, large-area storms) to be more spatially correlated than milder rainfall. This feature is empirically shown to occurr in available historical rainfall data. The extent of basis risk is then captured by simulating from the calibrated model and calculating the fraction of cases in which the insurance product would not pay even when rainfall at the farmer’s plot is within the payout region. The methodology is applied to an index product insuring against excess rainfall in Uruguay. To calibrate the model for this case study, the chapter uses 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 for the demand of index insurance and to farmer perceptions about the product. The results indicate that basis risk is not negligible in our case study. Depending on the farmer’s location, basis risk is such that the insurance product would fail to pay between 1 to 5 times out of 10 in which a farmer faces critical crop losses. Moreover, while spatial basis risk naturally increases with distance to the insurance reference gauge, it does so at a decreasing rate. In turn, farmers seem to overestimate the rate of increase, pointing to the presence of information asymmetries regarding the spatial properties of rainfall. In terms of the comparison to the theoretical model by Clarke (2016), spatial basis risk generally remains within the theoretical range in which a risk-averse farmer would demand a positive amount of insurance, even for plots located at a considerable distance from the reference weather station at which the index is measured. Finally, the results point to the importance of taking into consideration geographic variation in precipitation patterns—even within relatively small regions—when designing an index insurance product. This element is shown to considerably increase (or decrease) the degree of spatial basis risk, depending on the exact location of a farmer’s plot and its insurance reference weather station. This calls for a much more careful consideration of local climatologies before launching an index insurance product based on nearby weather stations. The fourth chapter, “Demand Heterogeneity for Index-Based Insurance: The Case for Flexible Products,” discusses a generally-overlooked yet important design issue in weather index insurance. Notably, most existing index insurance products are characterized by a relatively rigid payout structure, intended for a representative farmer’s standard risk profile. Albeit convenient, this one-size-fits-all structure comes at the cost of ignoring considerable heterogeneity in agricultural risk profiles, potentially lowering the product’s worth for many farmers. The chapter provides unique evidence on the ways in which heterogeneity in farmers' risk exposure affects their demand for agricultural index insurance. To achieve this, it analyzes a set of flexible insurance products against excess rainfall recently marketed in Uruguay during the 2013-2014 period to cover horticultural losses around harvest. The products were designed as independent insurance units―separately covering against the risk of excess rainfall across different calendar months and at different rainfall intensities―and were intended to be freely combined by the farmers to form optimal insurance portfolios that suited their particular risk profiles. The analysis exploits the substantial variation observed in the insurance portfolios demanded by farmers. The relevance of alternative sources of heterogeneity is explored by extending a simple expected utility decision model and relying on structural estimation to test the significance of each of these sources. The results show evidence for the presence of important aspects of farmer heterogeneity that directly affect their demand for risk-coping instruments, including the particular mix of crops chosen by the farmer, ex-post planting dates, soil drainage, product understanding, and spatial basis risk. The chapter concludes by quantifying the benefits of a flexible scheme by comparing farmer welfare to that achieved under alternative counterfactual insurance options. Overall, the value of providing flexibility in the form of an insurance units scheme is substantial. The findings underscore the need to provide flexibility when implementing index-based tools for hedging agricultural risks.