Efficiency of Weather Derivatives as Primary Crop Insurance Instruments

This study analyzes efficiency of weather derivatives as primary insurance instruments for six crop reporting districts that are among the largest producers of corn, cotton, and soybeans in the United States. Specific weather derivatives are constructed for each crop/district combination based on analysis of several econometric models. The performance of the designed weather derivatives is then analyzed both in- and out-of-sample. The primary findings suggest that the optimal structure of weather derivatives varies widely across crops and regions, as does the risk-reducing performance of the optimally designed weather derivatives. Further, optimal weather derivatives required rather complicated combinations of weather variables to achieve reasonable fits between weather and yield.agricultural risk management, crop insurance, index insurance, weather derivatives, Risk and Uncertainty,

Weather insurance design with optimal hedging effectiveness

I construct index-based weather insurance contracts with optimal hedging effectiveness for the insured or maximal profits for the insurer. In contrast to earlier work, I refrain from imposing functional form assumptions on the stochastic relationship between weather and yield and from restricting attention to (piecewise) linear contracts. Instead, I derive the shape of the optimal weather insurance contracts empirically by non-parametrically estimating yield distributions conditional on weather. I find that the optimal pay-off structure is non-linear for the entire range of weather realizations. I measure risk reduction of optimal weather insurance contracts for different weather indices and levels of risk aversion. Considering profit-maximizing contracts, I find that at modest levels of risk aversion (coefficient of relative risk aversion around 2), a loading factor of 10% of the fair premium is possible such that the insurance contract remains attractive for the insured. With higher levels of risk aversion, loading of more than 50% becomes possible.agricultural insurance, optimal insurance design, weather derivatives, weather risk, hedging effectiveness, loading of premium

Index Insurance for Agricultural Transformation in Africa

Over the past two decades, researchers, practitioners, and policy-makers have shown great interestin using index insurance to manage agricultural production risk in order to promote technicaltransformation of agriculture in the developing world. Unlike conventional agricultural insurance,which indemnifies policyholders for verifiable production losses arising from multiple perils, indexinsurance pays policyholders based on the observed value of a specified “index” variable, such asrainfall, that is highly correlated with losses. Index insurance is less susceptible to the structuralproblems that have rendered conventional agricultural insurance too expensive and financiallyun-sustainable for the developing world. Index insurance, however, offers less efective individualrisk protection than conventional insurance and faces non-trivial challenges for sustainableimplementation. This article summarizes lessons learned from index insurance projects undertakenin sub-Saharan Africa since 2000 and points the way forward for the use of index insurance tosupport African agricultural development in the 21st century

Contextual Centrality: Going Beyond Network Structures

Centrality is a fundamental network property which ranks nodes by their structural importance. However, structural importance may not suffice to predict successful diffusions in a wide range of applications, such as word-of-mouth marketing and political campaigns. In particular, nodes with high structural importance may contribute negatively to the objective of the diffusion. To address this problem, we propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, nodal contributions to the objective of the diffusion. We perform an empirical analysis of the adoption of microfinance in Indian villages and weather insurance in Chinese villages. Results show that contextual centrality of the first-informed individuals has higher predictive power towards the eventual adoption outcomes than other standard centrality measures. Interestingly, when the product of diffusion rate $p$ and the largest eigenvalue $\lambda_1$ is larger than one and diffusion period is long, contextual centrality linearly scales with eigenvector centrality. This approximation reveals that contextual centrality identifies scenarios where a higher diffusion rate of individuals may negatively influence the cascade payoff. Further simulations on the synthetic and real-world networks show that contextual centrality has the advantage of selecting an individual whose local neighborhood generates a high cascade payoff when $p \lambda_1 < 1$. Under this condition, stronger homophily leads to higher cascade payoff. Our results suggest that contextual centrality captures more complicated dynamics on networks and has significant implications for applications, such as information diffusion, viral marketing, and political campaigns

Risk management strategies to cope with climate change in grassland production: an illustrative case study for the Swiss plateau

In this paper, we assess climate change impacts on an intensively managed grassland system at the Swiss Plateau using the process-based grassland model PROGRASS. Taking the CO2 fertilization into account, we find increasing yield levels (in the range of 10-24%) and sharp increases in production risks for an illustrative climate change scenario that suggests a marked increase in temperature and decrease in summer rainfall. Climate change-induced increases in the coefficients of variation of grassland yields are in the range of 21 and 50%. This finding underpins that additional risk management strategies are needed to cope with climate-change impacts on grassland production. The outputs from the grassland model are evaluated economically using certainty equivalents, i.e., accounting for mean quasi rents and production risks. To identify potential risk management strategies under current and future climatic conditions, we consider adjustments of production intensity and farm-level yield insurance. The impact of climate change on production intensities is found to be ambiguous: farmers' will increase intensity under unconstrained production conditions, but will decrease production intensity in the presence of a cross-compliance scheme. Our results also show that the considered insurance scheme is a powerful tool to manage climate risks in grassland production under current and future conditions because it can reduce the coefficients of variation of quasi rents by up to 50%. However, we find that direct payments tend to reduce farmers' incentives to use such insurance schem

RISK, GOVERNMENT PROGRAMS, AND THE ENVIRONMENT

Nearly all farm business ventures involve financial risk. In some instances, private and public tools used to manage financial risks in agriculture may influence farmers' production decisions. These decisions, in turn, can influence environmental quality. This bulletin summarizes research and provides some perspective on private and public attempts to cope with financial risks and their unintended environmental consequences. Specifically, it examines the conceptual underpinnings of risk-related research, challenges involved with measuring the consequences of risk for agricultural production decisions, government programs that influence the risk and return of farm businesses, and how production decisions influence both the environment and the risk and average returns to farming.risk, agricultural production, government programs, environment, Agricultural and Food Policy, Environmental Economics and Policy, Risk and Uncertainty,

Weather insurance design with optimal hedging effectiveness

I construct index-based weather insurance contracts with optimal hedging effectiveness for the insured or maximal profits for the insurer. In contrast to earlier work, I refrain from imposing functional form assumptions on the stochastic relationship between weather and yield and from restricting attention to (piecewise) linear contracts. Instead, I derive the shape of the optimal weather insurance contracts empirically by non-parametrically estimating yield distributions conditional on weather. I find that the optimal pay-off structure is non-linear for the entire range of weather realizations. I measure risk reduction of optimal weather insurance contracts for different weather indices and levels of risk aversion. Considering profit-maximizing contracts, I find that at modest levels of risk aversion (coefficient of relative risk aversion around 2), a loading factor of 10% of the fair premium is possible such that the insurance contract remains attractive for the insured. With higher levels of risk aversion, loading of more than 50% becomes possible