Information Value of Climate Forecasts for Rainfall Index Insurance for Pasture, Rangeland, and Forage in the Southeast United States
Nadolnyak, Denis, Vedenov, Dmitry, Journal of Agricultural and Applied Economics
In this article, possible use of climate forecasts in rainfall index insurance of hay and forage production is considered in a geographical area (southeast United States) relatively heavily impacted by the El Nino Southern Oscillation (ENSO). Analysis of the stochastic properties of rainfall, yields, and the ENSO forecasts using the copula technique shows that the forecast impact depends on the proximity to the Gulf Coast where the impact of the ENSO is more pronounced and earlier in the year. Stochastic modeling shows that the use of skillful longterm climate forecasts by the insured producers creates intertemporal adverse selection that can be precluded by offering forecast conditional premiums. The impacts on the efficiency of the rainfall index insurance and results of sensitivity analysis with respect to model parameters are discussed.
Key Words: copulas, ENSO forecasts, rainfall index insurance
JEL Classifications: Q14, Q51, R51, R11, R38
Over the past two decades, several alternative designs of agricultural crop insurance have been tried in an attempt to increase participation rates and improve actuarial performance of the program. However, reducing moral hazard and adverse selection inherent in insurance contracts is frequently associated with reduction in the risk covered by insurance (Glauber, 2004). One example is the index-based agricultural insurance that largely avoids the moral hazard issues and can be especially applicable for crops and areas with limited yield/revenue records and where agriculture is more rainfalldependent (Skees, 2008).
In 2007, the U.S. Risk Management Agency (RMA) introduced a pilot program to offer Pasture, Rangeland, and Forage (PRF) insurance that provides protection against losses of forage produced for grazing or harvested for hay (RMA, 2012). Two types of PRF insurance contracts are currently available under the pilot program, both of which are designed to indemnify producers when yield-reducing drought conditions arise. Rainfall Index (RI) contracts indemnify policyholders based on gridded 0.25° latitude by 0.25° longitude rainfall data published by the National Oceanic and Atmospheric Administration Climate Prediction Center. Vegetation Index (VI) contracts indemnify policyholders based on gridded 4.8 mile ? 4.8 mile Normalized Difference Vegetation Index (NDVI) data published by the U.S. Geological Survey Earth Resource Observation Center. In the 201 1 crop year, RI insurance was offered in 16 states and VI insurance in nine states.
Both types of PRF contracts are examples of index insurance. Index insurance differs from the conventional insurance in that it indemnifies policyholders based not on verifiable individual producer losses, but rather on realization of a variable or an "index" that is highly correlated with these losses. Index insurance is generally considered to be free of the moral hazard problems that have undermined the actuarial performance of traditional crop insurance (Halcrow, 1949). However, with index insurance, it is possible for a policyholder to suffer a loss without receiving an indemnity as a result of the basis risk caused by imperfect correlation between the index and the losses. Properly designed index insurance products can minimize basis risk although not completely eliminate it. The benefits, limitations, and optimal design of agricultural index insurance have been thoroughly studied in the literature. Miranda (1991) was the first to analyze the demand for agricultural index insurance in a stylized setting, demonstrating that the optimal quantity of index insurance that a producer should purchase is generally proportional to the correlation between the index and the producer's yields. Mahul (1999, 2001) and Mahul and Wright (2003) extended Miranda's results, examining practical design issues and revenue insurance. Carriquiry and Osgood (2012) developed a theoretical model looking specifically at the impact of climate (weather) forecast availability on producer welfare and demand for index insurance. …