Using the Spatial Statistics Approach to Analyze Yield Risk Pooling in the US

Risk theory tells us if an insurer can effectively pool a large number of individuals to reduce the total risk, he then can provide the insurance by charging a premium close to the actuarially fair rate. There is, however, a common belief that the risk can be effectively pooled only when the random loss is independent, so that crop insurance markets cannot survive without government subsidy because crop yields are not independent among growers. In this paper, we take a a spatial statistics approach to examine the effectiveness of risk pooling for crop insurance under correlation. We develop a method for evaluating the effectiveness of risk pooling under correlation and apply the method to three major crops in the US: wheat, soybeans and corn. The empirical study shows that yields for the three crops present zero or negative correlation when two counties are far apart, which complies with a weaker condition than independence, finite-range positive dependency. The results show that effective risk pooling is possible and reveal a high possibility of a private crop insurance market in the US. Crop insurance has been an important instrument for protecting farmers’ income against low yield resulting from adverse weather and other natural disasters. Except for a few perils such as hail and fire, the multiple peril crop insurance (MPCI) has only been offered by the US government with a huge subsidy. MPCI was first introduced in 1938 on a trial basis, and extended in 1980 to most crops in the US. MPCI pays an indemnity to a farmer based on the difference between a pre-selected coverage yield level and the farmer’s realized yield