Basis risk has been cited as a primary concern for implementing weather hedges. This study investigates several dimensions of weather basis risk for the U.S. corn market at various levels of aggregation. The results suggest that while the degree of geographic basis risk may be significant in some instances, it should not preclude the use of geographic cross-hedging. In addition, the degree to which geographic basis risk impedes effective hedging diminishes as the level of spatial aggregation increases. In fact, geographic basis risk is actually negative in the case most representative of a reinsurance hedge, and the reduction in risk from employing straightforward temperature derivatives is significant. Finally, precipitation hedges are found to introduce additional product basis risk. The findings may be of interest to decision makers considering using exchange traded weather derivatives to hedge agricultural production and insurance risk.

Patterns in loss-ratio experience in the U.S. corn insurance market are investigated with a spatial econometric model. The results demonstrate systematic geographically related misratings and provide estimates of the impacts of several observable factors on the magnitude of misrating in the program. The model is used to estimate actuarial cross-subsidizations across the primary corn-producing states and counties. The impacts of the primary factors are substantial, resulting in net premium transfers of approximately 26 percent of total premiums annually. The misratings likely have important insurance demand, welfare, and land-use implications.Insurance markets are typically best suited for risks that are uncorrelated, occur with high frequency, and have a large number of like participants-among a handful of other standard conditions. Systemic risks (such as in crop production) induce correlation in losses, violating the standard insurability conditions and potentially leading to market failures (Glauber, 2004). Complementary causes of market failures in such systemic risk markets may include capital market imperfections, inadequate reinsurance capacities, capital and information shocks due to unexpected events, fattailed distributions that prevent diversification, and agency problems (see, e.g.,

Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near real-time, and that strongly relates to livestock mortality.Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related insurance premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record (1981 to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near-real time NDVI products; five from MODIS, and one from SPOT-VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R 2 statistics (leave-one-year-out) for the overlapping period 2002-2011. We found that division-specific models were more effective than a global model for linking the division-level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes.We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index.

Government-subsidized insurance is ubiquitous, yet estimation of demand in such markets remains challenging. The premium charged for a given deductible is determined by actuarial construction; thus, observed choicepairs are endogenous leading to biased estimation under standard econometric approaches. A theoretical model and simulation study are developed, and a new identification strategy proposed. An empirical application using Federal Crop Insurance Program-a $100 billion/year program-data reveals that demand is quite elastic after accounting for this endogeneity. Mistreatment of such endogeneity is likely partly responsible for pervasive faulty findings of inelastic insurance demand in related applications. Policy implications are also discussed.

Since the Agricultural Act of 2014, the federal crop insurance program (FCIP) has been the cornerstone agricultural policy in the United States, and is the largest such program globally, with about $100 billion in coverage annually. Given its scale and scope, the FCIP has the potential to have pervasive impacts on incentives and policy functioning if not designed and priced properly. Surprisingly, soil data are not considered by the government when establishing insurance guarantees or rates. Using soil data that could easily and feasibly be scaled nationally, we find that the pricing differentials caused by the government's failure to handle soil information leads to large errors in rating.