Ker, Alan P.

By: Ng, Horlick ; Ker, Alan P.
Although there is significant literature on technological change in U.S. crop yields, very little has been done with Canadian yields. We model the changing nature of county-level yields for barley, canola, corn, oats, soybean, and wheat in Canada. We use mixtures to allow and test for heterogeneous rates of technological change within the yield-data-generating process. While we tend to find increasing but heterogeneous rates of technological change, increasing and asymmetric yield volatility, and increasing absolute but decreasing relative yield resiliency, our results differ across crops and exhibit spatial bifurcations within a crop. Using a standard attribution model, we find changing climate has differing effects across crops.
By: Liu, Yong; Ker, Alan P.
Crop insurance is plagued by relatively little historical information but significant spatial information. We investigate the efficacy of using nonparametric Bayesian model averaging (BMA) to incorporate extraneous information into the estimated premium rates. Nonparametric BMA is particularly suited to this application because it does not make any assumptions about parametric form or the extent to which yields are similar. We evaluate the proposed estimator under small-to-medium sample sizes and various geographical restrictions on the distance of spatial smoothing for policy relevance. The nonparametric BMA consistently decreases error and enables statistically significant and economically important rents to be captured.
By: Racine, Jeffrey S.; Ker, Alan P.
The identification of improved methods for characterizing crop yield densities has experienced a recent surge in activity due in part to the central role played by crop insurance in the Agricultural Risk Protection Act of 2000 (estimates of yield densities are required for the determination of insurance premium rates). Nonparametric kernel methods have been successfully used to model yield densities; however, traditional kernel methods do not handle the presence of categorical data in a satisfactory manner and have therefore tended to be applied on a county-by-county basis. By utilizing recently developed kernel methods that admit mixed data types, we are able to model the yield density jointly across counties, leading to substantial finite sample efficiency gains. Findings show that when we allow insurance companies to strategically reinsure with the government based on this novel approach they accrue significant rents.
By: Ker, Alan P.; McGowan, Pat
Surprisingly, investigations of adverse selection have focused only on farmers. Conversely, this article investigates if insurance companies, not farmers, can generate excess rents from adverse selection activities. Currently political forces fashioning crop insurance as the cornerstone of U.S. agricultural policy make our analysis particularly topical. Focusing on El Nino/La Nina and winter wheat in Texas, we simulate out-of-sample reinsurance decisions during the 1978 through 1997 crop years while reflecting the realities imposed by the risk-sharing arrangement between the insurance companies and the federal government. The simulations indicate that economically and statistically significant excess rents may be garnered by insurance companies through weather-based adverse selection.
As traditional forms of agricultural protection continue to decline, agricultural interests will likely seek alternative protection in the form of technical barriers. A flexible framework for theoretically and empirically analyzing technical barriers under various sources of uncertainty is derived. Attention is focused on uncertainty arising from the variation in the product attribute levels, a source not yet considered by the literature. Ex ante and ex post densities of domestic and international quantities and prices as well as the densities of their respective extreme-order statistics are derived. An example is presented to illustrate the application of the developed framework.
By: Ker, Alan P.; Coble, Keith H.
For multiple peril crop insurance, the U.S. Department of Agriculture's Risk Management Agency estimates the premium rate for a base coverage level and then uses multiplicative adjustment factors to recover rates at other coverage levels. Given this methodology, accurate estimation of the base coverage level from 65% to 50%. The purpose of this analysis was to provide some insight into whether such a change should or should not be carried out. Not surprisingly, our findings indicate that the higher coverage level should be maintained as the base.