Volatility Spillovers in Agricultural Commodity Markets: An Application Involving Implied Volatilities from Options Markets

Replaced with revised version of paper 07/22/11 and 2/14/2012.Volatility Spillovers, Implied Volatility, Structural Change, Risk and Uncertainty,

Threshold Effects on Inter-Sectoral Migration of U.S. Farm Labor

Labor and Human Capital,

Snap Judgment: Recognizing the Propriety and Pitfalls of Direct Judicial Review of Audiovisual Evidence at Summary Judgment

Conflicting results in two recent police excessive force decisions by the U.S. Supreme Court—Tolan v. Cotton and Plumhoff v. Rickard—have sown confusion about the standards for summary judgment. This Note shows how the two decisions are consistent with each other and with longstanding summary judgment precedents. The key insight is that since the Second Circuit’s iconic 1946 decision in Arnstein v. Porter, appellate judges, including Supreme Court Justices, have listened to audio recordings, scrutinized artwork, and—as in the case of Plumhoff—watched video footage in order to decide for themselves whether there is a genuine issue of material fact for trial. These “objective” components of the record are considered vitally important to the decisions. When no objective evidence is available, appellate judges are left with “he said, she said” testimonial evidence in which demeanor evidence looms larger and are therefore more likely to allow the cases to proceed to trial. The presumed propriety of appellate judicial review of audiovisual evidence not only explains the different results in Tolan (no audiovisual evidence of police shooting and vacating the lower court’s finding for the defendant officer) and Plumhoff (video evidence of a police car chase resulting in the Court finding for the officer), but it also will have greater significance in current police excessive force cases given the omnipresence of smartphones and police recordings. At the same time, it is worth questioning whether appellate judges should continue to exercise limitless, de novo review of present-day audiovisual evidence, which may require as much understanding of context as traditional demeanor evidence

Analyzing the Effects of Weather and Biotechnology Adoption on Corn Yields and Crop Insurance Performance in the U.S. Corn Belt

Favorable weather and the adoption of Genetically Modified (GM) corn hybrids are often argued to be factors that explain recent corn yield increases and risk reduction in the U.S. Corn Belt. The focus of this analysis is to determine whether favorable weather is the main factor explaining increased and more stable yields or if biotechnology adoption is the more relevant driving force. The hypothesis that recent biotechnology advances have increased yields and reduced risks by making corn more resistant to pests, pesticides, and/or drought is tested. Fixed effects models of yields and crop insurance losses as functions of weather variables and genetically modified corn adoption rates are estimated taking into account the non-linear agronomic response of crop yields to weather. Preliminary results show that genetically modified corn adoption rates, especially insect- resistant corn adoption, have had a significant and positive effect on average corn yields in the U.S. Corn Belt over the last years. Furthermore, genetically modified corn adoption has not only increased corn's tolerance to extreme heat but has also improved corn's tolerance to excessive and insufficient rainfall.Crop Production/Industries, Farm Management,

The Implications of Binding Farm Program Payment Limits Associated with Income Means Testing

Replaced with revised version of poster 07/20/11.Agricultural and Food Policy,

Time-varying Yield Distributions and the U.S. Crop Insurance Program

The objective of this study is to evaluate and model the yield risk associated with major agricultural commodities in the U.S. We are particularly concerned with the nonstationary nature of the yield distribution, which primarily arises because of technological progress and changing environmental conditions. Precise risk assessment depends on the accuracy of modeling this distribution. This problem becomes more challenging as the yield distribution changes over time, a condition that holds for nearly all major crops. A common approach to this problem is based on a two-stage method in which the yield is first detrended and then the estimated residuals are treated as observed data and modeled using various parametric or nonparametric methods. We propose an alternative parametric model that allows the moments of the yield distributions to change with time. Several model selection techniques suggest that the proposed time-varying model outperforms more conventional models in terms of in-sample goodness-of-fit, out-of- sample predictive power and the prediction accuracy of insurance premium rates.Risk and Uncertainty,