MODELING TECHNICAL TRADE BARRIERS UNDER UNCERTAINTY

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.International Relations/Trade,

Trade Agreements, Political Economy and Endogenously Incomplete Contracts

We develop a political economy model of trade agreements following along the line of Grossman and Helpman (1995a) yet incorporating contracting costs, uncertainty and multiple policy instruments. We show that rent-seeking efforts do not affect tariff rates as they are offset by the substitution effect of domestic production subsidies. Similar to Horn et al (2010), we find the coexistence of uncertainty and contracting costs make optimal trade agreements incomplete contracts. Our model helps explain differential treatment on subsidies, countervailing duties, and the national treatment principle - all key provisions of the current WTO agreement.Trade agreement, political economy, contracting cost, uncertainty JEL Classification:, Agricultural and Food Policy, International Relations/Trade, Political Economy, Public Economics,

WEATHER-BASED ADVERSE SELECTION AND THE U.S. CROP INSURANCE PROGRAM: THE PRIVATE INSURANCE COMPANY PERSPECTIVE

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.Risk and Uncertainty,

Rating Crop Insurance Policies with Efficient Nonparametric Estimators that Admit Mixed Data Types

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.discrete data, insurance rating, kernel estimation, yield distributions, Risk and Uncertainty,

ON CHOOSING A BASE COVERAGE LEVEL FOR MULTIPLE PERIL CROP INSURANCE CONTRACTS

For multiple peril crop insurance, the U.S. Department of Agriculture'Â’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.Risk and Uncertainty,

Nonparametric estimation of crop insurance rates revisited,

With the crop insurance program becoming the cornerstone of U.S. agricultural policy, recovering accurate rates is of paramount interest. Lack of yield data presents, by far, the most fundamental obstacle to recovery of accurate rates. This article employs new methodology to estimate conditional yield densities and derive the insurance rates. In our application, we find the nonparametric kernel density estimator requires an additional twenty-six years of yield data to estimate the shape of the conditional yield densities as accurately as the recently developed empirical Bayes nonparametric kernel density estimator. Such methodological improvements can significantly aid in ameliorating the data problem

MODELING TECHNICAL TRADE BARRIERS UNDER UNCERTAINTY

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

Is There Too Much History in Historical Yield Data

County crop yield data from USDA-NASS are extensively used in the literature as well as practice. In many applications, yield data are adjusted for the first two moments then assumed independent and identically distributed. For most major crop-region combinations, yield data exist from 1955 onwards and reflect significant innovations in both seed and farm management technologies. These innovations have likely changed the yield distribution raising doubt regarding the identically distributed assumption. We consider the question of how much historical yield data should be used in empirical analyses. First, we use distributional tests to assess if and when the adjusted yield data result from different DGPs. Second, we consider the application to crop insurance by using an out-of-sample rating. Third, we estimate DGPs and then simulate to quantify the additional error. Overall, the results indicate that using yield data more than 30 years old can substantially increase estimation error. Given that discarding data is unappetizing, we propose three methodologies that can re-incorporate the discarded data. Our results suggest gains in efficiency by using these methodologies. While our results are most applicable to the crop insurance literature, we certainly feel they suggest proceeding with caution when using historical yield data in other applications. Acknowledgement

Insurance Subsidies, Technological Change, and Yield Resiliency in Agriculture

Innovation in the agricultural sector will determine our ability to reduce food insecurity and feed nine billion people by 2050. Concomitantly, most of the world's agricultural crop production is produced under heavily subsidized insurance. Changes in food security will be largely driven by the nexus of innovation, climate change, and the policy institutions under which production agriculture operates. In the United States, crop insurance subsidies increased from 30% to 60% between 1994 and 2000, bringing about a significant increase in program participation. We use this increase as a natural experiment (event) to empirically estimate the impact of insurance subsidies on rates of technological change and measures of yield resiliency in corn (maize) yields. Our event results indicate that subsidies caused an increase in the rates of technological change and, more surprisingly, an increase in yield resiliency measures. However, point identification fails if there exist any confounding variables. Therefore, we use the spatial heterogeneity in our estimated event parameters to identify causal effects from three sources: introduction of genetically modified seeds, changing climate, and insurance subsidies themselves. Quite interestingly, the increase in the rates of technological change dissipates and the yield resiliency effect is reversed (consistent with theory). Furthermore, we find that the positive effects of genetically modified seeds dominate the effects from both changing climate and increased subsidies