PREMIUM RATE DETERMINATION IN THE FEDERAL CROP INSURANCE PROGRAM: WHAT DO AVERAGES HAVE TO SAY ABOUT RISK?

This article reviews actuarial procedures used to calculate premium rates in the federal crop insurance program. Average yields are used as an important indicator of risk under current rating practices. The strength and validity of this relationship is examined using historical yield data drawn from a large sample of Kansas farms. The results indicate that assumed relationships between average yields and yield variation are tenuous and imply that rating procedures that rely on average yields may induce adverse selection.Risk and Uncertainty,

INSTABILITY AND RISK IN U.S. AGRICULTURE

The U.S. government has been extensively involved in providing income support and risk management policies for U.S. farmers over the last 65 years. Risk management policies have included crop insurance, disaster relief, and in recent years, revenue insurance. Recent policy changes signaled an intention on the part of policy makers, at least in principle, to move U.S. agriculture toward the free market. Low commodity prices and localized droughts, however, have brought about renewed calls for direct income assistance. In this paper, we discuss the role of the government in providing policies to address income shortfalls and risk in agriculture. Problems and inconsistencies with policies are identified and discussed. Implications for international markets are also highlighted.agriculture, crop insurance, government policy, risk management, Agricultural and Food Policy, Risk and Uncertainty,

FORECASTING CATTLE PRICES IN THE PRESENCE OF STRUCTURAL CHANGE

Recent empirical research and development in the cattle industry suggest several reasons to suspect structural change in economic relationships determining cattle prices. Standard forecasting models may ignore structural change and may produce biased and misleading forecasts. Vector autoregressive (VAR) models that allow parameters to vary with time are used to forecast quarterly cattle prices. The VAR procedures are flexible in that they allow the identification of structural change that begins at an a priori unknown point and occurs gradually. The results indicate that the lowest RMSE for out-of-sample forecasts of cattle prices is obtained using a gradually switching VAR model. However, differences between the gradually switching VAR model and a univeriate ARIMA model are not strongly significant. Impulse response functions indicate that adjustments of cattle prices to new information have become faster in recent years.Demand and Price Analysis,

Probabilistic Modeling of Catastrophic Weather Risks: Implications for Indemnification Plans for Animal Waste Spills

Replaced with revised version of paper 08/24/07.Livestock Production/Industries, Risk and Uncertainty,

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,

Spatio-Temporal Modeling of Wildfire Risks in the U.S. Forest Sector

In the US forestry industry, wildfire has always been one of the leading causes of damage. This topic is of growing interest as wildfire has caused huge losses for landowners, residents and governments in recent years. While individual wildfire behavior is well studied (e.g. Butry 2009; Holmes 2010), a lot of new literature on broadscale wildfire risks (e.g. by county) is emerging (e.g. Butry et al. 2001; Prestemon et al. 2002). The papers of the latter category have provided useful suggestions for government wildfire management and policies. Although wildfire insurance for real estate owners is popular, the possibility to develop a forestry production insurance scheme accounting for wildfire risks has not yet been investigated. The purpose of our paper is to comprehensively evaluate broadscale wildfire risks in a spatio-temporal autoregressive scenario and to design an actuarially fair wildfire insurance scheme in the U.S. forest sector. Our research builds upon an extensive literature that has investigated crop insurance modeling. Wildfire risks are closely linked to environmental conditions. Weather, forestland size, aspects of human activity have been proved to be crucial causal factors for wildfire (Prestemon et al. 2002; Prestemon and Butry 2005; Mercer et al. 2007). In light of these factors, we carefully study wildfires ignited by different sources, such as by arson and lightning, and identify their underlying causes. We find that the decomposition of forestland ecosystem and socio-economic conditions have significant impacts on wildfire, as well as weather. Our models provide a good fit to data on frequency and propensity for fires to exist (e.g. R-square ranges from 0.4 to 0.8) and therefore provide important fundamental information on risks for the development of insurance contracts. A number of databases relevant to this topic are used. With the Florida wildfire frequency and loss size database, a complete survey of four measurements of annual wildfire risks is implemented. These four measurements are annual wildfire frequency, burned area, fire per acre and burned ratio at county level. In addition, the national forestry inventory and analysis (FIA) database, Regional Economic Information Systems (REIS) database and the national weather database have supplied forestland ecosystem, socioeconomic, and weather condition information respectively. With our spatio-temporal lattice models, impacts of environmental factors on wildfire and implications of wildfire management policies are assessed. Forestland size, private owners’ share of forestland, population and drought would positively contribute to wildfire risks significantly. Cold weather and high employment are found to be helpful in lessening wildfire risks. Among the forestland ecosystem, oak / pine & oak / hickory forestland would reduce wildfire risks while longleaf / slash & loblolly / shortleaf pine forestland would have a mixed impact. An interesting finding is that oak / gum / cypress forestland would reduce wildfire frequency, but would enhance wildfire propensity at the same time. Hurricanes could intensify wildfire risks in the same year, but would significantly decrease the next year’s wildfire risks. Meanwhile, cross sample validation verifies that our method can forecast wildfire risks adequately well. Since our approach does not incorporate any fixed-effect indicator or trend as in the panel data analysis (Prestemon et al. 2002), it offers a universal tool to evaluate and predict wildfire risks. Hence, given environmental information of a location, a corresponding actuarially fair insurance rate can be calculated.wildfires, forestry, weather, socio-economic, Spatio-Temporal autocorrelation, Risk and Uncertainty,

Spatio-Temporal Modeling of Southern Pine Beetle Outbreaks with a Block Bootstrapping Approach

Our study focuses on modeling southern pine beetle (SPB) outbreaks in the southern area. The approach is to evaluate SPB outbreak frequency in a spatio-temporal framework. A block bootstrapping method with zero-inflated estimation has been proposed to construct a statistical model accounting for explanatory variables while adjusting for spatial and temporal autocorrelation. Although the bootstrap (Efron 1979) method can handle independent observations well, the strong autocorrelation of SPB outbreaks brings about a major challenge. Motivated by bootstrapping overlapping blocks method in autoregressive time series scenario (Kunsch 1989) and block bootstrapping method of dependent data from a spatial map (Hall 1985), we have developed a method to bootstrap overlapping spatio-temporal blocks. By selecting an appropriate block size, the spatial-temporal correlation can be eliminated. The second challenge arises from the fact that the SPB spots distribution has a heavy weight on 0. To accommodate this issue, the zero-inflated models are adopted in the estimation stage. With our saptio-temporal block bootstrapping approach, impacts of environmental factors on SPB outbreaks and implications of pine forest management are assessed. Almost all the explanatory variables, including drought, temperature, forest ecosystem and hurricane, have been detected to have significant impacts. Forestland size and government share of forestland would positively contribute to SPB outbreaks significantly. Meanwhile, our method offers a way to forecast the frequency of future SPB outbreaks, given the current environmental information of a county.Southern Pinebeetle, Block Bootstrapping, Risk and Uncertainty,

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

Labor and Human Capital,

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,