Browsing by Author "Kelly D. Zering, Committee Chair"

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    The Determinants of Exit Behavior Under a Structurally Changing Industry: Evidence from the U.S. Swine Industry
    (2005-07-22) Kuo, Heng Hung; Kelly D. Zering, Committee Chair; Michael K. Wohlgenant, Committee Member; Nicholas E. Piggott, Committee Member; David J. Flath, Committee Member
    This study estimates the determinants of the exit behavior in a concentrating industry that encounters industrial restructuring. The swine industry has experienced the dramatic change in many perspectives, especially farm size and operation numbers, in the United States in past decades and the process still continues. Characterized by industrialized hog production, hog industry provides available data and a significant case study for exploring the issue related to structural change and exit behaviors. This study uses U.S. swine industry data to explore the factors that affect small producers' quitting decision. Unbalanced panel data for 14 major hog production states from 1988-2003 was collected. Fixed effects models and random effects models, one-factor or two-factor are all considered in this study. By observing the aggregate leaving pattern: exit rates, we can evaluate how exogenous shocks, macroeconomic conditions, technological improvement and scale of production, drive small-farm operators' decision-making. Moreover, this study evaluates two driving forces of leaving behavior, voluntarily or non-voluntarily. It is implied that timing of leaving is important. In addition, this paper evaluates the existence of a crowding-out effect among large-scale modernized entrants and small traditional family producers' exit. Comparing the models by R-square, F-test and Hausman test, this study chooses one-factor random-effects model as the major results and two-factor fixed-effects model as auxiliary results. Whether the exiting behaviors of producers, especially for smaller producers, are volunteering or forcing to leave, that is related to the fairness of competition within the industry. In this study we find out new large-scale entrants do not displace the incumbents. It means that the crowding-out force does not happen between large-scale producers and small-scale producers. Alternatively, we find out that the expanding larger producers' hog operation sizes pressure the small producers to leave swine industry. As for this expanding is benign or hurtful, this study does not provide the evidences to judge. As for technology improvement, it affects the survival space for smallest hog producers. It implies that smallest category of producers have difficulty to access the improvement technology. Furthermore, technology improvement plays a buffer role for producers with scale of 100-499 head of hogs. It implies that for producers in this category need to change its efficient capacity, match the necessity of improved technology and/or raise the management skills to survive in this business. Illustrate the exiting behaviors might increase the forecasting precision of supply for the whole industry, especially established the relationship between exit behaviors and supply, that might help clarifying further hog cycles. In this study we conclude that hog price is the factor to affect the incentives of raising-hogs of producers with scale of 100-499 head. In addition, from this study, we do not observe that state-specific factors affect the exit behaviors of small producers strongly enough. It implies that state-level public programs or policies, such as environmental regulation, do not have crucial influence on small producers' exodus.
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    Spatial Econometric Analysis of a Watershed Utilizing Geographic Information Systems: Water Quality Effects of Point and Non-Point Pollution Sources in the Neuse River Basin, NC.
    (2005-12-12) Lee, Jong-Hwa; Montserrat Fuentes, Committee Member; Ada A. Wossink, Committee Member; Kelly D. Zering, Committee Chair; Walter N. Thurman, Committee Member
    This study utilizes elements of several different fields of study to facilitate more effective and efficient policy development for water pollution control. In order to implement efficient environmental policy, spatial aspects of watersheds should be carefully incorporated into empirical analysis. The geographical attributes of a watershed induce various spatial stochastic processes, causing surface water quality data in streams to have a unique spatial structure. In this study, geographical data of watersheds are collected and manipulated to find a consistent basis for comparing measures of pollution sources with variations in water quality across hydrologic units in the Neuse River basin in North Carolina. This research seeks to calibrate an empirical watershed model using available spatial (statistical) analytical techniques. Methods are demonstrated of utilizing Geographic Information Systems (GIS) to convert data from multiple sources to a common basis for water quality analysis. A spatial autoregressive response model is chosen considering spatial aspects of a regional watershed, and a corresponding structural watershed model is constructed. The empirical watershed model is designed to incorporate spatial effects and to produce accurate estimates. The model specifies that the spatially weighted sum of neighbor water qualities (total nitrogen [TN] concentrations) affects the TN concentration of each downstream monitoring unit, as do the standard covariates of local pollution sources and heterogeneous watershed characteristics. The completed standard econometric analysis includes cross-sectional estimation of several functions predicting TN concentration in streams conditional on watershed characteristics and potential sources of TN in the hydrologic unit. Results show that a clear understanding of regional spatial capacity will help avoid overuse of water resources. Specific knowledge of spatial information and empirical relationships allows improved design of controls on economic activity across regions (e.g., Total Daily Maximum Daily Load [TMDL] and nutrient trading programs) to preserve environmental resources. The study concludes by recognizing that a more robust watershed analysis would require more spatial data refinement and the option of panel data analysis.