A large array of literature has revealed the complexity of export performance analysis. Using the case of Ghana, this dissertation, divided into three essays, seeks to provide the methodological guidance, empirical and simulation evidence necessary to analyze a countrys export performance. The choice of Ghana was motivated by the countrys growth experience, strong export and agricultural sectors and implemented reforms and programs since 1983. In the first essay, we created a new trade-weighted Cedi index or real effective exchange rate (REER) that takes into account Ghanas most relevant patterns of trade and captures the evolution of Ghanas export price competitiveness overtime. Other factors of export performance have been identified collecting the perspectives of Ghanas agricultural export sector stakeholders and using grounded theory. This research showed that Ghanas export price competitiveness, as depicted by the REER, has improved since 1983 but has revealed many additional factors that played a role in the performance of Ghanas agricultural export sector. Following export demand theory and the procedure of Toda and Yamamoto (1995) and Dolado and Lütkepohl (1996) (TYDL), the second essay estimates causal relationships between exports, the REER and foreign activity over the 1970-2009 period. Two additional models (VAR-GARCH-in-mean) were estimated to investigate the impact of exchange rate volatility on Ghanas exports. The results support the view that some of the implemented macroeconomic reforms have been the cause of Ghanas export performance. Additionally, we found that third-country exchange rate volatility has hampered Ghanas export growth. The third essay tackles methodological shortcomings of the TYDL procedure. In a Monte Carlo experiment, we compared the Schwarz Bayesian criterion (SBC) and the likelihood ratio (LR) tests in terms of their lag order frequency distributions and the finite sample properties of the resulting modified Wald (MWALD) tests. We found that in general, the SBC selects the true lag length more often than the LR tests and that in large samples the choice of the lag selection method does not influence non-causality tests results. This research also revealed that in the presence of moving average terms or in the case of mixed unit-root processes, MWALD tests perform poorly.

As healthcare costs and premiums have increased in the recent past, hospitals are forced to try to provide healthcare on tight budgets. In many cases, quality is often sacrificed in an effort to manage patient wait-times and costs. This research attempted to add to the existing body of knowledge of quality of care by defining a relationship between quality of care provided and total hospital costs. This study used the 2006 American Hospital Associations Annual Survey Database and the 2006 Hospital Compare dataset to meet the data requirements for the study. A log-log, as well as a translog, cost function was used to estimate the relationship between quality of care provisioned for community acquired pneumonia and heart failure and total hospital costs. Regressors for the cost function included hospital outputs, inputs and wages as well as variables for patient-mix, case-mix, ownership status and medical school affiliation. Ultimately this study concluded that by increasing the quality of care score associated with community-acquired pneumonia by ten percent would decrease total hospital costs by 2.44 percent. However, several improvements were found that would improve the ability of the quality of care data and estimation methodologies to more comprehensively represent quality.

The federal government has recently approved legislation to develop revenue insurance products that are affordable and user-friendly for livestock producers. The features of the products will be the ultimate determinant of product acceptance among producers. The objective of the study was to determine the relative importance of livestock revenue insurance product attributes as well as to identify the characteristics of beef cattle producers who prefer certain levels of a given product attribute. Conjoint analysis was utilized to determine the importance of selected attributes. Conjoint Designer was used to develop eleven hypothetical insurance products from four attributes with three levels each. Producers rated each product from 0 (least preferred) to 10 (most preferred). The products were rated given four different economic scenarios to determine the influence of the cattle cycle on producers' preferences and purchase decisions for products. Data were collected via personal interviews with 52 beef cattle producers in 15 parishes in Louisiana. A two-limit tobit model was used to analyze producer preferences. The part worth utility values estimated were used in a cluster analysis to segment producers based upon their preferences. Univariate probit models were estimated for nine products to evaluate the influence of various producer characteristics on purchase decisions. The results of the aggregate conjoint analysis indicated that producers preferred products with a $2.24|$0.00/cwt premium|deductible, a 180-day policy length, a state price series, and an in-person method of marketing. The price series made the largest contribution to the preference rating, and the results suggested that the economic scenario did not significantly impact preferences. The cluster analysis identified three market segments that exhibited significant differences in primary source of income, farm size, marketing strategies used, and risk attitude. The results of the univarite probit models revealed that the economic scenarios had a significant impact on producers' insurance purchase decision. Producers who were risk averse and depended on income from beef cattle were more likely to purchase insurance. Producers who were older and/or had other means of mitigating risk were less likely to purchase insurance.

The structure of the U.S. hog industry is changing rapidly. U.S. hog farms have become smaller in number, larger in size and more specialized. This study examines the factors that influence the hog producer's choice among business arrangements offered in the U.S. hog industry. A national survey was mailed to 4,986 hog producers to determine these factors. The survey consisted of questions covering topics such as: production characteristics, autonomy, transaction costs, risk, social relationships, and demographics. A response rate of 21% was received from the mailed surveys. Four alternative business arrangements were used: independent production, cooperative farming, flat-fee contract, and incentive payment contract. The multinomial logit and binomial logit models were employed to determine factors influencing producers' choice of business arrangement. Results indicate that independent producers are, in general, more likely to be breeding sow operators, diversified, corn producers, located in the same counties as flat-fee contract producers, frequent checkers of market prices, have higher debt, value autonomy and relationships with feed merchants more, and be relatively more educated than incentive payment contract producers. Cooperative producers are also more likely to be breeding sow operators, diversified, corn producers, and located in the same counties as flat-fee contractees. They are also likely to have accumulated higher assets, have higher debt and greater farm assets, be risk averse, be concerned about autonomy and relationships with feed merchants, and be relatively more educated than incentive payment contract producers. Flat-fee contract producers are more likely to be finishers located in counties with independent and cooperative producers, work more hours off-farm, and be owners of greater farm assets. They are less likely to value autonomy and more likely to value relationships with neighboring farmers. Finally, incentive payment contract producers are generally larger, lower debt finisher or breeding sow operators who work more hours off-farm, value autonomy less and relationships with lenders more than other business arrangements. They are likely to be located in counties with cooperative producers.

The alternative rough rice marketing and storage strategies were evaluated using three methods of analysis. The methods used in this study were MOTAD, simulation, and stochastic dominance. Historical rough rice prices from 1980/81 - 2000/01 was used and tested for trends and seasonality in order for the methods to be formed properly. SAS was used to verify that the data used in this study did not follow any time trend or reflect seasonality. MOTAD, simulation, and stochastic dominance were used to determine the most efficient marketing and storage strategy that will enable rice farmers to make the best decisions and earn the highest average net returns. Results from the MOTAD analysis suggest that producers should take 100% of their loan deficiency payment in August. This study defined loan deficiency payments and storage usage as a type of marketing strategy. Once the statistical results clearly showed that loan deficiency payments were best taken in August, it was not utilized as a marketing strategy in the simulation analysis. The simulation analysis utilized fixed and flexible marketing strategies that incorporated storage usage. The fixed strategies were modeled and gave the lowest average returns. As more and more flexible strategies were incorporated into the study, average returns increased. When world export, production, and ending stock flexible strategies were incorporated, the average net return increased dramatically. The results of the simulation analysis were tested using first degree stochastic analysis. The stochastic analysis and simulation analysis both chose the same marketing strategy as the most efficient strategy a farmer should adopt. Results of this study indicate that farmers who store rice earn higher average net returns when they pay attention to monthly marketing year prices. Farmers that do not store rice should sell 100% of the rough rice in August. The study also suggests that average net returns are higher when loan deficiency payments are taken in August. These results are consistent with a similar study conducted on rice marketing strategies.

Structural-time series models have not gained much ground in commodity market modeling despite the overwhelming popularity of time series approaches in forecasting and dynamic analyses. This dissertation contributes by applying developments in seasonal cointegration and structural-time series analysis (e.g., Zellner and Palm (1974); Hsiao (1997); Lee (1992); Franses and Kunst (1999); Ghysels and Osborn, 2001) to the study of agricultural commodity markets. The focus is on three research themes. The first theme investigates the role of cointegration and seasonal cointegration for market data, an issue considered timely because most applications assume deterministic seasonal components. The second issue breaks new ground in agricultural commodity modeling by introducing a new dynamic simultaneous equation model (DSEM) that accounts for seasonal cointegration. Lastly, the research compares the out-of-sample forecasting performance and impulse responses of four multi-equation models for the U.S. wheat market. The forecasting comparisons apply recent developments on testing for differences in mean-squared-errors. The study adopts a structural model for the U.S. wheat market and estimates four econometric specifications: a vector error-correction model without seasonal cointegration (VECM), a VECM with seasonal cointegration (SVECM), a DSEM with cointegration (CDSEM), and a DSEM with seasonal cointegration (SCDSEM). The conclusions may be summarized as follows. First, quarterly data in the U.S. wheat market (1975:03-1999:04) have seasonal unit roots, therefore, a VECM or DSEM should be specified. Second, in a forecasting context, seasonally cointegrated VECMs perform uniformly better that their nonseasonal counterpart. DSEM with seasonal cointegration, however, perform better than VECMs at longer forecast horizons. Lastly, the impulse response analysis and dynamic multiplier comparisons lead to one salient conclusion, omission of seasonal cointegration components when significant generates unexpected response functions and dynamic multipliers. Of particular interest for future research is an assessment of the small sample properties of impulse response functions for structural-time series models with seasonal cointegration. From a more pure economic perspective, a similar structural-time series analysis to other agricultural markets seems timely given the new finding that these models may outperform other multiple time series models that are often used in empirical work.

The traditional view of the agricultural community as a good steward of the environment has been challenged by increasing concerns about the complex relationship between agricultural production activities and environmental quality. Agriculture provides a large range of products to satisfy human needs. It has also been singled out as major source of water pollution. Largely improved surface water quality has been assessed in the U.S. since the enactment of the Clean Water Act. However, efforts to reduce water pollution continue, targeting discharges from identifiable sources of water pollution and diffused discharges from nonpoint sources. Agricultural producers are encouraged to voluntarily implement site specific management practices known as best management practices (BMPs) to reduce the delivery and transport of agriculturally derived pollutants such as sediment, nutrients, pesticides, salt and pathogens to surface and ground waters. Louisiana is not a major U.S. milk producer. However, the dairy industry represents one of the most important animal agricultural industries in the state, and the need to adopt specific practices to improve water quality has become greater in the industry. This study examined the current implementation of BMPs by Louisiana dairy producers and investigated the likelihood of a dairy producer to adopt a conservation practice. Data for the analysis was based on a mail survey of the population of dairy producers conducted in Summer, 2001. Univariate, bivariate and multivariate probit analyses allowed for estimating the probability of a dairy producer adopting one, two or a set of BMPs, given the economic and non-economic factors hypothesized as determinant in the decision to adopt. Principal component analysis was used to reduce the number of explanatory variables needed for the multivariate probit analysis. Findings of this study emphasized the significant influence of farm size, milk productivity per cow, frequency of meetings with Louisiana Cooperative Extension Service (LCES) personnel, and producer's risk aversion on the increased adoption of BMP. Results also pointed out the need to address the lack of information regarding the legislation and the efforts to control nonpoint sources of water pollution through the use of BMPs, and the need of expanded incentives to induce producers' adoption.

Using a sample from the seven largest metropolitan areas in the United States, (Denver, Chicago, Los Angeles, Atlanta, New Orleans, New York, and Houston), consumer attitudes concerning agricultural biotechnology is examined. Conjoint analysis is used to examine consumer preferences for the labeling of biotech foods. The study examines the relationship between the consumers knowledge and attitudes regarding biotech foods and their preferences for food labels. Consumers attitudes regarding a healthy diet, and their risk perceptions regarding biotech foods were found to have a significant effect on the general use of food labels and preferences for labeling of biotech foods. The most significant finding of the study is that consumers prefer mandatory labeling of biotech foods, rather than FDAs current voluntary labeling policy. The conjoint results show that the most important attribute regarding a biotech label was the presence of a logo, contributing 48.7 % to the preference rating. A text disclosure describing the benefits of the biotech ingredient was determined to be the second most relevant attribute, accounting for 40.87% of the respondents preferences for labeling. The third most important attribute (contributing 10.43%) was the location of the logo on the principal display panel (PDP) of the product package.

Risk is an important concern in the management of a farm business. The rising input prices along with the variability in the farm commodity prices may result in a risk environment. Government programs have generally provided income support to farmers. However, there has been considerable discussion regarding this support in recent years. The farm act of 2002 and farm bill of 1999 are good examples of such discussions. These uncertainties emphasize the need to improve information for farm's income risk management, and make some one ask if there is not out there any alternative way of managing income risk besides government intervention. The literature shows that marketing strategies may be used to improve income risk management on farmers. This study is aimed at showing how pre-harvest marketing strategies may be used to manage income risk, using a portfolio approach in which three chosen marketing strategies are combined in a portfolio. The optimal marketing strategy combination is estimated assuming a safety first decision model. The optimal marketing strategy is then used to estimate optimal production portfolio under the specified scenarios. Cash marketing and optimal pre-harvest marketing scenarios are then evaluated in a financial model. Results generally indicate that opportunity to improve farm profitability, liquidity, and risk exist for the optimal pre-harvest marketing strategy. Results indicate that the optimal marketing strategy would include for the corn case 24% cash on spot marketing strategy, 54% forward contract marketing strategy, and 22% hedge to arrive marketing strategy. For the case of Soybean, the optimal marketing strategy would include 37% cash on spot marketing strategy, 30% forward contract marketing strategy, and 33% hedge to arrive marketing strategy. Comparison between optimal pre-harvest marketing strategy and cash on spot marketing strategy shows that the optimal pre-harvest marketing strategy has higher rate of returns to assets and equity, high debt repayment capacity, lower level of risk, higher level of liquidity, and represents a situation in which farmers has higher level of probability of repaying debt in nine out of 10 years.

The general objective of this study was to provide updated estimates of value of the green industry in Louisiana. Data collection was conducted for the production sector (Nursery Growers and Sod Producers, and Landscape Design, Installation and Maintenance Services) and the Golf Industry. In addition, expenditures on green industry products and services incurred by other sectors were obtained. Among those sectors were churches and cemeteries, public schools (elementary and secondary), public colleges and universities, private schools (elementary, secondary and college/university), parish/city grounds, state parks, road shoulder and median maintenance, and airports. To conduct the surveys, listings from those sectors were obtained from sources such as the Louisiana Department of Agriculture and Forestry, Louisiana Department of Education, Louisiana Department of Transportation, Louisiana Board of Cemeteries, American Business Directory and internet sources. Questionnaires were developed for the three main groups based on previous studies, and a single page questionnaire was developed to collect expenditures from other sectors. Survey procedures followed Dillmans methodology. Using IMPLAN, a 1999 input-output model was built. This model provided results of direct, indirect, induced and total effects of the green industry in specific sectors such as Production, Golf Industry,Retail, Other, and an overall impact on Louisianas economy. Those impacts were measured in four categories: gross sales, personal income, gross state product and employment, and multiplier tables were reported. Economic impact by the Production sector on gross sales was estimated at $605 million, which includes Greenhouse and Nursery Products ($119 million) and Landscape and Horticultural Services ($266 million), while the impact of the Golf Industry on gross sales was estimated at $151 million. The economic impact by the Retail Sector and Horticultural Expenditures Reported by Other Industries were estimated at $557 million and $872 million, respectively. Total economic impact by the green industry on Louisianas economy was estimated at $2.21 billion and 56,685 jobs were generated.