Previous studies on market power hypothesis strictly assumed that the data used in the analysis were a stationary process. This assumption has been argued that not all time series exhibit a stationary process such that conventional asymptotic theory cannot be applied. This study adopts the New Empirical Industrial Organization (NEIO) approach developed by Bresnahan (1982) and Lau (1982) to measure the degree of market power in the export market for soybean complex. The non-stationary properties of the data were accommodated by formulating the model in an error correction framework (ECM) developed by Bårdsen (1989) and applied by Steen and Salvanes (1999). The results can be summarized as follows. First, tests for stationarity on all the data used in this study show that each series exhibit unit root processes and variables under consideration are co-integrated with one co-integrating vector. Second, estimates of market power indices and the hypothesis tests of market power suggest that both soybean and soybean meal export markets are deemed competitive rather than behaving as a Cournot or any other forms of non competitive behavior. Third, estimates of own-price elasticities indicate that export markets for soybean and soybean meal are price elastic with the magnitudes fall in the range of previous estimates. The income elasticity of export demand is found to be inelastic in both markets. Bårdsens model results are compared to estimates from Johansens maximum likelihood and Engle Granger procedures.

This study investigates the effects of risk/benefit beliefs on consumer purchase intentions for genetically modified (GM) foods. A random, national, mail survey is conducted. Three main problems are addressed in the study. The first problem addressed is to analyze the relationship between consumers risk/ benefit beliefs regarding GM foods, and their willingness to buy GM crop and meat products. The second problem deals with linkages between a consumers risk/benefit beliefs about GM foods and willingness to pay (WTP) for GM foods with a benefit disclosure. The third problem addressed is to test a causal relationship between knowledge and trust toward GM institutions, and consumers purchase behavior toward GM foods. The cognitive factors associated with risk/benefit tradeoffs turn out to have significant impacts on consumer acceptance of GM foods. Results indicate that when consumers decide whether to buy GM crops and meat, the most crucial factor is food safety. Other important factors affecting consumer purchase intentions are ethical issues and concerns regarding the environment and wildlife. Depending upon product types, consumers show different levels of risk perceptions for GM foods. Results indicate that consumers have higher risk sensitivity for GM meats than GM crops, as expected. Consumers living in the Northeast region of the U.S. show a negative attitude about willingness to buy GM meat products. Benefits of GM foods on health and the environment have positive, significant impacts on the premium levels for GM potatoes. Similarly, benefits of GM foods, a positive evaluation of GM foods, and trust in GM institutions such as, government, food companies, consumer environmental groups, and scientists, are significant factors that affect consumers participation in the market for GM beef. Unexpectedly, however, health and environmental risk perceptions of GM foods and morality concerns stemming from unnatural way to produce them do not significantly affect either the decision to participate in the market or the premium level. The study found that consumer risk/benefit beliefs depend on their level of knowledge and credibility in GM institutions. Results also indicate that consumers perceive more risks than benefits for GM foods.

This study contributes to the literature on the export-led growth (ELG) hypothesis by adopting a semiparametric approach under two levels of temporal aggregation to investigate the ELG hypothesis in the Philippines. To assess the impact of model specification on the ELG hypothesis, parametric and semiparametric ECMs are estimated using Philippine annual and quarterly data on GDP, exports, exchange rates and gross fixed-capital formation, focusing on the role of exchange rates. The causal relationship between exports and economic growth is examined using the Granger-causality procedure. It can be concluded that for the Philippines, the ELG hypothesis is (a) sensitive to model specification, (b) affected by different levels of temporal aggregation, and (c) by the inclusion or exclusion of exchange rates. Under short-run and total causality tests, parametric and semiparametric analyses using annual data support export-led growth and bidirectional causality, respectively, and no causal relation between exports and output in the long run. Quarterly data analysis revealed that, in the long run, parametric and semiparametric procedures support bidirectional causality and growth-led exports, respectively, and that there is bidirectional causality between exports and economic growth for short-run and total causality tests. Using annual data, total causality tests support export-led growth and no causality, with the inclusion and exclusion of exchange rates, respectively. No change in results is evident for short-run and long-run causality tests. Using quarterly data, no change in results is shown in all Granger causality tests. The general results on bidirectional causality between exports and economic growth suggest that the Philippines could enjoy economic prosperity by strengthening their trade and investment policy and gearing it towards opening up the economy. Previous studies have argued that differences in outcomes of the ELG hypothesis tests may be due to different levels of temporal aggregation, methodologies, model misspecification, and omitted variables. This analysis introduces empirical evidence on these issues.

The objective of this thesis was to examine the validity of the Export-Led Growth (ELG) hypothesis in nine Southern African countries using annual data for the period 1980-2002. The thesis used time series econometric techniques to test for the causal linkage between exports and economic growth in Southern Africa. Dynamic econometric models were estimated to test for time series properties: unit root (ADF and PP tests), cointegration (Johansens procedure), and Granger-causality (Likelihood Ratio test-LR). The results of the unit root tests show that most of the series are stationary in first differences (series in levels have unit rootI(1)). Co-integration and causality between exports and economic growth were tested and compared using two types of bi-variate vector autoregressive models: models without exogenous variables VAR (p), and models with exogenous variables VARX (p, b). The results of the co-integration tests on both types of bi-variate models show that all three Granger-causality alternative models fit the ELG study for Southern Africa (stationary models; integrated but not co-integrated models; and Error Correction Models). In both types of models, the direction of causation (unidirectional or bidirectional) between GDP and exports was tested using a SUR system of equations by computing the LR test. Without exogenous variables, the ELG hypothesis is found to be valid in Lesotho and Swaziland, and, with exogenous variables, it is valid in Botswana, Lesotho, and Swaziland, implying that expanding exports can contribute to economic growth, poverty reduction, and job creation in all three countries. This research reveals that, even though most countries have adopted export-friendly policies, the long-term impact of such policies is yet to be observed for most countries.

With the recent proliferation of Regional Trade Agreements (RTAs) the tendency world-wide has been seemingly toward trade liberalization. This thesis is primarily concerned with the impacts RTAs have had in the Western Hemisphere regarding agricultural trade flows. Utilizing the framework of the Gravity Model, agricultural trade flows for 24 Western Hemisphere Nations were examined. In the course of the study it was expected that if RTAs were to have an effect it would be a positive Trade Creation Effect and a negative Trade Diversion Effect with positive effects for GDP of importer/exporter and population size of importer/exporter and a negative effect for that of distance. Of the five agreements examined (NAFTA, AC, MERCO, LAIA, and CACM), NAFTA and LAIA were the only positive (but non-significant) as to Trade Creation effects while AC, MERCO, and CACM were all negative (but non-significant). It was also interesting to note that of the agreements, NAFTA, had both a positive and significant (p=0.023) diversionary effect with the remaining agreements all being negative (as expected) and significant regarding trade diversion. It was also concluded that GDP (importer) and distance also had the expected signs (+, respectively) with distance also being significant (p=0.0001). It was concluded that RTAs had a more pronounced effect on inter-industry trade versus intra-industry trade and that with the passage of more time, further analysis may substantiate the claim of a positive RTA effect on agricultural bilateral trade flows.

Economic theory states that the exchange rates influences movements in agricultural prices and are in an important determinant of the agricultural sector trade. This paper reviewed the theory and economic models under which exchange rate fluctuations create shifts in the excess supply and demand between countries in the international market along with a history of relations between the United States and the DR-CAFTA region. The theory is econometrically tested through the OLS method to examine to what extent the real exchange rates between the United States and less developed countries were constant with accepted theory. The agricultural trade movements were investigated by examining imports and export values between the United States and the selected counties and proposing the exchange rate as one of the explanatory variables in the econometric model. Given the interdependence of the trade flows within the region, the OLS equations were run as a system of equations under Seemingly Unrelated Regression estimation for annual exports and imports from 1976 to 2004. The exchange rate effect entered into the model was expressed as the foreign countries' currency value with respect to the U.S. dollar. The expectation was that depreciations in the U.S. dollar would have a positive effect on U.S. exports and a negative effect on U.S. imports from the DR-CAFTA region. There were some major concerns of this study in the underlying interdependence of the factors and their effects on trade. Among the results of this study, U.S. agricultural exports are given more emphasis, as the more stable and larger economy. The developed economy of the United exhibited behavior conforming to economic theory. The less developed Central American countries could not be expected to comply with economic theory when there are underlying socioeconomic factors affecting trade. The results, while inconclusive on the U.S. import side, show that exchange rates were important in explaining U.S. exports to the DR-CAFTA region.

This dissertation focuses on two research themes related to econometric estimation of linear almost ideal demand systems (LAIDS) for U.S. meats. The first theme addresses whether nonstationarity (unit-roots and cointegration) contributes to a dynamic specification of LAIDS models. The results of the effect of nonstationarity are reported in two case studies. The second theme explores the relationship between age and household size with budget shares to specify semiparametric LAIDS model. The results are reported in a third case study that compares parametric and semiparametric models estimates of price and expenditure elasticities. The first case study conducts a comparative analysis of elasticity estimates from static and dynamic LAIDS models. Historical meat consumption data (1975:1-2002:4) for beef, pork and poultry products were used. Hylleberg et al. (1990) seasonal unit roots tests were conducted. Unit roots and cointegration analysis lead to the specification of an ECM of the Engle-Granger type for the LAIDS model. Marshallian and compensated elasticities were generated from the static and dynamic LAIDS models. The study found some model differences in elasticity estimates and rejected homogeneity in the dynamic model. The second case study evaluates the forecasting performance of static and dynamic LAIDS models. Forecast evaluation was based on mean square error (MSE) criteria and recently developed MSE-tests. The study found ECM-LAIDS model performs uniformly better under all forecasting horizons for the beef equation. However, in the case of the pork equation the static model performed better in one-step-ahead and two-step-ahead forecasting horizons while the dynamic model was superior in the three-step-ahead and four-step-ahead forecasting horizons using MSE comparisons. In testing, only the two-steps ahead was superior for pork. The third case study specifies a semiparametric LAIDS model that maintains the linearity assumption of prices and total expenditures and allows nonparametric effects of age and household size. 2003 U.S. Consumer Expenditure Survey data for four meat products (beef, pork, poultry and seafood) were used in the study. Model fit and elasticity estimates revealed negligible differences exist between parametric and semiparametric models.

Over the past decade, the peeling segment of the Louisiana crawfish industry has faced the challenge of remaining competitive in an increasingly global market. Since the mid-1990s, there has been escalated discussion among Louisiana crawfish processors on the need for a crawfish peeling machine. The International Trade Commission determined that the U.S. crawfish industry had been materially injured by the imported tailmeat and ruled China was, in fact, dumping crawfish in the U.S. by selling below fair market value in the host country. The development of a suitable crawfish peeling machine could potentially increase production and lower cost of production, therefore allowing the United States to be more competitive with the imported tailmeat from China. Using conjoint analysis, the preferences of Louisiana crawfish processors in adopting crawfish peeling machines are analyzed. Theses results were based on the various attributes a peeling machine would possess. According to the industry, whether the crawfish peeling machine deveins is viewed as being most important; devein constitutes 30.6% of the total importance. For this study, cluster analysis was used to categorize processors into homogenous groups to bring together crawfish processors with a relatively high similarity in attribute preference. The analysis suggests processors peeling a higher percentage of crawfish tailmeat tended to be grouped into cluster 2, which considered a machine that deveins and retains fat to be the most important. Thirty crawfish processors ex-ante adoption rates of hypothetical crawfish peeling machines are assessed using a polychotomous choice elicitation format. Adoption rates are estimated to range from 23 to 70 percent, depending upon the machine and whether it was purchased or leased. Processors most likely to adopt are determined using ordered probit analysis. Greater adopters would be larger and more diversified, have greater current resources, and have longer planning horizons. Early adopters of the machine would benefit from the reduced cost of production before the market becomes concentrated while late adopters would most likely experience lower profits or short-term losses prior to adoption.

This dissertation analyzed spatial agglomeration economies associated with the geographical distribution of the U.S. biotech industry. Three location issues associated with the biotech industry were addressed in the study. The first study utilized a Bayesian spatial tobit model and examined the overall and regional differences in factors affecting the location of the U.S. biotech industry. The second study examined the inter- and intra-industry spatial association of biotech related research and development (R&D) and testing facilities across all contiguous U.S. counties employing a Spatial Two-Stage Least Squares model. Finally, the interdependence between different subsectors of the U.S. biotech industry was analyzed using a Seemingly Unrelated Regression model. The first study confirmed the hypothesis of spatial agglomeration for the spatial structure of the biotech industry, indicating that biotech firms are positively correlated across counties, resulting in clustering of biotech production. Availability of venture capital firms, research institutions, and hospitals were found to have the most significant impact on the location of biotech firms. Results from regional models indicate that biotech firms willing to locate in the West prefer to establish in metro-counties with easy access to research institutes and skilled labor pool. Conversely, firms that are willing to locate in the Northeast prefer counties with easy access to funding sources and hospitals for research, testing and marketing of new biotech products. Spatial clustering of biotech research and testing activities was confirmed in the second study. Proximity to manufacturing firms and research universities, and availability of venture capital firms were found to have the most significant impact on the location of R&D and testing facilities. Results indicated that public as well as private spillovers are at work in the R&D and testing industry, resulting in their spatial clustering. Agricultural biotechnology firms preference to locate in counties with large farmland, low median housing values and average hourly wage, and a high unemployment rate was indicated in the third study. Conversely, results indicate that firms belonging to drug and pharmaceuticals, and medical devices and equipment subsectors prefer to locate in counties with high standards of living and in close proximity of research institutes and hospitals to access skilled-labor, and develop and test new drugs.

Fifty Louisiana dairy farmers were interviewed to gather production amounts, costs of production, management techniques, technologies adopted, and demographic information. These data were used to analyze what record-keeping systems the farmers were adopting and to what extent the systems were being used. Logit, ordered probit, negative binomial regression, OLS regression, and double hurdle models were used to determine adoption and intensity. In this study, age was found to decrease the probability that a farmer would believe their computer was not at all useful and also of limited usefulness, while increasing the probability that a farmer would believe the computer was very useful to the farm business. Older farmers were more likely to perceive the computer as more useful. Having a family successor to take over the dairy upon the operators retirement affected many things, including: decreasing experience with the internet; increasing the probability of a farmer perceiving the computer as of limited usefulness; decreasing the probability of a farmer perceiving the computer as very useful; increasing the hours spent per week reviewing DHIA output; increasing the number of financial measures tracked; increasing the intensity of use of DHIA after it has been adopted; and increasing the probability of adopting computerized record-keeping systems. If the operator himself kept the records for the farm, then fewer financial statements were generated and less time was spent updating computerized record-keeping systems. These farmers, however, devoted more time to reviewing DHIA output. When the farmer was a technology adopter he was more likely to have experience with the internet and to have adopted DHIA, but spend less time reviewing DHIA output. Also, technology adopters were more likely to view the computer as very useful and less likely to view the computer as not at all useful. The more statements a farmer generated for financial analysis, the more likely he was to adopt computerized record-keeping systems. Thus, farmers with a greater interest in record-keeping were likely to find the computer more useful because it can make financial analysis much easier compared to paper based records.