The capacity of Canada’s export-oriented agrifood sectors to cope with contemporary challenges may hinge on their ability to identify new export markets. This thesis uses an import demand model, developed by Hallak (2006), to assess how per capita expenditure on Canadian agrifood exports is influenced by income growth and the presence of a preferential trade agreement for a group of emerging economies, known as ‘the Next-11.’ Results reveal that while as a group the Next-11 does not appear to be distinct from other income groups or the BRICs in terms of their expenditures on agrifood imports, Vietnam and South Korea demonstrate expenditure elasticities notably higher then other Next-11 and BRIC members. Finally, the findings cast doubt on the capacity of PTAs to enhance Canadian agrifood trade. However, this result may be indicative of Canada’s longstanding commitment to multilateral trade liberalization and the corresponding delay in the adoption of PTAs.

For a long time logistics have been an important part of industrial activities. Dated from 1970s, with the emergency of third party logistics (TPL), companies began to think about insourcing or outsourcing. As the development and expansion of TPL, outsourcing logistics activities to third party logistic have became a worldwide trend. Companies could gain a competitive advantage from outsourcing. Outsourcing to TPL providers could make companies focus on their core tasks. An appropriate TPL provider could apply technology and expertise, which help companies reducing costs and improving services. Flexibility and efficiency can also be achieved by adopting good TPL providers. In this sense, helping companies to make outsourcing decisions and to select optimal TPL providers is meaningful. The purpose of this thesis is to bring a holistic view of third party logistics and to build models for companies to select optimal TPL providers. To achieve the purpose, relevant literatures were reviewed to gain theoretical bases. Qualitative and quantitative research methods are both used in the thesis. Two models for selecting TPL providers were built in the thesis. One was qualitative model that contained four steps: defining problems, formulate criteria, qualify selection process and finally make decision. The other one was a quantitative model with DEA method. It contained six steps: define problem, select candidates, select criteria and indicators, determine input and output and collect data, calculate the efficiency and make final decision. Each model was applied on a case study to examine its validity and feasibility. Both advantages and disadvantages were found during the case studies. The conclusion is that both of models could be used for companies and it allows a company to choose the most suitable one.

<p> The binary choice Fast Guess Model of Ollman and Yellott was generalized to a multiple choice model and six subjects were run in a choice reaction time task to test the model. Stimulus set sizes of two, four and six were used and response accuracy and speed motivation was manipulated through specific instructions which were changed from trial to trial. Three different motivational instructions were used. In all cases, subjects were to respond with maximum accuracy but were also told on each trial to either disregard the duration of their response, respond within 440 milliseconds or respond within 300 milliseconds.</p> <p> The generalized Fast Guess Model was rejected because response time parameters of the SCR state were found to change across response accuracy-speed motivation instructions and across stimulus set sizes. Implications of these results for other classes of models were also discussed.</p> / Thesis / Doctor of Philosophy (PhD)

Scholars often address the process of enlargement as one-sided argument. This work provides a general theory of organizational expansion by including strategies and actions of both, applicant states and members of international organizations. It is argued that dependent on the domestic characteristics of states an organization strategically implement a set of conditions to avoid the application and admission of states, which are either not able to conform with the rules or not willing to pay the costs of membership. This process incorporates two stages. I test this theory by utilizing a Heckman-Probit-Selection Model, which accounts for this two-stage procedure. The results confirm that conditions are important to avoid costs in the process of expansion.

The paper decomposes average income differentials between Roma and non-Roma in South East Europe into the component that can be explained by group differences in income-related characteristics (characteristics effect), and the component which is due to differing returns to these characteristics (coefficients or discrimination effect). The decomposition analysis is based on Blinder (1973) and Oaxaca (1973) and uses three weighting matrices, reflecting the different assumptions about income structures that would prevail in the absence of discrimination. Heckman (1979) estimators control for selectivity bias. Using microdata from the 2004 UNDP household survey on Roma minorities, the paper finds that a large share of the average income differential between Roma and non-Roma is explained by human capital differences. Nevertheless, significant labour market discrimination is found in Kosovo for all weight specifications and in Bulgaria and Serbia for two weight specifications. (author's abstract)

Univ., Diss.--Genève, 2007.

This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. We present two alternative approaches which can be implemented using Gibbs sampling methods in a straightforward way and allow us to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. In a simulation study we show that the variable selection approaches tend to outperform existing Bayesian model averaging techniques both in terms of in-sample predictive performance and computational efficiency. (authors' abstract) / Series: Department of Economics Working Paper Series

There are several well-known negative side effects associated with pesticide use such as health problems and environmental pollution.  Integrated Pest Management (IPM) seeks to minimize pesticide use while reducing pest infestation to economically tolerable levels.  The introduction of IPM CRSP activities in Ecuador to institutionalize IPM methods focused on priority crops in the country. This study analyzes adoption and the economic impacts of IPM technologies on potato production in the province of Carchi. A model is estimated in which IPM adoption is discrete and ordered and pesticides expenditures are estimated as a function of education, farming experience, wealth, plot size and farmer being sick due to pesticide use for each level of IPM adoption. Results indicate that farmers who were exposed to certain IPM information sources increased adoption of IPM practices on potatoes, but farmers\' education and experience were not important factors in explaining IPM adoption. The calculated economic benefits in terms of aggregate cost savings per production cycle were $823,000. / Master of Science

Metalearning aims to obtain knowledge of the relationship between the mechanism of learning and the concrete contexts in which that mechanisms is applicable. As new mechanisms of learning are continually added to the pool of learning algorithms, the chances of encountering behavior similarity among algorithms are increased. Understanding the relationships among algorithms and the interactions between algorithms and tasks help to narrow down the space of algorithms to search for a given learning task. In addition, this process helps to disclose factors contributing to the similar behavior of different algorithms. We first study general characteristics of learning tasks and their correlation with the performance of algorithms, isolating two metafeatures whose values are fairly distinguishable between easy and hard tasks. We then devise a new metafeature that measures the difficulty of a learning task that is independent of the performance of learning algorithms on it. Building on these preliminary results, we then investigate more formally how we might measure the behavior of algorithms at a ner grained level than a simple dichotomy between easy and hard tasks. We prove that, among all many possible candidates, the Classifi er Output Difference (COD) measure is the only one possessing the properties of a metric necessary for further use in our proposed behavior-based clustering of learning algorithms. Finally, we cluster 21 algorithms based on COD and show the value of the clustering in 1) highlighting interesting behavior similarity among algorithms, which leads us to a thorough comparison of Naive Bayes and Radial Basis Function Network learning, and 2) designing more accurate algorithm selection models, by predicting clusters rather than individual algorithms.

No description available.