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In September 2002, the Canadian Institute of Health Information, Health Canada and the National Task Force on Health Information created the Canadian Community Health Survey (CCHS) whose objective was to determine the status of the Canadian health care system and the health status of Canadians themselves. The CCHS was divided into two cycles (Cycle 1.1 and Cycle 1.2) of which CCHS Cycle 1.1 was general population health survey designed to provide information for 136 health regions covering all provinces and territories. For this particular report, data from Cycle 1.1 was analyzed in order to determine if Work stress, Type of Smoker, Body Mass Index (BMI) and Household Income were significant factors contributing to the general health of the population of Ontario. Each of the above mentioned variables was considered individually for a given age, gender and marital status of the respondents. Logistic Stepwise Regression was used to determine if these variables were significant predictors of general health and all the possible two way interactions were explored at the five percent level. Furthermore, a diagnostic check of the fitted models was conducted and the validity of the models was assessed once again after removing the influential points. In addition, the linearity of all the continuous variables was tested in the logit models followed by a comparison of the fitted models using weights. Overall, it was found that all of the potential predictor variables of concern were significant predictors of general health. The majority of two way interaction terms were included in the fitted models, however, the overall fit of some models was found to be poor. In some cases, upon removing the influential points, the overall fit improved significantly, while for others, the fit did not improve by much. Also, it was found that Household Income and Work stress were not linear in the logit model while Age was found to be linear. Upon comparing models with and without sampling weights, it was found that the model that included sampling weights consisted of the same main effects as the model without sampling weights, along with additional interaction terms which were not present earlier. KEY WORDS: CCHS, Sampling Weights, Logit Models, Fitted Models, Interactions, Stepwise Regression / Thesis / Master of Science (MS)

Establishing wetland gains and losses, delineating wetland boundaries, and determining their vegetative composition are major challenges that can be improved through remote sensing studies. In this study, we used the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to separate wetlands from uplands in a study of 870 locations on the Virginia Coastal Plain. We used the first five bands from each of two ASTER scenes (6 March 2005 and 16 October 2005), covering the visible to the short-wave infrared region (0.52-2.185υm). We included GIS data layers for soil survey, topography, and presence or absence of water in a logistic regression model that predicted the location of over 78% of the wetlands. While this was slightly less accurate (78% vs. 86%) than current National Wetland Inventory (NWI) aerial photo interpretation procedures of locating wetlands, satellite imagery analysis holds great promise for speeding wetland mapping, lowering costs, and improving update frequency. To estimate wetland vegetation composition classs of the study locations, we generated a Classification and Regression Tree (CART) model and a Multinomial Logistic Regression (logit) model, and compared their accuracy in separating woody wetlands, emergent wetlands and open water. The overall accuracy of the CART model was 73.3%, while the overall accuracy of the logit model was 76.7%. Although the CART producer's accuracy (correct category classification) of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), we obtained the opposite result for the woody wetland category (68.7% vs. 52.6%). A McNemar test between the two models and NWI maps showed that their accuracies were not statistically different. We conducted a sub-pixel analysis of the ASTER images to establish canopy cover of forested wetlands. The canopy cover ranged from 0 to 225 m2. We used visble-near-infrared ASTER bands, Delta Normalized Difference Vegetation Index, and a Tasselled Cap transformation in an ordinary linear regression (OLS) model. The model achieved an adjusted-R2 of 0.69 and an RMSE of 2.73% when the canopy cover is less than 16%. For higher canopy cover values, the adjusted-R2 was 0.4 and the RMSE was19.79%. Taken together, these findings suggest that satellite remote sensing, in concert with other spatial data, has strong potential for mapping both wetland presence and type. / Ph. D.

Two experimental procedures are employed to value both health and environmental benefits from reducing pesticides in Uganda. The first experiment, an incentive compatible auction involves subjects with incomplete information placing bids to avoid consuming potentially contaminated groundnuts/water in a framed field experimental procedure. Three experimental treatments (information, proxy good, and group treatments) are used. Subjects are endowed with a monetary amount (starting capital) equivalent to half the country's per capita daily income (in small denominations). Two hundred and fifty seven respondents were involved in a total of 35 experimental sessions in Kampala and Iganga districts. Tobit model results indicate that subjects place significant positive values to avoid ill health outcomes, although these values vary by region, by treatment and by socio-economic characteristics. Gender differences were important in explaining bidding behavior, with male respondents in both study areas bidding higher to avoid ill health outcomes than females. Consistent with a priori expectation, rural population's average willingness to pay (WTP) to avoid ill health outcomes was lower (by 11.4 percent) than the urban population's WTP possibly reflecting the poverty level in the rural areas and how it translates into reduced regard for health and environmental improvements. Tests of hypotheses suggest (i) providing brief information to subjects just prior to the valuation exercise does not influence bid behavior, (ii) subjects are indifferent to the source of contamination: WTP to avoid health outcomes from potentially contaminated water and groundnuts are not significantly different, and (iii) the classical tendency to free-ride in public goods provision was observed, and this phenomenon was more pronounced in the urban than the rural area. The second experimental procedure involved 132 urban respondents making repeated choices from a set of scenarios described by attributes of water quality, an environmental good. Water quality is represented by profiles of water safety levels at varying costs. Analysis using the conditional (fixed effects) logit showed that urban subjects highly discount unsafe drinking water, and were willing to pay less for safe agricultural water, a result not unexpected considering that the urban population is not directly involved in agricultural activities and thus does not value agricultural water quality as much as drinking water quality. Results also showed that subjects' utility increased with the cost of a water sample (inconsistent with a downward sloping demand curve), suggesting perhaps that they perceived higher costs to be associated with higher water quality. Some theoretically inconsistent results were obtained with choice experiments. / Ph. D.

This dissertation consists of three essays on forms of preference heterogeneity in discrete choice models. The first essay uses a model of heterogeneity conditional on observed individual-specific characteristics to tailor financial incentives to enhance weight loss program participation among target demographics. Financial incentives in weight loss programs have received attention mostly with respect to effectiveness rather than participation and representativeness. This essay examines the impact of financial incentives on participation with respect to populations vulnerable to obesity and understudied in the weight loss literature. We found significant heterogeneity across target sub-populations and suggest a strategy of offering multiple incentive designs to counter the dispersive effects of preference heterogeneity. The second essay investigates the ability of a novel elicitation format to reveal decision strategy heterogeneity. Attribute non-attendance, the behaviour of ignoring some attributes when performing a choice task, violates fundamental assumptions of the random utility model. However, self-reported attendance behaviour on dichotomous attendance scales has been shown to be unreliable. In this essay, we assess the ability of a polytomous attendance scale to ameliorate self-report unreliability. We find that the lowest point on the attendance scale corresponds best to non-attendance, attendance scales need be no longer than two or three points, and that the polytomous attendance scale had limited success in producing theoretically consistent results. The third essay explores available approaches to model different features of unobserved heterogeneity. Unobserved heterogeneity is popularly modelled using the mixed logit model, so called because it is a mixture of standard conditional logit models. Although the mixed logit model can, in theory, approximate any random utility model with an appropriate mixing distribution, there is little guidance on how to select such a distribution. This essay contributes to suggestions on distribution selection by describing the heterogeneity features which can be captured by established parametric mixing distributions and more recently introduced nonparametric mixing distributions, both of a discrete and continuous nature. We provide empirical illustrations of each feature in turn using simple mixing distributions which focus on the feature at hand. / Ph. D.

Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data become available. The primary goal of this thesis is to develop mode choice models using artificial neural networks and compare the results with traditional mode choice models like the multinomial logit model and linear regression method. The data used for this modeling is extracted from the American Travel Survey data. Data mining procedures like clustering are used to process the extracted data. The results of three models are compared based on residuals and error criteria. It is found that neural network approach produces the best results for the chosen set of explanatory variables. The possible reasons for such results are identified and explained to the extent possible. The three major objectives of this thesis are to: present an approach to handle the data from a survey database, address the mode choice problem using artificial neural networks, and compare the results of this approach with the results of traditional models vis-à-vis logit model and linear regression approach. The results of this research work should encourage more transportation researchers and professionals to consider artificial intelligence tools for solving transportation planning problems. / Master of Science

No / The existing empirical literature on foreign direct investment (FDI) entry strategies tends to allow a binary choice between wholly owned enterprises (WOEs) and equity joint ventures (EJVs) or between greenfield investment and acquisition only. The current study specifies a multinomial logit model for the choice from all four FDI entry modes in China. Five hypotheses are developed based on transaction cost economics and tested on a data set covering 10,607 foreign investment projects in China. A foreign investor seems to prefer the WOE mode given its large investment commitment, a high level of the host country's experience in attracting FDI, a good specific industrial location, and a high asset intensity in the host industry. If the conditions of host country experience and good specific location are not met, the EJV and the joint stock company (JSC) modes seem to be of greater use. A good specific location also makes the contractual joint venture (CJV) a preferable entry mode. Compared with overseas Chinese investors from Hong Kong, Macao, and Taiwan, other foreign investors prefer EJVs over WOEs and CJVs. The results have important implications for managers.

O mercado automotivo global em 2008 sofreu uma queda drástica na produção e nas vendas após a eclosão da crise do subprime nos Estados Unidos. Em todo o mundo, políticas de fomento foram sendo implementadas sob as mais variadas formas para recuperar o setor. Presumivelmente, em resposta à crise, o governo brasileiro resolveu agir decretando a política anticíclica de redução do IPI ao mercado automobilístico em 2008, sendo está repetida em 2012. Neste mesmo ano também ocorreu a modificação do acordo automotivo Brasil/México, e em 2013 foi implantado o INOVAR-AUTO. Sendo assim, o objetivo do presente estudo, será avaliar o efeito da diminuição do IPI sobre o comportamento da demanda, da oferta e os efeitos líquidos sobre os agentes de mercado. O estudo se relaciona com uma literatura que busca avaliar os efeitos de reformas tributárias em indústrias com produtos diferenciados, como Fershtman, Gandal e Markovich (1999) e Verboven (2002). É empregado o modelo logit aninhado de McFadden et al. (1973) e estendido por Berry (1994), combinado com uma estrutura de competição oligopolista pressupondo equilíbrio nos preços, segundo Nevo (1998), do tipo Nash-Bertrand. Adicionalmente, é elaborado uma análise econométrica preliminar de preços hedônicos, seguindo Griliches (1961), formulado num painel de efeitos fixos que avaliará o comportamento dos preços médios dos veículos novos nos períodos de modificação do IPI. Os resultados dos modelos de apreçamento hedônico demonstram que as montadoras não remanejaram os preços médios no mesmo percentual efetivo da queda do IPI. As variações dos preços foram mais baixas do que a da alíquota. Na metodologia discreta, os resultados apontam que empresas que detém maiores poderes de mercado possuem elasticidades preço próprias baixas. Lucros mais elevados estão associados a marcas que no grosso de suas vendas comercializam automóveis de menor porte que embutem uma alta relação markup preço-custo. Montadoras nacionais tiveram melhor desempenho que suas contrapartes importadoras. A carga tributária altíssima é o principal vilão para o desempenho ruim dos importados. Os excedentes gerados com a modificação do IPI foram positivos para todos os agentes. Consumidores, produtores e governo ganharam com a medida. Ou seja, há espaço para reduções de impostos com aumento da arrecadação do governo. / The global automotive market in 2008 saw a sharp drop in production and sales following the outbreak of the subprime crisis in the United States. Throughout the world, development policies have been implemented in a variety of ways to recover the sector. Presumably, in response to the crisis, the Brazilian government decided to act by decreeing the anti-cyclical policy of reducing the IPI to the automobile market in 2008, and is repeated in 2012. In 2012 also occurred the modification of the automotive agreement Brazil / Mexico, and in 2013 was implanted the INOVAR-AUTO. Therefore, the objective of the present study will be to evaluate the effect of the reduction of IPI on the behavior of demand, supply and net effects on market agents. The study is related to a literature that seeks to evaluate the effects of tax reforms in industries with differentiated products, such as Fershtman, Gandal e Markovich (1999) and Verboven (2002). The nested logit model of McFadden et al. (1973) and extended by Berry (1994), combined with an oligopolistic competition structure presupposing price equilibrium, according to Nevo (1998), of the Nash-Bertrand type, is used. Additionally, a preliminary econometric analysis of hedonic prices is elaborated, following Griliches (1961), formulated in a panel of fixed effects that will evaluate the behavior of the average prices of new vehicles in the periods of modification of the IPI. The results of the hedonic pricing models show that the automakers did not change average prices in the same effective percentage of the IPI fall. Price changes were lower than the rate. In the discrete methodology, the results indicate that companies that have greater market power have lower price elasticities. Higher profits are associated with brands that in the bulk of their sales market smaller automobiles that embody a high price-cost markup ratio. National car manufacturers performed better than their importing counterparts. The very high tax burden is the main villain for the poor performance of imported. The surpluses generated with the IPI change were positive for all agents. Consumers, producers and government won with the measure. That is, there is room for tax cuts with increased government revenue.

Com o crescimento da população em áreas urbanas, o transporte coletivo assume um importante papel nos deslocamentos diários nas cidades brasileiras. No entanto, o grande volume de pessoas no entorno desses sistemas podem resultar em pontos críticos de segurança viária. Recentemente, as pesquisas sobre melhorias na segurança viária estão se direcionando para a investigação da relação entre o ambiente construído - isto é, padrões de uso do solo; desenho urbano; e sistemas de transporte - e acidentalidade. Porém, a relação entre os sistemas de transporte coletivo por ônibus e o ambiente construído ainda é pouco explorada. O ponto mais crítico em segurança identificado na literatura em sistemas de transporte coletivo são as estações de embarque e desembarque. Assim, esta dissertação tem como objetivo verificar a existência de relação entre elementos do ambiente construído com a ocorrência e severidade de acidentes viários no entorno de pontos de parada em sistemas prioritários para ônibus. Para isso, aplicou-se os modelos de regressão Binomial Negativo (NB), na análise de frequência de acidentes, e os modelos Logit Ordenado (OL) e Logit Multinomial (MNL), para a análise de severidade. No modelo de frequência de acidentes totais, identificou-se duas variáveis significativas e, no modelo estimado para atropelamentos, três variáveis. O modelo de severidade resultou em 19 variáveis significativas para acidentes totais e oito variáveis significativas para atropelamentos. As variáveis referentes ao envolvimento de motocicletas, automóveis, presença de interseção e uso diversificado do solo foram as mais recorrentes. O estudo possibilitou também avaliar as diferenças e vantagens entre os modelos ordenados e os não-ordenados. O modelo Logit Multinomial teve ajustes levemente melhores do que o Logit Ordenado. Ressalta-se, entretanto, que o ajuste do modelo não deve ser o único critério a se considerar na escolha de modelos para o estudo de severidade de acidentes. / Public transport has played an important role in daily commutes in Brazilian cities as population grows in urban areas. However, high volumes of pedestrians near those systems may result in critical safety issues. Recent studies on road safety improvements have sought for evidence on the relationship between built environment - land use patterns; urban design; and transportation systems - and traffic safety. Nevertheless, the relationship between traffic safety at bus priority systems and the built environment is still incipient. According to studies, the most critical safety issues related to public transport are the stations for boarding and alighting. Thus, this thesis aims to verify the relationship between built environment and incidence and severity of traffic crashes in the surroundings of bus priority systems. To do so, we applied Negative Binomial regression models (NB) for the frequency analysis, and the Ordered Logit (OL) and Multinomial Logit (MNL) models for severity analysis. NB models resulted in two significant variables for traffic crashes in general and three variables for pedestrian crashes. The severity model resulted in 19 significant variables overall crashes and eight significant variables for pedestrian crashes. Variables related to the involvement of motorcycles, automobiles, intersections and mixed land use were significant in majority of model estimations. The results allowed to evaluate the goodness of fit between ordered and unordered models. MNL had a slightly better adjustments compared to OL. Goodness of fit, however, should not be the only criterion for selecting a model to assess crash severity.