This dissertation aims to contribute to the field of economics of competition policy by analyzing the demand structure and the market power in the Turkish beverage industry and in the cola market in particular. First, a demand system for the beverage products has been estimated by using a multi-stage linearized Almost Ideal Demand System (AIDS). Using the own-price elasticity of cola in a SSNIP test (Small but Significant Non-Transitory Increase in Price), it is shown that cola market consists of a distinct relevant product market. Then, the demand elasticities of cola products at brand and package level have been estimated by the simple and nested logit models. Finally, the estimated demand elasticities of cola products have been used in measuring the degree of market power and predicting the effects of a hypothetical merger between Pepsi and Cola Turca by using a merger simulation technique. The results show that all cola suppliers have large price-cost margins for most of their products. Prices of the merging parties increase in average by 15 - 21% after the merger. The merger also causes the market price to increase by 16- 22% and consumer surplus to decrease by nearly 5% in average. Finally, depending on these results, the thesis recommends a stricter merger control criterion than dominance criterion for competition policy in Turkey.

Recent advances in travel demand modeling have concentrated on adding behavioral realism by focusing on an individual’s activity participation. And, to account for trip-chaining, tour-based methods are largely replacing trip-based methods. Alongside these advances and innovations in dynamic traffic assignment (DTA) techniques, however, time-of-day (TOD) modeling remains an Achilles’ heel. As congestion worsens and operators turn to variable road pricing, sensors are added to networks, cell phones are GPS-enabled, and DTA techniques become practical, accurate time-of-day forecasts become critical. In addition, most models highlight tradeoffs between travel time and cost, while neglecting variations in travel time. Research into stated and revealed choices suggests that travel time variability can be highly consequential. This dissertation introduces a method for imputing travel time variability information as a continuous function of time-of-day, while utilizing an existing method for imputing average travel times (by TOD). The methods employ ordinary least squares (OLS) regression techniques, and rely on reported travel time information from survey data (typically available to researchers), as well as travel time and distance estimates by origin-destination (OD) pair for free-flow and peak-period conditions from network data. This dissertation also develops two models of activity timing that recognize the imputed average travel times and travel time variability. Both models are based in random utility theory and both recognize potential correlations across time-of-day alternatives. In addition, both models are estimated in a Bayesian framework using Gibbs sampling and Metropolis-Hastings (MH) algorithms, and model estimation relies on San Francisco Bay Area data collected in 2000. The first model is the continuous cross-nested logit (CCNL) and represents tour outbound departure time choice in a continuous context (rather than discretizing time) over an entire day. The model is formulated as a generalization of the discrete cross-nested logit (CNL) for continuous choice and represents the first random utility maximization model to incorporate the ability to capture correlations across alternatives in a continuous choice context. The model is then compared to the continuous logit, which represents a generalization of the multinomial logit (MNL) for continuous choice. Empirical results suggest that the CCNL out-performs the continuous logit in terms of predictive accuracy and reasonableness of predictions for three tolling policy simulations. Moreover, while this dissertation focuses on time-of-day modeling, the CCNL could be used in a number of other continuous choice contexts (e.g., location/destination, vehicle usage, trip durations, and profit-maximizing production). The second model is a bivariate multinomial probit (BVMNP) model. While the model relies on discretization of time (into 30-minute intervals), it captures both key dimensions of a tour’s timing (rather than just one, as in this dissertation’s application of the CCNL model), which is important for tour- and activity-based models of travel demand. The BVMNP’s ability to capture correlations across scheduling alternatives is something no existing two-dimensional choice models of tour timing can claim. Both models represent substantial contributions for continuous choice modeling in transportation, business, biology, and various other fields. In addition, the empirical results of the models evaluated here enhance our understanding of individuals’ time-of-day decisions. For instance, average travel time and its variance are estimated to have a negative effect on workers’ utilities, as expected, but are not found to be that practically relevant here, probably because most workers are rather constrained in their activity scheduling and/or work hours. However, correlations are found to be rather strong in both models, particularly for home-to-work journeys, suggesting that if models fail to accommodate such correlations, biased application results may emerge. / text

In this thesis, we develop a SKU level market basket model and apply the model to investigate cross-category reference price effects. This research extends previous work on the category-level multivariate logit model (Russell and Petersen 2000). Our model is a generalization of the multivariate logit model which allows for both complementarity and substitution effects at the brand level. The modeling effort in this thesis allows us to use conditional probability distributions of individual items to construct the final joint-distribution of all possible basket selections. The resulting model is very flexible and accommodates a large variety of market structure patterns. The model structure implies that the changes in brand-level marketing variables directly affect category incidence (by altering category attractiveness) and indirectly determine market basket composition. Because the model can be written in a closed form manner, we can easily study the pattern of brand price competition by computing a matrix of cross-price elasticities. We use scanner panel data for the yogurt category to demonstrate the structural flexibility of the model. The results from this application reveal asymmetric competition consistent with price-tier competition literature. We use this model to investigate how consumers' responses to reference prices within a category spillover into their choices across multiple categories. The notion is that a consumer's subjective judgment of the fairness of the price levels in one category influences the choice decisions of related items in other categories. We begin with building within-category SKU-level model based on previous findings from single category reference price models (i.e., internal versus external reference prices, asymmetric response due to loss aversion, and heterogeneity in response across consumers). We then develop four alternative model specifications for cross-category spillover effects and test competing theories about those effects. Using scanner panel data for detergent and softener categories, we discover valuable implications for reference price effects. First, SKU-level reference price effects exist and improve forecasting ability. Second, those reference price effects influence category attractiveness, but do no spillover across categories. Finally, category-level reference dependent evaluation may exist but not be important in forecasting.

No description available.

Cette thèse, portant sur le choix de localisation résidentielle des ménages, s’inscrit dans le cadre théorique de la microéconomie urbaine et de l’utilité aléatoire. Si l’approche des choix discrets reste la plus appropriée dans ce domaine, la difficulté réside dans l’adaptation de ce type de modèles au contexte spatial (autocorrélation spatiale, grand nombre d’alternatives de choix) d’une part, et dans la manière de définir l’échelle de désagrégation d’autre part. Pour ce faire, nous avons procédé en deux temps. Nous avons estimé un logit multinomial avec un échantillon aléatoire d’alternatives pour comprendre le processus de choix de localisation résidentielle, avant de tester l’apport d’une structure hiérarchique pour traiter les problèmes d’autocorrélation spatiale. Cela a aussi permis l’investigation d’une nouvelle méthode de correction des biais liés à l’échantillonnage d’alternatives dans le cas du modèle logit emboîté. En termes de résultats, nous avons montré que la qualité de l’environnement social est le facteur le plus déterminant, sans remettre en cause le poids toujours important de l’accessibilité à l’emploi. Au plan méthodologique, nous avons été en mesure de tester l’apport de l’utilisation d’un modèle logit emboîté pour analyser les choix de localisation de l’aire urbaine de Lyon. Cependant, nous n’avons pas pu dépasser la difficulté de séparer l’autocorrélation spatiale et entre les nids. Le calage du modèle de choix de localisation en 1999 et en 2007 a aussi permis de donner des éléments de réponse sur la transférabilité temporelle des modèles de choix de localisation et de questionner, dans les travaux futurs, le pouvoir prédictif d’un modèle de choix de localisation. / This thesis, focusing on the choice of residential location, is based on the theoretical framework of urban micro-economy and random utility. Though discrete choice modelling is the most appropriate in this field, the difficulty lies in choosing the appropriate model to the spatial context of residential location choice (spatial autocorrelation, large number of alternatives), on the one hand, and in the way of defining the spatial scale, on the other hand. For this purpose, we proceeded in two stages. We estimated a multinomial logit with random sampling of alternatives to understand the process of residential location choice before taking into account the spatial autocorrelation, and estimating a nested logit model. It also allowed to investigate the feasibility of applying a new method to correct biases of sampling alternatives in the case of nested logit model. In terms of results, we have shown that social environment are the most important determinants of residential location choice. Though job accessibility still weigh on household choice decision. In terms of methodology, we were able to test the feasibility of estimating a nested logit model with sampling of alternatives to analyze the choice of location of Lyon urban area. However, we could not overcome the difficulty of distinguishing spatial autocorrelation from nesting. The use of 1999 and 2007 databases to model residential location choice also helped to provide answers on the temporal transferability of location choice models and discuss in future work the predictive power of a location choice model.

Travel demand Management (TDM) focuses on improving the efficiency of the transportation system through changing traveler's travel behavior rather than expanding the infrastructure. An employer based integrated TDM program generally includes strategies designed to change the commuter's travel behavior in terms of mode choice, time choice and travel frequency. Research on TDM has focused on the evaluation of the effectiveness of TDM program to report progress and find effective strategies. Another research area, identified as high-priority research need by TRB TDM innovation and research symposium 1994 [Transportation Research Circular, 1994], is to develop tools to predict the impact of TDM strategies in the future. These tools are necessary for integrating TDM into the transportation planning process and developing realistic expectations. Most previous research on TDM impact evaluation was worksite-based, retrospective, and focused on only one or more aspects of TDM strategies. That research is generally based on survey data with small sample size due to lack of detailed information on TDM programs and promotions and commuter travel behavior patterns, which cast doubts on its findings because of potential small sample bias and self-selection bias. Additionally, the worksite-based approach has several limitations that affect the accuracy and application of analysis results. Based on the Washington State Commute Trip Reduction (CTR) dataset, this dissertation focuses on analyzing the participation rates of compressed work week schedules and telecommuting for the CTR affected employees, modeling the determinants of commuter's compressed work week schedules and telecommuting choices, and analyzing the quantitative impacts of an integrated TDM program on individual commuter's mode choice. The major findings of this dissertation may have important policy implications and help TDM practitioners better understand the effectiveness of the TDM strategies in terms of person trip and vehicle trip reduction. The models developed in this dissertation may be used to evaluate the impacts of an existing TDM program. More importantly, they may be incorporated into the regional transportation model to reflect the TDM impacts in the transportation planning process.

台灣自1993年公佈「有線電視法」後，有線電視產業發展快速，系統業者為因應市場的激烈競爭，紛採取垂直整合、合併或購併方式，致使廠商家數變化異常；同時由於採取整批收費方式，在價格上限管制之下，系統業者對市場具有壟斷力量，莫不提高收費管制上限的水準；而節目與廣告內容在不分級之下，可能影響青少年身心發展，依此多數學者與新聞局企圖引進「分級付費制度」及推出「付費頻道」與「計次付費頻道」以解決上述問題。有關「付費頻道」與「計次付費頻道」之供應，可增加市場服務之多樣性，然消費者的接受意願及真正的偏好。首先，本文應用巢式多項式羅吉特模型探討消費者對「分級付費制度」與現行整批收費方式的選擇意願；其次，再探討消費者選擇「分級付費制度」下，有關「付費頻道」與「計次付費頻道」的選擇意願模型；最後，應用台灣有線電視消費者的問卷資料對上述巢式羅吉特模型進行實證估計，並根據實證結果，嘗試對有線電視業者在提供「付費頻道」與「計次付費頻道」等節目時新服務管理策略的建議。

Submitted by Alexandre Otomo de Almeida (otomo.almeida@gmail.com) on 2017-03-04T15:15:48Z No. of bitstreams: 1 Dissertacao_vf2.pdf: 906019 bytes, checksum: 389d53353d9269653e591405747ca68f (MD5) / Rejected by Renata de Souza Nascimento (renata.souza@fgv.br), reason: Alexandre, boa noite Para que possamos aceitar seu trabalho, por gentileza, realizar os ajustes de acordo com as normas a ABNT: Diminuir os espaços entre as frases do título. A apresentação ocorreu em 2017. Alterar São Paulo 2016, para São Paulo 2017. Na contra capa e folha de assinaturas, em campo de conhecimento deverá colocar sua área: Economia. Em seguida, submeter novamente o arquivo. att on 2017-03-07T00:11:09Z (GMT) / Submitted by Alexandre Otomo de Almeida (otomo.almeida@gmail.com) on 2017-03-07T02:06:07Z No. of bitstreams: 1 Dissertacao_vf2.pdf: 905969 bytes, checksum: ddcaca60b92b7d4a93c6ae17626d4f8a (MD5) / Approved for entry into archive by Renata de Souza Nascimento (renata.souza@fgv.br) on 2017-03-08T23:41:02Z (GMT) No. of bitstreams: 1 Dissertacao_vf2.pdf: 905969 bytes, checksum: ddcaca60b92b7d4a93c6ae17626d4f8a (MD5) / Made available in DSpace on 2017-03-09T13:17:42Z (GMT). No. of bitstreams: 1 Dissertacao_vf2.pdf: 905969 bytes, checksum: ddcaca60b92b7d4a93c6ae17626d4f8a (MD5) Previous issue date: 2017-02-09 / This paper consists of a study over the impact of interest rates and maturities of loans for electronics and home appliances in Brazil, designed for consumers with credit constraints. Using micro data on loans of one of the largest retailers in the Brazilian market, some econometric models were built using conditional and nested logit approach, in order to estimate elasticities of loan demand with respect to the interest rate, the maturity of the loan and the terms value. Moreover, marginal substitution rates were also estimated between those attributes to evaluate consumer’s propensity to choose different combinations of loan conditions. Given the number of different product categories sold by this retailer, two of the most important were selected. The results suggest that the interest rate, the payment terms and the loan maturity are important factors on consumer decision. Loan demand is leveraged by loan’s smaller interest rates and payment terms. However, there is a material marginal substitution rate between payment terms and loan maturity or interest rate. This means that consumers are highly prone to change from loans with higher payment terms to others with lower ones, even if they face higher interest rates and longer terms to maturity. Results also suggest that consumers are not really aware of the real value paid for these products. / O trabalho tem por objetivo o estudo do impacto da taxa de juros e dos prazos de financiamento sobre a demanda de eletroeletrônicos no Brasil para os públicos com restrição de acesso a crédito. Utilizando dados de vendas por meio de crédito direto ao consumidor (CDC) de um grande varejista brasileiro, foram construídos modelos logit condicional e aninhado para dimensionamento das elasticidades da demanda às taxas de juros, aos prazos de parcelamento e ao valor das parcelas. Além disso, foram dimensionadas as subsequentes taxas marginais de substituição entre os atributos dos planos de financiamento oferecidos aos clientes, para avaliar a propensão à troca de condições de planos de financiamento pelos consumidores. Dada a extensão das categorias de produtos existentes no varejista em questão, foram selecionadas duas categorias de alta representatividade no resultado de vendas. Para as duas categorias selecionadas, foi constatado que a taxa de juros é fator importante na decisão de compra, bem como o número e valor das parcelas. A demanda se mostrou positivamente impactada por taxas de juros menores, bem como por valores de parcela menores. No entanto, os consumidores se mostraram altamente dispostos à substituição de planos com prazos de financiamento e taxas de juros menores por planos com valores menores das parcelas. Por fim, foi constatado que os consumidores têm baixa consciência do valor real pago pelos produtos comprados.

The quest towards resolving concerns about transportation energy consumption and emissions across nations has created more interests to investigate factors responsible for households’ car ownership. While literature holds an extensive body of investigation usually compartmentalised in individual different disciplines, limited efforts have been made to promote inter-linkages of this strand of research across different disciplines. To fill this gap, this study developed an integrating Multinomial logit (MNL) model to examine the impact of some rarely-investigated and conventional explanatory variables, including: ethnicity, accommodation tenure, settlement nature, mental belief, environmental concern, geographical regions, household structure, driving licence, number of household income earners and household income, on car ownership. Analysis based on rich data sets of British Household Survey and US Consumer Expenditure Survey found not only the conventional explanatory variables to be significantly linked to the number of cars in the US and UK households, but also the rarely-investigated psychological variables were found to be significantly linked as well. As Socio-demography, Geography and Psychology impact on how people and households process information and assess market offers (e.g., products and services), this study presents findings which have beneficial implications for policymakers and transportations planners, including those who would like to alter people’s behaviour from private car ownership to public transportation use, car sellers in terms of how to identify and reach potential customers, provision of alternative forecasting approaches to car ownership scholars as well as possible consideration for general car ownership decision making. Caution should be taken when interpreting the relationship between psychological factors and car ownership since the psychological factors adopted are measure representatives from databases used with limitations in the factor structure for a representative sample of the countries’ population.

Em ecologia, o comportamento dos animais é freqüentemente estudado para entender melhor suas preferências por diferentes tipos de alimento e habitat. O presente trabalho esta relacionado a este tópico, dividindo-se em três capítulos. O primeiro capitulo refere-se à estimação da função da probabilidade da seleção de recurso (RSPF) comparado com um modelo de escolha discreta (DCM) com uma escolha, usando as estatísticas qui-quadrado para obter as estimativas. As melhores estimativas foram obtidas pelo método DCM com uma escolha. No entanto, os animais não fazem a sua seleção baseados apenas em uma escolha. Com RSPF, as estimativas de máxima verossimilhança, usadas pela regressão logística ainda não atingiram os objetivos, já que os animais têm mais de uma escolha. R e o software Minitab e a linguagem de programação Fortran foram usados para obter os resultados deste capítulo. No segundo capítulo discutimos mais a verossimilhança do primeiro capítulo. Uma nova verossimilhança para a RSPF é apresentada, a qual considera as unidades usadas e não usadas, e métodos de bootstrapping paramétrico e não paramétrico são usados para estudar o viés e a variância dos estimadores dos parâmetros, usando o programa FORTRAN para obter os resultados. No terceiro capítulo, a nova verossimilhança apresentada no capítulo 2 é usada com um modelo de escolha discreta, para resolver parte do problema apresentado no primeiro capítulo. A estrutura de encaixe é proposta para modelar a seleção de habitat de 28 corujas manchadas (Strix occidentalis), assim como a uma generalização do modelo logit encaixado, usando a maximização da utilidade aleatória e a RSPF aleatória. Métodos de otimização numérica, e o sistema computacional SAS, são usados para estimar os parâmetros de estrutura de encaixe. / In ecology, the behavior of animals is often studied to better understand their preferences for different types of habitat and food. The present work is concerned with this topic. It is divided into three chapters. The first concerns the estimation of a resource selection probability function (RSPF) compared with a discrete choice model (DCM) using chi-squared to obtain estimates. The best estimates were obtained by the DCM method. Nevertheless, animals were not selected based on choice alone. With RSPF, the maximum likelihood estimates used with the logistic regression still did not reach the objectives, since the animals have more than one choice. R and Minitab software and the FORTRAN programming language were used for the computations in this chapter. The second chapter discusses further the likelihood presented in the first chapter. A new likelihood for a RSPF is presented, which takes into account the units used and not used, and parametric and non-parametric bootstrapping are employed to study the bias and variance of parameter estimators, using a FORTRAN program for the calculations. In the third chapter, the new likelihood presented in chapter 2, with a discrete choice model is used to resolve a part of the problem presented in the first chapter. A nested structure is proposed for modelling selection by 28 spotted owls (Strix occidentalis) as well as a generalized nested logit model using random utility maximization and a random RSPF. Numerical optimization methods and the SAS system were employed to estimate the nested structural parameters.