Promover a segurança viária no entorno escolar é uma estratégia que contribui para que sejam construídas cidades seguras, saudáveis e sustentáveis. Nesse sentido, este estudo é dedicado a identificar a influência das características da estrutura urbana na frequência e na severidade dos acidentes no entorno de escolas de educação básica de Porto Alegre. A análise da frequência e da severidade de acidentes foi conduzida através da estimação de modelos econométricos: binomial negativo e logit ordenado, respectivamente. Para esses, foram calculados os efeitos marginais, permitindo a observação da magnitude dos impactos das variáveis explicativas sobre as variáveis dependentes. As variáveis dependentes frequência e severidade foram extraídas dos acidentes registrados em Porto Alegre entre 2012 e 2014. Foram incluídas, simultaneamente, variáveis da estrutura urbana, das escolas, socioeconômicas e dos acidentes (para a severidade). A partir do geoprocessamento dos dados existentes, os entornos escolares puderam ser caracterizados para três diferentes áreas circulares de análise (buffer ring) de raios de 100, 150 e 200 metros, permitindo a comparação do uso das diferentes áreas. O conjunto de estimativas indica que áreas menores produzem modelos de melhor desempenho para ambas as técnicas empregadas. No entanto, áreas maiores permitem a análise de maior quantidade de variáveis relativas à estrutura urbana. Essa relação sugere os benefícios da escolha a partir do trade-off entre ajuste do modelo e sua capacidade de propiciar análises de variáveis. Foi identificado que a frequência e a severidade de acidentes podem estar relacionadas a uma única variável explicativa de formas opostas – a partir de sinais contrários. Essa diferença de resultados para frequência e severidade de acidentes indica que há maiores benefícios em analisá-las em conjunto. Identificou-se ainda que existem benefícios para a segurança viária em áreas de estrutura urbana com quarteirões menores e maior quantidade de interseções de quatro vias, em frequência e severidade, respectivamente. Já as áreas mais arborizadas tendem a apresentar acidentes de menor severidade nos casos de usuários de modos ativos. / Fostering road safety nearby schools is a strategy that contributes to build safe, healthy and sustainable cities. The aim of this study is to identify the influence of the built environment characteristics in the frequency and severity of accidents nearby elementary and secondary schools in Porto Alegre. The frequency and severity of the accidents were analyzed using econometric models: negative binomial and ordered logit, respectively. The evaluation of their marginal effects allowed the magnitude of the impact caused by the explanatory variable on the dependent variables to be observed. The measured variables frequency and severity were extracted from accidents registered in Porto Alegre from 2012 and 2014. Built environment, socioeconomic and school variables were also included, as well as accident data (for severity). Data geoprocessing allowed school surroundings to be characterized for three different buffer rings, measuring 100, 150 and 200 meters of radius. Thereby it was possible to compare the inclusion of different areas in the study. The estimations indicates that models based on smaller areas have better performances for both employed techniques, whereas larger areas allow the study of a bigger quantity of urban infrastructure variables. That indicates the benefits of choosing based on a trade-off between model adjustment and capacity to engender the analysis of variables. It was shown that frequency and severity of accidents could be related to a single explanatory variable in opposite ways – based on contrary signs. This difference in the results found for frequency and severity indicates that there are more benefits when analyzing them together. Moreover, there are benefits for road safety in areas where the city blocks are shorter and where there are more four-way intersections, in frequency and severity of accidents, respectively. Also, areas of more important afforestation tend to decrease the severity of accidents involving users of active modes.

Um dos desafios da educação brasileira é a grande quantidade de jovens que deveriam estar matriculados no Ensino Médio, mas que não estão frequentando a escola. Além disso, a taxa de abandono escolar nos anos iniciais do ensino médio é muito superior àquela encontrada no último ano do ensino fundamental. Nesse sentido, esse trabalho se propõe a investigar qual o papel da família no processo de transição e permanência no ensino médio. Para tanto, devido a disponibilidade de dados, será investigada uma coorte de alunos aprovados em 2010 no 9° ano do Ensino Fundamental no Estado do Ceará. Será estimado um sequential logit model, cujos regressores serão, além de algumas características individuais, informações referentes ao status socioeconômico da família e ao ambiente familiar. Existe uma vasta literatura que vem se desenvolvendo desde Mare (1980) buscando compreender o papel do background familiar na desigualdade educacional, tratando o processo de escolarização como uma sequência de decisões. Os resultados aqui encontrados, além de dialogar com essa literatura, apontam que a família é determinante, tanto para a entrada, como para a permanência no ensino médio, principalmente por meio da escolaridade dos pais. Esse efeito, todavia, é maior para aqueles que se defrontam com a decisão de entrada no ensino médio fora da idade ideal ou que exercem atividade remunerada quando não estão na escola. / One of the challenges of Brazilian education is the large amount of young people who should be enrolled in high school, but who are not attending school. In addition, the drop-out rate in the early high school years is much higher than that found in the last grade of elementary school. Thus, this study aims to investigate the role of the family in transition and permanence in high school. Therefore, due to data availability, a cohort of students approved in 2010 in the last grade of elementary school in the state of Ceará will be investigated. Will be estimated to sequential logit model, whose covariates are, individual characteristics, information regarding socioeconomic status of the family and the family environment. There is a vast literature, that has been developing since Mare (1980), trying to understand the role of family background on educational inequality, treating the educational process as a sequence of decisions. The results found here, as well as dialogue with the literature, show that the family is crucial both for entry and for staying in high school, mainly through parental education. This effect, however, is higher for those who are faced with the decision to enter high school outside the ideal age or to engage in paid work when they are not in school.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-10-10T14:49:33Z No. of bitstreams: 1 philipescherrermendes.pdf: 346564 bytes, checksum: 67ae50b48a6d6b505287fb67ce1da5a0 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-10-11T15:54:22Z (GMT) No. of bitstreams: 1 philipescherrermendes.pdf: 346564 bytes, checksum: 67ae50b48a6d6b505287fb67ce1da5a0 (MD5) / Made available in DSpace on 2016-10-11T15:54:22Z (GMT). No. of bitstreams: 1 philipescherrermendes.pdf: 346564 bytes, checksum: 67ae50b48a6d6b505287fb67ce1da5a0 (MD5) Previous issue date: 2009-12-15 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A proposta deste trabalho é abordar os fatores que condicionam a mobilidade de trabalhadores através do território. Nesse sentido, são abordados fatores de atração e repulsão pela literatura de Economia do Trabalho a fim de avaliar a extensão territorial da mobilidade do trabalhador, tendo em vista que a literatura de Economia da Tecnologia, e sua vertente regional, enfatiza que a mobilidade de trabalhadores qualificados é importante para transferir conhecimento de natureza tácita entre regiões, com claras repercussões sobre a possibilidade de desenvolvimento regional. Neste diálogo, estabelece-se conceitos teóricos que abordem questões relacionadas aos determinantes econômicos da mobilidade de trabalhadores e questões relacionadas à capacidade que os trabalhadores qualificados possuem de transmitir conhecimento tecnológico ao se moverem, favorecendo o desenvolvimento. Com o uso dos micro-dados da RAIS-Migra (MTE), acompanha-se uma amostra dos trabalhadores, empregados na indústria de transformação, ao longo de oito anos (1995-2002), e apresenta-se a composição do emprego formal no Brasil por setores com diferentes graus de intensidade tecnológica. A partir de modelos de regressão logística multinomial, o trabalho revela que a mobilidade é positivamente relacionada com o salário, com o sexo masculino e com o nível de escolaridade do trabalhador. Por outro lado, há uma relação negativa com o nível de senioridade do trabalhador. Além disso, todos esses resultados diferem significativamente em termos da extensão territorial da mobilidade, o que é possível observar pelo uso da metodologia multinomial. / The purpose of this paper is to study the factors that interfere on the mobility of workers through the space. Factors of attraction and repulsion are studied using the Labor Economics to verify the territorial extension of the mobility of workers, having in mind that the literature of Economics of Technology, and its regional approach, emphasize that the mobility of qualified workers is important to transfer tacit knowledge among regions, with strong repercussion on the possibility of regional development. In this dialog, theorical concepts that treat questions linked to the capacity of qualified workers to transfer technological knowledge when they move are established, improving the development. With the use of microdata from RAIS Migra (MTE), a sample of workers employed on the industry are followed eight years long (1995-2002), and the composition of the formal employment in Brazil divided in sectors with different levels of technological intensity are presented. Using multinomial logistic regression models, this paper reveals that the mobility is positively related to the wages, to the male gender and to the educational level of the workers. And it is negatively related to their level of seniority. Besides, all these results are significantly different when the territorial extension of the mobility is considered, what is possible to see through the use of the multinomial methodology.

Emerging automotive and transportation technologies, such as autonomous vehicles (AVs) have created revolutionary possibilities in the way we might travel in the future. Major car manufacturers and technology giants have demonstrated significant progress in advancing and testing AV technologies in real-life traffic conditions. Results from multi-population surveys indicate that despite enjoying moderate familiarity with AVs, more than 40% of the respondents were likely to use them when they become available. Simply looking at the demographic differences without paying any regard to the perceptions might suggest that the demographic differences are the primary causal factors behind the differences observed in the intended adoption of AVs. This study investigates the role of demographics and other factors (current travel characteristics, crash history and familiarity with AVs) on consumers’ perceptions and intended adoption of AVs with a view of disentangling one factor from the other. Results show that the observed demographic differences in intended adoption rates are due to demographic differences in the perceptions on the benefits and concerns of AVs. The study outcomes suggest that it may be beneficial to first address consumers’ perceptions on the benefits and concerns regarding AVs. The results from this study can be used to inform modeling decisions and policy discussions relevant to future market penetration of AV technology.

Correlated ordinal data typically arise from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. We investigate how sensitive the pairwise likelihood estimates are to the number of subjects and to the presence of observations missing completely at random, and find that these estimates are robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US companies as well as an incomplete panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework. / Series: Research Report Series / Department of Statistics and Mathematics

No description available.

Reflecting the growing interest from both consumers and policymakers, and building on recent developments in Willingness to Pay (WTP) methodologies, we evaluate consumer preferences for an archetypal traditional food product. Specifically we draw on stated preference data from a discrete choice experiment, considering the traditional Hungarian mangalitza salami. A WTP space specification of the generalized multinomial logit model is employed, which accounts for not only heterogeneity in preferences but also differences in the scale of the idiosyncratic error term. Results indicate that traditional food products can command a substantial premium, albeit contingent on effective quality certification, authentic product composition and effective choice of retail outlet. Promising consumer segments and policy implications are identified. (authors' abstract)

This paper aims to find macroeconomic and financial variables with ability to predict financial crises. A dataset covering 17 developed countries over the period 1870-2013 have been investigated using a logit model. We found commonly used macroeconomic variables such as terms of trade and consumption to be strong predictors within our sample. Whereas private debt and house prices are frequently found to be strong predictors, we found loans to business to be at least as good in predicting financial crises. Multivariate models are constructed as warning systems and used to analyze Sweden from 1975 up until 2016. The most efficient warning system give a strong signal before the first and moderate signal before the second crisis. In extension, regarding today’s climate the warning system provides no signal, suggesting low current risk. Policy makers can benefit from observing certain variables that are found significant in this study to improve financial stability and reduce socio-economic costs.

Credit Rating Agencies (CRAs) play a key role in financial markets by helping to reduce informative asymmetry between lenders and investors, on one side, and issuers on the other side, with regard to the creditworthiness of banks or countries. This crucial role has expanded alongside financial globalisation and received an additional boost from Basel II which integrates the ratings of CRAs into the rules for setting weights for credit risk. Ratings adjustment tends to be sticky, lagging behind markets, and often overreact when they do change. This overreaction may have aggravated the recent financial crises, contributing to financial instability and cross-country contagion. Criticism has been especially directed towards the high degree of concentration of the ratings industry. Promotion of competition may require policy action at the international level to encourage the establishment of new agencies and to discover alternative rules or regulatory requirements in order to achieve promising results. The recent growth of Middle Eastern and North African countries (MENA) and their commercial banking system has increased the need of paying widespread attention to this region of the world. This thesis crucially identifies, and estimates, the robust determinants of credit ratings for MENA countries and their commercial banks, incorporating a set of bank level accounting and financial risk factors, as well as country-specific characteristics, including indicators for regulatory, supervision, legal and economic environments. The research contributes, firstly, to the theoretical literature on credit ratings industry by reviewing extant methodologies specifically as they apply to banks and sovereign countries. Secondly, it conducts a systematic, cross-country empirical investigation using panel data econometric methodology for the purpose of estimating MENA countries sovereign and bank credit rating models. Thirdly, it provides tangible and statistically significant evidence on the different factors that determines the estimation of credit ratings and influencing bank's risk. The extant literature reviewed serves as a basis to achieve and develop the research aim, objectives and hypotheses of the thesis. The research then constructs an appropriate panel dataset from different sources, containing bank-level and country-level information for a sample of 108 commercial banks covering 13 MENA countries over the period 2000 - 2012. The methodological framework for estimating credit rating models (linear regression, logit and probit) is also reviewed and the procedures for panel data estimation are implemented using the econometric package STATA (version 13). All relevant data are drawn from public sources including Reuters, Bankscope, IMF and the World Bank. Using the random effects ordered probit and logit methodologies to estimate both sovereign (country) and bank level credit ratings models for the MENA countries, the evidence shows that real GDP growth, capital requirements, restrictions on banking activities and control of corruption all contribute negatively to the sovereign ratings. Furthermore, internal management and organisational requirements is considered as an additional regulatory factor not studied in previous research. The statistically significant and inverse relationship of the latter is considered an important and interesting outcome of MENA countries’ sovereign ratings. On the other hand, GDP per capita, investment (as a percentage of GDP), political stability, government effectiveness and the rule of law all reveal significant and positive impact on the sovereign credit ratings. In general, this research finds that improved macroeconomic conditions are correlated with higher ratings, while greater reserve regulations are correlated with lower ratings. The study also does find the significance of governance and regulatory variables plays a key role into the final credit rating. With regard to the impact on banks’ ratings, the results show that higher return on average assets and equity, larger bank size, more restrictions on bank activities, as well as higher official disciplinary power and higher standards of internal management, will yield higher credit ratings. Apart from having direct and positive impact on banks credit ratings, these variables are important for examining the risk-sharing incentives in MENA countries’ banks. In contrast, the estimation results indicate that net interest margin, net loans to deposits, liquid assets to deposits, capital requirements, deposit insurance scheme, liquidity requirements, unemployment rate and government effectiveness have an inverse and negative impact on banks ratings. In general, this study also finds various financial, macroeconomic, and regulatory effects on banks’ credit ratings. To a much lesser extent than government ratings, various macroeconomic variables also helped predict banks’ ratings, including real GDP growth and the unemployment rate. The thesis concludes by arguing that the combined use of financial and non-financial factors for estimating credit ratings models supports the relevant hypotheses examined and adds value to all stakeholders in improving and obtaining a better quality of credit ratings. This study also demonstrates that a diversity of bank-level and country-level factors influence the MENA sovereign and bank ratings differently, implying that policy makers, regulators alongside rating agencies should distinguish the different environmental factors between nations before any judgment and issuance can be model of the ratings. To conclude, there is no study which exclusively investigates credit rating models for the MENA region exploiting the richness of the data and methodology employed, and the current research aims to fill this gap.

Magíster en Gestión de Operaciones / Tanto para los clientes como para las empresas la falta de productos en góndola (o quiebres de stock) produce costos indeseados muy elevados. Éstos, en su mayoría, se generan por falta de información, errores en el pronóstico de ventas y problemas en el sistema de reposición de productos. Para las empresas de retail es necesario entregar una buena experiencia de compra ofreciendo productos y servicios adecuados a las necesidades de los consumidores dada la alta competencia existente. Por esta razón es importante cuantificar y estudiar el efecto que los problemas en la disponibilidad de los productos generan sobre los clientes. El objetivo principal de este trabajo es estudiar el efecto de la variedad de surtido en góndola en el comportamiento de los clientes mediante la incorporación de variables operacionales de stock a un modelo de incidencia de compra. Para ello, se utilizan datos transaccionales de los clientes y datos operacionales de quiebres de stock --medidos mediante fotografías en intervalos fijos de tiempo-- de los distintos tipos de pan ofrecidos por una tienda de una cadena de supermercados norteamericana. En primer lugar, se realiza un análisis agregado por clientes para los principales productos del supermercado para estudiar la relación entre las ventas de diferentes categorías de panes y los quiebres de stock, donde los resultados entregan evidencia preliminar del efecto agregado de la disponibilidad. Por lo anterior, se realiza un análisis a nivel desagregado por clientes, para el cual se utiliza un modelo mixed logit, que captura la heterogeneidad de los clientes. En este modelo, se estudian los patrones de sustitución entre los productos y los efectos cruzados entre las categorías de productos. Los resultados muestran una disminución en la incidencia de compra general de panes con quiebres de stock de rolls de la categoría de un 0,91% y un efecto de sustitución entre los productos de un 0,19 %. Finalmente, con los resultados obtenidos, se estudia el efecto de los quiebres de stock de panes dentro de otros departamentos del supermercado -en particular, se estudia el departamento Produce-. Para esto, se utilizan variables instrumentales, que corresponden a las probabilidades de compra estimadas de los panes de manera agregada o de las categorías de panes. Los efectos encontrados para estos dos modelos son distintos, en donde los productos empaquetados toman mayor protagonismo sobre los de venta a granel en las compras.