nÃo hà / O presente trabalho tem como objetivo identificar os fatores socioeconÃmicos e comportamentais dos alunos que mais influenciaram nos seus resultados na Prova Brasil de 2011. Para tanto, utiliza-se quatro modelos economÃtricos do tipo Logit para medir a chance destes alunos atingirem um ponto de corte nas disciplinas de lÃngua portuguesa e matemÃtica no 5 e 9 ano do ensino fundamental. Os resultados mostram a importÃncia das variÃveis relacionadas Ãs famÃlias nos resultados dos alunos. Medidas simples como incentivar os filhos a estudar tiveram mais importÃncia nas estimativas que variÃveis relacionadas a posses de bens. Os alunos que afirmaram que os professores passam e corrigem tarefas, bem como os que fazem o dever de casa de forma regular tÃm probabilidade maior de atingir os resultados almejados, chegando a 16 pontos percentuais a mais de chance para matemÃtica no 9 ano. NÃo ter reprovaÃÃo anterior eleva as chances de forma significativa em ambas as sÃries e disciplinas o que indica a importÃncia de um acompanhamento mais individualizado para as crianÃas com dificuldades de aprendizado logo nos primeiros anos de vida escolar. Por fim, cabe destacar a grande heterogeneidade dos resultados entre os municÃpios. Mesmo com PIB semelhantes alguns municÃpios tÃm notas mÃdias muito distantes indicando a importÃncia de polÃticas pÃblicas na Ãrea de educaÃÃo. / This study aims to identify the socioeconomic and behavioral factors that most influenced the students on their results of the âProva Brasil 2011â. Therefore, we use four econometric models of the logit type to measure the chance of these students to achieve a cutoff in Portuguese-speaking subjects and mathematics on the 5th and 9th grade of elementary school. The results show the importance of the variables related to families on student outcomes. Simple measures such as encouraging children to study had more importance in estimates that variables related to goods of possessions. Students who reported that teachers give and correct tasks, as well as those who do regularly homework are more likely to achieve the desired results, reaching 16 percentage points more chance for math in grade 9. Having no previous failure increases the likelihood significantly in both series and disciplines which indicates the importance of a more personalized support for children with learning difficulties in the first years of school life. Finally, we highlight the great heterogeneity of results among municipalities. Even with similar GDP some municipalities have very distant middle notes indicating the importance of public policies in education.

nÃo hà / Sendo a intermediaÃÃo financeira a principal atividade dos bancos, alocando recursos de clientes superavitÃrios a clientes deficitÃrios, à na incerteza quanto ao carÃter e a capacidade de pagamento dos clientes que se estabelece o risco e com ele a necessidade de se buscar novas alternativas para se proteger de perdas potenciais, que podem refletir em menores lucros para as instituiÃÃes. AlÃm da subjetividade dos analistas de crÃdito, o uso de modelos quantitativos, baseados em prÃticas estatÃsticas, economÃtricas e matemÃticas, vÃm cada vez mais se firmando nos mercados como ferramenta de apoio aos gestores de crÃdito na tomada de decisÃo. VÃrios modelos de avaliaÃÃo de risco sÃo adotados pelas instituiÃÃes, modelos de credit scoring, behavioral scoring, sÃo exemplos destes modelos. O modelo de credit scoring tem sido um dos mais usados, em especial para concessÃo de crÃdito a pessoas fÃsicas. Os modelos de credit scoring utilizam tÃcnicas como a anÃlise de discriminantes, programaÃÃo matemÃtica, econometria, redes neurais, entre outras, para atravÃs da anÃlise de caracterÃsticas particulares dos indivÃduos, estabelecer uma mÃtrica de separaÃÃo de bons e maus pagadores, atribuindo probabilidades diferentes de inadimplÃncia aos mesmos. A presente dissertaÃÃo tem como objetivo central analisar os determinantes de inadimplÃncia (pessoa fÃsica), usando uma abordagem economÃtrica com base no modelo Logit. O modelo utilizado foi um modelo para aprovaÃÃo de crÃdito na abertura de conta corrente, partindo de um estudo com uma amostra de 308 observaÃÃes (cadastros pessoas fÃsicas), baseados na experiÃncia real de uma instituiÃÃo financeira, cujo objetivo à atingir uma taxa de aprovaÃÃo de crÃdito tal que a receita mÃdia depois das perdas de emprÃstimos seja maximizada. / In the financial intermediation, banks focus on its main activity, allocating resources from clients with surplus to deficit clients. The uncertainty related to the characteristics or payment capacity of the clients establishes the risk and the need to search for new alternatives to protect the institutions from potential losses, which may reflect on lower profits. Besides the subjective issue of credit analysts, the use of quantitative models, based on statistical, mathematical or econometric practices are becoming an important tool to support credit managers on the decision making process. There are several models of risk evaluation, which are adopted by financial institutions such as the credit scoring and the behavioral scoring models. The credit-scoring model has been widely used, especially on the concession of individual credit. The credit scoring model uses techniques such as discriminant analysis, mathematic programming, econometrics, neural networks, among others, to analyze particular characteristics of individuals where it establishes a metric separation of good and bad payers, therefore providing different nonpayment status to each. This present dissertation has the main objective of analyzing the determinants of nonpayment status (individuals), using an econometric approach based on the Logit model. The model utilized was a model for approval of credit in the opening from the bill shackle, starting from a study with 308 observations (physical registers Persons), based in the real experience of a financial institution, whose objective is he reach a credit approval rate such that the medium prescription after the losses of loans be maximized.

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)

Dans la conjoncture économique actuelle un nombre croissant de firmes se trouvent confrontées à des difficultés économiques et financières qui peuvent, dans certains cas, conduire à la faillite. En principe, les difficultés ne surviennent pas brutalement, en effet, avant qu’une entreprise soit déclarée en faillite, elle est confrontée à des difficultés financières de gravité croissante : défaut de paiement d’une dette, insolvabilité temporaire, pénurie de liquidité, etc. L’identification des causes de la défaillance n’est pas évidente, puisqu’on ne saurait énumérer de manière limitative les facteurs qui la provoquent. Les causes sont multiples et leur cumul compromet d’autant plus la survie de l’entreprise. L’importance de ce phénomène et son impact sur l’ensemble de l’économie justifie le besoin de le comprendre, l’expliquer en analysant les causes et les origines. L’objectif de notre étude est de classer les entreprises en difficulté selon leur degré de viabilité et de comprendre les causes de la dégradation de leur situation. Nous effectuerons une comparaison entre trois modèles (Analyse discriminante linéaire, le modèle Logit et la régression PLS) ce qui nous permettra à partir des taux de bon classement obtenus, de choisir le meilleur modèle tout en précisant l’origine et les causes de ces défaillances. / In actual economic situation an increasing number of firms are facing economic and financial difficulties which can, in certain cases, drive to failure. In principle, difficulties do not happen suddenly, in effect, before a firm is declared bankrupt, it is confronted to financial difficulties of growing seriousness: default in payment of a debt, temporary insolvency, scarceness of liquidity, etc. Identifying the causes of the failure is not obvious, since one can not exhaustively enumerate the factors that cause it. The causes are multiple and overlapping compromise even more the company's survival. The importance of this phenomenon and its impact on the overall economy justifies the need to understand, explain it by analyzing the causes and origins The aim of our study is to classify firms in trouble according to their degree of viability and to understand the causes of the deterioration of their situation. We will do a comparison between three models (linear differential Analysis, the model Logit and decline PLS) what will allow us from the rates of good classification acquired, to choose the best model while specifying origin and reasons of these faults.

E-Commerce research shows that existing studies on online consumer choice behavior has focused on comparative studies of channel or store choice (online or offline), or online store choice (different e-tailers). Relatively less effort has been devoted to consumers’ online brand choice behavior within a single e-tailer. The goal of this research is to model online brand choice, including generating loyalty variables, setting up base model, and exploring the effects of Internet-specific attributes, i.e., order delivery, webpage display and order confirmation, on online brand choice at the SKU level. Specifically, this research adopts the Multinomial Logit Model (MNL) as the estimation methods. To minimize the model bias, the refined smoothing constants for loyalty variables (brand loyalty, size loyalty, and SKU loyalty) are generated using the Nonlinear Estimation Algorithm (NEA). The findings suggest that SKU loyalty is a better predictor of online brand choice than brand loyalty and size loyalty. While webpage display has little effect on the brand choice, order delivery has positive effect on the choice. Online order confirmation turns out to be helpful in choice estimation. Moreover, online consumers are not sensitive to net price of the alternatives, but quite sensitive to price promotion. These results have meaningful implications for marketing promotions in the online environment and suggestions for future research.

The transportation sector is considered one of the major contributors to greenhouse gas (GHG) emissions in metropolitan areas, and any efforts to reduce these emissions requires strategic management of multiple transportation modes. This paper presents a method to identify opportunities to reduce GHG emissions by expanding commuter bus services and incentives to shift commuters from private cars to transit. The approach uses a nested multinomial logit model for mode choice in a region that includes driving alone, carpooling, walking, cycling, and using four possible transit modes (ferry, commuter rail, rapid transit and bus) by walk access or driving access. A model of existing conditions was calibrated with data from the Boston metropolitan area. Using an emission factor model based on average speeds from the California Air Resources Board (CARB), the net effect of new commuter bus service on GHG emissions from transportation was estimated. Potential GHG reductions are weighed against the capital and operating costs of new transit services to quantify the cost-effectiveness of a new commuter bus service for isolated origin-destination pairs. This modeling framework is used to optimize fares and bus frequency in order to identify the corridors with the most cost-effective potential for GHG reduction. Results are presented for the Boston region, demonstrating the feasibility of implementation and the potential magnitude of benefits for cost-effectively reducing GHG emissions associated with transportation. The method is general and can be applied in other cities around the world.

This study used focus groups and surveys to provide a comprehensive evaluation of the Regional Traveler Information Center (RTIC) at UMass Amherst. The evaluation was completed by obtaining the awareness, usage, and perceived effectiveness of RTIC’s information by residents in the Pioneer Valley. It was found that awareness of RTIC is limited due to its lack of advertisement. Usage is focused primarily on its webcams and advisory information. Surveys showed that participants perceive RTIC to be useful, even though they may never have seen the information before (the survey provided a chance for them to become familiar with the service). Revealed preference data were collected regarding the travelers' most memorable instances where real-time traffic information was provided. A binary logit model of a traveler's switch decision (route, departure time, mode, destination, trip cancellation, or combinations of them) with real-time traffic information was specified and estimated. It was found that travelers have an increasing tendency to switch away from the original option when the resulting delay caused by congestion increases. Receiving congestion and crash information also provided a tendency to take an alternative travel method. It was found that males tend to switch more often than females, and young individuals switch less often.

A model that can be used to analyze both current and future high-speed rail corridors is presented in this work. This model has been integrated into the Transportation Systems Analysis Model (TSAM). The TSAM is a model used to predict travel demand between any two locations in the United States, at the county level. The purpose of this work is to develop tools that will create the necessary input data for TSAM, and to update the model to incorporate passenger rail as a viable mode of transportation. This work develops a train dynamics model that can be used to calculate the travel time and energy consumption of multiple high-speed train types while traveling between stations. The work also explores multiple options to determine the best method of improving the calibration and implementation of the model in TSAM. For the mode choice model, a standard C logit model is used to calibrate the mode choice model. The utility equation for the logit model uses the decision variables of travel time and travel cost for each mode. A modified utility equation is explored; the travel time is broken into an in-vehicle and out-of-vehicle time in an attempt to improve the model, however the test determines that there is no benefit to the modification. In addition to the C-logit model, a Box-Cox transformation is applied to both variables in the utility equation. This transformation removes some of the linear assumptions of the logit model and thus improves the performance of the model. The calibration results are implemented in TSAM, where both existing and projected high-speed train corridors are modeled. The projected corridors use the planned alignment for modeling. The TSAM model is executed for the cases of existing train network and projected corridors. The model results show the sensitivity of travel demand by modeling the future corridors with varying travel speeds and travel costs. The TSAM model shows the mode shift that occurs because of the introduction of high-speed rail. / Master of Science

<p>  </p> <p>Kentucky bluegrass is the most common cool-season turfgrass grown in the northern US. <br> The fact that Kentucky bluegrass requires s high quantity of fertilizers, pesticides, and irrigation to produce high quality turf has led to environmental concerns among policymakers, researchers, and consumers. To address this concern, turfgrass breeders have developed improved cultivars of low-input turfgrass species that aim to improve the sustainability of US lawns (Ghimire et al., 2019). For instance, tall fescue [(<em>Festuca arundinacea </em>Schreb.; syn. <em>Schedonorus arundinaceus</em> (Schreb.) Dumort., nom. cons.] and fine fescues (<em>Festuca </em>spp.) may represent viable options for residential and commercial buildings due to their outstanding performance under lower amounts of inputs such as irrigation, pesticides, and fertilizers (Watkins et al., 2011). Thus, adopting improved cultivars of low-input species may be a step towards reducing the use of inputs in landscapes (Simmons et al., 2011; Pooya et al., 2013). Yet, the production of low-input turfgrasses in the northern US is slow and limited, which leads to marketing and education obstacles that support their adoption. Thus, understanding factors that influence sod buyers to purchase low-input turfgrasses is imperative to increase the market share and the adoption of these turfgrasses. </p> <p>This study investigated the factors influencing sod buyers to purchase low-input turfgrass in the northern US, using tall fescue and fine fescue as low-input sod species. Using a logistic regression model, we assessed the determinants of low-input turfgrass purchase among sod buyers (i.e., athletic facilities, landscape contractors, garden centers, general contractors, lawn care, golf courses, and municipal parks). The logit model assumed the adoption decision to be driven by the buyers’ perception of the utility of buying low-input turfgrass species. Thus, the dependent binary variable Y equals 1 if the firm purchased tall or fine fescue in 2020, and 0 otherwise. The adoption is then expressed as a function of determinants, including the firm’s characteristics, supplier characteristics, sod attributes, and buyer’s perceptions. </p> <p>Data for this study came from a 2021 web-based survey of sod buyers located in 19 states of the Northern US. A total of 200 buyers completed the survey, including landscape contractors, golf courses, general contractors, lawn care services, and landscape maintenance firms who have purchased sod in 2020. The significant mean comparisons between adopters and non-adopters showed that adopters of low-input turfgrasses purchased most of their sod through contract agreements. The main suppliers of adopters were located at a closer distance to on-site delivery than the non-adopters. The logit regression results showed that low-input turfgrass adoption was positively influenced by the number of sod suppliers and managerial experience of the sod buyer. Landscapers were more likely to purchase tall fescue and fine fescue compared to golf courses and municipal parks. We found that distance from sod supplier to on-site delivery negatively impacted the purchase of low-input turfgrasses. Similarly, Kentucky bluegrass buyers were less likely to purchase low-input turfgrass species.</p>

The primary objective of this study is to develop an integrated public transportation planning framework to evaluate and plan Advanced Public Transportation Systems (APTS). With this purpose, a systems approach point of view is adopted to study the influence of new APTS technology in supply and demand transit variables. In this project the Systems Dynamics methodology is adopted to track the dynamic behavior of model variables and feedback loops forming among them. The proposed framework is illustrated in a case study involving automated vehicle location systems (AVL) applied to a small transit community. The proposed approach follows the same steps of the Systems Dynamics method; First, identify some key variables which are not only susceptive to AVL technology but also affect the supply-demand relationship of a bus transit environment. Second, trace and simplify the causal relationships of the variables considering impacts of facility supply changes to passenger demand responses and vice versa. To accomplish this, four detailed sub-models representing parts of the transit system are developed and combined under the Systems Dynamics methodology point of view. Theses Sub-models are: 1) demography, 2) urban transportation planning, 3) bus operations, and 4) evaluation. Finally, to validate the model procedure, the model is applied to a case study. This study attempts to encompass as many as possible factors around a bus transit system environment which can be impacted by new APTS technology to illustrate the use of the proposed framework. Some of these factors include: 1) Demographic characteristics; 2) urban or social activity of the study area and 3) changes to transportation facilities. The case study illustrates how the physical characteristics of the transit systems such as traffic demand, traffic conditions along the transit route, route layout, and bus performance can be affected by the new technology. Since APTS impacts are time dependent a continuous multi-loop simulation technique is adopted to track dynamic changes of all model variables. The analysis of the transit system is carried over a 20-year life cycle to illustrate the long term dynamics of the feedback structures inherent in the model. <i>[Vita removed Aug. 2, 2010. GMc]</i> / Ph. D.