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[pt] Esta dissertação é composta por dois capítulos. No primeiro capítulo, avalia-se o impacto de curto e médio prazo de um amplo programa habitacional brasileiro sobre a probabilidade de emprego e outros resultados dos beneficiários no mercado de trabalho. Nós usamos dados de sorteios realizados no Rio de Janeiro para identificar estes efeitos. Concluímos que o programa aumentou a probabilidade de emprego formal em cerca de dois pontos percentuais e não teve efeito sobre a probabilidade de emprego informal. Nós também encontramos evidências de que o programa aumentou salários e a qualidade dos empregos ocupados pelos beneficiários. Adicionalmente, usamos métodos de forma-reduzida para testar a importância de mecanismos que possam explicar o efeito do programa sobre a probabilidade de emprego dos indivíduos. Encontramos evidências de que o aumento dos custos de mobilidade do trabalho dos indivíduos aos projetos habitacionais construídos é um importante determinante dos impactos do programa. Em contrapartida, efeitos de vizinhança, fricções pela realocação dos indivíduos, migração e a distância para a sua residência anterior não parecem ser mecanismos importantes para explicar os impactos do programa. No segundo capítulo, complementamos a análise anterior construindo um modelo estrutural estático de oferta de trabalho. Nós incorporamos no modelo a decisão simultânea de participar do mercado de trabalho e do programa habitacional. Os dados do sorteio são utilizados para ajudar a identificar os parâmetros do modelo e para validá-lo fora da amostra. O modelo estimado é capaz de reproduzir de forma adequada o comportamento dos indivíduos que participam dos sorteios, tanto os utilizados na estimação quanto os sorteios mantidos apenas para sua validação. Então, utilizamos o modelo previsto para realizar experimentos de política pública. / [en] This dissertation is comprised of two chapters. In the first chapter, we assess how a large public housing program in Brazil affected short and medium-run employment probability and other labor market outcomes. We use data from lotteries in Rio de Janeiro to identify these impacts. We concluded that the program increased formal employment by about two percentage points and had no effect on informal employment. Moreover, we also find evidence that receiving a house increased wages and the quality of jobs held for the treated individuals and reduced participation in other social programs. Additionally, we used reduced-form models to test the mechanisms that might explain the observed increase in employment probability. We found evidence that the mobility costs from the individual job to the provided houses is an important determinant of the impacts of the program. On the other hand, neighborhood effects, relocation from the individuals house, migration and the distance from individuals previous homes do not seem to be important mechanisms in explaining the effect of the program on employment. In the second chapter, we complement the previous analysis by building and estimating a static labor supply structural model.We incorporate in the model the simultaneous decision to participate in the labor market and a housing program. We use data for lotteries to help identify the parameters of the model. The lotteries data is also used to out-of-sample validation. Our estimated model is able to reproduce well both the behavior of individuals in the data used for estimation and in the experimental hold-up sample. Then, we use this model to perform policy experiments and evaluate counterfactuals.

This dissertation is exploratory in design and employs an electronic survey and comparative case studies to examine the factors that shape the impact of a non-traditional data source that measures the market power of urban neighborhoods, the Social Compact Neighborhood Market DrillDown, on the policymaking process of local government officials concerned with neighborhood economic development. The four case studies are: Baltimore, MD, Louisville, KY, Detroit, MI, and Tampa, FL. The study examines the conditions that affect decision-making at the different stages of information use and considers instrumental, conceptual, and symbolic uses of information. The observation of seven variables (applicability to agenda of lead sponsor, congruence between findings and prior preferences, trust of information producer, availability of alternative information sources, information sustainability, costs of production, information as private sector "lure") provide the context for theory and hypotheses on information impact in which three factors are found to be significant (applicability to agenda to lead sponsor, information sustainability, and information as private sector "lure"). Overall, the study finds evidence that information use is inherently a political endeavor in which its use is dominated by the preferences of those who sponsor its production and use information toward initiatives that are important to them. / Political Science

Hundreds of steel products need to be scheduled on a single or parallel machine in a steel plant every week. A good feasible schedule may save the company millions of dollars compared to a bad one. Single and parallel machine scheduling are also encountered often in many other industries, making it a crucial research topic for both the process system engineering and operations research communities. Single or parallel machine scheduling can be a challenging combinatorial optimization problem when a large number of jobs are to be scheduled. Each job has unique job characteristics, resulting in different setup times/costs depending on the processing sequence. They also have specific release dates to follow and due dates to meet. This work presents both an exact method using mixed-integer quadratic programming, and an approximate method with metaheuristics to solve real-world large-scale single/parallel machine scheduling problems faced in a steel plant. More than 1000 or 350 jobs are to be scheduled within a one-hour time limit in the single or parallel machine problem, respectively. The objective of the single machine scheduling is to minimize a combined total changeover, total earliness, and total tardiness cost, whereas the objective of the parallel machine scheduling is to minimize an objective function comprising the gaps between jobs before a critical time in a schedule, the total changeover cost, and the total tardiness cost. The exact method is developed to benchmark computation time for a small-scale single machine problem, but is not practical for solving the actual large-scale problem. A metaheuristic algorithm centered on variable neighborhood descent is developed to address the large-scale single machine scheduling with a sliding-window decomposition strategy. The algorithm is extended and modified to solve the large-scale parallel machine problem. Statistical tests, including Student's t-test and ANOVA, are conducted to determine efficient solution strategies and good parameters to be used in the metaheuristics. / Thesis / Master of Applied Science (MASc)

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The correct use of morphological suffixes in obligatory contexts reflects linguistic knowledge and competence of speakers. Grammatical knowledge is acquired during a child’s period of primary language acquisition, and may be partial or incomplete due to normal linguistic variation found during acquisition, due to a child’s level of progression through typical chronological development, or due to the presence of language disorders, like specific language impairment (SLI). In the current study, we ask whether characteristics of verbs make it more or less likely that children will correctly use an inflectional morpheme. The morphemes of interest in the current study were third person singular –s (3S) and past tense –ed (ED). Data for analysis were taken from a database of spontaneous language samples collected from 40 children (20 with SLI and 20 developing typically; Hoover, Storkel, & Rice, 2012). Spontaneous language samples were analyzed for the presence or absence of each morpheme in obligatory contexts. For each word item, the uninflected base word was additionally analyzed for a number of phonological and lexical variables. After comparing children with SLI to typically developing peers group differences emerged with respect to the effect of phonological and lexical variables. Moreover, different variables were determined to predict the 3S and ED morphemes. The results are discussed highlighting relevant theoretical and clinical implications.

This dissertation will focus on three research projects: Neighborhood vector auto regression in multivariate time series, uncertainty quantification for agent-based modeling networked anagrams, and a scalable algorithm for multi-class classification. The first project studies the modeling of multivariate time series, with the applications in the environmental sciences and other areas. In this work, a so-called neighborhood vector autoregression (NVAR) model is proposed to efficiently analyze large-dimensional multivariate time series. The time series are assumed to have underlying distances among them based on the inherent setting of the problem. When this distance matrix is available or can be obtained, the proposed NVAR method is demonstrated to provides a computationally efficient and theoretically sound estimation of model parameters. The performance of the proposed method is compared with other existing approaches in both simulation studies and a real application of stream nitrogen study. The second project focuses on the study of group anagram games. In a group anagram game, players are provided letters to form as many words as possible. In this work, the enhanced agent behavior models for networked group anagram games are built, exercised, and evaluated under an uncertainty quantification framework. Specifically, the game data for players is clustered based on their skill levels (forming words, requesting letters, and replying to requests), the multinomial logistic regressions for transition probabilities are performed, and the uncertainty is quantified within each cluster. The result of this process is a model where players are assigned different numbers of neighbors and different skill levels in the game. Simulations of ego agents with neighbors are conducted to demonstrate the efficacy of the proposed methods. The third project aims to develop efficient and scalable algorithms for multi-class classification, which achieve a balance between prediction accuracy and computing efficiency, especially in high dimensional settings. The traditional multinomial logistic regression becomes slow in high dimensional settings where the number of classes (M) and the number of features (p) is large. Our algorithms are computing efficiently and scalable to data with even higher dimensions. The simulation and case study results demonstrate that our algorithms have huge advantage over traditional multinomial logistic regressions, and maintains comparable prediction performance. / Doctor of Philosophy / In many data-central applications, data often have complex structures involving temporal structures and high dimensionality. Modeling of complex data with temporal structures have attracted great attention in many applications such as enviromental sciences, network sciences, data mining, neuroscience, and economics. However, modeling such complex data is quite challenging due to large uncertainty and dimensionality of complex data. This dissertation focuses on modeling and prediction of complex data with temporal structures. Three different types of complex data are modeled. For example, the nitrogen of multiple streams are modeled in a joint manner, human actions in networked group anagrams are modeled and the uncertainty is quantified, and data with multiple labels are classified. Different models are proposed and they are demonstrated to be efficient through simulation and case study.