Becker's discrimination theory predicted that the discrimination by employers on competitive markets should cease to exist. However, in past decades, it was shown that discrimination on the labour market is a prevalent phenomenon. In this thesis I analyse what is the impact of agency problem on the theory of discrimination. I show that when an agent (in the thesis called 'agent employer') is deciding whether to employ or not to employ a worker, his motivation is different compared to principal's. The outcome of the analysis is such that under certain assumptions, the agent employer with non-zero taste for discrimination will always choose to discriminate.

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.

Memoria para optar al título de Ingeniero Civil Industrial / 18/03/2024

Tesis para optar al grado de Magíster en Gestión de Operaciones / Memoria para optar al título de Ingeniero Civil Industrial / Matching mechanisms for school assignment have been adapted on a global scale since the popular application in New York City in 2004, which used the Deferred Acceptance algorithm to assign thousands of students. Implementation of these systems is not a trivial task because of the large scale of the problem and educational regulations particular to each country. One of the goals of this thesis was to develop the implementation of the matching algorithm for the Chilean case and to show the nuances in the design of this mechanism. Then, taking advantage of the strategy-proofness of this system and the centralized data it produces, this work develops structural models for discrete choice to study families' preferences for schools, speci cally, a Bayesian multivariate ordered probit with a hierarchical Bayesian structure to model heterogeneity in the school choice. A methodology was developed to obtain choice sets for families using unsupervised learning techniques in the Coquimbo region where these structural models could be applied. This way, meta-analysis was conducted to evaluate what characteristics in school choice are consistently signi cant, where results indicate that, for example, while schools with poor academic performance are not preferred on average, schools with superior results on standardized test are only preferred by students with good academic records. The estimation of these preferences allows for a series of counterfactual analyses that can aid in the design and implementation of public policies and support the decision making of schools. A no-pricing policy was simulated for the Coquimbo region---which is actually in the process of being adopted by subsidized private schools in Chile---, where it was found that it would improve the social welfare of the assignment of the region, specially for families with children with disabilities, but would impact negatively students that are not in the lowest socioeconomic family groups.

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.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23178号 / 工博第4822号 / 新制||工||1753(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 山田 忠史, 教授 藤井 聡, 准教授 SCHMOECKER Jan-Dirk / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The purpose of this bachelor thesis was to compare three different methods for regression with binary outcomes. The three methods used for comparison are: Linear Probability Model, Logit and Probit. To compare the methods, data gathered from the World Value Survey when it last was done in Sweden in 2011 was used. The outcome variable in the creation of the models was whether the respondent preferred protecting the environment or economic growth. A Monte Carlo-simulation was also performed to strengthen the arguments in the comparison.  The results from the different models created was very small, but there are still differences. Two examples of the differences are the simplicity of interpretation between the models and errors that argues for not using Linear Probability Model under certain circumstances.

The challenges posed by climate change are a threat to human well-being as well as to natural ecosystems, but researchers indicate that awareness of and concern about climate change varies considerably. In this thesis, we investigate the socio-demographic factors associated with the perception of climate change, analysing four data sets collected from four surveys on urban middle-income households in Mexico. Our empirical strategy relies on the estimation of logit regressions on a binary variable defining whether respondents consider climate change as an important issue. Results indicate that age, gender, education, employment status, household size, and having in the household a member with a respiratory illness are important predictors that shape Mexicans’ perception of how important climate change is. It is important to know the determinants that effect climate change perception in order to develop sustainable policies to mitigate the risks of climate change.

Auditors must assess their clients' ability to function as a going concern for at least the year following the financial statement date. The audit profession has been severely criticized for failure to 'blow the whistle' in numerous highly visible bankruptcies that occurred shortly after unmodified audit opinions were issued. Financial distress indicators examined in this study are one mechanism for making such assessments. This study measures and compares the predictive accuracy of an easily implemented two-variable bankruptcy model originally developed using recursive partitioning on an equally proportioned data set of 202 firms. In this study, we test the predictive accuracy of this model, as well as previously developed logit and neural network models, using a realistically proportioned set of 14,212 firms' financial data covering the period 1981-1990. The previously developed recursive partitioning model had an overall accuracy for all firms ranging from 95 to 97% which outperformed both the logit model at 93 to 94% and the neural network model at 86 to 91%. The recursive partitioning model predicted the bankrupt firms with 33-58% accuracy. A sensitivity analysis of recursive partitioning cutting points indicated that a newly specified model could achieve an all firm and a bankrupt firm predictive accuracy of approximately 85%. Auditors will be interested in the Type I and Type II error tradeoffs revealed in a detailed sensitivity table for this easily implemented model.

This thesis is an exploration of the properties of shallow generative adversarial networks (GANs). We focus on several aspects of GANs to investigate the learnability of a class of distributions using shallow GANs and conduct experiments to explore the influence of these aspects on the performance of the GAN models. We identify and analyze several pathological phenomena in theoretical analysis and experiments, and propose potential solutions for them.