Diploma Thesis Abstract JEL Classification Z13, O12, O13, Z91 Keywords income shock, endogeneity, ethnicity, instrumental variables Title Income Shocks and Ethnic Group Bias Author's e-mail 61618416@fsv.cuni.cz Supervisor's e-mail bauerm@fsv.cuni.cz Abstract The thesis explores how individual income shocks impact ethnic group bias using a survey dataset from Uganda and meteorological data. Previous research has shown that the majority of civil conflicts break down along ethnic lines, and that ethnic tribalism harms economic performance of countries. A better understanding of the causal mechanism behind ethnic bias could help alleviate both of those problems. The author finds that income shocks have no measurable effect on ethnic out-group bias, but they might influence ethnic in-group bias. Since the regression of ethnic bias variables on income proxies likely suffers from endogeneity, the author employs the copula endogeneity correction model, which, however, does not prove to perform well with a binary dependent variable. In addition, the author demonstrates that ethnic group bias is most likely not directly influenced by variations in rainfall and that instrumenting for income with rainfall variation leads to non- robust results with even a small violations of model assumptions.

Thesis advisor: Donald Cox / I investigate the effect of the medical brain drain on health in sub-Saharan Africa. Such information would be invaluable to policymakers; if doctors are likely to emigrate, there is little benefit in investing in their training. Previous work has mostly been limited to measuring correlations, which fail to illuminate causal pathways; countries with weakly structured healthcare systems may have both poor health and high physician emigration. I address the problem with an instrumental variable. For African countries with historic colonial ties to the United Kingdom or France, I used immigration policy changes in these European nations to instrument for the medical brain drain. Higher rates of medical brain drain cause decreased physician density, decreased rates of measles immunizations among children, and increased rates of HIV prevalence in sub-Saharan Africa. Therefore, I conclude that targeting physician emigration would help improve health in the region. / Thesis (BA) — Boston College, 2013. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: College Honors Program. / Discipline: Economics Honors Program. / Discipline: Economics.

High-dimensional data, such as that relating to gene expression in microarray experiments, may contain substantial amount of useful information to be explored. However, the information, relevant variables and their joint interactions are usually diluted by noise due to a large number of non-informative variables. Consequently, variable selection plays a pivotal role for learning in high dimensional problems. Most of the traditional feature selection methods, such as Pearson's correlation between response and predictors, stepwise linear regressions and LASSO are among the popular linear methods. These methods are effective in identifying linear marginal effect but are limited in detecting non-linear or higher order interaction effects. It is well known that epistasis (gene - gene interactions) may play an important role in gene expression where unknown functional forms are difficult to identify. In this thesis, we propose a novel nonparametric measure to first screen and do feature selection based on information from nearest neighborhoods. The method is inspired by Lo and Zheng's earlier work (2002) on detecting interactions for discrete predictors. We apply a backward elimination algorithm based on this measure which leads to the identification of many in influential clusters of variables. Those identified groups of variables can capture both marginal and interactive effects. Second, each identified cluster has the potential to perform predictions and classifications more accurately. We also study procedures how to combine these groups of individual classifiers to form a final predictor. Through simulation and real data analysis, the proposed measure is capable of identifying important variable sets and patterns including higher-order interaction sets. The proposed procedure outperforms existing methods in three different microarray datasets. Moreover, the nonparametric measure is quite flexible and can be easily extended and applied to other areas of high-dimensional data and studies.

O trabalho infantil vem diminuindo desde meados da década de 1990. Foi também nesse período que a inspeção do trabalho no Brasil começou a dar maior importância ao combate ao trabalho infantil. Assim, o presente trabalho teve por objetivo analisar o efeito da inspeção do trabalho sobre a queda no trabalho infantil, em 2000 e 2010. Inicialmente, a ideia era utilizar o número de empresas fiscalizadas no município como uma medida da execução da inspeção no município. Entretanto, essa variável pode ser endógena já que as inspeções do trabalho não dependem apenas de ações fiscais planejadas, mas também de denúncias de violações da legislação. Para resolver esse problema considerou-se que a realização da fiscalização depende da disponibilidade de auditores fiscais do trabalho (AFTs) e da distância que eles precisam percorrer para chegar ao local onde será realizada a fiscalização. Os AFTs são distribuídos por estado e trabalham nas Superintendências Regionais do Trabalho (SRTs) ou nas Gerências Regionais do Trabalho (GRTs). De acordo com essas informações, criou-se duas variáveis instrumentais. A primeira foi a distância entre cada município e a SRT ou a GRT mais próxima. A segunda foi a quantidade de AFTs no estado. A partir dessas variáveis instrumentais utilizou-se o método de mínimos quadrados em dois estágios. As análises foram realizadas por faixas etárias. Os resultados encontrados para o ano 2000 mostram que o aumento de 1% na inspeção reduziu a proporção de crianças e adolescentes que trabalham em todas as faixas analisadas. A redução foi de 0,22% para a faixa de 10 a 17 anos, de 0,45% para a faixa de 10 a 14 anos, de 0,19% para aqueles com 15 anos e de aproximadamente 0,09% para a faixa de 16 a 17 anos. Em termos absolutos, esses valores representam aproximadamente 8.658 crianças e adolescentes de 10 a 17, 5.140 crianças e adolescentes de 10 a 14 anos, 1.233 adolescentes de 15 anos e 1.929 adolescentes de 16 e 17 anos. Os resultados foram estatisticamente significativos a 1% e a 10%. Para o ano de 2010 os resultados mostraram que o aumento de 1% na inspeção reduziu a proporção de crianças e adolescentes que trabalham em todas as faixas analisadas. A redução foi de 0,26% para a faixa de 10 a 17 anos, de 0,66% para a faixa de 10 a 13 anos, de 0,41% para a faixa de 14 a 15 anos e de 0,08% para a faixa de 16 a 17 anos. Todos esses resultados foram estatisticamente significativos a 1%, com exceção da última faixa etária. Em termos absolutos, esses valores representam aproximadamente 8.856 crianças e adolescentes de 10 a 17 anos, 4.686 crianças e adolescentes de 10 a 13 anos e 3.642 adolescentes de 14 e 15 anos. Esses resultados mostram a importância da fiscalização para a redução ou eliminação do trabalho infantil, principalmente das piores formas. / Child labor has been decreasing since the mid-1990s. It was also during this period that the labor inspection in Brazil started to give greater importance to combat child labor. Thus, this research aimed to analyze the effect of labor inspection in the reduction of child labor in 2000 and 2010. Initially, the idea was to use the number of inspected companies in the municipality as a measure of the implementation of inspection in the municipality. However, this variable may be endogenous since the inspections of the work don\'t rely only on planned fiscal actions, but also on complaints of violations of the laws. To solve this problem it was considered that the implementation of labor inspection depends on the availability of labor inspectors and the distance that they have to travel to get to the place where the inspection will be performed. The labor inspectors are distributed by state, and they work in the Regional Superintendent of Labor (RSL), or in the Regional Management of Labour (RML). According to this information, two instrumental variables were created. The first was the distance between each municipality and the nearest RSL or RML. The second was the number of labor inspectors in the state. We used the method of Two-Stage Least Squares. The analyses were performed by age groups. The results for the year 2000 show that an increase of 1% in the inspection reduced the proportion of children and adolescents working in all analyzed groups. The reduction was of 0.22% for the aged group of 10 to 17 years, 0.45% for the aged group of 10 to 14 years, 0.19% for those aged 15 and approximately 0.09% for the aged group of 16 to 17 years. In absolute terms, these values represent approximately 8,658 children and adolescents for the aged group of 10 to 17 years, 5,140 children and adolescents for the aged group of 10 to 14 years, 1,233 adolescents aged 15 years and 1,929 adolescents for the aged group of 16 to 17 years. The results were statistically significant at 1% and at 10%. For the year 2010, results also showed that the increase of 1% in the labor inspection reduced the proportion of children and adolescents working in all analyzed groups. The reduction was of 0.26% for the aged group of 10 to 17 years, 0.66% for the aged group of 10 to 13 years, 0.41% for the aged group of 14 to 15 years and 0.08% for the aged group of 16 to 17 years. All these results were statistically significant at 1%, except for the last aged group. In absolute terms, these values represent approximately 8,856 children and adolescents for the aged group of 10 to 17 years, 4,689 children and adolescents for the aged group of 10 to 13 years and 3,642 adolescents for the aged group of 14 to 15 years. These results show the importance of labor inspection to decrease or eliminate child labor, mainly its worst forms.

Ma Ning. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 49-57). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.5 / Chapter 2 --- Literature Review --- p.11 / Chapter 2.1 --- Problems about Using Sibling Samples --- p.19 / Chapter 2.2 --- Difficulties with the Within-twin-pair Studies --- p.20 / Chapter 3 --- Method --- p.21 / Chapter 3.1 --- Omitted Variable Bias (Selection Effect) --- p.21 / Chapter 3.1.1 --- OLS Model --- p.21 / Chapter 3.1.2 --- Fixed-Effect Model --- p.23 / Chapter 3.1.3 --- GLS Model --- p.23 / Chapter 3.2 --- Measurement Error --- p.24 / Chapter 4 --- Data --- p.26 / Chapter 5 --- Results --- p.29 / Chapter 5.1 --- "OLS, Fixed-Effect, GLS and IV estimates" --- p.29 / Chapter 5.2 --- Important findings --- p.34 / Chapter 5.3 --- Further Results --- p.35 / Chapter 5.3.1 --- Consistency of Fixed-Effect Estimate --- p.35 / Chapter 5.3.2 --- Smoking as an Instrument for Education --- p.39 / Chapter 5.3.3 --- Symmetry Test --- p.41 / Chapter 5.3.4 --- Hausman Test --- p.44 / Chapter 5.3.5 --- Selection Bias --- p.45 / Chapter 6 --- Conclusions --- p.48 / Chapter 7 --- Bibliography --- p.49

Nonlinear two-stage residual inclusion (2SRI) estimators have become increasingly favored over traditional linear two-stage least squares (2SLS) methods for instrumental variables analysis of empirical models with inherently nonlinear dependent variables. Rising adoption of nonlinear 2SRI is largely attributable to simulation evidence showing that nonlinear 2SRI generates consistent estimates of population average treatment effects in nonlinear models, while 2SLS and nonlinear 2SPS do not. However, while it is believed that consistency of 2SRI for population average treatment effects is a general result, current evidence is limited to simulations performed under unique and restrictive settings with regards to treatment effect heterogeneity and conditions underlying treatment choices. This research contributes by describing existing simulation evidence and investigating the ability to generate absolute estimates of population average treatment effects (ATE) and local average treatment effects (LATE) using common IV estimators using Monte Carlo simulation methods across 10 alternative scenarios of treatment effect heterogeneity and sorting-on-the-gain. Additionally, estimates for the effect of ACE/ARBs on 1-year survival for Medicare beneficiaries with acute myocardial infarction are generated and compared across alternative linear and nonlinear IV estimators. Simulation results show that, while 2SLS generates unbiased and consistent estimates of LATE across all scenarios, nonlinear 2SRI generates unbiased estimates of ATE only under very restrictive settings. If marginal patients are unique in terms of treatment effectiveness, then nonlinear 2SRI cannot be expected to generate unbiased or consistent estimates of ATE unless all factors related to treatment effect heterogeneity are fully measured.

<p>I develop an empirical model that estimates a firm-specific accrual noise ratio (ANR), an operational and statistically grounded measure of accrual reliability, and test the measure's construct validity. The model allows accrual reliability to vary across firms, which is particularly important because many reliability determinants vary in cross-section. Unlike metrics that measure relative perceived reliability, ANR measures accrual reliability independent of the perceptions of investors, creditors or auditors. I find that ANR relates in expected ways with multiple proxies of accounting reliability, that ANR's relation with the proxies of other accounting constructs is consistent with theory, and that ANR's sensitivity to percentage changes of accrual components is consistent with a subjective ordinal ranking of the components' reliability from prior literature.</p> / Dissertation

The rates of childhood obesity remain high in spite of the enormous efforts dedicated to tackling the disease. This dissertation investigates the effect of two of its causes, including parental time and children's obesity risk behaviors. Trends in these causes have changed over time and might explain changes in obesity. The two factors are analyzed separately given the differences in impact process and concentration of literature. The data for the investigation is drawn from the Parental Time, Role Strains, Coping, and Children's Diet and Nutrition project. In examining parental time, the attention is directed towards the mother's actual time spent with the child which has been associated with reduction in child weight status. The major aim is to test and correct for the problem of endogeneity stemming from unobserved health factors that can distort any meaningful causal impact of maternal time on child weight status. Using the household production theory, parental time allocation decisions towards child health are modeled and analyzed using instrument variable (IV) methods. Results indicate that the effect of mother's time allocation reduces child weight status. Her decision to allocate time to the child is affected by unseen factors. Father's work to family spillover was found to be a valid instrument for mother's time with the child. Results were robust across different estimators. In analyzing the relationship between childhood obesity risk behaviors and weight status, this study focuses on three child practices including breakfast intake, fast food consumption and sleep patterns. The main aim was to score their joint impact, while at the same time accounting for contextual factors. This work adopted the ecological systems framework which accommodates multiple factors. Based on this theory, a simultaneous system of equations considering child weight status, risk behavior and contextual factors was set up and analyzed using 3SLS. Findings indicated that dietary behaviors remain a major factor in affecting weight status. In addition, feedback mechanism from child weight status will influence the diet pattern adopted by the child. Sleep sufficiency had no effect on child weight status.

The effect of fertility on female labor supply decisions in Turkey is analyzed in this thesis. Taking the endogeneity between fertility and labor supply into account, the causal effect of fertility is interpreted in an instrumental variables estimation framework. Results of the analysis indicate that fertility estimates of sex preference and twin based instruments on short term labor supply of women differ substantially. While fertility increases due to sex preference instrument cause no significant change in labor supply decisions, unexpected fertility shocks by twin instrument have an adverse effect on female labor supply.

In this paper I propose Bayesian estimation of a nonlinear panel data model with a fractional dependent variable (bounded between 0 and 1). Specifically, I estimate a panel data fractional probit model which takes into account the bounded nature of the fractional response variable. I outline estimation under the assumption of strict exogeneity as well as when allowing for potential endogeneity. Furthermore, I illustrate how transitioning from the strictly exogenous case to the case of endogeneity only requires slight adjustments. For comparative purposes I also estimate linear specifications of these models and show how quantities of interest such as marginal effects can be calculated and compared across models. Using data from the state of Florida, I examine the relationship between school spending and student achievement, and find that increased spending has a positive and statistically significant effect on student achievement. Furthermore, this effect is roughly 50% larger in the model which allows for endogenous spending. Specifically, a $1,000 increase in per-pupil spending is associated with an increase in standardized test pass rates ranging from 6.2-10.1%.