Health Insurance on Social Welfare

This dissertation examines the causal effects of health insurance on social welfare, especially Affordable Care Act (ACA) Medicaid expansion and state-dependent health in-surance mandates. In 2010, the Affordable Care Act (ACA) was signed into law and intended to extend health coverage across the country by providing Medicaid to nearly all adults with house-hold income at or below 138 percent of the Federal Poverty Level (FPL). This expansion became effective on January 1, 2014. As of January 2017, 31 states and Washington D.C. adopted the Medicaid expansion. While the main goal of the ACA is to increase the health insurance coverage and improve the health of the population, this health insurance reform may also have effects on a broad range of non-health outcomes. The first two chapters of this dissertation investigate the effects of health insurance on criminal behaviors in the United States. In first chapter, using a one period static model of criminal behavior, I argue we should anticipate a decrease in time devoted to criminal activities in response to the expansion, since the availability of the ACA Medicaid coverage not only has a negative in-come effect on criminal behavior but also raises the opportunity cost of crime. This predic-tion is particularly relevant for the ACA expansion, because it primarily affects low-income childless adults, the population that is most likely to engage in criminal behavior. I validate this forecast using a difference-in-differences (DID) approach, estimating the expansion’s effects on a panel dataset of state- and county-level crime rates. My findings show that the ACA Medicaid expansion is negatively related to burglary, motor vehicle theft, criminal homicide, robbery, and aggravated assault. The value of this Medicaid expansion induced reduction in crime to expansion states is almost $10 billion per year. The second chapter, which is joint work with Scott Barkowski and Joanne Song McLaughlin, examines the negative effect of increasing health insurance coverage rates on arrest rates based on state-level panels of health insurance coverage and arrest data from 2000 to 2012 in the United States. To address the endogeneity of the health insurance coverage rates with respect to arrest rates, we use plausibly exogenous variation in the pre-dicted health insurance coverage rate by using state-level health insurance mandates and the federal dependent coverage mandate in the Affordable Care Act (ACA). The instru-mental variable (IV) approach estimates indicate that an increase in the health insurance coverage rate results in a statistically significant reduction in the arrest rates of aggravated assault, prostitution and commercialized vice, but an increase in the arrest rate for fraud. Overall, the state and federal dependent health insurance mandates are associated with a sizable reduction in arrest rates. Our findings suggest that the state and federal dependent health insurance coverage mandates are effective policy instruments to increase health in-surance coverage for young adults, and the increased health insurance coverage reduces arrest rates. The third chapter examines the effect of the Affordable Care Act (ACA) Medicaid eligibility expansion on the labor supply of low-income childless adults. In particular, I investigate that whether this effect is different between blacks and whites. I find that the Medicaid expansion decreased the labor force participation rate by approximately 3 percentage points for married whites and increased the labor supply of never-married blacks by 5.4 percentage points. However, expansion does not play a large role for other groups of blacks and whites. My finding suggests that the recent law change has different effects on the labor supply for blacks and whites

The effect of health insurance on crime: Evidence from the Affordable Care Act Medicaid expansion

Little evidence exists on the effect of the Affordable Care Act (ACA) on criminal behavior, a gap in the literature that this paper seeks to address. Using a simple model, we argue we should anticipate a decrease in time devoted to criminal activities in response to the expansion, because the availability of the ACA Medicaid coverage raises the opportunity cost of crime. This prediction is particularly relevant for the ACA expansion because it primarily affects childless adults, a population likely to contain individuals who engage in criminal behavior. We validate this forecast empirically using a difference‐in‐differences framework, estimating the expansion\u27s effects on panel datasets of state‐ and county‐level crime rates. Our estimates suggest that the ACA Medicaid expansion was negatively associated with burglary, vehicle theft, homicide, robbery, and assault. These crime‐reduction spillover effects represent an important offset to the government\u27s cost burden for the ACA Medicaid expansion

Using Sequence Mining Techniques for Understanding Incorrect Behavioral Patterns on Interactive Tasks

Interactive tasks designed to elicit real-life problem-solving behavior are rapidly becoming more widely used in educational assessment. Incorrect responses to such tasks can occur for a variety of different reasons such as low proficiency levels, low metacognitive strategies, or motivational issues. We demonstrate how behavioral patterns associated with incorrect responses can, in part, be understood, supporting insights into the different sources of failure on a task. To this end, we make use of sequence mining techniques that leverage the information contained in time-stamped action sequences commonly logged in assessments with interactive tasks for (a) investigating what distinguishes incorrect behavioral patterns from correct ones and (b) identifying subgroups of examinees with similar incorrect behavioral patterns. Analyzing a task from the Programme for the International Assessment of Adult Competencies 2012 assessment, we find incorrect behavioral patterns to be more heterogeneous than correct ones. We identify multiple subgroups of incorrect behavioral patterns, which point toward different levels of effort and lack of different subskills needed for solving the task. Albeit focusing on a single task, meaningful patterns of major differences in how examinees approach a given task that generalize across multiple tasks are uncovered. Implications for the construction and analysis of interactive tasks as well as the design of interventions for complex problem-solving skills are derived

Identifying critical testlet features using tree-based regression: An illustration with the Analytical Reasoning section of the LSAT

A Publication of the Law School Admission CouncilThe Law School Admission Council (LSAC) is a nonprofit corporation that provides unique, state-of-theart admission products and services to ease the admission process for law schools and their applicants worldwide. More than 200 law schools in the United States, Canada, and Australia are members of the Council and benefit from LSAC’s services

A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks

Early detection of risk of failure on interactive tasks comes with great potential for better understanding how examinees differ in their initial behavior as well as for adaptively tailoring interactive tasks to examinees’ competence levels. Drawing on procedures originating in shopper intent prediction on e-commerce platforms, we introduce and showcase a machine learning-based procedure that leverages early-window clickstream data for systematically investigating early predictability of behavioral outcomes on interactive tasks. We derive features related to the occurrence, frequency, sequentiality, and timing of performed actions from early-window clickstreams and use extreme gradient boosting for classification. Multiple measures are suggested to evaluate the quality and utility of early predictions. The procedure is outlined by investigating early predictability of failure on two PIAAC 2012 Problem Solving in Technology Rich Environments (PSTRE) tasks. We investigated early windows of varying size in terms of time and in terms of actions. We achieved good prediction performance at stages where examinees had, on average, at least two thirds of their solution process ahead of them, and the vast majority of examinees who failed could potentially be detected to be at risk before completing the task. In-depth analyses revealed different features to be indicative of success and failure at different stages of the solution process, thereby highlighting the potential of the applied procedure for gaining a finer-grained understanding of the trajectories of behavioral patterns on interactive tasks

Relationship of Family Environment, Psychological Resilience, Campus Bullying with Tobacco Use among Preadolescents

Objective. To explore the relationship between family environment, psychological resilience, campus bullying and tobacco use in early adolescence. Methods. According to the principle of cluster sampling, 4,792 students from grade 4 to grade 6 in five primary schools in Baise City and county were selected from February to November 2018, including 2,522 males (52.63%), 2,236 females (46.66%)and 34 missing genders (0.71%); the average age was (11.8 ± 0.5) years; 2,721 students in urban areas (56.78%) and 2,071 students in county towns (43.22%); 4,313 Zhuang (90.00%), 365 Han (7.62%), 98 other ethnic groups (Yao, Miao, Yi, etc.) (2.05%). The General Family Environment Questionnaire, Adolescent Mental Resilience Scale, School Bullying Questionnaire, and Tobacco Use Questionnaire were used for evaluation, and logistic regression was used to analyze the effect relationship between the study variables. Results. 467 people tried to smoke, and the total detection rate was 9.75%. The number of smokers was 334, and the total detection rate was 6.97%. Boys’ tobacco attempt and smoking behavior were higher than girls (χ2 were 57.230 and 56.013, P < 0. 001). Multivariate logistic regression analysis showed that the risk of tobacco attempt of boys was 2.37 times thanthat of girls (OR = 0.468, 95% CI 0.377 ~ 0.582), the risk of smoking in boys is 2.5 times that in girls 32 times (OR = 0.422, 95% CI 0.324 ~ 0.551); older adolescents had more tobacco attempts (OR = 1.609, 95% CI 1.446 ~ 1.791)and smoking behavior (OR = 2.026, 95% CI 1.776 ~ 2.310); campus bullying increased the risk of smoking behavior among adolescents (OR = 1.106, 95% CI 1.073 ~ 1.140). Psychological resilience (personal strength), family intimacy and family rules can effectively reduce the risk of adolescent tobacco attempts (personal strength, OR = 0.964, 95% CI = 0.951 ~ 0.976; family intimacy, OR = 0.946, 95% CI 0.892 ~ 0.984; family rules, OR = 0.949, 95% CI 0.930 ~ 0.965) and smoking behavior (personal strength, OR = 0.962, 95% CI 0.947 ~ 0.977; family intimacy, OR = 0.937, 95% CI 0.885 ~ 0.992; family rules, OR = 0.952, 95% CI 0.932 ~ 0.973). Conclusion. Campus bullying increases the risk of smoking behavior among adolescents. Psychological resilience (personal strength), family intimacy and family rules can effectively reduce teenagers’ tobacco attempts and smoking behavior

Mapping Background Variables With Sequential Patterns in Problem-Solving Environments: An Investigation of United States Adults’ Employment Status in PIAAC

Adult assessments have evolved to keep pace with the changing nature of adult literacy and learning demands. As the importance of information and communication technologies (ICT) continues to grow, measures of ICT literacy skills, digital reading, and problem-solving in technology-rich environments (PSTRE) are increasingly important topics for exploration through computer-based assessment (CBA). This study used process data collected in log files and survey data from the Programme for the International Assessment of Adult Competencies (PIAAC), with a focus on the United States sample, to (a) identify employment-related background variables that significantly related to PSTRE skills and problem-solving behaviors, and (b) extract robust sequences of actions by subgroups categorized by significant variables. We conducted this study in two phases. First, we used regression analyses to select background variables that significantly predict the general PSTRE, literacy, and numeracy skills, as well as the response time and correctness in the example item. Second, we identified typical action sequences by different subgroups using the chi-square feature selection model to explore these sequences and differentiate the subgroups. Based on the malleable factors associated with problem-solving skills, the goal of this study is to provide information for improving competences in adult education for targeted groups