Essays on Agglomeration, Homeownership, and Labor Market Outcomes

This dissertation consists of two essays on the labor market impact of agglomeration economies and that of homeownership. The empirical analyses use different U.S. data and econometric techniques to examine the impact on different labor market outcomes such as volatility of hours worked and men\u27s employment. The first essay is motivated by the labor market pooling model from Krugman (1991). The paper adds to a small but important literature that provides evidence on the microeconomic foundation of agglomeration economies. Using various years of data from the American Community Survey and the County Business Patterns survey, I show that the agglomeration of economic activities reduces the volatility of hours worked. Drawing on Krugman\u27s model, I argue that this implies that labor pooling contributes to agglomeration economies, and helps to explain why cities are productive places. In the second essay, I consider the extent to which homeownership affects men\u27s labor supply. Research on the labor-supply consequences of homeownership is complicated by the endogeneity of housing tenure status. To address this endogeneity problem, I use a set of family size instruments (the presence of the third and additional children in a single family household) to estimate the effect of homeownership in a bivariate probit model. Based on a sample of married white male household heads from the American Community Survey, the IV result suggests that men who own their homes are 1.2% more likely to be employed relative to those who rent. I also show that the relationship between homeownership and family size is highly nonlinear and nonmonotonic. The first two children have positive influence on both homeownership and men\u27s employment. The third and additional children are negatively associated with homeownership but have no significant incremental effects on men\u27s labor supply

UNDERSTANDING EDUCATION ABROAD WITH ADVANCED QUANTITATIVE METHODOLOGIES: STUDENT PROFILES AND ACADEMIC OUTCOMES

This three-study dissertation contributes to the research in the field of participation in education abroad, particularly as it relates to student profiles and academic outcomes. Through employing more robust methodologies across the three studies, this dissertation aims not only to understand what are the factors associated with education abroad participation and how these factors interplay with each other, but also to provide a less biased picture of the impact of participation in education abroad on postsecondary educational outcomes. The studies have implications for equitable and inclusive access to education abroad. The first study begins with the question: who studies abroad? Using logistic regression and classification and regression tree, the first study examines the average effect of each independent variable on the likelihood of education abroad participation, and also captures the complex interactive effects among independent variables. The findings of this study provide implications for education abroad policy makers and practitioners to understand student level barriers to education abroad participation. For example, students who academically performed well are more likelyto study abroad,yet students with lower academic performance also benefit academically from study abroad. This suggests policy changes to encourage flexibility in academic eligibility requirements for enrollment in study abroad. The long-standing gap in the likelihood to participate in education abroad between male and female students is replicated in this study. This suggests the need to examine how each gender is socialized to enhance their educational experiences during college. Additionally, the findings of the first study inform the methodological matching process to balance education abroad and non-education abroad participants to reduce the selection bias for future research. The purpose of the second and third studies is to examine the impact of participation in education abroad on college completion. To address the methodological challenges and limitations, both studies use propensity score matching (PSM) to reduce the selection bias—a threat to internal validity inherently existing within the nature of education abroad research—and to obtain samples of education abroad participants and non-participants who share a similar likelihood to participate in education abroad based on observed characteristics. The second study used the findings from the first study to select a comparison group that shares similar likelihood to participate in education abroad to examine the effects of education abroad on graduation rates. Moreover, this study used PSM to explore how education duration and times of education abroad experiences impact graduation rates, which have not been studied in this way previously. Overall, education abroad participants were more likely to graduate within four years or six years. Students who studied abroad for less than one semester or one semester were more likely to graduate within four years and six years than students who did not study abroad. For different numbers of education abroad experiences, the results indicate students who had one education abroad experience were more likely to graduate within four years and six years than students who had no education abroad experience and students who had more than one education abroad experience. Using two national datasets that were collected across multiple institutions, the third study first examines the association between both student- and institution-level factors and students’ likelihood to participate in education abroad. The findings of the first examination provide suggestions on what should be included in the PSM model in order to select a comparable untreated group to reduce the selection bias while assessing the effects of participation in education abroad on bachelor’s degree attainment. This study is unique in its attention to the participation and effects of education abroad by including both student- and institution-level characteristics while adopting PSM to reduce the selection bias that has existed in education abroad research. First, the results of this study confirmed that education abroad as one of the high-impact practices that enhances student success, measured as bachelor’s degree attainment. Second, by including a rich array of institutional-level variables from the IPEDS dataset, this study explores how various different institutional settings affect students’ participation in education abroad. For example, students from private not-for-profit 4-year institutions are more likely to study abroad than students from public and private for-profit institutions. Students from highly selective institutions have the highest likelihood to participate in education abroad. Whether the institutions accept advanced credits from high school is also a statistically significant predictor of participation in education abroad

Derivative Process Model of Development Power in Industry: Empirical Research and Forecast for Chinese Software Industry and US Economy

Based on [1], this paper analyzes the transferability and the diffusibility of industrial development power, puts forward the index of management strength, and sets up the derivative process model for industrial development power on the Partial Distribution ([2]-[3]). By the derivative process model, a kind of time series model, we can describe the process of industrial development effectively, and can forecast the future direction of industry or economy on using with [6]. Finally, by making use of the actual data of Chinese software industry and data of USA GDP (chained) price index, we give the examples of empirical analysis, and forecast the future of Chinese software industry and USA economic development. The conclusions in this paper are believed to be valuable and significant to guide the establishment of the industrial policy and to control the industrial development.development power (DP), partial distribution, derivative process, industry and macroeconomy, empirical research, forecast analysis

Derivative Process Model of Development Power in Industry: Empirical Research and Forecast for Chinese Software Industry and US Economy

Based on concept and theory of Development Power [1], this paper analyzes the transferability and the diffusibility of industrial development power, points out that the chaos is the extreme of DP releasing and order is the highest degree of DP accumulating, puts forward A-C strength, the index of adjusting and controlling strength, and sets up the derivative process model for industrial development power on the Partial Distribution [2]-[4]. By the derivative process model, a kind of time series model, we can describe the process of industrial development effectively, and can forecast the future direction of industry or economy on using with [7]. Finally, by making use of the actual data of Chinese software industry and data of USA GDP (chained) price index, we give the examples of empirical analysis, and forecast the future of Chinese software industry and USA economic development. The conclusions in this paper are believed to be valuable and significant to guide the establishment of the industrial policy and to control the industrial development.development power (DP), partial distribution, derivative process, industry and macroeconomy, empirical research, forecast analysis

Some results in probability and theoretical computer science

As typical examples for nonlinear dynamical systems, the logistic maps mapping x to cx(1 - x) with x is in [0,1] and c is a constant in [0,4] have been extensively studied. Bhattacharya and Rao (1993) studied the case that c is a random variable rather than a constant. In this case, each of the logistic maps above defines a Markov Chain on [0,1]. In this dissertation, we give some sufficient conditions for the existence of an invariant probability on (0,1) and some sufficient conditions for the nonexistence of invariant probability measures on (0,1) as well. When there exists an invariant probability on (0,1), we study the problem of the uniqueness of invariant probability measure on (0,1). We give some sufficient conditions for the invariant probability measure to be unique. We also provide an example where c takes only two values such that there exist two distinct invariant probability distributions supported by the open interval (0,1). This settles a question raised by R. N. Bhattacharya. In this dissertation, we also study the resource bounded measure that was introduced by Jack Lutz in 1992. It is shown that under Jack Lutz\u27s Strong Hypothesis, for any integer k that is at least 2, there is a sequence of k languages that is sequentially complete for NP, but no nontrivial permutation of this sequence is sequentially complete for NP. We also prove a stronger version of Resource-Bounded Kolmogorov Zero-One Law. We prove that if a class X of languages is a tail set, and has outer-measure less than 1, then it is measurable and has resource-bounded measure 0

Aligned Graphene Nanoribbons and Crossbars from Unzipped Carbon Nanotubes

Aligned graphene nanoribbon (GNR) arrays were made by unzipping of aligned single-walled and few-walled carbon nanotube (CNT) arrays. Nanotube unzipping was achieved by a polymer-protected Ar plasma etching method, and the resulting nanoribbon array was transferred onto any substrates. Atomic force microscope (AFM) imaging and Raman mapping on the same CNTs before and after unzipping confirmed that ~80% of CNTs were opened up to form single layer sub-10 nm GNRs. Electrical devices made from the GNRs (after annealing in H2 at high temperature) showed on/off current (Ion/Ioff) ratios up to 103 at room temperature, suggesting semiconducting nature of the narrow GNRs. Novel GNR-GNR and GNR-CNT crossbars were fabricated by transferring GNR arrays across GNR and CNT arrays, respectively. The production of ordered graphene nanoribbon architectures may allow for large scale integration of GNRs into nanoelectronics or optoelectronics.Comment: published in Nano Researc