Investing in the Homeland: Foreign Assets and Patterns of Immigrant Economic Incorporation

This dissertation consists of three separate studies that examine patterns of immigrant incorporation in the United States. The first study tests competing hypotheses derived from conflicting theoretical frameworks−transnational perspective and cross-national framework− to determine whether transnational engagement and incorporation are concurrent processes among Chinese, Indian, and Mexican immigrants. This study measures transnational engagement and incorporation as home and home country asset ownership using multi-panel, nationally representative data from the New Immigrant Survey (NIS) collected in 2003 and 2007. Results support a cross-border framework and indicate that transnational asset ownership decreases among all immigrant groups, while U.S. asset ownership increases. Findings from this study also indicate that due to disadvantaged pre-migration SES and low human capital, Mexican immigrants are less likely than other immigrants to own home country assets during the year after receiving their green card.

The second study examines the doubly disadvantaged position of elderly immigrants in the U.S. wealth distribution by applying the life course perspective to the dominance-differentiation theory of immigrant wealth stratification. I analyze elderly immigrant wealth in respect to U.S.-born seniors and younger immigrant cohorts using two data sets: the Survey of Income and Program Participation (SIPP) and the New Immigrant Survey (NIS). The Survey of Income and Program Participation (2001 to 2005) is a nationally representative survey of U.S. households. The first series of analyses reveals a significant wealth gap between U.S.- and foreign-born seniors which is most pronounced among the wealthiest households in my sample; however, U.S. tenure explains much of this difference. The second series of analyses suggests that elderly immigrants experience greater barriers to incorporation compared to their younger counterparts.

In the third study, I apply a transnational lens to the forms-of-capital and opportunity structure models of entrepreneurship in order to analyze the role of foreign resources in immigrant business start-ups. I propose that home country property use represents financial, social, and class resources that facilitate immigrant entrepreneurship. I test my hypotheses using survey data on Latin American immigrants from the Comparative Immigrant Entrepreneurship Project. Findings from these analyses suggest that home country asset ownership provides financial and social capital that is related to an increased likelihood of immigrant entrepreneurship.

Can a Broader Education Narrow the Gap? Evidence on Non-Academic Features of Schooling

Empirical studies of education programs and systems, by nature, rely upon use of student outcomes that are measurable. Often, these come in the form of test scores. However, in light of growing evidence about the long-run importance of other student skills and behaviors, the time has come for a broader approach to evaluating education. This dissertation undertakes experimental, quasi-experimental, and descriptive analyses to examine social, behavioral, and health-related mechanisms of the educational process. My overarching research question is simply, which inside- and outside-the-classroom features of schools and educational interventions are most beneficial to students in the long term? Furthermore, how can we apply this evidence toward informing policy that could effectively reduce stark social, educational, and economic inequalities?

The first study of three assesses mechanisms by which the Fast Track project, a randomized intervention in the early 1990s for high-risk children in four communities (Durham, NC; Nashville, TN; rural PA; and Seattle, WA), reduced delinquency, arrests, and health and mental health service utilization in adolescence through young adulthood (ages 12-20). A decomposition of treatment effects indicates that about a third of Fast Track’s impact on later crime outcomes can be accounted for by improvements in social and self-regulation skills during childhood (ages 6-11), such as prosocial behavior, emotion regulation and problem solving. These skills proved less valuable for the prevention of mental and physical health problems.

The second study contributes new evidence on how non-instructional investments – such as increased spending on school social workers, guidance counselors, and health services – affect multiple aspects of student performance and well-being. Merging several administrative data sources spanning the 1996-2013 school years in North Carolina, I use an instrumental variables approach to estimate the extent to which local expenditure shifts affect students’ academic and behavioral outcomes. My findings indicate that exogenous increases in spending on non-instructional services not only reduce student absenteeism and disciplinary problems (important predictors of long-term outcomes) but also significantly raise student achievement, in similar magnitude to corresponding increases in instructional spending. Furthermore, subgroup analyses suggest that investments in student support personnel such as social workers, health services, and guidance counselors, in schools with concentrated low-income student populations could go a long way toward closing socioeconomic achievement gaps.

The third study examines individual pathways that lead to high school graduation or dropout. It employs a variety of machine learning techniques, including decision trees, random forests with bagging and boosting, and support vector machines, to predict student dropout using longitudinal administrative data from North Carolina. I consider a large set of predictor measures from grades three through eight including academic achievement, behavioral indicators, and background characteristics. My findings indicate that the most important predictors include eighth grade absences, math scores, and age-for-grade as well as early reading scores. Support vector classification (with a high cost parameter and low gamma parameter) predicts high school dropout with the highest overall validity in the testing dataset at 90.1 percent followed by decision trees with boosting and interaction terms at 89.5 percent.