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.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.