This thesis studies the extent to which Hispanics and Latinos may be leading in entrepreneurial activity and new business formation within the United States, and gauging whether this trend is taking place due to culture and access to capital, among other factors. A two-model methodology along with a comparative analysis with Spain, a nation of Hispanic heritage with similar demographic patterns, is to be implemented in an attempt to answer this leading question. Ultimately, the hope of this study is to contribute to literature around the role of culture and geography in what people in America decide to pursue for a living, as well as call attention to the impact that entrepreneurship has on national economies.

As student loan debt in the United States increases, its influence on various decisions, particularly homeownership, has become a growing concern. While previous research has started to examine the link between homeownership and student loans in different contexts, little is known about how this relationship empirically varies across demographics. This thesis addresses two questions: (1) How does the relation between student loan debt and homeownership likelihood differ by race, marital status, and the combination of marriage and parenthood? (2) More broadly, how does having student loan debt or a high student loan balance connect to homeownership likelihood? Using the 2023 Survey of Household Economics and Decisionmaking (SHED), focusing on American adults aged 22 to 65, I apply binary logistic regressions to answer these questions. I find a statistically significantly negative correlation between having student loan debt and homeownership likelihood. However, I do not find a correlation between a high student loan balance and homeownership likelihood, nor do I observe significant differences across racial, marital, or parental status groups. Although the research has limitations, this paper provides implications for future researchers, policymakers, and the many American adults handling student loan debt.

Unemployment is perhaps one of the most tangible aspects of an economy for the average citizen. It upends their lives and is often almost completely out of their control, leading to frustration and increased demands for protection from one’s government. This thesis contends that this increased demand on government manifests itself in changes in political ideology, using evidence from both cross-sectional analysis for the years 2020, 2022, and 2024, along with time series analysis for the years 1997-2014. To measure ideology, this project debuts a novel ideology index created from the speeches of the members of the US House of Representatives using Natural Language Processing (NLP) techniques. When using the more traditional political mapping of the US House – the DW-NOMINATE score system (Poole & Rosenthal, 1985) – results showed that districts with higher rates of unemployment were associated more with Democrats than Republicans, although the directionality of this effect is unclear. These findings motivate the creation of a new, more dynamic measure of political ideology in congress using means unavailable to Poole & Rosenthal. Using this measure in a time series analysis with in-district fixed effects yields findings that suggest members of congress change the composition and content of their speech as unemployment increases, all the while getting more polarized along party lines. This thesis, with its innovative methods and question, fill unexplored gaps in the literatures of computational political science and economics, while also introducing exciting prospects for future research using similar NLP methods.