Investigation into U.S. Citizen and Non-Citizen Worker Health Insurance and Employment
This research addresses systemic inequalities in healthcare access for demographic groups, particularly non-citizens, but is incremental as it applies existing methods to new data.
This study tackled socioeconomic disparities in health insurance and employment among U.S. citizens and non-citizens, using statistical tests and machine learning to identify five distinct demographic clusters and reveal that non-citizens are disproportionately in precarious employment without benefits, with no association found between citizenship and workforce participation.
Socioeconomic integration is a critical dimension of social equity, yet persistent disparities remain in access to health insurance, education, and employment across different demographic groups. While previous studies have examined isolated aspects of inequality, there is limited research that integrates both statistical analysis and advanced machine learning to uncover hidden structures within population data. This study leverages statistical analysis ($χ^2$ test of independence and Two Proportion Z-Test) and machine learning clustering techniques -- K-Modes and K-Prototypes -- along with t-SNE visualization and CatBoost classification to analyze socioeconomic integration and inequality. Using statistical tests, we identified the proportion of the population with healthcare insurance, quality education, and employment. With this data, we concluded that there was an association between employment and citizenship status. Moreover, we were able to determine 5 distinct population groups using Machine Learning classification. The five clusters our analysis identifies reveal that while citizenship status shows no association with workforce participation, significant disparities exist in access to employer-sponsored health insurance. Each cluster represents a distinct demographic of the population, showing that there is a primary split along the lines of educational attainment which separates Clusters 0 and 4 from Clusters 1, 2, and 3. Furthermore, labor force status and nativity serve as secondary differentiators. Non-citizens are also disproportionately concentrated in precarious employment without benefits, highlighting systemic inequalities in healthcare access. By uncovering demographic clusters that face compounded disadvantages, this research contributes to a more nuanced understanding of socioeconomic stratification.