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Keywords: regression, STATA, poverty, education, family background, quantitative analysis
Understanding poverty as a state of being poor with regard to concretely being unable to meet basic needs such as water, food, clothes, shelter, and essential services (e.g., sanitation, health, and education), its concerted study in the United States not only stands tandem with constitutionally upheld values such as equality but is crucial for soundly informing and assessing law, policy, and programs that ensure a robust society. Especially amidst global challenges like COVID-19, it becomes all the more important to understand what factors may most impact a person’s movement into and out of poverty. For this project, I examine how family background and educational attainment interact to jointly affect poverty in the United States using data from a nationally representative panel study sponsored by the National Center for Education Statistics (NCES). After proxying poverty with receiving public assistance, I tested 18 independent variables consistent with demographic and family background by conducting a multivariate regression. Ten variables were found to have a statistically significant effect on the probability of receiving public assistance with dependents (under the age of 18), recent unemployment (within the past three years), and being female being among the strongest predictors of receiving public assistance (p<0.001). Roughly 36% of the variation in receiving public assistance is explained by the 18 independent variables tested, helping paint poverty with more color. More importantly, these results signal a need to buttress public programs through at least 2023 given the skyrocketing unemployment rate of the 2020 year. Leaders in education, non-profit, and government may ask how, while further research can expand the list of independent variables and/or focus on a single ascribed or achieved status to test varying hypotheses in response to the “causes” of poverty.