Unraveling the Relationship between Education and Health: Genetic Controls, Heterogeneity across Sociodemographic Groups, and Variation across Biomarkers of Health Risk

Abstract

Despite decades of research demonstrating better health among the higher educated, the causal effect of education on health is still debated. This is due in part to mixed evidence obtained in quasi-experimental work. These puzzling patterns could be explained by the influence of uncontrolled confounders in observational research, by effect heterogeneity across individuals or environments, or by variation in effects across manifestations of health. The empirical chapters of this dissertation draw motivation from these observations to further unravel the relationship between education and health among older adults in the United States. First, I assess the utility of a novel control variable: a measure of genetic selection into education. Genetic selection is operationalized using a polygenic score (PGS) that predicts years of schooling based on many hundreds of thousands of genetic variants across the genome. Among European-ancestry respondents to the Health and Retirement Study (HRS) and the Wisconsin Longitudinal Study (WLS), I find that controlling for the PGS significantly attenuates the association between education and later health. The level of attenuation I observe is comparable to that obtained when controlling instead for measures of other known confounders, including family background and childhood health. Additional results suggest that the education PGS reflects more proximal confounders of the education-health link that may not be adequately controlled using survey measures alone. Crucially, however, the positive relationship between education and health is robust to this particular measure of genetic selection into years of schooling. Next, I evaluate whether the association of education with health varies across sociodemographic groups defined by socioeconomic (SES) origin, race, and gender using data from the HRS. In so doing, I take a more complex intersectional perspective than has been used in prior work. This is important, as exposure to discrimination, which shapes opportunities to use resources in support of health, may depend on multiple sociodemographic characteristics simultaneously. Results underscore the importance of one intersection in particular: that between SES origin and race. In line with prior work, I find that the association of years of schooling with self-reported health is stronger for those from low-SES backgrounds; however, this is only the case among whites. Seen from the other angle, the association of education with self-reported health and mortality is weaker for blacks than for whites, but primarily among those from low-SES origins. For both self-reported health and mortality, I find the smallest gain in health per year of schooling among low-SES origin black men, the group with the highest risk of poor health and mortality overall. In the final empirical chapter, I use data from the HRS to assess whether educational disparities in biomarkers of health risk vary across their distributions. Fundamental cause theory implies that such disparities will be largest where related resources can most successfully be leveraged to improve outcomes. For many biomarkers, this could be in the unhealthy tail of the distribution, where unequal access to and efficacy of medical interventions may exacerbate disparities. Consistent with this theory, I find that educational disparities in blood sugar and blood pressure are largest at their least healthy levels, precisely the points where impacts on subsequent morbidity and mortality are greatest. Meanwhile, high-density lipoprotein (HDL) or “good” cholesterol—a biomarker that is not regularly targeted by medication—does not display such a pattern. These results are not only of theoretical and substantive interest; they also provide methodological guidance for future work on biomarkers of health risk, which is timely given the recent proliferation of such measures in social science datasets.