Understanding the Early Origins of the Education–Health Gradient A Framework That Can Also Be Applied to Analyze Gene–Environment Interactions

TLDR: A framework for analyzing the causal effects of interventions in the presence of latent factors that could affect outcomes, even in the absence of interventions is developed, which will be useful in situations in which genes are included among the latent factors.

Abstract: In this article, we develop a framework for analyzing the causal effects of interventions in the presence of latent factors that could affect outcomes, even in the absence of interventions. This framework will be useful in situations in which genes are included among the latent factors. We estimate the model and study the early origins of observed later-life disparities by education. We determine the role played by cognitive, noncognitive, and early health endowments. We identify the causal effect of education on health and health-related behaviors. We show that family background characteristics and cognitive, noncognitive, and health endowments developed by age 10 are important determinants of health disparities at age 30. We also show that not properly accounting for personality traits results in overestimation of the importance of cognitive ability in determining later health. Selection on preexisting traits explains more than half of the observed differences in poor health and obesity. Education has an important causal effect in explaining differences in smoking rates. There are significant gender differences. We go beyond the current literature, which typically estimates mean effects, to compute distributions of treatment effects. We show that the effect of education on health varies among individuals who are similar in their observed characteristics, and how a mean effect can hide gains and losses for different individuals. This analysis highlights the crucial role played by promotion of good health at an early age and the importance of prevention in the reduction of health disparities. We speculate about how the model can be applied to genetic studies.