Genetic risks, adolescent health, and schooling attainment.

We provide new evidence on the effect of adolescent health behaviors/outcomes (obesity, depression, smoking, and attention deficit hyperactivity disorder [ADHD]) on schooling attainment using the National Longitudinal Study of Adolescent to Adult Health. We take two different approaches to deal with omitted variable bias and reverse causality. Our first approach attends to the issue of reverse causality by estimating the effect of health polygenic scores (PGSs) on schooling. Second, we estimate the effect of adolescent health using sibling fixed-effects models that control for unmeasured genetic and family factors shared by siblings. We use the PGSs as additional controls in the sibling fixed-effects models to reduce concerns about residual confounding from sibling-specific genetic differences. We find consistent evidence across both approaches that being genetically predisposed to smoking and smoking regularly in adolescence reduces schooling attainment. Estimates for depression are more imprecise, but also suggest that a high genetic risk of depression and adolescent depression reduce schooling attainment. We find mixed evidence for ADHD. Our estimates suggest that having a high genetic risk for ADHD reduces grades of schooling, but we do not find any statistically significant negative effects of ADHD. Finally, we find no consistent evidence for a detrimental effect of obesity on schooling attainment.

The impact of BMI on mental health: Further evidence from genetic markers.

We estimate the effect of BMI on mental health for young adults and elderly individuals using data from the National Longitudinal Study of Adolescent Health and the Health & Retirement Study. To tackle confounding due to unobserved factors, we exploit variation in a polygenic score (PGS) for BMI within two related econometric methods that differ in the assumptions they employ. First, we use the BMI PGS as an IV and adjust for PGSs for other factors (depression and educational attainment) that may invalidate this IV. We find a large statistically significant effect of BMI on mental health for the elderly: a 5 kg/m2 increase in BMI (a difference equivalent to moving from overweight to obese) increases the probability of depression by 29 %. In contrast, for young adults the IV estimates are statistically and economically insignificant. We show that IV estimates likely have to be interpreted as identifying a weighted average of effects of BMI on mental health mostly for compliers on the upper quantiles of the BMI distribution. Second, we use the BMI PGS as an "imperfect" IV and estimate an upper bound on the average treatment effect for the population. The estimated upper bounds are consistent with the conclusions from the IV estimates.