Teen mothers experience disadvantage across a wide range of outcomes. However, previous research is equivocal with respect to possible long-term mental health consequences of teen motherhood and has not adequately considered the possibility that effects on mental health may be heterogeneous. Drawing on data from the 1970 British Birth Cohort Study, this article applies a novel statistical machine-learning approach-Bayesian Additive Regression Trees-to estimate the effects of teen motherhood on mental health outcomes at ages 30, 34, and 42. We extend previous work by estimating not only sample-average effects but also individual-specific estimates. Our results show that sample-average mental health effects of teen motherhood are substantively small at all time points, apart from age 30 comparisons to women who first became mothers at age 25‒30. Moreover, we find that these effects are largely homogeneous for all women in the sample-indicating that there are no subgroups in the data who experience important detrimental mental health consequences. We conclude that there are likely no mental health benefits to policy and interventions that aim to prevent teen motherhood.
Keywords: Bayesian methods; Causal inference; Mental health; Statistical machine learning; Teenage parenthood.
Copyright © 2023 The Authors.