Monotonic Path-Specific Effects: Application to Estimating Educational Returns

Conventional research on educational effects typically either employs a “years of schooling” measure of education, or dichotomizes attainment as a point-in-time treatment. Yet, such a conceptualization of education is misaligned with the sequential process by which individuals make educational transitions. In this paper, I propose a causal mediation framework for the study of educational effects on outcomes such as earnings. The framework considers the effect of a given educational transition as operating indirectly, via progression through subsequent transitions, as well as directly, net of these transitions. I demonstrate that the average treatment effect (ATE) of education can be additively decomposed into mutually exclusive components that capture these direct and indirect effects. The decomposition has several special properties which distinguish it from conventional mediation decompositions of the ATE, properties which facilitate less restrictive identification assumptions as well as identification of all causal paths in the decomposition. An analysis of the returns to high school completion in the NLSY97 cohort suggests that the payoff to a high school degree stems overwhelmingly from its direct labor market returns. Mediation via college attendance, completion and graduate school attendance is small because of individuals’ low counterfactual progression rates through these subsequent transitions.

💡 Research Summary

The paper introduces a causal mediation framework tailored to the sequential nature of educational attainment. Instead of treating education as a single “years of schooling” variable or a binary treatment, the author models education as a series of monotonic transitions: an initial treatment A (e.g., high‑school graduation) followed by K ordered mediators M₁,…,M_K (e.g., college enrollment, college completion, graduate school attendance). Monotonicity means that if an individual fails to achieve a given transition, all subsequent transitions are impossible (M_k(0)=0). This structural feature eliminates the need for the strong cross‑world independence assumptions that underlie traditional natural direct and indirect effect decompositions.

Under the monotonicity constraint and a sequential ignorability assumption—allowing each transition to have its own set of observed covariates that render the treatment and mediators conditionally independent—the average treatment effect (ATE) can be uniquely decomposed into K + 1 mutually exclusive “monotonic path‑specific effects” (MPSEs):

ATE = Δ₀ (direct effect of A on Y) + Σ_{k=1}^K