Monotonic Path-Specific Effects: Application to Estimating Educational Returns

2025-08-18

AI Summary

Conventional studies of educational effects often reduce education to years of schooling or a binary treatment variable, overlooking its inherently staged, progressive nature. Method: We propose a path-specific causal mediation framework that, within the potential outcomes framework, achieves full identification of monotonic path-specific effects—thereby relaxing the strong assumptions (e.g., sequential ignorability, no-interaction) required by traditional mediation analysis. Our approach integrates counterfactual progression-rate estimation with longitudinal cohort modeling to decompose the total effect of education into a direct effect and stage-specific indirect effects transmitted through subsequent educational transitions. Contribution/Results: Applying this framework to the NLSY97 dataset, we find that the earnings return to high school graduation is almost entirely attributable to its direct labor-market value; indirect effects mediated through college or higher education are statistically negligible. This framework establishes a more realistic, empirically tractable paradigm for investigating the causal mechanisms underlying educational attainment.

Technology Category

Machine Learning: Causal LearningReasoning under Uncertainty: CausalityMultiagent Systems: Mechanism Design

Application Category

Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystemsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

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.

Problem

• Estimating causal effects of sequential educational transitions
• Decomposing direct and indirect educational pathways
• Identifying labor market returns from high school completion

Innovation

• Causal mediation framework for education
• Decomposes ATE into direct indirect effects
• Identifies causal paths with less restrictive assumptions