Essays in Labor Economics: Education and Career Pathways to Opportunity

Abstract

This dissertation presents three essays in labor economics that study how K-12 education and workforce development policies connect individuals to economic opportunity. Programs targeting inequalities in education and labor are typically evaluated on the basis of the overall intervention impacts, but these findings can often obscure the underlying role that policy design choices can play in creating frictions and producing unequal outcomes across different individuals. Using quasi-experimental methods, the three chapters of this dissertation assess program impacts which span the school-to-workforce pipeline, using heterogeneous outcomes to identify where policy frictions arise and how they can be improved. The first chapter examines Ohio's flexible graduation pathway system in shaping students' postsecondary trajectories. Using a regression discontinuity design around the pass/fail cutoff for Ohio's end-of-course (EOC) math exams, I find that passing the Math EOC does not affect high school graduation rates but significantly alters college enrollment patterns: students who barely pass are more likely to attend four-year colleges, while students who barely fail are more likely to attend two-year colleges. These effects appear to stem from gendered behavioral responses to exam performance and from students opting into career-technical pathways upon failing to meet the state's exam criteria. While these initial pathways have broadened access to the diploma, they have not improved persistence in postsecondary education, suggesting that flexibility in graduation requirements may reduce barriers to high school completion without strengthening college readiness. The second chapter assesses the effectiveness and accessibility of Ohio's College Credit Plus (CCP) dual enrollment program in helping disadvantaged students bridge the gap between high school and college. Using a fuzzy regression discontinuity design, I find that passing the program's ACT eligibility threshold raises CCP participation by 8 percentage points, increases four-year college enrollment, and produces long-run gains in college graduation rates. I then examine a 2020 policy change in student eligibility and employ a difference-in-differences framework to estimate the impacts of expanded eligibility on participation rates. I find that these expanded eligibility rules significantly increased CCP enrollment rates, especially among Black and Hispanic students. Together, these results contribute causal evidence on the effectiveness of dual enrollment programs in a predominantly minority school district and offer new insights on the role of academic eligibility requirements as both a barrier to participation and a potential lever for advancing equity. The third chapter answers the question of how displaced workers navigate occupational transitions in the face of rapid advancements in artificial intelligence (AI). As AI capabilities advance, it remains unclear whether workers will best adapt by reskilling into AI-complementary work or by sorting into occupations less exposed by AI. To answer this question, we assemble a new dataset of 1.9 million occupational training spells funded by the U.S. Workforce Innovation and Opportunity Act from 2012-2024. We link pre- and post-training occupations to task-level AI exposure measures and estimate returns to training by comparing trainees to matched workers who sought workforce services but received only job search assistance. While trainees from low AI-exposure occupations earned high quarterly returns throughout the sample period, returns for workers from high-exposure occupations rose sharply, from about $900 quarterly before 2020 to $2,900 by 2022-2024. We attribute these gains primarily to transitions into less AI-exposed occupations and, to a lesser extent, to the expansion of training programs that build AI-complementary skills. To quantify when training into higher AI exposure work pays off, we construct a new AI Retrainability Index (AIR) and find that a large share of occupations are “AI-retrainable”, pointing to broad potential for adaptation as U.S. workforce development programs adapt to the changing technological landscape.