Research & Projects
Research & Projects
Abstract: In recent years, many US states have shifted away from rigid, exam-based high school graduation policies for their students. Some states have opted to add “alternative pathways”, including those focused on career technical education, which grant students the flexibility to demonstrate competency beyond standardized test scores and still earn a high school diploma. This study draws evidence from Ohio to investigate the effects of vocational training alternatives on students who struggle to meet the state’s high school exam standards. Using a regression discontinuity approach, I compare students who narrowly pass or fail Ohio’s end-of-course math exams in grades 9 and 10. I find that students who barely fail the math exam requirement are about 10 percentage points less likely to enroll in 4-year colleges, despite having similar high school graduation rates as their peers who barely pass. Students who narrowly fail are also more likely to pursue vocational training pathway options in high school, particularly in the form of earning industry-recognized credentials. These findings suggest that the presence of these vocation-based alternative pathways in combination with exit exam performance can induce marginal students to sort into career- or college-focused trajectories within high school.
With Benjamin Hyman (NY Fed), Benjamin Lahey (NYU), and Laura Pilossoph (Duke)
Abstract: We document the extent to which workers in AI-exposed occupations can successfully retrain for AI-intensive work. We assemble a new workforce development dataset spanning over 1.6 million job training participation spells from all US Workforce Investment and Opportunity Act programs from 2012 – 2023 linked with occupational measures of AI exposure. Using earnings records observed before and after training, we compare high AI exposure trainees to a matched sample of similar workers who only received job search assistance. We find that AI-exposed workers have high earnings returns from training that are only 25% lower than the returns for low AI exposure workers. However, training participants who target AI-intensive occupations face a penalty for doing so, with 29% lower returns than AI-exposed workers pursuing more general training. We estimate that between 25% to 40% of occupations are "AI retrainable" as measured by its workers receiving higher pay for moving to more AI-intensive occupations–a large magnitude given the relatively low-income sample of displaced workers. Positive earnings returns in all groups are driven by the most recent years when labor markets were tightest, suggesting training programs may have stronger signal value when firms reach deeper into the skill market.
Description: College dual enrollment programs have been rising in popularity within educational policies as a promising strategy to bridge high school and college education, aiming to increase college attainment rates and reduce equity gaps for disadvantaged student groups. This study examines the impact of Ohio's College Credit Plus (CCP) dual enrollment program on college attendance rates. Launched in 2015, CCP was created to lower barriers in college education by offering eligible students in grades 7-12 the chance to take college-level courses with local partner universities at no cost. In this study, I measure the impact of CCP participation upon students' four- and two-year college enrollment and completion rates, drawing from student-level administrative data and employing a regression discontinuity approach around GPA cutoffs for program eligibility.
With Benjamin Hyman (NY Fed), Benjamin Lahey (NYU), and Laura Pilossoph (Duke)
Description: Through linkage of nationally representative workforce training data from the Workforce Innovation and Opportunity Act (WIOA) and online job postings, we document the degree of mismatch in targeted skills by job training participants and those demanded by local labor markets.
With Benjamin Hyman (NY Fed)
Liberty Street Economics Blog Post. May 2020.