Research & Projects
Research & Projects
Abstract: Over the past decades, many U.S. states have moved away from high-stakes exit exams toward more flexible graduation requirements. In Ohio, this shift includes new career-technical pathways that allow students to demonstrate competency outside standardized testing. This paper examines how such career-focused alternative graduation pathways affect students who struggle to meet exam standards. 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 fail are less likely to attend four-year colleges and are about equally more likely to enroll in two-year institutions. These effects appear to stem from gendered behavioral responses to exam performance and from students’ use of career-technical pathways after failing. While these pathways have broadened access to a diploma, they have not improved persistence in postsecondary education, suggesting that flexibility in graduation requirements may reduce barriers to completion without strengthening college readiness.
Current Draft (Featured in the Harvard Gazette)
AI Retrainability (AIR) Index Data
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.