Highlighted Publications




 

Jacobs et al. (2026) ask whether collaborative robots (cobots) can be used to raise productivity while preserving jobs, rather than displacing workers. They examine existing research, industry case examples, and prior empirical studies on automation, worker safety, ergonomics, and labor markets, rather than analyzing a new dataset. They find that cobots tend to automate specific tasks instead of whole jobs, reducing physical strain and workplace injuries while increasing outp

Agan et al. (2025) ask whether clearing or sealing criminal records improves employment outcomes. They examine linked administrative court records from Maryland, New Jersey, Pennsylvania, and Texas merged with IRS tax data on earnings, employment, and gig work. They find that criminal charges—both convictions and non-convictions—are followed by large and persistent employment declines. However, removing records through Fair Credit Reporting Act rules or Clean Slate laws has l

Shekhar, Leder-Luis, and Akoglu (2026) ask whether unsupervised, explainable machine learning can effectively identify hospitals engaging in potentially fraudulent Medicare billing. They analyze millions of Medicare inpatient claims from 2017, combined with patients’ prior medical histories and hospital characteristics, covering over 2,200 hospitals. Using anomaly-detection algorithms, they rank hospitals based on suspicious coding and spending patterns. The authors find that

Darolia and Turner (2026) ask whether the rapid expansion of apprenticeships in the United States is being matched by credible evidence on program quality, outcomes, and skill development. They examine administrative data from the U.S. Department of Labor’s Registered Apprenticeship system, state-level apprenticeship records, and postsecondary enrollment and completion data. They find that apprenticeships have grown substantially since 2011, expanded into new industries, and