纽约联储-人工智能暴露工人的可再培训性如何？（英）

How Retrainable Are AI-Exposed Workers? Benjamin Hyman | Benjamin Lahey | Karen Ni | Laura Pilossoph NO. 1165 AUGUST 2025 How Retrainable Are AI-Exposed Workers? Benjamin Hyman, Benjamin Lahey, Karen Ni, and Laura Pilossoph Federal Reserve Bank of New York Staff Reports, no. 1165 August 2025 https://doi.org/10.59576/sr.1165 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 U.S. 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 the average earnings return to training among AI-exposed workers is high, around $1,470 per quarter. Low-exposure trainees capture higher returns, and trainees who target AI-intensive work face a 29 percent earnings return penalty relative to their high exposure peers who pursue more general training. We estimate that between 25 to 40 percent 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. JEL classification: J08, M53, O31 Key words: artificial intelligence, active labor market policies, job training, labor markets _________________ Hyman: Federal Reserve Bank of New York (email: ben.hyman@ny.frb.org). Lahey: New York University, Department of Economics (email: bpl9631@nyu.edu). Ni: Harvard Kennedy School (email: kni@g.harvard.edu). Pilossoph: Duke University (email: laura.pilossoph@duke.edu). The authors thank Joe Altonji, David Card, Lisa Kahn, Fabian Lange, Michael Lee, Kyle Myers, Steve Raphael, Daniel Rock, Wilbert van der Klauuw, Till von Wachter, and seminar participants at the annual SOLE conference, Federal Reserve Banks of San Francisco and New York, and UC Berkeley IRLE for helpful comments on an earlier version of the paper. They also thank Jonathan Lee for excellent research assistance. Ni gratefully acknowledges support from the Institution of Education Sciences, U.S. Department of Education, through grant R305B150012 to Harvard University. This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in this paper are those of the author(s) and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve Syst