We're sharing a review of the evidence on worker retraining programs, coauthored by independent researcher David Roodman and Anthropic's Maxim Massenkoff.
Retraining workers is the most popular policy option for mitigating labor market disruption from AI. In this report, the authors investigate whether these programs would work in the face of significant labor market disruption.
The review is part of our Economic Research team's work on AI's effects on the economy. Our Economic Index tracks how AI is being used across occupations and industries. Earlier this year, we published a framework for measuring AI's effects on the labor market and identifying the jobs most likely to be affected. Our Economic Policy Framework sets out possible policy responses, including worker retraining, across a range of scenarios. We want to understand the evidence behind each of these policy responses.
The review draws on 56 randomized US studies, combined in a new meta-analysis, along with experimental evidence from Europe. On average, job training programs produce positive but modest effects: for each person offered a training slot, employment rises by two to three percentage points and earnings by roughly $1,000 a year, against a cost of about $13,000. Counting the added tax revenue and reduced benefit payments, the government recovers more than half of what it spends, and programs roughly break even overall.
A small set of “sector programs”—programs that partner with employers in a high-demand industry and place people directly into jobs in that field—produce gains several times larger. But attempts to replicate them have often failed. The authors conclude that if AI displaces workers at scale, existing retraining programs would likely fall short.
Their central recommendation is to invest now in demonstrating, evaluating, and scaling the most promising programs, including through an effort to rapidly expand a leading program for a specific group of workers and rigorously measure the results. Our Economic Futures Research Fund is designed to fund investigations into questions like these.
Related content #
Patterns and problems in emerging multiagent systems
Here, we identify a few examples of behavioral tendencies in current frontier models and show how they can produce unexpected systemic failures, in hopes of starting a conversation about mitigating these risks.
Learning more about Claude's mathematical capabilities
An unreleased research version of Claude has made strides on a problem related to the Riemann hypothesis. It improved a longstanding lower bound for the fraction of zeros of the Riemann zeta function that satisfy the hypothesis, increasing it from 41.6% to 67.2%.
Discovering cryptographic weaknesses with Claude
cryptographic algorithms. The first attack significantly weakens HAWK, a digital signature scheme that was built for a future world where quantum computers are able to break existing standards. The second identifies a new way to attack round-reduced AES, the most widely used symmetric cipher.