What Do Over 3,000 Bank Runs Teach Us About Banking Crises?
Sergio Correia, Stephan Luck, and Emil Verner
Runs on financial institutions are one of the salient markers of financial crises. But the role of runs in crises is a topic of longstanding debate. Runs can be seen as the key turning point, whereby even small shocks can generate severe crises with widespread bank failures. Another view is that runs are mainly a symptom of deeper rot in the financial system, exacerbating crises rather than being their primary cause. Understanding this debate has first order implications for how to think about financial crises and the appropriate policy responses. In this post, we use a new database of more than 3,000 bank runs (introduced in our companion post) to show that poor fundamentals are central to explaining both when runs occur and when they have severe economic effects. We argue that this evidence tempers the view that small shocks can have outsized real effects through self-fulfilling run dynamics.
Using AI to Let History Speak About Bank Runs
Sergio Correia, Stephan Luck, and Emil Verner
Banking crises are commonly associated with bank runs and banking panics, yet our empirical understanding of bank runs is constrained by a lack of bank-level data. In a new paper, we use large language models (LLMs) to extract information on bank runs from millions of digitized historical newspaper pages, creating the most comprehensive database of bank runs in U.S. history. Every bank run episode that we identify is documented on a companion website where users can browse and examine individual episodes, and read the original newspaper articles. In this post, we describe how we built this dataset and discuss what its basic features reveal.
Remote Work Leaves Younger Workers Sidelined
Natalia Emanuel, Emma Harrington, and Amanda Pallais
Youth unemployment has risen dramatically since the pandemic—as has the prevalence of remote work. Our analysis suggests that these trends are related, with remote work making it more difficult for managers to train and mentor new employees. Accordingly, companies may be reluctant to hire less-experienced workers in distributed work arrangements. We estimate that remote work can explain 64 percent of the recent increase in unemployment among young college graduates. Further, the timing of this surge suggests that remote work—not generative AI—explains the bulk of the rise in youth unemployment.
AI’s Macroeconomic Challenges and Promises
Simone Lenzu
In the third quarter of 2025, America’s largest tech firms for the first time spent more on capital investment than they earned from operations. The implication is that AI, a technology with the potential to make the economy more productive, is, for now, absorbing resources faster than it is generating returns. This post discusses how the tension between AI’s long-run promise and its short-run costs affects the outlooks for inflation, real activity, and financial stability.
Do Job Postings Show Early Labor‑Market Effects of AI?
Richard Audoly, Miles Guerin, and Giorgio Topa
As generative AI tools become more widely used, a key issue is the technology’s impact on labor demand. Where might we find evidence of that impact? In this post, we examine whether early evidence of AI’s effect on the labor market appears in firms’ job postings. We combine an occupational measure of AI exposure with detailed U.S. job-posting data from Lightcast, which aggregates listings from company career pages, national and local job boards, and job-listing aggregators. Using this data, we test whether postings for AI-exposed occupations declined disproportionately since the release of ChatGPT in late 2022. We find that, while overall hiring has slowed since then, the evidence from job postings provides little indication of a distinct AI-driven decline in labor demand.
Will Mounting Supply Chain Strains Hamstring the AI Investment Boom?
Hunter L. Clark, Jeffrey B. Dawson, and Shad Turney
The conflict in the Middle East has precipitated a global supply shock—the third in six years following the pandemic in 2020 and Russia’s invasion of Ukraine in 2022. The current shock raises the specter of spillovers to the U.S. through both prices and physical shortages of goods. A critical conduit for spillovers through these channels is via Asian supply chains, especially from middle- to lower-middle income countries in southeast Asia, which are key suppliers for goods needed for the AI infrastructure build-out in the U.S. These countries are also heavily reliant on Middle East energy imports. This post examines key factors related to these Asian supply chain vulnerabilities.
In What Ways Has U.S. Trade with China Changed?
Hunter L. Clark and Gregory Simitian
Over the past year, U.S. trade policy with China has undergone enormous changes, but with surprisingly little effect on overall trade balances. In fact, the U.S.’s twelve-month trade deficit, while highly volatile due to import front-running early in the year, ended 2025 at $1.2 trillion, almost unchanged from 2024. At the same time, China’s trade surplus with the world actually increased from $1 trillion to $1.2 trillion. However, when looking at changes between individual countries, one sees large shifts in bilateral balances. In this post, we will focus on changing trade flows between the U.S., China, and southeast Asia.
Use of Gen AI in the Workplace and the Value of Access to Training
Ali Hashim, Gizem Kosar, and Wilbert van der Klaauw
The rapid spread of generative AI (AI) tools is reshaping the workplace at a remarkable rate. Yet relatively little is known about whether workers have access to these tools, how the tools affect workers’ daily productivity, and how much workers value the training needed to use the tools effectively. In this post, we shed light on these issues by drawing on supplemental questions in the November 2025 Survey of Consumer Expectations (SCE), fielded to a representative sample of the U.S. population. We find that adoption of AI tools at work is heterogeneous, that a sizable share of workers see AI training as important, and that a significant share of employers are nonetheless not yet providing access to AI tools or training on how to use them.
Are Businesses Scaling Back Hiring Due to AI?
Jaison R. Abel, Richard Deitz, Natalia Emanuel, Ben Hyman, and Nick Montalbano
The swift advancement of artificial intelligence (AI) has sparked significant concern that this new technology will replace jobs and stifle hiring. To explore the effects of AI on employment, our August regional business surveys asked firms about their adoption of AI and if they had made any corresponding adjustments to their workforces. Businesses reported a notable increase in AI use over the past year, yet very few firms reported AI-induced layoffs. Indeed, for those already employed, our results indicate AI is more likely to result in retraining than job loss, similar to our findings from last year. That said, AI is influencing recruiting, with some firms scaling back hiring due to AI and some firms adding workers proficient in its use. Looking ahead, however, layoffs and reductions in hiring plans due to AI use are expected to increase, especially for workers with a college degree.
Exposure to Generative AI and Expectations About Inequality
Natalia Emanuel and Emma Harrington
With the rise of generative AI (genAI) tools such as ChatGPT, many worry about the tools’ potential displacement effects in the labor market and the implications for income inequality. In supplemental questions to the February 2024 Survey of Consumer Expectations (SCE), we asked a representative sample of U.S. residents about their experience with genAI tools. We find that relatively few people have used genAI, but that those who have used it have a bleaker outlook on its impacts on jobs and future inequality.
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