At the New York Fed, our mission is to make the U.S. economy stronger and the financial system more stable for all segments of society. We do this by executing monetary policy, providing financial services, supervising banks and conducting research and providing expertise on issues that impact the nation and communities we serve.
Credit card delinquency rose sharply between 2022 and 2024, and the increase was steepest among borrowers in lower-income neighborhoods. Here, we check in on credit card performance by neighborhood income and trace when the borrowers who are now struggling with their payments were issued cards. We find that during the pandemic, an increasing share of new credit cards went to borrowers with lower incomes and credit scores, and those accounts are now showing higher levels of distress.
Has U.S. monetary policy actually improved over time? Or was the apparent success of the modern Federal Reserve mostly the result of more favorable economic conditions and better luck? In this post, we summarize our paper,* which develops a framework for comparing monetary policy across different economic environments. Our approach asks how close policy came to the best response given the economic conditions and shocks policymakers faced. Applying this framework to 150 years of U.S. monetary history, we find that meaningful improvements in monetary policy occurred only in the last thirty years. Most of the gains reflect, among other developments, substantially better responses to financial shocks and supply shocks.
The start of the Middle East conflict in early 2026 caused an unprecedented drop in global petroleum supply. In response, oil prices jumped—but not to unprecedentedly high levels. The U.S. oil sector was a key factor in moderating the shock. For one, its steady increases in drilling helped create a large oversupply in the global market before the conflict, making it less of a challenge to offset the drop in Middle East oil output. In addition, U.S. commercial and government inventories were rapidly drawn down, filling half the gap between global supply and demand in the second quarter by some estimates. Inventories, though, are a limited resource and U.S. stockpiles stopped falling in July. As a consequence, the continued shortfall in global production has been managed by running down inventories in the rest of the world.
Why is it that two borrowers who look much alike can be offered very different rates when applying for a credit card? In a recent paper, we address this question by examining how the rate offered changes when the lender’s funding cost falls. Data on 14 million newly issued cards show that when a lender’s funding costs decline, the lender passes less of that decline on to the offer rate when it holds a large market share. That is, two borrowers with similar credit scores and incomes borrowing from the same lender can see noticeably different interest rates on the credit card they are applying for.
In this post, we discuss insights from a recent staff report on the role of banking supervision in identifying and disciplining failing banks. We show that bank failures typically result from supervisory closure decisions based on hard information about book equity. Yet hard information is itself partly produced by supervision, as our evidence suggests that supervisors require troubled banks to recognize potential losses. We argue that supervision faces a trade-off: Stricter supervision can reduce the costs of resolving failing banks, but it can also lead to tighter regulatory capital constraints and hold back credit supply.
Measures of labor market tightness (LMT) describe how difficult it is for firms to find workers. The labor market is considered tight if there are few job seekers available relative to job openings, implying stronger wage pressures as employers compete for scarce job seekers. Thus, good measures of LMT are important for predicting wage inflation and calibrating monetary policy. Recently, the hiring platform LinkedIn began producing three innovative LMT measures based on its user data. In this post, we use public data from the Job Openings and Labor Turnover Survey (JOLTS) and the Bureau of Labor Statistics (BLS) to evaluate the ability of these novel measures to predict wage and price inflation relative to existing LMT measures, finding that one stands out as particularly useful and timely.
From the Great Recession until 2019, U.S. labor productivity growth disappointed. Nonfarm-business output per hour grew about 1 percent a year from 2010 to 2019, down from 3 percent in the late 1990s. This slowdown was especially pronounced in manufacturing, and it coincided with a slowdown in the growth of revenue per worker among public firms. However, since 2019, growth has picked up to twice the 2010s rate, around 2 percent. In this post, I use data from firms’ 10-K filings to unpack this recent acceleration. I first show that the acceleration appears in public firms’ revenue per worker, then document how the gains went into profit margins, and finally, what firm characteristics predicted significant profit margin increases. The increase in profit margins is consistent with ongoing declines in the labor share of output.
The AI boom is showing up in different corners of the U.S. economy, including international trade. Imports of semiconductors, servers, and computing equipment have climbed sharply yet the U.S. current account deficit has been largely unchanged. To better understand how the AI boom has not substantially affected the trade balance, consider that the current account is dictated by the gap between domestic saving and investment spending. It turns out that aggregate saving and investment spending have not changed much during the AI boom. Aggregate investment has not risen due to weakness outside the AI sector and business saving has remained robust.
As fall starts to show itself in cooler weather, I want to discuss a topic that usually raises temperatures: inflation. Like a budget-conscious consumer, the Federal Reserve is highly attuned to changes in the price of goods and services; minimizing such swings is one of the Fed’s primary duties, after all. But how does the Fed’s view of prices differ from that of a consumer? And how does the Fed go about stabilizing prices writ large when it lacks the ability to set prices directly? In this post, I explore that apparent paradox from the perspective of a central banker (though I remain a budget-conscious consumer when off duty).
There is little consensus on the effect of AI adoption on employment. Economy-wide measures of pay and headcount have moved little even as AI adoption has spread rapidly, yet narrower measures document declining hiring for workers at the start of their careers or in AI-exposed occupations. In this post, we revisit this topic for U.S. small businesses. We use data from the 2025 Small Business Credit Survey (SBCS) to examine the twelve-month-forward employment and revenue expectations of AI users. We find that firms currently using AI are significantly more likely than non-users to expect increased employment and revenues over the next year, even after accounting for firm and owner characteristics and location. Revenue expectations are most optimistic for AI users that report facing operational challenges in utilizing technology.
Liberty Street Economics is available on the iPhone® and iPad® and can be customized by economic research topic or economist.
RSS Feed
Follow Liberty Street Economics