This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since September 2024. As usual, we wish to remind our readers that the DSGE model forecast is not an official New York Fed forecast, but only an input to the Research staff’s overall forecasting process. For more information about the model and variables discussed here, see our DSGE model Q & A.
Anatomy of the Bank Runs in March 2023
Runs have plagued the banking system for centuries and returned to prominence with the bank failures in early 2023. In a traditional run—such as depicted in classic photos from the Great Depression—depositors line up in front of a bank to withdraw their cash. This is not how modern bank runs occur: today, depositors move money from a risky to a safe bank through electronic payment systems. In a recently published staff report, we use data on wholesale and retail payments to understand the bank run of March 2023. Which banks were run on? How were they different from other banks? And how did they respond to the run?
Do Import Tariffs Protect U.S. Firms?
One key motivation for imposing tariffs on imported goods is to protect U.S. firms from foreign competition. By taxing imports, domestic prices become relatively cheaper, and Americans switch expenditure from foreign goods to domestic goods, thereby expanding the domestic industry. In a recent Liberty Street Economics post, we highlighted that our recent study found large aggregate losses to the U.S. from the U.S.-China trade war. Here, we delve into the cross-sectional patterns in search of segments of the economy that may have benefited from import protection. What we find, instead, is that most firms suffered large valuation losses on tariff-announcement days. We also document that these financial losses translated into future reductions in profits, employment, sales, and labor productivity.
Using Stock Returns to Assess the Aggregate Effect of the U.S.‑China Trade War
During 2018-19, the U.S. levied import tariffs of 10 to 50 percent on more than $300 billion of imports from China, and in response China retaliated with high tariffs of its own on U.S. exports. Estimating the aggregate impact of the trade war on the U.S. economy is challenging because tariffs can affect the economy through many different channels. In addition to changing relative prices, tariffs can impact productivity and economic uncertainty. Moreover, these effects can take years to become apparent in the data, and it is difficult to know what the future implications of a tariff are likely to be. In a recent paper, we argue that financial market data can be very useful in this context because market participants have strong incentives to carefully analyze the implications of a tariff announcement on firm profitability through various channels. We show that researchers can use movements in asset prices on days in which tariffs are announced to obtain estimates of market expectations of the present discounted value of firm cash flows, which then can be used to assess the welfare impact of tariffs. These estimates suggest that the trade war between the U.S. and China between 2018 and 2019 had a negative effect on the U.S. economy that is substantially larger than past estimates.
Documenting Lender Specialization
Robust banks are a cornerstone of a healthy financial system. To ensure their stability, it is desirable for banks to hold a diverse portfolio of loans originating from various borrowers and sectors so that idiosyncratic shocks to any one borrower or fluctuations in a particular sector would be unlikely to cause the entire bank to go under. With this long-held wisdom in mind, how diversified are banks in reality?
Why Do Banks Fail? Bank Runs Versus Solvency
Evidence from a 160-year-long panel of U.S. banks suggests that the ultimate cause of bank failures and banking crises is almost always a deterioration of bank fundamentals that leads to insolvency. As described in our previous post, bank failures—including those that involve bank runs—are typically preceded by a slow deterioration of bank fundamentals and are hence remarkably predictable. In this final post of our three-part series, we relate the findings discussed previously to theories of bank failures, and we discuss the policy implications of our findings.
Why Do Banks Fail? The Predictability of Bank Failures
Can bank failures be predicted before they happen? In a previous post, we established three facts about failing banks that indicated that failing banks experience deteriorating fundamentals many years ahead of their failure and across a broad range of institutional settings. In this post, we document that bank failures are remarkably predictable based on simple accounting metrics from publicly available financial statements that measure a bank’s insolvency risk and funding vulnerabilities.
Why Do Banks Fail? Three Facts About Failing Banks
Why do banks fail? In a new working paper, we study more than 5,000 bank failures in the U.S. from 1865 to the present to understand whether failures are primarily caused by bank runs or by deteriorating solvency. In this first of three posts, we document that failing banks are characterized by rising asset losses, deteriorating solvency, and an increasing reliance on expensive noncore funding. Further, we find that problems in failing banks are often the consequence of rapid asset growth in the preceding decade.
To Whom It May Concern: Demographic Differences in Letters of Recommendation
Letters of recommendation from faculty advisors play a critical role in the job market for Ph.D. economists. At their best, they can convey important qualitative information about a candidate, including the candidate’s potential to generate impactful research. But at their worst, these letters offer a subjective view of the candidate that can be susceptible to conscious or unconscious bias. There may also be similarity or affinity bias, a particularly difficult issue for the economics profession, where most faculty members are white men. In this post, we draw on our recent working paper to describe how recommendation letters differ by the gender, race, or ethnicity of the job candidate and how these differences are related to early career outcomes.
Why Investment‑Led Growth Lowers Chinese Living Standards
Rapid GDP growth, due in part to high rates of investment and capital accumulation, has raised China out of poverty and into middle-income status. But progress in raising living standards has lagged, as a side-effect of policies favoring investment over consumption. At present, consumption per capita stands some 40 percent below what might be expected given China’s income level. We quantify China’s consumption prospects via the lens of the neoclassical growth model. We find that shifting the country’s production mix toward consumption would raise both current and future living standards, with the latter result owing to diminishing returns to capital accumulation. Chinese policy, however, appears to be moving in the opposite direction, to reemphasize investment-led growth.