The Federal Reserve Bank of New York is one of the 12 Federal Reserve Banks of the United States. It is responsible for the Second District of the Federal Reserve System, which encompasses New York State, the 12 northern counties of New Jersey, Fairfield County in Connecticut, Puerto Rico, and the U.S. Virgin Islands. Located at 33 Liberty Street in Lower Manhattan, it is by far the largest (by assets), the most active (by volume), and the most influential of the Reserve Banks. The Federal Reserve Bank of New York acts as the market agent of the Federal Reserve System (as it houses the Open Market Trading Desk, which executes transactions for the System Open Market Account), the sole fiscal agent of the U.S. Department of the Treasury, the bearer of the Treasury's General Account, and the custodian of the world's largest gold storage reserve, in addition to having the same responsibilities and tasks as the other Reserve Banks.Among the other regional Reserve banks, the New York Fed and its president are therefore considered first among equals. Its current president is John C. Williams.S.S.S...S.S.S.S.S.S.S.
Using highly detailed data on the loan portfolios of large U.S. banks, we document that these banks "specialize" by concentrating their lending disproportionately into one industry. This specialization improves a bank’s industry-specific knowledge and allows it to offer generous loan terms to borrowers, especially to firms with access to alternate sources of funding and during periods of greater nonbank lending. Superior industry-specific knowledge is further reflected in better loan and, ultimately, bank performance. Banks concentrate more on their primary industry in times of instability and relatively lower Tier 1 capital. Finally, specialization counteracts a well-documented trend in reduced lending by large banks to opaque small and medium-sized enterprises.
We infer risk-free rates from index option prices to estimate safe asset convenience yields in 10 G11 currencies. Countries' convenience yields increase with the level of their interest rates, with U.S. convenience yields fifth largest. During financial crises, convenience yields grow, but the difference between United States and foreign convenience yields generally does not. Covered interest parity (CIP) deviations using our option-implied rates are a similar size between the United States and each other country. A model in which convenience yields depend on domestic financial intermediaries, but CIP deviations reflect the funding costs of international arbitrageurs financed with dollar-denominated debt, explains these results.
We study the causes and consequences of bank runs using a novel dataset on bank runs in the United States from 1863 to 1934. Applying natural language processing to historical newspapers, we identify 4,049 runs on individual banks. Runs are considerably more likely in weak banks but also occur in strong banks, especially in response to negative news about the real economy or the broader banking system. However, runs typically only result in failure for banks with weak fundamentals. Strong banks survive runs through various mechanisms, including interbank cooperation, equity injections, public signals of strength, and suspension of convertibility. At the local level, bank failures (with and without runs) translate into substantially larger declines in deposits and lending than runs without failures. Our findings suggest that poor bank fundamentals are necessary for bank runs to translate into failure and for bank distress to generate severe economic consequences.
We propose a novel channel through which rising income inequality affects job creation and macroeconomic outcomes. High-income households save relatively more in stocks and bonds but less in bank deposits. A rising top income share thereby increases the relative financing cost for bank-dependent firms, which in turn create fewer jobs compared to other firms. Exploiting variation in top income shares across US states and an instrumental variable strategy, we provide evidence for this channel. We then build a general equilibrium macroeconomic model with heterogeneous households and heterogeneous firms and calibrate it to our empirical estimates. The model shows that the secular rise in top incomes accounts for 13% of the decline in the employment share of small firms since 1980. Through the new channel, rising inequality also reduces the labour share and aggregate output. Model experiments show that ignoring the link between inequality and job creation understates welfare effects of income redistribution.
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.
