Federal Reserve Economic Data

The H.8 Release from the Federal Reserve’s Board of Governors details aggregate balance sheet data (assets and liabilities) for all US commercial banks, and the data can be found in FRED. The first post on this topic examined recent trends in total bank assets for large and small banks. The second examined recent trends in the securities held by large and small banks. This post (the 3rd of 3) examines recent trends in bank loans and leases and deposits at large and small banks.

In the aggregate, the largest category on the asset side of commercial bank balance sheets is loans and leases. Loans and leases include (a) commercial and industrial loans, (b) residential and commercial real estate loans, (c) consumer loans, and (d) all other loans and leases. Our first FRED graph (above) plots loans and leases as a percentage of total assets for large and small commercial banks. A few things are worth noting in the FRED graph.

• Small banks have a larger percentage of their assets as loans and leases compared with large banks.
• Loans and leases tend to be sensitive to the state of the economy. During the past recession (February 2020 to May 2020), loans and leases as a percent of total assets fell sharply at small and large banks. However, the share of loans fell proportionately more for large banks—from 55.6% for the week ending January 29, 2020, to 51.4% for the week ending May 27, 2020 (roughly corresponding to the beginning and end of the recession). Over this period, loans and leases as a percent of total assets only fell from 68.3% to 66.4% at small banks.
• Since the end of the recession in May 2020, banks have once again been increasing loans and leases as a percent of total assets. However, the share of loans and leases on the asset side has not yet returned to its pre-pandemic levels for both categories of banks.

Fundamentally, commercial banks are in the business of intermediation. That is, they take deposits from customers—individuals or businesses—and use those deposits to finance loans or the purchase of other assets that increase bank earnings and thus profits. Deposits are the largest liability on a commercial bank’s balance sheet.

Our second FRED graph plots total deposits as a percentage of total liabilities since the beginning of 2015. Over most of this period, deposits (insured and uninsured) comprised roughly 90% of total liabilities for small banks. Beginning in mid-2019, though, small banks have increased their deposit-to-liability percentage (deposit ratio), which reached 94% in late February 2022. By contrast, large banks initially used other sources of funding, as their deposit ratio was much smaller in early 2015—roughly 83%. Over time, though, large banks have also sharply increased their deposit ratio, and it eventually rose to about 96% in early April 2022.

A good portion of the surge in bank deposits was pandemic related: fiscal stimulus and a decline in discretionary funding by individuals that led to increased personal savings. Acharya et al. show that bank deposits also increase when the Federal Reserve engages in large-scale asset purchases (quantitative easing), as it did from mid-March 2020 through June 1, 2022. Moreover, they show that a large percentage of these increased deposits were uninsured. From March 31, 2020, to December 31, 2022, uninsured deposits at US chartered banks increased by $1.56 trillion to $7.79 trillion.

The last few data points in the second graph are striking. Deposits as a percent of total liabilities have fallen sharply in response to numerous factors: One is the FOMC’s tightening actions, which reduce reserves, and another is recent market turmoil that was spurred, in part, by the recent failures at one large bank (Silicon Valley Bank, or SVB) and one “small” bank (Signature Bank).

Recent actions by the Federal Reserve, FDIC, and US Treasury have attempted to calm the fears of both depositors and investors in the U.S. commercial banking sector. Nevertheless, there has been some deposit flight from small commercial banks to larger banks and from commercial banks to money market mutual funds. In response, banks have greatly increased their borrowings (which include discount loans from the Reserve Banks). From the week ending March 8, 2023, to the week ending March 29, borrowings by small banks rose by 42.2%, or $175 billion, while borrowings by large banks rose by 47.7%, or by $304.4 billion.

FRED users who are interested in monitoring the US commercial banking sector can do so by analyzing weekly trends in commercial banks’ balance sheets in found in the H.8 data.

How this graph was created: First graph: Search FRED for and select “Loans and Leases in Bank Credit, Small Banks.” Click on “Edit Graph” and use “Edit Line” to search for and add to that new line the “Total Assets, Small Banks” series and apply formula (a/b)*100. Do the same for large banks. Second graph: Do the same as with the first, but use the “Deposits” series instead of the loans and leases series for small and large banks. Start the graph on 2015-01-01.

Suggested by Kevin Kliesen and Cassie Marks.

The FRED Blog frequently provides context to help tell the story behind the data. Today we need a bit of history as well to help clarify the names of the data series themselves.

In 1876, the city of St. Louis, Missouri, became its own county, separating its local government affairs from the rest of St. Louis County, Missouri. Since then, the U.S. Census has tallied population statistics across subdivisions, or tracts, of these two separate counties with very similar names.

The FRED graph above shows the racial dissimilarity index for St. Louis City (in blue) and St. Louis County (in red). The Census reports the index as a percent of the non-Hispanic White population that would have to move from one census tract in a county to another census tract in the same county to achieve an even distribution of racial groups across that county.

Consider 2009, when the first data in the series are available: At that time, about two out of every three non-Hispanic White residents in the city of St. Louis would have had to change where they lived for this specific type of racial dissimilarity to disappear within St. Louis City. Slightly more than half of the residents in St. Louis County would have had to do the same to eliminate this racial dissimilarity in that county.

Twelve years later, the population landscape has changed. Since 2020, in terms of their racial makeup, the tracts in St. Louis City are noticeably more like one another than the tracts in St. Louis County.

However, the overall racial makeup of these two similarly named neighboring counties is very different and should be taken into consideration when interpreting the data. To begin with, almost two out of every three County residents are non-Hispanic White. In the City, the ratio is almost one-to-one. The population trends are also different: Between 2010 and 2020, the city lost almost 6% of its residents, while the county added 5%.

So, at least two different population trends could be at play here. Perhaps, on average, neighborhoods in the City are becoming more racially integrated while neighborhoods in the County remain steadfastly less integrated. Or it could be that population loss of racial minorities in some City neighborhoods is making the overall racial distribution there more even. Of course, both trends can be at play here. An in-depth analysis of tract-level Census data is needed to come to a definite conclusion.

How this graph was created: In FRED, search for “White to Non-White Racial Dissimilarity (5-year estimate) Index for St. Louis city, MO.” Next, click “Edit Graph” at the top right corner and navigate to the “Add Line” tab. Search for “White to Non-White Racial Dissimilarity (5-year estimate) Index for St. Louis County, MO” and click on “Add data series.”

Suggested by Diego Mendez-Carbajo.

To help FRED users navigate the rapidly changing economic and financial environment, the Federal Reserve Bank of St. Louis has assembled two dashboards of FRED graphs. The first dashboard collects higher-frequency financial market variables. The second dashboard collects mostly monthly indicators that track expenditures, employment and unemployment, and key business and consumer surveys.

For some background on why and how economists and other analysts track economic and financial variables during stressful times, read on:

The World Health Organization declared the novel coronavirus—known as COVID-19—a pandemic. Johns Hopkins University is monitoring the spread of the virus and mapping the number of confirmed COVID-19 cases and fatalities worldwide.

The number of confirmed cases in the United States is rising, and U.S. financial markets have been tumultuous. For example, since hitting an all-time high on February 12, the Dow Jones Industrial Average has fallen by about 33 percent as of the writing of this post. Yields on 10-year Treasury securities plunged to an all-time low of 0.54 percent on March 9, though they have since rebounded modestly. Other key financial market indicators, such as commercial paper yields and yields on corporate bonds, have also exhibited stress. Financial market–based measures of inflation expectations have fallen sharply.

These financial market stresses have triggered numerous policy responses by the Federal Open Market Committee (FOMC), including two reductions in the FOMC’s federal funds target rate.

Clearly, the COVID-19 outbreak is a significant and rare event in U.S. history. It has led to widespread disruptions in economic activity, with an unknown duration and magnitude. But it can be characterized and monitored as an economic shock. So, economists and policymakers are monitoring key cyclically sensitive indicators such as initial claims for unemployment insurance, changes in employment, retail sales, and sales of light motor vehicles and new and previously sold (existing) homes.

During times of high and rising uncertainty, financial market variables often serve as reliable forward-looking signals of future economic conditions in the broader economy. A key example is the Treasury yield curve, which usually inverts prior to recessions. This forward-looking perspective is important because most of the important “real” data that economists and policymakers monitor—such as the unemployment rate or industrial production—are backward-looking. For example, the payroll employment numbers for March 2020 will be released on Friday, April 3. However, they will capture only payrolls for the survey week ending March 12. Labor market conditions could have changed dramatically since then, given the fast-moving nature of the COVID-19 outbreak and the responses by firms and the government. To get a more timely measure of labor market conditions, an analyst might instead look at the weekly initial claims data.

Our two new FRED dashboards collect these useful variables to help you monitor and better understand the trajectory of the economy and the state of financial markets. FRED account holders can create their own dashboards, either from scratch or by taking these two as starting points.

Suggested by Kevin Kliesen.