Federal Reserve Economic Data

FRED allows you to create complex data visualizations to help explain economic data. The FRED graph above shows one example, a “stacked area” graph, to show the evolution of the Federal Reserve’s total liabilities and its main components.

The graph clearly shows the impact on Fed liabilities from the monetary policy response to the COVID-19 pandemic:

• Early on, as the Fed implemented its quantitative easing (QE) program by buying Treasuries and mortgage-backed securities, bank reserves increased correspondingly.
• At the same time, the Treasury issued debt ahead of expenditures, leading to substantial accumulation of funds at the Treasury General Account (TGA). This account was slowly drained as the Treasury used the funds to make payments.
• Starting in the second quarter of 2021, the overnight reverse repo facility (ON RRP) was used extensively by certain financial institutions, such as money market mutual funds.

The graph also shows the next stage of Fed policy actions:

• The Fed tapered its pace of asset purchases from November 2021 to March 2022 and began the process of quantitative tightening in June 2022.
• The tightening led to the sustained shrinking of the Fed’s balance sheet. As expected, the decline in total liabilities was first supported by a decline in the usage of ON RRP and then by a decline in bank reserves.

The more recent data in the graph show the rebuilding of the TGA, following the increase of the debt ceiling in July 2025.

How this graph was created: From FRED, click on Browse Data By: / Release and scroll down to “H.4.1 Factors Affecting Reserve Balances” / “Table 5. Consolidated Statement of Condition of all Federal Reserve Banks.” Here, the assets and liabilities of the Fed are broken down by components.

From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Federal Reserve Notes, net of F.R. Bank holdings” (currency in circulation), “Other deposits held by depository institutions” (bank reserves), “Reverse repurchase agreements” (sales of securities to eligible counterparties with an agreement to repurchase at a specified date), and “U.S. Treasury, General Account” (deposits of the U.S. Treasury at the Federal Reserve).

Note that the four categories listed above explain most but not all of the Fed’s liabilities, so those stacked areas would not add up to 100% total liabilities. We need to add a line that consists of the difference between total liabilities and the four components we have in the graph. To display the difference between the total and the components, use the “Add Line” tab to search for and select “Total Liabilities”; then go to “Customize data” and add the four components, one by one. In the formula, type a-b-c-d-e (where a is total liabilities and b to e are the individual components).

In the “Format” tab, use the arrows next to each area to order the components as you wish. Here, the first component (Federal Reserve notes) is at the bottom and the residual (total liabilities minus the components) is at the top. From the “Format” tab, select “Area” as the graph type and “Normal” as the stacking mode. The start date is January 1, 2019, and the graph is set to show the latest available data.

Suggested by Fernando Martin.

FRED recently added 20 data series about work-from-home practices in the United States reported by Jose Maria Barrero, Nicholas Bloom, and Steven J. Davis. The data are organized by industry and worker characteristics and offer a broad range of insights.

The FRED graph above shows the frequency of three types of work arrangements:

• working fully on site (blue line)
• working partly on site and partly remotely (orange line)
• working fully remotely (green line)

Between November 2021 and August 2025, when these data are available, working fully on site was approximately twice as frequent as a hybrid working arrangement and five times more frequent than working fully remote.

Here are some other takeaways from the recently added data:

• The proportion of fully paid workdays worked from home by men and women is slightly different.
• At the time of this writing, the industries with remote work rates between 50% and 30% are (in descending order) finance & insurance; information; professional & business services; arts & entertainment; utilities; real estate; and wholesale trade.
• The industries with remote work rates between 25% and 10% are (also in descending order) health care & social assistance; government; education; manufacturing; retail trade; transportation & warehousing; and hospitality & food services.

For more insights into this topic, check out the work of St. Louis Fed economist Alex Bick and coauthors. They research the trends in work arrangements and provide a technical analysis of the differences in work-from-home rates by sex, education, state of residence, industry, and firm size.

How this graph was created: Search FRED for and select “All Full-Time Wage and Salary Workers: Working Fully on Site.” Click on the “Edit Graph” button and select the “Add Line” tab to search for “All Full-Time Wage and Salary Workers: Working Fully Remote.” Don’t forget to click on “Add data series.” Repeat the last two steps to search for and add “All Full-Time Wage and Salary Workers: Working in Hybrid (Some Days Working from Home, Some Days at Employer or Client Site).”

Suggested by Diego Mendez-Carbajo.

The FRED Blog has discussed how economists use the consumer price index (CPI) to adjust the dollar value of retail sales, credit card debt, and state tax revenue to account for rising prices over time.

Today, we discuss how the Social Security Administration uses a version of the CPI to calculate cost of living adjustments, which affect the benefits recipients receive.

Another way to put it: how SSA uses CPI-W to calculate COLAs.

The FRED graph above shows the annual percent growth in both the CPI and the related CPI-W (from the US Bureau of Labor Statistics) from 2014 through 2024:

• Blue bars represent the all-items price inflation for all households living in cities and urban settings. This CPI inflation metric covers over 90% of the total population, regardless of employment status.
• Green bars represent the all-items price inflation for all urban wage earners and clerical workers,* CPI-W. Those households amount to approximately 30% of the total US population.

As we might expect, the CPI and CPI-W annual inflation rates are very similar. But it’s important to note that neither is consistently higher or lower than the other. Also, in times of higher inflation, the differences are more marked than in times of lower inflation. Different spending patterns among the populations covered by each price index can help explain that.

Since 1975, the dollar value of the Social Security’s general benefits has increased by the average July-to-September annual growth rate of the CPI-W. The amount of adjustment is announced in October. Last year, it was 2.5%. More details are available here.

*NOTE: Urban wage earners and clerical workers are defined as “households in which at least one of the members has been employed for 37 weeks or more during the previous 12 months in an eligible occupation and for which 50 percent or more of the household income must come from wage earnings associated with an eligible occupation.”

How this graph was created: Search FRED for and select “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Consumer Price Index for All Urban Wage Earners and Clerical Workers: All Items in U.S. City Average.” Be sure to click “Add data series.” Next, use the “Edit Lines” tab to select each of the two graph lines from the dropdown menu and change the units to “Percent Change from Year Ago” and modify the frequency to “Annual.” Lastly, use the “Format” tab to change the graph type to “Bar.”

Suggested by Nylah Martinez and Diego Mendez-Carbajo.