Federal Reserve Economic Data

At the end of January 2025, FRED posted preliminary job growth data for US metropolitan statistical areas (MSAs) in 2024. These data from the BLS provide a useful glimpse into the differences in job opportunities and broader economic growth across the nation.

US stats

The FRED map above shows the wide range of job growth across 352 MSAs. Median MSA job growth was 1.1%. Most MSAs (220, or 62%) had job growth below the US average of 1.4%, and 41 MSAs had negative job growth (shown in red).

Strongest job growth: 6.5% in Rochester, Minnesota, followed by 5.3% in Stockton-Lodi, California.

Steepest declines: -6.7% in Ocean City, New Jersey, followed by -2.7% in Ithaca, New York.

Eighth Federal Reserve District stats

The median job growth rate of the Eighth Federal Reserve District (the home of FRED) matches the US median of 1.1%.

Strongest job growth: 2.4% in Columbia, Missouri.

Steepest decline: -1.0% in Pine Bluff, Arkansas.

The FRED graph below reports the job growth rates for the four most-populous MSAs in the Eighth District, along with the US average over the past two years. The graph shows that job growth in St. Louis and Little Rock has outpaced the national average over the past two years, while growth in Louisville and Memphis has been slower than the national average.

Of course, these data are subject to revision, as highlighted in this 2017 post. So, this analysis will be revisited in March after the benchmark revision.

How these graphs were created:
Map: Search FRED for and select series ID “STLNA.” Click “Edit Graph” in the upper right: Under “Units,” select “Percent change from year ago.” Click the “View Map” button to see the data across all MSAs. Click “Edit Map”: In the format section’s “Data grouped by” menu, select “User Defined Method” to choose your own data groups and colors.
Graph: Search FRED for and select series ID “PAYEMS.” Click “Edit Graph” then “Add Line”: Search for “STLNA” and click “Add series.” Repeat this for the three other metro areas shown: LRSNA, LOINA, MPHNA. In the “Edit Graph” panel’s “Units” menu, select “Percent change from Year Ago” and click “Copy to all.” Modify the frequency to “Annual” and select aggregation method “End of Period”; repeat this step for each line. From the “Format” section’s “Graph type” menu, select “Bar.” Return to the graph itself and, in the upper right,  modify the date range to “1Y” (1 year).

Suggested by Charles Gascon.

Employer-to-employer (EE) transitions are when workers move from one job to another without being unemployed in between. EE transitions are important for the aggregate economy for several reasons.

• Persons typically change jobs when they’re offered higher salaries, so an economy with a high EE rate may have a higher level of labor earnings and more demand for goods and services.
• EE transitions also facilitate the reallocation of workers across jobs, so an economy with a high EE rate may have higher productivity and thus a higher supply of goods and services.

As a result, the EE transition rate affects both aggregate demand and supply in the economy, and thus it’s potentially relevant for understanding inflation dynamics.

FRED has data that track the probabilities of EE transitions between two consecutive months. The FRED graph above shows two versions of these probabilities: the monthly probability in blue, which is highly volatile and seasonal, and its 3-month moving average in red, which is smoother by construction and allows for a better view of the trends. The Philadelphia Fed provides the data shown in the FRED graph above, backed by research from Fujita, Moscarini, and Postel-Vinay.

Several other research papers have investigated the role of EE transitions in inflation dynamics, including seminal work from Moscarini and Postel-Vinay. Work by Serdar Birinci, Fatih Karahan, Yusuf Mercan, and Kurt See contribute to this literature by studying the following:

• how the wealth distribution impacts the quantitative effects of EE transitions on inflation dynamics 
• how the monetary authority should respond to fluctuations in the EE transition rate

In particular, Birinci, Karahan, Mercan, and See highlight that periods with similar unemployment rates but different EE rates (as during the recessions shown in the graph above) should lead to different policy responses as the EE rate largely affects inflation dynamics.

How this graph was created: In FRED, search for and select “Average Probability of U.S. Workers Making Employer to Employer Transitions, Percent, Seasonally Adjusted.” From the “Edit Graph” panel in the top right corner, use the “Add Line” tab to search for and select “3-Month Moving Average of Average Probability of U.S. Workers Making Employer to Employer Transitions.”

Suggested by Serdar Birinci.

In past posts, we’ve looked at movement in household assets such as pensions and direct holdings of stocks and household liabilities such as home mortgages and consumer credit. Today, we look at how unexpected inflation can affect the value of these household assets and liabilities.

The FRED graph above shows data, adjusted for inflation, from the US Bureau of Labor Statistics: Blue bars show the net change in the dollar value of total assets, and red bars show the net change in the dollar value of total liabilities. (Btw, “consumer units” in the graph is just a less homey name for households.)

What the data show about inflation-adjusted changes in these assets and liabilities

1984-2000: In most years, assets and liabilities changed by similar amounts.

2001-2012: The value of liabilities consistently changed more than the value of assets.

2013-2020: The value of assets consistently changed more than the value of liabilities.

This alternating pattern strongly suggests that each side of a household’s balance sheet is impacted by different factors and may even respond differently to common shocks. Take, for example, the unexpected inflation recorded during 2021-2022.

What recent research shows about inflation’s effect on these assets and liabilities

Yu-Ting Chiang, Ezra Karger, and Mick Dueholm at the St. Louis Fed use survey data reported by the Board of Governors of the Federal Reserve System to study the impact of unexpected inflation on the payment streams related to different types of household assets and liabilities. They find that the net balance of wealth gains and losses from unexpected inflation is related to how wealthy a household is: In short, unexpected inflation makes the poorest and the richest households worse off, while middle-income households become slightly better off.

For more about this and other research, visit the publications page of the St. Louis Fed’s website, which offers an array of economic analysis and expertise provided by our staff.

How this graph was created: Search FRED for and select “Net Change in Total Assets: All Consumer Units.” The data, from 1984 to 2023, should be adjusted for consumer price inflation to compare their change over time. So, from the “Edit Graph” panel, use the “Edit Line” tab to search for “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average.” Click “Add.” Type the formula (a/b)*100 and click “Apply.” Use the “Add Line” tab to search for and select “Net Change in Total Liabilities: All Consumer Units” and repeat the steps to customize and adjust the data by consumer price inflation.

Suggested by Diego Mendez-Carbajo.