Federal Reserve Economic Data

The FRED Blog recently used research from the St. Louis Fed to discuss how pandemic-related immigration restrictions affected the number of job vacancies per unemployed person—a.k.a., labor market tightness.

Today, we revisit this topic by highlighting research pinpointing the urban centers with the tightest labor markets.

The FRED graph above shows data from Indeed.com, an aggregator of online job listings. Indeed reports job posting activity as a 7-day trailing average, presented as an index with a value of 100 on February 1, 2020. Job postings are highly correlated with job vacancies, and the FRED graph shows persistently elevated levels of job postings (as of May 2023) in three metropolitan statistical areas: Jackson, Mississippi; Omaha-Council Bluffs, Nebraska-Iowa; and Madison, Wisconsin.

Recent research from Cassie Marks, Lowell R. Ricketts, William M. Rodgers III, and Hannah Rubinton at the St. Louis Fed explores tightening in local labor markets during the recovery from the COVID-19-induced recession. Their work specifically identifies the cities of Jackson, Mississippi; Omaha, Nebraska; and Madison, Wisconsin, as the urban centers with the tightest labor markets as of May 2023.

For more about this and other research, visit the website of the Research Division of the Federal Reserve Bank of St. Louis, which offers an array of economic analysis and expertise provided by our staff.

How this graph wase created: Search FRED for and select “Job Postings on Indeed in Jackson, MS (MSA).” From the “Edit Graph” panel, use the “Add Line” tab to search for and add “Job Postings on Indeed in Omaha-Council Bluffs, NE-IA (MSA).” Repeat the last step to add “Job Postings on Indeed in Madison, WI (MSA)” to the graph.

Suggested by Diego Mendez-Carbajo.

The FRED Blog has discussed how household wealth increases and decreases when the values of financial assets and housing assets go up and down. It’s useful to also consider the concept of net worth, which is the difference between the value of your assets and the value of your liabilities. Our question today is, What impact does age have on the net worth of households?

The FRED graph above uses data from the US Bureau of Labor Statistics’ Consumer Expenditure Survey to track the net change in total assets and liabilities (i.e., net worth!) of six different age groups, from under age 25 to age 65 and over.

The data are plotted in stacked bars to show how changes in net worth differ across these age groups and how business cycles affect every group’s wealth. For example, those aged 25 to 34 (red bars) most frequently report decreases in net worth: At this age, the value of student, consumer, and mortgage loans tends to grow faster than the value of the underlying assets.

The observations in this data set don’t allow us to examine how different generations of these age groups have grappled with wealth accumulation, but recent research does. Victoria Gregory and Kevin Bloodworth at the St. Louis Fed explore how Baby Boomers, Generation Xers, and Millennials have balanced student loan debt and homeownership debt to accumulate wealth. Here’s what they found for the college-educated: Millennials and Generation Xers earn as much as Boomers did, but the larger amount of student loan debt the two younger generations carry can reduce their ability to own a home and, thus, accumulate wealth.

For more about this and other research, visit the website of the Research Division of the Federal Reserve Bank of St. Louis, which offers an array of economic analysis and expertise provided by our staff.

How this graph wase created: Search FRED for and select “Net Change in Total Assets and Liabilities by Age: Under Age 25.” From the “Edit Graph” panel, use the “Add Line” tab to search for and add the other five series. To save yourself some time, simply replace the age group after the colon with: “from Age 25 to 34,” “from Age 35 to 44,” “from Age 45 to 54,” “from Age 55 to 64,” and “Age 65 or over.” Last, use the “Format” tab to change the graph type to “Bar” and the stacking option to “Normal.”

Suggested by Diego Mendez-Carbajo.

The FRED graph above shows a data series that has been featured in recent FRED Blog posts: ”Dating a recession,” “Are we in a recession (yet)?” and “Assessing recession probabilities.”

Each data point represents the probability of the US economy being in a recession during the preceding month. In other words, these are backward-looking probabilities. These probabilities can range between 0 (complete confidence the economy is expanding) and 100 (complete confidence the economy is contracting).

As of October 2023, the latest observation available at the time of this writing, the data signaled a 2.2% probability the US economy was in recession during September 2023.

But what about forward-looking probabilities? Organizations of professional forecasters such as Consensus Economics synthesize available economic data to estimate the likelihood of an economic downturn occurring in the near future. Recent research from Christopher Neely at the St. Louis Fed investigates what variables that organization appears to use to predict the probability of recession occurring in the next 12 months.

Neely finds that although 10 economic variables are useful when forecasting recessions, they do not explain the Consensus Economics estimated probability of recession very well. Moreover, Treasury yield spreads are not among those best predictors, though they have a well-established record in predicting recessions. You can learn more about forecasting from yield spreads.

For more about this and other research, visit the website of the Research Division of the Federal Reserve Bank of St Louis, which offers an array of economic analysis and expertise provided by our staff.

How this graph wase created: Search FRED for and select “Smoothed U.S. Recession Probabilities.”

Suggested by Diego Mendez-Carbajo.