Federal Reserve Economic Data

The FRED Blog has discussed research on personal finance topics such as credit card debt, credit scores, and wealth accumulation. Our question today is: What has driven the changes in household debt since the 1990s?

The FRED graph above shows data from the Board of Governors of the Federal Reserve System’s Z.1 Financial Accounts of the United States release about the balance sheets of households and nonprofit organizations. Each stacked bar represents the sum of five categories of loans:

• Home mortgages (the blue segments)
• Consumer credit (the red segments)
• Depository institution loans not elsewhere classified (N.E.C.) (the green segments)
• Other loans and advances (the purple segments)
• Commercial loans (the teal segments)

The value of each type of liability has been divided by disposable personal income to represent its relative size.

There has been noticeable waxing and waning of these liability-to-income ratios, and recent research from Yu-Ting Chiang and Mick Dueholm at the St. Louis Fed explores how these ratios have changed. In the period they study, 1995-2019, mortgages were the single largest household and nonprofit organization liability; and, during the peak years of 2004-2010, all five types of loans amounted to between 120% and 134% of personal disposable income.

Chiang and Dueholm find that increases in the supply of loanable funds between 1995 and 2010 drove the liability-to-income ratios up and those ratios decreased between 2010 and 2019 when the demand for loans decreased.

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 was created: Search the alphabetical list of FRED releases for “Z.1 Financial Accounts of the United States” and select “Table B.101. Balance Sheet of Households and Nonprofit Organizations.” Select the data series “Home mortgages,” “Consumer credit,” “Depository institution loans N.E.C.,” “Other loans and advances,” and “Commercial mortgages.” Customize the data in each of the five graph lines by searching for “Households and Nonprofit Organizations; Disposable Personal Income, Transactions,” adding the series, and applying the formula a/b. 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 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.