Federal Reserve Economic Data

The federal minimum wage hasn’t changed since 2009. But in those 15 years, the real value of $7.25 per hour has changed considerably due to inflation, especially in the past few years.

The FRED graph above uses data from the Bureau of Labor Statistics to show the federal minimum wage adjusted for inflation using the consumer price index, or CPI.

Keep in mind that the CPI data here correspond to actual prices in 1982-84. Thus, the real federal minimum wage (the blue line) is measured relative to the prices of that period. But it is the visual here that really matters: The real federal minimum wage has never been as low as it is now, at least within the period shown in this graph. (If you move the date tracker below the graph, you’ll see that it was lower in 1947-49.)

Of course, the federal minimum wage is only impactful if the state-level minimum wage is not set higher. And most states do set a higher minimum wage. This FRED map identifies each state’s approach to the minimum wage, including some that do not set one at all.

Now, the lower a minimum wage is, the less it’s going to matter in raising overall wages. And the fewer employees who earn that minimum wage, the less it will affect the US economy. The line in red shows the proportion of US employees who earn the minimum wage or less. (There are exceptions for certain professions.) The red and blue lines do indeed follow a very similar downward path.

How this graph was created: Search FRED for “minimum wage.” Use the “Edit Graph” panel to add the series “CPI”; change the frequency to annual, taking averages; and apply formula a/b*100. Use the “Add Line” tab to search for and select the “paid at or below minimum wage” series. Use the “Format” tab to move the y-axis for one of the series to the right. Start the sample period in 1979.

Suggested by Christian Zimmermann.

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.