Federal Reserve Economic Data

The US Census reports the monthly value of retail sales grouped into 12 kinds of business activity. The terms for those activities, or industries, come from the outlets where the goods are sold. Think, gasoline stations, sporting goods stores, and home furnishings stores. But when consumers change where they go to purchase these products, the value of retail sales by industry may not tell a complete story about consumer spending.

The FRED graph above shows the inflation-adjusted value of retail sales in two kinds of businesses: furniture and home furnishing stores (the blue line) and electronics and appliance stores (the red line). The data are first available in 1992. Between then and 2008, they had similar values and identical directions of change during expansions and recessions (shaded areas in the graph), which strongly suggests consumers bought home furniture and appliances in synch.

After 2008, however, retail sales at electronics and appliance stores steadily declined while retail sales at furniture and home furnishings stores bounced back. Why? A number of reasons can help explain why consumers aren’t shopping at brick-and-mortar electronics and appliance stores as much as they used to:

• Consumers are using the online ordering portals of those traditional stores. As described by Jessica Nicholson at the US Department of Commerce, these sales are classified as non-store retail transactions.
• They’re buying online from businesses that do not have physical outlets and offer home delivery or pickup options at alternative locations, such as grocery stores or standalone lockers.
• And they’re patronizing big-box stores that cater to many different consumption needs under one roof and in large quantities. These retail sales are reported under “warehouse clubs and superstores,” a subcategory of general merchandise stores.

In short, the declining value of retail sales at electronics and appliance stores likely reflects consumer foot traffic redirected to other types of physical outlets, as well as its replacement by online browsing and digital shopping carts.

How this graph was created: Search FRED for and select “Retail Sales: Furniture and Home Furnishings Stores.” From the “Edit Graph” panel, use the “Edit Line” tab to customize the data by searching for “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average.” Don’t forget to click “Add.” Next, type the formula (a/b)*100 and click “Apply.” Next, use the “Add Line” tab to search for and select “Retail Sales: Electronics and Appliance Stores.” Repeat the steps described above to customize the data and adjust them by consumer price inflation.

Suggested by Diego Mendez-Carbajo

Life is full of choices. For example, should we go to the concert or the baseball game?

This question is most likely decided by whoever has the greatest bargaining power in the household, and the FRED graph above provides some history on this decision over the past 65 years.

Expenditures for live entertainment are split into sports (in blue) and all the rest (in red). While the overall result is roughly 50-50, there’s some variation over time. In 1971, 73% of spending on live entertainment went to sports, while in recent years it’s been barely more than 40%. What changed?

Did the rise in cable TV and then the Internet change preferences? Did the household power structure change? Or is it because nowadays it’s easy to see concerts in baseball stadiums? This is one of the many examples of interesting data questions without a definitive answer.

How this graph was created: Search FRED for “personal consumption expenditures admissions” and select the sports series. Click on “Edit Graph,” open the “Add Series” tab, search again, and select the live entertainment excluding sports series. Open the “Format” tab, select bar graph with percent stacking.

Suggested by Christian Zimmermann.

Economists measure labor market tightness as the number of job vacancies per unemployed worker, which is a key factor in monetary policymakers’ decisions.

In the FRED graph above, the blue line shows the seasonally adjusted number of job openings as a fraction of the number of workers in the labor force since January 2020, just before the pandemic. The red line shows the seasonally adjusted unemployment rate. The shaded area shows the onset of the pandemic-related recession, when job postings declined and the unemployment rate jumped to a historically high 15%. But soon after, the unemployment rate declined sharply and job openings became more abundant.

The second graph, below, shows labor market tightness as the ratio of job openings to unemployment. This captures how many job opportunities there are for each person seeking a job. Labor market tightness reached around 2 in early 2022, meaning a very tight labor market with two job openings for each unemployed worker. During this period, many firms faced high demand; as a result, they attempted to hire many workers. Since then, the labor market has cooled significantly, with recent labor market tightness approaching one job for each person seeking a job. This coincides with a general cooldown in demand.

The current labor market is slightly less tight than it was right before the pandemic, though it still remains very tight by historical standards.

How these graphs were created: For the first graph, search FRED for and select “Job Openings: Total Nonfarm (JTSJOR).” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Unemployment Rate (UNRATE).” Finally, adjust the time series to be from 2020-01-01 to 2024-08-01.
For the second graph, search FRED for and select “Job Openings: Total Nonfarm (JTSJOR).” From the “Edit Graph” panel, go to the “Customize data” field and search for “Unemployment Rate (UNRATE)” and click “Add.” Then, in the “Formula” field type a/b and select “Apply” to obtain the ratio of job openings to the unemployment rate. Finally, adjust the time series to be from 2020-01-01 to 2024-07-01.

Suggested by Mick Dueholm and Serdar Ozkan.