Federal Reserve Economic Data

Happy Halloween!

In the past, we’ve covered the cost of candy, costumes and pumpkins. Today we celebrate this holiday by showing how any FRED user can conjure economic oddities from the dark corners of FRED’s database.

Our first FRED graph above, in the form of a 10-legged spider, tracks resident population data for 5 spooky US counties:

• Graves, Kentucky
• Erie, Pennsylvania
• Crow Wing, Minnesota
• Lac qui Parle, Minnesota
• Malheur, Oregon

The last 2 counties are extra-spooky French names: “Lake that Speaks” and “Misfortune.”

Speaking of misfortune, our second FRED graph, above, uses data from the Centers for Disease Control and Prevention to reveal the rate of premature deaths in these counties. These data adjust for the age distribution in any given county compared with a standard county, as explained in this FRED Blog post.

The CDC defines premature death as any death before the average age of death in the US population. These malheurs can be accidents, diseases, murders, and other unnatural fatal occurrences that send people to their graves.

Speaking of Graves, that county in Kentucky has the highest rate of premature deaths among the 5 counties in this haphazard list.

How this graph was created: For the first graph: Search FRED for and select “graves resident population.” From the “Edit Graph” panel, use the “Add Line” tab to search for and add the same series for Crow Wing, Lac qui Parle, Malheur, and Erie. Under “Units,” choose “Index,” with 1991-03-01 as the date, and click “Copy to All.” For the second graph: Search for and select “graves premature deaths” and select the age-adjusted series. Add the same for the rest of the counties.

Suggested by George Fortier and Christian Zimmermann.

Our previous FRED Blog post covered declining sales at electronics and appliances stores that may be due to consumers’ increased online shopping and diverted foot traffic. Today, we examine those expenditure patterns more closely by comparing recent US Census data on retail sales at general merchandise stores.

The FRED graph above shows the value of inflation-adjusted retail sales at three different types of general merchandise stores—where a broad selection of different types of goods can all be purchased under one roof. There are three types of brick-and-mortar stores in this industry:

• Department stores (the blue line) are both the anchor stores at traditional shopping malls and the large stores where no single merchandise line predominates.
• Warehouse clubs and superstores (the red line) are the big-box, standalone stores where paying members can purchase food in bulk, along with apparel, furniture, and appliances.
• All other general merchandise stores (the green line) include establishments such as “dollar” and variety stores.

The data are first available in 1992. Between then and 2024, the general merchandise store industry has experienced very large changes. Department stores were once the primary home for consumer spending in items such as apparel, jewelry, home furnishings, and toys; but they have shrunk to the retail size of “dollar” and variety stores.

Consumer spending based on foot traffic is now primarily headed to warehouses and superstores. As of July 2024, retail sales at those businesses amount to almost three times the combined sales at all other types of general merchandise stores.

How this graph was created: Search FRED for and select “Retail Sales: Department 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: Warehouse Clubs and Superstores.” Repeat that step to add a third data series to the graph: “Retail Sales: All Other General Merchandise Stores.” Lastly, repeat the steps described above to customize the data in Line 2 and Line 3 and adjust them for consumer price inflation.

Suggested by Diego Mendez-Carbajo.

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