It’s common to think about cost of living when thinking about moving to a new city. One way to measure cost of living is through regional price parities (RPP), which are price levels for a region expressed as a percentage of the overall price level for the nation. An RPP of 100 is equal to the national average.

In 2022, Missouri had an RPP of 91.119, indicating a cheaper cost of living than the national average.  California’s RPP of 112.47, on the other hand, indicates a higher-than-average cost of living. Check your state’s score with this FRED map.

For a closer look, we can break down RPP into smaller components: Our first FRED map (above) shows the RPP for goods, and our second FRED map (below) shows the RPP for housing. As these two maps show, the differences in costs of living across the nation vary by component: There’s significantly less variation across states for goods than there is for housing.

One reason for this could be the differences in mobility for goods and housing. Goods are clearly easier to trade and ship around the country, which minimizes the differences in the cost of goods across states. But it’s difficult to move a house to a new location, and the location itself greatly affects the price. There are also many reasons people don’t choose to move to exploit lower housing prices elsewhere, which limits the ability for markets to equalize housing prices across states.

How these maps were created: First map: Search FRED for and select “Regional Price Parities: Goods for Missouri” (series: MORPPGOOD). In the right-hand corner, click the green “View Map” button then the red “Edit Map” button. Select 5 for the number of color groups and “User Defined Method” in the “Data grouped by:” dropdown menu. Change the bins to 69, 79, 97, 121, and 177. Click “Apply Intervals.” For Second map: Search FRED for “and select Regional Price Parities: Services: Housing for Missouri” (series: MORPPSERVERENT). Click “View Map” and “Edit Map” and again select 5 for the number of color groups. Select “Fractile Method” in the “Data grouped by:” dropdown menu.

Suggested by Maximiliano Dvorkin and Cassie Marks.

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.