Federal Reserve Economic Data

The FRED® Blog

Most unemployment measures are declining…

...while long-term unemployment is still rising

Many of us follow the unemployment rate closely, even more so since the pandemic began. But there are many definitions of unemployment, which depend on how people are attached to the labor force. To learn more, see this earlier blog post and this conversational account of unemployment measures.

Today’s FRED graph shows the recent evolution of 6 measures of unemployment. All increased dramatically, but not uniformly: The lines didn’t move in a parallel fashion—that is, the distance between them didn’t remain constant. Rather, the lines fanned out, showing that it wasn’t one particular type of unemployment that was responsible for the overall surge.

One detail worth noting, though, is that the long-term unemployed, which by definition take some time to accumulate, are still increasing, while all other unemployment groups are decreasing.

As of August, the long-term unemployed made up 5.1% of the labor force. And if the long-term unemployment rate stays high, the general unemployment rate must stay high, too. If the previous recession is any indication, reducing long-term unemployment may take a long time. Adjust the graph sliders to include the time period of the previous recession, and you’ll see what we mean.

How this graph was created: From the Alternative Measures of Labor Underutilization release table (A-15) from the Bureau of Labor Statistics’ Employment Situation release, select all (seasonally adjusted) series and click “Add to Graph.” Adjust the sample period as you wish.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: U1RATE, U2RATE, U4RATE, U5RATE, U6RATE, UNRATE

How much commuting time are we saving by working from home?

A back-of-the-envelope calculation of pandemic-related changes

The FRED Blog has looked at the wide range of commuting times across U.S. cities and counties, as well as the impact of shorter commutes on employment and happiness.

Given that many employees have been working from home during the COVID-19 pandemic, we’ll try to gauge the potential number of hours per week that are no longer spent commuting to work. Clearly, not every employee is working from home these days. So this is a “back-of-the-envelope” calculation, which uses available information to approximate answers to very complex questions.*

First, we use the latest data (which is from 2018) on the average daily commuting time for three suburban counties:

  • 29.57 minutes in DuPage County, IL
  • 24.30 minutes in St. Louis County, MO
  • 32.18 minutes in Fairfax, VA

Next, we multiply those average commuting times by the number of employed persons in those counties. Then we divide each figure by 60 to transform minutes to hours and multiply that by 5 to account for a 5-day workweek.

The FRED graph above shows the potential number of hours per week spent on commuting that are being saved by working from home: Between April and July, that potential weekly time savings in each county ranges from 1 million to 1.5 million hours.

*The physicist Enrico Fermi used this method to great effect in his research and teaching to get rough orders of magnitude. We wonder what Professor Fermi would have been able to accomplish if he had access to FRED… He received a Nobel Prize in physics in 1938 and passed in 1954, long before FRED came to be.

How this graph was created: Search for and select “Employed Persons in DuPage County, IL.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Employed Persons in St. Louis County, MO” and “Employed Persons in Fairfax County, VA.” Next, click on “1Y” above the graph to display the last 12 observations. Next, customize each line by applying the formula (a*average commuting time/60)*5. For Line 1, that formula is (a*29.57/60)*5. Last, from the “Edit Graph” panel, click on the “Format” tab and select line colors and mark types to taste.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: LAUCN170430000000005, LAUCN291890000000005, LAUCN510590000000005

What are the odds? Prices differ between hotels and casino hotels

The FRED Blog recently discussed the large reductions in travel related to the COVID-19 pandemic. Today we expand that analysis to include a specific aspect of travel: hotel stays.

The graph above shows producer price index (PPI) data from the Bureau of Labor Statistics (BLS) that measure price changes from the perspective of the seller. Percent changes in prices from a year ago are shown for both stand-alone hotels (in gold) and hotels attached to casinos (in red).

Seller prices began dropping for stand-alone hotels in February, and the downturn has persisted through the summer: During peak season (June to August) prices were, on average, 17% lower than they were a year ago.

But hotels attached to casinos show an increase for most of this time period. This disparity reflects the different drivers of consumer demand for stays at these two different types of hotel. Moreover, it may reveal different levels of risk aversion during a pandemic among these consumers.

Learn more about COVID-19’s impact across industries from this Economic Synopses by Matthew Famiglietti, Fernando Leibovici, and Ana Maria Santacreu.

How this graph was created: Search for and select “Producer Price Index by Industry: Hotels (Excluding Casino Hotels) and Motels.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Producer Price Index by Industry: Casino Hotels.” Use “Edit Line 1” to change “Units” to “Percent Change from Year Ago” and click “Copy to All” to apply this change to line 2. Use the “Format” tab to select “Graph type: Bars” and select colors to taste.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: PCU7211172111, PCU721120721120


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