Federal Reserve Economic Data

The FRED® Blog

Above-average wage growth in the leisure and hospitality industry

The COVID-19 pandemic greatly reduced employment in the leisure and hospitality industry, at both the state level and nationally. The recovery in leisure and hospitality was uneven at the regional level; and, at the time of this writing, it hasn’t matched the bounce-back in overall employment. At the same time, wage growth in leisure and hospitality has surpassed overall wage growth.

The FRED graph above shows data on monthly median wage growth, averaged over the preceding 12 months, reported by the Atlanta Fed. The dashed black line applies to all types of economic activities, and the solid red line applies to the leisure and hospitality industry alone. Between December 1997 (when data are first available) and July 2021, labor earnings growth in leisure and hospitality occupations was almost always lower than the all-occupations benchmark. Since then, workers providing arts, entertainment, recreation, accommodation, and food services have recorded higher wage growth than the average worker.

Is a shortage of leisure and hospitality workers driving up those wages? Perhaps, although evidence from other sectors contradicts that straightforward explanation. For example, employment in trade, transportation, and utilities services fully recovered from the past recession ahead of overall employment; and labor earnings growth for those workers is also above average. Manufacturing employment and wage growth data tell a similar story. In short, other factors might be at play here.

To learn more about the labor market landscape in the leisure and hospitality industry, read this March 2023 “Macro Minute” by John O’Trakoun at the Richmond Fed. Notice that you could replicate all the graphs shown in that publication using data in FRED.

How this graph was created: Search FRED for and select “12-Month Moving Average of Unweighted Median Hourly Wage Growth: Overall.” Next, click on the “Edit Graph” button and use the “Add Line” tab to search for and add “12-Month Moving Average of Unweighted Median Hourly Wage Growth: Industry: Leisure and Hospitality and Other Services.” Last, use the “Format” tab to customize the color and style of the graph lines.

Suggested by Sean McQuade and Diego Mendez-Carbajo.

Regional expenditures on entertainment fees and admissions

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The FRED Blog has examined how much people spend on recreation and what industries receive that spending. Today we’re having some more fun with data by comparing regional differences in spending on fees and admissions to entertainment events.

The FRED graph above shows annual data from the Consumer Expenditure Surveys reported by the Bureau of Labor Statistics. Data on household spending on fees and admissions to the arts, movies, sporting events, and other entertainment are available for each of the four regions defined by the Census Bureau. To compare data across regions, we present the dollar amount spent on fees and admissions as a fraction of overall expenditures in entertainment.

Between 1984, when data are first available, and the time of this writing, households in all regions gradually and unevenly spent less on fees and admissions to entertainment events. As expected, the COVID-19 pandemic represented a large-scale disruption to this type of spending.

Also noteworthy is the relatively low proportion of this type of spending in the South region (represented by the blue line) compared with the Midwest, West, and Northeast. This pattern could reflect several factors.

The BLS report on expenditures on fees and admissions to entertainment events by Bonnie Nichols documents higher spending among households with higher levels of educational attainment and income. Other factors beyond those explored in this regional analysis could also play a role: for example, the availability of entertainment options across the country (e.g., museums, amusement parks, sport venues) or regional preferences that could generate higher opportunity costs (e.g., outdoor activities such as boating or hunting vs. watching live sports or attending a concert).

And speaking of concerts, the FRED Blog can’t say for sure if there’ll be a “Taylor Swift effect” on household spending on entertainment during 2023. But be sure to come back and visit when we tour the data again. There’s no admission fee to the greatest show on FRED!

How this graph was created: Search for and select “Expenditures: Entertainment: Fees and Admissions by Region: Residence in the South Census Region.” From the “Edit Graph” panel, use the “Edit Line 1” tab to customize the data by searching for and selecting “Expenditures: Entertainment by Region: Residence in the South Census Region.” Next, create a custom formula to combine the series by typing in “a/b” and clicking “Apply.” Repeat the previous steps to add and customize expenditure data from the three other Census regions.

Suggested by Erica Wu and Diego Mendez-Carbajo.

Explaining the Fed’s recent conventional and unconventional monetary policy

This FRED graph chronicles the advent of “unconventional” monetary policy in the US since the 2007-08 financial crisis and the recent efforts by the Federal Reserve to normalize monetary policy. The graph shows the unemployment rate (in blue), the stock of mortgage-backed securities held by the Federal Reserve (in green), and the effective federal funds rate (in red). While the first series reflects an objective of monetary policy, the other two series reflect instruments of monetary policy: the federal funds rate being a “conventional” instrument and the large holdings of mortgage-backed securities being an “unconventional” instrument.

Three recessions are visible, represented by gray shaded areas. In each recession, the unemployment rate increased and, in response, the Federal Reserve lowered the federal funds rate to stimulate economic activity. After the 2007-08 financial crisis, the federal funds rate became so low (virtually zero) that there was no way to lower it any further. So, the Fed put in place alternative means of stimulating the economy—hence, the label “unconventional monetary policy,” which includes large purchases of mortgage-backed securities starting in late 2008 (again: the green line). These unconventional actions by the Federal Reserve are often referred to as quantitative easing (QE). By purchasing large quantities of assets, the Federal Reserve lowered their yields, which is a mechanism akin to lowering interest rates to stimulate economic activity.

As the unemployment rate declined from its 2010 peak, the Federal Reserve started, in 2016, to “normalize” its monetary policy first by raising the federal funds rate again and then, in 2018, by gradually reducing the stock of mortgage-backed securities it held.

In 2020, the COVID-19-induced crisis prompted the Federal Reserve to lower the federal funds rate to its lowest possible level once again and to further stock up on mortgage-backed securities.

The unemployment rate declined rapidly after 2020 and, in 2022, the Federal Reserve started to raise the federal funds rate and reduce its holdings of mortgage-backed securities again. In this instance, however, inflation (not represented on the graph) played an important role. Until 2021, inflation had changed little, particularly in comparison with the changes in the unemployment rate. With the rise of inflation in 2021, the Federal Reserve had an additional motivation to increase the federal funds rate.

How this graph was created: An “Unemployment Rate” link will likely be under the “Trending Search Terms” heading, which is below the search bar on FRED’s homepage. Click that link and then click the “Unemployment Rate” series link at the top of the results list. Click on “Edit Graph” and then “Add Line.” Use the series code EFFR in the search box and then “Add data series.” Click on “Edit Graph” and “Add Line” again and use the series code WSHOMCB in the search box and then “Add data series.” Click on “Edit Graph” yet again and use the “Format” tab to select “right” as “Y-axis position” for Line 3. Enter 2000-01-01 as the start date above the graph.

Suggested by Guillaume Vandenbroucke.



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