Federal Reserve Economic Data

Successful vaccines are bringing the pandemic effectively to an end. And, as economic activity resumes, firms everywhere appear to be having serious difficulties hiring: The news is filled with middling labor market reports, alarming anecdotes, and long restaurant wait times.

The FRED graph above quantifies this shift by depicting, across industries, the number of job openings at the end of each month. It’s very clear that across the board this number has jumped significantly, especially in the past few months.

Such a jump is a very positive development for the U.S. economy. The number of job openings at any given time is affected by both how difficult it is for firms to fill openings and how many openings firms offer in the first place. Insofar as the recent increase is caused by firms expanding, it’s clear that firms are expecting future business growth. Consult the graph to compare the recent jumps to the much lower number of job openings after the Great Recession and its lengthy “jobless recovery.”

The industry-level numbers are noteworthy: The leisure and hospitality industry, frequently the focus of news stories about worker shortages, has had the largest increase in job openings. But the other industries aren’t far behind, despite being significantly less impacted by the pandemic, as data from the same data release show. While leisure and hospitality lost over 3 million jobs in 2020, the others lost less than a million. Chalk that up to the pandemic’s lopsided impact on these various industries.

Stay tuned for part 2 of this post: “The health of labor markets post-pandemic: The supply perspective.”

How these graphs were created: First graph: Search for and select “Job openings: Health care and social assistance.” From the “Edit Graph” panel, use the “Add line” tab to search for and add “Job openings: Leisure and Hospitality.” Do the same for “Job Openings: Manufacturing” and “Job Openings: Trade, Transportation, and Utilities.” Adjust the date range to mirror the dates shown in the blog post. Second graph: Search for and select “Hires: Health Care and Social Assistance.” From the “Edit Graph” panel, use the “Edit Line” tab to modify frequency to be annual with “Aggregation method” set to “Sum”; then add “Total Separations: Health Care and Social Assistance” under “Customize data” and set “Formula” to “a-b.” Do the same for the other industries (“Leisure and Hospitality,” “Manufacturing,” and “Trade, Transportation, and Utilities”), adding hires for each using the “Add line” tab and then repeating the steps above. Finally, go to the “Format” tab and set “Graph type” to “Bar” and set the date range to mirror the dates shown in the blog post.

Suggested by Carlos Garriga and Devin Werner.

FRED just added a new family of data that can help us get a read on the U.S. economy.

The BBKI (Brave-Butters-Kelley Indexes) draw on about 500 indicators and search for some commonality among them, thanks to a technique called dynamic factor analysis. This analysis allows for an estimate of monthly GDP and decomposes it into different components. (GDP measures are typically quarterly, and this innovation is meant to be more timely.)

The graph above shows the monthly GDP estimate along with the coincident and leading indicators for a period spanning the past two recessions. Clearly, the leading indicator was able to accurately determine the direction of the changes in this current and strange recession. Anticipating the turning points, of course, is very difficult in forecasting.

The graph below shows a decomposition of the monthly GDP indicator into various components:

1. a trend, which varies very little through time
2. a leading component—that is, which current data will influence future GDP
3. a lagging component that was largely determined from the previous period
4. a cycle component—that is, a deviation from the trend that has some persistence
5. and an irregular component of random events and one-offs with no persistence.

As with many graphs lately, things are a little bit difficult to distinguish because of the scale of the data in our current environment. So let’s concentrate on the past year. The graph below shows that the large swings in 2020 were due to different components. The large downturn was due to the cyclical and irregular components, but the large upswing was mostly irregular, which then swings back down. This back and forth isn’t cyclical, at least not at the frequency that economists typically think a business cycle should last (2 to 8 years). And indeed, these wide swings didn’t have any economic fundamentals; they were tied to the evolution of health-related concerns.

How these graphs were created: Start from the BBKI release table, check the series you want displayed, and click “Add to Graph.” Adjust the time period to taste.

Suggested by Christian Zimmermann.