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Posts tagged with: "PAYEMS"

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Is the financial sector becoming more productive?

The Great Recession adversely affected employment across all industries. Since the recovery began in 2010, employment has rebounded and the unemployment rate started declining. But this recovery in employment has not been uniform across industries.

Employment in the financial sector has steadily declined as a share of total employment since the onset of the Great Recession. The financial sector averaged around 6.2% of total employment in the ten years preceding the Great Recession, from 1997 to 2007; in the recovery period, from 2010 to 2018, it averaged around 5.7%. It’s also interesting, but perhaps not very surprising, to note that the employment share in financial activities increased through the previous two recessions—in 1991 and in the early 2000s—but fell quite a bit during the Great Recession. And while total employment has grown by nearly 14% in the years spanning the recovery, from 2010 to 2018, financial employment has grown by only 11%.

So the question is, if there are fewer employees in the financial sector relative to the 1990s, how is that impacting output? One way to answer this question is by looking at the value added by the financial industry. The red line on the graph represents value added. It is interesting to note that while value added declined during the recession, it recovered shortly thereafter and has been on an upward trend since then. This implies that industry output has not declined because of slower employment growth, which in turn indicates that other factors must be responsible for this apparent increase in the productivity of labor.

How this graph was created: Search for and select the series USFIRE. From the “Edit Graph” panel, select a quarterly frequency and set the aggregation method to “Average.” Then add the series “PAYEMS” to the same graph and set the formula as a*100/b. Click on the “Add Line” option and search for the series “VAPGDPFI.” In the “Format” tab, scroll down to the formatting options for Line 2 and set the y-axis position to “Right.”

Suggested by Asha Bharadwaj and Miguel Faria-e-Castro.

View on FRED, series used in this post: PAYEMS, USFIRE, VAPGDPFI

How do government shutdowns affect employment?

A look at public and private payrolls

The Bureau of Labor Statistics (BLS) measures payroll employment with their establishment survey—or, more formally, their Current Employment Statistics survey. The establishment survey records the number of jobs on company payrolls on the 12th of each month. The recent partial U.S. government shutdown presents a good opportunity to look at how shifts in government payrolls might affect payrolls overall.

Because the establishment survey does not consider furloughed workers to be off the payroll, any decline in payroll employment as a result of a government shutdown will arise from either quits or formal layoffs in the government sector. Of course, contagion can also occur in the private sector: For example, demand for hotel rooms in Washington, D.C., may be lower until the government reopens or airlines may see a drop in demand if airport security lines become too long. The BLS estimates that the recent partial government shutdown (the longest in history, from December 22 to January 25, 2019) has not substantially affected federal employment. For now, let’s look at three previous government shutdowns highlighted in the FRED graph.

The dark blue line in the graph shows the monthly change in overall nonfarm payroll employment. Keep in mind that changes in overall payroll employment may not necessarily be caused by government shutdowns.
The light blue line shows the monthly change in federal government payroll employment, and the red vertical lines show three long-term government shutdowns: October 1978, November-December 1995, and October 2013.

During these shutdowns, federal employment as measured by the establishment survey didn’t change substantially: +7,000, -21,000, and -14,000, respectively. But shutdowns may have an effect on overall payrolls. The list below shows the changes in payroll employment in the month of, the month after, and the second month after these three previous government shutdowns:

  • October 1978 shutdown: October +335,000, November +435,000, December +280,000
  • November-December 1995 shutdown: November +144,000, December +146,000, January -5,000
  • October 2013 shutdown: October +225,000, November +267,000, December +67,000

As we can see from the data, in previous shutdowns, private payroll employment growth has tended to decrease in the second month after the shutdown, before rebounding a few months later.

During the most recent shutdown, the BLS remained funded and continued to conduct their surveys. In fact, they recently released their January numbers: +1,000 jobs on government payrolls and +304,000 on payrolls overall. If the current experience plays out in the same way as it did in the past, we can expect payroll growth to decline temporarily in March 2019.

How this graph was created: Search for “All Employees Total Nonfarm Payrolls” in FRED and select the series. Next, select the “Add Line” option in the “Edit Graph” menu. Search for “All Employees: Government: Federal” and click “Add Data Series.” From the “Format” tab, move the federal government employees series to the right axis. Using the “Edit lines” menu, click on each line and set the units to “Monthly change, thousands of persons.” Last, go back to the “Add Line” menu and select the link to create a user-defined line. Enter the dates of the three shutdowns in both the start and end categories. For example, for the October 2013 shutdown enter “2013-10-01” in both the start and end boxes. Then set the value start/end to be the max and min values of the left axis. Repeat this for all three shutdowns. Change the colors of the added lines to red on the “Format” menu.

Suggested by Michael Owyang and Hannah Shell.

View on FRED, series used in this post: CES9091000001, PAYEMS

Predicting the payroll employment numbers

Most people look forward to Fridays in general, but data analysts and economists eagerly await one in particular: the Friday when the BLS’s employment situation is published. Two headline figures in this release are the unemployment rate and total nonfarm payrolls. These numbers are still subject to revision after their initial release. For example, the payroll numbers are based on about 70% of the surveyed businesses, and that number gradually increases to about 94% through revisions. Thus, relying on this first release to tell the whole story may be a bit premature, given that something could be changed by the revisions.

ADP is a company that provides payroll services to many businesses. It uses its internal data, as well as other economic indicators, to predict a few days before the BLS’s release what the final payroll number will be. The graph here compares the BLS series (in red) and the ADP series (in blue) and shows that there are some spectacular hits…and misses. Note that the misses could be on either side―too high or too low―as both are imperfect measures. Yet, if both measures agree, that’s a strong indication that they hold some truth.

How this graph was created: Search for nonfarm payrolls, select the relevant series, and click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: NPPTTL, PAYEMS


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