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Posts tagged with: "PAYEMS"

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When initial claims, unemployment, and payroll employment clash

Not long ago, the FRED Blog discussed several details about the construction and interpretation of the data for initial weekly claims for unemployment benefits. As of May 30, FRED shows that the four-week moving average was 2.3 million new claims. Yet, the FRED graph above shows that for the entire month of May 2020, there was a decrease in the number of persons unemployed. And there was also a simultaneous increase in the level of payroll employment. How is all this possible?

First of all, data related to the labor market come from different sources: The U.S. Employment and Training Administration reports the number of initial weekly claims for unemployment benefits; and the U.S. Bureau of Labor Statistics, through the Current Employment Statistics (Establishment Survey), reports the payroll employment and unemployment figures.

Also, the data series have similar names but represent different concepts. Even if you file an initial claim for unemployment benefits, for example, it does not necessarily mean that you will be counted as unemployed.

Finally, keep in mind that changes in the number of persons listed on payrolls do not correspond to changes in the number of persons employed or unemployed. The FRED graph below shows that during May, June, July, and October of 2019 there were simultaneous increases in the level of payroll employment and increases in the number of persons unemployed.

How these graphs were created: Search for and select “All Employees, Total Nonfarm” anuse the “Edit Graph” menu to add two more lines: “Unemployment Level” and “Employment Level.” Next, change the units of any of the three series to “Change, Thousands of Persons” and click on “Copy to all.” Lastly, change the graph format to “Bar,” edit the colors to taste, and change the date range to match the time periods of each graph.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: CE16OV, PAYEMS, UNEMPLOY

Is the decline in manufacturing economically “normal”?

Deciphering the phases of economic development

The FRED graph above tracks the proportions of employees working in three industries—construction, mining and logging, and manufacturing—since 1939. Construction (the blue line) has remained roughly horizontal. Mining and logging (the green line) has steadily declined. And manufacturing (the red line) has noticeably declined as well. This trend may look like weakness for the U.S. economy, but is it something to worry about?

Let’s take a step back: Historically, economic development has led to a declining share of workers in goods-producing sectors. The first sector to decline is agriculture,* whose workers moved to manufacturing and mining during the Industrial Revolution (which pre-dates our graph by a century or so). In the 19th century and beyond, the U.S. economy grew further and progressed to the next phases of development, with mining and manufacturing losing relative importance.

So if the U.S. economy is growing, where is it growing? The graph below shows the service sector has taken up the slack. At the start of the graph, in 1939, this sector had already made up 50% of non-farm employees, and it has continued to grow. The remaining sector, government, has remained relatively flat over the 80 years of this data series. Clearly, the U.S. economy is now much less focused on “making things.” Rather, the emphasis is now on education, health, leisure, retail, information, and finance.

How these graphs were created: Search the Current Employment Statistics release table and choose Table B-1 (seasonally adjusted); select the series you want and click “Add to Graph.” From the “Edit Graph” panel, for each line add series “All employees, non-farm” and apply formula a/b*100.

*Why don’t we show agricultural employment here? For one thing, it’s really hard to count: Many are part-time/seasonal workers and relatives that work on family farms.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CES0800000001, MANEMP, PAYEMS, USCONS, USGOVT, USMINE

The big February employment miss

The Bureau of Labor Statistics (BLS) released its most recent employment report on March 8: In February of this year, the nonfarm economy, on net, created only 25,000 private-sector jobs and 20,000 jobs overall. One part of this report is the establishment survey, which contributed some of the weakest numbers since the past recession.

Forecasters failed to predict these anemic jobs numbers. In fact, before the report’s release, consensus market expectations foresaw 180,000 jobs being created in February. Thus, consensus expectations “missed” the February payroll number by 160,000 persons on the downside. The household survey component of the report was stronger, with the unemployment rate declining by 0.2%. According to the BLS, this decline in unemployment “reflects, in part, the return of federal workers who were furloughed in January due to the partial government shutdown.”

Another well-known statistic used to predict the BLS jobs number is the ADP national employment report. It’s produced by the ADP Research Institute, which is part of ADP. (ADP is an American company that provides human resource and payroll management software and measures nonfarm private sector employment using anonymous data from its clients.) The ADP report, which is released monthly a few days before the BLS employment numbers come out, predicted an increase in private nonfarm payroll of 183,000 in February. Thus, the ADP report differed from the BLS number by 158,000. This difference was one of the largest in 14 years, relative to forecast errors in months outside of U.S. recessions.

FRED provides an integrated picture of all this in the graph above, which combines three monthly series: BLS total nonfarm payroll, BLS government employment, and ADP total private nonfarm payroll. All three are presented as their month-over-month changes in thousands of persons. We combine the three series by first taking the difference of the first two. This gives the change in private nonfarm payroll according to the BLS. Second, we construct a “forecast error” by subtracting the ADP number (which we use as our forecast) from the actual BLS private payroll number.

Excluding five months during the recession (the shaded section in the graph), the downside difference between the BLS and ADP numbers for February employment was larger than the downside difference of any other month since 2003.

How this graph was created: Search for “total nonfarm payrolls,” select the series “All Employees: Total Nonfarm Payrolls,” and click “Add to Graph.” From the “Edit Graph” panel, use the “Edit Line 1” feature to “Customize data”: In this field, enter “government employees.” From the list of options, choose “All Employees: Government” and click “Add.” Again under “Customize data,” search for and add “Total Nonfarm Private Payroll Employment.” The first two series are from the BLS and the third is from ADP. For each of these three series, adjust the units to “Change, Thousands.” Next, compute the series shown here by subtracting the other two. FRED denotes the variables for each series in order: So, enter “a-b-c” into the “Formula” box and click “Apply.”

Suggested by Bill Dupor.

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


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