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The FRED® Blog

Quits are recovering

The dynamism of the U.S. labor market was threatened by the Great Recession. Clearly, the unemployment rate rose and stayed elevated. But flows from one job to another fell, with lower rates of hiring for both the unemployed and the already employed. Like the slow-to-recover unemployment rate, job flows also took a while to bounce back. However, not all job flows are the same. “Quits” are a very good sign for the economy. In principle, a worker quits a job only if she has a better offer elsewhere or has a strong belief she can find another job relatively easily. Also, the threat of quitting puts upward pressure on the wages of the currently employed because firms have to compete to keep the best workers. Even if a worker is replaceable, employers may offer a higher wage to avoid the costs of refilling an empty position.

The graph shows how long quits (as a share of total separations) have remained low. However, they seem to have just now recovered to their pre-recession levels, in line with many other labor market indicators such as wages. On the other hand, “layoffs and discharges” are generally ill signals for the economy. The Great Recession strongly affected layoffs and discharges, but their rise dates to 2006—no doubt, in part, because the housing market began declining before the rest of the economy. Layoffs and discharges aren’t quite back to their nadir, but they have dropped below their level at the start of the recession. For workers, this is good news: Often, those who are laid off take a wage cut on reemployment; but on average, those who quit gain a higher wage with the new job.

How this graph was created: For the blue line, search for “JOLTS” + “quits” and add “Quits: Total Private” (monthly, seasonally adjusted, level in thousands) to the graph. Modify this first series by using “Add Data Series” / “Modify existing series” / “Data series 1”  to add “Total Separations: Total Private” (monthly, seasonally adjusted, level in thousands). Then use the “Create your own data transformation” using the formula a/b. For the red line, do the same but search for and add “Layoffs and Discharges: Total Private” (monthly, seasonally adjusted, level in thousands) series. Modify this series (series 2) by again adding “Total Separations: Total Private” and using the transformation a/b. Convert the frequency for both to “Quarterly” to smooth some of the monthly wiggles.

Suggested by David Wiczer.

View on FRED, series used in this post: JTS1000LDL, JTS1000QUL, JTS1000TSL

Energy demand and supply

It’s no secret that energy prices have dropped dramatically, especially for oil and natural gas. The graph above looks at the consumer side and displays the same series under two concepts: real and nominal. The nominal series for personal expenditures (in red) shows that U.S. households are spending much less for energy. The real series (in blue) shows no significant trend. Indeed, energy is a very inelastic commodity: At least in the short run, consumption hardly changes even as its price changes. Fuel is actually a textbook example of an inelastic good, and recent data have proven it again.

Now let’s turn to the producer side. The graph below shows that consumption hasn’t changed much but that employment has taken quite a dive. It looks like this has to do mostly with prospecting and digging of new wells, as investment in this sector seems to have crashed. If this reduction in investment lasts for a while, production capacity will start to decrease, at least in the U.S. This is textbook economics as well: The relatively high price for oil generated a lot of investment in the sector, which has been producing much more than before. These conditions, at least in part, lead to a reduction in the price. The supply is adapting by not adding new capacity and letting existing capacity slowly wear out. The action here is all on the supply side, as demand is very price-inelastic.

How these graphs were created: For the first graph, search for “energy personal expenditures.” You should find the two series shown here: Check them both and click on “Add to Graph.” Restrict the sample period to the past five years. For the second graph, search for “real investment mining” and add the series to the graph. Then search for “employees mining oil” and add that series; set the y-axis for this second series to “right.” Then restrict the sample period to the past five years.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CES1021100001, DNRGRC1M027SBEA, DNRGRX1M020SBEA, E318RX1Q020SBEA

Grandfather of FRED

fred_19610517NEW

Long before FRED, there was Homer Jones.

Jones was the St. Louis Fed’s research director (1958-1971) whose mission to make economic data more accessible to the public eventually evolved into FRED. Jones began his mission 55 years ago by mailing typed reports, starting with the one shown above from May 17, 1961. He pledged to provide these data to “anyone who thinks that such time series may have value” with the chief intention of illuminating “current objectives and measures of monetary policy and action.”

By the late 1970s, the St. Louis Fed was mailing its U.S. Financial Data publication to over 35,000 subscribers in the U.S. and Canada. It was incredibly popular and was even mentioned on the Today Show in the 1970s and cited as a useful source in Money magazine. In the 1980s, the Bank began to offer data over a recorded line. The rest is (FRED) history. St. Louis Fed research directors since Jones have continued to place a high priority on enhancing data services and providing high-quality customer service.

Homer Jones, shown below on the right with former St. Louis Fed president Darryl Francis, is honored in an annual lecture series.

2016 homerjones

Suggested by Katrina Stierholz.

Why is the unemployment rate not decreasing?

The U.S. economy has been adding jobs continuously for several years. In fact, payroll employment growth has been consistently higher than measures of population growth, including the civilian population shown in the graph above. This is definitely an encouraging sign for the health of the labor market. The unemployment rate has steadily decreased over this period, yet it has hardly moved in recent months: It was 5.1% in August 2015 and is 5.0% as of April 2016. With this larger inflow of employed people than people in general, the unemployment rate should decrease, right? That would be correct if the proportion of people in the labor force remained constant. But it has not remained constant, as is visible in the graph below. The labor force participation rate has been increasing significantly in recent months after a decades-long decline. A large number of people who previously declared they were not in the labor force (not working and also not looking for a job) are now in the labor force. Some of these people are unemployed, and these additions to unemployment rolls have been large enough to almost exactly erase the gains made in employment.

How these graphs were created: For the first graph, go to the most popular series (shown on the FRED homepage, under “At a Glance” tab) and click on the payroll employment link there. Then add the civilian noninstitutional population series to the graph. Finally, change the units of both series to “Percent Change from Year Ago.” For the second graph, search for and add “Civilian Labor Force Participation Rate” to the graph, then add the unemployment rate series. Finally, set the y-axis to the right for the latter.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CIVPART, CNP16OV, PAYEMS, UNRATE

More on boundary changes

In our previous post, we mused about changes in country boundaries that can affect the time series of that region, taking up the example of German reunification. Such changes aren’t limited to international borders; they can also occur within a country. FRED has data for subnational units only in the United States. And although state boundaries haven’t changed much, some county boundaries have. The most dramatic changes happen when metropolitan statistical areas (MSAs) are redrawn. In 2010, the U.S. Census Bureau removed Scott County from the Louisville MSA, dropping about 24,000 residents, which can be seen clearly in the graph above.

The Census Bureau doesn’t recalculate population time series, but the Bureau of Labor Statistics adopts new definitions (after a delay) and recalculates its statistics. This is visible in the graph below, which shows the same series, total non-farm employees, with two data vintages. The ALFRED site allows us to see how data are revised over time and in this case shows the data as of March 17, 2014, and March 25, 2016. The difference is the deletion of Scott County.

How these graphs were created: For the first graph, simply search for “Louisville population.” For the second, Go to ALFRED, search for “Louisville employment,” change the graph type to “line,” expand the sample period to 10 years, and change the earlier vintage to 2014-03-17.

Suggested by Christian Zimmermann

View on FRED, series used in this post: LOINA, LOIPOP


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