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Help wanted…in measuring the availability of jobs

Posted on April 16, 2015

How easily can firms find workers? How long does it take to hire them? These are crucial questions for economists who study unemployment. Unfortunately, the available data are very bad, but for very good reasons.

The main workhorse models of unemployment include, at their core, “search frictions”—forces that prevent willing workers from matching up with available jobs. The models also rely on the following premises: The more willing workers out there, the more likely an available job is filled. And the more available jobs out there, the more likely a worker finds one. But how does an economist define an available job? Is it a posted job vacancy? In the stylized world of economic models, a worker who is hired fills a vacancy that was posted; the posted vacancy is necessary for the hire. However, as we see in the graph, hires almost always outnumber posted vacancies. Clearly, then, many hires occur without an explicit posting. Elsewhere in the labor statistics world, this reality is acknowledged: Unemployment is calculated every month by asking would-be workers how they searched for a job. Responding to a vacancy is only one of a dozen other methods of searching, including asking friends and relatives. The vacancy posting measure clearly undercounts the number of available jobs.

How this graph was created: Search for and select “Hires: Total Nonfarm, Level in Thousands” (first the seasonally adjusted and then the not seasonally adjusted series) and add them to the graph. To create the ratio, we must add the job openings series. In the “Add Data Series” section, search for and select “Job Openings: Total Nonfarm, Level in Thousands, Seasonally Adjusted” and select “Modify existing series” for series 1 (the smoother blue line, which is seasonally adjusted). Then enter the formula a/b in the “Create your own data transformation” section. Now do the same for series 2 (the rockier red line) with the job openings series that is not seasonally adjusted.

Suggested by David Wiczer.

View on FRED, series used in this post: JTSHIL, JTSJOL, JTUHIL, JTUJOL

The oil and gas extraction boom gives 101%

Posted on April 13, 2015

FRED recently added a large amount of data on industrial production, capacity, and capacity utilization. These series let you dig around to see how various industries are faring. Here we look at an industry that’s been in the news recently: oil and gas extraction. The graph makes it clear that a lot of capacity has been added since the boom in fracking. If you look closely, you’ll notice that capacity utilization (essentially a ratio of production to capacity) was over 100% in June 2014, an impossibility caused by the imprecision of the estimation procedure for both underlying series. Note that June 2014 is also the month when U.S. gasoline prices peaked.

How this graph was created: Search for “capacity oil gas,” and the three series you want should be among your top choices. Select the monthly, seasonally adjusted series and add them to the graph. Use the right axis for capacity utilization to make the graph easier to read.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CAPG211S, CAPUTLG211S, IPG211S

Does the market believe the change in oil prices is permanent?

Posted on April 9, 2015

Oil prices fell dramatically in the last half of 2014, from a high of $107.49 on June 13, 2014, to $54.14 on December 30, 2014, and continued to fall into early 2015. During the same period, a measure of 5-year inflation expectations declined in a similar way. The graph shows the unusual correlation between these two series from January 2014 to the present. The red line is the daily 5-year breakeven inflation rate from the beginning of 2014 to the present. (That breakeven inflation rate is computed from the difference between the 5-year Treasury inflation-protected security, or TIPS, and the 5-year Treasury and is a measure of market expectations of future inflation.) The blue line is the daily price of West Texas Intermediate crude oil.

Market expectations of the inflation rate 5 years out held steady for the most part from early 2013 to early 2014. On April 17, 2014, inflation expectations jumped up. After June 2014, oil prices fell precipitously, taking inflation expectations down with them. After January 27, 2015, oil prices stabilized and began to rise. Again, market inflation expectations rose.

While oil prices can pass through and affect other prices, the almost one-to-one movements in the two series seem to be unusual. Pass-through from oil to other prices is incomplete. If the price increase in oil was deemed to be temporary, the 5-year inflation rate would not move in unison with oil prices (little pass-through). In this case, it appears there’s at least some belief that the change in oil prices will persist, as there is substantial pass-through.

How this graph was created: Search for “crude oil prices,” select the series “Crude Oil Prices: West Texas Intermediate (WTI) – Cushing, Oklahoma,” and graph it on a daily frequency. Select the “Add Data Series” option: Search for “5-year breakeven inflation,” select the first series shown (“5-Year Breakeven Inflation Rate, Daily, Percent, NSA”), and add it as a new series. Select the “Edit Data Series 2” tab and change the y-axis position from left to right. Finally, set the start date to 2014-01-01.

Suggested by Michael Owyang and Hannah Shell.

View on FRED, series used in this post: DCOILWTICO, T5YIE


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