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Employment’s ebb and flow

When the unemployment rate rises, it’s partly that more employed persons are losing their jobs and partly that fewer unemployed persons are finding jobs. Although there’s no consensus on which is more important, some research finds that the flow of persons from unemployment into employment accounts for the lion’s share of changes in the unemployment rate. These unemployment-to-employment flows are cyclical and fell starkly in the Great Recession, as the graph above shows. Persons may also flow from outside the labor force (neither employed nor unemployed) directly into employment; this is also an indicator of the ease or difficulty of getting a job. This flow from “nonparticipation” to employment is expected: Some persons who’d like a job aren’t formally searching and so aren’t counted in the BLS measurement of unemployment. There are also new entrants into the workforce, such as recent graduates and parents returning after a hiatus for child care. The flow from nonparticipation into employment (that is, the proportion of nonparticipants taking a job) is much lower than the flow from unemployment to employment (graph above), but the two series track each other nearly perfectly in their cyclical fluctuations (graph below).

How these graphs were created: Search for Labor Force Flows and select the (seasonally adjusted) “Unemployed to Employed” and “Not in Labor Force to Employed” series and add them to the graph. Then in the “Add a Data Series” section, search for “Unemployed” (monthly, thousands of persons, seasonally adjusted) and select “Modify existing series” for series 1. Repeat these steps with “Not in Labor Force” for series 2. For both series, in the “Create your own data transformation” section, apply the formula a/b. Start with the first graph to create the second, but change the y-axis of series 2 from left to right. Note: These measures of the rates of flow aren’t precise because of “time aggregation bias”: That is, these measures compare employment status at two points in time (the beginning and the end of the period), but they don’t take into account any changes in employment that may have occurred between those two points.

Suggested by David Wiczer.

View on FRED, series used in this post: LNS15000000, LNS17100000, LNS17200000, UNEMPLOY

Help wanted…in measuring the availability of jobs

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%

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


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