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Posts tagged with: "CLF16OV"

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Losing your job doesn’t always gain you unemployment benefits

Requirements and trends behind unemployment insurance

Being unemployed does not guarantee that you’ll receive benefits from your local unemployment insurance program. Typically, there are eligibility criteria, such as previous work requirements, waiting periods, eligibility periods, and asset tests. These criteria can be stringent, depending on the political choices behind them. The graph above compares the U.S. unemployment rate with the segment of the labor force receiving unemployment insurance benefits. It is very clear that, most of the time, only a minority of the unemployed receive benefits.

The graph below focuses on that segment, showing the proportion of the unemployed that receives insurance benefits. Obviously, there are cyclical variations: At the start of a recession, proportionally more unemployed haven’t yet run out of eligibility. There also appears to be a longer-run trend that has been decreasing the segment of those eligible for benefits.

Update: The insurance claim numbers cover those who get regular state unemployment insurance benefits. There are also those who get benefits under the extended benefit and the emergency unemployment compensation programs, whose proportions tends to be higher during recessions. See this article for an analysis of these details.

How these graphs were created: Search for “unemployment insurance claims” and click on the series. From the “Edit Graph” section, add the “civilian labor force” series and click on “Apply.” Then enter formula a/b/10 (where the 10 makes it a percentage). Then open the “Add Line” tab and search for the unemployment rate; take the monthly, seasonally adjusted series. That’s the first graph. For the second, remove the line you just added, but add that series to the first line and apply formula a/b/c*10.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CCSA, CLF16OV, UNRATE

Measure for measure: Judging the economy

How do you know if the economy is improving? FRED has plenty of commonly used data to help you. Typically, you’d measure real gross domestic product (GDP)—in particular, its growth rate. This rate is almost always positive. Because population growth is also almost always positive, this isn’t too surprising. So FRED lets you measure real GDP per capita—that is, GDP divided by the population.

But let’s complicate matters, because economies can go through demographic transitions. In fact, because many industrialized countries now face a substantially older population, dividing GDP by the overall population may not be precise either. So, FRED lets you divide GDP by the working age population, age 15 to 64. FRED even lets you refine the measurement by considering only the working age population in the labor force—that is, by excluding those who choose not to work or who cannot work. Finally, FRED lets you measure only those who are actually working by excluding the unemployed still looking for work. (By the way, dividing real GDP by the working population corresponds to labor productivity.)

The graph above shows these five different measures of U.S. economic growth since 1948. Each has its merits, but their growth rates look remarkably similar—so much so that it may not seem worthwhile to distinguish between them. One possible exception is the last series, since the working population fluctuates much more than any of the other population measures.

How this graph was created: All five lines use real GDP, so add real GDP to the graph five times. For line 1, change units to “Percent Change from Year Ago.” For line 2, add the monthly population series, apply formula a/b, and change the units again for this new, transformed line. Repeat this for the remaining lines by searching for and selecting the other population data to divide with. Finally, use the “Format” panel to remove the many axis titles.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CLF16OV, GDPC1, LFWA64TTUSQ647S, PAYEMS, POP

Government employment in the US

This graph shows government employment as a share of the civilian labor force. The blue line is local government, the red line is state government, and the green line is federal government not including the postal service. (The latter two series use the right scale.) The regular upticks for federal employment correspond to temporary census workers. The recent evolution, however, looks uncharacteristic: local and state employment are currently on a slide that has not been seen since the late 1970s. On the other hand, federal employment follows abump up, likely as a result of the stimulus program.

How this graph was created
: Select Civilian Labor Force, add the series Local Government Employment to same line, then apply the equation b/a. Repeat for the other two series, selecting the scale to the right. Adjust the sample to start in 1955. Save.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CES9091100001, CES9092000001, CES9093000001, CLF16OV

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