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The unemployment bathtub

Economists often find a bathtub to be a useful metaphor for the behavior of unemployment. There’s some inflow of newly unemployed workers and some outflow as workers find jobs. A classic way to measure the inflow has been with initial claims of unemployment benefits, the blue line, in which we see spikes at the start of each recession. This inflow of newly unemployed persons initially reduces the mean duration of unemployment, the green line. But the green duration line rises as the blue initial claims line falls—since people who become unemployed early in the recession and remain so are unemployed for a while by the time the recession winds down. Every recession follows this pattern: Claims peak, then unemployment peaks, then duration peaks. The logic is essentially that of the bathtub: First it fills quickly; then, after some time, it begins to drain. But as this is happening, those left in tub have been there longer and longer.

How this graph was created: Search for and select the 4-week moving average of initial claims. Set its units as an index with scaled value of 100 at the 2007 pre-recession peak. Then use the “Add Data Series” option to add the other two series: the seasonally adjusted civilian unemployment rate (with the same units as the first series) and the seasonally adjusted mean duration of unemployment (with the same units as well).

Suggested by David Wiczer.

View on FRED, series used in this post: IC4WSA, UEMPMEAN, UNRATE

Is the PPI going crazy?

The graph above shows the producer price index since 1913. It measures the cost of items used in the production process and is thus different from the consumer price index, which measures the cost of final goods to consumers. Two aspects of the graph are striking: Prices have increased quite a bit since 1913, and prices in recent years seem to be subject to wild fluctuations. There’s no doubt the ups and downs of commodity prices such as oil and metals have an effect here, but are the recent years really as wild as they look?

In part, the second observation is a consequence of the first. Prices now are roughly 18 times greater than those in 1913. So a 1% increase will look 18 times larger now than before. This “optical illusion” can be fixed in two ways. 1. Look at percent changes. The first graph below shows these changes from the same month a year before, which takes care of any potential seasonal effects. Recent fluctuations are indeed somewhat larger than in preceding decades, but they’re nowhere close to the large fluctuations in the first years of the series. 2. Look at natural logarithms. The second graph below includes a transformation so that any change in the series looks the same in relative terms: that is, a 1% increase looks the same in 1913 and 2015. Again, we see that the fluctuations were much larger in the early years.

How these graphs were created: Search for and select the PPI for the first graph. Change the units to “Percent Change from Year Ago” and you have the second graph. For the third graph, start with the first graph, choose “Create your own data transformation,” and select “Natural Log” among the transformations.

Suggested by Christian Zimmermann

View on FRED, series used in this post: PPIACO

The Great Recession and trade collapse: Comparing Missouri and the nation

The Great Recession (Dec. 2007 to June 2009) not only shrunk U.S. GDP and employment levels, but also dramatically diminished international trade. This trade collapse was both national and global. But it also had regional effects. So we highlight the trade collapse’s effect on the state of Missouri and compare that with the decline for the U.S. as a whole.

The graph above maps the number of exporting firms, which is a measure of the extensive margin for exports. The graph below maps average export revenues, which is an approximation of the intensive margin, which refers to an individual firm’s exports. The total real value of U.S. exports dropped 17.9 percent between 2008 and 2009. The number of U.S. exporting firms and the real value of average exports dropped by 4.5 percent and 14.1 percent, respectively, during the same period. Missouri saw much sharper declines over the same period: 25.4 percent (total exports), 6.6 percent (number of exporting firms), and 20.2 percent (average exports).

What is intriguing is that changes to the number of exporting firms in Missouri roughly align with the national situation, but Missouri’s average export revenues exhibit some interesting differences. The nation’s average export revenues peaked in 2007, fell slightly in 2008, fell sharply in 2009, and smartly recovered after that. Missouri’s average export revenues, on the other hand, peaked in 2006 and then fell quite sharply until 2009; then they had a rather anemic recovery after that. This stark difference in Missouri’s intensive margin (relative to the nation) is worthy of further attention.

How these graphs were created: For the top graph, search for and select “number of exporters to all countries from the United States” and use the “Add Data Series” option to search for “number of exporters to all countries from Missouri” and add that series to the graph. Place the Missouri series on the right y-axis. For the bottom graph, search and select “CPI all consumers”: Under the “Frequency” menu, select “Annual.” Under the “Units” menu, select “Index (Scale value to 100 for chosen period)” and set the “observation date” to 2009-01-01. This preliminary step is necessary to report the value of exports in 2009 dollars. Now modify the existing series by adding two series, “value of exports to all countries from the United States” and “number of exporters to all countries from the United States,” through the “Add Data Series / “Modify Existing Series” options. Finally, use the “Create your own data transformation” option and insert the formula (b/a)*100/c. Repeat the same steps with the corresponding Missouri series and place this new series on the right y-axis.

Suggested by Subhayu Bandyopadhyay and Rodrigo Guerrero

View on FRED, series used in this post: CPIAUCSL, MOWLDA052SCEN, MOWLDA475SCEN, USWLDA052SCEN, USWLDA475SCEN

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