November | 2015 | FRED Blog | Page 2
Skip to main content
Explore Our Apps
FRED
Tools and resources to find and use economic data worldwide
FRASER
U.S. financial, economic, and banking history
ALFRED
Vintages of economic data from specific dates in history
CASSIDI
View banking market concentrations and perform HHI analysis
Release Calendar
Tools
FRED Add-in for Excel
FRED API
FRED Mobile Apps
News
Blog
About
What is FRED
Tutorials
Digital Badges
Contact Us
My Account
Explore Our Apps
Explore Our Apps
FRED
Tools and resources to find and use economic data worldwide
FRASER
U.S. financial, economic, and banking history
ALFRED
Vintages of economic data from specific dates in history
CASSIDI
View banking market concentrations and perform HHI analysis
STL Fed Home Page
Release Calendar
Tools
FRED Add-in for Excel
FRED API
FRED Mobile Apps
News
Blog
About
What is FRED
Tutorials
Digital Badges
Contact Us
Search FRED Blog
Search for:
Recent Posts
US manufacturing employment is down, but each state has its own story
Real GDP growth by state: First quarter 2026
Primary deficits: a short history
What US assets are held overseas?
Durable goods inflation and effective tariffs
Recent St. Louis Fed research
US manufacturing employment is down, but each state has its own story
Real GDP growth by state: First quarter 2026
Primary deficits: a short history
What US assets are held overseas?
Durable goods inflation and effective tariffs
Archives
July 2026
June 2026
May 2026
April 2026
March 2026
February 2026
January 2026
December 2025
November 2025
October 2025
September 2025
August 2025
July 2025
June 2025
May 2025
April 2025
March 2025
February 2025
January 2025
December 2024
November 2024
October 2024
September 2024
August 2024
July 2024
June 2024
May 2024
April 2024
March 2024
February 2024
January 2024
December 2023
November 2023
October 2023
September 2023
August 2023
July 2023
June 2023
May 2023
April 2023
March 2023
February 2023
January 2023
December 2022
November 2022
October 2022
September 2022
August 2022
July 2022
June 2022
May 2022
April 2022
March 2022
February 2022
January 2022
December 2021
November 2021
October 2021
September 2021
August 2021
July 2021
June 2021
May 2021
April 2021
March 2021
February 2021
January 2021
December 2020
November 2020
October 2020
September 2020
August 2020
July 2020
June 2020
May 2020
April 2020
March 2020
February 2020
January 2020
December 2019
November 2019
October 2019
September 2019
August 2019
July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
The FRED® Blog
← Older posts
Newer posts →
The unemployment bathtub
Posted on November 16, 2015
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
Tagged IC4WSA, UEMPMEAN, UNRATE
--
Is the PPI going crazy?
Posted on November 12, 2015
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
Tagged PPIACO
--
The Great Recession and trade collapse: Comparing Missouri and the nation
Posted on November 9, 2015
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
Tagged CPIAUCSL, MOWLDA052SCEN, MOWLDA475SCEN, USWLDA052SCEN, USWLDA475SCEN
--
← Older posts
Newer posts →
Back to Top
Filter
0
Subscribe to the FRED newsletter
Subscribe
Follow us
Saint Louis Fed linkedin page
Saint Louis Fed facebook page
Saint Louis Fed X page
Saint Louis Fed YouTube page
 
Need Help?
Questions or Comments
FRED Help
Legal
Privacy Notice & Policy