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FRED is looking good

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We asked for feedback, and our users delivered. Thank you!

The FRED series page has been revamped, and some of you have even been peeking at on our beta site over the past several weeks. We hope you like the results as much as we do. Here are the major changes:

  • FRED now lives at fred.stlouisfed.org. All traffic from the old address will be redirected, so no reason to worry about your bookmarks or old links.
  • We made the graph the star of the page—that is, BIGGER!
  • You can now see the edits to the graph as you make them. No need for scrolling. Click on the “Edit Graph” button to open the side panel.
  • We have reimagined and rewritten the FRED “Help” section: fredhelp.stlouisfed.org.
  • You can also get help from the short videos in the side panel. Just click on the info buttons.
  • When adding a line, the search box remembers your previous series.
  • Notes and related content remain beneath the graph.
  • The “Related Resources” section now includes thumbnails of relevant material. We’ll keep adding more to this section.
  • When sharing a graph, you can now control whether the sample window should update in the future.
  • All download options are available from a single location, including downloading several series at a time.
  • Plus many more small tweaks and improvements.

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Local unemployment dynamics in the Great Recession

The U.S. labor market as a whole has recovered from the effects of the recent recession: The national unemployment rate was 10% at the end of 2009, but now stands at 4.7%, a number FOMC participants consider to be close to its long-run value. Despite this overall recovery, regional patterns of recession and recovery differ, with some areas better off and some worse than average. These three GeoFRED maps show county unemployment rates at their pre-recession trough (December 2007), at their recession peak (October 2009), and at the time of the most-recent estimate (April 2016).

In December 2007, about 21 percent of counties had rates below 3.5% and about 66 percent of counties had rates below 5.5%. Certain regions experienced slightly higher unemployment rates, particularly the West Coast, Central South, and Upper Peninsula of Michigan. The Midwest and South (from Minnesota to Texas) had the lowest unemployment rates, with most rates below 3.5%.

By October 2009, only 15 percent of counties still had unemployment rates below 4.4%: Most counties had rates between 7.5% and 15.5%, and about 10 counties had rates greater than 20.5%. The regions with higher pre-recession unemployment rates also had higher levels of unemployment during the recession. And a strip of counties in the Northern Midwest maintained unemployment rates below 4.5%.

Today, some county-level unemployment rates remain slightly above their pre-recession trough, although most have recovered or even improved beyond pre-recession levels: 21 percent of counties have unemployment rates below 3.5% (the same fraction as in December 2007); 68 percent of counties have rates below 5.5% (slightly better than the pre-recession trough); and nine counties have maintained extremely high unemployment rates—above 20.5%. Newly developed regions in the West such as Arizona, New Mexico, and Utah have unemployment rates that are higher than the pre-recession trough, whereas Midwestern rates are lower than their 2007 levels.

How these maps were created: Select GeoFRED’s “Build New Map” option at the top right of the home page. Use the “Tools” menu on the top left to set “Region Type” to “County.” Type “Unemployment Rate” in the search box. Set the frequency to “Monthly” and the units to “Percent.” Select the desired date from the drop-down menu. You can select the color scheme for your map under “Choose Colors”: We used “Single Hue Red.” Finally, under “Edit Legend,” change the number of color classes to 9 and set the interval values to 3.5, 4.5, 5.5, 7.5, 9.5, 11.5, 15.5, 20.5, and 29.5.

Suggested by Maximiliano Dvorkin and Hannah Shell.

Income differences across countries: Is the gap closing?

Why are some countries rich and others poor? This question has troubled economists for a long time. Probably the most well-known economic model to study this question is the Solow growth model, which predicts that income per capita of countries that share similar savings rates, capital depreciation, and population growth (among other characteristics) should converge over time to similar values. This convergence hypothesis means that, holding all else equal, countries with low income per capita should grow fast and eventually catch up with countries with high income per capita. FRED data can help us evaluate whether this convergence hypothesis holds over time.

We take the U.S. as our “benchmark” high-income country and analyze whether other countries are catching up with it. The graph above shows real GDP per capita in low- and middle-income countries as a ratio to real GDP per capita in the U.S. For example, if Brazil’s ratio moves closer to 1, that means Brazil’s GDP per capita is converging to U.S. GDP per capita. We see some clear evidence of convergence in three countries: South Korea (solid blue line), China (solid red line), and India (solid green line). GDP per capita in Brazil (dotted blue line) moved toward convergence until 1980, then started decreasing and leveled out. The dashed lines at the bottom, representing Mozambique and Kenya, do not seem to converge at all.

The graph below shows GDP per capita in high-income countries as a ratio with GDP per capita in the U.S.: Japan (dashed purple line) and France (solid blue line) show some stronger signs of convergence, moving from about 20% and 55% of U.S. GDP per capita, respectively, to about 75% of U.S. GDP per capita between 1950 and 2010. The United Kingdom (red line) also shows some signs of convergence to the U.S., while Germany (dotted blue line) and Canada (green line) are mostly flat over the sample period.

Overall, the evidence on Solow convergence is mixed. Some assumptions important for the theory may not hold, and the lack of convergence could be related to differences in savings rates, depreciation, and population growth across some of the countries we analyze here.

How this graph was created: In the FRED search bar, search for “purchasing power parity converted GDP per capita (Chain Series) for China.” Add this series to a graph and search for the same series for other desired countries within the “Add Data Series” tab underneath the graph. After adding all the low- and middle-income countries, go back to the “Add Data Series” tab and modify the existing series by adding GDP per capita for the U.S. Next, for each series, under the “Create your own data transformation” option, type “a/b” in the formula box to create the ratio. You can turn off the title and axis titles on the “Graph Settings” tab to keep the graph clean. Individual series color and line styles can be changed on the “Edit Data Series.”

Suggested by Maximiliano Dvorkin and Hannah Shell.

View on FRED, series used in this post: RGDPC2CNA625NUPN, RGDPCHBRA625NUPN, RGDPCHCAA625NUPN, RGDPCHDEA625NUPN, RGDPCHFRA625NUPN, RGDPCHGBA625NUPN, RGDPCHINA625NUPN, RGDPCHJPA625NUPN, RGDPCHKEA625NUPN, RGDPCHKRA625NUPN, RGDPCHMZA625NUPN, RGDPCHUSA625NUPN


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