How clear is the public’s understanding of economic policy and its likely outcomes? FRED includes a data series that seeks to answer this question: In a recent paper, Scott Baker, Nicholas Bloom, and Steven Davis developed an index that estimates the level of uncertainty about economic policy by accounting for newspaper references to uncertainty, tax codes, and disagreement among forecasters.
The authors refer to spikes in the index that occurred during important events such as “tight presidential elections, Gulf Wars I and II, the 9/11 attack, and other major shocks.” More-recent events include the Lehman Brothers bankruptcy, the euro crisis, and the debt-ceiling deadlock.
The Federal Reserve has been making an effort to reduce uncertainty by increasing clarity and transparency with respect to its policies. As noted in a previous blog post, FRED also allows you to track the projections made by FOMC members. (Read full-text FOMC statements back to 2009 on the Board’s website.)
How this graph was created: Search for “Economic Policy Uncertainty Index” and change the frequency to “Weekly, Ending Friday.”
Suggested by Abhinav Chhabra
View on FRED, series used in this post:
By definition, “emerging” economies are in transition from “developing” status to “developed” status, which is generally a bumpy road that may involve more-frequent crises and stronger economic fluctuations. One way to visualize this transition in FRED is to graph per capita GDP growth rates for the BRIC countries (Brazil, Russia, India, China) and compare them with the rate for the U.S. (thick black line). While it is usually better to show fewer series on one graph, the idea here is simply to illustrate that these economies bounce around much more than the U.S. economy does. The U.S. rate is never the highest. On the rare occasions when it is the lowest, it is only barely so—with 2007 being the exception.
How this graph was created: Search for “Constant GDP per capita” for the various countries and add the series to the graph. Transform each series to “Percentage change” and emphasize the line for the U.S. so it stands out (in this case, it is thicker and black).
Suggested by Christian Zimmermann
Many economists argue that German labor market reforms implemented in the 2000s clearly paid off during the global recession, particularly the combination of less-generous unemployment benefits, wage moderation, and incentives to hoard labor. A long-established work program called Kurzarbeit (literally “short work”) is credited with helping to smooth Germany’s labor market adjustment much better than in previous recessions by allowing firms to reduce employee hours.
The graphs provide some evidence of the effect of this program at the aggregate level. Average annual hours per worker between 2008 and 2009 dropped by 1.87 percent in the United States, but fell more markedly—by 2.74 percent—in Germany. Massive layoffs occurred in the United States, but employment losses were barely noticeable in Germany. In addition, between the recession’s peak and trough, the U.S. employment-to-population ratio decreased by 2.6 percentage points (from 48.4 to 45.8 percent) while it increased by 0.6 percentage points in Germany (from 48.7 to 49.3 percent).
If this labor market feature works well in Germany, could it be adopted in other countries as well? One version of a short-time work program—called work sharing—already exists in the United States, with the goal of limiting job losses during difficult economic times. At the start of this year, twenty-six states and the District of Columbia were able to offer the program (now under the umbrella of the Layoff Prevention Act of 2011), though the levels of implementation vary.
How this graph was created: The graph of unemployment rates is a simple plot of the unemployment rates for the two countries since 2008. The graph of hours worked is plotted using the option “Index (scale value to 100 for the chosen period).” The data samples were shortened to highlight the previous recession cycle.
Suggested by Silvio Contessi and Li Li.
View on FRED, series used in this post: