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Staying up to speed on U.S. driving trends

The graph above shows how much Americans are driving. Because there’s a very strong seasonal pattern, which spikes in the summer, we use this 12-month “moving” series to achieve a smoother line. (Just one of the many options in FRED that helps you choose how to display the data!) We see that mileage has steadily increased over the years, with three exceptions in this sample period: Two were the massive gas price hikes—in the 1970s and 1980s—and the third is the aftermath of the Great Recession. In fact, never has a driving slump been as long and pronounced as this recent one. Does this indicate that something has changed?

The second graph looks at the same series, but this time it’s divided by a measure of population. Now we can see that yearly miles per person peaked around June 2005 at about 13,200 and then dipped all the way down to about 12,000 in March 2014. As of August 2018, it’s a bit higher, at almost 12,500 miles. But it’s been leaning downward again and may decrease even further. Are we seeing a change in commuting and traveling habits? As always, FRED will keep compiling the data so you can stay up to speed on these trends.

How these graphs were created: For the first, search for “miles traveled,” select the moving 12-month series, and click “Add to Graph.” For the second, take the first and go to the “Edit Graph” panel: Search for and add the “civilian population” series, and then apply formula a/b*1000. (Multiplying by 1000 achieves the correct units.)

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CNP16OV, M12MTVUSM227NFWA

The cost of servicing public debt: An international comparison

In a previous blog post, we looked at the cost of servicing U.S. debt. The metric we used is the gap between the real interest rate on debt and the growth rate of real GDP. We perform a similar exercise here, but we add a selected sample of OECD countries: Germany, Italy, Japan, and the U.K. This mix is interesting because Italy and Japan have high ratios of government debt to GDP, while Germany and the U.K. have more moderate ratios, which are all shown in the graph above.

The dotted line represents the 100 percent mark. As of 2017, three countries in our sample have debt-to-GDP ratios greater than 100 percent of GDP:  Italy, Japan, and the U.S. The U.S. debt-to-GDP ratio started to rise with the onset of the Great Recession in 2007, while the ratios for Japan and Italy started to rise in the 1990s.

As noted above, we calculate the cost of servicing debt for these countries as the difference between the real interest rate (measured as the difference between the interest rate on 10-year government bonds and the CPI inflation rate) and the growth rate of real GDP (measured as the sum of real GDP per capita growth and population growth). The second graph shows that, in 2017, Italy had the highest cost of servicing its debt, followed by Japan and the U.S. However, all of these countries have a negative cost of servicing their debt, which implies that they have a low burden of debt, since the growth rate of the economy is greater than the real interest rate for each of these countries.


It’s also worth noting that in the recovery period after the Great Recession, only Italy and Japan had positive costs of servicing their debt. Population growth in these countries is very low or even negative, which increases the cost of servicing the debt according to this measure.

How these graphs were created: For the first graph, search for and select the non-seasonally adjusted series “General Government Debt for Italy.” From the “Edit Graph” panel, select the “Add Line” option and repeat the above step for Japan, Germany, the U.K., and the U.S.

For the second graph, search for and select the series “Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States” and set the units to be “Percent” and frequency to be “Annual” (average). Then add three more series to this line: “Consumer Price Index: All Items for the United States” (with units set to “Percent Change”), “Constant GDP per Capita for the United States” (with units set to “Percent Change”), “Population Growth for the United States” (with units set to “Percent Change at Annual Rate”). Then, in the Formula bar, enter the formula a-b-c-d. In the “Add Line” tab, repeat the above steps for Germany, Italy, Japan, and the U.K.

Suggested by Asha Bharadwaj and Maximiliano Dvorkin.


Immigration and the Brexit vote: A look at the data

On June 23, 2016, a majority in the United Kingdom voted to exit (or “Brexit”) the European Union, defying most forecasts. One of the reasons cited for this outcome was concern by British citizens about too much immigration from Central Europe and the Baltics.* Is (or was) this concern valid? Let’s see what FRED has to show us about the patterns of net immigration for the U.K. and for Central Europe and the Baltics.

The graph tracks net migration—total number of immigrants minus total number of emigrants—from the early 1960s to 2017 (the latest available date point). Throughout the late 20th century, net migration for the U.K. was quite low, fluctuating between slightly positive (net immigration) and slightly negative (net emigration).

However, there was a sharp increase in the early 2000s. In 2004, the EU expanded to include several countries from Central Europe, and the U.K. was one of three EU nations (along with Ireland and Sweden) to immediately allow migration from these new member states. This policy may have contributed to the 2007 peak in net immigration into the U.K. After that, there’s a sharp decline as the Great Recession (2008-09) reduced economic opportunities. The overall picture of more-recent U.K. immigration is mixed: There was a sharp rise with the 2004 EU expansion, but the years immediately preceding the 2016 Brexit referendum show a decrease in net immigration. But, because net immigration adds to the number of foreign born in a nation (i.e., it is a flow), the immigration spike may have contributed to heightened anxiety in the U.K. about the relatively large immigrant population that had accumulated by the time of the 2016 referendum.

Central European nations and the Baltics had much more net emigration, with two clear spikes: The first and larger of the two was in the late 1980s/early 1990s, which was related to the breakup of the Soviet Bloc. The second was in the early 2000s, related to the EU’s enlargement in 2004. Since that time, migration from these nations seems to mirror U.K.’s experience, although in the opposite direction. This pattern suggests significant emigration from these nations to the U.K. after 2004, perhaps influencing the Brexit vote. Of course, our data aren’t detailed enough to provide a definitive connection between U.K.-Europe immigration flows and Brexit. More research may provide more insight.

* These nations are Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slovenia.

How this graph was created: Search for “United Kingdom migration,” check the series, and click “Add to Graph.” From the “Edit Graph” panel, use the the “Add Line” option to search for and select “Central Europe migration.”

Suggested by Asha Bharadwaj and Subhayu Bandyopadhyay.

View on FRED, series used in this post: SMPOPNETMCEB, SMPOPNETMGBR

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