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Posts tagged with: "CNP16OV"

View this series on FRED

How much cash is out there?

This graph shows U.S. dollars in circulation per capita—in other words, how much physical cash is held outside the Federal Reserve for each person living in the U.S. As of the November 2018 observation, that amount is $6,575. We checked, and no one on the FRED Blog team is holding that much cash right now. We assume not many of our readers would hold that much cash. So, who is holding it? Part of it may be lost. Much of it is held by domestic businesses and governments. And then there are all those dollars held abroad. In some countries, the dollar is valued over the local currency for its stability and low inflation rate. In fact, the following countries have adopted the U.S. dollar as legal tender and abolished their own currency: British Virgin Islands, Caribbean Netherlands, East Timor, Ecuador, El Salvador, Marshall Islands, Federated States of Micronesia, Palau, Panama, and Turks and Caicos Islands. Many more countries use the U.S. dollar alongside their own currency, either formally or informally. In addition, in those countries where the banking system is underdeveloped or not trusted, savings can be held mostly in U.S. cash in freezers and mattresses. (Note that foreign currency reserves held by central banks are rarely cash: They’re mostly held in Treasury bonds or accounts at the Fed.)

How this graph was created: Search for “currency in circulation” and click on the monthly series. From the “Edit Graph” panel, add a monthly population series, and apply formula a/b*1000.

Suggested by Christian Zimmermann.

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

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 puzzle of real median household income

The graph above shows two often-reported series that look at a measure of income adjusted for inflation and population: real median household income and real per capita GDP. They should be similar, but there are quite a few differences. For example, median household income has stagnated for about two decades while per capita GDP has steadily increased. Let’s try to straighten out this puzzle.

The blue line in the middle graph shows that the number of people in each household has decreased. So the number of households in the nation has increased faster than population, which means that any measure divided by population grows faster than one divided by number of households. To see how much this matters quantitatively, we divide both income concepts by the number of households in the bottom graph. Obviously, they still don’t line up, but at least the gap is smaller. What explains the remainder?

First, the income definitions are different: Household income is based on a survey that asks people about only their income, not their employer-provided benefits and retirement contributions. In a previous post, we showed that these benefits have increased relatively more than wages. Real GDP includes all income in the economy. Second, if the distribution of income becomes more unequal, then the median decreases while the mean stays put. How much each of these contribute to the remaining gap can only be determined with a look at the microdata.

How these graphs were create: Top graph: Search for “real median household income,” click on the series, open the “Edit Graph” panel, then select “Index (scale value to 100 for chosen date)” for units, with 1984-01-01 as the date. Then add a line after searching for “real per capita GDP.” Choose the same units. Middle graph: Search for “civilian population,” open the “Edit Graph” panel, then search for “number of households,” and apply the formula a/b. Bottom graph: Repeat the procedure for the top graph for the first line. For the second line, use the “Add Line” feature, search for “real GDP,” then add the “number of households” series, and apply formula a/b. Finally, choose as units “Index (scale value to 100 for chosen date)” with 1984-01-01 in the bottom field, as the units pertain to the result of the formula.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A939RX0Q048SBEA, CNP16OV, GDPC1, MEHOINUSA672N, TTLHH


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