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A plodding dollar: The recent decrease in the velocity of money
Posted on April 7, 2016
The velocity of money measures the number of times a dollar is spent to buy domestically produced goods and services per unit of time. It’s calculated as the ratio of nominal GDP to the average of the money stock. Nominal GDP measures the value of all final goods and services bought by consumers, firms, the government, and foreigners in a period of time; so, it’s used as a proxy for the value of all transactions that occur in an economy in that period of time.
Typically, statistical agencies calculate the velocity of money using one of two measures of the money supply: (1) M1, the supply of currency in circulation, is notes and coins, traveler’s checks (non-bank issuers), demand deposits, and checkable deposits. (2) M2, a broader measure of the money supply, is M1 plus saving deposits, small-denomination (<$100,000) certificates of deposit, and money market deposits for individuals.
The graph shows the evolution of the velocity of M2 for the United States from 1959 to 2015. During recessions (shown by gray bars), the velocity of money tends to decrease, since the amount of transactions in an economy decreases. Consumers tend to save more and firms tend to invest less—that is, they hoard cash instead of spend it. As the graph shows, this was the case during the “dot com bubble” crisis of 2001 and more recently during the financial crisis of 2007. In general, the velocity of money starts to increase after a recession is over, when confidence is restored. However, since 2007, the velocity of money in the U.S. has been decreasing, which means consumers and firms are still holding onto cash instead of spending it. This behavior, which also reflects a decrease in inflation, suggests that confidence in the recovery is still low. When confidence is restored, we should expect to see a rebound in the velocity of money.
How this graph was created: Search FRED for “M2 Money Velocity” and choose the series “Velocity of M2 Money Stock”, or M2V.
Suggested by Ana Maria Santacreu.
View on FRED, series used in this post:
M2V
Tagged M2V
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April is National Poetry Month…even for FRED
Posted on April 4, 2016
National Poetry Month is April.
Believe it or not, the bloggers for FRED
thought a lyrical post could be offered
to celebrate and also be helpful.
FRED provides a bounty of statistics
to the public and the academy
describing our wondrous economy
across numerous characteristics.
O, the Current Population Survey
(brought to us by the U.S. BLS,
a source that is diligent and zealous)
is a labor market data bouquet.
We graph the writerly occupation
(whether wage-earning or on salary)
where men and women have equality
in large part—but lo! A deviation!
Notice the gain for the fairer gender:
a 2014 female vertex!
Why is this so? Poets lack a context,
and economists are left to wonder.
How this graph was created: For clarity let us return to prose: A search for “writers” provides you with several results, including the two series shown here. Select the two series and click on “Add to Graph.” If you mouse over the data points, you’ll see that, although men and women had similar levels for most of the period shown, in 2014 the number for men is 42K and the number for women is 60K. (NB: We did not include technical writers in this graph, as their role seems less poetic.)
Suggested by George Fortier and Christian Zimmermann.
View on FRED, series used in this post:
LEU0254593200A, LEU0254700000A
Tagged LEU0254593200A, LEU0254700000A
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Replicating the Japanese Phillips Curve
Posted on April 1, 2016
An essential part of the scientific process is to verify results by trying to replicate them. FRED can be helpful in this regard, and we provide a simple example of this today. Gregor Smith published a 2008 study entitled Japan’s Phillips Curve Looks Like Japan, demonstrating that when you draw a scatter plot of the inflation rate minus the unemployment rate of that country for a sample period of January 1980 to August 2005, the resulting graph, commonly called the Phillips Curve, actually looks like a map of Japan. We replicate this result in the graph above, as one can clearly distinguish the large Japanese islands, Tokyo Bay, and various other features. So, yes, we have replicated the original result.
But the replication process is not only about reproducing results. You also want to check how robust they are to various modifications. The obvious modification here is to extend the sample to a more-recent date. We show this below, and it looks like the various islands have merged. With respect to Japan’s geography, this could only have happened if the seas lowered significantly or if new volcanoes emerged in the sea. As we do not believe this has happened in recent years, we must conclude that the original published result is not robust.
How this graph was created: Search for “Japan CPI” and select the monthly series. Then search for and add “Harmonized Unemployment Rate Japan”—again taking the monthly series. Modify the units of the first series to “Percent Change from Year Ago.” Modify the second series by applying the formula -a. Modify the date range of the graph to 1980-01-01 to 2005-08-01 and the graph type to “Scatter.” For the second graph, extend the date range to a more-recent date.
Suggested by Christian Zimmermann.
View on FRED, series used in this post:
JPNCPIALLMINMEI, JPNURHARMMDSMEI
Tagged JPNCPIALLMINMEI, JPNURHARMMDSMEI
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