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The FRED® Blog

Components of M2

What is money? Well, there are many statistical definitions and FRED’s new release tables can help us sort them out. The release table for monetary aggregates shows us the various components of M2, the broadest monetary aggregate currently measured. The major components are represented in the graph above, as shares of total M2. The strictest measure of money is currency, in red at the bottom. Add to that checkable deposit accounts in banks and elsewhere, and you have M1. Add savings accounts, small time-deposit accounts, and money funds to M1, and you get M2.

The graph shows how the composition of M2 has changed over time. Deposit accounts in banks used to be much more important. This has changed as the financial industry and its customers have become more sophisticated. Also, regulatory changes as well as amendments to accounting rules have had a direct impact on measurements or have nudged market participants to hold liquidity or savings differently. But currency has been largely unaffected by such changes.

How this graph was created: Go to the Money Stock Measures release, choose a table from the top, click the series you want graphed, and click on “add to graph.” Then, open the tab for the currency component and move it to the bottom of the pile by clicking on the “down” button. Finally, under graph settings, set graph type to “bar” with stacking set to “percent.” You will notice that the early data series have only currency; thus, start the sample in January 1959.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CURRNS, DEMDEPNS, NOM1M2N, OCDNS

Quits by industry

FRED recently introduced “release views,” which make it much easier to split an economic aggregate into various components or categories. Here, we use the Job Openings and Labor Turnover release to examine quits and hires by industry. In the graph above, it is striking how the ranking of industry quit rates remains the same no matter how well the economy is doing. Also, the quit rates of some sectors respond more strongly as the economy improves. Naturally, one is more likely to quit a job when it’s easier to find another. This is confirmed by looking at the industry hiring rates in the graph below, where the ranking and trend of the lines are the same as above. See the spike for government hiring around 2010? That corresponds to temporary workers hired for the decennial census.

How these graphs were created: For each graph, go to the Job Openings and Labor Turnover release, find the right release table from the top list, check the industry series you want, and click on the “add to graph” button.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: JTS3000HIR, JTS3000QUR, JTS4000HIR, JTS4000QUR, JTS6000HIR, JTS6000QUR, JTS7000HIR, JTS7000QUR, JTS9000HIR, JTS9000QUR

The KC Fed’s labor market index in FRED

FRED has just added two labor market indicators from the Kansas City Fed. They’re computed from a collection of 24 times series related to the labor market. Two principal components, which are extracted from this data set using factor analysis, are displayed in the graph above: They describe about 80% of what is happening in the labor market. When both components are above zero, the labor market is looking good. When both are below, there is definite cause for concern.

How this graph was created: Search for the Kansas City Fed (through source or release), select the two series, and add them to a graph.

Suggested by Christian Zimmermann

View on FRED, series used in this post: FRBKCLMCILA, FRBKCLMCIM

Overcoming the global crisis: USA, Japan, and Italy

Recent GDP data for Italy have rekindled concerns about how well some countries are moving out of the global financial crisis. Professor Justin Wolfers plotted a comparison between real GDP in Italy and the United States that shows the dismal Italian “recovery” and hints at the possibility of a triple-dip recession. (FRED lets you plot this graph pretty quickly.) Several Italian commentators have also made comparisons between Italy and Japan. But these FRED graphs show that the path of Japan’s GDP is more similar to that of U.S. GDP. And, as Professor Wolfers points out, U.S. GDP hasn’t been all that bad in an international context.

Italy’s GDP appears even more dismal if you consider real GDP per capita, which smooths out differences in population growth:

In terms of real GDP per worker (a ratio also used as a measure of labor productivity), Japan’s trend has diverged from the U.S. trend only since the global financial crisis. Because there is a tighter relationship between employment and GDP in the United States than in Japan, real GDP per worker in the United States hardly reveals a recession at all: As GDP was falling in 2008-09, the number of employed workers was also dropping. In Japan, however, workers were not being laid off in such large numbers, so the ratio declined more. Chalk that up to stark differences in the labor markets of these two countries.

Yet, the divergence of Japan from the United States is dwarfed by that of Italian real GDP per worker, showing a dismal protracted reduction since the global financial crisis.

How these graphs were created: The first and second graphs simply use data on real GDP and real GDP per capita, rebasing them to 100 in 2001 using the options under the “EDIT DATA SERIES” tab: Select “Index (Scale value to 100 for chosen period)” and choose the 2001 option. Note that this is a default option for rebasing the series, but one can also choose different dates. Construct the third graph as follows: Create the ratio of the original series (real GDP = a and civilian employees = b; a/b) and then apply the transformation “Index (Scale value to 100 for chosen period)” and again choose 2001. Finally, remove the legend axis on this last graph, which reduces the clutter.

Suggested by Silvio Contessi

View on FRED, series used in this post: CE16OV, GDPC1, ITAEMPTOTQPSMEI, JPNEMPTOTMISMEI, NAEXKP01ITA189S, NAEXKP01ITQ189S, NAEXKP01JPQ189S, NYGDPPCAPKDITA, NYGDPPCAPKDJPN, NYGDPPCAPKDUSA

On household debt

Some people are worried about high levels of U.S. household debt. When looking at aggregate numbers, there are two ways to consider this question. The first is how much it costs to service this debt as a fraction of disposable (after tax) income. This is shown with the blue line. The second is how much debt there is with respect to the same disposable income measure. This is shown with the red line. Whether these numbers are high is difficult to say; household-level data are more appropriate for that question. But in the aggregate, both measures have clearly decreased during the past crisis. Note the scale, though: While service payments decreased by almost one-third, the debt ratio decreased by only one-fifth. And whenever interest rates go back up, service payments will increase.

How this graph was created: Creating the blue line is easy: Search for “household debt” and select the series for debt service as percent of disposable personal income. The red line is more complex because it has to be constructed: We need the two components of household debt (consumer credit and mortgages) as well as nominal disposable income—nominal, not the real or per capita versions, because the debt measures are in nominal terms. So, from within the graph, search for “household consumer debt” and add this series (a) to the graph. We must combine more data here, so add “household mortgage debt” (b) and “disposable income” (c), being sure to select “modify series 2.” Then create your own data transformation by applying the formula (a+b)/c. Finally, switch the y-axis position to the right.

Suggested by Christian Zimmermann

View on FRED, series used in this post: DPI, HCCSDODNS, HHMSDODNS, TDSP


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