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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

The velocity of money

The velocity of money played an important role in monetarist thought. For example, monetarists argued that there exists a stable demand for money (as a function of aggregate income and interest rates). In some formulations, that translates into a stable relationship between the velocity of money and a nominal interest rate—for example, the short-term Treasury bill rate.

The velocity of money is defined by

V = (PY)/M,

where V is velocity, P is the price level, Y is real output, and M is a measure of the money stock.

The graph shows the velocity of M1, with nominal gross domestic product as the chosen measure of PY. There are at least two interesting features in the graph: First, before the early 1980s, there was a more-or-less predictable trend increase in velocity. But after 1980, velocity exhibits wide swings. Basically, this reflects a fairly stable money demand relationship before 1980 and an unstable one afterward. Second, there’s a dramatic decrease in velocity starting at the beginning of the Great Recession, shown as the shaded area in 2008-09 in the graph. This is perhaps surprising, as short-term nominal interest rates have been essentially zero since late 2008. If the demand for M1 had been stable, velocity would be roughly constant; but since the beginning of the Great Recession, M1 has grown at a much faster rate than nominal GDP. This can be explained partly by a flight to the safety of insured bank deposits during the financial crisis.

How the graph was created: There are measures of the velocity of money available in FRED, but we can learn some useful things about FRED by constructing M1 velocity ourselves. First, go to the Categories menu, look under the category “Money Banking and Finance,” and select the subcategory “Monetary Data”: There you’ll find “M1 and Components.” Select “M1 Money Stock, Monthly, Seasonally Adjusted” and the graph will appear. Because we use quarterly GDP as our nominal income measure, we need M1 to be quarterly as well. So in the Frequency box, select “Quarterly.” This will convert the raw monthly M1 data to a quarterly frequency. Next, select ADD DATA SERIES and check the “Modify existing series” box. In the search box, type “gross domestic product” and add it to the graph. (Make sure you select “gross domestic product” and not “real gross domestic product.”) Now click “EDIT DATA SERIES 1” and select “Create your own data transformation.” M1 is series “a” and PY is series “b,” so enter the formula “b/a.” (See the V = (PY)/M equation above.) Next, under “Create your own data transformation,” scale the result by selecting “Index (Scale value to 100 for chosen period)” and then add the initial date of the series, 1959-01-01, in the Observation Date box.

Suggested by Stephen Williamson.

View on FRED, series used in this post: GDP, M1SL


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