The FRED® Blog

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.

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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

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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

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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

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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

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Fertility in FRED

FRED recently added fertility data from the World Bank’s World Development Indicators. A few examples are listed above, and they show a general trend toward lower fertility. The measure used here is the average number of children a woman has in her lifetime. A rate just above two is necessary to replenish a population, taking into account that some children die before becoming fertile and having children of their own. In the graph above, a few observations are remarkable: 1. The United States remains at just two children per woman. 2. China experienced a big drop, no doubt due to the one-child policy; but the number is still quite a bit above one child, as the policy does not apply to everyone. 3. Mexico also exhibits a sharp decline. 4. This decline is more recent for Benin, which has still quite a ways to go.

How this graph was created: Look for the fertility series, select the countries you’re interested in, and add them to the graph. There are several pages of listings, so you may need to add some from the graph page itself.

Suggested by Christian Zimmermann.

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New life (expectancy data) in FRED

FRED recently added World Development Indicators data on life expectancy at birth, which are from the World Bank. The graph above includes some of the available data. The green line shows that life expectancy has been steadily increasing in the world, but that the experience varies across countries. Consider the three large countries shown in the graph: the U.S., China, and Russia. Life expectancy in the U.S. has also been steadily increasing, but not that fast, likely because it was already high to begin with. China had a big surge in the 1960s. The Russian experience has definitely been rocky, with several decreases at several points.

How this graph was created: Search for “life expectancy,” choose the countries you’re interested in, and add them to the graph. There are several pages of listings, so you may need to add some from the graph page itself.

Suggested by Christian Zimmermann.

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Where are the teenage employees?

When I was in high school, I had a job. As you can see from the graph above, up until 2001, over 50% of teenagers had jobs. Since then, the percent of teenagers employed—including part-time jobs—has declined and continues to decline. The most recent labor force participation rate for 16- to 19-year-olds is at just under 35%. Likely, most parents and teenagers see school as the first priority, as the rewards from finishing school have grown. Many years ago, teenagers participated in the labor force at a higher rate than adults over 55 years of age. But the percent of workers aged 55 and over has risen by almost 10% since then, at least in part because they are in better health during those later years and many retirees seek what they did as teenagers: part-time jobs.

How this graph was created: Search for “Civilian Labor Force Participation Rate” and then filter for the tag “sa,” which is “seasonally adjusted.” (NOTE: The seasonality of teenage employment is pretty extreme due to summer jobs.) And choose the ages 16-19 and over 55.

Suggested by Katrina Stierholz

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Manufacturing is growing, even when manufacturing jobs are not

The role of manufacturing in the U.S. economy is often discussed. As shown in the FRED graph above, as a year-over-year percent change, the level of manufacturing has generally grown. (One striking exception is during the recent recession.) The number of employees working in manufacturing is a different story, however. It has sometimes grown, but it has nearly always grown less than the growth in manufacturing. This suggests that growth in manufacturing does not equal growth in manufacturing jobs. What’s the explanation? A prime candidate is productivity growth. Another is that the sectoral mix has shifted toward industries with higher value added, such as computers and electronics. (See this previous FRED Blog post for more on this subject.)

How this graph was created: Search for “Industrial Production: Manufacturing” and “Manufacturing Employees” and add the series to the graph. Then convert both series to “Percent change from a year ago.” Finally, restrict the sample to a time period when both series are available.

Suggested by Katrina Stierholz

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More-severe unemployment in Southern Europe

“There is always someone who has it worse” is sometimes a consolation when bad things happen to you. Here, we contrast the U.S. unemployment rate with the rates in Greece and Spain. There were certainly reasons to complain about the high unemployment rates in the U.S. during the past recession, but they pale in comparison with the experiences in Greece and Spain—even outside recessions. This disparity doesn’t come from differences in definitions of unemployment, either; this graph uses the harmonized unemployment rates from the OECD, which are designed specifically to make the rates comparable.

How this graph was created: Search for “harmonized unemployment rate,” then modify the tags in the side bar to restrict the choices. Select the countries you want and add the series to the graph (using the button at the top or bottom of the list). Finally, restrict the sample period to when data are available for Greece or Spain.

Suggested by Christian Zimmermann

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