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The FRED® Blog

Labor market tightness

Unemployment is high during a recession, and job vacancies are numerous during an economic boom. That should surprise no one. This is why these two measures are useful in determining the state of an economy throughout its business cycle. One way to do this is to look at labor market tightness, defined as the ratio of vacancies to unemployment, which we show above. One should realize, though, that while the number of unemployed is reasonably well estimated from surveys, the number of vacancies is estimated with much less confidence. Indeed, at least in the U.S., it is not mandatory to post openings at an employment agency. In fact, some statistical agencies used to measure the square footage of job ads in newspapers, which obviously isn’t possible now that jobs are advertised in many different media and likely multiple times. In the U.S., a survey across businesses about their openings has been conducted only since 2000.

How this graph was created: Search for “job vacancies” and select the monthly seasonally adjusted series for the U.S. Then add the series “unemployment level,” making sure to check “Modify existing series 1.” Finally, create your own data transformation with the formula a/b/1000.

Suggested by Christian Zimmermann

View on FRED, series used in this post: LMJVTTUVUSM647S, UNEMPLOY

A good use of moving averages

Some data series are very volatile. That is, they don’t follow a smooth or step-by-step pattern. And it’s difficult to draw conclusions when new data are added to a volatile series. The weekly release of initial claims for unemployment insurance is a great example. In this and similar cases, it is useful to adopt some kind of smoothing mechanism: Here we provide a four-week moving average. Traditionally, a moving average is centered—say, the average of two periods before and two periods after. This moving average takes the last four observations, which allows you to better read trends, especially if you’re focusing on the most recent data. Of course, trends become more obvious if you look at longer spans of time. This graph shows a span of five years. Narrow or expand the sample with the slide bar to see how a moving average can help you interpret the data and avoid the pitfalls of volatility.

How this graph was created: Search for “initial claims,” select the two (seasonally adjusted) series, and add them to the graph. Finally, restrict the sample to the last 5 years, which is done by using the settings above the graph on the right.

Suggested by Christian Zimmermann

View on FRED, series used in this post: IC4WSA, ICSA

The diversity of U.S. state economies

The Federal Reserve Bank of Philadelphia computes a leading index for every U.S. state and for the nation overall. These indexes are intended to combine the information from several indicators that have (at least in the past) been good gauges of the economic development that will occur in the next six months. The graph above shows four U.S. states: one large and diversified state, New York, and three small states. The choices here are not innocent: These states all have contrasting fortunes. New York has a much smoother ride, while the small states are more often jerked around by the fluctuations of a particular industry. North Dakota and Wyoming recently benefited from the boom in oil but are now contracting even while the rest of the nation is expanding: The drop in the price of oil is the likely culprit. Nevada is also suffering quite a bit from fluctuations in commodity prices, but its current prospects seem positive.

How this graph was created: Search for “leading index,” select the states you want, and add them to the graph. We changed the color for New York to black to emphasize it.

Suggested by Christian Zimmermann

View on FRED, series used in this post: NDSLIND, NVSLIND, NYSLIND, WYSLIND

The composition effect in the labor force participation rate

In previous posts, we’ve used FRED to show how demographic factors relate to the current decline in the labor force participation rate. This post also uses FRED data to illustrate how the composition of the labor force relates to its current decline.

The first graph shows the aggregate labor force participation rate (thick black line), which leveled off in the 1990s, started to decline, and then declined further during the recent recession and thereafter. The other lines in the graph show the various age categories of the labor force participation rate. The so-called “prime working age,” which is 25-54 years, follows a similar pattern as the aggregate rate, but its decline is not as pronounced: It declined about 3 percentage points, while the aggregate rate declined almost 5 points. So the other age categories must also be contributing to the overall decline. The 16-19 years category declined dramatically and is certainly part of the story. Fewer students work during their high school years now, and more go to high school and college. To some extent, this same effect applies to the 20-24 years category.

What about the 56 years and older category? Their participation rate has increased, so does that mean they have counterbalanced the decline in younger workers? We look more closely at this question: The number of workers in this category has increased, as the Baby Boomers have gotten older and moved out of the prime working age category. And the participation rate of this age group is lower than the aggregate rate, so an increase in their numbers (and in their share of the labor force) implies a net negative contribution to the aggregate participation rate. Such an effect is called a composition effect.

We illustrate this effect in the graph below by comparing the reported overall participation rate (again: thick black line) with an artificial line (in blue) constructed by keeping the population shares of each age group constant. This constructed series does not show as much of a decline, which implies that, overall, changes in the shares of the age groups have contributed to the aggregate rate’s decline.

How these graphs were created: Search for “labor force participation rate years,” and all the series you need should be there. (FYI: This graph uses seasonally adjusted data.) Use the “Add to Graph” button to add these series to the top graph. For readability, thicken the line for the aggregate rate. For the bottom graph, add the aggregate rate as before. Create the other data series by adding the first age category to the graph and then adding all the other age categories (in the same order as in the first graph) with the “Modify existing series” option. Then use the “Create your own data transformation” option for this series to apply the following formula: 0.348*a + 0.5*b + 0.066*c + 0.088*d. (This formula reflects the recent population shares of each age category as determined by the figures in the civilian noninstitutional population data.)

Suggested by Christian Zimmermann

View on FRED, series used in this post: CIVPART, LNS11300012, LNS11300036, LNS11300060, LNS11324230

Government employment in context

The graph above shows the number of people employed in the U.S. government (excluding armed forces and intelligence agencies, but including the postal service). This number has increased almost continuously: The few exceptions are immediately after World War II, in the early 1980s, and since the previous recession. Note also that small spikes occur every ten years, owing to the temporary hiring for the census.

But does this picture tell a true story of an ever-expanding government? The graph spans almost 80 years, and over that period the U.S. population has continuously expanded. So a more-realistic picture would need to calculate the share of government employment in total employment. This is shown in the graph below. The picture looks quite different now: The current share of government employment is actually very low, and one has to go back to 1960 to find a lower number! The highest point is in 1975, not 2010 as in the first graph. Clearly, context matters.

How these graphs were created: Search for “government employees” and select “All Employees: Government” (series ID: USGOVT) for the first graph. For the second graph, add the series “All Employees: Total Nonfarm Payrolls” to series 1 through the “Modify existing series” option. Use the “Create your own data transformation” option to apply the formula a/b*100 to express the result in percentages.

Suggested by Christian Zimmermann

View on FRED, series used in this post: PAYEMS, USGOVT

Gamble on gambling?

Several U.S. states have considered expanding gambling operations as a new source of revenue, especially since the past recession. Is this a good idea? Is it viable? Many have questioned this plan for various reasons, but this post specifically examines whether there is room for expansion in the gambling industry to shore up state budgets.

The graph above shows the share of gambling in total personal expenditures. While there has indeed been a rapid expansion of these expenditures up to the mid-1990s, the trend has flattened markedly since then. It even decreased during the past recession, showing that this industry is certainly not recession-proof. That may not bode well for states that have a balanced-budget mandate and need countercyclical sources of revenue: Gambling does not appear to be a source that states can depend on.

How this graph was created: Search for “gambling expenditures” and select the series shown above, which is nominal and has an annual frequency. Add it to the graph. Then use the “Add Data Series” option to add “personal consumption expenditures” to series 1 by selecting “Modify existing series.” (Be sure to choose the personal consumption series that is nominal and has an annual frequency.) Then select “Create your own data transformation” and add the formula a/b*100. The result is then expressed in percentages.

Suggested by Christian Zimmermann

View on FRED, series used in this post: DGAMRC1A027NBEA, PCECA

GDP revisions

Measuring GDP and its components is tricky business. GDP is supposed to measure all economic activity of a country, but of course not all activity is well monitored. So one has to work with proxies and estimates based on various indicators and surveys. Those efforts take time, and GDP estimates must be corrected after they’re first released. Everyone knows the initial GDP measure is imprecise and that revisions can be rather dramatic, yet the initial release receives the lion’s share of press coverage. Later revisions receive little attention, yet they matter quite a bit.

The latest GDP release included revisions going all the way back to 2012. ALFRED, a sibling of our FRED database, allows comparison of different historical “vintages” of data for many series included in FRED. The graph above shows the quarter-to-quarter growth rates for the two latest releases of U.S. real GDP. One can easily see that there have been some stark changes: In particular, the measure for the first quarter of 2015 was initially a much-discussed negative growth rate but was then revised to reveal a positive growth rate.

If these revisions average out to zero, we simply have an imprecise measure of GDP and that’s that. But if these revisions lean in one particular direction, then we may have some systematic bias in the initial estimates. One way to look for a significant bias is to examine the levels of GDP across data vintages. (The levels are the cumulation of the growth rates.) The graph below shows these levels, and one can see a difference between the two series all the way to the last data point.

How these graphs were created: For the first graph, search “real gross domestic product” on ALFRED. Choose “Series” instead of “Site” on the drop down menu (which is to the left of the search field). Click on the first choice and then on “% Chg” under “Units” below the graph. The second graph is simply the default version of the first graph, which has units in billions of dollars.

Suggested by Christian Zimmermann

View on FRED, series used in this post: GDPC1

The volatility of GDP’s components

The four components of GDP—investment spending, net exports, government spending, and consumption—don’t move in lockstep with each other. In fact, their levels of volatility differ greatly. We can observe this in FRED by graphing the annual percent changes of each component. Investment (solid red) and net exports (solid yellow) are extremely volatile, varying greatly during economic contractions and expansions. In contrast, government spending (dashed blue) and consumption (dashed green) are highly stable; although they also vary with the business cycle, they do so to a much smaller extent. This pattern can be important for the effectiveness of monetary policy. According to economic textbooks, when the Fed lowers interest rates, investment spending and U.S. exports become cheaper, all else being equal. So, when the Fed lowers rates, it affects the two variables that disproportionately contribute to any given change in GDP.

How this graph was created: Add all of the series listed below to one graph with the “Add Data Series” function. Set their units to “Percent Change from Year Ago.” Use the “Line Style” option to give solid lines to the first two series and dashed lines to the last two and set “Line Width” to 1 for all four. Finally, take advantage of the “Color” option for each series to color the lines as you wish.

Series used in this post: GPDIC1, NETEXC, PCECC96, GCEC96.

Suggested by Ian Tarr.

View on FRED, series used in this post: GCEC96, GPDIC1, NETEXC, PCECC96

Stress test indicators

Screenshot from 2015-07-15 13:11:20
Click on image to get to dashboard

Sometimes you need to consider an assortment of data, and FRED’s dashboard tool lets you assemble and simultaneously view multiple economic variables. For example, the image above links to a dashboard of “stress test” indicators that help the Fed assess the resilience of banking institutions. As part the Dodd-Frank Wall Street Reform and Consumer Protection Act, the Fed crafts hypothetical economic scenarios (e.g., a spike in interest rates or a sharp contraction in GDP) and then analyzes banks to see how they would hold up. To learn more about the Fed’s stress test methods, click here.

How this dashboard was created: Go to the Fed’s stress test website, which is linked above, to see the variables used. Search for these stress test variables (or close equivalents) in FRED. Create each graph and then select “Save Graph.” If you have a FRED account, you will be prompted to log in. If you do not, you must create an account. Once you’ve saved as many graphs as you’d like, go to “My Dashboards” and click the “Create” button. To add the saved graphs, use the “Saved Graphs” option under “Add Widget.” If you are logged into your FRED account, you can save a copy of the dashboard to your account and then customize it. To learn more about FRED dashboards, click here.

Suggested by Ian Tarr.

Inflation’s dual cores

According to the Bureau of Labor Statistics, U.S. core inflation (i.e., excluding food and energy) is about 1.75%. Overall inflation measures combine the prices of both goods and services, but these two categories do not always behave in the same way. The graph above shows annual changes in the consumer price index for core services (purple) and core commodities (red). For about three years after the end of the recession, prices for goods and services changed at about equal rates. But the inflation environment has become a bit more complex in recent years: In 2012, growth in commodities prices began to slow and eventually turned negative. In contrast, prices for services have continued to grow at close to 2.5%.

How this graph was created: Add the two series listed below and use the “Graph Settings” option to set “Graph type” to “Bar.” Make sure that “Stacking” is listed as “None.” Then set “Units” to “Percent Change from Year Ago” for each series. Change “Frequency” to “Quarterly” and “Aggregation Method” to “End of Period.”

Suggested by Ian Tarr.

View on FRED, series used in this post: CUSR0000SACL1E, CUSR0000SASLE


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