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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


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