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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

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