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The demographics of the labor force participation rate

There is much lamenting about the decline in the labor force participation rate. As we recently discussed on this blog, while the rate decreased quickly during the previous recession and its recovery, the overall decline began several years before. This decline indicates there must be more than cyclical or even policy-related forces at work. One likely candidate is demographics. In the graph above, the proportion of the U.S. population 25 to 54 years of age follows a pattern similar to that of the labor force participation rate over the past 10 years. Why look at this 25-54 age range? Because this group has the highest labor force participation rate. So, if the share of this age group is declining, the aggregate labor force participation rate is likely to decline as well.

How this graph was created: For the first line, search for “population 25-54” and select “Civilian noninstitutional population—25-54 years.” To create the ratio, add the “Civilian noninstitutional population” series via the “Add Data Series” option: When you add this series, be sure to select “Modify existing series” for series 1. Then use the “Create your own data transformation” option using the formula a/b*100 so that the result is expressed in percentages. For the second line, simply add the civilian labor force participation rate as series 2.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CIVPART, CNP16OV, LNU00000060

Labor force participation: Is a trend or a cycle at work?

One major concern since the start of the recent recession has been the labor force participation rate. The graph above shows a clear and continuing decline. However, when you reveal the full sample, as shown in the graph below, you can see the decline started before the recession and the current level is not the lowest in postwar history. It appears, then, at least part of the current evolution of labor force participation has to do with a longer-term trend. What forces are at work here? Clearly, the rise in labor force participation had to do with many women entering the labor force. The subsequent decline has to do with the aging of the population, with a significant increase in the proportion of retirees. Also, the younger population is staying in school longer than before. Articles by Marianna Kudlyak and Maria Canon and Marianna Kudlyak provide more insight on this topic.

How these graphs were created: For the first, search for and add “Civilian Labor Force Participation” to the graph, but restrict the range to start in January 2008. (Note: The seasonally adjusted series is much easier to read.) To add the trend line, go to “Add Data Series” and select “Trend Line” from the pull-down menu: Start the line at 2008-01-01, with the value of 66.2. For the second, create the same graph but use the full sample, which starts in 1948.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CIVPART

Gender labor force participation gaps

In a recent article in the Regional Economist, Li Li and I plotted gender gaps in labor markets of G7 countries in 1991, 2001, and 2011. We focused in particular on two gender gaps: labor force participation (the difference in labor force participation rates for men and women) and unemployment (the difference between the unemployment rate for men and women).

While we used data from the World Bank and OECD, we could have just as easily transformed FRED data series to obtain these gaps. In the graph, we show the gender labor force participation rate gap in the United States and the G7 country with the largest gender labor force participation gap in the most recent data: Italy.

In the United States, the labor force participation rate for men has been trending down since World War II, while the labor force participation rate for women has been trending up—that is, until the Great Recession, when it began trending down in parallel with the rate for men. The gap has shrunk to about 10 to 12 percent. In Italy, the trend of the gap is very similar, but it’s shifted to the right and to date the gap is still almost twice as large as that in the United States.

How this graph was created: After finding the first series (men, United States) and the second series (women, United States), create your own data transformation using the formula a-b. After finding the same two series for Italy, apply the same transformation.

Suggested by Silvio Contessi

View on FRED, series used in this post: ITALFPMNA, ITALFPWNA, LNS11300001, LNS11300002


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