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Posts tagged with: "CIVPART"

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Why is the unemployment rate not decreasing?

The U.S. economy has been adding jobs continuously for several years. In fact, payroll employment growth has been consistently higher than measures of population growth, including the civilian population shown in the graph above. This is definitely an encouraging sign for the health of the labor market. The unemployment rate has steadily decreased over this period, yet it has hardly moved in recent months: It was 5.1% in August 2015 and is 5.0% as of April 2016. With this larger inflow of employed people than people in general, the unemployment rate should decrease, right? That would be correct if the proportion of people in the labor force remained constant. But it has not remained constant, as is visible in the graph below. The labor force participation rate has been increasing significantly in recent months after a decades-long decline. A large number of people who previously declared they were not in the labor force (not working and also not looking for a job) are now in the labor force. Some of these people are unemployed, and these additions to unemployment rolls have been large enough to almost exactly erase the gains made in employment.

How these graphs were created: For the first graph, go to the most popular series (shown on the FRED homepage, under “At a Glance” tab) and click on the payroll employment link there. Then add the civilian noninstitutional population series to the graph. Finally, change the units of both series to “Percent Change from Year Ago.” For the second graph, search for and add “Civilian Labor Force Participation Rate” to the graph, then add the unemployment rate series. Finally, set the y-axis to the right for the latter.

Suggested by Christian Zimmermann

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

How healthy is the labor market, really?

Economists, policy wonks, and the public often look at the unemployment rate to quickly assess the U.S. economy. Although the unemployment rate provides some understanding of the cyclical state of the labor market, it doesn’t account for those who have dropped out of the labor force. The labor force participation rate captures that information. Both rates (shown in the top graph) have declined since the end of the Great Recession, which may imply that there’s unmeasured slack in the labor market.

Andreas Hornstein and Marianna Kudlyak from the Federal Reserve Bank of Richmond and Fabian Lange from McGill University constructed a more comprehensive way of examining resource utilization in the U.S. labor market. Their non-employment index (NEI) counts those who are unemployed (as traditionally defined) and those who have dropped out of the labor force. The NEI weights those who have dropped out of the labor force according to their “attachment”—defined by the Bureau of Labor Statistics as the likelihood a person will transition back to employment, which is based on each group’s historical transition rate to employment relative to the highest transition rate among all groups. This weighting allows the authors to count all non-employed individuals without drawing “arbitrary distinctions on who is to be included.”

The BLS classifies the groups in the index as (1) unemployed, (2) out of the labor force but desiring a job, or (3) out of labor force but without the intention to reenter. The BLS further categorizes those who are out of the labor force but want a job as (2a) marginally attached because they’re discouraged by poor job prospects, (2b) marginally attached but haven’t looked for work during the most recent month, or (2c) temporarily out of the labor force for other reasons. Finally, the BLS classifies those who are out of the labor force but do not want a job as (3a) in school, (3b) not in school, (3c) retired, or (3d) disabled.

Hornstein, Kudlyak, and Lange recommend interpreting the NEI in comparison with the standard measure of unemployment. Generally, the two measures move in line with each other, with the exception of the period following the Great Recession, as shown in the bottom graph. This graph also includes a third series—the green line—that incorporates those who work part time in lieu of full time for economic reasons. More information on the NEI can be found here.

How these graphs were created: Top graph: Search for and select the monthly, seasonally adjusted unemployment rate. Use the “Add Data Series”/“Add new series” option to search for and select the monthly, seasonally adjusted labor force participation rate; be sure to set the y-axis position to the right. Bottom graph: Again, search for and select the monthly, seasonally adjusted unemployment rate. Then use the “Add Data Series”/“Add new series” option to add the two other series: Search for “non employment index” and select the base index (not the index that includes people working part-time for economic reasons). Then search for “non employment index” again and select the index that includes people working part-time for economic reasons.

Suggested by Travis May

View on FRED, series used in this post: CIVPART, NEIM156SFRBRIC, NEIPTERM156SFRBRIC, UNRATE

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


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