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Posts tagged with: "UNEMPLOY"

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When initial claims, unemployment, and payroll employment clash

Not long ago, the FRED Blog discussed several details about the construction and interpretation of the data for initial weekly claims for unemployment benefits. As of May 30, FRED shows that the four-week moving average was 2.3 million new claims. Yet, the FRED graph above shows that for the entire month of May 2020, there was a decrease in the number of persons unemployed. And there was also a simultaneous increase in the level of payroll employment. How is all this possible?

First of all, data related to the labor market come from different sources: The U.S. Employment and Training Administration reports the number of initial weekly claims for unemployment benefits; and the U.S. Bureau of Labor Statistics, through the Current Employment Statistics (Establishment Survey), reports the payroll employment and unemployment figures.

Also, the data series have similar names but represent different concepts. Even if you file an initial claim for unemployment benefits, for example, it does not necessarily mean that you will be counted as unemployed.

Finally, keep in mind that changes in the number of persons listed on payrolls do not correspond to changes in the number of persons employed or unemployed. The FRED graph below shows that during May, June, July, and October of 2019 there were simultaneous increases in the level of payroll employment and increases in the number of persons unemployed.

How these graphs were created: Search for and select “All Employees, Total Nonfarm” anuse the “Edit Graph” menu to add two more lines: “Unemployment Level” and “Employment Level.” Next, change the units of any of the three series to “Change, Thousands of Persons” and click on “Copy to all.” Lastly, change the graph format to “Bar,” edit the colors to taste, and change the date range to match the time periods of each graph.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: CE16OV, PAYEMS, UNEMPLOY

The recent evolution of labor force participation

Small movements from a lot of labor market churn

Since the early 2000s, labor force participation has been declining in the U.S. After peaking at 67.3 percent in March of 2000, the labor force participation rate declined consistently to 62.4 percent in September 2015 and has since flattened out. The first graph shows the period of decline in the labor force participation rate, which started in early 2000, flattened out in mid-2005, and then declined again from the onset of the Great Recession to 2015.

Several variables in FRED can illustrate the labor force dynamics at play behind the declining labor force participation rate. The next graph shows the annual change in the labor force (employment plus unemployment). While the labor force has mostly been increasing since 2000, it has not been increasing fast enough to keep up with population growth. Starting in 2014, however, the pace of growth in the labor force picked up, which led to the flattening out of the participation rate.

The last graph shows monthly flows into (red line) and out of (blue line) the labor force. These gross flows are very close to each other, with the net changes (green line) always close to zero. It is the net changes that explain the evolution of aggregate labor force participation. From 2009 to 2016, the positive values are not enough to offset the more negative values and more people flowed out of the labor force. More recently, however, the positive contributions more than offset the negative values, leading to an increase in participation. Despite this recent evolution, the graph does not seem to point to any particular new trend that’s different from the past. This suggests that more research is needed to understand the observed decline in the participation rate.

How these graphs were created:
Graph 1: Search for “Labor Force Participation.” Graph the first result and limit the date range from 2000 to current.
Graph 2: Search for “Unemployment.” Graph the series titled “Unemployment Level.” From the Edit Graph tab, type “Employment Level” in the customize data section search box. Click the series titled “Civilian Employment Level” and then click Add. Finally, type a+b in the formula box and change the units to “Change, Thousands of Persons.”
Graph 3: Search for “Labor Force Flows.” Graph the series titled “Labor Force Flows Employed to Not in Labor Force.” Repeat the process outlined in Graph 2 to modify the line by adding “Labor Force Flows Unemployed to Not in Labor Force” to the graphed series. Now, select the middle menu and search for “Labor Force Flows Not in Labor Force to Unemployed” and add this series as a new line. Repeat the process to modify the line by adding “Labor Force Flows Not in Labor Force to Employed.” Once again, use the middle menu to add “Labor Force Flows Not in Labor Force to Employed” as a new line and then modify the line by adding the remaining three flows as additional series on the new line. Use the letters assigned to each series to calculate the difference of the sum of those flowing into the labor force less those flowing out of the labor force (e.g., consider (a+b)-(c+d)).

Suggested by Maximiliano Dvorkin and Hannah Shell.

View on FRED, series used in this post: CE16OV, CIVPART, LNS17200000, LNS17600000, LNS17800000, LNS17900000, UNEMPLOY

The problem with U-U

About 50% of unemployed workers this month will be categorized as unemployed in the next month as well. Let’s call this concept of unemployment persistence the U-U rate and calculate this way: Take the number of workers observed as unemployed in two consecutive months and divide that by the number of workers unemployed as of the first month. The U-U rate is a potentially powerful tool for understanding unemployment dynamics, but we should test how accurately it predicts unemployment duration.

Here, the U-U rate is the “persistence” and 1 minus the U-U rate is the probability of exiting unemployment. We also apply properties of the exponential distribution to calculate its implied mean and median.*

Spoiler alert: The U-U rate poorly predicts unemployment duration. The graphs plot the implied mean and median durations using the U-U rate compared with self-reported unemployment duration. For both the mean and median, the implied duration from U-U is far from the corresponding statistic of self-reported unemployment duration. Why?

  1. The U-U rate we measure here isn’t really the persistence of unemployment, nor is 1 minus the U-U rate the rate of exiting unemployment. The Current Population Survey (CPS) provides these data by following workers for only four months at a time. So, the number of unemployed from one month to the next can’t include those who exited the survey in the fourth month. Hence, the number staying unemployed for 2 months is 1/4 lower, but the total unemployed in 1 month isn’t.
  2. A deeper problem is the way the CPS counts “unemployed” workers. To be counted as unemployed, a worker must be actively searching for a job. Very often, though, a worker won’t search for a month, despite still wanting a job. For example, a job seeker could have applied for appealing jobs last month but, since she’s waiting to hear back, hasn’t applied for any jobs this month. She could also be waiting for new postings or have gotten a job with a delayed start date. To further complicate things, a job seeker may find a temporary job that doesn’t feel like it truly interrupted her unemployment spell when she reports her duration of unemployment, but reporting it would break the U-U pattern.
  3. One feature of unemployment is called “duration dependence”: As workers are unemployed for longer, their individual probability to remain in unemployment tends to increase. This extends their duration further than would be expected using the average unemployment persistence. Even if we could accurately measure the persistence of unemployment, some exit unemployment much more slowly. These job seekers pull out the mean of unemployment duration more than they push up the average persistence.

*We treat the implied exit rate from unemployment as constant across individuals, so the exponential is the proper distribution for the number of people who exit unemployment at different times. The mean is (exit rate)-1 and median is log(2)*(exit rate)-1.

How these graphs were created: The top graph uses mean unemployment, and the bottom graph uses median unemployment duration and a slightly different formula for the implied median. Search for “flow from unemployed to unemployed workers 16+” and add that series to the graph. (FYI: We use the seasonally adjusted series.) In the “Edit Graph” section, add a line to this series: unemployment level. Divide the flow by the number of unemployed by using the formula a/b. Compute the implied mean duration by using the formula 1/(1-a/b). To add the reported mean duration, use the “Add line” option, search for mean unemployment duration, and then convert it to a monthly statistic (since it’s reported in weeks): Divide by 52 and multiply by 12 with the formula a/52*12. For the bottom graph, use the median versions and be sure to use the formulas as noted in the graph labels.

Suggested by David Wiczer.

View on FRED, series used in this post: LNS17500000, UEMPMEAN, UEMPMED, UNEMPLOY


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