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### Posts tagged with: "UNEMPLOY"

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## 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

## Seeking, missing, finding, and filling jobs

Bloomberg News recently suggested firms may be struggling to find qualified employees. The FRED graph above does show that, for the past year, the number of job openings in the U.S. has generally been higher than the number of hires. So, yes, some positions aren’t being filled. Also, the level of unemployment has been steadily decreasing since 2010, even though the BLS reports that the unemployment rate hardly fell from August to April of this year.

Civilian unemployment rate = (Unemployment level / Civilian labor force) * 100

As the equation shows, for the unemployment rate to hold steady and, at the same time, for the unemployment level to decrease, the labor force must also decrease. So, while fewer “unemployed” people might be taken at face value to mean more people are finding jobs, keep in mind that some people may have simply stopped looking for a job and left the labor force. And firms may not be finding their ideal applicants among the unemployed.

On the other hand, the unemployed may not be looking for the right jobs. For more insight into the current employment situation, visit FRASER (Federal Reserve Archival System for Economic Research) for the Employment Situation—May 2016. This and previous reports from the BLS tally which industries have added or cut jobs in that particular period—potentially useful information for those who want to know which industries are potentially looking to hire. In FRASER, you can explore plenty of interesting publications on employment throughout history. Happy hunting!

How this graph was created: Search for “unemployment level” and select the seasonally adjusted series and click “Add to Graph.” Adjust the timeline to start at January 2007. Add the next series by searching for “hires” and choosing the total nonfarm seasonally adjusted monthly series with level in thousands for units. Add the last series by searching for “job openings” and again choosing the total nonfarm series.

Suggested by Emily Furlow.

View on FRED, series used in this post: JTSHIL, JTSJOL, UNEMPLOY

## 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