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Posts tagged with: "IC4WSA"

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What can we claim about initial claims?

Keeping track of initial unemployment insurance claims

Initial unemployment claims is a much-watched indicator of the economy. It counts how many people have become eligible for unemployment insurance compensation in a particular week. The data are available quickly and at a high frequency (weekly), but the series has the disadvantage of being highly volatile. This is why FRED also offers a four-week moving average, shown in the graph above: Simply, it’s the average of the past four weeks. Included in the graph is also a red line that indicates the lowest value of this statistic in the course of its history—in May 1969. Currently, claims are around 230,000 per week; and, while this is low, it was lower for 126 weeks early in the sample period.

Of course, the population was much smaller in the 1960s, so the current statistics are even more impressive than they first appeared. Which is what the second graph shows, after dividing new claims by population. The red line indicates the lowest point before recent years, which occurred in April 2000. That low point has clearly been beaten—ever since May 2015, in fact. Keep in mind, though, this statistic is only part of the labor market picture. For example, average unemployment duration is still elevated (see a previous blog post on this). Also, unemployment insurance eligibility requirements may vary over time and, thus, distort the statistic.

How these graphs were created: Search for and select the 4-week moving average for “initial claims” and click “Add to Graph.” From the “Edit Graph” panel, use the “Add Line” feature to create a “user-defined line” and enter 179,000 for the start and end values. For the second graph, edit the first graph by adding a series to the first line, searching for “civilian population” and applying the formula a/(b*1000). Use the “Add Line” feature to create a “user-defined line,” and enter 0.00223 for the start and end values.

Suggested by George Fortier.

View on FRED, series used in this post: IC4WSA, LNU00000060

The unemployment bathtub

Economists often find a bathtub to be a useful metaphor for the behavior of unemployment. There’s some inflow of newly unemployed workers and some outflow as workers find jobs. A classic way to measure the inflow has been with initial claims of unemployment benefits, the blue line, in which we see spikes at the start of each recession. This inflow of newly unemployed persons initially reduces the mean duration of unemployment, the green line. But the green duration line rises as the blue initial claims line falls—since people who become unemployed early in the recession and remain so are unemployed for a while by the time the recession winds down. Every recession follows this pattern: Claims peak, then unemployment peaks, then duration peaks. The logic is essentially that of the bathtub: First it fills quickly; then, after some time, it begins to drain. But as this is happening, those left in tub have been there longer and longer.

How this graph was created: Search for and select the 4-week moving average of initial claims. Set its units as an index with scaled value of 100 at the 2007 pre-recession peak. Then use the “Add Data Series” option to add the other two series: the seasonally adjusted civilian unemployment rate (with the same units as the first series) and the seasonally adjusted mean duration of unemployment (with the same units as well).

Suggested by David Wiczer.

View on FRED, series used in this post: IC4WSA, UEMPMEAN, UNRATE

A good use of moving averages

Some data series are very volatile. That is, they don’t follow a smooth or step-by-step pattern. And it’s difficult to draw conclusions when new data are added to a volatile series. The weekly release of initial claims for unemployment insurance is a great example. In this and similar cases, it is useful to adopt some kind of smoothing mechanism: Here we provide a four-week moving average. Traditionally, a moving average is centered—say, the average of two periods before and two periods after. This moving average takes the last four observations, which allows you to better read trends, especially if you’re focusing on the most recent data. Of course, trends become more obvious if you look at longer spans of time. This graph shows a span of five years. Narrow or expand the sample with the slide bar to see how a moving average can help you interpret the data and avoid the pitfalls of volatility.

How this graph was created: Search for “initial claims,” select the two (seasonally adjusted) series, and add them to the graph. Finally, restrict the sample to the last 5 years, which is done by using the settings above the graph on the right.

Suggested by Christian Zimmermann

View on FRED, series used in this post: IC4WSA, ICSA

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