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How labor market flows changed

After the most recent recession, the volume of workers switching their employment status from unemployment to non-participation (that is, not in the labor force) and vice versa increased dramatically, reaching levels not seen in the previous two recessions.

The first graph shows the flows from non-participation into unemployment (NU) and from employment into unemployment (EU), both normalized by population. In the past, these two flows closely tracked each other and the contribution of both non-participation and employment to unemployment was roughly equal. However, in the Great Recession, the contribution to unemployment from both series increased significantly, with the contribution of non-participation becoming substantially larger, peaking far above the EU flow and taking a much longer time to return to lower levels.

The second graph shows the flows of workers that have switched from employment and non-participation into unemployment as a fraction of total population. We can see that the flows from employment to non-participation (EN) have decreased in the past recession, a behavior in line with previous episodes. The flows from unemployment into non-participation (UN) show a much stronger response, increasing to historically high levels after 2008.

The fact that both UN and NU flows are larger than usual, but of a roughly similar magnitude, implies that the labor market has become more dynamic on that margin. The levels of labor market variables (employment, unemployment, and non-participation) are very sensitive to the dynamics of labor market flows. Therefore, understanding the causes for the recent evolution of the UN and NU flows is central to understanding the dynamics of labor market variables in the past recession.

How these graph were created: Go to the labor force status flows category. For the first graph, find the flow called “Labor Force Flows Employed to Unemployed” and graph the seasonally adjusted values. Select the “Graph” tab and scroll down to the “Add Data Series” option. In the keywords box, search for “Labor flows,” scroll down to “Labor Force Flows Not in Labor Force to Unemployed,” and add the series as a new data series. Select the “Add Data Series” option again and search in the keyword box for “Civilian population.” Select the first series and add the data under the “Modify existing series option” for data series 1 and 2. Now, select “Edit Data Series.” Under the “Create your own data transformation” option, type the formula a/b and click “Apply.” Do this for each data series. Finally, restrict the time period to 1990 through 2014. To make the second graph, repeat this process but this time select the flows “Employed to Not in the Labor Force” and “Unemployed to Not in the Labor Force.”

Suggested by Maxiliano Dvorkin and Hannah Shell

View on FRED, series used in this post: CNP16OV, LNS17400000, LNS17600000, LNS17800000, LNS17900000

“Log in” to regional data

FRED has compiled regional U.S. data for many economic indicators. The vast amount of regional data can make searching through the categories a bit overwhelming. But there are simpler ways to find what you want: You can search the releases if you have a good idea what you’re looking for. You can also use tags to quickly narrow down your search results—for example, by selecting specific geographies and geography types. Here, we look at per capita income for a sample of metropolitan statistical areas (MSAs) across the U.S.

In the graph above, we took the natural logarithm for each series, for the following reason: If the economic aggregate you’re looking at is growing at a constant rate and you have a sufficiently long sample, the data series will look convex and may give the impression that growth has been explosive. But if you take the natural logarithm, then a constant growth rate will look like a straight line. The graph above uses the natural logarithm: All four metropolitan areas have a kink around 1980 with a subsequent slowdown. The graph below doesn’t use the natural logarithm: That kink is not visible. Also, below it looks like San Francisco is taking off and separating from the others. Above, the distance between the lines can be interpreted as percentage difference, and it is clear that in relative terms San Francisco stays within range.

How this graph was created: Go to the list of MSAs for which FRED has per capita income, select the series you want, and click the “Add to graph” button. That’s the bottom graph. For the top graph, go to the graph tab and, for each series, expand “Create your own transformation” and select “Natural Log.”

Suggested by Christian Zimmermann

View on FRED, series used in this post: BIRM801PCPI, LEBA142PCPI, SANF806PCPI, STLPCPI


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