FRED posted a tweet linking to a graph of the unemployment rate by education level. I thought I'd try the dynamic equilibrium model out on it:
One of the interesting things is that while the dot-com bust and the housing bust/financial crisis hit all levels at roughly the same time (vertical lines in 2001 and 2008), the 2014 boom hits the lowest education levels first, followed by the higher levels. That boom might have already passed for those with less than a high school diploma (and might be a leading indicator of a future recession).
Another interesting thing is that the logarithmic rate of decline is roughly the same in each case (from highest education to lowest it is 0.087, 0.070, 0.091, and 0.081 (multiply these by 100 to get a rough estimate of percent decline per year). That is to say that while the shocks may appear at different times, the process of the unemployed finding work is roughly the same for all education levels.
[Update 10 October 2018: In thinking about this a bit more, while these are roughly the same, the matching rate for "some college" (0.070/y) is significantly less than the others. Discussed more here.]
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Update 3 February 2017
I just wanted to note that the U6 unemployment rate tells basically the same story (rate of decline is 0.087 just like the others):
This means that given the U3, I could derive the U6 rate (as well as the rates for different education levels).
[Update 10 October 2018: In thinking about this a bit more, while these are roughly the same, the matching rate for "some college" (0.070/y) is significantly less than the others. Discussed more here.]
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Update 3 February 2017
I just wanted to note that the U6 unemployment rate tells basically the same story (rate of decline is 0.087 just like the others):
This means that given the U3, I could derive the U6 rate (as well as the rates for different education levels).
I'd point out that the LFPR enters into the unemployment rate in a way that complicates this picture a little bit. Is the "less than highschool" rate going up because they're losing jobs, or because a larger portion of the "less than highschool" population is reentering the "potential workforce", encouraged by the improving economy and now hopeful of finding a new job? This is one element that makes interpreting the unemployment rate a little hard, as the denominator is kind of fuzzy and can change size based on both business cycle length trends and generational demographics. Thoughts?
ReplyDeleteOne thing that is interesting about the info eq approach is that the unemployment rate is not an independent quantity, but rather a property of both the labor force (L) and the number of unemployed (U) in general equilibrium.
DeleteThese two variables are in information equilibrium so during the declines, both are increasing but increasing at differential rates (depending on the IT index) so that if L grows at rate r, U is growing at k*r and the unemployment rate grows (falls) at a rate of (k-1)*r (which is less than zero if k < 1). We can call these "normal times" as most of the time, this is what is happening.
Your question applies when shocks hit: in that case, we cannot distinguish an increase in unemployed from a decrease in the labor force from the unemployment rate alone. But we can look at the individual labor force and unemployment levels. Since the equilibrium is defined by a difference in logs, we should look at the log of the levels:
https://fred.stlouisfed.org/graph/fredgraph.png?g=cDMs
We can see from this graph that while there is some small variation in the labor force (notably in 2008-9), nearly all of the variation is in the unemployment level (10% variation vs 1%, a 10:1 ratio) so the shocks are primarily people losing (or gaining) jobs. This should make sense: the labor force is 160M while the unemployment level is about 10M. Changing 160M by 100k is a 0.1% change while changing 10M by 100k is a 1% change (a 10:1 ratio). A 1% change in the unemployment rate could be a 1% change in the number of unemployed (100k people) or a 1% change in the number people in the labor force (>1M people). The latter seems far more plausible. Basically it takes a lot of people to budge the denominator.
Another way to disentangle the effects might be to look at the matching function.