Suppose you divided workers into two groups, which we designate as type H and type L, and conjectured that the number of newly unemployed workers each month represents a mixture of the two types and further that there are differences in the probabilities that each of the two types will be successful in finding jobs. By keeping track of the dynamic accounting identities (somebody who’s been unemployed for 3 months at time t and doesn’t find a job will be unemployed for 4 months at t+1), observations on the 5 variables in the two graphs above are more than enough to determine 4 unknown magnitudes (inflows of type H and type L workers each month and fraction of each type who exit the pool of unemployed each month). Our statistical model also allows for measurement error (the true numbers may be different from those that are reported) and assumes that inflow and outflow probabilities for the two types change smoothly over time. We also use the extra information in the fifth observed variable to allow for the possibility that the process of being unemployed for a longer duration actually changes the individual, even if he or she started out initially just like everyone else.
We find that in normal times, 90% of those who are newly unemployed would be characterized as type H, more than half of whom will likely no longer be unemployed the following month. But in most recessions, the single most important development is an increase in the number of newly unemployed type L individuals who end up having a more difficult time finding work. For example, here’s how our model interprets the changes during the recession of 1981-82.
The overwhelming story behind the Great Recession is newly unemployed type L workers.
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