A few days ago I posted some skeptical notes about the comparison of unemployment rates over time within education categories. My comments were really purely statistical; I know next to nothing about unemployment numbers.
One of the advantages of running this blog is I sometimes get emails from actual experts. In this case, economist John Schmitt wrote in:
Your post looks at just how comparable the unemployment rates are in 2009 and the early 1980s. The specific issue concerns whether we should factor in the big changes in educational attainment between the early 1980s and the present –our working population is a lot better educated today than it was in the early 1980s.
According to the piece that motivated your blog post, the unemployment rate for workers at each level of education is higher now than it was in the early 1980s. So, in a mechanical sense, the unemployment rate is lower in 2009 than it was in the early 1980s only because a larger portion of the population in 2009 has shifted to the “low-unemployment” higher education groups.
You take the view that the aggregate unemployment rate is what matters, not the disaggregated unemployment rates by education. Dean Baker and I, however, did a recent analysis in the spirit of the education-based analysis you cite.
We focused on how much older the workforce is today (rather than how much better educated it is), and conclude that if you want to do a sensible comparison, you’ll want to factor in the age change.
The main argument from our [Schmitt and Baker’s] paper:
The official definition of unemployment has not changed between 1982 and the present. But, if we are interested in using the unemployment rate to assess the degree of underutilization of resources in the economy, the official measure does not tell the full story. Relative to 1982, the official unemployment rate today understates the true slack in the economy for two reasons.
First, the population is much older today than in 1982. The median age of the labor force was 42 years in 2008, compared to just 35 in 1982. Younger workers are more likely to switch jobs frequently and typically have fewer dependents and financial commitments so they can more easily endure periods of unemployment. As a result, all else equal, younger populations have a higher unemployment rate than older populations. We therefore expect a lower unemployment rate with today’s older population than we had with the much younger population in 1982, even if the economy were in an identical recession in both years.
“The second reason that the published unemployment rate today understates labor-market slack relative to 1982 is that the CPS misses a larger portion of the population today than in the past. The Census Bureau estimates, for example, that in 1986 the CPS covered 93.0 percent of the population, but by 2005 the coverage rate had dropped to 89.7 percent.3 For most purposes, the Census Bureau has techniques to compensate for the decline in coverage, but, for technical reasons, these fixes do not correct for the tendency of those excluded to be non-employed. The failure to take the lower employment rates of the uncovered population into account can lead to an important understatement of the unemployment rate today relative to 1982.
Obviously, the second issue is minor and not particularly relevant here, but there is an economic case for not taking the aggregate unemployment rates at face value. The key question is: how well does the economy do in converting inputs (workers of a particular age, say, or arguably of a particular education level) into outputs (jobs). If one economy (1982) has younger (or less educated) workers than the other (2009), we’d want to factor that in before we conclude that the 2009 economy is definitely doing better.
Interesting. I hadn’t thought of comparing by age; that makes a lot of sense. Certainly the aggregate numbers can never be the end of the story, once an appropriate analysis is done. As Kaiser commented:
The lesson of simpson’s paradox is to always look at the data at several levels of aggregation. If the analyses lead to different conclusions, that will lead us to explore more, and understand the data better.