Two recent Econlib posts of note

1. David Henderson discusses Scott Winship's skepticism about claims of the costs of inequality.

It's a good post to take a look at just to get the whole scope of the issue, but I think there are a lot of problems with Winship's claims and the bashing of several left-leaning economists is entirely inappropriate and unsupported by what he presents. He puts a lot of emphasis on work on inequality by the CBO and work by Burkhauser. These analyses are fine, but hardly the CBO/Burkhauser research speaks to fundamentally different questions than the stuff the list of economists he bashes at the beginning usually cite. It is household adjusted, which is good to look at but different, and it's post-transfer, which is good to look at but different.

If we are thinking about redistributive policy that latter point - that the CBO/Burkhauser numbers are post-transfer - is extremely relevant. One can't very well argue that redistributive efforts are unnecessary because inequality is illusory if your proof that inequality is illusory demonstrates that it's illusory because of redistributive efforts! Indeed, that may even show that the existing redistributive policies were necessary and did their job!

Inequality is a very complicated issue to discuss, and there are lots of different questions that people are legitimately interested in - is the wage distribution fair? do families have access to the resources they need? does redistribution work? is inequality a problem when you consider prospects over the life course?

All of these are important - but all call for looking at very different data. Too much of the accusations around inequality are derived from muddying the fact that people seem to be interested in fundamentally different questions.

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2. Bryan Caplan discusses job mismatch.

I think people need to approach this discussion very carefully too. This issue is actually exactly what the Sloan Foundation has funded me for three years to look into. The job relatedness questions he cites should not be interpreted as "mismatch", in my opinion. For one thing we should be skeptical about exactly what they're capturing. Mark Regets, an economist at NSF who works exclusively with the NSCG data that Bryan cites, always likes to refer to bizarre cases that come up in the data where surgeons report that their job is unrelated to their education (i.e. - medical school). Unfortunately, he refused to disclose the identity of these survey respondents so I could avoid these surgeons (something about privacy, etc.). More importantly for the purposes at hand, it raises questions about how people are interpreting the survey. Maybe most of what the surgeon does he feels he learned on the job or through inservices - that school only contributed a small component to his knowledge. Maybe these people are focusing on the detailed technical knowledge they gained outside of school and are ignoring the fact that school taught them how to "think like an economist" or "think like a lawyer" - a very important contribution even if people pick up the details of their work after school. It also has to do with conceptualization of what a certain major "does". An English major writing proposals for a big contractor may say that her job is unrelated to her major because she feels like she's gone off of the editor/journalist/writer track she thought she wanted. Of course that's nonsense. In all likelihood she does her job much better than someone that doesn't have the background in writing and editing that she does.

Another reason to not interpret these questions as evidence of mismatch is that mismatch has a specific meaning in economics related to a poor search-and-matching process in the job market. So do we think that everyone working outside their field is poorly matched to their job? Of course not. Lots of people choose to work outside their college major for lots of different reasons. This is especially important for interpreting the wage premium associated with working in a job related to your major. If you work outside of your major by choice (in other words - it's not a matching problem), it's likely because you are very passionate about this opportunity that has come up. Passion in the labor market leads to compensating differentials: they don't have to pay you as much because you are doing what you love.

There's also a question of the underlying skills distribution within a major as an explanation for the earning differential. If there are fewer engineering jobs than engineering majors, the best engineers may be more likely to get the engineering jobs (if they want them). These engineering majors are going to be more productive than the other engineering majors, so of course they are going to enjoy a premium. You would see this even if the cost of mismatch were zero. The NSCG data used in the paper that Bryan cites has no grades or ability information that I am aware of to account for this, and the notes on the wage regressions don't seem to account for it either.

So approach with caution.

Our Sloan work specifically concerns the "loose coupling" between major and occupation. Most people assume the science and engineering labor force is a pretty smooth pipeline, or if they know that people work outside of the field they assume it's always bad or a divergence. We're looking into the decision making behind working out of field. This should be the subject of one of my dissertation chapters (and then hopefully that will be an article), as well as a bunch of other papers we're putting together. We'll be using the NSCG, as well as the Baccalaureate and Beyond survey, which does have data on grades.