Does skill-based technical change explain growing wage inequality?

John DiNardo (of the University of Michigan) and I were troubled by the
fact that there are a lot of patterns and trends in the labor market
that don’t fit in very well with a skill-biased technical change
explanation. We were motivated to embark on a Don Quixote mission, a
noble cause that wasn’t going to go anywhere [laughs].

One thing we pointed out, for example, is that women are lower skilled
than men, if you take the fact that they have lower wages as evidence
of their skill. The SBTC theory says that people with lower skills
should have slower wage growth than people with higher skills. But over
the 1980s, women did much better than men. It’s also the case that over
the 1990s, women’s relative wages were fairly stable again. So there
was a long period of stability of women’s relative wages, then a period
of convergence of women relative to men that ended in 1991-92, and then
stability again. That’s an important set of trends that SBTC doesn’t
address. SBTC might be consistent with it; it might not be, but the
theory needs a lot of auxiliary hypotheses to work.

The same thing is true with respect to the black/white wage gaps.
Blacks earn less than whites, and many people believe that the reason
they do so is because they’re less skilled. Nevertheless, during the
1980s, the black/white wage differential was stable. It didn’t widen as
people had predicted it might.

Another trend that didn’t fit with the SBTC hypothesis concerns the
relative wages of people with different bachelor’s degrees. There are a
couple of different data sets that collect starting salaries for newly
minted B.A.s. What these data show is quite remarkable. Everyone knows
that the average wage of young college graduates went up over the
1980s. It wasn’t the case, however, that the gains were most pronounced
in engineering or science. They were actually greater for graduates in
the humanities, which doesn’t seem consistent with the idea that there
is increasing demand for technically proficient, computer-savvy people.

…A final puzzle concerned the age structure of the increases in the
relative wages of college versus high school graduates. Wages of young
college-educated workers rose relative to young high school workers,
but for people over age 40 or so, there really wasn’t any change in the
high school/college premium.

Hat tip to Greg Mankiw and Matt Yglesias.

Comments

Addressing only the last sentence here, but perhaps there are different seperating equilibria for different demographic groups? It should be status relative to those similar to you that matters most for signaling. Young college graduates are just starting to show their superior intelligence and skills and dominate hs grads in the job market and are thus VERY different, while the 40+ crowd competes mostly with each other for mid or senior level positions, and their peers grew up in an era where college was a stronger signal, so perhaps most of their earnings growth was concentrated early in their career, and thats why the premium for older workers hasn't changed much. The claim seems to be that there wasn't any change in the premium for older workers, not that the premium doesn't exist.

As for the rest, I'm flummoxed.

very interesting.....

There may be no advantage of a college education in the over 40 group. After 10 years out of college your experience (and track record) is really the education that is worth a premium. The signaling a college degree provides is necessary because of the lack of a track record. Once one exists, the signaling is then redundant and no longer matters.

"There may be no advantage of a college education in the over 40 group. After 10 years out of college your experience (and track record) is really the education that is worth a premium."

However, you were unable to get into that career track IN THE FIRST PLACE were it not for the fact that you had a college degree.

The college degre allows you entry into a career track which then allows you to accumulate the human capital which allows you to earn lots of money at the age of 40.

Card essentially calls the entire concept bullshit in a backhanded way.

The examples he choose aren't just out of the blue or small data quirks, they are the first places you would see effects if the concept were true at all. They are the first places you would look to verify this concept.

Look who he compared:

Women to men
Blacks to Whites
differently skilled college grads
high skill working class positions to low skill ones
high school to college grads

If you don't find effect in these groups, there really aren't many more places to look for effects.

So what has caused inequality to rise? Its pretty clearly not skill-based.

I would have expected the skill differential between men and women and between blacks and whites to have narrowed during this time period. So I'm not sure that Card's point holds water. But maybe that's addressed in the full interview, which I haven't read.

We had massive programs of affirmative action and anti-discrimination (which, in practice, usually turns out to include affirmative action to preclude discrimination lawsuits) precisely to give money to women and blacks, so why is it unexpected that they would get more money despite being less skilled?

Part of the problem is a simplistic notion of "technical skill". A humanities major with deep experience in supply-chain management definitely has a "technical skill", as do all sorts of other fields. Some of this may be simple "department-think" on the part of academics: "techies" are supposed to be engineers and scientists, not people who have deep process knowledge but with BA's in French literature.

Uh, folks, hasn't anyone read the lead article in the June 2006 American Economic Review? Thomas Lemieux, "Increasing Residual Wage Inequality: Composition Effects, Noisy Data, or Rising Demand for Skill?" From the abstract:

"This paper shows that a large fraction of the 1973-2003 growth in residual wage inequality is due to composition effects linked to the secular increase in experience and education, two factors associated with higher within-group wage dispersion....The magnitude and timing of the growth in residual wage inequality provide little evidence of a pervasive increase in the demand for skill due to skill-based technological change."

From the introduction:

"...there are already three possible reasons why residual wage inequality may be increasing over time. First, the "price" or return to unobserved skills may be increasing because of an increase in demand for skill...Second, the dispersion in unobserved skills may be growing over time. For example, if unobserved skills are more dispersed among older and more educated workers, dispersion in unobserved skills could increase because of composition effects linked to the aging and increasing educational achievement of the work force. Third, the extent of measurement error may be increasing over time.
In this paper, I show that all three factors played an important role in the increase in residual wage inequality over the last three decades. In other words, the growth in residual inequality cannot simply be equated to a rise in the demand for skill. In fact, I show that increases in the return to unobserved skills account for no more than 25 percent of the overall increase in wage inequality over the last three decades. Moreover, I show that all of the increase in the return to unobserved skills is concentrated in the 1980s."

So before speculating, it might be a good idea to read Lemiuex's paper and then decide if he missed something. Because if he's basically right, these questions have been largely answered.

Basically, most of the increase in residual wage inequality appears to be due

What a cool paper. It also happens to confirm of my major methodological biases: I believe that too many economists spend too much time worrying about statistical technique and not enough time checking and worrying about the quality of the data. Lemieux accomplishes a lot simply by using the May supplement to the CPS, rather than the March CPS. This allows Lemieux to get better data on hourly workers, because the March CPS clearly has more measurement error. Very cool.

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