Bryan Caplan  

International Evidence on the Human Capital/Signaling Split

PRINT
Daniel Goleman's Attack... Preferences, Planning, and Tra...
A Micro-Mincer regression estimates personal income as a function of personal education and controls:

ln Personal Income = a + b*Personal Years of Education + other stuff

Unless b is very large, b approximately equals the individual education premium.  b=.09, for example, indicates that an extra year of education raises earnings by about 9%.

A Macro-Mincer regression, analogously, estimates national income as a function of national education and controls:

ln National Income = a + b*Average National Years of Education + other stuff

In a pure human capital model, you would expect the Micro-Mincer and Macro-Mincer results to match.  More education makes people more productive, which increases personal income; more education makes nations more productive, which increases national income. 

In a pure signaling model, in contrast, you would expect the Micro-Mincer return to be positive, but the Macro-Mincer return to be ZERO (at least at the margin).  More education makes people look more productive, which increases personal income; at the national level, however, education is a rat race.  Only one person can be the best, and only 25% of the population can be in the top 25%.

Both cases are unrealistically extreme, but they do suggest a simple way to estimate the human capital/signaling split.*

1. Estimate Micro-Mincer regressions within countries.
2. Estimate Macro-Mincer regressions across countries.
3. Compare.

The implied human capital/signaling split is just (2) divided by (1):

Macro-Mincer coefficient/Micro-Mincer coefficient.

Of course, this computation is easier said than done.  (1) is pretty accessible: A big literature finds a global Micro-Mincer coefficient of roughly 10%.  But (2) is very difficult to get, because international education panel data is sparse, and researchers' samples and controls vary widely. 

Fortunately, after voracious but frustrating reading on the topic, I eventually hit pay dirt when the noble Ángel de la Fuente graciously emailed me an amazing appendix to his 2006 paper with Rafael Doménech.  In this appendix, they estimate the same Macro-Mincer regressions on 8 (!) different data sets for the same 21 OECD countries.  Their overall results:

Data Set

Macro-Mincer Estimate

Nehru et al (1995)

-0.7%

Kyriacou (1991)

1.0%

Barro and Lee (1993)

1.2%

Barro and Lee (1996)

0.3%

Barro and Lee (2000)

1.0%

Cohen and Soto (2001)

4.8%

de la Fuente and Doménech (2000)

2.4%

de la Fuente and Doménech (2002)

4.9%

Average

1.3%


Notice: The highest Macro-Mincer estimates imply a human capital/signaling split around 50/50.  The average estimate implies a split of 13/87. 

If, like me, you're a fan of transparent econometrics, you might prefer their log-levels with country fixed effects estimates.  The range is wider, but the average Macro-Mincer estimate remains very low.  In fact, it's eerily close to my best guest of the human capital/signaling split: 20/80.

Data Set

Macro-Mincer Estimate

Nehru et al (1995)

0.5%

Kyriacou (1991)

0.9%

Barro and Lee (1993)

2.0%

Barro and Lee (1996)

0.1%

Barro and Lee (2000)

1.3%

Cohen and Soto (2001)

7.2%

de la Fuente and Doménech (2000)

-0.2%

de la Fuente and Doménech (2002)

6.9%

Average

2.3%


At this point, you could reasonably ask: Are some of these 8 data sets objectively better than the competition?  Researchers predictably market their own data as new-and-improved, but outside certification is very scarce.  To the best of my knowledge, only one research team with no "dog in the fight" ever weighed in.  Their conclusion based on the data sets available back in 2003: "we are not convinced that any one of the available data series is clearly preferable to the alternatives."

Most researchers who don't like puny Macro-Mincer results emphasize that measurement error leads to attenuation bias, so the true effect of education is larger than it looks.  As I've pointed out before, though, this confident claim hinges on the crazy assumption that education is the only mismeasured independent variable!  As long as other variables are mismeasured, the observed coefficients on education could be too high, too low, or just right.

What does it all mean?  If you're a naive empiricist, the Macro-Mincer evidence is a big victory for signaling.  If you're a human capital purist, the Macro-Mincer evidence is a big indictment of the data.  The sensible Bayesian reaction, however, lies in the middle: While the Macro-Mincer evidence settles nothing, it's a little extra evidence in favor of an intrinsically plausible position

* A Micro-Mincer return in excess of the Macro-Mincer return also fits a scenario where education successfully trains workers for rent-seeking or other socially unproductive jobs.  HT to David Balan for reminding me of this possibility.  In my book, I explain why I doubt this is a big deal. 



COMMENTS (9 to date)
ThomasH writes:

Ought this move one's preferred position on policies to decrease inequality of income more toward redistribution and less toward education?

BC writes:

@ThomasH, actually, if one truly believed that the education premium was really due to signaling, then that would imply neither redistribution nor education to decrease inequality, at least not the inequality that results from inequality of opportunity. It would imply that we should replace our efforts on education with efforts on better signaling, e.g., having the poor take more tests and exams that could identify the high achievers. I'm not sure whether the poor's "access" to the SAT/ACT is less than their access to college education but, regardless, it would be much cheaper certainly to offer everyone the chance to take the SAT/ACT and to have a writing sample evaluated than to send everyone to college. If the signaling hypothesis is true, we don't actually need to send everyone to college, we just need to give everyone the opportunity to be accepted into college. (Maybe, that's not quite true; we might also need to allow everyone the opportunity to demonstrate that they would have done well in college courses had they actually gone.)

Jim Rose writes:

The trend rate of productivity growth did not accelerate over the 20th century despite a massive rise in investments in human capital and R&D because of the rising cost of discovering and adapting new technological knowledge.

The number of R&D workers and educated workers increased many-fold over the 20th century in New Zealand and other OECD member countries including the global industrial leaders such as the USA, Japan and major EU member states.

With knowledge ever accumulating, and a greater complexity of new innovations, each new generation of innovators and workers that used new technologies face an increasing educational and research burden.

R&D, human capital investments and on-the-job learning must be spread more and more thinly over a greater number of different sectors, products and production processes. More human capital must be accumulated to grow at the old rate.

human capital accumulation stops the growth rate from falling, as it did in the EU, because it did not keep up in investing in education and R&D.

DP writes:

The average for the first set looked a bit small, so I recalculated it.
It looks like a pretty standard Excel mistake. I get 1.3% only if I divide the sum of the first 7 sets by 8.
Otherwise, the average should be 1.9%.

Bostonian writes:

BC wrote, "I'm not sure whether the poor's "access" to the SAT/ACT is less than their access to college education but, regardless, it would be much cheaper certainly to offer everyone the chance to take the SAT/ACT and to have a writing sample evaluated than to send everyone to college."

Idaho, Maine, and Delaware require the SAT for all high school students. According to an NYT article "Testing, Testing: More Students Are Taking Both the ACT and SAT", "12 states now require, and pay for, all public high school juniors to take the [ACT]."

AS writes:

I suggest there exists decreasing marginal human capital gains with education. Human capital gains can still be quite large for individuals with low education, but gains taper off once the individual has a lot of education.

Yet even if human capital gains are small for high-skill workers, financial gains can be high. As the ideal, white-collar, professional jobs become more competitive, hiring decisions often rest on very small differences in perceived skill. Thus, individuals who improve their human capital just a bit face much greater probabilities of employment.

Is this "signaling", or just the obvious fact that at any elite level, small differences in performance have large impacts on individual outcomes. This is true in any field: professional sports, acting, music, art, etc. Why can't it be true for white-collar jobs?

Why, if there's an oversupply of educated workers, does the market not adjust by lowering wages until an equilibrium is reached and the surplus vanishes? What is propping up wages? Individuals are essentially playing a lottery where the outcomes are either (a) professional employment, (b) mal-employment, or (c) unemployment.

Zachary writes:

You have a significant TYPO:

In a pure signaling model, in contrast, you [*wouldn't*] expect the Micro-Mincer return to be positive, but the Macro-Mincer return to be ZERO.

Glen S. McGhee writes:

The Micro-Mincer equation has, at best, limitations.

Consider the guy at McDonalds' flipping burgers. According to Mincer, that graduate degree should net him more earnings than the high school drop-out working alongside him. But it doesn't work out that way, does it? (Hint: depends on labor supply, labor demand)

A lot depends, as well, on the organization that mediates the Mincer effect (if there is any), but none of this is in the equation.

This also applies to the security clearance example just referred to.)

When it comes to Mincer, I am a skeptic.

AS writes:

Are you sure that the the Macro-Mincer return will be ZERO in a pure signaling model? Shouldn't it be less than 0 because it wastes resources?

Comments for this entry have been closed
Return to top