Correcting For Ability Bias By Measuring Ability
By Bryan Caplan
Problem #1: Ability bias. People with traits the labor market values (intelligence, work ethic, conformity, etc.) tend to get more education. Since employers have some ability to detect these valued traits, people with more education would have earned above-average incomes even if their education were only average. Punchline: Standard estimates overstate the effect of education on worker productivity and income.
Problem #2: Signaling. People with traits the labor market values (intelligence, work ethic,
conformity, etc.) tend to get more education. Since employers have imperfect ability to
detect these valued traits, people with more education earn above-average incomes even if they personally lack these valued traits. Punchline: Standard estimates overstate the effect of education on worker productivity, but not the effect on income.
Neither of these stories enjoys much support from labor economists. They usually just ignore the signaling model – but when they’re being careful they’ll off-handedly admit that “Standard empirical tests can’t distinguish between the human capital and signaling hypotheses.” If you mention ability bias, however, labor economists will quickly point you to a massive literature that supposedly debunks it.
But if you pay close attention, there’s a bizarre omission. Despite their mighty debunking efforts, labor economists almost never test for ability bias in the most obvious way: Measure ability, then re-estimate the return to education after controlling for measured ability. For example, you could measure IQ, then estimate the return to education after controlling for IQ.
When I ask labor economists about their omission, they have a puzzling response: “IQ is a very incomplete measure of ability.” True enough. But the right lesson to draw is that controlling for IQ provides a lower bound for the severity of ability bias. After all, if the estimated return to education falls sharply after controlling for just one measure of ability, imagine how much it might fall after controlling for measures of all ability.
What happens to the return to education after controlling for IQ? I’ve done the statistics myself on the NLSY, and found that the estimated return to education falls by about 40%. I’ve talked to several other economists of widely varying political persuasions who reached very similar results. Only yesterday, though, did I discover an excellent publication that replicates this 40% figure – and shows it to be extremely robust: McKinley Blackburn and David Neumark’s “Are OLS Estimates of the Return to Education Biased Downward? Another Look” (Review of Economics and Statistics, 1995). Their conclusion:
Thus, in our NLSY data, OLS estimation of the standard log wage equation, including test scores, appears to provide an appropriate estimate of the return to schooling. Such estimates indicate an upward bias of roughly 40% in the usual OLS estimate of the return to schooling (that omits proxies for ability). In contrast to evidence from other recent research using different statistical experiments to purge schooling of its correlation with the wage equation error, our results show that one can address the issues of omitted-ability bias, measurement error, and endogeneity, and still conclude that OLS estimation omitting ability measures overstates the economic return to schooling.
Call me cynical, but I’m confident that if Blackburn and Neumark’s work had come out the other way, defenders of education would loudly include it on their list of reasons to ignore ability bias. Indeed, I wonder if their list would have grown half as long if the obvious test undermined education skepticism instead of supporting it.
To repeat: The straightforward way to test for ability bias is to measure ability, then control for it. If this approach failed to reveal ability bias, it would be reasonable to dismiss it. In practice, though, the straightforward test finds ability bias to be not merely real, but large. I’m not going to let anyone forget it. Expect me to invoke Blackburn-Neumark on a regular basis from now on.