Educational Signaling and Statistical Discrimination
By Bryan Caplan
The tacit assumption of signaling models of education is that employers engage in statistical discrimination. Instead of looking into each applicant’s soul and finding his true marginal productivity, employers rely heavily on more-or-less accurate stereotypes about “high school drop-outs,” “English majors from Harvard,” “community college graduates,” etc. As long as the stereotype leads to profitable hiring decisions, there’s not much reason for employers to wonder, “Are Harvard grads more productive because Harvard taught them job skills, or because people who graduate from Harvard were highly productive all along?”
The real-world importance of statistical discrimination is hard to dispute. Even people like me who think that markets thwart most taste-based discrimination usually admit that statistical discrimination is much more resilient to market pressure. Indeed, as I explain to my labor students:
Unlike taste-based discrimination, statistical
discrimination can survive and thrive in markets. If group differences
are real, and it is costly to judge case-by-case, then people who don’t
discriminate lose money.
If the typical economist read my notes on discrimination, his main complaint would be that markets do less to undermine discrimination than I imagine. Why then are most economists so quick to dismiss the signaling model of education as “implausible”? If you can believe that employers conserve on information costs by using their stereotypes about race and gender, why can’t you believe that employers conserve on information costs by using their stereotypes about education?