The Truth Hurts: What Harford Didn't Say About Statistical Discrimination

Best. Post. Ever.

In the same way that people are generally for health care reform only if it involves more redistribution from rich to poor, I suspect the political advocates against stereotypes are only interested if their group is given an aggregate boost. You can argue that making each male pay his fair share for insurance makes for a more efficient allocation, and in competitive markets, this cost savings will be shared with consumers, but I doubt that is sufficient.

Most egalitarians operate under the assumption that everyone is the same--statistically, by race, ethnicity, gender, sexual orientation, etc.--and so differences between groups must be due to ephemeral and arbitrary starting values--there can't be differences between men and women because they are the same in everything (see Gene Expression on the book Apes or Angels). Until the axiom of equality (all humans are the same, across all dimensions of human groupings or talents) can be safely rejected without being called a reactionary, stupid, mean, etc. your solution can't happen.

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The "black names don't get interviews" story was debunked in Freakonomics, I thought. The big point was that the names used to signify race were more accurately signals of class.

Still, even if I were to accept the theory that, say, "whites are better than blacks at software development on average," it doesn't do me any good as a filter for employment. Because all applicants have already passed through other screens, such as college, that filtered out the poor performers from both groups.

(You can legitimately say that a degree isn't a good test, but you could also use other filters, like previous experience on their resume.)

Excellent post.

Steven Levitt and Steve Dubner wrote in Slate in an excerpt from Freakonomics called "Would a Roshanda by Any Other Name Smell as Sweet?" about those super-black names that black mothers started giving their babies during the Black Pride era:

"The typical baby girl born in a black neighborhood in 1970 was given a name that was twice as common among blacks than among whites. By 1980 she received a name that was twenty times more common among blacks."

Levitt and Dubner show strikingly little sympathy toward blacks who have a harder time getting called in for a job interview because, as shown by numerous "audit studies", employers are dubious of DeShawns and Darnells.

Levitt and Dubner scoff:

"Was he rejected because the employer is a racist and is convinced that DeShawn Williams is black? Or did he reject him because ‘DeShawn’ sounds like someone from a low-income, low-education family?"

Sure, as the authors imply, a boy named DeShawn may indeed be, on average, more likely to goldbrick or to rip off his employer than a boy named, say, "Dov" (the male name with the most educated parents according to the book).

Following their Naturist inclinations, Levitt and Dubner conclude:

"And that's why, on average, a boy named Jake [the whitest common male name] will tend to earn more money and get more education than a boy named DeShawn. A DeShawn is more likely to have been handicapped by a low-income, low-education, single-parent background. His name is an indicator—not a cause—of his outcome. Just as a child with no books in his home isn't likely to test well in school, a boy named DeShawn isn't likely to do as well in life."

There aren't too many people who make _me_ sound like a diversity-sensitive multi-cultist, but sometimes Levitt is one of them! The authors could at least have a little compassion for the poor kid. DeShawn didn't ask to be given his name.

And I must point out that a recent study by economist David Figlio calls into question Levitt's assumption that DeShawns aren't hurt by prejudice. Figlio cleverly looked at siblings, and found that the ones with the blacker names tended to get rated more poorly by their schoolteachers even when their test scores were the same.

Blacks actually get a substantially larger return to education than non-blacks!

Similarly, I find that I am tremendously impressed by a black guy in a well tailored business suit, whereas I am apt to assume a white guy in well tailored business suit is a pointy haired idiot.

Black on black discrimination for 'acting-white' does have an intrinsic link to statistical discrimination, you may just be unaware of the statistics. Roland Fryer did the study several years ago.

In any case I tend to look at all matters of this type of research to exhibit a sort of confirmation bias and the bits about self-fulfillment or self-reversal to be post-hoc rationalizations. Stereotypes are, after all, a shorthand way of thinking and in the interests of generating policy, so are statistics. There are very few aspects of human behavior that can be accounted for on the basis of key performance indicators, nor are statistics likely to be taken on the rationale. And so we tend to focus on the few statistics that can be collected reliably and then, post-hoc, try to make sense of them.

Take the example of the federal consent decree set for the LAPD in the wake of the CRASH scandal and general public concern about the beating of Rodney King. There was a stereotypical assumption that white police officers were stopping black motorists because of racist reasons. And so in order to monitor this, a set of new reporting regime & requirements involving racial checkboxes was established. At some point, we would have a fairly representative sample of which officers were citing which people by race, but none of that statistical information gives any outsider a better understanding of the reasons why police do what they do. In otherwords we created a statistical abstract, an incomplete model of officer behavior, which is much more complex than can ever be statistaclly represented - especially on moral questions like discrimination.

So we can generate statistics on discriminations, but can we generate statistics on the rationales behind those discriminations?

It seems to me, that for the purposes of policy and remedy, we are always going to be at a loss with our abstractions, and that those who are close enough to the transactions in question cannot be both efficient at their jobs and effective in generating documentation bringing the layman close enough. We will always use some stereotypical and reverse-stereotypical thinking. It's how human mind copes with realtime performance without crashing.

Bryan, this is a good post, but you need to cite your sources.

I tested these claims using one of the world's best labor data sets, the NLSY. The results directly contradict Tim's self-fulfilling prophesy story. Blacks actually get a substantially larger return to education than non-blacks!

Citation? If this isn't published, Hartford can't be faulted.

But it is consistent with the small academic literature on counter-stereotypical behavior. If you clearly violate expectations, people not only notice; they often over-react.

Citation(s)?

There are about a half dozen federal longitudinal tracking data bases going back to 1967, for which interviews are conducted every year or two to see what people are up to now. The most famous is NLSY79, for which the U.S. military paid to have the entire nationally representative sample of about 12,000 15-23 year olds given the military's AFQT IQ test, its entrance exam. The military gave the test scores to Charles Murray and Richard Hernnstein and they matched it up with what the panel was doing in 1990. We now have another 15 or so years of data, and, amazingly enough, we even have data on about 6,000 children of the original panelists, including the kids' IQ scores.

Just Google NLSY and you can download it for free from the Bureaa of Labor. It's all out there for any social scientist to use. It's been used in countless studies, but they don't get much publicity because, on the whole, they just keep finding the same kind of thing Herrnstein and Murray found in The Bell Curve. And nobody wants to hear about that!

Wouldn't allowing employers to ask about child rearing plans (assuming they can get accurate information) help women overall by making them less risky? Some women may be helped and other hurt, but by the logic that you employ I would think that the overall effect would increase average female income.

Is the return on education for blacks significant for every level of education? For instance, it could be the case that there is not a significant return to a four year degree but that there is for post-doc work.

Public relations drives companies to recruit minorities for the top positions in a way that it does not for middle management.