The Truth Hurts: What Harford Didn't Say About Statistical Discrimination
By Bryan Caplan
I’m a fan of Tim Harford’s Logic of Life, and I’m a big promoter of the explanatory power of statistical discrimination (see here, here, and here for starters; also check out my lecture notes). So naturally I’m thrilled that Tim’s new book devotes a full chapter to statistical discrimination – or as he prefers to call it, “rational racism.” However, in the process of trying to make the idea palatable to his audience, I’m afraid that Tim failed to communicate some of the most important implications of the theory of statistical discrimination, and emphasized striking experiments at the expense of some basic facts about the labor market. Perhaps more importantly, Tim doesn’t mention some simple solutions to genuine problems he laments.
1. A key building block of statistical discrimination is the assumption of stereotype accuracy. For statistical discrimination to be stable, the stereotypes that market participants rely upon must be accurate statistical generalizations. But the average person could easily read Tim’s chapter without realizing that he is building upon this radically politically incorrect claim. Tim really should have spent some time poking fun at the idea that people make up stereotypes out of thin air, and pointing out some of the obvious but offensive evidence that stereotypes have been falsely stereotyped.
To be fair, several sentences in Tim’s chapter imply what I’m saying. For example, he tell us that “Statistical discrimination happens when employers use the average performance of the applicant’s racial group as a piece of information to help them decide whether to make the hire.” But the idea that racial groups have different average levels of performance is so objectionable, and Tim’s persona so likeable, that most people probably didn’t get the point. And that’s a sad missed opportunity.
2. An important implication of stereotype accuracy that Tim neglected is that the fundamental social conflict is not between groups, but within groups.* Take auto insurance. Young males are riskier drivers, so insurance companies charge them more. But if each young male were judged as an individual, the result would not be lower rates for young males. The result would be that young males who are more reckless than the average young male would pay even more, while young males who are less reckless than the average young male would pay less. The cause of the suffering of the cautious young male driver is not the “evil” insurance company, but the reckless young male drivers who make all young male drivers look bad.
This is a counter-intuitive claim that would probably offend a lot of readers. It is also one of the key implications of statistical discrimination. I wish Tim had stuck his neck out more.
3. In response, Tim could object that I’ve overlooked a subtler way for statistical discrimination to harm a group. After all, he heavily emphasizes a few experiments showing that statistical discrimination could be a “self-fulfilling prophesy.” For example, he describes a resume experiment where otherwise identical fake resumes with “black names” were less likely to get a response. “High-quality applicants were more likely to be invited for an interview, but only if they were white. Employers didn’t seem to notice whether black applicants had extra skills or experience.” If that is how employers treat black applicants, what’s the point of trying? As Tim asks, “Why bother to get a degree or work experience if you are young, gifted, and black?”
But is it really true that the market fails to reward blacks for getting more education? Is it even true that the market rewards them less? 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! The same goes for experience, though the result is not statistically significant. The real lesson of the data is that if you are young, gifted, and black, you should get a ton of education, because it has an exceptionally large pay-off.
Why would this be so? I’m not sure, but one simple story is that counter-stereotypical behavior stands out. When my sons were young, my wife was working a lot, so I often took my kids places on my own. Funny thing: Time and again, strangers came up and said, “Wow, you’re such a great dad!” But there were moms of young kids doing the same thing in plain sight, and the strangers rarely praised them. Why not? Because a dad taking care of two babies is counter-stereotypical, which grabs people’s attention.
Purely anecdotal, yes. 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.
The upshot is that stereotypes may actually be self-reversing rather than self-fulfilling. The marginal payoff of distinguishing yourself from the pack is high if people think poorly of the typical member of the pack.
4. Tim seems pretty fatalistic about statistical discrimination. He shouldn’t be. Are you disturbed by the fact that the market fails to judge people as individuals? Then allow employers to write contracts to solve the problem.
Example: Some young women are 100% focused on their careers, and don’t want kids. Most young women, however, do want kids, and intend to strike a balance between work and family. That balance often involves receiving expensive job training from a firm, then quiting before the firm can recoup its expenses.
Under current law, an employer isn’t even allowed to ask about a female applicant’s child-bearing plans. If you wanted to blow up the glass ceiling, though, you should not only allow employers to ask; you should allow them to offer deals like “We’ll hire you, but your health insurance doesn’t cover pregnancy.” The career woman would be happy to sign, reassuring the employer.
How will that help women? It won’t! On average, it’s a wash: It will help career-minded women, and hurt the rest. And if you want to judge female workers on the basis of individual productivity, that is exactly what should happen.
Once again, I expect that my proposed legal reform would appall many of Tim’s readers. From Tim’s point of view, it was probably a rational decision not to mention it. But the logic is compelling nonetheless.
Don’t get me wrong – on balance I’m glad that Tim raised the profile of the theory of statistical discrimination. And I’m glad to live in a world where he is one of the world’s best-selling writers of popular economics. But on balance, I’m afraid that Tim sugar-coated some hard lessons about discrimination and the market that most people are not yet ready to hear.
* To be fair, Tim did discuss black-on-black social sanctions for “acting white,” but that has no intrinsic connection to statistical discrimination.