[T]he inevitable existence of some statistical discrimination
doesn't make the practice immune to criticism. You can grant that it's
OK to some degree, but - even if the law is silent - still limited by
ethics and/or etiquette. But precisely what limitations do you think
are justified, and why?
Many readers took the bait. Here's my critique of the most interesting responses.
Perhaps if the variation within the group is much higher than the group
difference from the mean, the benefit (the amount of error reduced by
statistical discrimination multiplied by the cost of error) is much,
much less than the cost...
But don't market forces already provide incentives to take within-group variation into account? If half of employers act as if everyone of group X is average, that leaves remaining employees with plenty of cream to skim. So is there any obligation to go beyond this market outcome?
From HispanicPundit (who doesn't "necessarily agree with these reasons"):
What about when it harms someone else? That seems like a fair limitation.
Statistical discrimination always harms people who are above average for their group, so on this theory statistical discrimination is never permissible. Which is crazy for the reasons I gave in the original post.
If statistical discrimination leads me to give someone lower
preference in an interview, for example, maybe I should give that
person a deeper look to compensate for what could be my statistical
Statistical error is always possible, but it could go in either direction, so it's hard to see why compensation is typically in order.
Or how about, if it runs parallel to racial stereotypes. For obvious historical reasons.
Racial stereotyping is of course one common form of statistical discrimination. But everyone uses racial stereotypes some of the time; even black taxi drivers hesitate to pick up groups of young black male passengers. So blanket objections aren't plausible.
[T]he reasons for different standards mostly lack any philosophical depth
and are instead post-hoc reactions to past injustices. We frown upon
racial discrimination not necessarily because racial discrimination is
currently worse than any other kind, but because of what it used to be like.
I tend to agree. In signaling terms, people criticize statistical discrimination because they don't want people to think that they'd engage in taste-based discrimination.
I would make a distinction between mutable and immutable
characteristics. Then I would recognize a few of those are a little
fuzzy because while these characteristics may be changeable, they are
very difficult to change.
We should avoid discriminating based on truly immutable characteristics such as race, gender and sexual orientation.
But everyone statistically discriminates on the basis of immutable characteristics - when you prefer a female baby-sitter, market Maxim to men, speak Spanish to someone who looks Hispanic, etc. In fact, it's often seen as rude not to statistically discriminate; for example, focusing on stereotypically male interests in a mixed-sex conversation.
I would also argue that from a political economy standpoint,
libertarians should oppose statistical discrimination. The idea of
equality of opportunity, that anyone from any background can become
successful, is one of the best safeguards against redistributive
socialism. Destroy it and watch how the politics becomes a discussion
of class warfare.
The problem, though, is that market forces often strongly encourage statistical discrimination. So libertarians have two choices: either join the popular chorus against it, but insist that it should still be legal; or argue that it should be legal because it isn't nearly as bad as it's perceived to be. This might make markets less popular, as David suggests; but endorsing an ideal that markets will never meet seems counter-productive in the long-run.
It seems the "wrongness" of discrimination is inversely proportional to
its usefulness. The more you know about the individual(s) the less you
need statistics. It would be silly the assume someone is going to take
on the average value of the population when you know they are in the
95th percentile, for instance.
discrimination makes a lot of sense if rationally applied, but think of
the data requirements in order to apply the theory correctly. You need
a whole lot more than the means of two (or more) distributions, you'll
need variances too, along with skewness. You'll need to be able to
compute probabilities of Type I and II errors, obviously...
What most people call statistical discrimination is just the
application of rules of thumb, based on little or no data. Prejudice is
This strikes me as an unreasonably binary perspective. In the real world, there's a continuum between careful actuarial analysis and pure prejudice. And I'll bet (terms open to negotiation) that popular stereotypes correlate highly with higher-quality statistical work.
Relying on statistical inference is not unethical; it is simply
rational. But etiquette will sometimes require that we conceal some
statistical inference we have made about someone from that person; we
ought not needlessly to hurt other people's feelings.
Bad etiquette and bad ethics overlap. It's rude and wrong to tell people they're ugly, even (especially?) when it's true. The same goes for statistical discrimination. In fact, there's a good case for a broader ethic of "Don't ask, don't tell." It's rude and wrong to ask others if they think you're ugly - or whether they take statistics about your group into account when they decide how to treat you.
It's perfectly fine to discriminate. Rules of thumb are fine. Incorrect rules are fine. Prejudice is fine...
This seems to me to be the obvious position for any libertarian.
The obvious position for any libertarian is that these things should be legal, nothing more. "Fine" is much stronger. In any case, I'm not just presenting the libertarian position; I'm trying to find common ground between libertarians and reasonable people with other views.
Two points that I don't think anyone made in the comments:
1. While "Don't ask, don't tell" is the most reasonable limitation on statistical discrimination, a norm of "give people a chance" is also plausible when the cost is low. It's like letting others merge in front of you in traffic.
2. Many people have contractual or semi-contractual obligations not to statistically discriminate in some ways. Professors, for example, are supposed to grade students based on their classroom performance alone. In purely statistical terms, this is suboptimal - a weighted average of classroom performance and outside information would yield more accurate predictions. But part of the product universities sell is "judging people as individuals" - and it would be wrong to renege on that promise. In a free market, I suspect that many other businesses would pledge allegiance to similar principles - provided, of course, that's it's inexpensive to do so.