Rajiv Sethi points me to this column by Sendhil Mullainathan, who writes:
Tamir Rice. Eric Garner. Walter Scott. Michael Brown. Each killing raises a disturbing question: Would any of these people have been killed by police officers if they had been white? . . .
There is ample statistical evidence of large and persistent racial bias in other areas — from labor markets to online retail markets. So I [Mullainathan] expected that police prejudice would be a major factor in accounting for the killings of African-Americans. But when I looked at the numbers, that’s not exactly what I found. . . . what the data does suggest is that eliminating the biases of all police officers would do little to materially reduce the total number of African-American killings.
Then come the numbers:
According to the F.B.I.’s Supplementary Homicide Report, 31.8 percent of people shot by the police were African-American, a proportion more than two and a half times the 13.2 percent of African-Americans in the general population. . . .
But this data does not prove that biased police officers are more likely to shoot blacks in any given encounter.
Instead, there is another possibility: It is simply that — for reasons that may well include police bias — African-Americans have a very large number of encounters with police officers. . . . Arrest data lets us measure this possibility.
At this point I just have to interject that every time I see “data” used as a singular noun, it’s like fingernails on a blackboard to me. Yes, yes, I know that in modern English, “data” is acceptable as a singular or plural noun. So I’m not saying that Mullainathan (or the New York Times style guide) is wrong. It just bothers me. I’m not used to it.
Anyway, to continue from Mullainathan:
For the entire country, 28.9 percent of arrestees were African-American. This number is not very different from the 31.8 percent of police-shooting victims who were African-Americans. If police discrimination were a big factor in the actual killings, we would have expected a larger gap between the arrest rate and the police-killing rate.
This in turn suggests that removing police racial bias will have little effect on the killing rate. . . .
He continues with some sentences that explain the basic idea which should be unexceptional to the readers of this blog.
My Columbia University colleague Sethi was not happy with this reasoning. Sethi writes:
Sendhil Mullainathan is one of the most thoughtful people in the economics profession, but he has a recent piece in the New York Times with which I [Sethi] really must take issue. . . .
A key assumption underlying this argument is that encounters involving genuine (as opposed to perceived) threats to officer safety arise with equal frequency across groups. . . .
Sethi argues that it does seem that police officers often behave more violently toward black suspects. But then, he asks,
How, then, can one account for the rough parity between arrest rates and the rate of shooting deaths at the hands of law enforcement? If officers frequently behave differently in encounters with black civilians, shouldn’t one see a higher rate of killing per encounter?
Sethi answers his own question:
Not necessarily. . . . If the very high incidence of encounters between police and black men is due, in part, to encounters that ought not to have occurred at all, then a disproportionate share of these will be safe, and one ought to expect fewer killings per encounter in the absence of bias. Observing parity would then be suggestive of bias, and eliminating bias would surely result in fewer killings.
The discussion continues
This post by Jacob Dink is worth reading. Jacob shows that the likelihood of being shot by police conditional on being unarmed is twice as high for blacks relative to whites. The likelihood is also higher conditional on being armed, but the difference is smaller:
[Damn that’s an ugly y-axis. And boy is it ugly to label the two lines with a legend. — ed.]
This, together with the fact that rates of arrest and killing are roughly equal across groups, implies that blacks are less likely to be armed than whites, conditional on an encounter. In the absence of bias, therefore, the rate of killing per encounter should be lower for blacks, not equal across groups. So we can’t conclude that “removing police racial bias will have little effect on the killing rate.” That was the point I was trying to make in this post.
Some interesting discussion in comments to Sethi’s post. I have no idea where the error bars are coming from in Dink’s post—I assume the data are some total count, not from a sample.
What’s my take on all this? First off, data are good (or, as the kids today say, data is good). So I appreciate Mullainathan’s numbers and also Dink’s (even if I can’t be quite clear on what Dink is actually plotting). I’m also sympathetic to Sethi’s general argument. Suppose there’s a sliding scale of police aggression, starting with arresting, moving to violence, and culminating in killing a suspect. I could imagine #killed/#arrested not varying much by race, while at the same time the cops are disproportionately arresting, beating, and killing African Americans.
To put it another way, this is an argument over the denominator. Mullainathan is using arrests as the denominator, but it’s not clear this is appropriate. These are tough questions. In our stop-and-frisk paper we used previous year’s validated arrests as a baseline. But that’s not perfect.
It’s interesting that Mullainathan uses the example of Tamir Rice and then recommends using arrests as a reference point. I assume that had the officer gotten close enough to Rice to see what was happening, he wouldn’t have arrested Rice in any case, right?
Also it is notable that it is two economists having this discussion, given that the topic is not actually economics! I say this not because I think economists should be discouraged from studying such topics, it just seems surprising to me. I suppose what’s going on is that there are a lot more academic economists out there, than there are sociologists or criminologists or even political scientists. Also it’s my impression that quantitative political scientists are discouraged from working on this sort of policy research, but I might be wrong about that.