There are few things that makes us feel more justified in being authoritarian than an empirical study, all wrapped up statistical-ish words. We don’t know quite what he means, and hang on the technical jargon so the weasel words connecting the parts that don’t move well together can be easily overlooked. This is no more true than when there is an evil villain to blame for the death of human being, and Paul Cassell doesn’t disappoint.
As the Chicago Tribune reported this morning, University of Utah Economics Professor Richard Fowles and I have just completed an important article on the 2016 Chicago homicide spike. Through multiple regression analysis and other tools, we conclude that an ACLU consent decree trigged [sic] a sharp reduction in stop and frisks by the Chicago Police Department, which in turn caused homicides to spike. Sadly, what Chicago police officers dubbed the “ACLU effect” was real—and more homicides and shootings were the consequence.
It’s like a perfect storm, homicide spike, “multiple regression analysis and other tools,” and of all the people who could possibly be responsible, it’s the ACLU! The ACLU? Who doesn’t hate them? It’s even got a cool name, the “ACLU effect.” How could this not be right?
The analysis is relatively straightforward. It is well known that homicides increased dramatically in Chicago in 2016. In 2015, 480 Chicago residents were killed. The next year, 754 were killed—274 more homicide victims, tragically producing an extraordinary 58% increase in a single year. What happened?
What happened is, indeed, straightforward. There was a spike in homicide. That, however, isn’t analysis, just a fact. A fact worthy of a front page headline.
Well, that won’t outrage the unwashed much. So where’s the beef?
In our paper, Professor Fowles and I bring empirical research tools to bear in an attempt to identify what changed in Chicago during that time.
Appeal to expertise, plus “empirical research tools” (don’t worry your silly head about what these are. They’re “empirical,” so they’re science!) and then, the big wiggle.
While such analysis may be unable to provide absolutely definitive answers, it can suggest which factors are more likely than others to have been responsible.
By using the hyperbole of “absolutely definitive,” Cassell seeks to upend criticism as if only a complete hater would demand perfection. But his alternative to “absolutely definition” turns out to be “factors…more likely than others.” Not absolute. Not definitive. Not even remotely provable. Merely “more likely.” Maybe his empirical tools were rusty?
We next attempt to pinpoint the time when things changed in Chicago—what might be called the “inflection” or “break” point in the data series. We begin by seasonally adjusting Chicago homicide and shooting data, which show significant seasonal fluctuation from cold weather months to warm weather months. Once the data are seasonally adjusted, a change or “break” in the data series can be statistically detected around November 2015.
In less pretentious terms, when did the killings start? And this will surprise no one.
Good reasons exist for believing that the decline in stop and frisks caused the spike. Simple visual observation of the data suggests a cause-and-effect change. In the chart below, we depict the (seasonally unadjusted) monthly number of stop and frisks (in blue) and the monthly number of homicides (in gold). The vertical line is placed at November 2015—the break point in the homicide data. This is precisely when stop and frisks declined in Chicago. (Emphasis added.)
Don’t mutter correlation doesn’t prove causation when a “simple visual observation” does the trick. Cassell is using science, which you shouldn’t try at home or you could sprain something.
We then qualitatively search for other possible factors that might be responsible for the Chicago homicide spike. For various reasons, none of these other candidates fit the data as well as the decline in stop and frisks. In addition, Bayesian Model Averaging (“BMA”) provide strong statistical evidence that our findings are robust in the sense that they are not due to inclusion or exclusion of any particular variables in our equations.
Don’t ask what other factors, or why they don’t fit. He’s using Bayesian Model Averaging, and isn’t that good enough for you? And if that’s not persuasive enough, it not only costs lives, but money.
Our equations permit us to quantify the costs of the decline in stop and frisks, both in human and financial terms. We conclude that, because of fewer stop and frisks in 2016, a conservative estimate is that approximately 236 additional homicides and 1115 additional shootings occurred during that year. A reasonable estimate of the social costs associated with these additional homicides and shootings is about $1,500,000,000. And these costs are heavily concentrated in Chicago’s African-American and Hispanic communities.
And, indeed, even the ACLU says it’s to blame.
In our penultimate section, we explain why the ACLU settlement agreement with the Chicago Police Department is the most likely cause of the decline in stop and frisks. Indeed, the ACLU took credit for the decline in stop and frisks at the start of 2016.
As much as there is little in Cassell’s highly scientific study that’s persuasive, there is one rather significant “factor” that he neglects to mention. The tactic of “stop and frisk” was stopped because it was flagrantly unconstitutional. It was nothing more than cops deciding to randomly toss people for looking suspicious or for kicks. There’s no mention by Cassell of how many people were stopped, how many guns were seized, and how many constitutional rights were violated.
Is it likely that a facially unconstitutional tactic was effective, at least to some extent? Of course. If cops were allowed to make random house and car searches, there is a likelihood they would find guns and drugs as well. Randomly search enough people and you’re sure to find something eventually.
But wrapping up an unconstitutional tactic, and blaming the ACLU for pursuing an end to an unconstitutional tactic, in statistical jargon is asinine. And it’s doubly so when the best he can come up with is “more likely.” There was no “control” for Chicago cops being compelled to do their job while respecting the Constitution.
Or their sad feelings when they were told that they couldn’t toss black kids against walls anymore for kicks, so they decided to cash their paychecks and do as little policing as possible, ceding the streets to the gangs that festered for self-protection against the biggest chicago gang of all, the one that murdered Laquan McDonald and covered it up. Talk about regression analysis.