Nashville’s Policing Data Show Uneven Racial Burden from Operation Safer Streets

Operation Safer Streets (or OSS for short) has become the epicenter of Nashville’s debates over policing and racial justice. Every weekend, the MNPD funnels police officers to hotspots for gangs or crime, arguing that a visible police-presence deters crime. Since 2011, OSS has resulted in more than 5,000 arrests and 50,000 vehicle stops, according to WKLN.

Racial justice activists have criticized the program as disproportionately targeting black, immigrant, and low-income neighborhoods. As Nashville Black Lives Matter has said, Safer Streets “mostly targets innocent people for doing the same things that people in Green Hills do without punishment.”

MNPD touts OSS in regular press releases reporting the number of vehicle stops, arrests, and drug seizures they (in their words) “netted” each week. After extracting the text from these articles with some code, I identified which streets, blocks, and intersections have been targeted. [1]

And after looking at the numbers, what BLM and others have been saying is right: Nashville Safer Streets is not color-blind. Its burden falls disproportionately on communities of color.

In total, seventy-percent of Census Block Groups in Nashville are majority-white, according to the 5-year estimate of the American Community Survey. Yet 60% of areas targeted by Nashville Safer Streets were mostly non-white, and 25% of targeted areas were more than 90% non-white.

Plotting OSS actions alongside the racial composition of Nashville’s neighborhoods is striking. They are concentrated in three mostly black areas (North Nashville, the remaining black neighborhoods of East Nashville, and the Chestnut-Hill Area of South Nashville) and one mostly immigrant community (Antioch along Nolensville Pike).

Racial Burden of OSS

This matters. As Michelle Alexander notes, America’s syndrome of mass incarceration begins with overpolicing and ends with the economic and familial disruption of communities of color. Blacks and Hispanics are more likely to be stopped by the police, more likely to be arrested for drug-crimes despite lower drug use, and more likely to get jail time. They are more likely to be stopped in part because of policies like OSS.

My experiences as a Vanderbilt student highlight this disparity. On the weekends, I saw plenty of drug use. But the police didn’t regularly stop our vehicles and search us on the weekends, like they do in low-income, predominantly non-white neighborhoods.

And I’m glad they didn’t. Because plenty of us, who are now going to graduate school or law school or working good jobs, would have had our future prospects shattered if drug use was treated the same way on Greek Row as Lafayette Street, which has been targeted 77 times in the last few years by OSS.

The traditional response from the Police is that they target these predominantly black and low-income areas because they are known to be hubs of “gang-activity.”  But this is self-fulfilling. If police systematically stop cars in poor neighborhoods, that is where they will find cars with drugs.

At the end of the day, the question is, does Operation Safer Streets actually make us safer? I don’t think it does. OSS neglects the root causes of gang violence. It extracts tax revenue from the poorest neighborhoods in the form of citations and court fees. And it subjects tens of thousands of Nashvillians to arbitrary stops, undermining trust in law enforcement and, as recent events have shown, opening the door to a potentially violent encounter with law enforcement.

After all, what starts with a traffic stop ends too-often with a dead black man.

[1] I converted each street to a latitude-longitude coordinate that represents its central location, using the Google Maps API. (I deleted from this data some streets like Old Hickory Boulevard and Dickerson Pike that were too vague to make any real inferences about where police activity occurred.) Next I matched each of these areas with its Census Block Group (the smallest Census area with data, comprising, on average, about 2000 people) to see whether non-white communities were disproportionately affected.

Is there systematic gender discrimination in Metro Nashville Employment? Part 1

Using the same municipal employee data from as the previous post, here I am looking at whether there is evidence of gender discrimination in the salaries of Metro Nashville government employees. Obviously this is a pretty complicated issue, and I am only really going to scratch the surface of it. Mostly this is just a fun exercise for me.

Okay, so where to begin: First, I think, it makes sense to begin by just comparing mean salaries by gender. If this doesn’t show evidence of discrimination, then there isn’t much merit it going further.

In fact, as the next two figures show, there’s a pretty big difference in mean salaries between men and women.

salary v gender

t test salaries

The mean salary of men among municipal employees is $7,487 higher than for women. Not surprisingly, this is statistically significant, so the observed gap is more than what we would expect due to chance if mean salaries were equal. (It’s not shown but an f-test showed unequal variances between the two groups of salaries, which is why I assumed unequal variances for the t-test).

But wait – maybe men tend to be over-represented in full-time work while women tend to be overrepresented in part-time work. That could explain the observed difference, not discrimination (although I think that could still show evidence of systematic disadvantages [or at least disincentives] for women in employment).

And, indeed, it is the case that there is this kind of difference employment status between these two groups.

women in part time

chisq emply status gender

However, this doesn’t fully explain the difference in salaries. Even within these categories, men are paid more than women.more emply gender

A difference remains. Mean salary is $46,000 for full-time women vs $52,000 for full-time men – a difference of $6,000 or about 80% of the difference we observed without any adjustment. Tangent: why part time wages for women are slightly higher is a puzzle – when I looked at median instead of mean, this went away, and so the median part-time man earns more. This probably means there are outliers distorting the picture for part-time (some part-time women is getting paid much higher than average, perhaps??).

Another important check is difference in job. Using EEOC reported job categories, I can look at whether women and men tend to be working different sorts of jobs, thus explaining the different salaries.eeoc

And indeed, there’s a visually apparent (and statistically significant; Pearson’s X^2 < 0.0001) difference in the frequencies of jobs by gender. A much larger percentage of women are doing “administrative support” ($35,000 annually) and “para-professional” ($26,000) work. A larger percentage of men are doing “Professional work” ($53,000) or “technicians” ($58,000).

So this is another thing to account for, since it could be driving differences in salaries. I think the differential prevalence of women in the professionals category could signal some important inequities but is not per se discrimination in the sense of unequal pay for the same work. To try to give a more comprehensive account of what’s driving salary, I can do a multivariate regression.

I will do this tomorrow! Oh, the suspense.